pax_global_header00006660000000000000000000000064146037605610014522gustar00rootroot0000000000000052 comment=58b7a797367910cb890a7129ac27d2121d3b5a4b ipyparallel-8.8.0/000077500000000000000000000000001460376056100140555ustar00rootroot00000000000000ipyparallel-8.8.0/.binder/000077500000000000000000000000001460376056100153765ustar00rootroot00000000000000ipyparallel-8.8.0/.binder/jupyter_config.json000066400000000000000000000000601460376056100213140ustar00rootroot00000000000000{ "LabApp": { "collaborative": true } } ipyparallel-8.8.0/.binder/postBuild000066400000000000000000000010271460376056100172660ustar00rootroot00000000000000#!/usr/bin/env bash jlpm --prefer-offline --ignore-optional --ignore-scripts jlpm build IPP_DISABLE_JS=1 python -m pip install -e .[test,benchmark] jupyter serverextension enable --sys-prefix --py ipyparallel jupyter serverextension list jupyter server extension enable --sys-prefix --py ipyparallel jupyter server extension list jupyter nbextension install --symlink --sys-prefix --py ipyparallel jupyter nbextension enable --sys-prefix --py ipyparallel jupyter nbextension list jlpm install:extension jupyter labextension list ipyparallel-8.8.0/.binder/requirements.txt000066400000000000000000000001651460376056100206640ustar00rootroot00000000000000ipywidgets jupyterlab >=3.2.4 jupyterlab-link-share matplotlib networkx pidigits requests retrolab >=0.3.13 wordfreq ipyparallel-8.8.0/.coveragerc000066400000000000000000000003501460376056100161740ustar00rootroot00000000000000[run] parallel = True omit = ipyparallel/tests/* [report] exclude_lines = if self.debug: pragma: no cover raise NotImplementedError if __name__ == .__main__.: ignore_errors = True omit = ipyparallel/tests/* ipyparallel-8.8.0/.eslintignore000066400000000000000000000000531460376056100165560ustar00rootroot00000000000000node_modules dist coverage **/*.d.ts tests ipyparallel-8.8.0/.eslintrc.js000066400000000000000000000036001460376056100163130ustar00rootroot00000000000000module.exports = { env: { browser: true, es6: true, commonjs: true, node: true, }, root: true, extends: [ "eslint:recommended", "plugin:@typescript-eslint/eslint-recommended", "plugin:@typescript-eslint/recommended", "prettier", "plugin:react/recommended", ], parser: "@typescript-eslint/parser", parserOptions: { project: "tsconfig.eslint.json", }, plugins: ["@typescript-eslint"], rules: { "@typescript-eslint/no-floating-promises": ["error", { ignoreVoid: true }], "@typescript-eslint/naming-convention": [ "error", { selector: "interface", format: ["PascalCase"], custom: { regex: "^I[A-Z]", match: true, }, }, ], "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }], "@typescript-eslint/no-use-before-define": "off", "@typescript-eslint/camelcase": "off", "@typescript-eslint/no-explicit-any": "off", "@typescript-eslint/no-non-null-assertion": "off", "@typescript-eslint/no-namespace": "off", "@typescript-eslint/interface-name-prefix": "off", "@typescript-eslint/explicit-function-return-type": "off", "@typescript-eslint/ban-ts-comment": ["warn", { "ts-ignore": true }], "@typescript-eslint/ban-types": "warn", "@typescript-eslint/no-non-null-asserted-optional-chain": "warn", "@typescript-eslint/no-var-requires": "off", "@typescript-eslint/no-empty-interface": "off", "@typescript-eslint/triple-slash-reference": "warn", "@typescript-eslint/no-inferrable-types": "off", "no-inner-declarations": "off", "no-prototype-builtins": "off", "no-control-regex": "warn", "no-undef": "warn", "no-case-declarations": "warn", "no-useless-escape": "off", "prefer-const": "off", "react/prop-types": "warn", }, settings: { react: { version: "detect", }, }, }; ipyparallel-8.8.0/.github/000077500000000000000000000000001460376056100154155ustar00rootroot00000000000000ipyparallel-8.8.0/.github/dependabot.yml000066400000000000000000000010071460376056100202430ustar00rootroot00000000000000# dependabot.yaml reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file version: 2 updates: # Maintain dependencies in our GitHub Workflows - package-ecosystem: github-actions directory: "/" schedule: interval: monthly # jupyterlab extension - package-ecosystem: npm directory: "/" schedule: interval: monthly groups: # one big pull request lab: patterns: - "*" ipyparallel-8.8.0/.github/workflows/000077500000000000000000000000001460376056100174525ustar00rootroot00000000000000ipyparallel-8.8.0/.github/workflows/release.yml000066400000000000000000000034221460376056100216160ustar00rootroot00000000000000# Build releases and (on tags) publish to PyPI name: Release # always build releases (to make sure wheel-building works) # but only publish to PyPI on tags on: push: branches-ignore: - "pre-commit-ci*" tags: - "*" pull_request: concurrency: group: >- ${{ github.workflow }}- ${{ github.ref_type }}- ${{ github.event.pull_request.number || github.sha }} cancel-in-progress: true jobs: build-release: runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: 3.11 cache: pip - name: install build package run: | pip install --upgrade pip pip install build pip freeze - name: build release run: | python -m build --sdist --wheel . ls -l dist - name: verify wheel run: | cd dist pip install ./*.whl jupyterlab==4.* ipcluster --help-all ipcontroller --help-all ipengine --help-all jupyter labextension list 2>&1 | grep ipyparallel jupyter server extension list 2>&1 | grep ipyparallel # ref: https://github.com/actions/upload-artifact#readme - uses: actions/upload-artifact@v4 with: name: ipyparallel-${{ github.sha }} path: "dist/*" if-no-files-found: error upload-pypi: permissions: id-token: write environment: release runs-on: ubuntu-22.04 if: startsWith(github.ref, 'refs/tags/') needs: - build-release steps: - uses: actions/download-artifact@v4 with: path: dist merge-multiple: true - name: Publish wheels to PyPI uses: pypa/gh-action-pypi-publish@release/v1 ipyparallel-8.8.0/.github/workflows/test-docs.yml000066400000000000000000000041531460376056100221050ustar00rootroot00000000000000name: Test docs on: pull_request: push: branches-ignore: - "dependabot/**" - "pre-commit-ci-update-config" tags: - "**" workflow_dispatch: env: # UTF-8 content may be interpreted as ascii and causes errors without this. LANG: C.UTF-8 PYTEST_ADDOPTS: "--verbose --color=yes" jobs: test-docs: runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 with: # make rediraffecheckdiff requires git history to compare current # commit with the main branch and previous releases. fetch-depth: 0 - uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install requirements run: | pip install -r docs/requirements.txt . # readthedocs doesn't halt on warnings, # so raise any warnings here - name: build docs run: | cd docs make html - name: check links run: | cd docs make linkcheck # make rediraffecheckdiff compares files for different changesets # these diff targets aren't always available # - compare with base ref (usually 'main', always on 'origin') for pull requests # - only compare with tags when running against main repo # to avoid errors on forks, which often lack tags - name: check redirects for this PR if: github.event_name == 'pull_request' run: | cd docs export REDIRAFFE_BRANCH=origin/${{ github.base_ref }} make rediraffecheckdiff # this should check currently published 'stable' links for redirects - name: check redirects since last release if: github.repository == 'ipython/ipyparallel' run: | cd docs export REDIRAFFE_BRANCH=$(git describe --tags --abbrev=0) make rediraffecheckdiff # longer-term redirect check (fixed version) for older links - name: check redirects since 8.6.0 if: github.repository == 'ipython/ipyparallel' run: | cd docs export REDIRAFFE_BRANCH=8.6.0 make rediraffecheckdiff ipyparallel-8.8.0/.github/workflows/test.yml000066400000000000000000000115231460376056100211560ustar00rootroot00000000000000name: Test on: pull_request: push: branches-ignore: - "pre-commit-ci*" concurrency: group: >- ${{ github.workflow }}- ${{ github.ref_type }}- ${{ github.event.pull_request.number || github.sha }} cancel-in-progress: true env: # UTF-8 content may be interpreted as ascii and causes errors without this. LANG: C.UTF-8 IPP_DISABLE_JS: "1" JUPYTER_PLATFORM_DIRS: "1" jobs: test: runs-on: ${{ matrix.runs_on || 'ubuntu-20.04' }} timeout-minutes: 20 strategy: # Keep running even if one variation of the job fail fail-fast: false matrix: include: - python: "3.9" cluster_type: ssh - python: "3.8" cluster_type: mpi - python: "3.10" cluster_type: slurm container: slurmctld - python: "3.8" - python: "3.10" env: IPP_CONTROLLER_IP: "*" - python: "3.9" env: IPP_ENABLE_CURVE: "1" - python: "3.8" runs_on: windows-2019 - python: "3.9" runs_on: macos-11 - python: "3.11" steps: - uses: actions/checkout@v4 - name: Cache conda environment uses: actions/cache@v4 with: path: | ~/conda key: conda - name: Cache node_modules uses: actions/cache@v4 with: path: | node_modules key: ${{ runner.os }}-yarn-${{ hashFiles('yarn.lock') }} restore-keys: | ${{ runner.os }}-yarn- - name: Set environment variables if: ${{ matrix.env }} env: MATRIX_ENV: ${{ toJSON(matrix.env) }} run: | python3 <> $GITHUB_ENV - name: Install Python ${{ matrix.python }} if: ${{ matrix.cluster_type != 'mpi' }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python }} - name: Install ipyparallel itself run: | pip install --upgrade pip pip install --no-deps . - name: Install Python dependencies run: | pip install --pre --upgrade ipyparallel[test] - name: Install extra Python packages if: ${{ ! startsWith(matrix.python, '3.11') }} run: | pip install distributed joblib pip install --only-binary :all: matplotlib || echo "no matplotlib" - name: Show environment run: pip freeze - name: Run tests in container ${{ matrix.container }} if: ${{ matrix.container }} run: echo "EXEC=docker exec -i ${{ matrix.container }}" >> $GITHUB_ENV - name: Run ${{ matrix.cluster_type }} tests if: ${{ matrix.cluster_type }} run: | ${EXEC:-} pytest -v --maxfail=2 --cov=ipyparallel ipyparallel/tests/test_${{ matrix.cluster_type }}.py - name: Run tests if: ${{ ! matrix.cluster_type }} run: | pytest -v --maxfail=3 --cov=ipyparallel - name: Fixup coverage permissions ${{ matrix.container }} if: ${{ matrix.container }} run: | ls -l .coverage* ${EXEC} chmod -R a+rw .coverage* - name: Submit codecov report uses: codecov/codecov-action@v4 - name: Report on slurm if: ${{ matrix.cluster_type == 'slurm' && failure() }} run: | set -x docker ps -a docker logs slurmctld docker exec -i slurmctld squeue --states=all docker exec -i slurmctld sinfo docker logs c1 docker logs c2 ipyparallel-8.8.0/.gitignore000066400000000000000000000007021460376056100160440ustar00rootroot00000000000000MANIFEST build dist _build docs/man/*.gz docs/source/api/generated docs/source/config/options docs/source/interactive/magics-generated.txt examples/daVinci Word Count/*.txt examples/testdill.py *.py[co] __pycache__ *.egg-info *~ *.bak .ipynb_checkpoints .tox .DS_Store \#*# .#* .coverage *coverage.xml .coverage.* .idea htmlcov id_*sa .vscode node_modules lib ipyparallel/labextension tsconfig.tsbuildinfo dask-worker-space .yarn package-lock.json ipyparallel-8.8.0/.mailmap000066400000000000000000000250671460376056100155100ustar00rootroot00000000000000A. J. Holyoake ajholyoake Aaron Culich Aaron Culich Aron Ahmadia ahmadia Benjamin Ragan-Kelley Benjamin Ragan-Kelley Min RK Benjamin Ragan-Kelley MinRK Barry Wark Barry Wark Ben Edwards Ben Edwards Bradley M. Froehle Bradley M. Froehle Bradley M. Froehle Bradley Froehle Brandon Parsons Brandon Parsons Brian E. Granger Brian Granger Brian E. Granger Brian Granger <> Brian E. Granger bgranger <> Brian E. Granger bgranger Christoph Gohlke cgohlke Cyrille Rossant rossant Damián Avila damianavila Damián Avila damianavila Damon Allen damontallen Darren Dale darren.dale <> Darren Dale Darren Dale <> Dav Clark Dav Clark <> Dav Clark Dav Clark David Hirschfeld dhirschfeld David P. Sanders David P. Sanders David Warde-Farley David Warde-Farley <> Doug Blank Doug Blank Eugene Van den Bulke Eugene Van den Bulke Evan Patterson Evan Patterson Evan Patterson Evan Patterson Evan Patterson epatters Evan Patterson epatters Ernie French Ernie French Ernie French ernie french Ernie French ernop Fernando Perez Fernando Perez Fernando Perez Fernando Perez fperez <> Fernando Perez fptest <> Fernando Perez fptest1 <> Fernando Perez Fernando Perez Fernando Perez Fernando Perez <> Fernando Perez Fernando Perez Frank Murphy Frank Murphy Gabriel Becker gmbecker Gael Varoquaux gael.varoquaux <> Gael Varoquaux gvaroquaux Gael Varoquaux Gael Varoquaux <> Ingolf Becker watercrossing Jake Vanderplas Jake Vanderplas Jakob Gager jakobgager Jakob Gager jakobgager Jakob Gager jakobgager Jason Grout Jason Grout Jason Gors jason gors Jason Gors jgors Jens Hedegaard Nielsen Jens Hedegaard Nielsen Jens Hedegaard Nielsen Jens H Nielsen Jens Hedegaard Nielsen Jens H. Nielsen Jez Ng Jez Ng Jonathan Frederic Jonathan Frederic Jonathan Frederic Jonathan Frederic Jonathan Frederic Jonathan Frederic Jonathan Frederic jon Jonathan Frederic U-Jon-PC\Jon Jonathan March Jonathan March Jonathan March jdmarch Jörgen Stenarson Jörgen Stenarson Jörgen Stenarson Jorgen Stenarson Jörgen Stenarson Jorgen Stenarson <> Jörgen Stenarson jstenar Jörgen Stenarson jstenar <> Jörgen Stenarson Jörgen Stenarson Juergen Hasch juhasch Juergen Hasch juhasch Julia Evans Julia Evans Kester Tong KesterTong Kyle Kelley Kyle Kelley Kyle Kelley rgbkrk Laurent Dufréchou Laurent Dufréchou Laurent Dufréchou laurent dufrechou <> Laurent Dufréchou laurent.dufrechou <> Laurent Dufréchou Laurent Dufrechou <> Laurent Dufréchou laurent.dufrechou@gmail.com <> Laurent Dufréchou ldufrechou Lorena Pantano Lorena Luis Pedro Coelho Luis Pedro Coelho Marc Molla marcmolla Martín Gaitán Martín Gaitán Matthias Bussonnier Matthias BUSSONNIER Matthias Bussonnier Bussonnier Matthias Matthias Bussonnier Matthias BUSSONNIER Matthias Bussonnier Matthias Bussonnier Michael Droettboom Michael Droettboom Nicholas Bollweg Nicholas Bollweg (Nick) Nicolas Rougier Nikolay Koldunov Nikolay Koldunov Omar Andrés Zapata Mesa Omar Andres Zapata Mesa Omar Andrés Zapata Mesa Omar Andres Zapata Mesa Pankaj Pandey Pankaj Pandey Pascal Schetelat pascal-schetelat Paul Ivanov Paul Ivanov Pauli Virtanen Pauli Virtanen <> Pauli Virtanen Pauli Virtanen Pierre Gerold Pierre Gerold Pietro Berkes Pietro Berkes Piti Ongmongkolkul piti118 Prabhu Ramachandran Prabhu Ramachandran <> Puneeth Chaganti Puneeth Chaganti Robert Kern rkern <> Robert Kern Robert Kern Robert Kern Robert Kern Robert Kern Robert Kern <> Robert Marchman Robert Marchman Satrajit Ghosh Satrajit Ghosh Satrajit Ghosh Satrajit Ghosh Scott Sanderson Scott Sanderson smithj1 smithj1 smithj1 smithj1 Steven Johnson stevenJohnson Steven Silvester blink1073 S. Weber s8weber Stefan van der Walt Stefan van der Walt Silvia Vinyes Silvia Silvia Vinyes silviav12 Sylvain Corlay Sylvain Corlay sylvain.corlay Ted Drain TD22057 Théophile Studer Théophile Studer Thomas Kluyver Thomas Thomas Spura Thomas Spura Timo Paulssen timo vds vds2212 vds vds Ville M. Vainio Ville M. Vainio ville Ville M. Vainio ville Ville M. Vainio vivainio <> Ville M. Vainio Ville M. Vainio Ville M. Vainio Ville M. Vainio Walter Doerwald walter.doerwald <> Walter Doerwald Walter Doerwald <> W. Trevor King W. Trevor King Yoval P. y-p ipyparallel-8.8.0/.pre-commit-config.yaml000066400000000000000000000020651460376056100203410ustar00rootroot00000000000000ci: autoupdate_schedule: monthly repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.3.5 hooks: - id: ruff args: - "--fix" - id: ruff-format - repo: https://github.com/pre-commit/mirrors-prettier rev: v4.0.0-alpha.8 hooks: - id: prettier - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 hooks: - id: end-of-file-fixer - id: check-case-conflict - id: check-executables-have-shebangs - id: requirements-txt-fixer - repo: https://github.com/pre-commit/mirrors-eslint rev: v9.0.0-rc.0 hooks: - id: eslint files: \.[jt]sx?$ # *.js, *.jsx, *.ts and *.tsx exclude: ipyparallel/nbextension/.* types: [file] additional_dependencies: - "@typescript-eslint/eslint-plugin@2.27.0" - "@typescript-eslint/parser@2.27.0" - eslint@^6.0.0 - eslint-config-prettier@6.10.1 - eslint-plugin-prettier@3.1.4 - eslint-plugin-react@7.21.5 - typescript@4.1.3 ipyparallel-8.8.0/.prettierignore000066400000000000000000000001401460376056100171130ustar00rootroot00000000000000node_modules docs/build htmlcov ipyparallel/labextension **/node_modules **/lib **/package.json ipyparallel-8.8.0/.yarnrc.yml000066400000000000000000000000701460376056100161510ustar00rootroot00000000000000enableImmutableInstalls: false nodeLinker: node-modules ipyparallel-8.8.0/CONTRIBUTING.md000066400000000000000000000025671460376056100163200ustar00rootroot00000000000000# Contributing We follow the [Jupyter Contributing Guide](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html). Make sure to follow the [Jupyter Code of Conduct](https://jupyter.org/conduct/). ## Development install A basic development install of IPython parallel is the same as almost all Python packages: ```bash pip install -e . ``` To enable the server extension from a development install: ```bash # for jupyterlab jupyter server extension enable --sys-prefix ipyparallel # for classic notebook jupyter serverextension enable --sys-prefix ipyparallel ``` As described in the [JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/extension/extension_dev.html#developing-a-prebuilt-extension) for a development install of the jupyterlab extension you can run the following in this directory: ```bash jlpm # Install npm package dependencies jlpm build # Compile the TypeScript sources to Javascript jupyter labextension develop . --overwrite # Install the current directory as an extension ``` If you are working on the lab extension, you can run jupyterlab in dev mode to always rebuild and reload your extensions. In two terminals, run: ```bash [term 1] $ jlpm watch [term 2] $ jupyter lab --extensions-in-dev-mode ``` You should then be able to refresh the JupyterLab page and it will pick up the changes to the extension as you work. ipyparallel-8.8.0/COPYING.md000066400000000000000000000056121460376056100155130ustar00rootroot00000000000000# Licensing terms Traitlets is adapted from enthought.traits, Copyright (c) Enthought, Inc., under the terms of the Modified BSD License. This project is licensed under the terms of the Modified BSD License (also known as New or Revised or 3-Clause BSD), as follows: - Copyright (c) 2001-, IPython Development Team All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the IPython Development Team nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ## About the IPython Development Team The IPython Development Team is the set of all contributors to the IPython project. This includes all of the IPython subprojects. The core team that coordinates development on GitHub can be found here: https://github.com/jupyter/. ## Our Copyright Policy IPython uses a shared copyright model. Each contributor maintains copyright over their contributions to IPython. But, it is important to note that these contributions are typically only changes to the repositories. Thus, the IPython source code, in its entirety is not the copyright of any single person or institution. Instead, it is the collective copyright of the entire IPython Development Team. If individual contributors want to maintain a record of what changes/contributions they have specific copyright on, they should indicate their copyright in the commit message of the change, when they commit the change to one of the IPython repositories. With this in mind, the following banner should be used in any source code file to indicate the copyright and license terms: # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. ipyparallel-8.8.0/README.md000066400000000000000000000015201460376056100153320ustar00rootroot00000000000000# Interactive Parallel Computing with IPython IPython Parallel (`ipyparallel`) is a Python package and collection of CLI scripts for controlling clusters of IPython processes, built on the Jupyter protocol. IPython Parallel provides the following commands: - ipcluster - start/stop/list clusters - ipcontroller - start a controller - ipengine - start an engine ## Install Install IPython Parallel: pip install ipyparallel This will install and enable the IPython Parallel extensions for Jupyter Notebook and (as of 7.0) Jupyter Lab 3.0. ## Run Start a cluster: ipcluster start Use it from Python: ```python import os import ipyparallel as ipp cluster = ipp.Cluster(n=4) with cluster as rc: ar = rc[:].apply_async(os.getpid) pid_map = ar.get_dict() ``` See [the docs](https://ipyparallel.readthedocs.io) for more info. ipyparallel-8.8.0/asv.conf.json000066400000000000000000000003451460376056100164670ustar00rootroot00000000000000{ "version": 1, "project": "ipyparallel", "project_url": "https://github.com/ipython/ipyparallel/", "repo": "https://github.com/ipython/ipyparallel.git", "environment_type": "conda", "matrix": { "numpy": [] } } ipyparallel-8.8.0/benchmarks/000077500000000000000000000000001460376056100161725ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/.gitignore000066400000000000000000000001101460376056100201520ustar00rootroot00000000000000.DS_Store .idea ipyparallel/ env/ html/ __pycache__ .ipynb_checkpoints ipyparallel-8.8.0/benchmarks/asv.conf.json000066400000000000000000000003451460376056100206040ustar00rootroot00000000000000{ "version": 1, "project": "ipyparallel", "project_url": "https://github.com/ipython/ipyparallel/", "repo": "https://github.com/ipython/ipyparallel.git", "environment_type": "conda", "matrix": { "numpy": [] } } ipyparallel-8.8.0/benchmarks/asv_normal.sh000077500000000000000000000001471460376056100206740ustar00rootroot00000000000000#!/usr/bin/env bash ipcluster start -n 200 --daemon --profile=asv asv run ipcluster stop --profile=asv ipyparallel-8.8.0/benchmarks/asv_quick.sh000077500000000000000000000003151460376056100205150ustar00rootroot00000000000000#!/usr/bin/env bash echo "starting engines" #ipcluster start -n 200 --daemon --profile=asv echo "starting benchmarks" asv run --quick --show-stderr echo "Benchmarks finished" #ipcluster stop --profile=asv ipyparallel-8.8.0/benchmarks/asv_runner.py000066400000000000000000000055201460376056100207300ustar00rootroot00000000000000import atexit import os import resource import socket import sys from subprocess import check_call import googleapiclient.discovery as gcd from cluster_start import start_cluster from google.cloud import storage os.environ['OPENBLAS_NUM_THREADS'] = '1' DEFAULT_MINICONDA_PATH = os.path.abspath(os.path.join('..', "miniconda3/bin/:")) env = os.environ.copy() env["PATH"] = DEFAULT_MINICONDA_PATH + env["PATH"] ZONE = "europe-west1-b" PROJECT_NAME = "jupyter-simula" BUCKET_NAME = 'ipyparallel_dev' instance_name = socket.gethostname() compute = gcd.build("compute", "v1") def cmd_run(*args, log_filename=None, error_filename=None): if len(args) == 1: args = args[0].split(" ") print(f'$ {" ".join(args)}') if not log_filename and not error_filename: check_call(args, env=env) else: check_call( args, env=env, stdout=open(log_filename, 'w'), stderr=open(error_filename, 'w'), ) def delete_self(): print(f'deleting: {instance_name}') compute.instances().delete( project=PROJECT_NAME, zone=ZONE, instance=instance_name ).execute() def upload_file(filename): blob_name = sys.argv[1] storage_client = storage.Client() bucket = storage_client.bucket(BUCKET_NAME) blob = bucket.blob(f'{instance_name}/{blob_name}') print(f'Uploading {filename}') blob.upload_from_filename(filename) if __name__ == '__main__': # atexit.register(delete_self) benchmark_name = sys.argv[1] template_name = sys.argv[2] soft_limit, hard_limit = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (hard_limit, hard_limit)) ps = [] for i in [3, 4]: ps += start_cluster(i, 1030, '../miniconda3/bin/', log_output_to_file=True) # time.sleep(10) # ps += start_cluster( # 0, 'depth_0', 300, '../miniconda3/bin/', # log_output_to_file=True) # time.sleep(10) # ps += start_cluster( # 0, 'depth_1', 150, '../miniconda3/bin/', # log_output_to_file=True) log_filename = f'{instance_name}.log' error_log_filename = f'{instance_name}.error.log' def clean_up(): for p in ps: p.kill() atexit.register(clean_up) # cmd_run("ipcluster start -n 200 --daemon --profile=asv") # Starting 200 engines cmd_run( 'asv run -b DepthTestingSuite --verbose --show-stderr', # log_filename=log_filename, # error_filename=error_log_filename, ) clean_up() # cmd_run("ipcluster stop --profile=asv") print('uploading files') # upload_file(log_filename) # upload_file(error_log_filename) results_dir = f'results/{template_name}' for file_name in os.listdir(results_dir): if 'conda' in file_name: upload_file(f'{results_dir}/{file_name}') print('script finished') ipyparallel-8.8.0/benchmarks/async_benchmark.ipynb000066400000000000000000005440631460376056100224000ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results, BenchmarkType, SchedulerType, get_broadcast_source, get_async_source\n", "from benchmarks.utils import echo\n", "from benchmarks.throughput import make_benchmark, make_multiple_message_benchmark" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# time_async_messages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This benchmark comes from benchmarking the runtime of sending a large amount of messages asynchronously and after sending them all, waiting for the reply" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_multiple_message_benchmark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_view\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mmake_multiple_message_benchmark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_view\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mAsyncMessagesSuite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m 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}, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results from benchmarking on 64 cores\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "source = get_async_source(benchmark_results)\n", "dview = pd.DataFrame(source['DirectView']) \n", "dview['Scheduler name'] = 'DirectView'\n", "dview['Speedup'] = 1\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Duration in msNumber of messagesNumber of enginesScheduler nameSpeedup
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" ], "text/plain": [ " Duration in ms Number of messages Number of engines Scheduler name \\\n", "0 4.75 1 1 DirectView \n", "1 17.70 5 1 DirectView \n", "2 29.59 10 1 DirectView \n", "3 58.47 20 1 DirectView \n", "4 133.31 50 1 DirectView \n", ".. ... ... ... ... \n", "49 593.23 1 1024 NonCoalescing \n", "50 2398.68 5 1024 NonCoalescing \n", "51 4529.79 10 1024 NonCoalescing \n", "52 8927.69 20 1024 NonCoalescing \n", "53 22609.19 50 1024 NonCoalescing \n", "\n", " Speedup \n", "0 1.000000 \n", "1 1.000000 \n", "2 1.000000 \n", "3 1.000000 \n", "4 1.000000 \n", ".. ... \n", "49 5.581141 \n", "50 2.804805 \n", "51 2.870884 \n", "52 NaN \n", "53 NaN \n", "\n", "[162 rows x 5 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas = []\n", "for scheduler_name, scheduler_results in source.items():\n", " data = pd.DataFrame(scheduler_results) \n", " data['Scheduler name'] = scheduler_name\n", " data['Speedup'] = 1 if scheduler_name == 'DirectView' else dview['Duration in ms'] / data['Duration in ms']\n", " datas.append(data)\n", "data = pd.concat(datas)\n", "data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Duration in msNumber of messagesNumber of enginesScheduler nameSpeedup
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" ], "text/plain": [ " Duration in ms Number of messages Number of engines Scheduler name \\\n", "3 58.47 20 1 DirectView \n", "3 59.19 20 1 Coalescing \n", "3 60.54 20 1 NonCoalescing \n", "\n", " Speedup \n", "3 1.000000 \n", "3 0.987836 \n", "3 0.965808 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered = data[(data['Number of messages'] == 20) & (data['Number of engines'] == 1)]\n", "filtered" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data['Messages per engine per second'] = round(data['Number of messages'] / data['Duration in ms'] * 1000, 2)\n", "data['Total messages per second'] = round(((data['Number of messages'] * data['Number of engines']) / data['Duration in ms']) * 1000, 2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Duration in ms Number of messages Number of engines Scheduler name \\\n", "0 4.75 1 1 DirectView \n", "1 17.70 5 1 DirectView \n", "2 29.59 10 1 DirectView \n", "3 58.47 20 1 DirectView \n", "4 133.31 50 1 DirectView \n", ".. ... ... ... ... \n", "49 593.23 1 1024 NonCoalescing \n", "50 2398.68 5 1024 NonCoalescing \n", "51 4529.79 10 1024 NonCoalescing \n", "52 8927.69 20 1024 NonCoalescing \n", "53 22609.19 50 1024 NonCoalescing \n", "\n", " Speedup Messages per engine per second Total messages per second \n", "0 1.000000 210.53 210.53 \n", "1 1.000000 282.49 282.49 \n", "2 1.000000 337.95 337.95 \n", "3 1.000000 342.06 342.06 \n", "4 1.000000 375.07 375.07 \n", ".. ... ... ... \n", "49 5.581141 1.69 1726.14 \n", "50 2.804805 2.08 2134.51 \n", "51 2.870884 2.21 2260.59 \n", "52 NaN 2.24 2293.99 \n", "53 NaN 2.21 2264.57 \n", "\n", "[162 rows x 7 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 2, 16, 64, 128, 256, 512, 1024])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dview = data[data['Number of messages'] == 100]\n", "dview['Number of engines'].unique()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alt.Chart(dview).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log', base=2)\n", " ),\n", " alt.Y(\n", " 'Messages per engine per second',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Scheduler name:N',\n", " tooltip='Messages per engine per second',\n", ").configure_axis(labelFontSize=20, titleFontSize=20).properties(title='Runtime of apply using DirectView', width=1080).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alt.Chart(dview).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log', base=2)\n", " ),\n", " alt.Y(\n", " 'Total messages per second',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Scheduler name:N',\n", " tooltip='Total messages per second',\n", ").configure_axis(labelFontSize=20, titleFontSize=20).properties(title='Runtime of apply using DirectView', width=1080).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for scheduler_name in data['Scheduler name'].unique():\n", " scheduler_data = data[data['Scheduler name'] == scheduler_name]\n", " alt.Chart(scheduler_data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of messages',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Messages per engine per second',\n", " ),\n", " color='Number of engines:N',\n", " tooltip='Duration in ms', \n", " ).configure_axis(labelFontSize=20, titleFontSize=20).properties(title=scheduler_name, width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['combined']= data['Scheduler name'] + ' ' + data['Number of engines'].astype(str)\n", "alt.Chart(data[data['Scheduler name'] != 'DirectView']).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of messages',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Speedup',\n", " ),\n", " color='Number of engines:N',\n", " strokeDash=alt.StrokeDash(shorthand='Scheduler name', legend=None),\n", " tooltip='combined',\n", "\n", ").properties(title='schedulers vs directView scaling engines', width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for engine in data['Number of engines'].unique(): \n", " alt.Chart(data[data['Number of engines'] == engine]).mark_bar().encode(\n", " x='Scheduler name',\n", " y='Duration in ms',\n", " color='Scheduler name:N',\n", " column='Number of messages:N', \n", " tooltip='Duration in ms'\n", " ).properties(title=f'Runtime on {engine} engines:').interactive().display(renderer='svg')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/benchmark_result.py000066400000000000000000000315471460376056100221060ustar00rootroot00000000000000import json import os import pickle import re from datetime import datetime from enum import Enum from itertools import product from utils import seconds_to_ms from benchmarks.constants import DEFAULT_NUMBER_OF_ENGINES RESULTS_DIR = "results" class BenchmarkType(Enum): # ECHO_MANY_ARGUMENTS = 'EchoManyArguments' # TIME_N_TASKS = 'Engines' # TIME_N_TASKS_NO_DELAY_NON_BLOCKING = 'non_blocking' # TIME_N_TASKS_NO_DELAY = 'NoDelay' __order__ = 'PUSH TIME_ASYNC DEPTH_TESTING BROADCAST' PUSH = 'Push' TIME_ASYNC = 'Async' DEPTH_TESTING = 'DepthTesting' BROADCAST = 'Broadcast' class SchedulerType(Enum): __order__ = 'DIRECT_VIEW LOAD_BALANCED BROADCAST_NON_COALESCING BROADCAST_COALESCING BROADCAST' DIRECT_VIEW = 'DirectView' LOAD_BALANCED = 'LoadBalanced' BROADCAST_NON_COALESCING = 'NonCoalescing' BROADCAST_COALESCING = 'Coalescing' BROADCAST = 'Depth' def get_benchmark_type(benchmark_name): for benchmark_type in BenchmarkType: if benchmark_type.value.lower() in benchmark_name.lower(): return benchmark_type else: raise NotImplementedError( f"Couldn't find matching benchmarkType for {benchmark_name} " ) def get_scheduler_type(benchmark_name): for scheduler_type in SchedulerType: if scheduler_type.value in benchmark_name: return scheduler_type class BenchmarkResult: def __init__(self, benchmark_results_file_name): with open(benchmark_results_file_name) as results_file: results_data = json.load(results_file) self.results_file_name = benchmark_results_file_name self.date = datetime.fromtimestamp( results_data["date"] // 1000 ) # Date is in ms self.machine_config = results_data["params"] self.results_dict = { benchmark_name: { 'benchmark_type': get_benchmark_type(benchmark_name), 'scheduler_type': get_scheduler_type(benchmark_name), 'results': [ Result( duration_in_seconds, stats_for_measurement, param, benchmark_name, get_benchmark_type(benchmark_name), ) for duration_in_seconds, stats_for_measurement, param in zip( result_data["result"], result_data["stats"], ( product( result_data["params"][0], result_data["params"][1], result_data['params'][2], ) if len(result_data["params"]) > 2 else product( result_data['params'][0], result_data['params'][1] ) ), ) ], } for benchmark_name, result_data in results_data["results"].items() } def get_number_of_engines(benchmark_name): match = re.search('[0-9]+', benchmark_name) return int(match[0]) if match else DEFAULT_NUMBER_OF_ENGINES class Result: def __init__( self, duration_in_seconds, stats_for_measurement, param, benchmark_name, benchmark_type, ): if not duration_in_seconds: self.failed = True return self.stats_for_measurement = stats_for_measurement self.failed = False self.duration_in_ms = seconds_to_ms(duration_in_seconds) if ( benchmark_type is BenchmarkType.BROADCAST or benchmark_type is BenchmarkType.PUSH ): self.number_of_engines = int(param[0]) self.number_of_bytes = int(param[1]) elif benchmark_type is BenchmarkType.TIME_ASYNC: self.number_of_engines = int(param[0]) self.number_of_messages = int(param[1]) elif benchmark_type is BenchmarkType.DEPTH_TESTING: self.number_of_engines = int(param[0]) self.is_coalescing = True if param[1] == 'True' else False self.depth = int(param[2]) else: raise NotImplementedError('BenchmarkType not found in Result constructor') def get_benchmark_results(): results_for_machines = { file_name: BenchmarkResult( os.path.join(os.getcwd(), RESULTS_DIR, dir_content, file_name) ) for dir_content in os.listdir(RESULTS_DIR) if ( os.path.isdir(os.path.join(os.getcwd(), RESULTS_DIR, dir_content)) and "asv-testing-64-2020-04-28" in dir_content ) for file_name in os.listdir(f"{RESULTS_DIR}/{dir_content}") if "machine" not in file_name } return results_for_machines def get_value_dict(engines_or_cores='engines', bytes_or_tasks='tasks'): return { 'Duration in ms': [], f'Number of {bytes_or_tasks}': [], f'Number of {engines_or_cores}': [], } def ensure_time_n_tasks_source_structure( datasource, delay, number_of_cores, scheduler_type ): if delay not in datasource: datasource[delay] = {number_of_cores: {scheduler_type: get_value_dict()}} elif number_of_cores not in datasource[delay]: datasource[delay][number_of_cores] = {scheduler_type: get_value_dict()} elif scheduler_type not in datasource[delay][number_of_cores]: datasource[delay][number_of_cores][scheduler_type] = get_value_dict() def add_time_n_tasks_source(source, benchmark, number_of_cores): number_of_engines = benchmark['engines'] scheduler_type = benchmark['scheduler_type'] for duration, tasks_num, delay in [ (result.duration_in_ms, result.number_of_tasks, seconds_to_ms(result.delay)) for result in benchmark['results'] if not result.failed ]: ensure_time_n_tasks_source_structure( source, delay, number_of_cores, scheduler_type ) dict_to_append_to = source[delay][number_of_cores][scheduler_type] dict_to_append_to['Duration in ms'].append(duration) dict_to_append_to['Number of tasks'].append(tasks_num) dict_to_append_to['Number of engines'].append(number_of_engines) def add_no_delay_tasks_source(source, benchmark, number_of_cores): scheduler_type = benchmark['scheduler_type'] if benchmark['benchmark_type'] not in source: source[benchmark['benchmark_type']] = { scheduler_type: get_value_dict(engines_or_cores='cores') } if scheduler_type not in source[benchmark['benchmark_type']]: source[benchmark['benchmark_type']][scheduler_type] = get_value_dict( engines_or_cores='cores' ) dict_to_append_to = source[benchmark['benchmark_type']][scheduler_type] for duration, tasks_num in [ (result.duration_in_ms, result.number_of_tasks) for result in benchmark['results'] if not result.failed ]: dict_to_append_to['Duration in ms'].append(duration) dict_to_append_to['Number of tasks'].append(tasks_num) dict_to_append_to['Number of cores'].append(number_of_cores) def add_to_broadcast_source(source, benchmark): scheduler_type = benchmark['scheduler_type'].value if scheduler_type not in source: source[scheduler_type] = get_value_dict(bytes_or_tasks='bytes') for duration, bytes, engines in [ (result.duration_in_ms, result.number_of_bytes, result.number_of_engines) for result in benchmark['results'] if not result.failed ]: source[scheduler_type]['Duration in ms'].append(duration) source[scheduler_type]['Number of bytes'].append(bytes) source[scheduler_type]['Number of engines'].append(engines) def add_to_async_source(source, benchmark): scheduler_type = benchmark['scheduler_type'].value if scheduler_type not in source: source[scheduler_type] = get_value_dict(bytes_or_tasks='messages') for duration, number_of_messages, engines in [ (result.duration_in_ms, result.number_of_messages, result.number_of_engines) for result in benchmark['results'] if not result.failed ]: source[scheduler_type]['Duration in ms'].append(duration) source[scheduler_type]['Number of messages'].append(number_of_messages) source[scheduler_type]['Number of engines'].append(engines) def add_to_depth_testing_source(source, benchmark): scheduler_type = benchmark['scheduler_type'].value if scheduler_type not in source: source[scheduler_type] = { 'Duration in ms': [], 'Number of engines': [], 'Is coalescing': [], 'Depth': [], } for duration, number_of_engines, is_coalescing, depth in [ ( result.duration_in_ms, result.number_of_engines, result.is_coalescing, result.depth, ) for result in benchmark['results'] if not result.failed ]: source[scheduler_type]['Duration in ms'].append(duration) source[scheduler_type]['Number of engines'].append(number_of_engines) source[scheduler_type]['Is coalescing'].append(is_coalescing) source[scheduler_type]['Depth'].append(depth) def add_to_echo_many_arguments_source(source, benchmark, number_of_cores): view_type = 'direct_view' if benchmark['is_direct_view'] else 'load_balanced' if 'number_of_engines' not in source and benchmark['results']: source['number_of_engines'] = benchmark['results'][0].number_of_engines if view_type not in source: source[view_type] = { 'Duration in ms': [], 'Number of arguments': [], 'Number of cores': [], } for duration, arguments in ( (result.duration_in_ms, result.number_of_arguments) for result in benchmark['results'] if not result.failed ): source[view_type]['Duration in ms'].append(duration) source[view_type]['Number of arguments'].append(arguments) source[view_type]['Number of cores'].append(number_of_cores) def get_number_of_cores(machine_name): return int(re.findall(r'\d+', machine_name)[0]) def make_source(benchmark_type, add_to_source_f, benchmark_results=None): if not benchmark_results: benchmark_results = get_benchmark_results() source = {} for benchmark_name, benchmark in benchmark_results['results_dict'].items(): if ( isinstance(benchmark_type, list) and benchmark['benchmark_type'] in benchmark_type ) or benchmark['benchmark_type'] == benchmark_type: add_to_source_f(source, benchmark) return source def get_time_n_tasks_source(benchmark_results=None): return make_source( BenchmarkType.TIME_N_TASKS, add_time_n_tasks_source, benchmark_results ) def get_no_delay_source(benchmark_results=None): return make_source( [ BenchmarkType.TIME_N_TASKS_NO_DELAY, BenchmarkType.TIME_N_TASKS_NO_DELAY_NON_BLOCKING, ], add_no_delay_tasks_source, benchmark_results, ) def get_broadcast_source(benchmark_results=None): return make_source( BenchmarkType.BROADCAST, add_to_broadcast_source, benchmark_results ) def get_push_source(benchmark_results=None): return make_source(BenchmarkType.PUSH, add_to_broadcast_source, benchmark_results) def get_async_source(benchmark_results): return make_source(BenchmarkType.TIME_ASYNC, add_to_async_source, benchmark_results) def get_echo_many_arguments_source(benchmark_results=None): return make_source( BenchmarkType.ECHO_MANY_ARGUMENTS, add_to_echo_many_arguments_source, benchmark_results, ) def get_depth_testing_source(benchmark_results): return make_source( BenchmarkType.DEPTH_TESTING, add_to_depth_testing_source, benchmark_results ) if __name__ == "__main__": benchmark_results = get_benchmark_results() combined_results = { 'date': benchmark_results['CoalescingBroadcast.json'].date, 'machine_config': benchmark_results['CoalescingBroadcast.json'].machine_config, 'results_dict': {}, } for result in benchmark_results.values(): for benchmark_name, benchmark_result in result.results_dict.items(): combined_results['results_dict'][benchmark_name] = benchmark_result # source = get_broadcast_source(combined_results) # source = get_async_source(combined_results) # source = get_push_source(combined_results) # source = get_time_n_tasks_source(benchmark_results) # source = get_no_delay_source(benchmark_results) # source = get_echo_many_arguments_source() source = get_depth_testing_source(combined_results) with open("saved_results.pkl", "wb") as saved_results: pickle.dump(combined_results, saved_results) ipyparallel-8.8.0/benchmarks/benchmark_results.ipynb000066400000000000000000024335741460376056100227720ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "cannot import name 'get_broad_cast_source' from 'benchmark_result' (/Users/tomo/ipyparallel_master_project/benchmark_result.py)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseconds_to_ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms_to_seconds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mbenchmark_result\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_benchmark_results\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mget_time_n_tasks_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_no_delay_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBenchmarkType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSchedulerType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_broad_cast_source\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_echo_many_arguments_source\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbenchmarks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mecho\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbenchmarks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverhead_latency\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mecho_many_arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cannot import name 'get_broad_cast_source' from 'benchmark_result' (/Users/tomo/ipyparallel_master_project/benchmark_result.py)" ] } ], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results,get_time_n_tasks_source, get_no_delay_source, BenchmarkType, SchedulerType, get_broad_cast_source, get_echo_many_arguments_source\n", "from benchmarks.utils import echo\n", "from benchmarks.overhead_latency import echo_many_arguments\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipyparallel benchmark results ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### time_n_tasks ###\n", "The first benchmark comes from benchmarking the runtime of sending\n", "n tasks to m engines. Where the each task is just the echo function. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "??echo" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "### With a delay of 0.0s. :" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### 16 cores:" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "KeyError", "evalue": "'direct_view'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'#### {core_num} cores:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m alt.Chart(\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'direct_view'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmark_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m alt.X(\n", "\u001b[0;31mKeyError\u001b[0m: 'direct_view'" ] } ], "source": [ "source = get_time_n_tasks_source(benchmark_results)\n", "for delay, result_for_delay in source.items():\n", " display(Markdown(f'### With a delay of {ms_to_seconds(delay)}s. :'))\n", " \n", " for core_num, results in sorted(result_for_delay.items(), key=lambda key: key[0]): \n", " display(Markdown(f'#### {core_num} cores:'))\n", " alt.Chart(\n", " pd.DataFrame(results['direct_view'])\n", " ).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of engines:N',\n", " tooltip='Duration in ms' \n", " ).properties(\n", " title=f'DirectView',\n", " width=800\n", " ).interactive().display(renderer='svg')\n", " \n", " alt.Chart(\n", " pd.DataFrame(results['load_balanced'])\n", " ).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of engines:N',\n", " tooltip='Duration in ms' \n", " ).properties(\n", " title=f'Load Balanced',\n", " width=800\n", " ).interactive().display(renderer='svg')\n", "\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "### With no delay and 100 engines:" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-8ef75365ba7fbf4f4d54fa282803fabe" }, "datasets": { "data-8ef75365ba7fbf4f4d54fa282803fabe": [ { "Duration in ms": 8.65, "Number of cores": 16, "Number of tasks": 1 }, { "Duration in ms": 21.92, "Number of cores": 16, "Number of tasks": 10 }, { "Duration in ms": 162.5, "Number of cores": 16, "Number of tasks": 100 }, { "Duration in ms": 167.11, "Number of cores": 16, "Number of tasks": 1000 }, { "Duration in ms": 175.12, "Number of cores": 16, "Number of tasks": 10000 }, { "Duration in ms": 5.46, "Number of cores": 64, "Number of tasks": 1 }, { "Duration in ms": 17.3, "Number of cores": 64, "Number of tasks": 10 }, { "Duration in ms": 117.17, "Number of cores": 64, "Number of tasks": 100 }, { "Duration in ms": 124.92, "Number of cores": 64, "Number of tasks": 1000 }, { "Duration in ms": 123.48, "Number of cores": 64, "Number of tasks": 10000 } ] }, "encoding": { "color": { "field": "Number of cores", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of tasks", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector018": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "Ran with no delay on 100 engines Direct View", "width": 800 }, "image/png": 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "no_delay_source = get_no_delay_source(benchmark_results)\n", "display(Markdown(f'### With no delay and 100 engines:'))\n", "data = pd.DataFrame(no_delay_source[BenchmarkType.TIME_N_TASKS_NO_DELAY]['direct_view'])\n", "alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Number of cores:N',\n", " y='Duration in ms',\n", " tooltip='Duration in ms', \n", ").properties(title=f'Ran with no delay on 100 engines Direct View', width=800).interactive().display(renderer='svg')\n", "\n", "data = pd.DataFrame(no_delay_source[BenchmarkType.TIME_N_TASKS_NO_DELAY]['load_balanced'])\n", "alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Number of cores:N',\n", " y='Duration in ms',\n", " tooltip='Duration in ms', \n", ").properties(title=f'Ran with no delay on 100 engines Load Balanced', width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/markdown": [ "### With no delay and non-blocking map on 100 engines:" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-601f18975bb233a6a79bba11c16ea9ca" }, "datasets": { "data-601f18975bb233a6a79bba11c16ea9ca": [ { "Duration in ms": 1.14, "Number of cores": 16, "Number of tasks": 1 }, { "Duration in ms": 7.56, "Number of cores": 16, "Number of tasks": 10 }, { "Duration in ms": 74.67, "Number of cores": 16, "Number of tasks": 100 }, { "Duration in ms": 81.59, "Number of cores": 16, "Number of tasks": 1000 }, { "Duration in ms": 76.41, "Number of cores": 16, "Number of tasks": 10000 }, { "Duration in ms": 0.97, "Number of cores": 64, "Number of tasks": 1 }, { "Duration in ms": 7.07, "Number of cores": 64, "Number of tasks": 10 }, { "Duration in ms": 70.4, "Number of cores": 64, "Number of tasks": 100 }, { "Duration in ms": 72.49, "Number of cores": 64, "Number of tasks": 1000 }, { "Duration in ms": 71.21, "Number of cores": 64, "Number of tasks": 10000 } ] }, "encoding": { "color": { "field": "Number of cores", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of tasks", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector020": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "Ran with no delay on 100 engines Direct View", "width": 800 }, "image/png": 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", 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gqicHrXr1nT/jE7Q5pPOo79ttd565nZrDRh0Tc+NQxYeF8cm8PgMxv/zgam/rQ7W4Ssf7QOthjbEwt/p+1/pJxnWMXN1jP7U+BI2/4wDGbdX14M6f1avjvK+0U7z05Dln7ThQ8csZDojcls57brlNlw9w4mpr/aXNRD3KLxQ4JPILCx6sHQd/3uPKHmdd6/7n73lPK1dNx2/7rgfidueHtwdwFZu7B9gr9UFfnuPRajs9V6x5MHZ+mcEvCXgvM7+A4rVXvxKNsdUPs1PfQ1xvQ+Z1wJ0MvD+aX26wZ7iNmgMwB1KeB57P1vcQ10+ebxcbr2kOxBNdX/yc4Rc1ree69dVP9Xu865rw4X0csjnI85rmeebBa/GP1XnKA3G7Lss/yxXIFcgVaLgCeSBu+ARk+1yBXIGuVoDDBodI/q+Jg5+xXIHkg4m6FQMHKQ4u/KN7/AOTxufMQZ3DEh98xXugxx8cHKnFFWSueI4fCGs88yKOq868n3r8wSdPc2jmAFS/Fmii+teeHNS4Sj3RfboW54/nggM6txtzm3HrwQGIOf2mGi4n8+NDx6jDYWf8PbkT8TjAcnhdX23B5ypj/WCs1i92XPLkeWCPswe4yl0/AKvW4O943y77ot7K7KLfimXOXH1mfTj0tR6sK1dzJ+o/DsmMtc7XN4aad7/qSejMeaIerbGdYusUCx9Wx/jpxS8AWg9uC+cT0XlOef21HtxKz9qzR1p3a3Tyy7/PFcgVyBXIFYhcgTwQRy54tssVyBXIFZjiFahXvZlmff/lFE9ZSo8r7xzEubLJnQP1ym6712RJghmUK5ArkCuQK5ArkCsQXoE8EIfXMCvkCuQK5ArkCvxfBfigoSdW966Of3L2jl4n3kvKLcO815v3t15TvY6o3Wr9jl6rnH+uQK5ArkCuQK5AlArkgThKmbNJrkCuQK5ArkCuQK5ArkCuQK5ArkCuQK5AahXIA3FqZyTHkyuQK5ArkCuQK5ArkCuQK5ArkCuQK5ArEKUCeSCOUuZskiuQK5ArkCuQK5ArkCuQK5ArkCuQK5ArkFoF8kCc2hnJ8eQK5ArkCuQK5ArkCuQK5ArkCuQK5ArkCkSpQB6Io5Q5m+QK5ArkCuQK5ArkCuQK5ArkCuQK5ArkCqRWgW4PxNTfA8CfJ0mcr6C4Y9zve6p3GrZ75x/fj5nf6ZdaJ+V4cgVyBXIFcgVyBXIFcgVyBXIFcgVyBbazCnRzIH4xgJUAfgLgAQAWALixpT4HAPgkgF8DeASAYwFcD+C1AE4C8DsA0wEcDaAEcAGALRX2LACf2s5qncPNFcgVyBXIFcgVyBXIFcgVyBXIFcgVyBVIqALdHIg3AuDQezOAxwPgqi+H4/q4AsB7APCfhwM4AcAhAO6tVpX/AuAcAOurgZgryYsB9FY/mwlgc0K1zKHkCuQK5ArkCuQK5ArkCuQK5ArkCuQK5ApsRxXo1kA8AwC3O18M4IUAPgvgHQA2tdTmtwAOBsB/7g/gywCeAeCrAGZVuBMBPKUaiPnzYQCMmVumiblpO6p1DjVXIFcgVyBXIFcgVyBXIFcgVyBXIFcgVyChCnRrIH40gJ9X26S/AODDAP6jGozr9G8H8NhqtfdRAL4JYDaAi6qfE3cMgOdWW64vBPC5iszV54MA/GrlypVLi6J4Z2tNH/KQh+Bf/uVfEipzDiVXIFcgVyBXIFcgVyBXIFcgVyBXYApXoJw1axZ3xObj7yvA22B3rhZMk6tPtwbiBwO4FcDuADj4vgrA8wG8pqUC3wJwMoAbADwNwOkABqpt0Gwm3jfM3/PgdmnqfADAtOohXXxYV9uHaw0NDZULFizYYRrypptuGnvUox6VRL4xYrH2CNXz4btyVLyCUzDJfVIFBJRSvjFisfYI1fPhu3JUvIJTMAHtmBw1pXxjxGLtEarnw3flqHgFp2CSa/KAgFLKN0Ys1h6hej58V46KV3DEzJo1y2K24g7aqwD8DMATAdwD4EgAXBx8SXW7aUBnb6Nyh+6PqttVQ7U68V8P4N8AfAPA8zqBm/i9xUlrFzd1fwPg+Gor9EcAXANgTXU/8XUA3gvgDwDOrO4l5nbq0wD8AMAbq5N0OQCu/u4C4M0A+KAuNsQpAJ45UcE4EA8ODnYrtybO06Se//u//8tvpJLIN0Ys1h6hej58V46KV3AKJrkmDwgopXxjxGLtEarnw3flqHgFp2AC2jE5akr5xojF2iNUz4fvylHxCk7BJNfkAQGllG+MWKw9QvV8+K4cFa/gFIzYjvVATDhvHz23CwMxd9t+H8BLxZhCYFwE5S2wh1W3xoZodYXbzSGK9w6fXW13vhrAGwBwa/SV1c94DzB/zuO2asD9E4C5LVur1wE4qhqILwOwX/U6phdVA3bbouSBuCu9IokafhhM6GftEarnw3flqHgFp2Ckk72dgFLKN0Ys1h6hej58V46KV3AKZjtpfSnMlPKNEYu1R6ieD9+Vo+IVnIKRGm87AaWUb4xYrD1C9Xz4rhwVr+AUjNj6rQMxX037yGqnbb1CzN2y7wLwFgD/WS0e8rZTLha+HcArqltR+aae7wLg7atcdOTBhxh/BwAHYi5M8hZXPgSZz2qiHv2eA+BNAHj/KVd1+e88OItxsfLZAD4O4PyWfB5W3SJLDl+RyzcJcffvwipWQnkLba1VUzkfctGTi5/UX1TF8CwA7wdwYLVSfkY1H3K+XFE9p+qV1dz40DbxMrd51SIrb9FlHtx5fEu7c9DNgZh+1N8TAAddHnzYFp8cfVz139xPvm+1bM8t0vVBHLdb8wnTrcfDAWyonkQ9YU/lgVi83LoAM/wwmDA6a49QPR++K0fFKzgF04XWaEwypXxjxGLtEarnw3flqHgFp2Aaa+YuGKeUb4xYrD1C9Xz4rhwVr+AUTBfatDHJlPKNEYu1R6ieD9+Vo+IVnIIRm7keiLmLlg8l5uzEIbYeiPcC8JmW7dP/Xt2WuhOADwLgFmUuKvK1t/+vGjCJ5w5c7tJ9ejUQU4dbsx8C4DHV74njUMyf84HHHEQ5BP9r9UpcpsCHGnOW+3pLPhyOuYi5CsDjALwcQH81dPOhyNz2XcdV07g4+ovqjUSXVv6nVoNwPbhydfwIAE8CwKGbtWEdePDLAD5vikP9+HhfV+1W5rOpOGizRudVC7R/dxq6PRCPN+Qrkzjotr6PWOwNHZYHYr1W1kjDD4M8ELdUQK2rglMw1n3RpF5K+caIxdojVM+H78pR8QpOwTTZz9beKeUbIxZrj1A9H74rR8UrOAVj3aNN6qWUb4xYrD1C9Xz4rhwVr+AUjNjP9UDMIZSrw1zd5WDKf/IeYmUgfiAAPuT42uohxxyu+dwmDpV7VwMxFxn5pp/7Afh9NWB+rBo4OXRyXnttxeEqK/+bgzZXoLe05LJbNUSTw+dB7Vo9E+qSCsvX7vLtQxzEWw+u2L6vGsY51L6swnE+5YouB2jedsvVYn4hwBVsxkyfwWr4ru+tnihe+jEOvuaXQztf69v4QCz2QRgsD8Rh9QthG34Y5IE4D8QhrfhXboyeVAONEYu1R6ieD9+Vo+IVnIJRz/f2gEsp3xixWHuE6vnwXTkqXsEpmO2h79UYU8o3RizWHqF6PnxXjopXcApG7L3WgZiDHldkOQTz4ED8IAAXVA/E+lL1Fp7DAbSuEHNI5Qosn81U34fM4XgfANxxyy3TN1fbpflgXt6+yoGZgyO3JFO/9fW2fFsQH/K1strW3JoKV5g5UH+iWjnmDmDuDqYeH6I10UDMYZhDMXcLc1fw46tdwPwnV4y5+stX93Jo/171hQCHctaEwzNXsLklu128XKnmVvBjW75EYK0OyQOx2IXbG8zwAgxOPUYs1h6hej58V46KV3AKJrgREhJIKd8YsVh7hOr58F05Kl7BKZiE2js4lJTyjRGLtUeong/flaPiFZyCCW7KhARSyjdGLNYeoXo+fFeOildwCkZs79aBmFt961XQeiDmaitXTC8G8O1qlZW/cx2IOWS/rdpazYcY86HFHF65RZoDMVdneU8yV4N5n+/vACxruR+5NR1uS+a9xdyWzWc+cVDl/cofnWQgfkF17zK3NfM+59XVG4V4HzEHdq4av7tapeYW7CdU/2t92jYfuNwuXm4z55Zu5kU8V7Y5uHMb+d8dsbdMi30QBssrxGH1C2EbfhhMGIa1R6ieD9+Vo+IVnIIJ6YHUuCnlGyMWa49QPR++K0fFKzgFk1qPh8STUr4xYrH2CNXz4btyVLyCUzAh/ZgaN6V8Y8Ri7RGq58N35ah4BadgxB4fPxBzXuNDifnAKq4QcwjmAMr/5v2+v6zuseVAzEGQD6pqt0LMB2xxy3S9Qsz7enkrK19v+8PqbT4cRBdUwzEHZupzi/T/dBiI+QpdDp31SvZXqkGeW5Q5ZPP1uePfEMSt2hyE+UAwHoyBK7+8f5iv6uWXAfVxVnU/dbvXT7WLl8Mw713mfcw8uBrO4Z5vPMoDsdiI2xXM8AIMzjtGLNYeoXo+fFeOildwCia4ERISSCnfGLFYe4Tq+fBdOSpewSmYhNo7OJSU8o0Ri7VHqJ4P35Wj4hWcggluyoQEUso3RizWHqF6PnxXjopXcArGuL15nzAH1nsDdLldmkMs781tPfgk63+oHnzcer/wZFbkPKK6D5iDtXrwGVM7Vw/Ban3IMp88TT0OyMyzk3e7eB8MYCaAX09GzivE6qlKGNfABThhNWLEYu0RqufDd+WoeAWnYBJud+fQUso3RizWHqF6PnxXjopXcArGuQkTJqSUb4xYrD1C9Xz4rhwVr+AUTMLt7hxaSvnGiMXaI1TPh+/KUfGT4WYvXD23GCuee/bxB54ya9asKTlbOV882xlhSp60vGW6uS5UP1hCIrT2CNXz4btyVLyCUzAh5yc1bkr5xojF2iNUz4fvylHxCk7BpNbjIfGklG+MWKw9QvV8+K4cFa/gFExIP6bGTSnfGLFYe4Tq+fBdOSp+IlzfwuHXotz2zl2c/boDkQfi1K5iLZ48EGt1ShqlXswxkogRi7VHqJ4P35Wj4hWcgonRK7E8Uso3RizWHqF6PnxXjopXcAomVu/G8Ekp3xixWHuE6vnwXTkqXsEpmBh9G8sjpXxjxGLtEarnw3flqPiJcH0L11yCspybB+JYV2V3fPJA3J26RlVVL+YYQcWIxdojVM+H78pR8QpOwcTolVgeKeUbIxZrj1A9H74rR8UrOAUTq3dj+KSUb4xYrD1C9Xz4rhwVr+AUTIy+jeWRUr4xYrH2CNXz4btyVPxEuL7BYT486oV5II51VXbHJw/E3alrVFX1Yo4RVIxYrD1C9Xz4rhwVr+AUTIxeieWRUr4xYrH2CNXz4btyVLyCUzCxejeGT0r5xojF2iNUz4fvylHxCk7BxOjbWB4p5RsjFmuPUD0fvitHxU+E6xsc/hwAvgM4b5mOdWF2wScPxF0oamxJ9WKOEVeMWKw9QvV8+K4cFa/gFEyMXonlkVK+MWKx9gjV8+G7clS8glMwsXo3hk9K+caIxdojVM+H78pR8QpOwcTo21geKeUbIxZrj1A9H74rR8W3w73s1OFZ06bhZwD4pOY/n/26A/fM9xDHujptffJAbFvPRtTUizlGcDFisfYI1fPhu3JUvIJTMDF6JZZHSvnGiMXaI1TPh+/KUfEKTsHE6t0YPinlGyMWa49QPR++K0fFKzgFE6NvY3mklG+MWKw9QvV8+K4cFd8O1ze45nqgPADAuSND805UtVz6d+7g6v3HgINKFAeRV6C8pge45tKh+d930cnYySuQB+Ip0CHduAB9yxIjFmuPUD0fvitHxSs4BeN7/lPkpZRvjFisPUL1fPiuHBWv4BRMin3uG1NK+caIxdojVM+H78pR8QpOwfj2Yoq8lPKNEYu1R6ieD9+Vo+LH4/oGhz8G4DgAV+xa9vRftKr/L6qW2ut9g8NvLoD3lSV6UIDv+AVKbC0KjJXAKSND89WwkecAACAASURBVM7toDUdwP0AbG7Bcfbbgyvaahw7Ai4PxFPgLFtfgCEliRGLtUeong/flaPiFZyCCemB1Lgp5RsjFmuPUD0fvitHxSs4BZNaj4fEk1K+MWKx9gjV8+G7clS8glMwIf2YGjelfGPEYu0RqufDd+Wo+FZc34LVb0JRnFsAvyrHtg6MnHn0texdVUvp877B4RcDuLwD9iUjQ/OuaIPh8PzPAI4FsBXAyRWGmisB/ATAAwAsAHCjEs9Ux+SBeAqcYcsLMLQcMWKx9gjV8+G7clS8glMwoX2QEj+lfGPEYu0RqufDd+WoeAWnYFLq79BYUso3RizWHqF6PnxXjopXcAomtCdT4qeUb4xYrD1C9Xz4rhwVX+NmL1h9cFEUV21brC3Hjlm36qjP1j2ranXq8UMWfPaJPcV0Dtm7dMDeNVZuefplq175o3G43QC8G8DTANzQMhBvBMAt3jcDeHx17zOH4x3+yAPxFGgBqwvQohQxYrH2CNXz4btyVLyCUzAWvZCKRkr5xojF2iNUz4fvylHxCk7BpNLbFnGklG+MWKw9QvV8+K4cFa/gFIxFX6aikVK+MWKx9gjV8+G7clQ8cYPn3jDjzp3LnwHlw4DyjJGh+ae39qqq1am/5wyuObksy7P+uk16IsK27dPFqWuHBt4/AeRNAB5dDcQzAIwCuLh6TRQH+XcA2NQpnh3h93kgngJn2eoCtChFjFisPUL1fPiuHBWv4BSMRS+kopFSvjFisfYI1fPhu3JUvIJTMKn0tkUcKeUbIxZrj1A9H74rR8UrOAVj0ZepaKSUb4xYrD1C9Xz4rhwVT9xbP3bdWgBzAKwZGZo3b3yfqlqd+rtv4fD5KHFUJ9y23xe4YGTlvKOFgZiD8c+rbdJfAPBhAP8B4K8r3JLfFAXlgXgKnFirC9CiFDFisfYI1fPhu3JUvIJTMBa9kIpGSvnGiMXaI1TPh+/KUfEKTsGk0tsWcaSUb4xYrD1C9Xz4rhwVr+AUjEVfpqKRUr4xYrH2CNXz4btyVPy5F36r/PL3bmFrfr8HPf2XDvX/olsD8ZzB1Z8qUbxauQ4KlJ9eOzT/NcJA/GAAtwLYHcDtAF4F4PkAJuIq9lMGkwfiKXAq1Ys5RqoxYrH2CNXz4btyVLyCUzAxeiWWR0r5xojF2iNUz4fvylHxCk7BxOrdGD4p5RsjFmuPUD0fvitHxSs4BROjb2N5pJRvjFisPUL1fPiuHAU/Z+GaV5Rlya3GoyiK/pGVA5e160FFS+ndvsE1rwfwQaDkU6InOYotAE4cGRo4TxiIOfP9BsDxAL4M4CMArgHwCSWmqY7JA/EUOMNWF6BFKWLEYu0RqufDd+WoeAWnYCx6IRWNlPKNEYu1R6ieD9+Vo+IVnIJJpbct4kgp3xixWHuE6vnwXTkqXsEpGIu+TEUjpXxjxGLtEarnw3fldMIfsvjzj+jZuuVGoNy5LHDyupXzPjBRf3bSUvu679Thp2LatmG1w0CMLdiKg0bOmve9SQbiWQBOqX7/QgBnV0+YvhrAGwD8SY1rKuPyQDwFzq7VBWhRihixWHuE6vnwXTkqXsEpGIteSEUjpXxjxGLtEarnw3flqHgFp2BS6W2LOFLKN0Ys1h6hej58V46KV3AKxqIvU9FIKd8YsVh7hOr58F05nfB9C1dfg7J4+rP32wuDr3r+pHNTJy2XvuY7iO9bJZ70OFF4F/F4AeawZx6E/7YseSB26c5EsZYXYGiKMWKx9gjV8+G7clS8glMwoX2QEj+lfGPEYu0RqufDd+WoeAWnYFLq79BYUso3RizWHqF6PnxXjopXcAomtCdT4qeUb4xYrD1C9Xz4rpzJ8LMXDJ9XFDgBBb628pj9X/DE/R4bbSDmdXDfu4iLS1GW0//6xOkSW1Fwq3Q5d4J3EKd0CW03seSBeLs5VRMH6nrxdzPlGLFYe4Tq+fBdOSpewSmYbvZIbO2U8o0Ri7VHqJ4P35Wj4hWcgondw930SynfGLFYe4Tq+fBdOSpewSmYbvZrbO2U8o0Ri7VHqJ4P35UzEX7OguETygLnocBvix70f+BfD/zurFmzog7E7Pdt7yTumf6ioiz4TmGURXn92NiWr7R593Dsy2NK+eWBeAqcTteLv5spx4jF2iNUz4fvylHxCk7BdLNHYmunlG+MWKw9QvV8+K4cFa/gFEzsHu6mX0r5xojF2iNUz4fvylHxCk7BdLNfY2unlG+MWKw9QvV8+K6cdvi+d1z4dPSM8R5elMXYa9atPOrTiq6Cid3D2U+rQB6ItToljUrpAowRi7VHqJ4P35Wj4hWcgkm64R2DSynfGLFYe4Tq+fBdOSpewSkYxxZMGp5SvjFisfYI1fPhu3JUvIJTMEk3vGNwKeUbIxZrj1A9H74rZzz+gOM/stO+D9z9ZyXwj0VZrli7av5ito2iq2AcWzDDI1UgD8SRCt1Nm5QuwBixWHuE6vnwXTkqXsEpmG72a2ztlPKNEYu1R6ieD9+Vo+IVnIKJ3cPd9Esp3xixWHuE6vnwXTkqXsEpmG72a2ztlPKNEYu1R6ieD9+VMx7fN7jmC0B5KMrycyOr5vUDRZkH4thXXny/PBDHr7m5o+vFbx5Ai2CMWKw9QvV8+K4cFa/gFEw3eyS2dkr5xojF2iNUz4fvylHxCk7BxO7hbvqllG+MWKw9QvV8+K4cFa/gFEw3+zW2dkr5xojF2iNUz4fvymnFz14wfHpR4F0Aftgz1tN/6Zn9N9Y9p+gqmNg9nP20CuSBWKtT0qiULsAYsVh7hOr58F05Kl7BKZikG94xuJTyjRGLtUeong/flaPiFZyCcWzBpOEp5RsjFmuPUD0fvitHxSs4BZN0wzsGl1K+MWKx9gjV8+G7cmp834IL5qDouRTAPSiLI0ZWDaxtbRdFV8E4tiBG373v/ugpD0JRHrSNWxbXYKy4Zubpt3zfVSvjJ65AHoinQHd04wL0LUuMWKw9QvV8+K4cFa/gFIzv+U+Rl1K+MWKx9gjV8+G7clS8glMwKfa5b0wp5RsjFmuPUD0fvitHxSs4BePbiynyUso3RizWHqF6PnxXDvFv/sh3Hz592vSfliV2K1G8fd3QwHvH96Oiq2Bc+nx0ee+bALwfQA+AaRV3K4AxACfPXLzhQx30ZlbcO8bhplfvIr7VJZ6pjM0D8RQ4u9YXYEhJYsRi7RGq58N35ah4BadgQnogNW5K+caIxdojVM+H78pR8QpOwaTW4yHxpJRvjFisPUL1fPiuHBWv4BRMSD+mxk0p3xixWHuE6vnwXTnEv/Vj110N4OAC+OjaoXkntOtDRVfBqD2+6Yx9X1z0jF0+Gb4c63nJbqfdckUbzC4APgFg92p45mryO1twHPifCODFajxTHZcH4ilwhi0vwNByxIjF2iNUz4fvylHxCk7BhPZBSvyU8o0Ri7VHqJ4P35Wj4hWcgkmpv0NjSSnfGLFYe4Tq+fBdOSpewSmY0J5MiZ9SvjFisfYI1fPhu3JW/ceV5bf/+/dsu29sLXr6v7Syv+2qqaKrYJT+vnv53v+8FcV1JcDBdrLjrukoD9x58cYfjwO9BsDTAbwRAGe9QwFcAoCry3MBvB4AV4nzQFwVLg/ESmcmjrG6AC3SjBGLtUeong/flaPiFZyCseiFVDRSyjdGLNYeoXo+fFeOildwCiaV3raII6V8Y8Ri7RGq58N35ah4BadgLPoyFY2U8o0Ri7VHqJ4P34UzZ+HqY8uy+DiAW3qKov/SlQNXTdR7iq6CUXp78/J9Ti5RntWyTXoi2tYCxakzFq/nturW490AngbgAAA3A1gC4EsAHgXgPADLqp/lgTgPxEpLbh8YqwvQItsYsVh7hOr58F05Kl7BKRiLXkhFI6V8Y8Ri7RGq58N35ah4BadgUultizhSyjdGLNYeoXo+fFeOildwCsaiL1PRSCnfGLFYe4Tq+fBVTt+i4adiDDew10qUx64bmv/JyfpO0VUwSm9vXr7P+SXKoxRsgeKCGYvXHz0O++8AngPgEAD7A+Bw/RgAVwI4FsAeAJbmFeL/q1peIVa6LXGM1QVokWaMWKw9QvV8+K4cFa/gFIxFL6SikVK+MWKx9gjV8+G7clS8glMwqfS2RRwp5RsjFmuPUD0fvitHxSs4BWPRl6lopJRvjFisPUL1fPgSpyyL2YNrfl4UmPXCJ++Dk+Y/t+M8pOgqGKW3Ny3r/VRR4NUKtizx6d2WbOAW6dbjfQDuBrCw+uFGAAsAcFC+rrq3mAPyRwG0vWda8Z5KmI4NsD0mOzQ0VA4ODk7J3NqdD6sL0OJcx4jF2iNUz4fvylHxCk7BWPRCKhop5RsjFmuPUD0fvitHxSs4BZNKb1vEkVK+MWKx9gjV8+G7clS8glMwFn2ZikZK+caIxdojVM+Hr3D6Fqy5CEV5BFBe/P7jDjz0nx796I4zg6KrYJTeHl22z+tRlB+s7vOdjLIFZXHizCXruQ269TgGwOsAvBDAwwB8F8CjAfRWoKcAOAXAkdWWaiWsKY3p2ADbY/Z5IG7urFl9GEyWgbVHqJ4P35Wj4hWcgmmug+ydU8o3RizWHqF6PnxXjopXcArGvkubU0wp3xixWHuE6vnwXTkqXsEpmOa62d45pXxjxGLtEarnw+/Emb1g9aKiKJYD+AkfonXucQf8ZNasWR3noU667D4Fo3Tp6PJ9ngqU10gDMYqDZi5e/71xujsDOKfaMr25esL0cAuGD9zifcT5HuKqKB0bQDlxqWHyQNzcGbH6MMgD8d9WQK2rglMwzXWQvXNK+caIxdojVM+H78pR8QpOwdh3aXOKKeUbIxZrj1A9H74rR8UrOAXTXDfbO6eUb4xYrD1C9Xz4k3HmLBh+WVngMt40XPQUh61dOfBF1UPBKRi1S+9YvveJPSg41E54jKF8y/0Xb+RK8kTHngBur54urVrvkLg8EE+B0255AYaWI0Ys1h6hej58V46KV3AKJrQPUuKnlG+MWKw9QvV8+K4cFa/gFExK/R0aS0r5xojF2iNUz4fvylHxCk7BhPZkSvyU8o0Ri7VHqJ4PfyLOIade2NvTM/ZTFNgDRbFgZOXAmew11UPBKRiX/r5r5b4vHivHLi3Lba9ImlZxtxYFtvQUPXN3Wdj2HcQuFhlbVSAPxFOgFawvwJCSxIjF2iNUz4fvylHxCk7BhPRAatyU8o0Ri7VHqJ4P35Wj4hWcgkmtx0PiSSnfGLFYe4Tq+fBdOSpewSmYkH5MjZtSvjFisfYI1fPhT8SZM7jmmyXK5xQlPrF21bzj6l5TPRScgnHt8U3v3vuJPdN6XjhW4kByewpcN7Z17Ku7nb7xR65aGT9xBfJAPAW6oxsXoG9ZYsRi7RGq58N35ah4BadgfM9/iryU8o0Ri7VHqJ4P35Wj4hWcgkmxz31jSinfGLFYe4Tq+fBdOSpewSkY315MkZdSvjFisfYI1fPht+P0Da4+GyjeAuDbY1t7+i87q3/D9jIQp3hdTMWY8kA8Bc6qzwdGt9KOEYu1R6ieD9+Vo+IVnILpVn80oZtSvjFisfYI1fPhu3JUvIJTME30cbc8U8o3RizWHqF6PnxXjopXcAqmW73ahG5K+caIxdojVM+HP55zyODq1/Sg4OuGbu0piyMuXTXwrdZeUj0UnIJpoo+zZ+cKpDAQ3x/AHeNC7QGwK4DRcT+fCeBOAGOTpZYfqtX5xHcLEePDwNojVM+H78pR8QpOwXSrP5rQTSnfGLFYe4Tq+fBdOSpewSmYJvq4W54p5RsjFmuPUD0fvitHxSs4BdOtXm1CN6V8Y8Ri7RGq58Nv5fQtGH4yCvzXtt4py+NHVs3/2Pg+Uj0UnIJpoo+zZ+cKdHMgXgLgpQBuqcLg471/2BLSAQA+CeDXAB4B4FgA1wN4LYCTAPyuetz40WxjABcA2FJhzwLwqYnSywNx5xPfLUSMDwNrj1A9H74rR8UrOAXTrf5oQjelfGPEYu0RqufDd+WoeAWnYJro4255ppRvjFisPUL1fPiuHBWv4BRMt3q1Cd2U8o0Ri7VHqJ4Pv5XTNzj8PwAeW5ble9atmn9qux5SPRScgmmij7Nn5wp0cyA+H8AQgJ9Wg+z4aK4A8B4A/OfhAE6o3pd1L4A9APyleofW+mog5kry4uql0vwZV4v5bq2/O/JA3PnEdwsR48PA2iNUz4fvylHxCk7BdKs/mtBNKd8YsVh7hOr58F05Kl7BKZgm+rhbninlGyMWa49QPR++K0fFKzgF061ebUI3pXxjxGLtEarnw685fQuHV6PEPKC8dOvorf1f+uBb7u7mQHz3UO8Tbj7yqh8r7zRuopez5+QV6OZA/INqeOXW548DWATgrpZwfgvgYAD85/4AvgzgGQC+CmBWhTsRwFOqgZg/50ulGTO3TBNzUx6I9UfGx7gYfD68XOOy9gjV8+G7clS8glMwruckZXxK+caIxdojVM+H78pR8QpOwaTc766xpZRvjFisPUL1fPiuHBWv4BSMaw+mjE8p3xixWHuE6vnwyXnrx69fgLJcBZT/M1ZO679sVf+ET2RWPSbC3bGid79pJT5fAo/bMO8q5IE45St64ti6ORDzRdEfAfBHAJcA4Dbni1pC4YuiHwuAq72PAvBNALMrDH/O4xgAzwXwAAAXAvhc9fONAA4C8KuVK1cuLYrineNTPPLII7fPM5KjzhXIFcgVyBXIFcgVyBXIFcgVyBVwrsCNv7sdH77sxm28f33ho/HkR+7prOFC2P3aIex609ptlDwQu1QuLWy3BmI+FGsnAPX2hJMBPAbAG1rS51Pe+PMbADwNwOkABqpt0OTzvmH+nge3S3OA/kD1Yuo/V9uq2z5cK2+Zbq7J1G/aQiK09gjV8+G7clS8glMwIecnNW5K+caIxdojVM+H78pR8QpOwaTW4yHxpJRvjFisPUL1fPiuHBWv4BRMSD+mxk0p3xixWHuE6rny5yy94MEzcb9bN93FRw4Vi0aGBlZ26inVYyLc5hW9XytLPD8PxJ0qnfbvuzUQ8+sYPiyL251vrlZ2eU/xOgCPB3AdgPcC+AOAM6t7iTcBOA0At1q/EQC3N1wOgKu/uwB4M4AXA+DS7ykAnjlRafNA3FzTqR8sIRFae4Tq+fBdOSpewSmYkPOTGjelfGPEYu0RqufDd+WoeAWnYFLr8ZB4Uso3RizWHqF6PnxXjopXcAompB9T46aUb4xYrD1C9Vz5cxYMX1kWeF4JfGrd0Dw+pLfjoXpMhBtd1rsOBQ7JA3HHUicN6NZAzKTfUT0oi//+NQBvq7ZIX1ltgeY9wFdX1bmtGnD/BGAugM9WP+cAfVQ1EF8GYL/qdUwvAnBNHojvq4B6McfoxBixWHuE6vnwXTkqXsEpmBi9EssjpXxjxGLtEarnw3flqHgFp2Bi9W4Mn5TyjRGLtUeong/flaPiFZyCidG3sTxSyjdGLNYeoXou/L4Fw+9FgVMeufdu+OUfNj9sZHk/31bT8VA92uE2L9v34WVRfhcoH5oH4o6lThrQzYGYic+otjjX7xnmf58D4LiqKtMB7Fs9WItbpOuDuN2r+4tbC/hwbtEHwCdRT3jkFeLmek79YAmJ0NojVM+H78pR8QpOwYScn9S4KeUbIxZrj1A9H74rR8UrOAWTWo+HxJNSvjFisfYI1fPhu3JUvIJTMCH9mBo3pXxjxGLtEaqn8mcvWPPKoig/gxK3vbnvsXu89P/tL883qsd4XPm+h+16551briyBZ4yV5cU7TZt23u/6v315fqhWalexFo/cMJpcR1RvNejed7d7l448EHepsIKs+sEiSE0IsfYI1fPhu3JUvIJTMCHnJzVuSvnGiMXaI1TPh+/KUfEKTsGk1uMh8aSUb4xYrD1C9Xz4rhwVr+AUTEg/psZNKd8YsVh7hOop/DkLLngCip7vl8BORYHXf+C4A89zGUoVD/bleNzoit61KNGHovjqvcVO/Xss/M2fVa3U+jzHc98rjKbckQfi5k5pjA8Da49QPR++K0fFKzgF01wH2TunlG+MWKw9QvV8+K4cFa/gFIx9lzanmFK+MWKx9gjV8+G7clS8glMwzXWzvXNK+caIxdojVE/h9w0O/xjAE0qU7183NP8UhdPaKSq+FTe6vPffAbwGwPemjY3173La7/+33dBs35FZsVsVyANxtyobUVe9mGOEFCMWa49QPR++K0fFKzgFE6NXYnmklG+MWKw9QvV8+K4cFa/gFEys3o3hk1K+MWKx9gjV8+G7clS8glMwMfo2lkdK+caIxdojVK8Tv29wmM8cOrpAuQ673N6/dukJmztxxveOiq9xm5b1nlUUeHsB/KrsQf/MhRv4oOBth6oVq3+zj16BPBDrtUoWmdIFGCMWa49QPR++K0fFKzgFk2yzewSWUr4xYrH2CNXz4btyVLyCUzAebZgsJaV8Y8Ri7RGq58N35ah4Badgkm12j8BSyjdGLNYeoXqT8WcPrnlbgfI9AH4x1tNz5GUr+v/LZyhVYyRu7+FnDRbAEF8FW44VA7udtv7LPqvNHq2YKV2uQB6Iu1zgGPLqxTxVYrHON1TPh+/KUfEKTsHE6JVYHinlGyMWa49QPR++K0fFKzgFE6t3Y/iklG+MWKw9QvV8+K4cFa/gFEyMvo3lkVK+MWKx9gjVm4g/Z/CC55fo4RtsUJTFwNpVAxf6rtKqMd7ylbPL3a+977XGJcqjd1u88YLxfahqxerf7KNXIA/Eeq2SRaZ0AcaIxdojVM+H78pR8QpOwSTb7B6BpZRvjFisPUL1fPiuHBWv4BSMRxsmS0kp3xixWHuE6vnwXTkqXsEpmGSb3SOwlPKNEYu1R6heO/7swfP3LDCN9w3vi7I4bWTVwLKQVVolxtEVe89FWVxy3wCOE2cs2XBuu3ZStDzaMFMiVCAPxBGK3G2LlC7AGLFYe4Tq+fBdOSpewSmYbvdsTP2U8o0Ri7VHqJ4P35Wj4hWcgonZv932SinfGLFYe4Tq+fBdOSpewSmYbvdsTP2U8o0Ri7VHqF47/pyFa64oy/JFAD4zMjTvVaGrtJ1i3Lxsr4PLoudKADujwDtnLtrw7ol6sJNWzN7NXm4VyAOxW72SRKd0AcaIxdojVM+H78pR8QpOwSTZ6J5BpZRvjFisPUL1fPiuHBWv4BSMZysmSUsp3xixWHuE6vnwXTkqXsEpmCQb3TOolPKNEYu1R6jeeH7fwuFVKPEOANeg7OkfWdX/m24OxLeveOhjppVbrgSKh25+TD8ecuQ5k85Nofl6tmmmGVQgD8QGRWxaIqULMEYs1h6hej58V46KV3AKpumetvRPKd8YsVh7hOr58F05Kl7BKRjL/mxaK6V8Y8Ri7RGq58N35ah4Badgmu5pS/+U8o0Ri7VHqF4rf/ai4XnFGFYDGC1RvGLd0MBX251rV8+J8H9Z+rAHTp++9UoU5ZOLAp9ZP3DVMZ3eb+zqbdmrWSusAnkgDqtfEuyULsAYsVh7hOr58F05Kl7BKZgkGtsoiJTyjRGLtUeong/flaPiFZyCMWrNJGRSyjdGLNYeoXo+fFeOildwCiaJxjYKIqV8Y8Ri7RGqV/PnLrnwsWNbyu8B5YwCeNPaoXkfnugUu3pOhN+0vPerBfACAOtm3NvTf9Mx3x7NA7HRhZWgTB6IEzwpriG5Xvyu+i74GLFYe4Tq+fBdOSpewSkYl3OeOjalfGPEYu0RqufDd+WoeAWnYFLveZf4Uso3RizWHqF6PnxXjopXcArGpf9Sx6aUb4xYrD1C9Wp+3+AwX6n0ZKA8Z2Ro/lsn6xtXz3b4Tct6h4sCA0WB79451jPwoCW3/FbRVTCp9/yOGl8eiKfAmU/pAowRi7VHqJ4P35Wj4hWcgpkCl8VfU0gp3xixWHuE6vnwXTkqXsEpmHx9dKcCMWpv7RGq58N35ah4BadgutMdzaimlG+MWKw9QvXIP+lj1366RPHqEuWXp41t7r/0zGPv6OZAvHlF7wfLEm8ugZ9h61j/bqf//gf0U3JRMM10cnbtVIE8EHeq0Hbw+5QuwBixWHuE6vnwXTkqXsEpmO2g7eUQU8o3RizWHqF6PnxXjopXcApGbr7tAJhSvjFisfYI1fPhu3JUvIJTMNtB28shppRvjFisPUL1PnnJ1eUXvrvtuVm/LrH18HVDR9/Q6eS5erbiR5f3vhPAUpT4w1iB/vsv3vD12k/RVTCd4s+/b6YCeSBupu6mrildgDFisfYI1fPhu3JUvIJTMKYN2rBYSvnGiMXaI1TPh+/KUfEKTsE03NKm9inlGyMWa49QPR++K0fFKzgFY9qgDYullG+MWKw9QvTmLljznLGi/CZbYAzFUZcNDfCBWh0PV88aP7q8900AzkWBshgrjpyxZP3nW80UXQXTMYEMaKQCeSBupOy2pildgDFisfYI1fPhu3JUvIJTMLYd2qxaSvnGiMXaI1TPh+/KUfEKTsE029G27inlGyMWa49QPR++K0fFKzgFY9uhzaqllG+MWKw9fPXmvuOS+4/13PkjAI8AiqUjQwPvUjvB1ZP4vVYfPK+nKIa3eRTF8TMXrf/YeD9FV8GoeWRc3ArkgThuvbviltIFGCMWa49QPR++K0fFKzgF05VGbUg0pXxjxGLtEarnw3flqHgFp2AaauWu2KaUb4xYrD1C9Xz4rhwVr+AUTFcatSHRlPKNEYu1h6/e7MHVXypQvPSARz8I7zruRU6ziqvnb6++sHzg199y3ywMDM5YvGFVu3ZTdBVMQ62cbTtUwKnJtpdqDg0NlYODg1MyN9+LNNa5i/FhYO0RqufDd+WoeAWnYGL1SwyflPKNEYu1R6ieD9+Vo+IVnIKJ0bexPFLKN0Ys1h6hej58V46KV3AKJlbvxvBJKd8YsVh7+Oj1Da5eARQLAdxw+rwnHfD0p+zn9Pe8i+fdy/d60j332+MHPffcjqLAmTMWbVgwUV8pugomuoPI0gAAIABJREFURt9mD/cKODWZu3wzjDwQN1N3usb4MLD2CNXz4btyVLyCUzDNdZC9c0r5xojF2iNUz4fvylHxCk7B2Hdpc4op5RsjFmuPUD0fvitHxSs4BdNcN9s7p5RvjFisPVz1Zg8OH1kAFwK4p+wp5p5z7NO+3Ondv+PPuuo5uurB+xZbpl9ZAo9FWXxi5pL1x03WQYqugrHv0qxoUYE8EFtUsWGNlC7AGLFYe4Tq+fBdOSpewSmYhlva1D6lfGPEYu0RqufDd+WoeAWnYEwbtGGxlPKNEYu1R6ieD9+Vo+IVnIJpuKVN7VPKN0Ys1h4uenMHL3z0GMauB7B7UeIta1fN+6ALvz7xCqdcut/97tzpj1eWKJ5118Oeiwf+cs1OxVJsyQOx6eWzXYnlgXi7Ol3tg1Uu/lhpxojF2iNUz4fvylHxCk7BxOqXGD4p5RsjFmuPUD0fvitHxSs4BROjb2N5pJRvjFisPUL1fPiuHBWv4BRMrN6N4ZNSvjFisfZw0Zu9YPV1RVE8DWX5oZFV89/M8+vCdxmIR5fv80WgfHkJfPPWV4w895FPeFrHeUiJRcHE6Nvs4V6Bjg3gLtk8I2+Zbu4cxPgwsPYI1fPhu3JUvIJTMM11kL1zSvnGiMXaI1TPh+/KUfEKTsHYd2lziinlGyMWa49QPR++K0fFKzgF01w32zunlG+MWKw9VL05C4c/XpY4tizLr+x09139X/zAa2/r1kA8umyfj6Moj0WBH01HT//NA9/+qbItW8lFwdh3aVa0qEAeiC2q2LBGShdgjFisPUL1fPiuHBWv4BRMwy1tap9SvjFisfYI1fPhu3JUvIJTMKYN2rBYSvnGiMXaI1TPh+/KUfEKTsE03NKm9inlGyMWaw9Fr2/RmjdhrDwXwC0Y23royJlHX+uy2jv+hE/muWl571ABLADK3xVl2T9jye+vVmJUh3NVy7RJs5hJBfJAbFLGZkVSugBjxGLtEarnw3flqHgFp2Ca7Whb95TyjRGLtUeong/flaPiFZyCse3QZtVSyjdGLNYeoXo+fFeOildwCqbZjrZ1TynfGLFYe3TSm734ooOLrVuv4lkry+KYdasGPtt6Bjvx253tiTiblvW+vShwFoC7UZRHzFy0cUQddFWcT7y2HZvVfCuQB2LfyiXES+kCjBGLtUeong/flaPiFZyCSai9g0NJKd8YsVh7hOr58F05Kl7BKZjgpkxIIKV8Y8Ri7RGq58N35ah4BadgEmrv4FBSyjdGLNYek+nNOf4jM8oH7v4DAI8GijNGhgZOd1ntnejktvO8c0Xvq8dKfOq+wRuv2W3Jhk/XfDVnBadggpsyC3SlAnkg7kpZ44qmdAHGiMXaI1TPh+/KUfEKTsHE7eDuuqWUb4xYrD1C9Xz4rhwVr+AUTHc7Nq56SvnGiMXaI1TPh+/KUfEKTsHE7eDuuqWUb4xYrD0m0+sbHF4LoA/AmpGhefNcVnsnO+vjPUeX984GsG01uERxym6L17+/la/mrOAUTHc7Nqv7ViAPxL6VS4iX0gUYIxZrj1A9H74rR8UrOAWTUHsHh5JSvjFisfYI1fPhu3JUvIJTMMFNmZBASvnGiMXaI1TPh+/KUfEKTsEk1N7BoaSUb4xYrD0m0usbXPNuoDwNwA970HP4pUP9v+jGQLx5xd4HoSy+VgIzUZbLZi7ZSM+/OdScFZyCCW7KLNCVCuSBuCtljSua0gUYIxZrj1A9H74rR8UrOAUTt4O765ZSvjFisfYI1fPhu3JUvIJTMN3t2LjqKeUbIxZrj1A9H74rR8UrOAUTt4O765ZSvjFisfZop9c3uPpQoPjCfQu2Rd/IyoHLJjqLPvHUnNvO2GvWTj09VwL4BwAfnrl4w5tChm4lFgXT3Y7N6r4VyAOxb+US4qV0AcaIxdojVM+H78pR8QpOwSTU3sGhpJRvjFisPUL1fPiuHBWv4BRMcFMmJJBSvjFisfYI1fPhu3JUvIJTMAm1d3AoKeUbIxZrj/F6rxhc/Y9bgOuB4kFlgZPXrZz3gclOkk885Ox50dP22HnrzhyGn1oCw7st3jA/dOhWYlEwwU2ZBbpSgTwQd6WscUVTugBjxGLtEarnw3flqHgFp2DidnB33VLKN0Ys1h6hej58V46KV3AKprsdG1c9pXxjxGLtEarnw3flqHgFp2DidnB33VLKN0Ys1h7j9foGV18NFM8EyvNGhua/odPZ84mHnL1XH3xFURQvRlFeMaPnnv5i8M9/yQNxp2rv2L/PA/EUOP8+HxjdSjtGLNYeoXo+fFeOildwCqZb/dGEbkr5xojF2iNUz4fvylHxCk7BNNHH3fJMKd8YsVh7hOr58F05Kl7BKZhu9WoTuinlGyMWa49WvTmDwx8pgeOB4sqdirv6L1756j92Oqc+8fzhs68qd/31FZS+vmes7N/1tI2/tFiFVmJRMJ1yzr9vpgJ5IG6m7qauKV2AMWKx9gjV8+G7clS8glMwpg3asFhK+caIxdojVM+H78pR8QpOwTTc0qb2KeUbIxZrj1A9H74rR8UrOAVj2qANi6WUb4xYrD1qvb7B1a8Hin8DcCuKsZePrDzqu8qpdY1n0/J9PlCgfCuAX6Is+mcuWX99Jx/VQ8EpmE7x5N83U4E8EDdTd1PXlC7AGLFYe4Tq+fBdOSpewSkY0wZtWCylfGPEYu0RqufDd+WoeAWnYBpuaVP7lPKNEYu1R6ieD9+Vo+IVnIIxbdCGxVLKN0Ys1h7UO/Hj1z6jpyyuAjCtLIrXrFs58Nd3AHc6vS7xjK7YZwnK8oxypxnA3Xe9ZLfTbtm2TNzpUD0UnILpFE/+fTMVyANxM3U3dU3pAowRi7VHqJ4P35Wj4hWcgjFt0IbFUso3RizWHqF6PnxXjopXcAqm4ZY2tU8p3xixWHuE6vnwXTkqXsEpGNMGbVgspXxjxGLt8bOf/6I85RPX/xTA44uiWLF25cBil1OqxjO6bJ/Xoyi5Ao3bDn4XHvq8E+T5RvVQcArGJf+MjVcBuWHihRTuNDQ0VA4ODk7J3NpVJ6ULMEYs1h6hej58V46KV3AKJvwqTEchpXxjxGLtEarnw3flqHgFp2DS6e7wSFLKN0Ys1h6hej58V46KV3AKJrwr01FIKd8YsVh7LDzv8vJHv/ozUBafGxnq70dRlC5nV4nnjhW9R/SUuGibbok3bZh/1YdmzZolzwCKB6UVnIJxyT9j41VAbph4IYU75YE4vIa+CjE+DKw9QvV8+K4cFa/gFIzv+U+Rl1K+MWKx9gjV8+G7clS8glMwKfa5b0wp5RsjFmuPUD0fvitHxSs4BePbiynyUso3RiyWHnMWDp9elngXgJ/2jPUceumZ/Te6nuNO8dy5ove5YyW+xu3YKIrTZi5av6wTZ3wMKl7BKRjXGmR8nArkgThOnbvqktIFGCMWa49QPR++K0fFKzgF09WGjSyeUr4xYrH2CNXz4btyVLyCUzCRW7irdinlGyMWa49QPR++K0fFKzgF09WGjSyeUr4xYrHymP2O1XOLnuKS+1Zti7kjqwbW+py6yeK5e6j3CVu2bhuG9y5Qvn/G4o2nqCu5rbGoOSs4BeNTh8zpfgXyQNz9GnfdIaULMEYs1h6hej58V46KV3AKputNG9EgpXxjxGLtEarnw3flqHgFp2Aitm/XrVLKN0Ys1h6hej58V46KV3AKputNG9EgpXxjxGLh8fJTVj986/2KawH0vvygh+N1hz7Le9aYKJ5Ny/fau0DPlQD2Q4FPz1y04TV1W7jmoOIVnIKJ2L7ZyqEC3k3q4BEdmrdMRy/5Xw1jfBhYe4Tq+fBdOSpewSmY5jrI3jmlfGPEYu0RqufDd+WoeAWnYOy7tDnFlPKNEYu1R6ieD9+Vo+IVnIJprpvtnVPKN0YsFh6zB4e/WQDPQVl+7Ozjn/46l/t5x5/BdvGUF2LanT/vvbLc5oGRP82Y3v/wU26+Mw/E9v2/IynmgXgKnG2LDzCrMsSIxdojVM+H78pR8QpOwVj1Qwo6KeUbIxZrj1A9H74rR8UrOAWTQl9bxZBSvjFisfYI1fPhu3JUvIJTMFa9mYJOSvnGiCXUY/bg8IcK4I0Avn3PlnuO+Lc3PGuj9UA8urz38wAOA4qri3Ja/4wlN/+utVdcc1DxCk7BpNDXOYa/r0AeiKdAV6R0AcaIxdojVM+H78pR8QpOwUyByyLqrgW1XjFqb+0RqufDd+WoeAWnYNTzvT3gUso3RizWHqF6PnxXjopXcApme+h7NcaU8o0RS4hH3+DwcQA+hrK4Ddg6Z2TVUd8J0eM5Gs8fXb73R4Di+LLEjRgrj9zt9I0/UlaVJzvfaowKTsGovZdxcSuQB+K49e6KW0oXYIxYrD1C9Xz4rhwVr+AUTFcatSHRlPKNEYu1R6ieD9+Vo+IVnIJpqJW7YptSvjFisfYI1fPhu3JUvIJTMF1p1IZEU8o3Riy+HrMHLzygwNh3AOxSojx23dD8T7YbaF1PY2s8o8v3XgYUfI/xH3oKHLHrog3fbKfnmoOKV3AKxrUGGR+nAjEG4ukA9gRw6wQp3R/AHeN+1wNgVwCj434+EwDvExibrDz5HuI4zWPxQeQTqfUHTqieD9+Vo+IVnILxOS+pclLKN0Ys1h6hej58V46KV3AKJtVe94krpXxjxGLtEarnw3flqHgFp2B8+jBVTkr5xojFx2Pp0qU919/9uO+jxJNKFKvWDQ0M1ufTR6+1F2r+5hX7nFSW5fv5u6IsDpuxZP3FE/WMq6eKV3AKJtVe39HjijEQvxfAEwG8eFyxDwDAb5B+DeARAI4FcD2A1wI4CQDvCeAwffS2V20DFwDYUmHPAvCpiU5eHoiba+sYHwbWHqF6PnxXjopXcAqmuQ6yd04p3xixWHuE6vnwXTkqXsEpGPsubU4xpXxjxGLtEarnw3flqHgFp2Ca62Z755TyjRGLj0ff4PDnABwOFBdv2mWv/m8sfR7/Vt92+OiNH4j3HX72MWMoP8Of9xTlcbsu2viJyc60q6eKV3AKxr5Ls6JFBbo9EM8F8PpqsB0/EF8B4D0A+M/DAZwA4BAA9wLYA8BfAJwDYH01EHMlmVslequfcbV4c7si5IHYojX8NGJ8GFh7hOr58F05Kl7BKRi/s58mK6V8Y8Ri7RGq58N35ah4Badg0ux0v6hSyjdGLNYeoXo+fFeOildwCsavE9NkpZRvjFhcPeYMDi8ugWUAfjGt6Hn5JSv7/3v8QBvyUK2bv3N+uec337ZNsiyxYLclG87s1CmuOah4BadgOsWff99MBbo5ED8KwHm470JZ0maF+LcADgbAf+4P4MsAngHgqwBmVeU4EcBTqoGYPx/mbolqyzQxN+WBOPwbOMvWi/FhYO0RqufDd+WoeAWnYCx7ommtlPKNEYu1R6ieD9+Vo+IVnIJpuqct/VPKN0Ys1h6hej58V46KV3AKxrI/m9ZKKd8Ysbh49C1ccwjKch3PUVEUh65dOfDF8efLRW88d3TZPk8r7zfjuuLeUW4TXbXb4g1/3YqdV4ibvjKmnn+3BuJdAHy92gbN1d6lbQbi2wE8tlrt5fDMm+NnA7io+jmrfQyA5wJ4AIALAXBbBo+NAA4C8KuVK1cuLYrineNPzZFHHjn1zlbOKFcgVyBXIFcgVyBXIFcgVyBXoMEK3L75Xrzvkv/Gnzfdg7lPfxhe8OR9TKOZNroBD/z6iZi26RbcOWsu/nLgAlP9boqFrIh3M66sPXkFujUQc3v05QCuA7A7gMcA+Gi1LbqO6FsATgZwA4CnATgdwEC1DZoP1eJ9w/w9D26X5gD9AQDTAPy52lbd9uFaect0c20f8m2gGrW1R6ieD9+Vo+IVnIJRz8X2gEsp3xixWHuE6vnwXTkqXsEpmO2h79UYU8o3RizWHqF6PnxXjopXcApG7b3tAZdSvjFiUT36Fg5/FSVeUBT4xNqV8/i6pbaHqtdKLs/ae+bme4orATz9rof/Cx540/C0YunkD9Nt5bt6qngFp2C2h77fEWPs1kA8A8BDq4Jyy/MpALhkexuAx1eDMh+29QcAvB+A9xJvAnAagB/gvpd6891iHKq5+ssV5zdXq8zUod4zJzpheSBurpVjfBhYe4Tq+fBdOSpewSmY5jrI3jmlfGPEYu0RqufDd+WoeAWnYOy7tDnFlPKNEYu1R6ieD9+Vo+IVnIJprpvtnVPKN0Ysikff4JqzgfItAP7z3p57D7t8xTF81o/ZQDy6oncdShxSAF/feOi65z1yvwOcZhUlB58BWtFVMPZdmhUtKuDUZJ6GT6/uI+aqMVeC+a0Pt0DzHuCrK00Oyhxw/wSAD+L6bPVz3ptwVDUQXwZgv+p1TC8CcE0eiO+rQEoXYIxYrD1C9Xz4rhwVr+AUjOe1niQtpXxjxGLtEarnw3flqHgFp2CSbHTPoFLKN0Ys1h6hej58V46KV3AKxrMVk6SllG+MWDp59L1j+LXo2faGmNGiGJu9duVRbd8FXJ/MTnrjT/ro8t5PA3gVgB9OK6Yd+buBb93ougXZ1VPFKzgFk2Sj56C2PaAq5sGVYz45ut5ewdcq7Vs9WItbpOuDOG61Hv+t08MBbKieRD1h3HmFOOYp/VuvGB8G1h6hej58V46KV3AKprkOsndOKd8YsVh7hOr58F05Kl7BKRj7Lm1OMaV8Y8Ri7RGq58N35ah4Badgmutme+eU8o0Ry2Qeh5x6/lN6pk3/FlDeH2V5/Miq+R/rVHGXmDet6H1vUW7bAXpLUZaHz1iy8T9d+L5DuOqh4BRMp5rl3zdTgdgDMV+ZxEH3xm6mmwfiblZ3cu0YHwbWHqF6PnxXjopXcAqmuQ6yd04p3xixWHuE6vnwXTkqXsEpGPsubU4xpXxjxGLtEarnw3flqHgFp2Ca62Z755TyjRHLZB59C4ZvQIGnlmXxnnWrBk5Vqq3GvGn53gsLFCsA3APgsJmLN2x7erXKb43FlaPiFZyCUeqWMfErEHsgjpJhHoijlLmtSYwPA2uPUD0fvitHxSs4BdNcB9k7p5RvjFisPUL1fPiuHBWv4BSMfZc2p5hSvjFisfYI1fPhu3JUvIJTMM11s71zSvnGiGUij74Fw6tRYB6Aka2jDzjiSx885G6l2krMo2fs/Tr0FHzwLsqyeNVuS9Z/xne112eIVmJUdVUtpXYZE7cCeSCOW++uuKV0AcaIxdojVM+H78pR8QpOwXSlURsSTSnfGLFYe4Tq+fBdOSpewSmYhlq5K7Yp5RsjFmuPUD0fvitHxSs4BdOVRm1INKV8Y8TSzmPO4JoFJcohFPj12FjPnMtW9fOht9LRKebRFfu+AuXYxRQrUJ40Y/HGs1uFO/HbBeHKUfEKTsFIhcug6BXIA3H0ktsbpnQBxojF2iNUz4fvylHxCk7B2Hdpc4op5RsjFmuPUD0fvitHxSs4BdNcN9s7p5RvjFisPUL1fPiuHBWv4BSMfZc2p5hSvjFiGe/RNzhcv0YVBYoj1g4NfN7lbEwW853L9n32WDH2VQA7o8S7Zy7ZwLfK/M3hk7MrR8UrOAXjUr+MjVeBPBDHq3XXnFK6AGPEYu0RqufDd+WoeAWnYLrWrA0Ip5RvjFisPUL1fPiuHBWv4BRMA23cNcuU8o0Ri7VHqJ4P35Wj4hWcgulaszYgnFK+MWJp9XjZwgsfMq0c+08AjwKKRSNDAytdT8FEMd+9Yt/HbSnHvrbtwboFzp25aMOJFqu91HCtk4pXcArGtYYZH6cCeSCOU+euuqR0AcaIxdojVM+H78pR8QpOwXS1YSOLp5RvjFisPUL1fPiuHBWv4BRM5Bbuql1K+caIxdojVM+H78pR8QpOwXS1YSOLp5RvjFhaPeYMrvlSifKlBfCptUPzXutT+nYx37503wdP22nbMPykAsUFMxavP3oibZ+cXTkqXsEpGJ86Zk73K5AH4u7XuOsOKV2AMWKx9gjV8+G7clS8glMwXW/aiAYp5RsjFmuPUD0fvitHxSs4BROxfbtulVK+MWKx9gjV8+G7clS8glMwXW/aiAYp5Rsjltpj9uDq9xUoTgaKG7aO3fuKL535ypt9yt4u5k3Le68sgOeVwOV333tP/4OW/un2PBD7VDdzLCugDsQPAHASgOUAlgJ4PID3AOBWiuSO/JTp5k5JzA9sqyxDY/bhu3JUvIJTMFa1TUEnpXxjxGLtEarnw3flqHgFp2BS6GurGFLKN0Ys1h6hej58V46KV3AKxqo3U9BJKd8YsdDjpI9f+6qyLP6Dr0AqMPaytUNHXel7LsbHPLqi90KUOBLA9ff04Mg9F2741WTaPjm7clS8glMwvrXMvO5WQB2ILwUwB8DhAOob6vlt0cO7G56feh6I/epmwYrxYWDtEarnw3flqHgFp2AseiEVjZTyjRGLtUeong/flaPiFZyCSaW3LeJIKd8YsVh7hOr58F05Kl7BKRiLvkxFI6V8Y8Tynet/XA59/sd/QokHAuUbRobmnxdyLlpjHl3e+2EAbwDwK6A4fObi9d/rpO2TsytHxSs4BdMp5/z7ZiqgDMTTAdwL4AQAfNoch2KuEP8UwOMA3NhM6BO75oG4uTMS48PA2iNUz4fvylHxCk7BNNdB9s4p5RsjFmuPUD0fvitHxSs4BWPfpc0pppRvjFisPUL1fPiuHBWv4BRMc91s75xSvjFiedP71pa//v0oSpTvXzc0/5TQitYxjy7rfRcKnF4Ad4yNlYftdtpGPl264+GTsytHxSs4BdMx6QxopALKQDwNwC0APgTg7QC4deLTAL4AYB8AGxqJfBLTPBA3d0ZifBhYe4Tq+fBdOSpewSmY5jrI3jmlfGPEYu0RqufDd+WoeAWnYOy7tDnFlPKNEYu1R6ieD9+Vo+IVnIJprpvtnVPKt9ux9A0Oc5v0MShwebHzzMPWLp2zObSijHmf4YPfUqI4h1oFMDBj8YYLVV2fnF05Kl7BKRg194yLWwFlIGZEvF/4bVVohwBYA+DnAA6IG67mlgdirU7dQMX4MLD2CNXz4btyVLyCUzDd6I2mNFPKN0Ys1h6hej58V46KV3AKpqle7oZvSvnGiMXaI1TPh+/KUfEKTsF0o0+b0kwp327GMnvB6rcXRXHW7jN2wp/v2rr/ZSv6/8ui5r+78rxyj+/y0UMAyuINM5esd9qC7ZOzK0fFKzgFY1HXrGFfAXUgJu55bGcA3wDwRgD8hudW+5DCFfNAHF5DX4UYHwbWHqF6PnxXjopXcArG9/ynyEsp3xixWHuE6vnwXTkqXsEpmBT73DemlPKNEYu1R6ieD9+Vo+IVnILx7cUUeSnl261Y+t6x+gUoiq+gQPGaF8zCES86UJ0NJj1lm5bv9aICPVcQ1FMUi3ddtH6F6zn2ydmVo+IVnIJxrUHGx6mAS9M/CsDuVVhj1T9/DGBrnFB1lzwQ67WyRsb4MLD2CNXz4btyVLyCUzDWfdGkXkr5xojF2iNUz4fvylHxCk7BNNnP1t4p5RsjFmuPUD0fvitHxSs4BWPdo03qpZRvN2J5yckXPnCnnceuBvBYlMVpZx//tDNmzZrlMhu0PT2j7953f0wb+wqAB5Ul3rfbkg31LlOn0+mTsytHxSs4BeNUgAyOVgG16bn3/8Q2UXFAnvD9YdGyGGeUB+KmKg/E+DCw9gjV8+G7clS8glMwzXWQvXNK+caIxdojVM+H78pR8QpOwdh3aXOKKeUbIxZrj1A9H74rR8UrOAXTXDfbO6eUbzdi6Rscrt8ic/7I0LxXWnhsXvXQh41t2fq1AnjMnY88BA8+6pPqrPF3J9AnHleOildwCsa+S7OiRQWUJuVDtbYAuAnAR8etCHNQvsciEEuNPBBbVtNNK8aHgbVHqJ4P35Wj4hWcgnE762mjU8o3RizWHqF6PnxXjopXcAom7Y53iy6lfGPEYu0RqufDd+WoeAWnYNw6MG10Svlax9I3OHwmgFMB/HDs3q1zL3vv0b8O9SiX/uMud+5099dKlAcDuHRj/zfmPuqfHqfMGm0bwSceV46KV3AKJu2O33GjU5uUr1ZaByD4EewxSp0H4hhVbu8R48PA2iNUz4fvylHxCk7BNNdB9s4p5RsjFmuPUD0fvitHxSs4BWPfpc0pppRvjFisPUL1fPiuHBWv4BRMc91s75xSvpaxzBlcM79EeQGfDTQ2NvaSy848itubg3f5jS7v3bbiXKC8qsTWIzfMu+aWkC3YPjm7clS8glMw9l2aFS0qoA7Er6tWh68DcFeL8UsBBD+W3SKRVo08EFtXVNeL8WFg7RGq58N35ah4Badg9DOePjKlfGPEYu0RqufDd+WoeAWnYNLvej3ClPKNEYu1R6ieD9+Vo+IVnILRuy99ZEr5WsUyZ+HnHleO3ftNFMVeKMs3j6yaz9eqbjtCPEaX9X4SBV5bADdunYbD7z+44Scher7xuHqqeAWnYNLv+h0zQmUgJobvGt4LwO/HbZF+PIBNqZUuD8TNnZEYHwbWHqF6PnxXjopXcAqmuQ6yd04p3xixWHuE6vnwXTkqXsEpGPsubU4xpXxjxGLtEarnw3flqHgFp2Ca62Z755TytYqlb3D1t4Hi2UB5zsjQ/Le2Vs3XY9Py3jOL+7Zf/7GnKA7dddH6b/sOtKHxuOag4hWcgrHv0qxoUQFlIO4BcBuAj+ct0xYlt9dI6QKMEYu1R6ieD9+Vo+IVnIKx79LmFFPKN0Ys1h6hej58V46KV3AKprlutndOKd8YsVh7hOr58F05Kl7BKRj7Lm1OMaV8LWLpG1z9CaD4V6C4smdsl1dceubL7wgdQDct631HUWDVNp2yfMXMJRsvqTVDY/bhu3JUvIJTMM11c3aerALKQEz+GgD/DGAlgLtbBC+uHriVVJXzCnFzpyPGh4G1R6ieD9+Vo+IVnIJproPsnVPKN0Ys1h6hej58V46KV3AKxr5Lm1NMKd8YsViRYY1XAAAgAElEQVR7hOr58F05Kl7BKZjmutneOaV8Q2OZvXD4pKLE+wHcWqLnZeuG+m8YXzFXjztX9L52rMQn79Mpj525eGP17/f9xFUvNB4fTzVGBadg7Ls0K1pUQB2IN1Zbpsd75tcuWZyFQI2ULsAYsVh7hOr58F05Kl7BKZjAlkyKnlK+MWKx9gjV8+G7clS8glMwSTV4YDAp5RsjFmuPUD0fvitHxSs4BRPYkknRU8o3JJY5Cy94bln2XAHgfgWKo9YODaxuV2gXj9Fle89BUfAhWihLnLrbkg3vsRhoWzVc4ql5rhwVr+AUTFINnoP5awXUgfh5AHZuU7ev5hXi5rsppQswRizWHqF6PnxXjopXcAqm+a62iyClfGPEYu0RqufDd+WoeAWnYOy6s3mllPKNEYu1R6ieD9+Vo+IVnIJpvqvtIkgpX99YXrb0sw+Ydtf0q+7b6VksHRkaeNdEFVI9Nq/Y65ll2cMnU88EipUzF69fFDpgW/HVHFwHaEVXwdh1Z1ayrIA6EFt6dl0rb5nueoknNIjxYWDtEarnw3flqHgFp2Ca6yB755TyjRGLtUeong/flaPiFZyCse/S5hRTyjdGLNYeoXo+fFeOildwCqa5brZ3Tilf31jmLBz+fFnisLIoLly3cmBgsiopHn854yH/NL1nGhfE/gEoPzJz8cbXhw7Ylnwlh1Y/Fa/gFIx9l2ZFiwrkgdiiig1rpHQBxojF2iNUz4fvylHxCk7BNNzSpvYp5RsjFmuPUD0fvitHxSs4BWPaoA2LpZRvjFisPUL1fPiuHBWv4BRMwy1tap9Svj6x9C0YXokCgwB+WmJr37qho28KGYhvW/kPe+40dg+H4acWBS6asWhDf4hep5Plk7MrR8UrOAXTKef8+2YqkAfiZupu6prSBRgjFmuPUD0fvitHxSs4BWPaoA2LpZRvjFisPUL1fPiuHBWv4BRMwy1tap9SvjFisfYI1fPhu3JUvIJTMKYN2rBYSvm6xjJnwQX9ZdHDh+JiDGMvu2zoqC93Kmcnj9Hle18BFC8qgK/vuuv0I4pTbv5THoj/rwKd6tep/vn3zVXAZSDev3rSdGu0wwDubS789s55y3RzZyTGh4G1R6ieD9+Vo+IVnIJproPsnVPKN0Ys1h6hej58V46KV3AKxr5Lm1NMKd8YsVh7hOr58F05Kl7BKZjmutneOaV8XWLpO/X8f8K0aV8H8FAU5VtHVs4/R6nOZB6bV+xzQVmW81HiR1vKrYfvftqtP++k6RJzOy0fvitHxSs4BdOpZvn3zVRAHYj5DuJj24SYnzLdzHn7G9eULsAYsVh7hOr58F05Kl7BKZgE2toshJTyjRGLtUeong/flaPiFZyCMWvOBIRSyjdGLNYeoXo+fFeOildwCiaBtjYLIaV8XWKZs2D4yrLA81CW/zayav4b1YJM5LF5ee85JXBiUeAWoDxsxqKN1yiaLjHngVipaMZ0qwLKQDytepI0n1C3fNyK8DfyU6a7dWp03dAPHN2pMzJGLNYeoXo+fFeOildwCqbzmd5+ECnlGyMWa49QPR++K0fFKzgFs/10f+dIU8o3RizWHqF6PnxXjopXcAqmc9dtP4iU8lVjmb1w+LyixAko8O3pO+8894tLD71NrXg7j9EVvaejBJ9MfQ+AV8xcvOFLIXoqlzg151ZNV46KV3AKxiX/jI1XAWUgZjQ/AcAL4O3xQvN3ylum/WsXyozxYWDtEarnw3flqHgFp2BC+yAlfkr5xojF2iNUz4fvylHxCk7BpNTfobGklG+MWKw9QvV8+K4cFa/gFExoT6bETylfJZa+weE3A/ggiuI2bC1eMnJm/7Uu9RzvMbqs940o8CFqlGXxyt2WrD8/RM+Fmwdi12plfEgF1IGYL96eA+AiAH9sMTwZwF0hAXSDmwfiblRV01Q+sDWliVHWHqF6PnxXjopXcAom9BylxE8p3xixWHuE6vnwXTkqXsEpmJT6OzSWlPKNEYu1R6ieD9+Vo+IVnIIJ7cmU+Cnl2ymWuQvXPGusLC/nu4HLsjhm3aqBz7rWstXjjuW9/T3AtodyFSjfMmPxxg+G6Lly80DsU7HM8a2AOhBvBLBXG5N8D7Fv5Q15nT4kDa06SsWIxdojVM+H78pR8QpOwXQ80dsRIKV8Y8Ri7RGq58N35ah4BadgtqP27xhqSvnGiMXaI1TPh+/KUfEKTsF0bLrtCJBSvpPFMmfp2hm4a/Q7JbA/UJwxMjRwuk+Za487ztj7+T09xRUAeNvk0pmLN3DLtPMRWj8fvitHxSs4BeNcxEyIUgF1IJ6+7Quivz+Se8I0Q8wrxFF6p61JjA8Da49QPR++K0fFKzgF01wH2TunlG+MWKw9QvV8+K4cFa/gFIx9lzanmFK+MWKx9gjV8+G7clS8glMwzXWzvXNK+U4Wy+wFq9cURdEPFBePDA0c5lsJejzsooOftGVr8RUAexdFcc6MRevfGqI3a9Ysddb4Oxuf+rtyVLyCUzC+tcy87lZgsiblEPxJAJ8D8CIAXA0ef5wA4M7uhuiungdi95pZMWJ8GFh7hOr58F05Kl7BKRirfkhBJ6V8Y8Ri7RGq58N35ah4BadgUuhrqxhSyjdGLNYeoXo+fFeOildwCsaqN1PQSSnfiWLpW7j6DJTFEqD4RQ+Kl1061P8L39r98sfXlntdMve/AexXFOX5MxZtfKWvFnmh9fPhu3JUvIJTMCH1zNzuVWCygfh+AO4GsAjASXnLdPdOQqhyShdgjFisPUL1fPiuHBWv4BRMaE+mxE8p3xixWHuE6vnwXTkqXsEpmJT6OzSWlPKNEYu1R6ieD9+Vo+IVnIIJ7cmU+Cnl2y6W2QuHDytKfJ41K8uyb92q+et861d+BDvdVrz0nvvd+l+UuHz03q1H7LX01k2+ev+fvXeBr6sq0/+fddL0lrSUa9IWCgW8juN4RcA7XrgkBQHpBdSBnwKOl0ERadJUjdI0qRdUnBkvODpepkmLotIUBC+of2ZGRWdGHWdQQWghbcq1haYXmpz1/6z0FENIcp611rvfvVr2+Xz8gMn3eZ73Xfvdh67uc/YuNsQxK1dotVeg2scY3HcHygBK4xQ2pF0wk1dcIWZWKRtG4z8e0hmxfiF6Xw3LMxzDZDMd+bim1K9GLdIZsX4hel8NyzMcw+QzydmkptSvRi3SGbF+IXpfDcszHMNkM6n5uKbU7+haFixfO788WP6xAY4xsJev61ry6ZhVGuhouB4wZwP4VammdO60lk0bY/yKDXHs6hV6zRWotiHWrEUsq9gQiy2lt5HGfzykM2L9QvS+GpZnOIbxPvAJC1LqV6MW6YxYvxC9r4blGY5hEh5379JS6lejFumMWL8Qva+G5RmOYbyHMGFBSv2OrqW5pcfdUfqNAL7c27X44phlHOiYfS1g3zFUNxulgftfWN/WN3yZOPYVu34hel8NyzMcw8SuaaHPZgWKDXE266rqmtIJqFGLdEasX4jeV8PyDMcwqgOccVhK/WrUIp0R6xei99WwPMMxTMYjq2qfUr8atUhnxPqF6H01LM9wDKM6wBmHpdTvyFqaW3rcI5DcM4d/Xmt2N3+n829HPhLVa1W2r5jdaYxtAbD94ddeU3/UyQvF9gax6xei99WwPMMxjNfBKWC1FRAberWKiaDiCjGxSBkhGm8G0hmxfiF6Xw3LMxzDZDQeudim1K9GLdIZsX4hel8NyzMcw+QyyBmFptSvRi3SGbF+IXpfDcszHMNkNKq52KbU775ampb2/J0x+CcAAzClN/R2LvyP0MXZsXL25dbaTzm9MThv86J/uy7mrtCj64hdvxC9r4blGY5hQo9Voct2BdgNcQOAhQBeMKoc97dTxV2msz1GVd1TOgE1apHOiPUL0ftqWJ7hGKbq0O1HQEr9atQinRHrF6L31bA8wzHMfjT+VUtNqV+NWqQzYv1C9L4almc4hqk6dPsRkFK/rpb3ffH2k2yNuQnWzrLGXLi+c9HXQpdzZ0fj28pARW8urWvb/CXpfmP9QvS+GpZnOIYJPV6FLtsVYDfEPwJwyhiluEcxPZptif7uxRVi/zWTUmi8GUhnxPqF6H01LM9wDCM1Dyn4pNSvRi3SGbF+IXpfDcszHMOkMNdSNaTUr0Yt0hmxfiF6Xw3LMxzDSM1mCj4p9fuHP95pP/DVX/8S1p4Ai67eVYtbQ9doYGXjGbAYviO1hV1W37al0/27dL+xfiF6Xw3LMxzDhB6zQpftCjAbYnen6cHKM4kvB7BnREk7qpTnHt3knmc8ETcDwGOjfNxdracNfxzkya+6yhVpd+frcV/FhjjboZnIXePNQDoj1i9E76theYZjmPwmSD45pX41apHOiPUL0ftqWJ7hGEZ+SvNzTKlfjVqkM2L9QvS+GpZnOIbJb5rlk1Pqt/3LP7C/uvMhGGvXr+tavADG2JCOd6xoPMEa3ALgIGvxyfrl/R/c5yPdb6xfiN5Xw/IMxzAhx6zQZL8CzIbYVXEjgF0AzvEoqatyVdk94HsWgAtGbXBfXNlkbwBwNIC3u1u9A7io8tzjvspm2uncSb+6sjF37CcA/Mt4tRQbYo+jJIxqvBlIZ8T6heh9NSzPcAwjPBa52qXUr0Yt0hmxfiF6Xw3LMxzD5DrQwuEp9atRi3RGrF+I3lfD8gzHMMIjmqtdKv0uWNrzYWvwURhsKNWUTr1hxcI/hCzM1s6GY2vL5gcAjoXFV+uW9/+/kT7S/cb6heh9NSzPcAwTctwKTfYrwG6IfwngpZUruQ+MKOv5Y1zFdb92V3JvBXBChb0NwMcB3DBC6/526pPA8N9SnQvgUgBnVK5Auw30NgDXANhc2RC7K8ltABorP3MZY155LjbE2Q/OeAkabwbSGbF+IXpfDcszHMPkN0HyySn1q1GLdEasX4jeV8PyDMcw8lOan2NK/WrUIp0R6xei99WwPMMxTH7TLJ+cQr9NrWvPNLb8PdedLduz1n98ycg/S9NNP9R+yMyptZPdZvgEGNww/dD+N5tLn/Spz+Ij0xOsJjMLDEMfsAJUXQF2Q7wWwOFjVNZU5ePQz6tc8X0LgGcDeGSEx70ATgbg/vlCAN8HcCKAHwI4rsK9t3IjL3eF2P28x90ID4D7yLRj/jzWahUbYtUZelKYxpuBdEasX4jeV8PyDMcw+U2QfHJK/WrUIp0R6xei99WwPMMxjPyU5ueYUr8atUhnxPqF6H01LM9wDJPfNMsn593vWW3dRw0NGXcfn2ec9bKjcPHZL2f/3P6UxRjoaHSf9jwdMP9uMXROfdv9W0ZD0v3G+oXofTUsz3AMIz+lhaPECvieWDMBTAEw8irxRHW4K8h/V/m49JsA/HgE7G7G9azK1d5jAfwUgNtgX1f5uUPfCuDVAFyu25R/q6J3J/HLANzT2dnZboz5yOgizjvvPIn1KTyKFShWoFiBYgWKFShWoFiBYgWKFVBfgS/d/Cf8fuNWnPDMw3DBq+cH5x/086sw7Z7vY3DGPGx9ZScGZx4T7FUIJ14BycdWFWuttwLshthtWN13gvftMt1HLlYA+Nk4pc4F8BIAwx/xAHAVgMMqm+N9Eqd9P4BfV9gPA1hUueLsbqrlrgq737uX+7i020B/BoC7yZe70uw+Vj3mzbWKK8R6AzQ6SeNvx6QzYv1C9L4almc4hslvguSTU+pXoxbpjFi/EL2vhuUZjmHkpzQ/x5T61ahFOiPWL0Tvq2F5hmOY/KZZPjnPfpta1lxtYN8PmF/XDu4+45N/9/ItIZut7SsbPm2seR+Ah4Zs6U0zl29yX2Mc8yXdb6xfiN5Xw/IMxzDyU1o4SqwAuyF2V2fdZtj9c/uIjbH7Pu9Y3+M9GMAdlY9C9wP4RuUKsLsx1nMA3A7APQj8wcp3i913iZ3vhwD8BsC7APwOwM0A3NXfqQDcM4/fWMl2d7s+abwFKDbEEqMR5qHxZiCdEesXovfVsDzDMUzY0U9TlVK/GrVIZ8T6heh9NSzPcAyT5qSHVZVSvxq1SGfE+oXofTUsz3AMEzaJaary6re5dc3FsPZLAB6HLb2ud9XC20JqGeiY3QZYdwELMPasumVbJvz+cUjGREcu1i9E76theYZjmDQnvaiK2RC7ze3DAJYD6Kgs2dkArgfwIgD/Nc4yuiu+V1RujuU2ue7jz+57v+5j0+4j0O7f/72i3VrZ4LqcMwF8s/Jz94y08ysbYvfdh+dWHsf0BgC/KDbEe1cgpRNQoxbpjFi/EL2vhuUZjmEOpLfGlPrVqEU6I9YvRO+rYXmGY5ji/MhmBTTWXjoj1i9E76theYZjmGymIx/XPPo9s637JeUh4/7Me7iFefv6rkVfCfmz3sDK2ZfC2i84rQUuqm/rH/fpLPtWV7rfWL8Qva+G5RmOYfKZ5CK12gowG2L3HGH37GF3lfcdAIYAtFQ+Mv1MAH+aIMQ9S9hd3d13M63plTtHOx/3ct5zKjfWGvk8NccdVPl+8Uj7owC4K84jn4X8lPjiCnG1w57d7zXeDKQzYv1C9L4almc4hsluQvSdU+pXoxbpjFi/EL2vhuUZjmH0pzi7xJT61ahFOiPWL0Tvq2F5hmOY7KZV31m73/POW1uz87ihnwHG3XT2U71di92FJe+LH4+tmH1uydjh++4Yaz4wffnmq5nVk+431i9E76theYZjGOY4FIz+CjAbYlfVPwPY96yyxyrf6XUfn3bf+fV5uY9Yu41u0PPT2KBiQ8yulDyn8WYgnRHrF6L31bA8wzGM/GTk55hSvxq1SGfE+oXofTUsz3AMk980yyen1K9GLdIZsX4hel8NyzMcw8hPaX6O2v02tfR81QAXukeSbp/a0PST9tcO+m6Id3bNflV5yLpHmk4BbEdd2xb3CU/qJd1vrF+I3lfD8gzHMNSBKCD1FWA3xO5Kr7tLtNsAuw2te/yR2xCPfIySevHjBRYb4vwOhcabgXRGrF+I3lfD8gzHMPlNkHxySv1q1CKdEesXovfVsDzDMYz8lObnmFK/GrVIZ8T6heh9NSzPcAyT3zTLJ2v229yyphWwKwFsqjGlN3yvc+H/juyIqWX3ysbnDll7i4VxN7f9Ql1bv3vSC/1iMmgzga/0hdTjq2F5hmMYn/UrWL0VqLYhfoa7K13ljs7u7s6jX3eNd6dnvRaemlRsiPNbfY03A+mMWL8Qva+G5RmOYfKbIPnklPrVqEU6I9YvRO+rYXmGYxj5Kc3PMaV+NWqRzoj1C9H7alie4Rgmv2mWT9bqt2lpd5Mx5rt7v0poz+ntWvKdp/yB+6677ER3mX6sveGIUq1xT4Fxjzz9Vl1bv/fzR6X7jfUL0ftqWJ7hGEZ+SgtHiRWYaEM8GcBuAMsAuNu1HzFGoLta7B6HlNSr2BDndzg03gykM2L9QvS+GpZnOIbJb4Lkk1PqV6MW6YxYvxC9r4blGY5h5Kc0P8eU+tWoRToj1i9E76theYZjmPymWT5Zo98zr+yeY0vmB9bdONaitXfVYveY06degZpgQ2zbURqobfyhAV5rLX4yWDP5nFmtG70/xSndb6xfiN5Xw/IMxzDyU1o4SqzARBtid0V4aeVxSe6ZwnVjBLrHJT0uUYikR7EhllxNPy+NNwPpjFi/EL2vhuUZjmH8jnradEr9atQinRHrF6L31bA8wzFM2hPvV11K/WrUIp0R6xei99WwPMMxjN8Epk1r9Nu8tOc7MHgTrF3du2rJBeOtyES1DKxsXAuL82Dwuz1D5bNnfeh+9wlO75d0v7F+IXpfDcszHMN4H5RCoLIC1T4yva8I91zgWwH8X+UH7lFM7wbwucpjlVSKZUOKDTG7UvKcxpuBdEasX4jeV8PyDMcw8pORn2NK/WrUIp0R6xei99WwPMMxTH7TLJ+cUr8atUhnxPqF6H01LM9wDCM/pfk5Zt1vc0vPxwF80AK/GyztOfXmlW/d7LshHuho/DyAdw4/maWEs+pa+28PXTHpfmP9QvS+GpZnOIYJPTaFLtsVqLYhfi2AUwG4DfFPAOz7gr97/JF7PrD70v6mbEv0dy82xP5rJqXQeDOQzoj1C9H7alie4RhGah5S8EmpX41apDNi/UL0vhqWZziGSWGupWpIqV+NWqQzYv1C9L4almc4hpGazRR8suy3ubXnIlh8ZfjBSGbotes6z//pRD2PVcvAioarYIy7i/QeC7Ogvm3zzTHrJt1vrF+I3lfD8gzHMDHHp9BmtwLVNsRXAlg1Trz7G6gTsist3LnYEIevXaxS481AOiPWL0Tvq2F5hmOY2DlISZ9Svxq1SGfE+oXofTUsz3AMk9J8x9aSUr8atUhnxPqF6H01LM9wDBM7kynps+r3jGVrX2DKQzcamNkw5pLezkXXVut7dC07OhouszCfcTpTsudPb93SXc2j2u+l+431C9H7alie4Rim2jEofp/PClTbEE8B4B655O52948AfjiizG0AbD5lT5xabIjzOyoabwbSGbF+IXpfDcszHMPkN0HyySn1q1GLdEasX4jeV8PyDMcw8lOan2NK/WrUIp0R6xei99WwPMMxTH7TLJ+cVb/NLd0/AcyrAXtNb9eSy5jKR9ays3P2BeWy/WZF9566tn73Z/Tol3S/sX4hel8NyzMcw0QfpMIgkxWotiHeF+o2xRcBcI9hcq8SgOcAOAfA9kwqizAtNsQRixcp1XgzkM6I9QvR+2pYnuEYJnIMkpKn1K9GLdIZsX4hel8NyzMcwyQ14JHFpNSvRi3SGbF+IXpfDcszHMNEjmRS8iz6bV7a/SUYc7G1uLW8Y+bpN33uDPdEl6qvfbVs75h9qoH9/rDA4iN1y/s/VlVMAtL9xvqF6H01LM9wDEMeigJTXgF2Q9wLoGmM2mYUG2LlIzZGXEonoEYt0hmxfiF6Xw3LMxzD5D/VchWk1K9GLdIZsX4hel8NyzMcw8hNZ/5OKfWrUYt0RqxfiN5Xw/IMxzD5T7VcBdL9Lmhdc4W19hMAHqixpdd9b9XC37HVuloa//UVL0GNdd8TPtQac039ss3U1WWfjImedcz67ONi1y9E76theYZjGN81LHidFWA2xJPcl/Urj2A6E8ANAIYqN9Vyj2NK7mPTxRVineEZK0XjzUA6I9YvRO+rYXmGY5j8Jkg+OaV+NWqRzoj1C9H7alie4RhGfkrzc0ypX41apDNi/UL0vhqWZziGyW+a5ZMl+21auuZUY6z7CuI0A/PmdV2Lvu1T8T2//Td72Lpz/2iAZ1qY1fVtm8d9RJOP70hWsl/nG+sXovfVsDzDMUzosSl02a4AsyF2H492G+BPA9gJ4IUArgDw+8rHpu/ItkR/92JD7L9mUgqNNwPpjFi/EL2vhuUZjmGk5iEFn5T61ahFOiPWL0Tvq2F5hmOYFOZaqoaU+tWoRToj1i9E76theYZjGKnZTMFHqt+zr7j+iD2THr8FwN9Y2A+v71pylU9/tn3O9G2z/2qg9sHhC8o3T99TOse0b9rh48GwUv3uy4r1C9H7alie4RiGOQ4Fo78CzIbYVfV1AG8FsATAyLvYFR+Z1j9mT0lM6QTUqEU6I9YvRO+rYXmGY5gExlqshJT61ahFOiPWL0Tvq2F5hmMYseFMwCilfjVqkc6I9QvR+2pYnuEYJoGxFitBqt8FLT1rLXCeAa5b17V4oW+BAx2N7tOZCwD82kyqedP0pX33+XowvFS/xYaYWe2CSWkF2A2xu9v0GwH8CIA7kV8MYA2A21JqZl8txRXi/I6K9JvpWJ1IZ8T6heh9NSzPcAyT3wTJJ6fUr0Yt0hmxfiF6Xw3LMxzDyE9pfo4p9atRi3RGrF+I3lfD8gzHMPlNs3yyRL8LlnZ3WGOWAfaOofLQG276+Fu8NrPbOxq/aoALh+oaURrY9Df1bff/Vr7TvY4S/Y6sLdYvRO+rYXmGY5isjl3hG7cCzIbYfYf4YQDrAIh/XyGu/LHVxYY4i1XlPDXeDKQzYv1C9L4almc4huGO9v5BpdSvRi3SGbF+IXpfDcszHMPsH5PPVZlSvxq1SGfE+oXofTUsz3AMw03e/kHF9tu8tOdtMPia69ag/Lp1Xef/2KfzHSsbP2EtrjDA9odO+Vz9USe5i8zZvWL7HV1ZrF+I3lfD8gzHMNkdvcI5ZgXYE+uzAP4ewBsA/N+IG2ltLm6qFbP8MtqUTkCNWqQzYv1C9L4almc4hpGZzDRcUupXoxbpjFi/EL2vhuUZjmHSmGyZKlLqV6MW6YxYvxC9r4blGY5hZCYzDZeYfs9YuvavS6bsntIyz1q8a/2qxZ/36Wr7ioYWY0yn05StefP9S277luQdoMeqJabfLPxC6vHVsDzDMYzPDBSs3gqwG+ItAI4Yo6yDADyqVy6XVFwh5tYpC0rjzUA6I9YvRO+rYXmGY5gsZiMvz5T61ahFOiPWL0Tvq2F5hmOYvGY5i9yU+tWoRToj1i9E76theYZjmCzmNC/PmH6bW3p+AOD1sPh876rF7/LpYaCj4R2AuXZYY+wldcu2XBtTC5stnRHrF6L31bA8wzEMeywKTncF2A1xC4C6MUpbAYB6oLhmW8WGWHO1n5yl8WYgnRHrF6L31bA8wzFMfhMkn5xSvxq1SGfE+oXofTUsz3AMIz+l+Tmm1K9GLdIZsX4hel8NyzMcw+Q3zfLJof02tfT8owHeBdjbzNT6U9e1L6DvCD2wouEsGPNd140FWurb+le5fw+txWdVpDNi/UL0vhqWZziG8TkeBau3AuyG+ODhrz889eW+W5zcq9gQ53dINN4MpDNi/UL0vhqWZziGyW+C5JNT6lejFumMWL8Qva+G5RmOYeSnND/HlPrVqEU6I9YvRO+rYXmGY5j8plk+OaTf5qU9l8HgMxbYZkul19y4cuF/s5XtWDnn5daWb3YXoazFJ+uX939wnzakFjY3q4zYmkP0vhqWZziG8T0mBa+zAuyGuPjItM7xCEpJ6UvhxaQAACAASURBVATUqEU6I9YvRO+rYXmGY5igQUxUlFK/GrVIZ8T6heh9NSzPcAyT6KgHlZVSvxq1SGfE+oXofTUsz3AMEzSIiYp8+21q+dfXG9R8G8BMUzKL1q1ctJZtbddVc55VLpVvtsDRMPhq3bL+/zdS61sLm5tlRmzNIXpfDcszHMOEHJdCk/0KsBvijwJwzxx2r3oAiwHcC+BFxUemsz9I1RJSOgE1apHOiPUL0ftqWJ7hGKbazO1Pv0+pX41apDNi/UL0vhqWZziG2Z/mv1qtKfWrUYt0RqxfiN5Xw/IMxzDVZm5/+r1Pv2e3fu3QQUz5vrV4CWCv6u1a8mG210dXzj20xg7dUvlz9brpz+g/2yzEUJab1bFq8+mX6S3WL0Tvq2F5hmMYZt0KRn8F2A3x6MouAvAVAIcAeES/7IkTi49M53dENN4MpDNi/UL0vhqWZziGyW+C5JNT6lejFumMWL8Qva+G5RmOYeSnND/HlPrVqEU6I9YvRO+rYXmGY5j8plk+2affppbu1QZmCSy+27tq8dk+1WzvaLjFwLgnufzHdIOzzLL+B0brfWrxyc5y0x1bc4jeV8PyDMcwocem0GW7AuyGeAGAqZVSagC8BUATgPkA7sm2RH/3YkPsv2ZSCo03A+mMWL8Qva+G5RmOYaTmIQWflPrVqEU6I9YvRO+rYXmGY5gU5lqqhpT61ahFOiPWL0Tvq2F5hmMYqdlMwYftt7mlux0wH7EWd9lJQ6+7seOCDWz9O1bOXm2tXWKAPw6a0lkzl226YywtWwubq5ERW3OI3lfD8gzHMDHHp9BmtwLshnis7xD/G4BXFs8hzu7gsM4pnYAatUhnxPqF6H01LM9wDMPO3v7ApdSvRi3SGbF+IXpfDcszHMPsD3PP1phSvxq1SGfE+oXofTUsz3AMw87e/sAx/S5Y2n2+NeZfXT+lcvmNN3z8fPe4Jeo1sLLxc7B4D4CHjS0vmL78/n8fT8jUQoVOAElnxPqF6H01LM9wDBN7jAp9NivAbohfP+IKsa18f9j9Ddbj2ZQV51pcIY5bvxi1xpuBdEasX4jeV8PyDMcwMTOQmjalfjVqkc6I9QvR+2pYnuEYJrUZj6knpX41apHOiPUL0ftqWJ7hGCZmHlPTVuu36cqe58BgnTE4DsZe1tu55Bq2h4GOxo8AaB/mjV1Qt2xL70TaarWwuZoZsTWH6H01LM9wDCNxnAoP+RVgNsQNAM4H8EwAmwC4K8O3A3hMvhwZx2JDLLOOIS4abwbSGbF+IXpfDcszHMOEHPtUNSn1q1GLdEasX4jeV8PyDMcwqc56SF0p9atRi3RGrF+I3lfD8gzHMCFzmKqmWr/NLT03AjgdwJd7uxZfzPaxY0Xje6zB5xxvgb+tb+v/ejVttVqq6ZnfS2fE+oXofTUsz3AMwxyHgtFfgWob4mcD+L8xynKb4ZcD+J1+ydUTiw1x9TXKitB4M5DOiPUL0ftqWJ7hGCar+cjDN6V+NWqRzoj1C9H7alie4RgmjznOKjOlfjVqkc6I9QvR+2pYnuEYJqtZzcN3on6bW3o+A+AywP6iVJ7+hhs+fhZ1oeixjobFJZjuvZthc3l92+ZPM71prL10RqxfiN5Xw/IMxzDMsS4Y/RWotiG+FcBrAFzh7nwHoAzgFAAdAH4O4CT9kqsnFhvi6muUFaHxZiCdEesXovfVsDzDMUxW85GHb0r9atQinRHrF6L31bA8wzFMHnOcVWZK/WrUIp0R6xei99WwPMMxTFazmofveP02L+1+N4z5BwA7LEqvWt+18NdMfduvani9KZnvA6gBbEdd25bljM4xGmsvnRHrF6L31bA8wzEMe7wLTncFqm2I3feFbwZw2qiyrgbw/sr3infrllw9rdgQV1+jrAiNNwPpjFi/EL2vhuUZjmGymo88fFPqV6MW6YxYvxC9r4blGY5h8pjjrDJT6lejFumMWL8Qva+G5RmOYbKa1Tx8x+q3uaX7NUDpW4A91Fh7wbpVS1YztW3vOPwFBjVuM9wA2C/WtW15J6Pbx2isvXRGrF+I3lfD8gzHMD7HvGD1VoDZEH8IwIpRJV0K4AsADgLwqF65XFKxIebWKQtK481AOiPWL0Tvq2F5hmOYLGYjL8+U+tWoRToj1i9E76theYZjmLxmOYvclPrVqEU6I9YvRO+rYXmGY5gs5jQvz9H9vn7p2oOmGnsTYE+CRVfvqsWtTG07Vhw5t2wGbzHAcwF8u66t/82MbiSjsfbSGbF+IXpfDcszHMP4HveC11kBZkPsPhr901HlvAiAe4B4sSHWOU4TpqR0AmrUIp0R6xei99WwPMMxTAJjLVZCSv1q1CKdEesXovfVsDzDMYzYcCZglFK/GrVIZ8T6heh9NSzPcAyTwFiLlTC63+aWnq8BeJuBXb+ua0kzE2SvOX7Kzse232KBVxmDn+wq7X7TIS2PbGO0xYb4LnvcccdV26s8aSl9Z5TlGY5hfI97weusQLUhcx+ZnuhVbIh1jlOxIR6xAtJvOLF+IXpfDcszHMMkMNZiJaTUr0Yt0hmxfiF6Xw3LMxzDiA1nAkYp9atRi3RGrF+I3lfD8gzHMAmMtVgJI/td0NLTZvd+YnKjxdBr13dd8GcmaKCj4XrAnA3gf0ple+a0D225m9GNZjTWXjoj1i9E76theYZjmJBjX2iyX4FqG+Ljq5Tg3gzcjbaSehUfmc7vcGi8GUhnxPqF6H01LM9wDJPfBMknp9SvRi3SGbF+IXpfDcszHMPIT2l+jin1q1GLdEasX4jeV8PyDMcw+U2zfPK+fptaes4zsD2AKZVRPv3GrvPdd4GrvgY6Zl8L2HfAoh81prmudTN1862xjDXWXjoj1i9E76theYZjmKpDUwC5rEC1DXEuRcWGFhvi2BUM12u8GUhnxPqF6H01LM9wDBM+AekpU+pXoxbpjFi/EL2vhuUZjmHSm/LwilLqV6MW6YxYvxC9r4blGY5hwqcxPaXr971fvv1ZJYvvAXi2hblifdeiTzGV7lgxu9Ma22IM9pRsqWlq26YfMLrxGI21l86I9QvR+2pYnuEYJmYGCm12K1BsiLNbWzXnlE5AjVqkM2L9QvS+GpZnOIZRG16FoJT61ahFOiPWL0Tvq2F5hmMYhbFVi0ipX41apDNi/UL0vhqWZziGURtehSDX72XX/uoGwJ5pYL+2rmvJhUzs9hWNVxiDTzjWlOzi6a1b1jC6iRiNtZfOiPUL0ftqWJ7hGCZ2Dgp9NitQbIizWVdV15ROQI1apDNi/UL0vhqWZziGUR3gjMNS6lejFumMWL8Qva+G5RmOYTIeWVX7lPrVqEU6I9YvRO+rYXmGYxjVAc447OrVP7E//m0/YPGfk6ZNed1328/eWi1yZ0fjhWXgq8Ocxbvrlvf/UzUN83uNtZfOiPUL0ftqWJ7hGIY51gWjvwLFhlh/zcUTUzoBNWqRzoj1C9H7alie4RhGfEhzNEypX41apDNi/UL0vhqWZziGyXGcxaNT6lejFumMWL8Qva+G5RmOYcSHNCfDppaeSwzwRWvtHjOp5hW9HQt/Wa2UgZUNzbBm3TBn8JG6Zf0fq6Zhf6+x9tIZsX4hel8NyzMcw7DHu+B0V6DYEOuudyZpKZ2AGrVIZ8T6heh9NSzPcAyTyaDmZJpSvxq1SGfE+oXofTUsz3AMk9MoZxKbUr8atUhnxPqF6H01LM9wDJPJoCqbNi9d+wqY8nUAGq0xF67vXOQetzTha8eKhhOtMe5mW+4JLJ+ra+v/+2oan99rrL10RqxfiN5Xw/IMxzA+x7xg9Vag2BDrrXVmSSmdgBq1SGfE+oXofTUsz3AMk9mw5mCcUr8atUhnxPqF6H01LM9wDJPDGGcWmVK/GrVIZ8T6heh9NSzPcAyT2bAqGb/xiq/XTZ40+SYArzzl+Y24/PzXVP2z8rbOw4+fVK65GcCxBmb19LbNF0iXq7H20hmxfiF6Xw3LMxzDSM9F4SezAlVPcpkYXZfiLtO66z0yTePNQDoj1i9E76theYZjmPwmSD45pX41apHOiPUL0ftqWJ7hGEZ+SvNzTKlfjVqkM2L9QvS+GpZnOIbJb5plkpuW9nzZGLwdwC2fvfilbzzuuOMm/LPyI+2zZk2uneo2wyfA2lumD047y7Tfs0ummr+4aKy9dEasX4jeV8PyDMcw0nNR+MmsQNYb4kkA6gFMdBOCGQAeG9VOCcA0AAOjfl4HYGe1Zx8XG2KZ4Qhx0XgzkM6I9QvR+2pYnuEYJuTYp6pJqV+NWqQzYv1C9L4almc4hkl11kPqSqlfjVqkM2L9QvS+GpZnOIYJmcNUNM2ta66Etass7GYzVH71Z9954h+rbYgHOhpvBHA6gP/EpMEFdUsf3JRFPxprL50R6xei99WwPMMxTBazUXjGr0CWG+LLAVwK4BcAZgJYCuAPI0p+MYCvANgA4Ghg+G/jfgXgIgDvA9AHwG2o3cdOLIDVAAYrrLuV/b+M136xIY4fjFAHjTcD6YxYvxC9r4blGY5hQo9/irqU+tWoRToj1i9E76theYZjmBTnPLSmlPrVqEU6I9YvRO+rYXmGY5jQWcxbt6B1zZustT0Aplhrm9evWrK+Wr87VjZ+3Vq81QAbamCbp7Rt+Z+s+qhWi0SudEasX4jeV8PyDMcwEsep8JBfgaw2xJMB7K5cHXZXeZcDaADw3hEt3ALgk+4jKQDOrWyezwCwB8AsANsAXANgc2VD7K4kt7kbHFR+5q4W7xhrSYoNsfygsI4abwbSGbF+IXpfDcszHMOwx3t/4FLqV6MW6YxYvxC9r4blGY5h9oe5Z2tMqV+NWqQzYv1C9L4almc4hmFnLyWuqeVfjzWo+S6Av4ZFa++qxV2uvon63b6y4dPGGncBZ6Bs0DRjWf9Ps+xJY+2lM2L9QvS+GpZnOIbJckYK7/AVyGpD7Co6GMAjAKYD+BGAzwJwf/O273UvgJMBuH++EIC7M9+JAH4I4LgK5DbQL6hsiN3Pnd7VXK4wfy42xBO/YYePRphS481AOiPWL0Tvq2F5hmOYsKOfpiqlfjVqkc6I9QvR+2pYnuEYJs1JD6sqpX41apHOiPUL0ftqWJ7hGCZsEvNVNbes+RZgz4XB6t7OxU/cEGu8fnd2zG4rw65wVRtrzpm+fPN3su5AY+2lM2L9QvS+GpZnOIbJek4K/7AVyHJD7Cp6EfY+nNx9hMR9JHrkTQYeBfCsytXeYwG4v1lrAuBuc+9+7l5vBfDqykeu1wL4VuXnWwC8DMA9nZ2d7caYj4xu/7zzzgtbkUJVrECxAsUKFCtQrECxAsUKFCvwtFiB3tvvww/+ezPmHDIN72l6Nuqmum/rjf+afud3MfNX7pt7wLYTWrDz2AVPi3UqmuRWoNp3zjmXgtJegSw3xK+rfO/XPYdtzRiN/QzA+wH8GsBLAHwYwKLKx6DdTbXc94bd793LfVzabaA/A6CmcuXZfazaXSl+yqv4yLT2GP0lT+Nvx6QzYv1C9L4almc4hslvguSTU+pXoxbpjFi/EL2vhuUZjmHkpzQ/x5T61ahFOiPWL0Tvq2F5hmOY/KbZP7m5teci2OH72QCmdHJv58L/GOkyut/HVjSeVzJwF2hgrW2tX75l+KPVGi+NtZfOiPUL0ftqWJ7hGEZjVooM/xXIakPsfN13gF8P4JcjynJ3nH4OgNsBfArAgwA+Xvku8XYAHwLwGwDvAvA7AO429u7q71QA7wHwRgDu0q+7YddJ47VbbIj9B0FKofFmIJ0R6xei99WwPMMxjNQ8pOCTUr8atUhnxPqF6H01LM9wDJPCXEvVkFK/GrVIZ8T6heh9NSzPcAwjNZtZ+zS3rj4JKK2BxVEALu7tWvzl0Zkj+93Z2fiacnn4631TjMEnpy/r/2DWNU60Oc8iW/r4xvqF6H01LM9wDJPFcSs841cgqw3x8QD+NKq8rwH4BwA/rnwE2n1P+N8rjHssk9vgPgzgTADfrPx8PYDzKxtid1v751Yex/SGyt2rx1yBYkMcPxihDhpvBtIZsX4hel8NyzMcw4Qe/xR1KfWrUYt0RqxfiN5Xw/IMxzApznloTSn1q1GLdEasX4jeV8PyDMcwobOoqXtN+1en1u+auh4wpwD2mt6uJZeNlb+v390dDc8bHL7XjZnrnnJS19bvnoii+tJYe+mMWL8Qva+G5RmOYVSHpgijVyCrDfF4BbgbbLk7R7+jArgvasyp3FjLfUR638txB1W+XzzSy/0tXn/lTtTjNllsiOnjLw5qvBlIZ8T6heh9NSzPcAwjPhg5GqbUr0Yt0hmxfiF6Xw3LMxzD5DjO4tEp9atRi3RGrF+I3lfD8gzHMOJDmoFhc0v35wHzTmNx67ppd7we7e1jfgXP9dvw7RNnmz017hOLz4dF7/TB/rNM+9hf2cug1CcsNdZeOiPWL0Tvq2F5hmOYLGek8A5fAe0NsXtkktvojnwecXj14yiLDbH4ktKGGm8G0hmxfiF6Xw3LMxzD0Ad8PwBT6lejFumMWL8Qva+G5RmOYfaDsadLTKlfjVqkM2L9QvS+GpZnOIahhy8ncEHLmvdb2Kth8aAplV+5rvP8O8Yr5a47/2gb1rzqVgO81gD/MbindObM9k3u637qL421l86I9QvR+2pYnuEYRn1wikBqBbQ3xFRRsVCxIY5dwXC9xpuBdEasX4jeV8PyDMcw4ROQnjKlfjVqkc6I9QvR+2pYnuEYJr0pD68opX41apHOiPUL0ftqWJ7hGCZ8GrNXNi1d22RM2T3Cs94Yc/a6zkXu2cPjvh76+vl26r0/hoX5U9mUmmcu6/tj9lWOnaCx9tIZsX4hel8NyzMcw+Q1P0XuxCtQbIgPgAlJ6QTUqEU6I9YvRO+rYXmGY5gD4LR4ooWU+tWoRToj1i9E76theYZjmOL8yGYFNNZeOiPWL0Tvq2F5hmOYbKYj3rV56dp5KJW/A4sXWdgPr+9actVErgMdjZ8H8E4LPFKy9ozpy7f8PL6KcAeNtZfOiPUL0ftqWJ7hGCZ8AgpllitQbIizXF0l75ROQI1apDNi/UL0vhqWZziGURpdlZiU+tWoRToj1i9E76theYZjGJXBVQpJqV+NWqQzYv1C9L4almc4hlEaXe+Y5taeblgsNsB167oWL5xwM7yi4SoYs3yYMWiqW9bvbuya60tj7aUzYv1C9L4almc4hsl1iIrwcVeg2BAfAMOR0gmoUYt0RqxfiN5Xw/IMxzAHwGlRXCEWOoix8xKi99WwPMMxjNDSJmGTUr8atUhnxPqF6H01LM9wDJPEYI8qorml+2OA+RBg76gdnPLq73zynPvHq3P7itnvM8Z+2v1+24kfwpzXvTuJPytrrL10RqxfiN5Xw/IMxzApnh9FTe7vvQ7AV/Ed4vwOqsabgXRGrF+I3lfD8gzHMPlNkHxySv1q1CKdEesXovfVsDzDMYz8lObnmFK/GrVIZ8T6heh9NSzPcAyT3zSPndy0dM1bjLHfGP6tLb2yd9XC28arcWfH7LeUsZc1MJdvXnzb1ccdd1wSf1bWWHvpjFi/EL2vhuUZjmFSOz+KevauQBInufTBKDbE0ivK+2m8GUhnxPqF6H01LM9wDMMf8fTJlPrVqEU6I9YvRO+rYXmGY5j0p56vMKV+NWqRzoj1C9H7alie4RiGn77syQUtPS+1FmtgMN9avGv9qsXue8FjvgY6Gk8HsPej0dasrFu+uS2lfjVqkc6I9QvR+2pYnuEYJvupLxJCVqDYEIesWmKalE5AjVqkM2L9QvS+GpZnOIZJbMSjykmpX41apDNi/UL0vhqWZziGiRrIxMQp9atRi3RGrF+I3lfD8gzHMKmM+Gvab51Uv3tLLyxOhcHnezsXv2vczXBn40tRxk0ADgXsl+ratlzq2JT61ahFOiPWL0Tvq2F5hmOYVM6Poo4nr0CxIT4AJiKlE1CjFumMWL8Qva+G5RmOYQ6A0+KJFlLqV6MW6YxYvxC9r4blGY5hivMjmxXQWHvpjFi/EL2vhuUZjmGymQ5/1wWtPddYi/cC9rbNDz96yq+/dOmesVy2XtUwf1LJfN8AzwTw7bq2/jfv41LqV6MW6YxYvxC9r4blGY5h/Ce5UGisQLEh1ljljDNSOgE1apHOiPUL0ftqWJ7hGCbjkVW1T6lfjVqkM2L9QvS+GpZnOIZRHeCMw1LqV6MW6YxYvxC9r4blGY5hMh5Zyr65pec9AD5ngW0lW375ulXn/34sof3Hw+t3bp10s4U92QI/rZs0uMAsffCxYkNMLXNVKHZeQvS+GpZnOIapumgFkMsKFBviXJZdNjSlE1CjFumMWL8Qva+G5RmOYWQnNF+3lPrVqEU6I9YvRO+rYXmGY5h8J1o2PaV+NWqRzoj1C9H7alie4RhGdkL93ZqWrjnVGNsN4GALLFzftfi68VwGOhpvALDAWvy+BqZp2vLNG0ayKfWrUYt0RqxfiN5Xw/IMxzD+E10oNFag2BBrrHLGGSmdgBq1SGfE+oXofTUsz3AMk/HIqtqn1K9GLdIZsX4hel8NyzMcw6gOcMZhKfWrUYt0RqxfiN5Xw/IMxzAZj+yE9s1ta+diqPxtAC8DzFW9XYs+PO5meGXjV2BxEQz6YUpn1LVu+q/RbEr9atQinRHrF6L31bA8wzFMnudHkT3+ChQb4gNgOlI6ATVqkc6I9QvR+2pYnuEY5gA4LZ5oIaV+NWqRzoj1C9H7alie4RimOD+yWQGNtZfOiPUL0ftqWJ7hGCab6eBcm1q6v2Fg3gKL7/auWnz2eKrtKxo/YQyuADBYgj1tWtuWH43FptSvRi3SGbF+IXpfDcszHMNwk1xQ2itQbIi1VzyDvJROQI1apDNi/UL0vhqWZziGyWBMc7NMqV+NWqQzYv1C9L4almc4hsltmDMITqlfjVqkM2L9QvS+GpZnOIbJYEwpy6aW7g8ZmI9Zi7sGa2pfefPKczePJdy+oqHFGNPpfmesXTx9+ZY14wWk1K9GLdIZsX4hel8NyzMcw1DDXEDqK1BsiNWXXD4wpRNQoxbpjFi/EL2vhuUZjmHkpzQ/x5T61ahFOiPWL0Tvq2F5hmOY/KZZPjmlfjVqkc6I9QvR+2pYnuEYRn5Kqzs2t3QvAkyPI23JnLJ+5aJbx1INdDS8AzDXVn73nrq2/n+cyD2lfjVqkc6I9QvR+2pYnuEYpvo0F0QeK1BsiPNYdeHMlE5AjVqkM2L9QvS+GpZnOIYRHtFc7VLqV6MW6YxYvxC9r4blGY5hch1o4fCU+tWoRToj1i9E76theYZjGOERrWp3Zkv3C8t7N8PPhDGX9XYuumYs0Y4Vs8+2xl5f+V17XVv/R6uZp9SvRi3SGbF+IXpfDcszHMNUm7ni9/msQLEhzmfdRVNTOgE1apHOiPUL0ftqWJ7hGEZ0QHM2S6lfjVqkM2L9QvS+GpZnOIbJeaRF41PqV6MW6YxYvxC9r4blGY5hRAeUMGtu6VkHoBnAl3u7Fl88lmTnytmvLFt7E4A69zimurb+vyeskVK/GrVIZ8T6heh9NSzPcAzDzF3B6K9AsSHWX3PxxJROQI1apDNi/UL0vhqWZziGER/SHA1T6lejFumMWL8Qva+G5RmOYXIcZ/HolPrVqEU6I9YvRO+rYXmGYxjxIZ3AsHlpz6dgcDmAX2yfuvM1P2m/aNdofPdVs58zVLI3WeBoa0x3/bLN57M1ptSvRi3SGbF+IXpfDcszHMOws1dwuitQbIh11zuTtJROQI1apDNi/UL0vhqWZziGyWRQczJNqV+NWqQzYv1C9L4almc4hslplDOJTalfjVqkM2L9QvS+GpZnOIbJZFDHMF3Q2nOptfgCgB2lUvmkG1ae/9vR2GMrGw8vWXwfwIss7A/q9hzabNr/93G2xpT61ahFOiPWL0Tvq2F5hmMYdvYKTncFig2x7npnkpbSCahRi3RGrF+I3lfD8gzHMJkMak6mKfWrUYt0RqxfiN5Xw/IMxzA5jXImsSn1q1GLdEasX4jeV8PyDMcwmQzqKNMFLatPAWq6LewRxtoL1q1asnp0rgXMQEfDzQbmDQD+Exhsrmt7cMw7T49Xcyr9uvo0apHOiPUL0ftqWJ7hGEbj/Cgy/Feg2BD7r1lyipROQI1apDNi/UL0vhqWZziGSW7IIwpKqV+NWqQzYv1C9L4almc4hokYx+SkKfWrUYt0RqxfiN5Xw/IMxzBZD/mZy1Y3lMs13wLsK2DQ1du5uHWszB0rZ6+21i4BsLE8hDNmfLj/9761pdDvvpo1apHOiPUL0ftqWJ7hGMZ3BgteZwWKDbHOOmeaktIJqFGLdEasX4jeV8PyDMcwmQ6ssnlK/WrUIp0R6xei99WwPMMxjPIIZxqXUr8atUhnxPqF6H01LM9wDJPpwLq7Zy3t+QoMLjIw69d1LXI303rKa2Bl4+dg8Z7hj1Mbc9q0ZZv/v5C6Uui32BAf57VX8T1mLM9wDBMyh4Um+xXwGrLsy5FJ6Orqsi0tLQdkb2OtUEonoEYt0hmxfiF6Xw3LMxzDyJyJabik1K9GLdIZsX4hel8NyzMcw6Qx2TJVpNSvRi3SGbF+IXpfDcszHMPITObYLs1Le1pg0Omu+g6Va19+08fPvW80OdDR+BEA7e7nxppzpi/f/J3QmvLud2TdGrVIZ8T6heh9NSzPcAwTOouFLtsVOCA3jcWGONuhmchd481AOiPWL0Tvq2F5hmOY/CZIPjmlfjVqkc6I9QvR+2pYnuEYRn5K83NMqV+NWqQzYv1C9L4almc4hslqmptae86BtT0GphbAqb1di28ZnbVjReN7rMHnhn9etpfUfWjLtTH15Nnv6Lo1apHOiPUL0ftqWJ7hGCZmHgttditQbIizW1s155ROQI1apDNi/UL0vhqWZziGURtehaCU+tWoRToj1i9E76theYZjGIWxVYtIqV+NWqQzS+Lc7AAAIABJREFUYv1C9L4almc4hslieJuW9TzPlNED4K8szBXruxZ96imb4Y6GxRam2/3cGttav2xLV2wtefU7Vt0atUhnxPqF6H01LM9wDBM7k4U+mxUoNsTZrKuqa0onoEYt0hmxfiF6Xw3LMxzDqA5wxmEp9atRi3RGrF+I3lfD8gzHMBmPrKp9Sv1q1CKdEesXovfVsDzDMUwWA9zcsuZ6wJ5tYL+2rmvJhaMztncc8QaD0k0Aaizwqfq2/isk6sir32JDvHcFQtbfV8PyDMcwEnNZeMivQLEhll9TdceUTkCNWqQzYv1C9L4almc4hlEf4gwDU+pXoxbpjFi/EL2vhuUZjmEyHFd165T61ahFOiPWL0Tvq2F5hmMY6SFe0LKmy8IuhcV/Pj70+Ktu+eTbBkZmDHTMfhFgbwTQAOBf6tr6L5KqIY9+x6tdoxbpjFi/EL2vhuUZjmGkZrPwkV2BYkMsu565uKV0AmrUIp0R6xei99WwPMMxTC6DnFFoSv1q1CKdEesXovfVsDzDMUxGo5qLbUr9atQinRHrF6L31bA8wzGM5CA3Xdn9dlMyX7awe0zJnNi7cvF/jvTfsWLOUWVT/r4BnguD3rpl/Qsk87X7nah2jVqkM2L9QvS+GpZnOIaRnM/CS24Fig2x3Frm5pTSCahRi3RGrF+I3lfD8gzHMLkNcwbBKfWrUYt0RqxfiN5Xw/IMxzAZjGlulin1q1GLdEasX4jeV8PyDMcwUsPc1Nr9SmOHvxM81xpz4frORV8b6W2vPnLazp2D37fAqwD8fHDPpKaD2u97WCrf+Wj2W61ujVqkM2L9QvS+GpZnOIapdpyL3+ezAsWGOJ91F01N6QTUqEU6I9YvRO+rYXmGYxjRAc3ZLKV+NWqRzoj1C9H7alie4Rgm55EWjU+pX41apDNi/UL0vhqWZziGkRjQBR9YfZitLV0H4DUAPtXbtfgp3wke6Gi4HjBnA7izpjR0+tTWB+6UyB7podUvU7dGLdIZsX4hel8NyzMcwzDHumD0V6DYEOuvuXhiSiegRi3SGbF+IXpfDcszHMOID2mOhin1q1GLdEasX4jeV8PyDMcwOY6zeHRK/WrUIp0R6xei99WwPMMxjMSQLmjp+aIFLgFwS2/X4lNHew6snP0lWHsxgEeMsadPX7blFxK5oz20+mVq16hFOiPWL0Tvq2F5hmMY5lgXjP4KFBti/TUXT0zpBNSoRToj1i9E76theYZjGPEhzdEwpX41apHOiPUL0ftqWJ7hGCbHcRaPTqlfjVqkM2L9QvS+GpZnOIaJHdKmljUfMLCftLCbbU35pBs7Ltgw0nPHitmd1tgW97Mag6apy/rdDbUyeWn0yxauUYt0RqxfiN5Xw/IMxzDs8S443RUoNsS6651JWkonoEYt0hmxfiF6Xw3LMxzDZDKoOZmm1K9GLdIZsX4hel8NyzMcw+Q0ypnEptSvRi3SGbF+IXpfDcszHMPEDGrz0jULYKx73vB0a0vN61ctXD/Sb/uKxiuMwSfczyzwt/Vt/V+Pyaumzbrfavkjf69Ri3RGrF+I3lfD8gzHMD7HvGD1VqDYEOutdWZJKZ2AGrVIZ8T6heh9NSzPcAyT2bDmYJxSvxq1SGfE+oXofTUsz3AMk8MYZxaZUr8atUhnxPqF6H01LM9wDBM6rAtaVz/blkvdMHgBDFp7Oxd3PWkz3NF4oQG+OrwZtuYD9cs3Xx2axeqy7JetYR+nUYt0RqxfiN5Xw/IMxzC+x73gdVag2BDrrHOmKSmdgBq1SGfE+oXofTUsz3AMk+nAKpun1K9GLdIZsX4hel8NyzMcwyiPcKZxKfWrUYt0RqxfiN5Xw/IMxzChA9vUumaNsXYhDFb3di6+YKTPwIqGBTDmhuGfGbOybtnmttAcH12W/frU4ViNWqQzYv1C9L4almc4hvE97gWvswLFhlhnnTNNSekE1KhFOiPWL0Tvq2F5hmOYTAdW2TylfjVqkc6I9QvR+2pYnuEYRnmEM41LqV+NWqQzYv1C9L4almc4hgkZ2ObWNVfB2uUAfjc0MPiKmz73lkf3+exYecRJZVu6yQAHAfZLdW1bLg3JCNFk1W+qtUj3G+sXovfVsDzDMUzIsS802a9AsSHOfo0zT0jpBNSoRToj1i9E76theYZjmMyHVjEgpX41apHOiPUL0ftqWJ7hGEZxfDOPSqlfjVqkM2L9QvS+GpZnOIbxHdrmpT1vg8HwM4bLxp54Y+eSJ+4Y/ejKuc+cZIdutMBxAK6va+s/19c/hs+i39B6NGqRzoj1C9H7alie4Rgm9PgXumxXoNgQZ7u+Ku4pnYAatUhnxPqF6H01LM9wDKMyuEohKfWrUYt0RqxfiN5Xw/IMxzBKo6sSk1K/GrVIZ8T6heh9NSzPcAzjM7gLruw5ESV0W+AYABf3di3+8j79tvYjD5lUO3gTgBOsxc/qptgzzAe3DPj4x7LS/cbUo1GLdEasX4jeV8PyDMcwMTNQaLNbgWJDnN3aqjmndAJq1CKdEesXovfVsDzDMYza8CoEpdSvRi3SGbF+IXpfDcszHMMojK1aREr9atQinRHrF6L31bA8wzEMO7xvet9XZ+2ZMnWtMeYNgLmmt2vRZSO12zsabzTA6TD4vSmXTp++fNO9rLcUJ9lvbE0atUhnxPqF6H01LM9wDBM7B4U+mxUoNsTZrKuqa0onoEYt0hmxfiF6Xw3LMxzDqA5wxmEp9atRi3RGrF+I3lfD8gzHMBmPrKp9Sv1q1CKdEesXovfVsDzDMQw7wM2t3f8Aa95tLG5dt2rxKSN1O1Y2ft1avBXAllrUnDa5re+/WV9JTrLf2Lo0apHOiPUL0ftqWJ7hGCZ2Dgp9NitQbIizWVdV15ROQI1apDNi/UL0vhqWZziGUR3gjMNS6lejFumMWL8Qva+G5RmOYTIeWVX7lPrVqEU6I9YvRO+rYXmGYxhmgJtb1/w9rP0sgAcnTcKJ312x+K59uu0rGj5tjHkfgCFbsqfVt275IeOZBSPVr0RtGrVIZ8T6heh9NSzPcAwjMQuFh/wKaGyI6wG473zYccqfAeCxUb8rAZhW0Y38VR2Ane6+CxMtRVdXl21padHoTf6IBDimdAJq1CKdEesXovfVsDzDMUzAGCYrSalfjVqkM2L9QvS+GpZnOIZJdtgDCkupX41apDNi/UL0vhqWZziGqTaGZ7asPa2Mcg+Ag4wxZ6/rXPTdfZqdHbPbyrAr3P8vwy6Z0bbFcbm9JPqVKl6jFumMWL8Qva+G5RmOYaTmofCRXYEsN41HAHi+uysggOMB3D+q9BcD+AqADQCOBvB2AL8CcBEA9zeDfQAmAXDPonOb6dUABivsJwD8y3hLUWyIZYfEx03jzUA6I9YvRO+rYXmGYxifY546m1K/GrVIZ8T6heh9NSzPcAyT+sz71JdSvxq1SGfE+oXofTUsz3AMM9H8nf7BnuNqatAN4KUW9sPru5ZctY8fWDH7nTD288ObYYv3zlje/w8+s5wFG9uvZE0atUhnxPqF6H01LM9wDCM5E4WX3ApkuSF2t8Z/OYD3A2gYY0N8C4BPAnD/dKx7rtwZAPYAmAVgG4BrAGyubIjdlWT3IPbGys/c1eIdYy1FsSGWGxBfJ403A+mMWL8Qva+G5RmOYXyPe8p8Sv1q1CKdEesXovfVsDzDMUzK8+5bW0r9atQinRHrF6L31bA8wzHMRDPY3NLzTXehwwDXretavHAfu2Nl45utxXWV/99e19b/Ud9ZzoKP7VeyJo1apDNi/UL0vhqWZziGkZyJwktuBbLcEO+r0l3dHWtD7O4WeDIA988XAvg+gBMBuO+KuOfNudd7AbygsiF2P3cfnXE1u49MO+bPxYYYSOkE1KhFOiPWL0Tvq2F5hmMYubeY/J1S6lejFumMWL8Qva+G5RmOYfKfarkKUupXoxbpjFi/EL2vhuUZjmHGm87mlu6PAKYdwB0WQyev77rgEcfu7Gh8bRlwj1eaAoN/qFvW7/7sl8Qrpl/pBjRqkc6I9QvR+2pYnuEYRnouCj+ZFchzQ/wogGdVrvYeC+CnAJqA4b8hdD93L3eHwVcDmAlgLYBvVX6+BcDLANzT2dnZboz5yOjlOO+882RWqHApVqBYgWIFihUoVqBYgWIFihUIXoFf3/Uwvv7jvffNumzBs3Fso/vQHzBp61045KcfQGnnA9h19Oux9aQkLgwH91kIixU47rjjNPZWxUILr4DGQRvvCvHPKh+n/jWAlwD4MIBFlY9Bu5tqOZ37uLV7uXdOt4H+DIAaAO5vFd3Hqse8uVbxkWnhKfGw0/jbMemMWL8Qva+G5RmOYTwOefJoSv1q1CKdEesXovfVsDzDMUzyQ+9RYEr9atQinRHrF6L31bA8wzHM6PFraln7YrP3JlrHW4N3re9cPPw94YFVh83B4CR3Zfj5gPnB/23Y3PSSLw1/bS6ZV0i/WRWvUYt0RqxfiN5Xw/IMxzBZzUfhG7cC2htid8fp5wC4HcCn3O32AXy88l3i7QA+BOA3AN4F4HcAbgbgrv5OBfAeAG8E4C79Xg7gpPFaLzbEcUMRo9Z4M5DOiPUL0ftqWJ7hGCZmBlLTptSvRi3SGbF+IXpfDcszHMOkNuMx9aTUr0Yt0hmxfiF6Xw3LMxzDjJzH89rX1u/cVV4zfI8Yg8/3di52f76DbX/u5J2TH77JWpwCi/+ypnx6fdv97tN/Sb18+82yeI1apDNi/UL0vhqWZziGyXJGCu/wFdDaELs7Tj9QuRL848pHoN13gP+9UvrWygb3YQBnAnA3XXCv9QDOr2yIbwTw3MrjmN4A4BfFhnjvCqR0AmrUIp0R6xei99WwPMMxTPhbSnrKlPrVqEU6I9YvRO+rYXmGY5j0pjy8opT61ahFOiPWL0Tvq2F5hmOYkdO4YGnPp61xTw6xt/V2LXnlvt8NrGxcCzt8gePessFpM5b1/2/4FGen9O03u0p0/qwn3W+sX4jeV8PyDMcwWc5I4R2+Ahob4pHVTa/cOfodlR+6xyrNqdxYa+Rzih13UOX7xSP1RwHor9yJetyuiyvE4QMRq9R4M5DOiPUL0ftqWJ7hGCZ2DlLSp9SvRi3SGbF+IXpfDcszHMOkNN+xtaTUr0Yt0hmxfiF6Xw3LMxzD7JvJptaevzMW/2SBbSUz6cR1nW++w/1uoKPRfWT6nQbYMWTN6TOWb3ZfoUvy5dNv1g1o1CKdEesXovfVsDzDMUzWc1L4h62A9obYPTLJbXT/EFYupyo2xNw6ZUFpvBlIZ8T6heh9NSzPcAyTxWzk5ZlSvxq1SGfE+oXofTUsz3AMk9csZ5GbUr8atUhnxPqF6H01LM9wDOPmtKllzesNrHve8GEWWLi+a/HwI5UGVjRcBWOWu3831pwzffnm72Qx11KebL9SeRP5aNQinRHrF6L31bA8wzGMxqwUGf4roL0h9q8wQFFsiAMWTUii8WYgnRHrF6L31bA8wzGM0DgkYZNSvxq1SGfE+oXofTUsz3AMk8RgCxWRUr8atUhnxPqF6H01LM9wDNO8dO08mLLbDJ8MmKt6uxa5m6Zi+4rZ7zPGfnp4dI25pG7Z5muFxjgzG6bfzMJHGWvUIp0R6xei99WwPMMxjNa8FDl+K1BsiP3WK0k6pRNQoxbpjFi/EL2vhuUZjmGSHPTAolLqV6MW6YxYvxC9r4blGY5hAkcxSVlK/WrUIp0R6xei99WwPMMxzIKWnq9a4EJYfLd31eKz3eDv7Jj9ljLsN9y/W9hl9W1bOpM8IXLYhLLrwKw96zUeJ50R6xei99WwPMMxTOwxKvTZrECxIc5mXVVdUzoBNWqRzoj1C9H7alie4RhGdYAzDkupX41apDNi/UL0vhqWZziGyXhkVe1T6lejFumMWL8Qva+G5RmuGtO0tHuZMaYDBneZKeUT17Wf/+BAR+PpANyNUWGAT01v679Cdcgjwqr1G2HtLdWoRToj1i9E76theYZjGO8DXwhUVqDYEKssc7YhKZ2AGrVIZ8T6heh9NSzPcAyT7cTquqfUr0Yt0hmxfiF6Xw3LMxzD6E5wtmkp9atRi3RGrF+I3lfD8gw3EdPc2v1mlE0PDGpsyZyyfuWiWwc6G1+KMtyzhg+FwdfqlvVfmO1Ey7ozayKbOL6bRi3SGbF+IXpfDcszHMNozUuR47cCxYbYb72SpFM6ATVqkc6I9QvR+2pYnuEYJslBDywqpX41apHOiPUL0ftqWJ7hGCZwFJOUpdSvRi3SGbF+IXpfDcsz3HjMmctWP79cLvUAeA6Muay3c9E1uzobjh0sm5sM8Ez3GM26tv7mJE+CCYpi1kSrJ41apDNi/UL0vhqWZziG0ZqXIsdvBYoNsd96JUmndAJq1CKdEesXovfVsDzDMUySgx5YVEr9atQinRHrF6L31bA8wzFM4CgmKUupX41apDNi/UL0vhqWZ7ixmAvbb536wK4tPQY4C8CXe7sWX/xw18EHTRmaeiNgTwbwi+l7pp5m2u/ZmuRJUGyIn1gBZgZ8jmGsX4jeV8PyDMcwPutXsHorUGyI9dY6s6SUTkCNWqQzYv1C9L4almc4hslsWHMwTqlfjVqkM2L9QvS+GpZnOIbJYYwzi0ypX41apDNi/UL0vhqWZ7ixmOaWno8D+KDb+L5k6h0nt7e3lwc6Gm8AsMAAd+4pDZ1+UOsDd2Y2xBkaM2uSYfyTrDVqkc6I9QvR+2pYnuEYRmteihy/FSg2xH7rlSSd0gmoUYt0RqxfiN5Xw/IMxzBJDnpgUSn1q1GLdEasX4jeV8PyDMcwgaOYpCylfjVqkc6I9QvR+2pYnuFGM80tPe8A4B6ftKPGlk783qqFvxtY2fgVWFwE2K3G4LTpy7b8IsnhJ4pi1oSwEUE0apHOiPUL0ftqWJ7hGEZkGAoT8RUoNsTiS6pvmNIJqFGLdEasX4jeV8PyDMcw+lOcXWJK/WrUIp0R6xei99WwPMMxTHbTqu+cUr8atUhnxPqF6H01LM9wI5kFratfbW1NN2BnG1u6YN2qhau3r2j8hDHYdxfp5rq2/vX6Uy2XyKyJXNrEThq1SGfE+oXofTUsz3AMozUvRY7fChQbYr/1SpJO6QTUqEU6I9YvRO+rYXmGY5gkBz2wqJT61ahFOiPWL0Tvq2F5hmOYwFFMUpZSvxq1SGfE+oXofTUsz3D7mFOXfWN2rZ3cDWtfDYOu3s7FrTs6GpdaoMsNurW4sH55/9eSHHqPopg18bCLQjVqkc6I9QvR+2pYnuEYJmoICnFmK1BsiDNbWj3jlE5AjVqkM2L9QvS+GpZnOIbRm97sk1LqV6MW6YxYvxC9r4blGY5hsp9avYSU+tWoRToj1i9E76theYbbxzS39LiPSb/DwKxf17WoeaCj4R2AcT+DMeYD05dtvlpvirNLYtYku/QnO2vUIp0R6xei99WwPMMxjNa8FDl+K1BsiP3WK0k6pRNQoxbpjFi/EL2vhuUZjmGSHPTAolLqV6MW6YxYvxC9r4blGY5hAkcxSVlK/WrUIp0R6xei99WwPMM55rJrb78SgLuR1sZS2Z70zZmXv6zG2Ov3DrjprGvbvCzJYQ8oilmTANsgiUYt0hmxfiF6Xw3LMxzDBB38QpT5ChQb4syXOPuAlE5AjVqkM2L9QvS+GpZnOIbJfmr1ElLqV6MW6YxYvxC9r4blGY5h9KY3+6SU+tWoRToj1i9E76th+Ym4Ba3dV8CWXjf3sGmn3ffgjkEAkwCcet3M9+8sW3sTgDpYe23d8i2XZD+1egns2mlUpFGLdEasX4jeV8PyDMcwGrNSZPivQLEh9l+z5BQpnYAatUhnxPqF6H01LM9wDJPckEcUlFK/GrVIZ8T6heh9NSzPcAwTMY7JSVPqV6MW6YxYvxC9r4blx+OalnZfYYz5xJOH16zrnnlZS8niRgBHG4Prpy/rPze5AY8siF27yBhKrlGLdEasX4jeV8PyDMcw1MEuIPUVKDbE6ksuH5jSCahRi3RGrF+I3lfD8gzHMPJTmp9jSv1q1CKdEesXovfVsDzDMUx+0yyfnFK/GrVIZ8T6heh9NSzvuMu/8st5tlyaZy3mAZhnrT0aBm8GcPjI6avFnh9/c8YHZwF4kQF+tnVP6fQ57Zt2yE9ovo7s2mlUqVGLdEasX4jeV8PyDMcwGrNSZPivQLEh9l+z5BQpnYAatUhnxPqF6H01LM9wDJPckEcUlFK/GrVIZ8T6heh9NSzPcAwTMY7JSVPqV6MW6YxYvxC9r2Yk/8Yrvn7EpFLtPAMzDzWYB2vmGdjhze+MabUvfWznHmpGn1lzz4NXTf/MYQD+19jSadOXb7qXEu5nkO9aZ9meRi3SGbF+IXpfDcszHMNkOSOFd/gKFBvi8LVLRpnSCahRi3RGrF+I3lfD8gzHMMkMt0AhKfWrUYt0RqxfiN5Xw/IMxzACY5mMRUr9atQinRHrF6KfSHN6+zdnlh6fNM9YMw+Vq7wvPv6Q1l/f+eBtcJtgDF/5nehlLbDBwGwA7EYYu8FY86waDJ33jJp7hnV/GjoGS6b0oqn21vtNTem0utZN/5XMQAsXEnJ8hEt4wk6jFumMWL8Qva+G5RmOYbKaj8I3bgWKDXHc+iWhTukE1KhFOiPWL0Tvq2F5hmOYJAZbqIiU+tWoRToj1i9E76theYZjGKHRTMImpX41apHOiPXz1Z/XvnbyB876q90run/7+uENbmnv1d0Rm91jAEyuMlyb8MSGFxtQthtNyWy48pznrfvot/531g9XLdw2Ur9jReMJO1H7o6lmT737+W47GVPweNma8mn1bff/IIlBzqgI3+OTURnDthq1SGfE+oXofTUsz3AMk+WMFN7hK1BsiMPXLhllSiegRi3SGbF+IXpfDcszHMMkM9wChaTUr0Yt0hmxfiF6Xw3LMxzDCIxlMhYp9atRi3RGrN9Y+rPauo8a+T3eMuw84za9xn3EGfMBzJh4gOxDsGYDjNlgLDa86aSj3nf9Lzaea4fsRltTs+GmzoUPjKUfXcuj7XMOm1I7OGcPSlcb4HUjNRb4WX1b/6uTGeSMCok9vpJladQinRHrF6L31bA8wzGM5EwUXnIrUGyI5dYyN6eUTkCNWqQzYv1C9L4almc4hsltmDMITqlfjVqkM2L9QvS+GpZnOIbJYExzs0ypX41apDNC/E5vXXt4yWJ4k3vOSUde/+3/2PDp4e/0uk2v2/AauO/mjvuaUlvC7j3l/3Efa7bWbrTGbkDJbMCQ3Wgm1Wzo7VjYN1I8Vo326iMPGXh8z1zY0lxjy3MBM3fHsWd9dNpd31tvDOYAmAuLI8YrwgA/mt7W//rcBlcpOOT4ZlWaRi3SGbF+IXpfDcszHMNkNR+Fb9wKFBviuPVLQp3SCahRi3RGrF+I3lfD8gzHMEkMtlARKfWrUYt0RqxfiN5Xw/IMxzBCo5mETUr9atQinTGW3+nv/ebM0ox93+Mtj/pIs5kP2COrHPxBWHu3KZXuKZf3fo8Xxm401mxwG96rL3zRn59x/PHj/vlta+e8g2vKu+calOYalOdu/+tL/rn+d9d+YXiT+5f/NZAD+CCAEoBDRvFfqWvrfzvpsd9i0vMSsxAatUhnxPqF6H01LM9wDBMzA4U2uxUoNsTZra2ac0onoEYt0hmxfiF6Xw3LMxzDqA2vQlBK/WrUIp0R6xei99WwPMMxjMLYqkWk1K9GLRIZ7nu8u3dhngXmLXn10T/q/tndHxl+PJG7yrv3Cq/7WLPbRE702miBe054xqGvuv3Oh1egXN44/BFnU944ddekDdd9euHOscSPtM+a9eiZ33rk0PVvO7WmVJ7jruyWYf+y0TXDV3YbyQF6EMb0oWz7ANO3/a//38Uz/+fLF5fLZpO15b5yeVLfzPZND27/2BF/U6opfcdiuC/3utui/Kb6tvt/S+bst5jEvEg1r1GLdEasX4jeV8PyDMcwUvNQ+MiuQLEhll3PXNxSOgE1apHOiPUL0ftqWJ7hGCaXQc4oNKV+NWqRzoj1C9H7alie4Rgmo1HNxTalfjVqYTPOurz7qMenlOYZO7T3EUUw80qwR5dh5pu9G8NpVQ7YFgt7j4Fxt2be6L7Ta0p2eMM7ZWjShusqN64aWc/DXQcfVFueMrfW2DnWVja61gx/nBlu02txJAy32bUWDxpj7rPWbtp1/JlnTLvrhnZg78bXotxXZ2r7zLK+h0b2UG1tBj42+8UPnf7Pv5r30jMOyD87jnU8q62J5kmrUYt0RqxfiN5Xw/IMxzCaM1Nk8StwQL6pdXV12ZaWlgOyt+INW/5Oi7FvYCF6Xw3LMxzD8G8h6ZMp9atRi3RGrF+I3lfD8gzHMOlPPV9hSv1q1LIv4y/f4x0afhbv8Pd3jTkaxs6v3Ljq4IlW0QLbjMXdz59/8At+e88jn937mKKhjaZsNuwZ48ZVD7UfMrN+8tQ5ZTs0d6iywTUwc3cd+ap3T73vZ7cDcB+jns0cufKUWSjtfuS/3eZ2eJNrTF8Jts+YUt9geWhT2dT2zRyx2WXXleEYhulhf2FS6lejFumMWL8Qva+G5RmOYfaX2X+61XlAbhqLDXF+Y6zxZiCdEesXovfVsDzDMUx+EySfnFK/GrVIZ8T6heh9NSzPcAwjP6X5OabUr2QtZ175vRnW7JlnS7uPhi09seGdf0T9+Xdv2d4PVL3Sust9NBgW96CEe9z3d4eAjcaWN5Tt0MabPv6W+9xRczUf+40TZ+yuqZ27B0NzjSnNBcpz3WbX3ZzKuo2uwZGwwzeqqvoavrJbwr0A+mDtpuGPNMP2WVPqgyn3De2q7XvwrT996LjjjqP//MauK8MxTNUm9yMgpX41apHOiPUL0ftqWJ7hGGY/Gv+nVan0G+r+tCrFhji/o6XxZiCdEesXovfVsDzDMUx+EySfnFK/GrVIZ8T6heh9NSzPcAwjP6W/1b9wAAAgAElEQVT5OabUr08tf/ke7+DeZ/Ba477Te4z7/q4Bjhn+9wlfxsLae2Ds3caYu8tlbHR3anYbXovyxukvqt1w3cKFQ/YTDXWP7Zk019ih4U2u++hyyZg51tq5bqNbnnrYy0o73X2nqr+e2OyW0Qez9+PL205o/dghv+w6bXCovGmoXNt3UPt9D1dz8lmnfZt2ZgPN+DJMtfr3p9+n1K9GLdIZsX4hel8NyzMcw+xP8/90qrXYEB8ARzulE1CjFumMWL8Qva+G5RmOYQ6A0+KJFlLqV6MW6YxYvxC9r4blGY5hivMjmxUYvfanX/ntI0vm8XmoMfNQtkcbY4421sy3sO47vO5/k6tUsskCd5dg7i7DfX/XbPi7057xpS98/47nuBtXffag95m6mj1zS6iZO6lUmmOx9/FDbtNrgaP23g3a/X/q9QAM7oOFu3LsHnu0afjKLkrD39kdKk3pm9W68ZHRTiHz5qtheYZjGGq19hMopX41apHOiPUL0ftqWJ7hGGY/Gf2nXZnFhvgAOOQpnYAatUhnxPqF6H01LM9wDHMAnBbFhljoIMbOS4jeV8PyDMcwQkubhE2e/e79Hu+eeQal4Su8r/nrhs/c+j9bvmeGN7zu8USYUeUq70PG2Ltt2V3pNXfvfRYvNpZqhja8Ys9t9196yHX1k82kOdY9Z7dyk6rdR57yvqn33vpzwB4VvNk17sZU7ru7pb6HTv3azfN+evHB5v33bA05oCHr76theYZjmJB1SFWTUr8atUhnxPqF6H01LM9wDJPqrD/d6yo2xAfABKR0AmrUIp0R6xei99WwPMMxzAFwWhQbYqGDGDsvIXpfDcszHMMILW0SNln2u/d7vAPz4Da8JcwrWzvfwBwDY+bDDl/lPXziRbADgLl7+Lu87p9271XexpptmxdNuX7Pyyf9Zqrb7Jb33aTKGPdd3aMqN6iq9qzffdEjr+zeV4LpGyrbTXBXdm25b7A8te/g9vE3u7HrF6L31bA8wzFMEoMtVERK/WrUIp0R6xei99WwPMMxjNBoFjbCK1BsiIUXNA+7lE5AjVqkM2L9QvS+GpZnOIbJY46zykypX41apDNi/UL0vhqWZziGyWpW8/CN6feSL/6qtv/u/zvaomb4e7x7v7c7fGW3coXXfeR4wtfg3s2ue66tvefME4665Nbb//S2l035zc6Tza+Gnlvzp8kGpbmlkp1ryzjKfWfXAEfu/Tgz9XrSZnf4O7svW7Zi5i8631hryn2TS7v7TMsj2yincaCY9XOWIXpfDcszHMPErGdq2pT61ahFOiPWL0Tvq2F5hmOY1Ga8qGfvChQb4gNgElI6ATVqkc6I9QvR+2pYnuEY5gA4LZ5oIaV+NWqRzoj1C9H7alie4Rjm6XR+nH7lN48smb0bXjN8Zbc8313htRbzjXtEEUxp4vUwG91mtwR7dy0GNz570p+3Pa/2j48/z/zJHlPTN6Vkh+a4jW4J5sjB6YefXLPzfgtL/dnkyVd2jbnPfWd3yJY3uTsy7ynt7jtkjM2u9PGN9QvR+2pYnuEY5ul0fmj2qrH20hmxfiF6Xw3LMxzDaM5MkcWvQLEh5tcqWTKlE1CjFumMWL8Qva+G5RmOYZId9oDCUupXoxbpjFi/EL2vhuUZjmECxjBZyW9+/wfb9q//9WJ30ypYM9+UcIzb7FZuWuX+OX3i4u397uPMk8zQPUebTQ8dX3P39mfUbNgzv2ZT6QjzQN2U0mCDtaZyg6rhK7vMnzseGL45lbtJFdw/7X22bNxjiPpqUeqbPLS7z7Q//GjIokof31i/EL2vhuUZjmFCjkuqmpT61ahFOiPWL0Tvq2F5hmOYVGf96V4X8x+m/W6Niscu5XfINN4MpDNi/UL0vhqWZziGyW+C5JNT6lejFumMWL8Qva+G5RmOYeSnNDvHM6/85xnWTNv7PV6D+WW36XVXdt2m1wxvfA+ukr6txuDuxtKW/nmlTVuPLt23Y77pK8+ueWDyIaVH6yfj8QYLt+GF+3h0lavFw0lP2uyWYO91H2V2V3fvP6P7h/Xfe/NBhwZudplVlD6+sX4hel8NyzMcwzDHYX9hUupXoxbpjFi/EL2vhuUZjmH2l9l/utVZbIgPgCOe0gmoUYt0RqxfiN5Xw/IMxzAHwGnxRAsp9atRi3RGrF+I3lfD8gzHMCmdH5dc8sXa+484aN7QYPno8vBHmt2GF5WbVpWOAezsieqtrTE42D54Z2PpgQfmmc3bjp60efccs8UcVto6tR4DsyaZwQZjcKS1qCH6fspmd6iy2S2bmr4ZNY/3maUPPjaej8baS2fE+oXofTUsz3AMQ8zJfoOk1K9GLdIZsX4hel8NyzMcw+w3w/80K7TYEB8ABzylE1CjFumMWL8Qva+G5RmOYQ6A06LYEAsdxNh5CdH7alie4RhGaGlpG/c9XtTUHl0qD803pZpj3NVdd5W37P45fCOr8V+HmG04wjy0aXbNAw8dWdo8MLe0ZfCI0kM1s8z2umlm1yElgyNhy0wtT2x2DXCvgb0PpnQfTLnPlGr6HsPjmw6fYLPLBGisvXRGrF+I3lfD8gzHMMyx3l+YlPrVqEU6I9YvRO+rYXmGY5j9ZfafbnUWG+ID4IindAJq1CKdEesXovfVsDzDMcwBcFoUG2Khgxg7LyF6Xw3LMxzDCC3tEzYLPrD6sHLtpKONGRq+wus+ymysnW+fuGMzJo+VebDZhkNK23BE6aGH55bu3zrH3L/riNKD9lCzbfIMMzCj1uxxjzXyurLrNrvlymbXDpX7LGr6Zgzt6TPtD2yX7nu0n8baS2fE+oXofTUsz3AMk/WcaPqn1K9GLdIZsX4hel8NyzMcw2jOb5HFr0CxIebXKlkypRNQoxbpjFi/EL2vhuUZjmGSHfaAwlLqV6MW6YxYvxC9r4blGY5hfMfwvPa19TseLx9jhsx8lMrzbbmy6XVXd4c/3oyZoz1nmUeHN7uHmq3uCu+OOTUPPNpgHthzWOkRM8s8Nm2q2TXLhG52bem+GmPvM6amb9O5N//h2Gc9L4k/C2Sx9llvumNrDtH7alie4RjG9/xImU+pX41apDNi/UL0vhqWZziGSXnen861JfEfQekDUNxUS3pFeT+NNwPpjFi/EL2vhuUZjmH4I54+mVK/GrVIZ8T6heh9NSxfjXPP1b3ilFmPP/MZx3v9t/HFl3yxdvZB9ceUamuPseXyfLt3kzsfwzevGn4ur7tS+8RrlnkMh5S2Dm92Dy1txWGlR/bMNg8MHF56eOgQs7W23uyoM7BeV3YB3GvdlV1bus+6uzKbmr4dtYN9jR/cMjDRWVptTTTPcI1apDNi/UL0vhqWZziG0ZyZrLNS6lejFumMWL8Qva+G5RmOYbKe2cI/bAW8/qMfFqGvKjbE+mu+L1HjzUA6I9YvRO+rYXmGY5j8Jkg+OaV+NWqRzoj1C9H7alh+PO5N7d+ZNbTr8W/ONI81uQl81Natr9m16y3f/cxFW/dN5Flt3UfZwdL8oZL7OHPZPX+38mii4WfxujssD78OMo8Nb3IPcZvdv2x47RGlh3YdZh7BQWb7lBLKvndjftJmt7Y86b4pQ+U+075pR+wZw65dbA6j16hFOiPWL0Tvq2F5hmMY5ljvL0xK/WrUIp0R6xei99WwPMMxzP4y+0+3OosN8QFwxFM6ATVqkc6I9QvR+2pYnuEY5gA4LZ5oIaV+NWqRzoj1C9H7akbw5jXtt9YA90w6HHX/f3vnAi1JVZ3h71RV3zvMMIBkQGYIIKOLUUaNBgWUxCdGDT5D0ER8BZ9BzFJMDIgyg0HxFSVGXUaNEGMAEY1PAj4xImh8IcaI4mIQBgZ5OODMMHPv7arK+qtPDz1NN/dUdd++/dhnrbv6dtfe5+z91z516q99zqlkjlqSbtmepFGSRFPE65730I2nf/rqB9VmoyR39SQnSoiJV3HrulcvueBZa+Lrirj5RbqaD+w4/prb8hU35rkIb/F6ongPt7UguwXR9WS3IL6RpjX/tr5XdGeckIWMq40NqiJu1GdrZjfL0o3b0tpNq/pAdkP6cVmsQ+qsKjMIW/rdRq/1VdEvqxMqHyIXIlP1/A+j3jD5Owhb+t1Gr/VV0S+rEyofIhciM4xxbjZByMA9TDgtA7YD97klpmWIF++UDeJi0O82eq2vin5ZnVD5+eSee+pnf++MF669/eFr14xa368c1PNhUrniCooLZctxx10Yb91v9ySdvj1538uO3PrmT1y5gunlcXr3TJK5OMndXJLrk3oylU3FuUuTHJfkLkpy0oQ0SoiI4+K3LEGfaZbkziUvftLq8z9x2YaXRmTieklOXhyXfCGXZUlU6LkEybgoJssTXEP2qAfvc+IV19z6ceeiOM+lS/GXQxKRJ3nuJBtT1Nuo+8B9lh15w63brrrndxJyEufyRHZEjsSRJlGeJ44smYrdbllWFxONIjIicpw+XV78r9+c/2w/Jrkn177Lk2pX7nJGvz33qOL7TgIcbc4T0pB+05Xs1qL0xunp6ZvcyRs1jg1FWaiYrOLcIGzpdxu91ldFv6xOqHyIXIhMlXM/rDrD5O8gbOl3G73WV0W/rE6ofIhciMywxvqk2xUyuA8DRiuA84A6cBDwbuDcboaJEB828+2vP2Xdl48eBuMX2oZh6oCDsKXfbfRaXxX9sjqh8t3k1p+yfvVTl37zuw+OryvWMv48XX3bxbNHH3HmmW/ZsNDxuVD1r1+/PvoZhyY72Jy4u/eL02hrkrlakrmZZIlLkmxmLnHJkviU49Ze+7bP/PRhRTawniYifS52cYMQpkkuMkhWEMQkIi6Incig5PK0lkRpLXL1qSnyJHZ5LcnmpqIoq0Uuq8WZSFq9FpHruwherSBsUOhGUZO46TNPVq3c9+hbbtl0eeTy2BXkME9i8tg5YpenSfGZ5YmLiBxZHKHfdbz4i3b+n+Vx5PII6ZJHIn4FASxIXwcC6JrHmsSwTWYX/SahbMjsWncH/XvVHaLvZQrC2kJgdyGtOZGvu+mbSO1ClBuylRwYbdql6k6/AbflORtdh8yuyO5t09M3HTBEZDcEq9BrS0hdvcoMwpZ+t9FrfVX0y+qEyofIhcj0GgfDpD9M/g7Cln630Wt9VfTL6oTKh8iFyAxTfJst9yAwKoT4FGA5cBqwH6A7F2WLO66fEiF+bXo2l88ddsmV6x59DGdcFj3e+/ytn+0THar/1wKHwrb/W7ZzLdfszXdEq1YBK1dyw6bNUXMXlC13L3fTS5cVWG1bMuOmdswW/0/t2M1tn5l1e+zZqHz71JyrzS4pju2YmXPJdN3JzJla3cnYZK7uZmb121KSqbqbnas7dmvoptt17wu77QbZ9pmGTfqiYztmG9+nIU5rbq6eOv2/NE3dXD1xjzj2xF/+4PMfWDMNzMWZq6eJ0//ejmhqGqaYoh5lrp5lrvn+jplZF+llHjW14Y8tY4o0bUz5q6eZi5bEO2Mkn6t7rKSh7U3TqFAGXJa5LM3c2mNedcXVX/zwUUkN0jR2qlciSgmlWe6yLHKNY7mTbprFLsr8FMMYannu0ixqtBlDVHzPnXJXkT6BQ45+8aXXXHru05OksedM5nKXFmkmUP0Ne3KnRjOXKfVUvHeknjbX7cW4qFFXluXukCce/9lrv3X+sU29e4416opjrxvfs6Wry+VL7mJiDnrc887bcPmnjkf0JFMbDbsKG/QZNfxu1pXluXvAY48959eXX3SCRPVdx2qiMhku87Y5p98bkB9w+LM/csOVn3t1YXNUpNOkKErkIG0k64D9D3/mBzd+/wsnkecu922qhr3iu954ZHLVznWOkr2y/oibN+d7fFREzSnj50jyjJrT6XKuyMAVJLLIwjUyewVha5zOWIRNWb5ip9ucpCBoBbHL452fuU6wK4icjucFuXNRRB5RyGWRy53IX1QQPoeL5HRhfK4/p9+K4zlOmERkzqHfiv/vyQY2ieB9ZQUls5NkdSBj7t6Ez3cl+xgOBPJc3RqXNmYK5am+51PLl0ezW+7AkZIpHZyn6oG67BSXCHQZybN0zwesSbZc/1NdGBoyxWyj9PZsr0NXRHdqjNlZbsvvd/0K7jw9jvMbHenG6ZllG93663cMBwz9s2KYbuAGYUu/2+i1vir6ZXVC5UPkQmT6F52LX9Mw+TsIW/rdRq/1VdEvqxMqHyIXIrP4UW0WdEJgVAjxx4CvAReIX/ibmAcCjQVfbaVJiK/LDmB1dKOdeUNg4hHYkP4+B8cbd8Gh028TD1Q7AI5czxZyWv5ypzRlljn9JkLl9Eyj+K0gZw0i5n/PM+dcmjWIV5bvvv/BbL35l8VTDE/W8pzUicjleYpzqctzfdeTsxRyzYpJndNnnkbkdf05dDytu+J4nkY5ae5cGrk83brmBSfvfs157yzscFlakL9IdcnOrPiMHKmeYUUuS1M9Y9FzJ/0ekUa5fnNZ7FyaZmR3HbX+k3tcse75mmdd+BBlaUE7I5cWbWRZGkdOxmRJFKV5vd445nTMpXc87Zwf7v3Vlzw8n3XZVBKlM7hsKp1Ld+Cy6Vqc5vUozd2ObOlUnG7ZFmfLp5N0w3O/9NuDL3jy8k3UspV7L01/+L+/yg5bRerWd14uE3oT0k1u69tWPtWRX9J6+nPc03Y/bdOl494nQrEbBA6DsKXfbfRaXxX9sjqh8iFyITKDiJVBtTFM/g7Cln630Wt9VfTL6oTKh8iFyAwqdq2dcgiMCiG+ENDfRd693wBHANefddZZ651z61rdrtVqzM3NlUPCpA0BQ8AQMAQMAUPAEDAEDAFDwBCogMC+++7LCSecMCrcqoKH46syKiftdL0JAzjbz37drFmg3TbXmrRNtYbJ30HY0u82eq2vin5ZnVD5ELkQmXG65A2Tv4Owpd9t9FpfFf2yOqHyIXIhMtY/FgaBQWDf7zZ6ra+KflmdUPkQuRCZhYmOxal1mPwdhC39bqPX+qrol9UJlQ+RC5FZnEi2VudDYFQI8bOAk4A/AY4DTgYe0825SQvIYfJ3ELb0u41e66uiX1YnVD5ELkRmvgvHKB0fJn8HYUu/2+i1vir6ZXVC5UPkQmRGKf7ns3WY/B2ELf1uo9f6quiX1QmVD5ELkZkv5kbp+DD5Owhb+t1Gr/VV0S+rEyofIhciM0rxP0m2jgoh1u5SFze2wSq2oXoK8D0jxA0EhqkDDsKWfrfRa31V9MvqhMqHyIXIjNNFcJj8HYQt/W6j1/qq6JfVCZUPkQuRsf6xMAgMAvt+t9FrfVX0y+qEyofIhcgsTHQsTq3D5O8gbOl3G73WV0W/rE6ofIhciMziRLK1Oh8Co0KIm34cANyizZTvy7FJC8hh8ncQtvS7jV7rq6JfVidUPkQuRGa+C8coHR8mfwdhS7/b6LW+KvpldULlQ+RCZEYp/uezdZj8HYQt/W6j1/qq6JfVCZUPkQuRmS/mRun4MPk7CFv63Uav9VXRL6sTKh8iFyIzSvE/SbaOGiEOOjfaaOvUU09dHyQ8BkLD5O8gbOl3G73WV0W/rE6ofIhciMwYdIudLgyTv4Owpd9t9FpfFf2yOqHyIXIhMtY/FgaBQWDf7zZ6ra+KflmdUPkQuRCZhYmOxal1mPwdhC39bqPX+qrol9UJlQ+RC5FZnEi2VudDYCwJ8XxO23FDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwAixxYAhYAgYAoaAIWAIGAKGgCFgCBgChsBEIjAJhHh3YBuQj/EZvp9/LVU6xj6aa70hkABTwN0t1UR+kzr1j3ErS71Drf7qp+XAlnFz1vypjMAe/trZWkGnGJn2Y8hs5ZYWX7Fbn1gGbG97jeE4XxsW/0wMpwWdxohWS7v1gVGNn279oelztz7QyV8bW4YzpmVV1XufTuNA2ZgYXlTMsnshMM6EeF/g4cBngQcBt47h+T8Q+BRwG1AHfgScOYZ+mkvVEYiBhwIvA/TA5PW+qr8CXgfc5AeM430cVW9pODRrwLnAQcB1/r3l8n0t8HHg1/6YfvvBcJhsViwCAhoTHu9f4afYUDmsQ4xcBbwPeKSPJV1jX9tGHhfB/FJNdusTelh8nh871F/e7fvOuF4bSoE2QcLdxogmBCIUnfrA3iMaP936w46Wc96pDyip0qm/dLpu2Niy+B2o6r1Pt/NZJiYW33uzoDQC40yIjwWO8gTg/mNKiN8M6OK+Dljin/LvD9xcOhJMYVwR0E3vW4FHAT/0/UE3ONqpfS/gLuD9wCbgrDEA4XHA24E/8r58E/gQ8ArgPcBXAF0bXuXfaz4GLpsLFRA4BXgY8Aj/sERVKDbaY0SbM54NHO7b+AVwAvCdCm0ulkq3PvFAP2PiNGA/fw3Y018TxvHasFj4D3u7ncaIVpsf26UP/PGIxk+3/vBp73S38VGEWFnD1v6ibPHnbGwZyhCveu/TaRz40y73TN1ion1m2lACZEbtisA4E+KmpwrYcSXEeiez/NOTzWcD7/XZ8HGeHm59uBoCr/GxoQzxwcDXAN0QqyjjJWKgrOmoF93M6OHQVp8J/qnPkIvA6MbuRp/tu8RfF0bdX7O/OgKaQXR+CyFWbLTHyBv8g9W/9s3o5vczwL9Xb3bgmt36xOn+OnABoHuBDHiCz5KP47Vh4MCPWIOtY0Sr6S/s0gc0w0LjyKjFT7f+cIN3utv4qPuqdn/VT75lY8tQR3rZe59O48CRXe6ZusWEZqdZGTEEjBCP2AnrYK7WhZ4K6MbtOcA3Rt8l82ABEGgdFEQE9DR8jW/nRX766MsXoN3FqvL5wEcBZfj0oOh33l9lwlf7mxi919zK5CLQTog7xYhmTTzEPzQSUv8KaNbBJ0cQtvY+cSGgv4u8L78BXgL805hfG0bw1A3E5G6E+MQufeBZIx4/7f2hCXK38VH7DbT3lyOAq21sGUh8Vm2k7L1Pp3HgmC73TN1i4vqqxpre4iFghHjxsO9Hy8qE6QKtjV6U5dPNvhVDoBMCrYOCZhZoSo82iNATzua6Yq0TG4fyJkBrokX0teZT5b+9n5o2runjyo7phs7K5CLQTog7xYgeppzcEitf8EsQRm2NYKc+oT6gmz9NCdd6u83ASj+7YlyvDZMb7fN73o0Qa9ZApz6gaaSjGj+d+kMToW7jo6ZLt/urpQWX2dgyf3AtokTZe59O44AennS6Z+oWE5ptY2XEEDBCPGInrM3cVwLPsBv70T6JA7K+/WbnJ4Ce/GtK8aV+HbrWzox6OdRn8A7xayGb/vwjcDvwLr/eS1Oq3zLqzpr9PSHQTog7xYjI4gY//V6bCP0Y0MwCrb0fldKtT+iB0El+Lf1xnvQ8BhjXa8OonK/FsrN9jNDMCD0k0X4TnfqApkyPYvx06w+r/Bph7RPQqQ8oAdHJXxtbFitiw9oNufe5ws+C+D7Q7XyWiYkwy0xqqBCYFEKsHae1E/O4lXOAl7Y5JSJw7bg5av70jIAGBa130pN+Fd0MN6d9fhl4wZi8mkz9Qf2itWgTJD311aCnciegG//f9oyqVTDKCLQTYvWPTjHyDv/wSNkA3RB/cMSc7tYntPbzYkAEQVmxpwDfG+Nrw4idtoGb2z5GaDbEF/3Sk059QDEzivHTrT9oQ7lH+9lFncZHEeJO/na7bgz8BFqDHREIuffRztJabqgp0N3OZ5mYsFMxgghMAiEewdNiJhsCA0FA72HUTcCkTLXXZirKAmjTDNt4biAhNnKNdIuRFX5piqZMjltRxvsWnwls+jZp14ZxO6f98EebaSkutJGUSrc+MC7xo1exaanNGd7fbn2gk782tvQj4gZXR/u51Xe9baO5j0q381kmJgbnjbXUFwSMEPcFRqvEEDAEDAFDwBAwBAyBsUFAr1XS7vyTsh7yDwBthjRKyyHGJtgW2RG9dk7JAU2XtzKhCBghntATb24bAoaAIWAIGAKGgCFgCBgChoAhMOkIGCGe9Agw/w0BQ8AQMAQMAUPAEDAEDAFDwBCYUASMEE/oiTe3DQFDwBAwBAwBQ8AQMAQMAUPAEJh0BIwQT3oEmP+GgCFgCBgChoAhYAgYAoaAIWAITCgCRogn9MSb24aAIWAIGAI9I7A7sAOo91yTVWAIGAKGgCFgCBgCi4KAEeJFgd0aNQQMAUNg4hC4BHgq8KKWd2BrV0+9D1rvhe61fAx4GTCIcU3vJL4KWA083r/numn/MYB2rH17BYdWAjcDpwDvrKBvKoaAIWAIGAKGgCFQEoFB3DiUNMnEDQFDwBAwBMYQgSYhvtUTyW3+NRf9IsTnAC8dECF+IvAN4EP+vaXyqVn+BXglEFV437Xek30T8GbgbWMYA+aSIWAIGAKGgCEwdAgYIR66U2IGGQKGgCEwlgg0CbGcWwe8tYUQHwt8DjjXk8xnAm8BXg2sAM4ELgOUfd3ss6d/BxwMvAP4INAkxO8Fngf8HHgNcC3wIOBE4Hjf5uuAHwF/D6iti4HnAIe3If8q4I2ewH8XOMm3/1/AIcDVwCuA//F6zwY+AuwLfBk4ztv6XOBu4FJAbevdrn/hbVrjfXu9r6NJiFXPv/nflFVXNlo+Pw24whPmb49lpJhThoAhYAgYAobAABEwQjxAsK0pQ8AQMAQmGAER4kcCn/ck8kDga37K9F8CGzzxFRFWplcEV9ORlTU9H/gl8D0/5VowijCKxIp87g2ICEtP2dof++nZn/bk+JvAocD7fNv7AJqeLDItkqvyyZa69f3BnlR/H/gS8LfAJuBIT+alpzb1JxKrojZk11HAi4E5b/vZngSf7Emy/LgBkH0i4/L1w8A/+Lo0XfpJwKN9XVcC1/g2TgfeBCwB1tr65QnuUea6IWAIGAKGQF8QMELcFxitEkPAEDAEDIF5EGgS4ocBv/IZ4SNKEGJlcpV1VXZVmVZlSkVoleV9oM8oixDf35NikVhllJUdVnvK8H4VeALwx8DT/XERWxFYZXOiLI0AAANNSURBVF1bi4i5stgPBX4GnOrXBf8hcBDwn550f6VNr3XK9F4+Eyzy3Wx3PfDRFhKtBwSq4z+AZS2/q1oR37N8/Vpvray0ssJf99n0X1vUGQKGgCFgCBgChkBvCBgh7g0/0zYEDAFDwBAIQ6BJiEVYNZX5A15NRPX5gMidMqPaUKp5vDVDrA25RBxzT0b/zE8bFmnUdGJlTkWI7wfcCXzKZ4eP9pno7wDKFDeLjmtKtAjxHsCWNjfeA7zBT8u+3sv9s89aKyMdQohV9/uBz/jp1WcAIsT6lG/aBEx+KcutadgvbyHEsme7J/tbAWXUZe+fe2Ks4/v5qdhhZ8CkDAFDwBAwBAwBQ+BeCBghtqAwBAwBQ8AQGAQCrYQ4AX7ipxiLED8OmPXTokWKT2vZwbk5ZTqUEH8cuBzQ5xc9Sb7D132Cz9gqM631viLTIq16fZI2+WotkpFt5wEX+Wy0Mr77A88IIMSaWv03wAv81GZNoVY2W2RY66FFzrWW+kKf+dY0bk3T1vRr/X4LoGyzsuDCRFOs9UBAx2S36tUUba2VtmIIGAKGgCFgCBgCFREwQlwROFMzBAwBQ8AQKIVAKyGWojKkIoYinXrtkkifNrBS0bRgTWsWURYhvqBlevLvfMZXGWKtudWOzNpcS1OcRXi1WZamNSuD2tyAStOtlZnV7yrK/GrtrzK+3QixxkftIq2NvZpFm3Vp3a/WLnfLEGuzLK15Vnmynxqu1zRpmrdIvfS0iZh8Un0qG/0aZfktQqwHAu9qeWjwEK8rIqxssnyTbcqmWzEEDAFDwBAwBAyBHhAwQtwDeKZqCBgChoAh0FcElKnVlOj2bG3ZRkQa9Tqnepuiph3fXnKa8Z6ehGpKt7LYIWUpUAPuAqYBZZZ/00FRO2hr3XDoWmCN2fJBBDoNMcRkDAFDwBAwBAwBQ+C+ETBCbBFiCBgChoAhYAgYAoaAIWAIGAKGgCEwkQgYIZ7I025OGwKGgCFgCBgChoAhYAgYAoaAIWAIGCG2GDAEDAFDwBAwBAwBQ8AQMAQMAUPAEJhIBIwQT+RpN6cNAUPAEDAEDAFDwBAwBAwBQ8AQMASMEFsMGAKGgCFgCBgChoAhYAgYAoaAIWAITCQC/w+fqrBSEj2Q+wAAAABJRU5ErkJggg==", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "display(Markdown(f'### With no delay and non-blocking map on 100 engines:'))\n", "data = pd.DataFrame(no_delay_source[BenchmarkType.TIME_N_TASKS_NO_DELAY_NON_BLOCKING]['direct_view'])\n", "alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Number of cores:N',\n", " y='Duration in ms',\n", " tooltip='Duration in ms', \n", ").properties(title=f'Ran with no delay on 100 engines Direct View', width=800).interactive().display(renderer='svg')\n", "\n", "data = pd.DataFrame(no_delay_source[BenchmarkType.TIME_N_TASKS_NO_DELAY_NON_BLOCKING]['load_balanced'])\n", "alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Number of cores:N',\n", " y='Duration in ms',\n", " tooltip='Duration in ms', \n", ").properties(title=f'Ran with no delay on 100 engines Load Balanced', width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### time_broadcast ###\n", "The second benchmark comes from benchmarking the runtime of sending\n", "and array of n bytes to m engines. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mNumpyArrayBroadcast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", " \u001b[0;32mdef\u001b[0m \u001b[0mtime_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m 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"execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-6ccfa781aa255b2ec55c78dc8596470c" }, "datasets": { "data-6ccfa781aa255b2ec55c78dc8596470c": [ { "Duration in ms": 0.76, "Number of bytes": 10, "Number of engines": 1 }, { "Duration in ms": 0.84, "Number of bytes": 1000, "Number of engines": 1 }, { "Duration in ms": 1.47, "Number of bytes": 10000, "Number of engines": 1 }, { "Duration in ms": 8.71, "Number of bytes": 100000, "Number of engines": 1 }, { "Duration in ms": 68.98, "Number of bytes": 1000000, "Number of engines": 1 }, { "Duration in ms": 714.37, "Number of bytes": 10000000, "Number of engines": 1 }, { "Duration in ms": 4.74, "Number of bytes": 10, "Number of engines": 10 }, { "Duration in ms": 4.65, "Number of bytes": 1000, "Number of engines": 10 }, { "Duration in ms": 5.68, "Number of bytes": 10000, "Number of engines": 10 }, { "Duration in ms": 14.47, "Number of bytes": 100000, "Number of engines": 10 }, { "Duration in ms": 75.06, "Number of bytes": 1000000, "Number of engines": 10 }, { "Duration in ms": 722.53, "Number of bytes": 10000000, "Number of engines": 10 }, { "Duration in ms": 15.8, "Number of bytes": 10, "Number of engines": 50 }, { "Duration in ms": 14.41, "Number of bytes": 1000, "Number of engines": 50 }, { "Duration in ms": 15.61, "Number of bytes": 10000, "Number of engines": 50 }, { "Duration in ms": 21.82, "Number of bytes": 100000, "Number of engines": 50 }, { "Duration in ms": 88.37, "Number of bytes": 1000000, "Number of engines": 50 }, { "Duration in ms": 789.8, "Number of bytes": 10000000, "Number of engines": 50 }, { "Duration in ms": 28.54, "Number of bytes": 10, "Number of engines": 100 }, { "Duration in ms": 33.21, "Number of bytes": 1000, "Number of engines": 100 }, { "Duration in ms": 46.83, "Number of bytes": 10000, "Number of engines": 100 }, { "Duration in ms": 33.61, "Number of bytes": 100000, "Number of engines": 100 }, { "Duration in ms": 123.84, "Number of bytes": 1000000, "Number of engines": 100 }, { "Duration in ms": 844.51, "Number of bytes": 10000000, "Number of engines": 100 } ] }, "encoding": { "color": { "field": "Number of engines", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of bytes", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector001": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "Broadcast benchmark running on 16 cores with Direct View", "width": 800 }, "image/png": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-7b268d161e8775552bcb42811a989eb3" }, "datasets": { "data-7b268d161e8775552bcb42811a989eb3": [ { "Duration in ms": 0.71, "Number of bytes": 10, "Number of engines": 1 }, { "Duration in ms": 0.78, "Number of bytes": 1000, "Number of engines": 1 }, { "Duration in ms": 1.39, "Number of bytes": 10000, "Number of engines": 1 }, { "Duration in ms": 7.7, "Number of bytes": 100000, "Number of engines": 1 }, { "Duration in ms": 66.7, "Number of bytes": 1000000, "Number of engines": 1 }, { "Duration in ms": 694.22, "Number of bytes": 10000000, "Number 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19.42, "Number of bytes": 10, "Number of engines": 100 }, { "Duration in ms": 21.5, "Number of bytes": 1000, "Number of engines": 100 }, { "Duration in ms": 19.28, "Number of bytes": 10000, "Number of engines": 100 }, { "Duration in ms": 23.87, "Number of bytes": 100000, "Number of engines": 100 }, { "Duration in ms": 91.12, "Number of bytes": 1000000, "Number of engines": 100 }, { "Duration in ms": 718.6, "Number of bytes": 10000000, "Number of engines": 100 } ] }, "encoding": { "color": { "field": "Number of engines", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of bytes", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector002": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "Broadcast benchmark running on 64 cores with Direct View", "width": 800 }, "image/png": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "source = get_broad_cast_source(benchmark_results)\n", "for core_num, results in sorted(source.items(), key=lambda key: key[0]):\n", " data = pd.DataFrame(results)\n", " alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of bytes',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of engines:N',\n", " tooltip='Duration in ms', \n", " ).properties(title=f'Broadcast benchmark running on {core_num} cores with Direct View', width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### echo_many_arguments:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mecho_many_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_arguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mecho_many_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_arguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m 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"view": { "height": 300, "width": 400 } }, "data": { "name": "data-8604f17283614a508644e3660000a726" }, "datasets": { "data-8604f17283614a508644e3660000a726": [ { "Duration in ms": 15.84, "Number of arguments": 2, "Number of cores": 16 }, { "Duration in ms": 15.95, "Number of arguments": 4, "Number of cores": 16 }, { "Duration in ms": 13.94, "Number of arguments": 8, "Number of cores": 16 }, { "Duration in ms": 21.31, "Number of arguments": 16, "Number of cores": 16 }, { "Duration in ms": 17.97, "Number of arguments": 32, "Number of cores": 16 }, { "Duration in ms": 21.26, "Number of arguments": 64, "Number of cores": 16 }, { "Duration in ms": 27.26, "Number of arguments": 128, "Number of cores": 16 }, { "Duration in ms": 31.14, "Number of arguments": 255, "Number of cores": 16 }, { "Duration in ms": 11.98, "Number of arguments": 2, "Number of cores": 64 }, { "Duration in ms": 12.06, "Number of arguments": 4, "Number of cores": 64 }, { "Duration in ms": 12.72, "Number of arguments": 8, "Number of cores": 64 }, { "Duration in ms": 13.72, "Number of arguments": 16, "Number of cores": 64 }, { "Duration in ms": 14.43, "Number of arguments": 32, "Number of cores": 64 }, { "Duration in ms": 16.29, "Number of arguments": 64, "Number of cores": 64 }, { "Duration in ms": 21.2, "Number of arguments": 128, "Number of cores": 64 }, { "Duration in ms": 29.7, "Number of arguments": 255, "Number of cores": 64 } ] }, "encoding": { "color": { "field": "Number of cores", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of arguments", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector001": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "DirectView", "width": 800 }, "image/png": 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OXlcOG3wojHORTiACm8ylTF+C91AefOhwcbrw2GJNcabjpdS4JB+lnKEE76E8+NDh4nThscWa4Dw3ttR3/TTdmImu2FIzS40kbe5Ez8P+I30GlRpbauhoQ6aRGlvaSIpuKx66YkuvEdoJwA4Afg+AmkRqKGvTJpcaX+Kn24nprTVjNbb0ulZqHoeuLP84bcjp1l+6YvtMurlV4UcT3QJNDS1tfEUHeaArsfR8LT0zTE390HFB+rzxSLtDj+SXmlp6tpee86WDrk5Tk746NraSvjsE8GKykAPYMZKQ4j2ED18aHLyuHDb4UBijgpzgg20ylxKJBO+hPPjQ4eJ04bHFmuJMx0upcUk+SjlDCd5DefChw8XpwmOLNcExN7ZZly89R0uNJ92+a3vQbcjUjNKzq4UH7bz8CgD0vtvC52nH0iHMK9PnZKlBznrQ5sBT082edAGI3opDfNTo0jzH0x7J724AygH8ayxwvGKb9VSV4DiThSxtelK8h/DhS4OD15XDBh8KI63mi+nHJvNi+i3UluA9lAcfOlycLjy2WFOc6XgpNS7JRylnKMF7KA8+dLg4XXhssSa4IjW2kpZ5SXuJjW1Jn76xzZssZGkxSPEewocvDQ5eVw4bfCiMtJovph+bzIvpNza2fOlznXsXHlusKc50PF/KE4eplDOU4D2UBx86XJwuPLZYE1xsbEv78yo2tqV9/sZ0b7KQpcUgxXsIH740OHhdOWzwoTDSar6YfmwyL6bf2Njypc917l14bLGmONPxfClPHKZSzlCC91AefOhwcbrw2GJNcLGxLe3Pq9jYlvb5i42t5/Nn8mFoa8WXBgevK4cNPhTG9nxNRJxN5lJykOA9lAcfOlycLjy2WFOc6XgpNS7JRylnKMF7KA8+dLg4XXhssSa42NhK+sQx9xIbW/PMSgZhspClTUqK9xA+fGlw8Lpy2OBDYaTVfDH92GReTL/xii1f+lzn3oXHFmuKMx3Pl/LEYSrlDCV4D+XBhw4XpwuPLdYEFxvb0v68io1taZ+/eMXW8/kz+TC0teJLg4PXlcMGHwpje74mIs4mcyk5SPAeyoMPHS5OFx5brCnOdLyUGpfko5QzlOA9lAcfOlycLjy2WBNcbGwlfeKYe4mNrXlmJYMwWcjSJiXFewgfvjQ4eF05bPChMNJqvph+bDIvpt94xZYvfa5z78JjizXFmY7nS3niMJVyhhK8h/LgQ4eL04XHFmuC89XYzq/rODgHHKahDgM0EqVuHVDJbetbqu+eOJ8QxZ9JbGyLfw68OTBZyN5MWBJL8R7Chy8NDl5XDht8KIxlaU5ImE3mUoKQ4D2UBx86XJwuPLZYU5zpeCk1LslHKWcowXsoDz50uDhdeGyxJjgfjW1lXecZCviq1kigQO+Ipd52QCvklNaf625ffNE463wSgCkA+grGUQ+3M4AnJX1GFNtLbGyLfQY86pssZI82rKileA/hw5cGB68rhw0+FMaqMCcoyCZzKVFI8B7Kgw8dLk4XHlusKc50vJQal+SjlDOU4D2UBx86XJwuPLZYExx3YzuvdvXRSukNY63jJEmOWtNS/eMRxlAT/AYApwIYAPC5dMxRAFoB/AnAjgBqAdwr6bOiWF5iY1us5APomizkAHaMJKR4D+HDlwYHryuHDT4UxqggJ/hgm8ylRCLBeygPPnS4OF14bLGmONPxUmpcko9SzlCC91AefOhwcbrw2GJNcJyN7dzargMTlbsDwNRx1vEWneDQdS01fxw2biaA8wC8GcCdBY1tD4BDADwIYD8ASdrkSvq4KIqX2NgWJfYwoiYLOYyj7CpSvIfw4UuDg9eVwwYfCpO9Gif+SJvMpaQiwXsoDz50uDhdeGyxpjjT8VJqXJKPUs5QgvdQHnzocHG68NhiTXCcjW1lfeeZ0Kod0HQr8VgH3ZZ81rrWmq+PMuiTAPZJG9sZAHoBXA/gvQCuBnAOgM2SPiuK5SU2tsVKPoCuyUIOYMdIQor3ED58aXDwunLY4ENhjApygg+2yVxKJBK8h/LgQ4eL04XHFmuKMx0vpcYl+SjlDCV4D+XBhw4XpwuPLdYEx9nYVtWtXqWhF2dcw9d0t9WckKGxpQb3/vT24x8AWAHgyrTBzSg1cYfFxnbinluYLGRpMUjxHsKHLw0OXlcOG3wojLSaL6Yfm8yL6bdQW4L3UB586HBxuvDYYk1xpuOl1LgkH6WcoQTvoTz40OHidOGxxZrgOBvbyrrOlQBOyrKGNfQV69oWn5Khsd0NwKMAdgLwTMp/JICTs+hM9DGxsZ3AZ9hkIUuLQYr3ED58aXDwunLY4ENhpNV8Mf3YZF5Mv7Gx5Uuf69y78NhiTXGm4/lSnjhMpZyhBO+hPPjQ4eJ04bHFmuA4G9t59Z2nK41vAhj7VmSNASh9Rnfb4oszNLbUu/0bwMcA0KZUlwC4DcDlE+eTxn4msbG1z0480mQhS5uMFO8hfPjS4OB15bDBh8JIq/li+rHJvJh+Y2PLlz7XuXfhscWa4kzH86U8cZhKOUMJ3kN58KHDxenCY4s1wXE2tpXLOt+EnLptvGdsFbBNQR+2pm3xXWM0tnMAnJl+nZ6t/Ua6I/JvAJwO4ImJ80ljP5PY2NpnJx5pspClTUaK9xA+fGlw8Lpy2OBDYaTVfDH92GReTL+xseVLn+vcu/DYYk1xpuP5Up44TKWcoQTvoTz40OHidOGxxZrgOBtbWvlVtZ2f0ip/1Xb0Q6szutsXjfcu2+F46uF2iQ3tC2OJje3E+X7zopmYLGRpMUjxHsKHLw0OXlcOG3wojLSaL6Yfm8yL6Tc2tnzpc517Fx5brCnOdDxfyhOHqZQzlOA9lAcfOlycLjy2WBMcd2NLqz99l+0aaJRBgd5NC+RvP0Y/gPndbTU3TZxPieLOJDa2xc3fq7rJQvZqxIJcivcQPnxpcPC6ctjgQ2EsynLCQmwylxKGBO+hPPjQ4eJ04bHFmuJMx0upcUk+SjlDCd5DefChw8XpwmOLNcH5aGxpDR9X23XgQJJ7H3J4i1JKA7gjl+gfj/DuWklLvuS8xMa25E5ZdsMmCzk7a5iRUryH8OFLg4PXlcMGHwoTppJLQ8Umcykzk+A9lAcfOlycLjy2WFOc6XgpNS7JRylnKMF7KA8+dLg4XXhssSY4X42tpHU8kb3ExnYCn12ThSwtBineQ/jwpcHB68phgw+FkVbzxfRjk3kx/RZqS/AeyoMPHS5OFx5brCnOdLyUGpfko5QzlOA9lAcfOlycLjy2WBNcbGwlfeKYe4mNrXlmJYMwWcjSJiXFewgfvjQ4eF05bPChMNJqvph+bDIvpt/Y2PKlz3XuXXhssaY40/F8KU8cplAZVtWtrtVKHwTgaa3UtetaFt3immIo72P5DOXBhw4XpwuPLdYEFxtb15VWXHxsbIubv1d1k4Xs1YgFuRTvIXz40uDgdeWwwYfCWJTlhIXYZC4lDAneQ3nwocPF6cJjizXFmY6XUuOSfITIcF5dx1UK6oQXzFvjjd3tNfe4ZBHC+3j+QnnwocPF6cJjizXBxcZ2vCqW/fXY2Mo+P07uTBayk5AHsBTvIXz40uDgdeWwwYfCeCjbkqW0yVzKZCV4D+XBhw4XpwuPLdYUZzpeSo1L8hEiw8q61c8BeuoL5q3w+e7Wmq+6ZBHC+3j+QnnwocPF6cJjizXB+Wpse1v3PBgD+jAofRjtHKWUunWyTm6b0vDQ3ePVTfx69gRiY5s9q5IbabKQpU1OivcQPnxpcPC6ctjgQ2Gk1Xwx/dhkXky/hdoSvIfy4EOHi9OFxxZrijMdL6XGJfnwmeH8ZavfndO546HVJ180Z6Vqu1sXne+ShU/vWX2F8uBDh4vThccWa4Lz0dj2NVWcgQRf1RoJkL7uBxgAkIPGZ8uXb1oxTg2VA3nss8PGTUrfZfto1hqc6ONiYzuBz7DJQpYWgxTvIXz40uDgdeWwwYfCSKv5YvqxybyYfmNjy5c+17l34bHFmuJMx/OlPHGYuDN8/2e/t/O2aTMWJMACDT1v9KT0u7vbFv/MJUlu7zZeQnnwocPF6cJjizXBcTe2m1v3PErlcjeOVS8auaNmNjzy4xHGTANwOYCd8k0wcBeALxaM+wqAAwEcZVOPExETG9uJeFbTOZksZGkxSPEewocvDQ5eVw4bfCiMtJovph+bzIvpNza2fOlznXsXHlusKc50PF/KE4eJK8O5y7remAwMHK+UWqCB/fMJKTyhtVoJ3X+jUmVLALwJUE8pra9e215ziWuKXN5dfITy4EOHi9OFxxZrguNsbDe3zTpQDag7ALzw1vphRaSALQPQh+7Q0PPHYV86GcChAD6RXyHABwD8CINXe+cD+DgAumobG9s0uNjYunxCCceaLGRpU5HiPYQPXxocvK4cNvhQGGk1X0w/NpkX029sbPnS5zr3Ljy2WFOc6Xi+lCcOk2uGlXUdH1BI6OrsBwHQ1Sg6btMaK5Pp5SvXNlb1+UrL1TuHr1AefOhwcbrw2GJNcJyNbV/L7DOhdbsebD7HOgaUUmfNWLbx68MGnQfgzQAOAfAggOUAbgDwGgAXA2hK/y02trGx5fiIks1hspClzUSK9xA+fGlw8Lpy2OBDYaTVfDH92GReTL+xseVLn+vcu/DYYk1xpuP5Up44TDYZzm245pVlA5MW5KCPV8DhBWlchUSv7G5Z/JMQCdl45/YVyoMPHS5OFx5brAmOubFdpbVenKWOlNLXzFjW88LdwIHvAXgXgLkADgZwAYDXAfgpgFMB7AygMV6x/W/Cvq/YTkkvkQ//DRw9BL0lvV981PPd1tam6+rqfHvMUm8lOcZkIUuboBTvIXz40uDgdeWwwYfCSKv5YvqxybyYfmNjy5c+17l34bHFmuJMx/OlPHGYTDKsrOs4Qiu1AFp/UEHNphS0xt+h9Eqly1Z2t1f/O2QyJt59+QrlwYcOF6cLjy3WBMfZ2G5urlipgJOy1JMGrpjZsOmUYWNpJ/CtAOrTf+8BUIvBhpducaZnb6nRvRTAaVl0JvoYn01jG4AjAfw5/Y3CUgDTAawC0A/glelvHq4YLeTY2LqVn8lCdlPiR0vxHsKHLw0OXlcOG3woDH/Vli6jTeZSZivBeygPPnS4OF14bLGmONPxUmpcko/xMjz2U1fvOKm8bIGGOh5AZYH3G4BkZXdb9epizWc87yF8hfLgQ4eL04XHFmuC42xse1sqTofGN9OLfGOV2EBOqzN2WL6Rbi8uPE4E8FEA7wWwN4DfAtgHQEU66I0AzgSwML1VOUQZi9bw1djSFdlb0geeKYBfAaBt2mmDgB0ANKQnZSMAGjviMxWxsXWrHZOF7KbEj5biPYQPXxocvK4cNvhQGP6qLV1Gm8ylzFaC91AefOhwcbrw2GJNcabjpdS4JB+jZTh/2aqDBnRyvNKghvaAvGeFJ6D11RhQK7svqPldsech4fyH8uBDh4vThccWa4JjbWzPm30IyvSt4za2CtugksPK6x+mXY8LD9p0ihpjuhWZeiXaEbmzYABtLEXP2cZnbNNQfDW2Q5m/AQBdVqd7xl+fXqG9OT0ppE1bV88B8MBIH3ixsXX7NmCykN2U+NFSvIfw4UuDg9eVwwYfCsNftaXLaJO5lNlK8B7Kgw8dLk4XHlusKc50vJQal+RjeIZzazuPSxQWAFhUsPPrHdBq5fTpauW1jdWbpfiXcP5DefChw8XpwmOLNcFxNrZU+882zfp0otQ3xlkHZ5Q3bLpojDG7AHgm3Q1ZypIS6cN3Y3sQgNMB0G3I70+3pe4CcF2aBt0rfhiAf8bGlr8+TBYyv7oboxTvIXz40uDgdeWwwYfCuFXoxELbZC4lAQneQ3nwocPF6cJjizXFmY6XUuOSfFCGZ3739lcM9KsFULSzsXpH6k9D6w4otbK7reYmV8/PtOz1ujL0vz7R6tnpDZvoDkDnQ8L5D+XBhw4XpwuPLdYEx93YUuFubp59tIJeAwnknwcAACAASURBVKAs/UP/TK/s6de5ZP7Mcx92XjPOC2SCEPhqbPdKt6emdy3R8WUAuwGgW4/pNw60nTWd3CfT529zra2tjUqpwpcO54ELF9Jt4/GICcQEYgIxgZhATCAmEBMoVgJ/3/Qs7vnHk7jrgSfwTN+2vI3ddpyKt7x2Nxz62pdh1x3GfFVnZtvl967GDnfR3ZeDx7Zd98MT770YOhnvjSmZJeLAmMC4CcyZM4e1R9raNuvAbf3J+5DgzXS/aqJwxwByPx7h3bXjeosDRk+A9aQVyNAl87+mW1NvAnAVgJ8DoP8/I70XnDpWeuD5baPZi7ciu5WuyW+o3JT40VK8h/DhS4OD15XDBh8Kw1+1pctok7mU2UrwHsqDDx0uThceW6wpznS8lBovpo8jGrtmztySWwBFm0Hp+du9aNyooK9c276YNgRlPfqaZz2ooegCyX8PpT9SvqznchchCec/lAcfOlycLjy2WBOcjyu2LnUbsWYJ+GpsycUXAJwF4GkA9wCgnb2eA7A+3USKdkh+H72YOza2Zict62iThZyVM9Q4Kd5D+PClwcHrymGDD4UJVculoGOTuZR5SfAeyoMPHS5OFx5brCnOdLyUGi+Gj3nLOt+gcvp4DVWtBjf+pN2gHn/nAbu/7Nd/7XnLmubF/+fDl/4YJve9qmIrNF7w82miVMP0ZRtbXDQlnP9QHnzocHG68NhiTXCxsXVZZcXH+mxsaXbUvE5LbzkunO3L06u3g/eyjHLEK7ZuBWKykN2U+NFSvIfw4UuDg9eVwwYfCsNftaXLaJO5lNlK8B7Kgw8dLk4XHlusKc50vJQaD+mjsnZ1lVI5amhrtm8GpfTtSqmrtwwkV17ysUOe4r5Fs3B+T7fuvs+kXNnv058ft39Ja3XCzOUbr3HJQsL5D+XBhw4XpwuPLdYEFxtbl1VWfKzvxtZphrGxdYoPJgvZTYkfLcV7CB++NDh4XTls8KEw/FVbuow2mUuZrQTvoTz40OHidOGxxZriTMdLqXHfPiobuvZSOrdA51Q1oAc3g6KtoJTu1NBXrm9bsmHIg88Me5tnvwlaXwaFg/MO6IVBg2auL2/ooZ2XnQ6f3rMaC+XBhw4XpwuPLdYEFxvbrNUsc1xsbGWeFxZXJguZRZCRRIr3ED58aXDwunLY4ENhGMu15KlsMpcyaQneQ3nwocPF6cJjizXFmY6XUuO+fFTWrnqHQnK8VqCrsxWpzv1KoSOnJ69c13b8i17F6CvDLa0VR+RyuIxeAamAeyehf8lWlD0/SSfPTl++8V8cGfjybuItlAcfOlycLjy2WBNcbGxNKlre2NjYyjsnbI5MFjKbKBORFO8hfPjS4OB15bDBh8IwleqEoLHJXMrEJXgP5cGHDhenC48t1hRnOl5KjXP6qGq8ZEZuy84LlNLVAKqGuBXUhpzGNevaF109lp6PDHubZh0Hpb4DYHfad2Xr80n1rl96+N+c8yYuH95NPYby4EOHi9OFxxZrgouNrWlVyxofG1tZ54PVjclCZhVmIJPiPYQPXxocvK4cNvhQGIYynTAUNplLmbwE76E8+NDh4nThscWa4kzHS6lxDh/H1Xft36/18UrrxVDYb5BTPw6FTiRlV3Y3V9+eRYc7w80ts09MtL5MA1OgsaFvcn/17rWPPZvFi+kYbu+m+iGbax9z5eJ04bHFmuBiY2tT2XIwsbGVcy7YnZgsZHZxR0Ip3kP48KXBwevKYYMPhXEs0QkFt8lcSgASvIfy4EOHi9OFxxZrijMdL6XGXXzMq+2YpxQ9O5u/3XhKynWbpoZWD6xc17b0SRN+zgyfbao4I1H4Vr7FVqpj5rKNS0y8mI7l9G6qPTQ+lAcfOlycLjy2WBNcbGxtq1sGLja2Ms6DFxcmC9mLAQdSKd5D+PClwcHrymGDD4VxKM8JB7XJXEoIEryH8uBDh4vThccWa4ozHS+lxk19HL3sqtmTc5OP10CNAg5P8bn81Vmoa7pbF9FrF60Orgx7W2Ytg1bNqYkV5Q2bPmllyADE5d1A8kVDQ3nwocPF6cJjizXBxcbWpcKLj42NbfHPgTcHJgvZmwlLYineQ/jwpcHB68phgw+FsSzNCQmzyVxKEBK8h/LgQ4eL04XHFmuKMx0vpcaz+phX2/H2BMlCrfRiALPyOK3vzze0A7mrui9Yen9WrtHGcWTY11zRpoHaQQ3dXN7Qs9zVVxY8h/csOmONCeXBhw4XpwuPLdYEFxtb1yovLj42tsXN36u6yUL2asSCXIr3ED58aXDwunLY4ENhLMpywkJsMpcShgTvoTz40OHidOGxxZriTMdLqfGxfBzR+L1pM5+bfnx6q3FlwdgboNHZ3V5zJec8XDPsba74fwA+Tp4UcNaMhk1f4fQnoamU4MH1PI00By5OFx5brAkuNrahVqQfndjY+slVBKvJQhZhuMCEFO8hfPjS4OB15bDBh8JIq/li+rHJvJh+C7UleA/lwYcOF6cLjy3WFGc6XkqNj+Sjqv661yPXX52+qifdDAqPAeiEyq3qbl3yWx/+bTPUX917eu+WgcsU9OBztDn9kfJzey734XE0TlvvnB5DefChw8XpwmOLNcHFxpaz4sNzxcY2fObBFE0WcjBTGYWkeA/hw5cGB68rhw0+FCZjKb4khtlkLiUYCd5DefChw8XpwmOLNcWZjpdS44U+qmo7j6VmVmu9WCk1efBr+jatVOcUTLnq+tYFj/v0bZPh5sbdK9Sksu9Aga4ob1NK1cxYtvEHPn2OxG3jndtjKA8+dLg4XXhssSa42NhyV31YvtjYhs07qJrJQg5qLIOYFO8hfPjS4OB15bDBh8JkKMOXzBCbzKWEI8F7KA8+dLg4XXhssaY40/FSanz+slWzBgaSaqXyOxu/PfU1kL86q3Oru9uXrA3l1TTDZ1r2et0kPfAdDbwLwKM5YNEODZtuCeW3UMfUuw+PoTz40OHidOGxxZrgYmPro/LDccbGNlzWwZVMFnJwc+MISvEewocvDQ5eVw4bfCiMtJovph+bzIvpN/6wypc+17l34bHFmuJMx/OlbMdUdU7nW3WSb2bpz+BmUMB99KqesoFk1Zrzq++1Y7ZHmWTY2zT7zQAug9L/o6HvByZVz2x46G57dTekiXc3pdHRoTz40OHidOGxxZrgYmPrq/rD8MbGNkzORVExWchFMTiGqBTvIXz40uDgdeWwwYfCSKv5YvqxybyYfmNjy5c+17l34bHFmuJMx/OlnJ1pYWPXlC3P5Ra+aDMojRvoCm339L9ejcbGXHZG3pFZM9zcPOs9CuoyAK8CcHuiVfX05Rv/xevGjC2rdzNWs9GhPPjQ4eJ04bHFmuBiY2tW09JGx8ZW2hlh9GOykBllWaikeA/hw5cGB68rhw0+FIalUCcIiU3mUqYuwXsoDz50uDhdeGyxpjjT8SFrfP45V+2bSybVAIquzr4+1X5UKXT2Q62+oXXRr0P6GU0rS4Z9TbM/oJWmjaF20Vrf9Pyk56t3rXvy6WL7z+Ldt8dQHnzocHG68NhiTXCxsfW9Cvzyx8bWb75FZTdZyEU1OoK4FO8hfPjS4OB15bDBh8JIq/li+rHJvJh+C7UleA/lwYcOF6cLjy3WFGc6PkSNV9avngutaxRQo4F0MyjcSq/qGUiSVTe0Vj8awkdWjfEy3NJS8aGcxncBJFpj9czlm6hRF3GM5z2EyVAefOhwcbrw2GJNcLGxDbES/GnExtZftkVnNlnIRTc7zIAU7yF8+NLg4HXlsMGHwkir+WL6scm8mH5jY8uXPte5d+GxxZriTMfzpfxCpmPru3aflMvVpK/qGdwMSmMACp10hXZta023L21X3rEyfLZp1qcTpb6Ralxc3rDpdFc9TryE8x/Kgw8dLk4XHlusCS42tpyrLjxXbGzDZx5M0WQhBzOVUUiK9xA+fGlw8Lpy2OBDYTKW4ktimE3mUoKR4D2UBx86XJwuPLZYU5zpeJcar6pddQDUpLdADQz0D0y55Ybzj3+wsn7V26DztxrTLcd7pPz3AqpT5/TqdefX/MVFMwR2tAx7W2Yvh9ZfHvSgWssbNi4L4cdEI+T5H81XKA8+dLg4XXhssSa42NiarCp5Y2NjK++csDkyWchsokxEUryH8OFLg4PXlcMGHwrDVKoTgsYmcykTl+A9lAcfOlycLjy2WFOc6XjbGp9bt3pxAr2qAN+vlLpNa314wb+tV0p3Pjv13o6fNTb222qFxo2UYV9zxfkaODvf0gLnzGjYdEFoX1n0Qp3/sbyE8uBDh4vThccWa4KLjW2W1SR3TGxs5Z4bZ2cmC9lZjJlAivcQPnxpcPC6ctjgQ2GYS7ak6WwylzJhCd5DefChw8XpwmOLNcWZjret8crazpuh8J4R8I/Sq3qQ053r2hf/xpa/mLjhGfY2z7oEUB8b9KQ/Wt7QQzshizxCnf/Y2I59+l3Ogy3WBBcbW5HLN7Op2Nhmjqr0BposZGmzk+I9hA9fGhy8rhw2+FAYaTVfTD82mRfTb6G2BO+hPPjQ4eJ04bHFmuJMx9vWeGVt5wNQePUw/F1Jkjt2TcuSHlteCbihDPUFs8p7n1eX0aZXCnh+QGHpDss2XSfB42geQp3/2NjGxlbyOpjo3mJjO4HPsIQPcdt4pXgP4cOXBgevK4cNPhTGtjYnIs4mcyk5SPAeyoMPHS5OFx5brCnOdLxpjVd9ftVuenKyHMBnXozVX+puW9xoyiltPGVYcdVhe+nJk6ipPRYKj2itl8xs6PmJNK/D/fg+/1nmH8qDDx0uThceW6wJLl6xzVLJcsfExlbuuXF2ZrKQncWYCaR4D+HDlwYHryuHDT4UhrlkS5rOJnMpE5bgPZQHHzpcnC48tlhTnOl4kxqvqu1YopWipna/QZzeCKjZ+f+D3pBMm3n82saqPhNOiWP/dddP9W7rl/4G0G9XwH0aanF5w8bfSfQaG9s5rD/jc60fFx5brAkuNralsJpH98ha9NxRtLW16bq6OtEeuefMyWeykDl1ObikeA/hw5cGB68rhw0+FIajTicKh03mUuYuwXsoDz50uDhdeGyxpjjT8VlqfH5d1z4aermG/lA6/mZAN3e3Lf4ZvdYn0dv617UtfTILl/QxfU0Vh27b+dW3TXr6H2T19iSna6af25P/SykcPs6/6bxDefChw8XpwmOLNcHFxta0qmWNF900xsbWrVhMFrKbEj9aivcQPnxpcPC6ctjgQ2H4q7Z0GW0ylzJbCd5DefChw8XpwmOLNcWZjh+vxqvqOj+hQVdp9WwoPIOcau5uX3T+eLhS/Prm5j3ep5B8F8DeUPqmGc9PX6Qa//lUKc2F+/zbzD2UBx86XJwuPLZYE1xsbG0qWw4mNrZyzgW7E5OFzC7uSCjFewgfvjQ4eF05bPChMI4lOqHgNplLCUCC91AefOhwcbrw2GJNcabjR6vxqrrOtwBo0MBx6Zjv55KkaX1L9d1S1gWnj76m2cfnlP6eAnZ47hVH4mUnrhL9s+Noc+c6/y7ZhvLgQ4eL04XHFmuCi42tS4UXHyv6wylesXUrEJOF7KbEj5biPYQPXxocvK4cNvhQGP6qLV1Gm8ylzFaC91AefOhwcbrw2GJNcabjR6rxqrpOamgbAEwH8B8NNK1rq7lUynrg9rGlpeKUnAZdqaWnhS/dVPObj82Zw/vsJrfn2NgCHLU+PEcuThceW6wJLja2oVakH53Y2PrJVQSryUIWYbjAhBTvIXz40uDgdeWwwYfCSKv5YvqxybyYfgu1JXgP5cGHDhenC48t1hRnOr6wzuafs+p9A0myXAHvon9XCpdjW9K89sLqknnG1HTNbm6a/Vml9NfyLS3QPrNhU51Lhqb63OMleA/lwYcOF6cLjy3WBBcbW+6VF5YvNrZh8w6qZrKQgxrLICbFewgfvjQ4eF05bPChMBnK8CUzxCZzKeFI8B7Kgw8dLk4XHlusKc50PNX4wtqunfqglyulz0pr/g/p5lCrpawBHz56Wyq+AI0vpU1t3cyGTe30/zYZ+vBnwynBeygPPnS4OF14bLEmuNjY2qwuOZjY2Mo5F+xOTBYyu7gjoRTvIXz40uDgdeWwwYfCOJbohILbZC4lAAneQ3nwocPF6cJjizXFmY6fV9e5UAH0Cp+D8vWu8VWtBpomyi7Ho63hvuaKCzXw+cGvq9PKGzZuv9XaNEMpnxNSmvJQ+fnQ4eJ04bHFmuBiYytp1Zp7iY2teWYlgzBZyNImJcV7CB++NDh4XTls8KEw0mq+mH5sMi+m30JtCd5DefChw8XpwmOLNcVlHX9MXcerJkHRc7QfGaw1/Sutk6Z17YtulFL3vnz0Ns/+DqBp3s+rBCfMqN90rbT1Zjv3rOfflj8LLpQHHzpcnC48tlgTXGxss1Sy3DGxsZV7bpydmSxkZzFmAineQ/jwpcHB68phgw+FYS7ZkqazyVzKhCV4D+XBhw4XpwuPLdYUl2X8vLrOj6nBzaFeQc2dhm5a11rTBKW0lJr34UO377bDloFJl2uNhdB4pKxML51W33PzcK0sGfrwx8EpwXsoDz50uDhdeGyxJrjY2HKstuJxxMa2eNl7VzZZyN7NGApI8R7Chy8NDl5XDht8KIxhSU7o4TaZSwlEgvdQHnzocHG68NhiTXFjjZ+7rOuNSS5Htx0fn9Z2d5ma1PSj1g/eJqXWffnoa99rbz3Qfzm0OkoD9ymtlpYv3/h/I+mZZu7Lsw2vBO+hPPjQ4eJ04bHFmuBiY2uzuuRgYmMr51ywOzFZyOzijoRSvIfw4UuDg9eVwwYfCuNYohMKbpO5lAAkeA/lwYcOF6cLjy3WFDfa+Mr61edAa2pqdwDUI0rrprXtNd+SUuM+fTzbUrF/ovE9AIcCuH1bLrdk53Mf+ftomqaZ+/Ruyi3BeygPPnS4OF14bLEmuNjYmq4sWeNjYyvrfLC6MVnIrMIMZFK8h/DhS4OD15XDBh8Kw1CmE4bCJnMpk5fgPZQHHzpcnC48tlhT3PDxlXUdR0Cr5VB4D9WzgroaaqB5beuSv0qpb58++ppmvTUHdYVS2Bda39TfP3nxTo0PPjGWpmnmPv2bckvwHsqDDx0uThceW6wJLja2pitL1njfje0kADMBPGUz7ba2Nl1XV+fbo421ksCYLGRpE5LiPYQPXxocvK4cNvhQGGk1X0w/NpkX02+htgTvoTz40OHidOGxxZrihsZXNa6doZ/bvBxQ9Wkt3QeN5u72miul1LVvH5ubZx+ttL4CChUa6CpftqlGKXpd7diHaebj8YX8ugTvoTz40OHidOGxxZrgYmMbclXya/lsGs8EcBoAej5lRwC1AP4J4FEAG9Kp3Avg3NGmFRtbtxNuspDdlPjRUryH8OFLg4PXlcMGHwrDX7Wly2iTuZTZSvAeyoMPHS5OFx5brCmOxn/2sv/7gB687fiQfA0rfVGuv6xp/QXVm6TUtG8ffU0VC7XCSgDTAX1peUMP/ayW6TDNPBNpoEESvIfy4EOHi9OFxxZrgouNbaAF6UnGV2M7BcDW9GptLwbfAzcLwEUAzgOwBED/eHOKje14CcXfrLolND7a5MNwfLaRR/jS4OB15bDBh8LYnq+JiLPJXEoOEryH8uBDh4vThccWa4KrbOja6/DX7fbgr//yyFDp3qFV0rSutXqNlFoO4aO3edZHAPUd0tIa589cvokuOmQ+TDLPTBpooATvoTz40OHidOGxxZrgYmMbaEF6kvHV2JLdXQA8CWAGgJ8A+AaAzQBWDW7QgDvSq7i3jDa32Ni6nXWTheymxI+W4j2ED18aHLyuHDb4UBj+qi1dRpvMpcxWgvdQHnzocHG68Nhis+Iq6ztPAb3CR2NOvqEDmmdsTZqv/Vr1Fil1HMLH5qbZZyqlvzKYgV42s6Gn1VQ3a+amvCHGS/AeyoMPHS5OFx5brAkuNrYhVqM/DZ+NLbl+E5Dfbe+PAE4FcHj6b98GsAgAPd/y+sHvMy8+YmPrduJNFrKbEj9aivcQPnxpcPC6ctjgQ2H4q7Z0GW0ylzJbCd5DefChw8XpwmOLHQ9XVbvqAJ0ky6FRQ/W671474r5/P3XE2guW/FxK/Yby0dtU8SUofCGvp9Xp5cs3XmyjPV7mNpyhMBK8h/LgQ4eL04XHFmuCi41tqBXpR8dnY0u7DNLV2U8DWJ3ap1uUB9I/ZentyC8H8GBra2ujUuqLw6e5cOFCPzOPrDGBmEBMICYQE4gJTMgEfvaHHtx418Po29qPaVPKcPTBe+LIgyom5FzHm9SOv/sGZtzXBZ1MxtNvPRfPvSK/CXQ8YgIxgTESmDNnjs8eKWbvKQFfJ414nwbwXnovWoH3LwHYHcAnALwdwFXA4K1BIx3xiq3bWTf5DZWbEj9aivcQPnxpcPC6ctjgQ2H4q7Z0GW0ylzJbCd5DefChw8XpwmOLHQk3v3714VpjuYY+Jl+jWl8HqKbu9pp7bHWk1LqpD62h+loqvgvgZAA9OklOmln/8E2mPIXjSzlDCd5DefChw8XpwmOLNcHFK7YunxDFx/pqbPcBcP+w6dEOfHTr8c3plVraPIqu0HbHxtZPIZgsZD8O7FmleA/hw5cGB68rhw0+FMa+Oice0iZzKSlI8B7Kgw8dLk4XHltsIW5hY9eU57bmGqipBZAA+DeUaupuXZTfKIkOWx0ptW7i48nGnXeeMnnqdwH1AQXcqxOcWF6/ifY2cTpKOUMJ3kN58KHDxenCY4s1wcXG1ukjouhgX43teBOj+4HG3Vo/XrEdL8axv26ykN2U+NFSvIfw4UuDg9eVwwYfCsNftaXLaJO5lNlK8B7Kgw8dLk4XHlvsEK6qvrNSazQAeGtal5flyiY3rW8+/l+FdWqrI6XWs/rY0rbnK3Qu9z2tcSQUbusfGDhxp3MfHX6xISvdC8aVcoYSvIfy4EOHi9OFxxZrgouNrdVHgxhQsRrbTAHExjZTTKMOMlnIbkr8aCneQ/jwpcHB68phgw+F4a/a0mW0yVzKbCV4D+XBhw4XpwuPLfaeP/5VL7vm7m8pjU+l9fh7DTSta6u5dqT6tNWRUutZfGxtnvWGfqWugM6/p/fGgW3JCTs2PvxYFmyWMaWcoQTvoTz40OHidOGxxZrgYmOb5ZNA7pjY2Mo9N87OTBaysxgzgRTvIXz40uDgdeWwwYfCMJdsSdPZZC5lwhK8h/LgQ4eL04XHBjuvdvUJFbtMvarnqefypaiUurBsS1/zD79+ylOj1aaNjpQ6z+LjmZY9Dy/TOXrsa45SWD19n01LVXV+w062o5QzlOA9lAcfOlycLjy2WBNcbGzZPi6KQhQb26LEHkbUZCGHcZRdRYr3ED58aXDwunLY4ENhslfjxB9pk7mUVCR4D+XBhw4XpwuPCXZufefrksHnaE/MN7TAL+i9tN1tNeNuimSiI6W+s/roba44VmtcqRR2A/Ql5Q09H8+KNRlXyhlK8B7Kgw8dLk4XHlusCS42tiafCPLGxsZW3jlhc2SykNlEmYikeA/hw5cGB68rhw0+FIapVCcEjU3mUiYuwXsoDz50uDhdeLJiK+s6zwDyTe0sAM/Ne/Ne007/4Dsz/xyTVUdKbWf10dc6a5HOKXrLxGQFtM9o2FSXFWs6rpQzlOA9lAcfOlycLjy2WBNcbGxNPxVkjc/8DaEYtuMztm6pmyxkNyV+tBTvIXz40uDgdeWwwYfC8Fdt6TLaZC5lthK8h/LgQ4eL04VnPOzc+o7DEq1oc6iqtO7Woixp+saHD7nN5F2T4+lIqWkTH71fnn0aEn0xYRKl66cv62kzwZuOLeUMJXgP5cGHDhenC48t1gQXG1vTTwVZ42NjK+t8sLoxWciswgxkUryH8OFLg4PXlcMGHwrDUKYThsImcymTl+A9lAcfOlycLjyjY7WaV9e5XCHf1E6ld7ECaOpuq/k21Z+ppul4KTU+mo/NTRXnKIX2/Ne1Or18+cZ8g+vzKOUMJXgP5cGHDhenC48t1gQXG1ufnyD+uWNj6z/joimYLOSimRxFWIr3ED58aXDwunLY4ENhpNV8Mf3YZF5Mv4XaEryH8uBDh4vThWck7Lza1UcrpamhfWd6vq9Kctua15x/4r1D599U03S8lBofyUdv06xmKLUMwFYFffKMhp7OEH5LOUMJ3kN58KHDxenCY4s1wcXGNsQniT+N2Nj6y7bozCYLuehmhxmQ4j2ED18aHLyuHDb4UBhpNV9MPzaZF9NvbGz50uc69y48hdh5ddfsonTZciicmc7yXqV089rWxfQM6QsOU03T8Xwp8zL1NlfQFetP0hVsnaiTZ9Zv3MCrMDpbKWcowXsoDz50uDhdeGyxJrjY2Ib6NPGjExtbP7mKYDVZyCIMF5iQ4j2ED18aHLyuHDb4UBhpNV9MPzaZF9NvbGz50uc69y48Q9jKuo5FgKLNod6QzvDbSZJrWtOyhG5BftFhqmk6ni9lHibdhbIt98+6QkOdoIB7ofSHZizruY2HPRtLKWcowXsoDz50uDhdeGyxJrjY2Gb7LJA6Kja2Us8Mgy+Thcwgx0ohxXsIH740OHhdOWzwoTCsBVviZDaZS5myBO+hPPjQ4eJ04bn97j/r8zrv+S6gPpyvK61vB5Km7vZFa8eqM1NN0/FSapx8PN24966TJvdfkW6gdVuZKjtp2rKH7gvtsZQzlOA9lAcfOlycLjy2WBNcbGxDf6rw6sXGljdPUWwmC1mUcYtNQXz5D5GhLw0OXlcOG3wojK+aKUVem8ylzFOC91AefOhwcdryVNV3nrbTjCkXP9X7/GBJKd00MHunphs+PXfreDVmqmk6fjz9UF/f0lrxKp3DSg28C8CNuW36pB0aex4JpV+oU6oZ0hwkeA/lwYcOF6cLjy3WBBcb22J8svBpxsaWL0txTCYLWZp5Kd5D+PClwcHrymGDD4WRVvPF9GOTeTH9SvtBO1R+OFTb7wAAIABJREFUPnS4OE15Ks/ufJNK0KAVFqTn8yatdNO61sW/zFpbppqm47P68Dlua/MeB/Wrsiuh9f9ohdXl/9x0oroU23xqjsVdihkOzUeC91AefOhwcbrw2GJNcLGxLdanC49ubGx5chTJYrKQpU1AivcQPnxpcPC6ctjgQ2Gk1Xwx/dhkXky/sbHlS5/r3JvwVNZ21kGBdjyeCY2njnvry3f+6AcON/55xERTyhU7kzO3pWX2OzX0lVrjVYC+pLyh5+MmeB9jTTP34cGWU4L3UB586HBxuvDYYk1wsbG1XWEycMbfSELabmtr03V1daI9hszDVMtkIZty+x4vxXsIH740OHhdOWzwoTC+a7iU+G0ylzI/Cd5DefChw8WZhaeqbtWRGmUNgD6S6kcr1VWmBpq/duph98yZM8f4e30WTWm/BMm6bnpbZlXqHK5SSu2sgfaZDZvqsmJ9jjPN3KcXU24J3kN58KHDxenCY4s1wcXG1nRlyRpv/I0kpP3Y2LqlbbKQ3ZT40VK8h/DhS4OD15XDBh8Kw1+1pctok7mU2UrwHsqDDx0uzrF4FjZ2zex7bmC5gqqlulHAPzXQ3N1Wcxn93daDKc50fLFqfEvr7KW5nKbXGymtdP3MZT1txfIyXLdUMhwpLwneQ3nwocPF6cJjizXBxcZWyqeNnY/Y2NrlVhIok4UsbUJSvIfw4UuDg9eVwwYfCiOt5ovpxybzYvqVdhUuVH4+dLg4R+OZV9+5QGm9HFAH58+b1t8Bypq626v/PXQebT2Y4kzHF6PGe1srPoEcLhrMSp1evnzjxcXwMZpmKWQo2Xuo/HzocHG68NhiTXCxsZX0iWPuJTa25pmVDMJkIUublBTvIXz40uDgdeWwwYfCSKv5YvqxybyYfmNjy5c+17kfznPcmR0v75+qGpTGaanbe6CSpu7W6uu4rgKaejcdz5dyNqbeplnLoFQzgK056JN3aOjpzIYMN0p6hmMlIcF7KA8+dLg4XXhssSa42NiG+zzxoRQbWx+pCuE0WchCLG+3IcV7CB++NDh4XTls8KEw0mq+mH5sMi+m39jY8qXPde4LeebVrf5w/iqtwqtTpxc8p5Pmm9urnx7Jua0HU5zpeL6Ux2fa3FTRrhTOAdCjc+rkmedu3DA+KvwIyRmOl4YE76E8+NDh4nThscWa4GJjO95Kkv312NjKPj9O7kwWspOQB7AU7yF8+NLg4HXlsMGHwngo25KltMlcymQleA/lwYcOFyfxfOrSOw9KVI52O140WB/q57ncQPP685f82MeVNFPvpuND1Xhv86yLAXWa1rg3gT55xvKeW0Npm+pIzTDLPCR4D+XBhw4XpwuPLdYEFxvbLKtJ7pjY2Mo9N87OTBaysxgzgRTvIXz40uDgdeWwwYfCMJdsSdPZZC5lwhK8h/LgQ4eL87If/lr/8Nb/PAlgFwBbFNC8tq2Gbqsd97D1YIozHT+ucccBf2rcf8orJz+xUgE1Cri1XyWn7Ljs4b860nqFS8vQZLISvIfy4EOHi9OFxxZrgouNrcmqkjc2NrbyzgmbI5OFzCbKRCTFewgfvjQ4eF05bPChMEylOiFobDKXMnEJ3kN58KHjyjmvvuOdCkkDtD56sCb0GgXVtLat5o6sNWLrwRRnOj6rf5txzzTuuVvZ5NyVAI6FwgatcyfPbHikx4YrJEZShqbzluA9lAcfOlycLjy2WBNcbGxNV5as8bGxlXU+WN2YLGRWYQYyKd5D+PClwcHrymGDD4VhKNMJQ2GTuZTJS/AeyoMPHVvOYz+1fmrZzKeXQyu69VjtOGMynuntP6O7fdHgjr4Gh60HU5zpeIMpGA196st7zJmSlF2pod+ugM7p23b9kGr88/NGJEUaLCVDm+lL8B7Kgw8dLk4XHlusCS42tjarSw4ma2O7I4DPAqDbihoB7AfgQgBenwOJ77F1KxSTheymxI+W4j2ED18aHLyuHDb4UBj+qi1dRpvMpcxWgvdQHnzo2HBW1q6uAnLLodShaR1cubz6wJPe+qYDsv5M8YLysfFABKY40/E+ary3efaboPSV0DgAwMXlDZtO96Hji1NChrZzk+A9lAcfOlycLjy2WBNcbGxtV5gMXNZvQmsAVAE4HsD3U+sPAni5z2nExtYtXZOF7KbEj5biPYQPXxocvK4cNvhQGP6qLV1Gm8ylzFaC91AefOiYcM49u6siKctvDnVGev7pedCm7raaa0x4hteOLdYUZzqeu8a3NFe8WytcqTX2Vkq1zVi2sZ5bwzdfsTN0mZ8E76E8+NDh4nThscWa4GJj67LKio/N0thOArANyL+L7qi0uaUrtn8B8HoA9/qaRmxs3ZI1WchuSvxoKd5D+PClwcHrymGDD4Xhr1o+xvnndOw5oJIjVaIn53LJ/61vr/4DH/uLmWwy9+nHhFuC91AefOhk5ays7TwJCtTUvm7w/KhvTu7f2nz9hSc9Qn/LyjPSubXFmuJMx5vU4Xhje1v2fD907ioAMxVQN6NhU/t4GIlfL2aGrnlI8B7Kgw8dLk4XHlusCS42tq4rrbj4LI1tGYCHAdBzM2cB+CmAlQB+AGA2gE2+phAbW7dkTRaymxI/Wor3ED58aXDwunLY4ENh+KuWh3FeXdchCrlfAJgxxKiUPmlt62L6odjLYZO5FyMWpBK8h/LgQ2ckznm1q49WStOjR/sD+ilA0ff5g9PTc6tSaF7bWtNdeLpcvNliTXGm4y3KcTukr2nWWwEcqqH6k0TvmNOqdfD3Aerj5cs2XuLCXUxsyAy55ynBeygPPnS4OF14bLEmuNjYcq+8sHxZGltyRM/Tfj61NhfAagD3AzjEp93Y2Lqla7KQ3ZT40VK8h/DhS4OD15XDBh8Kw1+1PIxVtZ1f0yq/p8H2QwO/WNdW8788Ci9mscnclxdTXgneQ3nwoTMSZ2V95wPQeHXhuVBATkM1T5+mmq5trH7RRkcu3myxpjjT8aa1ODR+S3PFyTnge8Pwz6tEnzSjvod+firZI1SGPgKS4D2UBx86XJwuPLZYE1xsbH2svnCcWRtbGvdu2scfwM8AfAJAF4BHfVqNja1buiYL2U2JHy3FewgfvjQ4eF05bPChMPxVy8NYVddxlYY6YfgPxYBuGZikrr6hqebvPEr/ZbHJnNuDLZ8E76E8+NAp5FzY1VXWd8fAh1SZuvzF50O3drctXjbaeXLxZos1xZmOt63JzS0VtyiNI4bh15U3bKq05ZSCC5Whj/lK8B7Kgw8dLk4XHlusCS42tj5WXzjOrI0tOXoNgJ1Sa7n0v38EMODLbmxs3ZI1WchuSvxoKd5D+PClwcHrymGDD4Xhr1oexsq6jv8Aau/R2fRtgLp627Rk1Y2N1U9wqNpkzqHLwSHBeygPPnSI8zOX3/5e5FQVFCqhMWfE86KShd2t1dfFxnb8qu1trvgPgBesYa2xeubyTTXjo2WP8FGDoWYswXsoDz50uDhdeGyxJrjY2IZakX50sja23wTwqREsUKP7jB9rQGxs3ZI1WchuSvxoKd5D+PClwcHrymGDD4Xhr1o3xrn1HYclOfVbKNDn8nMApuUZNX6nk9w3lU6OBHDSMJX1Sutrpk0vu26k20OzOrLJPCu373ESvIfywKmTrzcklXvtOn35Q4/3FZwmtRHQvQD22f6PGv9Q059+w9rG0woHvuDUunizxZriTMeb1u6W5oqTBoCzFfCGF2GVOrd82cYmU05p431n6HO+EryH8uBDh4vThccWa4KLja3PVeifO0tjS5tH9QN4AMClw67QUsPr7aXisbF1KwCTheymxI+W4j2ED18aHLyuHDb4UBj+qrVnrKzv+DS0+kbKsH5gWv/i5DlVllOTJ93QWv2CRz6q6le/Hxof0dDzChUV9NUD0Nesb1uywdSJTeamGr7GS/AeyoOrTlXtqgOgyio1UAnodxScE/oF9doc1LpJuWnda84/7tmq+o4TtU5eC60fnzwwpeP6Cxfkdz8e7XDxZos1xZmOz1qzfS0VH9QaZ9NmUfQzkdb6aqXU/gBoAyk6rpuxbFO1UvnHuUr68JVhiFAkeA/lwYcOF6cLjy3WBBcb2xCr0Z9GlsaW1OmVPusAnOnPyouZY2PrlrbJQnZT4kdL8R7Chy8NDl5XDht8KAx/1doxVtZ1Xg1gaR6t0dbdXpPp3ZaNjY3JnVv3PUVr9ZGCH6Bp29XHgdw1CurqtW01d2RxZZN5Ft4QYyR4D+XBRmfu5695ZTIpqVQqqdTQx7zgnGj8cPH/vur9q3/1QMWaliU9LufLxtuQni3WFGc6frw8epsr5imFs/T252n1pZOgL5ra8Mjv88u58VXTgH/2q8b8xYEJcXBnGDIUCd5DefChw8XpwmOLNcHFxjbkquTXytrYfjS9Wks/JNEtckMHfZMc9dYkV7uxsXVL0GQhuynxo6V4D+HDlwYHryuHDT4Uhr9qzRiPbuzadfJzuduGbvnUCRava6npNGMZHF31+VW75SarUxXUqQBeu51D6/upwUWS61rbuuSvo3HbZG7j0wdGgvdQHrLqUG1N2tpfpXJllVCa3mSw/dVRAG7SGt3J5KR7bVP1P7JyjnfuXHhssaY40/GjzfnZ5op3q8Fbjo/NN7DQVycaF81Y3nPreDmV+te5MixGDhK8h/LgQ4eL04XHFmuCi41tMVYnn2aWxpbG0Dvs9gBAtyIV3nq8H4DNfHZeyBQbW7dkTRaymxI/Wor3ED58aXDwunLY4ENh+Ks2O2NVbeexWmF9ivj9gEoW39Ba/efsDKOPPOacq/adlEymBpeu5O5SMJJ+6L4GZcn13c3VDxUy2GTO4ZWDQ4L3UB7G0lnY2DVl69Zc5YBGlQI1s4q+Z6eH/pUGuhOtu9e2L/mTj3PvkoEt1hRnOn54ffa1zDpMa0W3HB+ffu0HSaIvml7f81OOWi4FDtcMizlHCd5DefChw8XpwmOLNcHFxraYq9RdO0tjmwB4CsBlFrciTwIwM8UXui0HsAXA0O7KI84kNrZuJ9hkIbsp8aOleA/hw5cGB68rhw0+FIa/arMxVtZ1NALqi+noq7rbaoZvCJWNKMOoyvqut0HrjwD6w8OG5zed2oKydTe3Vz9tk3kG+SBDJHgP5WEkncq6zqPooj0Gm1l6e8HgofC7wWYW3WPdks7l3YXHFmuKMx0/FOXWtlkHDvSrs7XCifl/09gA6BXly3vWBilyQSK2GUqYggTvoTz40OHidOGxxZrgYmMrYbXae8jS2BI7vVCcdvlrBbC1QO76dGOpkRzQ87inAaBb7XYEUAvgcQCrUswrAVwA4IrR7MfG1v7EEtJkIbsp8aOleA/hw5cGB68rhw0+FIa/asdnnFfXcYOCyj/nqJQ6e23rogvHR/GMqKxdXaWUPlUDxxUwagV9zceO2feEO3+2pqyxsXHMXzbyOOFlsakXXgfhPmuH5jqvtuPt9MwskJsHqIMK5vM3rdUPk0R1r22t/nmWeXLl58JjizXFmY5/unX3fSblkrMART/L0M5Pv9Bar9hheQ/9TPSSPEwzlBSSBO+hPPjQ4eJ04bHFmuBiYytp1Zp7ydrY0qYSBbc1bRca7XU/U9IGmK7W0msDlgOYBYBuf9sBQAOACgAbAdDV2xGf042NrfkJLUSYLGQ3JX60FO8hfPjS4OB15bDBh8LwV+3ojPPruvbJIfcbALtr4GkFVHe31dwU0sOQ1sKFXWXPzRk4MQf1EQUc/l8Pmn7xeI1WuG5d6+JfFsObjaZNvdjojIUJ4WH+slUHzT3kFfd03/Gf3wDq7QV+ejTw/SRB99qWmhtM58bl3YXHFmuKyzq+t323PVV/2Vka6rP5a9/A/+kcVsw8d9P3TPOdaOOzZihx3hK8h/LgQ4eL04XHFmuCi42txNWb3VPWxvbdAKaOQHvzGFds6dmuJ9NNK34CgF5l8V4AhKENUkibrg7Qy+DpVUIvOmJjm/1EjjTSZCG7KfGjpXgP4cOXBgevK4cNPhSGv2pHZqys66Qdj2nnYyiFW5TKLXbdgZbL+7H1XbsnWp9UsfPUC3ueKtgXMN10Sim9dk3b4ru49Hzw2NQLtw9fHubVXfOa/JVZrejVTnTL8dCxRQOrE6juZ6f1rftZ4ymFmzoaTY/LuwuPLdYUN9543fiqnfsmbTlbKfUZPfhLd3rufcWMhk0rVP6CbTzGy1ByQhK8h/LgQ4eL04XHFmuCi42t5FU8vresje34TCOPeBMA+g3nHwHQZiZXAuiid7qlw+lK8GEA/hkbW9uIR8eZLGR+dTdGKd5D+PClwcHrymGDD4Vxq9Bs6Kr6zm9qjU+lo7/R3VZDV4DEHZT55y658/W5RH8IoGdysXuByfymUwmSDWvaqv8mzbxNvXDPgdPD4C8bBqoUkrkYfD0PNVj5442v2RV3//2JD217Pum+8WvVT3DMg8u7C48t1hQ32njduP+UvslPng1oWp+70c8kGlixeYpeUXF2D911Fo80AdPMJQUnwXsoDz50uDhdeGyxJrjY2EpateZefDa270mfp/10+owuufsCAHoR/NcBlKVXdHemK7etra2NSm3fUGX7TBYuXGg+q4iICcQEYgJFTuCrP/wz/vXo4M/ES971ahy2L/28XBrHP3o24/b7HsNv730UuuA61f4v3wkHv2ZX7PfynbDD9MmlMZkScLmtP4c//vsp/PnfT+MvDz6NZ7ds2+56v713woGv2gUHvGIn7FxOT/nEgzOB8ntXY8a9q1HW14PctF3Qt88C9L12AXJT6UeTeMQEYgIv1QTmzJnjs0d6qcbqfd6+ThrxPp3eenx7wSzmAzgjvaWKOlbaYOpto80y3orsdv5NfkPlpsSPluI9hA9fGhy8rhw2+FAY/qodZJxb33FYotUtAKYD+gGNsup1bdV3+tLj4B0r86r6zkrkcIpWWFCgpTU0XcX94db+rRtuuvCkol3VsqkXjswKOWw95F/7lNBtxvoY6PxjOUPHb6Dx/VyC7vWtNfcN/aOtzljz5eJ04bHFmuIKx/e2zD5Naf05Deyr6LWFGisU1Irpyzf+i7s+JhKfaeaS5i7BeygPPnS4OF14bLEmuHjFVtKqNfdi0tgenO6MXKhCz8r+91fL//3KPgDuH2ZnJYDTgfy7G/cf/KEP70t3TR7ReWxszU8oxw9bbqo8aJMPIR7FkVlC+PClwcHrymGDD4XxUTeV9R2fhla0nwAd39/4xNOL77z0tJE+I33IW3NmyXxhV1fZljtzS6HUhwH9v/8Vo02nFD1DvL4YG2Jl8W4dTEagiYd59R3vTHQyTw/eZvw/BRJ/gNZdubKy7vUt1XePJG2ik9E62+75Lt5ssaY4Gj+r8/APKejPAeqN+X0+FFbkBtSKHc7d+Jesmb2Ux5lmLikrCd5DefChw8XpwmOLNcHFxlbSqjX3krWxpXfY0jOyw4/RdkUez8nLAWwapSnejo2N7Xgxjv11k4XspsSPluI9hA9fGhy8rhw2+FAY7qqtrOuk5o42iqLbd7+4rr3mPG4NX3ymmR911pV7TJ00eakGNbn5V8ENHfcBerXW2LCufTHtAu39MPXuw9B4HubXdRyco2ZW6WOG7URNVwdXaY3uLHmNp2MzNy5OFx5brAmur6Xig8+/7MBrJz/2h6GYvotErSiv3yj6bgqbc+oTY5K5Tx823BK8h/LgQ4eL04XHFmuCi42tzeqSg8nS2NKzsP0Afg2geVgz+rMxdkV2nmVsbN0iNFnIbkr8aCneQ/jwpcHB68phgw+F4araoxu7dp38XO5XAPYjTqXx/rXtNT/i4g/BY5P5kK+q+lWv11BLoRVtOkWvcRs6aNOpH+R0smF9e/X2boJ7Pi7eubyM5IFe8TSgcpUKoNuMj96upfAEcrpDq+SH69oW0VsCMh8+5srF6cJji82C622pmKs1zlQA7ftBr2PoVEqtmL5sY8m80ipzgQQYmCXzADasJCR4D+XBhw4XpwuPLdYEFxtbq+UlBpSlsSWzfwJA78c7K6Tz2Ni6pW2ykN2U+NFSvIfw4UuDg9eVwwYfCsNRtflnJFX+8Qo67lD9yaK1F1b/g4M7JIdN5iP5m9dw7dtVbuAErUHvyC3cXWo9XZVMBp7fsPbCk1jz4fLukveQh7lnd1UkZblKUDObv9VYDe1oTL8cXqWV/v5bptZ0NzYqetWd8eFjrlycLjy22LFwW1orjsjl8vt4VOWDVljz5Lu+On/vdyzJ+nOP8fl5KQBsz5WEbCR4D+XBhw4XpwuPLdYEFxtbCavV3kPWD/g16TeHawE8XiD3OQDW788bz3ZsbMdLaOyvmyxkNyV+tBTvIXz40uDgdeWwwYfCuFZtZV1HIzC4k7sCLl3bVnOaK2ex8DaZj+e1snZ1lU5wgtK6evtYDQ1Ft96qDUn/wIa1X1ny2Hg8433dh/fxNAu/vrCxa2b121/97JU//dtKDUXPzc4qmO8P6X2zM6Yn3dc2Vm824R1prI+5cnG68NhiR8L1tcw6LKcVXaEdrDulblY5rJixfOP1tjqu520i4Us5QwneQ3nwocPF6cJjizXBxca2tD+xsja29L7ZPUaYqu0ztplSi41tpphGHWSykN2U+NFSvIfw4UuDg9eVwwYfCuNStZV1netoA+Q8h9ZndLcvvsiFr9hYm8yzej6i8ZZJ5VsfWap07iRAHflfHG06lVyrtN7Q37fjhhu+NXdrVs7CcT69j+WHdovWOX0MNbNKFexorPATPaA7psyYuvb6xgWP2MxpNIyPuXJxuvDYYgtxm8+bdaAqU3SF9uR8Pwv1G5VgxfT6jdcM5Wmrw3kOS52rlDOU4D2UBx86XJwuPLZYE1xsbEv7UyprYztp8KLEiw6vu33GxtatuEwWspsSP1qK9xA+fGlw8Lpy2OBDYWyqlp6bzCH3UwC0Ad6TWunj1rUuLvln9Wwyt8pv2apZuYFkKRROGrYj8H1a6zVKY0P3+Yt/YsIdyjt5qqzrOEIDxyiVHA2taVfd/PGK3cvxr8d6P5dse/5H3Lda+27iufJz4bHFEm6vVW/bZyBJPp++dYF+VLmbdjouX7bxO8PryFbHpB4n+thSzlCC91AefOhwcbrw2GJNcLGxLe1PsbEaW2pmvwvguvS1PHR1dvhBt95t8RVBbGzdkjVZyG5K/Ggp3kP48KXBwevKYYMPhTGt2sq6TtrxmHY+hobekJs2sOiGxhOeMeWRON4mc9d5zDuncz8kWKqgPgTovQv4blUaN2idbOg+v7rwPegjSvr2PvecjjcnCseAmlnodxSY+KvW6EiQ+/7XP3bYH+fMmZP1F8XW0fmYKxenC48Ntrd5t9m9+57w8Mx7O7doYLrW+v5E4aLpu/WsUKeN+BpCtlcbWZ/ACQC0OVdSpi3BeygPPnS4OF14bLEmuNjYSlmxdj7G+kY8BQDdGrYMwGfjrch2ARcTZbKQi+lzJG0p3kP48KXBwevKYYMPhTGp+ar6zm9qjU/lMRpt3e019SZ46WNtMuec0/z61YfnoJdC528jpXecD0at9Y8V1IaBpH/DDa0n/DnUZ8X85V375rbljlFKHZO+b3ZQWqmHdA4didJda9tq7hjyEyo/HzpcnC48JtgnG3feecqkqZ+HUp8AsCugH4LGihmTnr9I1T359Fh1aaLDWd8TiauUM5TgPZQHHzpcnC48tlgTXGxsS/sTa7zfMNOrfmj3xmSUaQ74nH68YuuWrslCdlPiR0vxHsKHLw0OXlcOG3woTNaqrazr/AWAd+YbrQSL17XUdGbFlso4m8x9zW1efcd85LBUKfWCTacUcL1O9IayBBt+1Lz4P9xNZWVD114qN3CMRnIMNO1ojJmpxrNaYVWCXMfa1iU/D9Vch9LhOvcuPFmwunH/KX1THj8LGp8A1F4aeLL3gA/tMvNPl+9Z3vDYxiz1mEUnC89LeUwpZyjBeygPPnS4OF14bLEmuNjYlvYn3HiNbVFnFxtbt/hNFrKbEj9aivcQPnxpcPC6ctjgQ2HGq9q59R2HJTm1AQo7A/hbmUqO+1Fr9YhXDcfjkv51m8x9z+mQj10yec9dd1yaf0cu8N4CvccU1LpcojdMnjJ1w1dOPOhJ29uA31vbtdM0hWM0csfk3zdb8B5eDd2RKLVqbWtN93hzDZWfDx0uThee8bB9LbM/Sw2thn4t3UmmoVdMyumLHlry27+ZnPvxdMY7z/HrKOnbuSWc/1AefOhwcbrw2GJNcLGxLe1PutjYlvb5G9O9yUKWFoMU7yF8+NLg4HXlsMGHwoxV85X1HZ+GVt9Ix1zT3VZzgrQ1wunHJnNO/fG45tOmUzpZCq1PANTBQ+O1xt/fdcAec371l0eqnp361w0/a2yk98WOeSxc2FXW9xraACpHjSz92acA0K207ujv2+n7Jrs0h8rPhw4XpwvPaNjeltmnQWu65figwXOkL5lUhoum1vX8gf5mqmk6frxaeil+vZQzlOA9lAcfOlycLjy2WBNcbGxL+5MtNralff5iY+v5/Jl8GNpa8aXBwevKYYMPhRntfFXWda4E8rv2Qil19trWRRfanttSwdlkXqy5HVt/9f6TULZU6/yV3Fdu96FxNxJsUEg2aOhyBX2q1moPpfW9ZZPROjCQ7E1XZpGjjaCwfUdjDfwiUViV0wNd69qWPmkzr1D5+dDh4nThGY7d0jL7xNxgQ/vWfDurcdUA9IqdlvfcWnh+TDVNx9vUwkTHlHKGEryH8uBDh4vThccWa4KLjW1pf4plbWzphfP0vNP2HwbSaZ8Rd0WWWwAmC1naLKR4D+HDlwYHryuHDT4UZnjNH93Ytevk53I3A6Argpqu5nW31dwkbW348GOTuQ8fppyVtV3vOHz/3X756788srngmdiRaIZ//S4AHShLVnU3Vz9kqjt8fKj8fOhwcbrwDGGfban4oNL4hALenWb8/VxOr9jh3B56xdaLDlO5FjmuAAAgAElEQVRN0/GudTER8aWcoQTvoTz40OHidOGxxZrgYmNb2p9cWRtbeq/gkSNMlV4B5O11F/EZW7fiMlnIbkr8aCneQ/jwpcHB68phgw+FKazaqtrOY7XCGgD0mrNbcwPJB9ZfUL2Jv7JlMtpkLmUmQ97n1XfNT3TuBA0sHMmbhv6XQnKlzumOdefX/IXTf6j8fOhwcbrwPPirq/UuPz9rHYB56Xm5AUqvKF/WM+bzzaaapuM5a2SicJVyhhK8h/LgQ4eL04XHFmuCi41taX9aZWlsaWdkem6J3ml7JvCC98P1+Zx+bGzd0jVZyG5K/Ggp3kP48KXBwevKYYMPhRmq2sq6jkZAfXHw7/qb3W2LP8Nf0bIZbTKXMqPh3gvfN1zocVuybc8bW07MtHuu6dxC5edDh4vThmfLlyuOyCX4JIAP5lcf8HMNrNihYVNXlnNgqmk6PouHl9qYUs5QgvdQHnzocHG68NhiTXCxsS3tT7UsjS3NcD2A5wAsCDnd2Ni6pW2ykN2U+NFSvIfw4UuDg9eVwwYfCkNVW1nX8QNAfWDwh2p96rq2xfQLvJfcYZO5lJBe1NjSa3sGcv/QwOT/elS/6m5blH9lk48jVH4+dLg4TXj6mioOzQFnKIUT6Xxs23U/THriL6fMbNh0hcn5MdEkXtPxJl5eKmNLOUMJ3kN58KHDxenCY4s1wcXGtrQ/zbI2trcDeAuAZwE8WjBl2qmw11cEsbF1S9ZkIbsp8aOleA/hw5cGB68rhw0+BGZ+Xdc+OeRuSHfEfYzeXbqurfpO/kouDUabzKXMbCTvVWev+l+UqVM01O5a4z6dS9p93loeKj8fOlycWXg2nzfrQFVGV2jVafn6UfgTclixafGvLzJ5bc//b+87wOy6ymvXPnfULLmNhCVbVNsBAsShJBLlkVCCKTaGBNxICB0eLYUAli3KkFiWDKY8EpwXhyS02MIQQnBJ6IQEYQOC2CQPCM0Qq9iWZGNbfe7Z7/vvnGtfj+9o7v/vcv995z/fp28kzV7rX3vtffbMuvuU7twbpGbvPOW21zLHNeko2UMN2nNpSFEnFmcIjxTLwVmw1bTi8LUMGmzpsqD79KGn+2GSXY5swZY/oKPyQ5yzCIW5dGh0Dh2pasTgDeWQ4FNjei9VpfehHrvrtt++5JJXHUw5j7RzSzzX0icN2nNpSFEnFueheG77s2NOGHPudc45etLxfAA/9R4X3z5ZXXzcxNY9Ug1cHLe9ljmuSUfJHmrQnktDijqxOEN4pFgOzoKtphWHr2XQYNtlPgLAgmm7tvyqAyIs2A5o1AzNOCdyWKX4aC3ac+hIVSMGbyiHBJ8Sc+o5G98N13lWAL1C5O1XXXjWn8afveUxSjzX0ksN2nNpSFEnFmc/nt3rlh0LN+918HgN4I8CcBMcLm4fqC4+YmLrDunOqxQXq69a5v4wdJTsoQbtuTSkqBOLM4RHiuXgLNgOY2WJV3PQYHs8gA24+2mTnwdwPoCvxpNybyYLtmHuck7ksErx0Vq059CRqkYM3lAOCT4V5pRzP/455/3TaLY6j+deceFZ/xR/5pbJKPFcS081aM+lIUWdWJy9PH7D0UfuqRe+HlPvoj3WA3dUwMWuVV28aM3Wn0+fO1INXBy3vZY5rklHyR5q0J5LQ4o6sThDeKRYDs6CraYVh69l0GBLlyLTKxToK70PsPs6hRV2KTLf9FwIzomcS9OgdbRoz6EjVY0YvKEcEnxszLPOvWx15d2nAdB69QM3Vj3zivPP+Omgc3EutJN4rsUXDdpzaUhRJxYn8ez76LMXPGDertdXwKs9cAKANoCL2666+Ijztn5/pjkj1cDFcdtrmeOadJTsoQbtuTSkqBOLM4RHiuXgLNhqWnH4WgYJtkcD2AXgLQDWNSXoKaKfAvBoAPSi+ySH7diG2co5kcMqxUdr0Z5DR6oaMXhDOST4mJhTztn4eufw/maGfuTKDWe9KP5sLZ9R4rmWXmvQnktDijqxOLd99iJ/xLcu+k8Aj+jMDe/+hi47Xrx227dnmytSDVwct/1suufi90v2UIP2XBpS1InFGcIjxXJwFmzLXtkGCbZjzbtrPwrg5c0nsGuaS5EfDOCHqSywYBvmLOdEDqsUH61Few4dqWrE4A3lkOBjYU4957JL4Nwrml+yX3flhWd+IP5MHQ1Giedaeq5Bey4NKeqEct65bsWLHfBqAKtoTjjnLnO1u3jRW7b++6BzRKqBi+O2H1T/XGpXsocatOfSkKJOLM4QHimWg7NgW/aKNkiwpR7+DYCXNl2lV/4c3lyWfGbK7luwDXOXcyKHVYqP1qI9h45UNWLwhnJI8KGYp09cPj5vb30VHB5LH8R555981fqz/y3+LB0dRonnWnqvQXsuDSnqSDn3rFtxRg28xgG/SXNh/8onYv62rz19yblbP8edG1INXBy3Pbcfc6F9yR5q0J5LQ4o6sThDeKRYDs6Cbdkr2aDBdhGA5wKgIHskgI1NsL01Zfct2Ia5yzmRwyrFR2vRnkNHqhoxeEM5JPgQzLPP2fhM7/wnALcYwKb2wiOe+c8Tz7o9/gwdLUaJ51oc0KA9l4YUdbicu89f/mwP9xrn8IypOeA+D+cu3n7mv/2j5F20xMDV0J17XBy3vZY5rklHyR5q0J5LQ4o6sThDeKRYDs6CraYVh69ltmD7SwB2AqDH9Lf60P8YQM0vOxjCgu1gPs3UinMih1WKj9aiPYeOVDVi8IZySPBSzB/+9bfOA/wFNBs93Huv2nBm57U+dszugMTz2VnztNCgPZeGFHVm4txz/jGPr111ogN2Ll67/aq965Y/tUbnPbS/04mz8F9zwMWL1t50aUg4DcFy/eC2zzODy6pSsocatOfSkKJOLM4QHimWg7NgW9aaNF3toYItvUR9P4DzAPwRgGP6dJV2b5PthliwDZtcnBM5rFJ8tBbtOXSkqhGDN5RDgpdg3v7Xn/Obf0zPuKNP2uoXXL3hBZfFn5WjyyjxXIsbGrTn0pCiTj/OPReseLf3U+97bo5bANyn83eH78D7ixevvemDvXMgRJsUy8Vx22uZ45p0lOyhBu25NKSoE4szhEeK5eAs2GpacfhaDhVsaYf2HAD/CuDXANClfdOPiwAc4JcdDGHBdjCfZmrFOZHDKsVHa9GeQ0eqGjF4QzkkeA7mtDWXn1ij/sepJ7G6m1vOPfmf1p/x/+LPyNFm5HiuzQkN2nNpSFFnOqd/1/LFew44eq3ftMNvcQ7vXHTgpovdBCanfzdEmxTLxXHba5vrGvSU7KEG7bk0pKgTizOER4rl4CzYalhp5BpmuxS5y0yXH30ZwPea/6BXAL0WwJ8D+IW8/KGRFmzDnOWcyGGV4qOHrf3Ucy9/vveTD3jRk0+86Iynrxr0PBEZkaqvMXhDOST4QTHPWnPp2RWqzmWQj7j/UdjwmmckHSfR4BYCGtRzjd3RoD2Xhth1vvVKzDvmlVcfGP/sS18C+JMAdxKARzpg6b1zLV67+C3bL07xQaq0X1wct73G+T5sTSV7qEF7Lg0p6sTiDOGRYjk4C7bDXmXC6s/2i+CTATwdAAXbrwDo7oTcD8ALAKwEsDVMwsxoC7YyZ5+x5rIHzkP1209/9LHv+ezmbWdeceGZl8uYhofiLEKxVZ665uOfAjy9q3nqcO5fr1x/5pNi1+nypeprDN5QDgl+EMypay67AHDndoYHeMv7XvHr50sfXJNqXEviHcRzrf3RoD2XhpA6/v0nLtiz+86T4P1JHtVJqP1JzoGC7Pg9xtZhO3wn2M7r/f+6xpMPf+t2+j2g7xGiTYrl4rjttc75Yeoq2UMN2nNpSFEnFmcIjxTLwVmwHeYKE157tmD7ZgAXzlDmm9331oXL6M9gwZbv7KlrL1+Jtr8O8Hd94u4c/vyK9Wf9AZ9tGsJ796R3fKUF3DA2/85Wq7WkalX7XKta6FruAFr13oNjmL+o5dpouTG0UKPlWlNfD7j2mKtB/2p1vlZoOT/11bfrMVeNTbWv0IJH60VPPf6TH/7iT14AV4/Rv2tH/+8633MOLV/XY6haUxyd7zVf0f2+H6P/79aou9+v6f9dy9HD0O7CTfECnt7ZTK+yuleIrTzO/syFZ9HTwKMfnAWXUzwGbyiHBD8b5tQ1G68AcGrjxdOv3HDW52bDcHybi21L9k+D9lwaBq3jJ447bO9Y+yTvqpNoJ9ajE2BpJ5bWt95j6/5jH3vc/K3XvBNw16Our1/ytpu+u3vd8pd6uPe4qbcg0PGBxWu3v+5Q58ag2vpxSLFcHLf9XFwLZutzyR5q0J5LQ4o6sThDeKRYDs6C7WyrgO7vzxZsFwCgV/3QPWwfAPCFnu7QJcg+Zfcs2PLdPeWcja92DtMuF/M14L7ZCYSdAIeWB8b6hcLO0689OqGweRJ252snCE7hKr6q0UB44BcOuNY5XOPhrp2Hfdf+4/oX0VPDgw7OgsspFIM3lEOCnwnzrHMvW115fBxwD6DbIup29ZSr33XGdvJEUofj5ai3Ldk/DdpzaehXx1+47PC97fknedQnwVOApcuJ/aMBLLznvPVb4N11ngIs6ut97a4//G3b/+tQ2veuX/HAvW7/reNrbp31lqMQD6RYLo7bftTPe0n/SvZQg/ZcGlLUicUZwiPFcnAWbCUrgx7MbMG2q5TC7UsA0Ot/6KBw88vNY//7PGQiTgct2PJ9PHXNxjcBeCcfOTCi9kAb8O0KbrLzd482HP2foweKtIG6PfW1+2/6vpuE9204P9W++727sJ2HkdzF9ajjx5/3nZ/uupTaevi2o1pUo2lf1WjTv+n/qbbvcPZo8e02qmoS9ZS2zvcJX/u2r9Ae89U9+Dpt6na7qqp5tccnmjneY4rf3bwb9R5GeY8fO/hrHdw1fqy69sp1Z3xjYCebhpwFl8MdgzeUQ4Lvhzn13I+/At5fQv33wIeu2nAWrUd3HZI6HC9HvW3J/mnQnkvDT793nV/xD8/4Td/ZhfUnwblfBUAhlq42uftwuBEe3wH89bXHd+Gr6w9/67buMzLu0TSW9hAeKZaL47Yf9fNe0r+SPdSgPZeGFHVicYbwSLEcnAVbycqgBzNosL0SwCl9ZNNlTRZs9YwnTltz+TNq1P88TdL3HPAiBz+Jyrd9G21UaDs/NunH0PYH0cYY2vWB3e3W4gWT9T7frhf6dvvOun1gSbt9x9YD7c1/9cpJOJd0h15LUDl1zcaXA+7tgL/vkoVjuGNf+41XbTjz3ae8eeMvV5Vf7Z1bhRqr4Tq/UPY7rkUn7FbXYMxdc8X5Z/z0UFOEs+ByploM3lAOCX465tnnbny/93j9VN/dq6/ccOb/ne6DpA7Hy1FvW7J/GrSn0HD7BSuXOt8+yTUPdXIOj4LHo6bPRQf8j4f7NoVY53B9hdb1C8/b8t+DztlY2kN4pFgujtt+UA/nUruSPdSgPZeGFHVicYbwSLEcnAXbsle0QYItfRJ8sHn1z2kAPjO1G9d5eBS9BihZ2LEdW9nkOvXcy/4M3r18rFWtmGzXm+CqN165/oyvy9iGg+IsQqkUnj5x+ZINL3zMHTM9lOhJE18eW7T/5tVjvl7VE3Yf1EfPDtrVrYFrW1Xrmn0Hjtz0uYuevrvbLlVfY/CGckjwXczTJy4fn7ev/ge659kDB33tH3/1O8/+Vr/xltRJNW9K5C3ZPw3aQzXcMbH8GNfCSXDVSc757i7sI6bPpfZhy9Has/3TgLvOOXd95SavX3juLT8KmXOh2mOsY1INXBy3fYivo4ot2UMN2nNpSFEnFmcIjxTLwVmwLXv1GiTY0mXHFGTfC2Av0PnE+I0A/qu5HPn7s1iwBAD9Es8OwBZswyYX50QOqxQfrUU7V8fJb/zIMQvmzV/lvVsF+NWg3V3vj+rj0Pcp7HpU17z5dx72l7+x6lcGORdZRnO1pwiMEg2Eef1fXXtyVbUum3oImv/34x70S0+55FW/Rh+w9T0kdVhmjnjjkv3ToJ2jYfe6Zcd6zOs81AnePYruh3UOD+kzxX4O+M1w7j+A6vqWa1+/5YxNP4799G+O9kOdBiE8UiwXx20/4qe9qHsle6hBey4NKerE4gzhkWI5OAu2oqVBDWjQX6Y/AuCFAM4GcFmP+kNdinwMpp7E+CkAJwK4GQA9jOoWAP/ScPwAwFtncsOCbdg84ZzIYZXio7Voj6HjtDdf/hDfwiqP9mp0Ai9+fbpjjnYk4a8F3LWo/TUVsOkz7zw76FVaMbSHckjwH/z01/ynr/mfrkXvvnLDWfRB2iEPSZ3ZOOfS90v2T4P2mTTsuXDlff2B9klVi+6JdY+Gx6M9cEKfufUzAJvh3XdQ1ddXzl2/6NztN0xvl6KvsThDeKRYLo7bfi6tAYP2tWQPNWjPpSFFnVicITxSLAdnwXbQ1UBnu0GDLQXSkwF8EcAZAB4D0NNJ8e+H6NbzADwBwB8DWN4E24cC+NPmMmZ6WNAhDwu2szk0ur/scxahMJfyezgxMVFt3vPQVd5hNSqsWnb4ghfsuH1/HyFuGzyFXVxbwW26fdExm74y8eRZz5suUQwPQzm4+FPWbPw7B7yY+uCBM67acBY9yGvWg1tnVsI51qBk/zRoJw3HXfa/HljTk4mde+TUU4kd3YN//z4fYt3ggc0V/GYHd/2eA9V3x9+x9eeDTLkUfY3FGcIjxXJx3PaDjMlca1Oyhxq059KQok4szhAeKZaDs2Bb9qo2SLCle2x3AaB3R/6uoLt0CXI32NK7Jy9t3hVK78E9B8CXZ+K0YCtwuwfCOZHDKsVHa9GeQwfVeOMHr1t2EHtXw1erPLDadS5hxngfZ79LQdfDfb1yrU1XrH/+jLcCxNAeyjEo/rQ1l59Yo6b3BD/miMPm4Y4D7eNne+hWrzeD1ok/U0eDsWT/hqH9tvXLj5/nWyfB1xReH10vWnZKtXdHv8lwgwO+5ZzfXHt3nfNj1x/2lhu3SGdNir7G4gzhkWK5OG576TiNMq5kDzVoz6UhRZ1YnCE8UiwHZ8G27BVskGBLPfw/AP4AwNPo/ZE998tuG+De2d5g+9Tm1QR/AeBMAOcCoF1cv379+gnn6Em09zxOP/30sh029eaA0IFbbt+Hn928e+rPLXd2vk4/5o9VeMAxS/DAYxbjgcuX4PjlS3DYgnu++UNYPhvs+htuxYe/9GNMtj1+5QFH4eUnd98qlk2CFTIHZnSgdceNmHfbDzHv1h9gbNd/d75W+2+7V/v2kuNw8OiH4OD4QzF59C/h4JEnol601Jw1B8wBc8AcKNCB2M8zKNCCIiUPGmxvAkD3zE4/jgRw+yw97w2285sHUdHDqFpA592l9wNwYz8O27ENm1OcT6jCKsVHa9GeQwenxqlv/vtVqFqrHPxqj879ug/u4/7PHXDtcx97/9P/4dqfPfbq9WfT5cyig6OtX4HZ8M8+Z+PbvMM7prDuvCs3nLl+Noykjqjzcwgk8VyLPTG1337BcQ8dc+1HwXcuI360951Livs9/O0ncNjsPTYD9bdvee5Vn3vQw3990J+nYuti9rUrIhZnCI8Uy8Vx24sHaoSBJXuoQXsuDSnqxOIM4ZFiOTjbsS17ARv0B/EaAIv7dPV8AP1uDuxt2hts6RfY+wB4DYDHA/go+j9Io4O3YBs2uTgnclil+Ggt2nPoCKlxypq/PxrV2Cq0/erKuVXe05OYsexeI+Lxbe9wjQM2uclq0xUXHfrdurF+6T1U30495+OfgPPP70RaVE+9YsMZX6K/S/yQYOLP2nIZS/avn3YPuDvXLT+zgqMt058sXrt9+ru9cce65Y9wnftg6Y9/tEPnvdT9fs79mO6JpSAL7zd7564//Lzt9BDEu45c/qWoE4szhEeK5eK47cs9o9MpL9lDDdpzaUhRJxZnCI8Uy8FZsE23fuRgHjTYHt353fPeB917O9tBwZZ2e+kXgWMBfKHZqaXdWrr0+MqZCCzYzmbtob/POZHDKsVHa9GeQ0fsGqes+fvjPdzqpzzi2Eu/8p83bQKwurlConegfkH36gJuU1W5TfsO7Nv0uYt+/17XOodq64c/bc1lj6rh6OnqDwH8f+3zrSd84cIzSE/nkNSUYOLP2nIZS/avn/bd61bQMxzoPevd41Oo/dWoHAVYemUdPZ2YHop4j8MBP5oKsNjsKr954YJ5/+HecOOsP+dy+ZeiTizOEB4plovjti/3jE6nvGQPNWjPpSFFnVicITxSLAdnwTbd+pGDedBgG3Ipcr9+rACwfbYOWrCdzSELtmEOzY7mLIazs/VvkapGL++z3nzZr7UqunTZrfL0ft2pe9un/1b/Y+fd12vnN7kam6688KzrQrVNx596zsbfh8OHpwr7v71yw9kvmy5DUlOCkY7XKOJK9M+vv//R+yf3Lrv55A/+99LPv/K0yrml3mGZ93gsAHoq/yEPB/fDqffEYrPz2Lzw4MLvuIkb7n3z7GxEwg9jBqC9V5MU4xSLM4RHiuXiuO0lYzTqmJI91KA9l4YUdWJxhvBIsRycBduyV7FBgy1dQkzvrKVjCYCzANCLJunSrdkuRRY7ZMFWbF0HyDmRwyrFR2vRnkNHqhqH4n3mxMeOmLenWu2rqacwA6D7denp5fc4HnjMEvz05jveS0FX8m7dXg2nnHPZu5xznXfSeuBVV20465J+M0fihwQTf9aWyzhs/3ZOjB/RWjBv2fy2W1Y5LPXOUUBd5n29jC6td1OXFC+j4ArfudSe/tBzGgY+PD3Z3+Or3mHzkoMHNruJXbM9H2Jg7lz+pagTizOER4rl4rjtB54Ac6hhyR5q0J5LQ4o6sThDeKRYDs6CbdkL2qDBdnovXwLgb4HO60huTWWBBdswZzknclil+Ggt2nPoSFWDy/uMNZc9cB4F3Tbdp+tXed957dC8aaNL7zT5Ojw21XCb9iz63qavTEzc6926p09cvmTfnvoRb3r+w7/+nqt/dPTk3v0b4fB0+M4HYatpN3imWcPVXfqHOPHPHj6jxPOZqmx/1/LFC/ZXy+ZTQK3cMtTtTkjt3O9KO6oUSj2Wual7wenrUu/vfVnwbL1wwJ1w2HHg6Ic+cGzX9z8L73fAVTucxyI4/8rp83bx2u30fIckR0z/DiUwRZ1YnCE8UiwXx22fZLIUTlqyhxq059KQok4szhAeKZaDs2Bb9iI1aLB9NoCFTVfpU/LfA3AKgAcBuCGVBRZsw5zlnMhhleKjtWjPoSNVjRi8//qN7/p3feo/Xw3vVnmH1Q54WJ/R/p6joOv8psr5TfCth3v4j/WsGQcAzPfAV6/acNZvzjZbJLolmNl0zKXvz+Sff/+JC/bevm9ZXR9YhtbULmonoFZT4dR7twyuXgbvllUVlnb+DziM650D9gLY4eF3wlc7vPM7nMMOeNAHKTucn/p3u+V3VJPzdyw6YuEO9wc/6lwt1E/7neuOvcDBvwFT99HeWKE6Z9HarfQO9SRHrvmXok4szhAeKZaL47ZPMlkKJy3ZQw3ac2lIUScWZwiPFMvBWbAte5EaNNj2u8f2awCeOMB7bMUOWbAVW9cBck7ksErx0Vq059CRqkYM3ukcnZ3YfZi6T9dhlff1agdHD4XrPeiBcfdYW5zDl69Yf9ZTBpkpEt0SzCBahtXGX7js8DsOtJ481qrGXI3Ni96y7WehWvwExnbjPsv8vGrZGN2PWrtlvgmoux9yxvmH/WDjxyigugpLXXO5r7/7FpSByzvgAO2k1jV2Vg47fE9A7QZX+r5rY0dVYccvFtQ7Vrzppnu/pHnAioca+19M3Hf8yInZH/40YKkZm+WafynqxOIM4ZFiuThu+9B5MYr4kj3UoD2XhhR1YnGG8EixHJwF27JXrkGD7W/17L7QL610f+33Qb/AJDws2IaZyzmRwyrFR2vRnkNHqhoxeAfheM7ay+7XPojVqOjBVLSr654I+HsGW4/3XXHhWX88yEwZpOZ0HglmEC3DaLN/w4qHT9b4Evzd7w53Dqcfdt72T3b1dF5nc8GKZfNRLa1rLKs7AXXqkl84t8wBnct+774f1S+b4X2sh+6iQ5t2TT2ws7NzCtpB9Ts8qp2d3dRmZ9XV2FG1/I4F+w/uiHnv6iD+axj7XBpS1InFGcIjxXJx3PaDzL+51qZkDzVoz6UhRZ1YnCE8UiwHZ8G27FVtkGBLD5R5AYAHA9gKgHZq6VUKd6TuugXbMIc5J3JYpfhoLdpz6EhVIwavhOPUczbSk2bpwXJ3Hd77N1114dkXDTJTJDUlmEG05Gzj/wrz9u1asbJdY72bekBfj4GdEPkDDzd1XyoFV8Fxd0D1O4BqB+2c+trvvPNRr33zkus+8FJX+x2u8juqsfk7FrSrHe7cnyd7hoJAfl+IhrHPpSFFnVicITxSLBfHbR9rjo4ST8keatCeS0OKOrE4Q3ikWA7Ogm3ZK9ZswZZeCfK9Pl2kUPsEAN9N2X0LtmHuck7ksErx0Vq059CRqkYMXgnHKedtPMvVoPsZu+vL5kW+euonet5Ve6gZI6kpwcSftTMz3rb+/ke36v0rHaqVDvVKwK0E/Ep4+oqVqLCyd4d2EG3e+9ucc517UKd2T5udVdpF7fy7taMNv5Mu+UXL71hy3nbacaUrbu51aPcv9nwZxF9Om1z+pagTizOER4rl4rjtOXNgrrQt2UMN2nNpSFEnFmcIjxTLwVmwLXs1my3YfhnAkwDQKzq+DqAGQPfJrQNwDYDHpey+Bdswdzknclil+Ggt2nPoSFUjBq+U47fP/dTSg37/r7zhOQ//8lMed9Js68w9JpCkpgQTY9b6CVR7x45bCbRXAtVKdENr5TvhtfZupaMAO/gDleiqGDqO69XngG+0a5yDyu9w8+odi/fcssNN4F5Po5b2aVj+SfX24jRoz6UhRZ1YnCE8UiwXx20fY36OGkfJHmrQnktDijqxOEN4pFgOzoJt2avWbL9w0votjk4AACAASURBVKf7nwXwjGndfA8Aul+OnpRs77FVOgc4J7K2LmjRnkNHqhoxeEM5JPhcmNnmvJ8YP+JAa8HKg2N1Z2e1E1A7O6zNbivttALTH5w1E+1uD2wBsKWC3wK4LaCvrtoC1Fv21q2t45Nbt7gJ1H7D0UfubS/4Jw90niDt4H4I73/vsLds/8ZsmqXfl3gurRUbp0F7Lg0p6sTiDOGRYrk4bvvYc3UU+Er2UIP2XBpS1InFGcIjxXJwFmzLXqkGCbZvBXD+tG6+CsD/BXAkgGgvuZ9upe3Yhk0uzokcVik+Wov2HDpS1YjBG8ohwefA7L5w2XGox1bCVyvh77402AEr66nAutIN/kRgemp8J7R2/ji3xcNvGXPVFufqLXv9/K1HCe5V3bvhuPu7g5PzFr715h/HP8PuySjxPLWmQfk1aM+lIUWdWJwhPFIsF8dtP+gcnEvtSvZQg/ZcGlLUicUZwiPFcnAWbMte0QYJtnTJ8b9O6yY9GOZpFmx1Dz7nRNbWEy3ac+hIVSMGbyiHBB+C8e9avnj/wbGV3tOlwW5lG54C6tR9rPf8M9vaRzcI7+uG1dq5Laj9lqq704p6i0Nry3/9fNvWX7sEB7WdP1w9Es+5NVK116A9l4YUdWJxhvBIsVwct32qOVsyb8keatCeS0OKOrE4Q3ikWA7Ogm3JK9S0d0326UrfB430tLMdW8XjzzmRtXVDi/YcOlLViMEbyiHBz4S5c90xyx1azQ5rz/2s8Csnjzr+5NZtP6GHKR014FzeQTurncuBa7cFji4PnrpEeAz1lv0Hx7YeMbGVHsw0Jw7JOGkxRoP2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYjk4C7aaVhy+ltl2LU6chfInzQOl+JUHQNilyAOYdIgmnBM5rFJ8tBbtOXSkqhGDV8LhL0dr749WbKg9Vh849rFPXLDt669YvPamD840S/zEAxfub+1Z6V1rpUe98vZHve7SJd/5i/e5yk3d2+o8PUhppfeYN8BMowcqbXHwN951H2uzy+qaS4O37V+09UETN9BurB2NA5Jx1mKeBu25NKSoE4szhEeK5eK47bXMcU06SvZQg/ZcGlLUicUZwiPFcnAWbDWtOHwtswVbPmNEhAXbMDM5J3JYpfhoLdpz6EhVIwavhGP3uhV/B+DF02bFOzq7os3DlzxdIuzccfCdJwbTu1kHOPxt3je7rHBbnMONU7uu9ZadJ3/oivt9+cXL3ZtuunkAImsyzQHJOGsxUYP2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYjk4C7aaVhy+Fgu2fM+KQXBOZG2d0qI9h45UNWLwdjluuXDZ4UsOzhuvW268qtvjk3BLK+/GUdXjzrtx7zAOj6UAxgE8FsDYwHPKwcPf/fClPQ9+/u8s+u9PrmlV7ka6PNhNjm1Z0K63uImte2bijNHXgfWOYMOS/dOgPZeGFHVicYbwSLFcHLf9CJ7qwV0q2UMN2nNpSFEnFmcIjxTLwVmwDV4mhkpgwXao9qctzjmR0yrhs2vRnkNHqhr9eP3EfZbsWzhvvNV24wcwudR7N15Vbtz7erxybryusdQ5N+7hx53DeL1g6cOrfTsPAJjPH8V7IH429RC67mtu6i2Tk9WWsdbkjYvX7tjW21LihwQT2J+RgpfsnwbtuTSkqBOLM4RHiuXiuO1H6iSP1JmSPdSgPZeGFHVicYbwSLEcnAXbSIvFkGgs2A7J+BxlOSdyDj2cGlq059AhqUFP/927vzVeu4NLfV2Nt1oUTt145erxdu2WUijdd9/feOmiG7/61dpTWO3spNKfBZxx6GlL96TugsNO57HLw+/yvtpVVX5n7bHL1X6Xa1U7Xdvvqiu8FsDze+t4uGcsWbuN3ok96yHxQ4KZVcgcalCyfxq059KQok4szhAeKZaL47afQ0vAwF0t2UMN2nNpSFEnFmcIjxTLwVmwHXg5UNnQgq3KYYkjinMix6kYj2XY2v0ExvaNHbty2xlfvOH4Bz8s2XniJ4477GfP+4fdy/7xOY9stapOOPUVxn1dL6UdVPq3c/V4DbfUUTC9+5LfaAEVvtrlugHV+V0OUwG1XdW7dpz2mese8PnnH+becONezujuvWDF22rg1/fd76mnzv/5F84+fO1NGwfFS8ZeghlUz1xoV7J/GrTn0pCiTizOEB4plovjtp8L5z63jyV7qEF7Lg0p6sTiDOGRYjk4C7bcVUFX+2S/sMfopj08SuZi537IyXnPue0xb/jokm9e9NTD33rTl2RMw0NxFqHYKu9ct+JPHLDurt1N789f/Jab3nqoOlsnjjvsqEVuvJ48ON6qqvHJtltaVXWzi0pB1S2Fq8cd7nU/6kKh/nvtoHYDKmhH1bldtz3+/EvGv772ye263lUdnLdr0RFuJzegho6DBJ8LI/R9JGESz7UYoUF7Lg0p6sTiDOGRYrk4bnstc1yTjpI91KA9l4YUdWJxhvBIsRycBVtNKw5fiwVbvmeqEbetv//R8+oD3wJw/F1Cnbtg8Xnb1moU7idQ4WFwuBUVttHO6J5qGw5U+87+3O7DP/2kI5Ytqt1tu46oKkxWbr53Du0K87xzu+vKwVdubKED6sqNwcHXFSapja/2w1cLWvPdgc5X7w7s9532aHkHzKtcRV99hYO+chWm/l55531F70G9erpXDnh37dGm+0/R3H/qayyF81NhFVgk9HdfvWjZwmrfju92L/GlgOqd31UBOymguhq72p2d1HpnNTZv1217/a7jDvEgpa4OzkI+k/ZQDgk+F0Y4XiMJk3iuxQgN2nNpSFEnFmcIjxTLxXHba5njmnSU7KEG7bk0pKgTizOER4rl4CzYalpx+Fos2PI9U43YfcHyV8C7S6aJpHd7XgO4CqBAhwqgMNf5SrHOoaZ4N/U9D1QOcPS1ar7S/3f/zzk47xtsl4c4PCr6/873m/bdGndhe/g6dX1HxygcfXdQ+wXU+ah3tnsCKmfB5RgVgzeUQ4LPheF4OeptJZ5r8USD9lwaUtSJxRnCI8Vycdz2Wua4Jh0le6hBey4NKerE4gzhkWI5OAu2mlYcvhbVocIuReYP6J3nr3izc7iQjxwawgOgP3Xzp/N3P7ZwkZvcd2fn71NpvPYetB1bUwzvvCBmCtP5Sv/f+X7TvoPz3sO5uvt/d3HA1ah8fw64+fD+MdPd8PAfq+D+8x47qO16V7Vg3s5Fe/2uQ72KZjZnOQvubFy934/BG8ohwefCcLwc9bYSz7V4okF7Lg0p6sTiDOGRYrk4bnstc1yTjpI91KA9l4YUdWJxhvBIsRycBVtNKw5fiwVbvmeqEbvPX/4cOPfpaSJ/6lz1QmCyRk2bqa4+WMOPzXM1/d3XBzv/hzZtxLp6/gJX7287Px/09YBfAFfvazu/YIGr4araT1KYpK/7vD/M1TjovHdVvQSt+vYDzh+BsfrWhb/wR7tWjapVb9vd8sdifv2j8TF/4oIf1V/ZBv8kCqMTnWDa9+AsQrEH5M51Kz7sgN/v4b1i8drtp8Wu0+VL1dcYvKEcEnwuTKrxLJFX4rmWfmrQnktDijqxOEN4pFgujtteyxzXpKNkDzVoz6UhRZ1YnCE8UiwHZ8FW04rD12LBlu+ZesTudSs+AODVdLmxh/+hg/vDxWu3/7N64T0COYtQin7dsW7Fkx1wv9ue9N4P3/cJZyc9T1L1NQZvKIcEnwuTYt6UyinxXEtfNWjPpSFFnVicITxSLBfHba9ljmvSUbKHGrTn0pCiTizOEB4ploOzYKtpxeFrSfoLO1/OPRF2KbLcQXpdzQ1n/sfBB/3yI1WPscYd215NnMVQOlqpasTgDeWQ4HNhpOM1ijiJ51p80KA9l4YUdWJxhvBIsVwct72WOa5JR8keatCeS0OKOrE4Q3ikWA7Ogq2mFYevRXXosWDLH9DcoSxM4cxoziKUSgPx5tCRqkYM3lAOCT4XJuW8KY1b4rmWPmrQnktDijqxOEN4pFgujtteyxzXpKNkDzVoz6UhRZ1YnCE8UiwHZ8FW04rD12LBlu9ZMQjOiaytU1q059CRqkYM3lAOCT4XRtucH6YeiefD1KvtA7xc/qWoE4szhEeK5eK47bXMcU06SvZQg/ZcGlLUicUZwiPFcnAWbDWtOHwtFmz5nhWD4JzI2jqlRXsOHalqxOAN5ZDgc2G0zflh6pF4Pky9FmzjuR9r7EN4pFgujts+nsujw1Syhxq059KQok4szhAeKZaDs2Bb9nplwbbs8Tukes6JrM0GLdpz6EhVIwZvKIcEnwujbc4PU4/E82HqtWAbz/1YYx/CI8Vycdz28VweHaaSPdSgPZeGFHVicYbwSLEcnAXbstcrC7Zlj58F28Tjx1kMpVJS1YjBG8ohwefCSMdrFHESz7X4oEF7Lg0p6sTiDOGRYrk4bnstc1yTjpI91KA9l4YUdWJxhvBIsRycBVtNKw5fiwVbvmfFIDgnsrZOadGeQ0eqGjF4Qzkk+FwYbXN+mHokng9Tr+3YxnM/1tiH8EixXBy3fTyXR4epZA81aM+lIUWdWJwhPFIsB2fBtuz1yoJt2eNnO7aJx4+zGEqlpKoRgzeUQ4LPhZGO1yjiJJ5r8UGD9lwaUtSJxRnCI8Vycdz2Wua4Jh0le6hBey4NKerE4gzhkWI5OAu2mlYcvhYLtnzPikFwTmRtndKiPYeOVDVi8IZySPC5MNrm/DD1SDwfpl7bsY3nfqyxD+GRYrk4bvt4Lo8OU8keatCeS0OKOrE4Q3ikWA7Ogm3Z65UF27LHz3ZsE48fZzGUSklVIwZvKIcEnwsjHa9RxEk81+KDBu25NKSoE4szhEeK5eK47bXMcU06SvZQg/ZcGlLUicUZwiPFcnAWbDWtOHwtOYLtEgC7AfgeeYsB7AVQH0ryhg0b/Jo1a3Jo5DtXAIJzImvrjhbtOXSkqhGDN5RDgs+F0Tbnh6lH4vkw9dqObTz3Y419CI8Uy8Vx28dzeXSYSvZQg/ZcGlLUicUZwiPFcnAWbMter1KGxmMAnATgUwBOBHAzgGUALgUwCeABAN4F4EMzWWjBNmxycU7ksErx0Vq059CRqkYM3lAOCT4XJv6sLZdR4rmW3mrQnktDijqxOEN4pFgujtteyxzXpKNkDzVoz6UhRZ1YnCE8UiwHZ8FW04rD15Iy2D4PwBMA/DGA5U2wXQPgcABrAawAsA0A7d7u6Sfdgi1/QLXtZEh7wFmEpDUGweXQkapGDN5QDgk+F2aQ8Z8rbSSea/FGg/ZcGlLUicUZwiPFcnHc9lrmuCYdJXuoQXsuDSnqxOIM4ZFiOTgLtppWHL6WlMG2q4YuQe4G2w8C+AKAjQCoNl2KfAKAn1iw5Q/ebAjOiTwbV+7va9GeQ0eqGjF4Qzkk+FyY3HNacz2J51r6o0F7Lg0p6sTiDOGRYrk4bnstc1yTjpI91KA9l4YUdWJxhvBIsRycBVtNKw5fS+5gezkA+vPJRupNAFYDuGH9+vUTzrm3T+/C6aefzu+VIcwBc8AcMAfMAXPAHDAHzAFzwBwQOHDCCSfkyEgCZQY5lAM5Bq13x/ZtAG4H8D4ALQC3AjhqpodI2aXIYZOX8wlVWKX4aC3ac+hIVSMGbyiHBJ8LE3/Wlsso8VxLbzVoz6UhRZ1YnCE8UiwXx22vZY5r0lGyhxq059KQok4szhAeKZaDsx1bTSsOX0vuYHsagNcBOBkAbcW+AcDjZpJtwZY/oL0IzokcVik+Wov2HDpS1YjBG8ohwefCxJ+15TJKPNfSWw3ac2lIUScWZwiPFMvFcdtrmeOadJTsoQbtuTSkqBOLM4RHiuXgLNhqWnH4WnIFW3pC8i0AFgG4GsDDmr8/DcC1Fmz5AzcIgnMiD8KXs40W7Tl0pKoRgzeUQ4LPhck5n7XXkniupU8atOfSkKJOLM4QHimWi+O21zLHNeko2UMN2nNpSFEnFmcIjxTLwVmw1bTi8LXkCLb9VN0PwHYABw8l2XZs+QNqO7Zhnk1HcxZDaeVUNWLwhnJI8Lkw0vEaRZzEcy0+aNCeS0OKOrE4Q3ikWC6O217LHNeko2QPNWjPpSFFnVicITxSLAdnwVbTisPXMqxgO5BSC7YD2TRjI86JHFYpPlqL9hw6UtWIwRvKIcHnwsSfteUySjzX0lsN2nNpSFEnFmcIjxTLxXHba5njmnSU7KEG7bk0pKgTizOER4rl4CzYalpx+Fos2PI9KwbBOZG1dUqL9hw6UtWIwRvKIcHnwmib88PUI/F8mHq1XZmSy78UdWJxhvBIsVwct72WOa5JR8keatCeS0OKOrE4Q3ikWA7Ogq2mFYevxYIt37NiEJwTWVuntGjPoSNVjRi8oRwSfC6Mtjk/TD0Sz4ep14JtPPdjjX0IjxTLxXHbx3N5dJhK9lCD9lwaUtSJxRnCI8VycBZsy16vLNiWPX6HVM85kbXZoEV7Dh2pasTgDeWQ4HNhtM35YeqReD5MvRZs47kfa+xDeKRYLo7bPp7Lo8NUsocatOfSkKJOLM4QHimWg7NgW/Z6ZcG27PGzYJt4/DiLoVRKqhoxeEM5JPhcGOl4jSJO4rkWHzRoz6UhRZ1YnCE8UiwXx22vZY5r0lGyhxq059KQok4szhAeKZaDs2CracXha7Fgy/esGATnRNbWKS3ac+hIVSMGbyiHBJ8Lo23OD1OPxPNh6rUd23juxxr7EB4plovjto/n8ugwleyhBu25NKSoE4szhEeK5eAs2Ja9XlmwLXv8bMc28fhxFkOplFQ1YvCGckjwuTDS8RpFnMRzLT5o0J5LQ4o6sThDeKRYLo7bXssc16SjZA81aM+lIUWdWJwhPFIsB2fBVtOKw9diwZbvWTEIzomsrVNatOfQkapGDN5QDgk+F0bbnB+mHonnw9RrO7bx3I819iE8UiwXx20fz+XRYSrZQw3ac2lIUScWZwiPFMvBWbAte72yYFv2+NmObeLx4yyGUimpasTgDeWQ4HNhpOM1ijiJ51p80KA9l4YUdWJxhvBIsVwct72WOa5JR8keatCeS0OKOrE4Q3ikWA7Ogq2mFYevxYIt37NiEJwTWVuntGjPoSNVjRi8oRwSfC6Mtjk/TD0Sz4ep13Zs47kfa+xDeKRYLo7bPp7Lo8NUsocatOfSkKJOLM4QHimWg7NgW/Z6ZcG27PGzHdvE48dZDKVSUtWIwRvKIcHnwkjHaxRxEs+1+KBBey4NKerE4gzhkWK5OG57LXNck46SPdSgPZeGFHVicYbwSLEcnAVbTSsOX4sFW75nxSA4J7K2TmnRnkNHqhoxeEM5JPhcGG1zfph6JJ4PU6/t2MZzP9bYh/BIsVwct308l0eHqWQPNWjPpSFFnVicITxSLAdnwbbs9cqCbdnjZzu2icePsxhKpaSqEYM3lEOCz4WRjtco4iSea/FBg/ZcGlLUicUZwiPFcnHc9lrmuCYdJXuoQXsuDSnqxOIM4ZFiOTgLtppWHL4WC7Z8z4pBcE5kbZ3Soj2HjlQ1YvCGckjwuTDa5vww9Ug8H6Ze27GN536ssQ/hkWK5OG77eC6PDlPJHmrQnktDijqxOEN4pFgOzoJt2euVBduyx892bBOPH2cxlEpJVSMGbyiHBJ8LIx2vUcRJPNfigwbtuTSkqBOLM4RHiuXiuO21zHFNOkr2UIP2XBpS1InFGcIjxXJwFmw1rTh8LRZs+Z4Vg+CcyNo6pUV7Dh2pasTgDeWQ4HNhtM35YeqReD5MvbZjG8/9WGMfwiPFcnHc9vFcHh2mkj3UoD2XhhR1YnGG8EixHJwF27LXKwu2ZY+f7dgmHj/OYiiVkqpGDN5QDgk+F0Y6XqOIk3iuxQcN2nNpSFEnFmcIjxTLxXHba5njmnSU7KEG7bk0pKgTizOER4rl4CzYalpx+Fos2PI9KwbBOZG1dUqL9hw6UtWIwRvKIcHnwmib88PUI/F8mHptxzae+7HGPoRHiuXiuO3juTw6TCV7qEF7Lg0p6sTiDOGRYjk4C7Zlr1cWbMseP9uxTTx+nMVQKiVVjRi8oRwSfC6MdLxGESfxXIsPGrTn0pCiTizOEB4plovjttcyxzXpKNlDDdpzaUhRJxZnCI8Uy8FZsNW04vC1WLDle1YMgnMia+uUFu05dKSqEYM3lEOCz4XRNueHqUfi+TD12o5tPPdjjX0IjxTLxXHbx3N5dJhK9lCD9lwaUtSJxRnCI8VycBZsy16vLNiWPX62Y5t4/DiLoVRKqhoxeEM5JPhcGOl4jSJO4rkWHzRoz6UhRZ1YnCE8UiwXx22vZY5r0lGyhxq059KQok4szhAeKZaDs2CracXha7Fgy/esGATnRNbWKS3ac+hIVSMGbyiHBJ8Lo23OD1OPxPNh6rUd23juxxr7EB4plovjto/n8ugwleyhBu25NKSoE4szhEeK5eAs2Ja9XlmwLXv8bMc28fhxFkOplFQ1YvCGckjwuTDS8RpFnMRzLT5o0J5LQ4o6sThDeKRYLo7bXssc16SjZA81aM+lIUWdWJwhPFIsB2fBVtOKw9diwZbvWTEIzomsrVNatOfQkapGDN5QDgk+F0bbnB+mHonnw9RrO7bx3I819iE8UiwXx20fz+XRYSrZQw3ac2lIUScWZwiPFMvBWbAte72yYFv2+NmObeLx4yyGUimpasTgDeWQ4HNhpOM1ijiJ51p80KA9l4YUdWJxhvBIsVwct72WOa5JR8keatCeS0OKOrE4Q3ikWA7Ogq2mFYevxYIt37NiEJwTWVuntGjPoSNVjRi8oRwSfC6Mtjk/TD0Sz4ep13Zs47kfa+xDeKRYLo7bPp7Lo8NUsocatOfSkKJOLM4QHimWg7NgW/Z6ZcG27PGzHdvE48dZDKVSUtWIwRvKIcHnwkjHaxRxEs+1+KBBey4NKerE4gzhkWK5OG57LXNck46SPdSgPZeGFHVicYbwSLEcnAVbTSsOX4sFW75nxSA4J7K2TmnRnkNHqhoxeEM5JPhcGG1zfph6JJ4PU6/t2MZzP9bYh/BIsVwct308l0eHqWQPNWjPpSFFnVicITxSLAdnwbbs9cqCbdnjZzu2icePsxhKpaSqEYM3lEOCz4WRjtco4iSea/FBg/ZcGlLUicUZwiPFcnHc9lrmuCYdJXuoQXsuDSnqxOIM4ZFiOTgLtppWHL4WC7Z8z4pBcE5kbZ3Soj2HjlQ1YvCGckjwuTDa5vww9Ug8H6Ze27GN536ssQ/hkWK5OG77eC6PDlPJHmrQnktDijqxOEN4pFgOzoJt2euVBduyx892bBOPH2cxlEpJVSMGbyiHBJ8LIx2vUcRJPNfigwbtuTSkqBOLM4RHiuXiuO21zHFNOkr2UIP2XBpS1InFGcIjxXJwFmw1rTh8LRZs+Z4Vg+CcyNo6pUV7Dh2pasTgDeWQ4HNhtM35YeqReD5MvbZjG8/9WGMfwiPFcnHc9vFcHh2mkj3UoD2XhhR1YnGG8EixHJwF27LXKwu2ZY+f7dgmHj/OYiiVkqpGDN5QDgk+F0Y6XqOIk3iuxQcN2nNpSFEnFmcIjxTLxXHba5njmnSU7KEG7bk0pKgTizOER4rl4CzYalpx+Fos2PI9KwbBOZG1dUqL9hw6UtWIwRvKIcHnwmib88PUI/F8mHptxzae+7HGPoRHiuXiuO3juTw6TCV7qEF7Lg0p6sTiDOGRYjk4C7Zlr1cWbMseP9uxTTx+nMVQKiVVjRi8oRwSfC6MdLxGESfxXIsPGrTn0pCiTizOEB4plovjttcyxzXpKNlDDdpzaUhRJxZnCI8Uy8FZsNW04vC15A62CwDcAuBfGqk/APDWmWRv2LDBr1mzJrdGvotKEZwTWVsXtGjPoSNVjRi8oRwSfC6Mtjk/TD0Sz4ep13Zs47kfa+xDeKRYLo7bPp7Lo8NUsocatOfSkKJOLM4QHimWg7NgW/Z6lTs0PhTAnwJ4AYDJ2ayzYDubQ4f+PudEDqsUH61Few4dqWrE4A3lkOBzYeLP2nIZJZ5r6a0G7bk0pKgTizOER4rl4rjttcxxTTpK9lCD9lwaUtSJxRnCI8VycBZsNa04fC25g+2pAC4FcDiAbwI4B8CXbceWP3CDIDgn8iB8Odto0Z5DR6oaMXhDOST4XJic81l7LYnnWvqkQXsuDSnqxOIM4ZFiuThuey1zXJOOkj3UoD2XhhR1YnGG8EixHJwFW00rDl9L7mD7VACPBvAXAM4EcC4A2sX169evn3DOvX16F04//XR+rwxhDpgD5oA5YA6YA+aAOWAOmAPmgMCBE044IXdGEqg0yHQHcg/afADt5k+ruRz5fgBu7Dc0dily2ITlfEIVVik+Wov2HDpS1YjBG8ohwefCxJ+15TJKPNfSWw3ac2lIUScWZwiPFMvFcdtrmeOadJTsoQbtuTSkqBOLM4RHiuXgbMdW04rD15I72L4DwH0AvAbA4wF8FMAJM8m2YMsf0F4E50QOqxQfrUV7Dh2pasTgDeWQ4HNh4s/achklnmvprQbtuTSkqBOLM4RHiuXiuO21zHFNOkr2UIP2XBpS1InFGcIjxXJwFmw1rTh8LbmD7bEAvtDs1NLDo+jS4yst2PIHbhAE50QehC9nGy3ac+hIVSMGbyiHBJ8Lk3M+a68l8VxLnzRoz6UhRZ1YnCE8UiwXx22vZY5r0lGyhxq059KQok4szhAeKZaDs2CracXha8kdbLsKVwDYPptc27GdzaFDf59zIodVio/Woj2HjlQ1YvCGckjwuTDxZ225jBLPtfRWg/ZcGlLUicUZwiPFcnHc9lrmuCYdJXuoQXsuDSnqxOIM4ZFiOTgLtppWHL6WYQXbgZRasB3IphkbcU7ksErx0Vq059CRqkYM3lAOCT4XJv6sLZdR4rmW3mrQnktDijqxOEN4pFgujtteyxzXpKNkDzVoz6UhRZ1YnCE8UiwHU+rOYQAAHO5JREFUZ8FW04rD12LBlu9ZMQjOiaytU1q059CRqkYM3lAOCT4XRtucH6YeiefD1NtbW4P2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYjk4C7aaVhy+Fgu2fM+KQXBOZG2d0qI9h45UNWLwhnJI8Lkw2ub8MPVIPB+mXgu28dyPNfYhPFIsF8dtH8/l0WEq2UMN2nNpSFEnFmcIjxTLwVmwLXu9smBb9vgdUj3nRNZmgxbtOXSkqhGDN5RDgs+F0Tbnh6lH4vkw9Vqwjed+rLEP4ZFiuThu+3gujw5TyR5q0J5LQ4o6sThDeKRYDs6CbdnrlQXbssfPgm3i8eMshlIpqWrE4A3lkOBzYaTjNYo4iedafNCgPZeGFHVicYbwSLFcHLe9ljmuSUfJHmrQnktDijqxOEN4pFgOzoKtphWHr8WCLd+zYhCcE1lbp7Roz6EjVY0YvKEcEnwujLY5P0w9Es+Hqdd2bOO5H2vsQ3ikWC6O2z6ey6PDVLKHGrTn0pCiTizOEB4ploOzYFv2emXBtuzxsx3bxOPHWQylUlLViMEbyiHB58JIx2sUcRLPtfigQXsuDSnqxOIM4ZFiuThuey1zXJOOkj3UoD2XhhR1YnGG8EixHJwFW00rDl+LBVu+Z8UgOCeytk5p0Z5DR6oaMXhDOST4XBhtc36YeiSeD1Ov7djGcz/W2IfwSLFcHLd9PJdHh6lkDzVoz6UhRZ1YnCE8UiwHZ8G27PXKgm3Z42c7tonHj7MYSqWkqhGDN5RDgs+FkY7XKOIknmvxQYP2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYjk4C7aaVhy+Fgu2fM+KQXBOZG2d0qI9h45UNWLwhnJI8Lkw2ub8MPVIPB+mXtuxjed+rLEP4ZFiuThu+3gujw5TyR5q0J5LQ4o6sThDeKRYDs6CbdnrlQXbssfPdmwTjx9nMZRKSVUjBm8ohwSfCyMdr1HESTzX4oMG7bk0pKgTizOER4rl4rjttcxxTTpK9lCD9lwaUtSJxRnCI8VycBZsNa04fC0WbPmeFYPgnMjaOqVFew4dqWrE4A3lkOBzYbTN+WHqkXg+TL22YxvP/VhjH8IjxXJx3PbxXB4dppI91KA9l4YUdWJxhvBIsRycBduy1ysLtmWPn+3YJh4/zmIolZKqRgzeUA4JPhdGOl6jiJN4rsUHDdpzaUhRJxZnCI8Uy8Vx22uZ45p0lOyhBu25NKSoE4szhEeK5eAs2GpacfhaLNjyPSsGwTmRtXVKi/YcOlLViMEbyiHB58Jom/PD1CPxfJh6bcc2nvuxxj6ER4rl4rjt47k8Okwle6hBey4NKerE4gzhkWI5OAu2Za9XFmzLHj/bsU08fpzFUColVY0YvKEcEnwujHS8RhEn8VyLDxq059KQok4szhAeKZaL47bXMsc16SjZQw3ac2lIUScWZwiPFMvBWbDVtOLwtViw5XtWDIJzImvrlBbtOXSkqhGDN5RDgs+F0Tbnh6lH4vkw9dqObTz3Y419CI8Uy8Vx28dzeXSYSvZQg/ZcGlLUicUZwiPFcnAWbMteryzYlj1+tmObePw4i6FUSqoaMXhDOST4XBjpeI0iTuK5Fh80aM+lIUWdWJwhPFIsF8dtr2WOa9JRsocatOfSkKJOLM4QHimWg7Ngq2nF4WuxYMv3rBgE50TW1ikt2nPoSFUjBm8ohwSfC6Ntzg9Tj8TzYeq1Hdt47sca+xAeKZaL47aP5/LoMJXsoQbtuTSkqBOLM4RHiuXgLNiWvV5ZsC17/GzHNvH4cRZDqZRUNWLwhnJI8Lkw0vEaRZzEcy0+aNCeS0OKOrE4Q3ikWC6O217LHNeko2QPNWjPpSFFnVicITxSLAdnwVbTisPXYsGW71kxCM6JrK1TWrTn0JGqRgzeUA4JPhdG25wfph6J58PUazu28dyPNfYhPFIsF8dtH8/l0WEq2UMN2nNpSFEnFmcIjxTLwVmwLXu9smBb9vjZjm3i8eMshlIpqWrE4A3lkOBzYaTjNYo4iedafNCgPZeGFHVicYbwSLFcHLe9ljmuSUfJHmrQnktDijqxOEN4pFgOzoKtphWHr8WCLd+zYhCcE1lbp7Roz6EjVY0YvKEcEnwujLY5P0w9Es+Hqdd2bOO5H2vsQ3ikWC6O2z6ey6PDVLKHGrTn0pCiTizOEB4ploOzYFv2emXBtuzxsx3bxOPHWQylUlLViMEbyiHB58JIx2sUcRLPtfigQXsuDSnqxOIM4ZFiuThuey1zXJOOkj3UoD2XhhR1YnGG8EixHJwFW00rDl+LBVu+Z8UgOCeytk5p0Z5DR6oaMXhDOST4XBhtc36YeiSeD1Ov7djGcz/W2IfwSLFcHLd9PJdHh6lkDzVoz6UhRZ1YnCE8UiwHZ8G27PXKgm3Z42c7tonHj7MYSqWkqhGDN5RDgs+FkY7XKOIknmvxQYP2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYjk4C7aaVhy+Fgu2fM+KQXBOZG2d0qI9h45UNWLwhnJI8Lkw2ub8MPVIPB+mXtuxjed+rLEP4ZFiuThu+3gujw5TyR5q0J5LQ4o6sThDeKRYDs6CbdnrlQXbssfPdmwTjx9nMZRKSVUjBm8ohwSfCyMdr1HESTzX4oMG7bk0pKgTizOER4rl4rjttcxxTTpK9lCD9lwaUtSJxRnCI8VycBZsNa04fC0WbPmeFYPgnMjaOqVFew4dqWrE4A3lkOBzYbTN+WHqkXg+TL22YxvP/VhjH8IjxXJx3PbxXB4dppI91KA9l4YUdWJxhvBIsRycBduy1ysLtmWPn+3YJh4/zmIolZKqRgzeUA4JPhdGOl6jiJN4rsUHDdpzaUhRJxZnCI8Uy8Vx22uZ45p0lOyhBu25NKSoE4szhEeK5eAs2GpacfhaLNjyPSsGwTmRtXVKi/YcOlLViMEbyiHB58Jom/PD1CPxfJh6bcc2nvuxxj6ER4rl4rjt47k8Okwle6hBey4NKerE4gzhkWI5OAu2Za9XFmzLHj/bsU08fpzFUColVY0YvKEcEnwujHS8RhEn8VyLDxq059KQok4szhAeKZaL47bXMsc16SjZQw3ac2lIUScWZwiPFMvBWbDVtOLwtViw5XtWDIJzImvrlBbtOXSkqhGDN5RDgs+F0Tbnh6lH4vkw9dqObTz3Y419CI8Uy8Vx28dzeXSYSvZQg/ZcGlLUicUZwiPFcnAWbMteryzYlj1+tmObePw4i6FUSqoaMXhDOST4XBjpeI0iTuK5Fh80aM+lIUWdWJwhPFIsF8dtr2WOa9JRsocatOfSkKJOLM4QHimWg7Ngq2nF4WuxYMv3rBgE50TW1ikt2nPoSFUjBm8ohwSfC6Ntzg9Tj8TzYeq1Hdt47sca+xAeKZaL47aP5/LoMJXsoQbtuTSkqBOLM4RHiuXgLNiWvV4NK9guBrAXQH0o+zZs2ODXrFkzLI1ljywAzomsrbNatOfQkapGDN5QDgk+F0bbnB+mHonnw9RrwTae+7HGPoRHiuXiuO3juTw6TCV7qEF7Lg0p6sTiDOGRYjk4C7Zlr1e5Q+MyAJcCmATwAADvAvChmSy0YBs2uTgnclil+Ggt2nPoSFUjBm8ohwSfCxN/1pbLKPFcS281aM+lIUWdWJwhPFIsF8dtr2WOa9JRsocatOfSkKJOLM4QHimWg7Ngq2nF4WvJHWzXADgcwFoAKwBsA0C7t3v6Sbdgyx9QbTsZ0h5wFiFpjUFwOXSkqhGDN5RDgs+FGWT850obiedavNGgPZeGFHVicYbwSLFcHLe9ljmuSUfJHmrQnktDijqxOEN4pFgOzoKtphWHryV3sP0ggC8A2AiAatOlyCcA+IkFW/7gzYbgnMizceX+vhbtOXSkqhGDN5RDgs+FyT2nNdeTeK6lPxq059KQok4szhAeKZaL47bXMsc16SjZQw3ac2lIUScWZwiPFMvBWbDVtOLwteQOtpcDoD+fbKTeBGA1gBvWr18/4Zx7e28X5s2bh4MHD/J7ZQhzwBwwB8wBc8AcMAfMAXPAHDAHmA7c5z73wcte9rLcGYmp0pr3cyD3oL0NwO0A3gegBeBWAEfN9BApuxQ5bNKW7J8W7Tl0pKoRgzeUQ4LPhQk7u0YLLfFciwMatOfSkKJOLM4QHimWi+O21zLHNeko2UMN2nNpSFEnFmcIjxTLxXHbazpH57qW3MH2NACvA3AygNMBvAHA42YaBJtYYdOzZP+0aM+hI1WNGLyhHBJ8LkzY2TVaaInnWhzQoD2XhhR1YnGG8EixXBy3vZY5rklHyR5q0J5LQ4o6sThDeKRYLo7bXtM5Ote15A62iwBcDeBhAOjvTwNwrQXbNNOw5BNTi/YcOlLViMEbyiHB58KkOevKZJV4rqWnGrTn0pCiTizOEB4plovjttcyxzXpKNlDDdpzaUhRJxZnCI8Uy8Vx22s6R+e6ltzBtuv3/QBsB3DIG2htYoVNz5L906I9h45UNWLwhnJI8LkwYWfXaKElnmtxQIP2XBpS1InFGcIjxXJx3PZa5rgmHSV7qEF7Lg0p6sTiDOGRYrk4bntN5+hc1zKsYDuQ7/RAqXPPPXdioMbW6F4OlOyfFu05dKSqEYM3lEOCz4WxJeNuBySea/FPg/ZcGlLUicUZwiPFcnHc9lrmuCYdJXuoQXsuDSnqxOIM4ZFiuThue03n6FzXojrYzvXBsf6bA+aAOWAOmAPmgDlgDpgD5oA5YA7M7oAF29k9shbmgDlgDpgD5oA5YA6YA+aAOWAOmAOKHSgp2C4BsBuAV+ynNmlHN69XamsTNoAe0k6vg7LDHJjJgX5rwoJmjThgts3qQL/1oWoe7EdrrdaDNB4O4BfTBJagXauno6ir3/qwGMDeaa8YtHnTf/SXAtg57Vszra/9fB3WnJppfZhJO60ldwxLrNUNdmC2bNBvfGc65zXN42Bj5ipBCcH2GAAnAfgUgBMB3DxXB4vR7/sD+DiAWwBMAvg2gPMZ+GE2fSiAvwfwYwCHAfgYgI3DFGS11TnQb00YA/BeAI9q3pFNc/71M70jW12P8gqaaX14CYA/ArAFAPn5u80aklfdoav9DoA/a9Y0+sX7HADfBVCCdk0+jrKWfuvDMgCXNj8PHwDgXQA+ZPOm7zR4CoC/bM4x+kX/g83bLPqtr+Mz+Dqs+dVvffjeDD8b6GfF3wL4GQCaEy8D8K1hCbe6bAdmywa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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "source = get_echo_many_arguments_source(benchmark_results)\n", "display(Markdown(f'### With non-blocking map on {source[\"number_of_engines\"]} engines:'))\n", "display(Markdown(f'#### {core_num} cores:'))\n", "alt.Chart(\n", " pd.DataFrame(source['direct_view'])\n", ").mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of arguments',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of cores:N',\n", " tooltip='Duration in ms' \n", ").properties(\n", " title=f'DirectView',\n", " width=800\n", ").interactive().display(renderer='svg') \n", "\n", "alt.Chart(\n", " pd.DataFrame(source['load_balanced'])\n", ").mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of arguments',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of cores:N',\n", " tooltip='Duration in ms' \n", " ).properties(\n", " title=f'Load Balanced',\n", " width=800\n", " ).interactive().display(renderer='svg')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/benchmarks/000077500000000000000000000000001460376056100203075ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/benchmarks/__init__.py000066400000000000000000000000001460376056100224060ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/benchmarks/constants.py000066400000000000000000000000371460376056100226750ustar00rootroot00000000000000DEFAULT_NUMBER_OF_ENGINES = 16 ipyparallel-8.8.0/benchmarks/benchmarks/testing.py000066400000000000000000000000001460376056100223240ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/benchmarks/throughput.py000066400000000000000000000136551460376056100231040ustar00rootroot00000000000000import os os.environ['OPENBLAS_NUM_THREADS'] = '1' import time import timeit import numpy as np import ipyparallel as ipp delay = [0] engines = [1, 2, 16, 64, 128, 256, 512, 1024] byte_param = [1000, 10_000, 100_000, 1_000_000, 2_000_000] apply_replies = {} class ArrayNotEqual(Exception): pass def wait_for(condition): for _ in range(750): if condition(): break else: time.sleep(0.1) if not condition(): raise TimeoutError('wait_for took to long to finish') def echo(x): return x def make_benchmark(benchmark_name, get_view): class ThroughputSuite: param_names = ['Number of engines', 'Number of bytes'] timer = timeit.default_timer timeout = 120 params = [engines, byte_param] view = None client = None reply = None def setup(self, number_of_engines, number_of_bytes): self.client = ipp.Client(profile='asv') self.view = get_view(self) self.view.targets = list(range(number_of_engines)) wait_for(lambda: len(self.client) >= number_of_engines) def time_broadcast(self, engines, number_of_bytes): self.reply = self.view.apply_sync( echo, np.array([0] * number_of_bytes, dtype=np.int8) ) def teardown(self, *args): if self.client: self.client.close() return ThroughputSuite # # class DirectViewBroadcast( # make_benchmark( # 'DirectViewBroadcast', lambda benchmark: benchmark.client.direct_view() # ) # ): # pass # class CoalescingBroadcast( make_benchmark( 'CoalescingBroadcast', lambda benchmark: benchmark.client.broadcast_view(is_coalescing=True), ) ): pass # # class NonCoalescingBroadcast( # make_benchmark( # 'NonCoalescingBroadcast', # lambda benchmark: benchmark.client.broadcast_view(is_coalescing=False), # ) # ): # pass # class DepthTestingSuite: param_names = ['Number of engines', 'is_coalescing', 'depth'] timer = timeit.default_timer timeout = 120 params = [engines, [True, False], [3, 4]] view = None client = None reply = None def setup(self, number_of_engines, is_coalescing, depth): self.client = ipp.Client(profile='asv', cluster_id=f'depth_{depth}') self.view = self.client.broadcast_view(is_coalescing=is_coalescing) self.view.targets = list(range(number_of_engines)) wait_for(lambda: len(self.client) >= number_of_engines) def time_broadcast(self, number_of_engines, *args): self.reply = self.view.apply_sync( echo, np.array([0] * 1000, dtype=np.int8), ) def teardown(self, *args): replies_key = tuple(args) if replies_key in apply_replies: if any( not np.array_equal(new_reply, stored_reply) for new_reply, stored_reply in zip( self.reply, apply_replies[replies_key] ) ): raise ArrayNotEqual('DepthTestingSuite', args) if self.client: self.client.close() number_of_messages = [1, 5, 10, 20, 50, 75, 100] def make_multiple_message_benchmark(get_view): class AsyncMessagesSuite: param_names = ['Number of engines', 'number_of_messages'] timer = timeit.default_timer timeout = 180 params = [engines, number_of_messages] view = None client = None reply = None def setup(self, number_of_engines, number_of_messages): self.client = ipp.Client(profile='asv') self.view = get_view(self) self.view.targets = list(range(number_of_engines)) wait_for(lambda: len(self.client) >= number_of_engines) def time_async_messages(self, number_of_engines, number_of_messages): replies = [] for i in range(number_of_messages): reply = self.view.apply_async(echo, np.array([0] * 1000, dtype=np.int8)) replies.append(reply) for reply in replies: reply.get() def teardown(self, *args): if self.client: self.client.close() return AsyncMessagesSuite class DirectViewAsync( make_multiple_message_benchmark(lambda benchmark: benchmark.client.direct_view()) ): pass class CoalescingAsync( make_multiple_message_benchmark( lambda benchmark: benchmark.client.broadcast_view(is_coalescing=True) ) ): pass class NonCoalescingAsync( make_multiple_message_benchmark( lambda benchmark: benchmark.client.broadcast_view(is_coalescing=False) ) ): pass def make_push_benchmark(get_view): class PushMessageSuite: param_names = ['Number of engines', 'Number of bytes'] timer = timeit.default_timer timeout = 120 params = [engines, byte_param] view = None client = None def setup(self, number_of_engines, number_of_bytes): self.client = ipp.Client(profile='asv') self.view = get_view(self) self.view.targets = list(range(number_of_engines)) wait_for(lambda: len(self.client) >= number_of_engines) def time_broadcast(self, engines, number_of_bytes): reply = self.view.apply_sync( lambda x: None, np.array([0] * number_of_bytes, dtype=np.int8) ) def teardown(self, *args): if self.client: self.client.close() return PushMessageSuite class DirectViewPush( make_push_benchmark(lambda benchmark: benchmark.client.direct_view()) ): pass class CoalescingPush( make_push_benchmark( lambda benchmark: benchmark.client.broadcast_view(is_coalescing=True) ) ): pass class NonCoalescingPush( make_push_benchmark( lambda benchmark: benchmark.client.broadcast_view(is_coalescing=False) ) ): pass ipyparallel-8.8.0/benchmarks/benchmarks/utils.py000066400000000000000000000011141460376056100220160ustar00rootroot00000000000000import datetime import time from typing import Callable def wait_for(condition: Callable): for _ in range(750): if condition(): break else: time.sleep(0.1) if not condition(): raise TimeoutError('wait_for took to long to finish') def echo(delay=0): def inner_echo(x, **kwargs): import time if delay: time.sleep(delay) return x return inner_echo def get_time_stamp() -> str: return ( str(datetime.datetime.now()).split(".")[0].replace(" ", "-").replace(":", "-") ) ipyparallel-8.8.0/benchmarks/cluster_start.py000066400000000000000000000042061460376056100214440ustar00rootroot00000000000000import atexit import sys import time from subprocess import Popen import ipyparallel as ipp from benchmarks.throughput import wait_for def start_cluster(depth, number_of_engines, path='', log_output_to_file=False): ipcontroller_cmd = ( f'{path}ipcontroller --profile=asv --nodb ' f'--cluster-id=depth_{depth} ' f'--HubFactory.broadcast_scheduler_depth={depth} ' f'--HubFactory.db_class=NoDB' ) print(ipcontroller_cmd) ipengine_cmd = f'{path}ipengine --profile=asv ' f'--cluster-id=depth_{depth} ' ps = [ Popen( ipcontroller_cmd.split(), stdout=( open('ipcontroller_output.log', 'a+') if log_output_to_file else sys.stdout ), stderr=( open('ipcontroller_error_output.log', 'a+') if log_output_to_file else sys.stdout ), stdin=sys.stdin, ) ] time.sleep(2) client = ipp.Client(profile='asv', cluster_id=f'depth_{depth}') print(ipengine_cmd) for i in range(number_of_engines): ps.append( Popen( ipengine_cmd.split(), stdout=( open('ipengine_output.log', 'a+') if log_output_to_file else sys.stdout ), stderr=( open('ipengine_error_output.log', 'a+') if log_output_to_file else sys.stdout ), stdin=sys.stdin, ) ) if i % 10 == 0: wait_for(lambda: len(client) >= i - 10) if i % 20 == 0: time.sleep(2) print(f'{len(client)} engines started') return ps if __name__ == '__main__': if len(sys.argv) > 3: depth = sys.argv[1] number_of_engines = int(sys.argv[3]) else: depth = 3 number_of_engines = 30 ps = start_cluster(depth, number_of_engines) for p in ps: p.wait() def clean_up(): for p in ps: p.kill() atexit.register(clean_up) ipyparallel-8.8.0/benchmarks/depth_benchmark.ipynb000066400000000000000000002323361460376056100223640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results, BenchmarkType, SchedulerType, get_depth_testing_source\n", "from benchmarks.utils import echo\n", "from benchmarks.throughput import make_benchmark, DepthTestingSuite" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# time_broadcast on different levels of scheduler depth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This benchmark comes from timing broadcast on different levels of depth on the BroadcastScheduler" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mInit signature:\u001b[0m \u001b[0mDepthTestingSuite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mclass\u001b[0m \u001b[0mDepthTestingSuite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mparam_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Number of engines'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is_coalescing'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'depth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mtimer\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mapply_replies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreplies_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mArrayNotEqual\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'DepthTestingSuite'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/ipyparallel_master_project/benchmarks/throughput.py\n", "\u001b[0;31mType:\u001b[0m type\n", "\u001b[0;31mSubclasses:\u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "??DepthTestingSuite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results from benchmarking on 64 cores" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "source = get_depth_testing_source(benchmark_results)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Duration in msNumber of enginesIs coalescingDepthScheduler name
04.481True0Coalescing
17.121True2Coalescing
28.511True3Coalescing
39.971True4Coalescing
44.441False0NonCoalescing
57.301False2NonCoalescing
68.781False3NonCoalescing
79.561False4NonCoalescing
85.272True0Coalescing
97.372True2Coalescing
108.462True3Coalescing
119.672True4Coalescing
125.312False0NonCoalescing
137.602False2NonCoalescing
148.662False3NonCoalescing
159.842False4NonCoalescing
168.9916True0Coalescing
179.5816True2Coalescing
1810.6816True3Coalescing
1912.0716True4Coalescing
2014.8116False0NonCoalescing
2115.7816False2NonCoalescing
2215.9016False3NonCoalescing
2316.9816False4NonCoalescing
2427.4964True0Coalescing
2517.0164True2Coalescing
2616.7064True3Coalescing
2717.9264True4Coalescing
2853.3864False0NonCoalescing
2944.3464False2NonCoalescing
3043.9264False3NonCoalescing
3146.1864False4NonCoalescing
3259.50128True0Coalescing
3326.60128True2Coalescing
3425.24128True3Coalescing
3525.81128True4Coalescing
36113.06128False0NonCoalescing
3784.00128False2NonCoalescing
3878.83128False3NonCoalescing
3980.12128False4NonCoalescing
40147.38256True0Coalescing
4149.45256True2Coalescing
4242.14256True3Coalescing
4342.65256True4Coalescing
44254.80256False0NonCoalescing
45158.41256False2NonCoalescing
46152.24256False3NonCoalescing
47151.46256False4NonCoalescing
48444.02512True0Coalescing
49103.34512True2Coalescing
5082.23512True3Coalescing
5176.60512True4Coalescing
52617.53512False0NonCoalescing
53312.91512False2NonCoalescing
54299.75512False3NonCoalescing
55299.51512False4NonCoalescing
\n", "
" ], "text/plain": [ " Duration in ms Number of engines Is coalescing Depth Scheduler name\n", "0 4.48 1 True 0 Coalescing\n", "1 7.12 1 True 2 Coalescing\n", "2 8.51 1 True 3 Coalescing\n", "3 9.97 1 True 4 Coalescing\n", "4 4.44 1 False 0 NonCoalescing\n", "5 7.30 1 False 2 NonCoalescing\n", "6 8.78 1 False 3 NonCoalescing\n", "7 9.56 1 False 4 NonCoalescing\n", "8 5.27 2 True 0 Coalescing\n", "9 7.37 2 True 2 Coalescing\n", "10 8.46 2 True 3 Coalescing\n", "11 9.67 2 True 4 Coalescing\n", "12 5.31 2 False 0 NonCoalescing\n", "13 7.60 2 False 2 NonCoalescing\n", "14 8.66 2 False 3 NonCoalescing\n", "15 9.84 2 False 4 NonCoalescing\n", "16 8.99 16 True 0 Coalescing\n", "17 9.58 16 True 2 Coalescing\n", "18 10.68 16 True 3 Coalescing\n", "19 12.07 16 True 4 Coalescing\n", "20 14.81 16 False 0 NonCoalescing\n", "21 15.78 16 False 2 NonCoalescing\n", "22 15.90 16 False 3 NonCoalescing\n", "23 16.98 16 False 4 NonCoalescing\n", "24 27.49 64 True 0 Coalescing\n", "25 17.01 64 True 2 Coalescing\n", "26 16.70 64 True 3 Coalescing\n", "27 17.92 64 True 4 Coalescing\n", "28 53.38 64 False 0 NonCoalescing\n", "29 44.34 64 False 2 NonCoalescing\n", "30 43.92 64 False 3 NonCoalescing\n", "31 46.18 64 False 4 NonCoalescing\n", "32 59.50 128 True 0 Coalescing\n", "33 26.60 128 True 2 Coalescing\n", "34 25.24 128 True 3 Coalescing\n", "35 25.81 128 True 4 Coalescing\n", "36 113.06 128 False 0 NonCoalescing\n", "37 84.00 128 False 2 NonCoalescing\n", "38 78.83 128 False 3 NonCoalescing\n", "39 80.12 128 False 4 NonCoalescing\n", "40 147.38 256 True 0 Coalescing\n", "41 49.45 256 True 2 Coalescing\n", "42 42.14 256 True 3 Coalescing\n", "43 42.65 256 True 4 Coalescing\n", "44 254.80 256 False 0 NonCoalescing\n", "45 158.41 256 False 2 NonCoalescing\n", "46 152.24 256 False 3 NonCoalescing\n", "47 151.46 256 False 4 NonCoalescing\n", "48 444.02 512 True 0 Coalescing\n", "49 103.34 512 True 2 Coalescing\n", "50 82.23 512 True 3 Coalescing\n", "51 76.60 512 True 4 Coalescing\n", "52 617.53 512 False 0 NonCoalescing\n", "53 312.91 512 False 2 NonCoalescing\n", "54 299.75 512 False 3 NonCoalescing\n", "55 299.51 512 False 4 NonCoalescing" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.DataFrame(source['Depth'])\n", "data['Scheduler name'] = data.apply(lambda row: 'Coalescing' if row['Is coalescing'] else 'NonCoalescing', axis=1)\n", "data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for scheduler_name in data['Scheduler name'].unique():\n", " scheduler_data = data[data['Scheduler name'] == scheduler_name]\n", " alt.Chart(scheduler_data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Duration in ms',\n", " scale=alt.Scale(type='log')\n", "\n", " ),\n", " color='Depth:N',\n", " tooltip='Duration in ms',\n", " \n", " ).properties(title=scheduler_name, width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for engine in data['Number of engines'].unique(): \n", " alt.Chart(data[data['Number of engines'] == engine]).mark_bar().encode(\n", " x='Scheduler name',\n", " y='Duration in ms',\n", " color='Scheduler name:N',\n", " column='Depth:N', \n", " tooltip='Duration in ms'\n", " ).properties(title=f'Runtime on {engine} engines:').interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/engines_test.py000066400000000000000000000007111460376056100212320ustar00rootroot00000000000000import time from subprocess import check_call import ipyparallel as ipp n = 20 check_call(f'ipcluster start -n {n} --daemon --profile=asv --debug', shell=True) c = ipp.Client(profile='asv') seen = -1 running_engines = len(c) while running_engines < n: if seen != running_engines: print(running_engines) seen = running_engines running_engines = len(c) time.sleep(0.1) check_call('ipcluster stop --profile=asv', shell=True) ipyparallel-8.8.0/benchmarks/explore/000077500000000000000000000000001460376056100176505ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/explore/control_flow.py000066400000000000000000000017061460376056100227350ustar00rootroot00000000000000import numpy as np import ipyparallel as ipp def main(): client = ipp.Client(profile='asv') # client.debug = True direct_view = client[:] direct_view.targets = list(range(5)) direct_view_result = direct_view.apply(lambda x: x * 2, 13) print(direct_view_result.get()) # load_balanced_view = client.load_balanced_view() # result = load_balanced_view.apply(lambda x: x * 2, 13) # print(result.get()) broadcast_view = client.broadcast_view(is_coalescing=True) broadcast_result = broadcast_view.apply_sync( lambda x: x, np.array([0] * 8, dtype=np.int8) ) print(broadcast_result) print(len(broadcast_result)) # broadcast_view2 = client.broadcast_view(is_coalescing=False) # broadcast_result = broadcast_view2.apply_sync( # lambda x: x, np.array([0] * 8, dtype=np.int8) # ) # # print(broadcast_result) # print(len(broadcast_result)) if __name__ == '__main__': main() ipyparallel-8.8.0/benchmarks/explore/scheduler.py000066400000000000000000000002661460376056100222040ustar00rootroot00000000000000from ipyparallel.apps import ipclusterapp as app def main(): app.launch_new_instance(['start', '-n', '16', '--debug', '--profile=asv']) if __name__ == '__main__': main() ipyparallel-8.8.0/benchmarks/gcloud_setup.py000077500000000000000000000126361460376056100212540ustar00rootroot00000000000000#!/usr/bin/env python3 import multiprocessing as mp import os import sys from subprocess import Popen, check_call from time import sleep from typing import List import googleapiclient.discovery as gcd from benchmarks.utils import get_time_stamp CORE_NUMBERS_FOR_TEMPLATES = [64] ZONE = "europe-west1-b" PROJECT_NAME = "jupyter-simula" INSTANCE_NAME_PREFIX = "asv-testing-" MACHINE_CONFIGS_DIR = os.path.join(os.getcwd(), "machine_configs") CONDA_PATH = 'miniconda3/bin/conda' compute = gcd.build("compute", "v1") def generate_template_name(number_of_cores_and_ram): return f"{INSTANCE_NAME_PREFIX}{number_of_cores_and_ram}" def get_running_instance_names() -> List[str]: result = compute.instances().list(project=PROJECT_NAME, zone=ZONE).execute() return [item["name"] for item in result["items"]] if "items" in result else [] def delete_instance(instance_name) -> dict: print(f"Deleting instance: {instance_name}") return ( compute.instances() .delete(project=PROJECT_NAME, zone=ZONE, instance=instance_name) .execute() ) def delete_all_instances(): return [ delete_instance(name) for name in get_running_instance_names() if INSTANCE_NAME_PREFIX in name ] def gcloud_run(*args, block=True): cmd = ["gcloud", "compute"] + list(args) print(f'$ {" ".join(cmd)}') ( check_call( cmd # stdout=open(get_gcloud_log_file_name(instance_name) + ".log", "a+"), # stderr=open(f"{get_gcloud_log_file_name(instance_name)}_error.out", "a+"), ) if block else Popen(cmd) ) def copy_files_to_instance(instance_name, *file_names, directory="~"): for file_name in file_names: gcloud_run("scp", file_name, f"{instance_name}:{directory}", f"--zone={ZONE}") def command_over_ssh(instance_name, *args, block=True): return gcloud_run("ssh", instance_name, f"--zone={ZONE}", "--", *args, block=block) def run_on_instance(template_name): current_instance_name = f"{template_name}-{get_time_stamp()}" benchmark_name = sys.argv[1] if len(sys.argv) > 1 else '' print(f"Creating new instance with name: {current_instance_name}") gcloud_run( "instances", "create", current_instance_name, "--source-instance-template", template_name, ) sleep(20) # Waiting for ssh keys to propagate to instance command_over_ssh(current_instance_name, "sudo", "apt", "update") print("copying instance setup to instance") command_over_ssh( current_instance_name, 'wget', '-q', 'https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh', ) print('installing miniconda') command_over_ssh( current_instance_name, 'bash', 'Miniconda3-latest-Linux-x86_64.sh', '-b' ) command_over_ssh( current_instance_name, 'echo', '"source miniconda3/bin/activate"', '>>', '~/.bashrc', ) command_over_ssh(current_instance_name, f'{CONDA_PATH}', 'init') print('installing asv and google api') command_over_ssh( current_instance_name, f'{CONDA_PATH}', 'install', '-c', 'conda-forge', 'asv', 'google-api-python-client', 'google-auth-httplib2', 'google-auth-oauthlib', 'google-cloud-storage', 'numpy', '-y', ) copy_files_to_instance(current_instance_name, "instance_setup.py") for config_name in os.listdir(MACHINE_CONFIGS_DIR): if config_name == template_name + ".json": copy_files_to_instance( current_instance_name, os.path.join(MACHINE_CONFIGS_DIR, config_name), directory="~/.asv-machine.json", ) break else: print(f"Found no valid machine config for template: {template_name}.") exit(1) print("starting instance setup") command_over_ssh( current_instance_name, "miniconda3/bin/python3", "instance_setup.py", current_instance_name, template_name, block=True, ) command_over_ssh( current_instance_name, 'cd', 'ipyparallel_master_project', ';', "~/miniconda3/bin/python3", 'asv_runner.py', benchmark_name, template_name, block=False, ) if __name__ == "__main__": running_instances = get_running_instance_names() number_of_running_instances = len(running_instances) # atexit.register(delete_all_instances) print(f"Currently there are {number_of_running_instances} running instances.") if number_of_running_instances: print("Running instances: ") for instance in running_instances: print(f" {instance}") if "-d" in sys.argv: result = delete_instance(sys.argv[2]) elif "-da" in sys.argv: result = delete_all_instances() if "-q" in sys.argv: exit(0) if len(CORE_NUMBERS_FOR_TEMPLATES) == 1: run_on_instance(generate_template_name(CORE_NUMBERS_FOR_TEMPLATES[0])) else: with mp.Pool(len(CORE_NUMBERS_FOR_TEMPLATES)) as pool: result = pool.map_async( run_on_instance, [ generate_template_name(core_number) for core_number in CORE_NUMBERS_FOR_TEMPLATES ], ) result.wait() print("gcloud setup finished.") ipyparallel-8.8.0/benchmarks/instance_setup.py000066400000000000000000000030701460376056100215700ustar00rootroot00000000000000import os DEFAULT_MINICONDA_PATH = os.path.join(os.getcwd(), "miniconda3/bin/:") env = os.environ.copy() env["PATH"] = DEFAULT_MINICONDA_PATH + env["PATH"] from subprocess import check_call GITHUB_TOKEN = "" # Token for machine user ASV_TESTS_REPO = "github.com/tomoboy/ipyparallel_master_project.git" IPYPARALLEL_REPO = "github.com/tomoboy/ipyparallel.git" def cmd_run(*args, log_filename=None, error_filename=None): if len(args) == 1: args = args[0].split(" ") print(f'$ {" ".join(args)}') if not log_filename and not error_filename: check_call(args, env=env) else: check_call( args, env=env, stdout=open(log_filename, 'w'), stderr=open(error_filename, 'w'), ) if __name__ == "__main__": cmd_run( f"git clone -q https://{GITHUB_TOKEN}@{ASV_TESTS_REPO}" ) # Get benchmarks from repo print("Finished cloning benchmark repo") # Installing ipyparallel from the dev branch cmd_run(f"pip install -q git+https://{GITHUB_TOKEN}@{IPYPARALLEL_REPO}") print("Installed ipyparallel") # Create profile for ipyparallel, (should maybe be copied if we want some cusom values here) cmd_run("ipython profile create --parallel --profile=asv") cmd_run('echo 120000 > /proc/sys/kernel/threads-max') cmd_run('echo 600000 > /proc/sys/vm/max_map_count') cmd_run('echo 200000 > /proc/sys/kernel/piad_max') cmd_run('echo "* hard nproc 100000" > /etc/security/limits.d') cmd_run('echo "* soft nproc 100000" > /etc/security/limits.d') ipyparallel-8.8.0/benchmarks/logger.py000066400000000000000000000013531460376056100200250ustar00rootroot00000000000000import os from datetime import date from ipyparallel_master_project.benchmarks.utils import get_time_stamp LOGS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs') GCLOUD_DIR = os.path.join(LOGS_DIR, 'gcloud_output') PROFILING_DIR = os.path.join(LOGS_DIR, 'profiling') TODAY = str(date.today()) def get_dir(main_dir): target_dir = f"{main_dir}/{TODAY}" if not os.path.exists(target_dir): os.makedirs(target_dir) return target_dir def get_profiling_log_file_name(): return os.path.join(get_dir(PROFILING_DIR), f'profiling_{get_time_stamp()}') def get_gcloud_log_file_name(instance_name): return os.path.join(get_dir(GCLOUD_DIR), instance_name) if __name__ == "__main__": print(LOGS_DIR) ipyparallel-8.8.0/benchmarks/machine_configs/000077500000000000000000000000001460376056100213065ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/machine_configs/asv-testing-16.json000066400000000000000000000003131460376056100246660ustar00rootroot00000000000000{ "asv-testing-16": { "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU @ 2.30GHz", "machine": "asv-testing-16", "os": "Linux 4.15.0-1029-gcp", "ram": "16423396" }, "version": 1 } ipyparallel-8.8.0/benchmarks/machine_configs/asv-testing-32.json000066400000000000000000000003131460376056100246640ustar00rootroot00000000000000{ "asv-testing-16": { "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU @ 2.30GHz", "machine": "asv-testing-32", "os": "Linux 4.15.0-1029-gcp", "ram": "32423396" }, "version": 1 } ipyparallel-8.8.0/benchmarks/machine_configs/asv-testing-64.json000066400000000000000000000003131460376056100246710ustar00rootroot00000000000000{ "asv-testing-64": { "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU @ 2.30GHz", "machine": "asv-testing-64", "os": "Linux 4.15.0-1029-gcp", "ram": "64423396" }, "version": 1 } ipyparallel-8.8.0/benchmarks/machine_configs/asv-testing-96.json000066400000000000000000000003211460376056100246750ustar00rootroot00000000000000{ "asv-testing-16-16": { "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU @ 2.30GHz", "machine": "asv-testing-96-96", "os": "Linux 4.15.0-1029-gcp", "ram": "96423396" }, "version": 1 } ipyparallel-8.8.0/benchmarks/profiling/000077500000000000000000000000001460376056100201635ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/profiling/__init__.py000066400000000000000000000000001460376056100222620ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/profiling/profiling_code.py000066400000000000000000000041641460376056100235250ustar00rootroot00000000000000import sys import numpy as np import ipyparallel as ipp def echo(delay=0): def inner_echo(x, **kwargs): import time if delay: time.sleep(delay) return x return inner_echo def profile_many_empty_tasks(lview, n, block=True): lview.map(echo(0), [None] * n, block=block) # def profile_echo_many_arguments(lview, number_of_arguments): # lview.map( # lambda x: echo_many_arguments(*x), # [ # tuple(np.empty(1, dtype=np.int8) for n in range(number_of_arguments)) # for x in range(16) # ], # block=False, # ) def profile_tasks_with_large_data(lview, num_bytes): for _ in range(10): for i in range(10): lview.apply_sync(echo(0), np.array([0] * num_bytes, dtype=np.int8)) def run_profiling(selected_profiling_task, selected_view): client = ipp.Client(profile='asv') # add it to path on the engines # client[:].apply_sync(add_to_path, master_project_parent) print('profiling task: ', selected_profiling_task) print('profiling view: ', selected_view) if selected_view == 'direct': view = client[:] elif selected_view == 'spanning_tree': view = client.spanning_tree_view() else: view = client.load_balanced_view() # view = client[:] if selected_view == 'direct' else client.load_balanced_view() if selected_profiling_task == 'many_empty_tasks': for x in range(1, 5): profile_many_empty_tasks(view, 10**x) elif selected_profiling_task == 'many_empty_tasks_non_blocking': for x in range(1, 5): profile_many_empty_tasks(view, 10**x, block=False) elif selected_profiling_task == 'tasks_with_large_data': for x in range(1, 8): profile_tasks_with_large_data(view, 10**x) # elif selected_profiling_task == 'echo_many_arguments': # for i in range(100): # for number_of_arguments in ((2 ** x) - 1 for x in range(1, 9)): # profile_echo_many_arguments(view, number_of_arguments) if __name__ == "__main__": run_profiling(sys.argv[1], sys.argv[2]) ipyparallel-8.8.0/benchmarks/profiling/profiling_code_runner.py000066400000000000000000000055571460376056100251250ustar00rootroot00000000000000import datetime import os import sys import time from subprocess import Popen, check_call, check_output import ipyparallel as ipp def wait_for(condition): for _ in range(750): if condition(): break else: time.sleep(0.1) if not condition(): raise TimeoutError('wait_for took to long to finish') def get_time_stamp() -> str: return ( str(datetime.datetime.now()).split(".")[0].replace(" ", "-").replace(":", "-") ) def start_cmd(cmd, blocking=True): print(cmd) return ( check_call( cmd, stdout=sys.__stdout__, stderr=open('spanning_tree_error.out', 'a+'), shell=True, ) if blocking else Popen( cmd, stdout=sys.__stdout__, stderr=open('spanning_tree_error.out', 'a+'), shell=True, ) ) def stop_cluster(): if '-s' not in sys.argv: start_cmd('ipcluster stop --profile=asv') # atexit.register(stop_cluster) PROFILING_TASKS = [ 'many_empty_tasks', 'many_empty_tasks_non_blocking', 'tasks_with_large_data', 'echo_many_arguments', ] VIEW_TYPES = ['direct', 'load_balanced'] def get_tasks_to_execute(program_arguments): return ( [f'{program_arguments[2]} {view}' for view in VIEW_TYPES] if len(program_arguments) >= 2 else [f'{task} {view}' for task in PROFILING_TASKS for view in VIEW_TYPES] ) if __name__ == "__main__": print('profiling_code_runner_started') if '-s' not in sys.argv: n = int(sys.argv[1]) if len(sys.argv) > 1 else 16 client = ipp.Client(profile='asv') print(f'Waiting for {n} engines to get available') try: wait_for(lambda: len(client) >= n) except TimeoutError as e: print(e) exit(1) print('Starting the profiling') controller_pid = check_output('pgrep -f ipyparallel.controller', shell=True) number_of_schedulers = 15 scheduler_pids = sorted(int(x) for x in controller_pid.decode('utf-8').split())[ -number_of_schedulers: ] client_output_path = os.path.join(os.getcwd(), 'spanning_tree_client.svg') files_to_upload = [client_output_path] ps = [] for i, scheduler_pid in enumerate(scheduler_pids): scheduler_output_path = os.path.join( os.getcwd(), f'spanning_tree_{i}_scheduler.svg' ) files_to_upload.append(scheduler_output_path) ps.append( start_cmd( f'sudo py-spy --function -d 60 --flame {scheduler_output_path} --pid {scheduler_pid}', blocking=False, ) ) start_cmd( f'sudo py-spy --function -d 60 --flame {client_output_path} -- python profiling_code.py tasks_with_large_data spanning_tree' ) print('client ended') for p in ps: p.wait() ipyparallel-8.8.0/benchmarks/profiling/view_profiling_results.py000066400000000000000000000015601460376056100253430ustar00rootroot00000000000000import os master_project_path = os.path.abspath( os.path.join(os.path.dirname(__file__), os.pardir) ) ALL_RESULTS_DIRECTORY = os.path.join(master_project_path, 'results', 'profiling') def get_latest_results_dir(): return os.path.join( 'results', 'profiling', max( dirname for dirname in os.listdir(ALL_RESULTS_DIRECTORY) if 'initial_results' not in dirname ), ) def get_initial_results_dir(): return os.path.join( 'results', 'profiling', next( ( dirname for dirname in os.listdir(ALL_RESULTS_DIRECTORY) if 'initial_results' in dirname ), max(dirname for dirname in os.listdir(ALL_RESULTS_DIRECTORY)), ), ) if __name__ == '__main__': print(get_latest_results_dir()) ipyparallel-8.8.0/benchmarks/profiling_initial_results.ipynb000066400000000000000000000424331460376056100245260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML, IFrame\n", "from profiling import profiling_code\n", "from profiling.view_profiling_results import get_latest_results_dir, get_initial_results_dir\n", "from profiling.profiling_code_runner import profiling_tasks\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ipyparallel profiling results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running profiling with py-spy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_results(dir_name, file_name):\n", " return os.path.join(dir_name, file_name)\n", "\n", "def get_file_name(task_name, is_client):\n", " return f'{task_name}_{\"client\" if is_client else \"scheduler\"}.svg'\n", "\n", "def get_latest_results(task_name, is_client=True):\n", " return get_results(get_latest_results_dir(), get_file_name(task_name, is_client))\n", "\n", "def get_initial_results(task_name, is_client=True):\n", " return get_results(get_initial_results_dir(), get_file_name(task_name, is_client))\n", "\n", "def display_svg(svg_file_path):\n", " display(IFrame(src=svg_file_path, width='100%', height=800))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: many_empty_tasks:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m 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"execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('many_empty_tasks'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scheduler:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('many_empty_tasks', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: many_empty_tasks_non_blocking:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m 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"execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('many_empty_tasks_non_blocking', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: tasks_with_large_data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_tasks_with_large_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mprofile_tasks_with_large_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m 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~/ipyparallel_master_project/profiling/profiling_code.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "?? profiling_code.profile_tasks_with_large_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Client:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('tasks_with_large_data'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scheduler:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('tasks_with_large_data', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: echo_many_arguments:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_echo_many_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_arguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mprofile_echo_many_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_arguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mlview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m 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"display_svg(get_initial_results('echo_many_arguments'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scheduler:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_initial_results('echo_many_arguments', is_client=False))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/profiling_latest_results.ipynb000066400000000000000000000426431460376056100243740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import os\n", "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML, IFrame\n", "from profiling import profiling_code\n", "from profiling.view_profiling_results import get_latest_results_dir, get_initial_results_dir\n", "from profiling.profiling_code_runner import profiling_tasks\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ipyparallel profiling results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running profiling with py-spy\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def get_results(dir_name, file_name):\n", " return os.path.join(dir_name, file_name)\n", "\n", "def get_file_name(task_name, is_client):\n", " return f'{task_name}_{\"client\" if is_client else \"scheduler\"}.svg'\n", "\n", "def get_latest_results(task_name, is_client=True):\n", " return get_results(get_latest_results_dir(), get_file_name(task_name, is_client))\n", "\n", "def get_initial_results(task_name, is_client=True):\n", " return get_results(get_initial_results_dir(), get_file_name(task_name, is_client))\n", "\n", "def display_svg(svg_file_path):\n", " display(IFrame(src=svg_file_path, width='100%', height=800))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: many_empty_tasks:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mlview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mecho\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/ipyparallel_master_project/profiling/profiling_code.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "??profiling_code.profile_many_empty_tasks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Client: " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('many_empty_tasks'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scheduler:\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('many_empty_tasks', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: many_empty_tasks_non_blocking:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m \u001b[0mprofile_many_empty_tasks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m 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"source": [ "display_svg(get_latest_results('many_empty_tasks_non_blocking'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scheduler:\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('many_empty_tasks_non_blocking', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: tasks_with_large_data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_tasks_with_large_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m 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"#### Scheduler:\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('tasks_with_large_data', is_client=False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task: echo_many_arguments:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mprofiling_code\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile_echo_many_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlview\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_arguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mDocstring:\u001b[0m \n", "\u001b[0;31mSource:\u001b[0m \n", "\u001b[0;32mdef\u001b[0m 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"\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "??profiling_code.profile_echo_many_arguments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Client: " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('echo_many_arguments'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scheduler:\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_svg(get_latest_results('echo_many_arguments', is_client=False))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/push_benchmarks.ipynb000066400000000000000000004710661460376056100224270ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.9825750242013553" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "81920 / 41320" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results, BenchmarkType, SchedulerType, get_broadcast_source, get_async_source, get_push_source\n", "from benchmarks.utils import echo\n", "from benchmarks.throughput import make_benchmark, make_multiple_message_benchmark, make_push_benchmark" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# time_push" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This benchmark comes from benchmarking the runtime of sending arrays of various size to different numbers of engines and returning None." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { 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"outputs": [], "source": [ "source = get_push_source(benchmark_results)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "dview = pd.DataFrame(source['DirectView']) \n", "dview['Scheduler name'] = 'DirectView'\n", "dview['Speedup'] = 1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Duration in ms Number of bytes Number of engines Scheduler name \\\n", "0 6.30 1000 1 DirectView \n", "1 8.15 10000 1 DirectView \n", "2 12.97 100000 1 DirectView \n", "3 81.41 1000000 1 DirectView \n", "4 158.57 2000000 1 DirectView \n", ".. ... ... ... ... \n", "35 577.71 1000 1024 NonCoalescing \n", "36 598.62 10000 1024 NonCoalescing \n", "37 597.03 100000 1024 NonCoalescing \n", "38 666.20 1000000 1024 NonCoalescing \n", "39 806.00 2000000 1024 NonCoalescing \n", "\n", " Speedup \n", "0 1.000000 \n", "1 1.000000 \n", "2 1.000000 \n", "3 1.000000 \n", "4 1.000000 \n", ".. ... \n", "35 1.142511 \n", "36 1.082156 \n", "37 1.319331 \n", "38 2.073837 \n", "39 3.242618 \n", "\n", "[120 rows x 5 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas = []\n", "for scheduler_name, scheduler_results in source.items():\n", " data = pd.DataFrame(scheduler_results) \n", " data['Scheduler name'] = scheduler_name\n", " data['Speedup'] = dview['Duration in ms'] / data['Duration in ms']\n", " datas.append(data)\n", "data = pd.concat(datas)\n", "data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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36598.62100001024NonCoalescing1.082156
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" ], "text/plain": [ " Duration in ms Number of bytes Number of engines Scheduler name \\\n", "35 660.04 1000 1024 DirectView \n", "36 647.80 10000 1024 DirectView \n", "37 787.68 100000 1024 DirectView \n", "38 1381.59 1000000 1024 DirectView \n", "39 2613.55 2000000 1024 DirectView \n", "35 164.12 1000 1024 Coalescing \n", "36 164.27 10000 1024 Coalescing \n", "37 173.36 100000 1024 Coalescing \n", "38 284.16 1000000 1024 Coalescing \n", "39 718.34 2000000 1024 Coalescing \n", "35 577.71 1000 1024 NonCoalescing \n", "36 598.62 10000 1024 NonCoalescing \n", "37 597.03 100000 1024 NonCoalescing \n", "38 666.20 1000000 1024 NonCoalescing \n", "39 806.00 2000000 1024 NonCoalescing \n", "\n", " Speedup \n", "35 1.000000 \n", "36 1.000000 \n", "37 1.000000 \n", "38 1.000000 \n", "39 1.000000 \n", "35 4.021691 \n", "36 3.943508 \n", "37 4.543609 \n", "38 4.862014 \n", "39 3.638319 \n", "35 1.142511 \n", "36 1.082156 \n", "37 1.319331 \n", "38 2.073837 \n", "39 3.242618 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldata = data[data['Number of engines'] == 1024]\n", "ldata" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for scheduler_name in data['Scheduler name'].unique():\n", " scheduler_data = data[data['Scheduler name'] == scheduler_name]\n", " alt.Chart(scheduler_data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of bytes',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Duration in ms',\n", " scale=alt.Scale(type='log')\n", "\n", " ),\n", " color='Number of engines:N',\n", " tooltip='Duration in ms',\n", " \n", " ).properties(title=scheduler_name, width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ldata = data[data['Number of bytes'] == 1000]\n", "alt.Chart(ldata).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log', base=2)\n", " ),\n", " alt.Y(\n", " 'Duration in ms',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Scheduler name:N',\n", " tooltip='Duration in ms',\n", ").configure_axis(labelFontSize=20, titleFontSize=20).properties(title='Runtime of apply using DirectView', width=1080).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "## Results for duration[DirectView]/duration[scheduler]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['combined']= data['Scheduler name'] + ' ' + data['Number of bytes'].astype(str)\n", "alt.Chart(data[data['Scheduler name'] != 'DirectView']).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Speedup',\n", " ),\n", " color='Number of bytes:N',\n", " strokeDash=alt.StrokeDash(shorthand='Scheduler name', legend=None),\n", " tooltip='combined',\n", "\n", ").properties(title='schedulers vs directView scaling engines', width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for engine in data['Number of engines'].unique(): \n", " alt.Chart(data[data['Number of engines'] == engine]).mark_bar().encode(\n", " x='Scheduler name',\n", " y='Duration in ms',\n", " color='Scheduler name:N',\n", " column='Number of bytes:N', \n", " tooltip='Duration in ms'\n", " ).properties(title=f'Runtime on {engine} engines:').interactive().display(renderer='svg')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } 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ipyparallel-8.8.0/benchmarks/results/asv_testing-16-2020-05-04-17-43/000077500000000000000000000000001460376056100241425ustar00rootroot00000000000000ipyparallel-8.8.0/benchmarks/results/asv_testing-16-2020-05-04-17-43/results.json000066400000000000000000000047061460376056100265450ustar00rootroot00000000000000{ "results": { "throughput.CoalescingBroadcast.time_broadcast": { "result": null, "params": [ ["0", "0.1"], ["1", "10", "50", "100", "200"], ["10", "100", "1000", "10000", "100000", "1000000"] ] }, "throughput.DirectViewBroadCast.time_broadcast": { "result": null, "params": [ ["0", "0.1"], ["1", "10", "50", "100", "200"], ["10", "100", "1000", "10000", "100000", "1000000"] ] }, "throughput.NonCoalescingBroadcast.time_broadcast": { "result": null, "params": [ ["0", "0.1"], ["1", "10", "50", "100", "200"], ["10", "100", "1000", "10000", "100000", "1000000"] ] }, "throughput.SpanningTreeBroadCast.time_broadcast": { "result": null, "params": [ ["0", "0.1"], ["1", "10", "50", "100", "200"], ["10", "100", "1000", "10000", "100000", "1000000"] ] } }, "params": { "arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU @ 2.30GHz", "machine": "asv-testing-16", "os": "Linux 4.15.0-1029-gcp", "ram": "16423396", "python": "3.7", "numpy": "" }, "requirements": { "numpy": "" }, "commit_hash": "766bdabf611694c8bf1807677ed3a501bf0b87f2", "date": 1588108958000, "env_name": "conda-py3.7-numpy", "python": "3.7", "profiles": {}, "started_at": { "throughput.CoalescingBroadcast.time_broadcast": 1588607341576, "throughput.DirectViewBroadCast.time_broadcast": 1588607371588, "throughput.NonCoalescingBroadcast.time_broadcast": 1588607401077, "throughput.SpanningTreeBroadCast.time_broadcast": 1588607436141 }, "ended_at": { "throughput.CoalescingBroadcast.time_broadcast": 1588607371588, "throughput.DirectViewBroadCast.time_broadcast": 1588607401077, "throughput.NonCoalescingBroadcast.time_broadcast": 1588607436140, "throughput.SpanningTreeBroadCast.time_broadcast": 1588607459984 }, "benchmark_version": { "throughput.CoalescingBroadcast.time_broadcast": "fa34b3be29f11e8e92ff3f69ee3db3cef435c17dc271602df2b926f774e93a8e", "throughput.DirectViewBroadCast.time_broadcast": "fa34b3be29f11e8e92ff3f69ee3db3cef435c17dc271602df2b926f774e93a8e", "throughput.NonCoalescingBroadcast.time_broadcast": "fa34b3be29f11e8e92ff3f69ee3db3cef435c17dc271602df2b926f774e93a8e", "throughput.SpanningTreeBroadCast.time_broadcast": "fa34b3be29f11e8e92ff3f69ee3db3cef435c17dc271602df2b926f774e93a8e" }, "version": 1 } ipyparallel-8.8.0/benchmarks/results/benchmarks.json000066400000000000000000000240031460376056100227020ustar00rootroot00000000000000{ "throughput.CoalescingAsync.time_async_messages": { "code": "class AsyncMessagesSuite:\n def time_async_messages(self, number_of_engines, number_of_messages):\n replies = []\n for i in range(number_of_messages):\n reply = self.view.apply_async(\n echo, np.array([0] * 1000, dtype=np.int8)\n )\n replies.append(reply)\n for reply in replies:\n reply.get()\n\n def setup(self, number_of_engines, number_of_messages):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n \n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.CoalescingAsync.time_async_messages", "number": 0, "param_names": ["Number of engines", "number_of_messages"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1", "5", "10", "20", "50", "75", "100"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60, "type": "time", "unit": "seconds", "version": "7d58edbe3c5f17d8c8b9f56b07e36103b6aac63113a081844b4c8ae47c8f9912", "warmup_time": -1 }, "throughput.CoalescingBroadcast.time_broadcast": { "code": "class ThroughputSuite:\n def time_broadcast(self, engines, number_of_bytes):\n self.reply = self.view.apply_sync(\n echo, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.CoalescingBroadcast.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "bb46d525dc8242ec0b309ba7667c82bdca00f5c739fbd6e655a6642432442d25", "warmup_time": -1 }, "throughput.CoalescingPush.time_broadcast": { "code": "class PushMessageSuite:\n def time_broadcast(self, engines, number_of_bytes):\n reply = self.view.apply_sync(\n lambda x: None, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.CoalescingPush.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "d53ef539a1ba7dda53fcdfdd810c23bcde346a6e936436ffd5a03e8254a38640", "warmup_time": -1 }, "throughput.DirectViewAsync.time_async_messages": { "code": "class AsyncMessagesSuite:\n def time_async_messages(self, number_of_engines, number_of_messages):\n replies = []\n for i in range(number_of_messages):\n reply = self.view.apply_async(\n echo, np.array([0] * 1000, dtype=np.int8)\n )\n replies.append(reply)\n for reply in replies:\n reply.get()\n\n def setup(self, number_of_engines, number_of_messages):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n \n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.DirectViewAsync.time_async_messages", "number": 0, "param_names": ["Number of engines", "number_of_messages"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1", "5", "10", "20", "50", "75", "100"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60, "type": "time", "unit": "seconds", "version": "7d58edbe3c5f17d8c8b9f56b07e36103b6aac63113a081844b4c8ae47c8f9912", "warmup_time": -1 }, "throughput.DirectViewBroadcast.time_broadcast": { "code": "class ThroughputSuite:\n def time_broadcast(self, engines, number_of_bytes):\n self.reply = self.view.apply_sync(\n echo, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.DirectViewBroadcast.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "bb46d525dc8242ec0b309ba7667c82bdca00f5c739fbd6e655a6642432442d25", "warmup_time": -1 }, "throughput.DirectViewPush.time_broadcast": { "code": "class PushMessageSuite:\n def time_broadcast(self, engines, number_of_bytes):\n reply = self.view.apply_sync(\n lambda x: None, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.DirectViewPush.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "d53ef539a1ba7dda53fcdfdd810c23bcde346a6e936436ffd5a03e8254a38640", "warmup_time": -1 }, "throughput.NonCoalescingAsync.time_async_messages": { "code": "class AsyncMessagesSuite:\n def time_async_messages(self, number_of_engines, number_of_messages):\n replies = []\n for i in range(number_of_messages):\n reply = self.view.apply_async(\n echo, np.array([0] * 1000, dtype=np.int8)\n )\n replies.append(reply)\n for reply in replies:\n reply.get()\n\n def setup(self, number_of_engines, number_of_messages):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n \n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.NonCoalescingAsync.time_async_messages", "number": 0, "param_names": ["Number of engines", "number_of_messages"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1", "5", "10", "20", "50", "75", "100"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 60, "type": "time", "unit": "seconds", "version": "7d58edbe3c5f17d8c8b9f56b07e36103b6aac63113a081844b4c8ae47c8f9912", "warmup_time": -1 }, "throughput.NonCoalescingBroadcast.time_broadcast": { "code": "class ThroughputSuite:\n def time_broadcast(self, engines, number_of_bytes):\n self.reply = self.view.apply_sync(\n echo, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.NonCoalescingBroadcast.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "bb46d525dc8242ec0b309ba7667c82bdca00f5c739fbd6e655a6642432442d25", "warmup_time": -1 }, "throughput.NonCoalescingPush.time_broadcast": { "code": "class PushMessageSuite:\n def time_broadcast(self, engines, number_of_bytes):\n reply = self.view.apply_sync(\n lambda x: None, np.array([0] * number_of_bytes, dtype=np.int8)\n )\n\n def setup(self, number_of_engines, number_of_bytes):\n self.client = ipp.Client(profile='asv')\n self.view = get_view(self)\n self.view.targets = list(range(number_of_engines))\n wait_for(lambda: len(self.client) >= number_of_engines)", "min_run_count": 2, "name": "throughput.NonCoalescingPush.time_broadcast", "number": 0, "param_names": ["Number of engines", "Number of bytes"], "params": [ ["1", "2", "16", "64", "128", "256", "512", "1024"], ["1000", "10000", "100000", "1000000", "2000000"] ], "processes": 2, "repeat": 0, "sample_time": 0.01, "timeout": 120, "type": "time", "unit": "seconds", "version": "d53ef539a1ba7dda53fcdfdd810c23bcde346a6e936436ffd5a03e8254a38640", "warmup_time": -1 }, "version": 2 } ipyparallel-8.8.0/benchmarks/scheduler_benchmarks.ipynb000066400000000000000000032243241460376056100234220ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results,get_time_n_tasks_source, get_no_delay_source, BenchmarkType, SchedulerType, get_broad_cast_source, get_echo_many_arguments_source\n", "from benchmarks.utils import echo\n", "from benchmarks.overhead_latency import echo_many_arguments\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipyparallel benchmark results ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### time_n_tasks ###\n", "The first benchmark comes from benchmarking the runtime of sending\n", "n tasks to m engines. Where the each task is just the echo function. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001B[0;31mSignature:\u001B[0m \u001B[0mecho\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdelay\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", "\u001B[0;31mDocstring:\u001B[0m \n", "\u001B[0;31mSource:\u001B[0m \n", "\u001B[0;32mdef\u001B[0m \u001B[0mecho\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdelay\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0;32mdef\u001B[0m \u001B[0minner_echo\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mtime\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mdelay\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0mtime\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msleep\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdelay\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\n", "\u001B[0;34m\u001B[0m \u001B[0;32mreturn\u001B[0m \u001B[0minner_echo\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n", "\u001B[0;31mFile:\u001B[0m ~/ipyparallel_master_project/benchmarks/utils.py\n", "\u001B[0;31mType:\u001B[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "??echo" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/markdown": [ "## With 16 cores:" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### With a delay of 0.0s. :" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-1c48c1d1c479f23c984574997a86bdf2" }, "datasets": { "data-1c48c1d1c479f23c984574997a86bdf2": [ { "Duration in ms": 10.34, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 10.3, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 10.71, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 5.8, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 6.05, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 5.76, "Number of engines": 1, "Number of tasks": 100 } ] }, "encoding": { "color": { "field": "Number of engines", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of tasks", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector011": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "SchedulerType.BROADCAST_COALESCING", "width": 800 }, "image/png": 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-d67cc399a4f1dc20ed36ffdfa4f3150e" }, "datasets": { "data-d67cc399a4f1dc20ed36ffdfa4f3150e": [ { "Duration in ms": 13.07, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 13.48, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 13.6, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 6.07, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 6.17, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 5.96, "Number of engines": 1, "Number of tasks": 100 } 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-dc3718091d34f3c4bf53f7a397265104" }, "datasets": { "data-dc3718091d34f3c4bf53f7a397265104": [ { "Duration in ms": 12.35, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 12.74, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 12.52, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 4.92, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 5.22, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 4.95, "Number of engines": 1, "Number of tasks": 100 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Ijn3atrZlkd/IhfD1AG4H4IETFeEegF8F8EsAPgng5QDOAHBvAC8A8EgAHwXweAD/BODdAF5a9cOnAfg4ABHC3wLwFQCnVQJV8MTe/QA8E8D9AUgVV36X1/sB/C2AHwLwhwD+fKJvnwLgd6p7ZGnyHwF4GYAXVb7KpfJ5jSX/lzgeC0AqxrcB8GeVn7sBfBDAxQBOBSDYbwZwPgBZtfISAE8BcEUVi/gjlebfA7AC4DEAfrmKX/x9HIAvADgbwBcB3AHAM6rP/wPAcwD8M4B7AfiVanLgEwBeCeBj08YvCmGfozptrWPAYhBsC8Vt4SbmOEL65tO2pS0L7EUwD73ypOc74Dcmx4EkSSiG2zIwTsSxSDtpIR2rIVmLF5/cxZrjkBz7tG1tyyK/EQvhXwfwQgCvqwRfvTT6RAB/OrFMWkTiEwAMALy+EpbvA3AsgB+uxK1cL+Lv0wB+oBLCgiNLsE8AcMfqc7lOxLC8/zcAfq0Sv08C8L9VX74MwJMBfHiib4soFsF5AYA7AfgJAI+uxPaF1fJuEbziV/36kUpovwWAiOenA9hfiWwR6bV4FkEugljE8smV+P9GJcwlbnndFMDfA9hTLY3+TQDPA3BJ9f6zKqEsNsRvwZPKsQhqif+WAD5bYYmAP6daZv29AMYbxzAKYZ+jOm1RCDdsAxZ/IBqaVr0s5jhC+ubTtpatLxw4deXU3VcPrh3vWOnnh1eOuP7K9Q9/+3/f7H2PuXPqxitJ2hskLlvJkK4MEjfIXLICpCu5y1f6aTLInFtJkKwkiRvAJSs58pUkl2vcQN7P4VbSJBkc2vuIZ+766kV/nCTJwMHJjPCKc1hJEwxyYCUpZ4nlS8KKW/2/GwCCheOr5VRr7ThBBocbHZAl8kew/H8GrP2T9+Xz4j2XZEhceW31Xl68j+r9pMJIMsBlgpcgGcPlmUuS8jon7ydZkmBc4+XF+0nmkjxL5XrBTFzmxJ5DlqYVhvw/lffyzOVJ1uvV76N8P08y5/IMvWRc3JvnmSuuT7JePxkjzzKXp/J55vJx5rI0G+xIx8hHmRunGQa94mc+PpK5lV6WD/vZsTc7NMaRQYYbdmU4+WsZrkCWHECuOhgog2m1a2W3gsNZixefAcaa45Ac+7RtbcsivxELYRGfUg2Waq4IUvkpe4SbCOGbVZXPT1WVUBHVIvSkunqLSghfCeDuVRVVhKVUl/+gqjyL6JZq6ROre2TfrfxfhKxUnCfFoYhPEc9yj1Rj5dCqQwD+qrpWqrFySJYI8MmXiHYRqCKe5W+piE8R6g+tBLT48tSqavxWAI8AcM+q0iy8fA3Ae6tq+GZC+LYA/guA7In+doUtgl5ilaqziGyZLBCbv11NCEgV+EMARKDLvUe9KIR9juq0RSHcsA1Y/IFoaFr1spjjCOnbpV/9ilt5wwOOOeW4Q4PrsXOlh+FK2h8MEjdeGbneyo5ebzB22UqCdEV+DtJ0kLm8+D+SfJCLoIRbyV2y0ktc9f9SVIogFAFZC8Ybb3/mL+687D1/JiLTORGWyQqSUny6Qqy6FSTJAMVnWEkSDFxeikwRn/Kz+uMa9d8L1YZLMGFg3WQBCvFeCv96IqEU+uV1xWRBNZGQpG71fXmvmGRwyMr3q8kCmTRANeFQTCDk5aRAgix3LkuTcsKhnChYP+EgkwwH7/qUl+/5/BtfLJ+nxQTHBEYq7+WFrWz1/XIiIYVMCmTF7xsnEmRCoY9svHEiwfXTzGWjbEXurScSVo5kblxOJGQ7b8jGh/vZCdg9xsk3z3DFZ4NNJFiLF59dI+QYvVWcITn2advalkV+IxfCIi6lAiviV14ihG8OQIShHHT11wDeBeBRGyrCIk73ApADpOp9xiKKpaJ660oIf71aFi3Ljb9TCWU5iOtVFb7YrV9/Uh3edV4lWCebu1RURUi/qaoUyz5dEZ0ivGXJ8mZCWGw9qBLCowpQKsGyRFp+ylJoWVr90wDeWQlhwXtuJebFpiz7/sktKsLHAbiuEvGHq6q3LA2XivdkRfvt1XWydFzsSYVcxP1Jlahf172j/mLDw7J8/snxb8tiEPQfhY3FtnDjK45p1crdvWwwdL2VzaqV37nvee879uMvesws1cpaSDapViaoRWQpNOVeEZ/VHzipaC7bS2aNh4AbIknk5MthtvuWp6QHL/+PJClOwhwmwDB3GCXF526YIBk6uFECN3R5MkTqhkAycsAwlc/y6v+pK/+fuOF1p539hpt85oInpIl8lg7dOB+5fjrsjfMhBr2hy7ORS3rDPMmHg7Q/vPHQeLSyazzMeivDwSh7Wp4nr5gk1jn3ot078HrsGPWu+/Yx/XRl3Ev6WS8d7ugl/bx3xGX9ZJz3kt6gtyPNe8jy3hi9PlInNdUe0l4vSV1vPMr78rP4f+56SFwvR9rvVb8nadrLMtdPEvkMPeRpcY2D68OVv8u/RH6X92RPlUt69fV5Lvcmss9KPu8BaXUvemmS9PLE9VKH8j0kPTiUdpKk53LXl98Teb+4t7wucUlxTVLu3+q58vCRykb1npN7k+Ia+TdxzbK1z9j8LSYG6pUFTiYRIOJbqv8Yy896smFtoqBaQVBcW34+uYrBVSsNykkFWRHgykmGSuwXKxOA/NDeRzzpmEsv+t16IiGFm5hUyMvrgUzeX1vFIJMQ1WREtSKhWLmwYSJhdUVCmmQ9+TwtVy4Uqx2KFQuyIiHJBoN0LCsShr00W8nT7HA2ysoJBVmNkGY7jhmON04kHDl2R3bddcdmp+KSsaxI8PX3Y9aGYy0QYxHh1nFa5DdyISzLnusTo2shLNVVEXN/We1hrc+0mFwa3UQIi7h+frWEWvYZy1JiEa2yr1aE9u9Xe45ljJDKrVRS5W9lvd94stnJ/mHZqyvLr2XZsQhYEZVv3EIIS5Vb9vvK8m+pNsvv/6+yu5kQFlEr+5ZlyfNfTPiyWUV4oxAWIX11Jeql4n4WgNMB/GxVjRZxLlxIdVqWeksssq+YQnjWQY/X2zBgMQjaeOofNSQ3bh96Xz/llBWNauXB0375DTf5599+YSkAj65WlstbyyWxhciUymTuVhySlTRxA/kJJIOialkugy0qlNWS2GJ5bFHBFFGwVK8ECdwREY+FcBSBWYnJUmwmw0I8FiJSxGQpNAEUIrK4HoXYHDkkw7S8thKZpagUIepcOrr29P2/e/ynzv/Z4nOXjERsOqTDXpIPR3k+6qE3dEk2HCT94Y3ZeLSS9Id5Mh7uTncOv3nkxtEJOGGYHLhEbB/1smini2IefMVJL0hS/OiRk05/yMrlF//KnpdcuW7P8FI1k4DOOofks09D/+a3vG3vtrtv7OGGUQ879vQOumE/GWa9tL9+ImFnb9BDmvdGw7yf9Hq9eiJBJhWSYoLB9ZK01+unrpeN81K4p2kPE5MKcK6aGJD30ctFzBcTAOVEgvye52lffj/4vU88sOfzb3lV8Z4I/GKSoZpIkEmH6rpEJguKCQHXc04mAmSioJwgKCcQyt9lkqF8f+I6+ayYfEC/nkwoJhKKe8WmTB64agLiqImGgNlbftMyAZCng36Sjw6V2xZk9UC9pWD99obVrQ3FaoVywqGcRFhbxVBuTXDFBEK5iqGamKhWMaSpKycnilUJeYkxsSKhnLwoVx2kSS+77q5Petmez//BS7bb2oAUWQ/p2KVZiS1bG9K8nDiQCYQ0Kz5fN5HQq67LMuk12UrSG8sEAvqjzGFXftXDL7zshL8681b5qJftOXYwxvCGDIf7GY7dkeG6YzN84ZIseWcxkbLQq4tC+OCrTnoiXPLAq876+M8oPd6zfnySCDURwlKAlP2vsoxXKsKydFeEp/xfqpb/CeBu1YS57CeWfbDThLAcnCVLo+uKsPyNFqF4EwD/DuDBVaVYDpUSUSxCWfBlKfSXthHCsmRZlk3XlWtZdiwC/tpKCIuAlcO8Jl9iV/byyj5deYkPD6+WUYsQrqvPUu2WqrcsjZYKuBy4JYeCCaaIdIld9kOLQJbHJ8m+3nqP8EYh/H2VjQMA7lHZlckAmUyQA7ZEANdxy+Fesmf5qBcrwgsNE7x5EQYW/cK7iG2f91pWK9cJxkpIrr43sey1rEZWeytdJR7XlrwW4jJdrWAW1y3bS/7o12KxrFpWlce6Uln/dEkydM6Nhrf64YfuuPxj7ywFZzKEcyOpdNYiU64p/19gDTPnRlKlLIRn4oaZS0aFAE3SYQ/5cOTSUR/5UKqV/SQfHsl7o0GSDfN0PDymtzK8Znzj6PiV3UOc8vXhZaddOlb6I7ttniz7mQW2FqYWzrYE84IgDMSeX5lIwK+ih5vdoXfVkev7gxtGvd6OPb3jBuPeDQdH/XoiAaOsN4RMHgx65cqEvJc0WZHgXH/qREKa9q+756/8wZ7PXPDMXi6iXVYipD2ZZCgnFsrfyxUCSb/4XSYC5DqXVCsC5Pd68gG9PE/KiYbV1QrldUkxcVFOJNQTBmsrDqpJhdUJgw0rE+oVDeUqhqNWKgRpVG0xmsBNnoOwej7CUdsdJlcsrJ2D4FCeb5C4JDtywt3us+Nb//YRwZMtC8XZB4VgmZhIqFYNlJMIgunGGycSyi0VSZak5ZYFmag4eLennbv7339/f3n+Qnn2QVqciVBtc1g9c6E8W6E4BwHJeHUiYWIFgpyJIBMKV//Y733sZh966unFhEM1kYA8zZysTEjGxYSCG6SZGx/JZCLBrfSzbHhDNt7Vz3Zng19wzhWP67nyrH+Er7/RVbOTfcAiVOtlxfO0RlkWLcJP9gtPvqS/f3e1t/aow6I2MST3yIFWUrGWU6mbvo4BILFIxdltc5PsM5ZTreUgr29WEwLiv4j7WV8Snwhu2c9cv0TjyvuybHzTiSEK4Vmp5vVqDMzzRUarWimVx8S5lWKvZb0/Ut6TfZf52oE+5V7KsgI5eUDPqmhMqkN7nFtJIYf7lAf7SJVyooIpf+SX6lWKQQxzVNXKSZFZiUNXVReTcnlrUdFcXRpb/N8ND93x0U/f/eV3vLoWlVKVlGplISqRDpHkQ/k5dlKZTIulsfKzqFQm+dBBRGU2HOa9UYZsWFcrs9HK6AiODLeqVm5H+DztbzvMpp/7tG1pywJbC1MLp2lOeZ1fBpjf6XxbV/F8ZdkByWVf/XJ++/c/bCe+faj3bRzuy0RCemTcSwfj3mGX9Xf3d/RkIkG2OCAd9EZHZEIh7yVpv1iBMEbeH0xsZciQ95GXWx3qrQ2yIqEnIn/d1oZyRcLk1gaZKFhdkdBzvYN3fsKBPZf88avqbQn1ioRye8HadghZkXDURIKTiYUSv96+UK5WqLYtFCsO1iYfNm5tyHfd/JT08NXfmLZaQewt3wopX62quZ0AQri5c+25UvbsfrmqYtdR1RVdb1FSCHujOk5D81QrUZz2mhYH9NQH9eTOrfSSZCAnv9aVx3WH7Ui10pWnwBbLYOFWjtzyPo/ccfknP1i8V4jIcvlrLTiL38tlrxurlVG32ymZltNX16qVq4LRlXsp83JZ7Kr4dBgNT/nhh+z434+9q9pjWe63TJJhLlXKYtms3FNWK3OXFHsw5f9SoSwrl8XezGEvSYfjPB/lSTqsq5V5sceyP6yrlXnWH43SI8O6Wpk8evElVTUHMX9ZDembT9uWtiywtTC1cOIcuekV89tuISzRxZrjkJMNs9j+8AH073+zO/RkIgG7hr1r82P69USCTB4ko5Xe0GX9nTKRMB4UqxFGLu8nWb/c2jBI+1ff/w2fuOkHn37/fjFZ4HrjcVqelSD/l3MQZKuDrE6oVhDIcoY8ky0HR29n2HhGwg2n/txL91zyJxfIpMC6MxKmbG0oVjM0WJEwutmp9x5cc8lny9UN5UqDdNoZCeu2N6yejyBLcYvvlxTC3v7GSAVZljXLoWGyV1iehez1FbWgkMOynpW99gV7XnylrA+P/rVdtTLp9Qapk4Mn00Hi8pWsqEbmAzkJtjgRNkkHCfIV5BydRRsAACAASURBVOVPEZr16a5rjxgpTostHxWyevKr7K3EAEef9Lr6iJGyglmfDlueFFvtt1y6Q3uSBCPnZK+kGyaurESuLnut9lHK/8u9lOVeyUkhWb6HoSyRLfdWJkNksgy2WvZavJcO0yQfZi4dOeRFlbLYW5lX/6+qlfXBPQNkw1z2Vo5Howw7hsfiyBA4YYgDl8j+z+2WhxzVtmP9AjBrJ4w5jpC++bRtacsCWwtTC2fWNs/r/TDA/FII+2lpR1uZRYxq++jTtrUtiz68yGFZN7zq5JfAOTn0iUJYu+FGjBe9EH529logSV56zCB/4415f2WYj1d2jtceLZKk+UCeW9mvHikijwBJ0mSwVqFMVlZ/z+tTW8tDeMrDeKQCWTxSZK1aOfGIkeI6Vx3aI3su5RmYG/ZWiqCceE/W6C/TS0RaUWGcPJBnrYJZLnstlryWexdWK5dSoawO6Bkil0N76mvL6mS913JdhbLYU1ned839fuuvbvrR5/2YVC1FbMrBPjny4tAeObzHZaNRlg6GdbXyI9d9ffSAA0c/DHuZyG7qq8UfiKa2Na+LOY6Qvvm0bWnLAlsLUwtHsz8QS48B5pdCWK81zYZkLRC38sanbWtbFn14ESEsvN/wipNfiQQPvvKsj9/T8x7h2Rohr1ZjYDmEsFq4XoCKQ3vWTnYtHyGycb9l8fnkI0YqIVlcWzw2pBSSk4f4lPsrSyFZLoGVx5CUy18LIZn0ikN7XJavVi7lPalWHhr3RyvJaFhXK7969XGjO7z+q2Jj5mqlFosWg6CWb6Fx2sJNzHGE9M2nbUtbFthamFo4occC2p/OAPNLIRyqb1gLRArh+TO7qBAWy8659LLLLssohOfPwzLduSxCWMSlHNtdVCRX/9VC0pX7JOvnVtb7LUVsyoE+SbXktfx8ci9lKSTTQkiKoEyGLqseQ1KJzPpEWPkpeyzlJNi8OLRnMMzS8XBPemR4xeFdo5Nx+TDpSLVSq4Hzi8zmTLaFm5jjCOmbT9uWtiywtTC1cLTGO+LoMsD8UgjrtqjmaBTCzbna6kqLPkwhrJObLqEshRB2wNLsE+5S41k0VotBcFGfYrm/LdzEHEdI33zatrRlga2FqYUTy5hAP9YzwPxSCIfqExTCOsxb9OGYhfAj9r/r7jlGpzskpwuDCdzFKQYXX3T+T/+LDqNEmYeB6IXwL2WvfcExS3JY1jwJ6PI9FoNgW/hsCzcxxxHSN5+2LW1ZYGthauG0ZUxpWxzML4VwqDZNIazDvEUfjlUIn3n225+ZJO41DonsSKweqZmMHZxLgee85/yzfmcbVuUeearK5HNydRLRcZTohfD+/fuj9rHj7Weh8C0GwYUciujmtnATcxwhffNp29KWBbYWphZORMMCXZlggPmlEA7VISiEdZi36MMxCuEz91/4YAB/uxVraZ4/+KJff9wHp1zTA3AXAL8AFI+2fK4O+0SpGfAhMvfIQWyy/3yC9uMBXFclddNsyOOTKITb21gtBsG2sNUWbmKOI6RvPm1b2rLA1sLUwmnLmNK2OJhfCuFQbZpCWId5iz4cmxA+45wL75Lk+DQAeUbxVq/Dievf8z0X/PQXNlwkGupcAPcE8FkKYZ22N4liKYRPBHA3AO8GcAcA3wDw3QDeDuCbQPEYnH8G8IrNwqIQ1k94TIgWg2BM8S3iS1u4iTmOkL75tG1pywJbC1MLZ5F+zHvtGGB+KYTtWtfWyBTCOsxb9OHYhPDD97/9uc65VyOBVHY3fzlkLsUL3nfeWa/d5KJnVlqKFWGd5reKYimEHwXgvtXsxS0qIfwSlM/cfXk1O3IjgFsBuHxaXBTCytmODM5iEIwsxLndaQs3MccR0jefti1tWWBrYWrhzN2JeaMpA8wvhbBpA9tKsziXVI/XSX374FOEW9uy6MOxCeEz9r/trQmSxzZsJ3/+3vPP+hkK4YZsKV1mKYRrF2VJdC2Ed1VLpA8D+AkAv1XNcEx9li2FsFKWI4WxGAQjDXVmt9rCTcxxhPTNp21LWxbYWphaODN3Xt7ghQHml0LYS0ObYsRaIG4Vl0/b1rYs+nBsQvjh+9/2Fofk55u0VQf3lved/9gnUgg3YUvvGt9CWDyXU89eBOD5AB4J4O/lzQsuuODlzrkDG0Pbt2+fXrREIgNkgAyQATJABsgAGSADZIAMbMHA3r17F9ZIZ5x94dOTJHnd2knRmxh0yJC4Z733/Mf+HoWw32a5cJIbuDtZEZbN4u8AMATwbABX1PfLzNNGrAsuuCA/++yzvS89aRATL1Fg4LLLLstvf/vbM79TuGwLNzHHEdI3n7YtbVlga2Fq4SgMdYQwYID53ZRUWbY7vv3tb7/1nkSDnGhDRpzjkBz7tG1qyyK/smRdoe2n0oc0hPCZ51x4D+S4GED1yKTpvSQBRgnc6Red/9jNnikse4T3Aniedj/rOp5vIfxUAGcCeEQT4rk0uglLy3uNxbKY5WVjvedt4SbmOEL65tO2pS0LbC1MLZy2jClti4P5nZ5R6+WsPttRrDkOybFP29a2LPIb29Jo6S9nnP3WZydJ+rot+06aPOu9r3rMG3z2L9oqGfAlhOUEaTkp+s0AnrCB/DsC+Mq0hFAIt7uZWgyCbWGsLdzEHEdI33zatrRlga2FqYXTljGlbXEwvxTCodq0tUDcKi6ftq1tWfThGIWw5LN6lvBFQNJbWyadjAEnzwZ+xHvPP+sDodpz1+36EMJzc0whPDd1S3GjxSC4FIE3cLIt3MQcR0jffNq2tGWBrYWphdOgu/KSAAwwvxTCAZpdYdJaIFIIz5/ZWIWwRPSws//srin6D0qSRJ4JLO3oMznGH3z/BT/zufkj5p2LMkAhvCiDvH9uBvhFZnPq2sJNzHGE9M2nbUtbFthamFo4cw9wvNGUAeaXQti0gW0BTiGsw7xFH45ZCOuwRhRtBiiEtRklXmMGLAbBxsYjv7At3MQcR0jffNq2tGWBrYWphRP5UNFZ95hfCuFQjZ9CWId5iz5MIayTmy6hUAh3KduRxWoxCEYW4tzutIWbmOMI6ZtP25a2LLC1MLVw5u7EvNGUAeaXQti0gbEibL4E3KIPUwiH6hXLa5dCeHlzt/SeWwyCS09KFUBbuIk5jpC++bRtacsCWwtTC6ctY0rb4mB+KYRDtWlWhHWYt+jDFMI6uekSCoVwl7IdWawWg2BkIc7tTlu4iTmOkL75tG1pywJbC1MLZ+5OzBtNGWB+KYRNGxgrwqwI792rqpFuOPeWd0fqTkfiTi+al0suRp5cvPtll2/27OBQTbxTdlWTrM0cT43WZjQuPH6R2TwfbeEm5jhC+ubTtqUtC2wtTC2cuEZNelMzwPxSCIfqDawI6zBv0YdjrQjf8MqTngngNQmQOKAvDCbA2AEuB557kxdfud0zhOWeFQCHJthPAewCcINORrqJQiHczbxHEbXFIBhFYApOtIWbmOMI6ZtP25a2LLC1MLVwFLozIQwYYH4phA2aVSNICuFGNG17kUUfjlEIH/y1Wz44SfO/3YoQh/zBe178jQ9OuaYH4C4AfgGAPHP4udU1TwTwHAD/i1JYPx7AN7clnRccxQCFMBtFMAYsBsFgwSgbbgs3MccR0jefti1tWWBrYWrhKHdtwikxwPxSCCs1pZlhKIRnpmzqDRZ9ODYhfPDcW9w16SWfBrBjG9YO53D3usmLr/r8huv2ADgXgDx7+LOVEBbhOwJwUwDXAngdgCsAnKeTmW6hUAh3K99RRWsxCEYV4ALOtIWbmOMI6ZtP25a2LLC1MLVwFujGvNWQAeaXQtiweW0JTSGsw7xFH45NCB965cnPdXCvBiCV3a1eWZIkLzjmnCteu8lFsrT6DpUQvh2AvwOwt7r22QC+v6oa6ySnQygUwh1KdmyhWgyCscU4rz9t4SbmOEL65tO2pS0LbC1MLZx5+zDvs2WA+aUQtm1hm6NTCOswb9GHYxPCB19x8luTxD22CWMOyVv3vPgKWeI87TUphO8G4J0Avqe68GcB/AiAJzexw2vWM0AhzBYRjAGLQTBYMMqG28JNzHGE9M2nbUtbFthamFo4yl2bcEoMML8UwkpNaWYYCuGZKZt6g0Ufjk0I3/CKk96CBD/fiLEEb959zpVPaiCE5YAsOTRLDstyE/uGX9PIDi9axwCFMBtEMAYsBsFgwSgbbgs3MccR0jefti1tWWBrYWrhKHdtwikxwPxSCCs1pZlhKIRnpqy7QvhVJz0drtjDW5wUvcUrQ5I8c/c5V/x+AyEsl/wbgGcA+BwAOYjr5QA+oJOZbqFQCHcr31FFyy8ym6ejLdzEHEdI33zatrRlga2FqYUT1aBJZ1YZYH4phEN1BwphHeYt+nB0FeFXnnwPwF3cQAiPkKWnb/FMYVkaLXuCn1ex/wgAf1b9/j4Aj6uqwzrJ6RAKhXCHkh1bqBaDYGwxzutPW7iJOY6Qvvm0bWnLAlsLUwtn3j7M+2wZYH4phG1b2OboFMI6zFv04diEsDB16BUnPcsleP02rD1r9/bPEt4IcQyA46oTo3WS0kEUCuEOJj2WkC0GwVhiW9SPtnATcxwhffNp29KWBbYWphbOon2Z99swwPxSCNu0rO1RKYS356jJFRZ9OEYhLFwcPO+WD07y/KIE6LlqmXQCjB2QuTx9xJ6XXs5lzU0ajcE1FMIGpBKyGQMWg2Azy/Ff1RZuYo4jpG8+bVvassDWwtTCiX+06KaHzC+FcKiWTyGsw7xFH45VCBdi+Nxb3BVp+qA0LZ4JjDzHZ5DnH9zzsqtkny9fgRigEA5EPM0CFoNgW3htCzcxxxHSN5+2LW1ZYGthauG0ZUxpWxzML4VwqDZNIazDvEUfjlkI67BGFG0GKIS1GSVeYwYsBsHGxiO/sC3cxBxHSN982ra0ZYGthamFE/lQ0Vn3mF8K4VCNn0JYh3mLPkwhrJObLqFQCHcp25HFajEIRhbi3O60hZuY4wjpm0/blrYssLUwtXDm7sS80ZQB5pdC2LSBbQFOIazDvEUfphDWyU2XUCiEu5TtyGK1GAQjC3Fud9rCTcxxhPTNp21LWxbYWphaOHN3Yt5oygDzSyFs2sAohGEt+C36MIVwqF6xvHYphJc3d0vvucUguPSkVAG0hZuY4wjpm0/blrYssLUwtXDaMqa0LQ7ml0I4VJu2FohbxeXTtrUtiz5MIRyqVyyvXQrh5c3d0ntuMQguPSkUwt5SGLL9+bRtacsCWwtTC8dbg6ShmRhgfimEZ2owihdbC0QK4fmTRSE8P3ddvZNCuKuZjyBufpHZPAlt4SbmOEL65tO2pS0LbC1MLZwIhkq6MIUB5pdCOFTHoBDWYd6iD1MI6+SmSygUwl3KdmSxWgyCkYU4tztt4SbmOEL65tO2pS0LbC1MLZy5OzFvNGWA+aUQNm1gW4BTCOswb9GHKYR1ctMlFArhLmU7slgtBsHIQpzbnbZwE3McIX3zadvSlgW2FqYWztydmDeaMsD8UgibNjAKYR6WtXdv1BopVPtvm92ok3z++ee7/fv3R+1j2xqEz3j4RWZzttvCTcxxhPTNp21LWxbYWphaOD7HRNpqzgDzSyHcvLXoXsmKsA6fFn2YFWGd3HQJJWqRSSHc7qZoMQi2hbG2cBNzHCF982nb0pYFthamFk5bxpS2xcH8UgiHatMUwjrMW/RhCmGd3HQJhUK4S9mOLFaLQTCyEOd2py3cxBxHSN982ra0ZYGthamFM3cn5o2mDDC/FMKmDWwLcAphHeYt+jCFsE5uuoRCIdylbEcWq8UgGFmIc7vTFm5ijiOkbz5tW9qywNbC1MKZuxPzRlMGmF8KYdMGRiHMPcLcIxyqi3m1SyHslW4am2SAX2Q2bw9t4SbmOEL65tO2pS0LbC1MLRyO2nEywPxSCIdqmawI6zBv0YdZEdbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawFgRZkWYFeFQXcyrXQphr3TTGCvCzdpAW77kxRxHSN982ra0ZYGthamF06zH8irfDDC/FMK+21xtjxVhHeYt+jArwjq56RIKhXCXsh1ZrBaDYGQhzu1OW7iJOY6Qvvm0bWnLAlsLUwtn7k7MG00ZYH4phE0bGCvCrAizIhyqi3m1SyHslW4aY0W4WRtoy5e8mOMI6ZtP25a2LLC1MLVwmvVYXuWbAeaXQth3m2NFWJdxiz7MirBujrqARiHchSxHGqPFIBhpqDO71RZuYo4jpG8+bVvassDWwtTCmbnz8gYvDDC/FMJeGtoUI1warcO8RR+mENbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawLYApxDWYd6iD1MI6+SmSygUwl3KdmSxWgyCkYU4tztt4SbmOEL65tO2pS0LbC1MLZy5OzFvNGWA+aUQNm1gFMLcI8w9wqG6mFe7PoTwHgA3AHATke0GcCOAfKtozz//fLd//34fPnolncZKBvhFZvOW0BZuYo4jpG8+bVvassDWwtTC4XgdJwPML4VwqJbJirAO8xZ9mBVhndx0CcVSZJ4I4G4A3g3gDgC+AeC7ALwVwBjAbQC8GsBbNiOcQrjdTdFiEGwLY23hJuY4Qvrm07alLQtsLUwtnLaMKW2Lg/mlEA7VpimEdZi36MMUwjq56RKKpRB+FID7AngugFtUQng/gJsAeDGAkwBcAUCqw4emkU4h3O6maDEItoWxtnATcxwhffNp29KWBbYWphZOW8aUtsXB/FIIh2rTFMI6zFv0YQphndx0CcVSCNc8ypLoWgj/IYC/A3AhALEtS6P3AriMQrhLza6M1WIQbAuLbeEm5jhC+ubTtqUtC2wtTC2ctowpbYuD+aUQDtWmKYR1mLfowxTCOrnpEopvIfwOAPLvXRXJVwE4HcDXLrjggpc75w5sJH/fvn1dygdjJQNkgAyQATJABsgAGSADZCAgA3t5WFZA9v2Z9i2EXwbgOgCvBdADcA2Am252aBaXRvtrCCEsWcwGhojDwmZbuIk5jpC++bRtacsCWwtTC8eifxNzcQaYX1aEF29F8yGwIjwfbxvvsujDrAjr5KZLKL6F8CMAPAvAgwFIqfd5AO69GeEUwu1uihaDYFsYaws3MccR0jefti1tWWBrYWrhtGVMaVsczC+FcKg2TSGsw7xFH6YQ1slNl1B8CWE5QfqbAHYBeD+AU6vfHwTgYgrhLjW5tVgtBsG2MNkWbmKOI6RvPm1b2rLA1sLUwmnLmNK2OJhfCuFQbZpCWId5iz5MIayTmy6h+BDC0/i8NYArAYy2IpsV4XY3RYtBsC2MtYWbmOMI6ZtP25a2LLC1MLVw2jKmtC0O5pdCOFSbphDWYd6iD1MI6+SmSyihhHAjjimEG9G0tBdZDIJLS8YGx9vCTcxxhPTNp21LWxbYWphaOG0ZU9oWB/NLIRyqTVMI6zBv0YcphHVy0yUUCuEuZTuyWC0GwchCnNudtnATcxwhffNp29KWBbYWphbO3J2YN5oywPxSCJs2sC3AKYR1mLfowxTCOrnpEgqFcJeyHVmsFoNgZCHO7U5buIk5jpC++bRtacsCWwtTC2fuTswbTRlgfimETRsYhTCsBb9FH6YQDtUrltcuhfDy5m7pPbcYBJeelCqAtnATcxwhffNp29KWBbYWphZOW8aUtsXB/FIIh2rT1gJxq7h82ra2ZdGHKYRD9YrltUshvLy5W3rPLQbBpSeFQthbCkO2P5+2LW1ZYGthauF4a5A0NBMDzC+F8EwNRvFia4FIITx/siiE5+euq3dSCHc18xHEzS8ymyehLdzEHEdI33zatrRlga2FqYUTwVBJF6YwwPxSCIfqGBTCOsxb9GEKYZ3cdAmFQrhL2Y4sVotBMLIQ53anLdzEHEdI33zatrRlga2FqYUzdyfmjaYMML8UwqYNbAtwCmEd5i36MIWwTm66hEIh3KVsRxarxSAYWYhzu9MWbmKOI6RvPm1b2rLA1sLUwpm7E/NGUwaYXwph0wZGIczDsvbujVojhWr/bbMbdZL5HOG2Nbf18fCLzOb5bQs3MccR0jefti1tWWBrYWrhtHsUXt7omF8K4VCtlxVhHeYt+jArwjq56RIKhXCXsh1ZrBaDYGQhzu1OW7iJOY6Qvvm0bWnLAlsLUwtn7k7MG00ZYH4phE0bGCvCrAizIhyqi3m1SyHslW4am2SAX2RYEQ7ZI0K2P5+2LW1ZYGthauGEbKO03f4xUjvHIauV2rHE2odDcuzTtrUti/yyIqzdC9uPRyHc/hxHG6HFIBhtsDM61hZuYo4jpG8+bVvassDWwtTCmbHr8nJPDDC/rAh7ampHmbEWiFvF5dO2tS2LPkwhHKpXLK9dCuHlzd3Se24xCC49KVUAbeEm5jhC+ubTtqUtC2wtTC2ctowpbYuD+aUQDtWmrQUihfD8maUQnp+7rt5JIdzVzEcQN7/ItH/ZX8w5DumbT9uWtiywtTC1cCIYKunCFAaYXwrhUB2DQliHeYs+TCGsk5suoVAIdynbkcVqMQhGFuLc7rSFm5jjCOmbT9uWtiywtTC1cObuxLzRlAHml0LYtIFtAU4hrMO8RR+mENbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawCiEeWo0T40O1cW82qUQ9ko3jU0ywC8ym7eHtnATcxwhffNp29KWBbYWphYOR+04GWB+KYRDtUxWhHWYt+jDrAjr5KZLKBTCXcp2ZLFaDIKRhTi3O23hJuY4Qvrm07alLQtsLUwtnLk7MW80ZYD5pRA2bWCsCLMizIpwqC7m1S6FsFe6aYwV4WZtoC1f8mKOI6RvPm1b2rLA1sLUwmnWY3mVbwaYXwph322utseKsA7zFn2YFWGd3HQJhUK4S9mOLFaLQTCyEOd2py3cxBxHSN982ra0ZYGthamFM3cn5o2mDDC/FMKmDYwVYVaEWREO1cW82qUQ9ko3jbEi3KwNtOVLXsxxhPTNp21LWxbYWphaOM16LK/yzQDzSyHsu82xIqzLuEUfZkVYN0ddQKMQ7kKWI43RYhCMNNSZ3WoLNzHHEdI3n7YtbVlga2Fq4czceXmDFwaYXwphLw1tihEujdZh3qIPUwjr5KZLKBTCXcp2ZLFaDIKRhTi3O23hJuY4Qvrm07alLQtsLUwtnLk7MW80ZYD5pRA2bWBbgFMI6zBv0YcphHVy0yUUCuEuZTuyWC0GwchCnNudtnATcxwhffNp29KWBbYWphbO3J2YN5oywPxSCJs2MAph7hHmHuFQXcyrXQphr3TT2CQD/CKzeXtoCzcxxxHSN5+2LW1ZYGthauFw1I6TAeaXQjhUy2RFWId5iz7MirBObrqEQiHcpWxHFqvFIBhZiHO70xZuYo4jpG8+bVvassDWwtTCmbsT80ZTBphfCmHTBsaKMCvCrAiH6mJe7VIIe6WbxlgRbtYG2vIlL+Y4Qvrm07alLQtsLUwtnGY9llf5ZoD5pRD23eZqe6wI6zBv0YdZEdbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawFgRZkWYFeFQXcyrXQphr3TTGCvCzdpAW77kxRxHSN982ra0ZYGthamF06zH8irfDDC/FMK+2xwrwrqMW/RhVoR1c9QFNArhLmQ50hgtBsFIQ53ZrbZwE3McIX3zadvSlgW2FqYWzsydlzd4YYD5pRD20tCmGOHSaB3mLfowhbBObrqEQiHcpWxHFqvFIBhZiHO70xZuYo4jpG8+bVvassDWwtTCmbsT80ZTBphfCmHTBrYFOIWwDvMWfZhCWCc3XUKhEO5StiOL1WIQjCzEud1pCzcxxxHSN5+2LW1ZYGthauHM3Yl5oykDzC+FsGkDoxDmHmHuEQ7VxbzapRD2SjeNTTLALzKbt4e2cBNzHCF982nb0pYFthamFg5H7TgZYH4phEO1TFaEdZi36MOsCOvkpksoFMJdynZksVoMgpGFOLc7beEm5jhC+ubTtqUtC2wtTC2cuTsxbzRlgPmlEDZtYKwIsyLMinCoLubVLoWwV7ppjBXhZm2gLV/yYo4jpG8+bVvassDWwtTCadZjeZVvBphfCmHfba62x4qwDvMWfZgVYZ3cdAmFQrhL2Y4sVotBMLIQ53anLdzEHEdI33zatrRlga2FqYUzdyfmjaYMML8UwqYNjBVhVoRZEQ7VxbzapRD2SjeNsSLcrA205UtezHGE9M2nbUtbFthamFo4zXosr/LNAPNLIey7zbEirMu4RR9mRVg3R11ACyWEbwLgIAC3Fcnnn3++279/fygfu5D/oDFaDIJBA1I03hZuYo4jpG8+bVvassDWwtTCUezWhFJkgPmlEFZsTjNBcWn0THRterFFH6YQ1slNl1CaisxjATwHwCsBHABwZwC/AeCTM5J1PIA/AHA9gBMAvAvAWzbDoBCekd0lu9xiEFwyCrz+gQjBTcw5DumbT9uWtiywtTC1cEL0G9rcngHml0J4+1ZicwWFsA6vFn2YQlgnN11CaSqELwLwcACPAvAXFUFfB3DrGcl6AoCHAXg0gPsAeDOA76EQnpHFllxuMQi2hBq0hZuY4wjpm0/blrYssLUwtXDaMqa0LQ7ml0I4VJumENZh3qIPUwjr5KZLKE2EcB/ACMDTADy4EsNSEf4igDsB+I8ZCDsZwL8C+HsAPwTgtQB+k0J4BgZbdKnFINgWetrCTcxxhPTNp21LWxbYWphaOG0ZU9oWB/NLIRyqTVMI6zBv0YcphHVy0yWUJkK4B+ByAG8A8IJKxP4xgHcDEGF75QyEiZD+UwBvBHA6gCGAM+X+884770CSJC/bgJXs27dvy33EM9jmpfExIO2P+Z2el7ZwHBOnHAAAIABJREFUE3McIX3zadvSlgW2FqYWTnwjJz0SBpjfzdtBW7iJOY6Qvvm0bWnLAlsLM9nLU6M78ZemiRAWImQ/8PMrRmRp89sBfAXAaTOyJEuhvwTgAgByYNZ1AG4B4BvTcLhHeEZ2l+xyi9nAJaNgU3fbwk3McYT0zadtS1sW2FqYWjhtGVPaFgfzy4pwqDbNirAO8xZ9mBVhndx0CaWpEJbrHlBV7z4C4BkA3gHgmzOSJRXl2wN4JoDbALgYwK0AjCmEZ2SyBZdbDIItoKUIoS3cxBxHSN982ra0ZYGthamF05YxpW1xML8UwqHaNIWwDvMWfZhCWCc3XUJpKoSFExGwx1Xk5NXPzwPIZiBMqr/vqZZUy23nVqdIT4VgRXgGZpfwUotBcAlpmOpyW7iJOY6Qvvm0bWnLAlsLUwunLWNK2+JgfimEQ7VpCmEd5i36MIWwTm66hNJUCL8OwLOnECPCWJY3z/q6ZVVNlkO4Nn1RCM9K63JdbzEILhcDm3vbFm5ijiOkbz5tW9qywNbC1MJpy5jStjiYXwrhUG2aQliHeYs+TCGsk5suoTQRwnJYlixdvqw65GqyAiwCWQ68MnlRCJvQGg2oxSAYTXALOtIWbmKOI6RvPm1b2rLA1sLUwlmwK/N2IwaYXwpho6a1LSyF8LYUNbrAog9TCDeinhdNMNBECMvl8oik9wF4nk/2KIR9su3flsUg6D8KG4tt4SbmOEL65tO2pS0LbC1MLRybHk7URRlgfimEF21D895PITwvc+vvs+jDFMI6uekSSlMh/JSqGvxpAIcnCHoIgENWhFEIWzEbB67FIBhHZIt70RZuYo4jpG8+bVvassDWwtTCWbw3E8GCAeaXQtiiXTXBpBBuwtL211j0YQrh7XnnFesZaCKE5Rp5VvCJ1WOOJpdC3xnAQStSKYStmI0D12IQjCOyxb1oCzcxxxHSN5+2LW1ZYGthauEs3puJYMEA80shbNGummBSCDdhaftrLPowhfD2vPOK2YVwCuA7AP6QS6PZfDQZsBgENf0LidUWbmKOI6RvPm1b2rLA1sLUwgk5DtD25gwwvxTCofoHhbAO8xZ9mEJYJzddQmlSERY+3g7gLgDOA3BkgqC/3OwZwBoksiKswWK8GBaDYLzRzuZZW7iJOY6Qvvm0bWnLAlsLUwtntp7Lq30xwPxSCPtqaxvtUAjrMG/RhymEdXLTJZSmQviqamn0Rm7mfXxSI44phBvRtLQXWQyCS0vGBsfbwk3McYT0zadtS1sW2FqYWjhtGVPaFgfzSyEcqk1TCOswb9GHKYR1ctMllKZC+AEAdkwh5u9YEe5Sc9GN1WIQ1PUwHFpbuIk5jpC++bRtacsCWwtTCyfcKEDLWzHA/FIIh+ohFMI6zFv0YQphndx0CaWpEA7CCSvCQWj3ZtRiEPTmvLGhtnATcxwhffNp29KWBbYWphaOcVcn/JwMML8UwnM2nYVvoxBemMICwKIPUwjr5KZLKBTCXcp2ZLFaDIKRhTi3O23hJuY4Qvrm07alLQtsLUwtnLk7MW80ZYD5pRA2bWBbgFMI6zBv0YcphHVy0yUUCuEuZTuyWC0GwchCnNudtnATcxwhffNp29KWBbYWphbO3J2YN5oywPxSCJs2MAphWAt+iz5MIRyqVyyv3VmE8N2rk6Mno70QwMgqfC6NtmI2DlyLQTCOyBb3oi3cxBxHSN982ra0ZYGthamFs3hvJoIFA8wvhbBFu2qCaS0Qt/LBp21rWxZ9mEK4SQvmNZMMNBXC8gzhX5hCHU+NZnuamwGLQXBuZyK7sS3cxBxHSN982ra0ZYGthamFE9nQQHcqBphfCuFQncFaIFIIz59ZCuH5uevqnU2EcK86GfofAbxyQwX4Izw1uqtNZ/G4+UVmcw7bwk3McYT0zadtS1sW2FqYWjiLj3REsGCA+aUQtmhXTTAphJuwtP01Fn2YQnh73nnFegaaCGG54wsA/hrAC3wSyKXRPtn2b8tiEPQfhY3FtnATcxwhffNp29KWBbYWphaOTQ8n6qIMML8Uwou2oXnvpxCel7n191n0YQphndx0CaWpEL4IwMMBvBPA1RMEPRfAYSvCKIStmI0D12IQjCOyxb1oCzcxxxHSN5+2LW1ZYGthauEs3puJYMEA80shbNGummBSCDdhaftrLPowhfD2vPOK9Qw0FcJXAThxCnncI8wWNTcDFoPg3M5EdmNbuIk5jpC++bRtacsCWwtTCyeyoYHuVAwwvxTCoToDhbAO8xZ9mEJYJzddQmkqhPsApl1rdmK0JIEV4XY3RYtBsC2MtYWbmOMI6ZtP25a2LLC1MLVw2jKmtC0O5pdCOFSbphDWYd6iD1MI6+SmSyhbCWERv38E4F0AHgRAqr8bX08DcKMVYRTCVszGgWsxCMYR2eJetIWbmOMI6ZtP25a2LLC1MLVwFu/NRLBggPmlELZoV00wKYSbsLT9NRZ9mEJ4e955xXoGthLCKwCOADgHwHO4NJpNR5sBi0FQ28dQeG3hJuY4Qvrm07alLQtsLUwtnFBjAO1uzQDzSyEcqo9QCOswb9GHKYR1ctMllO2WRsujk3IA6SakZJZksSJsyW54bItBMHxUOh60hZuY4wjpm0/blrYssLUwtXB0ejRRtBlgfimEtdtUUzwK4aZM+Z/MohDWyU2XULYTwkG5oBAOSr+5cX6R2ZzitnATcxwhffNp29KWBbYWphaO+UBIA3MxwPxSCM/VcBRuohBWIBGARR+mENbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawLYApxDWYd6iD1MI6+SmSygUwl3KdmSxWgyCkYU4tztt4SbmOEL65tO2pS0LbC1MLZy5OzFvNGWA+aUQNm1gFMKwFvwWfZhCOFSvWF67TYXwLQA8GsD3bwj1WTw1enmTH9pzi0EwdExa9tvCTcxxhPTNp21LWxbYWphaOFp9mji6DDC/FMK6Lao5mrVA3MoTn7atbVn0YQrh5u2YV5YMNBXCHwLwwCmkySOVrrMik3uErZiNA9diEIwjssW9aAs3MccR0jefti1tWWBrYWrhLN6biWDBAPNLIWzRrppgWgtECuEmWZh+DYXw/Nx19c4mQlhOjh5XzxR+HoDRBFmHLImjELZkNzw2v8hsnoO2cBNzHCF982nb0pYFthamFk74kZIeTGOA+aUQDtUzKIR1mLfowxTCOrnpEkoTISx8vB/AYQA/5ZMcCmGfbPu3ZTEI+o/CxmJbuIk5jpC++bRtacsCWwtTC8emhxN1UQaYXwrhRdvQvPdTCM/L3Pr7LPowhbBObrqE0lQIfwrAvQBcD+CbEwTdDcANVoRRCFsxGweuxSAYR2SLe9EWbmKOI6RvPm1b2rLA1sLUwlm8NxPBggHml0LYol01waQQbsLS9tdY9GEK4e155xXrGWgqhN8B4IQp5J0BwGx5NIVwu5urxSDYFsbawk3McYT0zadtS1sW2FqYWjhtGVPaFgfzSyEcqk1TCOswb9GHKYR1ctMllKZCuObkWAA7NlSFzfiiEDajNgpgi0EwisAUnGgLNzHHEdI3n7YtbVlga2Fq4Sh0Z0IYMMD8UggbNKtGkBTCjWja9iKLPkwhvC3tvGADA02F8O0BnA9gX3X/BwG8AsA/WDJKIWzJbnhsi0EwfFQ6HrSFm5jjCOmbT9uWtiywtTC1cHR6NFG0GWB+KYS121RTPArhpkxtfZ1FH6YQ1slNl1CaCmFZGi0iWH4enBDEJ3FpdJeai26sFoOgrofh0NrCTcxxhPTNp21LWxbYWphaOOFGAVreigHml0I4VA+hENZh3qIPUwjr5KZLKE2E8PEAvg3gJQBeWZHzkwDeDeAeAP7FijBWhK2YjQPXYhCMI7LFvWgLNzHHEdI3n7YtbVlga2Fq4Szem4lgwQDzSyFs0a6aYFIIN2Fp+2ss+jCF8Pa884r1DDQRwv3q2cF/CuDJADIA+6ul0XcE8BUrUimErZiNA9diEIwjssW9aAs3MccR0jefti1tWWBrYWrhLN6biWDBAPNLIWzRrppgUgg3YWn7ayz6MIXw9rzzitmFsNzxJgBPqm6VRyjdpFom/RhLQimELdkNj20xCIaPSseDtnATcxwhffNp29KWBbYWphaOTo8mijYDzC+FsHabaopHIdyUqa2vs+jDFMI6uekSSpOKsPCxC8AjAYjwPQ7AhZUQvsaSLAphS3bDY1sMguGj0vGgLdzEHEdI33zatrRlga2FqYWj06OJos0A80shrN2mmuJRCDdlikJYhymiWDKwnRD+PwCuBnBTAL0pjlwKIJ/Dwd0AUgBSXd70RSE8B7NLdAu/yGyerLZwE3McIX3zadvSlgW2FqYWzhINqZ1ylfmlEA7V4CmEdZi36MOsCOvkpksoWwnhFQBHAJwD4DkATpxCjFSHr5uBsJ3VMmu5TwS0HLT18s3upxCegdklvNRiEFxCGqa63BZuYo4jpG8+bVvassDWwtTCacuY0rY4mF8K4VBtmkJYh3mLPkwhrJObLqFsJYSlAnw2gI8CuCcAqeJufP0GgOEMhD0BwA8AeAYAsS2nT/9VdQDXUTAUwjMwu4SXWgyCS0gDhXCgpIVsfz5tW9qywNbC1MIJ1DxpdhsGmF8K4VCdhEJYh3mLPkwhrJObLqFstzS65kKE64cBfLF6Qx6p9EwArwdw7QyEnVuJ6tMAfL16JNNfb3Y/hfAMzC7hpRaD4BLSQCEcKGkh259P25a2LLC1MLVwAjVPmqUQnqsNhBRpczm8xU2x9uGQHPu0bW3LIr8Uwtq9sP142wnhBwD48aqC+xEAl1SU3BrA4wDcCsDlM9D0ZgD3A/AwAHcH8GoA3w3ATcOgEJ6B2SW81GIQXEIaKIQDJS1k+/Np29KWBbYWphZOoOZJsxTCc7UBa/Eyl1Nz3hRrHw7JsU/b1rYs8kshPGdn6/Bt2wnhFwK4YBN+Pl0tc56Fvt+q9h2/qLrpKgD3BfDVCy644OXOuY37hZN9+/ZNFcmzGOW10TIg7Y/5nZ6etnATcxwhffNp29KWBbYWphZOtANoxx1jfjdvAG3hJuY4Qvrm07alLQtsLcxk796922mkjg/B7Qh/uyTvqB6d9JcA3gDg7ybCliXRs4qYnwXwFAA/BuAUAP8E4JbcI9yOxjRrFBazgbP6EOv1beEm5jhC+ubTtqUtC2wtTC2cWMeIrvvF/E5vAdZVPJ/tLtYch+TYp21rWxb5ZUXYZw9th63thHAdpTxH+IkA5HFK8pJHH90ZwE8BODgDFSKsX1ctjT5UnRgtzySe+uLS6BmYXcJLLQbBJaRhqstt4SbmOEL65tO2pS0LbC1MLZy2jClti4P5pRAO1aatBeJWcfm0bW3Log9TCIfqFctrt6kQfi+AM6aEeZMZhXANIYdtyWOXsq2ooxBe3obVxHOLQbCJ3WW4pi3cxBxHSN982ra0ZYGthamFswzjRRd9ZH4phEO1e2uBSCE8f2YphOfnrqt3NhHCfQCj6lFKjwBwUSVg5bAseazSrMujG3NNIdyYqqW8kF9kNk9bW7iJOY6Qvvm0bWnLAlsLUwtnKQfXDjjN/FIIh2rmFMI6zFv0YQphndx0CaWJEJZl0FK5fQ2AG6vTnl8A4AvV8ugvWRFGIWzFbBy4FoNgHJEt7kVbuIk5jpC++bRtacsCWwtTC2fx3kwECwaYXwphi3bVBJNCuAlL219j0YcphLfnnVesZ6CJEJY7/gSAHHT1WABvm4CYd2l0ozxQCDeiaWkvshgEl5aMDY63hZuY4wjpm0/blrYssLUwtXDaMqa0LQ7ml0I4VJumENZh3qIPUwjr5KZLKE2FsBxy9WAAHwLwaACnAXg7gI9bkkUhbMlueGyLQTB8VDoetIWbmOMI6ZtP25a2LLC1MLVwdHo0UbQZYH4phLXbVFM8CuGmTG19nUUfphDWyU2XUJoIYdkj/G0A7wHweJ/kUAj7ZNu/LYtB0H8UNhbbwk3McYT0zadtS1sW2FqYWjg2PZyoizLA/FIIL9qG5r2fQnhe5tbfZ9GHKYR1ctMllCZCWPj4bQC/BOBBAL44cUDWFTwsq0vNRTdWi0FQ18NwaG3hJuY4Qvrm07alLQtsLUwtnHCjAC1vxQDzSyEcqodQCOswb9GHKYR1ctMllKZC+CoAJ04h5rjqMUgmnLEibEJrNKAWg2A0wS3oSFu4iTmOkL75tG1pywJbC1MLZ8GuzNuNGGB+KYSNmta2sBTC21LU6AKLPkwh3Ih6XjTBQFMhvB/A7inMvQLAEStGKYStmI0D12IQjCOyxb1oCzcxxxHSN5+2LW1ZYGthauEs3puJYMEA80shbNGummBSCDdhaftrLPowhfD2vPOK9Qw0FcLHA5h2rewdNntRCJtRGwWwxSAYRWAKTrSFm5jjCOmbT9uWtiywtTC1cBS6MyEMGGB+KYQNmlUjSArhRjRte5FFH6YQ3pZ2XrCBgaZCmEuj2XTUGbAYBNWdDATYFm5ijiOkbz5tW9qywNbC1MIJNATQ7DYMML8UwqE6CYWwDvMWfZhCWCc3XUJpKoR/FYA8M1heewCcBeB/ANyDS6O71Fx0Y7UYBHU9DIfWFm5ijiOkbz5tW9qywNbAPPPsC7/vhY/63n+93w/ctenfuHCdnZbnYkCjncxlOPKbQoo0bWpizXFIjn3atrZlkV8KYe1e2H68eb8kPBHAHwG4GYBrrGji0mgrZuPAtRgE44hscS/awk3McYT0zadtS1sW2ItgnnHOhXdJcvwlgDtUvfCrLsVPvu9VZ31+8V5JhJgYWKSdxBSHti/W4kXb363wYs1xSI592ra2ZZFfCmGfPbQdtpoK4YcD2FmF3APwMwDOAHA7AF+zooJC2IrZOHAtBsE4Ilvci7ZwE3McIX3zadvS1izYpz319we33318/+B4OMhvvtLfMRwP8qTfH2fZYAW9fpbnA5ek/bN/6s7//uq/+OLpLsn6aeoGLkn6ee4GvSTtO+cGDkk/rX7KZ8XvvaT4LEHyWDj3A+t6YJJ8KnG40CV55lwyBjAG3DhxyTiBK37PXTpGz40T5GO4XvEzq34mrnwvHeTjkcvHCfrjxGXjxA3GqcvGh/PROMl2jtPjhuM+jhvvxI3j78Ul4wMHDuSLjwRE2IyBWdpel1i0Fi8+uYw1xyE59mnb2pZFfimEffbQdthqKoSn7RH+RwA/zOcIt6MhhIjCYhAMEYeFzbZwE3McIX1bZ9u5ZN+vvnNwGLv6N9544+Amab9/ZHztwCWD/o601x9nIhB7/V4/H+RI+7lzg37xMx84pKuiEEnST5wboJf0czcepBBxiMGj7nPb3333J772PBGQIhaL95Okj6QQjsV79b1yDVC/nw+ApJ/ADVya9FGJzwToAxgA6J98/K67X3HNjV8SO0lSv19g9+HKa+prLfpJ3JguB2rhjTEcxkhEeK+JcfndAeNExLl8nroxXP2eXCfXi3BPxolz4zwVsZ6U14qYRzLOi/vlvfLaJJX3BEc+T1evk/8X16YlbjEJ4NLi2uK64no3TnrpOCs+E9HfG8tEQCn+8/EwHWYbJwJ6+Wh8KMBEQMj+G3O7sxYvPmOPNcchOfZp29qWRX4phH320HbYaiqEf2yiIuyq/cFfAjC0pIEVYUt2w2NbDILho9LxoC3cLBrHgQMH0i/g1P61Bw8Pjuvt7B8ejwdZ2u8PVrJBnvT6WZYPBkhXq4lpVVWEVBNzN3D9Ujj2IOLQrQk7JP1H/9Bt/uDtH/uvX0oTEYYYOKCfiHhL0HcuL0SiiEIRh4XAlP+L2EtE4E2ISlSfFT9dv7iuwilEYCkQi/cK4Qk32DHoHX94ND5c2ig+78Jr7ErxNqrE3agSeiPnME4SFO+fcvNj7vL1qw99GsCoEInV+3AYiQCU/5fiTXBcKSSTdCRCzgEPSoBTN5D5BYfkA2ni+pJj5FWOJM9w/WJioMx1kYuiypw7aRPVe6s5qnMluSzbwsR9VR77MlFQ2Jn+pIUu5LmKcd1EwKicAHBjJNVP57K1iYByAqD8HGMnwr+YBFibNCir+a4U8g7jH/ye7/q5i//jW28sJwLq+0Xoi+AvJwbWJgKk6l9iFdemFf7qREA5KTBtIgDZKCtWBLh8PBocvSKgl+8YH9ozHqeYXBGwb3zgQBJkRYC1ePHZgBf9+2Hla0iOfdq2tmWRXwphq1bfXtwmQvgWAB4H4I4ALgcglWD5knK9NS0UwtYMh8W3GATDRqRnvQk3+/a9o3fwpD39PTc/OBhh0D9yZFSIw0GWDVwyKsThIOn1pWpYiMFBucQUSdpPip/1ktM1kViKwqwUf5XAW60irorCqtJXVRXXi8K6iliIgcGdTjnuYV/8n2s/XFYL6+WrIiImxeOq0NhQQVwVJ3rExoskX9JLgSjCT0RfWSksxOC6z8plvSMRlUAykmqgq+4VkbgqNvOyAni/u5z4tI9//huvdYWgdOMkSUa5iIRE7nVjl5eCsngfbpwmvVJUVp8XS4fzZCQVwTRJRxnkZ3nvCx9113++4N3/fudeLx0NXTbuj9KRVAiH/d6ol+8Z3+yYY0dfA8YfOfAAiaHRq0nb3wzo4S+88Add6t4LJDcvr3FXJ3ly5nt+/axPNjKueFE9iSOV/jGu7efXrvSP6fX7WXqkL/1U+qhU+mUixyXjvlT7MZbfs36veE8Eet6Xflr8lCXh0ueyvBDuRVW/EPKleK8nYVyel+K+eK+8r1hOXk8EiHiXyYAE0sdXP59pIkD6s0wGcCJgqxaTlRM+9b+JCn9SvVdU9td/Xk7srF0rS/jLCZ+J9+T3pKrwr64GEHFfjB/Zg+9+8jl/88//+7JyMqEU+FtPBKxfESD2xhky2SKwfmtAv9g2MLk1YNpEAL6A7J3vfLTEv9BrkbFgIcPb3GwtELcy79O2tS2L/FIIW7b8dmJvJ4TvBOCLU0IXEXxfAJ+zpIVC2JLd8NgWg2Ad1f0PHOgDt+kfeygfZOnOfp4MCoFYVBFFIO6oqohI+72qiljuQxRROBpAlpxWws31J/YkOgyKL54Oxd7F+nckeVFNLCpLaSkCiyWkxXuoqk4i/ooq0/qKYrWkVMSi4Mo1Jx6383u/ce2Nl5VfYsulquuWmZYVR9mv3/rXpBhcqxyWQlA+q75IFtVCEZHVUtO19+WLpauqiLX4QzK6751PfPInvvSN16+KwlzEn4jQtKo6ltVHWZIqorBeTlqKwjXhWArKfJxX2GmajMbDfNxLdxTiMUt7I/niuNLrjW502XjHjsHoVWfd5ZpnXPjxXSd8YfdI4wvjVo3Asp9ZYC+Kue+579h1eMf43s88484fetO7v3LMO1/z6Btb30liDPDAgXQfTu0fxjX98dW7+3l/pe96h/s708EcEwFJv4e8FPfVRMBjf+R2f/zWf/jPpx01ESDjbVHRbzIRUE4gTK4ISKpJBXeU0F9bNbC2ymPivWpSoFwRULy/3ferGLOm55NDJtV9GZ/LMXnDtgAR+PWEwMSEQXltWdG/062P+9H/+Pp17ytXgKxNGtQrAPJ0w7aAarLApcnY5Xk2dUXAVlsDejIxWE4ATDsjIHH98Qj5eKWH7CX7vu8r5/75v57SO2bH+BBkRcCecXbwW1nvyHfGe648aWw1rluL08kGYG1r0bF+WmOlENbrwl1B2m6g/jCA+wN4AYB/AiBLfR4I4JUAZIb93pZEiRD+0uHTd73lwAMOW9rpGvb9D3y4f8K13xwcGezoHxkdGUiVYrRjPMgh5QqpJo4LsdhPev28EolSoSj2JPbLPYl1laGoNCZ5Py8E3NoSUvkSMrmcNKmWnsqS06Lq6Nzg3nc68Rc/+aVvvkVEY7EksdhnWAq+4rq02r8ouBMicW3PYVEpKUVivRR1bUlqF0Sim77UdE0kyvLRunJYLkOtlpOKaMuLZYoiGCuRV1YERdih/mxS8BXVxwlRmIu4rMRiUXFYqzKWojAdP+Ohd3zfG9775QcWYtHl4zxNi5+9NB3JFwqpJMqBQ6NBb9S7fjze2e+Prs0Oj+XLxOk3//bI8sAhiz/CTccSn7YtbVlga2Fq4TTNKa/zy0D0+XUueerT3ti/Zvfxfflbm6UHiwnZXUmvP8wODlzSL1YEOIz7g1Haz5O0L1s65MA4WcXTk99llcDkioC8FPdbrQiQv7sPPe1Wv/b+z17+imri9agVAdXWj161/aMS7vVWgHrFD3pwpdBfE/flip9iG4lMxBafr90nWw06tt1j80YvEwFFhb6s5k9b6l9W8DdsAaivrZfyJ+We/9WJgATjH7jDzR//6a9e/aZ15wNU9orqf7F9YJOtAZtMBLgkKycOivvKiYB+iuy5P3Hnj/3mRZfcMy1WApQTAXJQoEzuyt9umQjAkeuz6/Nd47T62910IsCiDy8ihO9/4M079xze9RYAZ/72U+61e+/evdtpJL+DHq2ZMLBdkmU/8N8CeMgG678F4LnVvuEjJp4BECH88Wtu+608dWe+/7zHXmxlZw3XJU996hv7V9wSgzF293ceyQZZMuiPxvlADq0RoVifbCp7EWWJ6bSTTeUPhQjE1YpiUTl05aE0kxU+EXqYuK5evrZhL6P8oSmqjeVMdfGzFJnVXrdaSNZVyNUDasoDbgqBmor91cNr2i8SHVyxxHR1WWkpAgvh6CbE4GRFsV4emldisBaOk1XB6rNaXBZ45VLWsXMohKKIPlcJzmIPXCECi71to3y8USyWy03H1XJT+WOTjdPRS8+66yXnv+Pfb3ckz8aDfjrquZ3j6/PxeNeuXSM5lfadL983QpJI/4z6ZfGHTivgkL75tG1pywJbC1MLR6u9EUeXAeZ3Op/WVbzmWXTJaU99Y19Oip+cCJBVWTuTYbkqQLYADMf9fvFdSiYAxsXPcnVW1v/Fh974jv9nAAAgAElEQVT5Q7/7N18+o9gakMny/rWl/k4O5JPvTMXWgPq7UNJvsjWgmAhIkh5y+U60/syANSEvB/4V39FKcT9xZoBMBBy/e+W237lheGW5RaA+M2D1O1lXzn3YqjmsLfuXpf7VYX7FwX/VUv9b3HTHHa76zo1fmDxUcHX1gEwESMXfbZgIqFcMyNaA6pyByTMCHnT3k1/4gX+74typhwXWKwjqAwLzLFv31ACX/hzgHiVB/fZT7gUK4ea9fZmvbCKEXwpAZhYnX08D8HsAjgNwnRUBhRD+zm0FvjjsZN2exWK/0+QpqKXIk4piLTiBcvZy9cCbaqlqtaypXKYq4rIQi6tLVlOreGLBTYC83nd41DLSanlpvSex2HNY7mMaJSL2KnEpexKr/UjlfsbVQ2zKfY2rex1lwMnXKo01nvw864dv+3tv++jXfr6YwayWlYqIzGQvYlqKx9W9iemO0biqJEpFcdzrjVLIjOR4fOTG/igdD8d7+iujy264ZvzZNz5N7C/1qy1f8mKOI6RvPm1b2rLA1sLUwlnqgaTFzjO/sQvhxRtfrDluONmQnPbU368mAq7v11u08uRIf2fS7+dH1s4JEPHfL1YAyN5+mRDI+lJokUMeXbUVoF4ZkKbp4PH3u+2f/elHLn1y+f1WvvdWAjwvzxNofkbA5NaADSsCqgmC7z5h9w/+9zdu+NeNEwH1+QLVQZDVREE1KdCCAyAphBfvv8uC0EQIyxLoj24I6B4AHuRRCPvkUx55UZ5mOrlEtHyMxdr+w6Lqt1ZdrO+RpajFMpaq6lgdfFNUDYt9htXyUVctPXXV/4vDMIrKY7Wfsa4irh5gU/pUC8M8SUelWJQ9iGmxR7FYbnokHyd5Nu730pE833JQHFpzeHzdMekI+K/xRw4caHxojTXpsf6Rs467CX5buIk5jpC++bRtacsCWwtTC6dJf+U1/hlgfimE/be60mJDIWzink/bi9qSbXjA1/onYHdfDvTMcGM/Q78/uC4b5Omo/+LHnXbZr134uVNlO55sxSsnAsql/8VEwLjeKiD/L98XkV//Xq2w7Kd5WdSS6v2Z9zrlgvd86usHysmB6pBOEfWyklKu27ACYMNTA+QZ9HJAMCvCJq03TtAmQngrz/1UhJPkUw7Z26SyOHmyaebkhNNy36IIRBGK8v/i/TQZpWlaVhGzcl/itENrvvON0XhXb89oz5UHzQ43iDP14b3iF5nNc9AWbmKOI6RvPm1b2rLA1sLUwgk/UtKDaQwwvxTCoXrGogJxEb992ra2ZdGHF9kjfMY5F56V5HgbhfAiLXT57t1OCN9hm5Auqw7QMolclkZ/7JrbXooeHvm+V531eRMjBA3GgMUgGCwYZcNt4SbmOEL65tO2pS0LbC1MLRzlrk04JQaYXwphpaY0M4y1QNzKIZ+2rW1Z9OFFhLDw/vAXvfVHEte732uecs9zuUd45q6xlDdsJ4SDBsXHJwWl39y4xSBo7rQnA23hJuY4Qvrm07alLQtsLUwtHE9dnmZmZID5pRCescmoXW4tECmE50/VokJYLDvn0ssuuyyjEJ4/D8t0J4XwMmWrZb7yi8zmCW0LNzHHEdI3n7YtbVlga2Fq4bRs2G1NOMwvhXCoxkwhrMO8RR+mENbJTZdQKIS7lO3IYrUYBCMLcW532sJNzHGE9M2nbUtbFthamFo4c3di3mjKAPNLIWzawLYApxDWYd6iD1MI6+SmSygUwl3KdmSxWgyCkYU4tztt4SbmOEL65tO2pS0LbC1MLZy5OzFvNGWA+aUQNm1gFMLmp2Nb9GEK4VC9YnntUggvb+6W3nOLQXDpSakCaAs3MccR0jefti1tWWBrYWrhtGVMaVsczC+FcKg2zYqwDvMWfZhCWCc3XUKhEO5StiOL1WIQjCzEud1pCzcxxxHSN5+2LW1ZYGthauHM3Yl5oykDzC+FsGkDY0WYFeG9e6PWSKHaf9vsRp1knhrdtua2Ph5+kdk8v23hJuY4Qvrm07alLQtsLUwtnHaPwssbHfNLIRyq9bIirMO8RR9mRVgnN11CoRDuUrYji9ViEIwsxLndaQs3MccR0jefti1tWWBrYWrhzN2JeaMpA8wvhbBpA2NFmBVhVoRDdTGvdimEvdJNY5MM8IsMK8Ihe0TI9ufTtqUtC2wtTC2ckG2Utts/RmrnOGS1UjuWWPtwSI592ra2ZZFfVoS1e2H78SiE25/jaCO0GASjDXZGx9rCTcxxhPTNp21LWxbYWphaODN2XV7uiQHmlxVhT03tKDPWAnGruHzatrZl0YcphEP1iuW1SyG8vLlbes8tBsGlJ6UKoC3cxBxHSN982ra0ZYGthamF05YxpW1xML8UwqHatLVApBCeP7MUwvNz19U7KYS7mvkI4uYXmfYv+4s5xyF982nb0pYFthamFk4EQyVdmMIA80shHKpjUAjrMG/RhymEdXLTJRQK4S5lO7JYLQbByEKc2522cBNzHCF982nb0pYFthamFs7cnZg3mjLA/FIImzawLcAphHWYt+jDFMI6uekSCoVwl7IdWawWg2BkIc7tTlu4iTmOkL75tG1pywJbC1MLZ+5OzBtNGWB+KYRNGxiFME+N5qnRobqYV7shhfDNANwA4MhmEfM5wl7bgndj/CKzOeVt4SbmOEL65tO2pS0LbC1MLRzvAyMNNmKA+aUQbtRQDC5iRViHVIs+zIqwTm66hBJKCN8GwOcAPATAJyiEu9Tk1mK1GATbwmRbuIk5jpC++bRtacsCWwtTC6ctY0rb4mB+KYRDtWkKYR3mLfowhbBObrqEEkIIrwB4B4DbAXg6hXCXmtv6WC0Gwbaw2RZuYo4jpG8+bVvassDWwtTCacuY0rY4mF8K4VBtmkJYh3mLPkwhrJObLqGEEMK/BeBDAJ4N4FwK4S41Nwrhptm2+APR1LbmdTHHEdI3n7YtbVlga2Fq4Wj2B2LpMcD8UgjrtabZkCiEZ+Nrs6st+jCFsE5uuoTiWwg/CsBPAPg5AH9DIdylpnZ0rBaDYFsYbQs3MccR0jefti1tWWBrYWrhtGVMaVsczC+FcKg2TSGsw7xFH6YQ1slNl1B8C+F/AnAigKsB3AvAlwE8HsBnzjvvvANJkrxsA/nJvn37XJcS0rFYpf0xv9OT3hZuYo4jpG8+bVvassDWwtTC6diwvDThMr+bp6ot3MQcR0jffNq2tGWBrYWZ7OWp0Uvzx2ARR30L4VsD2Fk5/CYAvwPgIgCHpgXBU6MXSW3891rMBsYfdTMP28JNzHGE9M2nbUtbFthamFo4zXosr/LNAPPLirDvNlfbY0VYh3mLPsyKsE5uuoTiWwhPcvteAK/iHuEuNbf1sVoMgm1hsy3cxBxHSN982ra0ZYGthamF05YxpW1xML8UwqHaNIWwDvMWfZhCWCc3XUIJKYS35ZkV4W0pWuoLLAbBpSZkwvm2cBNzHCF982nb0pYFthamFk5bxpS2xcH8UgiHatMUwjrMW/RhCmGd3HQJhUK4S9mOLFaLQTCyEOd2py3cxBxHSN982ra0ZYGthamFM3cn5o2mDDC/FMKmDWwLcAphHeYt+jCFsE5uuoRCIdylbEcWq8UgGFmIc7vTFm5ijiOkbz5tW9qywNbC1MKZuxPzRlMGmF8KYdMGRiEMa8Fv0YcphEP1iuW1SyG8vLlbes8tBsGlJ6UKoC3cxBxHSN982ra0ZYGthamF05YxpW1xML8UwqHatLVA3Coun7atbVn0YQrhUL1iee1SCC9v7pbec4tBcOlJoRD2lsKQ7c+nbUtbFthamFo43hokDc3EAPNLITxTg1G82FogUgjPnywK4fm56+qdFMJdzXwEcfOLzOZJaAs3MccR0jefti1tWWBrYWrhRDBU0oUpDDC/FMKhOgaFsA7zFn2YQlgnN11CoRDuUrYji9ViEIwsxLndaQs3MccR0jefti1tWWBrYWrhzN2JeaMpA8wvhbBpA9sCnEJYh3mLPkwhrJObLqFQCHcp25HFajEIRhbi3O60hZuY4wjpm0/blrYssLUwtXDm7sS80ZQB5pdC2LSBUQjzsKy9e6PWSKHaf9vsRp1kPke4bc1tfTz8IrN5ftvCTcxxhPTNp21LWxbYWphaOO0ehZc3OuaXQjhU62VFWId5iz7MirBObrqEQiHcpWxHFqvFIBhZiHO70xZuYo4jpG8+bVvassDWwtTCmbsT80ZTBphfCmHTBsaKMCvCrAiH6mJe7VIIe6WbxiYZ4BcZVoRD9oiQ7c+nbUtbFthamFo4Idsobbd/jNTOcchqpXYssfbhkBz7tG1tyyK/rAhr98L241EItz/H0UZoMQhGG+yMjrWFm5jjCOmbT9uWtiywtTC1cGbsurzcEwPMLyvCnpraUWasBeJWcfm0bW3Log9TCIfqFctrl0J4eXO39J5bDIJLT0oVQFu4iTmOkL75tG1pywJbC1MLpy1jStviYH4phEO1aWuBSCE8f2YphOfnrqt3Ugh3NfMRxM0vMu1f9hdzjkP65tO2pS0LbC1MLZwIhkq6MIUB5pdCOFTHoBDWYd6iD1MI6+SmSygUwl3KdmSxWgyCkYU4tztt4SbmOEL65tO2pS0LbC1MLZy5OzFvNGWA+aUQNm1gW4BTCOswb9GHKYR1ctMlFArhLmU7slgtBsHIQpzbnbZw8//bOxfobdOpjF9TxvmcQYQYOUVihaSQKK3k2FCkZJxyWk7xjdMMhu8/CDnlVLSKGIREkUMHKimHWuVUTmEcQsZhZE6tK/e7vL75v//1/p9v7/u+n/v5PWvNmm993/tce+9r730/z/Xu+3nenuNo6VtN25m2MrCjMKNwJjcxJ6YyQH4RwqkFhhDmrdG8NbpVi1W1ixCuSjfG1hngRmZzPYzCTc9xtPStpu1MWxnYUZhROKzafTJAfhHCrSqTiXAM8xk9zEQ4JjdLQkEILynbncWasQh2FuJkd0bhpuc4WvpW03amrQzsKMwonMlNzImpDJBfhHBqgTERZiLMRLhVi1W1ixCuSjfGmAhvVwOj3OT1HEdL32razrSVgR2FGYWzXcfyqdoMkF+EcO2aW9ljIhzDfEYPMxGOyc2SUBDCS8p2Z7FmLIKdhTjZnVG46TmOlr7VtJ1pKwM7CjMKZ3ITc2IqA+QXIZxaYEyEmQgzEW7VYlXtIoSr0o0xJsLb1cAoN3k9x9HSt5q2M21lYEdhRuFs17F8qjYD5BchXLvmmAjHMp7Rw0yEY3O0BDSE8BKy3GmMGYtgp6Hu261RuOk5jpa+1bSdaSsDOwozCmffzcsJVRggvwjhKoW2ixG2Rscwn9HDCOGY3CwJBSG8pGx3FmvGIthZiJPdGYWbnuNo6VtN25m2MrCjMKNwJjcxJ6YyQH4RwqkFtgc4QjiG+YweRgjH5GZJKAjhJWW7s1gzFsHOQpzszijc9BxHS99q2s60lYEdhRmFM7mJOTGVAfKLEE4tMIQwzwjzjHCrFqtqFyFclW6MrTPAjczmehiFm57jaOlbTduZtjKwozCjcFi1+2SA/CKEW1UmE+EY5jN6mIlwTG6WhIIQXlK2O4s1YxHsLMTJ7ozCTc9xtPStpu1MWxnYUZhROJObmBNTGSC/COHUAmMizESYiXCrFqtqFyFclW6MMRHergZGucnrOY6WvtW0nWkrAzsKMwpnu47lU7UZIL8I4do1t7LHRDiG+YweZiIck5sloSCEl5TtzmLNWAQ7C3GyO6Nw03McLX2raTvTVgZ2FGYUzuQm5sRUBsgvQji1wJgIMxFmItyqxaraRQhXpRtjTIS3q4FRbvJ6jqOlbzVtZ9rKwI7CjMLZrmP5VG0GyC9CuHbNMRGOZTyjh5kIx+ZoCWgI4SVkudMYMxbBTkPdt1ujcNNzHC19q2k701YGdhRmFM6+m5cTqjBAfhHCVQptFyNsjY5hPqOHEcIxuVkSCkJ4SdnuLNaMRbCzECe7Mwo3PcfR0reatjNtZWBHYUbhTG5iTkxlgPwihFMLbA9whHAM8xk9jBCOyc2SUBDCS8p2Z7FmLIKdhTjZnVG46TmOlr7VtJ1pKwM7CjMKZ3ITc2IqA+QXIZxaYAhhnhHmGeFWLVbVLkK4Kt0YW2eAG5nN9TAKNz3H0dK3mrYzbWVgR2FG4bBq98kA+UUIt6pMJsIxzGf0MBPhmNwsCQUhvKRsdxZrxiLYWYiT3RmFm57jaOlbTduZtjKwozCjcCY3MSemMkB+EcKpBcZEmIkwE+FWLVbVLkK4Kt0YYyK8XQ2McpPXcxwtfatpO9NWBnYUZhTOdh3Lp2ozQH4RwrVrbmWPiXAM8xk9zEQ4JjdLQkEILynbncWasQh2FuJkd0bhpuc4WvpW03amrQzsKMwonMlNzImpDJBfhHBqgTERZiLMRLhVi1W1ixCuSjfGmAhvVwOj3OT1HEdL32razrSVgR2FGYWzXcfyqdoMkF+EcO2aYyIcy3hGDzMRjs3REtAQwkvIcqcxZiyCnYa6b7dG4abnOFr6VtN2pq0M7CjMKJx9Ny8nVGGA/CKEqxTaLkbYGh3DfEYPI4RjcrMklFZC+GKSTpV05l5k7+zsnH3gwIFWPi6pDprEmrEINgkkwego3PQcR0vfatrOtJWBHYUZhZPQ3kAGMEB+EcIBZTQJAiE8ibZznJTRwwjhmNwsCaW2yLy8pFdI+oKkMyS9R9KJmwhHCI9dihmL4CiMjcJNz3G09K2m7UxbGdhRmFE4o6wpo8VBfhHCrWoaIRzDfEYPI4RjcrMklNpC+NGSjpR0vKTzSjpN0mUlfWY30hHCY5dixiI4CmOjcNNzHC19q2k701YGdhRmFM4oa8pocZBfhHCrmkYIxzCf0cMI4ZjcLAmlthA+n6SzJX1T0m0kPU3SlcvfnYN3hPDYpZixCI7C2Cjc9BxHS99q2s60lYEdhRmFM8qaMloc5Bch3KqmEcIxzGf0MEI4JjdLQqkthM3tuSUdJ+mhkm4r6W1sjV5SyX0n1oxFcBQmR+Gm5zha+lbTdqatDOwozCicUdaU0eIgvwjhVjWNEI5hPqOHEcIxuVkSSm0h7O3QJ0v6lqQHSDplRfbBgwdPOOKII7xl+ruOY445Zkn5IFYYgAEYgAEYgAEYgAEYgIGGDBzN7wg3ZL+e6dpC+F6SbiXp1tuEyNbobVia72cyvg2cLxvf7fko3PQcR0vfatrOtJWBHYUZhTPKmjJaHOSXiXCrmmYiHMN8Rg8zEY7JzZJQagvhF0u62yEEX0XSR3YjHSE8dilmLIKjMDYKNz3H0dK3mrYzbWVgR2FG4YyypowWB/lFCLeqaYRwDPMZPYwQjsnNklBqC+F9cYsQ3hdds/twxiI4OxI2ODwKNz3H0dK3mrYzbWVgR2FG4YyypowWB/lFCLeqaYRwDPMZPYwQjsnNklAQwkvKdmexZiyCnYU42Z1RuOk5jpa+1bSdaSsDOwozCmdyE3NiKgPkFyGcWmB7gCOEY5jP6GGEcExuloSCEF5StjuLNWMR7CzEye6Mwk3PcbT0rabtTFsZ2FGYUTiTm5gTUxkgvwjh1AJDCCtb8Gf0MEK4VVfM1y5CeL65m73nGYvg7EkpAYzCTc9xtPStpu1MWxnYUZhROKOsKaPFQX4Rwq1qOlsg7hVXTdvZtjJ6GCHcqivmaxchPN/czd7zjEVw9qQghKulsGX91bSdaSsDOwozCqdaQWJoXwyQX4Twvgom8MPZAhEhPD1ZCOHp3C31TITwUjPfQdzcyGxOwijc9BxHS99q2s60lYEdhRmF08FSiQu7MEB+EcKtGgMhHMN8Rg8jhGNysyQUhPCSst1ZrBmLYGchTnZnFG56jqOlbzVtZ9rKwI7CjMKZ3MScmMoA+UUIpxbYHuAI4RjmM3oYIRyTmyWhIISXlO3OYs1YBDsLcbI7o3DTcxwtfatpO9NWBnYUZhTO5CbmxFQGyC9COLXAEMK8LOvoo7vWSK3qfzS7XSeZ3xEerdy+Ox5uZDbndxRueo6jpW81bWfaysCOwozCGXsVnm905Bch3Kp6mQjHMJ/Rw0yEY3KzJBSE8JKy3VmsGYtgZyFOdmcUbnqOo6VvNW1n2srAjsKMwpncxJyYygD5RQinFhgTYSbCTIRbtVhVuwjhqnRjbJ0BbmSYCLfsiJb1V9N2pq0M7CjMKJyWNYrt8dfI6By3nFZGx9JrD7fkuKbtbFsZ+WUiHN2F4+MhhMfPcbcRZiyC3Qa7T8dG4abnOFr6VtN2pq0M7CjMKJx9ti4fr8QA+WUiXKnUzmEmWyDuFVdN29m2MnoYIdyqK+ZrFyE839zN3vOMRXD2pJQARuGm5zha+lbTdqatDOwozCicUdaU0eIgvwjhVjWdLRARwtMzixCezt1Sz0QILzXzHcTNjcz42/56znFL32razrSVgR2FGYXTwVKJC7swQH4Rwq0aAyEcw3xGDyOEY3KzJBSE8JKy3VmsGYtgZyFOdmcUbnqOo6VvNW1n2srAjsKMwpncxJyYygD5RQinFtge4AjhGOYzehghHJObJaEghJeU7c5izVgEOwtxsjujcNNzHC19q2k701YGdhRmFM7kJubEVAbIL0I4tcAQwrw1mrdGt2qxqnYRwlXpxtg6A9zIbK6HUbjpOY6WvtW0nWkrAzsKMwqHVbtPBsgvQrhVZTIRjmE+o4eZCMfkZkkoCOElZbuzWDMWwc5CnOzOKNz0HEdL32razrSVgR2FGYUzuYk5MZUB8osQTi0wJsJMhJkIt2qxqnYRwlXpxhgT4e1qYJSbvJ7jaOlbTduZtjKwozCjcLbrWD5VmwHyixCuXXMre0yEY5jP6GEmwjG5WRIKQnhJ2e4s1oxFsLMQJ7szCjc9x9HSt5q2M21lYEdhRuFMbmJOTGWA/CKEUwuMiTATYSbCrVqsql2EcFW6McZEeLsaGOUmr+c4WvpW03amrQzsKMwonO06lk/VZoD8IoRr1xwT4VjGM3qYiXBsjpaAhhBeQpY7jTFjEew01H27NQo3PcfR0reatjNtZWBHYUbh7Lt5OaEKA+QXIVyl0HYxwtboGOYzehghHJObJaEghJeU7c5izVgEOwtxsjujcNNzHC19q2k701YGdhRmFM7kJubEVAbIL0I4tcD2AEcIxzCf0cMI4ZjcLAkFIbykbHcWa8Yi2FmIk90ZhZue42jpW03bmbYysKMwo3AmNzEnpjJAfhHCqQWGEOYZYZ4RbtViVe0ihKvSjbF1BriR2VwPo3DTcxwtfatpO9NWBnYUZhQOq3afDJBfhHCrymQiHMN8Rg8zEY7JzZJQEMJLynZnsWYsgp2FONmdUbjpOY6WvtW0nWkrAzsKMwpnchNzYioD5BchnFpgTISZCDMRbtViVe0ihKvSjTEmwtvVwCg3eT3H0dK3mrYzbWVgR2FG4WzXsXyqNgPkFyFcu+ZW9pgIxzCf0cNMhGNysyQUhPCSst1ZrBmLYGchTnZnFG56jqOlbzVtZ9rKwI7CjMKZ3MScmMoA+UUIpxYYE2EmwkyEW7VYVbsI4ap0Y4yJ8HY1MMpNXs9xtPStpu1MWxnYUZhRONt1LJ+qzQD5RQjXrjkmwrGMZ/QwE+HYHC0BDSG8hCx3GmPGIthpqPt2axRueo6jpW81bWfaysCOwozC2XfzckIVBsgvQrhKoe1ihK3RMcxn9DBCOCY3S0JBCC8p253FmrEIdhbiZHdG4abnOFr6VtN2pq0M7CjMKJzJTcyJqQyQX4RwaoHtAY4QjmE+o4cRwjG5WRIKQnhJ2e4s1oxFsLMQJ7szCjc9x9HSt5q2M21lYEdhRuFMbmJOTGWA/CKEUwsMIcwzwjwj3KrFqtpFCFelG2PrDHAjs7keRuGm5zha+lbTdqatDOwozCgcVu0+GSC/COFWlclEOIb5jB5mIhyTmyWhIISXlO3OYs1YBDsLcbI7o3DTcxwtfatpO9NWBnYUZhTO5CbmxFQGyC9COLXAmAgzEWYi3KrFqtpFCFelG2NMhLergVFu8nqOo6VvNW1n2srAjsKMwtmuY/lUbQbIL0K4ds2t7DERjmE+o4eZCMfkZkkoCOElZbuzWDMWwc5CnOzOKNz0HEdL32razrSVgR2FGYUzuYk5MZUB8osQTi0wJsJMhJkIt2qxqnYRwlXpxhgT4e1qYJSbvJ7jaOlbTduZtjKwozCjcLbrWD5VmwHyixCuXXNMhGMZz+hhJsKxOVoCGkJ4CVnuNMaMRbDTUPft1ijc9BxHS99q2s60lYEdhRmFs+/m5YQqDJBfhHCVQtvFCFujY5jP6GGEcExuloSCEF5StjuLNWMR7CzEye6Mwk3PcbT0rabtTFsZ2FGYUTiTm5gTUxkgvwjh1ALbAxwhHMN8Rg8jhGNysySUVkL4ApJOk3TWXmTv7OycfeDAgVY+LqkOmsSasQg2CSTB6Cjc9BxHS99q2s60lYEdhRmFk9DeQAYwQH4RwgFlNAkCITyJtnOclNHDCOGY3CwJpbbIvISkl0k6Q9IVJD1F0ks2EY4QHrsUMxbBURgbhZue42jpW03bmbYysKMwo3BGWVNGi4P8IoRb1TRCOIb5jB5GCMfkZkkotYXwAUkXkvQoSZeWdIokT4e/sRvpCOGxSzFjERyFsVG46TmOlr7VtJ1pKwM7CjMKZ5Q1ZbQ4yC9CuFVNI4RjmM/oYYRwTG6WhFJbCL9I0lskvVySbXtr9NGSPooQXlLZfTvWjEVwFBZH4abnOFr6VtN2pq0M7CjMKJxR1pTR4iC/COFWNY0QjmE+o4cRwjG5WRJKbSF8siT/96pC8uck3UDSxw8ePHjCEUcccfw6+UceeaROP/30JeWDWGEABmAABmAABmAABmAABhoxcNRRR+nYY4+trZEaRbtss7WT/FhJp0p6hqTvlfRlSRfd9NIstkaPXZzkd3N+R+Gm5zha+lbTdqatDOwozCicsVfh+UZHfjdPhE866aQzDxw48FvnbjgAAA6BSURBVD3zze63Pe81x54It+K4pu1sWxn53dnZOetwaz877rn35Wj+1xbCt5Z0f0k/K+kYSQ+RdMNNpGY0yWgJnHM85Bch3LJ+W9ZfTduZtjKwozCjcFrWKLbHXyOjczzSTXyvPdyS45q2s21l5BchHL2ijI9XWwifT9IbJV1Dkv98C0nvQgiPX2i7RZixCI7C5Cjc9BxHS99q2s60lYEdhRmFM8qaMloc5JeJcKuazhaIe8VV03a2rYweRgi36or52q0thFdMXU7SZyXt+QBwRpPMN1XjeU5+x5929Jzjlr7VtJ1pKwM7CjMKZ7yVd4yIyC9CuFUlZwtEhPD0zCKEp3O31DNbCeGt+PYLtI477rgTtvowH5odA+R3c8pG4abnOFr6VtN2pq0M7CjMKJzZLawLcZj8bhbCOzs7x49w79Rrji2EW3Fc03a2rYz8RmBmx72QJXo2YXYthGfDIo7CAAzAAAzAAAzAAAzAAAzAAAzMhgGE8GxShaMwAAMwAAMwAAMwAAMwAAMwAAMRDMxJCF9Q0tclnR0ROBhdMHD+4sU3uvCmHyfOLelckkbgxXFcTNIX+qEXTwZmwPXm/lnvHf+MjF/O6OsHx7wZ2HTNuICk0zb9FOO8Q97Ke9f4hSR95ZBPn6fcM31rK5R+PnQZSZ9Zc6fnOJZee/1Uze6enFfSmYe8k2jTNYFc9p7NBP/mIIQvKelHJP2JpCtL+nwCD0DWZeBISS+RdAVJHy2/KX2spG/WdaNLazuSbibp38tvbN9l5jfwvy3pWuUn03oh/K3lhvGM4tBdJf1vL87hxyQG/Lv015TkdcQ3PQ8uKL8h6UGSPl2+XHI/8aXMJIqbnrTpmuEvyF8myb3s68lTyrWlqbOVjd9e0hMkvUfS90l6hKQPSHq6pOuU66v/7QEz+aLgVpJeL8lixX3dQxyPlnTLNXF+Yvnz0muvcqnvy5xFre89nlxq6DV7XBM8YCOX+6J3nA/PQQjfQdKNyo3NpRDCQxTfjSU9SdJPlmjeLum5kl45RHTTg/DCbS6uXyDeURbxP50O2fRM/274fYoA8W+H93L8p6QfkuT1z6KJY/4MWBA9XtKPSfrncr3wdNi/THDR8sXHMyWdIung/MNdXASbrhlHl0nooyRduuTX6+gIu2m2TfLJkh4j6UOSHifJvfBqSc9Yu5b43+4u6Z3bgjb63A9KeqKkOxchfMNO4nipJH9J7S8YVl+gHqD2GlXJdmavLenXJd1G0sMkWQhvuiZYCHtHxZLXke1YHfBTcxDCK9pdqAjhMYrQi5G3q3ytfIv/r2Wa88kxwjvsKDzZ8iTrVyVdTdKXDxuxPsCVJD1Pkr8597fpvQhhb9P+kqSvFnHkL2R+tz49WExi4H5l55AnwleU9BZJFks+PBH70TI1TjIPbBIDm64Zjy05fnn5Yuuskm/vNFrS4XXNtf/wMrn0+usBwm8WEl5bxPEfdkyKtz+/sYiX/ypC2Ds4eojj/eWLFj9i8SJJj5T0bGqv42r6jmuvkuQvMiyEN10TrC98rVj6OjKLhEY7iRCOZhS8/TBwJ0kvlOSfyHrafk4c/LN+FMA3ML4JuK2kt80sXn/J4cm2t6l6Guf89iKEf0DSQ8v08PKS3ifJf+etsxzzZ2BdCLuPvMvkqiUsb4G/iaR7zD/MxUZw6DXD01D/55tdH5+TdANJH18YQxbCFsF3LELNzwpfvXz5Yyp+r6zJf9QxL79T1uMXl+eavTXa18Ee4niWpOdL+qKk15Ut+MdQex1X0+5CeNM14cLkcha5THESIZxCK6BbMOBvVC30fHPq55c4pMuWrZ2+0Prwc1+XWPtWfy4cWfS+SdK7JV1E0lUkvUDSvTsIwJMlr3veMuvD2879bJC/CeaYPwPrQtjTG2+R9Q21v/FfPTfsZw455sfAbtcMT4RPLdtn/Typd8/4yzdPhpdwOOZ7lvXVMXvt9Q4Xfwn5EEl+PGW1zvnRgX/qlBRvZ/cOMV8zfFyv/Nk5f2DjOLx++Bn11XskvI74mubHLJZce52W0jncWp8Ib7omeFs0uZxLRoP9RAgHEwrcVgxco3w77YvJoW+53Apg0A/5W/0PlhecfFaSt7H9dbnJmVPIfrOrRb0Pb0X1DZm/Pf9UB0HcXJInD/bLFz8/83VdJsIdZCbGhXUhbERvabyvJD9+4S9njpf05hhToFRkYNM1w0Lv/kUAeo3xWuPnSpd0+Jn440pd+50MP1G+9PlYefTo4pLeK+lyHV9vfS/ql6Gujg+Xx4I8gfU2d78IrVUcvi5/olwzfA1bCSu/pXzptTeHPlsXwpuuCd7FRi7nkM0EH+cmhP0Gad74mVAIlSHvJsnbn9YPv8jj0L+r7FYX5jzh8Isd/AWBb+I9MZ/jM8IrMv3iLz8n3MvWaK95flbI2+08Hfak+qQuMo8TEQxYCPuZYAsiHxZKq+2gbygv4eEn+CKYroux6ZrhnRx+rtRC2dOeW0h6V13Xmlu7naSnlp8N869q3Ku8MM4vd/KXQP7Czzf5z2nu6fYOuEdXOzl6iMPbzlc7mvyrA368xi/NWnrtbZ/Rdp+0EPY1wM/Jb7omWAiTy3Y5amp5TkK4KVEYh4GKDPiGzgvznAVwRbommfLPjPjLhtUbQCeBcNIsGPAOBW/R91ZGjjEZ8LTTu2hWjzyMGeXmqCwa/dbs9d/e9af9aI1/Q9jbPud89BCH1xFvRfeLFtePpdfeHOtq0zWBXM4xm4fpM0L4MAnkdBiAARiAARiAARiAARiAARiAgXkxgBCeV77wFgZgAAZgAAZgAAZgAAZgAAZg4DAZQAgfJoGcDgMwAAMwAAMwAAMwAAMwAAMwMC8GEMLzyhfewgAMwAAMwAAMwAAMwAAMwAAMHCYDCOHDJJDTYQAGYAAGYAAGYAAGYAAGYAAG5sUAQnhe+cJbGIABGICBeAYuKOmbvEU8nlgQYQAGYAAGYKBXBhDCvWYGv2AABmCgTwb+QtLPld+4Xv1G74ckfUnSDQNcfpGkYyXVuD75N1bfJ+lKkm4i6W/W/P8FSdeW9KQJMX1/+SmbA/xO9QT2OAUGYAAGYAAGKjBQ40ajQhiYgAEYgAEYqMTASgh/vgjIr0uKFMIvlnS3SkL4pyW9TdJzJT1OkmNaHc+XdC9J/o3Ws/fJ7WUkfVrSoyU9cZ/n8nEYgAEYgAEYgIEKDCCEK5CMCRiAARgYiIGVEHZIx0t6/JoQvoOk10p6SRGXvyjpMZLuI+kSkk6U9FeSPG39cpmW/pakK0rakfQcSSsh/DRJd5T0AUn3k/QRSVeWdF9Jdyk2HyTpPZIeIcm23ijptpKufwjf95b08CLc/0HS/Yv9P5d0FUn/Iumekv6xnHcbSS+QdElJb5B0TPH1dpK+IelNkmz7LEm/XHy6aontwQVjJYSN8wfl7+5afHDMt5T0d0Uo/+1A9UEoMAADMAADMDALBhDCs0gTTsIADMBANwxYCF9H0uuKeLy8pLeUrdG/IuljRfBaAHuya2Hrbceekv6xpA9LelfZWu2gLBQtXi06Ly7JAtjneTr73rIN+5VFFL9d0jUkPb3YPkqStyFbRFvc+vB2bQvO1XG1IqbfLenPJD1M0imSfryIeJ9nm/7P4tWHbdivG0n6NUmnF9+fUcTvQ4o4dhyflGT/LMId6/MkPaFgnSTpZpKuV7D+XtIHi43HSnqkpPNK+mGeT+6mvnEEBmAABmBgIQwghBeSaMKEARiAgSAGVkL4WpL+o0yAb7APIezJraesnqZ6surJqIWsp7pHlwmyhfClihi2ePUE2dNg2/NE9y8l3VTST0n6+fLvFrQWrp6yrh8W5J5aX1PSv0k6rjz3e11JV5D0miK233zIeetboy9aJr8W3Su7J0h64Zp49hcDxnippAus/b1hLXgPFnxvI/cU2lPgt5bp+SeCcgMMDMAADMAADMDAlgwghLckio/BAAzAAAz8PwMrIWyh6i3Lzy68WKDeSZJFnSehflHU6t/XJ8J+0ZYFo5+7tQi9fdkebLHol1Z5UmohfDFJ/yPpFWUafPMyeX6nJE+GV4f/3VufLYQvLOmrh+TpqZIeWrZff7x87lllSu0J9DZC2NjPlPTqso3azxNbCPv/js0v93Jcnmp7u/U91oSw/TmtiPyvSfIE3f7+UhHE/vdLly3XlBgMwAAMwAAMwEAlBhDClYjGDAzAAAwMwsC6ED6XpPeXrcQWwjeW9K2y/dli+FFrb2RebY3eVgj/vqR3SPL/X1/E8RcL9t3LhNaTaD/PaxFtseqfQfLLu9YPf8a+vUzSq8r02RPey0q61RZC2FuoHyjpzmULs7dKe3ptEeznnS3K/az0yWXS7e3a3o7tbdb++89K8nTZU29z4q3U/iLA/2a/jeut2H4WmgMGYAAGYAAGYKASAwjhSkRjBgZgAAYGYWBdCDskT0QtCC02/fNJFnt+MZUPb//19mULZAvhl69tQz61THg9EfYztX7Dsl+a5a3MFrp+CZa3L3tiunqxlLdVexLrv/fhSa+f7fWEd5MQ9nXOb4X2C7tWh1/C5ed6/WzypomwX4LlZ5p9/EzZAu6fW/J2bot5n+eXgzkm4/n4VHkG2XFbCPuLgCevfVlw9XKuBbCnx47Nvnl6zgEDMAADMAADMFCRAYRwRbIxBQMwAAMLYcCTWW99PnQ6u9/wLRb9+8RnHHKitxf/9z63E1+kiE9v3fbUepvj/JKOlPQVSeeR5Eny53Y50W/E9nPB2z7r62uvY7BwPnMbR/gMDMAADMAADMBALAMI4Vg+QYMBGIABGIABGIABGIABGIABGOicAYRw5wnCPRiAARiAARiAARiAARiAARiAgVgGEMKxfIIGAzAAAzAAAzAAAzAAAzAAAzDQOQMI4c4ThHswAAMwAAMwAAMwAAMwAAMwAAOxDCCEY/kEDQZgAAZgAAZgAAZgAAZgAAZgoHMG/g/3C5QVad4FzAAAAABJRU5ErkJggg==", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-528bab018118c0e7dedef9cdeb2813d7" }, "datasets": { "data-528bab018118c0e7dedef9cdeb2813d7": [ { "Duration in ms": 6.8, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 6.83, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 7.08, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 6.45, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 6.6, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 6.71, "Number of engines": 1, "Number of tasks": 100 } ] 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "text/markdown": [ "### With a delay of 0.1s. :" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-9a232fde5a4cab6816796779032d9105" }, "datasets": { "data-9a232fde5a4cab6816796779032d9105": [ { "Duration in ms": 110.69, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 111.59, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 111.08, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 107.01, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 106.91, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 107.14, "Number of engines": 1, "Number of tasks": 100 } ] }, "encoding": { "color": { "field": "Number of engines", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of tasks", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector016": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "SchedulerType.BROADCAST_COALESCING", "width": 800 }, "image/png": 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-97a6abfab327861ce324062846faebff" }, "datasets": { "data-97a6abfab327861ce324062846faebff": [ { "Duration in ms": 113.67, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 113.71, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 114.09, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 106.9, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 107.37, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 107.36, "Number of engines": 1, "Number of 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", 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For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-a66ebbe24cbc2c6a0595e6ea80ffc329" }, "datasets": { "data-a66ebbe24cbc2c6a0595e6ea80ffc329": [ { "Duration in ms": 114.01, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 114.54, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 115.15, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 110.94, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 110.83, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 110.96, "Number of engines": 1, "Number of 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", 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For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-87ceab5b19c2457c931903d15e2ca53b" }, "datasets": { "data-87ceab5b19c2457c931903d15e2ca53b": [ { "Duration in ms": 113.3, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 113.61, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 113.69, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 106.29, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 106.32, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 106.36, "Number of engines": 1, "Number of 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-064a17ae45e920f5c4b792fd7fe5c036" }, "datasets": { "data-064a17ae45e920f5c4b792fd7fe5c036": [ { "Duration in ms": 107.74, "Number of engines": 10, "Number of tasks": 1 }, { "Duration in ms": 107.87, "Number of engines": 10, "Number of tasks": 10 }, { "Duration in ms": 107.71, "Number of engines": 10, "Number of tasks": 100 }, { "Duration in ms": 107.57, "Number of engines": 1, "Number of tasks": 1 }, { "Duration in ms": 107.65, "Number of engines": 1, "Number of tasks": 10 }, { "Duration in ms": 107.59, "Number of engines": 1, "Number of 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "source = get_time_n_tasks_source(benchmark_results)\n", "display(Markdown(f'## With 16 cores:'))\n", "for delay, result_for_delay in source.items():\n", " display(Markdown(f'### With a delay of {ms_to_seconds(delay)}s. :'))\n", " for scheduler_name, results in result_for_delay[16].items():\n", " alt.Chart(\n", " pd.DataFrame(results)\n", " ).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " y='Duration in ms',\n", " color='Number of engines:N',\n", " tooltip='Duration in ms' \n", " ).properties(\n", " title=f'{scheduler_name}',\n", " width=800\n", " ).interactive().display(renderer='svg')\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/markdown": [ "### With no delay and 100 engines:" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-f4c8a6e7f35eaeba657c51b1351537da" }, "datasets": { "data-f4c8a6e7f35eaeba657c51b1351537da": [ { "Duration in ms": 75.76, "Number of cores": 16, "Number of tasks": 1 }, { "Duration in ms": 78.3, "Number of cores": 16, "Number of tasks": 10 }, { "Duration in ms": 77.84, "Number of cores": 16, "Number of tasks": 100 }, { "Duration in ms": 76.72, "Number of cores": 16, "Number of tasks": 1000 }, { "Duration in ms": 98.22, "Number of cores": 16, "Number of tasks": 10000 } ] }, "encoding": { "color": { "field": "Number of cores", "type": "nominal" }, "tooltip": { "field": "Duration in ms", "type": "quantitative" }, "x": { "field": "Number of tasks", "scale": { "type": "log" }, "type": "quantitative" }, "y": { "field": "Duration in ms", "type": "quantitative" } }, "mark": { "point": true, "type": "line" }, "selection": { "selector021": { "bind": "scales", "encodings": [ "x", "y" ], "type": "interval" } }, "title": "Ran with no delay on 100 engines SchedulerType.BROADCAST_COALESCING", "width": 800 }, "image/png": 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"text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v3+json": { "$schema": "https://vega.github.io/schema/vega-lite/v3.4.0.json", "config": { "mark": { "tooltip": null }, "view": { "height": 300, "width": 400 } }, "data": { "name": "data-db14e88f35cec334e11a5ca140df08f5" }, "datasets": { "data-db14e88f35cec334e11a5ca140df08f5": [ { "Duration in ms": 78.68, "Number of cores": 16, "Number of tasks": 1 }, { "Duration in ms": 84.5, "Number of cores": 16, "Number of tasks": 10 }, { "Duration in ms": 86.32, "Number of cores": 16, "Number of tasks": 100 }, { "Duration in ms": 85.67, "Number of cores": 16, "Number of tasks": 1000 }, { "Duration in ms": 105.36, "Number of cores": 16, "Number of tasks": 10000 } ] }, "encoding": { "color": { "field": "Number of cores", "type": 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", 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", 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", "text/plain": [ "\n", "\n", "If you see this message, it means the renderer has not been properly enabled\n", "for the frontend that you are using. For more information, see\n", "https://altair-viz.github.io/user_guide/troubleshooting.html\n" ] }, "metadata": { "application/vnd.vegalite.v3+json": { "embed_options": { "renderer": "svg" } } }, "output_type": "display_data" } ], "source": [ "no_delay_source = get_no_delay_source(benchmark_results)\n", "display(Markdown(f'### With no delay and 100 engines:'))\n", "for scheduler_name, results in no_delay_source[BenchmarkType.TIME_N_TASKS_NO_DELAY].items():\n", " data = pd.DataFrame(results)\n", " alt.Chart(data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of tasks',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Number of cores:N',\n", " y='Duration in ms',\n", " tooltip='Duration in ms', \n", " ).properties(title=f'Ran with no delay on 100 engines {scheduler_name}', width=800).interactive().display(renderer='svg')\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/throughtput_benchmarks.ipynb000066400000000000000000004401021460376056100240300ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "from IPython.display import display, Markdown, SVG, HTML\n", "import pandas as pd\n", "import altair as alt\n", "import re\n", "import pickle\n", "from utils import seconds_to_ms, ms_to_seconds\n", "from benchmark_result import get_benchmark_results, BenchmarkType, SchedulerType, get_broadcast_source, get_async_source\n", "from benchmarks.utils import echo\n", "from benchmarks.throughput import make_benchmark, make_multiple_message_benchmark" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#benchmark_results = get_benchmark_results()\n", "from benchmark_result import BenchmarkResult, Result \n", "with open('saved_results.pkl', 'rb') as saved_results:\n", " benchmark_results = pickle.load(saved_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipyparallel benchmark results ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# time_broadcast" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This benchmark comes from benchmarking the runtime of sending and returning different size arrays to different number of engines with the echo function on various scheduler implementations\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelay\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minner_echo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mFile:\u001b[0m ~/ipyparallel_master_project/benchmarks/utils.py\n", "\u001b[0;31mType:\u001b[0m function\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "??echo" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_benchmark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbenchmark_name\u001b[0m\u001b[0;34m,\u001b[0m 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{ "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "source = get_broadcast_source(benchmark_results)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "dview = pd.DataFrame(source['DirectView']) \n", "dview['Scheduler name'] = 'DirectView'\n", "dview['Speedup'] = 1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Duration in ms Number of bytes Number of engines Scheduler name \\\n", "0 9.45 1000 1 NonCoalescing \n", "1 9.47 10000 1 NonCoalescing \n", "2 17.83 100000 1 NonCoalescing \n", "3 89.25 1000000 1 NonCoalescing \n", "4 172.51 2000000 1 NonCoalescing \n", ".. ... ... ... ... \n", "35 677.21 1000 1024 DirectView \n", "36 661.01 10000 1024 DirectView \n", "37 760.37 100000 1024 DirectView \n", "38 1979.30 1000000 1024 DirectView \n", "39 3942.04 2000000 1024 DirectView \n", "\n", " Speedup \n", "0 0.484656 \n", "1 0.587117 \n", "2 0.722378 \n", "3 0.873501 \n", "4 0.858733 \n", ".. ... \n", "35 1.000000 \n", "36 1.000000 \n", "37 1.000000 \n", "38 1.000000 \n", "39 1.000000 \n", "\n", "[120 rows x 5 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datas = []\n", "for scheduler_name, scheduler_results in source.items():\n", " data = pd.DataFrame(scheduler_results) \n", " data['Scheduler name'] = scheduler_name\n", " data['Speedup'] = dview['Duration in ms'] / data['Duration in ms']\n", " datas.append(data)\n", "data = pd.concat(datas)\n", "data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ldata = data[data['Number of bytes'] == 2000_000]\n", "alt.Chart(ldata).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log', base=2)\n", " ),\n", " alt.Y(\n", " 'Duration in ms',\n", " scale=alt.Scale(type='log')\n", " ),\n", " color='Scheduler name:N',\n", " tooltip='Duration in ms',\n", ").configure_axis(labelFontSize=20, titleFontSize=20).properties(title='Runtime of apply using DirectView', width=1080).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
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\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for scheduler_name in data['Scheduler name'].unique():\n", " scheduler_data = data[data['Scheduler name'] == scheduler_name]\n", " alt.Chart(scheduler_data).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of bytes',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Duration in ms',\n", " scale=alt.Scale(type='log')\n", "\n", " ),\n", " color='Number of engines:N',\n", " tooltip='Duration in ms',\n", " \n", " ).properties(title=scheduler_name, width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/markdown": [ "## Results for duration[DirectView]/duration[scheduler]" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(f'## Results for duration[DirectView]/duration[scheduler]'))\n", "for scheduler_name in data['Scheduler name'].unique():\n", " if scheduler_name == 'DirectView':\n", " continue\n", " alt.Chart(data[data['Scheduler name'] == scheduler_name]).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of bytes',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Speedup',\n", " ),\n", " color='Number of engines:N',\n", " tooltip='Number of engines',\n", " \n", " ).properties(title=scheduler_name, width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/markdown": [ "### Running on 1 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 2 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 16 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 64 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 128 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 256 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 512 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Running on 1024 engines" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "for engine in data['Number of engines'].unique():\n", " display(Markdown(f'### Running on {engine} engines'))\n", " alt.Chart(data[(data['Number of engines'] == engine) & (data['Scheduler name'] != 'DirectView')]).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of bytes',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Speedup',\n", " ),\n", " color='Scheduler name:N',\n", " tooltip='Duration in ms',\n", " ).properties(title='schedulers vs directView', width=800).interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['combined']= data['Scheduler name'] + ' ' + data['Number of bytes'].astype(str)\n", "alt.Chart(data[data['Scheduler name'] != 'DirectView']).mark_line(point=True).encode(\n", " alt.X(\n", " 'Number of engines',\n", " scale=alt.Scale(type='log')\n", " ),\n", " alt.Y(\n", " 'Speedup',\n", " ),\n", " color='Number of bytes:N',\n", " strokeDash=alt.StrokeDash(shorthand='Scheduler name', legend=None),\n", " tooltip='combined',\n", "\n", ").properties(title='schedulers vs directView scaling engines', width=800).interactive().display(renderer='svg')\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Duration in msNumber of bytesNumber of enginesScheduler nameSpeedup
35617.3810001024NonCoalescing1.096910
36619.59100001024NonCoalescing1.066851
37652.451000001024NonCoalescing1.165407
381132.6710000001024NonCoalescing1.747464
394143.3020000001024NonCoalescing0.951425
35176.2110001024Coalescing3.843198
36221.57100001024Coalescing2.983301
37354.271000001024Coalescing2.146301
381699.8310000001024Coalescing1.164411
394317.6720000001024Coalescing0.913002
35677.2110001024DirectView1.000000
36661.01100001024DirectView1.000000
37760.371000001024DirectView1.000000
381979.3010000001024DirectView1.000000
393942.0420000001024DirectView1.000000
\n", "
" ], "text/plain": [ " Duration in ms Number of bytes Number of engines Scheduler name \\\n", "35 617.38 1000 1024 NonCoalescing \n", "36 619.59 10000 1024 NonCoalescing \n", "37 652.45 100000 1024 NonCoalescing \n", "38 1132.67 1000000 1024 NonCoalescing \n", "39 4143.30 2000000 1024 NonCoalescing \n", "35 176.21 1000 1024 Coalescing \n", "36 221.57 10000 1024 Coalescing \n", "37 354.27 100000 1024 Coalescing \n", "38 1699.83 1000000 1024 Coalescing \n", "39 4317.67 2000000 1024 Coalescing \n", "35 677.21 1000 1024 DirectView \n", "36 661.01 10000 1024 DirectView \n", "37 760.37 100000 1024 DirectView \n", "38 1979.30 1000000 1024 DirectView \n", "39 3942.04 2000000 1024 DirectView \n", "\n", " Speedup \n", "35 1.096910 \n", "36 1.066851 \n", "37 1.165407 \n", "38 1.747464 \n", "39 0.951425 \n", "35 3.843198 \n", "36 2.983301 \n", "37 2.146301 \n", "38 1.164411 \n", "39 0.913002 \n", "35 1.000000 \n", "36 1.000000 \n", "37 1.000000 \n", "38 1.000000 \n", "39 1.000000 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lol = data[data['Number of engines'] == 1024]\n", "lol" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "for engine in data['Number of engines'].unique(): \n", " alt.Chart(data[data['Number of engines'] == engine]).mark_bar().encode(\n", " x='Scheduler name',\n", " y='Duration in ms',\n", " color='Scheduler name:N',\n", " column='Number of bytes:N', \n", " tooltip='Duration in ms'\n", " ).properties(title=f'Runtime on {engine} engines:').interactive().display(renderer='svg')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/benchmarks/utils.py000066400000000000000000000001561460376056100177060ustar00rootroot00000000000000def seconds_to_ms(seconds): return round(seconds * 1000, 2) def ms_to_seconds(ms): return ms / 1000 ipyparallel-8.8.0/ci/000077500000000000000000000000001460376056100144505ustar00rootroot00000000000000ipyparallel-8.8.0/ci/slurm/000077500000000000000000000000001460376056100156125ustar00rootroot00000000000000ipyparallel-8.8.0/ci/slurm/Dockerfile000066400000000000000000000011741460376056100176070ustar00rootroot00000000000000# syntax = docker/dockerfile:1.2.1 FROM ubuntu:20.04 ENV DEBIAN_FRONTEND=noninteractive RUN --mount=type=cache,target=/var/cache/apt \ rm -f /etc/apt/apt.conf.d/docker-clean \ && apt-get update && apt-get -y install python3-pip slurm-wlm ENV PIP_CACHE_DIR=/tmp/pip-cache RUN --mount=type=cache,target=${PIP_CACHE_DIR} python3 -m pip install ipyparallel pytest-asyncio pytest-cov RUN mkdir /var/spool/slurmctl \ && mkdir /var/spool/slurmd COPY slurm.conf /etc/slurm-llnl/slurm.conf COPY entrypoint.sh /entrypoint ENV IPP_DISABLE_JS=1 ENTRYPOINT ["/entrypoint"] # the mounted directory RUN mkdir /io ENV PYTHONPATH=/io WORKDIR "/io" ipyparallel-8.8.0/ci/slurm/docker-compose.yaml000066400000000000000000000030301460376056100214040ustar00rootroot00000000000000version: "2.2" services: slurmctld: image: ipp-cluster:slurm build: . container_name: slurmctld hostname: slurmctld command: - tail - "-f" - /var/log/slurm-llnl/slurmctld.log volumes: - etc_munge:/etc/munge - etc_slurm:/etc/slurm - slurm_jobdir:/data - var_log_slurm:/var/log/slurm - ../..:/io expose: - "6817" networks: common-network: ipv4_address: 10.1.1.10 c1: image: ipp-cluster:slurm build: . hostname: c1 command: - tail - "-f" - /var/log/slurm-llnl/slurmd.log container_name: c1 volumes: - etc_munge:/etc/munge - etc_slurm:/etc/slurm - slurm_jobdir:/data - var_log_slurm:/var/log/slurm - ../..:/io expose: - "6818" depends_on: - "slurmctld" networks: common-network: ipv4_address: 10.1.1.11 c2: image: ipp-cluster:slurm build: . command: - tail - "-f" - /var/log/slurm-llnl/slurmd.log hostname: c2 container_name: c2 volumes: - etc_munge:/etc/munge - etc_slurm:/etc/slurm - slurm_jobdir:/data - var_log_slurm:/var/log/slurm - ../..:/io expose: - "6818" depends_on: - "slurmctld" networks: common-network: ipv4_address: 10.1.1.12 volumes: etc_munge: etc_slurm: slurm_jobdir: var_log_slurm: networks: common-network: driver: bridge ipam: driver: default config: - subnet: 10.1.1.0/24 ipyparallel-8.8.0/ci/slurm/entrypoint.sh000077500000000000000000000005311460376056100203630ustar00rootroot00000000000000#!/bin/bash set -ex # set permissions on munge dir, may be mounted chown -R munge:munge /etc/munge echo "starting munge" service munge start echo "hostname=$(hostname)" if [[ "$(hostname)" == *"slurmctl"* ]]; then echo "starting slurmctld" service slurmctld start else echo "starting slurmd" service slurmd start fi exec "$@" ipyparallel-8.8.0/ci/slurm/slurm.conf000066400000000000000000000030761460376056100176310ustar00rootroot00000000000000# slurm.conf file generated by configurator easy.html. # Put this file on all nodes of your cluster. # See the slurm.conf man page for more information. # SlurmctldHost=slurmctld # #MailProg=/bin/mail MpiDefault=none #MpiParams=ports=#-# ProctrackType=proctrack/linuxproc ReturnToService=1 SlurmctldPidFile=/var/run/slurmctld.pid #SlurmctldPort=6817 SlurmdPidFile=/var/run/slurmd.pid #SlurmdPort=6818 SlurmdSpoolDir=/var/spool/slurmd SlurmUser=root #SlurmdUser=root StateSaveLocation=/var/spool/slurmctl SwitchType=switch/none #TaskPlugin=task/affinity #CoreSpecPlugin=core_spec/none # # TIMERS #KillWait=30 #MinJobAge=300 #SlurmctldTimeout=120 #SlurmdTimeout=300 # # # SCHEDULING SchedulerType=sched/backfill SelectType=select/cons_tres SelectTypeParameters=CR_Core # # # LOGGING AND ACCOUNTING AccountingStorageType=accounting_storage/none ClusterName=cluster #JobAcctGatherFrequency=30 JobAcctGatherType=jobacct_gather/none SlurmctldDebug=debug5 SlurmctldLogFile=/var/log/slurm-llnl/slurmctld.log SlurmdDebug=debug5 SlurmdLogFile=/var/log/slurm-llnl/slurmd.log # # # COMPUTE NODES # Note: CPUs apparently cannot be oversubscribed # this can only run where at least 2 CPUs are available #NodeName=localhost NodeHostName=localhost NodeAddr=127.0.0.1 CPUs=2 State=UNKNOWN #PartitionName=part Nodes=localhost Default=YES MaxTime=INFINITE State=UP OverSubscribe=YES # COMPUTE NODES NodeName=c[1-2] RealMemory=4096 CPUs=2 State=UNKNOWN # # PARTITIONS PartitionName=normal Default=yes Nodes=c[1-2] Priority=50 DefMemPerCPU=2048 Shared=NO MaxNodes=2 MaxTime=5-00:00:00 DefaultTime=5-00:00:00 State=UP ipyparallel-8.8.0/ci/ssh/000077500000000000000000000000001460376056100152455ustar00rootroot00000000000000ipyparallel-8.8.0/ci/ssh/Dockerfile000066400000000000000000000024521460376056100172420ustar00rootroot00000000000000# syntax = docker/dockerfile:1.2.1 FROM ubuntu:20.04 RUN --mount=type=cache,target=/var/cache/apt \ rm -f /etc/apt/apt.conf.d/docker-clean \ && apt-get update \ && apt-get -y install wget openssh-server ENV MAMBA_ROOT_PREFIX=/opt/conda ENV PATH=$MAMBA_ROOT_PREFIX/bin:$PATH ENV IPP_DISABLE_JS=1 # x86_64 -> 64, aarch64 unmodified RUN ARCH=$(uname -m | sed s@x86_@@) \ && wget -qO- https://github.com/mamba-org/micromamba-releases/releases/latest/download/micromamba-linux-${ARCH} > /usr/local/bin/micromamba \ && chmod +x /usr/local/bin/micromamba RUN --mount=type=cache,target=${MAMBA_ROOT_PREFIX}/pkgs \ micromamba install -y -p $MAMBA_ROOT_PREFIX -c conda-forge \ python=3.8 \ pip \ ipyparallel # generate a user with home directory and trusted ssh keypair RUN useradd -m -s /bin/bash -N ciuser USER ciuser RUN mkdir ~/.ssh \ && chmod 0700 ~/.ssh \ && ssh-keygen -q -t rsa -N '' -f /home/ciuser/.ssh/id_rsa \ && cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys \ && chmod 0600 ~/.ssh/* USER root ENV PIP_CACHE_DIR=/tmp/pip-cache COPY . /src/ipyparallel RUN --mount=type=cache,target=${PIP_CACHE_DIR} python3 -m pip install -e 'file:///src/ipyparallel#egg=ipyparallel[test]' # needed for sshd to start RUN mkdir /run/sshd # run sshd in the foreground CMD /usr/sbin/sshd -D -e ipyparallel-8.8.0/ci/ssh/docker-compose.yaml000066400000000000000000000002171460376056100210430ustar00rootroot00000000000000services: sshd: image: ipyparallel-sshd build: context: ../.. dockerfile: ci/ssh/Dockerfile ports: - "2222:22" ipyparallel-8.8.0/ci/ssh/ipcluster_config.py000066400000000000000000000012531460376056100211570ustar00rootroot00000000000000import os c = get_config() # noqa c.Cluster.controller_ip = '0.0.0.0' c.Cluster.engine_launcher_class = 'SSH' ssh_key = os.path.join(os.path.dirname(__file__), "id_rsa") c.Cluster.controller_ip = '0.0.0.0' c.Cluster.engine_launcher_class = 'SSH' c.SSHEngineSetLauncher.scp_args = c.SSHLauncher.ssh_args = [ "-o", "UserKnownHostsFile=/dev/null", "-o", "StrictHostKeyChecking=no", "-i", ssh_key, ] c.SSHEngineSetLauncher.engines = {"ciuser@127.0.0.1:2222": 4} c.SSHEngineSetLauncher.remote_python = "/opt/conda/bin/python3" c.SSHEngineSetLauncher.remote_profile_dir = "/home/ciuser/.ipython/profile_default" c.SSHEngineSetLauncher.engine_args = ['--debug'] ipyparallel-8.8.0/docs/000077500000000000000000000000001460376056100150055ustar00rootroot00000000000000ipyparallel-8.8.0/docs/Makefile000066400000000000000000000034221460376056100164460ustar00rootroot00000000000000# Makefile for Sphinx documentation generated by sphinx-quickstart # ---------------------------------------------------------------------------- # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= --color -W --keep-going SPHINXBUILD ?= sphinx-build SOURCEDIR = source BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. # # Several sphinx-build commands can be used through this, for example: # # - make clean # - make linkcheck # - make spelling # %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) # Manually added targets - related to code generation # ---------------------------------------------------------------------------- # For local development: # - builds the html # - NOTE: If the pre-requisites for the html target is updated, also update the # Read The Docs section in docs/source/conf.py. # html: $(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS) @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." # Manually added targets - related to development # ---------------------------------------------------------------------------- # For local development: # - requires sphinx-autobuild, see # https://sphinxcontrib-spelling.readthedocs.io/en/latest/ # - builds and rebuilds html on changes to source, but does not re-generate # metrics/scopes files # - starts a livereload enabled webserver and opens up a browser devenv: html sphinx-autobuild -b html --open-browser "$(SOURCEDIR)" "$(BUILDDIR)/html" ipyparallel-8.8.0/docs/make.bat000066400000000000000000000015261460376056100164160ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=--color -W --keep-going ) if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=_build if "%1" == "" goto help if "%1" == "devenv" goto devenv goto default :default %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Open and read README.md! exit /b 1 ) %SPHINXBUILD% -M %1 "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS% goto end :devenv sphinx-autobuild >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-autobuild' command was not found. Open and read README.md! exit /b 1 ) sphinx-autobuild -b html --open-browser "%SOURCEDIR%" "%BUILDDIR%/html" goto end :end popd ipyparallel-8.8.0/docs/requirements.txt000066400000000000000000000002741460376056100202740ustar00rootroot00000000000000# pin jinja2 due to broken deprecation used in nbconvert autodoc-traits jinja2==3.0.* matplotlib myst-nb myst-parser numpy pydata-sphinx-theme sphinx sphinx-copybutton sphinxext-rediraffe ipyparallel-8.8.0/docs/source/000077500000000000000000000000001460376056100163055ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/_static/000077500000000000000000000000001460376056100177335ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/_static/IPyParallel-MPI-Example.png000066400000000000000000007116721460376056100246510ustar00rootroot00000000000000PNG  IHDR@ k jiCCPICC ProfileHWXS[RIhH ҫZJ A^\ "XUE Eł.(*oB+;7w9󟒙{gI6y|Y|DkLjd`y| 79)9_EW @A! n_Ǘ *SJ<b= 2%RJGm9_LdYhއzV? h~E"Kq _@}X^$%K!8Ɵ1e bU^B˥igi*|F"Ι4;%1ZC#J)"T1_΁L](!Dp.pSD ^$%m6&ū}u2[?ǓUzIbߊ\5?Y(JL U89bM9 Qj"ǸL x$"Dŏdy|M"17F#UNy\+B ;iG(=@{!$%yz!8U-J\<9.N?)͏KTʼnfFƩheI [:;/H8B Hg(@$y!BP_-蟑A@.% Bya)~@MÆhF15`I ##D{h }fOxFh%<& Lϓ(յ Cq&np臍AϞPQǭ eݿPPJ0Ǚ,Z_U ][b'Xv ;ăiOOSYIKKgXpjrq&IY|~,< [z}cCo8w.%p _'8WW T:\ iX;  $T0VY׹ L3\PJr @=8N3"n{pW :@ 3qD$ Cx$IG @f YlF_C <ҊA![4T5AmFDtҰLLŠr | k:8g, H< Y|-OGx@' ~.a !0PD('l#${H$2DoS%=&b+ D"IXO*"!"']%zd39F;Wɽm5ŏKPQQR))^Ֆ@MfSR+w5s4*4jxKsqhh RvZNjICz&CY)МYYyUEZ5AP\keNm6G=KR-nNN:u^tmtt t} Ϙ8h#qJvu{'Oկ?Ę6L.3yiCj\`AР` O,0p#FS667(h}CӍ_2615019ii4 66-3=fa0 47{gY )Vy|yyE<=,>e͖]VfVfXXݵXXW[`ckbЦ慭-׶ж.n]u{}z+ȡ#(v\:0wdXհ[N4'SS#gs۽cmO(υ͞_d^^Vo,9K {_>?swa;B8b'mMmAAml.Y?LNS(Z6aExVxMxWgHBdT[\.[=rSQQeэQ#Gu?:FS b+cM;<8:ntg3&0&&Lx,^]"9Y+y\ruДҔ1s1(UڐFJKNۖ=6l쪱<9v'MȝptDy*^w7c]F_% :R̀YY+:DArQ#^+~1CNlܔ=yC]I$IS'JEҶ~WMEɶxyC<_R)~R<*,,虒'bΎԹ9s2t_S7.0Y0g"~),Zp"|xQbk-_(q))/Ϯ?Wܷ4si2eK\bGNia铕V֕ʊZ5qr򍫩*+XYZ!{[z7n4X&ۛ#6UTo!n)lkֳRh[ɶ/%v8U]]xFQӱkܮ+Cw7:nSU}k7Ekﳿu됺iu]ԆC#577<|x#G.;F=XMҦY'4OlwrFj9uܙ3'ϲ?py.\u祃yvū+W[Gtĵkgs_sf۷j-N7w ޛsp~}OWG.=Nx| ɫ>W|5>)O? `=QǪ'/S9M&r^yO `SF(iNx@ASCIIScreenshot= pHYs%%IR$iTXtXML:com.adobe.xmp 832 2020 Screenshot 0P4iDOT($;y@IDATxun)QA@J%E APT%DPBB:E$ EE0sgl9wgΜ9];D׍"@@@@@@@@"zr1@@@@@@@Ly       Ā| rI@@@@@@@        1%@@@@@@@ O߁;w&^E@@@@@@@[#nn!      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[aބ^fz2>n9.-ҳ )*DBtB;u,$NqifVI11L[}|ӄ& <"| 縰 j4vF>-K.)MݜVB =`(&XO~ˋw=J^I^[4Rt^=͙nnb6[S *UC оn!(& ਱HUXQ; 3.OdkXr'*(l +~Fcm2n9j ԙ],) MFA;6&HLlkâ\P/[|}?#`F BI5oHB~ϕgA0^E36Y~ñ\]L-ϯ,nUlo[r<P;.j'<{8ik~,.)c;}*cK)!x$b\~-YEcAK)dkUnqSlp'C4da>zpiLSݼIGƜr82= ݖǓg`|Xſ@Wfd1V Ih%0s(>>LjO/og1x^驟'jؤvNHΎx|m{Pnٗ(W#ΣapYLڐ"_6gbD/|:VbϘG1lQ"A^_l0a*TMK+M#Ne+?6lN4lJoNxDwvbfeaAAhÔ) ]mU /W T%:EO^ moovlmvhd%@ avcCd!gd@9#jh"colrnclxpaspbtrtsttsstssctts stscTstsz8(=9':">(%%" "6" K; /%" !$ !$ !$ !$ !$ !$ !$ !$ !$ !$ K& %& '$ (9 4[qK$6!0/75!6:!4-(!!;#33(!!;!3,(!!";!3'(!!83# ("#hstco0budtaZmeta!hdlrmdirappl-ilst%toodataLavf58.76.100ipyparallel-8.8.0/docs/source/api/000077500000000000000000000000001460376056100170565ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/api/ipyparallel.rst000066400000000000000000000021251460376056100221260ustar00rootroot00000000000000API Reference ============= .. module:: ipyparallel .. autodata:: version_info The IPython parallel version as a tuple of integers. There will always be 3 integers. Development releases will have 'dev' as a fourth element. Classes ------- .. autoconfigurable:: Cluster :inherited-members: .. autoclass:: Client :inherited-members: .. autoclass:: DirectView :inherited-members: .. autoclass:: LoadBalancedView :inherited-members: .. autoclass:: BroadcastView :inherited-members: .. autoclass:: AsyncResult :inherited-members: .. autoclass:: ViewExecutor :inherited-members: Decorators ---------- IPython parallel provides some decorators to assist in using your functions as tasks. .. autodecorator:: interactive .. autodecorator:: require .. autodecorator:: depend .. autodecorator:: remote .. autodecorator:: parallel Exceptions ---------- .. autoexception:: RemoteError :no-members: .. autoexception:: CompositeError :no-members: .. autoexception:: NoEnginesRegistered .. autoexception:: ImpossibleDependency .. autoexception:: InvalidDependency ipyparallel-8.8.0/docs/source/changelog.md000066400000000000000000000504451460376056100205660ustar00rootroot00000000000000(changelog)= # Changelog Changes in IPython Parallel ## 8.8 ### 8.8.0 - 2024-04-02 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.7.0...8.8.0)) 8.8 is a small release. New: - `BroadcastView.map` is defined for API compatibility, but is not particularly efficient or recommended. Fixed: - `AsyncResult.join` is fixed. Improved: - Performance optimization disabling timestamp parsing in `jupyter_client` is not applied until ipyparallel classes are instantiated, rather than at import time. ## 8.7 ### 8.7.0 - 2024-03-04 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.6.1...8.7.0)) 8.7 is a small release, with a few improvements and updates, mostly related to compatibility with different versions of JupyterLab, Notebook, and Jupyter Server. Highlights: - JupyterLab 4 compatibility for the lab extension - Improved logging and deprecation messages for different versions of Jupyter Server and Notebook #### New features added - Update labextension to jupyterlab 4 [#833](https://github.com/ipython/ipyparallel/pull/833) ([@minrk](https://github.com/minrk)) - add `ControllerLauncher.connection_info_timeout` config [#872](https://github.com/ipython/ipyparallel/pull/872) ([@minrk](https://github.com/minrk)) #### Enhancements made - log launcher output at warning-level in case of nonzero exit code [#866](https://github.com/ipython/ipyparallel/pull/866) ([@minrk](https://github.com/minrk)) - improve deprecation messaging around `ipcluster nbextension` [#835](https://github.com/ipython/ipyparallel/pull/835) ([@minrk](https://github.com/minrk)) #### Bugs fixed - Use pre-3.10 serialization code on PyPy3.10 [#846](https://github.com/ipython/ipyparallel/pull/846) ([@mgorny](https://github.com/mgorny), [@minrk](https://github.com/minrk)) - fallback import when using notebook and jupyter_server is unavailable [#808](https://github.com/ipython/ipyparallel/pull/808) ([@minrk](https://github.com/minrk)) - don't propagate logs in IPython [#797](https://github.com/ipython/ipyparallel/pull/797) ([@minrk](https://github.com/minrk)) #### Contributors to this release The following people contributed discussions, new ideas, code and documentation contributions, and review. See [our definition of contributors](https://github-activity.readthedocs.io/en/latest/#how-does-this-tool-define-contributions-in-the-reports). ([GitHub contributors page for this release](https://github.com/ipython/ipyparallel/graphs/contributors?from=2023-04-14&to=2024-03-04&type=c)) @ellert ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Aellert+updated%3A2023-04-14..2024-03-04&type=Issues)) | @hroncok ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Ahroncok+updated%3A2023-04-14..2024-03-04&type=Issues)) | @mgorny ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Amgorny+updated%3A2023-04-14..2024-03-04&type=Issues)) | @minrk ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Aminrk+updated%3A2023-04-14..2024-03-04&type=Issues)) | @ottointhesky ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Aottointhesky+updated%3A2023-04-14..2024-03-04&type=Issues)) | @tornaria ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Atornaria+updated%3A2023-04-14..2024-03-04&type=Issues)) | @WernerFS ([activity](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3AWernerFS+updated%3A2023-04-14..2024-03-04&type=Issues)) ## 8.6 ### 8.6.1 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.6.0...8.6.1)) ### Bugs fixed - avoid errors when engine id cannot be identified [#793](https://github.com/ipython/ipyparallel/pull/793) ([@minrk](https://github.com/minrk)) - Disable variable expansion in %px [#792](https://github.com/ipython/ipyparallel/pull/792) ([@minrk](https://github.com/minrk)) - fix wait_interactive(return_when=FIRST_EXCEPTION) when there are no errors [#790](https://github.com/ipython/ipyparallel/pull/790) ([@minrk](https://github.com/minrk)) ### 8.6.0 A tiny release fixing issues seen building notebooks with jupyter-book. ([full changelog](https://github.com/ipython/ipyparallel/compare/8.5.1...8.6.0)) - Fix KeyError on parent_header when streaming output with %%px - Allow disabling streaming/progress defaults with IPP_NONINTERACTIVE=1 environment variable (e.g. when building notebooks in documentation) ## 8.5 ### 8.5.1 A tiny bugfix release ([full changelog](https://github.com/ipython/ipyparallel/compare/8.5.0...8.5.1)) - Fix error preventing creation of new profiles via the lab extension ### 8.5.0 A small bugfix and compatibility release. ([full changelog](https://github.com/ipython/ipyparallel/compare/8.4.1...8.5.0)) - Updates dependencies in jupyterlab extension to jupyterlab 3.6 - fix ResourceWarnings about closed clusters - Avoid some deprecated APIs in jupyter-client and pyzmq ## 8.4 ### 8.4.1 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.4.0...8.4.1)) 8.4.1 is a tiny release, adding support for Python 3.11 ### 8.4.0 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.3.0...8.4.0)) 8.4.0 is a small release, with some bugfixes and improvements to the release process. Bugfixes: - (`%px`) only skip redisplay of streamed errors if outputs are complete Compatibility improvements: - Avoid use of recently deprecated asyncio/tornado APIs around 'current' event loops that are not running. Build improvements: - Switch to hatch backend for packaging ## 8.3 ### 8.3.0 ([full changelog](https://github.com/ipython/ipyparallel/compare/8.2.1...8.3.0)) 8.3.0 is a small release, with some bugfixes and improvements to the release process. Build fixes: - Workaround SSL issues with recent builds of nodejs + webpack - Build with flit, removing setup.py Fixes: - Remove remaining references to deprecated `distutils` package (has surprising impact on process memory) - Improve logging when engine registration times out Maintenance changes that shouldn't affect users: - Releases are now built with pip instead of `setup.py` - Updates to autoformatting configuration #### Contributors to this release ([GitHub contributors page for this release](https://github.com/ipython/ipyparallel/graphs/contributors?from=2022-04-01&to=2022-05-09&type=c)) [@blink1073](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Ablink1073+updated%3A2022-04-01..2022-05-09&type=Issues) | [@dependabot](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Adependabot+updated%3A2022-04-01..2022-05-09&type=Issues) | [@jburroni](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Ajburroni+updated%3A2022-04-01..2022-05-09&type=Issues) | [@kloczek](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Akloczek+updated%3A2022-04-01..2022-05-09&type=Issues) | [@minrk](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Aminrk+updated%3A2022-04-01..2022-05-09&type=Issues) | [@pre-commit-ci](https://github.com/search?q=repo%3Aipython%2Fipyparallel+involves%3Apre-commit-ci+updated%3A2022-04-01..2022-05-09&type=Issues) ## 8.2 ### 8.2.1 8.2.1 Fixes some compatibility issues with latest dask, ipykernel, and setuptools, as well as some typos and improved documentation. ### 8.2.0 8.2.0 is a small release, mostly of small bugfixes and improvements. Changes: `len(AsyncMapResult)` and progress ports now use the number of items in the map, not the number of messages. Enhancements: - Show output prior to errors in `%%px` Bugs fixed: - Fix cases where engine id could be `-1` in tracebacks - Add missing `pbs` to engine launcher entrypoints [All changes on GitHub](https://github.com/ipython/ipyparallel/compare/8.1.0...8.2.0) ## 8.1 8.1.0 is a small release, adding a few new features and bugfixes. New features: - relay KeyboardInterrupt to engines in blocking `%px` magics - add `Cluster.start_and_connect(activate=True)` to include activation of `%px` magics in one-liner startup. - initial support for Clusters tab in RetroLab Fixes: - ensure profile config is always loaded for `Cluster(profile="xyz")` - build lab extension in production mode, apply trove classifiers - pass through keyword arguments to constructor in `Client.broadcast_view` ## 8.0 This is marked as a major revision because of the change to pass connection information via environment variables. BatchSystem launchers with a custom template will need to make sure to set flags that inherit environment variables, such as `#PBS -V` or `#SBATCH --export=ALL`. New: - More convenient `Cluster(engines="mpi")` signature for setting the engine (or controller) launcher class. - The first (and usually only) engine set can be accessed as {attr}`.Cluster.engine_set`, rather than digging through the {attr}`Cluster.engines` dict. - Add `environment` configuration to all Launchers. - Support more configuration via environment variables, including passing connection info to engines via `$IPP_CONNECTION_INFO`, which is used by default, avoiding the need to send connection files to engines in cases of non-shared filesystems. - Launchers send connection info to engines via `$IPP_CONNECTION_INFO` by default. This is governed by `Cluster.send_engines_connection_env`, which is True by default. - Support {meth}`EngineLauncher.get_output` via output files in batch system launchers - Capture output in Batch launchers by setting output file options in the default templates. - {meth}`LoadBalancedView.imap` returns a `LazyMapIterator` which has a `.cancel()` method, for stopping consumption of the map input. - Support for `return_when` argument in {meth}`.AsyncResult.wait` and {meth}`~.AsyncResult.wait_interactive`, to allow returning on the first error, first completed, or (default) all completed. Improved: - {meth}`LoadBalancedView.imap(max_outstanding=n)` limits the number of tasks submitted to the cluster, instead of limiting the number not-yet-consumed. Prior to this, the cluster could be idle if several results were waiting to be consumed. - output streamed by `%%px` includes errors and results, for immediate feedback when only one engine fails. Fixed: - Various bugs preventing use of non-default Controller launchers - Fixed crash in jupyterlab extension when IPython directory does not exist - `ViewExecutor.shutdown()` waits for `imap` results, like Executors in the standard library - Removed spurious jupyterlab plugin options that had no effect. - `%autopx` streams output just like `%%px` Maintenance: - Add BroadcastView benchmark code - Tag releases with tbump ## 7.1 New: - New {meth}`.Client.start_and_connect` method for starting a cluster and returning a connected client in one call. - Support [CurveZMQ][] for transport-level encryption and authentication. See [security docs][] for more info. - Define `_max_workers` attribute on {attr}`view.executor` for better consistency with standard library Executors. [security docs]: secure-network [curvezmq]: https://rfc.zeromq.org/spec/26/ Improvements: - {meth}`.Client.wait_for_engines` will raise an informative error if the parent Cluster object notices that its engines have halted while waiting, or any engine unregisters, rather than continuing to wait for engines that will never come - Show progress if `%px` is taking significant time - Improved support for streaming output, e.g. with `%px`, including support for updating output in-place with standard terminal carriage-return progress bars. Fixes: - Fix dropped IOPub messages when using large numbers of engines, causing {meth}`.AsyncResult.wait_for_output` to hang. - Use absolute paths for {attr}`.Cluster.profile_dir`, fixing issues with {meth}`.Cluster.from_file` when run against a profile created with a relative location, e.g. `Cluster(profile_dir="./profile")` - Fix error waiting for connection files when controller is started over ssh. ## 7.0 ### 7.0.1 - Fix missing setupbase.py in tarball ### 7.0.0 Compatibility changes: - **Require Python 3.6** - Fix compatibility issues with ipykernel 6 and jupyter-client 7 - Remove dependency on deprecated ipython-genutils - New dependencies on psutil, entrypoints, tqdm New features: - New {class}`.Cluster` API for managing clusters from Python, including support for signaling and restarting engines. See [docs](./examples/Cluster%20API.ipynb) for more. - New `ipcluster list` and `ipcluster clean` commands derived from the Cluster API. - New {meth}`.Client.send_signal` for sending signals to single engines. - New KernelNanny process for signaling and monitoring engines for improved responsiveness of handing engine crashes. - New prototype {class}`.BroadcastScheduler` with vastly improved scaling in 'do-on-all' operations on large numbers of engines, c/o Tom-Olav Bøyum's [Master's thesis][] at University of Oslo. [Broadcast view documentation][]. - New {meth}`.Client.wait_for_engines` method to wait for engines to be available. - Nicer progress bars for interactive waits, such as {meth}`.AsyncResult.wait_interactive`. - Add {meth}`.AsyncResult.stream_output` context manager for streaming output. Stream output by default in parallel magics. - Launchers registered via entrypoints for better support of third-party Launchers. - New JupyterLab extension (enabled by default) based on dask-labextension for managing clusters. - {meth}`.LoadBalancedView.imap` consumes inputs as-needed, producing a generator of results instead of an AsyncMapResult, allowing for consumption of very large or infinite mapping inputs. [broadcast view documentation]: ./examples/broadcast/Broadcast%20view.ipynb [master's thesis]: https://urn.nb.no/URN:NBN:no-84589 Improvements and other fixes: - Greatly improved performance of heartbeat and registration with large numbers of engines, tested with 5000 engines and default configuration. - Single `IPController.ports` configuration to specify the pool of ports for the controller to use, e.g. `ipcontroller --ports 10101-10120`. - Allow `f` as keyword-argument to `apply`, e.g. `view.apply(myfunc, f=5)`. - joblib backend will start and stop a cluster by default if the default cluster is not running. The repo has been updated to use pre-commit, black, myst, and friends and GitHub Actions for CI, but this should not affect users, only making it a bit nicer for contributors. ## 6.3.0 - **Require Python 3.5** - Fix compatibility with joblib 0.14 - Fix crash recovery test for Python 3.8 - Fix repeated name when cluster-id is set - Fix CSS for notebook extension - Fix KeyError handling heartbeat failures ## 6.2.5 - Fix compatibility with Python 3.8 - Fix compatibility with recent dask ## 6.2.4 - Improve compatibility with ipykernel 5 - Fix `%autopx` with IPython 7 - Fix non-local ip warning when using current hostname ## 6.2.3 - Fix compatibility for execute requests with ipykernel 5 (now require ipykernel >= 4.4) ## 6.2.2 - Fix compatibility with tornado 4, broken in 6.2.0 - Fix encoding of engine and controller logs in `ipcluster --debug` on Python 3 - Fix compatiblity with joblib 0.12 - Include LICENSE file in wheels ## 6.2.1 - Workaround a setuptools issue preventing installation from sdist on Windows ## 6.2.0 - Drop support for Python 3.3. IPython parallel now requires Python 2.7 or >= 3.4. - Further fixes for compatibility with tornado 5 when run with asyncio (Python 3) - Fix for enabling clusters tab via nbextension - Multiple fixes for handling when engines stop unexpectedly - Installing IPython Parallel enables the Clusters tab extension by default, without any additional commands. ## 6.1.1 - Fix regression in 6.1.0 preventing BatchSpawners (PBS, etc.) from launching with ipcluster. ## 6.1.0 Compatibility fixes with related packages: - Fix compatibility with pyzmq 17 and tornado 5. - Fix compatibility with IPython ≥ 6. - Improve compatibility with dask.distributed ≥ 1.18. New features: - Add {attr}`namespace` to BatchSpawners for easier extensibility. - Support serializing partial functions. - Support hostnames for machine location, not just ip addresses. - Add `--location` argument to ipcluster for setting the controller location. It can be a hostname or ip. - Engine rank matches MPI rank if engines are started with `--mpi`. - Avoid duplicate pickling of the same object in maps, etc. Documentation has been improved significantly. ## 6.0.2 Upload fixed sdist for 6.0.1. ## 6.0.1 Small encoding fix for Python 2. ## 6.0 Due to a compatibility change and semver, this is a major release. However, it is not a big release. The main compatibility change is that all timestamps are now timezone-aware UTC timestamps. This means you may see comparison errors if you have code that uses datetime objects without timezone info (so-called naïve datetime objects). Other fixes: - Rename {meth}`Client.become_distributed` to {meth}`Client.become_dask`. {meth}`become_distributed` remains as an alias. - import joblib from a public API instead of a private one when using IPython Parallel as a joblib backend. - Compatibility fix in extensions for security changes in notebook 4.3 ## 5.2 - Fix compatibility with changes in ipykernel 4.3, 4.4 - Improve inspection of `@remote` decorated functions - {meth}`Client.wait` accepts any Future. - Add `--user` flag to {command}`ipcluster nbextension` - Default to one core per worker in {meth}`Client.become_distributed`. Override by specifying `ncores` keyword-argument. - Subprocess logs are no longer sent to files by default in {command}`ipcluster`. ## 5.1 ### dask, joblib IPython Parallel 5.1 adds integration with other parallel computing tools, such as [dask.distributed](https://distributed.readthedocs.io) and [joblib](https://joblib.readthedocs.io). To turn an IPython cluster into a dask.distributed cluster, call {meth}`~.Client.become_distributed`: ``` executor = client.become_distributed(ncores=1) ``` which returns a distributed {class}`Executor` instance. To register IPython Parallel as the backend for joblib: ``` import ipyparallel as ipp ipp.register_joblib_backend() ``` ### nbextensions IPython parallel now supports the notebook-4.2 API for enabling server extensions, to provide the IPython clusters tab: ``` jupyter serverextension enable --py ipyparallel jupyter nbextension install --py ipyparallel jupyter nbextension enable --py ipyparallel ``` though you can still use the more convenient single-call: ``` ipcluster nbextension enable ``` which does all three steps above. ### Slurm support [Slurm](https://hpc.llnl.gov/documentation/tutorials/livermore-computing-linux-commodity-clusters-overview-part-two) support is added to ipcluster. ### 5.1.0 [5.1.0 on GitHub](https://github.com/ipython/ipyparallel/milestones/5.1) ## 5.0 ### 5.0.1 [5.0.1 on GitHub](https://github.com/ipython/ipyparallel/milestones/5.0.1) - Fix imports in {meth}`use_cloudpickle`, {meth}`use_dill`. - Various typos and documentation updates to catch up with 5.0. ### 5.0.0 [5.0 on GitHub](https://github.com/ipython/ipyparallel/milestones/5.0) The highlight of ipyparallel 5.0 is that the Client has been reorganized a bit to use Futures. AsyncResults are now a Future subclass, so they can be `yield` ed in coroutines, etc. Views have also received an Executor interface. This rewrite better connects results to their handles, so the Client.results cache should no longer grow unbounded. ```{seealso} - The Executor API {class}`ipyparallel.ViewExecutor` - Creating an Executor from a Client: {meth}`ipyparallel.Client.executor` - Each View has an {attr}`executor` attribute ``` Part of the Future refactor is that Client IO is now handled in a background thread, which means that {meth}`Client.spin_thread` is obsolete and deprecated. Other changes: - Add {command}`ipcluster nbextension enable|disable` to toggle the clusters tab in Jupyter notebook Less interesting development changes for users: Some IPython-parallel extensions to the IPython kernel have been moved to the ipyparallel package: - {mod}`ipykernel.datapub` is now {mod}`ipyparallel.datapub` - ipykernel Python serialization is now in {mod}`ipyparallel.serialize` - apply_request message handling is implememented in a Kernel subclass, rather than the base ipykernel Kernel. ## 4.1 [4.1 on GitHub](https://github.com/ipython/ipyparallel/milestones/4.1) - Add {meth}`.Client.wait_interactive` - Improvements for specifying engines with SSH launcher. ## 4.0 [4.0 on GitHub](https://github.com/ipython/ipyparallel/milestones/4.0) First release of `ipyparallel` as a standalone package. ipyparallel-8.8.0/docs/source/conf.py000066400000000000000000000307301460376056100176070ustar00rootroot00000000000000# # ipyparallel documentation build configuration file, created by # sphinx-quickstart on Mon Apr 13 12:53:58 2015. # NOTE: This file has been edited manually from the auto-generated one from # sphinx. Do NOT delete and re-generate. If any changes from sphinx are # needed, generate a scratch one and merge by hand any new fields needed. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # We load the ipython release info into a dict by explicit execution iprelease = {} exec( compile( open('../../ipyparallel/_version.py').read(), '../../ipyparallel/_version.py', 'exec', ), iprelease, ) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. needs_sphinx = '1.3' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.inheritance_diagram', 'sphinx.ext.intersphinx', 'sphinx.ext.napoleon', "sphinxext.rediraffe", # 'myst_parser', 'myst_nb', 'IPython.sphinxext.ipython_console_highlighting', 'autodoc_traits', ] myst_enable_extensions = [ 'colon_fence', 'deflist', ] # Add any paths that contain templates here, relative to this directory. # templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: source_suffix = { ".rst": "restructuredtext", ".ipynb": "myst-nb", } from traitlets.config import LoggingConfigurable # exclude members inherited from HasTraits by default autodoc_default_options = { 'members': None, "exclude-members": ','.join(dir(LoggingConfigurable)), } # Add dev disclaimer. if iprelease['version_info'][-1] == 'dev': rst_prolog = """ .. note:: This documentation is for a development version of IPython. There may be significant differences from the latest stable release. """ # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. from datetime import date project = 'ipyparallel' copyright = '%04d, The IPython Development Team' % date.today().year author = 'The IPython Development Team' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '.'.join(map(str, iprelease['version_info'][:2])) # The full version, including alpha/beta/rc tags. release = iprelease['__version__'] # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. default_role = 'literal' # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The style sheet to use for HTML and HTML Help pages. A file of that name # must exist either in Sphinx' static/ path, or in one of the custom paths # given in html_static_path. # html_style = 'default.css' # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'pydata_sphinx_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # https://pydata-sphinx-theme.readthedocs.io/en/latest/user_guide/configuring.html html_theme_options = { # "navbar_center": [], # disable navbar-nav because it's way too big right now "icon_links": [ { "name": "GitHub", "url": "https://github.com/ipython/ipyparallel", "icon": "fab fa-github-square", }, ], } html_context = { "github_url": "https://github.com/ipython/ipyparallel", } # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'ipythonparalleldoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, 'ipythonparallel.tex', 'IPython Parallel Documentation', 'The IPython Development Team', 'manual', ), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'ipythonparallel', 'IPython Parallel Documentation', [author], 1) ] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, 'ipythonparallel', 'IPython Parallel Documentation', author, 'ipythonparallel', 'One line description of project.', 'Miscellaneous', ), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { 'python': ('https://docs.python.org/3/', None), 'ipython': ('https://ipython.readthedocs.io/en/stable/', None), 'pymongo': ('https://pymongo.readthedocs.io/en/stable/', None), 'distributed': ('https://distributed.readthedocs.io/en/stable/', None), 'jupyterclient': ('https://jupyter-client.readthedocs.io/en/stable/', None), } import os rediraffe_branch = os.environ.get("REDIRAFFE_BRANCH", "main") rediraffe_redirects = "redirects.txt" # allow 80% match for autogenerated redirects rediraffe_auto_redirect_perc = 80 linkcheck_ignore = [ r"https://github.com/[^/]*$", # too many github usernames / searches in changelog "https://github.com/ipython/ipyparallel/pull/", # too many PRs in changelog "https://github.com/search", # github search links "https://github.com/ipython/ipyparallel/compare/", # too many comparisons in changelog r"https?://(localhost|127.0.0.1).*", # ignore localhost references in auto-links ] # myst_nb execution nb_execution_mode = "off" # avoid warning during linkcheck about image-only output nb_mime_priority_overrides = [ ("linkcheck", "text/html", 10), ("linkcheck", "image/png", 99), ] ipyparallel-8.8.0/docs/source/examples/000077500000000000000000000000001460376056100201235ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/examples/Cluster API.ipynb000066400000000000000000000635021460376056100232070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "80a38ff6-dfe6-4c7a-a378-d16a4fbb0786", "metadata": {}, "source": [ "# Cluster API\n", "\n", "IPython Parallel 7 adds a `Cluster` API for starting/stopping clusters.\n", "\n", "This is the new implementation of `ipcluster`,\n", "which can be more easily re-used in Python programs.\n", "The `ipcluster` script is\n", "\n", "Controllers and Engines are started with \"Launchers\",\n", "which are objects representing a running process.\n", "\n", "Each **Cluster** has:\n", "\n", "- a **cluster id**\n", "- a **profile directory**\n", "- one **controller**\n", "- zero or more **engine sets**\n", " - each of which has one or more **engines**\n", " \n", "The combination of `profile_dir` and `cluster_id` uniquely identifies a cluster.\n", "You can have many clusters in one profile, but each must have a distinct cluster id.\n", "\n", "To create a cluster, instantiate a Cluster object:" ] }, { "cell_type": "code", "execution_count": 5, "id": "ef0dff26-f6d2-4d79-901b-049debe0c0d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipyparallel as ipp\n", "\n", "cluster = ipp.Cluster()\n", "cluster" ] }, { "cell_type": "markdown", "id": "db2688db-d6e6-4bb2-8fec-cb38fe4d8c59", "metadata": {}, "source": [ "To start the cluster:" ] }, { "cell_type": "code", "execution_count": 2, "id": "23e385fc-6bc2-44ad-b544-0f39182c1a76", "metadata": {}, "outputs": [ { "data": { "text/plain": [ ")>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "await cluster.start_controller()\n", "cluster" ] }, { "cell_type": "code", "execution_count": 3, "id": "8f48261b-6787-424f-a9e7-b24fb04d076d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting 4 engines with \n" ] }, { "data": { "text/plain": [ ", engine_sets=['1623757384-b3pm'])>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "engine_set_id = await cluster.start_engines(n=4)\n", "cluster" ] }, { "cell_type": "markdown", "id": "af046a0c-281c-4dad-9dc7-582a8cda6b06", "metadata": {}, "source": [ "As you can see, all methods on the Cluster object are async by default.\n", "Every async method also has a `_sync` variant, if you don't want to / can't use asyncio." ] }, { "cell_type": "code", "execution_count": 4, "id": "e239a78a-2907-43e4-80be-eaba13dfac0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting 2 engines with \n" ] }, { "data": { "text/plain": [ "'1623757385-pe8h'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "engine_set_2 = cluster.start_engines_sync(n=2)\n", "engine_set_2" ] }, { "cell_type": "markdown", "id": "f289aaba-7417-40d3-84ea-88bf83715140", "metadata": {}, "source": [ "At this point, we have a cluster with a controller and six engines in two groups.\n", "\n", "There is also a `start_cluster` method that starts the controller and one engine set, for convenience:\n", "\n", "```python\n", "engine_set_id = await cluster.start_cluster(n=4)\n", "```\n", "\n", "We can get a client object connected to the cluster with `connect_client()`" ] }, { "cell_type": "code", "execution_count": 5, "id": "fe2dbd68-1dcb-4de9-a45f-757314261b8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc = await cluster.connect_client()\n", "rc.wait_for_engines(6)\n", "rc.ids" ] }, { "cell_type": "markdown", "id": "d9da9293-3a44-4f16-8f81-7faaf80f20cd", "metadata": {}, "source": [ "And we can use our classic `apply_async(...).get_dict()` pattern to get a dict by engine id of hostname, pid for each engine:" ] }, { "cell_type": "code", "execution_count": 6, "id": "a00c2116-269e-4016-a3d3-ec71ae1b093c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: {'host': 'touchy', 'pid': 24774},\n", " 1: {'host': 'touchy', 'pid': 24775},\n", " 2: {'host': 'touchy', 'pid': 24776},\n", " 3: {'host': 'touchy', 'pid': 24762},\n", " 4: {'host': 'touchy', 'pid': 24769},\n", " 5: {'host': 'touchy', 'pid': 24773}}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def identify():\n", " import os\n", " import socket\n", "\n", " return {\"host\": socket.gethostname(), \"pid\": os.getpid()}\n", "\n", "\n", "rc[:].apply_async(identify).get_dict()" ] }, { "cell_type": "markdown", "id": "1e71632f-d2d2-4c1b-b258-54ee639a8409", "metadata": {}, "source": [ "We can send signals to engine sets by id\n", "\n", "*(sending signals to just one engine is still a work in progress)*" ] }, { "cell_type": "code", "execution_count": 7, "id": "6ff6f7da-38e0-4fea-83af-ff875eebcc76", "metadata": {}, "outputs": [], "source": [ "import signal\n", "import time\n", "\n", "ar = rc[:].apply_async(time.sleep, 100)" ] }, { "cell_type": "code", "execution_count": 8, "id": "e898c30f-996b-44cd-bfad-84516d27a004", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sending signal 2 to engine(s) 1623757384-b3pm\n", "Sending signal 2 to engine(s) 1623757385-pe8h\n" ] }, { "ename": "CompositeError", "evalue": "one or more exceptions from call to method: sleep\n[0:apply]: KeyboardInterrupt: \n[1:apply]: KeyboardInterrupt: \n[2:apply]: KeyboardInterrupt: \n[3:apply]: KeyboardInterrupt: \n.... 2 more exceptions ...", "output_type": "error", "traceback": [ "[0:apply]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", "", "[1:apply]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", "", "[2:apply]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", "", "[3:apply]: ", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", "", "... 2 more exceptions ..." ] } ], "source": [ "# oops! I meant 1!\n", "\n", "await cluster.signal_engines(signal.SIGINT)\n", "ar.get()" ] }, { "cell_type": "markdown", "id": "ba286e10-0e9d-41af-9016-ceb8d7943cbe", "metadata": {}, "source": [ "Now it's time to cleanup. Every `start_` method has a correspinding `stop_method`.\n", "\n", "We can stop one engine set at a time with `stop_engines`:" ] }, { "cell_type": "code", "execution_count": 9, "id": "70574035-e97b-4a3b-acfd-ca404bab7e04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stopping engine(s): 1623757385-pe8h\n" ] } ], "source": [ "await cluster.stop_engines(engine_set_2)" ] }, { "cell_type": "markdown", "id": "89026a15-60a2-40b5-beff-8b281ae624b3", "metadata": {}, "source": [ "Or stop the whole cluster" ] }, { "cell_type": "code", "execution_count": 10, "id": "81275363-a1db-4d6d-9330-514d2355a219", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stopping engine(s): 1623757384-b3pm\n", "Stopping controller\n", "Controller stopped: {'exit_code': 0, 'pid': 24758}\n" ] } ], "source": [ "await cluster.stop_cluster()" ] }, { "cell_type": "markdown", "id": "7835fa97-59dc-4146-9bdf-43640bbb1801", "metadata": {}, "source": [ "## Cluster as a context manager\n", "\n", "Cluster can also be used as a Context manager,\n", "in which case:\n", "\n", "1. entering the context manager starts the cluster\n", "2. the `as` returns a connected client\n", "3. the context is only entered when all the engines are fully registered and available\n", "4. when the context exits, the cluster is torn down\n", "\n", "This makes it a lot easier to scope an IPython cluster for the duration of a computation\n", "and ensure that it is cleaned up when you are done." ] }, { "cell_type": "code", "execution_count": 11, "id": "31bf1cfb-9349-46a1-b412-50678368a017", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting 4 engines with \n", "Stopping engine(s): 1623757397-ng0s\n", "Stopping controller\n" ] }, { "data": { "text/plain": [ "{0: 24989, 1: 24991, 2: 24990, 3: 24992}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "\n", "with Cluster(n=4) as rc:\n", " engine_pids = rc[:].apply_async(os.getpid).get_dict()\n", "engine_pids" ] }, { "cell_type": "markdown", "id": "5d2d5f22-8116-45e8-8c82-c24e19f7e0de", "metadata": {}, "source": [ "It can also be async" ] }, { "cell_type": "code", "execution_count": 12, "id": "3c0cdb81-4288-4e3a-b25a-7b0b0ce6016a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting 2 engines with \n", "Controller stopped: {'exit_code': 0, 'pid': 24988}\n", "Stopping engine(s): 1623757400-5fq1\n", "Stopping controller\n" ] }, { "data": { "text/plain": [ "{0: 25058, 1: 25059}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "async with Cluster(n=2) as rc:\n", " engine_pids = rc[:].apply_async(os.getpid).get_dict()\n", "engine_pids" ] }, { "cell_type": "markdown", "id": "b7537226-dc39-4859-bcfb-16c79a16b951", "metadata": {}, "source": [ "## Launcher classes\n", "\n", "IPython's mechanism for launching controllers and engines is called `Launchers`.\n", "These are in `ipyparallel.cluster.launcher`.\n", "\n", "There are two kinds of Launcher:\n", "\n", "- ControllerLauncher, which starts a controller\n", "- EngineSetLauncher, which starts `n` engines\n", "\n", "You can use abbreviations to access the launchers that ship with IPython parallel,\n", "such as 'MPI', 'Local', or 'SGE',\n", "or you can pass classes themselves (or their import strings, such as 'mymodule.MyEngineSetLauncher').\n", "\n", "I'm going to start a cluster with engines using MPI:" ] }, { "cell_type": "code", "execution_count": 13, "id": "b85d2009-d60c-4642-a15c-84c80adcc361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Controller stopped: {'exit_code': 0, 'pid': 25057}\n", "Starting 4 engines with \n" ] } ], "source": [ "import os\n", "os.environ[\"OMPI_MCA_rmaps_base_oversubscribe\"] = \"1\"\n", "\n", "cluster = Cluster(n=4, engines='MPI')\n", "await cluster.start_cluster()\n", "rc = await cluster.connect_client()" ] }, { "cell_type": "code", "execution_count": 14, "id": "91062c98-3ff2-494f-ae02-6cb96648b6ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc.wait_for_engines(4)\n", "rc.ids" ] }, { "cell_type": "markdown", "id": "9274020a-a556-4d4c-a67b-170b448d1a91", "metadata": {}, "source": [ "Now I'm going to run a test with another new feature" ] }, { "cell_type": "code", "execution_count": 15, "id": "9a078e90-bdf8-4f8e-aedc-bcabf2c9ba87", "metadata": {}, "outputs": [ { "ename": "TimeoutError", "evalue": "Result not ready.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTimeoutError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_24747/824703262.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muhoh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/dev/ip/parallel/ipyparallel/client/asyncresult.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Result not ready.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTimeoutError\u001b[0m: Result not ready." ] } ], "source": [ "def uhoh():\n", " import time\n", " from mpi4py import MPI\n", "\n", " rank = MPI.COMM_WORLD.rank\n", " if rank == 0:\n", " print(\"rank 0: oh no.\")\n", " 1 / 0\n", " print(f\"rank {rank}: barrier\")\n", " MPI.COMM_WORLD.barrier()\n", "\n", "\n", "ar = rc[:].apply_async(uhoh)\n", "ar.get(timeout=2)" ] }, { "cell_type": "markdown", "id": "f69526d7-4c39-422e-8654-76fbac9ce14e", "metadata": {}, "source": [ "Uh oh! We are stuck in barrier because engine 0 failed.\n", "\n", "Let's try interrupting and getting the errors:" ] }, { "cell_type": "code", "execution_count": null, "id": "ebe3e6d1-10cf-49be-83b6-da8dbaf712b1", "metadata": {}, "outputs": [], "source": [ "import signal\n", "await cluster.signal_engines(signal.SIGINT)\n", "ar.get(timeout=2)" ] }, { "cell_type": "markdown", "id": "afd8854b-2138-458d-b2bb-69acdc009b7f", "metadata": {}, "source": [ "It didn't work! This is because MPI.barrier isn't actually interruptible 😢.\n", "\n", "We are going to have to resort to more drastic measures, and *restart* the engines:" ] }, { "cell_type": "code", "execution_count": 16, "id": "2ae1cb50-965b-400e-b39f-4567df667da2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stopping engine(s): 1623757404-oexv\n", "Starting 4 engines with \n" ] } ], "source": [ "await cluster.restart_engines()" ] }, { "cell_type": "code", "execution_count": 17, "id": "c676323b-1bf7-4983-8139-5b085c2fdf91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "engine set stopped 1623757404-oexv: {'exit_code': -9, 'pid': 25078}\n" ] } ], "source": [ "rc.wait_for_engines(4)\n", "rc.ids" ] }, { "cell_type": "markdown", "id": "7c49fa83-087c-4237-acbe-7d6c8ca208c9", "metadata": {}, "source": [ "We are now back to having 4 responsive engines.\n", "Their IPP engine id may have changed, but I can get back to using them." ] }, { "cell_type": "code", "execution_count": 19, "id": "7e261d39-0793-4207-813a-24caa4ec9a92", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{4: 0, 5: 2, 6: 3, 7: 1}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_rank():\n", " from mpi4py import MPI\n", "\n", " return MPI.COMM_WORLD.rank\n", "\n", "rank_map = rc[:].apply_async(get_rank).get_dict()\n", "rank_map" ] }, { "cell_type": "markdown", "id": "9a67ca12-67f5-4539-939f-80a6e61b5692", "metadata": {}, "source": [ "Finally, clean everything up" ] }, { "cell_type": "code", "execution_count": 20, "id": "060108bb-229b-4829-bf32-0dc7d0db0345", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stopping engine(s): 1623757404-oexv\n", "Stopping controller\n", "Controller stopped: {'exit_code': 0, 'pid': 25076}\n", "engine set stopped 1623757404-oexv: {'exit_code': -9, 'pid': 25154}\n" ] } ], "source": [ "await cluster.stop_cluster()" ] }, { "cell_type": "markdown", "id": "4607e5e6-e585-413f-85aa-281e606f7f82", "metadata": {}, "source": [ "## Connecting to existing clusters\n", "\n", "a Cluster object writes its state to disk,\n", "in a file accessible as `cluster.cluster_file`.\n", "By default, this willb e `$PROFILE_DIR/security/ipcluster-$cluster-id.json`.\n", "\n", "Cluster objects can load state from a dictionary with `Cluster.from_dict(d)`\n", "or from a JSON file containing that information with `Cluster.from_file()`.\n", "\n", "The default arguments for `from_file` are to use the current IPython profile (default: 'default')\n", "and empty cluster id,\n", "so if you start a cluster with `ipcluster start`, you can connect to it immediately with\n", "\n", "```python\n", "cluster = ipp.Cluster.from_file()\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "id": "0da44ee0-2a3f-4b98-b31a-9c71621b9777", "metadata": {}, "outputs": [], "source": [ "import ipyparallel as ipp\n", "cluster = ipp.Cluster.from_file()" ] }, { "cell_type": "code", "execution_count": 3, "id": "0df1b2b2-0d35-45a7-8ad8-c7f55eb52a5f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ ", engine_sets=['1624884556-z9qr'])>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster" ] }, { "cell_type": "markdown", "id": "947edd74-5b1c-42ca-a83a-1755a96c6469", "metadata": {}, "source": [ "`ipp.ClusterManager` provides an API for collecting/discovering/loading all the clusters on your system.\n", "\n", "By default, it finds loads clusters in all your IPython profiles,\n", "but can be confined to one profile or use explicit profile directories." ] }, { "cell_type": "code", "execution_count": 6, "id": "c8b30824-b833-449c-b1eb-71b56ff47ba6", "metadata": {}, "outputs": [], "source": [ "clusters = ipp.ClusterManager().load_clusters()" ] }, { "cell_type": "code", "execution_count": 8, "id": "6c5e2fe0-c45c-4528-8732-d6bd56580885", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'mpi:abc-123': , engine_sets=['1624884663-euj7'])>,\n", " 'default:': , engine_sets=['1624884556-z9qr'])>}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clusters" ] }, { "cell_type": "markdown", "id": "b75d78c5-5f49-436f-b313-c56085f70e43", "metadata": {}, "source": [ "This is the class that powers the new `ipcluster list`" ] }, { "cell_type": "code", "execution_count": 10, "id": "cbf3d3f3-70e1-4c92-b6d5-bf17ea913253", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROFILE CLUSTER ID RUNNING ENGINES LAUNCHER\n", "default '' True 4 Local\n", "mpi abc-123 True 4 MPI\n" ] } ], "source": [ "!ipcluster list" ] }, { "cell_type": "code", "execution_count": 11, "id": "08a6c241-8d05-45fa-bfe1-ba89f4257ade", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2021-06-28 14:53:00.591 [IPClusterStop] Stopping engine(s): 1624884663-euj7\n", "2021-06-28 14:53:00.592 [IPClusterStop] Stopping controller\n" ] } ], "source": [ "!ipcluster stop --profile mpi --cluster-id abc-123" ] }, { "cell_type": "code", "execution_count": 12, "id": "5ec2c3c2-153c-4fbf-b52d-567530533a3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROFILE CLUSTER ID RUNNING ENGINES LAUNCHER\n", "default '' True 4 Local\n" ] } ], "source": [ "!ipcluster list" ] }, { "cell_type": "markdown", "id": "3b3e53cf-4b30-4496-8b84-993daf4fc527", "metadata": {}, "source": [ "The same operation can be done from the Python API:" ] }, { "cell_type": "code", "execution_count": 13, "id": "7be40506-7108-4fbc-8b8f-16e4d478ed84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Stopping engine(s): 1624884556-z9qr\n", "Stopping controller\n" ] } ], "source": [ "cluster = ipp.Cluster.from_file(profile=\"default\", cluster_id=\"\")\n", "await cluster.stop_cluster()" ] }, { "cell_type": "code", "execution_count": 14, "id": "572b2e45-4b20-4f8d-81b0-9334a42e69e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PROFILE CLUSTER ID RUNNING ENGINES LAUNCHER\n" ] } ], "source": [ "!ipcluster list" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } ipyparallel-8.8.0/docs/source/examples/Data Publication API.ipynb000066400000000000000000010411141460376056100246650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython's Data Publication API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython has an API that allows IPython Engines to publish data back to the Client. This Notebook shows how this API works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by enabling matplotlib plotting and creating a `Client` object to work with an IPython cluster." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import ipyparallel as ipp" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = ipp.Client()\n", "dv = c[:]\n", "dv.block = False\n", "dv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple publication" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a simple Python function we are going to run on the Engines. This function uses `publish_data` to publish a simple Python dictionary when it is run." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def publish_it():\n", " from ipyparallel.datapub import publish_data\n", " publish_data(dict(a='hi'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "ar = dv.apply_async(publish_it)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{}, {}, {}, {}]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation loop" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation. To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def simulation_loop():\n", " from ipyparallel.datapub import publish_data\n", " import time\n", " import numpy as np\n", " for i in range(10):\n", " publish_data(dict(a=np.random.rand(20), i=i))\n", " time.sleep(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "ar = dv.apply_async(simulation_loop)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New data will be published by the Engines every second. Anytime we access `ar.data`, we will get the most recently published data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tVtzdKz8pJoSzeHh4YLFYnB3GZSRZ5WKOnz/ApuhVnL1wnM/WzmLy+yOI2jCP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zdhC/nhPakvxLxYqKJSrsHusMDZ8DP+jx4dweEwi818FV+/TuGFU38PLodH12vbbzLNqg7V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\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M1pxVOrOqZ6GM9Qnq6qYso3578A0eSVbRVQMkS4Czr8LFkrPlVh3ulZE/KGLhdRWsvvnx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\nYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 392, "width": 597 } }, "output_type": "display_data" } ], "source": [ "data = ar.data\n", "for i, d in enumerate(data):\n", " plt.plot(d['a'], label='engine: '+str(i))\n", "plt.title('Data published at time step: ' + str(data[0]['i']))\n", "plt.legend()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ipyparallel-8.8.0/docs/source/examples/Futures.ipynb000066400000000000000000000454711460376056100226360ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Futures in IPython Parallel\n", "\n", "The IPython Parallel AsyncResult object extends `concurrent.futures.Future`,\n", "which makes it compatible with most async frameworks in Python.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "107792592f044155ab2d9dec950ca6e7", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipyparallel as ipp\n", "rc = ipp.Cluster(n=4).start_and_connect_sync()\n", "\n", "dv = rc[:]\n", "dv.activate()\n", "dv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do some imports everywhere" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%%px --local --block\n", "import os\n", "import time\n", "import numpy\n", "from numpy.linalg import norm" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "def random_norm(n):\n", " \"\"\"Generates a 1xN array and computes its 2-norm\"\"\"\n", " A = numpy.random.random(n)\n", " return norm(A, 2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The basic async API hasn't changed:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = rc[-1].apply(random_norm, 100)\n", "f" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.854015134508366" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.get()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But the full Futures API is now available:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.854015134508366" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.result()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard futures API has methods for registering callbackes, etc." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I got PID: 12509" ] }, { "data": { "text/plain": [ "12509" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import os\n", "f = rc[-1].apply(os.getpid)\n", "f.add_done_callback(lambda _: print(\"I got PID: %i\" % _.result()))\n", "f.result()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A more complex example shows us how AsyncResults can be integrated into existing async applications, now that they are Futures:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ". [12507, 12506, 12508, 12509]\n", ". . [12507, 12506, 12508, 12509]\n", ". . . [12507, 12506, 12508, 12509]\n", ". . . . [12507, 12506, 12508, 12509]\n" ] } ], "source": [ "import asyncio\n", "from tornado.ioloop import IOLoop\n", "import sys\n", "\n", "def sleep_task(t):\n", " time.sleep(t)\n", " return os.getpid()\n", "\n", "async def background():\n", " \"\"\"A backgorund coroutine to demonstrate that we aren't blocking\"\"\"\n", " while True:\n", " await asyncio.sleep(1)\n", " print('.', end=' ')\n", " sys.stdout.flush() # not needed after ipykernel 4.3\n", "\n", "async def work():\n", " \"\"\"Submit some work and print the results when complete\"\"\"\n", " for t in [ 1, 2, 3, 4 ]:\n", " ar = rc[:].apply(sleep_task, t)\n", " result = await asyncio.wrap_future(ar) # this waits\n", " print(result)\n", "\n", "bg = asyncio.Task(background())\n", "await work()\n", "bg.cancel();\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So if you have an existing async application using coroutines and/or Futures,\n", "you can now integrate IPython Parallel as a standard async component for submitting work and waiting for its results." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Executors\n", "\n", "Executors are a standard Python API provided by various job-submission tools.\n", "A standard API such as Executor is useful for different libraries to expose this common API for asynchronous execution,\n", "because it means different implementations can be easily swapped out for each other and compared,\n", "or the best one for a given context can be used without having to change the code.\n", "\n", "With IPython Parallel, every View has an `.executor` property, to provide the Executor API for the given View.\n", "Just like Views, the assignment of work for an Executor depends on the View from which it was created.\n", "\n", "You can get an Executor for any View by accessing `View.executor`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex_all = rc[:].executor\n", "ex_all.view.targets" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12507\n", "12508\n", "12507\n", "12508\n", "12507\n", "12508\n", "12507\n", "12508\n", "12507\n", "12508\n" ] } ], "source": [ "even_lbview = rc.load_balanced_view(targets=rc.ids[::2])\n", "ex_even = even_lbview.executor\n", "for pid in ex_even.map(lambda x: os.getpid(), range(10)):\n", " print(pid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typically, though, one will want an Executor for a LoadBalancedView on all the engines.\n", "This is what the top-level `Client.executor()` method will return:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex = rc.executor()\n", "ex.view" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a few compatible Executor instances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Executors\n", "\n", "Let's make a few Executors. Aside: [dask.distributed][] is a great library. Any IPython Parallel cluster can be bootstrapped into a dask cluster.\n", "\n", "[dask.distributed]: https://distributed.readthedocs.io\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There *can* be serialization differences, especially for interactively defined functions (i.e. those in defined in a notebook itself).\n", "That's why we define our task function in a local module,\n", "rather than here. ProcessPoolExecutor doesn't serialize interactively defined functions.\n", "But for the most part working with functions defined in modules works consistently across implementations." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Generates a 1xN array and computes its 2-norm\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pycat task_mod.py\n", "from task_mod import task" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [], "source": [ "def task(n):\n", " \"\"\"Generates a 1xN array and computes its 2-norm\"\"\"\n", " import numpy\n", " from numpy.linalg import norm\n", " A = numpy.ones(n)\n", " return norm(A, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\n", "import distributed\n", "\n", "distributed_client = rc.become_dask()\n", "dist_ex = distributed_client.get_executor()\n", "\n", "N = 4\n", "ip_ex = rc.executor(targets=range(N))\n", "thread_ex = ThreadPoolExecutor(N)\n", "process_ex = ProcessPoolExecutor(N)\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [] }, "outputs": [], "source": [ "executors = [process_ex, thread_ex, ip_ex, dist_ex]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can submit the same work with the same API,\n", "using four different mechanisms for distributing work.\n", "The results will be the same:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for executor in executors:\n", " print(executor.__class__.__name__)\n", " it = executor.map(str, range(5))\n", " print(list(it))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This makes it easy to compare the different implementations. We are going to submit some dummy work—allocate and compute 2-norms of arrays of various sizes." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1048576, 1261463, 1517571, 1825676, 2196334, 2642245,\n", " 3178688, 3824041, 4600417, 5534417, 6658042, 8009791,\n", " 9635980, 11592325, 13945857, 16777216])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sizes = np.logspace(20, 24, 16, base=2, dtype=int)\n", "sizes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the work locally, to get a reference:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local time:\n", "CPU times: user 765 ms, sys: 403 ms, total: 1.17 s\n", "Wall time: 874 ms\n" ] } ], "source": [ "print(\"Local time:\")\n", "%time ref = list(map(task, sizes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then run again with the various Executors:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProcessPoolExecutor\n", "246 ms ± 86 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "ThreadPoolExecutor\n", "182 ms ± 32.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "ViewExecutor\n", "228 ms ± 24.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "ClientExecutor\n", "246 ms ± 27.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "for executor in executors:\n", " print(executor.__class__.__name__)\n", " result = executor.map(task, sizes)\n", " rlist = list(result)\n", " assert rlist == ref, \"%s != %s\" % (rlist, ref)\n", " # time the task assignment\n", " %timeit list(executor.map(task, sizes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this toy work, the stdlib ThreadPoolExecutor appears to perform the best.\n", "That's useful info, and likely to be true for most workloads that release the GIL and fit comfortably into memory.\n", "When the GIL is involved, ProcessPoolExecutor is often best for simple workloads.\n", "\n", "One benefit of IPython Parallel or Distributed Executors over the stdlib Executors is that they do not have to be confined to a single machine.\n", "This means the standard Executor API lets you develop small-scale parallel tools that run locally in threads or processes,\n", "and then extend the *exact same code* to make use of multiple machines,\n", "just by selecting a different Executor.\n", "\n", "That seems pretty useful. [joblib][] is another package to implement standardized APIs for parallel backends,\n", "which IPython Parallel [also supports](joblib.ipynb).\n", "\n", "[joblib]: https://joblib.readthedocs.io" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "107792592f044155ab2d9dec950ca6e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_2d6218bb526d44f0acda723f0c6dc861", "IPY_MODEL_acda3a9b8ac74e12bfcc6408aa8b11ad", "IPY_MODEL_30dc8b7299de4c6f869fa41aebba7f45" ], "layout": "IPY_MODEL_412b13dee37849cd97baf8ab5f0552f3" } }, 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"model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "97f0bee4e8c64f608146df6c4b9e30c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "a17b92b8857c4bd5b883c5615021c713": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "acda3a9b8ac74e12bfcc6408aa8b11ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_c80f0d14fe6542e0b25385efb22e54e5", "max": 4, "style": "IPY_MODEL_97f0bee4e8c64f608146df6c4b9e30c0", "value": 4 } }, "c80f0d14fe6542e0b25385efb22e54e5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "fd97190cd7094a429ec44137db60db39": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/Monitoring an MPI Simulation - 1.ipynb000066400000000000000000002034311460376056100266460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_start": false } }, "source": [ "# Interactive monitoring of a parallel MPI simulation from a notebook\n", "\n", "first, start a cluster with mpi.\n", "\n", "Or load an existing cluster with e.g.\n", "\n", "```python\n", "cluster = Cluster.from_file(profile=\"mpi\")\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_start": false } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from IPython.display import display\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b14b3b6f0cb4a67be4645c8274c4074", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00" ] }, "metadata": { "image/png": { "height": 365, "width": 572 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Monitoring interrupted, simulation is ongoing!\n", "Monitored for: 0:00:19.277042.\n" ] } ], "source": [ "monitor_simulation(refresh=1);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_start": false } }, "source": [ "If you execute the following cell before the MPI code is finished running, it will stop the simulation at that point, which you can verify by calling the monitoring again:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "slideshow": { "slide_start": false } }, "outputs": [], "source": [ "view['stop'] = True" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[stdout:0] \n", "{\n", " \"shell_port\": 57579,\n", " \"iopub_port\": 57580,\n", " \"stdin_port\": 57581,\n", " \"control_port\": 57583,\n", " \"hb_port\": 57582,\n", " \"ip\": \"127.0.0.1\",\n", " \"key\": \"944dac79-816e4a12e48b45baf11ab5e8\",\n", " \"transport\": \"tcp\",\n", " \"signature_scheme\": \"hmac-sha256\",\n", " \"kernel_name\": \"\"\n", "}\n", "\n", "Paste the above JSON into a file, and connect with:\n", " $> jupyter --existing \n", "or, if you are local, you can connect with just:\n", " $> jupyter --existing /Users/minrk/.ipython/profile_default/security/kernel-46128.json\n", "or even just:\n", " $> jupyter --existing\n", "if this is the most recent Jupyter kernel you have started.\n" ] } ], "source": [ "%%px --target 0 --block\n", "from ipyparallel import bind_kernel; bind_kernel()\n", "%connect_info" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "%%px --target 0 --block\n", "%qtconsole" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "02f2d94254d542bdbd3cff7f9bc035e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_4cdefd8ae5764be8a8bf1f3c374492f0", "max": 4, "style": "IPY_MODEL_d101b978f358462fbcc0ad51c4be1ee1", "value": 4 } }, 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"metadata": {}, "source": [ "# Interactive visualization of MPI simulaitons\n", "\n", "In this example, which builds on our previous one of interactive MPI monitoring, we now demonstrate how to use the IPython data publication APIs.\n", "\n", "\n", "\n", "## Load IPython support for working with MPI tasks\n", "\n", "First, we create a cluster using `mpi` to launch the engines.\n", "This launches the engines using `mpiexec` so they are in the same mpi universe.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da09992ca3fd438eadf96a6599d5384e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00" ] }, "metadata": { "image/png": { "height": 365, "width": 572 } }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Simulation completed!\n", "Monitored for: 0:00:11.457755.\n" ] } ], "source": [ "monitor_simulation(ar, refresh=1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "31b6b4a0105148e187aadfc70e0fe1c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_c872eb3aedc4492eb5a7d3b3c24249bb", "style": "IPY_MODEL_7b50ee6a201f48f796c337a68ba8730c", "value": " 4/4 [00:05<00:00, 5.23s/engine]" } }, "401e2a9344964c7082435def275db2c8": { "model_module": "@jupyter-widgets/base", 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"model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/Monte Carlo Options.ipynb000066400000000000000000005032141460376056100247120ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallel Monto-Carlo options pricing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to use `ipyparallel` to do Monte-Carlo options pricing in parallel. We will compute the price of a large number of options for different strike prices and volatilities." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "import time\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the basic parameters for our computation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "price = 100.0 # Initial price\n", "rate = 0.05 # Interest rate\n", "days = 260 # Days to expiration\n", "paths = 10000 # Number of MC paths\n", "n_strikes = 6 # Number of strike values\n", "min_strike = 90.0 # Min strike price\n", "max_strike = 110.0 # Max strike price\n", "n_sigmas = 5 # Number of volatility values\n", "min_sigma = 0.1 # Min volatility\n", "max_sigma = 0.4 # Max volatility" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "strike_vals = np.linspace(min_strike, max_strike, n_strikes)\n", "sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Strike prices: [ 90. 94. 98. 102. 106. 110.]\n", "Volatilities: [ 0.1 0.175 0.25 0.325 0.4 ]\n" ] } ], "source": [ "from __future__ import print_function # legacy Python support\n", "print(\"Strike prices: \", strike_vals)\n", "print(\"Volatilities: \", sigma_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monte-Carlo option pricing function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following function computes the price of a single option. It returns the call and put prices for both European and Asian style options." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):\n", " \"\"\"\n", " Price European and Asian options using a Monte Carlo method.\n", "\n", " Parameters\n", " ----------\n", " S : float\n", " The initial price of the stock.\n", " K : float\n", " The strike price of the option.\n", " sigma : float\n", " The volatility of the stock.\n", " r : float\n", " The risk free interest rate.\n", " days : int\n", " The number of days until the option expires.\n", " paths : int\n", " The number of Monte Carlo paths used to price the option.\n", "\n", " Returns\n", " -------\n", " A tuple of (E. call, E. put, A. call, A. put) option prices.\n", " \"\"\"\n", " import numpy as np\n", " from math import exp,sqrt\n", " \n", " h = 1.0/days\n", " const1 = exp((r-0.5*sigma**2)*h)\n", " const2 = sigma*sqrt(h)\n", " stock_price = S*np.ones(paths, dtype='float64')\n", " stock_price_sum = np.zeros(paths, dtype='float64')\n", " for j in range(days):\n", " growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))\n", " stock_price = stock_price*growth_factor\n", " stock_price_sum = stock_price_sum + stock_price\n", " stock_price_avg = stock_price_sum/days\n", " zeros = np.zeros(paths, dtype='float64')\n", " r_factor = exp(-r*h*days)\n", " euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))\n", " asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))\n", " euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))\n", " asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))\n", " return (euro_call, euro_put, asian_call, asian_put)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can time a single call of this function using the `%timeit` magic:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 174 ms, sys: 5.96 ms, total: 180 ms\n", "Wall time: 211 ms\n" ] }, { "data": { "text/plain": [ "(12.166236181100073,\n", " 7.6440122060909745,\n", " 6.8409606562778666,\n", " 4.4639448434953621)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time result = price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000)\n", "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallel computation across strike prices and volatilities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Client is used to setup the calculation and works with all engines." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ipyparallel as ipp\n", "rc = ipp.Client()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `LoadBalancedView` is an interface to the engines that provides dynamic load\n", "balancing at the expense of not knowing which engine will execute the code." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "view = rc.load_balanced_view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Submit tasks for each (strike, sigma) pair. Again, we use the `%%timeit` magic to time the entire computation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "async_results = []" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 127 ms, sys: 15.7 ms, total: 143 ms\n", "Wall time: 1.97 s\n" ] } ], "source": [ "%%time\n", "\n", "for strike in strike_vals:\n", " for sigma in sigma_vals:\n", " # This line submits the tasks for parallel computation.\n", " ar = view.apply_async(price_option, price, strike, sigma, rate, days, paths)\n", " async_results.append(ar)\n", "\n", "rc.wait(async_results) # Wait until all tasks are done." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(async_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Process and visualize results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Retrieve the results using the `get` method:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results = [ar.get() for ar in async_results]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assemble the result into a structured NumPy array." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prices = np.empty(n_strikes*n_sigmas,\n", " dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]\n", ")\n", "\n", "for i, price in enumerate(results):\n", " prices[i] = tuple(price)\n", "\n", "prices.shape = (n_strikes, n_sigmas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the value of the European call in (volatility, strike) space." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JmGFKxBmYCGEGxkeYgfkJM8BOwgxTI87ABAgzMD7CDAAsRphhisQZGDlhBsZH\nmIGjMTUDHCPMMFVepQ0jJszA+AgzcDTCDJCIMkzfZONMKeXKSU5JckKt9bL7HHO/JL+Y5JpJPpPk\n1CSPqLV+YtdxpyW51R6naJM8rdb64BUuHeYizMD4CDNwNMIMkAgzrMecDeGeSX45yXWSfC7JS5M8\nandDWMQkb2sqpVw/yVuS3OCAY56Y5OQkr0py5ySPSvL9SU4rpVxy1+FtkjcluXWS2+z4um2SZ6x0\n8TAHYQbGR5gBgKMTZliHORvCY5I8N8np+XpDuF2SN+3REI5scpMzpZSTkrwkyXuTvDzJPfY45sZJ\nHpLk3rXW5+7YfkqSd6X7IT9k18c+VWt9Y1/rBmC6hBk4OlMzgDDDOszZEK6Q5NeT/O9a6yN2bD91\n9rkHJ3nMMuuY4uTMPZO8PckPpLtVaS/3SXLmzjCTJLXWjyf5nSQ/W0qZXLhiGkzNwLgIM3B0wgwg\nzLBG8zSEayZpkrxm58Za64eSvCfJdyy7iCnGmfsmuX2t9UsHHHPrJK/eZ98pSS6T5EarXhgsS5iB\ncRFm4OiEGUCYYc3maQj/nOQ/klx/58ZSyrckuUa6QLOUyU2H1Fq/MsdhJ6YbPdrL+9IVsROTnLFj\n+81KKWcmuXKSjyd5cZLH1lo/v8RyYW7CDIyLMAMARyfMsG7zNIRa66dLKU9K8vhSyjlJXpfkKkme\nkOSzSZ657DqmODlzoFLKpZIcl2TPf2qutX4xyflJLrdj8xeS/Fm6+8h+LMkLk/xCkjeUUr651wVD\nhBkYG2EGFmNqBrbXBe/+lDDDoNVa/1e6FvAnST6Z5B1JrpfkpFrrfrdDzW1ykzNzOPYU5XMPOObc\nJJfe8fufqLWev+P3f1FK+askf5HkYVnywT9wEGEGxkWYgcUIM7C9RBnGoJTy5CQ/n+SR6d7YdMV0\nDwk+rZRyi1rrR5c5/9ZNzqSbgkmS4w845vh07yxPkuwKM8e2/WW659Nc6EnOsCrCDIyLMAOLEWZg\newkzjEEp5Tbp7qS5S631N2qtb6y1viTJTZJ8Jckzlr3G1sWZ2TNizk/30N8LmT3Q57gkn57jdH+Z\n5Bre7EQfhBkYF2EGAI5GmGFE7prkA7XWV+3cWGs9L12YuWMp5aABkENta1Q4M8l19tl33R3HHOa8\nJG2SC1axKDhGmIFxEWZgcaZmYDsJM9NxsRteZdNLWIcTknxwn30fTDf4ctUkH1j0Als3OTNzWpLb\n7bPvDukewQhuAAAgAElEQVQeFvyOYxtKKdfa59hbJnlfrVWcYWWEGRgXYQYWJ8zAdhJmGKHPpHuj\n816uteOYhW1rnHl2kmuWUn5258ZSyhWT3D/JH9Ravzrb9vgk/1BK+c5dx94iyV1m54KVEGZgXIQZ\nWJwwA9tJmGGk/jRdQ/hvOzfObmX6pSSn1VrneTTKvrbytqZa6xmllJOT/G4p5dpJ3pTkSkkemm5q\nZufbl34/yb3SPYH5CUnek+6hPw9P8tokT1/n2pkuYQbGRZgBgPmJMoxZrfVVpZQ/TPLCUspTk/xN\nkm9P8oAkl0tyx2Wvsa2TM6m1PiTJL6f7IdZ0Qeb0JLeaPTT42HFnp4sxr5wd//IkJd3rs+7oliZW\nQZiBcRFmYDmmZmC7CDNMQa31v6cb6LhDuobwG0n+b5Ib11rft+z5m7Ztlz0HC3r961/fJsnlTvyv\nm14KGyTMwLgIM7AcYQa2y7aHme++7xWSJCeddFKz4aWs3LE/z/79szb//+tT+Dlv7eQMDIEwA+Mi\nzMByhBnYLtseZuAoxBnYEGEGxkWYAYD5CTNwNOIMbIAwA+MizMDyTM3A9hBm4OjEGVgzYQbGRZiB\n5QkzsD2EGVjMVr5KGzZFmIFxEWZgecIMbAdRBpZjcgbWRJiBcRFmAGA+wgwsT5yBNRBmYFyEGVgN\nUzMwfcIMrIY4Az0TZmBchBlYDWEGpk+YgdURZ6BHwgyMizADqyHMwPQJM7Ba4gz0RJiBcRFmAGA+\nwgysnjgDPRBmYFyEGVgdUzMwbcIM9MOrtGHFhBkYF2EGVkeYgekSZaBfJmdghYQZGBdhBlZHmIHp\nEmagf+IMrIgwA+MizADA4YQZWA9xBlZAmIFxEWZgtUzNwDQJM7A+4gwsSZiBcRFmYLWEGZgmYQbW\nS5yBJQgzMC7CDKyWMAPTJMzA+okzsCBhBsZFmAGAwwkzsBniDCxAmIFxEWZg9UzNwPQIM7A5F930\nAmBshBkYF2EGVk+YgWkRZWDzTM4AMFnCDKyeMAPTIszAMIgzcASmZmA8hBkAOJgwA8MhzsCchBkY\nD2EG+mFqBqZDmIFhEWdgDsIMjIcwA/0QZmA6hBkYHnEGDiHMwHgIM9APYQamQ5iBYRJn4ADCDIyH\nMAMABxNmYLi8Shv2IczAeAgz0B9TMzB+ogwMn8kZ2IMwA+MhzEB/hBkYP2EGxkGcgV2EGRgPYQb6\nI8zA+AkzMB7iDOwgzMB4CDMAsD9hBsZFnIEZYQbGQ5iBfpmagXETZmB8xBmIMANjIsxAv4QZGDdh\nBsZJnGHrCTMwHsIM9EuYgXETZmC8vEqbrSbMwHgIM9AvYQbGS5SB8TM5w9YSZmA8hBkA2JswA9Mg\nzrCVhBkYD2EG+mdqBsZJmIHpEGfYOsIMjIcwA/0TZmCchBmYFnGGrSLMwHgIM9A/YQbGSZiB6RFn\n2BrCDIyHMAMAexNmYJrEGbaCMAPjIczAepiagfERZmC6vEqbyRNmYDyEGVgPYQbGRZSB6TM5w6QJ\nMzAewgyshzAD4yLMwHYQZ5gsYQbGQ5gBgAsTZmB7iDNMkjAD4yHMwPqYmoHxEGZgu4gzTI4wA+Mh\nzMD6CDMwHsIMbB9xhkkRZmA8hBlYH2EGxkOYge0kzjAZwgyMhzADABcmzMD28iptJkGYgfEQZmC9\nTM3A8IkygMkZRk+YgfEQZmC9hBkYPmEGSMQZRk6YgfEQZmC9hBkYPmEGOEacYbSEGRgPYQYAvpEw\nA+wkzjBKwgyMhzAD62dqBoZNmAF2E2cYHWEGxkOYgfUTZmDYhBlgL+IMoyLMwHgIM7B+wgwMmzAD\n7MertBkNYQbGQ5gBgK8TZYDDmJwBYKWEGdgMUzMwTMIMMA9xhlEwNQPjIMzAZggzMEzCDDAvcYbB\nE2ZgHIQZ2AxhBoZJmAGOQpxh0IQZGAdhBgC+TpgBjkqcYbCEGRgHYQY2x9QMDI8wAyxCnGGQhBkY\nB2EGNkeYgeERZoBFeZU2gyPMwDgIM7A5wgwMiygDLMvkDIMizMA4CDMA0BFmgFUQZxgMYQbGQZiB\nzTI1A8MhzACrIs4wCMIMjIMwA5slzMBwCDPAKokzbJwwA+MgzMBmCTMwHMIMsGriDBslzMA4CDMA\n0BFmgD6IM2yMMAPjIMzA5pmagWEQZoC+iDNshDAD4yDMwOYJMzAMwgzQp4tuegFsH2EGxkGYgc0T\nZmDzRBlgHcQZ1kqYgXEQZgBAmIFtUkq5cpJTkpxQa73sHvvPSnK1PT7aJvmftdanL3N9cYa1EWZg\nHIQZGAZTM7BZwgxsj1LK9dOFmask+fw+h7VJXpLkGXvs++dl1yDOsBbCDIyDMAPDIMzAZgkzsD1K\nKSeliy7vTfLyJPc44PB/rbX+dR/r8EBgeifMwDgIMzAMwgxsljADW+eeSd6e5AeSfGZTixBn6JUw\nA+MgzACAMANb6r5Jbl9r/dImF+G2JnojzMA4CDMwHKZmYHOEGdhOtdavHOHwnyillCSXT/KhJM9P\n8lu11vOWXYc4Qy+EGRgHYQaGQ5iBzRBlgDl9Ksnrk7wx3cOB75DkUUluNvvPSxFnWDlhBsZBmIHh\nEGZgM4QZ4AhuWms9f8fvTy2lvCPJs0op96q1Pm+Zk3vmDCslzMA4CDMAbDthBjiKXWHm2LZnJ/nH\nHPyGp7mIM6yMMAPjIMzAsJiagfUTZoAVel2S6y57EnGGlRBmYByEGRgWYQbWT5gBVuy8JBeaqjkq\nz5xhacIMjIMwA8MizMD6CTOwehf5L5ff9BLWopRyzVrrmXvs+r50tzYtxeQMSxFmYByEGQC2nTAD\nLKqU8sdJ3lJKueKu7T+V7m1Nz172GiZnWJgwA+MgzMDwmJqB9RFlgBX4P+lel/2WUsoTkpyd5IeS\nPCDJc2utL1/2AiZnWIgwA+MgzMDwCDOwPsIMsAq11renm5D52ySPTfLSJLdKcp9a671XcQ2TMxyZ\nMAPjIMzA8AgzsD7CDHBUtdbHJHnMPvv+Kcld+7q2yRmORJiBcRBmANhmwgwwNuIMcxNmYByEGRgm\nUzOwHsIMMEbiDHMRZmAchBkYJmEG1kOYAcZKnAGYCGEGhkmYgfUQZoAx80BgDmVqBoZPmAFgW4ky\nwBSYnOFAwgwMnzADw2VqBvolzABTIc6wL2EGhk+YgeESZqBfwgwwJeIMexJmYPiEGRguYQb6JcwA\nUyPOcCHCDAAAQyXMAFMkzvANhBkYB1MzMFymZqA/wgwwVeIMXyPMwDgIMzBcwgz0R5gBpsyrtBFl\nYCREGRg2YQb6I8wAU2dyZssJMzAOwgwMmzAD/RFmgG1gcmaLCTMwfKIMDJ8wA/0RZoBtIc5sIVEG\nxkGYgeETZqA/wgywTcSZLSPMwDgIMzBsogz0S5gBto04syVEGRgHUQaGT5iBfgkzwDbyQOAtIMzA\nOAgzMHzCDPRLmAG2lcmZCRNlYDyEGRg+YQb6JcwA20ycmShhBsZBlIFxEGagX8IMsO3c1jRBwgyM\ngzAD4yDMQL+EGQCTM5MiysA4iDIwDqIM9E+YAeiYnJkIYQbGQZiBcRBmoH/CDMDX9To5U0q5aZIf\nSHKVJK+utb5ytv34Wuu5fV57W4gyMB7CDIyDMAP9E2YAvlEvcaaUcuUkf5zkVkmaJG2SjyV55Wzf\n35RSfqXW+uI+rr8thBkYB1EGxkOYgf4JMwAXtvLbmkopl0xyWpIbJfnlJN+TLtAcc266UPOHpZRr\nrfr62+CD//YFYQZGQpiB8RBmAIBN6eOZM7+S5OpJTqq1nlxr/fudO2utn01yp9lvH9LD9SdNlIHx\nEGZgPIQZWA9TMwB76yPO/GSS1+yOMjvVWj+a5JVJfqiH60+WMAPj8LGzzxFmYCTOfe8nhRlYE2EG\nYH99xJmrJ/mnOY77YJJv7+H6k+M2JhgPUQbGQ5SB9RFmAA7WxwOBv5jk0nMcd8Uk/hRzCFEGxkGU\ngXERZmB9hBmAw/UxOfPmJD9eSrnUfgeUUq6U5CeSnN7D9SfBtAyMhzAD4yLMwPoIMwDz6SPOPCXJ\ntyV5VinluN07SymXS/LCJJeaHcsuogyMhzAD4yLMwPoIMwDzW3mcqbW+Mckjk9wlydtKKb8023WD\nUspjkvxjklsn+dVa69+t+vpjZloGxsNDf2F8hBlYH2EG4Gj6mJxJrfU3ktw1ybcmefps852SPCLd\nM2nuVGt9Uh/XHitRBsZDlIFx8UYmWC9hBuDo+nggcJKk1lqT1FLKjZJca7b5/bXWd/V1zbESZmA8\nhBkYF1EG1kuYAVhML3GmlHKRJG2tta21viPJO/bb38f1AVZNlIHxEWZgvYQZgMX1cltTkhckeeIB\n+38ryR/0dG2AlRJmYHyEGVgvYQZgOSuPM6WUn0r3vJmLH3DYNyW5RymlrPr6AKviob8wTsIMrJcw\nA7C8PiZn7p/k3bXWBx5wzIOSvCfJLx1wDMDGiDIwTsIMrJcwA7AafcSZ70rymoMOmD1r5i9mxwIM\nijAD4yTMwHoJMwCr09fbmpoVHwfQO1EGxkmUAQDGro/JmfcnudUcx90iyQd6uD7AkQkzME7CDGyG\nqRmA1eojzrw0yY1LKT+53wGllDsnuUmSl/VwfYAjEWZgnIQZ2AxhBmD1+rit6elJfj7J80spJyZ5\nVq31s0lSSvnWJPdL8ogkZyU5uYfrA8xFlIHxEmZgM4QZgH6sfHKm1vrFJD+c5ENJfjPJJ0spHy6l\nfDjJJ5M8PsmHk9xudizA2gkzMF7CDGyGMAPQnz5ua0qt9f1JbpBuSubUJJ+ffZ2a5L5JblhrfV8f\n1wY4yMfOPkeYgRETZmAzhBmAfvX1tqbUWs9L8vuzL4CNE2VgvEQZ2BxhBqB/vUzOAAyNMAPjJczA\n5ggzAOux8ORMKeVySS5Ra/3wCtcDsFKiDIybMAObI8wArM8ytzX9bZIrlFKuVmv92p9+SikXJGnn\nPEdba+3t1ipguwkzMG7CDGyOMAPLO+9dH0lyhU0vg5FYJoy8L8m/Jzl31/bnZ/44A7ByogyMnzAD\nmyPMwPK6MAPzWzjO1Fpvv8/2n1l4NQBLEmZg/IQZ2BxhBpYnzLCIld9SVEr5qSRv8iwaYN2EGRg3\nUQY2S5iB5QkzLKqPtzW9IMl9ezgvwJ4+dvY5wgyMnDADmyXMwPKEGZbRR5z5cJLL9XBegAsRZWD8\nhBnYLGEGlifMsKw+4swrktyplHLlHs4NkMS0DEyFMAPAmJ33ro8IM6xEH3HmEUnOTPKXpZSb9nB+\nYMuJMjANwgxsnqkZWJwowyqt/IHASU5O8v4kP57kzaWUDyU5K3u/XruttZ7UwxqACRJlYDqEGdg8\nYQYWJ8ywan3Eme9PF2I+PftKkqv3cB1giwgzMA2iDAyDMAOLE2bow8rjTK316qs+J7DdhBmYBmEG\nhkGYgcUJM/Slj8kZgJUQZWA6hBkYBmEGFifM0Kel40wp5SJJbpLkakm+lORva62fWPa8wHYTZmAa\nRBkYDmEGFifM0Lel4kwp5eZJXpDkhB2bLyilPDPJg2qtFyxzfmD7iDIwHcIMDIcwA4sTZliHheNM\nKeWqSV6d5JJJ/jrJGUmukOR2SX4xyblJHraCNQJbQpiB6RBmYDiEGVicMMO6LDM58z/ThZkH1lp/\n+9jGUsoVkvxNkgeUUp5Qa/3skmsEtoAwA9MgysCwCDOwOGGGdbrIEp/9wSTv2xlmkqTW+skkj0py\n8SS3XeL8wBb42NnnCDMwEcIMDIswA4sTZli3ZeLM1ZOcts++189+veYS5wcmTpSB6RBmYFiEGVic\nMMMmLHNb07ck2e+tTMf+Ce34Jc4PTJQoA9MhysDwCDOwGFGGTVpmciZJzt9r4463NDVLnh+YGGEG\npkOYgeERZmAxwgybttSrtAHmJcrAtAgzMDzCDCxGmGEIlo0zPzp7pfai+9ta632XXAMwcMIMTIco\nA8MkzMBihBmGYtk4892zr0X3t0l6iTOllCsnOSXJCbXWy+5zzP2S/GK6Bxd/JsmpSR5Ra73Qs3RK\nKXdO8tAk103ypSSnJ/m1WuuZfawfpkKYgekQZmB4RBlYnDDDToc1hFJKk+Tnktw/yXWSnJvuJUmP\nq7W+a9nrL/PMmWus4KuXtzmVUq6f5C1JbnDAMU9McnKSVyW5c7rXf39/ktNKKZfcdez9k7woyRlJ\n7pbkIUlOTPLmUsrV+vgeYOy8Ihum49z3flKYgQESZmBxwgw7zdMQkjw9ye8n+dskd03y4CQnJHlL\nKeV7ll3DwpMztdazl714H0opJyV5SZL3Jnl5knvsccyN0wWWe9dan7tj+ylJ3pUu1Dxktu3KSZ6U\n5PG11kfuOPZl6WLN/0kXd4AZUQamQ5SBYRJmYHHCDDvN2RBumO6um0fWWn9jx/aa5O+TPDHJScus\nY9m3NQ3RPZO8PckPpLtVaS/3SXLmzjCTJLXWjyf5nSQ/W0o5Fq7uleQ/kjxh17Ffnm370VLKFVe3\nfBgv0zIwLcIMDJMwA4sTZtjDPA3hYkmekuS3d26stf57kpcmucmyi5ji25rum+SCWut5pZT9jrl1\nklfvs++UJI9OcqN0kzG3TnL6LMbsdexFk9wyXWmDrSXKwHSIMjBcwgwsTphhH4c2hFrrGen6wF4u\nnuSryy5icnGm1vqVOQ47Md3I0l7et+OYM5JcK10J2+tany6lfGZ2LGwtYQamQ5iB4RJmYHHCDPuZ\nsyHsqZRysSR3SffCoKVM8bamA5VSLpXkuCR7/mmy1vrFJOcnudxs02X3O3bmnB3HwlZxGxNMizAD\nwyXMwOKEGXr0pCRXTffMmaVMbnJmDsfexHTuAcecm+TSO44/6Ngv7zgWtoYoA9MhysCwCTOwGFGG\nPpVSHpTkfyR5Sq31LcuebxvjzBdmvx5/wDHHJ/ncjuMPOvYSO46FyRNlYFqEGRg2YQYWI8zQp1LK\nPdI9IPjlSR62inNu3W1NtdbPp7tt6TJ77S+lfEu6254+Pdv0mf2Onbn0jmNh0oQZmBZhBoZNmIHF\nCDP0qZRyxyTPSfK6JHettV6wivNu4+RMkpyZ5Dr77Lvu7Nd/2fHrnseWUi6X7nkzZ650dTAwogxM\niygDwyfMwGKEmfU7/rpX2PQSkqzn/zNLKbdM8qdJ/ibJj9Vaz1vVuXubnCmlXLaU8vBSyutKKf9U\nSnnAjn3/ta/rzum0JLfbZ98d0j3k9507jr11KWWvW5vukG4K540rXh8MhjAD0yLMwPAJM7AYYYY+\nlVJulOSVSd6V5EeWecvTXnqJM6WU26Z7VfVvJvmeJN+R2a1BpZTrJ3lLKeUpfVx7Ts9Ocs1Sys/u\n3FhKuWKS+yf5g1rrsfeUPz/JxZI8fNexl0h3b9kra63/1v+SYf2EGZiOc9/7SWEGRkCYgcUIM/Sp\nlHKtJK9J8sEkPzx7y/NKrfy2plLKNdPVpPcm+cFa6ztKKV+7B6vW+o+llOcneVAp5c9rrW9Y9RoO\nU2s9o5RycpLfLaVcO8mbklwpyUPTTc08ZsexHymlPDTJyaWUyyd5dbrnzDwg3S1ND173+qFvogxM\niygD4yDMwGKEGdagpntR0G8muVEpZa9j3rLMbU59TM78WpLzktyh1vqOfY75pSQfT/faqY2otT4k\nyS8nuWO6H/Rjkpye5FazhwbvPPYZSe6W5HuTvDDdU5k/mOQWtdaz17lu6JswA9MizMA4CDOwGGGG\nNfnWJP8pyYuS/NU+X9++zAX6eCDwDyX5i1rrJ/Y7oNZ6Xinlz9OFkd7UWh+THVMwe+z/3SS/O+e5\narqIA5MkysC0iDIwHsIMLEaYYdX2awi11mv0fe0+4swVkpw1x3GfzMGvqAbWRJiBaRFmYBxEGVic\nMMPU9HFb08eTXH2O474zif9FwYYJMzAtwgyMgzADixNmmKI+4sxrkvzE7GnGeyqlfG+6W5pO6eH6\nwBw+dvY5wgxMiLcxwXgIM7CY8971EWGGyeojzvxmkv9I8uJSygm7d5ZSbpXkz5J8MckTe7g+cAhR\nBqZFlIHxEGZgMaIMU7fyODN7e9Gdk5yY5B9KKS+Z7bpjKeX0dE8xvmSSO9daP7rq6wP7My0D02Ja\nBsZFmIHFCDNsgz4mZ1Jr/YskN0ry0iS3nW2+cZLrJ/nDJN9Va319H9cG9ibKwLSIMjAuwgwsRphh\nW/TxtqYkSa31zCQ/U0ppklxuts3flWDNRBmYHmEGxkWYgcUIM2yTlceZUspFaq0XHPt9rbVNsuff\nkUopP1ZrfcWq1wB0hBmYFlEGxkeYgcUIM2ybPm5resFsWuZApZSfTlJ7uD4QYQamRpiB8RFmYDHC\nDNuoj9ua7prkq0nutd8BpZT7JHlmks/3cH3YaqIMTI8wA+MjzMBihBm2VR+TMy9NcvdSynP22llK\neXCS30vyyXz9YcHACggzMC3exgTjJMzAYoQZtlkfceYuSV6c7mHAv7dzRynlUUmenOTsJN9Xa31H\nD9eHreMV2TA9ogyMkzADixFm2HYrjzO11vOT3C3JnyS5dynlGUlSSnlKkkcleU+SW9Ra/3nV14Zt\nJMrA9AgzME7CDCxGmIGeXqVda72glHKPJP+R5P6llFsluX6StyW5fa31s31cF7aNMAPTIsrAeAkz\ncHSiDHxdH7c1JfnaK7R/Nslz0oWZ1yb5fmEGluc2JpgeYQbGS5iBoxNm4BstNDlTSnnkEQ7/1yRn\nJTkjya+UUnbua2utj1tkDbCtRBmYFlEGxk2YgaMTZuDCFr2t6dELfObX9tjWJhFnYA6iDEyPMAPj\nJszA0QkzsLdF48w1VroK4EDCDEyPMAPjJcrAYoQZ2N9CcabWevaqFwJcmCgD0yPKwLgJM7AYYQYO\n1tsDgYHlCDMwPcIMjJswA4sRZuBwC79Ku5RynSSXqbW+bdf2qx3lPLXWDy26BpgqYQamR5iBcRNm\nYDHCDMxn4TiT5PQk31pKuUqtdeffrc5K96DfeR23xBpgUkQZmB5RBsZPmIHFCDMwv2XizMuSnJDk\ns7u2PzZHizNAhBmYImEGxk+YgcUIM3A0C8eZWusv7LP90QuvBraQKAPTJMzA+AkzsBhhBo5umcmZ\nPZVSfjXJqbXWd6763DA1wgxMjygD0yDMwNGJMrC4lceZdLc1XSKJOAMHEGZgeoQZmAZhBo5OmIHl\n9BFnzkpy1R7OC5MgysD0iDIwHcIMHJ0wA8u7SA/nfGGSO5VS/ksP54ZRE2ZgeoQZmA5hBo5OmIHV\n6CPOPD7Ja5O8tpRy11KKV2Wz9T529jnCDEyQMAPTIczA0QkzsDp93Nb0mtmvl0zyx0meV0r5aPZ+\nvXZbaz2xhzXAYIgyMD2iDEyLMANHJ8zAavURZy6SLsS8vYdzw2iIMjBNwgxMizADRyfMwOqtPM7U\nWm+z6nPC2AgzME3CDEyLMANHJ8xAP/qYnIGtJszA9IgyMD3CDBydMAP9WXmcKaWcn+TRtdbHHXLc\nG5J8tdb6g6teA2yCKAPTJMzAtIgysBhhBvrVx9uamtnXYd6W5Ht7uD6snTAD0yTMwLQIM7AYYQb6\nt5HbmkopF0ly401cG1ZJlIFpEmVgeoQZWIwwA+uxdJwppdwryb12bf6ZUsptDrjmiUmulOQFy14f\nNkWYgWkSZmB6hBk4OlEG1msVkzOXSXKNXdu+Nfvf2tQm+VSSP0rymBVcH9ZOmIHpEWVgmoQZODph\nBtZv6ThTa31akqcd+30p5YIkT621PnbZc8PQiDIwTcIMTJMwA0cnzMBm9PFAYJgkYQamSZiBaRJm\n4OiEGdicPh4IfNskZ/VwXtgIUQamSZSB6RJm4OiEGdislceZWuvpe20vpVwqSVNr/dyqrwl9EWZg\nmoQZmC5hBo5OmIHNWyrOlFL+W5Jzaq1/dcAx90jy2CRXm/3+A0l+o9bqTU0MligD0yXMwHQJM3B0\nwgwMw8LPnCmlXD3dq7BfWEo5fp9jfiHJHyY5IclH/l97dx4vzVnXef+bRIclww4iS2QRWWYQER4Q\nHqPsIIvzQGQuFmFQNllGcEFAENnmYdGwiDCCCUhYAvMLCso2jAgBgzKACWEJESHDjpIQSRCiQHLP\nH9UHDidnP93V1VXv9+t1Xid3d3Vf151U6j79ua+qSvKBJNdIckJr7Sn7HRsWSZiBcbrgzLOFGRgx\nYQb2TpiB4TjIBYF/M8klk/xGVV2w8cnW2hWSHDv75VOq6keq6tZJrp3klCRPba3d/ADjw9wJMzBO\nogyMmzADeyfMwLAcJM7cKcnfVNWJWzz/4CSXSvIXVfX/rz1YVV9NckySf0ny2AOMD3Pz5c9+TZiB\nEbJaBsZPmIG9E2ZgeA4SZ45K8rfbPP+wJIeSPHfjE7NA8+Z0d3aCpRJlYJxEGRg/YQb2TpiBYTro\n3Zr+3WYPttb+nyTXT/KZqnr/Fq/9QpKrHHB82DdRBsZLmIHxE2Zgb0QZGLaDxJlPJvmpLZ57aLpV\nM6/b5vXXSHLuAcaHfRNmYJxEGZgGYQb2RpiB4TvIaU1vSXKL1tovrH+wtXbLdNebuSjdnZouprV2\nZJJ7JPm7A4wP+yLMwDgJMzANwgzsjTADq+EgK2dekO66Mq9vrR2X5LQkP5rkkUmOSPLHVfUPG1/U\nWjsiyfFJLpekDjA+7IkoA+MlzMD4iTKwd8IMrI59r5ypqnPT3bHprCSPSPLSJI9Pcpkk70zyuC1e\nev8k90nyd1X16v2OD3shzMA4uRsTTIMwA3snzMBqOdAFgavqo621/5gu0vzHJBcm+WBVnbLNa17d\nWrtpkuccZGzYDVEGxkuUgWkQZmDvhBlYPQe9W1Oq6jtJ3j772u1rfvOg48JOhBkYL2EGpkGYgb0T\nZmA1HTjOwNCIMjBeogxMhzADeyfMwOo6yN2aYHCEGRgvYQamQ5iBvRNmYLVZOcNoCDMwTqIMTIsw\nA3snzMDqE2dYeaIMjJcwA9MizMDeCTMwDuIMK02YgfESZmBahBnYG1EGxkWcYSWJMjBeogxMjzAD\ne5KcgtgAACAASURBVCPMwPi4IDArR5iB8RJmYHqEGdgbYQbGSZxhpQgzMF7CDEyPMAN7I8zAeDmt\niZUgysB4iTIwTcIM7I0wA+MmzjBoogyMmzAD0yPKwN4JMzB+4gyDJMrAuIkyME3CDOydMAPTIM4w\nKKIMjJsoA9MlzADA1sQZBkGUgfETZmCaRBnYP6tmYDrEGZZOmIFxE2VguoQZ2D9hBqZFnGFpRBkY\nP2EGpkuYgf0TZmB6xBl6J8rA+IkyMG3CDOyfMAPTJM7QG1EGpkGYgekSZeBghBnoX2vtyCRPStKS\nHJXknCRvTvLMqvpSX/M4vK+BmK4vf/ZrwgxMwAVnni3MwIQJM3Awwgz0bxZm3pfk0UmOS3KPJE9P\ncrckp7bWrtvXXKycYWEEGZgOUQamTZiBgxFmYGmenOQGSW5WVZ9Ye7C1Vkk+luTF6ULNwlk5w9xZ\nKQPTYbUMTNtFHz9HmIEDEmZgqVqSk9aHmSSpqvOS/EGSO7XWLtvHRMQZ5kqUgekQZWDaRBkARuCo\nJJ/Y4rkz0jWTq/cxEac1MReiDEyHKAMIMzAfVs3A0p2b5BpbPHfN2ffz+5iIlTMciFOYYDqcwgQk\nwgzMizADg/COJPdurV1u/YOttcOSPDTJF/q6Y5OVM+yLIAPTIsoAogzMjzADg/GMJPdMcnJr7TFJ\nTktynSTPSXLzJI/vayJWzrAnVsrAtFgtAyTCDMyTMAPDUVVnJblNku8kOTndKUzvT3J0krOT/FFf\nc7Fyhl0RZGB6RBkgEWZgnoQZGJ6qOj3JLVprRyW5fJIjk7wnyWOr6pt9zUOcYVuiDEyPKAMkogzM\nmzDDWF3tWpdf9hSSbxz8z6yq+nySz7fW/jrJaVX1ygO/6R6IM2xJmIHpEWaARJiBeRNmYDW01h6S\n5Nazr16JM1yMKAPTI8oAa4QZmC9hBlZDa+1K6S4EfEJVfbDv8cUZvkuUgWkSZoA1wgwAE3Zsukby\n28sYXJxBlIGJEmWANaIMLIZVM7AaWmtHJ3lgksdV1VeWMQdxZsJEGZguYQZYI8zAYggzsDqq6pQs\nuY+IMxMkysB0iTLAesIMLIYwA+yVODMxwgxMkygDrCfKwOIIM8B+iDMTIcrAdAkzwHrCDCyOMAPs\nlzgzcqIMTJcoA2wkzMDiCDPAQYgzIyXKwLQJM8BGwgwsjjADHJQ4MzKiDEybKANsJMrAYgkzwDyI\nMyMhygDCDLCRMAMAq0GcWXGiDCDKAJsRZmDxrJoB5uXwZU+A/RNmAGEG2IwwA4snzADzZOXMChJl\nAFEG2IwoA/0QZoB5E2dWiCgDJMIMsDlhBvohzACLIM6sAFEGSEQZYGvCDPRDmAEWRZwZMFEGSEQZ\nYGuiDPRHmAEWSZwZIFEGWCPMAFsRZqA/wgywaOLMgIgywBpRBtiOMAP9EWaAPriV9kAIM8AaYQbY\njjADAONj5cwACDNAIsoA2xNloH9WzQB9sXIGYACEGWA7wgz0T5gB+mTlDMASiTLAToQZ6J8wA/RN\nnAFYEmEG2I4oA8shzADLIM4A9EyUAXYizMByCDPAsrjmDECPhBlgJ8IMLIcwAyyTlTMAPRBlgN0Q\nZmA5hBlg2cQZgAUSZYDdEGVgeYQZYAic1gSwIMIMsBvCDABg5QzAnIkywG4JM7BcVs0AQyHOAMyR\nMAPshigDyyfMAEMizgDMgSgD7JYwA8snzABD45ozAAckzAC7JczA8gkzwBBZOQOwT6IMsBfCDCyf\nMAMMlTgDsA/CDLBbogwMgzADDJk4A7AHogywF8IMDIMwAwyda84A7JIwA+yFMAPDIMwAq8DKGYAd\niDLAXogyMBzCDLAqxBmALYgywF4JMwDAfjitCWATwgywV8IMDItVM8AqsXIGYB1RBtgPYQaGRZgB\nVo04AzAjzAB7JcrA8AgzwCoSZ4DJE2WA/RBmYHiEGWBVueYMMGnCDLAfwgwMjzADrDIrZ4BJEmWA\n/RBlYJiEGWDViTPApIgywH4JMzBMwgwwBuIMMAmiDHAQwgwMkzADjIVrzgCjJ8wAByHMAACLZuUM\nMFqiDHAQogwMm1UzwJiIM8DoiDLAQQkzMGzCDDA24gwwGqIMMA/CDAybMAOMkTgDrDxRBpgXYQaG\nTZgBxkqcAVaWKAPMiygDwyfMAGMmzgArR5QB5kmYgeETZoCxE2eAlSHKAPMmzMDwCTPAFIgzwOCJ\nMsC8iTKwGoQZYCrEGWCwRBlgEYQZAGBoDl/2BAA2I8wAiyDMwOqwagaYEitngEERZYBFEWZgdQgz\nwNSIM8AgiDLAoogysFqEGWCKxBlgqUQZYJGEGVgtwgwwVeIMsBSiDLBowgysFmEGmDJxBuiVKAMs\nmigDq0eYAaZOnAF6IcoAfRBmYPUIMwDiDLBgogzQF2EGVo8wA9ARZ4CFEGWAPgkzAMAqE2eAuRJl\ngD6JMrC6rJoB+B5xBpgLUQbomzADq0uYAfh+hy97AsDqE2aAvgkzsLqEGYCLs3IG2DdRBuibKAOr\nTZgB2Jw4A+yZKAMsgzADq02YAdiaOAPsmigDLIswA6tNmAHYnjgD7EiUAZZJmIHVJswA7EycAbYk\nygDLJMrA6hNmAHZn8nGmtXZkkiclaUmOSnJOkjcneWZVfWnDtp9J8iObvM2hJL9eVS9a7GyhH6IM\nsGzCDADQt9baHZL8ZZLHVNWL+xx70nFmFmbel+TaSZ6V5NQk10nyO0nu1Vr7f6vqrHUvOZTkDUk2\n+4/0qcXOFhZPlAGGQJiBcbBqBlglrbUjkrwoXRd4Sd/jTzrOJHlykhskuVlVfWLtwdZaJflYughz\ntw2v+UJVvbe/KcLiiTLAEIgyMB7CDLCCHpOuD9y6qg71PfjhfQ84MC3JSevDTJJU1XlJ/iDJnVpr\nl13KzKAHF5x5tjADDIIwA+MhzACrprV2lSS/m+T4qvrgMuYw9ThzVJJPbPHcGen+/Vy9v+lAf0QZ\nYCiEGRgPYQZYUc9N8u0kT1zWBKZ+WtO5Sa6xxXPXnH0/f8Pj92qttSRXTvK5JK9K8ntV9a3FTBHm\nS5QBhkSYgfEQZoBV1Fq7ZZIHJXl4km+31i5RVf/W9zymvnLmHUnu3Vq73PoHW2uHJXlouuvLrL9j\n0zlJ/keSX0lyTLqrOD81yRv7mS7sn1OYgCG56OPnCDMwIsIMsMJemOQ7SX4jydeTfLO19u7W2s37\nnMTUV848I8k9k5zcWntMktPS3a3pOUlunuTxG7a/VVVduO7Xb2utfTjJy1prD6qqE/qYNOyFIAMM\njSgD4yLMAKuqtXbnJLdKcl6SSvLRJNdKd3Hg97bWblNVH+pjLpNeOTO7TfZt0lWyk9OdwvT+JEcn\nOTvJH23Y/sINb5GqOi7dnZ0euODpwp5YKQMMkTAD4yLMACvuV5NckOSnqurpVfVnVfWCJD+Rrgn8\nQV8TmXScSZKqOr2qbpHk2un+A9whySWTPLGqvrnLt3lnkhsuZoawN6IMMEROYwIABuhWSV5fVZ9c\n/2BVnZ/udKdbtdau1MdEpn5a03dV1eeTfL619tdJTquqV+7h5d9KcrFVNdAnQQYYKlEGxsmqGSBJ\nrvPDl1n2FPLVT+/7pZdN8qktnlsLNldJ8tV9j7BLk185s15r7SFJbp1uadNmz193i5cene7UJuid\nlTLAUFktA+MlzAAj8cUkP7rFcz+W5KIkX+5jIuLMzGyp0nOSnFBVH9zk+dcm+dvW2lU3PH6/dEHn\nuF4mCjOiDDBkogyMlzADjMhJSe7TWrv2+gdba0emW7Txzqo6r4+JOK3pe45N9+/jt7d4/vlJ7p4u\n0DwnyWeT3CXdVZxfUVVv6mWWTJ4gAwyZKAPjJswAI/Pf0n2u/2Br7blJPp7ubk2/luQy2eKsmkWw\nciZJa+3odHdbenpVfWWzbarq79KtkPlAultw/1mSn03y8Kp6WF9zZbqslAGGTpiBcRNmgLGpqq8n\n+el0Z8I8IskbkzwlyXuS/GRVbXU9mrmzciZJVZ2SXfy7qKpPJLnv4mcE30+UAYZMlIHxE2aAsaqq\nbyR50uxracQZGDBRBhg6YQbGT5gBWDxxBgZIlAGGTpSBaRBmAPohzsCAiDLA0IkyAADzJ87AAIgy\nwCoQZmBarJoB6I84A0skygCrQJSB6RFmAPolzsASiDLAqhBmYHqEGYD+iTPQI1EGWBWiDEyTMAOw\nHOIM9ECUAVaJMAPTJMwALI84AwskygCrRJSB6RJmAJbr8GVPAMZKmAFWiTAD0yXMACyflTMwZ6IM\nsEpEGZg2YQZgGMQZmBNRBlg1wgwAwDCIM3BAogywakQZILFqBmBIxBnYJ1EGWEXCDJAIMwBDI87A\nHokywCoSZYA1wgzA8IgzsEuiDLCqhBlgjTADMEziDOxAlAFWlSgDrCfMAAyXOANbEGWAVSbMAOsJ\nMwDDJs7ABqIMsMpEGWAjYQZg+MQZmBFlgFUmygCbEWYAVoM4AxFmgNUmzAAbiTIAq0WcYdJEGWCV\niTLAZoQZgNUjzjBJogyw6oQZYDPCDMBqEmeYFFEGWHWiDLAVYQZgdYkzTIIoA4yBMANsRpQBWH3i\nDKMmygBjIMoAWxFmAMZBnGGURBlgLIQZYCvCDMB4iDOMiigDjIUoA2xFlAEYH3GGURBlgLEQZYDt\nCDMA4yTOsLIEGWBMRBlgO6IMwLiJM6wcUQYYE1EG2IkwAzB+4gwrQZABxkaUAXYiygBMhzjDoIky\nwNiIMsBORBmA6RFnGBxBBhgjUQbYDWEGYJrEGQZDlAHGSJQBdkOUAZg2cYalEmSAsRJlgN0SZgAQ\nZ1gKUQYYK1EG2C1RBoA14gy9EWSAMRNlgN0SZQDYSJxh4UQZYMxEGWAvhBkANiPOsBCCDDB2ogyw\nF6IMANsRZ5grUQYYO1EG2AtRBoDdEGc4MEEGmAJRBtgrYQaA3RJn2DdRBpgCUQbYK1EGgL0SZ9gT\nQQaYClEG2A9hBoD9EGfYFVEGmApRBtgPUQaAgxBn2JYoA0yFKAPshygDwDyIM1yMIANMiSgD7Jcw\nA8C8iDN8lygDTIkoA+yXKAPAvIkzEyfIAFMjygAHIcwAsAjizESJMsDUiDLAQYgyACySODMhggww\nRaIMcBCiDAB9EGcmQJQBpkiUAQ5KmAGgL+LMSAkywFSJMsBBiTIA9E2cGRlRBpgqUQY4KFEGgGUR\nZ0ZAkAGmSpAB5kWYAWCZxJkVJsoAUyXKAPMiygAwBOLMihFkgCkTZYB5EmYAGApxZkWIMsCUiTLA\nPIkyAAyNODNgggwwdaIMME+iDABDJc4MkCgDTJ0oA8ybMAP06bOfOSNJcqvcdMkzYVWIMwMhyACI\nMsD8iTJAn9aiDOyVODMAwgwwdaIMsAjCDNAXUYaDEmcAWBpRBlgEUQbokzDDPIgzAPROlAEWQZQB\n+iTKME/iDAC9EWWARRFmgL6IMiyCOAPAwokywKKIMkCfhBkWRZwBYGFEGWBRRBmgT6IMiybOADB3\nogywSMIM0BdRhr6IMwDMjSgDLJIoA/RJmKFP4gwABybKAIsmzAB9EWVYBnEGgH0TZYBFE2WAvogy\n09Vau3GSpyX52SSXTPLJJC9L8vKquqiPOYgzAOyZKAMsmigD9EWUmbbW2i2SnJzk1CS/luTcJLdM\n8rwkN07y2D7mIc4AsGuiDNAHYQboizBDkn9L8oIkT6mqQ7PH/mdr7UtJXtZa+8Oq+tSiJyHOALAj\nUQbogygD9EWUYU1VfSTJRzZ56pQkhyW5dhJxBoDlEWWAvggzQB9EGfbgZkm+leRjfQwmzgBwMaIM\n0BdRBuiLMMNOWmuXSHLFJHdNcmySJ1fVP/YxtjgDwHeJMkBfRBmgL6IMe/CPSS6X5FCSX6+qF/U1\n8OF9DQTAcF308XOEGaA3wgzQh89+5gxhhr26U5JfSPKqJMe21p7U18BWzgBMmCAD9EmUAfogyLBf\nVfWhJB9K8sbW2ruTvLK19uaq+uiix7ZyBmCCrJQB+vStj3xRmAF6IcwwL1X1qiRfTHK/PsazcgZg\nQgQZoG+iDNAHUWZ5rnmVI5c9hXz10wt76y+ku5X2wokzABMgygB9E2WAPogyHERr7fAkd0xydlWd\ntskm10vygT7m4rQmgBFz+hKwDMIM0Adhhjk4Mslrk7xwFmq+q7X2sHS31X5THxOxcgZghAQZYBlE\nGaAPogzzUlVfb609Oslrkvxla+34JF9Pcuckj0xyQlW9u4+5iDMAIyLKAMsgygB9EGVYhKqq1tqX\nkjwhyQuTXCbJmUn+a1W9rK95iDMAIyDKAMsizAB9EGZYpKo6Jckpy5yDOAOwwkQZYFlEGaAPogxT\nIc4ArBhBBlg2YQZYNFGGqRFnAFaEKAMsmygDLJoow1SJMwADJ8oAyybKAH0QZpgycQZgoEQZYAiE\nGWDRRBkQZwAGR5QBhkCUARZNlIHvEWcABkKUAYZAlAH6IMzA9xNnAJZMlAGGQpgBFk2Ugc2JMwBL\nIsoAQyHKAIsmysD2xBmAnokywJAIM8AiiTKwO+IMQE9EGWBIRBlg0YQZ2D1xBmDBRBlgSEQZYNFE\nGdg7cQZgQUQZYGiEGWCRRBnYP3EGYM5EGWBoRBlg0YQZOBhxBmBORBlgaEQZYNFEGZgPcQbggEQZ\nYIiEGWCRRBmYL3EGYJ9EGWCIRBlg0YQZmD9xBmCPRBlgqIQZYJFEGVgccQZgl0QZYKhEGWCRRBlY\nPHEGYAeiDDBUogywSKIM9EecAdiCKAMMmTADLJIwA/0SZwA2EGWAIRNlgEUSZWA5xBmAGVEGGDph\nBlgUUQaWS5wBJk+UAYZOlAEWSZiB5RNngMkSZYChE2WARRJlYDjEGWBSBBlgVQgzwKKIMjA84gww\nCaIMsCpEGWCRhBkYJnEGGC1BBlglogywSKIMDJs4A4yKIAOsImEGWBRRBlaDOAOsPEEGWFWiDLAo\nogysFnEGWEmCDLDqhBlgUYQZWD3iDLAyBBlgDEQZYFFEGVhd4gwwaIIMMBaiDLAoogysPnEGGCRR\nBhgTYQZYFGEGxkGcAQZDkAHGRpQBFkWUgXERZ4ClEmSAsRJmgEUQZWCcxBmgd4IMMGaiDLAIogyM\nmzgD9EKQAcZOlAEWRZiB8RNngIURZICpEGaARRBlYDrEGWDuRBlgKkQZYBFEGZgecQaYC0EGmBJR\nBlgUYQamSZwB9k2QAaZImAEWQZSBaRNngD0RZICpEmWARRBlgEScAXZBkAGmTpgBFkGYAdaIM8Cm\nBBkAUQZYDFEG2EicAb5LkAHoiDLAIogywFbEGUCUAVhHmAHmTZQBdiLOwEQJMgDfT5QBFkGYAXZD\nnIEJEWQANifMAPMmygB7Ic7AyAkyAFsTZYB5E2WA/RBnYIQEGYDtiTLAIggzwH6JMzASggzA7ggz\nwLyJMsBBiTOwwgQZgN0TZYB5E2WAeRFnYAWJMgC7J8oAiyDMAPMkzsCKEGQA9k6YAeZNlAEWQZyB\nARNkAPZHlAHmTZQBFkmcgYERZAAORpgB5kmUAfogzsAACDIAByfKAPMmzAB9EWdgSQQZgPkQZYB5\nE2WAvokz0CNBBmC+hBlgnkQZYFnEGeiBKAMwX6IMMG/CDLBM4gwsiCADsBjCDDBPogwwBOIMzJEg\nA7A4ogwwT6IMMCTiDByQIAOwWKIMME+iDDBE4gzsgyAD0A9hBpgnYQYYKnEGdkmQAeiPKAPMkygD\nDJ04A9sQZAD6JcoA8yTKAKtCnIFNiDIA/RNmgHkSZoBVIs7AjCADsByiDDBPogywisQZJk2QAVgu\nYQaYF1EGWGWTjzOttSOTPClJS3JUknOSvDnJM6vqS5ts/4gkj05y3STnJnlbkqdU1Vd6mzQHIsgA\nLJ8oA8yTMAMcRGvtiCRPSPJLSa6Z5J+SnJTk6VX1jT7mcHgfgwzVLMy8L11sOS7JPZI8Pcndkpza\nWrvuhu2fm+SF6eLNvZM8Ncntk5zcWrtMj1Nnjy76+Dnf/QJgeb71kS8KM8DcfPYzZwgzwDycmC7O\nvCLJMUmel+SBSd7WWuulm0x95cyTk9wgyc2q6hNrD7bWKsnHkrw4XahJa+3mSR6X5GFV9Yp12741\nyUfShZrH9Td1diLEAAyHIAPMkyADzEtr7Zh0iy/uXFV/te7xv0pyapJHpWsDCzXplTPpTmU6aX2Y\nSZKqOi/JHyS5U2vtsrOHH57krPVhZrbtPyV5SZJfbq1NPXYNghUyAMNhpQwwT1bKAAvw8CQnrw8z\nSTLrBK9L8og+JjH1OHNUkk9s8dwZ6f79XH3269skefsW2741yeWT3HSus2PXnLYEMCyiDDBvogww\nb7NrzRyd7jP9Zt6a5EattSsvei5TX+lxbpJrbPHcNWffz599/9EkZ26x7d8nOWy2zYfmNju2JcQA\nDI8gA8ybKAMs0NWSXDq7+6y/0A+gU185844k926tXW79g621w5I8NMkXqupLs1Objkjytc3epKr+\nJcmFSa604PlOnhUyAMNkpQwwb05hAnpwxdn3TT/rr3t84Z/1p75y5hlJ7pnubkuPSXJakuskeU6S\nmyd5/Gy7tTsxXbDNe12Q5HLbPM8+CTEAwyXIAIsgygA9uUySQ9n6s/43Z98X/ll/0nGmqs5qrd0m\nyfFJTk63XOmCJN9JcnaSP5pt+vXZ90tt83aXSnLeYmY6PYIMwLCJMsAiiDJAz76ergNs9Vn/0rPv\nC/+sP+k4kyRVdXqSW7TWjkp3Ud8jk7wnyWOr6puzbc5vrV04e/5iWmtHpjvt6av7mcNNjz5sPy8b\nt6OvsuwZALAtx2lg/m7l/hqwck5//ynLnsJBnDv7vuln/Xxvxcy+PuvvxdSvOfNdVfX5qvpokt9P\nclpVvXLDJmclucEWL7/hum0AAACA4ftyurNntvusfyg9fNaf/MqZ9VprD0ly69nXRicnuWuSx27y\n3N3TXSjow3sZ7w53uIMlMwAAAKycMXyeraoLW2unpPtM/4JNNrl7kjOr6uxFz8XKmZnW2pXSXQj4\nhKr64CabHJfkuq21X97wuqsmeVSSP6mqby9+pgAAAMCcvCzJ7Vprt1//YGvtRknul+SlfUzisEOH\nDvUxzuC11v4k3Z2bblBVX9lim2OTPDpdUTsl3T3RH59umdMtq+r8nqYLAAAAzEFr7aQkd0zy3HRn\nxPxYkiemO53ptlV14aLnIM4kaa0dne60pcdV1Qt32PZRSR6Z7pbbX0vytiS/s1XQAQAAAIartXZE\nkt9O8l+SXCPJV5K8IcnTquobfcxBnAEAAABYItecAQAAAFgicQYAAABgicQZAAAAgCUSZwAAAACW\nSJwBAAAAWCJxBgAAAGCJxBkAAACAJfqBZU9glbXWjkjyhCS/lOSaSf4pyUlJnl5V39jD+xyd5M+T\nvKeqjtlimyOTPC3JvZNcNckXkvxJkt+rqgv3/7tgSPrap1pr10ryf7Z4+aEkP1lVH9nb7Bmig+5T\nrbUfSnfsuUuSayT5XJJK8qyq+uaGbR2nRq6v/ckxajrmsE/9pyS/meSmSb6V5LQkz6mqd22yrWPU\nBPS1TzlOTce8fj6fvddRSc5McskkV6iq8zc87zg1YVbOHMyJ6f5HfUWSY5I8L8kDk7yttbarf7et\ntZbkL5NcbpttfiDJO5I8YDbGMbMxn5jk1QeYP8PTyz61zpOT3HbD1+2SfGpPs2bI9r1PtdaunOT9\nSe6Z5EVJ7pXkhCSPTvLO2Q8ra9s6Tk1DL/vTOo5R43eQfeq/JnlTug/ID0zy0CSfSfKXrbVjNmzr\nGDUdvexT6zhOjd+Bfz5f54VJvrbZE45TWDmzT7MD9L2T3Lmq/mrd43+V5NQkj0ry4h3e47eSPCfJ\n8UluuM2mj0ly8yQ3q6pPzB77n621D6T7gfbEqnrLvn8zDELP+9Saj1fVe/c9aQZtDvvUk9L9rc1N\nq+ofZo+9vbX210lOTnL/fO+HBcepket5f1rjGDVic9inPp/kUVX10nWP/Xlr7dLpAuCfrXvcMWoC\net6n1jhOjdg8fj5f95o7J/mZJM9KF182cpyaOCtn9u/hSU5e/z9pksz+R3pdkkds9+LW2iWTPDjJ\n71fVryS5aIexXrvuf9K1sd6V7gfabcdiZfS5TzENB9qnkpyR5LfWfZBee/17k3wxyU9tGMtxatz6\n3J+YhgPtU1X15xs+RK85JcnVWms/uGEsx6jx63OfYhoO+mdfkmS277woyVOS/PM2YzlOTZg4sw+z\npddHJ3nrFpu8NcmNZku4N1VV/5rk1lX1xB3G+uEk10/ytm3G+pkdJ82g9blPMQ1z2qeOr6r/vsXT\nl0jy7dlYjlMj1+f+xDTMY5/axs3SrWZwjJqQPvcppmHO+9RvJvnXJH+8xViOUzitaZ+uluTS6S7m\ntJm/T3JYkh9Ncs5Wb1JVm55vuMH10l1UbLux/n1r7apV9U+7eD+Gqc99ar1nt9Zeme6iZB9L8vyq\nev0e34Nhmss+tZnW2l2TXDnJe2YPOU6NX5/703qOUeM1132qtXapdBeZ/uUk901y93VPO0ZNQ5/7\n1HqOU+M1l32qtXbNdNcmuntVHeouD3kxjlNYObNPV5x93+qD8NrjV1qxsVieZfx3/mSSlye5T7qr\nz5+d5MTW2hPmOAbLs5B9qrV2hST/PcknqupNixyLQelzf1rjGDVuc9unWms/keQb6faZRyW5W1X9\n9SLGYtD63KfWOE6N27z2qecnefsO1yZynMLKmX26TLqyecEWz6/dDnQ3d8vZzVjZYazD5jQWy9Pn\nPpUkn6uqjRcMrtbaa5I8s7V2UlWdNaexWI6571Ozv0V8S5IfTvLTG8bKDmM5Tq22PvenxDFqCua5\nT30y3XL/a6e7u847Wms/v+7Wx45R09DnPpU4Tk3Bgfep1trt0626utEuxsoOYzlOjZyVM/vz9XT/\nc1xqi+cvPft+3pzGyg5jHZrTWCxPn/tUqurQFk89bvb9/vMYh6Wa6z41u73jnya5ZZIHVtWpUNfT\n0AAAC5NJREFUG8bKDmM5Tq22Pvcnx6hpmNs+VVUXVNX7quq1VXW7JK9P8up1F291jJqGPvcpx6lp\nONA+Nfuz7g+THFtVn9vFWNlhLMepkRNn9ufc2ffLb/H8WtH86oqNxfIM4r9zVf1juvOld3MbboZt\n3vvUq5LcOcmvVNUbFjwWw9Pn/rQlx6hRWeRx49nprhVxux7GYjj63Ke25Dg1Kgfdpx6c5CpJXtla\nu+ra17r3W3vssDmMxQiIM/vz5XRLzm6wxfM3TFc257GU8dPpiu12Y33DhaFWXp/71E6+leTCHsZh\nsea2T7XWXpzufPrHVtUrNtnEcWr8+tyfduIYNQ6L/HPv87Pv1559d4yahj73qZ04To3DQfepq6W7\nRsynZ++19vWC2fN/n+RLs+0cpxBn9qOqLkxySra+avvdk5xZVWfPYawvJ/mHbca6W5LNLlDGCulz\nn0qS1tqlW2tX2+Txyya5cZKPzmMclmde+1Rr7elJHpnkiVX1ki3GcpwauT73p9l2jlEjd9B9qrV2\nZGvtAa21zf6W+fqz72fPxnKMmoA+96nZ9o5TIzeHP/teneSuSX5uw9fzZ88fM3v+bMcpEnHmIF6W\n5Hazizx9V2vtRknul+Sl6x677OzChwcZ6/6tte9bHtlau1265ZUv3fRVrJpe9qnZ+dIfS/La1toR\nG57+vXTV/rX7eW8G50D7VGvtV5M8Jckzqur3dzGW49S49bI/OUZNykH2qZukOz3u8Zu87xPTXb9h\n/cVbHaOmoZd9ynFqUva9T1XVWVX1vzZ+5Xvh7l2zx769bizHqQk77NChra5lxU5aaycluWOS5yb5\ncJIfS3fwPivJbavqwtbapdMthfxKVW15le7W2ruT/HNVHbPJcz+Q5L3pllI+O11VvWmSJyR5R1Xd\nd56/L5anx33qV5K8JMn7krw4yb8leVi6mv/gqnr1XH9jLM1+96nW2k2SnJbu2PO0Ld7+a1V1+mx7\nx6kJ6HF/coyaiIP8uddae1G62xy/PMnbk/y7JA9KcpckD6mqE9Zt6xg1ET3uU45TEzHPn89n7/eg\nJK9IcoWqOn/d445TE2flzMHcN8nz0l3s6U+T/EaSE5P83GwZXJJ8J925hDtdoTvpzlm8mKr6TroL\nJ544G+NPZ2Mem+QXDzB/hqevfeplSX4+yUVJjkv3N0U/mOT2fpgYnf3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"text/plain": [ "" ] }, "metadata": { "image/png": { "height": 410, "width": 563 } }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.contourf(sigma_vals, strike_vals, prices['ecall'])\n", "plt.axis('tight')\n", "plt.colorbar()\n", "plt.title('European Call')\n", "plt.xlabel(\"Volatility\")\n", "plt.ylabel(\"Strike Price\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the value of the Asian call in (volatility, strike) space." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AByLMAGN22hfOE2aAqQkzLEKt9cIkd0ryjHSv1H5Tkmen26P2ZrXWD856j6ZtR/f2q7Wx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\nALBC4gwAAADACv1/Z26P1/NPQ6AAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 410, "width": 563 } }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.contourf(sigma_vals, strike_vals, prices['acall'])\n", "plt.axis('tight')\n", "plt.colorbar()\n", "plt.title(\"Asian Call\")\n", "plt.xlabel(\"Volatility\")\n", "plt.ylabel(\"Strike Price\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the value of the European put in (volatility, strike) space." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YeKQJM/KoNEYe5cjII/Vm5FEJ3LKlaWHgkaaAkUelMfIoR0YeqTfP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"text/plain": [ "" ] }, "metadata": { "image/png": { "height": 410, "width": 572 } }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.contourf(sigma_vals, strike_vals, prices['eput'])\n", "plt.axis('tight')\n", "plt.colorbar()\n", "plt.title(\"European Put\")\n", "plt.xlabel(\"Volatility\")\n", "plt.ylabel(\"Strike Price\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the value of the Asian put in (volatility, strike) space." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GmJc2DfSPLU/AJhDOADtPWAPMSpsG+kmbBlgX4QzQO3thjZAGmIY2DfSPNg2wasIZoLdG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\nSJwBAAAAGJA4AwAAADCg/w9Eb0cSCPNO6wAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": { "image/png": { "height": 410, "width": 563 } }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.contourf(sigma_vals, strike_vals, prices['aput'])\n", "plt.axis('tight')\n", "plt.colorbar()\n", "plt.title(\"Asian Put\")\n", "plt.xlabel(\"Volatility\")\n", "plt.ylabel(\"Strike Price\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ipyparallel-8.8.0/docs/source/examples/Parallel Decorator and map.ipynb000066400000000000000000000051651460376056100261150ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load balanced map and parallel function decorator" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import ipyparallel as ipp" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rc = ipp.Client()\n", "v = rc.load_balanced_view()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simple, default map: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n" ] } ], "source": [ "result = v.map(lambda x: 2*x, range(10))\n", "print(\"Simple, default map: \", list(result))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Submitted tasks, got ids: ['b21595ef-61f4-4ec3-ac7a-7888c025e492', '5e29335b-526d-4516-b22f-0a4b358fa242', '18cd0bd2-7aad-4340-8c81-9b2e384d73d9', '1ef7ccbc-6790-4479-aa90-c4acb5fc8cc4', '8d2c6d43-6e59-4dcf-9511-70707871aeb1', '58042f85-a7c1-492e-a698-d2655c095424', 'd629bf13-4d8b-4a54-996e-d531306293ea', '79039685-1b02-4aa5-a259-4eb9b8d8a65a', '16ffe6f3-fe82-4610-9ec9-a0a3138313a9', 'a3d9050b-faf2-4fa4-873a-65c81cab4c56']\n", "Using a mapper: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n" ] } ], "source": [ "ar = v.map_async(lambda x: 2*x, range(10))\n", "print(\"Submitted tasks, got ids: \", ar.msg_ids)\n", "result = ar.get()\n", "print(\"Using a mapper: \", result)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using a parallel function: [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n" ] } ], "source": [ "@v.parallel(block=True)\n", "def f(x): return 2*x\n", "\n", "result = f.map(range(10))\n", "print(\"Using a parallel function: \", result)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ipyparallel-8.8.0/docs/source/examples/Parallel Magics.ipynb000066400000000000000000154361031460376056100241230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Parallel Magics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython has a few magics to make working with your engines\n", "a bit nicer in IPython, e.g. via a Jupyter notebook.\n", "\n", "As always, first we start a cluster (or connect to an existing one):" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/benjaminrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86b22e270bf549349203129b31f247aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%px import time\n", "%px time.sleep(1)\n", "%px time.time()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But you will notice that this didn't output the result of the last command.\n", "For this, we have `%pxresult`, which displays the output of the latest request:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mOut[0:8]: \u001b[0m1635248507.7749069" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:41:47.776722", "data": {}, "engine_id": 0, "engine_uuid": "69b2e5ae-f96ddd1460d94415c8a16e91", "error": null, "execute_input": "time.time()", "execute_result": { "data": { "text/plain": "1635248507.7749069" }, "execution_count": 8, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_33", "outputs": [], "received": "2021-10-26T11:41:47.781347", "started": "2021-10-26T11:41:47.774307", "status": "ok", "stderr": "", "stdout": "", "submitted": "2021-10-26T11:41:46.756242" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[1:8]: \u001b[0m1635248507.7802722" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:41:47.782521", "data": {}, "engine_id": 1, "engine_uuid": "f86d40b0-1f3907b210428cfafdaca7a4", "error": null, "execute_input": "time.time()", "execute_result": { "data": { "text/plain": "1635248507.7802722" }, "execution_count": 8, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_34", "outputs": [], "received": "2021-10-26T11:41:47.785695", "started": "2021-10-26T11:41:47.779371", "status": "ok", "stderr": "", "stdout": "", "submitted": "2021-10-26T11:41:46.757260" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[2:8]: \u001b[0m1635248507.780295" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:41:47.782408", "data": {}, "engine_id": 2, "engine_uuid": "608a802c-2bb806ebe19459d6eea9542c", "error": null, "execute_input": "time.time()", "execute_result": { "data": { "text/plain": "1635248507.780295" }, "execution_count": 8, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_35", "outputs": [], "received": "2021-10-26T11:41:47.785329", "started": "2021-10-26T11:41:47.779449", "status": "ok", "stderr": "", "stdout": "", "submitted": "2021-10-26T11:41:46.757422" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:8]: \u001b[0m1635248507.780204" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:41:47.782258", "data": {}, "engine_id": 3, "engine_uuid": "89363e7a-3deecfc8a12bb00f66b0d3b4", "error": null, "execute_input": "time.time()", "execute_result": { "data": { "text/plain": "1635248507.780204" }, "execution_count": 8, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_36", "outputs": [], "received": "2021-10-26T11:41:47.784594", "started": "2021-10-26T11:41:47.779430", "status": "ok", "stderr": "", "stdout": "", "submitted": "2021-10-26T11:41:46.757528" }, "output_type": "display_data" } ], "source": [ "%pxresult" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember, an IPython engine is IPython, so you can do magics remotely as well!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%pxconfig --block\n", "%px %matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`%%px` can be used to lower the priority of the engines to improve system performance under heavy CPU load." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "%%px\n", "import psutil\n", "psutil.Process().nice(20 if psutil.POSIX else psutil.IDLE_PRIORITY_CLASS)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "%%px\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`%%px` can also be used as a cell magic, for submitting whole blocks.\n", "This one acceps `--block` and `--noblock` flags to specify\n", "the blocking behavior, though the default is unchanged.\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "dv.scatter('id', dv.targets, flatten=True)\n", "dv['stride'] = len(dv)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%px --noblock\n", "x = np.linspace(0,np.pi,1000)\n", "for n in range(id,12, stride):\n", " print(n)\n", " plt.plot(x,np.sin(n*x))\n", "plt.title(\"Plot %i\" % id)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[stdout:0] \n", "0\n", "4\n", "8\n", "[stdout:1] \n", "1\n", "5\n", "9\n", "[stdout:2] \n", "2\n", "6\n", "10\n", "[stdout:3] \n", "3\n", "7\n", "11\n" ] }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "engine": 3, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[0:13]: \u001b[0mText(0.5, 1.0, 'Plot 0')" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:14.943283", "data": {}, "engine_id": 0, "engine_uuid": "69b2e5ae-f96ddd1460d94415c8a16e91", "error": null, "execute_input": "x = np.linspace(0,np.pi,1000)\nfor n in range(id,12, stride):\n print(n)\n plt.plot(x,np.sin(n*x))\nplt.title(\"Plot %i\" % id)\n", "execute_result": { "data": { "text/plain": "Text(0.5, 1.0, 'Plot 0')" }, "execution_count": 13, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_61", "outputs": [ { "data": { "image/png": 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\n", "text/plain": "
" }, "metadata": { "engine": 0, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "transient": {} } ], "received": "2021-10-26T11:42:14.947221", "started": "2021-10-26T11:42:14.675655", "status": "ok", "stderr": "", "stdout": "0\n4\n8\n", "submitted": "2021-10-26T11:42:14.670796" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[1:13]: \u001b[0mText(0.5, 1.0, 'Plot 1')" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:14.946495", "data": {}, "engine_id": 1, "engine_uuid": "f86d40b0-1f3907b210428cfafdaca7a4", "error": null, "execute_input": "x = np.linspace(0,np.pi,1000)\nfor n in range(id,12, stride):\n print(n)\n plt.plot(x,np.sin(n*x))\nplt.title(\"Plot %i\" % id)\n", "execute_result": { "data": { "text/plain": "Text(0.5, 1.0, 'Plot 1')" }, "execution_count": 13, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_62", "outputs": [ { "data": { "image/png": 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\n", "text/plain": "
" }, "metadata": { "engine": 1, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "transient": {} } ], "received": "2021-10-26T11:42:14.952130", "started": "2021-10-26T11:42:14.675923", "status": "ok", "stderr": "", "stdout": "1\n5\n9\n", "submitted": "2021-10-26T11:42:14.671092" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[2:13]: \u001b[0mText(0.5, 1.0, 'Plot 2')" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:14.944902", "data": {}, "engine_id": 2, "engine_uuid": "608a802c-2bb806ebe19459d6eea9542c", "error": null, "execute_input": "x = np.linspace(0,np.pi,1000)\nfor n in range(id,12, stride):\n print(n)\n plt.plot(x,np.sin(n*x))\nplt.title(\"Plot %i\" % id)\n", "execute_result": { "data": { "text/plain": "Text(0.5, 1.0, 'Plot 2')" }, "execution_count": 13, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_63", "outputs": [ { "data": { "image/png": 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8ubHw5scxnng1QzMWyYxpXYNccdczscnWA5rA77ycVnSqk3iBVYpKr43Tus2cwXZMbbAdU+f1v6FrTb96ojfpXGcoBg0cgoN5bOyxhC5vlwoAkrgknmVYcZAfVO5x7olu97Sr74q/a1TP6enzTs5o475x9hHrI4Yh35ACAFIUKV5BFBizz6z4xPGJrs3bprpTf6dFy2ojOpkyHyi9Nqaw96WYjKEt0exUAoVn1aJTneTjuShByU+yWrdZEWdvV8bZ25U5A+/oulI2TbalXzkpspSIDLVhfpj7p/WfhjFhjGPAIE2R5nWLbnbUN8q9b38/tt3brr5Nf5uFksKEBI8ck1wTAKK+4HXt1M/xqWPxBcefeOysrFmz5p+zfW+kCGSo5fa3fH3n1zdXu6pTFYzCe2PhjW/dU37PUZZh0WhtjP3ZgZ9d2TPZk/PP8X9G/2nNn/61KHGRNWgX9nkYzbv3r1c0v72eASAqohx8/nn7vQu/dNSXtdoSGDrjAdj7D+lVtc+WKlreXR3j6NYta/qfBD5n/T7nJQ99BHUMTVGeItfPYCgN2tzqd+vN2fs7x3Kbzfbc4UlP8uePYRkI8VFKS1KMajgtTjOaHKseS4/T2HINUeP5xmhbcqzaDfhw1yd3Xds83lyq5tTOu0vvfu36wutbWIbFQfNBw68O/erqYQynqnP/OfnY+gf/lR2TYx+Z9Kh7rC5t24g9rtvi0g/aXHFDE574QZvbODLpMdq8otI2DnXLuKj2RyIiRs3ZchO1nYsz4jouLje2l6XG2ML972wu6DMYfqodv1moqv3npYzAK0RWwfuy1hzyLri+li/a3M8wLDgAAgC7pU2rqn22SNHw6mqNY8S4oOMRfenkriHnpQ+9Kcbnzbb0wX9w8A7utq233dzr7VVFK6In7l94/8uX5lzaDQDb+ral/O+R/71myDNkeHjyYfGJc554IkGT4AnWtQPoc0ikNp3P4IhrRHXHx3fcMeIbUcSp4iz/b/H/e2lD+oZBAHir862sv9T+5epub3fsPx3/dD997tNPaRQaSgqHiPLoC1nqbT+/lvFMRosAfCmLj3rLrqr2VtzQCYVGUMA/POWwD6uUR5/PUx57aaXS2pFT0PdKTN747knXpgde8WWtmfYGYOEyX74LOyc6o3+17Vd32QQbZ4wyDvxk6U9eWZa0bFQQBbzQ8kLhvxr+dWWzpznqcffj449ufLRKwSooKRwh5stnUM7279//s9m+V45JLgsAhclkiq2qqjpZJyoBgKeqqmrSZDKduByx7xTHAjPY3ZFEhn/U/WPhoeFDyziG47+7+LvPBDoiAFASX2J7bONjz9y17a4buia68n924GfXPnves48GY907M9Gvjnr5pms5S0u+CIDPO3e367zf7hBj0k46O0tIWzrmSlu6Bxt+ekCz9cdnKZre3KDo3L4y+slzMx3XPP+smJAftA4akTdBFLG73Wp8p85cfLhnvHhg3J0u4rPlhiwDISlGPZiTENVXmqLrW5YVN7AsWz+qVXFf+Ln+1cHfrmkeby5VsSr3L1f88sm1qWuHAq8tS1o2+vjGx5+445M7bjU7zWk/3PfDK58+9+lnk2LU7qQYtXtpVtx/JId5QWSO9tniDnWPJx/tn0hvH3Fk9I270ifcvtjavomFtX0TC5/c14t4rXK0LEXXcm5xYtNlC5O71QqWOjnEj3exUa/duknRvWsZAPiSF9a7zvvth0LKopMmRsWEfId7w0+r3Wu/X6Pa9fuFqsOPbeKGjxVHP7P5Xufmvz/jyztnOBhh/WTfT87ttffmaBXayQfOfuDR8oTy4wNgG9I3DObH5T92z7Z77hxxjaT8vz3/75JHNj7yajCuS4jc/HDvDzePuEaS41RxlofWP/Rodkz28WeZS3Mu7c6NyX38m7u+eWefvS/7h/t+eP4f1/zxAynjna/UH35vpfLo8xcyEBkhLqvHvf6n7/KFFw2e7Fgx2ujxrLq/0bPq/kZl9ZO56l2/v4ydHEiNeuXme91rf1DlXXZPa7jjn+94gWd+uPeHV9i8Nn1SVFL/vzb+68lETaIHAFiGxY1FN7ZkxWQ98eN9P769Zbyl5LeHf7v6J8t+skvquAmZD+SY5Gqb+rkEJ981cQmA9pMcW3uKY+0Ahk7yGolQreOtuhdbX7wYAK7Ou/q9ExNcAdHKaN+f1vzp5Vu33nqv2WlO/d3h3539q5W/2j6X6zLjPRrti1d/iZ3oTxMVUQ73uh+94l18W/vp3wlAo+ddm/+2U5F3Xrtm64+uYSf607QvXnW788qnnj5Vx47Mf4IoYmvjSOrrtUMLavpspeNOPj7wGstASI/T9Jak6DpW58Z3XFRm7J1pQfj9Q/sTP+j5YCMA3F1296snJrgCEjQJnt+f9fsX7t1+731dE135f67989JvV3774KnOqWAZcXFm3NjizLgxAE0A4PUJzK52q3F7y2huTd9EXueoI8fq8Bp2tVsNu9qtq/73ozZnSYqueWOR4dj1S9PaTpeYI/OY18lqX7z6Km6otlxkOJ+38tZ33Rt/fhjTqUfCqUTPuh/V8IWbOqPe/uo1rK0nI+qte253XfiHZ/iSy/vnEtY7Xe9k7hnacxYDRvxO5XdePDHBFZCpy3T8csUvn/v2rm/fW2+tr3i88fGG20tub5jLdQmRmycbnyyps9Qt5BiO/+WKXz53YoIrYIFhwdh3Kr/z4q8O/uqOvUN7V73T9c6xzdmbe6SId77SvH3femXTWxsAgM87f5fz0gc/xjRnzHkrb+3g889/KOqNuy/lhmrK1Tt+fQPjtr3qWfOd+pAGfYb5S+1flnROdBZoOI3zf8/63xcCCa4TnZ16tvnusrtf+3vd32/4oOeDjRdmXti0InnFiBTxEjKfyLHK3WH4lyFu+PwLJpOJAbAWU8mvqqqqfgAtJzt2ynoAO6uqqmiGgYz87vDvLvAKXnVebF7L/QvvP9XOmUjRpri+suArrwPAtv5ta6tHquNPdezpMLY+jfaFq25hJ/rTRI1+zHHtC49MO8F1Ar70ij7HdS8/KkQnmVnHaKL2lZtuZ0ebo0//TjKfHOoej//Oqw3rNzyw92v/9WrDPTtaLavHnXx8lJJ1LM6Irf76+pwXP75/1f+899UVj/3p6rJPrl2S2jnTBJcgCvhjzR8vFkSBW2RYdOSmopuaT3Vsob5w4qaim94GgLe73j6nd7L3i5aE/wclx4obCg3mn11ctO/Vu5c+v+vbq//nl5cUPb6+MGFXvFY56uKFqOpe26I/fdxxw/oH9nz7rmdrN79ZO5QpiDQr/4zCu1jtC1ddzQ3VloucyuO64PdPus/55fQSXCcQUpeM27/03pO+xOIWhndFad7/1q2KlvdTZhuWV/AyD9c/vBkA1qau/fSirIt6T3Xs8qTlo5fkXLIFAJ5vef5Cm8cmx8FCQmbF5rEpnmt57iIAuDTn0o+WJy0/5UqIi7Iu6l2buvZTAHi4/uHNXsFLO+8EieatL29QNr21QQQjupff97rzyse3TDfBFSDGpLkdN731Mp97zh5GFFjV3geuUe35U1moYj7T9Ez2aN/qfOs8ALix8MZ3ivRFE6c69qaim5orDZVHBFHg/lDzh4sFkbqlhMxVxCe5TCZTnMlkCtTOQlVVlQvAkwC+ajKZPv9QexuAHACPnPC7hwFcbzKZ/u2L22QybYQ/yfVwCMImIbKtb1vKMeuxCo7hfN9f/P13T7cbyeW5l3eVJ5TX+kQf9/ejf984q4t6nWzUyzdey04OpIqaeKvj6mcfF9KWjs3qXAAEY+mk4/rXnxBiUgcY15g+6pWbb2Qco1Rtcp5zeHzcP3Z0lV364IFbbnu65v4PGoY3WB1eQ5SSdZyVq9//i81Fj+/69ur/e+rWyjfuOTur0aBTzanez8ttL+d3T3bnqliV68fLfvzR6Y6/veT2Y1m6rA63zx31x5o/rpvLtaOUnHDlopTuv5kWbNnxrbP+9vANFX+/uNz4sSFaOezyClH7OseW/eitpjvW/2nP13/yVtNZ3VbnjJJqRJ6i3rjrfM58tEzkVG7XhX94il9gmv3MDo2ed9z49gu+5IXHGJ9HpXn/mzexA9Wz2kTm4fqHF424RpJ1Sp3th0t/uON0x39r0bcOJGoShya9k3F/qvnTqtlckxA5eqDmgZUT3om4RE3i0LcWfevA6Y7/4dIf7tApdbYR10jyw/UPLwpHjPOdetsvFiub314vghE9K7/2mmfdj2pmfTKGhfPKJz70Fl2yjQGg2vPAVYr6lzKDF+2Z64GaB852C25Nli6r/Y7SO047Q+5Hy370kZpVu3ome3Jfbns5PxwxEjKfRXSSy2QyRcO/9PDI5176KYAxADtNJtNdJpPpYpPJ9N8AHgTwh6qqqhNn9/xl6v1bTSbT/SaTaZPJZPo+gNcAvFxVVfV6qP8OEjyPNjy6EQBWJK3Yv8CwYGw67/lGxTc+YcEK9db6ij2De4wzvWbU67dfxFnb8kSl1u648omngrG8UNRnOZ1XP/usqNGPsRP9aVEvXX81BJ5GOeehml6b/hsv1Z+78c97v/Xgzq5rOy3OXAXL8IvSY2r/3/n5z+z8r9V/+OeNC9+7qjKlW8mxQZnaJIgCnm95/hwAODfj3J3p0enO072HZVjct+C+DwHggPnAsvbxdl0wYgGA1XnxI/9zRenOj7+x6h9/vqbsofUFCbujVdzEmJNPeL126ILLHjzw7S89WX3FO3XmdJrdNT+pP/7pUkXntlUiwwruc3/zHF965cnqZM6MMkpwXPviq4I+p4vxTOqiXr/9JmZySHX6N37G7XOzb3e9vQ4ALs+5/ONYVexpZ0wqWaX4peIvfQgA2/u3r7a4LDO6JiFyZHFZVNv6t60BgJuLbv5IySpP+2Udq4rlr8i9YisAvN359nq3zx3R/Y5Ipzz6fLby8KOXAIB38e1ve87+f0fnfFKGheuSf2znM1cfZEQfp9nygxu43n2zXvlA/GVV9pv3LweAL5d/+aPTDcgDQHp0uvOcjHM+BYDnWp47l2ZzETI3kX6z4QEMAvi30d6qqiorgHUAdgD4JYCXAVwB4DtVVVXf/dyxXgAXAngewLcBvArgLgB/BHBjaMMnwXTAfMDQZmsr4hiOv3/h/Z9O930LDAvGlhiXHAKAJxqfWDOTa6q3/bJS0f3pcpHhfK7zfvPCXGZwfZ5gKLI7L3nwGVGhcXEjDcWaD797VrDOTaT3QcNw6vWPHb7mS09W3/9x8+jZDo8v2hCtHL5mccp7735l+R+euW3xazevSG8LRUH21ztezx1yDqVpOI3j/or7TzvaHrA+bf1gYVxho0/0KR6sfzDon0eWYXBOceLQ365b8NH2b531wDc25LyQl6ht84ngqntti77/RuNdF/5t/90P7uwq8/oESvrOE4pjr2Yoq5+4GAC8S+9+y1tx/X/UUZw1dYzPcc3zLwjaxBHWMWyMevPuzZhB5+DxhsfLbR5bfJwqznJP+T0nq915UlfnXd2eqk3tdflcUQ/WP7h0VrETIiMP1j+41OVzRaVqU3uvyb+m7fTv8Lu77O6jcao4i81r0z/e+Hh5KGOcz1hzfYz645+aGFFg+ZyNe93n/PKU5TpmjGHhvOqp944vAX/3a9fCPcGd/o3kZB459shKn+hTFMYVNgZ2HZ2Obyz8xj4Np3GYnebU1ztezw1ljITMdxFRS6KqqurnAH5+kt+7AZz0hlhVVTUA4M5pnt8G4L+m/iEy9WzzsysAoCKh4ujJCp1+kTtL79xzaPjQsnpLfUXLWMvWQn3hKdfGB3DduxOURx6f6pjd9TZfds0p67TMli977ajnrP96Tb3zNzcojr1yriJrbTdfdlXQr0PCQxBFPH+wP//5g/1ruizOXMBfQL4iNabumiWpBy9fmNzNMqHP3bzc9vJqADg79ex9ceo470zee0vxLTt/sv8nJQfMB5ZYXJbtCZqEOS2bPBW1ghXuWpPVdNearKZD3ePxj+/pWbava2zxoM2d9o8dXdc+d6DPsnlB0q6vrc+p0akVVKheppjJQbXmk59e7e+YbdjrXv+T6mBfQ4zLdLk2/fnFqNduu4cbOLxQveO/O6ZzHUEU8F7Pe2cBwIWZF+6azsyUAJZhcVXeVTv/Xvf3G7b3b1/5ncrv7FNzahp6J/OS2+dmd/TvWAkAV+VdtXM6M1MClKxSvDDzwl1VbVWXvtf13up7yu45OpP3EwACz2je/soVDO/U+hIK2p1XPPph0K+h0AjOK596NfqpC+5lJwZSo96+7zzn1c/QrpgzZPfauQPmA0sAwFRg2j2T98aqYvmzU8/et6V3y8ZX2l5ZfVXeVR2hiZKQ+Y/uMkQWRlwjqiMjRyoB4PrC6/fN9P2LEhdZC/WFjQIE9qmmp5ac9g1eJ6t5/5tXM4JX6UuprHOv+1H1jIOeJs+KrzTzORv2MqLAaj7+ydWMfZiWvsiMIIp4dHdP8fl/3Xfv7z5su7nL4sxVcoxnbUHCnudvX/znZ29f/OqVi1LCkuCqHqmO75zoLOAYjr+n7J5pz+IKODfj3P40bVqPR/BoHmt8bGEoYvy8pVlx1r9dt+CjLV9f+cfrlqS+G6dRjI05+YRnD/Rfet5f9n3zx281rR5zeKlunQxFvfXlTYxrTC/oUgadlzy4JVTX8eWsH/EuueNdAFAefuxirmdPwune82HPhxnDzuFUDadx3lF6x7RncQVcV3BdS5wqzjLpnYx7vuX5otnETYgcPNfyXPGEdyIuThVnua7gupaZvv+usrtqNZzGMewaTvmg54OMUMQ4n2k++n+rOGtbnqiIcrguefA1cKqQrOsXY9NdrnN//bLIsIKic9sq5aFHCkJxnfnsqaanyl0+lzZRkzi0KWvTjOtO3lt+7wGO4XwdEx0F9Zb6WdWZJIRQkovIxGMNj1V6Ba8qPTq9a13auqHZnGNz9uaDAHDAfKCSP039K80H3z6bnehPE9Wx485LH35nprt/zZTz0oe3CLqUQcY9rte8/63ZFcgnYeefudWXd9Hf9t/1wCcd15snPClaFWe/ZEHS1nfuW/6nf1y34MOy1Jg513CbiWean1kGAGXxZfUZuozT1uI6mQuyLtgPALsGdi0OZmynExel5H+8qfDA1m+s+us9a7JeMepUQ3aPT/dG7dD5F/19//3//X7LMofHR0soZEK1/+/FXP/BRSKr4F0X/uEVqGNCOiPPve5H1b6UyjpG8Co1H33v0tMtW3yt/bVlALA8afmh6dTi+jwFqxDXp63fDwAf9nxISxbJvLWld8tiAFiXtu6AglXMOMGiU+r4ZUnLjgDAq+2vLg92fPMZ17XToKh/+VwA8Jz1zTcFY+lkKK/Hl17Zx5dc8QkAqPf86RIaeJ2ZLb1blgPAxvSN+2czYzE9Ot1ZrC9uAIAXWl6oDG50hJw5KMlFIp4gCtjRv2MZAGzK2rR/tue5IveKjmhFtM3mtenf6Xon61THcd27ExTN76wDAPfZ339DjE13zfaa06aK9rk3/uJNEYzIdW5bpWh4LT3k1yRz8mbtUOalDx689TcftH1pwOZOj1Ky9qsrU97f8vWVD/z28pJPU+M0of/cfI5X8DJHRo4sAoAr8648ONvzXF9wfaOSVbqHnENp+4b2JQYvwulRK1jh6xty6rbcv/Kh75yb91xyjGrA7vHpXjg0sPmCv+376gMfdyykml2RjbEPq1T7/+5f7l1u2uLLWT8S+ouycF78l/dERZSDtXbkqLf/6pRJWqvbqmwcaywFgOsLrv/85jbTdnPRzbUMGKFroiu/w9YRPdvzEBKpOmwd0Z22zgIGjHBj4Y0znvEYcFPhTYcAoMnaVGp1W2lm7nSIAjRbfngJI/o4Pn15tWfFV5vCcVnXBf+7W9ClDjBuW5zm/W+eE45rzgfb+ralDDgGMlSsyn1byW2z3hRgU9amwwCw37z/tIPyhJCToyQXiXi7BnclW9wWo4bTOG4svLFxtudRskpxqXFpDQC81/1e5UkPEgWot/xgMyP6OF/a0hpv5S1hWw/PF20e8OWdt5sBoN7+y8vgdVL7jEA1vTb9dY8evvZHbzXd0W115qgVrGvzgqSt731lxV9+vrloX4xGMeMZIcHyRscbuU7eGR2nirNckHnBrGu7xapi+fKE8noAeLX9Vcm2fWcZBreuymh5/2srH/nKuuyX4rXK0XEnH//onp4rL/jb/i+/eKifCrNGKM0H397AuG2xQkzqgPvcX896cGKmxPg8h2fpXe8DgLL6qQvY0eaTJp6qWquKvYJXlaJN6V1sXGyZ7fUydBnO/Nj8FhEi82Lriwtmex5CItULrS9UiBCZvNi81pnWQz3RosRF1lRtai8v8soXW18sCWaM85Vq528XsWMdOaIiyuG66E/Br8N1KgqN4D4nMPC6faWi4XUaeJ2GNzreqASAysTKmnh1/IzqoZ7o8tzLO2OUMWMT3gk9FaAnZHaoE00i3tudby8AgPKE8mMahWZOhX2vLbi2GgCOWY6Vn2zbd9XuPyzw1z3QOJ0X/jF8DxRTnBf9Ybuo0VtZ+3CSevsvaflLBLHYPcpvvXxs461PVX/t2OBkmYJl+POKE3e89eVlf/7d5SWfGnSqkBRon4mtvVsXAMDixMV1cy3suylrUw0AHB4+vNAreCUdSVSwjHjf2uxjH3195T9uXZn+RoyaGx+Z9CT9+v3WW0yPHjbV9Nr0UsZH/h3X9lES1/HJKhGM6F7/s7dCVT/mVDxrvnvUZyhsZXxujXrLD086C2HnwM6FALAqedWsR9sDNqRvqAWAvUN7JUsIExIqe4f2LgSADekbauZ6rlXJq2oBYOfAzoq5nmu+Y2x9GlX1kxcAgLfy1g9Ffc6syg/MFl948eDxgddPf3fRTHatPRPxAs/UWerKAGBT1qZZz3gE/EvhVyStqAaA97rfC2vZCELmC0pykYgmiAJqRmoWAMB5GefVzfV8S41LLWnatB5e5JXPtTxX+m8vuic41ZEnzgMAb8WNW8SE/FmPWM5aVILXs/TujwBAWf/yRma8RxP2GMi/EUQRD3zcsXDzgwe+vqVpZJ1PBFeRFnP0mVsr//qna8o+kWJZ4slMeicVDdaGMgC4PPfyOXfcN2dv7o5Vxo7ZeXvsGx1vRMRIolrBCt85L7/6g6+t/NvF5caPFSzjbRicLL316ZqvfuuVYxstdg8tgYkA6h2/uYCByPiy1hzkiy8ZCHsADAv3hl98IDKswPXuW8y1fph84stdE13aDltHAQNGvK7gujnfV0wFpmYVq3KbneZUKZb3EhIqewb3GIedw6kqVuW+ruC65rmez1RgqmfAiJ22zoKuiS5tMGKcrzRbf3w243Vohbjsbve6H845wTgbrvN/t0NURttZW2+Gatf/0UzVL/B+9/uZdt4eE6OMGT8/8/y+uZ7v+sLrqwGgeay5ZNw9Ts82hMwQJblIRNvauzXd5rXpoxXRExdnX9wdjHOuSvGPJO437/+3JJfmk5+tYNzjcUK00exe/+NZ12iZK8/KrzcI+pwuhndGabb+aJ1UcRBgX6fVcOmDB299dE/PlZNuX0xKrLr/vy8tfuy52xe/Wp4W3oLyp/Ny28sFHsGjNmqMgyuTV865/hHLsFiWtKwGAD7u/TiiHm5jNAr+f64o3fnsbZV/q0iLOeoTRMWWxpF1lzx44GuP7+mhXe4kpDzyeB5nackXOZXbdd5vtkkVhy9n3Ygv6+yDDERGs+PXF544C+HF1hcXiBCZnNicOS2/CtApdXxZQlk9ALzW/lpYdiQlJBze6HijAgDKE8rrdUrdnJfiZ8dkO3JictpEiMxLrS+Vzz3C+YkdOBzHdXy8CgDcZ3/vg1BvfnQqoi7Z4624/mMAUFU/cT5cYwpJApGBj3o/KgeAhYaF9XOdSQ8A5Qnl48lRyf0+0ad4veP1/DmfkJAzDCW5SER7r/u9BQBQYaioV7LKoCx5uSznsiYA6LB15AdGR5ix7ihF4xvrAMCz7L6Pwr285t8wLNxrf/CBCIDr3LaS7T+klyyWM5TD4+O+91rDunufO3pft9WZE6VkHbeuzHjjva+u+NdlC5NnvCV0OOzo37EAAJYnLZ/zLK6Ai7IuOgYATWNNRZFY/LQsNcb23O2LX/3VJUWPJceoBibcvtg/ftxxw/WPHb6mxWzXSR3fGcfnYVT7/nYBAPBFl3wqxueFfzbsCVzn/WabqNC4WGt7rvLwowWB3x80HywDgLUpa4PWVgIzjY9ajpae7lhC5KLOUlcCABvTN9YH65xrU9fWAsA+876IGjyJJJpPfn4OI/o4X0plHV9yeb+UsbjX/eiIEJ08xLhtsZpPfrZSylgilVfwMvWW+jIAuCjroqC1lUWJixoAYPfgbqphR8gMUZKLRCxBFHB09GgZAFyQeUHQbhpF+qKJwOjIG51v5AGAZtvP1jA+t0aIz+vwLr2rNVjXmi2+aPOAkLr4KCMKrHrnb9dKHc+Z5M3aocyL/7H/3veODW/0ieAWZ8ZWv3z30r9957y8agXLSJf8/AIO3sG12doKAeDKvCuD1lZWp6w2xyhjxpw+Z/SW3i0RW3j2ikUpPe99dcUjV1WmfKBgGW/9wGT5DY8f+ervP2pbLIgR+Z9sXlLt+dMC1j6ULKpjba5zfrlX6nhEfY6TL7pkJwCoDj2yEaKAAfuAps/el8WAEa7Iu6IlWNe6OOviLhWrclvd1sTqker4YJ2XEKnUjNTEW9wWo5JVui/OvrgrWOe9Ov/qZgaM0G/vz+yz90UF67zzBdf2URI3cHihyHA+18ZfbJU6HnAq0bPivo8AQNH8zhpmclAtdUiR5t2ud7McvEMXq4q1bkzfGLSk5MVZFzcCQPNYc7Hb56Y+OyEzQA2GRKxdg7uS7bw9VqvQTs5lp7iTWWhY2Aj4R0cYa7uW69i2AgDcq7+9Rapp4Z/nPvsH20QwIte7bzHXu486TSE27vQqvvz80Yt+9FbTHaN2r1EfpbD8+KKCp566pfKNrPiosBZ8nan3ut7L8gpelUFtMJcnlI8H67wsw6I0vrQJAD7p+6Q4WOcNBSXHir/YXLT3iS8t+kd+orbVzQuap/b3XXbZQwdvpcL0YeDzMMqjz60DAG/5tduh0Uu2y+iJXBt+ekBURtvZif401YGHit7sfLNAhMhk6DK6U7QpQaunp1FohPy4/BYAeK/7vYhuK4RMx3vd7xUBQEFcQatWofUF67zGKKM7Q5fRJUJk3u58m5ZhfY569x/XA4Ava80hIW3pmMThAAC8i+9oE+KyehjeFaXe/usVUscTabb3by8BgIqEimPBWKoYsCJ5xUi8On7ELbg173S9kx20ExNyBoiM3jwhJ/Fx78eFAFAYV9gSzJsGAFyUddHx0RF226/OYgSv0mcobpF6WviJfFmrLb70ZTUMREa987frpY5nPnv/2HDaZQ8dvHdXu3UlA4jnFBk+ffu+5Q9etzStQ+rYpmPX4K4iAChNKA3azJSAtalrmwCg3lIvi477oozYsVfvWfrs3WsyX41Sso4uizPn9mdq7vu/Le2VNKsrdFR7/1zOOkYTRXXcmPvs71dLHc9xUQleb8llnwKA8shjGw+YDxQBwELDwjkX0f685cblzQBQPVJNdeGI7B0ZOVIMAMuNy5uCfe5FhkXNABBoj8SPa9+axJmPlokM53Ov+9GnUsdzHMPCs/y+jwFA0fLeamain2ZznaDR2lgIAOvS1gX9vrIgYUEj8FkijRAyPZTkIhGrzlJXAAArklYEveO+MnnlcKwq1uryuaLqB3avBADPyq9tD/Z15sq99ofbRYYV2P5DC2k2V/A5vT72O68eW/+91xrusji8iQla5cj/Xln6rz9fW741LkoZETNRpiPwgLU2dW3QH7A2Z2/uUrEqt8VtMR4ZPpIQ7POHAsswuH9D7tGX7lr699IUXYPXJ6qe3Nd7+XWPHr6uc9RBO3oFm8Azytpn/bO4yq7eCWVURO017173o4OiSjchTA6mdIw1FwPAhZkXBr2tXJp7aSsDRuyd7M02O8zUCSSy5RAcTO9kbzYDRrw099Kgl3C4MMvf/trG2wppGdZn1Lv/sA4AfJlnHRaSyiekjudE3kVf6hT0uZ2Mz61Rb/9vqs01pXa0Vj/mGTOoWJU72KtOAOD8jPMbAKDB2lAiiBF1ayUkotGNhUSkQcegpt/en8WAETZnb24P9vlZhkV5fHkjAOzUKJW+hMI2vvTKOW/5G2xC+vIxX9rSWgYio9rzp7Okjmc+2ddpNVz20ME7P2gY2SACzJq8+L1vfnnZwxeVGSNmNt90HBk+kjDmGTOoWbUrFA9YJy7Derf7XVnM5grITohyvHDH4qo7zsp4TcUx7sYhe8l1jx35Cu3AGFyqfX8rYR0jRlEdO+4++/9JstX9F9LoeW/xpbtr1Go4RV6lV+lHlyUtGw32ZdKj051p0WndIkT2jc43Ck7/DkIiU4O7QS1CZNOj07vTo9ODvlx/qXGpJU4VZ3ELbs1HPR9lBPv8csR17TSwQ7XlETeL6wSeZfdsBwBF+0craadFvw97PiwAgJzYnHY1pw56FmpD+oYBDadxTHonYw+aDyYG+/yEzFeU5CIR6Z2ud/JEiEx6dHpPkjbJHYprnGVY1AkA+6LU8C67d0corhEMnpX37wYArnfvYna0JVrqeOaDBz7uWHjf83X3DtrcaTFqbvwnmwqefOiGig/kNHsr4IOeDwoBIC8urzUUD1jAZ8uw6i31squfwjIMvnVOXu3jX1r0YFZ8VKfD44v+48cdN9z7/NGLJt08J3V8sicKUNa9sAYAvCWX74IqOmi1e4LJs+Z7hz/R6XgAWKoyDoTqOoFlWPvN+ymRSmSr0d2oAUKzrDegNL60GQB2DOygtgJAtfcvZzEAfOnLa4TkCpvU8ZyMd+FNnUJMWj/jdWjVu35fKXU8kaB2pLYAABYnLg76qhMAULAKMS82rx0APun/JC8U1yBkPqIkF4lIB8wHCgGgwlARkpsGAFzeW69XiiIaVCp05KweDtV15sqXu3HYl1jSzAi8QrXr91Twcw7GHF7lrU9VX/bonp4rvYKoLE/V1b96z7IHTUvSOqWObbZqRmsKAWCpcWnI2soFmRd0AEDvZG+Wg3fIMjG0MD12/I0vL3vqioXJH7IMhN3t1pVXPHzozsM947QMeA4U9S9nsrbedFGhcbrXfLda6nhORYw2erbFxrsB4HJzd1yorrMxfWMrAHTYOvJoaQmRI1EU0eZtUwPA+vT1baG6zpqUNc0A0GBtKAzVNeSCHW2J5voPLAIAz8r790gdzykxLLwLrt8FAMrGN1bD52GkDklKdq+d65zozAOACzMvDFlbWWRY1AYAdaN1shtoJEQqlOQiEUcQBbSOtxYAwMb0jaHpuPs8jL7p3ZWVLjdEhsGHvVtyQ3KdIPEsvedTAFB0fLyCcYwqpY5Hjna2WoxX/PPg3Yd7bIs5luFvWp721nO3L345JVYdkpmC4WD32rneyd5sALg4++KQPWDlxeVNJqgThnmRV27p3SLbpSUKlhF/dWnxnt9dXvJorEYxNjThTr372dp7H9rZVSp1bHKlOvyv1QDA5248gKgEr9TxnErvZG9UFzzRKlHEanNLJte5PSTLPs5KOcus4TQOB+/Q0dISIkdmn5mbECZYDadxnJV8ljlU17ko66JujuH4UddoUtdE1xldK1G1+w/LGYFX+BJLmn0560akjueLeFZ+rUHUxFsZ11i8at/fzuh75/vd72fxIq80aAzmkviSkM2+Oz/z/HYA6J7sznHxLuq7EzIN1FBIxDloPpjo4B26KEWUfXXK6pA8YKkOPlzMuqwJy7xwAcDh4cMRPQWYX2DqEWIzehnepVHt/fNCqeORmz9ubV90/0v194zavcYErXLkgWvKHvn+BQWHWUbeg5Af932c7hN9igR1wnBOTI49lNcqiCvoAIB9Q/siOiE8HZvKk/pfvmvJwyXJ0Y0en6j++44u07NNQqxXoN0XZ4Lr3p3ADh8rERnO51n97QNSx/NFPur9KAcAikTVhFoEVAceDMmsWJZhkRub2wEAOwZ25ITiGoSEUrO7WQ0AOTE5HQpWEbIvxWhltC8tOq0HALb2bs0J1XUinmtMoWjfuhwAvItv3y11OKfFqURv8aV7AEB57OUzugD9PvO+fAAo0ZcEfXOGE5XEl9ji1fEjXsGrkvNAIyHhREkuEnE+Hfg0GwByYnI6WSY0H1FlfdVKAChPWlINAG22tojvuHtLr9oHAIrmt1aAlsFMi9PrY+95rnbT43t7r+AFUVGZEVvz6t1LH9lQaAjZ6HQ4HTAfyAGA/Lj8jlBfa4lxSTsANI01RXxbmY7UOI3rxTuXvHjN4pT3WQbCzgEx+v+OCInN5kmd1LHJherA31cwAHxpS44KiSWTUsfzRY4MH8kFgJzEBQ0AwPXuq2Qm+kOyA+KChAUdAFBvqZ8XbYWcWVq9rWoAqDBUhPy+Uqwv7gSA6pHqnFBfK1Kp9/5lAcM7tYIudcBbcUOX1PFMh+esb9WInMrNjndnKVreT5E6Hqm0jrfmAMBS49KQt5WCuIJ2ANgztCeiB+UJiRSU5CIRp95anwMAZfFlnaE4P9e5PZG1duSIrNJbsfrH21Wsyj3uGU+oG63Th+J6weJZ8dUGURltZ+3DScqjz2dLHU+kax9xRF/9yKFb9nSMrWAZCDcsS3v76VsrXzfoVB6pYwuWQMKp0lDZGeprXZB5QRcDRhywD2RYXBZVqK8XDizD4GcXF+377eUlj8WpIHRNQHnLkzX3vn10iEZKT4NxjCq5nr2VAOBdctd+icM5rXZbew4ALMq9uE7Q53Yyglep2vNAZSiutSFtQwcAdE105fICL+/pouSMwgs80+HpUAGffY5DaWXyyg5AHgONoaJofns5APAll+1HiAZ2g02MNnp8mWdVA4Dy0CNnZK1Yi8uiGnIMpTJgxHMzzu0J9fWWGZe1A0CjtZHqchEyDfL4NiVnDEEU0DXRlQMAa1PXdobiGqqDDy8HAF/aslqlPseVHZPdAQBb+7ZG9kOWKtrH52w4CADKmqfP6Cnip/NevTntpieO3NNjdWVHq7jJX19a/PgPLyw4JHVcwTTpnVT02/szAOC8zPNCPvqbok1xJUUl9YsQ2Q97PswK9fXC6eLypL4fLmWH82LhsXt8uh+91XTb/3zYtkTquCKZat9fKhifWy3EZvTyRZtDtlthMLSPt+ssbotRwSi8G9M39nvLr9kPAIrW91cgBEmoRYmLLDqlzubyuaJ2D+5OCvb5CQmVXYO7kp2ik41j43yLEhdZQn29jekb+xWMwmt1WxPbx9vPuFm0isY30tiJ/jRRoXG6V369Tup4ZsKz/Kv7AYAbOFTBjHVGSR1PuG3t25ohQmSNUcYBY5Qx5LVdL8q6qJMBIw46BtOsbivV5iXkNCjJRSLK4eHDhkA9rmVJy4JefJOxD6u43r2LAMC79K4DAFCWUNYBAMcsxyJ+dpRn1f2HRIYVWHN9CTt0NFbqeCLRH7e2L/rBG413TLp9sel6Tc9Tty56+NKK5F6p4wq2rb1b032iT2HQGMyZukxHOK5ZpC/qAICDwwfn3XT5ODUj/FclO3pWrn6/IIJ75kDfpXc9W7vZ4fHJcjfJkBIFKJrf8deQKbk8omtxAcCWvi05AJChy+jWKrQ+z7J7m0R1rI11WhKU1U8F/bPMMixyY/x1uT4d+DSyB08IOUHg85qnyvOEqlzEibQKrS9dl94NfNZOzyTKI0/4B12z1hyBRs9LHc9M+LJWW3yGwlZG4BXqvX9eLHU84XZ4+HAOABTEFYRliakxyuhOjEocFCGyn/R9QrPNCTkNSnKRiLJzYGcOAGTrskNSj0u1768VjM+jFuKyeviCC4cAYEXSih4A6JrsivjZKUJS+YRgLG9kIDKqgw8vkjqeSMILIvPNl+vPeXxv7xU+EdyK7LiDr9699MmiJF1E1wqarQPmA7kAkB8b+npcAUuNSzsBoN3WHvFtZTYULIN/3rjwvVtXpr/BMvDt6xxbZnr08M19Yy6N1LFFEkXD6xns5GCKqIhyeFZ89ZjU8ZxOzUhNLgCU6Ev8bUWhEficjYcAQHns5ZB0zhYaFnYAQIO1gZJcRDYarY05AJCvzA/brsMl+pJOAKgZqckJ1zUjATPWHcUNHlkAAJ7lXzkodTyzwZddewAAuI6PF59ptWJbxluyAaAyMfTlIgJyY3K7AeDI8JF5+QxGSDBRkotElHrLVD2uhNDU41K0vr8EALzFlx2ffbA6ZfWQklV6bB5bvBymy3vLrjoCAIrO7YtDsdRGjsadXsXNTxy5emvT6FoGEK9bkvruozcveker4nxSxxYqzWPNOQCwOHFxZ7iuuTF9Yy8AmB3mVJvHpgjXdcPtO+flV//mspLHtSpussvizLnx8SN3Hu4Zj5c6rkihrH12CQD4ss4+AnVsxM8+6JzozAKAlckrOwO/8yz/crUIgDXXl4Riqc36tPVdANBr780UzrDOH5EnQRTQZ+/LBPwzucJ13VXJqzoAoMPWkROua0YC1YG/VzACr/AlFLb5MlZapY5nNjxL7mgVldGTrGM0UXHslUyp4wkXm8emGHQMpgPAeRnndYfruhWGim4AaB1vpSQXIadBSS4SMabqcWUDoanHxbV/bGQn+tNETuX2LP9yQ+D3ak4tpGpTewFge//2iL9JeytvbRNVMROMyxqvrHvxjL/RtQ3bo6999PBt9QOT5UqO8Xz3/LznfrypMOKXUM2Fi3exA46BdAA4N+PcsO3GZIwyuhM1iUMCBHZ7//a0cF1XCpsXJPX966aKfyXqVGaLw5v45eeP3vVm7VDEfz+EGuMYVXKDR8oBwLP49mqJwzmtPntflNVtTeQYjl+btnYw8HshucImGApbGdHHqfb/Y2Gwr1uWUDamVWgn3T531KHhQ4Zgn5+QYDs8fNjg8rmidKxOSOQSwzZAtD5t/QDHcPyYZ8zQO9l7xtR2UrR/XAkAfPElRyQOZfYUGsGXtboGAJRHXzhjlixu7d2aIYgCl6hJHEzRprjCdd31aeu7AaDf0Z/h9rmpD0/IF6AGQiJGg7Uhzs7bY9Sc2rk8aXnQ63Gpap6sBABf6tK6z9c+KIgr6AGAo5ajkZ804lQin7WmGgCU9VVnzEPFyexstRhvearm7oFxd3qsRjH+wNVlj35pRUar1HGF2q7BXSk+0afQq/SjGboMZzivnR2T3QMAR4aPzPuET0Va7PiLdyx+LD9R2+r0Ctqfvt1069+2d5ZLHZeUlAcfLmV8HpUQm9Hry1kX9O/pYNvRvyMDAJK1yf1ahfbfOu580aX+WbHtW4O+1IZlWGToMnoAYO/g3nnfVoj87R3yf04zFZkehgnfJHGNQiOkaFP6gc/a63zHtX6YzE4OpIqc2uVZeneT1PHMhWfxHUcAgBs4XM7Yh+fFzsunc2j4UDYA5MXmhW2QEQAK4gomY1WxVq/gVe0e3J0czmsTIjeU5CIRY/fg7gwASItO6w16PS6vk+V6pgrOV9xQ/fmXKxMruwGgw9YR+UkuAN6ldx8BAHawppyZ6FdLHY8UXqkeyP7my8futLn4uNQ4dd9Ttyx6ZF2hwSx1XOFwcPhgBgBk6jLDXlC/PL68GwBaxltk0VbmKilG7X7xziXPr8zRH/SJ4B7+tPuaH77RuEYQRalDk4Sy9b3FAMDnX1AtcSjTUjtamwEAuTG5/7HFu2fp3U2iIsrJ2oeSFc3vpgb72oVxhT0A0DDWQEkuEvGOWY9lAkC2KjtsSxUDcmJyegCgdrT2jGgrqpqnKgHAl778qByWfH8RX/baUSEuq4cRvCrVgQfLpI4nHNpt7RnAZ8sHwylbl90NAPuG9p0Rz2CEzBYluUjEOGbxP2Dlx+YHveOurH6igPHaowWtYYQvveI/zr8hfUMvA0Y0O82pctia15ex0iroczsZgVeoDjy0QOp4wu2fn3aX/Ordli95fIK6JDm68aU7lzyRb4y2Sx1XuLSMtWQCQLG++D867qG2OnV1DwD02fvOmFpDagUr/PPGineuXJTyAQC8VWc+76sv1l3IC+IZVROP7T+kZ60dOSKr4D3LvyyL7e7bbe2ZALAgYcF/3lfUMT5fxopaAFDWPhP0WbFLjUt7AKB7ovuM6LgTeQt8TnOVuWFPclUYKnoAoGOiY/63FY+d43r3LQQA78KbqiWOJij4gosOA4Ci9f15v7qAF3hmwD6QAQBnJZ/VF+7rl8aXdgNA01gTJbkI+QKU5CIRo2uyKwMAFhoWBj/J1fhGJQD4cjZU4ySzxBI1iZ5ETeKQCJHZ1rctPdjXDwVv0cX+pTatH8z7h4oT/eaD1qV/295pCuyg+Nzti6viopSyHgmdqZ7JngwAWJ68POwzuRYkLBjTKrSTLp/rjKo1xDIMfnlJ0d57z856mWUgfNpmXXXrk9VXOjw+TurYwkV16JFFACAkVTSIMWlh231tttw+NxvojKxLW3fShLCn8rbDAMD1HVgI11hQN1NYm7p2gGM4n8VtMQ7YB2iHThKxBuwDGovbYuQYzpepzPSG+/rr0tb1TsWRPt9rDamOPFbI8E6toDUO80Wb+6WOJxg8y+87JrJKLzvencV17ZzXzwVHRo4kuAW3RqvQTpbEl4yH+/qrU1Z3A0DPZE/WmTLQSMhszOsbCZGPSe+kwuwwpwLAutR1QR0ZYaztWnb4WLEIRvQsvaf2VMflxk5tzTsij615PcvubRA5lZud6EvnOj4xSh1PqAmiiO+8emz98wf7LxEB5sJS47ZHblr4jpJjz6h1Y63jrboJ74ReySo9q5JXhX155om1hvYM7pFFWwmmr63Pqf/+BfnPKjnGU9s/UXHD40dusNg9ET/7c84EnlF07awEAG/Z1dXSBjM9ewb3JPEir4xVxVpzY3NPOtPTl3++WYhJ62d8brXq4D9Lgnn9aGW0Lzkq2V9raODMqDVE5Glb/7ZMAEjWJvcpmfB/neXE5NhjVbFWXuSVe4f2JoU9gDBSNL5ZCQC+3I1HTjboKkditNHjS11cBwDKI49XShxOSO0f2p8BAOnR6cEvrTINy5KWjahZtcvBO3RNY02xYQ+AEJmYH9+uRPZ2DuxMESCwCeqE4SRtUlBnCKgOPFTBiAIrGArbhKTyiVMdVxJf0gsAHbYOWczkQlSC15e6pB4AlLXPVkgdTijxgsjc89zRzR80jGxgAPHGZWlv/99VpdvZMBbHjRQ7B3ZmAkCqNrVPySolSfAVxRV1A0CjtXH+Ly05iRuWpbf/5rKSJ6OUrKN9xJF//WNHbu2yOLVSxxVKyroXshn3uF5Ux9q8C2/skDqe6ThoPugvpK3L/MJlvXzuxhoAULR9GPTv0dxYfy2wmpGaM7KtEHkI1MI6We26cMmM9teYPDB0YN62FXa0JZodaSwSwYieZfcclTqeYOLLr/V/j/buqwj2Rh6RpHGsMQMITWmV6WAZFinRKX0AsHdorzz6K4RIgJJcJCIcGT6SAQBZMVlBf8BSdG6vAAC+aHP1Fx23PGl5HwAMOAbS5DIFmC++rA4AFL37FszXhwo3L7C3PVV9xb7OsWUcA9/XN+RU/eDCgkNSxyWVeku9v5B2rHSdkcrEyj4A6LX3pkkVg9QuKjP2/9VU/lisRjE+YHOn3/pU9W3N5kmd1HGFiqLhdf/3aObqGnAqWcyebBprmlbtOu+SO+tFMCI72lzAjHVHBTOGBQkLeoDPaoMREokCg3snrV0XJkX6oh4AaBpvmrezHpWHHy1nIDJCYnGLkFgyKXU8weQtv7ZbVMVMMO7xOEXDq/P2v2H3ZHc6ACxKXCRZW8mJyekHgAZrAyW5CDkFSnKRiNAyfryQdlBvGlzf/nh2oi9dZJVez+Lbm7/o2MWJiy0qVuVy8A5d81izLKYAeyuu7xSV0ZOMyxqvaHpr3iUcHB4fd8uT1VfX9E0sVLKM9wcXFjx795qsRqnjklKnrTMTCE3tuulak7JmkAEjjrpGk8bd4/N/qd4prMyJH33s5oWPGqKVw6N2r/HOZ2pvr++fkMV3x4x47Bw3WF0KAN6KG2Uz+6DX3psOAEuNS79wCbxgKLIL8XkdjCiwqiOPlgYzhtUpq/sAYNAxKJvBE3JmEUQBg47BNAA4KyX8hbQDlict7wGAnsmeeZsQVnTtWAAAfP75sti4Y0ZYhehLX+5fsnjs1Xm5umDcPa4ccY6kMGDEtalrB6SKoyy+rA8Auia65t1zPyHBQkkuEhH67H0ZALAiaUVQO+7KmmfKAUBIKm9CVMIXFlNlGRYp2pR+ANg7tFceNw5OJfrSlvmXLNa/PK8eKsadXsVNTxy57tjgZJmKY90/31z09HVL02SxTCpU3D43O+QcSgWAtalrJUtyxanjvAaNwSxCZHYN7kqRKo5IUJysm3jylsonkmJUg2NOPuGe54/ecbhnPF7quIJJWftMHsO7ogStcdiXd86w1PFMx6BjUDPuGU/gGM63MnnlaWvX+fLO8c+K7fgkqLvVFuoLJ7QK7aRH8KhrRmoSgnluQoKhdrQ23iN4NFqFdrIgruCUJR1CbVXyKjPHcD6bxxY/6Bicdxs1sIM1sex4d6bIKnhv5W1NUscTCt7ya+sAgOs/VA7eNe/6mDsGdqSKEBmDxmBO0CSEfRfSgEAyetAxmM4L/JlXt4OQaZh3X0BEfjpsHdGT3slYJav0LEtaNhLMc3Pdu/yjZoWbpjVqlh2TLbspwN6yq6ceKg6Uw+eZFze70UmP6qYnqm9sHXYUapSs83eXFz952cJkyZbnRYqD5oOJPtGniFHGjGXoMpxSxpIRndEPALWjtfJICIdQdkKU46lbKp9MjVP32Vx83FdeqLt9d7s1Ueq4gkXZ8m45APgyz5LN7IPdg7tTASBRkzioVWh9pzves+SuBpFhBcbanssONwR12WmqNrUfAA4MHzjj2wqJPPvN+9MB/+dUikLaARqFRkjUJA4Cn7Xf+URV/aR/0DWxtFnUJUuWIAklvmhzv6CJtzBee7Ty6As5UscTbDUjNf7SKrosyQYZAaAgrmAyWhFt8wpe1Zm0yzUhM0FJLiK5fUP7UgEgKSppMJiFtLnOHYmsfShZ5NRuz6JbWqfzntL4UtlNAeZLr+gVNfoxxjMZo6yvypY6nrkanvSobn6y+uYuizNXq+Im/3hV2ePnlxolmxYeSY6MHEkFgNToVMn/feTH5fcDQJutTTZtJZTS9RrXs7ctfiozXtNl9/hivvly/e2fNI8mSx3XnLltCnboqH+p4sKb6qUOZ7rqLfWpAJChy5hWWxFj012CobiFAaA88nh5MGMJ1E9pHmumtkIiTuBzGRjkk1KmLrMfAOot9fOurXDdn/oHXQsukM336Iwx7PHBEEXTm0GdFRsJOiY60gCgUF8o2bLegPTo9D4AOGg+KJtBeULCiZJcRHLHrMfSgM8eboJFefQ5/+yD5IUNUMecdiQfAFYlr+oHZFY/hWHBp6/0P1Q0vCHrh4rRSY/qlierb+odc2XGqDnbX68tf3xtQYIslkeFQ+t4axoA5MbkSt4ZWZy4uA8A+u39864zMltGncrz3G2Ln801RLU5vYL2e6833LqtZTRJ6rjmQlX9ZAHj86gEXcqgL2vNqNTxTFeHbaozElc47bbCF1zg/x6dqpsTLGUJZf0A0D3RTW2FRJzA57Isvkzy+0pBXMEAAHTYOubVTC6ud188O9GfJrJKj6fy1hap4wkl78KbjgIAN1hTBvcEJ3U8wdRv708FgMrESskHGnNj/c+BTWNNlOQi5CQoyUUk1zXRlQoAhXGFwbtpiAK43j3+UbOii6c9alakL7JN1U/RHBk5Ipv6Kd6K66ceKo6UwWOX5UOFixeZLz1VfWPvmCtLp+Zsf7tuwRMrcvQWqeOKJD2TPf7OSIL0nZFVKavMLMP6rG5r4rBzWC11PJFCr1V6n7t98fMFRm2LyytEfe+1hls/bbMYpY5rthSt7/sHC7LWyGapIgD0O/zJ18XGxdNuK97FdzSJrNLL2noz2L4D+mDFEhg8GXIOpVL9FBJJeIFnAnUeVyWvkrzjHmivgfY7XyhrA/VhF5y2Pqzc+XLWjwjRyUOMz61WVT9ZIHU8wWJ2mNXjnvEElmF9y5OWSz74WmGo6AM+ey4khPw7SnIRyQ04BlIBYHHi4qA9YHFtW5JZx2iiqIhyehfe3D7d97EM+1n9FPMB2YyO+PLPNwta4zDDu6KUtc/mSh3PTLl9IvPXWiGhx+rK1qm5ib9cW/7kksw4q9RxRRK3z82aneYUADgr+SzJOyNahdZn1BiHAGDX4K55Neo+Vzq1wvfULZVVgRld33614VY51uhiHKNK1nysGAA8i26RzRKbAfuAxuaxxXMMx69IWjHtzoioNXiFpPImAFBNdUqDITc2165T6mxewauS0+AJmf8ODx82eAWvKloRbcuLy5uUOp4VSSuGOYbjbR5b/IB9YN4Un+e6d0/Vh71INt+jc+HLPvsoACha35f16oIT7RnakwIAiZpE83TqPIba2Sln9wPAsGs4xcE7ZDm4TUgoUZKLSKpnskc76Z2MUzAK79KkpUErOq+sr/LPPkipPAZl1IzWHebG5vYB8quf4stYeQwAFG0flkody0yMObzKv9YKCW02qKJV3OSfryl/Ynk2zeD6vEgqOh+QofMXnz86elRWbSUcYjQK/ulbK1/ISYjqcHh80f/1yrFb93eOySrBoax+oogRvEohNqNPSFs6JnU807VrcFcaABijjINqTj2j738+//xjAMD17gvq92hg8OSQ+RC1FRIxDg37P49p0WmSzw4GADWnFoxR/sGT+VJ8fjb1YeXOW3HjMQBgh48VynV1wecF6jymR6dLPsgIAEnaJHecKs4iiAJ30HxQdoNohIQaJbmIpPYM7kkF/J2RoBad7z9QCgB88SUzHjUr0ZcMAECfvS8lWPGEA196ZQMAcEO1xXLZZXHc6VV86anq61vHodIpITxwTRktUTyFSCo6HxCodxSof0T+XVyUkn/61srns+KjOu0en+4bL9ffdqh7PF7quKZL0f5xKQD4stbIavbBMcsxf9H56OkVnT+Rd+HNrSKr4FlbbzprrosJVkzHi8+Py2vwhMxvTWNN/jqPsdLXeQwI7Nw7X4rPK+urSgFASK5onG59WLnzZay0CtFJZsbnUSuPPie71QUn0zHhrxOXF5sXMc9gKdqUQQCoHa2VVX+FkHCgJBeR1DGrvzOSqcsM3lLFzh2J/qWKGpe33NQ10/cvS1o2CABmhzlFNsXnAfD55w+JGv0Y43VEKxpey5Q6ntOZdPPcLU/VXN856szTKSF8axE7uio3XjaFrcMtUHQ+0FmOBIsSFw0AwKBjkB6wTkGvVXqfvnXRc5l6Tfek2xfz9aq62470jOuljuu03BMcO9JQCADeBdc1Sh3OTAR2wCrSF824rYhag1cwFLUBgLL2uZJgxVSeUO4vPj9JxedJ5Oiz96UCQGl8acR03I8PnkzMj8ETrm9/CQDweec1SB1LOPnSljcAgKL1w6B9j0ppwO4vrbLIsChinsGyY7IHAaBtvG1ezHokJJgoyUUkFSg6XxBXELSbhrLhlWIAEIzlzTNdqjgVy4SG0zjcglvTaG2MC1ZcIcew8KUuaQAAZfM7Ef1Q4eYF9o5naq9uH3HkRylZxzcXsaPpOoaXOq5I1jPZkwoAZfFlEdMZWWZcNsyAEcY8Ywar26qUOp5IlRCt8j55a+Wz6XpNz4TbF/u1qvpbGwYngzZLKBSUdS/kMj6PSohOHvKlr5BVfbxAncdAEnamfNnr/LNiu3cF7Xt0RfKKQQAYdg6nUPF5EgkEUYDZaU4GgOXG5YNSxxNQafTvXBdox3LGDh2N9e+qqOA9C2+cdn3Y+YAvvbIRALih2hK5rC44FavbqrS6rYkMGHFVyiqz1PEEyHXlCSHhQEkuIqkB+0AaENzteLnefVOjZufMavYBy7BIikoaBIBDw4dkdePwFm32P1T0HypFhM5CE0QR9zxXe0nD4GSpimPdv7285OkMSnB9IV7gmWHnsL/ofIr0RecDopXRPoPGMAwA+4b2JUsdTyQz6lSep25Z9GxKrLrf5uL1971w9Eu9VmeU1HGdiqLtoxIA8KUtk9UsrmHnsNrmscWzDOtbbpzdDlieRbc0i2BE1tqey4x1B+W/UU5Mjl2r0E56Ba+q3lKvD8Y5CZmLOkud3it41VGKKHskFJ0PWG5cPswyrM/mscXLfede5dHn/YOuhqLW+b6r4ufxBRcMiuq4McZrl8Xqgi+yd2hvsgiRMWgMZp1SFzHPq8dXnjjltfKEkHCgJBeRzKBjUGPz2vQcw/HLk5YHpeg8O9ygY229GSKr4L0Lb551gc8MXcYgADSPNcsqycWXXtkjKqPtjHtcr2j9MOJiF0QRX3ux7oLDPbbFCpbhf7Kp4NlzixMjZgQ5UtWM1sTzIq+MVkRPZOoyHVLHc6IUbcoQAByzHKMk12kkxajdj9688BlDtHJ41O413vFs7U2jkx6V1HH9B4FnuMGaYuCz0Xi52G/enwQACeqEYY1CM6unflGf5RTjczsZiIyy9umiYMWWFJU0BABHRo5QWyGSOzx8OAXA8UG9SKFRaASD2j94EmjPcsX17PYPFmSvk9X3aFAwLHxpS2WxuuB06kbrUgEgLTotYgYZASAvNm8yiouyewSPus5Sp5c6HkIiCSW5iGQOmA8kAYBBYxie6Q5Yp6I8+px/1CyhoF3UGmY9alYQWzAIAN2T3RGXKPpCnEr0JS9sBABFw2sR91Dxgzca1+5ss57FMhD+69zcF69YlNIjdUxyUDtSmwx81kmOJIGaEB0THfJqKxLJio9yPnh9xdOxGsX4wLg7/bZnaq6bdPMRtfuUovHNdMZr14nquHG+4IKI6gCfToOlIQUAUrWpc2orfNYafz2Zzu1B+x5Nj04fBIDW8VZqK0Rygc9hRnRGxN1XAoMngfYsR4ytT8Na23NEMKJn0c3NUscjheOrCwYOR+zqgunosPmLzufE5ETU/ZBlWCRrkweBz5LWhBA/SnIRyTRaG5MBIFWbGrSbBtf96dSo2do5jZotSlw0CABDjiHZ3TT4ggumliz6d5iMFL96r2X5u/XD5wDAPWdnvfqlFRlnxFbawdAy3uLvjEzNMIwkJfqSQQDot/fLrq1IpTRFN/HANWVPaVWcvXPUmXf70zVXu3khYu7Hyua3/N+jqZWNYCImrGnpmOhIBj5Lvs6Wd+FNjQDAjjYXwGkJSr25/Lj8IQDomeyhmVxEcoHPYX5cfsTdV04YPJFtW1HWPlPIiAIr6nO6RH2OU+p4pOBfXaC1M64xvaLtI9n+txxwDCQDQGl8acS1lRNWnsi+hh0hwSSvp1cyr3ROdAY6I0EZRWQmB9WspS1PBCN6Ft7cNJdzLTEuGeUYzmfz2vSDjkFNMOILF2/FjR0ip3KzdnMS17MnQep4AODPn3RUVB0euBgAblqe9tZX1+XUSx2TnPTae5MBIC82L+JG3FckrxgCgGHncLJX8Mq6uGw4Lc/WW357WfHTKo51Nw7ZS+95rvYSQRSlDgsAwPYf9tc1LNwkuyU2A46BFAAoiS+ZU1sRkhZMCDHpfYzAK1Q1z+QHI7ZKQ+UgQLuRksgQ+BwuMiyKuI57oP0G2rMcBWaB8plnye57NGj8qwuaAEDR8FpEDbxOl1fwMiOukSQAWJa0LGKKzgcUxhUOAEDPZI9s2wohoUBJLiKZwMhIsb44KB13Ze2zBYwosGJcVo+YkD+nukVqTi0YNIYhANhv3i+v0SdVtE8wlrYAgOLYK0GrJzNbT+/vLXh0d8+VAHBZRfJH37+g4LDUMcnNkGMoGQAWGRZFXJIrU5fpiFZE23iRV9aM1EREUlUuzilOHPrJpoJnFSzDH+6xLf56Vf35UsfEde5IZJ2jBlGhcXrLrumWOp6Z4AWeGXH6OyPLk5bPua34Mlb4Z8V2fFI813MBwGLj4lGO4XwT3gm92WGWdUFtIm9mh1k94Z3QcwznW2JcMip1PJ8XaL8jzpEkWe5G6p7g2OGGAgDwLrhuToOucsfnnz+1uuBgUL5Hw61mpCbBJ/oU0YpoW3p0esTNyKtMpMETQk6GklxEElOdkWQgeCMjio5P/EtsMlcFZdQsLTptEACOWY7J7sbBZ61tBgBF335Jk1wfNAyn/mlrh0kEmA2FCbv++7Li3VLGI0cndkYWGxdHXGcEAJK1yUMAUDNaI6+EcAS4YlFKz7fOyX2RZSDsaLWs/vk7zSuljEd57OUSABCSFjRjloXbpRLsDRq8xZc1AwA3XF+IIHS0pwZPzACwz0y7kRLpBD5/Bo3BHKyaqME0NXgywYu8sma0Jl7qeGZKefT5XEbwqgRdyqCQtnRM6nik5F1wXYfIKnh2cjCFNdfHSB3PTB0dPZoEAEnayKuJCgCLExdbFIzCa+ftsb2TvRG7YzMh4UZJLiKJOkudPqi7xfEulh1p9I+alV4dlFGznJicIQDonOiUXZLLW3F9qwhGZMY6s5nJIUl2bzvSM67/6dvNN3kFUbkwPab2z9eWb5EiDrk7MOzfoCFBkxCRnREAyNRlDgJAy1iL7NpKJLhlZUbrbasy3gCAV6oHL3poZ5dkyzq4/oOFAMDnbJDd7IOakZrAbnFB6Yz48s41i+pYG+N1RCua3wlKvZNADcoGq3wLahP5O2b1D96ladMibqliQKAdB9q1nCg6Pi4CAF/qkjOy4Py/0eh5IaGgHQCU9S8VSB3OTLXaWpMBID06PeKWKgKAglWIBo1/N9LDI4dlvRspIcFESS4iiSMjR4LaGVE0vpnO8C6NEJVg8WWttgTjnIFllEOOIWMwzhdOoj7HKcZm9DKiwCrrXswL9/W7rc6ob7x87GaHxxedkxDV8ciNC99kGfmtOIgEwdotLpQK4vy7kfbae2XXGYkU3zonr/bicuPHAPDQzq6rX68ZzAx3DMx4j4YZ78kUGVbwVlzfHu7rz1XLeEsyEMQNGhgWvqQF/lmxLe8FZVZsoAZloCYlIVLosnWlAEB27Nw2aAilQDsOtGvZEAVwQ0f9gwWFmyjJBcCXsaoFALiePZKX0Jip3snIrYkaENiNtMnaREkuQqZQkotIom28zT8yoksPTpKr7cNCABCSK1qCcT4AWGpcagaAUdeoLGtC+NKW+h8qOreH9aFi3OlV3P1s7Y1Wh9dg1KmGHrlp4YtaFecLZwzzSbA3aAiFioQKMwAMO4fpAWsOfnt5yc7l2XGHfCK4/36/9Ya9HVZDOK+vrHsxn4HIiPrsblGX4g7ntYOhd9KfZA3sYhgMfO5G//fowOHCYJwvsDsX7UZKpDTgGEgCgBL93DZoCKVAO+6b7JNVkovr+jSRcY/rRUWUgy/a3C91PJHAW3ZNCwCwo815cE9wUsczE0NOf03UioSKiG0rmbpMMwB0T3bTMxghUyjJRSQR2Lo6WCMj3GC1f9Qs77ygjZpl6DKcWoV2khd55THrsbhgnTdc+KJL/J2z4fpCiOFZ5eb1Ccydz9Ze3T/uzohRc7YHr1/wbEqsWnad5UjS7+gP6gYNobAocZGFZVjfhHcibsQ1Isny2PmAZRg8eH3Fu4VGbbOLF6K+82rDzW3D9uhwXV/RvbMQAHxpy4I2WBBOZqc5CQAWJiwMWls5Xk9moj+NHWnUzfV8gRqUFpfFKMfBEyJ/gijA4rYYAWCJccmw1PGcSqAdDzmHZJUQVja94R90NZa1glNFxpa5EhNSK8eFaKOZEbwq5bFXsqWOZ7osLovK5rHFs2CFpcalI1LHcyqF+kIzAAzYB2SVECYklCjJRSQRzJER1lwXw04Opois0ustu6Zr7tF9xqgxmgGgdrRWdqMjfMEFg6IqZoLxTOoULe8FpZ7MFxFEEV95oW5T05C9RK1gXf9zRekzxcm6iVBfdz4TRAGjrtGg7RYXKmpOLSSoE0YA4JD5kOyW90YStYIV/nXTwpdTYtX94y5ef+/zdTeNTnpCnzgUeIYdbigEAG/xZbJLcllcFtWEd0LPghUqEyuDt0FDVIJXiM/rBABlXdWc68mkR6fLevCEyF+DtSHOK3hVUVyUPSg1UUNksXHxqBwHT7i+A/5B1+yzZfc9GkpCSqV/1+/2LUGZFRsO+837AzVRhzURvBHL0kT/ypMR10iSEKZBbUIiHSW5SNgFe2REWf9yAQAICQXtUMcEdVlcanSqGQBax1tll+Ty15Mp9z9UNL8T8oeKn7/Tsmpv59hyloHv+xfkv7C2ICFiR4jlotHa6O+MKCK7MwIASVFJZgBoHGuUX1uJMAnRKu9D1y94Li5KYR2acKfe9VztNW5eCOn9WtH8TirjdWhFddy4L2e97NrukZEjiQCgV+tHg90Z8WWs9O+y2LM7KEu/EzWJsh08IfJ3dPSoEQASoxIjup2rObUQr44fBeQzeMJMDqmYsc5sEYzoLb+uTep4Igmff2EzALBDtbKpy9VgbUgCPttBOlLlxOZMajiN0yN41E1jTbFSx0NIJKAkFwm7QGckXh0/EozOCNe717/EJnNV0EfNcmJyzADQO9kry86IL2eDv3M2cDikDxWP7+kpeq1m8EIAuHtN1uvXLE4N6oy6M9VRy1RnRBPZnREAyNBlmAGga6JLlm0l0uQbo+1/uKr0WY2SdbYOOwq/8VL9BaG8nqLlPf/3aPKCFjDyezRotDYaAcAYZQx6Wzmhnkw+PPY515NJ1aYOA0DbeBu1FRJ2gUG75KjkiL+vBAZPmsbkUVBbWV+Vx4gCK8Zl9or6LKfU8UQSb+kVvaJC42KdlgSue3eC1PFMR6AmapYuKyJ3VgxgGRaJmsTAbqSyaCuEhJr8nmSJ7AW1M+Kxc+xocz4AeEuvbp3z+T6nNL7UDHxW60VuvAuu6xAZzsfYetMZS5s2FNf4pHk0+a/bO68GgAtLjdu+tj6nLhTXORO1jrcaAXl0RvJj84cBYMA+IMu2EolW5sSP/uCCghdYBsKuduvK33zQujRU1+IGjviX2ORskOUSm86JTiMQmm3ehbSlY4I2cZjxeVTKY69kzfV82THZ/sETe68sZqeQ+SXwucuKieyOOwCkR6cPA0DXRJcs2grXud0/WJC6RJbfoyGl0AhCYnErACgaX5PFbK5Bx6ARAArjCiN6Jhfw2cqTlvEWegYjBJTkIhLonuxOBIC06LQ5d9yVDa9mMj6PSog2moXUyvG5R/fvlhqXDgOAxW1JdPEu2bUXMdroEeJzOxkAyroX51xP5vNazHbdj95svMHrE1UVaTF1/3tlyfZgX+NM1js51RnRZUV8kmuhYaF/h0UX7bAYTFdVpnTfsjLjTQB44WD/5ucP9uUF+xrsaEs0M9GXLjKcjy83dQT7/OEQ6IzkxuaGpK0Es55McXyxGQCGHEPUVkjYDTmGjABQpC+K+PtKXmyev6C2QwaDJ6IAzlzvHyyY2viH/Ds+y1+nLFC3LNKNukaNALAgYUHEFp0PCKw8CWzsRciZTnaddiJ/A/aBoHVGFG0fFQGAkFwZkgeKBE2CJ1YZOyaIAlc9Um0IxTVCzZe2zD9y1rsvP5jnHXd6FV+tqrt+wu2LS4tT9z58Q8UbLEObhQXTkNPfGSnQF0R8Z6Q8oXxMwSi8Dt6h67P3RUkdz3zy7XPzajYUJuwSAeYPWzuu3dthDep3kaL+pXwGgBCf1ylqDd5gnjtchl3DRgAoiy8LSVvhc89pBQDWXD/nJOPSRP/gidVtNXoFL31pkrA5cWfFxYmLI/6+UmGo8A+eOIcjfiaXou2jZMYzESOqdJN8wQWDUscTifiya9oAgB3rzIJrTCF1PF9k0DGocfAOnYJReEviS4I+iB5sxXoaPCHkRJTkImEX6IyU6EvmPDLCDh0tAAA+/9ygL1UMMEb5d1iss9TJ8sbBF17sf6gYacxHkHZdEUQRX36+7vKBcXd6rEYx/o/rFrwQo1HwQTk5AXB8Z0UjAFQmVkZ8Z0TBKsQEjX+zgcPDhyO+QyI3f7qmfGtxcnSjmxc0332t4cZuqzNoiURFz54CAPClL5fl7INx97jS5rHFM2CERYmLLKG4hrf0ym6RVfCsfSiZHW7QzeVcSdokd7Qi2uYTfVzNSI0satOQ+UEuOysGLDQstHIMx9t5e6zZYVZLHc8XUbS86/8eNZbJsq5hOAiGQrugSxlkBF6hbHhtzku/Q+l4/WBN/IiCVYhSx3M6y5OWmwHA4rYY3T43fQDJGU/SLLrJZFIA+B6A2wFkABgC8BKAX1RVVU1+wftyAJxuScXGqqqqbdM8fnFVVVX1dOMmszfpnVRMdUbEuW7zzprrY1jHsFFklV5vyZU9wYrx89Ki08xttraidlt7EoD6UF0nVHy5G4ZFlW6C8UzGcG1bkn0FF8y5tsD3XmtYXzcwsUDJMZ7/vqz4uXxjtD0YsZLPtI63xngFr1rDaZxZuixZ/PtN0aaYzU5zWtNYU9KluLRb6njmEwXLiA9fX/Ga6bHDt5snPCn3PV9neumuJc9oVdzcdpQVeIYdbcoDAL5osyx3Azs8cjiws6JFq9AGdYfd49QxPiE+t5MbbSlQNLyW5zGW1s7ldMYoo9k+YY+ts9QZlyUtm9O9kJDpCuzoGek7KwYoWaUYr44fGXGNpBwaOWTclLWpV+qYToUbOJIPAL7M1bL8Hg0XIamijZ0cTFF0bsv3Lr69Xep4TqV5rNkIAEmapIhfqggAxiijW6fU2Sa9k7F1ljr9UuPSkAz4ECIXUmd6nwXwAwBPALgawB8BfAnAe1MJsFMZBLDxFP/8CYAI4GRJj5+c4j0hmwVE/t2RkSMGESITp4qzRCuj59QZUTS+ngf4l9hANbdzfZHcmFwzAPTb++U5O4VhISSWtAOAsvX9OS+1+ceOrrIPGkY2MIB4/4aclzcUGiK+eK0c1YzWGAHAoDEMszIZFc7UZZoBoHuiW5azHiOdQafy/PXa8ue1Km6y2+rM+cqLdRcL4twGmBXtW5MYryNaVMVM+LLXyuJh/vMCm5kEdmILFV/q0jYgOEu/AzUppwZPCAmLtvE22WxmEhCItckawTssusYUzFhXlgjAW3aVLOsahgufu8G/umCoLqglNIKtZ7LHv5mJLl02bcWgNowAQIO1IVHqWAiRmmQzuUwm01UATAAurKqq+vCE328BcBjAVwD85WTvraqqcgHYdorz/hbA1qqqqpONpNQFZncRaTRYG4K2s6Kid18eAPjSloR0JKg4vngEAEZcI7K9afCZZ7Vx/QcXsQOH8wHsme15Pm4aSX5kV/cVAHDFopQPb1uVKcvlTXIgx85IQZy/dpgsigTLVFlqjO3nFxc+/4M3m24/1D2+5Nfvtw79dFPh/tmeT9H6vn/2gbGkTa5LbAI7KwZjM5Mvwhde3Kaqe+Gzpd9z+PeVrcs278Zu9Nn7qK2QsJHTzooB6bp0c721Ht2T3RE70Kg89mo2I/o4QZc6IMbnRfwyUCl5S6/sUX/yM//S75FGnZBYcsqVO1IK1A/OicmRzTNYsjZ5uGuyK6/D1pEIoFnqeAiRkpRPtPcC2HFiggsAqqqqjgF4EcCXZ3pCk8m0BMAqAP8ISoQk6Dpt/s5IqjZ1bjcNUQA7MrXEpmBTSKeGLzIsGgWAMfeYQa7r3PnSq9oBgB3rzJ5tsc9uqzPqJ283X88LorIyI7bm55sL9wY3SnKiwChihi5DNg9YCxIWHN+NVOpY5rNN5Un9d56V+QYAvHJk4MKXjwxkz/Zc7MAR/2BBxlmyXWITrs7I8aXfXns017ZlTjtYlcaXmgHA7DBHbMedzD+BotRy2FkxID823z94Yo/cwRNF13b/yoLkBRG7/C5iqGN5IT63EwAUDa8FfbfgYAkMbJfFl8lmhnOGLmMEAPrsfXRfIWc8STrsJpOJA3A2TjEbC8B2AKUmk2mmN7SvA+gF8ObsoyOh1O/oD0pnhGvfmsR47TpRpZvw5W4I6cNagibBo1PqbAIEts5Spw/ltUJlrsU+3bzAfuWFumttLl6fEqvu/8d1C96mnRRD6/jOinGRv7NiQLG+2MYxHO/kndGDjkGN1PHMZ1/fkFO3riBhtyCC/d+P2kxH+21xMz6J26ZgxzqzAYAvvVK2S2xCvbPicQwLX2JpGwAoW9+b01KbSmPlCACMecYMtMMiCYcTNzORw86KAYEdFkdcIxHbcQ8sveNzNsh2sCCcji/97tkbkUsWrW6rcsI7oWfBCgsNC2VT26ogrmAEAIadwzTQSM54Us1KSQOghX9Z4skcmvo57S8/k8lkAHA9gH9WVVWdqj7Tb0wmk9VkMjlNJtMBk8l0w7QjJkEx4vSPjJTEl8zpAUs5tcRGSCxuD8cSG4PGMAzIe527kFzhf6jo3Dbjh4pvvlx/fpfFmatVcfY/X1P2Iu2kGFqCKGDUOZoEAIsMi2TTGVGwCjFeHT8KADUjNQap45nv/nh12dZcQ1S70ytov/Vyw3XjTu+MZmkqG17LZAReIehSBgVDoSw2N/g8m8emGPeMJwRjM5Pp8GX6Z7yxU0WmZytRk+iJVkRP+EQfd8xyTB+U4Aj5Ag3Whjhe5JVy2VkxYEHCgrGpHRZjhp3DEbfDIjvaHM3ah5JFVsF7S0O3CdJ8csKu33nB2vU7mKpHqgObmYxqFJrIC/AUFiYsHAEAi8uSKETgv1dCwkmqmlyBLbNP9UAayJrPpJN0FwAOwL9O8XoL/IXuj8KfYLsVwHMmkymnqqrqtzO4zr/ZtWvXPbN9b6TweDyJQOj/Fl7kMeYeMwKA0CFcvqtz16wrJi/vrkswADimWZ7dF4b/Bql8amwXutDW0bZ51/CuDaG+XigkKpepluEjOAeaV+zatSt3uu/7tF+I+rRN1HMMcG+p6LS2116/K8gT8sP1GZQLm8/GugW3Rs2oxaG6oRvMjGzKpyBFTIkbwQjqmuqu0fXonFLHM11y/Qx+tURkfnMQvqEJd+o3n977X3eWMWPMNGdZlnTuiskB0K1bHNMos787oMfbowCABC7BV72/+s5QX0/lLWPPAQBrV96eHR/fI3Cz73MnMUnqDnRgb83emyc1k25Avp9DEvnqXHVqAEhhU5Rf9PmKxM+ggTXA7DPjo70f3ZWtyvZKHc+J0s2fRFUAsOiKfQcOHr1D6nhkQVRho1IvqL1jutqPnvvKRHTufwycSvk5rHZWRwFAClJiIqkdnI4oilAzatEtuDUf7Pzgy7FcLGW65iASvwvPNEqlctbvlWomV8zUz1ONJAV+P63lFyaTiYW/htfrVVVVAyc5pAdASVVV1X9XVVW9WVVV9UJVVdUmAM8B+JXJZCqYQexklob5YYUAAXpW71Mz6lknuFjBA/1EswoARvSL3MGL8NSSuCQeAIZ9w5Jt1jBXltgyj49RijHOHoXaY5lW228fF5UvtIh6ALg6nxkvjmci6uFyvhrgBxQAYOSM/HQTFpHCyBll31bkRKdkxC8vYC1KFuLBYTHqox4xerrvNdj8nd6RuIVh+R4NhUF+UAl89h0dah5VvDChzfJyohcJtvo5zSoxKvxtxewzU1shITfEDykAIEkRnrYSTImKRB4AhnxDEddWDONH1QAwGrdAtt+jYccwGI0tdwOAcaw64mbnmXn/d3K47ivBwjDM8WewQX4w4toKIeEkVQOYmPoZdYrXtVM/x6d5vksB5AC4/WQvfsHyxe/Av8PjjQB+Oc1r/Zs1a9b8czbviySBDHWo/5bHGx4vhQWmBF1C+5o1a56b7XmU1U/lcqL3FiE6ybxs46UPBTPGUzG3m3NQjVsH2IHRNWvWPBaOa4ZEW85NGG0pWK2o2+1Z84PaLzx02B79nSer7+FFYHl23KGfXLfo7VCFFa7PoFzsPrJ7OcZwcWJcYt2aNWtkVWOw/lj9AjTi6gHlQO+aNWtelDqe6ZLzZ3ANAIu2s/yhnd3XvNYuxpQX575mWpLW+UXvYSxtWt3e7u+KDOcrPu9LDxZr9LJ6mA94a89b58CGtUmJSfvXrFrzSTiuGTVcfB7autdUeg82Otd8/aPZnmfn4Z0r0YmLBjWDLWvWrHkbkPfnkES2p7c/fTnsqExPTf9kTeWag6c6LhI/g2/vfXsj+rHOrDPXhqudT4soQHf4zv8CgMwlFzyXWrBmSOqQ5EIlflqB0V1X5dsPDqeu+b9nPv+6lJ/Df27953UASnKycj5YU7amLtzXn4untz99ee9ob6UlwbL3i9o5Ob1I/C480+zfv/9ns32vVDO5Trcc8XTLGT/vawCOVVVVbZtJEFOzvuoAlMzkfWR2OiY6jACQok2ZW9H5zm3+XWySFoStwGeFoSKwzt0o53XuQsridgDg+vZ/4XJFNy+w97907JpJty82Q6/p+ZtpwXvhiZAAn+2smK5Ll009roBiffEI8NnORCQ8vroup35jkeFTEWB+v6X9tIXolQ2v+r9H9TldkGmCCwAGHOHf5p3PXu/frdZ8bNrLvk8mLzZvBADMTjO1FRJyQw7/ZiaFcYWyu69k6bJGAWDQMRhRbYXr3G5kPJMxolJr5/POlU9dgQjAl145tet3VxY8dk7qeE407PRvZlKin1v9YCmkadNGAKB3sjei2goh4SZVkmsAgBPAklO8Hvj9aSv/mEymYgDnAnhwlrGwAE4104sEUb/dv7Niti57bkkuc10eAPDZa8O2VXNebN6kklW63YJb0z3ZPe3lQJGGzzu3EwC4keacLzruu681bOy2OnO0Ks7+52vLqrQqjtpIGA06BmW3s2JApcFf/HvMPZbg4l1S3WPOSH+4qvTj/ERtq8srRH37lYZrJ938KTsOXM9ef5IrdbGst7wPdEZK40vD1la8JZf1iAwrMJMDqcx4z6x3EV2QsMA/eOK2UGeEhJQgCrC6rQYAWGjwF6eWk8K4wogcPFG0vOf/HjUUt4NVzLoMx5lIMBTZBW3iMCN4lYrmt9OljifAwTu4cc94AgCEYzOTYMuNzR0BgCHnUES1FULCTZIOSFVVFQ/gUwAbTnHIegCNVVVV05n2+zUAdgBPneoAk8kUbTKZ0k7y+zj4Z3HJaiqqXJmdZiMAFOmLZt0ZYSb61czEQKrIsAJfelVX8KL7YizDIkGdMAIAtaO1sr1x8PnnD4oKjYtxj+vZvgP6kx3z6O6e4k+aR89mAPG75+W9VJSkmwxzmGe8wDbvFQkVsktyxanjvDHKmHERIltnqYuXOp4ziZJjxb+Yyl+NUXPjAzZ3+rdePnbhqY7lRv2Jbj7vPNkmuVy8ix33jMcDYe6MRCV4xbjMHgaAsvH1nNmepkhfZOMYjnfyzuhBx+Csk2WEnE73ZHe0R/BolKzSnR2TLbudVBclLjo+eMILfMQUquQGa3IAwJe+rEPiUGRJMJZ2AICic9ucZsUGU81ITYIIkYlVxVpjVbGym+VcGl8akQlhQsJNylH2hwGsN5lM5534S5PJVArg+qnXA7+LM5lM2s+9HyaTSQfgFgDPVlVV2U52EZPJpII/ifWsyWT6fA2y/wEgAPiPteAkuLyCl7G6rYkAsMS4ZNajiIrGN7IZiIwYk94nag1hLYKeFJU0AgBt423yvXFwKlGIz+sEAGXLu//xUHG4Zzz+Hzu6rgSASyqStl6zODVsiUTi12fvi3L6nNEKRuEtiS+Zbl3CiGLQGEYAoMHaIN+2IlNZ8VHOn2wqrGIZ+PZ2ji3/y7aOis8fww7WxDIua7zIqdx8wQWDUsQZDHWWungRIhujjBkPd2fEl1TRCQBcz96c2Z5DwSrEeHX8CADUjNTMZDdpQmakbrTOAAAJ6oRRlpHfBFtjlNGtVWgnfaJP0WhtnNamVCEn8AxrbcsGAL5wU6fE0ciSL2NVJwBwQ3U50kbymQZrgxEAjBqj7AYZAaDCUGFlwQqT3sk4q9s6+63pCJE5ye50VVVVrwB4BcArJpPpByaTaZPJZPo6gK0ADgP4O+CfhQX/ssUjJznNrQBiAfzjC67jAfA7+GeHfWzyu9xkMr0F4G4A91VVVfUF8U8jJ3HMckzvE31ctCJ6whhlnPUONIqePTkA4Esq7wxWbNOVGp06AgA9kz2y7rj7Uhd3AADXf/DfklzjTq/iu681mDw+QV2cFN3060uLd0kT4ZmtbrQuAQD0ar0sOyMAkByVPAIAnROdsm4rcrWpPKn/uiVp7wPA43t6L93ZajGe+Lqi+Z0cABDi87rAqWS7xKZprOl4xz3c1/blrO8AAHZkbnW5jFHGUQBoGW+htkJCpt3WbgCARE2i7JZfBQRm09db6yOirSjatyYxvCtKVMfafGnLrFLHI0fekss7RQDMeHcmXGMRsRtghy049YOloubUgl6tHwWA6pHqiGgrhEhB6h7UDQD+AOBOAK/Cv9vhCwAurKqqCszS4QEMAug5yfu/AmBXVVXVF+4SV1VV9TD8OzCKAP4F4GkAagDnVlVVPTH3P4OcTuNYo78zoplbZ4QdacgBAF/W2Z1zj2pmcmOm1rk75L3Onc+/oBMA2NGWHJxQRP8bLx272DzhSYmLUlj/aip/nWUiZkXAGaXN1ib7zki6Ln0EAPrsfbJuK3L2/QvzDy5Mj6nlBVH5o7earhue9KgCr3H9B3MAQEhZ1ClVfMHQOdFpAD5LFIWTt+iSXpFV8Kx9OIkdbZl1ncZU7fwYPCGRrWeyxwAAKdoU2d5XkqKSRgGgw9YREbMeufatOQAgGAo7INMBKamJ+hynqEsZZEQfp2x8M1PqeACg39GfCADZMdmyq10XkKhJHAGAprEmuq+QM5akWfOpRNYvp/451TFuAOWneO2kvz/Fse8AeGemMZLgCDyUGDWz74wwtj4NMzGYIjKswBdferKkZ0jNl3XuvtyNZlGpdTCeyRiue5fBl7129P+2tFUe6hlfzLEM/8vNRS+mxmlcUsd5pgp0RpK1ybLtjOTH5o8AwLBzWNZtRc5YhsFfry1/+6pHDqWO2r3Gr71Yd/nzdyx+iWUYcKPNuQDA557TKXGYc9Jv7zcAnyVVw0od4xPisrs5a1ueounNHM/qb9fP5jRZMVkjQOTtGkfmF7PTbAA++7zJUboufeTwyOGIGTzhBqtzAcCXurRT4lBkTTCWdbCTgylc145cb+Utktc2G3GNGAAgPy5ftm0lNTp1pHm8Gd0T3RHRVgiRAg09kLAYcAwkAEBqdOqsO+7KxjeyGIiMGJsR9npcALDQsNDCgBEmvBP6cfe4fNe5MyyEhAJ/sc/WD3K3No2kPHOgfzMA3Lw87Z1zihOns+EDCZEhx5ABADJ1mbJNclUYKvy7xrksicIJswVJeCVEq7y/u7zkRSXHeI4NTpb98t2WlezA4TjGNaYXObWbzz9ftvW4AGDYOWwAgNyYXEnaipA8VZerd1/ObM9RFFc0LwZPSGQLfL4K4wple18JzKY3O83StxWBZ1hrezYAeKke15zwmWd1AgBnrs+RNhL/LqRj7jEDACxIWGCROp7ZytL5k9kDjgHp2wohEqEkFwmLwChidkz2rB+wuF5/gV+fsawzOFHNjEahEeJUcVYAqBmtSZAihmDxpS3tAABf3+H8n73TbPIJomJpZtyR75yXXy1xaGe846OIsfmy7YzkxeZNKlml2y24NT2TPf+xaQgJn1W58aN3r8l6AwBeqxm8oG3fe4sBQIjPlXU9LgCwuq0GACiJL5GkrfC550zV5WqcdV2uwK5x4+7xBLfPTc9kJOi8gpcZd48nAP7BOqnjma3S+NJRALC4LJIvV1S0bUlmeJdGVMeOC2lLx6SOR8740iu7RDAiM9GXzkwOqU7/jtDptHXqvIJXpWbVrvTodIeUscxFsb54GABGnDR4Qs5c9EBFwiLwUFKsL551Z4QdnqrHlb1WsunMiVHzY507X7CpAwD4kfZCm9MTnxSjGvzztWXvSh3Xme7fRhEN8h1FZBn2eJHg2tFaWbeV+eC+tdnH1uTF7xVEsJ1t9WcBgC+lUvJlIXNhcVlUdt4ewzKsrzS+VJJdSPmii/tFTuVhnRYDa66Lmc054tXxXp1SNy5AYOssdfogh0gI6i31egECG62InkjQJHikjme2yhLKxjiG89l5e8yIa0TSZAjXEajHVdRJ9bjmRtSluMXY9H5GFFhF4xtZUsZSb62X/cY/AFCZWDkKAFaP1eAVvFRgl5yR5NuCiWzYvXZuwjuhZ8CI5QnlY7M5BzPeo2EmB1JFhhW8xZf2BjnEaUvRpowAQNdEl6w77r7MVRY7F+eKESe5cq7X+79XlFbFRSl5qeM607WOt8bwIq/UcBpHenS6U+p45iIpKmkEAFrHW2XdVuaLP15dtiUlVt2/WGhQAYBX5vW4akdr/Z0Rld6iZJXSzEhTaARBn9sFAIqmt2Y9m8ugNowCQKO1kdoKCbpG6/GNf2RbYwgAlKxS1Kv8u8bVjNRI2la4geocAPClUT2uYPAZy/wlNHp2z2m32rk6vgtplHw3/gGABE2CJ1oRbRNEgau31OuljocQKVCSi4Tc0dGjCQAQo4oZ0yq0vtmcQ9n0ZjYDQIzN6EVUQtjrcQWcsM7dKFUMwfBxsyX5Q0+FGgB+lHm0fmlWHG1/HQHqLfUGAIhXx8v6AQvwFz4FgN7JXuq4RwCtivP9fZ3vg0x2GOOiFr9qzY2Inaxmq3m8OREADBqDpG1FSFnUAQBc3/5Zd86StckjANBh66C2QoIusAtpYHdCOTNE+dt7y1iLdEsWfZ4T6nFd3ClZHPNIYIUGO1wvaZKrz97n34U0KkW2M+kDEjQJFgBoGmuSfHkvIVKgJBcJucAXbGC0eja4nql6XEkLOoMT1ezkxeaNAsCoa1S2Nbksdo/y5++2XLtHKGUAYIVQo5Y6JuJ3fBfSqNnvQhopcmJy/LvGOWnXuEhRMfZxPADsE0pRVT184damkRSpY5qtnonI2IWUz93YBQDsaOusl9mkR/t3h+y391NbIUEX2I0wVTv7jX8iRarWP3jSPSndrnGK9i3JjM+tEdVxY1SPKzj44st6RIYVmInBFCnrch3f+CdGvhv/BCRq/LPRuia6ZNtfIWQuKMlFQq57snvOo4jsyFQ9rqyzO4MT1eyUJZRZAMDqtibIdde4b7587GKrw2toU/v/FtbangWZ/i3zzfFRRG2K7B+wAvX3IqFIMPFj+w/lAIA1tqRHEMH94t2Wa0YnPZLWtpmtQcegAQAyojOkTXLlnz8oskoP67ImsCONutmcIy82z79rnMtMbYUE3fFdSGOl2YU0mDJ1mZLvGse1f5wDAD5DUadUMcw3otbgFXWpAwxERtH8jmSzjC0uSwIAFMQWyL6tpGj9s9H67f2U5CJnJEpykZAbcAwkAEBadNqsbhrMWHcUMzmYIjKcz1t8iWT1uAAgS5dlV7JKt1fwquW4a9wDH3csPNJrq1SwDH/nJRtfEJVaO+N1RHPdu6hzFQGO70Kqm/0upJGiwlBhBQCbx6Z38S6610QAbrQ5FwDOXbf+g8Ropdnq8Bruf7l+syDKb5PFwC6kgdm1klFoBEGf3QsAiuZ3ZzWbqzyhPJAQpplcJOhGXaP+XUj10uxCGkyFcYWB2fSSPbNwg9W5ACBQPa6gEhKLuwGA69mdLcX1eYFnxjxjCQBQYaiQ/XLFLF2WBfgsyU3ImYY6HiTkAl+wObE5s3rAUja9mTVVj6tPynpcgH/XOL1abwE+q58kF/s7xxKe2t+7GQCuW5r63trCxGEhPt9fNLl9iyQPFeTfBR7cC/WFsu+M6JQ6XqfU2USIbONYY5zU8Zzp2P5DesY9HicqNC51yQX9v76s+CUFy3hr+yYW/nFrR6XU8c2EIAqwuP0zBCsMFZIX0xaM5V0AwPUfmNX3aEFcwQTHcLzL59I6BAfthEWCxuq2Ku28PZYFK5QllI1JHc9cLTIsGgEAq9tq4AU+/G3F52FYa4e/HlcR1eMKJl/GSv/36EiDJDssNo81x/pEnyJKEWU3RhndUsQQTEX6Iv/gidtCM7nIGYmSXCTkrG6rAQDK4stm1XHneo/X44qILe8TNYkWAOiwdcjmxjHp5rkfvNl4rdcnqspTdfXfOz//MAD4kiv8I2eD1ZJu20wAF+9ixz3j8QCw0LBQ9qOIABCvjqfCpxFC2fJuDgAI8XmdYBXimryEkZuWp70LAM8e6Lt4V7t8ZhF1TXRFewWvWsWqXJm6TIfU8fiyVvvrco00zyrJpWAVol7lHzwZ8Y0oghkbObMFNv6JU8dZ1Jxa9nUJkrRJ7iguyu4TfYqW8ZaYcF9f0fZRCuNzq0WNfkxIXTIe7uvPZ96izd0AwIz3ZsA9wYX7+sesx/y7kKoTZD/ICNBsekLoQ09CasQ1onLwDh3HcL5ifbFtNudgRxpzAMCXLW09roBAbbFee69sklzffrXhfPOEJyUuSmF94Jryt1jGPwDqyz0nUDSZZnJJrN5arxchsjqlbjxWFctLHU8wUOHTyMFN1ePypVR2Bn73X+fmVS9MiznKC6Lyx281Xzvu9MoiwVJnqTu+CynLSP8Y4y3a3CcynI+xDyUztj7NbM4R2AlrmB8Oe+eOzF/NY5GxC2kwBWbTN1obw35fUXQE6nEVdob72vOdqM9xCtFGMyP6OEXLu+nhvn7nRGcC8Nlzi9ydOJu+wdqglzoeQsJN+qdDMq8dHT1qAIA4VZxFwSpmXPiFmehX++txsYK3+FJJ63EFpEenWwDA7DTLouP+6O6e4t3t1pUsA+FHFxa8nBKrPj4Nm887Z0jkVG7GPa5nB2tipYzzTNdkbZpXo4jAZ4VPB+wDsmgr8xlr8e/+58vd0HX8dwyDB64tf1sfpbCMTHqSvvNqwwXSRTh97bZ2AwAkRkVIZ0Sj58XY9H4GgKLprVkVTTZGGS0AMOoblUWikchDYOOf5ChpdyENpsBs+kBSIpzYoaNZACCkLuk63bFk5gRDUTcAKLo/Dfvqgn57vwGYH7uQBsSr40cBoGmsiZ7ByBmHklwkpFrGWwzA7EcRFc3vZDIAxJj0fmj0ETG7JS82z98ZkbDw6XQ1DU3GPPRp1+UAcNnC5C2bypP6/+0ATiUK+pweAFC0vk+zuSTUOdE5511II02mLtOfEHbJIyE8X7Hm+hjGNRYvcioPn3fe0ImvGXUqz083Fb7MMhD2do4tf3Jvb6FUcU5X72RvxHVGfIkl/vqGfftm9T0aGDyh5YokmAYcAwYASI9Oj5i2MlfJ2mT/4IkjzIMnogDW2pEFAHz+BZTkCgFf6lL/6gJzXdifRwMb/2TGZM6LchEAYNQYAwnhiO+vEBJslOQiIdUz0eMfRdTObhSR693nHzVL9I/uRIKyhDILAIy5xxIEMXJLXPCCyHz3tYbLXV4hKtcQ1fazi4v2nuw4IWnBVNHkQ1SXS0KBzshsdyGNRIVxhRYAsLqslOSSkKL1g0wAEOKyesCp/mNG7fmlxoHNC5I+BoC/7+i8vH3EER3uGGdiyDmUCABZuqyIaSu+jFX+ztlw46w6Z9kx2RYAsPgstFyRBM2Ic2oX0jiJdyENIqlm03PduwwM79SKymi7L325NZzXPlPwhZu6AYAd68qC6AvrtS0uf4H2oriiedNWUqP9A0FhTwgTEgEoyUVCatA5mADMfhSRG2n0L7FJWx4xSa4sXZZdySrdHsGj7pns0Uodz6n8+r2WFR2jznyNknX+31WlrytY5qTLRfmss6fqcs2uaDIJjsAoYk7M7HYhjUQLEhYECp/Gu31uut9IJJDAFoxlp/we/eUlxbuzE6I6nV4h+tuvHrtcEGe8ujxsInEXUr74kh4RADPRlwbX2IxnY5XoS2i5Igm6Mc9YAjD7jX8iUX5svj8h7ArvrnGK9q3+79H43G5EQC3A+UhIrrCJGv0Y4/Oo4uwdynBd1+1zH9/4p8JQMW9mcmXpsvy1Hp3DlOQiZxz6liYhdXwUMXYWo4geO8fYetMBwFu4qSfIoc0ay7DHC58esx6LyBvHtpbRpNdrBs8HgHvWZL1ZlKSbPNWxfOGmfpFV8KxjxMiMdUaFL0pyIovLYgCAkviSedMZiVPHeaMV0RMCBLbR2hgndTxnKna02T9YkHnWKZNcCpYR/+fyktfUCtbVOuwo/O0HrcvCF+H0eQUvc7wzkhA5nRExJs0t6lIGGVFglc1vZ8z0/UX6IhvHcL5JYZJ1CS4mFDGSM8ugY1Dj9rmjFIzCmxebd8pnALkpTyiXZDY9O1jj/x6d2pWahIaQUNAFAAm2Y6pwXfOY5di82/gH+Gw2vcVtoeWK5IxDSS4SMoIowOq2GgBgQcKCGXfcFW0fpjACrxC0hhExIV/ybeJPFCh82j7eHnE3jgkXr/jFuy1X+0RwS7PiDt+9JqvxC9+gjvGJsRl9AKBsepuWLEpg3D2utPP2WBasUBpfOq+2JY9X/3/23ju8lS0r834rKGdZzpazT7g5p3Nz7twNjEkDA0McZoAZBhq6gZ4OdKChSUMYwjcwZAw0dLg5B9/bN8cTnaMsyVYoZanC90dp2z7nOEi2UlXt3/P0c562pKrla+1da71r7bV8MYA2Pm0WTDpsJsM7xLEPruz13kt7XMJ/vjn4LQD457fWHpicjQUaY2XlTCWm3LIiczbelvFb/cVm27Mdue2oevR7cbLqqlie5RW32R0HgHVpnR5ZpByaU7FTPgDwWDzxVphCWiu6Hd15C2fJiYpomhfmnY26LxufUUWugdupyFVHpK6rFgDAlzrTMJHrTOIMGfzTMomTWnBZm1pNnyqmvHkxr59NgEKpAPqFp9SNlcyKvSgXrSbWVBxwDWSq/Ty/8IJaGu4fazmHosPWEQOAlcxKywXun/z30/esp4sdXhsf+9onjj9WyWek9uNqcLbyKj2y2ATej73vAwCX2ZWwcJbWbfR2AMgEvMXUYsutFSPATz3Sx0BhFGd3SLG3lfZ7/8/cPnDqqj73O5Ks8J/59rnvThfElhJczibO+gDAa/a2XDAi9V6n9pOJnDpQsoAEWFExSo8sUg7NjDDjB/QXuANb679R1fTsxjkHm4v5FdZUEkfuW2vEPY2KOHLfIgB4U+fMaNCx+YXUgh8A2m3tuqmkBwCnySm6TK6kAoUhfiaFYhSoyEWpGydjJ/0A4LV4Nw6SRWQj76siV9dVLSdykR5j4Vy4pQL3v39tZfjFmfhNDKB86oHRr7c5zRVVOkjBW9S+XOsHa5pMORwzwowP2Kp60hNdtq4YAKxmV1tqrRgFbvk75eEdRyveR7/2Xccf8dj4eCRV7PqVfz9zd/2sqx4SjLRZ21qu8bN45EPqPppcDKKUq/qh125rpxMWKTWDJOHabe0tt1YOS5u1jUyNa8hzhZ96pB8AFE9wGbxVV4moVkMK3ryhmBwZs5hinbnlhiRZVjOrLText1b4LL4NAJhKTlEfjGIoqMhFqRvzwrwPAPwWf/UO1nmjmu9rOZFr2D2sNgnOb7TMQ2MhlrP/wbPznwCABy9pf/aDl3bseTRpO+KRDy8rYBQmFepmMtGGlYhTVJbSS34ACFgDugtGgs4gbXzaRLjoqfLwjusq3kc7XJbCL9838nUGUJ6bjt3yT2+sDtXPwuoIZUM+AOi0dbacICy3HcnINv8GI5dM/PRj3dV+vsfeQ5rPt1T1HEWbhLNhHwB02btabq0clk57Z0Or6bmV19V9dI/hHZQawbCQfcPlvlwnLY24ZTQXbQO2ptzqCZI8IQkiCsUoUJGLUjcOk0Vs9VHNl/gvIY1P2xrZ+HQ3ZEXBL3791EcyRcnZ57UuffGjR1+o5vOKo72ouLpDDBSGP0DTZMrh0HMwMuodJY1PqYPVaEo5lkku9QGAOPZgVcM7PnJ55/L9xwPPAcDvPTP3ieV4riWGUkRyETKxtyXXitx2ZAEA+IXnqz6y2O/qpxMWKTWDJOH0GLiT9U/2g3rDbpxVRa6+G6nI1QCkrisa2pcrXoz7AGDUM6q7tUL8SlKtRqEYBSpyUepGJBfxAUC3vbvqh0arj2rud/ZnTKypUJSLlpXMir3Z9nztydmrz4Qzx0wcU/zSR49+3cSxVTcykAPH1L5cSy/TI4sNZj2/7gOAoDPYcoLuYbnMf1kMAJLFpK8kl+jUuAbCzz7Zxcglk2zzx+S2I1X3RfziR4+90OOxLKcLkuuX/u3MB+thY7XECjEfAAy6B1tyrUjdV5f7cr1f9T561HtUFYSlGBW5KIcmXlAD9yOeIy25Vg7DkHuoYdX0THbDxKRC3QoYRRz74HK970cBpMG7tvpy1ZmCVGBTxZQHAC71X5qo9/0azYBrYAMAonlaTU8xFq2nHlB0w0ZhwwcAA66Bqh2sVh/VzDIsvBa18en7sfeb+uB4aynp/Yc3Vh8EgB+6ofehq4OexEGuI/VcuwQA3PqZYA3No1RAvBD3A8Cwe1h3wYjP4ivZeXtaVmTubPysp9n2GAmuXE0k+0cOtI9aeFb+0kePfd3EMqX3Q6nL/uj5+Utra2F1yIqMZCHpB4DjvuMtmXGXhu9VRa7EfBBVVvke8x1LsmAhyAIrFAUqdFEOjFAU+IyYcTNg5GO+Y7qa2AsAl/g2q+n99a6m5889VB7e0bWmODtbaqKrXhGH7w6LrEWxFTc4Nnq6rhM0z8TPeBQorIN3pJwmp1jPezWDI54jMQCI5+O0kotiKKjIRakbiULCD2xlp6uBjU23/KjmgDUQA4A5Ya5pIpcoK8yvfevsx0uSYr6ky3nq5+8aevfA1xq5fwkAmORSL6QirbhpECW5xKSKKS8AXOa/THciF7DV+PR04jTNJDYQLvzeoYd3XNvviX/PNd2PAcBfvbz8oXORdF0Djr1YzazainLRYmJNxaAzmG2WHXsh9V4fV0z2DFPK2rmV16qaZmXhLLKP80kAcDJ2kk7CohyYU7FTZGJvUm8TewEg6AxmSTX9ama1rkepueWX1X207UjL+qO6gzMrgmOkCAD8zON1TbxOJafUib2W1pvYWwsu9V8aZ8AoqVLKkxWztN8jxTBQkYtSF5KFpCkrZp0sWPmI94hQzWfZjXMONh9v+VHNHbaOGAAsp5eblh35jUembliM5wdsJjbzmx8/9hDLHFybktuPpxWLJ8HIJTM/90xHDc2k7MHZ+FmPDJm18/a0x+IpNdueekAEYdr4tIGowzuCACCWq4sOyq/cP/LGcMA+kxdl2y//+5mPyg0a634hp+OqSOoxe2IHmdjbEBgWsndwGQC42aeqDs78nF8E6CQsyuGYFqbJ4B9dBu4sw8Jr9saBrUnedbtX9HQQAKSea6nI1UASrtESAHCrb9RV5FpMLfqA1pzYWwscJofkNDmTChTmZOykt9n2UCiNokW9RIrWeT/2PskiJkysqaqISCujmnsdvRsAEM6FmxKMvDQbD/z7u+F7AeAnT/R/a7DNfujKBtk3rB5ZXHiBHllsEGcTZ3WdRQS2JmGFMiEauDcIbuU1H1PKOBWTPSsFbz7UWHSWYfCVjx37hoVn89PR7NhvPzl7da3srIYZYcYPAH7rASb2NhC5/RJ1Hw2/U/U+2sa1iQCwmFqkR0soB2bbxF7dPlf8Vn/9q+nFPMsmFtRkwegDVQ3voByOuOtoEQDYjam6DkMiE3s7bB0t/Vw5DD6LLw4As8IsrRCmGAYqclHqAtlIfRZf1Q6WVkY1k8ansXys4Q+Ngiizn33o3MclWeGv6nO/8+Mn+s/W4rpSx6VqBUL4PSpyNYjF9KIfANos+swiAkC/s59MwqKBe4PgZ55Qj9h4azO843iXM/VDN/Q+DAD/+Mbqg+8sC95DX7RKVjOrPgBot7a3dOAu9d20BADsxnTV+2iAC0gAsJZdo4Iw5cCQhEKnvVO3z5XNavrMct3WCj/3dAcjl8yK1RuXOy5N1es+lItJOFWRi0mt9qCQqtsxO9KQvVUn9tYCUqW2lFqiIhfFMFCRi1IXltLqRhqwBqpvOr85qvmmlha5jniOJAB1alyj7/2Zb589ERIKvU4LJ3z148cerdV1pYHb1L5c8dm6Zs4oW5DAnVQ76ZERz4gqCBdiNHBvEOza26rI1XlZzfbRn71z8L3jXc7TJUkxf/qbZz4uykpDe/eFs2rVbI+jp6UD99LYg6sKw8pMJtLBpMNVTQcLcAERANbz63StUA4MmdhLEgx6hIgS9aym5+afO9TwDsrBKZlcStraLTKKxPEzj3XX6z7xvDqFdNDVmhN7awGpUlvLrVGRi2IYqMhFqQskC111FjEXMzHptS51VPMHWnpU85B7KM0xnJiX8vb1/HrdxxwTnjq73vXoqeidAPBzdw5+o9tjzdfq2uLwPWGFNZXYfNzPbkw5anVdyu6QYKTVA/fDcNx7PA4AQlHw1nsSFkWFjalVRGLwRM2O2LAMg69+/Ni3bSY2sxjPD3zhkakba3XtSiATe1s+cLd6RcXZtcZAYfjpR3ur+WiAV0UuKghTDgOZ2DvqGW3ttXIIBt2DMQDYyG/UT+SKvK/24+q8gh5VbAJJ56jafH7p5bqcLpAVGYliwgcAl/gv0a0P1uvojQNANBelIhfFMFCRi1IXSOAedAarcrBM0493M4rMKo6OSKuPauZZXnGb3QkAOB0/3ZAHR7ogcl98dPrjsgL2xkHva99/Xe9sTW/AW2XF3bcCAPzM47SaqwHE8mowq+ssor2jYOWsOUmR+FlhtmnT+YwCI6xY2ex6u8Jwkjj6QKiW1x5ss2d/6taBbwHAN94N3/PSbDxQy+vvxbaJvS2/VuS2MbUv1/IrVQVnbVybxIBBupSmk7AoB6IgFVgysfdS/6Utv1YOynHv8RiwtS/UAzY+ryYLBm6nIlcTSLiOqH25IifrInKtZFbsJblkMbGmYq+jtyUn9taCIfdQHADihTgVuSiGgYpclLqQKKiZkRH3SFUOFrf8nT4AkP0jLV3FRdhs5phsTDPHX/3m2Tuj6WKnx8bHv/rxY0/U4x5S2xG1L9fq67QvV53ZnkU87juu24w7ALjN7jiw1WifUj/46cd6AUBx9YRgdki1vv6P3RI8e3Wf+21JVvj/9dC5TxREue6+xPaJvUd9R5P1vt9hkbquVvtyrZ+pah/lGR5u1i0B6uTVethG0TdnE2fdMmTWwTtSbrNbbLY99WLEM5LiGb6Ul/L2SDZiqfX12ehpJ1NIehTWVJQG74jW+vqU/Ym7jpUAgE3M96EOVeBn4md8QItP7K0Bx3zH4oDaXoVW01OMgn5XNKVplOQSIxSFA5X/sutnVJGr83JNiFykmeNyZrnugfu33wv3PXNu4wQA/OI9w//md5hL9biP1HtduWnyOVrJVWe2ZxGDzqBus4gA4LeoE/HIuG5K/eBWXysnC0brto9+9RPHH3VZuOSaUOj59W+dvbVe9yGQib1uszte7cTeZiCO3Kfuo4mFPshiVb3LfJxPAoCp5BRdK5SqOZc45wf0PbEXAFiGxWY1faL21fT87JN9AKB4givgzC2/5+iRtK1PVHhrnimmXeza2zUX/cmQLOKf6JVue3fOzJoLJblkXsms2JttD4XSCKjIRak55xLn3DJk1s7b0z6Lr3IhRpHBJsql4YN3aqI0vNOm9hwLZ8N1DUaSuRL/1SdnP64AzB2j/pc+fmVX3f77iKMPLAMAI6z0opSje0QdORU7ZYgsIgAEbIEEsNVon1I/SLJA6r6qbiJXl9tS+Lk7h74BAI+djt7x/NRGR73uBQAzwgyZ2KuJYETuulJQzC6BkQpWbnGyqqmifs4vAsByetlbF+MouoYkEkgSTs+Q/WBOmPPW+trc6pvqPlqubqc0AYaF7BlYAgB+9qmany7YnNhra9f1WmEZFh6zJw5sVa9RKHpH31EVpSmcTZxVs4hmb3VVXGvvephi2qnw1rwUvFkTGcg+Z18cADbyG3V9aPzqt87eGc+W2vx20/qXP3bsmXreS/ENZ2Wbf4ORRZ6ffbKrnvcyOnOpOT8A+K36ziICQLe9W218mo96m2yKvpFFhk0u9QGAOHxvXZMF33ddz9z1A543ZAXsFx6d/lg9jy0upZf8ABCwBTTxbAAA2Te0DAD83DNVBWekkiuUDdFghFI1oWxIHfxj0+/EXkKbtS0BACuZlZqvFTamVrNLPddSkauJyIFjaguN0Fs1F7nIZM4ue5fufbBNQTg1R58rFENARS5KzdmWRazKweLnnlKP2HiCy2B5TZSGD7uH4wCQKCa89brHIycjPc9PxW5hAOWT9438u8vK173HhuwbUZsmL7xA+3LVkc0sorVd98FI0BlsiCBsdLill/2MmLcqZmdK7rxcqPf9vvKxY0+4LJywJhR6PvfQuZvrdR9SLaulYETquFQ9shh+t+rm88DWABcKpRoiuYgfAHqdvZpZKwelw9ZRn2p6Mc8ywkovAIgj91ORq4lIfTeq+2hsqub+aCwfUyf2ulp8Ym8NCNgCcYBW01OMAxW5KDWHZJ+rLf/lQm+rIlc5a6MFLvFdkgAAoSj4xCr7rlRCuiByv/nEzMcVgLl11P+dD13WsVLre+wE6YnGRU/Svlx1JJxVs4g9jh7dByOjnlFVEC4PpaDUB37+2SAAyN7BZTTgCGyHy1L4mdsHvwkAD5+M3FWvaYvr+XU/UP3E3mYiBW9Rg7P4bLUilwjQSViUg0G+N8OuYc2slYOyWU1f2PDW8rr83NMdjCzystUXU/wjuu6X2eqIYx9YUcAoTGqti8lumGp5bbJWjniO6N4HIwmiSDZCnysUQ0BFLkrN2cwiOqrLIrKxqXJp+PWaEbn8Vn/RylmzkiJxs8Kss9bX/8y3z922kSm1e2187EsfOVrXY4rbEQfvKAdnc7SSq45sFNSqpn6n/rOIx7zHkgwYJSNm3JlShmu2PXqFDb+nJgvajzdsH/2PN/TOXBN0vyUp4D738LmPlSS55oI/CUZG3aOaCUbEkfvXFIaT2Ox6gEks2ir9XIALSACQLCa9dTOOoktkRUaymFQn9vqPa2atHJQh91ACqH3yhFt4MQgASvnIMaV5KI72ouLsDDNQGH760Z5aXTddSvMZMeNmwChHva0/sfew9Dv7iSBMRS6KIaAiF6XmxApq+e+ge7DywL2Q4phUqBsAxNEHGlKtVCu8FrX32NnE2Zo+OJ4+u9751Nn12wDgv9899E2v3VSXaYo7IQ3eEVU4c4EpCB428r6rUfc1GsmCGoyMecd0H4xYeavsNDmTAHA6ftrbZHN0CxubUftx9d3U0ODsKx879rjDzKVWk4W+zz8ydVMtr12e2OsFqp/Y21TMDklx964CAD/9aG+lH3OxLpln+FJBKtgi2YilfgZS9MZqZtVWkktmE2sqdtu7c822p94c96lCnlAUvLIi1+y6XEStYpc6LqUiVwsg+0fU0wXLr9Qs8XoqdsoLAC6zK2HlrbX78rQoI54RWk1PMRRU5KLUHBK4E+ejEvjZJ7sYReJkeyCquHvz9bOu9pDRwwuphZo9OHIlif3iY9MflxWwNw95X/3uq7oXanXtimB5Rfb0q02TZ56g1Vx1QCgKfEbMuBgw8lHv0br3TmoFvBZvAtialEepLUw6bGYykQ6FYWVx9IHVRt6722PN/9St/d8GgG+9F7n71fmEv1bXnkpMuRUorJ23p91md917EtYSue2I2t9w9bWK91GGYeA2uxMAcDpxmq4VSsWQZJvH7InrfWIvAASsAVJNz9eymp5JzKkiV/+tVORqAaSuq9TTBdFTNfNHp5PTPqD6IVla5bjveBIA0qW0OytmaTU9Rffo/wlIaSgrmRVbUS5aeYYv9Tv7M5V+jlt6Se0j4xvWnENBeo+tZddqFox87uGpWyKpYpfbyie//NFjT9XqutWwOdFm9U0qctUBUs3kMruSFs6i+ywiALRZ1JH2S+klb5NN0SX8zOM9DBRGcXatweptuBj0ozcHz13R63pXkhX+sw+d+5goKzU5tjiVnPIC2ByBriWk7mtUkWv9bHUTFsuTsGaFWSpyUSpmPjXvBbYSCkbAY/YkgNpV0zPxWTubi/kVlhfFobvDtbgm5XBIQ3erIldioQ81qthbyiz5ACBgDWjuuXIQ7LxdcpqcSQUKczZ+1tNseyiUekNFLkpNORM/o2YRLdVlEbnIKbWPTLnhuZbYbOaYq00zxxdnYu2PnIzcCQD/7Y6Bb7Y5zcVaXLdayNhsNj5T8TEbSuWQ4NUoWUQA6LB31FwQpmxBjnKQox3N4Dc/duxRu5nLLCXy/V98dOr6WlxzKa0GIz6LL1GL6zUSceS+ZQBghOUeVDGcpM3aRiZheetkGkWHkMlpRgncgS1BmEz2Piz89OO9AKC4e1dgshkiAdXqSL3XxxXelmXEnJ1bfb0mf+e1zJofADrtnYZZK8TfnEpOUR+MonuoyEWpKSSLSJyOSmET82ofmYHbNSdykWaOZBTxYShJMvP5R6Y+Jivgru33vPn91/XOHt7CgyGOqL3RmNRqN0o5ulfUmJX0ihcA/FZ/ormWNI5ue3ccANbz69TBqgPs+hn1iE3nlU3bR/t8ttyP3Rz8NgD8+zvhe99cSh76b72WXfMCQLutPXHYazUauf14WjG7BEYqWrjFybZKP9dp70wAQDgbpmuFUjGRXMQLAB22jkRzLWkcpJo+lA15a3E9LvSGmnT1H9GcP6pbGBayt38FALj552qSeCV+SJ+jT/eDfwgkebKcXvY22RQKpe7QwJVSU0jWuc3Slqj0M2z0tJMpJD0KZy5KA7dF62VbvTjiVUcPJ4qHb+b4+UembgolC71OC5f6yseOPX546w6O4u3PyVZfjJFFnp9/pqOZtuiRtZwauBspGBl0DcYBIJ6P08C91igy2OSiKnIN3dXU4Ownb+0/c2m386QoK6bPfPvcR2RFOdT1ormoF9iqmtUasndgBQD4hef7Kv1Mn6NPnYSVp5OwKJWzkd/wAkCvozfRXEsaR5e9KwHUrpqeXT+n7qPdV1ORq4WQ246oItfaOzURuRKFhBcABt2DiVpcTwt02DqIIEyfKxTdQ0UuSk0hwUg15b/8zBNq1swdXAZnPlw01ASOeI8IDBg5K2adQlHgD3qdV+cT/m+9F7kbAH7q1v5vdbkthdpZeTAU36DqVCy9TI8s1hgSjBAH3Qgc8x2LA9gccU+pHdzKaz6mlLUrJntW6r2+6WLQVz527GGric0txHJDv/PU3FWHuRaZ2Nvn6EvUwrZGIweOrQAAG3m/4n10yD2kCsIFKghTKodMTiPfHyPQ5+yrWTU9pCLDCkvqyYLyUWNKa7DZQiN2+BYasiJv+iHHvMcMs1Z6HD20mp5iGKjIRakpJOvc4+hJVPoZLvSWKnIFtFkabmJNisvsSgJbzcSrRZQV5rMPn/uoJCv8lb2ud3/kpuBUTY08IFLgOAnOKq5AoFQGCV4HXAOGcbCCzmCWZ/hSUS5aQpmQtdn26Alu7hl1H/X0L6MFpqoNttmzP3hd76MA8I9vrD4wE804DnqtZDHpBYBRz2iiNtY1Fqn3+nJwNltxcHaJ75IEAKRKKa9YRS8vinERZZFJlVJeYOv7YwRG3eq+kCiqlTmHgVt4oZ2RimbF4knK7cfTh70epXaIw/evAgCTDh26hcZKZsUuKqLJxJoKnfZOTU10Pwykmp4kjigUPdN8T5iiK4iTMeQeSlT6GTZGSsOv1aTIBQA+s9qDbCY5c6AHx28/OXP1Ujw/YDOxmS997NijtbXu4EjBm8vN5+dpJVeNIYH7Uc/RRHMtaRwsw8JjUSfknY6fpk5WDeHC75STBcdaZh/9ubsG3x0O2GcKomz9tW+de/Ag18iKWS5dSrsZMMpR79FkrW1sBOLI/SEFjMJkwp3IJyqq9vVb/UUrZ81KisTNCrPOettI0T5zwpxTUiTOylmzfqu/KQNrmsFR39EkA0ZJl9KerJjlDnMtcqRY9g62zD5KUVG8/TnZ5ldbaMw93XmYa51LnPMC6mTOaoZkaZ3NavoCraan6B/jrGxK3ZEVGUJR8AJVBO5inmWElV4AEEfvX6mbcXWGNHMkI4mr4Vwk7fznN9fuB4D/dFPfI/0+W67W9h0UcfiesMKwMpONtjPpNUuz7dEL0VzUUpAKNo7hxEH3oKGyxWQoxVxqjjpZNYSNzajJgt4bWiY4YxkGn/3g2Ld5lim9H0pd9lffWRqr9hpk1LnD5BCsvFWTk84UR3tRcbRHGUVm+Zknuiv9nNeiTsI6lzhH1wplX84ltwL35lrSWOy8XXKYHAKwtV8cFDasVq1LHZe0zD5K2UIp9zc8bAuNhdSCFwC8Fm/i8FZph6AzmDWxpmJRLlppNT1F71CRi1IzFlILDkmReAtryXfYOyrqJ8XPP9fOyCKvWH1xxTecrbeN9YL0IDvIJKxf/9a5DxQl2TLabp/6L7cNnKy9dYfA4hYVZ9caA6Ca4IyyN2cSZ7wA4Da7DZVFBLZG25NR95QaUBB4Jr3WpQAQRx9oqWTB1UFP4sOXdzwDAH/24uKHo+miuZrPzwgzXgDwmrUdjMjeITU4W3m14uCMCMILqQW6Vij7spha9AHGC9yBrf1hWpj2HuY6bGKuFwCkvhupyNWCSO2q+MhGTh5K5FrJqNOtqxmSpQdYhoXb7E4AtJqeon+MFV1R6gop/3Vb3BX3GOIWJ3uBrelTWqXX0as2c8xV18zx/768dOTUWvoSE8uUPv/hIw+xTOu1XpF9w+Xg7DXal6tGzAvzXsB4GXfgcIIwZWf42ac6GUVmFXsgqjg7W+6Y0q89OPZKp8sSShUk96996+zd1Xx2Kb3kBQC/1a/p3nVyx6XLAMBFT1UcnLVb28kkLG+dzKLoCPI9IYkEI0H2h6VU9dX0BCa7YWIy0XaFYWVx+N612llHqRVS301qn9iyGHlQDjIkSy+Q9iqkmo1C0StU5KLUjIW0mm32mX2JSj/Dlh1+qTx9SquQZo7VND6NpovmP59c/BAAfPSKzqcu73G3ZL8ZqfMKNXO2fpb25aoRm1lEq7GyiAAQdATjALBRUKdLUg4Pt/xKLwAo3sHVZtuyExaelT/1wMg3GUB5aTZ+47ffC1csmK9l13wAELAGEnUzsAFI/SfKwdlCxfsombwazUWpIEzZFxK4G2liL6HD1hEHgLXc2oHXCj/zRDcDhVEcHRFYvWLtrKPUCnH43jW1hcb6oVpoEP+jmiFZesFv9ScBYDW76m2yKRRKXaEiF6VmhDLlLKKt8mCEjZdLw3tv0LTIdcR7JAGozcRlpbK2Mb/2rbN3pwuSu8ttWf3VB0dfrad9h0EauE0NzpKVB2eUvYnkIl4A6LB1JJprSeMZdg8ngK1R95TDw62f7gEAqf2Slt1H7zkaWLtjzP8SAHztqdmPZItSRQ2iN/JqMNJt707Uz7r6Iw7eEVVYU4nJJ3xMbMZeyWf6Xf10EhalYsj3JOgMGq46hewP67l170GvQY4Sk+p1SgticUmbLTSmH+856GWI/zHoGkzUyjSt0G5rTwBbojiFoleoyEWpGVUH7rmYiclEOhQwijii7dLwHkdPjmf4UkkuWcLZ8L7NHB8+Gel9aTZ+IwMon7p/5JsmjlUaYedBkII3xxTOkmeKaRcbed/VbHv0wHpePdba4+gxXDBy3Hc8AQCpUsojymLrnc/VIExinvSRaeng7Dc+cvRZj42Pr2dKHZ996NyJSj4TK8S8ABB0BRP1tK3u8FZZcfWEAICffbKihMGoZ1StEKaCMKUCEgW1kpwkEozEgGvg0IIwqVaXWzhZQAFk/4h69Hv1tQMlXrcPyTrmPZaonWXagFSvkQQShaJXqMhFqRkkGOl19CYqeT8/82TXZmm4zV+qp231ZnszRzLhaDdyJYn9rSdnPwoAd4z5X7r7aCBcfwsPAcsrsie4CgD87FO0L1cNIMHIgHMg0VxLGo/H4ilZOWtWVmRuTphzNtsercOkVi1MdiOgMKwsDt3d0nuJx2YSf/aOwW8BwGOno7d/Zy7ett9nkkV11PmYZ0zzgrDsH1H7G66+WVFwdtR7VGDAKBkx48qUMhVVvlGMSUEqsOlS2gMAx3zHWrL1QT054j0SBwChKBxc5CpXq4vBW1ry2DdFReq4XD1dcMAWGovpxaqHZOkJUr1WTXsVCkWLUJGLUjOShaQXAIbdwxUFIyQLI/uGdJE1IxONSFPx3fj8w1O3rKeLHR4bH//Ch48+1wjbDovcNqZmztbepkcWD4msyEgW1bVy1Hc00VxrmgNpuD+VnPI21xLtw8882cMAUJxda7C4pGbbsx/fe23P3NVB99uyAu7zj0x9RJSVXav5hKLA58ScgwUrj3pGU420sx5IXVep/Q1j5yraRy2cRXaanAIAnEuc89TTNoq2OZs461agMA7ekXKanIbrJzXsHk5zDCfmpbwtmotW3auJic/amXzCq7CmkjR4e7QeNlJqgzR4R7mFxmIfKmwPsp2phOp3kMS00SDtVYSiUHF7FQpFi1CRi1ITRFlkhJJa/nvUe7SiLCIXPU1Kw3WRNfNb/Alg72aOry0k/I+cit4BAD9z28C3vXaTJirY5K6rVadiY5qKXIcknA1bS3LJYmJNxV5Hb7bZ9jQDIggvphe9zbVE+3ChN3oAbfWR+dJHjj5uM7GZpXh+4LeemLlmt/edjp/2AoDT7EyaWFPLHumuFHHoznJwttRbaXBGArEZYcZbN8Mommc6Oe0DjDmxFzi/mv5s4mzVgjA/82QPACiu7hB4K438Wxip70bSQsPJRk66q/38fEpNRBM/xGh027tzJtZULMklcygbsjXbHgqlXlCRi1IT5oQ5p6zInJWzZv1Wf0Uj7JnEgtosOXiLZoKzvdjWzHFHB0tWFHz+4akPS7LCX9XnfucHru+dbaiBh0AcuXcFABhhuQdSkfZROgRnEmd8AOA2uRMsY8wtmEyVXMuu0eqUQ7LZR6bjMs3so30+W+6Hb+x7BAD+5a21+2aiGcdO75sVZn0A4DV7NX9UEQDkrquSismRYcS8jVt+xV/JZ0jyZCWzQtcKZVeW0kteAPBZfbpYKwfBY/YkgS0Roxq4tbfUfdQ/qpl91LCoLTRWAICfe7rqxOvmkCxrwJBrhWVYuE3l9iqJvdurUChaxpgRFqXmkD5UlWYRmcSijc3H/QrLi+LgHZF62tYo9mvm+LtPz105H8sNWU1s7jc+cvTxRtp2WOS2IxnF4kkycsnMLbzQ3mx7tIzRs4gA0GnrTAB0uk8tYJOLah+Z/hOaqoj9mdsHTo4E7NNFSbZ85tvnHtjpPSvpFS+wJYpqHoYFCc64+ecqCs7arG1JAFjLrnnraBlF45DvR7tVTbYZEZ/FlwCA1czu1fS7wW6oR4ilzss1tY8aFbltTN1HQ9W30IjmVb+jw2686dYE4n8upBZo8oSiW6jIRakJS6lyFtFSWRaRny2Xhju7QzDZdFEaTpqIxwtx74WvzW9k7f/4+uoDAPAD1/U8OuC3ae6YmuwdWAYAfvFFemTxEBAHXDeB+wHocfQkgZ3XCqVy2PUzTqYguBXOXJT6T6w3255qYBkGn/ng2MMcy4jvrqYu/4fXV4YvfM9arhy42/QTuMuBY2p/w/B7Fe2jXfauBEAnYVH2Zj2nTuwl3xcjQvaJSC5SXeCuyGCTS+rJgnK/J0prI3Vfox79jlXfQoPspZUOydIjfmu5vcoBBGEKRStQkYtSE0JZtfy30sCdW32jXBo+rJus2ah3NAkAqWLKe+Frn/n2ufvyomwbbLPN/vxdQ+823LgaIAeOqU5F+H0qch2CSC7iBYAOW4chS+WBbdN9CnS6z2HgZ58u95HpXQVn1lzPqmuCnviDl7Q/BwB//PzCh1J5kd/++npeDdy77d26WStSz7XqPhqfqWhSLQnEqCBM2Qsy3TroDOpmrVRLl70rCVQvCLNrb3uYUtah8Lac1HOdYf/7aQlpeLOFRm+1LTQShYQP2PJDjEiHTa1iI/4ohaJHqMhFqQnRXNQHAJ129RjSfrAb59SsWecVusma9Tv7MxzDiQW5YI1kI5vTfb7+9lr/W8vCVRwD6dcfHHuIZbTZ0krqvX4VANjEfE+zbdEyG/kNH7B1vNWIkKmSqVLKQ6f7HBy2PO1Uy31kPvOBsZf9dtN6Iif6P/fwuRPbXyMi6IBLrZLVA+LwvasAwKRCXSjl9vXBRjwjCQBIFpP0WAllV4SiOvhnzDOWaK4lzSPoDCaA6pMnfPnosOzpW4FB+2RqDbltLKNY3ElGLpm4xclAxZ9TZKRKKQ8AHPMdS9TNwBanx676n7G8Ko5TKHqE7uaUmrAti5jY982KvNlHRuq/TbPB2YWcN90nedYLANmixP3Bs3MfBoB7jgVevGHQG2uehYdDHLo7pABgMuFOFFJcs+3RKsQBH3IPJZprSfMIWANFC2fJSYrEL6QWdmw6TtkfdmNK3Ue7rtBsRazdzEk/d9fgtwHgiTPrt706n9hsyJ4sJr0AcMSjjjzXA4onmFesvjijSBy/8Py+/Q2PeI8IAJApZdx5MU99NspFZEoZLiNmXAwYZdQzKjTbnmZBBD6hKFQlCHNr75STBWOa3UeNiOzpXwUAbunl7ko/My/MOyVF4q2cNRewBioakqVHSOIoUaTV9BT9wu//lvoxPj7OA/gkgB8F0AcgDOCfAXxuYmIivc9n7wTwzB5v8U1MTCS2vd8J4H8B+A8AOgEsA/hLAF+dmJgQD/5bUICtjXLItX/gzkZOuplSxqlwlrwUvEmzos9OeMyeRLwQD8wL897bum8L/8YjUzdtZErtXhsf++wHj7zYbPsOg+LsLCr2wDqbXQ/w8892ikc/Qh3CKpEVeTPjftSrVjMZFbfJnYxKUdu5xDnvkHso02x7NIcigxXUPjKixvvIfPdV3QvfeCf89lvLwlVfeHTqQ9/4qev+JlbYMBekgo1jOHHQPbinP6A1ZE//KpeP+7ill3vE0QfCe73XztslB+9IZcSMazo57b6s7bJEg8ykaISp5JQbABwmR8rKWw1bGjvsHk6zYOWclHMkC0mTx+IpVfI5NjbTAwBy11Wa3keNhtw2tspF3j/ORU72AKioDQhJQJOEtFEZ844lga1EEoWiR5qdFfw7AJ8C8FcAvhvA7wD4IQCPlAWwSvgJAHft8L9Np3h8fNwE4FEAPwzgd8v3+ksAvwLgb2vwexiaklxiUsXKy3/JyF/ZE1zVW2k46Um2ml31vLsieB45Fb0DAH76toGHXVZe82KqQjJny6/QI4sHYCm9ZBcV0WRmzfkue1e+2fY0EzLdZymtDq2gVAe38pqPEfM2xWTPyF1XJZttz2H5wkeOPmHl2dz8Rm74j55buGwqMeUBAJfJlWR19pyQ29SKETZ6qqJ9lARk08K0t35WUbTKrDDrBWjgzrO84jQ7kwBwNnG2smouqcgwwrKaLBi+mybuNITUdXUIANj4bMX+6GJq0QtUPiRLr/Q6erM8w5dKcsmyll2zNtseCqUeNK2Sa3x8/LsAjAN4YGJi4vFtP38SwJsAfgbAH1RwqdcnJibe3uc9PwvgegDXTkxMvF/+2cPj4+OvAHhyfHz87ycmJr5Z7e9AUZlOTrsUKKydt6fdZve+Qg679o6aNdNwH5ndIOO7I9mI93MPT31AlBXTpd3Okz94fe9Mk02rCVLg2CoXevMKdv1MxeXhlC2mElNegAYjgCoITyWnNodWUKqDW3heV8mCAb8tO35N9xN//erKR//2tZUHrW3zDwOAx+zRvIB3IVLPtaumU/9ScX9Dn8WXDGVDweX0Mu3LRbmIlfSKBwC8Zq/u1kq1eMyehFAUfHOpOe8NnTfsO3GWW5wMMHLJrFjcghw4pquKUb0jDt+9imc+Aya91gUxz6KCKsZqh2TpFdJeJVaItZ9NnPV22bvWmm0ThVJrmukZ/xSA57cLXAAwMTFxCsA/AfjpGt/rH7cJXOReTwF4vsb3MhxTySkfUHngzpVH/uqxNLzb0Z0AgNlYPHgukjlq4pji5z505LEmm1UzpJ7raPP5Q7CUXqLBSBky3Seai9LA/QBwa++q+2jbEd3so//jnuG3+7zWxWxRcjx0auUGAPBZfYkmm1VzxOF7QgDApMOdKGb27W9IArJwNuytr2UULRLOhT0A0GZtM/xzxWfxJYEt4W8/+MUXy8mCft3so0ZB8Q7mFKs3UU3z+Wgu6gWATltlQ7L0DKmmnxfmvc21hEKpD00RucbHxzkAtwJ4dpe3PAfg+Pj4eEcN7tUN4Mg+97ptfHxc+6nwJkGcCeJc7IksbpWGD92lu9LwoDOYBIBwWugGgI9d0fn00U5nqrlW1Q5p+J41BYzCZCIdKAhN7emnRVazq14A8Fv9hg9GyHTJjUJ1494pKmxc7SMjdV2tm+CMZxnlU/ePPMQykJeSiQEACFgDulsriqunINv8G4wicfz8s/v6OSQgi+bVAI1C2c56ft0L0MAd2KqmD+cqE4RZtZ8T5MBR3eyjRkJ2B1cBgF96qaLEaywf8wFAr6M3UUezNIHf4k8AW34phaI3mhWk9gCwQz2WuBNvlP8dARDZ51p/PT4+PgRVsHsdwG9MTEw8se310fK/e93LCbUZfWife13E5OTkT1b7mVajWCwGgIP/LmvpNScAtBXaBva7hiO7zN0mFS0Fk1d+YSrx/ZiaPMgtWxZZklkAkLkE1+tA6S5X5MrJyeiVzbarltxq7Zac+VX+5DP/8lMJ19GKGrvux2G/g1ohJsTcAODP+I9NTk4Gm21PMzGXzDwApDPpoVb4u2vqO6hIuC+11gUAL6V67y1OTt7dbJNqBQfgrl4mOynHnQDgTniunpycPNZks2rOVbYxe1fuFcy9953vXYx6s+TnO30PXQWXBQBSQuqSycnJzsZbS2llUkKqDQCcMectk5OT1x72epraCy/Am/XaACARS1w7OTk5st/7b95YCXgAvCMfuXp9cvLSuhtIqZhKvocj5mPOMbyH8OLUfScnJ2/Y75rpXLoDAEyrpvsmo5N31c5a7RHIBhwAsB5ev3VycvKKZtvTimh5L9QLJpPpwJ9tVvUSGRG+scvrZOJe2z7XeRPA/wPwfVB7eJkBPDY+Pv6DdbgXZRfiUpwDAB/rk/Z7ryczYwYAwT5QE3Gk1UhmXKwi82D5LL5nrJjkWKbZJtUcwTFUAgBPevrgO49B2Vwr3P5rRe+0cW0SACSkBKcoSrPN0RTO7DLPyUUmb/ZLRbNXd9PUPjLIpE2WBABgPubX5T6TdAyXAMCdmd339/Nz/s21Um+7KNojKSc5APCzfsM/V7atlX2T+IwswpldMgFAwjmqS59U7ySdo0UAcGfm991HFUVBUiqvFY6uFfLfgPilFIreaFYll6v8b3aX18nP9zpTPzkxMXFexmp8fPzvADwF4I/Hx8e/PTExkazRvXblxIkTf3aQz7USRKE+6O/yR0/80Q8ij9HgYPDhE0dOnNvrvbav/+n9AG72dgZfPnHixHMHuV+rIsoK8xt/8tqPKH4vGMs6Lr3WMXFz183RZttVayzZb96AjckPjImn5/pOfOkbtbjmYb+DWuGrj3z1JwF0Hzly5F9P9J8w/PGIL/77F3+lKBctA1cN/G3QGdxtj24IWvoOmp9/7goAnzB5uqdOnDjxT822px78xr99/pcKCuwvr/hM//XOK//heJd+jn0DgOm9xQEs/cOP9OTOJT3bvnM7fQ+ThaTpaw997dNJOcnccPMNf25iTVQVpgAARFlkEt9I/CoA3HPzPX/us/gOLdZoaS+8EM+Gx/vnz/35z8cQy+9nPz/9WCeniD+tWH3xG+944E8bZSOlMir5HjLJfivOfPmX3blF5sQN1/wFTLZdkz7zqXmH+IT4ixbWkr/ntnsM//fOLeV68Bp+Yp1dT2hxrTcCLe+FeuHVV1/9Xwf9bLMquYizatvldXv53117cUxMTFz0IJ+YmBAB/E8AbgAfq9W9KHuTLCa9ADDgGtj3vyEbn+sGAKnryqqPhrY6v/f07JXLiXw/JK8EAHPCnLfJJtUFqff6cvP5RTphsUpSxZQXAMY8Y3S/wdawiqnEFG0+XwVctNxHpm1Md30NASAv5tmikrFBAYp5j/kLj0zd32ybao04dHdIAcBkwp0opPbMpHssnpKNs2VkyOxMcsa113spxmI+Ne+QFZmzctZcLQQurTPqGRUYMEpGzLiyYnbPdcUtf0fdRz1BXe6jRkDxBPOK1RdnZJHnF55v3+u9M8kZDwC4zC7qfwEY844lAEAoCt7mWkKh1IdmiVz7HRHc74jhrkxMTLwOIAGA9PCo270ogKzISBVTHqCCwF0WGSa1oopcg3fqyqlYjOds//Rm6H4ACHo8iwCwklnxNtWoOiEO3R3ebD6fi+nyKFE9iBfipryUt7EMK/W7+jPNtqcV8JrV6T4LqQVvcy3RFnpOFgDAdHLarUBhbLwjzbG8+N5q6rKJN1cHm21XLVGcnUXF3rbOKDLLzz+7b58tt9mdBIAZYYYKwpRNppPTXgBwmWjgDgBW3io7TA4BAM4lzrn3ei8bPd0NAHLbEV3uo0ZB9vSvAlui5W4sphfJdOtEA8xqeYLOYJZn+FJRLlrXsmvWZttDodSaZolcIQA5ANfs8jr5+ewBr88CIOetZy645k73ygAIH/BehiaUDdlERTSZWFOhy96V3+u93PKrPkYqWhSzMy23H083ysZG8NmHzt2TL8m2Ab9t/tKutlmg8uk+msPmLymO9igDheHnnqZNkCvkXOKcmkU0uZI8y9PjRtiaMkmn+1SBVGSYVKgbAMShu3WVLCAQIcdrccfvPxZ4AQD++PmFD+ZKkq6mICuegXJw9sq+k8HIuPel1JK3vlZRtMRyerm8VmjgTvCYPaognJzx7vU+NjGvTqjtvlqX+6hRIBXNbPTUnvtoKBNSJ8FbK5gEbwBYht2spj+bOOttrjUUSu1pisNYPlb4IoA7d3nLHQDOTExM7Co8jY+Pt42Pj/t3+PlxqMcV3y/faxXA1D73emFiYkJ3zXsbATlm5Da5931ocEuTamm4u09XDsWjp6I9ry0kr2UZyJ96YOShLntXEgA28hveJptWN2RvOThbeb2isc0UYCG1oK4V8/5rxSi029Rx79Fc1NtcS7QDt/RyGyOXTIrFLSj+kab2MasXK5kVErgnf/0DYy95bHx8I1Nq/9Jj0zc227ZaIgWOqcHZ+pl9j363WdsSALCWW/PW1yqKlghl1cDdb/HT50oZIvjtWU0v5lkmvdYJqEeHG2IYpS5IPdeq+2h8bk9/dD2/7gGAgDVA10oZIggvpZf2rHqkULRIM7OifwrgjvHx8Xu3/7AsUn1f+XXyM8/4+Lh92/9vh1qh9fsXfJYH8NtQq7K+dcG9vm98fPySC95/F1SRy/ANCA8KKf91WyoQuSLvq6Xh/lHdOBSirDC/8/TsBwHg1hH/d04M+9f7nf0JAEgUEt5m2lZP5MBxVeRaP0NFrgohDrfPQrOIhB5HTwLQtyBca7ill3SZLNjOWlYVctqsbUmXlRd/8kT/wwDw0PuRO8+G07rpSSX1XBsCtipK9qLT1pkAqCBMOR/yfSAJAwoQsAYSABDOhXc92svNP9/OyCKvWL0JxRPc8xQCpbURh+8JAQCTDneimNm1D1ssH/MAAElEU7b8UVLlRqHoiWZNV8TExMS/jo+P/yuAfx0fH/8KgLcBjAL4FIA3AfwRAIyPjzugHltcB3C0/Nno+Pj4HwP41Pj4uAvA/4P6u/w8gGsBfGxiYmJ7hvsPAHwPgKfGx8e/DLWy60oAvwLgXyYmJv69vr+tflnNqMeMKskisvFZNTjrvEI3wdnvPjV7VShZ6HWYufRnP3TkOQAY9YwmAEAoCbp9aEi916/inb8Gk1igIleFRHIRmkW8gAHnQAIAksWkbtdKreEiJ8vJgjHdJAsuhGTcO2wdCQD44Rv7pr/5bvjs2Ujm6Ocenrrv73/06q831cAaIQ3dvbbZ37Ag8LC4xd3e2+PoSQJArBCja4WySbwQ9wBAt72bPlfKdNo6kwCwnlv37vYefvk76j7qpk3ntY7i6inINn+MzcX8/MJz7eLYB9d2el+imPAAQNAZpGulTMCm+qPEP6VQ9ESz+1t8P4CvAfgxAF8H8IsA/hHAA9umJ4oA1gAsbf/gxMTEpwH8JwC9AP4WajVWFMDNExMTj1/w3hKABwD8A9Tpi18H8OMAfgfAD9TjFzMKFZf/KjIYYVXtIzN4hy6Cs5VE3vrPb4XuBYAfuL7n8XanuQgAQ+6hNANGzok5R7qUbpqQXE/EobvCCsPKTHY9wGQ3aPP5CiBrhVRkUIAj3iMJYGtCK2V/yJEMqetK3QZnJHDvsfdsPld+/QNjj3IsI763mrr8n98MDTbNuBqiONqLij2gNp+f3bu/4aBrMAEAyQJdK5QtSIKg39VPA/cyvc7eBADEC3Hvbu9h10nTef0mC4yEQprPL+3efJ5MERxxj9C1UoYIwjR5QtEjTQ3Ay+LT58v/2+09BQCX7vLaXwP46wrvJQD4hfL/KDViI79Byn8Te72PW3nNx0gFi2JypOWOS1MNMa7OfO7hc3flSrI96LMu/Lc7Bt8jPzexJsVpcqZSpZRnKjHlvrr96the19EkVq+oODoibHqti5t9qku8bHxp/w8Zm0RBzSKSigwK0OPoyfEMXyrJJUskG7F02DsKzbappZFFhkmtdgGANHSnboMzIuQMuAY218qVfe7EfccCLzx6KnrXHz0//8EPX97xf2wmTvO9NBVv/yqy0XZu5dUe8fjHV3Z731Hf0QQApEopj6zIYJlm5ygprQAJ3EkFOQUYdg8ngL2TJ2ycNp3XE3Lg6Cq39vZlbPR0D9TTQOchFAU+L+XtLFh5yD2kq8FXh6HX2ZsEtvxTCkVPUC+JcijIxtjn7NszcOcWXyR9ZHQRmD11dr3rO3OJ6xlA+eR9Iw+zDHPe66S5+FxqTrcPDtkzEAIAbpU2n68EEowMu4epyFWGZVi4zK4kAEwlp3S7VmoFt/hSGyOXzIrZlZLbjmSabU89kBUZqVLKAwBHvEfOWyuf0WET+q3m86f33EcD1kDRwlrykiLxi+lFR2Oso7QykWzEUpSLFo7hxKAzqMshFAfhqPeoAADpUtpdkAoXxzlinmXSoS4AEIfu0oVPanSk7mvUfTS5c39DMt3aaXbS6dbbGHap/qhQ1G97FYpxoSIX5VCQYGS/wJ0Lk6bzI5p3KGRFwW8/OftBBWBuGfa9eudYW+TC93jN3iQArGZWdfvgkNtJ8/mzVOTah5JcYtKltBsAxrxjQrPtaSXcJrcAbA2xoOwOt/RSuY+MfpvOL6WX7JIi8WbWnG+3tZ9X2eey8uJP3NL/CKCfJvRS7/VqcJZY3HfCIhGEZ5IzdK1QcC6pBu4ukytJK/u2cJqcop23pxUozHRy+qI9glucDDCyyCsWT0LxDuaaYSOltojD96wpKDefL6Quaj5PEs6VTII3EqOeUQEAsmLWlRfzdBOh6Ar6haYcmHQpzWfFrJMBo4x6Rvc8gsjGZ3TTdP73n5m7YjmRD9pMbOazHzryzE7v8VvVRvzh7O7TfbSO1HfjKgAwSdp8fj+mk9MuBQpj5+1pp8m5a3NpI7Jtug8dYb0PXOSkuo+26WdC7YVMJ6e9wFY17IX8p5v6po52OM6WJMX8uYen7muocXVAHLo7vNl8Phfbs78hGfdOBWEKACykFrwA4LF4Es21pPXYrKYXLq6m55deVpMFnqBu91GjoTi7Coq9Te1vOP/sRf0NScKZTrc+HytvlR28I6VAYWaEGc0njSiU7VCRi3JgphJTbgBwmpyCiTXtXv6ryGBS+mg6vyYULP/4Ruh+APjea3ue6HJbduwh1GHrSALARmFDt4G7OHjnVvP5dNjcbHtamVlhdjPj3mxbWo02axud7lMhbGK2GwCkTv02nScCDhF0dmJ7E/p/eSs00Djr6oDNX1Ic7VEGCsPP7d18ngRoa9k1ulYom4F7JdOtjQbZP5YzyxetFTZ6qpwsGNPtPmpEFE+/2kJj+ZWLEq/EvyAJaMoWRBAmfiqFoheoyEU5MGRD3C3jTuBWX/cxYt6qmOwZueNSTR/V+uxD5+7MFiVHr9e69PN3Db272/tIc3EyJUyXWFyS4ugMMwD4uWe6mm1OK7OUXvICgNfipQ7WBXTaO4kgrN+1UgtkkWFSIVXk0nHT+VAmRIKRxG7vIU3oAeAPn5v/UK4kadqXkb0D6tHvlb37G5Ipxuu5dd0mTyiVQwJ3kiigbEGEv52q6dn4nLqPdl2t233UiGz1Nzxz0dHv9dy6F9iaJkjZgvilK+kV6oNRdIWmHUNKcyFZRK/Fm9jrfdzCi6SPTAga7hvx/NRGx0uz8RsZQPmle4cf5llm1+q1fme/IZo5yt5B1amgzef3hDjaNBi5mG57N53uUwHc8it+RiqaFbMzJQeO6XY61Hp+3QMA7db2PdfK9ib0X35s5obGWFcf5ADpb3hmz32UCMJ03DsFAGKFmBfYf7q1EWm3qfvHRv6CanqpyDDptXLTef0mC4yI1HNdub/hxc3nE0U63Xo3iCC8lqMVwhR9oV3FgdJ0wrly4G7ZO3DnIu+ppeH+Ec2WhsuKgt98YuaDCsDcMOh9/Z6jgbW93k+mgqWK6rh3vSK3H1fLw2nz+T3ZDNxt7Ykmm9JyDLoHDSEIHxZucXIrWaBjNvIbXgDosnft+VxxWXnxx28JPgIA334/fNe5SNrZAPPqAmk+zyT27m/Y6+gVACoIU1RIpfh+062NCNk/Lqym5xZeDDByyaRY3EnFN0wnUuoIaehutfl8JtKBgsBvfy1ZTHoAYNA1SNfKBWwThOlzhaIrqMhFOTDr+XL5r33v8l+mXBoud16h2eDsj55buGwxnh+wmdjs5z505On93t9uay+YWXNBVETTambV1ggbm4HUe4ManAn7TwYzMqTyglQtUbYY84xtjnsvySWm2fa0Klzk/XKyYFSzyYJKIBn3oDOY2O+9P3JTcOpIuQn9Fx6ZvrfuxtUJcfgetfl8dj3ASfld1wCZYkymGlOMTapYnm7t2nu6tREJOoMCsCVuELjl75STBbTpvN5QHO1FxR5Qm8/PPdNBfi7K4uZ066Peo3StXADxS2mFMEVvUJGLcmBINnnP8l9FBissq03nB27XZHAWTRfNf/fayv0A8D1Xdz/Z67XmK/kcGfc+nZzW7YNDHLwjojaf3wgw2Y09J4MZmWQh6QWAAdcAdbAuwG12izbOllGgsPPCvGarcerNVh+ZK3UdnJHAfcQzUtFa+fQDo4+xDKS3l4UrH3o/0ltf6+qExS0qjkCUgcK4MvP8bm8b8YykGDBKVsw6M6UM10gTKa1FVsxyGTHjKk+31nSv03pA9g+ynxC46EnadF7HKGXxklt9YzPxOp+ad8iKzFk5a9Zj8ZSaZ11r0u8qt1cp0Gp6ir6gIhflwJAM2YBz98CdDb3lZcS8TTHZs3Ln5Zp0xD7/8LnbM0XJ1e22rPzCPcNvV/o5Q4x7t7gkxd4eZQBw88/sORnMqMiKvFl5MeoZpSLXDhBBeEaY0e9aOQyyyDCpFVXkGrxTt8FZvBA35aW8nWVYadA1mKnkM9f2e+J3jLW9DAC//8zcB0RZ0WQ1IJkM5snM7possHAW2WFypABgOjlNm88bmHOJc24AcJgcKStv1W9PhAPS7+zPcAwnFeSCNZaPbU5/NkqywKjIbWMhAGDXz2wOQ5pJqn4F8TMo50P8UqFERS6KvqAiF+VAlMt/PcBW/6md4BdeKJeG965qsen8awsJ//PTsZsA4L/fPfTIXs3mL4SMew9lQ7p+cMhkbPO2zBlli3A2bC3JJbOJNRW77d25ZtvTinjMHgEAltMXj3unANzyqz5GKloUszMttx/XbdP5qcSUGoyYXEme5Svea3/9A2MvOMxcOiQUen//mbkr6mdh/ZDajqwBgDszv2dFrMukBmpzqTm6VgzMnDBHplsnmmxKS8IyLJwmZxIAppJTqiAsFTcn1IpDd+s2WWBkpK6r1gCATSxs+qPLGdWv8JrpdOud6LZ353iGL5XkkiWSjViabQ+FUiu0pzpQWoKl9JJdUiTOwllyfqu/uNv72HC56bxvRJNZs688PnO/rIC7us/99gcv7Vip5rNkkl40F9V1MCK3jalOxfo5KnLtwFRyK3BnNSj0NoLN6T5ZOt1nJ7ilSXUfdffpOjCbT82TwL2qYKTdaS5+77XdTwDAxJuh+yKpguYcdbnrqhAAuLILe4pcZNz7amaVVnIZmJXMiheggftekH1kIbXgAQBu6eW2ctN5QfGP0KbzOkQcvHMNAJhMuBNingWAUCbkBbb8DMr5sAy7WeV2LnmO+mAU3UAjLsqBmE5OewHAbdo7GGHjM6TpvOaCs797bWXkXCRz1MQxxV/7wOhT1X6+y6ZO94nl9d3MUeq5Vi0PTy5QkWsHFlILXmDr+CrlYgK2QBLYmkJJOR8u8r66j/pHNZksqBQSuJMq2Gr42TuH3uvxWJazRcnx+Yenbq+5cXVGHLprDQAcuWUTI4u7vo8EauFsmK4VA0MSAiSZRrkYr9krAMBKZqUscr1kiGSBkVG8/TnF4kkwsshziy+1AVt+BfEzKBez2V4lpeP2KhTDQUUuyoFYSi95AMBj2SNwV2SwwkoPAIgDt2sqOMuVJPbPJxcfAIAHj7c/f6TDWfURoV5nbxLYmhamV6TBO7fGNhdpM+QLIcdV/VZ/osmmtCxkuk+8EKfVKTvAxmfV4EyDyYJqiOQiajBiDSSq/SzPMsr/uHv4EQB4YSZ20yvz8bYam1dXFFdPQbH64pwiwpVb3LX5fIetgwrClK3p1rbORHMtaV0urKbnIifLyYIxTfmjlOqQ3X1rwNYkTTrden9IYmk1u0qfKxTdQEUuyoHYDNz3KP9l1971MGLOpvC2rNx1haYeLl99Yub6jUyp3WPj459+cPQ7B7nG5rj3or7HvSvOzqJi88cYRWb5hRfam21Pq0ECd5px352gM2gIQfhAKDIYYVXtIzOorWRBtRDhptPWeaC18uAl7avXBN1vyQrYrzw+80Btras/srsvBADu9NyuRxa77F0CAMQLcbpWDExF060NDhGEN/IbHgBgE6Tp/BW63keNjuxX26Nw0dPdwNZa6Xf2J5poVktjlPYqFGNBRS7KgSAbYbutPbHbe/jFF7sAQHb3rmmp6fxCLGf/1nuROwHgR28KPua08NJBrjPiVse9Z8SMKytmdV3htDm2eeUVemTxAshx1S57Fw1GdmHIPWQIQfggsKtveBmpYFFM9ozcfkmq2fbUExKMkCrYg/CZD4w9ZeaYwnQ0O/bXryyP1s66+iP7R9Tm89ndRa7Nce9FOgnLyJDp1oOuQfpc2QUiACaKCY86oTZUnlB7BxW5dIzUqYqYbGK+C8DmdOsRzwhdK7tAEktEEKZQ9IB2lAdKS0GyyHsF7my5j4ziHdKUQ/GFR6buKoiydajNNvujN/edPeh1rLxVtvP2FADMJmddtbOw9ZDbRi8a20xRSRQTXgDoc/RRB2sXBl2DGRasnJfy9mQhuWfjbaPBL72kHrFxaStZcBCEouAFgCHX0IHXyki7I/PBSzueA4C/eGnpwWxR0kyCQeq6Um0+v8eERTLuPVVKeWRFbpRplBZCVmRUMt3a6Ay4Bogg7OZW3/AyUtGsmBxpOXBMtxNqKYA0cJvafD612h3LrZsLUsHGMZwUdAbpsIFdMEp7FYqx0LfHTKkbicL+gTsbm+kCAKnjkrUGmXVonj673vnqfOJaBlA+ed/IoyzDHOp6ZLrPbGpW1w8OqfPKi8Y2U1RIxcWIZyTRZFNaFp7lFafZSab70L5c22AjJ7sAQPENamYfPQgFqcCmS2k3cPjA/VMPjL7qs5s24tlS25cfm76hNhbWH1Jh4soumSCLOz58to17N0dyEWtjLaS0AovpRYekSJyFteT3mm5tdMY8YwIApEtpD7P4UvlkQY+u91EKILcfTykme4aRCpaFuUeDAOA0OZM8yyvNtq1VIYklWiFM0RNU5KIcCFL+O+YZ213kSq2opeG9N2rCqZAVBV97au5BBWBuHva9euuIP3rYa5Jx7yvpFV0/OMRycMakQl2QiodTBnVEppThsmLWyYBRht3DNHu8B2RSKxn3TlFh47NqsqD9Uk1VxFbLrDDrVKAwdt6edpgcBzoiTrCbOeknTgQfBYCHTkbunIpknLWxsr7IbUcyBZNH5uU8w6285tvpPdvHvU8np6kgbEBmk7NuAJvfA8rO+K3+ooW15CVF4jYib/cDgOwd1oQ/SjkEDAvZ3RsCgGjotX6ATrfej1HPqAAAmVLGXZAKVBug6AL6RaZUTbwQN+WlvI1lWKnf1Z/Z6T1MYt7GFAS3wppKUvCmjUbbeBD+7MXF44vx3KDVxOY+84GxZ2txzc1x7zl9j3tX/CNZxewSGLlk4pZf8TfbnlZhKjnlBgCnySlYOAs9W7QHXos67n01Q6f7bIcVVlSRq08byYKDMp+aVwN3k0uoxfV+6Ia+6bF2+7mSpJi/8OjUPbW4ZiNI2QdKAMCVj6nuBKkQpuPejQmZbu0y12at6BkiBIZTC+qE2g59JwsoKkTM3BDU0wUk4UzZGYfJIdl5e1qBwswKs5pIClEo+0FFLkrVnEucUx0sk2vX8l9+/jn1iI2zKwzO3PIlwslcif+bV1ceAICPX9H5dK/Xmq/Fddtt7QIArOf0P+5ddveeN7aZAswJcyQYoQ7WPpDpPmQaJQVg4rN2pphSkwV9N8aabU89IdWutcy4/8r9o4+xDOS3loSrHjkZ6anVdeuJYB8sAVvHVHeCjns3Nmu5NTcA+Mw+KnLtA9lP1vMb7QAg9d2k62QBRYWImbH8RgAAAtYA9cH2YbO9iqDv9ioU40BFLkrVkONEbrN7VweLW3tbbZbs7deEQ/HFR6dvEfKip91pDn/yvpE3anXdbnt3EjDGuHfZrzaf5yInqchVJpQNuQHAa/bSYGQfyLj39bz+BeFK4eef11Sy4DCEc2F1rVhqt1ZuGPTGbh/1vwwAv/fM3AdEWWn5o9SCoyxyxed23UfbLG0CQMe9GxWSNCOJAcruEEE4CtGmsKai1HdDvNk2UeqP1H9CFbmkrAsAOm2diaYapAGIILycXqbPFYouoCIXpWrIcSKveffyX3ZjWm3y2Xa05UvDT4VS7ifOrN8KAD99W/+jJo6tWTBJxr2Tcd96Ru64TO3LFZ+jExbLkKok4mhTdmdz3HuBTvchcGtvq/uod6Dl99HDQgL3gDVQU0H4Mx888rzDzKVXk4W+P3x2/vJaXrseJJ0jqsiVWunGLtMTO+3quPdYPkbXigGJF+JuAOiwddDkyT6QCp4Qz0FxdYdBm48bAqnn2oTCmQsRRuGBLf+CsjtENA9n9d1ehWIcqMhFqZpoPuoGAL/Vv+tDgxGW1D4yvde3fCXXbzw6fa8oK6bjXc7T49f0zNfy2kYa9y4O3BYCADa1umtwZjQ28htuAAjYahu465EB59a492bb0iqwsWm1IjZwtOX30cNCAnci4NSKdqe5+B+u6X4SAP7pzdV7Y5miqZbXrzU5S6dU4uwKU8ra2egp107v2RSE6bh3Q5IoJtwADdwrgewnIZ43RLKAUoblFcXVs7bG8wCAAdcA9cH2gQjCtJqeoheoyEWpmo38hgcA2q3tOz80cjETk90IKGAUcfCOSEONq5J/f2ct+N5q6nKOgfSrD4w+XuvrG2ncu9x5uaDwthwj5mzs2rv0IQkgUVCDkS5bF3Ww9mHUaxxBuFIYYVlNFvRcp3uRK1lMugGg19Fb87Xy83cNvdvpsoTSBcn1pcemb6n19WsKwyBl71ebzy+8sOORRTru3dikiup060HXIH2u7APZT8IcB7lN/8kCyhYl70AozHMAgCPeI1QQ3gcjtVehGAMqclGqJllQg5FuR/eODhY//1wHA4VRHIEoLG6xsdZVjigrzB8+t/ABALjrSGDyyj53otb32D7ufSo5pe8HB8NCdvWEAIBffJEeWcTWMdWgM0gdrH0IWANFM2vOS4rEL6WX7M22p+moyYI2LSQLakGqpAbuQ+6hmq8VnmWUn7l94FEAeOrsxolToVRLVwtuTlgMv7fjPjrmHRMAIF1Ku0pyqeX7jFFqR0kuMelS2gVsfQ8ouzPsHt48rqiFkwWU2rHsH4iLDAOXAsltbt1YpFUwUnsVijGgIhelaoSSepxot8CdC72pNkt2t3bT+d99avaqcKrQ7bRwqV//wOiL9boPaeZohHHvim9YPbIYeZ82nweQKqnB9JB7iAYjFUCm+8wkZ3S/VvZDK8mCWpAVs1xWzDoZMMqwezhdj3t811Vdi5d0OU+JsmL60mPT99TjHrVCcAypfbnKx1UvxGlyijbOllGgsHPCHB33biDmhXmnAoW1ctas0+TU9b5QC8ZMvhyrKFjnOKR6r19vtj2UxjHl7ckAQLcoNdsUTUDaq9AKYYpeoCIXpSpkRd4slR9xj+wYuLPrZ7sBQGoba9n+B9F00fwvb6/dDQDfd23PE36HuVSve5Gm46FMSPcPDqnjkjVg9+DMSCQLSVNBKthYhpWCzmC22fZogU1BOK1/QXg/uNU3uoHWTxbUgtnkrAsA7Lw9beEsdTur+sv3jzzJMpDeWUld8cjJSE+97nNYko5hVeQSVnbdR0mF8IxABWEjMSvMugHAZXLRxEkF2JYmO9olCQrDYCq/5mi2PZTGMWWxKgDQUyxwjLCi63YhtaDX0ZvlGE4sykVrNBe1NNseCuWwUJGLUhWRXMQqKqKJZ/hSp70zv9N72MSCOhGs68qWDc6+9Oj0iWxRcna7LSv/9Y7B9+t5LzKxJJKP6D4YkfpuUiu5hBXDH1ecSk65AcBpcgo8nehUEUQQXsuu6X6t7Ae7ca48oXa0ZffRWjGXmlMD97JwUy+uCXrit4/6vwMAf/Ds/IOy0prLMm3vExWWF5lC0sMkFm07vYdMN6bj3o3FambVDWwlBCh7w4Xe6iKVPHPCHF0rBmItv+4CgE5JAj//jOF90v1gGRYuk/oMnk5Ot/SRfgqlEqjIRakKsvG5zK4ky+zw9RHzLJMJdwKAOHhnSwZn760KnmemNm4BgP92x+BjPMvUNdLptG2Oe9f9Q0MK3hxTWFORKabcTGzG0H2VFlILNONeJW3WNgGg032ArWSB1HVVy1bE1optgXvd18qvf2DsBZuJzSwn8sE/eWHh0nrf70AwHBRn1xqAXYMzn8WnNtSm494NxVpOTQB4LV76XKkAdv1sd5eonupczazStWIg1vPr6uAfUQIXesvwpwsqgYjnC6kFulYomoeKXJSqWE4v7xm4c0svtzGyyCsWT0LxBHes9Go2X3ls5h5JVvhLu50nP3pF51K979fr7DXOuHeWVxRXdxgA+PnnDO1UhLLq8VSvmQYjlULGvZMJroZle7Jg4I5ws82pN5GcWuVKhJt60uGyFD5xZdczAPD3r63el8qLfL3veRBk78AaAHBrb++4jwZsdNy7EVnPqYF7wBqglVwVwCYXNyu5wjkqCBuJeCHuBoBOSQS7MUUruSrAa1ErhIn/SqFoGSpyUapiM3DfJYvILb+iHrFx97VkFdfDJyO9766mLucYSJ+6f/TJRtxz0DVImjl6G3G/ZiN7B0IAwIXfMbRTEclF3ADgt/ppMFIhfY4+Ot0H5yULkoq3P9dse+oNybg3KnD/xXuH32x3msNCXvR88bHpmxpxz2qR246qR783pnfcR8m491ghZui1YjTI37vD3kGTJ/sh5lkmE+kglVzrOSoIG4lkQfUjukQJTHLJ0EnXSvFb/QIARHNR3Z88oegfKnJRqoIE7qTP1IVw0VPdACD7h1tO5JIVBX/w7PwDAHD7WNvLV/a5E42476hnNAUAmVLGEOPe5cDxcnA2ZWinYiO/4QaAdms7DUYqhIx7N/p0H27l1XKyoLfl9tF6EC/E1WDE3tWQtWLiWOUnT/Q/BgCPn47eNhXJtNyEQqn3hhAAMMLijvtor6OXCMI0GDEQ5O/d4+ihz5V94BZfamNkkW/nrBkAiBfjdK0YCDIJvkOUFSa7HkAuZmq2Ta1OwBoQACBW0H97FYr+oSIXpSpI+S/ZCC+ETcyrwVnH5S3XR+ZPXli4dCWRD9pMbObXHhx9sVH3dZqcopWzZhUozLww33LBVK2Rem9YAwBGWDZ0JVeioB5P7bR30mCkQkY8IykGjJIVs86smOWabU+z4KKn1H3UN9xy+2g9EIpqMEKEm0bwfdf1zB3pcJwtSYr5i49O392o+1aKOHhHRGFYmclu7BicjXhGkgA2px1TjAH5e5MKccrucCuvdANAwBqIAIBQMHbyxEiU5BKTKWVcANBu8UYZKAw//3xHs+1qdYh4nigkqMhF0TxU5KJUBQncd8wiKjKY1GoXAIgDt7VUBUIqL/J//9rqvQDwiSu7nulwWQqNvD/pYTafmtf9g0PsPxFVGFZmcrE2Jrth2MwZySIGnUEajFSIhbPIdt6eBoA5YU73gvBuMPFysqDz8pbaR+tFqphyA8Cwe7ihgvAn7xt+gmUgv7GUvPrJM+utJcpbXJJib1tnAOwUnA26BjMsWDkv5e3JQtKw+6yRKEgFNitmncBWhThld7jo6S4A6HQPLQOAUKIil1GYF+adChTWylmzFndfGAC40Judzbar1elz9AkAkCqldB+rUPQPFbkoVUEy7v3O/osCd3btXQ8j5m0Kb8vJHZe2VPXKlx+bvlHIi96AwxT5hXuG32r0/cnEkuXMsv4fHBaXpNjbowwAbv45wzoVJHAf8Yy01FpodVxmVRA27Lh3RQabWu0GADF4QvciV6aU4XJSzsGAkQfdg+lG3vvGQd/GzUO+VwHgd5+efVBW6jpot2oUd1BtPh968yIBjmd5xWlyCgAwlZzS/3OFgllh1qlAYey8PW3n7VKz7Wl1yMkCb+fVyxzDiSW5ZIlkI5Zm20WpPySh7DK5BNk/ugYA7Ma51kpktCBjnrHNCmFZkZttDoVyKKjIRakYWZGRLqXVwN19ceDOL76oVh+4ekJgWuerNRPNOB49Hb0dAH7iRP9jFp5t+M5NGvWHs2FDBCMKyZytvWVIkSuWj5mLctHKMZzY6+jNNtseLeE2uwUAWM2uGmKtXAgbfs/NiDmbwltzctcVuq8CJAKNw+RImVhTw1WmX//A2HNWns0txvMDfzG5dKzR998L2T8SBgB249yO+ygRhBfTi4ZcK0aDCP+7TbembEM9WdANAMrAbWsukysJAFPJKWMmTwwGmQTvMXuSUucVqsiVXKQi1z4EbIGCiTUVRUU0RXIRa7PtoVAOQ+soEZSWJ5QN2URFNJlYU7HD3nHRcT828n4XACi+oZaqPvjiY9N3lSTFPNpun/qB63tnm2EDmVhCpojpHdk/ojoV62cN6VSQwN1pcgpsCwm+WsBn9gkAEMlGDLFWLoRfnFT3UVfPWislC+rFfGreAwBuk7spgXuv15r/yBWdzwLAX7+6fH+6ILZMLzip80p1H00s7LiPkgrhUCZkyLViNFYyK5uBe7NtaXXKJwusCm/Lyh2XCm6z2zjV9BSs5dY2J8FL/beGAYBJhTohi7of/nQYWIYFqRCeSc7QtULRNPr3oCk1g2x4ZAO8EDY+2w0AUvvxlhG5np3a6Hh9IXkNAyi/eO/w482yo9OmNh8njfv1jtRVDs6SS4YUuUhlBalKolROwBZIAlvTKY0GG3lPnVDrHWyZfbSeEIHGY2le4P5L9w6/7reb1pM50feVx2dubJYdF0J6WzLptR2DM59FFYSjeTru3QiEs+HNwL3ZtrQ6204WrIFhN/+brWaMWSFsNIj/0GZtE+S2sYxidqYZuWTmVt/wNtm0locknJbSS3StUDQNFbkoFUPKf0lG7ELY8jQ9qe+mlpgIJisKfuep2QcUgLlpyPvaiWH/erNs6XZ0CwCQLCQNUSovDtymZs52Cc70TigT8gCAx+yhwUiVdNg6DCUIXwgbm1X30RZKFtSTcE49wk0Em2ZgM3Hyj90SfAwAHjkZuX1+I2tvli3bUfwj2c3gbOU134WvB2zqlGOjCsJGg1SC7zbdmrLFhScLyP5ilGp6oxPLx9wA0GnrTAKA7OpW+xsuvWzIxGs1eCyq3xrK0gphirahIhelYkJZNXAnx4m2wyTmbUxB8CisqSQFb95ovHUX8zevrIzNbeSGzRxb+NUHx55rpi0DzgFDTSxRfMNZxexKMXLJxC2/elFwpnciOfWond/ip8dKqoRMbk0Wk4ZYKxeymSzovbElkgX1ZiO/4QGaH7j/8I1908MB+0xRUiy/8ej0nc20ZTuyq0cNzpZfuagvF6kQpuPejQH5O3faO+lzZR+2ThZcEgKAdmu7AGyJHxR9Q/wHkmCWvarYyUZPUZFrH/wWY7VXoegXKnJRKoZseKS/1Hb4+efVrJmzMwLO3PQRVbmSxP7fl5fuB4APXtb+3IDf1tTm32PeMQEA0qW0SzRIZdNmcLbyiuGcilhBdaTbbe00414lQ64hslYM52AxwoqVKSS9CsuLUv8tLZEsqDckcO+ydzU9cP+fdw89zgDKq/OJ616ajQeabQ+wdWx1p+As6AwaWhA2GsmiWgne6+ilz5V9YIWV8smCG9eALbEjUaSCsBFIlVIeABh0DaoiV/txdYhHfM6Qw5Cqod3WTlpGGOLkCUW/UJGLUjEkA0Y2wO2w4Xc7AUD29LfEEZvffnL22li2FPDY+Pgv3zfyarPtcZqcopWzZhUo7Hxq3tFsexqB7B1UnYrIScOJXIlCwgMAXfYuGoxUybB7OAUAGTHjyot5Qz2j+Pln1GSBoyMC3mqI+d0kcCeCTTO5fawtcm2/5y0FYL721Oy9zbYHAOT2S1SRKz570T5qZEHYiJBK8CH3UNMF4VaGSSzamELSo7C8SE4W9Dn71Gr6ojGq6Y1MSS4xmVLGBQCjnlEBAKTeG9R9NLViOH+0Wrrtm+1V6FqhaBpDBRCUw5EoqoE72QC3w8Wm1SafbWNNF7lWEnnrN98L3wUAP3xD3+NOCy812yZgq2H/vDBviAeH3FEOzhLzhsucCUXBDQBBZ5AGI1Vi5a2ynbenAWBWmHU1255Gwq69o+6jLZIsaAREoBl2D7fEWvn0A6PPmFimdC6SOfrPb4YGm22P1HuDmixIrV60j/a7+jMMGDkv5e1CUeAbbx2lUWTFLJcVs04GjDLsHk43255Whl94rhMAFGdXmJwsGHYPG6plhJGZE+acChTGxtkyDpNDAgApePOGwvIiUxA8THLJ2mwbWxkiCAslga4ViqahIhelYkjg3u/sv7gnl7DcCQBS11XhRtt1IV98bPr2fEm29fusCz9+Inim2fYQSBNyo4yw3sycCcbLnBFHesQz0vTqFC3iMrlUQThlDEGYwG2cU0WuwFFD9ONKFpKmvJS3sWDloDPY1CPlhLEOR/qeY4EXAeBPX1y8X5SVph4vl4I3bQVniUXb9td4llecJmcKoOPe9c50ctoFAHbenrZwFkNUeR4Ubu1tdUKtp39zH+22d+c4hhNLcskSzUUtzbOOUm9mhVkPALjMri3/izMriqMjAgD8wnOG80mrgVS/pUtpt6zQrYaiXajIRakIWZE3M+6j3tHzM+6lHMtkou0AIA3c3lSR662lpHdyJnYDAPzcXUOPsUzrtL8iI6zXsmuGOOcu9d0YU1hTiSmm3BcGZ3ommotaSnLJwjN8qdvenWu2PVrEY/YkAeONe2eS5abz3VcbopJrKjnlBtQqV57lm97LkfCp+0dedpi5VDhV6P6j5+Yva6oxnFlRHJ1hAOAXn7+omosIwgvpBUOtFaMxn5ongXtLVDy2MuzGNEkWbO6jLMNuVtNTQVjfEL/BbXafl2QkFdLs2ruGO11QDR22jjzP8KWSXDKv59apIEzRLFTkolTESmbFLikSb2bNhYA1UNz+Grf4YoBRJE6x+uKKs7O42zUawVefmL1HVsBd0et694Hj7S1VDdFmbTPWuHfOrCjOTpI5M4xTMZ2c3gzcWYZusQeBCMLhXNgYawUACimOyUbbFQDS4F1Nr4htBAspVZhptcDd7zCXvufqrqcB4J/eWL0nmSs19Sig7AmqRxZ3CM48FlUQDmXouHc9QwJ3UhFO2R1GWCwnC649L1lARI/F9CJdKzpmLbvmBgCfxXfec4W0U2FjU7SSaw+2C8JTySlDJOUp+oRGYJSKmBVmNwP3C1/jVl9Xs2bu3qZWHzxyMtLzfih1GcdA+uX7Rp5upi070WHrSAJAvBA3jIMlu4PlzNk7hnEqFtOLHuDiLCKlcgwnCAPgFyfbGUVmFZt/Q7G3lZptTyMIZUMeAPCavS23Vn7+rqF32p3mcKogeX7z8Zkbm2mL3HZE3Uc3Lg7OfBafAACRXMQwa8WIRLLq39dv8beUINxyFFIck90IKADEwdsj21/ymr1JYEsEoegT4je0WdrOe66Qdipscskw/uhBcZvdScA47VUo+oSKXJSKWEovkfLfixwsbv2MOlnRN9S06gNZUfC/n5u/HwBuG/V/54rei+1sNqRhv5FGWMsBNTgjgwmMAKmo8Fq8Lfcd1Aqd9k4BMJYgzIbeUJslu3oNUcUFbAkzPquv5UQuE8cqP35L8HEAeOx09Lb5jay9WbZIXVeV+xteHJwFrAHDCcJGZD2/7gGAgC3QcmulleCXXgqQZAFs/vOSBUQQjuaidK3omFgh5gGADnvH+SLX4B1hAGAy0XaUcjT+3QNSTU8rhClahi5ySkWsZdQ+UsRJ2A6TWFArudovbVol1/99aenoUjw/YDWxuV99cOyFZtmxF/2ufsONsCaZM8ZAmbNoPkpK5WkwckB67D3qCOuicUZYcxvnOoDmJgsaDRFm2q3tLSkI/8D1vbMjAft0UVIsX3ps+o5m2bFXcNZpUwXhRME4yRMjQv6+XbaullwrrQIbenPXZAERCGOFGF0rOoYMyepx9JzngynOroJi9cYZReK4xRcDzbFOG/gtflUQzlNBmKJdqMhFqQiy0ZGNbztktLkYvLkpwVmuJLF//erKfQDw4cs6nutyWwrNsGM/xjxjAgBkShm3KIut0xG/jkgDt4cVAEw22o5ihmu2PY1gI7+hZhFtHVTkOiCDrkEiCBumHwSTWFArYtuPR/Z7r15IFBIeAOiyd7XsWvmFu4eeYADllfnEdd+Zi7c1wwbF2VlUrL4dgzMy7j1ZMo4gbESSxaQH2Pp7U3aGWz9bPlkweNE+SvYZI1UIGxEicg26Bi8ShGV3n3q6oNxmhbIz7bZ2VRDOU0GYol2oyEWpCJL5In2lCOz6GSdTyjoUzlKQu69ONMO233lq9pp4ttTmtfGxX7p35PVm2FAJbrNbtHLWnAyZXUwtOpptTyMoB2cxRpFZbnHSEJmzZEENNrvt3TTjfkCGPcMpAMiKWWdBKhjiOcWmQp0AIPXdaJhKLhKMBJ3Bll0rt4+1Ra7p97wlK2B/+6nZe5tlB+l5eWFwRgThdDFNgxEdkyqlSOBORa492EoWXHLRPtrn6DNcNb3RKEgFNitmXQAw6hlNXfg6qZQmbVYoO0MEYSO1V6HoD0MED5TDkyyoWcRuR/d5Dha3+KJaGu7sCqMJk+QiqYLlG++G7wSAH7y+90m7mZMabkQVkMb9c6k5wzw4FLd6bIBbfc0QmTOhpAbuNON+cOy8XbLz9rQChZkT5pzNtqfeMLEZO1PKOBXWVJS6r0k0255GQQL3Ec9IS6+VTz8w8gzPMqWz4cyxf307NNAMG2TfsCpyRU+fL3K5B9MMGCUn5RyZkjGqZY1GupTm81LezoCRB92D6Wbb08qw6d2TBWSfIfsORX/MCXNOBQpj420ZO2+/KB4gbVWY+Lwh/NGDQhJPQlEwTDU9RX9QkYtSESRw73f2ny9yhd9X+3F5+5vSj+tLj02fyJVkR6/XuvSTt/afboYN1eAxq+Pel9PGmVgi+0bU4Kx8jEDPyIq8ecRu1DPa0oF7q0ME4fnUvO7XCr84SZIFEbC80mx7GkEsHzMX5aKVYzix19GbbbY9e3Gkw5m+52jbJAD8nxcW7xdlpeHHzeWOS9W+XOVKFYKJNSkOkyMFANPJad2vFSMylZgi061TJtZkiP3hIDDxWTtTTDsV1lTaKVnQ6+jNcgwnFeWiNZaPmZtgIqXOkASyy+TasTpY7L9FHeKRDnVBkRtpmqYYcauCcLpEK4Qp2oWKXJR9kRUZmVLGDQBj3rHzjyvGZtTS8LYjDT9ic3ot7XpuKnYzAPzMbQNPsEzrt7kiE0vWcsYZYS13qJkzNqH/zFkkF7GKimgysaZih60j32x7tIzH7BEAYCWzovu1wkbeU/dRd59hjipOJTcDd4FtQhVwtXz6gdGXHGYuvSYUev7k+YVLG31/MXhC3UdTqxcFZy6TSwCAhfSC7teKESFCv8u8c+BOUeEXJzsAQHF2hndKFrAMC4fJIQDAjDDjarR9lPqzmll1A1v+w4XIXVclFc6SZ0pZOxs9Tb8Du9Bp78zzDF8qySVzJBuxNNseCuUgtL5nSWk6S+klu6RInIW15H0W33kjmRlhuQsApO5rGl7J9eXHp+8WZYU/3uU8/dErOpcaff+DQBr3r+fWDROMiP1qcMak9J85I5UUWgncWxkiCIezYd2vFTY2XU4WjBpG5FpILXgAwG12a6Li0e8wl77rqq6nAeAf31i9N5UX+UbeX+66Qg3OxNxFwRmpECYBHkVfrGZWPQDgNXs1sVaaBRsmyYLgrvuo26TuN4upRbpWdMhadvdJ8AAAhoXi6lZbaJQrqCkXwzLsZjX9jDBD1wpFk9AojLIvs8KsBwCcZuf5D42CwDPZ9YACRhEH72joRLBnzm10vrUkXMUykH/xnuEnG3nvw0Am7hlpuo/cebmg8NY8I+ZsbOSkrn/vpfSSGribtBG4tzIBqzrufSO/oevvDACwwkoHAMidVxpG5AplQ24A8Jq9mqlO+R93D70dcJgiQl70fOXxmRsaenOGheLqUY9+L754XlUsCeiiOTruXY9EchE3APgsPs2slWawdbJg92QBqfAJZUO015AOIf5Cm7Vt17UiewbUfTTynu5PFxwGkoBaSi/R5wpFk1CRi7IvZINzm93nPTT4hRfaGSiMYvdvwOoVG2nT7z49ex8A3DTke+2GQW+skfc+DKRxPxkHbggYFoqzuxyc6TtzFsqUA3eLdgL3VmVzhHUhpu+1IhUZJhPpAACx/0RDkwXNhAgyu2bcWxATxyo/dkv/4wDw6Kno7QuxnL2R95e9/WoFQuT98/bRNmubWiGcN06FsJHYyG94ACBgC2hmrTQDVlhSRa7OK3bdR31Wdb8hwiFFX5AEMkko74QcUNursLFZKnLtARGESSUphaI1qMhF2RdyXOjCUnlu9Q21WbKrsX1k/u61lZG5jdyImWMKn35g9PlG3vuwkMb9RhthLXtJ5ux9XTsV0bwauPutfhqMHJIeR48AAEJR0PVa4ZZf8TOyyCsWt6B4gobp4xbLq+IlETO1wn+8oXdmqM02U5RkyxcfnbqjkfeWA8fUvlwXBGed9k513HuBjnvXI4mi+nftsnVpaq00lPOSBbfu6pO2W9uTABDLx+ha0SEkgdzr6N11rUg915VbaCzrOul6WPxWfxKgyROKdqEiF2VfovmoB9jKFhPYjbPqZEX/cMP6cZUkmfn/Xlq6DwAeON7+woDf1tJTuS5kzDOmTiwR0y5RFlu/U36NkANH1cxZXN+Zs83A3aqtwL0VGXQNJgH9j3vnVl9Xqw/KfUKMQryoZty77d2aq3r8hXuGH2cA5ZX5xHWvzMfbGnVfqedaNTgr98IkkIAuWUzqeq0YFSL0B51Bza2VRsGtvO6rJFmwKQgXqSCsR0gCedA1uKsPJg7cFlXAKEw21oZczNQ467QF8WOpIEzRKg1tnHoh4+PjPIBPAvhRAH0AwgD+GcDnJiYm0hV8vhPA/wLwIIBOADMA/g7AH0xMTOS2vW8QwNwel7p6YmLi7YP9FvqHbHAdto7zJysmFtSm8x2XNSw4+/1n5q+MpoudLgsn/PL9I6806r61wmPxlCycJVeQCral9JJ9yD2UabZNjUDquX4N+FMwgr4zZ8Rx7nH00GDkkIy4R1IAkCllXCW5xJhY00XTsvQAGz2tilyeAUOJXEJR8ABA0BXUnCB851hb5Jqg5+03lpJX//aTs/f8849fO9GI+4r9t0UVhpWZ3EYbk90wKfa2ErAV0NFx7/okVUx5AGDYM6y5tdIouJVX1X3UuXeyoM/RZ4gKYSNSkApsVsw6AWDUM7r7WrG4RcURiLKZaAc//3yHePzjKw0zUkOQ9iokIUWhaI1mV3L9HYBPAfgrAN8N4HcA/BCAR8oC2K6Mj4+3A3gJwHcB+EMA41AFsk8B+Mb4+PhOv9uvA7hrh/9N1+B30S0kO9xt7956aCgymFSoEwCk8vS8epPIlkz/+nbobgAYv6bnKY/N1NA+YLWCjHufE+YM8+AQ+0+Ug7NYG5Pd0G3mjGQRybFUysFxmBySjbNlFCjMnDDnbLY99YJNzKvBWeCYYUQuWZE3BZlR96gmBeFPPTDyDM8y4plw5vi33gv3NeSmFpek2APrDABu4fkO8uMh91CaAaNkxawzK2a5hthCaQjxQtxUkAtWlmGlfme/IZJiB2EzWeDdO1kw5B6igrBOmRVmnQoUxs7b01beuucob8WttlnhQm/q+nTBYehz9hmyvQpFPzStkmt8fPy7oApTD0xMTDy+7edPAngTwM8A+IM9LvFpAD1Qq7DOlH/20Pj4+AsAnoEqlv2/Cz7z/sTExLO1+Q2MA8kiDrgGNgN3du1dDyMVrApvy8ntl6QaYceXH5++KV2QXJ0uc+i/3Tn4XiPuWQ/cZrewnl/vXM4suwGEmm1PQygHZ2wm0sHNP9cpXvJdy802qdbIioxUSV0rI54RKnLVAKfZKeRyOcecMOc+4j3SkH2m0bCpVTVZ0HOdYUSu9dy6pSSXzDzDlzrtnZrsQ3a005m6fdT/8tPnNm774+cX7vvQZR1/yTL1P4GuuPvWkIl0cKG3usTjn1gBAAtnke28PZ0RM67Z5KzrsrbLEnU3hNIQZpIzbgBwmpwCyzQ7L926sIm5ipIFQWcwy4KV81LeliwkTR6Lp9QYCyn1Zk6Y8wBbieS9kH0ja1zorcvZjXO6Pl1wGIbdauUo8WspFK3RzCfmTwF4frvABQATExOnAPwTgJ/e5/OnAfzSNoGLfP5ZACsAbqydqcZFlEWGZLxIPykA4JdeKjed715DAxyv2fWs44kz67cCwE+c6H+CZxnNHl0iDfzXsmuGenAo7qDafH7tLV06FauZVZukSLyJNRXabe2FZtujB7ZN99FlJpFJh81MPuFVGE6SgjdvNNueRjGVnPIA2g/cP/XA6KTVxOaWE/n+v3x5+Wgj7in7R9Xm8xcEZy6zKwkA86l5Xa4Vo7KYXnQDgMvk0qXIXytYcrKg59o9J9TyLK84TA4BAKaFaVcjbKM0hpXMihsAPGbPvtXBUteV6j6aXKSVXLvQbe/O8QxfKsklczQXtTTbHgqlWpriXY6Pj3MAbgXw7C5veQ7A8fHx8Y5dXsfExMSfTUxM/OEuL1sAJA5jI0VlKb1klyGzFs6S257xYiMn1abzDeoj86XHpu8oSYp5tN0+9b3X9uzVX63lIZP3jDaxRPaPqE7F+lldOhWzwiwJRmgVV40ggnA4F9blWuEWX+gAAMUeWIfJtufxCj2xkllxAWpVa7NtOQxdbkvhQ5d2PAcAf/3K8r0FUa67TyV1XqEO8Sj3xCRsCsJZfQrCRmUtu+YCtv6+lIthMlEzk4/7FIaVpeAt6/u9n+w7i6lFulZ0RCQXUSfBW7z7rhWpPIGTSYU6YaAhUNXAMiycJqcAbFWUUihaolkp1B4AdqjHEnfijfK/I9VeeHx8/AMAAgBe2+HlL42Pj8fHx8dz4+Pjr42Pj39/tdc3GrPC7I7lv2x8tlwafrTu/bi+Mxdve3U+cR0DKL9w9/AT9b5fvSEN/ON5YzVz1HvmbCm95AG0H7i3EmSi63p+XZdVj9zaO2pFbLk/iFEIZUMk4675tfLJ+0Ze99j4eCxbCvze03NX1ft+4sBt6oTF9Np5wZnP4hMAIJKNGOq5oneqCdyNCrdAkgXt0UqSBWTfIfsQRR+s51Q/IWAN7H9csW0so5idaUYumbmV13z1t06buMxq7EcqSikULdGsnlz+8r+7Hc+Ilf+tajT3+Pi4H8AfAzgL4JsXvDwFtdH9e1AFtv8E4O/Hx8cHJyYmvlzNfbYzOTn5kwf9bKtQLBYDwM6/y9n8WSsA+GW/d/vrtyfDHXYAr+f7TwiTk3U9GvqH70o+BWBu6mSybPj0JyY1Hg7aCjYzAKTT6aN6+P5UirnUzt4NQE5HeydffOEntx9z3es7qBWWs8t2APCX/N1a/j1aCU/OYwMAIS5cPTk5OVjPezXjO3jpyoInCGCGHx2cNdB3JpqOOgHAn/eP6GGtfGJAxl+dAb759sqHrrOErrfyBz9OX8n38C6TV7aUEua3nv63n87aeiQA8Ga8DgCIRWO3TE5OXnbQ+1Nai6SQ9ACAL+27bHJycqgR99Ta87h/7S37JQDC1mH3OxXY7M/73QAQXgnfO5mcvKnuBlIO39zcaQABAABJREFURLXfw1Qy5QMAd8J9XSV74PXWQVNb8X2ce/OF/7i2KGuyN2S9aRPbvEtYwsLcwkcmw5PZZtvTaLS2F+oRk+ngs8qaVclFzsHvtmDIzyvO3o+Pj9sBfAtqldgPTUxMSNteXgJwbGJi4osTExPfnJiY+MeJiYkPAPh7AF8YHx8frc5845CQEiwAuFn35n9PTsoxtkKUk8EibQ/WdcLh2bhiej8Gq4mF8rFhRhc9KbycVwIAQRIMNQWraPLIBZNH5uUCY8+v6e53T0pJDgA8rMcwx87qjYf1SACQlJLabdy0B87sEg8AKfuAJifFHhSyVrY/V7TMDZ1Mvs+BklAC+/iS4qj3/dK2vhIAuDPzm96fl/WStaK7vdXIkL2P7IWUi3FlF9V91NZfURN5D1d+rsh0regJsvcRH3s/Unb1++LKLuh24vdh0bsPRtE3zarkImKFbZfX7eV/KxotPj4+bgLwLwBuAvADExMT5x1VvEDw2s4vQp3w+AMAPl/JvS7kxIkTf3aQz7USRKHe6Xf5xsvfuBdpnPAGvK+cuOHE8wDAn/p6HwPlxxRHe+Tm2+6q2+8vKwq+8Mev/WcgjzuOBF748N2XPFOvezWSeCFu+tpDX/t0Ukni5ltu/jMtN16uFv5cx39ELDlyo2vtmeKN/2FzaMRe30Gt8DfP/c3HkMVVge7AsyeuPPHG/p+g7Idj3eHD8/i5BJPI1fu70fDvoCLD+drirwDAsRvv+8sj7cfTDblvC/DHT/7xDyCPsd6B3kdOHD1xttn21IIfsa0O/caj0z/8xDIs/+UDV/+fwTb7gbLelXwPbZHueyG8f+JSduadkRO/9BwAJBeSQbyB/xzjYhsnTpz4i4P9FpRW47ce+a2fANAzemT06yf6TzRkKrHWnseOM5/8EQCO/rHj3+y+5sTMfu8/d+bccZzC+IZpY/nEiRP/UH8LKQeh2u/hZ7/52V8AYLruyuv+9hL/JfvGj2b5hcuxhu8aFGeWOk6cmDikubrkO29/5zrM4kMxW2z6xIkTF56Q0j1a2wv1yKuvvvq/DvrZZkXX+x1H3O844ybj4+MMgP8H4EEAPz0xMfFPlRoxMTERAvA+gGOVfsZoxAtxFwB02Do2z7iT6XiKu6+u/bj+70tLR1cS+aDNxGY/df/IS/W8VyPxWXwlC2vJy4rMLaWX7Pt/Qj8oXnVQARs9pbsJi8li0gUAXfYu2julRox6RlMAkC6lXSW5pKvmsGz4PTcjFSwKb8vKgaOGEbgAIFVMuQGgz9Gnm7Xyvdf2zI0E7NMlSTF/+fGZ2+t5LylwtNx8fn5zHx10DQoAQKYhU/QB+XsOuAZ0s1ZqiiKDSYfVyYrBExU1syD7jlAU6FrRCXkxz2bEjAsAht3DFZ36kHquVZvPCyu680drRbe9OwkAiWKCrhWK5miWyBUCkANwzS6vk5/PVnCt/w3g+wH8j4mJiT8/gC0sAFoGvgvJQtINAJ32zk0Hi904p05W9I/UrTtWQZTZv3l15V4A+NBlHc91uCyFet2rGTjN6sQS0tjfKGwGZ/E53TkVJHDvdfTSYKRGOE1O0cpZswoUdiG1UPdjYI2EW3pZTRa4uiIwUDUnsBW4D7oGdXEEnfDzdw09CQDfmYtf/+ZSsm7NjDeDs9Tq5j5KAruMmHHlxbyxvlA6JS/m2ayYdQLAkGvIUEJ4pbCRk25GzFsV3parNFkw5B5KAkCqlKKBu06YS805AcDO29NW3lpRywgpeMuGwrAyk4/7mUzUXF8LtUmfkwrCFO3SFEdoYmJCBPAigDt3ecsdAM5MTEzsKaKMj49/DsB/BfCpiYmJ39/jfY7x8fGeHX7ugVrF9X6FphsO4gQEHcHNYIRNLKpZs84r6lbJ9XtPz10Vy5YCHiuf+KV7R3R39MttdicBYDm9bKgHh9R9cXCmF0jgPuwepiJXDSGTXeeFeV2tFS56Up1Q6+nX+CiN6kiX0nxeytsYMHK/qz/TbHtqyV1H2sJX9bnfkRWwv/XE7N31us9mcJaL+ZnshgkArLxVtvP2NADMCrOuva9A0QIHCdyNBrf0EkkWhCtNFgy4BjIMGDkv5e3pUrpZbVsoNWQhteAGAKfJWXnixOyQFHt7lAHALb7YXi/btMyIZ0QAqCBM0SbNzPb9KYA7xsfH793+w/Hx8eMAvq/8OvmZp9xYfvv7fhbAZwB8YWJi4iu73WR8fNwMVcT6u/Hx8QsfZr8JQAbwt4f5RfSKrMjIlNTyX7LRQRYZJr2milz9t9YlOEtkS6Z/e2ftTgD43mt7nrKbOd1V2nnN6jjwcDZsqAeH1H9i/cLgTA/E8jFzUS5aOIaTehw9uWbboyfcZrcAAEuZJV1VPbLxWVXkajtiKJFrOjntAgCHyZHiWf7AUwhblU/eN/wMx0B6P5S67NFT0YuSazXB7JAUe2CdAcAtvNBBfuwyuZIAMJ/SlyBsVMjf0Wly0sTJLnCR6pMFJtakOEyOFADMJGeoIKwDQpmQC9jyFypFcfdEAIALvaW7xGst6LZ35ziGE0tyyRLNRS3NtodCqYamZTAmJib+dXx8/F8B/Ov4+PhXALwNYBTApwC8CeCPALUKC+qxxXUAR8s/uxLA7wN4HsDT4+Pjd+5wi8TExMTbExMTxfL1/6T83j8EUADw4wA+CODHJiYmVur2i2qYcDZsFRXRZGJNxXZbewEAuNU3vIxcMismR1puG6tLFv43n5i5MVOUXJ0uc+i/3D5wsh73aDZt1jYBAKL5qLGCEbNDUuxt62wm2sEtvNAhHv+4LtbejDBDAnfBSIMEGoHXok9BmBHUakap+xpDiVyL6UU3sFWhpzcu73Enbx3xv/LcdOyWP3xu/t77jwf+mmVq305OcfeGkYl0cGtvdZJ91GP2COFcuHc1s6qrtWJUQpmQG1D/rs22pVVh43MdACD7x6raR10ml5AupT0L6QX3lYEr4/WxjtIowjnVPyAJ5EqRfSNhLvTW5WxsiopcO8AyLJwmp5AsJv0zyRl3u6092mybKJRKaXY09v0AvgbgxwB8Heq0w38E8MDExAQZBSwCWAOwtO1zPgAMgNsBPLPL/36PvHliYuJPAXwEgALgLwD8DQALgHsmJib+qi6/mQ6YFWbdAODgHZvlv9zyK2rWzNVTl8BsMZ6zPX56/VYA+LFbgk/yLKO7TD+w1cg/VogZLhhRXH1hYGuAgR5YSi/pOnBvJm3WtiQAbOQ39LNWCimOya4HFABi/wlDOY1EgHGb3brqx7WdTz0w+oKFZ/MLsdzQ3722MlqPe8g+tScmu7EVnG0Kwjl9CcJGhfwdPWaPbtfKYWFSy+VkwdVV+aREOFxNU0FYDxD/wG/1V7VWpI5L1X00uagbf7TWkPYqJEFFoWiFpp5FLwtZny//b7f3FABcesHPnoUqclVzr4cAPFS9lcZlJbNyUfkvmYpHpuTVmq88Nn1bUZItQ2222e+/rreSwQOahEzgI439jYTsHw5za29dvj040zrbMu40GKkxRBCOF+K6WSv84mQ7A4WRbf4N2Pyl/T+hH6K5qAsAfBafbgXhXq81/+Al7S98493wfX/58vK933dtz4yJY2uasJE6Lg2bTv3LecEZqRDWlSBsYDbyGy5g6+9KuYBihmOyG2qyYOC2qpIFZP+J5CJ0regAMgm+3dpe1VqRgreU+8SudUKRYbQhMJXgNXuFJSwhlA3RtULRFHQ1U3aFbGjbS+XJyHIyJa+WvLMseCdn4zcAwM/dOfREra/fSvQ7+9WJJSVBV32GKkFq11/mLJrXf+DeLLrt3QKgrxHWbOjNDgBQXL2GOqoIbAkweg/cf/m+kVddFi4ZTRc7//ez81fU+vqbwVm6HJxBn4KwkSF/x3ZbdYG7UeAWJwOMIrOKzR+rNlkQsAVUQbhABWE9kCyqCWOSQK4Uuf14SuFtOUYqWNnwe/S7sANeizcFAOu5dfrfh6IpqMhF2RWyoW0P3MlUPLkOfWR+68mZu2QF3OU9rvfuPRao2+TGVmDUMyoAQKaUccuKsYYmSf2bwVkHdPK7x/IxQwTuzWDQNahO9ynqZ7oPt3FW3Ud9g5Fm29JoiFjZbmvXddWjy8qL33N19zMA8C9vhe5O5cWaVs5vBmdi3spGTroBoNfRq1YIF41XIaxHhKLgBraEfsr5cKE3ypMVq2+f0WnrVJMnBf0kT4zM5iR4Z7C65wrDqpM5AXDL39FN4rWWkOq4WCFGhzRQNAUVuSi7Qja0gDWQAgAmu2FicjG/wrCyGLxlvZb3evrseuc7K6krWAbyL907/HQtr92K+K3+opk1FyRF4lYzq7Zm29NI5PZLUgpvzTFi3sZGT+nioUkCd+I4U2oHmeyaKWXcoizWvoN3E2ASC6rI1X6J4Sq5hKLgAoAee4/u18rP3jn4brvTHE4VJPdXHp+5oaYX3x6cLb3UCehTEDYy5O9YdeBuELh1kiwYqnof7XH0UEFYJ2yfBD/sGa76uUImc5JJnZTz6bB30LVC0SRU5KLsCgncyQbHLb7YzgBQbG0bsLikWt7rD56dvxcAbh7yvXZ10JOo5bVbFTIWnDT4NwwMC8XZrToViy/pwqnYzLg7umkwUmPcZrdo5aw5GTK7mFp0NNueWsCm19Rj3303Gk7kIoF7v6tf9yKXiWOV/3xz8AkAeOx09LbleK6mCQ3ZfX5wNuwZTgFAVsy6ClKB+ncaRlZkpMW0C9gS+innwyQX1MmKB0gWEEE4XUoby//SIauZVZukSLyJNRUC1kCx2s/LbUfUFhrxWV34o7Wm19GbArb8XApFK1AniLIrJBjpc/SpIldInYanuGvbR+af3lgdmlnPjpo4pvgr9488X8trtzIukysFACuZFcM9ODYzZ1F9ZM7SJTUYIY4zpbYQQXg+Na/5yj8mNmNnimmnwpqKUvc1iWbb00hKconJilk1cHePGEIQ/oHre2YG/ba5gihbv/z4zK21vLbcNlYOzuY6AMDO2yUbb8soUJg5Yc5Zy3tRGstSeskuKzJnZs15n8VnqOEUlcKmQmqyoPeGqn3SIfdQmgGjZMWsMytmudpbR2kUc6k5NwA4Tc4DPVOkcvsVRljRhT9aawZcA0QQ1rz/RTEWVOSi7Mq2wD0FAGxs6sCl4bshKwr+4qWlewHg3qOBycE2e7ZW1251PBa1of9ads1wD47N4Cym/cxZXsyzWTHrZMAoQ+6hdLPt0SN6EoT5xUk1WeDsjIDlazpxr9WZF+adChTGylmzDpOjptXArQrLMPhvdw4+AQCTM7Eb31ut3bARqfvq8mSw5c191GVyEUFY82vFyJC/H/l7Us6Hic/amWLapbCmktRzbaLaz1s4i2zn7SkAmE3OGs4H0xMr6fIkeJP7QGtF7D8RVcAoTHY9gEKKCp4XMOAayDBg5LyUt6dL6Zr2lqRQ6gkVuSg7ki6l+byUtzNg5H5XfwYA2ORSB7A1Ha8W/MkLC5euCYUeu5lL/8r9Iy/X6rpagDT0N+K4d6nrqghwfnCmVWYF1UG28baMhbPoo5N+i0EE4XAurPlghI28pyYL3EHDHVVcSC24gK3KPKPwwPH20OU9rvclBdxXn5i9q1bXFQduiyoAmOzGZnBGAr1QJqT5tWJkltPLqshldhmi4rFa+MVJdUKtszN80GSBy6wKiKQSiKJNNifBWzwHe67Y/CXF5osxUBh+cbK9psbpABNrUhwmRxoAZpIz9LlC0QxU5KLsCNnIHCZHimd5BYoMhpSG991Uk4lguZLE/sPrq/cAwMeu6HzW7zAbqiSfNPQ34sQSaeC2yIXBmVZZSKuBO8241w+/xa8bQZiNzah9ZNpGDSdyLWfUwN1jPmAwomF+6d7hp1gG8tvLwpVPn12vjbhv85cUm3+DUWSWX3opAAAeiycFAOFcWPNrxciEs6qg7zV7DbdWKoENk2RB34H9UbfZnQK2RBKKNiF+gc/iO7AgrLjUNixseWIn5XxIYmoxvWi4eIWiXajIRdmRhfQCKZVXjypunHMyYs6ucJaC3HVFshb3+O0nZ69N5kSfz27a+J/3DL9Vi2tqiU67cUdYK/a2kmLzx7YHZ1plNbPqBgC3+WCl8pT92RSE8zHNrxVWUKsX5c4rapIs0BKRXISIXIarTrk66EncPOR7DdgatFILNoOzVTU4a7O2EUGYBiMaJpqPqoG71UefKzvAxmbUfdR/8GQBERCjuShdKxpm2yT4A68V0oaFTOyknA9JTFFBmKIlqMhF2RFy1IEE7mQKnuLsDIM5/Ncmmi6av/Ve+A4A+MHre56y8KzhjnmRiSWpkjHHvSuunvOCM61CAnevhWbc60WXvYuMsNZ2MCIVGSYT6QAAsf9Ww1VyrefX3QDgt/oNuVY+9cDo82aOKcysZ0f/+c3QYC2uuRmcbajBGQn04oW4IZ8reoH8/Q4TuOsZVlhSRa6uKw+8j24ThOla0TAkUUwSxweBTOhkkoua9kfrhdfsTQFAJBuha4WiGajIRdmRaC5Kyn9VkSvyvnrEpjwV77D85uPTN+dKsqPHY1n+iRP9p2txTa0x5B4y9MSSC4MzrUIcZL/Fb7jqlEbR69SHIMytvOZnZJFXLG5B8QTzzban0cQLcRcAtFvbDRm4D/ht2XuOBl4CgD+fXLxXVg4/d2AzOEssdAJAl72LjnvXAclC0g1s/T0p25CKDJOJtgOHSxa029pTAJAoJAzpg+kF4heQxPFBEPtuVJOu5bYslPMhiSkjtlehaBcqclF25MLAnY1vloYf+ojNTDTjePrsxgkA+Mlb+59kGeawl9QkfY6+LAtWLkgFm1AUDDexZCs403bmjATuHbYOQwbujWDYPawLQZhbeVXdR53dhqviArYF7o4uw66VX75v5Dt2M5cJCYXev5hcOnbY60l9N0SAreAs6AwKAJAqpTS9VowO+fv1OfsMu1Z2g1t53cfIJZNidh0qWdBt7yYVwlQQ1jDELyCJ44Mgd1+dVDhzkSllHOzGlKN21ukD4t/SCmGKlqAiF2VHEkU1s9VuUzPujLCiNp0/RGk44SuPz9xRkhXTWLv93Hdf1b1w2OtpFZ7lFYfJkQKAmeSM4R4cEsmcpbWdOSMVE+RIHaX2dNu7cxzDiSW5ZFnPr5ubbc9BYaOnVZHLO2BIkYtk3IOOoGGrU9qc5uKHL+t4DgD+7rWVewqifCg/TOq+JqGwpiJTyjjZjSnHiGeECMJuWTFcFwDdQNbKkOvggbte2UwWlFseHJR+Z38KUNdKLeyiNB6hKPAFqWBjwcp9jr7sgS/E8ori6FRPFyy91FEzA3VCt6ObVAjT5AlFM1CRi7IjJHDvcfQIEPMsKQ2XBm47VCXXawsJ/6sLiWsZQPnvdw09VQtbtYyRJ5ZsBmfFtNNcTGh2L0oVy4G707iBe71hGRZEEJ5Nzmo2IGET82pwFjhmOJFLVmRkShkXABAhxqj8z3uG3/TY+HgsWwr8/jNzVx7qYiyvKM6uCKAGZwFroGhiTUVJkfhwNmyticGUhrKeXzeX5JKFYzixx9GTa7Y9rUatkgVkH8qUMi5RFo15pEDjkATx5iT4QyB7gmritTy5k7JFv7OfVAhr1v+iGA/NBpaU+kI2sn5nf4pbfsXPKBKnWDwJxdlVOMx1v/bU7N2yAvaqoPud28faDDdd7EIMPcJ6W3Dmzs5r8rimrMhIi2kauDcAl8klAMBSekmzgjCbWu0AAKnnOsOJXOFs2CoqosnEmorttvZDPUe0jt3MSd97Tc/TAPBv76zdlcyVDrX/XRickeTJjGC8CmE9QIR8h8mRYmsw6EdvbCULjh7Kh3Sb3aKFs+RkyOxyZtleG+sojYQkiIl/cBjktjF1Hy1P7qRsMeweTgFUEKZoC/r0pFyEKItMppRxAsCIZyTFrbxWLg3vPpRD8cjJSM/JUPpSjoH0y/eOPFMLW7UOaexv1BHWsrsvDACu7IKp2bYchKX0kl1WZM7MmvM+i6/UbHv0DBlhvZZd02TgzqTDZiaf8CkMK0vBmzeabU+jmRXKgTvvoBWPAP7L7QMnO1zmtXRBcn31idkbDnMt2T96XnDmMrlSALCSWdHkWjE6RMh3m9w0cbIDbGpVbZ9Rg2SB0+RMAcC8MG9IH0zrhDJqgphMgj8MUucVan9DYZmKXBfgNrtFK2fNKVDYpfQSFYQpmoCKXJSLWEwtOhQorJWzZp0mp8iunymXhg8e2KGQFQV/9PzCfQBw64j/lUt7Dp910QNGH2Ett42WRa5FTYpc86l5N1CbLCJlb3wWXwoAonltCsLc4gsdAKDY26Mw2QzXLGk5s1yzYEQP8Cyj/MhNfU8BwGOno7eFkvkDHy0kvTLZlBqcbROENblWjA4R8ulauRgmEzUz+Xg5WXDL+mGvR57dZH+iaItoXp0E77V4D508kQZuU4chZSIdEPM0Pr4AUiE8J8zRtULRBHQRUy5iLqVuYCTDVYs+Mv/w+urIQiw3aOHZ/KceGH2hNpZqn80R1sWEIR8acqcanDmzS5o8rriSXlFL5c0uWp1SZ4ggHMvHNLlWuLV3OwFAKVcvGg0iuBABhgL84PW90/0+23xBlK1feXzmxEGvsxWcRdsh5llSIbyeW9fkWjE6JHAnwj5lC27xxXagdskCr1kVR8LZMF0rGmQjv+ECtvyDw6C4egqKxZNkFInjll/xH946fUErhClag4pclItYyaiBO8kisqmQ2kem9/oDBWeirDB/+fLSPQBw//HAi71e64FHPusNMsLaqBNLxPIgA0du1cQoUrPNqRqScaeBe/3psHVoWhBmN86pyQLfkCFFrmhuM3Cna6UMyzD4mdsHngSA56djN50Npw/0HNgMzmSR55Zf9QesgRQAxAraFISNTiwfq1ngrje40FvlZEFvTfZRn7UsCOfXDemDaZ1EQfUHOmwdNVkrsqtbnbBYbtNC2cJr8QoAEM6F6VqhaAIqclEuIpKNbAbuTHrNwhSSXoXhJKnvxthBrvenLy5cEk4Vux1mLvXJe0dera212mbANbA57r3ZtjQDxd2bVyzuJKeU4Mitaq6aaz2vVkr4rX4ajNSZHkePpgVhNrmk9pHpvMyQIle8EHcDABFgKCofuqxj5Vin47QoK/xvPjFzx0Gvsxmcrb7W2WFXA75EMaHJtWJ0SODeaeukz5ULYGNT5WTBcE320YA1IABb+xNFWwgldRJ8t727Js8V0paFtGmhbEErhClag4pclIsgpfJ+iz/FLZT7yDjao+CtVZeG50oS+4+vr94NAB+7ovM5r91Em3NvY8Q9sjmxpCSXDDmxRHb1lPtyaW/CIqmUoIF7/dG0IKzIYNJrakVs8IQhRa7NwN1OA/cL+R93Dz/NAMrrC8lrQhmFO8g1NoOz6OnOXkdvCgBSRTruXYuQwJ0I+5QtNpMFHZfWZDp3l70rBQDJYpIKwhokVUy5AGDQNVibSq724+o+Wm7TQtmC+LlUEKZoBSpyUS4iUVCzv+22doFbe+dQpeG/9/Tc1Ymc6Pfa+Nh/v3vorVraqQccJodk5axZBQqzkFpwNNueZkCCM1dGe83niWPcZe+iwUidGXINpQEgK2adeY01hWXD77kZqWBVeFtODhxNN9ueZpAqqcFIr6OXrpULuGXYt35N0PO2AjDfmJMPFECQnplsYr5zyDVEBGEauGuQVEkVJwdcAzR5sh01WaCKXMFbapIsIPsRFYS1R0kuMVkx6wKAYc9wTdaK1HN9eYhHiIpcF9DtUNurUEGYohU0FShQGgPpedNl7xLY2HQHAMi+kaodikS2ZPrGu+E7AOD7rut52mbiDDdRrBJIg/+F1IIhnSwSnLlyi5qr5CKOMQ3c64+Vt8p23p4GgLnUnLPZ9lQDt/SymixwdYXBGPOxSyrwBl2DNHDfgU/eN/wsxzLi2+uwzglK1YI/6ZnJpkKd/a7+DANGzkt5e7qU1ty+amSyYpbLiTkHA0YZcg8ZUhDfDTZy0s2IeTVZ0H68JvvIkHsoBWi0QtjgzAvzTgUKY+WsWTtvr0lTV6nvhpjC8iJTSHqY1KqlFtfUC5uCcIkKwhRtYExvm7In2wL3FJtcLJeGX1Z1afhXn5y5IVOUXJ0uc+inbh04VWs79QJp8E8a/hsNEpw5s0uaq+QijvGwe5iKXA2AjLDWmiDMRU+qfWQ8/YY8qigUBT4v5W0MGLnf1Z9ptj2tyCXdLuHWYd+rAPD1GbnqZ4HUd2NMYTiJKSQ9pkzE7DA50gAwk5wx5HNFq8wJqoBv5+1pC2ehicFtcEsvqckCZ+2SBb2O3izHcFJRLlrihbjmfBAjs5BaUKdbm1y18794q6w42qMAwC28QKu5tkEFYYrWoCIX5TxkRd7cwEbcQwKTDqsiV391peEribz18dPrtwLAj94cfIpnGaX21uoDMpnPqCOspb4bYxLDw1bc4LSUOYvlY+aiXLRwDCf1OHpyzbbHCLjN7hQAhDIhTQXubHxOFbnajhhS5JoVZl0A4DA5UjzL02fBLvzK/SMvWjkoU0lY/uH1leGqPsxbZcXREQHU4IwIwovpRU2tFaNDBHzy96NswUVPqScLapgsYBkWDpNDAICZ5IwhfTCtspxZdgNbieJaIbv71CEe5XYtFJVue3eOYzixJJfM6/l1c7PtoVD2g4pclPNYz61bREU08Qxf6kquWRipYFF4W1ZuO1JV2fxXn5g5URBla7/PNv/91/XM1MtePeC3+FPA1qQ+w8Fb5aytpwQA3MKLHc02p1JmhBkSuAusQY+gNRqveXOEtabWCiOsqMmCrqsMKXKRwL2mGXcd0uez5e4NMmkA+P9eWr5XVqrTA7cHZyR5EsqGNLVWjM5qZpUE7vRY7wWwsdlysmCspvuoy+RKAcBSeokKwhoiklMnwXst3tqKXOX2LGxsmopc22AZdlN8n03O0ucKpeWhkRnlPGYFdeNymBwp08qr6mRFV3dVpeHnImnn89OxmwDgv9zW/xTLGHJoYMUEbOoIa9Lw34ikbEERALiwdjJnS+klGrg3GL9VFYQ38hvacbAKKY7JrgcUAOLAbdFmm9MMiNBCA/f9uS/IZNwmyOFUofv/vLB4STWflf1bwZnX7E0BQDQXNexzRYtEchEXAPgsPvpcuQAmtawmC7qvrqnIRSqBqCCsLTbyG2St1PS5Inderu6j5UmelC2IILyYXqRrhdLyUJGLch5LGTWT5Ta7ha0+MsGq+nF99YnZO0RZ4Y92Os58+PLO5XrYqSe67ZsTSwz70EjZ+0sAwG5MacapCGVUh9hj9tDAvUEErKogHC/ENRO480svBf5/9t47upHsPPN+bhUyUAUQBAHm3JNzng6TeoLstTbZ5jpssFf+ZDnqk6xob/SuLcmyLMte+5Ol3bXXa69sruyV5bWsmekZzUw3J+fR9IRu5giABIEqZFT4/ihckt1NshkAVAF1f+fMmXNIAPU2yRue533vfQl0onvDa/CGK2bHYwbJQlIEmHDfC26e6B8YIDIAfOPlxZOFirrnfZpWvTuTy8xHw56wBDSZIczY+H2F3WE2VrZSzvEkv1aXZAE1Seg8xWgOUqWUCAAdno6ajhWl/3gcAEh2JQpNYVn6LVBDeCW/0jR7MIZ9YSYX4wJWcisbZ9wPco/MS7Pp8Euz6VsIoH/0vqEn6xVnK9Hj75EBQCpLtt1gyb4Bw+RqosxZsphkGfcGQw3hZhor3NIr1c6K3ftu3tEq0Ix7u6edjZU9cE83yYe8jlS6oIS//OT0zXt9n9p/zBBnuXgs6onIALBeWm+ascLY7G4d9UbZWNkCPzcRIbrG6d5wqtbJAmoopkopJtybCLoP6PR31nSs6OGRvO70Z4lWcfHLr4Zq+dnNDt3v2vZ6FUZTwUwuxgUkixsZd/kg98h8+cnp+zUd3E294hsnRsO2PJqzX2hnvpySs+0GS/IPKoAhzqA3R0OpVNHIIjLh3jj6An2GIVxpHpOLX3vPSBa0DdnyPi4ASJeYcN8PDo7gx27rfhIAvv1m/L50vrKnrm9a+5Gc7vTniFp2DSiaBgBSWbLtutKMZEoZKtxZhfAW+OWNZEHN59GYL0avjGiadYUByGVZAIA+f1/Nx4omdBn3Gy682DT3xDaCdk97810ZwbAtzORiXAA9BtThDMmksNa+n9LwJ95b7XxrSb6OI1A/8eDwU/WMs5WI+WJF2rEkWUg2TXfBWlJ2tWklh6gRtezill8LmR3PXqAZ95g3xoR7gxgOVg3hSk7QmsQMJek5w+TquMa2Jhc1Jbt8XUy475GfPT5wNia4l3NlNfCFxyfv3Ov7qDi7Ql7zAKzde7MhVwzhPhAYYOvKFvjV943OiqHBms+jdF5qpgphu7O1EzxNFNcSPTRg3MuVPNs0pwsaAd3vZkoZljxhWB5mcjEugGayBisVst/S8N9/auYkANw91PbSDT1ipp5xthIXdCyRpmy7cGR9vRUAcCw83xSbCloh0eVnwr1RRDyRspNzllVddSznl71mx7MXuOyyURHbc4dtTS6ace8X+plw3yMOjugfOtp7CgAee2f1+MJ6YU9/73qwPw4Ag+lFATAMYYXdK9MUKJpCcpWcCAAjwRE2VrZAMjN1SxYMCIahmK1kbbv/ajYShYRH0RWnk3OWI95Iqdafr7ZfaZhc1WtbGAbd/m7jyogmqqZn2BdmcjEugGayhvNpF7D30vBvvrY8MLmaH3XypPyZh0dO1zPGVoR26LNzx5Kst18BAC7RHJkzmkWkG2RGY6CG8LQ0bXlBQlKTPlLOBnTOWVG7b02bHY8ZVLQKySt5AQBGxBFmCO+DH7+tZ2qw3TtVVjX35x+bPL6X96gRQ5wF0nMdbt5d0KBx89l5X30jZdSChdyCT4PGuXl3QXSJitnxWAlOriYLeu+suck1JA5lASCv5AMltcR0URNA13+/wy9z++j+vle07luN+w3lxabYjzaKAWFABpghzGgO2GTOuAA6cQ1lEgFgb6Xhmq7j6xNzDwLAA1e0PzvY7svXN8rWg3boi+fjtjW5ZH+1w2La+pmzolLk8ko+QED0IcHYIDMaAzWEF7ILlh8rjrkJ4x6ZQCwOzqGbHY8ZzEgzAR068fCevN/pV82Op9n4hXsGnwCAM1Prd5xdli/7N691bYqzgDMgA8CMPGP5scIApqVpEQDo741hQNIzXlLOCvVKFvgcPtXr8OZ06GRWnvXX+vMZtWc+Oy8CgOAS6pJkVPqOruqE00gh1Y5Cak93ItqBIXEoS0D0vJIPFJUi8xAYlob9gTI2yFVyfEEt+AmI3iUttgF7Kw3/k+cXrljKlHq9Ti7/6YdHn6t/pK1HyB2SACBZSNo2O0I7LNKGB1aGHiv1Orw5j8PTHJdDtQjUEF7Jr1heuHOJ7xv3yIh9tj2qSA0Wak4y9scHrulYuqYzcFbVdMdvPT513+Ver/QfS+ogOimk2oMOwyxZzC3adl1pJujvSXSKbKxswTF3hiYLEvVKFtAK4Vl51vLrCmNz/Q+5QvUZK25B1b3tawSAY/Z0R12e0YS4ebfmdXhzgL2vV2E0B8zkYmxAy399Dp/slFeq98jcvmvb+4qqkf/5wsJJAPjBa6PPdARc5fpH2npEvBHawtq2GyzZ169UxVkYxbTD7Hh2YzY7KwBMuJvBlhbWlt9gcanzxj0y4ZFd59FWZkO4u5hwPygfPzn0JAH0V+czNz07tR7Z9cVuQdV97asEQFTnKoC9K4SbCSrcg+4gGytb4FbeNOZRsbduyQLRKcoAsJCzfoUwY3P9D7lDdat61MUeo8Pi8muWT7w2ErrvpftgBsOqMJOLsQGdsESHL7tRGt5z+/pu7/kvT89cv5qrRAU3n/nEg8MvNybS1iPqjcqAvTuWaJwLhjjTiWP2jKUzZ0u5JRFgwt0MqCG8Xlq3vBjhpAVDnHXeaNtKrnjBMFhotSpj/9w52LZ220DwVR0gX35y6oHLvZ6Ksx7FuHB+tbhq+bHCANaKayIAhN1hNla2wKUmq8mC0brNo3R+ShQStt2DNRN0rEQ8kbqNFa1t2Lh8PnWOmVxboPteug9mMKwKM7kYG9AJqwN8BQB0f3TX0vBsSeG/+drK/QDwwzd3fS/gdrD7Vg5It8/oWJKpZGy9aOhCTwIA+OVXLb2pSBQSTLibRMwbkwEgXU5bW4yoZUJyiSgAKP3HbWtyUTHS5m5j9wwdgk8/NPK0gyPKu/Hc1X/3/UTPbq/VQkMJAOgrZl0AsF5at/ZYYQDY/D1FPBE2VrbASQvGse/Y9XWbR+n8ROcrhrXJlI29cswXq9seTK1e18Jl5iy9H2009MoIug9mMKwKM7kYG9AJq1tRdQDQgrvfI/OlJ6ZulYpKqN3vTP7yfYNvNiLGVmVLxxJbLxpa25CxqVh739KbirXimgAAYXeYiZEG0+PvkQBALl/+Em4z4RdfChNNceguQdKDfUWz4zGL9aJRcdfh6WCG8CG4MhaQT4yGnweAP3xm9kFN3/lqIjV6bRwA+vMZHwBkSvZOnjQL6VJaBIAufxcbK5QLkgUn6nbsO+qN0gphZgg3AVJZEgCgx99Ttz2Y2nd3tYnHSgw6u3qV0u5pl4DNfTCDYVWYycXYgGaweksFJwBobcM7biiS2bLr776fuBcA/vntPU84ec6WncNqBe1YUlAKfjt3LNGaJHNGj8p1eJlwbzSDwmBTGML84ovGERuh27ZVXACQKRtHsDv9nWysHJLPPjwy4XZwxbn1wuD/emlpZKfXqb13xgGgT14NAoBckZkYaQLkimHcUyOfAfCLL7cRreLUXYKkh/oL9XoOrQiSypKl1xWGAV3/B4SB+h1XjF0v6by7RJSCl1t9L1Cv5zQbdN9LTXkGw6rYVkwzLiVdMo7/9BUkLwBonTfsKM5+6/HJuwoVzdcddC/866N97zUqxlbFzbs1n8OXBezdsUTpu8vInGVXLG1y0Y1wl49l3BtNv9CfIyBaUS16s5WsZRsUcMl3jSM2oQFbm1xUuPf5+1jV4yHpCnqKj1zdcRoA/vj5+QcVTSfbvU7rvCmj865SZynvAaxvCDMM6O9pSBxiY6UKv/RSNVnQVdfmHXR+YiaX9clVcnxRLfoIiDYgDOTq9iDCQQ90GpfPz09Yek/aSDp9ncYdwmVWIcywNszkYmxAF/ferFGlstM9MrOpgu+J91aPAcD/c6z/FEe23Wcz9knAabR7n5FnbLtwUHFGKnkft/a+3+x4doIelesLMOHeaBycQ/c7/TIATGYmLWsIc+lpQ5xFrrKtyaXp2oZwHwmOMEO4BnzqoeEX/S5eTsjlzq+enr1m2xdVxVlQ0+AAp5W1sjtVTLkaHCpjHyQLSXdFq7h4witdvq66VSw1G1zynRgA6HVOFgyLwxIA5Co5QWNH0yzNpGSs+36nP+vknHU9RUKvbeETbzOTq0p/oN+4MoJVCDMsDjO5GBvQjHtnpeTQXQFZDw1uu9H6wmPnj1dU3TUS8Z3/kZu7ZhsbZetCO5Ys55ftu3AY4sy4fH7OmpkzTdeQU3ICwIS7WQhOQQaA2eysZQ1hTl6KAYDafWtdKxCsTDwf96i66nByznKHt6NkdjytQNDrVP7xjbGnAOAvX1l6oFBRt93HacG+BAHQRvgSsCkMGdaEVnAHnAGJI2xrTuHWjWSBWudkQcQbKTmIo6LoijNRSHjq+SzG4ZiVjXU/4AzUff+ltR+pdlicsuR+1AzovjdXyYnMEGZYGbaSMgBUhXvFEO5RVYUW2L40/K0lKfjs1PodAPCL9w4+0cgYW50tLawtK9wbwWbm7PuW3FQs5hZ9qq7yLs5VbHO3VcyOx45sGMI5axrCJBt3kWK6TSecpvYdXTU7HrOYlCYbJkbsxMceGH69zedcSxeU8JefmL5lu9doYUOcRVVDhcxn5229rlgd+vsRnAIbK1vYSBZ03VJXk4sj3MY8NS1NW3JdYRgs55dFAAi6gnUfK2rnjdXL5xej9X5Ws9Dmbqu4OFdJ1VV+MbfoMzseBmMnmMnFAABktSynQeP84BSPru9YGv7bp6buVXXw13YF3n7wqshKo+NsZWinPrt3LNHCo9XM2aQlTa4paYqJEZNpc7dJAJAsJC0p3Pm5iSgA6L6OJJxe26Y6F3OLdKywY701xO3gtB+/rftJAPibt+L3pvMV58WvUWPGnZpdlTIPACv5FVuvK1ZnJb9ChTsbK1VILulCcZ0mC9bq/TzBZazpzBC2NslCUgCAkCtU97GiVjt6klyyA5UC08xVmCHMaAbYgGUAANJamgeADg0qAKjtV1xick1MpSKvzUs3EUD/+APDTzY6xlaHdiyhnfvsihqrZs4ka2bOFrOLAgAILibczYIawqlSypIbLH7ldeMeGbHHtvdxAcByrnEZd7vxs8f7z8YE93K+rAa+8PjknRd/Xx04ngCAnmLODbAKYatDhXubp42NlSr83JkOgmqywOVX6/08ajBSw5FhTWgn+LAnXPexogdiZd0TShNd5fmF59vr/bxmgRrCNJHFYFgRZnIxAABpNc0BQGelzAGA1n3rJeLsd5+cfkAHyG0DwVfvGAylGh1jq0M7lti9uw8VZySXiEIpWm6O2pJxZ2LEJKxuCHNr7xuXzrcN29rkWi2uGsLdzYR7reEIwYeO9p4CgMffXT22mC5ecI+QLnSXdHcwE1MUAgCpYsqSY4VhkCoZv592TzsbK1X45dcamiyg8xSdtxjWhK77UW+0IWNFC3QZV2gsvWzJ0wVmwAxhRjNgOQHJMIeMluEBoLtcdOogunLRPTLfeTvR8248dzXPEeXTD408bU6UrQ3rWGJAxZmROXsxbHY8F7NaXG1YFpGxPV3+Lksbwpw0HwUANXadrU0uKtwjngireqwDP35bz9Rg2DtdUjTP5x87f/zi72tCVzymKACATDlj63XF6qRLaREAYt4YW1eqcKnzNFnQkOYd7Z52o0KYGcKWRipLArC5D6g3WtugcYVG8l1mclVhhjCjGWAmFwMAkFENkyumqNB9kVW4hY3ScE3X8YfPzD4IACdG2l64MhZggqUOsI4lm2gCzZy9ZLlNBRPu5jMQGLCuIaxrIPKKcVly79227awIAJlSxhDuPibc68XP3zNwCgBOT67f+c5K9oLxoIUG41HVWMqtaggzDOjvp8ffw9aVKlxmzphHo9c2JFlADUZmCFsb2gmeJobrjRa5KgEAXGbWkldomEGHp1pNX7RmNT2DATCTi1FF0qSqyaVAF7sv2FD8xctLw7OpwqDbwRU/8/DoGXMibH1Yx5JNtFA1c7b6ruU2FXQD3OnrZMLdJEaCIzIA5Co5QdGM41hWgYu/JRK15NEd3oLWcbWtBatU2RDubKzUiR+4Nrp0TWfgrKrpji8+Pnnv1u9pkaviMcUwuagwZFiTbMUwKAeFQTZWACNZkK0mC/qONsTkovMUM4Sti6IphHaCHw2ONmR9VbtvN/aj1U6fDCDmi8kAkC6nmSHMsCzM5GIAACRV4gAgqqoX3COjaDr5788tPAgAD10VmegJeYpmxWgHWMcSAy1ypZE5W5+x3KZCLstMuJtMwBlQPLynoEPn5uQ5v9nxbIVfeN64R0bojIPYe4mlwn1IHGJjpY587IGhJwmgvzyXueWFmfWNy5HV7lsTEVUF0XUUlIK/pJbs/QdpUXKVHF9QC34Cog+Kg1mz47ECXPKsQJSiR3d4GpYsGBAGZIAZwlZmPjvv06FzHt5TCDgDSiOeqfbekdIJr5KSFCTykrsRz7Q6dP9L98MMhhVhGx4GgM07uaKKCrXjmo0jNl87M3d1XC51+V189tMPjbxgXoT2QHAaHfvs3rFE7b7NspmzbCUrAsCwOMyEu4lQQ3hGnrGUIcwn3jbukQn22/o+LqksOUpqycuB0/oCfXmz42ll7hpqW7ulP/iaDpAvPzl9P/262nf3moPwaljVoEMn09J0wMw4GdtDk1o+hy/r5Jy62fFYAX7uWSNZEOhqWLJgUBzMEhC9qBZ9uUqOb8hDGftiWpoWgc31vyE4PJrujyYAgJ89bbk9qRnQxBXdDzMYVoSZXAwAgKzJPAB0qirU3rvjAFBSNO4vXll6AAD+4fWxp0M+Z8XMGO1A0G107FvJr1hKuDcate/uNSNzlgmR7IplMmepYspV1spunvBqt7+7YHY8dsaqhjC3PmWYXOEjtja5JjOTIgD4nX7ZwTmYcK8zn3pw+GmeQH17OXvto+8kuwBUxVlHMqYaBQ+z8qyt1xWrMiPPiAAguASWOKnCJxufLHByTt3n8GUBVk1vVeh63+ixoom9xj2x8Tctd4WGGfQF+vIc4dSSVvJkShmn2fEwGNvBTC4GilqRlPQScWsaBOIsaZ03ZADgd5+cvmk9X2kPeh3rHzs59KrZcdqBLR1LLCXcG05VnAEAP3vaMpuKSWlSAAC/0y9xNj+KZjYhd0gCgHghbikxQqRF4x6ZrpttbXLNZecEoMEZdxtzTZcgHRsJvwAAf/D0zIP067rYE49W7+Vayi/Ze12xKMv5ZQEARKfIxkoVLlVNFrSPNrR5BzVPZrPMELYidL0PuUINve9SDxsdPrm1c6ySCwBHOPgdfhkAzkvn2VhhWBKm0hhIa2njqKKqAoFYHIRDplBxfOvNlfsA4Mdu7X7S6+Tt3e6vQdCOfaxjiSHOAGtlzuaz80YW0cky7mazYQgXLGQIl2SeFNbadQDKwImk2eGYyXJuWQQA0SXa+vL9RvLZh0fOuHiuNL1WGP6Ll5eGAEBrG4nHqh0W4/m4dcYKY4NEISECQMjdWOFuZYhcTRZ0NjZZQI1GOn8xrAVd7+n63yjU6PXGFRrSAjO5qlBDmO6LGQyrwUwuBtJqmgOAmKJCC/YlAOCLp6buyJZUISq4Vj5yYuBtcyO0DzGf0cKadSwBtDbrZc7oxjfoCjIxYjIbhnDJOoawY/7ZCNE1TveGU/CGbX28O1lMmiJG7Exvm7fw4FXtEwDw356bf1DTdajR6xK0kmutuGb7dcWKpIopAQDaPe1srABAOceT/GpEB6AOnGhoJRc1GuMFZghbEbred3g7Gmty9RsdPkl2JQqd5fyBzX3wSs7e16swrAszuRhIq5uVXFr4SHw5U/R892zyBAD81F29Tzg4wu5TaRC9/l6jYwnr7rOZOcvMW8bkShaTRql89agcwzw6fZ0SAGTKGctssLilV6qdFbttfVQR2DRUmHBvLJ9+aOR5n4vPrUil7q9PzF2t9h/dqORKW8gQZmySLqcN4e5prHC3KvzchJEs8LSldF97Q5MFYXdYAjaNR4a1oOt9p6+zoYlGrf2KnO705YhadnMrrwcb+WyrEnIZ+2BaicpgWA1mcjEgaRIPGJVcauyG+BcenzxaUjRPf5tn9idv7zlvdnx2YlAclAEgW2YdS9S+u2nmLGaVzFmqmBIBIOKJMDFiMr0B6xnC/Nr7xj0ybUO2N7k2hLu3g1U9NpCw31X54HXRpwHgf7209EAhOJqP6HwRALLFtTZzo2NsR6aUEQGg099Y4W5V+OVXo8DmlQWNhM5XdP5iWAu63vf4exq+B9MDnQkAcMw/b5nEq5mEPWEZAFKlFBsrDEvCTC4GMtVKrpiq4HzgttzT51J3AcBHTgyc4ggxNzib0evvzXPgtJJW8khlyWF2PGaiRa7M6g5vnqglNxd/yxKLKM0iRr1RJkZMZkgcMgxhC7WwJulZw+SKXG17k0sqSyIAdPm6mCHcYD5+cvjVoNexnspXIl95avbGdk/7KgDkShKrQLAgckUWAKAv0MfGCgB+9T1jHg0NNnwe7fJ3Wa5CmLEJXe9HxJGGjxUtOGCcLkieZSYXgJi3er1KiV2vwrAmzORiIFdJOAAgTDz535jI3qVouvPKqP+9D14fWzA7Nrvh4By632l0LJnMTFpGvJsC4aALRuaMn3/OEpuKTNnIuNONMMM8unxdBZ7wSkWruFaLqy6z4wEALrtsXJbce6ftTS65bAj3/kA/M4QbjM/Fq//slu4nAeD/vLFyX5vfqIiRtKJfs0hVLMNA0zXklJwAAKPiKBsrAEhmxjC5Oq5p+DxKjUZWTW89koWku6JVXDzhlZgvVmz087X2I4bJtT5tif2o2dB9sFSR2FhhWBJTK0XGxsYcAD4F4KcB9AKIA/jfAP7j+Ph4dg/vjwH4TwB+EEAYwBSA/zI+Pv7VbV4bAPDvAfwogBiABQB/DOC3xsfHlZr8g5qUrLrOA4Df2Z5+cTZ9KwB89P6hJ8yNyr4ITkGSK3JwLjsn3Nxxc8rseMxEC/bHufXpQT55NlYBzpkdD80iDgQGmBgxGY5w8Dv9slSW2qYyU2LEE1k1Mx6yPuUj5WxA55wVtfvWtJmxmE1Fq5C8khcAYCQ4wsaKCfzcPQNv/583Vo4ns+XYC/mrPT7/DPIcx8XzcU+Xv6vhApGxPfPZeZ+ma7ybcxeD7qCtm1VQOHnFSBb03N7QS+eBTaMxq2QFTdfAEVYLYBWmpCkBAALOgGTG70XtusW4QqPa+dPu0H2wXLbOlREMxlbMnr3/HMBnAfwJgB8G8DsA/gWAv68aYDsyNjYWAvAMgJMA/iMM8+r/AvjK2NjY5y96rRPAdwH8SwBfrj7rjwF8BsCf1exf06Rk9DwHALP5QZemg7upV3zjxGg4aXZcdkV0VVtY51kLa639imrmbMr0TUVJLXEFpeAHgGFxmAl3C0Dbvc9n500vl3fMTRj3yARicXAOWzfrmJVn/Tp04uE9+YAzYOskklk4OKL/1F29pwDgf64MjMaqHRanpCnbrytWYlqaFgFDuJsdixUg6RkvKcuCzjkras/t641+ftAdrLg4V1HTNX4+O+9r9PMZOzOfnRcBIxFsxvOVgeNJHUQn+bV2lOx9nQgADAeNfXBeyQcqWoXdbcOwHKaZXGNjY/8UwBiAHx4fH/+N8fHx74yPj/8egAcA3A7g5y/zEb8Oo3rrxPj4+NfHx8f/bnx8/DPV931ybGzspi2v/aXqZ54cHx//SvVZvwngnwD4Z2NjY/+wtv+65qGoFLkMFMLpOp5Pj0Y4Au0TJ4efMjsuO0NbWCcLSduLEbXzJiNzJpmfOZuWpgM6dOJz+LIeh4ed+bEA1BBeya+YPla4+FvGERuxt+HVB1ZjVp6lwp2ZwSbyz+/oOd/X5pl9S+lxR6sdFpcyU+xeLguxmFs0hLtLYGMFgGPujNGhNhBLmJUsoPPWjDxj+rrC2GQlvyIAQNAVNGeseEKK7g2nCHTimDsTMSUGC+Fz+FQv783p0MmMNBMwOx4G42LMrOT6WQDPjI+PP7b1i+Pj42cB/CWAj+z0xrGxMTeAfwXgD8fHx5cu+vYfA5gD8OGLnvUX4+Pj37/oWU/AqAbb8VmtzpQ0JegEiKgq3tMHcNdQ20s39opps+OyM+2edgkA1oprplenmI0ycCKpAyCFtXaUZN7MWOiGlwl369DmbjMM4aL5hjCXOm+YXOFR29/HtZBdoMKdVaeYCEcIfu7EwKki3PApTgBAOvn9HpPDYmwhno9T4c7GCgBu5U2aLDBtHqXJk8Xsou33YFaCJn5D7pBpY4V2/OSWXzM98WoFmCHMsDKmmFxjY2M8gOMAntrhJU8DuHpsbCy6w/dvASBu9/7x8XENwGkA91Wf1QXgiss868TY2JjZRzdNYWHt+2EAiCgaFkh35dMPjZw2Oya7E/VGjY4lrIU14A1XdG84RXSNc8w/a2rmbDm3LACbR+QY5kMN4VQxZboY4aQFQ5x13mh7kytRSAgAEHKFmCFsMh+8PrZwZdT/HirGciLLczvtqxgmsFpcFQEg7AmzdQUAtz4VBQAtPGLaPEoNx3ghzvZgFiJVSokAEPFGTBsrtOMn7QBqdzYM4RwzhBnWwyxjpxuAD8CrO3z/ler/R3b4/mj1/7u9f2Qfrw3AuIzedqwvv9oLAH7FhRNXRJ8djvhyZsdkd7p8XTIASGWJLRoAdKG7mjl71dQxSje8ZmYRGRey0cLabENYLROSS0QBQOk/bnuTa0O4u5lwtwIfvX/oiULFyBGkcqkOk8NhbGG9tC4AQMQTYYYwAC4zbyQLYjeYNo9uVAizKyMsRaaUEQAg6o2aNlZox0+SYckCYHM/nCgk2FixECuZglvTbX01LADzuiuGq/9f2+H7tKNc+y7vV8fHxzO7vN9V7ai4n2ct7/CaHZmYmPjw5V9lXaYWltrhAXg1gB+IpK6cmJi4wuyY7I6qqDwA5Iv57mb/+9or5XI5Amw/no44RgMj+D6W56Yefndi4q7GR2eQko1qobZ821UTExO9ZsXB2MRRdjgBIJ/LDx92rOz2N3g5/PkF/oSmOIrONu3M9+f+pXFi3r7IaTkEAKIk3jwxMXGlyeE0FYf5O9wJDoDX1aUAs45UuRCxy7rSDGSlbBgAfKu+oxMTE7eaHQ9Qn7/BPaFreCiX7ASAiVT4A5WJCVNUWjAf9AGAlJJum5iYOGJGDIxL/w5z+VwEAJzLzgcm1iZOmBGTr9DJ3wNAkRJDbB4FQvlQAADW4msnJiYmbjI5nJpj2lx4CHRdxx8sfLJLB8GPBH5hrattsGx2TIfB6XQe+L1mVXLRCpX8Dt+nX9/pglRhl/de/P7DPqulkZQ1BwAIjnbd5yDM9rUAQT6oAoCsyZyms/vNZd+AAgBCft7UbjaSKvEAIHKiamYcjE1CfEgFAEmTTL2vTczPOgEg6+utmBmHVchoGR7YnMsY5nMkOpgDAJkrYVbWbd8ZzCrQuYuNFcBfXOZ5rUyKzpBWcYqm7UeDnPG7oGs+wxrQ3wdd980g74mpCufW3YrEucppW15zs5UQF2JjxWKcTeTc805gzgUI3qit96RmbXRoqal3h+/Ttr07VWrJu7z34vcf9lm7cuzYsa8d5H1W4fpb/tQ59HfjvzgQC5aa/d/SSnj+xvPJolr0dV3f9b9Gg6NZs+OpNzRLst3fID/PhXH+K78ULs6Uzfwb/cqjX/kplDAwMDzwt8dGjk2ZFQdjk6JS5D737c/926yW5W6989b/epiul7v9DV4Oz7f/9D4A94YinS8fO3bs1EFjaBV+4//+xi8CaL/m6mv+8ljXMdt3m9wPh/k73I3YekT42vf+4uMpB3Bqpij99488+Ge1/HzGwch8K/NpAI67b737TwaEgd2Stw2jXn+Dl8P1/FeuBjDmFKNTx44d+/NGPnsrypLSiefxs+vceobti81j699hUSlyuW/n/i0B0R869tDX3LzbtAww907HhyAt9B5tW3u0ctM/mDYrDiuwNLk0jDfwL1LOVPzYsWP/w+x4ao1Zc+FBUTSd/Pkf/MEvaT0EIVXXHzn50B+ZHdNhefHFF//9Qd9rlgu9l+OIwO5HDB1jY2M7nQEOAyiPj49na/Cslkb0tleu7bohF/Ab1TIMaxBwBiQAmJVnbX/OXe25fV3nnBVSzgokPbObuV1X5IosAEB/oJ/dM2QRPA6P5nP4sgAwLU+b1sKaS0/HAECNXGX7+7gAIKfkBAAYEUfYWLEIA8HhLKfrWOd5cOl3R/7q9eUBs2OyO5lSxlnWyh6OcGpfoM8SBpeZcMl3YgCghwZMnUcHhUEJALKVrO33X1ZhUpoUAMDn8GXNNLgAQBP7jHtiE2/Z8i7nrfT5++gdwmysWIA/OjN7jVedbAOAMHGUzI7HbMwyuZYBFGB0SdwO+vWdqiUmL3rddu+f2sdrcwCYOGFYBtElygCwkF1gCwfn0HV/NAEAjrkzpmwqNF3b2PAOB4eZcLcQVjCEOXnJMLm6b7P9OpIsJN0VreLiCa/EfLGi2fEwDJycU28DXwGAAcd5fH1i/iS7mNZczkvnBQAIOAIyR2x/8gncejVZ0H6lqfNoX6AvzxFOLWklT6aUOfiFMIyaQdd3ut6biRYeSQAAl5q0/eXzI0EjkZWr5AR2vYq5FCoq9xcvLz0QcSwAAEK8t+VPAV0OU1bV8fFxBcAZAPft8JJ7Abw7Pj6+00L3KoxjiJe8f2xsjAA4AeCp6rOWAJy7zLNOj4+Ps9HJsAy0hXWikGAdFgFowWrmLP6WKZuK5fyyV9VVh5NzliOeSFNf4thqUEN4KbdkislFckkXKabbdMJpat/RVTNisBJT0pQh3J0BiQl3axHkPTkA6HLPq4vpYt+fPL/AGs2YyJw8Zwh3l/nC3QoQeSkKAGr3raaaXBzh4Hf4ZWDTiGSYy3J+WQA213sz0TpvNPaj0oLtK7k6vB0lJ+csK7riTBQSHrPjsTNf+d70TemCEhbdSQ0AAu62dbNjMhszd6B/BODesbGxB7d+cWxs7GoAP1b9Pv1acGxsjN6dhfHx8SKA/wHgF8bGxjov+tyfAjAI4OsXPevHxsbGrrnoWffDMLma/swqo7Vo97RLALBaXGWVXAC08IixqUhNmrKpmJamN4S7Gc9n7EzIFTLVEOZnT0cBQPd1JOH02j5ZMpc1hLvgFEwXI4wLEVxiBgCi3uUSAPzPFxZOVlSNmBuVfVnJr4gAEHQFbT9WjGTBeriaLDD9+hA6f81n59kezALE83ERAELukOl7MKX/eBwASC4RhVq2/fxJDeHJzCQbKyaRKVQc33ojfh+gg3ekdQAICD22vw/VtA474+PjfzU2NvZXAP5qbGzs8wBeBzAK4LMwKrX+AADGxsb8MI4ergLY2or83wH4AIDTY2NjXwCwBOAYgF8B8KXx8fFXt7z29wD8CIAnxsbGPgejsutGAJ8B8M3x8fFv1emfyWAciIgnIgNAupRmWUQAWux6ozzcpMzZYm6RCnfTN1iMCwl7whIArBXXTNlg8SuvG/fIiD22P6oIbIoRWo3KsA4+f+cqiisDZSK5Qx6SWc1Vor//1MwNHz85/IbZsdmRZCEpAECbu832Y4WfO9NBAGi+yCpcftM7TQbdQWkpv4SV3Arbg1mAVCklAkC7u930saIH+4q6S5BIWRb5xZfCav8x001ZMxFdopQup9sXc4sCANsbK2bwW49P3ZErq8K1gWxijdejABAKDtn+ZIHZZwl+HMCXAHwIwF8D+ASAvwDwyPj4OG17qQBYATC/9Y3j4+PrAO4B8AyAXwfwTQD/GMAnxsfHP3nRaysAHgHwDRgm2F8D+BkAvwPgJ+rw72IwDkWnr1MCgHQ5zTIj2JI5y8aj0JSGZ85W8sZGl2XcrUeHp0MGgPXSuilihEudiwKA1jbMTC5sCncrZNwZFxLyd6UAIMmB//krsi8CwF+9vnJ/tqSw9u8mQIV72B22/VixWrKAVggni0m2B7MAdH3v8HZYYg+mCd1xAOAXX7L9vVw0oUUrUxmNZTlT9Dz6TvIEAPzK8OJbCd5YzmP+TtuvK6ZVcgEb5tOvV//b6TUlANfu8L1lGAbZXp4lAfh49T8Gw9L0+HtkAJDLMls0AOihwYLuCsiknBX4xZfa1L67U5d/V+1IFoyNLsu4Ww9qCGfKGVPGCpeZMy5Ljl7LMpjYknH3tFtCjDA2oQIxwfP4xfZ303/kvyu5lqt0fOmJqdv+/Q9e8YLZ8dmN9dK6CABRb9T2Y4VbOxcDAK1txBImV9gTlgHzKoQZF0LXd7rem40e6k9g7b0j1Y6g75gdj5mE3CEZ2NwnMxrLFx6fPFpSNE9/m2f2uPNd9XcchsnFOsGbX8nFYDC2YaNjiZJjpfJVNKHLtMwZFSP0GCnDOvQFjBbWcsUEQ1jXQOQVw+TqO2oJcWY26ZJRfRrzxmy/wbIaXb4uCQDiDgeca+9F/8UdvU8AwN99P3FPMlt2mRud/ZDKkgAAXf4u24+VjWRBxzWWmEej3qhRTc+ujLAE2bLR3Zqu92ajRq4y7olNz7DL5z0dEgCkSik2VhrMZDLnf/pc6i4A+MiJgVPc+vkYreQaFoctMVbMhJlcDIYFiXqjRQdxVCpaxZXIJ9xmx2MF9OCAcS9X8mzDNxXpsrHRjfmYcLcaw8Fh01pYc4m3RaKWPLrDW9A6rrb9hgIApIoh3Lv93WysWIz+QP9GJRe3Ph376bt73+sOuhcKFc33hccm7zY7PruRrRjCfSAwYO+5Q9dAsitGZ8V+ayQL6PwlVSRWnWIymq4hq2QFYDMBbDZq1y2GySUv2d7kivqiplbT25kvPD55j6Lpziui/vc+eH1sQZKXOgscBxfhKx3ejpLZ8ZkNM7kYDAvCEQ5+p9GxZEqaYgsHADVypWmZM3pstMffY4kNFmOTiCdSdnLOkqqrjuX8sreRz+bnnzXukRE64yBsOQW2CHfB5sLdglCBmHDw0OWlKEcIPny8/xQAPPn+6tGZtbxv909g1IqKViF5JR8AgOGgvTPuXPKsQJSiV3d4ClrHNZb4WVDjUS7LrDrFZOaz8z5N13gX5yq2udsql39H/VH7jq7phNNIcb2N5JK2roKl16tIZWYIN5LX5jOhF2bStwHAR+8bfBJKkVstrUcAwO8MWGIeNRu2K2cwLIroEiUAmM/Ns00WAK2aOSMmZM6ocB8UBtnCYUEC1QW90YYwn3jbuEdG7LdE9YHZFJUiV1AKfgKiD4lDWbPjYVyI6BIVN+cuKIQgXc6ESX7N+cM3dc2OdvjOVVTd9fnHJk+YHaNdmJFmAjp04uW9OZ/DZ3o3QTPh56rJgkBnwirJAmo85pW8UNEqDW92w9hkRp4Rgc113hK4/Krui6wCRmdQs8MxkyFhSAKAbCXLtEoD+dITU/drOrgbe4Q37znSnuAXXgwnecIDgOAKZsyOzwpYYzVhMBiXQE2ueD7OsiMAlL6jqzrhNFJIhVFIORv13Gwl6yiqRS8B0fqF/lyjnsvYO4JTkABgMbvY0E0Wtz5lmFztR5jJBWBKmhIAwOvw5ty8u7FnRxl7IuAyhGLSwYOfPR0FgF+6d/AJAHhuev32NxeloJnx2QVLCneT4JPVZEHQOskCn8OnenhPXodOZqSZgNnx2Bm6rtM9sVWgnUD55ddsfWSxX+jPERCtqBZ92UrW1IZ2duGpc2vRNxblGzgC7VdODn8PAPill2Lx6n1ctOOl3WEmF4NhUdrcbcb9KYUEM7kAwC2ourd9jQBwzJ5uWOZsMjMpAIDf6ZcdnENv1HMZeyfkDpnSwprIi8Y9Ml03W0acmckW4c42WBaFGsJx3gF+xRBnD1wZiV/fLbyl6eB/+9TUfaYGaBMWc9YU7mZg1WQBncdm5VlWoWIidF23mnDX2oaNKzRS521tcjk4h+53+rPA5n6ZUV9+/6mZBwDgzsHQyzf3BdMAwK2+G41XOyvSjpd2h5lcDIZFiXgiEgCsl9bZolFlI3O28nrDNhVz2TkBAASnwBYNi0IN4dXiauNMrpLMk/xaRAegDJxINuy5FmY5vywA1hMjjE2oIZxw8ODWzm90qv2Vk8Pf4wi01xekG0+fT9n6+E0joMmroCto+3WFSItGZ8VOayUL6Dy2mFtkiUYToes6XeetgtpxrWFyVTuD2hmaPKH7ZUb9+Pab8b73E7krnRypfPqhkWfo17n07EZnRdrx0u4wk4vBsChbWlizDVaVjczZ2vsN21Qs5ZZEgGXcrQw1hBvZwtox/2yE6Bqne8MpeMOWuAzXbJhwtz5hd1gGgBUHf4E4u7U/uH77QOgVHSBfeWr6AfMitAdrxTUBAMKesL3XlXKOJ/nViA5AHTiRMDucrdB5LFFIMOFuIjTR2+G1lnCnnUBJdiWKBnd2thp0f7ycX2Z6pY5ouo6vnpk9CQD3HAk/P9Lh37hChZOXYgmHcVqUdry0O8zkYjAsSq+/12hhzTqWbKB2XFPNnM1HL/faWpEsJEUACLlY+a9VifliDTeEueVXq50Vuy1VfWAmTLhbH2oIJ3geJLsS2yrOPv3QyDMOjlTei+eu+tu34r2mBWkDUqWUCLCMOz//bDvRNU73tKV0X7ulkgV0HmtohTDjEjLljAgAMW/MUmNF67hG1h2eIlGKXi551tZGKN0fJ/LsepV68ucvLY7OrxcHPA6u8JmHRyfo10k27iLFdGilelyRdry0O8zkYjAsyoBQbWFdkdmiUUXtu7vaYXG5s1GZM1od1O5pt9QGi7EJNYQbOVb41feMe2RCg8zkqrJeWqfCnW2wLEqnr9O4v87p1IhS8HLJdzbE2ZGoP3vvkfDzAPD/nZ59UNPZFYT1giavYr6YrccKv/SKkSyoXkVgJSKeiAxszmsMc6BjpSdgMeFOOOiBTuMKjfnnbH1kke6PG1lNbzcUTSd/8vzCSQB45JqOM52iu0S/x89NRAEg7nDqADAgDDC9AmZyMRiWZVAczBIQvagWfblKjjc7Hiugxa6XdN5dIkrBy62+15CORxvC3WKl8oxNBsVBGQCy5WzDxAhJzxomV7W6kLEpRrr8XWysWJTegGEILzvdKgDw889eIM4+8/DohMfBFebXiwN//tLiqBkx2gG5LAsA0Bfos/VY2ZIssNRRRWDTEM6UM0y4m0i2khUAYFgcttxY0YJ9hsmVeNvWJhfdHzNDuH589fTsNQm53Blw8/InHxx+cev3+JXXYyUCZDhCCIg2IAywTvBgJheDYVmcnFP3OXwyAExL02yTBZiSOdsi3K2VRWRs0OvvzXPgtJJW8khlqSEtrLnsstFZsfdOZnJVkStMuFudIXFIBoAkRzgA4BNvX3D0u1N0lx65puMMAPzJ8wsnFU0njY+ytdF0DdmKYciPiCO2HiskQ5MFV1tuHqXzmFxm1fRmUdJKpKyVPTzh1R5/T97seC5GCxsdQbn1SVubXHR/zK5XqQ+Fisr95StLDwDAP7oh9lTQ61S2fp9LnYvSS+f9Tn/WyTlZGTaYycVgWBrBZXT0m5Fn2MJRZTNz9lZDNhVUuPcH+m0tRqxMtYW1BACTmcm6jxWyPuUj5aygc86K2n1rut7PawY0XUOukqPCnRnCFqXL11XgCa8UiM7nCAG3PnXJPPrJB4dfDLh5KSGXO796evZaM+JsZVYLq25FV5wO4qhEvJHS5d/RunDystFZsecOy5lc1BDOKTmWZDSJtJbmAMDv8MtVX95SqJ03JQCASEsNuyfWigwEBuiVEWys1IHffXL65nRBCYe8jtTHHhh+/eLvc5n5WII38rsBZ4BplSrWmzEYDMYGWzqWsIWjitZOM2dTdd9UKJpCchVjgzsaHGXC3cIITsMQns3O1t3kclTvP9ADsQQ4B8uYAVjKLXlVXeWdnLMU9oTLZsfD2B6OcBub4LiDB5EWLzG5gl6n8k9u7HwKAP7ilaUH8mWVHZevIZOSYcQHnAHJisK9UZD0jJeUZSNZ0HP7utnxXEzUGy06iKNS0SquZCHpNjseO5JW0zwACC7BksKddgQl+WQHKgXbDuaRoJHYylVygqIprPq3hmQKFcffvBm/FwB+7LbuJ90O7sILiXUNJLsSTVQvnQ+6gpYcK2Zg2wHJYDQDIVfI6IRVYB1LKGrshmrm7FJxVmvms/M+HTrn4T2FgDOgXP4dDLMIuo2FfSW3UndDmIsbVYSa2Gu56gOzmJKmRAAQnNYUI4xNqCEc5/kdxdlH7x96o83nXMsUlLYvPzl1c+OjbF3ms/PGWKlWatsVx9wZ49J5f9SSyQKOcPA7/TLQmAphxqVktAwPbCZ8rYbu7yjrntA60TWOn382YnY8ZhFwBhQP7yno0Lk5ec5vdjytxG89PnVHrqwKUcG18rPHB85e/H0ueVYgStG77PIoAOsEvxVmcjEYFoZ2LFkrrrENVpWNzFku2QGlWNc5bFqa3si41/M5jMPTSEOYSxn3b2jhUWZyVVnMLVKTi22wLA7N9C55xbwhzp5rv/g1bgen/cRt3U8AwLffStybypWdjY6zVYnn4wJgXeHeKLj4W1Fg8woCK0JN+/ncPKumN4GMaphcbe42y64rmtBtXKGx9LKt7+Wi++QZeYaNlRqxnCl6Hn0neQIAfvquvlMOjlySDKD3Ey94A3kACHvCtl5XtsJMLgbDwnR4O2QASJfSbNGoogc6S7onlCa6ym8nzmoJE+7Nw5YW1vU3uaR5w+SKXW9ZcdZoVvJGBR2tqGNYl5DbMITnfWIe2Fmcffh4/zudonspX1YDv3Vq6s5GxtjKJItJEQDCbnuLkc1kwYhl51E6n8XzcZZoNIGMluEAa48VLTRkXKGx+p6t7+WiR0oXcgtsrNSILzw+ebSkaJ7+Ns/sT9zePbnda2jzmMVqJRfVjQxmcjEYlqbL10VbWLNFYwtaoKuaOXulrpuKeMHIuFNRyLAuUW9UBhrQwlotE5JLRAFA6T9hubb3ZkGrTa2ccWcYRLwRQ4y4vWUA4Fbf29bk4gjBh+7uOwUAj7+zenxhveBtXJStS6poGPHtnnZbjxUus5EssOw8SuezZCHJEo0mIKkSDwAxX8yyezCt46oEAHDpWVtXctFjcok8u16lFpxL5AJPn0vdBQAfOTFwiiPbX3VGm8fEeZ4AQLe/27JjpdEwk4vBsDADgtGxhLYbZxhobYPVzNm7dd1UbBHubNGwOHRhl8pSXcUIv/hyG9EUh+4SJD3UX6jns5qJLcKdjRWLQw3hJYfRjYnLzOw4j/7Ybd3TQ+3eqbKquT//2OTxBoXY0mTKGQEAot6ofceKpmxJFhy3bCVXxBNpWIUw41LonVxdvi7LGsK0MyiXXba1yUWr7VaLq8wQrgG/9fjkPYqmO6+M+t/74PWxhZ1eR+8nXiWqE2Cd4LfCTC4Gw8IMi8MyAGQrWdaxZAtahGbOdhZntYAK94gnYtkNFsOgUYYwv/iiUX0gdFm2+sAM0mXjSHWnr5NtsCxOr7/XECNEdwIAJy/vWhH78/cMPgEAZ6bW7zi7LDOxf0iksiQCQI+/x7brCr/4UhvRKk7dJch6aNCyyQJqRKZLafZ3bwKyJvPA5vpuRdSe21M651BISRJJZt5jdjxmQSuE615NbwNenc+0vTibvhUAfvm+wSd3fGGlwJH8akQDIKslH7CpGxnM5GIwLI3oEjc6lsxn531mx2MV1O7bjcyZXN/MGc24M+FufYbEoSwA5JV8oKSW6ra2ccl3jI5goQHLVh+YgVyRbS/cmwUqGNNq0VcVZ0EiLe4ozj5wTcfSNZ2Bs6qmO754aurexkXammQrWQEABoVB264rm8mCTkvPo9QQpsYko3GouoqsluWAzfXdkvAuXfdHEwDgmJuw7b1c7HqV2vGlU1P3azq4G3uEN+850r5jQpWff66d6Cq/6mvLaNVO8KJLZJ3gqzCTi8GwOFs6lrCFo4rae0dKJ7xKSpkgkZfc9XoOFe69gV7bipFmwefwqV6HN6dDJ7PybN1aWNPqQTVylaXFWaNhwr15GBKHsgREL6gFf8nXkQQAfu7MruLs4yeHniSA/spc5ubnp9fr2vCjlckreb6gFvwERB8UB60r3OsMl3wnCgB6cNDS8+iAMLBRTW92LHZD0iROhw6vw5vzOXyq2fHshib2GKcL4m/Y9sgiTXDJFZmNlUPw5HursTeX5Os5AvWTD458b7fX8suvGJ0VAx3rAOsEfzHM5GIwLA7t7LeQZR1LNnB4NJo542d3F2eHgW5sh8QhVp3SBNAFflqarttY4eQlw+Tqvs3S4qyRSGXJUVJLXg6c1hfoy5sdD2N3nJxT9zl8WQBYCHauAwC/srs4u3Owbe22/uCrOkC+/OT0A42IsxWZykwJAOBz+LJOznlJO3i7sJksuNLS8+igOLhhCOcqOd7seOxEWk3zQHMIdy08apwuWDtvW5NrWBxmdwjXgN97auZBALh7qO3lG3vF9G6vpR09ZwPhHMA6wV8MM7kYDItDO/vRTn8MA72aOePrlDlbLa66KlrFzRNe6fJ1WfbOEMYmQafR7n0pv1SXTRbJJV2kuN6mE05T+46u1uMZzchkZlIEAL/TLzs4h22FezNB273PCR1ZAOBSlxdnn3xo5GmeI8rZlew13z2b7K53jK3IbHZWAJpDuNcTelmy2nWLpe82rBrCMgBMSpNsD9ZA6KXzdF23Mlr0OsPkkhZsa3LFfLGigzgqFa3iShaSdTth0cp887XlgcnV/KiTJ+XPPDzyzOVeT5MFsz6xBABBt/XHSiNhJheDYXFoZ7/VwirLjmxBC48Ym4o9iLODMJWZ2hDuHGFTZTMQdAdlAIjn43UZK/zsaeOIja8jCadXq8czmpG57BwT7k2G6BSNqkd/qAgAXGb+shWxV3cG5OMjbS8AwB88M3OyvhG2Jsu5ZREARJdo24w7ya85SXE9rBNOU/uPWT5ZsGEIy3NsD9ZAMmrV5Kqu61aGdggl2ZUobNokiiMc/E6/DABT0hQzhPeJpuv42sTcQwDwwBWRZwfbfZetiqf3Es+73Bqw2eGSYcCUG4NhcTq8HaxjyTaoNHOWma+LyTWfnReATTHIsD7tnnYJANaKa3XZYPErrxsml9hj6SM2jWY5z4R7s0ErhGfcHg0ASC4eg3553/YzD41MuHiuNLNWGP7Gy4vDdQ6z5UgUEgKwmbyyI/zs6SgBoPsiq3D5LX3XErA5r9WrQpixPWktzQHNIdz18Ehed/pzRKu4uOXXgmbHYxaCs2oIZ5khvF/+27PzVy1nSj1eJ5f77MMjz13u9SS74ialTFAnvLpCVCfAOsFfDDO5GAyL0+nrlAHWseRi1P5jm5mzPYiz/bKSX6HC3fIbLIZBxFPfFtZc6pzREaxt2NJHbBpNspC0vXBvNto97UbVo1Zy605fjqhlF7f8Wuhy7+tt8xYeujpyBgD++3MLJzWdnU7dD6lSSgSaQ7jXC37lNaNDrdAcyYI2lzGv0XmO0RgkVeKBzUSv1dGErjgAOBZesO2RRXpcrl7V9K1KSdG4P3tx8SQA/ND1sWfaA67y5d6zcbLA35FMlyXWCX4bmMnFYFicHn+PBLCOJRejtV+Rq4ozN7fyes0zZ8liUgSANncby4w0CdQQrle7dy4zZ9wjE722KcRZo0gVDeFOK+kY1qfDs1khrAWoOHt+T+LsMw+NvOB38dkVqdT9tTNzV9czzlaDGvAd3g7brivcWjVZEB5uinl0S4UwE+4NRNIMk4uu61ZHD/YbpwuS79jW5KL7ZWYI74/f+970jal8JRL0ONKfODn8yl7eQ5vF6GJPnHWC3x5mcjEYFmdEHGEdS3ZAD3Qal8/XIXOWKqYEgAn3ZqI/0F8/Q1jXQLJxw+TqO9oU4qxRrJeZcG82uvxdEmAYwnqwPwEAXPKdPXWqDfmclX94fexpAPhfLy+dLCka20vukUw5Y/uMO73/Te1ojmQBuzLCHKjJ1Rfoa4qxorVfYZhc6Wnbmlx0v0wrVhmXJ1OoOP76jZX7AOBHb+l60ufi93SEm0udjwLGyQKqD1kn+AthGxMGw+LEfLEiT3iFdSy5FC04EAcAPvF2zTcV6XJaBICoN8oWjSZhOLjZwlqr8RFWLvG2SJSiR3d4C1rH1exvYgtStVS+y9fVFGKEAfQH+mUAkCuyqEWq4mx9as/z6MdODr0a9DrW1/OV9t99cvqmOoXZcmTLhhjpF/rtOVZ0DSRrXJas9jdHsqDb371hCJsdi13QdA2SKnEAMCwON8V6q3bfYlyhIS/uKVnQisS8MQkA0qU0Gyt75Iunpu7IllSxI+CK/8K9g9/f6/vofcRr7SNrFa3iYp3gL4WZXAyGxeEIt9G1jHUsuRCt/QjNnNV8U0GFO93gMqxPxBMpOzlnSdVVx3J+2VvLz+bnnzVKwwOdcbBumxdAs4jUOGFYn5GgUSGcq+SEcuzGqjhb2rPJ5XXy2o/d2v0kAHzrzZX7MoWKoz6Rtg6ariGrZAUAGBVHbTlWuORZgShFr+7wFLWOa5riZ7DFEGb7rwYRz8c9ChTiIi69w9tRMjuevaD0H1/VQXSST7WjmLblfNjj7zGujKhIbKzsgeVM0fPds8kTAPBTd/WecnBkb5dc6hpIdiUGAO+HegoA6wS/HeynwWA0AaxjyfaoXdXMmbRY80ouKtwHhAFmcjURAWeAtrCu6Vjhk2eN0vDqvRsMg4pWIblKLgAAI8GRphCtDEB0iYqbdxc0aNx0ZDBviLO1dpSkPYuzj5wYeDsquFayJVX44qmpO+oZbyswn533abrGuzhXMegOVsyOxwz4+eeaLllA57VcJScomkLMjscOTEqTRuMfTrR8980N3KKi+9rXCHTimD3TYXY4ZkD3y7RilbE7n39s8mhJ0Tz9bZ7Zf35Hz/m9vo+LvyUSteTWHd7CFKfyAOsEvx3NscIwGDaHdSzZHmXgeLIqziIoyXytPreoFLm8kg8AwJAwlK3V5zLqD13oF7OLNc0kcinjKBetHmQYzMqzfh065+E9+YAzoJgdD2Pv0OTJdGnNp3vDKQKdOObORPb6fgdH9J++q+8UAHz3bPLEcqboqVesrcCMPCMCm0a8HaFXCzRTsiDgDCge3pPXoXNz8pzf7HjswGJukZpctW+dXUd0odu4QqPaQdRuDIlDWQKiF9SCP6/ka7Ynb0XeT2QDz5xP3Q0AP3di4BRH9u6f89UmMXqgM77MOsHvCDO5GIwmgHUs2QFPSNkUZxM1y5xNy9MBAPA5fFmPw9NUmyy7Qw3h5fxyTQ1hIi8Y98h03pSo5ec2O7PyrO2Fe7MiOAUZABayC6Iu9hjibOnVfYmzn7i9e7K/zTtTUjTPFx6fPFqPOFsFarwLLsG2YoRbn6x2VhxtGpMLwMaVEdPyNEs0NoCV/IoAACLfRJVcALQ2o2Mot/q+LU0uJ+fUfQ5fFgCmMux6ld34wmOT9yqa7rgy5n/3h66PLeznvVtPFqwWV1kn+B1gJheD0QSwjiU7QzNn3PIrNdtUbBHuthUjzUqbu6327d7LOZ7k1yI6AHXgBDO5trCYY8K9WQm5Q0aFcCEuaKFBYx5d25844wjBz53ofwIAnj6Xuuv9RDZQ+0hbg3jBqMQOuUK2HSv0agG16+amMrlElygDm/Mdo74kC0kRAIJcsLlMro6rEwBAMnO2vXye7ptns7NsrOzAizPp8EuzmVsJoH/sgaEn9vv+zZMFownWCX5nmMnFYDQBrGPJzmhtQ0YFwtr7NdtULOWWaPkvy4w0GRFPRAaAVClVsw0WPzcRIbrG6d5wSve12/IunZ1I5BNUuLOx0mRQQ3i1sCpqHdcY9xtm5vadLPih62MLV8b87yqa7vytx6fuqXWcrcJqcVUAbJxxL+d4kl81kgX9x5Nmh7MfqDHJroxoDOul9ea7kwuA0nuXkSyodhC1I0FXtZo+V9tq+lbid56cekAHyM194uvHhsOr+30/kRfpyYI41YVUJzI2YSYXg9EEbHQsYS2sL2FDnKVna7apSBQSAmDvjHuzEvPV3hDmq1WCtGqQsQkV7mF3mI2VJqPD2yEBhqBUeu80xJl8MHH2/94/9CQB9Jdm07e+Mpdpq2WcrUKqaFRiRzwRW44Vfv7ZdqJrHDxt67q/o2x2PPsh7DHmN3o0iFFf0uW0sQfjQ01lcmldN6d13lUmlbyfW3vflve3hdxGwotWrjIu5NF3kl1vL2ev5QnUTz048tS+P6BS4LYmC6SKoQu7/F32TJ7sAjO5GIwmYFAYNDqWVLKs/Pci1N47EgDAVdvp1gJ61C3sCbNFo8no9fdKACBX5NqZXKvvGaXh1SNdjE1oxj3itadwb2Y6fZ0yAGTKGUHrujmjc84yqeT83Nq5fYuz4yPh5E194huaDu5LT0zdX/tom59MOWNk3H0xW64r/JKRLNCaMFnQ4dk0hM2OxQ7IZWP9DvLB5roTlXDQ/THjdMHcs7as5qIJL3qMjnEhf/D0zIMAcHQk/OK13fu/5oGfOxPZmiygupB1gr8UZnIxGE3AoDjIOpbsgNp1S1rnnGVSzgbI+pSvFp+5XloXgM2NLaN5GBQHZaC2LaxplaAWvbbpxFm9ocKdGiaM5uECQ5hz6HqgMwEA/PyzBzr6/ckHh5/iCNS3luTrn3hvtbOWsbYCclkWAKDH32PLdWUjWdA2tGJ2LPul098pAZvzHaO+ZCvG+h3imquSCwC0YJ9RFZv4vi3v5Yp6o0Y1fZldr3Ixf/Hy0tD0WmHYxZPSZx4eOX2Qz9hIFog9K7QTPAHRWSf4S2EmF4PRBLCOJbvAOXQ9EEsAgGNuoiabii3C3ZZipJnp9ffmOcKpJa3kyZQyzlp8Jr1fQ60e6WJsIlfsLdybmWFxmFYIiwCgib1UnB2oAuH6bjFzdLjtJQD4/admTtYqzlaB/pyHxCFbjhWSmTHEWfWKgWaiz98nA5tGJaN+ZCtZR1EtejlwEDihuSq5AGjhUeN0QWrSlpVcnf5qhXCJGcJb0XQd/+25+QcB4MGrIhP9bd7CQT5n68mCKcnQg16HN8c6wV8KM7kYjCaBdi9jHUsuRROrmbP4WzXZVNCjbn2BPlad0mQ4OIfud/hlADgvnT/0WCGpSR8pZwM65yyrXbekDx1gi0GF+4g4Ykvh3szEfLEiT3ilolVcyULSrbWPGvNo6vyB59FPPzRy2smT8uRqfvSbry0P1C7a5iZTyjhLWsnDEU7tC/TlzY7HDDh5uRNozmTBSHBkwxDWdKYl68lkZtLo2MsJKkeaT6aqnTdW7zdcsKXJ1RfooxXCTKts4aun565ZkUrdPhef/fRDI88f9HNIeqYTMJIFVA+yTvDb03yzB4NhU0SnKAGbnf8Ym2jhkZplzjRdQ66SEwBgODjMFo4mhBrC89n5Q48Vx9wZ49L5QGcCnEM/7Oe1EslC0l3RKi6e8ErMFyuaHQ9jf3CE29gcT0lTgha7wZhHpcUDV8QOtvvyD1wReRYAvj4x96CmsyEDbBrufodfbkbhfli2JAsqavetabPj2S8d3o6Sk3OWFV1xJgoJj9nxtDKz2VkRQFNWcQGA2n88AQAkl+yAUrTdYB8VR2UAyCk5QdEUYnY8VqBQUblvvLx4EgD+0fWxp8N+14G7dG89WUA7WNKOlowLsd3gYzCalZDb6PSXKCSYyXURGs2cSfOHNrmW88teVVcdTs5ZingiTdUBimEQdAVloDYtrGl1IL1ng7EJLZUPOAOSHYV7KyA4DUN4LjsnKv3HjU61uUQUavnA4uSzD48853Vy+aVMqfePn1u4slaxNjPUcBecgi2rgx1zE9VkQSzerMkCaghPZibZHqyOLOeWBQAQebHp7uMCAF3sKepuMUM0xcEvvBg2O55GE3QHK27OXdR0jV/ILdTkntxm53efnL45XVDCIa8j9bGTQ68e9HMuOFnQfWuadrCke17GhbBdKYPRJLR72mVgs/MfY5NNcZbsOIw4A4ApaUoEgIAzwBaNJiXkMgzhZCF56HJ5Wh2otR9pusuS643dhXsrEHQbGeB4Pi7qwb6i7hIkoikOfvGlA4uz9oCr/A+uiz4NAP/zxYWTFVWzfTZ/JbciAJs/b7vBJWiyoL9pkwV0nlvMLbJjWHUkWUgawp0LNqXJBQCa0GU08Vh62ZaXz1NDeEaasf1YSecrzr95M34fAPyzW7uf9Dr5A1cobiYLjJMFa8U1AQDCnrAt15XLwUwuBqNJoJ3+0qW07ReNi9kUZxUnv/hy22E+azFrbGBphQOj+Wj3tBstrEupw1dySca9GmrsxqYVZ/ViJV8V7qxUvmlpc7fJwGaFsCZ0xwGAX3zxUFWxv3Jy+BXR40iv5Sodv/fUzA2Hj7S5SRYN4U4NeLvBrRn3vGnh0aadR+k8t5w/fIUwY2doIrepTa7goHG6YPUdW97LRa+MWMwt2n6sfOHxyTtzZTUQE1zLHzkxcPYwn7WZLDBOFqRLRgfLDk8HSzRuAzO5GIwmocvfJQGAVJZsv2hsx2bm7KVDbSpW8ius/LfJiXqjMgCsl9YPN1aUIkdyiSgAqAPGPRuMTVaLq4Zwd9tTuLcCEU9EAoD10roAAHqo37iXK/nuoSoQAm6H+iM3d34PAP7qteX7syWFP2yszQwV7mFP2JbrykayoLN5kwVt7jYJAFYLq2wPVkfSZSORG+SDTXknFwBokSsNkys9a0uTK+QKycDmftquLKwXvI+9s3ocAP713X2nHBw51FHti5MFmXJGADb1IeNCmMnFYDQJ/YF+o4V1tfMf40L00ICxqUgeLnO2Wlxl5b9NTre/mxrCh6p65Oefaye6yuvuYFoXuku1ia51oMKdHqVmNB9Rb5RWCIsAoEauqoqz6UOLs1+8d/CtiN+ZkEtq8LdPTd122M9rZmgFNv1524oLkgUnmtbkingjNasQZuwMTeSG+FDTVnKp3bdVOywu2dLkovtnup+2K59/fPJYWdXcg2Hv9E/c3jN12M+7OFmwpRO8/daVPcBMLgajSaAtrHMV1rFkOzbE2frhxBndwNIjb4zmY0AY2Gj3fpjP4ZdeNrJmYk/TCrN6kikZWcSYN8bGSpPS6++9oEK4luLMyXP6v7yz9wkA+M7biXuS2bLrsJ/ZrEgV4+fb5euynSHML7wQboVkQaev0zCEy+zKiHpChXszH1dU++5e0wmvkmI6RLJx2817dP986Gr6JubssiyemVy/EwB+/p6BU4f+wIuSBdVO8CIAjIgjtltX9gIzuRiMJkF0iYqbdxc0aNx8dp51LLkItesW4/J5eelQx2wypYwIADFvjC0aTcqQOJQFgLySD5TU0oHXOS75rmFyhQaZybUNG8Ldbz/h3ioMCAMXVAirfUdXdcJppJhuI7nkocXZv7qr9/3ekGe+UNF8X3js/N2H/bxmRS7LAgAMCoO2M4T5xRc7gc373pqVHn+PMVbKrJq+XiiaQnKVXABo7kouOL2a7ousAgA/d9p2l893+jplYHM/bUd+69TUvaqmO67pDJz9gWujS4f9vIuTBUu5Ja+qq7yLc5XCnjDrBL8NzORiMJoI2vFvRp6x7cKxE2rf0TVDnK2HSX7NedDPkSuGGOnx99hOjLQKPodP9Tq8OR06mZamAwf9HC4zY5hcHVc3tTirF9lK1rbCvVUYFAezBEQvqkVfrpLjDXHWkQQAfvbw4owjBB8+3n8KAJ58f+3o1Gref9jPbDYqWoXklbwAAMPBYdsZwhvJgrbmThYMiUM1qRBm7MycPOfXoXMe3pN3kgNv4yyBLvYaTTxW3rTdkcXegFEhTPfTduPZqfXIq3OZmwmgf/zk0JO1+Ex+0bhvmCYLtnSCZ/uvHWAmF4PRRIhOkXYsseXCsSsuv6r7IqsEhxNndANLN7SM5oQu/LPy7IHHCicZR7bUnjuaWpzVg6JS5PJKPkBAdFo5x2g+nJxT9zl8MgBMSpPG5fNit9HEY+X1mlQg/JMbO+dGO3znKqru+q3HJ0/U4jObiRlpJqBDJx7ek/c5fM1bnXJANpIFkeZOFvT6e/McOK2oFr1SWXKYHU8rMiPPCMBmQreZ0dqGjKPfa+/bzuQaEuxtCH/5yamTOkBu6w++eudg21otPpNLvmNUxFaTBbRzpeAUmn6s1AtmcjEYTUTQbbSwtnvHkp3Qq3cn8SuvHWhTIZUlR1EtejlwWl+gL1/b6BiNJOg0xspSfulAY4Wk57ykLIs656yoPbenahtd8zMlTQkA4HV4c27e3bRdsBib7d7n5DkRALS2EUOcpc7VTJz98n1DTwDAc9Prt72xIIVq9bnNADXa7Zpx5+RlI1nQe2dTm1wOzqH7nX4JACYzk2wPVgcWcguGcK/OSc2MGrvOmEcz87Yzubr93QWe8GpZK7tTxZSt7iT71hsrfe/Gc1c5OFL59MMjT9fqczdPFlwTB4CV/IrRhbSqCxmXwkwuBqOJCLuNjiW0qxnjQrS24Wrm7PyBKhCocPc5fbKDcxyq1S/DXILuoAwA8Xz8QGPFUb1HQ/dHE+Bd7G/hImaz9hburYToEmUAWM4vCwCgVjfRtRRn91/RHr+hR3hT08H/9hNT99Xqc5sBmnGnP2c7QdJzXlKSRJ1zKK2QLKBVE3PZOVZNXwcS+YTRWdEVavp1Re29OwEAJLsShW6vPBBHOGwYwtUKYTug6Tq+embuIQC470j7c1fGaleRuJEsqJ4soDqwzd3W9GOlXjCTi8FoIiKeiAwAqSJrYb0dase1VXE2dyBxNpedY+W/LQLt7rNWXDvQBouLG/doaMG+pq4+qBfLuWWjA5aLZRGbHSooEwVDYKr9R40mHtmVWC3F2a+cHH6KI9BeX5BufObcmm0uY44X4lSMZMyOpdG0WrKAVk3Q+Y9RW1aLqwIAhN3hpt+DaR1Xy7rDWyBqycPF37Ld34vgNKrx5rPztvm3/7dn569aTBf7vE4u/9lHRp6t1eeSzLzn4mQB1YFUFzIuhZlcDEYT0eXvkgAgU87YJjOyH9S+jczZgcTZcs6oZBBdIhPuTU7EEzlUC2tu7bxx/0F4hJlc20CFe9AVZBusJmeLIWwcV+y4RtYdniJRih4uebZma80tfcH1OwdDLwPAV56aeaBWn2t1koWkCGxWYtuJjWSB2BrJAmoIJ4tJ2wj3RkLX64g30vxjhXDQA53GFRoLz9vuyCLdG9jFEC4pGvdnLy6eBIB/cF306ajgLtXqsx2zz8SAC5MF6XJaAICYL9b8Y6VOMJOLwWgiaMc/2u6dcSFa9FrJEGcFL5d8Z9/ijFYysPLf5qfLZxjCUlk6mMklGUe1tNgNLSHOag01RCKeiO2qU1qNDm+HDGwxhLeKs7lnayrOPv3QyDNOjlTeT+Su/Pab8b5afrZVoT/XqDdqu3WFS00a82iLJAsuNoQZtSVTzogA0OnrbInkiRbsN+bRxNu2M7noPjpZTNoiKf87T0zdnMpXIkGvY/2TD468UsvP3i5ZQHVgr7/XduvKXmEmF4PRRAyJQzJg344ll4Vw0AOxaubsuX0fh6Hlv61QKm93+gJ9MgBIlQOYXGqZkFwiCgDKwD0tIc5qjZ2Fe6vR7e+mhvCGGKHHdPnk2ZqKs5EOf+7eI+3PAcBXT88+qOlNf4LtslDhTiux7QS9103rvHHF7FhqAa2aSJfSthDujUauyALQOsJdax9NAAC3Pm2b49kUagjb4XqVVK7s/Nab8fsA4Mdv637C5+Jr2kV3I1nQProxj2bLhg4cFAeZXtkBZnIxGE1El6+rwBNeqWgVV7KQdJsdjxXZyJzFv79vcUaFe8wXY9UpTc5wcFgCgFwlJ2j7PLrKL74UJpri0N2ipAf7inUJsMnJlOwr3FuN/kD/JRXCWviIcb/h+mTNKxA+/fDIsx4nV5hPF/v/7MXF0Vp/vtWgP1f6c7YNapmQXDwGAEr/iYTZ4dSCLl+XDGwal4zaQhO4w+JwS4wVtfNm435DecF2lVxRb1QGgHQ53fJj5XOPTd6dL6uBTtG99HMnBt6u9edvJAuqJwuksuQoaSUPB07r9feyTvA7wEwuBqOJ4Ai30c2MdgJkXMimOJve96aCblxpZQOjeYl4ImUn5yypuupYyi159/NefvFFY0MhdLEqrh2gFXIDwgAbK03OaHBUBgxDWNEUAgBqV1WcSUs1r0DoFN2lD1zTcRoA/uT5hQcrqkZq/QyroOnahnAfCY7YaqxsJAtcgqSH+gtmx1MLBoVBCWDV9PUgWUi6K1rFxRNeifliLZFcUgdOJHQAJL8WQTnHmx1PI9muQrgVmVrN+594b/UYAHz4WP/jHKnxcrb1ZEE1WXAuc04EAL/TL7FO8DvDTC4Go8mwY8eS/aB23mRcPi/tP3NGN65DwpCtxEirEnAa7Zun5el9jRWuekRLCw4yk2sbKlqF5Co5Adg0SBjNS8AZUDy8p6BD5+bkOT8AqP3HkwBA8smOeoizTz048pLg5jPJbDn2+0/N3FDrz7cK89l5n6ZrvJtzF9vcbRWz42kkm8mC7paZR4fFYRkA8ko+UFJLTEPVEJq4DTgDEkda40er+9oruqctRXSN4+cmImbH00hoAqzVDeHPP3b+noqqu0Y7fOd+9JaumVp/vpEsqDi3JgvmZKMTPGuStTutMYswGDaCdixZya+09MJxUNQBI9NB8skOVAp7nuOKSpHLKYZwpxtZRnMjOo0NwGJ2cV+ZRG59xhBnHVe3xD0ytWZWnvXr0DkP78kHnAHF7HgYh4dWCM/IMwIA6P6Osu5pWye6xvHzz7bX+nmCx6H86C1dTwLAN19bfiBTqDhq/QwrMCVNBQFAcAm2OwLPJd8x5tFQ6yQLPA6P5nP4sjp0MivP+s2Op5WYy7amcNfFHuMKjeVXbXUv15AwlAUMQ7ioFFvSb3hpNh1+YSZ9GwH0j90/dKoez9hIFoibyYKl3FIQAIKuYEuNlVpj2qZibGzsAQD/HsCNABQALwL4tfHx8df2+H4OwE8D+AUAVwBYBTAB4NfHx8ffu+i1TwG4d4eP+sr4+Pj/e4B/AoNhCiF3tYV1wR4dS/aLIc5C66SYbuPnn2tXhx9I7uV9NIvoc/iyHodnf5c4MSxJ0B2UkAWW8/trYc3JSzEAUHtubxlxVktmpBkR2KwqZTQ/gkuQVoursYXcgghgGTCO6/LF9TZ++dWYOvJQze9U+qX7ht7627cSR5PZcuwLj03e+Zv/6KqJWj/DbBayC8ZYcdlvrNArA7SOq1pqHg04A3JeyQempWnxitAVLCFWI5Zzyy0p3LXQYIJPfP9qfvW9GIC3zI6nUVBDOK/kA9PydODqtqtb6vcKAL99auqkpoO7uU98/Z4j7XW5d3AjWbDlZEGymGSd4PeAKc7q2NjYBwE8CiAB4F8D+HkALgDPjI2N3bbHj/kKgK8DeBnAT8AwzK4E8PLY2NjV27x+AsD92/z3Xw7+L2EwGk/EGzE6lpRav2PJQaHHI/ill/d8ZHFG3hDutsu4typ0A7Cfdu9EXnKTUiakE15Ve+9M1S+65mU+Nx8EWi/jbmdCLiN5ksgnNi+fDw0Z8+jqu3W5NNnBEf1njvY9BgCPvpM8MZsq+OrxHDOJ5+MisPnztRMbyYLu21rK5KLz3lJ+ie3BakiykKTdrVtqD6Z1XG3cb5iZsd3l81sqhFturHzn7UTP2ZXsNTxHlM88NPK9ej2HS89ckiyge9qIN9JSY6XWNLySa2xszAvDnBofHx//yS1f/ysApwB8DcAtl/mMGwH8IoD/MD4+/h+3fP0vAbwG4A9hGFhbWR0fH3+qFv8GBsNMNjqWlFq/Y8lB0UKDCT559irOyJx9fy/vWcoZG9ZWyyLamYgnIgP7a2HNz56JAoDujybAKvq2hQr3Nncb22C1CGF3WAY2M8QAoHVclcC5vwNJz9XtmM1P3N4z9ZevLk9OreZHPvfo+RNf/fHrH63Xs8yA/jzbPe22WlcuSBb03b1mdjy1hBqWdB5k1Ia10oZwb6mxonYbFeGcvGI7k0t0iXKikMBybn/V9FZH03X8wdMzDwHAPaPh56/pql+l7nbJAqr/Yt5YS42VWmNGJdc/BhAD8G+2fnF8fFwF8OsAbh4bG7vjMp/hAvBlAL930WcUAfwVgMu9n8FoWnr9vUbHkkprdyw5DDRzxu0jcxYvVDPubvtl3FuVTl+nBADpcnrPY4Vfeb0TALRgb0tVH9QSmkUMe8JsrLQIHd4OY6yUNseK2nNHVZwt1VWcffS+wccB4Lnp9Ttenc+01fNZjWa9tC4CQIenw1ZjpZWTBXTe20+FMOPyUOHe5etqqbGi9ty2rnPOCinLAknP7avTc7OzUSFcSLSUXvnTFxaOzK0XBzwOrvDZh0fqdsyeZFfcpJi+JFlAO8FTPcjYHjNMrvsAzI2Pj09v873nAFSqr9mR8fHxl8bHxz8+Pj6+vs233QBYdpnRsmx0LCm3dseSw0AzHpy8vOcKhI3yX09rZRHtTI+/RwIAuSzveaxwqfNGaXh4lJlcO0CFe9QbZWOlRdhiCG+MFbXn9pTOORRSlkWSmffU69kPXBmJ39QrvqHp4L74+NQD9XqOGWwId39rCffLsZEsEFsvWdDh6ZABYL203lLC3WykshQEgN5Aiwl33qXr/o4kADjmTtvq8nlqCK8WV1tGr1RUjfzJ8wsPAcAPXBt9pivoKdbrWfzMM9smC2jHymFxuLXGSo0x4+L5UQCvbveN8fHx4tjY2NsARg7ywWNjY24A/wzAS9t8++6xsbFpAN0A4gD+N4xL6g9liE1MTHz4MO+3AuVyOQK0xr/FDqi6CgKCglrwP33m6Q87SPM3par536Cu4yHiBF+Sgi88/ejPKo6Afrm3SGmpDQCEtHDbxMTEdTWJg2EqBdXorpkr5zou97dF/wazqXgkBODNyuBtqxMTN9Q/yuYjm822A4An4blnYmLiLrPjaSVMW48rxn4wX8j3bX32UU+vLuZncHbi734mFby+XK/H/2ivzn1/Efj+snzdn37nTOdIkFTq9axGki/mowBAFsgHJlYmmqITaS3+Bq9bmgv2ApjkR4enW2xv6S15XQCQy+auZPvm2pEr5yIAUJws/uOJ6QmtlbTJ9e6RYI+8hJn33vqnM/JEzux4GkWwEPQCgLwu3zwxMTFkdjz7Zbu/wacXNe9aTg+1uaE+EEzcMDGRrNs+cWD5Dd/VAFbcw6E3qzGU9TKKatHHgcPiW4s/sUyW6/V4S+B0Og/8XjMqucIAdjufnwJw0HbVXwTQV/3/VrIA/g+AfwfjuOQ3APwcgKfGxsZa7qJTRmvDEx4BLqABQFpN82bHY0kIj5y3pwIAYm52TzNkRs3wABDiQy11tMLOBLiAxoNHUS+Skl4il32DriFQWHAAgOQbbApBagaSKvEA0Ma3qWbHwqgN9HcpqRKv65s5AdnXVwEAIT9b12xKu4do9/aQLAD89aQmbo2hWdF1fWOshLiQrcZKID/vBADZP9gSZuVWQnxoY6yYHUurUNSMNdoBB/zE33J7MNnXrwCAkJ9v/qz0PghyQRUAMlqmJcZKSdXJd2Z1EQA+OEhkJ3f5beVhEPKGfpF9/RvzKNV9AieoHDGlf2DTYMZgEwDkd/l+HkBwvx86Njb2/wL4JQBfHh8fP3PRt//J+Pj41oX278fGxp6A0eHx0zA6Mx6IY8eOfe2g77UK1KFuhX+LXfB/1/8zcl7u4Qf4vzs2eGzO7HgOSz3+Br3zHf8I+ZmbbnLNvFA69uHtqjsv4D98+z98HIDz1htu/bPr2q9L1yoOhrn4/tb3Ubkih4JXBv/3rR237tgtcWJi4sO+wjLv0EpR3RXI3n7/P/hqI+NsFhRNIdK3pH8DACfvPvl10SUyM7CGmLUea7qG//St//SrFb3ivOKWK/5H1BctAYA79627sIpHjqjnz/ceO/Z/6xnD8HVFz8RXX/7lSUnznncMfO+n7uo7V8/n1ZuF7IJXeUz5lItzlU6eOPlHZsezVw79N6gpJPDi3GcA4Kq7P/Df9fDIbnv+pmO9tO787b/77V+VdAl3H737a0xoHp4X4i9EkMQv+F3+1PHjx78GtJY2cb4xNYg5/Kuu8uS6eOzYfzU7nkbhS/rCOI1fWsd6oRl/jxf/DX72b949niknOmOCa/lj//TOrzs4UtdsjP/sJ/41AN/AFdd9q/umY9MA8K3pbw1iDf/K7/UvHjt27I/r+Xwr8OKLLx7Yozm0yTU2NvYJXFo5tR1Pj4+P3wdABrDbxXs+ANvdtbVbDP8CwO8A+DaAT138/YsMLvq1x8bGxv4OwL/AIUwuBsMMRJcoreRXelgL653RwqNxLL4ELnX+sncglNQSl1fyAQAYDg7L9Y+O0SgElyDJFTk0J8+Ju5lcACDmpp0AoAldK42JrvmYz877NGich/cUmMHVOnCEQ8AZkNLldPuUNCVGfdEkAGjR6+PAN8BJC3XvDNYV9BR/8LroM3/9+sojf/L8wkM/flvPpNvBNW1Vx5Q0JQJAwBmw1b0p/NIrIaJVXLorkG01gwsA2txtFRfnKpa1smcpt+TtDfQWzI6p2ZnLzomAsbc1O5Z6oPQfSwAAycaj0BQCztH8pap7YCQ4YtwhXMkKiqYQRxP/u2dTBd9j7ySPA8CHjvadqrfBBU0hJGt05FT6j2/cbcg6we+dWqQf/juAq/fw37+svv5yxxEvd5zxAsbGxn6oGsOTAP7Z+Pj4fjbdjwMYGhsbc+3jPQyG6YTd4QzAWljvhha9rtphcf6y4mxamg7o0InP4cv6HD5bHStpdehGYDl/+RbW9EiWHhxsucuSa8WUNBUE7Cfc7YDgEmRgU3ACgNJ/LA4AJLtiiLM688kHh18KehzptVyl43efnL6p3s+rJwvZhZYW7jvBL7xgNO8IdLXsPBpwBmRg08hkHI7l3HIQaF3hrrcN53VXIEu0ipNfeiVkdjyNQnSJiof35HXo3Iw84zc7nsPwuUfPnyirunuo3Tv547f1TNX7efzyq9smCxKFhNHd2s26W1+OQ1dyjY+Pp2AYV3tlEsDD232jenH8NQD+Yi8fNDY2dgLAOIDnAfzD8fHx/XY44ADoAJo2U8iwJ+2edtrCet9He+0CzXyQXDwGXQN2OVIwLU/bMuNuB9rcbbSF9R5MrjknAKiRK1tWnB0WKtyDriDrYtxiBF1BaR7zWMmvbIwVPTyS153+LKnkAvzyqyG15459Vdrvl4Dbof7Ybd1P/NGZuR/+P2+s3P//HOt7K+x3NeW9Tst5Q7iH3CFbjRUueTYGAHpooGXnUdElSqlSqmMxtyjCaGbFOATJQlIENtfrVkQLdMX51LkAv/BCTO29s67zqJUQnIJUVIu+aWk6OBoczZodz0F4bT4Tem56/Q4A+Oj9Q6ca8cydkgWpYkoENnUgY2fMOEj+FICBsbGxwW2+dzcAV/U1uzI2NnYTgL8F8H0A/2B8fHzHkuixsbEjO3zrLgDv7bP6i8EwnZg3JgHAemmdZRF3oCrOckQtu7jl10K7vZaV/7YuEU9kz4ZwoLBgmFzdtzLRsgPxglE9GnKH2FhpMajApIKTogldCQDgF1687NHvWvCREwNvd4rupVxZDXz+scm7G/HMerBaXLVlxp1Lz8SA1k4W0L3CVkOYcXBSJUO4d3g7WtYQ1kP9xumC1XfqfvTbStBK1oXcQtOOlS+emnpA08Hd2CO8efLKSEOus9gpWbBeWg8CQMwXs9W6chDMMLm+BSAB4D9t/eLY2BgHo/vh6+Pj4y9s+To/NjYWu+i1owC+C2AWwCPj4+M7/qLHxsZ+E8BbY2Nj11709WMAfgTA1w/1r2EwTKAn0JMBgEw507SLRiPQBCMD4lh4ftdNBT322cpZRLvS6evMAEC6lN51rPBqgXhLSV4nnKb2H19tTHTNh12Fux2gAnOttHbBWNGDVXFW3XTXGwdH9A8f638cAE69t3psajXflMdcaMa9w9thq7FCpEXD5Oq+rWXvNgx7wtsawoyDQRO2nb7Olh0rarth+nLr07YyudrcbU19vcq0pDvfWpKv5wjUTz008mSjnkv/TtTIVRfMo1JZEgGgz9/XsoZwrWh4d8Xx8fH82NjYzwL45tjYmBPAX1bj+BkAdwK4/6K3/CGAnxkbG7tnfHx8gn4MAD+A3wBw49jY2HaPer56fPFrAH4KwFNjY2OfA3AWwB0APgvgFIDfq+E/j8FoCEPCkAQAckVmxxV3QQ8NrCB1fphLvB0D8N5Or1strgYBVv7bivQF+iQAyJQzu44VMTfjINCh+aJJOL3sCPsO2FW42wEqMNOl9AVjRY1cteKYfhLc+lRno2L50Vu6Zr7x8uL755L5Kz736Pl7v/6TN3ynUc+uFTQJ1eXrss1YIbmkixTXwzrhNLXv6J7v1202aIUwrUBiHA65LBvCvbpetyJq923GFRpVE9gu0H01TZA1E7qu468nNREAjo2EX7yhR2yYsbSZLLjwZIFcMcbKoDjYsmOlVpjS93Z8fPxbAH4AQA+APwHwRzDuxrp3fHz8xYtevgggDWDrH1YYQACGQfa9Hf7rrD5rBoZ59m0AnwDwNwB+DMB/APBD7KgioxkZEoeyBEQvKAV/XsnzZsdjVdTI1SsALivOaBaRCffWgxrC2Up21w2WkJ91AoAu9rbsEZtaQIV7t7+bjZUWgwpMmimmqD23G/OotNgwkwsAPvbA8CkC6C/Opm99YWZ9t4ZFlmQj497Cwv1i+NnTUQJA93Uk4fK3bBMXOv9drkKYsTdownZYHG7ZsaL2H1vVCa9yxfUwycZt0/As6o3S61WaLin/xirc5zJwuR1c8VcfHjndqOfulCzIVrKOolr0ERBtQBjINSqeZqXhlVyU8fHxx2F0N7zc634dwK9f9LXBfT5rHsCH9vMeBsPKuHm35nP45JySE89nzgs3tN+QNjsmK6L23G6Uh0sLu4ozKkZ6/D2s/LfF6Bf6cxw4ragWvZlSxhl0B7e9xFrIGSaXFh5mJtcu0Cxif6C/ZcWIXRkRRzLA5u+YovYfX9UJr5LiehvJrrj1QGepEfGcGA0nb+kPvvbKXOaWLz0xfXL8Q23jjXhuLdB0bcNYHw2O2mas8CuvGffIiD0tPY/2B/ozACCVpaYT7lZjtbjqKmtlN094pcffs+P9yk2Py6/q/o4kya508jNPx5TrxubNDqkRdPu793RlhNUoKRr311NGFdcPXht9urfNW2jUs2myQLsoWXA+c14AjO6uTs6pNyqeZsWUSi4Gg3F46GWOM/IM22TtgNp3tCrO0m0ku+Le6XXU5BoQBmwjRuyCg3PotGvmucy5HTdZQmHOAQBq7MaWvUfmsGwV7iPBETZWWoxuf3eBJ7xS0SruZCG5OV86vZru70gCAD/zTEMun6d89uGRpxwcUd5ZyV797TfjfY189mGI5+MeRVecTs5Z7vB2NMQUtALc2vudAKCFR1t6HqXGZbaSFTWdnW4/DFOZqY3u1twuXbBbAU3sWwEAfuX1hlbFmgndV1+cPLE6X3pi6uZEAY6wG+qnHhp+qZHP3kgWBHsvmEep3hOcAtt/7YHWnk0YjBaGmly0MyBjG5xeTQ9Ejc5gM09vew9CSS1xeSUvAMCIOCI3MjxGYxBcQgYAZuXZ7ceKppBAft4JAMrAiZYWZ4dhLjvn13SNd3PuYpu7bduKOEbzwhEOgtMYK+cz5y/ssBik4uy1hoqzK2MB+b4j7c8CwB+enn1I05sjeT0pTW4Id7NjaSRceq4TANTO1k4WdHg7Sk7OWVJ0xbmcX/aaHU8zM5s11mU7CHet/Yhx9LtqBtuBI8EjEgDkKjmxolWI2fHshWS27PrWGyv3A8A/HiZSwO1o6NFrbvU9Yx4NH7lgHl3OLRud4N2sE/xeYCYXg9GkhN3hDAAk8glmcu3CRuZseXtxNivP+nXoxOvw5vzO1r1DxM7Qdu/L+eVtxwq/+FKbQyuRojOk6eGR1j0ucUimpWlDuLvsJdzthOAyhOZ8dv5CkytMxdm5houzzz4y8qzXyeUX08W+/zoxf1Wjn38QFrILIgCITtE+Y0UtE5JdiQGAMnCipY8rApumzGRmku3BDsFKbkUEgJA71PJjReu8wbhCIzNnG5PL7/SrXt6b06GTGWkmYHY8e+E3v3v+WKGi+QcEVG6PkmKjn0//PrSLkgWJQoJ2gmdXq+wBZnIxGE1KxBuhHUvYccVd2MycbS/OqHC3QxbRrtB274lCYtuxwi883wkAWV8/q07ahQ3h7mpchyFGYwm5QrRC+IKxQjfbZoizqOAuffD62FMA8GcvLT5YqKiW37tSQ91OGXd+8aUw0SpO3SVIettwyycLaDX9fHae7cEOAV2X29xtLT9WlIF7VwCA5BJRKEXLz2O1gibGpqQpyxvCZ5dl8alza0cB4EdGuAwhDS4+U8uEZOPVZMG9FyQL1oprIrDZ3ZWxO7YZYAxGqxHzxozuPuXmusyx0WidN1c7g81ve1xxMbcYBDY3rIzWo8PTkQE2NwgXwyXe7gQA2TfATK5diOfjRsbd1foZd7sS9hgVwsli8oKxogzeFwfME2efeHD4lZDXkVrPV9q/eGrqtkY/f7+sFldFAGj3tNvGEKbJAk3saemjipSQO5QBgJX8CtuDHYJUKWV0t/a0fndrPdhX1N3BDNEUB7/wQtjseBoFraan+20r87nHJh9QNN1xTWfg7JEQafiekF94wUgWuMWMHuq/4LJ7enk/1X+M3WEmF4PRpNBOgJlyhm2wdkEZvMcQZ9l4bDtxFi/EWflvi9Pp66Tt3rfdYHHp6U4AkPzM5NoNany0e9rZBqtFoe3e14prF4wVXewp6u5gmmiKg59/rr3RcXmdvPYv7+x9HAD+9q34fcuZoqfRMewH+vOzU8adS5w1TK62IVuYXO1uYx5MFpJsD3YI1kvrIgB0+jttsQejJjC/8IJtjizSxBjdb1uVU++udr6+IN3IEWiffnjklBkx0L8LTei+5Mh3pmLovd5Ar23WlcPATC4Go0kZFoeNjiVl2fKZETPRhe6S7gmlia7y/Nyzl4gzWt1DN6yM1oNuCKSKtH0ll7RUNbmGmcm1C3bKuNuVLl/Xju3eN8TZ4oumiLMPHe17t6/NM1usaN7//N3zJ8yIYa9kSoYY6fJ12Was0GSB1nFNy9/HBQBRbzQDAKlSiu3BDoFcNrru9frtIdy1kGEC88l3bGNyRTyRDACsFlYta3Jpuo7f/d70wwBwbLjthVv6gutmxMElabJg+JJkAdV7g8KgLcbKYWEmF4PRpAyKg1kCohXVoi9byTrMjsfKaMLO4ixVNIQ7rWBgtB7D4nAG2NxMb4WsT/lIWRYUzq3nPF2s8cAubAh3v32Eu92ghvB27d61tuE4YJ444wjBR+8fehQAzkym7nxtPhMyI469QA11O2XcOXkpBgBq7122qOSi8+B2hjBj79C5ZiQ4YouxokWvNe7lSs/YxuSK+oz9tZUN4T99YeHIbKow5HZwxV99ZPS0WXFsJAuqfyeUbCXrKKpFHwHRBsXBrDnRNRfM5GIwmhQn59T9Tr8MAOcz5wWz47EyWtjIiPCr716yqaDHPZlwb136An15nvBqWSt7UsWUa+v3HLOnYwCQ9fYqIGxJ3A0qRvoD/WystCijwdGNCmFN1y743hZxtu39ho3gkas7lm/sEd7UdPBfeHzyQbPiuBzZStYQ7qI9hDu3ds5PyllB55xlted2UyogGs2AMJABAKkiWVa4W51UMeUqa2UPT3i1x9/T8s0KAEDpu9u4J1Zeso3J1e3vNqrpy9tX05tNSdG4P35u4WEA+MFro0/3tnkLl3tPveDk5U4AUKt/J5TJzKQAAH6nX3ZyTt2M2JoNtqNnMJoY2p58Rp5hm6xd0Dp2zpxR4c7Kf1sXjnDwO/0SAJzLnLtgk8WvvMEund8Dmq5tCPfh4DAbKy1K1BstOjlnWdEVZzwfv+DeK6XvKBVnXbjIAGskn3149AmeI8rby9lrv/1mvM+0QHYgWUi6K1rF5SCOSswXa3j7eTPg587EAEAXuuLgHLYQYNQQzlVy4sWGMGNvnM+cFwHA7/RLnE2STFr3rWmdd5VIJefnVt8NmB1PIxgUBg1D2KIm15eemLo5la9Egl7H+qceGn7JrDi4tff9pJwN6LyrpHbdkt76vRl5hnWC3yf2mFEYjBaFtidfzi1bcuGwCkr/0c3M2ZbNaEWrkFwlR0vlZZPCYzQA0SlmAGAuO3fBWOFS5w2Ti106vyuLuUWfqqsOF+cqRTyRstnxMOoDRzgEnEa79/PS+QuSJ1rXzWmdd5dIJe/jVt8zTZxd2y1I9x0JPwcAf/DMzMOabi1PZTIzKQJAwBmwjXCnyQIt2G+Lo4oA0OZuq7g5d1HVVX4uO+c3O55mZDY7KwKb67MtIBz0QJdx9LtqDrc6I6Kxv84reaGkliw1KSazZde33li5HwB+8vaeUwG3w7RrK/jZM50AjL+Pi5IFVOfRTpWMy2OpPzQGg7E/aEdAq3csMRut86aMzruLpJL3ccl3No52zkgzAR068fCefMAZUMyMkVFfQu4QNYQvEO5Emq9eOj/ETK5dmJKmaBbRPmLEpoguQ3DOy/MXriuEgy50Gke/Z0+betTm1z5w5IzPxeeWMqXeP3xm9lozY7kYaqSLLtE2YoRLTRomV+RK25hcACC4jPlwKjPF9mAHYCW3Ygh3t72EuxYaMObR+Fu2OLLod/pVr8Ob06GTaWnaUtVrv/nd88cKFc3fHXQv/Ozx/rNmxsLH3zTm0erfx1bihXgQAMKesK3GymFgJheD0cTQ9uQXt3tnXAThjGMUAPi5iY3M2Ub5r4sJ91Yn7A5nACBRSGyKkZLkILnVDh1El3yDzOTahYXsAh0rbIPV4tB27yv5lUsvnw8OGuIs8X1TxVlHwFX+JzfGngSAb7y89KBcVCzTfGU5b7+MO00WqN232crkokbmQm6BmVwHIFlMigAQdttLuGvtVxinC6qV5HaAJsim5WnLjJWzy7L41Lm1owDwi/cOPsoRYmo89O+B/n1sZaMTvId1gt8rzORiMJqYmC/GuvvskY3MWWIzc0Y3pkGnfcSIXYl4DUM4Vdzs7uOYeaaDQCe6r31N493mBdcEUOFOK+IYrQvdRCeLyUuSJ7RSh1bumMmvnBx+LeJ3JqSiEvrcY+fvNDseSrJQFe52ybgX0w6SX4voILoycCJhdjiNhFbTr+RXWKLxANAEbYenwx5jpQo1g4m0aPo82iio6b+UW7KMXvncY5MPKJruuKYzcPaD18cWzI6HSAs7JgvSZUPndXo7WVJ+jzCTi8FoYvr8fRlgs0MgY2e0yFWXiLN4Ps6Eu03o8lXbvZc3DWF+6WXj/gOx11bVBwdhtbBqy4y7HenwdmQAIFVMXbKuqN23xoHNzbiZOHlO/9DR/scA4NGzyRMza3mf2TEBQKpk/NxopXWr45g9HTWSBZFVeEK2Ova/YQhXjU3G/qAJ2k6/vYS7MnhPUgfRSX6tHYWU0+x4GkGbu41WCFvCED717mrn6wvSjRyB9umHR06ZHQ8KKSfJr7XrILoyeE/y4m9TnUc7VTIuDzO5GIwmZlg0upxlK1lLLBpWRu2+/ZLM2WpxlZX/2oS+QN8l3X24tfeM0vDwKDO5LsN6aV0EgKg3ysZKi7PFEL5kXVEGTiR1wmmksNZO8mumi7N/fkfP5EjEd76s6u7f+O75+8yOB9gU7l2+LlsIdzsnC6LeaAYA1kvrbA92AOh63Ovvtde64hYV3R9JEujEMfNM1OxwGkG7pz0DbB67MxNN1/G735t+GACODbe9cEtfcN3smBwzzxjJAn8kCbd4SbIgWza6Ww+JQ/YaK4eAmVwMRhPTL/TnOHBaUS16M6WM6YLDyij9xy4RZ0y424eR4IgEAHJF3hAjXHrWKA2P3WA7cbZfNkrlfZ1srLQ4A8LAzu3e3aKi+yKrBAA/aw1x9isnhx8jgP7CTPq2Z6fWI2bHI5WlIAD0Buwh3LlVmiwYsd08SqsqtlYIM/aOXJFtK9x1sde4J3b5VdOrYhsB3WfTfbeZ/OkLC0dmU4Uht4Mr/uojo6fNjgfY/DvQxb5L5tFcJccX1IKfgOhD4lC28dE1J8zkYjCaGAfn0P1OP233Llzu9bbGLai6ryNJAPAzT8cAIFPKBAGgy99luw2W3ejydRV4wisVreJK5BNuaAoh2RXD5Bo4bjtxtl+ocO8P9LOx0uJQQzhbyYqarl3yfboJt4o4OzEaTt7WH3xVB8hvPzH1kNnxZCtGxp1WWrc6XGbOmEc7b7TdPDoQGDCSJ2WZVXLtk1Qx5SprZQ9PeLUv0Jc3O55GQyvIubX3Y5d7bSvQ4++RACBTzpg6VgoVlfvj5xYeBoAfvDb6dG+bt2BmPBT6d7DdyYLzmfMiAPidftnJOfVGx9asMJOLwWhyaHefWXmWbbIugx7svUCc0Swi3agyWheOcBvdfSalSZFffjVE1LJLdwWyWvsVObPjszKarm0Id2qAMFqXiCdSdnGukqqrjsXc4iX3XNGKHVrBYwV+7QOj33PypHwukbviL19ZGjIrjtXiqqusld084ZVuf7clxFNd0RRC5OUYAKj9x+Nmh9NojoSOUENYqGgVc1uzNRlbhLvEEfvJUTVmmMK0orzVGRQGqSFsaiXXF09N3ZbKVyJBr2P9Uw8Nv2RmLFvZOFmwTbJgNjtrdLd2su7W+8F+swqD0WJYsWOJVdHCo3HAyJgomkKylawAbG5UGa0NNYTnsnNBfv4544iN0GW76oP9spxf9qq66nByzlKHt6NkdjyM+rNhCGcmL718vroJ5zLzlhFnIx3+3ENXRc4AwNcm5h6uqJophsNUZkoEgIAzYAvhzi++1Ea0ikt3BWSt/YjtkgUBZ0Dx8J68Dp2bkWYCZsfTTMxl50QAEJ2iLe6uuxhl4IRxT2x2pROa0vIG6WhwVAKAvJIPlNSSKZPjYrro+ds34/cBwL+6s/exgNuhmhHHJWw5WaAMnLgkWUD1XdAVtOVYOSitvwIzGC0ObWHNuvtcno3MWWauc0ae8evQOQ/vyQecAVt1hLIrIXcoAxgbBi7xtmFyhYaYyXUZpqQplkW0GdQQns/Ob3f5vNFhMbsSg1q2jDj71UdGnxPcvJSQy51f+d7MjWbEsCHcqz+/VodfeME4YiP02HYeFVyGITwjz7A92D5Yzi0bwt0dtMVYuRg9PJLXXQGZaBUnv/hSm9nx1BuPw6P5HL6sDp1MZiZNuV7lP/39uXuLiuYdCHtnPnS0710zYtgOfuHFNqJVnLpLkPW24UuO7ibyCRHY7FDJ2BvM5GIwmpwOb4cEAGvFNXZc8TIog/dUM2fx2EzmfBAwMu7mRsVoFHSDsFpcFbn1KcPk6rjGtuJsr8zL84bJ5WIml12ghnA8H79EuOttw3ndJUhVcRZufHTbE/Q6lbFbup8AgG++tvxAKldueDMWKtztYnJxyWqyoM2+yQJaTb+QXWAm1z5IFo3EbNgdtsVY2Q5qDvMLz1umKrae0ESZGYbwCzPr7c9Nr98BAB97YOhRjlgmP7Px+9fE7m3n0bXSGusEfwCYycVgNDkxb8zoWFI2v2OJ1dFDgwXdLUpEqzjXVl7pBjY3qIzWh3b3WSuuBTl5ybj/oPdO24qzvbKSXxEBIOQKsbFiE+hmmgrRi9HEbqMzmMXE2S/eN/hWp+heypVV4TcfPX+00c+PF+JBAIh4IrY4VsKtT9s+WUCr6VcKKyzRuA9oYrbD02HbdYWaw7SyvNWhx+0Wc4sN1ytfeHzqYU0Hd2t/8NWTV0YsNV9xybO7nixIl1h364PATC4Go8npC/RlAPMvc2wWNMHIlGTWz3cDQMjNhLtd6PJ1ZQBAKqyFSEkSdc5ZUXvvTJkdl9VJFpNBAAh77JtxtxvUEE4VU9sKd7oZ55LvWKozmIMj+kdO9D8GAE+8t3bsvXi2ocdi6M+rw9NhD5NLWqwmC+6ylGhsJBFPxKgQLqyyPdg+oMI95ovZdl3ROq427olNz1hqHq0XdL8dz8cbagh/4+XF4XOJ3BVOnpR/7ZHR7zXy2XthI1kQvXbbeZR2pOz2d9t2rBwEZnIxGE3OcHCYtbDeBzRzlsnFIwAr/7UTfYE+CQBypfUwAOiBWBy8i7Vjvgw04x7zxmwh3BmbhnC6nN6+kqu6Gaebcyvxwzd1zV4V87+jaLrzP3/3/MlGPnu9tE7FSMuPFbI+5SNluZosuGPd7HjMIuqNZgAgVdreEGZsj1SRRGAzUWtHqDlMzeJWhxrCa8W1hhnCJUXjvnZm7hEAeOTqjtNHov5so569Vzh592QB7QRPO1Qy9gYzuRiMJqc/0J/jCKeWtJJnvbTe8DtImg2twxBn6YoUAjY3qIzWZyQ4IgGApBb8OgAt2G/b6oP9kC6lbSPcGQYDwsCuyZMNcSZbU5x99pHRxzkC9fUF6cbvvJ3oadRzacZ9QBho+bHimD0dAwA90BkH57BtsoBWV2TKGVbJtQ/o6YMhcci2wl3tuT2lc84KKcsiWZ/ymR1PvYn5jETZeqlx16t86Ympm1dzlajocaR/9ZHR5xv13L1CUpM+Us4KOucsqz23X5IsyFVyfEEp+AmIPhIckc2IsVlhJheD0eRwhNu4PP185jzbZF0GtffOOACs6WUvAPT4e1pejDAMot5o0ck5yyXovMQRaO1XXNKqmXEpNONuB+HOMDgSPCIBQFbJCso27e3VntvXdc5ZJuWsQFKTlhNnt/QF1+8ZDT8PAF/53vQHNL3+Hoyma8hWsiIAjAZHW1648ytvVO+RsXeyYEgYYtX0+yRVTLnKWtnDE17tC/Rd0k3ONvAuXQ/E4sCmadzKNNoQXpFK7m+9sfIAAPzk7T2PCx6H5TqpO2ZPdwI7JwsmJaMTpc/hk52c07bJhIPATC4GowUQnUYnp1l5lm2yLgMVZ3EOPAAMiUNMuNuErYbwCu+A2nWLrcXZXlA0hWTL2SAAXBG8ouWFO8Mg6A5WPLynoOkaP5+dv9TE4hy6Huik4syS1Vz/9geOnPa5+NxSptT7X56eub7ez1vKLXkVXXG6OFepw9tRqvfzzIZLTRomV/uVtp5HqaGZV/KBklpiumoP0ISs3+mXOGLvHxmtKOfjb1pyHq0lG4ZwpTGG8H/++3P3FCqarzfkmfvIif6zjXjmfuHjNFkwsO08Oi1NBwFAdIlMq+wTe88sDEaLEHQbHUto+3LGLnAOvSx0xZM8DwJgNDjKyn9thEhNLgcPdfBeVsl1GWbkGb8GjfPwnnzQHayYHQ+jcVBDeEqa2uHy+YGqOHvDkuIsKrhLP3xT5xMA8I2Xlx5M5cp1Pc5/PnM+CACCS7CFGCHSvHGPjM2TBR6HR/M5fFkdOpmSpgJmx9MMzGXnRGAzQWtntIhhEnNr5y05j9aSYXFYBgxDOK/k+Xo+65W5TNvpydRdAPDR+4ce5cglBcmWgEudryYLrth2Hl3MLQYBIOQO2WJdqSXM5GIwWoCwO7xru3fGhSwEu1IaIQjCUfI5fKrZ8TAaRyccZQBY8AVzuq+dmTaXYTIzaSvhztiEtnufz85vf/l8dVNuZXH2sQeGXo8KrpVsSRV/49Hzx+r5rPnsvH0y7iXJQfKrER1EVwbvSZgdjtkITmN+pFUXjN1Zyi0x4V6FmsREmrPsPForPA6P5nf4ZQCYlqbragh//rHzD2k6uJt6xTc+cE3HUj2fdRhIZsFIFnTfuq3JlSgkjO7W7rDtx8p+YSYXg9ECdHg6aMcStsHaA1NiTAaAmKYzg8tm9FaM+4XmvaJ97wHZBxtZRBcTI3Zjo917Ib6tyaX23F4VZ/NdjYxrPzh5Tv+Fewb/HgCefG/t2NtLct0SQcv5ZduIEcfs6Q6ia5zua1+DN2z7ZEHQHZQAYDG3yBKNe4AK93ZPe8uPlcuhDN6b0EF0klvtQDHtMDueeiO4BAmoryH8v19dHnw3nrvawZHKv/nA6BP1es6hKaa3JAvu3TZZsFpYDQKsSdZBYCYXg9ECdPo7G96xpJk5FwiVAKCnXGr5DQXjQgYKOScAzLs9lruA1IpQ4d7mbrP9sRK7QQUo3WRfjDJ4b0InnEbyqxGSX7NsZ99/elPn3DWdgbOKpjt+49HzD9XrOclC0jbCnV962bgsWey19VFFCjU24/k4SzTugdUiE+4beMMV3RdZJdCJY/qplr98XnQZR1SX8kt10SsVVSNfPTP7CAA8eFXkzJWxgGWvJHHMno4S6ET3ta/CE9p2T7peWg8CQJe/i42VfcJMLgajBej199LuPszk2gPTDgcBgL5S3kWycZfZ8TAax0AuFQCAJZ5j698eoAZHxBNhGyybEfPGJABIlVLbrytuUdF9HUkCgJ9+0tJHbX71kdHHeQL1rSX5um+/Ge+rxzNSpVQQ2Py5tTLc6rvGPTLhUWZyAWj3tEsAsFpcZXuwPZAupYMA0O3vZusKAD3YvwwA/NLLlq2KrRVbDOG6jJXffXL6poRc7hTcvPRrj4w+V49n1Ap+8aVqsqBvx3k0U84EAaAv0Nfy60qtYZt8BqMFGAmONLRjSbOTLBsVb12KAn7mKUuLM0YN0RTSJyfbAGANCjM39wAV7p2+TiZGbEaXv8to917K7LiuaFScLb9q6Xn0xl4xfd8V7c8CwO8/PfMBRdNrfgtxupQWAaAn0NPyY4Vbn+kCALXzpmWzY7EC1NikVReM3aHCfSAw0PJjZS+o7UeWAYBbfc/S82gtoIZwPa5XSWbLrm++vnISAH7stu5TIZ/T0kepudV3uwBAq/7+L0bTtQ1dNxocZWNlnzCTi8FoAXr8PXme8EpZK7tXi6tMvF8GuhHtVFTwS6+0fOaMYcAvvBDuLhedACApeUHTNbNDsjyZckYEgN5AL9tg2YxBYTADAFJF2jHjrkWuoOLM8vPov/3AkTN+F59dkUrdX/ne9A21/nypLAUBYFgYbu2xopYJya50AoAydB8zubBpbNL5krEzmq4hW8kawj3EhDsAaF03G/NoZtby8+hh2WII13ys/Oe/P3c8X1b93UH3wi/eO/hWrT+/1nBp4/etVn//F5MoJDwVreJyEEely9dVaGx0zQ8zuRiMFoAj3Ea798nMJNtkXQaaRexSFPBr77f8poJhwM8/2+XXdfh1aKquOhZziz6zY7I6ctnIIo6II0yM2IwjwSMSAOQqOaGiVbatfFK7bq2KsznLz6PtAVd57JauUwDwv19dfnAtW65ZQqiklri8khcAYDQ42tLHSvj559qJVnHq7mBGDw0y4QVgSBhi1fR7ZC4751d1lXdz7mLEEymbHY8VUIbuN5p4ZOMxVAotrc1pwixdTtd0rLw2nwk9fW7tKAD80r2Dj3Kk5sW6taVS4DaTBQ9se1zxXObcRndrjrT0n0VdYD8xBqNFoJc5zsqzbJN1Gahw71JUkCYQZ4zawCfe7gKAEHEVAWYIXw6pLDkKasFPQLRBcTBrdjyMxuJ3+lUv783p0LkZaWbbdu/q0P1xHQDJJaIoyXyDQ9w3v3z/0JtdonsxV1YDv/73547X6nMnM5OCDp34HX7Z4/C0dIkoP/+cccRG7GVVXFVGgiMyAdELSsGfV/KWHwdmMpmZ3BDuZsdiFXShu6R52lJEV3nH7DMdZsdTT2jCjO7Da8VvPnr+EVUHf0OP8OYPXR9bqOVn1wN+7kyE6Cqve0LruthT3O41c/JcEACCriAbKweAmVwMRosQcoUyALCYW2Qm1y4kC0l3WSu7HYRXgpquk1yyAyWJdVm0Adz6VBcABJ2BDAAs5BaYybUL5zLnRAAIOAOSk3PqZsfDaDy03fuUNLXtWNH9HWXd275GdI1zzD4TbWx0+8fBEf2X7hv8LgA8fW7t6GvzmVAtPndKmrKNcOeTZw2TKzzKTK4qTs6p+51+GQDOZ84LZsdjZeayTLhvhx40Lh/nF19s6cTrkDiU5cBpRbXoy5QyNenK+42XF4ffjeeucnKk8u9+4MipWnxmvXEsvFBNFvTtOI8u5za6W7OxcgCYycVgtAgRbyQNAIlCgplcu3A+c74q3IUMfO3Vts3fs7w4YxwSXQORl7oAwO+LrQKbGwjG9sxIM0EAEF0i22DZFFohvFvyRA8am/RmEWcfvD62cH238Jaqg//8Y5MP1eIzF3ILVLi39FFFAODWp6uXzt/ATK4tCE7D4JyRZ9i6sgsr+ZUgAIQ94bTJoVgKahpzyXeaYh49KA7OoQdcRqLxvfR7hx4rhYrK/dGZuQ8AwCPXdDxzZSwgH/YzGwH9PWvtOycLEkVDz7V72tke7AAwk4vBaBGi3mgGANaKayGTQ7E09Din6BIzdmrbbHe45ddCRCl6dKcvHxJ6kgAzhC/Hct4wAUPuENtg2RSaQV7Jr+x8+fxGZ7B3m2Ye/bVHRk85OKKcXcle883XlgcO+3mJfEIEbCBGNIUQedEwuQbvZSbXFkLukAQAS9klViG8C8lCMggAEU+ktcfKPqGdSrn0TNPMoweFVvHVwhD+wmOTt6/lKh1Br2P91z4w+tzho2sMXJomC27ccR5NFY3u1jFvjI2VA8BMLgajRejxG9190qXaXubYaizlljbKf7X2IysAwCWbR5wxDoZj7oyRNRN6lmO+zgwApEqpkKlBWZx4Pm5k3N3hlq9OYWwPFaKrxdUd15VNcTbb2ai4Dsu13YJ08sr2MwDw/52e/UBF1Q51SzH9+bS6cOeXXgkRtezWXYGsFrmK3dO3hTZ3mwQA8UKc7cF2IVUyhHtndR1mGChV05jIy51Qyxa/Nf1w0OtV6H78oMys5X1/+1bifgD46bv6Hg24HWot4qs7mkKIvGyYXAP3bnvpPLCp52j3Vsb+YCYXg9Ei0HbvtHMgY3to9U67pz2j2qhts93h4m8ZJlfb8PJGdx9mCO/KWnEtCGxWiTLsBxWiNKO8Hcog7Qy20gml2DT7yn/zgSPPBty8lJDLnV96Yurmw3zWemndaGbi62rpscLPTVTvkelhVVwXQQ3OteIaq+TahUzJ2KPSdZhhoIdH8rpLkIhWcfILL4TNjqee0IrXwxrCv/6dcw+UVc09HPFN/vTdve/VJrr6w88/FyZaxam7BElrP5Lb6XVSRQoCwJAwxMbKAWiazQiDwdidK0JXZAAgW8mKO7V7ZwBrpbWN8t9Ncdb6bZvtDr10XotdtzwijqQBQCpLzOTaBSrcu/3dbINlU/oCfbTde2in1+ih/oLuDqaJpjj4uWfbGxbcIQn5nJWfvL3ncQD469dXTi6mi56DfhZNLtGfV6vCJb5fTRaMMJPrIqjBSedNxvZQ4U677DE2oR1LaQfTViXmi6WB3ZMnl+Pxd5JdL81lbuUItE8/NPJdjjSP7NlLh9qSWuJylZwAAKPBUVZNfwCYqGMwWgTRJSq03ftkZpJ199kBWr3T7e/O6GJPUfe0rRNd5fnZ0y3dttnucNJiJwCo/ceWR0Sj3XtOyQms3fvOSBVJBID+QD8TIzZlNDiaBi5vCGsbl8+/0FTi7OfvGfh+f5tntlDRfP/xO+/ff9DPkcuyIdyDrS3cN5MF1zOT6yL6hf7LGsJ2J1vJOgpKwU9AtCFxiB13vQgtbJjHfPLtpppH9wtNnB3UENZ0Hb/z5PQPAMCx4bYXjg63rdYyvnqz2aF252TBZGZS0KETn8OX9Tv9zXEM02Iwk4vBaCFoFzTazpxxKVSsDQgDErCZSXE0mThj7B0u+U6AVHIBnXeX1O7b1j0Oj0bbvZ9Ln2NHS7ZB07UN4U6rRBn2Y1AYzHGEUy/X7l1rG1kBAD5xtqnmUY4QfOLBkb8ngP78dPr2772/FtvvZ6wWV10lreThCa/0Bfry9YjTEugaOMm4dF7pP8FMros4EjySBliF8G7Q9TbgDEhOzqmbHY/V0GLXG/fEVjuYtir0ehWpLIUO8v7/8vTM9QvpYp/XyeX+3Q9e8XRNg2sAm8mCnTvUUh3HulsfHGZyMRgtRNBtdCyZz86zTdY2VLQKyVVyIrAp3Gn73lZv22xn+JmnuwBAF7uXQYxlT3QyQ3g3lnJLXkVXnE7OWerwdpTMjodhDg7OoQecAQnYvd272nlD9fL55hNn91/RHr9zMPSyDpAvnpr8AU3fn/Y+nz4fBAzhzpHW3VZz8bdEohR8usNb0DpvYMLrIrr93QUHcVQqWsW9kl858NHXVmZanmbCfReUgXuMy+elxS7omtnh1I0rQ1ce+HqVVK7s/MbLSw8BwI/c3PVEp+hurv2JroHIS0ayYPCeHU2uhdxCENjsRMnYP627GjMYNqTd3Z4GNruiMS5kRpoJaNA4D+/Jiy5RATbb93LpmabpDMbYH3z8TSNrFhra2FCE3KE0ACzmFtlY2YbzGUO4UzOQYV+CrmAa2L3du7rRGWypE5rSPJejVPn3P3jkex4nV5hfLw784TOz1+3nvXPZORFofTHimD29eel8C5t5B4UjHESXmAaAc5lzbF3ZBtpNL+QOtfRYOSha9FpJd/ryRC15uOXXQmbHUy9El6h4eE9eg8ZNS9OB/bz31//+3IlsSRU6RffSx08Ov16nEOsGt/xaiChFj+705bSOa+SdXkd1HL2kn7F/2CrFYLQQHd6Oy7Z7tzOT0uQlWUTavpfIy13NKM4Yl4dLnTdaNUev3TC5Nrr75OMhk8KyNHPZOWOsuJnJZXfa3G0ZYHdDWItcldVdgSxRy25+6ZVQw4KrEb1t3sI/vbHzCQD485cWH07lyjsezbwY+nNpdeFOO9TqbUPsqOIO0L3FrDzL9mDbsCHc3Uy4bwvhoAlG51LH3Jmmq4rdD3Ss0H35Xnh1PtP21PtrRwHgl+8b/HsHR5ruyCv9vV4uWUB1HO3aytg/zORiMFoI2t0nVUqFTA7FkixkLy3/1dqP5HSXIBOt4uTnn2vpts12hZMWDJOr9+4NcRb1Rmm7dyZGtmElvxIEgLA7zDZYNodushOFRGi311Fxxs9NNKU4+8SDw6/GBNdytqQK//E7507s9X2JQsIWGXdufbKaLLiOmVw7QOfLpdxSyORQLAldb+n6y7gUrWoiU1O5VQm5jGp6ui/fC7/56PmHVR38DT3Cmx+8PrZQt+DqCP29Xq5DLb2Uv9PXycbKAWEmF4PRQtDuPrSdOeNCdhLumthji7bNdoSsT/lISQrqnLOi9t21Rr9Ou/ukSgdvYd3KJAtJWwh3xuWJeY1275erENbCw4Y4S3y/KedRJ8/pH71/6DsA8NS5taMvzab/f/beM06Sq7z3/1XonHtmenLOm4NWaSUUUUZIhEaIZGMw2Obei31xJBgDxlxjc33/2NhkMEmMAAnlnFfS7mrzTs45dM6pwv9FTe2upNndnp7urq6e8/189EIzVXWe2e5z6jzhPL+skh7+pH9TOO5nkgWNV5Ig13moNFQGgbOBT8KbkR13+f1LeDvCahBZbk5erjj10j58Kb5kz+b6Xx6ebxtejvVoaCrzpVs7nymocQXkTNP5iyQLwmmibr1RSJCLQCgjiLrPhTlT/mt4c/mv4OxYlW1WlzIY4eKwUy/WAIBorlkCoz1T2t5iaQkCZK6cDzn4V22oDittC0FZZIc0mApeOMjl2q565+xd26vndjVYTwgimH96cuzmbO4JpoNl77jT3iEzlY5aREab5uv3BZS2p1SRqy58SZ9dYVNKEjkB22xpLtu5slH4pv3SOhqeL+s+sXKFsJxQuxCJDE9/95WZWwHg5i1VL3VXm8/by6qkEYWzyYKmK5cudKm8N223tZO5kiMkyEUglBHnqvusxFd0SttTasiO+1vLf4Vq9TtnhLVhlo6vloa3vClr1mnvDAFAJBOxCWWsYpQrckCj3lxPNlibnDZrW1Zy71zzVdI6GllQtTLYl27tfEbDUOlRT7zrp6/PdV7s+jOOu7m5bAPCzNRLUrLAUrsImlVdH5xi0WBqINX050EQBUQyERsAdNg6ynaubBS+fl9AZHQpKhMz057BdTVlVxO1Jqm9ilzddyG+/sTYZf54ptJmYAOfv6XjtcJbVxhoz6CFysRNIqNL8XWXnDdZ4El4dGkhrWcohms0N8aLaWM5QYJcBEIZQVM0LFpLCACGQ8N2hc0pOULpkBU4uxGV4ZqukpXBVO2cEd4O7RuVglyVvW8KclXqK9M6WpfkRZ6dic6YlLGudJFL5dssbSTItcnpsnedkXtP8anz7huFml0hkdUnqUzcSK/0W4tnYX7pdJmit291vQAAP3h15pZoimPOdy0ncFQsE7MCZwPn5chaCrWEt9Nuaw8CpEJ4LeZj80Ze5FktrU1WGapSSttTslA0REvtEgAwUy+WbeJVPoZ3sYDw8HLU8mj/yrUA8Mkrmx4361i+COYVBGb6JUm8w1p3wabzY6ExKwBYNJYQTZRsc4b8yxEIZYbcVH0mMkM2WW8hko6sWf4rVG8Pi6whTnHJspZt3oxQodnVpvOXvc05s2gtQQAYC46RuXIOSS5Jx7m4BSAZdwJg0ph4I2uMihCpifDE+SsLKBqipU5SBlvdzKuVv72545DTqPEGE5zzH58Yu+J8181GZ428yDN6Rp9w6ByZYtpYTM4o1FZtIUGuC9Bh64hQoIQ4FzdHM1FWaXtKibHQ2NvUrQlrIweT5eByOdJh6wgCFw8I/8NjozdleFHb7TINf+zyhtGiGFcgsk0WyOqsZK5sDBLkIhDKDIfOEQSAhdgCcdzPIZQKaZJ80kiDFlosLbE3/ZKizzSfZ2cOlHUfhM0EFVnQ0cmAU6QYnmt+h+etv7dr7SEAmI3OkrlyDuPhcYsIkTKxpoie1ZPSRsJZuffQuP1C1wmONlkZTNXrqFHL8J+6qulxAHhywPOOwaWoZa3rxkPjNgAwa8xl7YzQZ5IFl5Mg1wXQ0BrRrDGHAWA4OKzaasZCICdez1W3JqwN75KCybR/vGyDXI3mxjhDMVxaSOs9Cc+a7VX6ji60nFqIbGNoivvirZ1PFNvGfCN/nrxr6wXXUdl/c+gcZK5sABLkIhDKjHPk3onjfg4joRErAJi15hC7Rk+Rs87Zybpi20YoDOzk86tN56uXoTG8LVgjq/ssxInc+7lMhCdsAM4cfSYQspV7lzfvtH9M9evovfvqJ3qqTUMZQdR89fHRm9a6ZjYmBcjlgHk5QgVnDFQqZBdpluObrvRd/I7NjRwQngpPkT3YOSzGF4njniVyMJkOz5ZtkIumaFg00h5jJDjytrmSyPD0d16avg0AbuqpfHlngzVYZBPzzpmm8w1XXDDIJftvRN16Y5AgF4FQZshN1f0pv11hU0qKqYi04bRq1i7/PdN83j+ueueMIEEvHpNKw23Na24o5ICwN+Elzsg5zEfny95xJ6yPM3LviaULB7ka9y8AAB2eK4t19Au3dD7J0BR3aiGy7bfHF5vf+vvFmOS423X2sj3Wy049f1ahllR2XhQ5iDMfm7crbEpJcUbdWl9J3isXgW/a7xVpTYZKhW20b7Rse4aeaa8SfXt7la8/MXaZL5apshnYwBdv7Xy1+NblF8o/bqRSYatIazJ84+UXTBb4Uj5Z3ZrMlQ1AglwEQpkhq6FdTO59syGX/57PGeFarpkHVp0z0ny+LGB8I1KQq6pnzSBXrak2CJxV3SRILCeWbcDZwAaB4DK45ICw/ULX8Q2XSspg6aiZXulf84ifmtjZYA3e0F1xAAC+/cLU7YkM/6Z9syfhsQOAy+AKFt+64sDIyQJ7CzmqmAUV+oogcHYdJUj4kquOu5E47heF0Ypyf0Nm8vmyreY6JyD8prkyuHS22fwf7296zKJnOQXMyyvsqoiAaKldAqO9oEKt7L/VmerIXNkAJMhFIJQZ7Vai7rMWFyv/lZTBDHGKSxhI8/nygArN1AEAX3fJms6ZrLIZTAXtRTSr5JEz7lWGKrLBIgAA6kx1QSCLgDDNiqK1fgEA2KkXyqKa60u3dr5iM7ABXyxT9fUnxi4793e+lM8OnA2YlyO0b7QOAITKtZMFhDcjV19cLCC82ZBV9BrMDeS9kgWCo20BAJil42Wxjq6FvB/3JDxveq98RW42X20a+uhlDWPKWJdfmKXskwWy/9ZibSFzZQOQIBeBUGa0W9sjFCgxxsUscS5+XunzzYacRazSn8dxp2gItoZV5+zFst1UbBao6LKWinsrRYoWuNbrl9e6ptPWGQKASCZCAsLn4EtKjnu9qT6orCWEUkGWe88meSI426Uji2XinNkMGu6P9zc9BgCPnl65bmAxcqahuJxxbzY3BxUyr+DQoek6AOAar1xQ2hY1IFdfBNOkmv5c5LWjzdpGHPcs4Ku3Seuob6ReaVsKRbWxOgic3Z8DUrP504uRbSxNcV+8pfNJxYzLM7RX+hx517b5C12XETJUNBO1AkCnrbNsj8EXA8Xkbd1u9/UA/h7ATgAcgEMAPt/X13csy/unALytP8Iqf97X1/dvb7n+fQD+GkAvgCiAFwH8XV9f33gu9hMIpYqe1QtG1hiJcTHraHDUurNyZ0Bpm0qBQCpgB4AGc0PwfNcIzo4FxjfawSyfqgMwUCTTCAWAnXimlgIgmKuXobPwa13TZGmK0RTNJ/mkISWkQjpad8ES8s1CKB2yA0CzpZk4IwQAQJe9Sw5y2QVRAE2dP0fKV+9YYMefLovm8zIfvaxh7OFTy0NDy7Gef3hs9OZf/9Ge+wEgko7YAaDD3lGWc4UKzhioZNAh0izHt7xdoZbwdlqtrUHgbOUSAYhlYkyci5spUGKbtS2qtD1qgG+6agEHv102/Q3Xot5U/6Zq+kSGp//jxenbAeCdZdJsXoYOz0onC5r2XzBZMBmeNIsQaQNjiFm1VtUf01QSRSq53G73uwA8CWAFwMcB/CkALYCX3G73Jet41G8BXLfGf795y3h/CqAPwFEA9wL4SwAdAA643e6WjfwtBEIpIjdznIxMkk3WKqGU5LjLG9C1EGp2SZmzAGk+r3aYhSPSERt763k3FCzNirK6j5/3k6pHAIIoIJwO2wGg29YdVNYaQqlQY6xJamhNihM5zUJswXCha7nmd0jraKi8+ht+6bbOJ1iaygwsRbf8/NB8uyfh0aWElJ6hGK7J3BRT2r5CwE4+K/WRMdeSpvNZ0m3vDgFANB21cQJHKW1PKTAWGrMCgEljCusYHfkeZQHfcJm/nPobrkWrtVVKnmSkKr9/fGLscn88U2k3sP5yaDYvQ/tGTKtN59N84xUXbDo/FhqzA4BFayFVXBuk6JVcbrfbAOD7APr6+vo+dM7PfwvgGQDfA7Any8fN9fX1vXCR8eoB/AuAf+rr6/v8OT//HYA3AHwLwHvW8zcQCKWOXWcPLcQXGmWVtM1ORshQkYx0xETegK4F13Ltgu7lfwIVnq+DwFGgWVLZo1Jo37AU5KracsHScJvWFgqlQ06/4GdqUbvps2az0VkjL/KsltYmXUZXSml7CKWDVWMN+VI+11hozNZgbkic7zqhdndwtb+hkV46bhNq95RFldP2Omvo1q1VLz58auXG774yfVtNDf8bALBqrcELVbapGWbxqLSOOtouuI4SzmLVWjkDY4gl+IRpIjxh7rJ3RZS2SWnkhKtVu7a6NWENVvsbUoGJVnbqhbq0a+uw0iblG/k4XiwTs5xc8Fsf61+5BgD++Kqmx8uh2bwMM/l8HQCI1vrFizWdn43OyiJZwSKYVtYo8Va+C0A1gC+c+8O+vj4ewFcA7Ha73ZfmcbyPAhAB/NNbxosB+GcAd7rd7rJVriBsToi6z5sZC41ZRIi0gb1w+a/g2hoRteYoxad0zNxBZzFtJOQXOjgj9T9ovOKCpeF2nV0ql+eDpJILwGhw1A5IjruylhBKDZtOqhCejkzbL3jhm/sbllU/mS/c0vm606jxBhOc82dvjFwOADatLaiwWQVDbjrPu7aSflzrwKKVKoTlqozNzlx0TnLctXYS5FoHgqO8+hu+FZPGxBtZY1SESH3lqcO3ZXhR21NtGvrIpeXRbF6GWTwmJwsuuo4uxhftAFCprwwW1qryR4kg17UAZvr6+ibX+N1rADKr1+RzvDf6+vrWOgP+IgAGwNV5HI9AUBxZFU1WSdvsjIfG7UB2zohglZwzZuaVstxUbAao0KyeSgYcIs1yXPOF+8jIG4kAHyBBLgAz0Rk7cDb4RyDIyHLvi/HFLJrPd0jr6PLJslpHjVqG/8w1LY8CQP+yZxsAOHXOsp0rdGg1WXCRPjKENyOvn3OxObIHA7AUX7IDQJWhKqisJeqCr94uBbnKqL/hW5Gr+8b8S90sTXFfvLXzCaVtyjfy5yd/nhfCk/DYAcBlcAULa1X5o0SQqwNSb6y30dfXlwTQD6A9y2fd7Xa7591ud8rtdo+63e4vut1u3TrGmwAQXMd4BIIqkJs5BpJSs/XNzmx01g4Adu3Fy3+Fiq55AGCWT5ftpqLcYSeelUrDzbVL0Bgu2P9DlnsnlVwSC7EFOwBU6KRqUAJBpkovOahvlXtfC6F656pzVn79Dd+/p3ZqR53lFNgADQCVhvLMuK+njwzhzcjr51JsiQS5AHiTXjtAHPf1wrVcU5b9Dc/FppECwpQmiJt6K1/aUV9mR1pFAXRotel88zsueuxbFski6tYbRwl1RSeAtaq4ZPwAKrJ4jg/ACwAOQ1JLvB2SWuOVbrf79r6+Pnk1cK5eu9Hx1uTAgQN/nOu9pUI6na4EyuNvIUgwGYYFgGgi2qSGz7XQ38GF6IIZACrTlc0XG8Mlduv2AIh45/YcPHDgfAquhBKmfe64uRPAnL7bMXCRz9uUMmkBIMgH9el0ulIN86WQ+MKSlLcz7uw9cOBAk9L2bDZK+X1sS9j0ABAKhnYfOHCg5ULX6lNN9LUA+PBiy4FXXvljUOXVf/veZpH+6qwkXByadVx+IH1gi8Im5Q35Ozj86mMf3wMgaGzFwdcPf1Jhs1SFM+Y0AYDP47vywIEDW5W2R2nC4XAFAFh8lv0HDhzISmCslNfCoiGKuJ4xCVouZjz63IN/ktBXr6kUrWZivsoKsIDZEBBurfD1HjhwoFdpm2Ty8R3Upzz0tZmYOcMYxZemMu/H9IELXh+JR6oAQLOgufGA58C1uY5bLmg0mpzvVSLIZQEQv8Dv4wCyyXxc3tfXlznn/x9xu93HAHwXwMcA/DjP4xEIqsHJOHkACPEhRhRFUGXmYKwXuUrHSTsvukEImjsyAGCJz2gg8gBFCnzUhi06oQGAsLktc7FrHYxDmitCaHNPklXkY5vyGkIgyMhzJZuqx6SuQkhpbIIuE6JNyQUmZqgvq++TXUcJdlOADwPM4XmH/o4KMaJnqbISKrFFx6V11HTxdZTwZs7MFYFUCAPn7MEYZ9k0Ey8KFIWIqSVTEe7X2aNjmnILcq3ERWbS59BoqoHWikBGQ5ffNsweHdMCQMTYkr5YskcURYT4EAsAFUxFWX3WSrDhIJfb7f4cgG9mcemLfX191wKIALiQ/LQRQOBiD3tLgEvm+wA+A+AjOBvkyma8nEsj9+/f/71c7y0V5Ah1OfwthLN87cGv/XVaSOsbdzb+vNnSfKFAr+IU+jv47ae+/REAbc2tzY/u79w/erHrxX7bZ9lUyPaOOu4Bvu3CPZ0IpYf52NhnAejbdl39y+bW/Rf8/GKZGPPNh7/5hZAQAqNhvJt9HfzGo9/4EwCuLT1b7t9fv39RaXs2G6X8Pq4KVln+67n/+ouAGEhnYx87VvtBeENdl+qnXk7vd58uho3FJP37L/4leBjDcSfz+xXH2P/3/q3PKm1TPpC/gy3pwQgAS01r9xPO/fvL7vMrJJn5TA0O4lN+yh8qxblcTFJ8ig79PvQFALj5ypu/Z9KYsnLeS3ktLCZ6X9N1CPe/Y6s4ONC2/6+eUdqefCGIIt77/SP3ZtIOlwYAZ/bM7t+//xdK23Uu+fgO6n//4+sBXG1xNRzev3//Bd8RE6EJM/cs9791jC5xw9U3fDfXMcuJQ4cO/X2u9+ajJ9ePAPRm8d9HV6+/2PHAix0vPC99fX0igGcB9Jzz44uN58h1PAKhlDnTzJGo+yCUDtkBoNXSGszmesHWKCmDzb5adv1kyh3aN2qiUmGbSGvSfNN+78WuP0fdByEhpESfypJBEAWEM2E7AHTZu4LKWkMoNdqsbVEatJDgE6ZwOnzRJKlQ0Sk1n18pv/6GoVRIk+STRhq0IHJmvDjqu/KVcX+V0nblDVE820em6SrSdH6ddNo7QwAQzoTtQpn2UsqWVXVrysSaItkGuAhnKdf+ht97ZaZ3zBPvZAVnGji7Ty835M9NqNl50XV0NDRqA4i6db7YcCVXX1+fH1IgKVvGAdy01i9Wm8ZvAXDfBkyiAZy7iI4D2HOe8VohBbkmNjAegVCS2LS2kDfprZ6JzNgAbNpNKidwVCQTsQFAt6M7mM09QkXXArNyupf29NcBOFFI+wj5hZl8Tmo6b61bBKPN6viQVWsNxbm42c/7lTjCXzIsxhcNGSGj1dCadK2xNqG0PYTSgqVZ0awxh8OZsH04OGzb59p3wQQhX7N7XjP8MOhAeTlnADAUHLIBgFlrDnU1OSYOz4T2/tOT47c9/CeOn9Jl0B5An/bRVCZmFhldkm+4bD17fAKAWmNtQkNr0hkho12OL+trTbVJpW1SCjnRShz33OBar13QvfINUOF5qfk8pf5cnCea1v7k9blbAeC2nvbnn0ng5nBaCgjTZfD3nUEUQIelZAHXfM1F/TBZ3dqhdQQLa9jmQIlv0gsAmt1ud8sav7sCgHb1mvPidrspt9t9PkXEywCcW1b9AoBL3G63aY1rrwEgAHj5ghYTCCqkQi+p+yzEJbW0zcpkeNIsiAKjZ/Rxh86RVW8RvnaPlDkLTJadc1buMEvHpayZoz3rwK5Na5PUSFf7UW1WRoOrWUSNNVhWG01C3pAd1anI1EV7mfKt1y0CABVeqAWfVn/k5xymIlN2QFLs/fIdXc/qNXRiJpBo+fYLU9sVNi0v2KKjWgAQrA0L5eBUFxuaomHRWEIAMBIasStsjqLI6tYOHXHcc0Go2hIRNaYoxad0zNxBp9L25IMvPjx8fSzNW2qtuvkv3rT7oI7WJXmRZ6cj02v56qqFXjhip7ikQdQY40LNjou2RlqMLdqBs/4bYWMo8eZ6EMAKgK+e+0O3200D+BKA4319fQfP+Tnjdrur3/KMXwB4ze12177lGR+EFOT6/jk//m8ADIC/ecu1RgB/BeDhvr6+TVvlQihfXAZXAABWEit2hU1RlLHw2LrLf7m2GxYAgIou1SCTIDt8FUH7RqUjNtXbs17XK/WVQYAEuc447jp7UFlLCKWKQ+cIAcB8dN5+sWuFis6YqLOGKCGjZWYOVBbcuCIyH523AZLj3uQwJNy7a58GgF8cnr95LpC4UB9YVSCLdwjO7JMFhDcjJ09Wq+k3LUvxJTtw9j1LWCcUfaaFBjP9suoTr08PempfnQhcSgHiX9/U/oiGoUV5fz4SLK+AMLv6eQnWxvlskgWepMcOAFWGqpx7hRPOUnTnra+vLw7gUwA+6Ha773O73Xe73e73A3gcUoDqU2+55TsAFtxu9/5zfvYtAHoAr7rd7k+53e5b3G73twD8HMCP+/r6fnfOeHOQglmfd7vd33G73e9yu90fglThVQngzwvzlxIIylJnqgsCgC/pcyhsiqLMRKTyX7vOnvVLQ7Q1JgW9w08JHMtOvegqmHGEvEOH59bdR6baUB0EAD/v39RBrsW4lEWUAxkEwlup0FeEAGA5sZyV4y7YmiTnrMz6Gy4nlu0AUGWoCgLAn9/QdrzRrp9JZATTFx4ZuVFJ2/KBLSYpKwrVO0iQK0ecemcQABbiC5s6yOVNeu0AUG2U3rOE9VMu/Q0zvED98zMTd4gAtb/dcfCG7solAHDoHAEAmI5Ol5W/wiyfkoJczo6s1lF/0m8HgHpTfbBwVm0eFKlQ6OvrexDArQDqAfwEwHcBiACu6evrO/SWy+cBBHGOAmJfX98bkI42HoZUEfYAgGsBfBrAH60x3rcB3AspiPYrAP8XwDSA/X19fZP5+rsIhFKi1So1WQ+mgnZlLVEW2XFfbxZRtK86Z3OvqXpTsZmgV05bqHTULDK61Hr6yDSYGwIAqeRaiUtVny6DK6isJYRSpcZYEwTOOq4X46xz1l9W66gv6bMDQLWhOgQALE2Jn7+142GagnBkJrTnd8eXmhQ1cCOIIiyxKQ0AcC3vIEGuHKnUV4YAwJOQqjM2K4FUwA4A9aZ6kjzJEb5m12oLDXX3N/zGU+OXLIVTdWYdE/7KHd3Pyz+Xj+fJx/XKBTowVg9k13QeAIJpyV9rsbYEC2fV5kGxJrt9fX1PA3g6i+u+AuAra/y8H4B7HePdh401tCcQVEW3XWqyXpbNHNeBNyE5Y+t13IXKngVm6cQ22jNYB+BoAUwj5Bl28oXVpvP16+ojIweEA3xgUzee96f8NgCoNdUGFTaFUKI0mhulgHAykFXGna/du6AZfKDs+hvKjnuDuSEo/2x/m9N7Q3fly08Pea/5f89Pvuum3sr/MutY1anJGVLLjJaP0SJriAs1u0hgIkfkgLAv6dvUlVyhlKSa12ZtCypriXrhW69fwItfPdvfMEtRnVJieDlqefDE0g0A8IeXNz5eZdam5d+5jNL+vKwCwgJHUeH5WgDgWq+7aJBLEAWE05K6dbctO5EswoXZnF4vgbAJqDHWJLW0NsmJnGY2OmtU2h6l8Kek8l/5+Ga28HWXkObzKoNePiEdVXR2zK/nvl5HbxAAQkKIyQiZsmqQvR5kx73Z3BxU1hJCqdJuaw8C2cu9n+1vuFhW/Q3D6bANADpsHcFzf/4Pt3e94jBqfP54pvIfHhvdv+bNJY49OiYdVbQ1kqbzG6DJ0hQEgEAqu4BwOZLiU3QkE7ECQI+jhwRMc6Qc+hv+/aMjN6d5UdfpMo18Yn/j0Lm/azBJyQJ5v14OMHMHnRSf1olaS0So6o1e7PrZ6KyRF3lWR+uSLqMrVQwbyx3y9iIQyhi5meNocNSurCXKIR/XbLGsr/yXa71+UQRAxZarkQpv6goftUD7VkvD19lHxqq1cmbaLAgQMBYasxTGutInko7YAaDD3kGcEcKadNu7wxQoMcbFLLFM7KLHe0VbY1LUOwKUwLHs9EtVxbCx0MQyMSbGxSwUKLHD1hE593cWPct95prmRwDg6UHPOw5OBSqUsTJ3bNHVflxZ9pEhrI1cjZFtQLgcGQ+NW0SItJE1Rs0aM6e0PWpGzf0Nf35ovr1/MbqVpanMl2/rfIym3pxLlPfn5dReRRYJkEUDLobsp1m0lmDhrNpckCAXgVDGyJLNU5GpTZlJFEQBkUzEBgDd9u51Oe6iuTotGiu9lCjQ7OTzpPl8qSMKoMOzdQDAtVy7bufMTtt5ABgNjW7KubISX9GlhJSeoRiuydwUU9oeQmmiY3SCWWMOAcBgYNCezT1nnbPy6G84EhyxAYBZYw7rGJ3w1t+799RN7W6wHudFMF99fOwOQVTXySJbbFwLAELNrnVVxBLeTIu1JcpQDJfiUwZPwqNT2h4lGAuP2QHAprUFlbVE/ai1v2EokWH/65Xp2wHgtq2uF3bUW9+2F5er6SOZiJ0TuLKopmeWT0lJ14qurNbR6ei0HTjrtxE2DglyEQhlzJlmjvHyauaYLdORaZNc/ltlqFp3+a9ob54HAGb+kKo2FZsRZvZ1J8UlDaLGFBVqdqy7EsnBODgAmI3M2vNunAoYDg3bAan6c7P27yNkh11nDwLARHjCns31QmX3PADQnoGyWEcnwhM24Gyl9Fp89V3dT+s1dGLan2j5v89N7iyacRuFT1Nnms6330iCXBuApugz35FsA8Llhvw+JY77xuFr96qyhcaXHhm5JpTgHJVm7coXbu14fa1rnHpnWs/o47zIM1ORKVOxbSwEdGC8AQD4ukuyWkcXYgt24KzfRtg4ZCdLIJQx1cbqAACsJCTVtM3GWEjKIl7IGbkQfGWPtKlYGajPn1WEQsBMv9gAAIKtaT6XPjIOxsEDmzogbANIxp1wcWSHdS46Z8/mer7ujHNWFuvoXEz6ux06x3mD6c1OQ/yDe+ueBID7jizcPO1PqKIvJjP1YhUrpKiEtoIXKrpIRecGsevsAQCYDE9uygrhpfiSHSCOez5QY3/D54a91c+P+PYDwF9c3/qwQcO8rfJVRt6njwRH1D9XUhGGiizWAADX/s6sThbITfeJunX+UMUkIRAIuVFvqg8CZ+XONxvTEan8V648WC98wxVSBUJwsiF/VhEKwZnS8NWqkfXiZJw8AHgSHvVvsHJgPjZvBwCnzkn6cREuiMvgCgDAUmIpq7nCtd+0KIISqdhyNZJB1fc3lB33Sn1l8ELXffb61hPNTsNkMiMYvvDw8E3FsG2jsDOvNABA2NSWUdqWcqBCJwV35PV1s+FJSo67rDRJyB3R1pgUDE4fJXAsO/FstdL2XIwUJ9D/9NT4nSJAXdFqP/Su7dVzF7peTp7MRGfsxbCvkLCTz1VTIs8IxgqvaK1PZnNPriJZhPNDglwEQhnTamkNAkAwFdyUjrtclZNrFpFru2FJpBieinsrqcjCpuypoRZo/3g9APD1+y64kTofFUwFB5xVGNxsyFnESsOFHXcCodZYGwQAb8Jrz+Z60VSVFk2uFUoUaHbsqdpC2lYM5EB4nakucKHraIrCl27tfIShwB+fC+/89ZGF1uJYmDv0Sn89AATNHWmlbSkH5KqMzVpNL79P5YQrYWOI9laphcbsqyWfeP3aE6OXLYVTdRYdE/7HO3uevdj1ctJATiKoGWb2tQbg7OeVDXLT/WZLM0k05gkS5CIQyphuh6TuE8lEbIJ43irhssWT8NiADZT/6iy8aKldogCwY0+VxVGbsiQVYajoUo0IgOu4OSdFMLmSK5guH3Wf9SAHLOpN9Rd03AmEJktTAAACqUDWyRPBIW322bnXS945uxjy391obrzoXLm0xe6/qbfqJQD49xen7ogkuZKuZJOPlIbMnSTIlQfk9dSX9G3KRGMoJSlLtlqlhCthY/CuLXMAwHj6S3o/emw2ZH/k1Mp1APDHVzU9UmXWXnQ9qTZUB4HyCAgzHqnFCe/amlXSVRAFhNNhOwB02yW/jbBxSJCLQChjKvWVaT2jT/Aiz06EJ8xK21Ns5I1lrbE258yI4GiXNhXzh1XvnJUr7MQzNZTIM6Kx0iOaa9YtMACcDXLFMjFrkktuunejP+V3AGelvAmE89Fp6wwCQCgtObDZILi2zQEA7R1S/Toq/93yv8PF+PLtXQcqTBpPMME5v/jI8DsKadtGoGIeLRVbcQmgETR3kOOKeaDFKq2ncpXGZiIjZKhIJmIF1q9uTVgbvvHKeQCgAqXbQkMQRfz9oyN3cIKo2VZrOf0HlzeOZnNfg7khAAD+pHRsT81QwSmp6fzq53UxFmILBk7kNFpam6ox1mR1vJFwcTbdRp5A2GxYtdYAAIyGRu0Km1J05KqcNltbztUpfM0OqS+Xb7ikM2ebGbk0XFhHafhbYSkWVtrKixCp4eCwLX/WlT6CKCCUDjkAoMfRQyq5CBekzdoWZSiGT/JJoz/p12ZzD9d89Wp/wylVr6OhVEgT5+JmGrTQae8MZ3OPUcvw/+u61kcA4PkR3/7nR3wl2U+HHX+qloJIRY0NGYEhp/PzQY9dWk9D6ZBjs1XTj4XGLCJE2sgao1atlVPannKAa71+WaQYnk74K6jQrF5pe9bi/z0/uWPSl2jXs3TiH+/sfiLb++Rqv/UkT0oRKjhjoBN+p0izHNd6/XI294wER+xA7iJZhLUhQS4Cocw508wxov5mjuuBEzgqnA47AKDX0Zt7kKvlWqkCITTTgE22SVULjHdQajrv2rohyXtZYVFW5dwsLMYXDRkho9PQmnS9qT6utD2E0oalWdGisQQBYDAwaM/mHr75ao/IaNNUKmyjvUOqrSqW/16L1hLU0Box2/vu3lkzc1mL/bAggv76k2N3pjih5PbfcrVy2NROqrjyRK2pNqmltSlO5DSz0VlVKGzmi42qWxPWQGviRUvdIgCw40+WXMJgyhc3/vKNhVsA4AN7655qqzRmrdDaY+8JAUAkHbFnhAxVKBsLDTvxVB0AiObaRWgMWTkNU5EpB5C7SBZhbUruJUsgEPKL3MxxMb64qXpCTIQnzLzIM3pGH6/UV+bcX4Sv3xcQWUOcysSN9MIRex5NJOQJKiBVh/BNV+bUdF7GQUtBrrnYnD0PZqmGocCQAwBsWluApsi2gHBx5M34ZGTSntUNjFYUrQ1SX67xZ0rOOcuW8fC47IysO3Hy9Tu7n7XomPBSOFX31cdHL8+/dRuD9g6RpvMFQK6mHwmObKo92ExkxgEADp2DHFXMI4KzQ2qhsXCk5I4sfv6h4VuSGcHQ4jRM/sUNrcfXc69NZ8sYWENMgECPh8YtBTKx4Mifi+DsyDrpOh+bdwBApb6SVNLnEbKbJRDKHLmZo6yetlmQN5S5OCNvgqIh2Jsk52zqhZLbVGx2qOCUgU4G5NLwlY08S67kWoovbSpnZCoyZQdIFpGQPU6dMwgAc9G5rOcK7+yUjiwuHlXtOioHwCt061fsdVl0qU9f3fwwADxyeuW6w9NBZ36t2xh0cLoBAIKWLlLJlUfkanp5nd0syOrWVfoqv8KmlBV8zc7V/oYjJZUs+O+Dcx0nFyLbWZrivnx718M0tf5iLJvWFgTUXU0vfy7y55QNy/FlBwDUGGtIkCuPkCAXgVDmyM0cfUmfXWFTisp0ZFrOIm74pSFUdEuZs6UTJbWpIJxVvRQtdQtg9Rs6T+pknByw+QLC81GSRSSsD5fBFQDWp4Ql1O6W1lH/qGqDXLIzIv/96+WjlzWM7ay3nOQFkf3yoyN3coJYEsdy6JXTFiodsYiMLhU1NJD+SXmkQi8FRBdiC5sqeeJJeCThH1Mtea/kEa71OilZUEItNHzRtPa/Xp6+AwBu2+p6fm+TLafPXN6vz0RV2l5FFKTPBWc/p2yQRbKyUewlZA8JchEIZU65NHNcL/LxzCp91YZfGnzdJdKmwj+uWuesXGFWq0LWUxp+PmSFxUAqYN/os9TESmLFAQDVxmqywSJkRZ2pLgisL3nCtd8oKYOF5urAp0siuLNevEmvAwDqzfU5z5Wvv7vnCYOGjs8Eks3//PT4nvxZlzvsxHNSX0NrwzzIkeW8Iq+r6wkIlwO+lOS4N5mbyHsljwi1u4OixhijuISBmT9cEoHTzz88fF0kxdtcFu3Sl27rfD3X58jtVZbiS/Z82VZMmPnDDopLGESNMS7U7g5me18gHXAAQLu1ncyVPELeZARCmdPr6A0C6m/muF7kapx8ZBG5jpsl5yyyUIt0jNno8wj5g/adKQ3fcJCrkq3kAZxRGtwsnMkimkgWkZAdLZaWAAAEUoGs54pQ2RMVdbYQJWS0zPTLVYWzrnAEU5Jib4ulJZjrM5ochsTHLm94DAB+c2zxpoHFiDU/1uUOs3RMShZUdG54HSW8mQZTQxAA/En/pnqvBFPScdwuexd5r+QTioZgk1poMJPPK554fax/pf7AROByChD/5p3tD+lYOufyMrW3V2GmXpSSBbamuWyTBSk+RUfSERsAbHFuCRbOus0HCXIRCGWOVWvlyqGZ43rJZxZRtNYnBUOFjxJ5hp14piTl3zclogA6NFsPAFzrdRtqOg8AdtrOU6CEOBc3RzNRduMGqoNgWnLc220ki0jIjk57ZxCQKoSFdRyZEezNcwDATr+suqPfgiicCYD3OHo2NFf+5Orm/m6XaTjDi9rPPzx8hyBmLdRYEGjfmOSc1e7Z8DpKeDNt1rYAcHad3QyEUiFNnIubKVBCl70rrLQ95YZQ2SMd/V4+qWiQK5rimH9+evzdAPCODudr7+ytWtzI8xrMqwHhlDoDwszyidVkQXfWyYLR4KhVhEibWFPErDGTo+J5hAS5CIRNgE0rnY8fDY3aFTalaIRSkjPSaZOcsY0iOlqkTcXsa4pnzggSzNxBp1QabooJNbs2rOBEUzQsWksIAAYDg7aNW1j6ZIQMFUlH7MDZqk8C4WI0mhvjLMVmMkJGtxxf1md7n1DVKx39XjmtunV0PjZvzAgZrYbWpGqNtYmNPIumKPzjnd2Pahk6NeaJd/7Hi9Pb8mXnuuHTFBWeqwPOHikl5I8eR08QkKrpOYHbFNX0g4FBOwBYtdaghtYoG8EtQ85poaFosuDzDw1f44tlqhxGje8f7+x+fqPPkxNtcsWs2pA/D77+kqyTBbJftmGRLMLbIEEuAmETIKv7yJLO5U44HWZjXMxCgRI67Z15ySLyrm1SkMszoLoKhHLlbGl4Y976yNi1ksLgRHhiU8yV0eCoVYBAG1lj1Kq1kiwiIStoij6TPBkKDmU9V7j6yyRlsMCE6oJcQwHp77RpbUE6D+tNd7U58oG9tU8CwM8Ozd065YsbN/zQHGCmXqyihIxW1NlCQkVXTAkbyhmHzpExMFI1/VhobFNU04+Hx/Ojbk1YE67jpnkRABVdqkEqokgLjScHPbXPj/iuAoC/uL719zaDZsP7h15Hb4gCJUYzUVuci6urNUg6xlCRxVoA4NpvWsj2tpmo5Jc5dU4yV/IMCXIRCJsAufm63Iy93BkKDNkBwKK1hHSMLi/yM3zjlVJfruCU6pyzckUu1ZdL9/OBU+cMAsBcdM6er2eWMiOhEdlxJxsswrqw66SA8FR4yp7tPVzHTYsiRQtUzFNFxTzaQtlWCKYi0t+ZD8Vemc/d2Has2WmYTGQE4989NHxrvp67HtiZV6R1dPUoKSH/2HS2ILB5qulno7NEsbeAiJa6lGis9Ky20Kgp9vjxNM9846nxu0SA2t/meP2unTWz+XiukTXyZo05LEKk1FZNz048U02JPCMYKnyitT6Z7X2LsVWRLMPGRbIIb4YEuQiETUCdqS4AAMuJ5U0R5JKziA5t/pwRrvX6ZZFmOTrhd1LBKUO+nkvIHTowIZWGr5bu5wN5o7EUX9oUc2U2MmsHAKeeZBEJ66NCXxEEgPnYfPZzRW/nRHP1MgWRYsefrCuUbYVA/jvz6bjTFIWv3NH1MEtTmVMLkW0/em22K1/PztqGlX6pInb1KCkh/8iB0c1STb8cl/aaLoOLvFcKhOBolZrPK9BC4wsPD1/tjaZdNgMb+Ma7e57L57PtOrsfAMZD46qaK8zc6/XA2dYm2eJJehzAWT+NkD9IkItA2AQ0W5r9AOBP+p1K21IM5CxiXh13jUEQLXWLAMCOPUWOLCpNKsxSkYVaEVLpfr4eW2+uDwCAJ+FR1QYrVxbiC1IWUV8VVNgUgsqQHdiVxIp9PfcJjnbp6PfcIVVVxcprQo2xJq/OyJ5GW+DOHdXPAcD3Xpm5Yz6YzLrHWT6gA+MNAMA1XE4quQpEpb4yCACL8UW7spYUB2/S6wCABlMDcdwLhHC2hUZR19Fnh701zw57rwaAz17X+nu7UZPJ5/Mr9BVyQFhV/gqzcroRONvaJFtk1dVmSzOZK3mGBLkIhE1At71bbua4KRx3uQqn2lCd15eG4OyQNhWLR1XlnJUj7OgTtZQo0KLJtSJa6lL5em6bpU0KCKc2R0DYm/CSLCIhJxrMkgPrTXrXNVf46u1S02TvkKqSBb6kzw6cVQDLJ1+4peNgnU03F0vzlr/5/dAt+X7++aDC83oq5nGJFMNzHTdvSBmNcH5qjDVBYPMkTwKpgAMAWq2t5L1SIPjG/dI6Gpwq2jqayPD0158ce7cggr6i1X7ofbtrp/M9hrxvV1t7FTowKQW5mt+xrqObsmKv7KcR8gcJchEIm4AOW0eEoRg+wSdM/qRfVX1QckF2RuSqnHzB1+ySmiZ7h0mQS2HY2VebAEBwduSlF4RMr7M3AEgbD0HMSzu3kkaW6m40N5INFmFddNo6/cBZhzZb+JZrpXU0ON0AFc2xYFpKErVZ2/I+VzQMLX7ljq4HGZrijs+Fd/7w1dnufI+xFuzY4/UUANFStwgdEZ4oFPL6Kq+35YwgCmcc9y2OLeS9UiC41mtXRFqToZJBB+0bNRVjzL9/ZOSqlUi6xqZng/90Z8+zhRij3lQvJ09UM1doz6CZSgbtIqNNc63XrWR7nyfh0SX5pJGhGK7N2hYtpI2bERLkIhA2ASzNilatNQAA/YF+1bw4cuVMFtGS3ywi13HTLADQoekG8OlNIQVeqtCeASlrVrNrJp/PrTfVJ7S0NpkRMtqZ6ExRNo5KEkqH7ADQZe8izghhXWx1bg0AQDgdtqf4VNb7Sb7xCp/IGuJUJmZmFt5QxfsoxafoSDpiA4BeR2+wEGNc1uLwvXtH9bMA8L0DM3fMBBIF7/3IzB1uBAChojOvyQLCm+myd/mBzVFNPxWeMvMiz+oZfcJldOWtyprwFli9INoa5gGAHXuisdDDvTDqcz056LkGAP7HtS0PVZi16UKM02ZVXzU9O/6UtI7amubAaMVs7xsMDNoBSfgnH4q9hDdD/kUJhE2C3PhUbc0c18u5WcRuR3cwr892bYuIOluI4tM6ZurFqnw+m7AORAF0cEoKcrXdkHfnTJY9HwwMlvVcCaVCmjgXN9OghQ5bR0Rpewjqwqq1cibWFBEh0sPBYWvWN1I0BHvLLAAwE88W3DnLB6PBUasIkTayxqhVW7iKpy/e2nmw0a6fiad58988OHRbocaRob1D0jpat5cEuQpIt707TIESYlzMEkqFNErbU0iGgkNEsbdI8JU9MwDALBwp6Dqa4gT6a4+P3SWIoC9ttr3xgb11k4Uaa4tTqv4LpdRTTS//+wuVPetaRyfCEw7grFIxIb+QIBeBsEmo1Ff6AWAuOqea7EguzEZnjRkho9XS2lStsTaR7+cLjtZZAGCnXmzK97MJ2cHMHXRSmbhR1BhjfP2+vG+knTqnHwCmwlNlPVcGAgN2ALBoLUENrck6+0ggyMgB4ZHgyLrmiuDaIgW5Fo+pI8gVGrUDgF1rL6jjztKU+A93dP1eVlv8r5enews2GJ+m6NCM1HS+/SYS5CogOkYnWLXWIACc9p8u6+TJdGQ6/8I/hDXh6/ZJpwu8wwVdR//+0ZErlyOpWouOCX3jrt6nCzlWjbEmqaN1SU7kNNORaVVU09O+USlZUH/putbRQij2Es5CglwEwiZBVoRaTiyX9QZrJDhyJotYiPJfvnq7lDlbPqUK56wcYSafk7Jm9pZZFOAzrjJUBYCzG5ByRc4iylWeBMJ6qdBX+AFgJjKzrrnCNV4pOWf+cVWsozNR6e9z6As/V/Y12/1376p5BgB+9NrsHVO+uLEQ47CTz7soIaMV9fagUNVL+sEUGIfO4QeA8XB5V9MvxBaI414kuK7b5wCAiizUIRVhCjHGc8Pe6sf7V64DgM9c0/JQVYGOKZ6LTWfzA8BgYLD0E43nKn133rIuZcXluOSPVRvzK5JFkCBBLgJhkyA3PvUm1qeEpTYmI5MFdUb45qsl5yygDuesHGGWTkhBrqotBak+qDPV+QFgJbFS1nNlNjrrAM5KdhMI66XaUO0HgMX44rrmCtdx84JI0QIVW66mIgu6wliXP2TF3ip9VbAY4/3dzR2Hm52GqURGMP71g0O3F2IMZvolaR1drU4mFJYqQ5UfAGajs2X9XllJrDgAoNZYS94rBUa01icFY5WHEnmGHXuiLt/Pj6Y45mtPjN0tiKAva7G/ce+++ol8j7EWTp1UBShXBZYy7NiTtZTIM7koffuSPgcANJgayFwpACTIRSBsEjpsHZISVnp9Slhqo9BZRK71+hWR1qSpZNBBe4fMhRiDcGFo/1gjcLYaJN80W5oDwNkNSLmyklixA4DL4CIbLEJOyAq2noRnfXNFb+dES90CBYAdfaLk1WrlgHe9qd5fjPFYmhL/8V3dv9cwVHpgKbrl31+c2prvMeRqZN61nQS5ioBcTb8UXyrrIJesINlkaSLvlSIgODtmAICdfTXvidfPPzR8rSearrYZ2MA/39XzVL6ffz7UVE0v/7vnovQti2QVQrGXQIJcBMKmYatzaxAAIumIbT1KWGpjJS5lEeUNZd5h9YJoa1xVtHmSVHMVGSqyoKNiKy6RogWu85bFQozRbe+WlLDSwbJ2RjwJj+y4BxU2haBS2iy5K2EJFV1SX665gyW/jvqSPicAtFpbixLkAoCdDdbg+3bXPgUAP3197vbRlVhekyp0YEIKcjW/gwS5ikCjudEPAN6kt+Qd940gK0h22YhibzEQanZKpwtW+vO6jj50crnx+RHffgoQ/+rG9gecJm0mn8+/EGqqpqdXZKXvnetaRzmBo8KZsB0422yfkF/K1tElEAhvxqwxcybWFBYh0kOBIZvS9hQKX0qqvmk0NwYLNQa/qqBSaEUbwtthR59ooACIlroF6AqjctZp64wwFMMnuITJn/RrCzFGKSAHJjpsHT6lbSGok15nbwAAgqmgc71KWHztntWmyUMlvY4KooBgSgp4b3FuKVqQCwD+5qb2Iy0VhokkJxj+9vdDdwhifvQhaO+QmUoGHSKtSXOt167k5aGEC9Jp65Sq6VOBknfccyWaibIxLmahQAmd9s6w0vZsBrjW66V1NDjViDypEfpjac03nxm/SwSoazqdr965o7qogfAms1QF6E/6SzsgfK7S9+rnkC1joTGLIAqMgTXEHDpH0QKImwkS5CIQNhFyg+mR0Ehpvzg2gLyB7LZ3F8xx5xsunQHOKqoQigczf0gqDV+tAikELM2KVq01AAD9gf6ynCsZIUOF0iEHAGyv2F5Ux51QPtSb6hNaWpuTEhbXcbPknIVmGsCnqcJYuHHmY/PGtJDWaWhNusncFCvm2DRF4et3dj+kZajU8Eqs+1vPTuzKx3PZsacaAEC0Nc6D1efHMyZckHOq6e1JLlmW/le/v98OABatJaRjdOR7VQT4xsv9osYYpzJxEzN/OC/7lb96cOidwQTnrDRrV/7p3T3P5+OZ66Hb3i0lT9LBkt5/MQtvOKhMzCRqjHG+4bJ17aNkP6zQir2bmbJcZAkEwtrIks4zkZmyzCSG02E2molaKVBCr6M3VKhxuM7b5kQAVGShFqkwW6hxCG9HrvqQq0AKhRwQHg+VpxLWSHDEKogCY2SNUZJFJGwEu86ekxKWUNUbFfX2ICVktOzEc9WFsW7j9Pv7nQBg19l9hVDsvRjb66yhD+2rfxwAfvnGwq1HZ0MbXpOYRakKWa5KJhSec6rpqaFgeVbTy+9Lh5Yo9hYNipaUpgEwE880bfRxvzw833ZwKriPpiB88ZaO35l1LL9xI9dHp70zTFM0H+fi5kAqoCn2+NnCTDybs9L3VHjKCZxtsk/IPyTIRSBsImQlLFkpqtzo90tVN1atNVjILKJoqUuJJtcKJfIMO/pEbaHGIbwFPk3RoZkGAODa37kuqeb1Uqmv9APAXHSuLAPCQ4EhJ3BW1p5AyBVZnXMqMrXu94rgaJsBzir9lSKT4UknAFToKhSbK5+9vvVEb415MMOL2s8/NHx3hhc2VPlGe0ekIFf9PhLkKiLyejsSHCnL98pMVEqgEsXe4iJUbZHW0VXl6VyZDyb1335x6i4AuGVL1QvXd1cu58G8daOhNaJVYw0CwIB/oGT9FWbpeM5K3/OxeScAVBurSbuIAkGCXATCJiJnJSyVMBoalTMjBXdGZCUVdvbVDWfOCNnBTL7govi0VtTZgoJra6SQY8nCBcuJ5bKcK1ORqQoAqNAr57gTyoMqfZUfABZiC+t23PlqSdlPVvorRWRnxGVwKTZXaIrCv7yn92GTlonOBZONX3xkZH/OD0tFGCqyUAdIVcl5M5JwUWTV53KtppeVI2uMNeS9UkRkpWnav7EWGn/14OCt0RRvqbPp5r5yR/eB/FiXG2eq6cOlW01P+2Sl7yvWHeSSm+o3mBrIXCkQJMhFIGwiNqKEpQZmIjMVAFBlqCr4S4Ov2SX15fIMlKxzVm6wq9UegqOt4NUHTeYmSQkr4S3LubIYX5SdEZJFJGyIOlNdzgFhrvkayTlbVforRZbjy7IKqaLOSJPDkPjMNS0PAsDj/SvXPTPkrcnlOez4k7WUyDOCscojWuuTeTWScEFcRilQuhhfLFnHfSPIjnuTpYm8V4oI13nLokgxPBXzuKjwvD6XZ3z3lenek/ORHSxNcV+5o+tBHUsr2lNNrgacjc6W5B6Mii6dVfruuHlhvff7kr4KAGi3tZMgV4EgQS4CYRPR4+gJAEAoHXKsVwlLDchZxFpTbcE3WHzbDXlXtCFcGLnag6/eVvAgV4etIwAAgXSgrJ0RWdaeQMiVZkuzHwB8Sd/6K7lar10RGW2KSoXs9Eq/Jf/WbRxfSvq7Wqwtis+VD19aP355i/2wIIL+6uOj7w0lMuvuCcnOvColC5zt5KhikZHXW0/SU5KO+0bxp/wVANBl61J8rmwqdFZOtNQtUgDY0cca1nv78HLU8oMDs+8CgLt31Tx9WYtD8SClXA24HC/Nanp29Il6CiIlWuoWobevS+n7XMXebc5tiv9blyskyEUgbCLqTHUJLa1NZYSMdiY6sy4lLDUgbxybzc2Fr+Sq3xcQNaYYlYkbmdnXy3LDWmrQgXEpyNV8dcGds63OrQGgfJWw/EmpmrPdSrKIhI3R6+j1AzizaV8XjFYUbE1zAMCOP1WS1Vzy3yX/nUrzzbt7n3YaNV5/PFP51w8O3bje++mV/iYAEGp2kiBXkWmztgWAs+tvOZHkknQkHbEDwLaKbaQnV5ERKrukvlzzh9e1jnKCSH3ugcG7kpxgaKs0jv/dzR2HC2Ph+mg0NwYAwJv0lmSQi5l7fTVZ0LnudXQiPGHmRE6jY3SJWlMtqaYtEGW3cScQCOeHpmjYdDZZCaskXxwbIZAKOAGg295deGfkHEUbdvJZ0perwNArpy1UMugQaU2aa71+pdDjmTQm3qwxl6USFidwVCgtKbTJwTwCIVfarG1RhmK4JJ80epNe7XrvF1YV/piFIyUX5FqMLeqTfNLAUmymzdoWVdoeALAbNZm/vbnjAZqCcGAicNkvD8+3ZX2zKIAOTDQBANd6PQlyFZmtjq1+oDyr6U/7TztEiJRFYwmZNeZ1VbYQNg5fu1daR1cVqLPlHx4buXzKl2gzaOj4N+/ueZClKbEwFq4POSAs7+tLDcYz0AwAfN36lb4HAgMVABH+KTQkyEUgbDJkuVpZvrZciGaibDQTtVGgxF5Hb7AYYwqurVJfrg0q2hAuDjv6eDOwKtXM6oviHdi19gBQfkpYo6FRCy/yrIExxKoMVSml7SGoG5ZmRZvWFgCAfn//+o8sNlwuHf32jZRcsmAgMOAEALvO7qfXKRFfSG7ZUrVwc2/ViwDw/70wdddcIGHI5j5m+uVKiksYRY0pyjdeQY7JFBmX0ZXSM/o4L/LseGi8JI/n5oos/OPQKX/UbTPCdd4yCwBUaLYBWVafPzvsrXno5PINAPCpq5of6nKZSyKQDwDbK7b7ASCcDpdeNX06xlChGanpfNcd0+u9Xfa/lFTs3QyU1peGQCAUHFkhSlaMKhdO+047AMCitQT1RQqCcE1XyYo2JeeclRvMwhtSkMu1dd0bilyRlQdnojNlVfU4FBiSnRGywSLkBVkJayI0sf7m8523zomgRCq6VEPFPOuuBCsk4+Hxoin2rpevvqv7lTqbbi6W5i2fe2DwdkG8eAEGO/GMtI4626dRQkG7zYRdJyVPBoODZbUHm45MO4HiCP8Q3o5Q0RUT9A4/JWQ07PjTFxWlCMYzmq88NvpeQQRzWYv9jT+6snG4GHZmi1Vr5cwac0iESPcH+u1K23Mu7NiTtZTAsYKxwitUdMbWe7/sf1Ubq0lAuICQNxyBsMloNDf6AGApvlShtC35RM4iOnXOor00uM5bF0Rak6HjvkraN1J2Pc5KCdo70gwAXPPVRQtyVRur/QCwEFsoq7kyFVnNIupJFpGQH2THdi42t27HXTRXp0VL7SIlCjQ78si6myYXkrnoXMk67jqWFr5+Z8/vNDSV6V+Mbv2/z03uvNg9zOJx6YhNzc6iraOENyMHTKfD02WVPJHfk3LDcELxESq6pgGAnXy++WLX/uUDg+/0xzOVTqPG+y939z5VeOvWj7yfHwmOlNQejJ1+aTVZ0JXTOrqcWK4AgAZzA5krBYQEuQiETUabtc0HAN6kt6yyiDPRmeI7IxqDINiapL5cI49edFNByA0qOGWg454qkWJ4rvO2+WKN22xu9gGAJ1FeSlgLsQU5i0g2WIS8UGus9QNnFW7Xi1DZMw0AzOyrJbWOLseXnQBQZ6orybmyt8kWuHdf/WMA8ItD87cfnAqc3xkUBdD+MSlZ0Ho9CXIpxJnkSXyhrN4rZ4R/LM2kOkUh+Lo90wBAL5+64Dr649dmu16fCu6jKQhfvLXzt3ajJlMcC9eHvJ+fikyVVJCLXjndBAB83d6c1lEi/FMcSJCLQNhkbK/Y7gOAQCpQUU6NT2XnSna2ioXg2iY5Z/OHSso5Kyc0w480AYBoa5yDzsIXa9xOe6cPAHxJX0ltsDbKSmKFZBEJeaXZIina5hoQ5hsuk9bRlYGWPJq1YXxJnxMAWiwtJTtX/uKG1uPb6yynM4Ko+buHht8XTXHMWtfRi8fsVDpiFVl9km+9zlNsOwkSdca6AACsJFbKKsgVSEoB1i5bV8nOlXKH67x1CgDo4GQzBI5a65pxT8z0ny9PvxsA3rW9+tkbeyqXimjiuqgz1fmAEqum59MUHZiUkgWdt647yCWIwhnF3m3ObSQgXEBIkItA2GTUm+rjOlqXzAgZ3XRkumyO2MnOlexsFQu++Sopc7Z6nI6Qf+QAIl+1pajVBzsqdpxRwiq5xqcbwJ8iWURCftni2HImeZLL/ZnuO6dFAFR4rh6pMJtX4zZAIC0pe/U4ekp2rtAUhW+9d8sjNj0bXImka/7qgcEb1rqOHXtiVbyjdRo0WxIKapuRVmurHyivavo4F2cimYiNAiVuq9hGFHsVQqjdExJ11hDFJfXMxHOut/1eFPG/fzd4VyIjGJudhskv3971mhJ2ZkspVtOzk8+7KD6lE3W2oFCzM7ze+8dD4xZO5DR6Rp+oNdUmC2EjQaJsNu0EAiE7aIqGXWf3AcBp/+nSyY5sENm56rR1FtUZyXTdPi9SDE/Flqup4ExWCleE9UF7h6QgV+OVRQ1ylXLj01w5N4u41bm1ZB13grrodnSHGIrhY1zM4k161908XrQ1JkVT9TIl8oxm+OH6Qti4XrxJrzbBJUwMxfCdts6I0vZciBqrLvU3N7X/lgLEl8cDV/zs0FzHW69hFo5KQa7q7eSoooLI1fTBVLBsqun7/f12ESJl0VhCRtZYtGprwtsRnJ2rfbmefVvi9auPj1467o136Fg6+c27ex9gaaqkg93djm4fAPhT/pLxVZjJ56R1tKIjp3VUFpyw6+xk/1VgSJCLQNiEVBoqfQAwEZ4omRfHRohlYmeyiFucW4JFHVxn5URbwxwFQDPyMFFZzDNUdFlLRRZrRYoWuO53zRZ7fLnx6XBguCzmynho3MKLPKtn9PEaYw3JIhLygobWiDatzQ8Ap3yncporQmW3dGRx5kBJVMX2+/udAGDT2vysCiqf7thePXfbNtdzAPDtF6buGl2Jmc/9Pe0flY7YtFxDglwK0mhujOsYXSIjZLQT4Qnzxe8ofeTG4ESxV3n42l3SOrp08k3r6LPD3prfHV+6CQA+cWXjQ7015pIO3APAVsfWIAVKiGaitnC6NCp85X9XvmZ3TuvoZHhSFv4hRxULDAlyEQibkFpjrQ8A5qJzZeG4n/afdgCARWsJKpFF5Ku2SpuKuYMl4ZyVE+zIw40UREq01C2KxoqiN0d1GVw+oPQan+bKQGCAZBEJBaFSX+kFgNHQaE5zha/ft9qX63RLHs3KmfHQuKzYq5q58tU7ug40Ow2TiYxg+t+/G7iLE0QKAGjPoJlO+J0irclwHTeXbA+ezYJD55Cr6SuVtiUfTEemZeEf4rgrDNd+8xQA0IHxZqxWCnqiae2XHx15vyCC2ddsO/Lpq5sHlbQxW/SsXrBqrUEAOOU7pfyRRVGQ/l0BcO035RTkmo/NVwBAtYEI/xQaEuQiEDYhjeZGH3C2AbXaGQ2NKuqMyMfo5GN1hPzBzL4ulYavqq8VG7nx6WJ8sSzmylRkSs4ikg0WIa9UG6t9ADATmclprnBdd0wDABWaaUQ6tmbz9GIyG50tvmLvBtEwtPiv7+l9QK+hE5O+RPuXHx25AgDY0cdW+3E1z4DVl8cZORVTpZeCQZPhybJ4r8jvx1pTcYV/CG+Hbz77xyIAAIM7SURBVLzcL2pMUSoTNzHTr1QKoojP3t9/RzDBOSvN2pX/+94tTyht43qQK55GQiOKzxVm5kAFlYmbRI0pyjdentN3XRacIMI/hYcEuQiETUinrbxU42YiM04AqNRXKvLS4LrfNStStEBFFmup6JJOCRvKFcYzIGXNVtXXik2LpaWsAsILMUm2nmQRCfmmwdTgA4Cl+FJuxxUrOmOCscJLCRzLjj1em1/r1o9aHffuanPkT69ufhAAHj61fMPj/St1zPxhKci1qgZMUJZaU60XKJ9q+jPCP6uNwgkKQtEQnFK/KHbi6eZvPTux6+RCZDtLU5l/fFf3/TaDhlPaxPUgV9PPRmYVr+Rix5+W1lFn+zSo3EIo/qQk/NNh7SBzpcCQIBeBsAk5RzXOmREya8oMqwnZGZGrboqNaKzIiJa6BQoixQ4/3KiEDWVJMshS4fl6EQDXdceMEib0OHqkxqfJ0ml8uhGWE8sVANBgIllEQn5pt7X7AMCb9OY8V4QKqS8XO/2y4lWxnoSnAgDare1epW1ZL394RePIFa32Q4II+h+fHHsfvCMtAMA3XUWCXCWAHAxaTiyXxXHFQEpSIe2yd5H3SgnA1+ycBoDozMmuXxxeuB0APnJp/aNXtjlUt5aVUjU9s3R8tR/XzpzWUSL8U1xIkItA2IQ49c60iTVFeJFnhgPDNqXt2SjehCTF3WRuUuylIVT1Sv1kZl9T3DkrFzQjj9ZTIs+Ipupl0daoSJP0XkdviAYtxLiYNZAKaJSwIZ94E95KAOiyd6lus0sobWTVuEAqUJmrahxft1c6+r18qiV/luWGP+WvBIDtFdtVOVf+9T1bnq4ya5eRCDrY+EqVSDF8puu2BaXtIgBd9q6yqaaPZWJMNBNVRviHsCZc241TAMD7Jjo4QWB3NVhP/MUNbScUNisnWi2tG06e5AvaPyadLGi9Pqcg13ho3MKJnEbP6BO1ploi/FNgSJCLQNikyI1PBwIDir84NoovJW0Ulcwicg2XS0EuzyAJcuUJWWVNVl1TAh2jE2y6M6pxipfLb4SMkKGCaSmLuKNyBymVJ+SVelN9XEfrkhkho50KT+WkGsd13iYFuYJTTeDTilUZz0ZnjSk+ZdDQmnSbtS2qlB0bwaJnuX96d/f9V7LDHAAsaltC0FlVdVSpXNlesd0PAOF02BHn4or3n9sIp/2nHSJESinhH8Lb4Vve4Y1RJr4KAXqHwRf4t/dueUxpm3Kl19HrBwB/StlqenrxqI1KhW0iq0/yrdd5cnkGEf4pLiTIRSBsUuRmumpXjQukAppoJmqjQQvbK7YHlLKD637XjAhKpCLz9Uj4VV/xUwrQK/1Safiq6ppSVOikxqe5qsaVCv3+frsgCoyJNUUq9ZVppe0hlBc0RcOhl47DnA6czq0vV/X2sKi3Byg+rWXHnqrJr4XZc8J7ohIAnDqnl86x90opcFmLw/dnrpMTAPC72HbH4/0rdUrbRACsWitn0ViCIkSq399vV9qejTASlBqCq0mFtNz59ktzW1/mtjAA8M2O08crzFrVvu+77F1hhmK4BJcweRIexXresqNPrIp3tE6DZsVcnjERnqgAzjbTJxQW9b65CQTChqg11vqAs42o1coJ74kKALDr7D4do1NMNUq01KVEc80SJQq0ZviRBqXsKBvSMYYOzTQCANd1u6JBLpfRtSHVuFJhwD9QCQAV+gpVHr8ilD6yatx4aDznucI7O6Wq2KkXFKuKHQ9L9lcaKlU/V7Zk+s0AcFDoob72xNj754NJvdI2EQCn3ukDgKHAkKr7cs1EZ2QVUuK4lwCHpoLOH78++65DQg8AoD12zKGwSRuCpVnRrrUrXk3PLBxpAQChelvO/WHnonOVAFBvqlf9e0UNkCAXgbBJabY0l4Vq3HBwuBIAKvXKOyNCZY/knK0esyPkDjvyaB0lZDSCsdIrVHTFlLRlo6pxpcJkZLISAFwGl+JzhVCe1BhrfAAwH5vPvfl83R5pHV0+2ZIns9bNbHS2EjibDFIrVHheT0UWa0WKFmaN25bCSc7+P+/vv0sQcypEIOSRakO1D1B/Nf18bL4SABpMDeS9ojCRJMf+7UND78/wojbk3D4OAIx/tEVhszaMXPmkZDU94xtuBQCu9YbJXJ+xlFiqBIBmSzOZK0WABLkIhE1Kt71bUo1T+Jz7RpmJzlQCQI2xRvGXBt9w6WpfrtMtCpuietip59sAQKjsyXlDkS/abG0+APAkPaqeKwuxBSmLaCZZREJhaLI0ScmTeO7JE67jZqkvV2CiGQKnSF+u5biketdiaVH1XNEMPtBMQaREa8PcV96z+9dahk6NrMS6//6RkSuUtm2zI1dzlIJq3EZYia9UAUCbtU3Vc6Uc+B/399+6EknX2Axs4NPvv/M3IqNLUcmgnV48rmqBqWqjFBCei84pMlfo+cN2Khm0i4wuyXXctJTrc3xJXyUA9Np7yVwpAiTIRSBsUrZVbAtQoMRIOmKPZWKqbXy6GFusBIAmS5PiL41M97unRQBUaK6B9OXaGMzyqVYA4BuvnFDalm2ObbJqnLqdkcRKJUCcEULhkFXjvElvzkew+LpLAqLOGqK4pJ4de1KRvlyy/WpXIWVmX5PW0eodk7sbbcE/ubrpAQD4/cnldz54YqlRWes2Ny3WFil5kvCo+riirEK6o2KHqueK2vnm0+O7j8yE9jA0xf3D7V19NQ5zUnC0TgOAZuThVqXt2wj1pno/oFw1vWb0sVYAEJwdU2C0OZXBhtNhNpwOOyhQ4o7KHaR/XREgQS4CYZNiZI28RSs1Pj3tP63aM/tnnBGb8s6IaG9KiJbaRUrkGc3gg01K26NaEn4NFZptEAFkeu5StB8XALRYW6IaWpNO8SnDbHTWqLQ9ueJPSlWb25zbFJ8rhPJkh1PavIfSIUeKT+W2x6Ro8JXdkwDAjj9ddOcszsWZcDrsAIBdlbtU7YzQ3gEpyNVyzSQAfGJ/0/DVHc7XRID6P0+Pv3/KF1fteqZ2tjq2qr6afjoybUzySYOG1qTbbe0Rpe3ZrDwz5K35xRsLtwHAh/fVP3pDd+USAPC1eyYAgJk/rOogV5u1TU6eKDJXmIU3pHW0dnfOJwtO+KT+wTatzU9USIsDq9TAbrf7egB/D2AnAA7AIQCf7+vrO5bFvS8AuOYCl/y0r6/vD7K8/v/19fV9NiujCYQyo0JX4Qunw47h4HDFZdWXqc7xzQgZSq6u2Vm5syR6pwhV2yboyGItO/1ya2bPx8eVtkeNaIYeaqREnhHMtYuivSmhtD00RcOhc/hWEiu1p3ynKhrNjXGlbVov87F5Q4JPmFiKzXTaOokzQigINp0tY9aYw9FM1DrgH7DvrtqdU5CIr79sgp0/vIteOtEG4NU8m3lBTvlOOUSIlFVjDZo1Zq6YY+cT2jdiomMel0izXKbrjjn55//6nt5n7vruG40LoVTD//rNwHt++8m9v2BpijTpKjLnqsatxFd0LqMrpbRN6+WE70QVADh1To+aVUjVzHwwqf/yYyNuXhDZvU22o5+7se24/Duu4+ZJ7alfgvaNtEEUAJV+Rtsqtp0JCAuigKJ+10QRtG9U6sfVcXPOQa7hQOn0D94sKPJtd7vd7wLwJIAVAB8H8KcAtABecrvdl2TxiM8CuG6N/+6AFDBbS/ngwHnu+fcN/CkEgqpxGSTVuOnItCoziYOBQRsv8qyJNUWqDFUlsUHkmq+eBADac7pNaVvUCjP9slQa7tqieD8umUp9pQ84KwGtNk76TlYAgEPn8LE5yl8TCNng1Dm9ADAYGMy9L1fPu6V1NDjVhHRxj9PLandqVyFlh34vraP2lhnoLGcqBwwaRvjWe7bcr9fQiQlvvP2vHxy8UNKYUCBYmhUdOocPAE74TqjyyOJ4aJyImSgIJ4jU/7i//+5QgnNUW7SL337/1sfP/T3fet2KqDHFqHTUzEy/rMrvGAA0mZtiOkaXyAgZ3Xho3FLMsc2JWZZKR82ixhTjW67x5PqcqciU1D/YpHz/4M1C0Su53G63AcD3AfT19fV96Jyf/xbAMwC+B2DPhZ7R19d3/DzP/hQACsAP1vi1t6+v74XcrCYQypMGc4P34MpBzMfmq5S2JRcG/AOVAODUO0vmpZHpvWtG98I/8FRksZYKzepFW2NSaZvUBuPpl7JmjftLJshVY6zxDQQGMBudVWWQayw4VgkAVYaqkpkrhPLEZXD5ZqIzbauqcaO5PEOo6o0KxkoPHfdWaYYfrs9svydn2fb1Ijsj1cZqVc8VZu6gFOSq3vG2dXRrnSX82etaf/uNp8Y//NSg95ofvza78IdXNI4U38rNTYW+wudNeqvHQmMV72x857zS9qwXWYW0zlSn6rmiVr7w8PD+0ZVYl46lk9+8u7fPomffXHlK0RAqOieZpePb2LEnWvmWa1T5OdEUjQp9hWchttB0wneiqtNevGr0itBJLQAIFZ2TG6mEW4yv9g82K98/eLOgRCXXXQCqAXzh3B/29fXxAL4CYLfb7b40x2f/GYBH+/r6irYZIhDUTKe90wMAK4kVVQa5JsITkjNiKCFnxODMiLbGOQqAZvCBFqXNURtUZEFHhefrRIoWuN67S2Ytl4UNluJLqsyGzsYkZ6TWWFs6c4VQltSZ6nzAWTXPXBGqpEpOZuqFolbFymp39ab6kjgCnyuMd1Xyvu2GNcU7PrSvfvz2ba5nAeDfX5x+z6GpoLOY9hGk5AkAzERnVJk8kd+HrdbWnCtcCLlx3xsLrY+dXrkeAD5zTfPvdjfagmtdx9ddIvXlWjiq6tMF8j5/LDRWVH+lItSvA87+O+aKLDDRYesge7AioUSQ61oAM319fWtl6F8DkFm9Zl243e5rAGwH8J2NGEcgbCZ2V+72AIAv6asSREFpc9bNfGy+EgDqzfUl9dLgq3dIm4rZV1W9qVACduj3suT9vGiqSittj0yvo9cDAJ6ER5UBYdkZabY0l9RcIZQfsnqnrOaZK7KyKrN8sqhNk2W7223tqp0r9OJRG5UMOERGl+I6bj6v5P3X7+x+pafaNJTmBd1fPTh4jy+a1hbTzs1Oo7nRCwDL8WVVBrl8SV8VAPQ6elU7V9TIqYWw7VvPTbxPBKgbuytf+oPLG89bMZvpfpd09Ns/1gI+TRXPyvzSYG7wAGf3/UVBFOCIDOkAINN1e84nCziBowKpQCUA7KrcReZKkVAiyNUB4Ohav+jr60sC6AfQnsNzPwNgHMBT5/n9FW63e9LtdqfcbveM2+3+V7fbbcthHAKhbGg0N8b1jD6eETLakeCIVWl71ovsjMhOVanAtVwrbSq8g6pWtFECduaApGLj2lYyRxUBYHflbh8FSgylQ85Yprg9gvKBN7GqQmpXXoWUUN5sr9juAQBv0ruhgHCm9+5pEZRIhecaqJinKMEXQRQQSErOiJpVSDUjj0pHFR1tU2D1581g0RSF//jAtgedRo3XF8tU/emvT79bFEnLvmLRaeuUVeNUVyEcSAU0kUzERoMWtju3B5S2Z7MQSmTYP//NwAcSGcHYWmGY+Oe7e1640PVC3d6gqLcHKT6lZ8eerC2SmXmn3druAYCl+FLREo222ASr4eOUqLMFhfp9wVyfMxQYsvEizxpZY7TGWENamBQJJdQVnQAu5Lz4Aawro+F2u+shHYP8276+vrXezlEAD0BqPu+FVCn2PwBc73a79/f19eWslHXgwIE/zvXeUiGdTlcC5fG3ENZPDV3DTvFTePnYyx/z6XyKNG/P9TsYjAerAUA/r7/pwNKBkilFo4Rq3EDrRDbuqzzy3O8/ndRVloxtpc7+5bEqC4DjzO6tvgMHOos1bjbfQQfjEPy8n3nkwCOfatA0qEZ1jRM5hNPhCgDgJri7DkweIF5siVIO72NRFKGltGKSTxqfeOmJT1sYS87r35XGZs4an9KMPPfLT604Lyn4+ynMh+mUkNLrKJ24dGrp3mVqudBDFoSdEyfttQDGjHtqJ7P4Ln1mqyj8n6MQB5aiWx7XCfyN9aKg5u+gWqAEigKAQDJQ/fIrL/+xmhQKZzOzLAA4Gadw5OCRT+T7+eWwFuYbURTx/QHRvhwRDU4d+D/rSRkPvf7aJy9233bzDm198iXMHX/unnGvM1oMW/ONgTfQABCIBxqL9Z1oDJxyAsCCZYf21AbG7E9JRx5dlEtLvs/rQ6PR5HyvEqupBcCFgkpxAOutsPo0JFXFH53n93f39fV9uq+v72d9fX2P9/X1/TWkoNguAH+9zrEIhLKiiqniAGCZW1Yi6J0zMSFGxcQYrYFGtNP2kgoiiTSLoKUrDQAVoZM6pe1RC9p0gLYk5lie0iBg7SmZo4oyLsaVAYAlbklVc8XLexkBAuy0nddROhLgIhQUiqLOvFcWucUNzRWfdWsKACpCp4qyjq7wKywAVDAVHEWp9GSPKMIZGdABgNe2PavAYJ2J4j/STQUB4OFpihkOQqV/vLow0AbRSlsFHjy8vFdVFcJL3JIGOLuHJBSeJ2dE01GPaNDSED+9jfabNVRW73OfbVsKAJzhftUeR3bQDkFLacW4GKcjfKQo8YuqcD8NAH7r1g3tR1c46b1C5kpx2fBG3e12fw7AN7O49MW+vr5rAUQAGC5wnRFA1mWvbrdbC+CTAH7d19fnX+uavr6+zBo/e8rtdj8K4CMA/j7b8d7K/v37v5frvaWCHFUuh7+FsH4OHDtw6eHJw7cu6hfH9u/f/7AiNuTwHXx85vEGePBHdoN96eqrri65764+uuUKhE7dtCV5ZLpl/xcfVNoeNaB9+RvbAbwHjpbxK66+7ufFHDub7+DDrz18/dDi0NXL5uVT+y/f/3zxrNsY/z38393w4R67yT65f//+XyhtD+H8lMv7+Gcv/uzd8775XT6H7+D+3fsP5/ocjXG0DUuPfqQxdjziLMK/yaETh/YggHc5rc7B/fv3P1Do8QoBM/60S5cJ/YmoNUd3vPPe72SrCLYfwCTff/2zw76rfzzC0N/bt71vZ4M1WFBjCXA87fhwOBJuj9ZEn7u7++5hpe3Jlkdff/Q6hPEOV6Xr8P7L9z+X7+eXy1qYL371xnzb7yfHPwwAn7yq+f4PXt08mO29tM9lwvh3PueMjjD7L9nxQ+gsfOEsLRyOxx2fXE4s16Wb0o/vb9k/XdDBUmHWeHDo8wDQ9o4P/KClojOW66N+9dKv7kAUe2tral/av2f/wfwZWf4cOnQo5xhNPiKhPwLQm8V/H129/mLHEZ0A1qNo835Iao25NJx/GkDraqCMQNiUyOfcl+PLqmqoPRYcqwSAKn1VSfZNybTfPAEAtHeoDSps6q8E7OxrHQDA1+0dU9qWtWixtHgAYCG+oKq5Mh2ZllVIVa0WR1APjebGFQCYic5ssC/XXTMixfB0dKmG9o2a8mPd+ZGbGqtZhVQz+lgHAAiVPePrlbz/5t29z/c4kIpzoP/8twOkEX0RqDXWegBgIjyhqvfKYnyxEgCazE2qnStq4ehsyPGtZyffJwLUDd0VL396HQEuABAqOmOCqXqZEjhWM/hAU6HsLDQug8sDnN3/FxLNwG+bGDGDiLEpI2wgwAUAy4nlkuwfXO5suJJrtXpqzQqq8zAO4Ka1fuF2u3UAtgC4bx3P+wyAI319fYfWcY8MDUAEQDxQwqZlZ+XONyksqqUnxEx0phIA6kx1JfnS4NtvXBY1piiVjlqYyeddfNsNK0rbVNKIAmjvUDsAcJ23jSttzlr0OHpUqbAoO+4N5oaSnCuE8iNvTYL1dk5wtE4z/rE2duC3bemr/+ZUXgw8D4uxxSoAaLG2qHau0IvHpHW08Yp1r6MahhY/sYUO/J8jQpUnmq7+1H2n7r7v43v6WDq7Y1GE9dNobvS8vvw65mPzqnqvyO/Bbnu3R2lbyhl/LK35378bvCfJCYbOKuPIv7xnS05V5EL1tnF6YrmanXy+PbProyUl7JMtDeYGzyn/qQ0nT7KBnXqhHQB81m2pjUqf+pN+1YuZqBElvNkXADS73e6WNX53BQDt6jUXxe127wFwOS5SxeV2u8/XvPhyAMN9fX3kjCxh09JmbYtqaW0yJaT0U+Eps9L2ZMtyXMqMtFhK1BmhaAhVPeMAoBl5pENpc0oddvzpaioTM4laS4RvvbYkN827K3f7ACCYCjrjXFw1/VNWEitVANBuay/NuUIoO3ZU7DiTPNnos4TVyk529tWCr6OepMcFANuc29SZlEj4NXRgshkAMlvel1OywKyhxD/dTvt0LJ0cXo71/OXvBq/Nq42EN9Fl7/IAZ9dpNZDiU3QwHXQCwK7KXaRCuEAIoog/ue/0Xd5o2uUwanzfuWf773INOHMt140DAL18UrX70VZra9EUFunlUx0A4LHv2pDgyXxs3pDgEyaWYjNd9q5wfqwjZIMSQa4HAawA+Oq5P3S73TSALwE43tfXd/CcnzNut7v6PM/6H5D6d/3qfIO53e6vAzjldru3vuXn+wG8D8D3c/gbCISygaZoVOgrPABw3HdcNZssT1LKInbZu0rWceca948BAL1wtF1pW0oddvRx6ahi1fqP2BQLm86WsWqtAREifcJ7wqm0PdkgiMKZQMPuyt3qdNwJqqPH0RPS0Jp0gk+YZqOzxo08K9N5u7SOeofaIXAFa4juSXh00UzUSlM0v6NiR9a9YUsJzcBvmymRZwRz7aLobM9ZObzWRPF/cX3r/RQgPjPsfce3X5jalk87CWfZVbnLC0jVHlwBv9/55LT/tF0QBcasMYedemfJicSUC3/5wOC1A0vRLVqGSn3j3T331Vh1OQdcMlveMyPSmgwdW66mPYOqSWifyzbntrwlTy4EvdJvoWMrLp7WigFr74a+3yd9JysAwKF3eFmaJRWxRaTonkRfX18cwKcAfNDtdt/ndrvvdrvd7wfwOIDLVn93Lt8BsLAalDqD2+2uAHAPgJ/09fUlLjDk9yAdp3zB7Xb/hdvtvsXtdn8JwDOr//1/efnDCAQVU22sls65h8ZUEeQKp8NsKB1yUqCEPVV7SjaLmNn6vgkRAB2caqbivtx1cDcBzKIUCORXA4OlSpW+ygMAA4EBVcyVwcCgLSNktAbGEGu2NOfs9BII64GmaDh1Tg8AHPMe29Bc4Vuv9YhaS5jKxE3s+FPnS3pumCOeI1UA4NQ5vTpGp8o2FuzUS9I6Wr1jw0e+791XP/He3TVPAMAPX5t99+P9K3UbfSbh7dSb6hNG1hjlRE4zFBhar7q8IgwGBisBoEJXUbJJRrXz7Remtj016L2GAsT/eW3rb69sc2zs31pn5QRH2xQAaAZ+q8rE61bn1iBDMXyMi1lW4isFU9zVDD7QBgABS09aoDfWlnA0OFoFlG7/4HJGkXR5X1/fgwBuBVAP4CcAvgupN9Y1a/TWmgcQBBB6y8//CIAOwH9eZKwpSMGzhwB8DsDvIQXHvgzgDnJUkUAAGkwNHgCYjc6qwnGXnRGHzuEzssaSVYkRHW1x0VK3QIk8ww78pkVpe0oVKu7TUMHpJhFSYFBpey5ErUlqEjwZnlTFXDnhPeECgEpDJaniIhQVOXkyEhzZ2FyhaPBVW8aBsxWfhWA4OFwFAC6DS7VzhV45JQW52q7PS7Lgi7d2HtrXZDvCCyL7D4+N3jO8HLXk47mENyNX05/yn1LFe0V+/8lznJBfHjm13PDDV2fuAoD37q558mOXN4zm47l8/SVjAMDMvabKI4saWiM6dFKw75j3WMGazzNzr3cAgNe2Y0NHFQFgKjLlAqR+Yht9FmF9bLjxfK709fU9DUnd8GLXfQXAV9b4+T8D+Ocsx5qFFBQjEAhr0GHrUJXC4oB/wAWowxnha3aO0ZGFOnbqpfbMJZ/Ky0al3GDlIzaWugXR3nKhylzFaTY3e17BK1iIqUNhcTw87gLOKngRCMWiwdTgOek7mZfkCd981Rg7f3A3s3isA8AreTDvbUxHplXtjNDLp6x03Fsl0pp0pueuubw8k6Lw7x/Y9vj7fnCkcjaQbP5MX/89v/nEnh/bDBqSIM4jNcYaz2x0tnU8NF4FoOT3CbPRWRcANJmbVDlXSpmT82HbVx4fvYcXwVzWYn/ji7d2Hrz4XdnBdd85rj35C9DekXbwaQqMVnXH51wGl8eb9FaPBEeqbm66eT7vAwgcRXuH2wDAa9+V3OjjFmOLLgBot7aXvL9SbpRm4xMCgVBUZMUPX9JXcFnefCBnRupN9SW/weLabpSafa6cUmXmrBiw0y9K/bhqdpb0UUUA6HX0rgDqUViUnZFmSzPZYBGKSoetYwUAluJLro0+K7PlfRMiKJEKzTRS0aWCHFNZjEvKim3WNlXOFc3g79oBQHC2T0FryluFs1HL8P95z/Y+q54NLoVTdX/8y1N3c4Koit5RaqHR3OgBgLnYnCreK8vxZRcA9Dh6VDlXSpWVSEr3v+7vvzeREUytFYaJb7u3Pk5T+ZtqfOMVPlFnC1JcwsCOPKbK48d1pjoPAExHpwsyV9jRJ2ooLmEUdbZQ1NCw4XVUFjPZWblzeePWEdYDCXIRCIS8NgkuBguxBRcAtNlK3xnhet49JzK6FJ3wV9Dzh+1K21OK0CunpSM2rTdsuI9ModldtdsLAIFUoDLJJUv+HbqcWJYFGkp+rhDKi+0V2z0A4E14N+yMiLbGpGhtmKNEgdb0F+botzfhdQHAduf2kk+erAUzd1BaR+suyfs62uw0xL/x7p5fahkqNbAU3fLnv+m/Id9jbGY6bZ2qqaZP8Snan/JXAcA+1z7yXskTGV6gPvWrU+/1xjIup1Hj/e4Ht99v0DD57Q1I0eBdWyW12vGnVNmXq9VSWIVFduyJVRGk3nFsMMA4F50zxLm4maXYzBbHlre2XSIUmJLfoBMIhMKz2iTYCwDHvcdLvppLzoxsd24v/Q0WqxeEis4JANAM/Z5Uc70FevGojY77KkVGm870vDsvR2wKiUPnyFg0lqAAgT7pO1nSCosZIUP5k5Izsrdqryodd4J66XX05jV5wtfuGgcAZvqlvK+jS/ElfYyLWRiK4bZVbFOfsiKXpGnfSDsAcN13FqQi9uoOp+dzN7b10RSEF0b9+7/+5NjeQoyzGdlbtXcFAHxJn6vUFRZP+k46eJFnzBpzqMpQteGeRQSJ/9HXf9OYJ96p19CJb713yy9rbfoNH5VbC775amkdlY5+q45eR68XKFw1vfzvwjddteF19IjniAsAKvQVK0RZsfiQIBeBQAAgnXMHziqBlCqyzDtDMfzOyp1+pe3JBr7+UqnZ5/whVWbOComm/zddACBUdI9BY1CFolmlodIDAP2B/pKeK6d9px28yLMm1hSpMdYUZMNMIJyPc5MnG1VYBACu/SZpHfUMdEDM71JxRllR7/RoaI3qnBF26PcNFJfUCwann2+6smDvxQ9eUj/xkUvrHwaA+95YuP2/D86p0lEuNRrMDWcUFk/7T9uVtudCnPKdqgbU0RNVLfzDYyOXHpgIXE5TEP7mpvZf722yFSzQntn6/kmRogUqPNdAhWb1hRqnUOyq3OWjQQuRTMTuT/o3Jn34FqjgjIEKzTSKFC1ktt+zYRGkkeCICwBqjDVkrigACXIRCAQAQIO5YQUApiJTBZNozwdveN6QZd5V44xket8j9eXyjbYhFWGUtqeUYOZe7wIAvmn/iNK2ZEudsU4VCosnfSddAFBlqCIbLIIiVBurV4D8JE+4rtsXRNaQoJJBOzP9cl4rjmVnpNqgTrU4zdgTUrKgemfB19HP3dh+/MaeypdEgPq35yff/+ywt6bQY24GXAbXMgCc8J4o6T2YLGZSZ6oj75U88N1Xpnt/c2zpVgD42GUND713V+10IccTzTUp0dY0S0GkNKd/rbrEq57VC0690wMAh1YObbjf47loTt/XTkGkRHvLtGiu2XCV4kx0xgUAjeZGMlcUgAS5CAQCAKDb3r0MAIvxxZLeYA0FhiRnZNV5UgNC7a6QYKpepoSMVtN/f4vS9pQKVNynof3jrSKAzLYPlHzTeZlmS7MHAOZj83ndYOWbc5QVVTNXCOWFLA4yE53ZeECY0YpC1ZZRANAMPtC14eedw3REamLcYGpQ5VyhF493AQDX/s6iJAv+9T29z++ot5zM8KL28w8N39u/ELEWY9xyps5UtwwAY6Gxkt6DzUel916bpY000t4gD55YavzPl2feAwC3ba167i9uaDtRjHH5+n0jAMBOv5TXdbRYVBurlwFgMDCY17nCTr8sraP1l+ZlHV2MS8qKnfZOVb5X1A4JchEIBADAnqo9ywDgSXiqhTwfBcknZ2TeVeaM8LW7pU3FxNOq3FQUAs3p+9ookWdEa8O8UNEZU9qebNnq3LoMAEvxpdJ2RmLzVQDQYmlR1VwhlA8dtg4PcFZGfaNwLe8YAQBm/nB3Pp4nsxBbqAbU6YzQ84ftdNxTJTLaVGbLe2eKMiZF4bsf3P5Qk0M/HUvzls/c3/+hlUiqIKqXm4U2qxQ0movNlXTyZDmxXA0AW5xbVDdXSolXJwKVX3ti7F5eENlLm21v/NO7e14u1tiZ3vcMAwDtGeiECgR03kqTuWkZACbDk/nbg3FJmvYOdgIA1/ueDQe5BFE4I2ayt3IvmSsKoLovNoFAKAxt1raontHH00JaNxQYsiltz/mQMyOyPL1a4Dpvk5yzpZPd+e4no1aYyeelo4q1e1VzVBEA9rn2eShQYjAVrAinw6zS9pyP5bjkjPQ6elU1Vwjlw+7K3VLyJJmf5Elm+wfHRIoWqNBMIxWcMmz4gZCcEU/CUwMAe6v2LuXjmcVEM/Bb6ahiZc84tKYNS95ni1nH8t+7d8d9TqPG642mXR//+ckPRFMcOY6fI1udW1eAs+t2KRJOh9lQOuSkQIl7q/Z6lbZHrYysRM1/+cDgh1OcoO90mUa+c8/2x+kNKvmtB75pv0/QO/wUlzRoBh9sKNrAeaIQJ080gw/KfQ19+ehrOBIcsaaFtF7P6BMt1pZoPmwkrA8S5CIQCACkJsFyT4ij3qMlucladUaqAWBn5U5VOe5cz53zosYYo1IhGzPxbElnaouCKIBZOS2Vhne/S1VBLrPGzDl0Dq8IkTq8crgkP8twOswGUoFKCpR4afWlqporhPKhy94V1jG6RIpPGUaCIxs+0iZa6lKivWWagkhpTv2qMx82DgeHrSkhpdcxukSHrSOSj2cWE2buoJQsaLyy6OtovV2f/Lf3bfmFUctEp/2J1j/6+cn3ZHihpNUBS5VLqi7xUKDEUDrkDKVCGqXtWYs3VqSeqHad3WfSFC+gWk54omntn9x3+kPhJGertenmf/ShHb/RsXRxM58UDaFml3S6YOyJvFbFFoNCnDxhx56UkgWr/y4b5YTvhAsAKvWVKzRFwi1KQP7VCQTCGeSeEKPB0ZIMco2Hxi1JPmnU0tpkr6M3pLQ964JmRd61VeonM/R71W0q8g07+ngtlY6aRa0lwnXcpLrqiRpjzTIAnPafLsm5cnjlsEuESNl1dp9Va+WUtoewOXlT8sSTn+QJ13D5MHC2f8pGOeo5WgNIjb/V5oxQMY+WDky0KNnXcHejLfhPd3b/XMtQqYGl6JY/ue/0bYKoCk2YksKkMfFy8kQW2Ck1BgIDskAD6ceVA4kMT3/iFyfdK5F0jd3A+r/3we2/tBs1GSVs4Tpuko4sLh1TXQuNc0+eDAeH89IPUP53yFdfQ6KsqDzqepsTCISCIveEmI3OlqTj/obnDdkZWVKbMwIAfMt1wwDALBxR3aYi37Ajj0jVB65tI1DhZ9lkbloCgMnwZEkqi8nBtxpjjeoCiITyot5UvwQAI6GRvMwVbst7RwCA9g51IJPY8OIxEhypBs7aqSY0p3/dutrXcE7JvobXd1cu/90tHb9iKPAHp4KX/NUDg9coZYuakRtqD/gHSrJCWO6BJKtxE7InwwvUH/385N0T3ni7QUPH/+19W3/eUmGMK2bPlvfNiIwuRcd9lczsa06l7MgFmqJRZahaBoBjnmMb9leY+UMOOu5d7Wv4vrz0NZyOTNcAQJu1TXXvlXJBfZ4FgUAoGNuc25aAs41FS43h4HANADSYG1T50shs/+CESDE8FZ5toH2jJqXtURI50Me3Xqeqo4oy3Q6pJ8R8bL4k54ocfJMbtBIISiFv8mciM3mZK3zDZQHBWOml+LRO039/80afNxOdkZ0R1c0VZuoFaR2tu0TxdfS9u2qn/+yalt9QgPjkoPfarz0+uk9pm9RGo7lRaqgdyWND7TwyF5urAYAue5cq92BKIYgiPv2rU7edWohs0zBU+it3dP9ib5MtoKhRGoMgVPWMAgA7+DvVJV7rTfXLADASGtnwXGEHftsJrPY11Bjycv5xMb5YCwDbK7aTuaIQJMhFIBDOcInrEm8pN9SWnSS1ZkZEU1VacLZNUgA0p+/LSz8ZNUJ7Bs10ZKFOpFkuvf2eSaXtyYU9lWd6QtSUohrpfGy+BgB6HD2qnCuE8mF7xXZZjTRvVY9CzW7pyOL4Uxt2zuRG33KSRzUIHMUsnyqpvoaf3N809OFL6x8GgF8fXbztP16a2qq0TWqi0yape8pqn6WEIApYia/UAsAlVZeoa64ozJ//ZuCGQ9OhSxgK/N/e1PGrW7ZULShtEwBwzZJaLbva109NtFpa83byhJk72A0AXNP+vKyjoVRIE0wFKyhQ4mXVl5GqR4UgQS4CgXCGUm+oLTtJstOkRviGKySVxZkDqttU5AvNyZ/3AIDg7JiAwalIP4qN0m5rj8g9IQYDgyWlRnquQIMa1eII5cVqQ20hmA5WBFKBvDTUznTesqpWe2JDarWhVEizqhYn7HPt8+TDtmLBDj7QQGViZlFnC3Ht7yyZd+JfvbP92O3bXM8CwPdemXnPLw/Ptyltk1qQ1UhXEisllzwZDAzaUkJKr2f08S57V1hpe9TC5x8avvK5Ed9VFCD+z+ta+96/p3ZKaZtkMtvvHRVBiVRwqpmKLOiUtmc9bHVuXQY2rkZKhWb1Ul9DSsxs/+BoPmw7tHLIJUKknDqnx6wxk56oCkGCXAQC4U3IDbX7/f0llUn0J/3aYDpYQYNWnTNyLpltH5CafXqHO5AMlly1XDFgpl/pBQC+5ZpBpW3JlXMbah/zbrwnRD4Z8A/Y00Jap2f08TZrG5GuJiiKSWPinXqnFwAOLR/KS/KE6717VmQNCSoZcDCTz+f8zHOcEa/a1OI0ww9L62jtnsFS62v49Tu7X9nf5nhdEEF/85mJe353fKlJaZvUQI+jJ6Rn9IkknzTkq6F2vjjiOaLqnqhK8I2nxvY8dGr5nQDw8SsaH/z4FY0lUXEpI9oak6K9eYYSBVpz4meqSrxe4pLUSAOpQOVGTp5oTvx3NyUKtOhonRIdbXnpkXbaf7oGAGpMpCeqkpBVikAgvAm5J8REeKKkGmofXDnoAgCn3ukxskZVOSPnItTsDAuWugVKyGi0J37WobQ9xYYKzhjowESrCEpM7/zYsNL2bIQzDbWD+WmonS9kZ6TaUE2cEUJJIAsgDAQG8jNXGK3IV28fAgBN//29uT7mHIGGkqmEygpRALN4tBcAMt3vGlDanLdCUxT+/QPbntrdaD3OCaLma0+Mfuix/pV6pe0qdWiKRrWhehEADq8crlXannMZCY7UAkCjuZE47lnwHy9Nbf3F4YV3AYB7T+1jn72+9aTSNq0F13jlIACwk8/nvI4qgVVr5Zw6p0eESL229FrOiUZ26sVeAOCarspb0lX2n1osLWSuKAjZ/RIIhDfR6+hdBIDZ6GxJbbAG/JJzVGusVf1Lg2+8YgAA2PGnVbWpyAfaEz/tpiBSgqNtUrQ3JZS2ZyPIjarlxtWlwmhoVFKLM6tPLY5QnjSbm6WG2uH8NdTmOm4ZAABm/tCWXJ9xRqDBoi6BBnbsiRoqGbSLGlOU2/KeOaXtWQuWpsQffmjHQ9vrLKczvKj90iMjH35myFtSa2Up0mhpXASA4eBwSe3BZiLSe67D1rGotC2lzk9en+383isz7wGAW7dUPf/FWzsPK23T+cjs/MggANDeoU4q7svLcfJiUWeqWwSAU/5TOc0VKubR0r6RdgDI7PjQUL7sknui9jp6yR5MQUiQi0AgvIkra65cAKSG2hkhQyltj4zsjLRY1Z8ZyWy7R9pUrJzuRirCKG1PMWFWs2Z8c/6yZkqxq3LXIgAsxhZLyhmZjc5Kzoi1Q/VzhVAeyAII8uY/H2R2fGhSZHQpOrbiYmYOVOTyjPnYfC0AdNu7VTVX2MEHpHW0ZucQaFZU2p7zoWFo8Ycf3vFAT7VpKMUJ+r99aOijL4/5q5S2q5TpsnUtAsBMZKak3itLCakn6q7KXaqaK8Xmvw/Odfzf5yY/IIigr253vPaNu3peUtqmCyFUbw8L1oY5SuBYzYn/VpUgUqu1dREAJsITOc0VzYmfdVACxwrWxjnBtTWSD5syQobyJrzVAHCp61IyVxSEBLkIBMKbaDA3JCwaS4gTOc0bK29UKm2PzEJ8QVKLs6tfLY5vvMIvmKqXKT6t05z65aZpyktFl7W0b7RdBJDZ8eG8Zc2U4hLXJV6GYrhIJmKfi84ZlLZHZim+VAsAOyt3qn6uEMqDK2quWASk5EmKT+Vn76k18YJr2zAAaE7ft+6q2BSfolcSKzXA2eSOWmDmD0tHbDpvK/lkgUHDCD/96K7fdFQZR5MZwfC5BwY/dnAqkFNQcjOwu2r3InB2HS8F5qJzhmgmamMpNrOnao9PaXtKlZ8fmm//1rMT9wgimCta7Yf+/QPbnqKpkskVnxe+8UrpdMHEM6o6XbDNuW0RAOaj83W53M9OPislC1ZPV+SD497jTk7kNGaNOdxgblD1aQW1Q4JcBALhbdQaaxcA4Jj3WElsspJckpbV4i6vvrwsHHe+4TJpUzH2RM5HbdSG5sTPOimRZ0Rb06xQ1av6hug6Rie4DK5FAHh16dWcNln5ZjoybYxkIjaWYjOXuC7xKm0PgQAA9ab6hFVjDeY7ecK1v1M6sjj7+rrX0cMrhyt5kWetGmtQTc4IM/VSJR33VomsPpnZ9oEppe3JBqOW4X/ykZ19zU7DZDzNmz77m4GPHpsN2ZW2qxTZWbEzoKE16RgXs0yGJ01K2wMAB5cP1gBApaFyWUNrSrZyUEl+9cZ82788O3EPL4K5rMV++L8+uP1xNQS4ACC9/YOrpwsGutQkiHRlzZVLAOBL+lyxTGx9pyJSYZZeGegCgMz2e/KWdD3uPX6mJ2q+nknIDRLkIhAIb6PZ0rwIAKPB0ZJw3A+tHKriRZ61aq2BWlNtUml78kFm2wck52z5VA8yiU2xFrOTz8lZs5KvPsiWRrPUP2UgMFASAeHXll6rA4BqY/UicUYIpUSt6UzyJG/vlfTOD4+LtCZNRxdrmflDjvXce8wj2SHbpRY0/X29ACC4tg1DYxCUtidbbAYN998f3fmrBrt+JprirX/269N/cHQ2tK7PbDPA0qwoJ08OrRwqiffKUHCoBjgrtkJ4M78+stD6f56e+CAviOy+ZtsRNQW4AECo3xcULLWLlJDRak/8vF1pe7LFqXemHTqHV4BAH1pZn3Kv5uQv2ighoxXMNUt8/aWBfNk0EhypA4AmSxPpXacwm8KxIhAI62OLc4vUfD5WGs3nj3qO1gNAval+Xmlb8gXfco1XMFZ6KC6p15y+r0VpewpOKszSnsEuAEiv9iQrBzpsHQsAMBWZKomA8EBgoA4AGs2NqnLcCeVPm7VtATjrBOQFvZ0TXFtGAEBz6lfrOmozGpKSOK2WVlXNFWb+oHRUsf2dqltHnSZt5qcf3fnLOptuLpLibX/269N/eGgq6FTarlJDTp4MBgZLYg82GZ6sA4A2axtx3N/C/UcXW77x1Pi9vCCye5tsR793745HWZpSXYKJbzgjiKSq0wW1xtpFADjuPb6uucKOPyUlXesvzes6Oh2ZrgeALY4tqnqvlCMkyEUgEN7G5dWXLwDASnylthSaz8vOiOwklQvyy5UdeUxVm4pc0B77cSclZDSCuXZRqN8XVNqefLG3au8CACzGFksiyDUVloJtXfausporBPWz1bl1AQDmYnN5nStc242DAMDMvraudVS2Q7ZLDTCzrznpyGKtSGvS6Z0fGVfanlxwWXSpn//B7p+tVnRZ/uf9/X94YMJfMv0/SwFZwXAqMlUSQa652Fw9AOyt2ls2icZ88Jtji81ff3LsXk4Q2T2N1mM/+NCOR9QY4AKAzLZ7BoBVQaT0Oo/+KUhOzedTEYZZOtkDAJmt789bkCsjZKjlxHItAFxZcyWZKwpDglwEAuFtNFua42aNOcyJnOao56jiDWLnotIGa0fFjrJ6aWS2vG/1yOKJ3nI/ssiOPr4dAPjmq08pbUs+ucR1iZel2EwkE7HNRmeNStuzGJeCbZdUXaIax52wObiy5sozzeeTXDJv611m50dHRZrl6PBcPb141JbNPat9HuWm86qpTtGc+Nk2ABBqdgxCZ+WUtidXqsza9C/+YNfPm52GqViaN//5bwb+4KVR37qOG5Uzuyt3l4xy71x0zhBOhx0MxXCXVV/mUdqeUuFnh+Y6vvbE2Ic5QdTsarCe+OGHdz6s1gAXAPBNV64KIqV0mhM/U40g0lbn1kUAmItmnzzRHv9pB8Wn9IKpeplvvS5v3+kjK0cqM0JGa9aYQ63W1li+nkvIjbJ2qggEQu7IzeePeo4qWqESzURZb9LrokCJ+2v2l1U/CL79xmXBWOGlMnGjmjYV64WKLOhoz2CXCCC9+w/7lbYnn2hojegyuJYA4LWl1xR1SEaDo5YYF7NoaE1qV+Uuv5K2EAhvpcZYk7RpbX5e5JmDKwfzFtAQjRUZoWrLMABoj/1kezb3rPZ5ZFTV51EUwMwc2AEAme53qT5Z4DRpMz//2K5ftlYYxhMZwfS/Hxj8g2eHvTVK21UKnJM8sU9FphRtPn9g6UA9AFQbqxd0jE41PeAKyfcPzPT8yzNSD65LmmxHf/ThHb9Xc4BLhm+8oh8ANCOP7FDalmy5slpKUniT3uo4F8+qAo0dfWwHkP+k6xueN+oBoM5UR5KMJQAJchEIhDWRm8/ntX9KDhxYPFAtQqQdOofXqXemlbQl71A0+KarTwKAZuRh1Wwq1ov2yA96KZFnRHvrlFC9Pay0PfmmwdywAJzth6UUry+/XgcANcaaRZZmVb/hJpQf9aZ6qfm8J3/N5wGA67rjJAAw0y/vgHhxP1xufi/bowbY0cdr6YSvQtQY45kdH55U2p58YDdqMr/4g933dVQZR5MZwfDXDw597IkBT0kc/VYSDa0Ra4w18wDwyuIr9Uractp3uh4AmsxNqpkrheTbL0xt+/YLU25BBL2/zXHwhx/e8bCGocvifZve/YenAIBeOd1DRZe1StuTDS6jK2XX2n28yDOvLb1WfbHrqeiSjvZIqorpXX9wOp+2yP5Sm6WtrE6dqBUS5CIQCGuyzbltHgCmo9OKbrBO+E7UA0CDuaEsXxrpXR+TNhXLp3uomEcVm4r1wkw8ux0AuLbrVV99sBadts5F4GxzXqUYDAxKqj7mprKcKwT102qVmryPhcbyOlfSuz42LrKGBB33VLHjT1/U0ZHHV1PTec3pX0tHvuv2ngarL5uKGoue5X72sV2/7naZhlOcoP+7h4Y+dv/RxRal7VKaFmvLPAD0+/sblLRjMjJZDwA9jp5N/17556fHd3/vwMx7RYC6obvi5e/cs+0JNakoXgyhbm9QsDXPUALHao7+sEdpe7Kl0dw4BwBHPEcuOlc0R3/YQwkcK9ibp4XaXaF82jEbna0HgO0V2zf9XCkFSJCLQCCsybV1184BwEpipTaWUa4J5XhovA4A2q3tqnFG1oNQvy8o2JpmKSGj0Rz9gWo2FdlCewbNdGCiVaRoIb3nEwNK21MILq2+dA4A5mPzDUIWVSSFYjoyXQcAPY6espwrBPWzvWL7AgDMxmbzGxDWmni+bo901ObULy9aFTsTmWkAgG0V29QxV/g0xcwf2gYAmS3vLbtkgVnH8j/7g119W2vN/Rle1P7jE6Mf/v6BmbJ7H66HrY6tcwAwGZ5ULMgliAIWYgv1AHCZ67JN7bh/6ZGRy392aP5OALhta9Vz//a+rc+VU4BLhmu7/iQAsONPqeZ0QZe9aw4ARoOjF50r7PgzUtK1Nb9J11gmxniSnmoAuKr2KtX0eSxnSJCLQCCsicvoSjl1To8gCsxLiy8p1mtIVvXZVblLHc5IDnCtq5uKMfVsKrJFc+zHWymIlFDZMyraGtXR+2ad7K3a69MxukSci5sH/AN2JWzgBI5aiJ9xRsp2rhDUzdW1Vy9QoERvwlsTSAU0+Xx2Zsv7pCOLcwe3g0+f1/v0J/1ab9JbTYESr6m7RhWOu6a/r5lKRy2i3h7ket8zp7Q9hcCgYYSffWzXby9rsR/mRTDffmHK/S/PjO9S2i6l2F+7fw4AFuOL9UqpXA8GBm1JPmnUM/rE9ortASVsUBpBFPGZvtM3PnBi6WYAeO+umif+z129LyttV6FI7/nkgEjRAu0fb6O9Q2al7cmGS1yXzAHAbGz2gkEu2jdiov1jbVLS9Y/ymnR9bem1akEUGIfO4a0yVKXy+WxCbpAgF4FAOC9N5qY5ADjmOaZIJnExtqgPpAKVNEXzV9ZcWVZN588lvfcT/dKmYqyN9o0o2mQ237BTL+wAAK7jlpNK21IoaIpGval+DgAOLB1QZK4c9RytSPEpg5E1Rrc4twSVsIFAuBgOnSNTZahaEiFSLy68mNej8NyW986KenuQSkctmlP3tZzvuhcXXqwTIVJVhqolh86RyacNhYIdfFCqPmi4/BSo8t26axha/N692x97Z0/liyJA/fTg/Ls//9DwlUrbpQQdto6oWWMOZ4SM9o2VNyqVsOH15dfrAaDWWDtPl/H37nwkMjz9sf8+cdeLo/79NAXhj65ofODLt3cdVNquQiLamxJCZc8IBZHSHP3hNqXtyYbLqy9fYSk2E06HHdOR6fOqXGuO/lBKulZ0jYn2lkQ+bTjuPV4PqKvPY7mz+VYsAoGQNd2O7lkAGA2NNiox/gsLLzQCQI2hZsGkMfFK2FAMRHtLQqjoHqUgUpojP1DFpiIbmJkDFXRkoU6kNen0no+PKG1PIWm3ts8CwEBgQJG58trya40A0GhunNmMzghBPbRYWmYB4Lj3eH7nCkWDa7xSqoodeuC8VbHyuM2W5tm8jl8oUmGWWTy2BQAyOz5UdkcV3wpNUfjWe7e88L7dNY8DwEOnlt/5Z78+/U5OEMvvbNhFkJMnh1cOK5I8GQwMNgBAq7VVFRWP+cQfS2s++KNjHzw+F96poanM525s+9Vnr28t22TduXCdt0nr6GqSstTRMTqhxlizAACvLL5y3rnCTr24mnS9Oe/r6HBwuBEAumxdZVlpq0bITphAIJyXS11Sr6G56JwiG6wT3hONANBqbZ1RYvxiwnXeurqpeH6n0rbkC+3RH+wGAL521wD0dk5pewrJzsqdcwAwHZlWJMg1FBhqBIBOW6c6HHfCpmWLc8ssAIyFxvI+VzI7PyIdWVw6sQXJILvWNePh8UYA2OLYooq5oj38Xz0Un9IL5tpFvvU6j9L2FIu/v63r0Cf3N/6OpiC8NOa/8sM/OfbeSJJb8zMtVzpsHXMAMBwcVkQAaCI80QgA2yu2byrHfdqfMN7zo2MfG/fGOwwaOv61O7t/+pFLG8aUtqtYpPd8fFRkdCk6sljLTL2oSBXhemmxtswBwGn/6TX9FWbiWRcdnqsXGW0qveePhvM9/kx0pgkALqu5rOz9FbVAglwEAuG87HPt82poTSrGxaxDgSFrscefCE80AcCOih2qcEY2QnrPx0fObCrGnrqoOljJk0nQzMyruwAgs/3eowpbU3Curbt2ngIlehKe6lAqlNdeQ9kwE51pBIB9rn1lP1cI6uaqmqtmAUmogRO4vFbn8M1X+wRL3QLFp7Xaw/+55a2/5wSOmo/NNwDAVbVXqWKuaEYe2QMAXMdNZb+OvpX/eW3rqb9+Z/svNAyV7l+MbnX/8OhHp/2J8x5HKjd2VuycB4DZ6IV7DRWCUCqkWYmv1FGgxOvrr1fFXMkHr08GKj70k2OfWAyn6q16NvRt99Yf3bbVtbkq2XRWjq/d3Q8A2mM/3q20OdlwMaEG+e/g6y45BYMzr8fUBwOD1kgmYtPS2tTl1Zev5PPZhNwhQS4CgXBeWJoV64x18wDw6tKrRd1kJbkkvRhfrAeAa+uvLf8Nlt7O8fX7TgKA9viP9yhtzkbRHvl+F5WJmQRjpZfb8t6y//yceme6Ql+xLEKkX1x8Mb/KcRdhLjpnCKQClQzFcPtr95dt7zpCedDj6AmZWFMkxacMRz1HK/L9fK7jliMAoBl+ZO9bf3du77peR29e5eMLATN30EEHJlpFmuXSl/5Z2R9VXIt799VPfOu9W35o0THhuWCy8cM/OfZHh6eDTqXtKgbvqHvHAg1a8Ca91UvxJX0xx35+4fl6AQJdoa9Y3iyNtO8/utjymb7+T4QSnMNl0S796MM7fnhZi8OntF1KkNnx4aMAwMwc2IVURDGF9Wy5oFBDKswyc6/vBIDMzo/mPVnw8uLLTQBQb6qf1dAaMd/PJ+QGCXIRCIQL0mZrmwWA0/7TRT2G9fLiy7W8yLN2rd3XaG6MF3Nspcjs/sMjAMDMHdqJhL/o1UD5hB36vVR90HbjkXJulHwuzZZmWaihqHPlxYUXpd51xpp5I2ss2951hPKApmg0mBtmAeD15dfzPlfSl/7ZaZHWpOnQdNNbj9rI4zWYG2bV0LtO88b39gCAULOrX7TUbYpAw1pc21mx8uOP7PyBy6JdCiY455/++vQnHjyxpMjR8GJi1Vq5amP1PAA8N/dcUf/eo56jTQDQZm3bFMev/uWZ8V1fe2L0IylO0He5TMN9H9/z4+5qc0Rpu5SC67lzXjBVL1Ncwqh947s9SttzMTpsHVGrxhrMCBntK4uv1Jz7O+0b3+2huKRBMNcscd13LOZ77H5/f+OqDZtirqiF0n/DEwgERdlVuWsGACbCE83FHPfwymG5OfCmeWlwHTcvC9aGeYpP6XSH/mOr0vbkCr10wkr7RjpEihbSl/7ppmjUCgC9jt6C9Rq6ECd9JxsBoM3aVvYVc4TyQO4dJ/eSyyeiuTrN111yCgC0R37wpmquoeBQw7njlzRckmZnXtkFAOntH9x0RxXfSne1OdL38T0/7qgyjiYzguHvHx352L8+O1E2PSzPR4etYxoAjnmPFXUPNhoabQKAbc5tZb0H4wSR+l/399/w04Pz7xZE0Fe3O1677+O7f11h1qaVtk1RKBpcx82rVbEPva0qthRpsbZMAcBrS6+9aa5ohh+Wkq7thTnyPRmebAKAPVV7ynquqA0S5CIQCBfkxoYbZylQgifhqSlmufxIaKQJOBs42CxwnbceAQB25DFVbCrWQvvGd3dRECnBtW1QdLRtiio8ALim7pppAJiNzja/rVy+gJzTHHhTzRWCetlbtXcWAKYiU02FeH5m18dWq2Jf24lU+Eyz8snwZMu545cy2qM/7KQyMbNgrPByW99PnCcAFWZt+r6P77nvilb7IUEE85PX5+769K9O3ZzihLL1Z/ZW7Z0CgPHweEuxxkzxKXohttAIANfUXVO23z1PNK394I+Oup8b8V1FAeIHL6l75Dv3bH9Kw9DkyBmA9GWfOSnSLEcHJlqZmVdL/ojwNue2aQAYCg61yD9j5g8V9Mj3SnxF50v6qmnQwrV11y7k+/mE3CnblwKBQMgPDp0jU2OsmRchUs/OPVsQh+StCKKAuehcIwBcUXNF2W6w1iJ96WdOi4w2RYdnG5jxp11K27NuBI5ip17cAwCZre/fVNUHW51bQ1aNNZgW0rqXF16uufgdGyecDrNL8aUGALi2bhP0riOUBdfUXbOooTXpYDpYMRIcseT7+Vz3HYuCuXaR4pIG7eH/7AWAkeCIJZQOOTW0Jn1d/XUl74ywgw/sAQC+9Yajm+XIdzboWFr43r07Hv/QvrqHaQrCgYnA5e/7wZEPzQUSBqVtKwTXN1w/S4ESl+PLtf6kX1uMMV9derU6I2S0Vq010GnvLMsjewenAhXv+/6RTw4tx3q0DJX665vaf/F3N3ccUdquUkK01KX42t2nAUB75Psl3yv2HXXvmAKA2chssyxqonnje7sBQKjeOSBa65P5HvOFhRcaRIiUy+hasOlseW1oT9gY5K1JIBAuSpe9awoAjnmPtRRjvEPLh6riXNxsYA2xvVV7N1XTT9FYkeHrL5Ua0B/7seqquTRHf9ROpUI2UWcLZnZ+ZFJpe4pNq7V1EgBeXXq1tRjjPT37dCMv8kyVvmqpwdyQKMaYBMJG0bN6ocHcMA0Az849W5C5wnXKDegf3nvuOA3mhmkdoxMKMWa+oBeP22jvcKdI0ULq0j87obQ9pcjf3NRx9Mu3d/3UoKHjU75E2z0/PvbJl8f8VUrblW8q9ZVpl8G1KEKkn51/tigCQAeXDzYBQLO5PNtF/OT12c4/va//k/54prLCpPF8555t3//Qvvpxpe0qRTI7PiJVxc4e2I10rKQb0G9zbguaNeZQSkjpX1161YV0jGGnX94NAJnt9xQk6XrUc7QZIO0iShES5CIQCBdFLpcfC421FGO8FxdfbAWAVkvrhBqaA+ebsw3oD+6k4j5VNaDXnP7VZQDAtd1wBDS76Ur+d1TsmAKAweBgSzHGO7RyqA0AOu2dE8UYj0DIF1scWyYB4KTvZEshnr/agD5DB6eamcnnq074TrSeO24po3v9/+2jIFJC9Y4B0dm+aY58r5e7d9bM/PDDO77nsmiXQgnO8b/u7//kt1+Y2qa0Xfmm3dY+DZx1qAvNYEB6f/U6essqyMUJIvWXDwy+41+fnbw3zQu6nmrT4P2f2PuDzaqgmA1c711zgsm1QmXiRu3h7/Qqbc+FoCn6/2/vvsOjqtI/gH/vnZ5eIYEkJCGUECD03kNHBBQv1rVg7+5a1i0/Xd1d3dV1BRVXVNaGi5cFpQkCkSjSQ8AAAZJACgmQhPQ29d7fH4FdVkNImWEyyffzPPvkeWbOuedlPXPmzntPQbRvdB4A7D6/O1q//+1+DUu+Q0ts/RfluaLNzMrMWAAYGjq03X+vdDad79cjEbVYUkTSGRHiNduX61jpsVgASAxO7JQ/3Bs2oI8sEBwWg3733wa5O57m0uR+H6IpzYpTRa3dMuaXnXLaf1JEUg4AFNQU9LA4LC7/js2qzIoBgGGhw3iDRR5lbNjYHAA4XXXaJTO5VJ8wi6P78IZZsfuXjby0H9eYsDG5rmjPaerLdJr8nUMBwDpk8V53h9PeDejmV7nm3qErBnTzPWpTVN3yXfk33rsyfU6Nxd6uZ520xKCQQbnAtXnQaHFYxLzqvBgAmBoxtcPcgxWU15ukD9Nu3pJRMhkAZieEfvvF4iFyaGffYP5qBBH2XrMOAIAuY81Id4dzNQlBCblAQ6JWl7G24aFrn7n7XLHk+3zdeWNRXVE3AYIyI3KGS5Jo1HpMchHRVV2+L9f2gu0u3ZfL4rCIeTV50cB/EwadkS3hpj0AoM3cOAoX9xZo7/QHlo0AAEe3YelqQHSnXDrXO6B3tb/ev8ym2PQphSnhrmyruK7YcOkGa3rk9A71xJ06vnHh44oMosFcbasOSC9ND3BFG9YRj+4FgHPFaYnVtuoAg2gwjwsfd94VbTmLYc+bAwW72aj4RRTa4xcUujseTxDgpbN9dtegNTcPDd8kCnDsy60YNv+9g4vTzlQGujs2Z0jqnpQPAOfqznUvt5S7dHb3jsId3ayK1eCv9y/rH9y/wpVtXSvr04sipQ/THswqru1t0IrmZ6bGrvzL/PidouARt1ZuZx39yx9VrdEsVhVEaDPWXpMls601sdvEXAAoqMqNQXVhd1VrNFtGP+mSU7635G+JVqEK3by7FQQZg5gsbWeY5CKiZukV0CsXANJK0ly611BKYUq4TbEZ/PX+Zf2C+lW6sq32zDri4ROqwb9SrC8L0h18v5e747kaoarQqCnYNwgArMMf2ufmcNwq1i82BwD2Fu116Wdla8HWHipUIdwrvJA3WORptKJWjfKNygWAHYU7XPJZcURPuOAI7p2dqtdqASDKNypXJ+ra7zJqVYH25IaRAGCLX9Cpx9GWEgUBv53ZK/Uv8+NX+Bm1FUXVlvD7Vh554INd+X3cHVtbhXuHm8O8wgoVVdFsztsc7cq2dp/f3bAE3r+Xx+9RZVdU4TfrT4z93YaTd1dbHH7h/obCD28b+N4vRkZkuzs2T6J6BdvsPSakAoA+7YNR7o6nKQODB5b76Hyq6lWb4YReB0fU+IMwBblkQ/i0krRYAIgPjPf4z0pHxCQXETXLyK4jTwNAZkVmT1e2c+kGK84/rsNMk28VrVGxx83YBwD6I/9q1zcVAGD44S/DBMWmUwJjcxyxScXujsedEkMScwHgePlxlya5LiWcewf07rQzHsmzJQQl5ADAkdIjLvus2BLv2LPf1LDKPiGgT7teUqI7+H4vsa4kVNX71FhHPJrh7ng80cx+oWflxUPe69PF+6TVoRiWpOTefO/K9DkVdTaP2t/yp/oF9ssGgH3F++Jc2U5GeUZPABgaOtSj78FyS+u8Fr5/8NYNR4qnqoAwIS5o97oHhq1IjPCrcHdsnsg65pf7VUFUxKIj/cRzh/3dHc+ViIKIeK/u+QCw02RSLaOeOOCqtrIrs2MBYHTYaI/+rHRUTHIRUbPMjJyZrxN11jJLWejR0qMBrmrnRMWJWAAYHDK40/9wt4x9Ok0VdVax/HSM9uSGbu6O54rMFVpt9pZRAGAddNcud4fjbrOiZp0GgILagh4XzBdcduR7VkVWLPDfBDSRp5kQPiEHAHKqcmLtLlqWXTfw1pwfvLxUAJhXVtyuEx36wx+PAwB73Mw90Hs73B2Pp+oeYDTL9w5ZNX9g162iAGVfbsWwee+l3r/teIlLl5C70rjwcdkAkFmR6bIk1wXzBf252nMRAgR1VtQsj70H+2jvmV6LVhx66NSFujijTqz/VVLM5+8s6r/NpNO061NV2zOlS/9qJSzxmABVMOx6bbS742nK3PISIwAkBwTXK+GDXLIiJKMsw7/CWhGsE3XWKd2nnHVFG9Q2THIRUbMYtUYlxi/mFABsyd/ikuVzZeYyfWFtYSQATI+c7rE3WM6i+nazOKInNkwR3/fWeHfHcyWGH/46WLDVeSs+4edsg+/q9NO2I30i68K9ws8oqqLZmLsx1hVtHCs75l9qKe2iE3XWqRFTC1zRBpGrjeg6osRX51tZ76j3Ti5IdkkiP7lwR/cKURC62+wYnJk8CHZzu7z31R79IkqszI9SNQazZfzzqe6Ox9OJgoCX5/bZ88aN/d4P9taVlNXZQp7+8vi9z687Mc7mUDxuM6akiKRCg2gwV1orgw6VHApyRRsX9xgSw7zCCrt4dbG4og1XKqmx6u/+9Me5f0vOubXO6vCJDDTmfXxH4j/uGhWZ5e7YOgLLiMd2AYDmzK6hYmmmt7vjaYxYmuk9teBYtFZVcUJ0mM7VnnPJYVnbC7bHAkAPnx45Bo2BydN2qF1+0RNR+zQkZEgWABwuPeySJNe6nHU9FVXRhHuFn4nwieiUG5f/lGX8r/eootauKcnoqzm1rYu74/kZu1nUnVg3BgBsA2/9wRUn2HiiAcEDsgBgX9G+3q64/qa8TX0AINYvNttbxxkf5JlEQUTfwL4nASDlbIpL9k5KKUzpDQBjrYpFYy4P0u9dkuCKdtpKn7q8YRZX7JT9qk9X7rHnJEl9Qs6ve2DY8tExAfsVFeLGo8VJ895LvfNgvmdtSq8TdWqsf+wpANhesN0l20YcKD7QE/DMPYbWHj4fteC91IdS8yuHiAIc8wd23brugWEf9wv3rXJ3bB2FI256kSO030lBsWsNO19pl7O59Dv/MsrXYdP2UzT1KiBszNvoks9K2oW0XgAwIHiAx31WOgv+GiGiZpvdY3YWAORV58VUWau0zr7+nqI9fQEgMTjxpLOv7amUkL41jsgxaQBg2Ptmu5vNZdj12kDBUhmgmIJKrSMeOe7ueNqLpIikTADIqszq5YplWIdKDvUBgGGhwzKdfW2ia2ls2NhMADhWdswlCeGM8ozeAJDYZWgaAOiOrBrf3k6s1Z7cGK4pPdlLFbV2y7hfc8N5J/M36ezLbx24+dlpsSu99JqaM+XmHveuTH/49xtPjvakWV2DQwZnA0B6abrTlywqqoLj5cf7AsD48PEeM/Opos6me+SLo9Ne2JR5d6XZHtDFV3/+rZsSlr88t88enUZsv4dMeCjriEe+BwBNTspwoSLf5O54LidU5Ju0uTuGA0Dv4AFHASC1ONXpD+WrrFXa05Wn4wDguujr+HulnWKSi4iaLc4/ribUFHrOoTq0m/Ode8KPQ3UgqzKrFwDMjJrJL43LWMY9t0sVREU8/2N/zelvQ90dz39YqjW6I6smAYCt/83fQ6PnDeVFY8PGFnlrvavq7HU+3539zqn7wJTUlxjO1JyJFiCo18dc7zE/RogaM6fHnFydqLNeMF/oeqzsmFM3NM4oy/C/YL7QVSfqrCPG/zFFNfhViXUlofq9S/o5s5220u/52xQAcPSYcEAN6lnn7ng6qjtGRGTLi4e8O7Cb7xG7omq/+rFo+pxlBxZ/n1Xa/mZJN2Jm1MxsoOFBY6Wl0qn7y+0o3NGtxlbj5631rk6KSPKIPYZWHijsed27Bx7+PrtsjACoSX2Cd254cPgHE3oFd+rDb1zJ3nfeWUdQ3ClBsekN373UrmZzGVNeGCs4rAZHYM/TI+JvSwWA7KrsOGc/aFyfuz7Wrtp1XUxdzsUHxnOmYDvFJBcRtUi/wH5ZALDr3C6nPnU/ZT2ltzgspgB9QOmIriMuOPPank4JS6xyRI45KAAw7Hwlyd3xXGLY+echgqXSX/EKKbGOffqIu+NpTy4uw8oEgB2FO5z6WVmfu76nAkUM9w4/E+kTyR/E5NG8dd6OS/s9fp33tVM/K5euF+Mbc8rbK9Rq63fj9wCgO/TRFNjq28U9sO7IqihNaVacqtFbzZNf/MHd8XR0PYJMdSvvHrz2V0kxn/sYNFXnqizdH1t97IGn12ZMrLHYNe6Orylx/nE1XU1dC+2qXbf29FqnzubaemZrPADEB8af0Iradv3AKre0zuu2jw4teHXrqdsrzfaAEB998Svz+n745sKEb730Gi7fdzHryMdTAEB7evtoseS4j5vDAQCIJcd9NDk7RgKAdcwvk8eEjSn20flU1dvrvbcVbOvuzLZ2ndvVFwAGBg884czrknO1iy94IvIcUyOmngSAjPKMeJtic9rTkaOWo0YASAhK4CyuRlgmv/i9KupsmgvH+2iPypHujgf1ZTpdxtoJAGAbfM+3nMX1c6O6jsoCgPTS9L7OvO6+on19AC7rpY5jWOiwkwBw6MIhp+7LlXYhrTcADA0dmgkAlvG/OaSYgkpFc3mQ4fs/DXFmW62iKtDveysJAOw9p+9RA2OZtL5G7hoVmbXugWHLRvTwT1VUiN8cvzBp1jv7H/54b4FL9hx1lmFdhh0DgJ3ndjp1b7mjZUf7AsDEbhPb7bYDdkUV/pZ8OvGmD9MeTS+sHqgRBft1/bskf/3w8Pfm9O9S6O74Ogt7vxsKHKH9TgiKXWvY8cJEd8cDAIaUP0wQFLvWEZpwwt533llRENE/qH8GAGzN39rfWe3YFJtwsuJkHwCYETmDSa52jEkuImqRyd0nn/XT+5XX2et81uesj3bGNVVVxXHLcSMATOo+iV8ajVBC+tbYe07bAwCGvW9Oherew1yMyb8dI9hqfRTfbmetIx/lf7NGzIuZd1ov6i0XzBe67j2/1ynLTGtttZqsiqzeADAjcgb346IOYV7MvCwBgppXnRdzpuaMlzOumVed55VXnddTgKDOjZnbsKxXZ1Jsg+/+FgB0x1ZPEupKnbrkq6X0+5f1ESvzolStsd4y+cU97oylM+ria7B8eHviphdm9/oo2FtXUlFvD3o9+fStN7x/8Jb2ujH9/Jj5GQCQXZnd21lLFvcV7Qspt5SHGDSG+jk95uQ545rOtvFIUcTsd/Yv/mhvwXyzTTFFB5ly3r91wLuvzOv7g0mn4el215hlwm+TVQiq5syeoZr83S457bO5NHk7gzVndg9VAVjGPfftpddnRM44CgBHy472c9aSxS35WyLNDrOXv96/bHTYaC6LbceY5CKiFhEFEYNDBh8BgOTCZKc8Hcmx5egqlAqNSWuqnR45vcAZ1+yILFNe2q1qTXViZX6Ufs+bbttTRjx32F+btXkcAFhHPbGVJyo2zkfnY48PjM8AgHW56wY445ryKbm3RbEYQ4whRVzWSx1FpE9kXbRv9CkVqvhF1hdOmaEiZ8sJKlQh2i86O9o3uvbS69ZRT2Qovt0LBVutt2Hbc+47zMNSpdWn/mMGANjib/hO9QmzuC2WTm7h4PC8zY+M+Mf8xK5bdRrBmlVc23vxZz8+8tS/MyaX1lj17o7vcglBCZXOXrK4OX9zPAD0Cehz0qg1tquE0fHzNb63fXRowfPrTy4+V2Xp7q3X1NwzOuLLdQ8O+2R4j4Ayd8fXWTmiJ15wdB9xWIAqGHb83wx3xmLY8cJMQVVER/eRhxyxU0ouvT4tclqhr863otZe6/t1/tdRzmgruSC5HwAkBCWcEHnv267xvw4Rtdi86HlHAeB42fF+dfa6Nu9hkVqf6gUAg0MGp+tEHZe9XYHqE2axDbjlWwDQH3x/plBb4pabb2Pyb6cJil3r6DrwmG3gbe3yqW97MS1y2hEAOFRyaIDihNl3KYUpAwFgZNeRP7b5YkTtyPhu49MBYHfR7oHOuN6eoj0DAWB8+Pj/3S9QEGEZ98wWANCe2jZGk78r2BnttZRx+29GC+aKQMUrtMQy5aUD7oiB/suk0ygvX9dnz6p7Br81KMLvR4cKzfaTFybMfvfA43/cnDW83uZoN7+ZnLlkUVEVpBanDgCAceHj2s1SRbNdFdbnKD63/vPQY+mF1QM1AhxTegfv3PTQ8LeemhKbLgoecyhmh2WZ+qdvVY3eorlword+/zKXnI57NboD/+ilKc2MUzUGi2Xaq8mXvycKIhJDEo8BwPYz29v8Wamz12nSS9MHAMCsqFnH2no9cq12M2ATkecYFTaqJMQYUmRRLMa1p9f2bMu1amw12iOWIyYAmB8z/7BTAuzALBN/l6b4djsrWKt9jVufueZ7Ieh+/DRaU/Rjgipq7eakP2+71u17mrnRc3O9td7VVbaqgG/OfBPRlmsV1BSYTlWe6i1AUBfFLeJG/9Sh3BJ3ywmdqLOerzsfcajkUJuWvxy+cDjwfN35CK2gtS2KW/Sz5dT2fgsL7N1HHBJUh8aQ/NtZ13r5t3jusL82c9N4ALCOfnIz2tnsmc6sdxefmk/vHPTVy9f1XhHuZyisszq8v0g7N3v6W/seefu73AS7oro9u3L5ksWS+hJDW671zZlvIsosZaEmral2YezCbOdE2HrVZrv2hU2Zo363T+nydZ7qa1dUXd+u3sdX3JH4zpKbEr4N9tFb3R0jNVBC+tbY+i3cAQD6A8tmwVyhvaYBmCu0+v3vzAQAW/yCFCW4V+1Pi8yOmn1pyWKCxWFpU97ji+wvepsdZq9gY3Dx5O6TPeIE0s6MSS4iapWhoUOPAkByQXJiW66zKmtVH7NqFsK14bZx4eO4vv1qNHrVMuG3m1QAmpxvR2mztoRdq6aFulKd4Ye/zAMAe6/ZO5XwQZXXqm1PpRN16sDggUcBYHPe5jbNUFmVtaq/AkXs4dvjdJx/XI1zIiRqH/wN/ra+AX2PA8Ca02va9FlZc6qhft/AvscDDYG2xsqYp7+2XdUYzJqy7J76Xa87bWPiq1IVGLc8eb2g2HSOLgMybIPuzLlmbVOzzU8MO7Pl0REfPDqxhxzopSutqLcHvfdD/sKZb++/7597zvRWVPdNOk8ISqiM8I7Is6t23ccnPm7TZ2V9zvohADAkZMhhdy5VrLc5xFe+yR46/e19j609fH5GjQ1ilA9sv58V9/Hqe4fKQyL9y90VG12ZZcpLBxTvLsWCuSLAuOWX1/TBq2nzE5NFc3mQ4hVSYpny8v7GykzoNuF8gD6g1Owwe32R/UWbDpVILkgeDABjwsYc4lLF9o//hYioVRbFLUoXIKiZFZl9MysyfVt7nR1ndyQCwBDjkHrnRdex2fvOO+uIGndAUBXRkPz8Aliqr8mx58avH5sqmCsCFO+uRebpr+26Fm12BHOj56YDwJHSIwPaslHw7qLdiQAwIXwClypShzQ1cmo6AKSWpA5s7UbBdsUuHCg5kAgAUyMartcYNahnnW3gbdsAQH9w+Ryx+Firv8dawpDyhyGasuxYVWusN89e8vW1aJNaRxQEPDCux/Ftj41cdtvwbhu89ZqaompL+Bvf5twydem+B9/+LjfB5lDcMrNrWuS0AwCQcjZleGuXwl8wX9AfKzvWHwAWxS065MTwmq2y3qb905asYdPe2vfo56lnr6uxOPxCffRFDySIZc8PFS9IQ7rluiMuaiatUbGMf36jCkB7attY7bHV1+T0b23GvyM0p78drUJQLZNeXAedqdEPgSiIGN9tfCoAfJ339YjWtpdVkeWbU5UTJ0BQbu116xW/V6j9YJKLiFqlb2Dfqj4BfTJUqOKK4yuGt+YaGWUZ/rlVuXEiRAw1DmWSqwXMc97ZphgDy8Taki6mzY9PdnV7usOfxGjzvh+hCqJiSfrTV9B7O1zdZkcxqfuk811NXc9aFIvx45Mft2rGyLcF34afrzvfXSfqLIt6/Xz5FVFHMD9mfo6vzreyyloVuPrU6lZtqr361Oq4KmtVoK/Ot3JB7ILTTZW1TH4xzRHcO1uwm43Grx+93tXLFsXCAwG6Hz+dAQDWofd/rQT3/tnyGmp/DFpR+fX0uLQtj4xYuiAx7Btvvaa6pMba9b0f8hcmLd33yOvbTw2qszquycOmS27rfdsJk9ZUW2YpC92Ut6lVm2p/dvKz/nbVruvu3T1/WJdhpc6OsSkF5fWmZ788PmHaW/ueWnXw3JzKentgoJeu9IFxUf/e+tjI9waHChaB+255BHvCTWccPaftEqAKhpSX5rv61FqhtkRvSHl5vgBVcPScutseP7+wqfJ39737sFbQ2vJr8mP3Fe0LaU2bn2d9nqhCFXr59zrZw7dHXesip2uJSS4iarUbe964DwD2F+8fVmWtavFa/BUnVoxQoQr9Df3rfTW+3JOkBVSvYJtl8gtfqhBUzaltY3VpK9q0N1pTxOJjvobv/3gj0LBM0d5r5nlXtdVRJUUk7QeA7QXbR7Tmqfuq7FWjAGBo6NBDV1p+ReTpdKJOHRc+bj8ArM9dP7I119iQu2EkAIwLH3fgqgeZCCLMc95Zp2qN9ZrSrDjj1mdGt6bNZrFUaU0bH5YEh1XvCE04YR379FGXtUUuEeCls710Xe+92x8fueSWYd02+hu1FeV1tuCP9xXOm7xk71NPrcmYfPx8zTWZEeil9XKM6DLiIAB8lfNVix80KqqCHYU7hgPAxG4T05wd35XszSkPfvBfR2Ze/4/UpzZnlEyutyleXX0N5x4a32N18uMj33l0YvQxrSjwACIPUz9r6Q7Fu0uxaC4PMq1bPMdlDwxUBaav7rlOrC8NVrxCSupnLUm5WpUwrzDzwOCB6QCwMnNli2dzme1m8YdzPwwHgJlRM6/ZZ4XahkkuImq1WVGzznQxdTlndphNH534aEBL6hbXFRtSi1OHAsAErwl8mt0K9n4LC+y953wnADDs/PNCTeH+QKc3YqsXTevvWyjY6ryVwNgc8+yl3zm9jU7grr53HTNpTbUXzBfCWjpDJaMswz+jLKO/AEG9s8+dje47QdRR3BN/zyGtoLXlVef1TClMadGegymFKWG51bk9tYLWdnffu5v1Y0QJja+xjnx8PQBoj8rTdIc/jmlN3FdjWnfvTLHmXLhqDKion/fBOnBPF4/lY9A6fjMj7mDyE6PeWjw68ssQH31xndXhvf3EhQmLPkx7atGHaTetOXyuh6v37fpFn18cFCCoJ8tP9ssoy/BvSd1/n/p3zxJzSZhJa6q9q+9dLj0prs7q0LzzfW7Cde8euPO+z488uut0+UiboupiQ7xO/W5m3CdbHxux/OEJPTJ0GpHJLU9l8HWYp/1ljSpq7ZqzqYmG7b9p9dLAJptJ/t1wzflDA1RRZzPP/PtqGPzszal3c6+b9wPAj6U/JhbXFbfosIYVJ1b0r7XX+gUZgkoW9lx4qjVx07XHb1giajVREDEtYto+ANiSv2VsS04uWXpk6WirYjVE+kTm9tD34MyUVjLPeft7R0jfTMFuNho3PHizUHO+TSct/Q9VgWnN7deJlflRqt6nun7eh2ug0fMmtBV8dD72Sd0m7QGA1dmrJ7RkNte7x94dp0AR4wPjjyaGJHLzXerQunt3rx/eZXgqAHx04qMJLal7qfywLsMORvhENHsJvHXU4ydscTN3ClAFw3d/vElTsM+pDwwMW58dqT2ze6gqaBzmaX+RVf9IszOvT+5h0IrKk1Ni0pMfH/nui3N6fdQvzCcDADLO1/R7cVPWXZPe3PvoM18en3DoTGWAK9qPD4yvig+MP6ZAEd899u645tZTVAXyKXkCAEwMn7jHR+fTrERBS+3MLgt9TD46ddKbe576x878hXll9dFaUbANjfQ/9ObCfu+te2DYZ4uGdssRuSyxQ3D0nFZsHfHIOgDQpa+cqTv8iVMfGOjSVvTUpX82EwCswx9a74iZXNLcuuPCxxVH+kTm2BSbfumRpc2esetQHdiYu3ECAEyPnL5HK2p5D+whru1Rnz8hSZIA4FUAzwJYIMvyVy2s/yCARwHEAigD8DWA38uyXNRI2YUAngMQD6AGwHcAfiPLMjOyRG2wOH7xkQ15GyZWWCuClx1dNuipxKeu+vQ8tzrXe+e5naMA4Pbet+9AIWa6PtIOStSq9Td8stbr01n3ibXFXbxW3XBr3a0bPlO9gtucODSuuzdJW7hvsCpq7eakP8uNHc9MzfdgwoOpOwp3jDlbdzbys8zP+vyizy9OXq1OanFq8KGSQ0MA4N74e3e6Pkoi93uo/0O79xfvH55ZmRm/OX9zxKyoWQVXq7M5f3NEZmVmvEbQ2B9MeHB3S9s0X7dsh/jpzHBNaWac6at77qxb+PkKJSyxqnX/gv/S73x1gP7I5w0/zIbdv8Hee865tl6T2hdREHDjoPC8GweF5x0/X+O7fFf+sN2ny4eU19mCt2SUTN6SUTI5IsCYPyEuKP22Ed0zogJNTtuD9L5+93331K6n+h8qOTTkYMnBPUNDh5Zdrc7KzJV9ztaejTJqjHUP9X8o1VmxAMCRs1X+/0o9239vTsWAkhpr10uvh/joi6f0Dk69b2xUepifweLMNqn9sI595qimOCNMe3rbWMOOF25W9V6f2vstvOr4fTXa4192N3z38iJBVUR7zJQ91nHPtni59+29b095Je2VmJ3ndo7Orc49EO0bfdV72n31+7wqrBX+/nr/svv73c9DfzyI22ZySZJkAPA5gGdaWf8vAJYC2ATgJgB/ADAFwHeSJPn/pOzDAGQAaQBuvdhmHIBdkiRFt/KfQEQAjFqjcmPsjckAsCF3Q1JBTYHpanVeTXt1qk2xGWJ8Y7LnRs/Nd32UHZvq281SP++Dz1S9b5VYmR/ltWrBLUJtib71F1RgXH/fZN2preNUCKpl/POyvd8Nbb5J6exCTaGWGVEzvgOAf2X9a/rV9rFTVAVvpr85Q4EiJgYnHhoVNqrZTy2JPFmcf1zN2LCxewBg+bHls2yKrcmpHjbFJiw/tnwmAIwNG7und0Dv6hY3qtGr9Qv/tVrxiygULJX+pjW33ykWHfFr1T/gIv3OVwfoDyybDwC2hJu2WSf8lj+SOrj4MJ/qv9/Yb8f3T43++7PTYlcO6OZ7RCsK9oIKc9TnqWevu27ZgWfmLDtw5wubMkc5Y4bXyK4jLwwMHnhYgSK+8eMbM682S7jKWqX9POvz6QAwPXL696Gm0DYlnBRVRfLJC2FPrz0+cerSffff+s/DT244Ujy1pMba1aAVzcOi/NP+NLfPiuTHR777+1m9DjDB1fHVX/9esiNs8BFBsemNW5+7XXv8q+5tuZ722OpI49ZnbhMUm84RPjS9fv6Kba25ztzoufkxvjHZNsWmfzXt1alXK1+r1Arf1HzjCwALYhfsMGqN3DvYg7glySVJUiCAbQCuB/BwK+oPRUOi6hFZlp+TZXmTLMvvAxgHIAjAC5eV7Q7gdQCvyLL8gCzL62VZ/hTABADlAN5o8z+IqJNbHL/4WIR3RK7ZYfb6w4E/zGrqJmtV1qq49NL0QaIgOn456Jebr2GYHZrSfXhF/bwPPlF13jVi+ekYr09n3iOeS2vRHh0AAGutxmvVgvm6rM0TVAiqdcQjX9qGPZDlgpA7pccGPHYwyBBUUmmtDPrDgT9Maarsu0ffTTxddbqXTtRZnh38bPK1ipGoPXh68NM/eGu9q4vqi7q9mvbqmKbKvpr26pii+qLu3lrv6qcHP/1Da9tUfbpa6xat+UzxCTsvmsuDvL5YeJ/2+Jct/4GmKjB+/fg4w/63bxBURbT1nPGDeebfWzy7jDyXQSsqd4yIyP787sFrtz428rW7RkV8FRNsOi0IUPPL66PXHj4/4xef/PjExL/veeT+z9Nnfbj7TJ/zVZZWbTfw3ODntutEnSWnKqfXsqPLBjVV9qXUl6ZUWiuDggxBJY8PfLxVs7gyi2t8lqbkDLjrkx+vH/e33U8++e+MB745XjKpqNoSrhUFW0K4z7EnJ8esSnly1Ov/vCNxw/UDu57hksRORKNX6xat/srRpf9xwWExGLc8eZd+1+sJrbmUfu+SeOPWZ34h2M0mR2i/k3U3rVqHNiwZ/OWgX24WBdGRXpo+aFXWqivuj6qoCtZWrfWvU+vECO+I3Hvj7+VBIR7GXTO5hgLoD2AGgC2tqH8/gDwAH17+oizL5wG8C+BuSZIuzWL4BQAVwCs/KVsL4K8ArpckKbwVMRDRRaIg4tnBz27UClrbsfJjA944/EajJ/0cvnA4cHnG8gUAMC1iWkpzptVT8zmixpbWz//nPxVTUKlYW9TVS150v37Xa/2be8qNNmtLmPeKCfdpzh5MVEWdzTLxd/+yjv/1EReH3al4ab0cTwx8Yp0AQd1TtGf0BxkfNHrjl1KYEvZF9hdzAOCmnjdtjvGL4VJR6lRCjCHWxfGL1wPAlvwtU9aeXtvo/i5fnv4yekv+likAsDh+8foQY4i1Le2qft3NdYvWfqIExuQKtlof4+Yn7zZueWoMbPXNumcWi4/6en087Vbd8bVJKgDrgFu+Mc//kEnqTizUR2/9VVLsj+sfHP7plkdGvHb/2Kg1CeE+x3QawVpWZwvZk1Mx4s0dOTdPf2vfc1OX7r3/3pXps99IPp24N6c82K6oV80OxfjF1N7U86bNACBny7N3FO5o9HfNiuMr+u0+v3u0AEF9dMCj6720Xo6rXbve5hC3HS8J//OW7GF3fnL4+ol/3/PIje+n/er9XWduOHimcnC1xeHvpdfUDIvyT/tVUsznKU+O+uuqe4b8e/GYyJM+Bu1Vr08dlNao1C1as8YeMSpNUOxaw943F5r+fcus5u4bK9QU6U2yNMew6zVJUOxae8Tog3W3rpehM7VpNtXQ0KFlUyOmpgDA8ozlCw5fONzo/otvHH5jeLol3aSDTn1m8DMbRR4U4nHcsieXLMvbJUmKk2W5rJXLBScB+F6W5cY6+ncA/g/AIAD7L5ZNlWW55gplNQDGo2E5IxG10rAuw0pv7XXrhk8yP7lhbc7a2QoU4elBT++/9MWw69yuLi+lvnSL2WH2ivaNPvXbob/d5eaQOyRH1Jiyuts2fWBae8dCTVl2T8PeJTdqT24cbBt81y5b4h05jT0B0+TtDNbvXTpaU7hvsKAqomoMqDBPfWW1vc/cs+74N3R00yKnFe4v3r9tU96m6R+d+OgGh+oQHkh44D9PCbfkb4n466G/3mJX7br+Qf3TH+7/MJc4Uad0c6+bsw+WHNy96/yuMUvSl9xsU2yrF8Utyr70vpwt93zn6DuSClUcGzZ29829bs5u6nrNpQZE1dfesfVT01d3z9Tm/zBcd2z1NE1uSqJtwK07rcMePA6D789+vIsXTvjod70+Qpvz7QjBYTWoWqPZOvLxddZRj59wRkzUMYT7G82PTYo++tik6KN1Vofmm+Ml3X84VRabca4m5mylOaKo2hpeVG0N35dbMfyfewug0wjWEG99Sbi/obhHkFdxbIipNDbYqzI+3Lci1Ef/n4Tuw/0f/jG9ND32aNnRgS+nvnx7vb1+1ewes89cen95xvL+n5z4ZAEAzO4xe9vMqJn/2YJAUVWcrTCbThbX+p8qqQs4daE29Ey5ObSo2hJaWmsLcSjq//xe1ImCLSrIlNe/m2/O+LignKQ+Iee1osANuel/6b0d9ZK8wbDt18W6I/+aoc3bOcL7n5P62eIXfG8d+Vi66tvtZ0tXhdoSvX7Xa4N1JzeMEazVfqogKvb4G5LNM9/Y7awTaX839He7Misyo3Orc3s+t+e5O/9v2P99PjZ8bDHQMIPr9cOvj/gq56tZADDfb37l8C7DS53SMF1Tbtt4Xpbltszg6Alg2RXeO3hZmf1o2Htr/RViOC1JUsXFskTURg/2f/BIcX2x35YzW6Z+lfPVrH1F+/rHB8afKqkvCTxWfqy/oiqaMK+wwqXjl8o8ocR1VP9Ic92d21cavnt5sC595VRN+alYzbe/jzXseq1SCYgpUPwiLgCqIJgrvMSy7CixtrgL0DDl1R417oB59lvbVe/QNs2GoKY9P+T5PaXmUr+9RXtHfXzy4xt3FO4Y0jugd25BTUGXkxUn+6lQhWjf6FNvjntzPZ8gUmf2yqhXtj/03UO+x8qPDViSvuS2jbkbM2P8Ygpzq3K7ZVdl9wGAhMCEI38e9eftTm1YZ1Lqb1r1te7g+5mGvUtmi7UlXQx7l9yoT11erwRE5ysBPYohahXBWmMQy7IjhOqz3QRVEQHAEdI30zxryUalS0LL9wajTsNLr3EsSAzLX5AYlg8gpbTGqv82s7TboTOV3bNKaiPOlJsjaq0On3NVlu7nqizd08787zkIBq1o9jVoqrz0mnovvabOy3C/2du0tKIW2QF/PPine945+GWRtxheU6mcCqxGThAABDnGnDtxdHaXhWkHb66zOky1VodXtdnub1NU3ZXiDDBpy6ICTYW9u3oXDo30L0zqG3LWpNNwfyK6OkGEZfpf9zlik3INO164TqwqiND/+Ols3ZFV05SAHmfUgB7nVa3JKtgtOrH8VDeh8kyEoNh0AKD4hJ8zJ/1pnSNu+s8OlGsLrahVl45fKt+fcv8vzted7/7rvb++v19gv6OhptDyE+Unep6tOxsJANO9p1ePMI1w2iERdG0Jqure35kXZ3LloJmnK17cVL4CwB2yLH92hTJ2AE/Ksvy2JEnlAP4my/Ifr1D2FIAvZVl+uiVxJycnqwBgs9k8/pQcq9UaAgB6vf6Cu2OhjiG1PtW0rnqdv1k1/880+yHGIXU3+N1QZRAM/zPwsA+6jt5WKUad3+IVUZLiZbSWaRor4xB0OBcypi43fG5NjVdkp1xe4I4+qKoqdtbt9NpSs8XPhv9urC1CxCjTqNq5vnOrtIJbD0Gma4xjYeMUVcHW2q0+KbUpvg78d4jSQINJ3pOqp3tPr3FlMlh0WBBZnOwVUfytt2/9mUY/lCoElAQMMud0u76m3K9fm0+3dRf2wfalyqqKhTXQnq1VtWdroSsxq5pyCzTlFmjtjaaZHDB02Qxd0G4Iwn8LqIoOluLZsJWPAvDzFZAGDdQAAxxBBji6egn2cC/YunkL9u7esHvprv0sLfbDDkhVEH5hlzGyeLt3UPWJKx6OVOHTy5oTPqe2KGik2VmztxpjUS3C2qq1fmnmNK/LXzcKRvV63+srEzWJ3gD7oDvpdLpwAEhKSmrxpn6eePfse/FvXRNl6gFc2nDZ9ypl6y4rS0ROMMw0rL6/ob/5sPmwscRRovUWvZV4fbwlXBdud3dsnY1V569kRy6qyY5YWONfm6P1q83ReZmLtIqgUR0ao1rh08tW4dvbqoitP4yRWkcQBEzwnlA3xDTEnG5ON5Y5yjQ+oo+SYEgwh2pDO2WykagxoiBips/MmpGmkXVHzUeNFUqFxl/0dwwwDjAHagJdPqNE0RiQFz67Li9sVp1PfYHGr/a0zqe+UAcADlGvVnlH28p94612rTdnKJNT+ekFxS8I1vgg4X9mV6uqikorxAoLxBobxFqbKtbYIZrtWsGuzhVq6saIpZrjOqtQJZjUICXEPsBm9PNWdQFQvbVQvbRQvHWC4qODEmKEwx2JLOpkBBHnQsebz4WONxstJWJATbbety5PK6gOQRF0aq2pm73ML8Fq0bt+TAcAg2BQb/G/pXKy9+SaDEuGsVapFUM1ofZBxkFmo2hUrVar97WIg1yjzUkuSZKeBvBaM4p+J8vypLa2B+DS1G9TE2VMACovK99UWa/LyrbY2LFjl7e2bnuxa9eu+4GO8W+h9iUJSc0qxz54rUz42Ss8daOBu/vgDMxwR7PUzri7H3qC63Cdu0P4mTB3B+BE7IMdyXx3B9Bq7IedU6yb2r0RN/7sNfZB99u/f/8Lra3rjJlcKwBsbEa5pmZTtUQVAAeA4MbevLicUQPg0iZxZVcqe1HgZWWJiIiIiIiIiMgDtTnJdXED+bZsIt/S9lRJkk4DGHKFIpdeP33x76krlZUkKQYNSa7Tjb1PRERERERERESewVOPbEoBMEGSpMY2IZuIhuWHhy8rO0ySpMbW1U4EoADY6fwQiYiIiIiIiIjoWmn3SS5JkjSSJHX9ycvvA4gGcM9PynYF8BCAj2RZtlx8+RM0LF/89U/KegF4FsAGWZbPuiB0IiIiIiIiIiK6RjzhdMVlAO6VJGmCLMu7AECW5QOSJP0dwDJJkuIA/ICGfT+fRcOeXS9eqizLcoEkSc8CWCJJUjCAzQD8ADwBIATAnGv5jyEiIiIiIiIiIudr9zO5ABQCqMBPTkCUZflXAH4F4HoAqwG8jIZk13hZlit+UvYtALcCGAngXwD+DiAPwFhZlnNcGz4REREREREREbma22dyybKcC6CxvbUuvf8SgJeu8N7bAN5uZjurAKxqRYhERERERERERNTOecJMLiIiIiIiIiIioiYxyUVERERERERERB6PSS4iIiIiIiIiIvJ4THIREREREREREZHHY5KLiIiIiIiIiIg8HpNcRERERERERETk8ZjkIiIiIiIiIiIij8ckFxEREREREREReTwmuYiIiIiIiIiIyONp3R0Aud+XX34ZAgBjx451dyjUSbEPkruxD1J7wH5I7sY+SO0B+yG5G/ugZ+NMLiIiIiIiIiIi8nhMchERERERERERkcdjkouIiIiIiIiIiDwek1xEREREREREROTxmOQiIiIiIiIiIiKPxyQXERERERERERF5PCa5iIiIiIiIiIjI4zHJRUREREREREREHo9JLiIiIiIiIiIi8nhMchERERERERERkccTVFV1dwweKTk5mf/HERERERERERG5QFJSktDSOpzJRUREREREREREHo8zuYiIiIiIiIiIyONxJhcREREREREREXk8JrmIiIiIiIiIiMjjMclFREREREREREQej0kuIiIiIiIiIiLyeExyERERERERERGRx2OSi4iIiIiIiIiIPB6TXERERERERERE5PGY5CIiIiIiIiIiIo/HJBcREREREREREXk8JrmIiIiIiIiIiMjjMclFREREREREREQeT+vuAMg1JEnSAngWwN0AIgAUAVgN4A+yLNc0o35XAC8DmA0gCMBpAG/LsvwPlwVNHU5b+qEkSZMA7GiiSKAsyxXOiZQ6OkmSugH4GkC0LMsBLajHsZCcojV9kOMgOYMkSQYAjwO4E0AMgLMAtgP4oyzLhc28BsdCapO29kOOh+QMkiT1B/AigIkAjAAyAbwH4ENZlh3NqM+x0ANwJlfHtRLA8wA+AnAjgDcA3AFg88XEwxVJkhQA4HsASQD+AOAmABsBLJEk6VXXhUwdUKv74WXuAzC5kf9dNVlLBPznhmYvgIEtrBcAjoXkBK3tg5fhOEitIkmSCOALAH8EsBnAIgB/Q8O4tv/iD7arXSMAHAupDZzRDy/D8ZBaRZKkEQD2AwgH8BQa+uEGNPTFN5tRPwAcCz0CZ3J1QJIk3QBAAjBDluWtl72+HUAagIcBLG3iEi+hITOdKMvy2YuvbZIkKQvAckmSVsmyfNglwVOH4YR+eEkq+xu1liRJSQDWoOFJ3To0JFmbi2MhtVkb++AlHAepteYCmAfgLlmWP770oiRJawAcA/AqGmZbN4VjIbWVM/rhJRwPqbUsaEhm/f6yWVtfS5JUiIax7C1ZljObqM+x0ENwJlfH9ACA7y9PLACALMsZaHiK8uCVKl6cSnwngGWXfXgv+SeAfAD3Ozdc6qBa3Q+JnOhOAIfQ8NSttLmVOBaSE7WqDxI5SR2A1wF8dvmLsiyXANgKYGRTlTkWkpO0qR8SOYMsyz/KsvybRpYl7rn4N/pKdTkWehYmuToYSZI0AMYBSLlCke8AxEuS1OUK7w8B4NdYfVmWFQA7AUxqa5zUsTmhHxI5y4MAZsmyXN3CehwLyVla2weJ2kyW5W2yLD9zhb1mDAAqrnIJjoXUZk7oh0SuNByADcCRJspwLPQgXK7Y8XQD4IWG5WCNOXjxb08AxY28H3fxb1P1F7U6Ouos2toPL/eJJEkxaEjKp6Jhg9JtTomSOjxZlutaWZVjITlFG/rg5TgOklNJkhQCYBaAD69SlGMhuUwL+uHlOB5Sm0mSZETD0sOZaNgz+HeyLJ9rogrHQg/CmVwdT9DFv1daElF28W9wE/UdsixXNlFfL0mSTyvjo86hrf3wkjQAHwO4GQ17eOkBfCNJ0m1tjpCoaRwLqb3gOEhOJUmSAOADNPSlN69SnGMhuUQL++ElHA/JWc4DKERDgvVFWZb/epXyHAs9CGdydTy+F/9e6cnxpdf9m6jf1FPny+vzFBO6krb2QwDYJcvy0MtfkCRpJYBkAMskSdrYxBcNUVtxLKT2gOMgucLf0bAJ+BOyLOdcpSzHQnKVlvRDgOMhOdd0AJEArgfwmiRJ3rIs/6mJ8hwLPQhncnU8l/b8MF3hfa+Lf6/0JVDdRN3m1CcC2t4PIcuyrZHX7AB+hYY18fPaEiDRVXAsJLfjOEjOJknS7wA8gYbNk5tzwjHHQnK6VvRDjofkVLIs75dleY0sy3cCeBLAHyVJSmyiCsdCD8IkV8fTnOWIQNPLyLSSJPk1Ud8qyzIz1NSUtvbDK5JlORUNG5T2bXlYRM3GsZDaLY6D1BqSJD0I4GUAnwJ4tJnVOBaSU7WyH14Rx0NqK1mW30HD0sVbmijGsdCDMMnV8ZwDUI+GEyAac+n101d4/9RPyjVW/0p1iS5paz+8GhFAYyf0EDkLx0Jq7zgOUrNJkiQBeAfAagB3y7KsNrMqx0Jymjb0w6vheEhtVQAguon3ORZ6ECa5OpiL03Z/wJWPMJ0I4IQsy0VXeD8NDdMxf1b/4gaR49HI0alEl3NCP4QkScGSJAU18no8GqalH3VCqERXwrGQ3I7jIDmDJEkz0DBr5msAt8my3JJkAMdCcoo29kOOh9QmkiRpJEmaIUnSz5JUF8eyOAAlTVyCY6EHYZKrY3oPwERJkqZe/uLFL4GbL75/6TV/SZIurSGGLMtmNJxa8ogkSWE/ue5daMhwv++asKmDaXU/lCQpFA1PTJb8pK4WwOsAigBscF3o1NlwLCR34zhIriBJ0igAawB8D2BhY/sa/aQ8x0Jyurb2Q46H5ATeAFYCWHqx31zuXjRssbLu0gscCz0bT1fsgGRZXiNJ0hoAayRJehXAYTRkp59HQxb6HQCQJMkbDdMqLwDoc9kl/g/ATAA7JUn6C4CzAMaiYWPHv8mynHaN/inkwdrSD2VZLpEkaRmA5yVJ8kXDl4oWDZuUDgUwT5blpk44IWo2joXkbhwHyYW+BlCFhpPsRjesFvtfsiynABwLyaXa1A85HlJbybJcJUnSowA+A7BVkqQP0DAzaxqAhwF8IsvydoBjYUfAmVwd1y0A/gZgMYC1AJ4GsArAjMuentgBnAdw5vKKsiyXA5iAhqctLwH4N4D5AJ6WZfmZaxE8dRht6Ye/AXAngO5o+EJ6Dw3TiEfLsrz1mkRPnQXHQnI3joPkKoEAwgFsArDjCv+7hGMhuYoz+iHHQ2oTWZZXAZiChn2Dl6Bhb7gJAB5Hw2ysSzgWejhBVZ213x8REREREREREZF7cCYXERERERERERF5PCa5iIiIiIiIiIjI4zHJRUREREREREREHo9JLiIiIiIiIiIi8nhMchERERERERERkcdjkouIiIiIiIiIiDwek1xEREREREREROTxmOQiIiIiIiIiIiKPxyQXERERERERERF5PCa5iIiIiIiIiIjI4zHJRUREREREREREHo9JLiIiIiIiIiIi8nhMchERERERERERkcdjkouIiIiIiIiIiDwek1xEREREREREROTxmOQiIiIiIiIiIiKPxyQXERERERERERF5PCa5iIiIiIiIiIjI4zHJRUREREREREREHu//ARdkgMO7NaShAAAAAElFTkSuQmCC\n", "text/plain": "
" }, "metadata": { "engine": 2, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "transient": {} } ], "received": "2021-10-26T11:42:14.950130", "started": "2021-10-26T11:42:14.676970", "status": "ok", "stderr": "", "stdout": "2\n6\n10\n", "submitted": "2021-10-26T11:42:14.671548" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:13]: \u001b[0mText(0.5, 1.0, 'Plot 3')" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:14.945829", "data": {}, "engine_id": 3, "engine_uuid": "89363e7a-3deecfc8a12bb00f66b0d3b4", "error": null, "execute_input": "x = np.linspace(0,np.pi,1000)\nfor n in range(id,12, stride):\n print(n)\n plt.plot(x,np.sin(n*x))\nplt.title(\"Plot %i\" % id)\n", "execute_result": { "data": { "text/plain": "Text(0.5, 1.0, 'Plot 3')" }, "execution_count": 13, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_64", "outputs": [ { "data": { "image/png": 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" }, "metadata": { "engine": 3, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "transient": {} } ], "received": "2021-10-26T11:42:14.951623", "started": "2021-10-26T11:42:14.677427", "status": "ok", "stderr": "", "stdout": "3\n7\n11\n", "submitted": "2021-10-26T11:42:14.671693" }, "output_type": "display_data" } ], "source": [ "%pxresult" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It also lets you choose some amount of the grouping of the outputs with `--group-outputs`:\n", "\n", "The choices are:\n", "\n", "* `engine` - all of an engine's output is collected together\n", "* `type` - where stdout of each engine is grouped, etc. (the default)\n", "* `order` - same as `type`, but individual displaypub outputs are interleaved.\n", " That is, it will output the first plot from each engine, then the second from each,\n", " etc." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ 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nAs6HlPjIqn9+ecB/kmP9h46UBdp+9bX1tx0prY4wnQsAAABoLii5AJw3D3+4dfTCzIKLPZZCP5rY7a1rB7bLMZ0JOJ+6tI6u+OvN/V9NiPIV5RVVdbzzP+tuKq2q8ZrOBQAAADQHlFwAzovHPt8x/LNNhyZI0j2jurz3lQs77TSdCTChT7u4kmdv6PtKTIS3LPtIRfpdr667vqomxP+PAQAAgHPEl2oAje65ubv6O6vzLpek24Z3+PjbY9I2mc4EmDS8a6v8x6/u9WqE11O19UBZn3teX39lyHVNxwIAAADCGiUXgEb16vK93f+5OOc6Sbq6f+rMh6Z0X206E9AUTOjV5sDPL+v+ms9j1azOKR78XWfTZNOZAAAAgHBGyQWg0Uzfcqj9H2bvsl3JGtcjadFvru612HQmoCm5dmC7nB9O6PaWx1Jowc78S3716fYRpjMBAAAA4YqSC0CjWJNT1OoXn2y/LRBy/QM6xq9/7sZ+s0xnApqi20d02nnnRZ0+lKR31+6/9IWFu/uYzgQAAACEI0ouAA1uT0FF9Pff2fzl8upgbFpS9K6/3zrgI49lmY4FNFk/nJC+/vJ+bedI0gsLd1//wbr9nU1nAgAAAMINJReABlVUEfB9/bX1txaUB5LbxkUc+PttA96KifAGTecCmrrHr+m9cHjXxFVBV97fTNt5y9JdBcmmMwEAAADhhJILQIMJBEPW3a+tvz63qKpTfKS3+K83X/Bau4TIKtO5gHDgsSz99eb+n/VoG7O9siYUfd97W76ceags1nQuAAAAIFxQcgFoECHX1bfe3HjZtgNlvSN9nsonr+3zn16pcSWmcwHhJNLnCf3jtgHvtEuIzC2qrGl17xsbbztSWh1hOhcAAAAQDii5ADSIX32646Kl2YXDPZaCD03JeHN096RDpjMB4SgpNiLwws0XvJ4Y7Ss4UFLV/muvr7+hqibE/68BAACAU+BLM4Bz9tKSnJ7vr9s/VZK+PrLLBzcMbr/bdCYgnGW0jS17+kt9Xovyeyp2Hirv8f23N00xnQkAAABo6ii5AJyTuduPpP5pfvb1kjS1T9t53xmbttF0JqA5GJHW+sjDU7q/6bEUWpRVMOK303cONZ0JAAAAaMoouQCctR0Hy+J++tHWWwJBN6J/h/iNT13Xe77pTEBz8qVB7fbcPqLTR5L05srcK95YuS/ddCYAAACgqaLkAnBWiioCvm87G28uqQomdkiM3Pu3W/p/6LEs07GAZufHE9PXjeuRtMiVrKdn77px6a6CZNOZAAAAgKaIkgvAGQu5rr7xxsZr8oqqOiZE+Yqev+mCN+OjfDWmcwHN1R9u6De7V2rs1qqaUNT972+5dU9BRbTpTAAAAEBT4zN5ctu2fZIekHSXpE6SDkh6W9IjjuOUnmS/NEm7TnH48Y7jzDvN7Qc7jrP2dHMDLd0D728ZuzGv5AK/16r+zdW9Xs9oG1tmOhPQnPk8lvu3m/u/b/9r9V0HS6rbffONjfbbXxvyn5gIb9B0NgAAAKCpMD2S6zVJD0t6WdL1kp6R9BVJn9cVYPXZL2l8PT9/kORKyjnBfj+vZ5+d534pQMvw/ILdfadvOTzOktzvjUt7Z1yP5IOmMwEtQXJcRPWfbuz3RkyEt3RPQUXat97aeHnIdU3HAgAAAJoMYyO5bNv+kiRb0lTHcWYcs3yWpNWSviXpjyfa13GcSknz6jnu45JmO46TeYLVG4+O7gJw5uZsO5z690V7rpWkawe2m3HnRZ13GI4EtCh928cX/+ryHm88/NG2u1btKRry62k7D/zish7LTecCAAAAmgKTI7nulbTg2IJLkhzH2SzpLUnfONMD2rY9RNJFkp5vkIQA/s+egoron3+y/eaakOsf1Clh3a+u6LHUdCagJbqsX0ru3Rd3/lCS3l2TN/WdNXldTWcCAAAAmgIjJZdt215Jo1TPaCxJ8yX1sW075QwP/V1JeyV9dPbpAByvqibk+dabG28srqxp1S4hMvf5my74hDcpAuZ8d1zaxjHdkxaHXHmemplpb8gtTjSdCQAAADDN1EiuDpJiVPtY4omsqvvMON0D2radLOlmSS86jlPfRLy/tW27wLbtCtu2V9i2fctpJwZasB+8s2ny7vyKbjER3rLnbuj7Fm9SBMx75vq+s7slR2dVBEIxP3xny01FFQGjL5MBAAAATDP1hTip7vNIPevz6z6Tz+CYX5PklfSPetbvUO1E9xtUW7DdIel127bTHMd5/AzO818WLVp0z9nu21RUV1e3kZrHtaDhfZEbiv4i023ltaR7+7gVBVnrb16U1bDn4B6EaeF6D367t2v9dqWCB0qq2v/g1aU/uruvVWgxyjJshet9iOaDexBNAfchTOMeNM/v95/1vqZKrvi6z/J61h9dflqPX9i27VHtHF4fOI6Td4JNciT1dhwndMyyN23bfk3SY7Ztv+04Dm9YBI6TVeT639zhtpKk6zOsol6trYDhSACOEee33G9c4Mn/3ZpQm5WH3OguOQpM6WKVmc4FAAAAmGCq5Cqp+4yuZ31M3WfRaR7vKklpku460cqTPL54n2rf8HirpEdP81z/ZeTIkS+ezX5NydGGujlcCxpO5qGy2Pv+vfaeGlca3jVx1c9vGvhJY52LexCmhfM9OFJSfkx2vxcW7rnh/Sw3vl+vbu/bQzpkm86FMxfO9yGaB+5BNAXchzCNe9C85cuX//Js9zU1J9epHkc81eOMx/uOpM2O48w7kxB1o742Sup9JvsBzV1VTcjzvbc331BaFUzo1Coq58/2BZ+bzgSgft8ek7ZpfM/kL1zJ+t2sLCaiBwAAQItkquTKk1QhaUg9648uP+XMP7Zt95I0UdJfzzKLR1J9I72AFun+97eM31NQkRYT4S177sa+TkyEl39HgCbu6S/1mZPRJmZnZSAU/eN3t9xYWlXjNZ0JAAAAOJ+MlFyO49RI+kLSuHo2GStpq+M4B07jcN+RVCbplfo2sG071rbtDidYnqjaUVwbT+M8QIvwz8U5veZuPzLKktz7J6W/3TMlrtR0JgCn5vd63D/a/d6Lj/QW5RVXdfzhO5unms4EAAAAnE+mRnJJ0t8kjbVte9KxC23b7iPp5rr1R5cl2rYdc9z+sm07TtLtkl5zHKf4RCexbTtCtSXWa7ZtHz8H2ZOSQpL+cy4XAjQXq3OKWj+/YPd1knRl/5TZNwxuv9t0JgCnr0vr6IqfX9bD8VgKLs0uHP7Hebv6m84EAAAAnC+mJp6X4zjv2rb9rqR3bdt+QtJaSd0lPSxptaS/SLWjsFT72OJhSb2OO8wdkhIkPX+S81TXHf+vkubYtv1nSVWSvibpckl3O46zrwEvDQhLRRUB3/3vb7Grg6HIXimx2359Va9FpjMBOHOX9UvJXZNTPO2NVblXvLRk71WDOyXuH9096ZDpXAAAAEBjMzmSS5JukfS0pLslvafatx2+KWmq4ziBum1qJO2XlHOC/b8laZHjOOtPdhLHcf6m2jcwupL+IelVSZGSJjqO8/K5XwYQ/r7/9ubLD5ZUt0uM9hX8ye73gceyTEcCcJYempqxckDH+PU1Idf/04+33XSotDrCdCYAAACgsRkbySVJdUXWo3U/9W1TJalfPetOuLyebT+V9OmZZgRagt/Pyhy0KqdosNdj1Tx6Rc+32idGVZrOBODseSxLf7qx3ydf+vuq9kfKAm2/89bGa9746uC3Ka8BAADQnJkeyQXAsNnbDrf7z4rcKyTpy8M7fDqhV5vTeeEDgCYuKTYi8MQ1vd/ye63qzftL+z762Y4RpjMBAAAAjYmSC2jB8ooqo3756XY7GHJ9QzsnrrlvUsZa05kANJyLurU+8vWRXT6UpPfX7Z/yyYYDnUxnAgAAABoLJRfQQoVcV99/Z/PVRRU1rVPiI/Y/d2Pfz0xnAtDwvjm66+aR6a2Xhlx5Hp+RecO+wsoo05kAAACAxkDJBbRQj0/fOWzL/tI+fq9V/dS1fZzEaH+N6UwAGscz1/ed1S4hMre4sibxB+9suibkuqYjAQAAAA2OkgtogeZsO5z69uq8SyXpqxd3/mhol8QC05kANJ6YCG/wyWt7vxPhtaq2Hijr/ZtpO4ebzgQAAAA0NEouoIXJL6v2/+qzHTcGXXmHdUlc/Z2xaZtMZwLQ+IZ0Tiz46iWdP5akd9bkTZ297XA705kAAACAhkTJBbQwP3hn8+UF5YHk5Fj/oT9c33ea6TwAzp9vj0nbNLxL4qqQK+8jn+244UhpdYTpTAAAAEBDoeQCWpBn5+wasGZv8SCfx6p57Mpeb7eK8QdMZwJwfv3hhr7T2sT6DxaUB5K/986mK5ifCwAAAM0FJRfQQizPLkx6ZfneKyTppqHtPx/dPemQ6UwAzr/EaH/Nr6/u9bbPYwXW7ysZ8MzsXYNMZwIAAAAaAiUX0AKUVtV4H/5o642BoBvRr33cpgcmZ6w2nQmAOSPTkw7fNrzDZ5L02op9ly/Kym9jOhMAAABwrii5gBbgx+9tmXywpLpdYrSv4Nkb+n3ssSzTkQAY9qOJ6WsHdIjfUBNy/T/7ePuNRRUBn+lMAAAAwLmg5AKauX8uzum1OKtghMdS6KdTu7/TLiGyynQmAOZ5LEvP3tjvk1bRvvzDpdUp9723ZYrpTAAAAMC5oOQCmrFtB0rjX/hi9zWSdPWA1FmX9UvJNZ0JQNPRNi6i+heX9XjHYym0NLtw+L+X7u1hOhMAAABwtii5gGaqJuRa97+/5ZrKQCi6W3J05i8v77nUdCYATc/kPm3zrrggZY4k/WVB9jVZh8tjTWcCAAAAzgYlF9BM/frzHRfuOlKREeX3VPz+S30+8Hks13QmAE3To1f2Wtw1KTq7IhCK/fF7m68JufznAgAAAOGHkgtohubtOJLywbr9kyXpnpFdPuqZEldqOhOApsvnsdwnr+n9fqTPU7nzUHmPx6fvHGY6EwAAAHCmKLmAZqakssb3yGc7rg+68g7tkrj66yO7bDWdCUDT169DfPFXL+78sSS9vWb/1EVZ+W1MZwIAAADOBCUX0Mw88MGWiYdLq1NaRfvyn76uz3TTeQCEj2+N6bp5UKeEdcGQ6/vFJ9uvL62q8ZrOBAAAAJwuSi6gGXl9xb70LzILLrIk9+Gp3d9LjouoNp0JQHh5+kt9Pk+M9hUcLKlu99AHWyeYzgMAAACcLkouoJnYnV8R88d52ddJ0qV92867vF/KPtOZAISflPjIqgcnZ7xnSe78nfmXvLUqt5vpTAAAAMDpoOQCmoGQ6+q+9zZfVVYdjOvUKirnN1f3Wmg6E4DwdVX/1L1T+rSZL0nPzt113d6CimjTmQAAAIBToeQCmoGnZ2UN3nqgrLffa1X/9upe7/m9Htd0JgDh7TdX917YITFyb2lVMP7+97debjoPAAAAcCqUXECYW5NT1OqNVbmXStJXLuz46eDOiYWGIwFoBiJ9ntBvr+79nt9jBTbmlVzwlwXZ/UxnAgAAAE6GkgsIYzUh1/rZx9uuDQTdiL7t4jZ/f3y39aYzAWg+hnZJLLhhSPvpkvTykr1XbD9YGmc6EwAAAFAfSi4gjP368x0X7imo7Brt95Q9eW3vTz2WZToSgGbmoSkZq9LbxGRW1oSiH/xg69Uhl6ehAQAA0DRRcgFhanFWQZsP1h+YJEn3jOzycVpyTLnpTACaH49l6Ylren8Y6fNU7jxU3uP3s7IGm84EAAAAnAglFxCGqmpCnl99uv3aYMj1DeqUsO5rI7tsM50JQPPVp11cyVcu7PiZJL25KvfSdXuLWxmOBAAAAPwPSi4gDP3ik20j84qrOsZFeoufurb3NNN5ADR/3x2XtqFPu7gtgaAb8ZOPtl5bE3J5PhoAAABNCiUXEGZmbzvcbtrmQ+Mk6Xvj0j5snxhVaTgSgBbAY1l66tren0T7PWV7Ciq7Pvb5jhGmMwEAAADHouQCwkhpVY33N9N2Xhty5RmR1mrFLcM6ZpnOBKDlSEuOKb93VNePJenD9QcmLs4qaGM6EwAAAHAUJRcQRn760bZxh0qrUxOjfQVPXdt7puk8AFqeuy/pvG1wp4S1wZDr++Wn26+rqgnxXQIAAABNAl9MgTDxyYYDneZuPzJSku6bmP5+UmxEwHQmAC3TU9f1mRYf6S3aX1zV4ecfbxtlOg8AAAAgUXIBYaGoIuB7albWta5kje2etPjage1yTGcC0HK1S4is+t64bh9K0vQth8Yu2HEkxXQmAAAAgJILCAM//XjbuILyQHJSjP/w49f0nms6DwDcPKzDruFdE1eFXHkem7bzGh5bBAAAgGl8IQWauM83HeywYEf+JZbkPjA544P4KF+N6UwAIElPXNN7Znykt3h/cVWHRz7dfrHpPAAAAGjZKLmAJqy0qsb75MzMa13JGtU9aekVF6TsM50JAI5KiY+s+taYtI8k6bNNB8fztkUAAACYRMkFNGG/+GT76CNlgbaton35v72qF48pAmhyvnxhx8whnRPWBF15H/ls+zWBYMgynQkAAAAtEyUX0ETN2XY4dfa2w6Ml6QcTun3UKsbP2xQBNElPXNN7RmyEtyS3qKrTo5/vuMh0HgAAALRMlFxAE1QRCHp+M33ntSFXnou7tVp+/aD2u01nAoD6tE+Mqrx3VJdPJOnjDQcnLM8uTDKdCQAAAC0PJRfQBD3y2Y5LDpZUt0uI8hU9fnXv2abzAMCp3HVx5+0DOsavD4Zc368+3X5NTcjlsUUAAACcV5RcQBPzRWZ+2883HRwnSd8Z2/Wj5LiIasORAOC0PHlN72kxEd6ynMLKLr+ZtmO46TwAAABoWSi5gCYkEAxZj36+45qQK+/QLomrbxnWMct0JgA4XZ1aR1fcfXHnTyTpg3UHJq3OKWptOhMAAABaDkouoAl59PMdF+UVVXWMi/SWPHFN7xmm8wDAmbpnVJet/drHbaoJuf5ffLL9qpDrmo4EAACAFoKSC2gilmcXJn284eAESbp3VJeP2yVEVpnOBABn44lren8W5fdU7M6v6PbM7F2DTOcBAABAy0DJBTQBNSHX+tVn268OhlzfwI7x6++8qPMO05kA4GylJceU3zas4zRJenNV7tTMQ2WxpjMBAACg+aPkApqA38/KHJxTUNk12u8p++01vaeZzgMA5+p749PWp7eJyayqCUX97OPtl5rOAwAAgOaPkgswbPvB0ri3V++fIkl3XNTp8y6toytMZwKAc+WxLP3q8h6f+DxWYGNeyQUvL83pYToTAAAAmjdKLsCwn3+8/bLqYCiye9uYHd8c3XWT6TwA0FAGd04svLJ/ylxJevGLPVceKq2OMJ0JAAAAzRclF2DQv5bk9Ny8v7Sv32MFHr2y56ceyzIdCQAa1M8u7bEsNT4yr6QqmPCzj7dNMJ0HAAAAzRclF2DIodLqiL8v2nOFJF09IHV2/w4JRaYzAUBDi/R5Qg9PzfjIktzFWQUjPtlwoJPpTAAAAGieKLkAQ3728bYJpVXBhHYJkbk/vbT7ctN5AKCxTOzVZv/YHkmLJenp2VlXlVcHvaYzAQAAoPmh5AIM+GzTwY6LswpGWJL78JSMj/xej2s6EwA0pl9f1WteYrSv4HBZIOVXn24faToPAAAAmh9KLuA8qwgEPb+blXW1JI3tkbR4Qq82B0xnAoDGlhjtr/nu2LSPJWn6lkNjlu4qSDadCQAAAM0LJRdwnj362Y5LDpdWpyRG+woeu7LXfNN5AOB8uWloh12DOyesDbnyPvr5jqtqQi5v2wAAAECDoeQCzqMVuwuTPt98aKwkfWt0109axfgDpjMBwPn026t6zYj2e8pyCiq7/m5m5hDTeQAAANB8UHIB50nIdfXoZzuuDIZc36BOCetuHd4xy3QmADjfOrWOrrh9RKfPJemdNfsnZx4qizWdCQAAAM0DJRdwnvxhzq6B2fkV3aL8nopfX9Vrhuk8AGDKt8Z03ZTRJmZndTAU+YtPtk81nQcAAADNAyUXcB5kHymPeXNl7lRJunVYh2ldk6LLTWcCAFM8lqVfXN7jM6/HqlmfW9L/jZX70k1nAgAAQPij5ALOg198sn1yZU0oOi05Ouv747utN50HAEwb0jmx4NK+bedL0vMLdl9RUlnjM50JAAAA4Y2SC2hk763d32XN3uJBXkvBn1/a41OPxcvEAECSfnFZjyVJMf7DhRU1SY98tn2k6TwAAAAIb5RcQCMqrw56/zhv15WSNLF3my8uTGuVbzoTADQVMRHe4PfGp30iSTO3Hh69PLswyXQmAAAAhC+jjwbYtu2T9ICkuyR1knRA0tuSHnEcp/QU+46TNPckm7R2HKfwmO3jJP1S0o2SUiXtlfSSpKccx6k5+6sA6vfrz3dcdKQs0LZVtC//V5f3/MJ0HgBoaq4f1H73h+sOrF2zt3jQY9N2XPHhvcNeZcQrAAAAzobpkVyvSXpY0suSrpf0jKSvSPq8rgA7HV+XNP4EP/9Xktm27Zc0TdLtkv5Qd66XJD0k6T8NcB3A/1i/rzjx882HxkrSN0Z3/Sw+ykeZCgAn8NhVvWZG+TwV2Ucq0v8yf/cFpvMAAAAgPBkbyWXb9pck2ZKmOo4z45jlsyStlvQtSX88jUOtdBxn7Sm2+a6k4ZKGOo6zsW7ZZ7ZtL5M0y7bt1x3H+ehMrwE4mUc+23FZTcj192sft+m24R0zTecBgKaqa1J0uT2k/cxXlu+7+j8r9l16w+B2O9snRlWazgUAAIDwYnIk172SFhxbcEmS4zibJb0l6RsNfK43jym4jp5rtqQFDXwuQP9YtKfX9oNlvfxeq/qRK3pON50HAJq6H05MX9upVdSe8upg7M8/2T7BdB4AAACEHyMll23bXkmjJM2rZ5P5kvrYtp3SAOdqL6nnKc412rZt049uopnIL6v2/2tJzmWSdM2A1Dm9UuNKTGcCgKbO57Hch6dkfOqxFFqWXTj8s00HO5rOBAAAgPBi6nHFDpJiVPtY4omsqvvMkHTwFMd6xbbtbqot7FZK+rXjODOPWd+97vNk54pT7WT0eac41/9YtGjRPWe6T1NTXV3dRmoe19IUODtD8SVVblzHWAXGxx8cuGjRoYGmMzV13IMwjXuwafBKGt/RKp+91417bubWO+Pytx/yelrOJPTchzCNexBNAfchTOMeNM/v95/1vqZGLx19RfiRetbn130mn+I4qyX9W9LNqp3DK0LSdNu2b2uEcwGnlFPi+ubtc+MsSbf29BS1pD+cAUBDuCrNKm0dqWBumXyz9rqxpvMAAAAgfJgayRVf91lez/qjyxNPcoxFjuMMPXaBbduvSZot6Xnbtj9xHKeogc5Vr5EjR754Nvs1JUcb6uZwLSbVhFzr139dcWfIrdSItFYrb798wKemM4UL7kGYxj3YtOyJyOn5zJxdt3yyW1G3jB/4jz7tWsZj39yHMI17EE0B9yFM4x40b/ny5b88231NjeQ6+mU1up71MXWfRfUdwHGcwAmW1Uj6saQESdc01LmA0/HsnKyBewsru0T7PWWPXdlztuk8ABCu7rq48/ZeqbFbA0E34rHPd0wxnQcAAADhwVTJdapHBE/1iGG9HMdZKalQUu/GPhdw1J6Ciui3VudNkaTbhnec3j4xqtJ0JgAIZz+/tMd0r8eq2ZBbcoGzOjfNdB4AAAA0faZKrjxJFZKG1LP+6PKsszy+R1Kw7q8zjzvmic5VJunAWZ4L0K8+3T6xMhCK7poUnf3dcWkbTOcBgHA3sFNC4ZTebRZK0vMLdl9eEQjyFmQAAACclJEvjHWPFX4haVw9m4yVtNVxnHqLJ9u2k23bTjrB8j6qfVxxY925ciXtOMW5FjqOEzrd/MCxpm0+1GHF7qKhHkuhh6dmfOqxmGweABrCzy/rsTgx2ldwpCzQ9rfTd44wnQcAAABNm8nfiv5N0ljbticdu7CupLq5bv3RZYm2bccc8/dtVTtC67nj9vVJ+r1qR2V9fNy5brZtu+9x249Xbcn1NwFnoSbkWs/MybpckkZlJC0dmZ502HQmAGgu4qN8NfeM7PKZJH268eC4bQdK40+1DwAAAFouU29XlOM479q2/a6kd23bfkLSWkndJT0sabWkv0iSbduxqn1s8bCkXnX7HrJt+3lJD9u2HS/p36q9lu9LGirpGsdxjn2b4h8l3SBptm3bj6t2ZNdASQ9JesdxnA8a92rRXP1hdtagvKKqjrER3tJfXdFzvuk8ANDc3D6i086P1h/Ytu1gWa9HPtsx+fW7Br9nOhMAAACaJtPzW9wi6WlJd0t6T9J9kt6UNPWYtyfWSNovKefYHR3H+YmkOyR1lPQf1Y7GOiTpYsdxZhy3bUDSVElvqPbti+9J+pqkZyTd2hgXhuZvX2Fl1Ntr8iZJ0q3DO8xoGxdRbToTADRHP7+sx7S6Sej7v706L810HgAAADRNxkZySf9XPj1a91PfNlWS+tWz7hVJr5zmuYol/ajuBzhnj3y2fXxFIBTTuXXU7u+MZbJ5AGgsAzslFE7u3WbhtM2Hxv9lQfblV/ZPeSHa72UuTQAAAPwX0yO5gLA0e9vhdkt3FQ63JPeByRmfMdk8ADSuXzAJPQAAAE6Bkgs4QyHX1e9nZV3uStYl6a2Xj+uRfNB0JgBo7uKjfDVfv6TL5xKT0AMAAODEKLmAM/Tc3F0D9hZWdo72e8p+dUXPuabzAEBLccdFnXb0SondFgi6EY98tmOy6TwAAABoWii5gDOwv7gq8s1VeVMk6aahHWa2S4isMp0JAFqSYyehf2dNXlfTeQAAANB0UHIBZ+BXn24fV14djO3YKirn++O7rTedBwBamqOT0EvSn+dnX1ERCPJdBgAAAJIouYDTtmDHkZTFWQUjLMm9f1L6Zz6P5ZrOBAAt0bGT0D8+PfNC03kAAADQNFByAach5Lp6cmbm5a5kXZjWauXEXm32m84EAC1VfJSv5muXdP5ckj7ZeGD89oOlcaYzAQAAwDxKLuA0/GX+7gv2FFR2jfZ7yh+5oucc03kAoKW786LOO3rWTUL/2Oc7J5nOAwAAAPMouYBTOFRaHfHain1TJOmGwe1ndWwVVWk6EwBA+snU7tM9loJr9xYP/HTjwY6m8wAAAMAsSi7gFB79bPuYsupgfPuEyH0/mpi+1nQeAECtoV0SC8b2SF4iSc/N3XVZTci1TGcCAACAOZRcwEms2F2YtGBn/kWS9IMJ3T5nsnkAaFp+flmPhbER3tK84qqOz83dNcB0HgAAAJhDyQWcxBMzMqeEXHkHd0pYe3m/lH2m8wAA/lvbuIjqm4a2nylJzuq8yQdLqiJNZwIAAIAZlFxAPV5bsS9j+8GyXn6vVf2zy7rPNp0HAHBi3x3XbUOHxMi95dXB2Ec/2zHGdB4AAACYQckFnEBFIOj5+6I9UyXp0j5tF/RMiSs1nQkAcGI+j+X+cEL655K0MDP/omXZBcmmMwEAAOD8o+QCTuCpmZnDj5QF2iZG+wp+cmn3pabzAABO7tK+bXOHdE5YE3LleWJG5lTTeQAAAHD+UXIBx9mdXxHz8YaD4yTpros6T4+L9AUNRwIAnIZfXNZjdoTXqtp5qLzHK8v2djedBwAAAOcXJRdwnMc+3zG+qiYU1S05OuuuizttM50HAHB6MtrGll3eL2W+JP1jcc6l5dVBr+lMAAAAOH8ouYBjzNl2OHV5duFQS3IfmJwxzWNZpiMBAM7Aw1O7L28d4z9SUB5Ifnz6zgtN5wEAAMD5Q8kF1Am5rp6evetSV7IuTm+9fFRG0iHTmQAAZyYmwhv8+sjO0yTp000Hx+04WBZnOhMAAADOD0ouoM6LX+zps6egIi3K76n4xWU95pnOAwA4O1+5sNPOHm1jtgeCbsRj03ZMNJ0HAAAA5wclFyCpqCLge3X5vqmSdO2A1DkdW0VVms4EADh7D03pPt1jKbQmp3jQ55sOdjCdBwAAAI2PkguQ9JtpOy8prqxJbBsXceCByRmrTOcBAJybC9Na5Y/pnrREkp6du+uympDLJIsAAADNHCUXWrzNeSUJM7ceHiVJ3xjdZZrf63FNZwIAnLtfXN5zQWyEtzS3qKrTn+dl9zedBwAAAI2Lkgst3q+n7ZxUE3L9fdrFbbGHdMg2nQcA0DDaxkVU3zik/SxJemt17qT8smq/6UwAAABoPJRcaNE+WLe/84bckv5eS8GfTu0+w3QeAEDD+v74butT4yPzSquC8b+dvvMS03kAAADQeCi50GLVhFzrz/N3XyZJ43u2WTSwU0Kh4UgAgAbm81jut8Z0nSZJs7cdGbk5ryTBdCYAAAA0DkoutFh/mJ016EBJVfu4SG/Jzy/r/oXpPACAxvGlQe329G0Xt7km5Pp/O33nRNN5AAAA0DgoudAiHSqtjnhn7f4JknTz0A4zk2IjAqYzAQAaz4NTMmZ5LAXX7SsZ8Pmmgx1M5wEAAEDDo+RCi/TbaTtHllcH49onRO779ti0jabzAAAa15DOiQVjuictlaQ/zsu+NOTyIl0AAIDmhpILLc6G3OLEuTuOXCJJ3xmbNt3nsfiTDgC0AD+/rMfCaL+nbG9hZee/Ltzdz3QeAAAANCxKLrQ4T0zPnBgMub5+7eM2XT0gNcd0HgDA+ZESH1l13cB2cyXp9RW5k0sqa3ymMwEAAKDhUHKhRfls08GO63NL+nstBR+e0n2W6TwAgPPrvknpq9vGRRworqxJ/M30nReZzgMAAICGQ8mFFiPkuvrjvOypkjSmR/KSgZ0SCg1HAgCcZ36vx71nZJfpkjRjy6HROw6WxZnOBAAAgIZByYUW468Ld/fbV1jZOdrvKfvZpd2/MJ0HAGDGzcM67OqZErstEHQjfjNt5wTTeQAAANAwKLnQIpRU1vheX5E7SZKuG9hubkp8ZJXpTAAAcx6YnD7TYym0Kqdo8Kyth9uZzgMAAIBzR8mFFuHx6TtHFFfWtGoT6z/4o4npa0znAQCYNSKt9ZGLu7VeLkl/mJN1acjlRbsAAADhjpILzV7mobLYaVsOjZGkr4/sMj3S5wmZzgQAMO/nl/WYH+XzVOwpqOz6j0U5vU3nAQAAwLmh5EKz95vpO8cHgm5E97YxO24d3jHLdB4AQNPQsVVU5VUDUudJ0ivL904prarxGo4EAACAc0DJhWZt3o4jKSt3Fw2xJPe+SekzTOcBADQt909KX5kU4z9cVFHT+okZmSNM5wEAAMDZo+RCsxVyXT0zO2uqK1kXdWu1YmR60mHTmQAATUu03xu6+5LO0yXp800Hx2QfKY8xnQkAAABnh5ILzdary/b12HWkIj3C66n66aU95pvOAwBomm4f0WlnepuYzOqgG/nraTvHmc4DAACAs0PJhWapIhD0/GtJzhRJuvyCtvO7JkWXm84EAGi6fjyh2wxLcpdnFw5bnFXQxnQeAAAAnDlKLjRLv5+VNTS/PNAmMdpX8ODkjOWm8wAAmrYxPZIPDu2SuMaVrKdnZ00ynQcAAABnjpILzc6+wsqojzYcGC9Jt1/YaUZcpC9oOhMAoOn7ydTuc/0eK7D9YFmvt1fnpZnOAwAAgDNDyYVm5zfTd46pDISiu7SO2v21kZ23ms4DAAgPPVJiSyf2bvOFJP3tiz1TakKuZToTAAAATh8lF5qVNTlFrRZl5l8oSd8b3226x+LPJwCA0/fwlIwlsRHekgMlVe3/Mj/7AtN5AAAAcPooudCsPDUza2LIlXdAx/j1U/u0zTOdBwAQXpJiIwI3DG43R5LeWpU7sagi4DOdCQAAAKeHkgvNxuebDnbYmFdygddS8MHJGXNM5wEAhKfvj++2rm1cxIGSqmDikzMyR5jOAwAAgNNDyYVmIeS6+tP87CmSNLp70tIBHROKTGcCAIQnv9fjfu2SzjMkafqWQ6Ozj5THmM4EAACAU6PkQrPwr8U5vXIKKrtG+T0VP720x0LTeQAA4e3W4R2zMtrE7KwOupG/nb5zrOk8AAAAODVKLoS9ikDQ88ryfZMl6coLUua3S4isMp0JABD+fjSh20xLcpdlFw5buqsg2XQeAAAAnBwlF8LeM7OzhhSUB5JbRfvy75+UsdJ0HgBA8zCmR/LBIV0S14RceX4/O2uS6TwAAAA4OUouhLWDJVWRH64/ME6SbhvecVZMhDdoOBIAoBn5ydSMuT6PFdh2oKz3u2vzuprOAwAAgPpRciGs/Xb6zpEVgVBsx1ZROfeM6rLFdB4AQPPSMyWudGKv5EWS9MLCPVNqQq5lOhMAAABOjJILYWvL/tL4+TvyL5akb43uOtNj8ecOAEDD+8nU7otjI7yl+4urOvx1we5+pvMAAADgxCi5ELYen7FzQk3I9fVpF7fl6gGpOabzAACap6TYiMCXBrWbI0lvrsqdVFJZ4zOdCQAAAP+Lkgthae72I6lrcooHeSyF7puYPst0HgBA8/bDCd3Wton1HyyurEl8YkbmhabzAAAA4H9RciEs/WFO1mRJuqhb6xUXprXKN50HANC8+b0e9+5LusyQpGmbD43ZnV8RYzoTAAAA/hslF8LOayv2Zew6UpER4bWqfjK1+wLTeQAALcOXL+yY2S05OrM6GIr8zbQdY03nAQAAwH+j5EJYCQRD1j8X50yWpKl92i7smhRdbjoTAKDl+NHE9BmW5C7LLhy2LLsg2XQeAAAA/H9GJ061bdsn6QFJd0nqJOmApLclPeI4Tulp7J8q6ZeSLpWUKilT0muS/ug4TsUx26VJ2nWSQw12HGft2V0Fzqfn5mYPPFRanRof6S1+cErGMtN5AAAty7geyQeHdE5cuyqnaPDvZ2VNfPtrQx3TmQAAAFDL9Eiu1yQ9LOllSddLekbSVyR9XleA1cu27baSFkv6kqQ/S7JVW5A9LOlD27ZPdG0/lzT+BD87G+Ba0MgKywP+d9fmTZAke0iH2YnR/hrTmQAALc/DUzPm+jxWzdYDZX0+3nCgk+k8AAAAqGVsJJdt219SbTE11XGcGccsnyVptaRvSfrjSQ7xE0kdVDsKa2vdsk9t214oaa5qy7J/H7fPRsdx5jXMFeB8e3zGzotKq4LxqfERed8Zl7bBdB4AQMvUKzWuZEz3pCVzth8Z/fyC3ZOvuCDlJY9lmY4FAADQ4pkcyXWvpAXHFlyS5DjOZklvSfrGKfbfIun+Ywquo/vPk7RP0oiGiwrTsg6Xx87ceniUJH19ZJeZPo/lms4EAGi5Hp7afVGU31Oxt7Cyy0tL9vYynQcAAACGSi7btr2SRkmaV88m8yX1sW07pb5jOI7zouM4f65ndaSkwnPJiKblt9N3jg0E3YjubWN23DS0w8nmVwMAoNG1S4isuqJfynxJemXZ3klVNSHTU0AAAAC0eKa+kHWQFKPaxxJPZFXdZ8aZHti27csktZG04gSrf2vbdoFt2xW2ba+wbfuWMz0+zr+luwqSl2cXDrMk90cT0meazgMAgCQ9MDljZWK0ryC/PNDm2Tm7BpnOAwAA0NKZmpMrqe7zSD3r8+s+z+jV3LZtJ0l6XtI2SR8dt3qHaie636Dagu0OSa/btp3mOM7jZ3KeYy1atOies923qaiurm4jNd1r+fP6YGtXsi5Ktco9B7Zct+iA6URoaE39HkTzxz2Is3Vd15Be3ip9tHbfFcMi84ZH+c7+cXruQ5jGPYimgPsQpnEPmuf3+896X1MlV3zdZ3k9648uTzzdA9q2HSPpY9WOEhvlOE7wmNU5kno7jhM6Ztmbtm2/Jukx27bfdhyHNyw2QdsKXP/GfEX5PXKvSbdKTOcBAOBYF6ZalbNy3MDeMvln5LixV3ezSk1nAgAAaKlMlVxHy4roetbH1H0Wnc7BbNv2S3pH0kWSbnUc578eVTyu8DrWfap9w+Otkh49nXMdb+TIkS+ezX5NydGGuqldS8h19djzK74qVWpszzYLr5zQd67pTGgcTfUeRMvBPYhzcWd0brdfT9t5+8y9ivzmZYNfSEuOqe+XeCfFfQjTuAfRFHAfwjTuQfOWL1/+y7Pd19ScXKd6HPFUjzP+H9u2LUn/lnSppG84jvPW6YZwHCdP0kZJvU93H5w//1qc02tfYWXnaL+n/OEpGYtN5wEA4ERuGtphV0abmJ2BoBvx+IzMMabzAAAAtFSmSq48SRWShtSz/ujyrNM41p8k3SLph47j/P0ssngk1TfSC4ZU1YQ8ry7fN0mSrrggZX5KfGSV6UwAANTn++O7zZKkpbsKhq/OKWptOg8AAEBLZKTkchynRtIXksbVs8lYSVsdxznpFOO2bT8i6duSHnYc57mTbBdr23aHEyxPVO0oro2nGR3nybNzdg3KLw+0SYzyFd4/KWPVqfcAAMCc8T2TDwzqlLAu5Mrzu5lZE0znAQAAaIlMjeSSpL9JGmvb9qRjF9q23UfSzXXrjy5LrJtY/tjtvivpF5IecxznifpOYtt2hGpLrNds2z5+DrInJYUk/edcLgQNq7A84H9/3f5xknTT0A6zYyK8jLQDADR5D0xOn+u1FNyYV3LBtM2H/ueXawAAAGhcpiael+M479q2/a6kd23bfkLSWkndJT0sabWkv0i1o7BU+9jiYUm96pYNlPScpAWS5ti2Pe4Epyh0HGet4zjVdcf/a922f5ZUJelrki6XdLfjOPsa7UJxxp6cmTmirDoYnxofkffNMV03mc4DAMDp6N8hoWhURtKy+TvzL/nz/OxJU/q0ecVjWaZjAQAAtBgmR3JJtXNpPS3pbknvqfZth29Kmuo4TqBumxpJ+yXlHLNfa0mWpDGS5tbz8+zRjR3H+ZukqyS5kv4h6VVJkZImOo7zcqNcGc7KnoKK6BlbDo+SpLsv6TzL57Fc05kAADhdD0/tvjDS56ncnV/R7bUV+7qbzgMAANCSGBvJJUl1RdajdT/1bVMlqd9xy+aptuQ6k3N9KunTM0+J8+mJ6TtHVwdDkd2So7NuGdbxdF48AABAk9GxVVTlpX3bLvxw/YHJLy3ZO+nmoR0y/V4Pv7ABAAA4D0yP5AL+z7q9xa0WZRVcKEnfG9dtpuk8AACcjQcnZyyPj/QWHSqtTv3TvOwBpvMAAAC0FJRcaDJ+NytzfMiVt3+H+A2TerfZbzoPAABnIz7KV3PD4PZzJemdNXkTSiprjI6cBwAAaCkoudAkzNl2OHXdvpIBHkuh+yelzzGdBwCAc/HdcWnr28ZFHCipCiY8MSPzQtN5AAAAWgJKLjQJf5yXPUmSLu7WesXgzomFhuMAAHBO/F6P+9WLO8+UpOlbDo3eW1ARbToTAABAc0fJBePeWpXbLfNweXe/16p+aErGAtN5AABoCLcO75CZlhS9q6omFPX4jMxRpvMAAAA0d5RcMCrkuvrH4pxJkjSpV5tFackx5aYzAQDQEDyWpe+MS5spSYsy80dsyC1ONJ0JAACgOaPkglF/Xbi73/7iqg4xEd7Sh6ZkLDGdBwCAhjS1T9u8/h3iNwZdeZ+amTXedB4AAIDmjJILxlQEgp43VuZOlKRrBqTOS4qNCJjOBABAQ7t/Uvpsj6XQ2r3FA+dsO5xqOg8AAEBzRckFY34/K2toUUVN69Yx/iM/npi+xnQeAAAaw+DOiYUXd2u9Qvr/L1oBAABAw6PkghGHSqsjPt5wYKwk3Ta8w+xInydkOhMAAI3l4andF0R4rarMw+Xd316dl2Y6DwAAQHNEyQUjnpyx8+KKQCi2Q2Lk3q+P7LLFdB4AABpT16To8om92iyWpL8v2jMp5LqmIwEAADQ7lFw47zIPlcXO2XZkpCTdM6rLLI9lmY4EAECje3ByxtKYCG9ZXnFVx38syultOg8AAEBzQ8mF8+6JGZljAyHX36NtzPbrB7XfbToPAADnQ3JcRPWVF6TMl6TXVuybWFUT4nsYAABAA+LLFc6rFbsLk5bvLhxqSe4PxnebbToPAADn048npq9OjPYV5JcH2jw3d9dA03kAAACaE0ounFdPz86aEHLlGdQ5Yd2YHskHTecBAOB8ionwBm8a0mGOJL2/bv/4ooqAz3QmAACA5oKSC+fN55sOdtiUV9rPayn44KSMuabzAABgwjfHdN2UEh+xv7QqGP/UzKwLTecBAABoLii5cF6EXFd/WbB7siSNykha1q9DfLHpTAAAmODzWO6dF3WaLUnTtxwanVdUGWU6EwAAQHNAyYXz4o2VuRm78yvSIn2eyoendl9oOg8AACbdNrzjzi6to7OrakJRT8zIHGk6DwAAQHNAyYVGVxNyrZeW5EyUpCl92nzRsVVUpelMAACY5LEsfWtM11mStGBn/kXbDpTGm84EAAAQ7ii50Oj+9sXuvgdKqtvHRnhLHpiUsdx0HgAAmoIrLkjZ1zs1dktNyPU9OTNzrOk8AAAA4Y6SC42qIhD0vLkyd4IkXTMgdX6rGH/AdCYAAJqKH05In2NJ7srdRUPyylyv6TwAAADhjJILjerZObsGF1bUJLWK9uX/YEK3NabzAADQlFyS3vrwkM6Ja13J+nBXKMF0HgAAgHBGyYVGU1ge8H+4/sBYSbp5WIc50X5vyHQmAACamgcmp8/zeqyatYcVtavY9ZvOAwAAEK4oudBonpqVeWFZdTA+NT4i795RXTebzgMAQFPUt3188aj01ssl6b3MEBPQAwAAnCVKLjSKfYWVUTO2HB4lSXdd3Hm2z2O5pjMBANBUPTQl44sor9wdRYp8Y+W+dNN5AAAAwhElFxrFUzMzR1bVhKK6tI7OvmVYh0zTeQAAaMo6tY6umNTZKpWkfy7eOynk8rshAACAM0XJhQa3/WBp3IKd+RdJ0jdHd5ntsSzTkQAAaPImd7bKEvwKHSipav/Cwj19TecBAAAIN5RcaHBPzcwaWxNyfb1SY7de2T91r+k8AACEg0iv5V7a1SqRpDdW7ptYEQjyPQ0AAOAM8OUJDWrF7sKkFbsLh1iS+/1x3eaYzgMAQDgZ08EqbxXtyy+sqEn6w5xdg03nAQAACCeUXGhQf5iza3zIlWdQp4R1o7snHTKdBwCAcOLzWLp5WIc5kvTR+gPjCssDftOZAAAAwgUlFxrM7G2H223ILbnAYyl436T0eabzAAAQju4d1XVzanxkXll1MO7JmZkjTOcBAAAIF5RcaDB/mpc9UZIu7tZ6xYCOCUWm8wAAEI58Hsu9+5JOsyRpxpbDo/YWVESbzgQAABAOKLnQIN5Zk9c183B5d7/Xqn5oSsZC03kAAAhntwzrmJWWHJ1VHQxFPjEjc5TpPAAAAOGAkgvnLOS6+vuiPZMkaULP5MVpyTHlpjMBABDuvj0mbbYkfZFVcOHmvJIE03kAAACaOkounLOXl+7tmVtU1Sna7yl/cEr3JabzAADQHFzat21u33Zxm4Mh1/fUzKxxpvMAAAA0dZRcOCeBYMh6ddneiZJ0eb+UBW3jIqpNZwIAoLn40cRucyzJXZ1TNGhxVkEb03kAAACaMkounJM/z8/uf7gskBIf6S26b1L6StN5AABoTkaktT4yrGvialey/jAna4LpPAAAAE0ZJRfOWmlVjfedNfvHS9L1g9vPjYv0BU1nAgCguXlwcsZ8n8eq2XqgrM+nGw92NJ0HAACgqaLkwll7enbW0OLKmlbJsf5D3xuXtt50HgAAmqNeqXElo7snLZWk5xfsnhRyXdORAAAAmiRKLpyVQ6XVEZ9uPDhWkr48vONsv9fDN24AABrJw1MyFkX6PJV7CirSXl+Rm2E6DwAAQFNEyYWz8tTMzIsqAqGYDomRe796SedtpvMAANCctU+Mqpzap+1CSXppac6kmpBrmc4EAADQ1FBy4Yztzq+Imb3t8EhJ+vrILrM8Ft+zAQBobA9MTl8eG+EtOVhS3e6Fhbv7ms4DAADQ1FBy4Yw9OWPnqEDQjchoE7PzhsHtd5vOAwBAS5AY7a+5dmDqPEl6a1XuhIpAkO9xAAAAx+DLEc7IhtzixMVZBRdK0nfGps02nQcAgJbkhxPS17aO8R8prKhJ+sPsXUNM5wEAAGhKKLlwRn4/K2ts0JW3X/u4TZN6t9lvOg8AAC1JpM8TumVYhzmS9OGGA2MLywN+05kAAACaCkounLZFWflt1uQUD7Ik90cT0ueYzgMAQEt076gum1PjI/PKq4NxT87MHGE6DwAAQFNByYXT9uycXRNcyRrWNXH1hWmt8k3nAQCgJfJYlu6+pNMsSZq59fDIfYWVUaYzAQAANAWUXDgtn2062HHrgbI+Xo9V8+DkjPmm8wAA0JLdMqxjVlpS9K6qmlDUEzN2jjKdBwAAoCmg5MIphVxXzy/YPUmSRme0XtYrNa7EdCYAAFq6b43pOkuSFmYWjNiyvzTedB4AAADTKLlwSm+uzE3fnV+RFunzVD40pfsXpvMAAADpsn4puX3bxW0Ohlzf72ZmjjWdBwAAwDRKLpxUTci1/rVk7yRJmty7zaKOraIqTWcCAAC1fjih2xxLclfuKRqyLLsg2XQeAAAAkyi5cFIvfrGnz4GSqvaxEd7SBydnLDOdBwAA/H8XdWt9ZEiXxDWuZP1hzq7xpvMAAACYRMmFelXVhDxvrsqdIElX90+d3yrGHzCdCQAA/LcHJqXP91oKbsor7Td9y6H2pvMAAACYQsmFej07Z9eggvJAcmK0r+CHE7utNp0HAAD8r77t44tHZiQtk6S/zM+eZDoPAACAKZRcOKGiioDvg/X7x0nSzUM7zIn2e0OGIwEAgHo8PCXjiwivp2rXkYr0N1fmdjOdBwAAwARKLpzQ72ZlXVhaFYxPiY/Y/43RXTeZzgMAAOrXqXV0xaTeyYsk6Z9LciaFXNd0JAAAgPOOkgv/I6+oMmra5kOjJenOizrN9nksvikDANDEPTg5Y2lMhLdsf3FVh78v2tPHdB4AAIDzjZIL/+PJmZmXVNWEorq0jtp92/COO03nAQAAp5YUGxG46oKU+ZL0+orcCVU1Ib7nAQCAFoUvP/gvOw6Wxc3fkX+RJH1jdNdZHssyHQkAAJymH01MX50Y7SvILw+0eW7uroGm8wAAAJxPlFz4L0/NzBxTE3L9vVJit13VP3Wv6TwAAOD0xUR4gzcN6TBHkt5ft39cUUXAZzoTAADA+WL0i49t2z5JD0i6S1InSQckvS3pEcdxSk9j/1RJj0m6XFKSpCxJf3Yc54UTbBsn6ZeSbpSUKmmvpJckPeU4Tk2DXFCYW51T1Hr57sKhkvT98d1mm84DAADO3DfHdN30/rr9ow6VVqf+flbW8Meu6rXEdCYAAIDzwfRIrtckPSzpZUnXS3pG0lckfV5XgNXLtu1WkhZImijpEdWWV59Ies627SeO29YvaZqk2yX9oe5cL0l6SNJ/GuxqwtzTs7LGh1x5BnVKWDe6e9Ih03kAAMCZ83ks986LOs2SpGmbD43eX1wVaToTAADA+WBsJJdt21+SZEua6jjOjGOWz5K0WtK3JP3xJId4VLWjtwY6jpNbt+xT27Z3SHrRtu03HcdZW7f8u5KGSxrqOM7GumWf2ba9TNIs27Zfdxzno4a6tnC0u8T1rc8t6e+xFLpvYvo803kAAMDZ+/KFHXe+uSp3d05BZdcnZ2Re8ocb+s41nQkAAKCxmRzJda+kBccWXJLkOM5mSW9J+kZ9O9q2HSnpDknPH1NwHfWSpD2S7jnuXG8eU3AdPdds1Y4Gq/dcLcUHWaEESbqoW+sVAzslFBqOAwAAzoHHsvTN0V1nSdK8HUcu3nGwLM50JgAAgMZmpOSybdsraZSkefVsMl9SH9u2U+pZP0RSwon2dxwnJGmhpHF152ovqecpzjXatm3Tj24as7XAjdhSoEi/xwo8ODljoek8AADg3F3VP3Vvr5TYbTUh1//UzMwxpvMAAAA0NlPFTgdJMap9LPFEVtV9ZtSzvnvd58n2zziDbeNUOxl9ixNyXX2QFYqXpHE9kxent4kpM50JAAA0jKMvklm+u3Do6pyi1qbzAACAxrG/uCoy5LqmYxhnak6upLrPI/Wsz6/7TD7J/kHHcYpOsn9E3RsVz+RcefVsU69Fixbdc+qtmq41h9zI7BJFxPpcXdYmv9eiRYt6ms6Elqe6urqNFP7/PiF8cQ+iKWiM+9AjaXiKVbHioBv93Ofrv35PP09hQx0bzQ//LURTwH0I08LxHnRdV79fE0oOSbqzt6cwNcYKms50Lvx+/1nva2okV3zdZ3k9648uTzzJ/vXte/z+53quZm1jvhslSZM7ucEYn0XtCwBAM3N1N6vEa0lrDrnRu0tcYy8dAgAAjWPDEUVmFiviQLn88X6FTOcxydQXnZK6z+h61sfUfdY3UqvkJPsev/+5nuukRo4c+eLZ7NdUjBwpvfjxF9/pkeipDvdrQfg6+lsS7kGYwj2IpqAx78NphzZcuiirYMTbe6IK379n2OsNfXw0D/y3EE0B9yFMC7d7sCbkWr/487J7pGpN6pM6a/K4XotNZzpXy5cv/+XZ7mtqJNfpPI4onfwRQ59t2wkn2b/acZzSBjhXs9cvyaqO8FqmYwAAgEby0JSMBX6vVb3zUHmPd9fmdTWdBwAANIy/fbG778GS6naxEd6SByanLzedxzRTJVeepArVviXxRI4uz6pnfeZx251o/6wz2LZM0oF61gMAAIS1tOSY8gk9kxdL0t8X5UxkYloAAMJfRSDoeXNl7gRJumZA6vzEaH+N6UymGSm5HMepkfSFpHH1bDJW0lbHceornlar9jHE/9nftm1L0mhJ8+rOlStpxynOtdBxnBb93CoAAGjeHpzSfUm031O+r7Cy88tL9/KiGQAAwtxzc3cNKqyoSWoV7cv/wYRua0znaQpMjeSSpL9JGmvb9qRjF9q23UfSzXXrjy5LtG376NxZchynUtK/JX3btu12xx33Tklpkv5+3Llutm2773HnGq/akutvAgAAaMbaxkVUX94vZYEkvbps78RAMMRcBQAAhKmiioDvg3UHxknSTUM7zIn2exm4I3MTz8txnHdt235X0ru2bT8haa2k7pIeVu1Irb9Ikm3bsap99PCwpF7HHOIXki6VtNC27Scl5UoaKenHkp52HGf1Mdv+UdINkmbbtv24akd2DZT0kKR3HMf5oJEuEwAAoMm4b1L6yplbD198uCyQ8qd52QN+NDF9nelMAADgzD01M+vCsupgfEp8xP5vjO662XSepsLkSC5JukXS05LulvSepPskvSlpquM4gbptaiTtl5Rz7I6O4xRIGiNpgaRHJb0j6VpJ9zmOc/9x2wYkTZX0hmpLsPckfU3SM5JubYTrAgAAaHLiIn3B6we1mytJ767dP760qsZrOhMAADgzeUWVUdO3HBotSXdd1HmWz2Mx2WYdYyO5pP8rnx6t+6lvmypJ/epZl6fagux0zlUs6Ud1PwAAAC3Sd8elrf9ow4GRR8oCbZ+enTXsl5f3XGY6EwAAOH1Pzsy8pKomFNWlddTuW4d3yDz1Hi2H6ZFcAAAAOI/8Xo/7lQs7zZakTzceHHOotDrCdCYAAHB6Mg+Vxc7fkX+RJH1jdNdZHospNo9FyQUAANDC3HVxp20dEiP3VgRCMU/OyLzYdB4AAHB6npyZOaYm5Pp7psRuu6p/6l7TeZoaSi4AAIAWxmNZumdUl1mSNGf74Uuyj5THnGofAABg1pqcolbLsguHSdL3x6XNMZ2nKaLkAgAAaIGuH9R+d/e2MTsCQTfiiRmZo03nAQAAJ/f07KzxIVeegR3j14/pkXzQdJ6miJILAACghfru2LTZkrRkV8Hw9fuKE03nAQAAJzZvx5GUdftKBngshX48MX2u6TxNFSUXAABACzWhV5sD/TvEbwi58v5+VtY403kAAMCJ/Wle9gRJGpHWauXgzomFhuM0WZRcAAAALdiPJ6bP9VgKrd1bPHDhzvy2pvMAAID/9tH6A523Hyzr5fdYgQcnZywwnacpo+QCAABowYZ2SSwY3rXVKleynpu3a4LpPAAA4P8Lua5e+GL3REka0yNpaUbb2DLTmZoySi4AAIAW7sHJGQt8Hiuw7UBZ7483HOhkOg8AAKj12op93XMKKrtG+TwVD03pvsh0nqaOkgsAAKCF65ESWzq2R9JSSfrrwt2TQq5rOhIAAC1eTci1Xl66d6IkTe3b9ot2CZFVpjM1dZRcAAAA0ENTui+K8nkqcgoqu762Yl9303kAAGjpXli4u+/Bkup2cZHekvsnpS83nSccUHIBAABA7RIiq6b2bfuFJL28dO/EmpBrmc4EAEBLVREIet5alTtBkq4ZkDovMdpfYzpTOKDkAgAAgCTp/knpy+MivcUHS6rbvbBwdz/TeQAAaKmenbNrcGFFTVKraF/+DyekrzWdJ1xQcgEAAECSlBjtr7luYLt5kvTmqtwJ5dVBr+FIAAC0OEUVAd+H6w+MlaSbh3WYE+nzhExnCheUXAAAAPg/3x/fbV3rGP+Rooqa1n+YkzXYdB4AAFqap2ZmXVhWHYxPiY/Yf++orptN5wknlFwAAAD4P5E+T+jWYR1mS9JHGw6OzS+r9pvOBABAS5FXVBk1fcuh0ZJ010WdZ/k8Fq88PgOUXAAAAPgv94zqsqVdQmRueXUw7qlZWSNM5wEAoKV4cmbmJVU1oaguraN23zq8Q6bpPOGGkgsAAAD/xWNZuvvizrMkaeaWw6P2FlREm84EAEBzt+NgWdz8HfkXSdI3Rned5bF40fGZouQCAADA/7h5WIdd3ZKjs6qDocgnZmSOMp0HAIDm7qmZmWNqQq6/V0rstqv6p+41nSccUXIBAADghL41Jm22JH2RVXDh5rySBNN5AABorlbnFLVevrtwqCR9b1zaHNN5whUlFwAAAE7o0r5tc/u2i9scDLm+383KGms6DwAAzdXTs7LGh1x5BnaMXz+mR/JB03nCFSUXAAAA6vWjid3mWJK7ak/R4KW7CpJN5wEAoLmZs+1w6vrckv4eS8H7J2XMNZ0nnFFyAQAAoF4j0lofGdYlcbUrWX+Ys2uC6TwAADQ3f5yXPUmSLu7WeuXATgmFhuOENUouAAAAnNT9kzPmez1Wzeb9pX2nbT7UwXQeAACai3fW5HXNPFze3e+1qh+akrHAdJ5wR8kFAACAk+rTLq5kVEbrZZL0lwXZE03nAQCgOQi5rl5ctGeyJE3o2WZxWnJMuelM4Y6SCwAAAKf00OSMRRFeT1X2kYr0N1buSzedBwCAcPfPxTm984qqOkb7PWUPT8lYYjpPc0DJBQAAgFPq1Dq6YnKfNl9I0r+W7J0Ycl3TkQAACFtVNSHPf5bvmyhJV/ZPXZAcF1FtOlNzQMkFAACA0/LQ5IxlsRHe0v3FVR1e/GJPH9N5AAAIV3+cu2tgfnmgTWKUr/C+iemrTOdpLii5AAAAcFpaxfgDV/dPnS9Jr6/MnVhVE+K7JAAAZ6ioIuB7b93+cZJ045D2c2IivEHDkZoNvpgAAADgtP1wYrfVidG+goLyQPKzc3YNMp0HAIBw87tZWReWVgUT2sZFHPj22LSNpvM0J5RcAAAAOG3Rfm/o5qEd5kjSB+v3jyuqCPhMZwIAIFzkFVVGTdt8aLQk3XlRp1k+j8Uklw2IkgsAAABn5Buju25KiY/YX1oVjP/drKwLTecBACBcPDEj85KqmlBUl9ZRu798YcedpvM0N5RcAAAAOCM+j+XedVHnWZI0bfOh0XlFlVGmMwEA0NRtP1gat2Bn/sWS9M3RXWd5LMt0pGaHkgsAAABn7NbhHTK7tI7OrqoJRT05M/MS03kAAGjqnpyRObYm5Pp6pcZuvbJ/6l7TeZojSi4AAACcMY9l6Zuju8yWpPk78i/afrA0znQmAACaquXZhUkrdhcNtST3hxO6zTadp7mi5AIAAMBZubJ/6t5eqbFba0Ku/6mZWWNM5wEAoKl6Zk7WBFeyBndOWDsyPemw6TzNFSUXAAAAztoPxnebY0nuit2FQ1ftKWptOg8AAE3N9C2H2m/KK+3ntRR8YFLGPNN5mjNKLgAAAJy1URlJhwZ1TlgXcuV5enbWeNN5AABoav4yP3uSJF2SkbS8X4f4YtN5mjNKLgAAAJyT+yelz/NYCm7ILek/e9vhdqbzAADQVLy5MrfbriMV6RFeq+qhKRkLTedp7ii5AAAAcE76d0gouiS99QpJ+tO87Imm8wAA0BSEXFf/XJIzSZIm9W6zqEvr6ArTmZo7Si4AAACcswcnZyz0e63qzMPl3d9Zk9fVdB4AAEx7YeGevvuLqzrERHhLH5ycsdR0npaAkgsAAADnLC05pnxCzzaLJenvi/ZMCrmu6UgAABhTEQh63li5b6IkXdM/dX5SbETAdKaWgJILAAAADeLhKRlLov2e8tyiqk4vLdnby3QeAABMeXbOrsGFFTVJraJ9+T+c2G216TwtBSUXAAAAGkRyXET1FRekzJekV5fvnRgIhizTmQAAON8KywP+D9cfGCdJNw3tMCfa7w0ZjtRiUHIBAACgwfx4YvqqhChf4ZGyQNs/zsseYDoPAADn25MzM0eUVQfjUuMj8r4xuutm03laEkouAAAANJi4SF/whsHt5krSu2vyxpdW1XhNZwIA4HzZW1ARPWPL4VGS9NWLO8/yeSwmqTyPKLkAAADQoL4zNm1Dm1j/wZKqYOLvZ2UNM50HAIDz5YmZmSOrg6HItKToXbcO75hlOk9LQ8kFAACABuX3etzbR3SaLUmfbTo45lBpdYTpTAAANLbNeSUJX2QWjJCkb43pOst0npaIkgsAAAAN7o6LOm3v1CoqpyIQinlyxs6LTecBAKCxPTUra2ww5Pr6tovbfFm/lFzTeVoiSi4AAAA0OI9l6Z5RXWZJ0pztRy7JOlweazoTAACNZXFWQZvVe4oGW5L7o4nd5pjO01JRcgEAAKBRXDew3Z7ubWN2BIJuxFMzM0ebzgMAQGP5w5ysia5kDeuSuHpEWusjpvO0VJRcAAAAaDTfG9dttiQt2VUwbN3e4laG4wAA0OA+WLe/89YDZb19Hivw4JSM+abztGSUXAAAAGg043smHxjQMX59yJX397OzxpnOAwBAQwq5rl74Ys9kSRrXI3lJr9S4EtOZWjJKLgAAADSqH09Mn+exFFq7t3jggh1HUkznAQCgofxzcU7vfYWVnaP9nvKHp2YsNp2npaPkAgAAQKMa0jmxYERaq5WS9Ny87Amm8wAA0BCqakKe/yzfN1GSrrggZX5KfGSV6UwtHSUXAAAAGt2DkzMW+D1WYPvBsl4frT/Q2XQeAADO1TOzswbnlwfaJEb7Cu6flLHKdB5QcgEAAOA8yGgbWza2R/ISSXph4e5JIdc1HQkAgLOWX1bt/2D9gXGSdMuwDrNjIrxBw5EgSi4AAACcJw9OyVgc5fdU5BRWdvnP8n3dTecBAOBsPT4j8+Ly6mBcu4TI3G+O7rrJdB7UouQCAADAedEuIbLq0r5tF0rSy0v3TgoEQ5bpTAAAnKmsw+Wxs7cdHilJ94zsMtNj8b+zpoKSCwAAAOfNA5MyVsRHeosOlVan/mle9gDTeQAAOFNPzNg5JhB0I7q3jdlx45D22abz4P+j5AIAAMB5Ex/lq7lxSPs5kvTOmrwJRRUBn+lMAACcrhW7C5OWZRcOsyT3h+O7zTKdB//N2JcK27YnSPqlpIGSaiQtl/RTx3HWnOb+Hkl3Sfq2pJ6SDktaJOlRx3G2HbftPElj6znUc47j/OAsLgEAAABn4bvjum34eMPBSw6VVqc+OSNzxG+v6b3IdCYAAE7H72dlTQy58gzunLB2TI/kg6bz4L8ZGcll2/ZVkqZLOijpq5K+JSlC0gLbtoed5mGek/R3SSsl3arawqyXpJW2bfc5wfaLJI0/wc+fz/5KAAAAcKZ8Hsv92iWdZ0jS9C2HRu/Or4gxnQkAgFP5bNPBjpv3l/b1eqyahyZnzDWdB//rvI/ksm07WrXllOM4zm3HLH9X0ixJL0oacopjDJT0HUm/chznkWOWvyVpjaTnVVtgHeuw4zjzGuIaAAAAcG5uHd4x663VeZlZh8szHp++c/QLt/SfbjoTAAD1Cbmu/jI/e7IkjemetLRv+/hi05nwv0yM5LpWUqqknx270HGcoKRHJQ22bfvCUxwjQtIfJP3xuGNUSnpX0qn2BwAAgGHfH5c2U5KW7Cq4cHVOUWvTeQAAqM8ry/b22FNQ2TXK56l4eEoGj9k3USbm5BonaY/jOLtOsG6JpEDdNsvrO4DjOCskrahndaSkonOLCAAAgMY2oVebA4M6Jaxbu7d44O9mZk1446uD3zWdCQCA4wWCIevlpXsnS9Jl/VIWtE+MqjSdCSdmua57Xk9o2/ZsScWO41xXz/o1kpY7jnPvWRw7UtIOSWscx7nmmOXzJPWRVC6pg6QDkt5W7ST1Z1WIzZ4925WkQCCQdzb7NyXV1dVtJCkiIuKw6SxombgHYRr3IJqClnofHql0Pb9cFkqtcaX7B3sOZyRaAdOZWqqWeg+iaeE+hGknugfn7wtFv7HDbdU6UsFHR3gO+j2WuYAtgN/vby9JEydOPON/0CYeV0ySdOQk6/MlJZ/lsX8nqXPd57FKJb0v6ReqfVzyDUnflDTPtm0mOgUAADAkOcoKje1olUrSe5mhhPP9C1gAAE6mKuhan+12EyTpqjSrhIKraTPxuGK8akdU1adcUuKZHtS27R9I+q6kPziO88Vxq69zHOfY3wp+XjeibLqkB1X7ZsazMnLkyBfPdt+mYtGiRfdIzeNaEJ64B2Ea9yCagpZ8H6ZfUBm16IWV38ssDkXv9HWde+dFnXeYztQSteR7EE0H9yFMO/4efPjDraOKqg+2S42PyPvhl0b83eex+G1MI1u+fPlZdzTnXHLZtn2f/nfk1InMdxxnnKQSSdEn2S5GUsEZZviKpGckfSTpgePXH1dwHV02w7btTyV9RedQcgEAAODctE+Mqrz8gpQF763dP/XlpXsn3zKsY2akzxMynQsA0LLtzq+ImbHl0ChJuvuSzrMouJq+hnhc8V+qne/qVD+3121/qscRT/U443+xbfvKugxzJN3kOE7NGWSfKambbdsRZ7APAAAAGtj9k9JXJEb5Co+UBdo+O2fXINN5AAB4fPrO0dVBN7JbcnTmLcM6ZpnOg1M755FcjuPkq7a4Ol2ZkqacaEXdxPF9Jb15OgeybXu0JEfSUklXO45zpm848EhyJfGbQgAAAIPiIn3Bm4d1mP23L/Zc//66/eO/PrLzhqTYCCahBwAYsSanqNWSXQUXStL3x3ebZToPTo+JiefnSepq23baCdZdLCmibpuTsm17kKSPJW2UdIXjOPXO82Xbdo96Vl0kadsZjv4CAABAI/jG6K6b2iVE5pZVB+OemJF5sek8AICW63ezsiaEXHkGdoxfP7FXm/2m8+D0mCi5PpB0UNJjxy60bduj2rcfrnUcZ9kxy722bacet213SdMk7ZY01XGc4vpOZtv2byVtsG2733HLR0q6QdLfz+lqAAAA0CB8Hsu9Z2SXmZI0a9vhkVmHy2NNZwIAtDy7il3/htyS/h5LwQcmZ8wxnQen77y/XdFxnHLbtu+V9I5t235Jb9Xl+JqkEZLGH7fL85K+Ztv2GMdxFh09jKRYSb+RNNC27ROdamnd44svSrpT0jzbth+XtFnShZIeljRL0h8b8PIAAABwDm4c0j77jZX7tu84VN7z8ek7x/79tgGfmc4EAGg5XNfVe5mhBEkamZG0fEDHhCLTmXD6TIzkkuM4H0i6TFJHSS9L+ptq58Ya6zjO8uM23yepUNKxN1aSpDjVFmRz6/lpV3eubNWWZx9Juk/Sh5JulvQrSVfyqCIAAEDT8sMJ6bMsyV2+u3DosuyCk72wCACABrXusCJ3FCki0uep/MmUjIWm8+DMnPeRXEc5jjNTtW83PNV2j0p69LhlaWd4rhxJd5/JPgAAADBjdPekQ0O6JK5ZtadoyNOzd0107m7tmM4EAGj+qmpCnveyakdxXd4vZX6n1tEVpjPhzBgZyQUAAACczMNTMub5PFbNlv2lfT5af6Cz6TwAgObv6dlZgw9WyJcUqeADk9NXmM6DM0fJBQAAgCanV2pcybgeyYsl6fmFuyeHXNd0JABAM3aotDrig3X7x0vStelWcVykL2g6E84cJRcAAACapIenZiyO9nvK9xVWdv7HopzepvMAAJqv307bObIiEIrtGq/A8BSr0nQenB1KLgAAADRJKfGRVVf1T50nSf9ZsW9SRSDId1cAQIPbnFeSMG/HkUsk6YYMT5FlWaYj4SzxRQEAAABN1n2T0le1ivblF5QHkn83K2uY6TwAgObn8RmZE2pCrq9vu7jNPVpZAdN5cPYouQAAANBkRfu9odtHdJopSR9vODAur6gyynQmAEDzMWvr4XZr9xYP9FgKPTglY5bpPDg3lFwAAABo0u6+pPPWzq2jdlcGQtG/nrZztOk8AIDmIeS6enburimSNDK99bIhnRMLTGfCuaHkAgAAQJPmsSx9f3y36ZL0RWb+iDU5Ra0MRwIANAOvLNvbY3d+RbdIn6fyJ1O7LzSdB+eOkgsAAABN3tQ+bfMGdoxfH3LlfXJm5iTTeQAA4a2qJuR5acneKZJ0eb+U+Z1aR1eYzoRzR8kFAACAsPDwlO6zvR6rZlNeab+P1h/obDoPACB8PT07a3B+eaBNYrSv4IHJ6StM50HDoOQCAABAWOjXIb54XI+kJZL0lwXZU0KuazoSACAMHSqtjvhg3f7xknTb8I6z4iJ9QdOZ0DAouQAAABA2fnppjy9iIrxluUVVnZ5fsLuf6TwAgPDz22k7R1YEQrEdEiP33juqy2bTedBwKLkAAAAQNtrGRVRfNzB1jiS9sTJ3Uklljc90JgBA+NicV5Iwb8eRSyTpO2PTpnssy3QkNCBKLgAAAISVH09MX9Mm1n+wuLKm1eMzdo4wnQcAED4en5E5oSbk+vq2i9t8Vf/UvabzoGFRcgEAACCs+L0e9+5LusyQpOmbD43OPlIeYzoTAKDpm7X1cLu1e4sHeiyFHpySMct0HjQ8Si4AAACEnS9f2DEzo03MzuqgG/mbaTvHmc4DAGjaQq6rZ+fumiJJI9NbLxvSObHAdCY0PEouAAAAhKUfT0yfYUnusuzCYYuzCtqYzgMAaLpeWba3x+78im6RPk/lT6Z2X2g6DxoHJRcAAADC0ujuSYeGdUlc7UrW72dnTTadBwDQNFUEgp6XluydIkmX90uZ36l1dIXpTGgclFwAAAAIWz+9tPtcv9eq3nGwrOdbq3K7mc4DAGh6fjcra1h+eaBNYrSv4IHJ6StM50HjoeQCAABA2MpoG1s2uXebLyTpxUV7pgSCId4FDwD4P/sKK6M+Xn9gnCTdMaLTjLhIX9BwJDQiSi4AAACEtZ9M7b4kPtJbfLCkut1zc7MHms4DAGg6Hvt8x9jKmlB016To7Lsv6bzVdB40LkouAAAAhLXEaH+NPaTDbEl6Z03ehPyyar/pTP+vvfuOr6o+/D/+vvfmJiQkZBASNgECYYMggqICgjJFcBz3aJ212trW2roVZ6u1tVpbt7bOgwMB2cgQlA2yQxhhhRDIgOzc3Ht/fwR+35Qysz65N6/n48EjD8+45337+PSDvvM55wAAzFuWntv0x52550nSby5pP8vpYLFvsKPkAgAAQMC7b0jS+uZNwjIKy7xRz8/adoHpPAAA8/40Z8dlPr+c/dpGrx6WEp9pOg9qHyUXAAAAAl6I0+G/56K2syVpXmr2oNQDBVGmMwEAzPl05b4OaVmFnd0uR9mjI5Lnm86DukHJBQAAgKBwVZ8Wu7okNt5c7vO7n525bZjpPAAAM0rLfc63Fu8eIUkjujb7vlNC4wLTmVA3KLkAAAAQNB4ekTzH6ZB37d4jvadvzGplOg8AoO79Zd6Ocw4VehKaNArJe2RE8lLTeVB3KLkAAAAQNPq2ic69ODluqSS9On/nSJ/fbzoSAKAOZR4pDZv8U+YlknRj/1ZzohqFlJvOhLpDyQUAAICg8vioTt9HhLoKMw6Xtn59YXpP03kAAHXn2RlpFxd7fBGtYxrtvueitptM50HdouQCAABAUEmICiu9qk/zeZL06cqM4TmFZW7TmQAAtW/V7sOx32/PGShJvx7afpbT4TAdCXWMkgsAAABB5zeXtF+bEBWaWVDqbfLcrG2DTOcBANS+F2dvu9Tnl7NP6yY/jezWLMN0HtQ9Si4AAAAEHbfL6f/lxUkzJOm71OxBGzPym5jOBACoPZNW70/acqCwa4jT4XlsZPI803lgBiUXAAAAgtKVfZrv7tY8clO5zx/y3Kxtl5rOAwCoHR6vz/GvxbtGSNLwLvGLUxIj801nghmUXAAAAAhaj4xInuNyyLs+I7/HlHUH2pjOAwCoeX/7bmefrPyy5lFhriOPjkj+0XQemEPJBQAAgKDVu3WTvCGdm/4gSa8tTB9Z7vPzFGIACCIHC8pCv1ibOUySrju35dyYCLfHdCaYQ8kFAACAoPb4yE6LG4e6CjKPlLZ8df7OXqbzAABqzrMz0i4sKvM2bhkdtve+wUnrTeeBWZRcAAAACGpNI0PLrL4t5krSpNX7h2cXlIWazgQAqL41ew7HLEzLvkCS7h+cNMvpYLFuQ0fJBQAAgKD3q6Ht17VoEravsMwbOXFG2oWm8wAAqu/5WdtGeP1y9WoVtW5sz8S9pvPAPEouAAAABL0Qp8N//5CkmZK0MC37gjV7DscYjgQAqIZPV+7rsOVAYRe30+F5YlSnuabzoH6g5AIAAECDcHnPxL09W0at9/rlenH29ktN5wEAVE2xx+t8c/HukZI0oluzRSmJkfmmM6F+oOQCAABAg/HoiOS5IU5H+abMgm5frNnfznQeAMDZ+9Ps7f2zCz3NosNDch8dmfyj6TyoPyi5AAAA0GB0bxl1ZFhK08WS9M/vd430eH08pRgAAkh6dlHE1PVZQyXpZwPbzIoMC/GazoT6g5ILAAAADcpjIzv9EBnmOpKVX9b8L/N2nGM6DwDgzE2cnnZJmdcX1iE+YvvPzm+dajoP6hdKLgAAADQoMRFuz439W82RpK/WZg7bl1fSyHQmAMDpzdl8sMWK3Yf7OR3y/eHSjjOdDhbj4r9RcgEAAKDBuffidhvaxjbaVezxRTw9fetQ03kAAKfm8/v1ync7R0nSoA6xyy7oEHvIdCbUP5RcAAAAaHCcDoceHN5xhkPyL92Z13/+1uxE05kAACf3+sL0nnvzStqEu52FT4zuvNB0HtRPlFwAAABokIZ2bnpgQFLMSr/keGnu9lE+v990JADACeQUlrk/XZlxqSRdfU6Lec2bhJWazoT6iZILAAAADdaTozvNb+R2Fu/JLWn3xqJdPUznAQD8r4kz0i4qKPVGNW8SlvHbYR3Wms6D+ouSCwAAAA1W69jw4it7N58nSR+v2HdZTmGZ23QmAMD/Wb3ncOyCrdkXSNKvhiTNCHE6WHaLk6LkAgAAQIP24PAOqxOjQvcXlHqjnp6edpHpPACA//P8rG2Xef1y9WoVte7ynol7TedB/UbJBQAAgAbN7XL6fz20/XRJWpCWfcGKXXlxpjMBAKRPVuzrkHqgsIvb6fA8MarTXNN5UP9RcgEAAKDBu7xn4t4+rZv85PPL9cKsbSNM5wGAhq7Y43W+uXj3KEka0a3ZopTEyHzTmVD/UXIBAAAAkp4Y1Wmu2+UoSztY1PnDpXs7mc4DAA3Z8zO3Dcgp8sRHh4fkPjoy+UfTeRAYKLkAAAAASZ0SGheM6Z6wQJLe+WH3yILScpfhSADQIKUeKIj6dmPWEEm684K2MyLDQryGIyFAUHIBAAAARz08Inl5XIT7UF5xedxzM7edbzoPADRET09Pu8zj9YemJDROvXVg6zTTeRA4KLkAAACAoyJCXd67L2w7Q5JmbTp48ebMgijTmQCgIbFXZyStz8jv4XI6yh8f1Wmm6TwILJRcAAAAQCU39G+1o0ti4y0en9/9zIy0y0znAYCGotjjdb6xaNdoSbqsS/z3vVs3yTMcCQGGkgsAAAA4zmMjO81yOR3l6zPye3y5dn8703kAoCF4fua2AdmFnmbR4SG5j4/q9IPpPAg8lFwAAADAcXq3bpI3LKXpEkl6bUH6mGKPl39vBoBatDnz/x42f9egttOjGoWUG46EAMRf1gAAAMAJPDGq0+Lo8JDc7EJPs+dnbhtgOg8ABLOJxx42n9h4yy0DWm8znQeBiZILAAAAOIHocHf5XYPaTpekbzdkDd20P7+J6UwAEIzs1RlJG/bn9whxOsofH9lpluk8CFwhpi5sWdYlkp6U1FtSuaTlkh61bXvNGZ6fLulkz0f4jW3bfzvu+Ksl/UFSV0kFkhZKesS27e1VyQ8AAIDgd8uA1tumrj+wZcuBwi5PT08b8fntfSeZzgQAwaTY43X+Y+GuMZJ0KQ+bRzUZWcllWdblkmZJypL0c0n3SgqVtMiyrHPP4qO+lDT0BH++OO5690qyJa2WdIOk30tKlrTEsqyk6nwXAAAABLcnRneaGeJ0eDZlFnT7aPm+jqbzAEAweW7mtoE5RZ74mPCQHB42j+qq85VclmWFS3pbkm3b9o2Vtn8paa6ktyT1PcOP22vb9oLTXK+VpJclvWDb9qOVtn8laaWkVyRdeTbfAQAAAA1Hz5ZNDo/q3mzh1PVZw99cvGv0Fb0S/8kDkQGg+jZnFkRN35g1WJLuurDtDOZWVJeJlVzjJSVKeqzyRtu2vZImSjrHsqzzavB6t0jyS3rhuOsVSvqzpHGWZbWowesBAAAgyDw2stPSuAj3obzi8riJM9IuMJ0HAILBsYfNd0lsvOXm83jYPKrPRMk1RNJu27Z3nmDfj5I8R4+pyeuttG274AT7FkpySbqoBq8HAACAIBMR6vLeNzjpW0mas/ngxat2H441nQkAAtnnqzLa//+HzY/qNNN0HgQHEyVXsiqejfU/bNsukbRR0pk+62CCZVn7LMsqtSwrzbKsxy3LCjuL6+2QlHcW1wMAAEADdU3fFum9Wkat9/rlenZm2iif3286EgAEpGKP1/nGol2jJemyrvGLerVqcth0JgQHE29XjJN0olVcx+RIanoGn5MtaYGkFap4W+IYVbyt8QLLssbYtu2rdL3sGrjeCS1ZsuSuqp5bX5SVlcVLwfFdEJgYgzCNMYj6gHEYGG5o53duPSD/toNFnV6bvPhX5yY4S0xnqimMQdQHjMOG4dt0X2ROkT8qvpG8o5pmd12yZElX05mOYQya53a7q3yuiZIrSlLRKfYXSYo+g88ZaNu2p9I/T7Msa42kNyXdKun9Gr4eAAAAGriYMIdvTJLjyJfb/dFfbPM36RHnL20U4mBJFwCcoawiv2vmbn+kJF3byXnY7XSYjoQgUu2Sy7KsByW9dAaHLrRte4ikfEnhpzguQlLu6T7suILrmLcl3SfpZv1fyXUm16vy0shBgwa9VdVz64tjDXUwfBcEJsYgTGMMoj5gHAaO87w+x/f/WH5XVn5Z82+yYrf9/Zru80xnqgmMQdQHjMPg5vP7ddXbq27w+IoSerSI2nDPuHO+NJ3peIxB85YvX/5kVc+tiWdyvSep6xn8ueXo8ae7PfB0txeelG3bfknzJHWptPl014ut6vUAAADQ8LhdTv9vL+kwTZIWpmVfsHh7TjPTmQAgELy1eHfXbQeLOoW6nKUTx3aeZToPgk+1V3LZtp2jiiLpTG2XdNmJdhx9aHw3SZ9VI5JTkve46/U9yfXaq6Lk2lGN6wEAAKCBGdMjYd+Xa/avWrH7cL8XZm0fPfUXsR86HdxyAwAnc7CgLPSDpXtHSdKVfRLndkpoXGA6E4KPibcrLpDUzrKspBPsO19S6NFjTsqyLIdlWSd7I+IASRuOu965lmU1PsGxgyX5JH1/ysQAAADAcZ4a23leI7ezeHducdJrC9J7ms4DAPXZ41NTLyks80a1aBK276FLO64ynQfByUTJNVlSlqRnKm+0LMsp6QlJa23bXlZpu8uyrMTjPuNjST9altXiuM+4XhUl19uVNv9bkkvSH487NkLSQ5Km2radUZ0vBAAAgIanbWx4sXVOizmS9PGKfSP25haf6jmwANBgzdl8sMUPO3LPc0j+P1zWcZrb5eSFHagVdf52Rdu2iyzLulvSF5ZluSV9fjTHHaooqIYed8obku6wLOti27aXHN32iqSxkn6wLOtFSbtUcQvkryW9b9v2V5Wut9eyrIckvWpZVlNJMyQ1OXpsvKQxtfRVAQAAEOR+M6zD2vlbs/vsyStp+9i0rcM/uLn3VNOZAKA+8Xh9jj/P3THWLzku7Bi7dFhKfKbpTAheJlZyybbtyZJGSWol6QNJb0rySxps2/by4w7fJylPld6AaNv2SlXc2rhCFSvCvpY0RNI9km4/wfVek3SDKkq0TyX9VRXF2CDbtnfW1PcCAABAwxLidPgfHZU81emQb9Xuw32/WpvZ1nQmAKhPXpy9/dzMI6UtI8NcRyaOTZlvOg+CW52v5DrGtu05kuacwXETJU08wfaNkqyzuN5nqt4D7QEAAID/MahD3KFhKfHfz9lyaPCr83deflnX+H9FhoV4T38mAAS31AMFUZN/yhwmST8b2GZGs8jQMtOZENyMrOQCAAAAgsnTYzovjo1wZ+cUeeKfnp42yHQeAKgPnvx264gyrz+sU0LjrXcMarPFdB4EP0ouAAAAoJqiGoWU3ze43TRJmrP54MXL0nObms4EACZ9tHxfx437C7qHOB2ep0Z3mu50OExHQgNAyQUAAADUAKtvy/RzWjdZ6/XL9cyMbWN9fl4eBqBhOlzsCfnX4l1jJGl094QFvVo1OXy6c4CaQMkFAAAA1JBnLk+Z08jtLN6VU5z01+929jadBwBMeGLa1sGHi8tj4yNDsx4blbzUdB40HJRcAAAAQA1pFxdedH2/lrMk6bNVGSN25RRHmM4EAHXpu9RDifO3Zg+SpN9e0n5quNvlM50JDQclFwAAAFCDHrik/U/t4sJ3lnh84Y9NTb3MdB4AqCul5T7nC7O3j/NLjvPbxyy/vGfiXtOZ0LBQcgEAAAA1yOlw6IlRnaa5HPKu3Xuk9+erMtqbzgQAdeHZmWkDMo+UtowKcx15blyXeabzoOGh5AIAAABq2HlJMTmXdW22SJJeX5g+Nr+kPMR0JgCoTWv2HI6Ztj5rqCTddWHbac0iQ8tMZ0LDQ8kFAAAA1IKnxnRe0rSx+2BecXnc49NSLzadBwBqi8/v15Pfbh1b7vO7e7SI2nDbwDZppjOhYaLkAgAAAGpBRKjL++uh7adJ0vyt2YPmb81ONJ0JAGrDq/N39tqZXdyxUYiz+LlxKTNN50HDRckFAAAA1JIJvZvvHpAUs8Lnl/P5WdvGlZb7+PdvAEElPbso4pOVGSMl6dp+LWd3iI8oNJ0JDRd/yQIAAAC16PlxKfOiwlxHMo+UtnxmRtpA03kAoCY9OiV1ZInHF54UF77zt8ParzWdBw0bJRcAAABQixKiwkrvuajdVEmatiFr6IpdeXGmMwFATfj3sr3J6zLye4Y4HeVPjek81elwmI6EBo6SCwAAAKhltwxova13q6h1Xp8/5Klvt44r9/n5L0EAAS27oCz0X9/vGitJo7snzO/XNjrXdCaAkgsAAACoA89f0WVmuNtZtDu3pN2f52zvazoPAFTHo1NTh+aXeqMTokIznxjdaanpPIBEyQUAAADUibax4cW3Dmw9XZK+WLP/sk3785uYzgQAVTF9Y1arJTtyBzok/x8v7TglLMTpM50JkCi5AAAAgDrzi4vabUxJaJzq8fpDH52aOtbn95uOBABnpaC03PXnOduvkKSLk+N+vLRrs/2mMwHHUHIBAAAAdcTpcOi5cSnfhrqcpdsOFnX6x8JdPUxnAoCz8eiU1MHZhZ5msRHu7OfGpcw3nQeojJILAAAAqEMpiZH51/ZrMUuS/rN876j07KII05kA4EzM2nywxfyt2RdK0m8vaf9NdLi73HQmoDJKLgAAAKCOPTi8w5p2ceE7iz2+iEempI4ynQcATqeozOt6cfb28X7JMahD7NLxvZvvMZ0JOB4lFwAAAFDHnA6HJo7tPDXE6fCsz8jv8d6PezqbzgQAp/LY1NSLDhWUJUSHh+S+eEWX70znAU6EkgsAAAAwoG+b6NxxvRK/k6S3Fu8euy+vpJHpTABwIvNSDzWfl3roIkl6YGj7b2Ii3B7TmYAToeQCAAAADHlsZPKyltFhewvLvFF//GbLSNN5AOB4xR6v8/lZ267w+eU8v33M8qvPabHLdCbgZCi5AAAAAEPcLqd/4tjOk11OR/navUd6v/vDnhTTmQCgsienbb0wK7+seXSjkLwXxnWZZzoPcCqUXAAAAIBBA5Jis6/olThPkt5asnvs7tzicNOZAECSFqRlJ8zafHCwJN0/JGlK08jQMtOZgFOh5AIAAAAMe3xUp2VtYhrtLirzRv5x8pbRpvMAQGm5z/nsjG3jfX45z2sXvfLafi13ms4EnA4lFwAAAGBYiNPhf3ps52+OvW3xX9/v6mo6E4CG7clvt15wIL+0RVSY6/CL47vOMZ0HOBOUXAAAAEA90L9dTM6EPs3nStJ7P+4Zm55dFGE6E4CG6bvUQ4kzNmYNlaT7BidNacZtiggQlFwAAABAPfHIiOQV7eLC04s9vog/TN4yxnQeAA1PQWm569mZ2yb4/HIOSIpZeUP/VjtMZwLOFCUXAAAAUE+EOB3+5y5P+cbtcpRtyizo9vrC9O6mMwFoWB6dkjrkYEFZYnR4SO6fx3eZbToPcDYouQAAAIB6pHfrJnlXn9NitiR9uHTvmLSswkjTmQA0DFPWHWgzf2v2IIfkf2h4x6/jGod6TGcCzgYlFwAAAFDP/PGyjquSmobvKCn3hT/8zZaxPr/fdCQAQS6nsMz90tzt4/2SY3CnuB/G9UrcYzoTcLYouQAAAIB6xulw6PlxKVNCXY7S1KzClFfm7ehjOhOA4PbQ5C2X5hWXx8VHhma9cEWX+abzAFVByQUAAADUQz1bNjl8Y/9WMyTpk5UZo1bvORxrOhOA4PTJin0dlqXn9Xc65Ht8ZPJXkWEhXtOZgKqg5AIAAADqqQcuaf9T1+aRmz1ef+ijU1IneLw+h+lMAILLvrySRq8tTB8vSSO7NVtwSUr8AcORgCqj5AIAAADqKafDoZev7Dq1cairYG9eSZvHp20dZDoTgODy0OTNowpKvVEto8P2ThybssR0HqA6KLkAAACAeqxtbHjxfYOTJkvSjI1ZQ+duOdTccCQAQeLNxbu6rtuX3yvE6SifOLbz5LAQp890JqA6KLkAAACAeu6m81ptH5gUs8Lnl/OZGWlXHS72hJjOBCCwpR4oiHpnyZ7LJWlCn+ZzBiTFZpvOBFQXJRcAAAAQAF6a0HVOXIT7UE6RJ/4Pk7cMN50HQOAq9/kdD369eXxJuS+8Q3zE9kdGJK8wnQmoCZRcAAAAQACIiXB7Hh6R/LXTId+SHbkDPlmxr4PpTAAC09PTtw5Mzy7uEO52Fr00ocvkEKfDbzoTUBMouQAAAIAAMbJbs4wRXZstlKS/L0gfvze3ONx0JgCBZV7qoeZT1h0YJkl3X9huSueEyALTmYCaQskFAAAABJBnLk9Z3DI6bG9hmTfqwa83j/H5WYAB4MzkFXncE6enXeXzyzUgKWbl7Re0STWdCahJlFwAAABAAAkLcfqeH9flK7fT4dm4v6D7X7/b2dt0JgCB4fdfb740p8gTHxfhPvTyhK6zTecBaholFwAAABBg+rWNzr2hf6vpkvTx8n1jlqXnNjWdCUD99v6PezovTc/r73TI9/ioTl/GRLg9pjMBNY2SCwAAAAhAvx3Wfm3PllEbPD6/+5EpqVcXlJa7TGcCUD9tP1jY+J/f77pCki7vmThveJf4TNOZgNpAyQUAAAAEIKfDoVeu6jYtulFIXlZ+WfOHvt48zHQmAPWPz+/X777aPL7Y44toFxe+86kxnX80nQmoLZRcAAAAQIBq3iSs9I+XdfzSIfm/3557/n+W7002nQlA/fLMjLTzth8qSg4LcZa8NKHr1yFOB2+rQNCi5AIAAAAC2NieiXtH90j4TpJeW5A+Pi2rMNJ0JgD1w7zUQ82/Wpt5mSTdcUGbKV2bR+abzgTUJkouAAAAIMA9M7bzknZx4TuLPb7Gv/tq0/hyn99hOhMAsw4WlIU+9e3Wa3x+ufq3i151z0XtNpvOBNQ2Si4AAAAgwLldTv9fruz6dSO3s3hndnHHp77der7pTADM8fn9emDSxrF5xeVx8ZGhWX+9qttM05mAukDJBQAAAASBlMTI/HsvajdZkqauPzBsxsasloYjATDklXk7+qzLyO8Z4nR4nrs8ZVJ0uLvcdCagLlByAQAAAEHiZ+e32Xp++5jlPr+cz83adnVWfmmY6UwA6tbi7TnNPl6RMUaSbj6v1bcXdIg9ZDoTUFcouQAAAIAg8pcru81pFhl64HBxeez99sZxPj8vUgMairwij/uxqanXlPv8IX1aN/npt8M6/GQ6E1CXKLkAAACAIBLVKKT8hStSJrldjrJNmQXdnpu5rb/pTADqxm++3DQyu9DTLDbCnf23q7pNN50HqGuUXAAAAECQGZAUm/3z89tMkaQv1uwfyfO5gOD32oL0Hit3H+7rcsj79JjOk5pGhpaZzgTUNUouAAAAIAjdNzhp48CkmBU+v5zPztx2zb68kkamMwGoHcvT8+LeX7rnckmy+rWcMbRz0wOmMwEmUHIBAAAAQeqvV3eblRgVtv9ISXnMryZtHM/zuYDgk19SHvLwlC3XeLz+0O4tIjf+8bKOq0xnAkyh5AIAAACCVGRYiPelCV3sUJezdGtWYcqT07aebzoTgJp1/6SNo7Lyy5pHh4fk/u3q7lOdDofpSIAxlFwAAABAEDunTXTeLy5q+7UkfbPuwKWTf8psYzoTgJrx0pzt56zafbivy+kof3pMZ7t5k7BS05kAkyi5AAAAgCB3x6C2qRclx/3olxx/mrP9mvTsogjTmQBUz9wth5p/vDJjtCTd1L/Vt8NS4jNNZwJMCzF1YcuyLpH0pKTeksolLZf0qG3ba87g3AWSBp/ikA9t277tDI9/1bbtB84oNAAAABCg/nJl17nj31zZJuNwaetff7Hpyi/v7PdxiNPBQ7qAALQvr6TRU9O3Wl6fP6Rf2+jVDw7vsNZ0JqA+MLKSy7KsyyXNkpQl6eeS7pUUKmmRZVnnnsFHPCBp6An+jFVFYbb7BOcsOck5r1fjqwAAAAABIdzt8r1yZbdJjdzO4h2Hijr+YfLmU/3SGEA9Ve7zO+6ftHHC4eLy2MSo0P2vXdN9hulMQH1R5yu5LMsKl/S2JNu27Rsrbf9S0lxJb0nqe6rPsG177Uk++25JDknvnGD3Idu2F1QtNQAAABD4ureMOvLA0PZfvjh7+02zNx8a/P6PezJ+dn6braZzAThzj01NHZSWVdg5LMRZ8tKErnZUo5By05mA+sLESq7xkhIlPVZ5o23bXkkTJZ1jWdZ5VfzsX0r61rbtE63kAgAAABq8G/u32j6mR8I8SXp94a4rl6fnxZnOBODMfLYyo/30DVmXSNJ9g9t9dU6b6DzDkYB6xUTJNUTSbtu2d55g34+SPEePOSuWZQ2W1FPSG9UJBwAAAAS758elLO6S2HhLmdcX9tDkzddlF5SFms4E4NTWZxyJfuW7HVf7JcfwlPhFtw1sk2Y6E1DfmCi5kiWtPtEO27ZLJG2U1LEKn3ufpO2SZp9k//mWZe20LKvUsqzdlmX9xbKs6CpcBwAAAAhoTodD/7i2x+S4CPeh7EJPs3s/33CF388z6IH66nCxJ+Q3X2y6ttjji2jfNHzHnyd0WWA6E1AfOer6LzPLstZIWmXb9h0n2T9PUq5t21efxWe2kpQu6WHbtl8+wf5pkvaq4uHzh1SxUux+SamSBtm2XXSWX0Pz5s3zS5LH49l/tufWN2VlZfGSFBoaesh0FjRMjEGYxhhEfcA4hAkZhX7Xn1b7mpV65RjXzucd3srvYwzCJObC/+X3+/X2Jn/M6oP+8LgweR8513kw0s2bUWsLY9A8t9vdQpKGDRvmONtz6/zB85KiJJ2qVCqSdLYrrO5RxVsV3zvJ/gm2bXsq/fOMo2XaLEl/kPTkWV4PAAAACHgtGzu8N6c48t7Z5I+dusvhatPY7++ZYDoVgMpm7fY3Xn3QHx7qlP+eHs4cCi7g5KpdclmW9aCkl87g0IW2bQ+RlC8p/BTHRUjKPYvrh0q6U9Lntm3nnOiY4wquY9tmW5b1raSbVY2Sa9CgQW9V9dz6YsmSJXdJwfFdEJgYgzCNMYj6gHEIUwZJ2undeMm81OyL3t/qcr7Vv6fdu3WTPNO50DAxF/63T1fu6/DNzu03SdKdF7abdP1F7TabzhTsGIPmLV++vModTU08k+s9SV3P4M8tR4/PkdT0FJ8XJyn7LK5/jSre1liVB87PkdT+aFEGAAAANEgvTeg6v0usSovK5fzNl5t4ED1QD6zeczj2lXk7r/ZLjmEpTb+/h4ILOK1qr+Q6unrqhCuoTmK7pMtOtMOyrDBJ3SR9dhafd58qnvG1/CzOOcYpyS/JV4VzAQAAgKDgdjn9d3Rz5v5pla/ZwYKyxLs/Wz/hs5/3tUOc3BYFmJBTWOb+3Vebrysp94V3ahax9eUru803nQkIBCberrhAUjvLspJOsO98SaFHjzkty7L6Shqo06zisiyr00l2DZSUatt2+ZlcDwAAAAhWkW6H/96ezuywEGdJ6oHCLr//avMQ05mAhsjn9+sXn20Yf6igLCE2wp39xnU9v6JwBs6MiZJrsqQsSc9U3mhZllPSE5LW2ra9rNJ2l2VZiSf5rPtV8fyuT092Mcuynpe03rKs7sdtHyTpaklvV+E7AAAAAEGnRWOH97eXtJ/kkPxzUw9d/NqC9B6mMwENze+/3jxkU2ZBt1CXo/TFK7p81rxJWKnpTECgqPO3K9q2XWRZ1t2SvrAsyy3p86M57pA0QNLQ4055Q9IdlmVdbNv2kmMbLctqKuk6Sf+0bbv4FJd8S9JtkhZYlvWCpE2SzpP0sKS5kv5eI18MAAAACAI39G+1I+1g4cwv1mSOevfHPVckN4vIGdU9IcN0LqAheG1Beo/Zmw8Ndkj+Xw1p/+UFHWIPmc4EBBITK7lk2/ZkSaMktZL0gaQ3VfFsrMEneLbWPkl5kg4ft/12SWGS/nmaa6WrojybIulBSd+oohx7StJYblUEAAAA/tvjozot7982epXX5w95enradakHCqJMZwKC3bT1B1q/+8Pu8ZJ01TnNZ906sHWa4UhAwKnzlVzH2LY9RxVvNzzdcRMlTTzB9j9L+vMZXmuPKkoxAAAAAKfhdDj0+rU9Zlz9zqr4Pbkl7e6zN173xR19348Od/MLYqAWrNt3JHrijLTrvH65BiTFrHx8VKdlpz8LwPGMrOQCAAAAUL9FhLq8/7yup92kUUhe5pHSlnd9sn5Cuc/vMJ0LCDZZ+aVhv5608YZij69x+6bhO16zus9wOvi/GlAVlFwAAAAATqhdXHjRi1d0+STU5SjdlFnQ7TdfbBxmOhMQTDxen+PuT9dfdajQkxAX4T705vU9J4W7XT7TuYBARckFAAAA4KQuSo47+ODwDrbTId+CtJxBz8/a1s90JiBY3G9vvGzbwaJOjdzO4leu6vZJi+hGJaYzAYGMkgsAAADAKV1/bqsdN5/XaqokfbYyY8y/l+1NNp0JCHRPT9963pIduQOdDvn+eFnHz/u1jc41nQkIdJRcAAAAAE7rweEd1w7vEr/ILzn+Nn/nNfNSDzU3nQkIVG8u3tX1izWZoyTp1gGtp1zVp8Uu05mAYEDJBQAAAOCM/OXKrvN7tYpa5/H6Qx+dknrDxoz8JqYzAYFm8k+Zbf75/e4rJWl092bf/XZYh59MZwKCBSUXAAAAgDPidDj05vU9p7SNbbSrsMwbdd+kjTdm5ZeGmc4FBIofduTGPztz2w1enz/kvHbRK1+4osv3pjMBwYSSCwAAAMAZiwwL8b51Q6/P4iLchw4VlCX8/KN11xaUlrtM5wLqu61ZBZG//3rzTaXlvkadEhpvfeO6njOcDofpWEBQoeQCAAAAcFZaxTQq+dvV3T6OCHUV7Mopbn/7R+uu9Hh9/Nc6cBIHC8pCf/HZhhuPlJRHt4gO2/fejb2+CAtx+kznAoINJRcAAACAs3ZOm+i8F8alfBTqcpRuyizo9ovPNoz2+f2mYwH1TrHH67zj43VWVn5Z85jwkJy3ru/5SUyE22M6FxCMKLkAAAAAVMklKfEHHhmZ/KnLIe+y9LxzH/p682DTmYD6xOP1OW7/aN2EHYeKOoa7nUV/u7r7R0lNI4pM5wKCFSUXAAAAgCq7qk+LXb8cnPSFQ/LP2nxoyLMz0vqbzgTUBz6/X/d8un70+oz8Hm6Xo2zi2JSP+7WNzjWdCwhmlFwAAAAAquXOQW233HReq6mS9Pnq/aP/sSi9u+lMgGm/+WLTsOW7Dp/rcsj78GXJn47s1izDdCYg2FFyAQAAAKi2hy7tuGZMj4R5kvTW4t1XfrJiXwfTmQBTHp2SesF3W7MvdEj+Xw1tb1/Tt0W66UxAQ0DJBQAAAKBGPD8uZfGgDrFLfX45X5q747qv1ma2NZ0JqGsvzt7Wd8r6A5dK0s/PbzP55+e32Wo6E9BQUHIBAAAAqBFOh0OvX9tj9jltmqwt9/ndz85Mu3H6xqxWpnMBdeUfi9K7f7wi43JJsvq2mP7AJe3Xmc4ENCSUXAAAAABqTIjT4X/3xl5TeraM2uDx+kOfmLb1prlbDjU3nQuobR8s3dPprcW7r5SkUd2azX98VKcVpjMBDQ0lFwAAAIAa5XY5/e/e1OvrLomNt5SW+xo9PGXLLd9vy2lmOhdQW/69bG/yX7/bea3PL+dFHWN/fHF8l0WmMwENESUXAAAAgBoX7nb5PrylzxfJzSLSSjy+8Ae/3nzrsvTcpqZzATXto+X7Or4yb8d1Pr9c57ePWf76tT1mOx0O07GABomSCwAAAECtiAh1eT+4ubfdLi58Z1GZt/EDX2y6Zc2ewzGmcwE15dOV+zq8PG/HdV6/XAOSYlb86/qeMyi4AHMouQAAAADUmuhwd/m/b+n9aeuYRrsLSr1Nfvn5httW7zkcazoXUF2fr8po/6c5O673+vwh/dtFr6LgAsyj5AIAAABQq+Iah3o+vKX3Jy2jw/bml3qjf/n5hp8tT8+LM50LqKpJq/cnvTh7+w1enz+kX9vo1W/d0OvbEKfDbzoX0NBRcgEAAACodQlRYaUf3XbOf46u6Ir61aSNP1uyIyfedC7gbH2xZn+752dtu6Hc5w/p26bJmndu7DWNgguoHyi5AAAAANSJZpGhZR/f1uejdnHh6YVl3sjffLHptkVp2QmmcwFn6j/L9yY/O3PbTeU+v7tP6yY/vXtT76kUXED9QckFAAAAoM7ENQ71fHRrn0/aNw3fXuzxNf7d15tvm5d6qLnpXMDpvL1kd5eX51Y8g+vcttGr37up1zcUXED9QskFAAAAoE7FRLg9H992zmfJzSLSSjy+8D9M3nLrzE0HW5rOBZzMawvSe7y2IN3y+eUc1CF22bs39ZrqdjkpuIB6hpILAAAAQJ2LahRS/p9b+3yektA4tbTc1+iRKVtunbR6f5LpXMDx/jxn+zlvLdl9lV9yDEtp+v0b1/WYyVsUgfqJkgsAAACAEZFhId7/3NbH7t4icqPH6w99bmbaTW8v2d3FdC7gmCembR34n+X7xknS6O7Nvvvb1d2/o+AC6i9KLgAAAADGhLtdvv/c2ufLAUkxK7x+uV5bkG69PHd7H9O50LD5/H7dZ28Y/vVPmSMk6ao+zWf+aXzX703nAnBqlFwAAAAAjHK7nP63bug5/dIu8Qv9kuPDZfuueHRK6gWmc6FhKvZ4nbf++6fxC9NyBjkd8t1+fpuvnxrTeZnpXABOj5ILAAAAgHFOh0OvXNVtwdXnNJ8hSVPWH7j0l59vuLTc5+feMNSZnMIy9/Xvrbl+7d4jvd1Oh+fB4R0+feCS9utM5wJwZii5AAAAANQbT47uvPzOQW2+cjrkW7Qt54KbPlhzVX5JeYjpXAh+u3KKI657b82t2w8VJYe7nUXPjkv58ObzWm8znQvAmaPkAgAAAFCv/GpI+/V/uLTjx26Xo2zj/oLu1rurb9mVUxxhOheC19KduU1v/GDNHfuPlLZq0ijk8GtW9/dGd0/YZzoXgLNDyQUAAACg3rmhf6sdr1zV7d2oMNeRvXklbW76YM3tK3blxZnOheAzafX+pPvsjXccLi6PTYgKzXzvpl7vDkiKzTadC8DZo+QCAAAAUC8N6dQ06/2be7+TEBWamVdcHnfv5xvumPxTZhvTuRA8Xp67vc+zM9NuLi33Neqc0DjV/nnf91MSI/NN5wJQNZRcAAAAAOqtlMTIfPvnfd9PbhaRVuLxhT/57dZb/zJvR2/TuRDYyn1+x68nbRz24bJ9V/j8cl7UMfbHz35+zudNI0PLTGcDUHWUXAAAAADqtaaRoWWf/bzvZ+e3j1nu88v1wdK94+/5dP2I0nIf/z2Ds3awoCz0+vdWW99tzb7QIfmvP7fltDeu6znb7XL6TWcDUD38pQAAAACg3gsLcfreuqHXjBv7t5zqdMi3ZEfuwKvfWXXj3tzicNPZEDiWpec2vfrtVXduOVDYJdTlKP3DZR0/fmRE8irTuQDUDEouAAAAAAHjj5clr35qTOcPw93OovTs4g7Xvb/mzu+35TQznQv13wdL93S697ONd+YUeeKbNnYffOO6Hm/f2L/VdtO5ANQcSi4AAAAAAWVC7+a7372p11sJUaGZh4vLY389aeOdry1I72E6F+qncp/f8fuvN1/8l3k7byjz+sK6JDbePOmOfu/wBkUg+FByAQAAAAg4PVs2OfzlHf3e69kyaoPH53e/tWT3VXd8vG5MQWm5y3Q21B97c4vDrXdXXzdz08GhkjS6e7PvPr+9r92MB8wDQYmSCwAAAEBAiolwez66rc+X1/Vr8a3TIe+y9Lxzx7+56vbVew7Hms4G86asO9DGenf1PWlZhZ3DQpwlvx/e4eM/je/6vdPhMB0NQC2h5AIAAAAQsJwOhx4d2Wnln8Z3fa9Jo5C8A/mlLe78eP3d7yzZnWI6G8wo9/kdj0zZMuixqak/yy/1NmkRHbbv3Rt7vXnLgNbbTGcDULsouQAAAAAEvJHdmmXYt/d9MyWhcWqZ1xf26oL06+74eN2YvCKP23Q21J307KKIq99edcPU9VnD/ZLj4uS4H765+9z3erdukmc6G4DaR8kFAAAAICi0imlUYt/R97PxvRJnOx3yLUvPO/eKN1feNWfzwRams6H2fbB0T6dr31vzi+2HipIbuZ3FvxvW/pN/XNtjTrjb5TOdDUDdCDEdAAAAAABqitPh0DOXp/w4pHPTnc/MSLsyu9DT7MGvN98xemv2/IljOy9xu5x+0xlRsw4WlIU+9PXmESt3H+4rSW1iG+16eULXr7q1iDpiOhuAusVKLgAAAABBZ1hKfOY3d5/71vntY5b7/HJO25A17Io3V966ajcPpQ8mX63NbDvhzZW/WLn7cF+nQ97xvRJnf3P3uR9ScAENEyu5AAAAAASl6HB3+Vs39Jrxn+V7015fuOuKPbkl7e74eN29Y3smfPfEqE5LWdUVuPKKPO5Hp6YOWbQt5wJJSogKzXxyVKevL+7UNMt0NgDmUHIBAAAACGo3n9d628XJTf/5yDdbRq7LyO85+acDly3bmdf9sZHJUyhFAs/HK/Z1/OeiXWMPl5THOCT/JSlNFz8/rsvCiFCX13Q2AGZRcgEAAAAIeu3iwos+/tk5X32wdM/6NxfvHrv/SGmr+ydtvPvSLvGLnhrTeXFkWAgFST2Xnl0U8ejU1BHr9uX3kqT4yNCsB4d1mDKmR8I+09kA1A+UXAAAAAAajNsGtkkb3T3hjYe/2TJ8+a7D587afGjIsvS8Xndc0HbmrQNbp5nOh/9V7vM7Xp2/s9dnqzJGlHh84S6no3xUt2YLnxjd6QfenAigMkouAAAAAA1KQlRY6bs39f72izX7N7y+MH1MdqGn2cvzdtzwzfoDWx8dkTyzX9voXNMZUWHa+gOt/74gfeT+I6WtJCkpLnznE6M7TevfLibHdDYA9Q8lFwAAAIAG6epzWuwa0yPhX8/P2jbg2w1ZQ9KyCjvf/tFPHYd2jl/y2MjkJU0jQ8tMZ2yoNmcWRD07M234sVsTG4e6Cq7t12LOr4e2X+d0OEzHA1BPUXIBAAAAaLDC3S7fM2NTfrz5vFbrn5mxbfjavUd6z009dPEPO3P7Xd4jYeHvhndYxS1xdaek3O+YvcffePaiNfeX+/xul0PewZ2a/vDEqE6LKR0BnA4lFwAAAIAGr3NCZMF/bu0zefJPmaveWLRrxP4jpa0+X71/9KzNBwde26/ld/dc1G5TiNPhN50zWOWXlIe8PG/HubM3+hIKPHJKUpfExpsfHpE8p28bbh8FcGYouQAAAADgqPG9m+8Z1yvxnbeX7O768YqMYblFnqZvLt599eSfDuy/sX/LBbcObL2V2+VqTrHH6/zbdzvPmbL+wMUFpd4mktQ2Up5bL0r+xOrbMt1wPAABhpILAAAAACpxOhy6+8J2m28b2Cb1r9/t6DP5pwNDD+SXtnjlu53X/2f5vgNX9mn+/d0Xtt3kdjlZ2VVFh4s9Ia8vTO8zY9PBCw4Xl8dKUrPI0ANXtit394lX6YUUXACqgJILAAAAAE4gLMTp++NlyavvubDd+le+29lv9uaDFxwsKEt8c/Huq+3V+7PH9UxYfO/FSesjQl1e01kDxd7c4vC/L0jvvyAte0CxxxchSbER7myrb4v591zUbtOyH3+403RGAIGLkgsAAAAATiEmwu2ZOLbz0ocu7bDi7wvS+0zfkHVhbpGn6YfL9l0xaU3m8As6xK66a1DblV2bR+abzlpfLd2Z2/SDpXv7L0/P6+vx+d2SlBgVtv/KPs0X3zmozWZWxQGoCZRcAAAAAHAGIsNCvI+MSF71u2Ed1vxz0a4e36w/MOhQQVnC3C2HLp635dBFXZtHbrb6tVg+oXfzXTy3Syoq87reX7qny4yNB8/dlVOcdGx7h/iI7Tec23LJNX1b7OR/JwA1iZILAAAAAM5CWIjT98Al7df9amjSuq9/ymxnr9p/3ubMgq6bMgu6PfVtWrdX56dnD0iKWXfDuS3XndMmOs903rr2/bacZvbqjN7L0vP6FHt8jSUpxOnw9G7VZMPNA1otH5YSn2k6I4DgZLTksizLIelFSQ9JmmDb9uSzPP8eSfdJ6iApR9J0SY/btn3gBMdeLekPkrpKKpC0UNIjtm1vr853AAAAANAwOR0OXdWnxa6r+rTYtTmzIOqtJbvP/WFHbt/cIk/TmZsODp256eDQ1jGNdl+cHLfuxvNabWobG15sOnNtWZ9xJPrTlRk9lu7M63mwoCzx2Pb4yNCsSzo3XXnnoLbrmjcJKzWZEUDwM1ZyWZYVJukDSddW8fw/SfqNpL9KWiSppSpKrIstyxpg2/bhSsfeK+l1SW9LekZStKQHJC2xLGugbdvpVf4iAAAAABq8rs0j8/96Vbf5peW+hfbqjA4zNh7stTmzoOvevJK2n6zMaPvpyowxbWLDd53bLjp1fK/ELYG+wsvn92v+1uzmMzYeTFm790jKgfzSFsf2hYU4S3q2jNo0oXfztWN7JuzhlkQAdcVIyWVZVqykbyT1k3SvpH+e5fn9JP1e0t22bb9daftUSeskPSnpt0e3tZL0sqQXbNt+tNKxX0laKekVSVdW5/sAAAAAgFRxK+PN57XedvN5rbcdLCgL/feyvV0XpmX32pVTnLQ7t+LPV2szR8RFuA+lJDbeMSApdseYHgnpgbDKaWtWQeTMTQfbr959pP3WrIIO+aXe6GP7QpwOT0pi462Xdmm2/tp+LbZFhoXwxkkAdc7USq5+knpIGiFpbxXOv0vSLknvVt5o23amZVn/lPQry7L+aNt2maRbJPklvXDcsYWWZf1Z0tuWZbWwbXt/FXIAAAAAwAk1iwwt+92wDj/9bliHn/YfLmn0xZrM5CU7crpszSrslFPkif9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\n", 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wBKciqfr/75FzH/3m4dHuhT6DiKgYFWOSqy93e9MCj98EoH8Jz40BmFyd0KiUjQXnKt77zdPvGZhNdFmMuvinH+z61hffvv3xeof5htsP7RWGzF//1pbnPvebm/+fs8IQ9IWTje/95ukPnh+PLJSsJSIqOYl0Vve+b556y+HB4M16CdnfuaXpp9/+wJ7vbWu0h2/0WoNOUn//vs4zX3vPzi83Os2j0WTW/gffv/CBn5+falqP2ImICsWnfnDh7u+f8r1OBaR7NrgP/eijN3/jvk01i5rPvH1v48D3fnfvl7c32M9lFNX4d0/2v/OLBwe3rXHIRETrphiTXCegbUO85+oHZFmWANwJ4BkA8Hq94wB6rvXcnLsBPOf1epU1iJNKyERoruJ93zz1Xl842ei0GAL/8q6d//LOm5v6b/zKV3v9Ns/4v71n5/+rsZmmAvF09Uf/4+wH+qZjlWsRMxFRIUlmFN37v3n6recnotuMein1Z2/c9I0/em3XiatXbt3IjkZHyPuhm76xodZ6ZS6jWP70p5ff+8Slmfo1CpuIqKB88gcX7vnVxZl7JED94G3NP/xHefsTZoNuSXOZeoc5+fAHdn//zm73i4oK3VeeH37bl54d2rpWMRMRraeCT3LJsuyUZfnlbV1er3cOwDcA/CdZlq++qH0/gHYA/zLvvq8AeIcsy68auGVZvhdakusraxA2lZBEOqv7yLfPypORVIPTYgh85R07vr6r2RFc7vtt9Nii33rf7q/VO8zjobmM62OPnHuXP5Za/vERRERF4L989/xrL/iiW016XfLP37jpm7+xs25kue/ltBgzD39gz3e2NdjOp7Oq6U9/evndZ8fDPESGiEra3zzet+exi1rLjN890PqD37+v88xy30snSfiivO2xB7fUPgMAX31+6C0/Ou1rWbVgiYgEKegklyzLldC2Hp686qH/CSAI4DlZlj8sy/JDsiz/JYB/BvB3Xq/3xLzn/kPu9U/Ksvx7siy/XpblPwbwQwDf83q9P1rr70HF7RPe868b9Cc6LEZd7Etv3/6NxWyruZEmV8Xcl9+x/dv5rYu/++2zb80oKo+4IaKS9L9/1bv3UH/gVp0E5U9e1/3tN2z3jK30PS1GvfKv7975w9Yqy2AslbV9wnv+3dPRlOnGryQiKj7fOznR9vDRsTcCwLtvaXr0E/e0n1vpe+okCX/z5s0Hb213Hcuq0P/lL3vfeXw4VLXyaImIxCnoJBeADAAfgFdVe71ebwDAXQCeBfBnAL4H4LcAfMrr9f7hVc9NA3gQwH8A+CSAHwD4MIDPA3jX2oZPxe5vn+jbfXgweIteQvZ/vH7DIzubHMs+ifNqXbWVsb99y5ZvmQ26uStTsU2fefTKbav13kREheLRs5PNjxwbfwMAvGdf80/fsrt+eLXe22Y2ZL/6rh3fcVuNM7OxdO1//d75NyiqeuMXEhEVkcuTUfvfPN4nKyp0d3ZVvfhHr+06ceNXLY5OkvBPb9/+i+5aa89cRrH80Y8u/XY0mdGv1vsTEa03g+gAAMDr9f4vAP/rGvcnAVyzEaLX650A8KFFvn8YwB/k/hAtypHBoPvbR8cfAoDf2df86Jt21I2u9mfs76ia/fhdbT/8wlMD73z03OT9t3W4ht+4Bp9DRCTCVCRp/txjfW9VAemOrqqXPvVA56nV/owmV8Xc//7NTd/5z985/5EzY5Gdn39yYGAtPoeISISMokqf+uHF30qkFWtHtaX/73972+Or/Rlmg0750tu3/+Bt/3rio5ORZMOnfnDxgS+/c8evVvtziIjWQ6Gv5CISIpHO6v7kp5ffmlZU445G+7k/uL/j1Fp91gdva7lyR1fVS4oK3V891vfWWW63IaIS8ckfXHx9aC7j8thNvv/z5i1PrNXnHOh0z/zOvqafAcDDR8ceOjoUdK/VZxERrafP/vzK/sHZRKfFqIv/7Vu2/NCo163JctUGZ8Xcn7yu+3s6Ccqh/sD+fz8y2r0Wn0NEtNaY5CK6hv/56JU7fOFko92sD/3dW7b8bKmnfy3V371l6xMeu8kXnsu4/uSnl+9b0w8jIloH/++FkU2nRsO7DDop82dv3Ph9m9mQXcvP++T9nad3NNrPZhTV+Nmf97yJ2xaJqNi9NBCo/smZyfsB4KN3tP1ko8cWXcvPe2ibZ+wN2zxPAcA/Pzv0RvY5JKJixCQX0VWODAbdj1+cvgsA/su9HT9ucFbMrfVnWk367Kdf2/1jCVAP9Qdu/dm5qaa1/kwiorUyG02Z/t8Lww8BwG/urHviQKd7Zj0+969+c/MvLUZdfMifaP/bJ/r3rMdnEhGtBUVV8ee/6H2jokK/p8Vx6kO3t1xej8/9zBs2vlhnN09Eklnnn7LwSkRFiEkuonkUVcWf/7LnDVkV+t3NjtNv39s4sF6f/cDmGt/dG9wvAMDfPtH3G4l0lv8+iago/Y+fXbknksw66uzmiT95XfeR9frcNrcl/jv7mn4JAN85PvFg33Sscr0+m4hoNf3fpwZ2DQcS7RajLv4Xb9r02Hp9rtmgUz79YNdPJEB9gYVXIipCnEQTzfOlZ4e2D84mOisMusR6XlDk/cWbNj3jtBgCM7G052+f6N+73p9PRLRSz/TMep7v9e+XAPUPX9P507XqH7OQ/3x3+9muGmtvKquY/+KXvVyFQERFZyI0V/HI8fHXAsDb9zY+1lplSazn59+/6ZXC698/M/B6bv8momLCJBdRTjSZ0f/HsfEHAOCte+qfaHNb4usdg9NizLx3X/NjAPCTs5P3jgXnKtY7BiKilfjCUwOvVQHp1nbXsQe31E6s9+frJAn/7TVdv9JJUI4Ph/Y8fWW2br1jICJaib/8Ve8dibRibXFVDP/+fR2nRcTwPx/a+KzVpI9OhJJNXzw4uENEDEREy8EkF1HOXz/Wty88l3HW2ExTn7y/86SoOD58oOVSa1XF0FxasfzlL3vuEhUHEdFSffvoWGf/TLzLpJeSf/r6Dc+IiuP2zqqZ/R1VR1VA+vyT/Q9yFQIRFYszY2Hn873+/QDwe/d2/GqtDz9aSK3NlHrr7vqnAOCRY+MPBONpo5BAiIiWiEkuIgCjgYTlFxe0ZvMfvK358fXeXjOfTpLw+/d1/hIADvUHbj09GnaJioWIaLHSWUX61xdGXgsAD26pfU7Eatj5/sfrug+aDbq5QX+i41tHxrpFxkJEtFh//XjffVkV+h2N9nOv21o7LjKW37+v45THbvJFklnHXz/ed6vIWIiIFotJLiIAf/1434FkRqlor7b0v/uWpl7R8Tywuca3s8l+RlGh+8JTA1zNRUQF75+fG9o+HU3V2c368B+9tuuw6HiaqyyJ122tfQ4A/v3I2L1czUVEhe6ZnlnPmbHITp2E7B+9putJ0fEY9Tr1g7e1PAEAT1yauX0qkjSLjomI6EaY5KKyN+RPWA/1BfYBwMfvbHtS1LLwq/2XezoOSoB6YiS0+/hwqEp0PEREC0lnFel7J313A8Bv7ap/xmkxZkTHBACfur/zqNWkj/nCycavvzS6UXQ8RETX88/PDt0NAPs7qo7tanYEBYcDAHjnzY19za6K4bmMYvnbJ/q5mouICh6TXFT2/u7J/tvSimrsrrX2vH6bR+iy8Pn2tbv8u1scp1VA+r9PD9wtOh4iooV85fnhbYF4utpRYQh+4p52IU2Sr8VlNaZft7X2eQB4+ChXcxFR4Xq2Z9ZzwRfdqpeQ/YP7Og6JjidPJ0n4wG3NTwPAU1dmb5sI8VAkIipsTHJRWRsOJCzP9/n3AcDvHmg9KDqeq/3+vR0HdRKU06PhnUeHgm7R8RARXS2jqNL3Tk7cBQC/saPuOYtRr4iOab4/uK/jWKVJH5mKpOr/7YWRTaLjISK6li89N3QXAOxrdx3fVGeLiI5nPvmmxsHWKstgMqNUfP6pgX2i4yEiuh4muaisfeGpgf3prGrqqrH2PrTNMyY6nqvtaXEGdzU7zqiA9JXnh28THQ8R0dX+9dDw5tlYutZRYQgV0iquPKfFmHlwa+0LAPC9U74DouMhIrraSwOB6vMT0W06Cdn/em/hrOKa7337mw4CwMGe2VtDibRBdDxERAthkovKVjCeNj7f678FAN57a/NzouNZyEfvaD0EAMeGQ7v7Z+KVouMhIspTVBU/OK0ljl63tfaQ1aTPio7pWn7v7vYTZoNubiw41/Kj074W0fEQEc331Vwh86YW5+mtDfaw6Hiu5W17GgbrHebxRFqxfvHg4G7R8RARLYRJLipbXzw4uHsuo1gaHOax39pVNyw6noUc6HTPbPRUXs4qquEfDw5yiTgRFYyfnJlsmQglmyoMusQn7m4/JTqehVTbTKk7u9xHAODfj4xxNRcRFYz+mXjliZHQbgD43QOtLwoOZ0E6ScJbdtcfAoBfXJi+PZ1VCuOkJiKiqzDJRWUpnVWkX12c3g8Av7Wr/oVCOVFxIe/Z13QIAJ7r9e/zx1JG0fEQEQHAt46O3Q4Ad3S5j7qsxrToeK7nE/e0H9FLyF6Zim061O+vER0PEREA/NOzg7dkVeg3eCqv3N5ZNSM6nuv58O0tF50WQyCUyFR99dDwVtHxEBFdC5NcVJa+9uLopmAi43ZUGIIfur3lkuh4buS3dtWPNDrNo8mMUvHl54d3io6HiOjIYNB9eTK2WSch+/G72o6KjudGOmussZtanacA4Gsvjt4qOBwiIoQSacNzudYZ77q58QXR8dyIUa9TX5/rcfjo2SmOo0RUkJjkorL0k7OTtwLAazbXvGQ26ArqJLCFvHF73WEAePzi9D5FVUWHQ0Rl7t9eHNkHALubHWc2eCqjouNZjPff2nwEAE4Mh3b5wkmz6HiIqLz9y6Hh7Ym0Yq2zmyfesrt+SHQ8i/GxO9rOmPS65GhwruWJSzP1ouMhIroak1xUdg71+2uG/Il2o05Kf+zOtlOi41msD93ectFq0kdnYmnPD0752kTHQ0Tlyx9LGY8OaT1k3ptLHBWDuzZUT7W5LYNpRTV+5fmh3aLjIaLy9quLM7cAwOu31R4u9NYZedU2U+qWNudJAPjWkTH2iiWigsMkF5Wdb7w0egsA7G5xnKl3mJOi41ksq0mfvaOr6jgAeE9McIk4EQnzL4dGdqSyirnRaR69f1ONT3Q8S/GmHZ4jAPDEpZl9GUUtjlklEZWcX5yfavSFk40VBl3iw7e3nBcdz1J88LaWowBwaiy8YziQsIiOh4hoPia5qKzMRlOm48OhXQDwO7c0FXwPmat95EDrcZ0E5ZIvuvnCRMQhOh4iKj+KquKxS9Pa6oOtnqIbR9+/v+Wy3awPBxMZ93eOj3eKjoeIytO3j43vA4B97a6TTosxIzqepdjX7vJ31Vh7s4pq+Orzw3tEx0NENB+TXFRWvnJoeEcqq5qbXRUj922qmRQdz1JtqrNFNtfZLqmA9I3Do7tEx0NE5edn56aapyKpeotRF//Q7S0XRMezVGaDTrmjy30cAH56dpKTMyJad8OBhOXsWHg7AHzwtpZjouNZjt/YWXcUAJ7r9e9hr1giKiRMclFZefrK7E0A8LqttUW3+iDvjTs8JwHgUH9gD7faENF6+/5J302AtvrAXmEoqtUHee/f33wKAC76opu51YaI1tvXXhzZkVWh76yx9u1tdQZEx7Mc776lqddq0kf98XTNo2enWkTHQ0SUxyQXlY3nev21vnCy0aSXku/f33xRdDzL9Y69jX02sz4cSmSqfnTa1yo6HiIqH/5YynhmPLwNAN51c9MpweEs29YGe7irxtqrqNB/7cWRnaLjIaLy8myvfw8AvHZLzUnRsSyX2aBT9rU5TwPAD075uCqWiAoGk1xUNh45Pr4bAHY1Oc4VW++D+Yx6nbq/veoUAPzotLaigohoPXzj8OiWdFY1NTrNo7d3Vs2IjmclHtxaexIADvZwqw0RrZ+nLs/UTUVS9WaDbu69+5ovi45nJd59S9NJADgzHt42G02ZRMdDRAQwyUVlIpHO6o7lGs6/ZXf9KcHhrNh7btVWUJyfiG71hZNmweEQUZl48vLsHgC4Z0P1KcGhrNh79jVdrjDqEtPRVN3jl2YaRMdDROXBe2JiNwDsbnacLdYt33n7O6pmm10Vw+msavraSyNbRcdDRAQwyUVl4j+OjXfHU9lKt9U489B2z6joeFbqphZnoM1tGcwoquHfXhzZLjoeIip9J0dCriF/ol2vkzIfuK3lnOh4VspmNmT3tmhbbb57YoKrYolozcVTWf2JkdBOAHjbnoZTgsNZFfdvqj4JAE9enuU4SkQFgUkuKgu/OD+1GwAOdFad0kml0av9gU1aH4dnrsyyDwIRrbmHj47tAoCt9baL9Q5zUnQ8q+EdNzeeBICTI+EdoUTaIDoeIiptDx8d25BIK9bqSuP0a7fUjIuOZzV8YH/LBaNOSo8G51peGghUi46HiIhJLip5Q/6E9cpUbJMEqO/b33xGdDyr5f37my+a9FJyIpxseq7XXys6HiIqXRlFlV4cCO4GgDft8JwSG83quWdD9VS9wzyeyirmbx4e2yw6HiIqbb+8ML0bAO7scp8slaJrtc2U2tFkPwcA3z6m9b8lIhKJSS4qeV9/aWSHokLXVWvt3VRni4iOZ7W4rMb0jibHeQD4/qmJHaLjIaLS9cNTvrbwXMZlN+vDv31T44DoeFbTnd3u0wDw9JUZjqNEtGb6pmOVPVOxjRKgvr+Eiq4A8Bs7604DwPHh0A4e5EFEojHJRSXvUF9gBwA8sLnmtOhYVtvrt9aeA4Bjw6HtvKggorXys/NTOwBgX7vrtEEnldRg8+6bG89LgNo3E+8eDSQsouMhotL08LHxbSogdddae7pqK2Oi41lNv7mzfthm1ofDcxnno2enWkTHQ0TljUkuKmknRkJVE+Fkk1Enpd99c9MV0fGstrfsrh+sNOmjoUSm6pcXpptEx0NEpSeeyurPjUe2AsBbdzecFR3PauuqrYy1V1sGFBW6h4+ObxEdDxGVphf7A9sB4J6N1UV/cMfVDDpJ3dPiPA8Aj56b5IFIRCQUk1xU0r57YmIbAGyut11yWY1p0fGsNqNep+5u1rYs/vQsLyqIaPV998REZzKjVFRXGqfv7HZPi45nLdzZ5T4HAM/1+TmOEtGqOz8ecYwG51oMOinz7pubLouOZy385s66swBwajS8LZlROMckImE4AFFJOzwY3A4A92+qOS86lrXyph3aRcXJkfC2dFYpjS6mRFQwnrg8sw0A9rW5Sm71Qd7v7Gu6qJOQHfInOq5MRW2i4yGi0vLI8fFtALDRU3ml2mZKiY5nLbxmc82Ey2LwJ9JK5Q9OTbSLjoeIyheTXFSyXugP1ExHU3UmvS759r0NvaLjWSuv31Y75qwwBGOprP1HpyfbRMdDRKUjMpcxnJ+IbAGAt+1pKNkkV4OzYm5DbWUvAPzHMW0ySkS0Wl7KFV3vLcGtink6ScLNuWLILy9Mc1UsEQnDJBeVrB+c0rYqbmuwXbSZDVnR8awVnSRhb6vzHAD84vwULyqIaNU8cny8O51VTR67ybev3eUXHc9aumdj9VkAeCHXN4eIaDUcHw5V+cLJRqNeSr3z5sYe0fGspbftqT8LAOfGo1ujyYxedDxEVJ6Y5KKSpKgqjg6FtgPAa7aU7lbFvDfv1i4qzo5HtsZTWV5UENGqePLyzHYAuLW9dLcq5r3r5sYrBp2UHg8lm0+OhFyi4yGi0vDdk7n+sHW2y06LMSM6nrV0oNM9U2szTaayivmRY+PdouMhovLEJBeVpIM9/jp/PF1TYdQl3ranoV90PGvtng3VU9WVxum5jGL53smJDtHxEFHx88dSxsuTsY0A8PabGku+WOCuNKW31NsuA68cWkJEtFJHclsVH9hcU/LFAgDY3+46CwBPXZnlqlgiEoJJLipJPzrt2wYAOxrsFyxGvSI6nvWwt9V5AQCevjK7RXQsRFT8/uP4+MaMohobnOaxXc2OoOh41sPdG9wXAODYSIjjKBGt2Pz+sPJNDX2i41kPb93TcAEALk9GN3B3ARGJwCQXlaRTo+EtAPDaLbUlv/og7w3bPBcB4PxEZBNPWSSilXqu178FAG5td5XNOPrbexp69TopMxFKNl30Re2i4yGi4vbjM74tALC1wXaplPvDzre31RmosZmmUlnV/P1T3F1AROuPSS4qOS/0B2r88XSN2aCbe/Pu+iHR8ayXezZWTzorDMFEWql89NxUi+h4iKh4RZMZ/ZWp2AYAeMuu+oui41kv7kpTurvG2gcA3z85sVl0PERU3E6MhDcDwD0bqstmHAWAPc2OiwDw9OVZjqNEtO6Y5KKS8+jZyU0AsKXedtls0JXFVkVAO2VxZ+6i4rGL09xqQ0TL9sPTvo50VjXV2kyTe1qcQdHxrKfbO6suAsDhwSDHUSJatgsTEYcvnGw06KT02/bUl8VWxbw3btd2F5ybiGzm7gIiWm9MclHJOT4S2gwAd3a5L4mOZb29dnPNRQA4MxbZrKiq6HCIqEg9fUWrvu9pcZTdOCrf1HBFAtQhf6J9NJCwiI6HiIrTD075NgFAd621r9RPVbwadxcQkUhMclFJuTIVtY2Hks16nZQpt6oZALxxR92o1aSPhecyrqevzNaLjoeIik9GUaXzE9FNAPDQNk/ZJbmaqyyJNrdlUAUk74mJjaLjIaLidGQouBkAbuuoKrtxVCdJ2NlkvwgAj1+c5pZFIlpXTHJRSfneSa1q1lVj7XNXmtKi41lvBp2kbqu3XQKAn52b4kUFES3ZL89PNcVTWZujwhC6d2O1T3Q8Iuxrd10EgBcGAhxHiWjJJkJzFUP+RLsEqG+7qeGK6HhEeM2W2ksAcGYssoW7C4hoPTHJRSXlyGC+auYqu6pZ3r2bqi8BwMncCZNEREvxy1zVfUej/ZJOKs9WKm/b03AJAPqm493BeNooOh4iKi7fPTmxQVGha3VbhlqrLAnR8Yjwxu2eEYtRFwvNZVzPXJmtEx0PEZUPJrmoZExFkuaB2XinBKhv21OeVTMAeOvuhgGTXkrORFOeo0NBt+h4iKh4KKqK07kE+f2basq2WLCl3hZpcJjHMopq8J6Y6BIdDxEVl0N92irQW9qcZTuOGvU6dVuD/TIA/Oz8FAuvRLRumOSikuE9MdGtqNA1V1UMt1db46LjEcVq0mc31dl6AOCnZ6fYT4aIFu3FgUBNMJFxVxh1id/YWTcsOh6R9rZqk9Pn+/ybRMdCRMUjMpcx9EzHugHgzbvqyzbJBQD3bqy+CACnRsMcR4lo3TDJRSXjhX6tanZza/luVcy7td11BQBOjoSY5CKiRXv0rNbLb0ud7bLZoFNExyPSg1trrwDApcnohoyilue+TSJash+e9rWns6rJYzf5djY5QqLjEek3d9YNGnRSZiqSqr88GbWLjoeIygOTXFQSkhlFd2VKq5q9aYenbLcq5r15d32vBKjDgUTbdDRlEh0PERWHU6PhDQBwoKuq7MfRu7rdU3azPpxIK5WPX5xuEB0PERWH53r9GwFgV5PjsuhYRHNajJmOaks/APz4zGS36HiIqDwwyUUl4Rfnp5qSGaWiymqcvaXN5Rcdj2itVZZEo6tiVFGh++EpX6foeIio8I0F5yrGgnMtOgnKm3fV94uORzSdJGFLve0KADxxeYarYonohhRVxQVfdAMAPLC5pkd0PIXg5lZXDwAcHQpyHCWidcEkF5WEp6/MbgCArfU2XlDk7Gqy9wDAC/0BXlQQ0Q398LSvSwWklirLsMduToqOpxDc0eXuAYAzY5ENomMhosL30kCwJjyXcVmMuvhrt9SOi46nELxpp6cHAPpm4p3RZEYvOh4iKn1MclFJODehTUDu6nYzyZXz4JZX+skoqio6HCIqcIcHgxsAYHezg+Nozpt31Q/odVLGF0429kzFbKLjIaLC9osLUxsAYKOnstegk3jxBWBHoyNUYzNNpbOq6adnJ9tEx0NEpY9JLip6F31R+1QkVW/QSenf2Fk3JDqeQnHPxupJm1kfiaWyticvzbCfDBEtKKOo0uVJbYvN67bWMsmV47Ia0+1uyyAA/PiMj/1kiOi6To1ofQ33d1RxHJ1ne4O20+Jgj5+rYolozTHJRUXvJ2cmNwBAZ42132Y2ZEXHUyh0koTNddpFxWOXZnhRQUQLevzidEMirVgdFYbQ7Z1V06LjKSR7W51XAODIEE+rJaKFTUdTpuFAok0C1N/aWdcnOp5Ccu/GmisAcH4iwnGUiNYck1xU9I4Na1ts9rY6WTW7yoFO7YS002NhXlQQ0YKevKwlwrfU23p0kiQ6nILyGzvqegCgdzrWFU9l2U+GiK7ph6d8nYoKXZOrYrS5ypIQHU8hecN2z6jZoJsLJjLuI4NBt+h4iKi0MclFRS2eyur7ZuKdAPCmHZ5e0fEUmjfvqh/QSchOhJJNg7Nxq+h4iKgw5RurH+jkFpur7Wp2BKsrjdO5fjKtouMhosL0wkBgAwDszB38Q68wG3TKhtrKXgD42fkpFl6JaE0xyUVF7adnJ1vTWdVUU2mc2tHoCImOp9BU20yptlw/mR+eZj8ZIvp1/TPxyolwskkvIfubO+sGRMdTiLY3aJPWg73sJ0NEv05RVVzyaX0NX7uFfQ2v5dZ2Vw8AnBwJcRwlojXFJBcVtfyEY1sjq2YL2d3s7AWA48OhLtGxEFHh+fEZXxcAtFVbB92VprToeArRnd3uXgC45It2io6FiArPM1dm62KprL3SpI/eu7HaJzqeQvQbuT5lw4G51lAibRAdDxGVLia5qKhdmIh0A8Dd3dXcqriA12yu6QOAnul4l6LyNGsierWjQ6FuANjT7GCxYAFv2O4ZNuikzHQ0VXdlKmoTHQ8RFZYnLs10A8Cmukr2NVxAZ4015rGbfFlFNfzs3BS3fhPRmhGaRZdl2QDgvwH4AIBmAJMAvgvgs16vN3qd17UDuNGWinu9Xu8zi3z+Hq/Xe2qxcVNhuOiL2mdj6VqjTkq/YbtnRHQ8hepAV9V0pUkfiaWy9oM9/rp7N1ZPio6JiApDRlGl3ulYFwC8ZksNTwNbgM1syLa5LYN9M/Hun52b6tx4n+2M6JiIqHCcGY90AcAtbS6Oo9extd7WNxXx1x/qD3S965amftHxEFFpEr2S62EAnwbwdQBvBfB5AO8B8ItcAmwhPgD3LvDnCwBUANdKevyPBV7DVUBF6OfnpzoBoK3aMmg16bOi4ylUOknCRk9lHwA8eXmGWxaJ6GUHe2brEmnFajPrI7d1VM2IjqeQ7Wp29AHAMW79JqJ5gvG0cSSQaAWAN+1gX8PruaPL3QcAF31RjqNEtGaEreSSZfktAGQAD3q93sfm3f8EgBMAPg7gH671Wq/XOwfgmQXe968APOn1eq9VSTmXX91Fxe9EbqKxs8nBStAN3Nzm7D85Gt59ZizcCeAF0fEQUWF46vJsJwBs8FT2cYvN9T2wqabvB6d86M1t/eb/vYgIAB49N9WqqNDX2U0TbW5LXHQ8hewN2z3Dn3usLzMdTdX1TMVsGzyVC+7cISJaLpEruT4K4Nn5CS4A8Hq9FwB8B8DHlvqGsizfBGA/gC+tSoRUsBRVRe9MvBPQJh6i4yl0b9xe1w8Aw4G5Njb7JKK8s+NhbYtNK7fY3Eh+63c8la082OOvEx0PERWGFwYCXQCwpd7GousN5Ld+A8DPzk3xIA8iWhNCklyyLOsB3IEFVmMBOAhgiyzLniW+9ScAjAL4yfKjo2JwsMdfF09lKytN+siBrqpp0fEUOjb7JKKrReYyhuHAXCsAvGG7h1tsboBbv4noWvKnrh7odLNYsAg7m/Jbv4McR4loTYhaydUIwAptW+K1HM/dLnrwk2W5GsA7AHzV6/Uu1J/pf8uyHJBlOSHL8lFZlt+56IipoDx1eSa/xaafW0YWZ2u9rQ8ADvUHeFFBRHj03GRrVlENHrvJ11ljjYmOpxjkm0qfGQtzHCUi9E3HKqejqTq9TsrwEKTF4anfRLTWRG1bcuduZxd43J+7rV7Ce34YgB7Avy7weA+0RvdnoSXY3gfg27Ist3u93r9awue8yqFDhz6y3NcWilQqVQMU13e5OJx1A8COykhbMcUt0g6LanoGQM+4f9+hQ4c6RMczXzH+Bqm0lONv8KVexQ4A2x1pezl975XoVlUdAIz4E51PP/v8R0z61S2ylOPvkAoLf4NL88KEYgGALoeaPXP88AdFx1MMJFWFwwQlnMpWfudXhz7eapcyVz+Hv0MSjb9B8YxG47JfKyrJZc/dLtScMX+/czFvJsuyDloPrx95vd6JazxlBMBmr9erzLvvEVmWHwbw57Isf9fr9fKExSKRyqroD8MEANvcUlJ0PMViowspow7qRByGYFLVucyScuNXEVGpuhxQzQCwhePoojlMktJUifRYDMbLQZh3VIP/tyMqYxcD0MbRKo6jiyVJEja5pOTRKdVy3q+ar5XkIiJaCVFJrkju1rLA49bcbWiR7/cmAO0APnCtB6+zffFT0E54fBeAP1vkZ73KgQMHvrqc1xWSfIa6WL7Ld46Pd6SV3vfW2ExTb7hv/5dFx1NMWi8ce3ffTLy7B00v/NcDHWdEx5NXbL9BKj3l9hscnI1bx5459od6Cdl3vubWf3ZajJxkLNKGsXMPjPX4D5xNuC597MD2x1fzvcvtd0iFh7/BxVNUFZ984YU/ALK4b++Wbx/YVDMpOqZicTw9sOPo1MhbzoQrpv/iwC3fuvpx/g5JNP4GxTty5MhnlvtaUT25brQd8UbbGa/2nwFc8Hq9zywliNyqr3MANi/ldSTWof5AJwBsqatkg88l2tFo7weAE6OhgtquSETr69HcqVYtbssQE1xLc3tnVT8AXJ6MchwlKmMv9AdqY6ms3WLUxe7aUD0lOp5i8oZtnn4AGAnMtcVTWb3oeIiotIhKck0ASAC4aYHH8/ff8CheWZY3AbgfwD8vMxYdgIVWelEBupg7xea23ESDFu/uDdUDANA3HefkjKiMHRsKdQKvJL5p8V6/1TOik5CdjKQaRgOJhVakE1GJezJ3CFJ3bWW/QSexg/oSdNVWxqorjdMZRTX88sJ0k+h4iKi0CElyeb3eDIDnAdyzwFPuBnDJ6/UuZtnvfwYQA/DNhZ4gy3KlLMuN17jfCW0V17lFfA4VAF84aZ4MJxt0EpQ3bPMMiY6n2NyzsXrSbNDNhecyzpMjIZfoeIhIjN6ZWDsA3L2hmkmuJXJZjekmV8UoAPziwnSb6HiISIxz45F2ANjT7BgQHEpR2uipHACAF/oDLLwS0aoStZILAL4C4G5Zlh+Yf6csy1sAvCP3eP4+pyzL1qteD1mWbQDeC+Bhr9cbvtaHyLJsgpbEeliW5at7kP01AAXAr+0Fp8L0i/NTbSogNTgrxtyVprToeIqNQSepHdWWQQB4/NIMLyqIytD58YgjlMhUmfRS8r6N1T7R8RSjrfW2AQA4OhTkOEpUhjKKKg3OJtoA4P7NNYOCwylKN7e6BgDgoi/CcZSIVpWoxvPwer3fl2X5+wC+L8vy5wCcAtAN4NMATgD4J0BbhQVt2+IMgE1Xvc37ADgAfOk6n5PKvf8/A3hKluUvAkgC+DCAhwB8yOv1jq3iV6M1dHQ41A4AmzyVg2IjKV7bG+0DlyZjm0+PhjsAnBQdDxGtr8cuTbcDQJvbMmTU67jFZhlu66wa/NXFGfRMaSviiKi8PNsz65nLKBa7WR/e3ewIiI6nGD20rXboHw8OYjQ41xxKpA3sD0lEq0XkSi4AeCeAvwPwIQA/gHba4SMAHvR6vflVOhkAPgAj13j9xwEc8nq91z0lzuv1fgXaCYwqgH8F8O8AzADu93q9X1/516D1cmVSm1Ds73ANio2keN23sUbryzUT71BUzm+Jys2p0XA7AGxrtA+KjaR4PbildtSgkzIzsbSnfyZeKToeIlpfz/b62wGgq7ZyQCdJgqMpTs1VloTHbvIpKvS/OD/dIjoeIiodwlZyAUAukfVnuT8LPScJYNsCj13z/gWe+zMAP1tqjFQ4JkJzFVORZL1OgvLgltprJT1pEQ50VU1bjLpYLJW1HRkMVu/vqFrsKaZEVALyB0/c1eUeFBxK0bKZDdnmqorhwdlE5y8uTLX/p7vaz4uOiYjWz7lxbYvdriYWC1Zik6dyYCqSqn9pMNDxjpsb2duMiFaF6JVcRIv2iwvTrSogNbIf14roJAmdNdZBAHjyMvtyEZWTM2NhZ2gu4zIbdHP3sB/XimxrsA8AwPHhEMdRojKSUVRp0J/rx7WJ/bhWYl97vi9XlOMoEa0aJrmoaBwbCrYDwOY6Gys9K7SzSTsJ6MwYm30SlZMnLs20A+zHtRru6KwaBIDe6Xi72EiIaD0d7JmtS2aUCkeFIbSnxRkUHU8xe2ibZ1gCVF842TgdTZlEx0NEpYFJLioaV3INfm9tZz+ulXpgk9aXa2A23p5RVDaTICoTp8Zy/bgauMVmpV6zpXbcqJdSgXi6+qIvahcdDxGtj3w/rvyqeFo+j92crHeaxxUVul+cn2oVHQ8RlQYmuagojAXnKiYjqQadBOV1W2tHRcdT7G5uc/ptZn0kkVasz/f5a0XHQ0Troy+36uiubvbjWimzQae0uS1DAPCrC9NcFUtUJs6NR9oBYFeTY1BsJKUhv0PjyGCQ4ygRrQomuago/PLCdBsANDorRl1WI/txrZBOktBVYx0AgGeuzPKigqgMnB4Nu8K5flx3b3BPio6nFGzP9eU6Mcq+XETlIJ1VpKFcP67XbGY/rtWwP9eX69JkjOMoEa0KJrmoKBwbzvXjqrcNio2kdOzMVSAv+KJtgkMhonXwxOWZNgBoZz+uVXOgyz0EAAMzcW6zISoDB3v8+X5cwV3NjqDoeErB67bWjugkKFORZD37chHRamCSi4pCvh/XfvbjWjV3b3APA8DgbLxVUTnfJSp1p0dz/bga2Y9rtdy7sdpn1EupYCLjvjIVtYmOh4jWVr4fVxf7ca0ad6UpXWc3T6iA9NjF6RbR8RBR8WOSiwreaCBhmYqk6nUSsq/bWjsiOp5ScUuba9Zq0scSaaXyyGCwWnQ8RLS2+mbiHQD7ca0ms0GnNLsqRgDg8UszXBVLVOLOTWinUu9qZj+u1bTBUzkEAEeHghxHiWjFmOSigvfLC9OtANDkqhh1WowZ0fGUCp0koT3XNPlgj58XFUQlLNePy6n146pmP65VtLnONgwAp0fD3LJIVMLSWUUammU/rrVwU4tzGACuTMU4jhLRijHJRQXv2HCoHQA217Ef12rb2mAbAoCz42EmuYhK2OOXZtoBoL3aMmjQSdyfvIpu7XANAUDvdJzjKFEJe+bKbH0qq5idFYbgziZHSHQ8peTBLTXDADAenGuOJjN60fEQUXFjkosKXs90rh9XB/txrbY7urS+XAOzCVbOiErY6TGtH9f2BvbjWm2v3Vw7ppOQnY6m6iZCcxWi4yGitfFsn9aPq7OW/bhWW3OVJVFTaZzKqtA/fmmmSXQ8RFTcmOSiguYLJ825flzKg1tqR0XHU2ru6nZPmvRSMjyXcZ0fjzhEx0NEa2NgVjv9747caYC0euwVhkyjs2IcAH7FpslEJeuiL9oKADsbHRxH10BXbeUwABweDLLwSkQrwiQXFbTHL2kThnqHeZz9uFafUa9TW92WYQB48gqbJhOVosuTUXsokaky6qXU3Rvc7Me1BjbmmiYfHw5xHCUqQYqqYtivrXq/d2M1k1xrYHezljy85ItyHCWiFWGSiwra8eFQKwBsyFV3aPWxaTJRaXvy8mwLALRUWUaMeh37ca2Bva3OIQC4MhXj5IyoBB0ZDFYn0orVatJH97Q4AqLjKUX3b6oZAoDhQKJVUfmfKiJaPia5qKD15E5Z2d3sYJJrjdzWUTUEAH0zbJpMVIpOj2kJ7I0eFgvWymu31I5IgOoLJxtDibRBdDxEtLqe7fW3AkCb2zKikyTR4ZSkLfW2iNNiCKSzqmkoAqPoeIioeDHJRQUrnsrqx0LJJgB4YHPNiOh4StUDm2vG9TopMxtL1w4HEhbR8RDR6uqb1ooFt7Q5meRaI/UOc7LWbppUVOgeuzjTLDoeIlpd58YjrQCwpd7GcXQNdVRbhwGgJ6iaRMdCRMWLSS4qWE9fma3PKqrBbTXOtFdb46LjKVVWkz7b5DSPAcBjF6a5ZZGohExHU6b84R0PbKoZEx1PKeuu1fpyHR4MclUsUYnJH95xW0cVk1xraEejfQgAekNMchHR8jHJRQXrpYFAKwB01Fh5QbHGNtbZhgDgxAibJhOVkicuTTergFRnN0+4K01p0fGUsj25psmXp6IsFhCVkL7pWGUwkXEbdVL63o3VPtHxlLJ7NmhN/ftCMKnsy0VEy8QkFxWsi5PaRGFHo51JrjV2S65pcs80+3IRlZJjucM7umtZLFhrr9lcMwwAo4G5lkQ6y+srohLxxOWZVgBoclWMmg06RXQ8pezmNqffatLHYhnoJuLQi46HiIoTL8KoIM0/qvmeDdWcnK2xB7fUjkqAOhlONsxGU1wiTlQirkzmD+9gP6611lVbGauyGmczimp8+spsg+h4iGh1nMqdPr2xjod3rDWdJKHdbdFOqw2qZtHxEFFxYpKLChKPal5f1TZTqs5hnlAB6VcXp9k0magEJNJZ3Vhwrhng4R3rpbPGOgQAL/YHuGWRqET0Tmv9uG5uZbFgPWxt0Fpo9AbBoisRLQuTXFSQeFTz+ttQq03Ojg6zLxdRKTjY469PK6rRZTH4O2usMdHxlIOdue31F31RjqNEJcAfSxknw8kGCVAf2Fw7KjqecnBHl3sYAPrCbD5PRMvDJBcVJB7VvP52NTtGAKB3KtYiOhYiWrkXeXjHurs7t71+OJBoUdg0majoPX5pplkFJI/d5Ku1mVKi4ykHd3W7J816qIEk9FemojbR8RBR8WGSiwoSj2pef/durB4BgLHQXFM6q3D5HFGRu+jLHd7RwMM71sueFkfAYtTFEmnFenIkXCU6HiJamaND2uEdXbXsx7VejHqd2mpDCgCevjLLwisRLRmTXFRweFSzGBs9tqijwhBMZ1XTc71+j+h4iGj5FFXFkD/RAgB3bXBzcrZOdJKE1irLKAA828vJGVGxuzIVbQGAXU0OjqPrqMMhpQHg9FiE4ygRLRmTXFRweFSzOG1uywgAvDQQ5EUFURE7ORKuiqeyNotRF7+lzTUrOp5ysrGucgQAzk9EOY4SFbFkRtGNBudaAOD+TdU8vGMddTmlFAD0z8R5GBIRLRmTXFRweFSzOJvrbaMAcMHHyhlRMXumZzZ/eMcwD+9YXze3ukYAoH8mznGUqIg91+v3pLOqyWkxBDbV2SKi4yknXU5tu+JEaK4xmszoRcdDRMWFSS4qODyqWZzb2rXJ2ZA/wcoZURE7mzu8YzMP71h392+qHtdJUGaiKc90NMXTwYiK1KF+7aTvjmoe3rHebEZJrbMgo6jQP31ltkF0PERUXJjkooISjKeNU5FkPY9qFuOuDdWTBp2UDiYy7v6ZeKXoeIhoeQZntVVEt7a5uMVmnTktxkyd3TyhAtKTl2aaRMdDRMtzyaedNr2l3sZxVIAOh7Zl8cgQW2gQ0dIwyUUF5akrsw2KCl2NzTTFo5rXn9mgUxqdFWMA8PSVWa7mIipCE6G5itlYulYvIXvfppoJ0fGUo84a6ygAHB8JcXJGVKSGA9rhHbd3VjHJJUCHQ9uyeHkyxnGUiJaESS4qKMeHQ80A0F5t4SouQbprrSMAcGqUkzOiYvTUldkmAKhzmCesJn1WdDzlaEeTfQQAeqY4OSMqRlemorbwXMZp1EupA51V06LjKUcbXNoJi8P+RLOiqqLDIaIiwiQXFZQrU9FmANhab2eSS5DdzY5RAOjjiTZERenkSLgZeGU1Ea2/e7q1k9hGg3PNGUVl53+iInOwx98MAI3OijGjXscMiwD1VmTMBt1cLJW1nxuPOEXHQ0TFg0kuKhiKqmIkoB3VfEcXl4aLct+mmlEAmAglmxLpLMcIoiLTOx1rBoDtjSwWiLKt0R62mfXhZEapODwYqBYdDxEtzekxrVjQXctigSg6SUJLVcUIADzb6+eqWCJaNE5gqWCcn4g4Y6mszWzQzd3c5vKLjqdctbkt8SqrcTajqIaDPf560fEQ0eJlFFUaDc41A6+sJiIxWqu0bfeH+gKcnBEVmf7cavZdTQ4muQTaUFs5CgBnxsIcR4lo0ZjkooLxbK+2NLzZVTFq0ElcGi5Qu9syAgCHBzk5IyomRwaD1cmMUlFp0ke2NNjCouMpZ5tzJ7Kdn4hwHCUqIsmMopsIJZsA4N6N1UxyCbS31TkCAP0zCY6jRLRoTHJRwTg3HsktDa/kBYVgW+ptowBwyRdjXy6iIvJCf6AZAFqqKkZ1EltBibSvzTUCAIOznJwRFZPnev2ejKIaXBaDv73aGhcdTzl7YFPNmASoU5FkvT+WMoqOh4iKA5NcVDDyS8P3tHBpuGgHcsdlD/njnJwRFZELPq1YsKnOxnFUsHs3Vvv0ErL+eLpmNJCwiI6HiBbnpQFtFXurmyd9i1ZtM6Vq7aZJFZCeujLbKDoeIioOTHJRQYgmM3pfONkAAPdtrB4THU+5u73LPW3SS8lIMuu86IvaRcdDRIszOJtoBoC9rU5OzgSzmvTZBmfFOAA8dWW2SXQ8RLQ4lya1k743s1hQEDqqteb/x4dDLLwS0aIwyUUF4WCPv15Roa+uNE43OCvmRMdT7gw6SW1yVYwCwDM9s7yoICoC09GUaSaa8ugkKPdtrB4XHQ8BXTXWEQA4OcKmyUTFYsivFQv2t7uY5CoA2xvsIwBweSrGcZSIFoVJLioIR4aCLQDQxqXhBWOjp3IE4Ik2RMXi6SszjSogeexmn9NizIiOh4BdzY4RAOiZ5uSMqBgM+RPWYCLjNuikzB3d7knR8RBw1wb3CACMBhLNispzqYjoxpjkooJwObc0fGu9nUmuArGr2TEGAIOzCW6zISoC+a0cHdUsFhSKuze4RwFgIjTXmFFUngRAVOCevjLTBAANTvOYxahXRMdDwO5mR8Bi1MUTacV6ajRcJToeIip8THJRQRgJzDUDwG0dXBpeKO7doPVG84WTDYl0lmMFUYHrmdYO79jWwGJBodjosUXtZn04lVXNhwcD1aLjIaLrOz2qHd7RWWPlOFogdJKEJlfFGAC80B9g4ZWIbogTVxLuylTUFp7LOI16KXVbZ9W06HhI01xlSbgsBn9GUQ2H+gIe0fEQ0cIUVcVoQOsjc0eXm5OzAtJcZRkDgBf7g82iYyGi6+vNbS3e2ciTvgtJV03lGACcG48wyUVEN8QkFwn3zBV/MwA0uSpGjXodN9sXkNbc5OzIUJAXFUQF7ORIuCqRVqwWoy6+p8UREB0PvWJDbeUoAFyajHIcJSpg6awijYXmmgDgno0sFhSS3c1a0nHQzxYaRHRjTHKRcGfGw80A0F1TyQuKArOxTquc5Y/TJqLC9HyfVixorrKM6iS2fioke1ry/Q3jnJwRFbAX+wO16axqclQYQhs9tqjoeOgV92xwjwNsoUFEi8NBgoTrn9H6yOxqZh+ZQnNzq9YjbZiVM6KCdn5C6yOzoZZ9ZArNvRuqJyRAnY6m6kKJtEF0PER0bS8OaFuKW6oqOI4WmHwLjayiGp7v9deJjoeIChuTXCRUMqPoJkLJJgC4d2PNmOh46NXu6nZP6iQos7F07VQkaRYdDxFd28Cs1o/rphYnJ2cFptpmSlXbTNOKCt3BHn+D6HiI6Nou+rRV65vqbBxHC1C+hcbRoRALr0R0XUxykVCH+vy1GUU1OC2GQJvbEhcdD72avcKQ8djNPgB4pmeWkzOiAhSZyximIsl6ALhvE4sFhagtNzk7PsLJGVGhGgpoq9ZvbmWxoBBtrGN/QyJaHCa5SKh8Q/N8dYYKT7tb+//NyZEw+3IRFaBne/11igpddaVxutZmSomOh37d5nptcnZlMsbJGVEB8sdSxtloqlYnQbmr2+0THQ/9uptbXWMAMBxgCw0iuj4muUioy7kL/u7aSia5CtTWBm3Zfu80J2dEhSi/OqilyjIuOha6tn1t2uRsJMjJGVEheqbH36ACUo3NNOW0GDOi46Ffd/cGt48tNIhoMZjkIqGGcg3Nb8qdPkWF57aOKm1yFpjj5IyoAF3Jbd3Y5GGxoFAd6HJPG3RSOpTIVA3Oxq2i4yGiVzuZKxa0ubmzoFDZzIZsvoXG01dmG0XHQ0SFi0kuEiYYTxtnoimPBKj3bKjm0vACdXOby2826OZiqaz9oi9qFx0PEb3aSGCuEQD2tjo5OStQZoNOaXCYJwDgYI+fBQOiAtOTW62+uc7GcbSAdVRbRgHg5GiY4ygRLYhJLhLmmZ7Z+vzScJfVmBYdD12bQSepjU7zOAA82zvLvlxEBcQXTpr98XSNToJyZ7d7UnQ8tLCOGusoAJwe4+SMqNCM5lar39Lm4rbvAralXktCsoUGEV0Pk1wkTL4K08ql4QWvq0ZrmnxuPMKLCqICcrBH27LhsZt9NrMhKzoeWti2BvsYAPTPxDmOEhWQIX/CGprLuAw6KX17Z9W06HhoYbd3ai00RgNzzYqqig6HiAoUk1wkTM9ULN9HhlWzArejKT85Y9NkokJyejTcCLCPTDG4s1ubnI0F55o4OSMqHPliQb3DPGE26BTR8dDC9ra+3ELDdskXdYiOh4gKE5NcJMxIINEIAPvaXZycFbi7u91jADARnmtMZxVJdDxEpMn3kclv4aDCta3BHrKa9LG5jGI5Phxyi46HiDRnxrRV6h3VLBYUulwLjTEAeK6P/Q2J6NqY5CIhRgMJSzCRcet1Uub2zqop0fHQ9XXVVsYcFYZQOquaXuwP1IqOh4g0Y0Gtj8yt7ewjU+h0koQmV8UYALzQH+DkjKhA9M1oxYKtDXaOo0Wgq0Y7SfgsW2gQ0QKY5CIhDvb6X14abjHquTS8CLRUVYwCwOHBIC8qiApAz1TMFklmHUa9lLq13TUjOh66sQ21WvP5C74ox1GiAqCoKsaC2gm1+X5PVNh25lpoDLCFBhEtgEkuEuJ0rul8u9vCqlmR2FCrVc4ucnJGVBCe69OKBQ2OinGjXscmT0VgV5NDm5zNxnlSLVEBODcecSbSSmWFUZfY3ewIiI6HbuzuDdVaC43QXBNbaBDRtTDJRUL0zcQbAWBrA/vIFIvdLY5xABjO9VIjIrHO5vvI1LCPTLG4e0P1OABMhZP1iXSW12BEgh3KbR1uclaM6STmS4pBZ401ZjfrQ2lFNR4eDNaIjoeICg8vsGjdKaqK0Vwfmf3tXBpeLO7qrp4AgOloqi6azOhFx0NU7vpn400AsI19ZIpGk6tizmkxBLIq9OxvSCTe+fF8scDKcbSINLkqxgHg6FCwQXQsRFR4DCI/XJZlA4D/BuADAJoBTAL4LoDPer3e6A1eew+Ap6/zlCqv1xuc93wbgM8A+G0AdQBGAXwNwN94vd7M8r8FLdUlX9QRT2VtZoNu7uY2p190PLQ4tTZTym01zvjj6Zrn+wJ1r9taywtCIkEUVcV4ro/MAfaRKSrNrorxUCJadWw41HjfpppJ0fEQlbOBWW11+o5GO8fRItJVWzl+aTK25dJkrBHAGdHxEFFhEb2S62EAnwbwdQBvBfB5AO8B8ItcAmwxfhfAvdf483KSTJZlI4BfAngvgC/kPutrAP4YwLdW4XvQEuSP/G10mse5NLy4NFdplbMTwyFuWSQS6ORIuGouo1gsRl1se6M9JDoeWrzu2spxALg8GeU4SiRQOqtIE2GtWHBnl5uFuyKyo9E+AQBDfrbQIKJfJ2wllyzLbwEgA3jQ6/U+Nu/+JwCcAPBxAP+wiLc65vV6T93gOZ8AcAuAvV6v91zuvp/LsnwYwBOyLH/b6/X+ZKnfgZbn/IR2Yd9RbWXVrMhsqK0cPzMW2XllOsbl4UQCvTiQ6yPjqmCxoMjsbLKP//jMJIYDc5ycEQl0eDBYk86qJrtZH97gqbzuDhIqLHd2u8c/91gfJsPJ+mRG0ZkNOp7UTkQvE7mS66MAnp2f4AIAr9d7AcB3AHxslT/rkXkJrvxnPQng2VX+LLqB/hmtjwyXhhef3c1a8/kRNp8nEur8hNZHpqumkuNokcn3N5yKJOviqSz7GxIJ8tJAsAkAmqt4eEexaa2yJJwVhmBGUY2HBwJsPk9EryIkySXLsh7AHQCeWeApBwFskWXZswqf1QBg4w0+605ZlkVv3SwLGUWVJkK5peHdXBpebO7qdvskQJ2JpjyRuYzQnn5E5Www10dmZxOLBcWm3mFOVlmNs4oK/aE+/4qvc4hoeS7ltgx313BnQTHKN58/MsQWGkT0aqImqY0ArNC2JV7L8dxtF4CpG7zXN2VZ7oCWsDsG4C+8Xu/j8x7vzt1e77Ns0JrRT9zgs37NoUOHPrLU1xSaVCpVA6zPd5mIqfpUVjU7TFBmek+/c6Z3rT+RVpvHiuxkHIYfPvXSR7ucUno13nM9f4NE11JMv0FFVTEZVuoBoDY++MChQ0P3iY6JlqbdmrUG4sCRs5febvVfiefvL6bfIZWmcvoNjs9kawCgXTez59ChQ9tEx0OvWMzvsKtCsV0A0Dc89ppDhyb2rVtwVBbKaSwsVEajcdmvFbV6yZ27nV3g8fyJe9U3eJ8TAL4B4B3QeniZAPxKluV3r8Fn0SoYjKgmAGi2YVWSI7T+Wm1aYmswrC5/5CGiZRuPwZBWILnMyDpMEvuQFKE2uzaODkXAcZRIgIyiYiKm/ftrt/OatBi12aUUAIxEeT1KRK8maiWXPXcbX+Dx/P3O67zHIa/Xu3f+HbIsPwzgSQBfkmX5Ua/XG1qlz1rQgQMHvrqc1xWSfIZ6Pb7Lw98591rAf1tLXc2LBw5sPbjWn0er74nQlX1Hp3yv703ZB/7ngd0/Xo33XM/fING1FNNv8AtP9e8ERt9c76rsOXBg73dEx0NL56ucaPvRQM/7+2PG0IED+1/+zRXT75BKU7n8Bp+6PFOXUS98zGkxBF57z+1fER0PvdpifoftwbmKL5498kejMUm6ad/+f7UY9Sz60Kopl7GwkB05cuQzy32tqJVckdytZYHHrbnbBY9F93q9v1Z18Xq9GQCfBOAA8Jur9Vm0eoZmEw0AsK3BvuStoVQYbmpxjgPAaGCOJywSCXB5MtYIAJ01VvY1LFL55vPT0VRdNJlh83midXZ8WOvj1Jzr60TFp8lVMee0GAJZRTW82B+oFR0PERUOUUmuG20RvNEWwwV5vd5jAIIANq/1Z9HSZBRV8oW1xMiBripeVBSpO7vck/nm88F4mkvEidbZkF8rFmxvZLGgWNXaTCm31TijqNA93xeoEx0PUbm5PBVrAIDOGivH0SKWT1Lmk5ZERIC4JNcEgASAmxZ4PH9//zLfXwcgm/vffVe957U+KwZgcpmfRYt0YjhUlcqq5kqTPrrRY4uKjoeWx2U1pmtspikVkJ7r83NyRrSO0llFmgwnGwDgzi6eUFvMmqu0ydkJTs6I1t1wIHdCbaOD42gR666tHAeAy1MxjqNE9DIhSa7ctsLnAdyzwFPuBnDJ6/UumHiSZblalmX3Ne7fAm274rncZ40D6LnBZz3n9Xq5j3uNHRkKNgJAo9PMC4oi11xVMQEAJ0fDvKggWkdHhoLVaUU12s36cHu1daFek1QENuQmZ1emY9z6TbSOkhlFNxVJ1QHAnd1uruQqYjub7OMAMORP8HqUiF4maiUXAHwFwN2yLD8w/85ckuoducfz9zllWbbO+3sttBVaf3/Vaw0A/hbaqqyfXvVZ75BleetVz78XWpKLDSfXwUVftAEAOrg0vOhtzE3Oelg5I1pXRwe1VT+N7CNT9HY1OyYAYCTAyRnRenqhP1CbVVSDs8IQbHJVzImOh5Yv399wKpKsi6ey7G9IRADEna4Ir9f7fVmWvw/g+7Isfw7AKQDdAD4N4ASAfwIAWZYroW1bnAGwKffaaVmWvwTg07Is2wF8A9p3+S8A9gL4Ta/XO7/C/Q8A3gbgSVmW/wrayq5dAP4YwPe8Xu+P1vbbEvBKlWVbg52TsyJ3U6tz/DsnJjg5I1pnlya1YkFnNYsFxe6uLrcv398wMpcx2CsMGdExEZWD48PBBgBoYrGg6NU7zMkqq3E2EE9Xv9AfqH1gc41PdExEJJ7IlVwA8E4AfwfgQwB+AOBTAB4B8OC80xMzAHwARua/0Ov1/ncA7wPQBOBb0FZjTQO4zev1PnbVc9MAHgTwH9BOX/wBgA8D+DyAd63FF6NXU1QVEyGtj8yBzipOzorcnV3uSZ0ExR9L1/hjKTafJ1onw7liwfZGFguKXbXNlHJXas3n2d+QaP3MO6GW16MloNmltdA4Nhxk4ZWIAAhcyQW8nHz6s9yfhZ6TBLBtgce+CeCbi/ysMIA/yP2hdXZyJFyVyipmq0kf3VRni4iOh1bGXmHI1NhMU1ORVP2zvf7639pVP3LjVxHRSmQUVfJFkvUAiwWloqXKMj4bS9eeGA41PrTNMyY6HqJyMBzQTqjdwWJBSeiqtY6fHY9sv6IlL0+IjoeIxBO9kovKxEuDgXzTeU7MSkRLlWUcAE6x+TzRujgyGKxOZ1WTzayPdNVWxkTHQyvXXWvNN5/nOEq0DnJN5+sB4A42nS8Ju5u1EzKH2UKDiHKY5KJ1kW86384+MiVjY23lBAD0cHJGtC6ODml9ZBqd7CNTKm5qcY4DwEhgjuMo0To4PBCoySiqwVFhCLZWWRKi46GVyzefn46m6qLJDJvPExGTXLQ+hmZzTefrbZyclYibWrXJ2SgnZ0Tr4tJktBEAOlgsKBl3drknJUCdjaZqg/E0+xsSrbGjw6F803mOoyWi1mZKua1af8Pn+wLsb0hETHLR2lNUFRNhren87ewjUzLu6KrSms/H0zXT0ZRJdDxEpW7Ir/WR2d7IYkGpcFmN6WqbaVoFJDafJ1p7l3PFgs4aK8fREtJcpa1wPjEcYuGViJjkorV3ajRclcwoFRajLra53hYWHQ+tDpvZkK21mSYB4Nne2QbR8RCVsoyiSr5w/oRa9pEpJS35ydkIJ2dEa204MJdvOs9xtIR051poXJmO8XqUiJjkorX30sDLfWQmdJIkOhxaRa255vOnRyO8qCBaQ8eGgu50VjVVmvSRDZ7KqOh4aPVsrK0cB4De6TiTXERrKJ1VpMmwdkLtHV0sFpSSfPP5ETafJyIwyUXr4KIvku8jw6XhJWZjXX5yxubzRGvp6JDWR6aRfWRKTr6/ISdnRGvrpYFgTUZRjXazPtTmtsRFx0Or564ut08C1JloyhNKpA2i4yEisZjkojU3mOsjs7XBxslZidnT4pwAgLHgHFdyEa2hi/k+MtUWFgtKTL75vD+WrklmVS53JlojR4eCjQCbzpeiapspVWU1zioqdC/0Bzyi4yEisZjkojWlqComQslGALi9o4qTsxJzoLNqKt983h9L8WQwojUyNKsVC7Y1sI9MqbFXGDL55vMjUXAFAtEauTyl9Wti0/nS1JxLXp4aDbPwSlTmmOSiNXV2LOLKNZ2Pb2lg0/lSYzMbstWVpmkAeL4vUC86HqJSpDWd11ZLHuhisaAU5SdnQxGVxQKiNTL88gm1LBaUos4a6wQA9LD5PFHZY5KL1tSLA4F80/lxNp0vTfnJ2emxMJNcRGvgxHCoKpVVzZUmfXSjx8am8yWos8bqA4CRCJjkIloDuabzDQBwZ5ebxYIStL3R7gOA0cAcr0eJyhyTXLSmLvq0PjLt1RZWzUpUV61WOeubjrNyRrQGjuT6yDQ6zZyYlagduZUlozGu5CJaC0eGgtVprel8uL3ayqbzJSh/YuZUNFWXzCic4xKVMQ4AtKYGZrXEx9Z6OydnJWpnk0NbgRBMMMlFtAYu+qINANCR24pBpeeOLrcPACZiMGYUVXQ4RCXn6GBIKxa4Kng9WqKaXBVzjgpDMKuohsMDgRrR8RCROExy0ZpRVBUTYa3p/G2dLk7OSlR+cjYTTXniqaxedDxEpWbIn2gEgG0NLBaUqnqHOem0GAJZFRiPsfk80Wq7NKkVCzqrWSwoZY1Osw8Ajo+EuGWRqIwxyUVr5tx4xDmXViwWoy6+rcEeEh0PrY3aecc2vzgQqBUdD1EpyZ1Q2wAAt3VUcXJWwpqcbD5PtFaGXykWcBwtYR25JOaVKTafJypnTHLRmjk8GGwAgHqH2cem86UtXzk7MRziRQXRKjo9GnalsorZYtTFNtVVRkTHQ2snvx11JMrm80SrKaOoki+SrAeA2ztZLChlWxtsEwAw7GfzeaJyxiQXrZlLk9F6AGh1s+l8qcsv/++ZjvGigmgVHRnSigUNzgoWC0rctgab1t8wypVcRKvp9GjYlc6qJqtJH93gqeQJtSXsto4qHwD4wskGRWV/Q6JyxSQXrZnB2UQ9AGyu0y7cqXRtz50MNhKY40ouolV0aVJLHLdWVXAcLXG357ajjmvN55nRJFolR4eC9QDQ4DBzHC1xGzyVEatJH0tlFfOp0XCV6HiISAwmuWjNTIS0hMdNLU5eVJS4A7nm85PhZH06q3ByRrRKhmbj9QCwqc7GFbElrqu2MuYwQklmIZ0cCXFyRrRK8k3n29wWXo+WOJ0kocFhngCAI4NB7i4gKlNMctGaGA4kLJFk1mHQSelb2pyzouOhtdXmtsRtZn04rajG48Mht+h4iErFRFhrOn9zK4sF5aDJhjTwyjZVIlq5IX+CxYIykk9mXvRFOY4SlSkmuWhNvNAXqAcAj900adTruCm+DDTmTgY7xubzRKticDZujSazdqNOSu9tdfpFx0Nrr8UmpQHg8iT7GxKtlomQ1nSexYLysLleS2YO+RO8HiUqU0xy0Zo4NxFpAICWKjadLxft1Vrl7BIrZ0Sr4oV+rVhQy2JB2WjJreTi5IxodfTPxCtjqazdqJdSN7U6A6LjobW3r801AQAT4SSLBURlikkuWhP9M1ofme5aK6tmZWJLbhvAUCDBiwqiVXB+QjuhlsWC8tHu0FZyTYTmeDIY0Sp4cUArFtTZzZMGncR/VGVgV7MjaNJLyXgqa7syFbWJjoeI1h+TXLQmxkNz9QCwu5lLw8vFbbmTwXhsM9HqGJiNNwDAhtpKjqNloqYCWYsBaiKtWC9Pxuyi4yEqdufHI7liQQWLBWXCoJPU+txJmi/2s78hUTlikotWXTCeNvpj6RoJUA90Vk2JjofWx5YGW7jCqEvMpRXL+YmIU3Q8RMVuLKgVC3Y1O5jkKhOSJKGpUtuy+NJAgJMzohUamE3kdhawWFBO8iugL/gi3F1AVIaY5KJVd6g/4FEBqbrSOG2vMGREx0PrQydJqM8d23x4gMc2E61EMJ42BuLpahYLyk9zrvn8BV+U4yjRCo2F5hoAYA+LBWVlo0dLag7Msr8hUTlikotW3enRcAMANLkqeEFRZtrcWuXs4iSbzxOtBIsF5as113w+v12ViJbHH0sZA/F0tU6CchuLBWXl5lbnBACMB+c4jhKVISa5aNX1zmhHn3fWVLL/QZnZXGdj5YxoFbBYUL5a7fnm80mOo0QrcKg/UAcA1ZWmaZvZkBUdD62fWzuqZvQ6KROay7gmQnMVouMhovXFJBetutGA1kdme6ONk7Myk6+cTeQOHiCi5ckXCzpqeEJtuWmsRMagkzLhuYxzNJCwiI6HqFidGQvXAywWlCOzQafU2kxTAPB8X4DXpERlhkkuWlXJjKKbiqbqAOBAp5sXFWXm5jaX36iXUtFk1jE4G7eKjoeoWOWLBTsa7VwRW2Z0kgSP3eQDODkjWoneaW3LbyeLBWUpf6LmmfEwV8USlRkmuWhVHR0KVmcV1eCoMASbXBVzouOh9WXQSWqd3TwJAIf6eTIY0XLMLxbc3lE1KToeWn8tVRYfAJybiHAcJVqm0SCLBeWsu1Zrm9I/E2exgKjMMMlFq+r4cKgeABqdZlbNylS+cnZ+nMc2Ey3HvGJBqLnKkhAdD62/7lorJ2dEK5DMKLrpSDK3s4DFgnK0O3ei5hibzxOVHSa5aFVdnoo1AECbm0vDy9VGT65yxpPBiJYlXyxocLBYUK72tGj9DTk5I1qewwOBmqwKvbPCEGxwcmdBOTrQWTUpAao/lq4JxtNG0fEQ0fphkotW1bA/UQ8AWxtsXBpepva0OH0AMB5KcgUC0TJcmdKazrdXWziOlqkDnVVTOgmKP87JGdFynBjRms43OM0cR8uU02LMVFcap1VAemEg4BEdDxGtHya5aNUoqorJsJbY2N/u4gqEMnVru2taJ0EJxNPV/liKkzOiJRr2JxoAYEu9neNombKZDVm31TgDAJycES3dlalorljAnQXlrNFZMQkAZ8bCdaJjIaL1wyQXrZrzExHnXEaxVBh1ic31trDoeEgMm9mQra40TQPAC/0BXlQQLYGiqvBFtGLBvnYnJ2dlrNFV4QOA06NhroolWqLh3Am1W+ttHEfLWEfuZM3eafY3JConTHLRqjk8EKwHgHqHeUInSaLDIYEandoJi2fGI0xyES3BxYmoYy6tWCoMusS2BntIdDwkTke1dRIA+mbiHEeJlkBRVfjCyQYAuJU7C8paPsk5ljtpk4jKA5NctGouTWpLw9tyR59T+erIbQ/om47xooJoCQ4PacWCOofZx2JBedvWoE3ORjk5I1qS8xMRZzKjVFiMujh3FpS3/R3ayZqTkWRdRlH5H1WiMsEkF62awVmtj8ymOjadL3fbGuycnBEtw4WJaAMAtLpZLCh3t3VU+QBgipMzoiXJ7yxgsYA6a6yxSpM+ms6qptOjYZfoeIhofTDJRatmPKQlNG5qdXByVuZu69QqZ9ORFCdnREsw5Nf6hmysreQ4Wubaq63x/OTs5EioSnQ8RMWCOwtovnqH2QcAR3MrpYmo9DHJRatiOJCwRJJZp0Enpfe1uWZFx0NitbktcZtZH0krqvHEMCdnRIs1HtKazu9tdXJFLL08OTs+HGJfLqJF4s4Cmi+/MvryFFtoEJULJrloVbzYH6gHgFq7acqo16mi4yHxXp6cjYR4UUG0CBOhuYrwXMal10mZfe0sFhAnZ0TLMRFOcmcBvWyjp3ISAIb9CRYLiMoEk1y0Ks5PROsAoDl35DlRa5VlEgAu57YNENH1Pd+XKxbYTFNmg04RHQ+Jt9GjbVsd8ic4jhItwmggYQnPZZwGnZThzgICgD0tWrJzIsQ+sUTlgkkuWhUDs1ofma4aK5NcBADYWKdNzlg5I1qcs+OReoDFAnrFTS3OSQDwheY4jhItwqFXdhZMcmcBAcC+NtesQSdlIsmscyw4VyE6HiJae0xy0aoYz12A72h0TIqOhQrD3hanVjnLbRsgouvLFwu6a63sI0MAgFvanC9PzkYDCYvoeIgK3bkJrVjQ4qrgOEoAAKNep9bYTFMA8EIuCUpEpY1JLlqxRDqrm4mmaoFXTtUj2tvq9Bt0UjqazDo4OSO6sdHgXAMA7GxiHxnSGPU6tdZmmgSAFweCXM1FdAP9M7mdBTyhlubJr5A+PxHhOEpUBpjkohU7PBisUVTonRZDoNZmSomOhwqDUa9Ta+25ytlAgBcVRNcRTWb0/liqBgAOsFhA8zS5KiYBTs6IFmM8qPVd2t3MYgG9ojPXTqU/t2KaiEobk1y0YqdGwvUA0JA7TY8or9mpVc7OjbP5PNH1HB4M1ioqdFVW46y70pQWHQ8Vjnyvy/wKFSK6tmgyo/fH0zUAcHtH1ZToeKhwbG+0TwLAWJDN54nKAZNctGI907E6AGhzW7n6gF6lq1abnA2wckZ0XadHw3UA0OAwcxylV9neaPcBwBhPBiO6riPazgJdldU467IaWSygl+VXSM9EU7WJdJbzX6ISx3/ktGIjAe1o88117H9Ar5Y/iICVM6Lr652J1wFAq9vCJBe9yoEuNydnRItweozFAro2j92cdFoMAUWF/vBgsEZ0PES0tnixRCvmCyfrAOCWNhcvKuhV8gcRzMZStfFUVi86HqJCNRpIeABgo6eSW2zoVWptphQnZ0Q31jetFQtaqiwcR+nX5Nuq5NusEFHpYpKLVqRnKmZLpJVKk16X3NFkD4qOhwpLrc2UclkMfkWFjpMzooVNRlJ1ALC31cliAf2axlx/Q07OiBY2EpyrA4BNdZUcR+nX5Nuq5NusEFHpYpKLVuTwoHakeZ3dNKmTJNHhUAFqcGong50cCXFyRnQNg7NxazyVtRn1UmpnkyMoOh4qPG1uiw8ArkzHOI4SLWAyt7OAxQK6lnxbleFcmxUiKl1MctGKXPBF6gGguaqC/bjomtpzk7NeVs6IrilfLKi1maYMOkkVHQ8Vns11tkkAGAkkOI4SXcOQP2GNpbI2o05Ks1hA17Kv3eUDgMlwsl5R+Z9aolLGJBetyOCsdsHdVcOl4XRtm+ttucoZm88TXcsFX7QOAJpyqx6Jrrav3cnJGdF1HB4MeACg1m6aZLGArmV7oz1kNujmEmnF2jMVs4uOh4jWDpNctCLjuSPNdzbZuZKLrunWtlzlLMLJGdG1DMxqzZI7aqxMctE1bWvg5Izoei5MsFhA16eTJNTZTZPAKyuoiag0MclFyxaZyxj8sXSNBKi3d1bxJBu6pi0NtrDZoJubSyuWS76oQ3Q8RIVmPKj1kdnWYOfkjK5Jm5xpJ4O9NBDkqliiq7BYQIvRXKW10Ljgi3IcJSphTHLRsr00EKhVAanKapx1WowZ0fFQYZo/OTsyxMoZ0XzprCLNxFK1AHBru4vFAlpQS5W2QuXiZJTjKNFVxkNasWBrg43jKC2oO5cEHZyNM8lFVMKY5KJlOz0WrgOABqeZVTO6rpbcwQQXWTkjepXjwyF3VlENdrM+3OSqmBMdDxWu7lrtZDBOzoheLZ1VpJloygMA+9ureE1KC9rd7PABwEQuKUpEpYlJLlq23mntQru92sp+XHRd3bWVucoZj20mmu/EiFYsqHOwWEDXl5+cjYeSHEeJ5jkxEnJnWCygRdjfUTUtAWognq4OxtNG0fEQ0dowiPxwWZYNAP4bgA8AaAYwCeC7AD7r9Xqji3h9HYDPAHgdgDoAfQAeBvAPXq83Me957QAGrvNWe7xe76nlfYvyNRLQEhZbckebEy1kT4vD943DwESYlTOi+XqmY3UA0FJl4ThK17W/wzWtk6AE4ulqfyxldFea0qJjIioEJ0fCHoDFAroxe4UhU11pnJ6JpT0vDAQ8D23zjImOiYhWn+iVXA8D+DSArwN4K4DPA3gPgF/kEmALkmW5FsALAN4C4IsAZGgJsk8D+LEsy9f6bv8DwL3X+NO7Ct+lrCiqislIqg4A9rW7uJKLruvW9ldPzkTHQ1QoRvyJOgDYkFvtSLQQm9mQdVeaZgDgxYGgR3Q8RIXiyhSLBbR4jbkTOE+PhrkqlqhECVvJJcvyW6Alph70er2Pzbv/CQAnAHwcwD9c5y3+O4BGaKuwLuXu+5ksy88BeBpasuwbV73mnNfrfWZ1vkF5Oz8RcSYzSkWFUZfYVFcZER0PFbb85GwmmvK80B+oe+OOulHRMREVAl9udePuZgcnZ3RDjU6zbyaa8pwZC9e/YTtXIBABwHCAxQJavPZqq+/MeGRH30ycuwuISpTIlVwfBfDs/AQXAHi93gsAvgPgYzd4/UUAfzgvwZV//TMAxgDcunqh0tWODoW0PjJ2s08nSaLDoSLQ5NROWDwzHuFFBRGA6WjKFJrLuHQSsje3OWdFx0OFrzPXA5OTM6JX5IsFu5rsPFmRbmhbg80HAGPBOa7kIipRQpJcsizrAdwB4JkFnnIQwBZZlhdcju/1er/q9Xq/uMDDZgDBlcRI13cpd0pe/khzohvpyE/OpmO8qCAC8NJAwAMA1ZWmGYtRr4iOhwrf1gatB+YoJ2dEAIDZaMoUSmSqdBKUW9pdM6LjocK3v0M7gXMykqzLKCor9UQlSNR2xUYAVmjbEq/leO62C8CSqjKyLL8eQA2Ao9d4+H/Lsvw1ABUAzgH4vNfr/Y+lvD9phnJ9ZPJHmhPdyLYGu+9HZyY5OSPKOZtb1djoZLNkWpzbOqp8ADCVm5wZdJIqOiYikV58pVgwzWIBLUZnjTVWadJHY6ms7eRIqOqWNpdfdExEtLpEJbncuduFtmfkB5vqpbypLMtuAF8CcBnAT656uAdao/uz0BJs7wPwbVmW271e718t5XPmO3To0EeW+9pCkUqlaoClfZepYNYDAPWp8QOHDk1wayjdkDOl6gBgJpJseu755z8yf5vrcn6DRKtJxG9weExxAkC7KdrO3z4Bi/sdOkxQwinV9JMnD32szipl1y86KgfF9t/jU6OKFQDaLGlHscRMN7bWv8MWa9Z4KQUcOn72d1Kjurm1+AwqbsU2FpYio3H5Z5WJSnLZc7fxBR7P3+9c7BvKsmwF8FNoq8Tu8Hq98y/8RgBs9nq98ys8j8iy/DCAP5dl+bter5cnLC7SXEaVZueg10lAkw0Z0fFQcXCYJMVhhBJOQzedgL7OCk7OqKyNx1QDADRVShxHadEarUiHUzCPRFUjk1xU7sZi2lym0Ya06FioeDTapPSloGoejcF4C8AkF1GJEZXkyp/GZ1ngcWvuNrSYN5Nl2QjgewD2A3iX1+t91VbFqxJe830K2gmP7wLwZ4v5rKsdOHDgq8t5XSHJZ6gX+10ePTvZrOLyh6orTVP33Lm/6L8/rZ+ac8d+JzwT75q1tj79lgOtLx8asdTfINFqW+/foKKqGHvu0B8DCu6/bffXNnps0fX4XCpsi/kd/sfYuQcuBf0HRtTq0//lwNaD6xcdlYNi++/xX54+8n5grnJLd+dPDuxr6hMdD62Otf4dHksN7HhqdOQtQ0nryIEDe71r8RlU3IptLCxFR44c+cxyXyvqdMUbbUe80XbGl8myLAH4BoDXAfiY1+v9zmKD8Hq9E9B6c21e7GvoldPxGnOn5REtVotLO6jg8mSMJ4NRWTs/EXGmsorZYtTFu2srmeCiRdtQWzkJAMOBOY6jVNYUVcVUJFUHAPvanextSIu2q9kxCbxyMicRlRZRSa4JAAkANy3weP7+/kW81z8CeCeA3/d6vf+yjFh0ALdNLUX/TLweADqqrbygoCXpfnlyluBFBZW148MhDwB47Oap+f3piG4kPzmbCDHJReXtki/qSGaUigqjLsFiAS3FvjbXrE6CEkxk3LPRlEl0PES0uoQkubxebwbA8wDuWeApdwO45PV6r5tEkWX5swD+E4BPe73ev7/O8yplWW68xv1OaKu4zi0ydAIwGtQurLfW27iSi5aEkzMizaXcasbm3OpGosWaPznzx1LL78pKVOSODoXqAKDObp5ksYCWwmrSZ6srTdMA8NJgsFZ0PES0ukSt5AKArwC4W5blB+bfKcvyFgDvyD2ev8+Zayw//3mfAPA/Afy51+v93EIfIsuyCVoS62FZlq/uQfbXABQA31rJFyknGUWVpiLa0t79HVWcnNGS3NrumuHkjAgYmtVWM3bVcEUsLY3VpM+6K00zAPDSYNAjOh4iUS5NRlksoGVrcJinAODseJiFV6ISI6rxPLxe7/dlWf4+gO/Lsvw5AKcAdAP4NIATAP4J0FZhQdu2OANgU+6+XQD+HsCzAJ6SZfmea3xE0Ov1nvJ6vanc+/9z7rlfBJAE8GEADwH4kNfrHVuzL1piTo+GXemsarKa9NHOGmtMdDxUXKwmfdZtNc7MxNKelwaDnoe2efhvj8rSRFhbzbijycHJGS1Zo8M8ORNNec6OReo4jlK5GvInPADQyWIBLUNbtWXyzHhkR/9MnEkuohIjciUXoPXS+jsAHwLwA2inHT4C4EGv15s/CjgDwAdgZN7rqgBIAO4C8PQCf/5v/sler/crAN4EQAXwrwD+HYAZwP1er/fra/LNStTxYW1peL3DzAsKWpYGp1ZxPTsW4UUFlaVoMqP3x9I1ALC/3TUtOh4qPm3VlkkA6JvhIR5UvvKtD3Y02nlNSku2pd42CbzShoWISoewlVwAkEtk/Vnuz0LPSQLYdtV9z0BLci3ls34G4GdLj5LmuzKlLQ1v4dJwWqZ2t2Xy7HhkBydnVK4ODwZrVUCqshpnXVZj+savIHq1LfW2yZ+encIYJ2dUpqLJjN4f14oFt3VUsVhAS7avzTUJAFORVJ2iqmBfN6LSIXolFxWZIb/WRyZ/Sh7RUm3OVc44OaNydXpU6//RwBWxtEz5ydlkbnJGVG6ODAZrFBU6l8XgZ7GAlmODpzJSYdQlkhml4uJE1CE6HiJaPUxy0ZL4wlrT+Z1NXBpOy3PLK5UzDydnVI76ZuIeAGipskyJjoWK0/zJ2SUfJ2dUfs7kWh7kWyAQLZVOklBn14pNx3LtWIioNDDJRYvmj6WMwUTGrZOg7Gt3zYiOh4rTprrKSIVBl5jLKJbLkzG76HiI1ttIQFsRu6mOK2JpeeZPzo4OcXJG5ad3Wmt50Fpl4ThKy5Y/mTN/UicRlQYmuWjRDg8GawGgymqctZkNWdHxUHHSSRI8L0/OgryooLIzGUnVAcDeVicnZ7RsTZycURkbybU8YLGAVqIrdzJnvh0LEZUGJrlo0c6Oa0vDG7k0nFaouSo3OfNxckblZXA2bo2nsjajXkrtbHIERcdDxWve5MwjOhai9TYZTnoA4KYWFgto+XY0OSYBYDzEPrFEpYRJLlq0/pm4tjTczaXhtDL5ydkgK2dUZg4PaqsXa22mKYNOYlM6WrYdjVpvzAlOzqjMDPkT1lgqazfqpPSuZhYLaPn2t7umJUD1x9I10WRGLzoeIlodTHLRoo0G5zwAsJlLw2mFtjfapwBOzqj8XMitXmziilhaods6qqYBwB/n5IzKy+HBgAcAau2mSRYLaCVcVmPaZTX6VUDKt2UhouLHJBctiqKqmIok831keCIYrcj+dtcUwMkZlZ/BWe1kxY7cakai5XJZjekqq3FWUaE7MhisER0P0Xq5mCsWNDoreD1KK9bg0PrEnh4Ns/BKVCKY5KJF6ZuO2xJpxWrS65LbGuwh0fFQcXNXmtIui8HPyRmVm/GQVizY2mDj5IxW7OXJ2RgnZ1Q+Bma0VgcsFtBqyLdh6c21ZSGi4sckFy3KkdwpeB67aVInSaLDoRLQkNuuxckZlYt0VpGmoykPAOxvr+LkjFbs5cnZNCdnVD7Gcq0OtjXYOI7Sim3yaG1YRgPsE0tUKpjkokW55It6gFeOLCdaqdYqTs6ovBwfDrmzimqwm/XhJlfFnOh4qPjlJ2cjnJxRmUhnFWmGxQJaRXtbtRM6JyMpjqNEJYJJLlqU/Cl4ndVWbrGhVbEpd4DBaJDN56k8nMz1+6jLbTEjWqmbcj0yOTmjcnFyJFyVUVSDjcUCWiU7mhwho15KxVPZyv6ZeKXoeIho5ZjkokUZzy0N3547spxopV6unIWTnJxRWbgyFasDgJbcKkaildrZ5AjmJmc2Ts6oHJwYCdUBQD2LBbRKDDpJ9di139ORwaBHdDxEtHJMctENJTOKbjaaqgWA/R0uruSiVZGfnMVSWVs4pXIsopI3klsRu6G2kpMzWhUGnaTW2kxTACdnVB5YLKC10OTUklwXcid3ElFx48SSbujYcNCdVaF3VBiCHrs5KToeKg3zJ2cjURhEx0O01nyRpAcAdjc7ODmjVZPvlcnJGZWDfP+57lq2z6DV05k7qXNwln1iiUoBk1x0QydHXu4jwwsKWlX5ydloVDWKjoVoLU1HU6ZQIlOlk6Dc3OacFR0PlY7Oak7OqHxM5Foc7G5isYBWz7YGrR1Lvj0LERU3JrnohnqntaXhrVU8WZFWV0ducjYWBZNcVNJeGgh4AKC60jRtMeoV0fFQ6djWmJ+csb8hlbbZecWCW9pdM6LjodKxv6NqCgBmoilPMqNwfkxU5PiPmG5oODCXWxrOPjK0ul6unMVUblekknZuPFIHAI1ONkum1XVbR9UkAMzEUrWcnFEpe2kwWAuwWECrr95hTjoqDKGsCv2x4aBbdDxEtDK8GKIbmgxrfWT2tHBpOK2u23IHGfgSMGYVVXQ4RGumf0bbStbmtnIcpVX18uRMUQ3Hh0OcnFHJOjuutc9oYLGA1kBd7sTOfJsWIipeTHLRdU1FkubwXMall5C9udXlFx0PlZYGZ8Wc3awPZRRgMsHm81S6xoLaitgt9TZOzmjVvTw5Gw1xckYlK18saGexgNZAvi1LT+4ETyIqXkxy0XW9NKAdSV5tM02bDTouDadVV5+bnI1EuWWRSpOiqpiMpOoAYF+7k5MzWnUtLk7OqPSN5ooFm+sreRASrboNHq0ty0iQzeeJih2TXHRdr/SRYdN5WhstVRY2n6eSdnEi6khlFXOFUZforq2Mio6HSk9+cpbvoUlUahRVxVS+WNDm4jUprbo9zVoRajLMQzyIih2TXHRd/bNxDwC0uy28oKA1kZ+cjbH5PJWoo8PBOgCos5sndZIkOhwqQfmemZycUam65Is6khmlosKoS2zwVEZEx0OlZ2+r06/XSZnwXMbpCyfNouMhouVjkouua+zlpeE2Lg2nNZGfnI3HuJKLStOlSW0LWbOLK2Jpbdzc6vLrJWQ5OaNSdXRI6zfnsbFYQGvDbNApNZWmaQB4cSDAggFREWOSixbEpeG0Hm5udfkNEhBIQs/JGZWiYX+iDgC6atgsmdaG2aBTamymKYCTMypNlyejHgBormKxgNZOo0vrE3s+166FiIoTk1y0oPMTEWcqq5gtRl28q9bKPjK0JswGnVJnRRoAXhoIeETHQ7TaxkPaitjtjXZOzmjN5HtncnJGpWiQxQJaBx3V2u+rfzbOcZSoiDHJRQs6PhzyAICHfWRojTVWShkAOD/ByRmVlmgyo/fH0jUAcFtH1bToeKh0tXNyRiVsIlcs2MFiAa2hLXU2rU8sT1gkKmpMctGC5vWRYT8uWlNNldpKrv4ZTs6otBwZDNaogFRlNc66rMa06HiodG2t1yZn46EkV8RSSYmnsnp/nMUCWnv7O7T2LNPRlCejqKzwExUpJrloQewjQ+ul2SalAWAsxJPBqLScHgvXAUC9w8xiAa2pfe2uKQCYiiTrODmjUnJ4MFijqNC5LAY/iwW0ltqrrXGrSR9NZ1XTmbGwS3Q8RLQ8THLRgthHhtZLiw0ZQJucKaoqOhyiVdM7ra1ObK2ycBylNdVZY41ZTfpYOquazo6FnaLjIVotp0e1YkGDk03nae3V2U2TAHAi17aFiIoPk1x0TdFkRh+Ip6sBLg2ntec0S4rNCEWbnEVcouMhWi0jAW1F7Ka6Sk7OaM3lJ2fHh0NcFUslo3c6lisWMMlFa68lV5S6PBXjOEpUpJjkoms6wqXhtM4ac325jnFyRiVkKpKqA4CbWpycnNGaa+bkjErQaHDOAwAbPTaOo7Tmumu1Ni35ti1EVHyY5KJrOjMW4dJwWlf5ExYvT0Z5UUElYXA2bo2lsjajTkrvanYERcdDpa+7hpMzKj2+sNavc28riwW09nY1OSYBYCLMPrFExYpJLromLg2n9dacW8k1xMkZlYjDg8E6AKi1myYNOonN5mjN7Wrm5IxKy3AgYYmlsnYWC2i97Gt3zegkKIF4ujoYTxtFx0NES8ckF10Tl4bTesufsMjJGZWKi76oBwCauCKW1smt7a5pCVA5OaNScXhAKxbU2E1TLBbQerCZDVm31TgDAC8OBGpFx0NES8ckF11Tfmn4nhYHj72nddFkQ0YC1GA87Q4l0gbR8RCt1MCsdrJie42V4yitC5vZkHVXcnJGpeOCT2ufwWIBracGZ8UUAJwdj7DwSlSEmOSiX5NfGm7QSek9Lc6A6HioPBh1EtyVxhkVkF4aDHJyRkVvPKQVC7Y1cEUsrZ8Gh5YMOMfJGZWAgRmthUFHrt8c0Xpoc2uHePTPxDmOEhUhJrno1+SXhtfauDSc1ld+cnZ2LMyLCipq6awizURTHgDY317FyRmtm7ZqbXLWx8kZlYDxkNY+Y2s9iwW0fjbXab+30eAcx1GiIsQkF/2aC76IBwAaXVwaTuur1a0tD++d5uSMituJkZA7o6gGm1kfbnJVzImOh8rHJk/lFMDJGRW/dFaRpl8uFri47ZvWzc25kzwnI8k6RWW9n6jYMMlFv2ZgNrc0vNrCCwpaV/nK2RgnZ1TkToxoqxHrHWYWC2hd3dzGyRmVhpMj4aqMohptZn24ucqSEB0PlY8tDbaw2aCbm0srlt7pmE10PES0NExy0a8ZD2kJhq31dk7OaF3tnVc5Ex0L0UpcmYrVAUBLlYXjKK2rbQ32kEmvS3JyRsXuxEioDgDq7GYWXWld6SQJHptpCgCODIZ4TUpUZJjkolfJKKo0E9GWht/a4eLkjNbV9kZ7yKSXkom0Yu2bjlWKjodouUYCCQ8AbKit5DhK60onSfDYTZMAcHSIkzMqXj3T+WIB22fQ+mvKtW25NBnlOEpUZJjkolc5PRp2pRXVWGnSR1q5NJzWmTY50yq2hweDvKigouULa6sRdzXZuQKB1l2TS+tveNHHyRkVr2G/1j5jg4fFAlp/+RM9B2fZJ5ao2DDJRa9yfDi3NNzBpeEkRr5ydpGVMypS09GUKZTIVOkkKLe0u2ZEx0Plp7Nam5wN+bUVhUTFaCJXLNjZ5GCSi9bdjkatbctEOMlxlKjIMMlFr/JyHxmerEiCdFRbcpWzBJNcVJQODwQ8AFBdaZq2GPWK6Hio/Gxt0A7xyPfYJCo2s/OKBfvaXLOi46Hys7+jagoAZqKp2kQ6yzkzURHhP1h6leGAlljoqrUyyUVCbGvIVc5Cc6ycUVE6Ox6pA4AGJ09WJDHmT86SGYXXelR0XhoM1gJascBq0mdFx0Plp9ZmSjkrDEFFhf74cKhadDxEtHi88KFX8eWW5O7i0nASZN7kzMPJGRWj/hmtf0e7m8UCEqPeYU46KgwhRYX+6FCQkzMqOmfHwywWkHB1Du33d2o0zFWxREWEE0h6WTCeNgbi6WoJUPexjwwJkp+cZbXKmVt0PERLNZZbhbil3sbJGQkzb3LGVbFUdPLFgja3hT1iSZjWKq2FRk+unQsRFQcmuehl+aXh7krjjM1s4NJwEiY/OTs5GuJFBRUVRVUxFUnVAcAtbU5OzkiY1qoKTs6oaI0FtX5yLBaQSPmTPUcCPMSDqJgwyUUvOzuWWxruYNN5Eit/8EHvVJwXFVRULk5EHcmMUlFh1CU2eCojouOh8tVdm5ucBdl8noqLoqqYzBUL9rW5eE1KwuxudkwBgC930icRFQcmuehlfTNaQqEtd7odkSj5yln+IASiYnF0OFgHAHV286ROkkSHQ2VsT4vWW3OSkzMqMpd8uWKBgcUCEmtvq9Ov10mZSDLr9IWTZtHxENHiMMlFLxvNVXs3eSq5xYaEYuWMitXlSW1rWLOLK2JJrJtbXX69hGx4LsPJGRWVY8MhDwB4WCwgwcwGnVJTaZwGgBcHArwmJSoSTHIRAEBVVUxFtITCzW1OTs5IqFvaXLM6bXLmmopwckbFY8ivrT7srOHJiiSW2aBTqm2maQB4aSDArd9UNC75olqxoIrFAhKv0an9Di9MRDiOEhUJJrkIABBKQZdIK1aTXpfc1mAPiY6HypvZoFNqXp6cBXlRQUVjPKStiN3RaOfkjITLT87OT0S4AoGKxmC+WFBt5c4CEq6tWjvhM3/iJxEVPia5CAAwEoURADx2E5eGU0HIT87OsXJGRSKazOgD8XQ1ANzWUTUtOh6idrfWY3Nglv0NqXhM5FoV7GhisYDE25o74XM8xBYaRMWCSS4CAIxGVQMANLkqWDWjgtDmZuWMisuRwWCNokLnshj8LqsxLToeos31tikAGOMJi1Qk4qms3h9L1QDA/nYXr0lJuPwJn1ORpEdRVdHhENEiMMlFAIDxmLaSq7OafWSoMGzJV844OaMicXosXAcADU72kaHCwMkZFZvD84oF7koTiwUkXFdtZcxi1MVSWdV8bjziFB0PEd0Yk1wEABiPaSu5tjbYODmjgnBL7gCEyUiqjpMzKgZ909qqw9YqC8dRKghdtdaoxaiLc3JGxeLMWNgDAPUOM8dRKhh1dvMUABwbDrHwSlQEmOQiZBUVk3FtJdf+jiouDaeC0F1bmZucKeaLE1GH6HiIbmQkt+pwo6eSkzMqCDpJgseuJQuOc3JGRaBnOlYHAG1uFguocORP+rw8GeU4SlQEmOQi+OIwZFTAUWEI1TvMSdHxEAGvnpwdHQ7yooIK3mSuWfLeVicnZ1Qwml3a5OwSJ2dUBEYD+WIBdxZQ4eiq0YpXQ34e4kFUDAwiP1yWZQOA/wbgAwCaAUwC+C6Az3q93ugiXl8H4M8BPATADaAfwBe9Xu+Xr/FcG4DPAPhtAHUARgF8DcDfeL3ezKp8oSKVbzpfx6XhVGCaXRVTQ/5Ex+XJWB2AHtHxEC1kyJ+wxlJZm1EnpXc1O4Ki4yHK66qxTh3qD2DYP8eTaqng+XLFgj0tDu4soIKRP+lzgicsEhUF0Su5HgbwaQBfB/BWAJ8H8B4Av8glwBYky7ILwLMA7gfwWWjJq0cB/L0sy5+76rlGAL8E8F4AX8h91tcA/DGAb63atylSo7mm8y0uNkumwtJVY2XljIrC4cGABwBq7aZJg05iEzkqGDuaHNrkLMxDPKiwDQcSllgqazfopPSeFmdAdDxEefvbXdMSoAbi6erIXEboIhEiujFh/0hlWX4LABnAg16v97F59z8B4ASAjwP4h+u8xZ9BW721y+v1jufu+5ksyz0AvirL8iNer/dU7v5PALgFwF6v13sud9/PZVk+DOAJWZa/7fV6f7Ja363Y5JvOb2AfGSow2xu1ytl4iJMzKmwXJrStYI3OCq4+oIKyv901DQD+WLommszobWZDVnRMRNdyeEBrTVBrN02xWECFxGkxZlxWoz8QT1cfHgzWPLC5xic6JiJamMiVXB8F8Oz8BBcAeL3eCwC+A+BjC71QlmUzgPcB+NK8BFfe1wAMA/jIVZ/1yLwEV/6znoS2GmzBzyoH47mVXLubuTScCsttHVXTABCIp6ujyYxedDxECxmY1U5W7MitPiQqFC6rMV1lNc6qgHR4MFgrOh6ihVzwRfLFAo6jVHAacm1dzoyFWXglKnBCklyyLOsB3AHgmQWechDAFlmWF+ofcRMAx7Ve7/V6FQDPAbgn91kNADbe4LPulGVZ9NZNIaYiSXMgCb1eAm5pc82KjodoPpfVmHZZDH5Fhe7IYLBGdDxECxnP9enY1sBmyVR46h3mKQA4PcrJGRWugdmEBwA6qnmyIhWe1tyJn70zcY6jRAVOVGKnEYAV2rbEazmeu+1a4PHu/5+9+w6T6yzvxv89U3fK7szOltle1YtVLFuy5W7AVAMJnAAJaRBIIQl5Q5I3IW9ISPIjPYGX8IYWSkgCJ5huwNjCTbJkVVt9JW2vM9tmZmenzzm/P86MtQjtasvMPlO+n+vSpcuzU+7dPX50nvt+nvvJ/L3U67tX8Fwn9Gb0ZedYf6AeALx2pKwmgyo6HqIbNWYqumdH58ry/1EqfMm0Kk2FE/UAcKCjmpMzKjht1frkrHcqwubzVLDGArFMsaCS4ygVnM2Zti4js1GOo1SwVI07vQFxPbk8mb8XWzk0k/m7ZonXpxVFCS7xekvmRMWVfNb4Is9Z1JEjR95362cVrpdHVDsANNlUQ7F/L1S8EolELXDz/5/aLarzEoD+oZHXHDkydmDdg6OysNQ1eCvj85oxpWomlwXqwIVTvziQ8+ioXKzlOlxKq6RWAMDE1MztR44c6czle1Npydc1eCuqpmEqrDYAQGWw97VHjvRxplbGRF2HS/FENCMATIWinYUUF+VHIV6Dy/Hnx9N1Rgnab+00zHgqpKJewGI2m1f9WlFJrsrM35FFvp593LXE6xd77Y2vX+tnlbTxef0aaHKANxNUkFqcSAHXD0ggKjQjYc0MAE0OJEXHQnQzrU4pCWiYyPTgJCo0/iiMSRWSywLVaWbTeSo8dTakLQZoc0kYQgnNUGUp7gQClZ5YSpN8EZgkCaiyoKyvT1GTxrnM37ZFvm7P/L3YSq25JV574+vX+llLOnjw4GdW87pCcecBVfrWoRd+3W4yaMX+vVDxylZJbnYNWgYDns9dPPvbwxFjgtco5ctS1+CtPPbYxQeAqfu9tZ6TBw/ueCrnwVHZWMt1uJSUqkl/dfLwH4eSmrll696vtHtsSxUKqYzl6xq8lU8fHtwKDMq1Vfa+gwf3/ed6fjYVHlHX4a3UvvTie8aC8ZZQVdcTr7u9qV90PJQ/hXoNLuX7F/zNGi6/t8Zh8d9/74GiiXsxx48f/8hqXyuqJ9dytiMCS28xNMmyXLXE6xOKooRz8FklzWw0aE0OKe22shpBhWlPq2vWZJCS84l05dBsdKnkNpEQQ7NRLwBszPTrICo0JoOk1VZa/ADw4sAs+8lQwbnin/cCQGs1T1akwtWc6RN7cSLMPrFUcM6PzdUD108CLWeiklzjAKLQT0m8mezjfYt8vfeG593s9X0reO48gLK/GIgKkckgaXXZyVl/gDcVVHAmQvrJirubq/jvCBWsVyZn45ycUeEZmtGLBRvqWCygwtVZa/cBwMA0D/GgwtObOfmz3WPzi45FNCFJLkVRUgAOA3hgkafcD+CyoiiL/UN3Gvo2xJ96vSzLEoB7ATyT+awxAFdv8VnPK4rClUxEBarplcrZHG8qqKBMhxOWYDRVbZCg3tHhnhIdD9FiOmpsmclZlEkuKjgToXg9AOxqYbGACte2RqcfAMaCcY6jVHBGMyfUbvayWCBqJRcAfBrA/bIsv2rhg7IsbwXwjszXs4+5ZFnO9s6CoigxAF8C8FuyLDfc8L6/DKADwGdv+Kx3yLK87YbPehB6kuvTIKKC1ZmZnPVzckYF5mi/vvWrxmGZtJmNLJZQwdreWOkDgNFgjOMoFZTpcMISiKY8Bgnqne3usmwfQsXhQEe1DwCmwon6ZFqVRMdDlKVqGnxzevL1jjZ32Se5hJ1WpijKY7IsPwbgMVmW/wbASwA2APhj6Cu1/hUAZFl2QN96OAVg84K3+DMArwXwvCzLfwtgDMBBAL8P4B8VRTm94LmfAPA2AIdkWf4Y9JVduwD8bwBfVxTlW3n6NokoB7Y1VPqBCYxxckYF5tzYnBcAGl3sf0CF7UCH2w8AU+FEXTKtSmajgSfYUUE4NhCoAwCPwzJltxjTouMhWkyzuyLmtBpD4Xi66vRw0LO/o5pJWSoI1ybnnbGkarMYDfGtjc6Q6HhEE7mSCwDeCeAfAbwHwDcAfAjAVwE8oihK9ij2FIAJAMMLX6goyiyA+wA8B+CjAL4O4C0APqQoyh/c8NwkgEcA/Df0JNg3ALwXwD8BeFcevi8iyqH9nXpFYnIuUZ9SNVbOqGD0Z/pydHjsTHJRQWuptkWdVuNcStXMZ4ZD1aLjIco6nykWNLFZMhWBhsx1emY4xBYaVDBODAa9AFBfafEZJE6VhK3kAl5JPn0082ex58QBbF/ka+PQE2TL+awQgP+V+UNERaSt2hZ1WIxz84l05ZnhYPUd7e6ZW7+KKP9GMv0PtjQ4yr7JJxU+b6XVF45HKk8PB713dnAcpcLQO6WfrNieaU1AVMha3BX+a5ORjZkTQS+JjocIAC5PhOsBoNnNE2oB8Su5iIiWhZUzKjSqpsE/l/ACwJ3t7H9Aha+lWr/5vTo5z63fVDCyzZK3eJ0cR6ngbazXm3oPz7JPLBWOgcwJtV01dhZdwSQXERWJFneFHwCu+MO8qaCCcHkiXBVPqRUVZkN0Y71jTnQ8RLeyoVZfcTg0w8kZFYZMsaAeAO5gsYCKwG3N+gmg4yGesEiFI9u3eFsjiwUAk1xEVCQ21OmVs6FZNp+nwpDtf+CttLL/ARWFXS365GwiFOeKWCoIPb75ylhKtVWYDNHNXhYLqPDd2e6eNkhQg9FU9XQ4YREdD1E8pRqmw4k6ADjQWc2VXGCSi4iKRHZyNs4TFqlAXPbpqwrZ/4CKxf4O95RBghqIpjwz8wmz6HiITgwGss2S/SwWUDGwW4zpGodlErh+MiiRSKeGgp60BmNVhSnYUGWNi46nEDDJRURFYeHkLBBJcnJGwg1mtnx11/JkRSoOdosx7bGbpwDg2ECAq7lIuMsTLBZQ8WmssvoB4NxYiIVXEu7MSHZngYXjaAaTXERUFBZOzo72z7JyRsKNB2P1ALCzqZI3FVQ0Gl16MuHc6BwnZyTc9WKBg+MoFY3sSaB9UxGOoyTcNX+kHgBaq23cqpjBJBcRFY1Gl958/vwYJ2ckVjieMs5EkrUAcFdn9aToeIiWq92j3wT3TUe4kouEG8s0797ZzGIBFY/sSaDZk0GJRMqe9Jk9+ZOY5CKiItLu0StnvayckWDHBwK1qgaD22aacdvNSdHxEC1XdnI2wkM8SLBIIm2cmU/UAsCBDjdXIFDRyJ4E6p9L1KuaJjocKnMTmWLBrszJn8QkFxEVkc1evUIxEohxBQIJdTaz1Su79YuoWOxrc+mTs3Dcy8kZiXR8MFCTLRZ4HBYWC6hobPY65qwmQyyWUm09vvlK0fFQ+ZoMJyzBWMptkKDua3dNi46nUDDJRURF4442vXLmm4s3cHJGIl2bnPcCQFs1k1xUXLY2OkNWkyEWS6q2q35Ozkicl0f0pt0NVVaOo1RUDJL0SpPvk0MBroolYV7sn60HgBqHZdJmNqqi4ykUTHIRUdHY2ugMWYyGeCyp2q5NzjtFx0PlazjTh2NTvZOTMyoqBklCvdPiB4Djg5yckThXs8UCD5slU/HJngh6KXNCKJEI58fn6gGg0cViwUJMchFR0VhYOTsxGORNBQnjC8XrAeD2zNYvomKSnZxdnpjn1m8SJtsXbhObJVMR6qq1+wBgcDrK+1ESJnvCZzuLBT+BSS4iKipNrJyRYIMzUft8Il1pNkjJXS1VAdHxEK1UZ2ZyNjDDQzxIHN+c3ix5byuLBVR8djRW+gFgPFP0IhIhe8Jn9lAZ0jHJRURF5ZXK2UyUNxUkxIsDev+D2kqL32SQ2ByOis7OpkofAIwH40xykRBDs1FbOJ6uNBmk5J5W16zoeIhW6q7Oaj8ATM8n6qLJNOfUtO5UTYN/LlEPXD/xk3T8H5KIikp2cjYWjHFyRkJcHNdXETbzZEUqUgc4OSPBXuzX+8HVOVksoOJU47QkXDbTrKrBcGIgUCs6Hio/Pb75ylhKtVlNhthmr2NOdDyFhDc2RFRUspOzqXCiLp5SOYbRuhvI9N/IbvkiKjZ1TkvCVWEKqBoMJweDNaLjofJzcUJvlpxtQUBUjLIng740GmLhldZd9mTP+kqLzyBJosMpKJwgElFRWTA5M54YDHByRutuNLOKcFsD+x9Q8XplcjbCyRmtv/5ssaDGxnGUilZbtX79XpuMsIUGrbvs4TEtLBb8FCa5iKjoeK9PznhTQesqmValqbDe/+BAh5sn2VDRan1lcjbPJBetu7FAtlhQyXGUitbGeocfAIZnecIirb/s4TGdNXaOozdgkouIik62cnbVz8kZra8zw6HqlKqZnFZjqKXaFhUdD9Fqbax3+ABgiJMzWmcpVZMmM8WC/Z1slkzFa29rlQ8AfCEe4kHrL3t4zI5Mv2K6jkkuIio6G+r1isVwgM3naX2dHg56AcBbaWXVjIra7pYqPwD4QnGuiKV1dWY4WJ1SNbPTapxrY7GAitjeVteMySCl5uLpqtFArEJ0PFQ+osm0YXo+UQtc71dM1zHJRURFZ0+Li5UzEuJKZvVgazX7H1Bxu73NNWPUJ2eu8SAnZ7R+FhQLOI5SUTMbDVqt0+IHgBcHAiwY0Lo5NRSsUTUYXRWmQJ3TkhAdT6FhkouIis7tba4Zo4R0KJZyTYTiVtHxUPnI9t3IbvUiKlZWk0GtdZgnAeAYJ2e0jq76M82SWSygEtDo0ld2XxifY+GV1k320Jhsn2L6SUxyEVHRsZoMarZydqx/lpMzWjfjmdWDu5ureFNBRa/JpScZLoxxckbrZ3AmUyyoY7GAil9njd0HAP1TEY6jtG6yxYLW6gpuVbwJJrmIqCg1uipYOaN1NR1OWILRVLVBgnpHh3tKdDxEa9VeY/MDQN80J2e0fiYyxYLbmqs4OaOit9Xr9AHAaJB9Ymn9ZPsSc2fBzTHJRURFqaNGP2Gxf5ong9H6OJpZNVjjsEzazEZVdDxEa7WtQZ+cjQXZfJ7Wx8x8whyIpjwGCep+FguoBBzInBA6GU7Up1RNEh0PlYdsX+LdLdxZcDNMchFRUcpOzkZ5wiKtk/Pjc/UA0Ohi/wMqDfs7qn0A4J+Le1VNEx0OlYFs/zePwzJltxjTouMhWquOGnvEbjGGk2nNcm405BIdD5U+/1zcGoqlXEYJ6X1t7hnR8RQiJrmIqCjd2e7OTs7qOTmj9dCX6bfR7rFxiw2VhK5a+7zNbJjXJ2dzbtHxUOk7N6q3GGhks2QqId5KvU/sqaEgC6+Ud8f69WJBjdMyaTUZuLPgJpjkIqKi1F3nmLeZDfOJtGY9PzbHyhnl3Uhm1eDWzCpColKQPZnpJCdntA56p+a9ANDhsXEcpZLRUq1fz1f88xxHKe+yOwuyh8fQT2OSi4iKlrfSysoZrQtV0+CfS3iB66sIiUpBi1s/xOOKP8y+XJR3owG9/9sWFguohHTX6icsZk8OJcon7iy4NSa5iKhotVTrFYzLvjBvKiivLk+Eq+IptaLCbIhurHfMiY6HKFe6ax2cnNG60IsFerPkO1gsoBKyq1lv/j0R4iEelH9jmZ0FW7wsFiyGSS4iKlrZytnQTIw3FZRXJwb11YL1TqvPIPHwJCodO5srfQAwHowzyUV51eObr4ylVJvVZIht9rJYQKVjf4d7SgK02UiyJhhNmkTHQ6VL1TT4w4l6ALizw8Uk1yKY5CKiorUzUzkbD/GERcqvHp++lSu7epCoVBzocE9mJ2dzsRQnZ5Q3J4cCXgDwVlpYLKCSUllhSlXbzdMaIB0bCNSJjodK16Xx6zsLNtQ5wqLjKVRMchFR0cpOzmbmk7XheMooOh4qXQOZrVzZ1YNEpcJlM6fcdvOMBkgvDgRqRcdDpevShN5aoNnNYgGVnkaXfohH9gRRonzIHhLDnQVLY5KLiIrWDZMzVs4ob8aD+mrBHU2VbPJJJacxc8Liy6MhTs4obwan9WJBF4sFVILaMyeGZk8QJcoH7ixYHia5iKiovTI5G+HkjPIjkkgbZyLJWgA40OFmkotKTlt2cjbJyRnlz1hI7/u2s6mKkzMqOZvqnX4AGAmwTyzlT3ZnQVeNnfejS2CSi4iK2iuTs6kIbyooL14cCNSqGgxum2nG47AkRcdDlGub6/UTFkcC7G9I+RFJpI0z84lssWBSdDxEubavTW8C7gvxEA/Kn/HM9bWjqZLFgiUwyUVERS07ORuejfKmgvIiu0qwIbNqkKjU3J6dnM0lOI5SXhwfDNSoGgwum2m2xmlJiI6HKNd2NlcGzEYpEU2qjt7JeYfoeKj0RJNpA4sFy8MkFxEVtb1tLj8A+Dk5ozy5ltnCle23QVRqdjZXBc1GKRFJpB19UxFOzijnssWCRhYLqEQZJAn1lfr1fXwwwHtSyrnszgIWC26NSS4iKmq3NVcFzEYpMZ9IOwdnonbR8VDpGc5s4drsdXJyRiXJZJC0OqfFDwDHBwLc+k05d/V6sWBCdCxE+dKcOWHx0sQ8x1HKuQXFAo6jt8AkFxEVtYWTsxcHZnlTQTmX7a+R3dJFVIqaXfpJTZd8Ya5AoJwbmdWLBZvqWSyg0tWZaQY+MB3hOEo5d5U7C5aNSS4iKnrZydnFcU7OKLcGpiP2+UTaaTZKiduaqwKi4yHKl85auw8A+jk5ozyYCMUbABYLqLRtzzQDHwuy+TzlHosFy8ckFxEVvY7abOWMzecpt47163016pwWv8kgaaLjIcqXbY1OPwCMB+NcEUs59UqxwCAld7WwWECl60CH2w8AU/OJunhK5TybcorFguXj/3xEVPS2N+oVjdFgjEkuyqmLE/rqwGZ3BW8oqKQd6Kj2AcBkOFGfTKuS6HiodLw4kCkWVFp8LBZQKWt0VcQqrcZgWtVMp4aCHtHxUOlYuLOAxYJbY5KLiIpednI2FU7UcXJGuZTtq9Fda2eTTyppze6KmNNqDKVVzXR6mJMzyh0WC6iceKusfgB4aSTEVbGUM68UC7izYFmY5CKiopednKVUzXxmOFQtOh4qHWOZ1YHbGys5OaOS11Clnwx2ejjEVbGUM/1TerGgq8bOcZRKXlu13hT8qn+e4yjlDIsFK8MkFxGVBG+lXjk7MxLkTQXlRDylGqbCiXoAONBZ7RcdD1G+tbgr/ABw1T/PFQiUM68UC5pYLKDSt6HO4QOA4Vn2iaXcyR4Kw2LB8jDJRUQlobVar2xcYeWMcuTEYKAmrcFYVWEKNFRZ46LjIcq3jfX65GyIkzPKkYXFgrs6qzk5o5K3u6XKBwATIZ6wSLkzFmCxYCWY5CKikvDK5GyGkzPKjZdG9C1bjZktXESlbnczJ2eUWyeHAh4WC6ic7Gt3TRskpIOxlHsynLCIjoeKH4sFK8ckFxGVhNuuT864zYZyIttPo9Vj4w0FlYU7OtxTBglqMJqqnubkjHLgzHCoAbje742o1NnMRrXGYZkCgGP9s7wnpTVjsWDlmOQiopJwZ7t72iBBDURTnpn5hFl0PFT8slu2NmVWCRKVuszkbBIAjnJyRjmQLRZkm3ETlYMml57UPT82x1WxtGZnMofBsFiwfExyEVFJsFuMaU+2cjYQ4OSM1iy7ZWtvaxVvKqhsNFbph3icH5/jOEprli0WbGSxgMpIu0dvDt6XOVmUaC2u+ucbABYLVoJJLiIqGU2ZCse5UVbOaG1GZqO2cDxdZTJIyb2trhnR8RCtl/YaGydnlDO+TLHg9jYWC6h8bG1w+gBgNNMsnGgtXtlZ4GWxYLmY5CKiktGe6Z3UNx3hCgRak+xqwDqnxW82GjTR8RCtly1eTs4oN0Zmo7Y5vViQYrGAyskd7S4/APjDiXpV4y0ErY2POwtWjEkuIioZWzKVs5FZTs5obS6M66sBm9wVvKGgsnJHu9sHAL65hJeTM1qLbLGglsUCKjMb6x1zFWZDNJ5SKy6Nh6tEx0PFazQQq2CxYOWY5CKikrGvLVs5i3NyRmvSPxVtAIDOGvY/oPKy2euYs5oMsXhKrejxzVeKjoeKV7ZY0OxisYDKi0GSUO/UW2icHAqy8Eqrdmxg1guwWLBSTHIRUcnY0uAMWU2GWCyp2q76OTmj1RsN6qsBtzdWcnJGZcUgSfBWWnwAcGIwwMkZrVr/tN5HprOWxQIqPy3VenL3si/McZRWLXtCJ4sFK8MkFxGVDIMkof6VyVmQfbloVZJpVZoKJ+oB4K7Oat5UUNlpdnNyRms3FnilWDAhOhai9dZVY/cDwOBMlPejtGoLigUcR1eASS4iKiktmcnZpQlOzmh1Tg8HPSlVM1VajaFmd0VMdDxE662r1u4DgMHMzTXRSiXTqjQZTngB4ECH2y86HqL1trNZXwk+HmSfWFq9BcUCFl1XgEkuIiopr0zOZiK8qaBVOT0c8gKAt8rKGwoqSzub9BOcxkNxrkCgVckWC5xWY6il2hYVHQ/Ressmd2ciydpIIm0UHQ8Vn0yxoB5gsWClmOQiopKyo7HSDwBjwTiTXLQqV3zzXgBoq2YfGSpPBzrckwAwPZ+o4+SMViNbLGhgsYDKlMdhSbptphlVg+HFgUCt6Hio+GSKBWYWC1aOSS4iKil3dVb7AX1yFk2mOcbRig3N6lu0NtY72P+AylKN05Jw2UyzqgbD8cFAjeh4qPhc8bNYQJRN8p4dDXFVLK0YiwWrZxL1wbIsPwTgIwB2AUgBOA7gw4qinFnm6w0AfgXAbwHYBGAKwBEAH1UUpeeG5z4D4P5F3urjiqJ8cBXfAhEVoOzkLBhNVZ8cDNbcu8EzKTomKi4TIX0V4J7WKt5UUNlqqLL6gtFU9dnRkPeBjTXcJkErMjTzSrGA4yiVrTaPzX/ZN7/16uS8F8A50fFQcWGxYPWErHKQZflNAJ4A4AfwqwB+E4AFwHOyLO9b5tt8HMBnAZwE8C7oCbPNAE7Ksrz1Js8/AuDBm/z55Oq/EyIqRNmKx0sjIW5ZpBWZCMWtoVjKbZSQ3tfmnhEdD5Eo2Zvqq5mbbKKVyBYLdrewWEDla1MmyTsyy+bztHLDLBas2rqv5JJl2QY9OaUoivLzCx5/DMBTAD4DYO8t3mMXgA8A+HNFUf5iweNfA3AGwKegJ7AWmlIU5ZlcfA9EVNhaq23+Ht/8lmt65ey86HioeBzrn60HgFqnxW81GVTR8RCJsrHe4X/y8hRGAjFus6EVWVgsuKPdPS06HiJR9rS4fADgm2OfWFo5FgtWT8RKrrcA8AL404UPKoqSBvBRAHtkWb7zFu9hAfDPAD5xw3vEADwG4FavJ6ISlq2cZXsrES3X+bG5BgBoclfwhoLK2t7Mdt3sTTbRcrFYQKTb01o1azJIyXA8XTkyG7WJjoeKh38ubg2yWLBqInpyPQBgSFGU/pt87SiAZOY5xxd7A0VRTgA4sciXrQCCawuRiIrZnkzFwxeKcwUCrUjfdMQLAJ01dia5qKztbXXNmAxSKhxPV40GYhXN7oqY6JioOLxSLHCxWEDlzWw0aLVOy+REKN50bCBQ/7Zq26DomKg4vNDHYsFaiEhybQBw+mZfUBQlJsvyBQDdq3ljWZatAH4ON0+A3SXLcj+AJgA+AP8DvUn9mhJiR44ced9aXl8IEolELVAa3wsVp1xfg6qmwWQA5uJp14+eOfx+h1nScvG+VLqy16B/JlgLAM2qf9+RI5O3iY2Kyk2h/XvcYNe0kTDww8Mn37ulWkqIjofyLxfX4NCY6gKAdstcV6Fcy1RcCm0sXIt2m+qaCAHne679TGOkb150PLQ8oq/BsyOqHQDabEl3Kfx/sBpms3nVrxWxXdEDYKkldzMAVntc9d8DaM38vVAYwDcB/Bn07ZL/DeA3ADwjy7J9lZ9FRAXKIElosCMJACNhrH6EpLKiasD4vF78aXUiJToeItGaHFJmHNWEncZNxWdsXjMDQEvm+iEqZ80O/X5iNCxkcQkVqdHM/KXZCY6jqyDif7ZKAJElvh4B4Frpm8qy/EEAvw3gnxVFOXzDl9+qKMrCC+QHsiwfgn7C4x9BP5lxVQ4ePPiZ1b62UGSzw6XwvVBxysc1WNf70ptHwqHdY0bvi796cONi25uJAOjXoC+iGROqWu+wGMOve/Dgv4mOicpPof17/KPAlQPHfROPDCarrh08uOt7ouOh/FvrNZhSNWns+cP/G9DwyMG9/95RY1/qnp/opgptLFyLUdtYxzf6rv3SYMwye/Dg/s+JjoeWR/Q1+JcvHf9VIGbfvrH7Wwf3Nd2szVPJO378+KpzNGtOcsmy/CH89Mqpm3lWUZQHAMwBWKrxnh3A7ApjeDeAfwLwHQB/eOPXb0hwZR/7kSzLjwN4N9aQ5CKiwtRZY/edGQ6hfzrCvly0LMNhffVBQ5V1QnQsRIVgW4PT9w0Ao8EYm8/Tsrw8EnIn05rFYTGGmeAiAvZ3VPsBYGouUZ9SNclkYAsNWlpK1SR/5kTOA51u9jZchVxsV/x3AFuX8ecXM8+/1XbEW21n/AmyLL8xE8OPAfycoigr2WLyJIBOWZYtK3gNERWBrV6nDwBGgzwZjJZnJLOVoKWazZKJAGB/h35zPRnWJ2ei46HCd2oo6AUAb5WV4ygRgHaPLeKwGMNJVTO/PBJyi46HCt/ZURYL1mrNK7kURZmBnrharl4Ar7nZFzKN47cB+Opy3kiW5XsBKACOAXhUUZSVnvxjAKAB4IkFRCUmW/nwz8W9qqbBIHF+RksbzfSR2Vjn4OSMCEBHjT1itxjDkUTaeXY05N7b6lrRSnsqP1f8YS8AtLpZLCDK8lZZfX1TEeepoaD39jaOo7Q0FgvWTkTj+WcAtMuy3HGTr90FwJJ5zpJkWd4N4LsAzgN4g6Ioi2Y5ZVneuMiXDgDoWeHqLyIqApnJ2XwyrVnOjc65RcdDhW98Xm/yuaulijcVRBneSosfAE4PBbn1m25pcCbqBYANLBYQvaIlk/S94p/n7gK6pR7fPIsFayQiyfUtAH4Af7nwQVmWDdBPP3xJUZQXFzxulGXZe8NzNwD4IYBBAI8oihJa7MNkWf7/AJyTZXn7DY8fBPA2AJ9d03dDRAXLW2nxAcDJTEWEaDGxlCZNx2A0SFD3d7inRMdDVChaqm0+AOjh5IyWYSKktwjY3VrF3oZEGRvq7D4AGJqNchylWxqciXgBYGO9g+PoKq376YqKokRkWX4/gK/LsmwG8LVMHO8FsB/Agze85FMA3ivL8n2KohzJvg0AB4C/BrBLluWbfdSxzPbFzwD4ZQDPyLL8MQAXAdwJ4I8BPAXgEzn89oiogLRW2yb6p6Ndl31hL4Ae0fFQ4RoOw6QBqHVYJm1mI7ewE2VsrLNPPH9tBoMz0QbRsVBhmw4nLIFoymOQoN7Z7l52f12iUre7xeUDRjDOQzxoGbLFAu4sWD0RK7mgKMq3ALwOQDOALwL4NPTeWPcrinL8hqePAggACC54zAPACT1B9vQifxoynzUAPXn2HQAfAvBtAO8A8OcA3sitikSla1OmAjIwHeHkjJY0kjlZscnF/gdEC+1pdU0AwFgwxnGUlnS0f7YeAGoclkm7xZgWHQ9Rodjf4Z4ySEgHoinPZDjBA89oUSwW5Ma6r+TKUhTlSeinG97qeR8F8NEbHutY4WcNA3jPSl5DRMVvT6YCMh6Kc3JGSxrJ9OPqqLEzyUW0QHZyFoymqv1zcWt9pTUuOiYqTGfH5rwA0MhiAdFPsFuM6VqnZdI/l2g40jvjfcuuhmHRMVFhYrEgN4Ss5CIiWg93dLinjAsmZ6LjocI1GtZMALC90cn+B0QL2MxGtdZpmQSAF/pm2XyeFtU3pa+a7qqxcxwlukGzq2ICAM6PzbHwSovKFgua3RUcR9eASS4iKln65MzqB4AjfbPsg0A3lVI1aSyzkuuuzmreVBDdIHuzfY6TM1rC8Kzet217YyXHUaIbdNXqyd/eKbbQoMX1Ts43AEAniwVrwiQXEZW0ZreVkzNa0pnhYHVCheSyQO2osUdEx0NUaLo5OaNbSKZVaXIu4QWAu7qquV2R6AbbGyt9ADASYH9DWlz2+tjRxJ0Fa8EkFxGVtGzlrG+SkzO6uZNDwQYAaHIgKToWokKUXZkzyskZLeL0cNCTVDWz02oMtXtsLBYQ3eDuLn2l+GQ4UR9PqZyD009JplXJnykW3N3JYsFa8H8wIippO5uq9MkZj22mRfT4wg0A0OKUmOQiuomDmZU5nJzRYrLFgsYqK1cfEN1Es7siVlVhCqZVzXRyKOARHQ8VnlNDQU9K1cyVVmOwpdoWFR1PMeONChGVtLuzk7O5uJeTM7qZwZloJsnFlVxEN9PoqohVVZgCaVUznRgM1IiOhwrPFZ/eR6bdwz4yRIvJJoFPD4W4KpZ+SrZY0OCq4CquNeKEj4hKWkOVNe6qMAXSGozHBzg5o582EYo3AEAbV3IRLarRlZmcDQc5OaOfki0WbPI6ODkjWkSbxzYBAD3+MMdR+ilX/NligY3FgjVikouISh4nZ7SYwZmoPRxPV1oM0Lx2pEXHQ1So2j02H3B9xQ7RQhOhuBcA9rW5ODkjWsQWr95MfHiW/Q3ppw1ligVbvA6Oo2vEJBcRlbzs9omrfk7O6Ccd7Zv1AkCTAymDJIkOh6hgZSdnQ7NR9jekn9A3FXHMJ9KVZqOU2NPqmhUdD1GhuqNdTwJnV5ATLZS9Lu5odzPJtUZMchFRyctWRIZmo7ypoJ9wYXyuAQCauVWRaEl3Zm66J0LxRlXTRIdDBeRYv14s8FZafSaDxIuDaBG7WqoCFqMUjyTSjqv+eafoeKhw9E7OO+YTaafFKMVva64KiI6n2DHJRUQl786OVyZnDZyc0UJ905EGAGhl03miJe1srgxYjIZ4NKnar01yckbXXRjPnFDrruDqA6IlGCQJ3kqrDwBeHAhwVSy94thAoAFgsSBXmOQiopK3o6kyaDUZYtGkar/qn68UHQ8VjrFALJPk4kouoqUYJAneKssEABzrD3BVLL2iP1Ms2FDHPjJEt9JSrSeDL2ZWkhMB13cWZK8PWhsmuYio5OmVMwsrZ/QT5mIp0/R8sk4CNK7kIrq11mr9xKdLEzwZjK4bC+rFgl0tVZycEd1CNhncP80WGnTdQOZ6YLEgN5jkIqKykJ2cXeTkjDJe6J+t0wCp2m6ethjZdJ7oVjbW2TOTswjHUQIABKNJ08x8slYCtLs63X7R8RAVumwyOJscJgKA0QCLBbnEJBcRlYVsZWSAkzPKeHkkpDedZx8ZomXZ1VzlA4CxIE8GI93R/kC9Bkgeh3nKZTOnRMdDVOju7qyelABtNpKsCUSSZtHxkHiBSNI8G0nWSIB2d2f1pOh4SgGTXERUFva0ZitnnJyR7trkfAMAdNTYmOQiWoYDndWTBgnqbCRZMzOf4OSMXikWNLlYLCBajsoKU6rGYZ7UAOmF/tl60fGQeC/0z9ZrgFTjME9WVphYLMgBJrmIqCzs73BzckY/YXhWXxq+vbGSkzOiZaisMKU8DssUoK/gER0PiZctFnTW2DmOEi1Tk6vCB1xPElN5e6VYwJ0FOcMkFxGVBafVlK5xWCYB4IW+WTafL3MpVZN8c/qqvgMdbt5UEC1Ts8s6AQAvj3JyRsBIIFsscHIcJVqmzlo9Kdw7FeH9KL1yHXSxWJAzTHIRUdnI9l46O8pjm8vd2dGQO5nWLA6LMdxd55gXHQ9Rsciu2OnNrOCh8pUpFngB4EBntU90PETFIpsUziaJqbxdLxZwZ0GuMMlFRGWjo8bmA4DeqXlWzsrcicFAAwA0VFl5Q0G0AtmbcE7O6MxwsDpTLJjrqrWzWEC0THd1Vk8AgD8Ub0ipGo93LmMpVZP8IX1nwV1dLBbkCpNcRFQ2dnByRhmXffoqlDYPm84TrUT2JnxyLuFNplVOzsrYqaGgFwAaXSwWEK1ER4094rAY55KqZj4zHKwWHQ+Jc3ooWJ1UNbPTapxr99giouMpFUxyEVHZuKsrUznj5KzsDc5EGwBgU72DkzOiFWj32CJOqzGUVDXz6eGgR3Q8JE5PtlhQzWIB0Uplk8Mnh4IsvJax7O+fOwtyi0kuIiobbdW2aKXVGEqpmvnUECdn5WwiqK/m29fm4k0F0Qo1VFl9ACdn5W5wJtIAAJu9bDpPtFLZ5HCPL8xxtIxlf//t3FmQU0xyEVFZyVZKTg1zclauhmajtrl4uspkkJK3t7lmRMdDVGyyN+NXfOxvWM7GM31kWCwgWrlNXocPAIZmohxHy9jQbJTFgjxgkouIykq7x56dnDHJVaaO9s02AEB9pcVnNho00fEQFZvszXj25pzKz+BM1B6Op6vMBim5t801KzoeomKTTQ5nk8VUnsaDLBbkA5NcRFRWNnv1HkzZnkxUfs6PzzUAQIu7gjcURKvwyuQsyMlZuTraN+sFgLpKi89kkFgsIFqhva2uGbNBSobj6arBmahddDy0/gamI/b5RLrSbJQSe1pZLMglJrmIqKzc3ubyAcBEKM7l4WVqYFrfGtBd6+BRzUSrsKfVNWs2Son5RLpyYDrCyVkZupApFrSy6TzRqpiNBq2u0uIDrieNqbwc7c/uLLCyWJBjTHIRUVlZODnrm4o4RMdD6280oDedv625kpMzolUwGSStvlJvPp+9Safy0j+tN53vrrVzHCVapeyK8gsTcxxHy9CF8TB3FuQJk1xEVFZMBknzZiZnx/pZOSs34XjKOD2fqAOAu7uquZKLaJVemZyN82SwcjSa2aq6q6WKkzOiVequ01to9E9FOI6WoezvfUMdiwW5xiQXEZWd7OTs/Phco+hYaH0d6w/UqRoM1XbztMdhSYqOh6hYZW/K+zg5KztzsZRpZj5RKwHa3Z3VftHxEBWrXc16kji7wpzKy2hQ/71nrwPKHSa5iKjsbKx3jANA/xSbz5ebl0aCDQDQ5LLyhoJoDXa3uLKTMxYLysyx/tlXigVuu5nFAqJVOthV7ZcAbXo+WReMJk2i46H1E4wmTTPzSb1Y0MViQa4xyUVEZWdvq2scAEaDnJyVm6uT+qqTzhouDSdai4Nd1X6DBHUmkqydmU+YRcdD6+elkVADADSzjwzRmrjt5qTHYZ7SAOkIm8+XlaP9gXoNkDwO85TLZk6JjqfUMMlFRGXn7q7qSYMEdTaSrJkMJyyi46H1MzwTbQSAbY1OTs6I1qCywpSqcVgmAeBwL5vPl5Ork/MNANBRw5MVidaqxV0xDgAvDYdYeC0j13cWsFiQD0xyEVHZsVuM6VqnxQ8Ah3tnODkrEylVkybm9GbJd3dWj4uOh6jYZSdnL4+GOI6WkaFZfRX0zqZKjqNEa9RVq7fQuJZJHlN5uOqfbwSA7lo7x9E8YJKLiMpSq9s2DgBnR1k5KxenhgKeZFqzOK3Gue46x7zoeIiKXXedPTs54zhaJpJpVfKF9GLBwW4PJ2dEa3Rbs54sHmZ/w7IynC0WNLNYkA9MchFRWdrwyuQswpuKMnFiMNgIAI2uCt5QEOXAruaqcQAY4eSsbBwfDNSkVM1cVWEKtlXboqLjISp293R7JgDAPxf3RpNpzs3LQDSZNvgzOwvuzfz+Kbf4PxIRlaXdLZyclZvLE+FGAOissTHJRZQD93Z7fAAwFU7Uh+Mpo+h4KP9OZooFTS4rx1GiHGiossbdNtOMqsF4tG+2TnQ8lH8vDgRq0xqMLptpttFVERMdTylikouIytI93R6fBGjT4UTdXCzFY5vLwGC26XwDl4YT5UKN05KotpunVQ2Go32z9aLjofzr8WWLBewjQ5Qr2ebjp9l8viycGtKLBc3cWZA3THIRUVlaeGzz4d4ZTs5KnKppGA/FGwHgrk43byqIciTbfJ6Ts/IwOKOvft7eyGIBUa50ZZqPX/GHOY6WgSuZpvOdbDqfN0xyEVHZas5Mzs6McHJW6s6NzrnjKbXCZjZEtjQ4Q6LjISoV2cnZVTafL3kpVZMmQnqS6+4unlBLlCs7MieVZpuRU2kbyuws2MFiQd4wyUVEZaur1j4BANf8nJyVumMDs680nTdIkuhwiEpG9iZ9eDbaIDoWyq+XR0LuRFqzOizG8MZ6R1h0PESl4mAmaewLxRuSaZU3KSUspWqSL7Oz4O6uajadzxMmuYiobN3WVMVjm8vEpUzT+Q4Pm84T5dI93dWZk8ESDfGUyvvKEvbiQCBTLGDTeaJc6qixR5xWYyipauZTQ0GP6Hgof04OBjxJVTM7rcZQV619XnQ8pYo3I0RUtrKTs0ke21zyBqb1peFbGpycnBHlUEu1LVpVYQqkVM10fCBQIzoeyp/LmabzHWw6T5RzTZkm5CcyJ5hSaTrxygm1bDqfT5zUEVHZanRVxFw202xag/FYf4DHNpew8VC8AQD2d7DpPFGuNWVW9mRPjKLSNDAdyZxQy2IBUa511ugrzbPJZCpN10+o5c6CfGKSi4jKWvb4Xk7OStcVf9gZSaSdFqMhvrulalZ0PESlprNG72/Yw5PBSpaqaZjI9JE5wBNqiXJua0PlBAAMZpqSU2nK/n63sel8XjHJRURlrZPHNpe8F/pmX+kjw6bzRLm3PXOzPsTJWcm6NB6uiiZVe4XZEN3eWBkUHQ9RqbkrkzyeCMUbVU0THQ7lgappGM8UC+7q5Am1+cQkFxGVtesng8V4MliJujCuJzDb2HSeKC/uzpwMNhGKN6RUjZnkEnS0P1MsqGKxgCgftjQ4QzazIRJPqRXnRufcouOh3Ds3OueOp9QKm9kwv9nrmBMdTyljkouIylr2+F5fKN7IyVlp6s/0kdnidTDJRZQHG+sdYYfFGE6kNevLIyG36Hgo9y5mTqhtZ7GAKC8MkoTGTAuNYwOzXBVbgrK/1yZXBYsFecYkFxGVta5a+7zTapxLqpr55GCAxzaXoLFArBEAbm9zcXJGlCeNmebzLw4EODkrQdmm85u9bDpPlC/ZJPKlCbbQKEWXrhcLJkTHUuqY5CKistfIY5tL1uBM1D4XT7tMBil5Z7t7WnQ8RKWqw2PnyWAlbCyo95G5s51N54nyZUsmiTwwzf6GpSj7e93CE2rzjkkuIip72WN8ezg5KzlHemcaAMBbZZ0wGw3s5EqUJ1sbspOzCMfREnPVP++cT6QrzUYpsbfNxRNqifJkf4eeRB4PxdkntsSomoaxoL6zIPt7pvxhkouIyt62Br35PI9tLj3nx+b0pvPVFbyhIMqjAzwZrGQd7Z9tAICGKuu4ySDxl0uUJ3taq2YtRkM8kkg7r/jDTtHxUO5c9c9XRpOqw2I0xHe3VLFYkGdMchFR2TtwvXLGyVmJ6cusKtlUz6bzRPm0o6kyaDUZYtGkar88Ea4SHQ/lzoXxbLGATeeJ8skgSWioskwAwAt9bD5fSrK/z0YXT6hdD0xyEVHZ29rIY5tL1Wim6fzeVjadJ8ongyShsUpvPn+0n83nS0nfVLbpPIsFRPnWlmk+f2GcLTRKSfb32cYTatcFk1xEVPb0ytkrJ4OxD0KJmAjFrYFoymOQkL6rq3pSdDxEpe76yWBzHEdLSLbp/L429pEhyrfN9exvWIqyv88tLBasCya5iIgAdNToJ4NdnJhrEh0L5cbhTNP5+kqrz2Y2qqLjISp12ROj+qajHEdLxMhs1BaKpdwmg5S6s4Mn1BLl2752feV5diU6lYZs03kWC9YHk1xERLh+Mlg/J2cl4+yo3kem1c2m80Tr4c529xgAjAViHEdLxOHe2UyxwDJhNRlYLCDKs/0d7imzQUrOxdOuvqmIQ3Q8tHYD0xH7XDxdZTZIyTvaXSwWrAMmuYiIABzsqh4FgLFgrInN50tD79R8IwBsYNN5onWxt801azEa4vOJtLPHF64UHQ+t3bnMCbWtbDpPtC7MRoOWbaFxuHeGq7lKwJFM03lvlXXCbDRwkrEOmOQiIoJ+MpjNbIjEkqqNzedLw8isvppkT0sVJ2dE68BkkLQml3UMAA73znI1VwnonZpvAnhCLdF6aq+xjQHAuTG20CgF14sF3FmwXpjkIiKC3ny+yVUxBgBH+jg5K3aT4YRlJpKsNUhQ793g8YmOh6hcdGQmZ+c5OSsJI5mtp3dktqISUf5tbXCOAUDvVKRZdCy0dr2T880AsLXBOSo6lnJhEvXBsiw/BOAjAHYBSAE4DuDDiqKcWebrBwC0L/Ll31MU5V9ueP7bAPwRgK0AwgCeBfAniqL0riZ+Iio9nTX2sd6pyIZM8/mLouOh1Xv26nQjANQ5rT6n1ZQWHQ9RudjWWDn2zNUZ9E9HmOQqciOzUVswmqo2GqTU3Tyhlmjd7O9wj332yDD7G5aI0czv8c4OFgvWi5CVXLIsvwnAEwD8AH4VwG8CsAB4TpblfSt4q8cAPHiTP1+/4fN+E4AC4DSAdwH4AwAbAByRZbljLd8LEZWOHU2VYwAwwObzRe/lkVATALR5KnhDQbSO7u6s1pvPs79h0Xvumt4PyMum80Tr6vY29wz7G5aG3sl5x1w8XWU2Sok723lC7XpZ95VcsizbAHwWgKIoys8vePwxAE8B+AyAvct8uxFFUZ65xec1A/gHAB9TFOXDCx7/BoCTAP4JwM+s5HsgotJ0T3f12L883Y/xYLwppWqSySBxhlakrk3qq0i2eLk0nGg97WyuDNjMhkg0qdrPj825bmuuCoqOiVbnbGbLabvHxmIB0ToyGSSt0WUdG5yJdh7unW3a7HX2iI6JVifbn7KxyjrOpvPrR8RKrrcA8AL404UPKoqSBvBRAHtkWb4zh5/3iwA0AB+74fPmAfwdgEdlWebJFUSEzV7nnMNiDCfSqvXUUMAjOh5aveFAtBkA9rW5ODkjWkc39DdkP5ki1jupN51nHxmi9cf+hqXh3Ji+s6C9xs770XUkIsn1AIAhRVH6b/K1owCSmefk8vNOKooSvsnXngVgBHBvDj+PiIpYk1ufnL04EOBNRZEaDcQqsn1k7mIfGaJ115m5mb84zslZMRsNxJoB9pEhEmFbg95Cg/0Ni1vflP7725Y5TIDWh4gk1wbovbF+iqIoMQAXAHQv873eKsvyqCzLcVmWr8qy/H9kWbau4PP6AARW8HlEVOK6a+2jAHBpIsybiiL13LXpJkDvI2MzG9lHhmidbW90sr9hkWMfGSKxDnaxv2GxUzUNo8F4E6AfJiA6nnIi4nRFD4CbreLKmgFQs4z3mQbwDIAT0E9LfAP00xrvlmX5DYqiZCc2nsxz1/p5N3XkyJH3rfa1hSKRSNQCpfG9UHEqpGuw06BZAWB0cnbvkSNHFjvBlQrY2QHVCQDd9kT1cq+pQroGqXyVynXYENMMADARjHYcPnz4fZIkiQ6Jlil7Df7whTO/CgCtDg3Hjx39NbFRUbkplbFwLTRNg90ENZJU7d85dOQ36mwST4peR7m4BmdimiGSUJ02I7T48Pm3HRnhv4UrYTabV/1aEUmuSgCRJb4eAeBaxvscUBQlueC/vyfL8hkAnwbwSwC+kOPPI6Iy0FmFJACMhmFWNQ0GTs6KzuCcZgaA9kokb/VcIsq96gpJrTJDDSVh8EVhbLCDk7MiMxjSx9G2SonjKJEAkiSh1YlkTwDWgZBmZpKr+PSFNAsAtDiRYLFnfa05ySXL8ocA/P0ynvqsoigPAJgDYFvieXYAs7d6sxsSXFmfBfABAO/G9STXcj5v1Sf/HDx48DOrfW2hyGaoS+F7oeJUaNfgX51+4YOhWMqFhm3fPLjBw55OReYPjh75IJCuOLh3+38d7F7e76/QrkEqT6V0HXovnnpnyD+/aczc8vzPHuw4LzoeWp7sNXgtVjEHzFdu7mz94UH+/midldJYuBZfH7/4YE9g6r7eVPXFDx7c8ZToeMpJLq7B//n6hYeA6XtbvDUnDh7cfih30ZWH48ePf2S1r81FT65/B7B1GX9+MfP8W20PvNX2wkUpiqIBOARgy4KHb/V51av9PCIqTc2Z5vPHB9l8vtj0TUUcc/G0y2yUEvs73FOi4yEqV12Z5vOXJth8vthomobRQIx9ZIgEY3/D4taf+b1lf4+0fta8kktRlBnoiaTl6gXwmpt9IdM0fhuAr64hJAPwE8viewHsXeTzOqEnufrW8HlEVGK6a+1jlybCW3t84SYAL4uOh5bv+WszTQDQUGUdNxsN7NRKJMhtzZWjT1yaxMAMJ2fFZjYOQySRdlpNhtjtba6V3OMTUQ4d7PaMffyZAYwF401soVFcVE3DWKZYcHfmEAFaPyJOV3wGQLssyx03+dpdACyZ5yxKlmVJluXFTkTcD2DhsupnAOyTZdlxk+feD0AF8PySERNRWbmtWT+2eZCTs6JzbkxfNdLhsfGGgkige7o94wAwEYo3JtMqZ2ZFpD8ECwA0uqxjnFQTibPZ65izW4zhRFq1nhoKekTHQ8v38kjIHUupNpvZENneWLnq1ki0OiKSXN8C4AfwlwsflGXZAODPALykKMqLCx43yrLsveE9/hPAUVmWG294j3dCT3J9dsHDXwZgBPC/b3iuHcAfAviuoiicDBHRK+7bUDMGAL65REM0mRYxTtIq9U1HmgBga0Mlx3Uigbpq7fOVVmMwmdYsLw4EakXHQ8uXPbyjM7PllIjEMEjSKy00jvWzhUYxOZr5fTW5KlgsEGDdT1dUFCUiy/L7AXxdlmUzgK9l4ngv9ATVgze85FMA3ivL8n2KohzJPPZPAN4I4AVZlv8GwCD0LZC/C+ALiqJ8Y8Hnjciy/IcAPi7Lcg2AHwCoyjy3FsAb8vStElGRanZXxNw200wgmvK80Ddb//Dm2gnRMdHyZJeGH+hkHxki0ZrdFWOXffOu4wOBpnuWeQgEiZdNcm1rYB8ZItG6auxjV/3zmzL9DXkIRJG4mOlH2VVrHxUdSzkSskJBUZRvAXgdgGYAXwTwaQAagPsVRTl+w9NHAQSw4ARERVFOQt/aeAL6irBvAngAwK8DeM9NPu//AngX9CTafwP4Z+iJsYOKovTn6vsiotLR4ta3u50cDLJyViQuTYQr5xNpp8VoiLOPDJF43XWOMQC4rPc3pCKgaRqGwzAD7CNDVAh2Nukr09nfsLgMTEebAWBHE3cWiLDuK7myFEV5EsCTy3jeRwF89CaPXwAgr+Dzvoq1NbQnojKyod4+dn58bkePP9wE4LToeOjWjvTOZJaGs48MUSHY1Vw59vh5P/sbFpGpGIyRFAw2syGyo4l9ZIhEu3eDZ+wfDvW90t+Qh+oUvpSqSePBWCMA3NPNYoEI7DVDRHQTu1uqxgBgiJOzonFhXF8t0sml4UQFgf0Ni09/SN+qyD4yRIWB/Q2Lz6mhgCeR1qxOq3FuU70zLDqecsQbDiKim7hvQ804AEyGE965WErYqldavr7pSDPAPjJEhaLZXRFz2UyzaVUzHe2brRMdD93awJy+VbGzlk3niQpFtvn88QE2ny8G2UMCml0VHEcFYZKLiOgm6pyWhMdunlI1GA73ztSLjoeWpmraK03nD3JpOFHBaMlMzk4Osb9hMRgMaRYA2NlUyRWxRAWC/Q2Ly8WJcDMAdNdxZ4EoTHIRES2izWMbBYDTw5ycFbqTg0FPLKXa7BZjeHsj+8gQFYqNdY5RAOjh5KzgJdOqlG06f/8GDydnRAViV7PevJz9DYtD/3SkBQB2t1RxHBWESS4iokVsqNO3a/T45ptFx0JLO9o/2wwALe6KUfaRISocuzL9DQdnohxHC9yRvtm6hAqp2op0d51jXnQ8RKRjf8PiEUmkjb5QvAEAHthYw50FgvB/EiKiRdzR7h4FgMGZaIvoWGhpF8bnWgBgA5eGExWUBzfWjEuANhlOeIPRJPsbFrDjA4FmAGivRFJ0LER0XbO7IlZtN0+nVc303LUZr+h4aHHPXp32qhqMHrt5qtFVERMdT7likouIaBH3bfBMGCSkZyLJ2olQ3Co6HlrcwLS+SmRPi2tEdCxEdF2N05KodVr8qgbD01emG0XHQ4u7NBFuAYCOSikhOhYi+klt1RWjAHBiIMDCawE7MRRsAa63PCExmOQiIlqE02pKe6usEwDw9JUpbrUpUOF4yuibyywN38Sl4USFpj1zs38qc/NPhSm7pbSzikkuokKz2escAYAeP1toFLIen950fovXwaKrQExyEREtobPGPgIAZ4ZDnJwVqGevzjRkl4Y3VFnjouMhop+0tUGfnF3xz3McLVDT4YRlKpyoN0hAZxW3KxIVGrbQKA5Dmd/Pvszvi8RgkouIaAnbG52jAHBtkpWzQnVyKNNHxmNj1YyoAB3o0G/2h2fZfL5QPX11ulEDpEY7khYjD+8gKjT3bfD4jBLSs5FkzWggViE6HvppI7NRWyCa8hgNUuq+DR6f6HjKGZNcRERLONjlGQGAkUCsRdU00eHQTVzx6atDtnidrJoRFaC7uqonzUYpMRdPu676552i46GfdnpY30raXilxFRdRAbJbjGlvlXUcAJ6+Ms2CQQF65upMEwA0VFnHbWajKjqecsYkFxHREva0Vs3azIZINKnaXx4JuUXHQz9tKLM65M4ON1dyERUgs9GgNbn0psnPXuXkrBBdzfT56awC+3ERFahsC42XRthCoxBlfy+dNWw6LxqTXERESzBIEprd+uTsSN8sbyoKzFBmabjJIKUOdlf7RcdDRDfXXWsfBYCzY3McRwvQSCDWDABdVVzJRVSodjRVjgBA7xRbaBSi7O9le2Mli66CMclFRHQLG+r0E1LOj83xpqLAPJNZss+l4USFbWdmctY3FWGSq8D0+MKV4Xi6ymI0xBsdSImOh4hu7t5uzyjAFhqFSNU0jARiLQBwsKuaK7kEY5KLiOgWdrdUjQJA/zQnZ4Xm7GhI32JTw6bzRIXs/o01owAwFow1JdMqO5sXkGevzjQDQJPbOmqQ+KshKlQ7mysDNrNhPpZUbWeGQ9Wi46HrXhoJVceSqs1mNkR2tVQFRMdT7pjkIiK6hYc26ZMzXyjeGEmkjaLjoeuuTeqJx+1NlayaERWwjfWOcFWFKZhMa5ajfbN1ouOh686N6X1ksltKiagwGSQJLdV6v6fDvTMsvBaQI72zzQDQ4q4YYbFAPCa5iIhuodFVEau2m6fTGozPXp32io6HdKqmYTSo95G5t9vDlVxEBa61umIEAI4NBLj1u4D0TemHd+xsqmKSi6jAbazTm89fGGd/w0KS/X1sqHNwHC0ATHIRES1Du0ffDndiKMibigJxaijoiSVVm91inN/RVBkUHQ8RLW1jvX7zf3EizHG0QCTTqjQeijUBwAMbWSwgKnTZFhoD01EWCwpI/3SkGQB2t1RxHC0ATHIRES3D5szkrMcX5k1FgXihT18a3uyuYB8ZoiJwe6trBAAGZ6JMchWII32zdcm0ZqmqMAW76xzzouMhoqU9uKlWb6ExF28Ix1NsoVEAIom00ReKNwLAg5tqxkTHQ0xyEREty/4O9wgADHFyVjAuTugJx+zSfSIqbA9uqhk3SFCnw4m66XDCIjoeAo5nto5mt5ISUWFrqLLGaxzmSVWD8dmrMw2i4yHguWsz3rQGY7XdPN3oqoiJjoeY5CIiWpZ7Nnh8RoOUCkRTnqHZqE10PHT9tMvs0n0iKmwumzlV57T6NED68ZXpJtHxEHAps3U0u5WUiApfm0dvPn9yKMDCawE4MagXC7KtTUg8JrmIiJbBZjaqjVXWcQB45so0tywKNhdLma4vDa/l5IyoSHTU6JOA08Psb1gIBjLFguxWUiIqfFu8zhEA6PHNcxwtAJcnwq3A9dYmJB6TXEREy9SZmZy9PBriTYVgh3qmGlUNhlqnxd9QZY2LjoeIlmdbg3MUAK5OzrNYINh4MFYxNZ+sN0hIP7y5dlx0PES0PPs73KMAMDTL5vOFYHAm2goAd3dVD4mOhXRMchERLdOOJv3ElGuTESa5BDsxGGwFgM4a27DoWIho+Q52V48AwPBsrEXVNNHhlLVDPfqq5IYq63hlhSklOh4iWp57uj1+k0FKBqOp6r6piEN0POXsij/sDMZSbrNRSty7weMXHQ/pmOQiIlqmBzfVDAPAyGy0JZlWeZyfQD1+fWn4jsZKJrmIisgd7e5pm9kQiSTSzpdGQtWi4ylnp4f1YkF3nYPjKFERsZoMarO7YhQADvVMtYqOp5w9fWW6FQCa3RUjZqOBlZsCwSQXEdEybW1wzlVVmIKJtGY90jdbJzqecqVqGoYyS8Pv3eDh5IyoiBgkCa3V+grM567OcHIm0NXJ+VYA2NXMYgFRsdlY5xgCgJdHQhxHBXp5dK4VADayWFBQmOQiIlqBdo8+OXuhd7ZNdCzl6tRQ0BNNqg6b2RC5vc01IzoeIlqZzfX6ZODcGCdnoiTTqjQaiLUA11cpE1Hx2NNaNQwA16YiHEcF6s0UC/a2ujiOFhAmuYiIVmBLg3MYAC5OzPGmQpDnr+mrP9qqbcMGibtGiYrNvnb3MAD0T0c5jgry/LWZ+mRas7gqTIFN9c6w6HiIaGUe3lw7AgDjwVhTOJ4yio6nHM3FUqaJzEnfD2+u4cmKBYRJLiKiFbi7Uz85ZWCGkzNRzo/rCcZNXi4NJypGD2+uGTNIUCfDCe9EKG4VHU85Oto/2wpcX51MRMWl2V0Rq3GYJ1UNxh/3TDeJjqccPX1lulHVYKx1mP2NroqY6HjoOia5iIhW4N4NHr85c6LNVf+8U3Q85Si7+uOOzGoQIiouLps55a2yjgPAoZ4pnlYrwMUJ/fCOrZnVyURUfDpr7MMAcHwwwMKrANmfe/b3QIWDSS4iohXInGgzAgBPX5nm5GydTYTi1qlwot4gQX1oU82Y6HiIaHU21NqHgOsn/NH6GsysRr6rq5qTM6IitaOpcggAenxhjqMCZH/u25t4eEehYZKLiGiFNmSaJr80GmLz+XWWXfXhrbKOu2zmlOh4iGh1bmvWmyZf8c9zcrbOrvrnncFoqtpslBL3dHv8ouMhotW5t1s/YXpwJtqqaprocMqKqmkYmo3pJ31386TvQsMkFxHRCt3e6hoCrp+oQusnu+qju5ZLw4mK2UOb9RP9RgOxlmRa5QkS6yi7CrnZVTFqNRlU0fEQ0ersa3fN2MyGSDSpOs4Mh6pFx1NOXhoJVUcSaYfNbIjsa+dJ34WGSS4iohV61Rb9RJuJULxxLpYyiY6nnFzNrPrY1Vw1JDoWIlq9TfXOsKvCFEimNctz12a8ouMpJy+PhlqB66uSiag4GSQJbdX64RHPXp3m7oJ19NxVnvRdyJjkIiJaoYYqa7zWafGrGoyHeqYaRcdTLpJpVRoJxFoA4MFNNSOi4yGitWmvsQ0BwNG+Wa6KXUe9U5FWANjbWsUkF1GR2+x1DgHXT56m9cGTvgsbk1xERKvQWWPLnmjDytk6Odw7W59Ma5aqClNgs9c5JzoeIlqbbZmT/bIn/VH+heMp43gw1gQAr9pcy2IBUZG7o901DAD9meQ1rY++zM+bJ30XJia5iIhWYWfmRJsrPvblWi/Z1R7tHhtvKIhKwN1d15smi46lXDx9ZbpR1WCscZgnG10VMdHxENHaPLy5dtwgIT01n6wfD8YqRMdTDvxzPOm70DHJRUS0Cvdt0JsmD83yRJv1cnFCXxq+NbP6g4iK2z3d1X6LUYqHYil3jy9cKTqecnB8INAKAJ01PLyDqBRUVphSDVXWcQA41DPdLDqecnCoZ6pZAySe9F24mOQiIlqFPa1Vs3aLcT6aVO0nB4Me0fGUg4HMao+7Oqs5OSMqAWajQWt2V4wAwI+vTHM11zq47Au3AcD2xkqOo0QlYkOdYwgATg0H2UJjHZwa4knfhY5JLiKiVdBPtKkYBoDne2c4OcuzSxPhymA0VW02Sol7N3j8ouMhotzYlDnh72zmxD/KH1XTMDgTbQOAezewWEBUKnY164dIXPOzhcZ66PHNtwPAbc08vKNQMclFRLRKW7In2ozNsXKWZ4d6ptoBoLXaNmw1GVTR8RBRbtzepjdN7p1k0+R8O9o/WxtNqna7xRi+o909LToeIsqNhzfrLTRGA7GWeErl/D6PIom0cSQQawWAR7bWDYqOh26O/xMQEa3S/g79RJW+qQiTXHn20kioHQC2eB28oSAqIa/aUjciAZpvLt44HU5YRMdTyp69OtMOAJ01tkGDJIkOh4hypLvOMe+2mWaSqmZ++sp0g+h4StmPe6YaU6pm8tjNU1219nnR8dDNMclFRLRKD2+pHTMZpORMJFnbOznvEB1PKbs2qS8NP9BZzSQXUQmpc1oS3irruKrB8MSlyRbR8ZSy82Nz7QCwvbGS4yhRiemu1YuAR3r1ZDblxwv9s+0A0F1r5zhawJjkIiJaJZvZqLZk+nL96PIUbyryZGg2apueT9YZJaRfvaV2VHQ8RJRbG+v1ydmJwSDH0TxRNQ3905F2ALh3g4eTM6ISc1uznry+OBHmOJpHlyf0wzt2tVRxHC1gTHIREa3BVq9zEADODHNyli8/ujjZBgDN7ooRp9WUFh0PEeXW7a2uQQDo8YU7BIdSss6NzrnD8XSV1WSI3dPtmRQdDxHl1qu21A4AwOBMtD2latyPnAfJtCoNzsTaAeBVm2uZ5CpgTHIREa3B/k73IABcm4wwyZUnpzMJxE2ZhCIRlZbXbtOb944GY81zsZRJdDylKHt4R7vHNmgySJroeIgot25rrgpWWo3BeEqteP7aTL3oeErR89dm6hNp1VpVYQpsb6oMiY6HFsckFxHRGrxmS92oUUJ6MpzwjsxGbaLjKUVXMwnEO9tdTHIRlaBmd0WszmnxqRqMT1yabBYdTyk6O6r349rWwGIBUanqyvSJeu7aNAuvefB8pt9ZF/txFTwmuYiI1qCywpRqcleMAMATl6Z4ymKOTYYTFl8o3miQoD6ytW5YdDxElB8b6vRJw4sDAU7O8qB3Sj+84+4uHt5BVKp2Nul9oi6Osy9XPmR/rjubeHhHoWOSi4hojTZnttGdGuLkLNeeuDjZqgGSt8o67nFYkqLjIaL82JPpy3V5gn25cu2KP+wMRFMes0FKPrS5dkJ0PESUHw9uqhkAgP7pSLuqcVdyLi08vOOBjTVMchU4JrmIiNYou43uKvty5dyJQT1xuKnOwRsKohL2yFa9L9dIINYaSaSNouMpJU9mTv9tqa4YspoMquh4iCg/9rW7ZuwWYziaVB3H+gO1ouMpJccHAjXRpOqwW4zhfe2uGdHx0NKY5CIiWqNHttYNGySovlC80T8Xt4qOp5Rc8etbbG5vYz8uolLWVWuf99jNUylVMx3qmWoUHU8peWk41A4AW9mPi6ikGSQJnTW2QQB45ir7cuVS9ufZWWMbNEg8vLLQMclFRLRGHocl2VBlHdMA6YlLk62i4ykVwWjSNBaMNQPAa7bWDYmOh4jyqzvTl+to3ywnZzl0bUpfZby/g/24iErdjka9X9T5sTmOozl0LvPz3N7IflzFgEkuIqIc2FSvb6c7MRjkTUWOPHl5qlnVYKxzWnzN7oqY6HiIKL92t2SaJrMvV86MzEZtU+FEvVFC+tVbasdEx0NE+XX/xlf6cnWwL1fu9GeKBfdu8DDJVQSY5CIiyoF9be5B4Pr2Olq77Clr2VPXiKi0vWpz7SAADM1G25JplftBciB76m+Tu2KkssKUEh0PEeXXXV3VUxVmQzQcT1e+NBKqFh1PKTg7GnLNxdMuq8kQu6fbMyk6Hro1JrmIiHLgkW11QxKgjQdjzYFI0iw6nlJw2acf1Zw9dY2IStu2xsqQy2aaTaY1y4+vTDeIjqcUnBrWVxdvrmc/LqJyYDJIWodH78v1454pFl5z4FDm59jusQ2aDBKXxxUBJrmIiHKgocoar6+0TKgaDE9cmmwRHU+xiyTSxpHZWCsAvGpzLftxEZWJrhp95eaRXvblyoWrmdXF+9pdHEeJysS2TN+os6Psy5ULL4/MdQDA1gYnx9EiwSQXEVGObMz05cpus6PV+9GlyaaUqpk9dvPUxnpHWHQ8RLQ+bmvO9OUa1ycVtHrjwViFLxRvNEhQX7eNh3cQlYt7uz0DANA3FekQG0lpuDY53wkA927w9IuOhZaHSS4iohzZm9lWd9nHpslrdbh3tgsANtU7eENBVEYe2lwzCAADM9H2lKqxL9caPH7e364BUpOrYsTjsCRFx0NE6+P+jR6fxWiIB2Mp97mxkEt0PMXszHDQHYyl3FaTIfbQppoJ0fHQ8jDJRUSUI6/bVjcIAKOBWAv7cq3NpYm5TgDY1+5ikouojOxuqZqttBqD8ZRa8XTPFPtyrcGJoWAnAGxtcHIcJSojZqNBa6/R+3I9cXGqU3Q8xeypy/rPr6PGNmA2GtiPq0gwyUVElCMt1baot9I6rmowPn7B3yY6nmIViCTNI4FYCwC8YXv9gOBwiGgdGSQJGzIrOJ+5OsPJ2Rpc8YU7AeBgdzWTXERl5ramyj4AODMS5Di6Bi+NhDoB4LbmKo6jRcQk6oNlWX4IwEcA7AKQAnAcwIcVRTmzjNc+A+D+JZ7yJUVRfnmZz/+4oigfXFbQRES3sKXB0eebizce7Z/t/Pk7mntFx1OMfnDR36pqMHorLeMt1bao6HiIaH3d3urqOzMc2n1+fK4LwAui4ylGvZPzjqn5ZL3JIKUe2Vo3IjoeIlpfD22q6X/spQn0Tka6VE2DQeLu75XSNA29U5FOQP95io6Hlk/ISi5Zlt8E4AkAfgC/CuA3AVgAPCfL8r5lvMUHATx4kz9vhJ4wu1lzzSOLvOaTa/hWiIh+wl2desW8xzfPytkqHesPdALAZi+32BCVo9dtr+sHgKGZaFskkTaKjqcY/eDiZAcAtFZXDDmtprTgcIhond2zweO3W4zz84m081h/oFZ0PMVoPALTfCLttFuM83d3VU+KjoeWb91XcsmybAPwWQCKoig/v+DxxwA8BeAzAPYu9R6Kory0yHu/H4AE4HM3+fKUoijPrC5qIqLlecP2+qG/e7JXnQjFm0YDsYpmd0VMdEzF5lJmi83+DjeTXERlaFO9M1zjME9OzyfrfnhxsvlndjfwZMAVOjUU7AKA7Y2VHEeJypBBktBda+8/Nza341DPVOfdXdVTomMqNhdnNAsAdNfa+7kSrriIWMn1FgBeAH+68EFFUdIAPgpgjyzLd67yvX8LwOOKovBmiIiEcNvNyWZ3xTAAPH7e3yE4nKIzEYpbJ4LxJoME9Q3b6zmWE5Wp7MmqR/pmukTHUoyu8sh7orK3K9NH6uxoiOPoKvQENCtw/edIxUNEkusBAEOKotzsYjkKIJl5zorIsnw/gJ0APrWW4IiI1mpbg145PzEY4E3FCv3gwitH3o/WOC0J0fEQkRh3tOsrOS+Oh7n1e4XOjoZcwWiq2mI0xB/eXDsuOh4iEuM1W2v7AKB/OtqRTKtcirQCqqbhWhBW4PrPkYqHiCTXBgCnb/YFRVFiAC4A6F7F+34AQC+AHy3y9btkWe6XZTkuy/KQLMv/KMuyaxWfQ0S0pHu6q/sA4IqffblW6vig3o9rC4+8Jyprb9xRPyAB2lgw1jIdTlhEx1NMfnTp+pH3VpNBFR0PEYmxp9UVcFWYAvGUWnGoZ7pRdDzFZGgOpmgKUlWFKbCn1RUQHQ+tjIjTFT0Alpq8zACoWckbyrLcDH0b5B8riqLd5ClhAN+E3nx+CvpKsd8G8JAsywcVRYms5PMWOnLkyPtW+9pCkUgkaoHS+F6oOJXaNVitarAYoM1EkrXfPXT41z0VEicZy9Q3nq4DgG2Wme1HjhzZuF6fW2rXIBUnXoc/qcWJ1HAY5q8dOv7+XbVSXHQ8xeJCn+oGgB3OSONKryVeg1QIeB3mzlaXajkWA547c/kdlbNXwqLjKRaXplMewIAtrrSF16EYZrN51a8VkeSqBLBUUikCYKUrrH4d+qmK/77I19+qKEpywX//QJblQ9BPePwjAB9Z4ecRES3KZJDQ7ULi0iysF2c16z2NUlR0TMUgGNcMYxGYTAZggwvcqkhU5ja5pfhwWDNfmtWsTHItj6ZpuJLpI7Olmj8zonK3pRrxYz7YewKa5Q2igykiPUHJAABb3LwfLUZrTnLJsvwhAH+/jKc+qyjKAwDmANiWeJ4dwOwKPt8C4NcAfE1RlJmbPeeGBFf2sR/Jsvw4gHdjDUmugwcPfma1ry0U2ex0KXwvVJxK8Rr84WzPXZdmfa85P185+EcHd39LdDzF4ONP9+8Ehn+mzWPvffC+fV9Zz88uxWuQig+vw580ZB3tOjTS++6LIfPcwYMH+DNZhmeuTteHkhd+w2Exhn/ukbs/tdITwXgNUiHgdZg7jVMRxxcvn/xQX0gy7tq3//NOqyktOqZCNxdLmXqfe+FPAOBnH7j9c1219nnRMZWj48ePrzpHk4ueXP8OYOsy/vxi5vm32o7oATC9gs9/O/TTGlfTcP5JAJ2ZRBkRUc48uLGmDwCu+ue7VO1mu6jpRicGAxsAYFdz1TXRsRCReK/fXj9klJD2zyUa+qYiDtHxFIOnLk9tAICN9Y5eHnlPRF219vlap8WfUjXT4+f9baLjKQbfPedrS6qQmh1IMsFVnNa8kiuzeuqmK6gW0QvgNTf7gizLVgDbAHx1Be/3AQCnFEU5voLXZBkAaADYL4eIcuqBTTU+u8UYnk+kKw9fm6m/b2ONX3RMhUzVNFydjHQDwKu31PaKjoeIxHPZzKk2j22wfzra9d1zvq7ffbDznOiYCt3Z0VA3ANzR7uI4SkQAgG0NzmvPXZupf753pvvnbm/iwT63cKRvthvglu9iJuJ0xWcAtMuy3HGTr90FwJJ5zi3JsrwXwAHcYhWXLMuLNS8+AKBHUZTUcj6PiGi5DJKETfWOXgB48vLUak6MLSvPXJn2RhJph9NqnDvYXT0pOh4iKgzZlZ3ZlZ60uEAkaR6aibYDwKM7vUxyEREA4J7u6l4AuDge5ji6DBcn9J/Tdg+TXMVKRJLrWwD8AP5y4YOyLBsA/BmAlxRFeXHB40ZZlr2LvNdvQ+/f9d+LfZgsy/8fgHOyLG+/4fGDAN4G4LOr+B6IiG7pznb3NQB4aTTEm4pbONQz3Q1wiw0R/aTXbK27BgBX/PPdKVXj4LCE7533t6U1GL2VlvGOGvuqTw4notLyxh3eIZNBSk6GE94r/rBTdDyF7Io/7JwKJ+rNBmgb2XS+aK17kktRlAiA9wN4pyzLX5Vl+a2yLL8dwA8A7M98baFPARjLJKVeIctyDYB3APiioihLnVz2GejbKZ+RZfl/ybL8WlmW/wzAU5k/n8jJN0ZEdINHb/P2AcDQTLR9Zj6x+nNwy8DZTCIwmxgkIgKAg93Vk06rcS6aVB3PXJlerOhJAI70zeirDxoruYqLiF5RWWFKddTYBgDgu+f83F2whOzPZ4MLCbOBdZViJWIlFxRF+RaA1wFoBvBFAJ+G3hvr/pv01hoFEAAQvOHx9wCwAvh/t/isAejJs+8A+BCAb0NPjv05gDdyqyIR5Uu7xxZpqLKOqRqM3z3n7xAdT6GamU+Yh2ajbcD1xCAREaBv/d5c77wGAE/1THFV7BIuTYS7AeCebg+TXET0E3a3VPUCwMnBIMfRJZwa0n8+W9mPq6itufH8aimK8iT00w1v9byPAvjoTR7/OwB/t8zPGoaeFCMiWlc7GiuvTYTiTS/0z3b/0oGWq6LjKUTfO+9vVzUYG6qsY23VtqVW5hJRGbqzw33t1HBwzzl9xedh0fEUoovjc1XT88k6s1FKvGFH/bDoeIiosLx2W921r5+ZwLWp+e5kWpXMRgOP/r5BStWka5PzXQCwo0aKiY6HVk/ISi4ionJx30a9on5pgs0+F/NC3+wGQE8Iio6FiArPm2/z9kmANhKItU6GExbR8RSix8/rW2w6auwDdosxLToeIiosd7S7p6sqTIFYUrU9dXmqUXQ8hehQz1RDNKnaqypMwUY7OI4WMSa5iIjy6PXb60csRkN8NpKseXkk5BYdTyHKbrG5dwO32BDRT2t2V8QaXdZRVYPh2y9PdIqOpxCdGg52A9e3JBERLWSQJGzxOq4BwI+vTLPwehM/zmyJ31Tv6JV4CFJRY5KLiCiPrCaD2lVr6wOAxy+w2eeNzo6GXDORZK3ZKCVet71uRHQ8RFSYdjZVXQOAYwMBTs5uEE+phmuTkW4AeG3mNEoiohsd6KzuBYDzY3McR2/i3Kj+c9nfwUOQih2TXEREeXZ7m+saAJweZrPPG33nrG8TAGyoc/TazEZVdDxEVJge3FRzDQAuT4Q3qBpbySz0/Qv+lnhKrXDbTDN3drhnRMdDRIXpzbd5+w0S1NFArGU8GKsQHU8hGZmN2kYCsVaDBPUtuxp4CFKRY5KLiCjP3rCjvhcA+qYiXeF4yig6nkJycii4CQDubHdfER0LERWu12ytHaswG6LBWMp9rD9QKzqeQvLjnqlNALC9sZLjKBEtqr7SGm9xVwxrgPSNlye4u2CBb77s69YAqbXaNthQZeXJikWOSS4iojzb2VQVrHNafMm0Zvn2WV+H6HgKxcx8wjwwHekEgLfu8vLkSSJalNlo0DbXO64CwOPn/ZtEx1NIzo3NbQKABzbWMMlFREva0+q6AgAv9M1yHF3gaL/+89ib+flQcWOSi4hoHdzWrFfYn706zZuKjG++7OtKazA2uqyj3XWOedHxEFFhu6ur+goAnBkObhYdS6E4Mxx0T88n68xGKfHobd4h0fEQUWF74476HgC44pvfGE+pzAVA72t4xT+/EQDeuKOeSa4SwAubiGgdvGpz7RUAuDAe3sx+MrrDvTObAGB3cxVvKIjoln52d+M1gwR1JBBrHZqN2kTHUwi+m1nVtrHOcc1uMfLIeyJa0p0d7mm3zTQTS6m2x8/7W0THUwgeP6/3Nay2m6fZ17A0MMlFRLQOXru9ftRmNsyHYinXc9dm6kXHI5qqabg0Ed4EAI9sq2OSi4huqaHKGm+rtg1qgPSNMxMbRcdTCE5l+xp2sK8hEd2aQZKwo0nfXfDjnimuigXw4yt6X8MdjU6OoyWCSS4ionVgMkjalgbnVQD44cXJst+yeOjyVON8Iu10Wo1zD26qmRAdDxEVh9vbXD0AcGyA/WSmwwnLwHSkAwDeuquBR94T0bI8uKmmB7jez6/cnc/8HO7fWMP+sCWCSS4ionVyT5enB2A/GQB44pJeNdvqdV4xSJLocIioSLxpp/cKAFz1RzZEk+myvo/95ssTnaoGY5PLOtJVa2dfQyJaljft9A5ZjIb4TCRZe2ooWC06HpFODwerp+eTdRajFH/TTvY1LBVlfXNARLSefnZ3Q59BQnosGG/pm4o4RMcj0sujoU0AcM8GD5eGE9Gy3d7mmvXYzVOJtGr9zllfu+h4RDqcOR1tdwv7GhLR8tnMRnVT5rTa75zzlXXh9bvnfBsBYEOdo5d9DUsHk1xEROukxmlJdNbY+wHgGy+Vbz+ZK/6wcyIUbzIapNTP7GroFx0PERWXnc2VPQDw9JXyPa02pWrSK30Nt7KvIRGtzF1deh+/bF+/cnVqSN9dsZ99DUsKk1xEROtoX5vrCgC8WMb9ZP7n9PgWAOiqsfW57eak6HiIqLg8nDmt9vz4XNmeVvv98/6WSCLtrKowBR/YVOMTHQ8RFZef3d14VQK04dlo+0QobhUdjwijgVjF4Ey0QwK0n93TyH5cJYRJLiKidfTWXQ09ANA7GdkQjCZNouMR4dhAYCsA3N3luSQ6FiIqPm/cUT9cYTZEg9FU9eEyPa32h5cmtwLAbc2Vl9jXkIhWqtldEWutrhhSNRiU02NlWXj92qmxzaoGQ5vHNtDusUVEx0O5wyQXEdE62t5UGWqoso4lVc2snB7fIDqe9TYyG7UNTkc7JUCTb2/sER0PERUfs9GgbWtwXgaA75zzbRUdz3pTNQ1nR0JbAeA1W+tYLCCiVbmzw30JAJ6/NlN24ygAvNA3uxUA9md+DlQ6mOQiIlpnd7S5LgLAM1eny+6m4qunxjdrgNReY+tvq7ZFRcdDRMXp4c21FwHg1HBom+hY1tuPe6YbgrGU224xht+00zssOh4iKk5v39N4CQCuTUY2zswnzKLjWU/T4YTl2lSkGwDkvY2XRcdDucUkFxHROnvr7oZLAHB5Irw5HE8ZRceznl7o16tmB1g1I6I1eNuexn6L0RCfCifqXxyYrREdz3p6/Lx/KwDsaHReNhmk8mxKRkRrtq2xMtTkso6kVM30tdPjZXUg0tdOj29Iq5qpyWUd2ex1zomOh3KLSS4ionV2R7t7ps5p8SXSmvWxMxNdouNZL5PhhKVvcr4bAN7OqhkRrYHdYkxvbXD0AMA3Xy6vLYtnRoJbAeDhzbUsFhDRmtzZ7r4IAM9dLa8ti9ktmne0s+haipjkIiIS4PbMlsVDV6bKZqvN106NbUxrMLa4K4Y31TvDouMhouL2wMaaiwBwcjBQNuPoC32ztdPzyTqryRB76+6GAdHxEFFx+5nM7oIef3jTXCxVFgcizcVSph5/eBNw/fun0sIkFxGRAG/Z5b0IAJfGw5ujyXRZjMWHe/Wq2b52F28oiGjN3r63sddslBK+uUTjmeGgW3Q86+HbZye2AsDWBmePzWxURcdDRMVtT6sr4K20jifTmkU5Pd4tOp718PUz413JtGapr7RM7G11zYqOh3KvLCZWRESF5mCXZ8pjN0/FUqrtmy9NdIiOJ9/mYinTFd+8XjXbxaoZEa2dy2ZObap3XAWAx16aKIvVXKeG9FMVH9hYw3GUiHJiX7u+u+DpMtld8HTm4Ke9rSy6liomuYiIBNmb2bL4VE/p31T818nRjUlVM9dXWib2tLoCouMhotJwf2bL4onBQMn3kzkxGPD45uKNZqOUeNuehl7R8RBRaXjrLu8lALg0Mb85kkiX9IFI4XjKeHE8vAUAHt3pZZKrRDHJRUQkyJt21F8EgPNjc1vjKbWkx+NDPdM7AeCuzupzomMhotLxc3sbr5oMUmosGG85NxZyiY4nn5TT4zsAYFuD87LLZk6JjoeISsP+jurpWqfFn0irVuX0WEkfiPTVk2Mb4im1os5p8d27wTMpOh7Kj5KeVBERFbIHNtX4PHbzVDSp2kv5pmIiFLde8ekNPt+1r+m86HiIqHR4HJbkZq9+yuJ/nxjbITqefFE1DS8OBG4DgNduqz8rOh4iKi13trvOA8CPLk3tFB1LPj15eeo2ADjQ6WbRtYQxyUVEJIhBkrC/Q/9H9ocXJm8THU++fOX4yNa0BmNbtW1gW2NlSHQ8RFRaXr2l7hwAvNA/u0vVNNHh5MWhy1ONs5Fkjc1siLx9b2O/6HiIqLS8a1/zOQC4NBHeOhlOWETHkw/+ubi1x6/3h30ni64ljUkuIiKB3rGv6RwAXJwIb5ku0ZuKZ6/N7ASAezdwqyIR5d479zVdqzAZotPzybpnrkx7RceTD98869sJALtbqs5bTQaeqkhEObWrpSrQ4q4YSqma6SvHR7aIjicfvnJ8dEta1Uyt7oqhnU1VQdHxUP4wyUVEJNDeVtdsi7tiOKVq5v84PlpyNxVX/GHn4HS00yBBffedLRdFx0NEpcduMaZ3tVRdAIDHXpoouVWxybQqnR4K7gCAN+30slhARHlx7wbPWQB4+sp0yY2jwPWi6z2Z75NKF5NcRESC3dNdfRYAnrk6XXJ9EP7rxNh2DZA21jmuNrsrYqLjIaLS9OhO71kAOD0c3JlMq5LoeHLpWy/72ucT6cqqClPgDTvqR0THQ0Sl6RfvbL5okKAOTEe7rvrnnaLjyaXeyXlH/1SkyyBB/YU7m1l0LXFMchERCfbu/S0XDBLUvqlId+/kvEN0PLl0pG/2NgB4cHMNVx8QUd68cWf9sKvCFAjH05XfeGmiQ3Q8ufT4Bf9OANjX5jpnkEoqf0dEBaSl2hbdWO+4ogHSV06MltRBHl85MbpdA6TuWvu1tmpbVHQ8lF9MchERCdZWbYtuqHNc1QDpP46Xzk3FiwOzNROheJPZKCV+4Y7mHtHxEFHpMkgS7mh3nwWAxy/4S2arzVwsZTo/NrcNAN6+t5HFAiLKq4c3154DgCO9syUzjgLXi64PZb4/Km1MchERFYCHMiudDvfO7hIdS6585fjoHgDY0Vh50WUzp0THQ0SlLZsEOj82ty0YTZpEx5MLXzw2siWeUivqKy0T93R7JkXHQ0Sl7RfuaL5iMUpx31y88UjfTK3oeHLhuavT9ePBeLPFKMXfta+JRdcywCQXEVEB+IU7mnuyNxVPl8DpYNFk2nB8MLgbAN62p/G04HCIqAzc3VU91VBlHUumNcsXjo5sEx1PLjxxaXIvADy0qeaU6FiIqPRVVphSO5v1gzz++8TYHtHx5MJ/ndS/j90tVec8DktSdDyUf0xyEREVAJfNnNrT6joLAP99cnSv6HjW6j9eHN0USaQdHrt56o0764dFx0NE5eGhTTWnAeBHlyZvFx3LWp0aClYPzkQ7TQYp9Z6727jFhojWxdszxcnjg4Hd4XjKKDqetZiLpUynhoK7AEC+vYlF1zLBJBcRUYF4176mUwBwaii0KxBJmkXHsxY/uOjfCwD3bfScZqNkIlov7z3Yds5slBLDgVhbsW+1+fKLI3sBYEdT5YWGKmtcdDxEVB5et71utM5p8UWTqv1LL45sER3PWnzpxZEtsZRqq6+0TLx6S+246HhofTDJRURUIB7aXOtrdFlHE2nV+rkXhreLjme1LozNVfVORjYYJKjvvbvtZdHxEFH5qHNaErubq84BwFeOjxbtaq54SjUcGwjsBoCf2d3A1QdEtG4MkvTKFukfXCjuVbFPXNS3fD+4sYZF1zLCJBcRUQF59ebaUwDw5OXivan44osjuzVA2trgvNTusUVEx0NE5eXnMqtiTw4Gd83FUkXZgP4rx0c3RhJpp8dunnrzbd4h0fEQUXl578G2syaDlBqciXYeHwh4RMezGqeHg9UDM9FOo0FKvefuVm75LiNMchERFZD33N163mKU4mPBeMszV6frRcezUilVk17om90LAI/u9HL1ARGtu0e21o17Ky3jsZRq+8Kx4a2i41mNx8/79C3fG7jlm4jWX0OVNb6zufI8AHz5+EhR9or90rGRzCnfzouNroqY6Hho/TDJRURUQDwOSzLbgP6/ThTfVpv/OjHaHYqlXFUVpoB8e1O/6HiIqDw9lFkV+8TF4lsVe24s5Lo2GdlokKC+5+5WbvkmIiHkPY2nAOD4QGBPJJEuqgb0kUTaeGwgsAcA3sot32WHSS4iogKzsAH9zHyiqBrQf+Olif0AcN8GzymTQdJEx0NE5em9d7eeNxuk5NBsrL3YGtB/5vDQHRogbW+svNhRY+eWbyIS4vU76kdqnRZ/NKna//3ocFE1oP/cC0PbIom0s8ZhnnzrroZB0fHQ+mKSi4iowDy0udbX5LKOJNKq9VPPDe4WHc9yvdA3W9s7FdlgMkip37iv/ZToeIiofNVXWuO7W6vOAsDnXxg+IDqe5QpEkuaj/YHbAeDn72g+JjoeIipfBknCqzbXnACA757zFc04CgDfO+ffDwCPbK17kVu+yw+TXEREBejR27zHAOCJS5MHUqpWFP86f+Ho8J0AsLul6mxbtS0qOh4iKm/vvbvtGACcGQ7tGpyJ2kXHsxz/7/nB2+IptaKxyjr6hh31o6LjIaLy9hv3tr9sNRliY8F4y/fO+VpEx7Mcj5/3N4+H4s1WkyH2G/e2nxUdD60/JrmIiArQe+9uu1RpNQYD0ZTnP14c2Sg6nluZCMWtp4aCuwHgVw60vCg4HCIi3N1VPdVda7+WUjXTp54bKPjeXKqm4YlLk/sB4I076zmOEpFwHocleXdX9UkA+MqJ0aJYzfWfmTgPdLpPue3mpOh4aP0xyUVEVICsJoP60Oba4wDw2EsTBX9T8Ymn+/clVc3c7rH137exxi86HiIiAJD3Nh4FgGevztxZ6I2Tv3J8dMP0fLLOYTGG33t320XR8RARAcBv3Nt+3CBBvTge3nZuLOQSHc9Szo6GXBfG57ZJgPb+e9pOiI6HxGCSi4ioQP3Wfe2nzUYpMTgT7fzhxckm0fEsJhhNmg5dmb4LAN65r+mI6HiIiLLesa+pr9Zh9s8n0s5/fXbgNtHxLOWrJ8fuBYCHN9cetVuMadHxEBEBwNYG59z2xsoLGiB98tnBu0THs5RPPjtwt6rBsLOp8vzOpqqg6HhIDCa5iIgKVKOrInZXp75E/HMvDN0rOp7FfPLZgd2RRNrhrbSMv3NfU6/oeIiIsgyShDff1nAEAL5zzndPMq0WZI/Db7080TociLVZTYbYBx/qPCk6HiKihX7tYNsRADg+ELi9d3LeITqem+mbijiODwb3AsD772k7LDoeEodJLiKiAvbBBzuPGg1Sqsc3v+WZq9P1ouO5UTylGn5wYfIgAPzsnsbDPMGGiArNb9zXft5lM80GoinPvz0/tF10PDfzpRdH7gWAe7s9x+ucloToeIiIFnpwU41vU72jJ6Vqpn95ur8gV3N9/On+/WlVM22sd1xh64zyxiQXEVEB21jvCN/Z7joNAJ9+vvBWc33y2YGdwVjKXW03T7/37tZLouMhIrqR1WRQ37TDexgAHntp/L5CO7H2yUuTjdcmIxtNBin1uw92sOE8ERWk99zd+jwAHOmbvWNkNmoTHc9CI7NR2+HemTsB4FcOtDwvOh4Si0kuIqIC97sPdB4xSFDPj8/teP7aTJ3oeLIiibTxGy9NPAAAb93lfc5sNGiCQyIiuqnffqDjZafVGJqeT9Z9+vDgNtHxLPSvzw0+BAB3dVWf6KixR0THQ0R0M6/fXj/aWWPrTaY1y98/1VdQq7n+9sneg4m0Zu2osfW9aad3RHQ8JBaTXEREBW57U2Xojnb3KQD4+NP9D4uOJ+ufDvXtDcVS7hqHefID93ecEx0PEdFi7BZj+k07vc8BwFdPjj0UTaYL4h74my9PtPVORTaYjVLij17dzR4yRFTQ3new7RkAeO7azIEr/rBTcDgAgKv+eefh3tn9APAb97b/WHQ8JF5B/ANPRERL+6NXdz9nMkjJHv/85u+c9bWKjicQSZq/e95/HwC8a1/zj7mKi4gK3e891HnGbTPNBKIpzz8f6t8rOh5V0/DZw0MPA8CDm2qOtntsXMVFRAXtjTu9I5u9jsspVTP/3ZN994uOBwD+/qnee1OqZtrsdVx+/fb6UdHxkHhMchERFYGN9Y7wAxtrjgLAvz0/+CpVE5tT+usnrt0VSaSdDVXWsfcebL0sNBgiomWwmY3qu+5oPgQA3z7nu39mPmEWGc8Xjo5sHg7E2ipMhugfvbr7qMhYiIiW6/ce6jwkAdqJwcDe4wMBj8hYXhyYrXlxILAPAH7n/o6nRcZChYNJLiKiIvHHj3S/UGE2RIcDsbZ/e35IWE+ZC2NzVU9dnroXAN5/T9uPeKIiERWL99/TdrGhyjoWSaSdH/3BVWGHeczFUqYvHBt+BADeuLP+2fpKa1xULEREK3GwyzO1p7XqJVWD4W+e7H1EZCx/86PeR1QNhttbXWd4oiJlMclFRFQk6iut8bfuajgEAP9xfOS10+GERUQcf/nDq69JqZppe6Pzwtv2NA6KiIGIaDUMkoTfvr/jhwDw9JXpu18cmK0REcdfP3HtQDCaqq5xmCf/92s2nBARAxHRan34tRt+bDZKiav++U3/fnR4k4gYvnhseOO1ychGi1GK/9nrNx4SEQMVJia5iIiKyB+8qut0Q5V1LBxPV37k8Svr3gtBOT3WcWE8vN1kkFIffu2GJ9f784mI1urR27zDmVUIxr/+4bXXrff273NjIdePLk7eBwC/fk/7D6wmg7quARARrdGmemf4TTu9TwPAv78w/LpgNGlaz88PRpOmz78w/FoAeMMO7zNdtfb59fx8KmxMchERFRGz0aD9r4c6HweA53tnDhzqmWpYr8+emU+YP/HMwKMA8Kottc/vbKoKrtdnExHl0p+/ftNTVpMh1j8d7f7XZwd3rNfnqpqGD3+n501JVTNva3BefMe+pv71+mwiolz6k0c2HK91WvzBWMr9f763voXXD3+n58FANOWpcZgn//iR7uPr+dlU+JjkIiIqMq/bXj92oMN9QtVg+KsfXH1rOJ4yrsfn/vF3eh4ORlPVdU6L7y/esOnIenwmEVE+dNXa59+2p+FJAPjSiyNv6PGFK9fjc//hqb49/dPR7gqTIfpXb9r8g/X4TCKifLCaDOrvPdj5PQB45sr0wfU6/ft753wtz12buUsCtD94Vfe3bWYjV8PST2CSi4ioCP3tW7Y86baZZqbmk/V//O3LD+b785TTYx0v9M3ulwDtTx7Z8C27xZjO92cSEeXTH766+3R3rf1aPKVW/NG3Lj+a722LL4+E3F87NfYIALx7f/MPNtY7wnn9QCKiPHv0Nu/wAxs9RzRA+vunet+S71NrZ+YT5r9/qu/NGiDdt9Hzwht21I/m8/OoODHJRURUhDwOS/IPX939TQnQnrk6c/A/T4x25+uzrvjDzn881P82AHjVltrnXrWldiJfn0VEtF4MkoS/e+uWb1eYDNHeqciGjzx+5a58fdZcLGX6/W9ckhNpzbrZ67j8gfs7zuXrs4iI1tPH3rzl6VqnxR+Ipjy/+/WLb8hXwUDVNPy2cuFNM5FkrcdunvrYo1ueycsHUdFjkouIqEi9aad35NVba58DgH/+cf/bTg8Hq3P9GdFk2vDB/7n49kgi7Wj32AY+9uYtz+X6M4iIRNlU7wy/92DrdwHg2y/7Xv21U2Od+ficD379wiO+uXhjVYUp8PG3bf+2QZLy8TFEROvOaTWlP/L6jY+ZDFLqpZHQrr/64bU78/E5H3vi2r6zY3M7TQYp+dePblYqK0ypfHwOFT8muYiIitjfvmXrsxvr7FfiKbXi979x6R3+ubg1V++tahp+46vn3zAciLU5LMbwx9+27es8BYyISs3772m/9NCmmsMaIP3Dob63nxrKbcHgL75/5c7jg8F9BgnpP3v9RqXZXRHL5fsTEYn2wMYa/3vubv02ADx2ZvwR5fRYRy7f/79OjHYpp8dfBwC/clfLd+/p9kzm8v2ptKzrUZ83kmVZAvA3AP4QwFsVRfnWCl//6wA+AKALwAyA7wP4P4qi+G7y3LcB+CMAWwGEATwL4E8UReldy/dARCSSySBpn3rHzm/Inz/9a1PhRP0vffnld/3nL+/+isdhSa71vX/v6xcfPjUU3GsySKkPv3bD17rrHDyemYhK0j/8zNYfv/1zpxt6pyIbfud/LvziZ9658wvbmypDa33fTzzTv/PrZyZeBwC/uL/lu49srRtfe7RERIXnA/d3nL88EW589trM3X/zo9532s3G/3jjTu/IWt/3+xf8zf/wVN87VA2Ge7urj/7OA53c7k1LEraSS5ZlK4D/AvAHq3z93wL4BIDHAbwdwF8AeAjAs7Isu2547m8CUACcBvCuzGduAHBEluWOVX4LREQFoaHKGv/427Z9xWk1hkYCsbZfATOXCAAAEfBJREFU/PLL75wOJyyrfT9V0/B7j1188MdXpu+RAO13H+xQ3pSDmxQiokJlNhq0z/78bf/T6LKOhmIp969/9dwvXRyfq1rLe37imf6dn39h+C0A8OhO75O//3DXyzkJloioQP3z27Y9tbOp8lwyrVk+8vjVX/j+BX/zWt7vu+d8LR/53pWfT6qa+bbmyrOfkHc8matYqXQJSXLJslwN4EkAjwL4zVW8/nboiarfUhTljxRFeVxRlM8CuAeAB8BHFjy3GcA/APiYoijvVxTlO4qi/AeA+wDMAvinNX9DRESC7Wl1Bf7lbdu+bLcY5wdnop1v//zpXz07GnLd+pU/KZJIG3/5yy+/+anLU/dJgPard7V+65cPtF7NR8xERIWkzmlJfOndu75SX2mZCERTnl/5ytlfe/z8yidoqqbhT759+eBnjwz/jKrB8OCmmsN//ejmF/IRMxFRITEbDdoX3r3rW1sbnJcSadX64e/0/PK/PjewfTXv9ZnDQ1v+z/eu/FIspdo21Tt6Pvfzt33bZJDyewwulQRRK7luB7ADwCMAfriK178PwCCAzy98UFGUCQD/D8CvyLKcXcXwiwA0AB+74bnzAP4OwKOyLDeuIgYiooKyv6N6+hNv3/aFart5ejKc8L73P8++75PPDmxf7ik3P+6Z8r7p306898xIaLfJICX/18Od//3BhzrP5jlsIqKC0eiqiH3p3bu+3FZtG4gk0s4Pf+fyr/zpd3vujibTy7pnvjQRrvzZz55613fP+18FAG/d1fDEJ96+/VB+oyYiKhxWk0H94rt3Pba3tepMStVM//b80Nve919nX7fcvrHT4YTlvf959g3/99mBn0urmmlfm+v0f/3KHsVmNrIvLC2LkCSXoihPAdigKMrhVb7FAwCeUxTlZhf6swDcAHYveO5JRVHCizzXCODeVcZBRFRQ9ndUT//3r+z5XGeNrTeaVO2fPjz0tjd/+uS7/+vEaFdK1W56nNeLA7M1v/qVl9/4e49dfJ9/LtHgqjAFPvbmLV/kCi4iKkct1bbo139t73/c2e46mdZg/PZZ36tf/6kT7//kswM7wvGU8Wav6Z2cd/ze1y8++AtffOm3rk1GNlpNhtgH7m9XPvrGTcfWO34iItHsFmP6C+/e9Z2f2d3whARoR/sDdz76byc/8Jc/uHrHROjmya7pcMLyF9+/sv9N/3bit14cCOwzSFAf3el98vO/cNt3efARrYSwxvOKosys4eXdAD61yNdOLXjOcei9t76zSAx9siwHMs8lIioJze6K2Dfet+8//+Gp3j1fPzPx6oHpaNfHftTb9clnB4LtHvtwk9s6rWlAKJZy9E1FWifDCW/2tQc63Cf+5s1bnqpxWhIivwciIpFsZqP6+V/Y9fiXXxzp+czhoTdMhRP1nz489LNfenEk2l5tG2qprvCbDFJ6PpGu6JuKtEyE4k2qphePN9bZr3zszVu+t9nrnBP9fRARiWKQJPzFGzYdu2+DZ+Bvn+x943gw3qycHn/9N16aeHVrdcVQW7XNV2E2JuKptHlgJto4OhtrTaqaGQC8lZbxD79247cf3FTzUwfKEd2KpC1zG0u+ZBq/92OZpytmmsoHALxbUZSvLPKcFIAPKorySVmWZwH8o6Iof7XIc3sBfFNRlA+tJO5Dhw5pAJBMJov+lJxEIlELABaLZUp0LFSeeA3mTyihGZ4Z1ewvTGj2QBw3XYFgMgD76qTIa9qkcJNDSq93jIWA1yAVAl6HhSmR1vD8mGY/PK45xiM3LxBLALZ7EHt1qyG8uVpa8+m2ovAapELA67D0qJqGEz6t4vlxzXEtiEUPR+qoROJVrdL83jopZpBuugFhXfAaFM9sNjcCwMMPP7ziC0HYSq41qMz8HVniOVEA2YbLlbd4bmTBc4mISkqVRVIf7ZTCb+zQwoNzMA/NaabJGExGCagwQu2skpJdVUhYjOJuJIiICpnFKOHhVinyUIsWGY/ANDinmcfn9XtoixFaq1NKbnQhYTezITIR0c0YJAn7G6TY/gbEpmOaoT+kWUbnYUqrkEwGaF47UpvdUsJtlbgtkdZszUkuWZY/BODvl/HUZxVFeWCtnwcgu/TbtsRzbACCC56/1HPtC567YgcPHvzMal9bKI4cOfI+oDS+FypOvAbXB5sPLo7XIBUCXockGq9BKgS8Dkk0XoPiHT9+/COrfW0uVnL9O4DvLeN5S62mWokQgDSAmpt9MbOd0QhgOvPQzGLPzahe8FwiIiIiIiIiIipCa05yZRrIr6WJ/Eo/T5NluQ/A3kWekn28L/N372LPlWW5E3qSq+9mXyciIiIiIiIiouJgEB3AKj0D4D5Zlm/WROZ+6NsPX1rw3H2yLDsWea4K4Pnch0hEREREREREROul4JNcsiwbZVn23vDwZwF0APjVG57rBfAbAL6oKEo88/CXoW9f/N83PNcO4A8BfFdRlLE8hE5EREREREREROukGE5X/BSA98qyfJ+iKEcAQFGUE7Is/zOAT8myvAHAYQAN0JNWIQB/nn2xoigjsiz/IYCPy7JcA+AHAKoA/C6AWgBvWM9vhoiIiIiIiIiIcq/gV3IBGAUQwA0nICqK8vsAfh/AowD+B8BfQk923asoSuCG5/5fAO8CsB/AfwP4ZwCDAA4qitKf3/CJiIiIiIiIiCjfhK/kUhRlAMDNemtlv/5RAB9d5GufBPDJZX7OVwF8dRUhEhERERERERFRgSuGlVxERERERERERERLYpKLiIiIiIiIiIiKHpNcRERERERERERU9JjkIiIiIiIiIiKiosckFxERERERERERFT0muYiIiIiIiIiIqOgxyUVEREREREREREWPSS4iIiIiIiIiIip6THIREREREREREVHRM4kOgMT75je/WQsABw8eFB0KlSlegyQar0EqBLwOSTReg1QIeB2SaLwGixtXchERERERERERUdFjkouIiIiIiIiIiIoek1xERERERERERFT0mOQiIiIiIiIiIqKixyQXEREREREREREVPSa5iIiIiIiIiIio6DHJRURERERERERERY9JLiIiIiIiIiIiKnpMchERERERERERUdFjkouIiIiIiIiIiIqepGma6BiK0qFDh/iDIyIiIiIiIiLKg4cfflha6Wu4kouIiIiIiIiIiIoeV3IREREREREREVHR40ouIiIiIiIiIiIqekxyERERERERERFR0WOSi4iIiIiIiIiIih6TXEREREREREREVPSY5CIiIiIiIiIioqLHJBcRERERERERERU9JrmIiIiIiIiIiKjoMclFRERERERERERFj0kuIiIiIiIiIiIqekxyERERERERERFR0WOSi4iIiIiIiIiIip5JdACUH7IsmwD8IYBfAdACwAfgfwD8haIo4WW83gvgLwG8HoAHQB+ATyqK8m95C5pKzlquQ1mWHwDw9BJPqVYUJZCbSKnUybLcBOD7ADoURXGv4HUcCyknVnMNchykXJBl2QrgdwD8EoBOAGMAngLwV4qijC7zPTgW0pqs9TrkeEi5IMvyDgB/DuB+ABUArgD4NIDPK4qSXsbrORYWAa7kKl3/CeCPAXwRwM8C+CcA7wbwg0ziYVGyLLsBPAfgYQB/AeDtAL4H4OOyLP9N/kKmErTq63CBXwPw4E3+3DJZSwS8ckNzDMBtK3ydGxwLKQdWew0uwHGQVkWWZQOArwH4KwA/APBzAP4R+rh2PDNhu9V7uMGxkNYgF9fhAhwPaVVkWb4TwHEAjQB+D/p1+F3o1+K/LOP1bnAsLApcyVWCZFn+GQAygEcURfnRgsefAnAawG8C+MQSb/FR6JnpXYqijGUee1yW5asAPiPL8lcVRXkpL8FTycjBdZh1ktcbrZYsyw8DeAx6pe7b0JOsy8WxkNZsjddgFsdBWq03AXgzgF9WFOVL2QdlWX4MwAUAfwN9tfVSOBbSWuXiOszieEirFYeezPo/C1ZtfV+W5VHoY9n/VRTlyhKv51hYJLiSqzS9H8BzCxMLAKAoykXoVZRfX+yFmaXEvwTgUwv+5836AoAhAO/LbbhUolZ9HRLl0C8BOAO96ja93BdxLKQcWtU1SJQjEQD/AOArCx9UFGUSwI8A7F/qxRwLKUfWdB0S5YKiKC8rivInN9mWeDTzd8dir+VYWFyY5CoxsiwbAdwD4JlFnvIsgK2yLNcv8vW9AKpu9npFUVQAzwN4YK1xUmnLwXVIlCu/DuB1iqLMrfB1HAspV1Z7DRKtmaIoTyqK8geL9JqxAgjc4i04FtKa5eA6JMqnOwAkAZxb4jkcC4sItyuWniYAdujbwW7mVObvbgD+m3x9Q+bvpV7/c6uOjsrFWq/Dhb4sy3In9KT8SegNSp/MSZRU8hRFiazypRwLKSfWcA0uxHGQckqW5VoArwPw+Vs8lWMh5c0KrsOFOB7SmsmyXAF96+FrofcM/lNFUcaXeAnHwiLClVylx5P5e7EtETOZv2uWeH1aUZTgEq+3yLLsXGV8VB7Weh1mnQbwJQDvgN7DywLgCVmWf37NERItjWMhFQqOg5RTsixLAD4H/Vr6l1s8nWMh5cUKr8MsjoeUKxMARqEnWP9cUZS/u8XzORYWEa7kKj2Vmb8XqxxnH3ct8fqlqs4LX89TTGgxa70OAeCIoii3L3xAluX/BHAIwKdkWf7eEv/QEK0Vx0IqBBwHKR/+GXoT8N9VFKX/Fs/lWEj5spLrEOB4SLn1GgCtAB4F8PeyLDsURfnrJZ7PsbCIcCVX6cn2/LAt8nV75u/F/hGYW+K1y3k9EbD26xCKoiRv8lgKwO9D3xP/5rUESHQLHAtJOI6DlGuyLP8pgN+F3jx5OScccyyknFvFdcjxkHJKUZTjiqI8pijKLwH4IIC/kmV51xIv4VhYRJjkKj3L2Y4ILL2NzCTLctUSr08oisIMNS1lrdfhohRFOQm9QemWlYdFtGwcC6lgcRyk1ZBl+dcB/CWA/wDwgWW+jGMh5dQqr8NFcTyktVIU5V+hb1185xJP41hYRJjkKj3jAKLQT4C4mezjfYt8vfeG593s9Yu9lihrrdfhrRgA3OyEHqJc4VhIhY7jIC2bLMsygH8F8D8AfkVRFG2ZL+VYSDmzhuvwVjge0lqNAOhY4uscC4sIk1wlJrNs9zAWP8L0fgCXFUXxLfL109CXY/7U6zMNIu/FTY5OJVooB9chZFmukWXZc5PHt0Jfln4+B6ESLYZjIQnHcZByQZblR6Cvmvk+gJ9XFGUlyQCOhZQTa7wOOR7SmsiybJRl+RFZln8qSZUZyzYAmFziLTgWFhEmuUrTpwHcL8vyqxY+mPlH4B2Zr2cfc8mynN1DDEVRYtBPLfktWZYbbnjfX4ae4f5sfsKmErPq61CW5TroFZOP3/BaE4B/AOAD8N38hU7lhmMhicZxkPJBluUDAB4D8ByAt92sr9ENz+dYSDm31uuQ4yHlgAPAfwL4ROa6Wei90FusfDv7AMfC4sbTFUuQoiiPybL8GIDHZFn+GwAvQc9O/zH0LPS/AoAsyw7oyyqnAGxe8BZ/BuC1AJ6XZflvAYwBOAi9seM/Kopyep2+FSpia7kOFUWZlGX5UwD+WJblSuj/qJigNym9HcCbFUVZ6oQTomXjWEiicRykPPo+gBD0k+zu0neL/SRFUZ4BOBZSXq3pOuR4SGulKEpIluUPAPgKgB/Jsvw56CuzXg3gNwF8WVGUpwCOhaWAK7lK1zsB/COA9wD4BoAPAfgqgEcWVE9SACYADC98oaIoswDug15t+SiArwN4C4APKYryB+sRPJWMtVyHfwLglwA0Q/8H6dPQlxHfpSjKj9YleioXHAtJNI6DlC/VABoBPA7g6UX+ZHEspHzJxXXI8ZDWRFGUrwJ4CHrf4I9D7w13H4Dfgb4aK4tjYZH7/9u5AxMAYBiGYfT/o9snBsMgnWFCZvfV3x8AAAAA/GHJBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAECeyAUAAABAnsgFAAAAQJ7IBQAAAEDeARWpumJJFLqkAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": { "engine": 2, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%px --group-outputs=engine\n", "x = np.linspace(0,np.pi,1000)\n", "for n in range(id+1,12, stride):\n", " print(n)\n", " plt.figure()\n", " plt.plot(x,np.sin(n*x))\n", " plt.title(\"Plot %i\" % n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you specify 'order', then individual display outputs (e.g. plots) will be interleaved.\n", "\n", "`%pxresult` takes the same output-ordering arguments as `%%px`, \n", "so you can view the previous result in a variety of different ways with a few sequential calls to `%pxresult`:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[stdout:0] \n", "1\n", "5\n", "9\n", "[stdout:1] \n", "2\n", "6\n", "10\n", "[stdout:2] \n", "3\n", "7\n", "11\n", "[stdout:3] \n", "4\n", "8\n" ] }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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\n", 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"text/plain": [ "
" ] }, "metadata": { "engine": 2, "image/png": { "height": 384, "width": 604 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%pxresult --group-outputs=order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Single-engine views" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When a DirectView has a single target, the output is a bit simpler (no prefixes on stdout/err, etc.):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [], "source": [ "from __future__ import print_function\n", "\n", "def generate_output():\n", " \"\"\"function for testing output\n", " \n", " publishes two outputs of each type, and returns something\n", " \"\"\"\n", " \n", " import sys,os\n", " from IPython.display import display, HTML, Math\n", " \n", " print(\"stdout\")\n", " print(\"stderr\", file=sys.stderr)\n", " \n", " display(HTML(\"HTML\"))\n", " \n", " print(\"stdout2\")\n", " print(\"stderr2\", file=sys.stderr)\n", " \n", " display(Math(r\"\\alpha=\\beta\"))\n", " \n", " return os.getpid()\n", "\n", "dv['generate_output'] = generate_output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also have more than one set of parallel magics registered at a time.\n", "\n", "The `View.activate()` method takes a suffix argument, which is added to `'px'`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "e0 = rc[-1]\n", "e0.block = True\n", "e0.activate('0')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[stdout:3] stdout\n", "stdout2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stderr:3] stderr\n", "stderr2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:15]: \u001b[0m83115" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:46.138918Z", "data": {}, "engine_id": 3, "engine_uuid": "89363e7a-3deecfc8a12bb00f66b0d3b4", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83115" }, "execution_count": 15, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_73", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:46.145021", "started": "2021-10-26T11:42:46.131553", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:46.128734Z" }, "output_type": "display_data" } ], "source": [ "%px0 generate_output()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[stdout:0] stdout\n", "stdout2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stdout:1] stdout\n", "stdout2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stdout:2] stdout\n", "stdout2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stderr:2] stderr\n", "stderr2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stdout:3] stdout\n", "stdout2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[stderr:0] stderr\n", "stderr2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 0 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[stderr:1] stderr\n", "stderr2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:1]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 1 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[stderr:3] stderr\n", "stderr2\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:2]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 2 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 0 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:1]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 1 }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[0:15]: \u001b[0m83112" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:49.088850Z", "data": {}, "engine_id": 0, "engine_uuid": "69b2e5ae-f96ddd1460d94415c8a16e91", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83112" }, "execution_count": 15, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_74", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.093793", "started": "2021-10-26T11:42:49.077001", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073629Z" }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:2]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 2 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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"stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073755Z" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[2:15]: \u001b[0m83114" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:49.090730Z", "data": {}, "engine_id": 2, "engine_uuid": "608a802c-2bb806ebe19459d6eea9542c", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83114" }, "execution_count": 15, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_76", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.098551", "started": "2021-10-26T11:42:49.077932", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073838Z" }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:16]: \u001b[0m83115" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:49.090833Z", "data": {}, "engine_id": 3, "engine_uuid": "89363e7a-3deecfc8a12bb00f66b0d3b4", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83115" }, "execution_count": 16, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_77", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.100480", "started": "2021-10-26T11:42:49.078468", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073911Z" }, "output_type": "display_data" } ], "source": [ "%px generate_output()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned above, we can redisplay those same results with various grouping:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[stdout:0] \n", "stdout\n", "stdout2\n", "[stdout:1] \n", "stdout\n", "stdout2\n", "[stdout:2] \n", "stdout\n", "stdout2\n", "[stdout:3] \n", "stdout\n", "stdout2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[stderr:0] \n", "stderr\n", "stderr2\n", "[stderr:1] \n", "stderr\n", "stderr2\n", "[stderr:2] \n", "stderr\n", "stderr2\n", "[stderr:3] \n", "stderr\n", "stderr2\n" ] }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 0 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:1]" ] }, 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"metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_75", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.095838", "started": "2021-10-26T11:42:49.077251", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073755" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[stdout:2] \n", "stdout\n", "stdout2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[stderr:2] \n", "stderr\n", "stderr2\n" ] }, { "data": { "text/plain": [ "[output:2]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 2 }, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 2 }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[2:15]: \u001b[0m83114" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:49.090730", "data": {}, "engine_id": 2, "engine_uuid": "608a802c-2bb806ebe19459d6eea9542c", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83114" }, "execution_count": 15, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_76", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.098551", "started": "2021-10-26T11:42:49.077932", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073838" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[stdout:3] \n", "stdout\n", "stdout2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[stderr:3] \n", "stderr\n", "stderr2\n" ] }, { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "HTML" ], "text/plain": [ "" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\alpha=\\beta$" ], "text/plain": [ "" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:16]: \u001b[0m83115" ] }, "metadata": { "after": [], "completed": "2021-10-26T11:42:49.090833", "data": {}, "engine_id": 3, "engine_uuid": "89363e7a-3deecfc8a12bb00f66b0d3b4", "error": null, "execute_input": "generate_output()", "execute_result": { "data": { "text/plain": "83115" }, "execution_count": 16, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "fc613eca-25dd1c96bcaa7d73d3f18b11_77", "outputs": [ { "data": { "text/html": "HTML", "text/plain": "" }, "metadata": {}, "transient": {} }, { "data": { "text/latex": "$\\displaystyle \\alpha=\\beta$", "text/plain": "" }, "metadata": {}, "transient": {} } ], "received": "2021-10-26T11:42:49.100480", "started": "2021-10-26T11:42:49.078468", "status": "ok", "stderr": "stderr\nstderr2\n", "stdout": "stdout\nstdout2\n", "submitted": "2021-10-26T11:42:49.073911" }, "output_type": "display_data" } ], "source": [ "%pxresult --group-outputs engine" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallel Exceptions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you raise exceptions with the parallel exception,\n", "the CompositeError raised locally will display your remote traceback." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "tags": [ "raises-exception" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[0:execute]: \n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)\n", "\u001b[1;32m/var/folders/9p/clj0fc754y35m01btd46043c0000gn/T/ipykernel_83112/1064401740.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 2\u001b[1;33m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'invalid shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random_sample\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32m_common.pyx\u001b[0m in \u001b[0;36mnumpy.random._common.double_fill\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer\n", "[2:execute]: \n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)\n", "\u001b[1;32m/var/folders/9p/clj0fc754y35m01btd46043c0000gn/T/ipykernel_83114/1064401740.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 2\u001b[1;33m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'invalid shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random_sample\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32m_common.pyx\u001b[0m in \u001b[0;36mnumpy.random._common.double_fill\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer\n", "[1:execute]: \n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)\n", "\u001b[1;32m/var/folders/9p/clj0fc754y35m01btd46043c0000gn/T/ipykernel_83113/1064401740.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 2\u001b[1;33m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'invalid shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random_sample\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32m_common.pyx\u001b[0m in \u001b[0;36mnumpy.random._common.double_fill\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer\n", "[3:execute]: \n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)\n", "\u001b[1;32m/var/folders/9p/clj0fc754y35m01btd46043c0000gn/T/ipykernel_83115/1064401740.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 2\u001b[1;33m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'invalid shape'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.random_sample\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;32m_common.pyx\u001b[0m in \u001b[0;36mnumpy.random._common.double_fill\u001b[1;34m()\u001b[0m\n", "\n", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer\n" ] }, { "ename": "AlreadyDisplayedError", "evalue": "4 errors", "output_type": "error", "traceback": [ "4 errors" ] } ], "source": [ "%%px\n", "from numpy.random import random\n", "A = random((100, 100, 'invalid shape'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, an error will occur on just one engine,\n", "while the rest are still working.\n", "\n", "When this happens, you will see errors immediately,\n", "and can interrupt the execution:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dv.scatter(\"rank\", rc.ids, flatten=True)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[0:execute]: \n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)\n", "\u001b[1;32m/var/folders/9p/clj0fc754y35m01btd46043c0000gn/T/ipykernel_83112/2811384868.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32m 2\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrank\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"rank 0 failed!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\n", "\u001b[1;31mRuntimeError\u001b[0m: rank 0 failed!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "75ab5444d541487a8e8d9f245e5c0347", "version_major": 2, "version_minor": 0 }, "text/plain": [ "%px: 0%| | 0/4 [00:00.has_closure'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_33018/935663835.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'make_closure..has_closure'" ] } ], "source": [ "pickle.dumps(closed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But after we import dill, magic happens" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import dill" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "b'\\x80\\x04\\x95\\xea\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x8c\\ndill._dill\\x94\\x8c\\x10_create_function\\x94\\x93\\x94(h\\x00\\x8c\\x0c_create_code\\x94\\x93...'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dill.dumps(closed)[:64] + b'...'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So from now on, pretty much everything is pickleable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using dill in IPython Parallel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, we start by creating our Client and View" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 2 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e05992ecd0ec4d2bab41ef48182f33bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_sync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/dev/ip/parallel/ipyparallel/client/view.py\u001b[0m in \u001b[0;36mapply_sync\u001b[0;34m(self, _View__ipp_f, *args, **kwargs)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0mreturning\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \"\"\"\n\u001b[0;32m--> 235\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_really_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ipp_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;31m# ----------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/conda/lib/python3.9/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m 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1218\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1219\u001b[0m )\n", "\u001b[0;32m~/dev/ip/parallel/ipyparallel/client/client.py\u001b[0m in \u001b[0;36msend_apply_request\u001b[0;34m(self, socket, f, args, kwargs, metadata, track, ident, message_future_hook)\u001b[0m\n\u001b[1;32m 1771\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"metadata must be dict, not %s\"\u001b[0m \u001b[0;34m%\u001b[0m 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\u001b[0mbuffers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPICKLE_PROTOCOL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbuffers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: cannot pickle '_io.TextIOWrapper' object" ] } ], "source": [ "view.apply_sync(closed, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Oops, no dice. For IPython to work with dill,\n", "there are one or two more steps. IPython will do these for you if you call `DirectView.use_dill`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc[:].use_dill()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's try again" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view.apply_sync(closed, 3)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33018: 10\n", "33046: 15\n" ] } ], "source": [ "cat /tmp/dilltest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yay! Now we can use dill to allow ipyparallel to send anything.\n", "\n", "And that's it! We can send closures, open file handles, and other previously non-pickleables to our engines.\n", "\n", "Let's give it a try now:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remote_closure = view.apply_sync(make_closure, 4)\n", "remote_closure(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33018: 10\n", "33046: 15\n", "33018: 20\n" ] } ], "source": [ "cat /tmp/dilltest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But wait, there's more!\n", "\n", "At this point, we can send/recv all kinds of stuff" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def outer(a):\n", " def inner(b):\n", " def inner_again(c):\n", " return c * b * a\n", " return inner_again\n", " return inner" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So outer returns a function with a closure, which returns a function with a closure.\n", "\n", "Now, we can resolve the first closure on the engine, the second here, and the third on a different engine,\n", "after passing through a lambda we define here and call there, just for good measure." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view.apply_sync(lambda f: f(3),view.apply_sync(outer, 1)(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And for good measure, let's test that normal execution still works:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 5]\n" ] }, { "data": { "text/plain": [ "[10, 10]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%px foo = 5\n", "\n", "print(rc[:]['foo'])\n", "rc[:]['bar'] = lambda : 2 * foo\n", "rc[:].apply_sync(ipp.Reference('bar'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And test that the `@interactive` decorator works" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting testdill.py\n" ] } ], "source": [ "%%file testdill.py\n", "import ipyparallel as ipp\n", "\n", "@ipp.interactive\n", "class C:\n", " a = 5\n", "\n", "@ipp.interactive\n", "class D(C):\n", " b = 10\n", "\n", "@ipp.interactive\n", "def foo(a):\n", " return a * b\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import testdill" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 10\n" ] } ], "source": [ "v = rc[-1]\n", "v['D'] = testdill.D\n", "d = v.apply_sync(lambda : D())\n", "print(d.a, d.b)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v['b'] = 10\n", "v.apply_sync(testdill.foo, 5)" ] } ], "metadata": { "gist_id": "5241793", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "482a88416c6f4f32b272165a7ff27da3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_64224d022bd94ac1806ed8f787780365", "style": "IPY_MODEL_fe9adbdc93c3486b94e22c87738e2b20", "value": "100%" } }, "4cdffdec17de419181264bbe14be7b02": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "64224d022bd94ac1806ed8f787780365": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "6e7318da3dd140288206e903e20e942f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_ea1e89f575bd4bc9bd38fb49807fcaf0", "max": 2, "style": "IPY_MODEL_d05bdbbd4a9041ce8e1f92c25bc3c326", "value": 2 } }, "d05bdbbd4a9041ce8e1f92c25bc3c326": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "de9d64c64a4e4c39b0ad9b219949f1d5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_e481dce1de5e46d0914b7c67836b8660", "style": "IPY_MODEL_f0ead7090f60468998a77b838c00f76c", "value": " 2/2 [00:03<00:00, 3.64s/engine]" } }, "e05992ecd0ec4d2bab41ef48182f33bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_482a88416c6f4f32b272165a7ff27da3", "IPY_MODEL_6e7318da3dd140288206e903e20e942f", "IPY_MODEL_de9d64c64a4e4c39b0ad9b219949f1d5" ], "layout": "IPY_MODEL_4cdffdec17de419181264bbe14be7b02" } }, "e481dce1de5e46d0914b7c67836b8660": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "ea1e89f575bd4bc9bd38fb49807fcaf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "f0ead7090f60468998a77b838c00f76c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "fe9adbdc93c3486b94e22c87738e2b20": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/Using MPI with IPython Parallel.ipynb000066400000000000000000000113321460376056100267050ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple usage of a set of MPI engines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example assumes you've started a cluster of N engines (4 in this example) as part\n", "of an MPI world. \n", "\n", "Our documentation describes [how to create an MPI profile](https://ipyparallel.readthedocs.io/en/stable/tutorial/process.html#using-ipython-parallel-with-mpi)\n", "and explains [basic MPI usage of the IPython cluster](https://ipyparallel.readthedocs.io/en/stable/reference/mpi.html).\n", "\n", "\n", "For the simplest possible way to start 4 engines that belong to the same MPI world, \n", "you can run this in a terminal:\n", "\n", "
\n",
    "ipcluster start --engines=MPI -n 4\n",
    "
\n", "\n", "or start an MPI cluster from the cluster tab if you have one configured.\n", "\n", "Once the cluster is running, we can connect to it and open a view into it:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "import ipyparallel as ipp\n", "rc = ipp.Client()\n", "view = rc[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define a simple function that gets the MPI rank from each engine." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "@view.remote(block=True)\n", "def mpi_rank():\n", " from mpi4py import MPI\n", " comm = MPI.COMM_WORLD\n", " return comm.Get_rank()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 3, 2]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpi_rank()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get a mapping of IPython IDs and MPI rank (these do not always match),\n", "you can use the get_dict method on AsyncResults." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 0, 1: 1, 2: 3, 3: 2}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpi_rank.block = False\n", "ar = mpi_rank()\n", "ar.get_dict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With %%px cell magic, the next cell will actually execute *entirely on each engine*:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mOut[0:8]: \u001b[0m{'data': 1, 'rank': 0}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[1:8]: \u001b[0m{'data': 4, 'rank': 1}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[2:8]: \u001b[0m{'data': 16, 'rank': 3}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\u001b[0;31mOut[3:8]: \u001b[0m{'data': 9, 'rank': 2}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%px\n", "from mpi4py import MPI\n", "\n", "comm = MPI.COMM_WORLD\n", "size = comm.Get_size()\n", "rank = comm.Get_rank()\n", "\n", "if rank == 0:\n", " data = [(i+1)**2 for i in range(size)]\n", "else:\n", " data = None\n", "data = comm.scatter(data, root=0)\n", "\n", "assert data == (rank+1)**2, 'data=%s, rank=%s' % (data, rank)\n", "{\n", " 'data': data,\n", " 'rank': rank,\n", "}" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/broadcast/000077500000000000000000000000001460376056100220655ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/examples/broadcast/Broadcast view.ipynb000066400000000000000000015332531460376056100260010ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "b9dd40a2-26ae-48b7-8dc7-3b4995b2b6e6", "metadata": {}, "source": [ "# Broadcast View\n", "\n", "IPython Parallel is built on the idea of working with N remote IPython instances.\n", "This means that most of the 'parallelism' when working with N engines\n", "via the DirectView is really implemented in the client.\n", "\n", "DirectView methods are all implemented as:\n", "\n", "```python\n", "for engine in engines:\n", " do_something_on(engine)\n", "```\n", "\n", "which means that all operations are at least O(N) *at the client*.\n", "This can be really inefficient when the client is telling all the engines to do the same thing,\n", "*especially* when the client is across the Internet from the cluster.\n", "\n", "IPython 7 includes a new, experimental *broadcast* scheduler that implements the same message pattern,\n", "but sending a request to any number of engines is O(1) in the client.\n", "It is the *scheduler* that implements the fan-out to engines.\n", "\n", "With the broadcast view, a client always sends one message to the root node of the scheduler.\n", "The scheduler then sends one message to each of its two branches,\n", "and so on until it reaches the leaves of the tree,\n", "at which point each leaf sends $\\frac{N}{2^{depth}}$ messages to its assigned engines.\n", "\n", "The performance should be strictly better with a depth of 0 (one scheduler node),\n", "network-wise,\n", "though the Python implementation of the scheduler may have a cost relative to the pure-C default DirectView scheduler. \n", "\n", "\n", "This approach allows deplyments to exchange single-message latency (due to increased depth) for parallelism (due to concurrent sends at the leaves).\n", "\n", "This trade-off increases the baseline cost for small numbers of engines,\n", "while dramatically improving the total time to deliver messages to large numbers of engines,\n", "especially when they contain a lot of data.\n", "\n", "These tests were run with a depth of 3 (8 leaf nodes)" ] }, { "cell_type": "code", "execution_count": 1, "id": "0e46054d-5082-40d9-ace3-413cef646622", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/minrk/dev/ip/parallel/ipyparallel/util.py:205: RuntimeWarning: IPython could not determine IPs for ip-172-31-2-77: [Errno 8] nodename nor servname provided, or not known\n", " warnings.warn(\n" ] } ], "source": [ "import ipyparallel as ipp\n", "\n", "rc = ipp.Client(profile=\"mpi\")\n", "rc.wait_for_engines(100)\n", "direct_view = rc.direct_view()\n", "bcast_view = rc.broadcast_view()" ] }, { "cell_type": "code", "execution_count": 2, "id": "339a80ab-bbeb-423c-abb3-6d429377017d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I have 100 engines\n" ] } ], "source": [ "print(f\"I have {len(rc)} engines\")" ] }, { "cell_type": "markdown", "id": "ff036951-029a-486e-ada9-ec0ee5edd3bc", "metadata": {}, "source": [ "To test baseline latency, we can run an empty `apply` task with both views:" ] }, { "cell_type": "code", "execution_count": 3, "id": "9c30c899-68fd-470d-80ec-c10e724bd27a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "311 ms ± 67.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit direct_view.apply_sync(lambda: None)" ] }, { "cell_type": "code", "execution_count": 4, "id": "ee82f5fa-a2a1-4b72-ad1f-f36427168317", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "259 ms ± 14.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit bcast_view.apply_sync(lambda: None)" ] }, { "cell_type": "markdown", "id": "b4f994af-21b5-440e-b0b3-97e3abd73493", "metadata": {}, "source": [ "With 100 engines, it takes noticeably longer to complete on the direct view as the new broadcast view.\n", "\n", "Sending the same data to engines is probably the pattern that most suffers from this design,\n", "because identical data is duplicated *once per engine* in the client.\n", "\n", "We can compare `view.push` with increasing array sizes:" ] }, { "cell_type": "code", "execution_count": 5, "id": "e5f49dd7-e903-4126-888c-0130261e5fd7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 16 x 16: 2 kB\n", "Direct view\n", "287 ms ± 72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Broadcast view\n", "266 ms ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", " 64 x 64: 32 kB\n", "Direct view\n", "598 ms ± 62.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Broadcast view\n", "274 ms ± 30.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", " 256 x 256: 512 kB\n", "Direct view\n", "5.99 s ± 199 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Broadcast view\n", "297 ms ± 43.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", " 512 x 512: 2 MB\n", "Direct view\n", "22.9 s ± 428 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "Broadcast view\n", "559 ms ± 107 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "import numpy as np\n", "\n", "dview_times = []\n", "bcast_times = []\n", "\n", "n_list = [16, 64, 256, 512]\n", "for n in n_list:\n", " chunk = np.random.random((n, n))\n", " b = chunk.nbytes\n", " unit = 'B'\n", " if b > 1024:\n", " b //= 1024\n", " unit = 'kB'\n", " if b > 1024:\n", " b //= 1024\n", " unit = 'MB'\n", " print(f\"{n:4} x {n:4}: {b:4} {unit}\")\n", " print(\"Direct view\")\n", " tr = %timeit -o -n 1 direct_view.push({\"chunk\": chunk}, block=True)\n", " dview_times.append(tr.average)\n", " print(\"Broadcast view\")\n", " tr = %timeit -o -n 1 bcast_view.push({\"chunk\": chunk}, block=True)\n", " bcast_times.append(tr.average)" ] }, { "cell_type": "code", "execution_count": 7, "id": "8926cf3e-a247-413b-be61-7a7db73437e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ndirect viewbroadcastbroadcast speedupMB
0160.2874950.2657861.0816790.195312
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" ], "text/plain": [ " n direct view broadcast broadcast speedup MB\n", "0 16 0.287495 0.265786 1.081679 0.195312\n", "1 64 0.598065 0.273835 2.184031 3.125000\n", "2 256 5.986274 0.297034 20.153521 50.000000\n", "3 512 22.946806 0.558653 41.075245 200.000000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "df = pd.DataFrame({\"n\": n_list, \"direct view\": dview_times, \"broadcast\": bcast_times})\n", "df[\"broadcast speedup\"] = df[\"direct view\"] / df.broadcast\n", "# MB is the total number of megabytes delivered (n * n * 8 * number of engines bytes)\n", "df[\"MB\"] = df[\"n\"] ** 2 * 8 * len(rc) / (1024 * 1024) \n", "df" ] }, { "cell_type": "code", "execution_count": 8, "id": "5a875b84-0d26-4e9d-94f6-23732095359e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'seconds')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 381, "width": 610 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot(x='MB', y=['direct view', 'broadcast'], style='-o', loglog=True)\n", "plt.ylabel(\"seconds\")\n" ] }, { "cell_type": "markdown", "id": "c7bee30d-84c8-4391-9d26-66517858e297", "metadata": {}, "source": [ "# Tuning broadcast views\n", "\n", "As mentioned above, `depth` is a tuning parameter to exchange latency for parallelism.\n", "\n", "This can be set with `IPController.broadcast_scheduler_depth` configuration (default: 1).\n", "The number of leaf nodes in the tree is `2^d` where `d` is the depth of the tree ($d=0$ means only a root node).\n", "\n", "With a DirectView, the total number of request messages is $ 2 N $ and the number of requests to deliver the first message is 2 (one client->scheduler, one scheduler->engine).\n", "\n", "With a Broadcast View, the total number of request messages is\n", "\n", "$$\n", "N + 1 + \\sum_{i=1}^{d} 2^i\n", "$$\n", "\n", "\n", "Since we are talking about delivering messages to all engines,\n", "the metric of interest for a 'cold start' is how many messages must the last engine wait for before its message is delivered,\n", "because until that point, at least some of the cluster is idle, waiting for a task.\n", "This is where the parallelism comes into play.\n", "\n", "In the DirectView, the message destined for the last engine N must wait for N sends from the client to the scheduler, then again for N sends from the scheduler to each engines, for a total of 2 N sends before the engines gets its message.\n", "\n", "In the BroadcastView, we only have to count sends along one path down the tree:\n", "\n", "- one send at the client\n", "- two sends at each level of the scheduler\n", "- $\\frac{N}/{2^d}$ sends at the leaf of the scheduler\n", "\n", "for a total of:\n", "\n", "$$\n", "1 + 2^d + \\frac{N}{2^d}\n", "$$\n", "\n", "sends to get the first message to the last engine (and therefore all engines). This is a good approximation of how deep your scheduler should be based on the number of engines. It is an approximation because it doesn't take into account the difference in cost between a local zmq send call vs a send actually traversing the network and being received." ] }, { "cell_type": "code", "execution_count": 9, "id": "680d971e-2a13-472c-9b08-c1be00b8304b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NGjWq4hpnzpzpHRERYYyJiTEEBQWZVSoVLz09XTJt2jTvnJwcwfz589X2ba9evcrr27dvQFlZGevTp48uMTFRq9PpWF5enmDz5s3SKVOmaPz9/U1jxowp9/LysiiVSkl8fLwuKiqqYsplHx+fO/4juW/fPjGfz6euXbtWuT6LxUJjx471USqVkqZNm5onTpyoMRgMtGHDBo9Tp0498M8fsdj6bxOf7zrX161bNwMR0Z49e8SOrwUAAAAAgIbi7M0yz7mpOfEnrqnb2dsuFmmbf3focqsXY5uccWdsDyNeYY5MvGtmf8HV3zo6tlukvkWGJ/+ZZnziRZRK1mNIijUwV7pEh9x5q/tPRXTHOEJ/y7x+r+fJzMwUTZkyRbNkyZJShzbNwIED/d966y3vhISEfC8vr0p/8tizZ494/fr1RQkJCXrH9vfee0+Rk5MjGDt2bPkXX3xRwuNZO0qeOXOmrE+fPgHvv/++V3x8vD48PNxMZE0InThxIr9JkyaVEkPFxcWsf//+/vPmzVOMHTu2omRQrVazN99805vH41FqampBdHR0RZLo9ddfV3z77bcy5+vLysoSvPPOO16enp5cWlpaQbt27UyO6/Py8ir15jx06NDNiIiISn9d0Ov1JU8//bTfF1984ZmUlKQJCwuzEBFt3rxZWlxczObNm1f66quvVhrrq6ysjPF4PI6IaOLEiVoiIqVSKUlMTNTZf6+JsrIylp2dLWzRooXJ1QD769atkyqVSsnjjz9u3LFjR4FUau2A9eGHH6p79eoV4OqYubm5/NWrV9eqDnP8+PHl9uftdr777jsPIqLevXu77KbWsmVLs0Kh4I4cOeKyJxkAAAAAQH2lN1l4H+88F51yMr+P0cxV/H9WyGPGhKjAvWO6ND7nzvgeOmYDE++Z01mYtbEvM+srKnw4nsBkavnUPl3/hYfQM6z+Q1IM6jW5XM598MEHase26Oho49ChQ7WbNm2SbtmyRTJhwoRKSZz4+Hidc0LMYDDQTz/9JJXJZNyCBQtK7QkxIqJWrVqZJ0+erFm+fLnn2rVrpbNnzy4jIpJIJOScECMi8vb25kaPHl0+d+5cRWZmpqh3794GIqKtW7dKiouL2fDhw7WOCTEiotmzZ6s3btzooVarmWP7ihUrPEwmE7322mtq54QYEVU5v3NCjMja++nFF1/UHDp0SJSeni5+4YUXKt0PqVRaJVlVVzNEXrlyhWc2mykoKMjlh/26deukRESzZ88utSfEiIj8/Py4N954Q20v23R08eJF/qeffupZmzh69uypv1NS7MiRI8JPPvnEUyaTcR9++KG6uu0CAgLM58+fF2i1WnKMGQAAAACgvvrp2I0mn2VcTCwoMwQ6trcN8fxr9qAIZZsQeWl1+0LtCbJ/ChXv/ziRV3Yj2LHdHND2tC5u4Q5LyBOYuKuBQFIM6rWoqCijQqGoksDp0aOHftOmTdLjx48LiahSEuiJJ56oUsZ36tQpgVarZZ07dzb4+flVOV7v3r31y5cv9zx58qTQsf3kyZOCZcuWeR45ckR08+ZNvl5fKddGV69erajDs8VC3bt3r7wRWRNpbdq0MTr3QDp69KiIiGjgwIFV9nHl4sWL/KVLl3oeOHBAdO3aNb5Op6uUZLt27VpFPIMHD9YtXLhQ/u6773r98ssv4n79+um7detmaNu2rckxKXgvCgsLeUREzr317LKyskQ8Ho969OhhcF7Xq1evKm1ERH379jWUlpbecy9DR6dPn+aPHj3a12QysRUrVhS5Si7a2QfYv3XrFs9VUhQAAAAAoL7IuanxnJt2Nu74VXV7x3ZfD2HByz2apo3q3OiCu2J7GLGi8x4S5cz+gqtHHnds5yQ+Kn3M9DRj5yT0xmtgkBRrYOqiJPFeaDQafyIimUxWcKdt60JAQIDL5EVQUJCFiKi0tLRKdsdVr6WSkhIeEVFgYKDLJEdISIjFcTsiokOHDgmHDh3qZzabWbdu3fTx8fE6uVzO8Xg8ysrKEiiVSonB8Hdep7S0lN3uHK7a7fGHhobesVvtuXPn+P369fMvKSnhdenSxdCrVy+9QqHg+Hw+5eXl8Tdt2iTV6/UVSbLmzZubd+/eXfDRRx/J9+7dK96xY4fEdq3mqVOnambMmKGp/mw1Y++F5pwstFOr1czLy8siElWtRgwJCXkgXYnPnDnDHzx4sH9JSQnv66+/Vg0dOvS2CUj7BAAeHh6YfRIAAAAA6rUz+WXejgkxAY8ZB7UJyPhgUMRhDxEfpXt1xWxg4r1zOwmzNvZjJl3lUsmIxP26uIWHSKyoUvkD9R+SYlCv3bp1y+WI6Pn5+TwiIoVCUSXRxBirsr2Xl5fFdjyXXaSuX79e5XhLliyR63Q6tnXr1sK+fftW6tW0cOFCT6VSWWlmSHuPtps3b7o8h6t22/n4V65c4bdp0+a2H6LLly/3VKlUvOXLlxc7j/v1ww8/SDdt2lSl1q9t27amdevWqYxGIx07dkyYnp4uXrlypWzWrFkKmUzGvfjii/c0mLw90adSqVxes1wu50pKSngGg4GcE2PXr193+dzW5Zhif/31l+Dpp5/2U6lUvG+//VY1bNiwO055qVKpeAKBgJxnrwQAAAAAqG+eahd0ZdOfN/48ernk8TbBntmzB0XsbNsIpZJ1SXBqS2Pxvo8TeWXXK42rbQ5oc0bXf+EOS6NOxW4KDeoAkmJQr2VlZQlLS0uZcwnl/v37xUREHTp0cDmjo7PIyEiTVCrlsrOzhUVFRcw54ZGRkSEmImrfvn3F8S5evMj39vbmnBNiREQHDx6s0vXJHsuBAwfEkydPrpS0Ki4uZtnZ2ULnfTp16mQ4efKkcMeOHeI7JcUuXLjAJyIaPnx4lcSOq3gcCYVC6tKli7FLly7GJ5980jB48GC/1NRUiT0pZp+N0Ww2V80o3kajRo0svr6+lvPnz7v8LImKijIcOHBAvH//flG/fv0q3ceMjAyXMdfVmGLHjx8XDB061E+tVvOSk5OLhgwZcscS1bKyMpafn89v3bq1sa5KTAEAAAAA6kJuQbnsj8sl/iMeD7nk2D4nMWL3wVzVX2O7NMYsh3WIqXI9JMqZ/QRXDj/h2G4tlZy23dj5Hznuig3qDr71Qb2mVqvZggUL5I5tmZmZwq1bt0rlcjlXk54/RNbB6IcNG6bVaDRszpw5lY6Xk5PDX7lypUwoFNLYsWMrklmhoaHm4uJiduzYsUoJn2+//dZj3759YudzDB06VOfl5cVt27ZNmpmZWSkBNm/ePLnzIPtERElJSeUCgYCWLVsmz8rKqpJYcpx9skmTJmYioj179lRKJm3fvl28fv36Kj2rDh8+LLxx40aV97i9l53jAPy+vr4WIqLLly+77L1VHR6PRzExMQaVSsU7e/ZslX3HjBmjJSKaP3++Qqv9O09YWFjIPv30U7nz9kR/jylWm8U5cfnHH38IhgwZ4qfRaNiaNWtqlBAjsg7GbzabKTY21uV4ZwAAAAAAD5rRbGHzt+d0efbbP6Yv3nX+uTyVtlKFSDM/j3IkxOqQ2cDE6bM6yVbHT3dMiHE8gcnYasieshcP/hcJsYcHeopBvRYdHW1Yt26dx9GjR4XR0dGG/Px8fkpKitRisdDSpUuLqxvg3ZWPPvqo9MiRI6Lvv/9eduzYMVFsbKy+qKiIl5KSItVoNOzjjz8uadGiRUVvo6lTp2r27dsnHjRokP/gwYO1CoWCO3bsmPD3338XJSQk6NLS0iqVT8rlcu6TTz4pTkpK8klMTPQfPHiwNigoyJyZmSk6c+aMMDo62pCZmVkpoRUVFWVatGhRycyZM7169eoVEB8frwsPDzepVCre8ePHhTKZjFMqlYVERElJSZqNGzd6TJ482Xfz5s3akJAQy6lTpwQZGRnip556SpeSklIpno0bN0pXrVoli4mJMTRv3tzk5eVluXjxomD37t0SkUhEr7zySpl9227duhmkUin3zTffyFQqFbOXRU6bNk1jH3i+OkOGDNFt375dolQqxS1btqxUjjlmzBjtli1bJLt27ZJER0cHDhgwQGc0Gik1NVXaoUMHw6VLl2qVhKuJoqIiNnToUP/i4mIWGxtrOHLkiMh5ggMiohkzZpT5+PhUurbdu3eLiYiGDh2qdd4eAAAAAOBBSzmZH/rvPRcSb6oN1lkOzURzUs/2+25ch5/dHNpDSXB6WyPxvo8SeeprjRzbzf6tz+j6f7zD0rhLsZtCg/sESTGo15o0aWJevnx5yezZs+Vr1qyRGQwGioqKMs6cOVM9aNCgGvX+sfPz8+PS09MLFi9e7JmamipdsWKFp0Qi4Tp27Gj45z//qXGeATIhIUG/du3aoqVLl8pTUlKkPB6POnbsaNi6dWvhhQsX+M5JMSKikSNH6ry8vIoWL17s+fPPP0tEIhHFxMTolUplwdKlSz0zMzOrxJWUlFTetm1b4/Llyz1//fVXkVKplPj4+FgiIyON48ePr0gydezY0bRt27aC+fPnK9LT0yVms5lat25tTE5OVnl7e1uck2IjR47UGgwG9ttvv4mysrKkOp2OBQcHm4cMGaKdMWNGWfv27SvKNX19fbnk5GTV4sWL5T/++KNHeXk5I7Imtby9vW87QOeIESO0s2bNUmzcuNFj2rRplZJiPB6P1q1bp1q8eLHnxo0bPZKTk2WBgYHm5557rnzWrFnqwMDAkOqOe7eKi4t5xcXFjMhaVlpdaen48ePLfXx8Kq7NbDbT5s2bPVq3bm2KjY2tUVkuAAAAAMD9cLGw3OPD1LP9/7hcWmmWQ2+poKhPS7/T7orrYcWKL0olO9/qx7/yayfH8h5O4l2sj35lu7HLy2fdFhzcV4zjMJb0g8AYO9qoUaOQ06dPr7jddrm5uUl8Pt+/adOmD2R2x9p60LNPQsOwcOFCz4ULF8r37Nlzq1OnTg1y1pVt27aJn3/+ed/PP/+8ePz48TXqKXbp0iV/s9lcEB4eftv3NdQ/Bw8eTCIiio2NxXMHAA0SPscAHk5Gs4X9a1dup5+O3+inN1kq/ujN5zFTfKT//lmDIg7JJYIG+f9tV9z+WWYxMfHeeU8IT67rx0y6irJUjvHNpohB+3Vxiw6SxPuhud8Pq8jIyKRr165d5ziuU233RU8xALhnr776atmqVas8FixYoNiyZUuRu+OpLYvFQosWLZK3a9fOOG7cOJROAgAAAMADl/bXzcaf/nIhMV+tr1RN0SpQdmbWoIgdHUIVxW4K7aFUfalk5Fl9/493mBtHq9wVGzw4SIoBwD2TSqX01VdfFWdkZIjKysqYp6dng+qCev36dd6AAQP0Q4YM0WLWSQAAAAB40MoNZv787TnPlenNFZNReUkFqhe7hW2f8GQYBnWvQ6z4olSinNmPf/lQ5VJJsVexIfqV7YboqSiVfIQgKQYAdaJ3796G3r17N8hZGxs3bmyZM2eO2t1xAAAAAMCjyUPEN4/u3GjXNwcvP8NnZO4f6b//w4SWBx+mUkm3s5iYOGP+48IT6/ozk7ZyqeRjAw/o4hcfQKnkowdJMQAAAAAAAIAH6M/LJd6Ph3kVO7ZN69XsZG5BecD4mNA/nwjzQuleHRKc+TlEvG9BIq/0SmPHdrNfqxx9/4+3m0NjcL8fUUiKAQAAAAAAADwAV1Ra6YepZ/v+dqmk06xBEauffSLkon0djzH694i26W4M76HDivOkkl1v9eXnHezsVCpZYoieut3Q5eUzxDB8yqMMSTEAAAAAAACA+8hk4diyX3I7/u/P6/21RosHEdEX+y4mPNUu8CupkG9xd3wPHYuJiTMWdBSe+KE/M2k97M3WUskBB3Vxiw6Q1NfozhChfkBSDAAAAAAAAOA+2X26IPhfu88nXivRhzq2e0uFJTfVBklTX2m5u2J7GAnOpoaIM+Yn8EqvVLrfZr+Ic/q+H203N+lW5K7YoP5BUgwAAAAAAACgjl0v0Ulm/3y275GLxZ05oorqPbmYXzKxa9iOyd3CTvMYu90hoBZYyWWJRGkvleQqbiwnVpQYury8wxD9ymmUSoIzJMUAAAAAAAAA6ojJwrHley6033j0Wry9VJKIiMfI3Kel36E5CS33e3sIUbpXVywmJt73cQfhibVxzFheuVSyRdwhXfyS/SiVhOogKQYAAAAAAABQR17fnN13z9nC7o5tzf2k598f+Nj2mGY+he6K62EkyEkLFu+dn8grvVy5VNI34ry+30dpKJWEO0FSDAAAAAAAAKCOTHgy9I99OYVdzRzxPcX80hdiQnckdW9yCqWSdYeVXpVIlG/24V860MWpVLLU0PkfOwwx00+hVBJqAkkxAAAAAAAAgLtg4TjSmyw8xxkknwjzUg1sG7i33GAWz0mI2OcrE6F0r65YTEy8f1F74fHV8ZVLJXkWU3jcIX38kn2chx/uN9QYkmIAAAAAAAAAtbTnbGHQkl3nEyKDPS8uG95mj+O6RU9HHnBXXA8rwbmdQeK9cxN5JXlhju1m38dy9X3np5mb9kBpKtQa+hM6YYy9whg7wRgrtS2/MsYS3R3XoyY3N5evUChCJk2a5O3uWOrKpEmTvBUKRUhubi7f3bHcDxMmTPBu1qxZUFlZWY37hSsUipC4uDi/+xlXTVy+fJkXEBAQMmvWLLm7YwEAAACA+u1GqV48Zf3JATP+99c/rhTrmqSfKYj97VKxr7vjelix0qsS6aaxAyXbXvyHY0KME8lL9d3e/LF8QvoaJMTgbiEpVtUVInqbiJ4gos5ElE5EWxlj7d0aFcB9lJycLFUoFCHJycnSu9k/MzNTuGXLFun06dPLPD09ubqOr7Z++uknyZgxY3w6duwYEBoaGhwQEBDSrl27wHHjxnlnZmYKnbcPCwuzPP/885qvv/5adunSJXwuAgAAAEAVFo6j5XsutHv669+nHcxVPckR2f8YzPadKwq77c5Qe5yFRPs+6iD7vs80waWMGPvYYRzjWYwt4g9oJh/43ND1VYwdBvcE5ZNOOI7b5tT0PmPsZSLqSkQn3BASQL03d+5cuaenJ/fyyy9r3B0LEVFqaqrk+PHjwg4dOhiDg4P1IpGIy83NFezYsUOakpIiXbp0aclLL71U7rjP66+/XpacnCxbuHCh/KuvvipxV+wAAAAAUP/syykMXLTrfMJlla6pY3tTX+nFdwe0SI0N9y1wV2wPI/45ZZBk75wEXkleE8d2s0+LXH3f+dvNzXrifkOdaHBJMcbYCCLqRUQdiagDEcmJ6AeO48bdZp9QIppHRAOJyI+IrhPRViKay3Gc6jb78YnoWSLyJKJDdXMFAA+X06dP8w8cOCAePXp0uYeHx513eAA+//zzYqm0aqe348ePC/r16xcwZ84cxfjx48vFYnHFutDQUEv37t31W7ZskS5atKjU29vb7T3eAAAAAMC9bpUZRB+mnu194FyRY88wkon4ZeOiG++c2rNpFmaVrDtMfU0sUb7Vh39xX3SlWSVFcrWh00s7DF1fzUbPMKhLDfHV9AERTSNrUuzqnTZmjLUgoqNENJGIMoloGRHlEtEMIvqVMVZlPCPGWDvGWBkR6YnoKyIaxnHcybq6AKid7OxswYgRI3zCwsKCg4KCgvv27eu3Y8cOsfN2jiWAaWlp4ri4OL9GjRoFKxSKEPs2xcXF7N1335V36NAh0N/fPyQ0NDT4qaee8lUqlSLn4+n1evrss888hgwZ4hsZGRno5+cXEhYWFjxo0CC/1NTUKue3UyqVon79+vkFBQUFh4WFBY8YMcInOzv7tgnow4cPC8eMGeMTERER5OfnF9KiRYugxMRE3w0bNkicr3HkyJE+bdu2DQwICAgJCQkJ7tOnj//q1atdlj2eO3eOP2XKFK927doFBgQEhISFhQV37tw5YOrUqV4FBQWMiCguLs5vxowZ3kREM2bM8FYoFCH2pSbjn61atcqD4zgaPny41tV6vV5P8+fP94yKigr08/MLadOmTeAHH3wg1+l0dzr0XXOVECMi6tChg+mxxx4zqdVqdvPmzSqff8OHD9dqtVq2YcOGuyojBQAAAICHx+ELKr/BX/42bf+5oq72hBgj4no85vtrystdPpvWqxkSYnWFs5Bo/8L2suQ+0wUXK5dKmsLjDmom7fvc0O11JMTq0Nqza1u+uOfF4SaL6ZF+ETe4nmJE9BpZx/06R9YeY3tuvzn9l4gCieifHMd9Zm9kjH1qO9ZHRDTFaZ8zZE26eRPRcCJaxRjrzXFcVh3ED7WQl5fHj4+P92/VqpVx3Lhxmvz8fH5KSop01KhRvl9++aVq9OjRVTIr27Ztk2ZkZIh79+6tHz9+vCYvL09ARKRSqVj//v39c3JyBO3btze+9NJL2sLCQl5KSor02Wef9Vu8eHHJlClTKkrqioqKeLNmzfLq1KmToWfPnno/Pz9Lfn4+f/fu3ZLRo0f7fvLJJ1VK8H788UdJUlKSj1AopMGDB2uDgoLMmZmZovj4eP/IyEiXUwOvWLHC4+233/bi8XgUFxenCw8PNxUUFPCOHz8uWrlypWzUqFEV1zhz5kzviIgIY0xMjCEoKMisUql46enpkmnTpnnn5OQI5s+fr7Zve/XqVV7fvn0DysrKWJ8+fXSJiYlanU7H8vLyBJs3b5ZOmTJF4+/vbxozZky5l5eXRalUSuLj43VRUVEm+zF8fHwszvE627dvn5jP51PXrl2rXJ/FYqGxY8f6KJVKSdOmTc0TJ07UGAwG2rBhg8epU6ce+OfP6dOn+bm5uXwfHx9Lo0aNqlxbt27dDEREe/bsETu+FgAAAADg0fN4mJdKKuJrNQaznIioiY/k0ttxLdJ6RvjddHdsDxO55oJAtnLmRF7JpUqlkhaf8Au6vvPTzM16oVSyDh0vOO6z9NjSgedLz7ckIvoy68vz09tPP+bmsNymwSXFOI6rSIKxO2TlGWPhRBRPRBeJ6Aun1R8SURIRPc8Ye4PjuIqxkDiOM5A16UZE9DtjrAtZE2iT7zX+e7X6rd9C7rzVA3HHOMb/q8v1ez1JZmamaMqUKZolS5aUOrRpBg4c6P/WW295JyQk5Ht5eVUqc9uzZ494/fr1RQkJCXrH9vfee0+Rk5MjGDt2bPkXX3xRwuNZ/8pw5syZsj59+gS8//77XvHx8frw8HAzkTUhdOLEifwmTZpUSp4UFxez/v37+8+bN08xduzYipJBtVrN3nzzTW8ej0epqakF0dHRFUmi119/XfHtt9/KnK8vKytL8M4773h5enpyaWlpBe3atTM5rs/Ly6v0p5BDhw7djIiIMDu26fX6kqefftrviy++8ExKStKEhYVZiIg2b94sLS4uZvPmzSt99dVXK431VVZWxng8HkdENHHiRC0RkVKplCQmJursv9dEWVkZy87OFrZo0cLkaoD9devWSZVKpeTxxx837tixo8Deg+vDDz9U9+rVK8DVMXNzc/mrV6+uVR3m+PHjy+3PmyOlUik6dOiQ2GAwUF5eHn/37t0SIqJ///vfJXx+1U5wLVu2NCsUCu7IkSNVeg4CAAAAwKNFLOBZpvdqlrpk1/lnR3dupJzeu9lJ9AyrO0x9TdzmwkpFaP5uGY/+/srFiTzVhk4v7TR0fe0v9AyrO6WGUsGSP5d0z7iW0d3MmSu+DO24vCP25aiXjwt4gkdy+JgGlxSrpb62RyXHcZUSGxzHqRljB8maNHuSiH65zXF4RFRtuZwjxtjRalZFisVi4cGDB5Nut7+Pj4+/VCoVaDQa/5qcrz67l2vQ6635LIVCQW+88YbI8Vht27alYcOGWf73v//xNm3aFDBq1CgLEZHFYuEREcXHx3O9evWSazQauX0fo9FIP/30k1Amk9H7778v1Gq1FccLDQ2lyZMnc8uXL2dr1qzxe/PNNyteK35+fqTRVB47XigU0qhRo9i8efPYoUOHArp27coREf3000+84uJiNmLECEvbtm29HPd766236Mcff6TS0lLS6/U+9nXffPMN32Qy0auvvmoJDw/3dj6X8/kbNWpUJR4iookTJ7JDhw7Rrl27/J977jkLERHHcTwiIrFYLNNoNJXKARljxHFcxbHs985iscg1Gk2V5F11cnNzyWw2U3BwMN/V871+/XoBEdF7773HLBaLv/18EomEXnvtNXrttdeIMSZ03PfcuXPs008/rdVnU7du3SRBQUFVPsTT09P5//3vfyv+JQ0MDKRly5aZ+vbt66nRaDxdHSswMJCdO3eOFRYW+kskElebVLBYLAKdTud/p/c11D8Gg8GfiAjPHQA0VPgcA6g7HMdR5k1O8uctTvqPtjyVY+eLICJaGMOpRfxrXX89dK2r+6J8iHAcNb61V9oyb52iiam04v/qFuJRXnC85lzocxoT5xFLh36NdWeYD5MTuhPiFHWKV7GluFLPgE6STuVPyZ8yH/n1yEvuiq0uiMVif7KOHV9rD3tSrJXt8Ww163PImhRrSbakGGNsERGlEtFlsg7iP4aIehNR4v0MFFyLioriPD2r5i66devG/e9//6OsrKwqf6p5/PHHq5TF5eTkMK1WS126dOF8fHyqHK979+6W5cuX87KysnhEf/+Z4vTp0+y///0v78iRI7z8/PyKZJ3d9evXGRFxREQnT55kRET2JJkjhUJBbdq04Q4fPlwp3qNHjzIion79+t2xTJGI6MqVK/T555/zDxw4wLt69So5j8t148aNip8HDBhgWbRoEf/999/n7927l9e7d29LdHQ016pVK+5OvSxrqqioiBEROffWszt58iTj8XgUExNTZX23bt0sRFSlu1ZsbCx3/fp1l6WmtTVr1izzrFmzzBqNhs6fP8++/PJL3rhx4wRvvfWW+bXXXnN5z20D7LOioiJq1KhRXYQBAAAAAPXUNQ0nWH/WosgpsXaCOHiDk3YPYZUqJ0R89A6rK3LNBUGbC995+ZSdrVSZUSSPNGQ3m1RSJmtqqm5fqL1bplv8reqtXmcNZyt18mkkaGQcJh9W0kzUrE6+dzVkD3tSzMv2WFLNenu7t0NbMBGttT2WENEJIhrEcdzOmpyQ47hOrtoZY0f1en1IbGzsitvtn5ubm8Tj8fxlMpnLuunx/+pSkzDuG3uPnuriqytisZhPRIH+/v46mUxW7Lw+JCRETES+paWlevt6Ho8nJSLv4OBgtUwmq/QPmVarFRGRX0BAgF4mk1WZcTQsLExARAElJSVGmUxWSER06NAh4dChQ/3MZjPr1q2bPi4uziSXyzkej0dZWVkCpVIpMZlMFecqLS31IiKP4ODgEplMpnc+R0BAgA8RScRisUomk5mJiEpKSgKJiB8eHl4ok8lu21313Llz/Pj4eP+SkhJely5dDD179jQqFAqOz+dTXl4ef9OmTVKDwaCVyWRqIqLIyEjavXu34KOPPpLv3btXvH37dr7t3pmnTp2qmTFjRkWXM/u94/F4Ve7d7SgUCgERBeh0OoOr+6pWq0O8vLws3t7eVV4vzZo1IyIK4Tiu4p7fLzKZjAIDA6lr1640ePBg33/961/iuLg4VUxMTJV/BGy9CIU+Pj53fE54PJ6/RCIpuNP7Guofe88KPHcA0FDhcwzg3hRpDMI5aTk9M3IKu1m4vyeg23lVqH9reMwKlEnWLVZ2QyzZ+VZv/sW9MY6zSuqF3pYzTcaWNH/67f90QKlknSk1lAoW/7m4+76b+yqVSor5Yu2QZkN+mdZu2h9CnvChKZfU6/V33Wv6YU+K3Yn9zVjxYuA4boJ7QgFXbt265XL2w/z8fB4RkUKhqNLbx1UvKC8vL4vteC4/aa9fv17leEuWLJHrdDq2devWwr59+xoct1+4cKGnUqmsVFunUCg4IiJXsxpW1247H//KlSv8Nm3a3PavIsuXL/dUqVS85cuXFzuP+/XDDz9IN23aVGXGxLZt25rWrVunMhqNdOzYMWF6erp45cqVslmzZilkMhn34osv3tNg8oGBgRYiIpVK5fKa5XI5V1JSwjMYDCQSVR6m6/r16y6f27ocU8yVfv366TMyMsT79u0TuUqKqVQqnkAgIF9f34fmHwkAAAAAsLJwHK04kNd69ZErA9V6s8Lezoi4ruE+mXMSIvYiIVaHOAuJDixpJ/ozOZ4Z/x6+hGM8i7l5n8P7fceFmwQeXHMkxOqEhbPQD2d/aLX27NqBaqPa23Hd4/6PH333iXd/CfUMrXEniEfBw54Us/cE86pmvcJpO6hnsrKyhKWlpcyecLLbv3+/mIioQ4cONeruGRkZaZJKpVx2drawqKiIOSc8MjIyxERE7du3rzjexYsX+d7e3pxzQoyI6ODBg1UGYrfHcuDAAfHkyZMrfdAUFxez7OxsofM+nTp1Mpw8eVK4Y8cO8Z2SYhcuXOATEQ0fPrzKjJuu4nEkFAqpS5cuxi5duhiffPJJw+DBg/1SU1Ml9qSYfdB5s9lcq/8BNGrUyOLr62s5f/68y8+SqKgow4EDB8T79+8X9evXr9J9zMjIcBnzxYsX+Z9++qnL8b6q07NnT31Nk2L2ZJxAUDXksrIylp+fz2/durXRPhEDAAAAADwcMi8W+36081xCbkF5C8f2xt6Sy2/1D0/r18r/RnX7Qu3xc38JlOyZncArvtTUsd3i0/yirs+8NHPzPrdMGBexzhwrOObzr2P/GnSh9EKEY3ugNPDatKhpqf3D+l9zV2z12cP+re+M7bFlNevtL5bqxhwDN1Or1WzBggVyx7bMzEzh1q1bpXK5nBs2bFiVBJErYrGYhg0bptVoNGzOnDmVjpeTk8NfuXKlTCgU0tixYyuSWaGhoebi4mJ27NixStmTb7/91mPfvn1VJl4YOnSozsvLi9u2bZs0MzOzUgJs3rx5crVaXSXhlJSUVC4QCGjZsmXyrKysKlkax9knmzRpYiYi2rNnT6Vk0vbt28Xr16+v0rPq8OHDwhs3blR5j9t72Uml0orEoK+vr4WI6PLlyy57b1XHNl6YQaVS8c6ePVtl3zFjxmiJiObPn6/Qav/OExYWFrJPP/1U7rw9EVHfvn0NpaWl12uzOCYudTodHThwoEoCkojoyJEjwtWrV3vweDwaMGBAlRLXI0eOCM1mM8XGxlZJhAIAAABAw2ThOHptc3afl9admOqYEJMKeeUvxIRu+/nlLslIiNUdVnZDLN38/ADplglTHBNinMizTP/kjM2aiRmrzM373HJnjA+TEn2J8L3D7/WZvn/6K44JMTFfrH22xbM//2/A/75FQqx6D3tPsT22x3jGGM9xBkrGmJyIYolIS0SH3REc3Fl0dLRh3bp1HkePHhVGR0cb8vPz+SkpKVKLxUJLly4trm6Ad1c++uij0iNHjoi+//572bFjx0SxsbH6oqIiXkpKilSj0bCPP/64pEWLFhW9jaZOnarZt2+feNCgQf6DBw/WKhQK7tixY8Lff/9dlJCQoEtLS6tUPimXy7lPPvmkOCkpyScxMdF/8ODB2qCgIHNmZqbozJkzwujoaENmZmalhFZUVJRp0aJFJTNnzvTq1atXQHx8vC48PNykUql4x48fF8pkMk6pVBYSESUlJWk2btzoMXnyZN/NmzdrQ0JCLKdOnRJkZGSIn3rqKV1KSkqleDZu3ChdtWqVLCYmxtC8eXOTl5eX5eLFi4Ldu3dLRCIRvfLKK2X2bbt162aQSqXcN998I1OpVMxeFjlt2jSNbeD5ag0ZMkS3fft2iVKpFLds2bJSOeaYMWO0W7ZskezatUsSHR0dOGDAAJ3RaKTU1FRphw4dDJcuXapVEq4mtFotS0hI8G/RooWpXbt2xkaNGpnLy8tZTk6O8NChQyIiolmzZpW66pm3e/duMRHR0KFD0aUYAAAA4CHBY4zUOpOHhbNO8sSIuJhm3r/PSWyZ3thbUqM/skMN3KFUUhe3JIPzDMIfn+uIhbPQ2rNrW/1w9geUSt6DhzopxnHcecaYkqwzTL5CRJ85rJ5LRDIi+prjOI2r/cH9mjRpYl6+fHnJ7Nmz5WvWrJEZDAaKiooyzpw5Uz1o0KAqPX1ux8/Pj0tPTy9YvHixZ2pqqnTFihWeEomE69ixo+Gf//ynZuDAgZWOl5CQoF+7dm3R0qVL5SkpKVIej0cdO3Y0bN26tfDChQt856QYEdHIkSN1Xl5eRYsXL/b8+eefJSKRiGJiYvRKpbJg6dKlnpmZmVXiSkpKKm/btq1x+fLlnr/++qtIqVRKfHx8LJGRkcbx48dXJJk6duxo2rZtW8H8+fMV6enpErPZTK1btzYmJyervL29Lc5JsZEjR2oNBgP77bffRFlZWVKdTseCg4PNQ4YM0c6YMaOsffv2FUkhX19fLjk5WbV48WL5jz/+6FFeXs6IrEktb2/v25YljhgxQjtr1izFxo0bPaZNm1YpKcbj8WjdunWqxYsXe27cuNEjOTlZFhgYaH7uuefKZ82apQ4MDAy5w9NWazKZjHvrrbfUhw4dEh8+fFhcVFTEY4xRcHCwediwYdp//OMfmq5du1YpuzWbzbR582aP1q1bm2JjYx/5WVgAAAAAHiZzE1v+MmzF7228PYSqt/qFp8a1Drju7pgeJtWWSno3v6jrMzfNHN4XPcPq0J+3/vRdenzpQOdSySBp0LVXol5BqWQtMI5rWGNJM8aGEtFQ26/BRDSAiHKJaL+trYDjuDcdtm9BRIeIKJCIthHRKSKKIaI+ZC2b7MZx3H2d+c4Wx9FGjRqFnD59+o6zT/L5fP+mTZve19kd79aDmn0SGpaFCxd6Lly4UL5nz55bnTp1apDTKG/btk38/PPP+37++efF48ePr9FfVC5duuRvNpsLwsPDMfNXA4NZ2wCgocPnGIBrxeVG4YId57q93KPJ7y0CZJU6P2ReLPZ9oomXSsBjDetLcD1W3aySnMizzPDE5J2Gbm9k0W0G0cdnWe2U6EuEi/9c3H3f9X2xFs7iPKvk7untpv8p4Akeudd3ZGRk0rVr165zHNeptvs2xJ5iHYnoBae2cNtCRHSJiCqSYrbeYp2JaB4RDSSiBCK6TkT/IaK5HMcV3a9AbSWa9nGThBzHYRoTeCi9+uqrZatWrfJYsGCBYsuWLfftPXW/WCwWWrRokbxdu3bGcePGoYsxAAAAQAO08tDlVt/9enlgqc7kna/W+6x5oeNWx/XRzbwb3P9T6y3OQqKD/2on+uO7qqWSzXof0cX/ay9KJeuOQ6nkILVRXWkiwSf8nzj6zhPvoFTyLjW4pBjHcXOIaE4t97lMRBPvRzx38AYRfWj/Ra1Wl91mW4AGSyqV0ldffVWckZEhKisrY56eng3qrxPXr1/nDRgwQD9kyBAtZp0EAAAAaFj+uFzis2B7zsCcW+UVE6wdu1LaYUf2rcyBbQJQRlbH+LnpAZI9sxN5xRdRKvkA/HnrT9+lx5YOuqC+8Jhje5A06Nq0dtNS+4X2w2v8HjS4pFgD8wkR2buB7pDL5QHuDAbgfurdu7ehd+/eDfKvQY0bN7bMmTNH7e44AAAAAKDmSrRGwfztObG7Txf0MNsG0Scikgh42uGPB+/uH+mPccPqECu7IZYoZ/biX9jzZJVSyccn7TTEvnnbUkmonRJ9iXDRn4u677++v1KppIQv0Q5pNmT3tHbTHslSybqGpNh9xHGcmojURESMMSNjqF0HAAAAAAC4V98fvhzx7aHLg0q0Jh/H9i5NvI7OearlL018pCglqyv2Usk/k+OZoezvUklinLl5n8O6+CUZnGdwrSZBg+pZOAutObMmcl3OuoHOpZKdAjr9/vbjb6ejVLLuICkGAAAAAAAADcKfl0u8F+w4N/DsTU0rx/Zghfjaa32bpyW0DbzqrtgeRtWXSja7ZC2V7HfTXbE9jG5TKnl1WrtpaSiVrHtIigEAAAAAAECDcORicYhjQkws4Ome6Ri8+63+4X8I+TxU5tQRVpYvkihts0pyloqaSJRK3h8olXQfJMUAAAAAAACgQUjq3uRU2l83z18o1LboFOb159ynWu5u6istd3dcDw3OQqKDS6NEf343AKWS99+dSiXfeeKd9MayxiiVvI+QFAMAAAAAAIB65+S1Uq+rxXqZ4wySPMZo1qCItPxSvcdT7YKuuDO+h421VPLDBF7xhWaO7SiVvD+O3jrq+8mxTwZdVF9EqaQbISkGAAAAAAAA9UaZ3sRfsONc153Zt3p5ivml3Vv4/NdTLDDb13dp6l1EREVuDPGhUm2ppFBWZnhiktIQ+9ZJlErWHVupZI/91/d3cy6VfLr507teiXrlGEolHxwkxe4jxpiciOS2X4Uc9/e0tQAAAAAAAFDZD79dbfH1gbxBqnKjHxFRsdbkO397TrfFQ1vvd3dsDx3OQqJDn0SJ/lgZzwxl8opmlEreFxbOQqvPrI5cl7NuYJmxDKWS9QSSYvfXG0T0of0XtVpd5sZYAAAAAAAA6qW/rqkV87bnDMi+UdbGsT3AU5TftbnPRTeF9dDi56YHSPZ+mMBToVTyQbCVSiZcVF9s4dge7BF8dVrUtNS+oX2vuyu2Rx2SYvfXJ0S0wvbzDrlcHuDOYAAAAAAAAOqTcoOZv2B7zpPbs2/1Mlk4ob1dxGf6Ie2D0t+Jf+x3sYBncWeMDxNWli+S7JrZi39hz5Molbz/VHqVcPEfi3scuHHAuVSy/OnmT+9GqaT7ISl2H3EcpyYiNRERY8zIGMOLHQAAAAAAgIg2/H6t+Zf7LyUUlRv9Hds7hiqOzx4UsTsiUIZKm7pyu1LJZr2P6Ab8ay9KJeuOhbPQqjOrWq/PWT8ApZL1G1LAUC/l5ubyFQpFyKRJk7zdHUtdmTRpkrdCoQjJzc3l33nrhmfChAnezZo1CyorK6vx2HkKhSIkLi7O737GVROXL1/mBQQEhMyaNUt+560BAAAA4F7lqbTSxbvOj3ZMiPl7im7OTWyZvOaFjluREKs7/At7AmTJvV4QH14+3DEhZvFuekk77PuvtMPX7ERCrO78fvN3v3G7x437JvubkY4JsWCP4KsLohes+KzHZ6lIiNUfSIoBACUnJ0sVCkVIcnKy9G72z8zMFG7ZskU6ffr0Mk9Pz3rXI/LWrVu88PDwIIVCEdK3b98qSbiwsDDL888/r/n6669lly5dwuciAAAAwH3WxEeqHdgmYB8RkZDPDM90DN6545Xor5/pGJzn7tgeFqwsXyTd8kKcdMsLUxzHDuOEsjJ99LSfNJP2f4+xw+qOSq8SvvPrO31fPfjqy45jh0n4kvJRj436vx/jf1yJscPqH5RPAsA9mzt3rtzT05N7+eWXNe6OxZVXXnnFS6vV3rYH2+uvv16WnJwsW7hwofyrr74qeVCxAQAAADwKjl8p9e4Qqih2bJs1KOLXUp1JOqNPs19bBnqiZ1hdQankA+VQKjmwzFimcFzXOaDzb28/8fYe9Ayrv5AUA4B7cvr0af6BAwfEo0ePLvfw8HB3OFWsWrVKumPHDsmiRYtK3nnnHa/qtgsNDbV0795dv2XLFumiRYtKvb29612PNwAAAICG5kx+mXxuWk78X9fVbf47KurL2HDfAvs6DxHf/MVzUbvcGd/Dhn9hT4Bkz+yqs0p6Nc3T9ZmTam4Rh55hdej3m7/7fXL8k0GX1JecZ5W8Mr3d9LQ+jfugZ1g9hzIhqPeys7MFI0aM8AkLCwsOCgoK7tu3r9+OHTvEzts5lgCmpaWJ4+Li/Bo1ahSsUChC7NsUFxezd999V96hQ4dAf3//kNDQ0OCnnnrKV6lUipyPp9fr6bPPPvMYMmSIb2RkZKCfn19IWFhY8KBBg/xSU1OrnN9OqVSK+vXr5xcUFBQcFhYWPGLECJ/s7OzbJqAPHz4sHDNmjE9ERESQn59fSIsWLYISExN9N2zYIHG+xpEjR/q0bds2MCAgICQkJCS4T58+/qtXr3ZZ9nju3Dn+lClTvNq1axcYEBAQEhYWFty5c+eAqVOnehUUFDAiori4OL8ZM2Z4ExHNmDHDW6FQhNiXmox/tmrVKg+O42j48OEu//qh1+tp/vz5nlFRUYF+fn4hbdq0Cfzggw/kOp3uToe+ZxcvXuS/9957XqNGjSofOHDgHf8aNnz4cK1Wq2UbNmy4qzJSAAAAALDSGs28WT+f6To6+c9pJ6+poywc8RbuPJ9g4fB3x/vhjqWSk/cnIyFWd1R6lfDtX9/u9+qBV6c6JsQkfEn56MdGb/sx/sfvkBBrGNBTDOq1vLw8fnx8vH+rVq2M48aN0+Tn5/NTUlKko0aN8v3yyy9Vo0ePrpJZ2bZtmzQjI0Pcu3dv/fjx4zV5eXkCIiKVSsX69+/vn5OTI2jfvr3xpZde0hYWFvL+n737jmvy3B4Afp43mxAIew8RcKCIKIgiiGywrd173rZ277p6b2t7OxTrqF2/Xtvejtvd2tpaAUEEFJAhgoiKouy9IYTs9/n9oeAbV2uJjcbz/Xz60Zwn45jGSE6ec55t27ZJbrnlFoe0tLTBRx99dGT0fvr6+piXX37ZdtasWdro6GiNg4MD29nZydu5c6f4jjvusF+/fv3gww8/PMJ97B9++EG8ZMkSO4FAANdee63KxcXFUFpaKkxMTHScPHmy7lx/xs2bN1stX77clmEYSEhIUPv5+el7enqYAwcOCD/99FPp7bffPvZnXLZsmTwgIEA3Z84crYuLi6G/v5/ZtWuX+Mknn5TX1tbyX3/9dcXodVtbW5nY2Fin4eFhsnDhQvWiRYtUarWaNDU18bds2SJ59NFHlY6Ojvo777xzxNbWls3KyhInJiaqp02bph+9Dzs7uz88/nr37t0iHo8Hc+fOPevPx7Is3HXXXXZZWVliHx8fwwMPPKDUarXw3XffWR05cuSSvv+wLAuPPPKIXCaTsW+//fZQb2/vH34JMG/ePC0AQG5uroj7WkAIIYQQQn/elsp2n/fzGlJ7lDpnblwm4iuHVHqB3Epwzp+L0V9AWRAWbQgS7v8k6exWyQUl6sS1eVTmjq2SJsJSFr6o+WLKt8eNWyUJEDrLada+laErd7lJ3S79t//IZLAodoXZ/NAdbn98rb/FH+ax5JNvx10ZLy0tFT766KPKtWvXDnFiyuTkZMelS5fKU1NTO21tbY2+bsrNzRV9++23fampqUZv/i+99JJNbW0t/6677hr54IMPBhnmZI3k6NGjwwsXLnT65z//aZuYmKjx8/MzAJwsCFVVVXV6e3sbFYYGBgZIfHy847///W+bu+66a6xlUKFQkBdffFHOMAxs3769Jzw8fOwf++eff97mk08+kZ7556uuruavWLHC1tramqanp/dMnz5dz11vamoyKuQUFRV1BQQEGLgxjUYzuHjxYocPPvjAesmSJUovLy8WAGDLli2SgYEB8u9//3vo2WefNZr1NTw8TBiGoQAADzzwgAoAICsrS7xo0SL16OU/Y3h4mBw+fFgwceJE/bkG7H/zzTeSrKws8cyZM3WZmZk9EsnJDVirVq1SLFiwwOlc91lXV8f78ssvL6oP89577x0Z/f826p133pEWFxcLf/zxx15bW1va29v7h/cTGBhosLGxoSUlJWftHEQIIYQQQhdW26W0fjX9WEJVqyKYG7e3EvQ8Hu2Tftss93pz5WaJeA35juJdL6cy/XUTuHFslbw0ztcq6Wbl1vLU9Ke2x3jEdJgrN/TXYVHsEiKEyABgtFovoJRecNA3OptMJqP/+te/FNxYeHi47vrrr1f99NNPkl9++UV8//33GxVxEhMT1WcWxLRaLfz8888SqVRK33jjjaHRghgAwKRJkwwPPvigctOmTdZfffWV5JVXXhkGABCLxXBmQQwAQC6X0zvuuGPktddesyktLRXGxMRoAQC2bt0qHhgYIDfddJOKWxADAHjllVcU33//vZVCoTB6DWzevNlKr9fDc889pzizIAYAZz3+mQUxAACRSAQPPfSQsqioSLhr1y7RfffdZ/R8SCSSs4pVpjohsqWlhTEYDODi4nJWXgAni2IAAK+88srQaEEMAMDBwYG+8MILitG2Ta6Ghgbehg0brC8mj+joaA23KFZdXc1fvXq1zd133z2SmJiovZj7cnJyMpw4cYKvUqmAmzNCCCGEEDo3jZ5lVu84HrbtYOdCrYGOjRkRMESXEuSc989k/xIrIe+cPy+ii0eGO4Xi7OULePW7Ighlxz7YUIFUqZ15f5Z2/vIqIDgpyVT6Nf2CNfvXRBe2F85j4fTzLeaJR26YcEP2Y9MeO8Bn+NgXfIXCotil9QIArBq9oFAo8ESVizRt2jSdjY3NWW8wUVFRmp9++kly4MABAQAYFYFCQ0PP2o595MgRvkqlIrNnz9Y6ODicdX8xMTGaTZs2WR88eFDAjR88eJC/ceNG65KSEmFXVxdPozHeedza2jo2c+tULjB//vyztifL5XI6depU3Zk7kMrLy4UAAH9m3hXAyYLRunXrrAsKCoRtbW08tVptVGRra2sby+faa69Vr169WrZy5UrbnJwcUVxcnGbevHnaoKAgPbcoOB6jLYln7tYbVV1dLWQYBqKios4qTC1YsOCcxarY2Fjt0NDQX95lqNVqYcmSJXJnZ2fDmjVrhv74FsZGB+x3d3cz5yqKIoQQQgghYw9+dWDxgTN2hwW5WR96JSUga6qb7KJ/HkPnMdYq+WkS0SqwVfISw1bJqwMWxS6t9QCw+dTvM2Uy2TnbxS6GKVoSx0OpVDoCAEil0p4/uq4pODk5nfMbJRcXFxYAYGho6Kzqzrl2LQ0ODjIAAM7Ozucscri5ubHc6wEAFBUVCa6//noHg8FA5s2bp0lMTFTLZDLKMAxUV1fzs7KyxFrt6brO0NAQudBjnCs+mr+np+cffnN2/PhxXlxcnOPg4CATFhamXbBggcbGxobyeDxoamri/fTTTxKNRjNWJJswYYJh586dPW+++aYsLy9PlJmZKT71ZzU8/vjjymeeeUZ5/kf7c0Z3oZ1ZLBylUCiIra0tKxSe3Y3o5uZ2Sb4tXLNmjfWhQ4cEW7du7ZXJZBf9jc3oAQBWVlb4bQ9CCCGE0J9wx2yP0gOtNcEAAHZWgt7HorzT75jtUWfuvCzJ+VslvZvUMa+mG/wTO82VmyUq6ypz2FC5IbVxuNGPG8dWScuDRbFLiFKqAAAFAAAhREcIwQ/ZF6m7u/ucpx92dnYyAAA2NjZnFZoIObtL1dbWlj11f+fcItXe3n7W/a1du1amVqvJ1q1be2NjY412Na1evdo6KyvL6GTI0R1tXV1d53yMc8VPPR6vpaWFN3Xq1LPaJ7k2bdpk3d/fz2zatGngzLlfX3/9teSnn346q9cvKChI/8033/TrdDqorKwU7Nq1S/Tpp59KX375ZRupVEofeuihcQ2THy309ff3n/PPLJPJ6ODgIKPVauHMwlh7e/s5/9+Od6ZYVVWVgFIKixcvdjjXdfft2ye0sbFxk8lktLW19ax/zPr7+xk+nw/29vb49xUhhBBC6AwaPcvwGEL5zOnPNoumObduO9hZYm8lUPwrJaAYWyVNhyi7heKspdgq+Te5UKvkjX43Zj8a9Ci2SloYLIqhy1p1dbVgaGiInNlCuWfPHhEAwIwZM/7UyTWTJ0/WSyQSevjwYUFfXx85s+CRn58vAgAIDg4eu7+GhgaeXC6nZxbEAAAKCwvP2vo0mktBQYHowQcfNCpaDQwMkMOHDwvOvM2sWbO0Bw8eFGRmZor+qChWX1/PAwC46aabztqie658uAQCAYSFhenCwsJ0ERER2muvvdZh+/bt4tGiGI93sj5lMBguau6du7s7a29vz544ceKc7yXTpk3TFhQUiPbs2SOMi4szeh7z8/PPmfN4Z4rFxMRo7O3tzyqWKpVKZtu2bWJHR0c2Pj5efa5Za8PDw6Szs5M3ZcoUnalaTBFCCCGELMVvVZ1em/LqUxMmO5atSPTfz1376I7pmebKyyJRFoR7NwYJyz85R6tkdKk68e1cbJU0HZay8HnN51O/rf02SalXGrVKznaeXbZi5opcbJW0TFgUQ5c1hUJB3njjDdkZp08Ktm7dKpHJZPSGG274U29MIpEIbrjhBtU333xj9eqrr8refffdsfurra3lffrpp1KBQAB33XXXWDHL09PTUFdXx6+srOSHhISMFaw++eQTq927d4vOfIzrr79e/dJLL9Fff/1VUlpaquQO2//3v/8tO3PIPgDAkiVLRr766ivpxo0bZYmJiZpp06addfrk6Fwrb29vAwBAbm6ucPHixWP/AGZkZIi+/fbbs3ZWFRcXC3x9fQ2urq5GBaLRXXbcotBoEam5ufmcu7fOh2EYmDNnjjYjI0N87NgxXmBgoNG3gnfeeaeqoKBA9Prrr9vMmzdv7PTJ3t5esmHDBtm57nO8M8WefPLJc+5+q6ur423btk3s6+ur37x58+C5rlNSUiIwGAwQGRl5UcP5EUIIIYQsWV3PiPTV9GPxFc1DIQAAWyo64u8M8zjibSf506eWoz8PWyX/XqWdpY4bDmxIaRpuOrNVsvnp4KfTF7gvwFZJC4ZFMXRZCw8P137zzTdW5eXlgvDwcG1nZydv27ZtEpZlYd26dQPnG/B+Lm+++eZQSUmJ8PPPP5dWVlYKIyMjNX19fcy2bdskSqWSvPXWW4MTJ04cK+o8/vjjyt27d4tSUlIcr732WpWNjQ2trKwU7Nu3T5iamqpOT083ap+UyWR0/fr1A0uWLLFbtGiR47XXXqtycXExlJaWCo8ePSoIDw/XlpaWGu2OmjZtmn7NmjWDy5Yts12wYIFTYmKi2s/PT9/f388cOHBAIJVKaVZWVi8AwJIlS5Tff/+91YMPPmi/ZcsWlZubG3vkyBF+fn6+6JprrlFv27bNKJ/vv/9e8sUXX0jnzJmjnTBhgt7W1pZtaGjg79y5UywUCuGJJ54YO/hh3rx5WolEQj/++GNpf38/GW2LfPLJJ5Wjg+fP57rrrlNnZGSIs7KyRIGBgUYFqTvvvFP1yy+/iLOzs8Xh4eHOSUlJap1OB9u3b5fMmDFD29jYeFFFuEtt586dIgCA66+/Hn/AQwghhNBVT2dgydrsE7N+OdAZp9GzYz9r6lkqyD3a63lfhGetOfOzNCdbJZdF8+pz5p7VKhlyX5Y2agW2SppQn7pPuKZiTXRRe9FcbJW8emFRDF3WvL29DZs2bRp85ZVXZP/73/+kWq0Wpk2bplu2bJkiJSXlorYLOzg40F27dvWkpaVZb9++XbJ582ZrsVhMQ0JCtE8//bTyzBMgU1NTNV999VXfunXrZNu2bZMwDAMhISHarVu39tbX1/POLIoBANx6661qW1vbvrS0NOvff/9dLBQKYc6cOZqsrKyedevWWZeWlp6V15IlS0aCgoJ0mzZtst67d68wKytLbGdnx06ePFl37733jhWZQkJC9L/++mvP66+/brNr1y6xwWCAKVOm6D777LN+uVzOnlkUu/XWW1VarZaUlZUJq6urJWq1mri6uhquu+461TPPPDMcHBw8tivN3t6efvbZZ/1paWmyH374wWpkZIQAnCxqyeXyC86EuPnmm1Uvv/yyzffff2915i4thmHgm2++6U9LS7P+/vvvrT777DOps7Oz4bbbbht5+eWXFc7Ozm5/8L/tb2MwGGDLli1WU6ZM0UdGRv6ptlyEEEIIIUu1vbrLY+OuukWdCq3Rz2uTXKQ1LycH7JjhaTNgptQsD7ZK/q1YysJnNZ9N/a72u3O2Sq4MXZnrauWKrZJXCUIpFj7/DoSQcnd3d7eamprNF7peXV3dEh6P5+jj4/O3nO54sf7u0yfRlWH16tXWq1evluXm5nbPmjXrgrPRLle//vqr6J577rF///33B+69994/tVOssbHR0WAw9Pj5+V3w7zW6/BQWFi4BAIiMjMT/dwihKxK+j6FLpbFPZbVq+7G48qbBUG7cVsLvf3iedwbuDjMtXsNuR/Guf121rZJ/93sZtkpapsmTJy9pa2trp5TOutjb4k4xhNC4Pfvss8NffPGF1RtvvGHzyy+/9Jk7n4vFsiysWbNGNn36dN3dd9+NrZMIIYQQuir9VtXp9XpG7R1qPTt2qjmPIfqEyY57XkkJKJKJ+Vfkl5+XI6LsFoqzl0Xz6rBV8u/wB62SWY8GPVqFrZJXJyyKIYTGTSKRwEcffTSQn58vHB4eJtbW1lfUPyjt7e1MUlKS5rrrrlPhqZMIIYQQulpFTJB38RgyNjoj0Fl69F/J/pkzvWwHzJiWZaEsCPe+M1VY/nES0SrGWvewVfLSuFCrZJhzWNmK0BXYKnmVw6IYQsgkYmJitDExMVfkqY0eHh7sq6++qjB3HgghhBBC5uQsE2nuCvPY8X15W9yD87wyHpjrdczcOVkSXsNuR9Gul1N4/SeMWvdYW+9mTcyq7Xr/JItulfy7nWqVTG0abjJqTXW3cm9+KvgpbJVEAIBFsUuKECIDgNFBiQJKKTFnPgghhBBCCCGkZylZv7Nu5vFupevHdwWnc9eeWOBTfX+EZw22SprO+VslrZTakPuytVErD2CrpOn0qfuEq/evjt7bsdeoVVLCkyhv9Lsx+7Fpjx1g8PlGp2BR7NJ6AQBWjV5QKBTDZswFIYQQQgghdJXbcaTbbX1O3aL2QY0HAMAP+9sO3xrq3jC6zhACWBAzkQu1SvpEl6oT1+ZRGw9s3TMRlrLw3yP/Dfr++PdJSr1y7BTPU62SpStCV+RhqyQ6ExbFLq31ADB6ikamTCZzMmcyCCHTwZN7EUIIIXQlaelXSV5Nr40taRiYzY1/U9YWyS2KIdPAVsm/V0lniePGAxvP2Sr5dPDT26Pdo/H5RueERbFLiFKqAAAFAAAhREcI+TOfog0AACzLEoZh8FM3QpcpTju04YJXRAghhBAyIz1LycacupAfK9rjVTrWajTOEDDETnIsXJUSUGDO/CwNtkr+vUZbJYs6iuZSoNgqiS4aFsUuM3w+v59lWXuVSiWQSqVX5NByhK4GKpVKQCnV8fn8fnPnghBCCCF0Ljtrelzf3nliUdugxpMb93O0OvHPJP/0cF95n7lysziUBWHxpqnCfZvP0SoZVaZOfDsXWyVNB1slkalgUewyI5FI6oaHh937+vqsAWBYIpHoCCGUEJzRj5C5UUqBUkpUKpWgr6/PmlI6LJFI6sydF0IIIYQQl0bPMk9+X51c0jAwmwKMfZCQiXiD90d4ZT4U6VXD4OcLk7lgq+SCV9L1Acl4yqEJnbdVUure9PT0p9OxVRJdDCyKXWbs7e2r1Wq1l16v9+nu7pYQQmz++FZ/H5Zl+QAADMM4mjsXhMyFUqqjlA7z+fxGe3v7anPngxBCCCHEJeIzbN+Izma0IMYQYGMCHApXpQbssZcKdebOz1KQkV6BKGtpNL9u57wzWyV1M+7N1kStqAKGjyNxTOS8rZJ8ifImv5uyHg16tApbJdHFwqLYZYbH4+nd3d0z+vr6pqlUKj+9Xm8HADxz5zVKrVY7AgCIxeIec+eCkJkY+Hx+v0QiqbO3t6/m8Xh4OhNCCCGELjv/SvbPfOCrqoneduKmfyb7p8/xtes1d04WY7RVsvzjJKIZwlbJS+xCrZLhzuGlK0NX5jpbOWvMmSO6cmFR7DLE4/H0Tk5OlQBQaeZUzlJYWLgEACAyMnLzH10XIYQQQgghdGl1DGlEa7NPzH0lJaBQbiUY2wU208t24KPbp30U7ivvxVZJ0+E17nEQ7Xo5ldd33LhV0sarWROzClslTayks8Rxw4ENqc3DzWe1Sj4T/Ex6lFsUtkqiccGiGEIIIYQQQghdYVhK4d3chuBvy9sSR7QGKaVANt48NZd7nYgJuDvMVC7QKjmim3FvFrZKmlaPukeYtj9tQVFHUQS2SqJLCYtiCCGEEEIIIXQFyavtdV6bdWJR84DaezS261hP5MG2of3T3W0GzZmbxaEsCIvfnSIs35x8dqvk/DJ14jpslTQhlrLw6ZFPg344/gO2SqK/BRbFEEIIIYQQQugK0KXQiFZtPxZTeKJ/DvdUSamQp7hnjseOIDcZFsRMCFsl/17FHcVOG6s2pmCr5KVHWRYUX345SZWzK9j5s//+RPhX7y5HLIohhBBCCCGE0GWMpRQ+yG+c9nVZa5JSa7AejTME2KiJ9sWrFgXmO1kLtebM0ZKcbJVcFs2vyz5Hq+Q92ZqolQewVdJ01KyavFj0YsLejr1ntUre7Hdz1iNBj2CrpAmpy8ocBt5el6yvr/cHABh8970Q+fPPVZg7L3PBohhCCCGEEEIIXaYKTvQ5rd5xIrWpX+XLjXvbSRpWJk1Mnz/RvttMqVmeC54qia2SpsZSFspUZZL04XSbYXbYdTROgNA5LnNKVsxckYetkqZj6O0V9r+1OkpdUDAP2NPF3pHMzHm2Tz9VebXuFsOiGEIIIYQQQghdpnbW9PhxC2JSIW/4rjCPHU8s8KnGUyVNh9ew21G06+UUXv+JM1slWzQxL2/XB6Riq6QJFbYXOm+q2pTaomyRc+MeUo+mZ4Kf2T7fbX6XmVKzOJRlYejjj4OGv/0uiSpPz2kDABCFzd5nt/KlXVdrQQwAi2KXFCFEBgCjLzoBpRT/1UIIIYQQQgj9aSuT/Mvyj/eF9g5rnSIn2pW8tigwz1kmwt0zJkKU3UJx9rJoXl3OXGyVvPS6RrpEayrWxJR0lsyhcPrzsRXfavhmv5uzlgQtOYitkqajKihwHtywMUXf3OzLjfPc3Fpsn3k63Sourt1MqV02sCh2ab0AAKtGLygUimEz5oIQQgghhBC6jBXV9TsCAMzzs+sZjYn4DPtS4sStAj5jiAlwwN0zpkJZEO5952SrpFZxRqtkVJk68W1slTQhlrLw0aGPgrfUbUlU6VXS0TgBAnMlc5XLY5a/7yRxwmKvieg7O0X9b74VoykungOczTlEIlFKb74p2/bxx6uu5t1hXFgUu7TWA8DmU7/PlMlkTuZMBiGEEEIIIXT56R3WCl9NPxa9+3jfXHdbcev2x8P+y22NTJjidNXv5jAlXkO+o3jXy6lMf53RKYesrXezZsEreKqkie1u2+3ybtW7i9pG2ry4cS9rr4abhTdL3QXueiyImQbV68ngBx/MUG75OZ6qThcfgRAqiogosfvnS3l8Fxd8rjmwKHYJUUoVAKAAACCE6AghWIlFCCGEEEIIAcDJUyX/U9A09cuSlqRhjcEGAKBlQO31zq76Gc/H+R0wd36Whgx3CsXZyxfw6ndFnNEqqdSG3JetjVp5ALB1z2Q6RjrEq/evXriva18Yt1VSypcqbvW/dceDUx48tLdo7xJz5mhJRnJy3AY3vZtqaG/35Mb53l4Nts89lyGZj3PazgWLYgghhBBCCCH0Nyuu73d4a8fx1PpeldFgd0+5uHmmly3uVDIlyoKwaEOQcP8nSUQ7PDZo/GSrZHSpOnFtHrZKmo6e1ZP/q/6/Gb/U/5KgNqitRuMMMOxc17l7V4au3G0vtteaM0dLom9ukfS/9VasZt++2dw4kUoV1nfcscPm4YcOEQaLveeDRTGEEEIIIYQQ+pv0KbWC19Jro/Nqe+exFMY+qUoEjPL2We7Zz8ZOOICnSpoOrz7XSZy7KuUcrZJNmphV6Xr/pE5z5WaJcltz3d47+F5qx0iH0W4lb2vvuudnPJ8R7hLec77bootDdToy8M6m0JFff42jGo1kbIFhWHFUVJHdyhV7eA4OWHz8A1gUQwghhBBCCKFLjKUUPi5smvJFcUuy4lSrJAAAAaARE+Rlry0KzHWzFeNuJRM52Sq5bAGvPveMVkmpUhtyX5Y2akUVtkqaTquyVbJm/5rY8u5yo91K1gLroTv878i8b/J9R/BUSdNRpqd7Dn3wYaqhq8uNG+dPmHBcvvTFTHFYWK+5crvSYFEMIYQQQgghhC6xfY2D9h/kN95CAca2gbnbilqWJUzcHjfJEdslTYWyICxcN01Y8d/Es1olfReUqBPX5lGZOw4aNxE9qyfvHXxv5raGbfFqg3pstxJDGMN81/lFy0OX77ET2enMmaMl0dXVSfvffCtBW1U1gxsnMtmA7N57MmX33nsUWyUvDhbFEEIIIYQQQugSC/eV90VMkJftrR8IlwiYkVtD3bKfjfU7wGfwMC5T4dXtchLnrkplBup9uXHW1qdJvfDV7YaJCTho3IR2Nu90/6D6g0Wdqk53btxX5nv8hZAXMmY5zeozV26WhlWrmYF168NHMjJiQKsVjS3weHpJ7MICuxUrChkbG70ZU7xiYVEMIYQQQgghhEyIpRSq2xS2wR42g9z4a4sCc9/IPK5/Kcl/j4ccWyVNhQx3iMRZp1olOaccUoF0WDvzgSzt/GUHsVXSdJqHm63W7F8TV9FTEcqNywSywbsC78q4O/Duo9gqaTrDP//iO7R5cwrb2+vMjQsCA2vky5fvEAVPHzBTahYBi2IIIYQQQgghZCLlTYN2b2TWpjT1qb1/eHDmexOdpMrRNTdbsfqD26ZlmzM/i0JZEBasnS6s/DyRaIetx8JAqME3pkSd9HYetXbFVkkT0bE6sqlq06ztDdvjNKxGPBrnEZ4h2j26YFnIskJbkS22SpqI9kiNTf+aNQm6w4enceOMXN4r+8c/MmR33H7CXLlZEiyKIYQQQgghhNA4Dap0/NfSa+fvOtoz30CBBwDwanptwv/uC9lq5tQsEq8ux1mc+0oqM9Dow42zct9G9cLX0g1+cdgqaUIZTRmeHx36KLVb1W002H2izcRjL4S8kBniGNJvrtwsDatU8gbWro0YycpeAHq9YGyBz9dZJSbmy5cvK2asrAxmTNGiYFEMIYQQQgghhMbhs73NgZ8WNacMqvVyblzIIzo9SwnODTMdomgTibOWxfAa8ucYtUoKrU+2SkYuxVZJE2pQNEjX7F8TX9VbFcKN2wht+u8JvCfzrsC7jpkpNYuk+OZbf8VnnyWzAwMO3LggKKjabuWKLOGkSQpz5WapsCiGEEIIIYQQQn9BRfOg/PXM4ym1XcpAbtzVRtT2fOyE7SlBzm3mys3iUBaEBWnBworPE4lOKR0LA6GGCQuL1Ylr87FV0nQ0Bg2zqWrT7PTG9IVaVsttldTHuMcULA9dXmgtsMbB7iaiOXBAPpC2NllXWzuJG2ccHLpslizJsL7xhgYzpWbxsCiGEEIIIYQQQhdBodbzX0uvjdx5tGe+gaVjn6nEfEZ1Y4hrzovxfvsFPAZ3h5kI73iWizjvtVRmsNGbG2flExpOtkrGdpsrN0v0e8Pv3psPb07tUfe4cOMBtgE1L4S8sCPYIXjATKlZHHZoiN//1ur5qry8SDAYTtdnhEKNNDU1V/7iC2VEJGLNmKLFw6IYQgghhBBCCF2EOz6ruKuxT+XLjc32tt3/6qLAHB97yYiZ0rI4J1slly7kNewOP6NVUqENfTBLO++FamyVNJ26wTrrNRVrEqr7qoO5cVuhbd/9k+/PuM3/tuPmys3SUJYFxRdfTFb876skqlDIuWvCkBmVditX7hT4+SnPc3NkQlgUQwghhBBCCKGLcO1059L38xt9AQBcZML252L9ti+a5txq5rQsB2VBuGf1DGHllwlGrZKEYQ0TYovVCWn51NpFa84ULYnGoGE2HNgQntmUGaNjdaLROI/w9HGecbuXhiwtkgqkONjdRNRlZQ4Db69L1tfX+3PjPGfndpsnn0iXpqS0mCu3qxEWxRBCCCGEEELoPFQ6AyMR8Izalx6O9D6SX9tXHeRm3bgsYWI5tkqaDv/4DhdR3muLmMEmL26ctfOrVy98LcMwYSG2SprQr/W/+nxy+JPUXk2vMzceKA88sixk2Y6p9lMHzZWbpTH09gr733orWl1QOBdYdmyLIxGJVNLrF+fYPvPMfiIQ4HvJ3wyLYpcQIUQGALJTFwWUnt7yixBCCCGEELq8fVXaOvE/BY2pD0d6Z9w7x3OsdYwhBL55YOYWc+ZmachQq1ictXQhr3FPmHGrpEyhnfXQDu3c5w5hq6Tp1A7UytIq0hIP9x+exo3LhfLeB6Y8kH7LxFvqzJWbpaEsC0ObN08b/u77RKpUyrhrorDZ++xWvrSL7+WpMld+Vzssil1aLwDAqtELCoVi2Iy5IIQQQgghhP6Eg21Dtq9nHE860jE8BQBgc2FT6g0zXD+Uifl42p6psXoi2rMmWHDgywSiGzmjVTJurzpx7W4qdcJWSRNR69XMusp1Edkt2Qt0rE44GucTvi7BKyH/hZAXiq34VtgqaSKqggLnwQ0bUvXNLT7cOM/NrcX2mafTreLi2s2VGzoJi2KX1noA2Hzq95kymczJnMkghBBCCCGEzm9Ea+C9nlE7N/Nwd7SepYLRuErLWhWc6HNOCXJuM2d+loZfm+kqynstlRlqNmqVNNhNrNPEvp5h8I3uMVdulujnup8nfHrk09R+Tb8jNz7FbsqhpSFLsybbTR4yV26WRt/RIe5/860YTUlJOHA6xohEopTefHO27eOPVRE+H1slLwNYFLuEKKUKAFAAABBCdIQQfNEjhBBCCCF0Gfp2X6vf/+1pSu0f0Tlw4yGeNgdeTQ3InugkxZPgTIQMNovFWUtjeU2Fs89olRzSznp4h3bus4exVdJ0avprbNZWrE2sGagJ4sbtRfbdD055MP0GvxsazJSaxaF6PRn84IMZyi0/x1OVamznIxBCxXMjSuQvvZTHd3HRmDFFdAYsiiGEEEIIIYSuWofbFTavpdcmHe4YnsqNO1kLO5+K8U2/YYZrk7lyszisnoh2vzVDUPVVAtGNWI2GKWFYvV98kSYhbQ+2SprOiH6E93bF23NzWnKi9VQ/tvNRwAi0iV6JeS/MeKFEzBezF7oP9OeN7NzpNvjue6mG9nZPbpzv7dVg+/zz6ZLISDwk4jKERTGEEEIIIYTQVem/e5sD389ruFnHaZUU8ojmumCX3BWJ/mUiPoMFAxPhH9vuJsp/PZUZajEqGBjs/es0sa+nG3yies2VmyX64fgPEz+v+TxlQDtgtPMxyC7o4PLQ5Vn+tv4479pE9M0tkv4334zTlJfP4saJtfWQ9e23Z9k8/NAhwuDOx8sVFsUQQgghhBBCV6U5PvKO9ziXZ3jIqlalBmYHOEuxYGAi522VFNkMaWctydRGPH0EWyVN53DfYdu1FWuTjg0em8KNO4gduh6e+nD6db7XNZorN0tDdToysPGdWSO//RZLNRrJ2ALDsOKoqCK7lSv28BwccOfjZQ6LYgghhBBCCKGrUpC7bChlqlP+3vr+4CdjfNNvCnHDgoGpsHoiyn8jRFD1dTzRq4xbJScmFmoS0vZQKwedOVO0JEqdkre2Yu28Xa27og3UMPY5X8AINCneKbnPzXiuTMQT4c5HE1Gmp3sOvf/BIkN3tys3zp8w4bh86YuZ4rAw3Pl4hcCiGEIIIYQQQsiiqXQG5q3M43NGdAbR+hun5nHXXk4N2AsAeyUCHhYMTIR/9Hc30e43FjFDLR7cuME+4IQm7o0Mg3ckFgxM6NvabwO+PPpl8qB20J4bn24//cCK0BXZE2wm4CERJqKrq5P2v/lWgraqagY3TmxsBmT33pMpu+eeo9gqeWXBohhCCCGEEELIYv24v933g90Nqb1KnRNDgN1zvO9QlL/92MBrLIaZDhlokoizXozlNRfNJpw4FdkMamc/mqmd82QNtkqaTlVvlXxdxbrk40PHJ3HjjmLHjkeCHklf5LOo2Vy5WRpWrWYG1q0PH8nIiAGtVjS2wOPpJbELC+xWrChkbGz0ZkwR/UVYFEMIIYQQQghZnGNdw9avbq9NPNimmD4aYykwnxY1z43yt//NnLlZHFZPRPmvzxRUfRNP9Kqx2UqU8Az6iQlF6sS1e0Bij62SJjKkHeKvrVgbmd+WP5/bKilkhOpFPot2PTvj2X0CRkDNmaMlGd7ys+/Qx5tT2d4+J25cEBhYI1++fIcoePqAmVJDJoBFMYQQQgghhJDF0OhZ5s3M4+G/V3cu1BmocDQu4BHtoiDnvJeS/UvMmZ+l4R/d5i7KfyOVUbQat0o6BBzXxL6ZYfCe12eu3CzRV8e+Cvzq6FcpQ7ohOTce4hBSsTx0+U4fmc+ImVKzONojNTb9a1Yn6g4fCeLGGbm8V/aPf2TI7rj9hLlyQ6aDRTGEEEIIIYSQRfi5ssP7vfyGRT3DWmduPMjN+tCq1MAdU1ytFebKzdKQgQaJeMfSOF7L3lnGrZK2g9qwRzO14U9gq6QJVXRX2K8/sD65bqgugBt3lji3Pxb02PYk76RWc+VmaVilkjeQtnbuSHZ2NOj1grEFPl9nlZiYL1++rJixsjKYMUVkQlgUQwghhBBCCF3Ruoe1wud+OrToQKsimBu3txL0PBblk377bPd6c+VmcQxaIsp/I1Rw8Js4olcbt0r6JxWqE9YUYKuk6QxqBgVrKtbM39O+J5KlLG80LuKJVNf6XJvzVPBT+7FV0nQU33zrr/jssxR2YMDo0AJBUFC13coVWcJJk7CwbmGwKIYQQgghhBC6osklfF3HkHZs3o+AIbrkIKf8fyUHFFsJebijw0T4R7Z6iPa8lcoo2ty5cYPDpFpN3BuZBq+52CppIixl4cujX07+tvbbZIVOYctdm+k4s3xl6MocT2tPlbnyszSaAwfkA2lrk3W1tUaHFjAODl02jzySbn3D9Y3myg1dWlgUQwghhBBCCF3RBDyGPh83YfvyrTUPTXW1PvxKSsCOIHfZkLnzshSkv85KnLUsjtdSHHpGq+SANvzxTG3YY0exVdJ09nXtc9hwYENKg6JhIjfuInFpfXL6k+lxnnFt5srN0rBDQ/z+t1bPV+XlRYLh9KEFIBRqpKmpufIXXygjIhGeUGvBsCiGEEIIIYQQumLU9YxIP9zdMHv14sm7BTxmrG0sNci51VbC/yDSz77HnPlZFIOWiPL+PUtQ/V3s2a2SyQXqxLQCEMv15kzRkvRr+gVr9q+JLmwvnMcCO1ZlFPPEI4snLN75xLQnKvkMH1slTYCyLCi++GKy4n9fJVGFQs5dE4bMqLRbuXKnwM9Paab00N8Ii2IIIYQQQgihy57OwJI1WSdmbz3QGac1sCIHad3wyiT/cu51sCBmOvwjv3iI9ry1iFG0u3HjBsfJxzRxb2YaPOf0mys3S8NSFj6r+Wzqd7XfJSn1SpvROAFCZznN2rc8dHmuh9QDWyVNRF1a6jCwbn2Kvr7eaCcez9m53ebJJ9KlKSkt5soN/f2wKIYQQgghhBC6rG072On5Tm79oi6F1nU09nNlR9yD87yqnWUijTlzszSk74SVOGtZPL+1ZCY3TsXyAW3Y4xna8MePmSs3S1TSWeK48cDG1KbhpgncuJuVW8tT05/aHuMR02Gu3CyNobdX2P/WW9HqgsK5wJ7eiUdEIpX0+sU5ts88s58I8NCCqw0WxRBCCCGEEEKXpYbeEatV24/F728eMirQyCX8viXzvTOwIGZCBi0R5b46W3Doh1iiV4tHw5Th6/X+yQXqhDWF2CppOj3qHmHa/rQFRR1FERToWIFGwpMob/C7IfvRoEersFXSNCjLwtDmzdOGv/s+kSqVMu6aKGz2PruVL+3ie+GhBVcrLIohhBBCCCGELis6A0vezq6b9fOBjjiNnh0r0PAYok+c7Ljn5ZSAIpmYjwUaE+Ef/tlTtGd1KjN8jlbJ+LcyDR7h2CppIixl4ePDH0/76cRPiUr96QINAULDnMNKV4SuyHO1clWbM0dLotqzx3lw48ZUfXOLDzfOc3NrsX3m6XSruLh2c+WGLg9YFEMIIYQQQghdNtIPdXlsyKlf1KnQGBVoJjlLj76cEpA5w9NmwEypWRymt1Yqyl4Wz28tC+HGqVjerwl/MkMX9mitmVKzSIXthc6bqjaltChbfLlxd6l70zPBz6RHuUV1mik1i6Pv6BD3v/lWjKakJBwoHTs0lUgkSunNN2fbPv5YFeHjTjyERTGEEEIIIYTQZeT3g51B3IKYrYTf/9A8r4z7I7ywQGMqBi0R5a6aLaj+IZYYNMatkgEpBeqEtEIQ2eBOPBPpVnWLVu9fHVPSWTKHwukCjRXfavgmv5uyHwl6pIohzIXuAv1JVK8ngx98MEO55ed4qlJJxxYIoeK5c4vlL63M57u4YNs1GoNFsUuIECIDgNEtsQLKqVAjhBBCCCGEzvbqosD86z7aN12tZ8XxkxwLVqUGFGKrpOnwD/3oJSpIS2WGO1y5cYPT1KPquDczWY+wATOlZnFYysJ/Dv0neEvdloQR/Yj1aJwAoREuEcUrQlfkO0mcsEBjIiM7d7oNvvteqqG93ZMb53t7Ndg+/3y6JDKy21y5ocsXFsUurRcAYNXoBYVCMWzGXBBCCCGEELqsZBzqcvd1sFJMcbVWjMacZSLNC3F+WyY6WQ2GetniLCsTYXprpaKsZfH8tjNbJe36NXOezNDNfgR34pnQ7rbdLu8efDe1TdnmzY17Sj0bnpvxXPpc17lYoDERfXOLpP/NN+M05eWzuHFibT1kffvtWTYPP3SIMLgTD50bFsUurfUAsPnU7zNlMpmTOZNBCCGEEELoctDUr5K8uv1YXFnj4KwgN+tD3/0j9Cfu+i2hbg1mSs3y6NWMKHfVbMGhn2KJQSMaDZ9slVy0R52wughbJU2nY6RDvGb/moVlXWVh3FZJKV+quMX/lqyHpjxUja2SpkF1OjKw8Z1ZI7/9Fks1GsnYAsOw4qioIruVK/bwHBy0ZkwRXQGwKHYJUUoVAKAAACCE6AghOMgPIYQQQghdtfQsJRty6kJ+rGhPUOtYCQDAofbhoO/2tZXfPtu93tz5WRp+9Q9eooK0RYyy04UbNzhNrVHHr97Bus8aMFNqFkfP6slHhz6a8UvdL/Eqw+lZVgQIO891XvHy0OX5jmJHLNCYiHL7dq+hDz5MNXR3G7UB8/38jstffCFTHBbWa67c0JUFi2IIIYQQQgihSy77SLfb2zl1i9oHNR7cuL+TVW2As3TATGlZJKb3mFSctTSB11Y+gxtnxXZ92oinM3SzHj5urtwsUV5rnut7B99b1D5iPMvK29q77vkZz2eEu4T3mCs3S6M7ccK6/63V8dqqKqPXNrGxGbC5794M67vvPoatkuhiYFEMIYQQQgghdMm0DqjFq7Yfiy1pGAjjxm3E/MEHIjwz/jHP6yhD8Dwqk9CrGdGuV8IEh39aSAxa41bJwGt2q+NXF4FIZjBnipakVdkqSduftrC8u3w2t1XSWmA9dLv/7Tvun3z/YWyVNA1WrWYG1q0PH8nIiAHt6dc28Hh6SWzsHrsVy4sYG2wDRhcPi2IIIYQQQgghk9OzlLyzq27GD/vbE1Q61mo0zhAwxAY6Fq5KDSiQWwl05szRkggOfuctLHw79axWSedpR9Txb+1g3UIHzZWbpdGzevL+wfdn/tbwW5zaoD792gaGjXSLLFoRumK3ncgOX9smMrzlZ9+hjzensr19RjO6BYGBNfLly3eIgqcPmCk1ZAGwKIYQQgghhBAyuV+rOry/KGldzI35OVqd+GeSf3q4r7zPXHlZGqanxlqctSyB174/mBtnJfZ92oin03WhD50wV26WKKclx/39g++ndqo6jdqAfWW+J56f8XzGbOfZOMvKRLRHamz616xO1B0+EsSNM3J5r+wf/8iQ3XE7vrbRuGFRDCGEEEIIIWRyN4W4NX5b1nb0aJdykkzEG7ovwjPz4UjvI9gqaSJ6NSPa9XK44PCWGONWSYFOP+ma3eq4t/Ziq6TptAy3SFbvXx1X0VMxixuXCWSDdwbcmXnPpHtqsFXSNFilkjeQtnbuSHZ2NOj1grEFPl9nlZiYL1++rJixssLX9jhRlkLbsSEnj8m23ebOxZywKIYQQgghhBAaF5ZSONI+bBPkLhvixl9OCcj8797mrlWpAXvspUJsJzMRwcFvfYSFa1MZZbczN25wnn5YHb86i3ULwVZJE9GxOvJu1buzfm/8PVZj0EhG4zzCM0S5RRUun7m8wFZki69tE1F8862/4rPPUtiBAXtuXBgUdFC+ckW2cNIkhblysyQNB/rc9qe3JCsHNO4pT0x539Hb+qp9z8CiGEIIIYQQQugvyz3W67I2+0TqkFpvu/2xsA+4c8JmeNoMbLolaJc587MkTPcRa3H2skRee8V0bpyVOPRq5z6brpv5QJ25crNEmU2Znh8d+ii1S9Xlxo372fjVvhjyYkaIY0i/uXKzNJrKSruBtW8n6WprJ3HjjKNDl82SR9Ktb7i+0Vy5WZL+jhHrki2NcV0NwyGjsdKtTfGpT0/dYsa0zAqLYgghhBBCCKGL1jGkEb26/VhMUV3/HApAAABeS6+dv/Hmqbnmzs3i6FSMeNe/5vCP/BJDDFrhaJgyAp1+8nX56ri3ikEoxXYyE2lQNEjT9qfFHeg9MJMbtxHYDNw96e6MuwPvPmau3CwN0WiIbeYO6+7du58Ag4E3tiAUaqSLFu2Sv/D8PiISsWZM0SJoVXp+yS+NEY1V/VGsgY69hwABViTlK1gDJQyPUDOmaDZYFEMIIYQQQgj9aSyl8F5ew/Rv9rUljmgN1qNxhgB+cL0EBAf+5yssWp/KjPQYnbxncAk+rI5fvYN1nTF0vtuii6MxaJiNBzaGZTZlLtSyp+e08QhPH+MeU7B05tJCG6GN3pw5WgrKsjC0efM09x9/cuYPDRkNYxOGzKi0e+mfOwUTfJXmys9SUJZCVU77lCN7OhK1KoOcuyZ3lRwLW+yd5eZvc1UfDoFFMYQQQgghhNCfsru213lN9onU5n61DzfuYy9pWJk0cXukn32PuXKzNEznQRtx9vJEXmeV0cl7rJVDjzbiuQzdzPuxVdKEttZv9f308KcpvZpeozlt/jb+R1+c+WJmsEPwgJlSsziq/HyXgY3vpBpaW725BQmei0ubzROPZ0hTUlrMlpwFaTzY57p/e0uyoldj9H4tlvG7g2PdMyfPd8H3EMCiGEIIIYQQQugPdA9rhau2H4spON4XMdoqCQAgFfKG7w732PF4tE81nippIhoFT5zzz7n8o9uiCasbO3nvZKvk4jx13Jsl2CppOjX9NTZvV76deKT/iFHx0VZo23f/5PszbvO/7bi5crM0+uYWSf/qt2I1+8pnAaVjbxgGqZS1vX7xNtsnnzxA+PyrsoXPlAY6VdKSnxtjO+sUodw4T8CoJs52yA271rucJ2BwZ+8pWBRDCCGEEEIInZfOwJKbPy5/uG9E5zgaIwB0vr998WuLAvOcrIVac+ZnSQT7P5koLH43hVH1OXDjBteQanXCmizWeRqevGciSp2St65y3dyclpxoPdWPFR/5hK+L94zPfyHkhWKpAIuPpkA1GmbgnXdmjWz7PZZqNOKxBYawQ/MiVYOpKQqfhIRK82VoGbRqA690a2NEQ2Vf9Jlzw9wDbMoibvLNs7YXqc2Y4mUJi2IIIYQQQgih8xLwGLow0GHflsqOZAAAbztx4/KEienRAQ5d5s7NUjBt5XLxzpVJvO7Dk7lxVurUpZ33Yrou+C48ec+Evqv9zv+Lo1+kDGoH7bnxqXZTq5fNXJYVKA/E4qOJDP+y1Wdo8+YUtqfHhRvn+/qekD//fGYja7jRXLlZCspSqM5tn3x4d2eiZkRvx12Tu0hqZ1/nleUeaIut7eeBRTGEEEIIIYTQGI2eZUR849aalUn+ZRUtQwFxkxwOPLnA9yC2SpqIZogvzl4eya/NmE9Y/dhnM8oTanRTb87VLHytDAQSbHMykcqeSrv1leuTTwydCOTGHcQOXQ9NeSh98YTFWHw0Ee3hwzb9aWmJusPGbanExmZAds/dmbJ77z1KGAagsNBcKVqE5kP9Lvt+b05W9Gh8uXGxNb9n2kK3HVOjXbH99w9gUQwhhBBCCCEELKXwf3sag74pa0t4/ZrAb2MnOXaOron4DPvrI7O/Mmd+lkZY+mGgsOzDZKIeMNrZYXCffUCduDabdQjEk/dMZFAzKEirSJu/p31PpIEaeKNxISPUpPik7Ho2+Nl9Ip4Ii48mwA4N8fvXpM1T5eZGgf50oRf4fJ0kLnaP3fLlexmZDE/wHKfBLpVV8c+NsZ0nFKHAmfPIExC1X6hjbth1Xvv4Qh6+pv8ELIohhBBCCCF0lSuq63dcnXU8paFX5QcA8PbOukXRAQ6f8RmCQ69NjNe8116061/JvJ6jAdw4a+3aoZm/PF0fdEuzuXKzNCxl4fOaz6d+d/y7xGHdsC13LcQhpGJZ6LIcX5kvFh9NgLIsKD7/fLLiq6+TqEIh564JgoKq7ZYvzxZOmTxkpvQshk5j4JVubQqvr+xdwOqpaGyBAHULsCmLuMEnT+YoVpkxxSsOFsUQQgghhBC6SvUptYJX02uj82t757EUmNF4r1JrX9U6JA/1su03Z34WRdUnEGcti+KfyJ5HOLuVKF+s1k27LUcTs6oceEIsQppIcUex0ztV76Q0DTdN4MZdJC5tj097PD3BK6HVXLlZGnXRXqeBDRuS9Y2Nftw44+TYabvkkQzp9diWOl6UpXAov2PSobyORM2I3mgWnq2z+MTsa713eEy27TZXflcyLIohhBBCCCF0lWEphc0FTVO+LGlJVmgMNqNxAkDn+tmVvpoakOdmK8ZTykyBsiAsfneKsHxzEtEMje1WogBg8IzYr05cm0Pt/EbMmKFF6VZ1i9Iq0hbs7dg7hwIdK/SKeeKRxRMW73xi2hOVfIaPxUcT0Hd0iPvfWr1AU1w8Bygda+EjIpHK6rrrdsmfe7acCAT4XI9Ty5EB533bmpOGutVGRUeRlN87baHrjqlRrrWEwTmPfxUWxRBCCCGEELqKlDT0O7yZeTylvlc1kRv3kIubl8X7befOEkPjw2vc4yDa9XIqr++40YdZVubRqol+KV0/eXGbuXKzNHpWT/5z6D/Bv9T/Ej+iH7EejRMgNMw5rGz5zOW5blI3LPSaANXryeC774Uot26NpyqV1dgCIVQUFrbP7qWVuXwPD2zhG6ehbrVV8c+NMR3Hh2YDd24Yn6gnhDrkh13nXSYQ8QxmTNEiYFEMIYQQQgihq8Tb2SdmflXWuoilMNa+JxEwI7fNcs96ZuGEKpwhZhpkuFMozl4RzavPmUsoO7ZbifIlI7rgu3ZqFvyrEnC3ksnktea5vnfwvdT2kXYvbtxD6tH4dPDTGVFuUVjoNRFlerrn0If/l2Lo7HTnxvleno22zz6bIYmOxud6nPRaA1P6a1N4/f7eBQY9FXOWqKu/zb6IG33ybJzEuLvURLAohhBCCCGE0FUi1Nu2/X+lrQzAyVbJiAnyslWpgbkecmyVNAnKgrBo/TTh/k8TiXZYNhYGQg3ekfvUCWtzqdwbd9CYSMtwiyStIi22vLt8Njcu5UsVN0+8OevhqQ9XM4Q5383RRdDVHrfuX7MmXltVNYMbJ9bWQ9a3355l8/BDhwiDz/V4HcrvCKjObU/SKPUO3LiNk7hu9rVeOzynyLvMlZulwqIYQgghhBBCV4m4SY4dERPkZY19Kvel8RO3x0927DB3TpaCV5fjLM59NYUZqPflxllb72bNglfS9QHJ+FybiI7VkXer3p31e+PvsRqDRjIaZ4Bh57nN27ti5ord9mJ7rTlztBSsUskbWPt2xEh2djTodMKxBR7PIFkQXShfsaKAZ2enM2OKFqG1ZtBp3+9NiYOdan9uXGTF7wuKcc0KWuB6FOeGXRpYFEMIIYQQQsjCDIzoBK9l1M53kgoVLyX77+Ourb9xarZEyDNgq6RpEEWbSJy1LIbXkD+HwOlh41QgVWpD7svSRq2oAtytZDLpjele/zn0n9RudbcrN+4r8z3+/IznM2c7z+41V26WRvHV1wGKL75IZgcGjE47FEyadES+9MUs0YwZA2ZKzWIoejWS4p8bYtprh8KAnp4bxvCJZkKIQ3749d6lODfs0sKiGEIIIYQQQhbk06LmSf/d25w8pNbLhTyiuW2W25GJTlLl6LpMzNebMz+LweqJsCAtWFj5ZQLRKaWjYQqEGnxjStSJaXlU5q4xZ4qWpG6wzjqtIi3+YN9Bo/Y9G4HNwF2Bd2XeFXjXUWyVNA31vn32A+vWJ+tPnAjgxhl7+26bBx/MtL71ljpz5WYp9FoDU/Zb8+y6/T0LDTrjuWEuE2X7I270ybV1lijPewfIZLAohhBCCCGEkAXY3zxo90ZGbXJt90jgaExroKIP9zSGrb9xap4ZU7M4/NpMV1Hea6nMULPRYHdWPqFBvfC1dINfbLe5crM0ar2aWX9g/Zys5qwYHXu6fY9HePoY95iCpTOXFtoIbbDQawKGnh5h/+rV0eqCwrnAnj4gAoRCjTQ1Jdf2hRfKGLGYNWOKFuHw7g7/6tz2JPWw3pEblzmKGmYt8sr0nmaHhxX8jbAohhBCCCGE0BVsUKXjv55RG7mzpifKwDlVUixgVDeHuO18Id6vwpz5WRIy0CQRZy9byGsqnG3UKimUKbShD+7Qznv+ELZKms6WE1sm/Lfmv6n9mn6j4kGgbeCRF0NezJrmMG3ATKlZFMqyMPTh/wUP//hjAh0ZseauCWfO3G+3cuUuwQRf3LU0Tu21gw5lvzYnDXSqjHbgCa14/VOjXbOmL3Srwblhfz8siiGEEEIIIXSF+ry4OeCTouaUQZXejhsP87Ytf/WawBxvOwmedGgKrJ6I8l+fKaj6Jo7oVVajYUoY1uAXt1edsHY3lTrhYHcTOdx32PbtyrcTjw4cncqN24nseu6ffH/GLROxfc9URnbudBt87/0UQ1ub0a5Hnptbi+1TT2ZYJSS0mSs3SzHcpxEX/9y4oO3YYDhQGKuaMzyi9Q2x3x1+vU+xUIxzw8wFi2IIIYQQQghdYQY1lPnfUda2uq/+Tm7c1UbU9lzshPTUIOdWc+Vmafg1v7qLdr+5iFG0uXPjBnv/E5rY1zMMPlE42N1EhnXD/LUVa+fltuZGGahh7LOqgBFoE70S856b8VypFd8KiwcmoGtstOp/a3Wcdv/+UG6cWFkNW998c7bNY48eJHw+HsYxDgYdy5Rta5p1Yl/vQoOOlXDXnCdYV0Tc5Jsjd8G5YeaGRbFLiBAiAwDZqYsCSinuhUQIIYQQQuMm4gFtUYJg7DKfUd8Y4rpzabzffgGPwQ+yJkD666zEWcvieC3Fodwf4qnIZlA7+9FM7Zwna7BV0jRYysI3td8EfnXsq+Qh7ZDRrscg+6Cq5TOXZ/vb+g+bKz9LwqrVzOD69WHK9IyFoNWKxhYYhhXPj9xrt3Llbp6jI+56HKeawk6/gzntSSqFzpkblzmIGkMXeWb6TLfvMFduyBgWxS6tFwBg1egFhUKBb+QIIYQQQmjcxHxCb55Ihj45TO1medlWvHZN4E4fe8mIufOyCAYtEeW+Oltw6IdYolePnQpHCc+g908qVCesKQCJvc6cKVqS8u5y+w0HNiTXD9UbzVlyFDt2LJm6JOMa32uazJWbpRn+8acJQ598ksL29Tlx4/yJE2vlL76QKZ49u89cuVmKjuND9qW/NiUNdKgCuXGhhDcwJco1KzjO7QjODbu8YFHs0loPAJtP/T5TJpM5XejKCCGEEEIInamqdcj2+/L2oDevm1TEjc9yIurQ24I+jPK3x5MOTYR/6EcvUUFaKjPc4cqNGxwnH9PEvZlp8JzTb67cLE2fuk+YVpEWVdhROJel7NgBESJGpF7kuyjnmeBnygWMAHc9moCm6qB8YO3aRN3Ro1O4cUYu75Pdd2+m7O67a82Vm6UY7teIS35ujG47OjiHGs8N0/kE2+2ec4NPsVDCx1NSL0NYFLuEKKUKAFAAABBCdIQQfFNHCCGEEEJ/yrBGz3s9o3Ze1pGeaD1L+YHO0u77IjzHPrwSQmA+FsRMgumpsRZnL4/ntZXP4MapWN6vCX8iUxf22DFz5WZpWMrCp0c+Dfrh+A9JSr1Sxl2b6TizfEXoil1e1l6469EEDAMDgoHVayJV+fmRYDg9ow0EAq1VQsJu+bKlxYxUijPaxsGgZ0n5782hx0t7YvU61oq75uxrXTnnRp8cOzcr7Bi7jGFRDCGEEEIIocvM12WtEz/a05g6oNLbj8Y+KWpKvn22+wkRn2HNmZtF0akYUe4r4YLDW2KI4fR8Jcrw9fqARXvUCauLQGSDuztMpLC90HlT1aaUFmWLLzfuauXa8uS0J9NjPWPbzZSaRaEsC4pPPp2q+PbbJDo8bMNdE06fXiVfsXynMDBQYa78LMXRvV0Tqna2JamGdC7cuLW9qDk01TPDd4Y9vp6vAFgUQwghhBBC6DJxsG3I9vWM40lHOoaN2pycrIWdT8f4bseCmOkIqr7yFRatT2GU3UaDsA3O046o49/awbqFDporN0vTMdIhTtufFlPaVRpO4fThYxKeRHmD3w3ZjwY9WsVn8KRDU1Dt2eM8uPGdFH1zsy83znN2brd5/LEM6aJFzWZKzWJ01insSn9tTOxvU03mxoVi3uDkKJfsGfHuh3Bu2JUDi2IIIYQQQgiZ2YjWwHs9o3Zu5uHuaD1Lx06VFPKI5rpgl10rEv33YUHMNJiuapk4e3kir+PANG6clTj0aiOeydCF/uOEuXKzNHpWT/6v+v9m/FL/S4LaoB5rLSNA6ByXOSUrZq7Ic7Zy1pgzR0uhb22V9L+1eqGmrGw20NOFRyKRjEgXL86xffqpCiLAGW3jMTKoFRVvaYhurRmcQymMzcFjeETnPd2uYM4NPkUiK5wbdqXBohhCCCGEEEJm9O2+Vr//29OU2j+ic+DGQzxtDryaGpA90UmqNFduFkWr5Ilz/hnBr/l1AWF1Y4VHygh0+smL89VxbxaDEOcrmcrO5p3uHx76MLVjpMODG/ey9qp/NvjZjLmuc3EenglQnY4MbHxn1si2bbFUrZaMLRBCRRERJXYvrcznu7qqzZjiFc+gZ8n+7S0za0u7Y/VaVspdc/KxPjDnBu8cew8ptqNeobAohhBCCCGEkBn9WtUZyi2IOVoLu55a4Lv9xhDXJnPmZUkEFZ/5Cfe+k8qoeo0KjwbXGdXq+DXZrMv0IXPlZmkaFY1WaRVpcZU9laHcuJQvHbo94PYdD0x+4DBDmPPdHF0E5a+/eQ/+5z8pbHe30WmpfB/vevnzL2SI52HhcbyOlXT7HMhqTVYN6YyeY2s7YcvMFM/MCTMdWs2VGzINLIohhBBCCCFkRq+kBGTd/XllIBCg10132bUyyb8MWyVNg2nfbyve+VISr6vaaEYba+XUrY18IV0XfHeDmVKzOBqDhnm36t1Z2xu3x2pZrXg0zhDGEOUWVbRs5rI9diI7nTlztBTaIzU2/WlpCbpDh4xagIlMNiC7+64dsvvvryEMFh7Ho6tBIS/d2pTY1zpi9N4hEPOGJs9z3hmS5HEQ54ZZBiyKIYQQQggh9Df5cX+7b0ygQ5uTtVA7GpvqJht6ZuGEH+f6ydsDna2HzZmfxdAM8cXZK+fxa7dHEVY/9pmH8oQa3dSb8jQL/10KAgkWHk1kW8M2748Pf5zao+4xOoVvos3EY8/PeH7HTKeZfebKzZKwCgW/P23tXFVOThTo9WMtwMDn6yULF+6xW7G8iLHB01LHY2RIKyz5uTGq5fDAXO7cMMIQvfc0ecGcG32LxFI+FnctCBbFEEIIIYQQusSOdg7LXkuvTTzYppiWV9tb9MFt07K56/dFeNaaKzdLIyj7KEBU+kEKUffbceMG91kH1AlpO1nHyVh4NJHagVpZWkVawuH+w9O5cRuhTf89gfdk3hV41zFz5WZJKMuC4n//mzT85f+S2KEho9e1YMqUQ3bLl2ULg4LwtNRxYA2U7E9vnnGspDtOr2GtuWuO3tKD4df77HT0kmKbtQXCohhCCCGEEEKXiEpnYN7acXzO9uquGJ2BCgEACk70Rew53lcZ5W+P835MiNdSYifK+Wcyr6cmkBtnrV07NJHL0vXTbm02V26WZkQ/wltfuT5iZ8vOaB2rE47G+YSvi/WM3fNiyIt7rQXWuGPJBNQlJY4D6zck6+vrJ3LjjKNDl82SR9Ktb7i+0Vy5WYra0m7vA1mtySODOjduXCoXts5M8cj0C3VsMVdu6NLDohhCCCGEEEKXwE8V7T7v5zcs6lXqnLjxIDfZYRcbocpceVkcVZ9AnL1iPv/4jkhCDWPtTpQnUuum3bZLs/DVfcATUnOmaEl+OP7DxM9rPk8Z0A4YHVowWT750NKZS7Om2E3B3TQmoO/sFA28tXqBunjvHGDp2IAwIhKpra69Zpf82WfLiUiELcDj0N04bFu6tTGht2UkiBsXiBjFpHnOO0OSPA8yPILvHRYOi2IIIYQQQgiZ0LGuYevXttcmVrUpjFrK7K0EPU8s8Nl+a6h7g5lSsyyUBWHJ+5OF+/6TTDSDtmNhADB4RuxXJ6TlUPuJI2bM0KJU9VbJ11euT6odrJ3MjduL7Lv/MeUfGTf63VhvrtwsCdXryeD7789Q/vxLPFWppGMLhFDR7Fnlditf2sX38sSi+jioFDphyc+Nkc2H++dR9nRNhDCg9wqyK4q40adAbC3AuWFXCSyKIYQQQgghZAIaPcus3nE8bNvBzoVaAxWNxgUM0aVOc857Kcm/xErIM5gzR0vBa9zjINr1cgqv77hRSxkrc2/TRL2Urp9yfau5crM0Q9oh/tqKtfPz2/IjDdQw9vlRwAg0yd7Jec8FP1cq5otxx5IJjGRmegx+8GGKoaPDgxvneXg0yZ95JkOyMKbDXLlZAtZASUVmS/CxvV1xOg0r4645eEmr51zvvdPR2xpns11lsCiGEEIIIYSQCXy0pzFoS2VHMjcW5GZ96JWUgKypbjJsKTMBouwWirOXRfPqcuYSyo61lFG+RKULvnOnZsHLFcDwsd3JBFjKwpdHv5z8be23SQqdQs5dC3YIrlw+c/nOCTYTlGZKz6LoTpyw7l+9Jk574EAIN06kUoX17bdl2SxZUk0Y5jy3Rn/GiX09XpU7WpOVA1p3btzKVtAekuSZ4R/miDMHr1JYFEMIIYQQQsgEHo/2qf69uiuiY0jjbmcl6H0syjv9jtkedebOyyJQFoRFG4KE+z9NIlrF2A4PCoQavCP3qRPW5lK5N7aUmUhpZ6njxqqNyY2KRqOdeM4S5/ZHgh5JT/FOwcHjJsCOjPAG1q0LH8ncEQO60wcWAI9nEEdFFdm9tHIPz84O2/jGoadZaVP6S2NCT7NyGjcuEDHDgRHOO2emeFbh3LCrGxbFEEIIIYQQukgaPcs09o1YBTpbD4/GBDyGPh87Yfue430T/pUSUIytkqbBq9vlJM5blcr01/ty46yNV4sm5uXt+oBUbCkzkR51jzBtf9qCoo6iCAqnh7uLeWLVtb7X5jw5/cn9AkaABQQTUHzzrb/i88+T2f5+owMLBIEBNfKlS7NEISH95srNEqiHdYKSnxsjmw4NRFKWcueGGTynyIsibvItkMgEWnPmiC4PJi2KEUJ4ACCilI6cEY8FgMUAMAIAmymlOIQRIYQQQghdkX6r6vTalFefyhDCpj8e9omAx4wVCVKCnNtSgpzbzJmfpSDDHSLxjqUxvIa8OQQoGY1TgVSpDbk3Wzt/eRW2SpoGS1nYfGjz9J/qfkoc0Y9Yj8YJEDrLada+5aHLcz2kHrgTzwQ0FRV2A2+/naSrPT6JG2fs7Hpk//hHpuz2206YKzdLwBooqcxqnXa0qCtBpzYYzQ2z97Q6POd6n2wnH+sBM6WHLkOm3im2DgAeI4S4UEoHAQAIIbcDwNcAMPoP2UOEkFBKKfbsIoQQQgihK0Zdz4j01fRj8RXNQyGjsbd31oW+lORfbsa0LA9lQbhnTbCw8otEolOOnb5HgVCD74ISdeLaPCpz15gzRUuS35bv+t7B91LalG3e3Li7lXvzU8FPpS9wX4A78UzA0Nsr7F+zZr569555wLK8sQWhUGOVnJwnX/piKSPGAwvGo25/r0dFZkuKsl9rdFCBla2gY0aCR2bAHKdGc+WGLl+mLopFA0DuaEHslFUAMAAAzwCAKwCsBoDnAeA5Ez82QgghhBBCJqczsCQt68TsrVWdsRo9Kx6N8xmi1xlYHEdiQvzaTFdR/r9TmcEmL26clfs2qmNWpRsmJnSZKzdL06pslaTtT1tY3l0+m3J24lnxrYZv8rsp+5GgR6oYgsPdx4uyLAx99NH04R9+TKBKpdHOJWFISIXdyhU5Aj8/PLBgHPpalbLinxvje5qUwdw4X8goAyKcckJTPCt5fAZ3laJzMvU/4l4AUDR6gRDiBwCTAODflNKvTsWiASAZsCiGEEIIIYQuc78f7PR8J7c+tVOhdePGJ7tIa15OCcgM9rAZPN9t0Z9HBpvF4qylsbymwtlGrZJCa4U29MEs7bwXqgELNCahY3Xk/YPvz9zWsC1ebVBLRuMECDvXdW7x8pnLdztJnHAnngmoduW6Drz7boqhtdVoFx7P1bXV9sknMqySklrNlZslUCv1gpJfGuc1V/dHsgYqGI0TAgaPKfLiiBt99ljZCvG1jC7I1EUxGwDgHjcdCQAUADI5sUMAsNDEj4sQQgghhJDJNPSOWL26vTa+vHlwJjduK+H3L4n0Tr93judxc+VmUVg9EeW/ESKo+jqe6FVWo2FKGNYwIW6vOmHNbmrtgsOwTSSzKdPzo0MfpXapuoyKvD7WPnXPzng2Y47LnB5z5WZJdE1NVgNvrY7VlJfP4saJRKKU3nxTtu3jj1cRPs7D+6soS6Eyq3VaTWFXgk5tsOGu2btbHQm73jvbZYIMDypAf4qpi2LtADCBczkeAFQAwJ2zYA0AehM/LkIIIYQQQibRO6wV3vrp/sdVOnZsnhWPIfrEyY57Xk4JKJKJ+fizrAnwj25zF+W/kcooWo3m/xjsJtZpYl/PMPhGY4HGROqH6qVpFWnxVb1VIdy4TCAbvCPgjsx7J91bg62S40c1GmZgw8bZyt9/Xwha7VirNTAMK543r9hu5crdPGfchTce9ZW97hUZLcnDfVqjFmuJjaAzON49c9Jc5wYzpXZFoiwLhLm6/+6buihWDADXEUKuAQA1ANwMADmUUh3nOn4AgNtEEUIIIYTQZcnBWqgN85Ef2H28bx4AwCRn6dF/JvtnzvSyHTBzahaB9NdZibOWxfFaikMJJ05FNoPa2Y/s0M556gi2SpqGWq9m3ql6JyyjKWOhjtWJRuM8wjNEu0cXLAtZVmgrstVd6D7QnzO85WffoY8/TmF7e525cf6ECcflL76QKQ4P7zVXbpagr21EVvJLY1x3w/AMbpwvZEb8w51yZi3yrMC5YX+eRjnML/7x69nNBytDbno17VOJzOaqfR8wdVHsLQBYDAC/nrrMAsCbo4uEEBsAiAGA70z8uAghhBBCCP0lGj3LiPiM0alvry4KzL/vy0qPW0PdCu+P8Ko1V24WxaAlorzXZgmqv48jevXYLhpKeAa9f2KhOiGtACT2V+0HM1P7pe4X30+PfJrap+lz4sb9bf2PvjDjhR0zHGdge5kJaKqrbQfS1ibqamqmcuOMrW2/7L57M63vuuvY1b4TZzw0I3p+yS+Nc5sO9kedMTeM9ZhsWzznRt/dUjnODfuzNCNKfslP38w6UVo0X6dWWwMAlPz4dVjMPx4r+qPbWiqTFsUopQcJIXMA4L5Toe8ppWWcqwQDQBYAfGvKx0UIIYQQQuhi6VlK1u08MXPbwa7oj++c/t+pbrKx2bhO1kJt+uPhn5sxPYvCP/yTp2jPmkXMcIcrN25wmFSriXsj0+A1t89cuVmaI/1HbN6ueDuxZqAmiBu3Fdr23T/5/ozb/G/DeXgmwA4N8fvXrIlU5ebNB73+9OdqPl9nlZCwW7582V5GKjWYMcUrGmUpVO1sm3qkoDNRqzLYctfs3CRHwxZ7Z7lOtMH3jT9Jq1LxSrZ8E3q8uDBKp1YZnYLaeqR6GmXZoqu1eGvyI6QppQcB4MXzrBUAQIGpHxMhhBBCCKGLkXm4231DTl1q+5DGAwDg3xm1id/9I/Qnc+dlaZjeY1Jx1rIEXts+o5YnKpYPaMMez9CGP37MXLlZGqVOyXu78u15u1p2RempfmxHDZ/wdfGe8fkvhLxQLBVgkWa8KMuC4r+fTVF8800SVSiMijXCadMOylcszxZOmqQwV36WoLGqz608vSV5uFdjdGqnRCboCo53z5w0z7neXLldaXRqNa90y7cza4sLorSqEaNDCQRiiSIgYv6e8Jvu2H+1FsQALkFRjIsQYgcA1pTS5kv5OAghhBBCCP0ZLf0qyartx2JLGwdnc+PN/WqP1gG12EMuVpsrN4uiVzOiXa+ECQ7/tJAYtGOzrCjD1+sDUveoE9YUgcgGDywwkW+OfRPwv2P/Sx7UDtpz41PtplYvm7ksK1AeiEUaE1AVFjoNbtiYom9q4h4uBzwnpw6bRx/NkF53bZO5crME/R0j1iU/N8Z21Q8bnfrLFzAjE8Mcc2df47WfJzBudUfnptdqmNIt34Uc27s7WjsyYlS8FYjFw/7hkXvCb75jv8hKetW/D5u8KEYIsQaA1wDgLgBwAgA6+jinWitXAcC/KKX7Tf3YCCGEEEIInYuepWRjTl3IjxXt8SodazUaZwgYYic5Fq5KCSiQWwlwnpUJCA5+6yMsfDuVUXYZDRw3OAXVqOPf2sG6zxowU2oWp7Kn0m595frkE0MnArlxB7FD10NTHkpfPGFxo7lysyT6tjZx/+rVMZqS0nCgdOx8CCIWj0ivX5xj+/TTFUQgwCHvfxGrB7L76xNRTQf757MGKhxbIMC6B9qWRtzkk29tJ8IvLP4EvVbDlP3yw4xjhfnRmhGlnLsmEImVfuFzCyJuvnOfSGp91RfDRpm0KEYIsYWT7ZFBAFAJAD0AMIVzlYMAEAUAdwAAFsUQQgghhNAlt7Omx/XtnScWtQ1qPLnxiY5Wx/+Z7J8R5iPHuTQmwHQdkomzVyTwOiqmc+OsxL5PG/F0ui70oRPmys3S9Gv6BWsr1kbtad8zj6UsbzQuZITqFJ+U3GeDn90n4olwR804UZ2ODL777kzl1l/jqFo9VkwHQqhoTnip3cqVeXx3dyzW/EUGPUsG6xmr/hqezKDpM5o3KHeVHAtb7J3l5m+Dp3b+CXqdltm39cfpRwvyFmiUw3bcNb5INOI3O6Ig4pa79omtZfjlzxlMvVPsn3CyIHY/pfRLQsgqAHhldJFSOkIIyQeAOBM/LkIIIYQQQmdZ8WvN/PTqrlgKMLa7QybiDT4w1yvzwXleNQwhF7o5+jO0Sp5417/m8I9sXUBY3dguD8oIdPrJ1+WrY98oBpEMZ1mZAEtZ+Kzms6nfH/8+aVg3bDQfKMQhpGJZ6LIcX5mv0lz5WRLltt+9hj76KMXQ1eXGjfO9vRpsn3suQzJ/fpe5crvSUZbC4T0dAYfyOxLUCr5Ra5/Ymt89Pc59x5T5LlhE/xMMej0p//XH6TV7cheohxVG7dN8oVA1Ydacwohb7y6VyGywGHYepi6K3QgAOyilX17gOo0AEGbix0UIIYQQQugsU1ysO7dXdxGAk62SCwMdil5NDdyDrZKmIaj43E9YvDGFGel15MYNLjMOqRPWZLEu04fOd1t0cYo7ip02Vm1MaR5uNppn5SJxaXt82uPpCV4JrebKzZJoqg7KB9ati9cdOWJ0eieRyQZld965Q/aPB45czUPJx6u+ste9ckdrgqJH48uNC0TMsH+YU25oqmclzg37Ywa9nuzftiXoyO5dMWrFkAN3jScUqibMDCuKuOWuUiu5ndZcOV4pTF0U8wSALX9wnWEAsP2D6yCEEEIIITRu90V41m6r7qzR6lnBP5P9M+b42mErjgkw7ZW24p0rE3ldB6dy46yVY7d27vMZupB78XQ4E2lXtovTKtJiyrrKwimcnmcl5olHFk9YvPOJaU9U8hk+zrMaJ0NPj7A/LW2+ek/BPDAYxlpSgcfTSxYuLJAvX1bEk8uxmP4XddYr7PZta47tbVZO48YJj1K5PzuccHvIe2Jr/LLij7AGA9m/7eepR/J3xqgUQ0ZfRvAEArXvzLC9EbfcVSK1s9eYK8crjamLYgoAcP6D60yAk7PGEEIIIYQQMomOIY1o1fZjC8O8besfivQ+yl375M7gn20kfB22SpqAeoAv3rkykl+bMZ+w+rHPEpQn1Oqm3JiriX29FAQS3OVhAjpWR96rei/098bfY9WG0/OsCBAa5hxWuiJ0RZ6rlSvOsxonqteTwfffn6H8ZWscHRmx5q4Jpk45JF+6NFs0bdqgufK70il61JKSrU3R7ccGwymF01vsCLBu/jblwok93nwxsFgQuzDWYCAV27dOOZy/c4FqcMCo5sITCDQ+M2btjbj17mJrewcshl0kUxfFygDgGkKIjFJ61rG/hBA3AEgFgN9N/LgIIYQQQugqxFIK7+Y2BH9b3pY4ojVID7YOTb55plsdtz0SWyVNgLIgLP1gsrDsoySiGZRzlwxus6rUCWuyWacpw2bKzuJsa9jm/fHhj1N61D1Gw8c9pZ6NTwU/lRHlFtVprtwsiXLrrz5DH3+cdObcMJ6LS5vNI49kSq+9ptlcuV3pNCN6ftmvTXMaq/qiDHoq4q7Ze1gdmXWNV46bv01vYWHPEnPleCWgLAsVGb9OPrwrK2ZkcMCFu8bw+Vqf4NDiiFvv3itzdMIC+V9k6qLYJgDIAIB0QojRi5sQMgUAPgYAMQC8a+LHRQghhBBCV5m82l7ntVknFjUPqL1HYwqNwfY/BU3TlydOxJPOTYTXsNtRlPtKMq/v+ERunLV27dBELs3QT7utyVy5WZoj/Uds1lWuSzjSf8SoxcxaYD14u//tWfdPvv8wQ3Ce1XhpDhyQD6xbn6CrqTFq/yVSqUJ64405to8/VkX42JL6Vxj0LKnMbA0+VtIdq1MbjA6DsLYXNc9IdM+aOMuxxVz5XSkoy8KBHb9POpSzI0Y5YHwyJ8Pna72nh5RE3HrPXhsnZ5W5crQUJi2KUUp3EEJeBYBXAaAaAHQAAISQHgCwg5On/iynlBaZ8nERQgghhNDVo0uhEa3afiym8ET/HO6pklIhT3F3uMeOx6N9DpkzP0tBhjtE4qxlC3gNeXMIZccqMZQvVumm35GjWfDyfuAJsXBgAkPaIf66ynXzcltzowzUMPYZjUd4+gXuCwqWhiwtshXZ4o7HcTJ0dYv609LmqwsK5gLLGs8NWxBdJF+2rJDn4ICDyf8CylI4UtDpfyi/I141pDPa0SSS8nunRrnsnLbQrYYw2MZ+IZRloSo7PaA6JzNG2dfrzl1jeHyd17QZpRG33lVk6+I2Yq4cLY2pd4oBpfTfhJA9APA0AEQAgAMAUABIB4CNlNJdpn5MhBBCCCFk+VhK4f38hulfl7UljmgNY7N/GAJs1ET74lWLAvOdrIX4gXa8WD0RFqQFCyu/jCc65djzTIFQg3fkPnXC2lwq98bdCSbAUha+qPliynfHv0tU6BRy7tpk+eRDL4a8mD3VfirOsxonqtORwffeD1Fu3RpHVSopd00YFHRQvvTFHGFQED7Pf1FjVZ9bRWZrwlC32uhkVL6QUU6c7Zg3a5Hnfr6Qh7MGL4CyLFTnZPofzM6IGe7r8eCuMTye3jNoRmnErXcVyl3dsRhmYiYvigEAUEpzASD3Utw3QgghhBC6+lQ0D8r/te3Y4qZ+lS837m0naViZNDF9/kT7bjOlZlH4R393E+1+M5UZavbkxllbnybNgpcz9AHJHebKzdIUtBc4v3fwvZTm4WZfbtxR7Nj54JQHMxZPWNxoptQsyvDPv/gOffJJEtvdbdSCxnN1bbV57NFMaWoqtvL9Rd2Nw7ZlvzXF9TQpp3PjDI/ovILkReHX+xRJZAL8ouICKMvCodxsv4NZ2xcqeruN3ncJw+g9g4LLIm65q9DO3VNprhwt3SUpiiGEEEIIIWRK1iK+vmNIPTYMWyrkDd8V5rHjiQU+1Xiq5PiRvhNW4uzlcbyW4lDus0mF1grtzH9kaSNfrAacZWUSrcpWSdr+tIXl3eWzKdCxp1vME6sW+Sza9XTw0+UCRoBtqeOkqaiwG1i/IUF39OgUbpxIpQrrW27JtnlkSTXODftrhvs04pKtjVFtNYNzKIXTbagEqIufbH/4Yu88OzcrPHjjDxzO2+l7YMe2hYruLm9unDCMwWPKtH1zbrmrwMHTG5/HSwyLYgghhBBC6LIX4Cwdvi7YJXdLRUfS/Il2xa8uCsx3lonw6Pnx0qsZUe6q2YJDPy0kBo14NEwJz6D3i9urSUjbQ6VOuNPDBHSsjmyq2jRre+P2WI1BIxmNEyA0zDmsbOnMpXkeUg9sSx0nQ1e3qH/Nmih1YWGE0dwwPl8viYkpkC9bWsSzs8P5bH+BVqXnl/3WHNZwoDfaoKNi7pqdm+TorEVeO90n2faYK78rxZHdu3wOZP62cKir04cbJwxjcJ8cVD7n5jsLHL19FebK72pj0qIYIYSFk/PDLoQCwBAAHAGAnwHgfUop/kCDEEIIIYQAAKCwrs8x/1if70vJ/vu48RWJ/mXxkx1PRPrZ44cuExBUfeUrLNqQwii7nLlxg+OkWs3C1zMN3vP6zJWbpfm1/lefT498mtKj7jEaQO5l7VX/9PSnMyPdIrvMlZuloDodGdy0KVT527ZYqlJZcdeE06dVyZcuyxFOmTxkrvyuZKyBksqs1mnH9nbFaVUGW+6a1E7YGhzvnhUQ7oSn0P6BowV53pUZv8UMdrYbzV4jhLBuk6bun3PznXucfP3wNfo3M/VOsd0AYAsAMwDAAADNANAJAC4A4AUAPACoOvW4M+HkIP47CCELKKXYI4sQQgghdBXrHdYKV6Ufi95zvG8upUDmTJC3xE1yHJthJeIzLBbExo/pOGAj3rkykddZFcSNs2K7Pm34E5m6sEdrzZWbpTnUd8h2XeW6xKMDR6dy4zKBbOCOgDt23Dvp3hoG21LHbfinLROGPv00ie0xLjry3NxabB9/LNMqObnVXLld6WoKO/2qc9sTRgZ1RjPZRFb8vsnznXOC49wP44mSF3Zs7x7Pyu1bFw50tPlx44QQ1jVgcmX4zXfsdvELwIMezMTURbE7AKAQAL4DgBWU0rFqMSHEGwDWAMAcAIgEACUAbACABwFgGQCsMnEuCCGEEELoCsBSCh/taZr6v9KW5GGNQTYaX7+zLiVukuNn5szNomiG+OKdL83lH9seRVidYDRMGYFOP+na3eq4N/eCSGYwZ4qWYlAzKHi78u3I/Lb8SAM1jH3m4hO+LsYjpuDFkBeLbIQ2enPmaAnU+/bZD27YmKirrZ3EjRNr6yHr227NtlmypJowWHT8K5qq+10qMloSBrvUE7lxvpAZ8Qt1yJ91jVe5QMTD94sLOF5S6LH/919iBtpb/Y0WCKGu/pMqw2+6fber/6QB82SHRpm6KJYGAH2U0jvPXDhVILuTELIPANIopfcRQh4FgCgAuAmwKIYQQgghdNUpru93eHPH8dSGXpXRN+iecnHT0oSJ6ebKy6JQFoRlHwUKyz5MJuoBO+6SwTWkWh2/Opt1mY4tOybAUhY+q/ls6vfHv08c1g0btZlNtZta/WLIi9mT7bCFb7z0HR3igTVp0eq9e+cAy56uevH5OklsbIF86Yt7eXI5zg37C3qalTZlvzXFdjcMz+DGCUP0XlPlReHXexdZ2Qpx/NEF1O0rdivf9vPC/tbmgDOWqIt/YFX4jbfnuwVO6TdLcugspi6KJQHAf//gOtkA8A8AAEqpgRCyGwDuMnEeCCGEEELoMtan1ApeS6+NzqvtncdSGPtQKxEwyjtmu2c9s3BCFZ4qOX68pkIH0a5/JfN6a412KrBSl05t5IsZuul3NJorN0uzp32Py3tV76W0KFuMhmc7iZ06Hp76cMY1vtfgzKVxohoNM7Dp3dCRbdsWUrXaeG5YcPAB+bKlOcJJk3BA+V8w3K8Rl25tmt9aMzCHskZ1AuriJ6sIu84rz95Dis/tBdSXl7qWb9sS09fSNOmMJeo8MeBg+A235btPDsJZjZcZUxfFZABg8wfXsT11vVH4okAIIYQQukqwlMLHhU1TvihuSVZoDGM/NxIAOtfPrvTV1IA8N1ux2pw5WgIy3CkUZ6+I5tXnzCX09E4ayherdUG37tLEvFIOfDFrzhwtRctwiyStIi12f/f+WRToWCVXzBOPXOt7bc6T05+sEDCCPzqMDP2B4e9/8Bv67LMktrfX6GAInrt7s+0Tj2daJSa2mSu3K5lWbeDt29YUVl/RF23QsRLumtxVcix0kedOz8nybnPldyVoqCx3Kf/1x5je5sbJZ645T/A/GHbDrfkeU6f3miM39MdMXRSrAYDbCCFvUkrPelMihHgCwG1w8uTJUV4AcFm9QAghKwHgRgCYBAAaACgGgJWU0mqzJoYQQgghZAG2Huicyy2IecjFzUvj/dK5Q/XRX0RZEBakBQsrv0gg2mHrsTAAGLzmlasT1uyidn4jZszQYmgMGmZT1abZGY0ZCzWsRjwaJ0BouHN46bKZy/LcpG5Y4B0ndVmZw+CGjYm648cDuXEikw1a33Zbts3DDx3CuWEXjzVQUrWzbWpNYWe8VmWQc9es5MK26bFu2ZPmOjeYJ7srQ1NVhfO+X39c0NNYP/XMNSdfv0Ozr78l32taCBYUL3OmLoqtB4D/AcB+Qsh7cHLo/ujpk/MB4CkAkMPJAftACOEDQDwA7DFxHuMVAwAfAkAZABAA+DcA7CSETKWU4s42hBBCCKG/iCEElsb7bX/2p8OPiAWM6tZQ9+xnYycc4DMEd9KME7823VWU/0YqM9jkxY2ztt7NmgX/StcHpGLR0UR+rvt5wmdHPkvu1RjvWvKx9ql7JviZzAjXCPwgPE76tjbxQFraAnVxcTiw1HhuWFzcHrtlS/cyNnhYwV9xrLjLtyqnPWFkQOvOjQslvIFJ85xzZiR4HGJ4+J58Ps3VlU5lv/y4oKexLujMNUefCYdnL74l3zt4Zpc5ckMXz6RFMUrp14QQdwB4E04WkrgIAOgB4J+U0q9PxeQA8AoAlJgyj/GilCZxLxNC7gGAQTh5auY2sySFEEIIIXSFYSmFb8raJt4S6lYv4jNjrXqxkxw7H4v2+em66S51HnJslRwvMtAgEWcti+U1F83mTmGjQuthbch92dr5y6uA4E4aU6jurZavq1yXeGzw2BRu3EZo039nwJ077g68+yiDz/W4UI2GGdi4cdbI9vSFVK02aucThoRU2C1btksQ4D9srvyuZC01A077t7ckDHSojAbA8wSMasJM+92zr/UuE4rxRMnzaTlU5Vi29YcF3fUnpp255uDlUzPrupvzfGfO7jRHbuivM/VOMaCUvk0I+QlODs8PgZMzxIYAoAIAvqGU1nGu2wMA/7nYxyCE3AwAC07d/ww4OaPsa0rp3Re4jSecLNQlA4ADALQDwFYAeI1S+kcnP8gAgAEAPCECIYQQQuhPKG8atHsjszblePdIwIkeZeaq1ECjL0Efi/I5bK7cLAWhBhDtfClMcOiHWKJXj7XvUcKwhgmxxeqEtHxq7aI1Z46Wol/TL3i74u35e9r3RBqogTca5xO+LtYzdveLIS8WWwuscdfSOCm+/W6i4vPPk9i+PidunOfh0WT71JOZVnFx7ebK7UrW16qUlf7atLCrfjgETm5WAQAAwoDec4q8OGyxd6G1nQi/oDiPtppD9qU/f7+gq652OnCePwAAe0/vo7OuvSlvwqxw3Il7hTJ5UQwAgFJaDwBvXIr7PuVfcLIYNgwALQBw1kA7LkLIRAAoAgBnAPgVTs4+CweAZwAgmRASSSm90FyzTQBQCQB7x505QgghhJAFG1Tp+K+l187fdbRnvoECDwDgt6rOhXfO9jgU4CzF3R0mYj9YLZzS8LmtUNWcyo0bHAKOaxb+O9PgE3VZzey9UrGUhU+OfDLtx+M/Jij1SqMDxYLsg6qWhizdGSgPxBP5xkldUuI4sGFjor6uzmgHE5HJBmR33pkl+8cDR3Bu2MUbGdSKSrc2RjYfHpxLWWr02d/J17oy7DrvXEcv6ZC58rvctR87Ylf683cLOk/UBgOlRsUwOw+v2tBrbsibGDYXD3i4wl2Sotjf4Dk4WQw7Did3jOX+wfU/hJMFsacppe+NBgkhG07d15sA8Oi5bnjqOvMBYD6lFLeSIoQQQgidx2d7mwM/LWpOGVTr5dx4iKfNQSshD3fRmADTVS0TZ69IDO+odODGqVjerw17bIc27LGj2CppGnmtea4fVH+Q0qps9ebGnSXO7UumLslI9UltNldulkLf2irpX7NmgaakNNyo6CAQaK0S4nfLX3yxhJHJ8L3jIuk0Bl75782z6sp7F+h1rBV3zdZFfDw0xXOnV5AdtvmdR8fxo/LSLd9Fdxw/GnJmMUzu5nF85qLr8wIi5reaKz9kWpesKHaqXdEDAETnWqeU7v6r900pHSuCEUIudFUghPgBQCIANADAB2csrwKAJQBwDyHkBUqp8ozbbgSA2wFgIbftEyGEEEIInVbRPCh/PfN4Sm2X0uh0OFcbUdvzsRO2pwQ54zfp46VR8MQ5/5zLP7otmrA6wWiYMny9PvCa3er4t/aCCIeOm0KjotFqbcXa2IqeilncuIQnUS6esDjn8WmPV/IZPg4hHwdWrWYGN2ycPZKevpBqTp/cCQAgnDlzv93yZbmCiRNxZ+lFoiyFqpy2qTUFXXGaEb09d83KVtAxbaFb9uRIF/xcex6ddbW2pT99G91RWxNCKTX6dkHu6l4Xsuj63MC5US3myg9dGoRS076fE0ISAWAj/EFLI6WUd6H1i3i8GDi5U+ycM8UIIQ8BwMcAsJlS+sg51nfAyaJZPKU0hxPfBCcLYjGU0iMXkU/5eZYmT5gwQfDhhx/2/Nn7uhxptVpHAAChUHhF/zkQQlc3fC9DyDS0BgoZjdQ6u4XK9OzpuBUf6LW+ZGiBBxlh/uALTPTHnPtKRZMb/2drpeky+vm5VR7O1k64p1stcmbPd1v05+mpHgpGCqQ5yhyZmqrHXrgMMBAhiVAmWScprBgrLIaNk+Rgtcjut19tBN09Rhs01BMmaPtvuH5Q6+2Nxd2/QNVNhL2HeDaaAUbAjfMk1GA/2aCQebOqy/Ht+HL4mUw7rGC6K0tlAyeOWgFr/HZq5eKudZ4ZrpC6eeJ8xsvY448/7lhfX3+QUjrrj69tzKQ7xQghcwDgdwDoBoD3AeApAMgHgKMAEAUAUwDgNzg5dP/vMunUr8fOs14LJ4tigQCQAwBACPkAAO4BgOsBoJ8Q4nrqusOUUvzGAiGEEEIIALKbqTSjicq4sQgXMnLjRKKwERIs1IyTVNXKm9Lwua3jYJVR58WwxEN/wPNe6JVNpUKhEJ9nE6jR1Ah/U/xm223oNvp85C/01yyWLR5y5btioWacBG1tfLtfttpIamuNXs96e3tD/7XXDI2EhKjhcqzaXOa0Q4Tfe4gnG+lkjHbcMXxK5QEGha0/q2RMsh3F8uiUCqa7cp/1wPEjUnpGMUzi5Kp1Dp2jsHb3wmKYhTN1++RLAKAGgDBKaRsh5CkAyKWU/puc7HN8FQBeAIB/mvhxL8T21K+D51kfjcs5scdP/ZpjfFV4DU7+Gc7rfJVJQki5RqNxi4yM3Hyh21/uCgsLlwAAXOl/DoTQ1Q3fyxAyjaCZOkHeh6VPKDQGWxeZsP25WL/ti6Y545yVcSLKbqEoe0UUv27nXMI56ZDyRGpd0M25eE1NrwAA8LVJREFUdOFr+xQl5Q8JAd/HxutAzwG7DQc2JNYO1hp1udgKbfvuDrx7xx0BdxxjcEbbuOiamqwG0tbGaMrKZhvNZxIKNVaJibvlL75Q4iuV4uzmi9TfMWJdurUpprNOEQqUc6IkAYP7ZNuS8MU+BTIHkcqcOf4Z5viZrLelybrkx6/ntx6pnk1Z1qhkKHNybpqRdE3elOi4ejzc4cqh0WiW/NXbmrooNhcAfqOUcudGMAAA9GSf5ipCSCqcLC7dbOLH/qtG30DGtkLTM4bpIYQQQghd7YY1el6vUifysZeMjMbkVgLdkvne21sH1LbLEiaWC3gMtpaNB2VBWLhumrDis0SiVYztwKMAYPCM2K9OSMuh9hNHLnAP6E/qU/cJ11asnV/YUTjPwCk8ChiBNt4zfvfzM54vlgqwUDMe7MgIb2DDxrCRjIwFoNWe3sVECBWFhu6XL1+eK5jgq7zAXaBzUCl0wtKtjfOaDw3MYw3UqFXS0VtaFXadd66Tj/WAmdK7rPW3tUiLf/hqfsvhg7MpyxrVQmQOTi3TExflBi1MqMNi2NXF1EUxWwBo4lzWAoD0jOsUAsCdJn7cCxndCWZ7nnWbM66HEEIIIYQ4viptnfifgsZUF5mo+6eHZ33HXbs/wqvWXHlZEv7xHS6ivH+nMoONRicdsjaeLZrof2boJ12LhxWYAEtZ2Hxo8/QtdVsSlHqlUevvdPvpB5bOXLrT39Yfx6WMA2VZGP7660DFl/9LZAcGjE5J5Xt719s+8/QOSXQ0nnx4kfRaA1O+vSX0xL6eGL2WNfqMbeMkrp+Z7JHtE2zfbq78LmcDHW1WxT98FdlyqCqcNRiMaiDW9o6t0xNS8qbFJR/HYtjVydRFsS4AsDvj8sQzriMAAImJH/dCjp76NfA86wGnfj3fzDGEEEIIoavSwbYh23+n1ybVdCqnAAAMqPT2nxc3B2AhzHTIQJNEnL1sIa+pcDaB090KVCBV6mbck62JWlEFeNKhSexq2eX2QfUHKe0j7V7cuIvEpfXRoEczkryTsPV3nFQFBc6DmzYl6Rsa/bhxxta2z/qeu7Nk99xzFAsPF4eyFKpz2ycf3tMZr1HqjYqMEpmga9pC16zJkS4nCIPNTmca7Gy3Kv7h63nN1QfCWYPeaFed1M6+fVp8Sm5wQmotviavbqYuih0D4yJYMQCkEEICKaXHTg2svwlODrf/u+Se+jWREMJQSscm6BFCZAAQCQCqU7kihBBCCF31RrQG3usZtXMzD3dH69nT7TkiPqMeUuvFF7ot+pMMWiLKfz1UcPC7OKJXjX1hTAnDGnxjStSJa/OptavGnClaigZFg3Tt/rVxlb2VM7lxCV+ivGHCDdmPBj1axcfC47joGhutBtLSFmr2lc86a25YUlK+/MUXShkrK2xHvUgnyns8D2S1JQ73aYwKuQIxbyhwjtOukGSPKh4f29bPNNTdJSn+4X9zmw5WzmH1eiF3zUpu1zEtLjlvRtI1WKBFAGD6olgmALxBCLGnlPYBwCYAuBEAKgghh+HkriwZACwz8eOeF6X0BCEkC06eMPkEALzHWX4NTrZ3/odSiv3sCCGEELrqfVPW6vdRQVNq/4jOaEfCTE+bylcXBe70c7TCn5nGiV/9g5eocG0qM9zhyo0b7P3rNAtfyzD4LugxV26WRK1XMxsObAjf0bwjRsfqxk48ZIBh57rOLV42c9luJ4kTFh7HgR0Z4Q2sWxc+siNrAWi1p0+VJISKZs8qly9fnivw8cE5eBep/fiQQ/nvzXF9rSNTuHGGTzS+wfZ7whZ7l4is+Hgi6hkUPd3i4h++mttYtT/irGKYrbxzamxi3syUxTVYDENcpi6K/QcAdgOADgCAUlpICLkFAF4HgGkA0AAAyyilX47nQQgh1wPA9acujv4wMZcQ8vmp3/dQSl/k3ORxACgCgHcJIXEAcAQA5gDAQji5u+2SnIZ5aifa6KwCAQ7wRwghhNDl6nC7wua19Nqkwx3DU7lxJ2th59Mxvtuvn+HabK7cLAXTdUgm3rkynte+P5gbpyLbAW3Yozu04U/UAJ50aBI/HP9h4hdHv0ju1/Q7cuN+Nn61zwY/u2O28+xec+VmCSjLguLLLycNf/V1Ijs4aM9d4/v41Nk++8wOyfz5XebK70o12KWSlm5tWtB+fGgWUBh7MyAEWLdA29Lwxd57bJzEWGQ8w3Bfr6j4h68iGg+UzzXoThfAAQAkNrbdU2MS8mYuuv4Iw+Phrjp0FpMWxSilQwBQckbsFwD4xZSPAwAhAHDfGTG/U/8BADQCwFhR7NRusdkA8G8ASAaAVABoB4B3AeC1U7vaLoUXAGDV6AWFQoFDOxFCCCF02TnSMSy7+/PKJ3WcVkkhj2iuC3bZtSLRf5+Iz7AXuj36A1olT5zzzwh+za/RhNWN7V6gDF+vD0gpUMevLgSxHHd9mEBFd4X9hgMbkk4MnTCaJywXynvvnXRv5u0Btx83V26WQrV7t8vgpneT9E1NE7hxRi7vld17T5b1XXcdw504F0c9rBOU/to0t+lgfyRroEY7nBy8pNWzr/Xa5TJB1m+u/C5Xyv4+UfGPX89pqCiba9DpjFr7JTKbnikL4vNCr73xMBbD0IWYeqfY34JS+ioAvHqRt2kGgAcuRT4XsB4ANp/6faZMJnP6mx8fIYQQQugPTXG1VkxysT5a3a6YBgAww0NWtSo1MDvAWYpf6I2ToPxjf2HJe8mMqs+oHdXgPP2wOv7NLNYtFE9AN4EedY9wbcXa6KKOogiWsrzRuIARaBO9EvOem/FcqRUfZ1qNh66+QTqQtmahZn9F6Blzw9TS1JQ82+ee24dzwy6OQccy+9NbQo6XdS/UaVhr7prMQdQYkuyRNSHEAU+ePcPI0KCw+Pv/hddXlM0zaLVGh/iJZTa9k6Ni82ddd1M1j4+zAtEfM2lRjBBiBwBuAHCCUqrhxB+Ak+2OIwDwDqW05Nz3YFkopQoAUAAAEEJ0hBD8S4kQQgghs9MZWCLgGQ9nfiU1IOu5LYdtH470yrkpxK3RXLlZCl5LiZ0o519JvJ4jk7hx1sqxWzv3uQxdyH315srNkuhZPfnPof8E/1L/S/yIfsSoqBDsEFy5LGRZjp+tHxZ3x4FVKnkDb6+bM5KdHX3W3LCwsDK7FSvy+F6eKjOmeMWhLIVDuzsCD+d3xKuH9UYbJ8QyfnfQAtfsqVGutXiipDGVYkhQ/MNX4fXlpfP0Wo0Vd01kLeubErUwf9biWw5iMQxdDFPvFHsLAO4GAOfRACHkKQB4BwBG/0YvJoTMppQeNvFjI4QQQgihC1DpDMybmccjdh/vC93y8KzNTtZC7ejaFFdrReYT4f81Z36WgIz0CkTZy6P4J7LnEWoY27FEeUKNburNuZqFr5WBQILtqCaws3mn+4eHPkzpGOnw5MZdrVxbHg96PCPeKx532IwDZVlQfP755OGvv0lkh4bsuGv8CRNOyJ99dod43txuc+V3paqv7HWvzGxNVPRqfLhxgYhR+Ic75YamelbiiZLG1MMKQfGPX8+u21c8X685oxgmte6fND8mf/b1txzkC4T43ooumqmLYpEAkEMp5X5T8CIAtALAnXByKP6XAPA8ADxk4sdGCCGEEELn8eP+dt8Pdjek9ip1TgAAq7Yfi/nwtmlZ5s7LYlAWhEUbgoT7P00kWoUNd0nvEV6hSUjLYR0C8OROE6gbrLNeW7k2rqq3KoQbt+JbDd/od2P2kqlLDvIZ3CkyHqrcPNfB995L0jc3+3LjjJ1dj+y++3bI7roTZ7NdpM46hd2+bc1xvS3KIG6c4RGt93S7gvDF3sVia4HOXPldjjTKYX7xT9/MrivdO1+nUUu5ayIr6UBg5ILdYTfceoAvFGExDP1lpi6KeQBAzugFQshUAPACgOWU0oJTsVsAINrEj4sQQgghhM7hWNew9avbaxMPtimmc+M1HcN+I1oDz0rIwxlA48Q7ke0szvt3CjNQ78uNszKPVk3Uygz9lOtbzZSaRRnRj/A2VG6Yk92SvUDHObCAIYwh0jVy79KZS/c4ih21F7oPdGG6ujpp/5q0WG1FRSg3TkQitVVqaq7t88/tY8RiLEBchKFutVXp1sbo9tqhMMo5URIIsG7+NvvCr/febesswYI5h2ZEyS/96dvQ46WFUTq12qgtWmhlNRg4N3p32A23HRCIxfjvFxo3UxfFJACg5lyOBAAKADs5sRMAcI2JHxchhBBCCHGodAZm9Y4T4b9Xdy7UcU4zE/CIdlGQc95Lyf4lEgEPP9yOAxlsFouzly/kNe4JI3B68DgVWCl1wXfv1ES/dABwx5JJfFv7bcD/jv4vaUA7YHRggb+N/9HnZjyXNdNp5qU6Tf6qwCoU/IF16+aMZO+MBt3pgiMQQkVzwkvtVqzI53t44Nywi6AZ0fPLfm2KaKjqm8/qqYi7Zu9hdXj2NV45rv42+Lrl0KpUvNIt34bWlhRE6VQqGXdNKLEaCoiYvyfsxtsrhBIJFsOQyZi6KNYKAJM5l5MAYAgADnBidgCAb6gIIYQQQpfIlsp2n/fzGlJ7lDpnbjzIzfrQqtTAHVNcrRXmys0iGLRElP/GTMHBb+OIXjU234YCoQbfBSXqxLV5VOauudBdoD9nX9c+h41VG5Pqh+oDuHE7kV3PfZPuy7zV/9YT5srNElCWBcWn/52q+O67BDo0JOeu8f38auXPPpslnhvRY6b0rkgGPUsqMltn1JZ0L9SpDUat1Nb2oqaQJPdsv1DHFnPldzkyaNQk/7P/zK3fXxqhVY0YPWcCsUThP2deQfhNd+wXWUn15soRWS5TF8VyAeA+QsiTcHLH2HUAsIVSyv0W0h8Amk38uAghhBBCCAAe+fZgclFd/xxuzN5K0PNYlE/67bPd8cTDceIf/tlTVLA6lVG0u3HjrJ1fvXrhaxmGCQtx8LgJdKu6RWsr1kbv7dgbwQI71nImYASaZO/kvOeCnysV87GNbzxGcnLcBt97P9nQ2urNjTP29ifnht15B84NuwiUpXCkoNP/UF5Hgkph/IWESMrvnRrtmj0txvUonih5WlvNIfu2wlybgbqjVlSvT+SuCcTi4Ylhcwvm3HxnuUhqjcUwdMmYuii2GgBuAoBNcPK0yWEAeHV0kRDiDAALAOBjEz/uZYkQIgOA0W2fAkopvgMihBBC6JLysZd0F9X1AwAAnyG6lCCn/H8lBxTj7LDxYXpqrMXZK+J5bftmcONUZDOonbVkhzbi6SNAmPPdHP1JelZPPjr00Yxf6n+JV+lVRoO1QxxD9i+buWyXr8wX5y+Ng672uHX/2rQ4beWBEG6ciMUqq0WpufLnnisnIhxcfjEaq/rcKjJbE4a61RO4cb6QUfqHOebOWuRVwRMw+JzCyd2Jh/N3TjiStzOir7U58Mx1vkiknDh7buGcW+7cJ7aW4cED6JIzaVGMUlpPCAkCgJtPhX6jlDZxruIDAB8AwDemfNzL2AsAsGr0gkKhGDZjLgghhBC6CiyN99ufe6w31N5KMPBKSsCOIHfZkLlzuqJplTzxrpfD+TVbY4hBOzZriTJ8vd4/uVAd/1YhSOzxg5sJ7Gja4fHRoY9SOlWdHty4m5Vb8xPTnsiI9YxtN1duloAdGuL3r317rionJwr0esHYAkNYcUREqXz58ny+u7v6AneBztDdOCwv+60ptqdJaXSQCcMjOq9pdoXhi733SmQCPPwBTp4kWb7t5+ATZXvnqAYHnM9cl9jYdk8Mn1c869obq3BnGPo7mXqnGFBKOwDg/fOslQFAmakf8zK2HgA2n/p9pkwmczJnMgghhBCyHHU9I9JXtx+Lv22W+75F05zHTjcU8Bj63QMzv3CwFuIHsXES7P/vRGHJpmRmpNeRGzc4BdWo49/awbrPGjBTahbl+OBx67cr3o4/2HfQaBeelC9V3DTxpuwlU5ccZHAX3l9GWRaGPv4kaPj77xOoQmHLXRP4+x+zff65LHFYWK+58rsSKXo1ktKtjVFtRwfDKQXe2AIB6jpRVh622DvfztUKN0QAQE9Tg2z/ti1hLYeqZuu1WsmZ61IPb43D1BnDSbfc/iFh8O85+vuZvCh2LoQQRwCIAoARANhJKb0qtu9TShUAoAAAIIToCCF4+hBCCCGExkVnYMmarBOztx7ojNMaWFHboMYlcYrjxwIeM/ZzBhbExodpLZOLc/6ZxOs+zD1AClgrhx5txLMZupkP1JkrN0ui1Cl56yvXR+S05kTr2NMnHvIIzzDfbX7R0pClBfZie3wtj8NIdrb74PsfJBva2ry4ccbBocvmgQd2WN92K76WL4JWpeeX/dYU3lDZF2XQUzF3zc5dUjNrkVeOe6AtHkwAACdKi9wPZmdEdDecCKKUGlW7GB5f5zZpSkVIynWlDX2DNwEAYEEMmYtJi2KEkMcA4H4ASKGU9p2KzQKATACwP3W1fYSQWEopzgJACCGEELoI2w52er6TW7+oS6F1HY11KjRu35S1+d8X4VlrztwsgqpPIM5eGck/sSOSsPqxn5MpT6jVT7khTx37RgkIJDgXaJxYysI3td8Efn3s66RB7aA9dy3ANqDm+RnPZ81wnNFvrvwsgfbYMdlA2to4bVWV0e47IhaPWF17ba78maf349ywP481UFK5o2X6sb3dsVq1wWi3ndRO2DIjwT3bP8yp6Xy3v1rodVqmMv23KbV7d89R9HR7nbkutJIOTpgZVjLrupsqrB0c1QAADYWFf3+iCHGYeqfYbQBARwtip7wNAHYA8BkAuADAIgB4FE62FiKEEEIIoT/Q0DtitWr7sfj9zUMzuXG5hN+3ZL53xj3hnnhK3HhQFoTFm6YKyz9OJJohow+8evewSk3C6hzWcTK2QplAaWep4ztV7yQ1KBr8uXF7kX33/ZPvz7x54s24c2kc2KEhfn/a2nmqXbvmG88NY1jx3Lkl8hXLd/NdXXFu2EWoKez0O7irPUE1pHPlxkVSft/kSJedwXFuR672EyWHursk5b/9FNpYWR6uVY3YnLlu4+TSFBgZXRycdM1RvkCIxVh0WTF1USwAALaPXjjVNrkAAD6hlD5yKlYCAHcCFsUQQgghhC5IZ2DJ2uwTs3450Bmn0bNjrTp8hugTpzjufjkloMhaxL8qxlJcKry6XU7ivFdTmP46o1PjWJl7m2b+igz91BtbzJWbJeka6RKlVaQtKO4snkPhdCuVkBGqU3xScp8NfnafiIc7l/4qyrIwtHnztOHvf0igw8NGRQlBQMBR2+efyxLPnt13vtujszVW9blV7miNG+xST+TG+UJmxG+WQ97sa7zK+ULeVf2abTlU5ViZ8ducjtqaGazBIOCuEUJYZ7+Ag9MTU0v8Zs3BQzLQZcvURTEHAOjiXI489esvnNgeONliiRBCCCGEziP3WK/Lm5m1izsVWjdufJKLtObl5IAdMzxtBsyUmkUgQ61icfayGF7D7nACdGybB+VLRnTBd+ZoFrxcAQwf58GOk57Vkw+qPwj5rf63OJVBJeWuhTqGli+duXSXj8xnxFz5WYKRzEyPwQ//L9nQ3u7JjTOODl02/3gw0/qWm+vNlduVqPFgn2tVdltMf7tqEjfO8IjOc6p8b/hi7yIrW6HGXPmZG2VZqN61Y+KR/JyIgfZW/zPX+SLRiPf0kLLQa2/aZ+/hhTts0WXP1EWxPgDgns6zAABYACjixCgAGA0lRAghhBBCxgQ8wnYPa11GL9tK+P0Pz/POwNlh48TqiWj3myGCqq/jiG5krEhDgVCDT3SpOmFNHrX1wvYyE0hvTPfafHhzSpeqy6iw6yH1aHpy+pMZC9wXdJgrN0ugPVJjM/D22jjtwepgbpxIJCPS667dZfvMM/uJQICF3T/pfMUwAKAufrKKsMXeefbuVgqzJHcZUCmGBPt/2zLjxL7iOWrFkOOZ61Zyu07/8HnFM6+5oVpkJdWbI0eE/gpTF8WOAMC1hJB/AoABTs4YK6OUDnGu4wsA+A8gQgghhNAFzJ9o3x010b64oK4/PGGy455XUgKKZGI+ftAYB/6RrR6iPW+lMoo2d26clU9oUMe8kmGYmNB1vtuiP+/YwDHZ2xVvJxzqPzSdG7cWWA/dMvGWrAenPHiIIXjS3F9lGBgQDKStnafKy5sP+tMHQgDDGMSRkcXy5cv28F1crtqdTBerqbrf9UB264L+NtXkM9fsPa0OhyR55HlOlnebI7fLQXdDnc3+bT+Htxw+OMug0561ucXBy6dmakxCyeSohQ14giS6Epm6KLYJALYCQAsA6AHACgCWjy4SQngAMB+Md45ZLEKIDABkpy4KKKVX9wRGhBBCCJ1TxqEu92NdSodnFk44yI2vWhSY39KvKpvpZTtgptQsAtN7TCrKXh7Pby0L4capyGbo/9m77/C4irNvwHPq9qZVr1bvslzlKtlyBdMhBEIICaEFQif5UkiA1PdNINQQQgtJSPJCIBDAXS6yLdtyky1LVu+9rLR999T5/lDxrix32avy3NelS9LM2bOPhLG1P808w8+9byu/+IlTCEKay+YUnPSLx19ctLN9Z76ITzd5pwhKzI/ML/lh7g9LDAqDEMgapzIsioT9z29nOT/5ZPUZfcNSU6uMTz+1XTFnDpzaeYHOG4atjSqOTjfOyKAcyzKqKy2Jrijasqi/tSkDjXkdS9I0H5mWeWzuhpsOhSenwZ85MKVNaCiGMf6CIIiHEEIPDA/9A2P8oc8lq9HQ1smtE/m8k9jTCKHnRj5xOBywpxoAAAAAo1oHParnN9auOtxim8eQhLA6NbglM1I3usI+RMvyIVqWD2SNU5rgIRW7fr6QOfXpCkLiFSPDmKAkMWltCbf6f/ZhtRlCmsskYxl9WPth6j/r/rnOzttNvnMpxpSqZ2Y/sy3LnGUNUHnTgvOzz+Mc77+/RurujvIdJ0NCuvX33bdVe8vNzQEqbcoZCsM6CwY73WeGYVHqU7PXRhXHZMzMMEzkObJs4+cZdQf2LXIO9EeNnVdotIPx8xaWzrv+1uMaUxCsRgTTwkSvFEMY47cRQm+fZW4rQsg03tw09RI6/b3YotPpQgJZDAAAAAAmB1HGxB92NOb+u6xrjVeQVQghJMiY+fXW+jX//M6cTwNd33TAHP9rPHvg5WtId7/fz19SSEYNt+pXW6WohbC6YQIc7D4Y8mr5q+tbnC0JvuNmhbn33vR7N9+cAGHN5fDs3Rtqe+OPq8XGxmTfcUKlcmluummH4dHvH4e+YRemtWIw7MT2zhUQhp3J1tOlPvrFp/NaThxbIHg9urHzhrDw5tRlKw9mr7m2lqLhABIwvUx4KAZOwxg7EEIOhBAiCEIgCAL+AgEAAABmuO1VfRG/39G4ocvG+f0WPilEXff0qoSdgapruiC7jhmURT9ZR/VWpPuOy6ogC5/36BZh3v31gaptOulydSl/V/a7FYd6Dy3EPqd3KkiF99q4a3c+lvPYUQWlkANZ41TGV1YarH94eSVfXj7bb4KiJOXSJQdN/+9He6nQEFipcwHaKgfDTmzrLBjodKePnQuKUlfNXhNZHJNp6glEbYHWerIs5MTmLxd119fkYFn2ywYIkpTCElPKc9ZdVzord96M/P6AmeGKhGIEQVyPELoLIZSOENJgjJOGx9MRQtejoW2VHVfiuQEAAAAAJqMOq1f53MbawtJm6wLfcb2Stn1nUfTme5fE1JAEtB+9ZF4rrdz+o6V0/ZZlhHy6+TgmGV5Mv6nYW/irUsRqpECWOB14RS/5SvkrC7a2bS3gJE41Mk4gAs8LmXfkh3N+uCtaG+0JZI1TmdjRobK++OIy74GDeUiSKN85dvbs48Ynn9jNZmbaAlXfVNJWORh2YntnwUAHhGG+ZEkiThZtTqreu2uRrbszYew8o1Q6Y7PnHJ57w61HTRFRrkDUCMDVNKGhGEEQBELoA4TQN4eHPAghlc8lgwih3yCECITQ/07kcwMAAAAATEaijIlXdjbO/vhY1xqPIKtHxkkCSYUpwSXPXZu8z6hmoK/VpcIyYg+8nMkee28NwdkNvlNSxLxy75r/2S6HpENf18skYxn9s+6fKf+s/edaK281+85Fa6KbH815dMvyiOUzLmCYKLLdTltf+kOeu6hoGeL9T/ijExLqDN9/pEi1fPmM3Np3sc4ZhkWqq3LWRBbHZs28MMxtt7FH//vv3Kajh/K8TkfQ2HmNKagredGyg3M23FzJKJXwCwQwY0z0SrGHEUJ3I4TeR0NN5p9ECP1sZBJj3E0QRAlCaAOCUAwAAAAAM4BXkMjPy3vyfQOxhGB1w0/XJW1aOMs4EMjapjq6dmOEYs9v1pO2lljfcVkb3s0t/eEmMev2tkDVNp0UdxaHv1nx5to2Z1u877ie1Q9+PfHr2+9Ju6eKhNM7LwkWBML25puzXZ//d+XYEyWp8PAO3Xe+UwRN9C9M2ylr6IltHQUDHe6MsXMzOQzraag1Hvvqs4Wd1ZVzJUFQjJnGwbGzqjML1x1MWZLfSpDw/zGYeSY6FPsuQugEQuh+jDE+Sw+tOoTQugl+XgAAAACASUmroKUHlsZu+n1R4106BWW/Z1H0lvuXxlbBVslLR1pqNYrtP15FdZTO8f0uYlrlFrLv3MkVPHsMUSz0cr1M9bZ67R+O/6HwuOX4HN9xlmS5NTFrip/IeeKQhoEtqZcCyzJy/vOfyY4P/7FatlhCfedIo3FAe/vtO3TfvfcUhBTnd64wzBSprp49FIZ1B6K2QMGyjGr374mt3Ll1UX9rcxoa2qk1imIYLio96+icDTcdDktMsQamSgAmh4kOxVIRQn/GGJ/rh5BehBCcwggAAACAaUfGGP3zcGfiNxZENviGXt/Ki67vd/Kff3tR9KkgDQtbJS8V56CUO3+WR9d8UUBIPDsyjAlSlmatKPWu/u0erI/yBrLE6cDO2+kXj7+4ZHfH7mUiFpmR8ZG+YU/nPr07ThfnDmSNU5l7y5Yo+9tvrxHb2uN8xwmVyqXesGG34fHHjpFKJRxScB7tVdbQ49s6CgbaxwvDVNWz10TNuDCM93ioso2fZdaXlixyDQ5EjJ1XanWW+Pl5pfNuuO2EWm/gA1EjAJPNRIdiIkJIeZ5rohBC0NcBAAAAANPKrlpL2O+2N1zbbvXGunjx4weXxVX5zj+1KuFEoGqb8rCM2MN/SmUOv7WW9A769cKRglPruBXPb5XillsCVd50Icoi8e6pd7P+0/Sf1U7BfytfvD6+7rHsx7blheX1B6q+qc57+LDZ9trrhUJ1tX+IwzC8akXBfuPTTx+gzGYIKs7jfGFYzurI4rjsoBkVhg12dWiOfvHJ/LaTx+cLXq927LwxPLIxLb/wYNaq9fUkRcEqWgB8THQodgohtIIgCGK81WIEQSgRQoUIobIJfl4AAAAAgIDotnOK5zfWrtzfOLgQD29R+evB9vVfmxNRD6vCLh/VuDNEUfyL9dRAvd8pabLa3M8v/P5WYd799YGqbTrZ2LIx5t1T767r8fRE+Y6bFebeu1Pv3nZ70u0NgaptqhMaGrTWl14q4I4cnYcwPr2ElCRkxfwFR41PP1XMJCTAKX/n0V5tDTmxtaPA0u7OHDtnilDV5KyJ3D3TwrDmsiNh5Vu/WtTTWJeNZdnvtFKCJMXw5LQTs9dfVxqbPacvUDUCMNlNdCj2d4TQGwihlwmCeMp3giAICiH0B4RQJELoRxP8vAAAAAAAV5WMMXptV3POv452rnXzkmZknCSQPDfWcDKQtU0HhLVVpSz6fyuoln0LCHQ6SMCUwitk3LqbW/nCYcSoYIvZZSq3lBtfOfHK6mprtV/QoKJUrutmXbfr4ayHyxSUAr7Pl0Dq72etv39xiWfPniVIPL0NFSGEmPT0SsNjj+5Uzp8Ph22cx3nDsNWRxXE5QV2BqC0QJFEkyrdtTKkt2b3I1tM9a+w8o1Q54mbPPTTvhluPGcIiYJszAOcx0aHYnxFCNyCEHkMIfQ0h5EAIIYIgPkEILUJDgdh/Mcb/mODnBQAAAAC4avbUWUL/Z3vDtW2DXr+eQHFBqqYfr0vctDQhCLaYXSrRSyp2PT+fOfXJSkL0jrblwIjAUuzSI97Vv9mNTQnwQu8y9bp7Fb8//vvlB3oOLJLx6RUmFEFJS8KXHHgm95l9IaoQLpA1TlWy203ZXn1tnnvz5gLs8ah95+iYmGb9Qw9uV69d2xmo+qaKjmpbyPGtHQWWdheEYQgh1+CA4ugXn+Q2HTucx7mcprHz2qDgjuTFyw7O2XDTKZqFIBuACzWhoRjGWCII4jqE0LMIoUcQQinDU7cghKwIoV8Ov80IBEHoEEK64U8Z7LtcGgAAAABTTq+DUzy3sXZFScNgHvY5zUvDUo5vLoza+nB+XCWcKnnpmLK/JLAHX11Puvv9DmWSTQlN3oKfbZES1/QGqrbpgpM48o2Tb8zd2LJxpVfy+gU26ab0yidznizKMmdZA1TelIZFkbC/916G69+frJJtNr/Qggw29+q+efd27Z131MOJkufWUW0LOb6tI9/S5soaO2eKUNVkr4osnjV75oRh3XXVpmMbP1/YWV05VxZF1m+SIHBIXPyprNXXHExetKw9QCUCMKVN9EoxhDEWEULPEwTxAhoKxcwIIRtCqBpjPNOObH4aIfTcyCcOhwMOGAAAAACmsGe/rCk80GRdOPI5SSB5WWLQwec3pBSHaFlokH2JqNb9QYpdP19H9Ven+I5jpXGQn//gNn7hI9WIgCDhcn1c/3Hi32r+tm6AG/ALHcPV4R33Z9y/5ZrYa+BF9SVyfvJpvOODD1ZLPT2RvuOETmfT3HTTLsP3HionGAYanJ/DucIwY7iqNmd15O6ZEoZhWUbVe3fNqty1bdFAe2vq2HmKYb3RmdlH5l53y+GQWQn2QNQIwHQx4aHYiOFG+zVX6v5TxEsIobeHP96i0+lCznUxAAAAACa3/7cmcc/t75XN5iVZEWtSNf9obeKm5UlB0MD4EhGOToWy6Mf5VNOuRQSWR1MvTDK8mHbDHm/hrw4ihW6m/VJ1wh3sPhjy2snX1jY7mpN8x7WM1nZrwq1F92fcX0FC6HhJPMXFYbY/vrlabGry+94SCoVXtXbNHuOTTx4mdToxUPVNBZ01tuDjWzsK+s8ehhXPmh00I7abcm4Xfeyrz7IaSksWuW3WsLHzKp2+P2HB4oNzr7+lXKXTw0EuAEyACQ/FCIKIRgg9iRDKRQhFI4SYcS7DGOPEiX7uyQZj7ECn+6oJBEHAb4cAAACAKaLPybMYYyJUpxjtq5QYonHdtSByM0OR0iMFcRWwVfISSTyh2PPrOczJfxUSgnv0kAKMEJKiFhznVv92hxycBivsL1OLo0X94vEXVx7rOzYP+xxWwJAMvzJq5d6nZj91UM/qIbC5BFxFhcH2h5cL+ZMnc/wmKEpULl1Sanz66X10ZKQ3QOVNCRCGnTbQ0aY9+sWn89sqji8QOU49dt4UGV2fXrDqYObKtQ2w/RaAiTWhoRhBECsQQpsQQkqEkIgQ6hl+f8alE/m8AAAAAAATRcYYvbmnJfPDQx3rcqJ0VW9/I2ez7/xTqxJOBKq26YA5+X+x7P4XryGd3eG+47I+po3L/8kWMfX6GfEi+EpyCS7qDyf+kFfUXpQvyILCdy43OPfYM7Of2ZVgSIDQ8RKIbe0q60svLfceOLAQyacPKEAEgdnZs48bn3xiN5uRAdvZzqGz1hZ8fGtHfn+rK3vsnDFcVZe9KmJ3fK55Rvw90Hi0NKJ828ZFfY31WRhjv7SLpCghPDntRO41N5RGZ+bA4S0AXCETvVLsdwghCiH0LYTQPzHGcOoFAAAAAKaM/Y2Dwb/dWn9t84AnHiGEDjZZFxRV95etTgvuDnRtUx3Zdcyg3PHsGqqn3O8kOczq7Pzce7fzS56ugL5hl0fGMvqg+oOMj+s/XmMX7EbfuVhtbNPDWQ9vzY/M7wlQeVOabLfT1pdeynMX7ViOeN4vaGSSkmr1jzy8Q7VsGRwEcQ4Qhg2RRJE4seWLtNqSPYvsfT2xY+dZldo+a8780nk33HZMFxwCqw0BuMImOhTLRgj9C2P84QTfFwAAAADgirE4efb5TbX5e+oHFssYjSYzKpZyd9m92kDWNtURbgujKPrxMrph2xJCFkd/9sQkLYpJ6/d5V/9mP1IFQW+cy1TUVhT51qm31nW6Ov1eZBtZo+WulLu23Zl8Zy30Dbt4WBAI2xt/zHX9978rscul852jIiLa9ffeW6S56caWQNU3FXTV2cxlWzoK+ltdWWjMjiFjmKoue1VEcfwcc0eAyrtqnJZ+5ZEvPpnTUnZkIed2GcfO64JD2lKW5B+cfc0N1TTDwuISAK6SiQ7FBhFCAxN8TwAAAACAK0LGGP15X2vG30rb1zk5ST8yTiCElySYDj2/IWVXuP50TzFwEbCM2H2/y2aP/3UNwTv8wgQpPLfCu+rX2+Xw2bDN7DJVDVbp/3DiD6sqByr9elspKIVnfez63Y9nP35ESSvhBfZFwrKMnB9+mOL4xz9XywP+p3WSRqNFe8fXd+i+850q6O90dhCGDemsrgwq2/TfvK6aqjmyJPr12yYIQg6JT6zMXn3NwcSFS6b9KjkAJqOJDsW+QggVTPA9AQAAAAAmXGnzoPnXW+qvabJ4/A7/iTYq236wOmFjYWowbDO7RHTV51GKff+znrS3R/uOy7qILm7JDzaLWbe3Baq26cIre4kdrh3avbv3PiphafRnehKRcl5YXukzuc/sidBEwNarS+DatCna/vY7a6SODr9Vd4RK5VJfd91uw2OPHiOVEDSeTVe93Vy2uX1km6RfGGYIU9ZnF0buTpg7vcMwLMvo1O7tCaeKdywa7GhLHjtPswp3dGbO0Xk33HrYHBPnCESNAIAhEx2K/QQhdJAgiD8ihH6IMXZN8P0BAAAAAC5bUXV/+NP/OXW/31ZJhnR/fV7ktsdXxpfTJJwYfSnI3kqdcsdPVlGdR2f7jmNG4xJy7iri8n9yApE0fG8vgyALxJ8q/pT7leWrUKfs9FumlGxIrn4s57Ht80Lmwc6NS+A9dMhse+31VUJNTbrfBMPwqhUrSoxPP3WQMpv5AJU36XXV283Ht7Tn97WcLQyLKE6YG9weoPKuCs7lpI9+8WlOw+EDizx2W8jYeZXB2Ju0YPHBudffclKh0cLJrwBMAhMaimGM+wmCWI8QKkUIfYsgiFqEkG38S/GqiXxuAAAAAIALtSLF3BNpUHa0W70xBEJ4Ubzx8AsbUnZFGJSwsuZScHZaWfSTxXTtxuWELIxuD8IEJUkJhQe9q3+7F2vDYRvqZfqs8bNZf6n+y7p+b7/fyZ0hqpCue9Pu3XpjPPS2uhRCXb3W+tJLK7hjx+YijE+HOSQpKxYsOGJ8+uk9TPws+GX/WUAYhlB/a7Pu2JefLmivLJ8v8rxq7HxQdGxtesGqgxkFq5tgyy0Ak8uEhmIEQWQihHYhhEzDQ3POcin8hhAAAAAAV40gyQRDkaM/f9AkgX+wOmHj74sar316VcJmOF3yEmEZsQdfS2ePvrOW4GxG3ykpJKOaK/zlNik6bzBA1U0bR3qPmF8rf21Nvb0+1XdcR+rk6xOv/+KhzIfKaViBd9Gk3j6F9cUXl3j27l2MRP9eT0xGRoXxicd3KubMgT+/Z9Fdbw8q29KR39fizEFjw7BQZUNWYcTuxHnTOwyrLy2JqijaktfX3JCJMfZLu0iKFiJS08vmXHtjaWRaJqzeBGCSmujtk39ACJkRQj9HCP0VIdSJMZYm+DkAAAAAAC6I1S0wL2yuW1bR6Uj66nsL3lPQ5GgfoMLU4J7C1OC/BLK+qYyu3xqmKP7lNaS1Oc53XNaE9PKLntgi5N7TFKjaposOV4fqxbIXCw71HlqA0ekX3BRBifmqfO8qzSpnYXbhiUDWOBXJbjdle/mV+e4tW/Kx16v2naNjY5v0Dz1YpF6zBpqen8VMD8NEgSePb/oivW7/nkUOS1/02HmFWmOdNWfBoXk33FqmNQfD6mMAJrmJDsUWI4T+gzH+1QTfFwAAAADgory3vy31/QNt6+1e0YgQQr/dWr/g+Q0ppQEua8ojBhrUyqIfF1JtB+YS6PRWM0yrPELW7Tu5gp8dQ3Da4WXxil7ylfJXFmxt3bqCkzml71x2UPaJJ2c/udNyynJHoOqbqrAoEvZ33sl0fvLpKmy3G33nyJDgHt3d39qu/frtDbC9bXzdDT5hGB4nDFsZUZw4P3jaHqJh7+tVHf3vJ3NbThxdyHvc+rHz+pCwlpRlBQdnr7u+hqJh5SYAU8VEh2I8Qqh5gu85ZREEoUMIjRxBzmDfHgUAAAAAuCKOtdlMv9pct76uz50yZjxFxriUJOCf40vCuyjlrp8vpKs+LyAkTjEyjBGBpVn5h7yr/2c3NsTAqojLIGMZ/aP2H6n/qvvXGitvNfvORWmiWh/KfGjrquhVnQghVIJKAlPkFOX89yfxjg8+WCP19kb4jhM6nU17yy079Q89eJKAIGNcMz0Ma6s4EXxiy5d53XXVubIk+b1+JkhSCk1IqshZs+Fg/LyFsA0fgClookOx3QihhRN8z6nsaYTQcyOfOBwOZwBrAQAAAKY1m0egf7m5bmlRdf9yCSNqZFzJkJ7bciOKnl6dUAaB2KVhjvw5mT30x3WkZ8AvqJGCkhu4lc9vkWYV9AeqtumiuLM4/M2KN9e2Odvifcf1rH7w60lf335P6j1VJAErmC6WZ9fucNubb64Wm5sTfccJhcKjWrduj/GpJw+TGg20exnHcBi2vK/FOXtsGKYPVTZmrYjYnbRgeoZhnMtJV+zYmtJw+MAca1dH0th5WqFwxWbnHpl7/a1HgqJi4DUeAFPYRIdiP0QIlRIE8SOE0P9ijGf6b1teQgi9PfzxFp1Od8axvAAAAAC4fB8cbEt+d3/bNTaPaPIdXxBnOPr8hpQdsSaVJ1C1TWVUc3GwYvfz6yhLnd+LQlkVZOEXPLxVmP9AHYKg5rLU2+q1Lx1/qfCE5YTfAVUsyXJrYtYUP5HzxCENA6HNxeLKTxptL79cyFdUZPtN0LSoXLr0oPEHz+yjw8LgRNRx9DQ6TMc2t+ePG4aFKBuzVoYXJy0IaQ1UfVeK4PVSp3ZvT2w6diirv7U5VRZFduw1aqOpOylv6cG5191SwapU8P8lANPARIdizyKEKhBCv0YI3U8QxHGEkG2c6zDG+LsT/NyTDsbYgRByIIQQQRACQRAzPSQEAAAAJpSMMfrGX8puq+xyZvqOh+sVnU8Vxm+8JjMUmmVfAsLWplQW/WgF1bJ3AYHl0dQLUywnpt9c7F35i0OIhaDmctg4G/PSiZcW7+7YvUzEp08+JBCB54fOP/xM7jPFMdoYdyBrnIqE1la19cWXlnOlpQuQLI+uGEUEgdnc3OPGp5/axaamOgJY4qTV0+gwlW1uz++dQWGYKPBk9Z5dsxqPHMzqa25IlwRBOd515pi46oyVaw+mLVvRAj3nAJheJjoU+7bPx/HDb+PBCKFpH4oBAAAA4MoiCQJF6JWWyq6h3SsKmvTekhte9IPVCccYioRfRl0s0Usqin85l6n4uJAQPaqRYYwQkqIXH+VW/2aXbE52BbDCKU+UReLdU+9mfdr46WqX6PJr1h2vj697LPuxbXlhebAd9SJJVitje+kPee6dO5chnlf4zjHJSTWG7z+6Q7lkcV+g6pvMepocprJNZw3DmjJXhO9OXjh9wjBZkoja/Xti6g/tz+ptrMsUOU493nVKnd4SmZpekbVq/Ynw5LTBq10nAODqmOhQ7GwhGAAAAADAFfHctcn7DrdYc5JCNY0vbEjZERekgtU1l4A5/rd49uAr60lXb6jvuGyc1cLlP7tFTF4PTaQv01fNX8W+V/Xeuh5PT6TvuFlh7r079e5ttyfd3hCo2qYqzHGk7Y0/5rq+/HIFdrl0vnNURES7/r77tmtuuH7aBDoTqafJYSrb3L68t9mZO93DMCzLqPHIwcjaA3uzuutrsgSPRzfedaxaY4tITqtIWVpQMSt3XjesCgNg+pvQUAxj3DKR9wMAAAAAGHGi3W787bb61f9vTWLRnBiDdWTcqGaE/z44/09mLcsHsLwpi2ovNSl2/mwt1XcqzXccKwxWft792/hFj1VB37DLU24pN7584uU1NdaaDN9xFaVyXTfrul0PZz1cpqAUcqDqm4qwLCPH3/6W6vzX/62WBwaCfedIk8mivfOOIt0991RDqHGm3maH8dim9vzeZudshJHfN0gfrGjKXBFRnJwXMuVf12FZRq0nj4fW7NuV1VVbncW5nKbxrmOUSmdYUmpF8qJllUkLl7TDnxkAZpaJXikGAAAAADChnJxI/XJz3ZJtVf35oozpX22ppz+9f97/+V4DgdjFI5w9rLLoR/lU485FBJZGey9hkhHElA17vat/cwAp9GIga5zqet29it8f//3yAz0HFsn4dH8riqCkJeFLDjyT+8y+EFUINHu/SK4vv4qxv/vuGqmzM8Z3nFCrnZrrr9ttePTRMkIBIeNYMyUM66iqMFfv2ZnZWXMqy2O3jXvQGc2yntCE5MrEhUsqUpbkt1I0DdvtAZihIBQDAAAAwKT14aGOxD/va7nW6hGDRsZqe12pO2v6wwpTg3sCWduUJYuEYu9vZzMnPlxFCC6t75QUOe+Ed9VvdsihmdCI/DJwEke+cfKNuRtbNq70Sl6/fkXppvSKJ3Oe3JFlzrIGqLwpy3vgYLDt9ddXC3V1qX4TDMOrClfuMz799EHKZBICVN6k1dvsMJZt7lje0+TIHRuG6YIVzZkrInanTPEwrKexznBq1/aszqrKLJd1IHy8ayia4YJnJVQlzM+rSM8vbKJZCE4BABCKAQAAAGASOtlpN/xyc/26qm5nuu94qI7tfnxF/CYIxC4NXfFxjGL/i+tJR6dfTytZH93BLft/m8X0mzsCVdt08VH9R0l/r/n72gFuwG+FSrg6vOP+jPu3XBN7TXugapuq+NpanfWlP6zgy8rmIIxP974iSVmxcOFh4w+e2cPExkIvwTH6WpzGY5vazx6GFYQXpywKbQ5QeZfN0t6qrdy5NbOj8mSWw9IXPd41JEUJ5phZNbPmLqjIXLm2nlWp4NRcAIAfCMUAAAAAMGm4eYn65ea6xVtO9eWLMmZGxlmK5G6cHbbz/61JPKKgSfjt/kUiu0/olTt+uobqPp7lO45ZrYPPvaeIX/qDk4iE7UOX40D3gZDXT76+ttnRnOQ7rmW0tlsTbi26P+P+ChJ6s10UsadHYX3xxaXefSWLkSj6vW5hMzNPGp58Yqdi9mxrgMqbtM4ZhpkVLZkrwndP1TDM1tOlrti5Nb294kSWrad71njXECQpBUXF1MXlzq/ILFxbq9LpYfUgAOCsIBQDAAAAwKTw+YnumD/sbLpx0C2Yfcdzo/Unnr82eXtiiMYVqNqmLM8Aoyz6yRK6fssyQj4dKmCSFsXENfu5Vb8pwZoQ6Md2GZodzZqXjr+04ljfsXkYnV7FxJAMvzJq5d6nZj91UM9Cb7aLIbtclPXlVxZ4tm7Nx16vyneOnhXXaPjew9tVhSvhNNQxpmsY5hywKCp2bElrLS/LsnZ3JvqtFhxGEIRsjIhqjM2ZU5FZuK5aG2SGXn0AgAsCoRgAAAAAJgVOlGnfQCxYy/Y+WjBr4y254a2BrGtKwjJi97+UxZb9ZQ3B2fW+U1JYzilv4S+3y5HzrAGqblpwCk765RMv5xW1Fy0XZEExMk4gAucG55Y9PfvpXQmGBGcga5xqsCgS9j+/neX89NNC7HAYfeeokJBu3T33bNd+/fbGAJU3afW1OA3HNrcv72l0zJkuYZjHYWcqd2xNaTlxNGugoy0Zy6cPqvBlCAtvjs6aXZFVuK7KEBYBW2gBABcNQjEAAAAATApfnxfZ9PmJnoqaXmfKDdlhO3+8LukwbJW8eHTNVxGKvb+5hrS1+p3MJ2vDu/klT28Rsu+c0g21A03GMvpL9V8y/l3/7zV2wW70nYvVxjY9kv3I1uURy6Hn3UVy/N9HiY6//W213Nfn1ySd0Oms2ttu3al/4IEKAk4I9DMShvU2OubgMWGY1qxozSwI35WSF9JMkGcsrJqUOLeLPrVre1Jz2ZFMS1tzqixJzHjX6YJD2qIzcioyCteeMkfHQvAMALgsEIoBAAAA4Kr7+FjnLA8vM/csiq7zHf/V9SlbJIy3poRq4YXORSL7q7WKoh+vojoO5/q+BMaM2i1k3bGDK3i2DFEshAqXYXvb9qg/V/55Xae70y9wNLJGyzdTvrn1juQ76qBv2MVx79gRYf/TW6vFlpYE33FCqfSo168rNjzxxBFSo4Hm6D76W52Go5umRxgm8hxZVbwjofFoaVZ/c1OaJJ5edelLYwrqikrPqkhfsboyLCHZdrXrBABMXxCKAQAAAOCqqelx6l7YVLf2ZKcjS8NSzmsyQ94I1SlGe79A37BLwDko5c5nF9E1X+YTEs+ODGOClKX4lQe9q3+7B+siob/OZagarNL/4fgfVlcOVmb7jisoheea2Gt2P5b92BElrYRVjReBO37cZH3l1UKhstLv8AdE06Jy2bIDxmeeLqHDwuDPrY++FqehbHP7sp5Gx9wzwrAgRWvmivDdKXkhTZM9DJNEkagtKY5rOLQ/q7epPkPkedV416l0+v7ItMyTafmFlVHpWZarXScAYGaAUAwAAAAAV5xHkMjfbK3P21jRu0KQMIsQQi5e0v5iU13+G1/P2h7o+qYkLCP20B/T2CNvryW8gybfKSk4rZZb+YutUuySgUCVNx0MeAfY3x///dJ9XfuWSFga/bmZRKScF5ZX+kzuM3siNBHeQNY41QgtLWrriy/mc4cOL0CyfDrYIQismDvnmOGpp4rZlBRHAEucVLCMUd3h/rja/b15A53uNISQX+KlDVK0ZRaE70pZNLnDMCzLqP7Q/ui6A3uzehpqMwWvVzvedQqNdjAiJa0iddnKitjs3F6ChJWXAIArC0IxAAAAAFxRnx7vintjd/O1/S4h1Hc8O1JX8eiKWQcDVddURjXuCFXufmE9OdgY7zsuq4P7+LzHtgpz720IVG3TgSALxJ8q/pT7RfMXhW7R7ffiPdmQXP1EzhPb54TMgcDxIkiDg4z1xZcWe3btWooEgfWdY1KSqw2PPbZDmZfXH6j6JhvOLdLlRZ3ZzScG8jx2IWzs/FQIw7Aso+ayI+G1+/dkddVVZ/Ful2G861iV2h6WlFKZsiS/ImFeXicEYQCAqwlCsSuIIAgdQkg3/CmDxzk+GAAAAJiuanud2hc21a0p73Dk+I4HqZn+RwriNt4+N7I5QKVNWcRgo1pZ9JMVVGvJfAKd/rkC00qvkHHrLm7F80cQo4JtfJfhP43/if+g+oN1/d5+vyAiRBXSdW/avVtvjL8RDiq4CJjjSOtrr89xf/XVCuz2DxipyMg2/QP3b9ds2NAWqPomm/42l/7kjs4FnbX2eZIgn7GtUB+saEpbFrYvdXFo42QNw9oqTgRX792V1VVzKsvrdJjHu4ZWKFxhCcmnkvKWViQvXt5GUhT0OwQABASEYlfW0wih50Y+cTgc0DQYAADAtMeJMvnbrfULvjzZs5KX8GjTZIYkhGuzQnf/ZF1SqZqloHH2xRA8pGLXcwuYU5+uICROOTKMEYGl2KVHvGv+Zxc2zvIEssSp7kjvEfNr5a+tqbfXp/qOa2iN48b4G3c8lPlQOU3C6YcXCssycnzwQZrz/z5aLQ8O+gUjZFBQv/Ybdxbp7r67BlYFDW2RrD/SH1sztEUyHWH/LZIESYih8doTmSvCS6PTjH2BqvNcuuuqTad2F2V2VldmuW3WM1a2IYQQxTDekFmJVYkLFlWkLl/ZTDMsBPgAgICDUOzKegkh9Pbwx1t0Ol1IIIsBAAAAroYeB6fYWNFb4BuIZUZoK39+TfK2jAidPZC1TUXMsXcT2dI31pPu/mDfccmU2Mit+PlWKWFVb6Bqmw46XB2qF8teLDjUe2gBRng0oaEISiyILCh5OvfpEpPCJASyxqnG9cWXsfZ3310jdXVF+44TGo1Dc8MNuwyPPHyCUChmfCDCe0S6vKgzq+n4QJ7HLoSPnWdVlDUm03Ro9prIMm2QYtL1rutradKf2rUto+NURZZzoD9qvGtImuaDY2fVxM9dWJGxYk0Do1TCL0QAAJMKhGJXEMbYgRByIIQQQRACQRDw20UAAADTXqxJ5bl1TnjRPw53Xm9SM5bvLY/ddOf8qMZA1zXVUK0lZsWun6+j+muSfcex0jTIL3hoK7/gezWIgFU2l8otuqlXy19dsK11WwEnn159hxBC2UHZJ57OfXpHihEavl8M7/4DIbbXX18t1Nen+E2wLKdetWqf4aknSymjccYHjJZ2l768qHN+V619nijI6rHzumBFc9L84NKMgvAaiiYn1euHwa4OTeWOrRntleVZ9r6e2PGuIUhSNMfE1c7KnV+RWbi2TqHRile7TgAAuFAQigEAAADgknGiTH52vDvujvmRTb7jz6xOLCMJAn+/YFY5bJW8OIS9Q6ks+nE+1bw7j8CnT+fDFMuLqTcUewt/WYoUOvieXiIZy+jD2g9T/1X3r7U23hbkOxeliWr5Xub3thZGF3YFqr6piK+q1ltffnkFf/x4LvLtoUuSknJR3mHjMz/YQ8dEz+jtvVjGqOGoJaZmf2+epcOVMe4WyVna8oyC8NKYDOOkWv3p6O9TVuzYkt528niWtbszHo05ARMhhAiCkE2R0fWxs+dWZBWuq1EbTXwASgUAgIsGoRgAAAAALsnnJ7pjXtvdvKHPyYdpFdR712WHtY/M0SSBf7gmsSyQ9U05Ek8oin81l6n4v0JCcI+uHsEIISkqr4xb/eudcnAa9Ce9DMWdxeFvVry5rs3ZNst3XM/qB+9IumPbt1K/VU3C6rsLJnZ3K62/f3Gpt6RkEZIkv9cVbHZWueGJJ3cpcrKtASpvUuA9In1yR1dm03FLntsmRIydZ5WULTrTeHj26shjumDlpAkO3dZBtmLnttTW8mNZgx1tSRjj8f7HwIbwyKbYrNkVmavWV+tDQidN/QAAcKEgFAMAAADARWnsd2ue31i7uqzdnjsy9squpmvXZYS8w1CTa6vPVMGU/yOO3f+Ha0hXj1+DatkQ28rl/3SLmLIBVi5dhnpbvfal4y8VnrCcmOM7zpIstyZmTfETOU8c0jAaWH13gcTubqXttdcXevfsWYw5/62ndHx8g+F73ytSrVzRHaj6JoOBTreufHvn/M5a23yRH2eLpFnRkjg/uDSzILyGYshJ0V+Ncznpyp3bkpuPH8mytLWkYFke97WiPiSsNSozuyKrcN0pU2S062rXCQAAEwlCMQAAAABcEEGSif/Z1jD/v+U9hZwoj74QpklCnB9nPCXKmGAoBKHYRSA7jxqVO55dQ/WezPAdxwq9nZ9z7zZ+yVOV0Dfs0tk4G/PSiZcW7+7YvUzEIjMyTiACzw+df/iZ3GeKY7Qx7kDWOJWIHR0q22uv53n27VuEeF7hO0eFhnbpvv3t7dqv3dZ0tsdPd1jGqLHMEl1d0ptnaXdlIIz8/uclSCSGxGlPZuSHl8ZmmXoCVacvweulThUXJTQdPZTV39qUJosiO9512iBzZ1R6dkXGyjWVIbMS4MAUAMC0AaEYAAAAAM7ry5M90a/satrQ6+D9TkhLC9NU/+ya5C05UXpboGqbighXH6so+vEyumH7EgJL1Mg4JmlRTL5mn3fVr/cjVdCMb0h+qURZJN459U72fxr/s8oluvS+cwn6hLpHsx/dlheW1x+o+qYaobVVbXv99UXekv15SBD8QhPSYBjU3HbrTv1991USND0jQ3HeK1EVOzszm8oG8lxWPnLsPKOk7DEZxsM5qyOP6UOUAQ9hRYEna/btjms8fDCrt6khQxJ45XjXqQ3G3si0zIr0glUVESnpg1e7TgAAuBogFAMAAADAWTVb3OrnNtauPtZm99t2ZlDRgw8sjd30rbzo+kDVNiXJIsHu+102e+JvqwneqfOdksLnnPSu+lWRHD4bVmFchq+av4p9r+q9dT2eHr9wwqw0934r9Vtbv5b4NTgJ9QIJTc0a2+uvL/YeOLAQiadX2iGEEGk0WjQ33LBH/8D9FYRCMSm2/11tg11ubfn2zvkdNbb5Ii9rxs5rzYrWxHnm0qwVEdWB3iIpSxJRd2BvTH1pSVZPY12GyHFn1IsQQkqtbiAiJb0idfmKitjsOX1Xu04AALjaIBQDAAAAwFn94LOq66t7XGkjn1MkIa5NC977s2uS9+uUtBjI2qYa+tR/ohX7/nc96eiI8h2XdZGd3NIfbBEzv9YWqNqmg6N9R4PeOPnGqhprjd9WVBWlcl0367qd38/+fhlDMjNyJdPFEurqtbY3Xl/qPXR4PhJFv9cLZFBQv+amG4v1991XSTAz8/vZeMwSVV3Sk2dpc2XiM7dISiGx2pPp+WGlcdlBAe2rhmUZNR4tjazdvyerp742k/e49eNdx6rVtvCktMqUpfkV8XMWdBEkbNkGAMwcEIoBAAAA4KyeKIzf8fD/VaTIGJEpoZqaZ9cnbZkTY7AGuq6phOyt0CmLfrKa6jqW4zuOGY1TmH13Ebf8R+WInJnbziZCnbVO92r5qwVl/WVzMcLEyDhFUNKS8CUHnsl9Zl+IKoQLZI1TBV9Vrbf98Y9LuSNH5iHp9LZehBAig8292ptvKdbd+52qmbhNUuAk6uTOroymY5Y8l5WPGjvPKClHdLrhcM7qyKOGUFVAt0i2lpeFVu/dldVVW5XFuZym8a5hlEpnWGJKZdKiZRVJC5d0kBQ14/6bAgAAQhCKAQAAAGBY66BHFaxheTVLjZ7CtzQhqP/GnPDt8WbVwHcWx9QGsr4px2ullUU/XULXbVpGyMLo1jNMUJKYsPoAt/o3e7E2jA9kiVNZl6tL+XL5y8sOdB/Ik7Dk9zNtuim94qnZTxVlBmVCr7sLwFdWGmx//OMy7uixOUiW/cIwKiSkW/O124p1d99dMxPDMGuPR3NiW8fQFklO1o6d1wYp2hLmmUuzVoRX0SwVsC2SndWVQVV7dmZ1Vldmeey2kPGuoVjWExqfdCpxweKK1GUrWqgZ+N8TAADGglAMAAAAmOFEGRMvFjXM+bSse/WatOCS39yYVuI7/4vrUg4GqrYpSRYJdt//5rDl/ygkOLvfdiUpNKuKK/zFdilqITStvkSD3CDzavmrebs7di/jZf8TEKM10c33pt+7Y33s+vZA1TeVcCdOGG1v/mk5f/x4LpJlvz1zVFhYp/brtxdr77qrdiZup2s6boms3teT19/qyjpjiySBpOA4bUX6srDSWbODugJVY09jnaFqd1FmR1VFlmtwIGK8a0ia5kPiEqri5y2syFixupFmZ2b/NwAAOBsIxQAAAIAZbGtVX8RLRY0buuxcFEIIbT7VV3DXgqiTmZE6aPZ+CZjjf41nS19fSzq7/U7plDWhvfziJzcLs+9uDlBpU55X9JJvVLwxd0vrlgK36PZbsROiCum6K/muHbcl3tZAEjMvwLlY3NFjQba3/rScP1E+G+HTW04RQoiKiGjX3nFHsfaOr9fPtDBM4CSqYldXeuMxS55rkI8eO88oSGdUuvHw7DWRRw2hKlcgahzoaNNW7tia0X6qPMvR3xcz3jUkRYnmmLiauDnzKzJXrq1XqDXQ/xEAAM4CQjEAAABgBmof9Kie21hbeKjFNt93XM1SruYBjw5CsYtDNe4MUez59RrKUpPsO44ZtVvIvH0XV/DsMUQrYYXGJRBlkXiv6r3Mz5o+K7Tzdr/+SAbWMHBrwq07v5327VM09GU7L++hQ2b7W3/O5ysqss8Iw6KiWnXf+Eax5rZbG2daGGbt8WjKt3fO66i2LhDG2yJpYtvj55pLs1ZGVDGK09vLrwZHf5+y8cjB2K7a6riBjtY4p6U/EiFEjL2OIEnJFBldH5c7ryKzcF2tWm+ArdkAAHABIBQDAAAAZhBRxsTLOxpz/13WtdojyOqRcZJAUmFqcMlz1yTvM6oZIZA1TiVkX5VWsfNnK6n20jmET5N3TNKimLD6ALfqVyVYGw5N3i+BjGX0cf3HSf+s++fqfm9/mO+chtY4ro27dvf3Mr93XAlh43l5SkpC7O+8my9UVmaNnaNjY5q1d32zWHPTjc0zLQxrPjEQUbW3J6+/1ZmFMfLrpUYQSA6O1VSkLQsrjc81d16tmgY62rRNR0tju+tr4wY72uLcNmvYWS8mCGwMj2yMzc6tyFq1vlprDvZerToBAGC6gFAMAAAAmCGKqvvDf1/UsKHTxvltC0oMVtf/dH3S5gVxxoFA1TbVEK4+VrHzZ4vp+q1L/ZroI4SkqAXHuZW/2CWHZcNqu0u0sWVjzAfVH6zucHXE+o4rSIW3MLpw7+M5jx/Ss3rYEnYenj17wuzvvJsvVFdnjJ2jZ8U16r55d7HmxhtaA1FboIi8RFbs7k5vPGrJcw5wZ2w/pBWkMyrNeCRndcRRU7jaeSVrwbKMehrrjC3Hj8b1NNTFWbs64rxOR9D5HqcPDWuJyZxdkblq3SljeGRAT7oEAICpDkIxAAAAYAb4+6H2pN9vb/wG9tl2o1NQtu8sjtny3SUx1SRxxm4cMB7RSyr2/CaXqfhoJSG4/LZZSUFJDfzyH28Xk9b1BKq8qW5f177QP1f+eVWDvSHFd5wmaGFJ+JKDT8x+Yn+4OhxWw5yHe8eOCMf77+cLtXVpY+fohIR63T3fKtZce+2MOozA1utRn9jeOa+j2rZA8Eq6sfMaE9uRMMdcmlUYcepKbZHEsozaT5WHtJ4oi+trboy1dnfG8R63/pwPIgisMQV1BUXFtIQnp7UkzM9rNYSGe65EfQAAMBNBKAYAAADMADflhDe/va910OoRg0gCySuSzSXPXZu8N0jDwlbJC4FlxBx9J5k9/NYa0t0X4jsla0J7+YUPbxPm3tcQqPKmunJLufGNk2+srBioyPEdJxAhzwuZd/SxnMf2JBmSruiqnenAvXVrlP0vH+SLDf6hIkIIMclJNbpvf3uPeu3aq7YVcDJoOTkQfmpPT15/qzMby2dukTTHaCrTloaVJsw1d0z0c4sCT7aeOBbWVnEirr+lKc7W0x0r8pz6XI8hSFLSmUPag6JjWyPTMloS5uW1qY0m6A8GAABXCIRiAAAAwDQkSDLBUORo43GdkhYfWBa7+d/Huhb9ZF3S5kXxJksg65tK6LpN4Yq9/7OWHGyM9x3HrNYhZN+5i1v+4+OIYqHJ+yVosjdpXil/Jf9o79H5MpL9GlplmjJPPpL9yK7c4NzBQNU3Vbg2bYp2fPDXArGpKWnsHJOaWqW/9949qsKV3YGoLRAkQSYrdnelNRyx5DkHuNix8zRLuqLSDEdyVkceMUVM3BZJ3uOhmo6WRnVUVcT1tzbH2vt6YmVRZM/1GJKmeX1IWJs5Jq4lKj2rJX7ewk44LRIAAK4eCMUAAACAaaTbzime21i7ssfOmf/zwLx/+G6LvHthdP1dC6LqYavkhSG7T+iVu35eSHYene37HcMkw4vJ15Zwhb84gNVmWGl3CXrdvYqXy19eUtJVsljEIuM7l6BPqHsg44Ed+ZH5sA31PJS1dWz3y6/cLba0JIydY9LTK/X337dHtXx5byBqCwR7n1ddXtQ5t+2UdYHglc7Ylqg2sp0Jc4JKswojK1nl5W+RdNttbOPhg9GdNZVxA+2tcY7+vmgsy9S5HkOzrMcQFtESHBvfGp2V0xKXO6+bZlg4LAIAAAIEQjEAAABgGpAxRq/uasr5v6Nda928pEEIobf3taY/tDyuyvc6CMTOj3B0KhQ7nl1GN+1cRMji6M9KGBFYill8lFv1q92yOcUVyBqnKjtvp18/+fqCovai5ZzEqXznItWRbfek3VN0/azrZ1Tj94uFZRm5Pvt8VthHH5mVTU2siJB5dJIgMJuRUaF/4IG9yiWL+wJY5lXVWjEYdmpPd15fizMby2Ne3xBIDo7WnEpbFloan2tuJ8hL/zvQ3terajxyMLartipusKM91jloiUQYn/OGjFLlMEZEtoTMSmiJzZnTEp2R009SFKwsBQCASQJCsSuIIAgdQmikkSeDz/OPJgAAAHApdtdZQn+3rWFDm9Xrt02opHEwdWwoBs5B8JCK4l/MYyo/WUGIHr++P1JwWi1X8LPt0qyC/kCVN5VxEke+VfnW7K+av1rhEl1+K3jMSnPvnUl3Ft2RfEcdSZBnu8WMh2UZuf79SYLjX/8qkDo6YpW+kwSB2ezscv2DD+xVLlw4I7ZGD22R7E5tPNqf57BwcWPnaZZ0R6YObZEMilQ7LuU5LG0tuqajh2K7G2rjBjvb4zw2a+j5HqPQ6gZMEVEtofGJrbPmzG8JS0wZJEj4cw0AAJMVhGJX1tMIoedGPnE4HNAgFgAAwITpdXCKn39Vu2J/42Ce76mSWgXluHth9JaHlseeCmR9UwaWEVv6Rhpz7N3VpGfA7Dsl6yK6+EWPbxNyvtkcoOqmNBnL6IPqDzI+afik0Mpb/b63ekZvvSn+pp33ZdxXQZM0rJw5CyzLyPmv/0tyfvRRgdTVFe03R5JIkZNTZnjooX2KeXMHAlXj1eSwcKoT2zvmtldaF/BeyTB2Xm1guuLnmEuzCyMqWRV9wb25sCyjnoZaU3PZkbjexvq4wa6OOM7lNJ3vcWqDsdcUGd0SlpTaEj9vYYs5OhZ+3gcAgCkEQrEr6yWE0NvDH2/R6XQh57oYAAAAuBAyxuj13c3Z/zzSudbNS9qRcZJA8vKkoAMvXJuyx6xl4bSyC0BXfRalKPn9WtLW6rfKDiv0Nj73nh38kqcrEAQ2l+SThk8SPqz9cFWvpzfSd1xFq1zrY9YXP5L9yDE1rb7svk7TFZZl5PzwwxTnx/8ukHp6/L6HiCRl5/x5XtuaNc68m276IkAlXlVtp6yhp4q783qbnTlYxmO3SGJzlOZU2tLQ0oS55rYL2SIpSxLRfqo8uLW8LK6vqTHO2t0ZJ3g9unM+iCCw1mTuDIqOaQlPTmtNmL+oVR8S6rmsLwwAAEBAQSh2BWGMHQghB0IIEQQhEAQBP1QDAAC4LG5eor727rFvtg56ZvmOxwWpmn+8LnHj0oQg2N53AaiOQybFrudXUT3lmb7jmFJwYur1e7yFvziEFHo4Ae4SFLUVRb5b9e7qVmer32mdDMlwK6NWljye8/hBk8IEBxScBRZFwvG3v6U6P/m0QO7rC/ebJElJMW/eMcMjD5e0WK1fD1CJV40kysSp4u7U+iP9eY5+btbYeZoh3ZGp+qPZqyKPmKM19nPdSxR4suX40fD2ivK4vpbGOFtvd6zE86pzPYYgSVEXHNJhjo5riUzLaImfv6hdrTfALxwAAGAagVAMAAAAmELULCUFaRhb6+DQ4gQNSzm/uTBq68P5cRXQRP/8CGurSrnjp/lUS/FCAsujjX4wQcpS3PLD3lW/LsbGWbDy4xKU9pQG/6niT4W1ttp033GKoMRFYYsOPZ7z+L5obTR8b88CCwJhf/8vGa7PP8uX+y3+vasoSlQsmH/U8P3vl7CpqUP9sUpKAlHmVeEc4JQntnfMbau0LuQ9Z26RVOmZ7vg55tKcVREVZ9siyblddNPRQ5EdVRVxlrbmOHtfb4wsiuy5npekaV4fEtYaHDurJSojuyV+7sJOVqWC1YwAADCNQSgGAAAATDEvbEjZfsf7x5LnxxlPvLAhZXcIbJU8P85BKXc9t5Cu/m8+IXF+Pcql0OxT3Iqf75BiFs+InkwTrWqwSv9a+Wsryi3luRidPlSIQATODc4tezT70eI0U9o5V/HMZFgQCPs772S5/vtFvjwwEOw3SdOicuHCw4bvf38/k5w07XtVtVdbQyp3d+f1Njlnj7dFMihKXZW2JLQ0cV5w69gtkm7rINtw5GBMV01V3EB7a5zD0heFZZk61/PRrMJtCAtvDZ6V0BKTNbslNmdOD82w8sR/ZQAAACYrCMUAAACASaqkcSD4tV3NK/9wa8aXUUald2Q8IVjt2vjwwlchDLsAWEbs/pey2ON/XUV4rUbfKVkf084teWqbmPm1tgBVN6W1OdvUr5x4Zdmh3kMLJSz5hQ9pxrTKh7Me3jU/dP6MOAnxUmCOI21/fjvb/eWXy2Wr/yEEiKYF5ZLFhwzff/QAEz/LFaASrwpJlImqvT0p9Yf78+x93vix8xRDeiJT9EezV0UeDo45vUXS1tOlbjxSGttdXxM70NEW5xociEDnOemdVantxvDIlpD4xJbYnDkt0RnZ/XAyJAAAzGwQigEAAACTjMXJs89tqs3fWz+wWMaIfG5jrevdu3I2+V4Dgdj5MSf/FcceeHkt6ej0a1IuK00DwtzvFvGLHqtCBLwgvlgD3gH2lfJXFhV3Fi8VZMFvO1qcLq7h/vT7dxRGF3YFqr7JTvZ6Sfuf3prt/uqr5bLd7n+6IcPwyqVLSw2Pfv8gExvrDlCJV4VzkFOWb++c01oxuJD3SMax8yo90zMrN6g0Z1XkSYWaFvtbm3WHP/sqq6ehLm6wsz3OY7ed9wArpVZnMUZEtYYlJrfMmjO/JTQ+yQohGAAAAF8QigEAAACThIwxemtva8bfD7Wvd3LS6Cloh1usc2t6nHtTw7SOQNY3VVAte82K4l+uofpOpfqOY1rlEdJvLuZWPHcEsRroE3SRXIKLev3k6/O3tW3L90pete9cmCqs4+7Uu3fcknBLU6Dqm+xkt5uyv/lmrmvT5mXY4TD6TbIsp1q+/KDh0e+X0lFR07rvWke1LaSyuGthb5NztixhZsw0DopSV6csCinVGvsdLSeK47a81rDB2tURx7mcpnFv6ENtNPWYIqNbwpNSWuLn5bUGRcVM+y2nAAAALg+EYgAAAMAkcLBp0PzrrfXXNls8Cb7j0UZl6w/XJG6CQOz8SEudRrHzZwVUa8l8wqe3FSYoSYovPOhd9at9WB/lPdc9wJkEWSDernw754vmL1Y6BIdf03OTwtT/tcSv7fhW6reqSVh1Ny7Z4aBtb745x71l6zLsdOp95wiFwqssKDhgeOzRUjosjAtUjVeaLGHi1N7u5PrD/Xn2Xm/C2HmSRt6gCEeVQtlotfc2hZZ82Hmb4PVqz3VPgiBkTZC5MygqtjUiJa0lYf6iVl1wCPz/DQAA4KJAKAYAAAAE0ICLZ57fVJdfXGdZImM0miqoGNJ1x7zI7U8Uxp+AUyXPwzPAKHf+fBFdt2kZIfF+2/mkiLnl3pXP75Qj5toCVd5UJWMZfVj7YerH9R+vGuAG/LaqaRmt/YZZN+x6MPPBEwzJ4EDVOJnJdjtte/2Nee7t25dil0vnO0colR7VypX7Dd///mEqNGTahmEuK684sb1jTluFdSHnFkdXemEsISz1IpLscFJUq9Nr7TS29/FzznUvgiRFfUhouzkmriUiNaMlYf6idpVOL1z5rwIAAMB0BqEYAAAAECAfHGxLfntf63UOThpdPUIghBfFGw+/sCFlV4RBCasezkXiCcW+3+Uw5f8oJHiH3woc2RjfzC37f9vE1Ougt9Ul+G/Tf+P+WvPX1d3u7mjfcSWldK+JWbP30exHj2gZrRio+iYzaXCQsb3+xnxPUdFS7PFofOcIlcqlWrVqv+H7jxyhzOZp2xews9YWXLm7e2FPoyNXljCDsYCw2I1ksQPJYjuSpU6MsEgghLTDb2egaIbTh4a1BsfGt0RlZLXGz13YySiVsO0ZAADAhIJQDAAAAAiQAZeg9g3EoozKth+sTti0KjW4O5B1TQVM2QcJbOnra0lXT5jvuKwO7ufnP7hdmP9gLTTRv3i7O3aHv33q7VXNjuYk33GGZPjlEcsPPJ7z+IEQ1fRd2XQ5pP5+1vb66ws8u3YvwR6PX881Qq12qtasKTF8/5GjlNE4LVc3yRImqvb1JNUf6suz9tgSZbFzKAQT2hGWehBCfnnWGctfaYXCbQyLaAmeldASkzm7JXb23B6KpmEVIgAAgCsKQjEAAAAgQJ4ojD+xvbp/rsXFB98+d2irJE0S8CLwHKiG7aGKvb9ZQ1nq/EIbzGhcQtbtu7j8n5YhWikHqr6pqqyvLOiNijdWVg1WZfmOkwQpLQxdePjxnMf3xunipvVpiJdK7OlR2F5/faF3d/FizHEq3zlCo3Go163bZ3jk4WOkXj8tV9a5bbzi8BenFrSdrMrjXO1aWWxHWOpDCJ37rzJWrbYZw6NaQuMTW+Jmz22JTMu0wMmQAAAArjYIxQAAAIArTMYYvb+/LTVMr3Bdnx3WPjJOEgT6zQ2pn4XqFN4oI2yVPBeyt1Kn2PnzlVTHoVy/JvokI4iJaw5whb8swdqwabsd7Uqps9bpXjv5Wv6xvmNzMcKjiQSBCJxtzj7xaPajuzODMqEf2zjE7m6l7dXX8jx79ixCPK/0nSN0Opt6/fp9hkceLiM10+ekU97jobrrq4P6mpqCu+q6Eq3dljiPvceMpYHzNj5U6vQWU0RUS1hickvcnPktYQnJ8OcKAABAwEEoBgAAAFxBh1usQb/ZWr++vs+dHKxhelenBf9ZxVCjK5nmxBisASxv0iNcfaxix7NL6IZtSwhZYEbGMUJIisor4wp/sUsOzYSTOS9Sl6tL+XL5y8sOdB/Ik7Dk9/NgsiG5+qHMh3YuDl/cF6j6JjOxrV1le/31RZ6SkjzE8wrfOUKvt2o2bNijf+jBclKtnpJhGJZlZGlv1fU21gUPdLSZ7b09wc4Bi9ljt5k5l8uIfELpc1EbTd1BUTEt4UmpLfHzFraaIqNdV7h0AAAA4KJBKAYAAABcATaPQD+/qW75zpr+pTJGFEII9buE0N9tb5j/3LUphwJd36QneknFnt/MYSo+WkkILr9m5ZI5uZ5b/pPtUuKa3kCVN1XZOBvzSvkrebs6di3lZf/VTdGa6OZ70+/dsT52ffvZHj+TCS0tattrry/2HjiwEAmC3ymnpMEwoL7+ur36Bx8sJ5VTY/uu2zrIdtfXmPtbW4LtvV1mR3+/2W0dDPY47GZZEpnz38EXiZTakMGQ+OjqqPT0poR5eW1aczCsfgUAADDpQSgGAAAATLD3D7SlvLe/7Rq7VzT6ji+MMxz57uKYkwEqa2rAMmKOvJ3MHnlrLenuD/adkjVhPXze97cJc77TGKjypiqv6CX/WPHHuZtbNxe4RbffaX8hqpCuu5Lv2nFb4m0NJBxOcAahsVFje/31Jd6DpQuQ6B8WkSZTv+bGG/fo7/tuJaFQTLowTBJForexztjX3Bg82NludvT1BrusA2a3zWYWvB7dpdyTIPWIIE2IYkyiJiioMyIlumr+9UuPqvT6aXmAAAAAgOkNQjEAAABgghxrs5l+tblufV2fO8V3PFyv6Hx6VcLG9RkhnYGqbSqgazdGKPb9z1pysGmW7zhmdQ4h564d3LIfliOKhYMILoIoi8R7Ve9lftb0WaGdt5t85wysYeDWhFt3fjvt26doEk75G4uvrdXZ3vjjEu7w4flIFP1+ZibNQX2am28u1t977ymCYQL+vbP1dKm762vNAx1tZntPd7BzYGjVl9flNGFZpi76hoQCEaQJEVQQIkkTIqihN0YR5A6OM1bNygmqTFoQ3EIx5KQLAgEAAICLAaEYAAAAcJlsHoH+5ea6pUXV/cul4a2SCCGkpEnPrXPCi55ZnVgGp0qeHdl13KDc9fNCqutYju84plheTL52n7fwFweRKghWoVwEGcvo4/qPk/5Z98/V/d7+MN85Da1xXBt37e7vZX7vuBJO6jwDX1Wtt73xxlLu6NF5SJL8AiUyOLhHe+utxbpv31NN0Fc3SOTcLrqnodbU39IUbO3qNPf19ho5h42u/sfbP5R4XnX+O/gjCEJm1Xo7SQXJomTWYmxiSSoIEaQJIUKFCGKodRjFkJ7gGE1VXI6pMnlhSDMEYQAAAKYTCMUAAACAy1Tf59aNDcQWxBqOPn9dyo5Yk8oTyNomM8LRqVDu+OlyqnHnIgKfDh8wIrAUu/QIV/jLYtmcDM25L9Kmlk0xf6n+y6oOV0ec77iCVHgLowv3Pp7z+CE9qxcDVd9kxVVUGOx/fHMZd+zYHDRmdRUVGtql+dptxbpvfrP2SoZhWJZRf1uLvrexzjzY2e7T5N4azLlcBoTQeE3uz9n/i1EqnSq9waIxmft1wSEWkg7HzsGgUFuvOkHgsBEjhKgxa8lGg7BsU2XSwuBmmqUgCAMAADAtQSgGAAAAXKZ5sYbBwtTgfdur+wvCdIqup1bFb7w2M7Qj0HVNWryLUux+YT5T9VkBIXr8VrhIIRk1XP6zRdKs/P5AlTdVlXSVhP658s+F9fb6VN9xmqCFJeFLDj4x+4n94epwaH4+Bnf8uMn25p+W8yeOz0Yy9muqRoWHd2i/fnux9hvfqCPIieu35tvk3tbTZXZa+oLdVqv50prcI0RSlKDU6S0ao8miDQq2GMIj+s0xcZbwpFSL2mDimssHIhuPWTK7m5x5vEcyDD3KP9ujGMJrjh4KwpLzQpogCAMAADATQCgGAAAAXASHV6S3VfdF3Zob0eI7/ty1yfsiDQrr4yvjTzAUCVslx4NlxB58LZ059t5q0jsY5Dsl6yI7+cVPbhOy72w528PB+Mot5cY3Tr6xsmKgwm/7KYEIeV7IvKNP5DyxJ8GQ4AxUfZOV98iRIPuf3srnT57MQRj7rcCiIiPbtHfcUaz9+u0NlxqGiQJP9jbWG/uaG83Wrg6zo6832Dk4YPbYrcGC16s9/x3OpFBrrCqD0aINMveLGn0aazCJ8/IW/c0cE+cgKWr07x0sY9RycjCy9LP+/N7m5gzeIxnHu99IEBabZTqVsiikEYIwAAAAMw2EYgAAAMAF+vuh9qS397Ve4+AkfbxZ/ebcGMPgyJxBxYjPrE48HsDyJjX61H+iFSW/X0va22J8x7HCYOXnfHsHv/jJSgTN3i9Kk71J82r5q/lHeo/Ml5Hsl9xkmjJPPpL9yK7c4NzBsz1+pvIeOBhsf/vtfL6yMuuMMCw6ukV3113FmltubrrQMGy0yX17a7Ctt9vsGrAMNbl3OoIwxhedqFEM61Xp9f0aU5BFZw6xGMMj+4Pj4i1hSSkDCrVmdNtrSUlJLEIIhcxKsCM0GoRFNB6zZPY2OTLPGoTRBBfksyKMUVDSxdYIAAAATBcQigEAAADncbLTbvjFprp11T2u9JGxX22pX/+f++f9K5B1TQVUe6lJsfuF1VRPeYbvOKYUXjHtxj3elS8cQgodvCi/CL3uXsUr5a8s2de1b7GI/bfaJegT6h7IeGBHfmR+T6Dqm6w8+/aF2t95J184VZU5do6OjW3SfvObxdqbbxp3pSLndtE99bVB/a1NZmtXp9lh6Qt2DQ6YPQ67+VKb3Cu0ugG1wWjRmoP7DaHhlqCoGEtYUkq/ITTcfaGBHMYItZwcCG88asnsaXJk8m7JNN51w0FYdWyWqTJlUUgjBGEAAADAEAjFAAAAgLNwciL1qy31i7ee6isQZTz6b6aCJr1zo/X1MsaIJMbrew0Ia7NKuePZfKplz0ICn17FhAlSluIKDnlX/XoPNsbCIQQXwSk46VfLX12wo33Hcq/k9QtiItWRbfek3VN0/azrWwNV32TlKS4Os7/zboFQU5M+do6Oj2/Q3X13seb669qwLKO+lqahJvcd7cH2vh6zT5N746U891CTe2O/1hRk0QWHWoyRUf0hsxIsoQlJVpphL2mrIpYxajtlDe8/SelcXaSq0d3w4HjXkTTBmaM01bHZxsrkvNBGVglBGAAAADAWhGIAAADAOP5xuCPxz/tarxl0C2bf8TnR+uPPb0gpSghWw6mI4+HstHLnzxfSNV/mExKn8J2SwnIquRXP7ZCi82BL30XgJI58q/Kt2V81f7XCJbr0vnNmpbn3zqQ7i+5IvqOOJCauEfx04C4qinC8/5cCoa7O7+ABkSSQOzGh1blwboNNwZDOEwfyXMWbrvU67GZZki6vyb05pN8QFmExx8RawpNSLRpTEDcRX8twEBbWeLQ/s6fJmcm5xCCEqDOuIymCN0erq2MyTZUpi0MbIAgDAAAAzg1CMQAAAMBHZadD/4vNdetOdTv9tvuFaNmex1bM2njT7PC2QNU2qckiwe5/KYs9/rdVBGcz+E0ZYlu5pT/YJqbfDCdyXgQZy+iv1X9N/3fDv1dZeatfOKtn9Nab4m/aeV/GfRU09GLz496yJcr6wV8LHG0tyS4Fi5zBBuRSMsilYJFToxJ5AtEIoVhUcSz2Yu6r0GisKr2xXxtktuhDwiymqOj+0IRkS3BMnH0iT6YcgWWM2qusYQ1HLBk9TY5MziWax7uOpAg+KEpdM7w1soFV0eJ41wEAAADgTBCKAQAAAMNkjNGTn566vcvORY2MsRTB3ZATtvNHa5OOKGgSTmYbB1P+jzj24KtrSUdnpO+4rAoaEObet53P+341glVMF+XThk/j/17799W9nl6/76mKVrnWx6wvfiT7kWNqWj3jVgFJokjYe7vV9r5ejXOgX+MaHNR6HDaN127X8G1tEWJPT4RbEtRuNYNwetx4tzjnz75jmtz3GyMiLcGxZza5v1KwjFFHjS20/nB/Zk/juYMwVZgkaaMk78rrF74JQRgAAABwaSAUAwAAAIaRBIHuWxpT9MvN9fcghNDsKF35c9embE8O1TgDXdtkRDXvCVYU/3I11V/ltz0N0yq3kHFrMbfiuSOIUUGQeBGK2ooi3616d1WrszXBd5whGW5l1MqSx3MeP2hSmIRA1XcluO021t7TpXFY+jXOAYvGY7NqPA67hnM5NZzLqeU9bg3v8WgEzqsROU59zpsxBEIMe85LCIKQlVrdgNpo6tcGBVv0oWGWoOjY/vCkFIshLMI9kV/bhWqvtoY0HOrP7GlyZHqdYvB415AUIQRFqmtiMo2VKYtD64+Uld6LEEIQiAEAAACXDkIxAAAAM1ZDn0sTH6x2+TbLv31uZPOBRuueZUmmxltzI8Y9iW6mIy21GsWOZ1dQbQfmEQiPfvMwSYtifOFBbtWv9mFd5IT0UpopDvUcCn6z8s3CWmutXzN4iqDERWGLDj2e8/i+aG30lDiY4KyruRwODedyani3ayjk8g4FXZfSx+tCMKzCpTKa+rSmIIsuJLTfFBltCZmV0B8Sn2i71Cb3E6mj2hZSf7gvo6fRkel1iiHjXUNShGCKVNfEZBhPpS4JrVOoIQADAAAAJhKEYgAAAGYcUcZoexvWbNp77LFvL4r+6rEV8Sd951++LWNXoGqb1DwDjHLHzxbTdZuWErIwuhwHI4TkiHnl3pUv7JQjcm0BrHDKqRqs0r9e/vqKE5YTudgnYCQQgXODc8sezX60OM2UZg9kjViWkcfpmLjVXJeJkbGs4AWSFSXEihJSiBJiBQmxCAnKuFmNhlWryiJXrWlSG038lazjUnTW2oLrDvVn9jTaM72OcwRhEara6AxTZeqS0Hqlhp5WKwMBAACAyQRCMQAAADPKx8c6Z719WA7p8Qz9G/iPw51r75gXWRuqU8DKprOReEKx939mMyf/WUjwTp3vlGxKaOKW/2ibmHxtd6DKm4ranG3qV068suxQ76GFEpb8jhFMM6ZVPpz18K75ofMtV+r5RYEnHX29Kntfr8Zp6dO6rFbNOVZzaWVJumI/MxIkKTFKpZNRqlysUuVSqDUuhUbrUmp1LpVe71J5ecSWHomny0+mMi63ikTIr0EdFRrapVq99rDuO9+uoIzGSRcgddXZzHWlQz3CPA4hdLxrCJIQTZGq2pgMY2Xq4tA6pZaZdF8HAAAAMB1BKHYFEQShQwiNvHhgMD79G2AAAABXV02PU/fCprq1JzsdWb7jKob01Pe5dRCKjY859n4ie+iPa0hXT5jvuKwO6eMXPLRdmHd/HTTRv3AD3gH2lfJXFhV3Fi8VfFbbIYRQnC6u4f70+3cURhd2Xex9L2o1l9erEfkru5qLZlkPo1C6GJXKxao0ToVG41JqtC6lTu9SG4wujSnIpTUHuwyh4S61wciNPb0RiyLh/OjjRNe/v1goNjQkn/EEJCmxmZmVmq/ddli9bl37lTj98XJ01dvNdaVDWyM9diFsvGsIkhBNEcNB2BIIwgAAAIBAgFDsynoaIfTcyCcOhwMaNQMAwFXmESTyt1sbFn5V0bNSkPBoCMGSCF+bFbb9J+uTSlUMFfD+QpMNVb8tTLH3N2uogfpE33HMaJxC9h27uOU/Po5oJXzfLpBLcFFvVLwxb1vrtnyP5NH4zoWpwjruTr17xy0JtzT5jk/a1VwqtUuhUp9ezWUwONUGk0sbZHbpQkJdhtAwN80qLunPhtjRobK/+94c754982W73XRGHTqdTbVs2RHdd+89xsTFBaQp/tl019uD6g71ZXY3nCsIQ6IpXF0XPRyEqXTMpNviCQAAAMwkEIpdWS8hhN4e/niLTqcbt3cEAACAK+PT411xb+xuvrbf5b9laU4w8nwtibRfvyr1QKBqm6zI3gqdYufPCqmOw7m+y5sxyQhi0roSrvCXB7AmBF7IXyBBFoh3Tr2T/d+m/6508A4jLRFIy9NIxZEoRDLYF9M5NbP6Qxzequq0z9w/nReY1VxqJ6tSu0ZXc+n1LrX+9GouY1iEU6U38FdyNZZ7+/ZI50cfL+ArKrLQOOEeHR/foLluwyHtnXfWEQyDr1ghF6m7wR5UV9qXMRyEhY93DUEi0Riuro9ON1SmLQ2rhSAMAAAAmDwgFLuCMMYOhJADIYQIghAIgpg0P8QBAMB092JRY+5fS9tv9B0LUjP9D+fHbYr2Nq0JVF2TFeHsYRU7n11KN2xfQsji6M8HGBFYis4r4wp/uUsOSZ+RK56xLCOvy8l4HXbW63SwnMvFcm4Xy3vcrODxsLzXwwqclxW9HCvwXlbkeVbgObbX2xvS4+0NIziJXcfrkZIzIlr2C5b0Eqpf0IDqJ6TOkdVcrFLlYnxXc+l0TpXe4BpZzaUPDXPpQ8PcgT6BUXY4aPtf/pLp2V60QOrujjrjApb1KufPL9N961tHFPPmDgSgxHH1NDpMdaV9md0N9gy3TYgY7xqCRJIxXFUXnW6sTF0SWqvWsxCEAQAAAJMQhGIAAACmpTvmRdR8dKzT4xVkFUMSwjWZobt/uj6pVM1SUklJ0/lvMFOIXlJR/Ku5TOXHKwjB7betTzKn1nH5P90uJRT2Baq8iyWJIuGx24bCK6eD5dwulnO7RwMsYTjAEjiOFXmOFXmeFXmelYTh96LASoLASqLIyuLIe5E9/zOPLxTR6HJ+3KJZ1sMoVae3LWo0LqVW51LqdKOruXTBIU5DaLjrSq/mmijciRNGx1//Np87fHgu9npVY+eHGuevnlSN83ubHcbag32Z3fX2zLMGYQSSjOGq+tEgzMBCn0IAAABgkoNQDAAAwLQgypigydMrcqNNKs/X5kRsP9ZmS/z5NcnbMiJ09kDWN+lgGbGH30phjv55Dem2BPtOydrwbj7v0W1C7j1XND0UvF7K47CzXqed5VxOlnMNrb7iPR6W93hYgfOcXn3F8UMhlsCzEs8PBVeCMBRi+YRXV7Kv1kQgSFIcOmVReXo1l1brHDppcXg1lznYpQ8JnRSruSbKaOP8L7+cMo3z+1qcxtoDvRldDY5Mt5WPHO8agkCSIUzVEJ1uqExdElajMUIQBgAAAEwlk/oHRwAAAOB8Gvpcmuc31a2hSEL64O7ZX/rOPbM6oYwkiLJA1TZZ0TVfRir2/e9a0toc5zuOWZ2dn333Dn7ZD08ikh4NGLEsI87tYjwOO8s5HazX5WJ590iA5WYFr3fojfOyou8KLGEkvPINscTRVVgY48AnHxeIpCiRpGmeommeopmhN4bhMUOJ3cSAvoPoC/ZQAi1QGIm0jAQKI8SQQrI+uXZ17OrjcREplqm0mmuiiG3tKvv770+Zxvl9LU5D7cHezO56R4bLyp+5pRMhRBBINoSp6qPSDJVpSyEIAwAAAKYyCMUAAABMSZwok/+7vWH+f0/0FPKSrEAIoa9O9pRdlx3WPnINSRBnv8EUJokiIXJeSuA4SuQ5SuA4WuI5SuR5ShQESuK5ofeCQEkCT0uiSEmCQBHOLp2mbWcWaW+PFmQSCXIc4mUK8ZiV7GykxUmFOKTNvfOkLx5fPHYFVqC/5osxXng18kYzLE+xLE+zCp5RKHhaoeAZhZJnlCqeVQ2/qTW8Qq3hlVrd0JtOz1M07dcXtMnepPlT5Z8Wl/aULhBkwe/7o6JUroLIggPfy/rekRBVyIwMTC6gcX695roNhydD4/z+Vqeh5kBfRneDPdM1eM4grCEqzVCZuiS0RmtSeK92nQAAAACYeBCKAQAAmHK+KO+JeXV307W9Dt7vtLft1f0pvqHYpZAliRA4LyWeDpwokecoaThwEnmOloYDJ1HgKUkQKFkUKUkUKEkQaEkSKVkUKVmUKEkUKFmSKFkSKVmSKEkUaSxJw2MSJcunP8ayRMuSTGFZomRZHh6TKSzLlCxL9OmPZQphfBlpnwIhlDh2kEJICEWoM3ScB1w5BIEpmuYomuFJmh4KrmiGpxmGpxiWp9mRAIvlGVbJ08qhAGsovFIPvak1vFKj4RVaHa/S6XmFWiNcyZVYFZYK49un3l5S1l82V8IS5TunY3S2NdFrSh7IfKBMz+rFK1bEJDWVGuf3t7n0tQd6M7rq7ZmuQT563IsIJBtDfYKwIAjCAAAAgOkGQjEAAABXjCxJhCyJhCQIpCRJpCyKhCxJpCyJhCSKpCxJpCwKpMgPhUvi2BVOgjAUNg2vdLK6BfWeNm9Gm12IjsESisMyorCENJTszTZIzWEtsuqL//3kBlkS6dNhkzwaQGFJpmRZokQZB2NZIur+7/0nZfl0+IRlmZpKW/quJoIkJYpmOIqmeZJhhlZiMaxfgEUPr8A6vfrKdwWWmldoNLxCo+WVWh2v0hl4RqGQpspWwoPdB0Per35/WeVAZTZG/qGkSWHq3xC3Ye+9afdWKGnltOgBdjG4EyeMjg/+uoA7cmTOZG6cb2l36WsO9GZ019kznecIwgyhysao1KEeYTqzwnOVywQAAADAVQShGAAAnIMsSYQkCEMhjk+oI4kCORT4DAU8WJLI4ZCHwLI8NCdLpCyKpCzLBJakofeyNBQEjVw3OiaTWPYdk0lZlkg8/PHQe3x6DOPROVmWSYyHr8N45PrTH2NMYDw8JmMC4ZGx4XtgmcQyJjHGBMJ4+F6YxMPXDY2N3otEI48dvu/ovP8ceXmrmc5u1vDbGEppAKV1Xvzt9JdZTsAQJCkRJCmRJCkRJCWSI59TlEQSGCslm1op2bQUIRMUISOKwIgkZIQYjYszJLZiXdQArfAPsFjl8AostZpXqIdXX2l1vFKn42lWMePCHoQQ2t62PerD2g+X1dnq0sbOhanCOm+Kv2nvXSl31dAkHdAtgFcbFkXC+X8fJbm++mrBuRrna2//2iHV2rUdgQo/Bzpcupr9vRld9Y5M5wAXM+5FBJINIcqmyDRDZdqSsGoIwgAAAICZA0IxcMGwLCPB7SIRQmigo02LMUYjL3oxxmj4xTPCWB4dG36BfJbrht4Pjw9dh0beI4QwIvA41yGMEUY+9xia8LkHOufzDz0HOj03dAPi9PVonLHRwXOPodPPNfQhJhDCCGN0us7R2ofHkN/9fL5+PHSMns/3CyNMIHnoeTAe/Zjw+XjkXsTw98q3tqHPx1w3/H0ertPvcSPfMwL5jPmNo9PX+3y9p+8x8nUMPWZkfPS608/t8zynrxuuE/ncE/l+j898HoSG7zFcDxp9zMi90ch9hv8cnHHtyHNdyVAHTH7+gRMpESQ19DHlEz6RlERSlERQQ3MkRYskdXqMoiiJpOihkIqmJYoenqdpiaJoiWIYiaQZiaJpiWIYkWIYiaIZiWIYiWYVEs0yEsWwEs2yEqNQijSrkGiFQmIUComiGXm8kIGwNqsUu3+xhG7alUfIAuM7J+ujO/j5D+4Ucu9pRMTUWJ0VKDKW0edNn8d/XP/x8lZna/zY+RhtTNPtibfvvTnh5iZyhn0vL6hx/vJlh3X33lsWqMb5A51uXc3+3vSuOnumc4CLHfciAmFDiLIxMtVQmbYktFoXrIQgDAAAAJiBIBQDF4z3eujaj/4ShhBCtQg9Heh6AABTBEFggiBkgiBkn48xQZAyIgiZIAmZJKnRoImgSIkkKXEkbCIoSqq00zE2idJIiEQMTXFpJqopWk9bSIqSKJqR/MImmhaHQihGIhlaohl2JHgaCpkYVmzs7LqFIEmclZH+z6EAaihwYpVK8WyB02RG2DuUit2/WEQ3bl9MSLxf03dZG9HFz/3uLmH+A3UQhp2bKIvEP2r/kfp50+fLejw9Z/TESjIk1dyVfNfedbHrOgJRXyBN5sb5zkFO2XpyMLqn0RE72OWe5Rzgz7YiDOtDlE1RKYbK1CWh1foQZcBPuwQAAABAYEEoBgAA5+Eb5pwOdUiZIAiMRgMeQkYkOfrx6JzfGHnmGEmOvD8dFJFD78nhsdH7kiNjJCZIYvg9KQ+/Yb85ipLJ0/NDj6Wo09eRJCZISiYpSiZHP/YZo6ih6ygKkyQlkzQlkySFR+YIipIpmhm6jqJkkqIxSVEyxQyNUTQ9MjYhL44jGweDH/244r7C1OD9P78meb9OSV9WE/NOb4mIEEKh8Um2iagvUAhnD6vY/YtFdP2WxYTEKX3nZE1YjzDn27v5hY9UQxh2bpzEke9XvZ+1sWXjsgFuIMR3jkAEzgjKOPnt1G+XLI1Y2huoGgNBtttp+wcfZHq2bV8o9fREjp0nFAqvYv78Mt3dd1+1xvlYxqi/zWVoO2WN7W91xtp6vLEeh3CuAyKwPkTZFJmiP5W6JLTKEKqCIAwAAAAAoyAUAxeMJClMKYYaCBMIewhEIEQgnxe8BB4aIk6PEQgjRCBieBPc6HUIDa0eGRoY+RwhNNy9mPC9x9muG/P8o9cRPs/lex0xWhMxvMPSt6ah256ubehS5PvYoTFi5LHDX5dPTcPzZ9bkO+b7XKMljXw8/tjIPYeDFjz6fuQ5fMcRQgRBDo+h4TECDYUqBB6939D1wzUOzw19jBBJDO1BJEg89PHQ44c/Hnn88D3JoT2pJDn0PEOBDiaIMY/3uW44EBoeG3ru4ZpHH4+G7034PM9waDT03EPhzvDXOPLx0Pjo9SOPJ6nRsZHrhoMgTPg+DzX0+ZUIdcCF2VzZG/m30o7F79yV/blWQUsj40sSTP2bH1n4cqhOwQWyvsmCcFsYRfEvFtK1m5YSosevsbmsDu4XZn9rF7/osSo0w/pcXSyn4KTfqnwrd3vb9qUOwWH0naMISsoNzi27P+P+khxzjjUwFQbGaOP8w4fnYI4LaON8SZSJjmpbWGeNLdbS7o6193tjBK90vl6AWB+saI5MHVoRZghVua5kjQAAAACYuiAUAxeMUSqltG/c14MQQkuXLn070PUAAKaP1kGP6vmNtasOt9jmIYTQr7fU9/z2xrR9vtdAIIYQ8lppRfGv5jM1XywjBLfGd0pWBQ0I2d/YzS95qgJRLIRh59Dn6VO8VfnW/N2duxd7RI/f95EhGX5h6MLD38v83sEEQ4IzUDVebaON87/8coHY2Biwxvlep8C0nByM7m5wxFq73DEOCxcjS5g954MIhNV6ptsYpmoNmaVtnTU7qAWCMAAAAABcCAjFAAAABIwoY+IPOxpz/13WtcYryKMrUoqq+5f8YDVfGqRhr+gqlCmDc1CKPb+ex1R/vpzgnVrfKaw0WoWsrxdzS39QjmjljDwh8kK1OdvUb1a8mXeg+8BCXub9tpsqKaVnWcSyg9/L/N6hCE2EN1A1Xm1iW7vK/t57czx79y7Adrtx7PyVbpw/2O3Wtp60xvS1OGOtPZ5Yt42P8F3HPR6SInitWdFuilC3hifoWmOzTR0qHcNPdG0AAAAAmP4gFAMAABAQ26v6In6/o3FDl43za2ieFKKue3Z98mYIxBBCvItS7PvfXObUJ/kEZ/fbMoYVeruQcdsebvmPyxCjgjDsHKoGq/RvV7695Gjf0bkiFv1O5dTQGseq6FUlD2Y+eMykMM2YP3PubdsinR//+9yN86+/7rD2jjsmrHE+ljHqqrMHt1fbYi3trlh7rzeWc4tnnGA5FqMgHfoQZWtQlKY1MkXfFp1u7KEYEv7MAwAAAOCyQSgGAADgquqwepXPbawtLG22LvAd1ytp272LYzZ/Z3F0DUmcc6HI9Cd6ScW+3+UwFR8VEJzN6DuFWa1TSL95D7f8J8eQQied5Q4AIXSk94j5var3lp60nJwtI9lvv5+BNQxcE3vNvvsy7itX0+oZ8X2U7Xba/pe/ZHm2Fy24Go3zea9EtVUMRnbV22MHOtyxDgsXI/msCD0blY7pNYQqW4NjNW0xmabW4BiNlSBn+N8JAAAAALgiIBQDAABw1byysynnn0c61nkEWT0yRhJIKkwNLnnumuR9RjUzY1bqjEviCXb/S9lM+T8LSO9gkO8UZtQuIe3GfVz+T48gpfGyTt+c7nZ37A7/a81fl9dYazLGzgUrg7tvmHXDvnvS7jnFkBOzAmqy444fNzn++rf5V7pxvsPCqVpODsT0NTtjBrs8sS4rF4VlRJ3rMQSJJI1J0WEKV7WGxuta47JNbdogxYzZvgoAAACAwIJQDAAAwFXTZfcafAOxxGB1/U/WJW1eOMt42atSpjRZJNiDr2YwJ/6+gnT3B/tOYVrlEVM2lHAFPzuE1eaZHRqex5fNX8b+q+5fy5sdzUlj56I0Ua23Jd6292uJX6sniSvTJH4yudKN87GMUW+L09R+yhrT3+aKtfV6Yr0OMeR8j6MY0qMPVrQGRapbw5P1rTGZpi5WSc2IlXoAAAAAmHwgFAMAAHDV/Oya5P37GwdzJRlT31kcs+W7S2KqZ/RWSSwjtvSNNOb4BytJV2+o3xSl8IrJ1+znCn5WirVh0ET8LGQso4/qP0r+tOHT5Z3uzpix8/H6+Lo7k+7cd92s61oDUd/VdqUa50uCTLadsoZ11dljLR2uWEefN1bgZO35HqdQ0wP6UGVrcIymNSrN0BaRpO+HrZAAAAAAmCwgFAMAADDhRBkTr+5qylmaYGpfFG+yjIxrFbT0vzel/ystTGOb0Y30sYyYI39OYY+9t5J0dof7TVEsLyasOcCt+PlBrI+CbWRnIcgC8UH1BxlfNn+5vN/bHzZ2Pt2UXvmt1G/tK4gs6A5EfVebe9u2SOdHHy/kKyuzkCSdsWXxYhvnu+0823pyMLqn0RE72OWJdQ5w0bKEmXM+iECyxsB2GcJUbaGztK2x2aZWY5jKdRlfFgAAAADAFQWhGAAAgAm1s6Y/7PdFjRvard6Y4jpL4+cPzv+772qwJQmm/gCWF1hYRkzZB4ns0bdXkvZ2v1M3MckIYnxhKbfiuf3YGOsJVImTnUtwUe+cemf21ratS228za/vGolIOSc45/h3079bMi9k3rTfkjuRjfMHOt261orB2P4WZ4y1xxPrtgnhCKFzLukiKYLXmRVtpkh1a1iCrjUu29Sh1M7wvoAAAAAAmFIgFAMAADAhuu2c4rmNtSsPNA4uxMMvppssnoR3S9rSHlgWWx3o+gKNOfH3WezhP60kba2xvuOYpEVpVsEhb8HPS3BQ4gVvZ5tpBrlB5k8Vf5q3q2PXEpfo0vnO0QQtzA+df/SBjAcOpJnS7IGq8Wq5oMb5a1Yf0n3725XjNc6XJUx01tmCO6ttsf1trlh7nzeW90jG8z0vo6Ts+hBlqzla3RqZYmiNSjP0UjQ5Iw4rAAAAAMD0BKEYAACAyyJjjF7b1Zzzr6Oda928pBkZJwkk5ycFHbg1N7wxkPUFGl3xcYzi0OuF5GDTLN9xTFCSFLvsCFfw7D45JN0ZoPImvQ5Xh+qtircWlnSX5Hklr18AxJKsd2nE0tKHMh86FKONmdaBIhYEwvnRx2dvnE9REpuZWaH92m2HxzbO5z0i3XJyMLKnwRE70OmOdVi8MZKAled7TpWe6TGEqlpDYjVtMZnG1uBYrW1ivyoAAAAAgMCCUAwAAMAl211nCf3dtoYNbVav3+qnuCBV04/XJW5amhA0Y7dK0lWfR7EHX11JDdQl+o5jgpSlmMXHuPxn98ph2dN+VdOlqrfVa9+qfGvx4d7D8wVZYH3n1LTaWRBZcOB7Wd87EqwMntaHEFxw4/zvfreMiY11I4SQvc+rbq0YjOltcsRauz2xLisfgTE6o8+Y331IQtSa2A5ThKo1NF7XGpttateaFNDTDgAAAADTGoRiAAAALlqvg1M8t7F2RUnDYB726TukYSnH3XlRW7+3PK5ypp4qSddtCmf3/2El1V+d4juOEYGlqIXHufyf7JEj51kDVN6kd6L/hOmdU+8sPW45nitj2S/I0TN665qYNSUPZj54XMtoxUDVeDVcaON8ze1fr+tt95pOlNtSLBurY2y9nlivUww+3/1plnTrgpWtQZHq1ohkfWtMprGbUVDSlflqAAAAAAAmJwjFAAAAXLSDTdbQfQ2Di0Y+JwkkL08MOvjchpTiEC07rVfunA3VsD1UUfLiSqqvMs13HCMCy5Fzy7nlPymWovMGA1XfZLeva1/oB9UfLKsarMrCCPslqkGKoL7r4q7b+53071QqKIUcqBqvtAtpnM/MX3jcUfiN5lZvkGmgw51r/8XJG0Re1ox3P18KDW0xhCpbg2M0rdHpxtawBN0AQc7M4BoAAAAAYASEYgAAAC7aDTlhbZ+UdR0va7fnxppUzT9el7hpWWJQX6DrCgSquThYse9/V1A95Zm+4xghJIfnVnBLf1gszcqfsdtIz2dL65bof9T+Y3mDvSFl7Fy4Orzj5vib996ZfGctTdLTtqH7UOP8v87nDh85o3G+SCmRPW7+wGDayr4BZYzSZRPnyXvlRQid/Y8UQSBZbWQ7jeGq1tA4bVtstqnNEKpyXfEvBAAAAABgioFQDAAAwDn1OXn2cIs15NrM0A7f8ec3pBRtrOhteKQgrmImbpWk2g4EKfb+toDsKssmxqxskkKzqrilP9gtJazqDVR9k5mMZfRp46cJ/67/9/J2V/ussfNx2rjG25Nu33tj/I3NJEGOc4epb6hx/kdJri+/8muc71UYkc2QiKzGJGwNy/a6KKMSISIIuVEQcp9xkCRCCCGKJjitWdEWFKluDU/Ut8ZmmzoUanpaby8FAAAAAJgIEIoBAAAYl4wx+tPelswPD3Wsk2RM5UTq3og2qTwj8wnBatejK2ZVBLLGQCA7DhsVe39TQHUcmX1GGBacXsMveXK3mHxtd6Dqm8xEWST+Xvv3tP82/Xd5r6c3Yux8siG5+u6Uu/eujlndGYj6rgaxrV1lf/fduZ59++bLdofRpYlA1sjlQ0GYIRFxyqCRSwmEkGq8e7AqyqYPUbaaozWtkamG1qhUQx9JEdN2JR0AAAAAwJUCoRgAAIAz7G8cDP7t1vprmwc88SNjz2+qK3z3rpyNgawrkMjuE3rFnl/nU+0H5xBY9lu+JJmT6/lFT+wS026ctmHO5fCKXvLdqnezN7duXjbIDfo1gScQgbOCssq/k/adkkXhi6btFlz31q1R1o8/W2hpd2XatLMoa9w3kd0Qj0Rafb6HYpWe6TGGqVpD4rStMZnGNnO0Bk4tBQAAAACYABCKAQAAGGVx8uzzm2rz99QPLJYxGg1+1Czlyo7UtQaytkAh+6q0iuJfLKda988jsP8pgLIpoYnLe3SXmPm1tkDVN5nZeTv9VuVbc4vai5Y4BafBd44iKHFO8JyyBzIe2J9lzrIGqMQrRuAkqvdkZ2jf1pLcwUZLho0O0TqM30Y46IyDJP2QFCFoTWy7MULdFpaga43LNrWrDSx3lcoGAAAAAJhRIBQDAACAZIzRn/e1ZvyttH2dk5P0I+MEQnhJgunQ8xtSdoXrFTPqhTlpqdMoin+5lGrZs4CQRb9/L2VDXCu/8OGdQs5dLYGqbzLrdfcq3qx8c8Hezr2LPJLH72REhmS4RWGLDj+U+dDBeH38lG7+Lgky2dfqNA50uINsPR6zc5A3uy2uEK/VE8pJtBoRJEIoEaGgxLPeg2ZJlz5E2RoUpW6NSNa3xmQYu2mWmrYnbAIAAAAATCYQigEAwAxX2jxo/vWW+muaLB6/V+7RRmXbD1YnbCxMDe4JVG2BQFibVcrdLyylmnYvJGSB8Z2T9dHt/PyHdgm532pE07QB/OVodjRr/lTxp0UHew4uEGRB4TunpJTu/Mj8gw9lPnQ4XB3uDVSNF0sSZWKgw22wtLmCrL0es9PCmd02IcjjFMycWzQinxWVp7FDHcHGoVTIVkOUoTE4RtManWFsDZ2lHSTImXdQBQAAAADAZAChGAAAzGCcKJNPfnLqbgcnjW5tUzGk++vzIrc9vjK+nCZnTvNuwt6hVOx+fjHdULSIkAXWd07WRXTxc+/bJcy7vw7CsDNVDlQa3j719pJjfcfmSljy+9lCy2jtq6JXlTyU8VCZQWEY//jEAJMlTAx2uXX9ba4gW4/H7LBwZreND/I4BDPnEk0Yo3PveRwPlpGCsyKVZOd1erIzeHHGsbgl8Q36EKX7CnwJAAAAAADgEkAoBgAAM5iCJuWvzY3Y+f6B9psJhPCieOPhFzak7IowKKfMSp7LRTi7FYrdv8ij67cuISTOb3WTrAnrEeZ8Zxe/8OEaCMPOVNpTGvx+1fvLKgYqsjHCft8gI2u0XBt37b570+89qabVUqBqHIFljKy9Hm1/qyvI2uMxOy1ckGuQN3scgtnrEoOwjC/pZyKWsyK1pxep3H1I7elFak8v0miJbmPmrErDXddWKuasG5zorwUAAAAAAEwMCMUAAGAGaehzaRJDNH59nB5fGV9e1e2Mun1uZNnqtODuQNV2tVGSh1BuemwZXbdpCSF6Vb5zsjq4T8i9Zzef92gVIukZs1ruQu1o3xH595q/L6u11aaPnQtRhXTdNOumvd9M/WY1QzJX9XuHZYwcFk7d1+oMsnZ7zI5+Lshl5YaCL6cYJEuYPf9dzsRgTlC5ukm1o5NSe/qGQ7BepPb0IUrmESIITMfGNinyFlRpbv52DZuS4pjorw0AAAAAAEw8CMUAAGAGsLoF5oVNdct21fYvffaa5L/fNiditEE8SRDo7W/kbA5kfVeV10rHd3yuie/6SsuIjnDfKVkVNCDk3LWbX/xkBaJYCMN8yFhGXzR/Meuj+o+WtThazugcH62Jbrkt8ba9tyXe1kBe4VV1zkFO2dfiDLJ2ecz2fm+Qy8qbPXbB7HUKQZKIlZdyT4ohPUotPaDS0gNaT7es6TipU9cfCVcNtKppycuc+QBKZBITGhSL8qq0t95aS0dFeS77CwMAAAAAAFcVhGIAADCNyRij9/e3pf7lYPs1dq9oQAihN4qbN2zICn1LxcywE+44O63Y8+u5TNXny1MFl9Z3CiuNViHrjt3c0mdOIlo5s74v5yFjGf2z7p8pnzV+trzL3RU9dj5Rn1h7Z/Kd+66Nu7ZtIp/X4xDY3mZH0GCXx2zvGw6+bLzZ6xSDREFWX8o9SZrglBp6QKVnLRojO6AzKyymCLUlOBjZ0M4vo7x796ULNTWpmOPGD9YYhmdSUmpVS5dWaW65uZ4ym/nL+iIBAAAAAEBAQSgGAADT1NFWm+lXW+quqe9zJ/uOszTJtw54NKlh2pmxxYt3UYq9v53DnPp0OcE79L5TWKG3Cxm3FXPLf3wcMSoIw3xwEkf+pfovmRtbNi6zeC2hvnMEInC6Kb3ynrR79i2PWH7Jp5NybpHubXYGDXa6zfY+b5BzkDN77EMnO4qcrD3/Hc5EUoSg0NADKh0zGnwZw9WWkDjNgD5Y6Ro56VHs6VG4Pv00xfuvA0vtDQ1JSBTPXA2GECKUSjeTnl6tKiio0tx0YxOp0QS8PxoAAAAAAJgYEIoBAMA0Y/MI9Aub6pbtrOlfJvmcmqdkSM9tuRFFT69OKJsRp0oKHlJR8rvZTMXH+QRnM/pOcYxRboy8wRF9/Y9fRwodhBw+nIKTfrvy7dnb2rcttfN2k+8cSZDSbPPs4/el37d/TsicgQu5H++VqP4Wp2mg02229Q4HXzY+yOMUzYJX0p//DmciSCSNBl8GdkBrVlqM4SpLcIxmwBSucowEX2MJTc0a138+TfUeOpwuNjcnIFked58nodXa2aysKlVhYZXmug2tBHN1e6MBAAAAAICrA0IxAACYRv5yoC3lvf1t19i8otF3fEGc4ejzG1J2xJpU07/vkcQTbMmL2ezJf60gvIN+oQ5mNC4h7ca9xcr1s2VKgaIhEBvV7+1n36p4a/7uzt2L3aLbb5UWTdDCwrCFRx7MePBAsjH5jBWGIi+R/a0uo6XDbbb3eoKcg7zZbeODPA7BzHskA0Jo/JTqHAgCyayaHlTpGIvaMLTiyxCqHAiO0ViCojR2krqwYJevrDS4Pv88zXvkaLrU0RGLMB63FtJksrA5OVXqdWurVKtWdRIknDYKAAAAADDdQSgGAADTxP/7vGr5psq+Qt+xcL2i86nC+I3XZIZ2Bqquq0YWCfbAKxlM+d9XkG5LsO8UplUeMWXDPq7gZ4ex2izIJSWzA1XmZNPubFe9VflWXklXSR4n+/fSUpAK79KIpQcfynzoUIQy0mtpdxmqKnoSbb2eIKeFM7ttQ1sdObdoRBhdfIpEIKxQ0ValjraoDeyA1qSwGMOUlqBozUBwtMZGMeQlbWn1lpYGu778Kp0vK0uXensjznYdFRraxc7JrdJsuK5KuXhR/6U8FwAAAAAAmLogFAMAgGni9rmRFVtP9RVIGFFKmvTckhu+45nVCccYipzeW7+wjNjS19OZ4x+sIF19fr2vMKXwisnX7OcKflaKtWHQFN1HrbVW91blW0uO9B6ZJ+LhflqYQBpej8L5OM8CIr8phZ/tEBpxxP4tffdyri4T9tmOezFYFWVTahmL2sAMaE0Kiz5UOWCO1liCYzRWRkFd9mo9LMvIs3NnhHvr1nS+/GS6PDAQfLZrqaioVsX8+VWaG26oVuRkWy/3uQEAAAAAwNQFoRgAAExRoowJ395g82INg6vSgvcNuATd8xtSdsQFqdyBrO+KwzJiD7+VwpS9v5J0dof7TVEsJyauPcit+PkBrIvkAlXiZHSs91jQ38r+VdDe1ZOl9wST87zXIIM3ZPSNlhmEEFIhhDJ60YX/EWKUlEOppS1qPTugNbEWfYhyIChKYwmJ0wyyKlqc6K8DCwLh3rgp1r1zZxpfUZGOHQ7DuBeSpEzHxTUqFy6o1txySzWTkOCa6FoAAAAAAMDUBKEYAABMMSfa7cZfbqlbNytI1f3iLRnFvnO/vzl9N0lcdPumqQXLiCl7P5E98s5K0tER5TdFMoKYsOogV/DzA9gYO/37pw0TOIly9HvVzkFe47byao9D0HicgoZziWreI2k4j6i2emxmD+c1UBzLzpVvR3Mv4XlolnQptYxFrWcGNCPBV6TaEhKnHVBqGWHCv7AxZLebcn3+33hPcXG6UFWVhj0e9fiF0iKTmFinXLy4SnPrLXV0eLj3StcGAAAAAACmHgjFxiAIIh8h9AxCaB5CKBIh9B2M8QcBLQoAABBCTk6kfrm5bsm2qv58UcZ0fa8r+VibrXxujGFw5JrpHogxx/8Wzx55ayVpa43xHcckLUqzCg55C35egoMSp/wKOd4rUY5+r8Y5wGnctqGQy+sU1Zxb1HBuUSN4JbXglTQCJ6tFXtJIIlac/64sUiH2vFdRDOkZWvHFDGiMCosuWDEafKkN7FVfdScNDjKuT/+T5N23L52vq0tBPD/+18qyXjY1tVa5fFmV5uabGyij8YqHdAAAAAAAYGqDUOxMWoRQBULob8NvAAAQcB8e6kj8876Wa60eMWhkTMKI+qqiN3lujOFQIGu7GuiKj2IVpW+sJK1Ns3zHMUFJUtyyI1zBz/bJwWnOAJV3XpxbpB39Xo1zkFe7bbzGN+TiPUOruQRO0gicpBY5WSNL+Pzp1WUQKU5mNdSAyaDv1g4HX6YItSUkTmvRBikCvqpK7OxUOj/5NJUrPZgmNDQmIUka9+cVQqVysZmZVcqCgmrNDdc3k2o1nCYKAAAAAAAuGIRiY2CMNyGENiGEEEEQHwS2GgDATHey02745eb6dVXdznTf8VAd2/3EyviN12eHtQeqtquBrvosij34aiE1UJ/gO44JUpZiFh/j8p/dK4dl2692XV6nwDgsnMY5yKndNkHjdQhqr1PQcG5Rw3mGV3F5JbXASRqRlzWyhJkrWhCBMMkgj4dxoEGqT+FkrJSHcSIv7UQexom8jBPJrOBNDI6vXp+85uiSpKXtBDm5VhUKdfVa52efpXGHD6WLra2zkIzHPc2S0OmsiuzsKtXq1VXq9evaCYaZ3gdJAAAAAACAK2bKhWIEQdyGECpACOUihGYjhHQIoX9gjL95jsdEI4R+gRBajxAyI4S6EEKfI4RewBgPnu1xAAAQKG5eon65uW7xllN9+aJ8OlBhKZK7aXbYjh+tTTwynU+VpGu+imAPvryS6q9J9h3HiMBSdF4Zl/+TPXLEXNtEPBeWMfK6RNbe79W4Bnm1284PhVwuUcO5RA3vkdS8VxzaqjgUcqmvQsglMyzpphWUi1FQbkZJuVgV5VaoaJdSS7uUWsat0jMuQityRY6NMTsGt2V1uNvjxrtVnC6uYXX06mN3Jt9Zo6Yn10oq7vhxk+uLL9K4o8fSpc7OmLNdR5qD+hSzZ1ep119TpSzI7ybIcfMyAAAAAAAALsqUC8UQQs+ioTDMiRBqRwilnetigiASEUL7EUKhCKH/IoSqEUILEUKPI4TWEwSxFGNsuaIVAwDARShrsxkf/+TUNwfdgtl3PDdaf+L5a5O3J4Zopu3peVT9tjDF/hdXUH2n/P5ux4jAcuS8cm75j4ul6Lxz/jIDyxh5HILCYeHULiuncdsEjcchqDmXqOHcopr3iBqniw6SeET+a8uxJ0VeUmP5yv57SBBIplnSRY8EXENvblZNu5Qa2q3SMS6VnnVpTKxbF6RwaYys91wrufZ07gl7r/HTuSdqT+TwMq8cO6+hNY75ofPL7ky+syzHnGO9kl/bxcCyjLz794e6N21K546fSJP7+sLPdi0VHt6hmDunSn3dddXKBQvg32kAAAAAADDhpmIo9iQaCsPq0dCKsV3nuf5NNBSIPYYxfn1kkCCIPwzf69cIoYeuTKkAAHDx0sK1dpJA8sjnIVq259EVszbdPDu8NZB1XUlU064QRcnvV1A95Rm+4xghJIXlVgzO+X/7rYrZnKub17pru0I9DkHDuQQN55bUvGeo8TzvHdqqKHKSGmNEnfsZR1YaSZe04osgkUSzlItRkG5GQbmGVnLRboWacik1jFupY1xqPePWmFiXzqx0qfUMd7nbFfu9/ezfa/6evadzz9weT0/kGTUhAicaEmvXxaw7dlvibfUKSiGPd5+rDYsi4SkqinRv357Ol59Ml63WoHEvJAhMx0S3KObNr9LcdGM1m5Fx1bfFAgAAAACAmWXKhWIY49EQjDjPKWsEQSQghNYihJoRQn8cM/0cQugBhNDdBEE8jTGetisvAABTi4qh5EfyZ236n231d9yQE7brR2uTDitoclIEHJcKyxgJnER7nSLLuUWG84is4JFY3FtrZuu+nCsNdsV55RTkkRcgj6xHHlmPnESU142DJaGHyMAnUBZCNVesPoJEIsNSLlpBjqzkcrMq2qVQ0y6FlnartIxLbWDcGqPCpQtWuFRahr8aPblkLKNtbdui/9v037mVA5VZIhbPCPH0rH5wUdiiY3cl33Ui2ZjsuOJFXQDMcaTrq42xnp070/nKynTscunGvZAkJTo+vlGZl1elueXmGiYubsqfHAoAAAAAAKYOAuOp25KGIIgVaGil2Lg9xQiCuA8h9A5C6G2M8YPjzG9FQ6HZaozxjnHmnQih72OMP7iImo6eZSotPj6eefPNN/sv9F6TEc/zwQghxLLslP46AJgsKgcwe7gHq+5JI2xjg363gAk1Q1zVv6SxjBCWECGLI28EgUVEyBIifT4eei8SJJaGPpdFRGCJGH6PCDxy/egcIhC6eo3dCQpjkkUyxWKZYpFMKYbekyyWKAWSES1oSQVGSjU9QCmQTNAIn+f3LFeVU3YSRzxH1Ic9h9W9Uu8Zv8CiEIUyFBmePFWeO5lN5kki8D22CEFAylNVCnV5uUpVVaWg3O5xi5JZFntTUzl3TrbHnZXFYZVq6v4gAkAAwc9kAIDpAP4uAxPh4YcfDm5qajqJMZ53sY+dcivFLlLq8Pvas8zXoaFQLAUhtAMhhAiC0CKEkobnSYRQLEEQuQihAYzxtN26BAC4uga8mPx3vawv60cqhBBKMSJ+SQTh8b3mbIEYxsPh1VBw5RdWySIisEiMBFfk0JhvmEUQsoRI7BtkDYdcWEIklidRMuSDoDAeDrVkSjEcdimQRI18rvB/T9LonEELz0sahBBiWDRpGs/LWEZ1fB1b6ilVn+JOqaRxSguhQsQFqgXuBaoFbi2pDXiYRHg8hLqiQqE+eVKlrK5RkDw/7h8gSaXCnowMrycn2+PJyOAwc2XPKQAAAAAAAOBCTPdQzDD8/mwnlI2MG33G5iP/PmUvDL/9FSH07fM94dmSSYIgjnIcF7F06dK3z3ePyaykpOQBhBCa6l8HAFeDLGGCc4uM1yWwvFtiOLfIulyC4ovK3pyjbbbZhEzRszFCDCZQZyXFeu2mU5KAGUmQWUmUWUmUGUnArCzJrCRiVhZlRpYwK0mYRfgqLruaICQSEEN4/d9ILyIYhQsbYjpIU0SvQk27FRrapdIxbrWBdWmDFC6dWeFWqGlxImuZTH+X1dvqtR/Wfph7sOfgXDtvN42dpwlayAzKrLgh/oZj62LWtQd6VZjQ2qp2/ec/Kd6Dpelic3MikqRx+7cRGo2DzcysVq1cWaW5/roWQjE5epwBMF1Mpr/HAADgUsHfZWAicBz3wKU+drqHYucz8qJy9LftGOPd6Gru8QEAXHWyhAlRkEmRlyiJlylRlIfeCzIlCZgSOInhvRIjeEVW4GRW5CRG4GRWEmRW5CVWHAmuhJHgSmYlCbOyiBlJlFlZwqwkYhbLeNy/YyMQQtchduywuqPKtuSKf/EXgKQIgaQInqQJnqJJnqIIgaRJnmIInqJJgWJInh5+o1hSoFmSZ1iKpxWkwCgonlFSPKukBI27Wm2o+yBL3VuSyWIHQxKnMxFM0qIUueAEn/f9g9Ksghm3XJ6TOPLThk8Tt7Rtmddga0jBCJ/x706oKrSzILLg2DdTvlkRogrhAlHnCL6qWu/6/PM07sjhdLGtPQ7hM+tFCCHSYBhgc3Kq1GvXVKlWr+4kaDrgq9kAAAAAAAA4m+keio2sBDOcZV4/5joAwCWSJUyIvESJvExJgkyKgjzy8VDoNBw4SaJMyeLQe0nElCzKlCQNvZcl7PcmSZjCks+4PPQey5iSJfT/27vz+LjO8u7/133OnNm0jEa7ZMmWJUve9yVespA9tlMIe6HQAu3z9FdooU/TPkBbmqQblKehhRYotNACgVIolCV2nIXs8RbvjjfJsiVb+y6NNKPZzv37Y0aOLMuxbEsaLZ/366XX0Zz1OrF1onxz39cxbTv5fWJpJL4XU1+2/s3PWieOES2pb8B0k5QSOxFcGRHDVFEzGWAZDhU1LSNiOpKhlWVEHZYRcTiNiMOZCK0cLjPidJsRy21ELbcj4vKaEZfXEXWlWRGXx4zeVAN5bYt1/Adl1uH/eJvRcbrq0pmS32grbSBWfs/+8OY/OqCzK2ZdU/U3Ot/I+kHND1a/3vb66oHYlc3nnYYzvCJnxbF3V7z70B3Fd7SkosYhgwcOZAd/+cvF4UOHF8dbWuZcbT8jL7fVtWrVKe/WrafcW7a0KWPa/3gBAABglpjpodjQq8qqrrK9Mrm8Ws8xICW0rcW2tbLj2rDjWsWTAVNiJNOby3jUToRL0auGTIYd14n1cW3aIwImPVrYZGtTx8W0dWL78HDJHhYuvRk8iam1NqfjdL7JYBsSH9TajCgtURGJKpGY0jo3zdlRkulqtSwzcmnkVXLUlcNpRJLhVTQ56iri9JhRl9cRcXkdEVeaI2q5zCnTC0tERCIDpnPvl5dap366yehvKRy52U4raI0tfmhveOMfHhdXxtSqfYIFY0HzP2v+c+FzDc+tqQ/UV4y2z5y0OfV3zrnz0IeqPnQy05k5rlNFx0rbtgy+9HJhcNeuReGjRxfbnZ35V9vXLC6+6Fqz5nTa23/tlGv16u7JrBMAAAAYLzM9FBvqDXafUsrQWl+au6OUyhCRLSISEpG9qShuJrHjWtlx+1KIc9lyWLij7eR6Wxs6uby03h7aLiM+a0NrrbQtl9Zpe9g+Wgw9tF5Lcvuw9Vou7Se2VloP22/4csR6ufRZjMS2RA0yfF+tk5/F0ForSS61vny/y9cP2y6Xb08sRQlTeCecUmIrQ8WVIXGlVFwZKm4kPxumihkOI5IcffXmyCvnmyOvLNebI6+SUwajTo8ZcXocEVeaIzHyyuuI/q//PLZtf33vuqHrLi1KP/HItqqnFxemB1J5/+NF9dR5XK/9/TrHuec2qEh/+sjt8dyFNdGVv7knuvLD52UKvCFxMu1v3Z/7o9ofrTnUfmjlYHzQO3K723QH1+atPfK+Be87tD5/fWcqatSxmAo+/XRJ6NnnFoePH1+s+/qyRt1RKe2YO/e8a/36U2nvfOiMs6pqRvz9BQAAwOw2o0MxrXWtUuoZSbxh8hMi8k/DNj8mImki8g2t9UAq6ptuBvuj1rknrULRIud+ceDPRGulJRHupLo2TA1KSTwRNCXDJfXm98nQ6VIQZST3M8xkGJVcGuaw9cO/DBU3HCpumEbcNId971Bx06HihsOImw4jbloqbjqMuMMy4qZlxE2Hsk2nEXdYZtzhNBJflhG/qSmC1+Gx7VXPv+tfDy7xWGbw926bt/PX1xWfn5QLTzCz/pUc575/2mg27l+l7Nhl/y7RhiMWn7PhSGTD7++Ll90+q/qF9YZ7rSeqn1jyQtMLa5oGmuaOtk9ZRlntvSX3Hnx/5furvQ7vpI+aswcHjeDPf1EWeunFxZETJxfpYPCKMFNEREwzZpWX17o2bjyV/u53VTvmzAmNuh8AAAAwTU27UEwp9ZCIPJT8ODRFZ5NS6j+S33dorf942CEfF5HdIvIVpdTdInJKRG4RkTslMW3yzya45BlDGUrrmBrqDDTt/u5MS0pspUS/OZJpROg09L1KbhsKmEaGS8bl4ZIyVdw0VVyZyjZMFTdNIxkyJQIlw5FYmkNLK/n9paDpzXBpKGgyHYY9WUHTVBSO2cYXn61d++ENc06U5Xgv9coq8XtCf/fQ4u/eUpbV4XVOsSmP10vbYh19Yr519DubzI4zlVdsttL6YxX37g9v/qOD2l8+q/qFvdj4YuFPz/10zbHOYysidsQ1cnuaI61vQ8GGwx+s/ODhpdlLJ7WPZby72wq9+FJx5MiRkujZsyWx+voyHQ67R93Z6Qw7Kyur3Vu2nE571zvPmjk5kcmsFQAAAJhM0zHYWCUivzViXXnyS0SkXkQuhWLJ0WLrROQvReQBEdkmIs0i8hUReUxr3TVRhSanaA41Urb0Vd7WNV0Ypnrrt4i9GeDYIqKVEluU0iq5XpRKLsVWyfWS3H/49suPu3I/pZQtxuWflRKtDPXmfsbl69XI9YbYylAj12nDUIlrDm1PHpsc4ZT4bLz52Uh+VoayjeR2w0wcbxhKKzOx3jCUrczE0jCVNszkcmi9qWzDNLRhKttMfp7NAdN087OjLaVfebFue3t/pOBs+0DRd35z1S+Gb7+zKqc1VbWNi3DAdO398jLHqf/ZZAy0FozcbKcXtkQXv3Nv5JZPvjGb+oW1h9pd3z3z3eWvNL+ypi3UVjRyuxJlL/AtqH5g7gOH3lPxnrOWYU34mxh1LKbChw5lD+7dWxI9dbokWl9fYnd0FFztbZEiIsrjCVqLFp323HHHqbSH3nHeSEubNX+GAAAAmN2mXSimtX5URB69zmMuishHJ6Kea3hYRB4Z+hAIBPpTUMO4cVhGvGxbpEUpkfXrb/k2AQ5mu3MdwbRHd1Tfc7ihb9XQukMX+1bvPNF2cNvS/MYUljYuVPc5r+u1x9c5zj23XkUHRukXtqg6uuq39kRX/EbdbOkXZmtbdl3YVfqLul+sOdl1cmlMx6yR+/icvq5NBZsOfajqQ0fLfeUT+tyPNTW5Qy++VBI5fnxOtLa2JN7UVHLVUWDDqPT0PueyZae8d999yrt92wVlTXxgBwAAAEw10y4Um2YeF5FvJr/flZGRkZfKYm6WMpSYTtEiIu40RzTV9QCpEo3b6gvP1K77+bHWu8Ix+1IA4TBU7P4leS/dviC7JZX13Syz7uVc5/5/3mg27l85ar+wkluORDb8/t74vNtS0hw+FS72X/R+78z3Vuxu2b2mK9x1xbPcVGZ8YdbCkw+WPXjo7WVvrzMmICTU4bAxuHtP/uDrr5dEz5wpiV28WGJ3d+eM5VgjJ6fNMXdug3PhwkbXLRsa3Js3tyljdgSZAAAAwNUQik0grXVARAIiIkqpqFLXmH4IYMp78nhryT+8cH57WyBSOHz9ooK0U3+xrfLp5cWZk9ovatxoW6wj3y23jn5vo9k5Sr8wZ3og0S/s4YM6q2xWNFyP2TH1s/M/m//UhafWnOk5s8jWtjlynxxXTtuWoi2HPrzww8fmpI1vI/pozdn00Csvl0TeOFESqztfEmtuKZbYlSPTRlJud9CcM6fBWlDR4Fq5qsFzxx1NZn5eeDxrAwAAAGYCQjEAGIO6zqD30R019xy82Lt6+Posj6Prf98696kPbyg5m6rabko4YLr2/MNyx+mfbTIG2vJHbrbTC1uiS969J3LLJ0+Ic3b0mqrpqcl4ovqJVfta963pi/ZljdzuUI7ospxlx99R9o5D95be2zgeo8LsQMAReuWVwvDBQyXRmpqSWENDiQ4EfNc80FC2mZff4igra3AuXtzg3rypwblyZTejwAAAAIBrIxQDgDF46mR72fBAzDRU7L5Fua98bmvl7gy3I5bK2m6E6qr1unY/vs5x7lcbVHQgbfg2LSJ27uIz0dUf2RNd/oH62dAvLBwPGz+u/fGCpy88vfZc37lKLVc2pi/wFDTeUXzHoQ8t/NAbue7cG34ro7ZtiRw75h/cvbskcurUnFhdfUm8ra1IbPua/6BVenqfo6SkwaqsbHCtXdPgue22ZiMzc9r9/QMAAACmAkIxABiD37117smdJ9rO1XWGyhfmp5353NbKXStLMntSXdf1Ms+/kOfc/9WNZtOBFVf2C7Oi8ZJbjkRu+eTe+NzNE/Zm3qnkaMdR/w/P/nD1gbYDqwZiAxkjtzsN5+Cq3FXH3l3x7kO3Fd12Q28RjXd0OEMvvjQnfORISay2tiTW2FiiQyHvNQ90OGJmYWGjNX9+g3PZ0gb3rbc2OquqAjdSAwAAAIArEYoBwAgXukOe2vZg5p1VOZdCEEMp+bP7F+w83dqf/ZGNpTWprO+6aVusI98pt45+d5PZWbPgis3OjEBswX37wpsePqSz5s74fmED0QHzYOige39ov/fcy+c+Odo+JWkldXfNuevQB6s+eCrTOfaRWDoWU4P79uWG9+0viZw+XRK7cKHE7uy8YlrqaIysrE7H3LkNVlVVg3v9+gbX5k1thtttj/XaAAAAAK4PoRgAJMVsrf7+udrVPzncco/HaQbXz1v/9XSX41IfrY3z/Z0b5/unzxsXw32OZL+wjcZA+5X9wjKKmqNL3pPoF2Z5Znz4srdlb96Pan+05kjHkZWD8UHPyO0eh2dgbd7aI+9f8P5Da/PWjmmkXPTCBe/gyy/PiRw7XhI9d64k1tw8RyIR1zUPdDoHHXPmNFrl5Q3OFSsaPHfc3uiYM76N+gEAAAC8NUKxCaSUyhCRoek4ltZX9qgBMDXsOtle/KVfndvW3BeeIyIyGLM9f73r7KYvvGPRq6mu7XoZnTVpzt2Pr3Ocf369igav7BeWt+R0dPXH9kSXve/CTO8X1h3utr5f/f2lLzS+sKY52Fw62j7zM+afvbf03oO/vuDXq92Oq4/MsoNBc/C11wrCBw6WRKurS2IXL86xe3uzr1mEUtrIzWmz5s5rsBYvanBv3NjgWru2UzkcvJEYAAAASCFCsYn1sIg8MvQhEAj0p7AWAKNo6A55HtlRfdf++t51w9dnuh09iwrSbqiHVKqY557Pc+7/6iaz6cAKpePm8G3asKLx0o2HI7d8cl+8dNOM7xf2fMPzRT8999O1x7uOL4/aUefI7ZlGZnyde13wgxs++K0l2Ut6RztH5NTpzMFXXy2JnDhREj1/viTe2lok8fg1/72pPJ4BR2nJRWvBggbnylWNnjtubzJzcm64MT8AAACAiUEoNrEeF5FvJr/flZGRkZfKYgC8KWZr9aVfnVv148PN9w5G7UtT6Uwl8bsW5r76yLbKV30ea+q/1U/bYh3+doV19IlNZtfZiis2OzP6Ygvu3x/e/PBB7SsdTEWJk6Ul2OJ+4swTy19pfmVN+2B74cjtSpRd6as8s3Xe1kPFLcV3GcqQoUAs3tNjDb70clH48OGS6NmzJbGGhhI9cGXj/SsYRtwsKGh2lJU1OJcuaXBv2dLgXLKkVxkzewQeAAAAMBMQik0grXVARAIiIkqpqFKKqTLAFPDsqfai//erc9ubexNTJYdU5HrP/tkDC55aPy9r6o+kGuxxuPb8wwrH6Z9vNIIdVwTudkZxU6Jf2B+cnMn9wmxty876nXN/WffLNSe7Ty6N6ytHcvmcvq7NhZsPfajqQ0fmZ84f0LYt+4/97F73+fPO9u//YFusvr4k3t5eKGOY4q4yM3scpaUNzsrKBtfaNQ3u225rMdLS4tc6DgAAAMDUQygGYFZp6Qu7Pv3z0x+JxvWlKXWZbkfvRzeWPPWxzaVnDDW1W/8ZndVpztceX+84//x6FQt5h29L9Atbejqy5mN7YkvfO6P7hdUH6r1PVD+xcnfL7jXd4e7ckdtNZcYW+RedfHDeg4e2ute3hl96ZU7kp/+6rqX2XEm8sXHOnHDYIyISFll/1Ys4HFFHcVGjY355g3P5sgbPbbc1WOXlAxN4WwAAAAAmEaEYgFmlMNMVvm9x3is73mi721ASv7MqZ/ej26peyfJa0VTX9lbM2mfzna9/bZPZdGj5KP3CIvHSzYcjGz+5L15yS3eqapxoMTumfnrup+VPXXhqTU1PzSJb7CtSv3wrp/UdwUU199X7+q2a+sLYha8/2Nb1N1eEZqMxsrPbHXPnNjgXLWxwbdjQ4N64sV1ZFiN8AQAAgBmKUAzAjHauI5hWnuu9bHTPnz+wYE9rX9j//902d/ctZf7OVNV2TXZMWYe+XWEd+/4ms7u2fORm7crsiy24f1948x8f0plzZmy/sNPdpzN/UPODVfta960JRAO+4dt8/VoWN5uxO5qyOpZdFO1q7syV2Eu3ahF5q872cY9HR+aWRrLnztvtWrmywX3H7U2OwsIZ+88QAAAAwJUIxQDMSM29g+5HdlTfub++d83X3r/sG5vL/R1D29Jdjvi/f3jlL1NZ31sa7HG4dn9ppePMLzYawY4rRjnZGXMao0vfuyey4ROnZmq/sMHYoPGj2h9VPnPxmbXn+84v0KKVI6alslVkQZOWqkYtS5qMuL83borEHSJtVzTWv8RQtpmb1+ooK2twLl7c4N60qeFAKPgeUUoqt2x5eRJvCwAAAMAUQigGYEaxtZZ/fP78yh8ebLo3FLXTREQ+/8zZrT//3XXfm/L9wjpOpzt3f2m94/wL667sF6a0nb/0dGTNb++JLXn3xZnaL+xIxxH/f9b855qDbQdWpXUG0yubtNzWqKWyScv8VhHrspb2l08jHaLS0gKOkpIGq7KywbV6dYP79tuazaysy6fHvvbaBN4FAAAAgOmAUAzAjPFCdWfBF5+t3dbQMzh35LaO/ogrP8MVTkVd12KefabA+frXN5rNV+kXNnfLofDGT+2z56zvSVGJE6o/2u/40eFvL63b/+wtafXtRRsbtXy4SUvWWFram2bMLCxssuaXNTiXLmtwb9nS6Fy8qG/CiwYAAAAw7RGKAZj2WvrCrkd3VL9t97nuW7TIpeFg6S4z8OENJbv+v9vmnpxyo8TsmLIOfWuB89j3Nxnd5+aP3Kxdmb2xyq37wpsfPqQziqdkmHe9tG1LrPZceuT0af/A+Zrc+obj8wPtjcWetp7se9q1MsbQ0t7IyupylJY0WFVVDa516xrct97aarjdM3IKKQAAAICJRSg2gZRSGSKSkfxoaa2n2H+VA9ObrbX804t1y39woOm+YCSePrTeUGLftiB7z2Pbql7OSXe+Vb/1yRfqslx7/mGF4/QvNhmhzpyRm+3Mkobo0vftiWz4+GlxTL+wxw4EHJE3TmRFa8/6Yxcu+mMtLX67vT073t3lj/f2+lUsfunfO0XJr6tyWmFHcXGjVV7e4Fy+osF9+22N1ty5wQm/CQAAAACzAqHYxHpYRB4Z+hAIBPpTWAsw4/zvHxzfvq+uZ93wdfOyPXWfvb9ix5by7I6rHZcKRvupdOfuxzc46l5ap2Ihz/BtWpS2C5afiqz52J7Ykvc0pKrGsdC2LbH6C2mRUyf9sfN1/lhjoz/e2pod7+z02z09fj0wkHG1Y9/q/wrYSiSQ4x1IK51/LmvxyvPuW25pcG1Y36EcjjGMHwMAAACA60coNrEeF5FvJr/flZGRkZfKYoCZ5u3LC44NhWJpTjPwoQ1znv747fNOTKWpko6aXYXOA1/faDQfXq60fVl3fG06I/G5tx4Mb/zUfrt4bU+KSryCPTBgRk6cyIrWnPXHLl7wx5tb/PHkaC+7p9cvsZh1o+fud4u0Zom0ZSkJZLlC7vyipjlly89uvP93Ds/NL5wR00QBAAAATA+EYhNIax0QkYCIiFIqqpRixANwg2yd+PEZHni9fUXBxZ8dbTnocZrhR7dXvZQ3VaZK2jFlHfzXSufxH2wyus+XjdysXb6eaNW2fZHNDx/W6ZMfBGnbllhDgzd68qQ/ev58crRXm9/u7MiOd/f4dX9/5o2eO65EOnwirVlK2rISy9bkssNvxHPy5tWtzF1ZfX/p/TWrcld1j99dAQAAAMD1IRQDMOW9WtuV94VnarfdviD72P+9t+Lw8G3/9qEVT06VkWEq2Gk593xppePMLzcaoa5R+oWVXowue9/eyPrfm/B+YXYwaEZPnfZFqqv9sQsX/PGWZn+8vcMf7+rMtnt6/RKNOm/45E4rHMzyhJr9IjX+wbQLWTFraPRXR6ZI3HzzzyPNkdZXlVVV80DBxpoHyx4853f5o+NwewAAAABw0wjFAExZnf0R5yM7qu94pbZro63F+PHh5oIPris+XeL3hIb2mQqBmNF2IsO5+0sbHPUvrVWxwVH6ha04GVn7O3tii9/ZOF7X1LYt8eZmT+TkSX/03Dl/vKHRH2ttvXy0142+3EMprdLT+0x/VreRk9tt5ud1txa6o/uyOjJf9TYWn9FNpbYKut484M1ZoUqULvYWNSzLWVZ9d8ndNVsKt7QayhjlIgAAAACQWoRiAKYcW2v5+iv1S5/Y33h/fzh+qXF7OGq7f/lG2/zfu23eyVTWN8RRvaPIeeAbG42WI8tG6RcWjs+77WB44//Zbxet6r2R89uDg0b09Glf9Ey1P3rhgj/e3OyPt7f77c7O7Hhvr18iEde1z3IVlhUxsnzdpj+728zL6zaLCrsdc+d1WZULup1LlvSGLC076neU7W7ZXXm658Dqvkif/9KxI6I2l+kKLchccHZt/tqaB+c9eLYkvSQkAAAAADDFEYoBmFJ2n+vO/fwzZ7fWdYbKh68vyXJf+L/3Vuy8syqnNVW1iYiIHVPOA9+oso7/cKPRM0q/MHdWT7Ry295Ev7CCa/Y4i7W0uCMnTvhjtef8scYGf7y11R/v6PTHu7uzdSDgu+HRXiLy5mivnG4zv6DbMae42yov77IWL+52lJYGlXH5CK7qnuqMHfU7qg7v+UplXV9deUxfvaF+rju3dYl/SfXtxbfX3Ft6b4NlWPRMBAAAADCtEIoBmBK6BiLWIzuq73j5bNcmW785H8/rNAd+fW3RM5+6c/6xVE6VVMFOy7n771c5qndsNEJd2SO32765F6LL3p/oF2Y6LwVEOhw2ImeqM6PV1f5YfZ0/1pQc7dXV5bd7erJ1OOy+4aIcjqjh83Wb2dndZl5ut1lY1O0oLe22qiq7nIsX9xoZGbG3OjxqR9WzF58tebnp5cqT3SerOgY7Cq56KeWIlmWWnVudu7pm69ytNYv8i/puuG4AAAAAmAIIxQCk3HOnOwr/4skzHwiE45feeqhE9KZy//5Ht1W+WORzD6aqNqP1eKZrz5c2mPWvrFWxwcsCLC1K24UrToQqPngwGJozGD1R64/t+vymxGivDr/d3Z1tB/p8Yusbbqql0tICRlZWt5mT020W5Hc7iud0O8rndzkXLep2lJUNjBztdS2NA42eX9b9csGBtgOVtX21C8LxsOdq+2Y6M7sXZS2q3ly4uWb7vO11aVZa/EbvAwAAAACmGkIxACm3rDijOz5sdFhJlvvin9xTvuOuhbkpmyrpOPNkkfPgNzYZLUeXStw2YiFTIv1OiQ6YEh5wxwcH87rD/e6Y3Rso14NfXnbDFzLNmOHzdRt+f7eZl9ftKCzsdswt7bYWVHY5ly7pMTIz33K017XY2pbXWl4r+FXDryrf6HyjqjnYXKJl9CmZhhh2SXpJ/YqcFdX3ld5XsyZvTSdN8gEAAADMVIRiAFKuMNMV/uC64me+/3rjA+9fW/zMp+6cf8xhqEnpUWUPDJixxkZvvKXFG29tSjNrX65QFw8tjncF/JEBU6L9uRIdMEXbl+VIpkh/rkj/mK6hvN7+S6O98vO7zeLibqusrNu5eHGXo6K8/3pHe11Ld7jberLuyfK9rXsrq3uqKwdiA5lX29fr8PZX+iprNhRsqHlw3oPn8jx54XEtBgAAAACmKEKxCaSUyhCRoTfnWfomGmYDM4GttfzbaxcXHWvsK/3n9y97dvi2P3hb2fH3ry2uLsx03VAoo21b7O5uZ6y52RNvbfXG2zu8dneXx+7p9dq9vV47EPDYAwNePdDv1cGQxw6FvDoU8krsas3k08Z+cdOMG5mZl0Z7mYWF3Y7Skm6roqLbuXRpt+n3R2/knq7HkY4j/l0XdlUd6zxWebH/Yllcx82r7VvoLWxclr2s+s45d9bcVnRbi8Nw0CQfAAAAwKxDKDaxHhaRR4Y+BAKBsQ0rAWag1+t7sv9m19mttR3BBSIiPz7UXPPeNUV1Q9sNpWQoENO2LfG2Nnd8KODq6PTa3d2eeE+P1+7r8+pAwGsPDHh0cMBrB4NeHRr06MFBr8SvHgTdLOXxDBhZWYmm9gXJ0V7zyrqtxYu6rIqKfuWY3GApGAuauy7smvta82uVp7pPVfVEenKutq/TcA5W+Cpq1+atrd4+b/vZeRnzgpNZKwAAAABMRYRiE+txEflm8vtdGRkZeaksBkiF7t6g80v/c/hth840bUgPB82NkQHJjASl/V9eeHtHet9Ju7/fqwf6PfZA0KsTo7cSAddkjaxUWkyXLabTTizdZkwyc9pl7upqY/7ilkujvXJyIpNSz1s413su/cn6JysPtR+qPB84XxG1o86r7Zvjymlb5F9Uc1vxbTX3l95/0WW67MmsFQAAAACmOkKxCaS1DohIQEREKRVVanJ6JAETxQ4GzXhjkyfW2uKNt7V77a4uj93T7Y339Hp1IOCx+/u99sCAVweDHh0MeiOhcLoRCTt/V0b9q+8fFNkyrgWaZky53SHl9QQNjzeovN6QmWZFnGZnulOac51GV9al8CsZhBmWFrHcoXjRmhOxxe86Hl36nosyRaYTxuyYeqHxheIXm16sPNF1oqot1FZ0tX1NZcbmZcw7vyp3VfXWuVtrlmYv7Z3MWgEAAABguiEUA2apeE+PFW9s9MZb2zzxjg5vvKvTa/f0eOy+Pq/dF/Dq/n6PHQx69cCA1x4avRW9+sik0dzUA8ayIsrtDhoeT0h5vUEjLS2o0tNDRkZG0PBlBo2srJDpzw6aeXlBsyA/ZM6ZEzSzsqIiIqq/xWUd+tYiR+1zy42us4vVKG9b1IYVtfNXnA5XbT8eXfnhc+JMi99MueOlJdjifrLuyYrX216vrOmtqRyMD3qvtm+GldG7MGth9abCTTXb520/n+m8uTdVAgAAAMBsQigGTHPatiXe3u6KNzd7421tnkSD+W6v3dvjsXv7vHZ/wGv3D3gS4VbQq4Mhrx4c9Eg8Pmk///2WRwy3eyA9zdVjpKUFjbS0oJGeHlQZGSHD5wuafn/QyM4OmXm5QUdhUdAsLgoZadcZUoUDpnPfPy1y1Dy13Gg/WaXs2BX3p5Vh2zmVtbGK+45HV3/sjE7LS/mUSFvbsq91X95zDc9VHe88Xtk40DhXjxLiiYgoUXpO2pwLy3OWV99bcm/NhoIN7YYa3zdXAgAAAMBsQSgGpJAOh414T4+l+wKW3R+w7EDAaQ8MWHogaOnggGWHQpYOhpx2X5/H7uvz2IFAYuRWcnqiDoW8OjzoEVtPTjKilFZud0h5PEHl8QQNrzek0tODRnp60MjICKksX/CfWzzrTkQ9xb3ONAk4vbKovODgX7xjya/m+j2hca8nHlHWG/81z3HqZyvMliOLVTzsHm032zfvQmz+nccjaz52UvvLU95kvi/S59hRv2P+npY9lWd6zlQFogHf1fZ1m+5gpa+yZn3++poHyx6sLfQWDk5mrQAAAAAwUxGKAVeho1Fl9/VZdm+vZQcClt3fb+n+fqc9ELR0MPFlD4YsHRq09OCgU4cHLR0OW3owbOlIxNKRiCWRiFNHI5aORC0djVoSjVo6FrN0NOqUWMwS207dMB/DiCfCLXfI8HiDKjGCK6Qy0oNGpi9o+HwhM9sfNHNzg2ZefsicUxw0c3PD13rL4rYLvXU/eeLoJ/IyXK1/etf8nduW5jeOa93aFkf1ziLrxI+Wm437l6lIf8Zou9lpBa3xebcej6z66Bt20aqU99c60XXC91T9U1WHOw9XXghcmB/XVx+pl+/Jb16avbT6bcVvq7lzzp1NjinS4wwAAAAAZhJCMUxL2rbF7uuz7L4+SwcClt0XsOyBfksPBC17YMCpQ0FLhwYTI60GBy0dHrT0YNipw4nASsJh61JYFYk4JZoIrXQsNhRcOSUeN1N9n2PmcESVxxM03O6QSvMGlTctaAz138rMDBr+rKDhzw6ZuTlBs7Aw6CgqChl+f0QZN57JBQZjjv/33Ll1D989/4DPY13qZbV2rq/7Cw8t/vbdC3OaLdMYtzDHvLA72zr63eXmxd3LjVBXzmj7aJevJ1a66Xh0+QfeiJff3TZe174Rg7FB4+mLT5e+2vxq5anuU1Vd4a6rvn3WMqzI/Mz5tWtz11Zvn7f9bLmvvH8yawUAAACA2YhQDONO27boUMi0e3qcdnJaoA70W/bAQGJ0VXDAqUNDI6xCiZFV4bClw4NOHY5cGmWlI5HE6KpIxJkcXXVppJXEYlaq73NcKKXF4YgqhyMqlhVVlhVRiWVUnM6osqyocjqjKi0tZAz138rKSvTfys8LmgUFQUdxccjIyJjUBuvf29+w4JuvXtjaE4plD0Ri7sffteTF4dsfWJLXNB7XMdpPpVuHv73MUffSciPQVDzaPtrhCcaL156ILn3PsdjidzVICnts1QfqvU/WPVl5sP1g5bm+cwsidsR1tX39Ln/HYv/ims2Fm2semPvABa/DOyUa/QMAAADAbEEohjGLd3Y6c7/7vSwViaiWf/rnD0oytEp+OS8LrPTojcKnHYcjEVAll8qyIsnwKhFWDX25XBHlckWVyxVVbnfiy+OOKq83Yni8UeX1Ro309KhKT4sa6RlRw5cZMXy+qPJ44jczWmuyHWvs8/3VUzUPnG4dWDS07lenO249fLH3yOpSX894XEMFmlzOg/+22HHuuRWq+3zZVd4cGbELVpyOLnzweHTFh86J5bHH49rXK2bH1MtNLxe+0PhC1Rtdb1S2hlrnXG1fU5nx0vTSupW5K6sfKH2gZmXuyu7JrBUAAAAAcDlCMYydYei0Q4c8IiIxkcpUlyOmGRs2wiox2srpTIy0clpR5XQlRls5nVHlHgqt3FHD444qtycZWqVFDK83qtK8USMjIxFcZWREzaysiEpPj02nwGoi9Ydj5l89VbP5mVMdt8dsfem54XIYgw+tKHh+WXHGzfXsCvc5nIe+Xek4u2u50X6qSukrp64m3hy5sCa24P7jkTUfqxZPdvSmrnmD2kPtrifrnyzf37q/sqa3pjIYC6Zfbd80R1rfwqyFNRsLN1Zvn7f9vN/lT0nNAAAAAIArEYphzK5rip5hxIeNroqK5YgqhxVVTmdEOa2oWM7EqCqnM6pczqhyuSPKnQitlMcTNTyeqPJ4oiotLWKkeaMqLS1qZGQmgqvMjKiRmRlVlkXz8Unw/dcbK77x6oWt3cHoZX28VpdmHnl0W9Vz5bnegRs6cTyirGPfn+84/YvlZuvRxSp+5VRDLSI6a35d4s2Rv3NKZ80d/zdYXoOtbTnYfjDnmYvPVB3rPFbZ2N84z5bRX5CgROkib1HDspxl1XeX3F2zpXBLq5HC6ZwAAAAAgKsjFMOYKYdDd77vfT3acuiKnJxdypMcYZWeHjHS05PTAn1RlZkRNdzulExnw/g50RTI/Munau4/2dK/ZPj6vHRn6x/eOX/H21cUXLzuk2pbHKd/Mcc6+ePlZuOBZSo6kDbabnZ6YUt83u3HIqs/esIuWN53g7dww/qj/Y6d9Tvn7W7ZXXm653RVX6TPf7V9XaYrtCBzwdl1+euqt8/bXluSXjLpwR0AAAAA4PoRiuG69G/eFBIRydiypTrVtWBi/deh5iXDAzGnaYTfsbLg+U/fW3HA5TCuK/Q0617OtY59f5l5cfdyY7A7e7R9tNvfHSvddDy64jeOx8vu6LjZ+sfK1rac7j7tO9B+oPBMz5miukBd8cXAxbKYvvrLHHLduS1L/Etqbi++vebe0nsbLINRiwAAAAAw3RCKTSClVIaIZCQ/WnqmNJ/HrPDZ+yv2v3y2c03nQDRvVUnm0Ue3VT5bkZc25qmSRtsbGc5D315m1r+y3OhvLhptH22lDcSL170RXfre47FFb2+c6DdHxuyYOtJxJPtwx+HC6p7qoov9F4vaQm1Fg/FBz1sd51COaFlm2bnVuaurt8/bXlOVVRWY0EIBAAAAABOOUGxiPSwijwx9CAQC/SmsBbiqk82BzIFI3LF+XlbX0DqPZdp/ck/Fz8Mx23zXqsILYzmP6r3odh781yWO8y8sVz3ny0ZLgbXpjMQLVp6MLXr78ejyD9SJY2Km2g7GBo3X21/PO9pxtPBs79mixoHGorZQW2HUjjrHcrzP6etalLWoZnPR5uptc7fVp1lp8YmoEwAAAACQGoRiE+txEflm8vtdGRkZeaksBhgpGImbf7Pr7C1PnWh7W2Gmq/nJj6//d0O9GWVtX5bfeM2ThLos5+F/r3KcfXq50XG6Uukrm9BrZcbt3EU1scqtxyKrP1Ij7qyxv7RhDPqj/Y69LXvzj3UeK6rtqy1qGmgq6hjsKIiP8hbL0ViGFS7wFLSUpJc0V/oqmzcUbGhYm7e269pHAgAAAACmK0KxCaS1DohIQEREKRVVStF3CFPGjw41lX31pfrtXcForojIxZ7BuV95oW7FH941/9g1D44NGtbR75VbZ55cbrQeW6RGGX2lRUT7y8/Hyu9OvDkyc87geNTdHmp37W3dW3Ci60TR+b7zRU3BpqKuwa48LWObnuw23cFCb2FzaXpp88Kshc1r8tY0L8te1uMwHPx8AgAAAMAsQigGzDJnWvszHttZc9/xpsCy4etz0qz2eTmenqseqG1xnPppiXXyJ8vNpoPLVDToHW03O6O4KVZ2x/Hoqo+csPOX3lTvrYv9F717W/cWnuo6VVTfX1/UNNBU1BvpHbVR/2jSrfTeQm9hy7z0ec2L/Iua1+evb17gWxAwJrh3GQAAAABg6iMUA2aJUDRu/O3TZ2/Z8Ubb26JxfWlkl2WqyIPLCl747P0V+z2WeUV/L/P8C3nWse8vdzTsXa4Ge7JGO7ftye6Ml24+Hl354Tfic7d0Xm9ttrblbO/ZjP1t+4tOd58uutB/oagl2FLUH+3PHOs5spxZnUVpRS1lGWXNi/2Lm28puKWlNL00eL21AAAAAABmB0IxYBb4yZHmef/8Yt22joFo/vD1y4sz3nhkW+UzCwvSLxvRZTQf8TmP/EfizZEDrQWjnVM70/vjczYcjy593/FY1bbmsb450ta2HOs85j/YfrBo6A2QLcGWosH44Kgjz0ZSonS2O7u92FvcPD9zfvPS7KXNGws2tuZ58sJjKgAAAAAAACEUA2a8ow19WY/tqPktLXKp51a21+r4xB3zdrxvTXHd0DrVfc7rPPTtJY7zLyw3euvnjnYubbrC8cJVJ2OL3nE8uvzX68R0vmUfrnA8bBxqP5RzuONw0dnes0UN/Q1FraHWwqgddY2ldlOZ8Vx3buuctDnNFb6K5uXZy5s3Fm5sS7fSx7VRPwAAAABg9iEUA2a4lSWZPRvKsg7uq+tZZxkqum1Z/ot/ev+CfV6nGVfBTss69K2Fjtpnlhud1QtGfXOk4YjZeUuqY5Vbj0dWfaRGXBnx0a4zEB0w97Xuyz/aebToXN+5osaBxqL2UHtBXMfH9JyxDCuS78lvKUkraV7gW9C8Mndl87q8dR1uh/uKKZ0AAAAAANwsQjFghqnvCnnnZXsu66X16LbK5//8l9Xm/723/MUluY5+68i/VljVO5YbbW8sUnbUGnkOLUrb2RXn4uX3HI+s/Z3TOr3wsqmJXYNdzj2tewre6Hoj8QbIgaaizsHOPC16THMo3aY7VOApaC7NKG2u9FU2r8lb07wyZ2U3b4AEAAAAAEwWQjFghqhtH0h7dGfNvada+hf+6LfX/HN5rndgaFuJzxp8Ys3pI9arf3ub2XRoqYqFPKOdw84saYiV3XE8uua3T9g5VQMiIo0DjZ69tT+Zf6r7VFFdoK6oaaCpqCfSkzPWutIcaX2F3sKWeRnzmquyqprX569vXpi1sI83QAIAAAAAUolQDJjmwjHb+Ltna9f9/GjrXZG47RIReXRn9T3f/c1VPzfPPlPgfOM/l5sN+5apcJ9vtONtb05HvPTW49FVv3n8TEZOdH/r/qLTtU+srT9SX9QSbCkKRAOjHjcan9PXVegtvPQGyA0FG1rKMsoGrn0kAAAAAACTi1AMmMZ+cay19Msvnt/WFogUDq0rUe3y/r7/KU77+sd+zwi25Y92nO3M6Ds7Z1XNy4VVnUdU2HOh/2Jp66E/Wx+MBdPHcl0lSvtd/o7itMQbIJf4lzRvKtjUku/N5w2QAAAAAIBpgVAMmIbOdQTTHt1Zfc/hi32rRERypFe2mfvkvdarsRVy1iFhuRSGxUWk3nLICU9a9IC/qOuY221fiPX7I3b9Wmmpv+a1DGXEc925bXPS5jTPz5yfeANkwcY2n8sXnbAbBAAAAABgghGKAdNING6rLz5bu/Z/jrbe7YgNuB8yDspD5mtyq3FcHMqWqIjjlNOS006nnHQ55aTLpc84nRJWokTEEgkWSCR41fM7lCOa78lvKUkvaa7IrGhembuyeUP+hnbeAAkAAAAAmGkIxYBp5De/tf/9hZ17Fv69+Zrc6j4k9S6R006n/JUrS045nVLjtCSm1PBD1NXO5TJdoQJPQUtpemnzAt+C5jV5a5pX5a7qsgyLN0ACAAAAAGY8QjFgqrNjqvfItxdUn/3lunea9RU1c5R8y2nJn1sFYqurZl6X8Tq8/YXewua56XMvvQFysX9xL2+ABAAAAADMVoRiwBTQMdjhPNtz1lffX+9rGmjy9fScyxvou1DcF+72d9rhtGaHqcQhIv5r98HPtDJ7CtMKm8syypoXZS1q3pC/oaXcV94/8XcBAAAAAMD0QSg2gZRSGSKSkfxoaa3HNqwHM8pgbNCo7avNONd3ztfY3+hrDbX6OgY7fN3hbl9fpC+zL9Lni9gR96gHGyJimKNuUiI6y+XvLPYWN5dlljUv8S9p3liwsaUorWhwAm8HAAAAAIAZgVBsYj0sIo8MfQgEAozWmWFsbUvjQKP3bO9Z38X+i5nNwWZfe6jd1x3u9vWEe3x9kT5fMBZM13JzgahDaykVK1iYNqehMH9V7bLcFU0bCza2+l1+3gAJAAAAAMANIBSbWI+LyDeT3+/KyMjIS2UxuH59kT5HdU+1ry5Q52saaPK1hdoyOwc7h0Z5+QLRgC+u4zf9c+S0tRTGY1IUi0thLCY5MZHBaJ60RefZKxYsf3n7237rNY8nOzYe9wQAAAAAAAjFJpTWOiAiARERpVRUKcVb/aaQqB1V5/vOp9f21foa+ht8rcHEtMaucJevN9KbGYgEfIPxQe/NXkdpLbnxuBTF4lIQj0tRLBF+FcViUpgMwby2Q78eX6j22ktkr71YjulyKcvLrPncQ5VPrSn1dY/H/QIAAAAAgDcRimFGsrUtbaE2d01vje9i4GJmU7DJ1x5q93UNdvl6Iolpjf3R/gwt+qZfv2gZVsRnegOFWmJzw4OOsoHetJJwv7swGXwVxOJijThGG1Y0nFnWsEuttv48sLTkuJ6vYskfx0y3o+cPNpc+9bFNpdU3WxsAAAAAABgdoRimpYHogHm292zm+cB5X2N/o68t1JY5rHm9LxAN+KJ21Hmz11Gi7HQrvS/Tmdmb5crqzXZl9+V78nvn20ZsUdeFzMr22lx/Z+1cFe7NeavzaMOK2lllF+yC5XWxsrfV/XBgtfOLLzS+OxS1L41EM5TE71qY+9ojWytfzfJa9AoDAAAAAGACEYphyonZMXWx/6K3trfWd7H/oq8l1JIY5RXu8vWGezMD0YAvGAumj8e13KY7mOnM7PU5fb1+l783z5PXW+Ap6CtJL+mtyKzoLcss67cMSxtNB7Osmp1lZt3BMqPzyeUq3Ot7q/OODMFilVubxPLYQ9sXNvX5ovGGSwPIKnK9Z//sgQVPrZ+X1TUe9wUAAAAAAN4aoRgmXddgl7O6p9p3of9CZuNAo68t1HZpWmNvpNc3EB3IjOu4ebPXcShHNN1K7xsKvLLd2b0FnoLe4rTi3rLMst5KX2VfpjNz1Ob1RtPBLOvoDyvMpoNlRmdN2c2GYCMtL87sfWBJ3ssv1XSu++im0l2/vbn0tKFu6gWVAAAAAADgOhCKYVyF42Gjtrc241zfOV/DQKJ5fedgZ2Z3uNvXG+n1BSIBX9gOu2/2OkqU9jq8geS0xr5sV3Zvnievt8hb1FuaXtq7wLegtzitOGSosbUMuzQSbJxDsJit1ZdfOL+isz+S/rfvWPTa8G2f21q550/uKd+XneZkqiQAAAAAAJOMUAxjZmtb+u1+1R3vNr975rsLW4JvTmvsCfdk9kX6fMFYMEOLvukhTy7DNZjhzLg0rTHXndub783vK0kr6S3PLO8tzywPuB3uq47EupaJCsGGe/5MR8H/e+7c9oaewVJDib19ef6ZLeXZHUPbvU4z7nWa8Ru9BwAAAAAAcOMIxTBmxzqP+R9rf6xQRES65Ndv9DyGMuJD0xqznFm92e7s3nxPfu+ctDm9c9Pn9i3IWtCb686NjFvhMjkh2JCWvrDrkR3Vd+45171BiygREVuL8dWX6m/fUp790/G4HwAAAAAAcHMIxTBmlb7KvrHs53V4+zOsjF6fy3dpWmOhp7C3NL20t8JX0VuaXhp0GA49YYVqOxmCPVVmNg+FYH3XDsH8ZfV2wYpECLbggeaxhmBDbK3lyy+cX/HDg833BSPxtKH1hhL79gXZex7dVvXyjd4SAAAAAAAYX4RiGLM0Ky2ebWbHTTF1mjetzu/y9+a4c/oKPAW9c9Ln9M7PmN+7wLegL81Km9wpgSkKwYZ7saYz/4vP1G6/2DM4d/j6edme85+9v2Ln8GmTAAAAAAAg9QjFcF0+m/vZNhGRLVu2fD9lRUyBEGxIbyjq+MzPT9/9Wm33LUNTJUVE0pxm4MO3zHn6926bd4K3SgIAAAAAMPUQimHqm0Ih2EhepxmvbhsoGwrEDCX2bRXZex/ZXvVSXrpzXPuiAQAAAACA8UMohqnnxkKwiO0vuzDRIdhIlmnoP7xz/o4//cWZ357r99R99v6KnbdWZLdP9HUBAAAAAMDNIRRD6k2TEKy9P+L855fq1nxua+U+h6EuvSjg15YXNBhK/dvWpXmNTJUEAAAAAGB6IBTD5JsmIdgQW2v52sv1S5/Y33j/QCSe4bXMyKfvqzg0fJ/ty/IbJ6MWAAAAAAAwPgjFMPGmWQg23GvnunI//3Tttvqu0Pyhdf99pPmeD22Yc3JOlntwsusBAAAAAADjg1AM4++GQ7D5b4ZglQ80i8M96SHYkM7+iPORndW3v3K2a5OtxRha73Wa/R9cV/xMkc9FIAYAAAAAwDRGKIabp20xmw74HTVPlZlNh8qMrpoyFe7LfMtDplgINsTWWv7llQtLvre/4f7+cPzSPSgRvbncv+/R7VUvFma6wqmsEQAAAAAA3DxCMVw/rcVs3D8jQrDh9p7vzvmbp89uq+sMlQ9fX5LlvvB/763YeWdVTmuqagMAAAAAAOOLUAxjZl7ck72y5h+z/H1nnO59XZ98q32nQwg20nf2NawbHoh5LGPgA+uKn/nUnfOP8VZJAAAAAABmFkKxCaSUyhCRjORHS2s9rZMVbTrtos49nlG3TcMQbKRHtlW9+I5vHFgWisTTNpX79z+6rfLFIh/N9AEAAAAAmIkIxSbWwyLyyNCHQCDQn8JabppdvLYn5MyJeyKd5nQPwfbX9WRneR2Rqvz0S38mhZmu8CffVvazwkzXwN0Lc1tSWR8AAAAAAJhYhGIT63ER+Wby+10ZGRl5qSxmPJwq+2hvxMq0l97zwa9PpxBsSE8waj22s+bW56s7tiwtyjj1g4+u/snw7b+xfk5tqmoDAAAAAACTh1BsAmmtAyISEBFRSkWVUjrFJd20tuz1iTcvTrNAzNZavr374sJ/39uwtW8w5hMROd4UWPajQ00H37emuC7F5QEAAAAAgElGKIYZ7+CFXv9f76rZerY9WDl8fZHP1Zib5gylqi4AAAAAAJA6hGKYsXpDUcdjO2tuff5Mx61xLebQerdlhN67uujZP7q7/IjDmP6j9wAAAAAAwPUjFMOM9O97LlZ9a/fFrb2Dsazh6zfM8x14bHvV8yV+DyPEAAAAAACYxQjFMOM8eby15EvPn//A8HWFma6mP7pr/o6tS/ObUlUXAAAAAACYOgjFMOM8uLyg4dt7LlbXtAer3A4j9K5Vhb/643vKD1mmwVRJAAAAAAAgIoRimAEudIc8c0dMh/zzrZW7vvpSXd9fbKt6YV62J5iq2gAAAAAAwNREKIZp62hDX9Zf7aq5v7k3XLjz4+u/6vNYsaFta0p93d/60ModqawPAAAAAABMXUaqCwCuV384Zn76Z6du+8j3jn7iTOvAor7BWNajO2tuS3VdAAAAAABg+mCkGKaVJ/Y3Vnzj1fptPaFY9vD1gcGYN1U1AQAAAACA6YdQDNPC8aY+31/urLn/dOvA4uHr8zOcLX945/wdv7a8oCFVtQEAAAAAgOmHUAxTWn84Zv71rrObnj7ZfkfM1pf+vjpNI/zQyoJffea+igO8VRIAAAAAAFwvQjFMWTFbq3d+8+DHWvrCxcPXry7JPPLItsrnKvLSBlJVGwAAAAAAmN4IxTBlOQylN833H/+foy3FIiJ56c7WT76tbMdDKwsvpro2AAAAAAAwvRGKYcqwtRZDqcvWffb+iv2v1/cs3jg/6+Rn7lvwusth2CkqDwAAAAAAzCCEYpgSfnigaf43Xq1/4C+2Vf30zqqc1qH1Hsu0d3x8/b+PDMsAAAAAAABuBqEYUupUS3/GYzur7z/R3L9UROSLz9Zuu6My+7IQjEAMAAAAAACMN0IxpEQoGjf+ZtfZjTtPtN0RjWvn0PrWQLhw7/me3M3l/o5U1gcAAAAAAGY2QjFMuh8fai776st12zoHonnD1y8vzjj+6PbKZ6ry0/tTVRsAAAAAAJgdCMUwaarb+tMf3VFz3/GmwPLh63PSrPZP3F62871riupSVBoAAAAAAJhlCMUwKb72cv2Sf9t94R3Dp0papopsX5r/4p8+sGCfxzJ5qyQAAAAAAJg0hGKYFJV53u7hgdiyoow3/mJb5TOLC9MDqawLAAAAAADMToRimBT3Ls5rvqWs+fWatoH5H7993s73ry0+n+qaAAAAAADA7EUohnEVjtnGF545u95rmZE/ubfi8PBtf/eORc95nGbc6zTjqaoPAAAAAABAhFAM4+hnR1tKv/Ji3fb2/kiB0zTC715dVF2e6x0Y2p6T7oyksj4AAAAAAIAhRqoLwPR3riOY9uHvHHnoc09Wf6y9P1IgIhKJ264vPX9uU6prAwAAAAAAGA0jxXDDwjHb+Ltna9f9/GjrXZG47Rpa7zBU7P4leS/9+QML9qSyPgAAAAAAgKshFMMN+eXx1pJ/fOH89rZApHD4+kUFaaf+Ylvl08uLM3tTVRsAAAAAAMC1EIrhugxEtfqvszpzf+uZ3x6+Psvj6Prft8596sMbSs6mqjYAAAAAAICxIhSbQEqpDBHJSH60tNYqlfWMB1OJVHfry6ZK3rc49+XPba3cne5y8FZJAAAAAAAwLRCKTayHReSRoQ+BQKA/hbWMC7dD6XdXqL5vndL+hQVppz/3QOXTK0sye1JdFwAAAAAAwPUgFJtYj4vIN5Pf78rIyMhLZTHjZV2+GqyorPrWry0vaEh1LQAAAAAAADeCUGwCaa0DIhIQEVFKRZVSOsUljQullBCIAQAAAACA6cxIdQEAAAAAAADAZCMUAwAAAAAAwKxDKAYAAAAAAIBZh1AMAAAAAAAAsw6hGAAAAAAAAGYdQjEAAAAAAADMOoRiAAAAAAAAmHUIxQAAAAAAADDrEIoBAAAAAABg1iEUAwAAAAAAwKxDKAYAAAAAAIBZh1AMAAAAAAAAsw6hGAAAAAAAAGYdQjEAAAAAAADMOoRiAAAAAAAAmHUIxQAAAAAAADDrEIoBAAAAAABg1iEUAwAAAAAAwKxDKAYAAAAAAIBZh1AMAAAAAAAAsw6hGAAAAAAAAGYdQjEAAAAAAADMOoRiAAAAAAAAmHUIxQAAAAAAADDrEIoBAAAAAABg1iEUAwAAAAAAwKxDKAYAAAAAAIBZR2mtU13DrKCU6nQ4HBn5+fkdqa7lZrhcrlwRkXA4PK3vA8DsxrMMwHTHcwzATMCzDOO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" ] }, "metadata": { "image/png": { "height": 397, "width": 610 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "N = np.logspace(0, 11, 12, base=2, dtype=int)\n", "N\n", "\n", "dview_sends = 2 * N\n", "\n", "def bcast_sends(N, depth):\n", " return 1 + (2**depth) + (N / (2**depth))\n", "\n", "plt.loglog(N, dview_sends, '--', label=\"direct view\")\n", "for depth in range(5):\n", " plt.loglog(N, bcast_sends(N, depth), label=f\"broadcast (d={depth})\")\n", "\n", "plt.xlabel(\"N engines\")\n", "plt.ylabel(\"messages\")\n", "plt.title(\"Serial messages for last engine\")\n", "plt.grid(True)\n", "plt.legend(loc=0)" ] }, { "cell_type": "code", "execution_count": 10, "id": "688f856f-c2b4-47ef-93cf-16a5f5abc496", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 397, "width": 616 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# \n", "plt.plot(N, [1] * len(N), '--k')\n", "for depth in range(7):\n", " plt.loglog(N, bcast_sends(N, depth) / dview_sends, label=f\"depth = {depth}\")\n", "\n", "plt.xlabel(\"N engines\")\n", "plt.ylabel(\"relative to dview\")\n", "plt.title(\"Serial messages for last engine\")\n", "\n", "plt.grid(True)\n", "plt.legend(loc=0)" ] }, { "cell_type": "markdown", "id": "5f065a9b-195c-49a7-92d7-670a39d81860", "metadata": {}, "source": [ "## Coalescing replies\n", "\n", "The broadcast view changes the number of requests a client sends from one per engine to just one,\n", "however each engine still has its own *reply* to that request.\n", "\n", "One of the sources of performance in the client, especially when sending many small tasks,\n", "is contention receiving replies.\n", "\n", "The IPython Parallel client receives replies in a background thread.\n", "If replies start arriving before the client is done sending requests,\n", "this can result in a lot of contention and context switching,\n", "slowing down task submission.\n", "\n", "Plus, deserializing all these replies can be a lot of work, especially if the only useful information is that it's done.\n", "\n", "For this, the broadcast view has a tuning parameter to 'coalesce' replies.\n", "Unlike depth, this parameter is set per-request instead of for the cluster as a whole.\n", "\n", "Coalescing reduces the number of replies in the same way that requests are reduced on the way to engines -\n", "by collecting replies at each node, and only sending them along to the next layer up when all engines have replied.\n", "\n", "As a result, when coalescing is enabled,\n", "a BroadcastView sends exactly one request and receives exactly one reply,\n", "dramatically reducing the workload and number of events on the client." ] }, { "cell_type": "code", "execution_count": 11, "id": "b5ecaea7-f8aa-4b93-9403-96a57bb2fa29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "288 ms ± 44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "bcast_view.is_coalescing = False\n", "%timeit bcast_view.apply_sync(lambda: None)" ] }, { "cell_type": "code", "execution_count": 12, "id": "aa4dadbe-2a76-4163-b8ae-83613fb1ff33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "216 ms ± 26.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "bcast_view.is_coalescing = True\n", "%timeit bcast_view.apply_sync(lambda: None)" ] }, { "cell_type": "markdown", "id": "1a597b4f-64b7-4f31-855e-3c8d44a8e371", "metadata": {}, "source": [ "Coalescing has its own trade-offs.\n", "Each send and receive at each level has a cost, but so does waiting to send messages.\n", "Coalescing behaves best when lots of (especially small) replies are going to come at the same time.\n", "If responses are large and/or spread out, the cost of leaving network bandwidth idle will outweigh the savings of reduced message deserialization." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } ipyparallel-8.8.0/docs/source/examples/broadcast/Dockerfile000066400000000000000000000002431460376056100240560ustar00rootroot00000000000000FROM jupyter/scipy-notebook:4a6f5b7e5db1 RUN mamba install -yq openmpi mpi4py RUN pip install --upgrade https://github.com/ipython/ipyparallel/archive/HEAD.tar.gz ipyparallel-8.8.0/docs/source/examples/broadcast/MPI Broadcast.ipynb000066400000000000000000002076761460376056100254620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# More efficient data movement with MPI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like [we did](memmap%20Broadcast.ipynb) manually with memmap,\n", "you can move data more efficiently with MPI by sending it to just one engine,\n", "and using MPI to broadcast it to the rest of the engines.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import socket\n", "import os, sys, re\n", "\n", "import numpy as np\n", "\n", "import ipyparallel as ipp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this demo, I will connect to a cluster with engines started with MPI.\n", "\n", "One way to do so would be:\n", "\n", " ipcluster start -n 64 --engines=MPI --profile mpi\n", " \n", "In this directory is a docker-compose file to simulate multiple engine sets launched with MPI.\n", "\n", "I ran this example with a cluster on a 64-core remote VM,\n", "so communication between the client and controller is over the public internet,\n", "while communication between the controller and engines is local." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rc = ipp.Client(profile=\"mpi\")\n", "rc.wait_for_engines(64)\n", "eall = rc.broadcast_view(coalescing=True)\n", "root = rc[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(rc)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "root['a'] = 5" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "%px from mpi4py.MPI import COMM_WORLD as MPI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We cn get a mapping of IPP rank to MPI rank, in case they mismatch.\n", "\n", "In recent-enough IPython Parallel,\n", "they usually don't because IPython engines request their MPI rank as their engine id." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 0,\n", " 1: 1,\n", " 2: 2,\n", " 3: 3,\n", " 4: 4,\n", " 5: 5,\n", " 6: 6,\n", " 7: 7,\n", " 8: 8,\n", " 9: 9,\n", " 10: 10,\n", " 11: 11,\n", " 12: 12,\n", " 13: 13,\n", " 14: 14,\n", " 15: 15,\n", " 16: 16,\n", " 17: 17,\n", " 18: 18,\n", " 19: 19,\n", " 20: 20,\n", " 21: 21,\n", " 22: 22,\n", " 23: 23,\n", " 24: 24,\n", " 25: 25,\n", " 26: 26,\n", " 27: 27,\n", " 28: 28,\n", " 29: 29,\n", " 30: 30,\n", " 31: 31,\n", " 32: 32,\n", " 33: 33,\n", " 34: 34,\n", " 35: 35,\n", " 36: 36,\n", " 37: 37,\n", " 38: 38,\n", " 39: 39,\n", " 40: 40,\n", " 41: 41,\n", " 42: 42,\n", " 43: 43,\n", " 44: 44,\n", " 45: 45,\n", " 46: 46,\n", " 47: 47,\n", " 48: 48,\n", " 49: 49,\n", " 50: 50,\n", " 51: 51,\n", " 52: 52,\n", " 53: 53,\n", " 54: 54,\n", " 55: 55,\n", " 56: 56,\n", " 57: 57,\n", " 58: 58,\n", " 59: 59,\n", " 60: 60,\n", " 61: 61,\n", " 62: 62,\n", " 63: 63}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mpi_ranks = eall.apply_async(lambda : MPI.Get_rank()).get_dict()\n", "root_rank = root.apply_sync(lambda : MPI.Get_rank())\n", "mpi_ranks" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sz = 512\n", "data = np.random.random((sz, sz))\n", "megabytes = data.nbytes // (1024 * 1024)\n", "megabytes" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3943864933941bbb65e6ad17e3c589f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "_push: 0%| | 0/64 [00:00 0: a = randint(0, nodes - 1) b = a while b == a: b = randint(0, nodes - 1) G.add_edge(a, b) if nx.is_directed_acyclic_graph(G): edges -= 1 else: # we closed a loop! G.remove_edge(a, b) return G def add_children(G, parent, level, n=2): """Add children recursively to a binary tree.""" if level == 0: return for i in range(n): child = parent + str(i) G.add_node(child) G.add_edge(parent, child) add_children(G, child, level - 1, n) def make_bintree(levels): """Make a symmetrical binary tree with @levels""" G = nx.DiGraph() root = '0' G.add_node(root) add_children(G, root, levels, 2) return G def submit_jobs(view, G, jobs): """Submit jobs via client where G describes the time dependencies.""" results = {} for node in nx.topological_sort(G): with view.temp_flags(after=[results[n] for n in G.predecessors(node)]): results[node] = view.apply(jobs[node]) return results def validate_tree(G, results): """Validate that jobs executed after their dependencies.""" for node in G: started = results[node].metadata.started for parent in G.predecessors(node): finished = results[parent].metadata.completed assert started > finished, f"{node} should have happened after {parent}" def main(nodes, edges): """Generate a random graph, submit jobs, then validate that the dependency order was enforced. Finally, plot the graph, with time on the x-axis, and in-degree on the y (just for spread). All arrows must point at least slightly to the right if the graph is valid. """ from matplotlib import pyplot as plt from matplotlib.cm import gist_rainbow from matplotlib.dates import date2num print("building DAG") G = random_dag(nodes, edges) jobs = {} pos = {} colors = {} for node in G: jobs[node] = randomwait client = parallel.Client() view = client.load_balanced_view() print("submitting %i tasks with %i dependencies" % (nodes, edges)) results = submit_jobs(view, G, jobs) print("waiting for results") client.wait_interactive() for node in G: md = results[node].metadata start = date2num(md.started) runtime = date2num(md.completed) - start pos[node] = (start, runtime) colors[node] = md.engine_id validate_tree(G, results) nx.draw( G, pos, node_list=list(colors.keys()), node_color=list(colors.values()), cmap=gist_rainbow, with_labels=False, ) x, y = zip(*pos.values()) xmin, ymin = map(min, (x, y)) xmax, ymax = map(max, (x, y)) xscale = xmax - xmin yscale = ymax - ymin plt.xlim(xmin - xscale * 0.1, xmax + xscale * 0.1) plt.ylim(ymin - yscale * 0.1, ymax + yscale * 0.1) return G, results if __name__ == '__main__': from matplotlib import pyplot as plt # main(5,10) main(32, 96) plt.show() ipyparallel-8.8.0/docs/source/examples/dask.ipynb000066400000000000000000001224631460376056100221200ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with IPython and dask.distributed\n", "\n", "[dask.distributed](https://distributed.readthedocs.io) is a cool library for doing distributed execution. You should check it out, if you haven't already.\n", "\n", "In many cases, dask.distributed should replace using IPython Parallel if you primarily use the LoadBalancedView.\n", "\n", "However, you may already have infrastructure for deploying and managing IPython engines,\n", "and IPython Parallel's interactive debugging features can still be useful.\n", "\n", "Any IPython cluster can *become* a dask cluster at any time,\n", "and be used simultaneously via both APIs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can turn your IPython cluster into a distributed cluster by calling `Client.become_dask()`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "12bcc9bb5f5849b281bc6b305c5cfe7d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00\n", "
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\n", "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dask_client = rc.become_dask(ncores=1)\n", "dask_client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will:\n", "\n", "1. start a Scheduler on the Hub\n", "2. start a Worker on each engine\n", "3. return an Executor, the distributed client API\n", "\n", "By default, distributed Workers will use threads to run on all cores of a machine. \n", "In this case, since I already have one *engine* per core,\n", "I tell distributed to run one core per Worker with `ncores=1`.\n", "\n", "We can now use our IPython cluster with distributed:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b9874c7d2ae04c6fa879f14f00135e30", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from distributed import progress\n", "\n", "\n", "def square(x):\n", " return x ** 2\n", "\n", "\n", "def neg(x):\n", " return -x\n", "\n", "\n", "A = dask_client.map(square, range(1000))\n", "B = dask_client.map(neg, A)\n", "total = dask_client.submit(sum, B)\n", "progress(total)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-332833500" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total.result()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I could also let distributed do its multithreading thing, and run one multi-threaded Worker per engine.\n", "\n", "First, I need to get a mapping of one engine per host:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 'touchy', 1: 'touchy', 2: 'touchy', 3: 'touchy'}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import socket\n", "\n", "engine_hosts = rc[:].apply_async(socket.gethostname).get_dict()\n", "engine_hosts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can reverse this mapping, to get a list of engines on each host:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'touchy': [0, 1, 2, 3]}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "host_engines = {}\n", "for engine_id, host in engine_hosts.items():\n", " if host not in host_engines:\n", " host_engines[host] = []\n", " host_engines[host].append(engine_id)\n", "\n", "host_engines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I can get one engine per host:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_engine_per_host = [engines[0] for engines in host_engines.values()]\n", "one_engine_per_host" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Here's a concise, but more opaque version that does the same thing:*" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "one_engine_per_host = list({host: eid for eid, host in engine_hosts.items()}.values())\n", "one_engine_per_host" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I can now stop the first distributed cluster, and start a new one on just these engines, letting distributed allocate threads:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc.stop_distributed()\n", "\n", "dask_client = rc.become_dask(one_engine_per_host)\n", "dask_client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And submit the same tasks again:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b6eddbe0edfb49e09f289ed49a29e988", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = dask_client.map(square, range(100))\n", "B = dask_client.map(neg, A)\n", "total = dask_client.submit(sum, B)\n", "progress(total)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Debugging distributed with IPython" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc.stop_distributed()\n", "\n", "dask_client = rc.become_dask(one_engine_per_host)\n", "dask_client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's set the %px magics to only run on our one engine per host:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "view = rc[one_engine_per_host]\n", "view.block = True\n", "view.activate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's submit some work that's going to fail somewhere in the middle:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "524ea13e69ef4fbc80650087f3f9b746", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox()" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "ZeroDivisionError", "evalue": "division by zero", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_14739/3426399645.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdask_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minverted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprogress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtotal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/conda/lib/python3.9/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"error\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 234\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cancelled\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/var/folders/qr/3vxfnp1x2t1fw55dr288mphc0000gn/T/ipykernel_14739/3426399645.py\u001b[0m in \u001b[0;36minverse\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" ] } ], "source": [ "from IPython.display import display\n", "from distributed import progress\n", "\n", "\n", "def shift5(x):\n", " return x - 5\n", "\n", "\n", "def inverse(x):\n", " return 1 / x\n", "\n", "\n", "shifted = dask_client.map(shift5, range(1, 10))\n", "inverted = dask_client.map(inverse, shifted)\n", "\n", "total = dask_client.submit(sum, inverted)\n", "display(progress(total))\n", "total.result()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see which task failed:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[f for f in inverted if f.status == \"error\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When IPython starts a worker on each engine,\n", "it stores it in the `distributed_worker` variable in the engine's namespace.\n", "This lets us query the worker interactively.\n", "\n", "We can check out the current data resident on each worker:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[0;31mOut[3:1]: \u001b[0mBuffer<, deserialize_bytes >>" ] }, "metadata": { "after": [], "completed": "2021-09-01T10:14:10.163710", "data": {}, "engine_id": 3, "engine_uuid": "f3a63abb-5985ad4fd1b9c71f02922bb4", "error": null, "execute_input": "dask_worker.data\n", "execute_result": { "data": { "text/plain": "Buffer<, deserialize_bytes >>" }, "execution_count": 1, "metadata": {} }, "follow": [], "is_broadcast": false, "is_coalescing": false, "msg_id": "867ada26-ed0b46a8d787906a2a56aca7_24", "outputs": [], "received": "2021-09-01T10:14:10.167670", "started": "2021-09-01T10:14:10.138718", "status": "ok", "stderr": "", "stdout": "", "submitted": "2021-09-01T10:14:10.130438" }, "output_type": "display_data" } ], "source": [ "%%px\n", "dask_worker.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we can poke around with each Worker,\n", "we can have a slightly easier time figuring out what went wrong." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/dependencies.py000066400000000000000000000061541460376056100231310ustar00rootroot00000000000000import ipyparallel as ipp from ipyparallel import Dependency client = ipp.Client() # this will only run on machines that can import numpy: @ipp.require('numpy') def norm(A): from numpy.linalg import norm return norm(A, 2) def checkpid(pid): """return the pid of the engine""" import os return os.getpid() == pid def checkhostname(host): import socket return socket.gethostname() == host def getpid(): import os return os.getpid() pid0 = client[0].apply_sync(getpid) # this will depend on the pid being that of target 0: @ipp.depend(checkpid, pid0) def getpid2(): import os return os.getpid() view = client.load_balanced_view() view.block = True # will run on anything: pids1 = [view.apply(getpid) for i in range(len(client.ids))] print(pids1) # will only run on e0: pids2 = [view.apply(getpid2) for i in range(len(client.ids))] print(pids2) print("now test some dependency behaviors") def wait(t): import time time.sleep(t) return t # fail after some time: def wait_and_fail(t): import time time.sleep(t) return 1 / 0 successes = [view.apply_async(wait, 1).msg_ids[0] for i in range(len(client.ids))] failures = [ view.apply_async(wait_and_fail, 1).msg_ids[0] for i in range(len(client.ids)) ] mixed = [failures[0], successes[0]] d1a = Dependency(mixed, all=False, failure=True) # yes d1b = Dependency(mixed, all=False) # yes d2a = Dependency(mixed, all=True, failure=True) # yes after / no follow d2b = Dependency(mixed, all=True) # no d3 = Dependency(failures, all=False) # no d4 = Dependency(failures, all=False, failure=True) # yes d5 = Dependency(failures, all=True, failure=True) # yes after / no follow d6 = Dependency(successes, all=True, failure=True) # yes after / no follow view.block = False flags = view.temp_flags with flags(after=d1a): r1a = view.apply(getpid) with flags(follow=d1b): r1b = view.apply(getpid) with flags(after=d2b, follow=d2a): r2a = view.apply(getpid) with flags(after=d2a, follow=d2b): r2b = view.apply(getpid) with flags(after=d3): r3 = view.apply(getpid) with flags(after=d4): r4a = view.apply(getpid) with flags(follow=d4): r4b = view.apply(getpid) with flags(after=d3, follow=d4): r4c = view.apply(getpid) with flags(after=d5): r5 = view.apply(getpid) with flags(follow=d5, after=d3): r5b = view.apply(getpid) with flags(follow=d6): r6 = view.apply(getpid) with flags(after=d6, follow=d2b): r6b = view.apply(getpid) def should_fail(f): try: f() except ipp.error.KernelError: pass else: print('should have raised') # raise Exception("should have raised") # print(r1a.msg_ids) r1a.get() # print(r1b.msg_ids) r1b.get() # print(r2a.msg_ids) should_fail(r2a.get) # print(r2b.msg_ids) should_fail(r2b.get) # print(r3.msg_ids) should_fail(r3.get) # print(r4a.msg_ids) r4a.get() # print(r4b.msg_ids) r4b.get() # print(r4c.msg_ids) should_fail(r4c.get) # print(r5.msg_ids) r5.get() # print(r5b.msg_ids) should_fail(r5b.get) # print(r6.msg_ids) should_fail(r6.get) # assuming > 1 engine # print(r6b.msg_ids) should_fail(r6b.get) print('done') ipyparallel-8.8.0/docs/source/examples/fetchparse.py000066400000000000000000000054101460376056100226210ustar00rootroot00000000000000""" An exceptionally lousy site spider Ken Kinder Updated for newparallel by Min Ragan-Kelley This module gives an example of how the task interface to the IPython controller works. Before running this script start the IPython controller and some engines using something like:: ipcluster start -n 4 """ import sys import time import bs4 # noqa this isn't necessary, but it helps throw the dependency error earlier import ipyparallel as ipp def fetchAndParse(url, data=None): from urllib.parse import urljoin import bs4 # noqa import requests links = [] r = requests.get(url, data=data) r.raise_for_status() if 'text/html' in r.headers.get('content-type'): doc = bs4.BeautifulSoup(r.text, "html.parser") for node in doc.findAll('a'): href = node.get('href', None) if href: links.append(urljoin(url, href)) return links class DistributedSpider: # Time to wait between polling for task results. pollingDelay = 0.5 def __init__(self, site): self.client = ipp.Client() self.view = self.client.load_balanced_view() self.mux = self.client[:] self.allLinks = [] self.linksWorking = {} self.linksDone = {} self.site = site def visitLink(self, url): if url not in self.allLinks: self.allLinks.append(url) if url.startswith(self.site): print(' ', url) self.linksWorking[url] = self.view.apply(fetchAndParse, url) def onVisitDone(self, links, url): print(url + ':') self.linksDone[url] = None del self.linksWorking[url] for link in links: self.visitLink(link) def run(self): self.visitLink(self.site) while self.linksWorking: print(len(self.linksWorking), 'pending...') self.synchronize() time.sleep(self.pollingDelay) def synchronize(self): for url, ar in list(self.linksWorking.items()): # Calling get_task_result with block=False will return None if the # task is not done yet. This provides a simple way of polling. try: links = ar.get(0) except ipp.error.TimeoutError: continue except Exception as e: self.linksDone[url] = None del self.linksWorking[url] print(f'{url}: {e}') else: self.onVisitDone(links, url) def main(): if len(sys.argv) > 1: site = sys.argv[1] else: site = input('Enter site to crawl: ') distributedSpider = DistributedSpider(site) distributedSpider.run() if __name__ == '__main__': main() ipyparallel-8.8.0/docs/source/examples/index.md000066400000000000000000000023271460376056100215600ustar00rootroot00000000000000--- file_format: mystnb kernelspec: name: python3 mystnb: execution_mode: "force" remove_code_source: true --- # examples Here you will find example notebooks and scripts for working with IPython Parallel. ## Tutorials ```{toctree} Cluster API Parallel Magics progress Data Publication API visualizing-tasks broadcast/Broadcast view broadcast/memmap Broadcast broadcast/MPI Broadcast ``` ## Examples ```{toctree} Monitoring an MPI Simulation - 1 Monitoring an MPI Simulation - 2 Parallel Decorator and map Using MPI with IPython Parallel Monte Carlo Options rmt/rmt ``` ## Integrating IPython Parallel with other tools There are lots of cool tools for working with asynchronous and parallel execution. IPython Parallel aims to be fairly compatible with these, both by implementing explicit support via methods such as `Client.become_dask`, and by using standards such as the `concurrent.futures.Future` API. ```{toctree} Futures joblib dask Using Dill ``` ## Non-notebook examples This directory also contains some examples that are scripts instead of notebooks. ```{code-cell} import glob import os from IPython.display import FileLink, FileLinks, display FileLinks(".", included_suffixes=[".py"], recursive=True) ``` ipyparallel-8.8.0/docs/source/examples/interengine/000077500000000000000000000000001460376056100224325ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/examples/interengine/bintree.py000066400000000000000000000154141460376056100244410ustar00rootroot00000000000000""" BinaryTree inter-engine communication class use from bintree_script.py Provides parallel [all]reduce functionality """ import re import socket from functools import reduce import zmq from ipyparallel.serialize import deserialize_object, serialize_object from ipyparallel.util import disambiguate_url # ---------------------------------------------------------------------------- # bintree-related construction/printing helpers # ---------------------------------------------------------------------------- def bintree(ids, parent=None): """construct {child:parent} dict representation of a binary tree keys are the nodes in the tree, and values are the parent of each node. The root node has parent `parent`, default: None. >>> tree = bintree(range(7)) >>> tree {0: None, 1: 0, 2: 1, 3: 1, 4: 0, 5: 4, 6: 4} >>> print_bintree(tree) 0 1 2 3 4 5 6 """ parents = {} n = len(ids) if n == 0: return parents root = ids[0] parents[root] = parent if len(ids) == 1: return parents else: ids = ids[1:] n = len(ids) left = bintree(ids[: n // 2], parent=root) right = bintree(ids[n // 2 :], parent=root) parents.update(left) parents.update(right) return parents def reverse_bintree(parents): """construct {parent:[children]} dict from {child:parent} keys are the nodes in the tree, and values are the lists of children of that node in the tree. reverse_tree[None] is the root node >>> tree = bintree(range(7)) >>> reverse_bintree(tree) {None: 0, 0: [1, 4], 4: [5, 6], 1: [2, 3]} """ children = {} for child, parent in parents.iteritems(): if parent is None: children[None] = child continue elif parent not in children: children[parent] = [] children[parent].append(child) return children def depth(n, tree): """get depth of an element in the tree""" d = 0 parent = tree[n] while parent is not None: d += 1 parent = tree[parent] return d def print_bintree(tree, indent=' '): """print a binary tree""" for n in sorted(tree.keys()): print(f"{indent * depth(n, tree)}{n}") # ---------------------------------------------------------------------------- # Communicator class for a binary-tree map # ---------------------------------------------------------------------------- ip_pat = re.compile(r'^\d+\.\d+\.\d+\.\d+$') def disambiguate_dns_url(url, location): """accept either IP address or dns name, and return IP""" if not ip_pat.match(location): location = socket.gethostbyname(location) return disambiguate_url(url, location) class BinaryTreeCommunicator: id = None pub = None sub = None downstream = None upstream = None pub_url = None tree_url = None def __init__(self, id, interface='tcp://*', root=False): self.id = id self.root = root # create context and sockets self._ctx = zmq.Context() if root: self.pub = self._ctx.socket(zmq.PUB) else: self.sub = self._ctx.socket(zmq.SUB) self.sub.SUBSCRIBE = b'' self.downstream = self._ctx.socket(zmq.PULL) self.upstream = self._ctx.socket(zmq.PUSH) # bind to ports interface_f = interface + ":%i" if self.root: pub_port = self.pub.bind_to_random_port(interface) self.pub_url = interface_f % pub_port tree_port = self.downstream.bind_to_random_port(interface) self.tree_url = interface_f % tree_port self.downstream_poller = zmq.Poller() self.downstream_poller.register(self.downstream, zmq.POLLIN) # guess first public IP from socket self.location = socket.gethostbyname_ex(socket.gethostname())[-1][0] def __del__(self): self.downstream.close() self.upstream.close() if self.root: self.pub.close() else: self.sub.close() self._ctx.term() @property def info(self): """return the connection info for this object's sockets.""" return (self.tree_url, self.location) def connect(self, peers, btree, pub_url, root_id=0): """connect to peers. `peers` will be a dict of 4-tuples, keyed by name. {peer : (ident, addr, pub_addr, location)} where peer is the name, ident is the XREP identity, addr,pub_addr are the """ # count the number of children we have self.nchildren = list(btree.values()).count(self.id) if self.root: return # root only binds root_location = peers[root_id][-1] self.sub.connect(disambiguate_dns_url(pub_url, root_location)) parent = btree[self.id] tree_url, location = peers[parent] self.upstream.connect(disambiguate_dns_url(tree_url, location)) def serialize(self, obj): """serialize objects. Must return list of sendable buffers. Can be extended for more efficient/noncopying serialization of numpy arrays, etc. """ return serialize_object(obj) def deserialize(self, msg): """inverse of serialize""" return deserialize_object(msg)[0] def publish(self, value): assert self.root self.pub.send_multipart(self.serialize(value)) def consume(self): assert not self.root return self.deserialize(self.sub.recv_multipart()) def send_upstream(self, value, flags=0): assert not self.root self.upstream.send_multipart(self.serialize(value), flags=flags) def recv_downstream(self, flags=0, timeout=2000.0): # wait for a message, so we won't block if there was a bug self.downstream_poller.poll(timeout) msg = self.downstream.recv_multipart(flags) return self.deserialize(msg) def reduce(self, f, value, flat=True, all=False): """parallel reduce on binary tree if flat: value is an entry in the sequence else: value is a list of entries in the sequence if all: broadcast final result to all nodes else: only root gets final result """ if not flat: value = reduce(f, value) for i in range(self.nchildren): value = f(value, self.recv_downstream()) if not self.root: self.send_upstream(value) if all: if self.root: self.publish(value) else: value = self.consume() return value def allreduce(self, f, value, flat=True): """parallel reduce followed by broadcast of the result""" return self.reduce(f, value, flat=flat, all=True) ipyparallel-8.8.0/docs/source/examples/interengine/bintree_script.py000077500000000000000000000054001460376056100260220ustar00rootroot00000000000000#!/usr/bin/env python """ Script for setting up and using [all]reduce with a binary-tree engine interconnect. usage: `python bintree_script.py` This spanning tree strategy ensures that a single node node mailbox will never receive more that 2 messages at once. This is very important to scale to large clusters (e.g. 1000 nodes) since if you have many incoming messages of a couple of megabytes you might saturate the network interface of a single node and potentially its memory buffers if the messages are not consumed in a streamed manner. Note that the AllReduce scheme implemented with the spanning tree strategy impose the aggregation function to be commutative and distributive. It might not be the case if you implement the naive gather / reduce / broadcast strategy where you can reorder the partial data before performing the reduce. """ import ipyparallel as ipp # connect client and create views rc = ipp.Client() rc.block = True ids = rc.ids root_id = ids[0] root = rc[root_id] view = rc[:] from bintree import bintree, print_bintree # generate binary tree of parents btree = bintree(ids) print("setting up binary tree interconnect:") print_bintree(btree) view.run('bintree.py') view.scatter('id', ids, flatten=True) view['root_id'] = root_id # create the Communicator objects on the engines view.execute('com = BinaryTreeCommunicator(id, root = id==root_id )') pub_url = root.apply_sync(lambda: com.pub_url) # noqa: F821 # gather the connection information into a dict ar = view.apply_async(lambda: com.info) # noqa: F821 peers = ar.get_dict() # this is a dict, keyed by engine ID, of the connection info for the EngineCommunicators # connect the engines to each other: def connect(com, peers, tree, pub_url, root_id): """this function will be called on the engines""" com.connect(peers, tree, pub_url, root_id) view.apply_sync(connect, ipp.Reference('com'), peers, btree, pub_url, root_id) # functions that can be used for reductions # max and min builtins can be used as well def add(a, b): """cumulative sum reduction""" return a + b def mul(a, b): """cumulative product reduction""" return a * b view['add'] = add view['mul'] = mul # scatter some data data = list(range(1000)) view.scatter('data', data) # perform cumulative sum via allreduce print("reducing") ar = view.execute("data_sum = com.allreduce(add, data, flat=False)", block=False) ar.wait_interactive() print("allreduce sum of data on all engines:", view['data_sum']) # perform cumulative sum *without* final broadcast # when not broadcasting with allreduce, the final result resides on the root node: view.execute("ids_sum = com.reduce(add, id, flat=True)") print("reduce sum of engine ids (not broadcast):", root['ids_sum']) print("partial result on each engine:", view['ids_sum']) ipyparallel-8.8.0/docs/source/examples/interengine/communicator.py000066400000000000000000000051161460376056100255070ustar00rootroot00000000000000import socket import uuid import zmq from ipyparallel.util import disambiguate_url class EngineCommunicator: def __init__(self, interface='tcp://*', identity=None): self._ctx = zmq.Context() self.socket = self._ctx.socket(zmq.XREP) self.pub = self._ctx.socket(zmq.PUB) self.sub = self._ctx.socket(zmq.SUB) # configure sockets self.identity = identity or bytes(uuid.uuid4()) self.socket.IDENTITY = self.identity self.sub.SUBSCRIBE = b'' # bind to ports port = self.socket.bind_to_random_port(interface) pub_port = self.pub.bind_to_random_port(interface) self.url = interface + ":%i" % port self.pub_url = interface + ":%i" % pub_port # guess first public IP from socket self.location = socket.gethostbyname_ex(socket.gethostname())[-1][0] self.peers = {} def __del__(self): self.socket.close() self.pub.close() self.sub.close() self._ctx.term() @property def info(self): """return the connection info for this object's sockets.""" return (self.identity, self.url, self.pub_url, self.location) def connect(self, peers): """connect to peers. `peers` will be a dict of 4-tuples, keyed by name. {peer : (ident, addr, pub_addr, location)} where peer is the name, ident is the XREP identity, addr,pub_addr are the """ for peer, (ident, url, pub_url, location) in peers.items(): self.peers[peer] = ident if ident != self.identity: self.sub.connect(disambiguate_url(pub_url, location)) if ident > self.identity: # prevent duplicate xrep, by only connecting # engines to engines with higher IDENTITY # a doubly-connected pair will crash self.socket.connect(disambiguate_url(url, location)) def send(self, peers, msg, flags=0, copy=True): if not isinstance(peers, list): peers = [peers] if not isinstance(msg, list): msg = [msg] for p in peers: ident = self.peers[p] self.socket.send_multipart([ident] + msg, flags=flags, copy=copy) def recv(self, flags=0, copy=True): return self.socket.recv_multipart(flags=flags, copy=copy)[1:] def publish(self, msg, flags=0, copy=True): if not isinstance(msg, list): msg = [msg] self.pub.send_multipart(msg, copy=copy) def consume(self, flags=0, copy=True): return self.sub.recv_multipart(flags=flags, copy=copy) ipyparallel-8.8.0/docs/source/examples/interengine/interengine.py000066400000000000000000000025461460376056100253220ustar00rootroot00000000000000import ipyparallel as ipp rc = ipp.Client() rc.block = True view = rc[:] view.run('communicator.py') view.execute('com = EngineCommunicator()') # gather the connection information into a dict ar = view.apply_async(lambda: com.info) # noqa: F821 peers = ar.get_dict() # this is a dict, keyed by engine ID, of the connection info for the EngineCommunicators # connect the engines to each other: view.apply_sync(lambda pdict: com.connect(pdict), peers) # noqa: F821 # now all the engines are connected, and we can communicate between them: def broadcast(client, sender, msg_name, dest_name=None, block=None): """broadcast a message from one engine to all others.""" dest_name = msg_name if dest_name is None else dest_name client[sender].execute('com.publish(%s)' % msg_name, block=None) targets = client.ids targets.remove(sender) return client[targets].execute('%s=com.consume()' % dest_name, block=None) def send(client, sender, targets, msg_name, dest_name=None, block=None): """send a message from one to one-or-more engines.""" dest_name = msg_name if dest_name is None else dest_name def _send(targets, m_name): msg = globals()[m_name] return com.send(targets, msg) # noqa: F821 client[sender].apply_async(_send, targets, msg_name) return client[targets].execute('%s=com.recv()' % dest_name, block=None) ipyparallel-8.8.0/docs/source/examples/iopubwatcher.py000066400000000000000000000054641460376056100232020ustar00rootroot00000000000000"""A script for watching all traffic on the IOPub channel (stdout/stderr/pyerr) of engines. This connects to the default cluster, or you can pass the path to your ipcontroller-client.json Try running this script, and then running a few jobs that print (and call sys.stdout.flush), and you will see the print statements as they arrive, notably not waiting for the results to finish. You can use the zeromq SUBSCRIBE mechanism to only receive information from specific engines, and easily filter by message type. Authors ------- * MinRK """ import json import sys import zmq from ipykernel.connect import find_connection_file from jupyter_client.session import Session def main(connection_file): """watch iopub channel, and print messages""" ctx = zmq.Context.instance() with open(connection_file) as f: cfg = json.loads(f.read()) reg_url = cfg['interface'] iopub_port = cfg['iopub'] iopub_url = f"{reg_url}:{iopub_port}" session = Session(key=cfg['key'].encode('ascii')) sub = ctx.socket(zmq.SUB) # This will subscribe to all messages: sub.SUBSCRIBE = b'' # replace with b'' with b'engine.1.stdout' to subscribe only to engine 1's stdout # 0MQ subscriptions are simple 'foo*' matches, so 'engine.1.' subscribes # to everything from engine 1, but there is no way to subscribe to # just stdout from everyone. # multiple calls to subscribe will add subscriptions, e.g. to subscribe to # engine 1's stderr and engine 2's stdout: # sub.SUBSCRIBE = b'engine.1.stderr' # sub.SUBSCRIBE = b'engine.2.stdout' sub.connect(iopub_url) while True: try: idents, msg = session.recv(sub, mode=0) except KeyboardInterrupt: return # ident always length 1 here topic = idents[0].decode('utf8', 'replace') if msg['msg_type'] == 'stream': # stdout/stderr # stream names are in msg['content']['name'], if you want to handle # them differently print("{}: {}".format(topic, msg['content']['text'])) elif msg['msg_type'] == 'error': # Python traceback c = msg['content'] print(topic + ':') for line in c['traceback']: # indent lines print(' ' + line) elif msg['msg_type'] == 'error': # Python traceback c = msg['content'] print(topic + ':') for line in c['traceback']: # indent lines print(' ' + line) if __name__ == '__main__': if len(sys.argv) > 1: pattern = sys.argv[1] else: # This gets the security file for the default profile: pattern = 'ipcontroller-client.json' cf = find_connection_file(pattern) print("Using connection file %s" % cf) main(cf) ipyparallel-8.8.0/docs/source/examples/itermapresult.py000066400000000000000000000043701460376056100234010ustar00rootroot00000000000000"""Example of iteration through AsyncMapResults, without waiting for all results When you call view.map(func, sequence), you will receive a special AsyncMapResult object. These objects are used to reconstruct the results of the split call. One feature AsyncResults provide is that they are iterable *immediately*, so you can iterate through the actual results as they complete. This is useful if you submit a large number of tasks that may take some time, but want to perform logic on elements in the result, or even abort subsequent tasks in cases where you are searching for the first affirmative result. By default, the results will match the ordering of the submitted sequence, but if you call `map(...ordered=False)`, then results will be provided to the iterator on a first come first serve basis. Authors ------- * MinRK """ import time import ipyparallel as ipp # create client & view rc = ipp.Client() dv = rc[:] v = rc.load_balanced_view() # scatter 'id', so id=0,1,2 on engines 0,1,2 dv.scatter('id', rc.ids, flatten=True) print("Engine IDs: ", dv['id']) # create a Reference to `id`. This will be a different value on each engine ref = ipp.Reference('id') print("sleeping for `id` seconds on each engine") tic = time.time() ar = dv.apply(time.sleep, ref) for i, r in enumerate(ar): print("%i: %.3f" % (i, time.time() - tic)) def sleep_here(t): import time time.sleep(t) return id, t # one call per task print("running with one call per task") amr = v.map(sleep_here, [0.01 * t for t in range(100)]) tic = time.time() for i, r in enumerate(amr): print("task %i on engine %i: %.3f" % (i, r[0], time.time() - tic)) print("running with four calls per task") # with chunksize, we can have four calls per task amr = v.map(sleep_here, [0.01 * t for t in range(100)], chunksize=4) tic = time.time() for i, r in enumerate(amr): print("task %i on engine %i: %.3f" % (i, r[0], time.time() - tic)) print("running with two calls per task, with unordered results") # We can even iterate through faster results first, with ordered=False amr = v.map( sleep_here, [0.01 * t for t in range(100, 0, -1)], ordered=False, chunksize=2 ) tic = time.time() for i, r in enumerate(amr): print("slept %.2fs on engine %i: %.3f" % (r[1], r[0], time.time() - tic)) ipyparallel-8.8.0/docs/source/examples/joblib.ipynb000066400000000000000000000134771460376056100224430ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using IPython Parallel as a joblib backend\n", "\n", "[joblib][] is a tool for running tasks, which includes support for implementing custom parallel backends.\n", "IPython defines one such backend, so you can use IPython parallel with joblib.\n", "\n", "[joblib]: https://joblib.readthedocs.io\n", "\n", "The simplest way to set this up is a single call:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import ipyparallel as ipp\n", "\n", "ipp.register_joblib_backend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This registers the 'ipyparallel' backend with all the defaults." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "be0e2704e2204783ae9435d63d6facff", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1 [00:00\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dea6ba0c1c7b419eae13a54f7b6c66fb", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00>> print list(mergesort([[1,2,3,4], ... [2,3.25,3.75,4.5,6,7], ... [2.625,3.625,6.625,9]])) [1, 2, 2, 2.625, 3, 3.25, 3.625, 3.75, 4, 4.5, 6, 6.625, 7, 9] # note stability >>> print list(mergesort([[1,2,3,4], ... [2,3.25,3.75,4.5,6,7], ... [2.625,3.625,6.625,9]], ... key=int)) [1, 2, 2, 2.625, 3, 3.25, 3.75, 3.625, 4, 4.5, 6, 6.625, 7, 9] >>> print list(mergesort([[4, 3, 2, 1], ... [7, 6, 4.5, 3.75, 3.25, 2], ... [9, 6.625, 3.625, 2.625]], ... key=lambda x: -x)) [9, 7, 6.625, 6, 4.5, 4, 3.75, 3.625, 3.25, 3, 2.625, 2, 2, 1] """ heap = [] for i, itr in enumerate(iter(pl) for pl in list_of_lists): try: item = next(itr) if key: toadd = (key(item), i, item, itr) else: toadd = (item, i, itr) heap.append(toadd) except StopIteration: pass heapq.heapify(heap) if key: while heap: _, idx, item, itr = heap[0] yield item try: item = next(itr) heapq.heapreplace(heap, (key(item), idx, item, itr)) except StopIteration: heapq.heappop(heap) else: while heap: item, idx, itr = heap[0] yield item try: heapq.heapreplace(heap, (next(itr), idx, itr)) except StopIteration: heapq.heappop(heap) def remote_iterator(view, name): """Return an iterator on an object living on a remote engine.""" view.execute(f'it{name}=iter({name})', block=True) while True: try: result = view.apply_sync(next, ipp.Reference('it' + name)) # This causes the StopIteration exception to be raised. except RemoteError as e: if e.ename == 'StopIteration': raise StopIteration else: raise e else: yield result # Main, interactive testing if __name__ == '__main__': rc = ipp.Client() view = rc[:] print('Engine IDs:', rc.ids) # Make a set of 'sorted datasets' a0 = range(5, 20) a1 = range(10) a2 = range(15, 25) # Now, imagine these had been created in the remote engines by some long # computation. In this simple example, we just send them over into the # remote engines. They will all be called 'a' in each engine. rc[0]['a'] = a0 rc[1]['a'] = a1 rc[2]['a'] = a2 # And we now make a local object which represents the remote iterator aa0 = remote_iterator(rc[0], 'a') aa1 = remote_iterator(rc[1], 'a') aa2 = remote_iterator(rc[2], 'a') # Let's merge them, both locally and remotely: print('Merge the local datasets:') print(list(mergesort([a0, a1, a2]))) print('Locally merge the remote sets:') print(list(mergesort([aa0, aa1, aa2]))) ipyparallel-8.8.0/docs/source/examples/phistogram.py000066400000000000000000000023501460376056100226520ustar00rootroot00000000000000"""Parallel histogram function""" from ipyparallel import Reference def phistogram(view, a, bins=10, rng=None, normed=False): """Compute the histogram of a remote array a. Parameters ---------- view IPython DirectView instance a : str String name of the remote array bins : int Number of histogram bins rng : (float, float) Tuple of min, max of the range to histogram normed : boolean Should the histogram counts be normalized to 1 """ nengines = len(view.targets) # view.push(dict(bins=bins, rng=rng)) with view.sync_imports(): import numpy rets = view.apply_sync( lambda a, b, rng: numpy.histogram(a, b, rng), Reference(a), bins, rng ) hists = [r[0] for r in rets] lower_edges = [r[1] for r in rets] # view.execute('hist, lower_edges = numpy.histogram(%s, bins, rng)' % a) lower_edges = view.pull('lower_edges', targets=0) hist_array = numpy.array(hists).reshape(nengines, -1) # hist_array.shape = (nengines,-1) total_hist = numpy.sum(hist_array, 0) if normed: total_hist = total_hist / numpy.sum(total_hist, dtype=float) return total_hist, lower_edges ipyparallel-8.8.0/docs/source/examples/pi/000077500000000000000000000000001460376056100205335ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/examples/pi/parallelpi.py000066400000000000000000000036561460376056100232440ustar00rootroot00000000000000"""Calculate statistics on the digits of pi in parallel. This program uses the functions in :file:`pidigits.py` to calculate the frequencies of 2 digit sequences in the digits of pi. The results are plotted using matplotlib. To run, text files from https://www.super-computing.org/ must be installed in the working directory of the IPython engines. The actual filenames to be used can be set with the ``filestring`` variable below. The dataset we have been using for this is the 200 million digit one here: ftp://pi.super-computing.org/.2/pi200m/ and the files used will be downloaded if they are not in the working directory of the IPython engines. """ from timeit import default_timer as clock from matplotlib import pyplot as plt from pidigits import ( compute_two_digit_freqs, fetch_pi_file, plot_two_digit_freqs, reduce_freqs, ) import ipyparallel as ipp # Files with digits of pi (10m digits each) filestring = 'pi200m.ascii.%(i)02dof20' files = [filestring % {'i': i} for i in range(1, 21)] # Connect to the IPython cluster c = ipp.Client() c[:].run('pidigits.py') # the number of engines n = len(c) id0 = c.ids[0] v = c[:] v.block = True # fetch the pi-files print("downloading %i files of pi" % n) v.map(fetch_pi_file, files[:n]) # noqa: F821 print("done") # Run 10m digits on 1 engine t1 = clock() freqs10m = c[id0].apply_sync(compute_two_digit_freqs, files[0]) t2 = clock() digits_per_second1 = 10.0e6 / (t2 - t1) print("Digits per second (1 core, 10m digits): ", digits_per_second1) # Run n*10m digits on all engines t1 = clock() freqs_all = v.map(compute_two_digit_freqs, files[:n]) freqs150m = reduce_freqs(freqs_all) t2 = clock() digits_per_second8 = n * 10.0e6 / (t2 - t1) print("Digits per second (%i engines, %i0m digits): " % (n, n), digits_per_second8) print("Speedup: ", digits_per_second8 / digits_per_second1) plot_two_digit_freqs(freqs150m) plt.title("2 digit sequences in %i0m digits of pi" % n) plt.show() ipyparallel-8.8.0/docs/source/examples/pi/pidigits.py000066400000000000000000000077751460376056100227410ustar00rootroot00000000000000"""Compute statistics on the digits of pi. This uses precomputed digits of pi from the website of Professor Yasumasa Kanada at the University of Tokoyo: https://www.super-computing.org/ Currently, there are only functions to read the .txt (non-compressed, non-binary) files, but adding support for compression and binary files would be straightforward. This focuses on computing the number of times that all 1, 2, n digits sequences occur in the digits of pi. If the digits of pi are truly random, these frequencies should be equal. """ import os from urllib.request import urlretrieve import numpy as np from matplotlib import pyplot as plt # Top-level functions def fetch_pi_file(filename): """This will download a segment of pi from super-computing.org if the file is not already present. """ ftpdir = "ftp://pi.super-computing.org/.2/pi200m/" if os.path.exists(filename): # we already have it return else: # download it urlretrieve(ftpdir + filename, filename) def compute_one_digit_freqs(filename): """ Read digits of pi from a file and compute the 1 digit frequencies. """ d = txt_file_to_digits(filename) freqs = one_digit_freqs(d) return freqs def compute_two_digit_freqs(filename): """ Read digits of pi from a file and compute the 2 digit frequencies. """ d = txt_file_to_digits(filename) freqs = two_digit_freqs(d) return freqs def reduce_freqs(freqlist): """ Add up a list of freq counts to get the total counts. """ allfreqs = np.zeros_like(freqlist[0]) for f in freqlist: allfreqs += f return allfreqs def compute_n_digit_freqs(filename, n): """ Read digits of pi from a file and compute the n digit frequencies. """ d = txt_file_to_digits(filename) freqs = n_digit_freqs(d, n) return freqs # Read digits from a txt file def txt_file_to_digits(filename, the_type=str): """ Yield the digits of pi read from a .txt file. """ with open(filename) as f: for line in f.readlines(): for c in line: if c != '\n' and c != ' ': yield the_type(c) # Actual counting functions def one_digit_freqs(digits, normalize=False): """ Consume digits of pi and compute 1 digit freq. counts. """ freqs = np.zeros(10, dtype='i4') for d in digits: freqs[int(d)] += 1 if normalize: freqs = freqs / freqs.sum() return freqs def two_digit_freqs(digits, normalize=False): """ Consume digits of pi and compute 2 digits freq. counts. """ freqs = np.zeros(100, dtype='i4') last = next(digits) this = next(digits) for d in digits: index = int(last + this) freqs[index] += 1 last = this this = d if normalize: freqs = freqs / freqs.sum() return freqs def n_digit_freqs(digits, n, normalize=False): """ Consume digits of pi and compute n digits freq. counts. This should only be used for 1-6 digits. """ freqs = np.zeros(pow(10, n), dtype='i4') current = np.zeros(n, dtype=int) for i in range(n): current[i] = next(digits) for d in digits: index = int(''.join(map(str, current))) freqs[index] += 1 current[0:-1] = current[1:] current[-1] = d if normalize: freqs = freqs / freqs.sum() return freqs # Plotting functions def plot_two_digit_freqs(f2): """ Plot two digits frequency counts using matplotlib. """ f2_copy = f2.copy() f2_copy.shape = (10, 10) ax = plt.matshow(f2_copy) plt.colorbar() for i in range(10): for j in range(10): plt.text(i - 0.2, j + 0.2, str(j) + str(i)) plt.ylabel('First digit') plt.xlabel('Second digit') return ax def plot_one_digit_freqs(f1): """ Plot one digit frequency counts using matplotlib. """ ax = plt.plot(f1, 'bo-') plt.title('Single digit counts in pi') plt.xlabel('Digit') plt.ylabel('Count') return ax ipyparallel-8.8.0/docs/source/examples/progress.ipynb000066400000000000000000001363521460376056100230440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "e06e6145-5d8a-42f0-a573-883ceefeb456", "metadata": {}, "source": [ "# Using widgets in IPython Parallel\n", "\n", "IPython Parallel 7.1 introduces basic support for using Jupyter widgets from a notebook to engines.\n", "\n", "This allows things like progress bars for incremental stages,\n", "when IPP's own task-level progress doesn't show you enough information.\n", "\n", "As always, we start by creating and connecting to a cluster:" ] }, { "cell_type": "code", "execution_count": 1, "id": "cc0f626a-4e9e-4cd7-b9d5-37aa72656045", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 4 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dfdbae12208c47ea98ef749d45d64f62", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipyparallel as ipp\n", "rc = ipp.Cluster(n=4).start_and_connect_sync()\n", "rc.activate()" ] }, { "cell_type": "markdown", "id": "9a68b37e-8f8c-4e21-87ff-ff993d74af4d", "metadata": {}, "source": [ "IPython widgets support updateable readouts of things like progress.\n", "\n", "There are [lots of widgets to choose from][widget list], but for our purposes,\n", "we are going to use `IntProgress`\n", "\n", "[widget list]: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20List.html" ] }, { "cell_type": "code", "execution_count": 2, "id": "a2ee17a5-ba31-4d3e-9a91-cba386b8e2a9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "358962d716384d2199b8522907c9033b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, description='Step 0', max=10)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import time\n", "\n", "import ipywidgets as W\n", "from IPython.display import display\n", "\n", "progress = W.IntProgress(min=0, max=10, description=\"Step 0\", readout=True)\n", "display(progress)\n", "\n", "for i in range(10):\n", " progress.value += 1\n", " progress.description = f\"Step {progress.value}/{progress.max}\"\n", " time.sleep(0.1)\n", "\n", "progress.bar_style = \"success\" # change color when it's done" ] }, { "cell_type": "markdown", "id": "6ff1fe0c-adb3-43f7-b89f-f259acc8a72a", "metadata": {}, "source": [ "IPython Parallel supports progress and interactive waits on AsyncResult objects,\n", "which is great when your tasks are small and you have a lot of them:" ] }, { "cell_type": "code", "execution_count": 3, "id": "2d510a7e-69af-4d5f-bdd3-bb35933933bd", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "05a2877edaee4d75bae10bfa7ada3c32", "version_major": 2, "version_minor": 0 }, "text/plain": [ ": 0%| | 0/1000 [00:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rc[:].scatter(\"rank\", rc.ids, flatten=True)" ] }, { "cell_type": "code", "execution_count": 6, "id": "307a2f86-0948-461e-94f1-748471707feb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[output:3]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e02c6493e62e4ed38d2dfd45b7115667", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, description='rank 3: 0', max=10)" ] }, "metadata": { "engine": 3 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:2]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "197376ab6c2247c1af0769eae93220cb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, description='rank 2: 0', max=10)" ] }, "metadata": { "engine": 2 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:0]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd5ef98076d94493b57b021455162c56", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, description='rank 0: 0', max=10)" ] }, "metadata": { "engine": 0 }, "output_type": "display_data" }, { "data": { "text/plain": [ "[output:1]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "697cf15dec6447beaa227add1fa986e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "IntProgress(value=0, description='rank 1: 0', max=10)" ] }, "metadata": { "engine": 1 }, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b5ac39bd6114df9b1820ff1402e5a6c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "%px: 0%| | 0/4 [00:002 # # # Eigenvalue distribution of Gaussian orthogonal random matrices # # The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices # from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest # neighbor eigenvalue distribution $\rho(s)$. # from rmtkernel import ensemble_diffs, normalize_diffs, GOE import numpy as np import ipyparallel as ipp # # ## Wigner's nearest neighbor eigenvalue distribution # # The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution # for the GOE: # # $$\rho(s) = \frac{\pi s}{2} \exp(-\pi s^2/4)$$ # def wigner_dist(s): """Returns (s, rho(s)) for the Wigner GOE distribution.""" return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.) # def generate_wigner_data(): s = np.linspace(0.0,4.0,400) rhos = wigner_dist(s) return s, rhos # s, rhos = generate_wigner_data() # plot(s, rhos) xlabel('Normalized level spacing s') ylabel('Probability $\rho(s)$') # # ## Serial calculation of nearest neighbor eigenvalue distribution # # In this section we numerically construct and diagonalize a large number of GOE random matrices # and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core. # def serial_diffs(num, N): """Compute the nearest neighbor distribution for num NxX matrices.""" diffs = ensemble_diffs(num, N) normalized_diffs = normalize_diffs(diffs) return normalized_diffs # serial_nmats = 1000 serial_matsize = 50 # %timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize) # serial_diffs = serial_diffs(serial_nmats, serial_matsize) # # The numerical computation agrees with the predictions of Wigner, but it would be nice to get more # statistics. For that we will do a parallel computation. # hist_data = hist(serial_diffs, bins=30, normed=True) plot(s, rhos) xlabel('Normalized level spacing s') ylabel('Probability $P(s)$') # # ## Parallel calculation of nearest neighbor eigenvalue distribution # # Here we perform a parallel computation, where each process constructs and diagonalizes a subset of # the overall set of random matrices. # def parallel-diffs(rc, num, N): nengines = len(rc.targets) num_per_engine = num/nengines print "Running with", num_per_engine, "per engine." ar = rc.apply_async(ensemble_diffs, num_per_engine, N) diffs = np.array(ar.get()).flatten() normalized_diffs = normalize_diffs(diffs) return normalized_diffs # client = ipp.Client() view = client[:] view.run('rmtkernel.py') view.block = False # parallel-nmats = 40*serial_nmats parallel-matsize = 50 # %timeit -r1 -n1 parallel-diffs(view, parallel-nmats, parallel-matsize) # pdiffs = parallel-diffs(view, parallel-nmats, parallel-matsize) # # Again, the agreement with the Wigner distribution is excellent, but now we have better # statistics. # hist_data = hist(pdiffs, bins=30, normed=True) plot(s, rhos) xlabel('Normalized level spacing s') ylabel('Probability $P(s)$') ipyparallel-8.8.0/docs/source/examples/rmt/rmt.ipynb000066400000000000000000001306031460376056100225750ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Eigenvalue distribution of Gaussian orthogonal random matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices \n", "from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest\n", "neighbor eigenvalue distribution $\\rho(s)$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from rmtkernel import ensemble_diffs, normalize_diffs, GOE\n", "import numpy as np\n", "import ipyparallel as ipp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Wigner's nearest neighbor eigenvalue distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution\n", "for the GOE:\n", "\n", "$$\\rho(s) = \\frac{\\pi s}{2} \\exp(-\\pi s^2/4)$$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "def wigner_dist(s):\n", " \"\"\"Returns (s, rho(s)) for the Wigner GOE distribution.\"\"\"\n", " return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "def generate_wigner_data():\n", " s = np.linspace(0.0,4.0,400)\n", " rhos = wigner_dist(s)\n", " return s, rhos" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "s, rhos = generate_wigner_data()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x3828790>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oUeqcDCkYQljOrILx8ssvs2DBAlq1asWMGTOIc/Kps1Iwai83N5gxQ33KcIGH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\ngvad9HfeeSc33nhjpe87wr2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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(s, rhos)\n", "xlabel('Normalized level spacing s')\n", "ylabel('Probability $\\rho(s)$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Serial calculation of nearest neighbor eigenvalue distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we numerically construct and diagonalize a large number of GOE random matrices\n", "and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "def serial_diffs(num, N):\n", " \"\"\"Compute the nearest neighbor distribution for num NxX matrices.\"\"\"\n", " diffs = ensemble_diffs(num, N)\n", " normalized_diffs = normalize_diffs(diffs)\n", " return normalized_diffs" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "serial_nmats = 1000\n", "serial_matsize = 50" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 1: 1.19 s per loop" ] } ], "source": [ "%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "serial_diffs = serial_diffs(serial_nmats, serial_matsize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numerical computation agrees with the predictions of Wigner, but it would be nice to get more\n", "statistics. For that we will do a parallel computation." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x3475bd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CWKxqWTAyczM5EH8Af2d/raMIU0lpDXtfhSeeB12+1mmEsEjVsmDsu7SPjk06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\nSfn7+yt3d3fl5+en1q5dW2I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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist_data = hist(serial_diffs, bins=30, normed=True)\n", "plot(s, rhos)\n", "xlabel('Normalized level spacing s')\n", "ylabel('Probability $P(s)$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallel calculation of nearest neighbor eigenvalue distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we perform a parallel computation, where each process constructs and diagonalizes a subset of\n", "the overall set of random matrices." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "def parallel_diffs(rc, num, N):\n", " nengines = len(rc.targets)\n", " num_per_engine = num/nengines\n", " print \"Running with\", num_per_engine, \"per engine.\"\n", " ar = rc.apply_async(ensemble_diffs, num_per_engine, N)\n", " diffs = np.array(ar.get()).flatten()\n", " normalized_diffs = normalize_diffs(diffs)\n", " return normalized_diffs" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "client = ipp.Client()\n", "view = client[:]\n", "view.run('rmtkernel.py')\n", "view.block = False" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [ "parallel_nmats = 40*serial_nmats\n", "parallel_matsize = 50" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running with 10000 per engine.\n", "1 loops, best of 1: 14 s per loop" ] } ], "source": [ "%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } ipyparallel-8.8.0/docs/source/examples/rmt/rmtkernel.py000066400000000000000000000022571460376056100233100ustar00rootroot00000000000000# ------------------------------------------------------------------------------- # Core routines for computing properties of symmetric random matrices. # ------------------------------------------------------------------------------- import numpy as np ra = np.random la = np.linalg def GOE(N): """Creates an NxN element of the Gaussian Orthogonal Ensemble""" m = ra.standard_normal((N, N)) m += m.T return m / 2 def center_eigenvalue_diff(mat): """Compute the eigvals of mat and then find the center eigval difference.""" N = len(mat) evals = np.sort(la.eigvals(mat)) diff = np.abs(evals[N / 2] - evals[N / 2 - 1]) return diff def ensemble_diffs(num, N): """Return num eigenvalue diffs for the NxN GOE ensemble.""" diffs = np.empty(num) for i in range(num): mat = GOE(N) diffs[i] = center_eigenvalue_diff(mat) return diffs def normalize_diffs(diffs): """Normalize an array of eigenvalue diffs.""" return diffs / diffs.mean() def normalized_ensemble_diffs(num, N): """Return num *normalized* eigenvalue diffs for the NxN GOE ensemble.""" diffs = ensemble_diffs(num, N) return normalize_diffs(diffs) ipyparallel-8.8.0/docs/source/examples/task_mod.py000066400000000000000000000002361460376056100222770ustar00rootroot00000000000000import numpy from numpy.linalg import norm def task(n): """Generates a 1xN array and computes its 2-norm""" A = numpy.ones(n) return norm(A, 2) ipyparallel-8.8.0/docs/source/examples/task_profiler.py000066400000000000000000000044101460376056100233400ustar00rootroot00000000000000#!/usr/bin/env python """Test the performance of the task farming system. This script submits a set of tasks via a LoadBalancedView. The tasks are basically just a time.sleep(t), where t is a random number between two limits that can be configured at the command line. To run the script there must first be an IPython controller and engines running:: ipcluster start -n 16 A good test to run with 16 engines is:: python task_profiler.py -n 128 -t 0.01 -T 1.0 This should show a speedup of 13-14x. The limitation here is that the overhead of a single task is about 0.001-0.01 seconds. """ import random import time from optparse import OptionParser import ipyparallel as ipp def main(): parser = OptionParser() parser.set_defaults(n=100) parser.set_defaults(tmin=1e-3) parser.set_defaults(tmax=1) parser.set_defaults(profile='default') parser.add_option("-n", type='int', dest='n', help='the number of tasks to run') parser.add_option( "-t", type='float', dest='tmin', help='the minimum task length in seconds' ) parser.add_option( "-T", type='float', dest='tmax', help='the maximum task length in seconds' ) parser.add_option( "-p", '--profile', type='str', dest='profile', help="the cluster profile [default: 'default']", ) (opts, args) = parser.parse_args() assert opts.tmax >= opts.tmin, "tmax must not be smaller than tmin" rc = ipp.Client() view = rc.load_balanced_view() print(view) rc.block = True nengines = len(rc.ids) # the jobs should take a random time within a range times = [ random.random() * (opts.tmax - opts.tmin) + opts.tmin for i in range(opts.n) ] stime = sum(times) print( "executing %i tasks, totalling %.1f secs on %i engines" % (opts.n, stime, nengines) ) time.sleep(1) start = time.perf_counter() amr = view.map(time.sleep, times) amr.get() stop = time.perf_counter() ptime = stop - start scale = stime / ptime print(f"executed {stime:.1f} secs in {ptime:.1f} secs") print("%.3fx parallel performance on %i engines" % (scale, nengines)) print("%.1f%% of theoretical max" % (100 * scale / nengines)) if __name__ == '__main__': main() ipyparallel-8.8.0/docs/source/examples/throughput.py000066400000000000000000000030621460376056100227070ustar00rootroot00000000000000import time import numpy as np import ipyparallel as parallel nlist = map(int, np.logspace(2, 9, 16, base=2)) nlist2 = map(int, np.logspace(2, 8, 15, base=2)) tlist = map(int, np.logspace(7, 22, 16, base=2)) nt = 16 def wait(t=0): import time time.sleep(t) def echo(s=''): return s def time_throughput(nmessages, t=0, f=wait): client = parallel.Client() view = client.load_balanced_view() # do one ping before starting timing if f is echo: t = np.random.random(t / 8) view.apply_sync(echo, '') client.spin() tic = time.time() for i in range(nmessages): view.apply(f, t) lap = time.time() client.wait() toc = time.time() return lap - tic, toc - tic def do_runs(nlist, t=0, f=wait, trials=2, runner=time_throughput): A = np.zeros((len(nlist), 2)) for i, n in enumerate(nlist): t1 = t2 = 0 for _ in range(trials): time.sleep(0.25) ts = runner(n, t, f) t1 += ts[0] t2 += ts[1] t1 /= trials t2 /= trials A[i] = (t1, t2) A[i] = n / A[i] print(n, A[i]) return A def do_echo(n, tlist=[0], f=echo, trials=2, runner=time_throughput): A = np.zeros((len(tlist), 2)) for i, t in enumerate(tlist): t1 = t2 = 0 for _ in range(trials): time.sleep(0.25) ts = runner(n, t, f) t1 += ts[0] t2 += ts[1] t1 /= trials t2 /= trials A[i] = (t1, t2) A[i] = n / A[i] print(t, A[i]) return A ipyparallel-8.8.0/docs/source/examples/visualizing-tasks.ipynb000066400000000000000000012041161460376056100246620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "c12b2b6a-f643-4cb9-b5b4-dca60086a8e7", "metadata": {}, "source": [ "# Visualizing an AsyncResult in progress" ] }, { "cell_type": "code", "execution_count": 1, "id": "91255a04-00e0-44d5-8817-e5346226b4ca", "metadata": {}, "outputs": [], "source": [ "import ipyparallel as ipp" ] }, { "cell_type": "code", "execution_count": 2, "id": "4bd7ec9e-285a-4e40-bfdf-89290a5e0e35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n", "Starting 8 engines with \n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a88e9002f12849979a16528e113e9a54", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/8 [00:00" ] }, "metadata": { "image/png": { "height": 384, "width": 612 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "df.plot.barh(y=['prepare', 'schedule', 'compute', 'reply'], stacked=True, width=1)\n", "plt.grid((True, False))\n", "yticks = [0, len(df) // 2, len(df)]\n", "plt.yticks(yticks, yticks);\n", "plt.xlabel(\"seconds\")\n", "plt.ylabel(\"tasks\")\n", "# plt.ylim([0, len(df)])" ] }, { "cell_type": "markdown", "id": "4e41ad4e-9af8-4a46-a745-2f0aa4af87eb", "metadata": {}, "source": [ "In this visualization, the blue indicates the time spent in the client preparing the tasks to be submitted.\n", "It takes 200ms to prepare and send all of these mesages.\n", "\n", "The blue area is mostly pure overhead of IPython parallel,\n", "and should be minimized. The same goes for red, which is the same thing, just in the other direction - delivering completed results from engines to the client.\n", "\n", "The orange indicates scheduling time - the time tasks are waiting in queues before starting.\n", "There are two contributors to this:\n", "\n", "1. available compute (if there are more tasks than engines, tasks need to wait). Allocating more engines can help with this.\n", " The optimal scenario here is one engine per task, which would mean no task ever has to wait for busy engines.\n", " On the other hand, your engines will probably spend a lot of time idle, so that's lost of wasted resources.\n", "2. Scheduler overhead - There's always a cost to delivering messages.\n", " There is currently no separate measure of when a task has been *assigned* to an engine,\n", " so it is difficult to measure this one except by comparing the time between completing one task and starting the next\n", " \n", "The green is time spent actually working on tasks. This is all the 'real' work. In a perfect world, the graph would be all green, but that's never going to happen.\n", "\n", "The little bits of red are the overhead of delivering results back to the client.\n", "That cost is usually very low." ] }, { "cell_type": "markdown", "id": "cca4b6af-36a4-4aca-8e45-7536bf43f2f9", "metadata": {}, "source": [ "We can also visualize the work on the engines,\n", "which should look like a solid block of color if there's no wasted time.\n", "\n", "The left and right edges are the times when the first task was created and the last result was received at the client (the total effective computation time).\n", "\n", "The y axis is the engine id, and the x axis is time since the beginning of the task.\n", "Space in the graph indicates idle engines." ] }, { "cell_type": "code", "execution_count": 7, "id": "d1d7a1ef-a541-4f17-b6ef-9bb9bd3a0138", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'engine id')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 401, "width": 632 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y = df.engine_id\n", "x = df.started\n", "width = df.completed\n", "height = np.ones(len(df))\n", "\n", "import matplotlib as mpl\n", "from matplotlib.collections import PatchCollection\n", "from matplotlib.patches import Rectangle\n", "from matplotlib import cm\n", "\n", "color_map = mpl.cm.ScalarMappable(\n", " norm=mpl.colors.Normalize(vmin=0, vmax=df.compute.max()),\n", " cmap=cm.viridis,\n", ")\n", "\n", "plt.colorbar(color_map, label=\"duration (s)\")\n", "\n", "\n", "def working_rect(row):\n", " \"\"\"Create a rectangle representing one task\n", "\n", " x = start time\n", " y = engine id\n", " width = duration\n", " height = 1\n", " color = duration\n", " \"\"\"\n", " duration = row.completed - row.started\n", " return Rectangle(\n", " (row.started, row.engine_id),\n", " width=duration,\n", " height=1,\n", " color=color_map.to_rgba(duration),\n", " )\n", "\n", "\n", "rects = df.apply(working_rect, axis=1)\n", "pc = PatchCollection(rects, match_original=True)\n", "\n", "ax = plt.gca()\n", "ax.set_ylim(0, df.engine_id.max() + 1)\n", "ax.set_xlim(0, df.received.max())\n", "ax.add_collection(pc)\n", "\n", "last_send = df.submitted.max()\n", "finished = df.received.max()\n", "first_result = df.received.min()\n", "half_done = df.received.quantile(0.5)\n", "\n", "ymin, ymax = ax.get_ylim()\n", "\n", "\n", "def annotate_milestone(ax, t, label, color=\"red\", dark=False, top=True):\n", " \"\"\"Add a vertical line annotation for a milestone time\"\"\"\n", "\n", " patch = plt.vlines(\n", " [t],\n", " *ax.get_ylim(),\n", " linewidth=4,\n", " color=color,\n", " )\n", " if top:\n", " xy = (t, ymax)\n", " xytext = (10, 10)\n", " else:\n", " xy = (t, ymin)\n", " xytext = (10, -30)\n", " full_label = f\"{label} ({t:.1f}s)\"\n", " ax.annotate(\n", " full_label,\n", " xy=xy,\n", " xytext=xytext,\n", " textcoords=\"offset points\",\n", " color=\"white\" if dark else \"black\",\n", " bbox=dict(\n", " boxstyle=\"round\",\n", " fc=color,\n", " ),\n", " arrowprops=dict(\n", " arrowstyle=\"wedge,tail_width=1.\",\n", " fc=color,\n", " patchA=None,\n", " patchB=patch,\n", " relpos=(0.2, 0.8),\n", " # connectionstyle=\"arc3,rad=-0.1\",\n", " ),\n", " )\n", "\n", "\n", "annotate_milestone(\n", " ax,\n", " df.submitted.max(),\n", " \"last task send\",\n", " color=(0.4, 0, 0.4),\n", " dark=True,\n", ")\n", "annotate_milestone(\n", " ax,\n", " df.completed.quantile(0.5),\n", " \"50% results computed\",\n", " color=\"black\",\n", " dark=True,\n", ")\n", "annotate_milestone(\n", " ax,\n", " df.completed.max(),\n", " \"All results computed\",\n", " color=\"black\",\n", " dark=True,\n", ")\n", "\n", "annotate_milestone(\n", " ax,\n", " df.received.min(),\n", " \"first result arrives\",\n", " color=(1, 0.5, 0.5),\n", " top=False,\n", ")\n", "annotate_milestone(\n", " ax,\n", " df.received.quantile(0.5),\n", " \"50% results arrived\",\n", " color=(1, 0.5, 0.5),\n", " top=False,\n", ")\n", "annotate_milestone(\n", " ax,\n", " df.received.max(),\n", " \"All done\",\n", " color=(1, 0.5, 0.5),\n", " top=False,\n", ")\n", "\n", "\n", "# plt.vlines(\n", "# [first_result, half_done, finished],\n", "# *ax.get_ylim(),\n", "# linewidth=4,\n", "# color=\"red\",\n", "# )\n", "# plt.legend(loc=0)\n", "plt.xlabel(\"seconds\")\n", "plt.ylabel(\"engine id\")" ] }, { "cell_type": "markdown", "id": "c4ecbaf9-d541-482e-8749-62aaa8a2192d", "metadata": {}, "source": [ "Sometimes in this example, a bubble of idle engines can be produced around when the last task is sent.\n", "\n", "This is caused when lots of results arrive in the client while the client is still trying to submit tasks.\n", "Because of IPython Parallel's threaded implementation,\n", "this can cause lots of contention,\n", "and receiving lots of results can significantly affect overal performance if the client is still trying to assemble tasks to submit.\n", "\n", "This is an opportunity for [optimization in the client](https://github.com/ipython/ipyparallel/pull/534)!\n", "\n", "A similar visualization can be done with altair." ] }, { "cell_type": "code", "execution_count": 8, "id": "a5ce43d6-2202-4fd6-a1d9-251d8572d380", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "" ], "text/plain": [ "alt.Chart(...)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import altair as alt\n", "\n", "alt.Chart(df).mark_rect().encode(\n", " x=\"started\",\n", " x2=\"completed\",\n", " y=\"engine_id\",\n", " y2=\"next_engine_id\",\n", " color=\"compute\",\n", ").transform_calculate(\n", " next_engine_id=\"datum.engine_id + 1\",\n", ")" ] }, { "cell_type": "markdown", "id": "7d5d9bda-a58b-43b9-9839-59811956a6f8", "metadata": {}, "source": [ "We can also visualize 'load' as a measure of how busy the cluster is.\n", "It's essentially collecting samples of the above plot by drawing lots of vertical lines,\n", "and counting how many boxes are intersected instead of empty space.\n", "\n", "That is, for any given point in time, how many engines are working,\n", "vs how many are waiting for their next task.\n", "\n", "We do this by sampling points in time and counting the number of 'active' tasks.\n", "This is computed as the average value across a given window (default: sample every 10ms, plot the average load over 100ms)" ] }, { "cell_type": "code", "execution_count": 9, "id": "bdabe160-5548-492d-910c-d1af39e88739", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 384, "width": 599 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_load(asyncresult_df, samples=None, window=10):\n", " if samples is None:\n", " samples = min(int(1e2 * df['received'].max()), 1001)\n", "\n", " timeline = np.linspace(0, df['received'].max(), samples)\n", " remaining = df\n", " working = []\n", " for t in timeline:\n", "\n", " remaining = remaining[remaining.completed >= t]\n", " active = remaining[remaining.started < t]\n", " working.append(len(active))\n", "\n", " s = pd.Series(working, index=timeline, name=\"load\")\n", " if window > 1:\n", " windowed = s.rolling(window).mean()\n", " else:\n", " windowed = s\n", " windowed.plot()\n", " plt.ylim(0, len(df.engine_id.unique()))\n", " plt.xlabel(\"seconds\")\n", " plt.ylabel(\"busy engines\")\n", " plt.xlim(0, timeline[-1])\n", " \n", "plot_load(df)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0421d9cb7f9b406d982ee0c005a9a532": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_b3cd9eef2db342308a832c022cbb620d", "style": "IPY_MODEL_8fd7b6a17e804c90961d59398b635244", "value": "100%" } }, "2325225341874e0b9cee528398d6424b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "5109f916201e46279f135d7403112689": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "6849ab149d6d41c99fe55794adfc8d23": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "7ba86883f6654cb8bd011b877aa43270": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "8fd7b6a17e804c90961d59398b635244": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "a304343ad95c4cf888b0052e1ac42917": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_c33ae892827943a9b19193d0fbbfa253", "style": "IPY_MODEL_2325225341874e0b9cee528398d6424b", "value": " 8/8 [00:09<00:00, 1.37s/engine]" } }, "a88e9002f12849979a16528e113e9a54": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_0421d9cb7f9b406d982ee0c005a9a532", "IPY_MODEL_f10a317efcce44cd9ddd950a291e1076", "IPY_MODEL_a304343ad95c4cf888b0052e1ac42917" ], "layout": "IPY_MODEL_7ba86883f6654cb8bd011b877aa43270" } }, "b3cd9eef2db342308a832c022cbb620d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "c33ae892827943a9b19193d0fbbfa253": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": {} }, "f10a317efcce44cd9ddd950a291e1076": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_5109f916201e46279f135d7403112689", "max": 8, "style": "IPY_MODEL_6849ab149d6d41c99fe55794adfc8d23", "value": 8 } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } ipyparallel-8.8.0/docs/source/examples/wave2D/000077500000000000000000000000001460376056100212535ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/examples/wave2D/RectPartitioner.py000077500000000000000000000461151460376056100247550ustar00rootroot00000000000000#!/usr/bin/env python """A rectangular domain partitioner and associated communication functionality for solving PDEs in (1D,2D) using FDM written in the python language The global solution domain is assumed to be of rectangular shape, where the number of cells in each direction is stored in nx, ny, nz The numerical scheme is fully explicit Authors ------- * Xing Cai * Min Ragan-Kelley """ from numpy import ascontiguousarray, frombuffer, zeros try: from mpi4py import MPI except ImportError: pass else: mpi = MPI.COMM_WORLD class RectPartitioner: """ Responsible for a rectangular partitioning of a global domain, which is expressed as the numbers of cells in the different spatial directions. The partitioning info is expressed as an array of integers, each indicating the number of subdomains in one spatial direction. """ def __init__(self, my_id=-1, num_procs=-1, global_num_cells=[], num_parts=[]): self.nsd = 0 self.my_id = my_id self.num_procs = num_procs self.redim(global_num_cells, num_parts) def redim(self, global_num_cells, num_parts): nsd_ = len(global_num_cells) # print("Inside the redim function, nsd=%d" %nsd_) if nsd_ < 1 | nsd_ > 3 | nsd_ != len(num_parts): print('The input global_num_cells is not ok!') return self.nsd = nsd_ self.global_num_cells = global_num_cells self.num_parts = num_parts def prepare_communication(self): """ Find the subdomain rank (tuple) for each processor and determine the neighbor info. """ nsd_ = self.nsd if nsd_ < 1: print('Number of space dimensions is %d, nothing to do' % nsd_) return self.subd_rank = [-1, -1, -1] self.subd_lo_ix = [-1, -1, -1] self.subd_hi_ix = [-1, -1, -1] self.lower_neighbors = [-1, -1, -1] self.upper_neighbors = [-1, -1, -1] num_procs = self.num_procs my_id = self.my_id num_subds = 1 for i in range(nsd_): num_subds = num_subds * self.num_parts[i] if my_id == 0: print("# subds=", num_subds) # should check num_subds againt num_procs offsets = [1, 0, 0] # find the subdomain rank self.subd_rank[0] = my_id % self.num_parts[0] if nsd_ >= 2: offsets[1] = self.num_parts[0] self.subd_rank[1] = my_id / offsets[1] if nsd_ == 3: offsets[1] = self.num_parts[0] offsets[2] = self.num_parts[0] * self.num_parts[1] self.subd_rank[1] = (my_id % offsets[2]) / self.num_parts[0] self.subd_rank[2] = my_id / offsets[2] print("my_id=%d, subd_rank: " % my_id, self.subd_rank) if my_id == 0: print("offsets=", offsets) # find the neighbor ids for i in range(nsd_): rank = self.subd_rank[i] if rank > 0: self.lower_neighbors[i] = my_id - offsets[i] if rank < self.num_parts[i] - 1: self.upper_neighbors[i] = my_id + offsets[i] k = self.global_num_cells[i] / self.num_parts[i] m = self.global_num_cells[i] % self.num_parts[i] ix = rank * k + max(0, rank + m - self.num_parts[i]) self.subd_lo_ix[i] = ix ix = ix + k if rank >= (self.num_parts[i] - m): ix = ix + 1 # load balancing if rank < self.num_parts[i] - 1: ix = ix + 1 # one cell of overlap self.subd_hi_ix[i] = ix print( "subd_rank:", self.subd_rank, "lower_neig:", self.lower_neighbors, "upper_neig:", self.upper_neighbors, ) print( "subd_rank:", self.subd_rank, "subd_lo_ix:", self.subd_lo_ix, "subd_hi_ix:", self.subd_hi_ix, ) class RectPartitioner1D(RectPartitioner): """ Subclass of RectPartitioner, for 1D problems """ def prepare_communication(self): """ Prepare the buffers to be used for later communications """ RectPartitioner.prepare_communication(self) if self.lower_neighbors[0] >= 0: self.in_lower_buffers = [zeros(1, float)] self.out_lower_buffers = [zeros(1, float)] if self.upper_neighbors[0] >= 0: self.in_upper_buffers = [zeros(1, float)] self.out_upper_buffers = [zeros(1, float)] def get_num_loc_cells(self): return [self.subd_hi_ix[0] - self.subd_lo_ix[0]] class RectPartitioner2D(RectPartitioner): """ Subclass of RectPartitioner, for 2D problems """ def prepare_communication(self): """ Prepare the buffers to be used for later communications """ RectPartitioner.prepare_communication(self) self.in_lower_buffers = [[], []] self.out_lower_buffers = [[], []] self.in_upper_buffers = [[], []] self.out_upper_buffers = [[], []] size1 = self.subd_hi_ix[1] - self.subd_lo_ix[1] + 1 if self.lower_neighbors[0] >= 0: self.in_lower_buffers[0] = zeros(size1, float) self.out_lower_buffers[0] = zeros(size1, float) if self.upper_neighbors[0] >= 0: self.in_upper_buffers[0] = zeros(size1, float) self.out_upper_buffers[0] = zeros(size1, float) size0 = self.subd_hi_ix[0] - self.subd_lo_ix[0] + 1 if self.lower_neighbors[1] >= 0: self.in_lower_buffers[1] = zeros(size0, float) self.out_lower_buffers[1] = zeros(size0, float) if self.upper_neighbors[1] >= 0: self.in_upper_buffers[1] = zeros(size0, float) self.out_upper_buffers[1] = zeros(size0, float) def get_num_loc_cells(self): return [ self.subd_hi_ix[0] - self.subd_lo_ix[0], self.subd_hi_ix[1] - self.subd_lo_ix[1], ] class MPIRectPartitioner2D(RectPartitioner2D): """ Subclass of RectPartitioner2D, which uses MPI via mpi4py for communication """ def __init__( self, my_id=-1, num_procs=-1, global_num_cells=[], num_parts=[], slice_copy=True ): RectPartitioner.__init__(self, my_id, num_procs, global_num_cells, num_parts) self.slice_copy = slice_copy def update_internal_boundary(self, solution_array): nsd_ = self.nsd if nsd_ != len(self.in_lower_buffers) | nsd_ != len(self.out_lower_buffers): print("Buffers for communicating with lower neighbors not ready") return if nsd_ != len(self.in_upper_buffers) | nsd_ != len(self.out_upper_buffers): print("Buffers for communicating with upper neighbors not ready") return loc_nx = self.subd_hi_ix[0] - self.subd_lo_ix[0] loc_ny = self.subd_hi_ix[1] - self.subd_lo_ix[1] lower_x_neigh = self.lower_neighbors[0] upper_x_neigh = self.upper_neighbors[0] lower_y_neigh = self.lower_neighbors[1] upper_y_neigh = self.upper_neighbors[1] # communicate in the x-direction first if lower_x_neigh > -1: if self.slice_copy: self.out_lower_buffers[0] = ascontiguousarray(solution_array[1, :]) else: for i in range(0, loc_ny + 1): self.out_lower_buffers[0][i] = solution_array[1, i] mpi.Isend(self.out_lower_buffers[0], lower_x_neigh) if upper_x_neigh > -1: mpi.Recv(self.in_upper_buffers[0], upper_x_neigh) if self.slice_copy: solution_array[loc_nx, :] = self.in_upper_buffers[0] self.out_upper_buffers[0] = ascontiguousarray( solution_array[loc_nx - 1, :] ) else: for i in range(0, loc_ny + 1): solution_array[loc_nx, i] = self.in_upper_buffers[0][i] self.out_upper_buffers[0][i] = solution_array[loc_nx - 1, i] mpi.Isend(self.out_upper_buffers[0], upper_x_neigh) if lower_x_neigh > -1: mpi.Recv(self.in_lower_buffers[0], lower_x_neigh) if self.slice_copy: solution_array[0, :] = self.in_lower_buffers[0] else: for i in range(0, loc_ny + 1): solution_array[0, i] = self.in_lower_buffers[0][i] # communicate in the y-direction afterwards if lower_y_neigh > -1: if self.slice_copy: self.out_lower_buffers[1] = ascontiguousarray(solution_array[:, 1]) else: for i in range(0, loc_nx + 1): self.out_lower_buffers[1][i] = solution_array[i, 1] mpi.Isend(self.out_lower_buffers[1], lower_y_neigh) if upper_y_neigh > -1: mpi.Recv(self.in_upper_buffers[1], upper_y_neigh) if self.slice_copy: solution_array[:, loc_ny] = self.in_upper_buffers[1] self.out_upper_buffers[1] = ascontiguousarray( solution_array[:, loc_ny - 1] ) else: for i in range(0, loc_nx + 1): solution_array[i, loc_ny] = self.in_upper_buffers[1][i] self.out_upper_buffers[1][i] = solution_array[i, loc_ny - 1] mpi.Isend(self.out_upper_buffers[1], upper_y_neigh) if lower_y_neigh > -1: mpi.Recv(self.in_lower_buffers[1], lower_y_neigh) if self.slice_copy: solution_array[:, 0] = self.in_lower_buffers[1] else: for i in range(0, loc_nx + 1): solution_array[i, 0] = self.in_lower_buffers[1][i] class ZMQRectPartitioner2D(RectPartitioner2D): """ Subclass of RectPartitioner2D, which uses 0MQ via pyzmq for communication The first two arguments must be `comm`, an EngineCommunicator object, and `addrs`, a dict of connection information for other EngineCommunicator objects. """ def __init__( self, comm, addrs, my_id=-1, num_procs=-1, global_num_cells=[], num_parts=[], slice_copy=True, ): RectPartitioner.__init__(self, my_id, num_procs, global_num_cells, num_parts) self.slice_copy = slice_copy self.comm = comm # an Engine self.addrs = addrs def prepare_communication(self): RectPartitioner2D.prepare_communication(self) # connect west/south to east/north west_id, south_id = self.lower_neighbors[:2] west = self.addrs.get(west_id, None) south = self.addrs.get(south_id, None) self.comm.connect(south, west) def update_internal_boundary_x_y(self, solution_array): """update the inner boundary with the same send/recv pattern as the MPIPartitioner""" nsd_ = self.nsd dtype = solution_array.dtype if nsd_ != len(self.in_lower_buffers) | nsd_ != len(self.out_lower_buffers): print("Buffers for communicating with lower neighbors not ready") return if nsd_ != len(self.in_upper_buffers) | nsd_ != len(self.out_upper_buffers): print("Buffers for communicating with upper neighbors not ready") return loc_nx = self.subd_hi_ix[0] - self.subd_lo_ix[0] loc_ny = self.subd_hi_ix[1] - self.subd_lo_ix[1] lower_x_neigh = self.lower_neighbors[0] upper_x_neigh = self.upper_neighbors[0] lower_y_neigh = self.lower_neighbors[1] upper_y_neigh = self.upper_neighbors[1] trackers = [] flags = dict(copy=False, track=False) # communicate in the x-direction first if lower_x_neigh > -1: if self.slice_copy: self.out_lower_buffers[0] = ascontiguousarray(solution_array[1, :]) else: for i in range(0, loc_ny + 1): self.out_lower_buffers[0][i] = solution_array[1, i] t = self.comm.west.send(self.out_lower_buffers[0], **flags) trackers.append(t) if upper_x_neigh > -1: msg = self.comm.east.recv(copy=False) self.in_upper_buffers[0] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[loc_nx, :] = self.in_upper_buffers[0] self.out_upper_buffers[0] = ascontiguousarray( solution_array[loc_nx - 1, :] ) else: for i in range(0, loc_ny + 1): solution_array[loc_nx, i] = self.in_upper_buffers[0][i] self.out_upper_buffers[0][i] = solution_array[loc_nx - 1, i] t = self.comm.east.send(self.out_upper_buffers[0], **flags) trackers.append(t) if lower_x_neigh > -1: msg = self.comm.west.recv(copy=False) self.in_lower_buffers[0] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[0, :] = self.in_lower_buffers[0] else: for i in range(0, loc_ny + 1): solution_array[0, i] = self.in_lower_buffers[0][i] # communicate in the y-direction afterwards if lower_y_neigh > -1: if self.slice_copy: self.out_lower_buffers[1] = ascontiguousarray(solution_array[:, 1]) else: for i in range(0, loc_nx + 1): self.out_lower_buffers[1][i] = solution_array[i, 1] t = self.comm.south.send(self.out_lower_buffers[1], **flags) trackers.append(t) if upper_y_neigh > -1: msg = self.comm.north.recv(copy=False) self.in_upper_buffers[1] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[:, loc_ny] = self.in_upper_buffers[1] self.out_upper_buffers[1] = ascontiguousarray( solution_array[:, loc_ny - 1] ) else: for i in range(0, loc_nx + 1): solution_array[i, loc_ny] = self.in_upper_buffers[1][i] self.out_upper_buffers[1][i] = solution_array[i, loc_ny - 1] t = self.comm.north.send(self.out_upper_buffers[1], **flags) trackers.append(t) if lower_y_neigh > -1: msg = self.comm.south.recv(copy=False) self.in_lower_buffers[1] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[:, 0] = self.in_lower_buffers[1] else: for i in range(0, loc_nx + 1): solution_array[i, 0] = self.in_lower_buffers[1][i] # wait for sends to complete: if flags['track']: for t in trackers: t.wait() def update_internal_boundary_send_recv(self, solution_array): """update the inner boundary, sending first, then receiving""" nsd_ = self.nsd dtype = solution_array.dtype if nsd_ != len(self.in_lower_buffers) | nsd_ != len(self.out_lower_buffers): print("Buffers for communicating with lower neighbors not ready") return if nsd_ != len(self.in_upper_buffers) | nsd_ != len(self.out_upper_buffers): print("Buffers for communicating with upper neighbors not ready") return loc_nx = self.subd_hi_ix[0] - self.subd_lo_ix[0] loc_ny = self.subd_hi_ix[1] - self.subd_lo_ix[1] lower_x_neigh = self.lower_neighbors[0] upper_x_neigh = self.upper_neighbors[0] lower_y_neigh = self.lower_neighbors[1] upper_y_neigh = self.upper_neighbors[1] trackers = [] flags = dict(copy=False, track=False) # send in all directions first if lower_x_neigh > -1: if self.slice_copy: self.out_lower_buffers[0] = ascontiguousarray(solution_array[1, :]) else: for i in range(0, loc_ny + 1): self.out_lower_buffers[0][i] = solution_array[1, i] t = self.comm.west.send(self.out_lower_buffers[0], **flags) trackers.append(t) if lower_y_neigh > -1: if self.slice_copy: self.out_lower_buffers[1] = ascontiguousarray(solution_array[:, 1]) else: for i in range(0, loc_nx + 1): self.out_lower_buffers[1][i] = solution_array[i, 1] t = self.comm.south.send(self.out_lower_buffers[1], **flags) trackers.append(t) if upper_x_neigh > -1: if self.slice_copy: self.out_upper_buffers[0] = ascontiguousarray( solution_array[loc_nx - 1, :] ) else: for i in range(0, loc_ny + 1): self.out_upper_buffers[0][i] = solution_array[loc_nx - 1, i] t = self.comm.east.send(self.out_upper_buffers[0], **flags) trackers.append(t) if upper_y_neigh > -1: if self.slice_copy: self.out_upper_buffers[1] = ascontiguousarray( solution_array[:, loc_ny - 1] ) else: for i in range(0, loc_nx + 1): self.out_upper_buffers[1][i] = solution_array[i, loc_ny - 1] t = self.comm.north.send(self.out_upper_buffers[1], **flags) trackers.append(t) # now start receiving if upper_x_neigh > -1: msg = self.comm.east.recv(copy=False) self.in_upper_buffers[0] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[loc_nx, :] = self.in_upper_buffers[0] else: for i in range(0, loc_ny + 1): solution_array[loc_nx, i] = self.in_upper_buffers[0][i] if lower_x_neigh > -1: msg = self.comm.west.recv(copy=False) self.in_lower_buffers[0] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[0, :] = self.in_lower_buffers[0] else: for i in range(0, loc_ny + 1): solution_array[0, i] = self.in_lower_buffers[0][i] if upper_y_neigh > -1: msg = self.comm.north.recv(copy=False) self.in_upper_buffers[1] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[:, loc_ny] = self.in_upper_buffers[1] else: for i in range(0, loc_nx + 1): solution_array[i, loc_ny] = self.in_upper_buffers[1][i] if lower_y_neigh > -1: msg = self.comm.south.recv(copy=False) self.in_lower_buffers[1] = frombuffer(msg, dtype=dtype) if self.slice_copy: solution_array[:, 0] = self.in_lower_buffers[1] else: for i in range(0, loc_nx + 1): solution_array[i, 0] = self.in_lower_buffers[1][i] # wait for sends to complete: if flags['track']: for t in trackers: t.wait() # use send/recv pattern instead of x/y sweeps update_internal_boundary = update_internal_boundary_send_recv ipyparallel-8.8.0/docs/source/examples/wave2D/communicator.py000066400000000000000000000036301460376056100243270ustar00rootroot00000000000000#!/usr/bin/env python """A simple Communicator class that has N,E,S,W neighbors connected via 0MQ PEER sockets""" import socket import zmq from ipyparallel.util import disambiguate_url class EngineCommunicator: """An object that connects Engines to each other. north and east sockets listen, while south and west sockets connect. This class is useful in cases where there is a set of nodes that must communicate only with their nearest neighbors. """ def __init__(self, interface='tcp://*', identity=None): self._ctx = zmq.Context() self.north = self._ctx.socket(zmq.PAIR) self.west = self._ctx.socket(zmq.PAIR) self.south = self._ctx.socket(zmq.PAIR) self.east = self._ctx.socket(zmq.PAIR) # bind to ports northport = self.north.bind_to_random_port(interface) eastport = self.east.bind_to_random_port(interface) self.north_url = interface + ":%i" % northport self.east_url = interface + ":%i" % eastport # guess first public IP from socket self.location = socket.gethostbyname_ex(socket.gethostname())[-1][0] def __del__(self): self.north.close() self.south.close() self.east.close() self.west.close() self._ctx.term() @property def info(self): """return the connection info for this object's sockets.""" return (self.location, self.north_url, self.east_url) def connect(self, south_peer=None, west_peer=None): """connect to peers. `peers` will be a 3-tuples, of the form: (location, north_addr, east_addr) as produced by """ if south_peer is not None: location, url, _ = south_peer self.south.connect(disambiguate_url(url, location)) if west_peer is not None: location, _, url = west_peer self.west.connect(disambiguate_url(url, location)) ipyparallel-8.8.0/docs/source/examples/wave2D/parallelwave-mpi.py000077500000000000000000000161611460376056100250770ustar00rootroot00000000000000#!/usr/bin/env python """ A simple python program of solving a 2D wave equation in parallel. Domain partitioning and inter-processor communication are done by an object of class MPIRectPartitioner2D (which is a subclass of RectPartitioner2D and uses MPI via mpi4py) An example of running the program is (8 processors, 4x2 partition, 400x100 grid cells):: $ ipcluster start --engines=MPIExec -n 8 # start 8 engines with mpiexec $ python parallelwave-mpi.py --grid 400 100 --partition 4 2 See also parallelwave-mpi, which runs the same program, but uses MPI (via mpi4py) for the inter-engine communication. Authors ------- * Xing Cai * Min Ragan-Kelley """ import argparse import time from numpy import sqrt import ipyparallel as ipp def setup_partitioner(index, num_procs, gnum_cells, parts): """create a partitioner in the engine namespace""" global partitioner p = MPIRectPartitioner2D(my_id=index, num_procs=num_procs) # noqa: F821 p.redim(global_num_cells=gnum_cells, num_parts=parts) p.prepare_communication() # put the partitioner into the global namespace: partitioner = p def setup_solver(*args, **kwargs): """create a WaveSolver in the engine namespace""" global solver solver = WaveSolver(*args, **kwargs) # noqa: F821 def wave_saver(u, x, y, t): """save the wave log""" global u_hist global t_hist t_hist.append(t) u_hist.append(1.0 * u) # main program: if __name__ == '__main__': parser = argparse.ArgumentParser() paa = parser.add_argument paa( '--grid', '-g', type=int, nargs=2, default=[100, 100], dest='grid', help="Cells in the grid, e.g. --grid 100 200", ) paa( '--partition', '-p', type=int, nargs=2, default=None, help="Process partition grid, e.g. --partition 4 2 for 4x2", ) paa('-c', type=float, default=1.0, help="Wave speed (I think)") paa('-Ly', type=float, default=1.0, help="system size (in y)") paa('-Lx', type=float, default=1.0, help="system size (in x)") paa('-t', '--tstop', type=float, default=1.0, help="Time units to run") paa( '--profile', type=str, default='default', help="Specify the ipcluster profile for the client to connect to.", ) paa( '--save', action='store_true', help="Add this flag to save the time/wave history during the run.", ) paa( '--scalar', action='store_true', help="Also run with scalar interior implementation, to see vector speedup.", ) ns = parser.parse_args() # set up arguments grid = ns.grid partition = ns.partition Lx = ns.Lx Ly = ns.Ly c = ns.c tstop = ns.tstop if ns.save: user_action = wave_saver else: user_action = None num_cells = 1.0 * (grid[0] - 1) * (grid[1] - 1) final_test = True # create the Client rc = ipp.Client(profile=ns.profile) num_procs = len(rc.ids) if partition is None: partition = [1, num_procs] assert partition[0] * partition[1] == num_procs, ( "can't map partition %s to %i engines" % ( partition, num_procs, ) ) view = rc[:] print(f"Running {grid} system on {partition} processes until {tstop:f}") # functions defining initial/boundary/source conditions def I(x, y): from numpy import exp return 1.5 * exp(-100 * ((x - 0.5) ** 2 + (y - 0.5) ** 2)) def f(x, y, t): return 0.0 # from numpy import exp,sin # return 10*exp(-(x - sin(100*t))**2) def bc(x, y, t): return 0.0 # initial imports, setup rank view.execute( '\n'.join( [ "from mpi4py import MPI", "import numpy", "mpi = MPI.COMM_WORLD", "my_id = MPI.COMM_WORLD.Get_rank()", ] ), block=True, ) # initialize t_hist/u_hist for saving the state at each step (optional) view['t_hist'] = [] view['u_hist'] = [] # set vector/scalar implementation details impl = {} impl['ic'] = 'vectorized' impl['inner'] = 'scalar' impl['bc'] = 'vectorized' # execute some files so that the classes we need will be defined on the engines: view.run('RectPartitioner.py') view.run('wavesolver.py') # setup remote partitioner # note that Reference means that the argument passed to setup_partitioner will be the # object named 'my_id' in the engine's namespace view.apply_sync( setup_partitioner, ipp.Reference('my_id'), num_procs, grid, partition ) # wait for initial communication to complete view.execute('mpi.barrier()') # setup remote solvers view.apply_sync( setup_solver, I, f, c, bc, Lx, Ly, partitioner=ipp.Reference('partitioner'), dt=0, implementation=impl, ) # lambda for calling solver.solve: def _solve(*args, **kwargs): return solver.solve(*args, **kwargs) if ns.scalar: impl['inner'] = 'scalar' # run first with element-wise Python operations for each cell t0 = time.time() ar = view.apply_async( _solve, tstop, dt=0, verbose=True, final_test=final_test, user_action=user_action, ) if final_test: # this sum is performed element-wise as results finish s = sum(ar) # the L2 norm (RMS) of the result: norm = sqrt(s / num_cells) else: norm = -1 t1 = time.time() print(f'scalar inner-version, Wtime={t1 - t0:g}, norm={norm:g}') impl['inner'] = 'vectorized' # setup new solvers view.apply_sync( setup_solver, I, f, c, bc, Lx, Ly, partitioner=ipp.Reference('partitioner'), dt=0, implementation=impl, ) view.execute('mpi.barrier()') # run again with numpy vectorized inner-implementation t0 = time.time() ar = view.apply_async( _solve, tstop, dt=0, verbose=True, final_test=final_test, user_action=user_action, ) if final_test: # this sum is performed element-wise as results finish s = sum(ar) # the L2 norm (RMS) of the result: norm = sqrt(s / num_cells) else: norm = -1 t1 = time.time() print(f'vector inner-version, Wtime={t1 - t0:g}, norm={norm:g}') # if ns.save is True, then u_hist stores the history of u as a list # If the partion scheme is Nx1, then u can be reconstructed via 'gather': if ns.save and partition[-1] == 1: import matplotlib.pyplot as plt view.execute('u_last=u_hist[-1]') # map mpi IDs to IPython IDs, which may not match ranks = view['my_id'] targets = range(len(ranks)) for idx in range(len(ranks)): targets[idx] = ranks.index(idx) u_last = rc[targets].gather('u_last', block=True) plt.pcolor(u_last) plt.show() ipyparallel-8.8.0/docs/source/examples/wave2D/parallelwave.py000077500000000000000000000164451460376056100243210ustar00rootroot00000000000000#!/usr/bin/env python """ A simple python program of solving a 2D wave equation in parallel. Domain partitioning and inter-processor communication are done by an object of class ZMQRectPartitioner2D (which is a subclass of RectPartitioner2D and uses 0MQ via pyzmq) An example of running the program is (8 processors, 4x2 partition, 200x200 grid cells):: $ ipcluster start -n 8 # start 8 engines $ python parallelwave.py --grid 200 200 --partition 4 2 See also parallelwave-mpi, which runs the same program, but uses MPI (via mpi4py) for the inter-engine communication. Authors ------- * Xing Cai * Min Ragan-Kelley """ import argparse import time from numpy import sqrt import ipyparallel as ipp def setup_partitioner(comm, addrs, index, num_procs, gnum_cells, parts): """create a partitioner in the engine namespace""" global partitioner p = ZMQRectPartitioner2D( # noqa: F821 comm, addrs, my_id=index, num_procs=num_procs, ) p.redim(global_num_cells=gnum_cells, num_parts=parts) p.prepare_communication() # put the partitioner into the global namespace: partitioner = p def setup_solver(*args, **kwargs): """create a WaveSolver in the engine namespace.""" global solver solver = WaveSolver(*args, **kwargs) # noqa: F821 def wave_saver(u, x, y, t): """save the wave state for each timestep.""" global u_hist global t_hist t_hist.append(t) u_hist.append(1.0 * u) # main program: if __name__ == '__main__': parser = argparse.ArgumentParser() paa = parser.add_argument paa( '--grid', '-g', type=int, nargs=2, default=[100, 100], dest='grid', help="Cells in the grid, e.g. --grid 100 200", ) paa( '--partition', '-p', type=int, nargs=2, default=None, help="Process partition grid, e.g. --partition 4 2 for 4x2", ) paa('-c', type=float, default=1.0, help="Wave speed (I think)") paa('-Ly', type=float, default=1.0, help="system size (in y)") paa('-Lx', type=float, default=1.0, help="system size (in x)") paa('-t', '--tstop', type=float, default=1.0, help="Time units to run") paa( '--profile', type=str, default='default', help="Specify the ipcluster profile for the client to connect to.", ) paa( '--save', action='store_true', help="Add this flag to save the time/wave history during the run.", ) paa( '--scalar', action='store_true', help="Also run with scalar interior implementation, to see vector speedup.", ) ns = parser.parse_args() # set up arguments grid = ns.grid partition = ns.partition Lx = ns.Lx Ly = ns.Ly c = ns.c tstop = ns.tstop if ns.save: user_action = wave_saver else: user_action = None num_cells = 1.0 * (grid[0] - 1) * (grid[1] - 1) final_test = True # create the Client rc = ipp.Client(profile=ns.profile) num_procs = len(rc.ids) if partition is None: partition = [num_procs, 1] else: num_procs = min(num_procs, partition[0] * partition[1]) assert partition[0] * partition[1] == num_procs, ( "can't map partition %s to %i engines" % ( partition, num_procs, ) ) # construct the View: view = rc[:num_procs] print(f"Running {grid} system on {partition} processes until {tstop:f}") # functions defining initial/boundary/source conditions def I(x, y): from numpy import exp return 1.5 * exp(-100 * ((x - 0.5) ** 2 + (y - 0.5) ** 2)) def f(x, y, t): return 0.0 # from numpy import exp,sin # return 10*exp(-(x - sin(100*t))**2) def bc(x, y, t): return 0.0 # initialize t_hist/u_hist for saving the state at each step (optional) view['t_hist'] = [] view['u_hist'] = [] # set vector/scalar implementation details impl = {} impl['ic'] = 'vectorized' impl['inner'] = 'scalar' impl['bc'] = 'vectorized' # execute some files so that the classes we need will be defined on the engines: view.execute('import numpy') view.run('communicator.py') view.run('RectPartitioner.py') view.run('wavesolver.py') # scatter engine IDs view.scatter('my_id', range(num_procs), flatten=True) # create the engine connectors view.execute('com = EngineCommunicator()') # gather the connection information into a single dict ar = view.apply_async(lambda: com.info) # noqa: F821 peers = ar.get_dict() # print peers # this is a dict, keyed by engine ID, of the connection info for the EngineCommunicators # setup remote partitioner # note that Reference means that the argument passed to setup_partitioner will be the # object named 'com' in the engine's namespace view.apply_sync( setup_partitioner, ipp.Reference('com'), peers, ipp.Reference('my_id'), num_procs, grid, partition, ) time.sleep(1) # convenience lambda to call solver.solve: def _solve(*args, **kwargs): return solver.solve(*args, **kwargs) if ns.scalar: impl['inner'] = 'scalar' # setup remote solvers view.apply_sync( setup_solver, I, f, c, bc, Lx, Ly, partitioner=ipp.Reference('partitioner'), dt=0, implementation=impl, ) # run first with element-wise Python operations for each cell t0 = time.time() ar = view.apply_async( _solve, tstop, dt=0, verbose=True, final_test=final_test, user_action=user_action, ) if final_test: # this sum is performed element-wise as results finish s = sum(ar) # the L2 norm (RMS) of the result: norm = sqrt(s / num_cells) else: norm = -1 t1 = time.time() print(f'scalar inner-version, Wtime={t1 - t0:g}, norm={norm:g}') # run again with faster numpy-vectorized inner implementation: impl['inner'] = 'vectorized' # setup remote solvers view.apply_sync( setup_solver, I, f, c, bc, Lx, Ly, partitioner=ipp.Reference('partitioner'), dt=0, implementation=impl, ) t0 = time.time() ar = view.apply_async( _solve, tstop, dt=0, verbose=True, final_test=final_test, user_action=user_action, ) if final_test: # this sum is performed element-wise as results finish s = sum(ar) # the L2 norm (RMS) of the result: norm = sqrt(s / num_cells) else: norm = -1 t1 = time.time() print(f'vector inner-version, Wtime={t1 - t0:g}, norm={norm:g}') # if ns.save is True, then u_hist stores the history of u as a list # If the partion scheme is Nx1, then u can be reconstructed via 'gather': if ns.save and partition[-1] == 1: import matplotlib.pyplot as plt view.execute('u_last=u_hist[-1]') u_last = view.gather('u_last', block=True) plt.pcolor(u_last) plt.show() ipyparallel-8.8.0/docs/source/examples/wave2D/wavesolver.py000077500000000000000000000265731460376056100240420ustar00rootroot00000000000000#!/usr/bin/env python """ A simple WaveSolver class for evolving the wave equation in 2D. This works in parallel by using a RectPartitioner object. Authors ------- * Xing Cai * Min Ragan-Kelley """ import time from numpy import arange, newaxis, sqrt, zeros def iseq(start=0, stop=None, inc=1): """ Generate integers from start to (and including!) stop, with increment of inc. Alternative to range/xrange. """ if stop is None: # allow isequence(3) to be 0, 1, 2, 3 # take 1st arg as stop, start as 0, and inc=1 stop = start start = 0 inc = 1 return arange(start, stop + inc, inc) class WaveSolver: """ Solve the 2D wave equation u_tt = u_xx + u_yy + f(x,y,t) with u = bc(x,y,t) on the boundary and initial condition du/dt = 0. Parallelization by using a RectPartitioner object 'partitioner' nx and ny are the total number of global grid cells in the x and y directions. The global grid points are numbered as (0,0), (1,0), (2,0), ..., (nx,0), (0,1), (1,1), ..., (nx, ny). dt is the time step. If dt<=0, an optimal time step is used. tstop is the stop time for the simulation. I, f are functions: I(x,y), f(x,y,t) user_action: function of (u, x, y, t) called at each time level (x and y are one-dimensional coordinate vectors). This function allows the calling code to plot the solution, compute errors, etc. implementation: a dictionary specifying how the initial condition ('ic'), the scheme over inner points ('inner'), and the boundary conditions ('bc') are to be implemented. Normally, values are legal: 'scalar' or 'vectorized'. 'scalar' means straight loops over grid points, while 'vectorized' means special NumPy vectorized operations. If a key in the implementation dictionary is missing, it defaults in this function to 'scalar' (the safest strategy). Note that if 'vectorized' is specified, the functions I, f, and bc must work in vectorized mode. It is always recommended to first run the 'scalar' mode and then compare 'vectorized' results with the 'scalar' results to check that I, f, and bc work. verbose: true if a message at each time step is written, false implies no output during the simulation. final_test: true means the discrete L2-norm of the final solution is to be computed. """ def __init__( self, I, f, c, bc, Lx, Ly, partitioner=None, dt=-1, user_action=None, implementation={ 'ic': 'vectorized', # or 'scalar' 'inner': 'vectorized', 'bc': 'vectorized', }, ): nx = partitioner.global_num_cells[0] # number of global cells in x dir ny = partitioner.global_num_cells[1] # number of global cells in y dir dx = Lx / float(nx) dy = Ly / float(ny) loc_nx, loc_ny = partitioner.get_num_loc_cells() nx = loc_nx ny = loc_ny # now use loc_nx and loc_ny instead lo_ix0 = partitioner.subd_lo_ix[0] lo_ix1 = partitioner.subd_lo_ix[1] hi_ix0 = partitioner.subd_hi_ix[0] hi_ix1 = partitioner.subd_hi_ix[1] x = iseq(dx * lo_ix0, dx * hi_ix0, dx) # local grid points in x dir y = iseq(dy * lo_ix1, dy * hi_ix1, dy) # local grid points in y dir self.x = x self.y = y xv = x[:, newaxis] # for vectorized expressions with f(xv,yv) yv = y[newaxis, :] # -- " -- if dt <= 0: dt = (1 / float(c)) * (1 / sqrt(1 / dx**2 + 1 / dy**2)) # max time step Cx2 = (c * dt / dx) ** 2 Cy2 = (c * dt / dy) ** 2 dt2 = dt**2 # help variables u = zeros((nx + 1, ny + 1)) # solution array u_1 = u.copy() # solution at t-dt u_2 = u.copy() # solution at t-2*dt # preserve for self.solve implementation = dict(implementation) # copy if 'ic' not in implementation: implementation['ic'] = 'scalar' if 'bc' not in implementation: implementation['bc'] = 'scalar' if 'inner' not in implementation: implementation['inner'] = 'scalar' self.implementation = implementation self.Lx = Lx self.Ly = Ly self.I = I self.f = f self.c = c self.bc = bc self.user_action = user_action self.partitioner = partitioner # set initial condition (pointwise - allows straight if-tests in I(x,y)): t = 0.0 if implementation['ic'] == 'scalar': for i in range(0, nx + 1): for j in range(0, ny + 1): u_1[i, j] = I(x[i], y[j]) for i in range(1, nx): for j in range(1, ny): u_2[i, j] = ( u_1[i, j] + 0.5 * Cx2 * (u_1[i - 1, j] - 2 * u_1[i, j] + u_1[i + 1, j]) + 0.5 * Cy2 * (u_1[i, j - 1] - 2 * u_1[i, j] + u_1[i, j + 1]) + dt2 * f(x[i], y[j], 0.0) ) # boundary values of u_2 (equals u(t=dt) due to du/dt=0) i = 0 for j in range(0, ny + 1): u_2[i, j] = bc(x[i], y[j], t + dt) j = 0 for i in range(0, nx + 1): u_2[i, j] = bc(x[i], y[j], t + dt) i = nx for j in range(0, ny + 1): u_2[i, j] = bc(x[i], y[j], t + dt) j = ny for i in range(0, nx + 1): u_2[i, j] = bc(x[i], y[j], t + dt) elif implementation['ic'] == 'vectorized': u_1 = I(xv, yv) u_2[1:nx, 1:ny] = ( u_1[1:nx, 1:ny] + 0.5 * Cx2 * (u_1[0 : nx - 1, 1:ny] - 2 * u_1[1:nx, 1:ny] + u_1[2 : nx + 1, 1:ny]) + 0.5 * Cy2 * (u_1[1:nx, 0 : ny - 1] - 2 * u_1[1:nx, 1:ny] + u_1[1:nx, 2 : ny + 1]) + dt2 * (f(xv[1:nx, 1:ny], yv[1:nx, 1:ny], 0.0)) ) # boundary values (t=dt): i = 0 u_2[i, :] = bc(x[i], y, t + dt) j = 0 u_2[:, j] = bc(x, y[j], t + dt) i = nx u_2[i, :] = bc(x[i], y, t + dt) j = ny u_2[:, j] = bc(x, y[j], t + dt) if user_action is not None: user_action(u_1, x, y, t) # allow user to plot etc. # print(list(self.us[2][2])) self.us = (u, u_1, u_2) def solve(self, tstop, dt=-1, user_action=None, verbose=False, final_test=False): t0 = time.time() f = self.f c = self.c bc = self.bc partitioner = self.partitioner implementation = self.implementation nx = partitioner.global_num_cells[0] # number of global cells in x dir ny = partitioner.global_num_cells[1] # number of global cells in y dir dx = self.Lx / float(nx) dy = self.Ly / float(ny) loc_nx, loc_ny = partitioner.get_num_loc_cells() nx = loc_nx ny = loc_ny # now use loc_nx and loc_ny instead x = self.x y = self.y xv = x[:, newaxis] # for vectorized expressions with f(xv,yv) yv = y[newaxis, :] # -- " -- if dt <= 0: dt = (1 / float(c)) * (1 / sqrt(1 / dx**2 + 1 / dy**2)) # max time step Cx2 = (c * dt / dx) ** 2 Cy2 = (c * dt / dy) ** 2 dt2 = dt**2 # help variables # id for the four possible neighbor subdomains lower_x_neigh = partitioner.lower_neighbors[0] upper_x_neigh = partitioner.upper_neighbors[0] lower_y_neigh = partitioner.lower_neighbors[1] upper_y_neigh = partitioner.upper_neighbors[1] u, u_1, u_2 = self.us # u_1 = self.u_1 t = 0.0 while t <= tstop: t_old = t t += dt if verbose: print( 'solving ({} version) at t={:g}'.format(implementation['inner'], t) ) # update all inner points: if implementation['inner'] == 'scalar': for i in range(1, nx): for j in range(1, ny): u[i, j] = ( -u_2[i, j] + 2 * u_1[i, j] + Cx2 * (u_1[i - 1, j] - 2 * u_1[i, j] + u_1[i + 1, j]) + Cy2 * (u_1[i, j - 1] - 2 * u_1[i, j] + u_1[i, j + 1]) + dt2 * f(x[i], y[j], t_old) ) elif implementation['inner'] == 'vectorized': u[1:nx, 1:ny] = ( -u_2[1:nx, 1:ny] + 2 * u_1[1:nx, 1:ny] + Cx2 * ( u_1[0 : nx - 1, 1:ny] - 2 * u_1[1:nx, 1:ny] + u_1[2 : nx + 1, 1:ny] ) + Cy2 * ( u_1[1:nx, 0 : ny - 1] - 2 * u_1[1:nx, 1:ny] + u_1[1:nx, 2 : ny + 1] ) + dt2 * f(xv[1:nx, 1:ny], yv[1:nx, 1:ny], t_old) ) # insert boundary conditions (if there's no neighbor): if lower_x_neigh < 0: if implementation['bc'] == 'scalar': i = 0 for j in range(0, ny + 1): u[i, j] = bc(x[i], y[j], t) elif implementation['bc'] == 'vectorized': u[0, :] = bc(x[0], y, t) if upper_x_neigh < 0: if implementation['bc'] == 'scalar': i = nx for j in range(0, ny + 1): u[i, j] = bc(x[i], y[j], t) elif implementation['bc'] == 'vectorized': u[nx, :] = bc(x[nx], y, t) if lower_y_neigh < 0: if implementation['bc'] == 'scalar': j = 0 for i in range(0, nx + 1): u[i, j] = bc(x[i], y[j], t) elif implementation['bc'] == 'vectorized': u[:, 0] = bc(x, y[0], t) if upper_y_neigh < 0: if implementation['bc'] == 'scalar': j = ny for i in range(0, nx + 1): u[i, j] = bc(x[i], y[j], t) elif implementation['bc'] == 'vectorized': u[:, ny] = bc(x, y[ny], t) # communication partitioner.update_internal_boundary(u) if user_action is not None: user_action(u, x, y, t) # update data structures for next step u_2, u_1, u = u_1, u, u_2 t1 = time.time() print( 'my_id=%2d, dt=%g, %s version, slice_copy=%s, net Wtime=%g' % ( partitioner.my_id, dt, implementation['inner'], partitioner.slice_copy, t1 - t0, ) ) # save the us self.us = u, u_1, u_2 # check final results; compute discrete L2-norm of the solution if final_test: loc_res = 0.0 for i in iseq(start=1, stop=nx - 1): for j in iseq(start=1, stop=ny - 1): loc_res += u_1[i, j] ** 2 return loc_res return dt ipyparallel-8.8.0/docs/source/index.md000066400000000000000000000046721460376056100177470ustar00rootroot00000000000000# Using IPython for parallel computing > ```{eval-rst} > > :Release: |release| > :Date: |today| > ``` (install)= ## Installing IPython Parallel As of 4.0, IPython parallel is now a standalone package called {mod}`ipyparallel`. You can install it with: ``` pip install ipyparallel ``` or: ``` conda install ipyparallel ``` As of IPython Parallel 7, this will include installing/enabling an extension for both the classic Jupyter Notebook and JupyterLab ≥ 3.0. ## Quickstart IPython Parallel A quick example to: 1. allocate a cluster (collection of IPython engines for use in parallel) 2. run a collection of tasks on the cluster 3. wait interactively for results 4. cleanup resources after the task is done ```python import time import ipyparallel as ipp task_durations = [1] * 25 # request a cluster with ipp.Cluster() as rc: # get a view on the cluster view = rc.load_balanced_view() # submit the tasks asyncresult = view.map_async(time.sleep, task_durations) # wait interactively for results asyncresult.wait_interactive() # retrieve actual results result = asyncresult.get() # at this point, the cluster processes have been shutdown ``` You can similarly run MPI code using IPyParallel (requires [mpi4py](https://mpi4py.readthedocs.io/en/stable/install.html)): ```python import ipyparallel as ipp def mpi_example(): from mpi4py import MPI comm = MPI.COMM_WORLD return f"Hello World from rank {comm.Get_rank()}. total ranks={comm.Get_size()}" # request an MPI cluster with 4 engines with ipp.Cluster(engines='mpi', n=4) as rc: # get a broadcast_view on the cluster which is best # suited for MPI style computation view = rc.broadcast_view() # run the mpi_example function on all engines in parallel r = view.apply_sync(mpi_example) # Retrieve and print the result from the engines print("\n".join(r)) # at this point, the cluster processes have been shutdown ``` ![IPyParallel-MPI-Example](./_static/IPyParallel-MPI-Example.png) Follow the [tutorial][] to learn more. [tutorial]: ./tutorial/index ## Contents ```{toctree} :maxdepth: 2 tutorial/index reference/index ``` ```{toctree} :maxdepth: 2 examples/index ``` ```{toctree} :maxdepth: 1 changelog ``` # IPython Parallel API ```{toctree} :maxdepth: 2 api/ipyparallel.rst ``` # Indices and tables - {ref}`genindex` - {ref}`modindex` - {ref}`search` ipyparallel-8.8.0/docs/source/links.txt000066400000000000000000000105351460376056100201720ustar00rootroot00000000000000.. This (-*- rst -*-) format file contains commonly used link targets and name substitutions. It may be included in many files, therefore it should only contain link targets and name substitutions. Try grepping for "^\.\. _" to find plausible candidates for this list. NOTE: this file must have an extension *opposite* to that of the main reST files in the manuals, so that we can include it with ".. include::" directives, but without triggering warnings from Sphinx for not being listed in any toctree. Since IPython uses .txt for the main files, this one will use .rst. NOTE: reST targets are __not_case_sensitive__, so only one target definition is needed for ipython, IPython, etc. NOTE: Some of these were taken from the nipy links compendium. .. Main IPython links .. _ipython: https://ipython.org .. _`ipython manual`: https://ipython.org/documentation.html .. _ipython_github: https://github.com/ipython/ipython/ .. _ipython_github_repo: https://github.com/ipython/ipython/ .. _ipython_downloads: https://ipython.org/download.html .. _ipython_pypi: https://pypi.python.org/pypi/ipython .. _nbviewer: https://nbviewer.ipython.org .. _ZeroMQ: https://zeromq.org .. Documentation tools and related links .. _graphviz: https://www.graphviz.org .. _Sphinx: https://sphinx.pocoo.org .. _`Sphinx reST`: https://sphinx.pocoo.org/rest.html .. _sampledoc: https://matplotlib.org/sampledoc .. _reST: https://docutils.sourceforge.net/rst.html .. _docutils: https://docutils.sourceforge.net .. _lyx: https://www.lyx.org .. _pep8: https://www.python.org/dev/peps/pep-0008 .. _numpy_coding_guide: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt .. Licenses .. _GPL: https://www.gnu.org/licenses/gpl.html .. _BSD: https://www.opensource.org/licenses/bsd-license.php .. _LGPL: https://www.gnu.org/copyleft/lesser.html .. Other python projects .. _numpy: https://numpy.scipy.org .. _scipy: https://www.scipy.org .. _scipy_conference: https://conference.scipy.org .. _matplotlib: https://matplotlib.org .. _pythonxy: https://code.google.com/p/pythonxy/ .. _ETS: https://code.enthought.com/projects/tool-suite.php .. _EPD: https://www.enthought.com/products/epd.php .. _python: https://www.python.org .. _mayavi: https://code.enthought.com/projects/mayavi .. _sympy: https://code.google.com/p/sympy .. _sage: https://sagemath.org .. _pydy: https://code.google.com/p/pydy .. _vpython: https://vpython.org .. _cython: https://cython.org .. _software carpentry: https://software-carpentry.org .. Not so python scientific computing tools .. _matlab: https://www.mathworks.com .. _VTK: https://vtk.org .. Other organizations .. _enthought: https://www.enthought.com .. _kitware: https://www.kitware.com .. _netlib: https://netlib.org .. Other tools and projects .. _indefero: https://www.indefero.net .. _git: https://git-scm.com .. _github: https://github.com .. _Markdown: https://daringfireball.net/projects/markdown/syntax .. _Running Code in the IPython Notebook: notebook_p1_ .. _notebook_p1: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%25201%2520-%2520Running%2520Code.ipynb .. _Basic Output: notebook_p2_ .. _notebook_p2: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%202%20-%20Basic%20Output.ipynb .. _Plotting with Matplotlib: notebook_p3_ .. _notebook_p3: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%203%20-%20Plotting%20with%20Matplotlib.ipynb .. _Markdown Cells: notebook_p4_ .. _notebook_p4: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%204%20-%20Markdown%20Cells.ipynb .. _Rich Display System: notebook_p5_ .. _notebook_p5: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb .. _notebook_custom_display: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Custom%20Display%20Logic.ipynb .. _Frontend/Kernel Model: notebook_two_proc_ .. _notebook_two_proc: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Frontend-Kernel%20Model.ipynb .. _Cell magics: notebook_cell_magics_ .. _notebook_cell_magics: https://nbviewer.ipython.org/urls/raw.github.com/ipython/ipython/1.x/examples/notebooks/Cell%20Magics.ipynb ipyparallel-8.8.0/docs/source/redirects.txt000066400000000000000000000012251460376056100210320ustar00rootroot00000000000000"asyncresult.rst" "tutorial/asyncresult.md" "dag_dependencies.rst" "reference/dag_dependencies.md" "db.rst" "reference/db.md" "demos.rst" "tutorial/demos.md" "details.rst" "reference/details.md" "development/connections.rst" "reference/connections.md" "development/messages.rst" "reference/messages.md" "intro.rst" "tutorial/intro.md" "magics.rst" "tutorial/magics.md" "mpi.rst" "reference/mpi.md" "multiengine.rst" "tutorial/direct.md" "process.rst" "tutorial/process.md" "security.rst" "reference/security.md" "task.rst" "tutorial/task.md" "transition.rst" "tutorial/index.md" "winhpc.rst" "reference/launchers.md" "examples/Index.ipynb" "examples/index.md" ipyparallel-8.8.0/docs/source/reference/000077500000000000000000000000001460376056100202435ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/reference/connections.md000066400000000000000000000142721460376056100231150ustar00rootroot00000000000000(parallel-connections)= # Connection Diagrams of The IPython ZMQ Cluster This is a quick summary and illustration of the connections involved in the ZeroMQ based IPython cluster for parallel computing. ## All Connections The IPython cluster consists of a Controller, and one or more each of clients and engines. The goal of the Controller is to manage and monitor the connections and communications between the clients and the engines. The Controller is no longer a single process entity, but rather a collection of processes - specifically one Hub, and 4 (or more) Schedulers. It is important for security/practicality reasons that all connections be inbound to the controller processes. The arrows in the figures indicate the direction of the connection. ```{figure} figs/allconnections.png :align: center :alt: IPython cluster connections :width: 432px All the connections involved in connecting one client to one engine. ``` The Controller consists of 1-5 processes. Central to the cluster is the **Hub**, which monitors engine state, execution traffic, and handles registration and notification. The Hub includes a Heartbeat Monitor for keeping track of engines that are alive. Outside the Hub are 4 **Schedulers**. These devices are very small pure-C MonitoredQueue processes (or optionally threads) that relay messages very fast, but also send a copy of each message along a side socket to the Hub. The MUX queue and Control queue are MonitoredQueue ØMQ devices which relay explicitly addressed messages from clients to engines, and their replies back up. The Balanced queue performs load-balancing destination-agnostic scheduling. It may be a MonitoredQueue device, but may also be a Python Scheduler that behaves externally in an identical fashion to MQ devices, but with additional internal logic. stdout/err are also propagated from the Engines to the clients via a PUB/SUB MonitoredQueue. ### Registration ```{figure} figs/queryfade.png :align: center :alt: IPython Registration connections :width: 432px Engines and Clients only need to know where the Query `ROUTER` is located to start connecting. ``` Once a controller is launched, the only information needed for connecting clients and/or engines is the IP/port of the Hub's `ROUTER` socket called the Registrar. This socket handles connections from both clients and engines, and replies with the remaining information necessary to establish the remaining connections. Clients use this same socket for querying the Hub for state information. ### Heartbeat ```{figure} figs/hbfade.png :align: center :alt: IPython Heartbeat connections :width: 432px The heartbeat sockets. ``` The heartbeat process has been described elsewhere. To summarize: the Heartbeat Monitor publishes a distinct message periodically via a `PUB` socket. Each engine has a `zmq.FORWARDER` device with a `SUB` socket for input, and `DEALER` socket for output. The `SUB` socket is connected to the `PUB` socket labeled _ping_, and the `DEALER` is connected to the `ROUTER` labeled _pong_. This results in the same message being relayed back to the Heartbeat Monitor with the addition of the `DEALER` prefix. The Heartbeat Monitor receives all the replies via an `ROUTER` socket, and identifies which hearts are still beating by the `zmq.IDENTITY` prefix of the `DEALER` sockets, which information the Hub uses to notify clients of any changes in the available engines. ### Schedulers ```{figure} figs/queuefade.png :align: center :alt: IPython Queue connections :width: 432px Control message scheduler on the left, execution (apply) schedulers on the right. ``` The controller has at least three Schedulers. These devices are primarily for relaying messages between clients and engines, but the Hub needs to see those messages for its own purposes. Since no Python code may exist between the two sockets in a queue, all messages sent through these queues (both directions) are also sent via a `PUB` socket to a monitor, which allows the Hub to monitor queue traffic without interfering with it. For tasks, the engine need not be specified. Messages sent to the `ROUTER` socket from the client side are assigned to an engine via ZMQ's `DEALER` round-robin load balancing. Engine replies are directed to specific clients via the IDENTITY of the client, which is received as a prefix at the Engine. For Multiplexing, `ROUTER` is used for both in and output sockets in the device. Clients must specify the destination by the `zmq.IDENTITY` of the `ROUTER` socket connected to the downstream end of the device. At the Kernel level, both of these `ROUTER` sockets are treated in the same way as the `REP` socket in the serial version (except using ZMQStreams instead of explicit sockets). Execution can be done in a load-balanced (engine-agnostic) or multiplexed (engine-specified) manner. The sockets on the Client and Engine are the same for these two actions, but the scheduler used determines the actual behavior. This routing is done via the `zmq.IDENTITY` of the upstream sockets in each MonitoredQueue. ### IOPub ```{figure} figs/iopubfade.png :align: center :alt: IOPub connections :width: 432px stdout/err are published via a `PUB/SUB` MonitoredQueue ``` On the kernels, stdout/stderr are captured and published via a `PUB` socket. These `PUB` sockets all connect to a `SUB` socket input of a MonitoredQueue, which subscribes to all messages. They are then republished via another `PUB` socket, which can be subscribed by the clients. ### Client connections ```{figure} figs/queryfade.png :align: center :alt: IPython client query connections :width: 432px Clients connect to an `ROUTER` socket to query the hub. ``` The hub's registrar `ROUTER` socket also listens for queries from clients as to queue status, and control instructions. Clients connect to this socket via an `DEALER` during registration. ```{figure} figs/notiffade.png :align: center :alt: IPython Registration connections :width: 432px Engine registration events are published via a `PUB` socket. ``` The Hub publishes all registration/unregistration events via a `PUB` socket. This allows clients to stay up to date with what engines are available by subscribing to the feed with a `SUB` socket. Other processes could selectively subscribe to just registration or unregistration events. ipyparallel-8.8.0/docs/source/reference/dag_dependencies.md000066400000000000000000000147651460376056100240430ustar00rootroot00000000000000(dag-dependencies)= # DAG Dependencies Often, parallel workflow is described in terms of a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph) or DAG. A popular library for working with Graphs is [NetworkX]. Here, we will walk through a demo mapping a NetworkX DAG to task dependencies. The full script that runs this demo can be found in {file}`examples/parallel/dagdeps.py`. ## Why are DAGs good for task dependencies? The 'G' in DAG is 'Graph'. A Graph is a collection of **nodes** and **edges** that connect the nodes. For our purposes, each node would be a task, and each edge would be a dependency. The 'D' in DAG stands for 'Directed'. This means that each edge has a direction associated with it. So we can interpret the edge (a,b) as meaning that b depends on a, whereas the edge (b,a) would mean a depends on b. The 'A' is 'Acyclic', meaning that there must not be any closed loops in the graph. This is important for dependencies, because if a loop were closed, then a task could ultimately depend on itself, and never be able to run. If your workflow can be described as a DAG, then it is impossible for your dependencies to cause a deadlock. ## A sample DAG Suppose we have five tasks, and they depend on each other as follows: - Task 0 has no dependencies and can run right away. - When 0 finishes, tasks 1 and 2 can start. - When 1 finishes, task 3 must wait for 2, but task 4 can start right away. - When 2 finishes, task 3 can finally start. We might represent these tasks with a simple graph: ```{figure} figs/simpledag.* :width: 600px ``` An arrow is repsented here by a fattened bit on the edge. As we specified when we made the edges, we can see that task 0 depends on nothing, and can run immediately. 1 and 2 depend on 0; 3 depends on 1 and 2; and 4 depends only on 1. Let's construct this 5-node DAG. First, we create a directed graph or 'digraph': ```ipython In [1]: import networkx as nx In [2]: G = nx.DiGraph() In [3]: map(G.add_node, range(5)) # Add 5 nodes, labeled 0-4. ``` Now we can add edges: ```ipython In [4]: G.add_edge(0, 1) # Task 1 depends on task 0... In [5]: G.add_edge(0, 2) # ...and so does task 2. In [6]: G.add_edge(1, 3) # Task 3 depends on task 1... In [7]: G.add_edge(2, 3) # ...and also on task 2. In [8]: G.add_edge(1, 4) # Task 4 depends on task 1. ``` The following code produces the figure above: ```ipython In [9]: pos = {0: (0, 0), 1: (1, 1), 2: (-1, 1), ...: 3: (0, 2), 4: (2, 2)} In [10]: nx.draw(G, pos, edge_color='r') ``` Taking failures into account, assuming all dependencies are run with the default `success=True, failure=False`, the following cases would occur for each node's failure: 0. All other tasks fail as impossible. 1. 2 can still succeed, but 3 and 4 are unreachable. 2. 3 becomes unreachable, but 4 is unaffected. 3. and 4. are terminal, and can have no effect on other nodes. Let's look at a larger, more complex network. ## Computing with a random DAG For demonstration purposes, we have a function that generates a random DAG with a given number of nodes and edges. ```{literalinclude} ../examples/dagdeps.py :language: python :lines: 24-40 ``` So first, we start with a graph of 32 nodes, with 128 edges: ```ipython In [11]: G = random_dag(32, 128) ``` Then we need some jobs. In reality these would all be different, but for our toy example we'll use a single function that sleeps for a random interval: ```{literalinclude} ../examples/dagdeps.py :language: python :lines: 16-21 ``` Now we can build our dict of jobs corresponding to the nodes on the graph: ```ipython In [12]: jobs = {n: randomwait for n in G} ``` Once we have a dict of jobs matching the nodes on the graph, we can start submitting jobs, and linking up the dependencies. Since we don't know a job's msg_id until it is submitted, which is necessary for building dependencies, it is critical that we don't submit any jobs before other jobs it may depend on. Fortunately, NetworkX provides a {meth}`topological_sort` method which ensures exactly this. It presents an iterable, that guarantees that when you arrive at a node, you have already visited all the nodes on which it depends: ```ipython In [13]: import ipyprallel as ipp In [14]: rc = ipp.Client() In [15]: view = rc.load_balanced_view() In [16]: results = {} In [17]: for node in nx.topological_sort(G): ...: # Get list of AsyncResult objects from nodes ...: # leading into this one as dependencies. ...: deps = [results[n] for n in G.predecessors(node)] ...: # Submit and store AsyncResult object. ...: with view.temp_flags(after=deps, block=False): ...: results[node] = view.apply(jobs[node]) ``` Now that we have submitted all the jobs, we can wait for the results: ```ipython In [18]: view.wait(results.values()) ``` Now, at least we know that all the jobs ran and did not fail. But we don't know that the ordering was properly respected. For this, we can use the {attr}`metadata` attribute of each AsyncResult. ## Inspecting the metadata These objects store a variety of metadata about each task, including various timestamps. We can validate that the dependencies were respected by checking that each task was started after all of its predecessors were completed: ```{literalinclude} ../examples/dagdeps.py :language: python :lines: 72-81 ``` Then we can call this function: ```ipython In [19]: validate_tree(G, results) ``` It produces no output but the assertions pass. We can also validate the graph visually. By drawing the graph with each node's x-position as its start time, all arrows must be pointing to the right if dependencies were respected. For spreading, the y-position will be the runtime of the task, so long tasks will be at the top, and quick, small tasks will be at the bottom. ```ipython In [20]: from matplotlib.dates import date2num In [21]: from matplotlib.cm import gist_rainbow In [22]: pos, colors = {}, {} In [23]: for node in G: ...: md = results[node].metadata ...: start = date2num(md.started) ...: runtime = date2num(md.completed) - start ...: pos[node] = (start, runtime) ...: colors[node] = md.engine_id In [24]: nx.draw(G, pos, nodelist=colors.keys(), node_color=list(colors.values()), ...: cmap=gist_rainbow) ``` ```{figure} figs/dagdeps.* :width: 600px Time started on the x-axis, runtime on the y-axis, and color-coded by engine-id (in this case there were four engines). Edges denote dependencies. The limits of the axes have been adjusted in this plot. ``` [networkx]: https://networkx.org ipyparallel-8.8.0/docs/source/reference/db.md000066400000000000000000000145731460376056100211640ustar00rootroot00000000000000(parallel-db)= # IPython's Task Database ## Enabling a DB Backend The IPython Hub can store all task requests and results in a database. Currently supported backends are: MongoDB, SQLite, and an in-memory DictDB. The default is to store recent tasks in a dictionary in memory, which deletes old values if it gets too big, and only survives as long as the controller is running. Using a real database is optional due to its potential {ref}`db-cost`. You can enable one, either at the command-line: ``` $> ipcontroller --sqlitedb # or --mongodb or --nodb ``` or in your {file}`ipcontroller_config.py`: ```python c.IPController.db_class = "NoDB" c.IPController.db_class = "DictDB" # default c.IPController.db_class = "MongoDB" c.IPController.db_class = "SQLiteDB" ``` ## Using the Task Database The most common use case for this is clients requesting results for tasks they did not submit, via: ```ipython In [1]: rc.get_result(task_id) ``` However, since we have this DB backend, we provide a direct query method in the {class}`~.Client` for users who want deeper introspection into their task history. The {meth}`db_query` method of the Client is modeled after MongoDB queries, so if you have used MongoDB it should look familiar. In fact, when the MongoDB backend is in use, the query is relayed directly. When using other backends, the interface is emulated and only a subset of queries is possible. ```{seealso} MongoDB [query docs](https://www.mongodb.com/docs/manual/tutorial/query-documents/) ``` {meth}`Client.db_query` takes a dictionary query object, with keys from the TaskRecord key list, and values of either exact values to test, or MongoDB queries, which are dicts of The form: `{'operator' : 'argument(s)'}`. There is also an optional `keys` argument, that specifies which subset of keys should be retrieved. The default is to retrieve all keys excluding the request and result buffers. {meth}`db_query` returns a list of TaskRecord dicts. Also like MongoDB, the `msg_id` key will always be included, whether requested or not. TaskRecord keys: | Key | Type | Description | | -------------- | ----------- | ----------------------------------------------------------- | | msg_id | uuid(ascii) | The msg ID | | header | dict | The request header | | content | dict | The request content (likely empty) | | buffers | list(bytes) | buffers containing serialized request objects | | submitted | datetime | timestamp for time of submission (set by client) | | client_uuid | uuid(ascii) | IDENT of client's socket | | engine_uuid | uuid(ascii) | IDENT of engine's socket | | started | datetime | time task began execution on engine | | completed | datetime | time task finished execution (success or failure) on engine | | resubmitted | uuid(ascii) | msg_id of resubmitted task (if applicable) | | result_header | dict | header for result | | result_content | dict | content for result | | result_buffers | list(bytes) | buffers containing serialized request objects | | queue | str | The name of the queue for the task ('mux' or 'task') | | execute_input | str | Python input source | | execute_result | dict | Python output (execute_result message content) | | error | dict | Python traceback (error message content) | | stdout | str | Stream of stdout data | | stderr | str | Stream of stderr data | MongoDB operators we emulate on all backends: | Operator | Python equivalent | | -------- | ----------------- | | '\$in' | in | | '\$nin' | not in | | '\$eq' | == | | '\$ne' | != | | '\$ge' | > | | '\$gte' | >= | | '\$le' | \< | | '\$lte' | \<= | The DB Query is useful for two primary cases: 1. deep polling of task status or metadata 2. selecting a subset of tasks, on which to perform a later operation (e.g. wait on result, purge records, resubmit,...) ## Example Queries To get all msg_ids that are not completed, only retrieving their ID and start time: ```ipython In [1]: incomplete = rc.db_query({'completed' : None}, keys=['msg_id', 'started']) ``` All jobs started in the last hour by me: ```ipython In [1]: from datetime import datetime, timedelta In [2]: hourago = datetime.now() - timedelta(1./24) In [3]: recent = rc.db_query({'started' : {'$gte' : hourago }, 'client_uuid' : rc.session.session}) ``` All jobs started more than an hour ago, by clients _other than me_: ```ipython In [3]: recent = rc.db_query({'started' : {'$le' : hourago }, 'client_uuid' : {'$ne' : rc.session.session}}) ``` Result headers for all jobs on engine 3 or 4: ```ipython In [1]: uuids = map(rc._engines.get, (3,4)) In [2]: hist34 = rc.db_query({'engine_uuid' : {'$in' : uuids }, keys='result_header') ``` (db-cost)= ## Cost The advantage of the database backends is, of course, that large amounts of data can be stored that won't fit in memory. The basic DictDB 'backend' stores all of this information in a Python dictionary. This is very fast, but will run out of memory quickly if you move a lot of data around, or your cluster is to run for a long time. Unfortunately, the DB backends (SQLite and MongoDB) right now are rather slow, and can still consume large amounts of resources, particularly if large tasks or results are being created at a high frequency. For this reason, we have added {class}`~.NoDB`, a dummy backend that doesn't store any information. When you use this database, nothing is stored, and any request for results will result in a KeyError. This obviously prevents later requests for results and task resubmission from functioning, but sometimes those nice features are not as useful as keeping Hub memory under control. ipyparallel-8.8.0/docs/source/reference/details.md000066400000000000000000000515371460376056100222250ustar00rootroot00000000000000(parallel-details)= # Details of Parallel Computing with IPython ```{note} There are still many sections to fill out in this doc ``` ## Caveats First, some caveats about the detailed workings of parallel computing with 0MQ and IPython. ### Non-copying sends and numpy arrays When numpy arrays are passed as arguments to apply or via data-movement methods, they are not copied. This means that you must be careful if you are sending an array that you intend to work on. PyZMQ does allow you to track when a message has been sent so you can know when it is safe to edit the buffer, but IPython only allows for this. It is also important to note that the non-copying receive of a message is _read-only_. That means that if you intend to work in-place on an array that you have sent or received, you must copy it. This is true for both numpy arrays sent to engines and numpy arrays retrieved as results. The following will fail: ```ipython In [3]: A = numpy.zeros(2) In [4]: def setter(a): ...: a[0]=1 ...: return a In [5]: rc[0].apply_sync(setter, A) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) in () in setter(a) RuntimeError: array is not writeable ``` If you do need to edit the array in-place, remember to copy the array if it's read-only. The {attr}`ndarray.flags.writeable` flag will tell you if you can write to an array. ```ipython In [3]: A = numpy.zeros(2) In [4]: def setter(a): ...: """only copy read-only arrays""" ...: if not a.flags.writeable: ...: a=a.copy() ...: a[0]=1 ...: return a In [5]: rc[0].apply_sync(setter, A) Out[5]: array([ 1., 0.]) # note that results will also be read-only: In [6]: _.flags.writeable Out[6]: False ``` If you want to safely edit an array in-place after _sending_ it, you must use the `track=True` flag. IPython always performs non-copying sends of arrays, which return immediately. You must instruct IPython track those messages _at send time_ in order to know for sure that the send has completed. AsyncResults have a {attr}`sent` property, and {meth}`wait_on_send` method for checking and waiting for 0MQ to finish with a buffer. ```ipython In [5]: A = numpy.random.random((1024,1024)) In [6]: view.track=True In [7]: ar = view.apply_async(lambda x: 2*x, A) In [8]: ar.sent Out[8]: False In [9]: ar.wait_on_send() # blocks until sent is True ``` ### What is sendable? If IPython doesn't know what to do with an object, it will pickle it. There is a short list of objects that are not pickled: `buffers/memoryviews`, `bytes` objects, and `numpy` arrays. These are handled specially by IPython in order to prevent extra in-memory copies of data. Sending bytes or numpy arrays will result in exactly zero in-memory copies of your data (unless the data is very small). If you have an object that provides a Python buffer interface, then you can always send that buffer without copying - and reconstruct the object on the other side in your own code. It is possible that the object reconstruction will become extensible, so you can add your own non-copying types, but this does not yet exist. #### Closures Just about anything in Python is pickleable. The one notable exception is objects (generally functions) with _closures_. Closures can be a complicated topic, but the basic principle is that functions that refer to variables in their parent scope have closures. An example of a function that uses a closure: ```python def f(a): def inner(): # inner will have a closure return a return inner f1 = f(1) f2 = f(2) f1() # returns 1 f2() # returns 2 ``` `f1` and `f2` will have closures referring to the scope in which `inner` was defined, because they use the variable 'a'. As a result, you would not be able to send `f1` or `f2` with IPython. Note that you _would_ be able to send `f`. This is only true for interactively defined functions (as are often used in decorators), and only when there are variables used inside the inner function, that are defined in the outer function. If the names are _not_ in the outer function, then there will not be a closure, and the generated function will look in `globals()` for the name: ```python def g(b): # note that `b` is not referenced in inner's scope def inner(): # this inner will *not* have a closure return a return inner g1 = g(1) g2 = g(2) g1() # raises NameError on 'a' a=5 g2() # returns 5 ``` `g1` and `g2` _will_ be sendable with IPython, and will treat the engine's namespace as globals(). The {meth}`pull` method is implemented based on this principle. If we did not provide pull, you could implement it yourself with `apply`, by returning objects out of the global namespace: ```ipython In [10]: view.apply(lambda : a) # is equivalent to In [11]: view.pull('a') ``` You can send functions with closures if you enable using dill or cloudpickle: ```ipython In [10]: rc[:].use_cloudpickle() ``` which will use a more advanced pickling library, which covers things like closures. ## Running Code There are two principal units of execution in Python: strings of Python code (e.g. 'a=5'), and Python functions. IPython is designed around the use of functions via the core Client method, called `apply`. ### Apply The principal method of remote execution is {meth}`apply`, of {class}`~ipyparallel.client.view.View` objects. The Client provides the full execution and communication API for engines via its low-level {meth}`send_apply_message` method, which is used by all higher level methods of its Views. f : The function to be called remotely args : The positional arguments passed to `f` kwargs : The keyword arguments passed to `f` flags for all views: block : Whether to wait for the result, or return immediately. False: : returns AsyncResult True: : returns actual result(s) of `f(*args, **kwargs)` if multiple targets: : list of results, matching `targets` track : whether to track non-copying sends. targets : Specify the destination of the job. if 'all' or None: : Run on all active engines if list: : Run on each specified engine if int: : Run on single engine ```{note} {class}`LoadBalancedView` uses targets to restrict possible destinations. LoadBalanced calls will always execute on exactly one engine. ``` flags only in LoadBalancedViews: after : Only for load-balanced execution (targets=None) Specify a list of msg ids as a time-based dependency. This job will only be run _after_ the dependencies have been met. follow : Only for load-balanced execution (targets=None) Specify a list of msg_ids as a location-based dependency. This job will only be run on an engine where this dependency is met. timeout : Only for load-balanced execution (targets=None) Specify an amount of time (in seconds) for the scheduler to wait for dependencies to be met before failing with a DependencyTimeout. ### execute and run For executing strings of Python code, {class}`~.DirectView` s also provide an {meth}`~.DirectView.execute` and a {meth}`~.DirectView.run` method, which rather than take functions and arguments, take Python strings. `execute` takes a string of Python code to execute, and sends it to the Engine(s). `run` is the same as `execute`, but for a _filename_ rather than a string. It is a wrapper that does something very similar to `execute(open(f).read())`. ```{note} TODO: Examples for execute and run ``` ## Views The principal extension of the {class}`~parallel.Client` is the {class}`~parallel.View` class. The client is typically a singleton for connecting to a cluster, and presents a low-level interface to the Hub and Engines. Most real usage will involve creating one or more {class}`~parallel.View` objects for working with engines in various ways. ### DirectView The {class}`.DirectView` is the class for the IPython {ref}`Multiplexing Interface `. #### Creating a DirectView DirectViews can be created in two ways, by index access to a client, or by a client's {meth}`view` method. Index access to a Client works in a few ways. First, you can create DirectViews to single engines by accessing the client by engine id: ```ipython In [2]: rc[0] Out[2]: ``` You can also create a DirectView with a list of engines: ```ipython In [2]: rc[0,1,2] Out[2]: ``` Other methods for accessing elements, such as slicing and negative indexing, work by passing the index directly to the client's {attr}`ids` list, so: ```ipython # negative index In [2]: rc[-1] Out[2]: # or slicing: In [3]: rc[::2] Out[3]: ``` are always the same as: ```ipython In [2]: rc[rc.ids[-1]] Out[2]: In [3]: rc[rc.ids[::2]] Out[3]: ``` Also note that the slice is evaluated at the time of construction of the DirectView, so the targets will not change over time if engines are added/removed from the cluster. #### Execution via DirectView The DirectView is the simplest way to work with one or more engines directly (hence the name). For instance, to get the process ID of all your engines: ```ipython In [5]: import os In [6]: dview.apply_sync(os.getpid) Out[6]: [1354, 1356, 1358, 1360] ``` Or to see the hostname of the machine they are on: ```ipython In [5]: import socket In [6]: dview.apply_sync(socket.gethostname) Out[6]: ['tesla', 'tesla', 'edison', 'edison', 'edison'] ``` ```{note} TODO: expand on direct execution ``` #### Data movement via DirectView Since a Python namespace is a {class}`dict`, {class}`DirectView` objects provide dictionary-style access by key and methods such as {meth}`get` and {meth}`update` for convenience. This make the remote namespaces of the engines appear as a local dictionary. Underneath, these methods call {meth}`apply`: ```ipython In [51]: dview['a']=['foo','bar'] In [52]: dview['a'] Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ] ``` ### Scatter and gather Sometimes it is useful to partition a sequence and push the partitions to different engines. In MPI language, this is know as scatter/gather and we follow that terminology. However, it is important to remember that in IPython's {class}`Client` class, {meth}`scatter` is from the interactive IPython session to the engines and {meth}`gather` is from the engines back to the interactive IPython session. For scatter/gather operations between engines, MPI should be used: ```ipython In [58]: dview.scatter('a',range(16)) Out[58]: [None,None,None,None] In [59]: dview['a'] Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ] In [60]: dview.gather('a') Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] ``` ### Push and pull {meth}`~ipyparallel.client.view.DirectView.push` {meth}`~ipyparallel.client.view.DirectView.pull` ```{note} TODO: write this section ``` ### LoadBalancedView The {class}`~.LoadBalancedView` is the class for load-balanced execution via the task scheduler. These views always run tasks on exactly one engine, but let the scheduler determine where that should be, allowing load-balancing of tasks. The LoadBalancedView does allow you to specify restrictions on where and when tasks can execute, for more complicated load-balanced workflows. ## Data Movement Since the {class}`~.LoadBalancedView` does not know where execution will take place, explicit data movement methods like push/pull and scatter/gather do not make sense, and are not provided. ## Results ### AsyncResults Our primary representation of the results of remote execution is the {class}`~.AsyncResult` object, based on the object of the same name in the built-in {py:mod}`multiprocessing.pool` module. Our version provides a superset of that interface, and starting in 6.0 is a subclass of {class}`concurrent.futures.Future`. The basic principle of the AsyncResult is the encapsulation of one or more results not yet completed. Execution methods (including data movement, such as push/pull) will all return AsyncResults when `block=False`. ### The mp.pool.AsyncResult interface The basic interface of the AsyncResult is exactly that of the AsyncResult in {py:mod}`multiprocessing.pool`, and consists of four methods: % AsyncResult spec directly from docs.python.org ```{eval-rst} .. class:: AsyncResult The stdlib AsyncResult spec .. method:: wait([timeout]) Wait until the result is available or until *timeout* seconds pass. This method always returns ``None``. .. method:: ready() Return whether the call has completed. .. method:: successful() Return whether the call completed without raising an exception. Will raise :exc:`AssertionError` if the result is not ready. .. method:: get([timeout]) Return the result when it arrives. If *timeout* is not ``None`` and the result does not arrive within *timeout* seconds then :exc:`TimeoutError` is raised. If the remote call raised an exception then that exception will be reraised as a :exc:`RemoteError` by :meth:`get`. ``` While an AsyncResult is not done, you can check on it with its {meth}`ready` method, which will return whether the AR is done. You can also wait on an AsyncResult with its {meth}`wait` method. This method blocks until the result arrives. If you don't want to wait forever, you can pass a timeout (in seconds) as an argument to {meth}`wait`. {meth}`wait` will _always return None_, and should never raise an error. {meth}`ready` and {meth}`wait` are insensitive to the success or failure of the call. After a result is done, {meth}`successful` will tell you whether the call completed without raising an exception. If you want the result of the call, you can use {meth}`get`. Initially, {meth}`get` behaves just like {meth}`wait`, in that it will block until the result is ready, or until a timeout is met. However, unlike {meth}`wait`, {meth}`get` will raise a {exc}`TimeoutError` if the timeout is reached and the result is still not ready. If the result arrives before the timeout is reached, then {meth}`get` will return the result itself if no exception was raised, and will raise an exception if there was. Here is where we start to expand on the multiprocessing interface. Rather than raising the original exception, a RemoteError will be raised, encapsulating the remote exception with some metadata. If the AsyncResult represents multiple calls (e.g. any time `targets` is plural), then a CompositeError, a subclass of RemoteError, will be raised. ```{seealso} For more information on remote exceptions, see {ref}`the section in the Direct Interface `. ``` #### Extended interface Other extensions of the AsyncResult interface include convenience wrappers for {meth}`get`. AsyncResults have a property, {attr}`result`, with the short alias {attr}`r`, which call {meth}`get`. Since our object is designed for representing _parallel_ results, it is expected that many calls (any of those submitted via DirectView) will map results to engine IDs. We provide a {meth}`get_dict`, which is also a wrapper on {meth}`get`, which returns a dictionary of the individual results, keyed by engine ID. You can also prevent a submitted job from executing, via the AsyncResult's {meth}`abort` method. This will instruct engines to not execute the job when it arrives. The larger extension of the AsyncResult API is the {attr}`metadata` attribute. The metadata is a dictionary (with attribute access) that contains, logically enough, metadata about the execution. Metadata keys: timestamps submitted : When the task left the Client started : When the task started execution on the engine completed : When execution finished on the engine received : When the result arrived on the Client note that it is not known when the result arrived in 0MQ on the client, only when it arrived in Python via {meth}`Client.spin`, so in interactive use, this may not be strictly informative. Information about the engine engine_id : The integer id engine_uuid : The UUID of the engine output of the call error : Python exception, if there was one execute_input : The code (str) that was executed execute_result : Python output of an execute request (not apply), as a Jupyter message dictionary. stderr : stderr stream stdout : stdout (e.g. print) stream And some extended information status : either 'ok' or 'error' msg_id : The UUID of the message after : For tasks: the time-based msg_id dependencies follow : For tasks: the location-based msg_id dependencies While in most cases, the Clients that submitted a request will be the ones using the results, other Clients can also request results directly from the Hub. This is done via the Client's {meth}`get_result` method. This method will _always_ return an AsyncResult object. If the call was not submitted by the client, then it will be a subclass, called {class}`AsyncHubResult`. These behave in the same way as an AsyncResult, but if the result is not ready, waiting on an AsyncHubResult polls the Hub, which is much more expensive than the passive polling used in regular AsyncResults. The Client keeps track of all results history, results, metadata ## Querying the Hub The Hub sees all traffic that may pass through the schedulers between engines and clients. It does this so that it can track state, allowing multiple clients to retrieve results of computations submitted by their peers, as well as persisting the state to a database. queue_status > You can check the status of the queues of the engines with this command. result_status > check on results purge_results > forget results (conserve resources) ## Controlling the Engines There are a few actions you can do with Engines that do not involve execution. These messages are sent via the Control socket, and bypass any long queues of waiting execution jobs abort > Sometimes you may want to prevent a job you have submitted from running. The method > for this is {meth}`abort`. It takes a container of msg_ids, and instructs the Engines to not > run the jobs if they arrive. The jobs will then fail with an AbortedTask error. clear > You may want to purge the Engine(s) namespace of any data you have left in it. After > running `clear`, there will be no names in the Engine's namespace shutdown > You can also instruct engines (and the Controller) to terminate from a Client. This > can be useful when a job is finished, since you can shutdown all the processes with a > single command. ## Synchronization Since the Client is a synchronous object, events do not automatically trigger in your interactive session - you must poll the 0MQ sockets for incoming messages. Note that this polling _does not_ make any network requests. It performs a `select` operation, to check if messages are already in local memory, waiting to be handled. The method that handles incoming messages is {meth}`spin`. This method flushes any waiting messages on the various incoming sockets, and updates the state of the Client. If you need to wait for particular results to finish, you can use the {meth}`wait` method, which will call {meth}`spin` until the messages are no longer outstanding. Anything that represents a collection of messages, such as a list of msg_ids or one or more AsyncResult objects, can be passed as argument to wait. A timeout can be specified, which will prevent the call from blocking for more than a specified time, but the default behavior is to wait forever. The client also has an `outstanding` attribute - a `set` of msg ids that are awaiting replies. This is the default if wait is called with no arguments - i.e. wait on _all_ outstanding messages. ```{note} TODO wait example ``` ## Map Many parallel computing problems can be expressed as a `map`, or running a single program with a variety of different inputs. Python has a built-in {py:func}`map`, which does exactly this, and many parallel execution tools in Python, such as the built-in {py:class}`multiprocessing.Pool` object provide implementations of `map`. All View objects provide a {meth}`map` method as well, but the load-balanced and direct implementations differ. Views' map methods can be called on any number of sequences, but they can also take keyword arguments to influence how the work is distributed. What keyword arguments are available depends on the view being used. ```{eval-rst} .. class:: ipyparallel.DirectView :noindex: .. automethod:: map :noindex: ``` ```{eval-rst} .. class:: ipyparallel.LoadBalancedView :noindex: .. automethod:: map :noindex: .. automethod:: imap :noindex: ``` ## Decorators and RemoteFunctions ```{note} TODO: write this section ``` {func}`~ipyparallel.client.remotefunction.parallel` {func}`~ipyparallel.client.remotefunction.remote` {class}`~ipyparallel.client.remotefunction.RemoteFunction` {class}`~ipyparallel.client.remotefunction.ParallelFunction` ## Dependencies ```{note} TODO: write this section ``` {func}`~ipyparallel.controller.dependency.depend` {func}`~ipyparallel.controller.dependency.require` {class}`~ipyparallel.controller.dependency.Dependency` ipyparallel-8.8.0/docs/source/reference/figs/000077500000000000000000000000001460376056100211735ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/reference/figs/allconnections.png000066400000000000000000000766201460376056100247270ustar00rootroot00000000000000PNG  IHDRN0*tEXtSoftwareAdobe ImageReadyqe<}2IDATx x\u6mlcldÏ/1N,!!$p|Dr44+ ?%/Me&Md@4H4hD6_lelcݳhhf4>̜g]k@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ dQ8,j9Y]DMk]Wx46;K-SV"qH(e{|(Vks\y]llkr֭[+&J V $ʂ)&Z켬Ăvk?'%ClsǵdQS-t^c6:;mζUkGD%L<(: p:NhpS@i9oomd&d:tZ;&HAۜe4:ON@p Ua|Si0j &MbPU+rQ^q1Cy-:S}h| sQBg b)ZZ%o' L m4zfbkt,0fOԹ"~_NҠ 4\:*a^Gۺ\*a4Ϲc5@ !0~smjsuRF%0n;k5q]tl5=6aWdvzL[T"*۲+ʣWc]~:TǢS3>Or'zi_cEd(7EڟC: (]O7oQcM!Q`@ `}O90=qa1+Oȏ<22,ᨽt 5gž` y$&,]&,U\ɓm0AmՒf"3#|/aҤybKXH~A %/!0@ bU B'(F_KTWy'JLu#m\XhZAR8 Q!\xX" DUW E!*e)%ҥ穋.>W͚5Cm|izMw^ =k,[^9|zjWf 7} >>!τ81n `(0Wg*B8*򷎕 ĺdBK#r-'Q)mt\s ;nT?p&1,z=K8f_sñLw$? >@F%:NR #Y|"f00,wOX6d(Ʒ2D4%؛HŹ-I o%+ggiw_ k: ~Jz0^S③=J R_ʨWCqC1 #>z8':@56mܡ>ݼ\YJG=r^PNKY?=xڊ\"Q*3I}}XY) ej⮯?vڧϪ ܭ\`,+Ji3מL9ıYs}% 2&k"#bcyuKkT"#s롂: ~/be Ps c:ԧn[>Η.zGݗԣs>SQ9j'%p*R/fB"&+55%l*ӺCu5l/4%EZVӃԒ(<|N yx 袥穝;6d@e4?{"yZYMF5>1#^P7F{PT?$aRYh \ 2Zvݑ93yڹ_O84ژ>얛su&*Rq@quSC̋tR9 !7+l#/8H-وڅoW .S3Бa|r;a1/U|>Jڧ~t\~gU/x4k>>|j!6ÌJ]=V'V,ΏWV0gAS;;GT"B,+ 2M0Bx}L5u7C-#}xާ*ĚvI|.IQתD"Q^Au vӶxDdh-2@PʾV'硑~Ш0H &L} dP.:-Z?ԑȬmȔ5ԈQEdy6U_xq?{s|T糭hMd$rQ`%edSTD#t mM!0(68 ˤ2nWE4lE#^ J$lc*"l)oSbdՃ䵁JwfV(MͬxJ ^9!LqğmW6} Wȸ+!a4WDGPـ?AƋ"462}y{)0j莞H, Ql r;).">oς $&*`Zc5@W|&$9 @CB+ZAbR+3p!2xEa<΢FF%ɫgBY:-DH{-AMfBWcDi,5rv4ND!:W,v g; @XgB_ Q}~\fO|^TsNBW 3d1ml( +ف2CAEMHQ03s/, acYk<[q7RwxSWWZgl+֐HfkH$cKܺ˄=1Z?qp?PpgO<*@LO6g q^XbJ$ӨUވ"u8* L_TiFcߒ`-% i.@L`1cq1H\Qyd O[- |`Y%&LWSIzj=eKw@L-_f.[KPЬ~J;õ(_  p]5iMvB::+r,JL Lɓ:5,J(hSe%W@ ?_y]/@q+<Qd[$Q@9`͵L>q}C_%`G0ʂhҥGԮ_w^_wڗnڍznƺ>U;#ꖛ=l26prX1¤X$U{v9Qv۝?Of7Cdh8` d{e]XEɫ,=e,X8W-7ߣYo5kFǃ@f8F}./D(2 d"npRl7`(=yȳ ESPHAK_$Î[I@\PbT%7Jj@p#5$?X9+wS+>zztnukkcNtS"ӣzM(a*-}lTE%`dBws5sUvJN^*K5WܳO5С]?lk>g:  ,f\JPvEWZ\v/%"%e$Zjp ~%//`J9j~lFQ>I~߳^Ӽ YDfBȰp*6:`T *B@GH &V6A@Xxr.ш#aLJx;d_.yPVk2Te2XZSMӣ,XKMzlFPA (ĠWBQP{t(=́^ڡ́Za8DRCL8D!%ۼ>c{=]h~͈B7 $b;WNʀ؀ZX'eKh0*~0DuxQ*TX$!/?i5Xs]F ?̓ptT9^̎T(ЌccY:@LlFPʉF;:}]t٤k Ոtpy\}M䊦\xHE k*|<6õzȯi\sH~B!0!/ * j .]syB`B^Ap匚<4JT@ *L?BšWNz;*L~ÏQF^ooe5yaur$y†j =㫜kmœ饚G0ļ[Tj:q[I<.LQ a7W Sb憭!خ_LCL[ "k.Lj|@b!ǑLb-oj-B ]0AZBFZvzCroFS` ]jśH.};j-+׊kƵ+G֫߬fQbmo*tΧ2&[vQWK)pҩ0S75-yxlrO5" fL%Җyo>|W*W&[̐H>'9`F|2 fUd.hVlC`)ܤTkaTWH)8`ʼÍq hMfݼ08qI7!e~ooS,t񀩐b67a%CdsjAo:#?kHdpؘs\[A39txfEf&61NYP|ݼJ=Ch$iԼƊ*0Y%CA9@}bXtF|Vފ5ƠF 27͐dZDLx.~hݓm#$ra m:5vB$$ݤ=F&vZM3HەYib&L-mC7/ZSkcBRg5S+"D ܿ"RAdw+jDEJA%ꬭ"kt\<>*H$0/=S (Ys THnFgqt[؅c{ &1(6Vnna_&ݻv&m~Z-\Ia""f:G=mO%xD"OQ+Y-1cd׺Dʬ#'0S!Ħö"ѣ# &HwqXoyM=/݂5N6hG\c6$mdyA &Z2Kt?=!Rc- yY@ԡ@7%_^]]''NCy@~8) [3ks".g$C4l]$H}U~AI1vSW 9r2tyPhDhC&, Lt ˯P!H8:26ޖ*RO劥\WUc|~ŷ:F5jd{~$ˬ0oPϧs5sisYKX5ѵѹ-fGul&bD-9• ^H5a&o]D :x6ܰ/ZS[[|m˖,O_?zl&D`lIŘJUժN}qG6K>뉰%w>ȟ}؎:*`ޯ;5D3oڻgCVO=xr$!&X-^D+ ,[Y )xi"d ͘2 ~b6=Ÿ ~o&DY9.1O?fyZ[ iU`+[b:.Q=&# *o<1Ɲy9~Qy=x_=!as 2Mn92º\<QK%+TW-V¯*<0t`br@>՜N^]'F3봂D~&c~/F(f (G`Y=)R2?D[2lB]ɘ &{)9l|^ '09Z|$nLF9c!H#E _n`sRha?, 20!œlq~0HQ^W/elQ̈0*Y4QQƍmIq/\Pz)@|律yBAW*y @\u{qo7}c xV1FV(+:I%^b(Y"n4fBKU ug#>KFbDb@vԳO1)V3o¯4YOZLegnO9Qj溦o犖 \+[IIXV<}n'OS&cz~_4&BQ<0!>8꼮 rRLgݰ~N`<v3^F՛ .Toο@G.3~5uڬ,2䱥FW Y78W8QN5xI.y \m޼I4 ܭzPjDnӯ sݭzw~7mW~բ;oБLVLX&9}L+eB")܃ELB%0uexvV+"QQ`Y*0Ͽ"6{xze^Ykuuy=O͙;7lYQM7t$U+l𩛾_έ;nT~<8dLMFنޛa yK("0f(.5j2etL!P%m3@Pw@tNaB`0LE,U_C׎W_M.Xwμyj9" +y6B^n >" +dd8c`Nz`:rQG=äBMj<0>x&1:oTi|i1=iQ"AB`W3<O?*}աÇԈ|s"Sw_o9u6 >fuɟfΪ$xߖVBՖkW.;0)yɗ/*;U\\wk!"7s]Jq$D%`?ͅ8Tϔ*r0ek5!/6j" T%d)Uʍ:Q=OklmW^Y<,G˒߿9J:9Qѷmqjƭ~轉ӊ?r0H1MbT_񓵃>:qzPpt@g)S֭[ºw G`|jȯ6ڵ{~QhǦB A*ME: 4(30qA}qWSd+~  gܩ&vz Mv7Wc~&OA`i>'c[W?8[kM &Hp]u 5쨮Î9k&ٳg'H(iNl{C^.9kl$C= $X>A 0«&9s=`[oK]ίwz&"|6zK yXTV PWp(W98閛I &BCym>1cǫȑM!eN&ѓ\uX2UWs,XmX<0AqjݛTRcjJ V10Ip,e'Q $ɠ6I%K1yN?2mq6aE*mKJ$p3:~v!3ܷo^#w`, &M!ԛ O lųuPP>+IfmZ&<ܞ+L! 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A Launcher has two primary methods: {meth}`~.BaseLauncher.start` and {meth}`~.BaseLauncher.stop`, which should be `async def` coroutines. There are two basic kinds of Launcher: {class}`~.ControllerLauncher` and {class}`~.EngineLauncher`. A ControllerLauncher should launch `ipcontroller` somewhere, and an EngineLauncher should start `n` engines somewhere. Shared configuration, principally `profile_dir` and `cluster_id` are typically used to locate the connection files necessary for these two communicate, though explicit paths can be added to arguments. Launchers are used through the {class}`~.Cluster` API, which manages one ControllerLauncher and zero to many EngineLaunchers, each representing a set of engines. Launchers are registered via entry points ([more below](entrypoints)), and can be selected via short lowercase string naming the kind of launcher, e.g. 'mpi' or 'local': ```python import ipyparallel as ipp c = ipp.Cluster(engines="mpi") ``` For the most part, Launchers are not interacted-with directly, but can be _configured_. If you generate a config file with: ```bash ipython profile create --parallel ``` you can check out the resulting `ipcluster_config.py`, which includes configuration options for all available Launcher classes. You can also check `ipcluster start --help-all` to see them on the command-line. ## Debugging launchers If a launcher isn't doing what you want, the first thing to do is probably start your Cluster with `log_level=logging.DEBUG`. You can also access the Launcher(s) on the Cluster object and call {meth}`~.BaseLauncher.get_output` to retrieve the output from the process. ## Writing your own Launcher(s) If you want to write your own launcher, the best place to start is to look at the Launcher classes that ship with IPython Parallel. There are three key methods to implement: - [`start()`](writing-start) - [`stop()`](writing-stop) - [`from_dict()`](writing-from-dict) (writing-start)= ### Writing start A start method on a launcher should do the following: 1. request the process(es) to be started 2. start monitoring to notice when the process exits, such that {meth}`.notify_stop` will be called when the process exits. The command to launch should be the `self.args` list, inherited from the base class. The default for the `LocalProcessLauncher` ```{literalinclude} ../../../ipyparallel/cluster/launcher.py :pyobject: LocalProcessLauncher.start ``` _ControllerLauncher.start_ is always called with no arguments, whereas `EngineLauncher.start` is called with `n`, which is an integer or None. If `n` is an integer, this many engines should be started. If `n` is None, a 'default' number should be used, e.g. the number of CPUs on a host. (writing-stop)= ### Writing stop A stop method should request that the process(es) stop, and return only after everything is stopped and cleaned up. Exactly how to collect these resources will depend greatly on how the resources were requested in `start`. ### Serializing Launchers Launchers are serialized to disk using JSON, via the `.to_dict()` method. The default `.to_dict()` method should rarely need to be overridden. To declare a property of your launcher as one that should be included in serialization, register it as a [traitlet][] with `to_dict=True`. For example: ```python from traitlets import Integer from ipyparallel.cluster.launcher import EngineLauncher class MyLauncher(EngineLauncher): pid = Integer( help="The pid of the process", ).tag(to_dict=True) ``` [traitlet]: https://traitlets.readthedocs.io This `.tag(to_dict=True)` ensures that the `.pid` property will be persisted to disk, and reloaded in the default `.from_dict` implementation. Typically, these are populated in `.start()`: ```python def start(self): process = start_process(self.args, ...) self.pid = process.pid ``` Mark whatever properties are required to reconstruct your object from disk with this metadata. (writing-from-dict)= #### writing from_dict {meth}`~.BaseLauncher.from_dict` should be a class method which returns an instance of your Launcher class, loaded from dict. Most `from_dict` methods will look similar to this: ```{literalinclude} ../../../ipyparallel/cluster/launcher.py :pyobject: LocalProcessLauncher.from_dict ``` where serializable-state is loaded first, then 'live' objects are loaded from that. As in the default LocalProcessLauncher: ```{literalinclude} ../../../ipyparallel/cluster/launcher.py :pyobject: LocalProcessLauncher._reconstruct_process ``` The local process case is the simplest, where the main thing that needs serialization is the PID of the process. If reconstruction of the object fails because the resource is no longer running (e.g. check for the PID and it's not there, or a VM / batch job are gone), the {exc}`.NotRunning` exception should be raised. This tells the Cluster that the object is gone and should be removed (handled the same as if it had stopped while we are watching). Raising other unhandled errors will be assumed to be a bug in the Launcher, and not result in removing the resource from cluster state. ### Additional methods Some useful additional methods to implement, if the base class implementations do not work for you: - {meth}`~.ControllerLauncher.get_connection_info` - {meth}`~.BaseLauncher.get_output` TODO: write more docs on these (entrypoints)= ## Registering your Launcher via entrypoints Once you have defined your launcher, you can 'register' it for discovery via entrypoints. In your setup.py: ```python setup( ... entry_points={ 'ipyparallel.controller_launchers': [ 'mine = mypackage:MyControllerLauncher', ], 'ipyparallel.engine_launchers': [ 'mine = mypackage:MyEngineSetLauncher', ], }, ) ``` This allows clusters created to use the shortcut: ```python Cluster(engines="mine") ``` instead of the full import string ``` Cluster(engines="mypackage.MyEngineSetLauncher") ``` though the long form will always still work. ## Launcher API reference ```{eval-rst} .. automodule:: ipyparallel.cluster.launcher ``` ipyparallel-8.8.0/docs/source/reference/messages.md000066400000000000000000000334531460376056100224040ustar00rootroot00000000000000(parallel-messages)= # Messaging for Parallel Computing This is an extension of the {ref}`messaging ` doc. Diagrams of the connections can be found in the {ref}`parallel connections ` doc. ZMQ messaging is also used in the parallel computing IPython system. All messages to/from kernels remain the same as the single kernel model, and are forwarded through a ZMQ Queue device. The controller receives all messages and replies in these channels, and saves results for future use. ## The Controller The controller is the central collection of processes in the IPython parallel computing model. It has two major components: > - The Hub > - A collection of Schedulers ## The Hub The Hub is the central process for monitoring the state of the engines, and all task requests and results. It has no role in execution and does no relay of messages, so large blocking requests or database actions in the Hub do not have the ability to impede job submission and results. ### Registration (`ROUTER`) The first function of the Hub is to facilitate and monitor connections of clients and engines. Both client and engine registration are handled by the same socket, so only one ip/port pair is needed to connect any number of connections and clients. Engines register with the `zmq.IDENTITY` of their two `DEALER` sockets, one for the queue, which receives execute requests, and one for the heartbeat, which is used to monitor the survival of the Engine process. Message type: `registration_request`: ``` content = { 'uuid' : 'abcd-1234-...', # the zmq.IDENTITY of the engine's sockets } ``` ```{note} these are always the same, at least for now. ``` The Controller replies to an Engine's registration request with the engine's integer ID, and all the remaining connection information for connecting the heartbeat process, and kernel queue socket(s). The message status will be an error if the Engine requests IDs that already in use. Message type: `registration_reply`: ``` content = { 'status' : 'ok', # or 'error' # if ok: 'id' : 0, # int, the engine id } ``` Clients use the same socket as engines to start their connections. Connection requests from clients need no information: Message type: `connection_request`: ``` content = {} ``` The reply to a Client registration request contains the connection information for the multiplexer and load balanced queues, as well as the address for direct hub queries. If any of these addresses is `None`, that functionality is not available. Message type: `connection_reply`: ``` content = { 'status' : 'ok', # or 'error' } ``` ### Heartbeat The hub uses a heartbeat system to monitor engines, and track when they become unresponsive. As described in {ref}`messaging `, and shown in {ref}`connections `. ### Notification (`PUB`) The hub publishes all engine registration/unregistration events on a `PUB` socket. This allows clients to have up-to-date engine ID sets without polling. Registration notifications contain both the integer engine ID and the queue ID, which is necessary for sending messages via the Multiplexer Queue and Control Queues. Message type: `registration_notification`: ``` content = { 'id' : 0, # engine ID that has been registered 'uuid' : 'engine_id' # the IDENT for the engine's sockets } ``` Message type : `unregistration_notification`: ``` content = { 'id' : 0 # engine ID that has been unregistered 'uuid' : 'engine_id' # the IDENT for the engine's sockets } ``` ### Client Queries (`ROUTER`) The hub monitors and logs all queue traffic, so that clients can retrieve past results or monitor pending tasks. This information may reside in-memory on the Hub, or on disk in a database (SQLite and MongoDB are currently supported). These requests are handled by the same socket as registration. {func}`queue_request` requests can specify multiple engines to query via the `targets` element. A verbose flag can be passed, to determine whether the result should be the list of `msg_ids` in the queue or the length of each list. Message type: `queue_request`: ``` content = { 'verbose' : True, # whether return should be lists themselves or the lengths thereof 'targets' : [0,3,1] # list of ints } ``` The content of a reply to a {func}`queue_request` request is a dict, keyed by the engine IDs. Note that they will be the string representation of the integer keys, since JSON cannot handle number keys. The three keys of each dict are: ``` 'completed' : messages submitted via any queue that ran on the engine 'queue' : jobs submitted via MUX queue, whose results have not been received 'tasks' : tasks that are known to have been submitted to the engine, but have not completed. Note that with the pure zmq scheduler, this will always be 0/[]. ``` Message type: `queue_reply`: ``` content = { 'status' : 'ok', # or 'error' # if verbose=False: '0' : {'completed' : 1, 'queue' : 7, 'tasks' : 0}, # if verbose=True: '1' : {'completed' : ['abcd-...','1234-...'], 'queue' : ['58008-'], 'tasks' : []}, } ``` Clients can request individual results directly from the hub. This is primarily for gathering results of executions not submitted by the requesting client, as the client will have all its own results already. Requests are made by msg_id, and can contain one or more msg_id. An additional boolean key 'statusonly' can be used to not request the results, but poll the status of the jobs instead. Message type: `result_request`: ``` content = { 'msg_ids' : ['uuid','...'], # list of strs 'targets' : [1,2,3], # list of int ids or uuids 'statusonly' : False, # bool } ``` The {func}`result_request` reply contains the content objects of the actual execution reply messages. If `statusonly=True`, then there will be only the 'pending' and 'completed' lists. Message type: `result_reply`: ``` content = { 'status' : 'ok', # else error # if ok: 'acbd-...' : msg, # the content dict is keyed by msg_ids, # values are the result messages # there will be none of these if `statusonly=True` 'pending' : ['msg_id','...'], # msg_ids still pending 'completed' : ['msg_id','...'], # list of completed msg_ids } buffers = ['bufs','...'] # the buffers that contained the results of the objects. # this will be empty if no messages are complete, or if # statusonly is True. ``` For memory management purposes, Clients can also instruct the hub to forget the results of messages. This can be done by message ID or engine ID. Individual messages are dropped by msg_id, and all messages completed on an engine are dropped by engine ID. This may no longer be necessary with the mongodb-based message logging backend. If the msg_ids element is the string `'all'` instead of a list, then all completed results are forgotten. Message type: `purge_request`: ``` content = { 'msg_ids' : ['id1', 'id2',...], # list of msg_ids or 'all' 'engine_ids' : [0,2,4] # list of engine IDs } ``` The reply to a purge request is the status 'ok' if the request succeeded, or an explanation of why it failed, such as requesting the purge of a nonexistent or pending message. Message type: `purge_reply`: ``` content = { 'status' : 'ok', # or 'error' } ``` ## Schedulers There are three basic schedulers: > - Task Scheduler > - MUX Scheduler > - Control Scheduler The MUX and Control schedulers are simple MonitoredQueue ØMQ devices, with `ROUTER` sockets on either side. This allows the queue to relay individual messages to particular targets via `zmq.IDENTITY` routing. The Task scheduler may be a MonitoredQueue ØMQ device, in which case the client-facing socket is `ROUTER`, and the engine-facing socket is `DEALER`. The result of this is that client-submitted messages are load-balanced via the `DEALER` socket, but the engine's replies to each message go to the requesting client. Raw `DEALER` scheduling is quite primitive, and doesn't allow message introspection, so there are also Python Schedulers that can be used. These Schedulers behave in much the same way as a MonitoredQueue does from the outside, but have rich internal logic to determine destinations, as well as handle dependency graphs Their sockets are always `ROUTER` on both sides. The Python task schedulers have an additional message type, which informs the Hub of the destination of a task as soon as that destination is known. Message type: `task_destination`: ``` content = { 'msg_id' : 'abcd-1234-...', # the msg's uuid 'engine_id' : '1234-abcd-...', # the destination engine's zmq.IDENTITY } ``` ### {func}`apply` In terms of message classes, the MUX scheduler and Task scheduler relay the exact same message types. Their only difference lies in how the destination is selected. The Namespace model suggests that execution be able to use the model: ``` ns.apply(f, *args, **kwargs) ``` which takes `f`, a function in the user's namespace, and executes `f(*args, **kwargs)` on a remote engine, returning the result (or, for non-blocking, information facilitating later retrieval of the result). This model, unlike the execute message which uses code as a string, must be able to send arbitrary (pickleable) Python objects. And ideally, copy as little data as we can. The `buffers` property of a Message was introduced for this purpose. Utility method {func}`build_apply_message` in {mod}`IPython.kernel.zmq.serialize` wraps a function signature and builds a sendable buffer format for minimal data copying (exactly zero copies of numpy array data or buffers or large strings). Message type: `apply_request`: ``` metadata = { 'after' : ['msg_id',...], # list of msg_ids or output of Dependency.as_dict() 'follow' : ['msg_id',...], # list of msg_ids or output of Dependency.as_dict() } content = {} buffers = ['...'] # at least 3 in length # as built by build_apply_message(f,args,kwargs) ``` after/follow represent task dependencies. 'after' corresponds to a time dependency. The request will not arrive at an engine until the 'after' dependency tasks have completed. 'follow' corresponds to a location dependency. The task will be submitted to the same engine as these msg_ids (see {class}`Dependency` docs for details). Message type: `apply_reply`: ``` content = { 'status' : 'ok' # 'ok' or 'error' # other error info here, as in other messages } buffers = ['...'] # either 1 or 2 in length # a serialization of the return value of f(*args,**kwargs) # only populated if status is 'ok' ``` All engine execution and data movement is performed via apply messages. ### Raw Data Publication `display_data` lets you publish _representations_ of data, such as images and html. This `data_pub` message lets you publish _actual raw data_, sent via message buffers. data_pub messages are constructed via the {func}`ipyparallel.datapub.publish_data` function: ```python from ipyparallel.datapub import publish_data ns = dict(x=my_array) publish_data(ns) ``` Message type: `data_pub`: ``` content = { # the keys of the data dict, after it has been unserialized 'keys' : ['a', 'b'] } # the namespace dict will be serialized in the message buffers, # which will have a length of at least one buffers = [b'pdict', ...] ``` The interpretation of a sequence of data_pub messages for a given parent request should be to update a single namespace with subsequent results. ## Control Messages Messages that interact with the engines, but are not meant to execute code, are submitted via the Control queue. These messages have high priority, and are thus received and handled before any execution requests. Clients may want to clear the namespace on the engine. There are no arguments nor information involved in this request, so the content is empty. Message type: `clear_request`: ``` content = {} ``` Message type: `clear_reply`: ``` content = { 'status' : 'ok' # 'ok' or 'error' # other error info here, as in other messages } ``` Clients may want to abort tasks that have not yet run. This can by done by message id, or all enqueued messages can be aborted if None is specified. Message type: `abort_request`: ``` content = { 'msg_ids' : ['1234-...', '...'] # list of msg_ids or None } ``` Message type: `abort_reply`: ``` content = { 'status' : 'ok' # 'ok' or 'error' # other error info here, as in other messages } ``` The last action a client may want to do is shutdown the kernel. If a kernel receives a shutdown request, then it aborts all queued messages, replies to the request, and exits. Message type: `shutdown_request`: ``` content = {} ``` Message type: `shutdown_reply`: ``` content = { 'status' : 'ok' # 'ok' or 'error' # other error info here, as in other messages } ``` ## Implementation There are a few differences in implementation between the `StreamSession` object used in the newparallel branch and the `Session` object, the main one being that messages are sent in parts, rather than as a single serialized object. `StreamSession` objects also take pack/unpack functions, which are to be used when serializing/deserializing objects. These can be any functions that translate to/from formats that ZMQ sockets can send (buffers,bytes, etc.). ### Split Sends Previously, messages were bundled as a single json object and one call to {func}`socket.send_json`. Since the hub inspects all messages, and doesn't need to see the content of the messages, which can be large, messages are now serialized and sent in pieces. All messages are sent in at least 4 parts: the header, the parent header, the metadata and the content. This allows the controller to unpack and inspect the (always small) header, without spending time unpacking the content unless the message is bound for the controller. Buffers are added on to the end of the message, and can be any objects that present the buffer interface. ipyparallel-8.8.0/docs/source/reference/mpi.md000066400000000000000000000105151460376056100213540ustar00rootroot00000000000000(parallel-mpi)= # Using MPI with IPython Often, a parallel algorithm will require moving data between the engines. One way of accomplishing this is by doing a pull and then a push using the direct view. However, this will be slow as all the data has to go through the controller to the client and then back through the controller, to its final destination. A much better way of moving data between engines is to use a message passing library, such as the Message Passing Interface ([MPI][]). IPython's parallel computing architecture has been designed from the ground up to integrate with MPI. This document describes how to use MPI with IPython. ## Additional installation requirements If you want to use MPI with IPython, you will need to install: - A standard MPI implementation such as [OpenMPI][] or MPICH. - The [mpi4py][] package. ```{note} The mpi4py package is not a strict requirement. However, you need to have _some_ way of calling MPI from Python. You also need some way of making sure that {func}`MPI_Init` is called when the IPython engines start up. There are a number of ways of doing this and a good number of associated subtleties. We highly recommend using mpi4py as it takes care of most of these problems. If you want to do something different, let us know and we can help you get started. ``` ## Starting the engines with MPI enabled To use code that calls MPI, there are typically two things that MPI requires. 1. The process that wants to call MPI must be started using {command}`mpiexec` or a batch system (like PBS) that has MPI support. 2. Once the process starts, it must call {func}`MPI_Init`. There are a couple of ways that you can start the IPython engines and get these things to happen. ### Automatic starting using {command}`mpiexec` and {command}`ipcluster` The easiest approach is to use the `MPI` Launcher, which will first start a controller and then a set of engines using {command}`mpiexec`: ```python cluster = ipp.Cluster(engines="mpi") cluster.start_cluster_sync() ``` or on the command-line ``` $ ipcluster start -n 4 --engines=mpi ``` ### Automatic starting using batch systems such as PBS or Slurm IPython Parallel also has launchers for several batch systems, including PBS, Slurm, SGE, LSF, HTCondor. Just like `mpi`, you can specify these as the controller ```python cluster = ipp.Cluster(engines="slurm", controller="slurm") ``` :::{versionadded} 8.0 The `controller` and `engines` arguments are new in IPython Parallel 8.0. In 7.x, these arguments had to be called `controller_launcher_class` and `engine_launcher_class`, respectively. ::: ## Actually using MPI Once the engines are running with MPI enabled, you are ready to go. You can now call any code that uses MPI in the IPython engines. And, all of this can be done interactively. Here we show a simple example that uses [mpi4py][] version 1.1.0 or later. First, lets define a function that uses MPI to calculate the sum of a distributed array. Save the following text in a file called {file}`psum.py`: ```python from mpi4py import MPI import numpy as np def psum(a): locsum = np.sum(a) rcvBuf = np.array(0.0, 'd') MPI.COMM_WORLD.Allreduce([locsum, MPI.DOUBLE], [rcvBuf, MPI.DOUBLE], op=MPI.SUM) return rcvBuf ``` Now, we can start an IPython cluster and use this function interactively. In this case, we create a distributed array and sum up all its elements in a distributed manner using our {func}`psum` function: ```ipython In [1]: import ipyparallel as ipp In [2]: cluster = ipp.Cluster(engines="mpi", n=4) In [3]: rc = cluster.start_and_connect_sync() In [4]: view = rc[:] In [5]: view.activate() # enable magics # run the contents of the file on each engine: In [6]: view.run('psum.py') In [6]: view.scatter('a', np.arange(16,dtype='float')) In [7]: view['a'] Out[7]: [array([ 0., 1., 2., 3.]), array([ 4., 5., 6., 7.]), array([ 8., 9., 10., 11.]), array([ 12., 13., 14., 15.])] In [8]: %px totalsum = psum(a) Parallel execution on engines: [0,1,2,3] In [9]: view['totalsum'] Out[9]: [120.0, 120.0, 120.0, 120.0] ``` Any Python code that makes calls to MPI can be used in this manner, including compiled C, C++ and Fortran libraries that have been exposed to Python. [mpi]: https://www.mcs.anl.gov/research/projects/mpi [mpi4py]: https://mpi4py.readthedocs.io/ [openmpi]: https://www.open-mpi.org ipyparallel-8.8.0/docs/source/reference/security.md000066400000000000000000000404341460376056100224410ustar00rootroot00000000000000(security)= # Security details of IPython Parallel IPython Parallel exposes the full power of the Python interpreter over a TCP/IP network (or BSD socket) for the purposes of parallel computing. This feature raises the important question of IPython's security model. This document gives details about this model and how it is implemented in IPython's architecture. ## Process and network topology To enable parallel computing, IPython has a number of different processes that run. These processes are discussed at length in the IPython documentation and are summarized here: - The IPython _engine_. This process is a full blown Python interpreter in which user code is executed. Multiple engines are started to make parallel computing possible. - The IPython _hub_. This process monitors a set of engines and schedulers, and keeps track of the state of the processes. It listens for registration connections from engines and clients, and monitor connections from schedulers. - The IPython _schedulers_. This is a set of processes that relay commands and results between clients and engines. They are typically on the same machine as the hub, and listen for connections from engines and clients, but connect to the Hub. - The IPython _client_. This process is typically an interactive Python process that is used to coordinate the engines to get a parallel computation done. Collectively, these processes are called an IPython _cluster_, and the hub and schedulers together are referred to as the _controller_. These processes communicate over any transport supported by ZeroMQ (tcp, pgm, ipc) with a well-defined topology. The IPython controller processes listen on sockets. Upon starting, an engine connects to a hub and sends a message requesting registration, to which the hub replies with connection information for the schedulers, and the engine then connects to the schedulers. These engine->hub and engine->scheduler connections persist for the lifetime of each engine. The IPython client also connects to the controller processes using a number of sockets. This is one socket per scheduler. These connections persist for the lifetime of the client only. A given IPython controller and set of engines typically has a relatively short lifetime, such as the duration of a single parallel computation performed by a single user. Finally, the hub, schedulers, engines, and client processes typically execute with the permissions of that same user. More specifically, the controller and engines are _not_ executed as root or with any other superuser or shared-user permissions. ## Application logic When running the IPython kernel to perform a parallel computation, a user connects an IPython client to send Python commands and data through the IPython schedulers to the IPython engines, where those commands are executed and the data processed. Via the client, a user can instruct the IPython engines to execute arbitrary Python commands. These Python commands can include calls to the system shell, access the filesystem, etc., as required by the user's application. From this perspective, when a user runs an IPython engine on a host, that engine has the same capabilities and permissions as the user themselves (as if they were logged onto the engine's host with a terminal). ### ZeroMQ and Connection files IPython uses ZeroMQ for networking. By default, no IPython connections are encrypted. Open ports listen only on localhost. When no encryption is used, messages are signed via HMAC digest using a shared key for authentication. As of IPython 7.1, all connections _can_ be authenticated and encrypted using [CurveZMQ][]. The key (whether the CurveZMQ key or HMAC digest key) is distributed to engines and clients via connection files. TCP connections can be tunneled over SSH. IPython supports both shell (`openssh`) and `paramiko` based tunnels for connections. In our architecture, the controller is the only process that listens on network ports, and is thus the main point of vulnerability. The standard model for secure connections is to designate that the controller listen on localhost, and use ssh-tunnels to connect clients and/or engines, or connect over a trusted private network. To connect and authenticate to the controller an engine or client needs some information that the controller has stored in a JSON file. The JSON files may need to be copied to a location where the clients and engines can find them. Typically, this is the {file}`~/.ipython/profile_default/security` directory on the host where the client/engine is running, which could be on a different filesystem than the controller. Once the JSON files are copied over, everything should work fine. Currently, there are two JSON files that the controller creates: ipcontroller-engine.json : This JSON file has the information necessary for an engine to connect to a controller. ipcontroller-client.json : The client's connection information. Similar to the engine file, but lists a different collection of ports. ipcontroller-client.json will look something like this, under default localhost circumstances: ```python { "ssh": "", "interface": "tcp://127.0.0.1", "registration": 54886, "control": 54888, "mux": 54890, "hb_ping": 54891, "hb_pong": 54892, "task": 54894, "iopub": 54896, "broadcast": [ 54900, 54901 ], "key": "7e99e423-c437d4daf7cf23ee84cae803", "location": "mylaptop", "pack": "json", "unpack": "json", "signature_scheme": "hmac-sha256" } ``` If, however, you are running the controller on a work node on a cluster, you will likely need to use ssh tunnels to connect clients from your laptop to it. You will also probably need to instruct the controller to listen for engines coming from other work nodes on the cluster. An example of ipcontroller-client.json, as created by: ``` $> ipcontroller --ip=* --ssh=login.mycluster.com ``` ```python { "ssh": "login.mycluster.com", "interface": "tcp://*", "registration": 55836, "control": 55837, "mux": 55839, "task": 55843, "task_scheme": "lru", "iopub": 55845, "notification": 55852, "broadcast": [ 55847, 55848, 55849 ], "key": "70bc97ac-e66ac5143885ca8b376d4cb7", "location": "mylaptop", "pack": "json", "unpack": "json", "signature_scheme": "hmac-sha256" } ``` More details of how these JSON files are used are given below. (secure-network)= ## Secure network connections ### Overview ZeroMQ supports encryption and authentication via a mechanism called [CurveZMQ][]. IPython Parallel supports CurveZMQ as of version 7.1. To enable CurveZMQ, set ```python c.IPController.enable_curve = True ``` in `ipcontroller_config.py`, or set the environment variable `IPP_ENABLE_CURVE=1`. When using CurveZMQ, all connections are authenticated using the controller's server key, which is distributed to engines and clients via connection files. This key is all that is needed to connect to an IPython cluster and execute code. Additionally, when using CurveZMQ, all communication is _encrypted_ using the controller's server key. Each client typically generates its own unique, short-lived key pair for its side of encrypted communication. When not using CurveZMQ, messages are not encrypted. ### Security without CurveZMQ When not using CurveZMQ, all ZeroMQ connections are not authenticated and not encrypted. For this reason, users of IPython must be very careful in managing connections, because an open TCP/IP socket presents access to arbitrary execution as the user on the engine machines. As a result, the default behavior of controller processes is to only listen for clients on the loopback interface, and remote clients must establish SSH tunnels to connect to the controller processes. ```{warning} If the controller's loopback interface is untrusted, then IPython should be considered vulnerable without CurveZMQ, and this extends to the loopback of all connected clients, which have opened a loopback port that is redirected to the controller's loopback port. ``` ### SSH Without CurveZMQ, ZeroMQ sockets themselves provide no security. SSH tunnels can be used to encrypt traffic across the network, but _at least_ loopback traffic will be unencrypted. A connection file file, such as `ipcontroller-client.json`, will contain information for connecting to the controller, possibly including the address of an ssh server through which the client is to tunnel. The Client object then creates tunnels using either [openssh][] or [paramiko][], depending on the platform. If users do not wish to use OpenSSH or Paramiko, or the tunneling utilities are insufficient, then they may construct the tunnels themselves, and connect clients and engines as if the controller were on loopback on the connecting machine. ### Authentication IPython uses a key-distribution model of authentication. Whether you are using CurveZMQ or message-digest signatures. In both cases, a single key is used for authentication, and the same key is distributed in `ipcontroller-{client|engine}.json`. There is exactly one shared key per cluster - it must be the same everywhere. Typically, the controller creates this key, and stores it in the private connection files `ipython-{engine|client}.json`. These files are typically stored in the `~/.ipython/profile_/security` directory, and are maintained as readable only by the owner, as is common practice with a user's keys in their `.ssh` directory. The key distribution model is the same for both security implementations, however the level of authentication is substantially different. If you are using CurveZMQ, _connections_ are authenticated using the server key. This means connections attempted without the key will be rejected, and no messages can be sent or received by unauthenticated clients. #### Authentication without CurveZMQ If not using CurveZMQ, connections are not authenticated, only _messages_ are authenticated. This means that _clients_ with access to the controller's ports, but without the key will be able to connect and send and receive messages, but the requests will be rejected. To protect users of shared machines, [HMAC] digests are used to sign messages, using the shared key. The Session object that handles the message protocol uses a unique key to verify valid messages. This can be any value specified by the user, but the default behavior is a pseudo-random 128-bit number, as generated by `uuid.uuid4()`. This key is used to initialize an HMAC object, which digests all messages, and includes that digest as a signature and part of the message. Every message that is unpacked (on Controller, Engine, and Client) will also be digested by the receiver, ensuring that the sender's key is the same as the receiver's. No messages that do not contain this key are acted upon in any way. The key itself is never sent over the network. ```{warning} It is important to note that the signatures protect against unauthorized messages, but, as there is no encryption, provide exactly no protection of data privacy. It is possible, however, to use a custom serialization scheme (via Session.packer/unpacker traits) that does incorporate your own encryption scheme. ``` ### Encryption Messages are only encrypted when using CurveZMQ, which provides perfect-forward security by issuing short-lived keys for each session. Knowing the distributed CurveZMQ key is _not enough_ to decrypt communication from another client. ## Specific security vulnerabilities There are a number of potential security vulnerabilities present in IPython's architecture. In this section we discuss those vulnerabilities and detail how the security architecture described above prevents them from being exploited. ### Unauthorized clients The IPython client can instruct the IPython engines to execute arbitrary Python code with the permissions of the user who started the engines. If an attacker were able to connect their own hostile IPython client to the IPython controller, they could instruct the engines to execute code. On the first level, this attack is prevented by requiring access to the controller's ports, which are recommended to only be open on loopback if the controller is on an untrusted local network. If the attacker does have access to the Controller's ports, then the attack is prevented by the capabilities based client authentication of the execution key. The relevant authentication information is encoded into the JSON file that clients must present to gain access to the IPython controller. By limiting the distribution of those keys, a user can grant access to only authorized persons, as with SSH keys. When using CurveZMQ, the connection will be rejected without the key. When not using CurveZMQ, _requests_ will be rejected if not signed by the key. It is highly unlikely that an execution key could be guessed by an attacker in a brute force guessing attack. A given instance of the IPython controller only runs for a relatively short amount of time (on the order of hours). Thus an attacker would have only a limited amount of time to test a search space of size 2\*\*128. For added security, users can have arbitrarily long keys. ```{warning} If the attacker has gained enough access to intercept loopback connections on _either_ the controller or client, then a duplicate message can be sent. CurveZMQ prevents replay attacks. To protect against this, CurveZMQ uses nonces. recipients only allow each signature once, and consider duplicates invalid. However, the duplicate message could be sent to _another_ recipient using the same key, and it would be considered valid. ``` ### Unauthorized engines If an attacker were able to connect a hostile engine to a user's controller, the user might unknowingly send sensitive code or data to the hostile engine. This attacker's engine would then have full access to that code and data. This type of attack is prevented in the same way as the unauthorized client attack, by requiring the authentication key to register the engine with the client. ### Unauthorized controllers It is also possible that an attacker could try to convince a user's IPython client or engine to connect to a hostile IPython controller. That controller would then have full access to the code and data sent between the IPython client and the IPython engines. Again, this attack is prevented through the capabilities in a connection file, which ensure that a client or engine connects to the correct controller. It is also important to note that the connection files also encode the IP address and port that the controller is listening on, so there is little chance of mistakenly connecting to a controller running on a different IP address and port. When starting an engine or client, a user must specify the key to use for that connection. Thus, in order to introduce a hostile controller, the attacker must convince the user to use the key associated with the hostile controller. As long as a user is diligent in only using keys from trusted sources, this attack is not possible. ## Other security measures A number of other measures are taken to further limit the security risks involved in running the IPython kernel. First, by default, the IPython controller listens on random port numbers. While this can be overridden by the user, in the default configuration, an attacker would have to do a port scan to find a controller to attack. When coupled with the relatively short running time of a typical controller (on the order of hours), scans would have to be constant. Second, much of the time, especially when run on supercomputers or clusters, the controller is running behind a firewall. Thus, for engines or client to connect to the controller: - The different processes have to all be behind the firewall. or: - The user has to use SSH port forwarding to tunnel the connections through the firewall. In either case, an attacker is presented with additional barriers that prevent attacking or even probing the system, because they must have access to localhost on either the controller node or the client's machine. ## Summary IPython's architecture has been carefully designed with security in mind. The capabilities-based authentication model, in conjunction with CurveZMQ, ipc file permissions, and and/or SSH tunneled TCP/IP channels, address the core potential vulnerabilities in the system, while still enabling user's to use the system in open networks. [openssh]: https://www.openssh.com [paramiko]: https://www.lag.net/paramiko [hmac]: https://datatracker.ietf.org/doc/html/rfc2104 [curvezmq]: https://rfc.zeromq.org/spec/26/ ipyparallel-8.8.0/docs/source/tutorial/000077500000000000000000000000001460376056100201505ustar00rootroot00000000000000ipyparallel-8.8.0/docs/source/tutorial/asyncresult.md000066400000000000000000000116671460376056100230610ustar00rootroot00000000000000(asyncresult)= # The AsyncResult object In non-blocking mode, {meth}`~.View.apply`, {meth}`~.View.map`, and friends submit the command to be executed and then return an {class}`~.AsyncResult` object immediately. The AsyncResult object gives you a way of getting a result at a later time through its {meth}`get` method, but it also collects metadata on execution. ## Beyond stdlib AsyncResult and Future The {class}`AsyncResult` is a subclass of {py:class}`concurrent.futures.Future`. This means it can be integrated into existing async workflows, with e.g. {py:func}`asyncio.wrap_future`. It also extends the {py:class}`~.multiprocessing.AsyncResult` API. ```{seealso} - {py:class}`multiprocessing.AsyncResult` API - {py:class}`concurrent.futures.Future` API ``` In addition to these common features, our AsyncResult objects add a number of convenient methods for working with parallel results, beyond what is provided by the standard library classes on which they are based. ### get_dict {meth}`.AsyncResult.get_dict` pulls results as a dictionary, keyed by engine_id, rather than a flat list. This is useful for quickly coordinating or distributing information about all of the engines. As an example, here is a quick call that gives every engine a dict showing the PID of every other engine: ```ipython In [10]: ar = rc[:].apply_async(os.getpid) In [11]: pids = ar.get_dict() In [12]: rc[:]['pid_map'] = pids ``` This trick is particularly useful when setting up inter-engine communication, as in IPython's {file}`examples/parallel/interengine` examples. ## Metadata IPython Parallel tracks some metadata about the tasks, which is stored in the {attr}`.Client.metadata` dict. The AsyncResult object gives you an interface for this information as well, including timestamps stdout/err, and engine IDs. ### Timing IPython tracks various timestamps as {py:class}`.datetime` objects, and the AsyncResult object has a few properties that turn these into useful times (in seconds as floats). For use while the tasks are still pending: - {attr}`ar.elapsed` is the elapsed seconds since submission, for use before the AsyncResult is complete. - {attr}`ar.progress` is the number of tasks that have completed. Fractional progress would be: ``` 1.0 * ar.progress / len(ar) ``` - {meth}`AsyncResult.wait_interactive` will wait for the result to finish, but print out status updates on progress and elapsed time while it waits. For use after the tasks are done: - {attr}`ar.serial_time` is the sum of the computation time of all of the tasks done in parallel. - {attr}`ar.wall_time` is the time between the first task submitted and last result received. This is the actual cost of computation, including IPython overhead. ```{note} wall_time is only precise if the Client is waiting for results when the task finished, because the `received` timestamp is made when the result is unpacked by the Client, triggered by the {meth}`~Client.spin` call. If you are doing work in the Client, and not waiting/spinning, then `received` might be artificially high. ``` An often interesting metric is the time it cost to do the work in parallel relative to the serial computation, and this can be given with ```python speedup = ar.serial_time / ar.wall_time ``` ## Map results are iterable! When an AsyncResult object has multiple results (e.g. the {class}`~AsyncMapResult` object), you can iterate through results themselves, and act on them as they arrive: ```{literalinclude} ../examples/itermapresult.py :language: python :lines: 20-67 ``` That is to say, if you treat an AsyncMapResult as if it were a list of your actual results, it should behave as you would expect, with the only difference being that you can start iterating through the results before they have even been computed. This lets you do a simple version of map/reduce with the builtin Python functions, and the only difference between doing this locally and doing it remotely in parallel is using the asynchronous `view.map` instead of the builtin `map`. Here is a simple one-line RMS (root-mean-square) implemented with Python's builtin map/reduce. ```ipython In [38]: X = np.linspace(0,100) In [39]: from math import sqrt In [40]: add = lambda a,b: a+b In [41]: sq = lambda x: x*x In [42]: sqrt(reduce(add, map(sq, X)) / len(X)) Out[42]: 58.028845747399714 In [43]: sqrt(reduce(add, view.map(sq, X)) / len(X)) Out[43]: 58.028845747399714 ``` To break that down: 1. `map(sq, X)` Compute the square of each element in the list (locally, or in parallel) 2. `reduce(add, sqX) / len(X)` compute the mean by summing over the list (or AsyncMapResult) and dividing by the size 3. take the square root of the resulting number ```{seealso} When AsyncResult or the AsyncMapResult don't provide what you need (for instance, handling individual results as they arrive, but with metadata), you can always split the original result's `msg_ids` attribute, and handle them as you like. For an example of this, see {file}`examples/customresult.py` ``` ipyparallel-8.8.0/docs/source/tutorial/demos.md000066400000000000000000000166571460376056100216200ustar00rootroot00000000000000(parallel-examples)= # Parallel examples In this section we describe two more involved examples of using an IPython cluster to perform a parallel computation. We will be doing some plotting, so we start IPython with matplotlib integration by typing: ``` ipython --matplotlib ``` at the system command line. Or you can enable matplotlib integration at any point with: ```ipython In [1]: %matplotlib ``` ## 150 million digits of pi In this example we would like to study the distribution of digits in the number pi (in base 10). While it is not known if pi is a normal number (a number is normal in base 10 if 0-9 occur with equal likelihood) numerical investigations suggest that it is. We will begin with a serial calculation on 10,000 digits of pi and then perform a parallel calculation involving 150 million digits. In both the serial and parallel calculation we will be using functions defined in the {file}`pidigits.py` file, which is available in the {file}`examples/parallel` directory of the IPython source distribution. These functions provide basic facilities for working with the digits of pi and can be loaded into IPython by putting {file}`pidigits.py` in your current working directory and then doing: ```ipython In [1]: run pidigits.py ``` ### Serial calculation For the serial calculation, we will use [SymPy](https://www.sympy.org) to calculate 10,000 digits of pi and then look at the frequencies of the digits 0-9. Out of 10,000 digits, we expect each digit to occur 1,000 times. While SymPy is capable of calculating many more digits of pi, our purpose here is to set the stage for the much larger parallel calculation. In this example, we use two functions from {file}`pidigits.py`: {func}`one_digit_freqs` (which calculates how many times each digit occurs) and {func}`plot_one_digit_freqs` (which uses Matplotlib to plot the result). Here is an interactive IPython session that uses these functions with SymPy: ```ipython In [7]: import sympy In [8]: pi = sympy.pi.evalf(40) In [9]: pi Out[9]: 3.141592653589793238462643383279502884197 In [10]: pi = sympy.pi.evalf(10000) In [11]: digits = (d for d in str(pi)[2:]) # create a sequence of digits In [13]: freqs = one_digit_freqs(digits) In [14]: plot_one_digit_freqs(freqs) Out[14]: [] ``` The resulting plot of the single digit counts shows that each digit occurs approximately 1,000 times, but that with only 10,000 digits the statistical fluctuations are still rather large: ```{image} figs/single_digits.* ``` It is clear that to reduce the relative fluctuations in the counts, we need to look at many more digits of pi. That brings us to the parallel calculation. ### Parallel calculation Calculating many digits of pi is a challenging computational problem in itself. Because we want to focus on the distribution of digits in this example, we will use pre-computed digit of pi from the website of Professor Yasumasa Kanada at the University of Tokyo (). These digits come in a set of text files () that each have 10 million digits of pi. For the parallel calculation, we have copied these files to the local hard drives of the compute nodes. A total of 15 of these files will be used, for a total of 150 million digits of pi. To make things a little more interesting we will calculate the frequencies of all 2 digits sequences (00-99) and then plot the result using a 2D matrix in Matplotlib. The overall idea of the calculation is simple: each IPython engine will compute the two digit counts for the digits in a single file. Then in a final step the counts from each engine will be added up. To perform this calculation, we will need two top-level functions from {file}`pidigits.py`, {func}`compute_two_digit_freqs` and {func}`reduce_freqs`: ```{literalinclude} ../examples/pi/pidigits.py :language: python :lines: 52-67 ``` We will also use the {func}`plot_two_digit_freqs` function to plot the results. The code to run this calculation in parallel is contained in {file}`examples/parallel/parallelpi.py`. This code can be run in parallel using IPython by following these steps: 1. Use {command}`ipcluster` to start 15 engines. We used 16 cores of an SGE linux cluster (1 controller + 15 engines). 2. With the file {file}`parallelpi.py` in your current working directory, open up IPython, enable matplotlib, and type `run parallelpi.py`. This will download the pi files via ftp the first time you run it, if they are not present in the Engines' working directory. When run on our 16 cores, we observe a speedup of 14.2x. This is slightly less than linear scaling (16x) because the controller is also running on one of the cores. To emphasize the interactive nature of IPython, we now show how the calculation can also be run by typing the commands from {file}`parallelpi.py` interactively into IPython: ```ipython In [1]: import ipyparallel as ipp # The Client allows us to use the engines interactively. # We pass Client the name of the cluster profile we # are using. In [2]: c = ipp.Client(profile='mycluster') In [3]: v = c[:] In [3]: c.ids Out[3]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] In [4]: run pidigits.py In [5]: filestring = 'pi200m.ascii.%(i)02dof20' # Create the list of files to process. In [6]: files = [filestring % {'i':i} for i in range(1,16)] In [7]: files Out[7]: ['pi200m.ascii.01of20', 'pi200m.ascii.02of20', 'pi200m.ascii.03of20', 'pi200m.ascii.04of20', 'pi200m.ascii.05of20', 'pi200m.ascii.06of20', 'pi200m.ascii.07of20', 'pi200m.ascii.08of20', 'pi200m.ascii.09of20', 'pi200m.ascii.10of20', 'pi200m.ascii.11of20', 'pi200m.ascii.12of20', 'pi200m.ascii.13of20', 'pi200m.ascii.14of20', 'pi200m.ascii.15of20'] # download the data files if they don't already exist: In [8]: v.map(fetch_pi_file, files) # This is the parallel calculation using the Client.map method # which applies compute_two_digit_freqs to each file in files in parallel. In [9]: freqs_all = v.map(compute_two_digit_freqs, files) # Add up the frequencies from each engine. In [10]: freqs = reduce_freqs(freqs_all) In [11]: plot_two_digit_freqs(freqs) Out[11]: In [12]: plt.title('2 digit counts of 150m digits of pi') Out[12]: ``` The resulting plot generated by Matplotlib is shown below. The colors indicate which two digit sequences are more (red) or less (blue) likely to occur in the first 150 million digits of pi. We clearly see that the sequence "41" is most likely and that "06" and "07" are least likely. Further analysis would show that the relative size of the statistical fluctuations have decreased compared to the 10,000 digit calculation. ```{image} figs/two_digit_counts.* ``` ## Conclusion To conclude these examples, we summarize the key features of IPython's parallel architecture that have been demonstrated: - Serial code can be parallelized often with only a few extra lines of code. We have used the {class}`DirectView` and {class}`LoadBalancedView` classes for this purpose. - The resulting parallel code can be run without ever leaving the IPython's interactive shell. - Any data computed in parallel can be explored interactively through visualization or further numerical calculations. - We have run these examples on a cluster running RHEL 5 and Sun GridEngine. IPython's built in support for SGE (and other batch systems) makes it easy to get started with IPython's parallel capabilities. ipyparallel-8.8.0/docs/source/tutorial/direct.md000066400000000000000000000565731460376056100217640ustar00rootroot00000000000000(parallel-direct)= # IPython's Direct interface The direct interface represents one possible way of working with a set of IPython engines. The basic idea behind the direct interface is that the capabilities of each engine are directly and explicitly exposed to the user. Thus, in the direct interface, each engine is given an id that is used to identify the engine and give it work to do. This interface is very intuitive and is designed with interactive usage in mind, and is the best place for new users of IPython to begin. ## Starting the IPython controller and engines In general, in this tutorial, each step will start with a fresh cluster. There is always a choice when starting an interactive session: Option 1. starting a new cluster ```python import ipyparallel as ipp cluster = ipp.Cluster(n=4) cluster.start_cluster_sync() ``` Option 2. connecting to an existing cluster, e.g. if it were started via {command}`ipcluster start` or another notebook, or a JupyterLab extension. ```python import ipyparallel as ipp cluster = ipp.Cluster.from_file() ``` No arguments are required for the default cluster (e.g. `ipcluster start` with no arguments), but `profile` and/or `cluster_id` would be typical arguments to specify a cluster. For more detailed information about starting the controller and engines, see our {ref}`introduction ` to using IPython for parallel computing. ## Creating a `DirectView` The first step is to connect a {class}`.Client` to your cluster: ```ipython In [2]: rc = cluster.connect_client_sync() ``` To make sure there are engines connected to the controller, users can get a list of engine ids: ```ipython In [3]: rc.wait_for_engines(4); rc.ids Out[3]: [0, 1, 2, 3] ``` Here we see that there are four engines ready to do work for us. For direct execution, we will make use of a {class}`DirectView` object, which can be constructed via list-access to the client: ```ipython In [4]: dview = rc[:] # use all engines ``` ```{seealso} For more information, see the in-depth explanation of {ref}`Views `. ``` ## Quick and easy parallelism In many cases, you want to call a Python function on a sequence of objects, but _in parallel_. IPython Parallel provides a simple way of accomplishing this: using the DirectView's {meth}`~DirectView.map` method. ### Parallel map Python's builtin {func}`map` functions allows a function to be applied to a sequence element-by-element. This type of code is typically trivial to parallelize. In fact, since IPython's interface is all about functions anyway, you can use the builtin {func}`map` with a {class}`RemoteFunction`, or a DirectView's {meth}`map` method: ```ipython In [62]: serial_result = list(map(lambda x:x**10, range(32))) In [63]: parallel_result = dview.map_sync(lambda x: x**10, range(32)) In [64]: serial_result == parallel_result Out[64]: True ``` ```{note} The {class}`DirectView`'s version of {meth}`map` does not do dynamic load balancing. For a load-balanced version, use a {class}`LoadBalancedView`. ``` ## Calling Python functions The most basic type of operation that can be performed on the engines is to execute Python code or call Python functions. Executing Python code can be done in blocking or non-blocking mode (non-blocking is default) using the {meth}`.View.execute` method, and calling functions can be done via the {meth}`.View.apply` method. ### apply The main method for doing remote execution (in fact, almost all methods that communicate with the engines are built on top of it), is {meth}`View.apply`. We strive to provide the cleanest interface we can, so `apply` has the following signature: ```python view.apply(f, *args, **kwargs) ``` There are some controls to influence the behavior of `apply`, called flags. Views store the default values for these flags as attributes. The `DirectView` has these flags: dv.block : whether to wait for the result, or return an {class}`AsyncResult` object immediately dv.track : whether to instruct pyzmq to track when zeromq is done sending the message. This is primarily useful for non-copying sends of numpy arrays that you plan to edit in-place. You need to know when it becomes safe to edit the buffer without corrupting the message. There is a performance cost to enabling tracking, so it is not recommended except for sending very large messages. dv.targets : The engines associated with this View. Creating a view is done as if the client is a Python 'container' of engines: index-access on a client creates a {class}`.DirectView`. ```ipython In [4]: view = rc[1:3] Out[4]: In [5]: view.apply view.apply view.apply_async view.apply_sync ``` For convenience, you can specify blocking behavior explicitly for a single call with the extra sync/async methods. ### Blocking execution In blocking mode, the {class}`.DirectView` object (called `dview` in these examples) submits the command to the controller, which places the command in the engines' queues for execution. The {meth}`apply` call then blocks until the engines are done executing the command: ```ipython In [2]: dview = rc[:] # A DirectView of all engines In [3]: dview.block=True In [4]: dview['a'] = 5 In [5]: dview['b'] = 10 In [6]: dview.apply(lambda x: a+b+x, 27) Out[6]: [42, 42, 42, 42] ``` You can also select blocking execution on a call-by-call basis with the {meth}`apply_sync` method: ```ipython In [7]: dview.block = False In [8]: dview.apply_sync(lambda x: a+b+x, 27) Out[8]: [42, 42, 42, 42] ``` Python commands can be executed as strings on specific engines by using a View's `execute` method: ```ipython In [6]: rc[::2].execute('c = a + b') In [7]: rc[1::2].execute('c = a - b') In [8]: dview['c'] # shorthand for dview.pull('c', block=True) Out[8]: [15, -5, 15, -5] ``` ### async execution In non-blocking (async) mode, {meth}`apply` submits the command to be executed and then returns a {class}`AsyncResult` object immediately. The {class}`AsyncResult` object gives you a way of getting a result at a later time through its {meth}`get` method. ```{seealso} Docs on the {ref}`AsyncResult ` object. ``` This allows you to quickly submit long-running commands without blocking your local IPython session: ```ipython # define our function In [6]: def wait(t): ....: import time ....: tic = time.time() ....: time.sleep(t) ....: return time.time()-tic # In non-blocking mode In [7]: ar = dview.apply_async(wait, 2) # Now block for the result In [8]: ar.get() Out[8]: [2.0006198883056641, 1.9997570514678955, 1.9996809959411621, 2.0003249645233154] # Again in non-blocking mode In [9]: ar = dview.apply_async(wait, 10) # Poll to see if the result is ready In [10]: ar.ready() Out[10]: False # ask for the result, but wait a maximum of 1 second: In [45]: ar.get(1) --------------------------------------------------------------------------- TimeoutError Traceback (most recent call last) /home/you/ in () ----> 1 ar.get(1) /path/to/site-packages/IPython/parallel/asyncresult.pyc in get(self, timeout) 62 raise self._exception 63 else: ---> 64 raise error.TimeoutError("Result not ready.") 65 66 def ready(self): TimeoutError: Result not ready. ``` ```{Note} Note the import inside the function. This is a common model, to ensure that the appropriate modules are imported where the task is run. You can also manually import modules into the engine(s) namespace(s) via `view.execute('import numpy')`. ``` Often, it is desirable to wait until a set of {class}`AsyncResult` objects are done. For this, there is a the method {meth}`wait`. This method takes a collection of {class}`AsyncResult` objects (or `msg_ids` or integer indices to the client's history), and blocks until all of the associated results are ready: ```ipython In [72]: dview.block = False # A trivial list of AsyncResults objects In [73]: ar_list = [dview.apply_async(wait, 3) for i in range(10)] # Wait until all of them are done In [74]: dview.wait(ar_list) # Then, their results are ready using get() In [75]: ar_list[0].get() Out[75]: [2.9982571601867676, 2.9982588291168213, 2.9987530708312988, 2.9990990161895752] ``` ### The `block` and `targets` keyword arguments and attributes Most DirectView methods (excluding {meth}`apply`) accept `block` and `targets` as keyword arguments. As we have seen above, these keyword arguments control the blocking mode and which engines the command is applied to. The {class}`View` class also has {attr}`block` and {attr}`targets` attributes that control the default behavior when the keyword arguments are not provided. Thus the following logic is used for {attr}`block` and {attr}`targets`: - If no keyword argument is provided, the instance attributes are used. - The keyword arguments, if provided overrides the instance attributes for the duration of a single call. The following examples demonstrate how to use the instance attributes: ```ipython In [16]: dview.targets = [0, 2] In [17]: dview.block = False In [18]: ar = dview.apply(lambda : 10) In [19]: ar.get() Out[19]: [10, 10] In [20]: dview.targets = rc.ids # all engines (4) In [21]: dview.block = True In [22]: dview.apply(lambda : 42) Out[22]: [42, 42, 42, 42] ``` The {attr}`block` and {attr}`targets` instance attributes of the {class}`.DirectView` also determine the behavior of the parallel magic commands. ```{seealso} See the documentation of the {ref}`Parallel Magics `. ``` ## Moving Python objects around In addition to calling functions and executing code on engines, you can transfer Python objects between your IPython session and the engines. In IPython, these operations are called {meth}`push` (sending an object to the engines) and {meth}`pull` (getting an object from the engines). ### Basic push and pull Here are some examples of how you use {meth}`push` and {meth}`pull`: ```ipython In [38]: dview.push(dict(a=1.03234, b=3453)) Out[38]: [None, None, None, None] In [39]: dview.pull('a') Out[39]: [ 1.03234, 1.03234, 1.03234, 1.03234] In [40]: dview.pull('b', targets=0) Out[40]: 3453 In [41]: dview.pull(('a', 'b')) Out[41]: [ [1.03234, 3453], [1.03234, 3453], [1.03234, 3453], [1.03234, 3453] ] In [42]: dview.push(dict(c='speed')) Out[42]: [None, None, None, None] ``` In non-blocking mode {meth}`push` and {meth}`pull` also return {class}`AsyncResult` objects: ```ipython In [48]: ar = dview.pull('a', block=False) In [49]: ar.get() Out[49]: [1.03234, 1.03234, 1.03234, 1.03234] ``` ### Dictionary interface Since a Python namespace is a {class}`dict`, {class}`DirectView` objects provide dictionary-style access by key and methods such as {meth}`get` and {meth}`update` for convenience. This make the remote namespaces of the engines appear as a local dictionary. Underneath, these methods call {meth}`apply`: ```ipython In [51]: dview['a'] = ['foo', 'bar'] In [52]: dview['a'] Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ] ``` ### Scatter and gather Sometimes it is useful to partition a sequence and push the partitions to different engines. In MPI language, this is know as scatter/gather and we follow that terminology. However, it is important to remember that in IPython's {class}`Client` class, {meth}`scatter` is from the interactive IPython session to the engines and {meth}`gather` is from the engines back to the interactive IPython session. For scatter/gather operations between engines, MPI, pyzmq, or some other direct interconnect should be used. ```ipython In [58]: dview.scatter('a',range(16)) Out[58]: [None,None,None,None] In [59]: dview['a'] Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ] In [60]: dview.gather('a') # This will show you the status of gather. Out[60]: In [61]: dview.gather('a').get() # This will give you the result. Out[61]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] In [62]: dview.gather('a')[3] # You can also direct call the result. Out[62]: [2] ``` ## Other things to look at ### Signaling engines New in IPython Parallel 7.0 is the {meth}`Client.send_signal` method. This lets you directly interrupt engines, which might be running a blocking task that you want to cancel. This is also available via the Cluster API. Unlike the Cluster API, though, which only allows interrupting whole engine 'sets' (usally all engines in the cluster), the client API allows interrupting individual engines. ```ipython In [9]: ar = rc[:].apply_async(time.sleep, 5) In [10]: rc.send_signal(signal.SIGINT) Out[10]: In [11]: ar.get() [12:apply]: --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) in KeyboardInterrupt: [13:apply]: --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) in KeyboardInterrupt: [14:apply]: --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) in KeyboardInterrupt: [15:apply]: --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) in KeyboardInterrupt: ``` ### Remote function decorators Remote functions are like normal functions, but when they are called they execute on one or more engines rather than locally. IPython provides two decorators for producing parallel functions. The first is `@remote`, which calls the function on every engine of a view. ```ipython In [10]: @dview.remote(block=True) ....: def getpid(): ....: import os ....: return os.getpid() ....: In [11]: getpid() Out[11]: [12345, 12346, 12347, 12348] ``` The `@parallel` decorator creates parallel functions, that break up an element-wise operations and distribute them, reconstructing the result. ```ipython In [12]: import numpy as np In [13]: A = np.random.random((64,48)) In [14]: @dview.parallel(block=True) ....: def pmul(A,B): ....: return A*B In [15]: C_local = A*A In [16]: C_remote = pmul(A,A) In [17]: (C_local == C_remote).all() Out[17]: True ``` Calling a `@parallel` function _does not_ correspond to map. It is used for splitting element-wise operations that operate on a sequence or array. For `map` behavior, parallel functions have a map _method_. | call | pfunc(seq) | pfunc.map(seq) | | ------------------ | --------------------------- | --------------------------- | | # of tasks | # of engines (1 per engine) | # of engines (1 per engine) | | # of remote calls | # of engines (1 per engine) | `len(seq)` | | argument to remote | `seq[i:j]` (sub-sequence) | `seq[i]` (single element) | A quick example to illustrate the difference in arguments for the two modes: ```ipython In [16]: @dview.parallel(block=True) ....: def echo(x): ....: return str(x) In [17]: echo(range(5)) Out[17]: ['[0, 1]', '[2]', '[3]', '[4]'] In [18]: echo.map(range(5)) Out[18]: ['0', '1', '2', '3', '4'] ``` ```{seealso} See the {func}`~.remotefunction.parallel` and {func}`~.remotefunction.remote` decorators for options. ``` ### How to do parallel list comprehensions In many cases list comprehensions are nicer than using the map function. While we don't have fully parallel list comprehensions, it is simple to get the basic effect using {meth}`scatter` and {meth}`gather`: ```ipython In [66]: dview.scatter('x',range(64)) In [67]: %px y = [i**10 for i in x] Parallel execution on engines: [0, 1, 2, 3] In [68]: y = dview.gather('y') In [69]: print y [0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...] ``` ### Remote imports Sometimes you may want to import packages both in your interactive session and on your remote engines. This can be done with the context manager created by a DirectView's {meth}`sync_imports` method: ```ipython In [69]: with dview.sync_imports(): ....: import numpy importing numpy on engine(s) ``` Any imports made inside the block will also be performed on the view's engines. sync_imports also takes a `local` boolean flag that defaults to True, which specifies whether the local imports should also be performed. However, support for `local=False` has not been implemented, so only packages that can be imported locally will work this way. Note that the usual renaming of the import handle in the same line like in `import matplotlib.pyplot as plt` does not work on the remote engine, the `as plt` is ignored remotely, while it executes locally. One could rename the remote handle with `%px plt = pyplot` though after the import. You can also specify imports via the `@ipp.require` decorator. This is a decorator designed for use in dependencies, but can be used to handle remote imports as well. Modules or module names passed to `@ipp.require` will be imported before the decorated function is called. If they cannot be imported, the decorated function will never execute and will fail with an UnmetDependencyError. Failures of single Engines will be collected and raise a CompositeError, as demonstrated in the next section. ```ipython In [70]: @ipp.require('re') ....: def findall(pat, x): ....: # re is guaranteed to be available ....: return re.findall(pat, x) # you can also pass modules themselves, that you already have locally: In [71]: @ipp.require(time) ....: def wait(t): ....: time.sleep(t) ....: return t ``` ```{note} {func}`sync_imports` does not allow `import foo as bar` syntax, because the assignment represented by the `as bar` part is not available to the import hook. ``` (parallel-exceptions)= ### Parallel exceptions Parallel commands can raise Python exceptions, just like serial commands. This is complicated by the fact that a single parallel command can raise multiple exceptions (one for each engine the command was run on). To express this idea, we have a {exc}`CompositeError` exception class that will be raised when there are mulitple errors. The {exc}`CompositeError` class is a special type of exception that wraps one or more other exceptions. Here is how it works: ```ipython In [78]: dview.block = True In [79]: dview.execute("1/0") [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero ``` Notice how the error message printed when {exc}`CompositeError` is raised has information about the individual exceptions that were raised on each engine. If you want, you can even raise one of these original exceptions: ```ipython In [80]: try: ....: dview.execute('1/0', block=True) ....: except ipp.CompositeError as e: ....: e.raise_exception() ....: ....: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero ``` If you are working in IPython, you can type `%debug` after one of these {exc}`CompositeError` exceptions is raised and inspect the exception: ```ipython In [81]: dview.execute('1/0') [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [2:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero [3:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero In [82]: %debug > /.../site-packages/IPython/parallel/client/asyncresult.py(125)get() 124 else: --> 125 raise self._exception 126 else: # Here, self._exception is the CompositeError instance: ipdb> e = self._exception ipdb> e CompositeError(4) # we can tab-complete on e to see available methods: ipdb> e. e.args e.message e.traceback e.elist e.msg e.ename e.print_traceback e.engine_info e.raise_exception e.evalue e.render_traceback # We can then display the individual tracebacks, if we want: ipdb> e.print_traceback(1) [1:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero ``` If you have 100 engines, you probably don't want to see 100 identical tracebacks for a NameError because of a small typo. For this reason, CompositeError truncates the list of exceptions it will print to {attr}`CompositeError.tb_limit` (default is five). You can change this limit to suit your needs with: ```ipython In [21]: ipp.CompositeError.tb_limit = 1 In [22]: %px x=z [0:execute]: --------------------------------------------------------------------------- NameError Traceback (most recent call last) ----> 1 x=z NameError: name 'z' is not defined ... 3 more exceptions ... ``` All of this same error handling magic works the same in non-blocking mode: ```ipython In [83]: dview.block=False In [84]: ar = dview.execute('1/0') In [85]: ar.get() [0:execute]: --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call last) ----> 1 1/0 ZeroDivisionError: integer division or modulo by zero ... 3 more exceptions ... ``` Sometimes you still want to get the successful subset, even when there was an error. 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C[Sj5l5}|$+(w}2jfhò~L<.ݸ R(t]֥Y6kˁo7@=@ xiJxՅxO!d(Q1VƒBˆ/RFd[~}%銱F#CHc>-ev}ȶ%(DlHݱk$^AZVVvGI[,wevJh\Yi<&0Z&DlȲiWP!pN1# Қ ž 6zXml؟HHHHHJщ{X @#l'DJGL7[cTG$@$@$@$@$@F9ߗ]LW{7f%$@$@$@$@$@$P Xߛ]g^C]iHHHHHl *QHHHHHJѩq=Ui٪QHHHHHJXs΢l؟HHHHHJ 88[9qf;"$@$@$@$xv^ݯf+Ёxq4'j*fuMvG1[8jfX.H;#-;#LFW,LH}@[Daovm4WNHHHHHH@٪pY;Vό$@$@$@$@$@$p{(R”IFr$@$@$@$@$@$p0;3B0-BHHHHHJ ӿn..ҴC$@$@$@$@$@('Th ڨGOsJ" } 檢#wŘO7Q@2JŁ/Gw lϸ˶͋ ]3y>Mߟ ^1

((a|b|rc)\d+))?
  (\.
    (?Pdev\d*)
  )?
  ''',
    re.VERBOSE,
)

_version_fields = _version_regex.match(__version__).groupdict()
version_info = tuple(
    field
    for field in (
        int(_version_fields["major"]),
        int(_version_fields["minor"]),
        int(_version_fields["patch"]),
        _version_fields["pre"],
        _version_fields["dev"],
    )
    if field is not None
)

__all__ = ["__version__", "version_info"]
ipyparallel-8.8.0/ipyparallel/apps/000077500000000000000000000000001460376056100173365ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/apps/__init__.py000066400000000000000000000000001460376056100214350ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/apps/baseapp.py000066400000000000000000000172631460376056100213340ustar00rootroot00000000000000"""
The Base Application class for ipyparallel apps
"""

import logging
import os
import re
import sys

import traitlets
from IPython.core.application import BaseIPythonApplication
from IPython.core.application import base_aliases as base_ip_aliases
from IPython.core.application import base_flags as base_ip_flags
from IPython.utils.path import expand_path
from jupyter_client.session import Session
from tornado.ioloop import IOLoop
from traitlets import Bool, Instance, Unicode, default, observe
from traitlets.config.application import LevelFormatter, catch_config_error

from ipyparallel import util

from .._version import __version__

# FIXME: CUnicode is needed for cli parsing
# with traitlets 4
# bump when we require traitlets 5, which requires Python 3.7
if int(traitlets.__version__.split(".", 1)[0]) < 5:
    from traitlets import CUnicode
else:
    # don't need CUnicode with traitlets 4
    CUnicode = Unicode

# -----------------------------------------------------------------------------
# Module errors
# -----------------------------------------------------------------------------


class PIDFileError(Exception):
    pass


# -----------------------------------------------------------------------------
# Main application
# -----------------------------------------------------------------------------
base_aliases = {}
base_aliases.update(base_ip_aliases)
base_aliases.update(
    {
        'work-dir': 'BaseParallelApplication.work_dir',
        'log-to-file': 'BaseParallelApplication.log_to_file',
        'clean-logs': 'BaseParallelApplication.clean_logs',
        'log-url': 'BaseParallelApplication.log_url',
        'cluster-id': 'BaseParallelApplication.cluster_id',
    }
)

base_flags = {
    'log-to-file': (
        {'BaseParallelApplication': {'log_to_file': True}},
        "send log output to a file",
    )
}
base_flags.update(base_ip_flags)


class BaseParallelApplication(BaseIPythonApplication):
    """The base Application for ipyparallel apps

    Primary extensions to BaseIPythonApplication:

    * work_dir
    * remote logging via pyzmq
    * IOLoop instance
    """

    version = __version__

    _deprecated_classes = None

    def init_crash_handler(self):
        # disable crash handler from IPython
        pass

    def _log_level_default(self):
        # temporarily override default_log_level to INFO
        return logging.INFO

    def _log_format_default(self):
        """override default log format to include time"""
        return "%(asctime)s.%(msecs).03d [%(name)s]%(highlevel)s %(message)s"

    work_dir = Unicode(
        os.getcwd(), config=True, help='Set the working dir for the process.'
    )

    @observe('work_dir')
    def _work_dir_changed(self, change):
        self.work_dir = str(expand_path(change['new']))

    log_to_file = Bool(config=True, help="whether to log to a file")

    clean_logs = Bool(
        False, config=True, help="whether to cleanup old logfiles before starting"
    )

    log_url = Unicode(
        '', config=True, help="The ZMQ URL of the iplogger to aggregate logging."
    )

    cluster_id = CUnicode(
        '',
        config=True,
        help="""String id to add to runtime files, to prevent name collisions when
        using multiple clusters with a single profile simultaneously.

        When set, files will be named like: 'ipcontroller--engine.json'

        Since this is text inserted into filenames, typical recommendations apply:
        Simple character strings are ideal, and spaces are not recommended (but should
        generally work).
        """,
    )

    loop = Instance(IOLoop)

    def _loop_default(self):
        return IOLoop.current()

    session = Instance(Session)

    @default("session")
    def _default_session(self):
        return Session(parent=self)

    aliases = base_aliases
    flags = base_flags

    @catch_config_error
    def initialize(self, argv=None):
        """initialize the app"""
        util._disable_session_extract_dates()
        self.init_config_from_env()
        super().initialize(argv)
        self.init_deprecated_config()
        self.to_work_dir()
        self.reinit_logging()

    def init_config_from_env(self):
        """Load any configuration from environment variables"""
        if "IPP_SESSION_KEY" in os.environ:
            self.config.Session.key = os.environ["IPP_SESSION_KEY"].encode("ascii")
        if "IPP_CLUSTER_ID" in os.environ:
            self.cluster_id = os.environ["IPP_CLUSTER_ID"]
        if "IPP_PROFILE_DIR" in os.environ:
            self.config.ProfileDir.location = os.environ["IPP_PROFILE_DIR"]

    def init_deprecated_config(self):
        if not self._deprecated_classes:
            return
        deprecated_config_found = False
        new_classname = self.__class__.__name__
        for deprecated_classname in self._deprecated_classes:
            if deprecated_classname in self.config:
                cfg = self.config[deprecated_classname]
                new_config = self.config[new_classname]
                for key, deprecated_value in list(cfg.items()):
                    if key in new_config:
                        new_value = new_config[key]
                        if new_value != deprecated_value:
                            self.log.warning(
                                f"Ignoring c.{deprecated_classname}.{key} = {deprecated_value}, overridden by c.{new_classname}.{key} = {new_value}"
                            )
                    else:
                        self.log.warning(
                            f"c.{deprecated_classname}.{key} is deprecated in ipyparallel 7, use c.{new_classname}.{key} = {deprecated_value}"
                        )
                        new_config[key] = deprecated_value
                        cfg.pop(key)
                        deprecated_config_found = True

        if deprecated_config_found:
            # reload config
            self.update_config(self.config)

    def to_work_dir(self):
        wd = self.work_dir
        if wd != os.getcwd():
            os.chdir(wd)
            self.log.info("Changing to working dir: %s" % wd)
        # This is the working dir by now.
        sys.path.insert(0, '')

    def reinit_logging(self):
        # Remove old log files
        log_dir = self.profile_dir.log_dir
        if self.clean_logs:
            for f in os.listdir(log_dir):
                if re.match(r'%s-\d+\.(log|err|out)' % self.name, f):
                    try:
                        os.remove(os.path.join(log_dir, f))
                    except OSError:
                        # probably just conflict from sibling process
                        # already removing it
                        pass
        if self.log_to_file:
            # Start logging to the new log file
            log_filename = f"{self.name}-{self.cluster_id}-{os.getpid()}.log"
            logfile = os.path.join(log_dir, log_filename)
            if sys.__stderr__:
                print(f"Sending logs to {logfile}", file=sys.__stderr__)
            open_log_file = open(logfile, 'w')
        else:
            open_log_file = None
        if open_log_file is not None:
            while self.log.handlers:
                self.log.removeHandler(self.log.handlers[0])
            self._log_handler = logging.StreamHandler(open_log_file)
            self.log.addHandler(self._log_handler)
        else:
            self._log_handler = self.log.handlers[0]
        # Add timestamps to log format:
        self._log_formatter = LevelFormatter(self.log_format, datefmt=self.log_datefmt)
        self._log_handler.setFormatter(self._log_formatter)
        # do not propagate log messages to root logger
        # ipcluster app will sometimes print duplicate messages during shutdown
        # if this is 1 (default):
        self.log.propagate = False
ipyparallel-8.8.0/ipyparallel/apps/ipclusterapp.py000066400000000000000000000004171460376056100224250ustar00rootroot00000000000000import warnings

warnings.warn(f"{__name__} is deprecated in ipyparallel 7. Use ipyparallel.cluster")

from ipyparallel.cluster.app import IPCluster, IPClusterStart, main  # noqa

IPClusterApp = IPCluster
launch_new_instance = main

if __name__ == "__main__":
    main()
ipyparallel-8.8.0/ipyparallel/apps/ipcontrollerapp.py000066400000000000000000000004061460376056100231250ustar00rootroot00000000000000import warnings

warnings.warn(f"{__name__} is deprecated in ipyparallel 7. Use ipyparallel.controller")

from ipyparallel.controller.app import IPController, main

IPControllerApp = IPController
launch_new_instance = main

if __name__ == "__main__":
    main()
ipyparallel-8.8.0/ipyparallel/apps/ipengineapp.py000066400000000000000000000003621460376056100222100ustar00rootroot00000000000000import warnings

warnings.warn(f"{__name__} is deprecated in ipyparallel 7. Use ipyparallel.engine")

from ipyparallel.engine.app import IPEngine, main

IPEngineApp = IPEngine
launch_new_instance = main

if __name__ == "__main__":
    main()
ipyparallel-8.8.0/ipyparallel/apps/iploggerapp.py000077500000000000000000000043341460376056100222300ustar00rootroot00000000000000#!/usr/bin/env python
"""
A simple IPython logger application
"""

from IPython.core.profiledir import ProfileDir
from traitlets import Dict

from ipyparallel.apps.baseapp import (
    BaseParallelApplication,
    base_aliases,
    catch_config_error,
)
from ipyparallel.apps.logwatcher import LogWatcher

# -----------------------------------------------------------------------------
# Module level variables
# -----------------------------------------------------------------------------

#: The default config file name for this application
_description = """Start an IPython logger for parallel computing.

IPython controllers and engines (and your own processes) can broadcast log messages
by registering a `zmq.log.handlers.PUBHandler` with the `logging` module. The
logger can be configured using command line options or using a cluster
directory. Cluster directories contain config, log and security files and are
usually located in your ipython directory and named as "profile_name".
See the `profile` and `profile-dir` options for details.
"""


# -----------------------------------------------------------------------------
# Main application
# -----------------------------------------------------------------------------
aliases = {}
aliases.update(base_aliases)
aliases.update(dict(url='LogWatcher.url', topics='LogWatcher.topics'))


class IPLoggerApp(BaseParallelApplication):
    name = 'iplogger'
    description = _description
    classes = [LogWatcher, ProfileDir]
    aliases = Dict(aliases)

    @catch_config_error
    def initialize(self, argv=None):
        super().initialize(argv)
        self.init_watcher()

    def init_watcher(self):
        try:
            self.watcher = LogWatcher(parent=self, log=self.log)
        except BaseException:
            self.log.error("Couldn't start the LogWatcher", exc_info=True)
            self.exit(1)
        self.log.info("Listening for log messages on %r" % self.watcher.url)

    def start(self):
        self.watcher.start()
        try:
            self.watcher.loop.start()
        except KeyboardInterrupt:
            self.log.critical("Logging Interrupted, shutting down...\n")


launch_new_instance = IPLoggerApp.launch_instance


if __name__ == '__main__':
    launch_new_instance()
ipyparallel-8.8.0/ipyparallel/apps/launcher.py000066400000000000000000000003751460376056100215160ustar00rootroot00000000000000"""Deprecated import for ipyparallel.cluster.launcher"""

import warnings

from ipyparallel.cluster.launcher import *  # noqa

warnings.warn(
    f"{__name__} is deprecated in ipyparallel 7. Use ipyparallel.cluster.launcher.",
    DeprecationWarning,
)
ipyparallel-8.8.0/ipyparallel/apps/logwatcher.py000066400000000000000000000060131460376056100220470ustar00rootroot00000000000000"""
A logger object that consolidates messages incoming from ipcluster processes.
"""

import logging

import zmq
from jupyter_client.localinterfaces import localhost
from tornado import ioloop
from traitlets import Instance, List, Unicode
from traitlets.config.configurable import LoggingConfigurable
from zmq.eventloop import zmqstream


class LogWatcher(LoggingConfigurable):
    """A simple class that receives messages on a SUB socket, as published
    by subclasses of `zmq.log.handlers.PUBHandler`, and logs them itself.

    This can subscribe to multiple topics, but defaults to all topics.
    """

    # configurables
    topics = List(
        [''],
        config=True,
        help="The ZMQ topics to subscribe to. Default is to subscribe to all messages",
    )
    url = Unicode(config=True, help="ZMQ url on which to listen for log messages")

    def _url_default(self):
        return 'tcp://%s:20202' % localhost()

    # internals
    stream = Instance('zmq.eventloop.zmqstream.ZMQStream', allow_none=True)

    context = Instance(zmq.Context)

    def _context_default(self):
        return zmq.Context.instance()

    loop = Instance('tornado.ioloop.IOLoop')

    def _loop_default(self):
        return ioloop.IOLoop.current()

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        s = self.context.socket(zmq.SUB)
        s.bind(self.url)
        self.stream = zmqstream.ZMQStream(s, self.loop)
        self.subscribe()
        self.on_trait_change(self.subscribe, 'topics')

    def start(self):
        self.stream.on_recv(self.log_message)

    def stop(self):
        self.stream.stop_on_recv()

    def subscribe(self):
        """Update our SUB socket's subscriptions."""
        self.stream.setsockopt(zmq.UNSUBSCRIBE, '')
        if '' in self.topics:
            self.log.debug("Subscribing to: everything")
            self.stream.setsockopt(zmq.SUBSCRIBE, '')
        else:
            for topic in self.topics:
                self.log.debug("Subscribing to: %r" % (topic))
                self.stream.setsockopt(zmq.SUBSCRIBE, topic)

    def _extract_level(self, topic_str):
        """Turn 'engine.0.INFO.extra' into (logging.INFO, 'engine.0.extra')"""
        topics = topic_str.split('.')
        for idx, t in enumerate(topics):
            level = getattr(logging, t, None)
            if level is not None:
                break

        if level is None:
            level = logging.INFO
        else:
            topics.pop(idx)

        return level, '.'.join(topics)

    def log_message(self, raw):
        """receive and parse a message, then log it."""
        if len(raw) != 2 or '.' not in raw[0]:
            self.log.error("Invalid log message: %s" % raw)
            return
        else:
            topic, msg = raw
            # don't newline, since log messages always newline:
            topic, level_name = topic.rsplit('.', 1)
            level, topic = self._extract_level(topic)
            if msg[-1] == '\n':
                msg = msg[:-1]
            self.log.log(level, f"[{topic}] {msg}")
ipyparallel-8.8.0/ipyparallel/client/000077500000000000000000000000001460376056100176515ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/client/__init__.py000066400000000000000000000000001460376056100217500ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/client/_joblib.py000066400000000000000000000043521460376056100216270ustar00rootroot00000000000000"""joblib parallel backend for IPython Parallel"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from joblib.parallel import AutoBatchingMixin, ParallelBackendBase

import ipyparallel as ipp


class IPythonParallelBackend(AutoBatchingMixin, ParallelBackendBase):
    def __init__(self, view=None, **kwargs):
        super().__init__(**kwargs)
        self._cluster_owner = False
        self._client_owner = False
        if view is None:
            self._client_owner = True
            try:
                # load the default cluster
                cluster = ipp.Cluster.from_file()
            except FileNotFoundError:
                # other load errors?
                cluster = self._cluster = ipp.Cluster()
                self._cluster_owner = True
                cluster.start_cluster_sync()
            else:
                # cluster running, ensure some engines are, too
                if not cluster.engines:
                    cluster.start_engines_sync()
            rc = cluster.connect_client_sync()
            rc.wait_for_engines(cluster.n or 1)
            view = rc.load_balanced_view()

            # use cloudpickle or dill for closures, if available.
            # joblib tends to create closures default pickle can't handle.
            try:
                import cloudpickle  # noqa
            except ImportError:
                try:
                    import dill  # noqa
                except ImportError:
                    pass
                else:
                    view.client[:].use_dill()
            else:
                view.client[:].use_cloudpickle()
        self._view = view

    def effective_n_jobs(self, n_jobs):
        """A View can run len(view) jobs at a time"""
        return len(self._view)

    def terminate(self):
        """Close the client if we created it"""
        if self._client_owner:
            self._view.client.close()
        if self._cluster_owner:
            self._cluster.stop_cluster_sync()

    def apply_async(self, func, callback=None):
        """Schedule a func to be run"""
        future = self._view.apply_async(func)
        if callback:
            future.add_done_callback(lambda f: callback(f.result()))
        return future
ipyparallel-8.8.0/ipyparallel/client/asyncresult.py000066400000000000000000001222061460376056100226020ustar00rootroot00000000000000"""AsyncResult objects for the client"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import concurrent.futures
import sys
import threading
import time
import warnings
from concurrent.futures import ALL_COMPLETED, FIRST_COMPLETED, FIRST_EXCEPTION, Future
from contextlib import contextmanager
from datetime import datetime
from functools import lru_cache, partial
from itertools import chain, repeat
from threading import Event

import zmq
from decorator import decorator
from IPython import get_ipython
from IPython.display import display, display_pretty, publish_display_data

from ipyparallel import error
from ipyparallel.util import _parse_date, compare_datetimes, progress, utcnow

from .futures import MessageFuture, multi_future


def _raw_text(s):
    display_pretty(s, raw=True)


_default = object()

# global empty tracker that's always done:
finished_tracker = zmq.MessageTracker()


@decorator
def check_ready(f, self, *args, **kwargs):
    """Check ready state prior to calling the method."""
    self.wait(0)
    if not self._ready:
        raise TimeoutError("result not ready")
    return f(self, *args, **kwargs)


_metadata_keys = []
# threading.TIMEOUT_MAX new in 3.2
_FOREVER = getattr(threading, 'TIMEOUT_MAX', int(1e6))


class AsyncResult(Future):
    """Class for representing results of non-blocking calls.

    Extends the interfaces of :py:class:`multiprocessing.pool.AsyncResult`
    and :py:class:`concurrent.futures.Future`.
    """

    msg_ids = None
    _targets = None
    _tracker = None
    _single_result = False
    owner = False
    _last_display_prefix = ""
    _stream_trailing_newline = True
    _chunk_sizes = None

    def __init__(
        self,
        client,
        children,
        fname='unknown',
        targets=None,
        owner=False,
        return_exceptions=False,
        chunk_sizes=None,
    ):
        super().__init__()
        if not isinstance(children, list):
            children = [children]
            self._single_result = True
        else:
            self._single_result = False

        self._return_exceptions = return_exceptions
        self._chunk_sizes = chunk_sizes or {}

        if isinstance(children[0], str):
            self.msg_ids = children
            self._children = []
        else:
            self._children = children
            self.msg_ids = [f.msg_id for f in children]

        self._client = client
        self._fname = fname
        self._targets = targets
        self.owner = owner

        self._ready = False
        self._ready_event = Event()
        self._output_ready = False
        self._output_event = Event()
        self._sent_event = Event()
        self._success = None
        if self._children:
            self._metadata = [f.output.metadata for f in self._children]
        else:
            self._metadata = [self._client.metadata[id] for id in self.msg_ids]
        self._init_futures()

    def _init_futures(self):
        """Build futures for results and output; hook up callbacks"""
        if not self._children:
            for msg_id in self.msg_ids:
                future = self._client._futures.get(msg_id, None)
                if not future:
                    result = self._client.results.get(msg_id, _default)
                    # result resides in local cache, construct already-resolved Future
                    if result is not _default:
                        future = MessageFuture(msg_id)
                        future.output = Future()
                        future.output.metadata = self.client.metadata[msg_id]
                        future.set_result(result)
                        future.output.set_result(None)
                if not future:
                    raise KeyError("No Future or result for msg_id: %s" % msg_id)
                self._children.append(future)

        self._result_future = multi_future(self._children)

        self._sent_future = multi_future([f.tracker for f in self._children])
        self._sent_future.add_done_callback(self._handle_sent)

        self._output_future = multi_future(
            [self._result_future] + [f.output for f in self._children]
        )
        # on completion of my constituents, trigger my own resolution
        self._result_future.add_done_callback(self._resolve_result)
        self._output_future.add_done_callback(self._resolve_output)
        self.add_done_callback(self._finalize_result)

    def _iopub_streaming_output_callback(self, eid, msg_future, msg):
        """Callback for iopub messages registered during AsyncResult.stream_output()"""
        msg_type = msg['header']['msg_type']
        ip = get_ipython()
        if ip is not None:
            in_kernel = getattr(ip, 'kernel', None) is not None
        else:
            in_kernel = False

        if msg_type == 'stream':
            msg_content = msg['content']
            stream_name = msg_content['name']

            if in_kernel:
                parent_msg_id = msg.get('parent_header', {}).get('msg_id', '')
                display_id = f"{parent_msg_id}-{stream_name}"
                md = msg_future.output.metadata
                full_stream = md[stream_name]
                if display_id in self._already_streamed:
                    update = True
                else:
                    self._already_streamed[display_id] = True
                    update = False
                publish_display_data(
                    {
                        "text/plain": f"[{stream_name}:{eid}] " + full_stream,
                    },
                    transient={"display_id": display_id},
                    update=update,
                )
                return
            else:
                stream = getattr(sys, stream_name, sys.stdout)
                self._display_stream(
                    msg_content['text'],
                    f'[{stream_name}:{eid}] ',
                    file=stream,
                )
        elif msg_type == "error":
            content = msg['content']
            if 'engine_info' not in content:
                content['engine_info'] = {
                    "engine_id": msg_future.output.metadata.engine_id,
                    "engine_uuid": msg_future.output.metadata.engine_uuid,
                    # always execute?
                    "method": msg_future.header["msg_type"].partition("_")[0],
                }

            err = self._client._unwrap_exception(msg['content'])
            self._streamed_errors += 1
            if self._streamed_errors <= error.CompositeError.tb_limit:
                print("\n".join(err.render_traceback()), file=sys.stderr)
            else:
                # single-line error after we hit the limit
                print(err, file=sys.stderr)
        elif msg_type == "execute_result":
            # mock ExecuteReply from execute_result on iopub
            from .client import ExecuteReply

            er = ExecuteReply(
                msg_id=msg_future.msg_id,
                content=msg['content'],
                metadata=msg_future.output.metadata,
            )
            display(er)

        if ip is None:
            return

        if msg_type == 'display_data':
            msg_content = msg['content']
            _raw_text(f'[output:{eid}]')
            self._republish_displaypub(msg_content, eid)

    @contextmanager
    def stream_output(self):
        """Stream output for this result as it arrives.

        Returns a context manager, during which output is streamed.
        """

        # Keep a handle on the futures so we can remove the callback later
        future_callbacks = {}
        self._already_streamed = {}
        self._stream_trailing_newline = True
        self._last_display_prefix = ""
        self._streamed_errors = 0

        for eid, msg_future in zip(self._targets, self._children):
            iopub_callback = partial(
                self._iopub_streaming_output_callback, eid, msg_future
            )
            future_callbacks[msg_future] = iopub_callback
            md = msg_future.output.metadata

            msg_future.iopub_callbacks.append(iopub_callback)
            # FIXME: there's still a race here
            # registering before publishing means possible duplicates,
            # while after means lost output

            # publish already-captured output immediately
            for name in ("stdout", "stderr"):
                text = md[name]
                if text:
                    iopub_callback(
                        {
                            "header": {"msg_type": "stream"},
                            "content": {"name": name, "text": text},
                        }
                    )
            for output in md["outputs"]:
                iopub_callback(
                    {
                        "header": {"msg_type": "display_data"},
                        "content": output,
                    }
                )
            if md["execute_result"]:
                iopub_callback(
                    {
                        "header": {"msg_type": "execute_result"},
                        "content": md["execute_result"],
                    }
                )

        try:
            yield
        finally:
            # clear stream cache
            self._already_streamed = {}

            # Remove the callbacks
            for msg_future, iopub_callback in future_callbacks.items():
                msg_future.iopub_callbacks.remove(iopub_callback)

    def __repr__(self):
        if self._ready:
            if self._success:
                state = "finished"
            else:
                state = "failed"
        else:
            state = "pending"
        return f"<{self.__class__.__name__}({self._fname}): {state}>"

    def __dir__(self):
        keys = dir(self.__class__)
        if not _metadata_keys:
            from .client import Metadata

            _metadata_keys.extend(Metadata().keys())
        keys.extend(_metadata_keys)
        return keys

    def _reconstruct_result(self, res):
        """Reconstruct our result from actual result list (always a list)

        Override me in subclasses for turning a list of results
        into the expected form.
        """
        if self._single_result:
            return res[0]
        else:
            return res

    def get(self, timeout=None, return_exceptions=None, return_when=None):
        """Return the result when it arrives.

        Arguments:

        timeout : int [default None]
            If `timeout` is not ``None`` and the result does not arrive within
            `timeout` seconds then ``TimeoutError`` is raised. If the
            remote call raised an exception then that exception will be reraised
            by get() inside a `RemoteError`.
        return_exceptions : bool [default False]
            If True, return Exceptions instead of raising them.
        return_when : None, ALL_COMPLETED, or FIRST_EXCEPTION
            FIRST_COMPLETED is not supported, and treated the same as ALL_COMPLETED.
            See :py:func:`concurrent.futures.wait` for documentation.

            When return_when=FIRST_EXCEPTION, will raise immediately on the first exception,
            rather than waiting for all results to finish before reporting errors.

        .. versionchanged:: 8.0
            Added `return_when` argument.
        """
        if return_when == FIRST_COMPLETED:
            # FIRST_COMPLETED unsupported, same as ALL_COMPLETED
            warnings.warn(
                "Ignoring unsupported AsyncResult.get(return_when=FIRST_COMPLETED)",
                UserWarning,
                stacklevel=2,
            )
            return_when = None
        elif return_when == ALL_COMPLETED:
            # None avoids call to .split() and is a tiny bit more efficient
            return_when = None

        if not self.ready():
            wait_result = self.wait(timeout, return_when=return_when)

        if return_exceptions is None:
            # default to attribute, if AsyncResult was created with return_exceptions=True
            return_exceptions = self._return_exceptions

        if self._ready:
            if self._success:
                return self.result()
            else:
                e = self.exception()
                if return_exceptions:
                    return self._reconstruct_result(self._raw_results)
                else:
                    raise e
        else:
            if return_when == FIRST_EXCEPTION:
                # this should only occur if there was an exception
                # any other situation should have triggered the ready branch above

                done, pending = wait_result
                for ar in done:
                    if not ar._success:
                        return ar.get(return_exceptions=return_exceptions)
            raise TimeoutError("Result not ready.")

    def _check_ready(self):
        if not self.ready():
            raise TimeoutError("Result not ready.")

    def ready(self):
        """Return whether the call has completed."""
        if not self._ready:
            self.wait(0)

        return self._ready

    def wait_for_output(self, timeout=-1):
        """Wait for our output to be complete.

        AsyncResult.wait only waits for the result,
        which may arrive before output is complete.
        """
        if self._output_ready:
            return True
        if timeout and timeout < 0:
            timeout = None
        return self._output_event.wait(timeout)

    def _resolve_output(self, f=None):
        """Callback that fires when outputs are ready"""
        if self.owner:
            [self._client.metadata.pop(mid, None) for mid in self.msg_ids]
        self._output_ready = True
        self._output_event.set()

    @classmethod
    def join(cls, *async_results):
        """Join multiple AsyncResults into one

        Inverse of .split(),
        used for rejoining split results in wait.

        .. versionadded:: 8.0
        """
        if not async_results:
            raise ValueError("Must specify at least one AsyncResult to join")
        first = async_results[0]
        if len(async_results) == 1:
            # only one AsyncResult, nothing to join
            return first

        return cls(
            client=first._client,
            fname=first._fname,
            return_exceptions=first._return_exceptions,
            children=list(chain(*(ar._children for ar in async_results))),
            targets=list(chain(*(ar._targets for ar in async_results))),
            owner=False,
        )

    @lru_cache
    def split(self):
        """Split an AsyncResult

        An AsyncResult object that represents multiple messages
        can be split to wait for individual results
        This can be passed to `concurrent.futures.wait` and friends
        to get partial results.

        .. versionadded:: 8.0
        """
        if len(self._children) == 1:
            # nothing to do if we're already representing a single message
            return (self,)
            self.owner = False

        if self._targets is None:
            _targets = repeat(None)
        else:
            _targets = self._targets

        flatten = not isinstance(self, AsyncMapResult)
        return tuple(
            AsyncResult(
                client=self._client,
                children=msg_future if flatten else [msg_future],
                targets=[engine_id],
                fname=self._fname,
                owner=False,
                return_exceptions=self._return_exceptions,
            )
            for engine_id, msg_future in zip(_targets, self._children)
        )

    def wait(self, timeout=-1, return_when=None):
        """Wait until the result is available or until `timeout` seconds pass.

        Arguments:

        timeout (int):
            The timeout in seconds. `-1` or None indicate an infinite timeout.
        return_when (enum):
            None, ALL_COMPLETED, FIRST_COMPLETED, or FIRST_EXCEPTION.
            Passed to :py:func:`concurrent.futures.wait`.
            If specified and not-None,

        Returns:
            ready (bool):
                For backward-compatibility.
                If `return_when` is None or unspecified,
                returns True if all tasks are done, False otherwise

            (done, pending):
                If `return_when` is any of the constants for :py:func:`concurrent.futures.wait`,
                will return two sets of AsyncResult objects
                representing the completed and still-pending subsets of results,
                matching the return value of `wait` itself.

        .. versionchanged:: 8.0
            Added `return_when`.
        """
        if timeout and timeout < 0:
            timeout = None
        if return_when is None:
            if self._ready:
                return True
            self._ready_event.wait(timeout)
            self.wait_for_output(0)
            return self._ready
        else:
            futures = self.split()
            done, pending = concurrent.futures.wait(
                futures, timeout=timeout, return_when=return_when
            )
            if done:
                self.wait_for_output(0)

            return done, pending

            # # simple cases: all done, or all pending
            # if not pending:
            #     return (None, self)
            # if not done:
            #     return (self, None)

    #
    # # neither set is empty, rejoin two subsets
    # return (self.__class__.join(*done), self.__class__.join(*pending))

    def _resolve_result(self, f=None):
        if self.done():
            return
        if f:
            results = f.result()
        else:
            results = list(map(self._client.results.get, self.msg_ids))

        # store raw results
        self._raw_results = results

        try:
            if self._single_result:
                r = results[0]
                if isinstance(r, Exception):
                    raise r
            else:
                results = self._collect_exceptions(results)
        except Exception as e:
            self._success = False
            self.set_exception(e)
        else:
            self._success = True
            self.set_result(self._reconstruct_result(results))

    def _collect_exceptions(self, results):
        """Wrap Exceptions in a CompositeError

        if self._return_exceptions is True, this is a no-op
        """
        if self._return_exceptions:
            return results
        else:
            return error.collect_exceptions(results, self._fname)

    def _finalize_result(self, f):
        if self.owner:
            [self._client.results.pop(mid, None) for mid in self.msg_ids]
        self._ready = True
        self._ready_event.set()

    def successful(self):
        """Return whether the call completed without raising an exception.

        Will raise ``RuntimeError`` if the result is not ready.
        """
        if not self.ready():
            raise RuntimeError("Cannot check successful() if not done.")
        return self._success

    # ----------------------------------------------------------------
    # Extra methods not in mp.pool.AsyncResult
    # ----------------------------------------------------------------

    def get_dict(self, timeout=-1):
        """Get the results as a dict, keyed by engine_id.

        timeout behavior is described in `get()`.
        """

        results = self.get(timeout)
        if self._single_result:
            results = [results]
        engine_ids = [md['engine_id'] for md in self._metadata]

        rdict = {}
        for engine_id, result in zip(engine_ids, results):
            if engine_id in rdict:
                n_jobs = engine_ids.count(engine_id)
                raise ValueError(
                    f"Cannot build dict, {n_jobs} jobs ran on engine #{engine_id}"
                )
            else:
                rdict[engine_id] = result

        return rdict

    @property
    def r(self):
        """result property wrapper for `get(timeout=-1)`."""
        return self.get()

    _DATE_FIELDS = [
        "submitted",
        "started",
        "completed",
        "received",
    ]

    def _parse_metadata_dates(self):
        """Ensure metadata date fields are parsed on access

        Rather than parsing timestamps from str->dt on receipt,
        parse on access for compatibility.
        """
        for md in self._metadata:
            for key in self._DATE_FIELDS:
                if isinstance(md.get(key, None), str):
                    md[key] = _parse_date(md[key])

    @property
    def metadata(self):
        """property for accessing execution metadata."""
        self._parse_metadata_dates()
        if self._single_result:
            return self._metadata[0]
        else:
            return self._metadata

    @property
    def result_dict(self):
        """result property as a dict."""
        return self.get_dict()

    def __dict__(self):
        return self.get_dict(0)

    def abort(self):
        """
        Abort my tasks, if possible.

        Only tasks that have not started yet can be aborted.

        Raises RuntimeError if already done.
        """
        if self.ready():
            raise RuntimeError("Can't abort, I am already done!")
        return self._client.abort(self.msg_ids, targets=self._targets, block=True)

    def _handle_sent(self, f):
        """Resolve sent Future, build MessageTracker"""
        trackers = f.result()
        trackers = [t for t in trackers if t is not None]
        self._tracker = zmq.MessageTracker(*trackers)
        self._sent_event.set()

    @property
    def sent(self):
        """check whether my messages have been sent."""
        return self._sent_event.is_set() and self._tracker.done

    def wait_for_send(self, timeout=-1):
        """wait for pyzmq send to complete.

        This is necessary when sending arrays that you intend to edit in-place.
        `timeout` is in seconds, and will raise TimeoutError if it is reached
        before the send completes.
        """
        if not self._sent_event.is_set():
            if timeout and timeout < 0:
                # Event doesn't like timeout < 0
                timeout = None
            elif timeout == 0:
                raise TimeoutError("Still waiting to be sent")
            # wait for Future to indicate send having been called,
            # which means MessageTracker is ready.
            tic = time.perf_counter()
            if not self._sent_event.wait(timeout):
                raise TimeoutError("Still waiting to be sent")
            if timeout:
                timeout = max(0, timeout - (time.perf_counter() - tic))
        try:
            if timeout is None:
                # MessageTracker doesn't like timeout=None
                timeout = -1
            return self._tracker.wait(timeout)
        except zmq.NotDone:
            raise TimeoutError("Still waiting to be sent")

    # -------------------------------------
    # dict-access
    # -------------------------------------

    def __getitem__(self, key):
        """getitem returns result value(s) if keyed by int/slice, or metadata if key is str."""
        if isinstance(key, int):
            self._check_ready()
            return self._collect_exceptions([self.result()[key]])[0]
        elif isinstance(key, slice):
            self._check_ready()
            return self._collect_exceptions(self.result()[key])
        elif isinstance(key, str):
            # metadata proxy *does not* require that results are done
            self.wait(0)
            self.wait_for_output(0)
            self._parse_metadata_dates()
            values = [md[key] for md in self._metadata]
            if self._single_result:
                return values[0]
            else:
                return values
        else:
            raise TypeError(
                "Invalid key type %r, must be 'int','slice', or 'str'" % type(key)
            )

    def __getattr__(self, key):
        """getattr maps to getitem for convenient attr access to metadata."""
        try:
            return self.__getitem__(key)
        except (TimeoutError, KeyError):
            raise AttributeError(
                f"{self.__class__.__name__!r} object has no attribute {key!r}"
            )

    @staticmethod
    def _wait_for_child(child, evt, timeout=_FOREVER):
        """Wait for a child to be done"""
        if child.done():
            return
        evt.clear()
        child.add_done_callback(lambda f: evt.set())
        evt.wait(timeout)

    # asynchronous iterator:
    def __iter__(self):
        if self._single_result:
            raise TypeError("AsyncResults with a single result are not iterable.")
        try:
            rlist = self.get(0)
        except TimeoutError:
            # wait for each result individually
            evt = Event()
            for child in self._children:
                self._wait_for_child(child, evt=evt)
                result = child.result()
                self._collect_exceptions([result])
                yield result
        else:
            # already done
            yield from rlist

    @lru_cache
    def __len__(self):
        return self._count_chunks(*self.msg_ids)

    @lru_cache
    def _count_chunks(self, *msg_ids):
        """Count the granular tasks"""
        return sum(self._chunk_sizes.setdefault(msg_id, 1) for msg_id in msg_ids)

    # -------------------------------------
    # Sugar methods and attributes
    # -------------------------------------

    def timedelta(self, start, end, start_key=min, end_key=max):
        """compute the difference between two sets of timestamps

        The default behavior is to use the earliest of the first
        and the latest of the second list, but this can be changed
        by passing a different

        Parameters
        ----------
        start : one or more datetime objects (e.g. ar.submitted)
        end : one or more datetime objects (e.g. ar.received)
        start_key : callable
            Function to call on `start` to extract the relevant
            entry [default: min]
        end_key : callable
            Function to call on `end` to extract the relevant
            entry [default: max]

        Returns
        -------
        dt : float
            The time elapsed (in seconds) between the two selected timestamps.
        """
        if not isinstance(start, datetime):
            # handle single_result AsyncResults, where ar.stamp is single object,
            # not a list
            start = start_key(start)
        if not isinstance(end, datetime):
            # handle single_result AsyncResults, where ar.stamp is single object,
            # not a list
            end = end_key(end)
        return compare_datetimes(end, start).total_seconds()

    @property
    def progress(self):
        """the number of tasks which have been completed at this point.

        Fractional progress would be given by 1.0 * ar.progress / len(ar)
        """
        self.wait(0)
        finished_msg_ids = set(self.msg_ids).intersection(self._client.outstanding)
        finished_count = self._count_chunks(*finished_msg_ids)
        return len(self) - finished_count

    @property
    def elapsed(self):
        """elapsed time since initial submission"""
        if self.ready():
            return self.wall_time

        now = submitted = utcnow()
        self._parse_metadata_dates()
        for md in self._metadata:
            stamp = md["submitted"]
            if stamp and stamp < submitted:
                submitted = stamp
        return compare_datetimes(now, submitted).total_seconds()

    @property
    @check_ready
    def serial_time(self):
        """serial computation time of a parallel calculation

        Computed as the sum of (completed-started) of each task
        """
        t = 0
        self._parse_metadata_dates()
        for md in self._metadata:
            t += compare_datetimes(md['completed'], md['started']).total_seconds()
        return t

    @property
    @check_ready
    def wall_time(self):
        """actual computation time of a parallel calculation

        Computed as the time between the latest `received` stamp
        and the earliest `submitted`.

        For similar comparison of other timestamp pairs, check out AsyncResult.timedelta.
        """
        return self.timedelta(self.submitted, self.received)

    def wait_interactive(
        self, interval=0.1, timeout=-1, widget=None, return_when=ALL_COMPLETED
    ):
        """interactive wait, printing progress at regular intervals.

        Parameters
        ----------
        interval : float
            Interval on which to update progress display.
        timeout : float
            Time (in seconds) to wait before raising a TimeoutError.
            -1 (default) means no timeout.
        widget : bool
            default: True if in an IPython kernel (notebook), False otherwise.
            Override default context-detection behavior for whether a widget-based progress bar
            should be used.
        return_when : concurrent.futures.ALL_COMPLETED | FIRST_EXCEPTION | FIRST_COMPLETED
        """
        if timeout and timeout < 0:
            timeout = None
        if return_when == ALL_COMPLETED:
            return_when = None
        N = len(self)
        tic = time.perf_counter()
        progress_bar = progress(widget=widget, total=N, unit='tasks', desc=self._fname)

        finished = self.ready()
        while not finished and (
            timeout is None or time.perf_counter() - tic <= timeout
        ):
            wait_result = self.wait(interval, return_when=return_when)
            progress_bar.update(self.progress - progress_bar.n)
            if return_when is None:
                finished = wait_result
            else:
                done, pending = wait_result
                if return_when == FIRST_COMPLETED:
                    finished = bool(done)
                elif return_when == FIRST_EXCEPTION:
                    finished = (not pending) or any(not ar._success for ar in done)
                else:
                    raise ValueError(f"Unrecognized return_when={return_when!r}")

        progress_bar.update(self.progress - progress_bar.n)
        progress_bar.close()

    def _republish_displaypub(self, content, eid):
        """republish individual displaypub content dicts"""
        ip = get_ipython()
        if ip is None:
            # displaypub is meaningless outside IPython
            return
        md = content['metadata'] or {}
        md['engine'] = eid
        ip.display_pub.publish(data=content['data'], metadata=md)

    def _display_stream(self, text, prefix='', file=None):
        """Redisplay a stream"""
        if not text:
            # nothing to display
            return
        if file is None:
            file = sys.stdout

        end = ""
        if prefix:
            if prefix == self._last_display_prefix:
                # same prefix, no need to re-display
                prefix = ""
            else:
                self._last_display_prefix = prefix

        if prefix and not self._stream_trailing_newline:
            # prefix changed, no trailing newline; insert newline
            pre = "\n"
        else:
            pre = ""

        if prefix:
            sep = "\n"
        else:
            sep = ""

        self._stream_trailing_newline = text.endswith("\n")
        print(f"{pre}{prefix}{sep}{text}", file=file, end="")

    def _display_single_result(self, result_only=False):
        if not result_only:
            self._display_stream(self.stdout)
            self._display_stream(self.stderr, file=sys.stderr)
        if get_ipython() is None:
            # displaypub is meaningless outside IPython
            return

        if not result_only:
            for output in self.outputs:
                self._republish_displaypub(output, self.engine_id)

        if self.execute_result is not None:
            display(self.get())

    @check_ready
    def display_outputs(self, groupby="type", result_only=False):
        """republish the outputs of the computation

        Parameters
        ----------
        groupby : str [default: type]
            if 'type':
                Group outputs by type (show all stdout, then all stderr, etc.):

                [stdout:1] foo
                [stdout:2] foo
                [stderr:1] bar
                [stderr:2] bar
            if 'engine':
                Display outputs for each engine before moving on to the next:

                [stdout:1] foo
                [stderr:1] bar
                [stdout:2] foo
                [stderr:2] bar

            if 'order':
                Like 'type', but further collate individual displaypub
                outputs.  This is meant for cases of each command producing
                several plots, and you would like to see all of the first
                plots together, then all of the second plots, and so on.

        result_only: boolean [default: False]
            Only display the execution result and skip stdout, stderr and
            display-outputs. Usually used when using streaming output
            since these outputs would have already been displayed.
        """
        self.wait_for_output()
        if self._single_result:
            self._display_single_result(result_only=result_only)
            return

        stdouts = self.stdout
        stderrs = self.stderr
        execute_results = self.execute_result
        output_lists = self.outputs
        results = self.get(return_exceptions=True)

        targets = self.engine_id

        if groupby == "engine":
            for eid, stdout, stderr, outputs, r, execute_result in zip(
                targets, stdouts, stderrs, output_lists, results, execute_results
            ):
                if not result_only:
                    self._display_stream(stdout, f'[stdout:{eid}] ')
                    self._display_stream(stderr, f'[stderr:{eid}] ', file=sys.stderr)

                if get_ipython() is None:
                    # displaypub is meaningless outside IPython
                    continue

                if (outputs and not result_only) or execute_result is not None:
                    _raw_text(f'[output:{eid}]')

                if not result_only:
                    for output in outputs:
                        self._republish_displaypub(output, eid)

                if execute_result is not None:
                    display(r)

        elif groupby in ('type', 'order'):
            if not result_only:
                # republish stdout:
                for eid, stdout in zip(targets, stdouts):
                    self._display_stream(stdout, f'[stdout:{eid}] ')

                # republish stderr:
                for eid, stderr in zip(targets, stderrs):
                    self._display_stream(stderr, f'[stderr:{eid}] ', file=sys.stderr)

            if get_ipython() is None:
                # displaypub is meaningless outside IPython
                return

            if not result_only:
                if groupby == 'order':
                    output_dict = {
                        eid: outputs for eid, outputs in zip(targets, output_lists)
                    }
                    N = max(len(outputs) for outputs in output_lists)
                    for i in range(N):
                        for eid in targets:
                            outputs = output_dict[eid]
                            if len(outputs) >= N:
                                _raw_text(f'[output:{eid}]')
                                self._republish_displaypub(outputs[i], eid)
                else:
                    # republish displaypub output
                    for eid, outputs in zip(targets, output_lists):
                        if outputs:
                            _raw_text(f'[output:{eid}]')
                        for output in outputs:
                            self._republish_displaypub(output, eid)

            # finally, add execute_result:
            for eid, r, execute_result in zip(targets, results, execute_results):
                if execute_result is not None:
                    display(r)

        else:
            raise ValueError(
                "groupby must be one of 'type', 'engine', 'collate', not %r" % groupby
            )


class AsyncMapResult(AsyncResult):
    """Class for representing results of non-blocking maps.

    AsyncMapResult.get() will properly reconstruct gathers into single object.

    AsyncMapResult is iterable at any time, and will wait on results as they come.

    If ordered=False, then the first results to arrive will come first, otherwise
    results will be yielded in the order they were submitted.
    """

    def __init__(
        self,
        client,
        children,
        mapObject,
        fname='',
        ordered=True,
        return_exceptions=False,
        chunk_sizes=None,
    ):
        self._mapObject = mapObject
        self.ordered = ordered
        AsyncResult.__init__(
            self,
            client,
            children,
            fname=fname,
            return_exceptions=return_exceptions,
            chunk_sizes=chunk_sizes,
        )
        self._single_result = False

    def _reconstruct_result(self, res):
        """Perform the gather on the actual results."""
        if self._return_exceptions:
            if any(isinstance(r, Exception) for r in res):
                # running with _return_exceptions,
                # cannot reconstruct original
                # use simple chain iterable
                flattened = []
                for r in res:
                    if isinstance(r, Exception):
                        flattened.append(r)
                    else:
                        flattened.extend(r)
                return flattened
        return self._mapObject.joinPartitions(res)

    # asynchronous iterator:
    def __iter__(self):
        it = self._ordered_iter if self.ordered else self._unordered_iter
        yield from it()

    def _yield_child_results(self, child):
        """Yield results from a child

        for use in iterator methods
        """
        rlist = child.result()
        if not isinstance(rlist, list):
            rlist = [rlist]
        self._collect_exceptions(rlist)
        yield from rlist

    # asynchronous ordered iterator:
    def _ordered_iter(self):
        """iterator for results *as they arrive*, preserving submission order."""
        try:
            rlist = self.get(0)
        except TimeoutError:
            # wait for each result individually
            evt = Event()
            for child in self._children:
                self._wait_for_child(child, evt=evt)
                yield from self._yield_child_results(child)
        else:
            # already done
            yield from rlist

    # asynchronous unordered iterator:
    def _unordered_iter(self):
        """iterator for results *as they arrive*, on FCFS basis, ignoring submission order."""
        try:
            rlist = self.get(0)
        except TimeoutError:
            pending = self._children
            while pending:
                done, pending = concurrent.futures.wait(
                    pending, return_when=FIRST_COMPLETED
                )
                for child in done:
                    yield from self._yield_child_results(child)
        else:
            # already done
            yield from rlist


class AsyncHubResult(AsyncResult):
    """Class to wrap pending results that must be requested from the Hub.

    Note that waiting/polling on these objects requires polling the Hub over the network,
    so use `AsyncHubResult.wait()` sparingly.
    """

    def _init_futures(self):
        """disable Future-based resolution of Hub results"""
        pass

    def wait(self, timeout=-1, return_when=None):
        """wait for result to complete."""
        start = time.perf_counter()
        if timeout and timeout < 0:
            timeout = None
        if self._ready:
            return True
        local_ids = [m for m in self.msg_ids if m in self._client.outstanding]
        local_ready = self._client.wait(local_ids, timeout)
        if local_ready:
            remote_ids = [m for m in self.msg_ids if m not in self._client.results]
            if not remote_ids:
                self._ready = True
            else:
                rdict = self._client.result_status(remote_ids, status_only=False)
                pending = rdict['pending']
                while pending and (
                    timeout is None or time.perf_counter() < start + timeout
                ):
                    rdict = self._client.result_status(remote_ids, status_only=False)
                    pending = rdict['pending']
                    if pending:
                        time.sleep(0.1)
                if not pending:
                    self._ready = True
        if self._ready:
            self._output_ready = True
            try:
                results = list(map(self._client.results.get, self.msg_ids))
                if self._single_result:
                    r = results[0]
                    if isinstance(r, Exception) and not self._return_exceptions:
                        raise r
                else:
                    results = self._collect_exceptions(results)
                self._success = True
                self.set_result(self._reconstruct_result(results))
            except Exception as e:
                self._success = False
                self.set_exception(e)
            finally:
                if self.owner:
                    [self._client.metadata.pop(mid) for mid in self.msg_ids]
                    [self._client.results.pop(mid) for mid in self.msg_ids]

        return self._ready


__all__ = ['AsyncResult', 'AsyncMapResult', 'AsyncHubResult']
ipyparallel-8.8.0/ipyparallel/client/client.py000066400000000000000000002634221460376056100215120ustar00rootroot00000000000000"""A semi-synchronous Client for IPython parallel"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import asyncio
import json
import os
import re
import socket
import time
import types
import warnings
from collections.abc import Iterable
from concurrent.futures import Future
from functools import partial
from getpass import getpass
from pprint import pprint
from threading import Event, Thread, current_thread

import jupyter_client.session
import zmq
from decorator import decorator
from ipykernel.comm import Comm
from IPython import get_ipython
from IPython.core.application import BaseIPythonApplication
from IPython.core.profiledir import ProfileDir, ProfileDirError
from IPython.paths import get_ipython_dir
from IPython.utils.capture import RichOutput
from IPython.utils.coloransi import TermColors
from IPython.utils.path import compress_user
from jupyter_client.localinterfaces import is_local_ip, localhost
from jupyter_client.session import Session
from tornado import ioloop
from traitlets import (
    Any,
    Bool,
    Bytes,
    Dict,
    HasTraits,
    Instance,
    List,
    Set,
    Unicode,
    default,
)
from traitlets.config.configurable import MultipleInstanceError
from zmq.eventloop.zmqstream import ZMQStream

import ipyparallel as ipp
from ipyparallel import error, serialize, util
from ipyparallel.serialize import PrePickled, Reference

from .asyncresult import AsyncHubResult, AsyncResult
from .futures import MessageFuture, multi_future
from .view import BroadcastView, DirectView, LoadBalancedView

pjoin = os.path.join
jupyter_client.session.extract_dates = lambda obj: obj
# --------------------------------------------------------------------------
# Decorators for Client methods
# --------------------------------------------------------------------------


@decorator
def unpack_message(f, self, msg_parts):
    """Unpack a message before calling the decorated method."""
    idents, msg = self.session.feed_identities(msg_parts, copy=False)
    try:
        msg = self.session.deserialize(msg, content=True, copy=False)
    except Exception:
        self.log.error("Invalid Message", exc_info=True)
    else:
        if self.debug:
            pprint(msg)
        return f(self, msg)


# --------------------------------------------------------------------------
# Classes
# --------------------------------------------------------------------------


_no_connection_file_msg = """
Failed to connect because no Controller could be found.
Please double-check your profile and ensure that a cluster is running.
"""


class ExecuteReply(RichOutput):
    """wrapper for finished Execute results"""

    def __init__(self, msg_id, content, metadata):
        self.msg_id = msg_id
        self._content = content
        self.execution_count = content['execution_count']
        self.metadata = metadata

    # RichOutput overrides

    @property
    def source(self):
        execute_result = self.metadata['execute_result']
        if execute_result:
            return execute_result.get('source', '')

    @property
    def data(self):
        execute_result = self.metadata['execute_result']
        if execute_result:
            return execute_result.get('data', {})
        return {}

    @property
    def _metadata(self):
        execute_result = self.metadata['execute_result']
        if execute_result:
            return execute_result.get('metadata', {})
        return {}

    def display(self):
        from IPython.display import publish_display_data

        publish_display_data(self.data, self.metadata)

    def _repr_mime_(self, mime):
        if mime not in self.data:
            return
        data = self.data[mime]
        if mime in self._metadata:
            return data, self._metadata[mime]
        else:
            return data

    def _repr_mimebundle_(self, *args, **kwargs):
        data, md = self.data, self.metadata
        if 'text/plain' in data:
            data = data.copy()
            data['text/plain'] = self._plaintext()
        return data, md

    def __getitem__(self, key):
        return self.metadata[key]

    def __getattr__(self, key):
        if key not in self.metadata:
            raise AttributeError(key)
        return self.metadata[key]

    def __repr__(self):
        execute_result = self.metadata['execute_result'] or {'data': {}}
        text_out = execute_result['data'].get('text/plain', '')
        if len(text_out) > 32:
            text_out = text_out[:29] + '...'

        return "" % (self.execution_count, text_out)

    def _plaintext(self):
        execute_result = self.metadata['execute_result'] or {'data': {}}
        text_out = execute_result['data'].get('text/plain', '')

        if not text_out:
            return ''

        ip = get_ipython()
        if ip is None:
            colors = "NoColor"
        else:
            colors = ip.colors

        if colors == "NoColor":
            out = normal = ""
        else:
            out = TermColors.Red
            normal = TermColors.Normal

        if '\n' in text_out and not text_out.startswith('\n'):
            # add newline for multiline reprs
            text_out = '\n' + text_out

        return ''.join(
            [
                out,
                f"Out[{self.metadata['engine_id']}:{self.execution_count}]: ",
                normal,
                text_out,
            ]
        )

    def _repr_pretty_(self, p, cycle):
        p.text(self._plaintext())


class Metadata(dict):
    """Subclass of dict for initializing metadata values.

    Attribute access works on keys.

    These objects have a strict set of keys - errors will raise if you try
    to add new keys.
    """

    def __init__(self, *args, **kwargs):
        dict.__init__(self)
        md = {
            'msg_id': None,
            'submitted': None,
            'started': None,
            'completed': None,
            'received': None,
            'engine_uuid': None,
            'engine_id': None,
            'follow': None,
            'after': None,
            'status': None,
            'execute_input': None,
            'execute_result': None,
            'error': None,
            'stdout': '',
            'stderr': '',
            'outputs': [],
            'data': {},
        }
        self.update(md)
        self.update(dict(*args, **kwargs))

    def __getattr__(self, key):
        """getattr aliased to getitem"""
        if key in self:
            return self[key]
        else:
            raise AttributeError(key)

    def __setattr__(self, key, value):
        """setattr aliased to setitem, with strict"""
        if key in self:
            self[key] = value
        else:
            raise AttributeError(key)

    def __setitem__(self, key, value):
        """strict static key enforcement"""
        if key in self:
            dict.__setitem__(self, key, value)
        else:
            raise KeyError(key)


def _is_future(f):
    """light duck-typing check for Futures"""
    return hasattr(f, 'add_done_callback')


# carriage return pattern
_cr_pat = re.compile(r'.*\r(?=[^\n])')


class Client(HasTraits):
    """A semi-synchronous client to an IPython parallel cluster

    Parameters
    ----------

    connection_info : str or dict
        The path to ipcontroller-client.json, or a dict containing the same information.
        This JSON file should contain all the information needed to connect to a cluster,
        and is usually the only argument needed.
        [Default: use profile]
    profile : str
        The name of the Cluster profile to be used to find connector information.
        If run from an IPython application, the default profile will be the same
        as the running application, otherwise it will be 'default'.
    cluster_id : str
        String id to added to runtime files, to prevent name collisions when using
        multiple clusters with a single profile simultaneously.
        When set, will look for files named like: 'ipcontroller--client.json'
        Since this is text inserted into filenames, typical recommendations apply:
        Simple character strings are ideal, and spaces are not recommended (but
        should generally work)
    context : zmq.Context
        Pass an existing zmq.Context instance, otherwise the client will create its own.
    debug : bool
        flag for lots of message printing for debug purposes
    timeout : float
        time (in seconds) to wait for connection replies from the Hub
        [Default: 10]

    Other Parameters
    ----------------

    sshserver : str
        A string of the form passed to ssh, i.e. 'server.tld' or 'user@server.tld:port'
        If keyfile or password is specified, and this is not, it will default to
        the ip given in addr.
    sshkey : str; path to ssh private key file
        This specifies a key to be used in ssh login, default None.
        Regular default ssh keys will be used without specifying this argument.
    password : str
        Your ssh password to sshserver. Note that if this is left None,
        you will be prompted for it if passwordless key based login is unavailable.
    paramiko : bool
        flag for whether to use paramiko instead of shell ssh for tunneling.
        [default: True on win32, False else]


    Attributes
    ----------

    ids : list of int engine IDs
        requesting the ids attribute always synchronizes
        the registration state. To request ids without synchronization,
        use semi-private _ids attributes.

    history : list of msg_ids
        a list of msg_ids, keeping track of all the execution
        messages you have submitted in order.

    outstanding : set of msg_ids
        a set of msg_ids that have been submitted, but whose
        results have not yet been received.

    results : dict
        a dict of all our results, keyed by msg_id

    block : bool
        determines default behavior when block not specified
        in execution methods

    """

    block = Bool(False)
    outstanding = Set()
    results = Instance('collections.defaultdict', (dict,))
    metadata = Instance('collections.defaultdict', (Metadata,))
    cluster = Instance('ipyparallel.cluster.Cluster', allow_none=True)
    history = List()
    debug = Bool(False)
    _futures = Dict()
    _output_futures = Dict()
    _io_loop = Any()
    _io_thread = Any()

    profile = Unicode()

    def _profile_default(self):
        if BaseIPythonApplication.initialized():
            # an IPython app *might* be running, try to get its profile
            try:
                return BaseIPythonApplication.instance().profile
            except (AttributeError, MultipleInstanceError):
                # could be a *different* subclass of config.Application,
                # which would raise one of these two errors.
                return 'default'
        else:
            return 'default'

    _outstanding_dict = Instance('collections.defaultdict', (set,))
    _ids = List()
    _connected = Bool(False)
    _ssh = Bool(False)
    _context = Instance('zmq.Context', allow_none=True)

    @default("_context")
    def _default_context(self):
        return zmq.Context.instance()

    _config = Dict()
    _engines = Instance(util.ReverseDict, (), {})
    _query_socket = Instance('zmq.Socket', allow_none=True)
    _control_socket = Instance('zmq.Socket', allow_none=True)
    _iopub_socket = Instance('zmq.Socket', allow_none=True)
    _notification_socket = Instance('zmq.Socket', allow_none=True)
    _mux_socket = Instance('zmq.Socket', allow_none=True)
    _task_socket = Instance('zmq.Socket', allow_none=True)
    _broadcast_socket = Instance('zmq.Socket', allow_none=True)
    _registration_callbacks = List()

    curve_serverkey = Bytes(allow_none=True)
    curve_secretkey = Bytes(allow_none=True)
    curve_publickey = Bytes(allow_none=True)

    _task_scheme = Unicode()
    _closed = False

    def __new__(self, *args, **kw):
        # don't raise on positional args
        return HasTraits.__new__(self, **kw)

    def __init__(
        self,
        connection_info=None,
        *,
        url_file=None,
        profile=None,
        profile_dir=None,
        ipython_dir=None,
        context=None,
        debug=False,
        sshserver=None,
        sshkey=None,
        password=None,
        paramiko=None,
        timeout=10,
        cluster_id=None,
        cluster=None,
        **extra_args,
    ):
        super_kwargs = {'debug': debug, 'cluster': cluster}
        if profile:
            super_kwargs['profile'] = profile
        super().__init__(**super_kwargs)
        if context is not None:
            self._context = context

        for argname in ('url_or_file', 'url_file'):
            if argname in extra_args:
                connection_info = extra_args[argname]
                warnings.warn(
                    f"{argname} arg no longer supported, use positional connection_info argument",
                    DeprecationWarning,
                    stacklevel=2,
                )

        if isinstance(connection_info, str) and util.is_url(connection_info):
            raise ValueError(
                f"single urls ({connection_info!r}) cannot be specified, url-files must be used."
            )

        self._setup_profile_dir(self.profile, profile_dir, ipython_dir)

        no_file_msg = '\n'.join(
            [
                "You have attempted to connect to an IPython Cluster but no Controller could be found.",
                "Please double-check your configuration and ensure that a cluster is running.",
            ]
        )

        if connection_info is None and self._profile_dir is not None:
            # default: find connection info from profile
            if cluster_id:
                client_json = f'ipcontroller-{cluster_id}-client.json'
            else:
                client_json = 'ipcontroller-client.json'
            connection_file = pjoin(self._profile_dir.security_dir, client_json)
            short = compress_user(connection_file)
            if not os.path.exists(connection_file):
                print(f"Waiting for connection file: {short}")
                waiting_time = 0.0
                while waiting_time < timeout:
                    time.sleep(min(timeout - waiting_time, 1))
                    waiting_time += 1
                    if os.path.exists(connection_file):
                        break
            if not os.path.exists(connection_file):
                msg = '\n'.join([f"Connection file {short!r} not found.", no_file_msg])
                raise OSError(msg)

            with open(connection_file) as f:
                connection_info = json.load(f)

        if connection_info is None:
            raise OSError(no_file_msg)

        if isinstance(connection_info, dict):
            cfg = connection_info.copy()
        else:
            # connection_info given as path to connection file
            connection_file = connection_info
            if not os.path.exists(connection_file):
                # Connection file explicitly specified, but not found
                raise OSError(
                    f"Connection file {compress_user(connection_file)} not found. Is a controller running?"
                )

            with open(connection_file) as f:
                connection_info = cfg = json.load(f)

        self._task_scheme = cfg['task_scheme']

        if not cfg.get("curve_serverkey") and "IPP_CURVE_SERVERKEY" in os.environ:
            # load from env, if not set in connection file
            cfg["curve_serverkey"] = os.environ["IPP_CURVE_SERVERKEY"]

        if cfg.get("curve_serverkey"):
            self.curve_serverkey = cfg["curve_serverkey"].encode('ascii')
            if not self.curve_publickey or not self.curve_secretkey:
                # if context: this could crash!
                # inappropriately closes libsodium random_bytes source
                # with libzmq <= 4.3.4
                self.curve_publickey, self.curve_secretkey = zmq.curve_keypair()

        # sync defaults from args, json:
        if sshserver:
            cfg['ssh'] = sshserver

        location = cfg.setdefault('location', None)

        proto, addr = cfg['interface'].split('://')
        addr = util.disambiguate_ip_address(addr, location)
        cfg['interface'] = f"{proto}://{addr}"

        # turn interface,port into full urls:
        for key in (
            'control',
            'task',
            'mux',
            'iopub',
            'notification',
            'registration',
            'broadcast',
        ):
            cfg[key] = f"{cfg['interface']}:{cfg[key]}"

        url = cfg['registration']

        if location is not None and addr == localhost():
            # location specified, and connection is expected to be local
            location_ip = util.ip_for_host(location)

            if not is_local_ip(location_ip) and not sshserver:
                # load ssh from JSON *only* if the controller is not on
                # this machine
                sshserver = cfg['ssh']
            if (
                not is_local_ip(location_ip)
                and not sshserver
                and location != socket.gethostname()
            ):
                # warn if no ssh specified, but SSH is probably needed
                # This is only a warning, because the most likely cause
                # is a local Controller on a laptop whose IP is dynamic
                warnings.warn(
                    """
            Controller appears to be listening on localhost, but not on this machine.
            If this is true, you should specify Client(...,sshserver='you@%s')
            or instruct your controller to listen on an external IP."""
                    % location,
                    RuntimeWarning,
                )
        elif not sshserver:
            # otherwise sync with cfg
            sshserver = cfg['ssh']

        self._config = cfg

        self._ssh = bool(sshserver or sshkey or password)
        if self._ssh and sshserver is None:
            # default to ssh via localhost
            sshserver = addr
        if self._ssh and password is None:
            from zmq.ssh import tunnel

            if tunnel.try_passwordless_ssh(sshserver, sshkey, paramiko):
                password = False
            else:
                password = getpass("SSH Password for %s: " % sshserver)
        ssh_kwargs = dict(keyfile=sshkey, password=password, paramiko=paramiko)

        # configure and construct the session
        try:
            extra_args['packer'] = cfg['pack']
            extra_args['unpacker'] = cfg['unpack']
            extra_args['key'] = cfg['key'].encode("utf8")
            extra_args['signature_scheme'] = cfg['signature_scheme']
        except KeyError as exc:
            msg = '\n'.join(
                [
                    "Connection file is invalid (missing '{}'), possibly from an old version of IPython.",
                    "If you are reusing connection files, remove them and start ipcontroller again.",
                ]
            )
            raise ValueError(msg.format(exc.message))

        util._disable_session_extract_dates()
        self.session = Session(**extra_args)

        self._query_socket = self._context.socket(zmq.DEALER)
        if self.curve_serverkey:
            self._query_socket.curve_serverkey = self.curve_serverkey
            self._query_socket.curve_secretkey = self.curve_secretkey
            self._query_socket.curve_publickey = self.curve_publickey

        if self._ssh:
            from zmq.ssh import tunnel

            tunnel.tunnel_connection(
                self._query_socket,
                cfg['registration'],
                sshserver,
                timeout=timeout,
                **ssh_kwargs,
            )
        else:
            self._query_socket.connect(cfg['registration'])

        self.session.debug = self.debug

        self._notification_handlers = {
            'registration_notification': self._register_engine,
            'unregistration_notification': self._unregister_engine,
            'shutdown_notification': lambda msg: self.close(),
        }
        self._queue_handlers = {
            'execute_reply': self._handle_execute_reply,
            'apply_reply': self._handle_apply_reply,
        }

        try:
            self._connect(sshserver, ssh_kwargs, timeout)
        except Exception:
            self.close(linger=0)
            raise

        # last step: setup magics, if we are in IPython:

        ip = get_ipython()
        if ip is None:
            return
        else:
            if 'px' not in ip.magics_manager.magics["line"]:
                # in IPython but we are the first Client.
                # activate a default view for parallel magics.
                self.activate()

    def __del__(self):
        """cleanup sockets, but _not_ context."""
        self.close()

    def _setup_profile_dir(self, profile, profile_dir, ipython_dir):
        if ipython_dir is None:
            ipython_dir = get_ipython_dir()
        if profile_dir is not None:
            try:
                self._profile_dir = ProfileDir.find_profile_dir(profile_dir)
                return
            except ProfileDirError:
                pass
        elif profile is not None:
            try:
                self._profile_dir = ProfileDir.find_profile_dir_by_name(
                    ipython_dir, profile
                )
                return
            except ProfileDirError:
                pass
        self._profile_dir = None

    def __enter__(self):
        """A client can be used as a context manager

        which will close the client on exit

        .. versionadded: 7.0
        """
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        """Exiting a client context closes the client"""
        self.close()

    def _update_engines(self, engines):
        """Update our engines dict and _ids from a dict of the form: {id:uuid}."""
        for k, v in engines.items():
            eid = int(k)
            if eid not in self._engines:
                self._ids.append(eid)
            self._engines[eid] = v
        self._ids = sorted(self._ids)
        if (
            sorted(self._engines.keys()) != list(range(len(self._engines)))
            and self._task_scheme == 'pure'
            and self._task_socket
        ):
            self._stop_scheduling_tasks()

    def _stop_scheduling_tasks(self):
        """Stop scheduling tasks because an engine has been unregistered
        from a pure ZMQ scheduler.
        """
        self._task_socket.close()
        self._task_socket = None
        msg = (
            "An engine has been unregistered, and we are using pure "
            + "ZMQ task scheduling.  Task farming will be disabled."
        )
        if self.outstanding:
            msg += (
                " If you were running tasks when this happened, "
                + "some `outstanding` msg_ids may never resolve."
            )
        warnings.warn(msg, RuntimeWarning)

    def _build_targets(self, targets):
        """Turn valid target IDs or 'all' into two lists:
        (int_ids, uuids).
        """
        if not self._ids:
            # flush notification socket if no engines yet, just in case
            if not self.ids:
                raise error.NoEnginesRegistered(
                    "Can't build targets without any engines"
                )

        if targets is None:
            targets = self._ids
        elif isinstance(targets, str):
            if targets.lower() == 'all':
                targets = self._ids
            else:
                raise TypeError("%r not valid str target, must be 'all'" % (targets))
        elif isinstance(targets, int):
            if targets < 0:
                targets = self.ids[targets]
            if targets not in self._ids:
                raise IndexError(f"No such engine: {targets}")
            targets = [targets]

        if isinstance(targets, slice):
            indices = list(range(len(self._ids))[targets])
            ids = self.ids
            targets = [ids[i] for i in indices]

        if not isinstance(targets, (tuple, list, range)):
            raise TypeError(
                "targets by int/slice/collection of ints only, not %s" % (type(targets))
            )

        return [self._engines[t].encode("utf8") for t in targets], list(targets)

    def _connect(self, sshserver, ssh_kwargs, timeout):
        """setup all our socket connections to the cluster. This is called from
        __init__."""

        # Maybe allow reconnecting?
        if self._connected:
            return
        self._connected = True

        def connect_socket(s, url):
            if self._ssh:
                from zmq.ssh import tunnel

                return tunnel.tunnel_connection(s, url, sshserver, **ssh_kwargs)
            else:
                return util.connect(
                    s,
                    url,
                    curve_serverkey=self.curve_serverkey,
                    curve_secretkey=self.curve_secretkey,
                    curve_publickey=self.curve_publickey,
                )

        self.session.send(self._query_socket, 'connection_request')
        # use Poller because zmq.select has wrong units in pyzmq 2.1.7
        poller = zmq.Poller()
        poller.register(self._query_socket, zmq.POLLIN)
        # poll expects milliseconds, timeout is seconds
        evts = poller.poll(timeout * 1000)
        if not evts:
            raise TimeoutError("Hub connection request timed out")
        idents, msg = self.session.recv(self._query_socket, mode=0)
        if self.debug:
            pprint(msg)
        content = msg['content']
        # self._config['registration'] = dict(content)
        cfg = self._config
        if content['status'] == 'ok':
            self._mux_socket = self._context.socket(zmq.DEALER)
            connect_socket(self._mux_socket, cfg['mux'])

            self._task_socket = self._context.socket(zmq.DEALER)
            connect_socket(self._task_socket, cfg['task'])

            self._broadcast_socket = self._context.socket(zmq.DEALER)
            connect_socket(self._broadcast_socket, cfg['broadcast'])

            self._notification_socket = self._context.socket(zmq.SUB)
            self._notification_socket.RCVHWM = 0
            self._notification_socket.setsockopt(zmq.SUBSCRIBE, b'')
            connect_socket(self._notification_socket, cfg['notification'])

            self._control_socket = self._context.socket(zmq.DEALER)
            connect_socket(self._control_socket, cfg['control'])

            self._iopub_socket = self._context.socket(zmq.SUB)
            self._iopub_socket.RCVHWM = 0
            self._iopub_socket.setsockopt(zmq.SUBSCRIBE, b'')
            connect_socket(self._iopub_socket, cfg['iopub'])

            self._update_engines(dict(content['engines']))
        else:
            self._connected = False
            tb = '\n'.join(content.get('traceback', []))
            raise Exception("Failed to connect! %s" % tb)

        self._start_io_thread()

    # --------------------------------------------------------------------------
    # handlers and callbacks for incoming messages
    # --------------------------------------------------------------------------

    def _unwrap_exception(self, content):
        """unwrap exception, and remap engine_id to int."""
        e = error.unwrap_exception(content)
        # print e.traceback
        if e.engine_info and 'engine_id' not in e.engine_info:
            e_uuid = e.engine_info['engine_uuid']
            eid = self._engines[e_uuid]
            e.engine_info['engine_id'] = eid
        return e

    def _extract_metadata(self, msg):
        header = msg['header']
        parent = msg['parent_header']
        msg_meta = msg['metadata']
        content = msg['content']
        md = {
            'msg_id': parent['msg_id'],
            'received': util.utcnow(),
            'engine_uuid': msg_meta.get('engine', None),
            'follow': msg_meta.get('follow', []),
            'after': msg_meta.get('after', []),
            'status': content['status'],
            'is_broadcast': msg_meta.get('is_broadcast', False),
            'is_coalescing': msg_meta.get('is_coalescing', False),
        }

        if md['engine_uuid'] is not None:
            md['engine_id'] = self._engines.get(md['engine_uuid'], None)

        if md['is_coalescing']:
            # get destinations from target metadata
            targets = msg_meta.get("broadcast_targets", [])
            md['engine_uuid'], md['engine_id'] = map(list, zip(*targets))

        if 'date' in parent:
            md['submitted'] = parent['date']
        if 'started' in msg_meta:
            md['started'] = util._parse_date(msg_meta['started'])
        if 'date' in header:
            md['completed'] = header['date']
        return md

    def _register_engine(self, msg):
        """Register a new engine, and update our connection info."""
        content = msg['content']
        eid = content['id']
        d = {eid: content['uuid']}
        self._update_engines(d)
        event = {'event': 'register'}
        event.update(content)
        for callback in self._registration_callbacks:
            callback(event)

    def _unregister_engine(self, msg):
        """Unregister an engine that has died."""
        content = msg['content']
        eid = int(content['id'])
        if eid in self._ids:
            self._ids.remove(eid)
            uuid = self._engines.pop(eid)

            self._handle_stranded_msgs(eid, uuid)

        if self._task_socket and self._task_scheme == 'pure':
            self._stop_scheduling_tasks()

        event = {"event": "unregister"}
        event.update(content)
        for callback in self._registration_callbacks:
            callback(event)

    def _handle_stranded_msgs(self, eid, uuid):
        """Handle messages known to be on an engine when the engine unregisters.

        It is possible that this will fire prematurely - that is, an engine will
        go down after completing a result, and the client will be notified
        of the unregistration and later receive the successful result.
        """

        outstanding = self._outstanding_dict[uuid]

        for msg_id in list(outstanding):
            if msg_id in self.results:
                # we already
                continue
            try:
                raise error.EngineError(
                    f"Engine {eid!r} died while running task {msg_id!r}"
                )
            except Exception:
                content = error.wrap_exception()
            # build a fake message:
            msg = self.session.msg('apply_reply', content=content)
            msg['parent_header']['msg_id'] = msg_id
            msg['metadata']['engine'] = uuid
            self._handle_apply_reply(msg)

    def _handle_execute_reply(self, msg):
        """Save the reply to an execute_request into our results.

        execute messages are never actually used. apply is used instead.
        """

        parent = msg['parent_header']
        if self._should_use_metadata_msg_id(msg):
            msg_id = msg['metadata']['original_msg_id']
        else:
            msg_id = parent['msg_id']

        future = self._futures.get(msg_id, None)
        if msg_id not in self.outstanding:
            if msg_id in self.history:
                print("got stale result: %s" % msg_id)
            else:
                print("got unknown result: %s" % msg_id)
        else:
            self.outstanding.remove(msg_id)

        content = msg['content']
        header = msg['header']

        # construct metadata:
        md = self.metadata[msg_id]
        md.update(self._extract_metadata(msg))

        if md['is_coalescing']:
            engine_uuids = md['engine_uuid'] or []
        else:
            engine_uuids = [md['engine_uuid']]

        for engine_uuid in engine_uuids:
            if engine_uuid is not None:
                e_outstanding = self._outstanding_dict[engine_uuid]
                if msg_id in e_outstanding:
                    e_outstanding.remove(msg_id)

        # construct result:
        if content['status'] == 'ok':
            self.results[msg_id] = ExecuteReply(msg_id, content, md)
        elif content['status'] == 'aborted':
            self.results[msg_id] = error.TaskAborted(msg_id)
            # aborted tasks will not get output
            out_future = self._output_futures.get(msg_id)
            if out_future and not out_future.done():
                out_future.set_result(None)
        elif content['status'] == 'resubmitted':
            # TODO: handle resubmission
            pass
        else:
            self.results[msg_id] = self._unwrap_exception(content)
        if content['status'] != 'ok' and not content.get('engine_info'):
            # not an engine failure, don't expect output
            out_future = self._output_futures.get(msg_id)
            if out_future and not out_future.done():
                out_future.set_result(None)
        if future:
            future.set_result(self.results[msg_id])

    def _should_use_metadata_msg_id(self, msg):
        md = msg['metadata']
        return md.get('is_broadcast', False) and md.get('is_coalescing', False)

    def _handle_apply_reply(self, msg):
        """Save the reply to an apply_request into our results."""
        parent = msg['parent_header']
        if self._should_use_metadata_msg_id(msg):
            msg_id = msg['metadata']['original_msg_id']
        else:
            msg_id = parent['msg_id']

        future = self._futures.get(msg_id, None)
        if msg_id not in self.outstanding:
            if msg_id in self.history:
                print("got stale result: %s" % msg_id)
                print(self.results[msg_id])
                print(msg)
            else:
                print("got unknown result: %s" % msg_id)
        else:
            self.outstanding.remove(msg_id)
        content = msg['content']
        header = msg['header']

        # construct metadata:
        md = self.metadata[msg_id]
        md.update(self._extract_metadata(msg))

        if md['is_coalescing']:
            engine_uuids = md['engine_uuid'] or []
        else:
            engine_uuids = [md['engine_uuid']]

        for engine_uuid in engine_uuids:
            if engine_uuid is not None:
                e_outstanding = self._outstanding_dict[engine_uuid]
                if msg_id in e_outstanding:
                    e_outstanding.remove(msg_id)

        # construct result:
        if content['status'] == 'ok':
            if md.get('is_coalescing', False):
                deserialized_bufs = []
                bufs = msg['buffers']
                while bufs:
                    deserialized, bufs = serialize.deserialize_object(bufs)
                    deserialized_bufs.append(deserialized)
                self.results[msg_id] = deserialized_bufs
            else:
                self.results[msg_id] = serialize.deserialize_object(msg['buffers'])[0]
        elif content['status'] == 'aborted':
            self.results[msg_id] = error.TaskAborted(msg_id)
            out_future = self._output_futures.get(msg_id)
            if out_future and not out_future.done():
                out_future.set_result(None)
        elif content['status'] == 'resubmitted':
            # TODO: handle resubmission
            pass
        else:
            self.results[msg_id] = self._unwrap_exception(content)
        if content['status'] != 'ok' and not content.get('engine_info'):
            # not an engine failure, don't expect output
            out_future = self._output_futures.get(msg_id)
            if out_future and not out_future.done():
                out_future.set_result(None)
        if future:
            future.set_result(self.results[msg_id])

    def _make_io_loop(self):
        """Make my IOLoop. Override with IOLoop.current to return"""
        # runs first thing in the io thread
        # always create a fresh asyncio loop for the thread
        if os.name == "nt":
            asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
        loop = ioloop.IOLoop(make_current=False)
        return loop

    def _stop_io_thread(self):
        """Stop my IO thread"""
        if self._io_loop:
            self._io_loop.add_callback(self._io_loop.stop)
        if self._io_thread and self._io_thread is not current_thread():
            self._io_thread.join()

    def _setup_streams(self):
        self._query_stream = ZMQStream(self._query_socket, self._io_loop)
        self._query_stream.on_recv(self._dispatch_single_reply, copy=False)
        self._control_stream = ZMQStream(self._control_socket, self._io_loop)
        self._control_stream.on_recv(self._dispatch_single_reply, copy=False)
        self._mux_stream = ZMQStream(self._mux_socket, self._io_loop)
        self._mux_stream.on_recv(self._dispatch_reply, copy=False)
        self._task_stream = ZMQStream(self._task_socket, self._io_loop)
        self._task_stream.on_recv(self._dispatch_reply, copy=False)
        self._iopub_stream = ZMQStream(self._iopub_socket, self._io_loop)
        self._iopub_stream.on_recv(self._dispatch_iopub, copy=False)
        self._notification_stream = ZMQStream(self._notification_socket, self._io_loop)
        self._notification_stream.on_recv(self._dispatch_notification, copy=False)

        self._broadcast_stream = ZMQStream(self._broadcast_socket, self._io_loop)
        self._broadcast_stream.on_recv(self._dispatch_reply, copy=False)

    def _start_io_thread(self):
        """Start IOLoop in a background thread."""
        evt = Event()
        self._io_thread = Thread(target=self._io_main, args=(evt,))
        self._io_thread.daemon = True
        self._io_thread.start()
        # wait for the IOLoop to start
        for i in range(20):
            if evt.wait(1):
                return
            if not self._io_thread.is_alive():
                raise RuntimeError("IO Loop failed to start")
        else:
            raise RuntimeError(
                "Start event was never set. Maybe a problem in the IO thread."
            )

    def _io_main(self, start_evt=None):
        """main loop for background IO thread"""
        self._io_loop = self._make_io_loop()
        self._setup_streams()
        # signal that start has finished
        # so that the main thread knows that all our attributes are defined
        if start_evt:
            start_evt.set()
        try:
            self._io_loop.start()
        finally:
            self._io_loop.close(all_fds=True)

    @unpack_message
    def _dispatch_single_reply(self, msg):
        """Dispatch single (non-execution) replies"""
        msg_id = msg['parent_header'].get('msg_id', None)
        future = self._futures.get(msg_id)
        if future is not None:
            future.set_result(msg)

    @unpack_message
    def _dispatch_notification(self, msg):
        """Dispatch notification messages"""
        msg_type = msg['header']['msg_type']
        handler = self._notification_handlers.get(msg_type, None)
        if handler is None:
            raise KeyError("Unhandled notification message type: %s" % msg_type)
        else:
            handler(msg)

    @unpack_message
    def _dispatch_reply(self, msg):
        """handle execution replies waiting in ZMQ queue."""
        msg_type = msg['header']['msg_type']
        handler = self._queue_handlers.get(msg_type, None)
        if handler is None:
            raise KeyError("Unhandled reply message type: %s" % msg_type)
        else:
            handler(msg)

    @unpack_message
    def _dispatch_iopub(self, msg):
        """handler for IOPub messages"""
        parent = msg['parent_header']
        if not parent or parent['session'] != self.session.session:
            # ignore IOPub messages not from here
            return
        msg_id = parent['msg_id']
        content = msg['content']
        header = msg['header']
        msg_type = msg['header']['msg_type']

        if msg_type == 'status' and msg_id not in self.metadata:
            # ignore status messages if they aren't mine
            return

        # init metadata:
        md = self.metadata[msg_id]

        if md['engine_id'] is None and 'engine' in msg['metadata']:
            e_uuid = msg['metadata']['engine']
            try:
                md['engine_uuid'] = e_uuid
                md['engine_id'] = self._engines[e_uuid]
            except KeyError:
                pass

        ip = get_ipython()

        if msg_type == 'stream':
            name = content['name']
            new_text = (md[name] or '') + content['text']
            if '\r' in content['text']:
                new_text = _cr_pat.sub('', new_text)
            md[name] = new_text
        elif msg_type == 'error':
            md.update({'error': self._unwrap_exception(content)})
        elif msg_type == 'execute_input':
            md.update({'execute_input': content['code']})
        elif msg_type == 'display_data':
            md['outputs'].append(content)
        elif msg_type == 'execute_result':
            md['execute_result'] = content
        elif msg_type == 'data_message':
            data, remainder = serialize.deserialize_object(msg['buffers'])
            md['data'].update(data)
        elif msg_type == 'status':
            # idle message comes after all outputs
            if content['execution_state'] == 'idle':
                future = self._output_futures.get(msg_id)
                if future and not future.done():
                    # TODO: should probably store actual outputs on the Future
                    future.set_result(None)
        elif msg_type.startswith("comm_") and ip is not None and ip.kernel is not None:
            # only handle comm messages when we're in an IPython kernel
            if msg_type == "comm_open":
                # create proxy comm
                engine_uuid = msg['metadata'].get('engine', '')
                engine_ident = engine_uuid.encode("utf8", "replace")
                # DEBUG: engine_uuid can still be missing?!

                comm = Comm(
                    comm_id=content['comm_id'],
                    primary=False,
                )

                send_to_engine = partial(
                    self._send,
                    self._mux_socket,
                    ident=engine_ident,
                )

                def relay_comm(msg):
                    send_to_engine(
                        msg["msg_type"],
                        content=msg['content'],
                        metadata=msg['metadata'],
                        buffers=msg["buffers"],
                    )

                comm.on_msg(relay_comm)
                comm.on_close(
                    lambda: send_to_engine(
                        "comm_close",
                        content={
                            "comm_id": comm.comm_id,
                        },
                    )
                )
                ip.kernel.comm_manager.register_comm(comm)

            # relay all comm msgs
            ip.kernel.session.send(
                ip.kernel.iopub_socket,
                msg_type,
                content=msg['content'],
                metadata=msg['metadata'],
                buffers=msg['buffers'],
                # different parent!
                parent=ip.kernel.get_parent("shell"),
            )

        msg_future = self._futures.get(msg_id, None)
        if msg_future:
            # Run any callback functions
            for callback in msg_future.iopub_callbacks:
                callback(msg)

    def create_message_futures(self, msg_id, header, async_result=False, track=False):
        msg_future = MessageFuture(msg_id, header=header, track=track)
        futures = [msg_future]
        self._futures[msg_id] = msg_future
        if async_result:
            output = MessageFuture(msg_id, header=header)
            # add future for output
            self._output_futures[msg_id] = output
            # hook up metadata
            output.metadata = self.metadata[msg_id]
            output.metadata['submitted'] = util.utcnow()
            msg_future.output = output
            futures.append(output)
        return futures

    def _send(
        self,
        socket,
        msg_type,
        content=None,
        parent=None,
        ident=None,
        buffers=None,
        track=False,
        header=None,
        metadata=None,
        track_outstanding=False,
        message_future_hook=None,
    ):
        """Send a message in the IO thread

        returns msg object"""
        if self._closed:
            raise OSError("Connections have been closed.")
        msg = self.session.msg(
            msg_type, content=content, parent=parent, header=header, metadata=metadata
        )
        msg_id = msg['header']['msg_id']

        expect_reply = msg_type not in {"comm_msg", "comm_close", "comm_open"}

        if expect_reply and track_outstanding:
            # add to outstanding, history
            self.outstanding.add(msg_id)
            self.history.append(msg_id)

            if ident:
                # possibly routed to a specific engine
                ident_str = ident
                if isinstance(ident_str, list):
                    ident_str = ident_str[-1]
                ident_str = ident_str.decode("utf-8")
                if ident_str in self._engines.values():
                    # save for later, in case of engine death
                    self._outstanding_dict[ident_str].add(msg_id)
            self.metadata['submitted'] = util.utcnow()

        if expect_reply:
            futures = self.create_message_futures(
                msg_id,
                msg['header'],
                async_result=msg_type in {'execute_request', 'apply_request'},
                track=track,
            )
            if message_future_hook is not None:
                message_future_hook(futures[0])

            def cleanup(f):
                """Purge caches on Future resolution"""
                self.results.pop(msg_id, None)
                self._futures.pop(msg_id, None)
                self._output_futures.pop(msg_id, None)
                self.metadata.pop(msg_id, None)

            multi_future(futures).add_done_callback(cleanup)

        def _really_send():
            sent = self.session.send(
                socket, msg, track=track, buffers=buffers, ident=ident
            )
            if track:
                futures[0].tracker.set_result(sent['tracker'])

        # hand off actual send to IO thread
        self._io_loop.add_callback(_really_send)
        if expect_reply:
            return futures[0]

    def _send_recv(self, *args, **kwargs):
        """Send a message in the IO thread and return its reply"""
        future = self._send(*args, **kwargs)
        future.wait()
        return future.result()

    # --------------------------------------------------------------------------
    # len, getitem
    # --------------------------------------------------------------------------

    def __len__(self):
        """len(client) returns # of engines."""
        return len(self.ids)

    def __getitem__(self, key):
        """index access returns DirectView multiplexer objects

        Must be int, slice, or list/tuple/range of ints"""
        if not isinstance(key, (int, slice, tuple, list, range)):
            raise TypeError(
                "key by int/slice/iterable of ints only, not %s" % (type(key))
            )
        else:
            return self.direct_view(key)

    def __iter__(self):
        """Since we define getitem, Client is iterable

        but unless we also define __iter__, it won't work correctly unless engine IDs
        start at zero and are continuous.
        """
        for eid in self.ids:
            yield self.direct_view(eid)

    # --------------------------------------------------------------------------
    # Begin public methods
    # --------------------------------------------------------------------------

    @property
    def ids(self):
        # always copy:
        return list(self._ids)

    def activate(self, targets='all', suffix=''):
        """Create a DirectView and register it with IPython magics

        Defines the magics `%px, %autopx, %pxresult, %%px`

        Parameters
        ----------
        targets : int, list of ints, or 'all'
            The engines on which the view's magics will run
        suffix : str [default: '']
            The suffix, if any, for the magics.  This allows you to have
            multiple views associated with parallel magics at the same time.

            e.g. ``rc.activate(targets=0, suffix='0')`` will give you
            the magics ``%px0``, ``%pxresult0``, etc. for running magics just
            on engine 0.
        """
        view = self.direct_view(targets)
        view.block = True
        view.activate(suffix)
        return view

    def close(self, linger=None):
        """Close my zmq Sockets

        If `linger`, set the zmq LINGER socket option,
        which allows discarding of messages.
        """
        if self._closed:
            return
        self._stop_io_thread()
        snames = [trait for trait in self.trait_names() if trait.endswith("socket")]
        for name in snames:
            socket = getattr(self, name)
            if socket is not None and not socket.closed:
                if linger is not None:
                    socket.close(linger=linger)
                else:
                    socket.close()
        self._closed = True

    def spin_thread(self, interval=1):
        """DEPRECATED, DOES NOTHING"""
        warnings.warn(
            "Client.spin_thread is deprecated now that IO is always in a thread",
            DeprecationWarning,
        )

    def stop_spin_thread(self):
        """DEPRECATED, DOES NOTHING"""
        warnings.warn(
            "Client.spin_thread is deprecated now that IO is always in a thread",
            DeprecationWarning,
        )

    def spin(self):
        """DEPRECATED, DOES NOTHING"""
        warnings.warn(
            "Client.spin is deprecated now that IO is in a thread", DeprecationWarning
        )

    def _await_futures(self, futures, timeout):
        """Wait for a collection of futures"""
        if not futures:
            return True

        event = Event()
        if timeout and timeout < 0:
            timeout = None

        f = multi_future(futures)
        f.add_done_callback(lambda f: event.set())
        return event.wait(timeout)

    def _futures_for_msgs(self, msg_ids):
        """Turn msg_ids into Futures

        msg_ids not in futures dict are presumed done.
        """
        futures = []
        for msg_id in msg_ids:
            f = self._futures.get(msg_id, None)
            if f:
                futures.append(f)
        return futures

    def wait_for_engines(
        self, n=None, *, timeout=-1, block=True, interactive=None, widget=None
    ):
        """Wait for `n` engines to become available.

        Returns when `n` engines are available,
        or raises TimeoutError if `timeout` is reached
        before `n` engines are ready.

        Parameters
        ----------
        n : int
            Number of engines to wait for.
        timeout : float
            Time (in seconds) to wait before raising a TimeoutError
        block : bool
            if False, return Future instead of waiting
        interactive : bool
            default: True if in IPython, False otherwise.
            if True, show a progress bar while waiting for engines
        widget : bool
            default: True if in an IPython kernel (notebook), False otherwise.
            Only has an effect if `interactive` is True.
            if True, forces use of widget progress bar.
            If False, forces use of terminal tqdm.

        Returns
        ------
        f : concurrent.futures.Future or None
            Future object to wait on if block is False,
            None if block is True.

        Raises
        ------
        TimeoutError : if timeout is reached.
        """
        if n is None:
            # get n from cluster, if not specified
            if self.cluster is None:
                raise TypeError("n engines to wait for must be specified")

            if self.cluster.n:
                n = self.cluster.n
            else:
                # compute n from engine sets,
                # e.g. the default where n is calculated at runtime from `cpu_count()`
                n = sum(engine_set.n for engine_set in self.cluster.engines.values())

        if len(self.ids) >= n:
            if block:
                return
            else:
                f = Future()
                f.set_result(None)
                return f
        tic = now = time.perf_counter()
        if timeout >= 0:
            deadline = tic + timeout
        else:
            deadline = None
            seconds_remaining = 1000

        if interactive is None:
            if ipp._NONINTERACTIVE:
                interactive = False
            else:
                interactive = get_ipython() is not None

        if interactive:
            progress_bar = util.progress(
                widget=widget,
                initial=len(self.ids),
                total=n,
                unit='engine',
            )

        # watch for engine-stop events

        engine_stop_future = Future()
        if self.cluster and self.cluster.engines:
            # we have a parent cluster,
            # monitor for engines stopping
            def _signal_stopped(stop_data):
                if not engine_stop_future.done():
                    engine_stop_future.set_result(stop_data)

            def _remove_signal_stopped(f, es):
                try:
                    es.stop_callbacks.remove(_signal_stopped)
                except ValueError:
                    # already removed
                    pass

            for es in self.cluster.engines.values():
                es.on_stop(_signal_stopped)
                engine_stop_future.add_done_callback(
                    partial(_remove_signal_stopped, es=es)
                )

        future = Future()

        def cancel_engine_stop(_):
            if not engine_stop_future.done():
                engine_stop_future.cancel()

        future.add_done_callback(cancel_engine_stop)

        def notice_engine_stop(f):
            if future.done():
                return
            stop_data = f.result()
            future.set_exception(error.EngineError(f"Engine set stopped: {stop_data}"))

        engine_stop_future.add_done_callback(notice_engine_stop)

        def notify(event):
            if future.done():
                return
            if event["event"] == "unregister":
                future.set_exception(
                    error.EngineError(
                        f"Engine {event['id']} unregistered while waiting for engines."
                    )
                )
                return
            current_n = len(self.ids)
            if interactive:
                progress_bar.update(current_n - progress_bar.n)
            if current_n >= n:
                # ensure we refresh when we finish
                if interactive:
                    progress_bar.close()
                future.set_result(None)

        self._registration_callbacks.append(notify)
        future.add_done_callback(lambda f: self._registration_callbacks.remove(notify))

        def on_timeout():
            """Called when timeout is reached"""
            if future.done():
                return

            current_n = len(self.ids)
            if current_n >= n:
                future.set_result(None)
            else:
                future.set_exception(
                    TimeoutError(
                        f"{n} engines not ready in {timeout} seconds. Currently ready: {current_n}"
                    )
                )

        def schedule_timeout():
            handle = self._io_loop.add_timeout(
                self._io_loop.time() + timeout, on_timeout
            )
            future.add_done_callback(lambda f: self._io_loop.remove_timeout(handle))

        if timeout >= 0:
            self._io_loop.add_callback(schedule_timeout)

        if block:
            return future.result()
        else:
            return future

    def wait(self, jobs=None, timeout=-1):
        """waits on one or more `jobs`, for up to `timeout` seconds.

        Parameters
        ----------
        jobs : int, str, or list of ints and/or strs, or one or more AsyncResult objects
            ints are indices to self.history
            strs are msg_ids
            default: wait on all outstanding messages
        timeout : float
            a time in seconds, after which to give up.
            default is -1, which means no timeout

        Returns
        -------
        True : when all msg_ids are done
        False : timeout reached, some msg_ids still outstanding
        """
        futures = []
        if jobs is None:
            if not self.outstanding:
                return True
            # make a copy, so that we aren't passing a mutable collection to _futures_for_msgs
            theids = set(self.outstanding)
        else:
            if isinstance(jobs, (str, int, AsyncResult)) or not isinstance(
                jobs, Iterable
            ):
                jobs = [jobs]
            theids = set()
            for job in jobs:
                if isinstance(job, int):
                    # index access
                    job = self.history[job]
                elif isinstance(job, AsyncResult):
                    theids.update(job.msg_ids)
                    continue
                elif _is_future(job):
                    futures.append(job)
                    continue
                theids.add(job)
            if not futures and not theids.intersection(self.outstanding):
                return True

        futures.extend(self._futures_for_msgs(theids))
        return self._await_futures(futures, timeout)

    def wait_interactive(self, jobs=None, interval=1.0, timeout=-1.0):
        """Wait interactively for jobs

        If no job is specified, will wait for all outstanding jobs to complete.
        """
        if jobs is None:
            # get futures for results
            futures = [f for f in self._futures.values() if hasattr(f, 'output')]
            if not futures:
                return
            ar = AsyncResult(self, futures, owner=False)
        else:
            ar = self._asyncresult_from_jobs(jobs, owner=False)
        return ar.wait_interactive(interval=interval, timeout=timeout)

    # --------------------------------------------------------------------------
    # Control methods
    # --------------------------------------------------------------------------

    def _send_control_request(self, targets, msg_type, content, block):
        """Send a request on the control channel"""
        target_identities = self._build_targets(targets)[0]
        futures = []
        for ident in target_identities:
            futures.append(
                self._send(self._control_stream, msg_type, content=content, ident=ident)
            )
        if not block:
            return multi_future(futures)
        for future in futures:
            future.wait()
            msg = future.result()
            if msg['content']['status'] != 'ok':
                raise self._unwrap_exception(msg['content'])

    def send_signal(self, sig, targets=None, block=None):
        """Send a signal target(s).

        Parameters
        ----------

        sig: int or str
            The signal number or name to send.
            If a str, will evaluate to getattr(signal, sig) on the engine,
            which is useful for sending signals cross-platform.

        .. versionadded:: 7.0
        """
        block = self.block if block is None else block
        return self._send_control_request(
            targets=targets,
            msg_type='signal_request',
            content={'sig': sig},
            block=block,
        )

    def clear(self, targets=None, block=None):
        """Clear the namespace in target(s)."""
        block = self.block if block is None else block
        return self._send_control_request(
            targets=targets, msg_type='clear_request', content={}, block=block
        )

    def abort(self, jobs=None, targets=None, block=None):
        """Abort specific jobs from the execution queues of target(s).

        This is a mechanism to prevent jobs that have already been submitted
        from executing.
        To halt a running job,
        you must interrupt the engine(s) by sending a signal.
        This can be done via os.kill for local engines,
        or :meth:`.Cluster.signal_engines` for multiple engines.

        Parameters
        ----------
        jobs : msg_id, list of msg_ids, or AsyncResult
            The jobs to be aborted

            If unspecified/None: abort all outstanding jobs.

        """
        block = self.block if block is None else block
        jobs = jobs if jobs is not None else list(self.outstanding)

        msg_ids = []
        if isinstance(jobs, (str, AsyncResult)):
            jobs = [jobs]
        bad_ids = [obj for obj in jobs if not isinstance(obj, (str, AsyncResult))]
        if bad_ids:
            raise TypeError(
                "Invalid msg_id type %r, expected str or AsyncResult" % bad_ids[0]
            )
        for j in jobs:
            if isinstance(j, AsyncResult):
                msg_ids.extend(j.msg_ids)
            else:
                msg_ids.append(j)
        content = dict(msg_ids=msg_ids)

        return self._send_control_request(
            targets,
            msg_type='abort_request',
            content=content,
            block=block,
        )

    def shutdown(self, targets='all', restart=False, hub=False, block=None):
        """Terminates one or more engine processes, optionally including the hub.

        Parameters
        ----------
        targets : list of ints or 'all' [default: all]
            Which engines to shutdown.
        hub : bool [default: False]
            Whether to include the Hub.  hub=True implies targets='all'.
        block : bool [default: self.block]
            Whether to wait for clean shutdown replies or not.
        restart : bool [default: False]
            NOT IMPLEMENTED
            whether to restart engines after shutting them down.
        """
        from ipyparallel.error import NoEnginesRegistered

        if restart:
            raise NotImplementedError("Engine restart is not yet implemented")

        block = self.block if block is None else block
        if hub:
            targets = 'all'
        try:
            targets = self._build_targets(targets)[0]
        except NoEnginesRegistered:
            targets = []

        futures = []
        for t in targets:
            futures.append(
                self._send(
                    self._control_stream,
                    'shutdown_request',
                    content={'restart': restart},
                    ident=t,
                )
            )
        error = False
        if block or hub:
            for f in futures:
                f.wait()
                msg = f.result()
                if msg['content']['status'] != 'ok':
                    error = self._unwrap_exception(msg['content'])

        if hub:
            # don't trigger close on shutdown notification, which will prevent us from receiving the reply
            self._notification_handlers['shutdown_notification'] = lambda msg: None
            msg = self._send_recv(self._query_stream, 'shutdown_request')
            if msg['content']['status'] != 'ok':
                error = self._unwrap_exception(msg['content'])
            if not error:
                self.close()

        if error:
            raise error

    def become_dask(
        self, targets='all', port=0, nanny=False, scheduler_args=None, **worker_args
    ):
        """Turn the IPython cluster into a dask.distributed cluster

        Parameters
        ----------
        targets : target spec (default: all)
            Which engines to turn into dask workers.
        port : int (default: random)
            Which port
        nanny : bool (default: False)
            Whether to start workers as subprocesses instead of in the engine process.
            Using a nanny allows restarting the worker processes via ``executor.restart``.
        scheduler_args : dict
            Keyword arguments (e.g. ip) to pass to the distributed.Scheduler constructor.
        **worker_args
            Any additional keyword arguments (e.g. nthreads) are passed to the distributed.Worker constructor.

        Returns
        -------
        client = distributed.Client
            A dask.distributed.Client connected to the dask cluster.
        """
        import distributed

        dview = self.direct_view(targets)

        if scheduler_args is None:
            scheduler_args = {}
        else:
            scheduler_args = dict(scheduler_args)  # copy

        # Start a Scheduler on the Hub:
        reply = self._send_recv(
            self._query_stream,
            'become_dask_request',
            {'scheduler_args': scheduler_args},
        )
        if reply['content']['status'] != 'ok':
            raise self._unwrap_exception(reply['content'])
        distributed_info = reply['content']

        # Start a Worker on the selected engines:
        worker_args['address'] = distributed_info['address']
        worker_args['nanny'] = nanny
        # distributed 2.0 renamed ncores to nthreads
        if int(distributed.__version__.partition(".")[0]) >= 2:
            nthreads = "nthreads"
        else:
            nthreads = "ncores"
        # set default nthreads=1, since that's how an IPython cluster is typically set up.
        worker_args.setdefault(nthreads, 1)
        dview.apply_sync(util.become_dask_worker, **worker_args)

        # Finally, return a Client connected to the Scheduler
        try:
            distributed_Client = distributed.Client
        except AttributeError:
            # For distributed pre-1.18.1
            distributed_Client = distributed.Executor

        client = distributed_Client('{address}'.format(**distributed_info))

        return client

    def stop_dask(self, targets='all'):
        """Stop the distributed Scheduler and Workers started by become_dask.

        Parameters
        ----------
        targets : target spec (default: all)
            Which engines to stop dask workers on.
        """
        dview = self.direct_view(targets)

        # Start a Scheduler on the Hub:
        reply = self._send_recv(self._query_stream, 'stop_distributed_request')
        if reply['content']['status'] != 'ok':
            raise self._unwrap_exception(reply['content'])

        # Finally, stop all the Workers on the engines
        dview.apply_sync(util.stop_distributed_worker)

    # aliases:
    become_distributed = become_dask
    stop_distributed = stop_dask

    # --------------------------------------------------------------------------
    # Execution related methods
    # --------------------------------------------------------------------------

    def _maybe_raise(self, result):
        """wrapper for maybe raising an exception if apply failed."""
        if isinstance(result, error.RemoteError):
            raise result

        return result

    def send_apply_request(
        self,
        socket,
        f,
        args=None,
        kwargs=None,
        metadata=None,
        track=False,
        ident=None,
        message_future_hook=None,
    ):
        """construct and send an apply message via a socket.

        This is the principal method with which all engine execution is performed by views.
        """

        if self._closed:
            raise RuntimeError(
                "Client cannot be used after its sockets have been closed"
            )

        # defaults:
        args = args if args is not None else []
        kwargs = kwargs if kwargs is not None else {}
        metadata = metadata if metadata is not None else {}

        # validate arguments
        if not callable(f) and not isinstance(f, (Reference, PrePickled)):
            raise TypeError("f must be callable, not %s" % type(f))
        if not isinstance(args, (tuple, list)):
            raise TypeError("args must be tuple or list, not %s" % type(args))
        if not isinstance(kwargs, dict):
            raise TypeError("kwargs must be dict, not %s" % type(kwargs))
        if not isinstance(metadata, dict):
            raise TypeError("metadata must be dict, not %s" % type(metadata))

        bufs = serialize.pack_apply_message(
            f,
            args,
            kwargs,
            buffer_threshold=self.session.buffer_threshold,
            item_threshold=self.session.item_threshold,
        )

        future = self._send(
            socket,
            "apply_request",
            buffers=bufs,
            ident=ident,
            metadata=metadata,
            track=track,
            track_outstanding=True,
            message_future_hook=message_future_hook,
        )
        msg_id = future.msg_id

        return future

    def send_execute_request(
        self,
        socket,
        code,
        silent=True,
        metadata=None,
        ident=None,
        message_future_hook=None,
    ):
        """construct and send an execute request via a socket."""

        if self._closed:
            raise RuntimeError(
                "Client cannot be used after its sockets have been closed"
            )

        # defaults:
        metadata = metadata if metadata is not None else {}

        # validate arguments
        if not isinstance(code, str):
            raise TypeError("code must be text, not %s" % type(code))
        if not isinstance(metadata, dict):
            raise TypeError("metadata must be dict, not %s" % type(metadata))

        content = dict(code=code, silent=bool(silent), user_expressions={})

        future = self._send(
            socket,
            "execute_request",
            content=content,
            ident=ident,
            metadata=metadata,
            track_outstanding=True,
            message_future_hook=message_future_hook,
        )

        return future

    # --------------------------------------------------------------------------
    # construct a View object
    # --------------------------------------------------------------------------

    def load_balanced_view(self, targets=None, **kwargs):
        """construct a DirectView object.

        If no arguments are specified, create a LoadBalancedView
        using all engines.

        Parameters
        ----------
        targets : list,slice,int,etc. [default: use all engines]
            The subset of engines across which to load-balance execution
        **kwargs : passed to LoadBalancedView
        """
        if targets == 'all':
            targets = None
        if targets is not None:
            targets = self._build_targets(targets)[1]
        return LoadBalancedView(
            client=self, socket=self._task_stream, targets=targets, **kwargs
        )

    def executor(self, targets=None):
        """Construct a PEP-3148 Executor with a LoadBalancedView

        Parameters
        ----------
        targets : list,slice,int,etc. [default: use all engines]
            The subset of engines across which to load-balance execution

        Returns
        -------
        executor: Executor
            The Executor object
        """
        return self.load_balanced_view(targets).executor

    def direct_view(self, targets='all', **kwargs):
        """construct a DirectView object.

        If no targets are specified, create a DirectView using all engines.

        rc.direct_view('all') is distinguished from rc[:] in that 'all' will
        evaluate the target engines at each execution, whereas rc[:] will connect to
        all *current* engines, and that list will not change.

        That is, 'all' will always use all engines, whereas rc[:] will not use
        engines added after the DirectView is constructed.

        Parameters
        ----------
        targets : list,slice,int,etc. [default: use all engines]
            The engines to use for the View
        **kwargs : passed to DirectView
        """
        single = isinstance(targets, int)
        # allow 'all' to be lazily evaluated at each execution
        if targets != 'all':
            targets = self._build_targets(targets)[1]
        if single:
            targets = targets[0]
        return DirectView(
            client=self, socket=self._mux_stream, targets=targets, **kwargs
        )

    def broadcast_view(self, targets='all', is_coalescing=False, **kwargs):
        """construct a BroadCastView object.
        If no arguments are specified, create a BroadCastView using all engines
        using all engines.

        Parameters
        ----------
        targets : list,slice,int,etc. [default: use all engines]
            The subset of engines across which to load-balance execution
        is_coalescing : scheduler collects all messages from engines and returns them as one
        **kwargs : passed to BroadCastView
        """
        targets = self._build_targets(targets)[1]

        bcast_view = BroadcastView(
            client=self,
            socket=self._broadcast_stream,
            targets=targets,
            **kwargs,
        )
        bcast_view.is_coalescing = is_coalescing
        return bcast_view

    # --------------------------------------------------------------------------
    # Query methods
    # --------------------------------------------------------------------------

    def get_result(self, indices_or_msg_ids=None, block=None, owner=True):
        """Retrieve a result by msg_id or history index, wrapped in an AsyncResult object.

        If the client already has the results, no request to the Hub will be made.

        This is a convenient way to construct AsyncResult objects, which are wrappers
        that include metadata about execution, and allow for awaiting results that
        were not submitted by this Client.

        It can also be a convenient way to retrieve the metadata associated with
        blocking execution, since it always retrieves

        Examples
        --------
        ::

            In [10]: r = client.apply()

        Parameters
        ----------
        indices_or_msg_ids : integer history index, str msg_id, AsyncResult,
            or a list of same.
            The indices or msg_ids of indices to be retrieved
        block : bool
            Whether to wait for the result to be done
        owner : bool [default: True]
            Whether this AsyncResult should own the result.
            If so, calling `ar.get()` will remove data from the
            client's result and metadata cache.
            There should only be one owner of any given msg_id.

        Returns
        -------
        AsyncResult
            A single AsyncResult object will always be returned.
        AsyncHubResult
            A subclass of AsyncResult that retrieves results from the Hub

        """
        block = self.block if block is None else block
        if indices_or_msg_ids is None:
            indices_or_msg_ids = -1

        ar = self._asyncresult_from_jobs(indices_or_msg_ids, owner=owner)

        if block:
            ar.wait()

        return ar

    def resubmit(self, indices_or_msg_ids=None, metadata=None, block=None):
        """Resubmit one or more tasks.

        in-flight tasks may not be resubmitted.

        Parameters
        ----------
        indices_or_msg_ids : integer history index, str msg_id, or list of either
            The indices or msg_ids of indices to be retrieved
        block : bool
            Whether to wait for the result to be done

        Returns
        -------
        AsyncHubResult
            A subclass of AsyncResult that retrieves results from the Hub

        """
        block = self.block if block is None else block
        if indices_or_msg_ids is None:
            indices_or_msg_ids = -1

        theids = self._msg_ids_from_jobs(indices_or_msg_ids)
        content = dict(msg_ids=theids)

        reply = self._send_recv(self._query_stream, 'resubmit_request', content)
        content = reply['content']
        if content['status'] != 'ok':
            raise self._unwrap_exception(content)
        mapping = content['resubmitted']
        new_ids = [mapping[msg_id] for msg_id in theids]

        ar = AsyncHubResult(self, new_ids)

        if block:
            ar.wait()

        return ar

    def result_status(self, msg_ids, status_only=True):
        """Check on the status of the result(s) of the apply request with `msg_ids`.

        If status_only is False, then the actual results will be retrieved, else
        only the status of the results will be checked.

        Parameters
        ----------
        msg_ids : list of msg_ids
            if int:
                Passed as index to self.history for convenience.
        status_only : bool (default: True)
            if False:
                Retrieve the actual results of completed tasks.

        Returns
        -------
        results : dict
            There will always be the keys 'pending' and 'completed', which will
            be lists of msg_ids that are incomplete or complete. If `status_only`
            is False, then completed results will be keyed by their `msg_id`.
        """
        theids = self._msg_ids_from_jobs(msg_ids)

        completed = []
        local_results = {}

        # comment this block out to temporarily disable local shortcut:
        for msg_id in theids:
            if msg_id in self.results:
                completed.append(msg_id)
                local_results[msg_id] = self.results[msg_id]
                theids.remove(msg_id)

        if theids:  # some not locally cached
            content = dict(msg_ids=theids, status_only=status_only)
            reply = self._send_recv(
                self._query_stream, "result_request", content=content
            )
            content = reply['content']
            if content['status'] != 'ok':
                raise self._unwrap_exception(content)
            buffers = reply['buffers']
        else:
            content = dict(completed=[], pending=[])

        content['completed'].extend(completed)

        if status_only:
            return content

        failures = []
        # load cached results into result:
        content.update(local_results)

        # update cache with results:
        for msg_id in sorted(theids):
            if msg_id in content['completed']:
                rec = content[msg_id]
                parent = util.extract_dates(rec['header'])
                header = util.extract_dates(rec['result_header'])
                rcontent = rec['result_content']
                iodict = rec['io']
                if isinstance(rcontent, str):
                    rcontent = self.session.unpack(rcontent)

                md = self.metadata[msg_id]
                md_msg = dict(
                    content=rcontent,
                    parent_header=parent,
                    header=header,
                    metadata=rec['result_metadata'],
                )
                md.update(self._extract_metadata(md_msg))
                if rec.get('received'):
                    md['received'] = util._parse_date(rec['received'])
                md.update(iodict)

                if rcontent['status'] == 'ok':
                    if header['msg_type'] == 'apply_reply':
                        res, buffers = serialize.deserialize_object(buffers)
                    elif header['msg_type'] == 'execute_reply':
                        res = ExecuteReply(msg_id, rcontent, md)
                    else:
                        raise KeyError("unhandled msg type: %r" % header['msg_type'])
                else:
                    res = self._unwrap_exception(rcontent)
                    failures.append(res)

                self.results[msg_id] = res
                content[msg_id] = res

        if len(theids) == 1 and failures:
            raise failures[0]

        error.collect_exceptions(failures, "result_status")
        return content

    def queue_status(self, targets='all', verbose=False):
        """Fetch the status of engine queues.

        Parameters
        ----------
        targets : int/str/list of ints/strs
            the engines whose states are to be queried.
            default : all
        verbose : bool
            Whether to return lengths only, or lists of ids for each element
        """
        if targets == 'all':
            # allow 'all' to be evaluated on the engine
            engine_ids = None
        else:
            engine_ids = self._build_targets(targets)[1]
        content = dict(targets=engine_ids, verbose=verbose)
        reply = self._send_recv(self._query_stream, "queue_request", content=content)
        content = reply['content']
        status = content.pop('status')
        if status != 'ok':
            raise self._unwrap_exception(content)
        content = util.int_keys(content)
        if isinstance(targets, int):
            return content[targets]
        else:
            return content

    def _msg_ids_from_target(self, targets=None):
        """Build a list of msg_ids from the list of engine targets"""
        if not targets:  # needed as _build_targets otherwise uses all engines
            return []
        target_ids = self._build_targets(targets)[0]
        return [
            md_id
            for md_id in self.metadata
            if self.metadata[md_id]["engine_uuid"] in target_ids
        ]

    def _msg_ids_from_jobs(self, jobs=None):
        """Given a 'jobs' argument, convert it to a list of msg_ids.

        Can be either one or a list of:

        - msg_id strings
        - integer indices to this Client's history
        - AsyncResult objects
        """
        if not isinstance(jobs, (list, tuple, set, types.GeneratorType)):
            jobs = [jobs]
        msg_ids = []
        for job in jobs:
            if isinstance(job, int):
                msg_ids.append(self.history[job])
            elif isinstance(job, str):
                msg_ids.append(job)
            elif isinstance(job, AsyncResult):
                msg_ids.extend(job.msg_ids)
            else:
                raise TypeError("Expected msg_id, int, or AsyncResult, got %r" % job)
        return msg_ids

    def _asyncresult_from_jobs(self, jobs=None, owner=False):
        """Construct an AsyncResult from msg_ids or asyncresult objects"""
        if not isinstance(jobs, (list, tuple, set, types.GeneratorType)):
            single = True
            jobs = [jobs]
        else:
            single = False
        futures = []
        msg_ids = []
        for job in jobs:
            if isinstance(job, int):
                job = self.history[job]
            if isinstance(job, str):
                if job in self._futures:
                    futures.append(job)
                elif job in self.results:
                    f = MessageFuture(job)
                    f.set_result(self.results[job])
                    f.output = Future()
                    f.output.metadata = self.metadata[job]
                    f.output.set_result(None)
                    futures.append(f)
                else:
                    msg_ids.append(job)
            elif isinstance(job, AsyncResult):
                if job._children:
                    futures.extend(job._children)
                else:
                    msg_ids.extend(job.msg_ids)
            else:
                raise TypeError("Expected msg_id, int, or AsyncResult, got %r" % job)
        if msg_ids:
            if single:
                msg_ids = msg_ids[0]
            return AsyncHubResult(self, msg_ids, owner=owner)
        else:
            if single and futures:
                futures = futures[0]
            return AsyncResult(self, futures, owner=owner)

    def purge_local_results(self, jobs=[], targets=[]):
        """Clears the client caches of results and their metadata.

        Individual results can be purged by msg_id, or the entire
        history of specific targets can be purged.

        Use `purge_local_results('all')` to scrub everything from the Clients's
        results and metadata caches.

        After this call all `AsyncResults` are invalid and should be discarded.

        If you must "reget" the results, you can still do so by using
        `client.get_result(msg_id)` or `client.get_result(asyncresult)`. This will
        redownload the results from the hub if they are still available
        (i.e `client.purge_hub_results(...)` has not been called.

        Parameters
        ----------
        jobs : str or list of str or AsyncResult objects
            the msg_ids whose results should be purged.
        targets : int/list of ints
            The engines, by integer ID, whose entire result histories are to be purged.

        Raises
        ------
        RuntimeError : if any of the tasks to be purged are still outstanding.

        """
        if not targets and not jobs:
            raise ValueError("Must specify at least one of `targets` and `jobs`")

        if jobs == 'all':
            if self.outstanding:
                raise RuntimeError(
                    "Can't purge outstanding tasks: %s" % self.outstanding
                )
            self.results.clear()
            self.metadata.clear()
            self._futures.clear()
            self._output_futures.clear()
        else:
            msg_ids = set()
            msg_ids.update(self._msg_ids_from_target(targets))
            msg_ids.update(self._msg_ids_from_jobs(jobs))
            still_outstanding = self.outstanding.intersection(msg_ids)
            if still_outstanding:
                raise RuntimeError(
                    "Can't purge outstanding tasks: %s" % still_outstanding
                )
            for mid in msg_ids:
                self.results.pop(mid, None)
                self.metadata.pop(mid, None)
                self._futures.pop(mid, None)
                self._output_futures.pop(mid, None)

    def purge_hub_results(self, jobs=[], targets=[]):
        """Tell the Hub to forget results.

        Individual results can be purged by msg_id, or the entire
        history of specific targets can be purged.

        Use `purge_results('all')` to scrub everything from the Hub's db.

        Parameters
        ----------
        jobs : str or list of str or AsyncResult objects
            the msg_ids whose results should be forgotten.
        targets : int/str/list of ints/strs
            The targets, by int_id, whose entire history is to be purged.

            default : None
        """
        if not targets and not jobs:
            raise ValueError("Must specify at least one of `targets` and `jobs`")
        if targets:
            targets = self._build_targets(targets)[1]

        # construct msg_ids from jobs
        if jobs == 'all':
            msg_ids = jobs
        else:
            msg_ids = self._msg_ids_from_jobs(jobs)

        content = dict(engine_ids=targets, msg_ids=msg_ids)
        reply = self._send_recv(self._query_stream, "purge_request", content=content)
        content = reply['content']
        if content['status'] != 'ok':
            raise self._unwrap_exception(content)

    def purge_results(self, jobs=[], targets=[]):
        """Clears the cached results from both the hub and the local client

        Individual results can be purged by msg_id, or the entire
        history of specific targets can be purged.

        Use `purge_results('all')` to scrub every cached result from both the Hub's and
        the Client's db.

        Equivalent to calling both `purge_hub_results()` and `purge_client_results()` with
        the same arguments.

        Parameters
        ----------
        jobs : str or list of str or AsyncResult objects
            the msg_ids whose results should be forgotten.
        targets : int/str/list of ints/strs
            The targets, by int_id, whose entire history is to be purged.

            default : None
        """
        self.purge_local_results(jobs=jobs, targets=targets)
        self.purge_hub_results(jobs=jobs, targets=targets)

    def purge_everything(self):
        """Clears all content from previous Tasks from both the hub and the local client

        In addition to calling `purge_results("all")` it also deletes the history and
        other bookkeeping lists.
        """
        self.purge_results("all")
        self.history = []
        self.session.digest_history.clear()

    def hub_history(self):
        """Get the Hub's history

        Just like the Client, the Hub has a history, which is a list of msg_ids.
        This will contain the history of all clients, and, depending on configuration,
        may contain history across multiple cluster sessions.

        Any msg_id returned here is a valid argument to `get_result`.

        Returns
        -------
        msg_ids : list of strs
            list of all msg_ids, ordered by task submission time.
        """

        reply = self._send_recv(self._query_stream, "history_request", content={})
        content = reply['content']
        if content['status'] != 'ok':
            raise self._unwrap_exception(content)
        else:
            return content['history']

    def db_query(self, query, keys=None):
        """Query the Hub's TaskRecord database

        This will return a list of task record dicts that match `query`

        Parameters
        ----------
        query : mongodb query dict
            The search dict. See mongodb query docs for details.
        keys : list of strs [optional]
            The subset of keys to be returned.  The default is to fetch everything but buffers.
            'msg_id' will *always* be included.
        """
        if isinstance(keys, str):
            keys = [keys]
        content = dict(query=query, keys=keys)
        reply = self._send_recv(self._query_stream, "db_request", content=content)
        content = reply['content']
        if content['status'] != 'ok':
            raise self._unwrap_exception(content)

        records = content['records']

        buffer_lens = content['buffer_lens']
        result_buffer_lens = content['result_buffer_lens']
        buffers = reply['buffers']
        has_bufs = buffer_lens is not None
        has_rbufs = result_buffer_lens is not None
        for i, rec in enumerate(records):
            # unpack datetime objects
            for hkey in ('header', 'result_header'):
                if hkey in rec:
                    rec[hkey] = util.extract_dates(rec[hkey])
            for dtkey in ('submitted', 'started', 'completed', 'received'):
                if dtkey in rec:
                    rec[dtkey] = util._parse_date(rec[dtkey])
            # relink buffers
            if has_bufs:
                blen = buffer_lens[i]
                rec['buffers'], buffers = buffers[:blen], buffers[blen:]
            if has_rbufs:
                blen = result_buffer_lens[i]
                rec['result_buffers'], buffers = buffers[:blen], buffers[blen:]

        return records


__all__ = ['Client']
ipyparallel-8.8.0/ipyparallel/client/futures.py000066400000000000000000000066301460376056100217250ustar00rootroot00000000000000"""Future-related utils"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import sys
from concurrent.futures import Future
from threading import Event

from tornado.log import app_log


class MessageFuture(Future):
    """Future class to wrap async messages"""

    def __init__(self, msg_id, header=None, *, track=False):
        super().__init__()
        self.msg_id = msg_id
        self.header = header or {"msg_type": "unknown_request"}
        self._evt = Event()
        self.track = track
        self._tracker = None
        self.tracker = Future()
        self.iopub_callbacks = []
        if not track:
            self.tracker.set_result(None)
        self.add_done_callback(lambda f: self._evt.set())

    def wait(self, timeout=None):
        if not self.done():
            return self._evt.wait(timeout)
        return True


# The following are from tornado 5.0b1
# avoids hang using gen.multi_future on asyncio,
# because Futures cannot be created in another thread


def future_set_result_unless_cancelled(future, value):
    """Set the given ``value`` as the `Future`'s result, if not cancelled.

    Avoids asyncio.InvalidStateError when calling set_result() on
    a cancelled `asyncio.Future`.

    .. versionadded:: 5.0
    """
    if not future.cancelled():
        future.set_result(value)


def future_set_exc_info(future, exc_info):
    """Set the given ``exc_info`` as the `Future`'s exception.

    Understands both `asyncio.Future` and Tornado's extensions to
    enable better tracebacks on Python 2.

    .. versionadded:: 5.0
    """
    if hasattr(future, 'set_exc_info'):
        # Tornado's Future
        future.set_exc_info(exc_info)
    else:
        # asyncio.Future
        future.set_exception(exc_info[1])


def future_add_done_callback(future, callback):
    """Arrange to call ``callback`` when ``future`` is complete.

    ``callback`` is invoked with one argument, the ``future``.

    If ``future`` is already done, ``callback`` is invoked immediately.
    This may differ from the behavior of ``Future.add_done_callback``,
    which makes no such guarantee.

    .. versionadded:: 5.0
    """
    if future.done():
        callback(future)
    else:
        future.add_done_callback(callback)


def multi_future(children):
    """Wait for multiple asynchronous futures in parallel.

    This function is similar to `multi`, but does not support
    `YieldPoints `.

    .. versionadded:: 4.0
    """
    unfinished_children = set(children)

    future = Future()
    if not children:
        future_set_result_unless_cancelled(future, [])

    def callback(f):
        unfinished_children.remove(f)
        if not unfinished_children:
            result_list = []
            for f in children:
                try:
                    result_list.append(f.result())
                except Exception as e:
                    if future.done():
                        app_log.error(
                            "Multiple exceptions in yield list", exc_info=True
                        )
                    else:
                        future_set_exc_info(future, sys.exc_info())
            if not future.done():
                future_set_result_unless_cancelled(future, result_list)

    listening = set()
    for f in children:
        if f not in listening:
            listening.add(f)
            future_add_done_callback(f, callback)
    return future
ipyparallel-8.8.0/ipyparallel/client/magics.py000066400000000000000000000435331460376056100214760ustar00rootroot00000000000000"""
=============
parallelmagic
=============

Magic command interface for interactive parallel work.

Usage
=====

``%autopx``

{AUTOPX_DOC}

``%px``

{PX_DOC}

``%pxresult``

{RESULT_DOC}

``%pxconfig``

{CONFIG_DOC}

"""

import inspect
import re
import sys
import time
from contextlib import nullcontext
from textwrap import dedent

from IPython.core import magic_arguments
from IPython.core.error import UsageError
from IPython.core.magic import Magics, no_var_expand

import ipyparallel as ipp

from .. import error

# -----------------------------------------------------------------------------
# Definitions of magic functions for use with IPython
# -----------------------------------------------------------------------------


def _iscoroutinefunction(f):
    """Check if a callable is a coroutine function
    (either generator-style or async def)
    """
    if inspect.isgeneratorfunction(f):
        return True
    if hasattr(inspect, 'iscoroutinefunction') and inspect.iscoroutinefunction(f):
        return True
    return False


def _asyncify(f):
    """Wrap a blocking call in a coroutine

    Does not make the call non-blocking,
    just conforms to the API assuming it is awaitable.
    For use when patching-in replacement methods.
    """

    async def async_f(*args, **kwargs):
        return f(*args, **kwargs)

    return async_f


NO_LAST_RESULT = "%pxresult recalls last %px result, which has not yet been used."


def exec_args(f):
    """decorator for adding block/targets args for execution

    applied to %pxconfig and %%px
    """
    args = [
        magic_arguments.argument(
            '-b',
            '--block',
            action="store_const",
            const=True,
            dest='block',
            help="use blocking (sync) execution",
        ),
        magic_arguments.argument(
            '-a',
            '--noblock',
            action="store_const",
            const=False,
            dest='block',
            help="use non-blocking (async) execution",
        ),
        magic_arguments.argument(
            '--stream',
            action="store_const",
            const=True,
            dest='stream',
            help="stream stdout/stderr in real-time (only valid when using blocking execution)",
        ),
        magic_arguments.argument(
            '--no-stream',
            action="store_const",
            const=False,
            dest='stream',
            help="do not stream stdout/stderr in real-time",
        ),
        magic_arguments.argument(
            '-t',
            '--targets',
            type=str,
            help="specify the targets on which to execute",
        ),
        magic_arguments.argument(
            '--local',
            action="store_const",
            const=True,
            dest="local",
            help="also execute the cell in the local namespace",
        ),
        magic_arguments.argument(
            '--verbose',
            action="store_const",
            const=True,
            dest="set_verbose",
            help="print a message at each execution",
        ),
        magic_arguments.argument(
            '--no-verbose',
            action="store_const",
            const=False,
            dest="set_verbose",
            help="don't print any messages",
        ),
        magic_arguments.argument(
            '--progress-after',
            dest="progress_after_seconds",
            type=float,
            default=None,
            help="""Wait this many seconds before showing a progress bar for task completion.

            Use -1 for no progress, 0 for always showing progress immediately.
            """,
        ),
        magic_arguments.argument(
            '--signal-on-interrupt',
            dest='signal_on_interrupt',
            type=str,
            default=None,
            help="""Send signal to engines on Keyboard Interrupt. By default a SIGINT is sent.
            Note that this is only applicable when running in blocking mode.
            Choices: SIGINT, 2, SIGKILL, 9, 0 (nop), etc.
            """,
        ),
    ]
    for a in args:
        f = a(f)
    return f


def output_args(f):
    """decorator for output-formatting args

    applied to %pxresult and %%px
    """
    args = [
        magic_arguments.argument(
            '-r',
            action="store_const",
            dest='groupby',
            const='order',
            help="collate outputs in order (same as group-outputs=order)",
        ),
        magic_arguments.argument(
            '-e',
            action="store_const",
            dest='groupby',
            const='engine',
            help="group outputs by engine (same as group-outputs=engine)",
        ),
        magic_arguments.argument(
            '--group-outputs',
            dest='groupby',
            type=str,
            choices=['engine', 'order', 'type'],
            default='type',
            help="""Group the outputs in a particular way.

            Choices are:

            **type**: group outputs of all engines by type (stdout, stderr, displaypub, etc.).
            **engine**: display all output for each engine together.
            **order**: like type, but individual displaypub output from each engine is collated.
              For example, if multiple plots are generated by each engine, the first
              figure of each engine will be displayed, then the second of each, etc.
            """,
        ),
        magic_arguments.argument(
            '-o',
            '--out',
            dest='save_name',
            type=str,
            help="""store the AsyncResult object for this computation
                 in the global namespace under this name.
            """,
        ),
    ]
    for a in args:
        f = a(f)
    return f


class ParallelMagics(Magics):
    """A set of magics useful when controlling a parallel IPython cluster."""

    # magic-related
    magics = None
    registered = True

    # suffix for magics
    suffix = ''
    # A flag showing if autopx is activated or not
    _autopx = False
    # the current view used by the magics:
    view = None
    # last result cache for %pxresult
    last_result = None
    # verbose flag
    verbose = False
    # streaming output flag
    stream_output = not ipp._NONINTERACTIVE
    # seconds to wait before showing progress bar for blocking execution
    progress_after_seconds = 2
    # signal to send to engines on keyboard-interrupt
    signal_on_interrupt = "SIGINT"

    def __init__(self, shell, view, suffix=''):
        self.view = view
        self.suffix = suffix

        # register magics
        self.magics = dict(cell={}, line={})
        line_magics = self.magics['line']

        px = 'px' + suffix
        if not suffix:
            # keep %result for legacy compatibility
            line_magics['result'] = self.result

        line_magics['pxresult' + suffix] = self.result
        line_magics[px] = self.px
        line_magics['pxconfig' + suffix] = self.pxconfig
        line_magics['auto' + px] = self.autopx

        self.magics['cell'][px] = self.cell_px

        super().__init__(shell=shell)

    def _eval_target_str(self, ts):
        if ':' in ts:
            targets = eval("self.view.client.ids[%s]" % ts)
        elif 'all' in ts:
            targets = 'all'
        else:
            targets = eval(ts)
        return targets

    def _eval_signal_str(self, sig_str: str):
        if sig_str.isdigit():
            return int(sig_str)
        return sig_str

    @magic_arguments.magic_arguments()
    @exec_args
    def pxconfig(self, line):
        """configure default targets/blocking for %px magics"""
        args = magic_arguments.parse_argstring(self.pxconfig, line)
        if args.targets:
            self.view.targets = self._eval_target_str(args.targets)
        if args.block is not None:
            self.view.block = args.block
        if args.set_verbose is not None:
            self.verbose = args.set_verbose
        if args.stream is not None:
            self.stream_output = args.stream
        if args.signal_on_interrupt is not None:
            self.signal_on_interrupt = self._eval_signal_str(args.signal_on_interrupt)

        if args.progress_after_seconds is not None:
            self.progress_after_seconds = args.progress_after_seconds

    @magic_arguments.magic_arguments()
    @output_args
    def result(self, line=''):
        """Print the result of the last asynchronous %px command.

        This lets you recall the results of %px computations after
        asynchronous submission (block=False).

        Examples
        --------
        ::

            In [23]: %px os.getpid()
            Async parallel execution on engine(s): all

            In [24]: %pxresult
            Out[8:10]: 60920
            Out[9:10]: 60921
            Out[10:10]: 60922
            Out[11:10]: 60923
        """
        args = magic_arguments.parse_argstring(self.result, line)

        if self.last_result is None:
            raise UsageError(NO_LAST_RESULT)

        if args.save_name:
            self.shell.user_ns[args.save_name] = self.last_result
            return

        self.last_result.get()
        self.last_result.display_outputs(groupby=args.groupby)

    @no_var_expand
    def px(self, line=''):
        """Executes the given python command in parallel.

        Examples
        --------
        ::

            In [24]: %px a = os.getpid()
            Parallel execution on engine(s): all

            In [25]: %px print a
            [stdout:0] 1234
            [stdout:1] 1235
            [stdout:2] 1236
            [stdout:3] 1237
        """
        return self.parallel_execute(line)

    def parallel_execute(
        self,
        cell,
        block=None,
        groupby='type',
        save_name=None,
        stream_output=None,
        progress_after=None,
        signal_on_interrupt=None,
    ):
        """implementation used by %px and %%parallel"""

        # defaults:
        block = self.view.block if block is None else block
        stream_output = self.stream_output if stream_output is None else stream_output
        signal_on_interrupt = (
            self.signal_on_interrupt
            if signal_on_interrupt is None
            else signal_on_interrupt
        )

        base = "Parallel" if block else "Async parallel"

        targets = self.view.targets
        if isinstance(targets, list) and len(targets) > 10:
            str_targets = str(targets[:4])[:-1] + ', ..., ' + str(targets[-4:])[1:]
        else:
            str_targets = str(targets)
        if self.verbose:
            print(base + " execution on engine(s): %s" % str_targets)

        result = self.view.execute(cell, silent=False, block=False)
        result._fname = "%px"
        self.last_result = result

        if save_name:
            self.shell.user_ns[save_name] = result

        if block:
            try:
                if progress_after is None:
                    progress_after = self.progress_after_seconds

                cm = result.stream_output() if stream_output else nullcontext()
                with cm:
                    finished_waiting = False
                    if progress_after > 0:
                        # finite progress-after timeout
                        # wait for 'quick' results before showing progress
                        tic = time.perf_counter()
                        deadline = tic + progress_after
                        result.wait(timeout=progress_after)
                        remaining = max(deadline - time.perf_counter(), 0)
                        result.wait_for_output(timeout=remaining)
                        finished_waiting = result.done()

                    if not finished_waiting:
                        if progress_after >= 0:
                            # not an immediate result, start interactive progress
                            result.wait_interactive()
                            result.wait_for_output(1)

                    try:
                        result.get()
                    except error.CompositeError as e:
                        if stream_output and result._output_ready:
                            # already streamed, show an abbreviated result
                            raise error.AlreadyDisplayedError(e) from None
                        else:
                            raise
                # Skip redisplay if streaming output
            except KeyboardInterrupt:
                if signal_on_interrupt is not None:
                    print(
                        f"Received Keyboard Interrupt. Sending signal {signal_on_interrupt} to engines...",
                        file=sys.stderr,
                    )
                    self.view.client.send_signal(
                        signal_on_interrupt, targets=targets, block=True
                    )
                else:
                    raise
            finally:
                # always redisplay outputs if not streaming,
                # on both success and error

                if not stream_output:
                    # wait for at most 1 second for output to be complete
                    result.wait_for_output(1)
                    result.display_outputs(groupby)
        else:
            # return AsyncResult only on non-blocking submission
            return result

    @magic_arguments.magic_arguments()
    @exec_args
    @output_args
    def cell_px(self, line='', cell=None):
        """Executes the cell in parallel.

        Examples
        --------
        ::

            In [24]: %%px --noblock
               ....: a = os.getpid()
            Async parallel execution on engine(s): all

            In [25]: %%px
               ....: print a
            [stdout:0] 1234
            [stdout:1] 1235
            [stdout:2] 1236
            [stdout:3] 1237
        """

        args = magic_arguments.parse_argstring(self.cell_px, line)

        if args.targets:
            save_targets = self.view.targets
            self.view.targets = self._eval_target_str(args.targets)
        signal_on_interrupt = None
        if args.signal_on_interrupt:
            signal_on_interrupt = self._eval_signal_str(args.signal_on_interrupt)
        # if running local, don't block until after local has run
        block = False if args.local else args.block
        try:
            ar = self.parallel_execute(
                cell,
                block=block,
                groupby=args.groupby,
                save_name=args.save_name,
                stream_output=args.stream,
                progress_after=args.progress_after_seconds,
                signal_on_interrupt=signal_on_interrupt,
            )
        finally:
            if args.targets:
                self.view.targets = save_targets

        # run locally after submitting remote
        block = self.view.block if args.block is None else args.block
        if args.local:
            self.shell.run_cell(cell)
            # now apply blocking behavor to remote execution
            if block:
                ar.get()
                ar.display_outputs(args.groupby)
        if not block:
            return ar

    def autopx(self, line=''):
        """Toggles auto parallel mode.

        Once this is called, all commands typed at the command line are send to
        the engines to be executed in parallel. To control which engine are
        used, the ``targets`` attribute of the view before
        entering ``%autopx`` mode.

        Then you can do the following::

            In [25]: %autopx
            %autopx to enabled

            In [26]: a = 10
            Parallel execution on engine(s): [0,1,2,3]
            In [27]: print a
            Parallel execution on engine(s): [0,1,2,3]
            [stdout:0] 10
            [stdout:1] 10
            [stdout:2] 10
            [stdout:3] 10

            In [27]: %autopx
            %autopx disabled
        """
        if self._autopx:
            self._disable_autopx()
        else:
            self._enable_autopx()

    def _enable_autopx(self):
        """Enable %autopx mode by saving the original run_cell and installing
        pxrun_cell.
        """
        self._original_run_cell = self.shell.run_cell
        self._original_run_nodes = self.shell.run_ast_nodes

        pxrun_cell = self.pxrun_cell
        if _iscoroutinefunction(self.shell.run_cell):
            # original is a coroutine,
            # wrap ours in a coroutine
            pxrun_cell = _asyncify(pxrun_cell)
        self.shell.run_cell = pxrun_cell

        pxrun_nodes = self.pxrun_nodes
        if _iscoroutinefunction(self.shell.run_ast_nodes):
            # original is a coroutine,
            # wrap ours in a coroutine
            pxrun_nodes = _asyncify(pxrun_nodes)
        self.shell.run_ast_nodes = pxrun_nodes

        self._autopx = True
        print("%autopx enabled")

    def _disable_autopx(self):
        """Disable %autopx by restoring the original InteractiveShell.run_cell."""
        if self._autopx:
            self.shell.run_cell = self._original_run_cell
            self.shell.run_ast_nodes = self._original_run_nodes
            self._autopx = False
            print("%autopx disabled")

    def pxrun_nodes(self, *args, **kwargs):
        cell = self._px_cell
        if re.search(r'^\s*%autopx\b', cell):
            self._disable_autopx()
            return False
        else:
            try:
                self.parallel_execute(cell)
            except Exception:
                self.shell.showtraceback()
                return True
            else:
                return False

    def pxrun_cell(self, raw_cell, *args, **kwargs):
        """drop-in replacement for InteractiveShell.run_cell.

        This executes code remotely, instead of in the local namespace.

        See InteractiveShell.run_cell for details.
        """
        self._px_cell = raw_cell
        return self._original_run_cell(raw_cell, *args, **kwargs)


__doc__ = __doc__.format(
    AUTOPX_DOC=dedent(ParallelMagics.autopx.__doc__),
    PX_DOC=dedent(ParallelMagics.px.__doc__),
    RESULT_DOC=dedent(ParallelMagics.result.__doc__),
    CONFIG_DOC=dedent(ParallelMagics.pxconfig.__doc__),
)
ipyparallel-8.8.0/ipyparallel/client/map.py000066400000000000000000000067361460376056100210140ustar00rootroot00000000000000"""Classes used in scattering and gathering sequences.

Scattering consists of partitioning a sequence and sending the various
pieces to individual nodes in a cluster.
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import sys
from itertools import chain, islice

numpy = None


def is_array(obj):
    """Is an object a numpy array?

    Avoids importing numpy until it is requested
    """
    global numpy
    if 'numpy' not in sys.modules:
        return False

    if numpy is None:
        import numpy
    return isinstance(obj, numpy.ndarray)


class Map:
    """A class for partitioning a sequence using a map."""

    def getPartition(self, seq, p, q, n=None):
        """Returns the pth partition of q partitions of seq.

        The length can be specified as `n`,
        otherwise it is the value of `len(seq)`
        """
        n = len(seq) if n is None else n
        # Test for error conditions here
        if p < 0 or p >= q:
            raise ValueError(f"must have 0 <= p <= q, but have p={p},q={q}")

        remainder = n % q
        basesize = n // q

        if p < remainder:
            low = p * (basesize + 1)
            high = low + basesize + 1
        else:
            low = p * basesize + remainder
            high = low + basesize

        try:
            result = seq[low:high]
        except TypeError:
            # some objects (iterators) can't be sliced,
            # use islice:
            result = list(islice(seq, low, high))

        return result

    def joinPartitions(self, listOfPartitions):
        return self.concatenate(listOfPartitions)

    def concatenate(self, listOfPartitions):
        testObject = listOfPartitions[0]
        # First see if we have a known array type
        if is_array(testObject):
            return numpy.concatenate(listOfPartitions)
        # Next try for Python sequence types
        if isinstance(testObject, (list, tuple)):
            return list(chain.from_iterable(listOfPartitions))
        # If we have scalars, just return listOfPartitions
        return listOfPartitions


class RoundRobinMap(Map):
    """Partitions a sequence in a round robin fashion.

    This currently does not work!
    """

    def getPartition(self, seq, p, q, n=None):
        n = len(seq) if n is None else n
        return seq[p:n:q]

    def joinPartitions(self, listOfPartitions):
        testObject = listOfPartitions[0]
        # First see if we have a known array type
        if is_array(testObject):
            return self.flatten_array(listOfPartitions)
        if isinstance(testObject, (list, tuple)):
            return self.flatten_list(listOfPartitions)
        return listOfPartitions

    def flatten_array(self, listOfPartitions):
        test = listOfPartitions[0]
        shape = list(test.shape)
        shape[0] = sum(p.shape[0] for p in listOfPartitions)
        A = numpy.ndarray(shape)
        N = shape[0]
        q = len(listOfPartitions)
        for p, part in enumerate(listOfPartitions):
            A[p:N:q] = part
        return A

    def flatten_list(self, listOfPartitions):
        flat = []
        for i in range(len(listOfPartitions[0])):
            flat.extend([part[i] for part in listOfPartitions if len(part) > i])
        return flat


def mappable(obj):
    """return whether an object is mappable or not."""
    if isinstance(obj, (tuple, list)):
        return True
    if is_array(obj):
        return True
    return False


dists = {'b': Map, 'r': RoundRobinMap}
ipyparallel-8.8.0/ipyparallel/client/remotefunction.py000066400000000000000000000230571460376056100232730ustar00rootroot00000000000000"""Remote Functions and decorators for Views."""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import warnings
from inspect import signature

from decorator import decorator

from ..serialize import PrePickled
from . import map as Map
from .asyncresult import AsyncMapResult

# -----------------------------------------------------------------------------
# Functions and Decorators
# -----------------------------------------------------------------------------


def remote(view, block=None, **flags):
    """Turn a function into a remote function.

    This method can be used for map::

        In [1]: @remote(view,block=True)
           ...: def func(a):
           ...:    pass
    """

    def remote_function(f):
        return RemoteFunction(view, f, block=block, **flags)

    return remote_function


def parallel(view, dist='b', block=None, ordered=True, **flags):
    """Turn a function into a parallel remote function.

    This method can be used for map::

        In [1]: @parallel(view, block=True)
           ...: def func(a):
           ...:    pass
    """

    def parallel_function(f):
        return ParallelFunction(
            view, f, dist=dist, block=block, ordered=ordered, **flags
        )

    return parallel_function


def getname(f):
    """Get the name of an object.

    For use in case of callables that are not functions, and
    thus may not have __name__ defined.

    Order: f.__name__ >  f.name > str(f)
    """
    try:
        return f.__name__
    except Exception:
        pass
    try:
        return f.name
    except Exception:
        pass

    return str(f)


@decorator
def sync_view_results(f, self, *args, **kwargs):
    """sync relevant results from self.client to our results attribute.

    This is a clone of view.sync_results, but for remote functions
    """
    view = self.view
    if view._in_sync_results:
        return f(self, *args, **kwargs)
    view._in_sync_results = True
    try:
        ret = f(self, *args, **kwargs)
    finally:
        view._in_sync_results = False
        view._sync_results()
    return ret


# --------------------------------------------------------------------------
# Classes
# --------------------------------------------------------------------------


class RemoteFunction:
    """Turn an existing function into a remote function.

    Parameters
    ----------

    view : View instance
        The view to be used for execution
    f : callable
        The function to be wrapped into a remote function
    block : bool [default: None]
        Whether to wait for results or not.  The default behavior is
        to use the current `block` attribute of `view`

    **flags : remaining kwargs are passed to View.temp_flags
    """

    view = None  # the remote connection
    func = None  # the wrapped function
    block = None  # whether to block
    flags = None  # dict of extra kwargs for temp_flags

    def __init__(self, view, f, block=None, **flags):
        self.view = view
        self.func = f
        self.block = block
        self.flags = flags

        # copy function attributes for nicer inspection
        # of decorated functions
        self.__name__ = getname(f)
        if getattr(f, '__doc__', None):
            self.__doc__ = f'{self.__class__.__name__} wrapping:\n{f.__doc__}'
        if getattr(f, '__signature__', None):
            self.__signature__ = f.__signature__
        else:
            try:
                self.__signature__ = signature(f)
            except Exception:
                # no signature, but that's okay
                pass

    def __call__(self, *args, **kwargs):
        block = self.view.block if self.block is None else self.block
        with self.view.temp_flags(block=block, **self.flags):
            return self.view.apply(self.func, *args, **kwargs)


def _map(f, *sequences):
    return list(map(f, *sequences))


_prepickled_map = None


class ParallelFunction(RemoteFunction):
    """Class for mapping a function to sequences.

    This will distribute the sequences according the a mapper, and call
    the function on each sub-sequence.  If called via map, then the function
    will be called once on each element, rather that each sub-sequence.

    Parameters
    ----------

    view : View instance
        The view to be used for execution
    f : callable
        The function to be wrapped into a remote function
    dist : str [default: 'b']
        The key for which mapObject to use to distribute sequences
        options are:

        * 'b' : use contiguous chunks in order
        * 'r' : use round-robin striping

    block : bool [default: None]
        Whether to wait for results or not.  The default behavior is
        to use the current `block` attribute of `view`
    chunksize : int or None
        The size of chunk to use when breaking up sequences in a load-balanced manner
    ordered : bool [default: True]
        Whether the result should be kept in order. If False,
        results become available as they arrive, regardless of submission order.
    return_exceptions : bool [default: False]
    **flags
        remaining kwargs are passed to View.temp_flags
    """

    chunksize = None
    ordered = None
    mapObject = None

    def __init__(
        self,
        view,
        f,
        dist='b',
        block=None,
        chunksize=None,
        ordered=True,
        return_exceptions=False,
        **flags,
    ):
        super().__init__(view, f, block=block, **flags)
        self.chunksize = chunksize
        self.ordered = ordered
        self.return_exceptions = return_exceptions

        mapClass = Map.dists[dist]
        self.mapObject = mapClass()

    @sync_view_results
    def __call__(self, *sequences, **kwargs):
        global _prepickled_map
        if _prepickled_map is None:
            _prepickled_map = PrePickled(_map)
        client = self.view.client
        _mapping = kwargs.pop('__ipp_mapping', False)
        if kwargs:
            raise TypeError("Unexpected keyword arguments: %s" % kwargs)

        lens = []
        maxlen = minlen = -1
        for i, seq in enumerate(sequences):
            try:
                n = len(seq)
            except Exception:
                seq = list(seq)
                if isinstance(sequences, tuple):
                    # can't alter a tuple
                    sequences = list(sequences)
                sequences[i] = seq
                n = len(seq)
            if n > maxlen:
                maxlen = n
            if minlen == -1 or n < minlen:
                minlen = n
            lens.append(n)

        if maxlen == 0:
            # nothing to iterate over
            return []

        # check that the length of sequences match
        if not _mapping and minlen != maxlen:
            msg = 'all sequences must have equal length, but have %s' % lens
            raise ValueError(msg)

        balanced = 'Balanced' in self.view.__class__.__name__
        if balanced:
            if self.chunksize:
                nparts = maxlen // self.chunksize + int(maxlen % self.chunksize > 0)
            else:
                nparts = maxlen
            targets = [None] * nparts
        else:
            if self.chunksize:
                warnings.warn(
                    "`chunksize` is ignored unless load balancing", UserWarning
                )
            # multiplexed:
            targets = self.view.targets
            # 'all' is lazily evaluated at execution time, which is now:
            if targets == 'all':
                targets = client._build_targets(targets)[1]
            elif isinstance(targets, int):
                # single-engine view, targets must be iterable
                targets = [targets]
            nparts = len(targets)

        futures = []

        pf = PrePickled(self.func)

        chunk_sizes = {}
        chunk_size = 1

        for index, t in enumerate(targets):
            args = []
            for seq in sequences:
                part = self.mapObject.getPartition(seq, index, nparts, maxlen)
                args.append(part)

            if sum(len(arg) for arg in args) == 0:
                continue

            if _mapping:
                chunk_size = min(len(arg) for arg in args)

            args = [PrePickled(arg) for arg in args]

            if _mapping:
                f = _prepickled_map
                args = [pf] + args
            else:
                f = pf

            view = self.view if balanced else client[t]
            with view.temp_flags(block=False, **self.flags):
                ar = view.apply(f, *args)
                ar.owner = False

            msg_id = ar.msg_ids[0]
            chunk_sizes[msg_id] = chunk_size
            futures.extend(ar._children)

        r = AsyncMapResult(
            self.view.client,
            futures,
            self.mapObject,
            fname=getname(self.func),
            ordered=self.ordered,
            return_exceptions=self.return_exceptions,
            chunk_sizes=chunk_sizes,
        )

        if self.block:
            try:
                return r.get()
            except KeyboardInterrupt:
                return r
        else:
            return r

    def map(self, *sequences):
        """call a function on each element of one or more sequence(s) remotely.
        This should behave very much like the builtin map, but return an AsyncMapResult
        if self.block is False.

        That means it can take generators (will be cast to lists locally),
        and mismatched sequence lengths will be padded with None.
        """
        return self(*sequences, __ipp_mapping=True)


__all__ = ['remote', 'parallel', 'RemoteFunction', 'ParallelFunction']
ipyparallel-8.8.0/ipyparallel/client/view.py000066400000000000000000001614301460376056100212020ustar00rootroot00000000000000"""Views of remote engines."""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import builtins
import concurrent.futures
import inspect
import secrets
import threading
import time
import warnings
from collections import deque
from contextlib import contextmanager

from decorator import decorator
from IPython import get_ipython
from traitlets import Any, Bool, CFloat, Dict, HasTraits, Instance, Integer, List, Set

import ipyparallel as ipp
from ipyparallel import util
from ipyparallel.controller.dependency import Dependency, dependent

from .. import serialize
from ..serialize import PrePickled
from . import map as Map
from .asyncresult import AsyncMapResult, AsyncResult
from .remotefunction import ParallelFunction, getname, parallel, remote

# -----------------------------------------------------------------------------
# Decorators
# -----------------------------------------------------------------------------


@decorator
def save_ids(f, self, *args, **kwargs):
    """Keep our history and outstanding attributes up to date after a method call."""
    n_previous = len(self.client.history)
    try:
        ret = f(self, *args, **kwargs)
    finally:
        nmsgs = len(self.client.history) - n_previous
        msg_ids = self.client.history[-nmsgs:]
        self.history.extend(msg_ids)
        self.outstanding.update(msg_ids)
    return ret


@decorator
def sync_results(f, self, *args, **kwargs):
    """sync relevant results from self.client to our results attribute."""
    if self._in_sync_results:
        return f(self, *args, **kwargs)
    self._in_sync_results = True
    try:
        ret = f(self, *args, **kwargs)
    finally:
        self._in_sync_results = False
        self._sync_results()
    return ret


# -----------------------------------------------------------------------------
# Classes
# -----------------------------------------------------------------------------


class View(HasTraits):
    """Base View class for more convenint apply(f,*args,**kwargs) syntax via attributes.

    Don't use this class, use subclasses.

    Methods
    -------

    spin
        flushes incoming results and registration state changes
        control methods spin, and requesting `ids` also ensures up to date

    wait
        wait on one or more msg_ids

    execution methods
        apply
        legacy: execute, run

    data movement
        push, pull, scatter, gather

    query methods
        get_result, queue_status, purge_results, result_status

    control methods
        abort, shutdown

    """

    # flags
    block = Bool(False)
    track = Bool(False)
    targets = Any()

    history = List()
    outstanding = Set()
    results = Dict()
    client = Instance('ipyparallel.Client', allow_none=True)

    _socket = Any()
    _flag_names = List(['targets', 'block', 'track'])
    _in_sync_results = Bool(False)
    _targets = Any()
    _idents = Any()

    def __init__(self, client=None, socket=None, **flags):
        super().__init__(client=client, _socket=socket)
        self.results = client.results
        self.block = client.block
        self.executor = ViewExecutor(self)

        self.set_flags(**flags)

        assert self.__class__ is not View, "Don't use base View objects, use subclasses"

    def __repr__(self):
        strtargets = str(self.targets)
        if len(strtargets) > 16:
            strtargets = strtargets[:12] + '...]'
        return f"<{self.__class__.__name__} {strtargets}>"

    def __len__(self):
        if isinstance(self.targets, list):
            return len(self.targets)
        elif isinstance(self.targets, int):
            return 1
        else:
            return len(self.client)

    def set_flags(self, **kwargs):
        """set my attribute flags by keyword.

        Views determine behavior with a few attributes (`block`, `track`, etc.).
        These attributes can be set all at once by name with this method.

        Parameters
        ----------
        block : bool
            whether to wait for results
        track : bool
            whether to create a MessageTracker to allow the user to
            safely edit after arrays and buffers during non-copying
            sends.
        """
        for name, value in kwargs.items():
            if name not in self._flag_names:
                raise KeyError("Invalid name: %r" % name)
            else:
                setattr(self, name, value)

    @contextmanager
    def temp_flags(self, **kwargs):
        """temporarily set flags, for use in `with` statements.

        See set_flags for permanent setting of flags

        Examples
        --------
        >>> view.track=False
        ...
        >>> with view.temp_flags(track=True):
        ...    ar = view.apply(dostuff, my_big_array)
        ...    ar.tracker.wait() # wait for send to finish
        >>> view.track
        False

        """
        # preflight: save flags, and set temporaries
        saved_flags = {}
        for f in self._flag_names:
            saved_flags[f] = getattr(self, f)
        self.set_flags(**kwargs)
        # yield to the with-statement block
        try:
            yield
        finally:
            # postflight: restore saved flags
            self.set_flags(**saved_flags)

    # ----------------------------------------------------------------
    # apply
    # ----------------------------------------------------------------

    def _sync_results(self):
        """to be called by @sync_results decorator

        after submitting any tasks.
        """
        delta = self.outstanding.difference(self.client.outstanding)
        completed = self.outstanding.intersection(delta)
        self.outstanding = self.outstanding.difference(completed)

    @sync_results
    @save_ids
    def _really_apply(self, f, args, kwargs, block=None, **options):
        """wrapper for client.send_apply_request"""
        raise NotImplementedError("Implement in subclasses")

    def apply(self, __ipp_f, *args, **kwargs):
        """calls ``f(*args, **kwargs)`` on remote engines, returning the result.

        This method sets all apply flags via this View's attributes.

        Returns :class:`~ipyparallel.client.asyncresult.AsyncResult`
        instance if ``self.block`` is False, otherwise the return value of
        ``f(*args, **kwargs)``.
        """
        return self._really_apply(__ipp_f, args, kwargs)

    def apply_async(self, __ipp_f, *args, **kwargs):
        """calls ``f(*args, **kwargs)`` on remote engines in a nonblocking manner.

        Returns :class:`~ipyparallel.client.asyncresult.AsyncResult` instance.
        """
        return self._really_apply(__ipp_f, args, kwargs, block=False)

    def apply_sync(self, __ipp_f, *args, **kwargs):
        """calls ``f(*args, **kwargs)`` on remote engines in a blocking manner,
        returning the result.
        """
        return self._really_apply(__ipp_f, args, kwargs, block=True)

    # ----------------------------------------------------------------
    # wrappers for client and control methods
    # ----------------------------------------------------------------
    @sync_results
    def spin(self):
        """spin the client, and sync"""
        self.client.spin()

    @sync_results
    def wait(self, jobs=None, timeout=-1):
        """waits on one or more `jobs`, for up to `timeout` seconds.

        Parameters
        ----------
        jobs : int, str, or list of ints and/or strs, or one or more AsyncResult objects
            ints are indices to self.history
            strs are msg_ids
            default: wait on all outstanding messages
        timeout : float
            a time in seconds, after which to give up.
            default is -1, which means no timeout

        Returns
        -------
        True : when all msg_ids are done
        False : timeout reached, some msg_ids still outstanding
        """
        if jobs is None:
            jobs = self.history
        return self.client.wait(jobs, timeout)

    def abort(self, jobs=None, targets=None, block=None):
        """Abort jobs on my engines.

        Note: only jobs that have not started yet can be aborted.
        To halt a running job,
        you must interrupt the engine(s) via the Cluster API.

        Parameters
        ----------
        jobs : None, str, list of strs, optional
            if None: abort all jobs.
            else: abort specific msg_id(s).
        """
        block = block if block is not None else self.block
        targets = targets if targets is not None else self.targets
        jobs = jobs if jobs is not None else list(self.outstanding)

        return self.client.abort(jobs=jobs, targets=targets, block=block)

    def queue_status(self, targets=None, verbose=False):
        """Fetch the Queue status of my engines"""
        targets = targets if targets is not None else self.targets
        return self.client.queue_status(targets=targets, verbose=verbose)

    def purge_results(self, jobs=[], targets=[]):
        """Instruct the controller to forget specific results."""
        if targets is None or targets == 'all':
            targets = self.targets
        return self.client.purge_results(jobs=jobs, targets=targets)

    def shutdown(self, targets=None, restart=False, hub=False, block=None):
        """Terminates one or more engine processes, optionally including the hub."""
        block = self.block if block is None else block
        if targets is None or targets == 'all':
            targets = self.targets
        return self.client.shutdown(
            targets=targets, restart=restart, hub=hub, block=block
        )

    def get_result(self, indices_or_msg_ids=None, block=None, owner=False):
        """return one or more results, specified by history index or msg_id.

        See :meth:`ipyparallel.client.client.Client.get_result` for details.
        """

        if indices_or_msg_ids is None:
            indices_or_msg_ids = -1
        if isinstance(indices_or_msg_ids, int):
            indices_or_msg_ids = self.history[indices_or_msg_ids]
        elif isinstance(indices_or_msg_ids, (list, tuple, set)):
            indices_or_msg_ids = list(indices_or_msg_ids)
            for i, index in enumerate(indices_or_msg_ids):
                if isinstance(index, int):
                    indices_or_msg_ids[i] = self.history[index]
        return self.client.get_result(indices_or_msg_ids, block=block, owner=owner)

    # -------------------------------------------------------------------
    # Map
    # -------------------------------------------------------------------

    @sync_results
    def map(self, f, *sequences, **kwargs):
        """override in subclasses"""
        raise NotImplementedError()

    def map_async(self, f, *sequences, **kwargs):
        """Parallel version of builtin :func:`python:map`, using this view's engines.

        This is equivalent to ``map(...block=False)``.

        See `self.map` for details.
        """
        if 'block' in kwargs:
            raise TypeError("map_async doesn't take a `block` keyword argument.")
        kwargs['block'] = False
        return self.map(f, *sequences, **kwargs)

    def map_sync(self, f, *sequences, **kwargs):
        """Parallel version of builtin :func:`python:map`, using this view's engines.

        This is equivalent to ``map(...block=True)``.

        See `self.map` for details.
        """
        if 'block' in kwargs:
            raise TypeError("map_sync doesn't take a `block` keyword argument.")
        kwargs['block'] = True
        return self.map(f, *sequences, **kwargs)

    def imap(self, f, *sequences, **kwargs):
        """Parallel version of :func:`itertools.imap`.

        See `self.map` for details.

        """

        return iter(self.map_async(f, *sequences, **kwargs))

    # -------------------------------------------------------------------
    # Decorators
    # -------------------------------------------------------------------

    def remote(self, block=None, **flags):
        """Decorator for making a RemoteFunction"""
        block = self.block if block is None else block
        return remote(self, block=block, **flags)

    def parallel(self, dist='b', block=None, **flags):
        """Decorator for making a ParallelFunction"""
        block = self.block if block is None else block
        return parallel(self, dist=dist, block=block, **flags)


class DirectView(View):
    """Direct Multiplexer View of one or more engines.

    These are created via indexed access to a client:

    >>> dv_1 = client[1]
    >>> dv_all = client[:]
    >>> dv_even = client[::2]
    >>> dv_some = client[1:3]

    This object provides dictionary access to engine namespaces:

    # push a=5:
    >>> dv['a'] = 5
    # pull 'foo':
    >>> dv['foo']

    """

    def __init__(self, client=None, socket=None, targets=None, **flags):
        super().__init__(client=client, socket=socket, targets=targets, **flags)

    @property
    def importer(self):
        """sync_imports(local=True) as a property.

        See sync_imports for details.

        """
        return self.sync_imports(True)

    @contextmanager
    def sync_imports(self, local=True, quiet=False):
        """Context Manager for performing simultaneous local and remote imports.

        'import x as y' will *not* work.  The 'as y' part will simply be ignored.

        If `local=True`, then the package will also be imported locally.

        If `quiet=True`, no output will be produced when attempting remote
        imports.

        Note that remote-only (`local=False`) imports have not been implemented.

        >>> with view.sync_imports():
        ...    from numpy import recarray
        importing recarray from numpy on engine(s)

        """

        local_import = builtins.__import__
        modules = set()
        results = []

        # get the calling frame
        # that's two steps up due to `@contextmanager`
        context_frame = inspect.getouterframes(inspect.currentframe())[2].frame

        @util.interactive
        def remote_import(name, fromlist, level):
            """the function to be passed to apply, that actually performs the import
            on the engine, and loads up the user namespace.
            """
            import sys

            user_ns = globals()
            mod = __import__(name, fromlist=fromlist, level=level)
            if fromlist:
                for key in fromlist:
                    user_ns[key] = getattr(mod, key)
            else:
                user_ns[name] = sys.modules[name]

        def view_import(name, globals={}, locals={}, fromlist=[], level=0):
            """the drop-in replacement for __import__, that optionally imports
            locally as well.
            """
            # don't override nested imports
            save_import = builtins.__import__
            builtins.__import__ = local_import

            import_frame = inspect.getouterframes(inspect.currentframe())[1].frame
            if import_frame is not context_frame:
                # only forward imports from the context frame,
                # not secondary imports
                # TODO: does this ever happen, or is the above `__import__` enough?
                return local_import(name, globals, locals, fromlist, level)

            if local:
                mod = local_import(name, globals, locals, fromlist, level)
            else:
                raise NotImplementedError("remote-only imports not yet implemented")

            key = name + ':' + ','.join(fromlist or [])
            if level <= 0 and key not in modules:
                modules.add(key)
                if not quiet:
                    if fromlist:
                        print(
                            "importing {} from {} on engine(s)".format(
                                ','.join(fromlist), name
                            )
                        )
                    else:
                        print("importing %s on engine(s)" % name)
                results.append(self.apply_async(remote_import, name, fromlist, level))
            # restore override
            builtins.__import__ = save_import

            return mod

        # override __import__
        builtins.__import__ = view_import
        try:
            # enter the block
            yield
        except ImportError:
            if local:
                raise
            else:
                # ignore import errors if not doing local imports
                pass
        finally:
            # always restore __import__
            builtins.__import__ = local_import

        for r in results:
            # raise possible remote ImportErrors here
            r.get()

    def use_dill(self):
        """Expand serialization support with dill

        adds support for closures, etc.

        This calls ipyparallel.serialize.use_dill() here and on each engine.
        """
        serialize.use_dill()
        return self.apply(serialize.use_dill)

    def use_cloudpickle(self):
        """Expand serialization support with cloudpickle.

        This calls ipyparallel.serialize.use_cloudpickle() here and on each engine.
        """
        serialize.use_cloudpickle()
        return self.apply(serialize.use_cloudpickle)

    def use_pickle(self):
        """Restore

        This reverts changes to serialization caused by `use_dill|.cloudpickle`.
        """
        serialize.use_pickle()
        return self.apply(serialize.use_pickle)

    @sync_results
    @save_ids
    def _really_apply(
        self, f, args=None, kwargs=None, targets=None, block=None, track=None
    ):
        """calls f(*args, **kwargs) on remote engines, returning the result.

        This method sets all of `apply`'s flags via this View's attributes.

        Parameters
        ----------
        f : callable
        args : list [default: empty]
        kwargs : dict [default: empty]
        targets : target list [default: self.targets]
            where to run
        block : bool [default: self.block]
            whether to block
        track : bool [default: self.track]
            whether to ask zmq to track the message, for safe non-copying sends

        Returns
        -------
        if self.block is False:
            returns AsyncResult
        else:
            returns actual result of f(*args, **kwargs) on the engine(s)
            This will be a list of self.targets is also a list (even length 1), or
            the single result if self.targets is an integer engine id
        """
        args = [] if args is None else args
        kwargs = {} if kwargs is None else kwargs
        block = self.block if block is None else block
        track = self.track if track is None else track
        targets = self.targets if targets is None else targets

        _idents, _targets = self.client._build_targets(targets)
        futures = []

        pf = PrePickled(f)
        pargs = [PrePickled(arg) for arg in args]
        pkwargs = {k: PrePickled(v) for k, v in kwargs.items()}

        for ident in _idents:
            future = self.client.send_apply_request(
                self._socket, pf, pargs, pkwargs, track=track, ident=ident
            )
            futures.append(future)
        if track:
            trackers = [_.tracker for _ in futures]
        else:
            trackers = []
        if isinstance(targets, int):
            futures = futures[0]
        ar = AsyncResult(
            self.client, futures, fname=getname(f), targets=_targets, owner=True
        )
        if block:
            try:
                return ar.get()
            except KeyboardInterrupt:
                pass
        return ar

    @sync_results
    def map(self, f, *sequences, block=None, track=False, return_exceptions=False):
        """Parallel version of builtin `map`, using this View's `targets`.

        There will be one task per target, so work will be chunked
        if the sequences are longer than `targets`.

        Results can be iterated as they are ready, but will become available in chunks.

        .. versionadded:: 7.0
            `return_exceptions`

        Parameters
        ----------
        f : callable
            function to be mapped
        *sequences : one or more sequences of matching length
            the sequences to be distributed and passed to `f`
        block : bool [default self.block]
            whether to wait for the result or not
        track : bool [default False]
            Track underlying zmq send to indicate when it is safe to modify memory.
            Only for zero-copy sends such as numpy arrays that are going to be modified in-place.
        return_exceptions : bool [default False]
            Return remote Exceptions in the result sequence instead of raising them.

        Returns
        -------
        If block=False
            An :class:`~ipyparallel.client.asyncresult.AsyncMapResult` instance.
            An object like AsyncResult, but which reassembles the sequence of results
            into a single list. AsyncMapResults can be iterated through before all
            results are complete.
        else
            A list, the result of ``map(f,*sequences)``
        """

        if block is None:
            block = self.block

        assert len(sequences) > 0, "must have some sequences to map onto!"
        pf = ParallelFunction(
            self, f, block=block, track=track, return_exceptions=return_exceptions
        )
        return pf.map(*sequences)

    @sync_results
    @save_ids
    def execute(self, code, silent=True, targets=None, block=None):
        """Executes `code` on `targets` in blocking or nonblocking manner.

        ``execute`` is always `bound` (affects engine namespace)

        Parameters
        ----------
        code : str
            the code string to be executed
        block : bool
            whether or not to wait until done to return
            default: self.block
        """
        block = self.block if block is None else block
        targets = self.targets if targets is None else targets

        _idents, _targets = self.client._build_targets(targets)
        futures = []
        for ident in _idents:
            future = self.client.send_execute_request(
                self._socket, code, silent=silent, ident=ident
            )
            futures.append(future)
        if isinstance(targets, int):
            futures = futures[0]
        ar = AsyncResult(
            self.client, futures, fname='execute', targets=_targets, owner=True
        )
        if block:
            try:
                ar.get()
                ar.wait_for_output()
            except KeyboardInterrupt:
                pass
        return ar

    def run(self, filename, targets=None, block=None):
        """Execute contents of `filename` on my engine(s).

        This simply reads the contents of the file and calls `execute`.

        Parameters
        ----------
        filename : str
            The path to the file
        targets : int/str/list of ints/strs
            the engines on which to execute
            default : all
        block : bool
            whether or not to wait until done
            default: self.block

        """
        with open(filename) as f:
            # add newline in case of trailing indented whitespace
            # which will cause SyntaxError
            code = f.read() + '\n'
        return self.execute(code, block=block, targets=targets)

    def update(self, ns):
        """update remote namespace with dict `ns`

        See `push` for details.
        """
        return self.push(ns, block=self.block, track=self.track)

    def push(self, ns, targets=None, block=None, track=None):
        """update remote namespace with dict `ns`

        Parameters
        ----------
        ns : dict
            dict of keys with which to update engine namespace(s)
        block : bool [default : self.block]
            whether to wait to be notified of engine receipt

        """

        block = block if block is not None else self.block
        track = track if track is not None else self.track
        targets = targets if targets is not None else self.targets
        # applier = self.apply_sync if block else self.apply_async
        if not isinstance(ns, dict):
            raise TypeError("Must be a dict, not %s" % type(ns))
        return self._really_apply(
            util._push, kwargs=ns, block=block, track=track, targets=targets
        )

    def get(self, key_s):
        """get object(s) by `key_s` from remote namespace

        see `pull` for details.
        """
        # block = block if block is not None else self.block
        return self.pull(key_s, block=True)

    def pull(self, names, targets=None, block=None):
        """get object(s) by `name` from remote namespace

        will return one object if it is a key.
        can also take a list of keys, in which case it will return a list of objects.
        """
        block = block if block is not None else self.block
        targets = targets if targets is not None else self.targets
        if isinstance(names, str):
            pass
        elif isinstance(names, (list, tuple, set)):
            for key in names:
                if not isinstance(key, str):
                    raise TypeError("keys must be str, not type %r" % type(key))
        else:
            raise TypeError("names must be strs, not %r" % names)
        return self._really_apply(util._pull, (names,), block=block, targets=targets)

    def scatter(
        self, key, seq, dist='b', flatten=False, targets=None, block=None, track=None
    ):
        """
        Partition a Python sequence and send the partitions to a set of engines.
        """
        block = block if block is not None else self.block
        track = track if track is not None else self.track
        targets = targets if targets is not None else self.targets

        # construct integer ID list:
        targets = self.client._build_targets(targets)[1]

        mapObject = Map.dists[dist]()
        nparts = len(targets)
        futures = []
        _lengths = []
        for index, engineid in enumerate(targets):
            partition = mapObject.getPartition(seq, index, nparts)
            if flatten and len(partition) == 1:
                ns = {key: partition[0]}
            else:
                ns = {key: partition}
            r = self.push(ns, block=False, track=track, targets=engineid)
            r.owner = False
            futures.extend(r._children)
            _lengths.append(len(partition))

        r = AsyncResult(
            self.client, futures, fname='scatter', targets=targets, owner=True
        )
        r._scatter_lengths = _lengths
        if block:
            r.wait()
        else:
            return r

    @sync_results
    @save_ids
    def gather(self, key, dist='b', targets=None, block=None):
        """
        Gather a partitioned sequence on a set of engines as a single local seq.
        """
        block = block if block is not None else self.block
        targets = targets if targets is not None else self.targets
        mapObject = Map.dists[dist]()
        msg_ids = []

        # construct integer ID list:
        targets = self.client._build_targets(targets)[1]

        futures = []
        for index, engineid in enumerate(targets):
            ar = self.pull(key, block=False, targets=engineid)
            ar.owner = False
            futures.extend(ar._children)

        r = AsyncMapResult(self.client, futures, mapObject, fname='gather')

        if block:
            try:
                return r.get()
            except KeyboardInterrupt:
                pass
        return r

    def __getitem__(self, key):
        return self.get(key)

    def __setitem__(self, key, value):
        self.update({key: value})

    def clear(self, targets=None, block=None):
        """Clear the remote namespaces on my engines."""
        block = block if block is not None else self.block
        targets = targets if targets is not None else self.targets
        return self.client.clear(targets=targets, block=block)

    # ----------------------------------------
    # activate for %px, %autopx, etc. magics
    # ----------------------------------------

    def activate(self, suffix=''):
        """Activate IPython magics associated with this View

        Defines the magics `%px, %autopx, %pxresult, %%px, %pxconfig`

        Parameters
        ----------
        suffix : str [default: '']
            The suffix, if any, for the magics.  This allows you to have
            multiple views associated with parallel magics at the same time.

            e.g. ``rc[::2].activate(suffix='_even')`` will give you
            the magics ``%px_even``, ``%pxresult_even``, etc. for running magics
            on the even engines.
        """

        from ipyparallel.client.magics import ParallelMagics

        ip = get_ipython()
        if ip is None:
            warnings.warn(
                "The IPython parallel magics (%px, etc.) only work within IPython."
            )
            return

        M = ParallelMagics(ip, self, suffix)
        ip.magics_manager.register(M)


@decorator
def _not_coalescing(method, self, *args, **kwargs):
    """Decorator for broadcast methods that can't use reply coalescing"""
    is_coalescing = self.is_coalescing
    try:
        self.is_coalescing = False
        return method(self, *args, **kwargs)
    finally:
        self.is_coalescing = is_coalescing


class BroadcastView(DirectView):
    is_coalescing = Bool(False)

    def _init_metadata(self, target_tuples):
        """initialize request metadata"""
        return dict(
            targets=target_tuples,
            is_broadcast=True,
            is_coalescing=self.is_coalescing,
        )

    def _make_async_result(self, message_future, s_idents, **kwargs):
        original_msg_id = message_future.msg_id
        if not self.is_coalescing:
            futures = []
            for ident in s_idents:
                msg_and_target_id = f'{original_msg_id}_{ident}'
                future = self.client.create_message_futures(
                    msg_and_target_id,
                    message_future.header,
                    async_result=True,
                    track=True,
                )
                self.client.outstanding.add(msg_and_target_id)
                self.client._outstanding_dict[ident].add(msg_and_target_id)
                self.outstanding.add(msg_and_target_id)
                futures.append(future[0])
            if original_msg_id in self.outstanding:
                self.outstanding.remove(original_msg_id)
        else:
            self.client.outstanding.add(original_msg_id)
            for ident in s_idents:
                self.client._outstanding_dict[ident].add(original_msg_id)
            futures = message_future

        ar = AsyncResult(self.client, futures, owner=True, **kwargs)

        if self.is_coalescing:
            # if coalescing, discard outstanding-tracking when we are done
            def _rm_outstanding(_):
                for ident in s_idents:
                    if ident in self.client._outstanding_dict:
                        self.client._outstanding_dict[ident].discard(original_msg_id)

            ar.add_done_callback(_rm_outstanding)

        return ar

    @sync_results
    @save_ids
    def _really_apply(
        self, f, args=None, kwargs=None, block=None, track=None, targets=None
    ):
        args = [] if args is None else args
        kwargs = {} if kwargs is None else kwargs
        block = self.block if block is None else block
        track = self.track if track is None else track
        targets = self.targets if targets is None else targets
        idents, _targets = self.client._build_targets(targets)

        pf = PrePickled(f)
        pargs = [PrePickled(arg) for arg in args]
        pkwargs = {k: PrePickled(v) for k, v in kwargs.items()}

        s_idents = [ident.decode("utf8") for ident in idents]
        target_tuples = list(zip(s_idents, _targets))

        metadata = self._init_metadata(target_tuples)

        ar = None

        def make_asyncresult(message_future):
            nonlocal ar
            ar = self._make_async_result(
                message_future, s_idents, fname=getname(f), targets=_targets
            )

        self.client.send_apply_request(
            self._socket,
            pf,
            pargs,
            pkwargs,
            track=track,
            metadata=metadata,
            message_future_hook=make_asyncresult,
        )

        if block:
            try:
                return ar.get()
            except KeyboardInterrupt:
                pass
        return ar

    @sync_results
    @save_ids
    @_not_coalescing
    def execute(self, code, silent=True, targets=None, block=None):
        """Executes `code` on `targets` in blocking or nonblocking manner.

        ``execute`` is always `bound` (affects engine namespace)

        Parameters
        ----------
        code : str
            the code string to be executed
        block : bool
            whether or not to wait until done to return
            default: self.block
        """
        block = self.block if block is None else block
        targets = self.targets if targets is None else targets

        _idents, _targets = self.client._build_targets(targets)
        s_idents = [ident.decode("utf8") for ident in _idents]
        target_tuples = list(zip(s_idents, _targets))

        metadata = self._init_metadata(target_tuples)

        ar = None

        def make_asyncresult(message_future):
            nonlocal ar
            ar = self._make_async_result(
                message_future, s_idents, fname='execute', targets=_targets
            )

        message_future = self.client.send_execute_request(
            self._socket,
            code,
            silent=silent,
            metadata=metadata,
            message_future_hook=make_asyncresult,
        )
        if block:
            try:
                ar.get()
                ar.wait_for_output()
            except KeyboardInterrupt:
                pass
        return ar

    @staticmethod
    def _broadcast_map(f, *sequence_names):
        """Function passed to apply

        Equivalent, but account for the fact that scatter
        occurs in a separate step.

        Does these things:
        - resolve sequence names to sequences in the user namespace
        - collect list(map(f, *squences))
        - cleanup temporary sequence variables from scatter
        """
        sequences = []
        ip = get_ipython()
        for seq_name in sequence_names:
            sequences.append(ip.user_ns.pop(seq_name))
        return list(map(f, *sequences))

    @_not_coalescing
    def map(self, f, *sequences, block=None, track=False, return_exceptions=False):
        """Parallel version of builtin `map`, using this View's `targets`.

        There will be one task per engine, so work will be chunked
        if the sequences are longer than `targets`.

        Results can be iterated as they are ready, but will become available in chunks.

        .. note::

            BroadcastView does not yet have a fully native map implementation.
            In particular, the scatter step is still one message per engine,
            identical to DirectView,
            and typically slower due to the more complex scheduler.

            It is more efficient to partition inputs via other means (e.g. SPMD based on rank & size)
            and use `apply` to submit all tasks in one broadcast.

        .. versionadded:: 8.8

        Parameters
        ----------
        f : callable
            function to be mapped
        *sequences : one or more sequences of matching length
            the sequences to be distributed and passed to `f`
        block : bool [default self.block]
            whether to wait for the result or not
        track : bool [default False]
            Track underlying zmq send to indicate when it is safe to modify memory.
            Only for zero-copy sends such as numpy arrays that are going to be modified in-place.
        return_exceptions : bool [default False]
            Return remote Exceptions in the result sequence instead of raising them.

        Returns
        -------
        If block=False
            An :class:`~ipyparallel.client.asyncresult.AsyncMapResult` instance.
            An object like AsyncResult, but which reassembles the sequence of results
            into a single list. AsyncMapResults can be iterated through before all
            results are complete.
        else
            A list, the result of ``map(f,*sequences)``
        """
        if block is None:
            block = self.block
        if track is None:
            track = self.track

        # unique identifier, since we're living in the interactive namespace
        map_key = secrets.token_hex(5)
        dist = 'b'
        map_object = Map.dists[dist]()

        seq_names = []
        for i, seq in enumerate(sequences):
            seq_name = f"_seq_{map_key}_{i}"
            seq_names.append(seq_name)
            try:
                len(seq)
            except Exception:
                # cast length-less sequences (e.g. Range) to list
                seq = list(seq)

            ar = self.scatter(seq_name, seq, dist=dist, block=False, track=track)
            scatter_chunk_sizes = ar._scatter_lengths

        # submit the map tasks as an actual broadcast
        ar = self.apply(self._broadcast_map, f, *seq_names)
        ar.owner = False
        # re-wrap messages in an AsyncMapResult to get map API
        # this is where the 'gather' reconstruction happens
        amr = ipp.AsyncMapResult(
            self.client,
            ar._children,
            map_object,
            fname=getname(f),
            return_exceptions=return_exceptions,
            chunk_sizes={
                future.msg_id: chunk_size
                for future, chunk_size in zip(ar._children, scatter_chunk_sizes)
            },
        )

        if block:
            return amr.get()
        else:
            return amr

    # scatter/gather cannot be coalescing yet
    scatter = _not_coalescing(DirectView.scatter)
    gather = _not_coalescing(DirectView.gather)


class LazyMapIterator:
    """Iterable representation of a lazy map (imap)

    Has a `.cancel()` method to stop consuming new inputs.

    .. versionadded:: 8.0
    """

    def __init__(self, gen, signal_done):
        self._gen = gen
        self._signal_done = signal_done

    def __iter__(self):
        return self._gen

    def __next__(self):
        return next(self._gen)

    def cancel(self):
        """Stop consuming the input to the map.

        Useful to e.g. stop consuming an infinite (or just large) input
        when you've arrived at the result (or error) you needed.
        """
        self._signal_done()


class LoadBalancedView(View):
    """An load-balancing View that only executes via the Task scheduler.

    Load-balanced views can be created with the client's `view` method:

    >>> v = client.load_balanced_view()

    or targets can be specified, to restrict the potential destinations:

    >>> v = client.load_balanced_view([1,3])

    which would restrict loadbalancing to between engines 1 and 3.

    """

    follow = Any()
    after = Any()
    timeout = CFloat()
    retries = Integer(0)

    _task_scheme = Any()
    _flag_names = List(
        ['targets', 'block', 'track', 'follow', 'after', 'timeout', 'retries']
    )
    _outstanding_maps = Set()

    def __init__(self, client=None, socket=None, **flags):
        super().__init__(client=client, socket=socket, **flags)
        self._task_scheme = client._task_scheme

    def _validate_dependency(self, dep):
        """validate a dependency.

        For use in `set_flags`.
        """
        if dep is None or isinstance(dep, (str, AsyncResult, Dependency)):
            return True
        elif isinstance(dep, (list, set, tuple)):
            for d in dep:
                if not isinstance(d, (str, AsyncResult)):
                    return False
        elif isinstance(dep, dict):
            if set(dep.keys()) != set(Dependency().as_dict().keys()):
                return False
            if not isinstance(dep['msg_ids'], list):
                return False
            for d in dep['msg_ids']:
                if not isinstance(d, str):
                    return False
        else:
            return False

        return True

    def _render_dependency(self, dep):
        """helper for building jsonable dependencies from various input forms."""
        if isinstance(dep, Dependency):
            return dep.as_dict()
        elif isinstance(dep, AsyncResult):
            return dep.msg_ids
        elif dep is None:
            return []
        else:
            # pass to Dependency constructor
            return list(Dependency(dep))

    def set_flags(self, **kwargs):
        """set my attribute flags by keyword.

        A View is a wrapper for the Client's apply method, but with attributes
        that specify keyword arguments, those attributes can be set by keyword
        argument with this method.

        Parameters
        ----------
        block : bool
            whether to wait for results
        track : bool
            whether to create a MessageTracker to allow the user to
            safely edit after arrays and buffers during non-copying
            sends.
        after : Dependency or collection of msg_ids
            Only for load-balanced execution (targets=None)
            Specify a list of msg_ids as a time-based dependency.
            This job will only be run *after* the dependencies
            have been met.
        follow : Dependency or collection of msg_ids
            Only for load-balanced execution (targets=None)
            Specify a list of msg_ids as a location-based dependency.
            This job will only be run on an engine where this dependency
            is met.
        timeout : float/int or None
            Only for load-balanced execution (targets=None)
            Specify an amount of time (in seconds) for the scheduler to
            wait for dependencies to be met before failing with a
            DependencyTimeout.
        retries : int
            Number of times a task will be retried on failure.
        """

        super().set_flags(**kwargs)
        for name in ('follow', 'after'):
            if name in kwargs:
                value = kwargs[name]
                if self._validate_dependency(value):
                    setattr(self, name, value)
                else:
                    raise ValueError("Invalid dependency: %r" % value)
        if 'timeout' in kwargs:
            t = kwargs['timeout']
            if not isinstance(t, (int, float, type(None))):
                raise TypeError("Invalid type for timeout: %r" % type(t))
            if t is not None:
                if t < 0:
                    raise ValueError("Invalid timeout: %s" % t)

            self.timeout = t

    @sync_results
    @save_ids
    def _really_apply(
        self,
        f,
        args=None,
        kwargs=None,
        block=None,
        track=None,
        after=None,
        follow=None,
        timeout=None,
        targets=None,
        retries=None,
    ):
        """calls f(*args, **kwargs) on a remote engine, returning the result.

        This method temporarily sets all of `apply`'s flags for a single call.

        Parameters
        ----------
        f : callable
        args : list [default: empty]
        kwargs : dict [default: empty]
        block : bool [default: self.block]
            whether to block
        track : bool [default: self.track]
            whether to ask zmq to track the message, for safe non-copying sends
        !!!!!! TODO : THE REST HERE  !!!!

        Returns
        -------
        if self.block is False:
            returns AsyncResult
        else:
            returns actual result of f(*args, **kwargs) on the engine(s)
            This will be a list of self.targets is also a list (even length 1), or
            the single result if self.targets is an integer engine id
        """

        # validate whether we can run
        if self._socket.closed():
            msg = "Task farming is disabled"
            if self._task_scheme == 'pure':
                msg += " because the pure ZMQ scheduler cannot handle"
                msg += " disappearing engines."
            raise RuntimeError(msg)

        if self._task_scheme == 'pure':
            # pure zmq scheme doesn't support extra features
            msg = "Pure ZMQ scheduler doesn't support the following flags:"
            "follow, after, retries, targets, timeout"
            if follow or after or retries or targets or timeout:
                # hard fail on Scheduler flags
                raise RuntimeError(msg)
            if isinstance(f, dependent):
                # soft warn on functional dependencies
                warnings.warn(msg, RuntimeWarning)

        # build args
        args = [] if args is None else args
        kwargs = {} if kwargs is None else kwargs
        block = self.block if block is None else block
        track = self.track if track is None else track
        after = self.after if after is None else after
        retries = self.retries if retries is None else retries
        follow = self.follow if follow is None else follow
        timeout = self.timeout if timeout is None else timeout
        targets = self.targets if targets is None else targets

        if not isinstance(retries, int):
            raise TypeError('retries must be int, not %r' % type(retries))

        if targets is None:
            idents = []
        else:
            idents = self.client._build_targets(targets)[0]
            # ensure *not* bytes
            idents = [ident.decode() for ident in idents]

        after = self._render_dependency(after)
        follow = self._render_dependency(follow)
        metadata = dict(
            after=after, follow=follow, timeout=timeout, targets=idents, retries=retries
        )

        future = self.client.send_apply_request(
            self._socket, f, args, kwargs, track=track, metadata=metadata
        )

        ar = AsyncResult(
            self.client,
            future,
            fname=getname(f),
            targets=None,
            owner=True,
        )
        if block:
            try:
                return ar.get()
            except KeyboardInterrupt:
                pass
        return ar

    @sync_results
    @save_ids
    def map(
        self,
        f,
        *sequences,
        block=None,
        chunksize=1,
        ordered=True,
        return_exceptions=False,
    ):
        """Parallel version of builtin `map`, load-balanced by this View.

        Each `chunksize` elements will be a separate task, and will be
        load-balanced. This lets individual elements be available for iteration
        as soon as they arrive.

        .. versionadded:: 7.0
            `return_exceptions`

        Parameters
        ----------
        f : callable
            function to be mapped
        *sequences : one or more sequences of matching length
            the sequences to be distributed and passed to `f`
        block : bool [default self.block]
            whether to wait for the result or not
        chunksize : int [default 1]
            how many elements should be in each task.
        ordered : bool [default True]
            Whether the results should be gathered as they arrive, or enforce
            the order of submission.

            Only applies when iterating through AsyncMapResult as results arrive.
            Has no effect when block=True.

        return_exceptions: bool [default False]
            Return Exceptions instead of raising on the first exception.

        Returns
        -------
        if block=False
            An :class:`~ipyparallel.client.asyncresult.AsyncMapResult` instance.
            An object like AsyncResult, but which reassembles the sequence of results
            into a single list. AsyncMapResults can be iterated through before all
            results are complete.
        else
            A list, the result of ``map(f,*sequences)``
        """

        # default
        if block is None:
            block = self.block

        assert len(sequences) > 0, "must have some sequences to map onto!"

        pf = ParallelFunction(
            self,
            f,
            block=block,
            chunksize=chunksize,
            ordered=ordered,
            return_exceptions=return_exceptions,
        )
        return pf.map(*sequences)

    def imap(
        self,
        f,
        *sequences,
        ordered=True,
        max_outstanding='auto',
        return_exceptions=False,
    ):
        """Parallel version of lazily-evaluated `imap`, load-balanced by this View.

        `ordered`, and `max_outstanding` can be specified by keyword only.

        Unlike other map functions in IPython Parallel,
        this one does not consume the full iterable before submitting work,
        returning a single 'AsyncMapResult' representing the full computation.

        Instead, it consumes iterables as they come, submitting up to `max_outstanding`
        tasks to the cluster before waiting on results (default: one task per engine).
        This allows it to work with infinite generators,
        and avoid potentially expensive read-ahead for large streams of inputs
        that may not fit in memory all at once.

        .. versionadded:: 7.0

        Parameters
        ----------
        f : callable
            function to be mapped
        *sequences : one or more sequences of matching length
            the sequences to be distributed and passed to `f`
        ordered : bool [default True]
            Whether the results should be yielded on a first-come-first-yield basis,
            or preserve the order of submission.

        max_outstanding : int [default len(engines)]
            The maximum number of tasks to be outstanding.

            max_outstanding=0 will greedily consume the whole generator
            (map_async may be more efficient).

            A limit of 1 should be strictly worse than running a local map,
            as there will be no parallelism.

            Use this to tune how greedily input generator should be consumed.

        return_exceptions : bool [default False]
            Return Exceptions instead of raising them.

        Returns
        -------

        lazily-evaluated generator, yielding results of `f` on each item of sequences.
        Yield-order depends on `ordered` argument.
        """

        assert len(sequences) > 0, "must have some sequences to map onto!"

        if max_outstanding == 'auto':
            max_outstanding = len(self)

        pf = PrePickled(f)

        map_id = secrets.token_bytes(16)

        # record that a map is outstanding, mainly for Executor.shutdown
        self._outstanding_maps.add(map_id)

        def signal_done():
            nonlocal iterator_done
            iterator_done = True
            self._outstanding_maps.discard(map_id)

        outstanding_lock = threading.Lock()

        if ordered:
            outstanding = deque()
            add_outstanding = outstanding.append
        else:
            outstanding = set()
            add_outstanding = outstanding.add

        def wait_for_ready():
            while not outstanding and not iterator_done:
                # no outstanding futures, need to wait for something to wait for
                time.sleep(0.1)
            if not outstanding:
                # nothing to wait for, iterator_done is True
                return []

            if ordered:
                with outstanding_lock:
                    return [outstanding.popleft()]
            else:
                # unordered, yield whatever finishes first, as soon as it's ready
                # repeat with timeout because the consumer thread may be adding to `outstanding`
                with outstanding_lock:
                    to_wait = outstanding.copy()
                done, _ = concurrent.futures.wait(
                    to_wait,
                    return_when=concurrent.futures.FIRST_COMPLETED,
                    timeout=0.5,
                )
                if done:
                    with outstanding_lock:
                        for f in done:
                            outstanding.remove(f)
                return done

        arg_iterator = iter(zip(*sequences))
        iterator_done = False

        # consume inputs in _another_ thread,
        # to avoid blocking the IO thread with a possibly blocking generator
        # only need one thread for this, though.
        consumer_pool = concurrent.futures.ThreadPoolExecutor(1)

        def consume_callback(f):
            if not iterator_done:
                consumer_pool.submit(consume_next)

        def consume_next():
            """Consume the next call from the argument iterator

            If max_outstanding, schedules consumption when the result finishes.
            If running with no limit, schedules another consumption immediately.
            """
            nonlocal iterator_done
            if iterator_done:
                return

            try:
                args = next(arg_iterator)
                ar = self.apply_async(pf, *args)
            except StopIteration:
                signal_done()
                return
            except Exception as e:
                # exception consuming iterator, propagate
                ar = concurrent.futures.Future()
                # mock get so it gets re-raised when awaited
                ar.get = lambda *args: ar.result()
                ar.set_exception(e)
                with outstanding_lock:
                    add_outstanding(ar)
                signal_done()
                return

            with outstanding_lock:
                add_outstanding(ar)
            if max_outstanding:
                ar.add_done_callback(consume_callback)
            else:
                consumer_pool.submit(consume_next)

        # kick it off
        # only need one if not using max_outstanding,
        # as each eventloop tick will submit a new item
        # otherwise, start one consumer for each slot, which will chain
        kickoff_count = 1 if max_outstanding == 0 else max_outstanding
        submit_futures = []
        for i in range(kickoff_count):
            submit_futures.append(consumer_pool.submit(consume_next))

        # await the first one, just in case it raises
        try:
            submit_futures[0].result()
        except Exception:
            # make sure we clean up
            signal_done()
            raise
        del submit_futures

        # wrap result-yielding in another call
        # because if this function is itself a generator
        # the first submission won't happen until the first result is requested
        def iter_results():
            nonlocal outstanding
            with consumer_pool:
                while not iterator_done:
                    # yield results as they become ready
                    for ready_ar in wait_for_ready():
                        yield ready_ar.get(return_exceptions=return_exceptions)

            # yield any remaining results
            if ordered:
                for ar in outstanding:
                    yield ar.get(return_exceptions=return_exceptions)
            else:
                while outstanding:
                    done, outstanding = concurrent.futures.wait(
                        outstanding, return_when=concurrent.futures.FIRST_COMPLETED
                    )
                    for ar in done:
                        yield ar.get(return_exceptions=return_exceptions)

        return LazyMapIterator(iter_results(), signal_done)

    def register_joblib_backend(self, name='ipyparallel', make_default=False):
        """Register this View as a joblib parallel backend

        To make this the default backend, set make_default=True.

        Use with::

            p = Parallel(backend='ipyparallel')
            ...

        See joblib docs for details

        Requires joblib >= 0.10

        .. versionadded:: 5.1
        """
        from joblib.parallel import register_parallel_backend

        from ._joblib import IPythonParallelBackend

        register_parallel_backend(
            name,
            lambda **kwargs: IPythonParallelBackend(view=self, **kwargs),
            make_default=make_default,
        )


class ViewExecutor(concurrent.futures.Executor):
    """A PEP-3148 Executor API for Views

    Access as view.executor
    """

    def __init__(self, view):
        self.view = view
        self._max_workers = len(self.view)

    def submit(self, fn, *args, **kwargs):
        """Same as View.apply_async"""
        return self.view.apply_async(fn, *args, **kwargs)

    def map(self, func, *iterables, **kwargs):
        """Return generator for View.map_async"""
        if 'timeout' in kwargs:
            warnings.warn("timeout unsupported in ViewExecutor.map")
            kwargs.pop('timeout')
        return self.view.imap(func, *iterables, **kwargs)

    def shutdown(self, wait=True):
        """ViewExecutor does *not* shutdown engines

        results are awaited if wait=True, but engines are *not* shutdown.
        """
        if wait:
            # wait for *submission* of outstanding maps,
            # otherwise view.wait won't know what to wait for
            outstanding_maps = getattr(self.view, "_outstanding_maps")
            if outstanding_maps:
                while outstanding_maps:
                    time.sleep(0.1)
            self.view.wait()


__all__ = ['LoadBalancedView', 'DirectView', 'ViewExecutor', 'BroadcastView']
ipyparallel-8.8.0/ipyparallel/cluster/000077500000000000000000000000001460376056100200545ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/cluster/__init__.py000066400000000000000000000000371460376056100221650ustar00rootroot00000000000000from .cluster import *  # noqa
ipyparallel-8.8.0/ipyparallel/cluster/__main__.py000066400000000000000000000001011460376056100221360ustar00rootroot00000000000000if __name__ == '__main__':
    from .app import main

    main()
ipyparallel-8.8.0/ipyparallel/cluster/_winhpcjob.py000066400000000000000000000244241460376056100225560ustar00rootroot00000000000000"""
Job and task components for writing .xml files that the Windows HPC Server
2008 can use to start jobs.
"""

import os
import re
import uuid
from xml.etree import ElementTree as ET

from traitlets import Bool, Enum, Instance, Integer, List, Unicode
from traitlets.config.configurable import Configurable

# -----------------------------------------------------------------------------
# Job and Task classes
# -----------------------------------------------------------------------------


def as_str(value):
    if isinstance(value, str):
        return value
    elif isinstance(value, bool):
        if value:
            return 'true'
        else:
            return 'false'
    elif isinstance(value, (int, float)):
        return repr(value)
    else:
        return value


def indent(elem, level=0):
    i = "\n" + level * "  "
    if len(elem):
        if not elem.text or not elem.text.strip():
            elem.text = i + "  "
        if not elem.tail or not elem.tail.strip():
            elem.tail = i
        for elem in elem:
            indent(elem, level + 1)
        if not elem.tail or not elem.tail.strip():
            elem.tail = i
    else:
        if level and (not elem.tail or not elem.tail.strip()):
            elem.tail = i


def find_username():
    domain = os.environ.get('USERDOMAIN')
    username = os.environ.get('USERNAME', '')
    if domain is None:
        return username
    else:
        return f'{domain}\\{username}'


class WinHPCJob(Configurable):
    job_id = Unicode('')
    job_name = Unicode('MyJob', config=True)
    min_cores = Integer(1, config=True)
    max_cores = Integer(1, config=True)
    min_sockets = Integer(1, config=True)
    max_sockets = Integer(1, config=True)
    min_nodes = Integer(1, config=True)
    max_nodes = Integer(1, config=True)
    unit_type = Unicode("Core", config=True)
    auto_calculate_min = Bool(True, config=True)
    auto_calculate_max = Bool(True, config=True)
    run_until_canceled = Bool(False, config=True)
    is_exclusive = Bool(False, config=True)
    username = Unicode(find_username(), config=True)
    job_type = Unicode('Batch', config=True)
    priority = Enum(
        ('Lowest', 'BelowNormal', 'Normal', 'AboveNormal', 'Highest'),
        default_value='Highest',
        config=True,
    )
    requested_nodes = Unicode('', config=True)
    project = Unicode('IPython', config=True)
    xmlns = Unicode('http://schemas.microsoft.com/HPCS2008/scheduler/')
    version = Unicode("2.000")
    tasks = List([])

    @property
    def owner(self):
        return self.username

    def _write_attr(self, root, attr, key):
        s = as_str(getattr(self, attr, ''))
        if s:
            root.set(key, s)

    def as_element(self):
        # We have to add _A_ type things to get the right order than
        # the MSFT XML parser expects.
        root = ET.Element('Job')
        self._write_attr(root, 'version', '_A_Version')
        self._write_attr(root, 'job_name', '_B_Name')
        self._write_attr(root, 'unit_type', '_C_UnitType')
        self._write_attr(root, 'min_cores', '_D_MinCores')
        self._write_attr(root, 'max_cores', '_E_MaxCores')
        self._write_attr(root, 'min_sockets', '_F_MinSockets')
        self._write_attr(root, 'max_sockets', '_G_MaxSockets')
        self._write_attr(root, 'min_nodes', '_H_MinNodes')
        self._write_attr(root, 'max_nodes', '_I_MaxNodes')
        self._write_attr(root, 'run_until_canceled', '_J_RunUntilCanceled')
        self._write_attr(root, 'is_exclusive', '_K_IsExclusive')
        self._write_attr(root, 'username', '_L_UserName')
        self._write_attr(root, 'job_type', '_M_JobType')
        self._write_attr(root, 'priority', '_N_Priority')
        self._write_attr(root, 'requested_nodes', '_O_RequestedNodes')
        self._write_attr(root, 'auto_calculate_max', '_P_AutoCalculateMax')
        self._write_attr(root, 'auto_calculate_min', '_Q_AutoCalculateMin')
        self._write_attr(root, 'project', '_R_Project')
        self._write_attr(root, 'owner', '_S_Owner')
        self._write_attr(root, 'xmlns', '_T_xmlns')
        dependencies = ET.SubElement(root, "Dependencies")
        etasks = ET.SubElement(root, "Tasks")
        for t in self.tasks:
            etasks.append(t.as_element())
        return root

    def tostring(self):
        """Return the string representation of the job description XML."""
        root = self.as_element()
        indent(root)
        txt = ET.tostring(root, encoding="utf-8").decode('utf-8')
        # Now remove the tokens used to order the attributes.
        txt = re.sub(r'_[A-Z]_', '', txt)
        txt = '\n' + txt
        return txt

    def write(self, filename):
        """Write the XML job description to a file."""
        txt = self.tostring()
        with open(filename, 'w') as f:
            f.write(txt)

    def add_task(self, task):
        """Add a task to the job.

        Parameters
        ----------
        task : :class:`WinHPCTask`
            The task object to add.
        """
        self.tasks.append(task)


class WinHPCTask(Configurable):
    task_id = Unicode('')
    task_name = Unicode('')
    version = Unicode("2.000")
    min_cores = Integer(1, config=True)
    max_cores = Integer(1, config=True)
    min_sockets = Integer(1, config=True)
    max_sockets = Integer(1, config=True)
    min_nodes = Integer(1, config=True)
    max_nodes = Integer(1, config=True)
    unit_type = Unicode("Core", config=True)
    command_line = Unicode('', config=True)
    work_directory = Unicode('', config=True)
    is_rerunnaable = Bool(True, config=True)
    std_out_file_path = Unicode('', config=True)
    std_err_file_path = Unicode('', config=True)
    is_parametric = Bool(False, config=True)
    environment_variables = Instance(dict, args=(), config=True)

    def _write_attr(self, root, attr, key):
        s = as_str(getattr(self, attr, ''))
        if s:
            root.set(key, s)

    def as_element(self):
        root = ET.Element('Task')
        self._write_attr(root, 'version', '_A_Version')
        self._write_attr(root, 'task_name', '_B_Name')
        self._write_attr(root, 'min_cores', '_C_MinCores')
        self._write_attr(root, 'max_cores', '_D_MaxCores')
        self._write_attr(root, 'min_sockets', '_E_MinSockets')
        self._write_attr(root, 'max_sockets', '_F_MaxSockets')
        self._write_attr(root, 'min_nodes', '_G_MinNodes')
        self._write_attr(root, 'max_nodes', '_H_MaxNodes')
        self._write_attr(root, 'command_line', '_I_CommandLine')
        self._write_attr(root, 'work_directory', '_J_WorkDirectory')
        self._write_attr(root, 'is_rerunnaable', '_K_IsRerunnable')
        self._write_attr(root, 'std_out_file_path', '_L_StdOutFilePath')
        self._write_attr(root, 'std_err_file_path', '_M_StdErrFilePath')
        self._write_attr(root, 'is_parametric', '_N_IsParametric')
        self._write_attr(root, 'unit_type', '_O_UnitType')
        root.append(self.get_env_vars())
        return root

    def get_env_vars(self):
        env_vars = ET.Element('EnvironmentVariables')
        for k, v in self.environment_variables.items():
            variable = ET.SubElement(env_vars, "Variable")
            name = ET.SubElement(variable, "Name")
            name.text = k
            value = ET.SubElement(variable, "Value")
            value.text = v
        return env_vars


# By declaring these, we can configure the controller and engine separately!


class IPControllerJob(WinHPCJob):
    job_name = Unicode('IPController', config=False)
    is_exclusive = Bool(False, config=True)
    username = Unicode(find_username(), config=True)
    priority = Enum(
        ('Lowest', 'BelowNormal', 'Normal', 'AboveNormal', 'Highest'),
        default_value='Highest',
        config=True,
    )
    requested_nodes = Unicode('', config=True)
    project = Unicode('IPython', config=True)


class IPEngineSetJob(WinHPCJob):
    job_name = Unicode('IPEngineSet', config=False)
    is_exclusive = Bool(False, config=True)
    username = Unicode(find_username(), config=True)
    priority = Enum(
        ('Lowest', 'BelowNormal', 'Normal', 'AboveNormal', 'Highest'),
        default_value='Highest',
        config=True,
    )
    requested_nodes = Unicode('', config=True)
    project = Unicode('IPython', config=True)


class IPControllerTask(WinHPCTask):
    task_name = Unicode('IPController', config=True)
    controller_cmd = List(['ipcontroller.exe'], config=True)
    controller_args = List(['--log-level=40'], config=True)
    # I don't want these to be configurable
    std_out_file_path = Unicode('', config=False)
    std_err_file_path = Unicode('', config=False)
    min_cores = Integer(1, config=False)
    max_cores = Integer(1, config=False)
    min_sockets = Integer(1, config=False)
    max_sockets = Integer(1, config=False)
    min_nodes = Integer(1, config=False)
    max_nodes = Integer(1, config=False)
    unit_type = Unicode("Core", config=False)
    work_directory = Unicode('', config=False)

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        the_uuid = uuid.uuid1()
        self.std_out_file_path = os.path.join('log', 'ipcontroller-%s.out' % the_uuid)
        self.std_err_file_path = os.path.join('log', 'ipcontroller-%s.err' % the_uuid)

    @property
    def command_line(self):
        return ' '.join(self.controller_cmd + self.controller_args)


class IPEngineTask(WinHPCTask):
    task_name = Unicode('IPEngine', config=True)
    engine_cmd = List(['ipengine.exe'], config=True)
    engine_args = List(['--log-level=40'], config=True)
    # I don't want these to be configurable
    std_out_file_path = Unicode('', config=False)
    std_err_file_path = Unicode('', config=False)
    min_cores = Integer(1, config=False)
    max_cores = Integer(1, config=False)
    min_sockets = Integer(1, config=False)
    max_sockets = Integer(1, config=False)
    min_nodes = Integer(1, config=False)
    max_nodes = Integer(1, config=False)
    unit_type = Unicode("Core", config=False)
    work_directory = Unicode('', config=False)

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        the_uuid = uuid.uuid1()
        self.std_out_file_path = os.path.join('log', 'ipengine-%s.out' % the_uuid)
        self.std_err_file_path = os.path.join('log', 'ipengine-%s.err' % the_uuid)

    @property
    def command_line(self):
        return ' '.join(self.engine_cmd + self.engine_args)
ipyparallel-8.8.0/ipyparallel/cluster/app.py000077500000000000000000000577041460376056100212260ustar00rootroot00000000000000#!/usr/bin/env python
"""The ipcluster application."""

import asyncio
import errno
import json
import logging
import os
import re
import signal
import sys
from functools import partial

import entrypoints
import zmq
from IPython.core.profiledir import ProfileDir
from traitlets import Bool, CaselessStrEnum, Dict, Integer, List, default
from traitlets.config.application import catch_config_error

from ipyparallel._version import __version__
from ipyparallel.apps.baseapp import BaseParallelApplication, base_aliases, base_flags
from ipyparallel.cluster import Cluster, ClusterManager, clean_cluster_files
from ipyparallel.util import abbreviate_profile_dir

# -----------------------------------------------------------------------------
# Module level variables
# -----------------------------------------------------------------------------

_description = """Start an IPython cluster for parallel computing.

An IPython cluster consists of 1 controller and 1 or more engines.
This command automates the startup of these processes using a wide range of
startup methods (SSH, local processes, PBS, mpiexec, SGE, LSF, HTCondor,
Slurm, Windows HPC Server 2008). To start a cluster with 4 engines on your
local host simply do 'ipcluster start --n=4'. For more complex usage
you will typically do 'ipython profile create mycluster --parallel', then edit
configuration files, followed by 'ipcluster start --profile=mycluster --n=4'.
"""

_main_examples = """
ipcluster start --n=4 # start a 4 node cluster on localhost
ipcluster start -h    # show the help string for the start subcmd

ipcluster stop -h     # show the help string for the stop subcmd
ipcluster engines -h  # show the help string for the engines subcmd
"""

_start_examples = """
ipython profile create mycluster --parallel # create mycluster profile
ipcluster start --profile=mycluster --n=4   # start mycluster with 4 nodes

# args to `ipcluster start` after `--` are passed through to the controller
# see `ipcontroller -h` for options
ipcluster start -- --sqlitedb
"""

_stop_examples = """
ipcluster stop --profile=mycluster  # stop a running cluster by profile name
"""

_engines_examples = """
ipcluster engines --profile=mycluster --n=4  # start 4 engines only
"""


# Exit codes for ipcluster

# This will be the exit code if the ipcluster appears to be running because
# a .pid file exists
ALREADY_STARTED = 10


# This will be the exit code if ipcluster stop is run, but there is not .pid
# file to be found.
ALREADY_STOPPED = 11

# This will be the exit code if ipcluster engines is run, but there is not .pid
# file to be found.
NO_CLUSTER = 12


# -----------------------------------------------------------------------------
# Main application
# -----------------------------------------------------------------------------

start_help = """Start an IPython cluster for parallel computing

Start an ipython cluster by its profile name or cluster
directory. Cluster directories contain configuration, log and
security related files and are named using the convention
'profile_' and should be creating using the 'start'
subcommand of 'ipcluster'. If your cluster directory is in
the cwd or the ipython directory, you can simply refer to it
using its profile name, 'ipcluster start --n=4 --profile=`,
otherwise use the 'profile-dir' option.
"""
stop_help = """Stop a running IPython cluster

Stop a running ipython cluster by its profile name or cluster
directory. Cluster directories are named using the convention
'profile_'. If your cluster directory is in
the cwd or the ipython directory, you can simply refer to it
using its profile name, 'ipcluster stop --profile=`, otherwise
use the '--profile-dir' option.
"""
engines_help = """Start engines connected to an existing IPython cluster

Start one or more engines to connect to an existing Cluster
by profile name or cluster directory.
Cluster directories contain configuration, log and
security related files and are named using the convention
'profile_' and should be creating using the 'start'
subcommand of 'ipcluster'. If your cluster directory is in
the cwd or the ipython directory, you can simply refer to it
using its profile name, 'ipcluster engines --n=4 --profile=`,
otherwise use the 'profile-dir' option.
"""
stop_aliases = dict(
    signal='IPClusterStop.signal',
)
stop_aliases.update(base_aliases)
stop_flags = dict(
    all=(
        {'IPClusterStop': {'all_cluster_ids': True}},
        "stop all clusters for the selected profile, instead of just one",
    )
)
stop_flags.update(base_flags)


class IPClusterStop(BaseParallelApplication):
    name = 'ipcluster'
    description = stop_help
    examples = _stop_examples

    signal = Integer(
        signal.SIGINT, config=True, help="signal to use for stopping processes."
    )
    all_cluster_ids = Bool(
        False,
        config=True,
        help="stop all clusters for the selected profile, instead of just one",
    )

    aliases = stop_aliases
    flags = stop_flags

    def start(self):
        """Start the app for the stop subcommand."""

        if self.all_cluster_ids:
            clusters = list(
                ClusterManager(parent=self)
                .load_clusters(profile_dir=self.profile_dir.location)
                .values()
            )
        else:
            if self.extra_args:
                cluster_ids = self.extra_args
            else:
                cluster_ids = [self.cluster_id]

            clusters = []

            for cluster_id in cluster_ids:
                try:
                    cluster = Cluster.from_file(
                        profile_dir=self.profile_dir.location,
                        cluster_id=cluster_id,
                        parent=self,
                    )
                except FileNotFoundError as s:
                    self.log.critical(f"Could not find cluster file {s}")
                    self.exit(ALREADY_STOPPED)
                else:
                    clusters.append(cluster)

        if not clusters:
            self.log.info("No clusters to stop")
            self.exit(0)

        async def _stop_all():
            tasks = []
            for cluster in clusters:
                self.log.info(f"Stopping cluster {cluster.cluster_id}")
                tasks.append(cluster.stop_cluster())
            await asyncio.gather(*tasks)

        asyncio.run(_stop_all())


list_aliases = {}
list_aliases.update(base_aliases)
list_aliases.update({"o": "IPClusterList.output_format"})


class IPClusterList(BaseParallelApplication):
    name = 'ipcluster'
    description = "List available clusters"
    aliases = list_aliases

    output_format = CaselessStrEnum(
        ["text", "json"], default_value="text", config=True, help="Output format"
    )

    def start(self):
        profile_dirs = None
        if (
            self.profile != "default"
            or "ProfileDir" in self.config
            and "location" in self.config.ProfileDir
        ):
            # profile-directory specified, only consider
            profile_dirs = [self.profile_dir.location]
        cluster_manager = ClusterManager(parent=self)
        clusters = cluster_manager.load_clusters(profile_dirs=profile_dirs)
        if self.output_format == "text":
            # TODO: measure needed profile/cluster id width
            print(
                f"{'PROFILE':16} {'CLUSTER ID':32} {'RUNNING':7} {'ENGINES':7} {'LAUNCHER'}"
            )
            for cluster in sorted(
                clusters.values(),
                key=lambda c: (
                    c.profile_dir,
                    c.cluster_id,
                ),
            ):
                profile = abbreviate_profile_dir(cluster.profile_dir)
                cluster_id = cluster.cluster_id
                running = bool(cluster.controller)
                # TODO: URL?
                engines = 0
                if cluster.engines:
                    engines = sum(
                        engine_set.n for engine_set in cluster.engines.values()
                    )

                launcher = cluster.engine_launcher_class.__name__
                if launcher.endswith("EngineSetLauncher"):
                    launcher = launcher[: -len("EngineSetLauncher")]
                print(
                    f"{profile:16} {cluster_id or repr(''):32} {str(running):7} {engines:7} {launcher}"
                )
        elif self.output_format == "json":
            json.dump(
                [cluster.to_dict() for cluster in clusters.values()],
                sys.stdout,
            )
        else:
            raise NotImplementedError(f"No such output format: {self.output_format}")


clean_flags = {}
for key in ('debug', 'quiet'):
    clean_flags[key] = base_flags[key]
clean_flags.update(
    {
        "force": (
            {"IPClusterClean": {"force": True}},
            """
            Force removal of files, even if clusters are running.

            WARNING: can leave orphan processes
            """,
        ),
        "all": (
            {"IPClusterClean": {"all_profiles": True}},
            "Clean all IPython profiles, not just current.",
        ),
    }
)
clean_aliases = {}
for key in ('log-level', 'ipython-dir', 'profile-dir', 'profile'):
    clean_aliases[key] = base_aliases[key]


class IPClusterClean(BaseParallelApplication):
    name = 'ipcluster'
    description = "Cleanup cluster files"
    flags = clean_flags
    aliases = clean_aliases

    force = Bool(
        False,
        config=True,
        help="""
            Force removal of cluster files, even if clusters appear to be running.

            WARNING: this can leave orphan processes.
            """,
    )
    all_profiles = Bool(
        False,
        config=True,
        help="Clean files in all profiles, instead of just the current profile.",
    )

    def start(self):
        if self.all_profiles:
            profile_dirs = None
        else:
            profile_dirs = [self.profile_dir.location]
        clean_cluster_files(profile_dirs, log=self.log, force=self.force)


engine_aliases = {}
engine_aliases.update(base_aliases)
engine_aliases.update(
    dict(
        n='Cluster.n',
        engines='Cluster.engine_launcher_class',
        daemonize='IPClusterEngines.daemonize',
    )
)
engine_flags = {}
engine_flags.update(base_flags)

engine_flags.update(
    dict(
        daemonize=(
            {'IPClusterEngines': {'daemonize': True}},
            """run the cluster into the background (not available on Windows)""",
        )
    )
)


class IPClusterEngines(BaseParallelApplication):
    name = 'ipcluster'
    description = engines_help
    examples = _engines_examples
    usage = None
    default_log_level = logging.INFO
    classes = List()

    @default("classes")
    def _classes_default(self):
        launcher_classes = []
        for kind in ('controller', 'engine'):
            group_name = f'ipyparallel.{kind}_launchers'
            group = entrypoints.get_group_named(group_name)
            for key, value in group.items():
                try:
                    cls = value.load()
                except Exception as e:
                    self.log.error(
                        f"Failed to load entrypoint {group_name}: {key} = {value}\n{e}"
                    )
                else:
                    launcher_classes.append(cls)
        return [ProfileDir, Cluster] + launcher_classes

    daemonize = Bool(
        False,
        config=True,
        help="""Launch the cluster and immediately exit.

        .. versionchanged:: 7.0
            No longer leaves the ipcluster process itself running.
            Prior to 7.0, --daemonize did not work on Windows.
        """,
    )

    early_shutdown = Integer(
        30,
        config=True,
        help="If engines stop in this time frame, assume something is wrong and tear down the cluster.",
    )
    _stopping = False

    aliases = Dict(engine_aliases)
    flags = Dict(engine_flags)

    @catch_config_error
    def initialize(self, argv=None):
        super().initialize(argv)
        self.init_signal()
        self.init_cluster()

    def init_deprecated_config(self):
        super().init_deprecated_config()
        cluster_config = self.config.Cluster
        for clsname in ['IPClusterStart', 'IPClusterEngines']:
            if clsname not in self.config:
                continue
            cls_config = self.config[clsname]
            for traitname in [
                'delay',
                'engine_launcher_class',
                'controller_launcher_class',
                'controller_ip',
                'controller_location',
                'n',
            ]:
                if traitname in cls_config and traitname not in cluster_config:
                    value = cls_config[traitname]
                    self.log.warning(
                        f"{clsname}.{traitname} = {value} configuration is deprecated in ipyparallel 7. Use Cluster.{traitname} = {value}"
                    )
                    cluster_config[traitname] = value
                    cls_config.pop(traitname)

    def init_cluster(self):
        self.cluster = Cluster(
            parent=self,
            profile_dir=self.profile_dir.location,
            cluster_id=self.cluster_id,
            controller_args=self.extra_args,
            shutdown_atexit=not self.daemonize,
        )

    def init_signal(self):
        # Setup signals
        for signame in ("SIGUSR1", "SIGUSR2", "SIGINFO"):
            try:
                signum = getattr(signal, signame)
            except AttributeError:
                self.log.debug(f"Not forwarding {signame}")
                pass
            else:
                self.log.debug(f"Forwarding {signame} to engines")
                signal.signal(signum, self.relay_signal)
        signal.signal(signal.SIGINT, self.sigint_handler)
        signal.signal(signal.SIGTERM, self.sigint_handler)

    def relay_signal(self, signum, frame):
        self.log.debug(f"Received signal {signum} received, relaying to engines...")
        self.loop.add_callback_from_signal(partial(self.cluster.signal_engines, signum))

    def sigint_handler(self, signum, frame):
        return self.relay_signal(signum, frame)

    def sigterm_handler(self, signum, frame):
        self.log.debug(f"Received signal {signum} received, stopping launchers...")
        self.loop.add_callback_from_signal(self.stop_cluster)

    def engines_started_ok(self):
        self.log.info("Engines appear to have started successfully")
        self.early_shutdown = 0

    async def start_engines(self):
        try:
            await self.cluster.start_engines()
        except BaseException:
            self.log.exception("Engine start failed")
            self.exit(1)

        if self.daemonize:
            self.loop.add_callback(self.loop.stop)
            return

        self.watch_engines()

    def watch_engines(self):
        """Watch for early engine shutdown"""
        self.engine_launcher = self.cluster.engine_set

        if not self.early_shutdown:
            self.engine_launcher.on_stop(self.engines_stopped)
            return

        # TODO: enable 'engines stopped early' with new cluster API
        self.engine_launcher.on_stop(self.engines_stopped_early)
        if self.early_shutdown:
            self.loop.add_timeout(
                self.loop.time() + self.early_shutdown, self.engines_started_ok
            )

    def engines_stopped_early(self, stop_data):
        if self.early_shutdown and not self._stopping:
            self.log.error(
                """
            Engines shutdown early, they probably failed to connect.

            Check the engine log files for output.

            If your controller and engines are not on the same machine, you probably
            have to instruct the controller to listen on an interface other than localhost.

            You can set this by adding "--ip=*" to your ControllerLauncher.controller_args.

            Be sure to read our security docs before instructing your controller to listen on
            a public interface.
            """
            )
            engine_output = self.engine_launcher.get_output(remove=True)
            if engine_output:
                self.log.error(f"Engine output:\n{engine_output}")
            self.loop.add_callback(self.stop_cluster)

        return self.engines_stopped(stop_data)

    def engines_stopped(self, r):
        return self.loop.stop()

    async def stop_cluster(self, r=None):
        if not self._stopping:
            self._stopping = True
            self.log.error("IPython cluster: stopping")
            await self.cluster.stop_cluster()
            self.loop.add_callback(self.loop.stop)

    def start_logging(self):
        # Remove old log files of the controller and engine
        if self.clean_logs:
            log_dir = self.profile_dir.log_dir
            for f in os.listdir(log_dir):
                if re.match(r'ip(engine|controller)-.+\.(log|err|out)', f):
                    os.remove(os.path.join(log_dir, f))

    def start(self):
        """Start the app for the engines subcommand."""
        self.log.info(f"IPython cluster start: {self.cluster_id}")
        # First see if the cluster is already running

        # Now log and daemonize
        self.log.info('Starting engines with [daemon=%r]' % self.daemonize)

        self.loop.add_callback(self.start_engines)
        # Now write the new pid file AFTER our new forked pid is active.
        # self.write_pid_file()
        try:
            self.loop.start()
        except KeyboardInterrupt:
            pass
        except zmq.ZMQError as e:
            if e.errno == errno.EINTR:
                pass
            else:
                raise


start_aliases = {}
start_aliases.update(engine_aliases)
start_aliases.update(
    dict(
        delay='Cluster.delay',
        controller='Cluster.controller_launcher_class',
        ip='Cluster.controller_ip',
        location='Cluster.controller_location',
    )
)
start_aliases['clean-logs'] = 'IPClusterStart.clean_logs'


class IPClusterStart(IPClusterEngines):
    name = 'ipcluster'
    description = start_help
    examples = _start_examples
    default_log_level = logging.INFO
    auto_create = Bool(
        True, config=True, help="whether to create the profile_dir if it doesn't exist"
    )

    clean_logs = Bool(
        True, config=True, help="whether to cleanup old logs before starting"
    )

    # flags = Dict(flags)
    aliases = Dict(start_aliases)

    def engines_stopped(self, r):
        """prevent parent.engines_stopped from stopping everything on engine shutdown"""
        pass

    async def start_cluster(self):
        await self.cluster.start_cluster()
        if self.daemonize:
            print(
                f"Leaving cluster running: {self.cluster.cluster_file}", file=sys.stderr
            )
            self.loop.add_callback(self.loop.stop)
        self.cluster.controller.on_stop(self.controller_stopped)
        self.watch_engines()

    def controller_stopped(self, stop_data):
        if not self._stopping:
            self.log.warning("Controller stopped. Shutting down.")
            self.loop.add_callback(self.stop_cluster)

    def sigint_handler(self, signum, frame):
        """Unlike engines, SIGINT shuts down `ipcluster start`"""
        self.log.debug(f"Received signal {signum} received, stopping launchers...")
        self.loop.add_callback_from_signal(self.stop_cluster)

    def start(self):
        """Start the app for the start subcommand."""
        # First see if the cluster is already running
        cluster_file = self.cluster.cluster_file
        if os.path.isfile(cluster_file):
            try:
                cluster = Cluster.from_file(cluster_file)
            except Exception as e:
                # TODO: define special ClusterNotRunning exception to handle here
                self.log.error(
                    f"Error loading cluster from file {cluster_file}: {e}. Assuming stopped cluster."
                )
            else:
                self.log.critical(
                    f'Cluster is already running at {self.cluster.cluster_file}. '
                    'use `ipcluster stop` to stop the cluster.'
                )
                # Here I exit with a unusual exit status that other processes
                # can watch for to learn how I existed.
                self.exit(ALREADY_STARTED)

        # Now log and daemonize
        self.log.info('Starting ipcluster with [daemonize=%r]' % self.daemonize)

        self.loop.add_callback(self.start_cluster)
        try:
            self.loop.start()
        except KeyboardInterrupt:
            pass
        except zmq.ZMQError as e:
            if e.errno == errno.EINTR:
                pass
            else:
                raise
        finally:
            if not self.daemonize:
                self.cluster.stop_cluster_sync()


class IPClusterNBExtension(BaseParallelApplication):
    """Enable/disable ipcluster tab extension in Jupyter notebook"""

    name = 'ipcluster-nbextension'

    description = """(DEPRECATED) Enable/disable IPython clusters tab in classic Jupyter notebook

    Only for the deprecated jupyter-notebook < 7.0.

    For current jupyter-server implementations (jupyterlab and jupyter-notebook 7):

        jupyter server extension enable ipyparallel

    for Jupyter Notebook >= 4.2, you can use the new nbextension API:

    jupyter serverextension enable --py ipyparallel
    jupyter nbextension install --py ipyparallel
    jupyter nbextension enable --py ipyparallel
    """

    examples = """
    ipcluster nbextension enable
    ipcluster nbextension disable
    """
    version = __version__
    user = Bool(False, help="Apply the operation only for the given user").tag(
        config=True
    )
    flags = Dict(
        {
            'user': (
                {'IPClusterNBExtension': {'user': True}},
                'Apply the operation only for the given user',
            )
        }
    )

    def start(self):
        if len(self.extra_args) != 1:
            self.exit("Must specify 'enable' or 'disable'")
        action = self.extra_args[0].lower()

        print(
            "WARNING: `ipcluster nbextension` is deprecated. Use `jupyter server extension enable ipyparallel`",
            file=sys.stderr,
        )

        from ipyparallel.util import _v

        try:
            import notebook
        except ImportError:
            self.exit(
                "Deprecated `ipcluster nbextension` requires `notebook<7`, no `notebook` package found."
            )

        if _v(notebook.__version__) >= _v('7'):
            self.exit(
                "Deprecated `ipcluster nbextension` requires `notebook<7`, found `notebook=={notebook.__version__}`."
            )

        from ipyparallel.nbextension.install import install_extensions

        if action == 'enable':
            print("Enabling IPython clusters tab", file=sys.stderr)
            install_extensions(enable=True, user=self.user)
        elif action == 'disable':
            print("Disabling IPython clusters tab", file=sys.stderr)
            install_extensions(enable=False, user=self.user)
        else:
            self.exit("Must specify 'enable' or 'disable', not '%s'" % action)


class IPCluster(BaseParallelApplication):
    name = 'ipcluster'
    description = _description
    examples = _main_examples
    version = __version__

    _deprecated_classes = ["IPClusterApp"]

    subcommands = {
        'start': (IPClusterStart, start_help),
        'stop': (IPClusterStop, stop_help),
        'engines': (IPClusterEngines, engines_help),
        'list': (IPClusterList, IPClusterList.description),
        'clean': (IPClusterClean, IPClusterClean.description),
        'nbextension': (IPClusterNBExtension, IPClusterNBExtension.description),
    }

    # no aliases or flags for parent App
    aliases = Dict()
    flags = Dict()

    def start(self):
        if self.subapp is None:
            keys = ', '.join(f"'{key}'" for key in self.subcommands.keys())
            print("No subcommand specified. Must specify one of: %s" % keys)
            print()
            self.print_description()
            self.print_subcommands()
            self.exit(1)
        else:
            return self.subapp.start()


main = IPCluster.launch_instance

if __name__ == '__main__':
    main()
ipyparallel-8.8.0/ipyparallel/cluster/cluster.py000066400000000000000000001073341460376056100221170ustar00rootroot00000000000000"""Cluster class

defines the basic interface to a single IPython Parallel cluster

starts/stops/polls controllers, engines, etc.
"""

import asyncio
import atexit
import glob
import inspect
import json
import logging
import os
import random
import string
import sys
import time
import traceback
from functools import partial
from multiprocessing import cpu_count
from weakref import WeakSet

import IPython
from traitlets import (
    Any,
    Bool,
    Dict,
    Float,
    Instance,
    Integer,
    List,
    Unicode,
    default,
    import_item,
    validate,
)
from traitlets.config import Application, Config, LoggingConfigurable

from .._async import AsyncFirst
from ..traitlets import Launcher
from ..util import (
    _all_profile_dirs,
    _default_profile_dir,
    _locate_profiles,
    _traitlet_signature,
    abbreviate_profile_dir,
)
from . import launcher

_suffix_chars = string.ascii_lowercase + string.digits

# weak set of clusters to be cleaned up at exit
_atexit_clusters = WeakSet()


def _atexit_cleanup_clusters(*args):
    """Cleanup clusters during process shutdown"""
    for cluster in _atexit_clusters:
        if not cluster.shutdown_atexit:
            # overridden after register
            continue
        if cluster.controller or cluster.engines:
            print(f"Stopping cluster {cluster}", file=sys.stderr)
            try:
                cluster.stop_cluster_sync()
            except Exception:
                print(f"Error stopping cluster {cluster}", file=sys.stderr)
                traceback.print_exception(*sys.exc_info())


_atexit_cleanup_clusters.registered = False


@_traitlet_signature
class Cluster(AsyncFirst, LoggingConfigurable):
    """Class representing an IPP cluster

    i.e. one controller and one or more groups of engines

    Can start/stop/monitor/poll cluster resources

    All async methods can be called synchronously with a `_sync` suffix,
    e.g. `cluster.start_cluster_sync()`

    .. versionchanged:: 8.0
        controller and engine launcher classes can be specified via
        `Cluster(controller='ssh', engines='mpi')`
        without the `_launcher_class` suffix.
    """

    # general configuration

    shutdown_atexit = Bool(
        True,
        help="""
        Shutdown the cluster at process exit.

        Set to False if you want to launch a cluster and leave it running
        after the launching process exits.
        """,
    )

    cluster_id = Unicode(help="The id of the cluster (default: random string)").tag(
        to_dict=True
    )

    @default("cluster_id")
    def _default_cluster_id(self):
        return f"{int(time.time())}-{''.join(random.choice(_suffix_chars) for i in range(4))}"

    profile_dir = Unicode(
        help="""The profile directory.

    Default priority:

    - specified explicitly
    - current IPython session
    - use profile name (default: 'default')

    """
    ).tag(to_dict=True)

    @default("profile_dir")
    def _default_profile_dir(self):
        return _default_profile_dir(profile=self.profile)

    @validate("profile_dir")
    def _validate_profile_dir(self, proposal):
        path = proposal.value
        if path:
            return os.path.abspath(path)
        return path

    profile = Unicode(
        "",
        help="""The profile name,
             a shortcut for specifying profile_dir within $IPYTHONDIR.""",
    )

    cluster_file = Unicode(
        help="The path to the cluster file for saving this cluster to disk"
    )

    @default("cluster_file")
    def _default_cluster_file(self):
        return os.path.join(
            self.profile_dir, "security", f"cluster-{self.cluster_id}.json"
        )

    engine_timeout = Integer(
        60,
        help="""Timeout to use when waiting for engines to register

        before giving up.
        """,
        config=True,
    )

    send_engines_connection_env = Bool(
        True,
        config=True,
        help="""
        Wait for controller's connection info before passing to engines
        via $IPP_CONNECTION_INFO environment variable.

        Set to False to start engines immediately
        without waiting for the controller's connection info to be available.

        When True, no connection file movement is required.
        False is mainly useful when submitting the controller may
        take a long time in a job queue,
        and the engines should enter the queue before the controller is running.

        .. versionadded:: 8.0
        """,
    )

    controller_launcher_class = Launcher(
        default_value=launcher.LocalControllerLauncher,
        entry_point_group='ipyparallel.controller_launchers',
        help="""The class for launching a Controller. Change this value if you want
        your controller to also be launched by a batch system, such as PBS,SGE,MPI,etc.

        Each launcher class has its own set of configuration options, for making sure
        it will work in your environment.

        Note that using a batch launcher for the controller *does not* put it
        in the same batch job as the engines, so they will still start separately.

        Third-party engine launchers can be registered via `ipyparallel.engine_launchers` entry point.

        They can be selected via case-insensitive abbreviation, e.g.

            c.Cluster.controller_launcher_class = 'SSH'

        or:

            ipcluster start --controller=MPI

        """,
        config=True,
    ).tag(alias="controller")

    engine_launcher_class = Launcher(
        default_value=launcher.LocalEngineSetLauncher,
        entry_point_group='ipyparallel.engine_launchers',
        help="""The class for launching a set of Engines. Change this value
        to use various batch systems to launch your engines, such as PBS,SGE,MPI,etc.
        Each launcher class has its own set of configuration options, for making sure
        it will work in your environment.

        Third-party engine launchers can be registered via `ipyparallel.engine_launchers` entry point.

        They can be selected via case-insensitive abbreviation, e.g.

            c.Cluster.engine_launcher_class = 'ssh'

        or:

            ipcluster start --engines=mpi

        """,
        config=True,
    ).tag(alias="engines")

    # controller configuration

    controller_args = List(
        Unicode(),
        config=True,
        help="Additional CLI args to pass to the controller.",
    ).tag(to_dict=True)
    controller_ip = Unicode(
        config=True, help="Set the IP address of the controller."
    ).tag(to_dict=True)
    controller_location = Unicode(
        config=True,
        help="""Set the location (hostname or ip) of the controller.

        This is used by engines and clients to locate the controller
        when the controller listens on all interfaces
        """,
    ).tag(to_dict=True)

    # engine configuration

    delay = Float(
        1.0,
        config=True,
        help="delay (in s) between starting the controller and the engines",
    ).tag(to_dict=True)

    n = Integer(
        None, allow_none=True, config=True, help="The number of engines to start"
    ).tag(to_dict=True)

    @default("parent")
    def _default_parent(self):
        """Default to inheriting config from current IPython session"""
        return IPython.get_ipython()

    log_level = Integer(logging.INFO)

    @default("log")
    def _default_log(self):
        if self.parent and self.parent is IPython.get_ipython():
            # log to stdout in an IPython session
            log = logging.getLogger(f"{__name__}.{self.cluster_id}")
            log.setLevel(self.log_level)

            handler = logging.StreamHandler(sys.stdout)
            log.handlers = [handler]
            log.propagate = False
            return log
        elif self.parent and getattr(self.parent, 'log', None) is not None:
            return self.parent.log
        elif Application.initialized():
            return Application.instance().log
        else:
            # set up our own logger
            log = logging.getLogger(f"{__name__}.{self.cluster_id}")
            log.setLevel(self.log_level)
            return log

    load_profile = Bool(
        True,
        config=True,
        help="""
        If True (default) load ipcluster config from profile directory, if present.
        """,
    )
    # private state
    controller = Any().tag(nosignature=True)
    engines = Dict().tag(nosignature=True)

    @property
    def engine_set(self):
        """Return the first engine set

        Most clusters have only one engine set,
        which is tedious to get to via the `engines` dict
        with random engine set ids.

        ..versionadded:: 8.0
        """
        if self.engines:
            return next(iter(self.engines.values()))

    profile_config = Instance(Config, allow_none=False).tag(nosignature=True)

    @default("profile_config")
    def _profile_config_default(self):
        """Load config from our profile"""
        if not self.load_profile or not os.path.isdir(self.profile_dir):
            # no profile dir, nothing to load
            return Config()

        from .app import BaseParallelApplication, IPClusterStart

        # look up if we are descended from an 'ipcluster' app
        # avoids repeated load of the current profile dir
        parents = []
        parent = self.parent
        while parent is not None:
            parents.append(parent)
            parent = parent.parent

        app_parents = list(
            filter(lambda p: isinstance(p, BaseParallelApplication), parents)
        )
        if app_parents:
            app_parent = app_parents[0]
        else:
            app_parent = None

        if (
            app_parent
            and app_parent.name == 'ipcluster'
            and app_parent.profile_dir.location == self.profile_dir
        ):
            # profile config already loaded by parent, nothing new to load
            return Config()

        self.log.debug(f"Loading profile {self.profile_dir}")
        # set profile dir via config
        config = Config()
        config.ProfileDir.location = self.profile_dir

        # load profile config via IPCluster
        app = IPClusterStart(config=config, log=self.log)
        # adds profile dir to config_files_path
        app.init_profile_dir()
        # adds system to config_files_path
        app.init_config_files()
        # actually load the config
        app.load_config_file(suppress_errors=False)
        return app.config

    @validate("config")
    def _merge_profile_config(self, proposal):
        direct_config = proposal.value
        if not self.load_profile:
            return direct_config
        profile_config = self.profile_config
        if not profile_config:
            return direct_config
        # priority ?! direct > profile
        config = Config()
        if profile_config:
            config.merge(profile_config)
        config.merge(direct_config)
        return config

    @default("config")
    def _default_config(self):
        if self.load_profile:
            return self.profile_config
        else:
            return Config()

    def __init__(self, *, engines=None, controller=None, **kwargs):
        """Construct a Cluster"""
        # handle more intuitive aliases, which match ipcluster cli args, etc.
        if engines is not None:
            if 'engine_launcher_class' in kwargs:
                raise TypeError(
                    "Only specify one of 'engines' or 'engine_launcher_class', not both"
                )
            kwargs['engine_launcher_class'] = engines
        if controller is not None:
            if 'controller_launcher_class' in kwargs:
                raise TypeError(
                    "Only specify one of 'controller' or 'controller_launcher_class', not both"
                )
            kwargs['controller_launcher_class'] = controller
        if 'parent' not in kwargs and 'config' not in kwargs:
            kwargs['parent'] = self._default_parent()

        super().__init__(**kwargs)

    def __del__(self):
        if not self.shutdown_atexit:
            return
        if self.controller or self.engines:
            self.stop_cluster_sync()

    def __repr__(self):
        fields = {
            "cluster_id": repr(self.cluster_id),
        }
        profile_dir = self.profile_dir
        profile_prefix = os.path.join(IPython.paths.get_ipython_dir(), "profile_")
        if profile_dir.startswith(profile_prefix):
            fields["profile"] = repr(profile_dir[len(profile_prefix) :])
        else:
            home_dir = os.path.expanduser("~")

            if profile_dir.startswith(home_dir + os.path.sep):
                # truncate $HOME/. -> ~/...
                profile_dir = "~" + profile_dir[len(home_dir) :]
            fields["profile_dir"] = repr(profile_dir)

        if self.controller:
            fields["controller"] = f"<{self.controller.state}>"
        if self.engines:
            fields["engine_sets"] = list(self.engines)

        fields_str = ', '.join(f"{key}={value}" for key, value in fields.items())

        return f"<{self.__class__.__name__}({fields_str})>"

    def to_dict(self):
        """Serialize a Cluster object for later reconstruction"""
        cluster_info = {}
        d = {"cluster": cluster_info}
        for attr in self.traits(to_dict=True):
            cluster_info[attr] = getattr(self, attr)

        def _cls_str(cls):
            return f"{cls.__module__}.{cls.__name__}"

        cluster_info["class"] = _cls_str(self.__class__)

        if self.controller and self.controller.state != 'after':
            d["controller"] = {
                "class": launcher.abbreviate_launcher_class(
                    self.controller_launcher_class
                ),
                "state": None,
            }
            d["controller"]["state"] = self.controller.to_dict()

        d["engines"] = {
            "class": launcher.abbreviate_launcher_class(self.engine_launcher_class),
            "sets": {},
        }
        sets = d["engines"]["sets"]
        for engine_set_id, engine_launcher in self.engines.items():
            if engine_launcher.state != 'after':
                sets[engine_set_id] = engine_launcher.to_dict()
        return d

    @classmethod
    def from_dict(cls, d, **kwargs):
        """Construct a Cluster from serialized state"""
        cluster_info = d["cluster"]
        if cluster_info.get("class"):
            specified_cls = import_item(cluster_info["class"])
            if specified_cls is not cls:
                # specified a custom Cluster class,
                # dispatch to from_dict from that class
                return specified_cls.from_dict(d, **kwargs)

        kwargs.setdefault("shutdown_atexit", False)
        self = cls(**kwargs)
        for attr in self.traits(to_dict=True):
            if attr in cluster_info:
                setattr(self, attr, cluster_info[attr])

        for attr in self.traits(to_dict=True):
            if attr in d:
                setattr(self, attr, d[attr])

        cluster_key = ClusterManager._cluster_key(self)

        if d.get("controller"):
            controller_info = d["controller"]
            self.controller_launcher_class = controller_info["class"]
            # after traitlet coercion, which imports strings
            cls = self.controller_launcher_class
            if controller_info["state"]:
                try:
                    self.controller = cls.from_dict(
                        controller_info["state"], parent=self
                    )
                except launcher.NotRunning as e:
                    self.log.error(f"Controller for {cluster_key} not running: {e}")
                else:
                    self.controller.on_stop(self._controller_stopped)

        engine_info = d.get("engines")
        if engine_info:
            self.engine_launcher_class = engine_info["class"]
            # after traitlet coercion, which imports strings
            cls = self.engine_launcher_class
            for engine_set_id, engine_state in engine_info.get("sets", {}).items():
                try:
                    self.engines[engine_set_id] = engine_set = cls.from_dict(
                        engine_state,
                        engine_set_id=engine_set_id,
                        parent=self,
                    )
                except launcher.NotRunning as e:
                    self.log.error(
                        f"Engine set {cluster_key}{engine_set_id} not running: {e}"
                    )
                else:
                    engine_set.on_stop(partial(self._engines_stopped, engine_set_id))

        # check if state changed
        if self.to_dict() != d:
            # if so, update our cluster file
            self.update_cluster_file()
        return self

    @classmethod
    def from_file(
        cls,
        cluster_file=None,
        *,
        profile=None,
        profile_dir=None,
        cluster_id='',
        **kwargs,
    ):
        """Load a Cluster object from a file

        Can specify a full path,
        or combination of profile, profile_dir, and/or cluster_id.

        With no arguments given, it will connect to a cluster created
        with `ipcluster start`.
        """

        if cluster_file is None:
            # determine cluster_file from profile/profile_dir

            kwargs['cluster_id'] = cluster_id
            if profile is not None:
                kwargs['profile'] = profile
            if profile_dir is not None:
                kwargs['profile_dir'] = profile_dir
            cluster_file = Cluster(**kwargs).cluster_file

        # ensure from_file preserves cluster_file, even if it moved
        kwargs.setdefault("cluster_file", cluster_file)
        with open(cluster_file) as f:
            return cls.from_dict(json.load(f), **kwargs)

    def write_cluster_file(self):
        """Write cluster info to disk for later loading"""
        os.makedirs(os.path.dirname(self.cluster_file), exist_ok=True)
        self.log.debug(f"Updating {self.cluster_file}")
        with open(self.cluster_file, "w") as f:
            json.dump(self.to_dict(), f)

    def remove_cluster_file(self):
        """Remove my cluster file."""
        try:
            os.remove(self.cluster_file)
        except FileNotFoundError:
            pass
        else:
            self.log.debug(f"Removed cluster file: {self.cluster_file}")

    def _is_running(self):
        """Return if we have any running components"""
        if self.controller and self.controller.state != 'after':
            return True
        if any(es.state != 'after' for es in self.engines.values()):
            return True
        return False

    def update_cluster_file(self):
        """Update my cluster file

        If cluster_file is disabled, do nothing
        If cluster is fully stopped, remove the file
        """
        if not self.cluster_file:
            # setting cluster_file='' disables saving to disk
            return

        if not self._is_running():
            self.remove_cluster_file()
        else:
            self.write_cluster_file()

    async def start_controller(self, **kwargs):
        """Start the controller

        Keyword arguments are passed to the controller launcher constructor
        """
        # start controller
        # retrieve connection info
        # webhook?
        if self.controller is not None:
            raise RuntimeError(
                "controller is already running. Call stopcontroller() first."
            )

        if self.shutdown_atexit:
            _atexit_clusters.add(self)
            if not _atexit_cleanup_clusters.registered:
                atexit.register(_atexit_cleanup_clusters)

        self.controller = controller = self.controller_launcher_class(
            work_dir='.',
            parent=self,
            log=self.log,
            profile_dir=self.profile_dir,
            cluster_id=self.cluster_id,
            **kwargs,
        )

        controller_args = getattr(controller, 'controller_args', None)
        if controller_args is None:

            def add_args(args):
                # only some Launchers support modifying controller args
                self.log.warning(
                    "Not adding controller args %s. "
                    "controller_args passthrough is not supported by %s",
                    args,
                    self.controller_launcher_class.__name__,
                )

        else:
            # copy to make sure change events fire
            controller_args = list(controller_args)
            add_args = controller_args.extend

        if self.controller_ip:
            add_args(['--ip=%s' % self.controller_ip])
        if self.controller_location:
            add_args(['--location=%s' % self.controller_location])
        if self.controller_args:
            add_args(self.controller_args)

        if controller_args is not None:
            # ensure we trigger trait observers after we are done
            self.controller.controller_args = list(controller_args)

        self.controller.on_stop(self._controller_stopped)
        r = self.controller.start()
        if inspect.isawaitable(r):
            await r

        self.update_cluster_file()

    def _controller_stopped(self, stop_data=None):
        """Callback when a controller stops"""
        if stop_data and stop_data.get("exit_code"):
            log = self.log.warning
        else:
            log = self.log.info
        log(f"Controller stopped: {stop_data}")
        self.update_cluster_file()

    def _new_engine_set_id(self):
        """Generate a new engine set id"""
        engine_set_id = base = f"{int(time.time())}"
        i = 1
        while engine_set_id in self.engines:
            engine_set_id = f"{base}-{i}"
            i += 1
        return engine_set_id

    async def start_engines(self, n=None, engine_set_id=None, **kwargs):
        """Start an engine set

        Returns an engine set id which can be used in stop_engines
        """
        # TODO: send engines connection info
        if engine_set_id is None:
            engine_set_id = self._new_engine_set_id()
        engine_set = self.engines[engine_set_id] = self.engine_launcher_class(
            work_dir='.',
            parent=self,
            log=self.log,
            profile_dir=self.profile_dir,
            cluster_id=self.cluster_id,
            engine_set_id=engine_set_id,
            **kwargs,
        )
        if self.send_engines_connection_env and self.controller:
            self.log.debug("Setting $IPP_CONNECTION_INFO environment")
            connection_info = await self.controller.get_connection_info()
            connection_info_json = json.dumps(connection_info["engine"])
            engine_set.environment["IPP_CONNECTION_INFO"] = connection_info_json

        if n is None:
            n = self.n
        n = getattr(engine_set, 'engine_count', n)
        if n is None:
            n = cpu_count()
        self.log.info(f"Starting {n or ''} engines with {self.engine_launcher_class}")
        r = engine_set.start(n)
        engine_set.on_stop(partial(self._engines_stopped, engine_set_id))
        if inspect.isawaitable(r):
            await r
        self.update_cluster_file()
        return engine_set_id

    def _engines_stopped(self, engine_set_id, stop_data=None):
        if stop_data and stop_data.get("exit_code"):
            log = self.log.warning
        else:
            log = self.log.info
        log(f"engine set stopped {engine_set_id}: {stop_data}")
        self.update_cluster_file()

    async def start_and_connect(self, n=None, activate=False):
        """Single call to start a cluster and connect a client

        If `activate` is given, a blocking DirectView on all engines will be created
        and activated, registering `%px` magics for use in IPython

        Example::

            rc = await Cluster(engines="mpi").start_and_connect(n=8, activate=True)

            %px print("hello, world!")

        Equivalent to::

            await self.start_cluster(n)
            client = await self.connect_client()
            await client.wait_for_engines(n, block=False)

        .. versionadded:: 7.1

        .. versionadded:: 8.1

            activate argument.
        """
        if n is None:
            n = self.n
        await self.start_cluster(n=n)
        client = await self.connect_client()

        if n is None:
            # number of engines to wait for
            # if not specified, derive current value from EngineSets
            n = sum(engine_set.n for engine_set in self.engines.values())

        if n:
            await asyncio.wrap_future(
                client.wait_for_engines(n, block=False, timeout=self.engine_timeout)
            )

        if activate:
            view = client[:]
            view.block = True
            view.activate()
        return client

    async def start_cluster(self, n=None):
        """Start a cluster

        starts one controller and n engines (default: self.n)

        .. versionchanged:: 7.1
            return self, to allow method chaining
        """
        if self.controller is None:
            await self.start_controller()
        if self.delay:
            await asyncio.sleep(self.delay)
        await self.start_engines(n)
        # return self to allow chaining
        return self

    async def stop_engines(self, engine_set_id=None):
        """Stop an engine set

        If engine_set_id is not given,
        all engines are stopped.
        """
        if engine_set_id is None:
            futures = []
            for engine_set_id in list(self.engines):
                futures.append(self.stop_engines(engine_set_id))
            if futures:
                await asyncio.gather(*futures)
            return
        self.log.info(f"Stopping engine(s): {engine_set_id}")
        engine_set = self.engines[engine_set_id]
        r = engine_set.stop()
        if inspect.isawaitable(r):
            await r
        # retrieve and cleanup output files
        engine_set.get_output(remove=True)
        self.engines.pop(engine_set_id)
        self.update_cluster_file()

    async def stop_engine(self, engine_id):
        """Stop one engine

        *May* stop all engines in a set,
        depending on EngineSet features (e.g. mpiexec)
        """
        raise NotImplementedError("How do we find an engine by id?")

    async def restart_engines(self, engine_set_id=None):
        """Restart an engine set"""
        if engine_set_id is None:
            for engine_set_id in list(self.engines):
                await self.restart_engines(engine_set_id)
            return
        engine_set = self.engines[engine_set_id]
        n = engine_set.n
        await self.stop_engines(engine_set_id)
        await self.start_engines(n, engine_set_id)

    async def restart_engine(self, engine_id):
        """Restart one engine

        *May* stop all engines in a set,
        depending on EngineSet features (e.g. mpiexec)
        """
        raise NotImplementedError("How do we find an engine by id?")

    async def signal_engine(self, signum, engine_id):
        """Signal one engine

        *May* signal all engines in a set,
        depending on EngineSet features (e.g. mpiexec)
        """
        raise NotImplementedError("How do we find an engine by id?")

    async def signal_engines(self, signum, engine_set_id=None):
        """Signal all engines in a set

        If no engine set is specified, signal all engine sets.
        """
        if engine_set_id is None:
            for engine_set_id in list(self.engines):
                await self.signal_engines(signum, engine_set_id)
            return
        self.log.info(f"Sending signal {signum} to engine(s) {engine_set_id}")
        engine_set = self.engines[engine_set_id]
        r = engine_set.signal(signum)
        if inspect.isawaitable(r):
            await r

    async def stop_controller(self):
        """Stop the controller"""
        if self.controller and self.controller.running:
            self.log.info("Stopping controller")
            r = self.controller.stop()
            if inspect.isawaitable(r):
                await r

        if self.controller:
            self.controller.get_output(remove=True)

        self.controller = None
        self.update_cluster_file()

    async def stop_cluster(self):
        """Stop the controller and all engines"""
        await asyncio.gather(self.stop_controller(), self.stop_engines())

    async def connect_client(self, **client_kwargs):
        """Return a client connected to the cluster"""
        # TODO: get connect info directly from controller
        # this assumes local files exist
        from ipyparallel import Client

        connection_info = self.controller.get_connection_info()
        if inspect.isawaitable(connection_info):
            connection_info = await connection_info

        return Client(
            connection_info['client'],
            cluster=self,
            profile_dir=self.profile_dir,
            cluster_id=self.cluster_id,
            **client_kwargs,
        )

    # context managers (both async and sync)
    _context_client = None

    async def __aenter__(self):
        client = self._context_client = await self.start_and_connect()
        return client

    async def __aexit__(self, *args):
        if self._context_client is not None:
            self._context_client.close()
            self._context_client = None
        await self.stop_engines()
        await self.stop_controller()

    def __enter__(self):
        client = self._context_client = self.start_and_connect_sync()
        return client

    def __exit__(self, *args):
        if self._context_client:
            self._context_client.close()
            self._context_client = None
        self.stop_engines_sync()
        self.stop_controller_sync()


class ClusterManager(LoggingConfigurable):
    """A manager of clusters

    Wraps Cluster, adding lookup/list by cluster id
    """

    clusters = Dict(help="My cluster objects")

    @staticmethod
    def _cluster_key(cluster):
        """Return a unique cluster key for a cluster

        Default is {profile}:{cluster_id}
        """
        return f"{abbreviate_profile_dir(cluster.profile_dir)}:{cluster.cluster_id}"

    @staticmethod
    def _cluster_files_in_profile_dir(profile_dir):
        """List clusters in a profile directory

        Returns list of cluster *files*
        """
        return glob.glob(os.path.join(profile_dir, "security", "cluster-*.json"))

    def load_clusters(
        self,
        *,
        profile_dirs=None,
        profile_dir=None,
        profiles=None,
        profile=None,
        init_default_clusters=False,
        **kwargs,
    ):
        """Populate a ClusterManager from cluster files on disk

        Load all cluster objects from the given profile directory(ies).

        Default is to find clusters in all IPython profiles,
        but profile directories or profile names can be specified explicitly.

        If `init_default_clusters` is True,
        a stopped Cluster object is loaded for every profile dir
        with cluster_id="" if no running cluster is found.

        Priority:

        - profile_dirs list
        - single profile_dir
        - profiles list by name
        - single profile by name
        - all IPython profiles, if nothing else specified
        """

        # first, check our current clusters
        for key, cluster in list(self.clusters.items()):
            # remove stopped clusters
            # but not *new* clusters that haven't started yet
            # if `cluster.controller` is present
            # that means it was running at some point
            if cluster.controller and not cluster._is_running():
                self.log.info(f"Removing stopped cluster {key}")
                self.clusters.pop(key)

        if profile_dirs is None:
            if profile_dir is not None:
                profile_dirs = [profile_dir]
            else:
                if profiles is None:
                    if profile is not None:
                        profiles = [profile]

                if profiles is not None:
                    profile_dirs = _locate_profiles(profiles)

            if profile_dirs is None:
                # totally unspecified, default to all
                profile_dirs = _all_profile_dirs()

        by_cluster_file = {c.cluster_file: c for c in self.clusters.values()}
        for profile_dir in profile_dirs:
            cluster_files = self._cluster_files_in_profile_dir(profile_dir)
            # load default cluster for each profile
            # TODO: only if it has any ipyparallel config files
            # *or* it's the default profile
            if init_default_clusters and not cluster_files:
                cluster = Cluster(profile_dir=profile_dir, cluster_id="")
                cluster_key = self._cluster_key(cluster)
                if cluster_key not in self.clusters:
                    self.clusters[cluster_key] = cluster

            for cluster_file in cluster_files:
                if cluster_file in by_cluster_file:
                    # already loaded, skip it
                    continue
                self.log.debug(f"Loading cluster file {cluster_file}")
                try:
                    cluster = Cluster.from_file(cluster_file, parent=self)
                except Exception as e:
                    self.log.warning(f"Failed to load cluster from {cluster_file}: {e}")
                    continue
                else:
                    cluster_key = self._cluster_key(cluster)
                    self.clusters[cluster_key] = cluster

        return self.clusters

    def new_cluster(self, **kwargs):
        """Create a new cluster"""
        cluster = Cluster(parent=self, **kwargs)
        cluster_key = self._cluster_key(cluster)
        if cluster_key in self.clusters:
            raise KeyError(f"Cluster {cluster_key} already exists!")
        self.clusters[cluster_key] = cluster
        return cluster_key, cluster

    def get_cluster(self, cluster_id):
        """Get a Cluster object by id"""
        return self.clusters[cluster_id]

    def remove_cluster(self, cluster_id):
        """Delete a cluster by id"""
        # TODO: check running?
        del self.clusters[cluster_id]


def clean_cluster_files(profile_dirs=None, *, log=None, force=False):
    """Find all files related to clusters, and remove them

    Cleans up stale logs, etc.

    if force: remove cluster files even for running
    If not force, will raise if any cluster files are found,
    because it's not safe to clean up cluster files while processes are running

    """
    if profile_dirs is None:
        # default to all profiles
        profile_dirs = _all_profile_dirs()

    if isinstance(profile_dirs, str):
        profile_dirs = [profile_dirs]

    def _remove(f):
        if log:
            log.debug(f"Removing {f}")
        try:
            os.remove(f)
        except OSError as e:
            log.error(f"Error removing {f}: {e}")

    for profile_dir in profile_dirs:
        if log:
            log.info(f"Cleaning ipython cluster files in {profile_dir}")
        for cluster_file in ClusterManager._cluster_files_in_profile_dir(profile_dir):
            if force:
                _remove(cluster_file)
            else:
                # check if running first
                try:
                    cluster = Cluster.from_file(cluster_file=cluster_file)
                except Exception as e:
                    if log:
                        log.error(
                            f"Removing cluster file, which failed to load {cluster_file}: {e}"
                        )
                    _remove(cluster_file)
                else:
                    if cluster._is_running():
                        raise ValueError(
                            "f{cluster} is still running. Use force=True to cleanup files for running clusters (may leave orphan processes!)"
                        )
                    else:
                        _remove(cluster_file)
        for log_file in glob.glob(os.path.join(profile_dir, 'log', '*')):
            _remove(log_file)
        for security_file in glob.glob(
            os.path.join(profile_dir, 'security', 'ipcontroller-*.json')
        ):
            _remove(security_file)
ipyparallel-8.8.0/ipyparallel/cluster/launcher.py000066400000000000000000002455141460376056100222420ustar00rootroot00000000000000"""Facilities for launching IPython Parallel processes asynchronously."""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import asyncio
import copy
import inspect
import json
import logging
import os
import re
import shlex
import shutil
import signal
import stat
import sys
import threading
import time
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache, partial
from signal import SIGTERM
from subprocess import PIPE, STDOUT, Popen, check_output
from tempfile import TemporaryDirectory
from textwrap import indent

import entrypoints
import psutil
from IPython.utils.path import ensure_dir_exists, get_home_dir
from IPython.utils.text import EvalFormatter
from tornado import ioloop
from traitlets import (
    Any,
    CRegExp,
    Dict,
    Float,
    Instance,
    Integer,
    List,
    Unicode,
    default,
    observe,
)
from traitlets.config.configurable import LoggingConfigurable

from ..util import shlex_join
from ._winhpcjob import IPControllerJob, IPControllerTask, IPEngineSetJob, IPEngineTask

WINDOWS = os.name == 'nt'

SIGKILL = getattr(signal, "SIGKILL", -1)
# -----------------------------------------------------------------------------
# Paths to the kernel apps
# -----------------------------------------------------------------------------

ipcluster_cmd_argv = [sys.executable, "-m", "ipyparallel.cluster"]

ipengine_cmd_argv = [sys.executable, "-m", "ipyparallel.engine"]

ipcontroller_cmd_argv = [sys.executable, "-m", "ipyparallel.controller"]

# -----------------------------------------------------------------------------
# Base launchers and errors
# -----------------------------------------------------------------------------


class LauncherError(Exception):
    pass


class ProcessStateError(LauncherError):
    pass


class UnknownStatus(LauncherError):
    pass


class NotRunning(LauncherError):
    """Raised when a launcher is no longer running"""

    pass


class BaseLauncher(LoggingConfigurable):
    """An abstraction for starting, stopping and signaling a process."""

    stop_timeout = Integer(
        60,
        config=True,
        help="The number of seconds to wait for a process to exit before raising a TimeoutError in stop",
    )

    # In all of the launchers, the work_dir is where child processes will be
    # run. This will usually be the profile_dir, but may not be. any work_dir
    # passed into the __init__ method will override the config value.
    # This should not be used to set the work_dir for the actual engine
    # and controller. Instead, use their own config files or the
    # controller_args, engine_args attributes of the launchers to add
    # the work_dir option.
    work_dir = Unicode('.')

    # used in various places for labeling. often 'ipengine' or 'ipcontroller'
    name = Unicode("process")

    start_data = Any()
    stop_data = Any()

    identifier = Unicode(
        help="Used for lookup in e.g. EngineSetLauncher during notify_stop and default log files"
    )

    @default("identifier")
    def _default_identifier(self):
        identifier = f"{self.name}"
        if self.cluster_id:
            identifier = f"{identifier}-{self.cluster_id}"
        if getattr(self, 'engine_set_id', None):
            identifier = f"{identifier}-{self.engine_set_id}"
        identifier = f"{identifier}-{os.getpid()}"
        return identifier

    loop = Instance(ioloop.IOLoop, allow_none=True)

    def _loop_default(self):
        return ioloop.IOLoop.current()

    profile_dir = Unicode('').tag(to_dict=True)
    cluster_id = Unicode('').tag(to_dict=True)

    state = Unicode("before").tag(to_dict=True)

    stop_callbacks = List()

    def to_dict(self):
        """Serialize a Launcher to a dict, for later restoration"""
        d = {}
        for attr in self.traits(to_dict=True):
            d[attr] = getattr(self, attr)

        return d

    @classmethod
    def from_dict(cls, d, *, config=None, parent=None, **kwargs):
        """Restore a Launcher from a dict

        Subclasses should always call `launcher = super().from_dict(*args, **kwargs)`
        and finish initialization after that.

        After calling from_dict(),
        the launcher should be in the same state as after `.start()`
        (i.e. monitoring for exit, etc.)

        Returns: Launcher
            The instantiated and fully configured Launcher.

        Raises: NotRunning
            e.g. if the process has stopped and is no longer running.
        """
        launcher = cls(config=config, parent=parent, **kwargs)
        for attr in launcher.traits(to_dict=True):
            if attr in d:
                setattr(launcher, attr, d[attr])
        return launcher

    @property
    def cluster_args(self):
        """Common cluster arguments"""
        return []

    @property
    def connection_files(self):
        """Dict of connection file paths"""
        security_dir = os.path.join(self.profile_dir, 'security')
        name_prefix = "ipcontroller"
        if self.cluster_id:
            name_prefix = f"{name_prefix}-{self.cluster_id}"
        return {
            kind: os.path.join(security_dir, f"{name_prefix}-{kind}.json")
            for kind in ("client", "engine")
        }

    @property
    def args(self):
        """A list of cmd and args that will be used to start the process.

        This is what is passed to :func:`spawnProcess` and the first element
        will be the process name.
        """
        return self.find_args()

    def find_args(self):
        """The ``.args`` property calls this to find the args list.

        Subcommand should implement this to construct the cmd and args.
        """
        raise NotImplementedError('find_args must be implemented in a subclass')

    @property
    def arg_str(self):
        """The string form of the program arguments."""
        return ' '.join(self.args)

    @property
    def cluster_env(self):
        """Cluster-related env variables"""
        return {
            "IPP_CLUSTER_ID": self.cluster_id,
            "IPP_PROFILE_DIR": self.profile_dir,
        }

    environment = Dict(
        help="""Set environment variables for the launched process

        .. versionadded:: 8.0
        """,
        config=True,
    )

    def get_env(self):
        """Get the full environment for the process

        merges different sources for environment variables
        """
        env = {}
        env.update(self.cluster_env)
        env.update(self.environment)
        return env

    @property
    def running(self):
        """Am I running."""
        if self.state == 'running':
            return True
        else:
            return False

    async def start(self):
        """Start the process.

        Should be an `async def` coroutine.

        When start completes,
        the process should be requested (it need not be running yet),
        and waiting should begin in the background such that :meth:`.notify_stop`
        will be called when the process finishes.
        """
        raise NotImplementedError('start must be implemented in a subclass')

    async def stop(self):
        """Stop the process and notify observers of stopping.

        This method should be an `async def` coroutine,
        and return only after the process has stopped.

        All resources should be cleaned up by the time this returns.
        """
        raise NotImplementedError('stop must be implemented in a subclass')

    def on_stop(self, f):
        """Register a callback to be called with this Launcher's stop_data
        when the process actually finishes.
        """
        if self.state == 'after':
            return f(self.stop_data)
        else:
            self.stop_callbacks.append(f)

    def notify_start(self, data):
        """Call this to trigger startup actions.

        This logs the process startup and sets the state to 'running'.  It is
        a pass-through so it can be used as a callback.
        """

        self.log.debug(f"{self.__class__.__name__} {self.args[0]} started: {data}")
        self.start_data = data
        self.state = 'running'
        return data

    def notify_stop(self, data):
        """Call this to trigger process stop actions.

        This logs the process stopping and sets the state to 'after'. Call
        this to trigger callbacks registered via :meth:`on_stop`."""
        if self.state == 'after':
            self.log.debug("Already notified stop (data)")
            return data
        self.log.debug(f"{self.__class__.__name__} {self.args[0]} stopped: {data}")

        self.stop_data = data
        self.state = 'after'
        self._log_output(data)
        for f in self.stop_callbacks:
            f(data)
        return data

    def signal(self, sig):
        """Signal the process.

        Parameters
        ----------
        sig : str or int
            'KILL', 'INT', etc., or any signal number
        """
        raise NotImplementedError('signal must be implemented in a subclass')

    async def join(self, timeout=None):
        """Wait for the process to finish"""
        raise NotImplementedError('join must be implemented in a subclass')

    output_limit = Integer(
        100,
        config=True,
        help="""
    When a process exits, display up to this many lines of output
    """,
    )

    def get_output(self, remove=False):
        """Retrieve the output form the Launcher.

        If remove: remove the file, if any, where it was being stored.
        """
        # override in subclasses to retrieve output
        return ""

    def _log_output(self, stop_data=None):
        output = self.get_output(remove=True)
        if self.output_limit:
            output = "".join(output.splitlines(True)[-self.output_limit :])

        log = self.log.debug
        if stop_data and stop_data.get("exit_code", 0) != 0:
            log = self.log.warning
        if output:
            log("Output for %s:\n%s", self.identifier, output)


class ControllerLauncher(BaseLauncher):
    """Base class for launching ipcontroller"""

    name = Unicode("ipcontroller")

    controller_cmd = List(
        list(ipcontroller_cmd_argv),
        config=True,
        help="""Popen command to launch ipcontroller.""",
    )
    # Command line arguments to ipcontroller.
    controller_args = List(
        Unicode(),
        config=True,
        help="""command-line args to pass to ipcontroller""",
    )

    connection_info_timeout = Float(
        60,
        config=True,
        help="""
        Default timeout (in seconds) for get_connection_info
        
        .. versionadded:: 8.7
        """,
    )

    async def get_connection_info(self, timeout=None):
        """Retrieve connection info for the controller

        Default implementation assumes profile_dir and cluster_id are local.

        .. versionchanged:: 8.7
            Accept `timeout=None` (default) to use `.connection_info_timeout` config.
        """
        if timeout is None:
            timeout = self.connection_info_timeout
        connection_files = self.connection_files
        paths = list(connection_files.values())
        start_time = time.monotonic()
        if timeout >= 0:
            deadline = start_time + timeout
        else:
            deadline = None

        if not all(os.path.exists(f) for f in paths):
            self.log.debug(f"Waiting for {paths}")
        while not all(os.path.exists(f) for f in paths):
            if deadline is not None and time.monotonic() > deadline:
                missing_files = [f for f in paths if not os.path.exists(f)]
                raise TimeoutError(
                    f"Connection files {missing_files} did not arrive in {timeout}s"
                )
            await asyncio.sleep(0.1)
            status = self.poll()
            if inspect.isawaitable(status):
                status = await status
            if status is not None:
                raise RuntimeError(
                    f"Controller stopped with {status} while waiting for {paths}"
                )
        self.log.debug(f"Loading {paths}")
        connection_info = {}
        for key, path in connection_files.items():
            try:
                with open(path) as f:
                    connection_info[key] = json.load(f)
            except ValueError:
                # possible race while controller is still writing the file
                # give it half a second before trying again
                time.sleep(0.5)
                with open(path) as f:
                    connection_info[key] = json.load(f)

        return connection_info


class EngineLauncher(BaseLauncher):
    """Base class for launching one engine"""

    name = Unicode("ipengine")

    engine_cmd = List(
        ipengine_cmd_argv, config=True, help="""command to launch the Engine."""
    )
    # Command line arguments for ipengine.
    engine_args = List(
        Unicode(),
        config=True,
        help="command-line arguments to pass to ipengine",
    )

    n = Integer(1).tag(to_dict=True)

    engine_set_id = Unicode()


# -----------------------------------------------------------------------------
# Local process launchers
# -----------------------------------------------------------------------------


class LocalProcessLauncher(BaseLauncher):
    """Start and stop an external process in an asynchronous manner.

    This will launch the external process with a working directory of
    ``self.work_dir``.
    """

    # This is used to to construct self.args, which is passed to
    # spawnProcess.
    cmd_and_args = List(Unicode())

    poll_seconds = Integer(
        30,
        config=True,
        help="""Interval on which to poll processes (.

        Note: process exit should be noticed immediately,
        due to use of Process.wait(),
        but this interval should ensure we aren't leaving threads running forever,
        as other signals/events are checked on this interval
        """,
    )

    pid = Integer(-1).tag(to_dict=True)

    output_file = Unicode().tag(to_dict=True)

    @default("output_file")
    def _default_output_file(self):
        log_dir = os.path.join(self.profile_dir, "log")
        os.makedirs(log_dir, exist_ok=True)
        return os.path.join(log_dir, f'{self.identifier}.log')

    stop_seconds_until_kill = Integer(
        5,
        config=True,
        help="""The number of seconds to wait for a process to exit after sending SIGTERM before sending SIGKILL""",
    )

    stdout = None
    stderr = None
    process = None
    _wait_thread = None
    _popen_process = None

    def find_args(self):
        return self.cmd_and_args

    @classmethod
    def from_dict(cls, d, **kwargs):
        self = super().from_dict(d, **kwargs)
        self._reconstruct_process(d)
        return self

    def _reconstruct_process(self, d):
        """Reconstruct our process"""
        if 'pid' in d and d['pid'] > 0:
            try:
                self.process = psutil.Process(d['pid'])
            except psutil.NoSuchProcess as e:
                raise NotRunning(f"Process {d['pid']}")
            self._start_waiting()

    def _wait(self):
        """Background thread waiting for a process to exit"""
        exit_code = None
        while not self._stop_waiting.is_set() and self.state == 'running':
            try:
                # use a timeout so we can check the _stop_waiting event
                exit_code = self.process.wait(timeout=self.poll_seconds)
            except psutil.TimeoutExpired:
                continue
            else:
                break
        stop_data = dict(exit_code=exit_code, pid=self.pid, identifier=self.identifier)
        self.loop.add_callback(lambda: self.notify_stop(stop_data))
        if self._popen_process:
            # wait avoids ResourceWarning if the process has exited
            self._popen_process.wait(0)

    def _start_waiting(self):
        """Start background thread waiting on the process to exit"""
        # ensure self.loop is accessed on the main thread before waiting
        self.loop
        self._stop_waiting = threading.Event()
        self._wait_thread = threading.Thread(
            target=self._wait, daemon=True, name=f"wait(pid={self.pid})"
        )
        self._wait_thread.start()

    def start(self):
        self.log.debug("Starting %s: %r", self.__class__.__name__, self.args)
        if self.state != 'before':
            raise ProcessStateError(
                'The process was already started and has state: {self.state}'
            )
        self.log.debug(f"Sending output for {self.identifier} to {self.output_file}")

        env = os.environ.copy()
        env.update(self.get_env())
        self.log.debug(f"Setting environment: {','.join(self.get_env())}")

        with open(self.output_file, "ab") as f, open(os.devnull, "rb") as stdin:
            proc = self._popen_process = Popen(
                self.args,
                stdout=f.fileno(),
                stderr=STDOUT,
                stdin=stdin,
                env=env,
                cwd=self.work_dir,
                start_new_session=True,  # don't forward signals
            )
        self.pid = proc.pid
        # use psutil API for self.process
        self.process = psutil.Process(proc.pid)

        self.notify_start(self.process.pid)
        self._start_waiting()
        if 1 <= self.log.getEffectiveLevel() <= logging.DEBUG:
            self._start_streaming()

    async def join(self, timeout=None):
        """Wait for the process to exit"""
        if self._wait_thread is not None:
            self._wait_thread.join(timeout=timeout)

    def _stream_file(self, path):
        """Stream one file"""
        with open(path) as f:
            while self.state == 'running' and not self._stop_waiting.is_set():
                line = f.readline()
                # log prefix?
                # or stream directly to sys.stderr
                if line:
                    sys.stderr.write(line)
                else:
                    # pause while we are at the end of the file
                    time.sleep(0.1)

    def _start_streaming(self):
        self._stream_thread = t = threading.Thread(
            target=partial(self._stream_file, self.output_file),
            name=f"Stream Output {self.identifier}",
            daemon=True,
        )
        t.start()

    _output = None

    def get_output(self, remove=False):
        if self._output is None:
            if self.output_file:
                try:
                    with open(self.output_file) as f:
                        self._output = f.read()
                except FileNotFoundError:
                    self.log.debug(f"Missing output file: {self.output_file}")
                    self._output = ""
            else:
                self._output = ""

        if remove and os.path.isfile(self.output_file):
            self.log.debug(f"Removing {self.output_file}")
            try:
                os.remove(self.output_file)
            except Exception as e:
                # don't crash on failure to remove a file,
                # e.g. due to another processing having it open
                self.log.error(f"Failed to remove {self.output_file}: {e}")

        return self._output

    async def stop(self):
        try:
            self.signal(SIGTERM)
        except Exception as e:
            self.log.debug(f"TERM failed: {e!r}")

        try:
            await self.join(timeout=self.stop_seconds_until_kill)
        except TimeoutError:
            self.log.warning(
                f"Process {self.pid} did not exit in {self.stop_seconds_until_kill} seconds after TERM"
            )
        else:
            return

        try:
            self.signal(SIGKILL)
        except Exception as e:
            self.log.debug(f"KILL failed: {e!r}")

        await self.join(timeout=self.stop_timeout)

    def signal(self, sig):
        if self.state == 'running':
            if WINDOWS and sig in {SIGTERM, SIGKILL}:
                # use Windows tree-kill for better child cleanup
                cmd = ['taskkill', '/pid', str(self.process.pid), '/t', '/F']
                check_output(cmd)
            else:
                self.process.send_signal(sig)

    # callbacks, etc:

    def handle_stdout(self, fd, events):
        if WINDOWS:
            line = self.stdout.recv().decode('utf8', 'replace')
        else:
            line = self.stdout.readline().decode('utf8', 'replace')
        # a stopped process will be readable but return empty strings
        if line:
            self.log.debug(line.rstrip())

    def handle_stderr(self, fd, events):
        if WINDOWS:
            line = self.stderr.recv().decode('utf8', 'replace')
        else:
            line = self.stderr.readline().decode('utf8', 'replace')
        # a stopped process will be readable but return empty strings
        if line:
            self.log.debug(line.rstrip())
        else:
            self.poll()

    def poll(self):
        if self.process.is_running():
            return None

        status = self.process.wait(0)
        if status is None:
            # return code cannot always be retrieved.
            # but we need to not return None if it's still running
            status = 'unknown'
        self.notify_stop(
            dict(exit_code=status, pid=self.process.pid, identifier=self.identifier)
        )
        return status


class LocalControllerLauncher(LocalProcessLauncher, ControllerLauncher):
    """Launch a controller as a regular external process."""

    def find_args(self):
        return self.controller_cmd + self.cluster_args + self.controller_args

    def start(self):
        """Start the controller by profile_dir."""
        return super().start()


class LocalEngineLauncher(LocalProcessLauncher, EngineLauncher):
    """Launch a single engine as a regular external process."""

    def find_args(self):
        return self.engine_cmd + self.cluster_args + self.engine_args


class LocalEngineSetLauncher(LocalEngineLauncher):
    """Launch a set of engines as regular external processes."""

    delay = Float(
        0.1,
        config=True,
        help="""delay (in seconds) between starting each engine after the first.
        This can help force the engines to get their ids in order, or limit
        process flood when starting many engines.""",
    )

    # launcher class
    launcher_class = LocalEngineLauncher

    launchers = Dict()
    stop_data = Dict()
    outputs = Dict()
    output_file = ""  # no output file for me

    def __init__(self, work_dir='.', config=None, **kwargs):
        super().__init__(work_dir=work_dir, config=config, **kwargs)

    def to_dict(self):
        d = super().to_dict()
        d['engines'] = {i: launcher.to_dict() for i, launcher in self.launchers.items()}
        return d

    @classmethod
    def from_dict(cls, d, **kwargs):
        self = super().from_dict(d, **kwargs)
        n = 0
        for i, engine_dict in d['engines'].items():
            try:
                self.launchers[i] = el = self.launcher_class.from_dict(
                    engine_dict, identifier=i, parent=self
                )
            except NotRunning as e:
                self.log.error(f"Engine {i} not running: {e}")
            else:
                n += 1
                el.on_stop(self._notice_engine_stopped)
        if n == 0:
            raise NotRunning("No engines left")
        else:
            self.n = n
        return self

    def start(self, n):
        """Start n engines by profile or profile_dir."""
        self.n = n
        dlist = []
        for i in range(n):
            identifier = str(i)
            if i > 0:
                time.sleep(self.delay)
            el = self.launchers[identifier] = self.launcher_class(
                work_dir=self.work_dir,
                parent=self,
                log=self.log,
                profile_dir=self.profile_dir,
                cluster_id=self.cluster_id,
                environment=self.environment,
                identifier=identifier,
                output_file=os.path.join(
                    self.profile_dir,
                    "log",
                    f"ipengine-{self.cluster_id}-{self.engine_set_id}-{i}.log",
                ),
            )

            # Copy the engine args over to each engine launcher.
            el.engine_cmd = copy.deepcopy(self.engine_cmd)
            el.engine_args = copy.deepcopy(self.engine_args)
            el.on_stop(self._notice_engine_stopped)
            d = el.start()
            dlist.append(d)
        self.notify_start(dlist)
        return dlist

    def find_args(self):
        return ['engine set']

    def signal(self, sig):
        for el in list(self.launchers.values()):
            el.signal(sig)

    async def stop(self):
        futures = []
        for el in list(self.launchers.values()):
            f = el.stop()
            if inspect.isawaitable(f):
                futures.append(asyncio.ensure_future(f))

        if futures:
            await asyncio.gather(*futures)

    def _notice_engine_stopped(self, data):
        identifier = data['identifier']
        launcher = self.launchers.pop(identifier)
        engines = self.stop_data.setdefault("engines", {})
        if launcher is not None:
            self.outputs[identifier] = launcher.get_output()
        engines[identifier] = data
        if not self.launchers:
            # get exit code from engine exit codes
            # set error code if any engine has an error
            self.stop_data["exit_code"] = None
            for engine in engines.values():
                if 'exit_code' in engine:
                    if self.stop_data['exit_code'] is None:
                        self.stop_data['exit_code'] = engine['exit_code']
                    if engine['exit_code']:
                        # save the first nonzero exit code
                        self.stop_data['exit_code'] = engine['exit_code']
                        break

            self.notify_stop(self.stop_data)

    def _log_output(self, stop_data=None):
        # avoid double-logging output, already logged by each engine
        # that will be a lot if all 100 engines fail!
        pass

    def get_output(self, remove=False):
        """Get the output of all my child Launchers"""
        for identifier, launcher in self.launchers.items():
            # remaining launchers
            self.outputs[identifier] = launcher.get_output(remove=remove)

        joined_output = []
        for identifier, engine_output in self.outputs.items():
            if engine_output:
                joined_output.append(f"Output for engine {identifier}")
                if self.output_limit:
                    engine_output = "".join(
                        engine_output.splitlines(True)[-self.output_limit :]
                    )
                joined_output.append(indent(engine_output, '  '))
        return '\n'.join(joined_output)


# -----------------------------------------------------------------------------
# MPI launchers
# -----------------------------------------------------------------------------


class MPILauncher(LocalProcessLauncher):
    """Launch an external process using mpiexec."""

    mpi_cmd = List(
        ['mpiexec'],
        config=True,
        help="The mpiexec command to use in starting the process.",
    )
    mpi_args = List(
        [], config=True, help="The command line arguments to pass to mpiexec."
    )
    program = List(['date'], help="The program to start via mpiexec.")
    program_args = List([], help="The command line argument to the program.")

    def __init__(self, *args, **kwargs):
        # deprecation for old MPIExec names:
        config = kwargs.get('config') or {}
        for oldname in (
            'MPIExecLauncher',
            'MPIExecControllerLauncher',
            'MPIExecEngineSetLauncher',
        ):
            deprecated = config.get(oldname)
            if deprecated:
                newname = oldname.replace('MPIExec', 'MPI')
                config[newname].update(deprecated)
                self.log.warning(
                    "WARNING: %s name has been deprecated, use %s", oldname, newname
                )

        super().__init__(*args, **kwargs)

    def find_args(self):
        """Build self.args using all the fields."""
        return (
            self.mpi_cmd
            + ['-n', str(self.n)]
            + self.mpi_args
            + self.program
            + self.program_args
        )

    def start(self, n=1):
        """Start n instances of the program using mpiexec."""
        self.n = n
        return super().start()

    def _log_output(self, stop_data):
        """Try to log mpiexec error output, if any, at warning level"""
        super()._log_output(stop_data)

        if stop_data and self.stop_data.get("exit_code", 0) != 0:
            # if this is True, super()._log_output would have already logged the full output
            # no need to extract from MPI
            return

        output = self.get_output(remove=False)
        mpiexec_lines = []

        in_mpi = False
        after_mpi = False
        mpi_tail = 0
        for line in output.splitlines(True):
            if line.startswith("======="):
                # mpich output looks like one block,
                # with a few lines trailing after
                # =========
                # = message
                # =
                # =========
                # YOUR APPLICATION TERMINATED WITH...
                if in_mpi:
                    after_mpi = True
                    mpi_tail = 2
                    in_mpi = False
                else:
                    in_mpi = True
            elif not in_mpi and line.startswith("-----"):
                # openmpi has less clear boundaries;
                # potentially several blocks that start and end with `----`
                # and error messages can show up after one or more blocks
                # once we see one of these lines, capture everything after it
                # toggle on each such line
                if not in_mpi:
                    in_mpi = True
                # this would let us only capture messages inside blocks
                # but doing so would exclude most useful error output
                # else:
                #     # show the trailing delimiter line
                #     mpiexec_lines.append(line)
                #     in_mpi = False
                #     continue

            if in_mpi:
                mpiexec_lines.append(line)
            elif after_mpi:
                if mpi_tail <= 0:
                    break
                else:
                    mpi_tail -= 1
                    mpiexec_lines.append(line)

        if mpiexec_lines:
            self.log.warning("mpiexec error output:\n" + "".join(mpiexec_lines))


class MPIControllerLauncher(MPILauncher, ControllerLauncher):
    """Launch a controller using mpiexec."""

    # alias back to *non-configurable* program[_args] for use in find_args()
    # this way all Controller/EngineSetLaunchers have the same form, rather
    # than *some* having `program_args` and others `controller_args`
    @property
    def program(self):
        return self.controller_cmd

    @property
    def program_args(self):
        return self.cluster_args + self.controller_args


class MPIEngineSetLauncher(MPILauncher, EngineLauncher):
    """Launch engines using mpiexec"""

    # alias back to *non-configurable* program[_args] for use in find_args()
    # this way all Controller/EngineSetLaunchers have the same form, rather
    # than *some* having `program_args` and others `controller_args`
    @property
    def program(self):
        return self.engine_cmd + ['--mpi']

    @property
    def program_args(self):
        return self.cluster_args + self.engine_args

    def start(self, n):
        """Start n engines by profile or profile_dir."""
        self.n = n
        return super().start(n)


# deprecated MPIExec names
class DeprecatedMPILauncher:
    def warn(self):
        oldname = self.__class__.__name__
        newname = oldname.replace('MPIExec', 'MPI')
        self.log.warning("WARNING: %s name is deprecated, use %s", oldname, newname)


class MPIExecLauncher(MPILauncher, DeprecatedMPILauncher):
    """Deprecated, use MPILauncher"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.warn()


class MPIExecControllerLauncher(MPIControllerLauncher, DeprecatedMPILauncher):
    """Deprecated, use MPIControllerLauncher"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.warn()


class MPIExecEngineSetLauncher(MPIEngineSetLauncher, DeprecatedMPILauncher):
    """Deprecated, use MPIEngineSetLauncher"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.warn()


# -----------------------------------------------------------------------------
# SSH launchers
# -----------------------------------------------------------------------------

ssh_output_pattern = re.compile(r"__([a-z][a-z0-9_]+)=([a-z0-9\-\.]+)__", re.IGNORECASE)


def _ssh_outputs(out):
    """Extract ssh output variables from process output"""
    return dict(ssh_output_pattern.findall(out))


def sshx(ssh_cmd, cmd, env, remote_output_file, log=None):
    """Launch a remote process, returning its remote pid

    Uses nohup and pipes to put it in the background
    """
    remote_cmd = shlex_join(cmd)

    nohup_start = f"nohup {remote_cmd} > {remote_output_file} 2>&1  0:
            self._start_waiting()

    def poll(self):
        """Override poll"""
        if self.state == 'running':
            return None
        else:
            return 0

    def get_output(self, remove=False):
        """Retrieve engine output from the remote file"""
        if self._output is None:
            with TemporaryDirectory() as td:
                output_file = os.path.join(
                    td, os.path.basename(self.remote_output_file)
                )
                try:
                    self._fetch_file(self.remote_output_file, output_file)
                except Exception as e:
                    self.log.error(
                        f"Failed to get output file {self.remote_output_file}: {e}"
                    )
                    self._output = ''
                else:
                    if remove:
                        # remove the file after we retrieve it
                        self.log.info(
                            f"Removing {self.location}:{self.remote_output_file}"
                        )
                        check_output(
                            self.ssh_cmd
                            + self.ssh_args
                            + [
                                self.location,
                                "--",
                                shlex_join(["rm", "-f", self.remote_output_file]),
                            ],
                            input=None,
                        )
                    with open(output_file) as f:
                        self._output = f.read()
        return self._output

    def _send_file(self, local, remote, wait=True):
        """send a single file"""
        full_remote = f"{self.location}:{remote}"
        for i in range(10 if wait else 0):
            if not os.path.exists(local):
                self.log.debug("waiting for %s" % local)
                time.sleep(1)
            else:
                break
        remote_dir = os.path.dirname(remote)
        self.log.info("ensuring remote %s:%s/ exists", self.location, remote_dir)
        check_output(
            self.ssh_cmd
            + self.ssh_args
            + [self.location, '--', 'mkdir', '-p', remote_dir],
            input=None,
        )
        self.log.info("sending %s to %s", local, full_remote)
        check_output(self.scp_cmd + self.scp_args + [local, full_remote], input=None)

    def send_files(self):
        """send our files (called before start)"""
        if not self.to_send:
            return
        for local_file, remote_file in self.to_send:
            self._send_file(local_file, remote_file)

    def _fetch_file(self, remote, local, wait=True):
        """fetch a single file"""
        full_remote = f"{self.location}:{remote}"
        self.log.info("fetching %s from %s", local, full_remote)
        for i in range(10 if wait else 0):
            # wait up to 10s for remote file to exist
            check = check_output(
                self.ssh_cmd
                + self.ssh_args
                + [self.location, 'test -e', remote, "&& echo 'yes' || echo 'no'"],
                input=None,
            )
            check = check.decode("utf8", 'replace').strip()
            if check == 'no':
                time.sleep(1)
            elif check == 'yes':
                break
        local_dir = os.path.dirname(local)
        ensure_dir_exists(local_dir, 700)
        check_output(self.scp_cmd + self.scp_args + [full_remote, local])

    def fetch_files(self):
        """fetch remote files (called after start)"""
        if not self.to_fetch:
            return
        for remote_file, local_file in self.to_fetch:
            self._fetch_file(remote_file, local_file)

    def start(self, hostname=None, user=None, port=None):
        if hostname is not None:
            self.hostname = hostname
        if user is not None:
            self.user = user
        if port is not None:
            if '-p' not in self.ssh_args:
                self.ssh_args.append('-p')
                self.ssh_args.append(str(port))
            if '-P' not in self.scp_args:
                self.scp_args.append('-P')
                self.scp_args.append(str(port))

        # create remote profile dir
        check_output(
            self.ssh_cmd
            + self.ssh_args
            + [
                self.location,
                shlex_join(
                    [
                        self.remote_python,
                        "-m",
                        "IPython",
                        "profile",
                        "create",
                        "--profile-dir",
                        self.remote_profile_dir,
                    ]
                ),
            ],
            input=None,
        )
        self.send_files()
        self.pid = sshx(
            self.ssh_cmd + self.ssh_args + [self.location],
            self.program + self.program_args,
            env=self.get_env(),
            remote_output_file=self.remote_output_file,
            log=self.log,
        )
        self.notify_start({'host': self.location, 'pid': self.pid})
        self._start_waiting()
        self.fetch_files()

    def _wait(self):
        """Background thread waiting for a process to exit"""
        exit_code = None
        while not self._stop_waiting.is_set() and self.state == 'running':
            try:
                # use a timeout so we can check the _stop_waiting event
                exit_code = self.wait_one(timeout=self.poll_seconds)
            except TimeoutError:
                continue
            else:
                break
        stop_data = dict(exit_code=exit_code, pid=self.pid, identifier=self.identifier)
        self.loop.add_callback(lambda: self.notify_stop(stop_data))

    def _start_waiting(self):
        """Start background thread waiting on the process to exit"""
        # ensure self.loop is accessed on the main thread before waiting
        self.loop
        self._stop_waiting = threading.Event()
        self._wait_thread = threading.Thread(
            target=self._wait,
            daemon=True,
            name=f"wait(host={self.location}, pid={self.pid})",
        )
        self._wait_thread.start()

    def wait_one(self, timeout):
        python_code = f"from ipyparallel.cluster.launcher import ssh_waitpid; ssh_waitpid({self.pid}, timeout={timeout})"
        full_cmd = (
            self.ssh_cmd
            + self.ssh_args
            # double-quote for ssh
            + [self.location, "--", self.remote_python, "-c", f"'{python_code}'"]
        )
        out = check_output(full_cmd, input=None, start_new_session=True).decode(
            "utf8", "replace"
        )
        values = _ssh_outputs(out)
        if 'process_running' not in values:
            raise RuntimeError(out)
        running = int(values.get("process_running", 0))
        if running:
            raise TimeoutError("still running")
        return int(values.get("exit_code", -1))

    async def join(self, timeout=None):
        with ThreadPoolExecutor(1) as pool:
            wait = partial(self.wait_one, timeout=timeout)
            try:
                future = pool.submit(wait)
            except RuntimeError:
                # e.g. called during process shutdown,
                # which raises
                # RuntimeError: cannot schedule new futures after interpreter shutdown
                # Instead, do the blocking call
                wait()
            else:
                await asyncio.wrap_future(future)
        if getattr(self, '_stop_waiting', None) and self._wait_thread:
            self._stop_waiting.set()
            # got here, should be done
            # wait for wait_thread to cleanup
            self._wait_thread.join()

    def signal(self, sig):
        if self.state == 'running':
            check_output(
                self.ssh_cmd
                + self.ssh_args
                + [
                    self.location,
                    '--',
                    'kill',
                    f'-{sig}',
                    str(self.pid),
                ],
                input=None,
            )

    @property
    def remote_connection_files(self):
        """Return remote paths for connection files"""
        return {
            key: self.remote_profile_dir + local_path[len(self.profile_dir) :]
            for key, local_path in self.connection_files.items()
        }


class SSHControllerLauncher(SSHLauncher, ControllerLauncher):
    # alias back to *non-configurable* program[_args] for use in find_args()
    # this way all Controller/EngineSetLaunchers have the same form, rather
    # than *some* having `program_args` and others `controller_args`

    def _controller_cmd_default(self):
        return [self.remote_python, "-m", 'ipyparallel.controller']

    @property
    def program(self):
        return self.controller_cmd

    @property
    def program_args(self):
        return self.cluster_args + self.controller_args

    @default("to_fetch")
    def _to_fetch_default(self):
        to_fetch = []
        return [
            (self.remote_connection_files[key], local_path)
            for key, local_path in self.connection_files.items()
        ]


class SSHEngineLauncher(SSHLauncher, EngineLauncher):
    # alias back to *non-configurable* program[_args] for use in find_args()
    # this way all Controller/EngineSetLaunchers have the same form, rather
    # than *some* having `program_args` and others `controller_args`

    def _engine_cmd_default(self):
        return [self.remote_python, "-m", "ipyparallel.engine"]

    @property
    def program(self):
        return self.engine_cmd

    @property
    def program_args(self):
        return self.cluster_args + self.engine_args

    @default("to_send")
    def _to_send_default(self):
        return [
            (local_path, self.remote_connection_files[key])
            for key, local_path in self.connection_files.items()
        ]


class SSHEngineSetLauncher(LocalEngineSetLauncher, SSHLauncher):
    launcher_class = SSHEngineLauncher
    engines = Dict(
        config=True,
        help="""dict of engines to launch.  This is a dict by hostname of ints,
        corresponding to the number of engines to start on that host.""",
    ).tag(to_dict=True)

    def _engine_cmd_default(self):
        return [self.remote_python, "-m", "ipyparallel.engine"]

    # unset some traits we inherit but don't use
    remote_output_file = ""

    def start(self, n):
        """Start engines by profile or profile_dir.
        `n` is an *upper limit* of engines.
        The `engines` config property is used to assign slots to hosts.
        """

        dlist = []
        # traits to inherit:
        # + all common config traits
        # - traits set per-engine via engines dict
        # + some non-configurable traits such as cluster_id
        engine_traits = self.launcher_class.class_traits(config=True)
        my_traits = self.traits(config=True)
        shared_traits = set(my_traits).intersection(engine_traits)
        # in addition to shared traits, pass some derived traits
        # and exclude some composite traits
        inherited_traits = shared_traits.difference(
            {"location", "user", "hostname", "to_send", "to_fetch"}
        ).union({"profile_dir", "cluster_id"})

        requested_n = n
        started_n = 0
        for host, n_or_config in self.engines.items():
            if isinstance(n_or_config, dict):
                overrides = n_or_config
                n = overrides.pop("n", 1)
            else:
                overrides = {}
                n = n_or_config

            full_host = host

            if '@' in host:
                user, host = host.split('@', 1)
            else:
                user = None
            if ':' in host:
                host, port = host.split(':', 1)
            else:
                port = None

            for i in range(min(n, requested_n - started_n)):
                if i > 0:
                    time.sleep(self.delay)
                # pass all common traits to the launcher
                kwargs = {attr: getattr(self, attr) for attr in inherited_traits}
                # overrides from engine config
                kwargs.update(overrides)
                # explicit per-engine values
                kwargs['parent'] = self
                kwargs['identifier'] = key = f"{full_host}/{i}"
                el = self.launchers[key] = self.launcher_class(**kwargs)
                if i > 0:
                    # only send files for the first engine on each host
                    el.to_send = []

                el.on_stop(self._notice_engine_stopped)
                d = el.start(user=user, hostname=host, port=port)
                dlist.append(key)
                started_n += 1
                if started_n >= requested_n:
                    break
        self.notify_start(dlist)
        self.n = started_n
        return dlist


class SSHProxyEngineSetLauncher(SSHLauncher, EngineLauncher):
    """Launcher for calling
    `ipcluster engines` on a remote machine.

    Requires that remote profile is already configured.
    """

    n = Integer().tag(to_dict=True)
    ipcluster_cmd = List(Unicode(), config=True)

    @default("ipcluster_cmd")
    def _default_ipcluster_cmd(self):
        return [self.remote_python, "-m", "ipyparallel.cluster"]

    ipcluster_args = List(
        Unicode(),
        config=True,
        help="""Extra CLI arguments to pass to ipcluster engines""",
    )

    @property
    def program(self):
        return self.ipcluster_cmd + ['engines']

    @property
    def program_args(self):
        return [
            '-n',
            str(self.n),
            '--profile-dir',
            self.remote_profile_dir,
            '--cluster-id',
            self.cluster_id,
        ] + self.ipcluster_args

    @default("to_send")
    def _to_send_default(self):
        return [
            (local_path, self.remote_connection_files[key])
            for key, local_path in self.connection_files.items()
        ]

    def start(self, n):
        self.n = n
        super().start()


# -----------------------------------------------------------------------------
# Windows HPC Server 2008 scheduler launchers
# -----------------------------------------------------------------------------


class WindowsHPCLauncher(BaseLauncher):
    job_id_regexp = CRegExp(
        r'\d+',
        config=True,
        help="""A regular expression used to get the job id from the output of the
        submit_command. """,
    )
    job_file_name = Unicode(
        'ipython_job.xml',
        config=True,
        help="The filename of the instantiated job script.",
    )
    scheduler = Unicode(
        '', config=True, help="The hostname of the scheduler to submit the job to."
    )
    job_cmd = Unicode(config=True, help="The command for submitting jobs.")

    @default("job_cmd")
    def _default_job(self):
        return shutil.which("job") or "job"

    @property
    def job_file(self):
        return os.path.join(self.work_dir, self.job_file_name)

    def write_job_file(self, n):
        raise NotImplementedError("Implement write_job_file in a subclass.")

    def find_args(self):
        return ['job.exe']

    def parse_job_id(self, output):
        """Take the output of the submit command and return the job id."""
        m = self.job_id_regexp.search(output)
        if m is not None:
            job_id = m.group()
        else:
            raise LauncherError("Job id couldn't be determined: %s" % output)
        self.job_id = job_id
        self.log.info('Job started with id: %r', job_id)
        return job_id

    def start(self, n):
        """Start n copies of the process using the Win HPC job scheduler."""
        self.write_job_file(n)
        args = [
            'submit',
            '/jobfile:%s' % self.job_file,
            '/scheduler:%s' % self.scheduler,
        ]
        self.log.debug(
            "Starting Win HPC Job: {}".format(self.job_cmd + ' ' + ' '.join(args))
        )

        output = check_output(
            [self.job_cmd] + args, env=os.environ, cwd=self.work_dir, stderr=STDOUT
        )
        output = output.decode("utf8", 'replace')
        job_id = self.parse_job_id(output)
        self.notify_start(job_id)
        return job_id

    def stop(self):
        args = ['cancel', self.job_id, '/scheduler:%s' % self.scheduler]
        self.log.info(
            "Stopping Win HPC Job: {}".format(self.job_cmd + ' ' + ' '.join(args))
        )
        try:
            output = check_output(
                [self.job_cmd] + args, env=os.environ, cwd=self.work_dir, stderr=STDOUT
            )
            output = output.decode("utf8", 'replace')
        except Exception:
            output = 'The job already appears to be stopped: %r' % self.job_id
        self.notify_stop(
            dict(job_id=self.job_id, output=output)
        )  # Pass the output of the kill cmd
        return output


class WindowsHPCControllerLauncher(WindowsHPCLauncher):
    job_file_name = Unicode(
        'ipcontroller_job.xml', config=True, help="WinHPC xml job file."
    )
    controller_args = List([], config=False, help="extra args to pass to ipcontroller")

    def write_job_file(self, n):
        job = IPControllerJob(parent=self)

        t = IPControllerTask(parent=self)
        # The tasks work directory is *not* the actual work directory of
        # the controller. It is used as the base path for the stdout/stderr
        # files that the scheduler redirects to.
        t.work_directory = self.profile_dir
        # Add the profile_dir and from self.start().
        t.controller_args.extend(self.cluster_args)
        t.controller_args.extend(self.controller_args)
        job.add_task(t)

        self.log.debug("Writing job description file: %s", self.job_file)
        job.write(self.job_file)


class WindowsHPCEngineSetLauncher(WindowsHPCLauncher):
    job_file_name = Unicode(
        'ipengineset_job.xml', config=True, help="jobfile for ipengines job"
    )
    engine_args = List(Unicode(), config=False, help="extra args to pas to ipengine")

    def write_job_file(self, n):
        job = IPEngineSetJob(parent=self)

        for i in range(n):
            t = IPEngineTask(parent=self)
            # The tasks work directory is *not* the actual work directory of
            # the engine. It is used as the base path for the stdout/stderr
            # files that the scheduler redirects to.
            t.work_directory = self.profile_dir
            # Add the profile_dir and from self.start().
            t.engine_args.extend(self.cluster_args)
            t.engine_args.extend(self.engine_args)
            job.add_task(t)

        self.log.debug("Writing job description file: %s", self.job_file)
        job.write(self.job_file)

    def start(self, n):
        """Start the controller by profile_dir."""
        return super().start(n)


# -----------------------------------------------------------------------------
# Batch (PBS) system launchers
# -----------------------------------------------------------------------------


class BatchSystemLauncher(BaseLauncher):
    """Launch an external process using a batch system.

    This class is designed to work with UNIX batch systems like PBS, LSF,
    GridEngine, etc.  The overall model is that there are different commands
    like qsub, qdel, etc. that handle the starting and stopping of the process.

    This class also has the notion of a batch script. The ``batch_template``
    attribute can be set to a string that is a template for the batch script.
    This template is instantiated using string formatting. Thus the template can
    use {n} for the number of instances. Subclasses can add additional variables
    to the template dict.
    """

    # load cluster args into context instead of cli

    output_file = Unicode(
        config=True, help="File in which to store stdout/err of processes"
    ).tag(to_dict=True)

    @default("output_file")
    def _default_output_file(self):
        log_dir = os.path.join(self.profile_dir, "log")
        os.makedirs(log_dir, exist_ok=True)
        return os.path.join(log_dir, f'{self.identifier}.log')

    # Subclasses must fill these in.  See PBSEngineSet
    submit_command = List(
        [''],
        config=True,
        help="The name of the command line program used to submit jobs.",
    )
    delete_command = List(
        [''],
        config=True,
        help="The name of the command line program used to delete jobs.",
    )

    signal_command = List(
        [''],
        config=True,
        help="The name of the command line program used to send signals to jobs.",
    )

    job_id = Unicode().tag(to_dict=True)

    job_id_regexp = CRegExp(
        '',
        config=True,
        help="""A regular expression used to get the job id from the output of the
        submit_command.""",
    )
    job_id_regexp_group = Integer(
        0,
        config=True,
        help="""The group we wish to match in job_id_regexp (0 to match all)""",
    )
    batch_template = Unicode(
        '', config=True, help="The string that is the batch script template itself."
    ).tag(to_dict=True)
    batch_template_file = Unicode(
        '', config=True, help="The file that contains the batch template."
    )
    batch_file_name = Unicode(
        'batch_script',
        config=True,
        help="The filename of the instantiated batch script.",
    ).tag(to_dict=True)
    queue = Unicode('', config=True, help="The batch queue.").tag(to_dict=True)

    n = Integer(1).tag(to_dict=True)

    @observe('queue', 'n', 'cluster_id', 'profile_dir', 'output_file')
    def _context_field_changed(self, change):
        self._update_context(change)

    # not configurable, override in subclasses
    # Job Array regex
    job_array_regexp = CRegExp('')
    job_array_template = Unicode('')

    # Queue regex
    queue_regexp = CRegExp('')
    queue_template = Unicode('')

    # Output file
    output_regexp = CRegExp('')
    output_template = Unicode('')

    # The default batch template, override in subclasses
    default_template = Unicode('')
    # The full path to the instantiated batch script.
    batch_file = Unicode('')
    # the format dict used with batch_template:
    context = Dict()

    namespace = Dict(
        config=True,
        help="""Extra variables to pass to the template.

        This lets you parameterize additional options,
        such as wall_time with a custom template.
        """,
    ).tag(to_dict=True)

    @default("context")
    def _context_default(self):
        """load the default context with the default values for the basic keys

        because the _trait_changed methods only load the context if they
        are set to something other than the default value.
        """
        return dict(
            n=self.n,
            queue=self.queue,
            profile_dir=self.profile_dir,
            cluster_id=self.cluster_id,
            output_file=self.output_file,
        )

    program = List(Unicode())
    program_args = List(Unicode())

    @observe("program", "program_args")
    def _program_changed(self, change=None):
        self.context['program'] = shlex_join(self.program)
        self.context['program_args'] = shlex_join(self.program_args)
        self.context['program_and_args'] = shlex_join(self.program + self.program_args)

    @observe("n", "queue")
    def _update_context(self, change):
        self.context[change['name']] = change['new']

    # the Formatter instance for rendering the templates:
    formatter = Instance(EvalFormatter, (), {})

    def find_args(self):
        return self.submit_command + [self.batch_file]

    def __init__(self, work_dir='.', config=None, **kwargs):
        super().__init__(work_dir=work_dir, config=config, **kwargs)
        self.batch_file = os.path.join(self.work_dir, self.batch_file_name)
        # trigger program_changed to populate default context arguments
        self._program_changed()

    def parse_job_id(self, output):
        """Take the output of the submit command and return the job id."""
        m = self.job_id_regexp.search(output)
        if m is not None:
            job_id = m.group(self.job_id_regexp_group)
        else:
            raise LauncherError("Job id couldn't be determined: %s" % output)
        self.job_id = job_id
        self.log.info('Job submitted with job id: %r', job_id)
        return job_id

    def write_batch_script(self, n=1):
        """Instantiate and write the batch script to the work_dir."""
        self.n = n
        self.context['environment_json'] = json.dumps(self.get_env())

        # first priority is batch_template if set
        if self.batch_template_file and not self.batch_template:
            # second priority is batch_template_file
            with open(self.batch_template_file) as f:
                self.batch_template = f.read()
        if not self.batch_template:
            # third (last) priority is default_template
            self.batch_template = self.default_template
            # add jobarray or queue lines to user-specified template
            # note that this is *only* when user did not specify a template.
            self._insert_options_in_script()
            self._insert_job_array_in_script()
        ns = {}
        # internally generated
        ns.update(self.context)
        # from user config
        ns.update(self.namespace)
        script_as_string = self.formatter.format(self.batch_template, **ns)
        self.log.debug(f'Writing batch script: {self.batch_file}\n{script_as_string}')
        with open(self.batch_file, 'w') as f:
            f.write(script_as_string)
        os.chmod(self.batch_file, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)

    def _insert_options_in_script(self):
        """Inserts a queue if required into the batch script."""
        inserts = []
        if self.queue and not self.queue_regexp.search(self.batch_template):
            self.log.debug(f"Adding queue={self.queue} to {self.batch_file}")
            inserts.append(self.queue_template)

        if (
            self.output_file
            and self.output_template
            and not self.output_regexp.search(self.batch_template)
        ):
            self.log.debug(f"Adding output={self.output_file} to {self.batch_file}")
            inserts.append(self.output_template)

        if inserts:
            firstline, rest = self.batch_template.split('\n', 1)
            self.batch_template = '\n'.join([firstline] + inserts + [rest])

    def _insert_job_array_in_script(self):
        """Inserts a job array if required into the batch script."""
        if not self.job_array_regexp.search(self.batch_template):
            self.log.debug("adding job array settings to batch script")
            firstline, rest = self.batch_template.split('\n', 1)
            self.batch_template = '\n'.join([firstline, self.job_array_template, rest])

    def start(self, n=1):
        """Start n copies of the process using a batch system."""
        self.log.debug("Starting %s: %r", self.__class__.__name__, self.args)
        # Here we save profile_dir in the context so they
        # can be used in the batch script template as {profile_dir}
        self.write_batch_script(n)

        env = os.environ.copy()
        env.update(self.get_env())
        output = check_output(self.args, env=env)
        output = output.decode("utf8", 'replace')
        self.log.debug(f"Submitted {shlex_join(self.args)}. Output: {output}")

        job_id = self.parse_job_id(output)
        self.notify_start(job_id)
        return job_id

    def stop(self):
        try:
            output = check_output(
                self.delete_command + [self.job_id],
                stdin=None,
            ).decode("utf8", 'replace')
        except Exception:
            self.log.exception(
                "Problem stopping cluster with command: %s"
                % (self.delete_command + [self.job_id])
            )
            output = ""

        self.notify_stop(
            dict(job_id=self.job_id, output=output)
        )  # Pass the output of the kill cmd
        return output

    def signal(self, sig):
        cmd = self.signal_command + [str(sig), self.job_id]
        try:
            output = check_output(
                cmd,
                stdin=None,
            ).decode("utf8", 'replace')
        except Exception:
            self.log.exception("Problem sending signal with: {shlex_join(cmd)}")
            output = ""

    # same local-file implementation as LocalProcess
    # should this be on the base class?
    _output = None

    def get_output(self, remove=True):
        return LocalProcessLauncher.get_output(self, remove=remove)

    def poll(self):
        """Poll not implemented

        Need to use `squeue` and friends to check job status
        """
        return None


class BatchControllerLauncher(BatchSystemLauncher, ControllerLauncher):
    @default("program")
    def _default_program(self):
        return self.controller_cmd

    @observe("controller_cmd")
    def _controller_cmd_changed(self, change):
        self.program = self._default_program()

    @default("program_args")
    def _default_program_args(self):
        return self.cluster_args + self.controller_args

    @observe("controller_args")
    def _controller_args_changed(self, change):
        self.program_args = self._default_program_args()

    def start(self):
        return super().start(n=1)


class BatchEngineSetLauncher(BatchSystemLauncher, EngineLauncher):
    @default("program")
    def _default_program(self):
        return self.engine_cmd

    @observe("engine_cmd")
    def _engine_cmd_changed(self, change):
        self.program = self._default_program()

    @default("program_args")
    def _default_program_args(self):
        return self.cluster_args + self.engine_args

    @observe("engine_args")
    def _engine_args_changed(self, change):
        self.program_args = self._default_program_args()


class PBSLauncher(BatchSystemLauncher):
    """A BatchSystemLauncher subclass for PBS."""

    submit_command = List(['qsub'], config=True, help="The PBS submit command ['qsub']")
    delete_command = List(['qdel'], config=True, help="The PBS delete command ['qdel']")
    signal_command = List(
        ['qsig', '-s'], config=True, help="The PBS signal command ['qsig']"
    )
    job_id_regexp = CRegExp(
        r'\d+',
        config=True,
        help=r"Regular expresion for identifying the job ID [r'\d+']",
    )

    batch_file = Unicode('')
    job_array_regexp = CRegExp(r'#PBS\W+-t\W+[\w\d\-\$]+')
    job_array_template = Unicode('#PBS -t 1-{n}')
    queue_regexp = CRegExp(r'#PBS\W+-q\W+\$?\w+')
    queue_template = Unicode('#PBS -q {queue}')
    output_regexp = CRegExp(r'#PBS\W+(?:-o)\W+\$?\w+')
    output_template = Unicode('#PBS -j oe\n#PBS -o {output_file}')


class PBSControllerLauncher(PBSLauncher, BatchControllerLauncher):
    """Launch a controller using PBS."""

    batch_file_name = Unicode(
        'pbs_controller', config=True, help="batch file name for the controller job."
    )
    default_template = Unicode(
        """#!/bin/sh
#PBS -V
#PBS -N ipcontroller
{program_and_args}
"""
    )


class PBSEngineSetLauncher(PBSLauncher, BatchEngineSetLauncher):
    """Launch Engines using PBS"""

    batch_file_name = Unicode(
        'pbs_engines', config=True, help="batch file name for the engine(s) job."
    )
    default_template = Unicode(
        """#!/bin/sh
#PBS -V
#PBS -N ipengine
{program_and_args}
"""
    )


# Slurm is very similar to PBS


class SlurmLauncher(BatchSystemLauncher):
    """A BatchSystemLauncher subclass for slurm."""

    submit_command = List(
        ['sbatch'], config=True, help="The slurm submit command ['sbatch']"
    )
    delete_command = List(
        ['scancel'], config=True, help="The slurm delete command ['scancel']"
    )
    signal_command = List(
        ['scancel', '-s'],
        config=True,
        help="The slurm signal command ['scancel', '-s']",
    )
    job_id_regexp = CRegExp(
        r'\d+',
        config=True,
        help=r"Regular expresion for identifying the job ID [r'\d+']",
    )

    account = Unicode("", config=True, help="Slurm account to be used")

    qos = Unicode("", config=True, help="Slurm QoS to be used")

    # Note: from the man page:
    #'Acceptable time formats include "minutes", "minutes:seconds",
    # "hours:minutes:seconds", "days-hours", "days-hours:minutes"
    # and "days-hours:minutes:seconds".
    timelimit = Any("", config=True, help="Slurm timelimit to be used")

    options = Unicode("", config=True, help="Extra Slurm options")

    @observe('account')
    def _account_changed(self, change):
        self._update_context(change)

    @observe('qos')
    def _qos_changed(self, change):
        self._update_context(change)

    @observe('timelimit')
    def _timelimit_changed(self, change):
        self._update_context(change)

    @observe('options')
    def _options_changed(self, change):
        self._update_context(change)

    batch_file = Unicode('')

    job_array_regexp = CRegExp(r'#SBATCH\W+(?:--ntasks|-n)[\w\d\-\$]+')
    job_array_template = Unicode('''#SBATCH --ntasks={n}''')

    queue_regexp = CRegExp(r'#SBATCH\W+(?:--partition|-p)\W+\$?\w+')
    queue_template = Unicode('#SBATCH --partition={queue}')

    account_regexp = CRegExp(r'#SBATCH\W+(?:--account|-A)\W+\$?\w+')
    account_template = Unicode('#SBATCH --account={account}')

    qos_regexp = CRegExp(r'#SBATCH\W+--qos\W+\$?\w+')
    qos_template = Unicode('#SBATCH --qos={qos}')

    timelimit_regexp = CRegExp(r'#SBATCH\W+(?:--time|-t)\W+\$?\w+')
    timelimit_template = Unicode('#SBATCH --time={timelimit}')

    output_regexp = CRegExp(r'#SBATCH\W+(?:--output)\W+\$?\w+')
    output_template = Unicode('#SBATCH --output={output_file}')

    def _insert_options_in_script(self):
        """Insert 'partition' (slurm name for queue), 'account', 'time' and other options if necessary"""
        super()._insert_options_in_script()
        inserts = []
        if self.account and not self.account_regexp.search(self.batch_template):
            self.log.debug("adding slurm account settings to batch script")
            inserts.append(self.account_template)

        if self.qos and not self.qos_regexp.search(self.batch_template):
            self.log.debug("adding Slurm qos settings to batch script")
            firstline, rest = self.batch_template.split('\n', 1)
            inserts.append(self.qos_template)

        if self.timelimit and not self.timelimit_regexp.search(self.batch_template):
            self.log.debug("adding slurm time limit settings to batch script")
            inserts.append(self.timelimit_template)

        if inserts:
            firstline, rest = self.batch_template.split('\n', 1)
            self.batch_template = '\n'.join([firstline] + inserts + [rest])


class SlurmControllerLauncher(SlurmLauncher, BatchControllerLauncher):
    """Launch a controller using Slurm."""

    batch_file_name = Unicode(
        'slurm_controller.sbatch',
        config=True,
        help="batch file name for the controller job.",
    )
    default_template = Unicode(
        """#!/bin/sh
#SBATCH --export=ALL
#SBATCH --job-name=ipcontroller-{cluster_id}
#SBATCH --ntasks=1
{program_and_args}
"""
    )


class SlurmEngineSetLauncher(SlurmLauncher, BatchEngineSetLauncher):
    """Launch Engines using Slurm"""

    batch_file_name = Unicode(
        'slurm_engine.sbatch',
        config=True,
        help="batch file name for the engine(s) job.",
    )
    default_template = Unicode(
        """#!/bin/sh
#SBATCH --export=ALL
#SBATCH --job-name=ipengine-{cluster_id}
srun {program_and_args}
"""
    )


# SGE is very similar to PBS


class SGELauncher(PBSLauncher):
    """Sun GridEngine is a PBS clone with slightly different syntax"""

    job_array_regexp = CRegExp(r'#\$\W+\-t')
    job_array_template = Unicode('#$ -t 1-{n}')
    queue_regexp = CRegExp(r'#\$\W+-q\W+\$?\w+')
    queue_template = Unicode('#$ -q {queue}')


class SGEControllerLauncher(SGELauncher, BatchControllerLauncher):
    """Launch a controller using SGE."""

    batch_file_name = Unicode(
        'sge_controller', config=True, help="batch file name for the ipontroller job."
    )
    default_template = Unicode(
        """#$ -V
#$ -S /bin/sh
#$ -N ipcontroller
{program_and_args}
"""
    )


class SGEEngineSetLauncher(SGELauncher, BatchEngineSetLauncher):
    """Launch Engines with SGE"""

    batch_file_name = Unicode(
        'sge_engines', config=True, help="batch file name for the engine(s) job."
    )
    default_template = Unicode(
        """#$ -V
#$ -S /bin/sh
#$ -N ipengine
{program_and_args}
"""
    )


# LSF launchers


class LSFLauncher(BatchSystemLauncher):
    """A BatchSystemLauncher subclass for LSF."""

    submit_command = List(['bsub'], config=True, help="The LSF submit command ['bsub']")
    delete_command = List(
        ['bkill'], config=True, help="The LSF delete command ['bkill']"
    )
    signal_command = List(
        ['bkill', '-s'], config=True, help="The LSF signal command ['bkill', '-s']"
    )
    job_id_regexp = CRegExp(
        r'\d+',
        config=True,
        help=r"Regular expresion for identifying the job ID [r'\d+']",
    )

    batch_file = Unicode('')
    job_array_regexp = CRegExp(r'#BSUB\s+-J+\w+\[\d+-\d+\]')
    job_array_template = Unicode('#BSUB -J ipengine[1-{n}]')
    queue_regexp = CRegExp(r'#BSUB\s+-q\s+\w+')
    queue_template = Unicode('#BSUB -q {queue}')
    output_regexp = CRegExp(r'#BSUB\s+-oo?\s+\w+')
    output_template = Unicode('#BSUB -o {output_file}\n#BSUB -e {output_file}\n')

    def start(self, n=1):
        """Start n copies of the process using LSF batch system.
        This cant inherit from the base class because bsub expects
        to be piped a shell script in order to honor the #BSUB directives :
        bsub < script
        """
        # Here we save profile_dir in the context so they
        # can be used in the batch script template as {profile_dir}
        self.write_batch_script(n)
        piped_cmd = self.args[0] + '<"' + self.args[1] + '"'
        self.log.debug("Starting %s: %s", self.__class__.__name__, piped_cmd)
        p = Popen(piped_cmd, shell=True, env=os.environ, stdout=PIPE)
        output, err = p.communicate()
        output = output.decode("utf8", 'replace')
        job_id = self.parse_job_id(output)
        self.notify_start(job_id)
        return job_id


class LSFControllerLauncher(LSFLauncher, BatchControllerLauncher):
    """Launch a controller using LSF."""

    batch_file_name = Unicode(
        'lsf_controller', config=True, help="batch file name for the controller job."
    )
    default_template = Unicode(
        """#!/bin/sh
    #BSUB -env all
    #BSUB -J ipcontroller-{cluster_id}
    {program_and_args}
    """
    )


class LSFEngineSetLauncher(LSFLauncher, BatchEngineSetLauncher):
    """Launch Engines using LSF"""

    batch_file_name = Unicode(
        'lsf_engines', config=True, help="batch file name for the engine(s) job."
    )
    default_template = Unicode(
        """#!/bin/sh
    #BSUB -J ipengine-{cluster_id}
    #BSUB -env all
    {program_and_args}
    """
    )

    def get_env(self):
        # write directly to output files
        # otherwise, will copy and clobber merged stdout/err
        env = {"LSB_STDOUT_DIRECT": "Y"}
        env.update(super().get_env())
        return env


class HTCondorLauncher(BatchSystemLauncher):
    """A BatchSystemLauncher subclass for HTCondor.

    HTCondor requires that we launch the ipengine/ipcontroller scripts rather
    that the python instance but otherwise is very similar to PBS.  This is because
    HTCondor destroys sys.executable when launching remote processes - a launched
    python process depends on sys.executable to effectively evaluate its
    module search paths. Without it, regardless of which python interpreter you launch
    you will get the to built in module search paths.

    We use the ip{cluster, engine, controller} scripts as our executable to circumvent
    this - the mechanism of shebanged scripts means that the python binary will be
    launched with argv[0] set to the *location of the ip{cluster, engine, controller}
    scripts on the remote node*. This means you need to take care that:

    a. Your remote nodes have their paths configured correctly, with the ipengine and ipcontroller
       of the python environment you wish to execute code in having top precedence.
    b. This functionality is untested on Windows.

    If you need different behavior, consider making you own template.
    """

    submit_command = List(
        ['condor_submit'],
        config=True,
        help="The HTCondor submit command ['condor_submit']",
    )
    delete_command = List(
        ['condor_rm'], config=True, help="The HTCondor delete command ['condor_rm']"
    )
    job_id_regexp = CRegExp(
        r'(\d+)\.$',
        config=True,
        help=r"Regular expression for identifying the job ID [r'(\d+)\.$']",
    )
    job_id_regexp_group = Integer(
        1, config=True, help="""The group we wish to match in job_id_regexp [1]"""
    )

    job_array_regexp = CRegExp(r'queue\W+\$')
    job_array_template = Unicode('queue {n}')

    def _insert_job_array_in_script(self):
        """Inserts a job array if required into the batch script."""
        if not self.job_array_regexp.search(self.batch_template):
            self.log.debug("adding job array settings to batch script")
            # HTCondor requires that the job array goes at the bottom of the script
            self.batch_template = '\n'.join(
                [self.batch_template, self.job_array_template]
            )

    def _insert_options_in_script(self):
        """AFAIK, HTCondor doesn't have a concept of multiple queues that can be
        specified in the script.
        """
        super()._insert_options_in_script()


class HTCondorControllerLauncher(HTCondorLauncher, BatchControllerLauncher):
    """Launch a controller using HTCondor."""

    batch_file_name = Unicode(
        'htcondor_controller',
        config=True,
        help="batch file name for the controller job.",
    )
    default_template = Unicode(
        r"""
universe        = vanilla
executable      = ipcontroller
# by default we expect a shared file system
transfer_executable = False
arguments       = {program_args}
"""
    )


class HTCondorEngineSetLauncher(HTCondorLauncher, BatchEngineSetLauncher):
    """Launch Engines using HTCondor"""

    batch_file_name = Unicode(
        'htcondor_engines', config=True, help="batch file name for the engine(s) job."
    )
    default_template = Unicode(
        """
universe        = vanilla
executable      = ipengine
# by default we expect a shared file system
transfer_executable = False
arguments       = "{program_args}"
"""
    )


# -----------------------------------------------------------------------------
# Collections of launchers
# -----------------------------------------------------------------------------

local_launchers = [
    LocalControllerLauncher,
    LocalEngineLauncher,
    LocalEngineSetLauncher,
]
mpi_launchers = [
    MPILauncher,
    MPIControllerLauncher,
    MPIEngineSetLauncher,
]
ssh_launchers = [
    SSHLauncher,
    SSHControllerLauncher,
    SSHEngineLauncher,
    SSHEngineSetLauncher,
    SSHProxyEngineSetLauncher,
]
winhpc_launchers = [
    WindowsHPCLauncher,
    WindowsHPCControllerLauncher,
    WindowsHPCEngineSetLauncher,
]
pbs_launchers = [
    PBSLauncher,
    PBSControllerLauncher,
    PBSEngineSetLauncher,
]
slurm_launchers = [
    SlurmLauncher,
    SlurmControllerLauncher,
    SlurmEngineSetLauncher,
]
sge_launchers = [
    SGELauncher,
    SGEControllerLauncher,
    SGEEngineSetLauncher,
]
lsf_launchers = [
    LSFLauncher,
    LSFControllerLauncher,
    LSFEngineSetLauncher,
]
htcondor_launchers = [
    HTCondorLauncher,
    HTCondorControllerLauncher,
    HTCondorEngineSetLauncher,
]
all_launchers = (
    local_launchers
    + mpi_launchers
    + ssh_launchers
    + winhpc_launchers
    + pbs_launchers
    + slurm_launchers
    + sge_launchers
    + lsf_launchers
    + htcondor_launchers
)


def find_launcher_class(name, kind):
    """Return a launcher class for a given name and kind.

    Parameters
    ----------
    name : str
        The full name of the launcher class, either with or without the
        module path, or an abbreviation (MPI, SSH, SGE, PBS, LSF, HTCondor
        Slurm, WindowsHPC).
    kind : str
        Either 'EngineSet' or 'Controller'.
    """
    if kind == 'engine':
        group_name = 'ipyparallel.engine_launchers'
    elif kind == 'controller':
        group_name = 'ipyparallel.controller_launchers'
    else:
        raise ValueError(f"kind must be 'engine' or 'controller', not {kind!r}")
    group = entrypoints.get_group_named(group_name)
    # make it case-insensitive
    registry = {key.lower(): value for key, value in group.items()}
    return registry[name.lower()].load()


@lru_cache
def abbreviate_launcher_class(cls):
    """Abbreviate a launcher class back to its entrypoint name"""
    cls_key = f"{cls.__module__}.{cls.__name__}"
    # allow entrypoint_name attribute in case the definition module
    # is not the same as the 'import' module
    if getattr(cls, 'entrypoint_name', None):
        return getattr(cls, 'entrypoint_name')

    for kind in ('controller', 'engine'):
        group_name = f'ipyparallel.{kind}_launchers'
        group = entrypoints.get_group_named(group_name)
        for key, value in group.items():
            if f"{value.module_name}.{value.object_name}" == cls_key:
                return key.lower()
    return cls_key
ipyparallel-8.8.0/ipyparallel/controller/000077500000000000000000000000001460376056100205565ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/controller/__init__.py000066400000000000000000000000001460376056100226550ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/controller/__main__.py000066400000000000000000000001011460376056100226400ustar00rootroot00000000000000if __name__ == '__main__':
    from .app import main

    main()
ipyparallel-8.8.0/ipyparallel/controller/app.py000077500000000000000000001262041460376056100217200ustar00rootroot00000000000000#!/usr/bin/env python
"""
The IPython controller application.
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import json
import os
import socket
import stat
import sys
import time
from multiprocessing import Process
from signal import SIGABRT, SIGINT, SIGTERM, signal

import zmq
from IPython.core.profiledir import ProfileDir
from jupyter_client.localinterfaces import localhost
from jupyter_client.session import Session, session_aliases, session_flags
from traitlets import (
    Bool,
    Bytes,
    Dict,
    Instance,
    Integer,
    List,
    Tuple,
    Type,
    Unicode,
    Union,
    default,
    import_item,
    observe,
    validate,
)
from traitlets.config import Config
from zmq.devices import ProcessMonitoredQueue
from zmq.eventloop.zmqstream import ZMQStream
from zmq.log.handlers import PUBHandler

from ipyparallel import util
from ipyparallel.apps.baseapp import (
    BaseParallelApplication,
    base_aliases,
    base_flags,
    catch_config_error,
)
from ipyparallel.controller.broadcast_scheduler import launch_broadcast_scheduler
from ipyparallel.controller.dictdb import DictDB
from ipyparallel.controller.heartmonitor import HeartMonitor, start_heartmonitor
from ipyparallel.controller.hub import Hub
from ipyparallel.controller.scheduler import launch_scheduler
from ipyparallel.controller.task_scheduler import TaskScheduler
from ipyparallel.traitlets import PortList
from ipyparallel.util import disambiguate_url

from .broadcast_scheduler import BroadcastScheduler

# conditional import of SQLiteDB / MongoDB backend class
real_dbs = []

try:
    from ipyparallel.controller.sqlitedb import SQLiteDB
except ImportError:
    pass
else:
    real_dbs.append(SQLiteDB)

try:
    from ipyparallel.controller.mongodb import MongoDB
except ImportError:
    pass
else:
    real_dbs.append(MongoDB)


# -----------------------------------------------------------------------------
# Module level variables
# -----------------------------------------------------------------------------


_description = """Start the IPython controller for parallel computing.

The IPython controller provides a gateway between the IPython engines and
clients. The controller needs to be started before the engines and can be
configured using command line options or using a cluster directory. Cluster
directories contain config, log and security files and are usually located in
your ipython directory and named as "profile_name". See the `profile`
and `profile-dir` options for details.
"""

_examples = """
ipcontroller --ip=192.168.0.1 --port=1000  # listen on ip, port for engines
ipcontroller --scheme=pure  # use the pure zeromq scheduler
"""


# -----------------------------------------------------------------------------
# The main application
# -----------------------------------------------------------------------------
flags = {}
flags.update(base_flags)
flags.update(
    {
        'usethreads': (
            {'IPController': {'use_threads': True}},
            'Use threads instead of processes for the schedulers',
        ),
        'sqlitedb': (
            {'IPController': {'db_class': 'ipyparallel.controller.sqlitedb.SQLiteDB'}},
            'use the SQLiteDB backend',
        ),
        'mongodb': (
            {'IPController': {'db_class': 'ipyparallel.controller.mongodb.MongoDB'}},
            'use the MongoDB backend',
        ),
        'dictdb': (
            {'IPController': {'db_class': 'ipyparallel.controller.dictdb.DictDB'}},
            'use the in-memory DictDB backend',
        ),
        'nodb': (
            {'IPController': {'db_class': 'ipyparallel.controller.dictdb.NoDB'}},
            """use dummy DB backend, which doesn't store any information.

                    This is the default as of IPython 0.13.

                    To enable delayed or repeated retrieval of results from the Hub,
                    select one of the true db backends.
                    """,
        ),
        'reuse': (
            {'IPController': {'reuse_files': True}},
            'reuse existing json connection files',
        ),
        'restore': (
            {'IPController': {'restore_engines': True, 'reuse_files': True}},
            'Attempt to restore engines from a JSON file.  '
            'For use when resuming a crashed controller',
        ),
    }
)

flags.update(session_flags)

aliases = dict(
    ssh='IPController.ssh_server',
    enginessh='IPController.engine_ssh_server',
    location='IPController.location',
    ip='IPController.ip',
    transport='IPController.transport',
    port='IPController.regport',
    ports='IPController.ports',
    ping='HeartMonitor.period',
    scheme='TaskScheduler.scheme_name',
    hwm='TaskScheduler.hwm',
)
aliases.update(base_aliases)
aliases.update(session_aliases)

_db_shortcuts = {
    'sqlitedb': 'ipyparallel.controller.sqlitedb.SQLiteDB',
    'mongodb': 'ipyparallel.controller.mongodb.MongoDB',
    'dictdb': 'ipyparallel.controller.dictdb.DictDB',
    'nodb': 'ipyparallel.controller.dictdb.NoDB',
}


class IPController(BaseParallelApplication):
    name = 'ipcontroller'
    description = _description
    examples = _examples
    classes = [
        ProfileDir,
        Session,
        Hub,
        TaskScheduler,
        HeartMonitor,
        DictDB,
    ] + real_dbs
    _deprecated_classes = ("HubFactory", "IPControllerApp")

    # change default to True
    auto_create = Bool(
        True, config=True, help="""Whether to create profile dir if it doesn't exist."""
    )

    reuse_files = Bool(
        False,
        config=True,
        help="""Whether to reuse existing json connection files.
        If False, connection files will be removed on a clean exit.
        """,
    )
    restore_engines = Bool(
        False,
        config=True,
        help="""Reload engine state from JSON file
        """,
    )
    ssh_server = Unicode(
        '',
        config=True,
        help="""ssh url for clients to use when connecting to the Controller
        processes. It should be of the form: [user@]server[:port]. The
        Controller's listening addresses must be accessible from the ssh server""",
    )
    engine_ssh_server = Unicode(
        '',
        config=True,
        help="""ssh url for engines to use when connecting to the Controller
        processes. It should be of the form: [user@]server[:port]. The
        Controller's listening addresses must be accessible from the ssh server""",
    )
    location = Unicode(
        socket.gethostname(),
        config=True,
        help="""The external IP or domain name of the Controller, used for disambiguating
        engine and client connections.""",
    )

    use_threads = Bool(
        False, config=True, help='Use threads instead of processes for the schedulers'
    )

    engine_json_file = Unicode(
        'ipcontroller-engine.json',
        config=True,
        help="JSON filename where engine connection info will be stored.",
    )
    client_json_file = Unicode(
        'ipcontroller-client.json',
        config=True,
        help="JSON filename where client connection info will be stored.",
    )

    @observe('cluster_id')
    def _cluster_id_changed(self, change):
        base = 'ipcontroller'
        if change.new:
            base = f"{base}-{change.new}"
        self.engine_json_file = f"{base}-engine.json"
        self.client_json_file = f"{base}-client.json"

    enable_curve = Bool(
        False,
        config=True,
        help="""Enable CurveZMQ encryption and authentication

        Caution: known to have issues on platforms with getrandom
        """,
    )

    @default("enable_curve")
    def _default_enable_curve(self):
        enabled = os.environ.get("IPP_ENABLE_CURVE", "") == "1"
        if enabled:
            self._ensure_curve_keypair()
            # disable redundant digest-key, CurveZMQ protects against replays
            if 'key' not in self.config.Session:
                self.config.Session.key = b''
        return enabled

    @observe("enable_curve")
    def _enable_curve_changed(self, change):
        if change.new:
            self._ensure_curve_keypair()
            # disable redundant digest-key, CurveZMQ protects against replays
            if 'key' not in self.config.Session:
                self.config.Session.key = b''

    def _ensure_curve_keypair(self):
        if not self.curve_secretkey or not self.curve_publickey:
            self.log.info("Generating new CURVE credentials")
            self.curve_publickey, self.curve_secretkey = zmq.curve_keypair()

    curve_secretkey = Bytes(
        config=True,
        help="The CurveZMQ secret key for the controller",
    )
    curve_publickey = Bytes(
        config=True,
        help="""The CurveZMQ public key for the controller.

        Engines and clients use this for the server key.
        """,
    )

    @default("curve_secretkey")
    def _default_curve_secretkey(self):
        return os.environ.get("IPP_CURVE_SECRETKEY", "").encode("ascii")

    @default("curve_publickey")
    def _default_curve_publickey(self):
        return os.environ.get("IPP_CURVE_PUBLICKEY", "").encode("ascii")

    @validate("curve_publickey", "curve_secretkey")
    def _cast_bytes(self, proposal):
        if isinstance(proposal.value, str):
            return proposal.value.encode("ascii")
        return proposal.value

    # internal
    children = List()
    mq_class = Unicode('zmq.devices.ProcessMonitoredQueue')

    @observe('use_threads')
    def _use_threads_changed(self, change):
        self.mq_class = 'zmq.devices.{}MonitoredQueue'.format(
            'Thread' if change['new'] else 'Process'
        )

    write_connection_files = Bool(
        True,
        help="""Whether to write connection files to disk.
        True in all cases other than runs with `reuse_files=True` *after the first*
        """,
    )

    aliases = Dict(aliases)
    flags = Dict(flags)

    broadcast_scheduler_depth = Integer(
        1,
        config=True,
        help="Depth of spanning tree schedulers",
    )
    number_of_leaf_schedulers = Integer()
    number_of_broadcast_schedulers = Integer()
    number_of_non_leaf_schedulers = Integer()

    @default('number_of_leaf_schedulers')
    def get_number_of_leaf_schedulers(self):
        return 2**self.broadcast_scheduler_depth

    @default('number_of_broadcast_schedulers')
    def get_number_of_broadcast_schedulers(self):
        return 2 * self.number_of_leaf_schedulers - 1

    @default('number_of_non_leaf_schedulers')
    def get_number_of_non_leaf_schedulers(self):
        return self.number_of_broadcast_schedulers - self.number_of_leaf_schedulers

    ports = PortList(
        Integer(min=1, max=65536),
        config=True,
        help="""
        Pool of ports to use for the controller.

        For example:

            ipcontroller --ports 10101-10120

        This list will be consumed to populate the ports
        to be used when binding controller sockets
        for engines, clients, or internal connections.

        The number of sockets needed depends on scheduler depth,
        but is at least 14 (16 by default)

        If more ports than are defined here are needed,
        random ports will be selected.

        Can be specified as a list or string expressing a range

        See also engine_ports and client_ports
        """,
    )

    engine_ports = PortList(
        config=True,
        help="""
        Pool of ports to use for engine connections

        This list will be consumed to populate the ports
        to be used when binding controller sockets used for engine connections

        Can be specified as a list or string expressing a range

        If this list is exhausted, the common `ports` pool will be consumed.

        See also ports and client_ports
        """,
    )

    client_ports = PortList(
        config=True,
        help="""
        Pool of ports to use for client connections

        This list will be consumed to populate the ports
        to be used when binding controller sockets used for client connections

        Can be specified as a list or string expressing a range

        If this list is empty or exhausted,
        the common `ports` pool will be consumed.

        See also ports and engine_ports
        """,
    )

    # observe consumption of pools
    port_index = engine_port_index = client_port_index = 0

    # port-pairs for schedulers:
    hb = Tuple(
        Integer(),
        Integer(),
        config=True,
        help="""DEPRECATED: use ports""",
    )

    mux = Tuple(
        Integer(),
        Integer(),
        config=True,
        help="""DEPRECATED: use ports""",
    )

    task = Tuple(
        Integer(),
        Integer(),
        config=True,
        help="""DEPRECATED: use ports""",
    )

    control = Tuple(
        Integer(),
        Integer(),
        config=True,
        help="""DEPRECATED: use ports""",
    )

    iopub = Tuple(
        Integer(),
        Integer(),
        config=True,
        help="""DEPRECATED: use ports""",
    )

    @observe("iopub", "control", "task", "mux")
    def _scheduler_ports_assigned(self, change):
        self.log.warning(
            f"Setting {self.__class__.__name__}.{change.name} = {change.new!r} is deprecated in IPython Parallel 7."
            " Use IPController.ports config instead."
        )
        self.client_ports.append(change.new[0])
        self.engine_ports.append(change.new[0])

    @observe("hb")
    def _hb_ports_assigned(self, change):
        self.log.warning(
            f"Setting {self.__class__.__name__}.{change.name} = {change.new!r} is deprecated in IPython Parallel 7."
            " Use IPController.engine_ports config instead."
        )
        self.engine_ports.extend(change.new)

    # single ports:
    mon_port = Integer(config=True, help="""DEPRECATED: use ports""")

    notifier_port = Integer(config=True, help="""DEPRECATED: use ports""").tag(
        port_pool="client"
    )

    regport = Integer(config=True, help="DEPRECATED: use ports").tag(port_pool="engine")

    @observe("regport", "notifier_port", "mon_port")
    def _port_assigned(self, change):
        self.log.warning(
            f"Setting {self.__class__.__name__}.{change.name} = {change.new!r} is deprecated in IPython Parallel 7."
            " Use IPController.ports config instead."
        )
        trait = self.traits()[change.name]
        pool_name = trait.metadata.get("port_pool")
        if pool_name == 'engine':
            self.engine_ports.append(change.new)
        elif pool_name == 'client':
            self.client_ports.append(change.new)
        else:
            self.ports.append(change.new)

    engine_ip = Unicode(
        config=True,
        help="IP on which to listen for engine connections. [default: loopback]",
    )

    def _engine_ip_default(self):
        return localhost()

    engine_transport = Unicode(
        'tcp', config=True, help="0MQ transport for engine connections. [default: tcp]"
    )

    client_ip = Unicode(
        config=True,
        help="IP on which to listen for client connections. [default: loopback]",
    )
    client_transport = Unicode(
        'tcp', config=True, help="0MQ transport for client connections. [default : tcp]"
    )

    monitor_ip = Unicode(
        config=True,
        help="IP on which to listen for monitor messages. [default: loopback]",
    )
    monitor_transport = Unicode(
        'tcp', config=True, help="0MQ transport for monitor messages. [default : tcp]"
    )

    _client_ip_default = _monitor_ip_default = _engine_ip_default

    monitor_url = Unicode('')

    db_class = Union(
        [Unicode(), Type()],
        default_value=DictDB,
        config=True,
        help="""The class to use for the DB backend

        Options include:

        SQLiteDB: SQLite
        MongoDB : use MongoDB
        DictDB  : in-memory storage (fastest, but be mindful of memory growth of the Hub)
        NoDB    : disable database altogether (default)

        """,
    )

    @validate("db_class")
    def _validate_db_class(self, proposal):
        value = proposal.value
        if isinstance(value, str):
            # if it's a string, import it
            value = _db_shortcuts.get(value.lower(), value)
            return import_item(value)
        return value

    registration_timeout = Integer(
        0,
        config=True,
        help="Engine registration timeout in seconds [default: max(30,"
        "10*heartmonitor.period)]",
    )

    @default("registration_timeout")
    def _registration_timeout_default(self):
        # heartmonitor period is in milliseconds, so 10x in seconds is .01
        return max(30, int(0.01 * HeartMonitor(parent=self).period))

    # not configurable
    db = Instance('ipyparallel.controller.dictdb.BaseDB', allow_none=True)
    heartmonitor = Instance(
        'ipyparallel.controller.heartmonitor.HeartMonitor', allow_none=True
    )

    ip = Unicode(
        "127.0.0.1", config=True, help="""Set the controller ip for all connections."""
    )
    transport = Unicode(
        "tcp", config=True, help="""Set the zmq transport for all connections."""
    )

    @observe('ip')
    def _ip_changed(self, change):
        new = change['new']
        self.engine_ip = new
        self.client_ip = new
        self.monitor_ip = new

    @observe('transport')
    def _transport_changed(self, change):
        new = change['new']
        self.engine_transport = new
        self.client_transport = new
        self.monitor_transport = new

    context = Instance(zmq.Context)

    @default("context")
    def _defaut_context(self):
        return zmq.Context.instance()

    # connection file contents
    engine_info = Dict()
    client_info = Dict()

    _logged_exhaustion = Dict()

    _random_port_count = Integer(0)

    def next_port(self, pool_name='common'):
        """Consume a port from our port pools"""
        if pool_name == 'client':
            if len(self.client_ports) > self.client_port_index:
                port = self.client_ports[self.client_port_index]
                self.client_port_index += 1
                return port
            elif self.client_ports and not self._logged_exhaustion.get("client"):
                self._logged_exhaustion['client'] = True
                # only log once
                self.log.warning(f"Exhausted {len(self.client_ports)} client ports")
        elif pool_name == 'engine':
            if len(self.engine_ports) > self.engine_port_index:
                port = self.engine_ports[self.engine_port_index]
                self.engine_port_index += 1
                return port
            elif self.engine_ports and not self._logged_exhaustion.get("engine"):
                self._logged_exhaustion['engine'] = True
                self.log.warning(f"Exhausted {len(self.engine_ports)} engine ports")

        # drawing from common pool
        if len(self.ports) > self.port_index:
            port = self.ports[self.port_index]
            self.port_index += 1
            return port
        elif self.ports and not self._logged_exhaustion.get("common"):
            self._logged_exhaustion['common'] = True
            self.log.warning(f"Exhausted {len(self.ports)} common ports")

        self._random_port_count += 1
        port = util.select_random_ports(1)[0]
        return port

    def construct_url(self, kind: str, channel: str, index=None):
        if kind == 'engine':
            info = self.engine_info
        elif kind == 'client':
            info = self.client_info
        elif kind == 'internal':
            info = self.internal_info
        else:
            raise ValueError(
                "kind must be 'engine', 'client', or 'internal', not {kind!r}"
            )

        interface = info['interface']
        sep = '-' if interface.partition("://")[0] == 'ipc' else ':'
        port = info[channel]
        if index is not None:
            port = port[index]
        return f"{interface}{sep}{port}"

    def internal_url(self, channel, index=None):
        """return full zmq url for a named internal channel"""
        return self.construct_url('internal', channel, index)

    def bind(self, socket, url):
        """Bind a socket"""
        return util.bind(
            socket,
            url,
            curve_secretkey=self.curve_secretkey if self.enable_curve else None,
            curve_publickey=self.curve_publickey if self.enable_curve else None,
        )

    def connect(self, socket, url):
        return util.connect(
            socket,
            url,
            curve_serverkey=self.curve_publickey if self.enable_curve else None,
            curve_publickey=self.curve_publickey if self.enable_curve else None,
            curve_secretkey=self.curve_secretkey if self.enable_curve else None,
        )

    def client_url(self, channel, index=None):
        """return full zmq url for a named client channel"""
        return self.construct_url('client', channel, index)

    def engine_url(self, channel, index=None):
        """return full zmq url for a named engine channel"""
        return self.construct_url('engine', channel, index)

    def save_connection_dict(self, fname, cdict):
        """save a connection dict to json file."""
        fname = os.path.join(self.profile_dir.security_dir, fname)
        self.log.info("writing connection info to %s", fname)
        with open(fname, 'w') as f:
            f.write(json.dumps(cdict, indent=2))
        os.chmod(fname, stat.S_IRUSR | stat.S_IWUSR)

    def load_config_from_json(self):
        """load config from existing json connector files."""
        c = self.config
        self.log.debug("loading config from JSON")

        # load engine config

        fname = os.path.join(self.profile_dir.security_dir, self.engine_json_file)
        self.log.info("loading connection info from %s", fname)
        with open(fname) as f:
            ecfg = json.loads(f.read())

        # json gives unicode, Session.key wants bytes
        c.Session.key = ecfg['key'].encode('ascii')

        xport, ip = ecfg['interface'].split('://')

        c.IPController.engine_ip = ip
        c.IPController.engine_transport = xport

        self.location = ecfg['location']
        if not self.engine_ssh_server:
            self.engine_ssh_server = ecfg['ssh']

        # load client config

        fname = os.path.join(self.profile_dir.security_dir, self.client_json_file)
        self.log.info("loading connection info from %s", fname)
        with open(fname) as f:
            ccfg = json.loads(f.read())

        for key in ('key', 'registration', 'pack', 'unpack', 'signature_scheme'):
            assert ccfg[key] == ecfg[key], (
                "mismatch between engine and client info: %r" % key
            )

        xport, ip = ccfg['interface'].split('://')

        c.IPController.client_transport = xport
        c.IPController.client_ip = ip
        if not self.ssh_server:
            self.ssh_server = ccfg['ssh']

        self.engine_info = ecfg
        self.client_info = ccfg

    def cleanup_connection_files(self):
        if self.reuse_files:
            self.log.debug("leaving JSON connection files for reuse")
            return
        self.log.debug("cleaning up JSON connection files")
        for f in (self.client_json_file, self.engine_json_file):
            f = os.path.join(self.profile_dir.security_dir, f)
            try:
                os.remove(f)
            except Exception as e:
                self.log.error("Failed to cleanup connection file: %s", e)
            else:
                self.log.debug("removed %s", f)

    def load_secondary_config(self):
        """secondary config, loading from JSON and setting defaults"""
        if self.reuse_files:
            try:
                self.load_config_from_json()
            except (AssertionError, OSError) as e:
                self.log.error("Could not load config from JSON: %s" % e)
            else:
                # successfully loaded config from JSON, and reuse=True
                # no need to write back the same file
                self.write_connection_files = False

    def init_hub(self):
        if self.enable_curve:
            self.log.info(
                "Using CURVE security. Ignore warnings about disabled message signing."
            )

        c = self.config

        ctx = self.context
        loop = self.loop
        if 'TaskScheduler.scheme_name' in self.config:
            scheme = self.config.TaskScheduler.scheme_name
        else:
            from .task_scheduler import TaskScheduler

            scheme = TaskScheduler.scheme_name.default_value

        if self.engine_info:
            registration_port = self.engine_info['registration']
        else:
            registration_port = self.next_port('engine')

        # build connection dicts
        if not self.engine_info:
            self.engine_info = {
                'interface': f"{self.engine_transport}://{self.engine_ip}",
                'registration': registration_port,
                'control': self.next_port('engine'),
                'mux': self.next_port('engine'),
                'task': self.next_port('engine'),
                'iopub': self.next_port('engine'),
                'hb_ping': self.next_port('engine'),
                'hb_pong': self.next_port('engine'),
                BroadcastScheduler.port_name: [
                    self.next_port('engine')
                    for i in range(self.number_of_leaf_schedulers)
                ],
            }

        if not self.client_info:
            self.client_info = {
                'interface': f"{self.client_transport}://{self.client_ip}",
                'registration': registration_port,
                'control': self.next_port('client'),
                'mux': self.next_port('client'),
                'task': self.next_port('client'),
                'task_scheme': scheme,
                'iopub': self.next_port('client'),
                'notification': self.next_port('client'),
                BroadcastScheduler.port_name: self.next_port('client'),
            }
        if self.engine_transport == 'tcp':
            internal_interface = "tcp://127.0.0.1"
        else:
            internal_interface = self.engine_info['interface']

        broadcast_ids = []  # '0', '00', '01', '001', etc.
        # always a leading 0 for the root node
        for d in range(1, self.broadcast_scheduler_depth + 1):
            for i in range(2**d):
                broadcast_ids.append(format(i, f"0{d + 1}b"))
        self.internal_info = {
            'interface': internal_interface,
            BroadcastScheduler.port_name: {
                broadcast_id: self.next_port() for broadcast_id in broadcast_ids
            },
        }
        mon_port = self.next_port()
        self.monitor_url = f"{self.monitor_transport}://{self.monitor_ip}:{mon_port}"

        # debug port pool consumption
        if self.engine_ports:
            self.log.debug(
                f"Used {self.engine_port_index} / {len(self.engine_ports)} engine ports"
            )
        if self.client_ports:
            self.log.debug(
                f"Used {self.client_port_index} / {len(self.client_ports)} client ports"
            )
        if self.ports:
            self.log.debug(f"Used {self.port_index} / {len(self.ports)} common ports")
        if self._random_port_count:
            self.log.debug(f"Used {self._random_port_count} random ports")

        self.log.debug("Hub engine addrs: %s", self.engine_info)
        self.log.debug("Hub client addrs: %s", self.client_info)
        self.log.debug("Hub internal addrs: %s", self.internal_info)

        # Registrar socket
        query = ZMQStream(ctx.socket(zmq.ROUTER), loop)
        util.set_hwm(query, 0)
        self.bind(query, self.client_url('registration'))
        self.log.info(
            "Hub listening on %s for registration.", self.client_url('registration')
        )
        if self.client_ip != self.engine_ip:
            self.bind(query, self.engine_url('registration'))
            self.log.info(
                "Hub listening on %s for registration.", self.engine_url('registration')
            )

        ### Engine connections ###

        # heartbeat
        hm_config = Config()
        for key in ("Session", "HeartMonitor"):
            if key in self.config:
                hm_config[key] = self.config[key]
            hm_config.Session.key = self.session.key

        self.heartmonitor_process = Process(
            target=start_heartmonitor,
            kwargs=dict(
                ping_url=self.engine_url('hb_ping'),
                pong_url=self.engine_url('hb_pong'),
                monitor_url=disambiguate_url(self.monitor_url),
                config=hm_config,
                log_level=self.log.getEffectiveLevel(),
                curve_publickey=self.curve_publickey,
                curve_secretkey=self.curve_secretkey,
            ),
            daemon=True,
        )

        ### Client connections ###

        # Notifier socket
        notifier = ZMQStream(ctx.socket(zmq.PUB), loop)
        notifier.socket.SNDHWM = 0
        self.bind(notifier, self.client_url('notification'))

        ### build and launch the queues ###

        # monitor socket
        sub = ctx.socket(zmq.SUB)
        sub.RCVHWM = 0
        sub.setsockopt(zmq.SUBSCRIBE, b"")
        self.bind(sub, self.monitor_url)
        # self.bind(sub, 'inproc://monitor')
        sub = ZMQStream(sub, loop)

        # connect the db
        db_class = self.db_class
        self.log.info(f'Hub using DB backend: {self.db_class.__name__}')
        self.db = self.db_class(session=self.session.session, parent=self, log=self.log)
        time.sleep(0.25)

        # resubmit stream
        resubmit = ZMQStream(ctx.socket(zmq.DEALER), loop)
        url = util.disambiguate_url(self.client_url('task'))
        self.connect(resubmit, url)

        self.hub = Hub(
            loop=loop,
            session=self.session,
            monitor=sub,
            query=query,
            notifier=notifier,
            resubmit=resubmit,
            db=self.db,
            heartmonitor_period=HeartMonitor(parent=self).period,
            engine_info=self.engine_info,
            client_info=self.client_info,
            log=self.log,
            registration_timeout=self.registration_timeout,
            parent=self,
        )

        if self.write_connection_files:
            # save to new json config files
            base = {
                'key': self.session.key.decode('ascii'),
                'curve_serverkey': (
                    self.curve_publickey.decode("ascii") if self.enable_curve else None
                ),
                'location': self.location,
                'pack': self.session.packer,
                'unpack': self.session.unpacker,
                'signature_scheme': self.session.signature_scheme,
            }

            cdict = {'ssh': self.ssh_server}
            cdict.update(self.client_info)
            cdict.update(base)
            self.save_connection_dict(self.client_json_file, cdict)

            edict = {'ssh': self.engine_ssh_server}
            edict.update(self.engine_info)
            edict.update(base)
            self.save_connection_dict(self.engine_json_file, edict)

        fname = "engines%s.json" % self.cluster_id
        self.hub.engine_state_file = os.path.join(self.profile_dir.log_dir, fname)
        if self.restore_engines:
            self.hub._load_engine_state()

    def launch_python_scheduler(self, name, scheduler_args, children):
        if 'Process' in self.mq_class:
            # run the Python scheduler in a Process
            q = Process(
                target=launch_scheduler,
                kwargs=scheduler_args,
                name=name,
                daemon=True,
            )
            children.append(q)
        else:
            # single-threaded Controller
            scheduler_args['in_thread'] = True
            launch_scheduler(**scheduler_args)

    def get_python_scheduler_args(
        self,
        scheduler_name,
        scheduler_class,
        monitor_url,
        identity=None,
        in_addr=None,
        out_addr=None,
    ):
        if identity is not None:
            logname = f"{scheduler_name}-{identity}"
        else:
            logname = scheduler_name
        return {
            'scheduler_class': scheduler_class,
            'in_addr': in_addr or self.client_url(scheduler_name),
            'out_addr': out_addr or self.engine_url(scheduler_name),
            'mon_addr': monitor_url,
            'not_addr': disambiguate_url(self.client_url('notification')),
            'reg_addr': disambiguate_url(self.client_url('registration')),
            'identity': (
                identity if identity is not None else bytes(scheduler_name, 'utf8')
            ),
            'logname': logname,
            'loglevel': self.log_level,
            'log_url': self.log_url,
            'config': dict(self.config),
            'curve_secretkey': self.curve_secretkey if self.enable_curve else None,
            'curve_publickey': self.curve_publickey if self.enable_curve else None,
        }

    def launch_broadcast_schedulers(self, monitor_url, children):
        def launch_in_thread_or_process(scheduler_args, depth, identity):
            if 'Process' in self.mq_class:
                # run the Python scheduler in a Process
                q = Process(
                    target=launch_broadcast_scheduler,
                    kwargs=scheduler_args,
                    name=f"BroadcastScheduler(depth={depth}, id={identity})",
                    daemon=True,
                )
                children.append(q)
            else:
                # single-threaded Controller
                scheduler_args['in_thread'] = True
                launch_broadcast_scheduler(**scheduler_args)

        def recursively_start_schedulers(identity, depth):
            outgoing_id1 = identity + '0'
            outgoing_id2 = identity + '1'
            is_leaf = depth == self.broadcast_scheduler_depth
            is_root = depth == 0

            # FIXME: use localhost, not client ip for internal communication
            # this will still be localhost anyway for the most common cases
            # of localhost or */0.0.0.0
            if is_root:
                in_addr = self.client_url(BroadcastScheduler.port_name)
            else:
                # not root, use internal address

                in_addr = self.internal_url(
                    BroadcastScheduler.port_name,
                    index=identity,
                )

            scheduler_args = self.get_python_scheduler_args(
                BroadcastScheduler.port_name,
                BroadcastScheduler,
                monitor_url,
                identity,
                in_addr=in_addr,
                out_addr='ignored',
            )
            scheduler_args.pop('out_addr')
            # add broadcast args
            scheduler_args.update(
                outgoing_ids=[outgoing_id1, outgoing_id2],
                depth=depth,
                max_depth=self.broadcast_scheduler_depth,
                is_leaf=is_leaf,
            )

            if is_leaf:
                scheduler_args.update(
                    out_addrs=[
                        self.engine_url(
                            BroadcastScheduler.port_name,
                            index=int(identity, 2),
                        )
                    ],
                )
            else:
                scheduler_args.update(
                    out_addrs=[
                        self.internal_url(
                            BroadcastScheduler.port_name, index=outgoing_id1
                        ),
                        self.internal_url(
                            BroadcastScheduler.port_name, index=outgoing_id2
                        ),
                    ]
                )
            launch_in_thread_or_process(scheduler_args, depth=depth, identity=identity)
            if not is_leaf:
                recursively_start_schedulers(outgoing_id1, depth + 1)
                recursively_start_schedulers(outgoing_id2, depth + 1)

        recursively_start_schedulers(identity='0', depth=0)

    def init_schedulers(self):
        children = self.children
        mq = import_item(str(self.mq_class))
        # ensure session key is shared across sessions
        self.config.Session.key = self.session.key
        ident = self.session.bsession

        def add_auth(q):
            """Add CURVE auth to a monitored queue"""
            if not self.enable_curve:
                return False
            q.setsockopt_in(zmq.CURVE_SERVER, 1)
            q.setsockopt_in(zmq.CURVE_SECRETKEY, self.curve_secretkey)
            q.setsockopt_out(zmq.CURVE_SERVER, 1)
            q.setsockopt_out(zmq.CURVE_SECRETKEY, self.curve_secretkey)
            # monitor is a client
            pub, secret = zmq.curve_keypair()
            q.setsockopt_mon(zmq.CURVE_SERVERKEY, self.curve_publickey)
            q.setsockopt_mon(zmq.CURVE_SECRETKEY, secret)
            q.setsockopt_mon(zmq.CURVE_PUBLICKEY, pub)

        # disambiguate url, in case of *
        monitor_url = disambiguate_url(self.monitor_url)
        # maybe_inproc = 'inproc://monitor' if self.use_threads else monitor_url
        # IOPub relay (in a Process)
        q = mq(zmq.SUB, zmq.PUB, zmq.PUB, b'iopub', b'N/A')
        add_auth(q)
        q.name = "IOPubScheduler"

        q.bind_in(self.engine_url('iopub'))
        q.setsockopt_in(zmq.SUBSCRIBE, b'')
        q.bind_out(self.client_url('iopub'))
        q.setsockopt_out(zmq.IDENTITY, ident + b"_iopub")
        q.connect_mon(monitor_url)
        q.daemon = True
        children.append(q)

        # Multiplexer Queue (in a Process)
        q = mq(zmq.ROUTER, zmq.ROUTER, zmq.PUB, b'in', b'out')
        add_auth(q)
        q.name = "DirectScheduler"

        q.bind_in(self.client_url('mux'))
        q.setsockopt_in(zmq.IDENTITY, b'mux_in')
        q.bind_out(self.engine_url('mux'))
        q.setsockopt_out(zmq.IDENTITY, b'mux_out')
        q.connect_mon(monitor_url)
        q.daemon = True
        children.append(q)

        # Control Queue (in a Process)
        q = mq(zmq.ROUTER, zmq.ROUTER, zmq.PUB, b'incontrol', b'outcontrol')
        add_auth(q)
        q.name = "ControlScheduler"
        q.bind_in(self.client_url('control'))
        q.setsockopt_in(zmq.IDENTITY, b'control_in')
        q.bind_out(self.engine_url('control'))
        q.setsockopt_out(zmq.IDENTITY, b'control_out')
        q.connect_mon(monitor_url)
        q.daemon = True
        children.append(q)
        if 'TaskScheduler.scheme_name' in self.config:
            scheme = self.config.TaskScheduler.scheme_name
        else:
            scheme = TaskScheduler.scheme_name.default_value
        # Task Queue (in a Process)
        if scheme == 'pure':
            self.log.warning("task::using pure DEALER Task scheduler")
            q = mq(zmq.ROUTER, zmq.DEALER, zmq.PUB, b'intask', b'outtask')
            add_auth(q)
            q.name = "TaskScheduler(pure)"
            # q.setsockopt_out(zmq.HWM, hub.hwm)
            q.bind_in(self.client_url('task'))
            q.setsockopt_in(zmq.IDENTITY, b'task_in')
            q.bind_out(self.engine_url('task'))
            q.setsockopt_out(zmq.IDENTITY, b'task_out')
            q.connect_mon(monitor_url)
            q.daemon = True
            children.append(q)
        elif scheme == 'none':
            self.log.warning("task::using no Task scheduler")

        else:
            self.log.info("task::using Python %s Task scheduler" % scheme)
            self.launch_python_scheduler(
                'TaskScheduler',
                self.get_python_scheduler_args('task', TaskScheduler, monitor_url),
                children,
            )

        self.launch_broadcast_schedulers(monitor_url, children)

        # set unlimited HWM for all relay devices
        if hasattr(zmq, 'SNDHWM'):
            q = children[0]
            q.setsockopt_in(zmq.RCVHWM, 0)
            q.setsockopt_out(zmq.SNDHWM, 0)

            for q in children[1:]:
                if not hasattr(q, 'setsockopt_in'):
                    continue
                q.setsockopt_in(zmq.SNDHWM, 0)
                q.setsockopt_in(zmq.RCVHWM, 0)
                q.setsockopt_out(zmq.SNDHWM, 0)
                q.setsockopt_out(zmq.RCVHWM, 0)
                q.setsockopt_mon(zmq.SNDHWM, 0)

    def terminate_children(self):
        child_procs = []
        for child in self.children + [self.heartmonitor_process]:
            if isinstance(child, ProcessMonitoredQueue):
                child_procs.append(child.launcher)
            elif isinstance(child, Process):
                child_procs.append(child)
        if child_procs:
            self.log.critical("terminating children...")
            for child in child_procs:
                try:
                    child.terminate()
                except OSError:
                    # already dead
                    pass

    def handle_signal(self, sig, frame):
        self.log.critical("Received signal %i, shutting down", sig)
        self.terminate_children()
        self.loop.add_callback_from_signal(self.loop.stop)

    def init_signal(self):
        for sig in (SIGINT, SIGABRT, SIGTERM):
            signal(sig, self.handle_signal)

    def forward_logging(self):
        if self.log_url:
            self.log.info("Forwarding logging to %s" % self.log_url)
            context = zmq.Context.instance()
            lsock = context.socket(zmq.PUB)
            lsock.connect(self.log_url)
            handler = PUBHandler(lsock)
            handler.root_topic = 'controller'
            handler.setLevel(self.log_level)
            self.log.addHandler(handler)

    @catch_config_error
    def initialize(self, argv=None):
        super().initialize(argv)
        self.forward_logging()
        self.load_secondary_config()
        self.init_hub()
        self.init_schedulers()

    def start(self):
        # Start the subprocesses:
        # children must be started before signals are setup,
        # otherwise signal-handling will fire multiple times
        for child in self.children:
            child.start()
            if hasattr(child, 'launcher'):
                # apply name to actual process/thread for logging
                setattr(child.launcher, 'name', child.name)
            if not self.use_threads:
                process = getattr(child, 'launcher', child)
                self.log.debug(f"Started process {child.name}: {process.pid}")
            else:
                self.log.debug(f"Started thread {child.name}")

        self.heartmonitor_process.start()
        self.log.info(f"Heartmonitor beating every {self.hub.heartmonitor_period}ms")

        self.init_signal()

        try:
            self.loop.start()
        except KeyboardInterrupt:
            self.log.critical("Interrupted, Exiting...\n")
        finally:
            self.loop.close(all_fds=True)
            self.cleanup_connection_files()


def main(*args, **kwargs):
    """Create and run the IPython controller"""
    if sys.platform == 'win32':
        # make sure we don't get called from a multiprocessing subprocess
        # this can result in infinite Controllers being started on Windows
        # which doesn't have a proper fork, so multiprocessing is wonky

        # this only comes up when IPython has been installed using vanilla
        # setuptools, and *not* distribute.
        import multiprocessing

        p = multiprocessing.current_process()
        # the main process has name 'MainProcess'
        # subprocesses will have names like 'Process-1'
        if p.name != 'MainProcess':
            # we are a subprocess, don't start another Controller!
            return
    return IPController.launch_instance(*args, **kwargs)


if __name__ == '__main__':
    main()
ipyparallel-8.8.0/ipyparallel/controller/broadcast_scheduler.py000066400000000000000000000212001460376056100251230ustar00rootroot00000000000000import logging

import zmq
from traitlets import Bool, Bytes, Integer, List, Unicode

from ipyparallel import util
from ipyparallel.controller.scheduler import (
    Scheduler,
    ZMQStream,
    get_common_scheduler_streams,
)


class BroadcastScheduler(Scheduler):
    port_name = 'broadcast'
    accumulated_replies = {}
    accumulated_targets = {}
    is_leaf = Bool(False)
    connected_sub_scheduler_ids = List(Bytes())
    outgoing_streams = List()
    depth = Integer()
    max_depth = Integer()
    name = Unicode()

    def start(self):
        self.client_stream.on_recv(self.dispatch_submission, copy=False)
        if self.is_leaf:
            super().start()
        else:
            for outgoing_stream in self.outgoing_streams:
                outgoing_stream.on_recv(self.dispatch_result, copy=False)
        self.log.info(f"BroadcastScheduler {self.name} started")

    def send_to_targets(self, msg, original_msg_id, targets, idents, is_coalescing):
        if is_coalescing:
            self.accumulated_replies[original_msg_id] = {
                target.encode('utf8'): None for target in targets
            }
            self.accumulated_targets[original_msg_id] = targets

        for target in targets:
            new_msg = self.append_new_msg_id_to_msg(
                self.get_new_msg_id(original_msg_id, target), target, idents, msg
            )
            self.engine_stream.send_multipart(new_msg, copy=False)

    def send_to_sub_schedulers(
        self, msg, original_msg_id, targets, idents, is_coalescing
    ):
        trunc = 2**self.max_depth
        fmt = f"0{self.max_depth + 1}b"

        # assign targets to sub-schedulers based on binary path
        # compute binary '010110' representation of the engine id
        targets_by_scheduler = [
            [] for i in range(len(self.connected_sub_scheduler_ids))
        ]
        for target_tuple in targets:
            path = format(target_tuple[1] % trunc, fmt)
            next_idx = int(path[self.depth + 1])  # 0 or 1
            targets_by_scheduler[next_idx].append(target_tuple)

        if is_coalescing:
            self.accumulated_replies[original_msg_id] = {
                scheduler_id: None for scheduler_id in self.connected_sub_scheduler_ids
            }
            self.accumulated_targets[original_msg_id] = {}

        for i, scheduler_id in enumerate(self.connected_sub_scheduler_ids):
            targets_for_scheduler = targets_by_scheduler[i]
            if is_coalescing:
                if targets_for_scheduler:
                    self.accumulated_targets[original_msg_id][scheduler_id] = (
                        targets_for_scheduler
                    )
                else:
                    del self.accumulated_replies[original_msg_id][scheduler_id]
            msg['metadata']['targets'] = targets_for_scheduler

            new_msg = self.append_new_msg_id_to_msg(
                self.get_new_msg_id(original_msg_id, scheduler_id),
                scheduler_id,
                idents,
                msg,
            )
            self.outgoing_streams[i].send_multipart(new_msg, copy=False)

    def coalescing_reply(self, raw_msg, msg, original_msg_id, outgoing_id, idents):
        # accumulate buffers
        self.accumulated_replies[original_msg_id][outgoing_id] = msg['buffers']
        if all(
            msg_buffers is not None
            for msg_buffers in self.accumulated_replies[original_msg_id].values()
        ):
            replies = self.accumulated_replies.pop(original_msg_id)
            self.log.debug(f"Coalescing {len(replies)} reply to {original_msg_id}")
            targets = self.accumulated_targets.pop(original_msg_id)

            new_msg = msg.copy()
            # begin rebuilding message
            # metadata['targets']
            if self.is_leaf:
                new_msg['metadata']['broadcast_targets'] = targets
            else:
                new_msg['metadata']['broadcast_targets'] = []

            # avoid duplicated msg buffers
            buffers = []
            for sub_target, msg_buffers in replies.items():
                buffers.extend(msg_buffers)
                if not self.is_leaf:
                    new_msg['metadata']['broadcast_targets'].extend(targets[sub_target])

            new_raw_msg = self.session.serialize(new_msg)
            self.client_stream.send_multipart(
                idents + new_raw_msg + buffers, copy=False
            )

    @util.log_errors
    def dispatch_submission(self, raw_msg):
        try:
            idents, msg_list = self.session.feed_identities(raw_msg, copy=False)
            msg = self.session.deserialize(msg_list, content=False, copy=False)
        except Exception:
            self.log.error(
                f'broadcast::Invalid broadcast msg: {raw_msg}', exc_info=True
            )
            return
        metadata = msg['metadata']
        msg_id = msg['header']['msg_id']
        targets = metadata['targets']

        is_coalescing = metadata['is_coalescing']

        if 'original_msg_id' not in metadata:
            metadata['original_msg_id'] = msg_id

        original_msg_id = metadata['original_msg_id']
        if self.is_leaf:
            target_idents = [t[0] for t in targets]
            self.send_to_targets(
                msg, original_msg_id, target_idents, idents, is_coalescing
            )
        else:
            self.send_to_sub_schedulers(
                msg, original_msg_id, targets, idents, is_coalescing
            )

    @util.log_errors
    def dispatch_result(self, raw_msg):
        try:
            idents, msg = self.session.feed_identities(raw_msg, copy=False)
            msg = self.session.deserialize(msg, content=False, copy=False)
            outgoing_id = idents[0]

        except Exception:
            self.log.error(
                f'broadcast::Invalid broadcast msg: {raw_msg}', exc_info=True
            )
            return
        original_msg_id = msg['metadata']['original_msg_id']
        is_coalescing = msg['metadata']['is_coalescing']
        if is_coalescing:
            self.coalescing_reply(
                raw_msg, msg, original_msg_id, outgoing_id, idents[1:]
            )
        else:
            self.client_stream.send_multipart(raw_msg[1:], copy=False)


def get_id_with_prefix(identity):
    return bytes(f'sub_scheduler_{identity}', 'utf8')


def launch_broadcast_scheduler(
    in_addr,
    out_addrs,
    mon_addr,
    not_addr,
    reg_addr,
    identity,
    config=None,
    loglevel=logging.DEBUG,
    log_url=None,
    is_leaf=False,
    in_thread=False,
    outgoing_ids=None,
    curve_publickey=None,
    curve_secretkey=None,
    depth=0,
    max_depth=0,
    scheduler_class=BroadcastScheduler,
    logname='broadcast',
):
    config, ctx, loop, mons, nots, querys, log = get_common_scheduler_streams(
        mon_addr,
        not_addr,
        reg_addr,
        config,
        logname,
        log_url,
        loglevel,
        in_thread,
        curve_serverkey=curve_publickey,
        curve_publickey=curve_publickey,
        curve_secretkey=curve_secretkey,
    )

    is_root = depth == 0
    sub_scheduler_id = get_id_with_prefix(identity)

    incoming_stream = ZMQStream(ctx.socket(zmq.ROUTER), loop)
    util.set_hwm(incoming_stream, 0)
    incoming_stream.setsockopt(zmq.IDENTITY, sub_scheduler_id)

    if is_root:
        util.bind(incoming_stream, in_addr, curve_secretkey=curve_secretkey)
    else:
        util.connect(
            incoming_stream,
            in_addr,
            curve_serverkey=curve_publickey,
            curve_publickey=curve_publickey,
            curve_secretkey=curve_secretkey,
        )

    outgoing_streams = []
    for out_addr in out_addrs:
        out = ZMQStream(ctx.socket(zmq.ROUTER), loop)
        util.set_hwm(out, 0)
        out.setsockopt(zmq.IDENTITY, sub_scheduler_id)
        util.bind(out, out_addr, curve_secretkey=curve_secretkey)
        outgoing_streams.append(out)

    scheduler_args = dict(
        client_stream=incoming_stream,
        mon_stream=mons,
        notifier_stream=nots,
        query_stream=querys,
        loop=loop,
        log=log,
        config=config,
        depth=depth,
        max_depth=max_depth,
        name=identity,
    )
    if is_leaf:
        scheduler_args.update(engine_stream=outgoing_streams[0], is_leaf=True)
    else:
        scheduler_args.update(
            connected_sub_scheduler_ids=[
                get_id_with_prefix(identity) for identity in outgoing_ids
            ],
            outgoing_streams=outgoing_streams,
        )

    scheduler = scheduler_class(**scheduler_args)

    scheduler.start()
    if not in_thread:
        try:
            loop.start()
        except KeyboardInterrupt:
            scheduler.log.critical("Interrupted, exiting...")
ipyparallel-8.8.0/ipyparallel/controller/dependency.py000066400000000000000000000147101460376056100232510ustar00rootroot00000000000000"""Dependency utilities"""

from types import ModuleType

from ipyparallel.client.asyncresult import AsyncResult
from ipyparallel.error import UnmetDependency
from ipyparallel.serialize import can
from ipyparallel.util import interactive


class depend:
    """Dependency decorator, for use with tasks.

    `@depend` lets you define a function for engine dependencies
    just like you use `apply` for tasks.


    Examples
    --------
    ::

        @depend(df, a,b, c=5)
        def f(m,n,p)

        view.apply(f, 1,2,3)

    will call df(a,b,c=5) on the engine, and if it returns False or
    raises an UnmetDependency error, then the task will not be run
    and another engine will be tried.
    """

    def __init__(self, _wrapped_f, *args, **kwargs):
        self.f = _wrapped_f
        self.args = args
        self.kwargs = kwargs

    def __call__(self, f):
        return dependent(f, self.f, *self.args, **self.kwargs)


class dependent:
    """A function that depends on another function.
    This is an object to prevent the closure used
    in traditional decorators, which are not picklable.
    """

    def __init__(self, _wrapped_f, _wrapped_df, *dargs, **dkwargs):
        self.f = _wrapped_f
        name = getattr(_wrapped_f, '__name__', 'f')
        self.__name__ = name
        self.df = _wrapped_df
        self.dargs = dargs
        self.dkwargs = dkwargs

    def check_dependency(self):
        if self.df(*self.dargs, **self.dkwargs) is False:
            raise UnmetDependency()

    def __call__(self, *args, **kwargs):
        return self.f(*args, **kwargs)


@interactive
def _require(*modules, **mapping):
    """Helper for @require decorator."""
    from ipyparallel.error import UnmetDependency
    from ipyparallel.serialize import uncan

    user_ns = globals()
    for name in modules:
        try:
            exec('import %s' % name, user_ns)
        except ImportError:
            raise UnmetDependency(name)

    for name, cobj in mapping.items():
        user_ns[name] = uncan(cobj, user_ns)
    return True


def require(*objects, **mapping):
    """Simple decorator for requiring local objects and modules to be available
    when the decorated function is called on the engine.

    Modules specified by name or passed directly will be imported
    prior to calling the decorated function.

    Objects other than modules will be pushed as a part of the task.
    Functions can be passed positionally,
    and will be pushed to the engine with their __name__.
    Other objects can be passed by keyword arg.

    Examples
    --------
    ::

        In [1]: @ipp.require('numpy')
           ...: def norm(a):
           ...:     return numpy.linalg.norm(a,2)

    ::

        In [2]: foo = lambda x: x*x
        In [3]: @ipp.require(foo)
           ...: def bar(a):
           ...:     return foo(1-a)
    """
    names = []
    for obj in objects:
        if isinstance(obj, ModuleType):
            obj = obj.__name__

        if isinstance(obj, str):
            names.append(obj)
        elif hasattr(obj, '__name__'):
            mapping[obj.__name__] = obj
        else:
            raise TypeError(
                "Objects other than modules and functions "
                "must be passed by kwarg, but got: %s" % type(obj)
            )

    for name, obj in mapping.items():
        mapping[name] = can(obj)
    return depend(_require, *names, **mapping)


class Dependency(set):
    """An object for representing a set of msg_id dependencies.

    Subclassed from set().

    Parameters
    ----------
    dependencies: list/set of msg_ids or AsyncResult objects or output of Dependency.as_dict()
        The msg_ids to depend on
    all : bool [default True]
        Whether the dependency should be considered met when *all* depending tasks have completed
        or only when *any* have been completed.
    success : bool [default True]
        Whether to consider successes as fulfilling dependencies.
    failure : bool [default False]
        Whether to consider failures as fulfilling dependencies.

    If `all=success=True` and `failure=False`, then the task will fail with an ImpossibleDependency
        as soon as the first depended-upon task fails.
    """

    all = True
    success = True
    failure = True

    def __init__(self, dependencies=[], all=True, success=True, failure=False):
        if isinstance(dependencies, dict):
            # load from dict
            all = dependencies.get('all', True)
            success = dependencies.get('success', success)
            failure = dependencies.get('failure', failure)
            dependencies = dependencies.get('dependencies', [])
        ids = []

        # extract ids from various sources:
        if isinstance(dependencies, (str, AsyncResult)):
            dependencies = [dependencies]
        for d in dependencies:
            if isinstance(d, str):
                ids.append(d)
            elif isinstance(d, AsyncResult):
                ids.extend(d.msg_ids)
            else:
                raise TypeError("invalid dependency type: %r" % type(d))

        set.__init__(self, ids)
        self.all = all
        if not (success or failure):
            raise ValueError("Must depend on at least one of successes or failures!")
        self.success = success
        self.failure = failure

    def check(self, completed, failed=None):
        """check whether our dependencies have been met."""
        if len(self) == 0:
            return True
        against = set()
        if self.success:
            against = completed
        if failed is not None and self.failure:
            against = against.union(failed)
        if self.all:
            return self.issubset(against)
        else:
            return not self.isdisjoint(against)

    def unreachable(self, completed, failed=None):
        """return whether this dependency has become impossible."""
        if len(self) == 0:
            return False
        against = set()
        if not self.success:
            against = completed
        if failed is not None and not self.failure:
            against = against.union(failed)
        if self.all:
            return not self.isdisjoint(against)
        else:
            return self.issubset(against)

    def as_dict(self):
        """Represent this dependency as a dict. For json compatibility."""
        return dict(
            dependencies=list(self),
            all=self.all,
            success=self.success,
            failure=self.failure,
        )


__all__ = ['depend', 'require', 'dependent', 'Dependency']
ipyparallel-8.8.0/ipyparallel/controller/dictdb.py000066400000000000000000000251061460376056100223650ustar00rootroot00000000000000"""A Task logger that presents our DB interface,
but exists entirely in memory and implemented with dicts.


TaskRecords are dicts of the form::

    {
        'msg_id' : str(uuid),
        'client_uuid' : str(uuid),
        'engine_uuid' : str(uuid) or None,
        'header' : dict(header),
        'content': dict(content),
        'buffers': list(buffers),
        'submitted': datetime or None,
        'started': datetime or None,
        'completed': datetime or None,
        'received': datetime or None,
        'resubmitted': str(uuid) or None,
        'result_header' : dict(header) or None,
        'result_content' : dict(content) or None,
        'result_buffers' : list(buffers) or None,
    }

With this info, many of the special categories of tasks can be defined by query,
e.g.:

* pending: completed is None
* client's outstanding: client_uuid = uuid && completed is None
* MIA: arrived is None (and completed is None)

DictDB supports a subset of mongodb operators::

    $lt,$gt,$lte,$gte,$ne,$in,$nin,$all,$mod,$exists
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import copy
from copy import deepcopy
from datetime import datetime

from traitlets import Dict, Float, Integer, Unicode
from traitlets.config.configurable import LoggingConfigurable

from ..util import ensure_timezone

# Python can't copy memoryviews, but creating another memoryview works for us
copy._deepcopy_dispatch[memoryview] = lambda x, memo: memoryview(x)

filters = {
    '$lt': lambda a, b: a < b,
    '$gt': lambda a, b: b > a,
    '$eq': lambda a, b: a == b,
    '$ne': lambda a, b: a != b,
    '$lte': lambda a, b: a <= b,
    '$gte': lambda a, b: a >= b,
    '$in': lambda a, b: a in b,
    '$nin': lambda a, b: a not in b,
    '$all': lambda a, b: all([a in bb for bb in b]),
    '$mod': lambda a, b: a % b[0] == b[1],
    '$exists': lambda a, b: (b and a is not None) or (a is None and not b),
}


def _add_tz(obj):
    if isinstance(obj, datetime):
        obj = ensure_timezone(obj)
    return obj


class CompositeFilter:
    """Composite filter for matching multiple properties."""

    def __init__(self, dikt):
        self.tests = []
        self.values = []
        for key, value in dikt.items():
            self.tests.append(_add_tz(filters[key]))
            self.values.append(_add_tz(value))

    def __call__(self, value):
        for test, check in zip(self.tests, self.values):
            if not test(value, check):
                return False
        return True


class BaseDB(LoggingConfigurable):
    """Empty Parent class so traitlets work on DB."""

    # base configurable traits:
    session = Unicode("")

    def close(self):
        pass


class DictDB(BaseDB):
    """Basic in-memory dict-based object for saving Task Records.

    This is the first object to present the DB interface
    for logging tasks out of memory.

    The interface is based on MongoDB, so adding a MongoDB
    backend should be straightforward.
    """

    _records = Dict()
    _culled_ids = set()  # set of ids which have been culled
    _buffer_bytes = Integer(0)  # running total of the bytes in the DB

    size_limit = Integer(
        1024**3,
        config=True,
        help="""The maximum total size (in bytes) of the buffers stored in the db

        When the db exceeds this size, the oldest records will be culled until
        the total size is under size_limit * (1-cull_fraction).
        default: 1 GB
        """,
    )
    record_limit = Integer(
        1024,
        config=True,
        help="""The maximum number of records in the db

        When the history exceeds this size, the first record_limit * cull_fraction
        records will be culled.
        """,
    )
    cull_fraction = Float(
        0.1,
        config=True,
        help="""The fraction by which the db should culled when one of the limits is exceeded

        In general, the db size will spend most of its time with a size in the range:

        [limit * (1-cull_fraction), limit]

        for each of size_limit and record_limit.
        """,
    )

    def _match_one(self, rec, tests):
        """Check if a specific record matches tests."""
        for key, test in tests.items():
            if not test(rec.get(key, None)):
                return False
        return True

    def _match(self, check):
        """Find all the matches for a check dict."""
        matches = []
        tests = {}
        for k, v in check.items():
            if isinstance(v, dict):
                tests[k] = CompositeFilter(v)
            else:
                tests[k] = lambda o: _add_tz(o) == _add_tz(v)

        for rec in self._records.values():
            if self._match_one(rec, tests):
                matches.append(deepcopy(rec))
        return matches

    def _extract_subdict(self, rec, keys):
        """extract subdict of keys"""
        d = {}
        d['msg_id'] = rec['msg_id']
        for key in keys:
            d[key] = rec[key]
        return deepcopy(d)

    # methods for monitoring size / culling history

    def _add_bytes(self, rec):
        for key in ('buffers', 'result_buffers'):
            for buf in rec.get(key) or []:
                self._buffer_bytes += len(buf)

        self._maybe_cull()

    def _drop_bytes(self, rec):
        for key in ('buffers', 'result_buffers'):
            for buf in rec.get(key) or []:
                self._buffer_bytes -= len(buf)

    def _cull_oldest(self, n=1):
        """cull the oldest N records"""
        for msg_id in self.get_history()[:n]:
            self.log.debug("Culling record: %r", msg_id)
            self._culled_ids.add(msg_id)
            self.drop_record(msg_id)

    def _maybe_cull(self):
        # cull by count:
        if len(self._records) > self.record_limit:
            to_cull = int(self.cull_fraction * self.record_limit)
            self.log.info(
                "%i records exceeds limit of %i, culling oldest %i",
                len(self._records),
                self.record_limit,
                to_cull,
            )
            self._cull_oldest(to_cull)

        # cull by size:
        if self._buffer_bytes > self.size_limit:
            limit = self.size_limit * (1 - self.cull_fraction)

            before = self._buffer_bytes
            before_count = len(self._records)
            culled = 0
            while self._buffer_bytes > limit:
                self._cull_oldest(1)
                culled += 1

            self.log.info(
                "%i records with total buffer size %i exceeds limit: %i. Culled oldest %i records.",
                before_count,
                before,
                self.size_limit,
                culled,
            )

    def _check_dates(self, rec):
        for key in ('submitted', 'started', 'completed', 'received'):
            value = rec.get(key, None)
            if value is not None and not isinstance(value, datetime):
                raise ValueError(f"{key} must be None or datetime, not {value!r}")
            if isinstance(value, datetime) and value.tzinfo is None:
                self.log.warning(
                    "Timestamps should always have timezones: %s=%s", key, value
                )
                rec[key] = ensure_timezone(value)

    # public API methods:

    def add_record(self, msg_id, rec):
        """Add a new Task Record, by msg_id."""
        if msg_id in self._records:
            raise KeyError("Already have msg_id %r" % (msg_id))
        self._check_dates(rec)
        self._records[msg_id] = rec
        self._add_bytes(rec)
        self._maybe_cull()

    def get_record(self, msg_id):
        """Get a specific Task Record, by msg_id."""
        if msg_id in self._culled_ids:
            raise KeyError("Record %r has been culled for size" % msg_id)
        if msg_id not in self._records:
            raise KeyError("No such msg_id %r" % (msg_id))
        return deepcopy(self._records[msg_id])

    def update_record(self, msg_id, rec):
        """Update the data in an existing record."""
        if msg_id in self._culled_ids:
            raise KeyError("Record %r has been culled for size" % msg_id)
        self._check_dates(rec)
        _rec = self._records[msg_id]
        self._drop_bytes(_rec)
        _rec.update(rec)
        self._add_bytes(_rec)

    def drop_matching_records(self, check):
        """Remove a record from the DB."""
        matches = self._match(check)
        for rec in matches:
            self._drop_bytes(rec)
            del self._records[rec['msg_id']]

    def drop_record(self, msg_id):
        """Remove a record from the DB."""
        rec = self._records[msg_id]
        self._drop_bytes(rec)
        del self._records[msg_id]

    def find_records(self, check, keys=None):
        """Find records matching a query dict, optionally extracting subset of keys.

        Returns dict keyed by msg_id of matching records.

        Parameters
        ----------
        check : dict
            mongodb-style query argument
        keys : list of strs [optional]
            if specified, the subset of keys to extract.  msg_id will *always* be
            included.
        """
        matches = self._match(check)
        if keys:
            return [self._extract_subdict(rec, keys) for rec in matches]
        else:
            return matches

    def get_history(self):
        """get all msg_ids, ordered by time submitted."""
        msg_ids = self._records.keys()
        # Remove any that do not have a submitted timestamp.
        # This is extremely unlikely to happen,
        # but it seems to come up in some tests on VMs.
        msg_ids = [m for m in msg_ids if self._records[m]['submitted'] is not None]
        return sorted(msg_ids, key=lambda m: self._records[m]['submitted'])


class NoData(KeyError):
    """Special KeyError to raise when requesting data from NoDB"""

    def __str__(self):
        return "NoDB backend doesn't store any data. "
        "Start the Controller with a DB backend to enable resubmission / result persistence."


class NoDB(BaseDB):
    """A blackhole db backend that actually stores no information.

    Provides the full DB interface, but raises KeyErrors on any
    method that tries to access the records.  This can be used to
    minimize the memory footprint of the Hub when its record-keeping
    functionality is not required.
    """

    def add_record(self, msg_id, record):
        pass

    def get_record(self, msg_id):
        raise NoData()

    def update_record(self, msg_id, record):
        pass

    def drop_matching_records(self, check):
        pass

    def drop_record(self, msg_id):
        pass

    def find_records(self, check, keys=None):
        raise NoData()

    def get_history(self):
        raise NoData()
ipyparallel-8.8.0/ipyparallel/controller/heartmonitor.py000077500000000000000000000251341460376056100236530ustar00rootroot00000000000000#!/usr/bin/env python
"""
A multi-heart Heartbeat system using PUB and ROUTER sockets. pings are sent out on the PUB,
and hearts are tracked based on their DEALER identities.
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import asyncio
import logging
import time
import uuid

import zmq
from jupyter_client.session import Session
from tornado import ioloop
from traitlets import Bool, Dict, Float, Instance, Integer, Set, default
from traitlets.config.configurable import LoggingConfigurable
from zmq.devices import ThreadDevice, ThreadMonitoredQueue
from zmq.eventloop.zmqstream import ZMQStream

from ipyparallel import util
from ipyparallel.util import bind, connect, log_errors, set_hwm


class Heart:
    """A basic heart object for responding to a HeartMonitor.
    This is a simple wrapper with defaults for the most common
    Device model for responding to heartbeats.

    Builds a threadsafe zmq.FORWARDER Device, defaulting to using
    SUB/DEALER for in/out.

    You can specify the DEALER's IDENTITY via the optional heart_id argument."""

    device = None
    id = None

    def __init__(
        self,
        in_addr,
        out_addr,
        mon_addr=None,
        in_type=zmq.SUB,
        out_type=zmq.DEALER,
        mon_type=zmq.PUB,
        heart_id=None,
        curve_serverkey=None,
        curve_secretkey=None,
        curve_publickey=None,
    ):
        if mon_addr is None:
            self.device = ThreadDevice(zmq.FORWARDER, in_type, out_type)
        else:
            self.device = ThreadMonitoredQueue(
                in_type, out_type, mon_type, in_prefix=b"", out_prefix=b""
            )
        # do not allow the device to share global Context.instance,
        # which is the default behavior in pyzmq > 2.1.10
        self.device.context_factory = zmq.Context

        self.device.daemon = True
        self.device.connect_in(in_addr)
        self.device.connect_out(out_addr)
        if curve_serverkey:
            self.device.setsockopt_in(zmq.CURVE_SERVERKEY, curve_serverkey)
            self.device.setsockopt_in(zmq.CURVE_PUBLICKEY, curve_publickey)
            self.device.setsockopt_in(zmq.CURVE_SECRETKEY, curve_secretkey)

            self.device.setsockopt_out(zmq.CURVE_SERVERKEY, curve_serverkey)
            self.device.setsockopt_out(zmq.CURVE_PUBLICKEY, curve_publickey)
            self.device.setsockopt_out(zmq.CURVE_SECRETKEY, curve_secretkey)
            if mon_addr is not None:
                self.device.setsockopt_mon(zmq.CURVE_SERVERKEY, curve_publickey)
                self.device.setsockopt_mon(zmq.CURVE_PUBLICKEY, curve_publickey)
                self.device.setsockopt_mon(zmq.CURVE_SECRETKEY, curve_secretkey)

        if mon_addr is not None:
            self.device.connect_mon(mon_addr)
        if in_type == zmq.SUB:
            self.device.setsockopt_in(zmq.SUBSCRIBE, b"")
        if heart_id is None:
            heart_id = uuid.uuid4().bytes
        self.device.setsockopt_out(zmq.IDENTITY, heart_id)
        self.id = heart_id

    def start(self):
        return self.device.start()


class HeartMonitor(LoggingConfigurable):
    """A basic HeartMonitor class
    ping_stream: a PUB stream
    pong_stream: an ROUTER stream
    period: the period of the heartbeat in milliseconds
    """

    debug = Bool(
        False,
        config=True,
        help="""Whether to include every heartbeat in debugging output.

        Has to be set explicitly, because there will be *a lot* of output.
        """,
    )
    period = Integer(
        3000,
        config=True,
        help='The frequency at which the Hub pings the engines for heartbeats '
        '(in ms)',
    )
    max_heartmonitor_misses = Integer(
        10,
        config=True,
        help='Allowed consecutive missed pings from controller Hub to engine before unregistering.',
    )

    ping_stream = Instance(ZMQStream)
    pong_stream = Instance(ZMQStream)
    monitor_stream = Instance(ZMQStream)
    session = Instance(Session)

    @default("session")
    def _default_session(self):
        util._disable_session_extract_dates()
        return Session(parent=self)

    loop = Instance(ioloop.IOLoop)

    @default("loop")
    def _loop_default(self):
        return ioloop.IOLoop.current()

    # not settable:
    hearts = Set()
    responses = Set()
    on_probation = Dict()
    last_ping = Float(0)
    lifetime = Float(0)
    tic = Float(0)

    def start(self):
        self.log.debug("heartbeat::waiting for subscription")
        msg = self.monitor_stream.socket.recv_multipart()
        self.log.debug("heartbeat::subscription started")
        self.pong_stream.on_recv(self.handle_pong)
        self.tic = time.monotonic()
        self.caller = ioloop.PeriodicCallback(self.beat, self.period)
        self.caller.start()

    def beat(self):
        self.pong_stream.flush()
        self.last_ping = self.lifetime

        toc = time.monotonic()
        self.lifetime += toc - self.tic
        self.tic = toc
        if self.debug:
            self.log.debug("heartbeat::sending %s", self.lifetime)
        good_hearts = self.hearts.intersection(self.responses)
        missed_beats = self.hearts.difference(good_hearts)
        new_hearts = self.responses.difference(good_hearts)
        if new_hearts:
            self.handle_new_hearts(new_hearts)
        heart_failures, on_probation = self._check_missed(
            missed_beats, self.on_probation, self.hearts
        )
        if heart_failures:
            self.handle_heart_failure(heart_failures)
        self.on_probation = on_probation
        self.responses = set()
        # print self.on_probation, self.hearts
        # self.log.debug("heartbeat::beat %.3f, %i beating hearts", self.lifetime, len(self.hearts))
        self.ping_stream.send(str(self.lifetime).encode('ascii'))
        # flush stream to force immediate socket send
        self.ping_stream.flush()

    def _check_missed(self, missed_beats, on_probation, hearts):
        """Update heartbeats on probation, identifying any that have too many misses."""
        failures = []
        new_probation = {}
        for cur_heart in (b for b in missed_beats if b in hearts):
            miss_count = on_probation.get(cur_heart, 0) + 1
            self.log.info(f"heartbeat::missed {cur_heart} : {miss_count}")
            if miss_count > self.max_heartmonitor_misses:
                failures.append(cur_heart)
            else:
                new_probation[cur_heart] = miss_count
        return failures, new_probation

    def handle_new_hearts(self, hearts):
        for heart in hearts:
            self.hearts.add(heart)
        self.log.debug(f"Notifying hub of {len(hearts)} new hearts")
        self.session.send(
            self.monitor_stream,
            "new_heart",
            content={
                "hearts": [h.decode("utf8", "replace") for h in hearts],
            },
            ident=[b"heartmonitor", b""],
        )

    def handle_heart_failure(self, hearts):
        self.log.debug(f"Notifying hub of {len(hearts)} stopped hearts")
        self.session.send(
            self.monitor_stream,
            "stopped_heart",
            content={
                "hearts": [h.decode("utf8", "replace") for h in hearts],
            },
            ident=[b"heartmonitor", b""],
        )
        for heart in hearts:
            try:
                self.hearts.remove(heart)
            except KeyError:
                self.log.info("heartbeat:: %s has already been removed.", heart)

    @log_errors
    def handle_pong(self, msg):
        """a heart just beat"""
        current = str(self.lifetime).encode('ascii')
        last = str(self.last_ping).encode('ascii')
        if msg[1] == current:
            delta = time.monotonic() - self.tic
            if self.debug:
                self.log.debug(
                    "heartbeat::heart %r took %.2f ms to respond", msg[0], 1000 * delta
                )
            self.responses.add(msg[0])
        elif msg[1] == last:
            delta = time.monotonic() - self.tic + (self.lifetime - self.last_ping)
            self.log.warning(
                "heartbeat::heart %r missed a beat, and took %.2f ms to respond",
                msg[0],
                1000 * delta,
            )
            self.responses.add(msg[0])
        else:
            self.log.warning(
                "heartbeat::got bad heartbeat (possibly old?): %s (current=%.3f)",
                msg[1],
                self.lifetime,
            )


async def _setup_heartmonitor(
    ctx,
    ping_url,
    pong_url,
    monitor_url,
    log_level=logging.INFO,
    curve_publickey=None,
    curve_secretkey=None,
    **heart_monitor_kwargs,
):
    """Set up heart monitor

    For use in a background process,
    via Process(target=start_heartmonitor)
    """
    ping_socket = ctx.socket(zmq.PUB)
    bind(
        ping_socket,
        ping_url,
        curve_publickey=curve_publickey,
        curve_secretkey=curve_secretkey,
    )
    ping_stream = ZMQStream(ping_socket)

    pong_socket = ctx.socket(zmq.ROUTER)
    set_hwm(pong_socket, 0)
    bind(
        pong_socket,
        pong_url,
        curve_publickey=curve_publickey,
        curve_secretkey=curve_secretkey,
    )
    pong_stream = ZMQStream(pong_socket)

    monitor_socket = ctx.socket(zmq.XPUB)
    connect(
        monitor_socket,
        monitor_url,
        curve_publickey=curve_publickey,
        curve_secretkey=curve_secretkey,
        curve_serverkey=curve_publickey,
    )
    monitor_stream = ZMQStream(monitor_socket)

    # reinitialize logging after fork
    from .app import IPController

    app = IPController(log_level=log_level)
    heart_monitor_kwargs['log'] = app.log

    heart_monitor = HeartMonitor(
        ping_stream=ping_stream,
        pong_stream=pong_stream,
        monitor_stream=monitor_stream,
        **heart_monitor_kwargs,
    )
    heart_monitor.start()


def start_heartmonitor(
    ping_url,
    pong_url,
    monitor_url,
    log_level=logging.INFO,
    curve_publickey=None,
    curve_secretkey=None,
    **heart_monitor_kwargs,
):
    """Start a heart monitor.

    For use in a background process,
    via Process(target=start_heartmonitor)
    """
    ctx = zmq.Context()
    loop = asyncio.new_event_loop()
    try:
        loop.run_until_complete(
            _setup_heartmonitor(
                ctx=ctx,
                ping_url=ping_url,
                pong_url=pong_url,
                monitor_url=monitor_url,
                log_level=log_level,
                curve_publickey=curve_publickey,
                curve_secretkey=curve_secretkey,
                **heart_monitor_kwargs,
            )
        )
        loop.run_forever()
    finally:
        loop.close()
        ctx.destroy()
ipyparallel-8.8.0/ipyparallel/controller/hub.py000066400000000000000000001550421460376056100217150ustar00rootroot00000000000000"""The IPython Controller Hub with 0MQ

This is the master object that handles connections from engines and clients,
and monitors traffic through the various queues.
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import inspect
import json
import os
import sys
import time
from collections import deque
from datetime import datetime

import zmq
from jupyter_client.jsonutil import parse_date
from jupyter_client.session import Session
from tornado import ioloop
from traitlets import (
    Any,
    Bytes,
    Dict,
    Float,
    HasTraits,
    Instance,
    Integer,
    Set,
    Unicode,
    default,
)
from traitlets.config import LoggingConfigurable
from zmq.eventloop.zmqstream import ZMQStream

from ipyparallel import error, util

from ..util import extract_dates
from .heartmonitor import HeartMonitor

# internal:


def _passer(*args, **kwargs):
    return


def _printer(*args, **kwargs):
    print(args)
    print(kwargs)


def empty_record():
    """Return an empty dict with all record keys."""
    return {
        'msg_id': None,
        'header': None,
        'metadata': None,
        'content': None,
        'buffers': None,
        'submitted': None,
        'client_uuid': None,
        'engine_uuid': None,
        'started': None,
        'completed': None,
        'resubmitted': None,
        'received': None,
        'result_header': None,
        'result_metadata': None,
        'result_content': None,
        'result_buffers': None,
        'queue': None,
        'execute_input': None,
        'execute_result': None,
        'error': None,
        'stdout': '',
        'stderr': '',
    }


def ensure_date_is_parsed(header):
    if not isinstance(header['date'], datetime):
        header['date'] = parse_date(header['date'])


def init_record(msg):
    """Initialize a TaskRecord based on a request."""
    header = msg['header']

    ensure_date_is_parsed(header)
    return {
        'msg_id': header['msg_id'],
        'header': header,
        'content': msg['content'],
        'metadata': msg['metadata'],
        'buffers': msg['buffers'],
        'submitted': util.ensure_timezone(header['date']),
        'client_uuid': None,
        'engine_uuid': None,
        'started': None,
        'completed': None,
        'resubmitted': None,
        'received': None,
        'result_header': None,
        'result_metadata': None,
        'result_content': None,
        'result_buffers': None,
        'queue': None,
        'execute_input': None,
        'execute_result': None,
        'error': None,
        'stdout': '',
        'stderr': '',
    }


class EngineConnector(HasTraits):
    """A simple object for accessing the various zmq connections of an object.
    Attributes are:
    id (int): engine ID
    uuid (unicode): engine UUID
    pending: set of msg_ids
    stallback: tornado timeout for stalled registration
    registration_started (float): time when registration began
    """

    id = Integer()
    uuid = Unicode()
    ident = Bytes()
    pending = Set()
    stallback = Any()
    registration_started = Float()

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if not self.registration_started:
            self.registration_started = time.monotonic()


class Hub(LoggingConfigurable):
    """The IPython Controller Hub with 0MQ connections

    Parameters
    ==========
    loop: zmq IOLoop instance
    session: Session object
     context: zmq context for creating new connections (?)
    queue: ZMQStream for monitoring the command queue (SUB)
    query: ZMQStream for engine registration and client queries requests (ROUTER)
    heartbeat: HeartMonitor object checking the pulse of the engines
    notifier: ZMQStream for broadcasting engine registration changes (PUB)
    db: connection to db for out of memory logging of commands
                NotImplemented
    engine_info: dict of zmq connection information for engines to connect
                to the queues.
    client_info: dict of zmq connection information for engines to connect
                to the queues.
    """

    engine_state_file = Unicode()

    # internal data structures:
    ids = Set()  # engine IDs
    by_ident = Dict()  # map bytes identities : int engine id
    engines = Dict()  # map int engine id : EngineConnector
    hearts = Dict()  # map bytes identities : int engine id, only for active heartbeats
    heartmonitor_period = Integer()
    pending = Set()
    queues = Dict()  # pending msg_ids keyed by engine_id
    tasks = Dict()  # pending msg_ids submitted as tasks, keyed by client_id
    completed = Dict()  # completed msg_ids keyed by engine_id
    all_completed = Set()  # completed msg_ids keyed by engine_id
    unassigned = Set()  # set of task msg_ids not yet assigned a destination
    incoming_registrations = Dict()
    registration_timeout = Integer()
    _idcounter = Integer(0)
    distributed_scheduler = Any()

    expect_stopped_hearts = Instance(deque)

    @default("expect_stopped_hearts")
    def _default_expect_stopped_hearts(self):
        # remember the last this-many hearts
        # silences warnings about ignoring stopped hearts for unregistered engines
        # harmless, but noisy if they happen on every unregistration
        return deque(maxlen=1024)

    loop = Instance(ioloop.IOLoop)

    @default("loop")
    def _default_loop(self):
        return ioloop.IOLoop.current()

    # objects from constructor:
    session = Instance(Session)
    query = Instance(ZMQStream, allow_none=True)
    monitor = Instance(ZMQStream, allow_none=True)
    notifier = Instance(ZMQStream, allow_none=True)
    resubmit = Instance(ZMQStream, allow_none=True)
    heartmonitor = Instance(HeartMonitor, allow_none=True)
    db = Instance(object, allow_none=True)
    client_info = Dict()
    engine_info = Dict()

    def __init__(self, **kwargs):
        """
        # universal:
        loop: IOLoop for creating future connections
        session: streamsession for sending serialized data
        # engine:
        queue: ZMQStream for monitoring queue messages
        query: ZMQStream for engine+client registration and client requests
        heartbeat: HeartMonitor object for tracking engines
        # extra:
        db: ZMQStream for db connection (NotImplemented)
        engine_info: zmq address/protocol dict for engine connections
        client_info: zmq address/protocol dict for client connections
        """

        super().__init__(**kwargs)

        # register our callbacks
        self.query.on_recv(self.dispatch_query)
        self.monitor.on_recv(self.dispatch_monitor_traffic)

        self.monitor_handlers = {
            b'in': self.save_queue_request,
            b'out': self.save_queue_result,
            b'intask': self.save_task_request,
            b'outtask': self.save_task_result,
            b'inbcast': self.save_broadcast_request,
            b'outbcast': self.save_broadcast_result,
            b'tracktask': self.save_task_destination,
            b'incontrol': _passer,
            b'outcontrol': _passer,
            b'iopub': self.monitor_iopub_message,
            b'heartmonitor': self.heartmonitor_message,
        }

        self.query_handlers = {
            'queue_request': self.queue_status,
            'result_request': self.get_results,
            'history_request': self.get_history,
            'db_request': self.db_query,
            'purge_request': self.purge_results,
            'load_request': self.check_load,
            'resubmit_request': self.resubmit_task,
            'shutdown_request': self.shutdown_request,
            'registration_request': self.register_engine,
            'unregistration_request': self.unregister_engine,
            'connection_request': self.connection_request,
            'become_dask_request': self.become_dask,
            'stop_distributed_request': self.stop_distributed,
        }

        # ignore resubmit replies
        self.resubmit.on_recv(lambda msg: None, copy=False)

        self.log.info("hub::created hub")

    def new_engine_id(self, requested_id=None):
        """generate a new engine integer id.

        No longer reuse old ids, just count from 0.

        If an id is requested and available, use that.
        Otherwise, use the counter.
        """
        if requested_id is not None:
            if requested_id in self.engines:
                self.log.warning(
                    "Engine id %s in use by engine with uuid=%s",
                    requested_id,
                    self.engines[requested_id].uuid,
                )
            elif requested_id in {ec.id for ec in self.incoming_registrations.values()}:
                self.log.warning(
                    "Engine id %s registration already pending", requested_id
                )
            else:
                self._idcounter = max(requested_id + 1, self._idcounter)
                return requested_id
        newid = self._idcounter
        self._idcounter += 1
        return newid

    # -----------------------------------------------------------------------------
    # message validation
    # -----------------------------------------------------------------------------

    def _validate_targets(self, targets):
        """turn any valid targets argument into a list of integer ids"""
        if targets is None:
            # default to all
            return self.ids

        if isinstance(targets, (int, str)):
            # only one target specified
            targets = [targets]
        _targets = []
        for t in targets:
            # map raw identities to ids
            if isinstance(t, str):
                t = self.by_ident.get(t.encode("utf8", "replace"), t)
            _targets.append(t)
        targets = _targets
        bad_targets = [t for t in targets if t not in self.ids]
        if bad_targets:
            raise IndexError("No Such Engine: %r" % bad_targets)
        if not targets:
            raise IndexError("No Engines Registered")
        return targets

    # -----------------------------------------------------------------------------
    # dispatch methods (1 per stream)
    # -----------------------------------------------------------------------------

    @util.log_errors
    def dispatch_monitor_traffic(self, msg):
        """all ME and Task queue messages come through here, as well as
        IOPub traffic."""
        self.log.debug("monitor traffic: %r", msg[0])
        switch = msg[0]
        try:
            idents, msg = self.session.feed_identities(msg[1:])
        except ValueError:
            idents = []
        if not idents:
            self.log.error("Monitor message without topic: %r", msg)
            return
        handler = self.monitor_handlers.get(switch, None)
        if handler is not None:
            handler(idents, msg)
        else:
            self.log.error("Unrecognized monitor topic: %r", switch)

    @util.log_errors
    async def dispatch_query(self, msg):
        """Route registration requests and queries from clients."""
        try:
            idents, msg = self.session.feed_identities(msg)
        except ValueError:
            idents = []
        if not idents:
            self.log.error("Bad Query Message: %r", msg)
            return
        client_id = idents[0]
        try:
            msg = self.session.deserialize(msg, content=True)
        except Exception:
            content = error.wrap_exception()
            self.log.error("Bad Query Message: %r", msg, exc_info=True)
            self.session.send(
                self.query, "hub_error", ident=client_id, content=content, parent=msg
            )
            return
        # print client_id, header, parent, content
        # switch on message type:
        msg_type = msg['header']['msg_type']
        self.log.info("client::client %r requested %r", client_id, msg_type)
        handler = self.query_handlers.get(msg_type, None)
        try:
            if handler is None:
                raise KeyError("Bad Message Type: %r" % msg_type)
        except Exception:
            content = error.wrap_exception()
            self.log.error("Bad Message Type: %r", msg_type, exc_info=True)
            self.session.send(
                self.query, "hub_error", ident=client_id, content=content, parent=msg
            )
            return

        try:
            f = handler(idents, msg)
            if f and inspect.isawaitable(f):
                await f
        except Exception:
            content = error.wrap_exception()
            self.log.error("Error handling request: %r", msg_type, exc_info=True)
            self.session.send(
                self.query, "hub_error", ident=client_id, content=content, parent=msg
            )

    # ---------------------------------------------------------------------------
    # handler methods (1 per event)
    # ---------------------------------------------------------------------------

    # ----------------------- Heartbeat --------------------------------------

    @util.log_errors
    def heartmonitor_message(self, topics, msg):
        """Handle a message from the heart monitor"""
        try:
            msg = self.session.deserialize(msg)
        except Exception:
            self.log.error(
                "heartmonitor::invalid message %r",
                msg,
                exc_info=True,
            )
            return
        msg_type = msg['header']['msg_type']
        if msg_type == 'new_heart':
            hearts = msg['content']['hearts']
            self.log.info(f"Registering {len(hearts)} new hearts")
            for heart in hearts:
                self.handle_new_heart(heart.encode("utf8"))
        elif msg_type == 'stopped_heart':
            hearts = msg['content']['hearts']
            self.log.warning(f"{len(hearts)} hearts stopped")
            for heart in hearts:
                self.handle_stopped_heart(heart.encode("utf8"))

    def handle_new_heart(self, heart):
        """Handle a new heart that just started beating"""
        self.log.debug("heartbeat::handle_new_heart(%r)", heart)
        if heart not in self.incoming_registrations:
            self.log.info("heartbeat::ignoring new heart: %r", heart)
        else:
            self.finish_registration(heart)

    def handle_stopped_heart(self, heart):
        """Handle notification that heart has stopped"""
        self.log.debug("heartbeat::handle_stopped_heart(%r)", heart)
        eid = self.hearts.get(heart, None)
        if eid is None:
            if heart in self.expect_stopped_hearts:
                log = self.log.debug
            else:
                log = self.log.info
            log(
                "heartbeat::ignoring heart failure %r (probably unregistered already)",
                heart,
            )
        else:
            uuid = self.engines[eid].uuid
            self.unregister_engine(heart, dict(content=dict(id=eid, queue=uuid)))

    # ----------------------- MUX Queue Traffic ------------------------------

    def save_queue_request(self, idents, msg):
        if len(idents) < 2:
            self.log.error("invalid identity prefix: %r", idents)
            return
        queue_id, client_id = idents[:2]
        try:
            msg = self.session.deserialize(msg)
        except Exception:
            self.log.error(
                "queue::client %r sent invalid message to %r: %r",
                client_id,
                queue_id,
                msg,
                exc_info=True,
            )
            return

        eid = self.by_ident.get(queue_id, None)
        if eid is None:
            self.log.error("queue::target %r not registered", queue_id)
            self.log.debug("queue::    valid are: %r", self.by_ident.keys())
            return
        record = init_record(msg)
        msg_id = record['msg_id']
        self.log.info(
            "queue::client %r submitted request %r to %s", client_id, msg_id, eid
        )
        # Unicode in records
        record['engine_uuid'] = queue_id.decode('ascii')
        record['client_uuid'] = msg['header']['session']
        record['queue'] = 'mux'

        try:
            # it's posible iopub arrived first:
            existing = self.db.get_record(msg_id)
            for key, evalue in existing.items():
                rvalue = record.get(key, None)
                if evalue and rvalue and evalue != rvalue:
                    self.log.warning(
                        "conflicting initial state for record: %r:%r <%r> %r",
                        msg_id,
                        rvalue,
                        key,
                        evalue,
                    )
                elif evalue and not rvalue:
                    record[key] = evalue
            try:
                self.db.update_record(msg_id, record)
            except Exception:
                self.log.error("DB Error updating record %r", msg_id, exc_info=True)
        except KeyError:
            try:
                self.db.add_record(msg_id, record)
            except Exception:
                self.log.error("DB Error adding record %r", msg_id, exc_info=True)

        self.pending.add(msg_id)
        self.queues[eid].append(msg_id)

    def save_queue_result(self, idents, msg):
        if len(idents) < 2:
            self.log.error("invalid identity prefix: %r", idents)
            return

        client_id, queue_id = idents[:2]
        try:
            msg = self.session.deserialize(msg)
        except Exception:
            self.log.error(
                "queue::engine %r sent invalid message to %r: %r",
                queue_id,
                client_id,
                msg,
                exc_info=True,
            )
            return

        eid = self.by_ident.get(queue_id, None)
        if eid is None:
            self.log.error("queue::unknown engine %r is sending a reply: ", queue_id)
            return

        parent = msg['parent_header']
        if not parent:
            return
        msg_id = parent['msg_id']
        if msg_id in self.pending:
            self.pending.remove(msg_id)
            self.all_completed.add(msg_id)
            self.queues[eid].remove(msg_id)
            self.completed[eid].append(msg_id)
            self.log.info("queue::request %r completed on %s", msg_id, eid)
        elif msg_id not in self.all_completed:
            # it could be a result from a dead engine that died before delivering the
            # result
            self.log.warning("queue:: unknown msg finished %r", msg_id)
            return
        # update record anyway, because the unregistration could have been premature
        rheader = msg['header']
        md = msg['metadata']
        ensure_date_is_parsed(rheader)
        completed = util.ensure_timezone(rheader['date'])
        started = extract_dates(md.get('started', None))
        result = {
            'result_header': rheader,
            'result_metadata': md,
            'result_content': msg['content'],
            'received': util.utcnow(),
            'started': started,
            'completed': completed,
        }

        result['result_buffers'] = msg['buffers']
        try:
            self.db.update_record(msg_id, result)
        except Exception:
            self.log.error("DB Error updating record %r", msg_id, exc_info=True)

    # --------------------- Broadcast traffic ------------------------------
    def save_broadcast_request(self, idents, msg):
        client_id = idents[0]
        try:
            msg = self.session.deserialize(msg)
        except Exception as e:
            self.log.error(
                f'broadcast:: client {client_id} sent invalid broadcast message:'
                f' {msg}',
                exc_info=True,
            )
            return

        record = init_record(msg)

        record['client_uuid'] = msg['header']['session']
        header = msg['header']
        msg_id = header['msg_id']
        self.pending.add(msg_id)

        try:
            self.db.add_record(msg_id, record)
        except Exception as e:
            self.log.error(f'DB Error adding record {msg_id}', exc_info=True)

    def save_broadcast_result(self, idents, msg):
        client_id = idents[0]
        try:
            msg = self.session.deserialize(msg)
        except Exception as e:
            self.log.error(
                f'broadcast::invalid broadcast result message send to {client_id}:' f''
            )

        # save the result of a completed broadcast
        parent = msg['parent_header']
        if not parent:
            self.log.warning(f'Broadcast message {msg} had no parent')
            return
        msg_id = parent['msg_id']
        header = msg['header']
        md = msg['metadata']
        engine_uuid = md.get('engine', '')
        eid = self.by_ident.get(engine_uuid.encode("utf8"), None)
        status = md.get('status', None)

        if msg_id in self.pending:
            self.log.info(f'broadcast:: broadcast {msg_id} finished on {eid}')
            self.pending.remove(msg_id)
            self.all_completed.add(msg_id)
            if eid is not None and status != 'aborted':
                self.completed[eid].append(msg_id)
            ensure_date_is_parsed(header)
            completed = util.ensure_timezone(header['date'])
            started = extract_dates(md.get('started', None))
            result = {
                'result_header': header,
                'result_metadata': msg['metadata'],
                'result_content': msg['content'],
                'started': started,
                'completed': completed,
                'received': util.utcnow(),
                'engine_uuid': engine_uuid,
                'result_buffers': msg['buffers'],
            }

            try:
                self.db.update_record(msg_id, result)
            except Exception as e:
                self.log.error(
                    f'DB Error saving broadcast result {msg_id}', msg_id, exc_info=True
                )
        else:
            self.log.debug(f'broadcast::unknown broadcast {msg_id} finished')

    # --------------------- Task Queue Traffic ------------------------------

    def save_task_request(self, idents, msg):
        """Save the submission of a task."""
        client_id = idents[0]

        try:
            msg = self.session.deserialize(msg)
        except Exception:
            self.log.error(
                "task::client %r sent invalid task message: %r",
                client_id,
                msg,
                exc_info=True,
            )
            return
        record = init_record(msg)

        record['client_uuid'] = msg['header']['session']
        record['queue'] = 'task'
        header = msg['header']
        msg_id = header['msg_id']
        self.pending.add(msg_id)
        self.unassigned.add(msg_id)
        try:
            # it's posible iopub arrived first:
            existing = self.db.get_record(msg_id)
            if existing['resubmitted']:
                for key in ('submitted', 'client_uuid', 'buffers'):
                    # don't clobber these keys on resubmit
                    # submitted and client_uuid should be different
                    # and buffers might be big, and shouldn't have changed
                    record.pop(key)
                    # still check content,header which should not change
                    # but are not expensive to compare as buffers

            for key, evalue in existing.items():
                if key.endswith('buffers'):
                    # don't compare buffers
                    continue
                rvalue = record.get(key, None)
                if evalue and rvalue and evalue != rvalue:
                    self.log.warning(
                        "conflicting initial state for record: %r:%r <%r> %r",
                        msg_id,
                        rvalue,
                        key,
                        evalue,
                    )
                elif evalue and not rvalue:
                    record[key] = evalue
            try:
                self.db.update_record(msg_id, record)
            except Exception:
                self.log.error("DB Error updating record %r", msg_id, exc_info=True)
        except KeyError:
            try:
                self.db.add_record(msg_id, record)
            except Exception:
                self.log.error("DB Error adding record %r", msg_id, exc_info=True)
        except Exception:
            self.log.error("DB Error saving task request %r", msg_id, exc_info=True)

    def save_task_result(self, idents, msg):
        """save the result of a completed task."""
        client_id = idents[0]
        try:
            msg = self.session.deserialize(msg)
        except Exception:
            self.log.error(
                "task::invalid task result message send to %r: %r",
                client_id,
                msg,
                exc_info=True,
            )
            return

        parent = msg['parent_header']
        if not parent:
            # print msg
            self.log.warning("Task %r had no parent!", msg)
            return
        msg_id = parent['msg_id']
        if msg_id in self.unassigned:
            self.unassigned.remove(msg_id)

        header = msg['header']
        md = msg['metadata']
        engine_uuid = md.get('engine', '')
        eid = self.by_ident.get(engine_uuid.encode("utf8"), None)

        status = md.get('status', None)

        if msg_id in self.pending:
            self.log.info("task::task %r finished on %s", msg_id, eid)
            self.pending.remove(msg_id)
            self.all_completed.add(msg_id)
            if eid is not None:
                if status != 'aborted':
                    self.completed[eid].append(msg_id)
                if msg_id in self.tasks[eid]:
                    self.tasks[eid].remove(msg_id)
            ensure_date_is_parsed(header)
            completed = util.ensure_timezone(header['date'])
            started = extract_dates(md.get('started', None))
            result = {
                'result_header': header,
                'result_metadata': msg['metadata'],
                'result_content': msg['content'],
                'started': started,
                'completed': completed,
                'received': util.utcnow(),
                'engine_uuid': engine_uuid,
            }

            result['result_buffers'] = msg['buffers']
            try:
                self.db.update_record(msg_id, result)
            except Exception:
                self.log.error("DB Error saving task request %r", msg_id, exc_info=True)

        else:
            self.log.debug("task::unknown task %r finished", msg_id)

    def save_task_destination(self, idents, msg):
        try:
            msg = self.session.deserialize(msg, content=True)
        except Exception:
            self.log.error("task::invalid task tracking message", exc_info=True)
            return
        content = msg['content']
        # print (content)
        msg_id = content['msg_id']
        engine_uuid = content['engine_id']
        eid = self.by_ident[engine_uuid.encode("utf8")]

        self.log.info("task::task %r arrived on %r", msg_id, eid)
        if msg_id in self.unassigned:
            self.unassigned.remove(msg_id)
        # else:
        #     self.log.debug("task::task %r not listed as MIA?!"%(msg_id))

        self.tasks[eid].append(msg_id)
        try:
            self.db.update_record(msg_id, dict(engine_uuid=engine_uuid))
        except Exception:
            self.log.error("DB Error saving task destination %r", msg_id, exc_info=True)

    # --------------------- IOPub Traffic ------------------------------

    def monitor_iopub_message(self, topics, msg):
        '''intercept iopub traffic so events can be acted upon'''
        try:
            msg = self.session.deserialize(msg, content=True)
        except Exception:
            self.log.error("iopub::invalid IOPub message", exc_info=True)
            return

        msg_type = msg['header']['msg_type']
        if msg_type == 'shutdown_reply':
            session = msg['header']['session']
            uuid_bytes = session.encode("utf8", "replace")
            eid = self.by_ident.get(uuid_bytes, None)
            if eid is None:
                self.log.error(f"Found no engine for {session}")
                return
            uuid = self.engines[eid].uuid
            self.unregister_engine(
                ident='shutdown_reply', msg=dict(content=dict(id=eid, queue=uuid))
            )

        if msg_type not in (
            'status',
            'shutdown_reply',
        ):
            self.save_iopub_message(topics, msg)

    def save_iopub_message(self, topics, msg):
        """save an iopub message into the db"""
        parent = msg['parent_header']
        if not parent:
            self.log.debug("iopub::IOPub message lacks parent: %r", msg)
            return
        msg_id = parent['msg_id']
        msg_type = msg['header']['msg_type']
        content = msg['content']

        # ensure msg_id is in db
        try:
            rec = self.db.get_record(msg_id)
        except KeyError:
            rec = None

        # stream
        d = {}
        if msg_type == 'stream':
            name = content['name']
            s = '' if rec is None else rec[name]
            d[name] = s + content['text']
        elif msg_type == 'error':
            d['error'] = content
        elif msg_type == 'execute_input':
            d['execute_input'] = content['code']
        elif msg_type in ('display_data', 'execute_result'):
            d[msg_type] = content
        elif msg_type == 'data_pub':
            self.log.info("ignored data_pub message for %s" % msg_id)
        else:
            self.log.warning("unhandled iopub msg_type: %r", msg_type)

        if not d:
            return

        if rec is None:
            # new record
            rec = empty_record()
            rec['msg_id'] = msg_id
            rec.update(d)
            d = rec
            update_record = self.db.add_record
        else:
            update_record = self.db.update_record

        try:
            update_record(msg_id, d)
        except Exception:
            self.log.error("DB Error saving iopub message %r", msg_id, exc_info=True)

    # -------------------------------------------------------------------------
    # Registration requests
    # -------------------------------------------------------------------------

    def connection_request(self, client_id, msg):
        """Reply with connection addresses for clients."""
        self.log.info("client::client %r connected", client_id)
        content = dict(status='ok')
        jsonable = {}
        for eid, ec in self.engines.items():
            jsonable[str(eid)] = ec.uuid
        content['engines'] = jsonable
        self.session.send(
            self.query, 'connection_reply', content, parent=msg, ident=client_id
        )

    def register_engine(self, reg, msg):
        """Begin registration of a new engine."""
        content = msg['content']
        try:
            uuid = content['uuid']
        except KeyError:
            self.log.error("registration::queue not specified", exc_info=True)
            return

        eid = self.new_engine_id(content.get('id'))
        self.log.debug(f"registration::requesting registration {eid}:{uuid}")

        content = dict(
            id=eid,
            status='ok',
            hb_period=self.heartmonitor_period,
            connection_info=self.engine_info,
        )
        # check if requesting available IDs:
        if uuid.encode("utf8") in self.by_ident:
            try:
                raise KeyError("uuid %r in use" % uuid)
            except Exception:
                content = error.wrap_exception()
                self.log.error("uuid %r in use", uuid, exc_info=True)
        else:
            for heart_id, ec in self.incoming_registrations.items():
                if uuid == heart_id:
                    try:
                        raise KeyError("heart_id %r in use" % uuid)
                    except Exception:
                        self.log.error("heart_id %r in use", uuid, exc_info=True)
                        content = error.wrap_exception()
                    break
                elif uuid == ec.uuid:
                    try:
                        raise KeyError("uuid %r in use" % uuid)
                    except Exception:
                        self.log.error("uuid %r in use", uuid, exc_info=True)
                        content = error.wrap_exception()
                    break

        msg = self.session.send(
            self.query, "registration_reply", content=content, ident=reg
        )
        # actually send replies to avoid async starvation during a registration flood
        # it's important that this message has actually been sent
        # before we start the stalled registration countdown
        self.query.flush(zmq.POLLOUT)

        heart = uuid.encode("utf8")

        if content['status'] == 'ok':
            self.log.info(f"registration::accepting registration {eid}:{uuid}")
            t = self.loop.add_timeout(
                self.loop.time() + self.registration_timeout,
                lambda: self._purge_stalled_registration(heart),
            )
            self.incoming_registrations[heart] = EngineConnector(
                id=eid, uuid=uuid, ident=heart, stallback=t
            )
        else:
            self.log.error(
                "registration::registration %i failed: %r", eid, content['evalue']
            )

        return eid

    def unregister_engine(self, ident, msg):
        """Unregister an engine that explicitly requested to leave."""
        try:
            eid = msg['content']['id']
        except Exception:
            self.log.error(
                f"registration::bad request for engine for unregistration: {msg['content']}",
            )
            return
        if eid not in self.engines:
            # engine not registered
            # first, check for still-pending registration
            remove_heart_id = None
            for heart_id, ec in self.incoming_registrations.items():
                if ec.id == eid:
                    remove_heart_id = heart_id
                    break
            if remove_heart_id is not None:
                self.log.info(f"registration::canceling registration {ec.id}:{ec.uuid}")
                self.incoming_registrations.pop(remove_heart_id)
            else:
                self.log.info(
                    f"registration::unregister_engine({eid}) already unregistered"
                )
            return

        self.log.info(f"registration::unregister_engine({eid})")
        ec = self.engines[eid]

        content = dict(id=eid, uuid=ec.uuid)

        # stop the heartbeats
        self.hearts.pop(ec.ident, None)
        self.expect_stopped_hearts.append(ec.ident)

        self.loop.add_timeout(
            self.loop.time() + self.registration_timeout,
            lambda: self._handle_stranded_msgs(eid, ec.uuid),
        )

        # cleanup mappings
        self.by_ident.pop(ec.ident, None)
        self.engines.pop(eid, None)

        self._save_engine_state()

        if self.notifier:
            self.session.send(
                self.notifier, "unregistration_notification", content=content
            )

    def _handle_stranded_msgs(self, eid, uuid):
        """Handle messages known to be on an engine when the engine unregisters.

        It is possible that this will fire prematurely - that is, an engine will
        go down after completing a result, and the client will be notified
        that the result failed and later receive the actual result.
        """

        outstanding = self.queues[eid]

        for msg_id in outstanding:
            self.pending.remove(msg_id)
            self.all_completed.add(msg_id)
            try:
                raise error.EngineError(
                    f"Engine {eid!r} died while running task {msg_id!r}"
                )
            except Exception:
                content = error.wrap_exception()
            # build a fake header:
            header = {}
            header['engine'] = uuid
            header['date'] = util.utcnow()
            rec = dict(result_content=content, result_header=header, result_buffers=[])
            rec['completed'] = util.ensure_timezone(header['date'])
            rec['engine_uuid'] = uuid
            try:
                self.db.update_record(msg_id, rec)
            except Exception:
                self.log.error(
                    "DB Error handling stranded msg %r", msg_id, exc_info=True
                )

    def finish_registration(self, heart):
        """Second half of engine registration, called after our HeartMonitor
        has received a beat from the Engine's Heart."""
        try:
            ec = self.incoming_registrations.pop(heart)
        except KeyError:
            self.log.error(
                f"registration::tried to finish nonexistant registration for {heart}",
                exc_info=True,
            )
            return
        duration_ms = int(1000 * (time.monotonic() - ec.registration_started))
        self.log.info(
            f"registration::finished registering engine {ec.id}:{ec.uuid} in {duration_ms}ms"
        )
        if ec.stallback is not None:
            self.loop.remove_timeout(ec.stallback)
        eid = ec.id
        self.ids.add(eid)
        self.engines[eid] = ec
        self.by_ident[ec.ident] = ec.id
        self.queues[eid] = list()
        self.tasks[eid] = list()
        self.completed[eid] = list()
        self.hearts[heart] = eid
        content = dict(id=eid, uuid=ec.uuid)
        if self.notifier:
            self.session.send(
                self.notifier, "registration_notification", content=content
            )
        self.log.info("engine::Engine Connected: %i", eid)

        self._save_engine_state()

    def _purge_stalled_registration(self, heart):
        # flush monitor messages before purging
        # first heartbeat might be waiting to be handled
        self.monitor.flush()
        if heart in self.incoming_registrations:
            ec = self.incoming_registrations.pop(heart)
            self.log.warning(
                f"registration::purging stalled registration {ec.id}:{ec.uuid}"
            )

    # -------------------------------------------------------------------------
    # Engine State
    # -------------------------------------------------------------------------

    def _cleanup_engine_state_file(self):
        """cleanup engine state mapping"""

        if os.path.exists(self.engine_state_file):
            self.log.debug("cleaning up engine state: %s", self.engine_state_file)
            try:
                os.remove(self.engine_state_file)
            except OSError:
                self.log.error(
                    "Couldn't cleanup file: %s", self.engine_state_file, exc_info=True
                )

    def _save_engine_state(self):
        """save engine mapping to JSON file"""
        if not self.engine_state_file:
            return
        self.log.debug("save engine state to %s" % self.engine_state_file)
        state = {}
        engines = {}
        for eid, ec in self.engines.items():
            engines[eid] = ec.uuid

        state['engines'] = engines

        state['next_id'] = self._idcounter

        with open(self.engine_state_file, 'w') as f:
            json.dump(state, f)

    def _load_engine_state(self):
        """load engine mapping from JSON file"""
        if not os.path.exists(self.engine_state_file):
            return

        self.log.info("loading engine state from %s" % self.engine_state_file)

        with open(self.engine_state_file) as f:
            state = json.load(f)

        save_notifier = self.notifier
        self.notifier = None
        for eid, uuid in state['engines'].items():
            heart = uuid.encode('ascii')

            self.incoming_registrations[heart] = EngineConnector(
                id=int(eid), uuid=uuid, ident=heart
            )
            self.finish_registration(heart)

        self.notifier = save_notifier

        self._idcounter = state['next_id']

    # -------------------------------------------------------------------------
    # Client Requests
    # -------------------------------------------------------------------------

    def shutdown_request(self, client_id, msg):
        """handle shutdown request."""
        self.session.send(
            self.query,
            'shutdown_reply',
            content={'status': 'ok'},
            ident=client_id,
            parent=msg,
        )
        # also notify other clients of shutdown
        self.session.send(
            self.notifier, 'shutdown_notification', content={'status': 'ok'}, parent=msg
        )
        self.loop.add_timeout(self.loop.time() + 1, self._shutdown)

    def _shutdown(self):
        self.log.info("hub::hub shutting down.")
        time.sleep(0.1)
        sys.exit(0)

    def check_load(self, client_id, msg):
        content = msg['content']
        try:
            targets = content['targets']
            targets = self._validate_targets(targets)
        except Exception:
            content = error.wrap_exception()
            self.session.send(
                self.query, "hub_error", content=content, ident=client_id, parent=msg
            )
            return

        content = dict(status='ok')
        # loads = {}
        for t in targets:
            content[bytes(t)] = len(self.queues[t]) + len(self.tasks[t])
        self.session.send(
            self.query, "load_reply", content=content, ident=client_id, parent=msg
        )

    def queue_status(self, client_id, msg):
        """Return the Queue status of one or more targets.

        If verbose, return the msg_ids, else return len of each type.

        Keys:

        * queue (pending MUX jobs)
        * tasks (pending Task jobs)
        * completed (finished jobs from both queues)
        """
        content = msg['content']
        targets = content['targets']
        try:
            targets = self._validate_targets(targets)
        except Exception:
            content = error.wrap_exception()
            self.session.send(
                self.query, "hub_error", content=content, ident=client_id, parent=msg
            )
            return
        verbose = content.get('verbose', False)
        content = dict(status='ok')
        for t in targets:
            queue = self.queues[t]
            completed = self.completed[t]
            tasks = self.tasks[t]
            if not verbose:
                queue = len(queue)
                completed = len(completed)
                tasks = len(tasks)
            content[str(t)] = {'queue': queue, 'completed': completed, 'tasks': tasks}
        content['unassigned'] = (
            list(self.unassigned) if verbose else len(self.unassigned)
        )
        # print (content)
        self.session.send(
            self.query, "queue_reply", content=content, ident=client_id, parent=msg
        )

    def purge_results(self, client_id, msg):
        """Purge results from memory. This method is more valuable before we move
        to a DB based message storage mechanism."""
        content = msg['content']
        self.log.info("Dropping records with %s", content)
        msg_ids = content.get('msg_ids', [])
        reply = dict(status='ok')
        if msg_ids == 'all':
            try:
                self.db.drop_matching_records(dict(completed={'$ne': None}))
            except Exception:
                reply = error.wrap_exception()
                self.log.exception("Error dropping records")
        else:
            pending = [m for m in msg_ids if (m in self.pending)]
            if pending:
                try:
                    raise IndexError("msg pending: %r" % pending[0])
                except Exception:
                    reply = error.wrap_exception()
                    self.log.exception("Error dropping records")
            else:
                try:
                    self.db.drop_matching_records(dict(msg_id={'$in': msg_ids}))
                except Exception:
                    reply = error.wrap_exception()
                    self.log.exception("Error dropping records")

            if reply['status'] == 'ok':
                eids = content.get('engine_ids', [])
                for eid in eids:
                    if eid not in self.engines:
                        try:
                            raise IndexError("No such engine: %i" % eid)
                        except Exception:
                            reply = error.wrap_exception()
                            self.log.exception("Error dropping records")
                        break
                    uid = self.engines[eid].uuid
                    try:
                        self.db.drop_matching_records(
                            dict(engine_uuid=uid, completed={'$ne': None})
                        )
                    except Exception:
                        reply = error.wrap_exception()
                        self.log.exception("Error dropping records")
                        break

        self.session.send(
            self.query, 'purge_reply', content=reply, ident=client_id, parent=msg
        )

    def resubmit_task(self, client_id, msg):
        """Resubmit one or more tasks."""
        parent = msg

        def finish(reply):
            self.session.send(
                self.query,
                'resubmit_reply',
                content=reply,
                ident=client_id,
                parent=parent,
            )

        content = msg['content']
        msg_ids = content['msg_ids']
        reply = dict(status='ok')
        try:
            records = self.db.find_records(
                {'msg_id': {'$in': msg_ids}}, keys=['header', 'content', 'buffers']
            )
        except Exception:
            self.log.error('db::db error finding tasks to resubmit', exc_info=True)
            return finish(error.wrap_exception())

        # validate msg_ids
        found_ids = [rec['msg_id'] for rec in records]
        pending_ids = [msg_id for msg_id in found_ids if msg_id in self.pending]
        if len(records) > len(msg_ids):
            try:
                raise RuntimeError(
                    "DB appears to be in an inconsistent state."
                    "More matching records were found than should exist"
                )
            except Exception:
                self.log.exception("Failed to resubmit task")
                return finish(error.wrap_exception())
        elif len(records) < len(msg_ids):
            missing = [m for m in msg_ids if m not in found_ids]
            try:
                raise KeyError("No such msg(s): %r" % missing)
            except KeyError:
                self.log.exception("Failed to resubmit task")
                return finish(error.wrap_exception())
        elif pending_ids:
            pass
            # no need to raise on resubmit of pending task, now that we
            # resubmit under new ID, but do we want to raise anyway?
            # msg_id = invalid_ids[0]
            # try:
            #     raise ValueError("Task(s) %r appears to be inflight" % )
            # except Exception:
            #     return finish(error.wrap_exception())

        # mapping of original IDs to resubmitted IDs
        resubmitted = {}

        # send the messages
        for rec in records:
            header = rec['header']
            msg = self.session.msg(header['msg_type'], parent=header)
            msg_id = msg['msg_id']
            msg['content'] = rec['content']

            # use the old header, but update msg_id and timestamp
            fresh = msg['header']
            header['msg_id'] = fresh['msg_id']
            header['date'] = fresh['date']
            msg['header'] = header

            self.session.send(self.resubmit, msg, buffers=rec['buffers'])

            resubmitted[rec['msg_id']] = msg_id
            self.pending.add(msg_id)
            msg['buffers'] = rec['buffers']
            try:
                self.db.add_record(msg_id, init_record(msg))
            except Exception:
                self.log.error(
                    "db::DB Error updating record: %s", msg_id, exc_info=True
                )
                return finish(error.wrap_exception())

        finish(dict(status='ok', resubmitted=resubmitted))

        # store the new IDs in the Task DB
        for msg_id, resubmit_id in resubmitted.items():
            try:
                self.db.update_record(msg_id, {'resubmitted': resubmit_id})
            except Exception:
                self.log.error(
                    "db::DB Error updating record: %s", msg_id, exc_info=True
                )

    def _extract_record(self, rec):
        """decompose a TaskRecord dict into subsection of reply for get_result"""
        io_dict = {}
        for key in ('execute_input', 'execute_result', 'error', 'stdout', 'stderr'):
            io_dict[key] = rec[key]
        content = {
            'header': rec['header'],
            'metadata': rec['metadata'],
            'result_metadata': rec['result_metadata'],
            'result_header': rec['result_header'],
            'result_content': rec['result_content'],
            'received': rec['received'],
            'io': io_dict,
        }
        if rec['result_buffers']:
            buffers = list(rec['result_buffers'])
        else:
            buffers = []

        return content, buffers

    def get_results(self, client_id, msg):
        """Get the result of 1 or more messages."""
        content = msg['content']
        msg_ids = sorted(set(content['msg_ids']))
        statusonly = content.get('status_only', False)
        pending = []
        completed = []
        content = dict(status='ok')
        content['pending'] = pending
        content['completed'] = completed
        buffers = []
        if not statusonly:
            try:
                matches = self.db.find_records(dict(msg_id={'$in': msg_ids}))
                # turn match list into dict, for faster lookup
                records = {}
                for rec in matches:
                    records[rec['msg_id']] = rec
            except Exception:
                content = error.wrap_exception()
                self.log.exception("Failed to get results")
                self.session.send(
                    self.query,
                    "result_reply",
                    content=content,
                    parent=msg,
                    ident=client_id,
                )
                return
        else:
            records = {}
        for msg_id in msg_ids:
            if msg_id in self.pending:
                pending.append(msg_id)
            elif msg_id in self.all_completed:
                completed.append(msg_id)
                if not statusonly:
                    c, bufs = self._extract_record(records[msg_id])
                    content[msg_id] = c
                    buffers.extend(bufs)
            elif msg_id in records:
                if rec['completed']:
                    completed.append(msg_id)
                    c, bufs = self._extract_record(records[msg_id])
                    content[msg_id] = c
                    buffers.extend(bufs)
                else:
                    pending.append(msg_id)
            else:
                try:
                    raise KeyError('No such message: ' + msg_id)
                except Exception:
                    content = error.wrap_exception()
                break
        self.session.send(
            self.query,
            "result_reply",
            content=content,
            parent=msg,
            ident=client_id,
            buffers=buffers,
        )

    def get_history(self, client_id, msg):
        """Get a list of all msg_ids in our DB records"""
        try:
            msg_ids = self.db.get_history()
        except Exception as e:
            content = error.wrap_exception()
            self.log.exception("Failed to get history")
        else:
            content = dict(status='ok', history=msg_ids)

        self.session.send(
            self.query, "history_reply", content=content, parent=msg, ident=client_id
        )

    def db_query(self, client_id, msg):
        """Perform a raw query on the task record database."""
        content = msg['content']
        query = extract_dates(content.get('query', {}))
        keys = content.get('keys', None)
        buffers = []
        empty = list()
        try:
            records = self.db.find_records(query, keys)
        except Exception as e:
            content = error.wrap_exception()
            self.log.exception("DB query failed")
        else:
            # extract buffers from reply content:
            if keys is not None:
                buffer_lens = [] if 'buffers' in keys else None
                result_buffer_lens = [] if 'result_buffers' in keys else None
            else:
                buffer_lens = None
                result_buffer_lens = None

            for rec in records:
                # buffers may be None, so double check
                b = rec.pop('buffers', empty) or empty
                if buffer_lens is not None:
                    buffer_lens.append(len(b))
                    buffers.extend(b)
                rb = rec.pop('result_buffers', empty) or empty
                if result_buffer_lens is not None:
                    result_buffer_lens.append(len(rb))
                    buffers.extend(rb)
            content = dict(
                status='ok',
                records=records,
                buffer_lens=buffer_lens,
                result_buffer_lens=result_buffer_lens,
            )
        # self.log.debug (content)
        self.session.send(
            self.query,
            "db_reply",
            content=content,
            parent=msg,
            ident=client_id,
            buffers=buffers,
        )

    async def become_dask(self, client_id, msg):
        """Start a dask.distributed Scheduler."""
        if self.distributed_scheduler is None:
            kwargs = msg['content'].get('scheduler_args', {})
            self.log.info("Becoming dask.distributed scheduler: %s", kwargs)
            from distributed import Scheduler

            self.distributed_scheduler = scheduler = Scheduler(**kwargs)
            await scheduler.start()
        content = {
            'status': 'ok',
            'ip': self.distributed_scheduler.ip,
            'address': self.distributed_scheduler.address,
            'port': self.distributed_scheduler.port,
        }
        self.log.info(
            "dask.distributed scheduler running at {address}".format(**content)
        )
        self.session.send(
            self.query,
            "become_dask_reply",
            content=content,
            parent=msg,
            ident=client_id,
        )

    def stop_distributed(self, client_id, msg):
        """Start a dask.distributed Scheduler."""
        if self.distributed_scheduler is not None:
            self.log.info("Stopping dask.distributed scheduler")
            self.distributed_scheduler.close()
            self.distributed_scheduler = None
        else:
            self.log.info("No dask.distributed scheduler running.")
        content = {'status': 'ok'}
        self.session.send(
            self.query,
            "stop_distributed_reply",
            content=content,
            parent=msg,
            ident=client_id,
        )
ipyparallel-8.8.0/ipyparallel/controller/mongodb.py000066400000000000000000000077241460376056100225670ustar00rootroot00000000000000"""A TaskRecord backend using mongodb"""

try:
    from pymongo import MongoClient
except ImportError:
    from pymongo import Connection as MongoClient

# bson.Binary import moved
try:
    from bson.binary import Binary
except ImportError:
    from bson import Binary

from traitlets import Dict, Instance, List, Unicode

from .dictdb import BaseDB

# -----------------------------------------------------------------------------
# MongoDB class
# -----------------------------------------------------------------------------


class MongoDB(BaseDB):
    """MongoDB TaskRecord backend."""

    connection_args = List(
        config=True,
        help="""Positional arguments to be passed to pymongo.MongoClient.  Only
        necessary if the default mongodb configuration does not point to your
        mongod instance.""",
    )
    connection_kwargs = Dict(
        config=True,
        help="""Keyword arguments to be passed to pymongo.MongoClient.  Only
        necessary if the default mongodb configuration does not point to your
        mongod instance.""",
    )
    database = Unicode(
        "ipython-tasks",
        config=True,
        help="""The MongoDB database name to use for storing tasks for this session. If unspecified,
        a new database will be created with the Hub's IDENT.  Specifying the database will result
        in tasks from previous sessions being available via Clients' db_query and
        get_result methods.""",
    )

    _connection = Instance(MongoClient, allow_none=True)  # pymongo connection

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if self._connection is None:
            self._connection = MongoClient(
                *self.connection_args, **self.connection_kwargs
            )
        if not self.database:
            self.database = self.session
        self._db = self._connection[self.database]
        self._records = self._db['task_records']
        self._records.ensure_index('msg_id', unique=True)
        self._records.ensure_index('submitted')  # for sorting history
        # for rec in self._records.find

    def _binary_buffers(self, rec):
        for key in ('buffers', 'result_buffers'):
            if rec.get(key, None):
                rec[key] = list(map(Binary, rec[key]))
        return rec

    def add_record(self, msg_id, rec):
        """Add a new Task Record, by msg_id."""
        # print rec
        rec = self._binary_buffers(rec)
        self._records.insert(rec)

    def get_record(self, msg_id):
        """Get a specific Task Record, by msg_id."""
        r = self._records.find_one({'msg_id': msg_id})
        if not r:
            # r will be '' if nothing is found
            raise KeyError(msg_id)
        return r

    def update_record(self, msg_id, rec):
        """Update the data in an existing record."""
        rec = self._binary_buffers(rec)

        self._records.update({'msg_id': msg_id}, {'$set': rec})

    def drop_matching_records(self, check):
        """Remove a record from the DB."""
        self._records.remove(check)

    def drop_record(self, msg_id):
        """Remove a record from the DB."""
        self._records.remove({'msg_id': msg_id})

    def find_records(self, check, keys=None):
        """Find records matching a query dict, optionally extracting subset of keys.

        Returns list of matching records.

        Parameters
        ----------
        check : dict
            mongodb-style query argument
        keys : list of strs [optional]
            if specified, the subset of keys to extract.  msg_id will *always* be
            included.
        """
        if keys and 'msg_id' not in keys:
            keys.append('msg_id')
        matches = list(self._records.find(check, keys))
        for rec in matches:
            rec.pop('_id')
        return matches

    def get_history(self):
        """get all msg_ids, ordered by time submitted."""
        cursor = self._records.find({}, {'msg_id': 1}).sort('submitted')
        return [rec['msg_id'] for rec in cursor]
ipyparallel-8.8.0/ipyparallel/controller/scheduler.py000066400000000000000000000150331460376056100231100ustar00rootroot00000000000000"""The Python scheduler for rich scheduling.

The Pure ZMQ scheduler does not allow routing schemes other than LRU,
nor does it check msg_id DAG dependencies. For those, a slightly slower
Python Scheduler exists.
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import logging

import jupyter_client.session
import traitlets.log
import zmq
from decorator import decorator
from tornado import ioloop
from traitlets import Bytes, Instance, Set, default
from traitlets.config import Config, LoggingConfigurable
from zmq.eventloop import zmqstream

from ipyparallel import util
from ipyparallel.util import connect_logger, local_logger

# local imports

# performance optimization: skip unused date deserialization
jupyter_client.session.extract_dates = lambda obj: obj


@decorator
def logged(f, self, *args, **kwargs):
    # print ("#--------------------")
    self.log.debug("scheduler::%s(*%s,**%s)", f.__name__, args, kwargs)
    # print ("#--")
    return f(self, *args, **kwargs)


class Scheduler(LoggingConfigurable):
    loop = Instance(ioloop.IOLoop)

    @default("loop")
    def _default_loop(self):
        return ioloop.IOLoop.current()

    session = Instance(jupyter_client.session.Session)

    @default("session")
    def _default_session(self):
        util._disable_session_extract_dates()
        return jupyter_client.session.Session(parent=self)

    client_stream = Instance(
        zmqstream.ZMQStream, allow_none=True
    )  # client-facing stream
    engine_stream = Instance(
        zmqstream.ZMQStream, allow_none=True
    )  # engine-facing stream
    notifier_stream = Instance(
        zmqstream.ZMQStream, allow_none=True
    )  # hub-facing sub stream
    mon_stream = Instance(zmqstream.ZMQStream, allow_none=True)  # hub-facing pub stream
    query_stream = Instance(
        zmqstream.ZMQStream, allow_none=True
    )  # hub-facing DEALER stream

    all_completed = Set()  # set of all completed tasks
    all_failed = Set()  # set of all failed tasks
    all_done = Set()  # set of all finished tasks=union(completed,failed)
    all_ids = Set()  # set of all submitted task IDs

    ident = Bytes()  # ZMQ identity. This should just be self.session.session as bytes

    # but ensure Bytes
    @default("ident")
    def _ident_default(self):
        return self.session.bsession

    def start(self):
        self.engine_stream.on_recv(self.dispatch_result, copy=False)
        self.client_stream.on_recv(self.dispatch_submission, copy=False)

    def resume_receiving(self):
        """Resume accepting jobs."""
        self.client_stream.on_recv(self.dispatch_submission, copy=False)

    def stop_receiving(self):
        """Stop accepting jobs while there are no engines.
        Leave them in the ZMQ queue."""
        self.client_stream.on_recv(None)

    def dispatch_result(self, raw_msg):
        raise NotImplementedError("Implement in subclasses")

    def dispatch_submission(self, raw_msg):
        raise NotImplementedError("Implement in subclasses")

    def append_new_msg_id_to_msg(self, new_id, target_id, idents, msg):
        if isinstance(target_id, str):
            target_id = target_id.encode("utf8")
        new_idents = [target_id] + idents
        msg['header']['msg_id'] = new_id
        new_msg_list = self.session.serialize(msg, ident=new_idents)
        new_msg_list.extend(msg['buffers'])
        return new_msg_list

    def get_new_msg_id(self, original_msg_id, outgoing_id):
        return f'{original_msg_id}_{outgoing_id if isinstance(outgoing_id, str) else outgoing_id.decode("utf8")}'


ZMQStream = zmqstream.ZMQStream


def get_common_scheduler_streams(
    mon_addr,
    not_addr,
    reg_addr,
    config,
    logname,
    log_url,
    loglevel,
    in_thread,
    curve_serverkey,
    curve_publickey,
    curve_secretkey,
):
    if config:
        # unwrap dict back into Config
        config = Config(config)

    if in_thread:
        # use instance() to get the same Context/Loop as our parent
        ctx = zmq.Context.instance()
        loop = ioloop.IOLoop.current()
    else:
        # in a process, don't use instance()
        # for safety with multiprocessing
        ctx = zmq.Context()
        loop = ioloop.IOLoop(make_current=False)

    def connect(s, addr):
        return util.connect(
            s,
            addr,
            curve_serverkey=curve_serverkey,
            curve_secretkey=curve_secretkey,
            curve_publickey=curve_publickey,
        )

    mons = zmqstream.ZMQStream(ctx.socket(zmq.PUB), loop)
    connect(mons, mon_addr)
    nots = zmqstream.ZMQStream(ctx.socket(zmq.SUB), loop)
    nots.setsockopt(zmq.SUBSCRIBE, b'')
    connect(nots, not_addr)

    querys = ZMQStream(ctx.socket(zmq.DEALER), loop)
    connect(querys, reg_addr)

    # setup logging.
    if in_thread:
        log = traitlets.log.get_logger()
    else:
        if log_url:
            log = connect_logger(
                logname, ctx, log_url, root="scheduler", loglevel=loglevel
            )
        else:
            log = local_logger(logname, loglevel)
    return config, ctx, loop, mons, nots, querys, log


def launch_scheduler(
    scheduler_class,
    in_addr,
    out_addr,
    mon_addr,
    not_addr,
    reg_addr,
    config=None,
    logname='root',
    log_url=None,
    loglevel=logging.DEBUG,
    identity=None,
    in_thread=False,
    curve_secretkey=None,
    curve_publickey=None,
):
    config, ctx, loop, mons, nots, querys, log = get_common_scheduler_streams(
        mon_addr,
        not_addr,
        reg_addr,
        config,
        logname,
        log_url,
        loglevel,
        in_thread,
        curve_serverkey=curve_publickey,
        curve_publickey=curve_publickey,
        curve_secretkey=curve_secretkey,
    )

    util.set_hwm(mons, 0)
    ins = ZMQStream(ctx.socket(zmq.ROUTER), loop)
    util.set_hwm(ins, 0)
    if identity:
        ins.setsockopt(zmq.IDENTITY, identity + b'_in')

    util.bind(ins, in_addr, curve_secretkey=curve_secretkey)

    outs = ZMQStream(ctx.socket(zmq.ROUTER), loop)
    util.set_hwm(outs, 0)

    if identity:
        outs.setsockopt(zmq.IDENTITY, identity + b'_out')
    util.bind(outs, out_addr, curve_secretkey=curve_secretkey)

    scheduler = scheduler_class(
        client_stream=ins,
        engine_stream=outs,
        mon_stream=mons,
        notifier_stream=nots,
        query_stream=querys,
        loop=loop,
        log=log,
        config=config,
    )

    scheduler.start()
    if not in_thread:
        try:
            loop.start()
        except KeyboardInterrupt:
            scheduler.log.critical("Interrupted, exiting...")
ipyparallel-8.8.0/ipyparallel/controller/sqlitedb.py000066400000000000000000000347451460376056100227540ustar00rootroot00000000000000"""A TaskRecord backend using sqlite3"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import json
import os

try:
    import cPickle as pickle
except ImportError:
    import pickle

from datetime import datetime

try:
    import sqlite3
except ImportError:
    sqlite3 = None

from dateutil.parser import parse as dateutil_parse

try:
    from jupyter_client.jsonutil import json_default
except ImportError:
    from jupyter_client.jsonutil import date_default as json_default

from tornado import ioloop
from traitlets import Dict, Instance, List, Unicode

from ..util import ensure_timezone, extract_dates
from .dictdb import BaseDB

# -----------------------------------------------------------------------------
# SQLite operators, adapters, and converters
# -----------------------------------------------------------------------------

operators = {
    '$lt': "<",
    '$gt': ">",
    # null is handled weird with ==,!=
    '$eq': "=",
    '$ne': "!=",
    '$lte': "<=",
    '$gte': ">=",
    '$in': ('=', ' OR '),
    '$nin': ('!=', ' AND '),
    # '$all': None,
    # '$mod': None,
    # '$exists' : None
}
null_operators = {
    '=': "IS NULL",
    '!=': "IS NOT NULL",
}


def _adapt_dict(d):
    return json.dumps(d, default=json_default)


def _convert_dict(ds):
    if ds is None:
        return ds
    else:
        if isinstance(ds, bytes):
            # If I understand the sqlite doc correctly, this will always be utf8
            ds = ds.decode('utf8')
        return extract_dates(json.loads(ds))


def _adapt_bufs(bufs):
    # this is *horrible*
    # copy buffers into single list and pickle it:
    if bufs and isinstance(bufs[0], (bytes, memoryview)):
        return sqlite3.Binary(
            pickle.dumps([memoryview(buf).tobytes() for buf in bufs], -1)
        )
    elif bufs:
        return bufs
    else:
        return None


def _convert_bufs(bs):
    if bs is None:
        return []
    else:
        return pickle.loads(bytes(bs))


def _adapt_timestamp(dt):
    """Adapt datetime to text"""
    return ensure_timezone(dt).isoformat()


def _convert_timestamp(s):
    """Adapt text timestamp to datetime"""
    return ensure_timezone(dateutil_parse(s))


# -----------------------------------------------------------------------------
# SQLiteDB class
# -----------------------------------------------------------------------------


class SQLiteDB(BaseDB):
    """SQLite3 TaskRecord backend."""

    filename = Unicode(
        'tasks.db',
        config=True,
        help="""The filename of the sqlite task database. [default: 'tasks.db']""",
    )
    location = Unicode(
        '',
        config=True,
        help="""The directory containing the sqlite task database.  The default
        is to use the cluster_dir location.""",
    )
    table = Unicode(
        "ipython-tasks",
        config=True,
        help="""The SQLite Table to use for storing tasks for this session. If unspecified,
        a new table will be created with the Hub's IDENT.  Specifying the table will result
        in tasks from previous sessions being available via Clients' db_query and
        get_result methods.""",
    )

    if sqlite3 is not None:
        _db = Instance('sqlite3.Connection', allow_none=True)
    else:
        _db = None
    # the ordered list of column names
    _keys = List(
        [
            'msg_id',
            'header',
            'metadata',
            'content',
            'buffers',
            'submitted',
            'client_uuid',
            'engine_uuid',
            'started',
            'completed',
            'resubmitted',
            'received',
            'result_header',
            'result_metadata',
            'result_content',
            'result_buffers',
            'queue',
            'execute_input',
            'execute_result',
            'error',
            'stdout',
            'stderr',
        ]
    )
    # sqlite datatypes for checking that db is current format
    _types = Dict(
        {
            'msg_id': 'text',
            'header': 'dict text',
            'metadata': 'dict text',
            'content': 'dict text',
            'buffers': 'bufs blob',
            'submitted': 'timestamp',
            'client_uuid': 'text',
            'engine_uuid': 'text',
            'started': 'timestamp',
            'completed': 'timestamp',
            'resubmitted': 'text',
            'received': 'timestamp',
            'result_header': 'dict text',
            'result_metadata': 'dict text',
            'result_content': 'dict text',
            'result_buffers': 'bufs blob',
            'queue': 'text',
            'execute_input': 'text',
            'execute_result': 'text',
            'error': 'text',
            'stdout': 'text',
            'stderr': 'text',
        }
    )

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if sqlite3 is None:
            raise ImportError("SQLiteDB requires sqlite3")
        if not self.table:
            # use session, and prefix _, since starting with # is illegal
            self.table = '_' + self.session.replace('-', '_')
        if not self.location:
            # get current profile
            from IPython.core.application import BaseIPythonApplication

            if BaseIPythonApplication.initialized():
                app = BaseIPythonApplication.instance()
                if app.profile_dir is not None:
                    self.location = app.profile_dir.location
                else:
                    self.location = '.'
            else:
                self.location = '.'
        self._init_db()

        # register db commit as 2s periodic callback
        # to prevent clogging pipes
        # assumes we are being run in a zmq ioloop app
        self._commit_callback = pc = ioloop.PeriodicCallback(self._db.commit, 2000)
        pc.start()

    def close(self):
        self._commit_callback.stop()
        self._db.commit()
        self._db.close()

    def _defaults(self, keys=None):
        """create an empty record"""
        d = {}
        keys = self._keys if keys is None else keys
        for key in keys:
            d[key] = None
        return d

    def _check_table(self):
        """Ensure that an incorrect table doesn't exist

        If a bad (old) table does exist, return False
        """
        cursor = self._db.execute("PRAGMA table_info('%s')" % self.table)
        lines = cursor.fetchall()
        if not lines:
            # table does not exist
            return True
        types = {}
        keys = []
        for line in lines:
            keys.append(line[1])
            types[line[1]] = line[2]
        if self._keys != keys:
            # key mismatch
            self.log.warning('keys mismatch')
            return False
        for key in self._keys:
            if types[key] != self._types[key]:
                self.log.warning(
                    f'type mismatch: {key}: {types[key]} != {self._types[key]}'
                )
                return False
        return True

    def _init_db(self):
        """Connect to the database and get new session number."""
        # register adapters
        sqlite3.register_adapter(dict, _adapt_dict)
        sqlite3.register_converter('dict', _convert_dict)
        sqlite3.register_adapter(list, _adapt_bufs)
        sqlite3.register_converter('bufs', _convert_bufs)
        sqlite3.register_adapter(datetime, _adapt_timestamp)
        sqlite3.register_converter('timestamp', _convert_timestamp)
        # connect to the db
        dbfile = os.path.join(self.location, self.filename)
        self._db = sqlite3.connect(
            dbfile,
            detect_types=sqlite3.PARSE_DECLTYPES,
            # isolation_level = None)#,
            cached_statements=64,
        )
        # print dir(self._db)
        first_table = previous_table = self.table
        i = 0
        while not self._check_table():
            i += 1
            self.table = first_table + '_%i' % i
            self.log.warning(
                f"Table {previous_table} exists and doesn't match db format, trying {self.table}"
            )
            previous_table = self.table

        self._db.execute(
            """CREATE TABLE IF NOT EXISTS '%s'
                (msg_id text PRIMARY KEY,
                header dict text,
                metadata dict text,
                content dict text,
                buffers bufs blob,
                submitted timestamp,
                client_uuid text,
                engine_uuid text,
                started timestamp,
                completed timestamp,
                resubmitted text,
                received timestamp,
                result_header dict text,
                result_metadata dict text,
                result_content dict text,
                result_buffers bufs blob,
                queue text,
                execute_input text,
                execute_result text,
                error text,
                stdout text,
                stderr text)
                """
            % self.table
        )
        self._db.commit()

    def _dict_to_list(self, d):
        """turn a mongodb-style record dict into a list."""

        return [d[key] for key in self._keys]

    def _list_to_dict(self, line, keys=None):
        """Inverse of dict_to_list"""
        keys = self._keys if keys is None else keys
        d = self._defaults(keys)
        for key, value in zip(keys, line):
            d[key] = value

        return d

    def _render_expression(self, check):
        """Turn a mongodb-style search dict into an SQL query."""
        expressions = []
        args = []

        skeys = set(check.keys())
        skeys.difference_update(set(self._keys))
        skeys.difference_update({'buffers', 'result_buffers'})
        if skeys:
            raise KeyError("Illegal testing key(s): %s" % skeys)

        for name, sub_check in check.items():
            if isinstance(sub_check, dict):
                for test, value in sub_check.items():
                    try:
                        op = operators[test]
                    except KeyError:
                        raise KeyError("Unsupported operator: %r" % test)
                    if isinstance(op, tuple):
                        op, join = op

                    if value is None and op in null_operators:
                        expr = f"{name} {null_operators[op]}"
                    else:
                        expr = f"{name} {op} ?"
                        if isinstance(value, (tuple, list)):
                            if op in null_operators and any([v is None for v in value]):
                                # equality tests don't work with NULL
                                raise ValueError(
                                    "Cannot use %r test with NULL values on SQLite backend"
                                    % test
                                )
                            expr = '( %s )' % (join.join([expr] * len(value)))
                            args.extend(value)
                        else:
                            args.append(value)
                    expressions.append(expr)
            else:
                # it's an equality check
                if sub_check is None:
                    expressions.append("%s IS NULL" % name)
                else:
                    expressions.append("%s = ?" % name)
                    args.append(sub_check)

        expr = " AND ".join(expressions)
        return expr, args

    def add_record(self, msg_id, rec):
        """Add a new Task Record, by msg_id."""
        d = self._defaults()
        d.update(rec)
        d['msg_id'] = msg_id
        line = self._dict_to_list(d)
        tups = '(%s)' % (','.join(['?'] * len(line)))
        self._db.execute(f"INSERT INTO '{self.table}' VALUES {tups}", line)
        # self._db.commit()

    def get_record(self, msg_id):
        """Get a specific Task Record, by msg_id."""
        cursor = self._db.execute(
            """SELECT * FROM '%s' WHERE msg_id==?""" % self.table, (msg_id,)
        )
        line = cursor.fetchone()
        if line is None:
            raise KeyError("No such msg: %r" % msg_id)
        return self._list_to_dict(line)

    def update_record(self, msg_id, rec):
        """Update the data in an existing record."""
        query = "UPDATE '%s' SET " % self.table
        sets = []
        keys = sorted(rec.keys())
        values = []
        for key in keys:
            sets.append('%s = ?' % key)
            values.append(rec[key])
        query += ', '.join(sets)
        query += ' WHERE msg_id == ?'
        values.append(msg_id)
        self._db.execute(query, values)
        # self._db.commit()

    def drop_record(self, msg_id):
        """Remove a record from the DB."""
        self._db.execute("""DELETE FROM '%s' WHERE msg_id==?""" % self.table, (msg_id,))
        # self._db.commit()

    def drop_matching_records(self, check):
        """Remove a record from the DB."""
        expr, args = self._render_expression(check)
        query = f"DELETE FROM '{self.table}' WHERE {expr}"
        self._db.execute(query, args)
        # self._db.commit()

    def find_records(self, check, keys=None):
        """Find records matching a query dict, optionally extracting subset of keys.

        Returns list of matching records.

        Parameters
        ----------
        check : dict
            mongodb-style query argument
        keys : list of strs [optional]
            if specified, the subset of keys to extract.  msg_id will *always* be
            included.
        """
        if keys:
            bad_keys = [key for key in keys if key not in self._keys]
            if bad_keys:
                raise KeyError("Bad record key(s): %s" % bad_keys)

        if keys:
            # ensure msg_id is present and first:
            if 'msg_id' in keys:
                keys.remove('msg_id')
            keys.insert(0, 'msg_id')
            req = ', '.join(keys)
        else:
            req = '*'
        expr, args = self._render_expression(check)
        query = f"""SELECT {req} FROM '{self.table}' WHERE {expr}"""
        cursor = self._db.execute(query, args)
        matches = cursor.fetchall()
        records = []
        for line in matches:
            rec = self._list_to_dict(line, keys)
            records.append(rec)
        return records

    def get_history(self):
        """get all msg_ids, ordered by time submitted."""
        query = """SELECT msg_id FROM '%s' ORDER by submitted ASC""" % self.table
        cursor = self._db.execute(query)
        # will be a list of length 1 tuples
        return [tup[0] for tup in cursor.fetchall()]


__all__ = ['SQLiteDB']
ipyparallel-8.8.0/ipyparallel/controller/task_scheduler.py000066400000000000000000000650231460376056100241360ustar00rootroot00000000000000import time
from collections import deque
from random import randint, random
from types import FunctionType

import zmq
from traitlets import Dict, Enum, Instance, Integer, List, observe

from ipyparallel import Dependency, error, util
from ipyparallel.controller.scheduler import Scheduler

try:
    import numpy
except ImportError:
    numpy = None

# ----------------------------------------------------------------------
# Chooser functions
# ----------------------------------------------------------------------


def plainrandom(loads):
    """Plain random pick."""
    n = len(loads)
    return randint(0, n - 1)


def lru(loads):
    """Always pick the front of the line.

    The content of `loads` is ignored.

    Assumes LRU ordering of loads, with oldest first.
    """
    return 0


def twobin(loads):
    """Pick two at random, use the LRU of the two.

    The content of loads is ignored.

    Assumes LRU ordering of loads, with oldest first.
    """
    n = len(loads)
    a = randint(0, n - 1)
    b = randint(0, n - 1)
    return min(a, b)


def weighted(loads):
    """Pick two at random using inverse load as weight.

    Return the less loaded of the two.
    """
    # weight 0 a million times more than 1:
    weights = 1.0 / (1e-6 + numpy.array(loads))
    sums = weights.cumsum()
    t = sums[-1]
    x = random() * t
    y = random() * t
    idx = 0
    idy = 0
    while sums[idx] < x:
        idx += 1
    while sums[idy] < y:
        idy += 1
    if weights[idy] > weights[idx]:
        return idy
    else:
        return idx


def leastload(loads):
    """Always choose the lowest load.

    If the lowest load occurs more than once, the first
    occurance will be used.  If loads has LRU ordering, this means
    the LRU of those with the lowest load is chosen.
    """
    return loads.index(min(loads))


# ---------------------------------------------------------------------
# Classes
# ---------------------------------------------------------------------

# store empty default dependency:
MET = Dependency([])


class Job:
    """Simple container for a job"""

    def __init__(
        self,
        msg_id,
        raw_msg,
        idents,
        msg,
        header,
        metadata,
        targets,
        after,
        follow,
        timeout,
    ):
        self.msg_id = msg_id
        self.raw_msg = raw_msg
        self.idents = idents
        self.msg = msg
        self.header = header
        self.metadata = metadata
        self.targets = targets
        self.after = after
        self.follow = follow
        self.timeout = timeout

        self.removed = False  # used for lazy-delete from sorted queue
        self.timestamp = time.time()
        self.timeout_id = 0
        self.blacklist = set()

    def __lt__(self, other):
        return self.timestamp < other.timestamp

    @property
    def dependents(self):
        return self.follow.union(self.after)


class TaskScheduler(Scheduler):
    """Python TaskScheduler object.

    This is the simplest object that supports msg_id based
    DAG dependencies. *Only* task msg_ids are checked, not
    msg_ids of jobs submitted via the MUX queue.

    """

    hwm = Integer(
        1,
        config=True,
        help="""specify the High Water Mark (HWM) for the downstream
        socket in the Task scheduler. This is the maximum number
        of allowed outstanding tasks on each engine.

        The default (1) means that only one task can be outstanding on each
        engine.  Setting TaskScheduler.hwm=0 means there is no limit, and the
        engines continue to be assigned tasks while they are working,
        effectively hiding network latency behind computation, but can result
        in an imbalance of work when submitting many heterogenous tasks all at
        once.  Any positive value greater than one is a compromise between the
        two.

        """,
    )

    scheme_name = Enum(
        ('leastload', 'pure', 'lru', 'plainrandom', 'weighted', 'twobin'),
        'leastload',
        config=True,
        help="""select the task scheduler scheme  [default: Python LRU]
            Options are: 'pure', 'lru', 'plainrandom', 'weighted', 'twobin','leastload'""",
    )

    # input arguments:
    scheme = Instance(FunctionType)  # function for determining the destination

    @observe('scheme_name')
    def _scheme_name_changed(self, change):
        self.log.debug("Using scheme %r" % change['new'])
        self.scheme = globals()[change['new']]

    # input arguments:
    scheme = Instance(FunctionType)  # function for determining the destination

    def _scheme_default(self):
        return leastload

    # internals:
    queue = Instance(deque)  # sorted list of Jobs

    def _queue_default(self):
        return deque()

    queue_map = Dict()  # dict by msg_id of Jobs (for O(1) access to the Queue)
    graph = Dict()  # dict by msg_id of [ msg_ids that depend on key ]
    retries = Dict()  # dict by msg_id of retries remaining (non-neg ints)
    # waiting = List() # list of msg_ids ready to run, but haven't due to HWM
    pending = Dict()  # dict by engine_uuid of submitted tasks
    completed = Dict()  # dict by engine_uuid of completed tasks
    failed = Dict()  # dict by engine_uuid of failed tasks
    destinations = (
        Dict()
    )  # dict by msg_id of engine_uuids where jobs ran (reverse of completed+failed)
    clients = Dict()  # dict by msg_id for who submitted the task
    targets = List()  # list of target IDENTs
    loads = List()  # list of engine loads
    # full = Set() # set of IDENTs that have HWM outstanding tasks

    def start(self):
        super().start()
        self.query_stream.on_recv(self.dispatch_query_reply)
        self.session.send(self.query_stream, "connection_request", {})
        self._notification_handlers = dict(
            registration_notification=self._register_engine,
            unregistration_notification=self._unregister_engine,
        )
        self.log.info("Task scheduler started [%s]" % self.scheme_name)
        self.notifier_stream.on_recv(self.dispatch_notification)

    # -----------------------------------------------------------------------
    # [Un]Registration Handling
    # -----------------------------------------------------------------------

    def dispatch_query_reply(self, msg):
        """handle reply to our initial connection request"""
        try:
            idents, msg = self.session.feed_identities(msg)
        except ValueError:
            self.log.warning("task::Invalid Message: %r", msg)
            return
        try:
            msg = self.session.deserialize(msg)
        except ValueError:
            self.log.warning("task::Unauthorized message from: %r" % idents)
            return

        content = msg['content']
        for uuid in content.get('engines', {}).values():
            self._register_engine(uuid.encode("utf8"))

    @util.log_errors
    def dispatch_notification(self, msg):
        """dispatch register/unregister events."""
        try:
            idents, msg = self.session.feed_identities(msg)
        except ValueError:
            self.log.warning("task::Invalid Message: %r", msg)
            return
        try:
            msg = self.session.deserialize(msg)
        except ValueError:
            self.log.warning("task::Unauthorized message from: %r" % idents)
            return

        msg_type = msg['header']['msg_type']

        handler = self._notification_handlers.get(msg_type, None)
        if handler is None:
            self.log.error("Unhandled message type: %r" % msg_type)
        else:
            try:
                handler(msg['content']['uuid'].encode("utf8"))
            except Exception:
                self.log.error("task::Invalid notification msg: %r", msg, exc_info=True)

    def _register_engine(self, uid):
        """New engine with ident `uid` became available."""
        # head of the line:
        self.targets.insert(0, uid)
        self.loads.insert(0, 0)

        # initialize sets
        self.completed[uid] = set()
        self.failed[uid] = set()
        self.pending[uid] = {}

        # rescan the graph:
        self.update_graph(None)

    def _unregister_engine(self, uid):
        """Existing engine with ident `uid` became unavailable."""
        if len(self.targets) == 1:
            # this was our only engine
            pass

        # handle any potentially finished tasks:
        self.engine_stream.flush()

        # don't pop destinations, because they might be used later
        # map(self.destinations.pop, self.completed.pop(uid))
        # map(self.destinations.pop, self.failed.pop(uid))

        # prevent this engine from receiving work
        idx = self.targets.index(uid)
        self.targets.pop(idx)
        self.loads.pop(idx)

        # wait 5 seconds before cleaning up pending jobs, since the results might
        # still be incoming
        if self.pending[uid]:
            self.loop.add_timeout(
                self.loop.time() + 5, lambda: self.handle_stranded_tasks(uid)
            )
        else:
            self.completed.pop(uid)
            self.failed.pop(uid)

    def handle_stranded_tasks(self, engine):
        """Deal with jobs resident in an engine that died."""
        lost = self.pending[engine]
        for msg_id in list(lost.keys()):
            if msg_id not in lost:
                # prevent double-handling of messages
                continue

            raw_msg = lost[msg_id].raw_msg
            idents, msg = self.session.feed_identities(raw_msg, copy=False)
            parent = self.session.unpack(msg[1].bytes)
            idents = [engine, idents[0]]

            # build fake error reply
            try:
                raise error.EngineError(
                    f"Engine {engine!r} died while running task {msg_id!r}"
                )
            except error.EngineError:
                content = error.wrap_exception()
            # build fake metadata
            md = dict(status='error', engine=engine.decode('ascii'), date=util.utcnow())
            msg = self.session.msg('apply_reply', content, parent=parent, metadata=md)
            raw_reply = list(
                map(zmq.Message, self.session.serialize(msg, ident=idents))
            )
            # and dispatch it
            self.dispatch_result(raw_reply)

        # finally scrub completed/failed lists
        self.completed.pop(engine)
        self.failed.pop(engine)

    # -----------------------------------------------------------------------
    # Job Submission
    # -----------------------------------------------------------------------

    @util.log_errors
    def dispatch_submission(self, raw_msg):
        """Dispatch job submission to appropriate handlers."""
        # ensure targets up to date:
        self.notifier_stream.flush()
        try:
            idents, msg = self.session.feed_identities(raw_msg, copy=False)
            msg = self.session.deserialize(msg, content=False, copy=False)
        except Exception:
            self.log.error("task::Invaid task msg: %r" % raw_msg, exc_info=True)
            return

        # send to monitor
        self.mon_stream.send_multipart([b'intask'] + raw_msg, copy=False)

        header = msg['header']
        md = msg['metadata']
        msg_id = header['msg_id']
        self.all_ids.add(msg_id)

        # get targets as a set of bytes objects
        # from a list of unicode objects
        targets = md.get('targets', [])
        targets = {t.encode("utf8", "replace") for t in targets}

        retries = md.get('retries', 0)
        self.retries[msg_id] = retries

        # time dependencies
        after = md.get('after', None)
        if after:
            after = Dependency(after)
            if after.all:
                if after.success:
                    after = Dependency(
                        after.difference(self.all_completed),
                        success=after.success,
                        failure=after.failure,
                        all=after.all,
                    )
                if after.failure:
                    after = Dependency(
                        after.difference(self.all_failed),
                        success=after.success,
                        failure=after.failure,
                        all=after.all,
                    )
            if after.check(self.all_completed, self.all_failed):
                # recast as empty set, if `after` already met,
                # to prevent unnecessary set comparisons
                after = MET
        else:
            after = MET

        # location dependencies
        follow = Dependency(md.get('follow', []))

        timeout = md.get('timeout', None)
        if timeout:
            timeout = float(timeout)

        job = Job(
            msg_id=msg_id,
            raw_msg=raw_msg,
            idents=idents,
            msg=msg,
            header=header,
            targets=targets,
            after=after,
            follow=follow,
            timeout=timeout,
            metadata=md,
        )
        # validate and reduce dependencies:
        for dep in after, follow:
            if not dep:  # empty dependency
                continue
            # check valid:
            if msg_id in dep or dep.difference(self.all_ids):
                self.queue_map[msg_id] = job
                return self.fail_unreachable(msg_id, error.InvalidDependency)
            # check if unreachable:
            if dep.unreachable(self.all_completed, self.all_failed):
                self.queue_map[msg_id] = job
                return self.fail_unreachable(msg_id)

        if after.check(self.all_completed, self.all_failed):
            # time deps already met, try to run
            if not self.maybe_run(job):
                # can't run yet
                if msg_id not in self.all_failed:
                    # could have failed as unreachable
                    self.save_unmet(job)
        else:
            self.save_unmet(job)

    def job_timeout(self, job, timeout_id):
        """callback for a job's timeout.

        The job may or may not have been run at this point.
        """
        if job.timeout_id != timeout_id:
            # not the most recent call
            return
        now = time.time()
        if job.timeout >= (now + 1):
            self.log.warning(
                "task %s timeout fired prematurely: %s > %s",
                job.msg_id,
                job.timeout,
                now,
            )
        if job.msg_id in self.queue_map:
            # still waiting, but ran out of time
            self.log.info("task %r timed out", job.msg_id)
            self.fail_unreachable(job.msg_id, error.TaskTimeout)

    def fail_unreachable(self, msg_id, why=error.ImpossibleDependency):
        """a task has become unreachable, send a reply with an ImpossibleDependency
        error."""
        if msg_id not in self.queue_map:
            self.log.error("task %r already failed!", msg_id)
            return
        job = self.queue_map.pop(msg_id)
        # lazy-delete from the queue
        job.removed = True
        for mid in job.dependents:
            if mid in self.graph:
                self.graph[mid].remove(msg_id)

        try:
            raise why()
        except Exception:
            content = error.wrap_exception()
        self.log.debug(
            "task %r failing as unreachable with: %s", msg_id, content['ename']
        )

        self.all_done.add(msg_id)
        self.all_failed.add(msg_id)

        msg = self.session.send(
            self.client_stream,
            'apply_reply',
            content,
            parent=job.header,
            ident=job.idents,
        )
        self.session.send(self.mon_stream, msg, ident=[b'outtask'] + job.idents)

        self.update_graph(msg_id, success=False)

    def available_engines(self):
        """return a list of available engine indices based on HWM"""
        if not self.hwm:
            return list(range(len(self.targets)))
        available = []
        for idx in range(len(self.targets)):
            if self.loads[idx] < self.hwm:
                available.append(idx)
        return available

    def maybe_run(self, job):
        """check location dependencies, and run if they are met."""
        msg_id = job.msg_id
        self.log.debug("Attempting to assign task %s", msg_id)
        available = self.available_engines()
        if not available:
            # no engines, definitely can't run
            return False

        if job.follow or job.targets or job.blacklist or self.hwm:
            # we need a can_run filter
            def can_run(idx):
                # check hwm
                if self.hwm and self.loads[idx] == self.hwm:
                    return False
                target = self.targets[idx]
                # check blacklist
                if target in job.blacklist:
                    return False
                # check targets
                if job.targets and target not in job.targets:
                    return False
                # check follow
                return job.follow.check(self.completed[target], self.failed[target])

            indices = list(filter(can_run, available))

            if not indices:
                # couldn't run
                if job.follow.all:
                    # check follow for impossibility
                    dests = set()
                    relevant = set()
                    if job.follow.success:
                        relevant = self.all_completed
                    if job.follow.failure:
                        relevant = relevant.union(self.all_failed)
                    for m in job.follow.intersection(relevant):
                        dests.add(self.destinations[m])
                    if len(dests) > 1:
                        self.queue_map[msg_id] = job
                        self.fail_unreachable(msg_id)
                        return False
                if job.targets:
                    # check blacklist+targets for impossibility
                    job.targets.difference_update(job.blacklist)
                    if not job.targets or not job.targets.intersection(self.targets):
                        self.queue_map[msg_id] = job
                        self.fail_unreachable(msg_id)
                        return False
                return False
        else:
            indices = None

        self.submit_task(job, indices)
        return True

    def save_unmet(self, job):
        """Save a message for later submission when its dependencies are met."""
        msg_id = job.msg_id
        self.log.debug("Adding task %s to the queue", msg_id)
        self.queue_map[msg_id] = job
        self.queue.append(job)
        # track the ids in follow or after, but not those already finished
        for dep_id in job.after.union(job.follow).difference(self.all_done):
            if dep_id not in self.graph:
                self.graph[dep_id] = set()
            self.graph[dep_id].add(msg_id)

        # schedule timeout callback
        if job.timeout:
            timeout_id = job.timeout_id = job.timeout_id + 1
            self.loop.add_timeout(
                time.time() + job.timeout, lambda: self.job_timeout(job, timeout_id)
            )

    def submit_task(self, job, indices=None):
        """Submit a task to any of a subset of our targets."""
        if indices:
            loads = [self.loads[i] for i in indices]
        else:
            loads = self.loads
        idx = self.scheme(loads)
        if indices:
            idx = indices[idx]
        target = self.targets[idx]
        # print (target, map(str, msg[:3]))
        # send job to the engine
        self.engine_stream.send(target, flags=zmq.SNDMORE, copy=False)
        self.engine_stream.send_multipart(job.raw_msg, copy=False)
        # update load
        self.add_job(idx)
        self.pending[target][job.msg_id] = job
        # notify Hub
        content = dict(msg_id=job.msg_id, engine_id=target.decode('ascii'))
        self.session.send(
            self.mon_stream,
            'task_destination',
            content=content,
            ident=[b'tracktask', self.ident],
        )

    # -----------------------------------------------------------------------
    # Result Handling
    # -----------------------------------------------------------------------

    @util.log_errors
    def dispatch_result(self, raw_msg):  # maybe_dispatch_reults ?
        """dispatch method for result replies"""
        try:
            idents, msg = self.session.feed_identities(raw_msg, copy=False)
            msg = self.session.deserialize(msg, content=False, copy=False)
            engine = idents[0]
            try:
                idx = self.targets.index(engine)
            except ValueError:
                pass  # skip load-update for dead engines
            else:
                self.finish_job(idx)
        except Exception:
            self.log.error("task::Invalid result: %r", raw_msg, exc_info=True)
            return

        md = msg['metadata']
        parent = msg['parent_header']
        if md.get('dependencies_met', True):
            success = md['status'] == 'ok'
            msg_id = parent['msg_id']
            retries = self.retries[msg_id]
            if not success and retries > 0:
                # failed
                self.retries[msg_id] = retries - 1
                self.handle_unmet_dependency(idents, parent)
            else:
                del self.retries[msg_id]
                # relay to client and update graph
                self.handle_result(idents, parent, raw_msg, success)
                # send to Hub monitor
                self.mon_stream.send_multipart([b'outtask'] + raw_msg, copy=False)
        else:
            self.handle_unmet_dependency(idents, parent)

    def handle_result(self, idents, parent, raw_msg, success=True):
        """handle a real task result, either success or failure"""
        # first, relay result to client
        engine = idents[0]
        client = idents[1]
        # swap_ids for ROUTER-ROUTER mirror
        raw_msg[:2] = [client, engine]
        # print (map(str, raw_msg[:4]))
        self.client_stream.send_multipart(raw_msg, copy=False)
        # now, update our data structures
        msg_id = parent['msg_id']
        self.pending[engine].pop(msg_id)
        if success:
            self.completed[engine].add(msg_id)
            self.all_completed.add(msg_id)
        else:
            self.failed[engine].add(msg_id)
            self.all_failed.add(msg_id)
        self.all_done.add(msg_id)
        self.destinations[msg_id] = engine

        self.update_graph(msg_id, success)

    def handle_unmet_dependency(self, idents, parent):
        """handle an unmet dependency"""
        engine = idents[0]
        msg_id = parent['msg_id']

        job = self.pending[engine].pop(msg_id)
        job.blacklist.add(engine)

        if job.blacklist == job.targets:
            self.queue_map[msg_id] = job
            self.fail_unreachable(msg_id)
        elif not self.maybe_run(job):
            # resubmit failed
            if msg_id not in self.all_failed:
                # put it back in our dependency tree
                self.save_unmet(job)

        if self.hwm:
            try:
                idx = self.targets.index(engine)
            except ValueError:
                pass  # skip load-update for dead engines
            else:
                if self.loads[idx] == self.hwm - 1:
                    self.update_graph(None)

    def update_graph(self, dep_id=None, success=True):
        """dep_id just finished. Update our dependency
        graph and submit any jobs that just became runnable.

        Called with dep_id=None to update entire graph for hwm, but without finishing a task.
        """
        # print ("\n\n***********")
        # pprint (dep_id)
        # pprint (self.graph)
        # pprint (self.queue_map)
        # pprint (self.all_completed)
        # pprint (self.all_failed)
        # print ("\n\n***********\n\n")
        # update any jobs that depended on the dependency
        msg_ids = self.graph.pop(dep_id, [])

        # recheck *all* jobs if
        # a) we have HWM and an engine just become no longer full
        # or b) dep_id was given as None

        if (
            dep_id is None
            or self.hwm
            and any([load == self.hwm - 1 for load in self.loads])
        ):
            jobs = self.queue
            using_queue = True
        else:
            using_queue = False
            jobs = deque(sorted(self.queue_map[msg_id] for msg_id in msg_ids))

        to_restore = []
        while jobs:
            job = jobs.popleft()
            if job.removed:
                continue
            msg_id = job.msg_id

            put_it_back = True

            if job.after.unreachable(
                self.all_completed, self.all_failed
            ) or job.follow.unreachable(self.all_completed, self.all_failed):
                self.fail_unreachable(msg_id)
                put_it_back = False

            elif job.after.check(
                self.all_completed, self.all_failed
            ):  # time deps met, maybe run
                if self.maybe_run(job):
                    put_it_back = False
                    self.queue_map.pop(msg_id)
                    for mid in job.dependents:
                        if mid in self.graph:
                            self.graph[mid].remove(msg_id)

                    # abort the loop if we just filled up all of our engines.
                    # avoids an O(N) operation in situation of full queue,
                    # where graph update is triggered as soon as an engine becomes
                    # non-full, and all tasks after the first are checked,
                    # even though they can't run.
                    if not self.available_engines():
                        break

            if using_queue and put_it_back:
                # popped a job from the queue but it neither ran nor failed,
                # so we need to put it back when we are done
                # make sure to_restore preserves the same ordering
                to_restore.append(job)

        # put back any tasks we popped but didn't run
        if using_queue:
            self.queue.extendleft(to_restore)

    # ----------------------------------------------------------------------
    # methods to be overridden by subclasses
    # ----------------------------------------------------------------------

    def add_job(self, idx):
        """Called after self.targets[idx] just got the job with header.
        Override with subclasses.  The default ordering is simple LRU.
        The default loads are the number of outstanding jobs."""
        self.loads[idx] += 1
        for lis in (self.targets, self.loads):
            lis.append(lis.pop(idx))

    def finish_job(self, idx):
        """Called after self.targets[idx] just finished a job.
        Override with subclasses."""
        self.loads[idx] -= 1
ipyparallel-8.8.0/ipyparallel/datapub.py000066400000000000000000000000671460376056100203700ustar00rootroot00000000000000from .engine.datapub import publish_data  # noqa: F401
ipyparallel-8.8.0/ipyparallel/engine/000077500000000000000000000000001460376056100176405ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/engine/__init__.py000066400000000000000000000000001460376056100217370ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/engine/__main__.py000066400000000000000000000001011460376056100217220ustar00rootroot00000000000000if __name__ == '__main__':
    from .app import main

    main()
ipyparallel-8.8.0/ipyparallel/engine/app.py000077500000000000000000001023641460376056100210030ustar00rootroot00000000000000#!/usr/bin/env python
"""
The IPython engine application
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import json
import os
import signal
import sys
import time
from getpass import getpass
from io import FileIO, TextIOWrapper
from logging import StreamHandler

import zmq
from ipykernel.kernelapp import IPKernelApp
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython.core.profiledir import ProfileDir
from jupyter_client.localinterfaces import localhost
from jupyter_client.session import Session, session_aliases, session_flags
from tornado import ioloop
from traitlets import (
    Bool,
    Bytes,
    Dict,
    Float,
    Instance,
    Integer,
    List,
    Type,
    Unicode,
    default,
    observe,
    validate,
)
from traitlets.config import Config
from zmq.eventloop import zmqstream

from ipyparallel import util
from ipyparallel.apps.baseapp import (
    BaseParallelApplication,
    base_aliases,
    base_flags,
    catch_config_error,
)
from ipyparallel.controller.heartmonitor import Heart
from ipyparallel.util import disambiguate_ip_address, disambiguate_url

from .kernel import IPythonParallelKernel as Kernel
from .log import EnginePUBHandler
from .nanny import start_nanny

# -----------------------------------------------------------------------------
# Module level variables
# -----------------------------------------------------------------------------

_description = """Start an IPython engine for parallel computing.

IPython engines run in parallel and perform computations on behalf of a client
and controller. A controller needs to be started before the engines. The
engine can be configured using command line options or using a cluster
directory. Cluster directories contain config, log and security files and are
usually located in your ipython directory and named as "profile_name".
See the `profile` and `profile-dir` options for details.
"""

_examples = """
ipengine --file=path/to/ipcontroller-engine.json     # connect to hub using a specific url
ipengine --log-level=DEBUG > engine.log 2>&1  # log to a file with DEBUG verbosity
"""

DEFAULT_MPI_INIT = """
from mpi4py import MPI
mpi_rank = MPI.COMM_WORLD.Get_rank()
mpi_size = MPI.COMM_WORLD.Get_size()
"""

# -----------------------------------------------------------------------------
# Main application
# -----------------------------------------------------------------------------
aliases = dict(
    file='IPEngine.url_file',
    c='IPEngine.startup_command',
    s='IPEngine.startup_script',
    url='IPEngine.registration_url',
    ssh='IPEngine.sshserver',
    sshkey='IPEngine.sshkey',
    location='IPEngine.location',
    timeout='IPEngine.timeout',
)
aliases.update(base_aliases)
aliases.update(session_aliases)
flags = {
    'mpi': (
        {
            'IPEngine': {'use_mpi': True},
        },
        "enable MPI integration",
    ),
}
flags.update(base_flags)
flags.update(session_flags)


class IPEngine(BaseParallelApplication):
    name = 'ipengine'
    description = _description
    examples = _examples
    classes = List([ZMQInteractiveShell, ProfileDir, Session, Kernel])
    _deprecated_classes = ["EngineFactory", "IPEngineApp"]

    enable_nanny = Bool(
        True,
        config=True,
        help="""Enable the nanny process.

    The nanny process enables remote signaling of single engines
    and more responsive notification of engine shutdown.

    .. versionadded:: 7.0

    """,
    )

    startup_script = Unicode(
        '', config=True, help='specify a script to be run at startup'
    )
    startup_command = Unicode(
        '', config=True, help='specify a command to be run at startup'
    )

    url_file = Unicode(
        '',
        config=True,
        help="""The full location of the file containing the connection information for
        the controller. If this is not given, the file must be in the
        security directory of the cluster directory.  This location is
        resolved using the `profile` or `profile_dir` options.""",
    )
    wait_for_url_file = Float(
        10,
        config=True,
        help="""The maximum number of seconds to wait for url_file to exist.
        This is useful for batch-systems and shared-filesystems where the
        controller and engine are started at the same time and it
        may take a moment for the controller to write the connector files.""",
    )

    url_file_name = Unicode('ipcontroller-engine.json', config=True)

    connection_info_env = Unicode()

    @default("connection_info_env")
    def _default_connection_file_env(self):
        return os.environ.get("IPP_CONNECTION_INFO", "")

    @observe('cluster_id')
    def _cluster_id_changed(self, change):
        if change['new']:
            base = 'ipcontroller-{}'.format(change['new'])
        else:
            base = 'ipcontroller'
        self.url_file_name = "%s-engine.json" % base

    log_url = Unicode(
        '',
        config=True,
        help="""The URL for the iploggerapp instance, for forwarding
        logging to a central location.""",
    )

    registration_url = Unicode(
        config=True,
        help="""Override the registration URL""",
    )
    out_stream_factory = Type(
        'ipykernel.iostream.OutStream',
        config=True,
        help="""The OutStream for handling stdout/err.
        Typically 'ipykernel.iostream.OutStream'""",
    )
    display_hook_factory = Type(
        'ipykernel.displayhook.ZMQDisplayHook',
        config=True,
        help="""The class for handling displayhook.
        Typically 'ipykernel.displayhook.ZMQDisplayHook'""",
    )
    location = Unicode(
        config=True,
        help="""The location (an IP address) of the controller.  This is
        used for disambiguating URLs, to determine whether
        loopback should be used to connect or the public address.""",
    )
    timeout = Float(
        5.0,
        config=True,
        help="""The time (in seconds) to wait for the Controller to respond
        to registration requests before giving up.""",
    )
    max_heartbeat_misses = Integer(
        50,
        config=True,
        help="""The maximum number of times a check for the heartbeat ping of a
        controller can be missed before shutting down the engine.

        If set to 0, the check is disabled.""",
    )
    sshserver = Unicode(
        config=True,
        help="""The SSH server to use for tunneling connections to the Controller.""",
    )
    sshkey = Unicode(
        config=True,
        help="""The SSH private key file to use when tunneling connections to the Controller.""",
    )
    paramiko = Bool(
        sys.platform == 'win32',
        config=True,
        help="""Whether to use paramiko instead of openssh for tunnels.""",
    )

    use_mpi = Bool(
        False,
        config=True,
        help="""Enable MPI integration.

        If set, MPI rank will be requested for my rank,
        and additionally `mpi_init` will be executed in the interactive shell.
        """,
    )
    init_mpi = Unicode(
        DEFAULT_MPI_INIT,
        config=True,
        help="""Code to execute in the user namespace when initializing MPI""",
    )
    mpi_registration_delay = Float(
        0.02,
        config=True,
        help="""Per-engine delay for mpiexec-launched engines

        avoids flooding the controller with registrations,
        which can stall under heavy load.

        Default: .02 (50 engines/sec, or 3000 engines/minute)
        """,
    )

    # not configurable:
    user_ns = Dict()
    id = Integer(
        None,
        allow_none=True,
        config=True,
        help="""Request this engine ID.

        If run in MPI, will use the MPI rank.
        Otherwise, let the Hub decide what our rank should be.
        """,
    )

    @default('id')
    def _id_default(self):
        if not self.use_mpi:
            return None
        from mpi4py import MPI

        if MPI.COMM_WORLD.size > 1:
            self.log.debug("MPI rank = %i", MPI.COMM_WORLD.rank)
            return MPI.COMM_WORLD.rank

    registrar = Instance('zmq.eventloop.zmqstream.ZMQStream', allow_none=True)
    kernel = Instance(Kernel, allow_none=True)
    hb_check_period = Integer()

    # States for the heartbeat monitoring
    # Initial values for monitored and pinged must satisfy "monitored > pinged == False" so that
    # during the first check no "missed" ping is reported. Must be floats for Python 3 compatibility.
    _hb_last_pinged = 0.0
    _hb_last_monitored = 0.0
    _hb_missed_beats = 0
    # The zmq Stream which receives the pings from the Heart
    _hb_listener = None

    bident = Bytes()
    ident = Unicode()

    @default("ident")
    def _default_ident(self):
        return self.session.session

    @default("bident")
    def _default_bident(self):
        return self.ident.encode("utf8")

    @observe("ident")
    def _ident_changed(self, change):
        self.bident = self._default_bident()

    using_ssh = Bool(False)

    context = Instance(zmq.Context)

    @default("context")
    def _default_context(self):
        return zmq.Context.instance()

    # an IPKernelApp instance, used to setup listening for shell frontends
    kernel_app = Instance(IPKernelApp, allow_none=True)

    aliases = Dict(aliases)
    flags = Dict(flags)

    curve_serverkey = Bytes(
        config=True, help="The controller's public key for CURVE security"
    )
    curve_secretkey = Bytes(
        config=True,
        help="""The engine's secret key for CURVE security.
        Usually autogenerated on launch.""",
    )
    curve_publickey = Bytes(
        config=True,
        help="""The engine's public key for CURVE security.
        Usually autogenerated on launch.""",
    )

    @default("curve_serverkey")
    def _default_curve_serverkey(self):
        return os.environ.get("IPP_CURVE_SERVERKEY", "").encode("ascii")

    @default("curve_secretkey")
    def _default_curve_secretkey(self):
        return os.environ.get("IPP_CURVE_SECRETKEY", "").encode("ascii")

    @default("curve_publickey")
    def _default_curve_publickey(self):
        return os.environ.get("IPP_CURVE_PUBLICKEY", "").encode("ascii")

    @validate("curve_publickey", "curve_secretkey", "curve_serverkey")
    def _cast_bytes(self, proposal):
        if isinstance(proposal.value, str):
            return proposal.value.encode("ascii")
        return proposal.value

    def _ensure_curve_keypair(self):
        if not self.curve_secretkey or not self.curve_publickey:
            self.log.info("Generating new CURVE credentials")
            self.curve_publickey, self.curve_secretkey = zmq.curve_keypair()

    def find_connection_file(self):
        """Set the url file.

        Here we don't try to actually see if it exists for is valid as that
        is handled by the connection logic.
        """
        # Find the actual ipcontroller-engine.json connection file
        if not self.url_file:
            self.url_file = os.path.join(
                self.profile_dir.security_dir, self.url_file_name
            )

    def load_connection_file(self):
        """load config from a JSON connector file,
        at a *lower* priority than command-line/config files.

        Same content can be specified in $IPP_CONNECTION_INFO env
        """
        config = self.config

        if self.connection_info_env:
            self.log.info("Loading connection info from $IPP_CONNECTION_INFO")
            d = json.loads(self.connection_info_env)
        else:
            self.log.info("Loading connection file %r", self.url_file)
            with open(self.url_file) as f:
                d = json.load(f)

        # allow hand-override of location for disambiguation
        # and ssh-server
        if 'IPEngine.location' not in self.cli_config:
            self.location = d['location']
        if 'ssh' in d and not self.sshserver:
            self.sshserver = d.get("ssh")

        proto, ip = d['interface'].split('://')
        ip = disambiguate_ip_address(ip, self.location)
        d['interface'] = f'{proto}://{ip}'

        if d.get('curve_serverkey'):
            # connection file takes precedence over env, if present and defined
            self.curve_serverkey = d['curve_serverkey'].encode('ascii')
        if self.curve_serverkey:
            self.log.info("Using CurveZMQ security")
            self._ensure_curve_keypair()
        else:
            self.log.warning("Not using CurveZMQ security")

        # DO NOT allow override of basic URLs, serialization, or key
        # JSON file takes top priority there
        if d.get('key') or 'key' not in config.Session:
            config.Session.key = d.get('key', '').encode('utf8')
        config.Session.signature_scheme = d['signature_scheme']

        self.registration_url = f"{d['interface']}:{d['registration']}"

        config.Session.packer = d['pack']
        config.Session.unpacker = d['unpack']
        util._disable_session_extract_dates()
        self.session = Session(parent=self)

        self.log.debug("Config changed:")
        self.log.debug("%r", config)
        self.connection_info = d

    def bind_kernel(self, **kwargs):
        """Promote engine to listening kernel, accessible to frontends."""
        if self.kernel_app is not None:
            return

        self.log.info("Opening ports for direct connections as an IPython kernel")
        if self.curve_serverkey:
            self.log.warning("Bound kernel does not support CURVE security")

        kernel = self.kernel

        kwargs.setdefault('config', self.config)
        kwargs.setdefault('log', self.log)
        kwargs.setdefault('profile_dir', self.profile_dir)
        kwargs.setdefault('session', self.session)

        app = self.kernel_app = IPKernelApp(**kwargs)

        # allow IPKernelApp.instance():
        IPKernelApp._instance = app

        app.init_connection_file()
        # relevant contents of init_sockets:

        app.shell_port = app._bind_socket(kernel.shell_streams[0], app.shell_port)
        app.log.debug("shell ROUTER Channel on port: %i", app.shell_port)

        iopub_socket = kernel.iopub_socket
        # ipykernel 4.3 iopub_socket is an IOThread wrapper:
        if hasattr(iopub_socket, 'socket'):
            iopub_socket = iopub_socket.socket

        app.iopub_port = app._bind_socket(iopub_socket, app.iopub_port)
        app.log.debug("iopub PUB Channel on port: %i", app.iopub_port)

        kernel.stdin_socket = self.context.socket(zmq.ROUTER)
        app.stdin_port = app._bind_socket(kernel.stdin_socket, app.stdin_port)
        app.log.debug("stdin ROUTER Channel on port: %i", app.stdin_port)

        # start the heartbeat, and log connection info:

        app.init_heartbeat()

        app.log_connection_info()
        app.connection_dir = self.profile_dir.security_dir
        app.write_connection_file()

    @property
    def tunnel_mod(self):
        from zmq.ssh import tunnel

        return tunnel

    def init_connector(self):
        """construct connection function, which handles tunnels."""
        self.using_ssh = bool(self.sshkey or self.sshserver)

        if self.sshkey and not self.sshserver:
            # We are using ssh directly to the controller, tunneling localhost to localhost
            self.sshserver = self.registration_url.split('://')[1].split(':')[0]

        if self.using_ssh:
            if self.tunnel_mod.try_passwordless_ssh(
                self.sshserver, self.sshkey, self.paramiko
            ):
                password = False
            else:
                password = getpass("SSH Password for %s: " % self.sshserver)
        else:
            password = False

        def connect(s, url, curve_serverkey=None):
            url = disambiguate_url(url, self.location)
            if curve_serverkey is None:
                curve_serverkey = self.curve_serverkey
            if curve_serverkey:
                s.setsockopt(zmq.CURVE_SERVERKEY, curve_serverkey)
                s.setsockopt(zmq.CURVE_SECRETKEY, self.curve_secretkey)
                s.setsockopt(zmq.CURVE_PUBLICKEY, self.curve_publickey)

            if self.using_ssh:
                self.log.debug("Tunneling connection to %s via %s", url, self.sshserver)
                return self.tunnel_mod.tunnel_connection(
                    s,
                    url,
                    self.sshserver,
                    keyfile=self.sshkey,
                    paramiko=self.paramiko,
                    password=password,
                )
            else:
                return s.connect(url)

        def maybe_tunnel(url):
            """like connect, but don't complete the connection (for use by heartbeat)"""
            url = disambiguate_url(url, self.location)
            if self.using_ssh:
                self.log.debug("Tunneling connection to %s via %s", url, self.sshserver)
                url, tunnelobj = self.tunnel_mod.open_tunnel(
                    url,
                    self.sshserver,
                    keyfile=self.sshkey,
                    paramiko=self.paramiko,
                    password=password,
                )
            return str(url)

        return connect, maybe_tunnel

    def register(self):
        """send the registration_request"""
        if self.use_mpi and self.id and self.id >= 100 and self.mpi_registration_delay:
            # Some launchres implement delay at the Launcher level,
            # but mpiexec must implement it int he engine process itself
            # delay based on our rank

            delay = self.id * self.mpi_registration_delay
            self.log.info(
                f"Delaying registration for {self.id} by {int(delay * 1000)}ms"
            )
            time.sleep(delay)

        self.log.info("Registering with controller at %s" % self.registration_url)
        ctx = self.context
        connect, maybe_tunnel = self.init_connector()
        reg = ctx.socket(zmq.DEALER)
        reg.setsockopt(zmq.IDENTITY, self.bident)
        connect(reg, self.registration_url)

        self.registrar = zmqstream.ZMQStream(reg, self.loop)

        content = dict(uuid=self.ident)
        if self.id is not None:
            self.log.info("Requesting id: %i", self.id)
            content['id'] = self.id
        self._registration_completed = False
        self.registrar.on_recv(
            lambda msg: self.complete_registration(msg, connect, maybe_tunnel)
        )

        self.session.send(self.registrar, "registration_request", content=content)

    def _report_ping(self, msg):
        """Callback for when the heartmonitor.Heart receives a ping"""
        # self.log.debug("Received a ping: %s", msg)
        self._hb_last_pinged = time.time()

    def complete_registration(self, msg, connect, maybe_tunnel):
        try:
            self._complete_registration(msg, connect, maybe_tunnel)
        except Exception as e:
            self.log.critical(f"Error completing registration: {e}", exc_info=True)
            self.exit(255)

    def _complete_registration(self, msg, connect, maybe_tunnel):
        ctx = self.context
        loop = self.loop
        identity = self.bident
        idents, msg = self.session.feed_identities(msg)
        msg = self.session.deserialize(msg)
        content = msg['content']
        info = self.connection_info

        def url(key):
            """get zmq url for given channel"""
            return str(info["interface"] + ":%i" % info[key])

        def urls(key):
            return [f'{info["interface"]}:{port}' for port in info[key]]

        if content['status'] == 'ok':
            requested_id = self.id
            self.id = content['id']
            if requested_id is not None and self.id != requested_id:
                self.log.warning(
                    f"Did not get the requested id: {self.id} != {requested_id}"
                )
                self.log.name = self.log.name.rsplit(".", 1)[0] + f".{self.id}"
            elif self.id is None:
                self.log.name += f".{self.id}"

            # create Shell Connections (MUX, Task, etc.):

            # select which broadcast endpoint to connect to
            # use rank % len(broadcast_leaves)
            broadcast_urls = urls('broadcast')
            broadcast_leaves = len(broadcast_urls)
            broadcast_index = self.id % len(broadcast_urls)
            broadcast_url = broadcast_urls[broadcast_index]

            shell_addrs = [url('mux'), url('task'), broadcast_url]
            self.log.info(f'Shell_addrs: {shell_addrs}')

            # Use only one shell stream for mux and tasks
            stream = zmqstream.ZMQStream(ctx.socket(zmq.ROUTER), loop)
            stream.setsockopt(zmq.IDENTITY, identity)
            # TODO: enable PROBE_ROUTER when schedulers can handle the empty message
            # stream.setsockopt(zmq.PROBE_ROUTER, 1)
            self.log.debug("Setting shell identity %r", identity)

            shell_streams = [stream]
            for addr in shell_addrs:
                self.log.info("Connecting shell to %s", addr)
                connect(stream, addr)

            # control stream:
            control_url = url('control')
            curve_serverkey = self.curve_serverkey
            if self.enable_nanny:
                nanny_url, self.nanny_pipe = self.start_nanny(
                    control_url=control_url,
                )
                control_url = nanny_url
                # nanny uses our curve_publickey, not the controller's publickey
                curve_serverkey = self.curve_publickey
            control_stream = zmqstream.ZMQStream(ctx.socket(zmq.ROUTER), loop)
            control_stream.setsockopt(zmq.IDENTITY, identity)
            connect(control_stream, control_url, curve_serverkey=curve_serverkey)

            # create iopub stream:
            iopub_addr = url('iopub')
            iopub_socket = ctx.socket(zmq.PUB)
            iopub_socket.SNDHWM = 0
            iopub_socket.setsockopt(zmq.IDENTITY, identity)
            connect(iopub_socket, iopub_addr)
            try:
                from ipykernel.iostream import IOPubThread
            except ImportError:
                pass
            else:
                iopub_socket = IOPubThread(iopub_socket)
                iopub_socket.start()

            # disable history:
            self.config.HistoryManager.hist_file = ':memory:'

            # Redirect input streams and set a display hook.
            if self.out_stream_factory:
                sys.stdout = self.out_stream_factory(
                    self.session, iopub_socket, 'stdout'
                )
                sys.stdout.topic = f"engine.{self.id}.stdout".encode("ascii")
                sys.stderr = self.out_stream_factory(
                    self.session, iopub_socket, 'stderr'
                )
                sys.stderr.topic = f"engine.{self.id}.stderr".encode("ascii")

                # copied from ipykernel 6, which captures sys.__stderr__ at the FD-level
                if getattr(sys.stderr, "_original_stdstream_copy", None) is not None:
                    for handler in self.log.handlers:
                        if isinstance(handler, StreamHandler) and (
                            handler.stream.buffer.fileno() == 2
                        ):
                            self.log.debug(
                                "Seeing logger to stderr, rerouting to raw filedescriptor."
                            )

                            handler.stream = TextIOWrapper(
                                FileIO(sys.stderr._original_stdstream_copy, "w")
                            )
            if self.display_hook_factory:
                sys.displayhook = self.display_hook_factory(self.session, iopub_socket)
                sys.displayhook.topic = f"engine.{self.id}.execute_result".encode(
                    "ascii"
                )

            # patch Session to always send engine uuid metadata
            original_send = self.session.send

            def send_with_metadata(
                stream,
                msg_or_type,
                content=None,
                parent=None,
                ident=None,
                buffers=None,
                track=False,
                header=None,
                metadata=None,
                **kwargs,
            ):
                """Ensure all messages set engine uuid metadata"""
                metadata = metadata or {}
                metadata.setdefault("engine", self.ident)
                return original_send(
                    stream,
                    msg_or_type,
                    content=content,
                    parent=parent,
                    ident=ident,
                    buffers=buffers,
                    track=track,
                    header=header,
                    metadata=metadata,
                    **kwargs,
                )

            self.session.send = send_with_metadata

            self.kernel = Kernel.instance(
                parent=self,
                engine_id=self.id,
                ident=self.ident,
                session=self.session,
                control_stream=control_stream,
                shell_streams=shell_streams,
                iopub_socket=iopub_socket,
                loop=loop,
                user_ns=self.user_ns,
                log=self.log,
            )

            self.kernel.shell.display_pub.topic = f"engine.{self.id}.displaypub".encode(
                "ascii"
            )

            # FIXME: This is a hack until IPKernelApp and IPEngineApp can be fully merged
            self.init_signal()
            app = IPKernelApp(
                parent=self, shell=self.kernel.shell, kernel=self.kernel, log=self.log
            )
            if self.use_mpi and self.init_mpi:
                app.exec_lines.insert(0, self.init_mpi)
            app.init_profile_dir()
            app.init_code()

            self.kernel.start()
        else:
            self.log.fatal("Registration Failed: %s" % msg)
            raise Exception("Registration Failed: %s" % msg)

        self.start_heartbeat(
            maybe_tunnel(url('hb_ping')),
            maybe_tunnel(url('hb_pong')),
            content['hb_period'],
            identity,
        )
        self.log.info("Completed registration with id %i" % self.id)
        self.loop.remove_timeout(self._abort_timeout)

    def start_nanny(self, control_url):
        self.log.info("Starting nanny")
        config = Config()
        config.Session = self.config.Session
        return start_nanny(
            engine_id=self.id,
            identity=self.bident,
            control_url=control_url,
            curve_serverkey=self.curve_serverkey,
            curve_secretkey=self.curve_secretkey,
            curve_publickey=self.curve_publickey,
            registration_url=self.registration_url,
            config=config,
        )

    def start_heartbeat(self, hb_ping, hb_pong, hb_period, identity):
        """Start our heart beating"""

        hb_monitor = None
        if self.max_heartbeat_misses > 0:
            # Add a monitor socket which will record the last time a ping was seen
            mon = self.context.socket(zmq.SUB)
            if self.curve_serverkey:
                mon.setsockopt(zmq.CURVE_SERVER, 1)
                mon.setsockopt(zmq.CURVE_SECRETKEY, self.curve_secretkey)
            mport = mon.bind_to_random_port('tcp://%s' % localhost())
            mon.setsockopt(zmq.SUBSCRIBE, b"")
            self._hb_listener = zmqstream.ZMQStream(mon, self.loop)
            self._hb_listener.on_recv(self._report_ping)

            hb_monitor = "tcp://%s:%i" % (localhost(), mport)

        heart = Heart(
            hb_ping,
            hb_pong,
            hb_monitor,
            heart_id=identity,
            curve_serverkey=self.curve_serverkey,
            curve_secretkey=self.curve_secretkey,
            curve_publickey=self.curve_publickey,
        )
        heart.start()

        # periodically check the heartbeat pings of the controller
        # Should be started here and not in "start()" so that the right period can be taken
        # from the hubs HeartBeatMonitor.period
        if self.max_heartbeat_misses > 0:
            # Use a slightly bigger check period than the hub signal period to not warn unnecessary
            self.hb_check_period = hb_period + 500
            self.log.info(
                "Starting to monitor the heartbeat signal from the hub every %i ms.",
                self.hb_check_period,
            )
            self._hb_reporter = ioloop.PeriodicCallback(
                self._hb_monitor, self.hb_check_period
            )
            self._hb_reporter.start()
        else:
            self.log.info(
                "Monitoring of the heartbeat signal from the hub is not enabled."
            )

    def abort(self):
        self.log.fatal("Registration timed out after %.1f seconds" % self.timeout)
        if "127." in self.registration_url:
            self.log.fatal(
                """
            If the controller and engines are not on the same machine,
            you will have to instruct the controller to listen on an external IP (in ipcontroller_config.py):
                c.IPController.ip = '0.0.0.0' # for all interfaces, internal and external
                c.IPController.ip = '192.168.1.101' # or any interface that the engines can see
            or tunnel connections via ssh.
            """
            )
        self.session.send(
            self.registrar, "unregistration_request", content=dict(id=self.id)
        )
        time.sleep(1)
        sys.exit(255)

    def _hb_monitor(self):
        """Callback to monitor the heartbeat from the controller"""
        self._hb_listener.flush()
        if self._hb_last_monitored > self._hb_last_pinged:
            self._hb_missed_beats += 1
            self.log.warning(
                "No heartbeat in the last %s ms (%s time(s) in a row).",
                self.hb_check_period,
                self._hb_missed_beats,
            )
        else:
            # self.log.debug("Heartbeat received (after missing %s beats).", self._hb_missed_beats)
            self._hb_missed_beats = 0

        if self._hb_missed_beats >= self.max_heartbeat_misses:
            self.log.fatal(
                "Maximum number of heartbeats misses reached (%s times %s ms), shutting down.",
                self.max_heartbeat_misses,
                self.hb_check_period,
            )
            self.session.send(
                self.registrar, "unregistration_request", content=dict(id=self.id)
            )
            self.loop.stop()

        self._hb_last_monitored = time.time()

    def init_engine(self):
        # This is the working dir by now.
        sys.path.insert(0, '')
        config = self.config

        if not self.connection_info_env:
            self.find_connection_file()
            if self.wait_for_url_file and not os.path.exists(self.url_file):
                self.log.warning(f"Connection file {self.url_file!r} not found")
                self.log.warning(
                    "Waiting up to %.1f seconds for it to arrive.",
                    self.wait_for_url_file,
                )
                tic = time.monotonic()
                while not os.path.exists(self.url_file) and (
                    time.monotonic() - tic < self.wait_for_url_file
                ):
                    # wait for url_file to exist, or until time limit
                    time.sleep(0.1)

            if not os.path.exists(self.url_file):
                self.log.fatal(f"Fatal: connection file never arrived: {self.url_file}")
                self.exit(1)

        self.load_connection_file()

        exec_lines = []
        for app in ('IPKernelApp', 'InteractiveShellApp'):
            if '%s.exec_lines' % app in config:
                exec_lines = config[app].exec_lines
                break

        exec_files = []
        for app in ('IPKernelApp', 'InteractiveShellApp'):
            if '%s.exec_files' % app in config:
                exec_files = config[app].exec_files
                break

        config.IPKernelApp.exec_lines = exec_lines
        config.IPKernelApp.exec_files = exec_files

        if self.startup_script:
            exec_files.append(self.startup_script)
        if self.startup_command:
            exec_lines.append(self.startup_command)

    def forward_logging(self):
        if self.log_url:
            self.log.info("Forwarding logging to %s", self.log_url)
            context = self.context
            lsock = context.socket(zmq.PUB)
            lsock.connect(self.log_url)
            handler = EnginePUBHandler(self.engine, lsock)
            handler.setLevel(self.log_level)
            self.log.addHandler(handler)

    @catch_config_error
    def initialize(self, argv=None):
        super().initialize(argv)
        self.init_engine()
        self.forward_logging()

    def init_signal(self):
        signal.signal(signal.SIGINT, self._signal_sigint)
        signal.signal(signal.SIGTERM, self._signal_stop)

    def _signal_sigint(self, sig, frame):
        self.log.warning("Ignoring SIGINT. Terminate with SIGTERM.")

    def _signal_stop(self, sig, frame):
        self.log.critical(f"received signal {sig}, stopping")
        self.loop.add_callback_from_signal(self.loop.stop)

    def start(self):
        if self.id is not None:
            self.log.name += f".{self.id}"
        loop = self.loop

        def _start():
            self.register()
            self._abort_timeout = loop.add_timeout(
                loop.time() + self.timeout, self.abort
            )

        self.loop.add_callback(_start)
        try:
            self.loop.start()
        except KeyboardInterrupt:
            self.log.critical("Engine Interrupted, shutting down...\n")


main = launch_new_instance = IPEngine.launch_instance


if __name__ == '__main__':
    main()
ipyparallel-8.8.0/ipyparallel/engine/datapub.py000066400000000000000000000033261460376056100216360ustar00rootroot00000000000000"""Publishing native (typically pickled) objects."""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from ipykernel.jsonutil import json_clean
from jupyter_client.session import Session, extract_header
from traitlets import Any, CBytes, Dict, Instance
from traitlets.config import Configurable

from ipyparallel.serialize import serialize_object


class ZMQDataPublisher(Configurable):
    topic = topic = CBytes(b'datapub')
    session = Instance(Session, allow_none=True)
    pub_socket = Any(allow_none=True)
    parent_header = Dict({})

    def set_parent(self, parent):
        """Set the parent for outbound messages."""
        self.parent_header = extract_header(parent)

    def publish_data(self, data):
        """publish a data_message on the IOPub channel

        Parameters
        ----------
        data : dict
            The data to be published. Think of it as a namespace.
        """
        session = self.session
        buffers = serialize_object(
            data,
            buffer_threshold=session.buffer_threshold,
            item_threshold=session.item_threshold,
        )
        content = json_clean(dict(keys=list(data.keys())))
        session.send(
            self.pub_socket,
            'data_message',
            content=content,
            parent=self.parent_header,
            buffers=buffers,
            ident=self.topic,
        )


def publish_data(data):
    """publish a data_message on the IOPub channel

    Parameters
    ----------
    data : dict
        The data to be published. Think of it as a namespace.
    """
    from ipykernel.zmqshell import ZMQInteractiveShell

    ZMQInteractiveShell.instance().data_pub.publish_data(data)
ipyparallel-8.8.0/ipyparallel/engine/kernel.py000066400000000000000000000253371460376056100215040ustar00rootroot00000000000000"""IPython kernel for parallel computing"""

import asyncio
import inspect
import sys
from functools import partial

import ipykernel
from ipykernel.ipkernel import IPythonKernel
from traitlets import Integer, Type

from ipyparallel.serialize import serialize_object, unpack_apply_message
from ipyparallel.util import utcnow

from .datapub import ZMQDataPublisher


class IPythonParallelKernel(IPythonKernel):
    """Extend IPython kernel for parallel computing"""

    engine_id = Integer(-1)

    @property
    def int_id(self):
        return self.engine_id

    msg_types = getattr(IPythonKernel, 'msg_types', []) + ['apply_request']
    control_msg_types = getattr(IPythonKernel, 'control_msg_types', []) + [
        'abort_request',
        'clear_request',
    ]
    _execute_sleep = 0
    data_pub_class = Type(ZMQDataPublisher)

    def _topic(self, topic):
        """prefixed topic for IOPub messages"""
        base = "engine.%s" % self.engine_id

        return f"{base}.{topic}".encode()

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # add apply_request, in anticipation of upstream deprecation
        self.shell_handlers['apply_request'] = self.apply_request
        # set up data pub
        data_pub = self.shell.data_pub = self.data_pub_class(parent=self)
        self.shell.configurables.append(data_pub)
        data_pub.session = self.session
        data_pub.pub_socket = self.iopub_socket
        self.aborted = set()

    def _abort_queues(self):
        # forward-port ipython/ipykernel#853
        # may remove after requiring ipykernel 6.9.1

        # while this flag is true,
        # execute requests will be aborted
        self._aborting = True
        self.log.info("Aborting queue")

        # Callback to signal that we are done aborting
        def stop_aborting():
            self.log.info("Finishing abort")
            self._aborting = False
            # must be awaitable for ipykernel >= 3.6
            # must also be sync for ipykernel < 3.6
            f = asyncio.Future()
            f.set_result(None)
            return f

        # put stop_aborting on the message queue
        # so that it's handled after processing of already-pending messages
        if ipykernel.version_info < (6,):
            # 10 is SHELL priority in ipykernel 5.x
            streams = self.shell_streams
            schedule_stop_aborting = partial(self.schedule_dispatch, 10, stop_aborting)
        else:
            streams = [self.shell_stream]
            schedule_stop_aborting = partial(self.schedule_dispatch, stop_aborting)

        # flush streams, so all currently waiting messages
        # are added to the queue
        for stream in streams:
            stream.flush()

        # if we have a delay, give messages this long to arrive on the queue
        # before we start accepting requests
        asyncio.get_running_loop().call_later(
            self.stop_on_error_timeout, schedule_stop_aborting
        )

        # for compatibility, return a completed Future
        # so this is still awaitable
        f = asyncio.Future()
        f.set_result(None)
        return f

    def should_handle(self, stream, msg, idents):
        """Check whether a shell-channel message should be handled

        Allows subclasses to prevent handling of certain messages (e.g. aborted requests).
        """
        if not super().should_handle(stream, msg, idents):
            return False
        msg_id = msg['header']['msg_id']
        msg_type = msg['header']['msg_type']
        if msg_id in self.aborted:
            # is it safe to assume a msg_id will not be resubmitted?
            self.aborted.remove(msg_id)
            self._send_abort_reply(stream, msg, idents)
            return False
        self.log.info(f"Handling {msg_type}: {msg_id}")
        return True

    def init_metadata(self, parent):
        """init metadata dict, for execute/apply_reply"""
        parent_metadata = parent.get('metadata', {})
        return {
            'started': utcnow(),
            'dependencies_met': True,
            'engine': self.ident,
            'is_broadcast': parent_metadata.get('is_broadcast', False),
            'is_coalescing': parent_metadata.get('is_coalescing', False),
            'original_msg_id': parent_metadata.get('original_msg_id', ''),
        }

    def finish_metadata(self, parent, metadata, reply_content):
        """Finish populating metadata.

        Run after completing a request handler.
        """
        metadata['status'] = reply_content['status']
        if reply_content['status'] == 'error':
            if reply_content['ename'] == 'UnmetDependency':
                metadata['dependencies_met'] = False
            metadata['engine_info'] = self.get_engine_info()

        return metadata

    def get_engine_info(self, method=None):
        """Return engine_info dict"""
        engine_info = dict(
            engine_uuid=self.ident,
            engine_id=self.engine_id,
        )
        if method:
            engine_info["method"] = method
        return engine_info

    def apply_request(self, stream, ident, parent):
        try:
            content = parent['content']
            bufs = parent['buffers']
            msg_id = parent['header']['msg_id']
        except Exception:
            self.log.error("Got bad msg: %s", parent, exc_info=True)
            return

        md = self.init_metadata(parent)
        reply_content, result_buf = self.do_apply(content, bufs, msg_id, md)

        # put 'ok'/'error' status in header, for scheduler introspection:
        md = self.finish_metadata(parent, md, reply_content)

        # flush i/o
        sys.stdout.flush()
        sys.stderr.flush()
        self.log.debug(f'Sending apply_reply: {msg_id}')
        self.session.send(
            stream,
            'apply_reply',
            reply_content,
            parent=parent,
            ident=ident,
            buffers=result_buf,
            metadata=md,
        )

    def do_apply(self, content, bufs, msg_id, reply_metadata):
        shell = self.shell
        try:
            working = shell.user_ns

            prefix = "_" + str(msg_id).replace("-", "") + "_"

            f, args, kwargs = unpack_apply_message(bufs, working, copy=False)

            fname = getattr(f, '__name__', 'f')

            fname = prefix + "f"
            argname = prefix + "args"
            kwargname = prefix + "kwargs"
            resultname = prefix + "result"

            ns = {fname: f, argname: args, kwargname: kwargs, resultname: None}
            # print ns
            working.update(ns)
            code = f"{resultname} = {fname}(*{argname},**{kwargname})"
            try:
                exec(code, shell.user_global_ns, shell.user_ns)
                result = working.get(resultname)
            finally:
                for key in ns:
                    working.pop(key)

            result_buf = serialize_object(
                result,
                buffer_threshold=self.session.buffer_threshold,
                item_threshold=self.session.item_threshold,
            )

        except BaseException as e:
            # invoke IPython traceback formatting
            # this sends the 'error' message
            try:
                shell.showtraceback()
            except Exception as tb_error:
                self.log.error(f"Failed to show traceback for {e}: {tb_error}")

            try:
                str_evalue = str(e)
            except Exception as str_error:
                str_evalue = f"Failed to cast exception to string: {str_error}"
            reply_content = {
                'traceback': [],
                'ename': str(type(e).__name__),
                'evalue': str_evalue,
            }
            # get formatted traceback, which ipykernel recorded
            if hasattr(shell, '_last_traceback'):
                # ipykernel 4.4
                reply_content['traceback'] = shell._last_traceback or []
            else:
                self.log.warning("Didn't find a traceback where I expected to")
            shell._last_traceback = None
            reply_content["engine_info"] = self.get_engine_info(method="apply")

            self.log.info(
                "Exception in apply request:\n%s", '\n'.join(reply_content['traceback'])
            )
            result_buf = []
            reply_content['status'] = 'error'
        else:
            reply_content = {'status': 'ok'}

        return reply_content, result_buf

    async def _do_execute_async(self, *args, **kwargs):
        super_execute = super().do_execute(*args, **kwargs)
        if inspect.isawaitable(super_execute):
            reply_content = await super_execute
        else:
            reply_content = super_execute
        # add engine info
        if reply_content['status'] == 'error':
            reply_content["engine_info"] = self.get_engine_info(method="execute")
        return reply_content

    def do_execute(self, *args, **kwargs):
        coro = self._do_execute_async(*args, **kwargs)
        if ipykernel.version_info < (6,):
            # ipykernel 5 uses gen.maybe_future which doesn't accept async def coroutines,
            # but it does accept asyncio.Futures
            return asyncio.ensure_future(coro)
        else:
            return coro

    # Control messages for msgspec extensions:

    def abort_request(self, stream, ident, parent):
        """abort a specific msg by id"""
        msg_ids = parent['content'].get('msg_ids', None)
        if isinstance(msg_ids, str):
            msg_ids = [msg_ids]
        if not msg_ids:
            f = self._abort_queues()
            if inspect.isawaitable(f):
                asyncio.ensure_future(f)
        for mid in msg_ids:
            self.aborted.add(str(mid))

        content = dict(status='ok')
        reply_msg = self.session.send(
            stream, 'abort_reply', content=content, parent=parent, ident=ident
        )
        self.log.debug("%s", reply_msg)

    def clear_request(self, stream, idents, parent):
        """Clear our namespace."""
        self.shell.reset(False)
        content = dict(status='ok')
        self.session.send(
            stream, 'clear_reply', ident=idents, parent=parent, content=content
        )

    def _send_abort_reply(self, stream, msg, idents):
        """Send a reply to an aborted request"""
        # FIXME: forward-port ipython/ipykernel#684
        self.log.info(
            f"Aborting {msg['header']['msg_id']}: {msg['header']['msg_type']}"
        )
        reply_type = msg["header"]["msg_type"].rsplit("_", 1)[0] + "_reply"
        status = {"status": "aborted"}
        md = self.init_metadata(msg)
        md = self.finish_metadata(msg, md, status)
        md.update(status)

        self.session.send(
            stream,
            reply_type,
            metadata=md,
            content=status,
            parent=msg,
            ident=idents,
        )
ipyparallel-8.8.0/ipyparallel/engine/log.py000066400000000000000000000011261460376056100207730ustar00rootroot00000000000000from zmq.log.handlers import PUBHandler


class EnginePUBHandler(PUBHandler):
    """A simple PUBHandler subclass that sets root_topic"""

    engine = None

    def __init__(self, engine, *args, **kwargs):
        PUBHandler.__init__(self, *args, **kwargs)
        self.engine = engine

    @property
    def root_topic(self):
        """this is a property, in case the handler is created
        before the engine gets registered with an id"""
        if isinstance(getattr(self.engine, 'id', None), int):
            return "engine.%i" % self.engine.id
        else:
            return "engine"
ipyparallel-8.8.0/ipyparallel/engine/nanny.py000066400000000000000000000233301460376056100213360ustar00rootroot00000000000000"""Engine subprocess that receives and relays signals

Currently the kernel nanny serves two purposes:

1. to relay signals to the process
2. to notice engine shutdown and promptly notify the Hub,
   rather than waiting for heartbeat failures.

This has overlapping functionality with the Cluster API,
but has key differences, justifying both existing:

- addressing single engines is not feasible with the Cluster API,
  only engine *sets*
- Cluster API can efficiently signal all engines via mpiexec
"""

import asyncio
import logging
import os
import pickle
import signal
import sys
from subprocess import PIPE, Popen
from threading import Thread

import psutil
import zmq
from jupyter_client.session import Session
from tornado.ioloop import IOLoop
from traitlets.config import Config
from zmq.eventloop.zmqstream import ZMQStream

from ipyparallel import error, util
from ipyparallel.util import local_logger


class KernelNanny:
    """Object for monitoring

    Must be child of engine

    Handles signal messages and watches Engine process for exiting
    """

    def __init__(
        self,
        *,
        pid: int,
        engine_id: int,
        control_url: str,
        registration_url: str,
        identity: bytes,
        curve_serverkey: bytes,
        curve_publickey: bytes,
        curve_secretkey: bytes,
        config: Config,
        pipe,
        log_level: int = logging.INFO,
    ):
        self.pid = pid
        self.engine_id = engine_id
        self.parent_process = psutil.Process(self.pid)
        self.control_url = control_url
        self.registration_url = registration_url
        self.identity = identity
        self.curve_serverkey = curve_serverkey
        self.curve_publickey = curve_publickey
        self.curve_secretkey = curve_secretkey
        self.config = config
        self.pipe = pipe
        util._disable_session_extract_dates()
        self.session = Session(config=self.config)

        self.log = local_logger(f"{self.__class__.__name__}.{engine_id}", log_level)
        self.log.propagate = False

        self.control_handlers = {
            "signal_request": self.signal_request,
        }
        self._finish_called = False

    def wait_for_parent_thread(self):
        """Wait for my parent to exit, then I'll notify the controller and shut down"""
        self.log.info(f"Nanny watching parent pid {self.pid}.")
        while True:
            try:
                exit_code = self.parent_process.wait(60)
            except psutil.TimeoutExpired:
                continue
            else:
                break
        self.log.critical(f"Parent {self.pid} exited with status {exit_code}.")
        self.loop.add_callback(self.finish)

    def pipe_handler(self, fd, events):
        self.log.debug(f"Pipe event {events}")
        self.loop.remove_handler(fd)
        try:
            fd.close()
        except BrokenPipeError:
            pass
        try:
            status = self.parent_process.wait(0)
        except psutil.TimeoutExpired:
            try:
                status = self.parent_process.status()
            except psutil.NoSuchProcess:
                status = "exited"

        self.log.critical(f"Pipe closed, parent {self.pid} has status: {status}")
        self.finish()

    def notify_exit(self):
        """Notify the Hub that our parent has exited"""
        self.log.info("Notifying Hub that our parent has shut down")
        s = self.context.socket(zmq.DEALER)
        # finite, nonzero LINGER to prevent hang without dropping message during exit
        s.LINGER = 3000
        util.connect(
            s,
            self.registration_url,
            curve_serverkey=self.curve_serverkey,
            curve_secretkey=self.curve_secretkey,
            curve_publickey=self.curve_publickey,
        )
        self.session.send(s, "unregistration_request", content={"id": self.engine_id})
        s.close()

    def finish(self):
        """Prepare to exit and stop our event loop."""
        if self._finish_called:
            return
        self._finish_called = True
        self.notify_exit()
        self.loop.add_callback(self.loop.stop)

    def dispatch_control(self, stream, raw_msg):
        """Dispatch message from the control scheduler

        If we have a handler registered"""
        try:
            idents, msg_frames = self.session.feed_identities(raw_msg)
        except Exception as e:
            self.log.error(f"Bad control message: {raw_msg}", exc_info=True)
            return

        try:
            msg = self.session.deserialize(msg_frames, content=True)
        except Exception:
            content = error.wrap_exception()
            self.log.error("Bad control message: %r", msg_frames, exc_info=True)
            return

        msg_type = msg['header']['msg_type']
        if msg_type.endswith("_request"):
            reply_type = msg_type[-len("_request") :]
        else:
            reply_type = "error"
        self.log.debug(f"Client {idents[-1]} requested {msg_type}")

        handler = self.control_handlers.get(msg_type, None)
        if handler is None:
            # don't have an intercept handler, relay original message to parent
            self.log.debug(f"Relaying {msg_type} {msg['header']['msg_id']}")
            self.parent_stream.send_multipart(raw_msg)
            return

        try:
            content = handler(msg['content'])
        except Exception:
            content = error.wrap_exception()
            self.log.error("Error handling request: %r", msg_type, exc_info=True)

        self.session.send(stream, reply_type, ident=idents, content=content, parent=msg)

    def dispatch_parent(self, stream, raw_msg):
        """Relay messages from parent directly to control stream"""
        self.control_stream.send_multipart(raw_msg)

    # intercept message handlers

    def signal_request(self, content):
        """Handle a signal request: send signal to parent process"""
        sig = content['sig']
        if isinstance(sig, str):
            sig = getattr(signal, sig)
        self.log.info(f"Sending signal {sig} to pid {self.pid}")
        # exception will be caught and wrapped by the caller
        self.parent_process.send_signal(sig)
        return {"status": "ok"}

    async def start(self):
        self.log.info(
            f"Starting kernel nanny for engine {self.engine_id}, pid={self.pid}, nanny pid={os.getpid()}"
        )
        self._watcher_thread = Thread(
            target=self.wait_for_parent_thread, name="WatchParent", daemon=True
        )
        self._watcher_thread.start()
        # ignore SIGINT sent to parent
        signal.signal(signal.SIGINT, signal.SIG_IGN)

        self.loop = IOLoop.current()
        self.context = zmq.Context()

        # set up control socket (connection to Scheduler)
        self.control_socket = self.context.socket(zmq.ROUTER)
        self.control_socket.identity = self.identity
        util.connect(
            self.control_socket,
            self.control_url,
            curve_serverkey=self.curve_serverkey,
        )
        self.control_stream = ZMQStream(self.control_socket)
        self.control_stream.on_recv_stream(self.dispatch_control)

        # set up relay socket (connection to parent's control socket)
        self.parent_socket = self.context.socket(zmq.DEALER)
        if self.curve_secretkey:
            self.parent_socket.setsockopt(zmq.CURVE_SERVER, 1)
            self.parent_socket.setsockopt(zmq.CURVE_SECRETKEY, self.curve_secretkey)

        port = self.parent_socket.bind_to_random_port("tcp://127.0.0.1")

        # now that we've bound, pass port to parent via AsyncResult
        self.pipe.write(f"tcp://127.0.0.1:{port}\n")
        if not sys.platform.startswith("win"):
            # watch for the stdout pipe to close
            # as a signal that our parent is shutting down
            self.loop.add_handler(
                self.pipe, self.pipe_handler, IOLoop.READ | IOLoop.ERROR
            )
        self.parent_stream = ZMQStream(self.parent_socket)
        self.parent_stream.on_recv_stream(self.dispatch_parent)

    @classmethod
    def main(cls, *args, **kwargs):
        """Main body function.

        Instantiates and starts a nanny.

        Args and keyword args passed to the constructor.

        Should be called in a subprocess.
        """
        self = cls(*args, **kwargs)
        asyncio_loop = asyncio.new_event_loop()
        asyncio_loop.run_until_complete(self.start())
        try:
            asyncio_loop.run_forever()
        finally:
            self.loop.close(all_fds=True)
            self.context.term()
            try:
                self.pipe.close()
            except BrokenPipeError:
                pass
            self.log.debug("exiting")


def start_nanny(**kwargs):
    """Start a nanny subprocess

    Returns
    -------

    nanny_url: str
      The proxied URL the engine's control socket should connect to,
      instead of connecting directly to the control Scheduler.
    """

    kwargs['pid'] = os.getpid()

    env = os.environ.copy()
    env['PYTHONUNBUFFERED'] = '1'
    p = Popen(
        [sys.executable, '-m', __name__],
        stdin=PIPE,
        stdout=PIPE,
        env=env,
        start_new_session=True,  # don't inherit signals
    )
    p.stdin.write(pickle.dumps(kwargs))
    p.stdin.close()
    out = p.stdout.readline()
    nanny_url = out.decode("utf8").strip()
    if not nanny_url:
        p.terminate()
        raise RuntimeError("nanny failed")
    # return the handle on the process
    # need to keep the pipe open for the nanny
    return nanny_url, p


def main():
    """Entrypoint from the command-line

    Loads kwargs from stdin,
    sets pipe to stdout
    """
    with os.fdopen(sys.stdin.fileno(), mode='rb') as f:
        kwargs = pickle.load(f)
    kwargs['pipe'] = sys.stdout
    KernelNanny.main(**kwargs)


if __name__ == "__main__":
    main()
ipyparallel-8.8.0/ipyparallel/error.py000066400000000000000000000177011460376056100201040ustar00rootroot00000000000000"""Classes and functions for kernel related errors and exceptions.

Inheritance diagram:

.. inheritance-diagram:: ipyparallel.error
    :parts: 3
"""

import builtins
import sys
import traceback

__docformat__ = "restructuredtext en"


class IPythonError(Exception):
    """Base exception that all of our exceptions inherit from.

    This can be raised by code that doesn't have any more specific
    information."""

    pass


class KernelError(IPythonError):
    pass


class EngineError(KernelError):
    pass


class NoEnginesRegistered(KernelError):
    """Exception for operations that require some engines, but none exist"""

    def __str__(self):
        return (
            "This operation requires engines."
            " Try client.wait_for_engines(n) to wait for engines to register."
        )


class TaskAborted(KernelError):
    pass


class TaskTimeout(KernelError):
    pass


# backward-compat: use builtin TimeoutError, but preserve `error.TimeoutError` import
TimeoutError = builtins.TimeoutError


class UnmetDependency(KernelError):
    pass


class ImpossibleDependency(UnmetDependency):
    pass


class DependencyTimeout(ImpossibleDependency):
    pass


class InvalidDependency(ImpossibleDependency):
    pass


class RemoteError(KernelError):
    """Error raised elsewhere"""

    ename = None
    evalue = None
    traceback = None
    engine_info = None

    def __init__(self, ename, evalue, traceback, engine_info=None):
        self.ename = ename
        self.evalue = evalue
        self.traceback = traceback
        self.engine_info = engine_info or {}
        self.args = (ename, evalue)

    def __repr__(self):
        engineid = self.engine_info.get('engine_id', ' ')
        return f"<{self.__class__.__name__}[{engineid}]:{self.ename}({self.evalue})>"

    def __str__(self):
        label = self._get_engine_str(self.engine_info)
        engineid = self.engine_info.get('engine_id', ' ')
        return f"{label} {self.ename}: {self.evalue}"

    @staticmethod
    def _get_engine_str(engine_info):
        if not engine_info:
            return '[Engine Exception]'
        else:
            return f"[{engine_info['engine_id']}:{engine_info['method']}]"

    def render_traceback(self):
        """render traceback to a list of lines"""
        return [self._get_engine_str(self.engine_info)] + (
            self.traceback or "No traceback available"
        ).splitlines()

    def _render_traceback_(self):
        """Special method for custom tracebacks within IPython.

        This will be called by IPython instead of displaying the local traceback.

        It should return a traceback rendered as a list of lines.
        """
        return self.render_traceback()

    def print_traceback(self, excid=None):
        """print my traceback"""
        print('\n'.join(self.render_traceback()))


class TaskRejectError(KernelError):
    """Exception to raise when a task should be rejected by an engine.

    This exception can be used to allow a task running on an engine to test
    if the engine (or the user's namespace on the engine) has the needed
    task dependencies.  If not, the task should raise this exception.  For
    the task to be retried on another engine, the task should be created
    with the `retries` argument > 1.

    The advantage of this approach over our older properties system is that
    tasks have full access to the user's namespace on the engines and the
    properties don't have to be managed or tested by the controller.
    """


class CompositeError(RemoteError):
    """Error for representing possibly multiple errors on engines"""

    tb_limit = 4  # limit on how many tracebacks to draw

    def __init__(self, message, elist):
        Exception.__init__(self, *(message, elist))
        # Don't use pack_exception because it will conflict with the .message
        # attribute that is being deprecated in 2.6 and beyond.
        self.msg = message
        self.elist = elist
        self.args = [e[0] for e in elist]

    def _get_traceback(self, ev):
        try:
            tb = ev._ipython_traceback_text
        except AttributeError:
            return 'No traceback available'
        else:
            return tb

    def __str__(self):
        s = str(self.msg)
        for en, ev, etb, ei in self.elist[: self.tb_limit]:
            engine_str = self._get_engine_str(ei)
            s = s + '\n' + engine_str + en + ': ' + str(ev)
        if len(self.elist) > self.tb_limit:
            s = s + '\n.... %i more exceptions ...' % (len(self.elist) - self.tb_limit)
        return s

    def __repr__(self):
        return "CompositeError(%i)" % len(self.elist)

    def render_traceback(self, excid=None):
        """render one or all of my tracebacks to a list of lines"""
        lines = []
        if excid is None:
            for en, ev, etb, ei in self.elist[: self.tb_limit]:
                lines.append(self._get_engine_str(ei) + ":")
                lines.extend((etb or 'No traceback available').splitlines())
                lines.append('')
            if len(self.elist) > self.tb_limit:
                lines.append(
                    '... %i more exceptions ...' % (len(self.elist) - self.tb_limit)
                )
        else:
            try:
                en, ev, etb, ei = self.elist[excid]
            except Exception:
                raise IndexError("an exception with index %i does not exist" % excid)
            else:
                lines.append(self._get_engine_str(ei) + ":")
                lines.extend((etb or 'No traceback available').splitlines())

        return lines

    def print_traceback(self, excid=None):
        print('\n'.join(self.render_traceback(excid)))

    def raise_exception(self, excid=0):
        try:
            en, ev, etb, ei = self.elist[excid]
        except Exception:
            raise IndexError("an exception with index %i does not exist" % excid)
        else:
            raise RemoteError(en, ev, etb, ei)


class AlreadyDisplayedError(RemoteError):
    def __init__(self, original_error):
        self.original_error = original_error
        self.n = len(self.original_error.elist)

    def __repr__(self):
        return f"<{self.__class__.__name__}({self.n} errors)>"

    def __str__(self):
        return f"{self.n} errors"

    def render_traceback(self):
        """IPython special method, short-circuit traceback display

        for raising when streaming output has already displayed errors
        """
        return [str(self)]


def collect_exceptions(rdict_or_list, method='unspecified'):
    """check a result dict for errors, and raise CompositeError if any exist.
    Passthrough otherwise."""
    elist = []
    if isinstance(rdict_or_list, dict):
        rlist = rdict_or_list.values()
    else:
        rlist = rdict_or_list
    for r in rlist:
        if isinstance(r, RemoteError):
            en, ev, etb, ei = r.ename, r.evalue, r.traceback, r.engine_info
            # Sometimes we could have CompositeError in our list.  Just take
            # the errors out of them and put them in our new list.  This
            # has the effect of flattening lists of CompositeErrors into one
            # CompositeError
            if en == 'CompositeError':
                for e in ev.elist:
                    elist.append(e)
            else:
                elist.append((en, ev, etb, ei))
    if len(elist) == 0:
        return rdict_or_list
    else:
        msg = "one or more exceptions raised in: %s" % (method)
        err = CompositeError(msg, elist)
        raise err


def wrap_exception(engine_info={}):
    etype, evalue, tb = sys.exc_info()
    stb = traceback.format_exception(etype, evalue, tb)
    exc_content = {
        'status': 'error',
        'traceback': stb,
        'ename': etype.__name__,
        'evalue': str(evalue),
        'engine_info': engine_info,
    }
    return exc_content


def unwrap_exception(content):
    err = RemoteError(
        content['ename'],
        content['evalue'],
        '\n'.join(content['traceback']),
        content.get('engine_info', {}),
    )
    return err
ipyparallel-8.8.0/ipyparallel/joblib.py000066400000000000000000000016601460376056100202110ustar00rootroot00000000000000"""IPython parallel backend for joblib

To enable the default view as a backend for joblib::

    import ipyparallel as ipp
    ipp.register_joblib_backend()

Or to enable a particular View you have already set up::

    view.register_joblib_backend()

At this point, you can use it with::

    with parallel_backend('ipyparallel'):
        Parallel(n_jobs=2)(delayed(some_function)(i) for i in range(10))

.. versionadded:: 5.1
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from joblib.parallel import register_parallel_backend

from .client._joblib import IPythonParallelBackend


def register(name='ipyparallel', make_default=False):
    """Register the default ipyparallel Client as a joblib backend

    See joblib.parallel.register_parallel_backend for details.
    """
    return register_parallel_backend(
        name, IPythonParallelBackend, make_default=make_default
    )
ipyparallel-8.8.0/ipyparallel/logger.py000066400000000000000000000001561460376056100202260ustar00rootroot00000000000000if __name__ == '__main__':
    from ipyparallel.apps import iploggerapp as app

    app.launch_new_instance()
ipyparallel-8.8.0/ipyparallel/nbextension/000077500000000000000000000000001460376056100207275ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/nbextension/__init__.py000066400000000000000000000005741460376056100230460ustar00rootroot00000000000000"""Jupyter server extension(s)"""

import warnings


def load_jupyter_server_extension(app):
    warnings.warn(
        "Using ipyparallel.nbextension as server extension is deprecated in IPython Parallel 7.0. Use top-level ipyparallel.",
        DeprecationWarning,
    )
    from ipyparallel import _load_jupyter_server_extension

    return _load_jupyter_server_extension(app)
ipyparallel-8.8.0/ipyparallel/nbextension/base.py000066400000000000000000000025521460376056100222170ustar00rootroot00000000000000"""Place to put the base Handler"""

import warnings
from functools import lru_cache

_APIHandler = None


@lru_cache
def _guess_api_handler():
    """Fallback to guess API handler by availability"""
    try:
        from notebook.base.handlers import APIHandler
    except ImportError:
        from jupyter_server.base.handlers import APIHandler
    global _APIHandler
    _APIHandler = APIHandler
    return _APIHandler


def get_api_handler(app=None):
    """Get the base APIHandler class to use

    Inferred from app class (either jupyter_server or notebook app)
    """
    global _APIHandler
    if _APIHandler is not None:
        return _APIHandler
    if app is None:
        warnings.warn(
            "Guessing base APIHandler class. Specify an app to ensure the right APIHandler is used.",
            stacklevel=2,
        )
        return _guess_api_handler()

    top_modules = {cls.__module__.split(".", 1)[0] for cls in app.__class__.mro()}
    if "jupyter_server" in top_modules:
        from jupyter_server.base.handlers import APIHandler

        _APIHandler = APIHandler
        return APIHandler
    if "notebook" in top_modules:
        from notebook.base.handlers import APIHandler

        _APIHandler = APIHandler
        return APIHandler

    warnings.warn(f"Failed to detect base APIHandler class for {app}.", stacklevel=2)
    return _guess_api_handler()
ipyparallel-8.8.0/ipyparallel/nbextension/handlers.py000066400000000000000000000131061460376056100231020ustar00rootroot00000000000000"""Tornado handlers for IPython cluster web service."""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import json
import os

try:
    from jupyter_server.utils import url_path_join as ujoin
except ImportError:
    # fallback on legacy Notebook server
    from notebook.utils import url_path_join as ujoin

from tornado import web

from ..cluster import ClusterManager
from ..util import abbreviate_profile_dir
from .base import get_api_handler

static = os.path.join(os.path.dirname(__file__), 'static')

APIHandler = get_api_handler()


class ClusterHandler(APIHandler):
    @property
    def cluster_manager(self):
        return self.settings['cluster_manager']

    def cluster_model(self, key, cluster):
        """Create a JSONable cluster model

        Adds additional fields to make things easier on the client
        """
        d = cluster.to_dict()
        profile_dir = d['cluster']['profile_dir']
        # provide abbreviated profile info
        d['cluster']['profile'] = abbreviate_profile_dir(profile_dir)

        # top-level fields, added for easier client-side logic
        # add 'id' key, since typescript is bad at key-value pairs
        # so we return a list instead of a dict
        d['id'] = key
        # add total engine count
        d['engines']['n'] = sum(es.get('n', 0) for es in d['engines']['sets'].values())
        # add cluster file
        d['cluster_file'] = cluster.cluster_file
        return d


class ClusterListHandler(ClusterHandler):
    """List and create new clusters

    GET /clusters : list current clusters
    POST / clusters (JSON body) : create a new cluster"""

    @web.authenticated
    def get(self):
        # currently reloads everything from disk. Is that what we want?
        clusters = self.cluster_manager.load_clusters(init_default_clusters=True)

        def sort_key(model):
            """Sort clusters

            order:
            - running
            - default profile
            - default cluster id
            """
            running = True if model.get("controller") else False
            profile = model['cluster']['profile']
            cluster_id = model['cluster']['cluster_id']
            default_profile = profile == 'default'
            default_cluster = cluster_id == ''

            return (
                not running,
                not default_profile,
                not default_cluster,
                profile,
                cluster_id,
            )

        self.write(
            json.dumps(
                sorted(
                    (
                        self.cluster_model(key, cluster)
                        for key, cluster in clusters.items()
                    ),
                    key=sort_key,
                )
            )
        )

    @web.authenticated
    def post(self):
        body = self.get_json_body() or {}
        self.log.info(f"Creating cluster with {body}")
        # clear null values
        for key in ('profile', 'cluster_id', 'n'):
            if key in body and body[key] in {None, ''}:
                body.pop(key)
        if 'profile' in body and os.path.sep in body['profile']:
            # if it looks like a path, use profile_dir
            body['profile_dir'] = body.pop('profile')

        if (
            'profile' in body
            and 'cluster_id' not in body
            and f"{body['profile']}:" not in self.cluster_manager.clusters
        ):
            # if no cluster exists for a profile,
            # default for no cluster id instead of random
            body["cluster_id"] = ""

        cluster_id, cluster = self.cluster_manager.new_cluster(**body)
        self.write(json.dumps(self.cluster_model(cluster_id, cluster)))


class ClusterActionHandler(ClusterHandler):
    """Actions on a single cluster

    GET: read single cluster model
    POST: start
    PATCH: engines?
    DELETE: stop
    """

    def get_cluster(self, cluster_key):
        try:
            return self.cluster_manager.get_cluster(cluster_key)
        except KeyError:
            raise web.HTTPError(404, f"No such cluster: {cluster_key}")

    @web.authenticated
    async def post(self, cluster_key):
        cluster = self.get_cluster(cluster_key)
        body = self.get_json_body() or {}
        n = body.get("n", None)
        if n is not None and not isinstance(n, int):
            raise web.HTTPError(400, f"n must be an integer, not {n!r}")
        await cluster.start_cluster(n=n)
        self.write(json.dumps(self.cluster_model(cluster_key, cluster)))

    @web.authenticated
    async def get(self, cluster_key):
        cluster = self.get_cluster(cluster_key)
        self.write(json.dumps(self.cluster_model(cluster_key, cluster)))

    @web.authenticated
    async def delete(self, cluster_key):
        cluster = self.get_cluster(cluster_key)
        await cluster.stop_cluster()
        self.cluster_manager.remove_cluster(cluster_key)
        self.set_status(204)


# URL to handler mappings


_cluster_key_regex = (
    r"(?P[^\/]+)"  # there is almost no text that is invalid
)

default_handlers = [
    (r"/ipyparallel/clusters", ClusterListHandler),
    (
        rf"/ipyparallel/clusters/{_cluster_key_regex}",
        ClusterActionHandler,
    ),
]


def load_jupyter_server_extension(nbapp):
    """Load the nbserver extension"""

    nbapp.log.info("Loading IPython parallel extension")
    webapp = nbapp.web_app
    webapp.settings['cluster_manager'] = ClusterManager(parent=nbapp)

    base_url = webapp.settings['base_url']
    webapp.add_handlers(
        ".*$", [(ujoin(base_url, pat), handler) for pat, handler in default_handlers]
    )
ipyparallel-8.8.0/ipyparallel/nbextension/install.py000066400000000000000000000045761460376056100227630ustar00rootroot00000000000000"""Install the IPython clusters tab in the Jupyter notebook dashboard

Only applicable for notebook < 7
"""

# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
from jupyter_core.paths import jupyter_config_dir
from notebook.services.config import ConfigManager as FrontendConfigManager
from traitlets.config.manager import BaseJSONConfigManager


def install_extensions(enable=True, user=False):
    """Register ipyparallel clusters tab as notebook extensions

    Toggle with enable=True/False.
    """
    import notebook

    from ipyparallel.util import _v

    if _v(notebook.__version__) < _v('4.2'):
        return _install_extension_nb41(enable)

    from notebook.nbextensions import (
        disable_nbextension,
        enable_nbextension,
        install_nbextension_python,
    )
    from notebook.serverextensions import toggle_serverextension_python

    toggle_serverextension_python('ipyparallel', user=user)
    install_nbextension_python('ipyparallel', user=user)
    if enable:
        enable_nbextension('tree', 'ipyparallel/main', user=user)
    else:
        disable_nbextension('tree', 'ipyparallel/main')


def _install_extension_nb41(enable=True):
    """deprecated, pre-4.2 implementation of installing notebook extension"""
    # server-side
    server = BaseJSONConfigManager(config_dir=jupyter_config_dir())
    server_cfg = server.get('jupyter_notebook_config')
    app_cfg = server_cfg.get('NotebookApp', {})
    server_extensions = app_cfg.get('server_extensions', [])
    server_ext = 'ipyparallel.nbextension'
    server_changed = False
    if enable and server_ext not in server_extensions:
        server_extensions.append(server_ext)
        server_changed = True
    elif (not enable) and server_ext in server_extensions:
        server_extensions.remove(server_ext)
        server_changed = True
    if server_changed:
        server.update(
            'jupyter_notebook_config',
            {
                'NotebookApp': {
                    'server_extensions': server_extensions,
                }
            },
        )

    # frontend config (*way* easier because it's a dict)
    frontend = FrontendConfigManager()
    frontend.update(
        'tree',
        {
            'load_extensions': {
                'ipyparallel/main': enable or None,
            }
        },
    )


install_server_extension = install_extensions
ipyparallel-8.8.0/ipyparallel/nbextension/static/000077500000000000000000000000001460376056100222165ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/nbextension/static/clusterlist.css000066400000000000000000000001201460376056100252760ustar00rootroot00000000000000.action_col {
  text-align: right;
}

input.engine_num_input {
  width: 60px;
}
ipyparallel-8.8.0/ipyparallel/nbextension/static/clusterlist.js000066400000000000000000000144451460376056100251410ustar00rootroot00000000000000// Copyright (c) Jupyter Development Team.
// Distributed under the terms of the Modified BSD License.

define(["base/js/namespace", "jquery", "base/js/utils"], function (
  IPython,
  $,
  utils,
) {
  "use strict";

  // use base utils.ajax in notebook >= 4.3
  // for xsrf settings, etc.
  var ajax = utils.ajax || $.ajax;

  var ClusterList = function (selector, options) {
    this.selector = selector;
    if (this.selector !== undefined) {
      this.element = $(selector);
      this.style();
      this.bind_events();
    }
    options = options || {};
    this.options = options;
    this.base_url = options.base_url || utils.get_body_data("baseUrl");
    this.notebook_path =
      options.notebook_path || utils.get_body_data("notebookPath");
  };

  ClusterList.prototype.style = function () {
    $("#cluster_list").addClass("list_container");
    $("#cluster_toolbar").addClass("list_toolbar");
    $("#cluster_list_info").addClass("toolbar_info");
    $("#cluster_buttons").addClass("toolbar_buttons");
  };

  ClusterList.prototype.bind_events = function () {
    var that = this;
    $("#refresh_cluster_list").click(function () {
      that.load_list();
    });
  };

  ClusterList.prototype.load_list = function () {
    var settings = {
      processData: false,
      cache: false,
      type: "GET",
      dataType: "json",
      success: $.proxy(this.load_list_success, this),
      error: utils.log_ajax_error,
    };
    var url = utils.url_join_encode(this.base_url, "ipyparallel/clusters");
    ajax(url, settings);
  };

  ClusterList.prototype.clear_list = function () {
    this.element.children(".list_item").remove();
  };

  ClusterList.prototype.load_list_success = function (data, status, xhr) {
    this.clear_list();
    var len = data.length;
    for (var cluster of data) {
      var element = $("
"); var item = new ClusterItem(element, cluster, this, this.options); element.data("item", item); this.element.append(element); } }; var ClusterItem = function (element, cluster, cluster_list, options) { this.element = $(element); this.cluster_list = cluster_list; this.base_url = options.base_url || utils.get_body_data("baseUrl"); this.notebook_path = options.notebook_path || utils.get_body_data("notebookPath"); this.update_state(cluster); this.style(); }; ClusterItem.prototype.style = function () { this.element.addClass("list_item").addClass("row"); }; ClusterItem.prototype.update_state = function (cluster) { this.cluster = cluster; if (cluster.controller && cluster.controller.state) { this.state_running(); } else { this.state_stopped(); } }; ClusterItem.prototype.state_stopped = function () { var that = this; var profile_col = $("
") .addClass("profile_col col-xs-2") .text(this.cluster.cluster.profile); var cluster_id_col = $("
") .addClass("cluster_id_col col-xs-2") .text(this.cluster.cluster.cluster_id); var status_col = $("
") .addClass("status_col col-xs-3") .text("stopped"); var engines_col = $("
").addClass("engine_col col-xs-3"); var input = $("") .attr("type", "number") .attr("min", 1) .attr("style", "width: 100%") .addClass("engine_num_input form-control"); engines_col.append(input); var start_button = $("', " ", "
", "
", '
', '
', '
profile
', '
cluster id
', '
status
', '
# of engines
', '
action
', "
", "
", "
", ].join("\n"), ); function load() { if (!IPython.notebook_list) return; var base_url = IPython.notebook_list.base_url; // hide the deprecated clusters tab $("#tabs").find('[href="#clusters"]').hide(); $("head").append( $("") .attr("rel", "stylesheet") .attr("type", "text/css") .attr("href", base_url + "nbextensions/ipyparallel/clusterlist.css"), ); $(".tab-content").append(cluster_html); $("#tabs").append( $("
  • ").append( $("") .attr("href", "#ipyclusters") .attr("data-toggle", "tab") .text("IPython Clusters") .click(function (e) { window.history.pushState(null, null, "#ipyclusters"); }), ), ); var cluster_list = new clusterlist.ClusterList("#cluster_list", { base_url: IPython.notebook_list.base_url, notebook_path: IPython.notebook_list.notebook_path, }); cluster_list.load_list(); if (document.location.hash === "#ipyclusters") { // focus clusters tab once it exists // since it won't have been created when the browser first handles the document hash $("[href='#ipyclusters']").tab("show"); } } return { load_ipython_extension: load, }; }); ipyparallel-8.8.0/ipyparallel/serialize/000077500000000000000000000000001460376056100203625ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/serialize/__init__.py000066400000000000000000000010411460376056100224670ustar00rootroot00000000000000from .canning import ( Reference, can, can_map, uncan, uncan_map, use_cloudpickle, use_dill, use_pickle, ) from .serialize import ( PrePickled, deserialize_object, pack_apply_message, serialize_object, unpack_apply_message, ) __all__ = ( 'Reference', 'PrePickled', 'can_map', 'uncan_map', 'can', 'uncan', 'use_dill', 'use_cloudpickle', 'use_pickle', 'serialize_object', 'deserialize_object', 'pack_apply_message', 'unpack_apply_message', ) ipyparallel-8.8.0/ipyparallel/serialize/canning.py000066400000000000000000000350151460376056100223550ustar00rootroot00000000000000"""Pickle-related utilities..""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import copy import functools import pickle import sys from types import FunctionType from traitlets import import_item from traitlets.log import get_logger from . import codeutil # noqa This registers a hook when it's imported def _get_cell_type(a=None): """the type of a closure cell doesn't seem to be importable, so just create one """ def inner(): return a return type(inner.__closure__[0]) cell_type = _get_cell_type() # ------------------------------------------------------------------------------- # Functions # ------------------------------------------------------------------------------- def interactive(f): """decorator for making functions appear as interactively defined. This results in the function being linked to the user_ns as globals() instead of the module globals(). """ # build new FunctionType, so it can have the right globals # interactive functions never have closures, that's kind of the point if isinstance(f, FunctionType): mainmod = __import__('__main__') f = FunctionType( f.__code__, mainmod.__dict__, f.__name__, f.__defaults__, ) # associate with __main__ for uncanning f.__module__ = '__main__' return f def use_dill(): """use dill to expand serialization support adds support for object methods and closures to serialization. """ import dill from . import serialize serialize.pickle = dill # disable special function handling, let dill take care of it can_map.pop(FunctionType, None) def use_cloudpickle(): """use cloudpickle to expand serialization support adds support for object methods and closures to serialization. """ import cloudpickle from . import serialize serialize.pickle = cloudpickle # disable special function handling, let cloudpickle take care of it can_map.pop(FunctionType, None) def use_pickle(): """revert to using stdlib pickle Reverts custom serialization enabled by use_dill|cloudpickle. """ from . import serialize serialize.pickle = pickle # restore special function handling can_map[FunctionType] = _original_can_map[FunctionType] # ------------------------------------------------------------------------------- # Classes # ------------------------------------------------------------------------------- class CannedObject: def __init__(self, obj, keys=[], hook=None): """can an object for safe pickling Parameters ---------- obj The object to be canned keys : list (optional) list of attribute names that will be explicitly canned / uncanned hook : callable (optional) An optional extra callable, which can do additional processing of the uncanned object. large data may be offloaded into the buffers list, used for zero-copy transfers. """ self.keys = keys self.obj = copy.copy(obj) self.hook = can(hook) for key in keys: setattr(self.obj, key, can(getattr(obj, key))) self.buffers = [] def get_object(self, g=None): if g is None: g = {} obj = self.obj for key in self.keys: setattr(obj, key, uncan(getattr(obj, key), g)) if self.hook: self.hook = uncan(self.hook, g) self.hook(obj, g) return self.obj class Reference(CannedObject): """object for wrapping a remote reference by name.""" def __init__(self, name): if not isinstance(name, str): raise TypeError("illegal name: %r" % name) self.name = name self.buffers = [] def __repr__(self): return "" % self.name def get_object(self, g=None): if g is None: g = {} return eval(self.name, g) class CannedCell(CannedObject): """Can a closure cell""" def __init__(self, cell): self.cell_contents = can(cell.cell_contents) def get_object(self, g=None): cell_contents = uncan(self.cell_contents, g) def inner(): return cell_contents return inner.__closure__[0] class CannedFunction(CannedObject): def __init__(self, f): self._check_type(f) self.code = f.__code__ if f.__defaults__: self.defaults = [can(fd) for fd in f.__defaults__] else: self.defaults = None if f.__kwdefaults__: self.kwdefaults = can_dict(f.__kwdefaults__) else: self.kwdefaults = None if f.__annotations__: self.annotations = can_dict(f.__annotations__) else: self.annotations = None closure = f.__closure__ if closure: self.closure = tuple(can(cell) for cell in closure) else: self.closure = None self.module = f.__module__ or '__main__' self.__name__ = f.__name__ self.buffers = [] def _check_type(self, obj): assert isinstance(obj, FunctionType), "Not a function type" def get_object(self, g=None): # try to load function back into its module: if not self.module.startswith('__'): __import__(self.module) g = sys.modules[self.module].__dict__ if g is None: g = {} if self.defaults: defaults = tuple(uncan(cfd, g) for cfd in self.defaults) else: defaults = None if self.kwdefaults: kwdefaults = uncan_dict(self.kwdefaults) else: kwdefaults = None if self.annotations: annotations = uncan_dict(self.annotations) else: annotations = {} if self.closure: closure = tuple(uncan(cell, g) for cell in self.closure) else: closure = None newFunc = FunctionType(self.code, g, self.__name__, defaults, closure) if kwdefaults: newFunc.__kwdefaults__ = kwdefaults if annotations: newFunc.__annotations__ = annotations return newFunc class CannedPartial(CannedObject): def __init__(self, f): self._check_type(f) self.func = can(f.func) self.args = [can(a) for a in f.args] self.keywords = {k: can(v) for k, v in f.keywords.items()} self.buffers = [] self.arg_buffer_counts = [] self.keyword_buffer_counts = {} # consolidate buffers for canned_arg in self.args: if not isinstance(canned_arg, CannedObject): self.arg_buffer_counts.append(0) continue self.arg_buffer_counts.append(len(canned_arg.buffers)) self.buffers.extend(canned_arg.buffers) canned_arg.buffers = [] for key in sorted(self.keywords): canned_kwarg = self.keywords[key] if not isinstance(canned_kwarg, CannedObject): continue self.keyword_buffer_counts[key] = len(canned_kwarg.buffers) self.buffers.extend(canned_kwarg.buffers) canned_kwarg.buffers = [] def _check_type(self, obj): if not isinstance(obj, functools.partial): raise ValueError("Not a functools.partial: %r" % obj) def get_object(self, g=None): if g is None: g = {} if self.buffers: # reconstitute buffers for canned_arg, buf_count in zip(self.args, self.arg_buffer_counts): if not buf_count: continue canned_arg.buffers = self.buffers[:buf_count] self.buffers = self.buffers[buf_count:] for key in sorted(self.keyword_buffer_counts): buf_count = self.keyword_buffer_counts[key] canned_kwarg = self.keywords[key] canned_kwarg.buffers = self.buffers[:buf_count] self.buffers = self.buffers[buf_count:] assert len(self.buffers) == 0 args = [uncan(a, g) for a in self.args] keywords = {k: uncan(v, g) for k, v in self.keywords.items()} func = uncan(self.func, g) return functools.partial(func, *args, **keywords) class CannedClass(CannedObject): def __init__(self, cls): self._check_type(cls) self.name = cls.__name__ self.old_style = not isinstance(cls, type) self._canned_dict = {} for k, v in cls.__dict__.items(): if k not in ('__weakref__', '__dict__'): self._canned_dict[k] = can(v) if self.old_style: mro = [] else: mro = cls.mro() self.parents = [can(c) for c in mro[1:]] self.buffers = [] def _check_type(self, obj): assert isinstance(obj, type), "Not a class type" def get_object(self, g=None): parents = tuple(uncan(p, g) for p in self.parents) return type(self.name, parents, uncan_dict(self._canned_dict, g=g)) class CannedArray(CannedObject): def __init__(self, obj): from numpy import ascontiguousarray self.shape = obj.shape self.dtype = obj.dtype.descr if obj.dtype.fields else obj.dtype.str self.pickled = False if sum(obj.shape) == 0: self.pickled = True elif obj.dtype == 'O': # can't handle object dtype with buffer approach self.pickled = True elif obj.dtype.fields and any( dt == 'O' for dt, sz in obj.dtype.fields.values() ): self.pickled = True if self.pickled: # just pickle it from . import serialize self.buffers = [serialize.pickle.dumps(obj, serialize.PICKLE_PROTOCOL)] else: # ensure contiguous obj = ascontiguousarray(obj, dtype=None) self.buffers = [memoryview(obj)] def get_object(self, g=None): from numpy import frombuffer data = self.buffers[0] if self.pickled: from . import serialize # we just pickled it return serialize.pickle.loads(data) else: return frombuffer(data, dtype=self.dtype).reshape(self.shape) class CannedBytes(CannedObject): def __init__(self, obj): self.buffers = [obj] def get_object(self, g=None): data = self.buffers[0] return self.wrap(data) @staticmethod def wrap(data): if isinstance(data, bytes): return data else: return memoryview(data).tobytes() class CannedMemoryView(CannedBytes): wrap = memoryview CannedBuffer = CannedMemoryView # ------------------------------------------------------------------------------- # Functions # ------------------------------------------------------------------------------- def _import_mapping(mapping, original=None): """import any string-keys in a type mapping""" log = get_logger() log.debug("Importing canning map") for key, value in list(mapping.items()): if isinstance(key, str): try: cls = import_item(key) except Exception: if original and key not in original: # only message on user-added classes log.error("canning class not importable: %r", key, exc_info=True) mapping.pop(key) else: mapping[cls] = mapping.pop(key) def istype(obj, check): """like isinstance(obj, check), but strict This won't catch subclasses. """ if isinstance(check, tuple): for cls in check: if type(obj) is cls: return True return False else: return type(obj) is check def can(obj): """prepare an object for pickling""" import_needed = False for cls, canner in can_map.items(): if isinstance(cls, str): import_needed = True break elif istype(obj, cls): return canner(obj) if import_needed: # perform can_map imports, then try again # this will usually only happen once _import_mapping(can_map, _original_can_map) return can(obj) return obj def can_class(obj): if isinstance(obj, type) and obj.__module__ == '__main__': return CannedClass(obj) else: return obj def can_dict(obj): """can the *values* of a dict""" if istype(obj, dict): newobj = {} for k, v in obj.items(): newobj[k] = can(v) return newobj else: return obj sequence_types = (list, tuple, set) def can_sequence(obj): """can the elements of a sequence""" if istype(obj, sequence_types): t = type(obj) return t([can(i) for i in obj]) else: return obj def uncan(obj, g=None): """invert canning""" import_needed = False for cls, uncanner in uncan_map.items(): if isinstance(cls, str): import_needed = True break elif isinstance(obj, cls): return uncanner(obj, g) if import_needed: # perform uncan_map imports, then try again # this will usually only happen once _import_mapping(uncan_map, _original_uncan_map) return uncan(obj, g) return obj def uncan_dict(obj, g=None): if istype(obj, dict): newobj = {} for k, v in obj.items(): newobj[k] = uncan(v, g) return newobj else: return obj def uncan_sequence(obj, g=None): if istype(obj, sequence_types): t = type(obj) return t([uncan(i, g) for i in obj]) else: return obj def _uncan_dependent_hook(dep, g=None): dep.check_dependency() def can_dependent(obj): return CannedObject(obj, keys=('f', 'df'), hook=_uncan_dependent_hook) # ------------------------------------------------------------------------------- # API dictionaries # ------------------------------------------------------------------------------- # These dicts can be extended for custom serialization of new objects can_map = { 'numpy.ndarray': CannedArray, FunctionType: CannedFunction, functools.partial: CannedPartial, bytes: CannedBytes, memoryview: CannedMemoryView, cell_type: CannedCell, type: can_class, 'ipyparallel.dependent': can_dependent, } uncan_map = { CannedObject: lambda obj, g: obj.get_object(g), dict: uncan_dict, } # for use in _import_mapping: _original_can_map = can_map.copy() _original_uncan_map = uncan_map.copy() ipyparallel-8.8.0/ipyparallel/serialize/codeutil.py000066400000000000000000000037161460376056100225530ustar00rootroot00000000000000"""Utilities to enable code objects to be pickled. Any process that import this module will be able to pickle code objects. This includes the func_code attribute of any function. Once unpickled, new functions can be built using new.function(code, globals()). Eventually we need to automate all of this so that functions themselves can be pickled. Reference: A. Tremols, P Cogolo, "Python Cookbook," p 302-305 """ # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import copyreg import inspect import sys import types def code_ctor(*args): return types.CodeType(*args) # map CodeType constructor args to code co_ attribute names # (they _almost_ all match, and new ones probably will) _code_attr_map = { "codestring": "code", "constants": "consts", } # pass every supported arg to the code constructor # this should be more forward-compatible # (broken on pypy: https://github.com/ipython/ipyparallel/issues/845) if sys.version_info >= (3, 10) and not hasattr(sys, "pypy_version_info"): _code_attr_names = tuple( _code_attr_map.get(name, name) for name, param in inspect.signature(types.CodeType).parameters.items() if param.POSITIONAL_ONLY or param.POSITIONAL_OR_KEYWORD ) else: # can't inspect types.CodeType on Python < 3.10 _code_attr_names = [ "argcount", "kwonlyargcount", "nlocals", "stacksize", "flags", "code", "consts", "names", "varnames", "filename", "name", "firstlineno", "lnotab", "freevars", "cellvars", ] if hasattr(types.CodeType, "co_posonlyargcount"): _code_attr_names.insert(1, "posonlyargcount") _code_attr_names = tuple(_code_attr_names) def reduce_code(obj): """codeobject reducer""" return code_ctor, tuple(getattr(obj, f'co_{name}') for name in _code_attr_names) copyreg.pickle(types.CodeType, reduce_code) ipyparallel-8.8.0/ipyparallel/serialize/serialize.py000066400000000000000000000144241460376056100227300ustar00rootroot00000000000000"""serialization utilities for apply messages""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import pickle try: PICKLE_PROTOCOL = pickle.DEFAULT_PROTOCOL except AttributeError: PICKLE_PROTOCOL = pickle.HIGHEST_PROTOCOL from itertools import chain from jupyter_client.session import MAX_BYTES, MAX_ITEMS from .canning import ( CannedObject, can, can_sequence, istype, sequence_types, uncan, uncan_sequence, ) # ----------------------------------------------------------------------------- # Serialization Functions # ----------------------------------------------------------------------------- class PrePickled: """Wrapper for a pre-pickled object Used for pre-emptively pickling re-used objects to avoid pickling the same object several times. """ def __init__(self, obj): self.buffers = serialize_object(obj) def _nbytes(buf): """Return byte-size of a memoryview or buffer""" if isinstance(buf, memoryview): return buf.nbytes else: # not a memoryview, raw bytes return len(buf) def _extract_buffers(obj, threshold=MAX_BYTES): """extract buffers larger than a certain threshold""" buffers = [] if isinstance(obj, CannedObject) and obj.buffers: for i, buf in enumerate(obj.buffers): nbytes = _nbytes(buf) if nbytes > threshold: # buffer larger than threshold, prevent pickling obj.buffers[i] = None buffers.append(buf) # buffer too small for separate send, coerce to bytes # because pickling buffer objects just results in broken pointers elif isinstance(buf, memoryview): obj.buffers[i] = buf.tobytes() return buffers def _restore_buffers(obj, buffers): """restore buffers extracted by _extract_buffers""" if isinstance(obj, CannedObject) and obj.buffers: for i, buf in enumerate(obj.buffers): if buf is None: obj.buffers[i] = buffers.pop(0) def serialize_object(obj, buffer_threshold=MAX_BYTES, item_threshold=MAX_ITEMS): """Serialize an object into a list of sendable buffers. Parameters ---------- obj : object The object to be serialized buffer_threshold : int The threshold (in bytes) for pulling out data buffers to avoid pickling them. item_threshold : int The maximum number of items over which canning will iterate. Containers (lists, dicts) larger than this will be pickled without introspection. Returns ------- [bufs] : list of buffers representing the serialized object. """ if isinstance(obj, PrePickled): return obj.buffers[:] buffers = [] if istype(obj, sequence_types) and len(obj) < item_threshold: cobj = can_sequence(obj) for c in cobj: buffers.extend(_extract_buffers(c, buffer_threshold)) elif istype(obj, dict) and len(obj) < item_threshold: cobj = {} for k in sorted(obj): c = can(obj[k]) buffers.extend(_extract_buffers(c, buffer_threshold)) cobj[k] = c else: cobj = can(obj) buffers.extend(_extract_buffers(cobj, buffer_threshold)) buffers.insert(0, pickle.dumps(cobj, PICKLE_PROTOCOL)) return buffers def deserialize_object(buffers, g=None): """reconstruct an object serialized by serialize_object from data buffers. Parameters ---------- buffers : list of buffers/bytes g : globals to be used when uncanning Returns ------- (newobj, bufs) : unpacked object, and the list of remaining unused buffers. """ bufs = list(buffers) pobj = bufs.pop(0) canned = pickle.loads(pobj) if istype(canned, sequence_types) and len(canned) < MAX_ITEMS: for c in canned: _restore_buffers(c, bufs) newobj = uncan_sequence(canned, g) elif istype(canned, dict) and len(canned) < MAX_ITEMS: newobj = {} for k in sorted(canned): c = canned[k] _restore_buffers(c, bufs) newobj[k] = uncan(c, g) else: _restore_buffers(canned, bufs) newobj = uncan(canned, g) return newobj, bufs def pack_apply_message( f, args, kwargs, buffer_threshold=MAX_BYTES, item_threshold=MAX_ITEMS ): """pack up a function, args, and kwargs to be sent over the wire Each element of args/kwargs will be canned for special treatment, but inspection will not go any deeper than that. Any object whose data is larger than `threshold` will not have their data copied (only numpy arrays and bytes/buffers support zero-copy) Message will be a list of bytes/buffers of the format: [ cf, pinfo, , ] With length at least two + len(args) + len(kwargs) """ arg_bufs = list( chain.from_iterable( serialize_object(arg, buffer_threshold, item_threshold) for arg in args ) ) kw_keys = sorted(kwargs.keys()) kwarg_bufs = list( chain.from_iterable( serialize_object(kwargs[key], buffer_threshold, item_threshold) for key in kw_keys ) ) info = dict(nargs=len(args), narg_bufs=len(arg_bufs), kw_keys=kw_keys) msg = serialize_object(f) msg.append(pickle.dumps(info, PICKLE_PROTOCOL)) msg.extend(arg_bufs) msg.extend(kwarg_bufs) return msg def unpack_apply_message(bufs, g=None, copy=True): """unpack f,args,kwargs from buffers packed by pack_apply_message() Returns: original f,args,kwargs""" bufs = list(bufs) # allow us to pop assert len(bufs) >= 2, "not enough buffers!" f, bufs = deserialize_object(bufs, g) pinfo = bufs.pop(0) info = pickle.loads(pinfo) arg_bufs, kwarg_bufs = bufs[: info['narg_bufs']], bufs[info['narg_bufs'] :] args = [] for i in range(info['nargs']): arg, arg_bufs = deserialize_object(arg_bufs, g) args.append(arg) args = tuple(args) assert not arg_bufs, "Shouldn't be any arg bufs left over" kwargs = {} for key in info['kw_keys']: kwarg, kwarg_bufs = deserialize_object(kwarg_bufs, g) kwargs[key] = kwarg assert not kwarg_bufs, "Shouldn't be any kwarg bufs left over" return f, args, kwargs ipyparallel-8.8.0/ipyparallel/tests/000077500000000000000000000000001460376056100175355ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/tests/__init__.py000066400000000000000000000101311460376056100216420ustar00rootroot00000000000000"""toplevel setup/teardown for parallel tests.""" import asyncio import os import time from subprocess import Popen from IPython.paths import get_ipython_dir from ipyparallel import Client, error from ipyparallel.cluster.launcher import ( SIGKILL, LocalProcessLauncher, ProcessStateError, ipcontroller_cmd_argv, ipengine_cmd_argv, ) # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. # globals launchers = [] # Launcher class class TestProcessLauncher(LocalProcessLauncher): """subclass LocalProcessLauncher, to prevent extra sockets and threads being created on Windows""" def start(self): if self.state == 'before': # Store stdout & stderr to show with failing tests. # This is defined in IPython.testing.iptest self.process = Popen(self.args, env=os.environ, cwd=self.work_dir) self.notify_start(self.process.pid) self.poll = self.process.poll else: s = 'The process was already started and has state: %r' % self.state raise ProcessStateError(s) # show tracebacks for RemoteErrors class RemoteErrorWithTB(error.RemoteError): def __str__(self): s = super().__str__() return '\n'.join([s, self.traceback or '']) # global setup/teardown def setup(): error.RemoteError = RemoteErrorWithTB cluster_dir = os.path.join(get_ipython_dir(), 'profile_iptest') engine_json = os.path.join(cluster_dir, 'security', 'ipcontroller-engine.json') client_json = os.path.join(cluster_dir, 'security', 'ipcontroller-client.json') for json in (engine_json, client_json): if os.path.exists(json): os.remove(json) cp = TestProcessLauncher() cp.cmd_and_args = ipcontroller_cmd_argv + [ '--profile=iptest', '--log-level=10', '--ping=250', '--dictdb', ] if os.environ.get("IPP_CONTROLLER_IP"): cp.cmd_and_args.append(f"--ip={os.environ['IPP_CONTROLLER_IP']}") cp.start() launchers.append(cp) tic = time.time() while not os.path.exists(engine_json) or not os.path.exists(client_json): if cp.poll() is not None: raise RuntimeError("The test controller exited with status %s" % cp.poll()) elif time.time() - tic > 15: raise RuntimeError("Timeout waiting for the test controller to start.") time.sleep(0.1) add_engines(1) def add_engines(n=1, profile='iptest', total=False): """add a number of engines to a given profile. If total is True, then already running engines are counted, and only the additional engines necessary (if any) are started. """ rc = Client(profile=profile) base = len(rc) if total: n = max(n - base, 0) eps = [] for i in range(n): ep = TestProcessLauncher() ep.cmd_and_args = ipengine_cmd_argv + [ '--profile=%s' % profile, '--InteractiveShell.colors=nocolor', '--log-level=10', ] ep.start() launchers.append(ep) eps.append(ep) tic = time.time() while len(rc) < base + n: if any([ep.poll() is not None for ep in eps]): raise RuntimeError("A test engine failed to start.") elif time.time() - tic > 15: raise RuntimeError("Timeout waiting for engines to connect.") time.sleep(0.1) rc.close() return eps def teardown(): try: time.sleep(1) except KeyboardInterrupt: return while launchers: p = launchers.pop() if p.poll() is None: try: f = p.stop() if f: asyncio.run(f) except Exception as e: print(e) pass if p.poll() is None: try: time.sleep(0.25) except KeyboardInterrupt: return if p.poll() is None: try: print('cleaning up test process...') p.signal(SIGKILL) except Exception: print("couldn't shutdown process: ", p) ipyparallel-8.8.0/ipyparallel/tests/_test_startup_crash.py000066400000000000000000000001311460376056100241620ustar00rootroot00000000000000import os import time time.sleep(int(os.environ.get("CRASH_DELAY") or "1")) os._exit(1) ipyparallel-8.8.0/ipyparallel/tests/clienttest.py000066400000000000000000000131011460376056100222610ustar00rootroot00000000000000"""base class for parallel client tests""" import os import signal import sys import time import warnings from contextlib import contextmanager import pytest import zmq from decorator import decorator from ipyparallel import Client, error from ipyparallel.tests import add_engines, launchers # simple tasks for use in apply tests def segfault(): """this will segfault""" import ctypes ctypes.memset(-1, 0, 1) def crash(): """Ungracefully exit the process""" os._exit(1) def conditional_crash(condition): """Ungracefully exit the process""" if condition: crash() def wait(n): """sleep for a time""" import time time.sleep(n) return n def raiser(eclass): """raise an exception""" raise eclass() def generate_output(): """function for testing output publishes two outputs of each type, and returns a rich displayable object. """ from IPython.core.display import HTML, Math, display print("stdout") print("stderr", file=sys.stderr) display(HTML("HTML")) print("stdout2") print("stderr2", file=sys.stderr) display(Math(r"\alpha=\beta")) return Math("42") # test decorator for skipping tests when libraries are unavailable def skip_without(*names): """skip a test if some names are not importable""" @decorator def skip_without_names(f, *args, **kwargs): """decorator to skip tests in the absence of numpy, etc.""" for name in names: try: __import__(name) except ImportError: pytest.skip("Test requires %s" % name) except Exception as e: warnings.warn(f"Unexpected exception importing {name}: {e}") pytest.skip("Test requires %s" % name) return f(*args, **kwargs) return skip_without_names @contextmanager def raises_remote(etype): if isinstance(etype, str): # allow Exception or 'Exception' expected_ename = etype else: expected_ename = etype.__name__ try: try: yield except error.AlreadyDisplayedError as e: e.original_error.raise_exception() except error.CompositeError as e: e.raise_exception() except error.RemoteError as e: assert ( expected_ename == e.ename ), f"Should have raised {expected_ename}, but raised {e.ename}" else: pytest.fail("should have raised a RemoteError") # ------------------------------------------------------------------------------- # Classes # ------------------------------------------------------------------------------- @pytest.mark.usefixtures("cluster") class ClusterTestCase: timeout = 10 engine_count = 2 def add_engines(self, n=1, block=True): """add multiple engines to our cluster""" self.engines.extend(add_engines(n)) if block: self.wait_on_engines() def minimum_engines(self, n=1, block=True): """add engines until there are at least n connected""" self.engines.extend(add_engines(n, total=True)) if block: self.wait_on_engines() def wait_on_engines(self, timeout=5): """wait for our engines to connect.""" n = len(self.engines) + self.base_engine_count self.client.wait_for_engines(n, timeout=timeout) assert not len(self.client.ids) < n, "waiting for engines timed out" def client_wait(self, client, jobs=None, timeout=-1): """my wait wrapper, sets a default finite timeout to avoid hangs""" if timeout is None or timeout < 0: timeout = self.timeout return Client.wait(client, jobs, timeout) def connect_client(self): """connect a client with my Context, and track its sockets for cleanup""" c = Client(profile='iptest', context=self.context) c.wait = lambda *a, **kw: self.client_wait(c, *a, **kw) return c def assertRaisesRemote(self, etype, f, *args, **kwargs): with raises_remote(etype): f(*args, **kwargs) def _wait_for(self, f, timeout=10): """wait for a condition""" tic = time.time() while time.time() <= tic + timeout: if f(): return time.sleep(0.1) if not f(): print("Warning: Awaited condition never arrived") test_timeout = 30 def setup_method(self): self.context = zmq.Context.instance() if hasattr(signal, 'SIGALRM'): # use sigalarm for test timeout def _sigalarm(sig, frame): raise TimeoutError( f"test did not finish in {self.test_timeout} seconds" ) signal.signal(signal.SIGALRM, _sigalarm) signal.alarm(self.test_timeout) add_engines(self.engine_count, total=True) self.client = self.connect_client() # start every test with clean engine namespaces: self.client.clear(block=True) self.base_engine_count = len(self.client.ids) self.engines = [] def teardown_method(self): if len(self.client): self.client[:].use_pickle() # self.client.clear(block=True) # close fds: for e in filter(lambda e: e.poll() is not None, launchers): launchers.remove(e) # allow flushing of incoming messages to prevent crash on socket close self.client.wait(timeout=2) self.client.close() if hasattr(signal, 'SIGALRM'): signal.alarm(0) signal.signal(signal.SIGALRM, signal.SIG_DFL) self.context.destroy() ipyparallel-8.8.0/ipyparallel/tests/conftest.py000066400000000000000000000143021460376056100217340ustar00rootroot00000000000000"""pytest fixtures""" import inspect import logging import os import sys from contextlib import contextmanager from pathlib import Path from subprocess import check_call, check_output from tempfile import NamedTemporaryFile, TemporaryDirectory from unittest import mock import IPython.paths import pytest import zmq from IPython.core.profiledir import ProfileDir from IPython.terminal.interactiveshell import TerminalInteractiveShell from traitlets.config import Config import ipyparallel as ipp from . import setup, teardown def default_config(): """Return a config object with good defaults for testing.""" config = Config() config.TerminalInteractiveShell.colors = 'NoColor' config.TerminalTerminalInteractiveShell.term_title = (False,) config.TerminalInteractiveShell.autocall = 0 f = NamedTemporaryFile(suffix='test_hist.sqlite', delete=False) config.HistoryManager.hist_file = str(Path(f.name)) f.close() config.HistoryManager.db_cache_size = 10000 return config @contextmanager def temporary_ipython_dir(prefix=None): # FIXME: cleanup has issues on Windows # this is *probably* a real bug of holding open files, # but it is preventing feedback about test failures td_obj = TemporaryDirectory(suffix=".ipython", prefix=prefix) td = td_obj.name with mock.patch.dict(os.environ, {"IPYTHONDIR": td}): assert IPython.paths.get_ipython_dir() == td pd = ProfileDir.create_profile_dir_by_name(td, name="default") # configure fast heartbeats for quicker tests with small numbers of local engines with open(os.path.join(pd.location, "ipcontroller_config.py"), "w") as f: f.write("c.HeartMonitor.period = 200") try: yield td finally: try: td_obj.cleanup() except Exception as e: print(f"Failed to cleanup {td}: {e}", file=sys.stderr) @pytest.fixture(autouse=True, scope="module") def ipython_dir(request): with temporary_ipython_dir() as ipython_dir: yield ipython_dir def pytest_collection_modifyitems(items): """This function is automatically run by pytest passing all collected test functions. We use it to add asyncio marker to all async tests and assert we don't use test functions that are async generators which wouldn't make sense. """ for item in items: if inspect.iscoroutinefunction(item.obj): item.add_marker('asyncio') assert not inspect.isasyncgenfunction(item.obj) @pytest.fixture(scope="module") def cluster(request, ipython_dir): """Setup IPython parallel cluster""" setup() try: yield finally: teardown() @pytest.fixture(scope='module') def ipython(ipython_dir): config = default_config() config.TerminalInteractiveShell.simple_prompt = True shell = TerminalInteractiveShell.instance(config=config) yield shell TerminalInteractiveShell.clear_instance() @pytest.fixture() def ipython_interactive(request, ipython): """Activate IPython's builtin hooks for the duration of the test scope. """ with ipython.builtin_trap: yield ipython @pytest.fixture(autouse=True) def Context(): ctx = zmq.Context.instance() try: yield ctx finally: ctx.destroy() @pytest.fixture def engine_launcher_class(): """override to test an alternate launcher""" return 'local' @pytest.fixture def controller_launcher_class(): """override to test an alternate launcher""" return 'local' @pytest.fixture def cluster_config(): """Override to set default cluster config""" return Config() @pytest.fixture def Cluster( request, ipython_dir, controller_launcher_class, engine_launcher_class, cluster_config, ): """Fixture for instantiating Clusters""" def ClusterConstructor(**kwargs): log = logging.getLogger(__file__) log.setLevel(logging.DEBUG) log.handlers = [logging.StreamHandler(sys.stdout)] kwargs['log'] = log kwargs.setdefault("controller", controller_launcher_class) kwargs.setdefault("engines", engine_launcher_class) cfg = kwargs.setdefault("config", cluster_config) cfg.EngineLauncher.engine_args = ['--log-level=10'] cfg.ControllerLauncher.controller_args = ['--log-level=10'] kwargs.setdefault("controller_args", ['--ping=250']) kwargs.setdefault("load_profile", False) c = ipp.Cluster(**kwargs) if not kwargs['load_profile']: assert c.config == cfg request.addfinalizer(c.stop_cluster_sync) return c yield ClusterConstructor @pytest.fixture(scope="session") def ssh_dir(request): """Start the ssh service with docker-compose Fixture returns the directory """ repo_root = os.path.abspath(os.path.join(ipp.__file__, os.pardir, os.pardir)) ci_directory = os.environ.get("CI_DIR", os.path.join(repo_root, 'ci')) ssh_dir = os.path.join(ci_directory, "ssh") # only run ssh test if service was started before try: out = check_output(['docker-compose', 'ps', '-q'], cwd=ssh_dir) except Exception: pytest.skip("Needs docker compose") else: if not out.strip(): pytest.skip("ssh service not running") # below is necessary for building/starting service as part of fixture # currently we use whether the service is already started to decide whether to run the tests # # build image # check_call(["docker-compose", "build"], cwd=ssh_dir) # # launch service # check_call(["docker-compose", "up", "-d"], cwd=ssh_dir) # # shutdown service when we exit # request.addfinalizer(lambda: check_call(["docker-compose", "down"], cwd=ssh_dir)) return ssh_dir @pytest.fixture def ssh_key(tmpdir, ssh_dir): key_file = tmpdir.join("id_rsa") check_call( # this should be `docker compose cp sshd:...` # but docker-compose 1.x doesn't support `cp` yet [ 'docker', 'cp', 'ssh_sshd_1:/home/ciuser/.ssh/id_rsa', key_file, ], cwd=ssh_dir, ) os.chmod(key_file, 0o600) with key_file.open('r') as f: assert 'PRIVATE KEY' in f.readline() return str(key_file) ipyparallel-8.8.0/ipyparallel/tests/test_apps.py000066400000000000000000000201001460376056100221020ustar00rootroot00000000000000"""Test CLI application behavior""" import glob import json import os import sys import time import types from subprocess import Popen, check_call, check_output from unittest.mock import MagicMock, create_autospec, patch import pytest import zmq from ipykernel import iostream, kernelapp from ipykernel.ipkernel import IPythonKernel from IPython.core.profiledir import ProfileDir from tornado import ioloop from zmq.eventloop import zmqstream import ipyparallel as ipp import ipyparallel.engine.app def _get_output(cmd): out = check_output(cmd) if isinstance(out, bytes): out = out.decode('utf8', 'replace') return out @pytest.mark.parametrize( "submod", [ "cluster", "engine", "controller", ], ) def test_version(submod): out = _get_output([sys.executable, '-m', 'ipyparallel.%s' % submod, '--version']) assert out.strip() == ipyparallel.__version__ @pytest.mark.parametrize( "submod", [ "cluster", "engine", "controller", ], ) def test_help_all(submod): out = _get_output([sys.executable, '-m', 'ipyparallel.%s' % submod, '--help-all']) @pytest.mark.parametrize( "subcommand", [ "list", "engines", "start", "stop", "clean", ], ) def test_ipcluster_help_all(subcommand): out = _get_output( [sys.executable, '-m', 'ipyparallel.cluster', subcommand, '--help-all'] ) def bind_kernel(engineapp): app = MagicMock(spec=kernelapp.IPKernelApp) with patch.object( ipyparallel.engine.app, 'IPKernelApp', autospec=True ) as MockKernelApp: MockKernelApp.return_value = app app.shell_port = app.iopub_port = app.stdin_port = 0 app._bind_socket = types.MethodType(kernelapp.IPKernelApp._bind_socket, app) if hasattr(kernelapp.IPKernelApp, '_try_bind_socket'): app._try_bind_socket = types.MethodType( kernelapp.IPKernelApp._try_bind_socket, app, ) app.transport = 'tcp' app.ip = '127.0.0.1' app.init_heartbeat.return_value = None engineapp.bind_kernel() def test_bind_kernel(request): ctx = zmq.Context.instance() request.addfinalizer(ctx.destroy) class MockIPEngineApp(ipyparallel.engine.app.IPEngine): kernel = None context = ctx app = MockIPEngineApp() app.kernel_app = None app.kernel = MagicMock(spec=IPythonKernel) def socket_spec(): spec = create_autospec(zmq.Socket, instance=True) spec.FD = 2 return spec app.kernel.shell_streams = [ zmqstream.ZMQStream( socket=socket_spec(), io_loop=create_autospec(spec=ioloop.IOLoop, spec_set=True, instance=True), ) ] app.kernel.control_stream = zmqstream.ZMQStream( socket=socket_spec(), io_loop=create_autospec(spec=ioloop.IOLoop, spec_set=True, instance=True), ) # testing the case iopub_socket is not replaced with IOPubThread iopub_socket = socket_spec() app.kernel.iopub_socket = iopub_socket assert isinstance(app.kernel.iopub_socket, zmq.Socket) bind_kernel(app) assert ( app.kernel.iopub_socket.bind_to_random_port.called and app.kernel.iopub_socket.bind_to_random_port.call_count == 1 ) # testing the case iopub_socket is replaced with IOPubThread class TestIOPubThread(iostream.IOPubThread): socket = None iopub_socket.reset_mock() app.kernel_app = None app.kernel.iopub_socket = create_autospec( spec=TestIOPubThread, spec_set=True, instance=True ) app.kernel.iopub_socket.socket = iopub_socket assert isinstance(app.kernel.iopub_socket, iostream.IOPubThread) bind_kernel(app) assert ( app.kernel.iopub_socket.socket.bind_to_random_port.called and app.kernel.iopub_socket.socket.bind_to_random_port.call_count == 1 ) def ipcluster_list(*args): return check_output( [sys.executable, "-m", "ipyparallel.cluster", "list"] + list(args) ).decode("utf8", "replace") def test_ipcluster_list(Cluster): # no clusters out = ipcluster_list() assert len(out.splitlines()) == 1 out = ipcluster_list("-o", "json") assert json.loads(out) == [] with Cluster(n=2) as rc: out = ipcluster_list() head, *rest = out.strip().splitlines() assert len(rest) == 1 assert rest[0].split() == [ "default", rc.cluster.cluster_id, "True", "2", "Local", ] cluster_list = json.loads(ipcluster_list("-o", "json")) assert len(cluster_list) == 1 assert cluster_list[0]['cluster']['cluster_id'] == rc.cluster.cluster_id # after exit, back to no clusters out = ipcluster_list() assert len(out.splitlines()) == 1 out = ipcluster_list("-o", "json") assert json.loads(out) == [] @pytest.mark.parametrize("daemonize", (False, True)) def test_ipcluster_start_stop(request, ipython_dir, daemonize): default_profile = ProfileDir.find_profile_dir_by_name(ipython_dir) default_profile_dir = default_profile.location # cleanup the security directory to avoid leaking files from one test to the next def cleanup_security(): for f in glob.glob(os.path.join(default_profile.security_dir, "*.json")): print(f"Cleaning up {f}") try: os.remove(f) except Exception as e: print(f"Error removing {f}: {e}") request.addfinalizer(cleanup_security) n = 2 start_args = ["-n", str(n)] if daemonize: start_args.append("--daemonize") start = Popen( [sys.executable, "-m", "ipyparallel.cluster", "start", "--debug"] + start_args ) request.addfinalizer(start.terminate) if daemonize: # if daemonize, should exit after starting start.wait(30) else: # wait for file to appear # only need this if not daemonize cluster_file = ipp.Cluster( profile_dir=default_profile_dir, cluster_id="" ).cluster_file for i in range(100): if os.path.isfile(cluster_file) or start.poll() is not None: break else: time.sleep(0.1) assert os.path.isfile(cluster_file) # list should show a file out = ipcluster_list() assert len(out.splitlines()) == 2 # cluster running, try to connect with default args cluster = ipp.Cluster.from_file(log_level=10) try: with cluster.connect_client_sync() as rc: rc.wait_for_engines(n=2, timeout=60) rc[:].apply_async(os.getpid).get(timeout=10) except Exception: print("controller output") print(cluster.controller.get_output()) print("engine output") for engine_set in cluster.engines.values(): print(engine_set.get_output()) raise # stop with ipcluster stop check_call([sys.executable, "-m", "ipyparallel.cluster", "stop"]) # start should exit when this happens start.wait(30) # and ipcluster list should return empty out = ipcluster_list() assert len(out.splitlines()) == 1 # stop all succeeds even if there's nothing to stop check_call([sys.executable, "-m", "ipyparallel.cluster", "stop", "--all"]) def test_ipcluster_clean(ipython_dir): default_profile = ProfileDir.find_profile_dir_by_name(ipython_dir) default_profile_dir = default_profile.location log_file = os.path.join(default_profile.log_dir, "test.log") with open(log_file, "w") as f: f.write("logsssss") cluster_file = os.path.join(default_profile.security_dir, "cluster-abc.json") c = ipp.Cluster() with open(cluster_file, 'w') as f: json.dump(c.to_dict(), f) connection_file = os.path.join( default_profile.security_dir, "ipcontroller-client.json" ) with open(connection_file, 'w') as f: f.write("{}") check_call([sys.executable, "-m", "ipyparallel.cluster", "clean"]) assert not os.path.exists(log_file) assert not os.path.exists(cluster_file) assert not os.path.exists(connection_file) ipyparallel-8.8.0/ipyparallel/tests/test_async.py000066400000000000000000000016511460376056100222660ustar00rootroot00000000000000"""tests for async utilities""" import pytest from ipyparallel._async import AsyncFirst class A(AsyncFirst): a = 5 def sync_method(self): return 'sync' async def async_method(self): return 'async' def test_async_first_dir(): a = A() attrs = sorted(dir(a)) real_attrs = [a for a in attrs if not a.startswith("_")] assert real_attrs == ['a', 'async_method', 'async_method_sync', 'sync_method'] def test_getattr(): a = A() assert a.a == 5 with pytest.raises(AttributeError): a.a_sync with pytest.raises(AttributeError): a.some_other_sync with pytest.raises(AttributeError): a.sync_method_sync def test_sync_no_asyncio(): a = A() assert a.async_method_sync() == 'async' assert a._async_thread is None async def test_sync_asyncio(): a = A() assert a.async_method_sync() == 'async' assert a._async_thread is not None ipyparallel-8.8.0/ipyparallel/tests/test_asyncresult.py000066400000000000000000000456751460376056100235430ustar00rootroot00000000000000"""Tests for asyncresult.py""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import os import time from datetime import datetime import pytest from IPython.utils.io import capture_output import ipyparallel as ipp from ipyparallel import error from .clienttest import ClusterTestCase, raises_remote def wait(n): import time time.sleep(n) return n def echo(x): return x class TestAsyncResult(ClusterTestCase): def test_single_result_view(self): """various one-target views get the right value for single_result""" eid = self.client.ids[-1] ar = self.client[eid].apply_async(lambda: 42) assert ar.get() == 42 ar = self.client[[eid]].apply_async(lambda: 42) assert ar.get() == [42] ar = self.client[-1:].apply_async(lambda: 42) assert ar.get() == [42] def test_get_after_done(self): ar = self.client[-1].apply_async(lambda: 42) ar.wait() assert ar.ready() assert ar.get() == 42 assert ar.get() == 42 def test_get_before_done(self): ar = self.client[-1].apply_async(wait, 0.1) with pytest.raises(TimeoutError): ar.get(0) ar.wait(0) assert not ar.ready() assert ar.get() == 0.1 def test_get_after_error(self): ar = self.client[-1].apply_async(lambda: 1 / 0) ar.wait(10) with raises_remote(ZeroDivisionError): ar.get() with raises_remote(ZeroDivisionError): ar.get() with raises_remote(ZeroDivisionError): ar.get_dict() def test_get_dict(self): n = len(self.client) ar = self.client[:].apply_async(lambda: 5) assert ar.get() == [5] * n d = ar.get_dict() assert sorted(d.keys()) == sorted(self.client.ids) for eid, r in d.items(): assert r == 5 def test_get_dict_single(self): view = self.client[-1] for v in (list(range(5)), 5, ('abc', 'def'), 'string'): ar = view.apply_async(echo, v) assert ar.get() == v d = ar.get_dict() assert d == {view.targets: v} def test_get_dict_bad(self): v = self.client.load_balanced_view() amr = v.map_async(lambda x: x, range(len(self.client) * 2)) with pytest.raises(ValueError): amr.get_dict() def test_iter_amr(self): ar = self.client.load_balanced_view().map_async(wait, [0.125] * 5) for r in ar: assert r == 0.125 def test_iter_multi_result_ar(self): ar = self.client[:].apply(wait, 0.125) for r in ar: assert r == 0.125 def test_iter_error(self): amr = self.client[:].map_async(lambda x: 1 / (x - 2), range(5)) # iterating through failing AMR should raise RemoteError with raises_remote(ZeroDivisionError): list(amr) # so should get with raises_remote(ZeroDivisionError): amr.get() amr.wait(10) # test iteration again after everything is local with raises_remote(ZeroDivisionError): list(amr) def test_getattr(self): ar = self.client[:].apply_async(wait, 0.5) assert ar.completed == [None] * len(ar) with pytest.raises(AttributeError): ar._foo with pytest.raises(AttributeError): ar.__length_hint__() with pytest.raises(AttributeError): ar.foo assert not hasattr(ar, '__length_hint__') assert not hasattr(ar, 'foo') assert hasattr(ar, 'engine_id') ar.get(5) with pytest.raises(AttributeError): ar._foo with pytest.raises(AttributeError): ar.__length_hint__() with pytest.raises(AttributeError): ar.foo assert isinstance(ar.engine_id, list) assert ar.engine_id == ar['engine_id'] assert not hasattr(ar, '__length_hint__') assert not hasattr(ar, 'foo') assert hasattr(ar, 'engine_id') def test_getitem(self): ar = self.client[:].apply_async(wait, 0.5) assert ar['completed'] == [None] * len(ar) with pytest.raises(KeyError): ar['foo'] ar.get(5) with pytest.raises(KeyError): ar['foo'] assert isinstance(ar['completed'], list) assert ar.completed == ar['completed'] assert all(isinstance(dt, datetime) for dt in ar.completed) def test_single_result(self): ar = self.client[-1].apply_async(wait, 0.5) with pytest.raises(KeyError): ar['foo'] assert ar['completed'] is None assert ar.get(5) == 0.5 assert isinstance(ar['completed'], datetime) assert isinstance(ar.completed, datetime) assert ar.completed == ar['completed'] def test_abort(self): e = self.client[-1] ar = e.execute('import time; time.sleep(1)', block=False) ar2 = e.apply_async(lambda: 2) ar2.abort() with pytest.raises(error.TaskAborted): ar2.get() ar.get() def test_len(self): v = self.client.load_balanced_view() ar = v.map_async(lambda x: x, list(range(10))) assert len(ar) == 10 ar = v.apply_async(lambda x: x, list(range(10))) assert len(ar) == 1 ar = self.client[:].apply_async(lambda x: x, list(range(10))) assert len(ar) == len(self.client.ids) def test_wall_time_single(self): v = self.client.load_balanced_view() ar = v.apply_async(time.sleep, 0.25) with pytest.raises(TimeoutError): ar.wall_time ar.get(2) assert ar.wall_time < 1.0 assert ar.wall_time > 0.2 def test_wall_time_multi(self): self.minimum_engines(4) v = self.client[:] ar = v.apply_async(time.sleep, 0.25) with pytest.raises(TimeoutError): ar.wall_time ar.get(2) assert ar.wall_time < 1.0 assert ar.wall_time > 0.2 def test_serial_time_single(self): v = self.client.load_balanced_view() ar = v.apply_async(time.sleep, 0.25) with pytest.raises(TimeoutError): ar.serial_time ar.get(2) assert ar.serial_time < 1.0 assert ar.serial_time > 0.2 def test_serial_time_multi(self): self.minimum_engines(4) v = self.client[:] ar = v.apply_async(time.sleep, 0.25) with pytest.raises(TimeoutError): ar.serial_time ar.get(2) assert ar.serial_time < 2.0 assert ar.serial_time > 0.8 def test_elapsed_single(self): v = self.client.load_balanced_view() ar = v.apply_async(time.sleep, 0.25) while not ar.ready(): time.sleep(0.01) assert ar.elapsed < 1 assert ar.elapsed < 1 ar.get(2) def test_elapsed_multi(self): v = self.client[:] ar = v.apply_async(time.sleep, 0.25) while not ar.ready(): time.sleep(0.01) assert ar.elapsed < 1 assert ar.elapsed < 1 ar.get(2) def test_hubresult_timestamps(self): self.minimum_engines(4) v = self.client[:] ar = v.apply_async(time.sleep, 0.25) ar.get(2) rc2 = ipp.Client(profile='iptest') # must have try/finally to close second Client, otherwise # will have dangling sockets causing problems try: time.sleep(0.25) hr = rc2.get_result(ar.msg_ids) assert hr.elapsed > 0.0, "got bad elapsed: %s" % hr.elapsed hr.get(1) assert ( hr.wall_time < ar.wall_time + 0.2 ), f"got bad wall_time: {hr.wall_time} > {ar.wall_time}" assert hr.serial_time == ar.serial_time finally: rc2.close() def test_display_empty_streams_single(self): """empty stdout/err are not displayed (single result)""" self.minimum_engines(1) v = self.client[-1] ar = v.execute("print (5555)") ar.get(5) with capture_output() as io: ar.display_outputs() assert io.stderr == '' assert '5555\n' == io.stdout ar = v.execute("a=5") ar.get(5) with capture_output() as io: ar.display_outputs() assert io.stderr == '' assert io.stdout == '' def test_display_empty_streams_type(self): """empty stdout/err are not displayed (groupby type)""" self.minimum_engines(1) v = self.client[:] ar = v.execute("print (5555)") ar.get(5) with capture_output() as io: ar.display_outputs() assert io.stderr == '' assert io.stdout.count('5555'), len(v) == io.stdout assert '\n\n' not in io.stdout, io.stdout assert io.stdout.count('[stdout:'), len(v) == io.stdout ar = v.execute("a=5") ar.get(5) with capture_output() as io: ar.display_outputs() assert io.stderr == '' assert io.stdout == '' def test_display_empty_streams_engine(self): """empty stdout/err are not displayed (groupby engine)""" self.minimum_engines(1) v = self.client[:] ar = v.execute("print (5555)") ar.get(5) with capture_output() as io: ar.display_outputs('engine') assert io.stderr == '' assert io.stdout.count('5555'), len(v) == io.stdout assert '\n\n' not in io.stdout, io.stdout assert io.stdout.count('[stdout:'), len(v) == io.stdout ar = v.execute("a=5") ar.get(5) with capture_output() as io: ar.display_outputs('engine') assert io.stderr == '' assert io.stdout == '' def test_display_output_error(self): """display_outputs shows output on error""" self.minimum_engines(1) v = self.client[-1] ar = v.execute("print (5555)\n1/0") ar.get(5, return_exceptions=True) ar.wait_for_output(5) with capture_output() as io: ar.display_outputs() assert io.stderr == '' assert '5555\n' == io.stdout assert 'ZeroDivisionError' not in io.stdout def test_await_data(self): """asking for ar.data flushes outputs""" self.minimum_engines(1) v = self.client[-1] ar = v.execute( '\n'.join( [ "import time", "from ipyparallel.datapub import publish_data", "for i in range(5):", " publish_data(dict(i=i))", " time.sleep(0.1)", ] ), block=False, ) found = set() tic = time.time() # timeout after 10s while time.time() <= tic + 10: if ar.data: i = ar.data['i'] found.add(i) if i == 4: break time.sleep(0.05) ar.get(5) assert 4 in found assert len(found) > 1, ( "should have seen data multiple times, but got: %s" % found ) def test_not_single_result(self): save_build = self.client._build_targets def single_engine(*a, **kw): idents, targets = save_build(*a, **kw) return idents[:1], targets[:1] ids = single_engine('all')[1] self.client._build_targets = single_engine for targets in ('all', None, ids): dv = self.client.direct_view(targets=targets) ar = dv.apply_async(lambda: 5) assert ar.get(10) == [5] self.client._build_targets = save_build def test_owner_pop(self): self.minimum_engines(1) view = self.client[-1] ar = view.apply_async(lambda: 1) ar.get() ar.wait_for_output() msg_id = ar.msg_ids[0] assert msg_id not in self.client.results assert msg_id not in self.client.metadata def test_dir(self): """dir(AsyncResult)""" view = self.client[-1] ar = view.apply_async(lambda: 1) ar.get() d = dir(ar) assert 'stdout' in d assert 'get' in d def test_wait_for_send(self): view = self.client[-1] view.track = True with pytest.raises(TimeoutError): # this test can fail if the send happens too quickly # e.g. the IO thread takes control for too long, # so run the test a few times for i in range(3): if i > 0: print("Retrying test_wait_for_send") # inject delay in io loop so send doesn't complete immediately self.client._io_loop.add_callback(lambda: time.sleep(0.5)) data = os.urandom(10 * 1024 * 1024) ar = view.apply_async(lambda x: x, data) ar.wait_for_send(0) ar.wait_for_send(10) def test_return_exceptions(self): view = self.client.load_balanced_view() def fail_on_2(n): if n == 2: raise ValueError("two!") return n amr = view.map(fail_on_2, range(4), return_exceptions=True) assert amr._return_exceptions rlist = list(amr) error = rlist[2] assert isinstance(error, ipp.RemoteError) expected = [0, 1, error, 3] assert list(amr) == expected assert amr.get() == expected def test_return_exceptions_postmortem(self): self.minimum_engines(3) dv = self.client[:] bad_id = dv.targets[1] dv.scatter("rank", dv.targets, flatten=True) def fail_on_bad_id(rank, bad_id): if rank == bad_id: raise ValueError(f"{rank} is bad!") return rank ar = dv.apply_async(fail_on_bad_id, ipp.Reference('rank'), bad_id) with raises_remote(ValueError): ar.get() result = ar.get(return_exceptions=True) assert result[:1] == dv.targets[:1] assert result[2:] == dv.targets[2:] assert isinstance(result[1], ipp.RemoteError) def test_split(self): self.minimum_engines(3) dv = self.client[:] parent = dv.apply_async(os.getpid) children = parent.split() for child in children: assert len(child.msg_ids) == 1 # split is identity when only one message assert children[0].split() == (children[0],) result = parent.get(timeout=10) # get doubly-nested lists because these are split_results = [ar.get(timeout=10) for ar in children] assert split_results == result joined = ipp.AsyncResult.join(*children) assert joined.get(timeout=1) == result def test_split_map_result(self): v = self.client.load_balanced_view() amr = v.map_async(lambda x: x, range(5)) splits = amr.split() amr.get(timeout=10) for i, ar in zip(range(5), splits): assert type(ar) is ipp.AsyncResult # not a MapResult! assert ar.done() assert ar.get() == [[i]] def test_wait_first_exception(self): dv = self.client[:] def fail(i): print(i) import time if i == 0: print(1 / i) time.sleep(1) return i dv.scatter('rank', range(len(dv)), flatten=True, block=True) amr = dv.apply_async(fail, ipp.Reference("rank")) tic = time.perf_counter() done, pending = amr.wait(timeout=5, return_when=ipp.FIRST_EXCEPTION) assert done assert len(done) == 1 first_done = done.pop() assert first_done.msg_ids == amr.msg_ids[:1] assert amr.wait(timeout=10) is True done, pending = amr.wait(timeout=0, return_when=ipp.FIRST_EXCEPTION) assert pending == set() assert len(done) == len(amr) def test_map_wait_first_exception(self): dv = self.client[:] def fail(i): print(i) import time if i == 0: print(1 / i) time.sleep(1) return i amr = dv.map_async(fail, range(len(dv))) tic = time.perf_counter() done, pending = amr.wait(timeout=5, return_when=ipp.FIRST_EXCEPTION) assert done assert len(done) == 1 first_done = done.pop() assert first_done.msg_ids == amr.msg_ids[:1] assert amr.wait(timeout=10) is True done, pending = amr.wait(timeout=0, return_when=ipp.FIRST_EXCEPTION) assert pending == set() assert len(done) == len(amr) def test_wait_interactive_first_exception(self): dv = self.client[:] ar = dv.apply_async(time.sleep, 0.2) ar.wait_interactive(return_when=ipp.FIRST_EXCEPTION) assert ar.done() def fail(i): print(i) import time if i == 0: print(1 / i) time.sleep(1) return i amr = dv.map_async(fail, range(len(dv))) tic = time.perf_counter() amr.wait_interactive(timeout=5, return_when=ipp.FIRST_EXCEPTION) toc = time.perf_counter() assert toc - tic < 4 done, pending = amr.wait(timeout=0, return_when=ipp.FIRST_EXCEPTION) assert done assert len(done) == 1 first_done = done.pop() assert first_done.msg_ids == amr.msg_ids[:1] def test_progress(self): dv = self.client[:] amr = dv.map_async(time.sleep, [0.2] * 2 * len(dv)) assert len(amr) == len(dv) * 2 assert amr.progress == 0 amr.wait_interactive() assert amr.progress == len(amr) def test_error_engine_info_apply(self): dv = self.client[:] targets = self.client.ids ar = dv.apply_async(lambda: 1 / 0) try: ar.get() except Exception as e: exc = e else: pytest.fail("Should have raised remote ZeroDivisionError") assert isinstance(exc, ipp.error.CompositeError) expected_engine_info = [ { "engine_id": engine_id, "engine_uuid": self.client._engines[engine_id], "method": "apply", } for engine_id in self.client.ids ] engine_infos = [e[-1] for e in exc.elist] assert engine_infos == expected_engine_info def test_error_engine_info_execute(self): dv = self.client[:] targets = self.client.ids ar = dv.execute("1 / 0", block=False) try: ar.get() except Exception as e: exc = e else: pytest.fail("Should have raised remote ZeroDivisionError") assert isinstance(exc, ipp.error.CompositeError) expected_engine_info = [ { "engine_id": engine_id, "engine_uuid": self.client._engines[engine_id], "method": "execute", } for engine_id in self.client.ids ] engine_infos = [e[-1] for e in exc.elist] assert engine_infos == expected_engine_info ipyparallel-8.8.0/ipyparallel/tests/test_canning.py000066400000000000000000000051021460376056100225610ustar00rootroot00000000000000import os import pickle from binascii import b2a_hex from functools import partial from ipyparallel.serialize import canning from ipyparallel.serialize.canning import can, uncan def interactive(f): f.__module__ = '__main__' return f def dumps(obj): return pickle.dumps(can(obj)) def loads(obj): return uncan(pickle.loads(obj)) def roundtrip(obj): return loads(dumps(obj)) def test_no_closure(): @interactive def foo(): a = 5 return a pfoo = dumps(foo) bar = loads(pfoo) assert foo() == bar() def test_generator_closure(): # this only creates a closure on Python 3 @interactive def foo(): i = 'i' r = [i for j in (1, 2)] return r pfoo = dumps(foo) bar = loads(pfoo) assert foo() == bar() def test_nested_closure(): @interactive def foo(): i = 'i' def g(): return i return g() pfoo = dumps(foo) bar = loads(pfoo) assert foo() == bar() def test_closure(): i = 'i' @interactive def foo(): return i pfoo = dumps(foo) bar = loads(pfoo) assert foo() == bar() def test_uncan_bytes_buffer(): data = b'data' canned = can(data) canned.buffers = [memoryview(buf) for buf in canned.buffers] out = uncan(canned) assert out == data def test_can_partial(): def foo(x, y, z): return x * y * z partial_foo = partial(foo, 2, y=5) canned = can(partial_foo) assert isinstance(canned, canning.CannedPartial) dumped = pickle.dumps(canned) loaded = pickle.loads(dumped) pfoo2 = uncan(loaded) assert pfoo2(z=3) == partial_foo(z=3) def test_can_partial_buffers(): def foo(arg1, arg2, kwarg1, kwarg2): return f'{arg1}{arg2[:32]}{b2a_hex(kwarg1.tobytes()[:32])}{kwarg2}' buf1 = os.urandom(1024 * 1024) buf2 = memoryview(os.urandom(1024 * 1024)) partial_foo = partial(foo, 5, buf1, kwarg1=buf2, kwarg2=10) canned = can(partial_foo) assert len(canned.buffers) == 2 assert isinstance(canned, canning.CannedPartial) buffers, canned.buffers = canned.buffers, [] dumped = pickle.dumps(canned) loaded = pickle.loads(dumped) loaded.buffers = buffers pfoo2 = uncan(loaded) assert pfoo2() == partial_foo() def test_keyword_only_arguments(): def compute(a, *, b=3): return a * b assert compute(2) == 6 compute_2 = roundtrip(compute) assert compute_2(2) == 6 def test_annotations(): def f(a: int): return a f2 = roundtrip(f) assert f2.__annotations__ == f.__annotations__ ipyparallel-8.8.0/ipyparallel/tests/test_client.py000066400000000000000000000572471460376056100224430ustar00rootroot00000000000000"""Tests for parallel client.py""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import os import signal import socket import sys import time import warnings from concurrent.futures import Future from datetime import datetime from threading import Thread from unittest import mock import pytest import tornado from IPython import get_ipython from ipyparallel import AsyncHubResult, DirectView, Reference, error from ipyparallel.client import client as clientmod from ipyparallel.util import utc from .clienttest import ClusterTestCase, raises_remote, skip_without, wait @pytest.mark.usefixtures('ipython') class TestClient(ClusterTestCase): engine_count = 4 def test_curve(self): if os.environ.get("IPP_ENABLE_CURVE") == "1": assert not self.client.session.key assert self.client.curve_serverkey assert self.client.curve_secretkey else: assert self.client.session.key def test_ids(self): n = len(self.client.ids) self.add_engines(2) assert len(self.client.ids) == n + 2 def test_iter(self): self.minimum_engines(4) engine_ids = [view.targets for view in self.client] assert engine_ids == self.client.ids def test_view_indexing(self): """test index access for views""" self.minimum_engines(4) targets = self.client._build_targets('all')[-1] v = self.client[:] assert v.targets == targets t = self.client.ids[2] v = self.client[t] assert isinstance(v, DirectView) assert v.targets == t t = self.client.ids[2:4] v = self.client[t] assert isinstance(v, DirectView) assert v.targets == t v = self.client[::2] assert isinstance(v, DirectView) assert v.targets == targets[::2] v = self.client[1::3] assert isinstance(v, DirectView) assert v.targets == targets[1::3] v = self.client[:-3] assert isinstance(v, DirectView) assert v.targets == targets[:-3] v = self.client[-1] assert isinstance(v, DirectView) assert v.targets == targets[-1] with pytest.raises(TypeError): self.client[None] def test_outstanding(self): self.minimum_engines(1) e = self.client[-1] engine_id = self.client._engines[e.targets] ar = e.apply_async(time.sleep, 0.5) msg_id = ar.msg_ids[0] # verify that msg id assert msg_id in self.client.outstanding assert msg_id in self.client._outstanding_dict[engine_id] ar.get() assert msg_id not in self.client.outstanding assert msg_id not in self.client._outstanding_dict[engine_id] def test_lbview_targets(self): """test load_balanced_view targets""" v = self.client.load_balanced_view() assert v.targets is None v = self.client.load_balanced_view(-1) assert v.targets == [self.client.ids[-1]] v = self.client.load_balanced_view('all') assert v.targets is None def test_dview_targets(self): """test direct_view targets""" v = self.client.direct_view() assert v.targets == 'all' v = self.client.direct_view('all') assert v.targets == 'all' v = self.client.direct_view(-1) assert v.targets == self.client.ids[-1] def test_lazy_all_targets(self): """test lazy evaluation of rc.direct_view('all')""" v = self.client.direct_view() assert v.targets == 'all' def double(x): return x * 2 seq = list(range(100)) ref = [double(x) for x in seq] # add some engines, which should be used self.add_engines(1) n1 = len(self.client.ids) # simple apply r = v.apply_sync(lambda: 1) assert r == [1] * n1 # map goes through remotefunction r = v.map_sync(double, seq) assert r == ref # add a couple more engines, and try again self.add_engines(2) n2 = len(self.client.ids) assert n2 != n1 # apply r = v.apply_sync(lambda: 1) assert r == [1] * n2 # map r = v.map_sync(double, seq) assert r == ref def test_targets(self): """test various valid targets arguments""" build = self.client._build_targets ids = self.client.ids idents, targets = build(None) assert ids == targets def test_clear(self): """test clear behavior""" self.minimum_engines(2) v = self.client[:] v.block = True v.push(dict(a=5)) v.pull('a') id0 = self.client.ids[-1] self.client.clear(targets=id0, block=True) a = self.client[:-1].get('a') with raises_remote(NameError): self.client[id0].get('a') self.client.clear(block=True) for i in self.client.ids: with raises_remote(NameError): self.client[i].get('a') def test_get_result(self): """test getting results from the Hub.""" c = clientmod.Client(profile='iptest') t = c.ids[-1] ar = c[t].apply_async(wait, 1) # give the monitor time to notice the message time.sleep(0.25) ahr = self.client.get_result(ar.msg_ids[0], owner=False) assert isinstance(ahr, AsyncHubResult) assert ahr.get() == ar.get() ar2 = self.client.get_result(ar.msg_ids[0]) assert not isinstance(ar2, AsyncHubResult) assert ahr.get() == ar2.get() ar3 = self.client.get_result(ar2) assert ar3.msg_ids == ar2.msg_ids ar3 = self.client.get_result([ar2]) assert ar3.msg_ids == ar2.msg_ids c.close() def test_get_execute_result(self): """test getting execute results from the Hub.""" c = clientmod.Client(profile='iptest') t = c.ids[-1] cell = '\n'.join(['import time', 'time.sleep(0.25)', '5']) ar = c[t].execute("import time; time.sleep(1)", silent=False) # give the monitor time to notice the message time.sleep(0.25) ahr = self.client.get_result(ar.msg_ids[0], owner=False) assert isinstance(ahr, AsyncHubResult) assert ahr.get().execute_result == ar.get().execute_result ar2 = self.client.get_result(ar.msg_ids[0]) assert not isinstance(ar2, AsyncHubResult) assert ahr.get() == ar2.get() c.close() def test_ids_list(self): """test client.ids""" ids = self.client.ids assert ids == self.client._ids assert ids is not self.client._ids ids.remove(ids[-1]) assert ids != self.client._ids def test_queue_status(self): ids = self.client.ids id0 = ids[0] qs = self.client.queue_status(targets=id0) assert isinstance(qs, dict) assert sorted(qs.keys()), ['completed', 'queue' == 'tasks'] allqs = self.client.queue_status() assert isinstance(allqs, dict) intkeys = list(allqs.keys()) intkeys.remove('unassigned') print("intkeys", intkeys) intkeys = sorted(intkeys) ids = self.client.ids print("client.ids", ids) ids = sorted(self.client.ids) assert intkeys == ids unassigned = allqs.pop('unassigned') for eid, qs in allqs.items(): assert isinstance(qs, dict) assert sorted(qs.keys()), ['completed', 'queue' == 'tasks'] @pytest.mark.skipif(os.name == 'nt', reason='timing out on Windows') def test_shutdown(self): ids = self.client.ids id0 = ids[-1] pid = self.client[id0].apply_sync(os.getpid) self.client.shutdown(id0, block=True) for i in range(150): # give the engine 15 seconds to die if id0 not in self.client.ids: break time.sleep(0.1) assert id0 not in self.client.ids with pytest.raises(IndexError): self.client[id0] def test_result_status(self): pass # to be written def test_db_query_dt(self): """test db query by date""" hub_n_before = len(self.client.hub_history()) client_n_before = len(self.client.history) for i in range(4): self.client[:].apply_sync(lambda: 1) new_entries = len(self.client.history) - client_n_before hist = self.client.hub_history() for i in range(10): if len(hist) < hub_n_before + new_entries: time.sleep(0.5) hist = self.client.hub_history() else: break else: raise TimeoutError( f"Timeout waiting for {new_entries} entries in the hub history" ) print(hist) middle = self.client.db_query({'msg_id': hist[len(hist) // 2]})[0] tic = middle['submitted'] before = self.client.db_query({'submitted': {'$lt': tic}}) after = self.client.db_query({'submitted': {'$gte': tic}}) assert len(before) + len(after) == len(hist) for b in before: assert b['submitted'] < tic for a in after: assert a['submitted'] >= tic same = self.client.db_query({'submitted': tic}) for s in same: assert s['submitted'] == tic def test_db_query_keys(self): """test extracting subset of record keys""" found = self.client.db_query( {'msg_id': {'$ne': ''}}, keys=['submitted', 'completed'] ) for rec in found: assert set(rec.keys()), {'msg_id', 'submitted' == 'completed'} def test_db_query_default_keys(self): """default db_query excludes buffers""" found = self.client.db_query({'msg_id': {'$ne': ''}}) for rec in found: keys = set(rec.keys()) assert 'buffers' not in keys assert 'result_buffers' not in keys def test_db_query_msg_id(self): """ensure msg_id is always in db queries""" found = self.client.db_query( {'msg_id': {'$ne': ''}}, keys=['submitted', 'completed'] ) for rec in found: assert 'msg_id' in rec.keys() found = self.client.db_query({'msg_id': {'$ne': ''}}, keys=['submitted']) for rec in found: assert 'msg_id' in rec.keys() found = self.client.db_query({'msg_id': {'$ne': ''}}, keys=['msg_id']) for rec in found: assert 'msg_id' in rec.keys() def test_db_query_get_result(self): """pop in db_query shouldn't pop from result itself""" self.client[:].apply_sync(lambda: 1) found = self.client.db_query({'msg_id': {'$ne': ''}}) rc2 = clientmod.Client(profile='iptest') # If this bug is not fixed, this call will hang: ar = rc2.get_result(self.client.history[-1]) print("mark") ar.wait(2) print("mark2") assert ar.ready() print("mark3") ar.get() rc2.close() def test_db_query_in(self): """test db query with '$in','$nin' operators""" hist = self.client.hub_history() even = hist[::2] odd = hist[1::2] recs = self.client.db_query({'msg_id': {'$in': even}}) found = [r['msg_id'] for r in recs] assert set(even) == set(found) recs = self.client.db_query({'msg_id': {'$nin': even}}) found = [r['msg_id'] for r in recs] assert set(odd) == set(found) def test_hub_history(self): hist = self.client.hub_history() recs = self.client.db_query({'msg_id': {"$ne": ''}}) recdict = {} for rec in recs: recdict[rec['msg_id']] = rec latest = datetime(1984, 1, 1).replace(tzinfo=utc) for msg_id in hist: rec = recdict[msg_id] newt = rec['submitted'] assert newt >= latest latest = newt ar = self.client[-1].apply_async(lambda: 1) ar.get() time.sleep(0.25) assert self.client.hub_history()[-1:] == ar.msg_ids def _wait_for_idle(self): """wait for the cluster to become idle, according to the everyone.""" rc = self.client # step 0. wait for local results # this should be sufficient 99% of the time. rc.wait(timeout=5) # step 1. wait for all requests to be noticed # timeout 5s, polling every 100ms msg_ids = set(rc.history) hub_hist = rc.hub_history() for i in range(50): if msg_ids.difference(hub_hist): time.sleep(0.1) hub_hist = rc.hub_history() else: break assert len(msg_ids.difference(hub_hist)) == 0 # step 2. wait for all requests to be done # timeout 5s, polling every 100ms qs = rc.queue_status() for i in range(50): if qs['unassigned'] or any( qs[eid]['tasks'] + qs[eid]['queue'] for eid in qs if eid != 'unassigned' ): time.sleep(0.1) qs = rc.queue_status() else: break # ensure Hub up to date: assert qs['unassigned'] == 0 for eid in [eid for eid in qs if eid != 'unassigned']: assert qs[eid]['tasks'] == 0 assert qs[eid]['queue'] == 0 def test_resubmit(self): def f(): import random return random.random() v = self.client.load_balanced_view() ar = v.apply_async(f) r1 = ar.get(1) # give the Hub a chance to notice: self._wait_for_idle() ahr = self.client.resubmit(ar.msg_ids) r2 = ahr.get(1) assert r1 != r2 def test_resubmit_chain(self): """resubmit resubmitted tasks""" v = self.client.load_balanced_view() ar = v.apply_async(lambda x: x, 'x' * 1024) ar.get() self._wait_for_idle() ars = [ar] for i in range(10): ar = ars[-1] ar2 = self.client.resubmit(ar.msg_ids) [ar.get() for ar in ars] def test_resubmit_header(self): """resubmit shouldn't clobber the whole header""" def f(): import random return random.random() v = self.client.load_balanced_view() v.retries = 1 ar = v.apply_async(f) r1 = ar.get(1) # give the Hub a chance to notice: self._wait_for_idle() ahr = self.client.resubmit(ar.msg_ids) ahr.get(1) time.sleep(0.5) records = self.client.db_query( {'msg_id': {'$in': ar.msg_ids + ahr.msg_ids}}, keys='header' ) h1, h2 = (r['header'] for r in records) for key in set(h1.keys()).union(set(h2.keys())): if key in ('msg_id', 'date'): assert h1[key] != h2[key] else: assert h1[key] == h2[key] def test_resubmit_aborted(self): def f(): import random return random.random() v = self.client.load_balanced_view() # restrict to one engine, so we can put a sleep # ahead of the task, so it will get aborted eid = self.client.ids[-1] v.targets = [eid] sleep = v.apply_async(time.sleep, 0.5) ar = v.apply_async(f) ar.abort() with pytest.raises(error.TaskAborted): ar.get() # Give the Hub a chance to get up to date: self._wait_for_idle() ahr = self.client.resubmit(ar.msg_ids) r2 = ahr.get(1) def test_resubmit_inflight(self): """resubmit of inflight task""" v = self.client.load_balanced_view() ar = v.apply_async(time.sleep, 1) # give the message a chance to arrive time.sleep(0.2) ahr = self.client.resubmit(ar.msg_ids) ar.get(2) ahr.get(2) def test_resubmit_badkey(self): """ensure KeyError on resubmit of nonexistant task""" with raises_remote(KeyError): self.client.resubmit(['invalid']) def test_purge_hub_results(self): # ensure there are some tasks for i in range(5): self.client[:].apply_sync(lambda: 1) # Wait for the Hub to realise the result is done: # This prevents a race condition, where we # might purge a result the Hub still thinks is pending. self._wait_for_idle() rc2 = clientmod.Client(profile='iptest') hist = self.client.hub_history() ahr = rc2.get_result([hist[-1]]) ahr.wait(10) self.client.purge_hub_results(hist[-1]) newhist = self.client.hub_history() assert len(newhist) + 1 == len(hist) rc2.close() def test_purge_local_results(self): # ensure there are some tasks # purge local results is mostly unnecessary now that we have Futures msg_id = 'asdf' self.client.results[msg_id] = 5 md = self.client.metadata[msg_id] before = len(self.client.results) assert len(self.client.metadata) == before self.client.purge_local_results(msg_id) assert len(self.client.results) <= before - 1, "Not removed from results" assert len(self.client.metadata) <= before - 1, "Not removed from metadata" def test_purge_local_results_outstanding(self): v = self.client[-1] ar = v.apply_async(time.sleep, 1) with pytest.raises(RuntimeError): self.client.purge_local_results(ar) ar.get() self.client.purge_local_results(ar) def test_purge_all_local_results_outstanding(self): v = self.client[-1] ar = v.apply_async(time.sleep, 1) with pytest.raises(RuntimeError): self.client.purge_local_results('all') ar.get() self.client.purge_local_results('all') def test_purge_all_hub_results(self): self.client.purge_hub_results('all') hist = self.client.hub_history() assert len(hist) == 0 def test_purge_all_local_results(self): self.client.purge_local_results('all') assert len(self.client.results) == 0, "Results not empty" assert len(self.client.metadata) == 0, "metadata not empty" def test_purge_all_results(self): # ensure there are some tasks for i in range(5): self.client[:].apply_sync(lambda: 1) assert self.client.wait(timeout=10) self._wait_for_idle() self.client.purge_results('all') assert len(self.client.results) == 0, "Results not empty" assert len(self.client.metadata) == 0, "metadata not empty" hist = self.client.hub_history() assert len(hist) == 0, "hub history not empty" def test_purge_everything(self): # ensure there are some tasks for i in range(5): self.client[:].apply_sync(lambda: 1) self.client.wait(timeout=10) self._wait_for_idle() self.client.purge_everything() # The client results assert len(self.client.results) == 0, "Results not empty" assert len(self.client.metadata) == 0, "metadata not empty" # The client "bookkeeping" assert len(self.client.session.digest_history) == 0, "session digest not empty" assert len(self.client.history) == 0, "client history not empty" # the hub results hist = self.client.hub_history() assert len(hist) == 0, "hub history not empty" def test_activate_on_init(self): ip = get_ipython() magics = ip.magics_manager.magics c = self.connect_client() assert 'px' in magics['line'] assert 'px' in magics['cell'] c.close() def test_activate(self): ip = get_ipython() magics = ip.magics_manager.magics v0 = self.client.activate(-1, '0') assert 'px0' in magics['line'] assert 'px0' in magics['cell'] assert v0.targets == self.client.ids[-1] v0 = self.client.activate('all', 'all') assert 'pxall' in magics['line'] assert 'pxall' in magics['cell'] assert v0.targets == 'all' def test_wait_interactive(self): ar = self.client[-1].apply_async(lambda: 1) self.client.wait_interactive() assert self.client.outstanding == set() def test_await_future(self): f = Future() def finish_later(): time.sleep(0.1) f.set_result('future') Thread(target=finish_later).start() assert self.client.wait([f]) assert f.done() assert f.result() == 'future' @skip_without('distributed') @pytest.mark.skipif( sys.version_info[:2] <= (3, 5), reason="become_dask doesn't work on Python 3.5" ) @pytest.mark.skipif( tornado.version_info[:2] < (5,), reason="become_dask doesn't work with tornado 4", ) @pytest.mark.filterwarnings("ignore:make_current") def test_become_dask(self): executor = self.client.become_dask() reprs = self.client[:].apply_sync(repr, Reference('distributed_worker')) for r in reprs: assert "Worker" in r squares = executor.map(lambda x: x * x, range(10)) tot = executor.submit(sum, squares) assert tot.result() == 285 # cleanup executor.close() self.client.stop_dask() ar = self.client[:].apply_async(lambda x: x, Reference('distributed_worker')) with raises_remote(NameError): ar.get() def test_warning_on_hostname_match(self): location = socket.gethostname() with mock.patch('ipyparallel.client.client.is_local_ip', lambda x: False): with mock.patch('socket.gethostname', lambda: location[0:-1]): with pytest.warns(RuntimeWarning): # should trigger warning c = self.connect_client() c.close() with mock.patch('socket.gethostname', lambda: location): c = None try: with warnings.catch_warnings(): warnings.simplefilter("error", category=RuntimeWarning) c = self.connect_client() finally: if c: c.close() def test_local_ip_true_doesnt_trigger_warning(self): with mock.patch('ipyparallel.client.client.is_local_ip', lambda x: True): c = None try: with warnings.catch_warnings(): warnings.simplefilter("error", category=RuntimeWarning) c = self.connect_client() finally: if c: c.close() def test_wait_for_engines(self): n = len(self.client) assert self.client.wait_for_engines(n) is None f = self.client.wait_for_engines(n, block=False) assert isinstance(f, Future) assert f.done() with pytest.raises(TimeoutError): self.client.wait_for_engines(n + 1, timeout=0.1) f = self.client.wait_for_engines(n + 1, timeout=10, block=False) self.add_engines(1) assert f.result() is None @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_signal_engines(self): view = self.client[:] if sys.platform.startswith("win"): signame = 'CTRL_C_EVENT' else: signame = 'SIGINT' signum = getattr(signal, signame) for sig in (signum, signame): ar = view.apply_async(time.sleep, 10) # FIXME: use status:busy to wait for tasks to start time.sleep(1) self.client.send_signal(sig, block=True) with raises_remote(KeyboardInterrupt): ar.get() # make sure they were all interrupted for r in ar.get(return_exceptions=True): assert isinstance(r, error.RemoteError) ipyparallel-8.8.0/ipyparallel/tests/test_cluster.py000066400000000000000000000277721460376056100226460ustar00rootroot00000000000000import asyncio import json import logging import os import signal import sys import tempfile import time from pathlib import Path from unittest import mock import pytest from traitlets.config import Config import ipyparallel as ipp from ipyparallel import cluster from ipyparallel.cluster.launcher import find_launcher_class from .clienttest import raises_remote _timeout = 30 def _raise_interrupt(*frame_info): raise KeyboardInterrupt() def _prepare_signal(): """Register signal handler to test with Registers SIGUSR1 to raise KeyboardInterrupt where available """ if hasattr(signal, 'SIGUSR1'): signal.signal(signal.SIGUSR1, _raise_interrupt) # returns the remote value of the signal return int(TEST_SIGNAL) try: TEST_SIGNAL = signal.SIGUSR1 except AttributeError: # Windows TEST_SIGNAL = signal.CTRL_C_EVENT async def test_cluster_id(Cluster): cluster_ids = set() for i in range(3): cluster = Cluster() cluster_ids.add(cluster.cluster_id) assert len(cluster_ids) == 3 cluster = Cluster(cluster_id='abc') assert cluster.cluster_id == 'abc' async def test_ipython_log(ipython): c = cluster.Cluster(parent=ipython) assert c.log.name == f"{cluster.Cluster.__module__}.{c.cluster_id}" assert len(c.log.handlers) == 1 assert c.log.handlers[0].stream is sys.stdout async def test_start_stop_controller(Cluster): cluster = Cluster() await cluster.start_controller() with pytest.raises(RuntimeError): await cluster.start_controller() assert cluster.config is not None assert cluster.controller.config is cluster.config assert cluster.controller is not None proc = cluster.controller.process assert proc.is_running() with await cluster.connect_client() as rc: assert rc.queue_status() == {'unassigned': 0} await cluster.stop_controller() proc.wait(timeout=3) assert cluster.controller is None # stop is idempotent await cluster.stop_controller() # TODO: test file cleanup async def test_start_stop_engines(Cluster, engine_launcher_class): cluster = Cluster() await cluster.start_controller() n = 2 engine_set_id = await cluster.start_engines(n) assert engine_set_id in cluster.engines engine_set = cluster.engines[engine_set_id] launcher_class = find_launcher_class(engine_launcher_class, "engine") assert isinstance(engine_set, launcher_class) with await cluster.connect_client() as rc: rc.wait_for_engines(n, timeout=_timeout) await cluster.stop_engines(engine_set_id) assert cluster.engines == {} with pytest.raises(KeyError): await cluster.stop_engines(engine_set_id) await cluster.stop_controller() async def test_start_stop_cluster(Cluster): n = 2 cluster = Cluster(n=n) await cluster.start_cluster() controller = cluster.controller assert controller is not None assert len(cluster.engines) == 1 with await cluster.connect_client() as rc: rc.wait_for_engines(n, timeout=_timeout) await cluster.stop_cluster() assert cluster.controller is None assert cluster.engines == {} @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) async def test_signal_engines(request, Cluster): cluster = Cluster() await cluster.start_controller() engine_set_id = await cluster.start_engines(n=2) rc = await cluster.connect_client() request.addfinalizer(rc.close) rc.wait_for_engines(2) # seems to be a problem if we start too soon... await asyncio.sleep(1) # ensure responsive rc[:].apply_async(lambda: None).get(timeout=_timeout) # register signal handler signals = rc[:].apply_async(_prepare_signal).get(timeout=_timeout) # get test signal from engines in case of cross-platform mismatch, # e.g. SIGUSR1 on mac (30) -> linux (10) test_signal = signals[0] # submit request to be interrupted ar = rc[:].apply_async(time.sleep, 3) # wait for it to be running await asyncio.sleep(0.5) # send signal await cluster.signal_engines(test_signal, engine_set_id) # wait for result, which should raise KeyboardInterrupt with raises_remote(KeyboardInterrupt) as e: ar.get(timeout=_timeout) rc.close() await cluster.stop_engines() await cluster.stop_controller() async def test_restart_engines(Cluster): n = 2 async with Cluster(n=n) as rc: cluster = rc.cluster engine_set_id = next(iter(cluster.engines)) engine_set = cluster.engines[engine_set_id] assert rc.ids[:n] == list(range(n)) before_pids = rc[:].apply_sync(os.getpid) await cluster.restart_engines() # wait for unregister while any(eid in rc.ids for eid in range(n)): await asyncio.sleep(0.1) # wait for register rc.wait_for_engines(n, timeout=_timeout) after_pids = rc[:].apply_sync(os.getpid) assert set(after_pids).intersection(before_pids) == set() async def test_get_output(Cluster): n = 2 async with Cluster(n=n) as rc: cluster = rc.cluster engine_set_id = next(iter(cluster.engines)) engine_set = cluster.engines[engine_set_id] out = engine_set.get_output() print(f"---engine output---\n{out}\n---end engine output---") assert out assert 'Completed registration with id 0' in out assert 'Completed registration with id 1' in out async def test_async_with(Cluster): async with Cluster(n=5) as rc: assert sorted(rc.ids) == list(range(5)) rc[:]['a'] = 5 assert rc[:]['a'] == [5] * 5 def test_sync_with(Cluster): with Cluster(log_level=10, n=5) as rc: assert sorted(rc.ids) == list(range(5)) rc[:]['a'] = 5 assert rc[:]['a'] == [5] * 5 def test_load_profile(tmpdir): profile_dir = tmpdir.join("profile").mkdir() # config cases: # - only in profile config (used) # - in profile config and direct config (direct used) # - in profile config and kwargs (kwargs used) with profile_dir.join("ipcluster_config.json").open("w") as f: json.dump( { "Cluster": { "controller_args": ["--from-profile"], "n": 5, "engine_timeout": 10, } }, f, ) print(profile_dir.listdir()) config = Config() config.Cluster.engine_timeout = 20 c = cluster.Cluster(profile_dir=str(profile_dir), n=10, config=config) print(c.config) assert c.profile_dir == str(profile_dir) assert c.controller_args == ['--from-profile'] # from profile assert c.engine_timeout == 20 # from config assert c.n == 10 # from kwarg @pytest.mark.parametrize( "classname, expected_class", [ ("MPI", cluster.launcher.MPIEngineSetLauncher), ("SGE", cluster.launcher.SGEEngineSetLauncher), ( "ipyparallel.cluster.launcher.LocalEngineSetLauncher", cluster.launcher.LocalEngineSetLauncher, ), ], ) def test_cluster_abbreviations(classname, expected_class): c = cluster.Cluster(engines=classname) assert c.engine_launcher_class is expected_class async def test_cluster_repr(Cluster): tmp = tempfile.gettempdir() c = Cluster(cluster_id="test", profile_dir=tmp) assert repr(c) == f"" await c.start_controller() assert ( repr(c) == f")>" ) await c.start_engines(1, 'engineid') assert ( repr(c) == f", engine_sets=['engineid'])>" ) async def test_cluster_manager(): m = cluster.ClusterManager() assert m.clusters == {} tmp = tempfile.gettempdir() key, c = m.new_cluster(profile_dir=tmp) assert c.profile_dir == tmp assert m.get_cluster(key) is c with pytest.raises(KeyError): m.get_cluster("nosuchcluster") with pytest.raises(KeyError): m.new_cluster(cluster_id=c.cluster_id, profile_dir=c.profile_dir) assert list(m.clusters) == [key] m.remove_cluster(key) with pytest.raises(KeyError): m.remove_cluster("nosuchcluster") async def test_to_from_dict( Cluster, ): cluster = Cluster(n=2) print(cluster.config, cluster.controller_args) async with cluster as rc: d = cluster.to_dict() cluster2 = ipp.Cluster.from_dict(d) assert not cluster2.shutdown_atexit assert cluster2.controller is not None if isinstance(cluster2.controller, ipp.cluster.launcher.LocalProcessLauncher): assert cluster2.controller.process.pid == cluster.controller.process.pid assert list(cluster2.engines) == list(cluster.engines) es1 = cluster.engine_set es2 = cluster2.engine_set # ensure responsive rc[:].apply_async(lambda: None).get(timeout=_timeout) if not sys.platform.startswith("win"): # signal tests doesn't work yet on Windows # register signal handler signals = rc[:].apply_async(_prepare_signal).get(timeout=_timeout) # get test signal from engines in case of cross-platform mismatch, # e.g. SIGUSR1 on mac (30) -> linux (10) test_signal = signals[0] # submit request to be interrupted ar = rc[:].apply_async(time.sleep, 3) await asyncio.sleep(0.5) # send signal await cluster2.signal_engines(test_signal) # wait for result, which should raise KeyboardInterrupt with raises_remote(KeyboardInterrupt) as e: ar.get(timeout=_timeout) assert es1.n == es2.n assert cluster2.engine_launcher_class is cluster.engine_launcher_class # shutdown from cluster2, shouldn't raise in cluster1 await cluster2.stop_cluster() async def test_default_from_file(Cluster): cluster = Cluster(n=1, profile="default", cluster_id="") async with cluster: cluster2 = ipp.Cluster.from_file() assert cluster2.cluster_file == cluster.cluster_file with await cluster.connect_client() as rc: assert len(rc) == 1 async def test_cluster_manager_notice_stop(Cluster): cm = cluster.ClusterManager(log=logging.getLogger()) cm.load_clusters() c = Cluster(n=1, log=cm.log) key = cm._cluster_key(c) assert key not in cm.clusters await c.start_cluster() cm.load_clusters() assert key in cm.clusters c_copy = cm.clusters[key] await c.stop_cluster() # refresh list, cleans out stopped clusters # can take some time to notice tic = time.perf_counter() deadline = time.perf_counter() + _timeout while time.perf_counter() < deadline and key in cm.clusters: await asyncio.sleep(0.2) cm.load_clusters() assert key not in cm.clusters @pytest.mark.skipif(os.name == 'nt', reason="Does not work on Windows") async def test_wait_for_engines_crash(Cluster): """wait_for_engines is cancelled when the engines stop""" c = Cluster(n=2, log_level=10) crash_on_startup = str(Path(__file__).parent.joinpath("_test_startup_crash.py")) with mock.patch.dict(os.environ, {"PYTHONSTARTUP": crash_on_startup}): c.start_cluster_sync() rc = c.connect_client_sync() with pytest.raises(ipp.error.EngineError): rc.wait_for_engines(3, timeout=20) @pytest.mark.parametrize("activate", (True, False)) def test_start_and_connect_activate(ipython, Cluster, activate): rc = Cluster(n=2, log_level=10).start_and_connect_sync(activate=activate) with rc: if activate: assert "px" in ipython.magics_manager.magics["cell"] px = ipython.magics_manager.magics["cell"]["px"] assert px.__self__.view.client is rc else: if "px" in ipython.magics_manager.magics["cell"]: px = ipython.magics_manager.magics["cell"]["px"] assert px.__self__.view.client is not rc ipyparallel-8.8.0/ipyparallel/tests/test_db.py000066400000000000000000000250071460376056100215370ustar00rootroot00000000000000"""Tests for db backends""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import logging import os import tempfile import time from datetime import datetime, timedelta from unittest import TestCase import pytest from jupyter_client.session import Session from ipyparallel import util from ipyparallel.controller.dictdb import DictDB from ipyparallel.controller.hub import init_record from ipyparallel.controller.sqlitedb import SQLiteDB from ipyparallel.util import utc class TaskDBTest: def setUp(self): util._disable_session_extract_dates() self.session = Session() self.db = self.create_db() self.load_records(16) def create_db(self): raise NotImplementedError def load_records(self, n=1, buffer_size=100): """load n records for testing""" # sleep 1/10 s, to ensure timestamp is different to previous calls time.sleep(0.01) msg_ids = [] for i in range(n): msg = self.session.msg('apply_request', content=dict(a=5)) msg['buffers'] = [os.urandom(buffer_size)] rec = init_record(msg) msg_id = msg['header']['msg_id'] msg_ids.append(msg_id) self.db.add_record(msg_id, rec) return msg_ids def test_add_record(self): before = self.db.get_history() self.load_records(5) after = self.db.get_history() assert len(after) == len(before) + 5 assert after[:-5] == before def test_drop_record(self): msg_id = self.load_records()[-1] rec = self.db.get_record(msg_id) self.db.drop_record(msg_id) with pytest.raises(KeyError): self.db.get_record(msg_id) def _round_to_millisecond(self, dt): """necessary because mongodb rounds microseconds""" micro = dt.microsecond extra = int(str(micro)[-3:]) return dt - timedelta(microseconds=extra) def test_update_record(self): now = self._round_to_millisecond(util.utcnow()) msg_id = self.db.get_history()[-1] rec1 = self.db.get_record(msg_id) data = {'stdout': 'hello there', 'completed': now} self.db.update_record(msg_id, data) rec2 = self.db.get_record(msg_id) assert rec2['stdout'] == 'hello there' assert rec2['completed'] == now rec1.update(data) assert rec1 == rec2 # def test_update_record_bad(self): # """test updating nonexistant records""" # msg_id = str(uuid.uuid4()) # data = {'stdout': 'hello there'} # self.assertRaises(KeyError, self.db.update_record, msg_id, data) def test_find_records_dt(self): """test finding records by date""" hist = self.db.get_history() middle = self.db.get_record(hist[len(hist) // 2]) tic = middle['submitted'] before = self.db.find_records({'submitted': {'$lt': tic}}) after = self.db.find_records({'submitted': {'$gte': tic}}) assert len(before) + len(after) == len(hist) for b in before: assert b['submitted'] < tic for a in after: assert a['submitted'] >= tic same = self.db.find_records({'submitted': tic}) for s in same: assert s['submitted'] == tic def test_find_records_keys(self): """test extracting subset of record keys""" found = self.db.find_records( {'msg_id': {'$ne': ''}}, keys=['submitted', 'completed'] ) for rec in found: assert set(rec.keys()), {'msg_id', 'submitted' == 'completed'} def test_find_records_msg_id(self): """ensure msg_id is always in found records""" found = self.db.find_records( {'msg_id': {'$ne': ''}}, keys=['submitted', 'completed'] ) for rec in found: assert 'msg_id' in rec.keys() found = self.db.find_records({'msg_id': {'$ne': ''}}, keys=['submitted']) for rec in found: assert 'msg_id' in rec.keys() found = self.db.find_records({'msg_id': {'$ne': ''}}, keys=['msg_id']) for rec in found: assert 'msg_id' in rec.keys() def test_find_records_in(self): """test finding records with '$in','$nin' operators""" hist = self.db.get_history() even = hist[::2] odd = hist[1::2] recs = self.db.find_records({'msg_id': {'$in': even}}) found = [r['msg_id'] for r in recs] assert set(even) == set(found) recs = self.db.find_records({'msg_id': {'$nin': even}}) found = [r['msg_id'] for r in recs] assert set(odd) == set(found) def test_get_history(self): msg_ids = self.db.get_history() latest = datetime(1984, 1, 1).replace(tzinfo=utc) for msg_id in msg_ids: rec = self.db.get_record(msg_id) newt = rec['submitted'] assert newt >= latest latest = newt msg_id = self.load_records(1)[-1] assert self.db.get_history()[-1] == msg_id def test_datetime(self): """get/set timestamps with datetime objects""" msg_id = self.db.get_history()[-1] rec = self.db.get_record(msg_id) assert isinstance(rec['submitted'], datetime) self.db.update_record(msg_id, dict(completed=util.utcnow())) rec = self.db.get_record(msg_id) assert isinstance(rec['completed'], datetime) def test_drop_matching(self): msg_ids = self.load_records(10) query = {'msg_id': {'$in': msg_ids}} self.db.drop_matching_records(query) recs = self.db.find_records(query) assert len(recs) == 0 def test_null(self): """test None comparison queries""" msg_ids = self.load_records(10) query = {'msg_id': None} recs = self.db.find_records(query) assert len(recs) == 0 query = {'msg_id': {'$ne': None}} recs = self.db.find_records(query) assert len(recs) >= 10 def test_pop_safe_get(self): """editing query results shouldn't affect record [get]""" msg_id = self.db.get_history()[-1] rec = self.db.get_record(msg_id) rec.pop('buffers') rec['garbage'] = 'hello' rec['header']['msg_id'] = 'fubar' rec2 = self.db.get_record(msg_id) assert 'buffers' in rec2 assert 'garbage' not in rec2 assert rec2['header']['msg_id'] == msg_id def test_pop_safe_find(self): """editing query results shouldn't affect record [find]""" msg_id = self.db.get_history()[-1] rec = self.db.find_records({'msg_id': msg_id})[0] rec.pop('buffers') rec['garbage'] = 'hello' rec['header']['msg_id'] = 'fubar' rec2 = self.db.find_records({'msg_id': msg_id})[0] assert 'buffers' in rec2 assert 'garbage' not in rec2 assert rec2['header']['msg_id'] == msg_id def test_pop_safe_find_keys(self): """editing query results shouldn't affect record [find+keys]""" msg_id = self.db.get_history()[-1] rec = self.db.find_records({'msg_id': msg_id}, keys=['buffers', 'header'])[0] rec.pop('buffers') rec['garbage'] = 'hello' rec['header']['msg_id'] = 'fubar' rec2 = self.db.find_records({'msg_id': msg_id})[0] assert 'buffers' in rec2 assert 'garbage' not in rec2 assert rec2['header']['msg_id'] == msg_id class TestDictBackend(TaskDBTest, TestCase): def create_db(self): return DictDB() def test_cull_count(self): self.db = self.create_db() # skip the load-records init from setUp self.db.record_limit = 20 self.db.cull_fraction = 0.2 self.load_records(20) assert len(self.db.get_history()) == 20 self.load_records(1) # 0.2 * 20 = 4, 21 - 4 = 17 assert len(self.db.get_history()) == 17 self.load_records(3) assert len(self.db.get_history()) == 20 self.load_records(1) assert len(self.db.get_history()) == 17 for i in range(25): self.load_records(1) assert len(self.db.get_history()) >= 17 assert len(self.db.get_history()) <= 20 def test_cull_size(self): self.db = self.create_db() # skip the load-records init from setUp self.db.size_limit = 1000 self.db.cull_fraction = 0.2 self.load_records(100, buffer_size=10) assert len(self.db.get_history()) == 100 self.load_records(1, buffer_size=0) assert len(self.db.get_history()) == 101 self.load_records(1, buffer_size=1) # 0.2 * 100 = 20, 101 - 20 = 81 assert len(self.db.get_history()) == 81 def test_cull_size_drop(self): """dropping records updates tracked buffer size""" self.db = self.create_db() # skip the load-records init from setUp self.db.size_limit = 1000 self.db.cull_fraction = 0.2 self.load_records(100, buffer_size=10) assert len(self.db.get_history()) == 100 self.db.drop_record(self.db.get_history()[-1]) assert len(self.db.get_history()) == 99 self.load_records(1, buffer_size=5) assert len(self.db.get_history()) == 100 self.load_records(1, buffer_size=5) assert len(self.db.get_history()) == 101 self.load_records(1, buffer_size=1) assert len(self.db.get_history()) == 81 def test_cull_size_update(self): """updating records updates tracked buffer size""" self.db = self.create_db() # skip the load-records init from setUp self.db.size_limit = 1000 self.db.cull_fraction = 0.2 self.load_records(100, buffer_size=10) assert len(self.db.get_history()) == 100 msg_id = self.db.get_history()[-1] self.db.update_record(msg_id, dict(result_buffers=[os.urandom(10)], buffers=[])) assert len(self.db.get_history()) == 100 self.db.update_record(msg_id, dict(result_buffers=[os.urandom(11)], buffers=[])) assert len(self.db.get_history()) == 79 class TestSQLiteBackend(TaskDBTest, TestCase): def setUp(self): tmp_file = tempfile.NamedTemporaryFile(suffix='.db') self.temp_db = tmp_file.name tmp_file.close() super().setUp() def create_db(self): location, fname = os.path.split(self.temp_db) log = logging.getLogger('test') log.setLevel(logging.CRITICAL) return SQLiteDB(location=location, filename=fname, log=log) def tearDown(self): self.db.close() try: os.remove(self.temp_db) except Exception: pass ipyparallel-8.8.0/ipyparallel/tests/test_dependency.py000066400000000000000000000067211460376056100232720ustar00rootroot00000000000000"""Tests for dependency.py""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import ipyparallel as ipp from ipyparallel.serialize import can, uncan from ipyparallel.util import interactive from .clienttest import ClusterTestCase, raises_remote @ipp.require('time') def wait(n): time.sleep(n) # noqa: F821 return n @ipp.interactive def func(x): return x * x mixed = list(map(str, range(10))) completed = list(map(str, range(0, 10, 2))) failed = list(map(str, range(1, 10, 2))) class TestDependency(ClusterTestCase): def setup_method(self): super().setup_method() self.user_ns = {'__builtins__': __builtins__} self.view = self.client.load_balanced_view() self.dview = self.client[-1] self.succeeded = set(map(str, range(0, 25, 2))) self.failed = set(map(str, range(1, 25, 2))) def assertMet(self, dep): assert dep.check(self.succeeded, self.failed), "Dependency should be met" def assertUnmet(self, dep): assert not dep.check( self.succeeded, self.failed ), "Dependency should not be met" def assertUnreachable(self, dep): assert dep.unreachable( self.succeeded, self.failed ), "Dependency should be unreachable" def assertReachable(self, dep): assert not dep.unreachable( self.succeeded, self.failed ), "Dependency should be reachable" def cancan(self, f): """decorator to pass through canning into self.user_ns""" return uncan(can(f), self.user_ns) def test_require_imports(self): """test that @require imports names""" @self.cancan @ipp.require('base64') @interactive def encode(arg): return base64.b64encode(arg) # noqa: F821 # must pass through canning to properly connect namespaces assert encode(b'foo') == b'Zm9v' def test_success_only(self): dep = ipp.Dependency(mixed, success=True, failure=False) self.assertUnmet(dep) self.assertUnreachable(dep) dep.all = False self.assertMet(dep) self.assertReachable(dep) dep = ipp.Dependency(completed, success=True, failure=False) self.assertMet(dep) self.assertReachable(dep) dep.all = False self.assertMet(dep) self.assertReachable(dep) def test_failure_only(self): dep = ipp.Dependency(mixed, success=False, failure=True) self.assertUnmet(dep) self.assertUnreachable(dep) dep.all = False self.assertMet(dep) self.assertReachable(dep) dep = ipp.Dependency(completed, success=False, failure=True) self.assertUnmet(dep) self.assertUnreachable(dep) dep.all = False self.assertUnmet(dep) self.assertUnreachable(dep) def test_require_function(self): @ipp.interactive def bar(a): return func(a) @ipp.require(func) @ipp.interactive def bar2(a): return func(a) self.client[:].clear() with raises_remote(NameError): self.view.apply_sync(bar, 5) ar = self.view.apply_async(bar2, 5) assert ar.get(5) == func(5) def test_require_object(self): @ipp.require(foo=func) @ipp.interactive def bar(a): return foo(a) # noqa: F821 ar = self.view.apply_async(bar, 5) assert ar.get(5) == func(5) ipyparallel-8.8.0/ipyparallel/tests/test_executor.py000066400000000000000000000035061460376056100230100ustar00rootroot00000000000000"""Tests for Executor API""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import time from ipyparallel.client.view import LazyMapIterator, LoadBalancedView from .clienttest import ClusterTestCase def wait(n): import time time.sleep(n) return n def echo(x): return x class TestExecutor(ClusterTestCase): def test_client_executor(self): executor = self.client.executor() assert isinstance(executor.view, LoadBalancedView) f = executor.submit(lambda x: 2 * x, 5) r = f.result() assert r == 10 def test_view_executor(self): view = self.client.load_balanced_view() executor = view.executor assert executor.view is view def test_executor_submit(self): view = self.client.load_balanced_view() executor = view.executor f = executor.submit(lambda x, y: x * y, 2, 3) r = f.result() assert r == 6 def test_executor_map(self): view = self.client.load_balanced_view() executor = view.executor gen = executor.map(lambda x: x, range(5)) assert isinstance(gen, LazyMapIterator) for i, r in enumerate(gen): assert i == r def test_executor_context(self): view = self.client.load_balanced_view() executor = view.executor with executor: f = executor.submit(time.sleep, 0.5) assert not f.done() m = executor.map(lambda x: x, range(10)) assert len(view.history) == 11 # Executor context calls shutdown # shutdown doesn't shutdown engines, # but it should at least wait for results to finish assert f.done() tic = time.perf_counter() list(m) toc = time.perf_counter() assert toc - tic < 0.5 ipyparallel-8.8.0/ipyparallel/tests/test_joblib.py000066400000000000000000000027321460376056100224130ustar00rootroot00000000000000import warnings from unittest import mock import pytest import ipyparallel as ipp from .clienttest import ClusterTestCase, add_engines try: with warnings.catch_warnings(): warnings.simplefilter("ignore") from joblib import Parallel, delayed from ipyparallel.client._joblib import IPythonParallelBackend # noqa except (ImportError, TypeError): have_joblib = False else: have_joblib = True def neg(x): return -1 * x class TestJobLib(ClusterTestCase): def setup_method(self): if not have_joblib: pytest.skip("Requires joblib >= 0.10") super().setup_method() add_engines(1, total=True) def test_default_backend(self): """ipyparallel.register_joblib_backend() registers default backend""" ipp.register_joblib_backend() with mock.patch.object(ipp.Client, "__new__", lambda *a, **kw: self.client): p = Parallel(backend='ipyparallel') assert p._backend._view.client is self.client def test_register_backend(self): view = self.client.load_balanced_view() view.register_joblib_backend('view') p = Parallel(backend='view') assert p._backend._view is view def test_joblib_backend(self): view = self.client.load_balanced_view() view.register_joblib_backend('view') p = Parallel(backend='view') result = p(delayed(neg)(i) for i in range(10)) assert result == [neg(i) for i in range(10)] ipyparallel-8.8.0/ipyparallel/tests/test_launcher.py000066400000000000000000000110231460376056100227440ustar00rootroot00000000000000"""Tests for launchers Doesn't actually start any subprocesses, but goes through the motions of constructing objects, which should test basic config. """ import logging import os import sys import time from subprocess import Popen import entrypoints import pytest from traitlets.config import Config from ipyparallel.cluster import launcher as launcher_mod # ------------------------------------------------------------------------------- # TestCase Mixins # ------------------------------------------------------------------------------- @pytest.fixture() def profile_dir(tmpdir): return str(tmpdir.mkdir("profile_foo")) @pytest.fixture() def cluster_id(): return str(time.time()) @pytest.fixture() def work_dir(tmpdir): return str(tmpdir.mkdir("work")) @pytest.fixture() def build_launcher(work_dir, profile_dir, cluster_id): default_kwargs = dict( work_dir=work_dir, profile_dir=profile_dir, cluster_id=cluster_id, log=logging.getLogger(), ) def build_launcher(Launcher, **kwargs): kw = {} kw.update(default_kwargs) kw.update(kwargs) return Launcher(**kw) return build_launcher @pytest.fixture(params=launcher_mod.all_launchers) def launcher(request, build_launcher): return build_launcher(Launcher=request.param) BATCH_LAUNCHERS = [ cls for cls in launcher_mod.all_launchers if issubclass( cls, (launcher_mod.BatchControllerLauncher, launcher_mod.BatchEngineSetLauncher) ) ] @pytest.fixture(params=BATCH_LAUNCHERS) def batch_launcher(request, build_launcher): return build_launcher(Launcher=request.param) SSH_LAUNCHERS = [ l for l in launcher_mod.all_launchers if issubclass(l, launcher_mod.SSHLauncher) and l is not launcher_mod.SSHLauncher ] @pytest.fixture(params=SSH_LAUNCHERS) def ssh_launcher(request, build_launcher): return build_launcher(Launcher=request.param) WINHPC_LAUNCHERS = [ l for l in launcher_mod.all_launchers if issubclass(l, launcher_mod.WindowsHPCLauncher) and l is not launcher_mod.WindowsHPCLauncher ] @pytest.fixture(params=WINHPC_LAUNCHERS) def winhpc_launcher(request, build_launcher): return build_launcher(Launcher=request.param) def test_profile_dir_env(launcher, profile_dir): env = launcher.get_env() assert "IPP_PROFILE_DIR" in env assert env["IPP_PROFILE_DIR"] == profile_dir def test_cluster_id_env(launcher, cluster_id): env = launcher.get_env() assert "IPP_CLUSTER_ID" in env assert env["IPP_CLUSTER_ID"] == cluster_id def test_batch_template(batch_launcher, work_dir): launcher = batch_launcher batch_file = os.path.join(work_dir, launcher.batch_file_name) assert launcher.batch_file == batch_file launcher.write_batch_script(1) assert os.path.isfile(batch_file) def test_winhpc_template(winhpc_launcher, work_dir): launcher = winhpc_launcher job_file = os.path.join(work_dir, launcher.job_file_name) print(job_file) assert launcher.job_file == job_file launcher.write_job_file(1) assert os.path.isfile(job_file) def test_ssh_remote_profile_dir(ssh_launcher, profile_dir): launcher = ssh_launcher assert launcher.remote_profile_dir == profile_dir cfg = Config() cfg[launcher.__class__.__name__].remote_profile_dir = "foo" launcher.update_config(cfg) assert launcher.remote_profile_dir == "foo" def test_ssh_waitpid(capsys): proc = Popen([sys.executable, '-c', 'import time; time.sleep(1)']) pid = proc.pid def _wait_one(timeout): launcher_mod.ssh_waitpid(pid, timeout=timeout) captured = capsys.readouterr() return launcher_mod._ssh_outputs(captured.out) # first run, it's alive assert _wait_one(timeout=0.1) == {"process_running": "1"} # second run, process exits assert _wait_one(timeout=5) == {"process_running": "0", "exit_code": "-1"} # third run, process has already exited assert _wait_one(timeout=5) == {"process_running": "0", "exit_code": "-1"} @pytest.mark.parametrize("kind", ("controller", "engine")) def test_entrypoints(kind): group_name = f"ipyparallel.{kind}_launchers" group = entrypoints.get_group_named(group_name) assert len(group) > 2 for key, entrypoint in group.items(): # verify entrypoints are valid cls = entrypoint.load() # verify find method assert launcher_mod.find_launcher_class(key, kind=kind) is cls # verify abbreviation roundtrip abbreviation = launcher_mod.abbreviate_launcher_class(cls) assert abbreviation == key ipyparallel-8.8.0/ipyparallel/tests/test_lbview.py000066400000000000000000000213331460376056100224400ustar00rootroot00000000000000"""test LoadBalancedView objects""" import time from itertools import count import pytest import ipyparallel as ipp from ipyparallel import error from .clienttest import ClusterTestCase, crash, raises_remote class TestLoadBalancedView(ClusterTestCase): def setup_method(self): super().setup_method() self.view = self.client.load_balanced_view() def test_z_crash(self): """test graceful handling of engine death (balanced)""" self.add_engines(1) ar = self.view.apply_async(crash) with raises_remote(error.EngineError): ar.get(10) eid = ar.engine_id tic = time.time() while eid in self.client.ids and time.time() - tic < 5: time.sleep(0.01) assert eid not in self.client.ids def test_map(self): def f(x): return x**2 data = list(range(16)) ar = self.view.map_async(f, data) assert len(ar) == len(data) r = ar.get() assert r == list(map(f, data)) def test_map_generator(self): def f(x): return x**2 data = list(range(16)) ar = self.view.map_async(f, iter(data)) r = ar.get() assert r == list(map(f, iter(data))) def test_map_short_first(self): def f(x, y): if y is None: return y if x is None: return x return x * y data = list(range(10)) data2 = list(range(4)) ar = self.view.map_async(f, data, data2) assert len(ar) == len(data2) r = ar.get() assert r == list(map(f, data, data2)) def test_map_short_last(self): def f(x, y): if y is None: return y if x is None: return x return x * y data = list(range(4)) data2 = list(range(10)) ar = self.view.map_async(f, data, data2) assert len(ar) == len(data) r = ar.get() assert r == list(map(f, data, data2)) def test_map_unordered(self): def f(x): return x**2 def slow_f(x): import time time.sleep(0.05 * x) return x**2 data = list(range(16, 0, -1)) reference = list(map(f, data)) amr = self.view.map_async(slow_f, data, ordered=False) assert isinstance(amr, ipp.AsyncMapResult) # check individual elements, retrieved as they come # list comprehension uses __iter__ astheycame = [r for r in amr] # Ensure that at least one result came out of order: assert astheycame, reference != "should not have preserved order" assert sorted(astheycame, reverse=True) == reference, "result corrupted" def test_map_ordered(self): def f(x): return x**2 def slow_f(x): import time time.sleep(0.05 * x) return x**2 data = list(range(16, 0, -1)) reference = list(map(f, data)) amr = self.view.map_async(slow_f, data) assert isinstance(amr, ipp.AsyncMapResult) # check individual elements, retrieved as they come # list(amr) uses __iter__ astheycame = list(amr) # Ensure that results came in order assert astheycame == reference assert amr.get() == reference def test_map_iterable(self): """test map on iterables (balanced)""" view = self.view # 101 is prime, so it won't be evenly distributed arr = range(101) # so that it will be an iterator, even in Python 3 it = iter(arr) r = view.map_sync(lambda x: x, arr) assert r == list(arr) def test_imap_max_outstanding(self): view = self.view source = count() def task(i): import time time.sleep(0.1) return i gen = view.imap(task, source, max_outstanding=5) # should submit at least max_outstanding first_result = next(gen) assert len(view.history) >= 5 # retrieving results should submit another task second_result = next(gen) assert 5 <= len(view.history) <= 10 self.client.wait(timeout=self.timeout) def test_imap_infinite(self): view = self.view source = count() def task(i): import time time.sleep(0.1) return i gen = view.imap(task, source, max_outstanding=2) results = [] for i in gen: results.append(i) if i >= 3: break # stop consuming the iterator gen.cancel() assert len(results) == 4 # wait self.client.wait(timeout=self.timeout) # verify that max_outstanding wasn't exceeded assert 4 <= len(self.view.history) < 10 def test_imap_unordered(self): self.minimum_engines(4) view = self.view source = count() def yield_up_and_down(n): for i in range(n): if i % 4 == 0: yield 1 + i / 100 else: yield i / 100 def task(t): import time time.sleep(t) return t gen = view.imap(task, yield_up_and_down(10), max_outstanding=2, ordered=False) results = [] for i, t in enumerate(gen): results.append(t) if i >= 2: break # stop consuming gen.cancel() assert len(results) == 3 print(results) assert all([r < 1 for r in results]) # wait self.client.wait(timeout=self.timeout) # verify that max_outstanding wasn't exceeded assert 4 <= len(self.view.history) <= 6 def test_imap_return_exceptions(self): view = self.view source = count() def fail_on_even(n): if n % 2 == 0: raise ValueError("even!") return n gen = view.imap(fail_on_even, range(5), return_exceptions=True) for i, r in enumerate(gen): if i % 2 == 0: assert isinstance(r, error.RemoteError) else: assert r == i def test_abort(self): view = self.view ar = self.client[:].apply_async(time.sleep, 0.5) ar = self.client[:].apply_async(time.sleep, 0.5) time.sleep(0.2) ar2 = view.apply_async(lambda: 2) ar3 = view.apply_async(lambda: 3) view.abort(ar2) view.abort(ar3.msg_ids) with pytest.raises(error.TaskAborted): ar2.get() with pytest.raises(error.TaskAborted): ar3.get() def test_retries(self): self.minimum_engines(3) view = self.view def fail(): raise ValueError("Failed!") for r in range(len(self.client) - 1): with view.temp_flags(retries=r): with raises_remote(ValueError): view.apply_sync(fail) with view.temp_flags(retries=len(self.client), timeout=0.1): with raises_remote(error.TaskTimeout): view.apply_sync(fail) def test_short_timeout(self): self.minimum_engines(2) view = self.view def fail(): import time time.sleep(0.25) raise ValueError("Failed!") with view.temp_flags(retries=1, timeout=0.01): with raises_remote(ValueError): view.apply_sync(fail) def test_invalid_dependency(self): view = self.view with view.temp_flags(after='12345'): with raises_remote(error.InvalidDependency): view.apply_sync(lambda: 1) def test_impossible_dependency(self): self.minimum_engines(2) view = self.client.load_balanced_view() ar1 = view.apply_async(lambda: 1) ar1.get() e1 = ar1.engine_id e2 = e1 while e2 == e1: ar2 = view.apply_async(lambda: 1) ar2.get() e2 = ar2.engine_id with view.temp_flags(follow=[ar1, ar2]): with raises_remote(error.ImpossibleDependency): view.apply_sync(lambda: 1) def test_follow(self): ar = self.view.apply_async(lambda: 1) ar.get() ars = [] first_id = ar.engine_id self.view.follow = ar for i in range(5): ars.append(self.view.apply_async(lambda: 1)) self.view.wait(ars) for ar in ars: assert ar.engine_id == first_id def test_after(self): view = self.view ar = view.apply_async(time.sleep, 0.5) with view.temp_flags(after=ar): ar2 = view.apply_async(lambda: 1) ar.wait() ar2.wait() assert ar2.started >= ar.completed ipyparallel-8.8.0/ipyparallel/tests/test_magics.py000066400000000000000000000451431460376056100224200ustar00rootroot00000000000000"""Test Parallel magics""" import re import signal import sys import time import pytest from IPython import get_ipython from IPython.utils.io import capture_output import ipyparallel as ipp from ipyparallel import AsyncResult from .clienttest import ClusterTestCase, generate_output, raises_remote @pytest.mark.usefixtures('ipython_interactive') class TestParallelMagics(ClusterTestCase): def test_px_blocking(self): ip = get_ipython() v = self.client[-1:] v.activate() v.block = True ip.run_line_magic('px', 'a=5') assert v['a'] == [5] ip.run_line_magic('px', 'a=10') assert v['a'] == [10] # just 'print a' works ~99% of the time, but this ensures that # the stdout message has arrived when the result is finished: with capture_output() as io: ip.run_line_magic( 'px', 'import sys,time;print(a);sys.stdout.flush();time.sleep(0.2)' ) assert '[stdout:' in io.stdout assert '\n\n' not in io.stdout assert io.stdout.rstrip().endswith('10') def test_px_var_expand(self): ip = get_ipython() v = self.client[-1:] v.activate() v.block = True ip.user_ns['x'] = 'client' v['x'] = 'engine' ip.run_line_magic('px', 'y=f"{x}"') assert v['y'] == ["engine"] def test_cell_px_var_expand(self): ip = get_ipython() v = self.client[-1:] v.activate() v.block = True ip.user_ns['x'] = 'client' v['x'] = 'engine' ip.user_ns["out_name"] = "theoutput" ip.run_cell_magic('px', '-o {out_name}', 'y=f"{x}"') assert v['y'] == ["engine"] assert "theoutput" in ip.user_ns assert isinstance(ip.user_ns["theoutput"], ipp.AsyncResult) def _check_generated_stderr(self, stderr, n): expected = [ r'\[stderr:\d+\]', '^stderr$', '^stderr2$', ] * n assert '\n\n' not in stderr lines = stderr.splitlines() assert len(lines), len(expected) == stderr for line, expect in zip(lines, expected): if isinstance(expect, str): expect = [expect] for ex in expect: assert re.search(ex, line) is not None, f"Expected {ex!r} in {line!r}" def _check_expected_lines_unordered(self, expected, lines): for expect in expected: found = False for line in lines: found = found | (re.search(expect, line) is not None) if found: break assert found, f"Expected {expect!r} in output: {lines}" def test_cellpx_block_args(self): """%%px --[no]block flags work""" ip = get_ipython() v = self.client[-1:] v.activate() v.block = False for block in (True, False): v.block = block ip.run_line_magic("pxconfig", "--verbose") with capture_output(display=False) as io: ip.run_cell_magic("px", "", "1") if block: assert io.stdout.startswith("Parallel"), io.stdout else: assert io.stdout.startswith("Async"), io.stdout with capture_output(display=False) as io: ip.run_cell_magic("px", "--block", "1") assert io.stdout.startswith("Parallel"), io.stdout with capture_output(display=False) as io: ip.run_cell_magic("px", "--noblock", "1") assert io.stdout.startswith("Async"), io.stdout def test_cellpx_groupby_engine(self): """%%px --group-outputs=engine""" ip = get_ipython() v = self.client[:] v.block = True v.activate() v['generate_output'] = generate_output with capture_output(display=False) as io: ip.run_cell_magic( 'px', '--group-outputs=engine --no-stream', 'generate_output()' ) assert '\n\n' not in io.stdout lines = io.stdout.splitlines() expected = [ r'\[stdout:\d+\]', 'stdout', 'stdout2', r'\[output:\d+\]', r'IPython\.core\.display\.HTML', r'IPython\.core\.display\.Math', r'Out\[\d+:\d+\]:.*IPython\.core\.display\.Math', ] * len(v) assert len(lines), len(expected) == io.stdout for line, expect in zip(lines, expected): if isinstance(expect, str): expect = [expect] for ex in expect: assert re.search(ex, line) is not None, f"Expected {ex!r} in {line!r}" self._check_generated_stderr(io.stderr, len(v)) def test_cellpx_groupby_order(self): """%%px --group-outputs=order""" ip = get_ipython() v = self.client[:] v.block = True v.activate() v['generate_output'] = generate_output with capture_output(display=False) as io: ip.run_cell_magic( 'px', '--group-outputs=order --no-stream', 'generate_output()' ) assert '\n\n' not in io.stdout lines = io.stdout.splitlines() expected = [] expected.extend( [ r'\[stdout:\d+\]', 'stdout', 'stdout2', ] * len(v) ) expected.extend( [ r'\[output:\d+\]', 'IPython.core.display.HTML', ] * len(v) ) expected.extend( [ r'\[output:\d+\]', 'IPython.core.display.Math', ] * len(v) ) expected.extend([r'Out\[\d+:\d+\]:.*IPython\.core\.display\.Math'] * len(v)) assert len(lines), len(expected) == io.stdout for line, expect in zip(lines, expected): if isinstance(expect, str): expect = [expect] for ex in expect: assert re.search(ex, line) is not None, f"Expected {ex!r} in {line!r}" self._check_generated_stderr(io.stderr, len(v)) def test_cellpx_groupby_type(self): """%%px --group-outputs=type""" ip = get_ipython() v = self.client[:] v.block = True v.activate() v['generate_output'] = generate_output with capture_output(display=False) as io: ip.run_cell_magic( 'px', '--group-outputs=type --no-stream', 'generate_output()' ) assert '\n\n' not in io.stdout lines = io.stdout.splitlines() expected = [] expected.extend( [ r'\[stdout:\d+\]', 'stdout', 'stdout2', ] * len(v) ) expected.extend( [ r'\[output:\d+\]', r'IPython\.core\.display\.HTML', r'IPython\.core\.display\.Math', ] * len(v) ) expected.extend([(r'Out\[\d+:\d+\]', r'IPython\.core\.display\.Math')] * len(v)) assert len(lines), len(expected) == io.stdout for line, expect in zip(lines, expected): if isinstance(expect, str): expect = [expect] for ex in expect: assert re.search(ex, line) is not None, f"Expected {ex!r} in {line!r}" self._check_generated_stderr(io.stderr, len(v)) def test_cellpx_error_stream(self): self.minimum_engines(6) ip = get_ipython() v = self.client[:] v.block = True v.activate() v.scatter("rank", range(len(v)), flatten=True, block=True) with capture_output(display=False) as io: with pytest.raises(ipp.error.AlreadyDisplayedError) as exc_info: ip.run_cell_magic( "px", "--stream", "import time; time.sleep(rank); raise RuntimeError(f'oops! {rank}')", ) print(io.stdout) print(io.stderr, file=sys.stderr) assert 'RuntimeError' in io.stderr assert len(v) <= io.stderr.count("RuntimeError:") < len(v) * 2 printed_tb = "\n".join(exc_info.value.render_traceback()) assert printed_tb == f"{len(v)} errors" def test_cellpx_error_no_stream(self): self.minimum_engines(6) ip = get_ipython() v = self.client[:] v.block = True v.activate() v.scatter("rank", range(len(v)), flatten=True, block=True) with capture_output(display=False) as io: with pytest.raises(ipp.error.CompositeError) as exc_info: ip.run_cell_magic( "px", "--no-stream", "import time; time.sleep(rank); raise RuntimeError(f'oops! {rank}')", ) print(io.stdout) print(io.stderr, file=sys.stderr) assert 'RuntimeError' not in io.stderr assert io.stderr.strip() == "" printed_tb = "\n".join(exc_info.value.render_traceback()) assert printed_tb.count("RuntimeError:") >= ipp.error.CompositeError.tb_limit def test_cellpx_stream(self): """%%px --stream""" self.minimum_engines(6) ip = get_ipython() v = self.client[:] v.block = True v.activate() v['generate_output'] = generate_output with capture_output(display=False) as io: ip.run_cell_magic('px', '--stream', 'generate_output()') print(io.stdout) print(io.stderr, file=sys.stderr) assert '\n\n' not in io.stdout print(io.stdout) lines = io.stdout.splitlines() expected = [] expected.extend( [ r'\[stdout:\d+\]', 'stdout', r'\[stdout:\d+\]', 'stdout2', r'\[output:\d+\]', r'IPython\.core\.display\.HTML', r'\[output:\d+\]', r'IPython\.core\.display\.Math', r'Out\[\d+:\d+\]:.*IPython\.core\.display\.Math', ] * len(v) ) # Check that all expected lines are in the output self._check_expected_lines_unordered(expected, lines) assert ( len(expected) - len(v) <= len(lines) <= len(expected) ), f"expected {len(expected)} lines, got: {io.stdout}" # Do the same for stderr print(io.stderr, file=sys.stderr) assert '\n\n' not in io.stderr lines = io.stderr.splitlines() expected = [] expected.extend( [ r'\[stderr:\d+\]', 'stderr', r'\[stderr:\d+\]', 'stderr2', ] * len(v) ) self._check_expected_lines_unordered(expected, lines) assert ( len(expected) - len(v) <= len(lines) <= len(expected) ), f"expected {len(expected)} lines, got: {io.stderr}" def test_px_nonblocking(self): ip = get_ipython() v = self.client[-1:] v.activate() v.block = False ip.run_line_magic('px', 'a=5') assert v['a'] == [5] ip.run_line_magic('px', 'a=10') assert v['a'] == [10] ip.run_line_magic('pxconfig', '--verbose') with capture_output() as io: ar = ip.run_line_magic('px', 'print (a)') assert isinstance(ar, AsyncResult) assert 'Async' in io.stdout assert '[stdout:' not in io.stdout assert '\n\n' not in io.stdout ar = ip.run_line_magic('px', '1/0') with raises_remote(ZeroDivisionError): ar.get() def test_autopx_blocking(self): ip = get_ipython() v = self.client[-1] v.activate() v.block = True with capture_output(display=False) as io: ip.run_line_magic('autopx', '') ip.run_cell('\n'.join(('a=5', 'b=12345', 'c=0'))) ip.run_cell('b*=2') ip.run_cell('print (b)') ip.run_cell('b') ip.run_cell("b/c") ip.run_line_magic('autopx', '') output = io.stdout assert output.startswith('%autopx enabled'), output assert output.rstrip().endswith('%autopx disabled'), output assert 'ZeroDivisionError' in output assert '\nOut[' in output assert ': 24690' in output ar = v.get_result(-1) # prevent TaskAborted on pulls, due to ZeroDivisionError time.sleep(0.5) assert v['a'] == 5 assert v['b'] == 24690 with raises_remote(ZeroDivisionError): ar.get() def test_autopx_nonblocking(self): ip = get_ipython() v = self.client[-1] v.activate() v.block = False with capture_output() as io: ip.run_line_magic('autopx', '') ip.run_cell('\n'.join(('a=5', 'b=10', 'c=0'))) ip.run_cell('print (b)') ip.run_cell('import time; time.sleep(0.1)') ip.run_cell("b/c") ip.run_cell('b*=2') ip.run_line_magic('autopx', '') output = io.stdout.rstrip() assert output.startswith('%autopx enabled'), output assert output.endswith('%autopx disabled'), output assert 'ZeroDivisionError' not in output ar = v.get_result(-2, owner=False) with raises_remote(ZeroDivisionError): ar.get() # prevent TaskAborted on pulls, due to ZeroDivisionError time.sleep(0.5) assert v['a'] == 5 # b*=2 will not fire, due to abort assert v['b'] == 10 def test_result(self): ip = get_ipython() v = self.client[-1] v.activate() data = dict(a=111, b=222) v.push(data, block=True) for name in ('a', 'b'): ip.run_line_magic('px', name) with capture_output(display=False) as io: ip.run_line_magic('pxresult', '') assert str(data[name]) in io.stdout def test_px_pylab(self): """%pylab works on engines""" pytest.importorskip('matplotlib') ip = get_ipython() v = self.client[-1] v.block = True v.activate() with capture_output() as io: ip.run_line_magic("px", "%pylab inline") assert ( "Populating the interactive namespace from numpy and matplotlib" in io.stdout ) with capture_output(display=False) as io: ip.run_line_magic("px", "plot(rand(100))") assert 'Out[' in io.stdout assert 'matplotlib.lines' in io.stdout def test_pxconfig(self): ip = get_ipython() rc = self.client v = rc.activate(-1, '_tst') assert v.targets == rc.ids[-1] ip.run_line_magic("pxconfig_tst", "-t :") assert v.targets == rc.ids ip.run_line_magic("pxconfig_tst", "-t ::2") assert v.targets == rc.ids[::2] ip.run_line_magic("pxconfig_tst", "-t 1::2") assert v.targets == rc.ids[1::2] ip.run_line_magic("pxconfig_tst", "-t 1") assert v.targets == 1 ip.run_line_magic("pxconfig_tst", "--block") assert v.block is True ip.run_line_magic("pxconfig_tst", "--noblock") assert v.block is False def test_cellpx_targets(self): """%%px --targets doesn't change defaults""" ip = get_ipython() rc = self.client view = rc.activate(rc.ids) assert view.targets == rc.ids ip.run_line_magic('pxconfig', '--verbose') for cell in ("pass", "1/0"): with capture_output(display=False) as io: try: ip.run_cell_magic("px", "--targets all", cell) except ipp.RemoteError: pass assert 'engine(s): all' in io.stdout assert view.targets == rc.ids def test_cellpx_block(self): """%%px --block doesn't change default""" ip = get_ipython() rc = self.client view = rc.activate(rc.ids) view.block = False assert view.targets == rc.ids ip.run_line_magic('pxconfig', '--verbose') for cell in ("pass", "1/0"): with capture_output(display=False) as io: try: ip.run_cell_magic("px", "--block", cell) except ipp.RemoteError: pass assert 'Async' not in io.stdout assert view.block is False def cellpx_keyboard_interrupt_test_helper(self, sig=None): """%%px with Keyboard Interrupt on blocking execution""" ip = get_ipython() v = self.client[:] v.block = True v.activate() def _sigalarm(sig, frame): raise KeyboardInterrupt signal.signal(signal.SIGALRM, _sigalarm) signal.alarm(2) with capture_output(display=False) as io: ip.run_cell_magic( "px", "" if sig is None else f"--signal-on-interrupt {sig}", "print('Entering...'); import time; time.sleep(5); print('Exiting...');", ) print(io.stdout) print(io.stderr, file=sys.stderr) assert ( 'Received Keyboard Interrupt. Sending signal {} to engines...'.format( "SIGINT" if sig is None else sig ) in io.stderr ) assert 'Exiting...' not in io.stdout @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_default(self): self.cellpx_keyboard_interrupt_test_helper() @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_SIGINT(self): self.cellpx_keyboard_interrupt_test_helper("SIGINT") @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_signal_2(self): self.cellpx_keyboard_interrupt_test_helper("2") @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_signal_0(self): self.cellpx_keyboard_interrupt_test_helper("0") @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_SIGKILL(self): self.cellpx_keyboard_interrupt_test_helper("SIGKILL") @pytest.mark.skipif( sys.platform.startswith("win"), reason="Signal tests don't pass on Windows yet" ) def test_cellpx_keyboard_interrupt_signal_9(self): self.cellpx_keyboard_interrupt_test_helper("9") ipyparallel-8.8.0/ipyparallel/tests/test_mongodb.py000066400000000000000000000025571460376056100226040ustar00rootroot00000000000000"""Tests for mongodb backend""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import os from unittest import TestCase import pytest from . import test_db c = None @pytest.fixture(scope='module') def mongo_conn(request): global c try: from pymongo import MongoClient except ImportError: pytest.skip("Requires mongodb") conn_kwargs = {} if 'DB_IP' in os.environ: conn_kwargs['host'] = os.environ['DB_IP'] if 'DBA_MONGODB_ADMIN_URI' in os.environ: # On ShiningPanda, we need a username and password to connect. They are # passed in a mongodb:// URI. conn_kwargs['host'] = os.environ['DBA_MONGODB_ADMIN_URI'] if 'DB_PORT' in os.environ: conn_kwargs['port'] = int(os.environ['DB_PORT']) try: c = MongoClient(**conn_kwargs) except Exception: c = None if c is not None: request.addfinalizer(lambda: c.drop_database('iptestdb')) return c @pytest.mark.usefixtures('mongo_conn') class TestMongoBackend(test_db.TaskDBTest, TestCase): """MongoDB backend tests""" def create_db(self): try: from ipyparallel.controller.mongodb import MongoDB return MongoDB(database='iptestdb', _connection=c) except Exception: pytest.skip("Couldn't connect to mongodb") ipyparallel-8.8.0/ipyparallel/tests/test_mpi.py000066400000000000000000000011721460376056100217340ustar00rootroot00000000000000import shutil import pytest from .test_cluster import ( test_get_output, # noqa: F401 test_restart_engines, # noqa: F401 test_signal_engines, # noqa: F401 test_start_stop_cluster, # noqa: F401 test_to_from_dict, # noqa: F401 ) # import tests that use engine_launcher_class fixture # override engine_launcher_class @pytest.fixture def engine_launcher_class(): if shutil.which("mpiexec") is None: pytest.skip("Requires mpiexec") return 'mpi' @pytest.fixture def controller_launcher_class(): if shutil.which("mpiexec") is None: pytest.skip("Requires mpiexec") return 'mpi' ipyparallel-8.8.0/ipyparallel/tests/test_remotefunction.py000066400000000000000000000026101460376056100242060ustar00rootroot00000000000000"""Tests for remote functions""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import ipyparallel as ipp from .clienttest import ClusterTestCase class TestRemoteFunctions(ClusterTestCase): def test_remote(self): v = self.client[-1] @ipp.remote(v, block=True) def foo(x, y=5): """multiply x * y""" return x * y assert foo.__name__ == 'foo' assert 'RemoteFunction' in foo.__doc__ assert 'multiply x' in foo.__doc__ z = foo(5) assert z == 25 z = foo(2, 3) assert z == 6 z = foo(x=5, y=2) assert z == 10 def test_parallel(self): n = 2 v = self.client[:n] @ipp.parallel(v, block=True) def foo(x): """multiply x * y""" return x * 2 assert foo.__name__ == 'foo' assert 'ParallelFunction' in foo.__doc__ assert 'multiply x' in foo.__doc__ z = foo([1, 2, 3, 4]) assert z, [1, 2, 1, 2, 3, 4, 3 == 4] def test_parallel_map(self): v = self.client.load_balanced_view() @ipp.parallel(v, block=True) def foo(x, y=5): """multiply x * y""" return x * y z = foo.map([1, 2, 3]) assert z, [5, 10 == 15] z = foo.map([1, 2, 3], [1, 2, 3]) assert z, [1, 4 == 9] ipyparallel-8.8.0/ipyparallel/tests/test_serialize.py000066400000000000000000000142571460376056100231460ustar00rootroot00000000000000"""test serialization tools""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import pickle from collections import namedtuple import pytest from ipyparallel import interactive from ipyparallel.serialize import deserialize_object, serialize_object from ipyparallel.serialize.canning import CannedArray, CannedClass # ------------------------------------------------------------------------------- # Globals and Utilities # ------------------------------------------------------------------------------- def roundtrip(obj): """roundtrip an object through serialization""" bufs = serialize_object(obj) obj2, remainder = deserialize_object(bufs) assert remainder == [] return obj2 SHAPES = ((100,), (1024, 10), (10, 8, 6, 5), (), (0,)) DTYPES = ('uint8', 'float64', 'int32', [('g', 'float32')], '|S10') # ------------------------------------------------------------------------------- # Tests # ------------------------------------------------------------------------------- def new_array(shape, dtype): import numpy return numpy.random.random(shape).astype(dtype) def test_roundtrip_simple(): for obj in [ 'hello', dict(a='b', b=10), [1, 2, 'hi'], (b'123', 'hello'), ]: obj2 = roundtrip(obj) assert obj == obj2 def test_roundtrip_nested(): for obj in [ dict(a=range(5), b={1: b'hello'}), [range(5), [range(3), (1, [b'whoda'])]], ]: obj2 = roundtrip(obj) assert obj == obj2 def test_roundtrip_buffered(): for obj in [dict(a=b"x" * 1025), b"hello" * 500, [b"hello" * 501, 1, 2, 3]]: bufs = serialize_object(obj) assert len(bufs) == 2 obj2, remainder = deserialize_object(bufs) assert remainder == [] assert obj == obj2 def test_roundtrip_memoryview(): b = b'asdf' * 1025 view = memoryview(b) bufs = serialize_object(view) assert len(bufs) == 2 v2, remainder = deserialize_object(bufs) assert remainder == [] assert v2.tobytes() == b def test_numpy(): pytest.importorskip('numpy') from numpy.testing import assert_array_equal for shape in SHAPES: for dtype in DTYPES: A = new_array(shape, dtype=dtype) bufs = serialize_object(A) bufs = [memoryview(b) for b in bufs] B, r = deserialize_object(bufs) assert r == [] assert A.shape == B.shape assert A.dtype == B.dtype assert_array_equal(A, B) def test_recarray(): pytest.importorskip('numpy') from numpy.testing import assert_array_equal for shape in SHAPES: for dtype in [ [('f', float), ('s', '|S10')], [('n', int), ('s', '|S1'), ('u', 'uint32')], ]: A = new_array(shape, dtype=dtype) bufs = serialize_object(A) B, r = deserialize_object(bufs) assert r == [] assert A.shape == B.shape assert A.dtype == B.dtype assert_array_equal(A, B) def test_numpy_in_seq(): pytest.importorskip('numpy') from numpy.testing import assert_array_equal for shape in SHAPES: for dtype in DTYPES: A = new_array(shape, dtype=dtype) bufs = serialize_object((A, 1, 2, b'hello')) canned = pickle.loads(bufs[0]) assert isinstance(canned[0], CannedArray) tup, r = deserialize_object(bufs) B = tup[0] assert r == [] assert A.shape == B.shape assert A.dtype == B.dtype assert_array_equal(A, B) def test_numpy_in_dict(): pytest.importorskip('numpy') from numpy.testing import assert_array_equal for shape in SHAPES: for dtype in DTYPES: A = new_array(shape, dtype=dtype) bufs = serialize_object(dict(a=A, b=1, c=range(20))) canned = pickle.loads(bufs[0]) assert isinstance(canned['a'], CannedArray) d, r = deserialize_object(bufs) B = d['a'] assert r == [] assert A.shape == B.shape assert A.dtype == B.dtype assert_array_equal(A, B) def test_class(): @interactive class C: a = 5 bufs = serialize_object(dict(C=C)) canned = pickle.loads(bufs[0]) assert isinstance(canned['C'], CannedClass) d, r = deserialize_object(bufs) C2 = d['C'] assert C2.a == C.a def test_class_oldstyle(): @interactive class C: a = 5 bufs = serialize_object(dict(C=C)) canned = pickle.loads(bufs[0]) assert isinstance(canned['C'], CannedClass) d, r = deserialize_object(bufs) C2 = d['C'] assert C2.a == C.a def test_tuple(): tup = (lambda x: x, 1) bufs = serialize_object(tup) canned = pickle.loads(bufs[0]) assert isinstance(canned, tuple) t2, r = deserialize_object(bufs) assert t2[0](t2[1]) == tup[0](tup[1]) point = namedtuple('point', 'x y') def test_namedtuple(): p = point(1, 2) bufs = serialize_object(p) canned = pickle.loads(bufs[0]) assert isinstance(canned, point) p2, r = deserialize_object(bufs, globals()) assert p2.x == p.x assert p2.y == p.y def test_list(): lis = [lambda x: x, 1] bufs = serialize_object(lis) canned = pickle.loads(bufs[0]) assert isinstance(canned, list) l2, r = deserialize_object(bufs) assert l2[0](l2[1]) == lis[0](lis[1]) def test_class_inheritance(): @interactive class C: a = 5 @interactive class D(C): b = 10 bufs = serialize_object(dict(D=D)) canned = pickle.loads(bufs[0]) assert isinstance(canned['D'], CannedClass) d, r = deserialize_object(bufs) D2 = d['D'] assert D2.a == D.a assert D2.b == D.b def test_pickle_threshold(): numpy = pytest.importorskip('numpy') from numpy.testing import assert_array_equal A = numpy.ones((5, 5)) bufs = serialize_object(A, 1024) assert len(bufs) == 1 B, _ = deserialize_object(bufs) assert_array_equal(A, B) A = numpy.ones((512, 512)) bufs = serialize_object(A, 1024) assert len(bufs) == 2 B, _ = deserialize_object(bufs) assert_array_equal(A, B) ipyparallel-8.8.0/ipyparallel/tests/test_slurm.py000066400000000000000000000020221460376056100223040ustar00rootroot00000000000000import shutil import pytest from traitlets.config import Config from .conftest import temporary_ipython_dir from .test_cluster import ( test_get_output, # noqa: F401 test_restart_engines, # noqa: F401 test_signal_engines, # noqa: F401 test_start_stop_cluster, # noqa: F401 test_to_from_dict, # noqa: F401 ) # put ipython dir on shared filesystem @pytest.fixture(autouse=True, scope="module") def ipython_dir(request): if shutil.which("sbatch") is None: pytest.skip("Requires slurm") with temporary_ipython_dir(prefix="/data/") as ipython_dir: yield ipython_dir @pytest.fixture def cluster_config(): c = Config() c.Cluster.controller_ip = '0.0.0.0' return c # override launcher classes @pytest.fixture def engine_launcher_class(): if shutil.which("sbatch") is None: pytest.skip("Requires slurm") return 'slurm' @pytest.fixture def controller_launcher_class(): if shutil.which("sbatch") is None: pytest.skip("Requires slurm") return 'slurm' ipyparallel-8.8.0/ipyparallel/tests/test_ssh.py000066400000000000000000000032451460376056100217470ustar00rootroot00000000000000from functools import partial import pytest from traitlets.config import Config from .conftest import Cluster as BaseCluster # noqa: F401 from .test_cluster import ( test_get_output, # noqa: F401 test_restart_engines, # noqa: F401 test_signal_engines, # noqa: F401 test_start_stop_cluster, # noqa: F401 test_to_from_dict, # noqa: F401 ) # import tests that use engine_launcher_class fixture @pytest.fixture(params=["SSH", "SSHProxy"]) def ssh_config(ssh_key, request): c = Config() c.Cluster.controller_ip = '0.0.0.0' c.Cluster.engine_launcher_class = request.param engine_set_cfg = c[f"{request.param}EngineSetLauncher"] engine_set_cfg.ssh_args = [ "-o", "UserKnownHostsFile=/dev/null", "-o", "StrictHostKeyChecking=no", "-i", ssh_key, ] engine_set_cfg.scp_args = list(engine_set_cfg.ssh_args) # copy engine_set_cfg.remote_python = "/opt/conda/bin/python3" engine_set_cfg.remote_profile_dir = "/home/ciuser/.ipython/profile_default" engine_set_cfg.engine_args = ['--debug'] c.SSHProxyEngineSetLauncher.hostname = "127.0.0.1" c.SSHProxyEngineSetLauncher.ssh_args.append("-p2222") c.SSHProxyEngineSetLauncher.scp_args.append("-P2222") c.SSHProxyEngineSetLauncher.user = "ciuser" c.SSHEngineSetLauncher.engines = {"ciuser@127.0.0.1:2222": 4} return c @pytest.fixture def Cluster(ssh_config, BaseCluster): # noqa: F811 """Override Cluster to add ssh config""" return partial(BaseCluster, config=ssh_config) # override engine_launcher_class @pytest.fixture def engine_launcher_class(ssh_config): return ssh_config.Cluster.engine_launcher_class ipyparallel-8.8.0/ipyparallel/tests/test_util.py000066400000000000000000000012521460376056100221230ustar00rootroot00000000000000import socket import pytest from jupyter_client.localinterfaces import localhost, public_ips from ipyparallel import util def test_disambiguate_ip(): # garbage in, garbage out public_ip = public_ips()[0] assert util.disambiguate_ip_address('garbage') == 'garbage' assert util.disambiguate_ip_address('0.0.0.0', socket.gethostname()) == localhost() wontresolve = 'this.wontresolve.dns' with pytest.warns( RuntimeWarning, match=f"IPython could not determine IPs for {wontresolve}" ): assert util.disambiguate_ip_address('0.0.0.0', wontresolve) == wontresolve assert util.disambiguate_ip_address('0.0.0.0', public_ip) == localhost() ipyparallel-8.8.0/ipyparallel/tests/test_view.py000066400000000000000000000721131460376056100221240ustar00rootroot00000000000000"""test View objects""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import base64 import platform import sys import time from collections import namedtuple from tempfile import NamedTemporaryFile import pytest import zmq from IPython import get_ipython from IPython.utils.io import capture_output import ipyparallel as ipp from ipyparallel import AsyncHubResult, AsyncMapResult, AsyncResult, error from ipyparallel.util import interactive from .clienttest import ( ClusterTestCase, conditional_crash, raises_remote, skip_without, wait, ) point = namedtuple("point", "x y") @pytest.mark.usefixtures('ipython') class TestView(ClusterTestCase): def setup_method(self): # On Win XP, wait for resource cleanup, else parallel test group fails if platform.system() == "Windows" and platform.win32_ver()[0] == "XP": # 1 sec fails. 1.5 sec seems ok. Using 2 sec for margin of safety time.sleep(2) super().setup_method() def test_z_crash_mux(self): """test graceful handling of engine death (direct)""" self.add_engines(1) self.minimum_engines(2) eid = self.client.ids[-1] view = self.client[-2:] view.scatter('should_crash', [False, True], flatten=True) ar = view.apply_async(conditional_crash, ipp.Reference("should_crash")) with raises_remote(error.EngineError): ar.get(10) tic = time.perf_counter() while eid in self.client.ids and time.perf_counter() - tic < 5: time.sleep(0.05) assert eid not in self.client.ids def test_push_pull(self): """test pushing and pulling""" data = dict(a=10, b=1.05, c=list(range(10)), d={'e': (1, 2), 'f': 'hi'}) t = self.client.ids[-1] v = self.client[t] push = v.push pull = v.pull v.block = True nengines = len(self.client) push({'data': data}) d = pull('data') assert d == data self.client[:].push({'data': data}) d = self.client[:].pull('data', block=True) assert d == nengines * [data] ar = push({'data': data}, block=False) assert isinstance(ar, AsyncResult) r = ar.get() ar = self.client[:].pull('data', block=False) assert isinstance(ar, AsyncResult) r = ar.get() assert r == nengines * [data] self.client[:].push(dict(a=10, b=20)) r = self.client[:].pull(('a', 'b'), block=True) assert r, nengines * [[10 == 20]] def test_push_pull_function(self): "test pushing and pulling functions" def testf(x): return 2.0 * x t = self.client.ids[-1] v = self.client[t] v.block = True push = v.push pull = v.pull execute = v.execute push({'testf': testf}) r = pull('testf') assert r(1.0) == testf(1.0) execute('r = testf(10)') r = pull('r') assert r == testf(10) ar = self.client[:].push({'testf': testf}, block=False) ar.get() ar = self.client[:].pull('testf', block=False) rlist = ar.get() for r in rlist: assert r(1.0) == testf(1.0) execute("def g(x): return x*x") r = pull(('testf', 'g')) assert (r[0](10), r[1](10)) == (testf(10), 100) def test_push_function_globals(self): """test that pushed functions have access to globals""" @interactive def geta(): return a # noqa: F821 # self.add_engines(1) v = self.client[-1] v.block = True v['f'] = geta with raises_remote(NameError): v.execute('b=f()') v.execute('a=5') v.execute('b=f()') assert v['b'] == 5 def test_push_function_defaults(self): """test that pushed functions preserve default args""" def echo(a=10): return a v = self.client[-1] v.block = True v['f'] = echo v.execute('b=f()') assert v['b'] == 10 def test_get_result(self): """test getting results from the Hub.""" c = ipp.Client(profile='iptest') # self.add_engines(1) t = c.ids[-1] v = c[t] v2 = self.client[t] ar = v.apply_async(wait, 1) # give the monitor time to notice the message time.sleep(0.25) ahr = v2.get_result(ar.msg_ids[0], owner=False) assert isinstance(ahr, AsyncHubResult) assert ahr.get() == ar.get() ar2 = v2.get_result(ar.msg_ids[0]) assert not isinstance(ar2, AsyncHubResult) assert ahr.get() == ar2.get() c.close() def test_run_newline(self): """test that run appends newline to files""" with NamedTemporaryFile('w', delete=False) as f: f.write( """def g(): return 5 """ ) v = self.client[-1] v.run(f.name, block=True) assert v.apply_sync(lambda f: f(), ipp.Reference('g')) == 5 def test_apply_f_kwarg(self): v = self.client[-1] def echo_kwargs(**kwargs): return kwargs kwargs = v.apply_async(echo_kwargs, f=5).get(timeout=30) assert kwargs == dict(f=5) def test_apply_tracked(self): """test tracking for apply""" # self.add_engines(1) t = self.client.ids[-1] v = self.client[t] v.block = False def echo(n=1024 * 1024, **kwargs): with v.temp_flags(**kwargs): return v.apply(lambda x: x, 'x' * n) ar = echo(1, track=False) ar.wait_for_send(5) assert isinstance(ar._tracker, zmq.MessageTracker) assert ar.sent ar = echo(track=True) ar.wait_for_send(5) assert isinstance(ar._tracker, zmq.MessageTracker) assert ar.sent == ar._tracker.done ar._tracker.wait() assert ar.sent def test_push_tracked(self): t = self.client.ids[-1] ns = dict(x='x' * 1024 * 1024) v = self.client[t] ar = v.push(ns, block=False, track=False) assert ar.sent ar = v.push(ns, block=False, track=True) ar.wait_for_send() assert ar.sent == ar._tracker.done assert ar.sent ar.get() def test_scatter_tracked(self): t = self.client.ids x = 'x' * 1024 * 1024 ar = self.client[t].scatter('x', x, block=False, track=False) assert ar.sent ar = self.client[t].scatter('x', x, block=False, track=True) ar._sent_event.wait() assert isinstance(ar._tracker, zmq.MessageTracker) assert ar.sent == ar._tracker.done ar.wait_for_send() assert ar.sent ar.get() def test_remote_reference(self): v = self.client[-1] v['a'] = 123 ra = ipp.Reference('a') b = v.apply_sync(lambda x: x, ra) assert b == 123 def test_scatter_gather(self): view = self.client[:] seq1 = list(range(16)) view.scatter('a', seq1) seq2 = view.gather('a', block=True) assert seq2 == seq1 with raises_remote(NameError): view.gather('asdf', block=True) @skip_without('numpy') def test_scatter_gather_numpy(self): import numpy from numpy.testing import assert_array_equal view = self.client[:] a = numpy.arange(64) view.scatter('a', a, block=True) b = view.gather('a', block=True) assert_array_equal(b, a) def test_scatter_gather_lazy(self): """scatter/gather with targets='all'""" view = self.client.direct_view(targets='all') x = list(range(64)) view.scatter('x', x) gathered = view.gather('x', block=True) assert gathered == x @skip_without('numpy') def test_apply_numpy(self): """view.apply(f, ndarray)""" import numpy from numpy.testing import assert_array_equal A = numpy.random.random((100, 100)) view = self.client[-1] for dt in ['int32', 'uint8', 'float32', 'float64']: B = A.astype(dt) C = view.apply_sync(lambda x: x, B) assert_array_equal(B, C) @skip_without('numpy') def test_apply_numpy_object_dtype(self): """view.apply(f, ndarray) with dtype=object""" import numpy from numpy.testing import assert_array_equal view = self.client[-1] A = numpy.array([dict(a=5)]) B = view.apply_sync(lambda x: x, A) assert_array_equal(A, B) A = numpy.array([(0, dict(b=10))], dtype=[('i', int), ('o', object)]) B = view.apply_sync(lambda x: x, A) assert_array_equal(A, B) @skip_without('numpy') def test_push_pull_recarray(self): """push/pull recarrays""" import numpy from numpy.testing import assert_array_equal view = self.client[-1] R = numpy.array( [ (1, 'hi', 0.0), (2**30, 'there', 2.5), (-99999, 'world', -12345.6789), ], [('n', int), ('s', '|S10'), ('f', float)], ) view['RR'] = R R2 = view['RR'] r_dtype, r_shape = view.apply_sync( interactive(lambda: (RR.dtype, RR.shape)) # noqa: F821 ) assert r_dtype == R.dtype assert r_shape == R.shape assert R2.dtype == R.dtype assert R2.shape == R.shape assert_array_equal(R2, R) @skip_without('pandas') def test_push_pull_timeseries(self): """push/pull pandas.Series""" import pandas ts = pandas.Series(list(range(10))) view = self.client[-1] view.push(dict(ts=ts), block=True) rts = view['ts'] assert type(rts) == type(ts) assert (ts == rts).all() def test_map(self): view = self.client[:] def f(x): return x**2 data = list(range(16)) ar = view.map_async(f, data) assert len(ar) == len(data) r = ar.get() assert r, list(map(f == data)) def test_map_empty_sequence(self): view = self.client[:] r = view.map_sync(lambda x: x, []) assert r == [] def test_map_iterable(self): """test map on iterables (direct)""" view = self.client[:] # 101 is prime, so it won't be evenly distributed arr = range(101) # ensure it will be an iterator, even in Python 3 it = iter(arr) r = view.map_sync(lambda x: x, it) assert r == list(arr) @skip_without('numpy') def test_map_numpy(self): """test map on numpy arrays (direct)""" import numpy from numpy.testing import assert_array_equal view = self.client[:] # 101 is prime, so it won't be evenly distributed arr = numpy.arange(101) ar = view.map_async(lambda x: x, arr) assert len(ar) == len(arr) r = ar.get() assert_array_equal(r, arr) def test_scatter_gather_nonblocking(self): data = list(range(16)) view = self.client[:] view.scatter('a', data, block=False) ar = view.gather('a', block=False) assert ar.get() == data @skip_without('numpy') def test_scatter_gather_numpy_nonblocking(self): import numpy from numpy.testing import assert_array_equal a = numpy.arange(64) view = self.client[:] ar = view.scatter('a', a, block=False) assert isinstance(ar, AsyncResult) amr = view.gather('a', block=False) assert isinstance(amr, AsyncMapResult) assert_array_equal(amr.get(), a) def test_execute(self): view = self.client[:] # self.client.debug=True execute = view.execute ar = execute('c=30', block=False) assert isinstance(ar, AsyncResult) ar = execute('d=[0,1,2]', block=False) self.client.wait(ar, 1) assert len(ar.get()) == len(self.client) for c in view['c']: assert c == 30 def test_abort(self): view = self.client[-1] ar = view.execute('import time; time.sleep(1)', block=False) ar2 = view.apply_async(lambda: 2) view.abort(ar2) ar3 = view.apply_async(lambda: 3) view.abort(ar3.msg_ids) with pytest.raises(error.TaskAborted): ar2.get() with pytest.raises(error.TaskAborted): ar3.get() def test_abort_all(self): """view.abort() aborts all outstanding tasks""" view = self.client[-1] ars = [view.apply_async(time.sleep, 0.25) for i in range(10)] view.abort() view.wait(timeout=5) for ar in ars[5:]: with pytest.raises(error.TaskAborted): ar.get() def test_temp_flags(self): view = self.client[-1] view.block = True with view.temp_flags(block=False): assert not view.block assert view.block def test_importer(self): view = self.client[-1] view.clear(block=True) with view.importer: import re # noqa: F401 @interactive def findall(pat, s): # this globals() step isn't necessary in real code # only to prevent a closure in the test re = globals()['re'] # noqa: F811 return re.findall(pat, s) assert view.apply_sync(findall, r'\w+', 'hello world') == 'hello world'.split() def test_unicode_execute(self): """test executing unicode strings""" v = self.client[-1] v.block = True if sys.version_info[0] >= 3: code = "a='é'" else: code = "a=u'é'" v.execute(code) assert v['a'] == 'é' def test_unicode_apply_result(self): """test unicode apply results""" v = self.client[-1] r = v.apply_sync(lambda: 'é') assert r == 'é' def test_unicode_apply_arg(self): """test passing unicode arguments to apply""" v = self.client[-1] @interactive def check_unicode(a, check): assert not isinstance(a, bytes), "%r is bytes, not unicode" % a assert isinstance(check, bytes), "%r is not bytes" % check assert a.encode('utf8') == check, f"{a} != {check}" for s in ['é', 'ßø®∫', 'asdf']: try: v.apply_sync(check_unicode, s, s.encode('utf8')) except error.RemoteError as e: if e.ename == 'AssertionError': self.fail(e.evalue) else: raise e def test_map_reference(self): """view.map(, *seqs) should work""" v = self.client[:] v.scatter('n', self.client.ids, flatten=True) v.execute("f = lambda x,y: x*y") rf = ipp.Reference('f') nlist = list(range(10)) mlist = nlist[::-1] expected = [m * n for m, n in zip(mlist, nlist)] result = v.map_sync(rf, mlist, nlist) assert result == expected def test_apply_reference(self): """view.apply(, *args) should work""" v = self.client[:] v.scatter('n', self.client.ids, flatten=True) v.execute("f = lambda x: n*x") rf = ipp.Reference('f') result = v.apply_sync(rf, 5) expected = [5 * id for id in self.client.ids] assert result == expected def test_eval_reference(self): v = self.client[self.client.ids[0]] v['g'] = list(range(5)) rg = ipp.Reference('g[0]') def echo(x): return x assert v.apply_sync(echo, rg) == 0 def test_reference_nameerror(self): v = self.client[self.client.ids[0]] r = ipp.Reference('elvis_has_left') def echo(x): return x with raises_remote(NameError): v.apply_sync(echo, r) def test_single_engine_map(self): e0 = self.client[self.client.ids[0]] r = list(range(5)) check = [-1 * i for i in r] result = e0.map_sync(lambda x: -1 * x, r) assert result == check def test_len(self): """len(view) makes sense""" e0 = self.client[self.client.ids[0]] assert len(e0) == 1 v = self.client[:] assert len(v) == len(self.client.ids) v = self.client.direct_view('all') assert len(v) == len(self.client.ids) v = self.client[:2] assert len(v) == 2 v = self.client[:1] assert len(v) == 1 v = self.client.load_balanced_view() assert len(v) == len(self.client.ids) # begin execute tests def test_execute_reply(self): e0 = self.client[self.client.ids[0]] e0.block = True ar = e0.execute("5", silent=False) er = ar.get() assert str(er) == "" % er.execution_count assert er.execute_result['data']['text/plain'] == '5' def test_execute_reply_rich(self): e0 = self.client[self.client.ids[0]] e0.block = True e0.execute("from IPython.display import Image, HTML") ar = e0.execute("Image(data=b'garbage', format='png', width=10)", silent=False) er = ar.get() b64data = base64.b64encode(b'garbage').decode('ascii') data, metadata = er._repr_png_() assert data.strip() == b64data.strip() assert metadata == dict(width=10) ar = e0.execute("HTML('bold')", silent=False) er = ar.get() assert er._repr_html_() == "bold" def test_execute_reply_stdout(self): e0 = self.client[self.client.ids[0]] e0.block = True ar = e0.execute("print (5)", silent=False) er = ar.get() assert er.stdout.strip() == '5' def test_execute_result(self): """execute triggers execute_result with silent=False""" view = self.client[:] ar = view.execute("5", silent=False, block=True) expected = [{'text/plain': '5'}] * len(view) mimes = [out['data'] for out in ar.execute_result] assert mimes == expected def test_execute_silent(self): """execute does not trigger execute_result with silent=True""" view = self.client[:] ar = view.execute("5", block=True) expected = [None] * len(view) assert ar.execute_result == expected def test_execute_magic(self): """execute accepts IPython commands""" view = self.client[:] view.execute("a = 5") ar = view.execute("%whos", block=True) # this will raise, if that failed ar.get(5) for stdout in ar.stdout: lines = stdout.splitlines() assert lines[0].split(), ['Variable', 'Type' == 'Data/Info'] found = False for line in lines[2:]: split = line.split() if split == ['a', 'int', '5']: found = True break assert found, "whos output wrong: %s" % stdout def test_execute_displaypub(self): """execute tracks display_pub output""" view = self.client[:] view.execute("from IPython.core.display import *") ar = view.execute("[ display(i) for i in range(5) ]", block=True) expected = [{'text/plain': str(j)} for j in range(5)] for outputs in ar.outputs: mimes = [out['data'] for out in outputs] assert mimes == expected def test_apply_displaypub(self): """apply tracks display_pub output""" view = self.client[:] view.execute("from IPython.core.display import *") @interactive def publish(): [display(i) for i in range(5)] # noqa: F821 ar = view.apply_async(publish) ar.get(5) assert ar.wait_for_output(5) expected = [{'text/plain': str(j)} for j in range(5)] for outputs in ar.outputs: mimes = [out['data'] for out in outputs] assert mimes == expected def test_execute_raises(self): """exceptions in execute requests raise appropriately""" view = self.client[-1] ar = view.execute("1/0") with raises_remote(ZeroDivisionError): ar.get(2) def test_remoteerror_render_exception(self): """RemoteErrors get nice tracebacks""" view = self.client[-1] ar = view.execute("1/0") ip = get_ipython() ip.user_ns['ar'] = ar with capture_output() as io: ip.run_cell("ar.get(2)") assert 'ZeroDivisionError' in io.stdout, io.stdout def test_compositeerror_render_exception(self): """CompositeErrors get nice tracebacks""" view = self.client[:] ar = view.execute("1/0") ip = get_ipython() ip.user_ns['ar'] = ar with capture_output() as io: ip.run_cell("ar.get(2)") count = min(error.CompositeError.tb_limit, len(view)) assert io.stdout.count('ZeroDivisionError'), count * 2 == io.stdout assert io.stdout.count('by zero'), count == io.stdout assert io.stdout.count(':execute'), count == io.stdout def test_compositeerror_truncate(self): """Truncate CompositeErrors with many exceptions""" view = self.client[:] requests = [] for i in range(10): requests.append(view.execute("1/0")) ar = self.client.get_result(requests) try: ar.get() except error.CompositeError as _e: e = _e else: self.fail("Should have raised CompositeError") lines = e.render_traceback() with capture_output() as io: e.print_traceback() assert "more exceptions" in lines[-1] count = e.tb_limit assert io.stdout.count('ZeroDivisionError'), 2 * count == io.stdout assert io.stdout.count('by zero'), count == io.stdout assert io.stdout.count(':execute'), count == io.stdout def test_magic_pylab(self): """%pylab works on engines""" pytest.importorskip('matplotlib') view = self.client[-1] ar = view.execute("%pylab inline") # at least check if this raised: reply = ar.get(5) # include imports, in case user config ar = view.execute("plot(rand(100))", silent=False) reply = ar.get(5) assert ar.wait_for_output(5) assert len(reply.outputs) == 1 output = reply.outputs[0] assert "data" in output data = output['data'] assert "image/png" in data def test_func_default_func(self): """interactively defined function as apply func default""" def foo(): return 'foo' def bar(f=foo): return f() view = self.client[-1] ar = view.apply_async(bar) r = ar.get(10) assert r == 'foo' def test_data_pub_single(self): view = self.client[-1] ar = view.execute( '\n'.join( [ 'from ipyparallel.datapub import publish_data', 'for i in range(5):', ' publish_data(dict(i=i))', ] ), block=False, ) assert isinstance(ar.data, dict) ar.get(5) assert ar.wait_for_output(5) assert ar.data == dict(i=4) def test_data_pub(self): view = self.client[:] ar = view.execute( '\n'.join( [ 'from ipyparallel.datapub import publish_data', 'for i in range(5):', ' publish_data(dict(i=i))', ] ), block=False, ) assert all(isinstance(d, dict) for d in ar.data) ar.get(5) assert ar.wait_for_output(5) assert ar.data == [dict(i=4)] * len(ar) def test_can_list_arg(self): """args in lists are canned""" view = self.client[-1] view['a'] = 128 rA = ipp.Reference('a') ar = view.apply_async(lambda x: x, [rA]) r = ar.get(5) assert r == [128] def test_can_dict_arg(self): """args in dicts are canned""" view = self.client[-1] view['a'] = 128 rA = ipp.Reference('a') ar = view.apply_async(lambda x: x, dict(foo=rA)) r = ar.get(5) assert r == dict(foo=128) def test_can_list_kwarg(self): """kwargs in lists are canned""" view = self.client[-1] view['a'] = 128 rA = ipp.Reference('a') ar = view.apply_async(lambda x=5: x, x=[rA]) r = ar.get(5) assert r == [128] def test_can_dict_kwarg(self): """kwargs in dicts are canned""" view = self.client[-1] view['a'] = 128 rA = ipp.Reference('a') ar = view.apply_async(lambda x=5: x, dict(foo=rA)) r = ar.get(5) assert r == dict(foo=128) def test_map_ref(self): """view.map works with references""" view = self.client[:] ranks = sorted(self.client.ids) view.scatter('rank', ranks, flatten=True) rrank = ipp.Reference('rank') amr = view.map_async(lambda x: x * 2, [rrank] * len(view)) drank = amr.get(5) assert drank == [r * 2 for r in ranks] def test_nested_getitem_setitem(self): """get and set with view['a.b']""" view = self.client[-1] view.execute( '\n'.join( [ 'class A: pass', 'a = A()', 'a.b = 128', ] ), block=True, ) ra = ipp.Reference('a') r = view.apply_sync(lambda x: x.b, ra) assert r == 128 assert view['a.b'] == 128 view['a.b'] = 0 r = view.apply_sync(lambda x: x.b, ra) assert r == 0 assert view['a.b'] == 0 def test_return_namedtuple(self): def namedtuplify(x, y): return point(x, y) view = self.client[-1] p = view.apply_sync(namedtuplify, 1, 2) assert p.x == 1 assert p.y == 2 def test_apply_namedtuple(self): def echoxy(p): return p.y, p.x view = self.client[-1] tup = view.apply_sync(echoxy, point(1, 2)) assert tup, 2 == 1 def test_sync_imports(self): view = self.client[-1] with capture_output() as io: with view.sync_imports(): import IPython # noqa assert "IPython" in io.stdout @interactive def find_ipython(): return 'IPython' in globals() assert view.apply_sync(find_ipython) def test_sync_imports_quiet(self): view = self.client[-1] with capture_output() as io: with view.sync_imports(quiet=True): import IPython # noqa assert io.stdout == '' @interactive def find_ipython(): return 'IPython' in globals() assert view.apply_sync(find_ipython) @skip_without('cloudpickle') def test_use_cloudpickle(self): view = self.client[:] view['_a'] = 'engine' __main__ = sys.modules['__main__'] @interactive def get_a(): return _a # noqa: F821 # verify initial condition a_list = view.apply_sync(get_a) assert a_list == ['engine'] * len(view) # enable cloudpickle view.use_cloudpickle() # cloudpickle prefers client values __main__._a = 'client' a_list = view.apply_sync(get_a) assert a_list == ['client'] * len(view) # still works even if remote is undefined view.execute('del _a', block=True) a_list = view.apply_sync(get_a) assert a_list == ['client'] * len(view) # restore pickle, shouldn't resolve view.use_pickle() with raises_remote(NameError): view.apply_sync(get_a) @skip_without('cloudpickle') def test_cloudpickle_push_pull(self): view = self.client[:] # enable cloudpickle view.use_cloudpickle() # push/pull still work view.push({"key": "pushed"}, block=True) ar = view.pull("key") assert ar.get(timeout=10) == ["pushed"] * len(view) # restore pickle, should get the same value view.use_pickle() ar = view.pull("key") assert ar.get(timeout=10) == ["pushed"] * len(view) @skip_without('cloudpickle') @pytest.mark.xfail(reason="@require doesn't work with cloudpickle") def test_cloudpickle_require(self): view = self.client[:] # enable cloudpickle view.use_cloudpickle() assert ( 'types' not in globals() ), "Test condition isn't met if types is already imported" @ipp.require("types") @ipp.interactive def func(x): return types.SimpleNamespace(n=x) # noqa: F821 res = view.apply_async(func, 5) assert res.get(timeout=10) == [] view.use_pickle() def test_block_kwarg(self): """ Tests that kwargs such as 'block' can be specified when creating a view, in this case a BroadcastView """ view = self.client.broadcast_view(block=True) assert view.block is True # Execute something as a sanity check view.execute("5", silent=False) ipyparallel-8.8.0/ipyparallel/tests/test_view_broadcast.py000066400000000000000000000063021460376056100241430ustar00rootroot00000000000000"""test BroadcastView objects""" import pytest from . import test_view needs_map = pytest.mark.xfail(reason="map not yet implemented") @pytest.mark.usefixtures('ipython') class TestBroadcastView(test_view.TestView): is_coalescing = False def setup_method(self): super().setup_method() self._broadcast_view_used = False # use broadcast view for direct API real_direct_view = self.client.real_direct_view = self.client.direct_view def broadcast_or_direct(targets): if isinstance(targets, int): return real_direct_view(targets) else: self._broadcast_view_used = True return self.client.broadcast_view( targets, is_coalescing=self.is_coalescing ) self.client.direct_view = broadcast_or_direct def teardown_method(self): super().teardown_method() # note that a test didn't use a broadcast view if not self._broadcast_view_used: pytest.skip("No broadcast view used") @pytest.mark.xfail(reason="Tracking gets disconnected from original message") def test_scatter_tracked(self): pass class TestBroadcastViewCoalescing(TestBroadcastView): is_coalescing = True @pytest.mark.xfail(reason="coalescing view doesn't preserve target order") def test_target_ordering(self): self.minimum_engines(4) ids_in_order = self.client.ids dv = self.client.real_direct_view(ids_in_order) dv.scatter('rank', ids_in_order, flatten=True, block=True) assert dv['rank'] == ids_in_order view = self.client.broadcast_view(ids_in_order, is_coalescing=True) assert view['rank'] == ids_in_order view = self.client.broadcast_view(ids_in_order[::-1], is_coalescing=True) assert view['rank'] == ids_in_order[::-1] view = self.client.broadcast_view(ids_in_order[::2], is_coalescing=True) assert view['rank'] == ids_in_order[::2] view = self.client.broadcast_view(ids_in_order[::-2], is_coalescing=True) assert view['rank'] == ids_in_order[::-2] def test_engine_metadata(self): self.minimum_engines(4) ids_in_order = sorted(self.client.ids) dv = self.client.real_direct_view(ids_in_order) dv.scatter('rank', ids_in_order, flatten=True, block=True) view = self.client.broadcast_view(ids_in_order, is_coalescing=True) ar = view.pull('rank', block=False) result = ar.get(timeout=10) assert isinstance(ar.engine_id, list) assert isinstance(ar.engine_uuid, list) assert result == ar.engine_id assert sorted(ar.engine_id) == ids_in_order even_ids = ids_in_order[::-2] view = self.client.broadcast_view(even_ids, is_coalescing=True) ar = view.pull('rank', block=False) result = ar.get(timeout=10) assert isinstance(ar.engine_id, list) assert isinstance(ar.engine_uuid, list) assert result == ar.engine_id assert sorted(ar.engine_id) == sorted(even_ids) @pytest.mark.xfail(reason="displaypub ordering not preserved") def test_apply_displaypub(self): pass # FIXME del TestBroadcastView ipyparallel-8.8.0/ipyparallel/traitlets.py000066400000000000000000000057611460376056100207710ustar00rootroot00000000000000"""Custom ipyparallel trait types""" import entrypoints from traitlets import List, TraitError, Type class Launcher(Type): """Entry point-extended Type classes can be registered via entry points in addition to standard 'mypackage.MyClass' strings """ def __init__(self, *args, entry_point_group, **kwargs): self.entry_point_group = entry_point_group kwargs.setdefault('klass', 'ipyparallel.cluster.launcher.BaseLauncher') super().__init__(*args, **kwargs) _original_help = '' @property def help(self): """Extend help by listing currently installed choices""" chunks = [self._original_help] chunks.append("Currently installed: ") for key, entry_point in self.load_entry_points().items(): chunks.append( f" - {key}: {entry_point.module_name}.{entry_point.object_name}" ) return '\n'.join(chunks) @help.setter def help(self, value): self._original_help = value def load_entry_points(self): """Load my entry point group""" # load the group group = entrypoints.get_group_named(self.entry_point_group) # make it case-insensitive return {key.lower(): value for key, value in group.items()} def validate(self, obj, value): if isinstance(value, str): # first, look up in entry point registry registry = self.load_entry_points() key = value.lower() if key in registry: value = registry[key].load() return super().validate(obj, value) class PortList(List): """List of ports For use configuring a list of ports to consume Ports will be a list of valid ports Can be specified as a port-range string for convenience (mainly for use on the command-line) e.g. '10101-10105,10108' """ @staticmethod def parse_port_range(s): """Parse a port range string in the form '1,3-5,6' into [1,3,4,5,6]""" ports = [] ranges = s.split(",") for r in ranges: start, _, end = r.partition("-") start = int(start) if end: end = int(end) ports.extend(range(start, end + 1)) else: ports.append(start) return ports def from_string_list(self, s_list): ports = [] for s in s_list: ports.extend(self.parse_port_range(s)) return ports def validate(self, obj, value): if isinstance(value, str): value = self.parse_port_range(value) value = super().validate(obj, value) for item in value: if not isinstance(item, int): raise TraitError( f"Ports must be integers in range 1-65536, not {item!r}" ) if not 1 <= item <= 65536: raise TraitError( f"Ports must be integers in range 1-65536, not {item!r}" ) return value ipyparallel-8.8.0/ipyparallel/util.py000066400000000000000000000546721460376056100177400ustar00rootroot00000000000000"""Some generic utilities for dealing with classes, urls, and serialization.""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import asyncio import functools import inspect import logging import os import re import shlex import socket import sys import warnings from datetime import datetime, timezone from functools import lru_cache from signal import SIGABRT, SIGINT, SIGTERM, signal from types import FunctionType import traitlets import zmq from dateutil.parser import parse as dateutil_parse from dateutil.tz import tzlocal from IPython import get_ipython from IPython.core.profiledir import ProfileDir, ProfileDirError from IPython.paths import get_ipython_dir from jupyter_client import session from jupyter_client.localinterfaces import is_public_ip, localhost, public_ips from tornado.ioloop import IOLoop from traitlets.log import get_logger from zmq.log import handlers utc = timezone.utc # ----------------------------------------------------------------------------- # Classes # ----------------------------------------------------------------------------- class Namespace(dict): """Subclass of dict for attribute access to keys.""" def __getattr__(self, key): """getattr aliased to getitem""" if key in self: return self[key] else: raise NameError(key) def __setattr__(self, key, value): """setattr aliased to setitem, with strict""" if hasattr(dict, key): raise KeyError("Cannot override dict keys %r" % key) self[key] = value class ReverseDict(dict): """simple double-keyed subset of dict methods.""" def __init__(self, *args, **kwargs): dict.__init__(self, *args, **kwargs) self._reverse = dict() for key, value in self.items(): self._reverse[value] = key def __getitem__(self, key): try: return dict.__getitem__(self, key) except KeyError: return self._reverse[key] def __setitem__(self, key, value): if key in self._reverse: raise KeyError("Can't have key %r on both sides!" % key) dict.__setitem__(self, key, value) self._reverse[value] = key def pop(self, key): value = dict.pop(self, key) self._reverse.pop(value) return value def get(self, key, default=None): try: return self[key] except KeyError: return default # ----------------------------------------------------------------------------- # Functions # ----------------------------------------------------------------------------- def log_errors(f): """decorator to log unhandled exceptions raised in a method. For use wrapping on_recv callbacks, so that exceptions do not cause the stream to be closed. """ @functools.wraps(f) def logs_errors(self, *args, **kwargs): try: result = f(self, *args, **kwargs) except Exception as e: self.log.exception(f"Uncaught exception in {f}: {e}") return if inspect.isawaitable(result): # if it's async, schedule logging for when the future resolves future = asyncio.ensure_future(result) def _log_error(future): if future.exception(): self.log.error(f"Uncaught exception in {f}: {future.exception()}") future.add_done_callback(_log_error) return logs_errors def is_url(url): """boolean check for whether a string is a zmq url""" if '://' not in url: return False proto, addr = url.split('://', 1) if proto.lower() not in ['tcp', 'pgm', 'epgm', 'ipc', 'inproc']: return False return True def validate_url(url): """validate a url for zeromq""" if not isinstance(url, str): raise TypeError("url must be a string, not %r" % type(url)) url = url.lower() proto_addr = url.split('://') assert len(proto_addr) == 2, 'Invalid url: %r' % url proto, addr = proto_addr assert proto in ['tcp', 'pgm', 'epgm', 'ipc', 'inproc'], ( "Invalid protocol: %r" % proto ) # domain pattern adapted from http://www.regexlib.com/REDetails.aspx?regexp_id=391 # author: Remi Sabourin pat = re.compile( r'^([\w\d]([\w\d\-]{0,61}[\w\d])?\.)*[\w\d]([\w\d\-]{0,61}[\w\d])?$' ) if proto == 'tcp': lis = addr.split(':') assert len(lis) == 2, 'Invalid url: %r' % url addr, s_port = lis try: port = int(s_port) except ValueError: raise AssertionError(f"Invalid port {port!r} in url: {url!r}") assert addr == '*' or pat.match(addr) is not None, 'Invalid url: %r' % url else: # only validate tcp urls currently pass return True def validate_url_container(container): """validate a potentially nested collection of urls.""" if isinstance(container, str): url = container return validate_url(url) elif isinstance(container, dict): container = container.values() for element in container: validate_url_container(element) def split_url(url): """split a zmq url (tcp://ip:port) into ('tcp','ip','port').""" proto_addr = url.split('://') assert len(proto_addr) == 2, 'Invalid url: %r' % url proto, addr = proto_addr lis = addr.split(':') assert len(lis) == 2, 'Invalid url: %r' % url addr, s_port = lis return proto, addr, s_port def is_ip(location): """Is a location an ip? It could be a hostname. """ return bool(re.match(location, r'(\d+\.){3}\d+')) @lru_cache def ip_for_host(host): """Get the ip address for a host If no ips can be found for the host, the host is returned unmodified. """ try: return socket.gethostbyname_ex(host)[2][0] except Exception as e: warnings.warn( f"IPython could not determine IPs for {host}: {e}", RuntimeWarning ) return host def disambiguate_ip_address(ip, location=None): """turn multi-ip interfaces '0.0.0.0' and '*' into a connectable address Explicit IP addresses are returned unmodified. Parameters ---------- ip : IP address An IP address, or the special values 0.0.0.0, or * location : IP address or hostname, optional A public IP of the target machine, or its hostname. If location is an IP of the current machine, localhost will be returned, otherwise location will be returned. """ if ip in {'0.0.0.0', '*'}: if not location: # unspecified location, localhost is the only choice return localhost() elif not is_ip(location): if location == socket.gethostname(): # hostname matches, use localhost return localhost() else: # hostname doesn't match, but the machine can have a few names. location = ip_for_host(location) if is_public_ip(location): # location is a public IP on this machine, use localhost ip = localhost() elif not public_ips(): # this machine's public IPs cannot be determined, # assume `location` is not this machine warnings.warn("IPython could not determine public IPs", RuntimeWarning) ip = location else: # location is not this machine, do not use loopback ip = location return ip def disambiguate_url(url, location=None): """turn multi-ip interfaces '0.0.0.0' and '*' into connectable ones, based on the location (default interpretation is localhost). This is for zeromq urls, such as ``tcp://*:10101``. """ try: proto, ip, port = split_url(url) except AssertionError: # probably not tcp url; could be ipc, etc. return url ip = disambiguate_ip_address(ip, location) return f"{proto}://{ip}:{port}" # -------------------------------------------------------------------------- # helpers for implementing old MEC API via view.apply # -------------------------------------------------------------------------- def interactive(f): """decorator for making functions appear as interactively defined. This results in the function being linked to the user_ns as globals() instead of the module globals(). """ # build new FunctionType, so it can have the right globals # interactive functions never have closures, that's kind of the point if isinstance(f, FunctionType): mainmod = __import__('__main__') f = FunctionType( f.__code__, mainmod.__dict__, f.__name__, f.__defaults__, ) # associate with __main__ for uncanning f.__module__ = '__main__' return f def _push(**ns): """helper method for implementing `client.push` via `client.apply`""" user_ns = get_ipython().user_global_ns tmp = '_IP_PUSH_TMP_' while tmp in user_ns: tmp = tmp + '_' try: for name, value in ns.items(): user_ns[tmp] = value exec(f"{name} = {tmp}", user_ns) finally: user_ns.pop(tmp, None) def _pull(keys): """helper method for implementing `client.pull` via `client.apply`""" user_ns = get_ipython().user_global_ns if isinstance(keys, (list, tuple, set)): return [eval(key, user_ns) for key in keys] else: return eval(keys, user_ns) def _execute(code): """helper method for implementing `client.execute` via `client.apply`""" user_ns = get_ipython().user_global_ns exec(code, user_ns) # -------------------------------------------------------------------------- # extra process management utilities # -------------------------------------------------------------------------- _random_ports = set() def select_random_ports(n): """Selects and return n random ports that are available.""" ports = [] for i in range(n): sock = socket.socket() sock.bind(('', 0)) while sock.getsockname()[1] in _random_ports: sock.close() sock = socket.socket() sock.bind(('', 0)) ports.append(sock) for i, sock in enumerate(ports): port = sock.getsockname()[1] sock.close() ports[i] = port _random_ports.add(port) return ports def signal_children(children): """Relay interupt/term signals to children, for more solid process cleanup.""" def terminate_children(sig, frame): log = get_logger() log.critical("Got signal %i, terminating children..." % sig) for child in children: child.terminate() sys.exit(sig != SIGINT) # sys.exit(sig) for sig in (SIGINT, SIGABRT, SIGTERM): signal(sig, terminate_children) def integer_loglevel(loglevel): try: loglevel = int(loglevel) except ValueError: if isinstance(loglevel, str): loglevel = getattr(logging, loglevel) return loglevel def connect_logger(logname, context, iface, root="ip", loglevel=logging.DEBUG): logger = logging.getLogger(logname) if any([isinstance(h, handlers.PUBHandler) for h in logger.handlers]): # don't add a second PUBHandler return loglevel = integer_loglevel(loglevel) lsock = context.socket(zmq.PUB) lsock.connect(iface) handler = handlers.PUBHandler(lsock) handler.setLevel(loglevel) handler.root_topic = root logger.addHandler(handler) logger.setLevel(loglevel) return logger def connect_engine_logger(context, iface, engine, loglevel=logging.DEBUG): from ipyparallel.engine.log import EnginePUBHandler logger = logging.getLogger() if any([isinstance(h, handlers.PUBHandler) for h in logger.handlers]): # don't add a second PUBHandler return loglevel = integer_loglevel(loglevel) lsock = context.socket(zmq.PUB) lsock.connect(iface) handler = EnginePUBHandler(engine, lsock) handler.setLevel(loglevel) logger.addHandler(handler) logger.setLevel(loglevel) return logger def local_logger(logname, loglevel=logging.DEBUG): loglevel = integer_loglevel(loglevel) logger = logging.getLogger(logname) if any([isinstance(h, logging.StreamHandler) for h in logger.handlers]): # don't add a second StreamHandler return handler = logging.StreamHandler() handler.setLevel(loglevel) formatter = logging.Formatter( "%(asctime)s.%(msecs).03d [%(name)s] %(message)s", datefmt="%Y-%m-%d %H:%M:%S", ) handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(loglevel) return logger def set_hwm(sock, hwm=0): """set zmq High Water Mark on a socket in a way that always works for various pyzmq / libzmq versions. """ import zmq for key in ('HWM', 'SNDHWM', 'RCVHWM'): opt = getattr(zmq, key, None) if opt is None: continue try: sock.setsockopt(opt, hwm) except zmq.ZMQError: pass def int_keys(dikt): """Rekey a dict that has been forced to cast number keys to str for JSON where there should be ints. """ for k in list(dikt): if isinstance(k, str): nk = None try: nk = int(k) except ValueError: try: nk = float(k) except ValueError: continue if nk in dikt: raise KeyError("already have key %r" % nk) dikt[nk] = dikt.pop(k) return dikt def become_dask_worker(address, nanny=False, **kwargs): """Task function for becoming a dask.distributed Worker Parameters ---------- address : str The URL of the dask Scheduler. **kwargs Any additional keyword arguments will be passed to the Worker constructor. """ shell = get_ipython() kernel = shell.kernel if getattr(kernel, 'dask_worker', None) is not None: kernel.log.info("Dask worker is already running.") return from distributed import Nanny, Worker if nanny: w = Nanny(address, **kwargs) else: w = Worker(address, **kwargs) shell.user_ns['dask_worker'] = shell.user_ns['distributed_worker'] = ( kernel.distributed_worker ) = w kernel.io_loop.add_callback(w.start) def stop_distributed_worker(): """Task function for stopping the the distributed worker on an engine.""" shell = get_ipython() kernel = shell.kernel if getattr(kernel, 'distributed_worker', None) is None: kernel.log.info("Distributed worker already stopped.") return w = kernel.distributed_worker kernel.distributed_worker = None if shell.user_ns.get('distributed_worker', None) is w: shell.user_ns.pop('distributed_worker', None) IOLoop.current().add_callback(lambda: w.terminate(None)) def ensure_timezone(dt): """Ensure a datetime object has a timezone If it doesn't have one, attach the local timezone. """ if dt.tzinfo is None: return dt.replace(tzinfo=tzlocal()) else: return dt # extract_dates forward-port from jupyter_client 5.0 # timestamp formats ISO8601 = "%Y-%m-%dT%H:%M:%S.%f" ISO8601_PAT = re.compile( r"^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})(\.\d{1,6})?(Z|([\+\-]\d{2}:?\d{2}))?$" ) def _ensure_tzinfo(dt): """Ensure a datetime object has tzinfo If no tzinfo is present, add tzlocal """ if not dt.tzinfo: # No more naïve datetime objects! warnings.warn( "Interpreting naïve datetime as local %s. Please add timezone info to timestamps." % dt, DeprecationWarning, stacklevel=4, ) dt = dt.replace(tzinfo=tzlocal()) return dt def _parse_date(s): """parse an ISO8601 date string If it is None or not a valid ISO8601 timestamp, it will be returned unmodified. Otherwise, it will return a datetime object. """ if s is None: return s m = ISO8601_PAT.match(s) if m: dt = dateutil_parse(s) return _ensure_tzinfo(dt) return s def extract_dates(obj): """extract ISO8601 dates from unpacked JSON""" if isinstance(obj, dict): new_obj = {} # don't clobber for k, v in obj.items(): new_obj[k] = extract_dates(v) obj = new_obj elif isinstance(obj, (list, tuple)): obj = [extract_dates(o) for o in obj] elif isinstance(obj, str): obj = _parse_date(obj) return obj def compare_datetimes(a, b): """Compare two datetime objects If one has a timezone and the other doesn't, treat the naïve datetime as local time to avoid errors. Returns the timedelta """ if a.tzinfo is None and b.tzinfo is not None: a = a.replace(tzinfo=tzlocal()) elif a.tzinfo is not None and b.tzinfo is None: b = b.replace(tzinfo=tzlocal()) return a - b def utcnow(): """Timezone-aware UTC timestamp""" return datetime.now(utc) def _v(version_s): return tuple(int(s) for s in re.findall(r"\d+", version_s)) @lru_cache def _disable_session_extract_dates(): """Monkeypatch jupyter_client.extract_dates to be a no-op avoids performance problem parsing unused timestamp strings """ session.extract_dates = lambda obj: obj def progress(*args, widget=None, **kwargs): """Create a tqdm progress bar If `widget` is None, autodetects if IPython widgets should be used, otherwise use basic tqdm. """ if widget is None: # auto widget if in a kernel ip = get_ipython() if ip is not None and getattr(ip, 'kernel', None) is not None: try: import ipywidgets # noqa except ImportError: widget = False else: widget = True else: widget = False if widget: import tqdm.notebook f = tqdm.notebook.tqdm_notebook else: import tqdm kwargs.setdefault("file", sys.stdout) f = tqdm.tqdm return f(*args, **kwargs) def abbreviate_profile_dir(profile_dir): """Abbreviate IPython profile directory if in $IPYTHONDIR""" profile_prefix = os.path.join(get_ipython_dir(), "profile_") if profile_dir.startswith(profile_prefix): # use just the profile name if it's in $IPYTHONDIR return profile_dir[len(profile_prefix) :] else: return profile_dir def _all_profile_dirs(): """List all IPython profile directories""" profile_dirs = [] if not os.path.isdir(get_ipython_dir()): return profile_dirs with os.scandir(get_ipython_dir()) as paths: for path in paths: if path.is_dir() and path.name.startswith('profile_'): profile_dirs.append(path.path) return profile_dirs def _default_profile_dir(profile=None): """Locate the default IPython profile directory Priorities: - named profile, if specified - current IPython profile, if run inside IPython - $IPYTHONDIR/profile_default Returns absolute profile directory path, ensuring it exists """ if not profile: ip = get_ipython() if ip is not None: return ip.profile_dir.location ipython_dir = get_ipython_dir() profile = profile or 'default' try: pd = ProfileDir.find_profile_dir_by_name(ipython_dir, name=profile) except ProfileDirError: pd = ProfileDir.create_profile_dir_by_name(ipython_dir, name=profile) return pd.location def _locate_profiles(profiles=None): """Locate one or more IPython profiles by name""" ipython_dir = get_ipython_dir() return [ ProfileDir.find_profile_dir_by_name(ipython_dir, name=profile).location for profile in profiles ] def shlex_join(cmd): """Backport shlex.join to Python < 3.8""" return ' '.join(shlex.quote(s) for s in cmd) _traitlet_annotations = { traitlets.Bool: bool, traitlets.Integer: int, traitlets.Float: float, traitlets.List: list, traitlets.Dict: dict, traitlets.Set: set, traitlets.Unicode: str, traitlets.Tuple: tuple, } class _TraitAnnotation: """Trait annotation for a trait type""" def __init__(self, trait_type): self.trait_type = trait_type def __repr__(self): return self.trait_type.__name__ def _trait_annotation(trait_type): """Return an annotation for a trait""" if trait_type in _traitlet_annotations: return _traitlet_annotations[trait_type] else: annotation = _traitlet_annotations[trait_type] = _TraitAnnotation(trait_type) return annotation def _traitlet_signature(cls): """Add traitlet-based signature to a class""" parameters = [] for name, trait in cls.class_traits().items(): if name.startswith("_"): # omit private traits continue if trait.metadata.get("nosignature"): continue if "alias" in trait.metadata: name = trait.metadata["alias"] if hasattr(trait, 'default'): # traitlets 5 default = trait.default() else: default = trait.default_value if default is traitlets.Undefined: default = None annotation = _trait_annotation(trait.__class__) parameters.append( inspect.Parameter( name=name, kind=inspect.Parameter.KEYWORD_ONLY, annotation=annotation, default=default, ) ) cls.__signature__ = inspect.Signature(parameters) return cls def bind(socket, url, curve_publickey=None, curve_secretkey=None): """Common utility to bind with optional auth info""" if curve_secretkey: socket.setsockopt(zmq.CURVE_SERVER, 1) socket.setsockopt(zmq.CURVE_SECRETKEY, curve_secretkey) return socket.bind(url) def connect( socket, url, curve_serverkey=None, curve_publickey=None, curve_secretkey=None, ): """Common utility to connect with optional auth info""" if curve_serverkey: if not curve_publickey or not curve_secretkey: # unspecified, generate new client credentials # we don't use client secret auth, # so these are just used for encryption. # any values will do. curve_publickey, curve_secretkey = zmq.curve_keypair() socket.setsockopt(zmq.CURVE_SERVERKEY, curve_serverkey) socket.setsockopt(zmq.CURVE_SECRETKEY, curve_secretkey) socket.setsockopt(zmq.CURVE_PUBLICKEY, curve_publickey) return socket.connect(url) ipyparallel-8.8.0/jupyter-config/000077500000000000000000000000001460376056100170225ustar00rootroot00000000000000ipyparallel-8.8.0/jupyter-config/jupyter_notebook_config.d/000077500000000000000000000000001460376056100241735ustar00rootroot00000000000000ipyparallel-8.8.0/jupyter-config/jupyter_notebook_config.d/ipyparallel.json000066400000000000000000000001301460376056100273760ustar00rootroot00000000000000{ "NotebookApp": { "nbserver_extensions": { "ipyparallel": true } } } ipyparallel-8.8.0/jupyter-config/jupyter_server_config.d/000077500000000000000000000000001460376056100236615ustar00rootroot00000000000000ipyparallel-8.8.0/jupyter-config/jupyter_server_config.d/ipyparallel.json000066400000000000000000000001261460376056100270710ustar00rootroot00000000000000{ "ServerApp": { "jpserver_extensions": { "ipyparallel": true } } } ipyparallel-8.8.0/jupyter-config/nbconfig/000077500000000000000000000000001460376056100206075ustar00rootroot00000000000000ipyparallel-8.8.0/jupyter-config/nbconfig/tree.d/000077500000000000000000000000001460376056100217705ustar00rootroot00000000000000ipyparallel-8.8.0/jupyter-config/nbconfig/tree.d/ipyparallel.json000066400000000000000000000000741460376056100252020ustar00rootroot00000000000000{ "load_extensions": { "ipyparallel/main": true } } ipyparallel-8.8.0/lab/000077500000000000000000000000001460376056100146135ustar00rootroot00000000000000ipyparallel-8.8.0/lab/schema/000077500000000000000000000000001460376056100160535ustar00rootroot00000000000000ipyparallel-8.8.0/lab/schema/plugin.json000066400000000000000000000003601460376056100202430ustar00rootroot00000000000000{ "jupyter.lab.setting-icon-class": "ipp-Logo", "jupyter.lab.setting-icon-label": "IPython Parallel", "title": "IPython Parallel", "description": "Settings for the IPython Parallel plugin.", "properties": {}, "type": "object" } ipyparallel-8.8.0/lab/src/000077500000000000000000000000001460376056100154025ustar00rootroot00000000000000ipyparallel-8.8.0/lab/src/clusters.tsx000066400000000000000000000555411460376056100200200ustar00rootroot00000000000000import { showErrorMessage, Toolbar, ToolbarButton, CommandToolbarButton, } from "@jupyterlab/apputils"; import { IChangedArgs } from "@jupyterlab/coreutils"; import * as nbformat from "@jupyterlab/nbformat"; import { ServerConnection } from "@jupyterlab/services"; import { refreshIcon } from "@jupyterlab/ui-components"; import { ArrayExt } from "@lumino/algorithm"; import { JSONObject, JSONExt, MimeData } from "@lumino/coreutils"; import { ElementExt } from "@lumino/domutils"; import { Drag } from "@lumino/dragdrop"; import { Message } from "@lumino/messaging"; import { Poll } from "@lumino/polling"; import { ISignal, Signal } from "@lumino/signaling"; import { Widget, PanelLayout } from "@lumino/widgets"; import { newClusterDialog, INewCluster } from "./dialog"; import * as React from "react"; import { createRoot } from "react-dom/client"; import { CommandRegistry } from "@lumino/commands"; import { CommandIDs } from "./commands"; /** * A refresh interval (in ms) for polling the backend cluster manager. */ const REFRESH_INTERVAL = 5000; /** * The threshold in pixels to start a drag event. */ const DRAG_THRESHOLD = 5; /** * The mimetype used for Jupyter cell data. */ const JUPYTER_CELL_MIME = "application/vnd.jupyter.cells"; const CLUSTER_PREFIX = "ipyparallel/clusters"; /** * A widget for IPython cluster management. */ export class ClusterManager extends Widget { /** * Create a new cluster manager. */ constructor(options: ClusterManager.IOptions) { super(); this.addClass("ipp-ClusterManager"); this._serverSettings = ServerConnection.makeSettings(); this._injectClientCodeForCluster = options.injectClientCodeForCluster; this._getClientCodeForCluster = options.getClientCodeForCluster; this._registry = options.registry; // A function to set the active cluster. this._setActiveById = (id: string) => { const cluster = this._clusters.find((c) => c.id === id); if (!cluster) { return; } const old = this._activeCluster; if (old && old.id === cluster.id) { return; } this._activeCluster = cluster; this._activeClusterChanged.emit({ name: "cluster", oldValue: old, newValue: cluster, }); this.update(); }; const layout = (this.layout = new PanelLayout()); this._clusterListing = new Widget(); this._clusterListing.addClass("ipp-ClusterListing"); // Create the toolbar. const toolbar = new Toolbar(); // Make a label widget for the toolbar. const toolbarLabel = new Widget(); toolbarLabel.node.textContent = "CLUSTERS"; toolbarLabel.addClass("ipp-ClusterManager-label"); toolbar.addItem("label", toolbarLabel); // Make a refresh button for the toolbar. toolbar.addItem( "refresh", new ToolbarButton({ icon: refreshIcon, onClick: async () => { return this._updateClusterList(); }, tooltip: "Refresh Cluster List", }), ); // Make a new cluster button for the toolbar. toolbar.addItem( CommandIDs.newCluster, new CommandToolbarButton({ commands: this._registry, id: CommandIDs.newCluster, }), ); layout.addWidget(toolbar); layout.addWidget(this._clusterListing); // Do an initial refresh of the cluster list. void this._updateClusterList(); // Also refresh periodically. this._poll = new Poll({ factory: async () => { await this._updateClusterList(); }, frequency: { interval: REFRESH_INTERVAL, backoff: true, max: 60 * 1000 }, standby: "when-hidden", }); } /** * The currently selected cluster, or undefined if there is none. */ get activeCluster(): IClusterModel | undefined { return this._activeCluster; } /** * Set an active cluster by id. */ setActiveCluster(id: string): void { this._setActiveById(id); } /** * A signal that is emitted when an active cluster changes. */ get activeClusterChanged(): ISignal< this, IChangedArgs > { return this._activeClusterChanged; } /** * Whether the cluster manager is ready to launch a cluster */ get isReady(): boolean { return this._isReady; } /** * Get the current clusters known to the manager. */ get clusters(): IClusterModel[] { return this._clusters; } /** * Refresh the current list of clusters. */ async refresh(): Promise { await this._updateClusterList(); } /** * Create a new cluster. */ async create(): Promise { const clusterRequest = await newClusterDialog({}); if (!clusterRequest) { return; } const cluster = await this._newCluster(clusterRequest); return cluster; } /** * Start a cluster by ID. */ async start(id: string): Promise { const cluster = this._clusters.find((c) => c.id === id); if (!cluster) { throw Error(`Cannot find cluster ${id}`); } await this._startById(id); } /** * Stop a cluster by ID. */ async stop(id: string): Promise { const cluster = this._clusters.find((c) => c.id === id); if (!cluster) { throw Error(`Cannot find cluster ${id}`); } await this._stopById(id); } /** * Scale a cluster by ID. */ async scale(id: string): Promise { const cluster = this._clusters.find((c) => c.id === id); if (!cluster) { throw Error(`Cannot find cluster ${id}`); } const newCluster = await this._scaleById(id); return newCluster; } /** * Dispose of the cluster manager. */ dispose(): void { if (this.isDisposed) { return; } this._poll.dispose(); super.dispose(); } /** * Handle an update request. */ protected onUpdateRequest(msg: Message): void { // Don't bother if the sidebar is not visible if (!this.isVisible) { return; } const root = createRoot(this._clusterListing.node); root.render( { return this._scaleById(id); }} startById={(id: string) => { return this._startById(id); }} stopById={(id: string) => { return this._stopById(id); }} setActiveById={this._setActiveById} injectClientCodeForCluster={this._injectClientCodeForCluster} />, ); } /** * Rerender after showing. */ protected onAfterShow(msg: Message): void { this.update(); } /** * Handle `after-attach` messages for the widget. */ protected onAfterAttach(msg: Message): void { super.onAfterAttach(msg); let node = this._clusterListing.node; node.addEventListener("p-dragenter", this); node.addEventListener("p-dragleave", this); node.addEventListener("p-dragover", this); node.addEventListener("mousedown", this); } /** * Handle `before-detach` messages for the widget. */ protected onBeforeDetach(msg: Message): void { let node = this._clusterListing.node; node.removeEventListener("p-dragenter", this); node.removeEventListener("p-dragleave", this); node.removeEventListener("p-dragover", this); node.removeEventListener("mousedown", this); document.removeEventListener("mouseup", this, true); document.removeEventListener("mousemove", this, true); } /** * Handle the DOM events for the directory listing. * * @param event - The DOM event sent to the widget. * * #### Notes * This method implements the DOM `EventListener` interface and is * called in response to events on the panel's DOM node. It should * not be called directly by user code. */ handleEvent(event: Event): void { switch (event.type) { case "mousedown": this._evtMouseDown(event as MouseEvent); break; case "mouseup": this._evtMouseUp(event as MouseEvent); break; case "mousemove": this._evtMouseMove(event as MouseEvent); break; default: break; } } /** * Handle `mousedown` events for the widget. */ private _evtMouseDown(event: MouseEvent): void { const { button, shiftKey } = event; // We only handle main or secondary button actions. if (!(button === 0 || button === 2)) { return; } // Shift right-click gives the browser default behavior. if (shiftKey && button === 2) { return; } // Find the target cluster. const clusterIndex = this._findCluster(event); if (clusterIndex === -1) { return; } // Prepare for a drag start this._dragData = { pressX: event.clientX, pressY: event.clientY, index: clusterIndex, }; // Enter possible drag mode document.addEventListener("mouseup", this, true); document.addEventListener("mousemove", this, true); event.preventDefault(); } /** * Handle the `'mouseup'` event on the document. */ private _evtMouseUp(event: MouseEvent): void { // Remove the event listeners we put on the document if (event.button !== 0 || !this._drag) { document.removeEventListener("mousemove", this, true); document.removeEventListener("mouseup", this, true); } event.preventDefault(); event.stopPropagation(); } /** * Handle the `'mousemove'` event for the widget. */ private _evtMouseMove(event: MouseEvent): void { let data = this._dragData; if (!data) { return; } // Check for a drag initialization. let dx = Math.abs(event.clientX - data.pressX); let dy = Math.abs(event.clientY - data.pressY); if (dx >= DRAG_THRESHOLD || dy >= DRAG_THRESHOLD) { event.preventDefault(); event.stopPropagation(); void this._startDrag(data.index, event.clientX, event.clientY); } } /** * Start a drag event. */ private async _startDrag( index: number, clientX: number, clientY: number, ): Promise { // Create the drag image. const model = this._clusters[index]; const listingItem = this._clusterListing.node.querySelector( `li.ipp-ClusterListingItem[data-cluster-id="${model.id}"]`, ) as HTMLElement; const dragImage = Private.createDragImage(listingItem); // Set up the drag event. this._drag = new Drag({ mimeData: new MimeData(), dragImage, supportedActions: "copy", proposedAction: "copy", source: this, }); // Add mimeData for plain text so that normal editors can // receive the data. const textData = this._getClientCodeForCluster(model); this._drag.mimeData.setData("text/plain", textData); // Add cell data for notebook drops. const cellData: nbformat.ICodeCell[] = [ { cell_type: "code", source: textData, outputs: [], execution_count: null, metadata: {}, }, ]; this._drag.mimeData.setData(JUPYTER_CELL_MIME, cellData); // Remove mousemove and mouseup listeners and start the drag. document.removeEventListener("mousemove", this, true); document.removeEventListener("mouseup", this, true); return this._drag.start(clientX, clientY).then((action) => { if (this.isDisposed) { return; } this._drag = null; this._dragData = null; }); } /** * Launch a new cluster on the server. */ private async _newCluster( clusterRequest: INewCluster, ): Promise { this._isReady = false; this._registry.notifyCommandChanged(CommandIDs.newCluster); // TODO: allow requesting a profile, options const response = await ServerConnection.makeRequest( `${this._serverSettings.baseUrl}${CLUSTER_PREFIX}`, { method: "POST", body: JSON.stringify(clusterRequest) }, this._serverSettings, ); if (response.status !== 200) { const err = await response.json(); void showErrorMessage("Cluster Create Error", err); this._isReady = true; this._registry.notifyCommandChanged(CommandIDs.newCluster); throw err; } const model = (await response.json()) as IClusterModel; await this._updateClusterList(); this._isReady = true; this._registry.notifyCommandChanged(CommandIDs.newCluster); return model; } /** * Refresh the list of clusters on the server. */ private async _updateClusterList(): Promise { const response = await ServerConnection.makeRequest( `${this._serverSettings.baseUrl}${CLUSTER_PREFIX}`, {}, this._serverSettings, ); if (response.status !== 200) { const msg = "Failed to list clusters: might the server extension not be installed/enabled?"; const err = new Error(msg); if (!this._serverErrorShown) { void showErrorMessage("IPP Extension Server Error", err); this._serverErrorShown = true; } throw err; } const data = (await response.json()) as IClusterModel[]; this._clusters = data; // Check to see if the active cluster still exits. // If it doesn't, or if there is no active cluster, // select the first one. const active = this._clusters.find( (c) => c.id === (this._activeCluster && this._activeCluster.id), ); if (!active) { const id = (this._clusters[0] && this._clusters[0].id) || ""; this._setActiveById(id); } this.update(); } /** * Start a cluster by its id. */ private async _startById(id: string): Promise { const response = await ServerConnection.makeRequest( `${this._serverSettings.baseUrl}${CLUSTER_PREFIX}/${id}`, { method: "POST" }, this._serverSettings, ); if (response.status > 299) { const err = await response.json(); void showErrorMessage("Failed to start cluster", err); throw err; } await this._updateClusterList(); } /** * Stop a cluster by its id. */ private async _stopById(id: string): Promise { const response = await ServerConnection.makeRequest( `${this._serverSettings.baseUrl}${CLUSTER_PREFIX}/${id}`, { method: "DELETE" }, this._serverSettings, ); if (response.status !== 204) { const err = await response.json(); void showErrorMessage("Failed to close cluster", err); throw err; } await this._updateClusterList(); } /** * Scale a cluster by its id. */ private async _scaleById(id: string): Promise { const cluster = this._clusters.find((c) => c.id === id); if (!cluster) { throw Error(`Failed to find cluster ${id} to scale`); } // TODO: scale not implemented // should add an engine set void showErrorMessage("Scale not implemented", ""); // const update = await showScalingDialog(cluster); const update = cluster; if (JSONExt.deepEqual(update, cluster)) { // If the user canceled, or the model is identical don't try to update. return Promise.resolve(cluster); } const response = await ServerConnection.makeRequest( `${this._serverSettings.baseUrl}${CLUSTER_PREFIX}/${id}`, { method: "PATCH", body: JSON.stringify(update), }, this._serverSettings, ); if (response.status !== 200) { const err = await response.json(); void showErrorMessage("Failed to scale cluster", err); throw err; } const model = (await response.json()) as IClusterModel; await this._updateClusterList(); return model; } private _findCluster(event: MouseEvent): number { const nodes = Array.from( this.node.querySelectorAll("li.ipp-ClusterListingItem"), ); return ArrayExt.findFirstIndex(nodes, (node) => { return ElementExt.hitTest(node, event.clientX, event.clientY); }); } private _drag: Drag | null; private _dragData: { pressX: number; pressY: number; index: number; } | null = null; private _clusterListing: Widget; private _clusters: IClusterModel[] = []; private _activeCluster: IClusterModel | undefined; private _setActiveById: (id: string) => void; private _injectClientCodeForCluster: (model: IClusterModel) => void; private _getClientCodeForCluster: (model: IClusterModel) => string; private _poll: Poll; private _serverSettings: ServerConnection.ISettings; private _activeClusterChanged = new Signal< this, IChangedArgs >(this); private _serverErrorShown = false; private _isReady = true; private _registry: CommandRegistry; } /** * A namespace for DasClusterManager statics. */ export namespace ClusterManager { /** * Options for the constructor. */ export interface IOptions { /** * Registry of all commands */ registry: CommandRegistry; /** * A callback to inject client connection cdoe. */ injectClientCodeForCluster: (model: IClusterModel) => void; /** * A callback to get client code for a cluster. */ getClientCodeForCluster: (model: IClusterModel) => string; } } /** * A React component for a launcher button listing. */ function ClusterListing(props: IClusterListingProps) { let listing = props.clusters.map((cluster) => { return ( props.scaleById(cluster.id)} start={() => props.startById(cluster.id)} stop={() => props.stopById(cluster.id)} setActive={() => props.setActiveById(cluster.id)} injectClientCode={() => props.injectClientCodeForCluster(cluster)} /> ); }); // Return the JSX component. return (
      {listing}
    ); } /** * Props for the cluster listing component. */ export interface IClusterListingProps { /** * A list of items to render. */ clusters: IClusterModel[]; /** * The id of the active cluster. */ activeClusterId: string; /** * A function for starting a cluster by ID. */ startById: (id: string) => Promise; /** * A function for stopping a cluster by ID. */ stopById: (id: string) => Promise; /** * Scale a cluster by id. */ scaleById: (id: string) => Promise; /** * A callback to set the active cluster by id. */ setActiveById: (id: string) => void; /** * A callback to inject client code for a cluster. */ injectClientCodeForCluster: (model: IClusterModel) => void; } /** * A TSX functional component for rendering a single running cluster. */ function ClusterListingItem(props: IClusterListingItemProps) { const { cluster, isActive, setActive, scale, start, stop, injectClientCode } = props; let itemClass = "ipp-ClusterListingItem"; itemClass = isActive ? `${itemClass} jp-mod-active` : itemClass; let cluster_state = "Stopped"; if (cluster.controller) { cluster_state = cluster.controller.state.state; if (cluster_state == "after") { cluster_state = "Stopped"; } } // stop action is 'delete' for already-stopped clusters let STOP = cluster_state === "Stopped" ? "DELETE" : "STOP"; return (
  • { setActive(); evt.stopPropagation(); }} >
    {cluster.id}
    State: {cluster_state}
    Number of engines: {cluster.engines.n || cluster.cluster.n || "auto"}
  • ); } /** * Props for the cluster listing component. */ export interface IClusterListingItemProps { /** * A cluster model to render. */ cluster: IClusterModel; /** * Whether the cluster is currently active. */ isActive: boolean; /** * A function for starting the cluster. */ start: () => Promise; /** * A function for scaling the cluster. */ scale: () => Promise; /** * A function for stopping the cluster. */ stop: () => Promise; /** * A callback function to set the active cluster. */ setActive: () => void; /** * A callback to inject client code into an editor. */ injectClientCode: () => void; } /** * An interface for a JSON-serializable representation of a cluster. */ export interface IClusterModel extends JSONObject { /** * A unique string ID for the cluster. */ id: string; /** * the cluster file for `Cluster.from_file` */ cluster_file: string; cluster: { /** * The number of engines (null = auto) */ n?: number; /** * The cluster id */ cluster_id: string; /** * The profile directory */ profile_dir: string; /** * The profile, abbreviated */ profle: string; /** * The class import string */ class: string; }; controller?: { class: string; state?: { state: string; }; }; engines: { n: number; class: string; sets: { [key: string]: { n: number; state: string; }; }; }; } /** * A namespace for module-private functionality. */ namespace Private { /** * Create a drag image for an HTML node. */ export function createDragImage(node: HTMLElement): HTMLElement { const image = node.cloneNode(true) as HTMLElement; image.classList.add("ipp-ClusterListingItem-drag"); return image; } } ipyparallel-8.8.0/lab/src/commands.ts000066400000000000000000000012451460376056100175550ustar00rootroot00000000000000export namespace CommandIDs { /** * Inject client code into the active editor. */ export const injectClientCode = "ipyparallel:inject-client-code"; /** * Launch a new cluster. */ export const newCluster = "ipyparallel:new-cluster"; /** * Launch a new cluster. */ export const startCluster = "ipyparallel:start-cluster"; /** * Shutdown a cluster. */ export const stopCluster = "ipyparallel:stop-cluster"; /** * Scale a cluster. */ export const scaleCluster = "ipyparallel:scale-cluster"; /** * Toggle the auto-starting of clients. */ export const toggleAutoStartClient = "ipyparallel:toggle-auto-start-client"; } ipyparallel-8.8.0/lab/src/dialog.tsx000066400000000000000000000111231460376056100173770ustar00rootroot00000000000000import { Dialog, showDialog } from "@jupyterlab/apputils"; import * as React from "react"; export interface INewCluster { /** * The cluster id */ cluster_id?: string; /** * The profile */ profile?: string; /** * The number of engines */ n?: number; } /** * A namespace for ClusterDialog statics. */ namespace NewCluster { /** * The props for the NewClusterDialog component. */ export interface IProps { /** * The initial cluster model shown in the Dialog. */ initialModel: INewCluster; /** * A callback that allows the component to write state to an * external object. */ stateEscapeHatch: (model: INewCluster) => void; } /** * The state for the ClusterDialog component. */ export interface IState { /** * The proposed cluster model shown in the Dialog. */ model: INewCluster; } } /** * A component for an HTML form that allows the user * to select Dialog parameters. */ export class NewCluster extends React.Component< NewCluster.IProps, NewCluster.IState > { /** * Construct a new NewCluster component. */ constructor(props: NewCluster.IProps) { super(props); let model: INewCluster; model = props.initialModel; this.state = { model }; } /** * When the component updates we take the opportunity to write * the state of the cluster to an external object so this can * be sent as the result of the dialog. */ componentDidUpdate(): void { let model: INewCluster = { ...this.state.model }; this.props.stateEscapeHatch(model); } /** * React to the number of workers changing. */ onScaleChanged(event: React.ChangeEvent): void { this.setState({ model: { ...this.state.model, n: parseInt((event.target as HTMLInputElement).value || null, null), }, }); } /** * React to the number of workers changing. */ onProfileChanged(event: React.ChangeEvent): void { this.setState({ model: { ...this.state.model, profile: (event.target as HTMLInputElement).value, }, }); } /** * React to the number of workers changing. */ onClusterIdChanged(event: React.ChangeEvent): void { this.setState({ model: { ...this.state.model, cluster_id: (event.target as HTMLInputElement).value, }, }); } /** * Render the component.. */ render() { const model = this.state.model; // const disabledClass = "ipp-mod-disabled"; return (
    Profile { this.onProfileChanged(evt); }} />
    Cluster ID { this.onClusterIdChanged(evt); }} />
    Engines { this.onScaleChanged(evt); }} />
    ); } } /** * Show a dialog for Dialog a cluster model. * * @param model: the initial model. * * @returns a promse that resolves with the user-selected Dialogs for the * cluster model. If they pressed the cancel button, it resolves with * the original model. */ export function newClusterDialog(model: INewCluster): Promise { let updatedModel = { ...model }; const escapeHatch = (update: INewCluster) => { updatedModel = update; }; return showDialog({ title: `New Cluster`, body: , buttons: [Dialog.cancelButton(), Dialog.okButton({ label: "CREATE" })], }).then((result) => { if (result.button.accept) { return updatedModel; } else { return null; } }); } ipyparallel-8.8.0/lab/src/index.ts000066400000000000000000000362701460376056100170710ustar00rootroot00000000000000// IPython Parallel Lab extension derived from dask-labextension@f6141455d770ed7de564fc4aa403b9964cd4e617 // License: BSD-3-Clause import { PageConfig } from "@jupyterlab/coreutils"; import { TabPanel } from "@lumino/widgets"; import { ILabShell, JupyterFrontEnd, JupyterFrontEndPlugin, } from "@jupyterlab/application"; import { ISessionContext, IWidgetTracker } from "@jupyterlab/apputils"; import { CodeEditor } from "@jupyterlab/codeeditor"; import { ConsolePanel, IConsoleTracker } from "@jupyterlab/console"; import { ISettingRegistry } from "@jupyterlab/settingregistry"; import { IStateDB } from "@jupyterlab/statedb"; import { INotebookTracker, NotebookActions, NotebookPanel, } from "@jupyterlab/notebook"; import { Kernel, KernelMessage, Session } from "@jupyterlab/services"; import { LabIcon } from "@jupyterlab/ui-components"; import { Signal } from "@lumino/signaling"; import { IClusterModel, ClusterManager } from "./clusters"; import { Sidebar } from "./sidebar"; import "../style/index.css"; import logoSvgStr from "../style/logo.svg"; import { CommandIDs } from "./commands"; const PLUGIN_ID = "ipyparallel-labextension:plugin"; const CATEGORY = "IPython Parallel"; /** * The IPython Parallel extension. */ const plugin: JupyterFrontEndPlugin = { activate, id: PLUGIN_ID, requires: [IConsoleTracker, INotebookTracker, ISettingRegistry, IStateDB], optional: [ILabShell], autoStart: true, }; /** * Export the plugin as default. */ export default plugin; /** * Activate the cluster launcher plugin. */ async function activate( app: JupyterFrontEnd, consoleTracker: IConsoleTracker, notebookTracker: INotebookTracker, settingRegistry: ISettingRegistry, state: IStateDB, labShell?: ILabShell | null, ): Promise { const id = "ipp-cluster-launcher"; const isLab = !!labShell; const isRetroTree = PageConfig.getOption("retroPage") == "tree"; const clientCodeInjector = async (model: IClusterModel) => { const editor = await Private.getCurrentEditor( app, notebookTracker, consoleTracker, ); if (!editor) { return; } Private.injectClientCode(model, editor); }; // Create the sidebar panel. const sidebar = new Sidebar({ clientCodeInjector, clientCodeGetter: Private.getClientCode, registry: app.commands, }); sidebar.id = id; sidebar.title.icon = new LabIcon({ name: "ipyparallel:logo", svgstr: logoSvgStr, }); // sidebar.title.iconClass = 'ipp-Logo jp-SideBar-tabIcon'; if (isLab) { labShell.add(sidebar, "left", { rank: 200 }); sidebar.title.caption = CATEGORY; } else if (isRetroTree) { const tabPanel = app.shell.currentWidget as TabPanel; tabPanel.addWidget(sidebar); tabPanel.tabBar.addTab(sidebar.title); sidebar.title.label = CATEGORY; } sidebar.clusterManager.activeClusterChanged.connect(async () => { const active = sidebar.clusterManager.activeCluster; return state.save(id, { cluster: active ? active.id : "", }); }); // A function to create a new client for a session. const createClientForSession = async ( session: Session.ISessionConnection | null, ) => { if (!session) { return; } const cluster = sidebar.clusterManager.activeCluster; if (!cluster || !(await Private.shouldUseKernel(session.kernel))) { return; } return Private.createClientForKernel(cluster, session.kernel!); }; type SessionOwner = NotebookPanel | ConsolePanel; // An array of the trackers to check for active sessions. const trackers: IWidgetTracker[] = [ notebookTracker, consoleTracker, ]; // A function to recreate a client on reconnect. const injectOnSessionStatusChanged = async ( sessionContext: ISessionContext, ) => { if ( sessionContext.session && sessionContext.session.kernel && sessionContext.session.kernel.status === "restarting" ) { return createClientForSession(sessionContext.session); } }; // A function to inject a client when a new session owner is added. const injectOnWidgetAdded = ( _: IWidgetTracker, widget: SessionOwner, ) => { widget.sessionContext.statusChanged.connect(injectOnSessionStatusChanged); }; // A function to inject a client when the active cluster changes. const injectOnClusterChanged = () => { trackers.forEach((tracker) => { tracker.forEach(async (widget) => { const session = widget.sessionContext.session; if (session && (await Private.shouldUseKernel(session.kernel))) { return createClientForSession(session); } }); }); }; // Whether the cluster clients should aggressively inject themselves // into the current session. let autoStartClient: boolean = false; // Update the existing trackers and signals in light of a change to the // settings system. In particular, this reacts to a change in the setting // for auto-starting cluster client. const updateTrackers = () => { // Clear any existing signals related to the auto-starting. Signal.clearData(injectOnWidgetAdded); Signal.clearData(injectOnSessionStatusChanged); Signal.clearData(injectOnClusterChanged); if (autoStartClient) { // When a new console or notebook is created, inject // a new client into it. trackers.forEach((tracker) => { tracker.widgetAdded.connect(injectOnWidgetAdded); }); // When the status of an existing notebook changes, reinject the client. trackers.forEach((tracker) => { tracker.forEach(async (widget) => { await createClientForSession(widget.sessionContext.session); widget.sessionContext.statusChanged.connect( injectOnSessionStatusChanged, ); }); }); // When the active cluster changes, reinject the client. sidebar.clusterManager.activeClusterChanged.connect( injectOnClusterChanged, ); } }; // Fetch the initial state of the settings. void Promise.all([settingRegistry.load(PLUGIN_ID), state.fetch(id)]).then( async (res) => { const settings = res[0]; if (!settings) { console.warn("Unable to retrieve ipp-labextension settings"); return; } const state = res[1] as { cluster?: string } | undefined; const cluster = state ? state.cluster : ""; const onSettingsChanged = () => { // Determine whether to use the auto-starting client. // autoStartClient = settings.get("autoStartClient").composite as boolean; updateTrackers(); }; onSettingsChanged(); // React to a change in the settings. settings.changed.connect(onSettingsChanged); // If an active cluster is in the state, reset it. if (cluster) { await sidebar.clusterManager.refresh(); sidebar.clusterManager.setActiveCluster(cluster); } }, ); // Add a command to inject client connection code for a given cluster model. // This looks for a cluster model in the application context menu, // and looks for an editor among the currently active notebooks and consoles. // If either is not found, it bails. app.commands.addCommand(CommandIDs.injectClientCode, { label: "Inject IPython Client Connection Code", execute: async () => { const cluster = Private.clusterFromClick(app, sidebar.clusterManager); if (!cluster) { return; } return await clientCodeInjector(cluster); }, }); // Add a command to launch a new cluster. app.commands.addCommand(CommandIDs.newCluster, { label: (args) => (args["isPalette"] ? "Create New Cluster" : "NEW"), execute: () => sidebar.clusterManager.create(), iconClass: (args) => args["isPalette"] ? "" : "jp-AddIcon jp-Icon jp-Icon-16", isEnabled: () => sidebar.clusterManager.isReady, caption: () => { if (sidebar.clusterManager.isReady) { return "Start New Cluster"; } return "Cluster starting..."; }, }); // Add a command to launch a new cluster. app.commands.addCommand(CommandIDs.startCluster, { label: "Start Cluster", execute: () => { const cluster = Private.clusterFromClick(app, sidebar.clusterManager); if (!cluster) { return; } return sidebar.clusterManager.start(cluster.id); }, }); // Add a command to stop a cluster. app.commands.addCommand(CommandIDs.stopCluster, { label: "Shutdown Cluster", execute: () => { const cluster = Private.clusterFromClick(app, sidebar.clusterManager); if (!cluster) { return; } return sidebar.clusterManager.stop(cluster.id); }, }); // Add a command to resize a cluster. app.commands.addCommand(CommandIDs.scaleCluster, { label: "Scale Cluster…", execute: () => { const cluster = Private.clusterFromClick(app, sidebar.clusterManager); if (!cluster) { return; } return sidebar.clusterManager.scale(cluster.id); }, }); // Add a command to toggle the auto-starting client code. // app.commands.addCommand(CommandIDs.toggleAutoStartClient, { // label: "Auto-Start IPython Parallel", // isToggled: () => autoStartClient, // execute: async () => { // const value = !autoStartClient; // const key = "autoStartClient"; // return settingRegistry // .set(PLUGIN_ID, key, value) // .catch((reason: Error) => { // console.error( // `Failed to set ${PLUGIN_ID}:${key} - ${reason.message}` // ); // }); // }, // }); // // Add some commands to the menu and command palette. // mainMenu.settingsMenu.addGroup([ // { command: CommandIDs.toggleAutoStartClient }, // ]); // [CommandIDs.newCluster, CommandIDs.toggleAutoStartClient].forEach( // (command) => { // commandPalette.addItem({ // category: "IPython Parallel", // command, // args: { isPalette: true }, // }); // } // ); // Add a context menu items. app.contextMenu.addItem({ command: CommandIDs.injectClientCode, selector: ".ipp-ClusterListingItem", rank: 10, }); app.contextMenu.addItem({ command: CommandIDs.stopCluster, selector: ".ipp-ClusterListingItem", rank: 3, }); app.contextMenu.addItem({ command: CommandIDs.scaleCluster, selector: ".ipp-ClusterListingItem", rank: 2, }); app.contextMenu.addItem({ command: CommandIDs.startCluster, selector: ".ipp-ClusterListing-list", rank: 1, }); } namespace Private { /** * A private counter for ids. */ export let id = 0; /** * Whether a kernel should be used. Only evaluates to true * if it is valid and in python. */ export async function shouldUseKernel( kernel: Kernel.IKernelConnection | null | undefined, ): Promise { if (!kernel) { return false; } const spec = await kernel.spec; return !!spec && spec.language.toLowerCase().indexOf("python") !== -1; } /** * Connect a kernel to a cluster by creating a new Client. */ export async function createClientForKernel( model: IClusterModel, kernel: Kernel.IKernelConnection, ): Promise { const code = getClientCode(model); const content: KernelMessage.IExecuteRequestMsg["content"] = { store_history: false, code, }; return new Promise((resolve, _) => { const future = kernel.requestExecute(content); future.onIOPub = (msg) => { if (msg.header.msg_type !== "display_data") { return; } resolve(void 0); }; }); } /** * Insert code to connect to a given cluster. */ export function injectClientCode( cluster: IClusterModel, editor: CodeEditor.IEditor, ): void { const cursor = editor.getCursorPosition(); const offset = editor.getOffsetAt(cursor); const code = getClientCode(cluster); editor.model.sharedModel.updateSource(offset, offset, code); } /** * Get code to connect to a given cluster. */ export function getClientCode(cluster: IClusterModel): string { return `import ipyparallel as ipp cluster = ipp.Cluster.from_file("${cluster.cluster_file}") rc = cluster.connect_client_sync() rc`; } /** * Get the currently focused kernel in the application, * checking both notebooks and consoles. */ export function getCurrentKernel( shell: ILabShell, notebookTracker: INotebookTracker, consoleTracker: IConsoleTracker, ): Kernel.IKernelConnection | null | undefined { // Get a handle on the most relevant kernel, // whether it is attached to a notebook or a console. let current = shell.currentWidget; let kernel: Kernel.IKernelConnection | null | undefined; if (current && notebookTracker.has(current)) { kernel = (current as NotebookPanel).sessionContext.session?.kernel; } else if (current && consoleTracker.has(current)) { kernel = (current as ConsolePanel).sessionContext.session?.kernel; } else if (notebookTracker.currentWidget) { const current = notebookTracker.currentWidget; kernel = current.sessionContext.session?.kernel; } else if (consoleTracker.currentWidget) { const current = consoleTracker.currentWidget; kernel = current.sessionContext.session?.kernel; } return kernel; } /** * Get the currently focused editor in the application, * checking both notebooks and consoles. * In the case of a notebook, it creates a new cell above the currently * active cell and then returns that. */ export async function getCurrentEditor( app: JupyterFrontEnd, notebookTracker: INotebookTracker, consoleTracker: IConsoleTracker, ): Promise { // Get a handle on the most relevant kernel, // whether it is attached to a notebook or a console. let current = app.shell.currentWidget; let editor: CodeEditor.IEditor | null | undefined; if (current && notebookTracker.has(current)) { NotebookActions.insertAbove((current as NotebookPanel).content); const cell = (current as NotebookPanel).content.activeCell; await cell.ready; editor = cell && cell.editor; } else if (current && consoleTracker.has(current)) { const cell = (current as ConsolePanel).console.promptCell; await cell.ready; editor = cell && cell.editor; } else if (notebookTracker.currentWidget) { const current = notebookTracker.currentWidget; NotebookActions.insertAbove(current.content); const cell = current.content.activeCell; await cell.ready; editor = cell && cell.editor; } else if (consoleTracker.currentWidget) { const current = consoleTracker.currentWidget; const cell = current.console.promptCell; await cell.ready; editor = cell && cell.editor; } return editor; } /** * Get a cluster model based on the application context menu click node. */ export function clusterFromClick( app: JupyterFrontEnd, manager: ClusterManager, ): IClusterModel | undefined { const test = (node: HTMLElement) => !!node.dataset.clusterId; const node = app.contextMenuHitTest(test); if (!node) { return undefined; } const id = node.dataset.clusterId; return manager.clusters.find((cluster) => cluster.id === id); } } ipyparallel-8.8.0/lab/src/sidebar.ts000066400000000000000000000027541460376056100173730ustar00rootroot00000000000000import { Widget, PanelLayout } from "@lumino/widgets"; import { CommandRegistry } from "@lumino/commands"; import { ClusterManager, IClusterModel } from "./clusters"; /** * A widget for hosting IPP cluster widgets */ export class Sidebar extends Widget { /** * Create a new IPP sidebar. */ constructor(options: Sidebar.IOptions) { super(); this.addClass("ipp-Sidebar"); let layout = (this.layout = new PanelLayout()); const injectClientCodeForCluster = options.clientCodeInjector; const getClientCodeForCluster = options.clientCodeGetter; // Add the cluster manager component. this._clusters = new ClusterManager({ registry: options.registry, injectClientCodeForCluster, getClientCodeForCluster, }); layout.addWidget(this._clusters); } /** * Get the cluster manager associated with the sidebar. */ get clusterManager(): ClusterManager { return this._clusters; } private _clusters: ClusterManager; } /** * A namespace for Sidebar statics. */ export namespace Sidebar { /** * Options for the constructor. */ export interface IOptions { /** * Registry of all commands */ registry: CommandRegistry; /** * A function that injects client-connection code for a given cluster. */ clientCodeInjector: (model: IClusterModel) => void; /** * A function that gets client-connection code for a given cluster. */ clientCodeGetter: (model: IClusterModel) => string; } } ipyparallel-8.8.0/lab/src/svg.d.ts000066400000000000000000000001271460376056100167730ustar00rootroot00000000000000// svg.d.ts declare module "*.svg" { const value: string; export default value; } ipyparallel-8.8.0/lab/style/000077500000000000000000000000001460376056100157535ustar00rootroot00000000000000ipyparallel-8.8.0/lab/style/code-dark.svg000066400000000000000000000003661460376056100203320ustar00rootroot00000000000000 ipyparallel-8.8.0/lab/style/code-light.svg000066400000000000000000000003661460376056100205200ustar00rootroot00000000000000 ipyparallel-8.8.0/lab/style/index.css000066400000000000000000000071201460376056100175740ustar00rootroot00000000000000:root { --ipp-launch-button-height: 24px; } /** * Rules related to the overall sidebar panel. */ .ipp-Sidebar { background: var(--jp-layout-color1); color: var(--jp-ui-font-color1); font-size: var(--jp-ui-font-size1); overflow: auto; } /** * Rules related to the cluster manager. */ .ipp-ClusterManager .jp-Toolbar { align-items: center; } .ipp-ClusterManager .jp-Toolbar .ipp-ClusterManager-label { flex: 0 0 auto; font-weight: 600; text-transform: uppercase; letter-spacing: 1px; font-size: var(--jp-ui-font-size0); padding: 8px 8px 8px 12px; margin: 0px; } .ipp-ClusterManager button.jp-Button > span { display: flex; flex-direction: row; align-items: center; } .ipp-ClusterListing ul.ipp-ClusterListing-list { list-style-type: none; padding: 0; margin: 0; } .ipp-ClusterListingItem { display: inline-block; list-style-type: none; padding: 8px; width: 100%; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; cursor: grab; } .ipp-ClusterListingItem-drag { opacity: 0.7; color: var(--jp-ui-font-color1); cursor: grabbing; max-width: 260px; transform: translateX(-50%) translateY(-50%); } .ipp-ClusterListingItem-title { margin: 0px; font-size: var(--jp-ui-font-size2); } .ipp-ClusterListingItem-link a { text-decoration: none; color: var(--jp-content-link-color); } .ipp-ClusterListingItem-link a:hover { text-decoration: underline; } .ipp-ClusterListingItem-link a:visited { color: var(--jp-content-link-color); } .ipp-ClusterListingItem:hover { background: var(--jp-layout-color2); } .ipp-ClusterListingItem.jp-mod-active { color: white; background: var(--jp-brand-color0); } .ipp-ClusterListingItem.jp-mod-active a, .ipp-ClusterListingItem.jp-mod-active a:visited { color: white; } .ipp-ClusterListingItem button.jp-mod-styled { background-color: transparent; } .ipp-ClusterListingItem button.jp-mod-styled:hover { background-color: var(--jp-layout-color3); } .ipp-ClusterListingItem.jp-mod-active button.jp-mod-styled:hover { background-color: var(--jp-brand-color1); } .ipp-ClusterListingItem-button-panel { display: flex; align-content: center; 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When multiple engines are started, parallel and distributed computing becomes possible. ### IPython controller The IPython controller processes provide an interface for working with a set of engines. At a general level, the controller is a collection of processes to which IPython engines and clients can connect. The controller is composed of a {class}`Hub` and a collection of {class}`Schedulers`. These Schedulers are typically run in separate processes on the same machine as the Hub. The controller also provides a single point of contact for users who wish to access the engines connected to the controller. There are different ways of working with a controller. In IPython, all of these models are implemented via the {meth}`.View.apply` method, after constructing {class}`.View` objects to represent subsets of engines. The two primary models for interacting with engines are: - A **Direct** interface, where engines are addressed explicitly - A **LoadBalanced** interface, where the Scheduler is entrusted with assigning work to appropriate engines Advanced users can readily extend the View models to enable other styles of parallelism. ```{note} A single controller and set of engines can be used with multiple models simultaneously. This opens the door for lots of interesting things. ``` #### The Hub The center of an IPython cluster is the Hub. This is the process that keeps track of engine connections, schedulers, clients, as well as all task requests and results. The primary role of the Hub is to facilitate queries of the cluster state, and minimize the necessary information required to establish the many connections involved in connecting new clients and engines. #### Schedulers All actions that can be performed on the engine go through a Scheduler. While the engines themselves block when user code is run, the schedulers hide that from the user to provide a fully asynchronous interface to a set of engines. ### IPython client and views There is one primary object, the {class}`~.parallel.Client`, for connecting to a cluster. For each execution model, there is a corresponding {class}`~.parallel.View`. These views allow users to interact with a set of engines through the interface. Here are the two default views: - The {class}`DirectView` class for explicit addressing. - The {class}`LoadBalancedView` class for destination-agnostic scheduling. ## Getting Started To use IPython for parallel computing, you need to start one instance of the controller and one or more instances of the engine. Initially, it is best to start a controller and engines on a single host. To start a controller and 4 engines on your local machine: ```ipython In [1]: import ipyparallel as ipp In [2]: cluster = ipp.Cluster(n=4) In [3]: await cluster.start_cluster() # or cluster.start_cluster_sync() without await ``` ```{note} Most Cluster methods are async, and all async cluster methods have a blocking version with a `_sync` suffix, e.g. `await cluster.start_cluster()` and `cluster.start_cluster_sync()` ``` You can also launch clusters at the command-line with: ``` $ ipcluster start -n 4 ``` which is equivalent to `ipp.Cluster(n=4, cluster_id="").start_cluster()` and connect to the already-running cluster with {meth}`.Cluster.from_file` ```python cluster = ipp.Cluster.from_file() ``` For a convenient one-liner to start a cluster and connect a client, use {meth}`~.Cluster.start_and_connect_sync`: ```ipython In [1]: import ipyparallel as ipp In [2]: rc = ipp.Cluster(n=4).start_and_connect_sync() ``` More details about starting the IPython controller and engines can be found {ref}`here `. Once you have a handle on a cluster, you can connect a client. To make sure everything is working correctly, try the following commands: ```ipython In [2]: rc = cluster.connect_client_sync() In [3]: rc.wait_for_engines(n=4) In [4]: rc.ids Out[4]: [0, 1, 2, 3] In [5]: rc[:].apply_sync(lambda: "Hello, World") Out[5]: [ 'Hello, World', 'Hello, World', 'Hello, World', 'Hello, World' ] ``` When a client is created with no arguments, the client tries to find the corresponding JSON file in the local `~/.ipython/profile_default/security` directory. Or if you specified a profile, you can use that with the Client. This should cover most cases: ```ipython In [2]: cluster = ipp.Cluster.from_file(profile="myprofile", cluster_id="...") In [3]: rc = cluster.connect_client_sync() ``` If you have put the JSON file in a different location or it has a different name, create the Cluster object like this: ```ipython In [2]: cluster = ipp.Cluster.from_file('/path/to/my/cluster-.json') ``` Remember, a client needs to be able to see the Hub's ports to connect. So if the controller and client are on different machines, you may need to use an ssh server to tunnel access to that machine, in which case you would connect with: ```ipython In [2]: c = ipp.Client('/path/to/my/ipcontroller-client.json', sshserver='me@myhub.example.com') ``` Where 'myhub.example.com' is the url or hostname of the machine on which the Hub process is running (or another machine that has direct access to the Hub's ports). The SSH server may already be specified in ipcontroller-client.json, if the controller was instructed at its launch time. ### Cluster as context manager The {class}`~.ipyparallel.Cluster` and {class}`~.ipyparallel.Client` classes can be used as context managers for easier cleanup of resources. - Entering a `Cluster` context 1. starts the cluster 2. waits for engines to be ready 3. connects a client 4. returns the client - Exiting a `Client` context closes the client's socket connections to the cluster. - Exiting a `Cluster` context shuts down all of the cluster's resources. If you know you won't need your cluster anymore after you use it, use of these context managers is encouraged. For example: ```python import ipyparallel as ipp # start cluster, connect client with ipp.Cluster(n=4) as rc: e_all = rc[:] ar = e_all.apply_sync(task) ar.wait_interactive() results = ar.get() # have results, cluster is shutdown ``` You are now ready to learn more about the {ref}`Direct ` and {ref}`LoadBalanced ` interfaces to the controller. [zeromq]: https://zeromq.org/ ipyparallel-8.8.0/docs/source/tutorial/magics.md000066400000000000000000000226641460376056100217470ustar00rootroot00000000000000(parallel-magics)= # Parallel Magic Commands We provide a few IPython magic commands that make it a bit more pleasant to execute Python commands on the engines interactively. These are mainly shortcuts to {meth}`.DirectView.execute` and {meth}`.AsyncResult.display_outputs` methods respectively. These magics will automatically become available when you create a Client: ```ipython In [1]: import ipyparallel as ipp In [2]: rc = ipp.Client() ``` The initially active View will have attributes `targets='all', block=True`, which is a blocking view of all engines, evaluated at request time (adding/removing engines will change where this view's tasks will run). ## The Magics ### %px The %px magic executes a single Python command on the engines specified by the {attr}`targets` attribute of the {class}`DirectView` instance: ```ipython # import numpy here and everywhere In [25]: with rc[:].sync_imports(): ....: import numpy importing numpy on engine(s) In [27]: %px a = numpy.random.rand(2,2) Parallel execution on engines: [0, 1, 2, 3] In [28]: %px numpy.linalg.eigvals(a) Parallel execution on engines: [0, 1, 2, 3] Out [0:68]: array([ 0.77120707, -0.19448286]) Out [1:68]: array([ 1.10815921, 0.05110369]) Out [2:68]: array([ 0.74625527, -0.37475081]) Out [3:68]: array([ 0.72931905, 0.07159743]) In [29]: %px print 'hi' Parallel execution on engine(s): all [stdout:0] hi [stdout:1] hi [stdout:2] hi [stdout:3] hi ``` Since engines are IPython as well, you can even run magics remotely: ```ipython In [28]: %px %pylab inline Parallel execution on engine(s): all [stdout:0] Populating the interactive namespace from numpy and matplotlib [stdout:1] Populating the interactive namespace from numpy and matplotlib [stdout:2] Populating the interactive namespace from numpy and matplotlib [stdout:3] Populating the interactive namespace from numpy and matplotlib ``` And once in pylab mode with the inline backend, you can make plots and they will be displayed in your frontend if it supports the inline figures (e.g. notebook or qtconsole): ```ipython In [40]: %px plot(rand(100)) Parallel execution on engine(s): all Out[0:79]: [] Out[1:79]: [] Out[2:79]: [] Out[3:79]: [] ``` ### %%px Cell Magic \%%px can be used as a Cell Magic, which accepts some arguments for controlling the execution. #### Targets and Blocking \%%px accepts `--targets` for controlling which engines on which to run, and `--[no]block` for specifying the blocking behavior of this cell, independent of the defaults for the View. ```ipython In [6]: %%px --targets ::2 ...: print "I am even" ...: Parallel execution on engine(s): [0, 2] [stdout:0] I am even [stdout:2] I am even In [7]: %%px --targets 1 ...: print "I am number 1" ...: Parallel execution on engine(s): 1 I am number 1 In [8]: %%px ...: print "still 'all' by default" ...: Parallel execution on engine(s): all [stdout:0] still 'all' by default [stdout:1] still 'all' by default [stdout:2] still 'all' by default [stdout:3] still 'all' by default In [9]: %%px --noblock ...: import time ...: time.sleep(1) ...: time.time() ...: Async parallel execution on engine(s): all Out[9]: In [10]: %pxresult Out[0:12]: 1339454561.069116 Out[1:10]: 1339454561.076752 Out[2:12]: 1339454561.072837 Out[3:10]: 1339454561.066665 ``` ```{seealso} {ref}`pxconfig` accepts these same arguments for changing the _default_ values of targets/blocking for the active View. ``` #### Output Display \%%px also accepts a `--group-outputs` argument, which adjusts how the outputs of multiple engines are presented. ```{seealso} {meth}`.AsyncResult.display_outputs` for the grouping options. ``` ```ipython In [50]: %%px --block --group-outputs=engine ....: import numpy as np ....: A = np.random.random((2,2)) ....: ev = numpy.linalg.eigvals(A) ....: print ev ....: ev.max() ....: Parallel execution on engine(s): all [stdout:0] [ 0.60640442 0.95919621] Out [0:73]: 0.9591962130899806 [stdout:1] [ 0.38501813 1.29430871] Out [1:73]: 1.2943087091452372 [stdout:2] [-0.85925141 0.9387692 ] Out [2:73]: 0.93876920456230284 [stdout:3] [ 0.37998269 1.24218246] Out [3:73]: 1.2421824618493817 ``` ### %pxresult If you are using %px in non-blocking mode, you won't get output. You can use %pxresult to display the outputs of the latest command, as is done when %px is blocking: ```ipython In [39]: dv.block = False In [40]: %px print 'hi' Async parallel execution on engine(s): all In [41]: %pxresult [stdout:0] hi [stdout:1] hi [stdout:2] hi [stdout:3] hi ``` \%pxresult calls {meth}`.AsyncResult.display_outputs` on the most recent request. It accepts the same output-grouping arguments as %%px, so you can use it to view a result in different ways. ### %autopx The %autopx magic switches to a mode where everything you type is executed on the engines until you do %autopx again. ```ipython In [30]: dv.block=True In [31]: %autopx %autopx enabled In [32]: max_evals = [] In [33]: for i in range(100): ....: a = numpy.random.rand(10,10) ....: a = a+a.transpose() ....: evals = numpy.linalg.eigvals(a) ....: max_evals.append(evals[0].real) ....: In [34]: print "Average max eigenvalue is: %f" % (sum(max_evals)/len(max_evals)) [stdout:0] Average max eigenvalue is: 10.193101 [stdout:1] Average max eigenvalue is: 10.064508 [stdout:2] Average max eigenvalue is: 10.055724 [stdout:3] Average max eigenvalue is: 10.086876 In [35]: %autopx Auto Parallel Disabled ``` (pxconfig)= ### %pxconfig The default targets and blocking behavior for the magics are governed by the {attr}`block` and {attr}`targets` attribute of the active View. If you have a handle for the view, you can set these attributes directly, but if you don't, you can change them with the %pxconfig magic: ```ipython In [3]: %pxconfig --block In [5]: %px print 'hi' Parallel execution on engine(s): all [stdout:0] hi [stdout:1] hi [stdout:2] hi [stdout:3] hi In [6]: %pxconfig --targets ::2 In [7]: %px print 'hi' Parallel execution on engine(s): [0, 2] [stdout:0] hi [stdout:2] hi In [8]: %pxconfig --noblock In [9]: %px print 'are you there?' Async parallel execution on engine(s): [0, 2] Out[9]: In [10]: %pxresult [stdout:0] are you there? [stdout:2] are you there? ``` ## Multiple Active Views The parallel magics are associated with a particular {class}`~.DirectView` object. You can change the active view by calling the {meth}`~.DirectView.activate` method on any view. ```ipython In [11]: even = rc[::2] In [12]: even.activate() In [13]: %px print 'hi' Async parallel execution on engine(s): [0, 2] Out[13]: In [14]: even.block = True In [15]: %px print 'hi' Parallel execution on engine(s): [0, 2] [stdout:0] hi [stdout:2] hi ``` When activating a View, you can also specify a _suffix_, so that a whole different set of magics are associated with that view, without replacing the existing ones. ```ipython # restore the original DirecView to the base %px magics In [16]: rc.activate() Out[16]: In [17]: even.activate('_even') In [18]: %px print 'hi all' Parallel execution on engine(s): all [stdout:0] hi all [stdout:1] hi all [stdout:2] hi all [stdout:3] hi all In [19]: %px_even print "We aren't odd!" Parallel execution on engine(s): [0, 2] [stdout:0] We aren't odd! [stdout:2] We aren't odd! ``` This suffix is applied to the end of all magics, e.g. %autopx_even, %pxresult_even, etc. For convenience, the {class}`~.Client` has a {meth}`~.Client.activate` method as well, which creates a DirectView with block=True, activates it, and returns the new View. The initial magics registered when you create a client are the result of a call to {meth}`rc.activate` with default args. ## Engines as Kernels Engines are really the same object as the Kernels used elsewhere in IPython, with the minor exception that engines connect to a controller, while regular kernels bind their sockets, listening for connections from a QtConsole or other frontends. Sometimes for debugging or inspection purposes, you would like a QtConsole connected to an engine for more direct interaction. You can do this by first instructing the Engine to _also_ bind its kernel, to listen for connections: ```ipython In [50]: %px import ipyparallel as ipp; ipp.bind_kernel() ``` Then, if your engines are local, you can start a qtconsole right on the engine(s): ```ipython In [51]: %px %qtconsole ``` Careful with this one, because if your view is of 16 engines it will start 16 QtConsoles! Or you can view the connection info and work out the right way to connect to the engines, depending on where they live and where you are: ```ipython In [51]: %px %connect_info Parallel execution on engine(s): all [stdout:0] { "stdin_port": 60387, "ip": "127.0.0.1", "hb_port": 50835, "key": "eee2dd69-7dd3-4340-bf3e-7e2e22a62542", "shell_port": 55328, "iopub_port": 58264 } Paste the above JSON into a file, and connect with: $> ipython --existing or, if you are local, you can connect with: $> ipython --existing kernel-60125.json or even just: $> ipython --existing if this is the most recent IPython session you have started. [stdout:1] { "stdin_port": 61869, ... ``` ```{note} `%qtconsole` will call {func}`bind_kernel` on an engine if it hasn't been done already, so you can often skip that first step. ``` ipyparallel-8.8.0/docs/source/tutorial/process.md000066400000000000000000000733001460376056100221530ustar00rootroot00000000000000(parallel-process)= # Starting the IPython controller and engines To use IPython for parallel computing, you need to start an IPython cluster. This includes one instance of the controller and one or more instances of the engine. The controller and each engine can run on different machines or on the same machine. Any mechanism for starting processes that can communicate over the network Broadly speaking, there are two ways of going about starting a controller and engines: - As managed processes, using {class}`.Cluster` objects. This includes via the {command}`ipcluster` command. - In a more manual way using the {command}`ipcontroller` and {command}`ipengine` commands directly. Starting with IPython Parallel 7, some features are only available when using the `Cluster` API, so this is encouraged. Such features include collective interrupts, signaling support, restart APIs, and improved crash handling. This document describes both of these methods. We recommend that new users start with the `Cluster` API within scripts or notebooks. ## General considerations Before delving into the details about how you can start a controller and engines using the various methods, we outline some of the general issues that come up when starting the controller and engines. These things come up no matter which method you use to start your IPython cluster. If you are running engines on multiple machines, you will likely need to instruct the controller to listen for connections on an external interface. This can be done by specifying the `ip` argument on the command-line, or the `IPController.ip` configurable in {file}`ipcontroller_config.py`, or the `controller_ip` argument when creating a Cluster: If your machines are on a trusted network, you can safely instruct the controller to listen on all interfaces with: ```python cluster = ipp.Cluster(controller_ip='*') ``` or ``` $> ipcontroller --ip="*" ``` Or you can set the same behavior as the default by adding the following line to your {file}`ipcontroller_config.py`: ```python c.IPController.ip = '*' # c.IPController.location = 'controllerhost.tld' ``` :::{note} `--ip=*` instructs ZeroMQ to listen on all interfaces, but it does not contain the IP needed for engines / clients to know where the controller is. This can be specified with the `--location` argument, such as `--location=10.0.0.1`, or `--location=server.local`, the specific IP address or hostname of the controller, as seen from engines and/or clients. IPython uses `socket.gethostname()` for this value by default, but it may not always be the right value. Check the `location` field in your connection files if you are having connection trouble. ::: ```{versionchanged} 6.1 Support hostnames in location, in addition to ip addresses. ``` ```{note} Due to the lack of security in ZeroMQ, the controller will only listen for connections on localhost by default. If you see Timeout errors on engines or clients, then the first thing you should check is the ip address the controller is listening on, and make sure that it is visible from the timing out machine. ``` ```{seealso} Our [notes](security) on security in the new parallel computing code. ``` Let's say that you want to start the controller on `host0` and engines on hosts `host1`-`hostn`. The following steps are then required: 1. Start the controller on `host0` by running {command}`ipcontroller` on `host0`. The controller must be instructed to listen on an interface visible to the engine machines, via the `ip` command-line argument or `IPController.ip` in {file}`ipcontroller_config.py`. 2. Make the JSON file ({file}`ipcontroller-engine.json`) created by the controller on `host0` available on hosts `host1`-`hostn`. 3. Start the engines on hosts `host1`-`hostn` by running {command}`ipengine`. This command may have to be told where the JSON file ({file}`ipcontroller-engine.json`) is located. At this point, the controller and engines will be connected. By default, the JSON files created by the controller are put into the {file}`$IPYTHONDIR/profile_default/security` directory. If the engines share a filesystem with the controller, step 2 can be skipped as the engines will automatically look at that location. The final step required to use the running controller from a client is to move the JSON file {file}`ipcontroller-client.json` from `host0` to any host where clients will be run. If these file are put into the {file}`IPYTHONDIR/profile_default/security` directory of the client's host, they will be found automatically. Otherwise, the full path to them has to be passed to the client's constructor. ## Managing `Clusters` The {class}`~.ipyparallel.Cluster` class provides a managed way of starting a controller and engines in the following situations: 1. When the controller and engines are all run on localhost. This is useful for testing or running on a multicore computer. 2. When engines are started using the {command}`mpiexec` command that comes with most MPI [^cite_mpi] implementations 3. When engines are started using the PBS [^cite_pbs] batch system (or other `qsub` systems, such as SGE). 4. When the controller is started on localhost and the engines are started on remote nodes using {command}`ssh`. Under the hood, `Cluster` objects ultimately launch {command}`ipcontroller` and {command}`ipengine` processes to perform the steps described above. The simplest way to use clusters requires no configuration, and will launch a controller and a number of engines on the local machine. For instance, to start one controller and 4 engines on localhost: ```python cluster = ipp.Cluster(n=4) cluster.start_cluster_sync() ``` ## Configuring an IPython cluster Cluster configurations are stored as `profiles`. You can create a new profile with: ``` $ ipython profile create --parallel --profile=myprofile ``` This will create the directory {file}`IPYTHONDIR/profile_myprofile`, and populate it with the default configuration files for the three IPython cluster commands. Once you edit those files, you can continue to call ipcluster/ipcontroller/ipengine with no arguments beyond `profile=myprofile`, and any configuration will be maintained. There is no limit to the number of profiles you can have, so you can maintain a profile for each of your common use cases. The default profile will be used whenever the profile argument is not specified, so edit {file}`IPYTHONDIR/profile_default/*_config.py` to represent your most common use case. The configuration files are loaded with commented-out settings and explanations, which should cover most of the available possibilities. Additionally, each profile can have many cluster instances at once, identified by a `cluster_id`. You can see running clusters with: ``` $> ipcluster list ``` ### Using various batch systems with {command}`ipcluster` {command}`ipcluster` has a notion of {class}`Launcher` that can start controllers and engines via some mechanism. Currently supported models include {command}`ssh`, {command}`mpiexec`, and PBS-style (Torque, SGE, LSF, Slurm). In general, these are configured by the {attr}`Cluster.engine_set_launcher_class`, and {attr}`Cluster.controller_launcher_class` configurables, which can be the fully specified object name (e.g. `'ipyparallel.cluster.launcher.LocalControllerLauncher'`), but if you are using IPython's builtin launchers, you can specify a launcher by its short name, e.g: ```python c.Cluster.engine_launcher_class = 'ssh' # which is equivalent to c.Cluster.engine_launcher_class = 'ipyparallel.cluster.launcher.SSHEngineSetLauncher' ``` The shortest form being of particular use on the command line, where all you need to do to get an IPython cluster running with engines started with MPI is: ```bash $> ipcluster start --engines=mpi ``` Assuming that the default MPI configuration is sufficient. ```{note} The Launchers and configuration are designed in such a way that advanced users can subclass and configure them to fit their own system that we have not yet supported ``` ### Writing custom Launchers TODO: example writing custom Launchers (ipcluster-mpi)= ### Using IPython Parallel with MPI The mpiexec/mpirun mode is useful if: 1. You have MPI installed. 2. Your systems are configured to use the {command}`mpiexec` or {command}`mpirun` commands to start MPI processes. you can usually start with MPI pretty readily without creating a profile. ```python cluster = ipp.Cluster(engines="mpi", n=4) client = cluster.start_and_connect_sync() ``` This does the following: 1. Starts the IPython controller on current host. 2. Uses {command}`mpiexec` to start 4 engines. More details on using MPI with IPython can be found {ref}`here `. ### Starting IPython Parallel on a traditional cluster For cases that require more extensive configuration, IPython can use "profiles" to collect related configuration. These profiles have names, and are located by default in your .ipython directory. IPython supports several batch systems, including PBS, LSF, Slurm, SGE, and HTCondor. Many "PBS-like" systems can be supported by configuring the PBSLauncher. Additional configuration options can be found in the BatchSystemLauncher section of {file}`ipcluster_config`. We'll use PBS as our example here, but all batch system launchers are configured the same way. We will start by creating a fresh profile: ``` $ ipython profile create --parallel --profile=pbs ``` And in {file}`ipcluster_config.py`, we will select the PBS launchers for the controller and engines: ```python c.Cluster.controller_launcher_class = 'pbs' c.Cluster.engine_launcher_class = 'pbs' ``` IPython does provide simple default batch templates for supported batch systems, but you may need to specify your own. Here is a sample PBS script template: ```bash #!/bin/bash #PBS -N ipython-parallel # set the name of the job (convenient) #PBS -V # export environment variables, required #PBS -j oe # merge stdout and error #PBS -o {output_file} # send output to a file #PBS -l walltime=00:10:00 # max runtime 10 minutes #PBS -l nodes={n//4}:ppn=4 # 4 processes per node #PBS -q {queue} # run on the specified queue cd $PBS_O_WORKDIR export PATH=$HOME/usr/local/bin /usr/local/bin/mpiexec -n {n} {program_and_args} # start the configured program # program_and_args is populated via `engine_args` and other configuration # or you can ignore that and configure the full program in the template /usr/local/bin/mpiexec -n {n} ipengine --debug ``` There are a few important points about this template: 1. This template will be rendered at runtime using IPython's {class}`EvalFormatter`. This is a subclass of {class}`string.Formatter` that allows simple expressions on keys. 2. Instead of putting in the actual number of engines, use the notation `{n}` to indicate the number of engines to be started. You can also use expressions like `{n//4}` in the template to indicate the number of nodes. There will always be `{n}` and `{profile_dir}` variables passed to the formatter. These allow the batch system to know how many engines, and where the configuration files reside. The same is true for the batch queue, with the template variable `{queue}`. 3. Any options to {command}`ipengine` can be given in the batch script template, or in {file}`ipengine_config.py`. 4. Depending on the configuration of you system, you may have to set environment variables in the script template. The controller template should be similar, but simpler: ```bash #!/bin/bash #PBS -N ipython #PBS -V #PBS -j oe #PBS -o {output_file} #PBS -l walltime=00:10:00 #PBS -l nodes=1:ppn=1 #PBS -q {queue} #PBS -V cd $PBS_O_WORKDIR export PATH=$HOME/usr/local/bin {program_and_args} # or ipcontroller --ip=* ``` Once you have created these scripts, save them with names like {file}`pbs.engine.template`. Now you can load them into the {file}`ipcluster_config` with: ```python c.PBSEngineSetLauncher.batch_template_file = "pbs.engine.template" c.PBSControllerLauncher.batch_template_file = "pbs.controller.template" ``` Alternately, you can define the templates as strings inside {file}`ipcluster_config`. Whether you are using your own templates or our defaults, the extra configurables available are the number of engines to launch (`{n}`, and the batch system queue to which the jobs are to be submitted (`{queue}`)). These are configurables, and can be specified in {file}`ipcluster_config`: ```python c.PBSLauncher.queue = 'veryshort.q' c.Cluster.n = 64 ``` Note that assuming you are running PBS on a multi-node cluster, the Controller's default behavior of listening only on localhost is likely too restrictive. In this case, also assuming the nodes are safely behind a firewall, you can instruct the Controller to listen for connections on all its interfaces, by adding in {file}`ipcontroller_config`: ```python c.IPController.ip = '*' ``` You can now run the cluster with: ```python cluster = Cluster(profile="pbs") ``` ``` $ ipcluster start --profile=pbs -n 128 ``` ### Starting a cluster with SSH The SSH mode uses {command}`ssh` to execute {command}`ipengine` on remote nodes and {command}`ipcontroller` can be run remotely as well, or on localhost. ```{note} When using this mode it highly recommended that you have set up SSH keys and are using ssh-agent [^cite_ssh] for password-less logins. ``` As usual, we start by creating a clean profile: ``` $ ipython profile create --parallel --profile=ssh ``` To use this mode, select the SSH launchers in {file}`ipcluster_config.py`: ```python c.Cluster.engine_launcher_class = 'ssh' # or 'sshproxy' # and if the Controller is also to be remote: c.Cluster.controller_launcher_class = 'ssh' ``` The controller's remote location and configuration can be specified: ```python # Set the user and hostname for the controller # c.SSHControllerLauncher.hostname = 'controller.example.com' # c.SSHControllerLauncher.user = os.environ.get('USER','username') # Set the arguments to be passed to ipcontroller # note that remotely launched ipcontroller will not get the contents of # the local ipcontroller_config.py unless it resides on the *remote host* # in the location specified by the `profile-dir` argument. # c.SSHControllerLauncher.controller_args = ['--reuse', '--ip=*', '--profile-dir=/path/to/cd'] ``` Engines are specified in a dictionary, by hostname and the number of engines to be run on that host. ```python c.SSHEngineSetLauncher.engines = { 'host1.example.com' : 2, 'host2.example.com' : 5, 'host3.example.com' : (1, ['--profile-dir=/home/different/location']), 'host4.example.com' : {'n': 3, 'engine_args': ['--profile-dir=/away/location'], 'engine_cmd': ['/home/venv/bin/python', '-m', 'ipyparallel.engine']}, 'host5.example.com' : 8 } ``` - The `engines` dict, where the keys are the host we want to run engines on and the value is the number of engines to run on that host. - on host3, the value is a tuple, where the number of engines is first, and the arguments to be passed to {command}`ipengine` are the second element. - on host4, a dictionary configures the engine. The dictionary can be used to specify the number of engines to be run on that host `n`, the engine arguments `engine_args`, as well as the engine command itself `engine_cmd`. This is particularly useful for virtual environments on heterogeneous clusters where the location of the python executable might vary from host to host. For engines without explicitly specified arguments, the default arguments are set in a single location: ```python c.SSHEngineSetLauncher.engine_args = ['--profile-dir=/path/to/profile_ssh'] ``` Current limitations of the SSH mode of {command}`ipcluster` are: - Untested and unsupported on Windows. Would require a working {command}`ssh` on Windows. Also, we are using shell scripts to setup and execute commands on remote hosts. #### Moving files with SSH SSH launchers will try to move connection files, controlled by the `to_send` and `to_fetch` configurables. If your machines are on a shared filesystem, this step is unnecessary, and can be skipped by setting these to empty lists: ```python c.SSHLauncher.to_send = [] c.SSHLauncher.to_fetch = [] ``` If our default guesses about paths don't work for you, or other files should be moved, you can manually specify these lists as tuples of `(local_path, remote_path)` for `to_send`, and `(remote_path, local_path)` for `to_fetch`. If you do specify these lists explicitly, IPython _will not_ automatically send connection files, so you must include this yourself if they should still be sent/retrieved. ### Starting the controller and engines on different hosts When the controller and engines are running on different hosts, things are slightly more complicated, but the underlying ideas are the same: 1. Start the controller on a host using {command}`ipcontroller`. The controller must be instructed to listen on an interface visible to the engine machines, via the `ip` command-line argument or `IPController.ip` in {file}`ipcontroller_config.py`: ``` $ ipcontroller --ip=192.168.1.16 ``` ```python # in ipcontroller_config.py IPController.ip = '192.168.1.16' ``` 2. Copy {file}`ipcontroller-engine.json` from {file}`IPYTHONDIR/profile_/security` on the controller's host to the host where the engines will run. 3. Use {command}`ipengine` on the engine's hosts to start the engines. The only thing you have to be careful of is to tell {command}`ipengine` where the {file}`ipcontroller-engine.json` file is located. There are two ways you can do this: - Put {file}`ipcontroller-engine.json` in the {file}`IPYTHONDIR/profile_/security` directory on the engine's host, where it will be found automatically. - Call {command}`ipengine` with the `--file=full_path_to_the_file` flag. The `file` flag works like this: ``` $ ipengine --file=/path/to/my/ipcontroller-engine.json ``` ```{note} If the controller's and engine's hosts all have a shared file system ({file}`IPYTHONDIR/profile_/security` is the same on all of them), then no paths need to be specified or files copied. ``` #### SSH Tunnels If your engines are not on the same LAN as the controller, or you are on a highly restricted network where your nodes cannot see each others ports, then you can use SSH tunnels to connect engines to the controller. ```{note} This does not work in all cases. Manual tunnels may be an option, but are highly inconvenient. Support for manual tunnels will be improved. ``` You can instruct all engines to use ssh, by specifying the ssh server in {file}`ipcontroller-engine.json` This will be specified if you give the `--enginessh=user@example.com` argument when starting {command}`ipcontroller`. Or you can specify an ssh server on the command-line when starting an engine: ``` $> ipengine --profile=foo --ssh=my.login.node ``` For example, if your system is totally restricted, then all connections will be loopback, and ssh tunnels will be used to connect engines to the controller: ``` [node1] $> ipcontroller --enginessh=node1 [node2] $> ipengine [node3] $> ipcluster engines --n=4 ``` Or if you want to start many engines on each node, the command `ipcluster engines --n=4` without any configuration is equivalent to running ipengine 4 times. ### An example using ipcontroller/engine with ssh No configuration files are necessary to use ipcontroller/engine in an SSH environment without a shared filesystem. You need to make sure that the controller is listening on an interface visible to the engines, and move the connection file from the controller to the engines. 1. start the controller, listening on an ip-address visible to the engine machines: ``` [controller.host] $ ipcontroller --ip=192.168.1.16 [IPController] Using existing profile dir: u'/Users/me/.ipython/profile_default' [IPController] Hub listening on tcp://192.168.1.16:63320 for registration. [IPController] Hub using DB backend: 'ipyparallel.controller.dictdb.DictDB' [IPController] hub::created hub [IPController] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-client.json [IPController] writing connection info to /Users/me/.ipython/profile_default/security/ipcontroller-engine.json [IPController] task::using Python leastload Task scheduler [IPController] Heartmonitor started [IPController] Creating pid file: /Users/me/.ipython/profile_default/pid/ipcontroller.pid Scheduler started [leastload] ``` 2. on each engine, fetch the connection file with scp: ``` [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ./ ``` ```{note} The log output of ipcontroller above shows you where the json files were written. They will be in {file}`~/.ipython` under {file}`profile_default/security/ipcontroller-engine.json` ``` 3. start the engines, using the connection file: ``` [engine.host.n] $ ipengine --file=./ipcontroller-engine.json ``` A couple of notes: - You can avoid having to fetch the connection file every time by adding `--reuse` flag to ipcontroller, which instructs the controller to read the previous connection file for connection info, rather than generate a new one with randomized ports. - In step 2, if you fetch the connection file directly into the security dir of a profile, then you need not specify its path directly, only the profile (assumes the path exists, otherwise you must create it first): ``` [engine.host.n] $ scp controller.host:.ipython/profile_default/security/ipcontroller-engine.json ~/.ipython/profile_ssh/security/ [engine.host.n] $ ipengine --profile=ssh ``` Of course, if you fetch the file into the default profile, no arguments must be passed to ipengine at all. - Note that ipengine _did not_ specify the ip argument. In general, it is unlikely for any connection information to be specified at the command-line to ipengine, as all of this information should be contained in the connection file written by ipcontroller. ### Make JSON files persistent At fist glance it may seem that that managing the JSON files is a bit annoying. Going back to the house and key analogy, copying the JSON around each time you start the controller is like having to make a new key every time you want to unlock the door and enter your house. As with your house, you want to be able to create the key (or JSON file) once, and then use it at any point in the future. To do this, the only thing you have to do is specify the `--reuse` flag, so that the connection information in the JSON files remains accurate: ``` $ ipcontroller --reuse ``` Then copy the JSON files over the first time and you are set. You can start and stop the controller and engines any many times as you want in the future, as long as you make sure to tell the controller to reuse the file. ```{note} You may ask the question: what ports does the controller listen on if you don't tell is to use specific ones? The default is to use high random port numbers. We do this for two reasons: i) to increase security through obscurity and ii) to multiple controllers on a given host to start and automatically use different ports. ``` ### Log files All of the components of IPython have log files associated with them. These log files can be extremely useful in debugging problems with IPython and can be found in the directory {file}`IPYTHONDIR/profile_/log`. Sending the log files to us will often help us to debug any problems. ### Configuring `ipcontroller` The IPython Controller takes its configuration from the file {file}`ipcontroller_config.py` in the active profile directory. #### Ports and addresses In many cases, you will want to configure the Controller's network identity. By default, the Controller listens only on loopback, which is the most secure but often impractical. To instruct the controller to listen on a specific interface, you can set the {attr}`IPController.ip` trait. To listen on all interfaces, specify: ```python c.IPController.ip = '*' ``` When connecting to a Controller that is listening on loopback or behind a firewall, it may be necessary to specify an SSH server to use for tunnels, and the external IP of the Controller. If you specified that the IPController listen on loopback, or all interfaces, then IPython will try to guess the external IP. If you are on a system with VM network devices, or many interfaces, this guess may be incorrect. In these cases, you will want to specify the 'location' of the Controller. This is the IP of the machine the Controller is on, as seen by the clients, engines, or the SSH server used to tunnel connections. For example, to set up a cluster with a Controller on a work node, using ssh tunnels through the login node, an example {file}`ipcontroller_config.py` might contain: ```python # allow connections on all interfaces from engines # engines on the same node will use loopback, while engines # from other nodes will use an external IP c.IPController.ip = '*' # you typically only need to specify the location when there are extra # interfaces that may not be visible to peer nodes (e.g. VM interfaces) c.IPController.location = '10.0.1.5' # or to get an automatic value, try this: import socket hostname = socket.gethostname() # alternate choices for hostname include `socket.getfqdn()` # or `socket.gethostname() + '.local'` ex_ip = socket.gethostbyname_ex(hostname)[-1][-1] c.IPController.location = ex_ip # now instruct clients to use the login node for SSH tunnels: c.IPController.ssh_server = 'login.mycluster.net' ``` After doing this, your {file}`ipcontroller-client.json` file will look something like this: ```python { "url":"tcp:\/\/*:43447", "exec_key":"9c7779e4-d08a-4c3b-ba8e-db1f80b562c1", "ssh":"login.mycluster.net", "location":"10.0.1.5" } ``` Then this file will be all you need for a client to connect to the controller, tunneling SSH connections through login.mycluster.net. #### Database Backend The Hub stores all messages and results passed between Clients and Engines. For large and/or long-running clusters, it would be unreasonable to keep all of this information in memory. For this reason, we have two database backends: [^cite_mongodb] via [PyMongo], and SQLite with the stdlib {py:mod}`sqlite`. MongoDB is our design target, and the dict-like model it uses has driven our design. As far as we are concerned, BSON can be considered essentially the same as JSON, adding support for binary data and datetime objects, and any new database backend must support the same data types. ```{seealso} MongoDB [BSON doc]( https://bsonspec.org) ``` To use one of these backends, you must set the {attr}`IPController.db_class` trait: ```python # for a simple dict-based in-memory implementation, use dictdb # This is the default and the fastest, since it doesn't involve the filesystem c.IPController.db_class = 'ipyparallel.controller.dictdb.DictDB' # To use MongoDB: c.IPController.db_class = 'ipyparallel.controller.mongodb.MongoDB' # and SQLite: c.IPController.db_class = 'ipyparallel.controller.sqlitedb.SQLiteDB' # You can use NoDB to disable the database altogether, in case you don't need # to reuse tasks or results, and want to keep memory consumption under control. c.IPController.db_class = 'ipyparallel.controller.dictdb.NoDB' ``` When using the proper databases, you can allow for tasks to persist from one session to the next by specifying the MongoDB database or SQLite table in which tasks are to be stored. The default is to use a table named for the Hub's Session, which is a UUID, and thus different every time. ```python # To keep persistent task history in MongoDB: c.MongoDB.database = 'tasks' # and in SQLite: c.SQLiteDB.table = 'tasks' ``` Since MongoDB servers can be running remotely or configured to listen on a particular port, you can specify any arguments you may need to the PyMongo {py:class}`~.pymongo.Connection`: ```python # positional args to pymongo.Connection c.MongoDB.connection_args = [] # keyword args to pymongo.Connection c.MongoDB.connection_kwargs = {} ``` But sometimes you are moving lots of data around quickly, and you don't need that information to be stored for later access, even by other Clients to this same session. For this case, we have a dummy database, which doesn't store anything. This lets the Hub stay small in memory, at the obvious expense of being able to access the information that would have been stored in the database (used for task resubmission, requesting results of tasks you didn't submit, etc.). To use this backend, pass `--nodb` to {command}`ipcontroller` on the command-line, or specify the {class}`NoDB` class in your {file}`ipcontroller_config.py` as described above. ```{seealso} For more information on the database backends, see the {ref}`db backend reference `. ``` ### Configuring `ipengine` The IPython Engine takes its configuration from the file {file}`ipengine_config.py` The Engine itself also has some amount of configuration. Most of this has to do with initializing MPI or connecting to the controller. To instruct the Engine to initialize with an MPI environment set up by mpi4py, add: ```python c.MPI.use = 'mpi4py' ``` In this case, the Engine will use our default mpi4py init script to set up the MPI environment prior to execution. We have default init scripts for mpi4py and pytrilinos. If you want to specify your own code to be run at the beginning, specify `c.MPI.init_script`. You can also specify a file or python command to be run at startup of the Engine: ```python c.IPEngine.startup_script = u'/path/to/my/startup.py' c.IPEngine.startup_command = 'import numpy, scipy, mpi4py' ``` These commands/files will be run again, after each It's also useful on systems with shared filesystems to run the engines in some scratch directory. This can be set with: ```python c.IPEngine.work_dir = u'/path/to/scratch/' ``` [^cite_mongodb]: MongoDB database [^cite_pbs]: Portable Batch System [^cite_ssh]: SSH-Agent [pymongo]: https://pymongo.readthedocs.io ipyparallel-8.8.0/docs/source/tutorial/task.md000066400000000000000000000402001460376056100214300ustar00rootroot00000000000000(parallel-task)= # The IPython task interface The task interface to the cluster presents the engines as a fault tolerant, dynamic load-balanced system of workers. Unlike the direct interface, in the task interface the user has no direct access to individual engines. By allowing the IPython scheduler to assign work, this interface is simultaneously simpler and more powerful. Best of all, the user can use both of these interfaces running at the same time to take advantage of their respective strengths. When the user can break up the user's work into segments that do not depend on previous execution, the task interface is ideal. But it also has more power and flexibility, allowing the user to guide the distribution of jobs, without having to assign tasks to engines explicitly. ## Creating a `LoadBalancedView` As always, the first step is to start a cluster and connect a {class}`.Client` instance. For more detailed information about starting the controller and engines, see our {ref}`introduction ` to using IPython for parallel computing. ```ipython In [1]: import ipyparallel as ipp In [2]: cluster = ipp.Cluster() In [3]: cluster.start_cluster_sync() In [4]: rc = cluster.connect_client_sync() In [5]: rc.wait_for_engines(4) ``` For load-balanced execution, we will make use of a {class}`.LoadBalancedView` object, which can be constructed via the client's {meth}`~.Client.load_balanced_view` method: ```ipython In [4]: lview = rc.load_balanced_view() # default load-balanced view ``` ```{seealso} For more information, see the in-depth explanation of {ref}`Views `. ``` ## Quick and easy parallelism In many cases, you want to apply a Python function to a sequence of objects, but _in parallel_. Like the direct interface, these can be implemented via the task interface. The exact same tools can perform these actions in load-balanced ways as well as multiplexed ways: a parallel version of {py:func}`map` and {func}`@view.parallel` function decorator. If one specifies the argument `balanced=True`, then they are dynamically load balanced. Thus, if the execution time per item varies significantly, you should use the versions in the task interface. ### Parallel map To load-balance {func}`map`, use a LoadBalancedView: ```ipython In [62]: lview.block = True In [63]: serial_result = map(lambda x:x**10, range(32)) In [64]: parallel_result = lview.map(lambda x:x**10, range(32)) In [65]: serial_result==parallel_result Out[65]: True ``` ### Parallel function decorator Parallel functions are just like normal functions, but they can be called on sequences and _in parallel_. The direct interface provides a decorator that turns any Python function into a parallel function: ```ipython In [10]: @lview.parallel() ....: def f(x): ....: return 10.0 * x ** 4 ....: In [11]: f.map(range(32)) # this is done in parallel Out[11]: [0.0, 10.0, 160.0, ...] ``` (parallel-dependencies)= ## Dependencies Often, pure atomic load-balancing is too primitive for your work. In these cases, you may want to associate some kind of `Dependency` that describes when, where, or whether a task can be run. In IPython, we provide two types of dependencies: [Functional Dependencies] and [Graph Dependencies] ```{note} It is important to note that the pure ZeroMQ scheduler does not support dependencies, and you will see errors or warnings if you try to use dependencies with the pure scheduler. ``` ### Functional Dependencies Functional dependencies are used to determine whether a given engine is capable of running a particular task. This is implemented via a special {class}`Exception` class, {class}`UnmetDependency`, found in `ipyparallel.error`. Its use is very simple: if a task fails with an UnmetDependency exception, then the scheduler, instead of relaying the error up to the client like any other error, catches the error, and submits the task to a different engine. This will repeat indefinitely, and a task will never be submitted to a given engine a second time. You can manually raise the {class}`UnmetDependency` yourself, but IPython has provided some decorators for facilitating this behavior. There are two decorators and a class used for functional dependencies: ```ipython In [9]: import ipyparallel as ipp ``` #### @ipp.require The simplest sort of dependency is requiring that a Python module is available. The `@ipp.require` decorator lets you define a function that will only run on engines where names you specify are importable: ```ipython In [10]: @ipp.require('numpy', 'zmq') ....: def myfunc(): ....: return dostuff() ``` Now, any time you apply {func}`myfunc`, the task will only run on a machine that has numpy and pyzmq available, and when {func}`myfunc` is called, numpy and zmq will be imported. You can also require specific objects, not just module names: ```python def foo(a): return a * a @ipp.require(foo) def bar(b): return foo(b) @ipp.require(bar) def baz(c, d): return bar(c) - bar(d) view.apply_sync(baz, 4, 5) ``` #### @ipp.depend The `@ipp.depend` decorator lets you decorate any function with any _other_ function to evaluate the dependency. The dependency function will be called at the start of the task, and if it returns `False`, then the dependency will be considered unmet, and the task will be assigned to another engine. If the dependency returns _anything other than `False`_, the rest of the task will continue. ```ipython In [10]: def platform_specific(plat): ....: import sys ....: return sys.platform == plat In [11]: @ipp.depend(platform_specific, 'darwin') ....: def mactask(): ....: do_mac_stuff() In [12]: @ipp.depend(platform_specific, 'nt') ....: def wintask(): ....: do_windows_stuff() ``` In this case, any time you apply `mactask`, it will only run on an macOS machine. `@ipp.depend` is like `apply`, in that it has a `@ipp.depend(f, *args, **kwargs)` signature. #### dependents You don't have to use the decorators on your tasks, if for instance you may want to run tasks with a single function but varying dependencies, you can directly construct the {class}`dependent` object that the decorators use: ```ipython In [13]: def mytask(*args): ....: dostuff() In [14]: mactask = dependent(mytask, platform_specific, 'darwin') # this is the same as decorating the declaration of mytask with @ipp.depend # but you can do it again: In [15]: wintask = dependent(mytask, platform_specific, 'nt') # in general: In [16]: t = dependent(f, g, *dargs, **dkwargs) # is equivalent to: In [17]: @ipp.depend(g, *dargs, **dkwargs) ....: def t(a, b, c): ....: # contents of f ``` ### Graph Dependencies Sometimes you want to restrict the time and/or location to run a given task as a function of the time and/or location of other tasks. This is implemented via a subclass of {class}`set`, called a {class}`Dependency`. A Dependency is a set of `msg_ids` corresponding to tasks, and a few attributes to guide how to decide when the Dependency has been met. The switches we provide for interpreting whether a given dependency set has been met: any|all : Whether the dependency is considered met if _any_ of the dependencies are done, or only after _all_ of them have finished. This is set by a Dependency's {attr}`all` boolean attribute, which defaults to `True`. success \[default: True\] : Whether to consider tasks that succeeded as fulfilling dependencies. failure \[default\] : Whether to consider tasks that failed as fulfilling dependencies. using `failure=True,success=False` is useful for setting up cleanup tasks, to be run only when tasks have failed. Sometimes you want to run a task after another, but only if that task succeeded. In this case, `success` should be `True` and `failure` should be `False`. However sometimes you may not care whether the task succeeds, and always want the second task to run, in which case you should use `success=failure=True`. The default behavior is to only use successes. There are other switches for interpretation that are made at the _task_ level. These are specified via keyword arguments to the view's {meth}`~.LoadBalancedView.apply` method. after,follow : You may want to run a task _after_ a given set of dependencies have been run and/or run it _where_ another set of dependencies are met. To support this, every task has an `after` dependency to restrict time, and a `follow` dependency to restrict destination. timeout : You may also want to set a time-limit for how long the scheduler should wait before a task's dependencies are met. This is done via a `timeout`, which defaults to 0, which indicates that the task should never timeout. If the timeout is reached, and the scheduler still hasn't been able to assign the task to an engine, the task will fail with a {class}`DependencyTimeout`. ```{note} Dependencies only work within the task scheduler. You cannot instruct a load-balanced task to run after a job submitted via the MUX interface. ``` The simplest form of Dependencies is with `all=True, success=True, failure=False`. In these cases, you can skip using Dependency objects, and pass msg_ids or AsyncResult objects as the `follow` and `after` keywords to {meth}`client.apply`: ```ipython In [14]: client.block = False In [15]: ar = lview.apply(f, args, kwargs) In [16]: ar2 = lview.apply(f2) In [17]: with lview.temp_flags(after=[ar, ar2]): ....: ar3 = lview.apply(f3) In [18]: with lview.temp_flags(follow=[ar], timeout=2.5) ....: ar4 = lview.apply(f3) ``` ```{seealso} Some parallel workloads can be described as a [Directed Acyclic Graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph), or DAG. See {ref}`DAG Dependencies ` for an example demonstrating how to use map a NetworkX DAG onto task dependencies. ``` #### Impossible Dependencies The schedulers do perform some analysis on graph dependencies to determine whether they are not possible to be met. If the scheduler does discover that a dependency cannot be met, then the task will fail with an {class}`ImpossibleDependency` error. This way, if the scheduler realized that a task can never be run, it won't sit indefinitely in the scheduler clogging the pipeline. The basic cases that are checked: - depending on nonexistent messages - `follow` dependencies were run on more than one machine and `all=True` - any dependencies failed and `all=True, success=True, failures=False` - all dependencies failed and `all=False, success=True, failure=False` ```{warning} This analysis has not been proven to be rigorous, so it is likely possible for tasks to become impossible to run in obscure situations, so a timeout may be a good choice. ``` ## Retries and Resubmit ### Retries Another flag for tasks is `retries`. This is an integer, specifying how many times a task should be resubmitted after failure. This is useful for tasks that should still run if their engine was shutdown, or may have some statistical chance of failing. The default is to not retry tasks. ### Resubmit Sometimes you may want to re-run a task. This could be because it failed for some reason, and you have fixed the error, or because you want to restore the cluster to an interrupted state. For this, the {class}`Client` has a {meth}`rc.resubmit` method. This takes one or more msg_ids, and returns an {class}`AsyncHubResult` for the result(s). You cannot resubmit a task that is pending - only those that have finished, either successful or unsuccessful. (parallel-schedulers)= ## Schedulers There are a variety of valid ways to determine where jobs should be assigned in a load-balancing situation. In IPython, we support several standard schemes, and even make it easy to define your own. The scheme can be selected via the `scheme` argument to {command}`ipcontroller`, or in the {attr}`TaskScheduler.schemename` attribute of a controller config object. The built-in routing schemes: To select one of these schemes: ``` $ ipcontroller --scheme= for instance: $ ipcontroller --scheme=lru ``` lru: Least Recently Used > Always assign work to the least-recently-used engine. A close relative of > round-robin, it will be fair with respect to the number of tasks, agnostic > with respect to runtime of each task. plainrandom: Plain Random > Randomly picks an engine on which to run. twobin: Two-Bin Random > **Requires numpy** > > Pick two engines at random, and use the LRU of the two. This is known to be better > than plain random in many cases, but requires a small amount of computation. leastload: Least Load > **This is the default scheme** > > Always assign tasks to the engine with the fewest outstanding tasks (LRU breaks tie). weighted: Weighted Two-Bin Random > **Requires numpy** > > Pick two engines at random using the number of outstanding tasks as inverse weights, > and use the one with the lower load. ### Greedy Assignment Tasks can be assigned greedily as they are submitted. If their dependencies are met, they will be assigned to an engine right away, and multiple tasks can be assigned to an engine at a given time. This limit is set with the `TaskScheduler.hwm` (high water mark) configurable in your {file}`ipcontroller_config.py` config file, with: ```python # the most common choices are: c.TaskSheduler.hwm = 0 # (minimal latency, default in IPython < 0.13) # or c.TaskScheduler.hwm = 1 # (most-informed balancing, default in ≥ 0.13) ``` In IPython \< 0.13, the default is 0, or no-limit. That is, there is no limit to the number of tasks that can be outstanding on a given engine. This greatly benefits the latency of execution, because network traffic can be hidden behind computation. However, this means that workload is assigned without knowledge of how long each task might take, and can result in poor load-balancing, particularly for submitting a collection of heterogeneous tasks all at once. You can limit this effect by setting hwm to a positive integer, 1 being maximum load-balancing (a task will never be waiting if there is an idle engine), and any larger number being a compromise between load-balancing and latency-hiding. In practice, some users have been confused by having this optimization on by default, so the default value has been changed to 1 in IPython 0.13. This can be slower, but has more obvious behavior and won't result in assigning too many tasks to some engines in heterogeneous cases. ### Pure ZMQ Scheduler For maximum throughput, the 'pure' scheme is not Python at all, but a C-level {class}`MonitoredQueue` from PyZMQ, which uses a ZeroMQ `DEALER` socket to perform all load-balancing. This scheduler does not support any of the advanced features of the Python {class}`.Scheduler`. Disabled features when using the ZMQ Scheduler: - Engine unregistration : Task farming will be disabled if an engine unregisters. Further, if an engine is unregistered during computation, the scheduler may not recover. - Dependencies : Since there is no Python logic inside the Scheduler, routing decisions cannot be made based on message content. - Early destination notification : The Python schedulers know which engine gets which task, and notify the Hub. This allows graceful handling of Engines coming and going. There is no way to know where ZeroMQ messages have gone, so there is no way to know what tasks are on which engine until they _finish_. This makes recovery from engine shutdown very difficult. ```{note} TODO: performance comparisons ``` ## More details The {class}`LoadBalancedView` has many more powerful features that allow quite a bit of flexibility in how tasks are defined and run. The next places to look are in the following classes: - {class}`~ipyparallel.client.view.LoadBalancedView` - {class}`~ipyparallel.client.asyncresult.AsyncResult` - {meth}`~ipyparallel.client.view.LoadBalancedView.apply` - {mod}`~ipyparallel.controller.dependency` The following is an overview of how to use these classes together: 1. Create a {class}`Client` and {class}`LoadBalancedView` 2. Define some functions to be run as tasks 3. Submit your tasks to using the {meth}`apply` method of your {class}`LoadBalancedView` instance. 4. Use {meth}`.Client.get_result` to get the results of the tasks, or use the {meth}`AsyncResult.get` method of the results to wait for and then receive the results. ```{seealso} A demo of {ref}`DAG Dependencies ` with NetworkX and IPython. ``` ipyparallel-8.8.0/examples000077700000000000000000000000001460376056100216022docs/source/examplesustar00rootroot00000000000000ipyparallel-8.8.0/hatch_build.py000066400000000000000000000025141460376056100166770ustar00rootroot00000000000000"""Custom build script for hatch backend""" import glob import os import subprocess from hatchling.builders.hooks.plugin.interface import BuildHookInterface class CustomHook(BuildHookInterface): def initialize(self, version, build_data): if self.target_name not in ["wheel", "sdist"]: return cmd = "build:prod" if version == "standard" else "build" osp = os.path here = osp.abspath(osp.dirname(__file__)) lab_path = osp.join(here, 'ipyparallel', 'labextension') if os.environ.get("IPP_DISABLE_JS") == "1": print("Skipping js installation") return # this tells us if labextension is built at all, not if it's up-to-date labextension_built = glob.glob(osp.join(lab_path, "*")) needs_js = True if not osp.isdir(osp.join(here, ".git")): print("Installing from a dist, not a repo") # not in a repo, probably installing from sdist # could be git-archive, though! # skip rebuilding js if it's already present if labextension_built: print(f"Not regenerating labextension in {lab_path}") needs_js = False if needs_js: subprocess.check_call(['jlpm'], cwd=here) subprocess.check_call(['jlpm', 'run', cmd], cwd=here) ipyparallel-8.8.0/install.json000066400000000000000000000002671460376056100164230ustar00rootroot00000000000000{ "packageManager": "python", "packageName": "ipyparallel", "uninstallInstructions": "Use your Python package manager (pip, conda, etc.) to uninstall the package ipyparallel" } ipyparallel-8.8.0/ipyparallel/000077500000000000000000000000001460376056100163735ustar00rootroot00000000000000ipyparallel-8.8.0/ipyparallel/__init__.py000066400000000000000000000062041460376056100205060ustar00rootroot00000000000000"""The IPython ZMQ-based parallel computing interface.""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. # export return_when constants import os from concurrent.futures import ALL_COMPLETED # noqa from concurrent.futures import FIRST_COMPLETED # noqa from concurrent.futures import FIRST_EXCEPTION # noqa from traitlets.config.configurable import MultipleInstanceError from ._version import __version__ # noqa from ._version import version_info # noqa from .client.asyncresult import * # noqa from .client.client import Client # noqa from .client.remotefunction import * # noqa from .client.view import * # noqa from .cluster import Cluster # noqa from .cluster import ClusterManager # noqa from .controller.dependency import * # noqa from .error import * # noqa from .serialize import * # noqa from .util import interactive # noqa # ----------------------------------------------------------------------------- # Functions # ----------------------------------------------------------------------------- def bind_kernel(**kwargs): """Bind an Engine's Kernel to be used as a full IPython kernel. This allows a running Engine to be used simultaneously as a full IPython kernel with the QtConsole or other frontends. This function returns immediately. """ from ipykernel.kernelapp import IPKernelApp from ipyparallel.engine.app import IPEngine # first check for IPKernelApp, in which case this should be a no-op # because there is already a bound kernel if IPKernelApp.initialized() and isinstance(IPKernelApp._instance, IPKernelApp): return if IPEngine.initialized(): try: app = IPEngine.instance() except MultipleInstanceError: pass else: return app.bind_kernel(**kwargs) raise RuntimeError("bind_kernel must be called from an IPEngine instance") def register_joblib_backend(name='ipyparallel', make_default=False): """Register the default ipyparallel backend for joblib.""" from .joblib import register return register(name=name, make_default=make_default) # nbextension installation (requires notebook ≥ 4.2) def _jupyter_server_extension_paths(): return [{'module': 'ipyparallel'}] def _jupyter_nbextension_paths(): return [ { 'section': 'tree', 'src': 'nbextension/static', 'dest': 'ipyparallel', 'require': 'ipyparallel/main', } ] def _jupyter_labextension_paths(): return [ { "src": "labextension", "dest": "ipyparallel-labextension", } ] def _load_jupyter_server_extension(app): """Load the server extension""" # localte the appropriate APIHandler base class before importing our handler classes from .nbextension.base import get_api_handler get_api_handler(app) from .nbextension.handlers import load_jupyter_server_extension return load_jupyter_server_extension(app) # backward-compat load_jupyter_server_extension = _load_jupyter_server_extension _NONINTERACTIVE = os.getenv("IPP_NONINTERACTIVE", "") not in {"", "0"} ipyparallel-8.8.0/ipyparallel/_async.py000066400000000000000000000060001460376056100202150ustar00rootroot00000000000000"""Async utilities""" import asyncio import concurrent.futures import inspect import threading from functools import partial from tornado.ioloop import IOLoop def _asyncio_run(coro): """Like asyncio.run, but works when there's no event loop""" # for now: using tornado for broader compatibility with FDs, # e.g. when using the only partially functional default # Proactor on windows loop = IOLoop(make_current=False) try: return loop.run_sync(lambda: asyncio.ensure_future(coro)) finally: loop.close() class AsyncFirst: """Wrapper class that defines synchronous `_sync` method wrappers around async-native methods. Every coroutine method automatically gets an `_sync` alias that runs it synchronously. """ _async_thread = None def _thread_main(self): loop = self._thread_loop = IOLoop(make_current=False) loop.add_callback(self._loop_started.set) loop.start() def _in_thread(self, async_f, *args, **kwargs): """Run an async function in a background thread""" if self._async_thread is None: self._loop_started = threading.Event() self._async_thread = threading.Thread(target=self._thread_main, daemon=True) self._async_thread.start() self._loop_started.wait(timeout=5) future = concurrent.futures.Future() async def thread_callback(): try: future.set_result(await async_f(*args, **kwargs)) except Exception as e: future.set_exception(e) self._thread_loop.add_callback(thread_callback) return future.result() def _synchronize(self, async_f, *args, **kwargs): """Run a method synchronously Uses asyncio.run if asyncio is not running, otherwise puts it in a background thread """ try: loop = asyncio.get_running_loop() except RuntimeError: # not in a running loop loop = None if loop: return self._in_thread(async_f, *args, **kwargs) else: return _asyncio_run(async_f(*args, **kwargs)) def __getattr__(self, name): if name.endswith("_sync"): # lazily define `_sync` method wrappers for coroutine methods async_name = name[:-5] async_method = super().__getattribute__(async_name) if not inspect.iscoroutinefunction(async_method): raise AttributeError(async_name) return partial(self._synchronize, async_method) return super().__getattribute__(name) def __dir__(self): attrs = super().__dir__() seen = set() for cls in self.__class__.mro(): for name, value in cls.__dict__.items(): if name in seen: continue seen.add(name) if inspect.iscoroutinefunction(value): async_name = name + "_sync" attrs.append(async_name) return attrs ipyparallel-8.8.0/ipyparallel/_version.py000066400000000000000000000011641460376056100205730ustar00rootroot00000000000000import re __version__ = "8.8.0" # matches tbump regex in pyproject.toml _version_regex = re.compile( r''' (?P\d+) \. (?P\d+) \. (?P\d+) (?P