pax_global_header00006660000000000000000000000064142342036470014517gustar00rootroot0000000000000052 comment=676b77d0668c7ea0da455bdef052ae1936b39a0a gplearn-0.4.2/000077500000000000000000000000001423420364700131525ustar00rootroot00000000000000gplearn-0.4.2/.coveragerc000066400000000000000000000001261423420364700152720ustar00rootroot00000000000000[run] branch = True source = gplearn omit = */gplearn/tests/* */gplearn/doc/* gplearn-0.4.2/.github/000077500000000000000000000000001423420364700145125ustar00rootroot00000000000000gplearn-0.4.2/.github/ISSUE_TEMPLATE/000077500000000000000000000000001423420364700166755ustar00rootroot00000000000000gplearn-0.4.2/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000022161423420364700213700ustar00rootroot00000000000000--- name: Bug report about: Create a report to help us improve title: '' labels: bug assignees: '' --- **Describe the bug** **Expected behavior** **Actual behavior** **Steps to reproduce the behavior** **System information** gplearn-0.4.2/.github/ISSUE_TEMPLATE/feature_request.md000066400000000000000000000013371423420364700224260ustar00rootroot00000000000000--- name: Feature request about: Suggest an idea for this project title: '' labels: enhancement assignees: '' --- **Is your feature request related to a problem? Please describe.** **Describe the solution you'd like** **Additional context** gplearn-0.4.2/.github/pull_request_template.md000066400000000000000000000010321423420364700214470ustar00rootroot00000000000000 **Reference Issues/PRs** **What does this implement/fix? Explain your changes.** gplearn-0.4.2/.github/workflows/000077500000000000000000000000001423420364700165475ustar00rootroot00000000000000gplearn-0.4.2/.github/workflows/build.yml000066400000000000000000000033411423420364700203720ustar00rootroot00000000000000name: build on: schedule: # Every friday at 4am UTC - cron: '0 4 * * 5' push: branches: [ master ] pull_request: branches: [ master ] jobs: test: strategy: fail-fast: false matrix: os: - ubuntu-latest python_version: - '3.8' - '3.9' - '3.10' include: - os: windows-latest python_version: '3.10' - os: ubuntu-latest python_version: '3.10' coverage: true runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} on ${{ matrix.os }} uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - name: Install global dependencies run: | python -m pip install --upgrade pip python -m pip install pytest pytest-cov coveralls python -m pip install pandas - name: Install minimal dependencies if: ${{ matrix.python_version == '3.8' }} run: python -m pip install scikit-learn==1.0.2 joblib==1.0.0 - name: Install gplearn run: python -m pip install . - name: Describe Python environment run: | python --version python -c "import sklearn; print('sklearn %s' % sklearn.__version__)" python -c "import joblib; print('joblib %s' % joblib.__version__)" python -c "import numpy; print('numpy %s' % numpy.__version__)" python -c "import scipy; print('scipy %s' % scipy.__version__)" - name: Test with pytest run: pytest -v --cov - name: Coverage if: ${{ matrix.coverage }} env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: coveralls --service=github gplearn-0.4.2/.gitignore000066400000000000000000000014151423420364700151430ustar00rootroot00000000000000# Hidden files *~ .#* .DS_Store # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg setup.cfg *.egg .ipynb_checkpoints/ # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage* .cache nosetests.xml coverage.xml /.idea/ .idea/* .idea/workspace.xml # Translations *.mo *.pot # Django stuff: *.log # Sphinx documentation doc/_build/ # PyBuilder target/ gplearn-0.4.2/CODE_OF_CONDUCT.md000066400000000000000000000064231423420364700157560ustar00rootroot00000000000000# Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at @trevorstephens. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq gplearn-0.4.2/CONTRIBUTING.md000066400000000000000000000043771423420364700154160ustar00rootroot00000000000000Contributing ============ ``gplearn`` welcomes your contributions! Whether it is a bug report, bug fix, new feature or documentation enhancements, please help to improve the project! In general, please follow the [scikit-learn contribution guidelines](http://scikit-learn.org/stable/developers/contributing.html) for how to contribute to an open-source project. If you would like to open a bug report, please [open one here](https://github.com/trevorstephens/gplearn/issues). Please try to provide a [Short, Self Contained, Example](http://sscce.org/) so that the root cause can be pinned down and corrected more easily. If you would like to contribute a new feature or fix an existing bug, the basic workflow to follow (as detailed more at the scikit-learn link above) is: - [Open an issue](https://github.com/trevorstephens/gplearn/issues) with what you would like to contribute to the project and its merits. Some features may be out of scope for ``gplearn``, so be sure to get the go-ahead before working on something that is outside of the project's goals. - Fork the ``gplearn`` repository, clone it locally, and create your new feature branch. - Make your code changes on the branch, commit them, and push to your fork. - Open a pull request. Please ensure that: - Only data-dependent arguments should be passed to the fit/transform methods (``X``, ``y``, ``sample_weight``), and conversely, no data should be passed to the estimator initialization. - No input validation occurs before fitting the estimator. - Any new feature has great test coverage. - Any new feature is well documented with [numpy-style docstrings](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) & an example, if appropriate and illustrative. - Any bug fix has regression tests. - Comply with [PEP8](https://pypi.python.org/pypi/pep8). Currently ``gplearn`` uses [GitHub workflows](https://github.com/trevorstephens/gplearn/actions/workflows/build.yml) for testing, [Coveralls](https://coveralls.io/github/trevorstephens/gplearn) for code coverage reports, and [Codacy](https://app.codacy.com/gh/trevorstephens/gplearn/dashboard) for code quality checks. These applications should automatically run on your new pull request to give you guidance on any problems in the new code. gplearn-0.4.2/LICENSE000066400000000000000000000027111423420364700141600ustar00rootroot00000000000000Copyright (c) 2015, Trevor Stephens 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 gplearn 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 HOLDER 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. gplearn-0.4.2/MANIFEST.in000066400000000000000000000000201423420364700147000ustar00rootroot00000000000000include LICENSE gplearn-0.4.2/README.rst000066400000000000000000000064111423420364700146430ustar00rootroot00000000000000.. image:: https://img.shields.io/pypi/v/gplearn.svg :target: https://pypi.python.org/pypi/gplearn/ :alt: Version .. image:: https://img.shields.io/pypi/l/gplearn.svg :target: https://github.com/trevorstephens/gplearn/blob/master/LICENSE :alt: License .. image:: https://readthedocs.org/projects/gplearn/badge/?version=stable :target: http://gplearn.readthedocs.io/ :alt: Documentation Status .. image:: https://github.com/trevorstephens/gplearn/actions/workflows/build.yml/badge.svg?branch=master :target: https://github.com/trevorstephens/gplearn/actions/workflows/build.yml :alt: Test Status .. image:: https://coveralls.io/repos/trevorstephens/gplearn/badge.svg :target: https://coveralls.io/r/trevorstephens/gplearn :alt: Test Coverage .. image:: https://app.codacy.com/project/badge/Grade/02506317148e41a4b68a66e4c4e2b035 :target: https://app.codacy.com/gh/trevorstephens/gplearn/dashboard :alt: Code Health | .. image:: https://raw.githubusercontent.com/trevorstephens/gplearn/master/doc/logos/gplearn-wide.png :target: https://github.com/trevorstephens/gplearn :alt: Genetic Programming in Python, with a scikit-learn inspired API | Welcome to gplearn! =================== `gplearn` implements Genetic Programming in Python, with a `scikit-learn `_ inspired and compatible API. While Genetic Programming (GP) can be used to perform a `very wide variety of tasks `_, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations. gplearn retains the familiar scikit-learn `fit/predict` API and works with the existing scikit-learn `pipeline `_ and `grid search `_ modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, `reading the documentation `_ should make the more relevant ones clear for your problem. gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification. gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required for `installation `_. If you come across any issues in running or installing the package, `please submit a bug report `_. gplearn-0.4.2/doc/000077500000000000000000000000001423420364700137175ustar00rootroot00000000000000gplearn-0.4.2/doc/Makefile000066400000000000000000000151561423420364700153670ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) endif # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " xml to make Docutils-native XML files" @echo " pseudoxml to make pseudoxml-XML files for display purposes" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/gplearn.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/gplearn.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/gplearn" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/gplearn" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." latexpdfja: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through platex and dvipdfmx..." $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." gplearn-0.4.2/doc/_static/000077500000000000000000000000001423420364700153455ustar00rootroot00000000000000gplearn-0.4.2/doc/_static/favicon.ico000066400000000000000000000074761423420364700175040ustar00rootroot00000000000000  ((@ nnVnnnnnnnnG``````````!``nninnnnnnRh``````````-nnxnnms.p<``````````-`nnnmLfo``````````$`Pi.a````````````````````````f[^`````````````o1g 4lBn```1`````````b}ksnnn1n``Q`````````hH.mnnnnn!n``d`````````p"n@nnnnnnnnn```b`````````` nnnnnnnnnn n``E`````````j`_o+nnnnnnnngnn```````````````m-nngmm}lXi 3Xnnnnnnnn`````aU"mnnnnnnnn\n1nn1nnnnnnn5`````b-5nnnnnnnnnnnnnnSnnnnnnnn``````V$< e)mannnnnnnnnnnnnnnnnnn`````````bQNln3nlnnnnnnnnnnnnnn````````````[`n nDn|nnnnnnnnn`|``````````````V`` wnnnnnnn`2``````*`c``````````X`!`_onnnnnn`k````*``f``````````brj0nnnnnn``*``````b```````fng ]nnnnnn`````$`Y```a?-mnnnnnnQ``$`LaAi Xnnnnnnn`[nnnnnnn`nnnnnnnnnnvnnnnn`nnnnnnonnx?x`80 ~8xgplearn-0.4.2/doc/advanced.rst000066400000000000000000000355401423420364700162250ustar00rootroot00000000000000.. _advanced: Advanced Use ============ .. currentmodule:: gplearn.genetic .. _introspection: Introspecting Programs ---------------------- If you wish to learn more about how the evolution process came to the final solution, ``gplearn`` provides several means to examine the best programs and their parents. Most of these methods are illustrated :ref:`in the examples section `. Each of :class:`SymbolicRegressor`, :class:`SymbolicClassifier` and :class:`SymbolicTransformer` overload the ``print`` function to output a LISP-style flattened tree representation of the program. Simply ``print(est)`` the fitted estimator and the program will be output to your session. If you would like to see more details about the final programs, you can access the underlying ``_Program`` objects which contains several attributes and methods that can yield more information about them. :class:`SymbolicRegressor` and :class:`SymbolicClassifier` have a private attribute ``_program`` which is a single ``_Program`` object that was the fittest program found in the final generation of the evolution. :class:`SymbolicTransformer` on the other hand has a private attribute ``_best_programs`` which is a list of ``_Program`` objects of length ``n_components`` being the least-correlated and fittest programs found in the final generation of the evolution. :class:`SymbolicTransformer` is also iterable so you can loop through the estimator itself to access each underlying ``_Program`` object. Each ``_Program`` object can also be printed as with the estimator themselves to get a readable representation of the programs. They also have several attributes that you can use to further understand the programs: - ``raw_fitness_`` : The raw fitness of the individual program. - ``fitness_`` : The penalized fitness of the individual program. - ``oob_fitness_`` : The out-of-bag raw fitness of the individual program for the held-out samples. Only present when sub-sampling was used in the estimator by specifying ``max_samples`` < 1.0. - ``depth_`` : The maximum depth of the program tree. - ``length_`` : The number of functions and terminals in the program. For example with a :class:`SymbolicTransformer`:: for program in est_gp: print(program) print(program.raw_fitness_) div(div(X11, X12), X10) 0.840099070652 sub(div(mul(X4, X12), div(X9, X9)), sub(div(X11, X12), add(X12, X0))) 0.814627147552 Or if you want to access the individual programs:: print(est_gp._best_programs[0]) div(div(X11, X12), X10) And for a :class:`SymbolicRegressor`:: print(est_gp) print(est_gp._program) print(est_gp._program.raw_fitness_) add(sub(add(X5, div(X5, 0.388)), X0), div(add(X5, X10), X12)) add(sub(add(X5, div(X5, 0.388)), X0), div(add(X5, X10), X12)) 4.88966783112 You can also plot the programs as a program tree using Graphviz via the ``export_graphviz`` method of the ``_Program`` objects. In a Jupyter notebook this is easy using the ``pydotplus`` package:: from IPython.display import Image import pydotplus graph = est_gp._program.export_graphviz() graph = pydotplus.graphviz.graph_from_dot_data(graph) Image(graph.create_png()) This assumes you are satisfied with only seeing the final results, but the relevant programs that led to the final solutions are still retained in the estimator's ``_programs`` attribute. This object is a list of lists of all of the ``_Program`` objects that were involved in the evolution of the solution. The first entry in the outer list is the original naive generation of programs while the last entry is the final generation in which the solutions were found. Note that any programs in earlier generations that were discarded through the selection process are replaced with ``None`` objects to conserve memory. Each of the programs in the final solution and the generations that preceded them have a attribute called ``parents``. Except for the naive programs from the initial population who have a ``parents`` value of ``None``, this dictionary contains information about how that program was evolved. Its contents differ depending on the genetic operation that was performed on its parents to yield that program: - Crossover: - 'method': 'Crossover' - 'parent_idx': The index of the parent program in the previous generation. - 'parent_nodes': The indices of the nodes in the subtree in the parent program that was replaced. - 'donor_idx': The index of the donor program in the previous generation. - 'donor_nodes': The indices of the nodes in the subtree in the donor program that was donated to the parent. - Subtree Mutation: - 'method': 'Subtree Mutation' - 'parent_idx': The index of the parent program in the previous generation. - 'parent_nodes': The indices of the nodes in the subtree in the parent program that was replaced. - Hoist Mutation: - 'method': 'Hoist Mutation' - 'parent_idx': The index of the parent program in the previous generation. - 'parent_nodes': The indices of the nodes in the parent program that were removed. - Point Mutation: - 'method': 'Point Mutation' - 'parent_idx': The index of the parent program in the previous generation. - 'parent_nodes': The indices of the nodes in the parent program that were replaced. - Reproduction: - 'method': 'Reproduction' - 'parent_idx': The index of the parent program in the previous generation. - 'parent_nodes': An empty list as nothing was changed. The ``export_graphviz`` also has an optional parameter ``fade_nodes`` which can take a list of nodes that should be shown as being altered in the visualization. For example if the best program had this parent:: print(est_gp._program.parents) {'parent_idx': 75, 'parent_nodes': [1, 10], 'method': 'Point Mutation'} You could plot its parent with the affected nodes indicated using:: idx = est_gp._program.parents['parent_idx'] fade_nodes = est_gp._program.parents['parent_nodes'] print(est_gp._programs[-2][idx]) graph = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes) graph = pydotplus.graphviz.graph_from_dot_data(graph) Image(graph.create_png()) .. _parallel: Running Evolution in Parallel ----------------------------- It is easy to run your evolution parallel. All you need to do is to change the ``n_jobs`` parameter in :class:`SymbolicRegressor`, :class:`SymbolicClassifier` or :class:`SymbolicTransformer`. Whether this will reduce your run times depends a great deal upon the problem you are working on. Genetic programming is inherently an iterative process. One generation undergoes genetic operations with other members of the same generation in order to produce the next. When ran in parallel, gplearn splits the genetic operations into equal-sized batches that run in parallel, but the generations themselves must be completed before the next step can begin. For example, with three threads and three generations the processing would look like this: .. image:: images/parallel.png :align: center Until all of the computation in Threads 1, 2 & 3 have completed, the next generation must wait for them all to complete. Spinning up all these extra processes in parallel is not free. There is a substantial overhead in running `gplearn` in parallel and because of the iterative nature of evolution one should test whether there is any advantage from doing so for your problem. In many cases the overhead of creating extra processes will exceed the savings of running in parallel. In general large populations or large programs can benefit from parallel processing. If you have small populations and keep your programs small however, you may actually have your runs go faster on a single thread! .. currentmodule:: gplearn .. _export: Exporting --------- If you want to save your program for later use, you can use the ``pickle`` library to achieve this:: import pickle est = SymbolicRegressor() est.fit(X_train, y_train) Optionally, you can reduce the file size of the pickled object by removing the evolution information contained within the ``_programs`` attribute. Note though that while the resulting estimator will be able to do predictions, doing this will remove the ability to use ``warm_start`` to continue the evolution, or inspection of the final solution's parents:: delattr(est, '_programs') Then simply dump your model to a file:: with open('gp_model.pkl', 'wb') as f: pickle.dump(est, f) You can then load it at another date easily:: with open('gp_model.pkl', 'rb') as f: est = pickle.load(f) And use it as if it was the Python session where you originally trained the model. .. _custom_functions: Custom Functions ---------------- This example demonstrates modifying the function set with your own user-defined functions using the :func:`functions.make_function()` factory function. First you need to define some function which will return a numpy array of the correct shape. Most numpy operations will automatically do this. The factory will perform some basic checks on your function to ensure it complies with this. The function must also protect against zero division and invalid floating point operations (such as the log of a negative number). For this example we will implement a logical operation where two arguments are compared, and if the first one is larger, return a third value, otherwise return a fourth value:: def _logical(x1, x2, x3, x4): return np.where(x1 > x2, x3, x4) To make this into a ``gplearn`` compatible function, we use the factory where we must give it a name for display purposes and declare the arity of the function which must match the number of arguments that your function expects:: logical = make_function(function=_logical, name='logical', arity=4) Due to the way that the default Python pickler works, by default ``gplearn`` wraps your function to be serialised with cloudpickle. This can mean your evolution will run slightly more slowly. If you have no need to export your model after the run, or you are running single-threaded in an interactive Python session you may achieve a faster evolution time by setting the optional parameter ``wrap=False`` in :func:`functions.make_function()`. This can then be added to a ``gplearn`` estimator like so:: gp = SymbolicTransformer(function_set=['add', 'sub', 'mul', 'div', logical]) **Note that custom functions should be specified as the function object name (ie. with no quotes), while built-in functions use the name of the function as a string.** After fitting, you will see some of your programs will have used your own customized functions, for example:: add(X3, logical(div(X5, sub(X5, X5)), add(X9, -0.621), X8, X4)) .. image:: images/ex3_fig1.png :align: center In other mathematical relationships, it may be necessary to ensure the function has :ref:`closure `. This means that the function will always return a valid floating point result. Using ``np.where``, the user can protect against invalid operations and substitute problematic values with a default such as 0 or 1. One example is the built-in protected division function where infinite values resulting by divide by zero are replaced by 1:: def _protected_division(x1, x2): with np.errstate(divide='ignore', invalid='ignore'): return np.where(np.abs(x2) > 0.001, np.divide(x1, x2), 1.) Or a custom function where floating-point overflow is protected in an exponential function:: def _protected_exponent(x1): with np.errstate(over='ignore'): return np.where(np.abs(x1) < 100, np.exp(x), 0.) For further information on the types of errors that numpy can encounter and what you will need to protect against in your own custom functions, see `here `_. .. _custom_fitness: Custom Fitness -------------- You can easily create your own fitness measure to have your programs evolve to optimize whatever metric you need. This is done using the :func:`fitness.make_fitness()` factory function. Let's say we wish to measure our programs using MAPE (mean absolute percentage error). First we would need to implement a function that returns this value. The function must take the arguments ``y`` (the actual target values), ``y_pred`` (the predicted values from the program) and ``w`` (the weights to apply to each sample) to work. For MAPE, a possible solution is:: def _mape(y, y_pred, w): """Calculate the mean absolute percentage error.""" diffs = np.abs(np.divide((np.maximum(0.001, y) - np.maximum(0.001, y_pred)), np.maximum(0.001, y))) return 100. * np.average(diffs, weights=w) Division by zero must be protected for a metric like MAPE as it is generally used for cases where the target is positive and non-zero (like forecasting demand). We need to keep in mind that the programs begin by being totally naive, so a negative return value is possible. The ``np.maximum`` function will protect against these cases, though you may wish to treat this differently depending on your specific use case. We then create a fitness measure for use in our evolution by using the :func:`fitness.make_fitness()` factory function as follows:: mape = make_fitness(function=_mape, greater_is_better=False) This fitness measure can now be used to evolve a program that optimizes for your specific needs by passing the new fitness object to the ``metric`` parameter when creating an estimator:: est = SymbolicRegressor(metric=mape, verbose=1) As with custom functions, by default ``gplearn`` wraps your fitness metric to be serialised with cloudpickle. If you have no need to export your model after the run, or you are running single-threaded in an interactive Python session you may achieve a faster evolution time by setting the optional parameter ``wrap=False`` in :func:`fitness.make_fitness()`. .. currentmodule:: gplearn.genetic .. _warm_start: Continuing Evolution -------------------- If you are evolving a lot of generations in your training session, but find that you need to keep evolving more, you can use the ``warm_start`` parameter in both :class:`SymbolicRegressor` and :class:`SymbolicTransformer` to continue evolution beyond your original estimates. To do so, start evolution as usual:: est = SymbolicRegressor(generations=10) est.fit(X, y) If you then need to add further generations, simply change the ``generations`` and ``warm_start`` attributes and fit again:: est.set_params(generations=20, warm_start=True) est.fit(X, y) Evolution will then continue for a further 10 generations without losing the programs that had been previously trained. gplearn-0.4.2/doc/changelog.rst000066400000000000000000000151231423420364700164020ustar00rootroot00000000000000.. currentmodule:: gplearn .. _changelog: Release History =============== Version 0.4.2 - 3 May 2022 -------------------------- - Require keyword only arguments for all public methods and functions to comply with ``scikit-learn`` SLEP009. - Replace ``n_features_`` attribute with ``n_features_in_`` to comply with ``scikit-learn`` SLEP010. - Update test suite to ensure compatibility with ``scikit-learn``. ``scikit-learn`` 1.0.2 or newer will be required due to recent changes in their testing requirements. Also requiring ``joblib`` to 1.0.0 or newer to align with next release of scikit-learn. - Added the `class_weight` parameter to :class:`genetic.SymbolicClassifier` allowing users to easily compensate for imbalanced datasets. Version 0.4.1 - 1 Jun 2019 --------------------------- - Fixed a bug with multi-processing and custom functions, allowing pickling of models with custom functions, fitness metrics or classifier transformers. ``joblib`` 0.13.0 or newer required in order to take advantage of this release in order to wrap functions for pickling saved models. Version 0.4.0 - 23 Apr 2019 --------------------------- - Added the :class:`genetic.SymbolicClassifier` to use symbolic regression to solve binary classification problems. This passes the outputs of a program through a sigmoid function in order to translate the result into a probability of either class. - Allow users to express feature names as strings rather than X0, X1, etc. Graphviz and ``print()`` output can now be customized by setting ``feature_names=[...]`` in :class:`genetic.SymbolicRegressor` or :class:`genetic.SymbolicTransformer`. - Allow users to exclude constants from their programs by setting ``const_range=None`` in :class:`genetic.SymbolicRegressor` or :class:`genetic.SymbolicTransformer`. - Record details (similar to the verbose output) of the evolution in the estimator attribute ``run_details_`` dict in :class:`genetic.SymbolicRegressor` and :class:`genetic.SymbolicTransformer`. - Pearson and Spearman correlation coefficients added as first-class metrics to :class:`genetic.SymbolicRegressor`. These metrics allow for evolution of value-added features for second-stage estimators. - Added a `low_memory` parameter in :class:`genetic.SymbolicRegressor` and :class:`genetic.SymbolicTransformer` which can reduce memory use for cases where there are large populations or many generations by removing early generation program information. By `Bartol Karuza `_ and `wulfihm `_. - Drop support for Python 2.7 and Python 3.4 to ensure compatibility with ``scikit-learn``. ``scikit-learn`` 0.20.0 or newer will also be required due to recent changes in their testing suite. Additionally joblib 0.11 or newer will be required due to scikit-learn devendoring it. Version 0.3.0 - 23 Nov 2017 --------------------------- - Fixed two bugs in :class:`genetic.SymbolicTransformer` where the final solution selection logic was incorrect and suboptimal. This fix will change the solutions from all previous versions of `gplearn`. Thanks to `iblasi `_ for diagnosing the problem and helping craft the solution. - Fixed bug in :class:`genetic.SymbolicRegressor` where a custom fitness measure was defined in :func:`fitness.make_fitness()` with the parameter `greater_is_better=True`. This was ignored during final solution selection. This change will alter the results from previous releases where `greater_is_better=True` was set in a custom fitness measure. By `sun ao `_. - Increase minimum required version of ``scikit-learn`` to 0.18.1. This allows streamlining the test suite and removal of many utilities to reduce future technical debt. **Please note that due to this change, previous versions may have different results** due to a change in random sampling noted `here `_. - Drop support for Python 2.6 and add support for Python 3.5 and 3.6 in order to support the latest release of ``scikit-learn`` 0.19 and avoid future test failures. By `hugovk `_. Version 0.2.0 - 30 Mar 2017 --------------------------- - Allow more generations to be evolved on top of those already trained using a previous call to fit. The :class:`genetic.SymbolicRegressor` and :class:`genetic.SymbolicTransformer` classes now support the ``warm_start`` parameter which, when set to ``True``, reuse the solution of the previous call to fit and add more generations to the evolution. - Allow users to define their own fitness measures. Supported by the :func:`fitness.make_fitness()` factory function. Using this a user may define any metric by which to measure the fitness of a program to optimize any problem. This also required modifying the API slightly with the deprecation of the ``'rmsle'`` error measure for the :class:`genetic.SymbolicRegressor`. - Allow users to define their own functions for use in genetic programs. Supported by the :func:`functions.make_function()` factory function. Using this a user may define any mathematical relationship with any number of arguments and grow totally customized programs. This also required modifying the API with the deprecation of the ``'comparison'``, ``'transformer'`` and ``'trigonometric'`` arguments to the :class:`genetic.SymbolicRegressor` and :class:`genetic.SymbolicTransformer` classes in favor of the new ``function_set`` where any combination of preset and user-defined functions can be supplied. To restore previous behavior initialize the estimator with ``function_set=['add2', 'sub2', 'mul2', 'div2', 'sqrt1', 'log1', 'abs1', 'neg1', 'inv1', 'max2', 'min2']``. - Reduce memory consumption for large datasets, large populations or many generations. Indices for in-sample/out-of-sample fitness calculations are now generated on demand rather than being stored in the program objects which reduces the size significantly for large datasets. Additionally "irrelevant" programs from earlier generations are removed if they did not contribute to the current population through genetic operations. This reduces the number of programs stored in the estimator which helps for large populations, high number of generations, as well as for runs with significant bloat. Version 0.1.0 - 6 May 2015 -------------------------- - Initial public release supporting symbolic regression tasks through the :class:`genetic.SymbolicRegressor` class for regression problems and the :class:`genetic.SymbolicTransformer` class for automated feature engineering. gplearn-0.4.2/doc/conf.py000066400000000000000000000221201423420364700152130ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # gplearn documentation build configuration file, created by # sphinx-quickstart on Sun Apr 19 18:40:35 2015. # # 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. import sys import os # Add the local code to the Python path, so docs are generated for # current working copy rundir = os.path.dirname(__file__) sys.path.insert(0, rundir[:-4]) # remove '/doc' from end of path # 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('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # 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.autosummary', 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'numpydoc', ] numpydoc_show_class_members = False # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'gplearn' copyright = u'2016, Trevor Stephens' # 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. import gplearn version = gplearn.__version__ # The full version, including alpha/beta/rc tags. release = gplearn.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # 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 = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # 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 # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # on_rtd is whether we are on readthedocs.org on_rtd = os.environ.get('READTHEDOCS', None) == 'True' # only import and set the theme if we're building docs locally if not on_rtd: try: import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] except: pass # Hide the "Edit on GitHub" or "View page source" links html_context = { 'display_github': False, 'show_source': False, 'html_show_sourcelink': False, } # 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. #html_theme_options = {} # 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 = 'favicon.ico' # 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 = False # 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 # Output file base name for HTML help builder. htmlhelp_basename = 'gplearndoc' # -- 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': '', } # 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 = [ ('index', 'gplearn.tex', u'gplearn Documentation', u'Trevor Stephens', '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 = [ ('index', 'gplearn', u'gplearn Documentation', [u'Trevor Stephens'], 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 = [ ('index', 'gplearn', u'gplearn Documentation', u'Trevor Stephens', 'gplearn', '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 = {'http://docs.python.org/': None} gplearn-0.4.2/doc/contributing.rst000066400000000000000000000044441423420364700171660ustar00rootroot00000000000000.. _contributing: Contributing ============ ``gplearn`` welcomes your contributions! Whether it is a bug report, bug fix, new feature or documentation enhancements, please help to improve the project! In general, please follow the `scikit-learn contribution guidelines `_ for how to contribute to an open-source project. If you would like to open a bug report, please `open one here `_. Please try to provide a `Short, Self Contained, Example `_ so that the root cause can be pinned down and corrected more easily. If you would like to contribute a new feature or fix an existing bug, the basic workflow to follow (as detailed more at the scikit-learn link above) is: - `Open an issue `_ with what you would like to contribute to the project and its merits. Some features may be out of scope for ``gplearn``, so be sure to get the go-ahead before working on something that is outside of the project's goals. - Fork the ``gplearn`` repository, clone it locally, and create your new feature branch. - Make your code changes on the branch, commit them, and push to your fork. - Open a pull request. Please ensure that: - Only data-dependent arguments should be passed to the fit/transform methods (``X``, ``y``, ``sample_weight``), and conversely, no data should be passed to the estimator initialization. - No input validation occurs before fitting the estimator. - Any new feature has great test coverage. - Any new feature is well documented with `numpy-style docstrings `_ & an example, if appropriate and illustrative. - Any bug fix has regression tests. - Comply with `PEP8 `_. Currently ``gplearn`` uses `GitHub workflows `_ for testing, `Coveralls `_ for code coverage reports, and `Codacy `_ for code quality checks. These applications should automatically run on your new pull request to give you guidance on any problems in the new code. gplearn-0.4.2/doc/examples.rst000066400000000000000000000271561423420364700163020ustar00rootroot00000000000000.. _example: Examples ======== The code used to generate these examples can be `found here `_ as an iPython Notebook. .. currentmodule:: gplearn.genetic Symbolic Regressor ------------------ This example demonstrates using the :class:`SymbolicRegressor` to fit a symbolic relationship. Let's create some synthetic data based on the relationship :math:`y = X_0^{2} - X_1^{2} + X_1 - 1`:: x0 = np.arange(-1, 1, 1/10.) x1 = np.arange(-1, 1, 1/10.) x0, x1 = np.meshgrid(x0, x1) y_truth = x0**2 - x1**2 + x1 - 1 ax = plt.figure().add_subplot(projection='3d') ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) surf = ax.plot_surface(x0, x1, y_truth, rstride=1, cstride=1, color='green', alpha=0.5) plt.show() .. image:: images/ex1_fig1.png :align: center We can create some random training and test data that lies on this surface too:: rng = check_random_state(0) # Training samples X_train = rng.uniform(-1, 1, 100).reshape(50, 2) y_train = X_train[:, 0]**2 - X_train[:, 1]**2 + X_train[:, 1] - 1 # Testing samples X_test = rng.uniform(-1, 1, 100).reshape(50, 2) y_test = X_test[:, 0]**2 - X_test[:, 1]**2 + X_test[:, 1] - 1 Now let's consider how to fit our :class:`SymbolicRegressor` to this data. Since it's a fairly small dataset, we can probably use a large population since training time will still be pretty fast. We'll evolve 20 generations unless the error falls below 0.01. Examining the equation, it looks like the default function set of addition, subtraction, multiplication and division will cover us. Let's bump up the amount of mutation and subsample so that we can watch the OOB error evolve. We'll also increase the parsimony coefficient to keep our solutions small, since we know the truth is a pretty simple equation:: est_gp = SymbolicRegressor(population_size=5000, generations=20, stopping_criteria=0.01, p_crossover=0.7, p_subtree_mutation=0.1, p_hoist_mutation=0.05, p_point_mutation=0.1, max_samples=0.9, verbose=1, parsimony_coefficient=0.01, random_state=0) est_gp.fit(X_train, y_train) | Population Average | Best Individual | ---- ------------------------- ------------------------------------------ ---------- Gen Length Fitness Length Fitness OOB Fitness Time Left 0 38.13 458.57768152 5 0.320665972828 0.556763539274 1.28m 1 9.97 1.70232723129 5 0.320201761523 0.624787148042 57.78s 2 7.72 1.94456344674 11 0.239536660154 0.533148180489 46.35s 3 5.41 0.990156815469 7 0.235676349446 0.719906258051 37.93s 4 4.66 0.894443363616 11 0.103946413589 0.103946413589 32.20s 5 5.41 0.940242380405 11 0.060802040427 0.060802040427 28.15s 6 6.78 1.0953592564 11 0.000781474035 0.000781474035 24.85s The evolution process stopped early as the error of the best program in the 9th generation was better than 0.01. It also appears that the parsimony coefficient was just about right as the average length of the programs fluctuated around a bit before settling on a pretty reasonable size. Let's look at what our solution was:: print(est_gp._program) sub(add(-0.999, X1), mul(sub(X1, X0), add(X0, X1))) Interestingly, this does not have the same structure as our target function. But let's expand the mathematics out: .. math:: y = (-0.999 + X_1) - ((X_1 - X_0) \times (X_0 + X_1)) .. math:: y = X_1 - 0.999 - (X_1 X_0 + X_1^{2} - X_0^{2} - X_0 X_1) .. math:: y = X_0^{2} - X_1^{2} + X_1 - 0.999 Despite representing an interaction of :math:`X_0` and :math:`X_1`, these terms cancel and we're left with the (almost) exact relationship we were seeking! Great, but let's compare with some other non-linear models to see how they do:: est_tree = DecisionTreeRegressor() est_tree.fit(X_train, y_train) est_rf = RandomForestRegressor() est_rf.fit(X_train, y_train) We can plot the decision surfaces of all three to visualize each one:: y_gp = est_gp.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape) score_gp = est_gp.score(X_test, y_test) y_tree = est_tree.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape) score_tree = est_tree.score(X_test, y_test) y_rf = est_rf.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape) score_rf = est_rf.score(X_test, y_test) fig = plt.figure(figsize=(12, 10)) for i, (y, score, title) in enumerate([(y_truth, None, "Ground Truth"), (y_gp, score_gp, "SymbolicRegressor"), (y_tree, score_tree, "DecisionTreeRegressor"), (y_rf, score_rf, "RandomForestRegressor")]): ax = fig.add_subplot(2, 2, i+1, projection='3d') ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) surf = ax.plot_surface(x0, x1, y, rstride=1, cstride=1, color='green', alpha=0.5) points = ax.scatter(X_train[:, 0], X_train[:, 1], y_train) if score is not None: score = ax.text(-.7, 1, .2, "$R^2 =\/ %.6f$" % score, 'x', fontsize=14) plt.title(title) plt.show() .. image:: images/ex1_fig2.png :align: center Not bad :class:`SymbolicRegressor`! We were able to fit a very smooth function to the data, while the tree-based estimators created very "blocky" decision surfaces. The Random Forest appears to have smoothed out some of the wrinkles but in both cases the tree models have fit very well to the training data, but done worse on out-of-sample data. We can also inspect the program that the :class:`SymbolicRegressor` found:: dot_data = est_gp._program.export_graphviz() graph = graphviz.Source(dot_data) graph .. image:: images/ex1_child.png :align: center And check out who its parents were:: print(est_gp._program.parents) {'method': 'Crossover', 'parent_idx': 1555, 'parent_nodes': [1, 2, 3], 'donor_idx': 78, 'donor_nodes': []} This dictionary tells us what evolution operation was performed to get our new individual, as well as the parents from the prior generation, and any nodes that were removed from them during, in this case, Crossover. Plotting the parents shows how the genetic material from them combined to form our winning program:: idx = est_gp._program.parents['donor_idx'] fade_nodes = est_gp._program.parents['donor_nodes'] dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes) graph = graphviz.Source(dot_data) graph .. image:: images/ex1_fig3.png :align: center Symbolic Transformer -------------------- This example demonstrates using the :class:`SymbolicTransformer` to generate new non-linear features automatically. Let's load up the Diabetes housing dataset and randomly shuffle it:: rng = check_random_state(0) diabetes = load_diabetes() perm = rng.permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] We'll use Ridge Regression for this example and train our regressor on the first 300 samples, and see how it performs on the unseen final 200 samples. The benchmark to beat is simply Ridge running on the dataset as-is:: est = Ridge() est.fit(diabetes.data[:300, :], diabetes.target[:300]) print(est.score(diabetes.data[300:, :], diabetes.target[300:])) 0.43405742105789413 So now we'll train our transformer on the same first 300 samples to generate some new features. Let's use a large population of 2000 individuals over 20 generations. We'll select the best 100 of these for the ``hall_of_fame``, and then use the least-correlated 10 as our new features. A little parsimony should control bloat, but we'll leave the rest of the evolution options at their defaults. The default ``metric='pearson'`` is appropriate here since we are using a linear model as the estimator. If we were going to use a tree-based estimator, the Spearman correlation might be interesting to try out too:: function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max', 'min'] gp = SymbolicTransformer(generations=20, population_size=2000, hall_of_fame=100, n_components=10, function_set=function_set, parsimony_coefficient=0.0005, max_samples=0.9, verbose=1, random_state=0, n_jobs=3) gp.fit(diabetes.data[:300, :], diabetes.target[:300]) We will then apply our trained transformer to the entire Diabetes dataset (remember, it still hasn't seen the final 200 samples) and concatenate this to the original data:: gp_features = gp.transform(diabetes.data) new_diabetes = np.hstack((diabetes.data, gp_features)) Now we train the Ridge regressor on the first 300 samples of the transformed dataset and see how it performs on the final 200 again:: est = Ridge() est.fit(new_diabetes[:300, :], diabetes.target[:300]) print(est.score(new_diabetes[300:, :], diabetes.target[300:])) 0.5336788517320445 Great! We have improved the :math:`R^{2}` score by a significant margin. It looks like the linear model was able to take advantage of some new non-linear features to fit the data even better. Symbolic Classifier ------------------- Continuing the scikit-learn `classifier comparison `_ example to include the :class:`SymbolicClassifier` we can see what types of decision boundaries could be found using genetic programming. .. image:: images/ex4_comparison.png :align: center As we can see, the :class:`SymbolicClassifier` was able to find non-linear decision boundaries. Individual tweaks to the function sets and other parameters to better suit each dataset may also improve the fits. As with scikit-learn's disclaimer, this should be taken with a grain of salt for use with real-world datasets in multi-dimensional spaces. In order to look at that, let's load the Wisconsin breast cancer dataset and shuffle it:: rng = check_random_state(0) cancer = load_breast_cancer() perm = rng.permutation(cancer.target.size) cancer.data = cancer.data[perm] cancer.target = cancer.target[perm] We will use the base function sets and increase the parsimony in order to find a small solution to the problem, and fit to the first 400 samples:: est = SymbolicClassifier(parsimony_coefficient=.01, feature_names=cancer.feature_names, random_state=1) est.fit(cancer.data[:400], cancer.target[:400]) Testing the estimator on the remaining samples shows that it found a very good solution:: y_true = cancer.target[400:] y_score = est.predict_proba(cancer.data[400:])[:,1] roc_auc_score(y_true, y_score) 0.96937869822485212 We can then also visualise the solution with Graphviz:: dot_data = est._program.export_graphviz() graph = graphviz.Source(dot_data) graph .. image:: images/ex4_tree.png :align: center It is important to note that the results of this formula are passed through the sigmoid function in order to transform the solution into class probabilities. Next up, :ref:`explore the full API reference ` or just skip ahead :ref:`install the package `!gplearn-0.4.2/doc/gp_examples.ipynb000066400000000000000000046171341423420364700173060ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 1: Symbolic Regressor" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from gplearn.genetic import SymbolicRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.utils.random import check_random_state\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import graphviz" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAADzCAYAAACi/XuyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACgTElEQVR4nOz9d5Rl+Vnei392DidVncpVnXume4ImaYJAYOEA2MsWI5sgRBIYsK11Fwb7rmsbX3v552sblm3+ufhiY3thX3QtmSBAyNiIYJAMkhhpgmZ6umd6pnNXVVc8Oe28f398a+86YZ/u6p6unkFTD4ul6apz9tnn1Hm/b3re55XiOOYABzjAuwPy230DBzjAAe4dDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcR1D085qCMf4AD7D+ke/EiBx7+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4A9wgHcRDgz+AAd4F+HA4N8mxHFMEAREUcSBcvAB7hX2IoBxgLuMKIrwfZ9er0ccxzQaDaanpzEMA0VRkCQJSboneggHeJfhwODvIeI4JgxDrly5gqIoTE9PA3Dt2jXy+TxBEAAgyzKqqqJpGoqiIMsHgdgB7g4ODP4eIY5jPM9LQ/goigBSby7LMoqipOG953l4ngccHAAHuHs4MPh7gCiK8DyPOI5T4x6XtyehvKIoAAcHwAHuKg4Mfh+RFOaCIEgNHYRRJx4++fftHABJtNB/AGiahqqqBwfAAW6KA4PfJ/SH8FlFuMTbw80NfhjD10oOANd1cV0XEIdD4v1VVT0oAB4gxYHB7wOCIMD3fWDUQJOf9Rv4WzHIrAMgiiIcx0l/lhwASQRwcAC8e3Fg8HcRwyH8OMPK8uh3qxe/lwOg1WoxMzNzcAC8C3Fg8HcJSW99XAjfD0mSaLfbPP/88+Tz+TQcN03zrt9X1gHw5ptvUigU0p8dRADvHhwY/FtE0ltPQvhbFcyiKGJtbY12u83jjz+O67qcP3+eixcvEoYhpVKJyclJJiYm0HX9rt9vcgD0FwGjKKLX6w0UCA8OgK9NHBj8W0Acx/i+TxiGN/XqlUaFG9s3WF5f5qUzL/HYfY/xwLEHMAwD0zTJ5XKcOHEC0zRpNBrUajVWVlYIw5CJiYn0AFDVu//n6ucBJO/p4AD42sWBwd8hhnvrw0ZQaVR47eprvH71dTZrm8RBzPLqMjMzMzx/6XmmJ6c5whFgN6eXZZnJyUkmJycBCMOQer1OrVbj6tWrAOnvS6VS6qXvJvZyAKiqmv7/wQHwpwsHBn+bGNdbT7BWWeOPXvkjbmzfoNPtEMURle0KURRx4vgJ/NCn2+nyP577H9x34j5K+dJYg1EUhampKaampgBR/a/X61QqFS5duoSiKGkEUCqV9qX/nnUAhGGY0oCBlASkqiqyLB8cAO9gHBj8bSCOY7a2tvA8j+np6YEv9lZ9iz946Q+4sHwh/ZmpmVy8cpF8Ps9EaQJJltAkDYB2r82vf/7X+b5v/b702reCqqpMT0+nHHzP86jX62xubnLhwgU0TUsjgEKhsK8HQILhA0CSpIEI4OAAeGfhwOD3iKQw1+l06PV6zMzMpD//wtkv8MVXv0jeyqePb7fbLNeWue/YfbiBIMR4gYdt2KkhrlfX+cwff4aH5x++o7acruvMzs4yOzsLgOu61Go1bty4QavVwjCM9ADI5/Op4d3NcdysA2CYh5BQgXVdPzgA3mYcGPwtMBzC9w+4rFXW+K0v/RYbtQ0AGp0GhVyBS1cuEUURS0tLeJFH0S7S7DYB6LpdclaOdrsNwMWVi7TrbY4cOfKW79UwDObn55mfnweg1+tRq9W4fv067XYb27aZnJxMB3j2w/CyDoDl5WUkSWJhYeEgAnibIe3htH/XqjNk9da3trao1Wp05A6f++rncD2XIBThrOd5bG5usjCzgKLvFtQUWcHQDLpOFxCGSAi6qUMM9Vqdb/vAt/Fn3vtn9u29xHFMt9ulVqtx+fJlDMMgn8+nEYBlWfv22isrKyiKwsLCwsCkIOymAP2DQO/SA+CevOkDD5+B4d56v9eK45g/PvfH3OjcACBn5IiciHqjTqPRYG5uDt3UBww8jEL80MfQDFzfRVM0giCgXCzT7DQJ45A/eOkPKE+Wefj4w/vyniRJIpfLkcvlWFtb46mnnqLdblOr1XjzzTdxXZdCoZAeAIZh3LXXHp4b6O8uJK3N/s86KQAmcwDv0gNgX3Bg8EO4WW/d8Rx+40u/wetXX2d6RhTOWr0W3WYXp+ewtLSELMsjBg7gBz6TBWFIW5UtWk4Lva2neX8URfzWF38LXdO5/9D9+/4+JUmiUChQKBQ4cuQIURTRarWo1Wq89tpr+L5/10hAN0sfsg4Az/PSQaBkEjCJAA4OgLeGA4Pvw8166z23xyf+8BPUO3XinSzHdV02NzeZmJjg/pP302g30sf7gY+lW6iKimVYhFFIrV3DNm1kSU6n3NrdNrZpp6//6f/1ab77L3w3R+eP3tP3LssypVKJUqnEsWPHiKLorpGAkpRoLxhmAQLpAdBfADzQArgzHBg8gyF8Vm+943T45B9+ko3aBq7rkjNyNOoNWq0W83PzaLpGo9uglCvR6OwafUxMIV+g0WoQxSJv7TpdLMuiXq+nj3F8h6ncFBERQRjwa5/7Nb73W7+XhamFe/YZDCOLBJQcAAkJqP8AuBkJ6E4LhAdiIHcf7/qi3a3m1tu9Np/4g0+w1dgCRMFtY2ODufIcZs5EkncfLyNjGRadXodSvkSr1yKKIopWkVa3lUYGnuvhdlx6Xg9FUfB9n2KxyGRxMs37bdPm+7/1+5memL7r7/n555/n6aeffkvXSEhAtVqNer1+UxLQ5cuXKRaLKX/gbiH57vYXAf8UHwAHRbv9xq3osY7n8Gtf+DUqrYr4t+OwtbWFpmnkSrnUuNPrERETM5GfoN6ppz9v9poD3t91XbbqWxw7dIxWu0Wn02Fzc5N2q03eziOrMo7k8Jt//Jt8x5/9DiYLk/v/YdwmbocEdDsh/e3gZmpASTRy6NChAzWgPrwrDf5W9FgQIeynvvAplreXKZgFlleX6XQ7zMzM0GiIEN31XUzNxPHFrLlt2viBTxAFGLqB67np9RrdBsVckesr12m1W1imRazELM4v4gc+hmFQzBfxPUG93dzcZGNrg63KFn/9g3+d+en5e/b53AluRgLa3t6mXq/T6XRGSEB3E/2HdhiGdLvdETWgfjmwd6Ma0LsupN/L3Hocx/zmn/wmZ6+dJQxDNjc2KRfL6JZOEARUKpWU3GLoBmEQYhs2baed5uqGZhCFEX7gp6+7sbHBZGESWZWp1qrMzswiSRKhExJJEYomPJWmaigoNNoNnJ6DFEn8hUf/AkcWj1Aul5mYmEDTtDv+DO5GSH87uHDhAoVCgSiKqNVqAySgyclJbNu+64ZXq9XY3t7m/vt3Ox5JBJB85+M4Hgj/3+YD4CCkv5u4WW99GH/4yh9y9tpZet0e29vbTE1NYeQMSlaJSqMyQE11PZep4hS1Vi01dgDXd7F0iziO6XRFyD45OYmZN7E0i1qtRjFXpON0qHQrzEzMECAIPH7ggwKlQinth7+29RrHjh+j2Wxy/fp14jje98m5u4U4jjFNk4mJCRYXF0dIQN1u966TgKIoGoncDuTA3iUGn4Tw586d44EHHrjpH/HVq69y9tpZqtUqvV6PhcWFtAXV7DUp2AU248308aVciUqrQsEq0O6208IciFae1/PYrmwzPz+feuWYmJyZo9ltpvWDZq/JwvRCmuf7oU9MjKVb9LweHbfDbz//23zPN38PJ0+eTItm29vbXLp0CVVV931w5k4xXKXvJwEdOnRItCfvMgkoy+CH8W48AL7mDb4/hK/Vajf9g23WN/nMc59h9cYq5UKZ8mR5INCKien5PQxVfAFL+d1CXKvXopgr0uw009fd3NxEkRVO3XeKbq+LLMvkrTyVZoWW02J+YT7t3cdxnOb5yTUSyq5t2HTdrmgP/t4n+Z5v/h7mp+YHimbDgzOmaTI5OUm5XN6XkPl2cKu2XBYJqN1uU61W75gEtBeDz7qPrAPga0kM5GvW4G/VWx+G67v84u/+IteXrzM9PU0ul0NXdHpeb+BxURwRxAGT+UlqndrA75pdUY3frG6mhJxCoUDH7VAuluk4HRrdBtLOKdLsNinmimlPvv9n/UYfRzHFXDGtpnzq85/i2W94doCc0z84E8dxOjjTHzKXy+W0r34vkYh77BWyLFMsFikWiwMkoHq9npKA+g+ArHrGnRj8ML4W1YC+Jg3+Vr31YURRxH/4zf/AldUrLC4uoigKYRQSyiGaouGHfvpYSZLQVZ1e0ENV1NQLJ1heXyZ0Q8Gp3/FEJbtEvVMnb+bxfA+k3R5yYuD9B0vyM8dzsAwLx3VoO21yRo5WtwXAL//BL/PB938wk3svSRK2bWPbNktLSwMh8/nz5+l0OrzxxhtpyPxWCoB7wVudzOsnAR0/fnyABHTt2jVglAR0Nwx+GHs5AJKVYf2LQd9J+Joz+Fv11ofR7Xb51d/9Va5uX2VxcXHgd8n8ehiFaUGuaBdZdVdxfAfbsIndWPw+itja2kKSJGZmZpjITdDoDLLvkhpAs90ceJ2W02IiP5Hm/4ZuEBNjaAatbit97bbTppgv0mw3CaOQ//bF/0az0+Tr3/P1N32PwyHzV77yFWZnZ6nVaiwvLxPH8Z5Zc3eCuz2KqygK5XKZcrkM7JKAqtUqly9fTvvt+Xx+Xww/QdYBUKlUqFarnDx5kv/9f//f+df/+l/fdcLRW8HXjMHvpbc+jI2NDZ4/8zxXOlc4snQk9Z796LpdClaBVreFbdh03E5qmF23S97IU2/WWVtfo1QqUSwWAeGlp4pTVJqVgeu1ei1yVm6gNiAh0eq2ODx3mDAOaXVb6dBNzszhuA5hFBITi7ShUKLRahDHMZ/76udodpt869Pfelt89X7abJbBJPn/3SgA7tfsfYIsEtCFCxdotVq88MIL90QJCHa1CZM23/nz5+/q1OHdwNeEwd9uCB+GIW+88QbdXpeVYAVVV+m6XWzTTqmt/Wj1WkzmJwf67AnWttbwul7KqQdhwHk7T6VdEYW9vqEaEJ7a1EwxWReGgDDsltfC1u2Bx3acDqZuooQKXiD4442OKO61OoKu++IbL9LqtvjQN34ITb398DzLYLIKgJOTk+Ryuds23v1i2o2DruvYts3s7CwzMzN7VgK6GwjDMI2Qut3uvuoM3An+1Bv8rdY69UOSJFqtFmfPnmVxcZHNcJOtluDIh/HoSGsCWZJxApFP+13xWnEUs7W9RRzFzMyKEL7ZaabG3uqJaKHRbYwYvSRJdL0uS8YSjueQt/O03TYFvZCG/Z1eJz1cHM9BUzQsw6Lnily/2WtSzBeRYolYilneWuYTv/8J/tqf+WtM5Cfe0meq6zpzc3PMzc0BYn6gWq1y9epVOp3ObffM99vDZ6E/lN+rEtDdIAH1G3wcx+84fsSfWoPf61qnfgRBwJkzZ3jPe95DO2jzxa98ceD3fuhjaRaKLIp2CQp2gUa3QY8epVyJ69F1VldX00oy0o4B5orEcZwae4Iso4/jmM3tTXRFp+N2BoZwWk5L0HQ9Py0Y+qFPFEfkrbyo8ksi8tA1HRmZntuj5/b4xc/+Is9+47OcWDhxR59rFizLYmlpaaQAmPTMi8ViygDMapm93QY/DMuysCxrTyQg0zRv697DMETX9buqG3g38afS4G9nrRMIQ3/99dfxfZ+v+7qvQzd0fun3f2kkPAfo+T3yZp5Or0Mcx5TsEo3urqGubq6iyRrl2fJIfiZJ0oDh9qPf6IMgQEEhlmKcyMH3fQIvoNfrpV/GbtzF0A0M2RjI573Aw9CM9PBwPVe0sXZ087pul1/5w1/hmx77Jt7/nvff1ue6F2T1zJvNZjo3H0XRSAHwnWbw/RhHAqrX61y4cAHHcSgUCkxMTFAuj/7Nh9Hv4d+JYh1/qgz+dtc6gVic+Oqrr3LkyBFcVxjHF17/Ao7vICENMOMStJ02JbuE53u03Xb62tvb20KSWQqYmpii3WunzynmiunBMDwXn6DRbaBICtValYCAfCGfcrtt26aUF9TdarVKEIgBnEK+QLlYFgW7nqjuO75DMVek3WsTRZEwup0Io9VtEccxn3/586xV1vjg+z+Ioe1f4UiWZSYmJpiYmOD48eMEQUCj0RgoADqOQ7PZZHJy8p4xAO+0Ot9/oB0+fHiABPT666/jed5NSUCJwfdz9t9J+FNj8Htd69T/+JWVFZaXl3nkkUcoFApsbW2x1djiS298iTAKxxom7LTKchP0Wj1832djY4NCoUCpVGJ5eZme1yNn5Oi4HYp2caDC3+iNimEAVKtVFBQOHzvM+TfPpz+XJIkgCgjlkMW5Rdq9tni/no8cy1xauYSlWIRSiGVZmIZJs9fE0i2iKEqn8prdpogCfNGWXK2s8quf+1W+5alvYb58b6btVFUdWJ7heR4vvvgim5ubXLp0KS2YlcvlOyoA7hV3qx3XTwJKrtsf0QyTgBKDdxznHVewgz8lBn+7vfUgCDh79iyqqvK+970vDbFkWeZ3X/7dND/PUqlJULALVDtV5EhmfX2dmZmZge2uYRTihi4lu0TLaY1ECv1GH4YhGxsbTE9Mo+dEzm6b9kCVPnmfHbdDKSeii9gQajjz1jxRFKGhsVXZolatIcsylmWRz+Up5Uu0e20URRGkD8kQBcpui3avzcd/9+N84yPfyNc/9PX3nGOv6zqapnH69GlkWU4LZteuXaPdbpPL5VIG4N00kP3qvw9HNP0koOvXr9PtdnEch1dffZVcLnfb15ck6S8BPwsowC/Ecfwvh35vAP8f8CRQAb47juOre73+O9rgk3wqCAIsy9rTH7DRaHDu3DmOHTs2QqS5tHWJq1tXB/KwZrdJwSzQcnY9dM7I0eg02K5sE/gBD9z3AB23wzAkJLzYw9RMut5oO6/Ra6ArOpeWL3Fs6RihsmvcXa/LIeMQPaeXvtfkf2MpRtf1lF4L4osWEnLo0CECP6Dn9ET1vF6lsi3afyEhruemwz5J2B9GIf/rlf/FpRuX+Ctf91du+RnebfTn8MMFs06nM1IATApmb0U4cz8JN/0YJgG98sor6LrO5z73Oc6cOcM3fdM38f3f//38jb/xN255LUmSFODfAt8CrADPS5L03+I4fq3vYT8C1OI4vk+SpI8A/wr47r3e7zvW4JPe+vr6OnEcc/TozUUd4zjm2rVrrK+v89hjj42cro7n8OUrX0Y1B99yTEzX66YtL1mScTyH1dVVcrkc01PTopBn5QdydhCMuLbbRpXVdKqt78I0mg1azRYPnX5IHBhDNcKO20FXdXRNpxeK55ZyJRo9EXHkrBye5w1Qe7tuF03RKOVLGLrB4vyiIOp4Lmqo0mq06DpdTNOk0+kwUZwQB5LbZWVrhf/82f/MornI09y7eXggMyqTJIl8Pk8+n0/z5VarRbVaZXV19S0LZ74dE4NxHHPy5En+7t/9u6iqyr/5N/+GtbW1vT79GeBiHMeXASRJ+mXgQ0C/wX8I+Kc7//1rwM9JkiTFeywYvCMNPinMJQIFiVrJOHiex9mzZ7Esi2eeeSbzD/3F81/E8R1yxmiYFcYhYRSiKzqBH3B19aoI4S0RwkdEImc3c3ScDpIkiQGXnSJaEA1OtcVRzObWJpIkceq+U7S9NjkzR8/tDYTwIApwSGIPXTFXTI0ddg8EWxXXTeCHPpZhkdfyNDoNYuLUG5o5kyP6EbYqW/ScHtdXrgMIbTxZ3OOZ62cw/sjgzz/x5ykXyrf8e9wr9CvnJuFy//bchCFYLpcpFos3Nei3y+CTHL7T6QiH0Udo2gOWgOW+f68A7xv3mDiOA0mSGsAUsL2XF3hHGXwWPVZRlBEj6Ueio37fffelRJFhbDW3eO7icxTsAmGcfS3Xd/E7PtV2lcWlxRHCRBiHuIGLZVjYhk2j0xhowQVRQEyMJmtcWb5CsVRkbnoOLxC1h7bTxtJF4S1hzPU/V9f0TM0TL/AIpCCtByiyImbpdw6bnJkjCIO0cBdFES2nxfTUtFB3DTxM3cR1XVrtFsury7S7bZ4/9zznr53nGx/9Rr7h4W+4I4befmN4e67v+9RqNTY2NnjzzTdvyph7uww+ed2EoPROwzvG4Mf11scZfBzHXL58me3tbd773vfetODzB6/+gSiIeR2milMjBbYgCNjc3KRULHHi2Akc18ns0QdRgKZo6bSbNGShzWaTer3OscPHkBRJyE5Hu9N0Pa+HrupY+u69SpJEzsxRa9VQHXWAMpt+NnFEo9egXCjT9bqpsYOIAhRZGSk+tp02pVwJW7Kpt+ugQL6UJ1/MI2/J6JrOdmWbT/3ep/iD5/6Av/j0X+Tph59+x3G/+6Fp2oBunuM4VKvVlDGXy+XSA+Bu9v7dwEWVVRT51qy55HUTD3+bWAUO9/370M7Psh6zIkmSCpQQxbs94W03+FtJT2UZvOu6vPrqqxQKBZ5++umbnuSXNy5zYe1Ceu2O22F6cjoNkbvdLpXtCscPHyeQAzEsYxZoO+3MPqqqqfiRj6mZeJGXvodqpYrneWJ/mhpTsArU2rWR53uBl+b8kiSRs3JsVjdTA2/2mpm5e8EqUHfqIsQf4vyHUUij1yCfy+M6LrqmExGl6YFt2mJG3u2lIh7TxWnmZ+Zpdpq4nstvfeW3+PK5L/Po4Uc5ung0Zc7dTt58r2GaJouLiyMFwIsXL9Ltdnn99dfTFOB2C4Atp8WZG2fQVZ0TUyeYyk3d1vPb7fadePjngfslSTqOMOyPAN879Jj/Bvwg8CfAdwJ/uNf8Hd5mg99Lb12W5QHd8Uqlwvnz5zl16lS6svlm1/+fZ/5n+m9JktI1ULqis765juM4HD96nEiKUoGJltOiaO2IUPTdUtESeXtEhBd52LpNq9tifWMd27JZWFgACfJWnkZvUL2mH0EU4IYu5WI51bvvx3DuXswVafVa6dIKCYlSvkSz0xw4lHpej7ydJ0Z8+RN0vS4Sou7Q6XZAEodEx+2IvfVI1Nt13Njl+bXn2fA2ON09jXRVSmfR79bk3H5huAD4la98hcXFRWq1GmfPnt1zAXCrvcVLKy/hBz5fd+zrmM7f2WjrnYT0Ozn5jwG/i2jL/ec4js9JkvTPgBfiOP5vwH8C/oskSReBKuJQ2DPeNoPfa2898fBRFHHp0iXq9TpPPvnkQE98HF659gobjY3038n4ouM51LZrqKrK4uIilmkNtOVgR0u+T8JKV3S6fje9ThAGNNoNats1ypNlLFuE6QWrkDLu0mu0R/v8tmFT7VQp5Uq4PXdEGzjJ3aeL02y3B+sxMTGNXgPbtAmDENd3yZk5/Njf9eqWTRzF6bBNwtRTFIW8lRctO6edqvbYhliB1e61uV69znJtmYeOPMQz9z+DHMoDk3NJG8qyrHccdTSBJEkDq7OSfnkyBNQ/IlwqlbhcucxLqy9hyibvP/H+OzL0/jSi0+lw+PDhWzwj8xq/Dfz20M/+Sd9/O8B33faFd3DPDf52pacURcHzPF544QXK5TJPPfXUnr5kfuDzx6/98cDPJEnCdV0ajQZTU1PMlmeRkWk6o14YBok5hm4MHAqNRgPHcVhaWBIhttvFUA16fi/zGolgJQgRjWV3GX/bZy1Yo2gVR+oBIMg/291tClYBx3MGQnwQnluRFaYnpqk0KwN5f9ft8+q9DmEk1HtMw2TFX8GNXPJWPt2Ik/AIbNNGRqbttHnt+mtc27jG/Yfu59Hjj/LAAw+kk3MXL15MeeYJceat9M33G8P98qQA+KXzX+JPrv4Jh4qH+Mb7v5GjC0fJ5+6s2NZv8N1uF9u2b/GMe497avC3O7cOpKuMnnzyyfSPtRd85dJXCOMw9eog/giu67K0tISqqnS9LiW7dNPrNLoN4WVbwssmqqaSJAlijySKOjkjRyzFmS3ERq+RjryamkmtXcP3fQrFAoV8gVarhed6hJshyGDZFnPludRbt9wWmqwJIY6+STwJUQPY7mxjWzZEDLTvEq+uKRqlgkgBmr0mMTGu74pFGoaJJmvpdZO0oFQoQSwm8r56+at89fJXmZ+c54kTT/DwkYc5dOjQSN88GZwpl8vvaOnsOI750vUv8dz155gvzvNPP/xPCfwgVQBqtVrYtj3AANzLd7V/cKbdblMoFPb7rdw27pnB3y49Nooi3nzzzbT4cTvG7vouz114jq4rDLrarLK5KaSlC4VCmr8VrAK1Tm1kIq4fqqKmNNmt2hYbGxtomsbExMTulpMoRNd0/MjPvAYIwylYBRrNBjfWbqCoCqViiTAKU0751NQUaqzS7XZ54+obSJKUMtPQwY98irbQso/jGNuy04p9f57edbqp1p6u6mi6RrVbxdAMLMNinfX0vhzfwcHBNm1M3aTdaw9EMqqqUtAL9Nwe67V1PvviZ/nDM3/II8ce4bFjjzE3OZf2zbOks8vlMmEY3tOJuXE1rFqnxhcuf4Hz2+eJpZgPPvRBHp4TmoCqorKwsCCKrn0js/2RzK1ks4fFL96Vbbk7kZ7qdru8+uqrzM7OcurUKZ577rnbes0vX/xy6uk2qht0G910+CHxwJqi0XYEc27csAuIPnej12BlcwW/6zM3NzegMgtgGRb1nvjZuMNDQmKrskWz1eTk8ZNcunqp/5fEO/9n5SzsnI1ZMPF8j16vR7PZxHM9NF2jZbUoF8vkc/kR1dyYmKbTRJVVSmaJMBR8/4QW7AYuTuSg6RoFe1dHv2gXcQKHareKqqgUTSGg6fmeqFX0GmmhUJEUvNDjpcsv8cKlF5jMT3Jq8RSnFk9xaOrQiHR2tSq6F1/5ylf2jTc/jP4efNNpcm79HFerV9lobyApQqDkrz74V1koZm/nHR6ZHZbNDoIgHZiZnJxMHUi/wd9hW27fsa8Gf7tz6wDr6+tcunSJhx9+mImJidt+Tcdz+PKFLwOk+8zm5uaYzE+yUdlIT3/LsAb62VmDNLZp0+g22NreIgxDZmdnhThlo5FeJ3lPSf7ccEar81EU0W12cUKHmXmxYaaf8Zfk77Zh03aFjJau6BT1oiiy5fMQg+d7uI7LVnWLa2vXKBfKKKqCqqkjJCBkQRYyVVOo9PR99H7o03Jb5O08uqpT79bTTkgQBjR7u8o9UiwRxcKAmq5IB2RJxrZFrt/qtfjym1/my29+mYJV4MHDD3J4+jCLk4sUrAILCwusrKzw1FNP0el0qFarKW++32jupnLuemOdM1tnOP/iebY6W2iKhqzIyKqMoRn8tQf/GrP52T1fb1g2e3hgBoRqrmEY6UHT6XTePSH97ax1ShCGIefPn8fzPJ555pk7/gL8yYU/oet209B7cXFRSEq5XUF97Xax9NGqPOzoyu94aEmS8H1fcOrzOWamZ1Jlm4JVSA2+YBVGin7Jtthmt4nrujSrTcyCyXRZeL4ojuj4HY7YR1IFW1UW/f2E8OOFog9fypVodVpEUoSpm0yWJsVEXVzCcRwRybSEcIdpmdiWzfTkNA2n7+CybBSUgfdcsAp0/S5tv42qqOTtPJ7n4Xhi60qMUOOVVRk3cLFVm5yco9vrEsVRGh2psko5L+b1/dDnxSsv8pVLXwFEtLM4uUhro8VSbYmpwhRHjhxJhTMSo1leFmzS/vx/L5FgHMe03Bb1bp2t9hbrzXW2u9tUO1WqnSrzxXkCKcBQDdzQRZIkvv2hb2cmd/N27q2QVQCs1+usra3RaDT48R//cba2trh48SLHjx+/7VqGJEll4FeAY8BV4MNxHI+QOiRJCoFXd/55PY7jZ2957bu9TDKOYxqNxm1t52y327z66qssLS1x+PDhked86Utf4v3vv7V6S9ft8jO/+TOsrq2mdMt+hH5Iq9ni8KHDIzJUCSRJIm/k6Xa7I5z6BJXtCnPlOTRDw/GdTBENAHyhjDszP4OsDn6BV5ZXWFxaxFRNet0e7U6b/ER2zmdpFlIkIWtyamjD0CQNx3FwHZdau4au62n+n3zhbF0U9tbX1ylOFzOvkzNySEjIkkzLbY3kw4qskNNFdCLLMi2nNSAHJkkSpmqiqRpSLFK4y9cuU54Rh4Kt25TsEhO5Ccr5MgWrgK7oEEO306XT7NDtdLFMCztvY+UtVF3FD338yBdMQ7dJ22lTd+oEUSBatwjqs63bdL0uKxsr3Hf0PoIooOt3mbAm+PDDH2Y6t3+S0dvb2zSbTWRZ5gd/8Ad56KGHePXVV/nCF76wF2+ffuklSfrXQDWO438pSdJPApNxHP+DkSdIUjuO49sqFNxVD59U4S9evMiRI0colW5eAQdYXV3l2rVrvOc970nz7Dt97d/60m9xY/3GwB63fgSROO2Ht8kMX+fGxg1CLxRLKdSM03lHT26psCSuNXymxVCpVvBcj9P3nxb5cOiNXicWxTbLtOj2RsdrE/R8oaUHQlAzk/ZLIHaxE1EOyjTbTXq9HltbW0RRhGmaeLZHsVjEl/yBdt3gLcUEBARhQMEWkUzX6aaPi+MYFNLUw9AMbMVGQsIPfTzPQ9M0/Min5/eIIyHjJatyOhnohA7rrXVuNG+k10x0+hRZIZIivLZHd7OL67h0nA6yJmMYBlOTU5iGYDl6oSA/xcREsYiA3NBFkRVs3cYJHJEamUU+ePqD+2rssJvDHz16lDAM+fjHP44sy3dSrPwQ8Gd3/vvjwOeBEYO/E9xVg0/emKqqA+y4LARBwGuviam/Z5555pYUzptVeYMg4IWvvsBXLn2FB+57YKwXlCQJPxLKtFEcjdxjGIZsbm4yVZrCnDTFppeM0D9ph1W7VYr2jtTUzuxrInZhGianT56m6Yi2WKKO03cRYgQFt9qq0vW7zNvzdJxRIyzZuyOzuqZjKMZIhFKwd1MLCYmpiSmCfEDP64nCqR/g+R6Xrl8iCAKWN5bJ2Tkm85MEgXhc0S7S9naluJPrybJM3syjyipBFAykMG7g4gaiEFqwCoRyiIeHoioUtaJQiDGaWIaFqqpiYhBBpIriCEM1MDUTJ3SEyisKiqRgmAaGZRDGIZPRJIEf0Oq0uLJ2BU3WKNpFVFMVobosUbbLeJGHEzkQilRDUzTaQZtnTz/LkdKRzO/E3UR/0S753O6wMzEXx3EyU7sOZE+FgSlJ0gtAAPzLOI5/81YXvus5vCRJKIpCEARjH9OvM7e0tHTLDyXppWc9rtlscvbsWTbjTSbKE3S9bjrGOoyCVaBaq9LxOuSNfNreAlFR3tzcZGFuAd3SRZ7tdUbEMUC0ujpuB8u2aDpCVsr1XbEWemOT8lSZyeJkSmbxQ58gCgYq+BIShmaIx0i7FXZDNTAkI+0y5M08TXfXwLxATMDlrbz4b98TRcI+I4wRua2ERNEuEsWCCqwFGlbeYn19HUVRqNVrbGxuoOs68+V5gjhAVdSRab6YGEVVaDhiFFfTNUzVRJZkgjAgDENUVaXljR6OqqSKiCBsQ985pigKJbNE02mmBwZAXs3jhUJLUEJKD0tN01AMBbNoktfzbDe3CdwAr+GBDPVmHduyKRfL9JwenaiDGZh84OgHeM/se7K/WHcZyeewlxbkN3/zN7O+vtsePXfu3Nmd//xH/Y+L4ziWJGlcWn00juNVSZJOAH8oSdKrcRxfGvNYYB8NftyE2/LyMqurqzz66KN77lMm1+sv5CSadSsrKzz48IP8yRf+BBAFMS8QKjSO7wxcJ5Z2hQXbbjvlyzdbTZrNJvPz85RL5dR4EqNPOPQJTEN4pAQdt4PX86hX6yKd0LURZl4cCzpsEhFIsshvwyAU4ezOn9QN3NRQXd/FCZzMvnLbbSNLMtPFaWrd0SEdEIbqhA6xJNZW6ZpOq9Ma4J1LkoQqq2zVt3CqDlEUUcqVmMhPIKnioNUNfaAI6Ie78tlFq0gvFl47Z+ZQZEWIg0ZCk73n9Ygk4cnjWBQCbcMmiiPc0MXUxUIOWZLRFI0gDtBlHUnZ2dUmydRc8f5kZMp2GT/2yeVzhJHonLTdNvhiQceV+hWCWKRuD0w8wDcc/oabfbXuKsIwTHv0tzL6//k//+fwj9JTSZKkDUmSFuI4XpMkaQHYHH7wzmus7vzvZUmSPg88AdxbgwcR0g97eN/3OXfuHJqm8cwzz9xW5TIx+CQvT9IBSZJ45plnePHqiyMCEYqmDCx7LFgF6t36gPE0ug3RLnMdlpaWyFv5kYp7FEe7wzQ94c1XG6sDywYSNdtDhw6hqzqqoo6l6ybik3kjnymLBcJQO26HnCVm3QM3O1rKmTkqvYrwlrrQteuf99cUDUmR8AIP3xMGqukaeTOPpmhif51u0PE6aQs03tk8u15fJw5iVE2lkC+I9Vgy6SEqS7KYy/d2DscwSg8BVVYxdZOm1yQMRIjvxi4yMnl7MGIBsBUbN3Jp+bsHZN7IE0Qi4ihqRZHmSFBxxCSohMSkPYkf+cRyjGRKzOfnCeOQbruL23U5Ehzh+eefv2f03ySkvwubdpKJuH+587+fGX6AJEmTQDeOY1eSpGngG4B/fasL74vBD3v4RqPB2bNnOXHihJgou030T8y1223OnDnD0aNHWVpaIoxCnrswSsxxfIecmSN0RL4YRoM0W98TSrTFUpH7j9+fKsdkISYWRm8W8SIvvU4QBGxsbJDL5ZiZmSGIA5RYEWHsLd5PN+wyZ8yJyEFi5LVzVo6m00y9fdftDmyqtQyLji/23AVRQMNppMbRdoT3VzV1hNvvhR5tv01ZKZM3xbLF/sUb0s7m2enJaZETew7dXpfKZgXfF0o708VpcoUcsixjGzZBGOAHflqFD+KAtrcr7y0hoSs7B+GQsZesEj2/J3j+mlj6oMgKHb+DGwmikCIpmKrQDczr+VT/v+pUkz8Qk9YkXujhBA4+Ps/e/yxPPfTUPaX/JlFor9d7qzz6fwn8qiRJPwJcAz4MIEnSU8DH4jj+UeBB4D9IkhQBMiKHf23cBRPsm4dPaLSJztwTTzxxxx9CcoAkFf1Edhrg7PLZsbTYjiPC8cRgJUmQSDptQQCZnZ3FMA0avQbTpV2+fBZiYiRFwlCEIqzneTQaDaanpwdYY0koP465J8uykKSOQi5cv4CpmCJEDkSFW9d0sVRiJ0JI2XOKSskQNQBN0QjjcKRaH0QBTbeJruqZ0Up6D8homkbdqYt/SzJ5K4+MTNcTQ0BOJBZYqqqa6rQDxKFQ71ldXiWOYyxTtP4s02LSFkanSzqGJj4nX/EJrRDLtIjjmIJaSO9bUzRaXosw3lUBKpmlXUMGFEmhaBYJ4xA1VmkHbYpGkaa7Qw7SRbTS/5wPLH2A2dxs+nn3y2aNo/+Wy+W3vGcuyeHfKssujuMK8Bcyfv4C8KM7//0l4JHbvfa+5fCu6/LSSy9h2/ZYnbnbueaFCxdQFGWgoh/HMX9y4U9u+txmr8lUYVe8IAgCms0mS0tLyMrOPcUiJ74Zp16RFbpeV6QLkUKn3WHp0NJAd8E27FSyutFrCHKL2x2ouufNPJv1TVzXZWZmBsuyiIKIZlWo5RBC1aximMaAUm8QBjRCMRKrq3pK5c2CaZhUe1VkSaZoFQnCIE0fJCQM3UhHfUFo9iUeOW/kiaSIvCy8v+PvTumZmkmohUi6hF2000GiTreD03JYZz3t/SfplyZptMM2htvHP4+haBbTAydBySzR9tvk9byIOuKQiCjN4QGKuqABF40ivaBHLMXUHPF7VVZ5aPohHio8NLZoPLw4M6H/9qvm3Cn9N/Hw71RaLeyTh+92u9y4cYNHHnkklSO6U3Q6Hba2tlhYWOCBBx4YOIEvrF/I7m/3IW/lqXQq5PQcF65cQEJKhSoSFGxRiXd8Z6zR56089U6dzc1NgiBgcXYRTdMGC2pDoXnLbWFqJlqs4XhCQHN1c5VGs4FpmulMfyzF+Pjcf+x+wiik0qqkHHoQyi62bWMYRuqZ81aeMAhHQvaSXaLhivuP4igNoW3DRpVUojhiY3uDLNi6TS/sjej+6bpOQS8QEgpPG6rp527bNgtTCzQcsUIr0Z33fV+0/ORg4HoSEgWzQMfrpHv8ZElGlVV6YU8cPkEbYigYhYGDqqgXiaQIP9qJhmQdJ3CEgpBmERPz54//eWqbtT07GMMwBoZmsui/ifrPrdifiYdPDo53Iu66wa+srLC6ukq5XH7Lxr62tsaVK1eYmZlhenp6JNz68qUv0+w2R6ro/YiJcXoOy9eXObZ0jKurV0d2s/fPmTd6jRGj1xWdarPK2vpaqpba9tocnjos+spRKJ7jjB4Uju+gyApFq8jK+go9r8fS0hIbGxsDk4MxMUjgxR7TpWlaRouJiQmiKKLX69FqtWg32mwpW5iWSRjshNtmAT/0U0Xe4Rw5QdfvUrREfm/rNkWzSMfb7fmbmokXe9kinxI0/ebINKCqqhQNUdcoWAXRbSiCFEtICDpzs9nECz3qW3VM06RUKNENu4RSKKS5w50w3q0OXDu5v4IhrutHPp2gQxAH6d/NVE2QoO23aXpNvvOB78TWbCpR5Y6kufq7F8P0337O/Dj6b7+HfydOysE+GPzs7CyFQoGLFy/e8TWiKOL8+fO4rsvTTz/N9evXR9p8G40Nrm5dBUQ4nkhE98M2bFY3Vum0OywsLBApEbY2WEfI4sIPT89FQcSNtRvp9plEPipRoiWCjjfa90/geR6VjQr5Qp6JyYmUpNMPXdFpeS3RVgx3+uy+6LnncjlKxRKSLNFxOiMMOtu2mZ2YJZIi4iC78FgwCzS9JlEc0Q26NL0mMqLSrkoqQRyMtDFhp9IvSZmRlK3b1NzaaMFRz9Hze0RyhGRJEECxXEQNVW7UbuA4DqqqYlkWM6WZgUPKUAxymlDviaWYltdClmRMxUyN3VRNbNWm7tXTesDD0w9zauoUIFK9u7VmKhnugV3O/ObmJhcuXBhZmxVFUSpR/a4xeMMwCILgptLSN0O32+XMmTMsLCzw4IMPju3rJwMaIELX4d3uURSxsb6BH/ksLi2mXtQJHPJGPiV2uGG25n2jK3rmG5sb1Jo1QbPtq+gmoXzP61HOl8GHwBvNG13HpVqpMjk1iWqrYr1zLA90DAChcttXhEv67IlXtgyLlttC13V0XadUKgkJK6dHt9vlSvsKAYEYnbXzeJGXGqKlW3SD0RZgordvGibdoIuu6sJrImoGXuihKVrmc/N6npY3umLL0iycwNk91GLhjSesCZpuc0ByOvZESzMKIzRDQzVVzLw5cojkNfFaRaOYRiD9jynoBb71xLemjx/mbNwtaJrGzMxMqqWYpC9Xr16l0+ngui7nz59Pl5jcKSRJ+i7EsokHgWd2inVZj7vpWqos7Ftb7mZMu3HY2Njg4sWLvOc97xng4Q/vYOu4Hc6unB14rh8KJVlVUen2utQrdeyizUxhcDIqIqLn97B1+6b98iiKePPKm0wVplhYXBhIJ/qN1dRMar2ayDGHUotWs0Wj0eDksZO4sThYHN8RVXFDjLxKkiTENSqjYpZJDj6ZmxRf9JjBdEQWLbSF6QUabkOsoOr1qG5UIYSJvMg7AzUYq8eft3b74l7oDXjyolXEj3wKZgEZOZ2gSyjKw9t3dUUXHYide1VkBVVWyRtiS27RLKZ/AwxwQxe9IHapu66L67gs15cJ4gDTMkUEUJwR5CBFTaOShEMA4uD5iyf+Ipa2W2C7V5r0w2uznnvuOS5fvsx//I//kWq1SrPZ5Ed+5Ed45JHbLqafBb4d+A/jHiDtbS3VCO4p024cEnWbbrebORqrKEo6agvw4pUXB3rSCRzfIXIjNjc3OXH0BF6cXdALoxAv9LIHYxAh+MbGBouziyiWQtEQXjbxwP0Gr6kajuuk7bOSLVKB7e1tgiDg9MnTdILBcD8Zj12YWEBTtbRCnoUk1A+iQPDRJZWW27cHT9/N21VNpaAVKBTF0IvruARRQH2jjiZryIaYCU+MpWgVx+b8RauYkmr6ockayEJ9R8y77CwMkRU0WcMLPcGbiCNCQhRZoRk2aQWD0ly2Zqc1AUmSsEyLqcIUHb+DJmlEXkSn2+HNq28iq2Jxpm3ZlHNlmm6TvJEnjELmcnNpKJ/gboX0t4Pke//BD36QtbU1ZFnm4YcfvqNrxXH8enLNm2Ava6lGsC8e/nZ6mb1ejzNnzjA7O8vp06czn5us3wVhrC9eeXHkMf2Mt4fue4i2P2aAZscr2Yadevp+xltSpZ2bm2OiNEHLbdF0m+QMwZcPwiC9R9uwB4wPoNqu0qw0MXSDmZkZQsYcfJLI+6fMKfLK+HzP0I30NZKKvG2I6TTXd/HxMwlDkiQJXTxXLLAIwxCn5+D1PNRIpVPvCI090xwhnxSMQiYvXkIScwT+7gGWeP2clhs5IBRZEbn40P0VjWLaSUiuWzbLBAQYsZhdR4OpmSnMkokf+PR6PXr1Htcr11ENla7RJW/n+Zbj3zJyn2/X1pl+xdqjR4/yLd8yem93EXtZSzWCt1WXfmtrizfffJOHHnooLYxkoT9iOLdybmQabpjxJqkSBbWQLTUlCZ63Hwk+eEycLoKsVCp4npfSbPuNueN1xMCILOM4gt/erzIDuwM4U1NTTBQnyBt5qt3q8C2kSKrqcRyjqzo5PTdQ/CuYhZEDBUgPqMncZKqzP2xUpmYOGK2iKIJ/boVUggr2hBADadfbEIFmaCi6QiFXoBf2Mg+RglnIjAiKZnakYGs2m53NAUXegl6g43dEr10SvXZZkqm61YHXTK8pCV7BdF4w/7qeECLt9Xoc4xhXXrtCo9xI981JkrRvOfzN0F+P2Usf/mbDM3Ecj1Bp7xb2JaS/FaIo4sKFC7RaLZ5++ulb8pv7c/hXrr+Cpmr4gQgHu90ulUolraDLskzH7YxMp/XfX87MpTPxQRgQBiHVrSqSIqXU36wvTDJfbekWoR4OjLu2221qtVo6ix/HMU13p2XYHVxoATveL/IFKRKRz3b9LgWrIO4tZmBAZxh5M58STjRVw1ZFxOKFXhpmR2H2iLKlWUiqRK4ovpRRFOH0HOFF2z00XcO2bEzLRFZl0SnQs1t+OT2XGQ2UjBK9sIelCgGLklFKFX7DOEwjMEVSUGRlwNgN1aDttbFVUWdJUp6e3xMCG6bJ/MQ8P/TEDxGHMdVqlRs3bvDGG29gWRaO4wykgPcCw3p2txK8uNnwzB6xl7VUI9hXD581MeQ4DmfOnGFqaoonn3xyTwdEMpCwVl/j6vZVbMMmDEMqVUFQ6a+gF8xC2g/P6qkn22cSJF55ZnqGcqlM1+2SM3JiAisDXjjaq+6PDJKDwjZsGk5DjM9aOQI/GOgI2LqNG7jpYRfHcUoBVhWVCXuCSjd7ZZgiK+maKxAFy0bYQEamYBbQFI1aL3uCrmgV2apuUWD3CynLMnbOZn5qnqbbxPd9Wr0Wm41NgjAQk3s5F9MyUWUVWRJdBlVSU/48sahNRERC9tpvESFagF7gobkaOT03cojZmj1wYKiSSk7L0aMnugOBOAAGohVZ4VuOfwuqrIIMc3NzzM3NpWqz586d4+rVq1y8eDHtm+/32qxhg78Hbbm9rKUawb59AomR9ueH29vbvPHGGzzwwANpe2av1wrDkJeuvgRAq9uiXRNFtIWFwQr6cJut2Rsk5li6RcftoGkarZaooideOdGXHw7Vh2EbNtu1beaNed648gamaQ4MBZmaOZgO7Cx8TO5DV3SqYRXVF7xry7IGQkJd0al0K0IhRtHFnrs+D5gzs71tRCS8p9MWfW7VIoxCul6XmDjNv7OWXqSeWhITdZquUSwVIQIpkqi0KmxWNpFlOa1OT+WnBibcQHjs5F6AlFBUMksDeTvsttt0WRe03ShEkzWqvcE0SJPF30aVVHJGjmlrmvvL94+8h0Rt1jRNTp8+jaZpA9tmZFlOefOFQuGuymbfTcVaSZL+GvD/ADPA/5Ak6eU4jv+iJEmLiPbbXx63lupW195Xgw8CoTcWxzGXLl2iVqvx1FNP3faG0mSu+uz62dQjl8tljkwfGfDeeTM/UvGOiWm7bfE7p50eRFtbQol2cXEx9cpJy+lmkJAIYsEzeO3SaxxZOIKkDvbQdU1Pi4wJwiik6YjDR5IkXE+sb56YmKDdbuP7Ppubm9iWjT6pExOnajKmvrMswmmJfXYZeX1yb7EUp4KS6biqomJrIjzO8vyqpGYW12CXqpsINib0Wb/jc377PIZhpAeALMsi9O8r3sVxjKkO1hMSlpymaNjYdP2ukOFWtJEDu2AUBOXZEDz7pisYdTdDksMPE2c8z6NarbKyskK73U6XTZTL5T2tLrvVa95OSH8zxHH8aeDTGT+/Afzlvn+PrKW6FfYth0+8suu6nDlzhomJiT2viRqGLMu8sfEG29XtVKhC07TRkH3MpaNYEEwm7Ak2Njeob9YpFAqZyyglRWyYHUfXLVgFVjZXcByHQ4cOEasxpm4SRRGuL/bHZ8liJQiigPXtdbRYozBVwLIs8vk8rusyMTGBEilcW70mJtF2DCmOYxzJwdItTN2k18vW5CtY2UW1RLa66lTTlVNFswixyKkNzchswdmaPfJzVVWZmpgiJMQObTzPo9sVFFpLsajrdSzbwtAN8feIhYfOabk0neoFYhw2qUEkMBVzJLxXJCFQ2fDE3/iRmUeYy41TfBIY15bTdZ35+Xnm5+cHePPnz5/H9/2UNz85OXnbY7N/GpZQwD625VRVpVqtcu3aNU6fPp1OJ90pXll+BVdyRySxmk6TvJknDMOxeTcIjfZur0vohhRLxUyBTUuz0vA58cb9Ri8hcWPjBq22WKqY5N89r5cSTJBHZ9sTRFHE2voagRQgmZKQblJFuJr0omVFZsFeSCfR2u02lUpF7EafnKXtCdadqZr0vF7ayzYUY2w/X1cGW2l+5KeEo4JRoBN0sFRL0Gh32pZhJLykFmmEUjhQ99AUDc/30nvOWTmhWCNrdDtdel3BQFN1FU3SaEktFG/XgHJabiRKScL7ZOQ10b+vu/X0Maqs8oEjH8h8j8Of815k0/p588PLJhVFYXJykqmpqT2NzQ6vmXpXGXwcx7TbbZrN5p43vY5Dp9Ph97/0+zTcBkePHs18rZ7fo2gWx3o+Ymi32jTbTQzbYCI/kWmUuqaLgY4d9BNpojiiXWvjSz7z8/NsbAxOnCVeVFGUEdosiFC4vl3HKBhMFaaoVoVwpaVYFK0ia6yJ3LxPONK27VRDIAoi2m5b1C6iSBBRbJvp4rRobckyrpdNEzY0I7OSrshKOvfei3r0gt33XjQHiTeSJKSrC4ZoqymykhbpojiiqO+00QzRRjMxIYTtyrYQ0eh2Be/fsjFVc+DzTwaIioYYfW35Iq9PuPMJnl54WlT7b4E7Id4Ma827rjuway5Zd1YulzNT0v5WoOu6t5223ivcdYOP45iXX34ZgFOnTr0lY0+oth2jc8s/YNcXwg39gogAcRSzubVJ0SyysLhAtVql5/WYmpgaaKvpqp6ZGzd6DQzF4PK1y8zOzlI0hRJrls5cLO2scdZtojDCCUQe7zgOW5tbHDl0hFgdfF4STSRqL+MwUZxAdmXyxXzaRmu1W2xXtinZJXRLZ7I4mVKHE+SNfKaxA2On6yzNGnlOTIwiKalgxcB1Mjw27KQYZhPDMsjlcjiOg+zLXLomhCeKuSLlQhnLsEb486ZqDhw4eS3P+5duvZvgbsEwjIHwf3jVVH/1P0lf+7sAbwfxZy/Ylxz+wQcfZGVl5Y4HaJI+fbvd5j2PvYc/+aObi1wkntFQDXRFT/ngvu+zsS5krErlkuhR7+SRyThpYvSWYaVS0P3odDosV5c5ffw0PqIQluXB+6+VrHEu2SWW15dpNpucOnGKXjQYgQwMz8gSDacxUKBLYGnWgEElbTQ7Jxh3cRzTaDeoLleJoig1JEUfbN8N3K+ebaTJvcTh6IGWFSlIiILlcMRUMHZJQ8lOwYnCBKqsMjM7Q6fXodausbyxTBAGaLqGZVmYlikOkJ3XMRQDQzF4auEpDPXt8ZqSJKWqP4nmfL1ep1qtcvnyZVRVFcSmnYm5t1L9v43BmatAC6EFHMRx/NRerr8vIX2y7eRODD4p8pXLZd773vfy/JXn03ByHJJqtBu4WJqFGqs0200q2xVmZmeYnZhNPVlirGEsjD5v5nF8Z1TLPharqpM+f6zGSJGEpVjZiyyGbi8IAy5ev4ipmBw9fJRQDumfiu3/UtiGzY3gBhNM7G5z1W1kSRZTc4pMHGXXBYqWoKkmMk5J7r9WW0OJFEI5ZLIwKTbUKhKtrsiTI0aNFEZD+QTJdNzI44dosiBac0ldwpANDN2gaBSRkETxLRCfV6FYYGl6iYbbwHVcMfTUqJNTcxiWQTFfFO04WeXJ+Scz3//bAUVRmJqaSlvLruty4cIFqtUq3/d930en0+ETn/gE3/It38L8/PztXv6WgzN9+HNxHI/XZcvAvsUdWcq1t0KtVuOFF17g+PHjnDx5EkmSeOX6K3S9LqaWnRoknPgEPa8nptTqDRYXFzFNcyAX7PfOYSx61BP2xECYGkUR6+vrhFHIwuICJbtEz+vhBR5u4FKySgPeOWcOLpkIw5C1tTUhUFEuULSL6dhpP4YXUvaj63dpe23KubLIcTNaEMlgTT+S3H9pfonJ+UmKE0VaXosrq1e4ePUijUYDW7XTSn1ez2OoRio02V/cS6+JnBkp6LI+MK+e1/MUjSIT5kQ6R++EDh1fMB+HD5Kk0ChJEqZlUi6XOX30NPPz8wQErG6tsrq6yrw3T2VLkJveiTAMA9u2OXbsGP/rf/0vcrkcq6ur/PRP//RtXyuO49fjOH5jH24T2Oc+/F7/QInY5cbGBu9973tTLbG1+hrrDcE37vpdTtgnRqiyqrz7FqIoYnNjE1VTue/4fWKNk2YNDMcMh+MxfRTYXjNNA0oTpV3hxr49AFEc0XAaqXpqHMcD7cBk0i4RRZAlmY7fEaIWRp4gDHACJzXyvDHKHUjvFYlO0BGMPFXHUi06njAeEASfLG8M4sB1PLEauqSX0vl5p+dQbVRpXBH7/2zLxrItdFXH1u007UnYdMSiKu9HPqayW2yL41iMIvtC588JxcYXXdFpx7uThQnxJlX16YOu6GI1lKRgazZhJCS0elGPfCFPvpAnp+X45vu/mWatyauvvkocx2nxLFEfeicgIZm5rsvk5CQ/+ZM/ud8vGQO/t7Ok4j/Ecfwf9/KkfW3L7SWkD4KAs2fPous6Tz/99MAf8MzymYHHDvfd+3nWiaFNTkySL+TFAgmzOJJrJ8MzCQqmULzxAx81UlleW07VbEFQYAdWRO2g7bc5ZBxKly7C4KRd0rIrWLtU37YnRC1KVolmo5mmFuOQhOuwu3FGlmSKZhEZmbpXz3zeuENEkiWxaKPbZG52TkyhdXtsb2+jobGtb2PZFqa5W0DUFZ223x5R6clpuRERShgtthGLgttw5JBTxSKJol6k43doeS3y+uh9f/3S1zM1McXUxBTHjx/H931qtRrr6+u88cYb2LbN1NRUSp7Zw3LUfUHSlruTwRlIh2duZ3DmG3e2zswCvy9J0vk4jv/oVk/ad6bdzZBozB87dozFxcWB3wVhwKsrr6b/TrTpk75722mTM3I0nEY6uDI3O4du7A7iJOOv/UINiVR1+jo73rJWr9HpdHjg5AMDE2uqosKYOYyO26Fkl8gbeZbXl0d4/bIkj4hMJhGCpoiFEP2tsH7IkpytUhOLz8A2bLGWSjHwQz+9joSUvqdhWFofKUgSCi5aSaNYLGJqpthx1+1SrVZTCarZidlRSa4d3vwwhll2CYI4QEF8JpZqoStilddwOjK8U6+gF3jv/HsHfqZpGrOzs8zOzqbc+UqlMkCeCcNwZM/bfuN2DD5jcAZuc3imb+vMpiRJn0bMx7+9Bn8zD3/jxg2uXr06oDHfjzfX3xwojiXhc9J3T6SUKtsVPN9jabFPdnoHtmGL9U5WkVZPyDH1h/S2Ibz3xsYGiqKwuLhIx++kslK6qt+UNWfpFvWu0DgrWkXKi+WBsLXfuw/DCz06foeclhOHyhCGd8r1o2DuzqsnbUhDNTBVE0VShEZ7RqE4iZ6G6wHJ9fr7/r7vgwfX1q4RhiGmKRRoTNMUO+EyDDurCJhTc6iySkEv4IQOvaCXpjkD96APzuCbisn7D71/IGUbRsKdz+VyKXmmUqmwtbXFSy+9hKZpqfe3bfuucueHkRj8vSDdSJKUA+Q4jls7//2twD/by3P3NaTP8vD9ApU32xr78vWXR66ZFtuiEEmRWFlZQdEVFuYXRr/g8e5apIQ11+q1Bq4TBRGrq6sUi8WBVdVNRwheqLJ60xFVYli9sUqxUMQqWaKwGO+OcQ57937kjBzrtXW2altiHj7WibwISZPS3DgLiYLrMNxAqNtIslBesVRLLHuMAnp+T1TZ/dHDS0bO1PXTVA3bttHymsj9HbGBpl6rU9WqaKb4ffL3K+pFmn4TVVIxVANVFpLYq8EqQRAQe4N1k2EkUYmlWqiK2D786MyjYz+/LCiKwsTEBPl8nsceewzHcahUKly+fJler0exWEzz/7s9OXc7Hv5m2MvgDGKb7Kd3DjAV+K9xHP/OXq5/Tz284zi88sorzM3NpQKVWWj2mlzeujzws/51U67rUtmoiLzNMgdkphMMD9IkRt/tdsWaKC/gwuaFdI5+GK7vEqkRlmZlGq6KyptX3xzYPNPze2mODoy0qxKEQchWdUtsPZkqIyGxemOVeruOHIo96oEWDBhUgnF8eRC5e/Ka/e9dkRXRBtMLBHJAT+1hKmLHel7PZ3rr/vacJEtYtoVlW5SMEtttsbm1U+8QBRG2bdO1u+iGjhd5BL4w3pJRIogCdHbTrIJWGDl4iroQp8xpOeH5A/jA4Q+gKTfXgc9Cfx/cNE2WlpZYWloS6WCzmS6dSAZrpqam7srk3N0y+L0MzuzIWj12J9ffN4Mf9vDJaOyDDz6Y0hfH4ezq2exiWxzTarVoNVvMLcyhKAqaogn9tKH8L+sP2HSa5K081VoVtaGOKNH2wzZtmk4TRVJGVka3Wi1kZBbmF1C1wY8wybEn7AlM1UzZdgk816NVa6GZWrp3LeGlT0wIYoof+ei+TrfRpe20U0FH27JHrpdAU8Zr4/Xn1oEf0At6OKGDJms4gYOhGigISrCMvEt11cWarhiRSimSghu6GLpYiGHlxWCPGqpsNjfpOT1URU0HgjpeJ02jAIghYLBFmtcEb74/xNcV/Y777uPkrWRZZmJigomJCU6cODEwObcX6uxeX/etTsrtN/YtpE88fBzHXL58mUqlsufR2DPLZwaYa8k1q9Uqsixz+uTptDLe9YRgRc/rpYUkXdEzB2niKGZlfQVd1pmen84MjZPXSgwrjMVQTskS3YFKtSI2pcr+iLEnKJgFak5tV2a6J6rc3Y4oMB05fAQvFLvpbty4QRiKgZ7+hRaSIZE38pTVMpEXUW1WcdsunuSlPPr+w8rSrUzPL0vy2MKgpVniIBiqv5WM0dl12F3z1A9FVpBVmfLUzvisH9DtdenVe6w5a6Ktp2rouk7RFJwAQzUwFTMtSg4X7x6fe3xAhfZ2sFc9u+HJuYQ6e+7cOaIoSvXmsxZOZCERe3knr5mCffTwiUd+6aWXyOfzPPXUU3v64Nbqa2y1ttL1QT2/RxAE6Qc5MzMz0srquB0KZoG22xZjpYaF1xvkAAR+wPrGOrZlY8gGui5y5uHIAEa122Jiap0arWoLRVU4cegEZy+eHXle+lo7RJ/E2xuqQbvRplavcf/x++kGXRRFQVEVVE1lbm4Ox3Vo1BpUKhV0U8eyLAzDEEsWZZiamsLSLRzPodqsposoLMuilC/RUrKLi3ljMGRP8mdd1jMjAkVSRlR2Ybc9N3L9oZRA1VTKuihe2pHNxsYGruvSaXfoGT00S8PXfVxN1A00eUihWFZ4ZuGZsZ/trTAsurIXDFNngyCgVqulCydMU5CCpqambrlv7p28hAL20eAbjQadTof77ruPubmbzy/3I9GbD6JA6L65Puub62kF2dTNzL3qyQ73Vq81Qn11eg5bW1vMzMyIPKvVoeOK3WZhtLu5NMHwgeJ7Pusb60xOTjIzKQ4cSSySGykW5o38gGHEcczy2jLEcOrYqZTSWtkW8lUL80KxxzAMinNiwaLTc9L2mKYJfbnZiVmx+CGOmShNMD81TxRHVJoV3J5LdbuKruvpDL2iCK24zOKfNJ60k9fz6ex5P0zVHIkgFEnJjB5yWo6GJ4aOSkaJ0kQJ0zSpdqr0ej261S5BEDCZm6Srd7EsKw373zP9HopGceSae8Vd2M2OqqoDCyeSv0Wyby4ZnOmfm+9XrL0DOu09w74tk3zttdewLOu2jD2OY87d2FHpiWFze5PACVhaXKLZahJFkVhlnLESCUSxbzo/zXZnO71Go9mg1WqxsLgghhxQ2Pa3mWCCnt8TDLa+wpylWwN9+G63S2W7wuzcLIZhpFN1OT2XqdnXfwCEYcjG+ga2bVMsFoklsWq5VW1hGRa5Yi5tkcmyaFXJ0u5YbEyM7wmJ5uvr18WyDUuMmAZxADFMlaZQZIWZmRnaTpt6S7QJAWZKM4RauCtGsQNDzh6XVSU104sbSvbjh/vusiRjK6L9Zas23aBLO2ijBipqpA6sno7jGCVU2G5uU6/XxVrnXIlHTj2S/bnuEfshUZ38PQ4dOkQURengzJUrV9J100n6+lY8vCRJPwN8G+ABl4C/HsdxPeNxt71xJsG+GHwul+N973sfzz333G398S5vXRbrlndGWmVJZnpumpJVotVqQUymd++HGwque71TZ2tbtLyWFpdSnTpTH2RjeYGHKqtpzUBXdeG1Yqg36nQ6HRaXdot7Xuil+9mWjKVU7hoEK2+Y+Vcul0UYGIuf3Vi7weTkJMWC4LG33BZRFA2IbyaQkNB1nenSNE1XHHjdXpdWuyVmrnUDZUIhUAJkWSaWYsoTZcxpEymWaPVaNFtNtp1tdENPq/6qomb3zI1cZu6eUGD7oUgKQRRQ1IsgiQEmN3BRFGVAtCKOY3JabiTSyOniZ+VyGUM1kCMZO7JprDVYv7xOqVRiamrqttVn9luTvl8XD0THaHt7G9/3+cmf/EleeukldF3nAx/4wC2L0xn4feAf7ujV/SvgHwL/oP8Bd7pxJsG+hfSJptjt5FTnVs8NjLQmvfFEiCIIg8wWXAJN0Wi7Qh+uXW1j6IZQttk5b2RJput3R8UpooDIj9L9Z3Ecs7W5BRIsLi7uLp7Q7fSLK0kSLaeFqZvpKKiqqBD0RQWzs7tbdEK4siY24RqGQRiFNJyGIKVYhfF9d0lKw2ZZlsnn8uRzorIdh2IsttMVEYlt21iWJQzRKIIGpckSU/IUUijhOi69uhgCMgwDy7ZQ1B2J6B3egi7raZVeRkaRFCQkSkaJmDjd42dp1oBhAygo2Z2CHamrgcfKgiugyoIeHRPzVx/6qxyfOJ5uba1UKqkXTabTbkWguddLKAxDLBzZ3Nzkp37qp/gbf+NvsLGxwYc+9CF+4Rd+gdOnT+/5WnEc/17fP58DssT77mjjTIJ93TyTVOr3YvB+4PPixRdZ31gf4LInaHttctrNq58pmWVri+npaebL8wPDNgWzQL1Xz+RbR3EEsiB+vHnlTfL5/MBhATv97OH73hGLnLQmafliSq/dbrOwsCC+eJLYMRe5EfPz8yN99SAKiKQd1RhTVMH7awrjlj9ISEzkJ5A1mYnJCcIwpNvt0mg0CP2QhtXAsAxxuMQhSKBYCuVcmdXNVXphj+pWFd/3MQ2TuYk5fMUfUewtGqNLJhRJyQz9swQ1DNmg63cH3ndeE7vm+vP/GXuG4xPHgdGtrY7jpLPnCYEm8f7Dn+fbsXUmEb9QVRVJkvjxH//xO9knN4wfBn4l4+d3tHEmwb7q0ie9+FstmojjmD944Q/Y3N4cCJ8HrqWo1Ht1jk4cHbsAcrO6yXZlm4UFka8PDNtIQsstS7wiQa1dY+XGCseWjhEr8aASrTrY6hu+TkjI9vY2KuqAZHWlUkGOZCZnJjPlryWkdDlDYiwFo5Budr1Z371fwEJRlDQ/LhgFNpubdLtd6vW68KY71fz1yjq6rmPn7LR9FPohlXaFTreDrMjpBF3ezJ6Bz+LMy8iZUUrS19cVMe3nBA6yLI8cDE8vPJ35PkEQaBYXF1lcXEy9f6KXmMym969sfjsMPnnNW/XhswZnAM6dO/ehZHBGkqR/hFAN+OTdvtd9bcvtRQTD931effVVXl9/fWRLaz/yZp5Or0Pba6frnhPEcUy70abrdUdELhMuvR/6N83/Iy9idWuV+YV5QiXE0ixBKd0xOFMzBzarDhh8BBeuXkA3deySTV4XohoraysYhsGxQ8fGs+MyjCr594S1u0t+eD+7rduZ+XbSd7dMC8sULaQgCGh32mxvbNMLe+RyOTzPSzkRk/lJZF2mUCwINaCe8KgdOkRalHLok1A/6xAqGIWR+zFVk2bcJKflcCMXz/VQJXWgKApCHfc9M3ubHRn2/q7rpqF/tysiiXw+TxAE+7p4oh9RFKWvdSvF2jGDMwCJsf8Q8EHgL8TZnumONs4kuCcefhySabnFw4uwDZIzPjdL8sco3ln3bNh03S5hGLK+vs58eZ7CZPbJ2nSaTBemcTpOpnevVCpokjawOabn7yrRdr3uyGGRGLzv+7SqLfLFfNqjrXfrbG1usTSzhGKMyWt3MCzU2A8ndNKORKIr3/PF3rdxOnV5I2PoRgK365KbyDGVmxpo+xmagZNz0E1d8PBlIdU0UZwQPPyd/fPJBN1saZauNhiiS+zWGRRZwVbFhKIsyXTDLt2gm36uOX20MPjE3BM3HZK5GQzDGPD+Fy5cwHVdXn75ZWRZTnP/XC63b8MzQRAMePi3UKX/S8DfB74pjuNx3umONs4k2FeDv5mHX19f5/LlyzzyyCNcrF7E9d1MEUrYIX147XSOPYxF71wKJW7cuMHM1AxG3hg7FipLMvVeXTDy3N28MYoiNjY2sC2b0mRpQOgCRH7d8TpM56bZ6ozub3cch0ajwZGlI0LCCuFxkp5/rMcUrIKYjPNGySw5I5epMAPCMPp/1/W76Zhu2S4TxmE6GJNEAVlhted7bG5scmzxGIEiPp/+tp8lW6zX12luN1Mij2VZ6aYY0zTTWYMwCHE9l3qjThRGokVo28wWZsW4razR83u0vBaarBHG4aBuX9/BkECRlZER2DuFLMsYhsHk5CSzs7O4rpvKTnc6nTT3v9vDM/2F6f7I6Q7wc4CBmG8HeC6O44/djY0zCfY1pM8SwUhO4U6nw9NPP42mabx29rW08qvIygj7zTJ29OL7vjy1Ro1Os8PRQ0fJ2/mxeT3sjpq23bbQvJNV0Q3Y2KBUKnFo7tDYMdaYmE7QIW/mUxEKEKmI53mcPHoSJxJeOJHmThZlSEh0PKF2Y+ti7HQgUriZwxnzO1mSaXrN9HCTkMjpYrJPkQejCcdx2Nre4tD8ISI1Gq2USwqBFDBRmmCiNEEURfR6PZyOw5vVN1E1Fdu2MQxDTKLZE/SMHpMTk2LEtdOh3q5zqXqJWI3T3F9RhIJN4slTdZ8hXTxFUnhk5hEK+t3jnvfn8IZhsLCwwMKC0PlvtVpUKpV0eCZhz+1Fd/5mSDYsvVXEcXzfmJ+/5Y0zCfbdw/eH9J7n8corr1Aul3niiSeQJImu2+Vq5Sogwui8kafjdgb6xG7gIkuyUEeNYyqVCoEfMLswi6bfeqKq/1o9vyeusVVJW2TjiDzAgESWpmjkjTxXV68ShiEzMzPCU3hCj8/zvN3qPIMS0ck18kaeMArFQZLh9YFUyioLWVTZjif2xWmahhd6yLKM3/PpNXqcPnoay7BwQidV2EmkpJLwX5ZEN0FVVEzNJDedwwvF1Fu73aZT64h2nO2DDoEmSD+osDC7QMfr4Ac+Ts8RpJ8Y2rl2OiCUIGEwqpKaRjCPzLzlavYAbjY8kwh9JsMzlUqFa9euDXj/ycnJ3VbqbbxmslJtP2fu7wbuWUjfaDQ4e/Ysp06dGljxdH79fDr2CuwOquxIRicsOEmWiMKItRtrWJbF9Pw0SKKd5kUeuqqPUGRhtLreaDRwPIcTx09gGzayLN80x9Y1PQ1DXd/l2vI1ZkozQlG22RbFwG4XTdOYm5sb+INH0qgqTHIv5VwZOciubKuqKsSHhyAhjZ3P7z9cqrUqvW5PcPRjB9d1R6cJ2S2+RbHw/hFRKtmV1Ba0vMZEfgJTNtlqbtFtdvE8D9MQE3w5LSfCeVVDK2gUCgXyep7NxibNZhPP89je3maqMIWLS9Eo0vE6NNwGs7lZDhcPczdxO8MzifeP45hms3nH3r9fk/6dbvT7HtL3ej1WVlZYXl7miSeeSBVVErx2Y5Qv0N9OSwzO931c12Vufm5gGsnWxVpmQzMyjd7UTTzHI45jtre2ieJInOA7u8pLdkkQGTOQyESDCOHX1wWfXrEU5gpz4MOFlQtC635HqzyXy4nR0Z0ttVlQFIV6r05EhK3ZIhR3BfnkZmOu49pkILxnjIh+olD0/CVJrG3K3Otu5DJfZ9x8vKqqFPIFCnlBi3Vdl9ANubRySSjlWoL0o6gKQRykJCDP85idnCVyI1bXVkXdYGfa7/Hjj2d/8G8Bd9KWkyRpxPsnc/PtdptCoZDm/lnePwxDwXe4x7Jad4J99fCSJLG+vk4ul+OZZ54Z+TA6bodrlWuZz212m2J9lNej3RKTZqqqjoweJiOuSdFv2Oi9wEsr+Tk7x8TEBCsrK7u7y3r1dPR1ZBDGFLWBXk8IPfYz59q9NtVKlfsO30eohvi+n1azgyBgujhNpEdYpjVy4uf0XYJK4uE1WRP6e5KUKQ4Jo0M9CRJK7+bmJrqmMzU7hbTzf+NGY7M06QDcaLRoaqiDe+skScI0TYqlIrlSjiAI6Ha7Yp9crIIOhVwBLdYwVRNJlUCGWWs2rRN0mh16V3ucb5wfS6K5E9yNPvzw6GwinLGysgKQev9EOKNf/OKdPCkH+2jwjuNw8eJFNE3j0UcfzQxzXl97fSCc70dMLAYVqnVa3RZLS0usrg62G011cHLODYTRJ9V+27Cpt+tsbGwIWmZORBeSLJRrbVtEB0mvvu22RwQum81mWojr3w7qtB2mZqbwZR9N0sjZuZT8IiHRc3t02910EWTOzgkuu6Jm9rH9yKfltJAVsXI5oQEnoXg/rXfks4pi1tfWyefzA1Jd49ZM2Vr2tcap3ZqKOdI96S8QqqpKsSgWdE4YEzTaDVa2VnBdF9M0iZyIUBVGkejmP378cb7+xNfTarVGSDR7odCOw90m3vR7/+PHj6fef3l5OfX+nudRKpXSFdRv4bX2OjxzlTvYOgP7aPDb29scPXqURqMx9g+XFc4niKKIG2s3iNWYE0dOZMpMGbqB0xs0nn6jd7oOG5sbzM3PDbD9kh56P5ml6TQF2SYWZBtLs7h+4zpBELCwsEsIajQadLtdTh49mXrPhF6bN0Ql39RNYikWssk7E2+dbkfM42s26KShf/9nk3QTAi9I7zNv5G+6dUeOZS5cu8BkeZKcPRj9jOvxjzOILI6CIimZWnh5TchpabKWfm5BFFBzalRbVRRF4dixY/i+L2b9Gw3CMEzbfu+dE624ZIPr0aNHUwnqhEI7MTHB1NRUur9tL9hvpt2w92+1Wpw/f56vfOUr/LN/9s8wDIPnn3+eJ5988k7u45bDM3247a0zsI8Gf/jw4XSMMAttp831yvXM3yWTZnOzc+iW2CKatbllHO3UDVzajTaem61mK0kShmqMHCIJ2cZWbdbX15FlmdnZWUAYw/b2NpIkcfTw0cxQue2KLSo5OZcODiUTb7quMzkxiaEabDe206p+ks9aljWy3SWOY9puG13R8WNfkG9kNd2x3u116da7abehH+MigoTTMAxLszI5Af3pB4jDwlJFmpIUVH1XpFVFo8jyxjJxHDM7O4skSZTzZdp6G7tgp+F8Lsyx+sYq9Vw9NeiEi94/h56E0pcvX0bX9dT730yE4l5SayVJolgsYts2f/bP/lkMw+Bnf/Zn+bmf+zkeeOAB/uE//Ie3db09Ds+8JbxtTLs31t/I9CjJMocTR07gS+KL5Ic+sioPqKPYhp1JlY2iiM3NTUq5ErMLs+iqnsmS0xQtk6jTc3tc37zO0uwSkSrEKsIwZHNzk1wuJ/7AO4XCLNi6Ta1XQ5OF7nzLbaXv09RMnNAZmAnv9QSTrdvoEihBGvr3ezRTN/Fcb8CAu90uvUaPw4cOo+qC7+BFXjpNOO5Lb2riWsPIYrrJO5vIkoUeXuilLdKsOsOVlSvIqqC+ZkV1siyTy+X44P0f5JGZR2i322xvb/PaayLSS8ZObdsmjuNUTViSpHR985tvvonneakA5bAE1dvFpVcUBV3Xeeihh/i3//bf3o3LjhuegTvcOgNvI9Pu9fXX0/VOCarVKq7rsrS0hG0NGpUbuMiyGNcM4zBT0TQIAtbX1ykWi8zOzorZ+iAe4d7LkkzH6yCrg18MxxHKOCcOn8CNXWzdpuf0WF9f39U2RxqbS8OuofmRj+/66IouOOU7Ulf9bTVJklLWW97IU22LRRAbGxtCIsq2yefydJRBz9tsNmm325w6eop20Ia+QEeRFXKaEOdIFjj2r4cCwXsf1qaXJImCLoZ2gkiMIduanVnhH667RHFEu9JG0YVMdIJhGa2clsNQDB6cenBAVirZKLO9vc3q6iqtVivtiycGret6uoACRGqVSFBZlpV6/7fT4O906wzc9vDMHW2dgbdpeKbn9UR1PhbtoVavxcbGRtobBTKNyos8dE3H870Rr50Y68zMjDCSnZZYGIViQ4xZSg+QnJHDD/2B9cOtVotGoyFGWDUV13OpNCo0ag1OHj6JG4ui1c32rauyOiKe6YVeyrSLiQcMMIGmaHS8Thr6T0xMEEZi3NVtuaxvrmOagsbqOA5BEDA/P4+fsRInjEIkSco01KI5OuoK40Ursyr5pmYOhP5hJFR9ZsuzqNbg18nSLHzXJ6/nBU3Z73B66nTmYa1p2kBfvNFosL29zfXr11EUJa2MG4YhZL4mJiiVSmKYx3Go1Wq89tprtNttrly5wvT0dBod7Df6Df5WirV3YXjmjrfOwD57eFmWM8P2NzfeTL1Eu9dma32LQrGQtjRyZi6TaSZJEh2nQzlfHqDSJpX0ZCw2Z+QGdr3HxDSchvjCO02QBgtUlUoF3/dZWFjA0Ay6Xjf1ovPz87ixS8EsCAaglD1aCyLNGDcVpyoqTbcpPL5m0vE6aQXe0kYVZxVZoZAvYOs2OT9Hr9ejUqmkW2BiL6YTd0ZaWTJy5qw6MZlzChJS5korUzUzD11D2WUmBqGIqOan51HN0fuAnTHevgPy8bnHMz6doXvakexOooVkoUQyEZcoyiYGbZpmelgkoqk3btzg/Pnz5PP51PvfLoNur0jINt1u960uobjl8Mxb2ToD+2zw4/DGutiGm+TrR5aOCMJGKHLqLKEJIKXXRkRiTZTTYWt7iyAIBsZixw3RJHrx1WoVK7bS4ZkkXATxRV/bXCOKogGabMtpiVA5QxoqQdYGlwQJXyDx+LIkUzJLuIE7tldu60IXLtFRS1pfnucR+iHbtW3iOE7TAl3XRaU/gzgzblAnp+cyDwhd0UeKorK0y0pMZhGmpqaYKk6lwpe2KgqLEtJI1DBrz7KQX+B2MbxQol6vs7W1xeXLlzEMIyXFdLvdAQmqRDa6Vqtx5oxYTFoul5menn7L/PkstNvtgZTmDnDL4RnewtaZ5An7gnEfphd4XN66TK1WS5cvxnKMoRmpxxu3okmSpVTXzvVdmpUmmqYxPT2dPkZXdbru+Bw7JsbWbaIw4saNG5RKpfRUjqKIK8tXUHTRDx5+D6qq0nAaFEwhSdWfrtjG+D551vaaZKlkwSggI6ec/f7DSpGVtC6RrFACyFt5PMNjMb+YKt3U63U8z6OdbyMbMpZlDeSy41p7I4sidx6bdTgkrD3Xc9nc3GR2ZhbLFDz9olHEDdz0M8jrowSUx+buaFnK4L0Nacp1u122t7c5d+4c7XZb7AfsI8Akh2FyWPQz6O7W9Fy/Yu2hQ4fu+Dp7GZ55K1tn4B55+H5+8Rtrb7C8uoyqqgP97Y4rljhGUTR+X/pOK6jjdFKByNnJWTEiupMiWPqoJn0//NCn2WsSuAGHFg4RyeJ5QRDQqDQwC+ZYtlTi8VqO0K9LUwTGGxSIsHYs440orUdI7PTdd/a9VdtV1jfWR9ZhWZqVVtr7lW4s1UoLf7WaYCbats1EYSKbUDMmbB9Hx/Uij57To7Jd4cjCEQp2AU3SqDiVgXRBU7SRqEGRlD2LXNwOkpamJEm8//3vp90WjMM333yTXC43wuKbnp5menpaaBK2WtRqtbRO8FZJP+9qpl2CJI9Pcpzf/uJvk8vlMosbicz0OIOXJVlUsasb6Wrojtchp+dwPCedkx8HUzXZqG7Q7XWZKE2AKvatbdW32N7e5sTh3VbgMCzNGjDaIAxohmLpZEw8Vk0n6QhkQVf1gd/FxGnRT4s1GpUGp46cQlKlVPhCJtv7Aumap6RP7Xme8P7bdVp+Swy75HLoup5yEbKGcbKEQm3Vptvp4tQdjiwdIZIimm5TEImGH6uNqvGcmjqV+di3io2NDa5fv84TTzwhZhgsS2gR7EhGb21t8dprrxFFUVr4S6SwEtLPkSNHRkg/e1XN7e9Y3Ert5p2AfQ/pkxHZZrPJa6+/hmd6FJTxlcyG0xiYlksRCzGHrdrWyE64xOiJGWsMAM2GKMRNlCbo9sSEW7PZJHRClhaXhIjFmBQ9UaQdRhKZFIwCHb8zUj/IVKDZgaVZI9JVIDoGvW6P8kyZXtwDf0fzXbMxVRM3dFNZ6KS7lqi/9kPXdSFlrShp4S8J/W3Lxsk76IY+EPrbqo0TOtiajSaL8VY/8um0O9yo3mB+fj6dAjS17AghK5p5bPath/PDWF9fZ3l5mccff3ykICdJUmrQSduvUqmkbb9kIKZUKo0l/dRqNa5cuXJT0k+/+MU7fc0U3AMPrygKV69epV6vM318GurjH5uQaZq95kCPPo5iNjc3MRUTs2Bmnrgdr8NUbgo3dEeMLiHj5Kwcc3NzQoNMU6lWhWqrbdvo6KKGkLVXXcoeY03gRz49v4ciKZTMktCa32lpjd1fl9HPj4nFUgZf6PEPEEriKF3OmDxPlmUMRaxmNlUTN3LTGYQwComIMBWTTtDB0AxMzaRcFLmvFEjUW3Ua9UY6lGTlLHRFTCf231ur0aLb67IwvzBwT/0V+wTDSj0gGHgnJk6M/fzuBDdu3GBtbY0nnnhiT/m3pmkjAzHb29ssLy+nUljlcll0QPpIP8eOHUv588nmmYT0MzExMTAhdy92w79V7KvBJyojiqLw1FNP8Ttnb15MTPqzMTEtpyWWMrZrgkxTKGKpFi7jK+Ftr42qCOWXJKdMil5zU3MolgKxMLZWq4VlWyxNLuF5Hh2nQ71Vx1ANJF0il8ulX6SckRtQiO1Hf0EujHe15vNGHj/0xxYgh/v5CXUX4Pih45mV8+GcO4qjdGmGG7mZ0YKqqGLKbihysQwLS7awSha+79PsNtmsbxLFu9JVhmFQq9XQY53ZudmBvFaSpLGt0wSKpJDTczw2+9hdrYivrKywubnJ448/fkfjqP0DMSdPnkyFMK9evUq32005/EnbL9E6SOjCCUcgGQ4LgiBdrXanm2MlSfrnCH35CNgEfminWDf8uB8E/vHOP/9FHMcfv63XGSfZ3IdbPiDzSXHMF7/4RaIo4vTp00xMTPCrz/9q2pLLgqEbuP6uQXuuR6PSID+ZJ2flaDTFCGuhOPqh9ufYmqKhSRrVVjXVqJ+dnKXREwMcGxsbFIvF9I+jKmqqQhMEAZIvsVkTX37Lspgtz+JL/gg7DRCRyJiQfdKaJCSk7bVH2Gn9k2lRLNqDpmkyXZ4WXjqjep7ozA0jp+UyF0AaipE57jpu533JKFHtif1vnU6HXq+HLMscmjsE+mBhsqAXRghIifdXZRVTMWn7Yvrwbz3xt5i2p7kbuH79OpVKhUcffXRfZs+Ttt/29jbVanUgnNd1Pf07JnWpZHLu53/+5/nCF77Ad3/3d/Md3/EdvP/9778txp8kSaU4jps7//3jwENxHH9s6DFl4AXgKYRdvgg8Gcdxba+vs685/BNPPMGlS5dSPv23PfZtVNoVttujQz6WPvglTJhviwuL5Myc0KVvZi+RAHZXRCGKTo1Og26zy9zcHKqi0vWFUsvm1ibT09OphDOIfndSbVdVFVQ4WTqJ53u0O21WN1fxfA/LtLBzdjrjfjOarYREy2ulSzFLZoleILa+9A+wBGGQbtop5AuZyxxgfKsMbs6bd90MUVBZp8eowXuRJ/L9XC71VuVimWqrSm+7J7TqbJucnSPWR/8OBb0gaLZeO402FvOLd83Yr169SqPR4LHHHts3+uxw2y/RQkjUcJPfJc4iCAIKhQL//t//e77pm76Jb/iGb+BTn/oU73//+2/rdRNj30GObEf7F4Hfj+O4CiBJ0u8Dfwn4pb2+zr6G9LquD9BrLd3iu5/5bv7fL/y/I1VtXdVTg0+Yb4uLi8iyTBAFGJohZK6C7Pn5lCQS73Ly5+bmKBgFYmI26hvU63Xm5+ZHCjxZVemu10WRFQ7NHMLMibyu5/TSHe+6pjNbniWIswUM+xVlkrVSEhIFo4CmaFR71VRRtr8YlBWWg8iNsyi9w8KVCcYdRuOYeEmElJCRLMsS9FWjhKSJyCYR+aht19gIxWOK+SKThUnCKBRyX0OsvbvVirt8+TLtdptHHnnknnLlLcvi8OHDHD58mDAMqVZF1HjhwgVUVcVxHA4dOsTZs2dZW1vj2Wef5Tu/886G3CRJ+ingo0AD+HMZD8naOrN0O69xT4p2/QSVcq7Mh5/+MJ947hMpsw52ljTufNkMwxhYuRtEAY4vZtSzZrOT6CCOYjY2N1BVlbn5OYgRubcn1j0tLCyMsPiyxmQThFFIy2+RM3IEUSCGXSzBifdcD9/zxdLLnSkw27bTwyQrZ42JabktdFUn9EM6lQ5HFo+kXICEWZeFcQo1tmZnHgTjOP/j6hG6IrT/kuGjQqEwElVomiZy31mx56/T7bDV2GJtc42CVUCztAHCjyzJPDT9UOZ97xVxHHPp0iUcx+GRRx55W/XiFEVJK/nVapXz588zNzfHD/zAD3Dt2jU+8pGP8Oqrr/Lkk09mPn/c4MxP/dRP8aEPfYg4jv8R8I8kSfqHwI8B/7+7/R723eCzRmQPlw/z7GPP8umvfpo4jjFUg2anmWrGDVc6c7rwlhHRwIhsAk3WaAWttLiXL+QR+xFFIUxVVU4dP0XH7YwYjqmZYymxli68nouLjEzJKonQPxaEjyiOOFQ4lHq+rS2xrTafy+PlPDRdG8n7bd1ms76ZRhuRHGFqJrqii33uGQZvqMbYcH5cF2AcBXhYzBJ2dfvX1taYnJxMW0s5bTeqUGQhPZ0IhLihCzqi502MERus1dao1Wpp6P/I4iOiXXqHiOOYCxcuEAQBDz/88DtGHLJer/Pmm2/y5JNPpinrb//2b7O8vMyLL7441uBvMjgzjE8iZKiHDX4V+LN9/z4EfP527n3finYg8pvl5WVc1+X48eMjv//jC3/M589/HjVSuXLjilgimSHiX7JFX77X6+H2XBbmFga8chzGrNxYYXp6WiyhjMUXe3NjU0xx2eJcszVbjK0Gu0ZiaNnLL9LXHZp7N1UzHdPN8qBhGKKEChv1jcy8P+yFbDQ2mJubG4g2ZEkGWRiWqYi1To7v4EXe2GKdrWVHBJqiZR4EhmJkHm6mZHJx5eJAaiEhMWFOpAsvkvTA1rN1CHRVT9OR5AB8X/59HDIOMTU1xfT0dDrdthfEccwbb4gC7+nTp98xxt5oNHj99dd5/PHHuX79Oh/96Ef55Cc/+ZaXR0qSdCqO4ws7//23EQM03zn0mDKiUJds7ngJUbTLVpnJwD3x8J1Otnf6xvu+kTevvMmry6+OkGn6kXzBZFnGD32CKEgrzYEbsLa1JopzO2003/fZ3NykPFVmcnIyDWG7fhdN0dIv7TB7buR1M3LgpFZQtsvoij6ScyuKWNw4Z8yN5P0SEoqqMDc7N5JaJHrzURgN1BQM1RDKKkZRSHKHO8swpGzRCshmuiXXGjZ41xMtqaOLR8lZgrwUxAFhHFJzRou/qjT6msMHj6ZpzE7N8qGnPwSRqKmsrq7y+uuvUywWmZ6eZmpqamz/PI5jXn/9dVRV5f7773/HGftjjz3G2toaH/3oR/n4xz9+NzbFAvxLSZJOI9py14CPAUiS9BTwsTiOfzSO4+pO++75nef8s9sxdrgHHn5zc5OtrS0eeOCBgd9FUcS5c+eI4oiz3bNc3r6ceY1+qSbP86jVasK4ZZVWvYXv+diTdrpModftUa1W02hBVuRMTfaCKUQgxinX3GwNVGI4MjIFs0Dbaw+Mug4fIlEsiD8Fo4Abu3R7YqorZ+ewczaaqo0dCc7r+ZEimyyJYRtFUlJjiInFookoRFVUgjgYSSc0RSOKhexWRCQWTG5tU5wpjhQys9ZEy5LYBDv8eRaN4siE3uNzj/NX7vsrAz9LCC9bW1upuOf09DQzMzNpZBHHMefOncM0TU6ePPmOMfZWq8W5c+d47LHH2Nra4ru/+7v5hV/4BZ5+evzW29vEPXmj+y5TnZXDJ8v+5ufnOXLkCA9FD/GJ5z7BSm1l5BqaqqU71RLxyTiOWV1bxdAMlhaWUg/ebDTpdDqiOKco2LqdabQxMc1ek8ncZDpyO4xxI7pASm+NEBNviqRQNIXq7fCgTCIQkc/nmZ2ZpeN1KFPGD3y6HZH3y7GMYRvYOTvluaefYcaK6SiOUCQlewxWz27r5Y38gCxVpyvGRk8dOUU3yhAbyegW9Of0CbLahZqsZVbn+wkv9913X9ryev311/F9n8nJSZrNJpOTk5w8eXLk+W8XWq0WZ8+e5bHHHqNarfKRj3yEn//5n7+bxn7PcM+r9MkGmtOnT6djrZqi8ZGnP8J/ee6/sNHcGHh+/0x2Igx548YNYUDlWTq+4NFfW7tGHMcDE3g3a9/YhtCeM1VTyEr31QTGscjSexoaOAnjkKbTRFM0ZFlODxE/8NlY32CyPMlkYXLgmpqqpV/+vJ5nvb6e8twTYcucne31k9fMwripvf5DpNVupYIhoTR6HUvNTnWyCoH97UJLtdJZ+CPFI5n3MfA6fS0vz/N4+eWXiWNBo+71eszMzNw09L8XaLfbnD17lkcffZRms8mHP/xhfvZnf/a2++zvFNxTg19bW+PKlSs8/vjjI0MGlm7xvc98L7/4pV+k1q2lP+s3RN/3cRyH+fl5TNNMp80ur11mujSNYilpGCtJN9eeUxUVfHGgyMgD3P1xW1lgfKEMRGur3qujyApGbLC+sc70zLRYy9Q30joMHz8d9OgXtnQaDr7ipzPdSY1DV/TMyGUcOUeWdyf2EpnthYUF8no+870kfPqBz0tWx2+61XJISOln9szSM7cViodhyLlz51hYWODw4cMDoX+iVz8zM8P09PRb0n2/XXQ6HV599VUeeeQROp0O3/md38nP/MzP8E3f9E337B7uNu5JSO/7PhcuXKDZbPLMM8+MPbHzZp7v+7rv4+Nf+jgtpzVAxknUcVRVTWfDO04nbSWp9s58+h6MlnhQZCMiouk0yRv5my6WhJ0UY8xK9ySiaLab1Ko15ufnmcwJUsq4nXDDvfd+YUtLs6h36nQ6HdbX14UEdi7H/OR85rWyQm4QdYCG26BWreH7frqGKmsCUELKNOxhQUtZlilqRdzIHeExPDz9cPYHlIEwDHnllVeYnZ1NxSP6Q3/Y1St84403cF03rfpPTEzsW47f7XY5c+YMjzzyCI7j8J3f+Z389E//NN/8zd+8L693r7CvRbsoiuh0OvzRH/0Rhw8f5tSpU3v6A200N/gvz/0XQBhmoo4zOzvL2tqamO4yc9Tb9ZFWXskULbyCVRg78HKzLS66uqMym8WPj0UFOouZJyMjKRK1eo12uy3abjseORGw1GSR3/c/fxwXP6uinrS7Alfshbdte2C+PadnpwC2bnNtTaz0mp6eRkJClkTqMbysIq9nb5+xNZte0Eu9ecfvkNdG5bQmrUn+tyf/t9HPLgNBEPDKK6+wsLDA4uLinp4ThiGVSoXt7W0ajQaFQiGt+t8tzbper8crr7zCww8/TBAEfPu3fzv/+B//Y77t277trlx/DO5J0W5fDb7dbvPCCy/ged5th0ErtRU++eVPsnxjd3wxQa1WI3IinNjZ5Xf3TbcVjAK9oDdW265kjfbXExQMMRRSMAo4vjPQz84Z43PqvJHn+vp1PN9jdnZ2IJcefr2cnkORFHEtOTsfv1XvPQzDdMjF8zwKdgHDNjBNc+BQ1WSNlfUVNE1jsjyZpjzJ+xx5/0NDMRISOU2s0ep4g/P+OW20k/H+w+/nzx3NYoUOwvd9Xn75ZQ4fPjzAqrwdJJtfkqq/oihp1f9OQ//E2B966CHiOOY7vuM7+D/+j/+Db//2b7+j690G/vRX6VVV5aGHHuLcuXO3/dwZe4aH9Ido5Boplx1IlzUeO3os3Srb6XTY3NwkjmNyuRzGpIGhGxBkCFrG2f31FDsfe8ttCeVYs0Cr1wJpfOU+jmPWN9aJ4kisjO7722Vx2pNDo2AUiKRIaNeFgeAbSOIex/EDNEWkFIqiDOT9WqSxXltnu7KNrukp2adVb2GaZhoe734Mo+e4LIsaQKKlH8di9l6RlZEDUlO0zNB/L1TaxNiPHj2aiofeCZLNL8VikZMnT+I4Dtvb22noXy6XmZmZGVlWMQ6O4/DKK6/w4INCN/+7vuu7+Imf+Il7Yez3DPvq4eM4xvM8vvSlL91WVbPVanHmzBlOnTpFhw6/9MIv4Xgij9N1nbmZuUwmWRiGguTjC4XaUr5EsVgkkqLU690snJdlOd2T3o+EUupF3og3DsOQ7a1tLNuiWCoyjJu9XsEc9KaJkIUma7iRixM4Ay1DCaH1nxW5mKqJEziC5+95tNttmo2mmHvPW2m/HwbHgWVkTNVEVVQ0WWjRDTMPk+Wc/ejX80swk5vhbz7xNzPfa4KkGn/8+PFUXWY/0D/o0mg0yOfzadU/K/R3XZevfvWrPPDAA2iaxoc//GF++Id/mB/4gR/Yt3scwp9+D38n2Nzc5OLFizz66KOYpskEE3zHo9/B//PZ/yfVwksWHAxDURSKhSKqqpIP8nS7XTa3N5EjmVgV3r9ojhplgnFFr67fJW/kyWm5AWXZIAjY2Njg0OyhzHFRuLnk9nB6EEQBba9N0Symh4Su6CnPXpEU3NBFlQSxJjFaW90t/ElIKLJCr9fj2NIxQiXE6Tm0Ki2IxfLGXD6HZEgEYYAb7ijN+uJwGjbs/msP3Gs4eug8OPVg5ntNkPAv7rvvvoEUbT/QP+jSH/pfv34dWZbTqn8ul0vv6/Tp0xiGwYc//GG+//u//14a+z3DPTF4SZJuuQIojmOuXr3K9vY2733ve1EUwSJrNptsXtnkb/2Fv8X/eON/4AZupjhEgkQuWpblgZBXjVTWKmvUa3UUTUmn2waKiDc5YyVJouk0Uz35SqvC2vqayBcLdrrpph8y46Wxckb24TKcAiQ69iDy6+FDQlVUDNUgREQevu9T2aywMLvAZGGSptcUtY0Cad6/Vlmj0WsM8Px1Rc+81ySF6IehGpmPfWhmfDjvOA4vv/wyp06dSmfN7xWGQ3/XddMR116vJ2oghQKyLPN93/d9fNd3fRc//MM//JZf94d/+If57//9vzM7O8vZs2dHfh/HMT/xEz/Bb//2b3Pp0qUzCJWbl97yC98E+zpYnBjTzZZKwi7NttPppLJFkiSxsbHB+fPnefzxx3nwyIN879PfO7YSnSCLXy5JEqEScv/R+zl0+BCFQoFer8fKygobGxu026IqnWW0MGi4URyxXltna2uLU0dPUcgVxj7PNu3M6TQYP82WjOKO3MOYHnsQBmkY3uw2ubZ6jYnpCVRdTRVnEiiKQrlYpjhV5NDSIeycUKJdWV2htl2j1WoNrgaLyZ7eU0YHnBYKC0xZ2V671+vx8ssv88ADD9xzY8+CYRgcOnSIhx9+GFmWOXz4MJ/97Gd58sknBQW6UKDbvUmdZ4/4oR/6IX7nd8bLun32s5/lwoULXLhwAeBvAj//ll/0Fth3JYGb7ZgDkdO98MIL5PP5Ab79lStXWFtb48knn0x51osTi3zkqY9gaePXBY/rdyeQJZnpktAmP3ToEBMTE4Kjv1FjdW1VqNgO3att7Bpuq92iWq0yMzdDL+qR1/IUzWImwy1LEgt2SCxjDq1xz8nr+UwKcE4Xe/J6To+tzS3m5+YxdIOcln1wmJrgMCSz/cnnUJwo4vs+6+vr3Lhxg0ajgS7pmS3IftqtrdkUjMLYcL7b7aaFsLe4leWuwvd9vvrVr3LixAkOHz7Ma6+9xt/5O3+H//yf/zNXrlwZO/B1O/jABz5w0wPuM5/5DB/96EcTyvhzwIQkSbe/muc2cE9C+nEG3263OXPmDPfddx/lcpk4Foqrr7/+Orqu8/jjj4/07edL83z0fR/ll1/4Zeq9+sDvbjX9lvTA/dAXs+29JoZhYBgGBbNAtV1NSS4AuVyOXC6HZEqpoqzjOAMrqLzYo+N2UGV1V6o6DAbYbcOwdTuTB38zzflxAhiKrKS8+Pn5+bQ1mcXBh+z5eUMx8CQv/XIGQUCn02F7a5um10xpvrqhY2kWru9S0AuEcZhGPqenTo9ct9PpcObMGd7znvfcsbjjfiAIAl5++WWOHTvG5OQkP/IjP8JTTz3F3/t7fw9Jknj88cfvyX2srq5y+PDh/h8lCjZr+/Wa98Tgs0L6JId65JFHUmlg3/c5c+YMCwsLN13ZM5Wb4ge/7gf55Rd+mY3WLve+X9duGMOHQaPXwNZtgjAgiAM6bgdN09IlhkEQ0O12qWxX2NreIoojoaQzN5d682TrK4iCW8NpIEsyRbOIIimZ46XACNklwbi8fhytVZIk1qvr1Bv1ATWfccsvTNXMVPcZ3hmvqioTpQmxejrI0ev2qDfqRH7ETH4GTGjGzfQwnsvNUbYGPVm73U5pqe8k6ebE2I8cOcLU1BQf+9jHeOihh/g//8//8x0zmbefuCfiYP0ePinOXblyhSeeeCIlinQ6nTTE2st+rryR5wfe9wMcLR9NfzZODw7EYTCMrtcljEMmrImRQqCqqqLIc/QkSKQtrdXVVSqVCo7jZKYWUSxoun7kY2kWRbM4UFfQ1eziGIzP65O24DD8jk+j2RiR7srpucyIoH89dj+yPjdbtwmiAE3RWJxa5OShkywsLiBZQs9+ZVXUP1rtFvdNDK5Eazab6cDJO8nYwzDk5Zdf5tChQ0xPT/O3//bf5vDhw/zTf/pP3xZjX1paYnm5X6KOQwhVm33Dvnv4hE8fhmEarkdRxBNPPJH+fnt7m0uXLvHII4/c1uYOQzX4nqe+h8+c+QyXti6N1aaD8ZtdE/HFvJHHC7yBL38QBKxurVLIF9KQNIojer0ezVaTdr2dtvuS/WawE2b7HTEI4/fSnXFJyyxriCZl3mVgOBePialVa+joKS9+LxjeBgvCu2dtiTUUA1kXKUZC/TVVE0dymJ6eTvv9nU4Hf9XnxeqL6Q68y5cv8+ijj97TQZdbITH2paUlZmdn+bt/9+9SLpf56Z/+6bfNsz/77LP83M/9HB/5yEeQZfnrgEYcx/sWzsM+E29AFEcuXbqEoihsbGwwPT2dTkQBLC8vs729zSOPPIKuj3rhvSCOYz5/4fN86cqXMn+vKzpelO39E+GMKI6QJVmIPzqtdEPqzMyMkM0agqZoeKGH4zhCw93poes6uVyOucm5sbm4rYtWYMqu8wW7LktEIr33vkMo0emTJZnpmekRz5/UFoY9/Lh+esko0fAaWKqVCmT0L9boR5YoRkK2cRyH69evs7KyIrT8Z2eZmZmhUCi87aFyMqCTbJ75B//gHyBJEv/m3/ybfVXA/Z7v+R4+//nPs729zdzcHP/X//V/4fuihvKxj32MOI75sR/7MX7nd36Hy5cvnwX+ehzHL+zbDXGPDP6NN95gdXWVBx98kKmpqVTEItEse+CBB+7KB//Vla/yu6//7qgiizl+WUT/QogEUihagkuLS2OXSxatQaZZomTb6XSIvRhP8kTRz86lQzRZAzGqrKYilk7ojLDr+o0smRXXdI2j80cz8/1xB0fJ2OXmK7IiVlQpakrjHZgZyODIp/c/RMz5M0f+DB848oF0FVPSVq1UKmxtbaU702dnZ5mcnLynEtMgWr6vvPIKMzMzLC4u8k/+yT+h3W7z7//9v7/n93ILfG0w7ZIFfvPz82klPgxDzpw5w/T0NEeOHLlrHuCJQ08wnZvm117+tQGhxXFiETAqktHpdKjVayzMLVDMFak79cxeepZslmEYWKZFLMW4nisq/hvrYgAll2NharTjEkQBTuCke+NARA/97Lq8nicIA5ZvLGNaJqViiVjKPocjhISVpmhCjEKSkJHT6CVJW7pRF5PRcB6yRTQS6u4wHph6gEqlwsWLF3niiSfSycXEm0ZRRK1WY2trK13hnLDc7tZ02zhEUZR+z5aWlvjn//yfU61W+U//6T+904z9nmHfPfzq6qpgd62tcfLkSVRV5ezZs5w8eXLfuNSNXoNfe/nXWG+uo8rq2Kr4MDe90WjQ6XZSzbz+8da2004Le6qijp3Ey4omgkBouAeOMO5kui+RsR4XgSQRQRiFu3rx+QK6ooM0WuRL0oTh92tr2Xz+rGggqTMMv7+scH7KnuI7Dn9HKmpyq5QsjuN0f3sy3ZbQX4e3sr5VRFHEq6++yuTkJIcPH+Zf/at/xaVLl/j4xz/+tiro3AR/+sdjQbCJklHDS5cu0e12U7GD/RQw8EOf/372v7PSWBkZ8kiQbKuNialUKkRhxMzMzO5seV9Ym87JO82RcL4feTOf7njvR7/xdjtdcQD4AZZtMTs5SyCPik4WzSLVTpX1DaHXn7NFQXNc2L6XcH7gnjIkuseF81ke/qH8QxzyD2Wua94LEmGLra0tfN9PR1vfat4fRRFnz56lWCxy9OhR/u//+//m5Zdf5r/+1/+671HFW8DXhsH/zM/8DJ/61KeIogjf9/nUpz6FYRjpFNPExARzc3NMTEzsS5j15atf5nMXP5cZlpesErVeja3NrZF58XEz86Ym5KpqvdEeuyzJxFKc2ULL8uJRHOE5Hs1OE9d1MU1TVPx3NOwVFK6tXmNqempgF964QyVrVTPxoGZ8Aku16IWjXY0sT56Vu/s9n6+zvo5v/rpvviseMwgCtre333Len6je5nI5jh07xr/7d/+OL37xi/zqr/7qHReF7xG+Ngwe4O///b/Pa6+9xvve9z4++9nPYhgGzz77LM8++yy6rrO5KTaxlEolZmdnKZfLd9X41xprfPrVT6daeQkUWWF5dZl8Pk+xODhFp6nZyjaKohBFEbqqY6jGQKh/s+LgsD5fguQ5cRwPVPxt3abttpmbnUslvWB8FV6RFaI4Ggnzx26KzRDY2Es4n9NztJotOq0O/+Lb/sW+hMfJBtfNzU1qtdqe8/44jnnttdcwTZMTJ07wC7/wC/ze7/0ev/Ebv5G54OQdhq8dg3/ppZd44oknUpnpa9eu8eu//uv85m/+JgDf9m3fxoc+9CFyuRxbW1tUq1UKhQJzc3OUy+W7shbYDVx+5/Xf4eyamFpSUbm6epXJ8m6onOBmM+zDRq0pGrZm0/baWLqV6XmzPGyCLBWdXq9Hr94j1ENcxxWSXjmhYT9pT2YeKuMOmyyPDdn993HhvK3aqIqKF3psVjfpdDp88IkP8pfu+0uZ7+luIsn7t7a22N7eHpv3J8srdF3n5MmTfPzjH+czn/kMn/nMZwYOzHcwvnYMfuyF45gbN27w67/+63z605/GcRw++MEP8uyzzzI1NZUWd/L5PLOzs0xPT79l4z9z4wyfefkz1Ct17Ekb0xj9MtxMAiurjQfC8C3NIoiDkVVM43L+LK+c8OKPHz6OH4sIw/M8Ot0O3U6XnC64/Tk7N+Bdx91X1nYcUzUzh4yGc31LszBkg07QESu4Gw26PbGC+6OPfpSjpaMj19hvDOf9U1NTzMzMsLq6mm6q+eQnP8mv/Mqv8Fu/9Vt3jfzzO7/zO/zET/wEYRjyoz/6o/zkT/7kwO9/8Rd/kb/39/4eS0timeuP/diP8aM/+qO38xJf+wY/8CI7PeZPf/rT/MZv/Ab1ep2//Jf/Ms8++ywLCwvpCd9P6riTcHJ7e5sXz73IqrbKWieb1DRu39y4cBoG82pdEUMmTujg+u7YiGHYK7daLZqtJkeXjhJkSOPKsowf+LTbbTrdDnEUY+dsSvkSiqaMhPNjyTZZenmxOLRkSU4LdG7ophFCvV7HcR3mZuewdZu/88zfGauBf6+Q5P2XLl3C930+97nPAfDcc8/x2c9+9q7ResMw5NSpU/z+7/8+hw4d4umnn+aXfumXeOih3fn/X/zFX+SFF17g537u5+70Ze6Jwb9jmpGSJDE3N8fHPvYxfu/3fo/PfvazHD58OFUL/ZVf+RUKhQInTpyg2+3y4osv8tWvfpUbN26k7KVbYWVlhStXrvDnvv7P8de//q/zrae/dYRjf7Plkjktm6MOg2OtXujRcBpiqswooCoqtjbqafqvVW/UaXfaLCwsYBvZXikRkyyVSiwuLIrpOEWlXW+zvLJMtVrFcZy0aKgp2fnuAPknFgW8slVGUzTc0KXhNtLHhHFIrVbD9VzmZv//7Z17cFTl+cc/Z7O5sLlvNlkCyD2BALmgoNJWBwVKBZKNHQjaFqiEDmWqI1Zbsc4g/hyqtmn7KwPKrwVHnFYjSQiEglhkEIoiKGQIctMQEiCX3U2ybG5kk919f38se5qQ3RAh2WzI+cxkMCev57xcvnve93mf7/PokSSJ8dHj+13s4IqnNDQ0EBMTw0MPPYRGo2H37t1YLBZWrFjRbQ2G78Lx48cZP348Y8eOJSgoiCeeeIJdu3b1yr19jV8eSALExMSQnZ1NdnY2165do6ioiPXr11NRUcGcOXN4/PHHGTduHGazmeLiYtRqNXFxccTFxXWJxrqPBJubm+VqOgDTR04nMS6Rfef3UWouBTxHpOX7eEl26bbphYS8nFer1LLhxu60y8k2lnoL7fZ2hupdefE9bQEdEBBAeHg4YbowwlpdJb2sVqvcvcYR4UAVqOp0xBWsDkYIQUSQqzllq72V6+3XPVa8CZACuGK8gt1uJy4uTv5QS4xJ9Px79SHuv1O73U5SUhJ79+5l//79HD16lKioKEpLS3stoHizjXXEiBEcO3asy7iCggIOHz5MYmIif/nLX262vvoF/f8x3QOioqJYunQpO3fu5NChQ0ydOpU///nPzJkzh61btyJJEhMmTJDrnJ84cUJuU+2upuNwOEhJSekSA4gMiWRx2mIykzNlu6wnJEnyas7RBHmvbNPxLW532mm0NdJoa3RlwgWoabG0EBoQyrgR4+QUW0/PUUmey2WpVKpOJb30ej0jRoxAG6HF0mChtrqWZkszqnYVGrWGIQFDXNVxbA00tTXJEfmbbcVCCFquteCwO1y5CTfEHhgQyLio/u/7VlZWhs1mIykpif3795OTk8Pu3buJjo5GkiQSEhJ8Op/09HTKy8spKSlhzpw5LFu2zKfP7yl+s4e/HVpaWvjoo48oKCjg9OnTzJw5E4PBQGpqKnV1ddTU1NDc3Ex0dDQTJky4ZTbX9fbrHL54mOKq4i4C7q4mvbcIeXcZeZogDZeuXiIwKND1j9R9/h8SSaujFbVK7WoWceMzOUAK6LKvFwg5KCdJkqvirnAihCBIHcS11ms4hRObzZXme/36dcKCwwgY4qrl77bU3nwmL4QrEUmj1jAkakin7UqCNoGsSVnd/jn2Ne6KNJMnT+bgwYO8+uqr7Nmz545KXnfH0aNHWbduHR9//DEAr7/+OgAvvfSSx/EOhwOtVovV6jnw64XBFbS7U1pbW9m/fz95eXnyMeDJkyd55513iI6Oxmg04nS6Muni4uK6jd7Wt9RzsPQg503n5WvdZdd5O7P31uzBHaAMGdK1Xry3AJ+3KPzNjSPceDp2C5QCabY109zcTEuLa1WgCdUwTDuMZkezPDf38Zfb6NSR+QnzSdOndf1D8BHl5eU0NDSQnJzMf/7zH15++WX27Nlz280seoLdbicxMZEDBw4wfPhwpk+fzvvvv8/kyf9tqVVdXU18vMsrUVhYyJtvvskXX3zxXR6jCP52KS4uZuHChaSlpXHhwgUeeOABMjMzmTFjBhaLBZPJJKdy6vV6rx78Smsln3z7CVevXfUq6u7O7D2J1OFw0FDXgDpUTXhY57JP3lYEkuRqDXWzCUi29jo7BxI9ufIAIoI6p962211NPBytrt53Go0Gm81GUFAQI4eO7DJ3laTi2fuf9RiA9AUVFRVcu3aN5ORkjh07xgsvvMC//vUv+SisL9m7dy+rV6/G4XCwfPlyXn75ZdauXcu0adPIyMjgpZdeoqioCLVajVar5e233+5Uo7EHKIK/XXbu3MnEiROZOHEidrudQ4cOkZeXx5EjR7j33nvJzMzkoYceoqGhAaPRSGtrq/zmDwsL65LHfcF0gSPlR6hprOnyLG9n9h199m7sdjs1xhpGDh2JU9012u9ta+AxZdZ93cM2w9t9PPWNc9cKsNvt1NTUyOXE4yLjEEGCoOAgeUk/KmoUP5vysy739QVXrlyhrq6OlJQUTpw4wbPPPktRUREjR966LfUAQRF8b+NwODhy5AgFBQUcPHiQKVOmkJmZySOPPCK7uFpaWoiJiSEuLo6IiIhO4r9Uf4mjFUe5VH9JvubtGC88pHMzy7b2NkxGEzqdjrDQMI9vcm/L9u+aRefpA0KlUsl1CG6+h9VmxWg0uhp/REUhnAJbqw1rkyvif0/UPTw87mEeHPug16O+vuTq1auYzWZSU1M5deoUq1atYufOnYwdO9bnc+lDFMH3JU6nky+++IKCggL2799PYmIimZmZzJo1C5vNJter12q16PV6IiMjZfHXNNZw7PIxLtVf8lrZpqPgbTYbJrPJZQYJi/YcbfewInATGBDY5ajOW967t22Bt73+EPUQyq6WodFo5HiCJlBDq72VBG0C9w29j3BHOCaTCavVSkREhOx36I2U51tRWVmJ0WgkNTWVs2fP8otf/IL8/HwSE/v/aLCXUQTvK5xOJ8XFxeTl5bFv3z5Gjx5NRkYGc+fOxW63YzKZaGhoIDo6WnZwuTvRnK45TUlNCfUt9fL9JElCpVLhcLo6vdTV1aHX6wkMDPS+3Pbydnd3i/V4vYced7gRQLypfbYKFZU1la4WXDfMQ+FB4aTp00jRpxAVEtVpvBACq9UqpzyHhobKKc99YaKprq6mqqpKjsUsX76c3NzcThludxGK4PsDt5c6Ly+Pjz76iLi4OAwGA/PmzQPAaDRitVqJjIxEr9fL9s1KayUlNSWcNZ11FbFsa5ar57gz4sC7kcab5dVb22hvHnePy3nJlXzT8ajR6XTSWNeIaogKvVZPki6JSbpJ3BNxT4+86B2LWdTW1hIYGCinPPeGM62mpobKykrS0tK4ePEiS5cu5Z///CfJycl3fG8/RRF8f+N2YOXn57Nnzx4iIyMxGAzMnz8ftVot2zfdy9yYmBicwsnF+osc//Y4p6+eJiwmTD7v9va27rgiuBlvEXdPphhv24KwwDCa2v/7YeJwOmisbeS+Ufdx/7j7GR01+o5TZVtaWjCZTJjNZgA56/F2KtkYjUauXLlCWloaly9f5ic/+Qnbtm2TKx3fpfi/4PPy8li3bh3nzp3j+PHjTJs2zeO4WzmNBgJCCEpLSykoKKCoqIiQkBAyMjJIT08nJCQEk8lEfX29HOVva2tzJQBdr+OS5RIX6y9ibbVSf72+y729JfV4E7u3+nJhQZ2F7SYiOAK7sDMqchQjQkdgvWwlNSG1zxJV3M0aOx5/ejsBuRmTyURFRQVTp06lsrKSJ554gi1btjB9+vQ+masf4f+CP3fuHCqVipUrV5KTk+NR8D1xGg00Onr6CwsLUalUpKenk56ezu7du0lJSSEwMBCNRtNpj+twOjC3mDE2GTE2GalpqsHYZGSIeojH5bm3/X5kiOejwLDgMJrbmokIjkCn0REbGotOo2NY2DB0Gh3t7e0+6c3ekfb2drmSTXNzs3wC0jEI6sZsNssNSkwmE4sWLeKtt97ie9/7nk/m2s/4f9XapKTu+4FDZ6cRIDuNBrLgJUli9OjRPP/88/z617+mqqqKvLw85syZQ2xsLDabjczMTHQ6HWazmYqKCoKDg9Hr9eh0OoaG/TcrTAhBY1sjTbYm169tTTTaGuU89zZnm6vn3g3fvFM4CVGHcE/EPQwJHIImUIMmUMMQ9RAiQyLRaXSuIpc34cve7B0JDAwkPj6e+Ph4HA4H9fX1VFZWcu7cuU4VjiwWiyz22tpasrKy2LBhQ6+K/VYrTZvNxtKlSzlx4gQxMTF8+OGHjB49utee7w/0uVuup06jgYokSQwfPhyr1Up2dja//OUvKSws5LnnnqOhoYF58+ZhMBgYNmwYJpOJ4uLiTgGuoKAgIoIjiAiOuPXDbpP+7M3ekY7VatxlrMxmM+fPn5ctzuXl5Tz11FPk5OTw8MMP99qzHQ4Hv/rVrzqtNDMyMjq9eLZu3Up0dDSlpaXk5uby4osv8uGHH/baHPyBWwp+9uzZcjfVjqxfvx6DwdAnkxqIrFmzRo5Or1q1ilWrVlFbW8vOnTv53e9+h8lk4rHHHsNgMDBq1CjMZjOnTp1yZbXdCHD1Rd2169evc+rUKSZOnOhX7ZpVKpX84WOxWEhKSmLDhg384x//YOTIkVRUVNDc3PydWo91R09Wmrt27WLdunUALFy4kKeffhohRL93zulNbin4Tz755I4ecHPDvKtXr/ok99nXeBKrTqdjxYoVrFixAovFwu7du3nttde4cuWK7OkfP348ZrOZkpISJEmSxd8bddhaWlooKSkhKSmpi0nHH7h27RrffPMNU6dOpbm5mUOHDvG3v/2NiRMnUlhY6LHF+O3Sk5VmxzFqtZrIyEjq6urQ6XS9No/+ps+X9NOnT+fbb7/l0qVLDB8+nNzcXN5///2+fqzfER0dzdKlS1m6dCkNDQ3s2bOHnJwcSktLmTVrFgaDgcmTJ1NbW8uZM2dkZ59er7+toy1/7c3uxmq1cv78edLS0mhtbWXRokWsWbOG9PR0wFXpWKH3uaPD18LCQkaMGMHRo0eZP38+c+fOBaCqqkpOVFGr1WzcuJG5c+eSlJREVlZWJ1vhYCQiIoInn3yS/Px8Pv/8c2bMmMFbb73FI488wqZNm2hra2PKlCmo1Wr5yNPtAe8JTU1NlJSUkJyc7LdiP3fuHKmpqbS3t5OVlcXq1av58Y9/3GfP7MlKs+MYu92O1Wr1aYDTFyiJN35Ea2sr//73v8nPz6e4uJjvf//7ZGZmMn36dCwWC0ajkba2tk623pv3lw0NDZw9e/Y7t972FY2NjZw5c4bU1FScTieLFi0iOzubJUuW9Olze+Jp37RpE6dPn2bz5s3k5uayY8cOtm/f3qfz6oD/n8Mr9B1tbW0cOHCA/Px8jh07xoMPPih7+q1Wl7vt+vXrclJLeHg4DQ0NnDt3zu96s7txiz0lJQVJksjKyuKnP/0py5cv98nzb+Vpb21tZcmSJRQXF6PVasnNzfWlI08RvCfq6+tZvHgx5eXljB49mu3btxMdHd1lXEBAgJx3PXLkSIqKinw91V6jvb2dw4cPy57+++67T/b0NzY2yk42u93OpEmT5P54/kRTUxOnT5+W6wo++eSTPP7446xcudLv5tpPKIL3xG9/+1u0Wi1r1qzhjTfewGKx8Oabb3YZFxYWRlNT1zTTgY7b05+fn8+nn35KcnIyCQkJlJWVsXbtWiwWC42NjWi1WuLi4vq0YWdPcQcQk5OTCQwMZMmSJfzwhz/kmWee6fe5+RGK4D0xYcIEPv30U+Lj46murmbmzJlcuHChy7i7VfAdcTqd/PWvf+WPf/wjw4YNY8yYMWRmZjJ79myXB//Gm/92GzP2Bi0tLZw6dYrk5GSCg4NZtmwZP/jBD3j++ecVsXfG/1Nr+wOj0SgXCxw6dChGo9HjuNbWVqZNm4ZarWbNmjVkZmb6cJa+QZIkLly4QElJCVqtlpMnT5KXl0dOTo7s6f/Rj36Ew+HAaDTyzTffEBERIffs62vxu5N+pkyZQkhICMuXL+f+++9XxN6P+OUbvrvsvmXLlnHt2jX5WnR0NBZL19bNlZWVDB8+nLKyMh599FEOHDjAuHH9X0/dFzidTk6fPk1+fj579+5l6NChsqdfkiTZ2RceHi7benu7eo1b7JMmTSI0NJSVK1eSkJDAunXrFLF7RlnSe6KnS/qO/PznP2fBggUsXLjQR7P0H2729EdFRcmefner7traWkJDQ2Vzz52K3527n5SURFhYGM888wzx8fH8/ve/V8TuHUXwnvjNb35DTEyMHLSrr6/nD3/4Q6cxFosFjUZDcHAwtbW1zJgxY8A79HoDt6c/Pz+f3bt3ExISgsFgYMGCBYSGhsriv5OGnTabjeLiYiZOnEhERATPPfcc4eHh5OTk+Dx+MMBQBO+Juro6srKyuHz5MqNGjWL79u1otVq++uorNm/ezJYtW/j8889ZuXIlKpUKp9PJ6tWryc7O7u+p+xVCCMrLyykoKGDXrl2yp99gMBARESGLPygoCL1eT2xsLIGB3VesdVtwExMTiYyM5MUXX0SSJDZs2KCI/dYoglfwDUIIqqqqKCgoYMeOHbS3t7NgwQIMBgOxsbFy6aruGna2tbVRXFxMQkICUVFRrF27lqamJjZv3qyIvWcoglfwPUIIjEYjhYWFFBQU0NjYyPz58zEYDAwfPlwuXeW29cbGxhIQEEBxcTHjxo1Dq9Xy2muvUVNTw9atW/uklPVdmnylCL4/UaqjuHB7+gsKCqitreWxxx4jIyODMWPGYDabMRqNNDY2EhISgk6nY8eOHVy8eJH33nuvz+rW36XJV76JZrq7kXTzNeiw2+1i7Nix4uLFi8Jms4mUlBRx5syZTmM2bdokVq5cKYQQ4oMPPhBZWVn9MVWfUl9fL959912Rnp4upk6dKlavXi3uvfdeceLECbFz506RnJwsdDqdWL9+vSgrK+uzeSQmJoqqqiohhBBVVVUiMTHR47jQ0NA+m0Mf0BMt3vGX8ob3QE/aA8+dO5d169YxY8YM7HY7Q4cOxWw2D5pjp8rKSmbPnk18fDz19fVotVo0Gg1///vf2bdvH2FhYSxatKhPnh0VFSXnYgghiI6O7pSb4UatVpOWljZQkq+UTLv+QqmOcmtOnjzJq6++SlZWFk1NTfzpT3/ihRdeIDQ0lKeeeuqO799d8lVHJEny+iFbUVHRKfkqOTl50CRfeUMRvMJt4a5MA6698iuvvNKr9++utJper5f7sVdXV3utr+8ucDF27FhmzpwpBxYHM8p5iQeU6ij+TUZGBtu2bQNg27ZtHoupWiwWbDZXE4/a2lo+++yzQZ94BShBO0+0t7eLMWPGiLKyMjlo9/XXX3cas3Hjxk5Bu0WLFvXHVAcltbW14tFHHxXjx48Xs2bNEnV1dUIIIb788kuRnZ0thBDis88+E1OmTBEpKSliypQpYsuWLf055Z6gBO36Ez+vjqJw96GcwysoDCJ8InhlD6+gMIhQBO9n7Nu3jwkTJjB+/HjeeOONLj9/9913iY2NJS0tjbS0NLZs2dIPs1QYqCjHcn5ET/qfASxevJiNGzf20ywVBjLKG96P6Nj/LCgoSO5/pqDQWyiC9yM8ZfhVVlZ2GVdQUEBKSgoLFy7slC+goHArFMEPMNLT0ykvL6ekpIQ5c+awbNmy/p6SwgBCEbwf0ZMMv5iYGLlT7YoVKzhx4oRP56gwsFEE70d07LTb1tZGbm4uGRkZncZUV1fL/11UVERSUpKvp3nH5OXlMXnyZFQqFV999ZXXcbc6sVD47vQk8UbBh0iSNA/4XyAAeEcIsV6SpP8BvhJCFEmS9DqQAdiBemCVEOJ8v034NpAkKQlwAv8HvCCE6KJ6SZICgG+AOcBV4EvgSSHEWV/O9W5DEbxCvyFJ0qd4F/wMYJ0QYu6N718CEEK87tNJ3mUoS3oFf2U40PEI4uqNawp3gCJ4BQAkSXpHkiSTJElfe/m5JEnSBkmSSiVJKpEk6d5b3O8TSZK+9vDV1cuq4DOUTDsFN+8CG4H3vPz8MSDhxtcDwNs3fvWIEGL2Hc6nErinw/cjblxTuAOUN7wCAEKIw7iCgN4wAO/d8G5/AURJkhTfh1P6EkiQJGmMJElBwBOAX9eZHggoglfoKb22p5Yk6XFJkq4CM4A9kiR9fOP6MEmS9gIIIezA08DHwDlguxDizB3MXwFlSa/QDwghCoFCD9ergHkdvt8L7PXh1O56lDe8Qk9R9tR3AYrgFXpKEbD0RrT+QcAqhKi+1f+k4F8oS3oFACRJ+gCYCehu7K9fAQIBhBCbcS2t5wGlQAtw58XnFXyOkmmnoDCIUJb0CgqDCEXwCgqDCEXwCgqDCEXwCgqDCEXwCgqDCEXwCgqDCEXwCgqDiP8HVEzrXXxeYaoAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Ground truth\n", "x0 = np.arange(-1, 1, .1)\n", "x1 = np.arange(-1, 1, .1)\n", "x0, x1 = np.meshgrid(x0, x1)\n", "y_truth = x0**2 - x1**2 + x1 - 1\n", "\n", "ax = plt.figure().add_subplot(projection='3d')\n", "ax.set_xlim(-1, 1)\n", "ax.set_ylim(-1, 1)\n", "ax.set_xticks(np.arange(-1, 1.01, .5))\n", "ax.set_yticks(np.arange(-1, 1.01, .5))\n", "surf = ax.plot_surface(x0, x1, y_truth, rstride=1, cstride=1, color='green', alpha=0.5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "rng = check_random_state(0)\n", "\n", "# Training samples\n", "X_train = rng.uniform(-1, 1, 100).reshape(50, 2)\n", "y_train = X_train[:, 0]**2 - X_train[:, 1]**2 + X_train[:, 1] - 1\n", "\n", "# Testing samples\n", "X_test = rng.uniform(-1, 1, 100).reshape(50, 2)\n", "y_test = X_test[:, 0]**2 - X_test[:, 1]**2 + X_test[:, 1] - 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " | Population Average | Best Individual |\n", "---- ------------------------- ------------------------------------------ ----------\n", " Gen Length Fitness Length Fitness OOB Fitness Time Left\n", " 0 38.13 458.578 5 0.320666 0.556764 1.56m\n", " 1 9.97 1.70233 5 0.320202 0.624787 58.17s\n", " 2 7.72 1.94456 11 0.239537 0.533148 53.89s\n", " 3 5.41 0.990157 7 0.235676 0.719906 48.94s\n", " 4 4.66 0.894443 11 0.103946 0.103946 45.92s\n", " 5 5.41 0.940242 11 0.060802 0.060802 42.23s\n", " 6 6.78 1.09536 11 0.000781474 0.000781474 38.58s\n" ] }, { "data": { "text/html": [ "
sub(add(-0.999, X1), mul(sub(X1, X0), add(X0, X1)))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "SymbolicRegressor(max_samples=0.9, p_crossover=0.7, p_hoist_mutation=0.05,\n", " p_point_mutation=0.1, p_subtree_mutation=0.1,\n", " parsimony_coefficient=0.01, population_size=5000,\n", " random_state=0, stopping_criteria=0.01, verbose=1)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est_gp = SymbolicRegressor(population_size=5000,\n", " generations=20, stopping_criteria=0.01,\n", " p_crossover=0.7, p_subtree_mutation=0.1,\n", " p_hoist_mutation=0.05, p_point_mutation=0.1,\n", " max_samples=0.9, verbose=1,\n", " parsimony_coefficient=0.01, random_state=0)\n", "est_gp.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sub(add(-0.999, X1), mul(sub(X1, X0), add(X0, X1)))\n" ] } ], "source": [ "print(est_gp._program)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
RandomForestRegressor(n_estimators=10)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "RandomForestRegressor(n_estimators=10)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est_tree = DecisionTreeRegressor()\n", "est_tree.fit(X_train, y_train)\n", "est_rf = RandomForestRegressor(n_estimators=10)\n", "est_rf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_gp = est_gp.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)\n", "score_gp = est_gp.score(X_test, y_test)\n", "y_tree = est_tree.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)\n", "score_tree = est_tree.score(X_test, y_test)\n", "y_rf = est_rf.predict(np.c_[x0.ravel(), x1.ravel()]).reshape(x0.shape)\n", "score_rf = est_rf.score(X_test, y_test)\n", "\n", "fig = plt.figure(figsize=(12, 10))\n", "\n", "for i, (y, score, title) in enumerate([(y_truth, None, \"Ground Truth\"),\n", " (y_gp, score_gp, \"SymbolicRegressor\"),\n", " (y_tree, score_tree, \"DecisionTreeRegressor\"),\n", " (y_rf, score_rf, \"RandomForestRegressor\")]):\n", "\n", " ax = fig.add_subplot(2, 2, i+1, projection='3d')\n", " ax.set_xlim(-1, 1)\n", " ax.set_ylim(-1, 1)\n", " ax.set_xticks(np.arange(-1, 1.01, .5))\n", " ax.set_yticks(np.arange(-1, 1.01, .5))\n", " surf = ax.plot_surface(x0, x1, y, rstride=1, cstride=1, color='green', alpha=0.5)\n", " points = ax.scatter(X_train[:, 0], X_train[:, 1], y_train)\n", " if score is not None:\n", " score = ax.text(-.7, 1, .2, \"$R^2 =\\/ %.6f$\" % score, 'x', fontsize=14)\n", " plt.title(title)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "program\n", "\n", "\n", "\n", "0\n", "\n", "sub\n", "\n", "\n", "\n", "1\n", "\n", "add\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "\n", "\n", "\n", "4\n", "\n", "mul\n", "\n", "\n", "\n", "0->4\n", "\n", "\n", "\n", "\n", "\n", "2\n", "\n", "-0.999\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "\n", "\n", "\n", "3\n", "\n", "X1\n", "\n", "\n", "\n", "1->3\n", "\n", "\n", "\n", "\n", "\n", "5\n", "\n", "sub\n", "\n", "\n", "\n", "4->5\n", "\n", "\n", "\n", "\n", "\n", "8\n", "\n", "add\n", "\n", "\n", "\n", "4->8\n", "\n", "\n", "\n", "\n", "\n", "6\n", "\n", "X1\n", "\n", "\n", "\n", "5->6\n", "\n", "\n", "\n", "\n", "\n", "7\n", "\n", "X0\n", "\n", "\n", "\n", "5->7\n", "\n", "\n", "\n", "\n", "\n", "9\n", "\n", "X0\n", "\n", "\n", "\n", "8->9\n", "\n", "\n", "\n", "\n", "\n", "10\n", "\n", "X1\n", "\n", "\n", "\n", "8->10\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dot_data = est_gp._program.export_graphviz()\n", "graph = graphviz.Source(dot_data)\n", "graph.render('images/ex1_child', format='png', cleanup=True)\n", "graph" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'method': 'Crossover', 'parent_idx': 1555, 'parent_nodes': range(1, 4), 'donor_idx': 78, 'donor_nodes': []}\n" ] } ], "source": [ "print(est_gp._program.parents)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "add(-0.999, X1)\n", "Fitness: 0.35180331907500284\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "program\n", "\n", "\n", "\n", "0\n", "\n", "add\n", "\n", "\n", "\n", "1\n", "\n", "-0.999\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "\n", "\n", "\n", "2\n", "\n", "X1\n", "\n", "\n", "\n", "0->2\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = est_gp._program.parents['donor_idx']\n", "fade_nodes = est_gp._program.parents['donor_nodes']\n", "print(est_gp._programs[-2][idx])\n", "print('Fitness:', est_gp._programs[-2][idx].fitness_)\n", "dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)\n", "graph = graphviz.Source(dot_data)\n", "graph" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sub(sub(X1, 0.939), mul(sub(X1, X0), add(X0, X1)))\n", "Fitness: 0.17080204042764768\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "program\n", "\n", "\n", "\n", "0\n", "\n", "sub\n", "\n", "\n", "\n", "1\n", "\n", "sub\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "\n", "\n", "\n", "4\n", "\n", "mul\n", "\n", "\n", "\n", "0->4\n", "\n", "\n", "\n", "\n", "\n", "2\n", "\n", "X1\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "\n", "\n", "\n", "3\n", "\n", "0.939\n", "\n", "\n", "\n", "1->3\n", "\n", "\n", "\n", "\n", "\n", "5\n", "\n", "sub\n", "\n", "\n", "\n", "4->5\n", "\n", "\n", "\n", "\n", "\n", "8\n", "\n", "add\n", "\n", "\n", "\n", "4->8\n", "\n", "\n", "\n", "\n", "\n", "6\n", "\n", "X1\n", "\n", "\n", "\n", "5->6\n", "\n", "\n", "\n", "\n", "\n", "7\n", "\n", "X0\n", "\n", "\n", "\n", "5->7\n", "\n", "\n", "\n", "\n", "\n", "9\n", "\n", "X0\n", "\n", "\n", "\n", "8->9\n", "\n", "\n", "\n", "\n", "\n", "10\n", "\n", "X1\n", "\n", "\n", "\n", "8->10\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = est_gp._program.parents['parent_idx']\n", "fade_nodes = est_gp._program.parents['parent_nodes']\n", "print(est_gp._programs[-2][idx])\n", "print('Fitness:', est_gp._programs[-2][idx].fitness_)\n", "dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes)\n", "graph = graphviz.Source(dot_data)\n", "graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2: Symbolic Transformer" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from gplearn.genetic import SymbolicTransformer\n", "from sklearn.utils import check_random_state\n", "from sklearn.datasets import load_diabetes\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "rng = check_random_state(0)\n", "diabetes = load_diabetes()\n", "perm = rng.permutation(diabetes.target.size)\n", "diabetes.data = diabetes.data[perm]\n", "diabetes.target = diabetes.target[perm]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.43405742105789413\n" ] } ], "source": [ "from sklearn.linear_model import Ridge\n", "est = Ridge()\n", "est.fit(diabetes.data[:300, :], diabetes.target[:300])\n", "print(est.score(diabetes.data[300:, :], diabetes.target[300:]))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " | Population Average | Best Individual |\n", "---- ------------------------- ------------------------------------------ ----------\n", " Gen Length Fitness Length Fitness OOB Fitness Time Left\n", " 0 11.37 0.126618 5 0.612827 0.68 29.88s\n", " 1 6.63 0.344375 3 0.659086 0.451797 28.15s\n", " 2 5.36 0.473731 3 0.669019 0.321485 25.31s\n", " 3 4.74 0.587613 3 0.673354 0.31249 23.44s\n", " 4 4.39 0.597151 13 0.675282 0.49482 23.46s\n", " 5 4.31 0.611995 15 0.686134 0.148203 21.01s\n", " 6 4.89 0.611891 9 0.685649 0.199002 19.14s\n", " 7 6.49 0.617031 9 0.688309 0.287286 21.08s\n", " 8 9.19 0.628618 17 0.720606 0.26362 17.73s\n", " 9 11.44 0.637057 18 0.701906 0.607692 15.93s\n", " 10 15.19 0.646744 31 0.709565 0.485812 14.10s\n", " 11 18.69 0.654609 28 0.71732 0.373906 12.77s\n", " 12 21.14 0.660923 28 0.714525 0.388124 11.31s\n", " 13 23.66 0.664435 24 0.717779 0.499734 10.34s\n", " 14 23.80 0.665121 40 0.717637 0.454897 8.18s\n", " 15 24.05 0.668179 32 0.71817 0.357258 6.73s\n", " 16 24.56 0.66613 26 0.718859 0.382447 5.74s\n", " 17 24.83 0.666171 27 0.715718 0.487776 3.55s\n", " 18 25.56 0.665736 30 0.720983 0.479341 1.70s\n", " 19 26.20 0.669084 45 0.722443 0.365526 0.00s\n", "0.5336788517320445\n" ] } ], "source": [ "function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log',\n", " 'abs', 'neg', 'inv', 'max', 'min']\n", "gp = SymbolicTransformer(generations=20, population_size=2000,\n", " hall_of_fame=100, n_components=10,\n", " function_set=function_set,\n", " parsimony_coefficient=0.0005,\n", " max_samples=0.9, verbose=1,\n", " random_state=0)\n", "gp.fit(diabetes.data[:300, :], diabetes.target[:300])\n", "\n", "gp_features = gp.transform(diabetes.data)\n", "new_diabetes = np.hstack((diabetes.data, gp_features))\n", "\n", "est = Ridge()\n", "est.fit(new_diabetes[:300, :], diabetes.target[:300])\n", "print(est.score(new_diabetes[300:, :], diabetes.target[300:]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3: Customizing your programs" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from gplearn.functions import make_function" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def logic(x1, x2, x3, x4):\n", " return np.where(x1 > x2, x3, x4)\n", "\n", "logical = make_function(function=logic,\n", " name='logical',\n", " arity=4)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "function_set = ['add', 'sub', 'mul', 'div', logical]\n", "gp = SymbolicTransformer(generations=2, population_size=2000,\n", " hall_of_fame=100, n_components=10,\n", " function_set=function_set,\n", " parsimony_coefficient=0.0005,\n", " max_samples=0.9, verbose=1,\n", " random_state=0)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " | Population Average | Best Individual |\n", "---- ------------------------- ------------------------------------------ ----------\n", " Gen Length Fitness Length Fitness OOB Fitness Time Left\n", " 0 56.27 0.134535 7 0.639079 0.667244 2.73s\n", " 1 9.44 0.387482 7 0.658126 0.740852 0.00s\n" ] }, { "data": { "text/html": [ "
[add(sub(X3, X6), add(X2, X8)),\n",
       " add(X2, X8),\n",
       " mul(X1, mul(X1, add(logical(X9, sub(X8, X4), add(X3, logical(div(X5, sub(X5, X5)), add(X9, -0.621), X8, X4)), add(X2, X9)), X2))),\n",
       " sub(X2, sub(-0.278, X7)),\n",
       " logical(X6, X0, X2, add(X9, X2)),\n",
       " mul(X1, mul(X1, add(sub(X9, X6), X2))),\n",
       " sub(add(mul(X3, X9), mul(X6, X8)), add(X2, X3)),\n",
       " sub(add(X3, X2), sub(X5, X8)),\n",
       " add(X2, 0.706),\n",
       " add(add(X3, X8), X8)]
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "SymbolicTransformer(function_set=['add', 'sub', 'mul', 'div',\n", " ],\n", " generations=2, max_samples=0.9,\n", " parsimony_coefficient=0.0005, population_size=2000,\n", " random_state=0, verbose=1)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gp.fit(diabetes.data[:300, :], diabetes.target[:300])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "add(X3, logical(div(X5, sub(X5, X5)), add(X9, -0.621), X8, X4))\n" ] } ], "source": [ "print(gp._programs[0][3])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "program\n", "\n", "\n", "\n", "0\n", "\n", "add\n", "\n", "\n", "\n", "1\n", "\n", "X3\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "\n", "\n", "\n", "2\n", "\n", "logical\n", "\n", "\n", "\n", "0->2\n", "\n", "\n", "\n", "\n", "\n", "3\n", "\n", "div\n", "\n", "\n", "\n", "2->3\n", "\n", "\n", "\n", "\n", "\n", "8\n", "\n", "add\n", "\n", "\n", "\n", "2->8\n", "\n", "\n", "\n", "\n", "\n", "11\n", "\n", "X8\n", "\n", "\n", "\n", "2->11\n", "\n", "\n", "\n", "\n", "\n", "12\n", "\n", "X4\n", "\n", "\n", "\n", "2->12\n", "\n", "\n", "\n", "\n", "\n", "4\n", "\n", "X5\n", "\n", "\n", "\n", "3->4\n", "\n", "\n", "\n", "\n", "\n", "5\n", "\n", "sub\n", "\n", "\n", "\n", "3->5\n", "\n", "\n", "\n", "\n", "\n", "6\n", "\n", "X5\n", "\n", "\n", "\n", "5->6\n", "\n", "\n", "\n", "\n", "\n", "7\n", "\n", "X5\n", "\n", "\n", "\n", "5->7\n", "\n", "\n", "\n", "\n", "\n", "9\n", "\n", "X9\n", "\n", "\n", "\n", "8->9\n", "\n", "\n", "\n", "\n", "\n", "10\n", "\n", "-0.621\n", "\n", "\n", "\n", "8->10\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dot_data = gp._programs[0][3].export_graphviz()\n", "graph = graphviz.Source(dot_data)\n", "graph.render('images/ex3_fig1', format='png', cleanup=True)\n", "graph" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "\n", "# Example 4: Classification" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from gplearn.genetic import SymbolicClassifier\n", "from matplotlib.colors import ListedColormap\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.datasets import make_moons, make_circles, make_classification\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.gaussian_process import GaussianProcessClassifier\n", "from sklearn.gaussian_process.kernels import RBF\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.datasets import load_breast_cancer" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Modified from https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html\n", "# Code source: Gaël Varoquaux\n", "# Andreas Müller\n", "# Modified for documentation by Jaques Grobler\n", "# License: BSD 3 clause\n", "\n", "h = .02 # step size in the mesh\n", "\n", "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n", " \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n", " \"Naive Bayes\", \"QDA\", \"SymbolicClassifier\"]\n", "\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " SVC(kernel=\"linear\", C=0.025),\n", " SVC(gamma=2, C=1),\n", " GaussianProcessClassifier(1.0 * RBF(1.0)),\n", " DecisionTreeClassifier(max_depth=5),\n", " RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n", " MLPClassifier(alpha=1, tol=0.001),\n", " AdaBoostClassifier(),\n", " GaussianNB(),\n", " QuadraticDiscriminantAnalysis(),\n", " SymbolicClassifier(random_state=0)]\n", "\n", "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n", " random_state=1, n_clusters_per_class=1)\n", "rng = np.random.RandomState(2)\n", "X += 2 * rng.uniform(size=X.shape)\n", "linearly_separable = (X, y)\n", "\n", "datasets = [make_moons(noise=0.3, random_state=0),\n", " make_circles(noise=0.2, factor=0.5, random_state=1),\n", " linearly_separable\n", " ]\n", "\n", "figure = plt.figure(figsize=(27, 9))\n", "i = 1\n", "# iterate over datasets\n", "for ds_cnt, ds in enumerate(datasets):\n", " # preprocess dataset, split into training and test part\n", " X, y = ds\n", " X = StandardScaler().fit_transform(X)\n", " X_train, X_test, y_train, y_test = \\\n", " train_test_split(X, y, test_size=.4, random_state=42)\n", "\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", " # just plot the dataset first\n", " cm = plt.cm.RdBu\n", " cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", " ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n", " if ds_cnt == 0:\n", " ax.set_title(\"Input data\")\n", " # Plot the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n", " edgecolors='k')\n", " # Plot the testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,\n", " edgecolors='k')\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " i += 1\n", "\n", " # iterate over classifiers\n", " for name, clf in zip(names, classifiers):\n", " ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n", " clf.fit(X_train, y_train)\n", " score = clf.score(X_test, y_test)\n", "\n", " # Plot the decision boundary. For that, we will assign a color to each\n", " # point in the mesh [x_min, x_max]x[y_min, y_max].\n", " if hasattr(clf, \"decision_function\"):\n", " Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", " else:\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", "\n", " # Put the result into a color plot\n", " Z = Z.reshape(xx.shape)\n", " ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n", "\n", " # Plot the training points\n", " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n", " edgecolors='k')\n", " # Plot the testing points\n", " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n", " edgecolors='k', alpha=0.6)\n", "\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " ax.set_xticks(())\n", " ax.set_yticks(())\n", " if ds_cnt == 0:\n", " ax.set_title(name)\n", " ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n", " size=15, horizontalalignment='right')\n", " i += 1\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "rng = check_random_state(0)\n", "cancer = load_breast_cancer()\n", "perm = rng.permutation(cancer.target.size)\n", "cancer.data = cancer.data[perm]\n", "cancer.target = cancer.target[perm]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
sub(div(worst fractal dimension, mean concave points), mul(mean concave points, area error))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "SymbolicClassifier(feature_names=array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n", " 'mean smoothness', 'mean compactness', 'mean concavity',\n", " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n", " 'radius error', 'texture error', 'perimeter error', 'area error',\n", " 'smoothness error', 'compactness error', 'concavity error',\n", " 'concave points error', 'symmetry error',\n", " 'fractal dimension error', 'worst radius', 'worst texture',\n", " 'worst perimeter', 'worst area', 'worst smoothness',\n", " 'worst compactness', 'worst concavity', 'worst concave points',\n", " 'worst symmetry', 'worst fractal dimension'], dtype='\n", "\n", "\n", "\n", "\n", "\n", "program\n", "\n", "\n", "\n", "0\n", "\n", "sub\n", "\n", "\n", "\n", "1\n", "\n", "div\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "\n", "\n", "\n", "4\n", "\n", "mul\n", "\n", "\n", "\n", "0->4\n", "\n", "\n", "\n", "\n", "\n", "2\n", "\n", "worst fractal dimension\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "\n", "\n", "\n", "3\n", "\n", "mean concave points\n", "\n", "\n", "\n", "1->3\n", "\n", "\n", "\n", "\n", "\n", "5\n", "\n", "mean concave points\n", "\n", "\n", "\n", "4->5\n", "\n", "\n", "\n", "\n", "\n", "6\n", "\n", "area error\n", "\n", "\n", "\n", "4->6\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dot_data = est._program.export_graphviz()\n", "graph = graphviz.Source(dot_data)\n", "graph.render('images/ex4_tree', format='png', cleanup=True)\n", "graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 1 } gplearn-0.4.2/doc/images/000077500000000000000000000000001423420364700151645ustar00rootroot00000000000000gplearn-0.4.2/doc/images/ex1_child.png000066400000000000000000001003571423420364700175400ustar00rootroot00000000000000PNG  IHDR[ȫDzbKGD IDATxwxTUL )B .i;Xhti VD]TX@AQ@P*B'!'`B2ɝL̐|{EQB3jB!gIqB!|w! /rݤBFFN& L&.]%JYC qV+;?ϱItu~D*CըSAWjB3B"ιnv/º_7ǞqmBЇWC ."Ah !z \6Ś;sRpDy[ \v A!hޜmZӥKx_!D! ];}v.\Ȓe˹xy( 0DG7D~Ǖ߱@hzΠAZgB )B!;;3},$c*Um1Vy]x q$Şԯ3/ЪM[FAnd^q)BENNsᭉD_5~=1ov+7s<_,DŽ2dzYF#.5.3g2ױ؜kǿΓhL!jG˓+ =a; 1#:vv,!ʤ ޽aO?Cx ĿP4~jǺcsXwNzt w3>NՎ%PwQ\.&OoC.ڱNl[9_УG# !T ]Y.]G^رSj4jǺoӆeX~3#F20 jB )H:qytЗv$_ i՜eKv$!D.xZnPN3)8/cY9jٴWBBwqs"4jҌ,}i;Ϻkse!w`Ԯk0LjGB3+(2hcdiF((?ӯO(2F ؍E.>b̜9S8B|&HXp!|.0ThvX~Ϲw111jB)f*WFvqnb@Aؐ יƼb˲~G*>> =+S=EsqKT4ZM_ukYvm'Pwl6>ChԎ aԜ)SS;"Hq>mŊdee#۰^݋M7pi!wӾ[QMFū+ŋՎ"rgEa˶m{Ma?wv20_LOZFO9}mdJnه+$[Ñ$s@G[!6m¸q<׮+]Ǐ~2u<֦y;X- x† ɟЅU"{8g.5Th~e\Dt+\ƿӄ @pd.=]رӣm !wᳮO-\gw ! ڀp.GW94}}h #b(ػryGJV̌4rrr<ڮB}R܅JMMEա1zMc8a Aq:p%r }}oxL^ m`#͗k}B+·9w, : 4 f2u% kP1X}Vmے6}\[l6{=!.|VXXN\<ԪWqp;q/y[ՂqS -;ۭݹih_۴fPBx>+<}t61W1D5X-7 (; sSqe'پřz\6_D_F%FsuB)gƢjq^Lh oJ=㶤ac`( 6_ĺG.̳7OFHYcǼ8,8/ƓehG!*VhhBɍcO@MBZyL=bI{pg%]hEL1{8Ŗ."/c&΋? 7)crgJ(.j][AI+Dh;ף !']g}/" x%/2/߿q&§ 4DՎUl +[njGB)§=CԪ}|xס<9?jB)Mkp&v`=-QDy]tYضMS8r/3^DEEGOdA(;F:uÿgV:PJ{Xbj'B"Zj|чX~rqTa\{Y;)B8)6lCŲ"wz`?gԫWO8B|&])gϢc8@X'wd&MĐ!CԎ#(REX":<҆ð'v|k_Y/D! Dr=|7zKaxh$~t(S#E8><jBH9}4?o-X< }ahFv(tKp/<\..ĭ=zޞȷ~zLхWU7Ƒ{8ogɘ1cW7kHq6.\ܹs93Ο=De4a}DtL+NΤסބ-"u7`gӧAQHqno?wKqi e)]}D U7s.řv nDу5kz !|w!A||<+o6.7`PnS8F:# ΈEq;amƒ67SV4Z-墰Y̼[tԉ˫^BF111_=J`` СC;v .pY/!'; Պjaa`0Db(W,Q*PlxjժEtt4njժqq)B"#w!ANNUVe'|رcIHHr֏·Hqۼ{?~|pCƍ?~#-޿C^&==iӦK/ka0 L8 w|K;d.]z7o'N( cEYfz|OQ]ĬY0aBh2e k֬aҧpwa]#GW[vԉϳw^Z\.ț;v7x ;)Sc޷pw'`š׫EٴiVpw,Yɓ'V&Nŋ={jOFB܁:ݻh4fy嗙;w.Ǐ'$$D,B$#w!֭[Yz5SLQ(ԩSՎ"R2ro4k MԎr|9" !n"]?Ν;iܸqԬYm矫Ge MzR K.U;MkQ;‹Hq" ,੧DGG&n P\9VXv!.-8j֬I˖-;wqfڷoφ hӦq^B0c ƌÇTqnG!++]vyj~! jժѫW/Mv~իOՎ#R܅I&1yd?NRԎsGA\\A8B&6B\'##>1cW>>}9sE q)Sj;vQJŊ1b:jBLyW V;]0a6PB/)BoMhh(#GT;=)Qƍ… jBH Q;B%R܅Ϟ={W~_0j(Vq*.RN[zvr]6111,^X8BHqE^Ν9w{Ee(+V[nl߾&MGQ"m4oޜիWcǣڴifjGB0)HkӦ .-[~74iʕ+رqH(~:uiڴqE9|0( wN(E~DEE|r䛣Gg}SO=v!D.E1`KڵՎFʕ+9z(jB)q:ҰaCϟv|wEVʄ 7nq7n7k֬9sp ^uR1yd._|v;;vP)"Hq>Ӵoߞ͛sNV+'NgJ**',8ƍϏɓ'W>|7TR{NtBOg=zrIXӦMر#իW'==ǫ`1aƎKڵy9x iii(FQ9S u kײj*իGNN ^-\2DsuMnn.IIIDFFP)2-/|ՑUNEQؿ?5j`$''={~zꑘ\y-F4!D>.|V||QN'.ʕ+{祖.2vXT¼ypݷ|= R!"ȴYE!00* [.oI=!~=z+WinFFez~PB* .,phM!hߕ=7Âb;2 N:1NF?[Utؑ:uxʦMX~=ngq]0+2( @-Ś;79w%dĕv9NOtl-ZlN6mxG TGIq*.. drL}teꡏD {eKXbI?Q`˺Hd ޕ}ҨQ#Yf.]ʲeYv VK.l]\0V}}Zm3Ok! F#[GnݛP+DQ%]xbaѢEs~߽ SX9c6MT\64:S^:NXF>4 "$$$_~/`Ѣol7FW Ɗ-+}ñˎFg̣%q[qڌfgM6tm۶B')crrr3gL~˗1Tn1'cx{8Qa!+)SFhm6&NšU?W ]ֿns'n}|%vR3:WƽH~nIqp'oa9TwR9X/yk.+^z_~ݗܿƐp(J-1l}czl Q*)SXB:R}ٸq##F&1~ub7͗y3iz`=R2<MGjǺk1Gs svr'6kO>vjА.je/Un8t!Վu_ܹXwLÚ#OY3 xEQ5kc_| Ebl6C&jgAlőW_}UꅸR];r] Zǯzg#yv&P"ğ~OÆ ՎTφ 0Ñ`ݷ]A- *J&]ܕ-[?b+v|ᶤcYo=|pݻwW;Mh߱3)vLLE_wKJ;uK4iB[^ܱe˖#hH?lay]٫fR; nJF %"/ 5Zn?v$!Yz5w|5߯U&?òk:_~% R;vÏ.ۄG-ҽ&w$ aR:wBBxwvMVViO@k eG~ɏ?cǎ刏qf8K#4<~r7O[VZH"]VFF֮Kg_実!i3#**Φ^ϋވ7c8ȁ&DB܏ZܑaÆs1ӂEX^v όLr*Gߓn"n!Uk-nkڵ,]=~m&S5*.cIQ ބ鑩;_}GڼSׯEmAڷ'X~uHcjA};w0|+Da'\.jsQ皬LK{;7r2Xܟt g]e꽸mM'lyIrQ B[Zr%G'tQQddfHq;v/Hde3gQ R-3L vC_1>9@>c&J-х"Bc A@g>>B˗/v5kE$! ~Ν;͛G˝_l/NL~S;+Ɋؾ};nٛ]؏(Qbz`秊nnp;ED_y6/}Zٺu+jʷ~6oތFPGڳ'J_:lZ>qj+ + տQl٘7Oqw4 Zbc ?%wtekbS(ŇoH;/QRl޼ƍXBx#l߾jhL#6W|CB!1ļu*{ñ3d~8uF[jrwƕyxhї֭[n݊_X4zG3ViGsq+_Yn2C `;/c.%l&f\kǿ3?C .}DG-MlڼE&RMN<;5kCO FvwIJo=q;?BeU QMЅW#葉(K7{{Rc'}c'pTʷbFcǯ«8S݁K@cmp$S_4QHq7I6Z˛וn'ˉs26Thvñ7n>ŋh[R7| ,yǃJJ/T)D~s&fs.xh*ŞeWŝsŖ}+벣ht~h 7 ǝq.ۛ{V IDAT$!s9_ھʜ&Ct@т;i XQHq7)Y8,hIUtD hw+/itF4@ő{SV7|}{ۚA>@xxq97܎뇷ⶤ3B2-/nQkT8DPSʝ ?~K:ϧQD%Q, (; sSqe'GQdIU›Hq7ŝzs j"MŲKK:ӆnBc e 3;Q^-x=ˇW|kNz0CyhT=P:-MvIӦM 3:-HS,T Z ,e# sػ2N]nov QX.Jvo|d|֔E,wiV֭[ǣ>JЍd~ X>qMl=2 Fh4 ¼=زEeyʇC h}?k\2?oʧ>aȐ!kWBHnDD)\?T8^vd%_CsDD]f3%#Jm0{zS'O%[MMF# ĝPV|- Wb.:w.RE-5/p^89@=j'X F΋XѣԎ"WiyÚ]FgT;0/CغeS٧o?F`ht~o`^s64ځPEޝ2Ŝ -O>T4Xwmf 'cR؅)"OQQQ޻X~=jQ+-ɼԫW@.W}8 ˞9 ym2~SG.*( ݺ`ՆmpڑTX3ablA,^#ݾB^U B4_kVԎ$א.Vff&[ CPBHJ琻iY޵e˪%77G}?ſ\tUˢ&wn*? BIoLEߤG#/^I$e*wX#ŚAȒ̎[ZUrvv6m~Oa4}jG*PsXM۷ntjG9wqG"""زi#L/set46oXb[&c0/=W#G偲lݼQ y.Xdd$;wle: ~|-%}X?~5k] n{eS;VQX|Nat|5;oUN+g^7wYܹjŞC<ٗm[7ST)cݒ`>/>G{t)W)3)aٲ% Q9wq϶mF~HtC#3"o[ }D;.]ꎝ:uc6L]15y/|TlX~ہԬQE,VZjP.Knn.SLݩ-V}g{h eI p1#9z05EQXhc_zԴt a$Znz{?w&UPH'O2?EC#_Nz?l؏/cLO sssyP~+^Eh:5{q1_&44ThB:R܅G\.Ǝˌ3ѣ'?,_[C_=~5/ ֠K'ϸm9tу ̓>v4X,̝;wOYmWQ4&(݌F\Gc=}yFIHH(M~X~=9 -- 09|(Rh*X $pNoĚv C4hPXqvٸq#s~ҥp8"뢩cW ?x28MFF cv>C{".Kbb"]t!##˗ӠA9p˗/?w`Z21(u0x+; •ycWV~!Fjգ]vaÆE#YYYY+~dϿ! ]:hKF_h≂ξ+$#/CIُ-AoߞBN(^8>/'Ndd(¤{m6wNdd$+V **olٲ۷-;RC`iܦhK1+hFۖ\YIسq\Dc֜5nVKhۺ͛7yTrػw/;v`+]H:_(P @c /n[6(h,ih-pa d)6nLhڴ) 4h6³fbѼkRs{_0zht¼ysv#Gp!>̹sHJNd@Qr3Q4h %88-SrQzuj֬I͚5/i_Ç'11ϓ|!3gϓCfF:MG(YrePLTBtt45k$<<3|g9^z)SS§Iqwr1~xNʸq4iZmn:}Q ej&88iӦ1t|i… 41c0u|C_&;v¹3`5Nys:zyt<0cƌ"NB{!]ܑmٲ Br _qk]N\.f͚U DB)o]pnΝwpt+;;\kI~իg}&^; %ⶾ ڵkGVؾ}*dZޛnnwm۩S'-[… ߿HqrxW>|8// {P{wЁUVrJ((dggӵkW>#ϟϔ)ST iyq=(inݚ_~UVѽ{wl6IAbb"7f޽lٲ@WߎL{wikѢVb˖-t Z( kmF&M0ܹWߎL{5׬Y36lݻС999›Iq,ˋL{W~}֭[G\\:u";;[,Bx#)E-ˋL{GWխ[-[p1:t@VVy&R܋0o\80rf͚lܸSNѶm[Ԏ$Wο"y¹ȴPsAݭT^[?LjjڑP"țE彇L_RJlܸ,ZlIrrڑP"E彇7M_B lܸA۶mIJJR;^Dsyiym+_<[nEѦMΝ;v$!T!Ž(L "Wti6l؀͛7ĉjGsyiy׋`DDDЦM?v$! wVE彇7O_/,,kRLZhA||ڑ(0R}Ta]8NFΞxP֬YCʕi׮T;B) nGɴ(,#BBBXv-111jՊ={I|'݇¹2r޾V駟x衇xGԎ$DE,)ޡ0,~'Zlc=Ν;Վ$D|iȴw(l￧m۶<#lذAHB )-{8-=w}G׮]yY~ڑ8)//ˋwPX寧7o={sOjG£ G".;;~~zϟoECaNcܹt:zŋڵڱY%&&ҥK222زeϞ_{tt̙3@z͢Eѣڱo2-_syNM|F?fԨQ<|jGȽ/=z4]ta޼y>~VdZ;\-Dq: W;wwC p./:NF^rܴ_hZՎ$](Bh۶mtޝHVXeڴiOn222p\9sJ(AFFpe4?{lڷorbGΝ} &;;˗/SJ4 ŋGШQ#LrbϚ5kG7^S;wLF^F]QzuƌsYYY\h4ҬYV`ٸpϟݻwf+#GD1rHrss}Ë]Ese*;Σ>JxxmtЁbŊPGЯ_?Fm>Yg}{ǸqԎ#^@6nܘb_U 8ErѧOLU4 ݞz=;wcСCYp!ӦMclޠhWB׮]o9\nz|@Ν 0QԨQ#ʔ)].O=TRI>}o1bnc^u,XB:!BnNSeߺuRdIN:ӧULjԨh4?NtUxEƘ1cx(jU;b駟???eС;ＣJJөbB!E{>۾} I)ׯW>sh4*={Tf1ԩS^SAh4ʷ~v"cΝ,FQ=z /L&o߾PkVUk#"N.G.u뒐p|V% Fرcy|fDo#55 -IrrM۵k5R!֮]KnhժVV%**cǎf?»9|4{loXHvZ/^^ ([,[aӔ䥰VWP J]>(?O?N ^cǎ2%FC߾}1 hZ6mJJԎU.]J>}n9+WHB\.~k ʗ/ϛoc=VP Ap8p\<j)z@Q vU4k֌^z.sݜ8qBFB>ZvFzz:YYYdgg_;e6+4 F}Wb #,,W^__֫;zt3$,] BCC)UJ*Rsrr3g'OҥK(W.-ᘫ+?~w3͜? PڍtBdd$DW(رc?~ӧOs9Ξ=ˉSgHNN&-29Y+X0!a)SUon2qD;Na*^ղ`g}'Pq $?%|WyL.ItK͚5[.>^_pݻww^xN?yu`H JI@(CKc*Vc`(~p ~v+ ō=7Ռ-' 99[N:iI䦝Ú~sd]+VB<Ct_FŰaØ3gzyRB^M( +Wd6PB$822+C@XYC"0GaN;GfQaa3aI@nz2d&!'ig[ -Ni׶ ]trj^̙3a#6o!#-ހD\!64h#ИM5J;bIG_( nk&2ns*nE\gd$J;=2:uѮM+ڷoOV|ȸn~g~m~kZ-+Wȑ#7|$y3% (6btѱ|i DHH+n@{FF?#ukr cRVJlyG"I6r!~a5ӰqE^nVl6͛oEjj*wZ!i^0]$̑539wkD+߿L+ʕ+o?+>'~5zHAsev\ кm z|v9Mp950@߃a_05 :ħއ*ߊfݺu̘9UF##D5N1G^r~:Nsju9.]\,(yi+dfe@a>>SpI)\~ߖS3ӢE cݗ;v0b?9x`k0}jʓ`=/Ѻ,ˌ7ɤv{p8Oxͷڜc`;qP*b΃_uYҋMt>sfS'iIV ѾDepnZ7쾵(YÇ1rB)9>>gFdTx۫_eOrn: x>x% O~rƋ/cW_bj_Еx@XwLqZ_}ϧD-çf= 6nȈQIL<_ FW)N+aLh=zP;K+/_fwG8n*4IFV>ܴ$/غX2/1xS?\rjGk3g1c +C^ڑWǂ0i]|kڵkv;qF@f c0Vkv{I]g彩S[XVƽ2L_֘CR^Xŝu4 ?D̞5S"]h % IDATf3&MÏ>Fz؈"3~\+6`ɼO7$<<\h+''OnP+h7 H1/`իU?xVGk$<_Uv$rގuJ] *K,_ #L/R1|wv9i>OBS&3tP]ŝB9v -]@ՎqG7e1<ѻ7sιvjs8 2EߘjV;ǹ2a]A4֬z[lt-曋eݖt,o.{jG㞊{rr2ukrN,TWÒžHX3z}GQn6R!!@h" ]H;;Pz)WXQ:E顗( ^ $^6# Iy{2fޝ3gy6XN +۷oӸ$A']ѓ&yFN{v ùhK5P:$Ht;F~p۷̄ӽGOʼӛ_SBvvfdJG$rH=:C?xbt8<'rInJh~QeQ &twݻ7ÆW|tڶ{{1IX ;MY[GJcذqNm~.@ŹtLiV+ܹX(G}h+Ź Tap31p mۦt8<ӕl>`9d؄: jז[ؽ> EbP̓kُ`yEbظq#:tt*V$%IRNkSF-5%pn7G^Ξ>ERGxL]$ ȯF㑋)]bui{BFٺyٻw/i0h&gվmɁ#xpz ΟXbV;22WT IVۖ\"yMO$XΝPEaվmdLCp>[PZܿk1ïF ~&pA$"P M\9kIw F-K@' fٲL%=Ojbw{u[ۓ-ڟS;9l,Io$8>MJ?o}=nРn.=6*ғXn߷ y1`[_jCBBhٲ%--I$ JcmL1WI\م1`S~ڼy3ƏNV+efh Stqe=3C2]W9̫3&dJ7j{KEa0o<ϟ/K9Yl.v-~ӏm8E'+OY {R v}ۙ+Ǔp`…\p*};wŋvW9-sX]$ƍ*L&>p N/[9%_xU! ~9=rXܓ+?GWl9ydL[Ξ]&l2nܸ![?+Wīde{M~ {wl:<>~r{(͛IqVXoCΝU k?yC\<g*/y6l؀h|wL8{e{3cLB\ IZBP-{i$?Z{9"Z>L~wfQNŨg:Qii4Z[ˡ'ETg߾}t*ﲯaaa8TAhM;$ԣ1\݁9hC7ԣ;Zuɸ:NGƌt*Gw<˱SMtaߐ~ӱ+VckM%_܎u$%%QWIoJlIZ\}s^_'lAvɻŎW1}6'i{9Sx.=mF瞲xdБ8ݸv^9-HsboMfGq~+X!|I;>F[.}61O:6ﳴ5"auOQx PŔ"Tj ,,/{XϜ=G[[}.wج^/\ >9-..cڣCrw\p ڛݻw1qy1[ǷX[[(cGBUmP;{cO 87;ZyWpi`JGײsx.9.Hٍ71q%`TXū^>f Q~t#uzO0[Pȓ8fl}L&Q;Vaw1F)#~w-Q祏8`Wz4^PxcFYChT?S\;.+`6bZzxհt,m.ިKg;v=-- AI==JVos+.6Ѻ،+bru,k=浞Y;ϳ^5^ c/&%o _=;B8c*1<c!&iii8;;/ 55l 8lv5_H jr_*'7raS;{bN95vT9"m9Z!K0\߅99Iu%ɘ' HTT1G-%Τ)Vm@͓ދoMz8tg[\/Cw??yFY<<2\<<K8 h`R3:r=7(MlPBi< Wp Q I$mKksz#<^f],j'ˎothO4K6$\:6?pi?-2n4]LzERZ4%UxݯD g{ħrc*~8SzZčkq*AZr=NZR KzdSg1[j ®n穀$gwSd]JPu2J^TZb4V8{x"Z V1soTmT ,mE1'gcEcJ}9kScWEڑ̤(RD AkJfD\Y>cC2ek-w=0q;D{Kt-"ȱф{wX;T*5 AǾCI\$) Fwx,A8K{bIOgbnz0Ib߼$F]ǔ'1B7ae1Ωm4mTIߧe28`EJDw#>5)jTjU1=dc임) ɀ95  mɬOFhK7rӿ!0%Dw j'_)N3H'i۶6cƘ>~9ڂK h`Ezb1RjQO#]sQ9:[3bޟ~ Zz qP6mJoN(gߧ9z/LrQl$?g݇Uxgs>@(g% l?StESm0x6%6 y78q"g.ƹe1F_FfwaN vhX#U;rɐLJ7OD]&{ϩ@TNY6)'tu$v>2g[RHG 1>Fcc?tA4h[XhBC1DvtC[1j"˘_b] 3[8 t4^p7H8CՎj>ڿ#~~~m>&n6)_:4P\Xa[ay/&}MײtgY=hԨ}=+{aV`Ma0()?N]9_Bܽso+w ֗㰼ZDɸb⎙|9ywRul5WZBiذa_Rƍ)_nי"I ?Ƞn5jooOb6A!+ㅵnC EiF GGzeg /lJ!-[a߭LJmpyG*ɱCD}QR:x ˛dפ^Wڶ!CᆼE3ogp*9+7mĶm[iժU~^*$_}R4U@?sQу;'vjY}QkצV5;Gє|aYDI3ڵie_yҧw/O02\ !u{T(E_)S! x+G>}'qFw>ko竓$38i:.f䉲/gjIWf!8zŸowNp``=Xz%kV::q]vvT &c!4sj{&b&|qln!ȤI)87RgM!԰餞_Сqr Y?t&MC٦ς9IҒbw,"ul'|aLp uɩS'}w;w˱r:zܹs,HNNfXt NZԅoc$3ib8E< 4nX騞ɾ}޳7QbmN5{:^Hf7b8<{Xh!oQ 7o`ؘhJlAz(Y;{q4^S; ?prr}6lU<ܹsq Sgk z!(. grq c3lb܋?:|$SݕHn~tWL0B )stL:iӿA]WP5|$ 7"\_faod <;YC`/?~՛S;Uo{1 XCэ9czMA=֭..y67QQQL8 Y>\6y%ɥ?p9d6Z ~1G1o~1cSN'%5 8Vҡeg6a G]MvL6*U䴛7o2nZ[q45P ɤpO. mڶc_Yu%Ld-gƍ\ݻva0)Z:嬨hUQBc=j ӣ{W:vX`vMuSLa%5i؝YqIfE:WW7ƌ? ?dfΜɔHJJ ڪ]/@bs.cCr4m۶c nׯ3u4-_Y|+*wη:% 1^ނILΝ^ҡ /jQ:PBBBؾ.]8JƫD%|`V) IDATYG88»0\ʸxx~&nqljO`6yvtHN(_~3d20`Vbՙn޼YfGFRSUŪƽK_ٛSc1E_}ӽWQt@ǎi֬Y\̙3lذ59wjUkfxC^^vل)!SU/@ 0)W]:u}ԭ[@Ν cΝԫW"(3L\rsE_v v.nEp,}!Oܱ/䁃'Qkh3&4vɐ)=c ^ɨ']>9}JcPIdHœ|UZ (blLGV_o4QF4jԈre`0ХK۩_!XM&""É$&&8c∉%..HrRRu)88:1 ₽=nnxzzP'xzzKR(Q%KX=Oiqqq;wsU"## uȨ(ѧ`VkKBvDϏJ*@*U ݻ¦M |_ϟҥKܾ};w~6III$'%b2ѥ$cggs!.Jb>,Q' ||lp 3 \| .dܽw#1!IHNJ@$ B;*Gqb(QJ*Qre*W ]ex yaA6y 3^O׮]e`ڴi 2D suu~`?_7oLf,އ ?1>&N#((Pv)T9(|{{{VZE˖-i׮wA^ BBPPǎ#$$DL*,ZիWӡC?eKG*HBBmڴڵkݻW,:R@L&ٷh4,]4cfƍO)Q!UV{|;ٌlc%KV b͚5ޯDq<(..-[EXXXn_dERܹ3k֬ Bs}ZhABB{lٲJ$Xh}XQ ZK.Z[.yHdd$o&}Q˭'=WS,\'''uʕ+СUc(GDDDмysj5{OX{XQ*3gf^/_=zX=!w BNͱg׮]*%R駟PL^Ex2Qݼy͛NHHEQ:$AAJ^?RQTL>}GNwAa/_y+V;wtH”rawvvHĻﾫhL¿DquE7oNٲeٶmJ$&=_ZfftH M:u-Zrlݺ… +`#laXq_~efOMMeJTΧC?N˖-V7oPBJ$[ɓQՌ9I1b!h 9p[aÆ[@I/< ?jĉ8991j(f3FR:>P@Ѷm[6mڵkqppP:$>S}f3Lf޽k׎֭[b Z! 6V駟V1|JT. ۱c;vwa6{E&[رcQՌ=N_C*Pl!֭[ܹ3zbjClay[r裏>BVGa67n! B֬YC^߿?s]x&yѣGĈ#$+R7>Ϭ\>}0x`f͚JR:$! u96ljÇc60a!{y!Ċ+ׯGfJ#1yaB]N Jbذa2uTCDq+Z`Æ cر&6,V2d$1m4CʷާCs2b&L %5p@xw1|7J/O 1~-cǎ/?W:!˫ٳg;CwDqM6>>@p<./?{T*zNcbbO ظ'2yd~'ζ`y9t ZMϞ=$9so! LƏϔ)SXp!W:!0h4|StJgϞfΝ+|Q$Ib̘1,Zw}W鐄|aqO:w#;wF$͛' K],H$}ΝҥKի! dsҮ];֯_Oǎ1,X@>!_Yz5:tP:$!2iӆ`:vHjj*˖-˷"AĀXbk֬]dwjݺ5ӧO̧磒$IR:AL&}%88 6ТE C#Gƣk׮QhQ2_jTTI0sѮ]JRR111+WJ'*zUCBBh߾=AAAXB\?'Q% wNHH6m"00P,j֬Yf$ŋkٳg3|p+De={]vnݚ+Vj)z=]ta݄еk\۷(2xSѵkW+Ee=M6e۶ml߾=ztHy(OtΝ;iР#Eo0ѣ*Xt`xvvvk///+Fe=7f턄СCzۊ]`̘1^:)))q1BBBWYO޽ERJX1^z>}D~5j cǎek3~xϟ@t6J!;wHZVlْz||ԠA[:s挂ZObbdoo/jZ1c}pppP *kРwȑ#oߞT&O̗_~ @TTK.U8J f cݻG2e2Oj;;;j֬Idd$v| Gi]t)J^ȑ# FV0HޡC=zzʕ+sU*ܼy3IrL:5K3FN:7|S ;d5F#ͷ'͚/]4uU(*ԩSd)%(22~Ml(HΝ푛J.]d).f.](UмVe~>$Ib„ ~(ǯe6r1+G={fFCfQ8nݺ/^ FST*zVEVӠA˪~FLm'Nl9"% cڴiO}[VgNؙ8qb;+EZMҥ HĀwη~7[šf_ڵkݻwGa0HIIAZ|||^xsHJ.FvvvF^y>̫9s>uW_Y,BϿ^O>$%%n: *T o0Xr%'OƍjL&S6jsZ9.؄K.q Ξ=spwFPxL2Ԭ@@@ժU^z8;;?gGͬY kZөW9ڵ˷A'bf3[n/ԩSh4,E^Rnݺ\`Ώ]Pݻwٴiwa_{~Fk_E +xɽq(䁝j;-v.HfIH>%"IGBU^">) 1iZф6mаa,###)SLZHV7n\\7e)ddH 틤%>b}G۷;vd~1h4+={6s[:EwjX|9׬ر#;8SޕS1^5Qk,;ܭK0.#r1P;CϞ=iڴ)GDѠRӧ~iۛR756,/(,$$ɌJ`E^Ra6ټy3uԱ\Dqdw^fϞCpp0v.Dަx@4ZGƒp2G7q_?Irȑ#»R} [!Yĥ?s|[.׮[r$,eLwI'|w}G`Ϸ1Gמ](ȿD%;Q܅0{<?ZCo*Ӓm'q78tp?KV4kW֋?aV2s,XT xcL^x"/om"z+w9r Q$}t8V_-XW//$e=O5'NhN,M\J~?B>!ip+SBUfEE&ɿrٗ_~WSoOcn6/fyͿ~g _޹=y"Y>#ƒt&}MXlOrhĝʍW?6 'VMHf3EסVqxWo'/o)#L(W~Dw>Saci2rev?sR ,[$?Qb?lS?˸qdVQv|يRRtT-'V}yszgxg2o|o^\޲`ػ~dֱĆV9=7.~UkƊڏ)j{&/oi#0 yxQ}9nZ!_hdETmFh>Y'ܽ _=g&c" oI#Q|!)oқ5[WrxײZ˻b}Dq t 9d{15CJ|""_? ~o Vjy1[彧2\}+.[y2,p]ULL <_ԥH8= )Vy8R[ͿF}Jz]Fw#K/Q܅\tYiHzZ2YTm~5"5>>JK֠w$%% Rl5n+=]݌6\E_ߒGw!Wew_o@3jh\2>Litxzwea''扌6dW_ߒGw!WE!-IagwS\JPWeT$"["Eևߧrc*~8SzZčkq*AZdY_RGw!W5U%!}t X0FCu,彊).GY~ɗ)T*+W[ͿJ97w(Q $E^h-/o #֖rd AP[GϼGPv;?Z d673*Gqr;q Mf,>õ\pN>l9 ܿ|$3EʽF.eݸ!_? ߟRVkñĦOjѻS+f̘![7"O ,e1,/t4i.U:uwЯ_?YϙȿD[Eqɐ8ĨJb3.IZUKDWȿr˿(3֭*TԪ/&7ȺT^`6sl5nJ۶mҧD%gͿ(3k׮]v%(bN;7wU ,e=kEq˜9qucMJcu'svw Y}%+y/\<<<^9`#O"> 0A)ȿȿRDϹ /$$$v(]ZtHzp0țM^FW_ߚD"W y뭷Xf57`O0RI6Ƿǔ4o֘U+W"JWĕRO۠q,MKqyIk&lS4xgͲۣD%,'WKiذ!ǎr=l!YDJmҖeƌ7w͝@_i"2ĉ'PPxzzҿ__bY3kWU5 {)s39'~C5!;wo+S+K_Y"9Eg;GGy̴4e+K_ g27okbbc8ʭFPhCƔ'H07KKtڍ)_ҡ0e+K?(l_2u:ݡDTh6-h-UHKK.|xE搋ȿDU/ ;Ȗ-[3wc!jTw U$Lܭs:6~~%:dO",e.XUDDYEU!E+ýD܊W@ѾT?Q>Ogxpy$5)b~tԁN:Ѹqc,'e+ _wA1фBp,&q,k1\:bJFha2H%pԸ{]b,EѸQC6mB&MQ*J6+K_Y= 6`0pE.\ŋ^d"nZ2tIDATHRb"&$8… ClٲPZ5-g+K_Y- B>#VA|FwAAgDqA|Xt  X\>>TIENDB`gplearn-0.4.2/doc/images/ex1_fig1.png000066400000000000000000001353261423420364700173070ustar00rootroot00000000000000PNG  IHDRdG}*sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.1.0, http://matplotlib.org/mߕ IDATxYdy;VK/wsHɔIH(P Y ۀm6ໄ@@EIjr h%EZ/3gzzTٷtUWuO,d<@Suz>T5jԨQ݇n@5j F5v jBQF]kԨQc&5j% F5v jBQF]kԨQc&5j%Pw}WF;F5v jBQF]kԨQc&5j% F5v jBQF]kԨQc&5j% F5v jBQF]kԨQc&5j%ةP[( ,@Q$i[*5jԠ&W y1EQEQŋ9x ( ,#2$d]Ԅ\㊐9ie?яЇ>TnQ>}}$IBeEAU՚kԠ&(($I`"x( <'28yMM5~^Qrm$4M$`mF@ND]FBM5.D\ uQ# n4떨a{ZnKo7.jBf(x+snsp@nUD . g2mg@pt]~siy QA@;wr<6~>y(Jso줙v+qy>r;mz -jLlw{W,m!B<U,B $IVpZNeX5s~U}g*6M~ϱcv]cii [w\0j~ױ ]G)v̑f)~ۼ~uuq<4"[7P333IioQ"*vڸ;>x'1 xgOpqt5UD= xW|߿ǵpS\J1$Iy333aӓm͒f).qX".WU>';U|Hih6EAx`0HԶmh4)QO*^)ꦗ5!_'i3G|W?bć|ߧ頪*Ӵ-1)eqdE Vhmz~ 8t, 1|8൹@;$ՇeYXU,a=cuuumqec_$B).n1M&kw1;c#A{xܛcIDbHgϢC5$DWuL\! AhF?E@wiZJcF)?St.^ Cԓy%2:}7Q/,,`YZr yJ&s[=W~z~ݢ)X/i"esQ4!W8%^"]`6Id~G44U8Nc,1L4V2Zꐯ0DӴkv|W,&02XW7&]a /ȱc&. zw^c94LQu9VVWFăp doqHQAgY6$%^ !1"Ӱ AU$|/ُ~==x%n gΜǵN!e5!Q~Q?u^_x}1u 4Mfдq8QËvpH7oƨJl0cw} )]Q,./"gGppA4"Nb%SU ia6MID˟k\拫=\oͻv|;EM3R ?7 [<Ï|V~{A<J55q.-E=yITMď=iMl{8Y3aq!g~WM%i=_ƐI;m[HUUGj%QU6s6 u_Yti1qDf!plF ds-gM˼}؋ɤtH$]I -qq}y4 Q8\x'O$ UdQ;m^tNE!jG0 (bqqqF%!&C\I30&EȮrqw=fbX)B6Qgzkl16 z4˫}Yq*'žᨤ^[l0 =l&MRLh$4L)"CBS{|OpСqW",u}$vgƥNNO0Q( zoFSiV4MϿ&+ĕ6sl(Uyuu'N 2 aif"IҘDul˞Vz++m& [lw^2)nb[6Zopf]G( a#58c="ӯpi~_AȃOO_Iԃ<#F+5-IaYV5#rmæLך 4b]ץn_]mԄ|dBez< apwWyǶ C50T(&6Q0J? /?72ۚe?YѤ8Ytקa:Sʖ3H? 105ɒzAC70$?/sm{?^D!Ia`333s+++ 0䥗^,z}$vsvRpD=;;[=^QUٳQ,zSSSW})ԄCd2Nwiiz Y{qWμ/<4BW&z*]vu?K0 ۏ.\֐`0Q CCtBJH+vڰt s vcm_Eo<˩ xG?[uR4 C^u9`0<a7q0M#`.vN)mNMQrSNÄ\:]O y(\",='R-H#7 %y$uR14!Nb.OO> w|ಈZ~˹sYGhT߃w;6 K)'0FVD=Lup3Dž HÇ_yOfϞ=w}Ay?*^LB%Ec:i.J*G>Ų,}sA|?PEQGf^-E(u( |g0 2{aʙiZMqn RqOLENEL58x$KJH+y>,#JF%^*- `6$&!n4PDk70y'xՂfҼK-w{bfDOsw/o'> |'\o,?#|IyxGx{SmggLOO[ocK_WMlCUmSN<2^9 /{=W*&u5/"Q ЭH( >bHE| jL_t.y芎wߕhmQ> /?QdeBUUPa7JU sERF",p}[G> + '#hVҼ8IWn]"˲]߀S?s=ooO~rG3<ñc8rxGOO_Weu5N9TUvE'Odyy[nz?<c<)EZ^$|m]oҹKe\XX͑e++"VdS7YV# KYLUWS4L ݑm$aߞ}ʢAH[M@De*FUq#S'Y a r_$I^O4yO n;tۦH K6.Ki9u/qxv9JinFBze'laan{~~~zmUʶ"sIȥt-˲5sl'eo6n[onm[N|홯a6E<3$i6 EWUi89r\몎c:^?9pK D"*B䝖t .Ǖ6sl<'Ny殻zHHR|+E^`x"-i4!2Qab1Ȓci(P$eKuEn dMMbUmUSӜ")B?qLfIgA4$Ia)*6tZkS i7d$MHr!=Nj'_3rm>TE v(y`Si^IWSwTEQ {yZPwop]w$e]q̙g2777qy4uݑU炐V3Ǥs (ٳg?WξD'^Axjߺ))F ͠whY-N?MףlVD<(YWNty(' +ȴFIT/.YaϞ=dy$I 6f AG H]k4 C7nNcAh5Mѐ% mYv$Ic^8Cw};lv#y歮ri85Z*A4୕0@;zqx0tvLp7fnn{˿G_"=_W?~Un|74!o~'~mi9Gi \{AoKӟ-=i9D_((Y5p},pLgXN湛}044O;wYnxTϲKdI&]4 ]ױLQfxHb AeUcL?W ‧^x>pXŨy΍˕ VE+QYI_ GTIc|k]6FB/O Y{{}oyG}~cǎ133c=v=. j-4gEe=zE@EQ?jI>VoB%O(!!a6 p} p'hMlݦ;VA*o>~%vcmY-E$ ,a5HҤK5qv$wX,WљL7)$QHrTA翉H{ʰFvyϟ:Qy^XWyӜ\=I?)lTD|;@0!+˽<<#6M|+y"L `yy'N1i6W?x^96n kOg^Ȃt*".1XEcfC-53mD!Kr5OVWWQ{igQc8Bw1y1UޤEQPTOdC"Bc1*IB eYFu4]#NbtMg9#d$ B)pt7uY/}xk]Qz'$y%#gj*sͮٲ̏N33þ[QJxÿľ澫v>#S!92(rxii~F= /0L|T+$ArgY!aI0@ҶUmUYa5F>iO,,,`6,V X^^5 .yi%VUQK > o KۼʗaBͮy[&n$ =>U6/Be].ݮD 9_׉C%\DnWLn{)a2DBH%<ŋqM Ng!*mcrna6]`$Sxr&!3414"J#lFB $t=h N8ЈH0U]3dfZ3syOW~/E;v`7Mǵo߾c,ypߦIC1H%AG(Dԫw| IDATa/}&qGgesȕ!" ~*Ani){-4f,ȋ|[?i7Ә89}4H0;;;r b-emY-,рhưѮNNЩ`0S,Q S7"0*+ѤYxŋ}z^5V5<'oOy߭c~vj`|12O!yZS qm־ 0\gīq#hyi:'d0,ٳ={} J,|;bt](IJ-o bR`SaNx'&pbV;=,Ϙtf 4Vy;/#CS "V]9ߒLn ]jY\Xpn$IX]]=tUWE*,*#4MxAgqEv22匑~0C) M- ɋsaV1 {.$$P:m5M˴ư*Fuáu*ObnzCs:`0ﳸHcF]jEIijgy«, Hk#{ SV1l&{駹8y$>,?<o~;n?ϯʯ裏ƶ,B|9 yc< x9rddž?p9/)X޽{^$22!m8kcM!%ǐ03f{S͍=tEg=Je6L1 qoٖ EVh"9iH e[+UFnHxQ뺎YqSNFj ")4m uI%$o]r"EtNu|S9ϼH4fqhΩ;%yu)0$MSxm3_8~ _X5٤a784{C3PUR rWu2 k#|5qra, 0ȑ#}W-PV7z?c&Fٜc8[Ca6*BONGLOOWMY /[iix+$)A H]jrl`$Kp3˰05S&|>tT$JǣȪc9,JVd?B$ZK(=ᨧ4&$sw OS3i )iM1.ݻw,gZؗD(8|'N`0S{\j?o%kg٦CN[z/( Vz+̌MVeY4ߴZߚ}/9eYF$\pݤ2_L; 2cKb6oXYt4i@mڨ*$~n`nQ]K5qI G/#'d$I{խRsq<ѣ;H@xNSt{4nV.Q,t:DQ4wv~䏑c8?Rc_JڨC !S?6oobe~R!TMN#Zn4LQ GLp}wԛcM{mh&4F>7%^(6b,l N?b5yu<ï4 d9 _ IID*R)%S2?+P!U2S GD#$"5ᯅT`ik" R/u[ؖJ X\VA,R@MICk>;>t'ؘخ7Mۿ579]Sz%9r}c?%~ӧr.C<A#G*O<肂n#?[G}n$j;BeETɺ%b\7[vU,7$H2ys<}71;3EUkCFWSwsD USu]/|ٚMcd1mGE6rk>)}Q~7~??[n_2>,7/ꫯȲL<#Pw랐K\IN!?<] fePdA< Vii.̃p@i}3qS,;U!1*B.⊏wvvzj￟/| |C_ 'doW~7|`߾iN+Gabh(yu݈,˪{p;Eja X$ըEEV(₳gi4ϏFKQȯp|ThX6H3щ'2-EUeG}tCG7D!Ʊ0DR i$!+]="CVdlMK2ҵt Iţi2XDf ?ɢ ""vS3()T[p+|xF"!TU,:eiXh= G)4ISZnIMKq?=̱ccjg'SF)L2_ɫk!z=?Ne9r0 7%0 kߥa6BoK {غMF_}gjj=]ձU{y $ H=o[늊~OŶmn;z)1N{DQ)rA4"dlxy {, F 4؎]wQ((dqFvs|隐&nV7ȫ&>BhqO\30ޤfM%A:n)fq?_EC5Ȋ p@ZP0RTeH$O2Qlbed8CS*hH96s ({<,k4X8&y!6 dII<<7ѣ۪~W4tpKѨt~Іj!<&J?2WkHDƤRPJ4rnˁb󐕧[B6yt:LСC(B&n nyX7 ^1Sqg7{2t!|*ߴmSe@hw^tE'[tNu(誎jt񛁬(J&Ϸ(1?URv=Cf),fʪH!!dJmآ[E=lk*%0("*2Z@(Kхoxw{ypM?,FV0 GURD0 eiDs=z! B Wk.oGݶr]~r'^$$FqO$Y҈<ˑsˊcxy"UIBBU12<BsM }"shՅؚyZjY qӶeml Q^Uyզ#LBD4 =Ui8$}! suES5*_9Y! oh-QKWI$0#ݏEt5dd)ᷡUaQB˔*8#Vag$7n)h1W*?לl(;,gy^yLB&iq-/MjW\{pCvTAb c;|XIcѕ-s\ץCw~fjmP^YJ ?I_$OHPxoF. Aժ`" Ftn([V(n0dO|ZFJIdE6%"ʢӨTp97Y0`_pHt-qm=\֛p0.°pqEnu~vg@8IFE>UVyGNWƄIcMј6 9 #VVij:cy$W~9l'KH)/rzz8I$ؔ7"SȫY. Wo-% *g1Mj$ Vs==XW9M|\7&lJMI"E ©$DJJR$dEFvkH mMEzE>99"MY0rOJkmIn'dym$i!prWWW9~8rwl;TʷMEi蕧eX[EfJM hYz;(TF$$NXaH.VF3ɳA-sm*v)KbA=9xXZ,t%iS&k4[6A0HD1E𱶌&9%5x]ߥNaR7 XYYu]~biiF3G[b9UAy _mCnUNꪢr* 3i\6 Mք۴mMiЋzӶ(n&IUD̼ ..@IE5ttTU%1ՄDC_,V]E!heMw9t7vim>%0v|'Qv?^~eovFcs_?Mɲ??'d~~xGy۪9( VVV8~8e粉0!v-$EAף?fY3o@7bd iN\AXE6,X,!Q ^G)car3L&9Ȳ 䬚(ܶx7VU$O6kNj22δp`-# H0^Q!2, YKi.t\lYlYM%#c"e)ZvZgH aNi I_#aI)4e$Np4J۔5aVeERw,vu48x v|#|3뮻.3<ñc8rxkB.1LEQ̉'m{粖.P,DFnEAu]cj¤G-Q \Gq#ʶ2, K"2,67i"OحYZfF.yv$&˯WmIU=ȵ<2umvey< A > 8}4}D\BQ${'7Qsض͡CUTHʦj'5qUuaUTRM^kXZZniBalpnSL[1V*(ٺMZN7 F@e 0椢9A?&#nN .SqC~OUYYou!)Ҙ6z*k2?ٔ[xCB ZhWetʉܘeEƴLf[Hľ}B+1zs47ft7淯v077Ǚ3SϞ=[_.nB~UUٿ?wym'⪪닯W1kͱLjx$mX-7/0TS6h.i;j#lPvER IZg` qq+&q`]yPuI‘ 7d`#݋Q@QcR늃ȫs MհT UQIS5q*:o$B97i^h! M " l|KD;q5F,kWwO\vgmc;am9@(8>(/D$#9I, l6q^ef/sUUUݳz+YNpy2J#E*Vu;Ly#>< Lpf^ `d/*Mr_X߆fV!,M;OONut4PJEQv#"(",p D y@,7EY/ =ت-4)~afPVG9)ܧB5h0)‡SS4"w?toc<}}`8d6*JZy^׫/"#?#/} .]>SR]$̪Y 0^1XXhE0߇0u!̜f3؁><˗a*&)M^fl*G$ ** @BXQ*ǀJ~#Adۋ\k.i#GqI3wrt\NnW>Ϝf]@Ϋ}Ӑ7W^m۸{khn<aHwgLb)hqPq4Nj;Zvѐz49x$NRXE$h B)k,/OLʬFm KF®ܗ,¢Qe:p(dg k:,B"$J*$=9&oUVlǧggĠf*ԄPI-M1ak6ٳg%/ݨMӜIKOȯg7`6kQFzz  \299lq;P>aV0 ta0 j4sz@Zvt:mo8$$KVda/)K[gf&"bdf "ҕ6/@Hh0qO#[p Bj `WXhR>N "tYЌߘ j*RfBdCQBA@=8%A)y &(>]$p8UUg8]n ZVIYh #e;3K[%B@gJ kz up<[ /`j}63Iawԓ&yЊ,7NDL\H,JUԹϠFɫUpٹ\x*3!bdaU[CDZ`z}C2_LҖa,u"gdơ'_G~&v^|K_OB|yT:|߇(Lr אoLy j_tvo7OƛA qIQp:jq9peNM<$R~=TYVGfR b},w$rPh56 {GrFdK͏:~GY+@4%1ntb/B2wIGDfMa̙0 a6)@Yv/2dY޵97|c&M񼩚L[GBE:k˾VN L8oxӌ ul4!+2V<'`iĄTjb<'5/TqE(ӢJ lQ4/0sAVĤpd"#&fwI?X*CL%kY5mxIi/d^_3}ӐyQx4!~ϚS?L"F*exD*tځ265[:nmrB֩^,p}TKh0,tqq1KcjmhUQszI 0x$ E02 q(1E~Q Wڐ M9x dqh{3yxRA@@eaW(0 YF_3F®hbT& $ Ce.kexsh7!J̤Bp[6^_Oeލ4tpa~k;BQorZ_F$ה$+OTɒ$NvQM))v'/3Ys{%ZoFTcL (Ń @@$X,:;1& SRKa6|G{Ed 0e$L qqDSDZo@!#2UFo?2dD`QqF!$Tulg8NMᚬ4! R"1TTL2(Jm{O=(f'Z, B}QB|>o| '^_~Q[o7<{8f8(J8tԄ38P$ab_rS3. qȁ#UyFy®H v<^لy먕k}:Oapvq*[pw*֨ c( ɢ2ep*@XFR- FA+ZRZ Ŧ  [Җ c*p ڈ.ߋ7-I,.\ "UͰNJ*"ߌv׾5akk <N8~xO<]C/^[wZT:-T  )zn, o"ayΠq(IXYZCX01&L6 kA)`Gq?<‚J2\Rd='fil4l0 4ju/M"OyUfluOE6ݞL߮F>=q0%)0ˏ|Jj ?pj«M_۶1nfbvwu|^2{1<3kȼ s=~۶q=\qSO&rE8< 6}K^ZҦSEghhz$t:z(˸28h4)l/GYqkۆb iw@MI#=Eǿ8q2HDpyh$)CC8ɳD@0yjEd"6]ER qz*Qh7t3.TUE^Ν;lx/o^ aض`z G~MCVGl[/^~Q\b[1<n-%,(uMPD$&IJ`IT*ammm0i`6.Eb3af ˴P;Pcj=IFAU UhB|01 E›$D%$IbԀj16UɅ<{B"լYWtIVAGpBg&SC%tǚVܻ|o,"SpmS]"Iڌƍ\ !ˋOH0DلHV9׹(pC:Q+{}q(&3p/Yi!MӖ "C6dHahL{>./ \HBQJ46*B_ا`A_ܼs[;s $o=֙i/+!a`^ݜ4u}O ?k̽,N/^~Q;4MDQZGI/kt.,-2=Y0~oz.ҚAw\fPm=wSjd$AB"qm.<3aNn$~l:]TLsqTzj4FayJ5(lY{>0)%4_gOmXԂD$QaCd(DM@P3j3^nzRd< F.EuinȮ*t_/^x":9lR 97 ԜMXa Xzjf0!2VWW Q52e򌗮+zf 9I4MGZݷ 'tryR|BMqHݰ*2 B&uܨ4_He+HW*3Iѥm2!byT ZNKBIdXJa0d|cH1ڪ 't/ZB$xhaZio 4,~<.^( i77.n}N7v}վ7[|D64M058f:Q8~vbλ30ii'7KzYxOL,*&jlddN4uTYN;?%l-fxljvC'>ZxpZEPiou six~_x޽}֐yqn/aܹsy>tx.v 0J_.q((ө><$ ..lFu S53ii_b^qtS2E>vvvaKC06-ČCA}rƉ'T8/EV+5Qޞ[5JJpHhP%9a03S榀{8qU3=~wUUq-s/~_%DQ}3& IDAT?|ݯnN=unoQW^O? UUqCƌk<̶m:3ow'g(*aBa0DYg8D$NJBCT ';:8Y3MRz![/CݪdQ^AoqD%&Z;'f PL°jER`Q eVep{yY579͑Iu]5&e\r/{(#O.lIiK󻺺5\8Oc}c|_kT|8pLĝwމO|8qmloϽGy~;>ONE_?#^x_^xᅽxikB.r|KWX__:CAe qHyV̼l`jA|*H?N+e;lE < &L 8v& 0 GG!QȌj [f M Iiame4sq*zUQE.Ld&~Iؒ7FM3<[oǏO/;嵯2/JԄ1.]x衇ƪ3n2ӛktJZ ~c.EJf λ^YPRsJƎ{\DNV/? P UR$?va,ӂ;t.(uaHА$ {JR$;k*E ̓Dg &}8A\X.p!ƥ Nc69e_ <D$M3$ ۶E^l,d)ynس?#O/^Ç:tO?]Kϒ 9I\rgϞ"|slMdӡm}~(il#KKj7ᦓ(XYXA!FY y$x*Q 'VwALD/~DDys|B~3OfomCgO=4V[!pqpuw}3;켂Yx{m6EL񄣐# Vڭg:sYFePy*bdYƁDS*5"Sٓ%K2EB4@$q*9&~ P.pp11 0 r]~yLF!Y}ݷ"+ B*CebpT`t¸Ҧb2ELpQ27c>xMtzs0j!H^/wwկ~5sI<ӧOٳX[[__ϯqyː뮻ͷO=׎TO n1P]" /(_\LIy, ^ROUT6 @X8Q3ӿsԄ0GdxzALqH]:c%5bx͝Vi.+8Dc`NY̚2K" #~ Ȓ;LV.Ĵ UVՖ>3Q!˗/h@UkAnQ/Q0ӌ\hXZDsX_ {}\\i8tPf~e 7pbǼrL!& ʡPh!aΝNՅ*ECl3^ڝ_w&{8HWtXɖ2z}A$NϱiT @Rat&!Tq9&xbi+A(4.: 9IPG-%އ~ঀ&1yY1ݤo^>l@VTg{ 6K4 +BLMػ&& "Lc `vX]\Ł3+]Zd@ 1QfcF.]A=%Z5c99o"4B쬲ԔQylpah In߅j*U!+2SQRptr$=dpX=)4Um&܋ePA<☖2YϮ? C5N硽ӆSA8\JϾt%)OvW4G˲PZ(͵DI#D `ˣ7r]W(ش8JCe3ϋr߄39d5A\ UKuDQgCh+ .-&Whv }AN;#jT f0H)agC n?vsLޮqG!{$7]CM NoF00 hNA0:QYMi$IB`uiCoKv^qS J(.o^FXZ^BLjxWTM07+Jٚ _| LS CƵ\^^Μ8` [h a.UtkRshlX$O&tClj'D,WY?e~Ϧ peZ@'CFzUUQ6"Ƃ$N@b !Zu&jqEWQǝ/ݫxOH77m_|^\;Cao"ˆӯV ,O=&%eqye+up#&aΜp!Bt][l;v(## :\/caaaƚYVԄ"+7 {O4Ia D~/2Ol.Ae,,ˢBPV"y/ aGSŕ0zk nl6O;|SO=/Pڭc`6dY(8uut+2ް(p)MoKo6_c8iwX[BuA4^+#qu#ްQ.TQ"C;mt]cd.^K1E{>v;(i%U /؄}q3\YcY͠Kyq NuP"Nb+Cq.I, nlg,1緢=! NV3' CgA FKLeɲPk,ՂLdllomG߆/2!$IׂK/aee妄^z%,//4Mٟz)yxO~vuz o}[( ~W~;;Sw1:u*sE! ;]e[8n^JK xx "7… ,kS8qX? 'pr1ZUV "i(È䈽n 6M(J09`  O*\7O&kbjO q{a2g)PtTX=8̕9jR"-AhP$5ctDϛ12OJ(%<*8_:2VA#^'NDqٲLK7]Bf,!JzGvcyk5d^y&A?/'s)ka,601fmgǐ-͚Pn5$ Ѕ3tl6afxL3ЊҫIҼ8Rn~nhc7j.^4q3 }!!`H2MzT JW^y"e!!HB G@X^P Sv^19:mEe:度|UYE$i\w1!eH 47tu=oA BęBe4M߳w Ȥ>I|o̹3m)yF!MvN C 8[7m~Ƴ8Ct.,b_L줚^vrY,4q|9f.K-_pͬ83'/MyQI'i۴c2$f6`P)~6AǠ7:!$dMxeq|_ ,%Z*ٙY{/k I@C*Tr}z_#.\Od^$/daF0 X_đ#aW锑J]6~0%&[-t]TUj/")h;mĈ!e0 EaEbt:9IstX<&rɹM͜p5p% 3y%8$"P  Ï|Hdl+Ixa{= f 5U8j[6nb\*NbǓekv!*³$ dyS^^tcmm-3yFT54-{;饧`Jif6 CLIΗQ \&Qu]y4^^Cvc{ x׻ޅwy`qqAfըf݋ Lg6$EXeT*[1(fӧĎKw?ip8"X  eQϛy<]9֔l3ITE) `ǒ`$5ѐcQJ8x`F)C1f6YmnݙWb N‰a+S1{R|h粛"dd^\TpAPiSau+++b?r)h9oi?W&L _*|Is]̪}אUUśfqI_x 3+ (^eq#Z\Ỷ<FAK-mZ$^KԲ.Wq ,d5W%7g"EqtTĢXseޓ'B4ufJ KW\mnULD:MEO`P, )XI&¯>j '8PڻPtzR㢊n+`0MS4W~EC:E}_@/_II(e/P!I]wAS4|[+ .qab"G}PJg֝EüaBk&X褑$lBvT*j9k.B[gy&H0L#8NIU!#Fţ\|%[>8cMe'Y\Qx,R1(tE%@ih"qf0^΅OJtVo;;;" `WFE!KfFZ&\׽Y鯪eCion]z]Nɢr)c[=Ucy?3N]^ؚ=5sB80ƑO0Ө ;S5rZI0?IfC2wg2QH tC"̻XLwz\[^4v,S7#Ƣ(]'uqe(T֧v~5IBcl5NUN@'PafLVYVIM(I`w]T*e |,^B`۶PI!Tk-3ߋڷ 9oBuv_d *Lf(jz.:쒍|9D.\r2D$sP6hiRUEV,OC5ﲸwEQ vf%:n$fI+ͼb7~DžoaicDlFwl KGؒt ѢYkRJQB FB!vٗWV\h0( |qJ)noȬ+YbNHRr&51 ” \q z3]a_`0w-,&W\<NԒVW `gg 6_ IDATMWޤ#Fc^n#p`X.zCCekLȒ L,Za 3u|W+-0 F!(hh ZҼ"IZW:bZ "1BDQ*^ ޴]][[[8s =;#Puhe=ZQK!5kWqU/񞷼Ï|T$A>i ^iH~dJٷK}5 a6rpE\Z0th<0 nKrM2x=\ 0q iJ<2YLyza R*l1F#0b+$AE?,..đ׫:^'/L^r]_Ipk]0mB߇(bՊZ0ct:k߹cǷx[2Rf g?ss hBumn۰K6t5Vgqй`0s i g;P K%/B\ey"6mpɫ;G>/| $ ?թ?og+^|ETU?~5Uq!ǥ{^PӴ)昸'?I(o RW)^x|SNߏSN'Onv=71{rӧG>oO{:we"Oya8B\E DQzN{,u19x(@GI&/a00k6 dB5i>k)49$[坸 &_XZ^J/g-̀`gM׆b̼4xlFi ӃB4;t,i*K%*U\}ndEQgϢjĉIkUi#I+49*htDQ庖<|/GAV<#韾ǝ}ِyq[8s >n6qQ, xŧm)+ŗy Cfa9OFL{ZV __euXگOJ_nx">,}!\x/uCΣagb{{Ǐǝw9}U|g0$IXˮۅwYe%Hct: VE`z]!y^e:2tDRIQ+DP~8<$Bb6P53q$l qa^0yӷ&k'ooW_!y"qulllx衇f.n[ y{SEBSFHICHpf ˴(XmvsE4\H!E5+:Zn1fǘ7[W5Q $$B"%"c73RlJJW\spg8]`{{?s?˲OOW4z׻njkkkW"R/cAs΁R IpY,//رcWA:]|8p83tP_djS5굄p#-5(0s:UJFX`?E=IyvVɂ[0M219;vHH2'lr.%B2 hۨT*XYX)CVep*/mƛތ7MlJYee~==ɜ>}Zd}Wթf'O[o~Ss{K=uqy,--ɓׄ/2{w;tL74![u:&&lawv,$"÷oW^47cuS+Դ5/ U{>:mo6K Mކfz`X:u ԧjG>xя~tx׵/'dq)*Y._5&$Sʾj*VA7FQP1zZ¾Q4%㜬󅠪sKI=zFQVsE(&577GXR2K7Y("0j 'Hy$Ib%8bv Ur[hZp]XN:>dl*e;Əj*M4<)8r*M[<8ƹs簳#Ǐ؏nw!qF 'N}9}o1jncqqkKk]q AECZEi,!阨1[^)\޹GGYyμsdrOH $\z9]/]՞Zu݊gWzvz-[[bQHn{L/L2 9'03o2fy>FVNlGT9^% sY3ωG6ruS t{ f2+Mթ#S5e0'OrHKK,aLk&,PܼffLOK8fϞu5uܓjt>jVKII 'O{!++'|gm3,\x~}X4ab34ܡ!p:\L2^1:d0$GdRD[봚#vd吞Tِμ~)rz̓ILZ&jf9  /{I:M0|ܾǧS\\Laaa\o߯e@ :#cO?ͣ>ʗGs֐!'viki+(-fo^=Ài=-fMqEĝHtz=sy]9ZohGl  '==޾$,o4G LEVJNa_K0Y7zX|(F2/'3IVtMv54@`tKzgmAmµ~)'yc[GG~_k㡩|6ߔZm@ĞȟGkk+PJN' " 5F"ǺiSzC3@l.YM֤(*-VSQ8!]j})#b(]7RJ5Rlͅl=h%q k yUѥť" " FjJ͢y7F,tttp1Jȱ-:Hwvv45u|ND"vY`6旿%=\stɊϚiW 8p$IH ,_7$';G$Ikmphb>hSDHWqd;=.ђRGn|a=D("/7/F2e3ْeQLy5mv&3NL^s/fH?FQ)9x:ьMT?=5~P|K/ sb6 ͤpޛH, j'i-:H;~AAuuu?~n"6mtΓ:6 ùVG70w\eCtv*-)G =P edlF~n6':3ZUѪ+~iHx#~4S'O188Hff& bcrhҩ2[چsGrC QF;].n7`d`ٕ!._n0&{_f%|ѠORYY9l5CIwuuz1#t+IhoopBV+oyx'XdG3m7BH>K.dԿ'˲f؝GIIn8=[MoĜVgLӆ:'.F3"cb (ހp$<*KIVd|^2bQd<=_ΠH|!߈ƑT(&erRg6磫KHA<6GtvkfS$ݒND Fl%%su+vJZRO̘1Yfzxc64M1k,:::Xlg'ruz/!//{x\e/_Ά Y[ƅ8[OdPL[[[ZAֆn,=9wfhTF%ʴTfCM&gZV;j$'8!ݚ%ݒ{qx&0 F$YiyñI/6і{.p3r&P$X"^A,5iH.uK$V\WyU9gB A}?Ǐ#I/2N?zkv̲M6t:馛 6Oanٺ5yVYud-^V$I6"HA߿P(DEEEJ [jl 8F5P{͠>"rӅd0YrÇk8Ѧv%+9;vYIiφf+gc1ќ4 Y:0HA^#xьQ0b2(f*-ͮL%\hsDË,JњheqQU'bΜ9OKxQS\\LQQ288%\ƒ>Ȗ-[x衇d 9tP׭[M7݄ |sNt9`(Q@Bw.h5.4u{֥%~Ѩ5VM油*"ݾn BvZ6_ 2Ryܸ\.T Ʃ |@ JG8dVrG" 4"62#A_PiTq1|9B8&BZyar$,u8N&V+S Kz\tEX,~_zj~qUWiͳ:?$#"P(O蠤yذS%Ff͢S ]x{l?=f `0(J3a3c7HW!EF#FQaZ,&K\UC )4јXAˊ1~iq2R\ #kNm&rBRKoO/HlLRH4=*Z e\8qt8夦N"d20xE/V8q)U޳f͢#Gp]wQYY͛q:1O+dx,sI8@~~>g9]*===CCCyfk6j,`þ 8V$j^D" EifFOwFk 8ށdj*>blNHݔZ4ÖMtŽDY pP<+ђ0ylP4#I4aؑe||!C! `hբ?ó^MKK |3v!ֆ罹իo~ÓO>_>oQQG9vEEE"iUr?---8Njkk ;YVFڊb欇$BW!~V=Fq`SnAjDgW'R@0$kB!̲:*YZc4SSSl%ԭӭ.(,*Q$2WIu2_D붚8E'^gdRjNV|j8)S(..Cl2ټy}}K.gn`֭\ /W4VYmmmtwwc67o#&̝;wܴ^WFIa{C aqXp+VAnPeӞ~E! #Íj$I>dd˗S9B"$P]hq O)đYMV7,G4sTCD"8m|\wKUL&V^o[z)>OhV|7{M~~>>v//Ȳ]w[on7 c [ طoN";;*$IjեmMhݼ.ސ7/E2@PbnKǖnK0;0̸0;Xh{"9UV-+KeYf``4 I%22$ #x^ Ù m=:2ݍj$i!Y 8x2htmI3'!q#B>z(1ɉ6W;Az,f" ҢCEQ_$ۍjZ_ZZusM[hj&˲h^\  g -y7d0T_z?WF͉ll1NwV9HDoo/Pb3YFtGh6E<ň1B4HW)SV-ݢV'(++;,7zIwV>Ut{{;˖-~8]'.v@nllq̘1'Or5p-(SOy"==5&ygü6'/mv22L툗qg4-ΙLD(&_FF K1"Ȱg0@eK3^Gf k"%e^W_[~- k1xD t:9HAZZZeJF#=k׮]vyzEӞ ; }l޼SNk. /Z2IDATPLil6[L,{ vGLHK 7WۤEhٙTW,FQkٴh!DP(b!;;;}NVrPKLnt4믿O?}Vf]:z@-,IccO:Eyy9uuuQQQc?h0bhvy38IYc4FsLDDhQ#&RLzV'P!gN0fG(;lV6 N J/> Y49n"ƫIny3ʆ)?!ICU)((ﾛK/|pLKzo˗/G$nvVX5k{o?= $֭[ٺu+;w$ h"ꨭHlzX, }.n7ڦ|1O Hh>39z f`bo5Ȫg!)eȧt"BN o G4$v3H?jLi P ۇ$Il6|A;_:cve(I9s& fo3<3&kctsh4RUUEUUCsNygطoiiiQ__OMM ~9B0phߏg#EH:.fω=|xC`Yw#ed%H5j;*ׇ';; v;^z)^z)|{zzhlld֭+=zbD+"2iiiZVF3`0pQE,֮VwlpE8?jNԣGzz69\dY`PLL ZƙͪdkdP$DIF ;d0>0L#?X~=>, \uUcxk?i&͛ǓO>;=  õ^˵^ kuVyq6 ssszܹsa߾},[+ /L$_WX, |oeW8 ٽ{V޳gf9s7M.]cw’!E$N5cG&2|p$αSzJsǰ TtO4/Ugd 4Ȳg2R-d^<{ioo׺:¦]2~?MMMX,v7gk?|ᇬZ7?$۝|zoMV"oo??A{{;3f .DŸ;US: .>9 ~_c#"IS5AN4μ9u% Sp2<ɁnghhDUUդh:dY=zy摕޽{8Yq8f޼y;+BuuvLD7x-[LԒ= Of6me]U l˖-466Hoo/Js% HTEhw4j^ovh&0!z{H%4'ArHޅh 36Dy=ʬJ*s+wѣG9rHE㱐9DuV,o=X56l{A$nV~rJYt)?ׯGEx稬e= Ou0MMM6zΝȲE]D}}=hޝ<` x,344Dvv6I7R雓oD,;ւd0Q*fn\2Ȱf;44DSSS\ Uvx<|>,KLlYt5+޳gw}7\s ?L53= O7TG}e---dffHl6V?#tû>/=Z EDX'?=S՞Տg8gP^Hyf9%LƑhQWkv ϶>|۷Iyy9$ӟ~э9oB@eN:mn۶N̙#IVH$S<+Dazzz2To˚$Y *Z,)tRVHQZi8}}}PPP@qq95>ȲɤvBT3鱔˩*++?f|_fŊzV<1B%֦գw؁gZ5kbhAz(0mъ-F@ 5ԅ?) EVf &ʀ;|-d2wkϥ0 /6wݚuf1I)a~ /hѢt>%; Z&uR_(vڥգ݋nz/^ Tdl^}UfϞ=fLo/z@***x1c\~崴8Nȣ' k~[lp饗/}ZR*GOvuL__UUU8>^nf-[6)bO' #xYnG#"555Ȋ+׾6Kw==]]]Z&} EOnn5#w^^|EΝ;K9wz+:iN:'і,[_?N0|'-Ν;A3cƌZ|HFK?j{V_|Q?9O?<{dggkz<1a۱X,twwsj}v-nii!+++Fzmg#IgppP|Ybw;ϋΤCAOO_9r%%%kdee}vyV^|w܁` p=pmM/HT;h'NPZZ骪*\ΪfEEE̜9!V\y饗3g8b B: MDhmm11%%%p׻tvV$6^/ji&VXwwqˊudyB?޽[z7taO>á;d9s088u6M |__"ph,[JV\IEENb޼yw};vW_}~.]:>y$3f'O=S__= ' V[޵kEEEB!vrJV|7:!UÇct .ԥx9Mb46LxGġ,ttRp*/ke3@i^CI .rNg?L,?l@ QdoߞL YGGGgאuttt& z@љ$?sd 筘zIENDB`gplearn-0.4.2/doc/images/ex1_fig2.png000066400000000000000000006500601423420364700173050ustar00rootroot00000000000000PNG  IHDR5rsBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.1.0, http://matplotlib.org/mߕ IDATxyCDT+$Q7 b@ƬM̡lb6Q7&Q7&Jċc[E/r}OGuuf|$qcAꫯ&\p^{P(UW]EII !UUU$If Z`?O3g>;s+WRYYI0Eoڵ=oaÆq? h$ITUUq}j*nv^/_|ݖ-[:u*@.d2';v.br84iEEElݺܶ Mss3֭cĈy睔R^^w^|E&L@QQ~{ޱK.1}t>C? PPP)SXzukנ`0ȷ.e+EQعs[oٳ)((`ڴiy{2||>sַOF _Ab Ǝc̘1??LSS_۷o~+WRWWGkk+555ƚ(u믿Kh435kְg6oʕ+}Nc„ X,կK/ngٲe(7of?g޽}Y.Rڸ˹袋HRR)_;&~rWNNYhTUUQ[[˲e˺fQYY @mm- .[n_\r%477psYg˻:1JS~KD"6niO~;<۩;mmm,\~} jʕ %1{Bqq1Vݮ70 3g,cx';|=$1a\.__ʢE?>r-V+˗/nroaÆ Yhy@~?6l@$ZJJJXx1|_'0V\ɕW^ifƲehiin1eN>3Xd 6}{$ z-z-(7|3v/| ,Z'xswx<8NΝk>[oQPPxG)--G . .@e=\8 ^|Eϻm݆ngܹ,^/_r?fmF<)S};^xN:+e]ƤIx_}L2Պf; D0%χqF:::yޠ#Gnii!JݡVVVR[[c6&PWWw,C0< ˡ t@W&O̊+a۶mq72sLn7֭O>a׮]y. bX̛2y˕rc,ˌ1:3&K|2k,:::hoogŬ_|n߾}EQz|b_ݷow}i?x7ؿ?P;dy6l؀z뭃Z=:m4>#nJ<g?Yeee/O>ˬ'`֬Y@-3<ã>UW]5cm޼ohp85k;IR[{[k.gu|(B"7vۢ"sms+NI$[*++93X|9iӦm<(Y觞z| $ Y رc?8j*o΢Em !=馛;)++Wguox<;sr{,]SO=3fRP2e )//0v|G?bL8=?B,Y2h'6>~=k,N9. q>}:$1o޼ /dժUrJ{9^z%yG4iRX,0Dl"0lO"qcO0|Z#H$PH$B8&H$l@ l L&zvg6V~1e|IbǏի裏X`9K  =0|} GBtW$s&(Q]]-@ 8nd2jzX',SSSڵk͎;رc3gog׮](B{{;555H$hiiAUUsuN (B=u>70mYq0tx<(a0!XAK&3H@b1~aƍDze\wul۶z, LV+wfڵ|_$DRB0`g8/8Zپb\nF +hu)+GfOIزe k׮?̚5D"/K|kl6 vw7o^5^Gw1[mAo1{1Ѐh0lgnq +@ 8Z֥Iݲj* 3g/[oeԩTUUb ڸ{5kVO"ŐA_b8b0P}pv3-gĐ@p Z.G2!2p,_kpM71eWi<̘1<(PX,bM'ĭq@=Vqeft`bD"BHX,f9֯]  kR{0t~Gp:,Z|+dL4|믿Sr73{l{ni$ j`LM6@v-F By\Lpb 20rL[[---;vLW1bȃ@ 8RE~!ӦMԘݿG~wy7HRl6ZZZ|l۶iӦ`Yt)3f`ԩ۷~q^N>d.\9CII VkT9!$aĪq(/`~@2MxCa`P+<&8qbjeƌ\qqKXnw{饗RXXȺu{OQQfb…,Xn_kCuu5k֬pyQTTV)..8Bc knF 8l)Z4j[koz2zHtV|n8餓HR|>4,3l03fFeٌ>}ٳgb x ֭[7 0cǎs_"sO=/ˬX;w2}^`0hf [Y"0nC!f0bO Ǝ|#l$qlgwؒL$!zո>peXywȩTp8L$x<v}ޙqq0  f-]״{I'X,N:aRczJBd?o~ȲlaMӈD"al6s;ߏyI'I'5\C2d׮]_^{?Owc]^zj>3n8󹎎*++O>}z޹B<䓬^]vQPP-nTD"'d=1;9TUo6OPdth!YZRMGG; c*ǠjY[;┓Oa҈Il6 `мd2I8jRn|.UšCc#Jl)Hl޹;79>n-ªG(ֈdI%bT5W;/q/[V TU5G֢*N>ٟ)S2e 7=^il۶뮻.Z[[F9yv |Tm% ERX,$nU t&3/^>6/$xx</U]](y^.ߏ֗:8h= \'WR\3X]hGhX|< hkd2A<bn1:pڝk7lۀeg؀crQVVdc' Iv$c2f:::jkkټy3z*^׌a|f|4qQSSCcc#eee?eڵ|_f<455qw`ZYfmŪۑC٣`D3[RVMDQ NTw(X(xj2,d %c]n󵪦O1k,x]Ͻ7/ݻxRԐ' U{2yd@n%tD;x״k19x) I8-*d*:N!6ˁ%W{2ttέ'n6^ ++ 6bj==O>d\.?<\pٵk?яҘ&f<}'|e˖1a~m^|EFMii)LGy?\tEl69ϟϹOUUWf͚5\|,]l٘d2y4UUywbd0=VLwC*d~`]vV݊ΊT*EKK Q %m(x6<./x|}48 hc/myG8e˕L&MdV+ ;Tڄ yD `wn6l&R \d[\RRBUUǏ75p6] IDAT ߘpB*++yghoog̘19=S+tbX2&(5s=ڎ$IDHRWn^FKK HP\\L$^oze . %i!ji9)! MM<' rty-1c dŝaO4a rJOs5nn[ck( y`01V>#F=`{UUS}լ@tlivb QUt@!KdlVJB5ڊ#JJB( ٕ'^{ fQ0y[E_|1.z+W2f˖-K.a466R[[KCCHf^cҥ/,HLss3=ӧO'Lo|}{]q58=YQ ~̎&حn. rĒ133yڡ,h;s`cvW'N=cϸ)7ZMMM`ѢElݺ+Vgx3vmp 477ظqiyo}YuKӄaJKKZTUU1qā#ǖd2TU5컫.]~:橍On dp:>/6xHxcd\(nYfF@uBJP}[=O 5qH51˙8q"gq3f`ٞl޼*ihh0EH0a9X,D":::PU7|;㨝@qL&Z7_17Xm-d 555 &(>w?5Қ 4^7,Ϗd8MM|]HE(xذatI̘138#G"Iuuulٲ[D'x||<<䓜sNv\]{yinn'`ٲer5ט^YYɫ5\CUU̟?qaۉD"epw^btoz~_aN;4l7#nYMӨ'JF#=VO50B{>Pҍ8?^dԨQ贵g[ñ0~jNW9fS A}@1^D*AȴQĒ1߸hEG;/׾}PUD"Ass3xnf /ב` L X )KAol׶NuFŏDz'EG&M< xD85WܒZՁjGT$$b Edƹ'׌ij_g) ;+XEÇgB!K*2bnQp1UesojܨQPUo~X,***1c?է^/@o}[ + PXX 7NQQYv- ]tnO>;wO2~xӧc3C=i3fGQ^p!c,M 9yvdG[vi )Tk\( kщiVvKSp2׎UN$"= ZGk{kK&{{0iҤ_h4ʢEl|'?a\xᅦeٲeX,|A{1&L@yy9q޽|>L1r?~<6mbك&E"3yzP2~*+@KԂjvQRb);D˟5`,Y!f)#0(ԓͤ3ٿdf1rH2 JB]m'X$-Q٥jd*aGQV+ι)UJJJm /WKK {%x@z?&70+w9z;k>\)DKcK(P҅ю*x9}li 8m>I :r ZZ+qv'#+Ym1(nC" LR\d,6 G>%.aҨI/鸣mۻ' "HEƍj5Zx_o'̸q뮻YazUWqUWpwo>Yg̙3wy'[nK/ñ=DrJ@4;;]dc?spX-0^ |n R%8x<4gwT1e_)+cѠwyr<C]+Qps2}b䵞뎶h\Ek[+p"&*l/EQSHxl*%x*NJH6Y}mGUTZ;Z5|TA<-2#$&5ZtIi ˉ%dJ ~ >]A:څw$Ix<[Y:&D&bXLX,MƴiӺ7H_Yfzyט`Ap~!Y,nc/]G<ҕ!tcN[a$%%6&Ɍ$5**=N GXj0o`i2JR=ŏ%IT6:rّ.t:m1uȲ $7Z(.Xʸ\,5 eY諗!('Ko1;Hp].[ )!!uy d,n?Xr_=h: xZinnFeƏR6#d_ә415#bZF#uzƭ,h &p $MFD7Ca(**|sQp*2TUn3g̙/#9ƽދb .`۶mZ>oۃ~ tJbG@43i~ -3|\.LV8.jlnLwd'BbyW5l#F}k.gp#8ݝ$Kh#4NtܼĚQ,ݻM_aOJbH ޼\px<Ύ;>HiZgXctGwbVujkkygh72i$J1>kYg_>,"6pξ}dl `Y^ۮp<;ձsWAI*]Z%jiN2DX455h62S4H՚'֌p8L[[UUUy ~&G#[Qpkk+dn7{1FL{hZ͉,7{0Tcw BTWWy [dfa56 $( G 'rJbjD{ou`-xpW˔M夤$k!H{m'$PT5j" A2v㶻DZ:XhV vL3o>)s{~wpɞ޽{v.CHVB !D"444(?t 1y3/kjjb޽8 2 ۠JS*cv* M8mNl[^`)1ZZ]~$pa:bh1X,Aˏb|l6ƍG*B$6|.I-*[2Zv MX6FKK XVdIΎ2L8h[9^@ t=럵 ,+ŻVVlc :Hh 3k0nݸ5 dTCDn@PY͢`cbw+=E'=ԀhZ,fٹs'̜9Őeb{MT8Yp%Ne"tC2DQ4cƌf)$$|nmJѸm d)9mrE|n̶Oot=[;hT"-Τ͂2Ãb!k罧x=H BJG(cɳX9T{5 C-0)r\ev;dZVk/ 7@:_:7|sd͛7 YjG% ":zP?giCh^l8н$8=htP(i9 cc8V9Ȏ#oKx)b6ʽ!lVN;{S86MF{;YjVDRܴDR8%Na9d`x ȅoL,1IE4`uZht(8;xeI%Ybus}Ω:ťX$E@,~-gy}y[p}/2A1jW+ k]qUQmKwGv}Fף'4kݴὙ`0 T*DQ콈?z8VS<ӧO#I1TQ^*|r\b5)BO0R4Z8%a QLTƺA죫2C#I`)~ nṮ-K-β0 AxM,"Kvdiʷ6/PMkF􄩩m~Wn{Xzj5.^[oşɟ099oorQ~7~#] I}l߾G3paΟ?ϋ/?yo>9Be$\xE<طA\^ odu @WX;p8_Y"ClFS9\q1>Os-U~lO6]}Vv̦iŋy7ٱcGY6f91ybg)^a֪+٥\NM255LLL(^Qt &' N^!1(6~H ^5ft:Ơ0u] KDIu^&!eg9ILӔۤhQe ^_{sMÑ#G8v{E$*'N^LOO/;e8ԧ9?y ~o>ك:_WWg?Y~W~_~=:<͡ǍY\ϩk-M8~D˯ פlWhmB+)0dbb˲iW[AJB% /ʲQi($Iy8hF)NdU!NbASv9u7z=v?ѣGyQUNokɓ'vV래;\O^0 4M|[b۶m1[lVU̾ FpnJ™3gؼy3O?(_ȟO+KG1Ql؞$vC5sJcF`R2|Z$b[1IE\ C֪l6}9,N =Ȓܧ; "iﶶ:~'4P$$HH&.?|F`{7KO_r{{~jj;v޾};ǨJ\Rxo#˲v;nf8uO,8p.UIf}Μ9C$9rj~vp].ǥnP̲FAbllLʙE^;,$`8<(Âcyǐ=DQH$T+$i #\kJ*mͥKhhA!zWurf MSȧtp~mM&~uN֤m =aCTڰ]ʄ%iWߝ筩&E_}b+Cv=HYVp34Mtsss8p ^ fqqul9#,5[t;.JR(fYN904 ĄDTB ;Uv}׋UVq,z޷6IXv(8TUjuV;yy2X#4 00MEQhK i֧q.kxGͭ npDMɕ]g?JԠ70\ۥ3 U6sduwV0hCx?̅T$ñZ4իLMMߐ0ߜsk: A`}=Eaˮ.~%?I0UDrf|\pr4LfxdS`jz6l@ 2MXXIբi44&Ec!S75w#U̫]MՄע5=čߠQROh4߲zpA2?dCo^mal}vo`lZNLL09l|ڵ\Qa1۷o'c Ye[EΞ=˖-[A"#0S4K ′S֩ ąE`붭yAΡ ILz,f6kN&e4bplfvjZEU UtJv /)וV%87Z-$ɓ[۲.h^QjqdI$J"7yʻ|0{c];7+y72w(8{;^~YH_~;ן۽u2{'P8}4ccc<7]U[_$DS5ddRhT+a͛ń tZ1Z$MpCwbmXeW7\_rVkU[n]E 8EƖB4PTDW x[Y[O̾zFO̢2CrN7NG4:aNч(yk7fw:vW^2(\{0 9s A8W@|heKܶLOQضiVǙz@fbh+bAPUh Pn;W/nM:nVZsw%Ju}x]f=X>O/UL0)˶-"+96C^9 Ya|h߇;F(zF HdqyԽ/M[Jdftn9UU$I;;<#| _੧gww-/-]6R0+4Mr 333,ĉ|hN>CqUrf?OO}gG4Mn]wڕ+Wem۶ݑLo2[V9s ?~7y_=?+! !Ҿ0MSOUaӦMNXԮD*QmUȰe AJadY;v.*tbLcMՄ{c10MH4z])s-,kJL'rxl>4ۡZ(11@.+:  K{]v?֑lz"2=YfgOP#^ߕ݋}%ݪl}<(j5.\0Ǐ_׮['큾sk,ͨkHSvkm0S=(Y%~ I8 IoriYeTm3HV'dI߮Ukj&Hqh,'Yi& -_u-PIJXYQ5Q 0 K44M9q^}Oߴ%Fnojr^!ޞy'(:.101Rqo^ə~tJ (DQ[n{].-\Y.@'营N;:&N[ 砄P 1 -ѩh{bߵ5jT(]FׅJ C23 aSX0eB<] ^AW`th7J*NNõk(l]s+W/W8ć]W=[ k1KOp]s4=!2N- fxi&PESL[n)bF 6MdiF^=n$IIF0 Lxʻv'>hUtmvQ*fwc[o¡MsP$Mh\+T{q_& x]Ͳ)\a<#~wD\˿;NX,ڏ$K ZMh32E!h$aqa۰41GJ"0."w3(MtJh u9ѶtTJqU\Ehm$Yb릭޽;?8IҌLΨL'ٳ}mfDF -'E/I]$2q,ʰ6eKlX$"If id2ڈ=BSIL)md.tEG4Z~Y@Ӵr$IBQs=tK ApGwP/ ptk$LO{neu LB4^v*%0mVBQAMH7pV2/Z$Lä嵖nUQ)[eZn+|˲Dd VY&! < 0FKry'8~86ߖ|A`ezl>M9,Ӝkdž;ܯ60ٳxǣ>Jټoݭ?D. IkK$ꦾfQ05wAI?fgd"t.^ȦM#f) ҁ[t:1/u@JYͫ}Eez,AlzBdts%4مgY,.\`3sj266i]MJ Jh5E[5ۍui 4GGGU^)ᬲ*']ȋ _PZnkaY~/Pu¥+rjJiLF[ie(BUվ"LaUgrb QUڦd5,)8 x$Irp'^nn ,ߚ"*)\~Ͼ+'YhMIp G/2ߴnA 7'DQ!\w;fߗ,pK\Y;v`70'0:5^h7Z-aiuf)^aihLDBp) VPV)FD 7tsnT7uz,hm66:HK] Y6qJViY&) ń4E Y6VТ(, JK"i-UV|44ːe ˰(%R^:?ItMGS5vth :ͷ\;?>aLNNM$K$ bVqU"۽FQAUUzi$l)i1KUKbDA*n(Y`{- /$pO H$8*@\/Iv? <ذWQ?qg*frz4'.@$661{Ȏܨrj|stW_zJ' v Wꗩ{5*ǥLJr@ qg')-Ob yjWfo+ vÇ$ =_?s>O7}޻dK3^JK.]Vkv5zni,Ed4Uku$I$,KWuFkma*{~w[]QjuyB:lreN_EVd<:Y똦at,x{FB&TEcmQ6ghˊi$UYGI -&MjշoEl̷y5~<s~~ZfP*YCBTé-8Q0w4z,??cݷQ1LfمΝ;֭[9~7W$R}{a65F^!Z I-ac(^0+Vsu?EHŭPK,âְ KDAt"B!o-ȒHyɲ0 }Z&xS(&aiV#䶋0 8MΕ^祠Y躎$/WP22*; ]7Tyẃ#ˈ.][$pAPk.M4jP$MkXNf`a&)1<1J&뢪*P,YŴo ;T\vM1Ixgr)dSؚM'pP%G7ptV#àK' ΃?|_-IҚʕ+LLLpE>яcw>lz߯ANéS0 G^n%mZ͗Xh.=yj-h&4SuYN˰u FK-[ l:bgQt3=SMXq,3::JEȲeJDەsy1;a( Q9"US,}~TJ15$Ke{B̲, BSlݹQLcA,0ƪ;""I~[! Zs(3 ?5꾅I$*lOޤ9٤ rK*FQWҩ'>6 =nsQNAUpM(V~~)jTw@wa5dvP<ӧO#Is:Jt) ͨI$kLK\qJhKV[S{wz}zN]hMl@ȊPX2x-ZJ'tipIH')G߰tA+scġR’CX&x_]ZhFQډ-=IMa:"d;yjAi$JH?+2j$IY. [ITpc?R%GuHJQDKQ;NDADEȚsP2vQQ)4D=HrRI*g1fюBS͓ IyxYoApƚyffffغu+333yp%22{#od=n\~.$ŋ,..r"7nw=t) 9cQtئMkxs5ı]훶#s˒,lp;28C3Œ0ŒKUڊ7&I8CQ*fۺbsЈd9&E硑q*E$HщJqj{l'Djbb &L#b:A"یZ"#A5s !/+\$tMP ddڒV5x4bE(M$0x !!ah.ނGp- CaEJHc9 ɔL$%DƤr#;C$Y֐ Kh-vm- MGȒD|of7Ry/+vXi˗e}?#hPP(0: 3u2Y\vʢ;NP;djV2<ڵ^z _˿K_N ݳ>Ç7Ykvt8y$OVf>NBez)w[f)^Jh վyɲ/uʖZp-H6ҧ;2܋[bAQj1>6ix*"b*9s7ZJRnZ~}?22Y|קZB1[S5a]|RKJddȪLͯ8q$#Jb-bjqn Zd&cfF]s8p,mm2?Pa[hY,ɘIG>n"0ؖMj8%A%UY)͂,6%pqNԡfg3|藩WG=b|g7[kXd}\ʕ+9r/plV&]p]`וZa9Uj0&۶mxנhvƇ;DUvʄ ͷmj" ~Hp [hGUh-ʶ8r^sHJ)nm6DiJb8ButCGUĄc8YH;sg qP0Rʦ؄I(8gk2,#f hE-4Z-B6I=7KM)!vtEхYET&LB6mm_`a4GDI*޺߅b#+˫~SO/} ~;Yi뺜tؤ5YM^UYQeN??PԋdRHi7v):Q(h-yŽ4EUU;w.wn2Α#GG7Ͳi._޽{ټysNV'0asddxm؄\֠dQ-m۷ ԕEoQ0 nߤz*))&)I^yx} !͒fm.5[uL2Q,A/%fM o*fQ1TleW.RJ5(UV݆8c5T5V`4]ѵ?$fȊuT 4·8x`lj&~ +Cbb_(-]]1l^A ###uro$zlo0t:^udYl^ԩSqsD^xXV̎ӘI e.4]!g[mW\ CSsSԚ\k 2ꍴ3:Qn.WB?v^Y4f"tcUDX=6SQY5f)cjƛj4Fcr Y(%ZA+誠Fi$4sR0eZ f+tE,Q uP IDAT鎖_ԅ"TƝq,C5* O=Ikk^#IuRFqٻ2駟7 I;̆a믿8Eת"8`bIW4 &LO-$ SAk"4L99y-0TZ(˰XHpCI V@WutRZ An4 ]!xs`%4]-S(RLJvv1 N;vig&hz&UZ&M)yUH⏪$YB&e/d-4CL]H.Y")dJF=+*{(B#hr |hÆSn>99 Q1HԜĉjUJ2 Nԡf׆ /׏wΧ|:z}=\xK,f0t3(x077 K/7N*PiHթq lh PM v8 ۴wsxq_6)?"^uvǕWHEzb҇<$K@ Zsss;wnX[#eTzɳ+Eݭs9`pK3~M%&$(JQc1(~`&V<3&R&=u}*N˰hw3޺sf Ù$q :hE1H`X0I$,I&x*@R-ੵz4kVs [{Ȓa̷|q]UؤOݮ[T*_(0[H2 ۶YYYx|&OwK0fvcg!snܸ۷t?T4Ky I[/(빔~5\qPkP Y+zGRW4㱇#&2J#NS޳F]n4JWY 8Km8CkHet]Kqm[e?4M:q*HM 8ƕMv9p)PT ]HM"T^ۚ~!>qae]drl;9RwuM3q`L`V9rC?VaH?!t*V_xۓNheT }:[[[z=4MV5۶mڿfVJ&<3}G)LSSMge%sT(LRŽiNJ[oEgVP*'NCahT]YO+]y@LڦM; )'dx_vh <{5!H͢6ec:%5C,V%[ !lCAH'0d#9`cıt\]Yѷ@6R7S<kX hBbUȥzńi%-as.fbjfY,}SݤLMM{qg>˂O»9 8N38=DaNh4x9s̘%iSuzU8{6 5q]XZIŨ@ũLL ~cN2cD5 )UӼX[2QA@`A6 ,H; ǮKEDYd*ALV6+ $ybu v80hFũ (uN;.LĴL*njoqеY@Mͭ] ۶!L1q=VzH -~Qg:=ZI!He6iD$rY\UQ1G KJ͚U9_;ϓON;ߦ {visKxW6ju{h1͠pexHڼؿI@Ů-Tvt=% OU`IkpUr֭-E,邇STMfDD&[%I蚎F! U4MÒq_!y.𬄚W)O&ͣg{ϠgzDi4QܪNU *dB58ƒeme~yд ]L䨣 uG巣ާݸ;1\ _FT,M v/Sg~j^$;qoAS-|w-u51T6"q]kSueH'jڃJ {Sv묬`Khr3eEw\ƈkhc;O #Esl4K6s-I =̔i0Y14rf ,]oJ~k0 UjQV7>,eق&քF'quMiC3UMq@`&BQ]3?3ox74 %d0t}6W^;Q諾{-p4Mz*vzjMjپ{S3{Qo?zK G*ӘaC{'oΠÜ?7t8%C4sZرa9otR]ӉsU 7[#QጝN [c-{pM[YHlS3 hrMN=G!@ӵH"ҬT(0ut2ò u ^`p~b&He?fהt҈ $y =cloG6G r]s}nvp ׾5pe^xe|?e_җ?û{0@H)bccc"qӚۗ9O6q̢*aeYH)~/i_2u*;N_K5癛Ԋ4uaD:m尤Ղ98o7u[ S,SYTEfmjb=&r8 .a+udyIdHMX]Yt¢0t0`&Y\ #_[0 sXBd u^Q aޝ'H:I{ǘ4tByqrʲnKڵkA8cњNGW.\'8qUD?/ AK{NҘE˰Xhi(JeeF ɀ\-e \GmG_[[j t.&n.D҉:̹s䨦mZ a3 cCVVCChb/UgK"][5M#}zO1glI){[Iyj@s[b# VlƳ<ɠlA'ЄFc0|?̈́Kc{' Pn V뮛٧~|_,oq9}Y>?Hn&^^{JrmZ3kߞ0 ,<ɕ@mjZÚ˜~D_ hF"MR0 %iL.Hb<E>AeᲳC LL͙٠l*v):UwS^PC3R7zSPs( ʛc8c+ Ԧp [ xzEa,싾g mawģiLb$4eY1Y0 nɗlj lZLlTrIڃ6vC7ʚm6BS( o+^؛ΪfSs c)U ?*P!7i6~&?0ynrأױҕxh8C-\cj 0 iyNݭӳz8\eN% nML-"LC 3=$ؤ\t )YqBqR+t5L?!ַoo_%QfS~lllO)1i4)Nӄ(ć1nz2tlt]/_ŮY pv3>t&a>|OծR* (<+ǟ6HǔVh*,V55]t{NJi‚]8va,UhjR[pf{q0U7Lf{3=.:b+б$Ss;A,,,GG|Jƽܹ֘s|{''=={;ݦZ$>sw?~,.MY|4u䪞@5(8fko ux걧f%B/G˼L4qe[8S3kxG}nxF􃣄. yr-qtC/'Dݰgy3ic;U9&y+jgydZFݮM&EW PXS[{+Hi*6mn.prJ0 L^!|g*,,, [ti>/9m ŅG 5.6j5VNu-bNc]S(M'WcOyNZg~ظuk}qVb0 Zi~W JmI>&0g{4MHec AYP&YbQX̲]?`ԯ5k25/Ǟq9 #u-۱I41Z^Ӟj%$˵N91-,*B3jk:a,ʓs"RZHG@HL-[It*7jUUH \B'2-é:8U$¦k!fS:7cUNj5nE5lu՝b.|z>)vw'CHկ/|]=i0 yבRwwZ;'Ne$a0y'@M Yp͡Wk;l J5~ԧhcӍMhC=[^\cyy0Sࡲ?Ler 0*~E}+v>IuKvieY$$cb9<\=c&ئN%l5X8ʂ-9A\M{]S5ף$*SeMrTO ˘bg}:iPrA8 &7ƇcA3E#[:$=l I5u~oTݬ1|j}N+Io<i( no@zЕ `.Y;6vjnSWE;n˜^4݆BY)mKie T~F \ԾS"WujqLF4[M4a7L qY. 4N3M C"K;AyCDlj52Rli#2nޟ{uzi0 9W=Y0ݯZW$Gϵf߷n<>"n"qFgH!'RJڭ6^ew%턝Z˰&p҆ @G@ ĘI(#qXqugYVI DGۡ(w kq]w'q9W%3anȤF.5Kg1B@d0ք:` 0I?HdBŮ R"[e|gz@fzYS i 23:Q- 8$E4Mw3!QaPq+v|e QkrI2ɓ+~j}Ny)" _#P IDAT<25hf]-հ5ghviZc qjkК(tB¿-lIӔ~O$#冠O?Vr:4M#MSf<9~v!ҴSB\>::dr%(mRjԨ:UA0 {65]qYI=xH[4`u8Be6sZcBhķǷGus^ CAP|rpMH]ۥ50qƜb$*Xn m<9RR00g[+8S[8泏~X>ቩ탆2^qh>{O\k9`BymNA}8gv~Ol\Ul4YXXP~{gfZ.HfNQ<0ʰo`dK9Ǔ;To<  S.l_!a%$T*Ǐߺ2ǓmxՅNu S7rlXkti=`aэ jՋzQpf['xb<`'bKѠjpR{骩Olܢ(R=I]ap&1R:ۍipϞLWm%./9f]hK'q{7nCJW:|]^ixVԢPNKEի$^gAhZl6~DOV%mh5MpF$ {{{ضO>4͉@JU0GvfCL.tajXLc kS)_'MA[jкbi2_'c*2\|0nbƴL*^ěIՙ,lq&TgqklnQL=r汍)avY=V,*j{IMhcV]s?I:Nϓ7YX\%$)/ Sd d0 0HXk*qKȳk4!Bc5Be*饷 SIXBmU ,Oq)Pk^7 *33y$K<1T4$ X1UQp |.L8çt}|/C $)C Jͷ9na(_7%ݽcķ]׹Eq:ӡuR;aR6LB4.L277DXA*Q^d͇R 䠷Q;Z"/۴ a-31ySITYz/]]G& `{H7?_sf!a6r HQ|Wl[q GqMw}/ЩXr?=ZDי/Y/b&;;;t2&^1$y_mf/\ w=Nj7^q‚ʩxՋ{jBS4Ur":T5j1ƊcBϱb Jdl51 ̠d2 WslnjTܤ؆m؄iXN+v$ѸB,¶mjWi[q 2V\3$M-z׆5%q) !9u/#crlI]hd-]qJKG7'^YR8"4Ime~-$RŲRk縷Gwi{'~:oQVX?>u;Q4t4헵l6; Su;ݨ[ƜK9IMldOc69 ,rQD=u@I5+'Fi5aݛx-GfU%nDQsvMFw5niFө &8Ck}a[K#6o^M AFMV~ҧŋxbd,H)1M5րm8󼲹}6nwmfM|۱w(xWy _=jϥPqzIVq*,)Mz k[w" HrL=ci1ڭi>BMO+,xi7b$ķ}ԬSđ:+",*UEIi ]LzކFSf:Qf:B$QD0MDt>څT_Uzr}G 6 A7'V~UN03k&KKK v~O͛}Lގz(+ˁy~W1!snܸ0 O||Jz,vfcۻASB9tNz;a[\*Ao'BFTC43"H`Q) K]zi1^bkkkL/Q{5=~N}4힉_lmmKMA%qyNll#))1O0m `ЉR5#mRj*Q J~KE4M^–#^o<݇1GW6XLojG_CaPf!чA4= >{Bؒ}w.!m<~f )9C4I蚎gxeXX{lqm a(QA tO^z*j{Q)?yNa}ZC:A[={0S0K2{= ݸg8iaQ*u0Ǩ;؝1Lij],Ú)ܲt ?~x(1Cρu1F*T,x 5Sjd2+)MVթj aCeI@?O}bT_β,IVz=6oA`Yh0 )47ߤ3ҭpM8Z` I mũp=`F]t!7E߻hBr* :i5No {ky&5w,EC#cv [BQ\Ϣ9u{Wͩǡ2>q idf T筭R<-8뱲R&hBͩM KO>HRV8{5l;9TR 0P 4ۚ5F+nÖRI63Y>~Lx娩aT5%-.i/%2LD7u\ץ%[^&wk4^p=4ns;ϡYs..y3IWۉBP(h4 z2hn=ϛ8я Lf`b2Ǖ+Wx衇tRJ^2Ym= g?h,//ysS7n^u^n41 !R_אR` 3mz5l#G9&ZͦG|Aͬ1$yeh lsB" Ҁ> [фA3i]055ꆎo/ :نi[:e`jXS Y8qݤhGM 6F2axIA^l!DI(CqbVTƦk~޷lwR&6~t0 }4LE9$=^_QGb i3̍go(modaaaMtQt"Sh R˳, \y%,8 ',P)i' 9 ެ ]EI,c*VE]"QH6 %5-s2LIX^8Ul;U5xlC.s*fe,** hf^%x?Oۏ_.lfeenKA8VMƻ~k_+_ /_^=TUt]0 ^|Ż{0 |2Y{F9ԅۏc5qr+/0r3ϝHU(PDJ$~Aw?D@Ϟγ/$XѴ 9rsʥ@VA0@:>5,#-Xf lp|@1:-[g c 13B zuZpW AB1ID7JtEMkޙ'#q`2:c{'H3+mrOw3ܲz1DQ6}3{7+Q4KjJՍM-VB"ji[؆)x3 oh;I`%mREA;mK&ЍԪ)lFZoG E'6y}.co[Q,WEI?v8MW?jhN !a##KY\QNih]ӥ !Ԫ5HN8w[t gmvبyβuuՄeX3c0M[UIgyF!QBLlf2eyB;jOtl#IM@ Hs6}/nNK4M666/͛7ķf߷݊ n߾իWyᇹtDxmh8b%SIHh:=֖טM4= Ȼ/TS=5{{XUrc[99K MSJZlчZC\mШ|GX]EF!.-Iq&wgJ(p /v;uLWl!@A/ߚ>-64c,\sy>A;| àkAfx0UJeY@pFŪ4GOazzF4܉B8ʍի=o˿|K_}ҥw~B]{Z{nb7qpc߂8TGԓ(p&gp 鴚]xJ0-lJ} @8̙3IFkAO:T8&"^Lջ,,/'O}X.cY&~H y-ӨМ{Q9ٝ3gpI։ajQ87hVN72Wo}MTOq#1ʦvM]I hDYwP`zn߾͵k?S._g?Y>Ow{j}8k=u/o\)hhGyWH},3ؖ=5bPӴ@-[w8Os^c1JU{w4ձ&]$k[gv[;k/Zk)=|m:A4L9<<,y̶m3pXŜ;GNc n;[E?Yp(mќ95]踆mtZGs*TjeSbnKlv{$.뤹)t/!Vۿ?<3c\{ !4b IDATgB/zW9fy穧*7͗jitBŽ : _u.}^zwvk}̞F3R(:{l6&cf tRl5jxeWt Ss8ؕI@{ЦjW ,H~O[?7Q$b Yx|M2 ClI {;iaЋ{yDnIH*2M港aeV 2:Os=,RS$[=;5M._ډQY+cV !t]/VNA!zl2]˥9h Fcu D/aJKA9A?!Fؙn\/=0i**M)σ$ b9:a<4Lk CB7vQLn[&۶mhjx&Ig .0w9YBaɌA\ &pb }&2t8heeo?gG)zr|'VG h(>5mfGiI2=}ꫯRקct*; uڟDZqcMfAEȤZp}g^QW;=_??V4Ȗn>IPuDI4Fȗ.>F&38g3^/: jvr.V˲J[oEXO/!fTa+ǣU OK{Iҳ\bb̪ګgg<s.B2W 0⿱@\ !aYp \!dYF ~84==3זY{w.\"cYU~ǧ^ HJS*l 9XVan{ nfNG.eae~pe| _K% q˗/}\xH[o$ӱP3js)K2M 7N73SFP$2qs&67Q]9y mw ?Y)(F㙳8 ~1EX fT1{|#^+a8#MSU9G0OL,u!>jI\TT'LAe*%I(LYfjUR|yR*b"ahRv^ 6͠( |r'MSߟ۞Gbd'G.ik6Naiii0NEHyA0ȃx`4r8˴̓W: ͘@ I(8 On>3+sAmH!s AFSfZE(]au,F\-@bw Nװ?JKxr(v=Y; +_+Y!8H:$<,*]BDG}a0un|OcĩS9+f!q<d2Mp]7 s]7߽gwdsΞ=~0Nː"ɧV1+ڱQeus{ ;M$tH7MSDsOg~i1sI ]ٌ:np2~$!lC~p~.LzXС2=t EbWՄ t1MCxYܔe9ÞA}A8Ui5P (7C7ߘ8yqΫLdȒP A\"d!4BF0~T%"Mr 97v4slW^E\aZs1fgp8m܋P'Uƛoyd{jm `σ,~EE􇊃}'NHOevGQ )da1vBI)TLD c8 t*$KHP! l0emovrEa@,a`uq5oEeԄN > SڸZg:ŅX'cIgh4A$, 2*D)Ud%OTiLbÉlQa6kB b4_=b3 dR)o Z-.^W/">ܵky^u(ؚ\?>" }Í]oVŊGsJn;SX}VXZZȰ^&H<3~z) 2<߇& @$b&ɬ+y 5KhpSf ::EW#:Y(}%gڶC!e}?[+D,'™& ,d1Eht‚h*Zm1\T(<>s6sߛ,dΛf/dײ8Dr'tto+f?8wvq㭷BjOM׎:S5qu*0**fpqe{$i"Sgh6u=86q@9V` =$ C_d3ł5DFÕ$)w~ 3TM,*o88Ӎ}l U..*E$@EwH'q)w  f'i‰SŸt,U ] 3 hD$~d(v]7tkGAJf/AoxΝ+r} r9Ǎ7p-<-[?)bNWfuUNk^zo<^(52A8yk| 2ΠBg6ɄəSH n0c6p1D,0:(\  q_aPc|"AWg**}_C"ޗ% 7u$ qMt,ɈX4! _QJ"( N*guE/o|\,VM9< ,mǹă5NtfW~٬2; p%={kkk/p:,͂N&Y`$ ӄfiy PVU; !N] Wu] BB7rJhMq`fW!^ª)0wP=kmFכMЀݭAnjE RFܦ5 .jVX)W%e6 ŕ Xp+[__ ^^] En\},d}OliʭW^ F3 }(DDR6*rO7!^J-a4m̔ƈxb @9GӁ8q\R*/Y0P,y a\@c!RFn:cL9ng] ౕ!pլ= Yg`(jUD}O6Wó>Wo& ayֶFEx(p˗}Wma8 ^KK#dCaL YE 5"c,@'G/mIŨUl'o@T:* bÐ|䱷 fy9!!.n]"@WQl&HRU !mVpmozxj:.?^;I9(Rufr "ZHUӗ%9_?Yʠzjθ^a<6>vEqj9GVnwM1sz:nýC޸qOOȦiW^ xh4Ύ^(ܹ {BHkFQXUd(e=A?]feaccaϴL RT쨳הIP"YA']х+O‹/saXFa190Pef#NY~x좳8s,ctUs*he\k*ЕC3$M`j&8-uT*C= Ie,U\!CPRg1[5( M$T2-YURDl:8+*F!O""KDʟS5S+?UziBU]>)5~Nf|Iq+WWLpVcCS41X5W A 0M++M4ş*y5&&nbyy]& 2p^ibii ]rkb語KJed 3H$HqUMfyӘvHYW Pzlьh.L:Ki 0TRQjpc ]c~#MvE)ݨ:4Y&ky /&bDTM:I{TP AʎBQ[[[x0ϸ6#{"'+7HoUkGV[YʄϴLFj 8@mAӄK0pt# K_)"*]!nl oqʂfP#hfC' _RV*QkN x Z[/@&*6hYkn$7E`~KKKW/üp)[eI8z$Qu ib}Pi˭^3T K[㰨5+Ak:4 YDXFX$%Bov؅϶ L$*V&:\aΟ>GZG%TFC4` 7v,Nh΄ݠAcj4)"U'$CT5H$@VjK$$fQ"Zw`).)CzXUѡ;4$ `3g ! gv`mTcV-\~zIJ\{{{x5ظWp0nvsd[rmA%؆-|wf lpp7ev&~؇`)CD36E0 շ EWDpzkSv$/A5cM'G%&=ENϿNDFzDmpМ<UIEFP$)OxRS3%-6*T &$t]Ǣ!nw]蚎VAFCj1#i'LB8)rJn'~;a>PJ CiiWE^AFafASҖ8)`^͓l<8/>LՄjM0Q^.9:B o]d~{*vo!T d.C*ByYeSy֙,UA2PdBm(XLqH_k$$F&" n ;g)nګp$nV?3xG r <יkRF} bI%(!\@T3Yq,C/a1ď}kȘ Xy1DMemQ0JvAC7(B>g(ݧcc&Oy*LhR-"aӐ$f ʓPT3 \"יr)OKlIUœsMm",f!y[[GNf18.^ (BcV؇c:1Q|iͪUfVCXaa lY3qNؼl6nDi"h.٪1$IBр@Bէ:̡$xK#2"Rh%Vg$&Dv=z1#S-;pQ\%W0lL> ~:\4HtWW_~R?ַ ]i77Wo/(2ap\rlLuu :i M֎4#A87օTTa,F& jm AmጧZ5eWRDQPoԁhZg-pj">>`RkY`dg3)gEPVBHR"oxmWhI* eV).._[9{it#2ph>> < (`Yyd6sno_} MWu*2AB 88縺uR"!,BT1/j#kdE_q@677ssGHtqm -]8TUEH/`MPf(CAq^"lVZUeT@O@tJ,lmm!I|a]Hڧ*_|Njۈ ^7 (7 kc_u-677s>޵}i퀥 ݭCMׂG*/b=2)j tޠCpp8+6:n'  U a!ua*y K\,LBP]Q!DIIXJM&\3d"Ȓ\i.UF5E5Z)R^_\ZhZy4Ŧ,~-9 IDATل7^,ҵh8z%_yb9:N{OFi" *aI^e-9:abkk $TEDBqd;Р4ytal4E̒B&9_82llnjGs%b64ujJZ dB`0@ NNR`Dxngbd :]ܷslj QV :LG(z=a5]E=Qxl6>\p8}4q%\x_җ&§>|S/֭[3t.\s΁R__wv]pop4(WWka}}N8~y*S!mXЉ?c p]+X.J4/ ANvwvs/Q 3'8zEXf@GkDj>һ#eli8X\Y* @8f~_hwsQ+syjgmj#@&kxc*DUk \< 7Ym8,>Y2Ϙy;m0aA_8w89 Kh*( $ Wn#.v^2[" {]SN!`Eԩ>Ad^2MY?aj?tc]:Gw0-UE9!zzQM)U4T:S1rmu:뗝tRvLJ"6 F8&CRY{P<,Zy|*-Ty;(N1UUsa{{Nڵk'>o~8 Mۿۉf9^~dⵊ^^x}wyބ8JRǓc` W+|666p[nn$A7oĭ[O^nW9]& 7N6LB,NTc$캉/☉Oٚ`&l!&5 WQmxED\:J5f^M\!EEۨsQ,LRUSPEbάȏ Q@e*b^OfmB,Jk& 1T4NЙY 2LR: b:f}PcCf(kYZ-\|=z)Bo @,j!әG H;\ ˘U=O<4FiI{0Mz]sOqq!3EHO|84M)ˏ??Pf} (P+Pbi" K׃=Uќ`ք>aʅ.,浸z4 M C. ҇-x}qZ JpUf$ɟp3gNf}̏7o6^ _ bxPT},_`3q2D܋4d%I*pe xBqdLƦm}#3m!%IGs&aOfei~"v%1P<0T`8XfQl=>9]FmJRE(0 A  )[HaTT1UH B=g!1 (%VL%ZWvRL#[2p~|=ʶmضf,t{nIg~$(o1~".Os+IM[X[BrH{M&0%8e2]f,/X/x]2~RL)`j-XTh6yn!IA۶)8p4'O|vaGQPY9~n K@U( %,זjǖ5OoqvhwF+0BNQHi0MkkEz\׃-ӄ~/1P''WPGO`Oߵky{阝)]=}1!) ML&u}9wpP; S1dI7ar54N[gwȒ|U):Lӄ$IT*yB% #LqDG~qCUUppPJAMDžy]12 UhW ]%u ^:[adݼyI:ٽr1Nfisʕ+pBp#c ̉?1V*BRZpR*ʤAfÏws$I9VU-%W8888@EX_[f90 Ďym,dh#}hRy"!nM)d j d!* 8BB߇w$]d|LNFV\٬R?^8Gݨ^R%,--@.v[o!P*ZspNjyﯯ_οg?~'zGx d_G>٫ۯTZ$A \yeU@$f ";sg& 7 CUIy5vB<d;|`B*,]XrpT$E@pxa4H{{{k̟{f`6Ld,Zh{퉟giJFC,~ؖec@윫CRm c偉Rzx]\A92Z|0UV';ɝ8z !j`0@xB$:|E$A"s4daI̜T\JN!2jZ^c{{fs5NMx9\w+0>Yet:xK׶^+4 ~$Dǥspnji @e ] XTUҌ&!d"jTp( Mj 2 YND%X̮U6Z-'?Pm ܁ZB5hY ˜=Oðud@it[aXv ˰F clbpLTx3jGu o6vP%:x=Cl\,UW`: l "nm-G!kTd+C56Ż4Muk zc~Ї dP=ë e)Fh8c;{A ^My:duc\$8W(HհXYE3<4}CzOl662Y6wQ3j`Pak'r}B*T䰔;4MR q\rbI2küi3g__|ۯ똃hYAF kq8gbOPF3 eã{uNR؃,12X*f`>j 5,,,bTD\'b6,`yatC8*]I8sU(**B9Ue8zB@&amxY%1..{Œb†67j#EA45{\IE'9u2 9#M^~m}lx:ҝݝIQ"(څ\4Mqpp6NongZ7,Xg;o5")mnlHa*SpƦiVK H|J'GQ-nATYr*U(݁\8CG` CmkTUX#~6q+:c{0+2> @e *QtîpjS,ˢ=z]E%0Y/00eT^v $q˼<f&:^B 3,v&_1.0JyA8@ը¸ F?2>F` >Wsð3c kkݿ~\*Xpta#ĜA[W3jA"+5K%Q%e!CE=$ILA`UVM@A5U;e>x((qMup 4M QY:N>m($qr # v'EF7d85bA:nlAVUT, ;ؙxM^0I@Ve8aL>4Š;z[%HH)\\KJ+$)Phz\ !z=PB/@YFü94i-h\u$DF .Ao5HbTG*Kun0 a^VkKpsY0N2T#ӘYFCe"a Xc IVW.ۅiH4^X6_^^G̲ P:N'FY[:M m2z=lg&T5hGЩ^ЃıV ڞl?!BgtU3MZs)%ʜ {L0颱ԀF\}9 HY5^CCk:LÄ :h4!K2I)MX(^@h jr"c?.("&'ߗ{a'8L9܉6Cf*ﾞNOzppӫغр bouqLڅm -WBN7!a LϜK{$@Ũ}.)¦[l9* üB GsE^8 늎(,$J8h\~k>mKHD&BJc4 3WvT*BGި ,j ɫؽ@$~Õ$9^|k_w]PJqy|[ߚcqȲ EQwv!E o6qfgMWB)E$>Vyۻt.m;)!@?\Bjog+ E,6. G^+T-)\޲*)c ;heؚ-ddò!e[o2-XH} B7BVd \FeTنf|XWtDK&r*Lxh4=aIj ˲w1vdv< LN2pcct8!|c $i;f^5P"1k H-O`ggqѝqWfBkBs ҈E]DۿVq!WxҔ@8D#)~u}w}ll > *0AC1ެ<34sde `l`mtZV@Hni^TMI/q๢*OXr\[8'xl'6vM!d6$rCIr' U~S#ń_pO&k3 "<gZ!=\.rªe T@꺎Fm| //}Kя~??Ɵ>O~>)}0Cلq/pሙUN3 aPI^Â7 NX,KzW $@WxE(1J"lGy,fSb E[jLh;#Fo΋c*sic=w ' Dj>srxi=WAsQur|hRpUٸoB+ oB0+\[[[-f7Uݮ@BEKp(dsGvU0YV]̇F)-plmoaaa(Aٙ" 6뚢[ xc[(uf)mlk7CUv8>r^TB'b6KPđeRTZs8!&,˹-Ua_8)pr,.5I(*vǕ+W=%͐mه߹#}2Rs̺A 8x6AI^"IehJ^$!v1DB&*{Q(W뫅fRbÒЉ!1$xrmf!{-0Ca) }O=MՠJj~2̰^RdwE^E@6P5},WE0+A4 ٫lĂ&Xq=kHLid*#VcTmxe"OgWh?O7!KWW$S h۸z**Zo듟$vwgݝ|oEQ/ 4MKx'?wJ^!\ (T"бqjcf<6l?{2D$pC]$M`i(tLHյչB9=XB{w꽛ېfhdZH6k*lKX1A""(Ab \ٖQ,$KRьFE}k6K9v?}N$rhvwթ<dbVCN| Cܭvq{5hˬ55dY&eI5(JN*50NcYc%B+,'WsdEYo9{%#ĩlۛ"",]\׷ECv\f>8cx$:3^4npTP4O{McvTnGm2sssº+q j\r0 ris;Nx= RE(5ifUdzs0 $&fiYLcvzZ虡?jMN 蓸ī[yn(Ukm|mL6ș'g8!VO/AQwTkUڭ6nڍE@W*a...93G\G6dECwy/: mo2vi) %ZnXEr/t#휢HEғ`' O|qiuA$lF5TLD'ղ;e\m1Aɘty spnkj[vت\z5M-cGPamfDUWՉ|??k_מL?w,SA,IVKlL?SXCUP-;}Lt?#N[$k7 ^0H Y@kzTMZVbX6nJUb _iӨ6t+JdazV&d0MLLGq"GTZYFZuښU녆$dtU>A*R+6X,ppicqMQauP FȎpMhy-\NZ 5[m*&.<;}Qnӧ &ilob[vG@˜ߘX.#mYT"paaUUtBQgw/ Cn]:Q'-ia+B6"~4:ԭIPuWYSiJWQub)Ȓhđ pKƮ)X'aЋ1Le?h77mLdIYJYvd*iByz_*nWa<{_ZZ_"]uĚ&Ǐ\4vQsjغn!2j$B<yA`:af_8>8 I+4lEF܆8E?_GW69 R2"VE^gff&Zq@ N ,uz|}۱Ų5A*M8*bFĴr\,23n<$k.quR,"I'Kti%zEe;nkvof ^u]N׹`ZGלPC8Fil/,MKLK^+fP BQƩTJ?Dɾ@#RXxɺ˵k"p 8f[EsM\Iyjbu,c2Ի)!d$O0 q3Y6lnn2==,f/{ޢܑBBXX,5(*m;^Q٭"ono;w.MJ4V+;&kCZ9iLOOH5/niDCu֛3:Q)-Tzc n`hy-0Hqu]*5~8ޡm3r(BVN:LHVڹL|[acc A2Y;+S42Ҭ>aYb/*KKKX566kzlak6~0i7'6g E£J^j׮d[Y;PV)˩l(W8FQVDeɐ0fn]ꖪV"VToԁfڵk>}EQĠ]$IE\Su^:;?sI]o,-p;*]xmp0CM޲j`&c&$Nԁ: {,pGjLWu2zf,WIdSRnIXb h4ˆcj%MI ߨƳ5-34m`C0 lVPte(WWt{퓐p\Sk|>k;$*[[[̙3iÝPv*oJ%:N\n_|cK+^TZl4wŨAWtXv;i4.{!iJ_D'HjvDD7t\ezvzg'C5FNkya44đ$ 01458C\|^% IDAT,âa60LVy,.l-[_G!$O銎tnzű>2xt 233NVZڵktݔnD|vDeW(By;1NRo.]DE}rPTvc.~s|no}\*UannK<۩ML2MWD7Dy3O0wzZ4A8&(%\{˲سk$#7\PF!Bs(-8x{m %4NDɵ5*D-hE6tcp&g V ], }ݾTALn=ik=n[ ܹs`*ؠc`f0HDq ޮ5if|GQVsh%:UAh_:6t$͵M$]J`n&'ނ8U)[JO=# ڠk wd1{WZ$ճȲL#' u_(EI[4 ;0DUՔ5``S|F $ ʎ!^UoLLVi0b( EQf)3gp]o `&躎w1AӴ >l6-n8_e9>ϤT^'_Mx+!I#ib(Ix&]d"k"[ի5!%,Z1fF l>m"`A頾`DR *¾Ƿ5MQ$܊4vNZCU|T2Bf+2V !F_* ]YJG8#$$:JYԨ՝8j˗/j0M3mhM$I"6;&L%^Dn]Io~L.\O<18wxP[:&K,">P@ݭ@VLaa13_ݐl)laSŽP&Kʧ3q4f\ͣ:(4ș9ܮq8+7 $>+@N $Uykmvi9-:1UȲLL:f ;rxG' ĝ`Y]L{}5:+:jkOQkRefffd2}ZN6vZ*aoꫯǽCuk}3wqC'w9qbݖJ%2|ҸJH&S5c՝ɚ6mfg58D uz/a j Rn<7&$O?7v=&/}}{_җ;nof vm;uH`hFV nTEO,.{wŏ}:ݎचyNo1r~FS8 |sU{-'!b< NpLFz] vbv~ERҍ[QntMGt<11A k5ZN Mrv.JA/n'..|{X%.,.vBkzMfwmT*y衇h6CBD+qeΞ=K6̙38q^z??#zÏ{}̎$Pd{,uNFllnȸ[UVӦ/Ytj!Kr%(4vvv|_njjSfA+~RFT^qa3hpC6gcqGL#w 904c"P:#ʛe(ྃt?w$q-l>K o]OLV*h 2V\š%'$hV6VhZϧ=kk=(K2{BRԩS'K㷚^6YJDoח B*(Ԫ5~MRV[]mm.BVVWeqicճQ0}h!#q˗Y__ѣcm`AHDx[IW#ԧ>>)>OO_[Oiv:Y<\|xV\.si:4&n^SVS]BvGI^E3 [\Ap9/=a-xy/dYTo{( [+Aq Q"F\t 7i^T=ʭq 2*4^y'0 E$7ĩ_'^ȯ({Ow² hk C!_/>|._ea&am3fgו4n(BٚRÐ'N:qnnIr AE2)nP{կ/2?S?5T7 ??zw"l%"9΍}dP%IRj=8eOE'L&>3$\qx\]JݭcS\O2`h.- E z7e1qP5D;:LG}NG$I[22ݸK.#mo8]9$o{ᚦ, d@,.ĵvJ$pI)-qsE* O?ME+2$JD+NO `1F7g{/ r7xzÇ?zcK1FR'CݩIECwaLm+8X3WWK??0lj ĉѣG47\g߾}Vq4:=$F{nՁ~ݾ/I 8}43Ϥ[w2ߟe3qP$VEƭXkfTB˭˻ bgfhy-v-fDFߤְ,C4c# Ԅ >IAT}egtE;C+:n IV\lXD MPOj13Qe~{$ "՗ϰ=Sc_A"8|KM|衇Rwjt:d2tr8&* cvoos)dYf޽"./HT8='I}0J1+++\rOO,PC;bz4~4hU[Dr4$ Vdx`<<:~EWؓCi̵ۘю8PfeY}#;5~ZJN }ۯ~[m7`.L%$GJ0wv %SҩݣZIY!f|}o63bn[/]y ns1ݛRn7!p.NV\zf&9 2LJI6M'] kdo}JYJ7x={,Z B ڬ $rJq9g丰zennR4$m{^ $3[ɇʙ9:AE^Cu<qp]1A4M-J]8븞PN1gFY$SsFawbgfqlm"rk"KͿ䯱[z /=2??}ݷ89$48Y]]ŋZuOrFh\#^YTS%$d>7YC칸K^CEEѕt(w$sds9Yfq̹sh6<37r'0{3 jeE6W^%!wfWLPT8s vfuw, bjnev~Ur0m ɫ!N.,dtms*H`L&ͱEҒ<_ۧ Z5^oK&62M(.Á A037ځrE!>bRn[iA|gq$gn Vy衇n:hD]px/\*8wa{{T՝ 1Eu]:?i zڳgGlvM\a.rF}0$oi8b 4q){)QpȩaRWJ@ :ܭm[ Ѵ! bhjH4YY]aqvxJ46qHS҃It3UQj")X.lPmTCն_{@)ؖZruסQoihh(ُ[t6j*~}}cǎQ(xꩧa?T* /?q~wվ- "C?C]x_} ;͝=YAЭ^u* NnQ_+ }mscEdӰE3K\P(fy d}Ao]Yϣh_ s= ֪պmKjEVjV[mv/&c d8w?A/En7򸡋$@! CZ3==W'( xX~tji9^ۈ4>:m6MN8n{*O<}W?_^ۗk}̶Z-Ls^?P),,.`Bo  v rYZy Pmy hu e2S!<[(DShNԒ$a&!!I"1!IESWJ5"4EDʲtl|2H+q #ӏ?zjDQb[FQٳgt:>|XQ.ǎczz{r)>~➼;{fGQ/ͱ[~8/sׯf%oG%윙##_ǝ@냃i1inqŦ/ z% ^oU p5._̑#GnY0V#"N>M4M~(˾ IDAT߽#ѹ h4\t7CIEٙY Bowm0\%W{m$$.>O?#_wE4ʹfK.knw Cns/ CEǹs{?~{_/Xbq#)`7UV⹋X58"c XL쑶T6mR6Mg؆LWͬxmn XMFFxKo$Ȋl~P A?SZ8݁^=JpE9xRqh/h,ّ,rT3z؆@S4-=:@w~B98)?[b1&mbߙfӚmv=<; >! ۿ?sss|?˻m/v΅ 1M=a~Eѣe>?|WVpCVom?~R;NdYp0no~NWqS%hTkUfg-ΎnG8Bp: M8X{C$dycHHU(W#cUE 5pEQ:`0ҾF7~"(cZN&DR;m IYMFX9jho={… T*4MceevMXk+WO?$I|^կ~MUlUU{AM޽l\d$AMc}gc};cHFLNMrFMF )Lx)㊞LWutYGՑ#P e(&D,UUI֪"iLgiW@ "T"'ѹ nNd2>~kU[ }855Ń#Vv9{,ijKz7RV"*\q-$v͉' ~~ۗoszKf¡CЅf'?_ $ oG6k>!A(#K1Y{qH'%yft +:j :tXܛ@ ""._(ȏ, j/2@" #ۈ7x8ySkoYI-3^9 jS,a0?Ę$pvf;{'o@q+0 פx GqDũeo{6[M: (h؝iqos> bJI8Y[?r:zcטʶy N:~fgg,Yx™mbty#O}qǧ{T*:u{,J4u!P*Zsh48y$<R/~|䳟,GyKO u]N8mq;R__ڵ/Fʕ+4M Hkhn.:sss޽ӧO|}cɝ}73gPyꩧ&r8Fu5.\^kٳdiN<$40qӁ6&-ԪҨ4C=S###twẅ́1xӧO344Mi.l正o=nՈ9V5c̶[X\E"t-wB>&Vʢ(^RӾ5&̽Aإt*>҄@!r6yJ֜l,h7FnO(zip<( IaQ!ʌ?Q8iƵ3vٞqouRb0mT*Egg'6icPv;UUUTU[ ~A:$Iw75rƍu{EQصkWjॆK*U2bnݚju]G,^J3?Whd׋Rl$ISNC (-+_zdoR]]}gB2j A}mwstH# P5,)6-?FqA\g62';%Vj2VxGtﳀyz5gإ4&Q5=;бnXZXP`|NZň$$daOd@!a;,H"!\##iV5%esUBER N=k2]d+iҐzWO'ىT”JXP$ Eb`wMF$DyH'KL[ꊰk${6ߓvV0k֬YXPŌ`=65k8mmmTUUQ[[Kgg';v67x㴭gDf6Hގng˖-9O9H$Bgg'N[6,_bG1/f6;v@nJN- ;}]d/"QTl˙%;}}$专 e;37Io$F+kxo[D i]!9?韍@((aҏ}b%024B %<% J2|>Ej*ε: E pD)ٳl ^\PŜްaÜ9g炙Y(k׮c߾}޽{ZoV.n挷7>||O1w!aX4Gi0w,( ]]]HĦMr҂Rn| 5Cu `Xe+nhg00Tl0u&Fk,q.n|~ ,ثkpL@ MZqX3TCTC K%! qI̘c-8%'Y%, XL Mں{]^=$ Xl]Lx\mhh(]R/($b`w%"Drhj*@zw=̄kW٢"ECACaU-VH5 RtBkhdYA89xҔJ#I9 ,$ ɭA4kpbE);-dzAQ)**bÆ sz@e,X`08q2oo>gdk>K&5>ڊil޼9gw>HAQQ7o>oMH|,,fgq=>U DQ4e= $-z޴uޟiRUp݄a  A9j{յF@@gXmrĄlCc%}L)W:dZ ;edh.o9Dh @EUMRYXym&bppcǎ|A* Ŗ-[@$/_+07tE[S__ .4/^0B^yn~ ͋6S[\ˋ/[-T'V4]Ê`tMH$Aۡ=!322BmE-E9+i@E#u$4 ]XV9H3N$SvJf&7I}麎'JeJ=Dt˞ Mmr#f[<| @.H%#XDy'|ҥK3`0p[m۶}|;u]s)n_2,"K.y13M %5r E"#2Sj<3::p<.'%P4`ewG-ق0V Z֕{)w+2$69Mrg}~Qs>w,cddѬ{psqZAfa(XP@X L۱%dAW ]%׫ν7(F"9U:DQڨ~|{?Y^e (`Y{07 Emm-+W Uŗ;;w9 d<{%LĴl֙¬UG24YCQD*hjDYjYɉk0Ң0׮=H$n&):8s@nr!MVIR  R.G*2^B9!.a IDATo,vs0ndIUaC;-)^b 9y$|@* Se菸{Ena9dY#u:,"T"6mĤ[C9w`ʞ)Wb,$w-EQ/,#KiڊZdULfrVl_)84b YOa~)]lqmDAs =]Fr[L1 "k2Q?Ra5M&QRRW\1KTa8/ /+fum555:uwd򘛈b|S[[5;&uŴj5Q6S͢Z 6F,]Fբ*5gۗ)8.g\Ś3QFfȅq{eSȥP(VJ8w̘]zn10Hlܸ 3fXVSiPۿ[>OTܞ?)PQQAii)fN<~Yt;J[{ԕֱk'ddptU؝vS% 2N:(ubs2TCnnHjjlP53`VJuUښ1[+';Ȧ81vN$Q"cp N.7=A7.˜3 Lu)hjjBegϞ.7 7x#?8ٿ?n;bpð=zhVIʼn1;сHf$炦Mr9IՑQր,@5]maZ*BvȤHe^Meg6ΐ{rvVu|~Dzx"C:LdƪX%+vEH2&B#~Z[s"C॥+sAp\<<Ӧ2sKqIf}Ci06gua"Ȕ6ӁUXXv,aM\ṰlA p!:&!j", $$4T$JqxL6%ù ӷ. EUocKB~Er>^SNqW䜰k4nTUx<Ύ;Xp!կ.K/|^{-#X[o L/w?ivq:g?(cvaX2d룷הITUpۖx؛;/#8s; 'h#JQ[[khlPUIZlDhJ39&EYųIiÁpPY*6XLʅ,ZF$1{Y9̱j+<+ضzxQۈنE}}*\ dž Zo洫L/YMP>˗9OpNdbA^9 Td/`C~|>_FرH$cJڽY*ne g<Գ\ڲ~l0$`A9CrnJ}06*" 2 X%+x`r9ܹNIMBx^PU <gNNEQhmmŋM7Ď;رcǼb) flH3g'NbP[[ d2ܙ\18:ȯ;@hl''^wpfq1ݔ$3.wH\gU j)'=(67CkghrtJuX#e oӧD""<ǜyFKD"`͚5$IկRZZy51Mtf'ʼd:qCCCYf֦WJdud dww+AA&i.B#!b6ԹڳS hHjqA-.l5D` R lé0Th*1-rdzR" >ŋS[[K(K*2ŬKJJ\rkH-[ ~^~p)1fpȑYm(O7)ox={Ii)[ vu֬҃ GDQ",%gkTUfMf>8UUU\O '5n),䨩N "T"-($J{'^pB6mdEEEf̞kɭ(Q\\L}}=tM|__Z\%Lbkmt9#jd|hi֤$"؅I'{ Lwu!% %;5mI_\M+ߏE" t9֡`uDB,)pғU?X3'Y)SDqVEN6Vɚ?,\YsJ-TU% n-vx<jrk$k׮g}<,Yd5p9s6fCzr``Q,Ybr!Eas8_;ܟЎkqJJ3RYD ZVk[#j"J\!RSig2=c-'kU29J!eIQ)A@CKcSXD ф@'=mRf-mnRzǴL(ewKV~obUVVftuQt3STTd%dsD"aTf.S\~,‹"l6v)Ǒpl9_e˻%IB55өN~[ vtUR$F Qs$dVuBHXet@8% ױفbMb%;DAA&ΩSOmX̖8|>N8iۋQu'Nzijjbͫ:'c", @P(Daa!`pB#pl}xI jǎv؃ @ mlLWh檖2u ʱlM "{1`l,6DDje\]gLfsI=fR1ZV^u˅bfr8x x|Jn^/2_x_OfȌK&x`ddp8jeҥ~_Ǟ{84|USAEV-٨/1{?+5lg5MH( UT6--d=fsɶqV1<<(,Y2Lyç+-#Y4]׉ڱV  iɘ$ٕH31.[fU-n1VU.DQdժU૪J 0[#5,jf}ʕD"vիyG.zɊ.lVU>e˖144@AXx13>{!+q{#b1***ZY% 텄[pXu]hUPlV}|m"k:@zKjƓ>/"gF`KQ f@nAQĤ͕u`)eX,fE-aX͵8q?iM1Dbt dhhu!I<>O?4%%%z/dv_zO?MUUL6xδEV{94Faa!nGҍ\M[mHƓ)N"12)MLWA@+wfVON[7@$B5gAR'~-[khkkDNRq-0UD"Akk+,\'Np7sٚe\blȑ#<p fN&!bU8~z)AJK(тkYg%r982C/ewᏞ٩d dӉa7_?J?ЏjS$c,`;5\R4MfC :q~dPdۃv~-+KW՟1J}}DuV^M,;`…nm۶ƍi@ j5ۢYu]7׬YC{^GєEƳ/$4gDE$D"aNܐnOi&`o%]'J*ɔDmMkْl-Rي444̸䏁Lɭ$9nϼ_я~W_=oa>"AG??'uVsѣZP T,'::S4S~dYF$(E2*H7;W Y4ær}SȢg fBZfs"?dI"f+Mo8Y2HB~(L~ll2^[ \Ys%MMX-V9zi&0[4m(p6Qxwy{bӶ1f_RT0ݻw{n:;;Yz5477xx^I]Qdmj Jr> Iou,r횊ń:e'h!\.N<'L1I4gDQ#i;^k&*QW1ƽ^ĄՄ~$W]TյW³´1p|Ygx&HnM[Euݜ={!xf$a$eJu]k}'18:hb4t$IԌdҴURJL̖$iF x}%EE J8q>15Abಹ*WG%t]nj{sE€|$顱s=Ǐ~#{9.]:ce՘!K.?3TU>زe ۷og۶mԩShFII Y󁪪s}1f#['" "Zq*!˲LHJcC#Ci 1A(u]EhF<'D &cwupV-Ah&SvV2'c=Yk?Ғ`2{MȲ̂ LmMEQ ڊb#G//lٲ~y/_nKcyA??NHNoo/OR\*hha<&}U l9 lr [ 8lY9{hxM(raYp8x"4,T :69UG',Ylh+ "6m+ѸєSvذaÜ,[,3q 7\0.;ƞ={]\ceNbaÆ lذ￟D"}hii?!r5͛b!TZZJQQy1{cO+W?6& bNMod4k%"NdIgZ'ad,63&CW4D1"j=}n3QUSp)}%nn~oh)|+Sb,E500ill4CeZN8C=ĝw-B4ޛRٗUЄ۽{7{?۷}v||>(EEEfrkƝg_$$t- ~kNΨip{tQDLb-ф̿5 [(J2-+QR896J܌xQ)*(B8V,BZ@A>5l ϊ?qbk֬wRU`۷???>}|2igN:ƍinnf۶mXqyPSSÒ%Kf90:@P''>ؠ9/Eb͊h Mflp۲'ѹ2ɴeĔ>뱈:("2.ޭڲ+OB!:;;Yl٬yTlr4e͚5fgA6Brq1{1E15E)--`N9M㤴Tvv i###`$RTy4-;e8—pN-Ż]ڰ"ۊ\%`4 =_ IDAT]9z(pkCqI?uFrdvn7hBhJxAl6I88Qh1G|>,Xlu-!,,s0ó_xXX%+ՅQ箣HqPMbg9v)..* DFikk3-=җă>g?yT!\.@o<Xfnj&H{88I_̗u CCCn :(+(->-,F7@\XD ԸjXZ:U'{Obqn[ԸB<daќO0N'˗/' /} 6o;o'ёOfg/\s5No{,Yb&^/Hr{<RAB}=M_첝'H&`l1!Dj]TUS,?MUUFFF8t 8|>Yf .={o|'|^|G>#+:::طo xiii7IHXVsFb& x*N_gTF#A'I劽Z rUҜV'""Z3nWV"cu]' qA6j&b,Z*zzz[?syOf24M,o~zw4oH&gr1wB gP"?'JZ徦j1:ŔK(qxp̦{20 n{Ν{{{gݺuXVx ^|Ey) 師c4 !quQSScƚp8|T*;O).e45J  OKi]ݶmwԒb_>j<Jjkk`j!6Th 7'? [n] ' P}c477} Mz~t]`ddÇj*Sy,t]' TESPTDRj AVd,X ~0;̉L[& ȲlI5k.`q!RWFUU/D"O~>Ofd"$6l۹k/ !Dhoo `4%zl6 dբllŊCr`f5P`&50lulUe2B>ü8]~=&h$dV44MP(4'-tuu!"+W7VǸ0.H6S6/|[3̴V@>c6'$݋nGHa^8BrrN&444̹TPUNdYd2i`pܵ<_gsA>dSe8W'f2k0p~;=X*P[[͛yg._bCL(o:tu֙ɭ6X,2ƲFg>Jx^:d*M7t]'-`.ΰE6luw?SO=źu]1d"#FSYY… `,\yx˗/gҥ|祗^d(S__ϝwI*ߧoϕW^Iss4EUUI&$ ŴX|3~A(,,EE"wFmdd&$I?ohiiWy11;K***qd%$:d$zYf͌g=zt"v;UUUTUUYm;vH8jA]"7M~ӟq{?y;U2{.c…ϵ߿W4$-[eoFٻw/---=m۶Luu5oM'e(ogٰaE] \.\.uuuhfNnAn5M Aذapw""v|m"ownt̹o'> &?YXGu^/{g޽lݺd2u]NJ+>}.ӛPPP0&䮡7E4hXt)<Ǐ7䡪*pBg>}7yʶ7x8EII Gg_:=ܴ!}aFH qY`0u]ƍF|>=b(tttPPP f}?Tm B~:;;I&f[vAt]gƍR)^8---Ӯ29޽_SSéS̟{{{})CXr%P?EQؽ{7=]]]444Lss3~=:G4۩bahh֬YCQQѬ#, f(FFF&rdxx}-R}饗xg?]w݅볾1 5\C=į~+V\ɞ={x4m'vݜMB"nEv6CĉansO~3rh ÜKf/7o;v}Y~^֬+`Ϟ=fIjٲe~ʇ~HKK sCCClٲ۷s5 I~Y6GGGٸq㼚ܕe :~&z{{y'xXj\qӮIx.e۱$ ۍ륬lZגGS!'pw^3fssӓx<~zzzD"BH r1su@EsofڵO~r>7|ܾ0̫d_fxxgڵӧOcv܉$I

Mk& slƍX,ڵT*e2oF4Q-<.cGG-˖-HH}Y >1F`T2Ν;7 2Ȁy%͕Ń(koSZZjNܮ^P(5 9 @_oPUl6+V Hp]wvx뭷xٵk<>yn~T*Eee%p\ce믿0H&@q]]ݼ#cUr֭[b?!;w?EQ팸4uf=RwfϞ=rJ3g8`HZ~N<ڵk)((w43㴶tM|s(A'JQ__Ϟ={a<خg}_<3Y%s /S+!ai}TB" ep$zEk_ WvFٙsǏIX,466h"^~勽i lڴfmۆn7x<6D31c-L&A:;;Yj%%%8p|+['3bs`3M8J$x|3{ssiӛ,]"?OLP(dz{<HRSXXȲeˈbqTUU}o^N^☛E>fA>n2DeVq s5f{lllDE^{{n^^'\6r˸ XRRt߾>jjjcϞ=,[lF=F?)++455}vZ |>(. *b1Z[[Yx1>}??[oo2Glb6LA4//lܸV-[f֚sPUvN'˗/'swr.L S6 \m***7Ud; K}v8pEHk}Sbǎ(߿ݻws뭷F>Fss34 G{{;Fņ磻n7.w}7u]wדGy3T*f͆ituu7ͬт5ۄD,ZgEe&E˿KJKKAwq8Nl6###\uUȌQx ZZZػw/N۷}v֯_O$ n{ƧO{{{隣ojM$MѦ{іP LY*Ed"CEEUT|}2D "~Qf[Qt@K[J..MyNΝssVX-[ ::AM~Ģ7 |įZmDII]PTx"ڴi///9s'OƲe0p63*3hH5jnܸp|wP*8y$VZ5kѣxWp80c ⋍&1|ԩS&EFFBVs7s/_j>.\soW\4(Jfc{C"77Сb1̦M! Y<9ףu.'st{F߾}P(^T%w7sk]oooDDD@_FTT,Y$ĽOQ2K]v-[BRbKnt7I\Ʉvځ*3M%]RR^uvL4 fryl6c8u|||uVDDD4NcféSVV{ӧ}Q|eĚ6_e˖GVVƍɓ'oZ}qD,iTCjܹshٲ][evd9hժ8l޼٥kʕ8{,۱uFlݡqa$''СCpss&ҥ 7sުVbQQ^vA&رc1cKٳ^-`(%vC Alsv1̛7{,^r 8? ͆@W6c(**~ ((ZJ \,--EQQ:tPok֬w}=%{~A1Aqfj! vA.ɓx駟_~ n jrrr ʼn'= բ<~~~3f ƌ-/`ٲex"ڵk>}o߾AAAN< ><8p(((! "??L&tr/NHS@qf<==1p@ 8ХCb۶mx뭷\:$ZjJsfncҥP(عs'~ǻ E,ox$|Mxyya˖-Ά;x |s[n{6W+鲸n!qD">;lقDD"Ν;K{(n߻(mBBBxLhh(l6ju$a͉@ C=zC رc1n8T*9W\~qn#55zfiqwB/|ѣ1bo6mO<Ӹx"uD0Xm{jel6:sιb +0ۼy39rdc4ɸ~:KKKn;VPP(m9s&[x1cŋz1%%%d21+,,dwMPm]sGhdplFiQXGڽ{7a-[d .d16{lcX#XTTc׮]kZ(**b}eѬ_~1Xjj*{c9roߞuЁoߞY1T4vbI}vEqA8j3% !MUcvndO!BHdԋ$n+=n:SNԩ֬Y$P& f@ncԩ.;ǻlGƊ+B٤YRg)))F˖-!0fرE! IsC,vɩt?:`Ĉ.-B{(f憒YrW 28{, &4v!Tb6iJ(%uVm||| &M©Sj !MJfIʕ+HOOb-[rL^^;wD6mv3 !b6i~(%u+V`hӦ FvaΜ9عs'/@vбcG|Xn]6m۶ ڵɓ'=vKBplvsC;RG/^+>]ttnGV\ټyspH!(n75ٴ,!uTK¡H!w H /۷qOhtGKB^e4<' Jё=`'t9c[5@`1dU*x# bM8ck#>cSܾOPicx[2eA8x<^c,⁌؜an}!o|Tf@g!ok} !@ Njx"clkBHst7c6@qɠd:xx<^6x{?Ƙ k;j3!(n7/Tf@g2`{碬y{=o&!?zfl'2"(f*Q,ixKRbĉtݬZ)rss322`ٸ֭[@T \;b׮]wMyd%&&B"33f’%K/6vjttxǰb JǗjw?t:rrr^zJ})_]bb"t:Μ9ӧOcōݤZsZ=!? NJ)S`̘1PT~Ǝ:wNdP([b8wf3fΜ-Z 'Oh䞳ct rQQQHJJk֬\zzB/F=իZXp!Gn:<裘9s&Fk͚5ٳ'OR rFVV .RDll,~*-H0j(9s5;?VZAP`ڴixGnݺ;jB:… ]vM6d ŧ~ʵaժU~i( <+WDtt4bcckBV`` Śݻwsΐ üycׯG-EqFL8h۶-RSS]uE^^^h׮v=6qDL2T*#<7obƌFll,N>]״sNk^^^ݻ7.^=%KCfCnn.???DFF/OIIA.] 7xгgOR);>qAʕ+ǏG=兎;cٳ'd2qL:={vZh}֭[-[B&!22~-[v=zqqqP(ѣGz{<<<cLղ`~zfZɓ'Rdiii1ƎΝ;fr׻չT*e?X,?fnnn,!!ںk.T*,//1Ƙ/;rcbv)c{e~~~h4W_}Ӈ1ƘjeYII 3 "Sx;T>>gee۳ӧ3;p;{,?dl16i$f0ؙ3gH$b.\`1;G}7nvڱcEEEE1ϤR)&L|||ɓ'hd}alf{ݻPޥK۷oX,lɒ%,**fy;vd7n`vfv5ɒcuޝmذ1V;^XV Ga1f؊+P(d1ƲRdwfv۷)JVPP]7df:td26vX7t:f0ny>Nd2s1ƪ,..f^^^lÆ jM61///VTT+ cΝcVY,Zrj(06Mn… z*wѣGYDDc_~͘1O*Ǎ^z%U8g2k٘P(dϟ[jիc,DEEqzKܪիYdd}?8[nwj2HIJƍ]1c l…dV,3B-[F^v-2Ƙ`.lmںw^ֺukvqf]V^4Ǐg.w[V3>ϲd~cA4vbIl<==T*eX߾}YiiiǾ\{,S(^B` :1ȑ#v; fvZǏ0'2{؜9sXaa1%) ֭[?CsϹglݺu,33 Ǝ[)vZΧB`}΃>;7l\޽;9ҫW/6{lFEc܃rrr``0C ^^^4h YYY.] vvotLQQV+ùÑ j<",,vff&Nʽ.___|dgg#33G֭[x5kT*! ]nu\xJk`xW2V ؾ};v܉-Zw8q 77}vy+8~'hZ8.K$J+~ * * ?w 6eff"77%.]/_Fll,n;{PT(--E||<:rm۶\Cnn.J%Tյyzzb֭XjO"-- @;ψbG=&55999x!Hpy.j.Pڵk=_`` V^\|ט2e W' PL7n $$^^#]XX֮]˽.Jшnݺ!,, syLaŊO?ӦMl=1jV1c;w.\'|֭v܉+o'OƢE * = >>ϟǦM`ZaZK.Uyחvs?S?͆?龓 %%6 D0شi4 B!d2_ڵkqYfxhIBq3f@rr2OhZ(J#%%6myFŋX|9XnၥKjHLL חQFaؿ?V+>cbѣv L%Kh4nܹs䵍7|>^^^z{o9 JTbҤI?>w^vL&v^X T>;x \|6mf֭[q^xA^ȑ#o`ȑС+ ݻ7nDPPr=Uy뭷dX,[; [no\v ;wX,T*BDDYׯGxx8r9֮]7 9s`ذa 7ٳ{Əcʕ3gd2ϟQF~~~ òe˸oǎbѷ:'|`(JoꫯT]v?.]]v׷.?RGVr/{C8G14kGFll,n 53KnpԸ)HJJJlƂ  ѵkn! *55׮]@RRv؁~E[c7ܛcl0Lwww|-rw&֗ÇgfCv}[QBHSnbl@ ǃH$[ݼy<  ;wnk{m///vɓ'Cq B.p6D*Ht::t:uBPPP_4KTf@%V|j!-- dj777Kp)fzR_N% %$qəL&6w Mp=@RR|A 8իWj*ddd| ZhQo!Ü#h6 V f; .fvf..;c6 .lrY5rssaZT<x<ܖUq8x'q1xxx111XhYDX` /^\_o ig4 Βx<F#.]:jҥK BAfh4BA@VCsu.󑑑d|F^^{1̛7eqqDl3g;/6N!͍bj][Ul>ύzjh4t:L&n9G1 (OUlE% k:\F|hݺ5H&OgϺb cȐ! 1 ̍Uw1|}-6UѣGMg*)p^Ndo'N}p |޽;V+ص\.۷cܹ={6~zo+!46gIV1#Wc'|nݺjb&Iâd>QUխ wRzPS<}|||лwoݻo6 ""ѢE h4dddݻwovBHc)?)&1}CuJ8K náhN14(JfbMuoCF>ZD̟??<7Dqq1f͚X0Ƹ5|>V\Xh[ƭXL!2gInwYMNUj1rHv|׈FqNImQJ3v[n:H$<}=~7p]bxG NV Z VRnEgPtY8έ gvsU*nVSDW9PjJ%f̘|'ObM8'bP&F=PMknn2MII_ÇVBEEEP(8w:vDǠAТE eqnz6l~<4h"##h5*.!7g̲Z5*yk#FSNxٌ1lܸ%f8$4٤:63J*0 Btcǎ3@;_ΫdgEHH8̛7x衇0x`!-- :t(B_yiXU뙽bnzVPn ܪ(fߟhӄfbpCL|03Thl6|9sm۶H$ϦW… x?_~\`˃fCXXX8pww+))DZ~o0ضmZnS~={`ʔ)h߾=j5_#>>~R;,SS'N@n`X󑞞OOOWom4qU<Sט ݻ1uTG (mc0Ju F:SNA)QkhWj\.GII ZnDV}(iBE'N@tt4|||n:9ro4 Aw1tIΝmqyt~!|||pBl޼gϞvG.CaΒH1ΘPXX]x}ѣP*(--\.lm}m̾U2T1[TbѢEؼy3O(j,Jf;gbAii)n޼mLz1B“`iPHtr b1l6!!!  V&,dddȑ#_"o߾۷/_)X9㏈1tPlڴ ݻww)Orrr Jo~d})_vqѣ^Λ~3׳^jG6mT*鐙ɕe B-Zf0pڵ[&@۷#::1?ؼy3vz˭)fߓj J5MPŒ@pǓJ>k7a0P\\ www{܃/rssѪU+bL&niFFl6<==`)J.kFDD ""cǎfCzz:=W_g}B֮]зo_lْ{j5ݻl6b ;;QQQz7ի3f f̘jTURPF-'ЙC P+#nnnVH$B`` , sWnn.f3!ɸ]욼ّ\oJBXXӹ , .]Xx<.f'$$(f;.^S̾wP2ۄTWcU"\ gq9@̷aw@`5 ((vj||۷{lRDDeyyytTxzzbbb &p#Ul8I̮bP)f_˗/ǓO>'x ^~e,]{Fii)j5)f7+Gf̚,&$J(֖nfM8ldRH2Z2y,6 O$Bk P*<]M6djx"RRR_!##%%%܇Ҷm[HR$&&r^9r˱BX,HIIFo t j3gDtt4x<QTTCBTwӧGCHH:uꄏ>j/,v!Lbfsnp0f!)% 2L^6W9֫.rD,:wCʥv;s4-44m۶E\\:v???f\v 8}4]B:ڪM.Yyݺukx<\p۷ܹs1vX]bVNÑ#G0uTCx%SRgs'5V2p6,\<N %*/d207wxK+=OgSɝ_.Q|U>~:[oCMb~~~o4 f3Ҡ'xzzb֬YxwЧO(Jb…uT;upƒ%K^W> |DGGcΜ9x8y$6mڄB'B!233]  %; U󩟑U`vyS&7xJ 'udCg!LA^=UGUW({oz=Z-T*L& *rP׀DGǷn݊[bܚ۷oG.]0{lx<ҥKo3ϠuXz5>Çcԩ8~84 9 b`޼y׿\͆RlݺǎCTT |QgLqUܺқؓOÃa(6 agjR'CoY-tD4&9;%T*RRRd1{$bvBB.aaaڵ+q [\Gt6lƏ֭[d2aҤI8rڶmAaС޽;A\;o8v&aa,KkVH%RgΜBiŋPT\Ell,7ܔs3f ҥKB3mh׮D"zqq1f͚b̘18qlقqajRtk(Bsj{]l\N߷b@TB$` & L:e#CRBQ;ÁGw`lRVR`&JC6m*zswܳ]]7V1h,0|$%%aРAx7g?G\\VVsP\v={HNNƾ}0ak qY%mq,͊m'!0byyjP*sX(ZӤ3 X N QB P̵iO+qn7,??&`WxHTLD">#O_>1//GLL  N۷uP(\E->\ gϞܞΝåKrk׮Œ%K#@.<;кuj7y L.wCZzd2 !3*=OmPC&D, ia4%`& R%f[ll}=>Rom[)q4f;; x衇V¢Eq :ׯG aZ1uT$''cǎرcM1?VZUV:u*6n܈_F@@JKKsNL&m۶v⊒FR5V|>FoDvI6LFJK E~f"7E0YL$wCWgRa'**b?`d9')J> FZ,XVH$pʯMT~ɭߍ;＃ _~%BCCɓ'{FAA~W̛7ׯ_G>}+?9sm۶x>D˗/k׮HJJʕ+hk&LP(\7ԕBQq8Glڋd00qbbšu.K&H hOȫ\&wZtw}&5\&1fqbvzz:R+.+Vp#b˗/ÇrJ<;w.;kڵ K. /^z 8|0,Xm`1K.u9O-hݿQ2{5DΤCRZ$^awbwH -6 !$" #|RhMF(<(є}`ڴidHJJ7`ZR"+J.@Op IoԖǕ+شnDp@0rE^X\/Zb71D|elv<ĕ&=i5WMi@m'ݪS3DRPUDUjBCC1dܹ @6m~[zCE@@FѣGscNnnn0L?>bcckq}GP(={6:uꄿ /"6n܈{9Gdyԍٻ9z n 0;`7@)Vﺜn F˷w^ 7M:aeVӲp"vn5(u:x(U);ڭU.ZCU'vcH!we$]MWJ\ܮjs4ԩu/2ѿA" ((>^ Zz,GTT`EnsC]0 psss)OkRl#͘2e ? &`ܹw͛7HHHvIHHe0f(J(..FNNnܸ@ٳgs?ٳgcذa/R?\ݗl6d2$B C$1T'~<#rspyZR6 ťPx**%V[Y's'4J.{q92;=<)uJqvJ8\r-["!!Ǐhģ> OOO|?0j( ,fC֭.clڴ 3f@VhD-p5 "9̄bX,oߎkٳVEPP˺j:% krTFi*s"f6l6||o7f-pb`ZP교:{{fw]eC|KfP O{NFGVM@ V+7uMhڲZO0kݺ5`̙_O?aÆ G=0ydnL ,,{^߾}c8~8jyr,\sE|x1m4xyyA,>Ap&&?5ނ] ij*)p}߉RYFPغg/]J"CRykeV3@1e6 2 ofΜ Áɓؿ?  ]˗/_E!** eH{osl^)m.n6[Xxjջ%WW  =\T{; DRm˹&}k~OwO6x{@c`6`[0*wkB!|||^\L&HRT*nKBU\gCŋVo>ٳFǎ%~i8ٳŋѪU+L8z:t(ۇÇcqYcرr ~i̞=eT:W(%)2a:Y%|}(f9P^@+@`2@PBkԺN7P2eGFWV'.S#+[n:V.ĆLg3ш˗/CTBv՛Zn pիWѲeKl?w\r̚5v#F7V^> -Z@xx87rUVСCظq#PTT~v [uJ4Q7Jf_ g`M2[bh0uI:on7HRhlVݎ`,HأRptj e֝.,) `eV[IF-y;\S"\R3綴|> #GqIXt)bbbpa := DEEq(,,? lMC᭷—_~sz[%yyyZիWqiK5 i@[YNg1"3v`0x~lAц0X!w!= B Rz1IwLsՊ,zr1[*BePATJ@ocD'>=ӽusTN V)?A&q_m۶ GΝ;+__~\eː޽{^}Ut۷o֯_@h-+njdddp# .G}Ts7JfIMڪ͐s; B @77zJ˳m(֘Q w7Մ? 7lv&=܅0YMXii)<<< :rR) [||{~l])ۘJx<<==ɭ p8j`\, =gL<HOOرcѿxe[v Vٌqd.]B^{q-ɘ8q"7lU`Y~,\x&jJkS(UCy\wy99}z}$H%YMo8@]!$~S"E*[ERu$zI7 I0llm-mf$YYo4ݧ9|)N)\2ˊA;h# qMf i qƫFgq+Q:^1T* șL'=ih溵RV'1<2uZ\{km/UBw3?3=\;~;m /=ok_<O\.wgyi7s;߁5E$6w}Əw2be[.t~ GN!hjLAcXFRH-,;/JdrJ@JI 4'i̓4Aۡl "۷o'MSI!Rf?q~೴Y2IŴ$qr$<5 b FE6~ضm?~olbgV?)v[>7Sҗ>*JvsUWȆx-juIjujrbj0/|v?DK;8 !S˄Q.={$HDQ&iLY rO76J" B>1#UKJ^ymL.iKF8/:t݂G=9{?vCkaUo%WO8ٟq5011O?^:>#I}ky׿C=?qϞQy/s /Ŷ79YW ðPP8s zO1iX=X_J%>o>z!?=/m 7wO:3- -@A}泌*Y%$&\;|S:SS5QŒ8 pi> +z+II)tUGE,"RA\u&[n 1q=Dmq;:™N2 wz^XΥެvxFT=կ2??sm]Rk_Q;{СCE5ubvE̶m[%`=]k,x|9x a2>>^Гmﾞw//SO}w|S6T2)333cw;kqǞ*Nk$M~8q#혚Ime+' Y.ӜGz`l?MbxxrLJ9 Ӑ4^9,˰\jDQD"#Nq=G,?S$=xc!ToWTq;k{'f޽;~w~щGett??=4+^6 fL _W8tfmHgO!1T^3TC2XnQ;Bl} IDAT`wu1!N%FM Ґeˍe:%tնULn 8QdO6I*QH"P5,b'Mv 爛*$IRowvZmM{y A Af=z,(TnEg5;']wşyM[V6t:6区h48|0ccc0 +P*F{:^|W S%Ht)e"NS.ʫ aZDzZ;ZݷiTA2$@YȒsួ0R9/JT 5MN:U%$I*d<_J=<$IŚ״R)нCo=-w{/̣*SSSy}l7OI-rZVADWC㘥%vE%y骎,G,BG>ã}vkBn y+"ZY㩳!n)jFb(*Rx%(0;z(KKK9s%6hy[[V`syQ--^.'faȑ#G[n'qw?7aQbl @^MӰvu$G<BϲV6˨$IXފ JAs.raHEV"b"uK~f K2Պ$f T*M̷G!ez($ҁzibA1á WJay|srۀVvVř3gp>s+NO9;fwtp7yM VDdv:7AQ#?;$ wPeOM,*lh;g=B~+MSY&K3$-i%_L9Ф?9p6KGr` ٨Wu K2@@U4A+A\`"?}rq8qN>.d|C266sX["'8k\4AY- /9Wɐ@*IԢzk|I]=k[;CԩS= ٚws>YsOO^1Q@DTUOIK8$ x?Vev j,elRhZ>|Z7LA3Fk,eUIp<鬪U, Desr)z 1Dq2՛*l\aXckzo?m>4/zY JeY6DY/Nmf4:=yHvW(-n6q1 Nc3ˣ\;t#O\4aHsq!Kb-@.eFO$kXeKpB AquS`VXNaѢ u;w.s=G1C¯W=Hې`=R$/ݱh@\\.X 7mTs+K&0 Jm'}Գ|ɯr)BBd&sC r{xկxLYadd]23sa9$I$w~wxᇙ;mo{7pCO}S sQ~|3[2p>e֝>}'NpWs_rs90 Րh%ûD 211 Dqċ/zQ(e~TcɓiAp5T WCHm9L2!2/X;7mQ)Up}oIvmLǍdWoA~blle-Cz¥Ԙ8߀YwQ}fff8x e]TeՔAKIf%ng߾}ٳ___bO`?ew¥ae:mRV뮻gufŞǬBRFU,+_# rA3tL ]561mjxC7<fE^yAu5vv UJ:nR`mJz(]w. p}8ׯy l zD3s9 BTMT =cj5{18n{wxT/^L@dO~H nndw59?KÎpxh^#Q  ת ;;Dccؒ ktdIOIHxn[%5?2p/:0/r֋Aܴcv#_|h$b$Qꑫ1=c{x7]R`\4MQ뮻u{?8tMͱsi{5#2Zb•4(8z(eqׯ9ح3.'hٓi-t%yIPGIĐ9Wϩr+BL(rE5-iXնHJ_h&jQ W APTE_zrX)J8qЩ^.XO2)# CQ6vv0Qc'ǢbRxb}2|d6I?ǞGݡS߉]vQ95T!hyP ,+S?Z aȑ#ƘQT$MhV,5y^hM:a&h VVTUE,KIꈒHB芎s;0E+Y"#cDWj[I7Z jC q7nخ 3yv\^en_g [`j;=vw;Н̮v @r-~]љ_'#mFFEr`vd#b[[`};n2W +؞гȒ,yBnhA|Q@{ $S+r~jWAraxd7ff_goYL}EHDVVIS0;;Kٳÿ ¥&g3K3zΆaԣyH oU>lۦVmIF:Ԁx"?zmi:nl6rX"JTLU@dYծBNjj@;fb.NI\VːBU"a N5$8:\Ūޏ +ݓ ߿UUWu?֟qL]+DqIyEĭӷk~~u09Fe:]d9IbRb)~ejjjsVuE"ntmСC]wuAڦ(qÙMhmJc% vL!2iR`Q$THI$GBRūghG*b!nF!QQ-U(ddJ#I,t(5e&άĴ6#W@("ޛIL̡CZ:Žu׽}С'mTUerr',,, ¥i#;y9źګ+17,Lݲ 4_c̮ci(I~;3O;OSk.Ȓl{83Jb&2;;ciر.2!ulR.,r{ ^,OD?JT¶l<߃,V+$J!g(2%%#R._#IȩL=tu`@unf X/: mUz.|:w;4!ZQX(Ȣ*_~>#DIFU3k}#kKb/wy'/Ǐgjj￟y6׼5|s~.+N^A)!%qsQ&_} ˅ ]h)U0?igmJyBy(RKvq}ڱ))EVò,.e1fc^a9cCcm  FM0 #8׍ҕ5CH4erALrf")R|{/.o>?_'t';3Q3p|pI~5i:xi~6F̋݃򒋩R1*lU?~yڞD7iȈrCf0CPA~8{8NqWw][*N;k! p u, zsH,,.eHb&:A+ 㜋dV*躎$HvXIF)J+܌X|۟d:Ɏڎpixq6 k)3ITj'tG|ms ETPdk_YY %Πb]{B^/Y ^$׹?w6~7~__e߾}p뜷b(d{+2Ξ=˱cصk^{% =-7u2Ri;T ahFO"ۍVТ\)+Aͫ)KKKA01::Jm1??OR}: h t]G4DI$J"TY@ϲ 2_A/]L4R=ͩK蔇fqjZjo 6-ð(^ <9$?^̓qsTJ:{gC֑+lX,--ϳm6^W[LLȰdgߣfj>pΡK/'1VҰAzVkEq@A?yYZ^B\UR#,b4QZFQD,5v jf d"nsRIR% ɩeYTUsY2i mLMs5皪ɮ]ܹNǦѸ.X7=CHͭ+KO|=(:-P@5$CFF5!K3vd6$Qɼ uӎ l1li(_'Fo-o-oyK|]ynGw[/R80 uۥbyY^^Zrͭ/yw 2bp}k#9fh(Pv$Ic73|2R6ʄQy2E&i*S+אD(MeD$t*N)j ޵ \tKl/og遊8/'.V`zBdP.{f&.=^*M҄_qB,Q 9,e (ajCוqܗ_W|2ٲ-s!nV cp[{uePЪzq-,DLz4c,w*k垊 $i3d f{=ZYZZZi@n Qhe-HdW/rΗ0Z ]4V ۷Yl.Dg"K54QRCm*XuSQ%l( b= Qj IDATӿẇ`uwŲCOHxW'j5*@NgaόTMYɮ]$YdyrXw_}Cg$vrӎݿ+If j֋ΠV;ve`A7\Y؎,˸˴WTlʥ2QpHI.юk7a n hq"$qqzSMf b^%RR ۳s0|Rp;m*R/-9RV )A%`dzY;O`6}f,6t<=GOXy>QsqNӘ4K)eJB,N6U7?ADךWl2ٔ,8vgϞe/)qdR-̲m=&\/΍- [ @DEbB>Zݶ)+;0~]\Z$ #&M( % \Kr7(/P!"UXL']uZFӭL&:Q&2/QSy\( m")2N5D _LDA5,͹ȁ\߾o .웁,XzBZ\.)8:R;GUE0 EǮNjs͓+5C#ovH-+XA$I,..rvɫ_ =hml4[ytpU $a=jn(3tkzVU,Ǣlbv5Fo%4pÿu|USQ<{h5ȳxcDEڤ_d VlQ 9I1&h-Yfk*Z)Wzզl zBGұjq鋦'|/}Y4c1o&oL̾ Wd2)gϞ-*T_^^.`Q :!##,:PZrzt!dC5%  ¿>{ 3ۅEm{zNxH8`t(N T oPTYExdqqD\LRh4DQ$Ic0:6/ǽM/|jb, (Y6ua \E$teE?X%4f:áqƾ+wsmJf"˲jX.7;.KKKwܱC:08 EF*#ym []^utYaR dȫNnd-nIASDY `4KBC]YK24b\ɋR] X a+:f$SMe:fǗirbsl_/^.9Nv|gnn%^ŏ|,E 츅_-orKw2kV2YH0 cAu}9B$T* ̞+FDJp(8jZ$JAϲ Y tZV-wx,-.!(r>l& P2A-,HDr]Y8$CdA (HB?V?V.#ۦjj'A@k$JQAM5QCdZ^~U=7]uWAceGmnzBGޏdRn˾J.4Tlj})R6iBٍ$I±cXZZbxxm۶mJ"g1oHu:hW$Q5d~rz(g7(]YQo=`85* soa-j~$MG@B )i0TZt AXEuqx Q*" >!R"a5>))em/X o_nuo鍔;Z?8irI?8}Ĥ$rۤqL$ʝ777:f_)+.C!9/iʩSkabbMƞMf7mڔ6Te4Mall 4 /QvmaGmS. jw`t;$qK !J#b5:^9؉v^ci9gZ^JRtd:U4IIsͭvk`B%& pͬ |FK{F"T9: aȦL(aV> DAհ㐔4!JHDH 229 /y{ܾt(H4gL ɋ;PpI-!lMm ^&txVb9;z#G0>>Ϋ_xǿl2Mpqr.JӔ4M(. vt$ =4K0 c|hJ#s%iR> xח82vlSwꨱZ\@ 4QC*r^mq`7/R5Ǝ$XaP?]0!EArVby(G__})3_ɛ{y[:ƱcAFL McɁA?Oݫ 9YIw܌!y)P*(.aW])-l,6n9|p7\ Šz@Z75`VfaL΢*hrcS39qq}vdw}0 ˴iO{ ze 01xm ׆IEUPd{spi8ܟSCL\tr9\(8ߜ}[ GQh ;n`w޴M1uGpԩ>åu[4$]R`LӔfggٷo^(l۾ QQ8jT}Ӣl/ m,}v-x 1Cr[^B,ݸV& 3LďsDURQ#r݁,ȫ%DƄqHZJ5ù@DjZ )oFw EUzx\a4VEEpYoy3XEE0l  &khrEZS:NbǿO&. ?\|_cya0 Y\\DQ1L41K!!yg3;ArgK$̪da~Qj4cUgYŎlYAuDA$K2B7嶊dת58)K<1%/U4&ji$Qw:=1DT+Y;vPc.A׏/qb"A4rL+ .W]͞=ء,ܼ-hG=,Μ9sɎ+)f_쥴fttR094Ucjz9 $:ZEo=ɠx=6c ңnjf&)KK$q= &jJ[ͧaMDbs )2tIG DN=ߥPOӥ+:r&pl;Xs'y<1{nArﴫ.eR-,%bFF҈FL4$&iE<>LU1Uٳ}juY|owoJ*(q5#'K2lȏigq)v0d,3*fe: )qp9C{ezBضMbqq ղ/"'N`˿ Œ$q͹v]x.XA8quac,8}4'N`޽LNNإ tւ*əJ%ݴȎSȩ׆. fbO \RDݶx(aT Za Sˇ M9$ď5e)חؓ[*2{:ɲh2ءM+ZID欿AP$;OK,,. Bn:$ay~3G>>я=0 zK-l.f̑#Gִ?ד:)}fvخEy)Ap+/ԙ@UUCHmX'1(3sRҽ l :A6(BI-N"C gs$_җ|2O<]덎d2 nYu:Ξ=X`]I`ry~CKýj/(3(gN8mnvGNWWquf ŮsR `8JfbREM&ս+– ^ٚS.PÂ'6,iJD<^T(qE|w w qcR)2Y\(_:$'gUrU_R3j8ppSGNq1R) !8vMR$8!ɓ1z! |ۧ!zmkş^ "G[n5FMe= 9qk#;1/Pxnx-̠D)QuT82I {tjC0Z%=8\[fqY=,3mu;q}KK\k_[+ vA<AjFclYu)}zqOq4BX($4yby!}}KTΜ92 koo}_|F:OӔ!<]OOK)r}!/sP:v{hP2mf^CgkG M5j j0h4Emuͮᚮ2wek uzi eaZ<gK\Ŗ^w^eEF^4&ݨ{]J_ .Yme*tMaDFɼfg#(JABlO/])Y5lic(TÙgPg .}rV6ˍAHve}\W/]%{1x!ܾ/’ QjFNo0S!''+%J(&?Oޔ ^!|SK_ַ Mbmm%9Կ{4yG?z!YWVV8}]kM2;,jNƍƾɬaR@SF7EQR%Wǒ!"45WUEXk:F]m};BӪuRmc^Khb:\.%%64)BW/|si\ D.+2!A WA 0qb%-V:&I+%T:ae`K%\%/r:h@MMG8NX6s7Yjygyb ,SϮ{kci5 010(ʂ@ 5,M.$)7[v@NN '(c? IDATQ`0deƎK.Œٵ=Q%s?s|_~x;vF23ƍ\rSNSO.FJy߭N7yTKým&a\x5h0b'%NCjO 0]%^߽7Am'mhT0c`peL4b^EQe &pqM ϖ9E+â芶g{;ziNWVdckD"i?r0pMS9S%)x.ex)7: Xwd1q>0:8Ara5Ʒ^MM6tMBKIo`)%B ,m,vM\_S8E([~=KbA%Y,&?zz?妍z>u~ՉAѕ+WX^^ŋ|g׷9w}{=I2;m6vik=pKXNvfmX077Wm8ٷ[1`~nNC/eAi5dTYVE6DvUc[f;CFҚD5S:=VuD*(݁z7&4 h'X"H MG\miRAdYP ("K`ޟgޟG 5G Si'82iwyY\\2%s"/vv4>|'9/]?04(*4 /V1BS4)Oliseqщ}+2lٳ4ͻLíZbsmZTNl@5{,^71{Ц[H 3? ʀ܌)Ida@?[Ԏ03ߜ' m.Q /o\-Br#c2, J, c82d_>@\৞9z]V_WÇYYYaiiưĩSx0} "IΝ;GQ]N2ի\~mV"k^EY<ǣ4A4 4dY6!U<࢔"s2ܺK3>f)i ht]3M a ;H_ryLKwa>kSFB In[:iBmiA-ءBa2T0C OAg|f,u8d+Y+FݩjcTFLajK0 Ww đ5J X82{>iRsL P2@(UEy_L@~X@k(,Tu<#;DychL4'G|sr?q'jo@'Y/yꩧA J)ָp'NgKX{i-ܚ"=BmOu=W٭$,vB ]9-5!I*yl >n( MfIlqM qY8@b&L;V&sH NBt95 U`aԉx,IZ N7u!+peXR )lFDxEfJUPJa5Q,bo83n6yA>Ғ,!l' Fx\Kю:Ur5?_m[Yqiً$EiĹg}5ɯ|0?OL27VZ8lnn}__s>&ҥKܼy_0 (fy?(t;T!yQ(XY_aو)iSW$NbփL P9vfniu*m˲p\fHA2@,Y.`x5Y1 ,]jNMkN0!s3 pz04p̀B;k4I^Kؖon[yX,jv~ѯ!b<(,,!m:YI_IB'Y6$`V\RS:<Ϯ&a`Mm8dhţ3GzOQlVE*UUYvÿ?|?3r1|_~lXNF6]YYs |Nޯ2!gϞ.J?m\\@ Jq@2s١'* YZ.mfgguvM𘖷SeXU;AIJ]^;~ŅE@>i N&*^i"ܟ^AR^'`8E$p*qpe=Bz~]+hPp%/%ҔIT",LBmp7]F;fn%W9IQ jN G:E- 6sΨ-u>Y [-S4"÷}T0 G@K1"áy8riVChҔLN҄8t;i)L9V*m_CD0*M ,Hsb6t((M'=',7N~ahp7xc̞?$I}5nO},-Ç2™Lh#\;^NLTvUJOOVJjȢYlǦfȻd TA2`[teL7vL1N̝ 2z[bjeirZ,.MStM u#cZ0Hs]-% 28RR )2e&XnwͿh(#Oryy}H)Y++A)U2/bG .c^x~}wݷ8[{JfơC̙3wmi{'A(3ęk jZTB7Ѥa^\6ǖl4?-rla<*"꬝_c=6LuhM נ_Q89 [ڤ**iZ.ZMNqf -g`v&XE"n񛆉,$Rp o' 7 skvLT"%3 ACZ&*?_?=A~[ d2;,IgM7:s i8pa'qzWAVӞRWF)QMX !$advo1'Zab7lj֏RH>ڎEͨQJZI !3c'{>41m᭴2bհ=txGnv^%;_ֲ,}[)#e98݉ j/R-$X5qJk1bvwZ6{4 <Òp bjEn;w]ŝWr_L(Lk>Qf<|FMVgN2C9jJ]kp0X ^^9FZH\s@'yDZ \; ?vY;yAlĘI`$*[{IO';غXUÇy1CvI8O?4N0 9s eUAֽܵbTCj :EyDmw*0 +IġqWRHnM7+mq[8˭v) IrX`Ґc-OayL ^g"ec C!D ̗(IɅ7EihU Fϝ:6QU4>vC A@` !jz\VJ` tuj 5٫Øgzc!|'u5fo0 Z?J+!9BlhhuMB 2Oz*A0;;Kףp0uݱF~OCo4dv40ޯ]4KNV76nQe};MAaq050RH)/!ǃ<Ҥ( <õ\,4hVJmMY!L@vQC1k1oy !ʈ$OEH,c--+9$d,@DiDP &LCi%--bQUNL (0mvWyQu,"#T;iz=jjJswaS"߻a p9}ˡ_FeN֠0 k6i ,E\ʷWTSF&MI/WLwjRNn.B 4uđOKǽB>W..yvv$ %jGJ|!*@vM!a'X@.+x5jyDYRvYձM+aUNMf4epѮIIu}\%BniI:qgyOv‡wqm2gj׳vhK(3# h'm=ax YVJp-ӪL<틛%1$T6%kq."JmLyLz&Ґv,EaP,JnAk{}$+3M+%҉+]p-zfY>>ǟ߽}hB\uJ,K>N+W0 &(e :.nkj~^qQ BY1 c˕\u,Q}s eY \ϥ'OEQT~Ǐ׭)0ǠҮ7gܙʢoݰKSӐicXZs0%~c3[h!,6~x@'dPU8`u`꥽J:NbzEY`[6XDkfݘέ5 fФusK/>F^;A;Lmί,!kx 벮+9;6lt ""keiYUVhme%aryO;4Mj@F)EEt]׹p=FcC1MV=]L렍NQ^T Earq9Do< ~aC BC9TeEZ>1377X} IDAT% m˴&e@ҦBulijpk/<1ffȰӇn)S^J*d:{u˳A4kUq"K]4Jj1 7Ж# b@ZtEƼ_X\8#yJ.rngxI>fjaC9^~>ԤBQvQ蹇̱Eq۔nitt:\z<}JnQ&o0fp*hڕ1jgmJLؓM-&gFnhxӮaTIM\gNVBjl@p | t $q&-_R(4"T;ZY,( o^$f\EJtR"īyq@s=<8CkHaEBaYGgCeN_sf!}i ZUeY5@`H3.1O?z{BQ v yEQv\p(py\r`|!=u666xWqe6߿2P}Z-fclne1CV@?j8vWYuNB^jϹn׉҈,vx%Rj{EQjrq2Nw.雔-UTlLi 0pbT%ϙ3yM^{5sG:\¬O= SѶm,ˊRvu>R*^7 $M`]K!nu+Alnh cDaTMtNfqd4ڕ)ɳ4! BnG_aZ&tQZ\9OR\ !xe'Omĝ0{ _җg>ٳg7s=7O}SE/ݛ| w旅v'lYc}R]=tgw g?.irڪ Nd*ɺYō>ɒ.UQ\xXMjiאiTaRfJH8;r,q~wqeQ[%}?PHVs,eX8ü7  LߵC ilim;IfR $eccu9| ۴+50S:OP 5&˳K5MӔyLL=c{﫭 *vt.0L2#3CĽRRZ%bPk+Ikv 8v%%5Y[l -m\ՉU-8'ZYqm"֢&q3k6hoG1 ky`nL܋@ H.Ϸ}P\ /j5yT(!UXӀ^J8F${1:b_ʕ+;weO7#f? \kixޛd kIJ,|m[ۭejC?vM"^Drai:Zǟx՞j8tS)YzcIJQimH!P˾Ϯ'ir7B  kG EFK^v۫c~ܶa`7і0A @ĚUJ(%k˽mMbcF!as }~AoyZpYV)4wP mYEE9yen vͫ}jX򏝚xoV^4M'>q[ƍ?~c7!{頍0ۯfQI*1y,A.yUO4~$ P `wiCeɥ;V#q|ܤLHݔ 0A;iKM}~]"0u3`-npN[mÆK)eBX(ek*H-vJ>R FFe*f&EQ`63u-*ׯhm.24$J)cqr$l0gi !_?2y~+!`7%??G?\;f?,LVsqС۶4nܼ8,֍U;fW[ꍫaȡC4 Mɦ9rLgdطj[N[,--Umt:ZnQS3~zF1.14BrlƱ i3 K(k, LlBbB:[3ݙ51!01,̮scse,M[U@Uc6Cb%VbםZk<5W,P_SB"#O~|3ǒ,VdEGxSzފ,R~N,G^{7BuQIܢMvґ%=|&SrGϯ/4t6;c2S˴&t]. :Y‘f,y`0q& jNCod$/$5;A)Pd`ȌC^ZO+Q 16O1zeZZߵ,81.nԝZmwapdÑVnN\oiO/:1"T@m9Xy;5HylƜ[(u[-f?h94LwXV,x"\ba~_b@|ؾ=qLka?v`>KWhHJB ]u_09p[*!,:P½!o$Fo5Vl2;dYƍ7, N VٳgYZZ䩓\l]kd,KIG9(_MZ !ȋ^nޠGͮa(2Or~%(STD~|rH>4McUsn#M9]ҐxS 1eZ%=٢a$ec9մN0Ukf]V[8aqW/Q@ C[@I{G[XE{&3"߼|BiY[GxjhٲdfFaYMRR\[ٙg2pW[2 g|kFʬ3[~XlZY5XmSq9N%ͨs*M|o_>B(:B^lCU"X5n;5f1vyy"C=[-[9P؁ '}C/#WAȍ"" q8G-YvKf79EVsZU"M*| CͪT{/m&2VG8/CrR%I$jVmd@Yxm=k}x?2ɵ5[,md:J;wDd$NȢ{ &ꍦi5fӀq6>&8FvJ¥KzPLq̕+W}7|J;g1!P2W3('< jl6YYY6_#c,B&&^$Hjc;^/,Mr|Ӆg'"|YMӊ~+BHÌ8h0 AFEkic1qc 2 0\ { Aw|ia* vwS\H 8ZZ77u2kgu$\>V4#Kf%'d>Nggy7?C6777ɷ- _OO)k 9w}^:hØJ)y&#2ÄɬTEd <66Ghgһ#Ћ$sssk5eȬ:.x$,rjlZ-u,G:"vaR5t Pk¾JJs٪9JJ  S1 t$ GP %e fS%`j&};ݠwvSzA/غ25Sɗk|;ת  ]שu^Zxss8CA0v#9gssfIRy(p2; N Y6q*`<80 eY:#;A_UIX_XGd G[ĩFŊWOͭDiDE$4N-oߺ5j5lLw1f&"fSZ"`4fI;jF!2۰M[Sm)Yq -SR77Ȳ C!MF~{Ia1Hf^T̊U! }vCjDYFFZc{a6l86Z1m.,1!t= t:ܽ{^aEdG^{z/˼{29 򘝦)| v{Qq K*# CZjIN7mP5LӠ­oIlm(w±D["Œ`?`Vӊj0\˝CXtI\IIgX&Ңʅ,$d 8ua|Ф^^]k4ܘLf4܆J>Q&0L8qFAymEQmzu J5tyqqP 6n r Mxi%lCuъaN]n6 A<+Om2[TAi[[[ܾ}^z+(F8NQ-kJ*lA4&He^hc  0/62uB֛F,h{yGmX{CԔvD?ŝѱu{&@T׶hWOoT H>$oo IDATi|Y.N 0Sm ;N]0$z[V?w{4gx~[E&Χ jZeooOKo+y,ߥQk z35N 2`Z&MYUiip!&Chݘh""5L( >{Р'7OW7/V}m Jv/[ߚ|8Ldii%n:n̓ep20gC` ">#<+_H%G9=,]vZke͊"0- o` &bl&3:^pUvAk#i*eYeNJŦaKd}T'j>vl9 SkpSDԗcXZLC3fZ !hVvF~7l4G(Gw3@8z_u昛cmm t]O&%>mX1> Om2;}irU8w;)̖qW8janG#Ȅ luuwi$YBM"q+^$Ӳv^TT+S>Ij?8Ok;3lBqTT*E##Ghc:ئM?MJ$;e[MNU4 `Drvjƒ 25pOthܺu$IZ>@$Ɉ˳drRɍ7pAB3sN3Ȳ=z : MnvBW,foi:uj" v2L 1yOh{4Ah5iVW8_I_*#3 UIB!*jc;62^Nj!y<~d"3 }alP1+CyV7SuosT*tr.qa1;_NRC$t].'yzX:nrhS9RG}.]nᄕY {{{۹nŝDN?ex}4!1ݝRfT <I/A`&56TZgu; K35$Z}GsT ,em~O$u>S;0G0cmjfi ,8lSJAyaRs:C&jT݄MwFHQn!9;g-_h/R*"%HXS gc/Mqe\hgYFߧp J—e zod}nĪ*P o밶t)yDS( ^OXuwiZj5p,51L7\h-~%:W_'Aծs7r؆]]ăJu]ިSp8 BƝie[O+IOHDR& !Mh37$BR[ yi05s ɷQb9>qczkkJ6Wt:MZ-q`\i+@. 楗^*v6Ӡ4M҄=?Pm#[}r2l?Mf%Da,< S_oNf[ݦY}XkV08O|n>^/\BNݥE4趕~k굙SsT>.!f\Ýv[E aakYE-, !Fx[RJZ^%D""&.0:oIw{${/Wi u]\-4t.d*@E{Pgqe}]/N} Y0w}0xmZ\rg/?4b v=.-L6Q_Yagڳf&FŠm52N/jۛCjšb9B?dc~;-v.565DZzxS+PajǬbUJˠkzn͜7  Mӊ[ ᎛WLmB W:_U53$4cg7N-ZVo W\9s{ǯگL-gx2 x!/__]BAY0+JℝsB(J@ieDW\C+ I|uI Sq!^^Ϲ]2LtSv] xa>uGR,"tjZ oIU܎qi8S k쩳HsLNa Vz*mU_k5cY3LG#TlUg35 ʨ" .LЄFխ"#9ASU J1d6K @Ơl]w,g±2Gխh5! #Ф1{mb;6i8(>5F%*$2!L94+_}2/7>Ry2|1Fn:Sxw.\Y>=jG}ę3gJ`8{.͹&iٲ/B4{^T.2XƤKF>#IZj.i \%Խ{-2;Յ*v& &nFA7STWHgFS3K)2S(kȳR:J?W ı+kv[$az3WQC׿uگrgx8ٲDe0Pոt6/{6"ZAթN&jB+ DiH;aPQ\$Ӓƀgс]iWl/X1u06K'qS[Y,.eR[ Du=<х>5,PNejj_JO0a  kt`&*ᕩ$$ \ iN `jvv,տP1Iنao?\xOr } k֣Om2;L3ƿ?4MchyJӔݝ],i @:Xif`jU@`oGѤR=LB)=&Kbn|n !@ y1 0-,R!ˤi zl8C#mلJץ^hApk3[Nq{_Ufe:Q2Lݜ FM&Dz1;$ۉ`V!>j$q5MӦrԿqo&7u??e}}mz-^~eկ>M]=v>M`P= }O4$Ir 7x%]?QL(M֗lvZ4 z40mإWnщ$RɃHӐnrvdnUUmӦ6H0 I@MLNը> Ħy4Ka+!uN;>`VBd 9uM[襕YUjr'Aj(8jW4f袎$> Cy1 DEX_$K3NOSqm~077w"'MGHf8ڵk3 &cVNL, QkuLm&ޓ.>0&%T.g6֘sAaTIIG06Ooӧ0 N⩲W0H׸(M9bm~!f𞳌 0qS412fn!Mf*.2:IVHe[TGW궒"+ڨJfy,M'*0J)yӄiZ,o&&7ַo6oGv/hŭ->Ο?+xy}._g/5epu2+3.Q O G Ymq̄ 6pf ,`هn%I5o*Ήr>6٘6l0!(8jS{ 1Ywy~y ]SrT?)$Yԛ9Yaj& nɬ@P3k lVCgt*uneYa\"Gϳ\_Ê4a٣:nOr2 =gdVyZ-*ʉ`ʐ\tݛpsђ* ,fޡ4wG1w#B= R1+T /qۏ|0`nuN3[%7V1T]o!B~88L^=%b\7*թ4Yl)zC٦m]f!A`vi2[&r }bn0`us4KՂ& sD0kXb[HӴn6T*c?i8U? IDATOoAokkk|[… {'gT籵ŗGfc<T*/lgu̎RBVc FbP@?4?nGP_zq3?AɌuxφ۠,Qjm9g?&牒]z Alpt.5+FS$ereNCŮ1<ܠB9dZkDYDEnɒ  Q=]~I;m"u# &zv" ڪb4}d9چZ7:8hhA;iӏ˂@%I%3cns\ZEŜ8yt:޽;qK䉊OS̮qܹ:^{ 2 ęe:ejp~<>=Z555Gu66YXXŌ dYL7蒦`Tݗ8!?}`J>iU@Q8)ܤnj/uu,V&I2bA,Q ǁ,<>_IY]ӑ*[ۘ:Jm$ cVzϪfiFk! &P:?p5u(G}BU AT*ajQ8˽<^d!xne[=)Ph] ] hQk#-A8Pm a ŻEvbUFA4Fm+?|YfO~XcN3&jS ggpQZ'E/VWW~6eLK)nlڜ9jiVi\;)%6贈)ԉx/+y}01@zPWffA:]aNTmk=O7p5 )%/9&j6h>@ NKRLiY\l&\Mu5kLG:%uY5%0lREH)t:\z IOQyT'*>N>!Dʓ2/bX+Ɩ fIIy]p@yLb% b I((Ei #AbE\zcw }jIծ>e177iDQD/t8nՉHP ՝:^iTKqc:vCtyƗZaXh{q߸qmHncQ@a/ ?z!l\|HN(ҝB}r7i(hmh̭֚MƓucÕYD9 FEMT+U aea%?~UvYM-A4,ˢ^$ A:~zJ]EqG>jt4ɾ7xASL$% +G!y0: |=\i#v?+<ư믿ݻwKKe/eЅN&l,tDq<Ͳ4emm(% ش BWц #lӞTu7|rg,l ˶T n}ڃ6aumMP81Q |-]IEJRA'Kfa ӼsjBի#I<+n3<0rn| ˼Ek104!Mx"cN:ePUkhڼ!hw{}Yl.'OM|% (*q2i`:&Mb#R'Ȫve+9eɻ`/`^HJ1iJ@)` ^Л]ȡ'*ә{Lf79.v۷o$IS>mи44_,v9_+BUXS( kʗ>nV$IRtݻGETՑ =gq \]Rַ.kT^,(Y.aPsH) jIǹWS|. N )Tu8:Q.>aeĢќӘ_o9sY2{8VFʬi8. }2p(n5皬.r3}頰l2-67q;1처jV%eUtCqg::` =oE@uλ{Tl?MׂxҒqhݻqLZ-Ƈ,+2`| lE|ᇤi:U%Dzv  VrMdFuF;%z ÐSu\ vAs NcŻ6lA I[;Qep gݭP^(k(ۃ6Qak6s9?b#d n' fŷT|ah#OX?/?$MSoO?9>ç ,looꫯ=e1088VQUΩ MyE̯O(M*{ 6--ll>wQM3T Ml0 trluZERf˶T*$͠,y35x:K1ipMenyHwAY: QJ%n}U҉a8;WD(w~2US7'Lyfj3Y9f0TRZto%Uv?i]H#suhh|1ԭD5 AXըݡt]ԝ:M*S_ ȵ<$s8>B˗/x୷~0 1[?|5i?6|K_~xW>4\]feeK.~5MM#> aj5CanlQi8E)MR[躎a3٣ʩF $eoHDS!DiR_vvtmR6=21;,t܊[Ky0:X6 ff@y;MDX}Ao@EX >kqqw$ ~˗/seo}>q?'^ /p~d aqu(c15f-[v.'dmtYgPYgoE* w'lG;#&iyE,w۶9RQ4x_^*Mf!*۱9Ԧ;"Z;-}52&>FÏN2; `בRr5>C>+տ:vwOu2ƥ['efAӴx}?S6^#a1+-2(ަZ^-:z`yq\puEkM 2y.ҐEr;X-*fItn nI ?'c`QP>y֋K)1dj#"%8*ڶ"ۓ#Y=ؓ"MSl歷?ݿ//sƍw~w_߽{3g;Ή JQݴ=>C u`Z̾u05nj+(fY\t}rٵRHHӔk뇦5Lf*ŔlpF6)N$:u ۴1\۶"5xZTO=43wDNw3M4LQn)*_t]P39-vle=##S|_8ww|?T'9fA%\>jny ,L}*yVOyv 4B$Aas@oY^YRxD6"ͩ+++looJdeq0׷5Z!ҁN8[/F(ḫH) `d1, TEa&1/Aլ"lKTi'z>Xōk7hiGa\ܹsضK/4گ}kܿ7 ~?çnZqR7'O5Y4RGjv S8SE;  10?r3瘚Y Ԉywi", }bqjnX:<-/Yzܣvn4nsE<20T.irAfujqcʶVikԘ#̎F)%7odssW^yE}OdOo)~bhN!Ɗ{2 8~XģUdTx4I*⛆8y&(kuA  ,|ަ,#3@ A!"+8#޲ʫc8Eı$(~VjDiTTKpPvm:8ttnTj!K(v]Ljbad(5??*=ֿ??>un>ܹsPax'٣e߽]u4}>{{%\wbe`LIgelߢ1Zi5ut~LFhbRӅ>Aaii 8,hĉrI̩vBHqL>ݠaT!TYlZ53[EB?rm+0&ejfjVzV/$ W^-rXku.7u q*yu37گo}ktܾ} .[[oS?2?gѹwcMUT`a>E.rfl1N~:H1$f}3rlBYYʉեV?gl566!WOay7,Y A"7st]ZRkT*qS(4MlSQ}߬EDZVi:Ǻ)..~9O]ԼPس_h0 :aen|3ji~?+xLզ^3*b WaDUJ%GsdYğ`ha<ݻn\p,BS,K8n׹pa1n*x)\~)@ZEAhFw hxPaI:$KD|᪘<+є7_GNN]ki2"{wwx%+f筹>Lʯ!_җ%(_uvww_eyt]?4M.]竊ls֭ry=l6ܠl1HHc{[gpFŬ0f o@jUų1ULF,˯cpȴx]6]I6a,/>ZhјeAF){{ t0RrvS +!ިGS"/\99num]=;͜G͞?Pƒ֭[/Cֳ7ٳ_x0 1NS[-$ MS Pڶm#k4Lԫu)p=ǓɤlJ2KUQycH{XG zUkNNB/ X fd,h^;weY lF(?n7u1>ƍK;_~~e}<:Q}]]qjoseN4M9wƂs% IDATZQe~䓤SgRjPSoԱp4T@0ɅjgK΀Bsm*sZW ;vθH݆K5.pD' I`YN ndYskn=Jnؖ}RK}-`]?qmH`1g?I*Ei2 E==`S*?Ξƒ4My!^~@\^R4j)U;0;Zg{B[$I8::Br"jl\^EsDF @Y_yȳrMomI(p}ѐ⤹'ѢQN Rf$YRZkI)B`Jt _˲+dXu gAv5c)0}ig=r]+BMgV(VV/[L&^'ryTq~5fɌfDQNcj]_kp0BJM_ޞ9^O`jA(QCÒ' 5hVǿ6Oƴ(x=X/qc8KXu(smd<ј4KLY=rgp=d'Kj5Ml,˘N n߾t:q\U M;/<˿K* fYp߹8WqNĶcP.RqV x$ -4yzpWu„w YYPl0Z&p ÐNSF 5UIJz5ʚ9=f<C|p666jlajRdJ$D0Ye0M?=}ZZT+U*zrE7?DeQ1|T CAz դkD1]ḥ !0M4I_0\KS]VYUe#uY&OZ ۶٪m){%S7';e;=](M'XEũ tՀ44R>gzgx< (#T4 1Q!-+[4IJyYjKhA0݇wWr,,_$=0t31u(Z֝~[ ,`<3IeZ&IDc!*j 2^lZfX5>aοW>|T_qT4`ݻwe^/WKe=kԺ٧[[|/Fh5IϷ&rg&a4$H\eT*GլzZszWV/iؚ]:Qgyqpr#;iZʫӐ=TMOu[")ZdI`F9'H3B,HHdsL*~<wmkZⰹɬ`ȸa7Vak>0 J4M ~!ච=$P>`IsOWn*d2jј X)''\)bW0,Tb-@ Byn 3Wa 6 TM0-5].wzwhZXp&ņfvDKN5FM[9M4 2MSz^x~ 1>:иwQ|q/zB >l*lq5s%Qy !hX %4N9m-O+^YYE뿺Qɡc54s4aNc,mCR2Bae&5,<\mfuYbU=C:adӔfYӢ2uZU] [<Υ&Ip/\ı/f^篒iyrOdI UJp;_hr mmӜŠӄq5h4"2nL$[f-yT&C{O8Mh _fO1LeRܸqlǼx^|͇|zuzNzѤ)~TyW`L1, 4]LZ6͝# ׎hU4bjz:+b(fN10l5ؼIB=1GGGl ]4bT]aqqdyy56_H0c0v{F7#uPg_yIzUj+EeY:};m0Gi#8koٚakq TJ[qui:xd`0`kk$ۆ] "i$sLL Uu$\h RHf&in-}g7 xxNj,loo^n߿_6yBqv2(^:];}%>&+B0>cmbcjm叴ȸ{.i价t@ 9eP1+9T聚z%$Fc7IYfJ26uvvvFS̊YLAy֤FŬy]Y-D8zGd: ~/4SC70+g nlkj_?9<<$2oui/uU! dIkj~ e鐘Ifk^Ǵ7%+`U}/dt]gssr+p8R1xB/oLڵk_Kj|]rΟٟ= }Y?zZ0{u۷^Cmd 'PzճmUMjFi-wPzBδm:?sh91Uıg)lYMAg@4iweY\zE%bK "rAGgbviY$P*̒" Ao$; vDYxf dJ{i,U!ʴϋ¢HQ]o.h%*Aۉt;]vϤUHQuϞߦm6+4ˁiٟ0[&wB5zj3<;dD2'N-vj7T#z ]*?wtZFRmZNeyHIAe 7rU_EjnmWp$ ,]M,,NI&~;cscFI:93p0^f,8&&A@<? L=7sjD?UTo Ð 鐜|AiGjWY6v`?i1//M>O=}=EqAxlnn"5I%}x$O|Wk]jO\p&qQ1Uw78COHjU$pJ$G '9ϨmԐ?e 4-3=7󽕜y)T,i!eYȪ$j[GV±1tJBa{E\awFyspҗ[o<>ߦsM^{5IrT f%?j%\fu@, .F\' P}C͊_۾u lDT= ֹߏ9'L%o^} q0 Y2+ !Or7*>_zM\pADX!c_'l#A!IhӬH!POqc:Pvk13=#ˌt]$I¦I%X3u[*uF3.-$IJqd2YW{{k7~7׿7͕Ih|+_OO^b1_4VJo]n3~w_AL9Y,L$r=?%j2n?gqko!DʕY{=ܧ嵔xS,Zp 8#59!eBEQVmJWSg(^&Eh(j IDAT xh2,ϔ-sٝuk! ]ܵ_Or@ruHbo&"e$iJ,Ma5keJE+``6qaCZ6дvW|2%OU# fj'ըhTfqt8%vC-'M)L4 rRF2 B]cNsej*Ru,&Ȃ3u-Ayt]>qcf6KA< !Jǭ[fg,T]?zV7_5o-5Nɲjt:w~w7wŶlZZ&&s,%Gl~{coP1CTÃklQՄ² )n ܊u.B2!(kpȏxr PV.XPkVx!ThƖ]NnAZ2H$UQe<3g{U1+Pݑd:!Tlk庼ッmr49lk(.?Xh1z/'MjئZ%U<ϙg 'CΝ;`UFhPfp81{g&"p(V<(-TF*yŤc2p__! C/JP˿˼;|P___y!/#yAgՙuV͊B\ :dJ=&l=1i!rb?7 L=<2C.4u]lݦwCƒyAEܸqNOO^ )ȒEkhh4v}[&,v̹ĪYeoR!S`;%-TI8K3Lkx9yarN aH%Tsb& F'KtAXR95FbĦu/~t.J[؊{}, Y1N@K8ȅIwh6fe$!mO'` SfѬ˦VWgU#Nc5'!H7MSiMΝ;0;,DPy&o]{~_W2s `{{QD>K>?m,7ywxWtOe}U [n-Wwz2{8J#6Z H5}HA.cU.4O]FFoc6]8&  !0YJijҤ[$ ͅl&5d8`d;LLgSrTQvZ:%LFԇ ÜӘ <&ئ4Iƒ٢8++fpH!suAQ.ar7nHn&4TÔ)y.sq|;~F)l|NO_'4!}fp#\tܮY0bh&ʻh`JVM4X\ aÇ, <"Ub()~S%KEU,Ϙ%3h2KfT*az~OؖMVͨ8tMW(29rYd+iu)x3|Y|OӶ ΅]i>m$V\v;keri~$BwxQ ef)%iZ=!YI(S(?y΍7ۣnꫯ>ﭣHThLgҡhq u:v>i:~:,HR5dz $ €s;JL4GJ?ڧ?j[׆8IzX'\,[y4Ĵ3MK4A?y>arqD60ȣ\[wz"I!nVqF&'kP?L|}N[acj&y$qS/< LV#A7+NVa4{:q]qq3 NEl4ZTl6lll ;[[[ C>|Hg~\Yqf_'~:i~?lUTq ^b,5i75XU+M Cq*$rt11!4Hi4--^لIԊs~᭝ʚr o¶5cA8r8<$ceI7jdY۲O;+L [4 ʭk{jv|/&2tZn$JuRS3ׂct <8-EϭXugɹsfܼysA+h4~uZ۴^x0+(jNZG38-dJVɸtʷ^R븛RH˭*uD'xzM6Ø>vc цވ\ϗ@ YOyYRH„lhq6f lWxy  d`iJ|inئ55uJ.1ŰV0o%4pM NN^aW [Mj0{-2-LTqҿ àjjQLvwwiU[4B ^\W^=7;eVi7`e-+4ywX??k{ؼΠ8v\xuTsh$Yvϼ@#M2mlEL{S$(YP QP׮\#QXD*2Aʥm NՕP)$蔑n0 =!5\GFYV+P1 vAJ dy~81􉒈<i[")ADمUz=oggsT*iᘰU2ٜci1ּhw:rʕgr?DY!|tM'4s&X. 7-y0Xnpeer ƲXTeY^PC^տU=:88|s+9B VVƆ<>vL(x%cgfq`v8Ҍ!ptB*⿦ψXiR[m1ag9Ð&ZVza鮝~b5)^ﲓrq !c.Q14IZU;;;cJE݊Y8#Ŵ[m20M,=3Յ빣n3GJ:m6n)qsczuǔU*c1 &cnÄѡj;d[bTK N)qsxxeY/q KȲ"nwo iddeS, /,eH[z8qL= /Zo@{.T9f{xU2k8mvvvk_XQ'OnBFڍ》H¨p StO.  L _vZ]?DSю+0yzkjǵxہu= RFAT7<d~ima6Pd%Wd+l07oko] 8ٳMv:Vg7꺷x 8&g?X,Oj'?I^ ;=z{>Ln^xA8p/"Tb*ސ5qL䥗^bll,t6 Ӄ") 7aw2teQ\*bK6- }*RM>:"TȖX@Tkp`۷I͐ jZGVQ\)t!a$t}˕2Lޞ^R,mSnZk# IDATʡCI-)Ԍkmku\Ŭ8($cIffT-!Bh%* 芖y:>b033{ ȗc_Ϋ߹3EAtɗ^zd2#p}$IknfӦ (a YfggeAL&i,&_5굕:fj ?Q[n8a;"蒊VDa,B^g|z۶4-mURd.լҺV-tDfBX,FOo7 Ѳ- wC@$(|((w`%Z;܋"+Ak2=Z4\.zGI["{z)Vu_BK`j5:@WhnNfiN:H$ٝ8;;w^_}MYI,v?UdA2Šz$ rb>K:[̖2Kt[m vo ^<=X8eKC%(q-N$BU//x_j#BQ/[C6<&`2v])me#|k' x0#vM'~7az D"A<Ţ,Y.bj|)OV'ǽGUl,0P`{|;FvR; LOO333-ܲlA F./җĻ277?w| _>(.k~Y^8BKBՎg Y,L qdY1tｋGj< #pb>ܥBf9~8;w  9۶͹s~ƓO>Ɂ:66z;&l*9GX psuM7̠iZ`QeYkž7nyӮIOGlTݒ(D N!_ EAD7t @ӵ@WQ-OiYb v^I %d>NbH>o)L܊%XTkU;{IOss,ZĴUv]Y۶9~8٢0 q`޽tuuBE~}[ا~oo8223<=551/:^t4 =uCI k0HvJ..^KErq^MRtl[qb%V(]Y-T\"nn$I3u!G/пfJZa1( 4J4+^]D|=6G6FxON৶aDd 0 PzqXGuGr,V:,g$z~Ir—LMM122–-[ַ/%m>yFzu]Ξ=<{%{ۥ!CQW(rX*PX ՝Ѝ8zNn.]>tٻ9vDD"{޶m*r%DQ$h ɒunA8pnG*9Ue5̮'#yt;pC6B?dFDn VwF3|*#`[w*bhh(ɓ'ٳgX/<|/vo)ʶ( bvKesTn'ay*T^FؠTH$/IlKZT.yV1t=| ˵عugPIBɨ.T;t֕] 4).Q =5hM,k^-"GT+$!G__ccX{d2N8]꺠ǾV; oJ&_s{?͉'E-[S/=Mww7g? wY׽h&ǎqnvTU%]Nw$ .n}})ÿu\.S/Ivu5-uY`5*J@鉦mT Kl݆AEHP! ؘRY3l844 t{pp<h1tC7H`SP.'HJHR,T*>|`jٳgx' m۶ ~P k.?R*aϞ=$ɖi{ϣKz E$ {Yz:SAL/bD"F;ZQuk +w޲d"`5WVkq[MeQP TI(u,amu~@`½R\.ԅ09L׽ǥ8Z1z#ml6u] ٷo_hF8?~uٵkl}Co~6zhy_ZlȚ `nnbHWW'N`۶mA6áh!V|KzTi2??EPn[,q]#)Yu~u'tQu߹-˔J%JQޔ:0"yQ(n2:.X:VMh $QBuSSq#5Ѕ@O!kCm7mzSw_?[mݱEN<ݻpI曑$gvvۿ}W/k.rq;~,ax"iGd( zz5OC [Ӧ$aZ&ci&TIP$)'[u[q jn:% oiJfWvfhhIDQB")Gkޅ,U$uOmfLNNbYV 4 ˲8z(N6l޼Ǐ#~wW<:6A`~~|>ρZt򓩟Q"^¢kQ3kAmSD* d ߏyѿVD2$!Hte=ݰNX=T+"RyHB@Z q}m_róKdIqV۱)1VÁ *ԩc&\&,"zC$YHe( Ň{?nl, jX,l;>>>N&Zq^y,bE~Wj=k۞sss069.L3ʒLVc~adbYg*eL:F50A~_\;꺚O5s+$8X~w\!Zm۶:YJK֠dj33->$Q |*5J5w$SaryG,1,jB@^G=KdbyVY&ްY͗ ~6jC/Qd)]y7wԓޠc(Fg=\dAT bR4[H(+N>zGR[[@FgΜa߾}>ʓO>SO=k}zq"˱{n|dz1Bͮ?]'gΜ!sm+[跎?跺o]n8l8'p]z^ĶmۨT*k ʍ2H< O:}MeIFUfpLX,-XB-X_ ֵe=4,&LFVIa5 =qX*RVq=NP""{1z)zb=`5)Zpt°) ҫ&ɲ 39?$ImºŊ`kk߹M hlߨ:]HBZ ]i.hѰd UQ:QiTڞ7,ΆIȚBͪ|Plu]N:ETkddl6˹s(MR+R/⥥@ۿ ~ s9pMYI@e,43^p2#ᝳ02+ bh{Z[YZziÈrZC $H$p]˲K%2$$@BM \J|vmCYUUQQ[E9¶m?ǬߪT*DѠPFKNn:/r{{y/%=uf??<OuLtAgoA .}qp{qƆaM:7RBHrZ%;N7Tێ̮ڻ5J2Bv\F1,г Q٠TBe(QCpa1 bz W%7h4>|d2-r^9F|֏VU5ىD⒓[q8v p뭷Ї>E!VFfmǦT,zL\EVeR],,x[P,Y,Qqd'im&֓ t,~JhEXA^̶9'&& YJn: =GYX*"*"/..R혊e+zY85O+VfºA ) Q1;;(f͛e3g.~P(pȑ '<޽{_^um1d<;Yf 3Fүz$C喯W >rxD4Kլ֊N1yo A#ɾnVc֎> tד t\$N̡ʤ95?HmL"iUX$,ɁOX)1?Hy\&%huv ͣ)gUܡ0L5r׫jF =L/nxx3,333?~EQd2yQzCϦM8qG?KKN{p͐Y8c;Lh8YgfH *RaR2T-2{O1t8,kjщB a u c\$禳LgR2] =rZFl**R)W0 ͣ1MtRu"+4Yp.b!ΛvNtu,+ȲsX,ra} IDAT?8z%}2j"=")OuBCVY HťQox2XmF˗4 V8]eye_| 뼮'tNBS$loeLx_X$ɠ( J9Aח;߂&ih$G #" .h3X$aVP:WAY&''-.u9}t,}cD"]Oŏ|#knw]6Hc\edd d2LdhhY[`١eyu],Bh4at'14cMc K> k 4_S|I3( Eu^bTEn @W.<A-rk u\L:+^Bf/r-b;. ;hI+ %C_w^iL. }yam4vm!h 'ofiN:(-]SP1 _+ַtȬs[7̢z k%!$e )ұlHF6'..lj($⢢X\ .P0 Y "WX,9DRWURVy&4#s`Z!L5==lq5=J$ ox<ʯ ԧ.!?'> ~ᶷ;ɓ==U 'aX%AA1|nվqfb{F؎Z"P›R1#F{ARA}5j5>L__;w|MUUe`` :4Ml6gΜh!reSeSO\,}͐YX?H`DkuҞ]OoPb=-e1E)ZG(BV:AN4)Z)VK23Юmm\f><, ] \O+Kr¸me[ض14B\]|/G[#b7p".],['^^$x^~vy5F`7cjjn 91I555a|^??mo{%=c||7x\.l-w߸$0 ;#h>˾}LLLp=`xvYX X=1[ J2D@rضiĢ1ݨ-}WC5FC\df~D"|l:av®kRj,eZ0Š 9jyٚ.dcْi*9tǖdͳr֊L5*vT-QpW>۞5|+D6`AQa4rd29 ?~ɻ.~w~z]Mu"aD?cOQ3keō7H5h%] KdVЭ+rQ"F#_I-L37 .kumwee?04krA6LP$#vΠ!1q4t˅l1|zm*-Z\AX|8rl4, : ժ&u[,m-W/8Ǐq8pŶ.@TnC4ȲE,x;i6m=::u2:Ff8}4;w۶mQj9r.|ϛ ̗Np6{ź(i1IR SS#;ģqHDͪDH]ץRgz_#p|zt!ܱ(v K\\- [s+03;C4%0 YX&K:Jbb-XHVwgI1[d]t4EC%r\ xSSSq뭷^.ޝ={o&&&k*_E߹絑5Cf;r̓ 0 R=)jQ"9"C߁̮e9gfYWUrRƛ486 O:v>.#mT*l1KRs0#=p]ZFahpL2AN֣Leq]Qk0=@Vd P%{u>NZC188U51:==4z+_?!,..v􎼎ɱcp'+;wn `| wnq)p6s83 k5 ROD.)UJ,fV].dQ^c۞LBOt`>V:7l U(T 99ti=KNarlѓߵ#9C5,G}{=du՝Y۶9y$biZK7vii'O}#iEVֽmok|"7xv񌧷 #|q՝vb:fbڳ LĴ15FլaQh\bW@<뺈ltYoqXղ?HO`\׵p !řYwRDCUՠfN%^yvM2yi$J,__CK2sHu!jo0{}8rssL'`vhØnWVV #Ȓ_!Q7{C;) xi( ժg-fY= LZ?ް[X D:C]9f)\\W`lt.`Wb=p]B@Tb``E ?㉗LnuY'a&r84sT* msTUeWU\СClݺ^|E~7~??/.|gyEFGG> گo͎;D"߮y]kf2v 166 ܤ#/bRWtvdgNbеώDAY-[Y4|>O2$yĬP9E!bDNt1"8CŬ6Y%p GFAHD7 dI]J.LPz޶8kKh bX@nnۂA8Llw,ÇŸYXX=yԧjzHpAwp/饗^b팏8vB+ssssNlfbiKgLPꈂ16Q-X(KB)wV1~*c:ts] gl޼6".֞={b կ&R1aÆ9={UU${F8eˮBc|Vgdj}z2 R\.Gooo1d 6; fj5 fHԀG]J҉nb1FKWIEg;d$IC=f??DhhP,w _n7wͰ4D~W*bvjs[<:t-[088/>1/oͿyOJO~BVcǎ ,Ν;`sόsf ӹ?J'_][ YP \E$rܚ@޴L/0bޏq5XUJѠkz_vB17*m #Ry]]]A7@TRw. =Aѹ#r'd3;??ɓ'R`YVJ6jߟڝg߾}Ȳ'x^B\' zӧO۶y[~kKKK޽/%\etm'.SDPiTpEⱷw%[@@W;s>1me:=BOM0!nrhxERֽ4/zk")Vg)zdOiYLa6umT*/H$xln!Hĉ8V|e˖-|߼qkjj^vR*G{M @lwlq-2g*?ʼn϶kN$ PdEf '1"ߴMov\@ݰgU"[-jJf먇IV;BFoΜ9þ}[R%}oZF"jF!SSS.3ʳ>O?\-fjmD ɓ %[ڻXX]Y*/y*ȒݺY_ɘv!EPT V?TKK8R+*Ӡ:uԪ52|[bw5Mk)я^qͰm1 B4SSSڵEÉa&''\Lro oȌ3[qj43) x`wEkS㠸 |jN<'O!I^ LM"r9DS b!*bº"hazb:l HxN [l f7ڑ+=kMdu>2sfd vff3gE$avv6mt/Zq̸( eQFWtJWykRxĤd@z!M"_^v7Vc- Sڭˆ NOF͕Nu^'N3A־%Q2D>A::>[y&Ak܎ i}, K](|rdrCx9 STر^{9>O裏r}]^Ex=2 [Bb1$KKKD"Ʈ*cȑ#MRn9;ǹ9dΐ..ZUU/0bY/8ժgcUyG蠵wjQVuJTU̓HG W1x$vhOlדt(ضXym۶ ;CX$V`͝InMСCR)nx衇?!\f6^كO7d躎 R)zzzlr5[&Q-Jjt\]e $zkHdvAJjmEV;`GbVP|j|{P<{FlGq\SYoXCh uAaT*RޢXtnYo`/]]k^-4Og/}}klݺʰakv:8w<?O<>!L U÷ڱcGrhmfϒ(Pixt LH4膎zm u&Nȳ'nP5|*񦓨Z"t97{oo^ :[B"bl۶~___.3W^dh'x1.7swPՂn,E |+Q"2fäZ[T/| EJˆ1-TJNUC˶iX2o?P5]O;I VU 4M~q/ֱv<²rpŐ[u`iioQ>+ uyy'yu{|{Yd2d2 SԆ:ӧ)ٳU\5tmdzm;a뭒0˲RDZ@zzeUUeQ0d/:fߖ]zׯ՚bH>wy[4I&,9u|+]WqGfKx[ʟɟ{nϒL&yصkW, AOO݁Gݥe[L8m s+Eɕ ՝8Ѽ~۵95+bx>lqtqi,4M#rd;;y+r>  Eji.</|a]:[ym3??򷍽FnK 4YB>v|rveYfllr#<ݻ?Kvwoob6<[n <%9%u2~|G?駟gh7x`MC׮_ΆzT*9r>lrIrf2Rj:@j-~A fp,Lq9tGBw",Uk?~e;BUD钊yMZ"1ɸzZuYZZ¶mEl2;PC>=_oIK/WK2zI633K/Ď;* x< +f֘Lpv,lljĴ5"UUUq)!:nXV,=C|5^)1 EX$bc.uK䮱S'Zr̠:Zz_i-[Q@2 z/LLLCO~~]X2::Wv񖢕4 IDAT^G$ǼNf7 ly晶 ݻw[>\ f211qE\e8Y-!7%b/:@D` 6SS UU[8Rt]'raLl޺T_8]ٜL\}==g'KcEDbzZFBuYvmJUm. cn }UT&W7>.P: 円ޡ ヸ־Ód=tP`=IPX,2882^myEQb:}rɓ'$H$B&aΝo|%=;v{7R:.g2]zz׻Z{g^U}׹kBCIQm˴(.g,X[ 3Z}ԅ,me&H]E$%@I {ps܅}ېWC"U( ;I'>2듯GԶvګu#DQ:hr4G10} . ߃Q2rqfdYh4or0 [I8t2Ts+7kip ѩ͇Y0j,p:w,`ڴi\uU̚5kXf͐^|M֜=h&;;u8ts=tvvrSPPEl6$IFEE]VЅ,33S]Z %wz.Vjk5u:*+[t;zIC1KS >c0kZIx?tKzQP %=4~c(aa:d,"I#$> Ǩ'o<o jk(>%âBƖ Ѕm]]UUUf֯_Oyy97̛7uuu$%%9tP?q222o~1r Pl6ې6$ڨ`ƌL>}_j$G'M܄㤨G}L t)uyO=B!CHvq\$}LBhAChHׯC pXVB(+njj>L cG [Ayy9QQQܹ;vt:yۿ!_E9{Rٸq#8p"z)@^^444p F5Lb~UB=ĄsyZG->هV%LFw6ppܲV(|uZ݀lF4 mV])Ŧ,sSb,˸.'cP4$n%1ƆW;8ZCDFF=a"cC[[- ..4~yG<;/̪U_2q&#ِn<M(//l6cqIo߾ג9{5Dqq19rduvkƌ8V+Nɤ& E___SZ99Nd !N#Х IRچAн_-ʬYDy]$>J@Gqi$ިǭ|=/7n֝ݟ| 7nd߾}l޼{X- ΍"bfvн!ِn!x8z(&1B~χ>e?_ӿ!4^Q 2<졦%'g 䀂,kY>QB 4. @s0Ss\|}PUU 77Fÿ˿pqzaU9[H RLVYYW_&Iz 餥!=]ɾZohFcpz- n,j FI%mkttT2kxۋG*w.AU&E#BEW`0`4z tYG"h~t(*?3uT,~7DwWݠ^Hr(++#11ӧsiVZţ>ˇ}'##?>o1 w}vWָ.QNB.!h&==)Sn/^:LIܿVҢh7I+ofP Ȉz3G1!zB%ىj%66Ș M"2Gh4deeya9[Wȑ#jqʢETƝՒ0qՙ,q{!$ikBAOsMLb EQ*+hrY:k%yܗ|x5 @S z):mtz%BeDGu %\r׆J@f4 SLAUqG: cl6~z^y,X0bqٳu!2W'`޼y,]{ݻwX,dffX|(fmH >c"#Lk9b kю=Jbb"VJYsZ^-Ѩ3zSqF#G5bpn}Ęt#CȒ^wCggs1>%2طgXqݔ@BBgϞeʕ]vHUf.d٢bN'~!EEEvN-[Ƶ^KGG{h%rչUitJu)C %Fg"._N̍4v4p$sgp9j,նe!g9Z$)h$9S'”)DDD d\m465BnL֒hW_eǎܹS\2{Cf…XnA(2" ˥fpcG%+K~Ѵt>WD"hw63L%"fMr?@DhM8e|jĨ Wۺxtcb 233VI(fG^{;w3P^^NII &>>| 9soNXX*':,mp3YkL/a@1("Nʗ[!hHno=;,W͜u:" @#?!D㶜REAJ YQ SX,4\ 8mqk\ PYY&;;@ q8kŬ_ ֬Ys=館t:,YBAA^{-N: !2Ǐ'CuWބN/+̚ο.D*>7T;fgsx\{( \]Fqya0a2n8 dǏhoӰs#$$7x_;w$ n?:"RVI_(@˝W%Dp%-` 3>T?nwQ)VuaP$Ϩ$.0Fhkfa6@h4EvF_糬,KyEQ8uN~SSSb2F- (f,_RTTDqq1---,XB/^$Ijq+2fa&9s{ #m*p+\|v6{{iy$8rn x{)NNG76Se=祹Yt?hph4L&,f x/}#6ᤳ2RRR:u*'NPgZg1DiHhHʳ"g/ ׫ygJ5.;ή:FVEѣ萐oW7h x|9ՙU@Q4ܜWFxCgxqNSۍ֯H)Ё"u},fUK⭼d~?L&fΜI[[Wfܹlڴi̍SD1;q\|G I/srl̑WԵx<UW]գpjno?&Dat~rsPKՏt.m_/6}@m|! 1JU1pǃV%;! c2xh8y$g&22qnʵ^;LpD1+R7$JJJhnnTCl6_{X ĉ|>fϞo)>j;.Lr3 On (^' (Z (W'cfhihl@gg>Ԝv хmocUsGpt\2gΜaժU?w}h> (f/ %%%uφQsv[[!!!9Xjkkihh 77kƶmعs'CLbرch4~y>,dddPTTDbb"g۶m̞={">VCCϧŋa6+WEQ(//W7E^>"N)D|BPESٺfnTy˹Crr2VUVq-d}Q̎0"g@ bccdNGC`0PYYX$v|yT٫oGV(6W_fp5g_j[0~ٳ lذ믿.TfY71^D'PTTￏgpB<Vv%11<%pZZڨԥ /.v蠼3gKEEk֬aӦMyC:]+Wrbbbرc)))CcQ̎"gf &1cƸ1ox(++cʔ)$''*$}lUԶУYXlϯ( NSPe|>y,YgCzy#}#))IȡCF1h4A<DEEW_ɓ'1 ۡ\j ֕Fl6c6RpٳzƄfN>ݻM6o7d1ʲC=ԣӵtW_}hN:Y~=;v2YsFaΜ9̙3G}GCⷿ-~TJKKy S$I=jC"bALLLzR-Zƽ\=Վ e-pC8ߚKA$L&&$EFEE^WUPtRVVFjj*qqqOS-[6J1W~ߤz!\Md$Il6s]wq]wwߍbp7nM>Nuu$]+q Η~}t)%;gΜQO:^NZt:?<{!äurX|9N;qF-[ڵkQe^."g >G}?ŋ|rfgϞM{{mH(B]]̙3#P22ɌWmJ bgn{`$IuLIIV[[Nt:Ο?ONNî2#1+z|BB555Ior8;wk|n&pquF4o;\ v)++cԩ$%%:Xbcj=|>yG l6{JL1:(V먌n7"g=dz>FQâ"^~e^ 1&A72`Hr;-w(EնT~?ףwCTT555;̜9<:"o_cRϟOee%UUU$$$}vzkTIJJDѐCNNꫯv)((c8w܈z%P  iӦ版 )) N 7?ڡ ٽ1 ^Z-I{{c!r(++P6l,8y$7p~L),,f<#BU1k.fnv̙þ}g͚5ٳNǖ-[[eիW=ڡiz=/flڴ68@QQO>$W^^p)t:]ġH^PSSCSS\s͸ nTbPVV}͛ۀy0`:]c;&\."gڐe: fyVV`>6 ÁVUm/!( gϞE_osQӉQUf6n]w5"1'[kl?vItttZ-]s>3f`#\ ɜ:u9smDDD D/6 rf.hDFNNZ_8p;v\Ӟ={Xnzy'7oK.vb , ۷oW7L`D1;y{8~ 4%<<|Tf۽p† x駱Wud>S۶6: j[=[ZZT-,sQBBBHOO撱UIDATvCa6yG|㜠OD1;yjH8NIJJb.n'77V˖-[ػw/;wre!d֬Y߿xĉI/:::)**+%KPXX5\Zv$Ibx$//o\}nR>}:X˗vq b3?BlCfTfWpȲv|˶mۘ5kh'(f'8"o-{a޽dffro~3gp8Za4tC 233VUf~_rvxވbv8rUwol='*'N_[6 RPP7ވVU[C+q4ɃEygg'999H͛9r۶msS`L0ά#_!zYx1ׯ'77bJJJ8~8tM|!,nG!s#$$w}~RpcQ'>(111Fƿۿ8nh뮻5dEQ:::{˥.o8pIXd ̟?˥*cyk,n~?eeeDFFJgg'< <=6 Ĉ}ee?\mHXV-Z4pi:::AʌpbZϹsHNNfΝX,>3^z%^y>c~4 @upC7(fSm<ȴi@׫# òIYY)))L::VX}7_!s hbbb!)//'<<4n7k׮%**J̌D1+X(BUUXQQAVVɴA+fqIfϞMdd$~)k׮瞣p^`Ŭ@0B\!1k,Z[[!MJJ">>^̌OD1+ȲLiiȂ gɒ% v;VU&z5^SSCcc*c^xoNZZ0B00^"g WҐ nsqU\̌[D1;ٻw/?0,f6l~ʕ+9r111ر vx<|~dYf`|>:%r~,_ |a3wNE#(f_shTstC:0#2# ى,dddPTTDbb"g۶m=6) λvbǎȡ( STT'|bQiooj$FQVVb!99ɏ~#y駇up*6D1+5D .ؐTVVxF$~i>So>*3"o |'lܸ}yf{1[n7ru6m͓rNHQjkk V[[zNIII+V{gCT4lL/c999`ݎF`6IOOGe~=" "o qH]]IIID=Fj8 $իy7)--eӦM~6l@~~>O>$ǎĉ߿ӧ{Caa!z+z눜T`ڴi455yf޼y,Z~{ۃG$w /֭[IHH_odݺu8pΛoIll, ̝;yyFL.QEb &\rssyGz|'&;３s{ԩSihhP<._|!@0Fy{h4RUUűc0}z4\---Jaa|PV(駟*?EQ>HQ򔜜W^͐ &` !EaaйNdlq-+B@ @0Ŭ`Hػw/f"--~[n%669s0g^yQR  r`b! W,LZZ /_;3a DL4D1+bl?<-[CoQ #ق(f#wAuu5 V@"g \1qh4f921   Q 8444޽{7YYY#@ 9[0Ŭtlٲ[n,O<ݻxꫯg֭?l4 }Y]L G FɞA퉆pcǎhyg7o^cdY&##ζmۄ/" 8a9[ W`ߺKH@0=ά@0n$5yEQr_n:{E o?ϊZ$ip(kX(}>@ HtoGqLdp~^@I%I*!X &/[ "oO&Ę`¢(IR8N?剃$%IWES$u@ۓљLf6_n8H$*I r`0=@ LDF2gQ W$Iߑ$xO}_~$I{Ek}1`(GG+f@ ̈=c̐,!) ڨsa !\y{b!:n`"+@ :"g DtfI~EyۀStɼ;: @l"tf@ 1f @ bV @0n |5IENDB`gplearn-0.4.2/doc/images/ex1_fig3.png000066400000000000000000001130001423420364700172720ustar00rootroot00000000000000PNG  IHDR[ bKGD pHYs  tIME   IDATxw\U]pY q^h*p dJn4 w="aK̉ "2OKJss Ƞ"""""DDDDD> [IIx ^ڟc)KhMI&@ d"WD\U̙^ӧ'q16j!X rn3$۹@daC_ }A&ty B Uv j%*:TF[o/o={@D,܉^%** aaai<~k߁'!f7(˾ M/B~7} aPZ5&Xb˖-&xn\kF['XI9}h zqСED,܉`ѢEv a_a4PCs{Qx07h)A_b1#"DDdz=BBB4u jH݇"yymuhbCq(i׮Af͘L"bNDD)>>?Kba#X5hگN2f1c̸qX0X"DDDAcA4 ù$_P݊R8;bϮxwd"DDD-77 Ç`zˈ)΄&-]?eX] .+ uldb=E1K1vX|r$ Ox$%%=vêjmMP=w];!!D,܉Ѳ*¦:>}#1x`=D"DDd ѱxMUfQ6]#,, 3f@ bNDDd6i`c-Dr{껬{j; ,ѣG9/!""^zrXlq( q5s`0θQ?̺h$_/XAȰ^azQPvDr{X6+A0z,^!.j`Aq{'۔f-W!bNDDd~WdX= M.+{>ijɝݐʁBlegg?+Kt;TyP=Z3K~lVUo /|4P\TbYɓ(D,܉VQQѳU*/ضT¨4YBbE[%WKT(8PX+ݎE.(?x/dU[BCJN6%nA8PXF.(rJt;  r} Eg.4+?SWb=C%"DDDe^zCX+r`YDvsH*~{mMک[q-.蕗7?޽qkD4{  rDDd4,--Ѵ[Hxx zvSyW@ S6^ݸzy 4w¦×T I*Ы 走i7 ʫې`"]! ._ 6m3ˌG68PLHa ""c1c ,]`l|4΀ Ȩ@ uI.4l;aY&Zy07 Ç1D&щF۶ma5p6x nH;ooot 3_Xi^f$ϛâqƝRJJ z4VOX{O1o8y"ȄqƝR͚5qWàs v=gV-Yp'""2\8q"AkV}W,>+W` 2T 4x !3P^܄{> 2q'""&9|У+ 0fZ(͂J(BC`Ndn;θIԴz= Xrϑ %ӿ,As?&; Ř rhK!.j}R':j6Ԭ=vAL4 w"""w-0al Z]?tOBuj1'0fX,[r &bNDDd:Z-1c,-!=}Q6:~'֭ ˋI%bL1mtl  y{$,2/Ȃ*. (ggӧaܸqdL$p'""pm̝7۶maٸ/$˶a{砹 P S&cѰf∈;,b HK kF,juġUB4wCz t˜#ѳgOΰ w""Yp9޽w{Y-BR!u4: OnC}Aq^0xUVeR;qqqXv- qV@*EE7謜 u" W}dBT QBB"j(*ŋŅ'"DDDV;7n{";; vp?=iw#5- ʢ"TJUockXJQ+jTCjаaC4n 6DQQԩ1c`޼y :p'""6n܈q!!!+,\s[xy p'""' Qn] k׮-m)JԫW]t |"zmbʕ+QPPķ%1{llڴ ׮]cqƝZff&֭ &J:]6~'&^ g܉Ȭ-X666>>(,,ę3g "bNDD2Fll,]V&O+=p $&&///HD,܉%$$2dѴW^HMM˗!V"bNDD&|k׮x Ξ=kTO%"DDD 11W\D"1~vB߾}x"DDD̙3xw~t;tP={ dD,܉C۶m!JqQozz:֭e˖a̘1DfAkɓ'ꩢ7nfϞNV;Q˃:tK.t:L~Ӭ1}thLz 0D&H1HLLF'мys-[͛7O?]<1e̙3jœ9s ^/rp'""*]"@ۇpxxx7- C 33!2!T_XQB$$$^z5j".]Bѽ{w}pښul<==qi8::PbޠJtqEQFa`p4h_~/2 ׯ_gpL,CZHKKCQQ Jeߗ+WpuuFDF';;ʂNC^^^rXYY5jԀh4{.@$aʔ)?>-U\\\.] ..jBݻwF%b1ʗ/LTV VVV 0 wRx$$$j\n%%qV&zk .U{#4qo&M 4`ݸq/_F||<$% P^=vvvtDXr%>S*U'NO>8t.^ >>׮] w7 O-o_ժ@܋ҢE TTA'*E"fA^ӧ'q16j!X rn3$۹@daC_ }A&ty B Uv j%*:TF[o/o={@D%.%%GTTbbb#rFQ\\\ggհx!p="%%999]ҷo_t[6-Çs.A$@^ P&`_ b;@lDO?:o Bxz4( N>>>ҥ "!** aaai<~k߁'!f7(˾ M/B~7} aPZ5&ޘ4|{{{xzzYfxwPV7^P?zOFbb".^d8::W^󃷷YDP`ǎعk7;NoCRFUk TfO?Z%@~X(_D"ǀxy  wSXX-[Uq]n`Q#$jnc:hCs)G)|OǡCLkGU?B С:t͛t 񒔚Gѣq4iqf^C6nAR,jȲ\EPB}Rc`kg`ر{E5Aiþ5 I9@ zhp}/ oF 0% e"z-:aaaXx1nܸsIMM?Baر2e M6//_̙p +eYƲ[mt ͅw*"K#$$AS@}< 76k]&v=-~4k֌$Wx"ƎXt_777[nΝ;aee `&̓'O0u4¡$oeNznEBwy#٩1r?*TF½cǣpR,,}3:tIP,y7 DD􂼼<̘1k֬A˖-1aԮ]ۨ[֭C&M>/7oP簨p IB}flz|b)p/c 88ԥ,g%1V$4{v;$Ο?9:ud}III %K 00(gsrr0lxr?[Hf7} "(ϭV B3KX\ 4U 7`? ʧ(::SXt ƏDlŊ {ｇYf|F'AuVZ ]vŖ-[P\9i`ioX> paO{ѼysD,] .+ uldb=E1K1vX|rEADeKO>ƍ1qD 8x5L<8p\]] 0m`qQ]ZgU>L];ѣGD,$yByXX U|C_Gؽk'R>Ph4… e}'|B(nӦM1b$, w\w=@o ~ȝ?w}l.f:^F &^LJ~~ k֬18NAAƎBĠJ*={`cb, &ݳ|wN;'* t_eE;H]<`u90c "7eڵ K,1lmm ҥ }111<MM֭?e0pΞ=˝/p>1۹] Pt|>"t!tfB^̮2d 5kDrssȽ)`c^*Ǡ qW&bDDD 仍eE;}aY}OrP :;v4ˢ\]]1uT[Ai\'xV_"1|!q.>t}ΝgF3tO8'Au# ̩pwZ"믿"a:M#"%͆B HBR8HJ?޽{C&T$  R/49rLqw;7ԥь%IZ\b/ Slذ;. ?۵{7Uނ #ZBl]?翲l 9OABd?O{> TPԷwww޽Է'<PIJdݱkϞ2%;zDU0\aN IDATU& w"5kٙ-[ĉݫW /2 "W]=A||ҥK(,,Dxx8VXQ?gٲeG:u0qDX4idggrr9Ȭ X5 &t`l( O, @ЪPtj׀m!Xgyp4ʄ=(YZZi8ԁTns᭷LqOKK TYbȏ㛰nl;̓ @A1dtX&/|Q5SR ɩS\jj*jԨQ Bbb"&L xyyARo߾pumllkۿ?1~xܸq%•A(URSS!P㍯.ZB[=I}C=n+P_(YP}!)_?VokB$EHMs#%47`$F4ªPg3 ARZرO) : Y5K;<IJMD,+>[E_.̓xWU˱ɬNRz؁S9{{{1sرc<==>m߯9\r;//AiPĚ<W(R  w#)ENm "XyV ohzu^=%E_9<}Dիcݺu7ot:ݟVRCyv$W~fFFwKn啜$fN1l^3<UjOK='D, \z`a)6+_e.􅏠}xk{gεQ g hU?W|.)1&S\&Mp֭[H.]...ZqFܽ{;wDAAg+<^wnnnqAVZw%DXYYnݺwww(@Pt?z4E.ȟ{+(ίiWAP?˕& !hV]"PIP^ յ[%,jxAldU[{$媔5'"`?ZlɽȈݿ{쁿?D%tLpp09=zʕ×_~ [[[#)) G+Wk׮߆jԨڵk˗q"((b{naĈceUoI977YT >AS[? eMdM]. 2&=> yAf]̵9vP~mf<u>T C}pXbF$ofbƌX*zr3Mz,~]CJ^\\z-lڴ  ot:wѣG㫯*תSv-`=xN Ju^&ԩ' kܳv"дiSػw/h<~Æ +͈KOt7cԈլYڼ m|}~w "Gرcb0~}vx{{ͭL?d ng2~z C a0X¯Czh TZÇg0 H$*Co5 ...Xj ** /^… ˬ UVرc9m LP@sa5>$f"b'ҵg9> H̟7q_~H$$$u,T*[ٳo>}: ~*ίLԩ柈XzdGPZa1PN;cРAD&:t̙3+V@NN˼-NNNX|&=ls˛rE>pY&B6njwaծlJ5غe3/ 2ŃX-[@P`0Dž>ݻw#$$5j069gt}A&TGw/$b|}}1qD 2ESO`ߏ{9Adœ{n?~+W4_p3gDPPcPmjUu3*ʟGn glܸ;(_:/; }?újHm=L07a=8 mG}ٳ @@@ƌct>3bzz:<[FH^޴>ʧ(y,$p oH%qE9|У+ 0fZ(͂J(BC`NdFM6~yLرcѳgO쥀:v%9P4{K"N:vE;Ԩq^/+Vs$%iq|}}p/9Z1syeFq(ʋAuņkŤp'h=߿?  *ĄNcƍܹsTj9g&b퐻 ˖:51sgU} eY <˗-3w4"6}:mEj=u߇HbxOdAMv0n8d2&;+i4c…P(0` `׿j srQMKcaU=do@a@s;݉;-xoߞ; qm̝7۶maٸ/$˶a{砹 P S&cѰfX tEѷo_;6wDDD ӧVZ&`9xA@da[uT@S> ^.]pg"bn6 -6]n>:Jf(A%h>ԅ91GgϞagNm]vEzz:ߏwb8z(*Vvڡ}xaiYzܹsQQQ8vn޼zaĈ:tY>8ҥKذa#ATFk@ CA_ԓЧ* Vr AA~>v؁-[`ܡXAp9޽w{Y-BR!u!.i}rf&@8ʌ:xho߾FySp'ÐΝ;B5޽{سgvڅsA"aÆpwwGƍQvmԪU?M۸y&{Tn nY9Ad D6$+Ȅ(Pe߅^D"E `gclڴ`N]|]vE*U ''W8qbbb7n@B"Fpqq#PRW~NAA233,PJxyyރ6mboӧOɓ: .@Q,!.Wzgm!q$WN 2/Ȅ({PfmТE k ///nb.YAAAX|9>3&ax"Zh-[`FBB]$OOGHMK*jA,ReGT PZ54l7FÆ aii-[࣏>Ѽys;ѿvIakϮVTHLLDbb"_{ݻHOOǣG鐟_~\\kkkԨQUV+իƍ+VdbAk׮ڵkHKK{ h5Ax rdpvvAM^*jԨQ77iڵ7nΝӧ3)D, OVSC v;vgNcѣJuH;wr-o{.vU"a3f—_~ɀ Rd/8qN:U?D"\mڴ?޽{3D􏄄`ԨQ6l֭[D 5rHH$9  .dP!1CiZL4 {F֭KeZ A0i$T*&^ے%K0b|ذav*1 òe0yd{ _~Th"4h< SNŋb ?A7p@H$ 2Xf o6&b^60k,=RjUL4 s΅?)MDj#,, ?>CJMamm~Aaݺue@/"c [x1t:]x *`LRD~sNٳE;ݻ#<<[n?Z-B½ܽ{˗/ǴiPB2iϟqqqL  //ݺu^z1(TfvHDDDϏ; 3sLO>)v 2Z.=p1k׎A2׶m[8pAh"%+66[naaaQmDXt)o>&p=x{{#++ |X///s.Idn޼{2 gΜA 28mڴcpY A!b߿XlAkѢE~$gϢM6puuETTT j޼99Xt  7G`߿?ZliPmsuuŤI0gddd0YDfСCر#<==qaTXA!h$%%k׮cPX7nDjj>!+& DǐOaٳ'w0(d46lcǎ!%%>>>x Bٳ5kd_rƍ\Llذ °aðm۶2aߨ_>bbbN:!;;A! h"z̘1à&L`҈Lٳ1zh̟?֭D"aPh!** ر#=zĠ wҰb ̜9VHe˖㈈`#AJ!m ^1w\|7 bP$T^ǏGaa!X?3c T^cǎ5lĉ<$h4:t(֮]2ћVZ5 07ofP.WX~=>|,;wQ)Fnݐ#Gә x_llmm1|pt: 6!x KL0f Ç\<'Xa_}ϟZ!W2eY!PR%T+ɓ'Ơ b|t?~ &ʕ+ҥ TH899d?x^BaiiYZ v)G߾}QTTTZ^^\růY[[c޽hҤ}͚5 ܹsMidF3֫W/ J?\( Zl&H$!>>^Ah4ի{{{!88Xh^رo}3g ceD"ШQ#R9Mׯb0{l2*f=^zuܻw H ɰi&Ϝ4iA0`l߾x9NAxx8=z ,,YgS:uؕJ1ydRҬY3A׿K.dCBB0rHL< . `7hT*QXX@lذ3s(t )))ϟ^Clڴ Ν;MhZjy_}t8p H)˜H$le˖ #p7tׯ_^ǣG0j(i/^4<|N /|IMM}H"z}K.'bƍH$fU$> '"[Fj8HJrJכb۶mo0vؗQQQ|2 p/K i@v9 2lbn:ԭ[;v j/Mbb"E*Up^SL_| `ů,bM@zHd3J*xw_R" ...&#<<=FS IDAT—t}N!e%>>RWÔdpuuExxd͚5(**ziիs^bĉￏxVñl2| 28::m۶+*KlУGc˖-j믿쩩X+W@VhoР.\FMLN>;W\C N< AT*퍫WرcكCut)T*EN܈2ү_,ѷo_E׮]}=z{]:F.vEcǮXc]&5ĆbMņ]PT(4Y`w?|14a2uq)}f9̼Nwzϟ[0*Ç4m۶ƍbsFalĝ# B̙3Ӷm[.\cѣGשw*$addDǎ)uuu:uꄑQё rڵ }D^$$$ӧO_*+:۶mcڵ4"%@' zw.''thڴ}t邆F6---z% <[S>>>,Z(AgJ%}u^|H~DOyuuuQAJեgϞhhhA^˨HN1Je˖ 3a ۸C^<gϞeȑ6-Z燅Eye ## \.ٳ>|kMϞ=ӣcǎ"ONN999߿}---:wD"y'}=,, wwwQibE\\QQQ͛w^ZZZ"H066cc㿅zܿ\ 8u&ժUtؑ Ѹ'$$Ill,rw^:::RreJ"4Ւׯ '##Ln``T* ʗ/ηaÆ=999ԨQӧʔDEEMVV2J*`ooٳg)SL~S\9Q \^|ILLL:::x"cee`ڵlٲkג3`4rrr$ Jǣ,Bcaaa EhcIMIWǔH/o95kT ?:::>}&MZ𑤦ҧOΟ?ϟߟ"#^!161fԭS;;;ԩCFJRL&Ϗ=zģGן4%_|kצN:ԯ_ZjbmmMfohԨQ7ӧOzhkkcjj 5k֤VZԬY `bb"nO ..__?Ӂ?xī$I-U4g]O&MJlTv͊+x.Hرc JqOLLʕ+ܼy{7IHT~hF߸:]Ffhc+rRbJ##)x2H #31`dQɺ*ҨaChҤɿnAtBCCXa/Q*hhP΢:500~+YWf 7npY|||#++ramm5VVVbffctJ% ~X '<<0066GGGwߎ3}t6oޜ1L:cffV"#66s nݺE@@-[KK#˗ E.@\\Edd$^"&&RIʕi޼9M6CԮ][ 1gϞҥKxy<I6UlU$ȇD)jiQŠHF oNfbZZsl:uL2%Jl:IJe A*"QSSܜP455g W^ӓ.(j)61ԀUPJCZB"UqH!1̟ ObQ/ӧUVwjG>}RJ ḹqį!CH(9p~\'OڬUGb^۱{}x!Q2es̡lٲn.$%%j*6oތ:z`@Pˉ'pFFF|̚5ąq=|WWW<<<044[nt"{\t3gp=6lԩS4hPgE9{]jP{ Z5:Y4:”iȂ΢ t'39]:wQ~fS qfٲeq}wwwW䋮ӱqT` =0g,&NXlcRRR7[~:mi4xe-mg!ZHض:u;qcذa >} a͚51qDb΂ pwwƆӦMb?Ν;p 4`ʕtСX~{2uL2h5fE0@#lXСCBAa V\ɪW[֌/ϢZH5KpI<:˻hnO%ܹCILͤnֻؖGrS2۬q]ͤID{Q~(lܸٳgS^=̙S0ϝ;Ǻu(S q 7ofܹ)Sӭ[ƍeȐ!_^i $FɓЮ?&.iώ"eNY&~z];|_PbΘN}ҥKKUxYjj*֭ӓe˖1w"p_Y@Efc+T2.΃9An݄BAA:wFPs쇹R0J/ 9ԩSYvmrɡO~~'òA[Qxfn2>W.l`100`Æ z͛GΝ(RYM~ƎK-XlYSP>|WWW޽{{wf̘hn'ݣ m. & ʸ?yՅzUwnHϞ=8_ȠP(:t~WkR!;#KȍWKUFDD-[ȈM6lLăpqq_~^͛73ydFĉK<___ON۶m9zgKGҿǡԥTC͍#}ZA 0ߧS{vƱbb_{Zhʩ߲nYaĿ@4Iqd&O*/e >}#8~wp Cֿ$14s3f`=p}XClq<{X&TzXTkLϞ=7nV*􆃿?@[[[TՕB~MZl~uhڴ/ύs>[nѤI)_i|YyI#7 n֛*2|( /^Ms4/ٶm͛dɥ^ CCCFņ />}'N0ga?ՙ={vw73дhXdM2;ΥYK41mRRZ {{ _!Pn*J! ̪y6Tm[owd e* KM@HߣW޲)di6.sfMc…RӧOgcp}űb\m>@*$lͣS|p]i50u֌("^ "ԻP46 )qC% +Wu̜ݻw9|0.\VZ <8Q___Í7hݺ5]v񊍍ܸ͛q٣rMѣÆ ,rhԨ6T*߸q+W&!!{{{:wKIIȈ7oސԩS155}gǏaɓ'/;w L*UT^F7nܠe˖݇y"tɼDS~B;uN/)GpKJ{{Bj1'{˱c ~'VJZLa?x9<L,S [kdNiZj;rr~ _>~9jvj5.lGlE[3[æVDڎH5>GM;nTAd^ܯm'^ ŋW!Ν#22}Ԕ\.] J`ooÇgݺuжm[.\@jjjhIϞ=ٵk2Lr 5cv 8q"ӦMcӦM3YYY 6 cccƍٳgРA3vɄ عs'8qŋq+:tbŊlذPŎ;6-Z@"E rC@(SkXڣmZw*դZh7<)>IA۶SQʘVU_Jj8BS/GES,5ڍbdi\Ą`ݤWjrOAR8d9=zT{eR$aj4jU.ϯ uh<`'_2/L k`ZGw 9ru֥r*~j,\Zjgb``)SޛsdM֭\{nݺTV@͎;ݻwv8;;iӦvH߿/_Ҿ!t\֭[|rׯOZXd ܽ{_ {f޽r\.HU+դZHt>OzW=B(Uk!-) ȸrLJJM{/B!ϥ]wv$WA~v VРByaJeSh闣][N>rm}||(oY*:掆>$#1~<#7״NL#~rʕBիe#=ʼy"CX[[qiݺu̙3MX'++'NsyusmrEFk2oqr'g%5@Cփ YiҽP~#id=:J5m?*rc"P=Yoߦ^zW7ad\_Ԩ i1趙zCƝ(eɨiyʜ̿#) sAAA)gA<3):(%dBK/oh.56콟3 ׻_F=/şR!/} r&XjyBL Dz~G^?N: |oAaP:JP{&Q**+=zUVӴiXTT0#$$U ΐ? 1zndd{?n߾n+u(ˌ3777vͱcP*| *-04*4oxDa&zmY J m~H NïgdO~Ct[MGÐQa=ViTYJ9y3t[LC=%?uQ*H"-=6V(O1_P@ׅQC_m+]שI;ؠdg~W^DM"EYS3*6)1-M/TCg7HVz2yxP @ Qf`&<{Hcdd* IDATzzzv̷R:''罟:t(7r}ҸtnB"P»D5b̞=D/_o)rRuKBBJMCCRBCT|2jRͷTqw5FhC4ywJ]UVJap8ڍFaNYo:dO=yu ÁHjhC\?f⪿{ZZR-?щcXNjeDT*qi+Xq~W]Dvf J/ >X ==]fm< .;Qs4yvfdff' kr;ZZZ,[\ϟ_([>E򺗞^yߦFkT^-[`ffĉ9r$(JJk|UZ` / )-M333QJUw\6?|dsP}@&/VZ(WUYyg+Jy61z%ψq1Wu1ԂoaZsoݔO~;I<mJ/"+%jm>H11QaYPa"/f⪿__7SUG妽PSWH5aRs?|cdw`ZRE?_y(Qum呥&J%J/qF^/k5D?_*} S11U͛i8j*VZf^9s(S \~H铔TaQ(ZNNNH$ܹv___Y(~z4h@Noˎ)7$&&dQ|yJR92}#-[w\ 97zkLG!OԼ֭T~ta~au8?.9/?}2M6%+=*?^9 EХ]d d.?ᯫ84ΊO&ȸm};G*a3155eĈ;v,?/==cǎ1zΈ]vѮ];vl,Y˗/'־}?L&cݺuу޽{q l?~LÆ UZNvvvdƇV]d^IZ5UA^M>w"NwyA90|Ù7/޽v]2;xf* Y(ȍGx ,/,7&AMBƵ5似2WFNW4?) ȸ׫Wc3 geɆ}Q n}=︾}uϠ^H5u5@/1uM:/_J>GܱumѼ9!*9 yLZK^7ė:Fm3pl,ɬqA_ W^qyƍGtt4# ̟XȘ1cYח(_(YJ%4o\144^z...1+Vyf,Y_͘1cFOOo!:AAA1 vQǔlBquue֭=ŋ3! x2,?TGU4oBNnl\=yg >+ѰLzаlB>AbClЪ ,"=yhVmK]ZDZaVfc}RPz,4t3F$Դ ȸ 0pލԨ*gf_7r^vPDnG+=VLpq |?ji贸0"QĪaWU} 0"G.\ÇԫW΋/bZo{?乜Vo&aɒ%B`vũST^Aqݻ3T;xbmɓ'S&~"##ԤuԨQcX|9O<ѣG*קjĔinW~2ӟgO%XAPWA۩rPrEE4n޸V(kа1i5i5a^zaϟ?Z"BCCa888A`޼y$&&-*ښEѥK!G@׮]ټy3GVVZ%?PfB|XTQd֖3sL _鼷+J*ӇɕT@A+?ϼb(Q8n#LR -[СCB?x5/_fĈr>sssٳg> |FI!Ȟ"W%\AwסL$; ׷w>*I[!ƍGEss^, TQj !F!?pM^*6n܈%#G,sDEEgQӰZWL OCK34r|73e$ͅ%\Aw333֭qn^=%w[nPgqB=&K[ċkWA}tM6hݺ5]taƍdggj->|ȹsXti~ʊ EYb5믿.sϟ?ue6;J}9dيDܹsDAw#F0|pm˽~8,!CɉUz{EOx*lڴx6l(pBB 33fޞӧ,*X|9۷'TnYzߵ9TC__tqر};:rՙB^8OWv&I$ۋ$'[&ZE[xzqwrʢb"UTa:t//R ˖-#''{ְPWWȑ#L>],amƩSpwwl;;^.].ͣ_N&/ иsaZ6k̹$ꑗPT*7 ߱iӦBv}_o±y~JUYDzsmhfϞ~1}tϟϽ{Jw_v-W^رcؘ'O+&OȑlٸqgϾ{NZ'zJ;SB֩TlƎۄTt#771cDZ~/:g=*}<1.q;={,2vQOwa/Oqٵ}{w)xۘU2x`N:ņ _~?3 G-2ρǏ䄑k̬֬\\]]9z(۶m+ԏCdd$MV.P6,J2NP^훟}B_LH-|t :nwrrd D+E#\)RO>ͣB^rSą>΅٣+ws/йsg&LPfrssYd _~)Rρ/k׮!J2d>3n8Μ9Çi"$2"5Ė"%C1Jb0BA1o1cW rCvY 9s˻Ќ/:|pN>EqVu'#d=\,OØ>m*!"&dҤI̞=["KwoooΜ9Ð!C5p-:v숋 ׯG&zxHzz:wܡwEkԨ[Դ4$@r^,q~c[7V_/Pqh,Ϝ~=dܦ*u)ryza;3 nws1ʖ-[/yMyut(0.Б ?wVZ%zڋG"Օ]vqƍGTTX ۛ۷qrr*ת;۶mĉߟ;wkf̘9spvv[["{\_9PʋZU\F֍zgg^D/|$!߿τu:u<KaS%F1ŅKbhXb2331s&[lr.42U]y28ǧaW6>_fy Æ 믿FKK}W^f^ hk5j.]UVL2D憇ʕc׮]tԩX}C1fxr5ʢbUeYd"j4rSصc; mr9~7]ŢvkjtUnH%Fx!v9r~-UT)LJ& jKfmP7<+geK4i..\6nȢE044dرtTZ=!!777=Jٲe Ŷ,N< cǎ :ZjÇ9tjjj̞=)S[,OLL Ӿǡh[D$M<?~vk-8g~XZZ2j(:tP~,xxxၞcĉ%b.BÃӬY3 @͋{'N}}}&Nȴiӊ|r%fϙ˽~شE(M}{;xMcjm۶ Eɸ%**]vm."_SݩԸ4@MRt{r2S~˻'?KfZ-[e8pǒΖ-[qܸۍƬv$RbBr#^3R2t ϧjժN.!?>_}z;wp1\B 5kǏ/=˗/vZΞ=>ڵ/aÆEćp%Ο?Ohh(vvvL:!CO̙3L:!}~h脚g.ev3(!3 ME.(S 4iӦ4k֌My-ׯg̜9&ƍ\z+>KzfH͐虀TC](^H{$#E+dɯyql7o^b/J^ٳgvZM& H'22ϟBXXļ&&6(R ^j|Zh"H`l 055ܜjժaccCj000_d2?~Ǐy ^",šPIKM_KK-mttu\2,FԮ];;;ʕ+'| DFFAxx8dee2eJ`aaVVVRvmlmmKUBPPAAA<{(B&uuu ̰FԪUK4Eyf&LIPG,^Eh8_ǐCzZ ?:$ zkh`ffNJTrikkkѡR߲e 'Ndٲe̟?_0@ Tw}Dzeغu+cƌQ9>|3jo΄ Xh/SDk@ 0sa͚5ݻ Ac"J;v,\R"@ b̙lذAdFBOOC"YzEw@ BĉٵkW"( T*e? ¸ @ }۷'NеkW!@۷/\֭[KM:Ua@  >'Np @etڕǏӻwoٳgO Py%A1C ӓg .P9;wٳxzz2drss(¸ @ 'd2Μ9s3gЦM!PhӦM~8p 999Ba@ ,~:^^^888Q;ϟW^,\'] I^}6.\qBgExyyq-z a@ (դѩS'޽ŋiذEYiԨ/^ϏΝ;&D] MJJ ;w&$$+WP~}!HP~}|||sΤQqtxyyakk+D)lmm"44'''(¸ @PC$$$p5j֬)DIj֬ɵkHHHȯa@ (Ѿ}{Z"(X[[Mrr2۷'..N"@ %(ZnMNNW^RJBARJ\|tZnMTTEw@ J&muuu077VVV\zuuuڶmKDDEw@ J/^.]EP,155ҥKhkk/(¸ @P2 m۶peLLL(b ޘжm[BBB(¸ @P  Ο?EP"022ܹsTX?~,D] I@@NNNr (Eٲex"k׎!0@ /BD]H sQ  ֭CMMM" 555~'R)[Sw@ >{2`&O,L@? Fnn.EU{(򨈀bm+/74^*ml[R׶7k]Mk{W)sSR{ej(he *(( 0# sX嵔nx1gu期9g>ցիWkAʈA8OEEnɄ۷3fC5#22jBCCc :B\.Wk566#Gmw1+WRYYɥn7---رnh e„ m!oooRRR4;v,cyVTTrJo3EDs8dee?2_:v޼yMzzz].hԿ36Kn7YYY8NCo/:UFDDe˖1sz///|}}cذajcوnOMM pԿ3n8酨f%K0c n/z]DD4Ivv6WzrqRRRZ@jj*ԩS5Z`ۯxMvv0]DDKwnX0LL8???5JKKpp8HKKFח'b6X,WYja*#""nsqp8׷b6 bEll,j9׏SN]vp72{lf͚Edd!ֶ~|rÿDuu5yillm&9r!?EDS9s wRrǎq=NHh8}e$$$@bb"WKfɌ={dΜ9dddW6Ο?ϲeXp!Ohoŋ0\ ""p7nHnn.y 8!L&3}@Pчn}& 7^>},Fk%5'h9Pucv3;fǏ'%%w۽p\3oF}:zY~S,Z8qqq 7/G__=.N|!6W wЄz/_=_ܹ7+= ?+__Aqqש2""7u3=}^M_ߣkp1BFɤ\zH*˨C_m )]*/QYءψK$PY nmUoRu0MKaÆm+,B^b?M1\32M_SeDD l%wU;h~c^I~|K8 g3ѵM6in<.Mzp4=nŋ ݿ䝅 #_д,/`Orp"W:wr\;č/db/o5Gpd&xg6L_UvowDk׾EKK!7g0>! y{|?_Ap,7.g׍a'|?^QoV8`Gwr;a\Lj8g/>^O."bPEEE}]-[8{ml{ <_-ߣڄ\o@EEE_y!am@ݱt@ ""U^^Np5~kC̐ TAA7xq$&!ܐui?]O."bPwȲ}eEX>>A'7~ů[\t@ ""e1wK7ŗQ, f3dhnowRkp1Pu̓?'1ޗ%Olg| f!`x%t@ ""NSCm-ܩzm6c^/gB6{TZ ٿLfNy0{YfD?_A%$$`DžW}Y6I6/LҫH<|ѯ4a<̠A ?0,AS~Kqటܥ ~A?]DĠiiqQ{tU]Nulzz" 5ǩ;~?ʮlJ.;æ rϫnhc49F?d5G?BRRa6ASeGf;^Šɏ2xW:vu1k7by,/TWz]Dfޟт7p6"gS#eWyG."b`89#G1.2 v__=wY(Yz5αlzA__=w{'w3|>ɜ9s__=w^vv8UG?^G_w~:m:[dSmulJ6=9I!g*WW]DDgQi[g3>XƳlξޡ>l/'((H_#T 88nYM˯sSO6n谡E_]DDZEGG5w l-4֜p{3N+[s]4__K ""ƀ(.Il@6>9r:VMO&.*]ǫ{| ""5lchr\`')lZ*yӳgOWWN_gh)}7u,yOߩkpGMNjb'⹟}s]K_տ.""\~7ɏ#>#/vq)߳ay7N_տ.""؆ z*Ży`oΤ_Xwrqp-G.t~' ywED[ۻw/.^_XEӅ&oO̍?&fH sћl5DŞܿ???]dff2dWW._|g6 6kX\GX$"'|Cr;)u?n_@&~;ӧOcҤIwED{?RPPvލtX¢ 덗elV[I u'h.\IȘѣ5j(wEDrݔQZZJii)ǎĉ ʎ AzZZZ0t b+*>зo_HBBqqqL&UC."""" SEDDDD4w """"")1L\IENDB`gplearn-0.4.2/doc/images/ex3_fig1.png000066400000000000000000001260511423420364700173040ustar00rootroot00000000000000PNG  IHDRzdbKGD IDATxwtTU $T RQ*"5P  =$z @B($#RdrSg-˙s~3J3QEA!Ev!0*B)B!D.Ы@ ##/Jzz:vTjxxxE2epwwW9aIA"v9tDGsdgj,gWJ*C͠ "((uV+KBG9WGڵkٶ=vNoɿvJh}*({ 4^h .Ѣ1X* P(fJN(YIⱧCzkgYxxy'yUKڷoOPP&)B<h"._IܹЖz]zKG\^EmX[~ 0g$Qbe"=znݺ;HA?3+t.Gٿm0Vz }Z_@bz0[Qm&yj׭O~oӳgOVHA>Ҙ5k'L$3ӄB+]0m hԎw1K\3Av4! )BENNSL_bQCzR'g/ݗB֡_F-¨3OprrR;T!aڵ |}.'`&ε_EctS;CQdZH΁(S4?v,! 59^dddͷСWx\K~h4g5$Ю];z/jGВCxs$j4mԎg6meJ Q;PEn:l֜v^^Q)r_^Aٛ&MdÆ jGБ* R-n/u+v$Ѻ`/ך;pB# QHAQd-]>}Ĺ~\v38p7FΝN%D ߡ"i֭$^(dW & k^_3:3g\FIyk֍(L4:'4;ѺaO9{sBF܄\G!U ~gbߏ[O0C h>̰) Ŝb1Q$aM9l|! 3EB?JVR ر$Dsm' X8P[d%cK;ˇ=>(YI/(̤"vI17Ftkd%+Ś.pצ8{6K\8ń- [I1Ԯ(̤(֭[Gyk;Z\SJ> 0:{a(?Y/7 -܍J/D݂ί*.}p 8{e7]'y^K֯_ϳ>+c QHAEBZZŊ8wS;N}7Sz%QȖ(<==ޭ+;c ++RLxDRPE;Nf4j@-_Qv<ݖCp}i!Zעyӊ^4&֯CB<)Hr M6#>]k8{)O))VAv_4?T#(/-5daZڑ=*}s6uF)B)*Wa۷R%ؒp0-I)w+;oL2jGА*ʕ+oO*'9P;NWy" {#éXڑ(T"Ϗ7n ANQ;VQS\3h;lٴ_g,9)I?uӳ06zFv{f OJBK رc۩|+bx.8Uc>'X1b q'Nφr *ƪϣ;k6sԹ _|1V6G /bbbb,^]v8xM (Vb=ŚCW^O?ZjjH*r ,`y9ugPJO/Y7V5kgJNJUN߷ݻ7Ŋ˓BIA߿UVtJNDq. %F[wyo"[ylOcMăd_BYLח^SNԫW/w^IA1YN:ѦM]̩6:=N~v?n8yq@t$!{N:JNJN:+h29cYjuT Nrٴi;:tP5 !$U`iڴ)NNNl۶ FCNN111pq.^H|e.\"--Ԕdn\ ^^^-SҥJRtijԨA5^:NN7۴iCjj*t:u^RPx ӧOgȐ!:t5jɜ'OVZL2ɜB'UGtejԨ/tO?3fCRtn!IA+[ΝEpp05bѢEy: H!۷xbOŅ3f믿iӦ<_q'Y f3k&((˗sDEEqU"DQ'+T!җ_~I\\SLQ; SN%!!I&E"O L0? *e2j(Əω'Ԏ#D&[B<ǁ0 jjҠA/Ά Ԏ#D%+T!В%KXnzBCCٴiK.U;EPxԨQsz-֮]R;EPxÇ';;/R(4i$l6FR;ET!ő#G9s&'O_8ĉ>}:T;El qvf͚ CѨEudffV++M e޽L>=SF9tsQ;EPDjԨA߾}0aqСC %&&%KG"A ЫW/ˆ]8d2LfX`q(dW _~aڴi7߰pBl٢v!Y f:uPBtؑǏsԎ#D&+T!᫯̘1C(mڴiFBvRP_2rH*VvV\9θq8{q(dWy_Zԯ_RJn:Qh U,_k2mڴBSLF3faVXv! -Y LxꩧՎ:7o&&&@,D~'+T!#FĉՎ0SL!;;ѣGEBI (򢢢6m'Nxjq???ƏԩS9tq(tdWiЬY3V+ᅾnyE o(͛Ǟ={ -E2{l<?v! Y"T^^{ɓ''O 2~ǏSX1Q(HAEoMY7jnݺT%Kعs'gԩESL᧟~b֭jP(rV+O<%J`jQUa %Zd*)SpIfΜvM>s7ߨEO (R7nÇRJjQ]O3f wcF@pp0͚5c*'̟zɾ}faXуO|O (t~g^{5v;z5jtN+%KT;bƛoIll,6*Uę3gTJ&D![Љ@7.IHH`Ŋ/_٬r%))~VZqܹ;)@ll,III*`* ;vV8v;111TVѣGKa8@JPg*޽{8TQdee}, 9997ڵkz1rHh4W(Bwݶ;VKNNy*YnnnndXؽ{w'* thтSF}X~=>>>y*\2]vwMKKԩS*$* ;wޱ1 ̙3箫0Zǣhh4j=B (4ILL1/;w_W'Xh:t(/ևN'RPEq۪JSvm>LÆ ULVtڕp/`bcc ޗP]RR ENN& ^:ooo@uH fFCnݘ7o...y :up:wLXXǎd2zqIII$''d"''Ǜ7NNNホ[޼(!H p18@TTGFs/hu:PR%j &88ZjѠAv܉lF2uT}]l6lڵN:a"!2IIOKɕ#^Q|Yʗ+Kٲe)We˖%00r\B8sy֬Y[k NG JUͯ .%p):3Z+vkv&b''#+d%'`JN 5$Ip SzFVɓ0`mۿΝ;Gdd$XqXlݓnN1g1i^%;[YIg%th4*Vi4iܘ6mPZ<'MRPcS72c,N^ߑr :RfKz3&էgw/Dw.RѫW/111_ nddgp)k+> ] 4zg>0{UQX(`ΠD2<3}i}Y|}}Վ*)lڴ>TߤrWlj@crrZ3.|A&fҥ,m NĠwrEP]+B[Y툹Kcz˅p$^iب1/wJ)Q)E!&U@sN>1m[p.Y].8Uk?\D3sb ߰f^嗻3bpՎ& 8)L0GPN[ڑ7\:6mڰ_ -6nCq\*Xm%`YȎﱦ%УgO12eʨLPRP}l6牞_@(+zz/;A>a]z9!Zspj} clv9'eL49b8~!NNNj'TqOV.]nOՎ0/N8 ۾ʕvɓ'3bh%0>9 C'ՎU(|`K䗅yIyŃ*JQ^ ~[x`o`LaӄSE@@ڑwyWop.u_ݽo.=2Ya>ѣF駟޳'UÙ8+ҵV;NNƆm(W̃]8;`ǎt經d{7(d^DiҤ1WҋUt.Jl͌Km*Ξx'NeGe)cn{b.|oƷ'!j*/vpTgw-&2atijDU=|ag:`Ȼ,wS۠~fʘY~´s2Zْ+~}Jv P%]rVqWRP-T^='Rm\?+ >GNF-pGc6gߗn4&H?ʏR*5 ^g*8d gw/fi\ 7bGuzWyvoո8,Z>MLS=;9 b1ktϑϞve=xMsV\Q ǒ}$q˴ip)I`72Wq6zs¿f̹ULozg|38Q"ޙ/͒㐼ں_M9iw1ipieUPZ8?5kV~S;ȇ X, L:JOvld$[G!vx`ێ*U 0H7W%aow>m۶9l6m?`|?uk8K(P>5;3ÏP;g ƙIרUiݼ9g"Wo ISˈlJ%|+鰬^Q"-r7gا8Wi\S%sA$1w\|F_$;m\Kа$vKď [YHF@;˅jvX%k߿s9}ΌD.>|*Tƪϣ;݆Fc~%'w9kslk]|Uy >E%Ukw|UZʹ\<5W^9K?DP Z6x`c6>ׯ瘛,^i IDAT+/MH]ٸCM0of4N5_Gh .x} 4:#oP |v= @fqIǹN/w#\G)fMĞqf\+l8SvdŬR@ѣ/')8-iD¿sfʿVoīT5spX{ _˵k6sq^aߏ&ZFgP5]X1n q6uzT%u[]?s&ΈC?dG|&zcdgsv_5ҵK$rG4?)[q{Ws:JvXޛdff:lT4^ŜFxgvӵͼq]B8=a6mǚ3:3g\FIy냯sً(Y }ybn0J?Emht .ٱ =8:\r2󦜌:leJaϼE31rPw~`իuXZL!lFuO+w`bN;lˡ͸X n{\\Dw{sMv5)zN˹wbĞHֱ3R7hJ7nFցQo|{qG8x mi0| =3l~ɻU0%WwͦTUikU[\ E 5tz#%Rы=7%]c:N~#JKKX}Sι>ߛҠ/M]'-3їySRݸB$حUǵ`"g`|0|uN:섻ΩuP \λc1O|> {5^7GO־߷a lwחLͳJrr/ &)GRV-jҩ&`ی3ơsګ7576š H]֋?TȿdWBu9}QK$#2{v\tC>v5Bzi^c5[(vrvMe;HA#aJNP;Jq|tZjOo*S ~2 YXDI#cQX#4Fw\v<3˕̜>M("-_qKvv6UZ){WwIرc͚ـl6w`[ֽܺx0,">+֫DZg^A䉡l\DUxz5D?`̙N Tq^{vc_ f#Ѵn wlcJNN~F$dpya6Z]*.LNwjTqEQh~n .5Z~\ڳ#RB'Q[t}E,~Ν;ƧN?E_ڑ 'N%LBR,_SB 7C&]iwpAC.]X0' :?;}AqnY.üs֤|0}ƌcpE W'_g 7sifD!C0iҤ|25k{$^<&ZwyMAaM8;j֬v,QIA$**.]w2ߜNԎϰkf.cjGz`&Yf1aWbz ڽyQ;Z`QH"iظ)ɳ>v0QIAd2̛;OAɸ+vfdstLZj,]իWW;#f޼y0p.}PO$r~|9ӢS9zJh*֭[y@Ν?Ogn,ڵk5;?7l拦3Ph 9s6g6b>o֛}֮URP#X,L6&aZwL?|_9p)%W^;ҥ_O?/.c8yCS Ɗ-їzhcHMѠкMzA.]d#RPcd֬YLj ׯ]lԛF3/8ٙكÇI"&&e˖1pZRuДnL#tkՎYXb&>gh4k֌>oAǎQ;(䤠\a6Yj3ffG6\}(]tD͖A۸~0q{Wsi\x*Ny7px&>>w}UVѭ[7ZnͮݻYa#W.ǣp?}@b5n;_cK:51 [Qz hZCjmX"3gӌ5!CH#pRPEeŊ,]{"@x:>I* )G*hu ΔOc\=ȵr"lSeU[tܙ&MEa…|xyyJ۶mo;ԩSٳ={swGbAWū2|ʣu/qGI4z'JƖ=2xlgѤÞt+xѨQC4nDÆ iԨjeƌ|gT\yѠAgETPl߾;vi6Nn3-U28yշ4F .h: .,9Y,9Md^| sZ")JO diZhF-hѢ*ju9s~}v _|f;Ɖ'8vǎp4:sgϐu8zNh<8{Vxs. Z'ņbμ݆bkT44t4drR.aoG`` CjRzu^:+Vt!Ò*Tvv6111DGGsq.^ȥ\x42ӱZ-230쌇/[%JPjUj֬Ipp0~~ ܹsiذa}5.^Ȯ]/ҥ Vd^OdlcWdquQ F#w??|||t+V 44f͚Q|\9+vooofϞ}NJ]%UBü[DEE1tP>S?٨K.8qÇ;d+nS~}*Tʕ+s}˗/{l2vʬY5E%! , O<3d)Geʕ;aKkZƎ˪UK(ҥKYz5ԬY ۓ'+⬬,&Niذ!sΥFS>BHHHK.t֭ؑ[gtΝlڴq|7M0|rѣٻw/999ԫWѣGc6PyGhh(O?tfhժ:͛7  ::::J*1o޼\HnB":s O?4o&*Gb}vF4~xΝ;gz}9B@@M4_~dddYQ0 U|j2eFM``-4i/k׮Ue~wy?SN]bn|xzz2{ly< Y :tƍ3f޽{U+W&""BMF"99ٳg޽{Efxg֭׮],"@VVcƌa4mڔsRZ5(BzX"+VP-MC~̙3xzzc͚5 0ŒgYAVBlԩSٳg3emۦj1XlGQ}uz'|foQ5 /@TTݻw7ޠ}ũIRPP O=DEE9AÃ;nݺQvmU͐!C#FI9vڱ x饗ر#mڴ!::.]ƸqիWg}ƬY6:u0l0iQDIA"(œ9s^:fƍ,X___ٳ1B(wկ_?J*vԪUݻwW_1c ׯOddڱD*>}6m0p@^u>L6mԎuӧ*UR;] FO?DLLqns!Ç)Y$M6ET!b0qDIJJ"<>P>S\]]Վk>rrr:uQ͆k׮%22jժwQ!UXnAAA2sLnJ``ڱrرc)VoQr?~!'Ojڵرcۗsyc{*_ޠ!$$ՠA?~Çv\7d8qQr&L`ǎ$ !)#B8ҥK8p zӧӹsg#9D>}غu+'Nh4![>N>~o.\(A׮]9~8:tst֍+WH* ZhAr刎.4 X,\vSB̙S(/e˖mۖ(;^j%66VxKhh譆իWgΜ9~+d!fڵJr%44TjG!!! p?7>v\u)㾯5k֨233C*:NiٲrI#zBΕ+Wݻ7۷QF8q5h[}iZ^x&LGF*Uغu+>>>h4rjҥ ˗//ׯݻ`0EQDAI&:t#G2j(4hԎU(IAރiܹs}iZâEYl իIٲe:- *$SB899j}ߟKIIxqoRPENYf]٠!$$gϲeBCC =>ř3gjkj߿?s̹Mrػw/!!!w-oPԬYݻw3cƌ[ !nz{=>}:#GdwDBB)))deedBNۛ UuѧOha0xgXfC/AU&E IDAT+/Rbb"DGGsQD8RjOoʔ-G퐚ԪB͚5iԨ]? ‹/Hjjl6rrrhРA.ݻcǎ͑Ǹx)IjހqRUT!99^ϲeر^Aje̘1DFFpB8r4'zۘ:\kLZhР|5Ѷm[|||v- ֭cĉ 6QcǎqB dݺulڴq hp) UVAQ {qnѺ14N7ΔUrŜ=+{UWǣ$Fr䋠(TZ[hsPyܹs{Nc޽׮]cڵlٺ;(ےMz: "HBhҋA^K%tMZHnvG$?1ؖdQf{>{w=cO$܊!FZRH<{ Uf(@*CH׀^!%aPbHy!5CBGEIF*SVm5iDVhڴi&[صkGT/.>q(7n>:!S(mcHפ"RP'>GU%96 7Q%accKm:hF6m[nH,3u$ UVo)bŀh4lٲ7qat:-J] Eȋ'hAB"/“ h\GUGwtc"kһwmrpUMϟvZgHeTϸ>5DaoT:BzQ<=]hۦ ݻwm۶*FFFzj6oʕQ:PR#kJD*3D]Jc:ų[}.R.;HjXܹs*UT*իW)Uk}+5­D|1#ݻǢEX|%Iؔl(4GjfV-u<HxmX0.  Æ lٲYοz*uAضL&C> A8t,eΝHJd%(EFFQ 'gȐ!^0Bލ kuTnTncV=<AąD?_j>>}hV-`0дiSH$9&W(TR,b@W&rbԠ|@6:C=7bod̨Opqqt9y$'N 88JB`0d;,OXdI求1abbb ^ RkBkXi&@ڝ_Z61 o߾}"15~LÏ--魉IȪ1<1 bn3o <Ғ^Ɍ34[UoI!KP:yXZһ#=˛ҢE 6nXUejab$ΖdTM"γiڷooiI/>l?Fgŭ33= ф,?g̙V^2 1˖S;'|? V>{Sp^r}+VҒ2zĀXj֯M'wo`@u{Br kiE@v&OSh2;{PN;~Zr'UKOOk#WSGd2Rc#8:#dN8f[DP۷ӥKWlk ľhK1 _^^Ŷm[i׮EDDDPA#b.ص[f^."#٣WHU xUs653boH];ӡNܹsgȴScDn= $hIL vm` (CX~,\doe4@l\ DVA}[c#?wGVJRRUV'ZZ 4 NaS_%tރm#RBfo-Kꁯزe ;w6#G?p7}K!{&5d@@S)ǜϽ ]:0Ql-GH;gRN،trt8|ozLuM}оE]6m`6søqX| f_3_"!KJJQk8GLh4l r!^5{LE\[Gw}O_G&Y5yM5ߨpnx6e}%7| ^{WaEu}jbW"W0?2l߳vkE1{j*t:I&}L7Ya8ήy)ݠ;^^FZ,mTp`=&tvƧJ /9_ V^g*vCRo0?"/*G656e[ S:qF9rq1ksw8v~;m]ą]FoSLAbԝ,%FMŧ"Ynl_MB-{dr}&'[>H&ׯ_'%)祏tc=?>"k[. =DmS%NmUx=fJ*U RRRv21ɺA!4Ե]PVewn۞Hd6H}jq >s9uE+aTv3EC8r,R_lFZu 8W\8֣He}l%Q7RD7^/nRԸHB@֣P;/T+̙3(윐?͘>1z!Qf"0$Ezjėlʶc wF([' mjBѕT|ی P<<T4Ƅsa48b@"T*Y AEЦ""Qd].f?t k E^r}̎O|Kj5Ҩm Tg瑰q y:ox}MȨk*4*r^WӤh ͧrQg[usCe#^į9WTm$U%VeԂ\<-Xҳٙ2 P777:OId6Hli:UMg[5R'o4W!h.F+c$3 ҒfΑ/B׾{}7ph?gd}kdk&0pqu#-)sESZK Ι~yKrb=n2>Ju,X@n396]_ V'hLҶd# _ ?|Ƥ2zcPǡPV3zAgқ'x5G0{r'jH܀bBzozzMAD"1Dd)D^;L/mp(`,N  *I1mLMr_<==MfZo b@"*WLZlX#wžX$JRO@ N>>'d;eJnHlQHT4ăt-{iP"EQ*x4t\ߔ7Fƾ%y(ʔ49kU&!Ъ ^:FCe~Q!|P?/3r(sh)/@MZJ_`_[m[}x[V[ߊXb8w^r 2,Gy@sy VM/CV3^: ڊ̳] &**"EĆ` jBY89Yu<3sцĐT邢dc/dl={qF5rl17mA5-3֙_IMۂ MCE5ЪY7(kg&?EGG1U&`bڇ3Gᦺ͙ࠜO~K/8|w,r^ѪrNkW_ VFy}--ŪH=ԗέO~:tġ+O`}yCsW}ңGٹ~:UVdv}:V䳑:uϞ/NZG^RA 49 I ''g:t`R;UTj;& W}Ԏb@2>-]b5܁SӧOjݺ5>E" C72d@Jo:dagbr[yѴYIߟ@VrP .d>Nmi9GЩ]Xȑ#6=LƏӧ{oK/DW_ހ*T+[7=k'A"oiߙŞɍŀjL<A:<E(Hc7ߘf>}ZӍ&Ox*2… ŦRsܿ`֊^i׾52MOn/T+ˋ2"YZE^@sy5ITʪ1D:lvA8Uf Շ?:qn%BN@լvEg[J2d;v"D ),-9%]taf_zu~u%hd_pP)[l2H$X .# sx˗.TRf-/T+fŊe.zP6u<]C)[0˖.aÆ1h TؙՉg˦fۛ-7yi!k[Dv%C/֭E4Ͻ}VNdd$u5 N]˿qwS&Kk={׶طDٓ'PIڵlڴ.]L[ 7_tуmPMpqs>[NY8Br-ʉcGIAKK2ǨꍗG[DnDHOC}WWo~,?Ήm۶AIm#r殺dkќC5ظaޙ7nе[G>yҒޙg9`I7Y0W"?{Ā?6|z[wlMȳUhO@M`ZZRO~?v o' 3]mJS2i~B}o7|\.QT;eKRGcu]PbE6o{giY9"?+b@ͣDFF_yFea?e硤Iz|ܓ2Kz#A`Æ |xQT닲Z [b4y_߼чͱc6|$xH|یFdZB0ypz׷.9Ma( KK{#D?b@9r_MʥؕmEjXZV.Ԭ]3gмysKz'RSS5k74iz$ȋhF8e˕g̟1oOt:Νˏ3~&%EEŖèP܋ZZZZ57qs/$={D}-j}Zs ĀOػw/_MȍWQzGV QZTI y72mZnmQ]&))~%.6e+uFQ sRv{7[>]*W⯿bڵkBjj* .d_x)| >U[ Yv~{W h-TzI̒\t5pMZhAzduhZl#-RM6j2$?A$I ֖^ :ZjEtܹ%KqAd % @QR{sM c}x!k8:9ӯOo DIOOgر,Xɓ'3e[QhZo8u8vnцGWyȋ;CRe6t0|r"[1n߾M@@eʔa޽8;;̞={V݋ZҽH  IDATEbn+jm*{E2<&>;{>#uB6mpt4ڈbΝuǏ']γ4BȽ#(̽ w!)G 'ѩpsStЁ-[f[vmɒ%|tܙ+V`oozuV6oJ U:R?n|q.Tn TqQ$D$eb4$(Eܹ3G*-X ŀǹu+W{uQF̙3ƍNxD$Qƒ`0?T +ZŋQX1*WK7Ӆ@=z͛7 %""H£sRRiթt(Q(lwp "ޔ,^oooʕ+/+Wܻw:mۨSuhuܾ}"<%""$RIOסJMA[''' .y ʗ/[B~P(/_e˖T\={dJut K i&z聹~ݛdk""5Ot-[VZ߿d'_lOOO7I"և[neر׏1c`0]""oP/^e˖Ԯ]۷cgg|/^7 C&1c ֭[ҥKi۶-|"ɓ'3eK┯1+ֈwXhÆ 3)_#Vψ̙3#F0m4[Ky#)_S!VɏՄ/|L>I&YZ#NkJ,)VD̚53g&Nhi9oL&G"&ÃkD$?_믿2zhKy'rPELXF$ P̋`` #TqWkD:b@5"}_5O? PE+֜;wҒDDrPɓ6mgĈc4Ā*bn]iӦb<PIYbÇ"NXbH^D ĉ1c+Vct+ֈ5Āرc9s&+Wo߾dĀ*bizѣGŊ5"VPA3f cҒL8+b ԫWOX#bHq!BfQ?={ZZј>}:GA1 <|\]]3r9֭AVm۶tc>>hZBCC|\dYbkF%l26nܘ)@ǎqppx9 ]70ѷoʯB"ЫW/3)>Ċ5"֊PA׿2^g]͛7ӱcG33vvvtW޽U,zwZjQti3Nޤbӧ"&G aG"nذ-[о}{ )4}Eվs'''7onFEŋSn]2,߮(rSŋP(RD^//_^QF AAHOOz- J`0E @? B0`%{ϟ/dlT*>}jiVNF)H$aʔ)ᨨ(p‚D"܄x +ψUM6 $5vX!==] CYZY8qP(߿=TL&XZUxbAP=zbccڵkgeB!1E1~ی T\;wd6JԪU7o{n5kfYfݻTX1q'''bbb^U8jՊ#G>U&lٲ|˘:t=zPZ5^JL"H8{,uԱBJ}vnݺRP h߾} *TFH$c "K"ƣwYK| ΘlْQFqĉ,Yd2CPNL&r\6l_~iUg/-W;5I.GjAUy0}lWs5-[fe"PwիW_MAfϞw}g^aV@Ϟ=_:;;{͈ڵ z޽{[Xsvs Ǐ'::LD :N2%S2{l3<<|;ũR8<ׯ_ƍ\v V?*)C*PZU*WL5\+3X$ YkעVJ3SR9s0zW۷yDDD(1Mb|iiw*puuÃbERdqJ,I)Q*TdɒlGt% Ν;޽{9r8/զakq_}*S{Wo܊`Ⅽ;LNz CNCZJ<( OI !6)Q^ݣM4& 9۷D/iܸ1gϞ%===Ǐ'000y ^Opp0\x}7d\ s-ԡRG/$vH. AESZ4$ 1DcHy!>haHNƍi"=={~ÑFJŶmXjGE&ppp޽{*T7orܹЛ\J' <|({ }kײu=­H UlBJFgЧvgx~nJzZtօ}7nD"A6mɓ 0K#Ǐgu;(=K?PԱpnZx !=" .U%PJ5zmoЧ#<%_+_ŪUXp!xy&%5Uj 6J܋W©he\|*V2.E+Q,308"oy[$E"!"gHe2ޫTҰaCZl)^g a􀚒š5k`7CST@ lLS9bH$/ Rp'::3i$j׮mVM&>>+W2bܻ]?$[`S}dnf)"ϣQ)1|A+FG}/m׎#ӲeKj]ƾ}鴸.r%%t{\H '~BBK͇ЪU+5k&&d1F ))),_~ABBj||cgHoC _*Lv]vXp! 6y6V遼{`@q->L4qBb ?]l޼7rm\<Ԅ"~,-hj=ÓLjugBޭZ yj0Xx1'}Fb[DJ+\/f-.:9s0u@^jX;}}B Uby6iD^/OUdzj*-^;p)Tb(U3C>xAjL8o#V+}{2|p|}}--/NҥK :+WPÑTi?ުo$%U.[A 2?R8q!ÆClj @YK5h|ܳs ޖko}\xyaF)c6Eru L})1yxf3$6j§#ӵkw6$[TA5k|3 u7\U2>#?'du4hҪ^NoeY(K7d.ywJ3pjY>CKKz-b>Μ9~d=@ƽ0`c; )eذaZZ]j||<{|̱cǨ;>/4ɱ/Lӏ?Ғ%22;qz(FҒMEu|:iwv3nxfU.ZuqU7#Qzu 悫!Ńu-ZO#000߿W7oPUkbi2z=ekRBo2tε3$(?cUf Ǧҡ}{֮RiiIzHLLd̛?re2E{xk %:kkߠ!̧J*u@wM6Gp%`v>vqj~ڵ[ZsYEbӔ"/;:sp>H(aΝ 4VOSI|1K` >LȪD?6mZg rP;wlZ/HKK/dܹoڇځ3w&@ =k'Pj6m\OR,-+O .b*U <]p,[jX-͚YDGtt46&YQȭg ԔH=ξ<~F-C/x)g#4& )s#H,Szt#EZ([Y=9ѣGsT0&ޭdYP:{iڴٟA[|+zo):FQ~\rTZoGѴY`}XDGACI&Y$]́Nڵ:lZJhN]Vʼnфp3Y͟?cy"fPm޸n֛3Tx! 5!֝/wbeV"S׌%Y@KKZ^PZ-E^ԟF#V/kqcw5#8G4{kp$u2Vmj;UeĐO9sώ8J)Kzn7co*TA:z6l z-]iϦLfXk znXȽR5qZ$''S^b52ޟl®qi=`0YΟ]'Ucu`N'=d6JFÖ ͺe Sec *s!ϟo2/HJJb(jSMMl}2k/&e~z/0lq,TCRgVAJ4^sM6kN^g`aػhb\Q:yP8yuijډ=_{YFÖ Sd]_i_|?ka~!M>]`sa7Yοy^`vY[!Hi4b:}Yl5 &21>12 {PGqvgW}NPϤ܆Mrt:Immذ]ڙN^Cۍ;vmR;x6/7$PV[ <:$Z5 㿜@})\|i "!vBfcy\"QA7Rc#/vԉϹsteWn@= ,yYq/Y5*Ǚ̜BDٴi#N2@uÆ xW +a6_E\UjtߔrMcǎ|;( oMV Umi *.̱s(*NɴZgϣmI'',S3ge2bRkո͒њbU[0;dxs) Wnv3EC8r,R_lvZt8IO#q/GSaju,[ĆNs(M4Z^:d ڿcH~2[>5MA"Eՙȋz‚H1 !A?DujK`!}ȋ0(Pɓ2d/Nh//T$)L9?{\[y(1E|"3V{92n9GgB;7oJjK.rѹTcn IDATU(b'~ $** 5M2B!}5rMzSz+4I8.G߄ 4vIJqr'[ɵGW+WQ5͕?qh nCq [iwBy;xD򓯢dG^&hU; vOZ2us>|?f}| {4CJz}Ϲ[*4OzEN #;v찴"K@ C\|ʛUw9:㋻_y^x2zdH\KM]YdPDnF㐹2J6cDaܫ2N~ՉbRRDE<kǣt ^탋IK#5&>CS 7ޢbr2[): 2-ɫ46.mHHz7{~{OKЌx6Potd %s KK*Kۧ ف8|Ԅ7jTN_kt}уX$+]fSOz;ҟ^k(s-r<#u"=T-"*]IL4jVk2ݏo+f& 4;&jd)oKή skNv4U9bSyECk~@CTbh2r%uVP#qeXYޡj|/u ֒Grskx\}&ɴ{J*MjFQ|"Iү;6e[s;{rڻcH}Aԡ;(2$k҉ڽ~v_:IҦͨew+Kul?{M3U\ ?3 ñPI (3~/y>cbCaK:BH\PMa[Ɠᛎߖ?'PįyfQ硰suپ+z'@+ IZr.n3^B ù2܇ũ\AsyN!!YXlTqH/&_buoI&S"EAn2]9#GpY>}x)\\}G4gٚR(Y> NK43?70/(JϺ %zx0'(cywqQU?0þ @f悤ifjrKKͥ )2KMmrkq--5--TwEAEvYegaR>/g_ιCʩ]u6'Oz7nxP=v ~R*xFg<==i1T1˹x텵U\{g8eO "2.<+k6)8{G34_ŊN-#vruk]^5vAQd6nvNkA A U>qꁣ7'pģg̏An sRu_,QUTP4i=YaMUz,)a# ('C+'𳬋95iKE/u-zū_Khv @wLp2c14苳)90`{ݬ6 B^j "tW)3 k[Jl(cei'1h)MoW(rJ=?{` 1R28"x< <ήn$hm܊5YLiAKP^F~Z!rLAFvfd]lxlbJ 0]%~jNnm 1q{dEjd_0iFqnW.sJbV*\}py.Ahb$?ڛn&94{|Gp5\>MQv2*;<iHBڗ}{Fkƫv]<ã#+xe(=may)C?  E{i/J=?kxr)wu1Z ƿk?dPvS~ʔ}G־Ǖs8; 2XZ  5|oyCiJpՈFP7n1?Iw?jܮu9iBF-i[ߛ3- fL2Ӱ#jSN:ϗ,yg¸)zpԩ1 2 HLOiFzmvWT׮]t֍SRE'#OYi&bXd 1bY)2ޖ\UQѶF=xa8eܤhWŝe!Oxݩԩcڥ![0$qO{Z?NN >mY[[iـ.۫ @/^+4oޜ/\>,_V(NlV[PzÑotwuw}h ]2dAAQ1|eJw5oj2sIM3_%-F SRI~?~/7G~\J-H7%]HPˇicgs-)`;! gaeu<+ˑ#Gm voQat=}"u;pmM_?g.nVΏ*e+RM6mڭ~F r۸y7aS`гo /}VMھ7b&zmPVm*y?w45JEINNb)8\{ąʈ;Į9}=iР!T@nn.WjdДwpk\>Ϳn" @ipp0g8sT;bTS8PGWUtp힂޽{ccfSɹt=ZM-X"]:M&Rv:^СX&::Փ9S4FC'Ed z/A\xL #?a…;VX+)""g{[˔Lц&ogMq|駊SoN:v |>jjaYmIMoS2qD)SZF\|:M u:YJ2dM׸;w [[[#66u[q7W~GUK/ǻ!|8::ү_?zֳoIe5 w1fd7_# ߳_˖-c+ccRԥ8t}YK 0 |Ǽ=y RwsQ6q?%;Ok 88XnIt||L/':n,ȃ]%vRb~^SĄx7WAaгǹXHP7̶CTq$QG3O>wE3"zmƢKؼW]=m?Si0VVwuۈ)ȸĥI8' jq/3x`\]]bĨ ?[ Oψ^ $$D,Rp VZ3 ;>OӮ#piQYMNKZJⱭx?&2vX2>}uP;jQ7T60m4g~B WxӻwKfڵ'bϞޑNP7vo@Y¬$bzzwF /[njôhZPKOOgٲe,k.'\ӧ~>x6|$W[OZ^.Gm$ r5/o߾ZuD| WRcfS0A[69 텝/FSMp?e `%MMyKHH`l۶;v35ZmhvE҂rOɺE~V*66wg<nk9ͭz=_m 91{'7j5W<7u]iq$!!2c#ɸx zڵ߿? 6/VEt:S+i)8뺭Q{5G].>>4o޼r{(::-[ɓ'9s 񤦦rriW(+Ei588:V삋 Mzn!C8w'N;_hZRRRHLL$!!D222&;;)**Mi)pvu+++ݱFӣ5j___|}}_> 46׺:E 0gyXAU\IAF=J6mذaQ<,]aÆ!0'RP(ڷoϖ-[ٳ9r$wܹs !D!޽;DDD6mʂ 5jIB%IA"""ԩvk׮&kwܸql޼XTaV]tAVc?sa„ &m[!LM ۺu+={d޽tY8yNaѤZ`<==ټy"gddϻĉA!LA`6mСC{Ÿq={6!&#T e0hݺ5~~~[NXcҤIL2EXXdj~'N<ɻﾫt(xzzt8Ba2B@:-[zj>}!DZz5Ν3unnngqBJ'#T  C,_\pnPXX?ÇgJ#JFf\pS*M4i_|)))J#JFDмysx ,Yt8*))I&<3̛7OpՂ|פ)[,YDBJ##T QF~Ua$-JFbѢEdee1i$C#[[[&O̲e˸p!DwϚBq?djϟOaa!JrT*SLٳJ#LFU\U^WtB%#*>C+=STL65kp B"#*,''???&Nh֏]q5jG!PٳgVyWYYY?s!B&#**##}]&Nt8,88OOO6oެt(Bq_dZE͜9'''ƍt(g˖-۷OP JMMߟٳg+(Nҥ j;v(B3)Uиqؼy3)NSNڵ]*B)ULBBM6e5Jp*])**"22RPHAb^|EvŹs簵U:JEٲe ={T:!kRPҥK6lM޽IKK#** +++B"w$9rӦMϏ^Lg̙;vM6N_U(*!=)fСCmۖ}r)>}:jZe˖<Ӽ;zz=k׮yNtBq5CseQ%%%'N`mmEGGӲeKx lBLL VVVz.\@FQ!n`g*(..+++0 lذ۷Qm6NMMۛ?sa0+BL :{,ZV`0~7oСC-FDDбcGwNZZ SjZ ,IA5Cϟ}VNc͚54oޜđNo߾tԉPVVv笭3uxBqGRPLiiiVoyϚ(*ST[8;;sZXE%wO x"OLV̎;pss3adסC8-d6 ={đ !ĝIA53Ttޝ۷q$WU'666~&!!t! 5gƍ+4k֌]a.JKKIMMU 2!5)f&>>9r$-GmA8p +07RPLll,5/2_}UXԩCdd$AAA7UJ%w !N:CWʊ7| VE=<<ؽ{7[./,Y&HOO'==\t:舝Sn]<<KAJ gЗJ֕z+E3;vJVYtZ ^t$B)ɪUXa#i)ԨWPjHp[Z*V*GzLEyzQ |aÆ]m[VOl'xg9W޴^WFvIFHL (((`ʕ,\3ѧj>mRm_jmϘ4F$HiUzqc޽QcQ]`@WAecgXcO Rd IDAT_ Xt)3f~Dnn.>Ex)p~ȯ^Fҩ={SaqO/}A|L~g*%%ZG`\Cn)qNn#[hh[/Z@۶mHZԣG2z?~=Soe_:U!%&ѣGѬY+] !,I,s2e;jڞv/ݧa8dܝYjBBB$BKT jNN}ݻwȠiz ,XI~~9;5k&o!UH/Tժ&&&D'IɼJSQC\ѿ}S=f4 JtT$BKVm j\\q'͟q /} _O^[nmJ!555!)~M鐌.l$;eϱo}Cl6m40":w #ooRw>a6.^k.!W6BӱÉtIwT:iՃv?>?P$ɿBEO/ۗ.^EvG1{$13ѧL/0--fPkKq*?Ozܤka_Y ֙H=gόHLƕ7x [h s8%F`Μ9~ ۔S"Bӳ)E#Ncsg_*$t9@ɛy|\4fRl 6?)BEPz=4½e?h\\ޅ#sܣ8ՎίV~g˴n6~WEe6?3(I3S_ ر4 av{G{QŹ寕`4 t\Ij[chZ%)/PE~:mN$K7p/d'Sjy(YWؽ{?$3Uʰ%\vGVLҖ{ CĒXjxۍ#VcKZG => Ǟ5j5S?<FkG_1S_ fffp1ZMڛơCiՃ \k~^Sc% p/εw~nX=Y]A{οB9WP0 y71iuG~5m'~gVp۔Ncb.&#.ʸqmKv|4rʱ PH)$!j#m`W1ģ[*=s$;+hǗߞ/PbT&iO[R@ĒфBVOP8u(ɻb0;Q\ThKoB( j_[ Jl\߷cqsRwe8+ƭF3vʱymL|r)vvȬ<}h;L@StgW˔d0p:|[˨1g$g !cqI&ٓuQ]ϯql]nYӰxQʡnYXy%\Meɺx=ոQιtV?dKoB("lBK#k'4(;uG{v_Rݛ~94{|Gp5\>MQv2*;<iHBڗ}{FkF &ʿBYPNKXYlzp)֎bʿB7 0bRH9KPJѶF?K+f !aϏ:r/lf'phcFd$73eʰȂ 0$AJb8:k3|p'/0=-݇#B-Q:Ee"~j>5;[/_$S"BӳțKLL$v?Q:EhK NmoXYYmɿBŎP|}}r"b- =KF.+໕ߚd.W6BӲP,X8 {OuU:$9uRNlemԮ][$_a:={^gay]XKJd\WO XOOp$_a={5+V,_^ݏT:$їiطh$-滕+d.BTiӦMS:SPTߟ-tt| 2/'Ȏ/6ҷo_C*'BXj1-[ƄW^'.å!=K7ppz5*-IZT=%詉~M{F Q+I8Ffy88fs!BXj[PZ-cӱsMӓ T%!K2<רK/K.JuO$BKR uIIILV­}ia *{CIaV1[k\ygی?Co!%qqq|8cWޙiP4.NKZJⱭx?&2vX2IU l2|5.ӌzmQM<>l)ImI>i{c[(.ȡK0ƾ<}bkkk"BTERPoCqwrVx5 a+}qQѕGN2"36'] ПӰabU_QHAOfϞ=ٳ"HOK7غ{W/lPءu@ec8^8Mq>EYISDvytZ *ZЭkgBCC %A/0gRP@VVN"::8HHL&==<4jptrFWWW|}]>>>4oޜ@d{{ B)B!D%k !&U!RPBJ,w/-!DmK|mlIENDB`gplearn-0.4.2/doc/images/ex4_comparison.png000066400000000000000000022762531423420364700206450ustar00rootroot00000000000000PNG  IHDRZ4sBIT|d pHYs  ~9tEXtSoftwarematplotlib version 2.1.0, http://matplotlib.org/mߕ IDATxwx\߳\dݖ 6ؘb`:z $! ~H!NB4\ []lɲjUJ:|ٽ99cPEQEQEQEQEQEQEQO+(((((((* +((((((( Ȋ((((((( Ȋ((((((( ((((((((((((((PD =PQDkog9FD]X""ȷɮ(J[ADK]ܶ'""28uR:"2UD6$iō@D>Mv=CKEQ"ܺZ,׹EĈ~(+BL-Z[Ywjwrx"(^wTMD:HD>Jbci#"uYӜgc4hkG4c>1쟌:#"]E1""e"BD~[."E"2#gp? WSAϽDi3qBDe"2_Dtx()"%"RuWEO+|i1oFsE |_""ɨ1c̓.7"rrWV1OE$j۪mcY܄AW]."YJijl8cW+MU,3m}a?4-@*ű)wHFʻEDly )F}^\k"KD>1~(mصDd+-Y"lv"rc=m9u6Aǖ-^N Ȋ /GMsB3%A4>r@T;ܓ9b:LG_k%Μb ;s37v&Z+x^@j`1f#?lڇ'RNpcDZ; 8 HOblw^n阘b ?7`gݿplkM(Mm\mkKo6&hK"ykƊ]c/gDg W ZN>NnuT8΍H\ȡLjF8-Bq#"7b@U2-Zl Ӱk1K@!$Bmk9'"r DD"Ӱ"z$;dLj9ע?^D8@\7ySD;u{SD9NhW< "띺cu~%")Ddx|+"gΝ$"_9`:7:>pOttsYN!֞ݿ/MDm;8҄=Dv}?+"eN[ ;D9q9DM5ik E<[D?C%N[!"7gh)"K4\G8mTT׹'.#fLDzNyEd|k>g^9֘v9kl_wHm`zQbA4B4k&@D.]SDfc%tN $9w/mtw"%a -i[Ƙ@&bc'Ef$ۑ蜱ʰ{ a #]""~X/99o59FXﻀA!LIs.{ӱN󥾈|1IQF 0wC>b1| l`aaU7]PÎ[a ta=#W;}7eӱ.5ؿ#:SbYy+fgwx??N7!qSҰL]toq;;F(8 hbqKmfo?qƘ=q+zSK4hl#!5;3yӱg5g1!PT+)XclBC@%߹qӬ%Yt:a׌ %VЎŚXG=ubFNgLv>͎p= Z 9&iƚcC6Fz+p1]c!绯w/:nu%78%!Bi@@D501B׵3Ƅ@M:OƿAcuP?-c{Y?|;t^qCcvUT(Ƙ!U'yyoBZc?/'5bƘŎ[l.LҀ%DcL1ecL1 k@Y{q1An'k1;WYƘ18_c-Ƙ=Ƙmc=a'':"e -)"2*@16'o5?7BknƦtr9XLjplj/l{Mv/3,p:ztcL\`8 ƘeƘ2fc&}HˁcpΞ.N0ƼtFVdI k_ ,펄$P_~ cfkPkbۨ+o>'D/31|+,v ؉J91%;15Rc8ъ'"Ƙrc~N7;fcwƘ1Utog7ԧFbη`Ӎ1sAwγz2gcL_8hż# sD.q7eXӦ7cG(Ϗ],z{:v˹9m` s;c5eO1unt>C%vQ,g x3ù]"|vGF,¾#Q꫗a z6c5/JTmwswnF)6Wx4ض8YMC-!|Mz`yas> @vXoz쇷-wT;;5^v + ~RuRbLkmfROњ]pBW]K}tx.%uij[9Vh?lNz:a-/8s=&yu>|Z1<#>ڱKlU&"nűu ӓF^HKSk `Jۥ:؄8 q 1f'/>^DzĬJqcaz`#S1h?TbS6 @ūͳ="0]cEvUQ3lt=^t)~&؁]lp]wGfHp\_2;tG/v_^ݜz1g 9Ƙ9$c`߁ +:cvoc4lg^Fsq7!Ea5ƜX/ZBR( &l'*SFu>1/8;ἮuQZz瘻dcOhSomԺMZ~ +]Y#$lއ16VhѿaPֹ1Nر4T Eo|CmCY8Nf\GX l%y jB$/N-eXcakmae6FvUQ3Lد2\("#E$^+'9( H^-6M]X*ys+us>Ш(0|Mv0yoi"r c*_ 6q"TD4Mw&PUTG!/@?4糔b^:%P&"7Hg8/9<Z8ss7a[KEd}tpz8lU@B?'Str\k"r)uE&:8]l/Rތ!zoIXȆڒL7hQ&"~"Oey2lD]ؽM,SSߕ_bHaDB)0֓5J0("1+Eorc.KbCfld8ݡ}`/{nb^4tQqR^aR'ܣzJ/e9K잷`6nyswsDp pCDNu=5ا@s~حFB wl11|?X"ہF7gyt16Mn[EK?Bmb퇍fkX܋]:etw5%8 e$vq vn:-?=Z;~m"s:ܬEkFJbiki6ֹ6yST(bԶ\XGƘ -^6S]slkN%}'`Xl p:?,=}4ICDȵ!SDc3ў >;9Cz/mj=:Qv朻֞t{FvUQg& 6W&n^tWz:z؇xXDdl6PwCv`H +^u;5ùf6|$3nõ@mlcib6a].D_&v`};]`6aȑN>N消Hc\nDg0"l[6ENx=`vժ¯m`7_MxS_vB}Ct.Ķbl<:~niƘyXO;l?1f)Qa6`m#DZq0 84^!>zn0̭_ľn2TƦ ۗ^DEd?ySKb= wcpڱތTC\btb;^[b @۲s%"R+u>zavcS<"rF[[OC94Q'[zt9@H\ns76/Q_ݓ&qz 1ttqg~ra,G[ ¹ Wc#<l&bm9z"NǶȂkQ4*"-O¸;.Ž@Ǟ= xDޯ;fvhv;)6g(Y`TqZj{߇m:ƉٚƕnSeݚ*qlVufbl•[kmN:DZcʱbg861+Ʈ\]84۱^3RK>Q p vn:T]i7ƘwsŠ$eص#$v=Ohjͱ!Li8v_ExG{ lvbF`[v1l 9 "cQa p`9>}cvM i&cuyNtol/:Z{v-\ !|2X5fWṳ!9FD>F +uQT+Ŧ)Jv}EQEQEQEQEI,"r6P3s)"Cafc6-X*lfM{ح6o&ܷƘ_%(FD6c~(Ed9#(Cur((((( [=,2ܖ(حn3ƬKv}D/ haNCeT{Z惵`|SXXm1m`J#[STo&@*b^nGdd#o(j:L}6dt%×}-nm fq./\TSSQh=!o өknb6o6nmtIk6?V٢{tkS} PI>J *IE`ǣ,TFz͵[$ 8y浾VJEDƢbQLz {i㏍I,Gߌ2"J9~n=\;<哳̘B>XpŴh}mTo˜)dwNv5$VmP烩 COe`^dW#"3xVAuXjUf9'78޲⯖5ZFcj[CCON/Xm *ͧCPx=1S（<عʢUdb[4toI;$K"tN$>|E۠T vI-*L!TDSS[ㄊȊ(6}((Ҿyr&>T޺?dt|Dbo7&S<>~)̎]eTRdh%ӧ;q}DP9_z(ʘ\^γ99~|'S:ejn=rR /\MJܤիp4|1/rڤxS)e+WsZ^dfp㵧qVOS{ܘx=d钕SY46~de(}"rTnqh؍1[_ qO?󏝒EP\w׭` u+9pN8_; Ʊfߏ;w8pGr;窐ċ/vlX_ƞSxъ"x:a0b+)J$!cU&Ix67l1/j?o2YR:Uڳ p 2w J10d+׆cK2t@RQ+Iuڗڒbx=›to %axzm0oTq>)AHC1[ 5K'*1>c1hUYTmM=s:+US:z 8{Dץ; >3y*'c92r{L}z3x@9TDxulpˤ\!8*I箹<:V ܝGOJVDNZ:#;qXabɪҁ/m"u[ÔɬҁH5-J9nBٴê$={}M߬(1A#kFͱA|4~;⠤)VƭN>4g3b|N 2=eiTKS!+rX>>@t>ӃM>/yW"s;8F!hH8N94 |ީd׻>Tc [fP:=:qސLX5`3\< 2ulTOL>$~-Cdf ؟!ݒ]=189zax arLޫAc9Gu_y&z9dp.:Jd%1hAܾ`oj Gӕ 'jJᢡ㸽lVWrx0,,n:6UKXXRQ+Iuڗb%̜9˗sȑL6 VDNeqKى {%%ԫŬ,M[|#x_%|>iG}B!Fqk"E?|.;i*Yl>d,Y-.+&"r;i:Uc7WM©oJqȡtjZplL(͗;6#}h *J,9~0ԏK}K:%ZJbT||sobOzпSTXi xk7l۵'!$Yo+̶ ..ݓŝ1霞’l(Oj 3(uIVM՝{ڗx^N8N8dWE須c0[ABɚq:4W* ׾2Q*O"B)1_|O|1@\.\q ),ٸ1P2ytIS>X}[Zua.yTTsHN7buHDVxPZ]K]'@8`<*) `OEKZQN`>ҝc ç Jv9RK9]8np2>S?d˒a>7zsNjҹUQb1O6cfFLYެ=33PR9Mqp}*Yb`EM ȭIeX'(Pruw,h5 YUfbFci"Lxd Jlݵ2jyD8&=GJ$[8/#c@""LKO[1f/x\S8.)cWK(>nTTU)eMVNynEUtsӚ5T[E9'"oW9E;v_3%⭕ET@nv/?sW2lذ˭"=L@ CUcYr39`F#`E8غKHn o_LM Frȡxtr I~i"hrebfm,}9GZa`6t\l _,dmfbjQ fA 6gcE۰ V1}ɪmA$Q%y94&Xذ~|d)-NKn76A&@zؼ+}RJX%ԙiPH0$-]~DGKTwWVܯz:zuI a'M͢(!ڒVm]i?LضmObNݻ>=(y5MUL!྅lbС[Sí?d@eAsz.=YFeQ6f6Z~;~QtM.2OfleF=7SŨ1;,xǒYx9'Ϋsi>x{oZ.ch֗\}}OŲen's;o*_n۵JKWٛIw9"_LeA%(>Ł{+9_.<Ç"&i٘c`ڷ~hZI[E&9fV큚K/eQ&L`qq{qaNZ|3隚Fشa{x-t Ӄb! >}r(я?mD\D]8}Hq}iGCi_~-Y{Z|c8 *yߝƝϽ$lS۫hζ&c,|;EUՄ֋\tgdep)Gqcr`sex^.WTWm//mM !j?,⋢ LܿeyL?P|S0`| ާQUPoAB&%ѳmPD80^ @Xd rr|wͬX*5La(=شC5`^n1+_`9X1G?;.5˹dXtѵ{vYª=g?0:ʷSn?2k&Oʣ7\UCxXo Ks#nxn\U77AeWrQQ=[3vײ!lyL0L?nofF?ñ<ʌ rd0ȕ/742O/ ݫp0B^5t_׿E[J** 7_牐xp,Ps'k֬aРA.p5װXhٝ:'SP),پ- DzK>+vGu#/F-5|5muhl3>⊽uOKc9󄣣zNej1F2_W8$ `)cntcQ F=j?g -6|4uH%09 8ϬoWE% ݃gWk7#{` ABx,.$0^@zgpvW1ܮuaT:6K6onNܺm# IDAT$/sE]i?l縰?2vl(䜴tNvY%*K!L߇̴٦ǏP823e0Q)3k.`M.M/䑓ANJ-, [E#7 VLO'='ִVߞtڏJ ***|Fvܙa*=999QAD5jF,QH4vD8 fQ/ۗy#)Kvf܌ J 6cL@V3>#VG$h U^/,P u 1 I&S8c(SAfF:$|>/>/;Wc犱 堡9(4JCU0O<:Oe{}pDbapnט%=Vzp3 ;nWh4u& 2wVo߄7Ÿ>ҹ^cs896W<8M-c ̣wѵ3'vQ3fUTVV{1W!i}q ~0[~ċfPZκRޚ;cFA8sLj%"34M3e:ܨR\\C_ϴ"ߟ?Ao]wE?/ٜ٭\x!oz?֩ 0sb.ܞɸ(ZJ_x9(7e/x03k1K!û#/hx\gd [S~˸r]RYUU cF_228S6#|>^NaMwؓ"<)vx@*&cx:gw<)+6o_ϼA鞊z(+/䘌t`>< ¶P.<~y|WpkV&e_4_0ܙŵ"nX%[#^QN\xLSC3XVʋE?CXqYj-oϽzpslֽwF:0AYA" ň]Xz}?^YU9_229-6?}py=p( c1t.Kv)J3R%;[241F24F-Y<›|te?iZomw ȟ|+*8>Oɼy[w/[Fɓڹ5?yef>wtهVދU,Kr۸a0&PBBKB Bw:6r,[$wVe| ɲ\-{stw{wuww߻r%'=!V0D `M:Z(niK3yn|B"c`0Es_ 8Jk׻VS<\iDgx S\NXLhEsT[{{xzȔD>ɫs[X0iﰵ8oUU)*gmޞﱷy*@(rDf1En>BgLp8Fwi-Σ1Pfobc%)VD#r%JSK'$1' n ~?7@uI{"}͙ tcᆮ Ċ"uuu$$$x,ˤiOLR 뙣ղi 22h;v_]~9+/g. b$9x<:Sd;BZ _W2jY.e * 70Od,R`0c&? Sk2/"tȢ` oXüSⱪ<|ｼˌɓ$FLPЩlwEn.1N_qFþ#^hUuq= pc۲ٌ ^{;/}> Y!6:75!{6n]}Dn88 "]G>LA8``MC R\rz),j X7WR:Uoy'IK<{]?*vY¬t&hӈ"R6E,ovmJ^޻U4k4adzo;H#Z=dۭ2?OVcz~llmzݳ"឵[yuisοkm-n'1ˁDIHo .E'頸FOPBU%`ń|A]U q6[:nfj;.XdvZ$ jӼ:+x_TW}5xQ~yMޣ Vu>hH=ڷHƟ: $ȳ1){&ͳ{6R@ *,̥ə*[#cvA FLQbr 5";z, _8w}ղaɨLh.>qj8ӵƁyuߨLr.ܲLw䇫O{iapm._N*B3WŒvQ*0yT@  "{yEi.XQX/{(UUwpWH ؘB|T8.YC F\,#2n1?m3;g|+w:YHAz{GVw|Ćq@Q둉znI¿d_e ryorK;)tS\ OvNfv"#"wQ=/t3dLݞ:DUE>̻D)-Gfn_Wr wgSOVO[ ſ"Y`Ah/zZ7Gb!W;}Y (b2(=糒[:>(&\5qx$ܵ${e$5۹_\(~f <f?^DY驸 9Dcp?䎆F%xȫ:ƋӇre ;xXGs?޲[<2^ oTc;](.$7VEf #cz=7&;#a{9o,RUeZk0->[ R[VĆ:+ b ]Z-sT[d`qc3 e>k#HQvbSd.ޖeFF8slTd q~Gvwxko,(&L:n|e1]'^̛3oVSr+ ,B#Ie(`$em@f‘7eܒ *jWQ;)]v;o=FFcA,IJd<^q~f OcPRU6site)U?" xLYU};~s_ښ<:]bk"5#Ѐkb\X*$b_ν<둻h%<|1kgVr&pYXsן|?֬keμyg0;c1 ͒~4&NnOhN<Uk5HJcӼsRX[ujZfZOO<>6՝㞸jt~ e.7FI#1& ϝ %TZIj8 =Wwk 5hoj!8D7H̺͜ݙ?2M|{Š!\?|S\;&Nf͘v}>[-.AFXB;<-S G"?ː1k!Z=#^0;G@m[3oms1|sNĖz+L1epA&}-Yu,#3wN;-^x xܕ# ǒGf"K+XJpVFda; ؜Ϟj܂ȼX .Gk=>g0)d^'Ͻ@0,{kdNFcYQ.?1 h*S߾{vVDž#oc]K#vT[l2Rx`IRGSAyuTANUU)jiɊN08(6X,!gm&FâhJq]o14$q"wd4_ ǩEUUk-@F$YfJ E5jD.JM[iV< Ƞ0& 7ڍe'},oT9Zȷա*!tc&Tq)VGCɟxs3x 0>1dj\+z 'Z*ٲm'NaH1Ciʒ3I5عf;aKȤx(2mT]\ն}:\7QVAz#Ƨ5GEofC-n'&>#L( ÕA ˂as8~[S qF,?Ə8kN!f<ok"ȸh4^:2BVSoq:qi%:|n[99||F ?38}tx1IE>#<)) Nq ϟ^rL&Vj Ի,;(tAj Sӎް hzkJqM9[ Ff'ez-pxyZM"3[U(j{Ȩ.w"aO(֧xlm;#&MHϷٷ oP?mFu|l6sm718&467sQ_z ˓-r_RVpQC I 0bF%]{Z㊋1<)CN eV6E1Ew2$.X~8DӎU+% 3{8QA aJMyF &Obɪ鴖Y_]¡8F%i@я[oNJCD2#.},f~9u6dq$ "hxǧ[w-e!|r6O OoȢ>fQUHO ga~$d/b;Nէ IDAT=<{Mޞ-LUB^]F^`wN0lGC-X8/1\A4/F$ww*27Z-tSKQw[Ib 'CatLݫ"x$eŤ ?Ϸ!A\;g*~މS{\~24"i`WOggVr&p Ȫ?ՊV!c޽,k.ң\w6nȡ>౸8Z-7Oxx 1}tn΋O?sٌ4N|oWʖC\(KLn+> Xtq'`(niF&YnFy%X` s5oфĶ$%r,~op߮bpZ>z/tofh5곯׿=Nw|_իKN?#G8񸍾bUr{J&,㾔_/ӽlOv8sk6iɳKE3 0IMGLsM(>;9۴33N@Z43  쳀Yh6cl%s!+zBB6<█iEtd/ *۶P֡(,2 S3S*f_15.!NeA`V˗ &c)s V#&c[9h%Tl'Fi^,-(k> kgyy vA^BN{ǃO(>:gVr&1WZ$tR>_Fe7\GUK)FnǯU} ,Ue]u) 1Q ̌JDD$UEi T4"csFo=t:ee nw۳y54̚4t-Z[Q>[>.,.$uG<>s|:Oބu9xUۘ:f(/8^=IVABS'௪]y1R#;ǒ|4V7͗}}nx6VsQUU\N_xNp (u;r [jJZ8FbYF^DZxOY>x-|[r[=O.PÜ h==:DvDg# n'$`?sD=M׀QUo:!+>q LMaVGSQCPrK#&q=,+/dwu)iɤBEU.[]bAUQzmtOd"2dCV6(pp椧d A.EAu1-(BV"ܵ9X[O+9(\z4_vcle}o3CQ:vZn"W :kS@&l& +6?eI6ѷ`#~ 5#>7DIL)sY@j ,d}rpG_qpelt! K&+g68B fx3_!Ш%Ue30YpZ>N?rxj:nS&c{JmV;r}6^P-{hVTBp4UJ5ew8$ZE.7Z s͖>gI#ms+tQrp\𨪐u]CRdZMS:F dg67\VH {9:(-5,[) `qp r(-K?QT%J6fri%*:]ziߴC+sP85qOVҕv Xyʕܸv{ǎ [ZH fVֲAbD\Y&sbf̜vgϝk|Nz<6pKgE6Uȴ/Td4[%ľfAU찾׈K%muTX'OL];> JIڗ\nTTcm#O[w*)2~Cŋ!l1'Wm`F]A袌O<>uxlw[eMz۱_66;qø?+tD*P¹i9}y ć0lX_颈`E4"NJ_-{"5'>VG$dSa}L=v%zT\3cqقRH"¢&LY>UCh4qg`^^Tdl{=X{%y̋O2T7-@Vό 4}5ħ\~Α$lQU0&Q1=z.*̃Y9 yj]xP)X02]SB 0)! 2 ^V1:S| ([˕?hvLVd.:GMㅢ:h}Ș4}LKl b\Uɟߎm]w㳇eL)h"3Yٛ,}-b+C%We$uuNN*j$k4eYnƄ%uեۛZQ:?)2)?[^FP`/@r@6囦zjvEUeVǬأitѸ3 ().gI+$#lwJ OUIHeRRlS߅%\]HHQiVVrf2`U+WrIss"?`CVV<,ձ`[k0IĶm(s!8 KKK 13Ej1Llٲ={sόvm4ԲHU,i+e:MAZI~D[EU)hnVǐϒ"Җ&bLA'jЈ"S&+zAaSSXsgBjDv +N__}PXRδhRڲsޓޠg¸QE͏Ǡh.Y0֚4=; zhc^'"NкhsH+=ۑ=OEU3&BZld̯ag^!(2zHI?GEa՞<6+ "8!8u,9g<RٝW Mj!wǘrThX{&.7ݷ%\UY__/; 쯪%DZdXpWj EVH&5<l/.g[Lxf "ZdU!=0~[$7EaoK#v;~%g[=w"Bas,z:$C9DavM l@0%"cNFɟZ$C9&Vݎ-jj!#&gOizqQnjjǍet`ó j.2h)na_C f7#32>jU$@5Z.G~S-ۜZ<jcեda5#M,2} mxz'en6kc= ;rr4}0AZNźqL3SUu|Xm̞:)cGȝ7\}#22 ғ73JR.#BH áf|@ۜܲL^c=VG3PL,YW]$=(Vԑձތp3]4b.qKk%BvE_4UXv&%2*)Ǹx,vQRHfIA1t<;K+ȭI~qbP3+ EDhg׌"x|'xd Ç̿Mff&<~{0JRyIKKOޮ]0Vod@'&lz, :S2u.mիٺu+,^7iEucs])E@H`/>[$ߏ;\5sߵػvYUyg6͚̂Il9@b>X.q٬;@Zl$R)lhfcbt$}G-oq |cTE8th4!~&>۞ͳ߯ Ps&r +`LSA/ |{Ҹhpܲ2 foB#r[A(5pك%RrT&t,¢í'qT51.֗i D eL?Yձ$̋Mlx?TK 4TU%JUs,UF33PШ.6֖= "֗FQ,t޿_D%5u;&egv! NO܏F#_$T뷳<"=Aq_|E@aҌɼ?m20{ta}rKʡb9<|Bni4۳U(*p]F&ig P9)3Q&+_W3T>*M@wNԠh!]75˪*[ `lHT]pN|qτQՀkC qz+ã 18"חRYHQRUyiG;rjں1D:^ __EрNa|b>UUeSa J+ T ^SO>>d~ZW^y%pπ- 7 1+B ݝ;Yx.U[|&+k5{ G3UGl&c,3TUyLVp!FG xXp,N.n>Ȫ ٛȯfp@N.=ke(n'̤!Ix-<[Yí^9b$XYQŵ?H٨^?GG.exWeq˫j*W?39y쏏f+jHq8UQ]UY#+li{ΰ[Y^rW~6%ca}e[YaQ(* y1YuY?EZQd U X*Knj Ѫk5o" t`G&`LX7n-U r•O2'.?"Jpw(2#.߽)<,<2j,E&ꍼ5[wQZ\F՚MWʶᷪJ&pr0r&^8 yն2n~*q͢%#as,JHWdp a =:IUsmO9H IQ|eobGMPYt DO]Qĸ82~r,UgG&#g^g{ȲBGg 9ZyZ1Ok+*2-̿znt156snX0ɏ񢖕5HHJ`՛2[(Y?2~YVL`Qj֛euV -JioCfϹ1Ix`~)!p9[`uE+*ضg3BYiJ*kp™WpMEUqydٲ̔e ~~J5ܳhVqJ71U|W:޽$ogC}zJL춣H>  eLF@_ݼvNkFu. Ӄ(gˊ_GVE6SOqN܈6oiw\|{GߟxinMM~i845 &R5j\1VG"NEfch4;XknGbce~,0p;XPPW=geEj+2ϩ*_^%۳ ӗۃI1\?N>n:z ݺn 2b]çjr.( o)2 ; ) s<>{:+S "N1H i篊\Z`2"xكYC:*jՑhfs!7KV @0S^}B)2)2 e4ԞIDE%6D[H$F2 x# A WSO}Chq_{̟‰;<JX EaeQy}?9o|x^V'y9O'd35< *hEvYBH0DU j`oh%Cbk ؛Ȯ,[ FiC@7)2J:,<`w[⟊^\Tۛx|Fu:Ø(j'j/% e݊`e aS/*#kNmف!V|߼)w!u oL-vwjc?dN%<'q䆌hђ$jxLnObuLUd+3P!k55:mdK~2N%QQH~5O̶#Y;idϨ*e9,ݿd|BRO?zoq8f^v 31Ԡ']N?->^ZL5(;CJ˫,.՛(2'@0Owef,eUd%QV%nkqp_bq'Vr8ɋs7k, *+B0zh>shtS:fʡR"wqTg~ffnKr؀IF B:@-tBPB7؀{M}gc%jIڒ>ݙ;{oщ"Y w<ҙOy+yeNn#^q=`zT#bki iH6YY>ST|p""Sno%`FhPN;&A .(ۑ{)p:.ck UrÔ@gFz2n;˫DhؽC+"l;)ZklfNVp* -M>ՄmcYoZɡJC|y/{]{jmH>h%'mr;)S0qDDQdѬxs` {<'`@&rCkm-.YFN [9K쇬(=xb^` pb! Aad#"q& n!l.Q!EU{4(/`f3HA`fD\gEU{J ]ROװWZťg͢ix{ihh9XhglJ"f}JVwxf L18ˑ`NIb(2b47sM2)}Y17 ?Zxfz۸75 `q),%ą飨7xXUyEy\@,(0]N&g(.U]Ӥv{Cr\"7_1ÒcvO$vLiQW_S; nE& guϘtN!̞= +fjgA5{}ˣ[R1s ͗e:-;~~6 )<³NO;]<*\@5O.Z2#bBI+e=4yWvL` PUf'' >ƺ7>>7}85-6b%?onzl+ \xjxjb!{D*/O=ӱ*uQdZd56=?lxm^M+5hgfs(gJLQbi2xxR,Ea) z#JKNO9O-g"9 V5DžGZUX3(1%z`%|Rr7}c~`}mߩŅ(3d$3::Ozd^q&#)#8(#x x]D !!5%R% |?Z-&0$xeʚR=o]tTja^!_6+tZ \ d/n8\iwbT<:#fN㿒ȣגDE8k^i똕S,*F⌓>/ 0lo~Mq\w b6s 3uAإq*/Y~EaF9`IrP E wEi}^wZD,vsy iQ*GpiDiv"™YMVr NˍfSd2,Cn?\^VdUeRZ[5wۀq:-\sr <&I]p",?#&6)_x9FnIݶ/[ \QWy￟{fS-G%**gI-t3"455: ,n[XI`<Ӊ X`!SXYLVvͬ7 pX_[6'EfQk7WqDW̱|f0sQ=FZ+}4& #YWE$)dj^ƒh6qOC @qh`&Yca RVSƘWdDraY7K{k<^UVp>zr$/#,,~:&[19, hD/ ƐG*RD9#IJ#39 a pƂ|r,F=8tWka2 'x᤮WZɒn72 JXf+fd ͚A޷>@u9qQʦkrs,OQKA>#='.?XoH 榅39}/(eN kԣ>nQ?u<nCI5XF[È Q#PK_D,-DUѨ*%'e| Gz G. )t6;6|?+ ՜> R-6~/M-D,N쭸,4QbDu95$x8F*LAĘ{d2FZLKy!7u: EX"L£ )}p Egp𑢐N13'# }35e-[V]^er#`ш"睹Ox i,jjy!$MWQ͒aVc Lg\Z 09#:P.a$e=a|t~v 5%M^hm/E5q:{|yf^.UUDqO(BzRC#8@+)\NFF'V +Mf$_X2}řc d$ % wo8);Ǜֺ< nxIީ y_td(L]soanf*6/"X4ls0qd:z..'F(HK""< G33=Ϙ_;0%n۴h${oS!o: v'!#j9)*&Ϝ.ƞ%gP{ YC=֎{kjY_VYNb wI}oHM?tCC7R׾tڣ# oc"LVw\ ²Hl1l©(|pbNNcpz<7ai 9Ӹ?W2{ PTiq-Ïiu|v`o~7&ږ+oVz䰞_W^dIs3NǨ|UZ洝[ư(NI«T:Zъ!7{i绪R\1d3I =Bu ,b|jLQoa|Ə1lUY>rJMd6F //[[Epem8Z6>ڶLKMb N]Whc4 n⭝k!hێξi҂ ե4X"<-,VrؐUT Tlhr8x*ZܬT#N_k㴠0 4=(5ؖoSd9=xQ) ｌCoehc|eYB%-66Zø PTV"51J/lNBAE RX2y Zǁ=IqXr__Qn; Ḏ,K۵RXIb7S23PrDLC=̚r[71=2 omY}#;x_lpLB5izp{~tJ.HDΞ۵ jxoY,+u|)\x\RaT5pDap{^Ya<{<$FY9oTƀ`l0CoxIuW$1lxW t]<,"oN$u.vxO* gff'>bΘu +66s:lܸZSR3fLdFҥKYtPrtO{)pf}^C⬱쨭F| IDAT`mk3 #c5F:}WQoi \o$c, £Ñc%wn%%] a!||{ݾ.:ym`Bzn`Ȳ̶=(-$Q]"=Iqlo$=O<~Ot{lҊ.7CD{[wcy0j$Jc 9Dҳ)` ɽidd##{Q{,P1n KIC)e dCAo܆NcJv r2:C/}WUºFX!38 Fۣ])Sl'"B=x`o]f@ HnAXΠ0ޫF¢ hDsbBNQK#I"#(^YGL嗾8쮭N1@$0[Xhm͊KA(ZO_e&i8p롮窚[Y_RAU-uLU3ر ;=TUXWyRdj hreM s[㡥-u`r^GCk;LAs 郪ݹ!5U;aܗ-P 1v3Q< 8˅&sVKyy$YIuw<d!ަJl58 u - =(G>kM <(Jh{- Ve4Tp,&&UU~`;nOŧ( <JF qV֮­fu؛R_6riI|$,o{#0-hLcZC͗m&3&b_?k_Zo>x8w^v 9$i5̲Ii9c7C5|anfڸ|PwTĻ#>y54&#IZ ؑsz9,6* T54ݶٹ rʍG8>)bxc33&8I#Qab\r}Ѿ.".(fFbվI$IC(9LQT 6Ֆvr-p*ey4Zr% #$ :GlY,~j~ܩ[>8rDAqL(1^0W|xR,C[otѱwkg`7}K:t]4?]4NHѸ;O\v6F0UcV g13#eHeNfg^J4o:?3ɠC&;ۿkE᭗_fӇ"2 3fp7[l6:e /_FUY Vuy&7ktibd塿LD%Y e~VãXVSF n×J!~SԴGMk a?=nv $)ٹ~?iTĔD~+_ 8$(6;Q@T .'!qǝ &*tPё_SNf/jȆ4:&J.khWC6؟pAHvWVqgBy wojC-32ڙ'_#Fp#T;J@@q5rCace^NtwIYQ3i|_].' ObT{K@@>AI١m]ʫk!B`KF0sܸ(^EBZZqG@ڀ| "#B̑7Z=n)ڇdk*fI&E#lD!?;2&5iu-| #f S|<':G҂̑7'/:U[w#{ɳ2E\xE:d$R"$Yao \C ZB~^,j5cx}o^y;Cʊrn~ᴅsyjA>~ }8|8p ̘?]Ĕ)SI?OGCjz=WT_F6qzS}<=x~2̙:;oxbE!Ԥ)6Wں 쯫'Vc\|3Ff1.6D+wK_}v!Pqw UDN?[mkV!#:be_SϿw>$fsWMS9ƪ*{OS>O'E!(DV1vq$be\L2ah{y~+NqUrKg+岗 1r!D-&DS܇/+4U%sTfoj!H g\L£0H?`6TUkXd3Jo@\\+bfΌIOLnRLEwA=*ZxX7 0*.D+1DžMm͜f't:Μ;G`>(Kgd}ϱkz^DE%7&csNcwHx|F+~C"hFgY=еE5 W*ntRu`.9`tzEsʌ ^Ŋʛ̟sCw- 4VrހETqz<6PbC[IF=6߳)B#1_X~K2I<Jpd|I7^P`mssԔURc"OPno`on`0 rFH0k0*.sN%.%C}#"|[+u}n~Կ\Ee.Д_Luu-,KB9Fbr£1kx.ϫk}[+.^~ I%S1+"CQts6ڄZ \n:rϭAV|mLYy9?ŋv'%9džtdY/g׊ID֗U(2mq*i$^G..{GJ{Ƞ 7ET 5<<܅/`{S3kKI&jFGqƤ~u#"?Wq#x^~-mY)h|O޾оFRi%3:odAY)vKF?Em$a– BENGKH8aNvؼL $"fw:D mDž{D FĮ&D/8~PPW?l UVvD50~DQm RXFgjzDsMt+*Dil`F ZYSdًSQ(P0>օpt;1!$r%U:-tlMxET`3D=s $ . v(>@Qc- T#EaGeMدSjk!EigdqJuյX;q/|Wnh~_VIɏcOENKjBu<z.8|7hީn6뺴///Gua40F9x+?5xcoiJёӤ+Q ҧ4cO3~aEfrX }^:-`9S)S HuGyeqU89cFAU0Nj3BZEf򝷢ӵw9{qWY6U#d֙3ۛZYF}pvv&SQru#"peך :jپf#s<=z1.粲ASH=OV*Nf#kJx; "V%N!l8ibf*AGbkpR)Z}fQ7A^zX^Ѻjhi9*A`׉";9⤶K8sMTOdk` .j5,_w-)85;fN9qj5Gl=}xaT_ :pXhTT)pe[[2J۲G֫{c֠:2d2ˋ#*zD%늎v}O!0B"3]\E!ٕz jrΑ̍hq$,]A`PdG>9i\^E4L% ]Eʫ<|( &-pJ}P.޹32דJCypwua{sۂxbE%@ZW݁i^libP\Vk98jCIB@ՌM4p*?.ٍW3ɅvmE3RՌ+ǁ]x 88VLPA,Pb"K!mCqj= `1&2.۸{t*jXx$#3[٬0 m_~f $^ZpCz67ߏ܄+)\F\_j8.'ӹ98f3]ZŒ:A`xY'n'Ah 䪪*|)Y%b((@ DT_~'Iտ?ޡ<6f ~~#m~iiisӈxٕFF vY4QDMЀB^^t,"L6+;r)*-3E: FnޯV0oKX׫N~L_`TfIdo7Rncwqof,+/&HL:G:9b@YI%' p& 8䋦q祙}aıd5ٲ|%1 HiP5E=Eq{ʝC){C7˖Mnn.]vmv?svvfM7ْ%DLcMCBI={KWs<77wwB#:]gE}lؔrS$Iveesgl 7Օ#Gȋz?vS o&MA=y;y/D"hWtA_Mq^6:Gg"iʹpl$z"l y zӥj iʸ+W^M2s7 p'A๹7qKҋJ p#ˣ q]x-}W)2TlEx6iӑhqڐ8vƧSu"Qa՗ dcJvej X0mcIi\Yۥ7N.vCv[ ˜NC@sAY"ځL%1@sd 0;CY-\|/@Ȏ(f{Pgp?>]p#MIFK/&rm bRbTdT֐ O㛇cVZ[m\$ iX t4lLÇ$6+ + EZlb63/]T*RLX?D58k[87p>7a,lAQQO!Dz"{$HXdN.{9@QFvg3(GJϘNl g3.:ΆξO^d\EOTjUڳSo~#X]?(24* uqؘg(dffR]]MNZBQ-fVSA6 6+ΑQ&Ճ]-9Of{!ٍ]Q}%fI'f *59P8&@NA~voN[Ѥ(dPqg[U&Rʥ+)F3J`ĺNC%WꃟW~a~lҲo8;6۽3~A&SOr%=.STVAA~ͫIchʕdeZDޓn$j4l ʼnۢ#to&gwξ(5/ĬTSw~d?;v`wA) b t0`doUQv L>H qqqgUY~==5js,89\L&xm+D#92ӦVdR+ܭu!ЪUD)V{8}'OcطV#l-f"Gٺy^J~/'7ܽ(;\Nl6~xA"%Ε¡N̥*]QqnA!<<+w$WyNjуȃy,{CN^Qc;6:yGDwqE~_L CD߾#/XQ/"]B"~pb>.[R*e\j0þ4\5. RT\LùGORhӢOHz4J&U_}1۬| ~B+޾ތ|,./*5^xulI J RoLs3>!wrG?>tB=\KC2\)POV~>I_oTurFΝ@R~ vKNz"YprC1TuNهX.xsՃԵ=XDzvkhQROד Q+6#KdsWҋqV`eJx)2E=|9,X[[M_#:Cl͓:Zs`/#AF!\뀃'}Bd\:qc;lfշY:3dEaYas3p%F{B?Qᗲ Fz`fIhL芓ZCdh\N"iɴ '/a5yMVqrӕ(ND6%/6dddҋS]hj4h=WQ~9z$+#pa. T,,eкaej4>sۃj['%=X4kO/j=+ ux21LJtѓŅ|Tz(”[F3(aDs9`{qM`11F`?' DQv&h}tb1b@9{ܝ;QgLP5.~)R^|qAXM7 믽$ eE|"S-7;TGʊ4ldE!IQ܉퐭n ںB-oۋm\S%5ՈJ Fb„X=p{r x׿H+*e|'v1mIEQe23kwES@#xϏ _'_Ǥ},S B&s$=掙Si&Mb8~n̿y,BtFz4ǕC-F=[k*I8hֳ+Oqm hwMNȕ,g3G+A%4x*%.j`0O?N=2e ZG2+y\ Q 0ݻ{{Ւl6S+`sի)++Qqssft,͛]  "k,&44oUYZ@NMN. Aj=?*``Y#bY'g?14>vȸ&g"Gz vjJ FՎ پΆKEQ8+~\Ɵtu{cIs6v=@p?'iw0ѲL7pīx(sv$ɣ3 ),|um! | c@ǘm*.㍬lFFPT]f'n?$y MDrl,dm@H2A`bx B(3QK"U5򸦴v8jW੧>b/2U,)@$^Hߋ8_*2xYb]y1|љ &V3JYZ=yq{020+C)5n /LIOֶ=4qpg̹dm~]s8aг807HNK!s,ܦ(,C% vdʘAQX( 'IsVں5ĕݢDUȲ  )o%ud2r'vyM.d:&[cO7/A`lf,x5.ĸxz6 {w;g=_EAU 6IBbAwѓ _%5US ;- ~ B#oi9bf[q-&y(+/䧌|!K < l!ʿC [V5*=.<<ʼn*f (Yoy X|DGѷoǒ3jmVh >H*I`Qa{2ٍ7 zfT;e*w/H;:մQMZG{ݶE7xBHA@,[60zjH_e+s\ս3ZJ|)ZvDܢ26Z1v w+iG{Qb<*/˓JYe5U5(\av %,p: bs`m(ibbOY}C͓+ ĸH3'^R"'H2#I DќM^᜔TKR(t}>`JN,xN8ש듓ȁV[@E..䓬۱E 3݀/!,).!t(*/'{wTj5֭k׮DFF^ooo-BCQ~}!vǒ(Lbj [L$*~Jv0V,:J=f JMpjnj` pp@N"͝(@$rڋ텋Fn[$5QoS ?O?CI\z]% FnKmV6 F6::w8CK2@MX^>a7 *W"4П4;>Jw%/w;Pddeӳ{z ~ϐnW ڣgSC/$t!2sP uw?!D^YjqCžgef5Hx9U( EadRY(Dn>Kj ٛO$Q@,s(8}}:2 =8E#Euh9J^R\rCxDIVFTpfzF<8I$wC/\p߉*/[d Tan06呭%USIOottf/EZ̈́kk?nh.P`JD$D-)c. Sujڭ֗V?ʒf( :E FD1-;+2%KTaD \]Izχh4x{Pǁ<m[sEPSҲVxpjNNN dǎL4o݅Î|JbI(}[?,|V+x K8`fcKf6=:h awvdc8?qy8hfX IDATe%n'cAE |Sq q,ֹy1/n,+QIDzH l6Dwjjz{s!]^g"CQQ:KhqvǪU1rnb,[XRS}G"0O〕ԣDy3?E|IV}++e!\P"[֧e}jldQ&ƹ =$EaOY!+q:Х?Q\тJx nM)FQ 2/l۟|wQ֛F_w'_CN֯8fZ_KN;OZSo,%0YCѳ}8:4%#;qV'8c6@F *Ҧ>ζmG\lE,-hVVw<1X |%Jr\nCe}NBbJxHӤU~z3e"'fŎh* Od$z63br Y]|d WEf K'k*Zjm6T<4~4݇ڻߐ].G%O_类myo_ 0FLG [_şQg1GWztLږcBJtAYrGN $UPRzM%ɕ@QK&c_6+Ea 2/?Gg HIݞm6i Kuf#dG<.K(.f=6 %xR_R@_!f3YFQZ0,:?SZd?`Aͽ# rqIy%߭XCbcRA7b1f*' g.%Ȋ§v,Q,19 N׸D,V[ $LȔ cY-w>߉$DDy%1ńj8_D\T|kL&DdI=jgjkg\$mڬ̮3SlmcgA$-KDy0<( f[*'v_p?W5$϶ 0Y$c #Ͻ @Kq=V+Bjs}r$_bNg}|I;R^V͒ƕ"5XIcI>\K >V_7\yip%V+m%yAB1JH{#2F3jguVǕƕ*`Y9,8:0kPE# S4"ˁҲ6 BnJ+ DZF>Ε$sld M'7ޝb;qm6q PdE<nfuxL|4g~GO۩{i׸eee̝{ޡECOP[[(2hP7 4^T[|9agq>ou$Wʺ8ؤ6גS1/p.~_Em~ /d6ru=Qը~AG1$Ӯg2K6*KW(Db.B}avm׸F#޽xǩ(oM],v5HP/곭y|,'0/$ߗu(@Vb~gVd Gx |}Xjk.yl6ٶm{An,̾&6 ޣWڧ+.jћadx=D~WTWmm[>_ϙ/Xpz7b~=y\1͆Fb`JQ$M?L}8;;؛^K`egC|:){rʑr$že#7]}SZek|ݯr\Y,ˬKOƒ=l],kޚˎ^,']|C@؍"zɲAHŧ}rG[]H)/Af L^y*DϡHu]F886bCȮ!걧vBwgkNksq_Pŏ$11u brJ˲YAu1@e $y y\Y8 | i|@yp#Bȫ$>.1` cѦ"jm[c'ؘ6ߝz>n#/_}Q?^oyUWM7|7vhr0NL*ż}q4 <@WCk gc_|U^őƌFYE%~o FXux?'u@AY%ӎPPֶ5YP̠yϱ s?/^Jƒ/S\>t^7?0b:>j6 @uѓEz Æ3rZf5&2sdUv=+5($ڬ,IcA}7됀>6Q`w|#O1Q-ezۏ}Zτb͊u9OWЮ3w1q`8AYYҲ ;I5ˑIq%tZr5$g9BXxB3.sUٔBzvޙo$x/<6K=V:Үq*1Qx/5OT. KqٮFoaRYq%(lSV}?-7~Q X 5[CpO<,ر'< krf1qxֽ;#/!8+awk~\<+Wrf .5 ]ᇼh2K<gjBdffRZJHȩ%Fn]2!!![WWW*t:,^97[H͛KxgVKtZjM_z.YY|m6F#5[uu_=f3$/oV:̇Ė-ٱcmEgOСGr?rK p;Bdn lfd& T"1GӝҊ* pAQiG* c) Ntf1r`j%,&KO=z/QQ!hg|%ÆVyE=*g J%f!h0H_*ՙ#[Ea̝qHf @ǟ?E|j5Z]ģ&\)*(>$'c^L8[AP9(O#;-V&Tdz̬yp@}1-lO'>Ak6DŽQXyf'g@f?@b:||=[ӧYHLLٳ Q_ѣGm^͞ZVXx>F\u5Nm}{ٺ{c$nلGuI>V[!Z6n;QC1[1Jl.VI.!DpCF6ϙ-\<+lvr{k&ڵ}lvl~o1eb0e`7$Ib*:z|je!CSkHJ56“ NMeV"sG˙)E3V"/Lb)ٶg@8}G5=LG0GJ}"Wc1fƵ~FPQ6s83G&fhuCG'0c8D UkuW߮PSS]YIھ$+0!(Ž kXK"&O ~sWv66+@>0[?ئX˯هHexj}\ݻ;qAm|1Ld 3EFtiox a=SMyQ5E6=~ ZGg!3Aw R3!$ 3y_]i&/pg7A`'L)'5%/-0}Y1{$R׮fepBl٫5ky+e?A SQ5'[@[-|L{Yo ˋݶl-#`U'ֱ;^#? 8ō"CEDPl : W7Ds6 TȴNݙyD_`8cØ'E]*Шơذeh0_Dmi-YyD:E w9%L+-C{2 C"3%+S"ZҶ2 f,Q_YbtI߭C'iuk2moHKmG xfx{{s |D1A`mkBi׳_5hDAIŇp+(0GW߃!hT*CШNٟZ**,FVQTB'}CRyJ#b|7'md c=)th8`pu#XGQ~ޫ5RR513>'yMؿ?c\" 55i?3 {%=t\|MP\Zk={74$[SX! 2 4rZKu&"S# #L1Wӱs_*6[t^'iݑK?J#) !Q9o~c=}fxo}~k?'oͻP$|k%/>UcYEMM_iJ VٜD$$]m "0! O-I\@ ݛQa JWTlHPҫVDV(Rn62?z Ѯ: S׼[3eYXk?=j%~!5Zu}̫smEFA? 6ˊ+Ѩ ')<ѿ.dSVE}G@wɉCiWU/غ(]#{Մm o,GF1DyVk"x ] jk0Wgml0tj YyW|?=a֬)O|;W{fNI9|{El֏(͆vZ=xIIͫ~dz#1ݯAp;`ڴb'e>:GğKre*۬A\EJ5`=[oO!=]MmLD1L᭷c:={fj\\]:u*G[hd =ƑzAwՓu Qٵu3 cn]C= 0%K/coyͿxٿ'qϘ}|8c)Vf.ך-'&-}:dkwLN\ba{Me-2NLna C x!ٞcܷ w"լnfϛ>ؤ2>w?SѨT\拮A~̂?jc\ݵMx}8]3Xu[:D+ma9+ْ% (ʣHb 6/#ͶjzMÔA8G28;dzWv`(*1XRi23,*Nߘ!-fZZ=3Lb AQ|LլcF<@agٴjKzeYn07`SVY}yja&SNꃻ 0@X @ bXvY޻k1w1c Kb/7; x=9qNCpM$/g)!~<Y>0;  ʬfyTT:hO݃,Kx߱YBޏ [ͤ_mrm%$^( ٿF=a?2SqqW}T0PΩhF'YbeQ^dEQX m 0 d/3 dA9T'# g3A 2~Ұ-|(.85,>DH'g7A>~qqK?aFAz0zm!ˁl;W=úϞ5wqABx /b,.w<2'@g7LYS$z$mXG^h|NR0=y;vu6QP0fyU"ƒ\qb3zxv9au*DU@^K=}xupFwmZ(_VJ̈0GxXm")1lZg["qTN@o`y IDAT@:EzF dDz\g3[(/[ɧ5}R}(\f3}} I஺+A ՇC-W8PLP[@զz|ZA"Xw",I{D+CH߇O2@'PH2 'w HQH׿=Rga XojźٶiA(a4G'9o$_P9V.VzTkw{laӑlf? Ooc _ya&hݕ D>, OK9 w{ 3_2֞:]Yr/" vr}G43gTk2,;5~U 'X;訪i;-ދ MtZ +VP슊tAjJ 4Ҧ1ԙ$(/ks9<^\D#Mwfyf)srnӁ0׊8:qlZp4r_|4OPZYiәr#o5kf_ GUd^LJb:{Q\gWysOvZˇ=͗L4o72`py(˾x]bdzxu";΅ Ĥj^*csTl\Cx?'@o=Y6,|-wJ=I:~F톫KEfm9b4n4a==O_u:/<.X˙\%$n[wٲr#+)=z8x;wsb*1!t:r%?=F+ EHt=WrK>>;+7z=۵ZTQQlxV˷˗:FLL ~~m[$իIIنx0r,HLqL2QN ٓTՕOgΝٮh\^*))O:)f ##0 NHm0[YLqq_RͽIIIqJ M"du"F6y4[O!rE&`(灱g$>:r:YM[ɽg7G~ -lm?/=~7g[ :r uXc{))DN;ʳ-'|e "I MzqA֘2Pj22>ڇ>~~Cubw" 2?C<Z-뙈˺x:j0#[sZUJc[a!/6z_:C;.c-+|5@s/چD9FȐ+l2{t n΢76k;9wr7E/CY$b62l~VǑ2-[VAWFHB 'ل;ʺe+ILtN`,Eq,h$ e,R%t- 6цU}~a 1q8ZBwxZ?0LLnP?4uVqp7t)Cr/ et868ކ ɻk5f_fAjCd޾pALl A8[TդI*"&g s݁u)tҭWń$ ɎNstJ9A?7duuxӸ5]NbRQqDy-0n>zj'Jh1SdHT `|mfٽo;vz%rK 8~=äw$E[ګQ,J&>MI"di #;v(BE$g\e]y "Yy?SY@j}hZcHfQ]ɻv Tk:" >OFyiK_Y.t UY3u#[*|Y^dWe]%4"Aa$1<xh/:6 b'KVZYP!.PIͤ,6iٮJ[dGYjʅ s،yd8)PF[0dKsyu4pSՈ/^JSiPM9:faxy_v {d3|=8WM ɘ^@FswNbP5aoIĴh[J4-BkgzaO}M$ZHV*xy19ܶvV9U@%`F[օl Q*c/bsI<YxYv畔2nb\қ#G:%ƕ :J5Zo[^&W[Bl: e6p.̖\!0G@b?̻_HMXUVr|:f)UnuD^bY|'n"WGF%v t~ش)HRMjJ0k?!C* ~-s. vq:yu֑͛ӯ_?BP(9s 3g.$==Y!)OPA d ֏"-mx{ל8̞ѣl5l_yq1 JJwXv^Fp^T9$%I"װyiKוѸq,[nyV'ۯ^c[T1m/es. (<8'3ޱ|"p)MEuI< D*DRWUob=0ꕤ_śei2|yS"rXx7pW[geɘvyp`0r8 u1)f7@eXhuǦx͐Yy_x`,n0rG>\'Eހ2@nNڂbGFĴhQh\9Hu0:޾7W5&j5.ŰsD$I Y{(9|߀6 vZ`1|2k^dY(&QXt #*Y99PE{رv@Z4:2),KJz2/s@nIia8[wt-; , xGD; rZTy,j>CHX-jչ3c"rc3ݛ^~7tP""]YrqȲ Y>Y Te$i+Atf/CCdyVmÉ#7jAlܚQz`jY4y N$.0*+IXiXYQf?$~0o}?gӯ0A4};a䙩xHl,{!pAHhN A{(ΦO%owcK;s_M&[ǂ{mK_+E9$%I@op̕hԵJ_alElvV+*Abbns3Õzx髥g!ˁA:+X"˱TJ&Ne\%>!F>j1ŖY4Y @AQ 3yvRW1WR(4 _ @!nzRڇϟٸ% ӧO8(n^^5c;5jW_)))I,DVwg'#}qvN˖xR2Jv=;3~>^|8k ɩ[@xHS<ٱBce={=;,)Bt ;v 11ҥ<\RRTl$ft^s2K;Pllج Ѻԝo2F: fHQ~bu_܊EAI)Q[5_ؾy9yH[@S/MclN>@ٗ8sQPSRսfpbf&V"L~pMh‹ȫKy/Xܰ9:z^cԆjE8+XVݽiu1:3P<'+h\иb$8xb&Bi+}$+۲.'̓%.Aya |i缘Raa73q (x;gW&c$PGlJzH̶4=I@X?m[|p= q Ai_0yҝ}'pq I㞬^ 51]`AD?d+-OpW;  $v,V*ޡJ'ć.2[1ח؈_dچא!A!TIu7>' R3vsTȲѼlR.7rqEP v$6&! 6VpVY2wc2 LIt~ēS˃0__ɪR¸Xg!)ko0 n|G>'pT͓oM?y7}zylGy]iO$Iq#sPmוUt:7PI`0(U(3&WdGн];߷FѮ]X2gı;wdjM|zqq$$ῂ4q 8}1+syַ v4 WЉ3si݅g^r+ߊ5[Xd'0MyYzӣ[g\",J8a6[s%ՃJ>'7_{~6d6vPWRކ{8t E׈ o+r؞T* F"?xqOMff4$MbY|2͖*\I tjƙR=qHu+s&~Qi`59lDީs6nLwzz:>4}8xd5|A`N;|R6غH^;gt2޽OoCִ-.mJKlHr\]Ǖhhަ=n-q %QkDT%IkP(w`77)GurGR}B8uaBj`!M!22*Wru4<-8nMsQR)WoкS22QCӄ5,\c2N ̝> a{#l}˸>Pkє{uI޿? V"4h -ӶmmrV dhAe*Vt<Dzi 0 DwA!ҳ!*UғM?e?;nEؚ-V+ ~XCPr*o{Aɩ|,xk=E+.4ԋ͈o wlvPk!*eh֢Pm.2k@ˠ@EMa-442_ǎi̦}Juccr~n#W˲̦ v*hPƴcH O^}iЖfa 7T:JBT/@+=y IDATX=k-7ŕQtV|,*DdWO:Bas=)D=G^`(5 (Dw|o^agqͼ QmvV DFH|BqpF!#*?FcKّ(~E|ԌBd!6Oa 5. rz0[(wdztǁtnRAe0M iEX|B}OwsYa =|pʋUȗ] )LBh (hشe>>: 䃛z3/HRrRt UJb+alXr:\BB,0 hM$H!,Զk^ώI#^~ƏE>GFۡېIN\&)O yKh1E=EӵA$.Zz4E!|\ETވ\*vyT/1sU;߄.-'1=&9/<$ ,tuaS ܢ#JTlM@[zܜKk#P _W{/VcγeF&|$7r?<K3lTк *`+f(AwM pueRz`R WW^X ,,V+YȓO=c ޞO@(DwtHte7{'#sc20W?䥧tUhOk |5.t BxR r:U@}2u&vgFâ:u$_&1yMClsu *,KdrNy8$67l\ETzVG0pP1WcA!ؾ.V.4;y' Rܲ,[_ŅnmZص=ѡWO(j~ WzۻuAwtl`Z` 0Pa3vBL{P(E\IT@OgsÃ;ӡúrWms%NP_6QԪ)qT8Czu@\R\VQ|f "(TwΕTv͹+RpNcP7C$TEbv~ZqgAᏠ+t1Djr-Wh&֮qoXϯXQkLj#ؽ\O;]m?GVwjM!>>?_t&Μ9,1ʻ  ,{;z?7n$(<O/R&9AwEs؞^[=rs1eؽo_:V$o\gV.C**PysZG.\s^5Ω.H*>+WO/F>4?|/+ U1,Q(B!^b˿sYJԏ,Q\$l4Ax5/KY@HZsgV̸rf aD_׿W2q,~j&~=hESpZ܆K=ecN,_/AY\pr٠\ZYFÌ9sxD$dk*Yh;U6hJY>+|EGD~;_[*d?_\>Y* !T KtL?PC|p;Jfĥ](E %h]'v!ߎBiб5$&#yVL02ш7/dQj6?Y!hEaa(tV@RuCEA<1a6ri:Yа>?d&i^pícb~OMctuP?|+p'fLh]sϋ?'/yQټ3[ȥ5 aAlSޅUmۼ;ߓ Yչ5:ɒ>!_KVaKW,^ mJ 0_G,)M D&t! ڱ?VYdV:`Cq.Fˠ]ou^ؑPj:j*q c\+AlØԼBڰrAP|u!{ʞj RQԧc6|> Yo3h٪)Ǎ 4ي&?xjZ,U*;yrާmz>)EVG:֮ /]hֺ]>uls!VgbuZ#x~59>73}`T꣨TKyg|8g>P4bog½;GҧkWeE20[QʏKHH`|<>siܪ{ZhԼ9O&'f6#ɣ7jׅW}IE("J'-aԗrhzߊ&m;ȿee0LQYEdZtZ!HvdJi' D! QG[^ )(HhsU4kT>ԅ 8؝D`P?A7JŔa3m:1+aÆl۾o+ٰaǏ#&6!CZCp>:jʰY5%i(F<>aEz>[ZYN#Ow_,?W)3|6͘fKĪdˡj<4xztmUg1[{߲fKWR2k*.h}h{XF%ב%+ZUos% F=*ޒDnnM!EQdݺeY+=lEBt n]{`2hFڜ8Rth4l*ǝ;w@޽=f M]Y&_|RI 03f 炂Yù7ju&>8޽{:='??.].RZ nӧ=[ I["m@ pdE-<Zo|^ l=7=Bf6HH EwH] id l^%Glf?0YAfQT^3Rj1GZ;D4(xl:ϭeѤ+ $LAouj6\X̓UG9'ڏ{7[cl r&1dA JruyYPwU$IbL9Z,_%Y8{GUe~>glGB!fqpw&᭺)c -e L6_kDIE>Ȏ#q gsvZJ_$*a ;ATJ™{$6Ѻ(^#ӝgmdT2,`hd\'@DA; <}RS7ԓ1Ya,s _Ytpv K!5B0hZ*]-+CF#F~_<^SY u@ Tt }naM08yٿִ#Bqבq97uQx -`#,Q(IsLoWZ% MąFs-24̦P4IG\{Ht=}SqrbdyELۉ:wAހ,vDU-ii[f鏜?;-[3`[yxb^&PeKE9 ~q>I&3+JZ5bH `6s>'ow7ZD0U"BqBBֻ/'$s%M"=Q\LR~Wʏ$gΠqqС;['M"lD , ݷqVGnekj@ͣ\3y`S+Lfo|Xm]P/!Z'=F[ hD%oi 彏Zɔvp?zp:+k4L'oŸW\TLO=*],j+MCev=Nמ]k`ob0էyl\RQ3Wx 9YYYb[oMhhhyHNnBX@A^wߝGhhSg}Ob_fڴذaz J,f:;RY$\em^_ g˽-ł+żRHEq×  G(p(2ѽ!'Zp p]{4+ϧ2QesH4prA`TLSD6!ϤGWr P(MY4C QdY X`5BrϬ~ 7% Jpx(g$=,sV2.8#:uDNj5o,nY9ӬwW}x6DŽrMh"fKU?\ |mk/=۱f>vuloi!{3xfF2MV~T\a74R TS;2hǨ}IMGP戮u[rA^P:dDFbԺ߈t1ϹIXM'V `?}ڟVB-:H{n/? SfFX%ӢhI¶Uwej!=oB*?=#>4.DFzvԎ@F1ܾڼ(U9]=x}zdm#bodGI5/ݎJn>ȲiNCp瘍z{ i=iA=7;_d@)(j1C#**E%eKE;=]ӥf  hv4oͱYe'sxyڷ*E%>q3T'Y߰iӴQ*^dggc0]Ǝqqa!YFȲLA^2%Ͻ,j9suo(}ow>Q!$ж7߽4(З3׋>S'cI7 дeMk!*#22'Ozj.?͛X^m2k`$%1 x}:\ DP0d1fXZ"EgX&>:7ˢfPRȶ=Qg4z KMXd{+ VW܋z) BA&ux^((ЯNo Sg9h?o7DMC$޷ݹ1#H/dދs"v\|OYXH^^{e? /I?7 tLLr I }K`l>P~ܭ׺",v Leb*Bƕxڭ>E!:3W}MPDNeu!fx?tBt?'l & oxx¨ZFüT l{[9WR_؎$H\ qcDž9?*g&`6JW>_T<}\AO6ئsa֝{P(h_T¬M}o,C iߒg:U~ƽ=;UXL.jUE vLW{/ү_+6mJ&=y<: IDATz FH"= Dž /[Q xpDl؀F }F߾e6 ̻7➙Iz=Krr6yrN[ ~94Z?x .\HJiwQTlM@H)Hi XX_WQ]. *b!PB!޷X6uC{>0{s=9bv{Uts"mr gЬ @R9UWI ߘL([Aby |0UoY`X{j?S V?,~;x<#O*52\UVC I3Vh#6rØeA8+H.S}e3lN3Y5޴ugqKx% QHZHj 8ybEfGvi֮fjk혊Ix$O'H8{ ,.7Nµ6lf3Eh@dOăob7._'zIJlo *Ktc$WX%%te7DC0_" ZWq!2^ìQI2&Qa}yaj^͛/^Li67f~DWؙ,}K\*vV#–I+B=8j#FDp` KJl%7׬oVB߂\>OknVUg* Bj8WvQqUBTv)#soN볻6c6L ;!H;ǖUø^:pڿ3l=0C4/AonrZGW˦ZFm4pRX;ÍچUzY>W,f$9-j.NWC`;<ۜ6wP*Q0RNTbR;c1<C8o3_nrNBo1aoSƷESU˟_2V"7٥T1UG-Cٷe?&#/_+ [,8s m:mS S_{<{QB}OK_ PɽR3 a#G=y^"2;㪭UG耏J"kJi]`glsP; 8ԂJd9dsxh\o]i}j3q̐6|j5~|l`i!˽Pk{jApN^vrRl\ͅ{ln Oq|[@>Ï'Ia"]=ymGuKǑl@GmݡFu(@2SdDZ/EL(,("ڄ1(k?Wĺm{8`6z3]pu[v ZV&^<˗mR:){o'l2}-[f+^ҕIx7tzIpf`܀jٕ;p!ݕt?s>?JE~];ЯkJ</p+l1nόߋ@Ym4 W\̒D;r%*@֮Q%vش_j#]}iH&t, urv̖Rd&f!Ŵ!kU*$iOBkpALz* \31z5Fz>II#8~xx˙| 5vСC,YFExPv$.,]QU EhFIvvaZ'qH%IȐ$4w2c3lpV2n^i6r@(=WIVwUB|i_|V/l#m(J%=qEٗ>֮{4 %ϬY Idؾ1e- Ey'56*ؑNY7?sDo^B>h&\_} /2]ZPGf3.w1quȫ y|<˖-ѣuzFMؼy3J\s(*bk^uqcbәPGg5U-E*۝ V1?o.r Kg%z#˗lmP<5f.1k0K;35jqqn&)BƙJ}COdda(G,rN}~U9+,FT'GD+Y.K޻ %vFߌXy߯j4&3gն~Ao'[fG<" u#rnf.nn Q*[JDzOh0"<wφ;T*᭛tOҨ}tZu]cq t[TJԚBݍ%`- FF-^DŽ!64clXV=ꊄ,[,ATvBɷӿvrq<Qfdbĺ*k X#c+W,љLtiC-'$PtGw}{N/}{Y^AhǿV2wwr?ĚSL,~lI"03\ZB(ѷ)}Q۴M P.3OoFD2TkΫ[7yiP9-LD.߰pJV+Gl̈́v&0lViNK'Jf p{x_.صio> HS3k:ZSZA) &,˜J~!dٹQwhs.ڇ,w*b.@NnQi>J3ֽk56]Jf$(`8oKLγ `OUӘHHIy1[!*\0[ճw7JJa,JYtrr2)xxzҦMZr<jURzƵX !C_Xh:tEˁ!WPjӶZFbDM<[,db۫S۵hNUgO),"OГO6J*,S%--gBddd+¯y(,#3,[I f!\J WZP?k ٓ7d`2 ƴ\B+-_QRn>YIZQdY.^W'F@Uf~H̬,N%jSiŲjHea!,l*@*ȲɸVQUޔ*m{pgWdQ1aݪ Ht[r\D/fL5'@v^>C&(m1g0f`O{cb#q% E,ZUepIDu(\IfjP*,KNU9r-\3_*ٵ:/~¶1gѧS$ >ymޘD֓H) kSJrEzG. I2mP*[qy3σ/m+Fp%ׄ@NIIJUhk_?`2q*ׁ+;f3WV23F(w XDZ,?ZOJLIUFaҤL69 Pd2㎪ɩ?~m$-;wsz ۨ(j)O3m cp F+K3z| ~d&@ J- {o6VֺwϜ>a&@20OVAL^6{SNFnh"횇Ѻ" uu 'dˌ|p_"ppjɱsgP:#"vH; &fDc1 C:C)uA6Q^A!H*sˍguybeHscCy/I<G0Jλ2* 5+7k[$&HTda=>fA6eR o\[hCZؗ.{2f']`1yy$aG:24j0z' M:v|(w0W;vJх?ɾ%BRU 鼋s{Z#"*2%x5TuFj^A9L΀Q‘,#XL k>|37gW,八׌Mݛg46~zd\=ݬ3jWKȆ]I?6Qi[P:#0>6F EGŹ3/2E+T6TO _Oda30wWk < zV"]=y'22Vzp ݯ|b5I'p). 8 U-ɓԆȿr* Q l!K$b5Dy?'w@GE'\4K9 UEixOM _w]sgɥ04 o;<+i(2 <U}dͻ1^d iz>m:oTAi;b8ٯ:2i?2Ð( hu{[B:SWH He2Ⱥk ĸ9+(0J)IzI_s.5vlV+QM4+!f3s6F', bқoV[8!$1}+TQ88 Iom ~=gzM! iF^Uo\!NqHJz }Hc'X)Q"cchߩcF=3_FLLL},`\,^L(%,e´i4mڰ߂?1=ٵj Ʃ9hb_2w 㰘cG.' ԕР\d)XYb:eku"ufD7MdpTMMu6'mc^m8&ܾ5:w.т@!ŅG|-/Qql+_s StVQzZGW ۚX$1;n3~[xn\+ B+q%19 Ȯxo8Kl\kG M߹R+wԎ[ID"`j2>h4<9a(fsd W2{{quO W׫4ث{\jOM|Wckz:t+Wqh7>~shLxV%Wuw/7cD(e [({][ _UGP>«\5דYұW]wfrLTJ{/kl4>2T1 -=iii,>׽ZYxEa<\Y@N.ֻɱc{Y|9&,aFYhg.2I@x@۵=ʫ'ⳤh 2]ȖPr ?#P"˅ʇDsޓU)c-22dDzZ`92cBT]]l6/)4Q[#bazJ".M&w& Y(*s99j >JCuyNu /b J.(;7+ Wg'd疫- koNa|yst]^edYF2j(n,2w{N{(L&K>'W'F#ΛMV48 صuFbYkhǤ^׿IJ%wM# $ڃ[k37c˔5JO *̷fO_&`sȉT/b(/[L^5U<׆@tY$Q n,YlI&XX.խv,1&#WN ؑbBʓ,6,G˲L֙ J,&C1W3*A۸i'#Pi8:vPu>j$D7cřr{GOg+К JoUr"d;]ݷ$[m;-,X%V#͋u.aAZce~J㘓+-|M?{}!H58Gעzhս/gmBT8e>7BݼXQ\H2d_k۸1JcB|`-:_6G-l2,Kl4.L -_{Wy:3H H,d^ocS0A֢/8Byoëyj`U IDAT(܍$ o("CYʨ2͹>}nxa?PJc-Z?^%IFГ.\"*#ޒQّyw^[xA0He'-^U-RȽ QurCfgY؛^H5tH{,"HlbOJ^ı OC1@&8-];όwߒ$!"O7{q,N(eoްռ9ʫ3/\o^ڐiSHJLEdDaqocU'Mx%$\.?fm_' "qQP"^RuːD K =mkhCed  \}p!4kjͮt~3|Y O&!JA7;~c'Uo@h8+IO#Kx{3o$Z7VglRQA .8'%;3X(>Lo1g;5`ь;ֿ0d/(íj+N"~jv>o}lc 8F~N6ݏq^AY&FU9 {6s)1v>엤J\ g]nnLEJ'D p%!y}'+$fM{ OR1V#yJx}͙`d}INL:Fd\GТ僌1_&{T8.15a5荇)˕ 1{q6?;z^o~QD#,d|Z yAC3O8]o{UYȒę#usMǕ\ ۤI~3S(XLINCT&6RXoIHMM奉Yj0j0>5wQT6y onY,% Ņ\ kV &#>ĖTLLgl˗]73 ,?0 󈑤[lnd ;7m"a8cʸ'\CTDCbiXt(=Y,O L:EETn`3\\\̴W__~@Oμ7s&ǓXt)ݡ'CBN&yAA y VK8gw{wae?"(ĺI%VeL& پV1e]LGr&zہW*?򆎫bccqPQ>Qp\&3J"T} _ i|0Wo%SgO9)]TPvNN%״H_OU"^uY=}qz k޺@^[ռn4& ɸߑLfTJ^ (xV_&ڗ6^ڿ%A_PH9( >+2.fEr"f#aN2/"!+LI.z MF r)*~J /SV_.ͩqtAPt뿧i(&g<]Đ^KObfpxIT0"U3"d,a,_f̆K'8z4ZFFQ̾B܈t.7-ĥ &i%EAZ͗YD u$:Rg\6H;!Q(kؿ8ܹq'Ҏag$sxMdTl[gc2U1&/yyysͱDf̉<xwO|=/rrO{y+,Esɱd @?W֛81$,\J4>V{f6fsvߧܖ^7)7xF(!ԥzgE\R*f?,lwbdx]>8͞KE hB܈+NƯ^NGaa!}H%%Iby"JM =!Es@qV+AJtwD0/cRxd_\꺠 Q,^}߿'OfKB{a>5 ?϶m!C*ѱ<ÊgIKK# ~|,׿L@5N'Y} ݇zs0ٝV> `d%Qkx̯aqC-kހB{T_&mj"j/{_&0Z⁻nWΎ|Г)m}ޗ.qiZ. -ql?jg V̤|ˁ=JqY(UjZ>glhӫ#9 xWo Y!ɑ] +`@J 뗥+dɎ}^]p3 {;+ATYcѕG6M*s%yy:t]% CVV$%e.Wm@+W2l0 yZq%f؞oFptYFg(WR߹8')[1Z$:מ_Ye&i=;̯<_wYL&y}:p+~_|Avn.ە 6 ܹ@)AP!.3 ѥ 8i?ܻg2a„*ۚM&vjR&@] 2VIw|5ȷ2גOm4ݛCġMlqzǵ D֛s2qlS8 JJ'/Yǹ2JBǀ?sy-~#_t@*2KNv)nsׄڼw'v(pѪ5}JLLp!xxx0cOM#62/FFjZG±D<uE|7gi|>{0+6(˕3%.&%7RRBٗ] r- Qsdf.^Ǹ-{eޫ#_]WeծC'Lǁw 0CTt#â"~6w1i,]JdXiCy9IW `2'kL.'q)MR49{cg"iA'BZUWD7J!%Zv2 l[>6rYh2/,S21g:sg ARfk H]c;4XQTpv,lէoO1+br l~:g1^KxKRjE'uo_ F#̨lNZ$_7ѓX6'F[UGA0S~.M&쥳4Wfu<?\AJBWu\ еk_v,ϕlzVwVe?1I8l6[}S^ Uu@lAm A[ h$%@o$ݤOs?kfEQɉyrkCfϞI\(L=L1hpuʻns-"(7-f$/xed>*cnsl Hauw\hF(/Ph1 =Ww^*[]>yd:x;*V_ zZw nEc[1xQDc'٥R nIW)$+DoޓQj|f=3^8k~6djק;q}|c^gƾ]BJZ=o}%&&{vv|C*>E1W!!jyUAO-CӪMNEtB;J N3舼"2M&jLQ0VZ=Wd/ OGv[=1Pjfpp\$gdaVyYbCDy⣵gP"^쭐 h$a.dT8$]ͅw̀6ӳO늾x uY[LyMf_Hش~miU|wxH NcH ==%e'ل9(AVJC1<nWlk cւNqr{3{:9_ހ t7hZlFUkGsyٹć/#JKԅ<+"ؙh(]Wpjr{}k6t T(YaO93 S2^e&!#|yh+s3<v$$RbN ׫\[Xf@]7)RԊAOթUP]=xA̓hUu9jG0i0I (I|r&Onc$rveUt5Ԣ"R4 Y,H ڷLz AeBCy|ͫ#?L{a<ɼ 9f9NxO ;*,bE*J2_f0哯٥$n˼y"W" n۰S>Wb+*d:/f}0N`4ގFoaǺ50yl.ǕӧoT;FJ!&nWf jQ ^.fDQM % YrC%.QUzšC9qb!zu`„xF+9ʤIڼgq'XX/XB,^F˜G;(zJ}tw C{}u ֭iڴԕ+橱p+VaK -",7ģȇ6[Y rL7G)׃3g18Ux4>aƱ=f3\=Ьenj aMC,ݰV 4o^{r*Ԏ*Xp.^34͛7]lv`&NBH}蜜"-Zba;}:͚5#11*t dONb0мuy|"MKw4ϚN,-8"X:sҲds{ ;0uٍao˹tt^D%Ms"9+.f{odo ٓw?"/^"DbD7>,*杵1M1:&P5V) jߎo죓EA6-\n%*޻"<[s>iPiݰZj2  ϲFĖ3GwW~H 8L&>b6w̟,s kʽXUFO$`%mւsP٣ @ϺeӢ){v"KDlBO٬G PpOIܹSjuGY˲ YHHk'W &II1)ĚG98ȓ,~"V3 H`P=B^G'egH ;4Gv"Yz#*R [nO-@$:\uħ p̳hĭ8z8ޓ#K큿+(8}j"2y:r%נpТR*!;Z߮ ?] 5ptWkФaRC#ĿrK$nZof7y V{oH~Zb{s=R]q# 2j&ɩɌo^j4f2Cﻏ#G(`fnj'EReLxʚ S_Yp(jSqpTY`4l'"y|+G#2&s#/g/wɐ͛y륗w8M==yxG2db39h E?Ul02pp?>TWGbLd4x W/8[#苶>Q㊈jE'?ZEjbz0h4j}'rN?Vq2i=ڇx'/bM!*ڏi=SI)t,UJ<%wbG0s[<8P"nj?+`v qO?`lV+IOL߾ K_ǵ&m]|Op|l4g2僯X}o9͕lTJ$GO1w1՚'߻t=xϥtCFv#пa 6#b6&׺C8xjA֮=Xr$ݑ-[dƌk/==ޝ;R^?2f3cgƍJ *4OIlJ cee;wq~~Vg ~n֍(J҅M.i9~?9.\@hr&M/6汍M3u<;df0_`^!'N7h/<(Dd@^fϥs}[Dz7eꁟ=akv۰9!G@%>X[h[f#ARs@c6x{{3e5'I34QWd};~֭[`֌;΋"aY=;o˼Lh*ļlx:81U砃7X,&#@pJe{E{d7^-{& (]@Aj#bA,4QQDJHdǒ^ .e2gٙs<'"Xբ[ZtSoE,Z$L`X^~t%_aP* Օ5u-:du[B =e7XoCTL )$d0WZ7"W7ZL/ |]j~p^ pv<6[>gLoxZ:X*2y7nd$=s8 XGGGN:E\L }|ک...7 4(ILjј%K CyzA e(ͰIq4=͔c[=[8;ص+,ȝoFf/>_~`kddٲILLCso4U]W`Dcgذa$>S `$2́''F"_ΝWK ɛK"()7kgj˷OU`[]Afcپ{z:͸?o) lg/'i'RQc 9K;/t΄s^4ݺxxB%GGZ%WC*>^Eɟm#NƐ wʫfq_ףDz,fjV̟Ojz:|sҬYu/SSSUD_M)6K\<ذgǍGޜڵ I8$2Md,JK3^g%]Ç^L;8JLe<<]i-\݊Xص47)hPՐǮ_~ y\X%MY&lfͪ57i8$M\ 6< <)<(lL@7>8/)_»EĿ㦣&3> Fu٪__bkf=|^x>:&Uhгcz˙7l br"pYY}8olc~M8p )߽+n5 ;zdׅ0sSiUIq]oA@CP:G\ y!]^aG*m#Z-Dɥ'QIiY*\yHl|,v-\0#R}/A F]W*RE+/[Py'jtp {\ϟc˟qQ= >^ifұ|kNn"?dE?2WW/?L[ Wd@J㓧ݵ5"5*/Q1\{mӉ H:IQ37uo:X^ԝq,?ʾO13eJʲ̮#'Y AApq3)l2ҟ`^8J́[qj=q"C&NCX:YpV>4y -^3gK/yt5[0BD'dFf<5(NPЮEڵ^ 7E.B{߸ f|0;΁4>q֔t+ "C]`妽dJb̤66>Nfcb,V)D x e4d|<+W7(< ůG]oW 09΅ώ£N=j6RGųnxlXj:~s&wݩY9R+EbZ.K?7# c1+g"Lfq `2eQd4dw9~8qIҹ[JnfR@|਒kAc~plXwYܼ<ۗsWv-1cϟaD$Պ|"=A۵}\Y.d%LkYƝb(YW>WWWr'O$&&<OZux3kX-"zNk @t䣏"9/F#NUxhNN=NW_1fsX,k) r6@sѺuk bt|OmA^=8"d{bVJnVVK~x gB궿Ye4R Xu3/NJ=G.M؉ 7QhR'@P"qI֏c,|54F{ gL~|LIO-d.ū;bA]AMJun_}x|{t_HZʹv歯cʷ)gߧ/' c6@b0 366/Fm7g \' fZTgFs x\ZWW&A~c}&!<,|4{wbf{SJtj~xY!)-z |M,TPZ*(8N7=yBq5O =dl^^=Sitbml6oODpPy"YҰ3P8`ݮ9 ͏;bU!?7Ec\ Jxע>x'qZ^>dْDRw[ELL, f^nYy;q v r802<&3-`Rkޘ17OOl$#O-0RgϮ]%riU<.g z MEP:28R#ΟϑA| -kCZ`*  ֯2{ 4ٹx96%pP 5ٽV͝=;ULIJ,ِσB1SEʅol$,;2Ch@Չ;vaێ-,rJ%EH(Rk)Fm\vc(S"z:ޢs+`ق߻s$G7`waNF5D|eXI8Z7n0~MJ Pn]2dʄ[@t떞^3POWV_x6`?Ϟ= 0<6tV3X,PB'R6d<ߎͻv3z ,[QH2mN_3{Ixծlb쫙" &3[0gU݉ӑltJX?b#Ig|fZGg$v+`Q|V_A=ǥu߲h˦bXH@a"DдE2mcYtfY ey2i>(@qk׮mե#Oٲ7lb4JR(pFxFk;!+l63Du'_+1{`IywBy~H+-;tM}<\-!>ρ⫛ɀW9W 8Lg`DGs}>&<>LUdK0ؤI(Q_`-=wxoe%nnn/nhߡo Z:DfٸaKd˗i#2M~ ~9D+`%jm_#q׹-,5,$+eXMͨc].K:/}2@оfs(5$$)(P?Р ( #)`qjiөm.Z4k֌kh̩ce^C''Z}ZUPϝf3N/MFZ )eu|V!o.vs)/7c)$K8`&Jz׽ >~m9Qd!(fjGGGS_TVk6ͥ"ۙC (uq',#G‹pL9YsbZfxC*պo3&JW̊]BD Km}p,(_:=Wgf"5;6+:=.upjST>Ⱦ::tMk8<.kIeBWj{ 1b}!(J}IGn<,VУ5<ɎAdp$02':,}R3OG'|\;}*=sF=&}..eEL$rk~Jٚ+ oLO]Rz<-&vD+e` pE{e}GBO2f+Q*IMۯQ6vhwY^h5x})%.$5bz&-~} Y6 U~g=םbl31hC<HǁI:Gޚ6 !#FЩ{wΞ9(th:xAFOσV`("O /QMwD/t4elHa49ӧ)I|7aљt>;6#W(1j\PLF#> 'Nd%:/NvKvNcC& Tg"b7`|`jDD8e%L&~=r6$%%qmVF[>}Ҙׯχ˗F^n.8};\Jf8:2FoR2Yi\qX\ɍź}| )'~?7qp`ɼh47nT118`0 Sr/ё˷tvvf$J.dbrw3/gqƱ/˴t<݊M6<1p 4Q*9n6ܤI7n񦕙dVZwTL6?/Gr,5YN51ܿ)0BE2*3R CJE4ZGdbGGX  ӝoO-J_lŷGI+A,wmկN8.xєx3jcpprGߩc,X_mh0o/}EjxUct.呋Ҩ@!@  j9(^:ڑF fc\@PS)ThcDžttN V+;w9TJc>>/T[o}$I?:ڭm0P5z0oM# 3r30X,a \<+T3b[@PMlGl_-5ksbBc.p :)${e_{ъqvvvf5c5Q;͂%2AhWi{rqUJj. %~(Vv@ZGœV::ӳ?uqJ.Y3V+~u89VeP;Qgɍ94U7fbĉe׬Y>N~ۼq#GRGq.H߯YCݺ%m<% }v:JCAE.$-ՙatNy^`, exb@OSuN(eARAizzoW؋=kxmܹCe^ 0ȼ/ѱ-ߑ IDAT@JtW/@et@F|Ƌg LIo |QTo4븸bdSPgNsD. ,LzEޙ5~t߷laL=f3ϼ:zEvU,s% {P2s|=Ffx}H4[q]O\hhW~Y'#6[O}j3ogXz=_M,EϺ48pBZѭY51{b{HfAl?L)J͇u)Psfǻج?b" o([, CC֗JlYZ LџK 8hR'MQ@QEudk%F?,I$9$d"Ms?:\ wV?Ʀ}A|e8V+&S[-g*-\K/<ܛo4ޣQhk=phn\L({С# YQDžaII #\aǷrpTwrEwVx#2A<d@2 ԌfPA]%Dz!*o$큝YWx/ 3q1m1?%,nt}ͱRU*:l[8u0;<Pj8(!| wpc>u`zA $h*B`݁\AwR5 T+FȕL}ZtwƓfՓk:~Yӕ*?2=M1]8AA}+UGd1m,9c,wZͿYqA\ԥ2qFeУ8j~oۤ!^aKJBEzVr@P/ U_N t>g :jv`s6S#R `߷ÌTfX'<R ]᤹gDzkTWbQ[5_[#b@: @Kcxzx:L{ 2.G`Xhٲ%jw gE.  iF!C'@ ͜ϫ&3sd<=Wn 1-飊--Vuz5<]^˙ӉYԪMǮ{3fbE{z-;O`Njժ11K-wJb~LF H1#pQN_M+j2jk j杋gHfEs.,s2=sWpsPx^ jsi</ W} w2`vp\!O; hֻuA?FDde|`#jֻg^uc}Bf.2{zl{'1\cLg^[j:DJ~gBϰagɲ̿B}8n<#ԭ]񁫨 u[ j7_"/},h?fާ6{ɵVkԨG !! kXɷ  hyQlY.nEK/H!S9~1Q_-Yݻؽoʫ!Y9v[nAaq4Z:yYP+YRryxxн{/X8~ʬ嗧\wrvvfDEEzhԨQ P{Yg8j)ҥ5BYh|e멛20eHCCl6 }xJ'>?Ĉ'ظq#0 ,_7&M~oo n3:#/o-ڵkwC˗//JbҤI|:>9ʨ@,3gbSk(JY|za4DC>c_e&=2Rx6,|R(#aFboX%%ᚃY舞s_?ԝzd{_~sÞ 3YMࡎnf[l ޤG@O]5nc4N\XckUdhvF5[aŊk3a(>z囆Ǐht E[6/*O±DGE!Ii8;g0hzqqz\DE݇ógqut-[?g.  [~Mvi1]gEf˾c\"ɤ- =G?xVI0;曗V>=9+ X9Jӌ%("Sm ۶~t,Ig"b?azQe$~eY:(3gb0[Xn^ CMŔ+.ze_]Yqu.;HG4.Pl$FA8 beɾvo` \,>sQL3Y◤Kqݪ&QNKgQ#T dm[>|Œd1( $bN3C d/љ?VY=S NCP"^xjJwC APʇYh!GSb9|%Gg5m_&{v*7'{ 7dJ̟QwqhCt%J]# r>٧$M-9/ev޸:TZJ2CD:"c}v[٩YuPÇ G!(Z3OJɦwX6 wD#hҤ U}08)بHV11Eƾv)ˮZu=!8yGgNT[ϝu=v73XJ(7ofcqD&*;Ngx (J\xO+y[t;|UV"GQǃV PeX>x5RSQT7⎲>]FttyBINN?Zw_+d%w 80'+SPгD㿫`Lhv,"23?2lƠToRޛ=+V}׮](+<ܯ V\f友ֵ?iivV"Ixz*ٳ ?RSۜ|BÇ3o"3Ek8rߺϣPK̏Z|+.GZh]]p#e /qjL6m5bl!{M,&}ǏgAv7#Պ$90{ľM5omRRRy)#1Ej֬üyY*uÇSOhhʕϒUnoX/^$2Rw`ὸ7ҥ&LxMfM,aDeѫ+B8|Hu@f$6&5)߷JB%A!`Z@v00&J!'+MFФL&wO/d o-V_O8ѐWaY!x@浴DYLݶGsѯ-dH%, 2odf1䒝Q~LCDS_wC8nlnPp9z+J/ 4 >s3o|DqﯙL^?G on V+v j-fyN7T6;;^4l w,n۷>Hx~Ӽm@ xLӳY0-HEǥUз*?/bqpz~lv(AΣ=hBA! IfcZԇT8n)Ml`j<׮ IJ 6f`M9Oih*|mڽ'Z76=>}h\(ɫ]Yȥ%2:cF=[+t퇫|HTQvaqUuu$Xj{BIEFg`h!O  :7y!X)Tn1ܐhFWO$Gd&c`"vIA]i,IVgگ@xREG[Å4U͊{AZfw<^ =gP[PE ZH=q ܻevy (m 0Ԙ,$|Y6ޒlL1p 9 Z-`Ղ}L8za8BYMo_drH6Jzٹch¯-df.\=%%^|GC8kRؙʸ>füthrndhAT,˷?׳})?Wk gfOf*xcI^n'=^cw?1Jo`տOXktnNzyY^9舃ÊJ;A> ER:61OW~-@p$5Q/M4t{_u-]/X?dn+.Jtw pO ȗTb[=RItR3W>-VVZ'vΟY-aXЎ|°UX]?APtGd|j?mW+vT*O/]9se0X=kZ\ۡg0ZSم+G ;z޽%L-]K95 9w^{f%<4,=ϋAK}0J]NAVQv]_rrt:>Sq Xq;l4 \Zxfeƽq}s{Cvj:"'QGwӨK4~#¾Z_׭#++oRS@e grecaUx#ݝZN)[/p*2/Q #R-@_1\YG{!^}A[#,Ne.si @Wz g@lX"Zw)5UDTNI ﵴbo1h1"YƐ}T )҉زG`+?]*?: VNvc(`nF[El~S퍎]ݢ1GK+2qqqɓ!r7j'A 8:jmNDD+݀oD+xN;zy-fE*RL'E~g6/z?n{{)(U.0u|8p:wcWljz89_xTG@]t2/`$+-Z? j/\ܽP=@̺]*OnZ9r\pl[-_t>(*4DdUm%>/Q$YbWBt5b0Pf#Y)]'[7xǮfUACxma@-&ȇȲ(n/2ǯ\/Ŗ@ޅ*hqu88| ！oФBŨ(rS Z|!vѱUS7 DT&`{Ќ*6^ :u)%BFȹ?xbի4[ue/2B!*cIdr]0;wy~oaM Z1W/?{[֐+jUiQp d6~y][:}gQh q8eSQx g ).؄jߟ& Nn rnq9 IDAT>gpvNBMdt jQX 1y&)'VM />t;Btp@&l,d>"V|AF,'WQxc&**˸ڴ$YfAQ; GyZEcp.en;N9CGl,]TbOLB<;󩨨h|&hhK)\k7^!cku%(n,SEQfwR,:>ch &&;Ezu+EVoض`S&趯,df35f l5Ȑ1Gom.F\Gf3ŹJ**Ew@!v*7]/G┝}ՋW䧥=r%cU"?[~n HژTn#FxJ1}D}E#ܹJ֮ي$ #<,|}|񉧺*V}o7Zn\;V+֯С]o>^ QRYTŁ*6>|NV9?Uu+vħk+:M$͘+ c-H@[jk̙=JlH>###+gάۯ6;v#ab%eU Yws}tre5Yw$<o_>2ltj*9~Ev> +9v}YHGb47efo?UO;_pӉiY4p9=@t|dmHگmtՎncϲ(< b+v(=ЩE,fXcqLe &*ni:lduu%tξ 6Х Rq>BeIEFemTeǏߓ1k5O5{A0xL-tZ2_ld"ñh]ߧ9aft%>>Yv:ٹ' q(J>vd9Iq4 f |G*dO6j(JG5Mp\8'2wet*`  wb.κ>g7^p ބS+I]ohy Z 2 ҁ94Zr%0&[(,̕F$5<80vX;ntw0E-1O*K'}s=R~G\f1O \;|8) ױ:BUQ["*(h-u@XFDdQ^E [c巯b $6%휵C' ;˓>A$苗f{i}h(kNjG^•34kPըy՗9} HwbFҶ~.{^^nbIn?q k"_ )?NE I'fLf=HIk<7^jWOM?}ٸD,~Uw>vzjvd)"o?3 Ppp t-6~0WQeoZ~X2p Jl)3潟~+=y-7?b^HoB=Ȳl&Ng(z$&!JK人ħCoʁh/5hHsߨNN"@7ȟ～0򋙋k[| IbE-(2{/a`bof5WPkJlZI GcRwORm$Kjۤ/7@~h~wQ~cjZDt"j=5'#;# }SUl゚-?V ԱJ"HR1hiH mpL9(KRsn)ݷಫq2TwY[qO/x /)AMZlfYXQGzϺc=$㌾ m_E1! @^!b2l(*{JQ^( OX*c(:sFo* DFCT(ԌKH k*Bfc,Yj(Դ \' ic1tn*׼!&o,^7` xAX*bdŁչ4Sj}٬4Y,D #% Ta>0AP%ഥS~.@+D?6A*@Q$C%3 ɯb"3Wc RC}d2y*^lvC6=}[IV?+9v5>x?@AH*+SZ\(2U+Ժ$ԘNƞK%6ʢ,ݍ #h(΋XCy1y 0[Ld IZk3n8Y%'p2xI 0) +=|2ߙcN'sӌM<7=߽~ﲜB};^?DDW͢u)BaFj+@pp0NDH!F[.=,#jP] 퍤G!Ɛпk}|RK$%|fs4ނo`@b NX퍽ZJ?x}Yםیlٲ1}HO Rл ]~-eU6_i+iqqh2 -}uۃĥ(ʲݔ@Q~C>`δAܣ3/ "C+mgq}H@vIf[`Q^F~~vnrz2ba]KX“?Z}9yf AQ₂VW^1<Iݚ̀ORrcF'(>Treyہ߰ VO"z} ]HA~sKZ"D'}rL4\H. ɣ[o?ʝrs>e'\BD, "&`6,;G+Iۗp%6d21cr9ٵo#o22P_śq%+e~ڼcqbB ɵhF뮻~rK~*WqHwc^/q8rG0zK"!m@jVnZu.Oh.D H-FV:W{pfF[YJ:M лwoEqq1j__ bP($$\sJ]ܹ@ԩ\1gN&dһwoeffg$22n^e|On(J/D_LVg GUUL2{:7f͚Fb&~y;vJn;h4~ZPS=<>* >U3HQ5jlVpr\/,A( (B("}0&!sJ 8rir ~hKbmƍEvq3N$!;l0a@uc#/(9q`HM}TATYWwz5zSth)5e2=T0JТ(ZdU3!g^ NP@=Q#.} .۴1\5#;8 ̜[omWmxsf_[K˱_Ir:l[`lf&MG/=V79TttHr'ȧ#kڋwmЁ܁Z{*RP!,l4{lj%׻7Nd}i9A闕[T* q\uxR [kԹ:7[uo@cg߀KNu(n?,U;<և}e"a{6m:)P Pa݁ ,TE3 ؼº76ZԒǵcp\: B clNpX="Щ%:&b% Q8yhs&oW@w`r:.iZĬ%YM&ϻ =|nD]XA1LFvn}*ԹSzHmN\;9Di<.,Wk6;.Ƴ}uޖWxrs#7nELP|4 ń`ŹJ؃ݨQkPEyd,"TWdsQtWBQ'Xcf??y7dDkc8e([THi3< wĹ Zk >$Kѡf.F3r j]}p.L;Aӕ QITحxOo?*>~z?:\2( Q$Z3g2 Yw!JQ}z e?"IS YժԇINuW+?{ \4w6Y6rxh fbnF]?GT&խgD/ AQ\پ5IÚg3z2_ʫ60rM{ S^˵ ѼG"C(k!"`(BܩK-voۊ͚N@`<7_a!?/E [R9seN_\7o/ Vwp4yWvn/18>Fœ0c([ъSD`5`S%r]DD(DInIg676 sTT'$ ޾M9DR'(8n9#kqսs`13 ܦ3gCnkmA -?PpO9cֹ Tswr"|rԒk]aڨ3WVq< 7bO>$9ɞٹRϥ; AQz#frk5opǃ21VW3S\wyNFr??MЈYu\ZGY$W\IJB8*ODڵ+=vgO9#r4?a%J]\{ vZh#<2冷&ng,+f\Axxo*u]Ub (U0-W#?.YzCt:}= /UW3xXBr%WTOĩ4(&x<{a6j/g„adffB߾{|&l^a: A ##^ͺuH joWv|Aߝ+3\}wA`jtWK&;A`Yq6Jjuf" F ,DhJ"Ht=X}ҋէybHdͰi$u膭]2{1_eHjG&0!*vy ~U7OH,>!h:!9GAҞCGǻε7K+kq)>RQo,QD8yYYd h4̿NijEQ4V{g k.tK?[xy x1.Z+zWpCKZD3xBBX<YJ =Zz33mw& mv6H*f oiu{΢{Ρ.>DWHVEqhcJZkd;@y+&]`S" u-,I|^x?-EMKw܀3 -S Z}wp YR08]:UA8w@ZQ]x '{@((vXu8۰P`i ņq}DIk`P}*ьp-jo:bzۆVւ pW`,lLߠpOEQkVZxH=]Yu촠+us ¤u˂܃6~"M$k\ntt:9t=LOIGAVaXUABc5(9|zdBw|56QO&@ٻd~(`^)͟$M7Eѽ&&kv:P|fJJ&뛎;Qy%UT<$tV_1C 5rM՘p Bk0ubzc;)Bp\Yu4NGB% 綂bq:%LOMb`a))N'r~_GecuZM IDATC5v/׹Zj4~%Hqq1cHMMmU]y0s G֫pVGBa_GŸvfº.i!%y\ Q 1ɒؽ vgN6.KF &HJZg-#iEiZ[]T:2(,߿?Cj)S~?(>LrRo?u繀$>"O\c]; 4IH蘒å,5`$qE>%ƊE%ͺHh:uBqP}F`N@_JL4uZ w_{%,zD-Y7d^QSϏ1C3<0[mj#C6%c ƍb4v!=}fd~Zg|g6n$,(,`μysm@>}ާ/ZĮ\-$u\|^FCڱDn 6$/ mM6A@!*.3'r~0`l>BxXUx{3nߘ66WZqܰ;dHL&wǚsΕ7@#,xACBg-( #=ߞ}.dRNΚ$ ,pݛovܥKTaӹ>"cgHLL={%>OgaΜKپ?Օ#t+l A^,5YНC9"6E!CQxDB+-"+-<"ȷ"OZ*'~Q!EGAk{NY V@n/14\ \̙pm/,g r *M2NG>AdVי0sѼcIxn.JBT*BwY/`u`2SR,uLhh(Uj?h=j-BBy7x|:R)g[>YY#.-A(>6;=K7M 6lj3$;e+'ե})Q|ݎYUDŽs2w+zu͕-їnTc$bF$ BnD"xccD'ԑgP7k![W{oXAPY?Jjet Ni9 !z/KvTy1>i xga'P VحVWmlj4\xRFptx֗p0o#(. CW߬PHU~]uZ+R2! :ԭ~n(li./Z˓}/CoiQŰ8|كJ Zj2RMMX:1['}t6kAOP8V;l5Te1],΄7$? 2_G#"Njb[ ~!@RX|~xP "CA[ZA@'  ЌkA{ˉ̍茩u$yub*Qe#Q[mEeҵ[Isb]ϑ屈\i`;I7Jr4Yiy\1c0f̘3x "\1yyjMVv6sݼEĈiOBJFL$KWs TH]c.rùz[FGL/ Tp(vԜRvOz]tM!fM϶!&t`Ji?]CXʹM,}LL/=I$1O}"V+*YSo~4KLj [:UX%asl$$$iii|Ogg&E'Jb9ZIW;|+9t48@ɶ}n`-S!7DxMHD$25깒JhD$F<-AF8sF sudY#G&@cȲ)^^^rD^|Y$ɈŲ%|"""xYd9/#!!q%$$\70(8AY V׉}egk0;Ŗe \DI#$M[X*.AP0f=mƃIΎ#dְfnZ0i  ~'NOt7Ȉg~i{2j`~,7/$~~u}0PpĉN!A[.瑇{:m8f8:e{㳅Zڷo?/JpTl62,G\+oR,_>l{_RQIN(H>n9t:-_~CuY*):V3.w9LpW_4$Ӓ-'!DD);HCx L-jh/9m,4Hl6ZH N9't2CSY\J~ n==FMk DPžS J*a+?O`[_`]R=5"a6+*OO ɞ?\izN)ޟ,@T$_/-sսt* k9_z@H"2ΛBsG;Hn[6(d5=5^ƭPD,V6+%ԏ1SFl@DF(sd>x l<ǩ6"v~LQH~m-^-j*}Ǒ>-'U |©)(7 y–k9` m*/rC;GSo7sǵ0j5c1iDݶ=y8NVU0"j ?fpͳ%1%3d.֬ynNg Nt6ZKs"kv,UºD:IۺR JOШ%9gNۖ]XDE!T{ p}-ӷq ͊F";q83qj/B.lW &;c kހ˳sӅCsdr bOYUV4ޤDf]-DQ0vlx5>;j;Z4 A=+Їh; .ǼmegsOmе[f;+񊞼Q[X60pH>mw8Аqi/&2&n?g+9!.F$rUȞ/Iqd_*+/F-cn!k \ֱ7}ilm0<|J (J \^Ȕet: #HH?SYyѯ_۬h&***/g >U#GC  00!"y9Is. XKJ֢kg!ĆRيOhq-T=.A$YasZ!5ⲵ~ v̔3jN1O`\O嶎j Gk4!i ^hN݈9N[AVU6JB1!TVcݧUes *DEPYYwMQ?{E"JB8INAT8 ohk=ﲎht#`29hj@X*QqQ;<Pg\|6l؍A˰s י䱧@mAHȸЖM+L}8vBSQIjL᱘c XqjǷ,Ay5PAyWѫNl܅J%wdtel6+i/!"n#{h*btE2|j8^kdi)s8h4 X쎺z!kǿ_>ݻf'gϞMbMCcsEb B1Np*ΥX>!qHZ=ZFJDNit:j(,=Ih\3(+72J+ 8p C {vˢ08]S5Kh Wr47hRN7uՇ a˺eѵ{<@=D1raDO#ts+9o1WW/It8X.IEFO5(-[0b!I /|Fsz򤼼Sg~F4.X[Y&_~aՄ0{iUro?TW 8$ "N>cB٩L{bҸqŸ im y`욄6T! 3\ucpFǂjbY]vMN{_T_0U#iO;;|Cdd^B &u\F(fN$!}/lfcoTWKw:Y(DDGw!BBB1|W2k$NSS33~xxC|OGd'&m?)'5?{Vѐ1z"15,2['8)(Dc1,+Deϸ|?'Ժ#[\+Xk%3w&r+kp#kn=< TWP'cāc/޾cEQ8'm^JFPf$BLD8/%7N.jXVuOx轈wNZ[YkB# M k"x&j>l1{6vdcLAǦd㯴H5/XWUIr~!Lyh!MzeyXJ|!'>oH7^q:ﲂ$iwFF`ܙ=(2;|ƒ#\~7F@'gϯ(PD\~KE y{Vc&0;=j3imѢZUN(=|xFmKcPD0K~pWɓʍsVEoؿw=|%P}u;sй$k;{YUv+~IMՔ{ 2LQI%mv9[LKׇ窪/ x 2yF‚;a'o(3X]7'< gVAb!|Md^.!I$!B'J ?ELv"[kHM^[nt|Mu|l~Xx(q:̓˸Fm8V[YsT!"Hz959Nv)D# ~_)=h:jU*:P$"_&$!nʸtlNDO븒TU~Wس́ղP| b/b @w׵ _?M@$i~~EawcsX8]g-%Ip @ ~@(ХjA+Bѡ؄Ȅ!4y@\] l`}?`@v,g70a^fkWEȎe@@\6?}aT곫dv^V3W7̻3t`eQ,+ 44&N6m/2o\ZNLɊ#Мx'ooj-cG O" 2C3׏k+)}sCq2{E̖lzB'@%TJ DDQ *"`D ZB @Hy? d }]!g33{rs?m`8CzwjŅ{JY,T aݗ>ϟp!xޟ{,~d"@=lپU#"Z1Nl$/ym<e dg V5 b䋘 u0u/+~\?RNo4OT8IJ*uSi]%/\o :"#x/r{fv@{)\<ԒSWP#xsGnj?DKnG`4/ S^9$c~p IDATtO<0٣3+/$(\Hy7"Tjr7;o$h3S#":#G +99\I~ߍ3`Рވqhj&+㗮TQǞWp]vV&c /Z ,-8F-hr%PH"QZ '"ryaUj;(>|2Fp%VGd& dд KAUNi*W͛7k_ yeԨ{Wll,1lĞ=Hm[Gf;EJjpga;SPǍvmGol֯_ɔ@k9?/>fȐeɓ?aڴϱX<| ;I{s%?PwOjU+SIt ݹ fvF33H7L:ŕW zsuiKY+_k;ֹ8LnDSŘONYGi1/>'V㙓BMxWpeېf% G//ĹːnHDFqz]G5ۍ5w[*T߄ 5ooeyTLNN-[t xVzM>0ҰcKEJpix9kZ5 f˖a6 أx\Z j[˴1LY$HUct|H * lwT͕!zMn y+n.e/ lV ?{+^X/I<) z^ m(dWX" Y$2(9R>qVJp%"9 L޵$$^$4 4̤9+g{tz@N zSU7዆׈"eg| fZJMdxio=NcYXxdb1_Z"u *0n8#oGʺ8 e7^9j֯X8<6\S.=ٓIruǵE'-'µXM*d}R~ =!JI;_3si,5&A*5p tRx@`( :\｡̆H&&3kRbɑhH%bsl]=ɪ$FDBb~ eOPDK^Z>/cCfR[CHӱGfÚ Ehٴu~ȪXMo$yïP1?[/dT D=|ɮXKFEzǠ'@ǵscn:Q';9CY!/ ZQz:@Yj2x0/!ӢG6=-g(N,\o5a \_سwST\ny\Hil~~!(K:T:w(ËGv"}E=C̎4Ey9lQAEa1n#*{O=T=Z5\N#T-jX.vn[/ldLԑ#˷l$,;}`;y /*հUs/8^8y}K>VvޝhOpdd`2bʢ2-zSՆ65KtL#LPX(}QǞb~e0oeػwKbulǕAN+osØ1/2|{2F֯_OFF۷Kl+A`RiLh(gʜ n(ߢHF_uΜ̘$Jk#`yyO~?8>hLO)|&*w09;ĝ9 Ydi&4Ԛ %lž5\;ڰ%u/a+ QP6p>-N:jiRg٠6ub K_>dqDB=H5q GUhБe ɪ4)xKXM?̹훐:@&:=uH&<( ylGH,\ȅ pqqJ*e2g\\Ѹ${ \t:|~k,_ @gz3lh\0!I"r<"QaQNˎyn=S&hL?XgC~&F $LGMkHX`0' u[_}0A@J49.!UQ `q;Ed_qjތĚjy J۹LX4Z&)RIdM9C͚5iݦM|(9Xl=k{ .w1)L!(8 xl[63`>zGlH< V<vu&\5!2\= dvSINb:(m,'֤CHY31]N+]=Qakukp9?NڨŐdphѧQ։e@4e4WN~h doWvoOrV&3Oz~38O,12~]nK܁-jD&e]#P?zu s9>;vE2}q%QQQӗ g-FrҬElreK@؇bT Yꙑ;wM兎1W &='""{^C (F/ yu˔)_{koRD*n8sV^}"u@~AͶ ;u+HR͌d>,~6jŨxxC!n'*]<ШUQ?ҌԬ\KJoѰ ;f2>Mm$?|?O#I*Vcڵ= 6n؀,tڕ;OoT"Q՝jJfy,*R\B*~M!jYN|P* I}0YXE>Qk9yttw#Jѽ$Fx}(W0b w|OI%C ׆ұG2cAϥc5h6eG>! q_]⧋TxbÕCLI"f}w Tr@<f}6Z{^OCzwiGVMIHIӃr>֐q}<s%u=}(\ɇS?gmx{0r :q%7n$3#Gy+iҡ|d3oK} -+` 퇿ZN+\zwq.nŕE(d)Ka_$Vٞfh7_7)7* tQ7%fDjЮm^nfTY6%Ѕ.c/n)J"dJ~Ju"""⾑;vlF+{0ͤѨQk~ٝWُx5j]}1U?ѣi;c #CJCZ (9EW'^[\}|hj0p`>!!!x;U%tO?+ P dFy=.*5aޔsu/A5MR~q!J;c10a8G$Ô'UߕoQFU_@q! q`l4a|f+ތ< ezIx)RVǣL^#-*-H("(QsoLN$Tyl uԡZj̓mq<,GC9{$“}Y0Ws5y> ㅧ"IqҮҿm3za ݧz0傋bk\[hئ;鄰T^!]ttSk߻D5aQUBΦEb6Bbur 28XkXV}Qj!R΁. A כZ<H>~;y@4޾Ϳ}u6Vwէ玪'LuÆyM2u*W~˂Zm;!37kRqG%t% VX]T(NZv#AVr,\)(넀D00+jSߗ-[JU ;y@hʕ[زy3={ `HM]ˮO9gG^Nr<׶1&Ⱥߡ[ZR W6#>b"^A!@ \cpT @ѽ74#;%h$FDsi6(=VᾐIYG$e7n dza϶v__ǩ/3(/Zr|9uVeѬ=zի_\?E{$i'(މSZA$R.;kռH)R*-Ar8}\;'7i:*_+y9s G5ٺu Gs'z}}m2I\̻LLϊi '˰fOsާ,у/Uoe m͇iA&T:o3K@N%@NDqL(jecQ+C{yQ}#vى,?_[l(y؏ygϼǏSJ~6}oE&Mx̕ԬY@s%T U @ /o 9zءn`py<<(Ε4mݏ _SW"fQ4أ)IAl؆Z| vƜN(ks84u3&m,>0u{]{XTܗ\8|3 iF\ЗNG ]\Iه) 4hE'b eD.J"T^K`oFqFwd9.)^!h]gt>b VcN: ޢ Praӵ*(>6 PiQ2ePr bKxp'?g :^>۱KqMP L8Oc D'NaĠZx{Ш$h]0gfhQcйMG~ ؀e.) c.@`;r3c- tU yc{Dpq<=kU^d$J)lu@[m!R e]Hd($D ;u|ˣٳvo@lrѨ]&E8(IYKA0DvCj0QݛZ"*ґKDE (sQq)aMo>pFPaY'_AsǔIɝH-(U=diX{HѾPhloT%]J$beQY9lSGsD>_͆%xOqu##"F~j)3Vl Tg?oQtкvddbFM7D<(ܱ\طɲ%ڧ6`5¾J3q={߬& Lgs~e6֔I``TpBJ<؍ZI^ZՊBm5#۬uk/QX GiI]ycp43mp<נH ^Yd(%1 =m]mVΞY_z6}v6b Q]A_pggYhDfqtT 74e*Mkj+ jQ2tm Q1 ;xl-F`Lp;C 9P18qN{ϟ z=Xo3!| pcjmcD脹xɗ1¬w\+Vfh\vHTnԆTTJj$!SCMpf;3_F̏g`4LF_GϢ/eʹ:%%Çhde^+o42v(._.ۇ-Pt|Ѻh!/Ax#/@ ~ -cj4.Fd>gg!|P1EQHFB={EcΉְҢ4 j3T!RpsA~3~4hXǂ/gb49͜a,&?ßF=[zV XD/KJ;Jvy?l5?Mq lfC.Q ^m'aͺ"Y-ج(v風 $j 7[r%sλL(W`»JS+Y`W_Xdo$AV\22I8p ",(2!h^1nƕzVn,_ؠ}>G-7i҄XfFX :r;ͺrXG%uKJ'ٓ={6Яy"#g1f/NR%%jQH`MZ_jկ~þT之'O@ZR٣q.gI,ԅ5m-+8ht $#w ݲm_7Y>y. IDATuK.-_~]I'RSZt;{P"ZIԚc0 8Zæ|g+y|/$o's"Mnr;#jbo|4.ِ$[;C$+\WLD$6/Y;0Ks{{phoK0d*0lfݟ+KډotZFIi`B%C6rMI~VuY%֐\I/-OR 'TsMO!KkҮ,V+؊~f^3 ĤgaCIjC~C'h:T9.P| dX&ˌ5}usZ7DlN>XL2]<ǮF38j9M.$P̚\=w{kEuԾZd涗)'.2DCuL2LEZr3Hw 1P71B<r^GKǹQ8FJrQ2kgvsIrrr\A!hT9Dt>'>BU;zuhOd{ѹ^-7jS s9,{Fu3 [ݶ:~ܲN,B2qbh@5Wcr!: L3,QU]W?6Iԓdۍ2VABڡ[}-(+y˱sdd| ,nݺT(~AL3^{MB="__p!D{1'1:׭EvXlqg-+L"e-VHɽenҳ?fD6T1'~:.G R_P[ڽО>@E+Uf9r) %uuJ#/$u@ #[VO4\>`Kp:(УXdddY~Y<1T֐/TyժUg$?мK>ZCEsRCǕK.۱+W9+V.QװD\MՈ/yƌ+ZrQb@Teo[;P%LԬvc(}>4M9Oҵ%Wg釋{pэCǑc*f7q1gJS`(: sƜc>y45måRJZ Jwxb6ƔU'N ܜ`Q>*A5W]~2U[AuDTkq^`5x̥~ϡeχ \J}K/?+0`(kR;v8+c(\А{r%ƭdye~ƍt>F߆FG>I߾OqLQD61 uΠPW~޸f&ƕxLa9I,M> \. a= JD{TIg`58Wb!Glгs,̦oaq CJ%e^ =+~TҸ>ѧ|%^: ow˹})ߗ/gK/kn^ZuGR'KlKYKS(y5f)r"%?0L4oށFXyÇwQQI3 z=Gi#Z2 IY9x:y_0dx'Xe ZxGъjT8E(ߞ_Jセ Lt dlT)0Uoۛ&cX@~8 BD$Cr7y/ҕZ__~Xf͚v[ݺu׎uj`%\oR|uNj1G7L_uټn>vg]x:z+lVd{&g(NۡVk ǥ=QakkHR>:m?8gp~y{}+?p]"Zg^$_JqoPMpu^zeRar2<{GVd594Q,u3i̧ W@d 'Jgqs9,i@3b-,\A߶N(`X p,NKWC,*gǜXb~2r;.7d FS@EO@²|ڴkNU3"^\vL'|h[>szPfF!OaBZ%iSyIgHX?@obb#y y|HI1,^H' n=]3ʞ7ZbHK"O/FU!ٲ.c> 7'QO#?R;g 5"Ɏl 0]_ýéUf1,\5+FV;ʈފ@U~Gg2tӱS'^\0O< s S + ̚<̴ {md񮱘x:!ARҖ"5[wf뒪:w/՝>@AX?ft|;ϦWRI!l{N|ݷyԷ'g$`ۅ4ܺۥ)VFk YԴ'm- /Hl}?($%e8~& >.q/鹸9quٸ琂6M&H)L+1Yx#q䗒?<á48㎂b׺]0y)tjgӬ7ڪ|y6f˨c|5LrE++-azy Vw'$!ob(\|9w>*ьdٗI;Hoߟ ok;ڝ!jsH֏Pڶ"SHoš=0_Xyo~8_I&jV0Μ\&gr.5֭ Cmڴd %G\߿?p%mt%)+'ׇ={7XLF#nx\ɑ\== $;ӝLWb `T k;95gҬN8^81YD~g dq8Y+8øHV2x2^K8]NѩiZ),*yRW|P5Pe~4听?a9bG{jF'wdjXrMW]O8!CS9JiQQ҅CgYѰt㉎FԨQ(5ЪUsU+ʑQ(1c$&jZkOL/̎;СC-Z,s=V;77t{/XrECN3zWrFFFo&MԪU>XM֭o]!Eѝϐ_h J5mlQ7bD޷4}du Zɹ H{J9`j*$%E5MP5 #+# GJkfAOe20[} 6 b6K`K,!LJU/Dp+O!g-ɋyQ{~~ǹ<٭3Nj:C\Wx/ P~Â~æ(9Wgo8^_+rhnvm9$ Dۃ$=^~au^"ߨմ^ܛt؎Cǀ'8I8 dȽhF~RFLi9|ϙC14XGߧ|efYFlk8P5mkծU#;w ;U"x΀*4LAA\RhS^!aN:rT- %dw770p(ҮM;=3j;sj DUl^RN"YS{2 Bs rz.FFQMo 2_K\ IXylFѡQNj m܆zJUpkg`%9ׁi( ظ]5F!gO`2NYPn}x"y!s5{>|'QcX ԨX ?RNşC&b <%Z1uxG.Q}D}585D;gQ03uͲ+Sz~ũٛ1_,KB#n\](9}*jCb"n`"8 lS~I]~;f^J@a+Ҽz%g$N @2&|j`ȣw( G7tb4d|Tn> Ό4_nGՆWn.@oBX/b۱CjTd%f1j+zmFz#GRB7 6JȒ+;V eh3}ݞ|`}Z~a^_p 8m2eDZK9$dD0,KDlciFlڀJ7դ' 'ϡ79{L Yޞ5!CFmndVdhWJ0?dZ늠JE4 %]Tc (c^o}xiүHS2ߗ/g/' ѶcTN334l |;c>68{L6镄JI@BUTET bر6""UzBzd I$$a@@X.-IΜ9g=6yc?jY]ژ8*byC*xJrVS)&R ,S4^˗o!/]b\Ȃⶅ)Hsc0x->TUD~׹}fv 2YӳV:߹}Z=N9#ofsƉ3N>x7i7]>!$PJHgCgpaz_y6' ȎѮp ܴ@ :߯Dev:qhrw1PM 7>>``^> vj,Q*Yآ8+WUiHbJTp.~sӯssJUi̝tSKv6QjR΢ca,V-1j#0^y+9}q:Hݐ_o]O?\C@yi}&m6v>lם+$r"iI= 0 ޜWGcroVJ;_v&5f#8sTt4Q.CoJttE,f}v! kZ3~;l6"Vxx-g`k\QᨮUn|SBd^F52+$VrVMBLmj "Ưf|XRBa΄r-ZB+V& tƽ|7:$[-|q:8& |)J+J@Ƞ9B"+dg\IJJb,[V@Ek@(JrʕB$Fz-qq5Fff}鹹<3x0CEhX`Ҥ|L+_Ń\w]M k4Qj5ox= w5h /)}9cdEԟ&[> vQ ۦ4`=jB""ұ _ȯ?|Ǻӧ۹= E=Tӑ̽AYF殭?V.x K\EM 8gx\Z~Oߝ! A{o)ao' >mQ(Xӂ E dcCtJm#FWn(#֗ټ,>D7tuCF_ok_0󾯾*jcgV}u t]d˴(O:Z*j ArD# : @ht:8{񁄆G0m,^w e203UQiJUn"ʷo}1շ+bKQPZ(^Uyuy\mNm//k@ pQ0F?=Ogqy 0MȨؒYFZaQ޾e:<~<֮6 ~M mJAƛHG) P^ 2up?ۀQ;V $(i_}I5{urwD#$(*[aHǐ׃!3^7 /{x3߉+v{nzBቧo! 'Lmx/VT=(q8pU\q(BHk=!!9$&v|39Ǹe@zj3fC$rEJF <fKXl/ߟWssEZtA~vg;+%!g#;,GNrE'hx Sf\&)7 Ih9ܐs7J&"94=h!zh xx y nf+/z ](;/G0P][ y, I'k{m.\켼c0P %%%k#}_v2C`$̀uڤ6'9EN]t=^ޣ0 ײ7b8@ӈH׾g6`0C2Rj:ԇX,[O gPZO~EOy5ƠhV_n!>Qڥ;-"9r V*G4 }t2vD1 N"iGoo"e*Wd c?"Zwb[fq4/duj%}n/s`ZI}s_vJ&J5$:Zk]}.UsiޓJXϧ>QA!٫k% \9 OJe8've܁P/z*J(,XM9FY:7Oh40b^|q5^^r@NN!v䊏b| 4k܋/6yl޼n}"[w^;VwJP*O?hصkS HNFݨCҡ--67I99-@dKN+Тx,'}D$hT,[+LX (ECQ (W^;I(>Ҡ,d_Oɣ|o ?_6#CPx{1"EK<;\1@.]G͟Ygf.F}c'N%4,{,ڤ's&#PVF^؎JQ(VsWk?;Ǝػa#~t~4M#|8$)cmac$2Sls5YDz !og"Zk\+/2.Q g$`^kVlK0A]pLlyǴݨ]HE] gf?n@X:WH{j;ptltZķi z D1ɾ Kl(!JsQWmC~nq][{yidU' :Uj/6U_b(@F|)YMt^I@.yz 6ɳDᬈ 2nxBjS,IYi5RA$>=!e )iҬ֊r83JEpb4w{^nBu ׳U AhjC[Ъf+ i.ϻvцu eu=@T( iw=(>BZђCogܷ1ŁXGfJ;(LFE#m}.JB VHLT5'@Zσ3;FV?i_?MK0f}0w?0!7–2^}G2[;{ׂ%@ﺇ˫];-LS8ap'bw_pH&^IxF"h,`ʍ@ҐЇa2ݔI3<Ϩ=4T<"{R&c>%11?_Ogņ{1jXQY{:cy3d,d#x3*mmcJnODr{BI)XvwC x[#Q(p$ "wac#ǷfC\uW]kkn[''__4ir](T<;~@r˄WhW*!7jZ p9ٴVm²e;(9${ j%r7ͺk{vÔ=bf᧟a8NPp L+;&Ogf6S֨p*'|'|6%?|la~F]͟.At߯833{RZ:&^z OLĘ1Yr%z޽{R 7ɚ5{Y}yg5XO1wWcq!bߩ(Z3fc9,ˈ[U6ܫ(rK|+;A`BWVCbIuPOB4{HӐ{ܛ4 i{+cah83BI8η?KiQL%yx DUc(LA$Y8e9tlZՍу?gOZm+D>hX6}*pl7O%l: TV6f{t~tEFJ"_oHGۆd~U5ֻ節4U5>8f'Ͻ)3g<: ʹ}Q,7Ci<$IYh7#P֑c^xr2bt2-Vz:fyⱓЦ"EO?O[~h&"Pju/?)7lCxYwiTӭ?npٹu@.1h>% >gٹu3wt3F8zsGo7OyߛЏch=S>o q7GQ|zzGn?ɍ m ʍhrxA~Ntz~ ~쉳Ǫ&d@El!mZQϾۨîXlӤ/z /`,| ⻍ m/OD_DsQAޑoCQ@z_6r_ yfZN}c_Sw&Z_*Mcclٺ<WZ~rVf3y0 em0E7t ~8 ֡h׹J[{n@ٵKq%&S`S~beQAjPS"<\ڋgSVdf<+iXۜ̆2:!6#WVrE ^^^ٶ~  >ȨQ/##ӟeҥhL2z qvAud8ϑ{K8hڶL 7`|&TTno2bĈ$>[T$+Kd_y{|W3?/'jx*%xO_KVaʍC7hpj-wWVaLydzONi(1y#HMf+K;~#[} N_EPgU݈P堻BŤ#s4{MR 믾ͯB$FN'W br[$wDdCX)ۏWId{f(Z}|5W;ӒX_9v-[p8o),%E'L! Ɛwr'ȶcj%_oH9ً9z56n@,yA-z I8a-t&e)|dg$z^X?vҷi A:fl"/W[P$NYͱa.ŻI"}F۴nkƐ{p ܋)]x7qU8c/a߇Ӗn{ ku߰D|B dP{B=<OVרlXZ9m۶fդnWc ϙEi vDcHsՀsNsoj  t Q5 \\\nIvT Ov 4Wp^!Y:>TAXʲߡ xux{2㖑[wc-a+j=4{|EJZ+iHe+]#|W(*5c1k?=T:1I/sg hS+/0~] lJQv=Q@%*K1HfL{=)un_ԥ'L% Uv17Z qj%Whwzqq1ii=((}=%%E̜ ۶M`J_AXedXVWwdSF]%[9{:M1 g@4'v%]{|4)fa"xkqgh`L2Yf*hQrt ɟ?1Q*xU Yk<<[Qj*5 ?!ׇuֱwofrB>Evc̀ƅg`ONkQQD|Ϲ55A a@q-JS9Y-E46Ԛt]Nq}l~؋r/G?8l d]ǐuUۈlJo[΅ $vⓟԁDd-eKv5Q\04LEY NK3PR_W}G G9~ܛ MVpf.=͒ϖGzd~r@!HHPMcUK|NE*B9JulՌ"# @H,t {{.6"m]z0H[ӚGr ~acj.QB j׃ vJ&*?k}5ϱYz9r65^q$lU8Q:}ϭjܓP*'jSuWn*cz41ǭnevl{{-t樄B=mVn *$yAXyPRͥiw_޷_/?j~J1+FHu`PL 4U*\˭Rk4H `zbky}U:O"Iv2eto…p~w/&ӭH3G0ta,!DW;8gb³D5}5'ADH:~AV 9*s,潋W?Nyyvgh**re wj.}Yz.0J:uV@]+]h%j,Ub&c&M#/N]<>UJoHFۻJRuݕQd!(yNRծ\F? vb2ۃQRٝ;w~ ^^/V? J5 OϧW~ nA}@Bzanp?@T2fLOl.<;}*8ܖ욁8pro$E־}- aB_#( rI՘tBrcZ!*G;&^1h<|]v,ڭX?U<صkv{/WП;w~ﺋ hcy+huCyWP;=F\ZP<}'^Ŵ c %I듊W.'rES،E i4GfI(T/P:-#I[= ~Khˮ. KDيٰL򂅊s]Cb`(:P~>yߪ107͒}o_KfţoUޑ|֮q&1h=O`.[$ِ$Q돸rRsމl #*xxyqOBGwpny)-Ay׹~M `(Ì$90lEFX6ĩC&*J*ZDe`>ܛHDVmU{W *#=jep8LH /j\\ `]X`P9qP]%yf 4j O:ˉI'tAgP]mGT(^zi[f۶f@Oa3QQz>Z=z$iyy ^6eBhulL 6d;Dz 5#-Gzp"|FxmyghX9=[R\UxNhCAؽ%bl=.6!KK2w}\B-'6%EGNkT[+ 8ROh pT_,!g9XG3A++q3 uCd[齟Pos&qU7NkI;ɱ=efFsEmD%뀺\A}eD}Yf^(tg*22m|lEܳILokՏ&:IEwbH P(5:{%8 m?[)>T;Ϡ}T*4wRO wpE#nDt>N%K8"9i(~ Eض,٢;ԇv\V8vrN`6+8QYrq)cX rt?z7zrxdILJb[a*-#SG4;{$= t3]~#s $I$ —A7jl^5 )I20y_r5* (g r?b'HB4^w?d1+23ӵwGƌ ok ͏gdgC۞QIdjW-?PVѶϚ'IƢ,LŹ(:CbPR|qc-bٶm v{yЄrЈŎSʭ3d@扴 Kli9p1:jDBk6=]S CkHI0b?RjBfQ\C+>{&%%u:>Sm6)͸ﾵJηFiu;$=̈́i9^HoX"J}Eۏfm\?74GCTѬUzsߌXB{{hVX"S(U\u&m1Ӏ@j4oޔ[ƍ[IKK宻 ..z=V͛ٵk ѣGE1cF0jP ^^^˜9 ={#v{`Fy'{ұduyux93q8l33͆3ߔ {LT uW9cgrQQwӇz ! BAoC`l26K Kݜ|Iǜ(IdZ"1'IyƊ2Sc? Qk1dԛn_Iq;l!I>;fIjgΥ!1ip8؄oO;D{#F|vTQku(k9wK Agepk2 .r]i݊Vؾn,W{Ao_>P{

}uJ )}9fX`%rk) Zu AT^%jێK;|h{06 Ak}>C+c1#IQ~!k&p})6oeIt$aa;b$<+p\E~]ķnOWrZﺃ_"gY*ծ-[mf#V-X Rpn{[O\\j`Ƴ31:"xdVpe۵I)&V&0r,5[ITEE7vpå@cY8v9eHR !"z,/gx'<\ D|(>o49¬Y[ J% >̞t6;BX+'l4={㾗7,ŧpHHxž!*gU_FW!ɂ2P7W 5^Axս9>ATҺV@On9Z(R[OҷwH X_t.b+g$IUۮ"p8z : sEñ6k)絯nx\h#t(=kGff-Eoj]Ǐ=Gh"vKFYAyQn'y[=a;/zcm._3st:J۰m' FcP>8 & wBPt38t6b,嘍-6H!uDy4HDnzZAF_{OI얱x=s:2|BmDf_];܄B[Sq-A47VnS2ER:*/4sRi=m٭)S mV޲$4{$ֆjXԢ}xɴ9'7?'w2u rq-ޱ k`nm±=1uFSY-f*Z[lף3/_ `єx晱yȴyч*l6=6(@GW~C籃xּ |RS=D>%sZ2%ۀ z) |}Bz:B}I$ì9$ǹ0龡m9B.M]TjPqSAhn?[wqr{ bi.x `?g3ϓ>RŀcxF}V# u?ĦX dK#[6BUBi1rKh#ZёcJQW+9f6mʲ勸}? 2dH>X?+fqL2zCՐ5 LiW[JO?ϧNc=?R)dz+7Ns꟏kUǵW)׹<>$Ix6IBcwp5k%Wn۶-[`0RPݟ^TT/&(h2wKtF\\ypya6Yã)EE ٳ#0Ue [LLM&W!@E""UPYE]ۊV]]YQ, Eς{ {d9cH$@x_p3y~)#ECPiBWr,i){y5fjƱ{גL@i0ҴT^H)+*XfDQĹ@$Ib'ߢR;ޯFp"n`hlCwrbvͫ:)Ty4PxxxrX,T1aIg?(ޅO_LV&r.}CC5rstX xՍ߂BHJRҸ`c/ǚP,Gsk:ܼyL|'AB| dxkmM&%|tґUfhʋ,:[ht<ց@K}2)VEU>cQSP0~?Yw9"DrnuvAP;_Kqgm  |/oɀIsCr ''{2Tލk7r_QD,˔uut иN_$<̘qzJeD O&͆㟈h%r|dߎu(kmڈڱ ըN;w!&9$'%}c.AsQ(ߏ8埽K>}:d=ޑ(G!ٮvg,+R {5c݀|m *4-룦jKsfY\^D{Dk,&[Otޭ{if ըZ@Se\ǻrPΕ,Z_W \I{. 'ZILeE7&9-kD>r9N$ܯ]b.+•\QQ]\\ZnVI[..(LMM̙3tޝnX>}QT*g`G,j &F@@Y&r3*e ᓚ dt=UJmQ; re<(XTgط# Φqh=З>q'i.gEjEE IDATE?E y\^}eIqOϟ^1ƃJQM%[C˲LCr B"wlqN0(@kQe4c0pkAɇSml(4"m-=0p^B:^ـԊծsuRSeuAޙ/%kI\H8Kq&rP;uES #;u y|>JR9߸o0lXUS7BF`!7n\?6o>ѪĈh5SuJghjwY(G_^2l۳5Dqf2SW'=Lp\$ 7mjW*Mr%Ǐj|9je-`nLr#~Ͼo| `6ټyj@c\^UHR!MU+ZQV?Lvv6Æ #66Uɖ i3xyk>NE^]t7 U_bU!WUeLtr+ y*$Ƒ/d`>tҶjW"zgZLX˲D†%GP Dqb'P;'B re 兴h((|Ӣ]C'/9D;7TкtL)kQ V8{C}n@TDY_IS@e>.+>f0xPOv%AJOJY6IԎ(vD2Jݢl,:q &/=ǝO݅_ȥ'CHDw cFa&/K\QmOah7ynFto8o6ٽOک8qsR>q\/,TQA]c,;NqZ.>U}A SNsr14qr PƱeT.D!C6b1cFIE7W y|)"kNnO6I 7z 䶪ZB^~6,'PBYiO\ 6J㱻)o~{9lL%)]f:N08 F>L_:O<KǨ,ٰw޷jU٘ Bɴ.ь JxNYrdNz ߈z*q,YEz¼((NtƹƊ"mߏ44"h9F@AZG'yк?,c5$_Ǭ$Ȳ]bA%$#rƤn{"?s-@-!8r:ԳWcծ*IۉR"r\ZN+ؽ;l[pv ZN5+6glp@;R ۍxsN]y1ӿgǶ~Gޜ;шMxx}|TKBgO?@y(X-11&bPe- J I9 Y< .V=_}b Ξ KFwFeO rgUv !;~'J],K(ٹXeǍcxysYlHH΋PA!j bmAȵ(\PEmL@Wtsc,ˤFE~w4.wTp=Ҭ;,󻐥 :jJETγ-Tl8œLf6 y/_hϵl{fuN4X5*z\ɯJNNC aĈ~7Wv+n8|x捧qi=/kfYB6Qk5'q%5]GxV\ @C]\;"(**buӔX,X, ܳ #G"b\^gӦj *+h`6矯'$:ٌTRaDXXwfZܹyL81cnl67{|nM&,l&:]j !!(-ˮ]{;yvʃ%?523#3s^-Gw1YT^|jSg} ѽZQ$yty%Hby+-Zvwj\]Q_D',[jFY19APpf*xstV®O_p.I K?6 rRl텰иlTc̕d ~ݱ=f 6-V>̢6w$oG4.D#Z!Y{Jj ŰY pΞ8s|}=_et[8W, 3j \}FڑLՕ!:Y ʍGPe~ [x?m;O[ 9aNZK>ģI۰جېDcp &aZ͞ƞ_n9gNb(*WT?#7}q~c'q?T1VlT& k?;:ē>Ȓ|fRf*|îyTވ͜ոɖLƾ#߶v"{Z&}9\rՃ6sàn.tݰY26!Z3HޟDZuj ByvՔBu.=;ϗ&sIVYWٴpCYZoGBt}<*^Zt! J+c\b^[v[~GS)(t7-_VغՄɔAe v;}bbbjJ2d gϞ[d }ɓ S~7%EM' !P\ ~?t텐.xG_Jޠ  rWb4|Ij%/ƕql&bQR,J~?_-_D^EuVƏ\x)&nƊI>K⣏ NW7PQPT'S))yY~ ]Ă 9\o̝֭g&-th8wDOCȲLBBGģѨBxx i&;x4 a+ s%9x jr%NTGƷJ!g={Vx7je|v r 5 D%>%'+NCN~AY>[ =<}Z6Xo9\/7?^Hja\=qk>N.fm)!IlV{v'8"{Prf+6t^EgkS@|Jkm"57r>*klvhyQ HT*@;LaS@p p7}KI'u'&x[uJ8RrPi:vW"^sINJD( dJΌd$QZQk غ>ߓg.Xf.Jv-|Vcbs 9'w#KIIr E aOb+UE@ `c=UHֳ3Vj־! % RBԠu ' ANPV *MZ$XYOX(]{lB1ُPv 1xՎW&IlN$R32J"/?C$ LT?KS3@.@Iz<9Hs'?9;82FRDn!@F댫Py ݰк͖f6Puo/-'mAui!gip-xQ;q) w?~Y.jYb2pXjǏaȈO,U,YmK#qid)N[sn3qr@֙,LB>6w#5X/_}%fIdg# :X{I׬|1XMFdHX4ND]wեXѺA d!ZOaO&YTN@wG09Քe3BivY2 AN㚻nVijTE6Ys}sJ{j6 KK`l:'RMq+ 1UE :h}?vłЋ^*%UŽ̀*bIԣPkmZ cEvjBهsgpXGP5ᢡPYdUU`[ UY$Inej5@nbm(c. 4\6X.PK{mbHYټ10 lD'44nm+%)rBP F%>x4=sW 23s17鍑$2]} Ja9u(=+IMMAg;Ε|3n]x'1\j=.nVǎ, k[lȲϫe<p̖W0uH]7c|?n[]q%DdSuLY IDATG\>J~?V+W(J9ÆU@v`?G=6=GRRNh\Jdl'a+,0eD[7*tjѐݻY_DrnkѺ6(PxE:l/,*jk= 8hjtN+-a*)T-Ejo([ڶI_J4.D?$ !/];Nlܸ?ʲ2N5#F$at 6C < etGP*ORѳO$Խf~$QD_V]ݼ᫥X̳@~¾O_@P0t0T&"k8zjXg]zEp_=$a אWpO1Z]XV>SXneͭFP67\5H_\MCI&^\Mg_8& \}'d~ ZH겂Z_ǃƅ}.7.Σ;VcVwD\3M)6?B5k8#ȆiX;/Ÿ3+RHT}2)1V?܏^](F :M:}/˦b*d>%|h} j m'|þ o o$Q؂ZIean-|v&LHz.\wՔII)2Gلn/lAQWdYz {zR>]uWt<O"ů ~E`(+,9dqucDt?Vz5!1]ڬ@Y#_ K~aL1@쌿˱s7 Ԑ 2*r"x`)KHRk' K{) M|WsfzDyn"K{{B{OGT' M5hS p!p` Z_1=tؽ~='?b0dXDE.?xP{_)Kqz~UtB$D~ +d)ER`ˏb2ڒw=^$2Efqifn%d-縠+e%\Ch4lR6.nn#e}i2W Zx4%]9RJ{@sPD޷VJpdIB `/"$ъBF%wd!2 iʲW5APc:,9+R(rx8!t X6VϿnFT2mD yhrJ)_Ǵ|>zv^̩u9î$W2t;9D_YhdM\,Tܿ |ʕSmzdn/(.p%5-ͽqQa^q[J%7ͥ+H79sgwɇf{&xTpLX!qwF$i^O 8NFҳ@ gOca>&7TVJN :?fS(ԒZMbbbu`qQ6%evyUp*Q:2M 8ܫQ bˆmE6M $_3J&{]{j(3@B mUXҠ Lڄ-dz|BmP_A|1gݯmPm22"B,F_rҫ:/s7nxG6j8;8 ]&˛!1G Ct; [89kZݺIx(=i[8kW⧝ND58^Tt,Ʒ)+L}+am;,% 5b{F)KSWlǬy2)5g"nY%zYwV\e!rD<KPjcPjF2`,Fm2 6T7`|7{DLaJ aK(6$T>e%& Ʒsڂtd1<; fm~?g=C;vsCa6oQ'2knMj]A`gs7ξ*k>7,T!l&BMA=>/]l[ݳsXM @d#RƠ=i*b0S]BRTp*Jx%n;3/{y^ΘЮLS(Y=)Bɔ-ؕ}P%(ChA|1|`gεW`W_x_F< S H@-=۶fht -J3ZCjBra }'ODZqzZr15ZKpv%|D`!Ju,J"]תsGE䩜ذ]3E&dKJF'z 1NequLfj\Ra}/ySX;gS3=v~?Ĵq7#]{!~ҁ/CIl5dY$3:G7DW`(}uF4ֆ1@if>5it1TY:JFEEc #i|:rQ3k|X1O {M:O>4nnoP>iI7QccR\_ ɸ{^p vb E<|tV+Y(ԑ`/{e%%QVOS?¡krJjX .//å4$f37Ͷ_fܲe ̘Aia!;x^ ̘[XZ"A`gPiAPF~;c¯s6Kn: }8Ml#6:o پjz s\Ï}&[4\kD['ƿ`$ILV,|s{đ#*UW3t(8-p[F3;Wnn`[:9D_=7@YpS4ŕXAO#(ϝ,ΞND<h\Izً`UGC빒+|.Zђ#ks+۷oǪkVWcZZu?Ĕիj4<v/$&eψ{2gN:.B}`Eٞ$/I}%K>&8+G[ ~||[)/.>>z 櫜k`1T Z*/Þ'!q1讧qb8F]E7@. IwTibb$6rq%|& cMGIfsm[FƧ1G>Xlډ7칗jxxz Ajzn(!6'Q^  ,~dek߬&0gHWYLH։8{㺛@,WFW#(TxykldɄ"+Q}|9}kI &HF39Um<;eydm NtE[(UJd OOz"c&SWb4cA~&m˔{Ъy׼fw9իǎE9vkc+9sEQ|9|sw 1WX? rIGLSLQ7͕\+eЉ;@yy#gM͑Zڹ};#\6'N `‹ a7/RpLﱿptj;k%=Ü9`0|,o'OdqwaLfǎ,\8wн{F1bPV)'':ӧuUjWNq 29j`h#bOܴr=ѹuhVQJp),5v'ݒ)jE`a,/-G)M?Bt׳x4rB%>,y(Gq'ǡC`[ 6mϿfCs:qSpusg#6-z>(xeP=}Y a1&yoXܽKa5߁BāәֻČhl: s1j (&]3.65h 9!#p{6K`{kD8Rbj ׇRt5mX=ਬDyN=Ĺ]cӀrWcqlbs#Z!gݳu3{w#%vdYʥ'pr[I*$q;=/nz%۫2@^(Ұ= Mzk.N"+JG}iܢYnu1(}NF_jV^Xr*{E DϤ-C[NgVuZ¦]=7{nRIc?Թhmk%>Mb*,~o-?t=κR Ws7{o q_3 ?%S{ֲ|qƠRj`0'%M=cVrIZݸXأN [FjDD@6lnQ"8,^| ڰ٦CWq-7w;~/TUέ^9..;EUJ~_yZHMxHYۗQgr-x>_'q Eym<02Sh}݅ߩ(3%}0DinqaE6g.nki ז vgiOLZmsBiE9uH-8'$ LҐbrC ˁ+A{?W&=3EK?F oi~*fw'fZEެ jh=Ө>c=:cܞuDfHp k-6.|/D-EBhm^FhfO~w_QQ8͞%kjʞl {>g-N `%5klҩW-*#QZ? - DI+#,S1A %$L)޳g7_:A0Ge-\O 2s&az (=I#"mD6RߋAk5:A}R,d#2/{%LM* x]?R\);qffđ9$!{YհszڷnCFѿ^i :3|1$Md!$G>9kYϗ.`>^׷x*l}H@Dj.cGӘ"&qV? xb{@, IDAT#M@aЪo}'{i:dxg:<!rǸuƤWx -\Л?%yX:*m oa:'~x]eoUY?4t ?6v)#{#iG"H49dNELߦDUU|ųUy  ٷzY0G$88w8Y2-ƎU.CllAF&<$0͐CzJ4G9Ιw ld e W:yW_ۢ?0Gx^.~v^xV% ?II()|A+ OgGcLV<'ͷ.#߸}'Q; 3,;fR[ <xavۓCUUVU[d?_i@Uojs,'O ;{;v@ѐc_#bx}`% LLni}5ڲQ"+y(L~E@v,Q36#QL9.ր؆VYfPѬBuյjMexjJi9mZ*;Wh(/9ܨ&\y)#g^~=<oqy;Axt>z ?';ZY:qj|WN(!&}y=/Bqgl/"hrZl,aEA_c fNnƺ3[Y1i?1fOgА}g4 V~θAYyxH9xpZ|[hQ(,g;1}[¢s5T>s):SRqcf3w e~^Nsڋ?* `G4DI?݋{(Ar o^z ŋ_C69U4}E&oj2yuT:[5^<>]Hdn<|( &M2d'9 mygM)o#~wՇ#)/ϋ`i&ϥ3D뱢*s)ل%|uzO>ڊBϕR!m U%L"Oyc!\sg|Xƛe&1ۙۻ]hVȡl|F"> wNI-D^'ӷgu)CИS~+fkf֒_zTHHO'3{35}p3q֕Iii#iRv" WMnG%MAUxd1>C K)bD{;=R+LiHL@d2Gsp9[j%=eG^ )5gWΧ^liS+پqivU?gx,(I&YE%0'ɏFob5>>C~\D͙ %r>N%VOemS< R9(J܃AKH|M!Yc:e _F wlYdGhWp8BQHhsı]BM<;??ܦx 5X&CH!~'S];V`2o<.VzX$ 9x>ػU<[A9ާD"g#if .BcA]E '*:95i7P ǔz0B{c =d 2ib4e,}=xgx;InzÕra_Ji<>|G⏋^YW1[wX\kbMǍ乿,-2L&^II7C6Oqj?(6c >r’m"Di|%hVT5WPۗ/SgG79G$UuhL1= DH\Cp ,"xŘMw}uw0 yͿ3nZ:hAdGP^z kIEpAvMZhoPzg ۡ3ZɩDV'շaԩ/,dڵȲСCSaf̘1,[6EQoaj+veDFT,Y]} Wgɒ;q8DkYﮇH_SWQH"")ީ5fUUIMY>EqVAoKKl_s7cY'^.{EO=[Ǔ4l֭[ّ_JCѵb2њx\Otb Wܳ+YD-BQCGw0Rq%gI?In4Ӌp;SG9kgF(>Z9:'O"J LIFX٭A$ f"0qԁl e70ݹnB.r6nHDD}z\s>s.U_/o> {.ks(_a+9k6KNM#)5^X1Cl_|Zؑx\ .`Y[ٌ+W?Nx\OD "Rli/,gt QA%k)ܹa_7<*P(qG;2r/F$d7JN >G( |\6q9!~779R 9 n_f_ p^JV/i[-TG+ -2F";;#"ӦMc>CY~o<W:ӧxOKDA J_[1ytd}p Ҽt<݈A 41:5h M ˺4Un|u2jQ"= Q-fθz A0e0lԘSZ%۬Sm\{^H?@H\ '(7dked94Yh 3YBcmY$ݝȔģqW!>#yƌG3;آ?| XD&sF@&'"9Iބ0a(-Hqc"FFqstPN mU"2:A%As~=!soF?Nf"&F&qsf|T<3 ,2ȜFgF֎GL1@6ۚ¸;-w5xlj &;aȚ>Gz$ Jr 㫯t΍#i i J=B{fƍwJL?g6LGXCغ1~^zHHI;QJ'qp"Z5szt:cƌOΝh1$>1(^3W5Tno`kD@Ҥ iD 'i. =4 #f2,wZɩ}ܡDEȬ܅-{svo;a7W'aBEXziM+ +vDXwңtoqD,8 QuuDDoC z_};vk.!##X8} zv2ۭԾ=F.GrMeQtBj+)>!dZuz{˜'}cL;8Ј@\Մ49(FgKƘil_Fg v:Kx| k @Tp&2HXmZ ߢ]U1X;g>aٛoaMM6k]q=kVg4]B` 3iI*wuxeOMa4Q Ń(6C]R"";UL1u1љ:[5Zud#iUq H -`5X=A s.=-2=A̸\ ZY-y&R{IxvʭFx|Y_}r[h%8̛7W^7 ףјq픔nlܸ܌V+3qH[Iff.?R&Nj?=t9Ӄ},nsڵ7Et  IQ(uj#6+A)ͣ"?TMHB3Dc i.XQ|lUS-6Wx+~1 #ػc$hwd8?6q{IߛČ~-,Cbj?DQ^#jK HLj:2DuFpV^n,$u̩L|@6~ǙFz\NXcH$" wnU"WOh:R{F%ghs'3ژdC{՝?*߃7+YΔVYMX| 2n^Т>h&5m[zJpV,1$ F c-ÉF wf4k75s.:;w᪮BhB$!uGa*5XH(=@xb:opT2p9vѭ%|um0Ⳇ. YDvI^W]JYfdȴ'}IxmѝuXE: Aq@V8p^)/ncO77eQ",_r9(ݿALF~lPJ!&DQC]!3.`nC~#Rtyl~%߾QM}Oֈ-L潽E󯤮Ru8;wKcm#N, !V,dE.ec4k;zGTdNE-lGP'"6 6BDVͼ?P_Ga9?77 $TXd!wUgW}^ )v%d%8ʵd3fϣ,k 7Q%l`̥[q~9i#Oћaޣ/clZ߶'BQ|]z4:zODOPWb U5r2Κrv^hD)Ҝ>SHPUm/A8A،(K{8񨊂_s38DIb%~G}S9ywocco Pрr/'_Hamte>?qJUU|^9W|ƀ-bDQ~tC@(TNRv,)_%!XTNKXb`灡*w:z&i Zr";4nqӦQ[!~  3j$?qe<Uure7}Z@cݻc^v)O:/ Ey/s`h%LJTUeŊxO).!:UW=hȕW^̤pF#۶m/x/c{"ƏEhhh;kŔ=\qK3m{̝;7qt%=}CHSkGe^rTeE;\ FP>t /'9;Ӆ]<޻k[ 3ȌͰӂP2"c0&f+jVkmb/O9b I]({榏~o)IY]95=hV{sm%^DY qPRGMBg[I,Cљ{.z/ ?qUE%;c ]g'TM h n !QdΘQ hCv} z1kP>ht;P=Żonx ?Em|$K!SR9}Ne(F^˦eSxcߠNKۆS"ΛN\Jp F#Wp s/ <2 F÷˗wWq _سX-]ggoxa#Maԯ` gUUٿ{v^aql .Ad'.ˁhC5T爐 z?|'lHylx|ӏfO IhBÉCUUVZ7AT+ĉ(yL:kUBC߽V{Qy#B:aw/'+V;c9#6#%t=ZG@իװx7DD@RRv{.FAA>$s璚>=F#FcÆ[GD֊g;Snذr/иP z8`/eHYzxK8?jVuU*>"Ӈ!ZF1҃갣 " PԔuzQtTPS Qᶓ~Q<qA^.=BVRYDɍQCI >G5F5dQ@ea;wwZ@~MV*!3Fy}?8#%t[USɺ-C#\1t0A/pTi :cpE SXC'a#Ed(J H@޷K*1{P?0lqW9%"׋ݕ}\OYZV//ڙF.r}AU['SSVDtZ? (FE̍栣J-# 5` [ԋ{/:Ǻ{P|^gdt&[_Eme2T[t@UEMi :K&q8W0c[#>]5%Oˮʾ^<ɰSh4_ŗK]q>>N"hkjcY#&mxfjۣ`@cLT5mD:֏ݍ7yI!{򆏗5S%6ݛo{l_c"čva4݄.~u&edT5 G[x]uȝt9} r%82ABɞ8{8[ 9 %:?0YCȞ6KHX@Zm!MĐu?bd3[)<01qd: c TrvDTRxϫa 1Gr㣗_" -YD:C08?Qn9xQhԣV>qwnr!&Zډs=>IAA6?;H3Y߽ 9M` w2T6}QGи%\ȟ g;QU D+Z4\y<k cUU))<%:./=]^|= u6n}mߣB~Nom/nATYl>LϨQFo8~M)ѣ!cޥ(O#{jQ|RóEVN .YϺUu-]P>QtW{{mXoB8l fٿȺͬZ <Q)Y6FisI~S=@*-砪^Uu:ЙC{jzG<:n܇)tdD(ش:m?=w1.}nK$ꜸF*:PIzPՖ'T|Apx]uΗX9te;+>/Vr7/rN.@+سns-Y6FȨJ=BUUA ksDcԕ?μ ml>܂ FW" ?t9^nmj1O>2xS}ݛ ʁmp)AyP]U1H&yu#vޒVoDg5jU@J#*vN~+?l!T~ܚΐ9Ҳ~3z^SӸذb[&ĥhQhU(&cА_FZH瀬7S_?k_:!tsFp[tƾlĩm-;\IpxPD<<׵>|q7Gbi_{F}M\J8A|i*Q风 ܁dx摻OP9KZ ԆX*֔U Mߺ7QqNΞ]ӓ}}Uj&m{Q}P|sY}6;_% a8_SQ͙جAKQwb)֘ܛ/^ [?]͎{Tp?=^Mܒ};q:xC~f~KJޏQUQUH:9I*xsp aɁ~d/ɷj?zD]uK<rD1x Pw]2 p>ͧ>rt 7R)ĤTqڗ+Ӷѽ"iՍZ1iC:MaG_lpEnG5aRûUq~.J>ﵠ.n뽿}ӆS[^CUUopB;Y:eFDuEQG~W/9}ꓱexk+:t^골Q+B9^i(ކ]C*?:4%osPUUo~ί]/(iTV}{E Y iC#i&aYGʨ׋?B;t$up*_-%:9 w]mei1)w$0㪻5~dY-Y*K^U3BHd/(qٿ^Ǒ/"fy@UN>q^w>Z1176<G\@}v^5~ 9 egpTqN-W@}e_tz[9HK33Ǖv>8j.#&vѱQQz>/<\ι ɍH]\| Й]IjڪɜELF F\0K/hӀ4 7_8{gAID.Fϓ i"(Ѕ9*[$9ڊBTU>$ kUU)q2dMTYa0(A׈y pr9cٻu*Kmq;@cQk렪XCù[(=tCtlt-ǪR]I  WҟԑYX{+o,?J8PUKH\J=$c+c)NɆR|Ѐb*>[umM eXlUSz1Dgjx ppf0l!l)A BᡟپeCyd֋mBTxK2;=skK>2 4T<%1]Wm 1-הS܊(}?2)7Ȩ;hƾ]۩(%*af뺵zݨD%a؂} G˿ÿ9Ӹ^qA edhRWULmeg Y5;Hxz9h$h8/y ^+H2dN(/:sx糝z_NH|Ǥ*%{1݅ cEр8/60>%l'kzQߔQ}5@ywgҲ󨊏 B2,DDb פ&rG(>/a >!e;v{.)꫞]#JCg9iSD?3yh_+ZөͱJfsYC5$e۹oU0Qk3u5ROBy,d/JeLf8z,՛z֣AVr|SOI_oFֲoj\ƛ{8 eǨʋ/#,nb eѢ?pͿ h+ dP3/pO@5SZjGo:$Ip8Jy-[XYV*jI X2ghm GihAY"D5b3oj}ǮoSW(5L aqw[@c ; "q׹$ADB𺃯}"8c,b[/;*gnMBJۺuW]]eu tC@:BzOn3ϋk: )7M̜s.{_r/#,Ɠ둬4yb&w_uz>y9*sPL*.Pk*l*Kv;XJq od›^RkBZApzMvw]*dP7_{<}ؾr ?_ZXW5˂ZSwTAč˄kӁ,I^_K@ Z2F]z-ށa Z;,:$u놷/[Tn*&TY1k9gN^OȯT7+@yl=JOcEge qfqΟx,2^́ G Ԑ^c. TW>B ŭ&  AI:û)~Cji<$3Z;Se1N?z$Ci^vg"#*uuG#ń : b6Sk)tA?/=|ChqJG`5ٯ /=i~1VEvI;x\wm@8Jرu>mu=_BTc3u=ںnWI:xBa5Rĉ}F%1Gz K`߷oSxx#H<I ~s)H!Ȳ/Iİөv"mȒAh? M%+kI`#,Mߕ+Pk+M|ZJO<ן|n%4Pav8Kd<>0^KV&֖ZrZeX.K^ycm%~pٍU]UAA?'+CPGMfM7vo14ψS+M=:́tH(VһZ" waÆ1lX㏏@.GWsDEuoïa4~ <^pK>#7wΰwYٱ=/+g„>xC\õ\lw>zJC=vBE-|6Iǝ IDATT~\,y ?~~RŁ_!zm!IKIIMP>#,c,;5A7< ^ xo^@h?j^"`[( ΙÐ3:_ց=,'1?6k:vxc gYȋ ĞĆͪ00\}F<DdWEN jp^hO@\u%uH>U/ގ,ÖJD&~Xحˑ2rv c̻n$WAtzk2_{# tq=NšUːe$%܅[Yð3lS ?a: NoG#9?Aj<871"YTjgc,y<.eOy5yؖe IE|ʌf*{FGЉDI\3m ydTu+IpvحliA^ Ȕxm>?>᚟RvzQvtDv͡/HveYF'lI Q&,m,tAye[ e2d#v2^$|.* f!9><(|_>s޶T!aM0Gu!!n7ܽ3"*e_=n=1SH26+yEC[/@=ɂ{wQ;>]DDdG6@]ۙtӓxR#Fm;z::c.^'/3pz:OtvK6}fKH{ &>H%o7vX@llp @p|Voϟڵ HLB$i?~9.lWGF!$bTStd hl.D`>qi8Rqbh\tY'y]Ю hgRQr\b(PkG!9J{Wݡǧ#}tYkW`!}N36Z҆#~~XQfzR<yyN,;lfcW"0WrA$qV8)wFz8.ȋ:.cDwG|[ \ZД NZ#9fY&(>718#CRMUa.amٔ5:Ԗ}C~)~<g1@|R2WѸs㈌n{l$Uq_ Krz:9o,5TgbG^CeI( v |d?owj/SS:Joдs 18SMI|r[1EtsE- %qPV{!=KO«7pxJzB\!c<w?J=Tp8t]Q5|XjK6ۮ9':t{B>qNL5&|BJF}z AOA|K#v(6~>^{|4hŸ,%'3]WvlLUXuh=bӈLi %uTJa܂ghLJ# 1nJެkkt߈F-݊,4\UxƶEfTU =6B?isQy}xz8/ٙ_CdlLwf$k9^^č=--'bmh%=(r3oc68ǹٲe9~|5wILFN8#>~(֬+g^,˔ka.b.M5X,9_Z=MUqɾAUv}"S8*75y*FATU‘pئ!#82q:=|,u J#$i$!Ijqg(=}`< c1~K]OABqOT<6U/0׎EEU܋+Sv}'"~f)McBr.Kc]O}SD>s*yc>Jh-k_J2W!ٛfA//mk3ױϨ+KG՘HMj?<}GaIX3s弎ZE,T*FO m:kdꮦ"2wa~@KP\ ))߲'(qT8qB,V7_LP<-u:W@^:u]ڷ^ o0#١*9>^?2:PKSo@Qg*8Skճ@Va\DDdf"<|ﯼhkVabxF6w/vIиΈUo{i2 98qEHGwy9ә;Еz%uħ9/ɑT0j$^?Vn|A fZCE޿ yQ,Kڱ01c GG Vȇ}+VS+<vo~%1CmDƴf=[>a?n46 z?Qk郫1hZ:OoR&$eR?[cwEJ:G8/6uuvENN?8^{+Fc}Xf<<̢E٦,,^͍`„d֮};#&f!p,\؋H:7wINހ+PDմSGI~!;?ɮ;Hv B;)Oۍo})?Mu3x] d7d1LǍR$u8cp+o,2~ XGWވޫ2mu-kgpTlJsObz#zЯzE&B24[9mQ/"wk4do_ŠYpdXQ Ox};+wsiҰ(~rb"q> 1G!wv-qSL4r{ػ-hu_ m_~v/tPJa xEٻcBAαO ;+h{&@wX`6l]+oFv<zOm;VPo@s YIL*- CP{Co(Hcfp'+\b’(8z9A\k]V]LE}3rTc.|Q@ B52jt۬ٴY/. t8,@/AxQOٮFQNT:tI l', ' {,U4:)xb1̗'[nw.dzHB/ݽ6%}z>rFERՏv/ we׈O9;6 ZdKa=S/Kg~6%nI)}@~fg8[q-~M;E<[N|6۔e?F9Z\CĠ[u YcCFW<~.qǻݯtE@aJKˁ ju\|E|񅆇~HJJ_zs=tPSSCNN5QQ7L~qvg0bj忸#̸ȊrKa\@ ьn{ڒBIr!ۮa#2% JM@<+q>a 0PTVⰧ.IUUoϗBց' fu1MjgawQ MOuw$M:W@0A7j~)7qD(3zLQֺjB{4R<8w)8jfǐyWFJ2O-;i%E=;7Lv["湎Br=]1k[AvDcF5Ͻ kTj1%32{Ln 4x4z LsDF OڻR`3"4Da3"*_6|Ei#yCk Bc^1 N&8^AEi7͗فQҹ) Su%?[?ka0fSoxGKeȣcd9l'4igx)6er|x 'l:O L8W@2aFloGCߧ,vs-/Z631{XjN u_j1]/j>჏wpt=Bq9gey%J4TWV0mzMÛ/"?Dƥp}/1qFZ k bZ5b,u2C`}b>[cwEJ:Gm? fqGX,\݋ ,XP8&n&%%K.U?ݺν< ٯ1%ac'~ƄZ |m!jQJxj<|MOпP֤Oc1N%ѿ :y0}>:@R[J$ա3x7f&b/;{th3Kc8ɌZ9'4w<&s) %FU1OCw-SWw}YdIֺ2":g,Lx(h0Y[gʟb 5= ~mͮ2huV33.x ODr߮ʈ Ivz^k^ wV3P WR0\I7Ѐ_x *hJC8Kuz\SX<QLC꺺!Gw3p$Ćb9pI G:3I06FBIX_t2ɝ+c01v;|4]>&}TDy6ƛƛ;ܦ҂\ܪ{->Q$@/{ W P5Z/_^:SH6U5rTOx8)+ʞaeB+%ga̎{iZT*G U[O%8]ʕgxS\J:ow zurE<=Wu-*N1HNIr1m:*+r%+8M9d~y󀯀ň$NG !XqsdɂBS]3~"af;~6.:LbB۟/m#ӱ-Gr vU9,IS/ 4#(.#PN%|p2!|)ֆ~ju{n}k8e&Oܸ9g<$7zbRҙ=,Ϭ.и %t7 )-ަm1L̡_c3[H4w{\s,\HRqIzw=P'cc&wRF#)iD  ˏ#*P-xI4 g')ﾢbearFOdmysu a^-ccGx.u;0vo|+hb<ݭJMҴd hQkI:U;Ra5|8U IVuWO&gJ@0 o GE]Z\`&;WRč}YcN!{ !b>1S s܎f,`Ɋ,_LHxW}Gv/cGyh"GP<3' CJ:" <<.kSsM%:K|/fkj{5}Z'v̹ĎivgE1^ P4zoߟP3VkrL&ĉ1SU):a xRlSZA7qg:ўhܬMA` ;Sxxy#+㳁D\tt4bqp{#NǨ 2ry=<  Wc6 I' &ݥm'0XLň*5z_cQ&sݮ^ 9RL xu5]% ǧsbȹwi_>A w1$ɁDʼnF "cy詧\ڦӇԖ#m #՚3?8@3':oRft6Q:O묇l1s9r$}(=;;r8lE1+1Vc;\okD6ߏ φVqLp()b/Z1"*_(/{@,]E]OGh=dS=OgQmOHn)[{۵LeI>kaU/"z:1 I=ޏ"+( a(X_OpU&6 *(#zCo@ȺպTdkny0$yϟ /=}zfǧlhZ" +GkÔl ]C]OgB{z3tBr=A6%wF4vJqTg p޸"upl3V*(te/geSpW1]f(" +VqKl ="E<>{芀TM9 \?z[,nJp Ϧө"x(uoz%XA=kzGsztB.^/7OlB(Y-cPcWouΦn&Cw6OJrPcĞoNw3e ;9dSǙX2C* XeBEffEJӳsL{BSe1?qGg&1SUh=zΉ?va`DF9(9ldmYƩԖ "}* /El*؆O*'4WccS[ E0| ~dY&g'_N`І{jKOrϨ-fEC`&.Dccs)ݐv5e4:t^=UY3$Q|*FM`HXƎphwԖ  ԿyO Z=[I4D݌QZ,[HIn!<<6`-`q z uy_[K^OH/eO@Ic޺u!+Kٿ{A޻tXٰF@L*幇ٳ-]UG?k{~?OD*> E*V~9yǪ:rιr CF\GE㦼ꋼsy Jʷ_}#FDPXwߗ2*ؕs=d/}N!h\xlO#}X"nLǧ|K/eu3{̝o-by=2.Cr8{3e9e!E':G֓:R~x*Y8n%ǷBbƧ2xq,osi[w;쩺Dz,SxdPdJ $fdDU ;gӮOMa66~\Gڜ0U' g {7L<|]>.e|*l $㢸t3C޶b/]c;>a ح&<|<.(k;[}\;3[@&~44zom݌#/1⓷ YE3v:K&]}(>/.RWÌhvo脫K&(g^4V(i%֙D݈2wDTT2,G}7'*܈&}Y?Y8at4CC\gR_D„JA]I27}@'ٳ|W`$>.@` ǒ`ϲXj/ `5e-&.KǑm%/%}5?1꿱in# .Կ H7,{/QgtLg ]iYe~X c XL|ޫ\so0]YKt_Nu0o^s"" l/=G|R Oו/ǎp^Dvh|߉(XJW "q`e5[;yG3g_O?KETT3ϐ]:&ԯ) Es'ErԐ6:D /2ťcPp?2o&3A@Ty"9:M$Mp34{q_S1@Ea3AT%+_l'jdWpp#7c`-4s7v DInbF)][ |dg֯OϷ/a'gwĎY@J]y>Y[1bs_Dr(OyԖq W8L_`,&uw7#5%e=)3sS*^ %!2yP̿O.Ȧ w9.-+Z ZpR s wJ3 g6mbs3&aaS((}-?frs/^Op:Dj5Ud8Ap%Pѝ5[\֏,ȒL\w>{l֬YyXVVƚ5k+\6&@r)CgAtEBv?]* lmۇBDSUx-.l=!SzEGP/DT9Eg<$Gj=jиGQJ۸Viʹd~/;&,Rvm˦ҥ}*qXĤ7;ڝ+כFiQ`6j3+ gR[\]uP%{wfy)0!e?F2 c/P]VӸpQ^^JxRS)#}}nhZb@n-ro.D;=Q ;>Wtd+ |?3PcB9(XS/ȆeۋlhGGA!.ȋ1Zmc>A"$yǤ1ۦB+Lubh'4Z-6ܜ>m6t]uEdwMuu<=VDEYmo9DHu[ZCVj%;;,[W^1)݆P#cj?l&s~1? ֽz+,a|L }X \kty_ghu{|m3ͮ C0g\IٜXEح&*g%ѣ7:ZǑ5cRW5K4cR[*j雯A|0չztuPt,KH;udn4Zfh3YUn BmN_dE[k%j1ٵ @9/9uxV ?{6n<@ᆷ~P㸳V[gwUh`d"˳ v8Hr};YY?؃, 8YOצ  2[*8tz#y#/ߖЮx `B̈ӣTe$i7=c;[-m |R0lqANù۟Q=/q2cZ/?F\z/Z7C_j4Mi .G! _bU")eYjͅ31qKb>a]TUVzFd~\#G$))2=]>+ewӫ$Nouջ6놹8]?~|;m/8+ـJGf>FߘfD׭ttO(5DGPpZOO {2E5Џ I4w~L{Mvy_gu{߈ J3pn9G,U̸neG6\5)770峜,*gGR? }Б ,|WC$^^un@uavE誖BM)uf31{7X%} L dyHZW#x>u*$155mEg Gke+p]ߔa#76\6J&\dž{Vat wJzLnȑ#7n۶-g:v'z !!!}=g\biV{O0Q*t &hsi_'+1Rp`e9CjXj+(;Hʌ\:@<|OζWQAXMN0z9_sQIZKV159^d̕ѭ1/sM$[}+dIg˨5 v+c1zk/ .Aiw_֍AdJ ٹu3O=Kd]ztqSADV\z>|5cCPدLyWkK8ח+ofa|g]<赜lܸOB!1GrlXj#jqXm$l2;'y3xncǀt^Z6E9n[wQkϞK>9 f-g^TwAé<j#*m*=u?r}é?3hMc)I|Vwً&}Ә'v {7Pa0휙l2x{b)a_wZZsZI l( B g]9::l:l}Q|OnѩfSԺ1HR }%1Q#*$9}o@xyOT]l]àIxk=3n~Js˧ī` " ;j%}&Qd7BRwaϬ_^˼y3fL_ӗM-7~zA n P=qECsndﲗ}IMku_@I.Kc $M!aGXLD״1Tgٛ^R]̦ 4@/!wȒِF?z0[޻]+H84tW u9wa!Ƶۨ42r\*\NCO>˽.(p}8Ջ~,\ve.Kgǎ@{=}wlYj cԻ2OǙDZbz&0"e55|,:Cq. {VFsD"S{):!zdl};y}J%y|rRq+4z 0ĺ/eto*_Rqoe϶-,s?${E aD պPkk]_նZu_h[PP$!!@2HrNθ!$8'9;㑇$Ws>W@3yG^8{Xwo<ԟˈ? F0u#0X/`YkzVΟ} (9Ls)'XyV^O4(mHBbgk YSLe:ڶQޜS R0p$)%mk78sTL"&9:cւ,dwݭKw F_t‹|4i$p>,)id]x0a99/)9]{0~'qT7 )**⩧"##c>?KB*c316q./&1cErweđ5kjDS]K?z]R9&Ħr]b$6%~oظxν vxN=؎n;&eoY[+}Pd |`WVת7EJ'rکGN=khؿ!n%H)6mb|XR>Lލ.gEtT[Aʀ1ox Lš"#+Ȧf7^+fe\>d[ڦ}Fp{ }߁}ܕldN|" ӿۚ6 ]w1_JPDOƄX@|F]$A_'Gpg- )TCu9뒚rRJ:QqIigoO:p[SĕzX:<=g^@=ƥfph62x`6Ot(-nP&YDY1DچP,g9떧$bT~Xk([ NGKٶjR q|K_+=ə䌢~$F8l͡=> m8LGY'7lI< 77=bJ> 9÷_֓Go^>U3znN1[oݶ>e_xFFl`]cYhkq7eV8 طoO=z_lP wN1-.JʏH+:/Ed>^AO=}0m8v.z i'6-#K)Yw5 _Gso69Dةںm_7>%>KTTlGj)_;Eё7ϻyE99)[4ҋ?[_D9_)BE3?g>!>-ӦIDu}:`8E k ED+CSF|8JS4wj0I+ǠWc0Y$  }CT4ƒk9[ٻ*IkS=DGL;O/`wwl9bS3_o%u(4˅gڌ Xb:v6SN%%%fSz6r(܃Zhɾ Rն~~*=$$$pEq{̶E0&9ҕQq!.O ?w!YA{LPsZcMNa& w,y<Y k. i֩i[c-zSxL_ĥPn>D%6TN17 wnF09Զ{gν/xX\ꪉMJ#qTF}e62 Km&?)&mapM"hykUt3k=r*n%GLxV> }g2 YxmGAbФN]R0rn_"E~o]kGt|>늫u@簰kx bJ7]qw&I?.,z,_}uv+O}\;{Gۆz?'c̡g9L?L{1D`x̱}`(>#% 9'ag5쬎3M$snQ|}`q(<<yPx_cagbٳ:>7G2t]@yxPp& ~Ҿqf {x,BO_)edqiK[s?mI׀׽uo#BD$Ǒ"'27k:A;ؑA; ǡiqW݅]Cp OF`!rO"taV D8RD E*5|) }OBHO$c!"|RVq+ps' LC}]YN!<%Ix^C՞Uu|@c!B^${ݚB/// 9PFf |("N(>^]nC)f"w %0B!/Ejx,B 2PgoH,"NެnF"x$<%Hxݵ6 <Ϋ !"dc!/Pp̵ăCz/ot<ح (O2 μP"HoѬ *6c}bޫ8У(Odw=9lGknILI2R|$ǬY>c'&I=ⲵjZB@e^C0D)k:_ CM=wZOz>}͓_DK&5;Ėx}c~;ަ_V 쵇;fJNW5k huSr( zu}P7_ߟ1YccBܧiZ yshis!6A5Wg|V^V>KQ2_'-*N=}%_>̓Lz$I}ьiCӉ>Q}Oġ|{iiY=&i]^F?Kn>te|+Xι'}b~/v%w ۯ0 q~_4R bX`-x1Oe}vm /~7Zr}rfk@Z[5vz=kM4.xn Rubݟ VW}3}9@<9;?E߮{/[e9^EPyzMG\ux,w$8޲~gIH>uB!G@[H-B?HN*.G" !8P{hqW] gZc- }= HY!B, *g*ABF09\d "rq'iN{_arwBB!R`Bwn?`]-MBp5g%7{K- r{,"H[ZwV~/$DNd!B`$K OBd!8 HCm}BQ""Lj]*Ab0Q\d$@ /g6F#.N"pv*xfo4YӬ^eӲ.@Fff%„/4nub.)'9.KG#71WMfA쥾45TS`brV>1n !r_ibuu O vDOoE4f9(fi(]]`OLh[WݡN6Lްwo}1CDB%v_Ν̋fa0ȭY8նUBabgǤY}V:>^{pm__Sͥf.rFe?c V쏲R:HV9o$f9b1yfj2%<p}kfŪ%:wOc-,mhzZ.~1ŇFO?SN-v7mDx䲙L.AK;Qf#\}SUytuܠ߿kF3534ʅJ4M_[WQVSMf౪2f fV~q'g:ۘ6vxzgt{i`ܕv knr8iy:QwW!26>~][䕏?/}&S:kS!ڼk4ɚ;5YPߝ2h,Hƽ =wf2.q:Ͽ? SpǴ1ЁZK$+^`}Ԝ9[w[Zu{o&wnv7U*q:xm`7M@8Tq^rX<|xizNGס/NMՔTVuqpZu^6jl-nvֱ^`gk62KRei=jWGTAan8_~25;6;7`mn[13'gmL){VϹ/Vq:X܌nFysn'Uxqj.Tn.FZl߮`7O Vxmi}?ߚOk+Z[wPso[{?P:dz.,CI'ٲ]^? JD߳uy`U}5Iy\Q}7 v(x8^u9]Ȝ ĺ\쨮1~ c*Euu*3(li_쉽e}ox_z?7.^fw޹5dW:]|]9௠=Up|VXAN^W]u [QD(W5I|jxSsH0ɺ3QQ4typj&gH4VX΢󉍋c.b1pu=e4]@abYsCtIk#C1Io#ً*[ݡJTM,c7R֑Ŭ7Pt nTm-|{p.c)OkG,F#e::WhH3y^2j}4m KLcdb^@q?ݖ2nJNM]zSw&iuRq!(R!bQUѠӃ %Zw)+)aȈ̸b)-oYi|<2^{HM 9ѣ}<n;ıd%Wg.oXJZବqu:&xvپaBKY,=[ߓDgeNo[`1.9ã},9W|y6mKx~yJ[xd">`kw Gxey8K6S\7/rΟ3dw׆f}]ShĊ~@{z׆ld)eZF2ָx^w{x,:K~QwF{~E?s~΅gYDԨhy&~Y-`}URo[թ Vuql$ v3h4_gl\rs9cDoɭĸv 0Jg%Ŷ'.dN~2(~>c,k+s^L {߾Nm+ _.1%wH `(S TW_$cbnc*p΁D% IDATb١J)e-<2={f&%a u(_.}u{"63.U}uODSPŸ> !Fiz;EQPf;'rPxG(91h,v18v( CN9!:qMCC 䎙ζ߹CυY?a'`~İh)fUox09,eOx,KnYYnxr*_xvEp8,qX\DJz>LKp n)pcNtgƞAƧ3Gt }MB価/f%}t"~3 /&Og^tŸu+6lv3LǮo_ر.r}B(1tn~FK,7$$sQFyQQ̌Pje<piC#m;EQ#煏>C?pwv:7^q%yٹLNz6L\iIBB|oQ!jrة6aEa FjCp {Df ]~Sg+!nWKS]@ᱭ jFq ]rj3. $F{ܞ`,!r8Jr`sr(omKUpi$Eq|.M㐭֖^ݧ5EQesM(C#de+Ë᥏cYǚQ]UA^dJu^{) _%D]xp/=QRR‹?a~YYx=z_:h|'LDVUq ;cU_HKi SLgѓ{|íVhmƌBE&x K bsMQ5v; Ff%x,B`y=8۶Qkf-13WJj9&ڢ9368ud S|H-vZGuP81qp m'WDOgOafARc,2.ˡr5(2.-M(0LfRt:]Nu9o23"44J"8dd2!3}ߡ9t;Q6r9y`ho΃"hƗvz625J|.=c4f#e~IqNzʛVfkǁHXƚލ1|5cFrWsMPm[q#x~R43<6z7y(,Q^˒ԂXz6{7 9Ckfr>$[[hRUb ?u5tJ2H 哄%`VY碲VIӁ GT gԃcPB2,'i&3N4[9G%ńǑR4mR9?'#'}ccfVU01%Ej7-YCGzémi=g=Q &4đ< $+ v8 Ç~/HtO ~ܵx1C&NΛnV>NYӉb;:.Mcޭ\bsmFHl%i)^?( fR;FR\"dc1HJ4M<]eLB(on%ӃXV>_a'IW̦_TbD_ _*bP;V&s# 7YB"YK1\t?FYRt4WU`b\Z/W^1TYM ~ޟSE߰/>x1w^>3Q^//6G^:PfEg0R=1)~hp!1:owKoDRp*+znɹ5\|U5& x&w!r$;}eQ~@VF&`V^{Q~~0)9^nٌb21‹.=[ZG[˃"v6ĝFF4McqJriwae9M^?R(d&3=#UNJj?dpZ;yGc-e%zsÊw_Hsx5f%FNB&YIIk֬Q9L}^I&yDnqy'})?&#^I5y9}1<h;Cإ&nE&Ogbȡ HNkõhPUJL>csR(z@[uլĨsfF.,yڻҋ>0CC㌴2_{V+]3cu:.VG2652.f1:1-bEj_~= }֖>R@syQNG[Z[ Dr%Uռr|0ÇI6vhj݌4#4cWQ ~Q֒qOi-/t}V:Qҩ=ɽ L,>PJusLL%J߷P am!Cs3SMc}|ݛH#|n"x(Sf]AL|g#+2 9$+nshYt :E/cfsఓCim[7-{\.#\סJ4ZN &Gk6:ZI Rxd3Oѕ%óڮAkӁO}0&k[^nk%_<1r&̯.}m:}E"teL.YIzh|m[oRUF3ac{.bcQMM̘Aaapfkvn> Ưn2(mZv{9^ N)<;1n_q9y>N'Q,#_yw6Z7MU[W7TUW_p+nώYy&F+_\Egq/w7ڞrع﹝3ȡ>jk7;3ӸQ>c6v.]  vcIaOOUg[oS5SL+ @tr}mr.DÎI`J*RlVzv\|k02#O5쬮dͪ{2!=$ Z]{;SjŠ蘞UG)ySHJ^OBE^|٦q]u1t^ zVU؄i\W4Kltgb^(ۏCLF*NT.=cLPfm-zUks\.`ნ%f(8޼y3w|3+6l@(\p<2]~I]KW.~#^Qؼl;ٹ|awX[+J?(̔TZ_̹V3s3&;ƍo8vu-D-kH¶Ukw\\nk\?$mt'i\b察.`rTM㔌T~w9 HeUv,VUeŐE$t_lh5)s}"ݱ\Nn:;6ۻ3 #vk3k_~]+*iy^ҋFqiԌ+21ʂ[ GFM 5{p]gutj73Oz4+Yy?/{Ȳ9TswobAUNM%lᚢь\ hc}M曙9sOy2ϛGv*+4Mr]]g%,Ip0-_ȝ<߲N6?2n^JYOϥ$&q*R8'!*X]UyOȺak+DW[ilCi|yo,_Cu1ٙ:y<8H뒶Z5pBBF;Wv7yt2Yp>q3&N⛯_nrWr? Ⱥ>{GM,6 {H,-ZƧfY߮d_p ;H`/d0pŘ>;Θ$MR[蟑O r|HB~8jQFo-Q#3U^ʺ'3R7=[D% cTRƃ+k֎uv9oqa>;NnL<Ѭ=2x.HN瀵7wmʠt@Օ-<S>9goew5潒-4Փnp~ƧlMpo^ks ϪJygCNq-1(ni".:4:켰} -MK)i %W.VSF-̶Zfqvf^Jkwx@-wVsώíaν|r_^v IDAT~\W<׏!fepS])PWEG>7.ٓYՇyt|~ (fϜΟ^^}Mx\]]ͅg͟K.;\Y G|I/[D|r"7N9ؘhlX ƫ)!E< WK[Zf5N9Q[X[cBf>f㼙Z::+eqչg`|h6Yb>_KfLNzf&O?k}EZN.s]`sNɸcmw/sݛ `Ws#7tyMcR-V>~{uZr .S|z̮3yCڥOi6hJ*}x=( C-+Q4 ?X˿DMٗ3n&"H^t瞧U?X)+W*{qZde%fo^oƜEQ|ώ#YI4@K.^+V+q/̤/`Ͷm੻۷frZ[;nXo?ώ0aBfSZZZh~`︃zg=(#v{ZM5K;R}UYˇ*Cszlѓ2g:5u =Jrfk o?1Ea`\"(4'7A~PkXSŭCc u+BwV2oZw8)-)J^G$wxesIJB #*uյuײ+쀠J/J( 5rz$$ؽ?dfޙ{}?zl&dJɃ<_O?~ /b9iԱjM˺II9>3a-wKs maH"n;޹fAmg 1p?lݣ2@|PA#>/۹Ϸ'Ge>_$Fq2ZߨB>I`oU)W%)vC -C1&$OaM^DgK"c O !GPGk4ώI$ Xa汢l{44 \g _TD}5?$@ݙ{01UoǼ<)i |.IėrUa1Yr2^?%Ȭ13Pi%jzhJV/(.{tvow cd5|} ٤j4mLJ8d9ҫmwdUv/ {mk'ڦA>DI"_JfZk<䪉[pH"OgIAr`$qڂn#DBInc;nV 3$r >x)$UvFv2M  mWMe>>O%' x_XmvfۈQW<`rX)o;D`&$D՟/bԲk@ԫmcz^;xASxf~vFX!BɔڽxO[c$v> +[ >n^rae}l-ItNP+oO>mFo:EޱXy}kO"EfǪW5Źr=m$;%ˤ $ ٹy25ն?SU'V+ %-nNg"BCs xVdBTHe(-F>%Rq9C}&KhfҫKu5k w: :*;餠kC;J N'WN^ݸcruNVbT@O#4:' &Esqk#s&g.V٬M\N'Cjٺjyח1DnÕLXzi.n@ލ`ĕY8ۏL4\_Ν+ɚȂw=řT,(mһVJyQ8Cd R jz-9zuN~$Z0Vxngs+%CBY@uI~4n UX[ `Rky2zh+|_ܽ JWVA [E=TY,)f\$(h5&[svu/^IisY %:=.]s+=qŊhCZ,g/5u4p=؜" 9_DNDP c8jQ)JbtNfq[cJbNeP( A+/(\Q!0T F?*ٱVħ(I!j?/AzzzB/%,vxLe:V}9yxw qF" [dZbbB3)n!P,˄erj*YBo[5v+u[AsKhTA$K4hT$WXc@ Pb1ѯpE!˛͉`,q{+iY9ZZԣe\B4c"Q{$#I2; K_r2]VaXphc?d2S:}|h6;ZT~XbbBOK}QFө'.raڽĉذц3aD&LVs[x^j >z~S{DV([Y#DK2?IϜQqXzyUke9Z1u`^B6<ŝC/D`q0Ko;Z& M69Y'0fۆL[Lt,ˬG2=@12W[(`$X9#5'=EJxשUj.5p.Yم84jF$1%5cnG&ȿ}mNg ?0D`I:O?EѼVF2 ݾ{u#+g1&},CiNw$~pp%?4o?>b5Rv7yG1 J<]ְq҆+TQrd/&_ޥ+zI^>IcLAf@ȃykzR|&LJ"Rd ۾} C5FY&'aiǕ4@NLLd I7C\\ KD(bm X?p" #x3 ]SO-aʔInk0U*hIX${{]صkܔ¤Ik)8hiA5Ts絗sF _o55uʻdSf$JQܳ T>j ( 6W6orWV(dj2԰PͰ>)p כbQhN; zJo![k6;ÁG:y!F^~yg/E/1{Y-7kh,  MpP! w srMwrdnf1bj(3 (rE@_ cgua pnj)ܸg^w: lVxr{:S-.*!ցZW|wP^abZ =S_33}2F#_ד<*'he}cvSҁH% `6 gӁ;J7ĭ]$ˬ?$H&Ȗ C ,( GRڬE]⺧qal&ǰ BɒpgPvk_ BEG> "Y%<^Y(8 ܣPrqL,(GE'Q/e3K &1DTak"}Jx,#[U , u3 az4IDp&DY\% %CiQ9D|7i{ WsaZx;)!23'Ϗ⺺U . <<1?G1xh@ Is!z_ OAH Ѕ3|L7݁ 2}lפÏ/DJdz^t;ѣӷY) @[5N'~OJD}焹.nQ bI:WbR bD C7bXd8"YT\SNeaPpNjw.wN >_̿W_q%pRo O♛3ߩ;J> e/bIƿ[ɞύEFmV3w:Y{ycdVKHb.>!${~߂7Z-f7VRRƖXc@m9ї&bg~(*hq)J_˖~/Cp{Qc00{,"mCfVW#UxO&U&\w!W > _P7|3Sz+NFZt̚u;@&Nĉ=6!˗@n qٕ=+YxQ$Đ:l(]WQV[~ݷv&WU-GRKO!kSb7+Mc뀭{o+! snt|DU{#>O^ϜAI\P"1Zs ,^̾E )ܓF$2P27_sH"UR5e _t}5j 32R SU)W8L @e,Xʼna8ʴv3͡WΟx[PN}XdZA#t07~OT"ʀ &D׭%q4Rs /Ϳjq*Ӣ$nkfgr]R;r 㫽Y<%gcR7 _8g+yp *ysSMc݌ꀇTJRŒ3c7e{&$Q7d_sޚ5ZZ}f{?lY >7s8'^33pgXm6؁Z]v)<=FOHdZR;׹Zx?ʇ ht bY9y6maa^s/_g?MdorF ~<$ǿQBUɓwq=ڃ囮MNp*"38#rhb A`$*h<CԐĉWB!Umv}™V|( HY3: JwQXQ͛|$z-dz٫?@|D(ġ~[KLAJCu-Ծ18[ZG^gw}'\LIb;:_`!'"7Ւt;poI>J%GGUo87Gtr:]9dBARɞ%İkh5% f+u%Z$,Hǡ@~5bq 'HMIM澛1/IU(%|B*OQl=J\Egr [>뽄 yozqEV?`6֐p澰D4Ygg(2@tS,j N>5m@To~>| Y^ =ݶ J.e jUii*bj A!~: mq ,MJM3}F ?b4q85$I|kGB&"58/Xv- O0l:TШ<6w̜ PuU?oaP_BX^AKe}&,aBFP#(>)Gbˑߓnch? L̟N`Ӣt3yB7Dneqʼ!zXY L7ie©Z,ceAMW~ADVF'\՝ әA\ӎJ$Vj1H*$:y(~ߨ"ΑeT \1B>(SnohiF3â S{<˭jl@O<#2ivTjԽUecN4%K"/!90]'JRE OBviQkxt [Osq_c\Hc#w+Uh!eZ/q$$Z9''\aG)^R+pծywDP /Cѱ"\(IHl&hM 2& ,.l-oJ`>9 F>WH>nD@H( C dIz ny٠NGSYžO=&k5$fJ_\~ #ilGO!TZa ;jD>{r%c(5JF,-qB+iA3y]D~A:Чr#||.Ԑ*o|` wvd aʂlyMP$E! JYP&^V{Ԯٮzf̘w+8өgƺW>[U<wK"f|D0>ПeEcx1lp9C)G'F#+4b-E6z}>sx%\؞7HR!9η ҥH(I18齈yS5*GuO{'esˠܸ_s,Y^#2dzѨL352JD0Ty\3x:~&en+JƌogdI(K]5^D%}#({ y JT^:-:F_Verʫe3FyI1-F ʏ}JjxdbBAgwP: dY& /=C]*dg" G٥=jfr|TY[ mV9[l4bp>*bӄaD!rYK]ȖʮlkB eYjb[&ۿf*'] ]ps0]l1+$b"}jA*UV|hHFCMnofm t j3fV0ZZ( Efcqʉ)1HЙ6⭉L2]B]^'ƢLCFBA# O!Amm&OK}8Hh,V(-?~=YղP]M eDS jMiWYFx1vTlcMQQQDE@'HZ;r^h-UL_|Nd琂Dk-0Vv dC}=D:j4n QVIr^ &{p7"(8EtkEe=ǔY]!qXq9*-U 0VeǑv dFvq9 5*UQdv ?l)Dkswl~zfn=gqWݩH$gS9^jLDy!h@J=z:[6|$((hz1J!n34:xj%20 D%#$7U^ W#56״{( *F9crr8fXC; bI]V) iDzLŕQ;4^NdyqCq e\sUA͆OPklwLS2c<Ă-H†#\gdxO>_,_dy}RbbZZ_JLe%!^/e|p-bTϚc AXS dAIi;j'~?QԊ"&`4YgN%? ~Ik'!$9!t쥅$-E*oꆽ]2;M%$Q\3pxnc]N&V3>dT!\;չko8ZTJlp NpuMnF^zjN"kjNܫͭމ$ǒ+b?e5>WsUctX^N'߿ /` uH/6vo%8A N$ e7`闽t2D6;d[HuZj &›o,!ؿeǧޠǭ.dr2P$ TfMᢩ4eY泽=-  殙1!\ls|# GI9p %П?Lｬ{F OYYZˌ:lzNY(Zϗ!\S'&RC3Mv̺8H%j|ڮmdY浟xW()q87<Ϝ!Ul@>ؐ<𵨃NLl_ {HYCY&Al`[n&)N'>FsCwYT?z<-\d Zy|aGhgms|J/ejIXO&#m#)KF}@3Mo#T_NJ>WEM -ƕEl|*D,+9Hdw+Vд#D]Y̲J"L?cُm ~\7Xhg1Uyba,cf&?{= %&O-[XBlK.isMxXg6 ?o/?Hff&2j(+DȲ̼iH9rv;:ൕ+s'Nۻ_x@,/EJMيuU$)l2xGGMo֕ q!./ kir:9n{yژ؁g±+ %u`?B?zg{$uW,=$wX[vI"'nZe1jfjwv,qM%uHjnWtp$[N1%ؤժ_%,RMINmsnIif2 ^c'sr/(ׇ)8<5kgD`Ezg}F@)h03(A-ˬ9JOm;C|.5yV.u8yY8f y#&j{RQ*q2k%#'EV xMwf4q=.(%oAć=64'C5j޼2FƜ6"O)Ա6=;G?Tȭar:_hwYxd}Dc]%HB$lWX dxpX߹:v I 'yH**eKp&;u 9˟XSV_F3,+ W"|FAu Ov@&'“<.y,'GC{xc){>ZHrʯ$λyo9~(VqVf28l& B:V%}>E;]!s"5lDۿE6+")/3)iN%$5/%SOSqƟyfe,:8˾ ;s/t,Aa(:QmcGr䓗j6q%wV?lB 5x5"˲ 7.d|I:\\ɫ{y\'Y\i22"Pyן8>(cϙzȭ\Z0K?8$E(x'#x+1ְJ R@i?u2>7L)!v6p%g,I60plÁO}==-_{p]oHZ EJ%'Fܷ^|c/_v;1۶aZ:@z`ӦMgeT9Q@}=+V]8rr[T2ۛmןyCtY&Fy=fk*Z]Gċg3m|>lJnw8Y<-J,/.# m3518s+n߮BPOJH^Z%dhm%Dc>QId}Q6]fq:aR3+ 0C㚲s@~svy Yf?odwxmG3qkyt~b"YfȤ;&_@Kbaۮ4.;#@?lϫEBM&nуf `wkqh=)!MAZA΃;Cl[k~a DIdɃL i7cbIMD#S!67]}V|oMcdIcWI"?`䀄ncO;$)l^v;4lW5N'l7#YyoA\BJ2ҎYuEٌ[4S{ mqOmtQ*Y;8%ƷU||6lԛ \'I|ϙȞ=A$ IqEw$PvF 9*Uy 0^emeRselc 7Y-Mk$~%̍mkޛ!x0H,AmS=e&/4$0D6fsy2O2;7;rILfF'6ù\T4Z*sV|*KbgO?Ϳ_x", <޾-UV{Zo-!&.ݶdIn"{H>M{|ɍ7mA®tn>A.BzV>$<Ԩ8ZQؘss=XgeFIb¦8Q1݋ XlI䑆rI"PCgc$5*1Iv)CM&:I$,QV3{m#.Wnb-_ÖBjs''qӌ}Oxl1xjw+y0cnKQIM IDAT_kbdrwȟfpr:#mV1A^l6崓= iugܨ> Hg/S\j曯bҝwq`Ov Mvly . 86"Gu9~4W#U!W(h`+`duy)|GkJݱP5+ 8).YjɕJ]`bܻatU{G%H>[3@.--i/$mxZᾧrQWGll,^^8% luLj4z>yN'7ؿ$I(M K%G?I9lcQ㋁ {B̸Gfk/#$isKJʙ$l@RF/iCӶQ!{> |R`MDTH@-X]X8x kF`=0oC! Wؼ'6-¼ /(:mw)8Wu {ssPI. ?vsԛ@$5yLc]Z]k]ti>掵 'ӑ]$1@fY;Ch0 ZDݞ2!~h.ꙷE- u@,iYPTxh׹=h<I5Xu`بxT jP뾚?AS=V$ؙx+ v N?{:e|e֚'.Y%.x;n"n}`Z oc#8%KdֹBAIcD妳6-pΉ}uڞJU-`wov"s0f%O>p8oJ ˝`w砟NOPm\!;)7 J2YϙɅuf u`,c1sD~;V>$٩QmG:)5P( #$"AoԹѻOdr%oE(u I[i}w:DE{/N[mDWgDr@&V]+8" uITU@2r=U< ́ >2ђ}`Ekޝ:_5,?rb8YxweP=@,Pm+'ze|ՀN>)IԵtnyԚYǗRGJ/Q{o}Y^g 85\ZPiY|Ig2 xs_y%|(Ix+ }Ɋ\0 D$ή:Wz)1oݱV>T8S*UEݘtX>9xdxQǨ#ڹeCkX2EW(QO:w2Q` L'_L4po>O`Mf2DށD Ჿˉ"xqH"axVPUK{qcǕ$Aا'7:s&Jc;1fmi@D`Wf"Wbg|2ϯ\0\dw><<wCYlX RѩfUXL'dsqwϊn@12eq$MElMDwGa9ڼaL#wuWj≯e n(˒VPiIl |7@Y`PЃ᝸3$K"C %3"귫J d!pLfdp;ӵRzD@3E H [ZgDj ;T޽J5i9H,!Q><ޥ?"8I"#= %:5A s T28"^v!Aw`hXRn^*ۀ_JOmK\q]~ĝǛF# :=mV%JJÔ3XDBǚe y_1lv"&nҁ'5jnM`@Ow;[3@d}J'SHnnY^ hx #T^Ly''dd,Wx[^1f)<ƅgmrDUj^1a|LxIPw%1R%=b(Q f$B=M@_^NEcjo]5QӮl=ʫ/0j.LF7"'7u:xrͳh^1Kn,#ٞ6(IF&2 ),c F fE@ځ%i %}C:y9[ӿE߁]IEmJ97VKW򬃖jg5N7{֢4q(⠥G%Ȳѥ߶d*,_.= Q=ˣ9mhO bt:4Bgɧ;+O>ݫg^+ ?qKHbfk̜v~^B[.XӳS7S7+W;WrX=,ZlBf3O?wse?wɍg /p7_+ׯgŜmDXs'/>˗cE& kgtЁ7PRTĤNҥK Gtt" eL k[f2P~kWƵ80=~%kBK%7B%C"j5֬8h4̸N6 /+og~J(t8߽}˷ c}/׋:ޗHOk<(Ap/,_CPVA %Iē24ڮm.yܜ@f=}$(,O{e_9)rs ~JZ\"6^?5|.>p;o/__v03Ww wv+7q%dJ6\?uYt`پ$|_郄$ltv|<].ahݼt]:N%jZ\ɌcB-#ېd39^"ye26A@plm"Jj5-ʲ5ʇU"]\ >}ȇFjj*;w~$ٴlyiiw̸[o%";qϴi_QS~..j#F*odY%wA8,rjMA;w>-\j ,& L7r2;C9ΎND|Q!j]#n@P(/{/,v@!pm _߭EdE~qs+s+7+UtЃ~IY(GIVtv56ȕ(2H*(EsuOj.9; #@a~Vm,* Aն=Ehӫ~\m|[GxZ|_ )Ik{g- }ѝm2{G{])=% Ǔf%PF־SE_qjG zK-5 %e2M)r7kna}ق@Rh^{]5I[6aQ 軯"$Wkjѫ2dV򧫻aiKH?45R=XҪ~o.mA>ڼښb_?xLAUZ;RߡνjMH,+BTl9`8AFa AA=P]%e-.dy&$ZU+oi9řrAII*ӻVmHL(~$/lЭwWFtjߨI6SӤ(ԃgʇ76d&y&մLvI,S4v q7U]Z`o[ X+3+!2/cXީRFPMڵXL& θqqkJ,Uۮ-&͆zwPM׸ D݃ 9H9)ED1%ErϿj3Zn(`wEU&,|o"28v!E` 72,܎ֲo| ?QSx t#Nߧ$pYɸ;?Vs&y\EQS;iɲ3qCqa øx7;җRu8eMPM\ Sz SZ Lq]B*w+%m5#:w-Tep*+ȦUWDV6z҇S闩铩j*C Rx+R jx;ogf#!z=g׼ymF"Fbbbhj  `Q#] ?-Zğba*5)eŬ()хnUzO7UPmY=qBIn=P* ^=LOaB{w;Ir-:A0tZz5zJv ȭz=iov3CzNO(rIV+z@nOݢ͆N`$b&Sݪ:. ;]ܝKYv 7;hE%{Rܳ+#k܍2eYӔӧgwt?;2l 5j_nQ&FcgO`08,~cG]׺7Q$hn7dY&?;Bo1 TROj5fo ۷,ϩ>n *5aJ@t**T9u8TM"2/'p"'ςR$%Y̘>na.2ii&C99,29Zo| oI"hZHNvh!d`BI9Ak68N1ևOGj}7:O=Xjd4J22(--%\ND]"D++ls++$dPO[JqJAFPnw j.`»N[RJ*pЦ"R/]#X͘D^uNFaXq" z2乻4VFS棬eٿ>!h I/g5O2zd^z]Jjww@B{,m9xzx޽;Syr(*3P\nNTƮTd(vƕEU'2B`dx5YqKAM^CR1ё$bd-E^xVF,~VvYj%deቻgZ7NNk~MyudND`qmx$:ʗ.$oZp1bFېYŊr?FMD#1 7+]܉M{=j\๪eYԪoJu$[S LRbFOJʹZ -EL!>g}e#!!0ϧNY TB~<JCM6K{vn++#[g} Yr6œo(%ՋсҎ0/=B@R݇a=5e mD_P~J, y9YØ+ [C*.V;֡C~}7ȗbA߭^eYZJ7~lpĝ=Ͳ+(.tDE:1>Um Fc2[(C[]T]ZƯGNK{vG260B~m3 Q(pjأ3Hr=Hq`ūI9@3>p Y hpӷѹkFJMbw+V #`_MEr/bEzvmv)-_O`c8IZ+.½$'S+cUh˖,cBQ RI\2I޿(m y,"O~GQ>#]^t:'ҐggEpAŃN߀{QY|ٯ[D_o3O '* wgr$"IV?7ʖd qupdtPdJ5(>,cˊA@"{.82Kqwi q\iLn"gΎEgQ $2QDwb-{%J2~'PȮ(Uwp]?n/KVB`>}?q@U*r7-[D IGwO\s0Ƀoor{f!. 1(]9W&$rw^KB@D@7]I>#ҹ3JGfu(buxfbvRY/_P;macIMQF(U#v&"lV~Vk'zʫ,3]~(IMhN!aD{?YY}?lDzGgI&Q.i\7Pz)_wX)X̎@1HzNFE1bZYoqA1xTg6Ap1t1EK~c8*f cB'UN9wrŸim^&ɑe܌=Z,ˬ?|?v 0qxUN>^|2ne,cڷoOVHpj->^]u9 0oݼ?߼9fW$o+!I̤0xjMȢ(ןGDgʔzdVSάu'j0QQ-yVJwœVJ7\FOnrbMfn?)7jOރl$DYF{荰zA\/Ȟ.kJo<$[>z1h5!2e)(K9<^QȲLYIJґeozk al6rBeZ__}KiG[R V ]L;e sapi<#yÖ|t'rg$aQ 8] ̉X SbTZ!ɤfW2Rbϱ%dgnt+:GYSDA .} srƖƱ-[JJs 2BXxJNNۆ'$INmڅ,Fq7UsOFggsHJ͏ fU?NMڦ$򕘅|*5ErdYg_6F-Uh,r]dڧQבkӝJR99!BW@գ]vv+I}j)Ș qgRg-']}隝ʽxUr%r .5-,߶!ܚu˔qZ^΍,s"'ADZcjAxVDRR.;r~懩Xd!JKb5Μ9×p,=汧& vLVV2&00Y:Yp Jܞ&00I9xO>`!s<_NT?زe ׮ݝCwEXXX?k%&2kry1-9}G|E}dEs6+ Q5 vnNr\ I\+0 I]2fZ *Xq dE$`_Y9w=Dp%Vo/h hD>|s>y=쓊[egK{ K?g?ǔG爱[, Dv֬ZAfI)/YHm"GR)m"ꗡ􈊤||YlK\\B/zY&fckR*#ȗ![:T򸨸5vp(.uFqg7,Gyo ߢB~C6ČGEje,&=m1};R1JCHQX<@a ,xԵVZϚɴZ!>AKsxtˍRYbI?UW ?njTjʡyߥgXʁylA} [V폿ְ/nqccRCCScY9r`?;hg`G~-&22Q4vbЃϢ1F׎Xט:y:oNvfE~sP$v*qk%W^ˊVg_ṅcǀـ+ ȖFΜبv,?SmGg%Z ,dIy,] {|z~iI*hǯOxϢa "6;//,Ew$eg#ޣw Sϒd#]wY(J ˛(zdY\OD} IeV}ٻa#=vgU;7OocUƠ̇>k;!lJgu)Dor98n_8> INv1]CYjJQ|,C`DOgEg(oT=KB| ?ߊ+h=e|ޘV8'_e<=@ }- 8y-߼Xh/zp/o.]̛'%c,&n+9^R#;@,4cb(+-HGɕLbH#49?ּ}Q+֟JRP;Qk=Y1 }m4S8 "99>Bd8}vt!7mjA"Õ|Cyyqh6ys|&*7gRr%f"bsԄW?$ a2.o'c1l5WRt'_\ks_bpOUU۳'88>OҕRt1%`f*ߋ Փ_\b=YVAV U4+-}w$Lz2fLJb櫯6'O+ߣTN@cÆs=nu0 m6nf7Ax}-bgL Uرmz}DٳDG_t7l؅ \^_T(MȑW̬E%Gftd\͐eof$V`wHK'ffkˠ$ 30 E>cʝӛ_Fj2Yh$xas9hS\XPrwCP(?g} [>qGW?"*/;&S|.Έtӟ]BN8v0u^Yx"; ^}#1$jbaſg"D7ۚ]='/ 1Œ8'Iİnb)hb^x;e汸!#+ 0`r2+95#ob Ǐ˙\7z=:w4T;S\ҏC1øSG1wۿ:գ7y1$=ETeK;Fޱ >߶f޹1C+Iv ͼ=J/lf;dXpIlON y\TRZo,$9{n`)fEXmBnVXa蟟&ʼOz[Hr<(*c4췟H%7/,ewn&xܑOwWM|/WG^/z0߄f =hIfAj&zZ yl dYw/c(/ln2xn8Bxd$x&<=TNQA>[))aÍvn}V(5I,bS d4yPjLUہc#MV 6<9o 2l5Wh2SmlYBP7PD=G׳_CE~ħqE&EȒ͛K Ex,qxzdVn F26%IdM NY^#9v6өٸ{ r;&ʓKWci|0H0}DՃ);áLʊ9$U3$). YΚכ(I22r(щ.&J*e>{6op (tfMc./b"+t*Su (?y}wU,+(2JSر dkbBm4J=-=n[N=2#Zp\2[Rey<O@zEŇ3d< *|pE:U훒}]&*G/MAsfF-h.7$U8|}5/ =vȗ+סVW;7=TBw;7[X=&,2+W祪4gna*dG0prq-/[ Řm6+b~%%gE5[G1WdA~tچ2i0Yl6ޡYfOd̥(]9 z2P;?GWh!Xޣ[pZ'/`&b8cU#H܉-(w"{kܧ@@̖%X+`Gz dc&HF|c?k6+?'$Q饽[]ZNxڸ_LQ3p0@_\¹B^VI6q|`>>hXu+7)Q0V?o߿ /Xt$~?s˗2뗍zTQ IDAT sc}(ףVTEĞAfz:ASePu%uj+{VO$?}F<* {3apoL)o]/ʷo>_&c5Wb22#;e୏6z=Ts%V r3qp_+H[Q;p|!k*jj8p +?J_YbMA ?qtc#$C-A A@җ'l_w> Ơ\O8c(ѭ:Lp xMTm+% rr%e%tl׽VKIɼ䕍F4C|26J$1Xr.kHθS|Onzk1*|![zaSKW_\3yϮ?nE-PP֋\S Z ttkӂ4-3gpj f3/oOȍT}aYk~|嗌_Rmwɬ9spv˲W_DFLzz.`Tn9X,:znL2nXx륗a%%/Nyyy=' (P(<)))E ?*Pl5m]ăȌjQy&D!Th Σ",̆]xw"Fۣͧ +6"I0tN[xf2?5zNko[0Jeƛm6:l|5%Xh0 `"C=*䂊kڊDz$!^5]NEBPWr:N{2v升o'Էa ?RfG`@ac艍SoWFyg.(0^c4ӏ3*O SRp ecy<ؓ۲\4cã?پB{D Xk/xzE DQAz{ $!پ3?ME?灳gLΞ9}[RF͎5Wυ ZDZa*(r-N'}GAE%Zi y du'6I˱#t l5#G_}; ((`y6W<c'Lql" ba P]u:k, 4lk(e]*$xd[˭ j42ɮУuO6 dWmw8BjF411Ӫ? 8* ~Eah +|ClUיf[r0x>݆ QdI@QVHY\XuR>+B 2X+цQX9ss lRnbfy|m$Yti,q9i܏P'JX-d٭L\t8UQS!B#ؠl7"_רmw`8FSmMp:H3S=ɷ\vg-tx 2~UY*9Q#ؒ^ rO9LE7m p$!M#kt;㵑e^lݒ[>#EcD[ƣvp{X(щo!uj'}kUjvK X:G${={9f%Gl6+I[6LN߳"EArGxIdLyi%3pl PQ"v"ve֝ RkcLt} ]{" w>T;j'Nd޽Dq0iURTĪ<T*޴Tn޴ʲWھ%t@k(E|ɿIu:e_c3Xʭ5$q~IDDVr+-|m,.Z6SOpWdep p/]ZZc6 GIB/'zV%&8=csÝp=tY N^K_lʧ`ܿVռU <}guxq2BJ3꾟AyI &@a^wB`Hy@3=Trtև]= TE)M[]H)+ @]"3<=ɤNI iӬ鬬\&zd& y:Eٯ/݀AiӸ5qxb gQQpLX-!{n}7ĵiXvsF]6ல~[7>,.̦RP6R;LD1ƭ"s/(V([h,&K~צɗu֩=۝YA rJv) -!!"FڻVk7Q\r*m+@` 6*Ⱦ\"o" rTce܎&BŴz619],PnEDWB nGhr+*IDq,>rBSjVtw£ fIU` Ҷ8v|F+"4zS Nd#Ou'ZtxkKe^[k6+/}"c_y:6ܲLJ~z}7@rw=\_ZTA8 ~~&(`u_ubLM]/07^w\ѿ?+L&*U`.VMee..&3x0pt"55@_ɰ+1 x_c4bS)RVVƉ Ɣ(ˬ]֧K6\;Qj9R]. r֭K-eO^ZCt_S ԍ H0B%"ÀWn uZNH̒,`ŊvF4 KdTeƉ#FD/n^j t 5 ^O^N6J2`Lv#|` &H*ǁ^vy[.rrnۊ{'-|%Q&BII8ƮcY/+R/nJ]߭dIi9mv7'3⮧(*k\{(߬v7kAo 7 Cյ9qlpm]~S=xDǷjAnku(.ڶkFq8y|^&,['BѦY( 3sJΠgYPPA=92T ǹqiL~uE7L1Ķ[^Omm7UgReSz)KVQH!8\\3ΔlEh=6.8S*:DV /:Qx,/KXUP>n3R*(9x|:*.'=z|wLjRV09t0..tRPD#(4q24 NfdvՏH޷Wjc@7wV7`,fˆ]ÂM$ GRq(ȆT:D4,Y#~he!qj5usEFIMZf xxz3}lawncoQY9V }\" I 娅[13*9 y FPi/( ٨tfU߸*@VV)izןZCC3 ¤ h4k &]+fqż ~}ѱs p""Z-E*IFz 1^g$ /_;״{)c}+fcòO{5"i.F,XǨz[4x ,JRDQA%G^vv2~ZMZφ^GAGZ|Ѻpă@=X.3׀I``_ii||덎 &VvA *aT҃ D3htpwVK;RQ{nVU>};uOxO[iZ&d~Vq,#"PXtR_rS6BVu`j(h@u&O1/S:/*:fuN Kx*TVyN<=4ѱΜ GS' &q j>$/+huGL}Wm tVMܒ 9hapUg6i,?BB+;B{lٺ8=׵s[Hd@ /WvY,,-,ƳAKHυrŻm3 :`6Z0n!PVTXoS.ZNu8wD;nə -mQՀEܺkkJ|X,(Vj&1WяRADgt`NJN$ mϖ_IZZYN?;QJ֯ELDM~: ?ע0ET{QTq%*uZta)7aW2UQ(߿'G  f^ KjzDqo+ * QɗD[\y `"t#2v{YZ7rX|o,yH~UBd.*AX ɣrFdԻVB 0*+vHD67DFŶ{vv {#"+KedZc~Vl֕3*c?g{!(+2!-<—nwQ5 z}x& BXPOMA].s8U]a9v,ZWNN&i*+& kF)u2 1KwZ$Ԛq\u8Q""ػL<_~4Ebm'v]#Ib5\;:yD#Leoa4@/ WlomZ!"Y^ {${ld k Tw~ҳ.[ږE"lbTQ'EcT!?Y@IQGQk@GPHG d <A0" p @ @`+p{؜*wY$&͸yʊknbrl;Zɵо7L/糏Edܲ6ѧ9n(' b.)`Pd<1AoQkiw2 p]B C7` bp;Y${xd@=[_7F_\G}16 [Q qv_3'㆙U3#˹(BN k$Hօ_ADQ<^Um;iO$JTF֜, ++϶"_'=Ǚ{c·1l\'+ th׎waf#~W;IBBfS,׬]n=$*~?h ;%p;=_K=9tӒQOF:Kܚ8OPa ` U:p v~@mGLڍLg9Br8'ͧ(ϊΐÞA䩛 ,*7m; )IֵLކmwM2aM>^OVKE4N ;J6s~Sz)Qx˲LdэJXOy?qmiq:vfq`Zdጝ:{OۍMg߽oطCR?Av#73Q]N0.HFolNfY@ C)/B>n/IY7yVtO5\SZ#s+32L!۳?j4  r%Z/,橔G#FvU`sefյ"]72}Iy"4R<٣^ťnɕL,^E*4zN+Q[mlc\)wHHQ^7QR1w,^|%^}u2N(#( W\11ZVVƢEHMMW\}ռcZ!0Ș1IJjx7mw^߿ ;ޡ\pA~Ӽt37%# ??f0a|>|753h8wRGncwI=cBݏ8kT|S&ϸc)hjN_݆ tk#݀(.߯߆Z st{"M#>»A݊0("{>3ѽUӯHIwqė#ҏGKJ:CwQd,%:Y.\& ν~XCdT<B wX~q+:19|;v,",j}s4Aeԋ_rIT)ěF0^!QFc4FO<Q=O?>2'MYUofĶ!H&2N;97Qs$tѠ3 Ӎ^{uT~!1)Rq@i[^=8?߇} ҆yC qӎp{>mUG.b,bde&6āYwF*+N1edrҐ=2Ӈ2n^iDԉ6p0᳏P_ X(MdaDjZx<87bpdn~=dp:?AzRQ!Zp4z "'QPk<ڄ' IDATC kFӪ%N^E%bxg'r)u\+ 理6H'gW }*`2'R-`kC|z zuTv8>׷fADۂH]XFs> ͎d$uIrvPyb7k֬#((\DŽGBy#&Thh..7) )0mh~H.P!yc~oZÿҎg$mgL6^9:rg Tl/؈/-xc62p%ھSƲ"*\nxs p#^IO8,]'?hfjrOzj$xE!]hT*6SPArv~GKU@נpUZ Mlu'lGWQ~EޗЋS %&?=%ީL}=Z}\}x ӓZEarwdpS@ . MGNTl2.W9=u DwZ#VUk*owHMF]d ݁o{ۏ SqcБ#,DQd+y?J2 .niQ-:C`h0Rh3^a8? q 7?r??Qhc{I6-_Ҿ{n{NvoLVFyTڷ֞_(<б?O[p`'p^toyǪ׮`31ظT*>C W.tԎk6Q2{O/BQp9͸m {&EplHЫ91o߉'5 o?|u5Zn}'_"Ev#| WD^ XݷɧO >g^uk6v`)sDZ{ sUM%/^:>/ȣ ;.H\ESRkB cwJOg׋ܶmÇ_(A7>|Fpj+Nn]E nEQ.{$7_q|8? oX_M;yn2>i,M$*¦UYt3v](S.5:sGBZzn,7䙇q,P nEtp64Z92G(3-[ƌn[7 \BgS+RC'Qlߏ\Ao jƯx@ŮC<&^\bNtrX;ΔϓpR 9K-av4uR:n_ Xb`_qo[Ŭ|'!swy6xĭU'X~=Z9tѓ\7㤸{b\Q,|![q#ƒ@eH =b".f@^f71Aך߸nqʖ*dq,(3mկ%}SXBq(rr8>-<ݻy(pd:xE6t/7O|K=!s@lhWk8v}Y~aΑ(yE֣$1JhQWht$*\|ϥBc>..vH)/!H~ .K6EE ᇵ|6RNK)̓9ge-|J6Kp980(;3ڵDIMAaēPG~仇o"o<$^Rt|z-!y⎙~ u2Zm*R^AHk8su@3uf1s^$skʦU+.蜢I`@}z8gfOS5J ^6;*]d50FWUQ@ݸExlAP]np~Bkm6^ {QiOH bCX#plt!oc<Q"Hz6ڠmZL(i h2}ʉeM}tu\=aǨp{*vg$zEnvfc⛭$t{s'N2VCP?3%kpwcoHK)4r+@K~Ac0o,{+^yD +3 uSI<^ΞU6 rBȇ$2{,`! y.yzc\TmwW!ϴ].lx :pI4ԭ# :J\4jׯt:xc#2e%O:OQn!QϠ:oߊ vc1DoXJS> %g҂YHS ݆yg>ف E^}q^w_~x<+&\7''Bw@nj$ NgPxRm$ѥK:U.Y.p8У ~(^7nվNrh\c%Oג= F6<67ΒS,vhƯfM] DAh d HH%1DNL&bj3H(w#0i8)2W# tg-8JGfɫPq]x? 6/nMKAu/A $?Dv삤])z/Pbs]Z.\hϕ4\VIr,xk>t'=p%( %O#iHQT[Q{Rucn!]Iw?b3lYKQ5-|,Ey|lmq;7ldNeU4A)cܬjLiJ_$X2xz}L?u{d$Q̕W^y(2u$&MEkQ**&q[S\_@qNf,+끿`BN>M>^y]lxD@YLzO(3X搚:{7x:Z?|$MFȾݘ's=vG7N4 +F{ l޵X-3^eejs Aߗ5>r?% ;|9.mxE ZKE^'7/Qy (㚃~]Y>_}D|HMDXsvnK|hfLeJ[Ɲ;q> ۃnWsHiRi%.'"k8),|k!5aŸݣζxlΚU:߀ o?$|7FZ\ u"BOm;NoD2MQ x|Q,%& >q]Uq9pq%g,";׮h((]e2,eedtT8t|3aFw.~rUum-c] ޡ_v|~Q:2ܴr#Zkz)uV51@8ADPQ(ȨL:4{Z] ,i<FLS46  Ћa 2("K/vF_CWoԇN˰6' zJ=.w,lS=IS=OM8!HYɗ s!@bLggJPKkP^dg|Mm12:pHRshn)cʟ]Ԭ=8_ o΁<xmk^x3C3^H?n"ƥϕ|9A0K;tq\o|;;vm1Hn0FcB>kE9>gn<k( ZEy`ª(ĠIĵmne.U%'CuY[춷ࡼ?p8v%n{cz&! f!Xt@R nz yŕ o9cl>ݳf=x]@0SFޖV{A+x0Lٺu$Iդ-[:N:!GƄ@+R|NNAqb6 EaÆ5J:uEPՀFǑ#G8|x?R[FxE^cJMRt#\RRS;V~&DjZ>Z͖766V Ÿ]9q9[bнFcl<"Rxj;(~ i<HOnoغIdgeOr=HQr'GU@cǗ38Z-&[Wб}F*Y h 7j <{uKEiB@p6o>ffԫ1@l\bpؒ(fϿtUo~  *̼|7LAQSs%gW] q>q(`oS(J;n@Tk Q@~Z-zֲmgV#9\+v=x3Ef>Rܵ Z7Y~o4Q=Ƣ?~Z#:Q3çӼ}˺> ohmAIqC_k "*m?JnG@Dc{y!5Y({4k&P{EP78sбWQb6CL9DB n ] d! yHg *0=DIQTQx<HAI#;Dk*U(;(/ux{#[`$qkZ0Ld|:( Zm y; hDʞĶDb*8Vy1"P,:V{q4z? 68܄Ѭmw)ctoK7dQn6`8'>lW~ lb:Xhl>&~>jO/֝ U$~ *I_X4p\ !J|ZlIS $icpكDbڀR(g!NmxTs/'GH8E ȪNXzf_ISIj a4+WZ[gSC= x\6c%%%(r]?,'R^pkQ}>x`^Y[;퓹.h򟅄o؀nGK.$&FqC\/h3h422>dY1PjCQ$zР;kpL\Sno2Μ7.b +[]!A,zyN'U_qpKQDQmȞ:R6!$$ԽQdnTԚ:/_(_ s  ZQ<;kv5޸W\ (.ڢ$y yc5FSo2ܻy`ZiOonfnzݛLܹ IDAT3w0(jd Tp6iՍ%?6IDWөԳcԝ^1Gy hx:Uv4u#A`[߷יwIzDQޛhE5e?chn;D%՟u |!A._$QiU,ox\꼏f!׍EFŸ#c]x,IG^Qj }3aBWuPXuhnRELXKF#Jcã(6ٚM$!C#ҥXcy^w/""RAz 5zH߾3Kzk fggv̜s>v= DD"KPTTjV퀢kl :**xף߁$M8A1a3P۵Gבm'2u r?jJ6ʲQQt A`ZT3$HMkPǽ_a{~=k2N{.ʗ ESQ: nwAe~kͯ)x瀲/A\K`6 pK-( ECǎѣiVF=:Ʃܽs;sm3lc V084UaJ-,WV%2Pg?p3# 7 f[sM]'QBq \pt(di%\،g@ P:# qrVNY7xYXnk W5q%wLҘ*VV|)(QV͔'6o8J.M -P/LƯe"Q^:ywS:ͮd2 )*8K-jVܕ`=pqY8Qy+ѹҤe(M\^ ڥJ sI_ߪ *a3wմ)Ox sf[/K=棷z,c0f[\Z}v*W V1cC~]ntفpݷL|Nۨ~huu+$I#7bQ=j" n(*q*݉ybzO2h]hQjz||o{A=7S貆[C\93(\j2 qUZ]]tRUxPo> x4W7dzKo4πU#>$Sf,s>+H*Z:, W@c6F TVz}?؟vGȷ2€!9!RSsHIN&3s5"7+}Up£z 1'qr;K~pn&THCTн{EUe7SC/Ba.V悷QRԠ^a1XDmt9ZO& `sʒ e(sV 2Qu+8T5vJ`hyY:Ox;d/l5DQT<|Z2pzQDx &3[fx s-#HKSz,Y1o!HC%@yikn(.zbǾQcDT]$_ߠQ}Qi0ISL6cX!xs,n]cAPk,:oMCl;JK5u 1}C/dCQPi&]~ o7tU:8{IuodCP,c*ގgn1p r =ʇi5hA$~> W4mm3 7 ǎ!;+obVI@>$Od١TM A,>mYlNg7\5ΐP4UKbⒹ]}p遨\nK?K ک3#*+t*|||X~yyyzT+#F cĈaaҥocW%xcJD9w?_N}{W8,\=} p&SGt³>Œ%پZQz0x +K9K"fDEˊ:OFEe;.O,m|5YzJ>Vgiwh$ޡJH/0#I+&MuLEg30Av&k_; @P).HR_D.&Z|qstߞ" .Y>IsGed'gmߋ&N7/ƥfejXe-dՊPD{)^|MX,UGqq/$f.jо{kƩarֹlt&fYBp iѠnvEWFmqMExFv0&MP\9ݺ=Z%}}Y9kAdY(Ȟ݇MF9JܮGeiLTZ-Ƒq8ZcކR}/3GS֣%Cߵ5z;b0 (U|<~8݂Pdmo$e]]0L:B8b+8hˈ`M8~] QLZ@,aQK+C߈%nz@}9G\QvDJ![!IP*}||oOܾ#deе[cƧ%r]*d<qŨM&O`5yqrc 0mzc*CuFۜ=p*ƹ5 1|=yWB[g^j(ĻY$ax5|dy cA6ϞXhyf#ODXGA0y5xQ]JPdV!û9}(NHi]0t9۲@ p*eWCMh1" jKW9t2C;ص A Z^x,f|7,4b 40E< v;MBA$1eP/߁Dt⣥B^^QeVF}1Ey?),'A5[Gƚ璕!-0|dÓOמ(C9+xz˜96@/&OJ6uDʮD1<,ߴfj-z}p Ő|4ۻDDԺ87MY\,Y-X;ɿHBGv5Wp[&>KgOCOD c؊;R+o &G\: &Mj[?H'Oeтh֐`6q۸|QZWUXy WIޠ`3cadC^ ڏPl֒zC-qʤݰUJEo N}w4'?{tYجZ"P(#cưe:F[Ϟ`N\:h컟z'HF`5x3.qy(y\ΕηOb,FQ;{r>SeldhN[/DMIorq:$GNh[ov#oӱ"IMn.Vz3MZzrn GضsǔR!j#D{ޮNy$&Ė_$]~O?ٱ:hݽY~{=O3g{WgeXxb >7(IqXxp= u Ϻ㘳ٳ"+@Rq-UWZ G.`C4v Muu I7GGPBNB,JQiQ ߈$؈lIRH:"jo{} /~-gR|2CTGJbkukf~1 '!s ;Sؿm wG\|k,z(.޵EP;ޮǬ/' {܎ɁGRrj#&ѽ?:/]sWf} 8gpN@-."gHpHmOSoOFFBC۷9FQ +26C$N@PpW x~Z3>\:~5;j{\vG{g3dß`20d cm4gO싿ɇ5Yj۟"kff21-;U3i/jPHجf:\Auj{J>[iJT͉X}z6{s2j2j'7mi*m[ss(N]Àq?~;cy _|իۇצկ?YWWBf_(v{*}Ǖu9G B:w!<.n?'W j q@r,U$sám΢Mh܌g_zb wޗɸ cluzَ,]V+!;>k#+m$)SmPr W@9ĕcA iA^Yrq (nynd/}F YߍG7##b֛]Sͥ3G8 R X=%;r H ;*-V+N]"a~4Q!V[_<.Gy wO"?X Gq[ O+qB {, dj[ՋZ.)).]PP0u41 < fZVs޽yz֭{ tL%~<P[,;ƅ "/XPՒ6EDtjlҁbOZf6|&,Q27>*_l?(TĂB{S@ǐCAZ l?ɞ';]|{,.ͮ 7 ](JƐྯAc<: kY9Z!I?.xg1s6?Mzjpε $9C2j'~+>%<+6 Fz0}9+>n9Aw۟Iye]ؾT|G.>R/XZut,Y3۹l /xgf=nmW^AڪLFFObJrjBk%k ,I.YbN`s;񪅗N,b"+|n< :g5:牤?fvs*-q$I_QHYbs<,bi7 !6͛`FJ`{E'CCK bW!B6I.Gj<\B܂;{b6Xd;޾oV{9T+޻:B> u <&IlKK oQO,˜ݺ{v89:OxﰆjC%Ĝf !A.}>~,깫=.1(5Ao(T~H͜Ǟm\*.cҠdcuJ x D%k#@Vx7orſOM`Ձc,dжQ=NJ^*}olch߾./~ QQuW!l6-]EDI|e02Lw,w.d%\?և7/#vVdɓݟy-`q%4V|d}(Ƙpk :FG`O;qZ7{B͛=?`܏_b?Bw]c7zA}lR;j]}A%A fGT>+#'&\*[}lA$Cj #ПNS/E#0dsiXMA!(G͢h8.B_<~Ri u1 1عmV~N #&oUוFD =;&(+ 廄4scE@:$fZMxQ"ۅ#@ΝN>Oyp}4@K= IDAT?WtΓ>9ӺU'Z3\ gX'>LNnl]ķ~sާj݂]9&=AVVVptJe ($%9nONJT@JEFF}'>;!t"nCՠ (}] Q@T*9#ҡ5_IϚD>k'8q ?Μ, ΏN& fŽ@:Z#}BtzJ=AJZ*^*AA\JM.nZM4yfS5GVڇ"C,fl I˳6\dob7 9=}>̴Tm)y vYۓPʮ:hm㮥͊>?#_TEnlXWWV^_~31OEz'U7y |l&@S$kwo'nךzSʟS֥4!Sm)U+ۜUNH뉨g kKkEWySzYo+^)dYy\ҷ8SW뽺rOcf^Ro{d$$dgaxx Ul'l^uuB.L!R9srmpHH9,+YVpl7HƭV7|d+6]Cn6mԹJ`50R )&JEss dhy8{ǖ(T:$g#F"l]l# kj6p~粫r/,ݨs@eSqXсg/_BARf@vaqO#/᫯c2h'̦|5S.^}`ՁRKfy V߽}'ȟUジ)W\.'qDƛ  1@FAÞBa`9$&l?lsgI9@^F Y_8d$o%(*@k^Tzcx,[%G X,7ڮ.jw?x.Fj,b6#z 7ﵒы~jz߰d bw\.3).'\.*˩T. p)=NKkfnA5ƛ'Gw/6vd۷H8ts]ܷq"[s%3f}AP(IEz'*EٗϜU%OJ eE}SQY}ԷHϺͳ0s1._yڼ_ŕ83 8@{T E ؚL)hQ+_r=s%7bg^6oIKKsxN/leYF%Iy1+Y:].XtpcMC^%q u Q*}5{)@T*K_faZ~,7ټju Y%3ƾsbBtduSgЧ8b!,W;vM:κz޼_[MAzM9 r` D `}MH6+<hꮩԄj|d 6[YUlFOiڮ%$62𛨠ǩjj{y) Bs%tΕz8 \:}SQ6Y(H ajQW* ҂dx5C3w2/DZy7P{yl] a@Gu@sF=ŒD!{,}-~!79̪?|Y)J!arS`8 O/wQqqX?6C/6Hþ`ۦu ʓ[aجi͊-u qQ㢦(VcqgcNE]te̔DGc,|M<0TZs.NZ8` sܔ/u1r3rOW"Y~`_̽Z=.9˥{=XMc$btXEfX<(K&WWzvβJVpxM߈`^nf0VLغع1cGĞ8҅c}^6)C*Eyy`1_bYI{ݏ>tnJ7^imUU?[VmR /? NmccjT5 mX Y=Z~V{lu*]]rJ `-0\ߗ~ͮ=i}=XpI0TRWsjQ]w@UY> V\I:QŜٺy$mFlPoYs[ߖd] U@qQ6:F(j VzqgW*X l&"Z8ޫFB||<_|9})gm'U_T/r-@sG2@62WL,&⶯#ng:GùӛVc{NQIPLKlW@蜔*&a'#A7~KڐWvdֽĦ^nqm YzQk 5Jd.drqfn?O0.8a5Lj[%/W&ɔM~bsßATԧQ-xAU-FBff_{3g#5džϱwl6UXG˓ez)Pn"pI̚>߾Zˎߝش ?ͥĚ%XD>?E0k̅ ݋|?xZ-];ȇ:'}yǺ_P'ٸu{=ۋD{S!Ws-J^Xhnl#\ Pʕ$Б|$a$SGlyKsMeu.rff&_VXʶh-AF@//.^ IDD ǎ)W1\~q oaa!_|w ce$}H׮rjeժUlڴ@?~BCC)̜++zE0e3,ˬ\,dܸq5a|嗼a| 6q(4ȆHoG^ĒNZ>}EdD5IGd>8-խ2JW YgtCA\ h`OD#*B+eiq[*˲Dd?JD}! sKٕLMQ]3~h,ZsQ#aP*U{?՟sz> 'AuV F7w!*]>zua/ھ,ݦg08k,j<N"OcQTTʲg.bسs6ӻo*˲́}{Yr-*hف{~sfd0z҃\Ugfss$˼f3.rJBm;d0lUѽ{P3i!e|Cۤ*xbfw Q xxxA3ߩ-[0Np~!9f?T;GVnElvo̸G'erN|IRӮ+<1 &MfLboHM-yx0j[gy$6Ibp6٩bndiOa^M<;2igP{p #){݈Jc"y$jC{c/ʛo^؁]I~O;Gx/zչ?"Td03TDRLRcwb] hY>Df4$ ڈW"F=S/p(ESMҥCZƼx Fx0-9|=b3F&2vNmyw:{օA#Frd灒J£\T:7A,_vmߩN=')h,o*>FU|[87SVSMHB0MlY1yZ#ˠAPH֕<.[CLI‚yl\G_-%7NBXٿ;zğ{r/1J 2xj":bQ\_Ό>n R#wP}\y vW )xbW qQu$8y=Xt.|? tؖ]O1'3n[xYW[jb/H*vEWP} z #za.ǾO&` TI|+=N{xp&Fg5tf/q# /ށ2z;s'}`0EY"MMn5t:tq旼Cx6,zeڶ*Qᘜσ?/J7n=ޞm*^k}\xwbɌXӌQcYqX * ݕA"eszk ZPQ2>'9ZW {:u9r1eܝxs3Xh9r11fx{{!jn2NZ",T9f:^?rj׎.AUs7** W4+cQvkÐrN<!d8`ȯȕ/oƮ:wWǕXDy Ur䗸g^-Eld!'W͇rt< 5VE-n+Ig_={a>&@DQLxGO8ܶ<P>:S@6 8SMDN/㏅ >Y"//JUA)pp U΄G<Պ{ߦ~bWH,3? H"+"C{;^|8iIMǁ6GqRU hQ<ˬz)ɽ).S,rSU]Kbfϱ>E@(E]W̆bsp*{ ROέ@>b2P)pOy~ީb1ѹ2ܞܰ~=o>Ϙ !~3-˒pU]!E&vlĎm}ԆP" ;"'%> LDi_]J*;;H%8w 4X˨#RH}Yeg?8^aUJbjZןw.;9PLdǏYF8s6!3w)hދ`jHgn;fZx=b󬷩 ]0,a~-Nb:j;*$M.K[d Vlˌ?C'!Btj~v4mߓ6QX-FTIyF8|?$;dy9LMO.s!۟euě7jNzb3mߊjqx|d>B xABgH-s||& x6ؕ"}$^@T1~8+>x6߸kjJA(*, J $r l!p,Vmbӷ}]ݛҤgjF&yU{ߨІ%,+<:K F_Gm&&i߯A!a<++6}Hj!<-DW_qM1@yH~y x0prATYD :זey_pX/%cXc1෮? IDAT-|ll\Gֻ7E蜝Q_˹đh]q ,aּy_sd+~I O?lEP ȲcEA!n{Xsy內i|֙6C'qfSdd Q3~-\=5H%tB T{9^G=HZhҶf!&!r{d2d&}g<6}kb߃#*h6%2@yQ<PðZPQOX^cYf՜d™kX, * Y%g``9z=lb-z}U{-xoZky`Jusx)u-6ib?"c"z, > zpaEY& , h[mHR1B,%RJ֝o(c͛b, j&E ?ҼmGyI1B'6ƏlnWP~7`oTVt+K8+9'L(JgM^X8}z=s:{ y/{<,-FAZ.JtSWV0-WZ 0B[ty~&ux :dSsI@Y)jl;In?bӢ0&= ; _eF6ٝ?>}nǟۮ9t$82I]+l|-.=@ʆǵ5Ϸ~ǡCj Հqy榡T*z޽ՋqDX4֋{n:usb=3q%2/$@=CMYuNwgȋչWsG!Kwj~罹(_zX]o RlT KqtU7e_lyȖdoH56`cQ}K93񸒑f ' hnhЬYçw2:q}ƣ^݀++ cŌ}FsSFroﮝ, ,jK# ó-WEY)6MFICϲ +5)ǙjT vgErG//oVn^ٿg?Q &~.o9.d{Mo0Kb\}sgMSŁe?cqҶԑ4kN}~ZYj0\vȔB\Wmеh>x\Is` r(po_]ӊf_Zg?fWoi}0M{#8udž.XrӰ`"~w?v#(5s1_GZN>;ٶ[[Oq3yھ'Yegzoz &3/[|8zV+˗/g P5O! d6W, Hs!8gx<||ʋ); 2cp'ZoW}jsnAf{$:Jϩxfr KI/ /%nP$L(Ruuw?^dYЛ~_mG'b1~=?(ͻ?ͷ^ܳ}2w+p'ltxә:nU'RO_I.$ǻ!f(/܅. o\}{y^zٯl6X@|eۣ>v%ں ɺX]Sc~4jݵwJh.29]}} ?2?T* ~钓,seA}F=߀Lr06s~b ew5'vl܏ox+vyIH֯?b `qq>ԍ0'lH'ؚ$$ p$۫ΙMӬ0KYfD]KӍ;j,$Ym ش=9ũQ}HNp;tV'ybJi vɼW,;DB֗w [<\ܿD_B_: @|JA>:kW onv+.ƿnLLTfהW.y>mF^a$Zk#: kB've{kԏ& >;@>VczY}DpdHy~u%@DR~@G# TZ5GBzagPg'KZ@=?s&]s3+yxA_1 7,PE`Mɬ_ѸpuBjj%'"Vr2Yͥ6tyg/`x>X,߿ߥfƛLO`Ě5kn ȕtJ_ݯUӋQOf֎tnHa5H2uWKV3.L.؀ix/G["Pv;YT{Ż,l ltc<}s D\ `d7J \ݹPVsǀ1r`0Y=c| v1MWaaDD*G`W$Y(_GN_|FdTQM^Љw;I=v*MMpkW+yR* 5FW[@WJ{,fd {S;a3%FLVGp ӶW˲|]\Ld !f̬·ΖO'OjkSf_)6fg\UȢ`7XՀS_Qg] `֎Z7f{gC(5 0#)8=0/29۪j/B!۪cb1[7ھ'vsoW/L֥s6+5u$1h_덆(H.; .m H׶WjA=Phþ؃H% ҹ2Icڈw% -G;U?,(sGD%{KX[c੒oD\gq`BK/yԞ=F֗Vڶ'=1kG:֮:"K>_v u8 qbk>!C .]Ƴ)`Jws,F[edy " Wgú Gu40%b+rE+ *TڴE< r!YMm/ա+,m@ڢU1Z$N%qAVL jmظsڹr98J^ ǏMKUm뢡64PjdQyso0]nsb"Y-R'CW6AM%5&x2(#9$ Bg\.dd"*s,{q%Mv*{wU]*j܅5k>VUB垇.JZ8{!Z,ؓ`_7. 6ۇ`AaCj.@s59k+g*)yC['] y V< U(ؐ{P34nW V=p5S))m3v.tBZ&@x% Fl[gFa Dcƴ8**`'_Ơ8HJŖBBAQ,wKR$]Ǘ"]pr?_,?S̽B"ATpʶ`,)|pW}\{˪#7lb)Şj@=3ǝ4n ]NPSx'UӾR Rk\^FEJࣻ)85j2UesJn;ݰko0*vDdJDn 1- OT KF뵞BTVWd-|WtBA'rMnIbWm*;rvɵVjJIO  [:lDd#Qox,&qp'OR#fӎhWCxy9݃CHZ߳S[SVj4Ve&Ӑj_Gq!\4^w'm&0:/ާh)ШfIDJfcZ*r}A3PdSpz!zս R}Y_M*{ #&SDz/!-ڂdGcjYl0m0W|t)yHӥ# spNG{Ġ&Ɯ* ؟3^W) %O;$q>gX6 | ~l Pb?Jl!~>p>s1vx74.z*\K,Ȳ1O@ 9.rD_W BMl灼TV{~WT,WƷEY+idnz"R%s;c,]WhܾK&{KIgYB ;Sʲz=|0~ZwEWKm̘14i 'Of; FOqq0ܹs'~jҮs+Óػw#T}7jU8q"=<1fwA ;Խ1n>ڟ< Rعy?ytݙ=_@\mif%}J2lG @T rƯq GQu cFPc1:Oh^Aq 9gxuskѾF8ӕ )\w EC, c#qEv[۶dǩLdMBd(խA3W8'7p2m"yr@"]SI7))-e؀T}$&q bޢH^@Dv/͛:WΧwJ oG{ tρӇCspyȻ 'mЗt@{  ;I&M@ŌI_b[vAMd'}GFҲu>7l6D/o>cpxKoA- yyy|W[MyFG6ߕ'gf؈FZ,-Ɓ=  gQ* nYS(4=, eP{ѩ: '4֊2.v,AÝw0 <ϝa$.<-VD:ׇ@>b 'I"Lя\WvWKǣS(!Ur,OMaմ(F߉urA|A]68Dq:M['&&:λWe^lٱ8P0t@j^NI`ô/cDZC}@^/}+miVp=>U"1P_4Ko=F׸9VcԈA7ͰpJsӐR{t}FDiHZLT <"\IbZ cF䖨8>*ʣuE m ^kչ -y_KEEx{{{;\_~umۘ=90 " ˖p+0EL8&g9|e=Uo$<M{H*ÂݯZM=O̞3Ќ3'! =aFg&EaEwzۇ ;ckұ#"]x3)蓽jA&9$9ߚ4i^쭋?ُ>O~b٪qڲ@+NL:/AxyX?wlK ڪ< ?UU~w?g86%3ox::-_;VG@s\H |BWEz;9?b֗  p~n>RIHl\MFz^hjCd7Ȩm> B"}9<8n4hPlf0PV䴿q Ϫ-Osɬ>1zd,8pb˖dkU}nM4dZdЁ+)xwXYiFm{b!f}>tO8!K@N֮P($N@& Q BV{hwI0ic}87؃iThd>* X>Y?4V}] eWEV[$7jBο ~|O h¥$Ad<##˭x4jNY3MDCǩ][Yj5GVZ5cSrLh#=Ƿ.g[֝aX8p4G0e!{vm)1C^$ҟC(/"hny0"YԨ== t ߰ \^xV] R;ȓ_xSB^{$) DT\ϱ G55lFG3gĵlŦY2rΟNv&,]5<&?9'sj:fPpAE_o6d@6z?xr,+"/gpQy[cȒ;uPknW4ݟH D DDь՜9Fh=9[Q+ HΉc; aԺbvN›O>Nrdf̙]!5I/zǼz2dΖEӾCʊٷGe A=yK(ٷ&,w)od @)LNsL#"-"llD.=JS]SjtSu( 9ǚCijStisBwe73@?KB+}HPb<u=֞4L@3NoςsZBpv/׃V"~'<߬ ˓ԏM _Q$jӗyDW2B`Ĉ>[&)=l0 '+k 3g?Dkc͚5=ZѸ8cYf Cݻ:m!--X,9df.cҤN=KJJ S-e1tHƷ2l0uL&=7-[ 2zBi ԁ:{li31Ce< 04\)LKEmcDαm4n>' Q);ʣ2K÷aI y]=´]fID(%^Q.}eD<=;133't.x(nY оQݥd@e~i`LXQߠs91o)++?Mb>(-?d*%*oUykm22WKixz;)UqLS>^C(-IxܴrE\Hq!3?fDEEO4/v9Ɲ}%$kXM,Cu;VHqٌZ7 Qap>6{Y@g Mw9Y :kUbW/YǬ ሣ#fDF];U{Ϛm -HjI4ӛܕ(0,H4:`]$ œ7؁7$(b[uhs!@o*gJBB FwY}o4 w*y b[B#ZftZg-3H]@#JE6L'nݚ֭[#K7:,H,Vڌia.Ɂ%-Uj #(Xؠ֨:rO,Ta$?qZ?ٝCLGցHֵ@ pQ:|qUu^7?a2լP;p~i`s:NCtW&W{b5=Ѿ5PS^+)']]+8e6/@" kH9wH4jfr1Ny^TP f Ȁ[#IQ ,/H6>Qt}$i{wRF~$ 'ԽR^p ;} ߰pO@T ڼ5gb6B66,I$@4_y&غQC<v"q[(9[zҢNPO+ܿǘڃwIdej70 ME@E(6XܺrUEIe |s욼G,յ:37X@.92Zr %9j* { T Pu¿r^sb&zrPЅcYLf~؈͒K^X'H;פPiD.|n42+ʀ`jj%f$VyMzJ%rUBJK<=e2.עucc}M AAAHRڝKQL'<򪧪kX{z&*>>>uD]19c|1Jؽ6n_?q"`4ƍѤ֫M8|X'tj"5 B9m;PiDX|?Ke/,,$J>"d cܑdln Z96}q<ř+Mf}S(9]=zꪊ63!BCE;9H zGF4j=;ܥ{X8Dغ*1|w"ⳏ?Ͳ{<9q ~e9V,a{a~cY?d7֯߈[Za㺽D\YkALT}&U獹Ӫe,g!W\^ENq% e&Vjz9wCL~lLťY X-87bY4~\(OTI2VF Rx)q.y`=.A 4`.ه_}o!Y>dm')<ƻNɟGpp 'Of=l5`Qh>uUF_`',^y'p/)/XιWvu>dXdMR6$9Noq Kܽ#($AHCV(aea} TTٚ Ȩt5}=s$=)Hα74|Bc8m6a{>9'7OPH!,,*K 3QL{0Q#r3ݫl-f=onDۡcJΧǰ KP̃Oo4n݅+b6~82zc6dɷ}8.*! 4 vUGjwbѤXţB!dy\VdZo-|/T^̎j,M<Eo?V++[8:"Y31Zuwˣj˦p? B!:hJU~AΉ A:*zsZMDn5XMzv}]lӀl~ aԝX:Yn 'i}{t媊ݡz 2AF|ݺuIM! g$Z@NBwL;%l5$=sEEQ$w)$?wItԉF<i~?7%`,—h47n\]/ *`0 &QQ~gϲq5FiP8~U=\>a!$nJ"{ u^Tf>`t""jPq<$!Y6QFɹG܃lD~]r{b:}"J0W4J9Y1/[жU,*Փ<XF%?tbYmJ=,E߄uFrNӳzrNb4z-T$k~=cDGC(PZ'xI >o P  :g\Ci1j  ETǹTecBfMaG%S(b^NJ'N _ŀV$FE[WQ"@D=Gp 4ѰmwجfNd]< B}0]+(:l%؎,XoXɧ@{Ki3 YZlx/خCUg+ET=@lWH Ae:x,}'7-XZ \Ջ= s-6 y >n< 8ϣTO͠[Q^ec|k@fk`1YM\D_yU&;`ʔ(?+hYt~ݩZfP=@Dx4M;,,{9lYȲLEY' B)$vZ#*bqZ`yX nbԘȎ++Y >WpHdٝ+nCSa!JE9fȲ7Z9j Zs^؂M1 겯3Q=>hD@QJ@T= Fls!}XMٷx&223 yZYn AZuڢb@T# %/ub_$rZdʹ-J(ClA}:ظnNu`Nc5ONtx&U7FrWtܥs``t4P"=΂Y*:ضAJ*Sټ~ zl̎VLtkÅnmSS?M#s&lקbtOhF[y9g >n^ &ɂAC=ٮMc[U7ǧZm$[y} WiQ GK>kNyA& T$ N30PQ mPhٺ}پWY9YZg~Oo45_|;h϶^tatnBB*Sׯ IDATN-Tw`XΥJ)8'YrjL;U=+!g~6eWҟF$|x镄NCVYlN0kE!vԺ(5P[ҺE`}e=mOAѡPj$d;HF *{ %\~E`Bz}3|e"B=uMSK&M'ã6|CBJI "*@PQ;(@PP@.JBm/3KBRH0}].svf̜.O1:5'4cj2Zt^~7))"bkgedD|Y!Rpm_ًJK\/U I;X BlZVՍn}OGP@k3,_ڕp SQMEG7'$G?QG"'!I,? n{ʽ 8Pf)>bC*bcylL Z;>r]Oլ|t-bhٙ){jb__P@Hhz~ WmW gx9})P A$7,ٙӻ!oSQDxO"J{f(?FT7XpǓ/c3f,BL&a Ѻ{#UPC mFqYwa!: |B7-o[Ve זl#5ѱlAAmS_&r%^*;w<~gLFN˅ |}'g=ND^/4Mծq :o/>I0I7k~s8 - A_9RA@]2ULfԴ*\ҁz3Y u iH oN}ӣWoX%OL{0_5cF߈:u֢\*MAs~"ЫVRF[s|,D'08L1}uVf~#C<=I`Cn={UM@zW||.Giм_$XcG+Pik_Zܸ|SÞݛN#ǓxIP`,B5}qbKO~-XYm7 &&X쬏ϣQ3:5 k/O™߾%b4kzAoLXZ_kֻ#␮ZBI@kOX[Ox@&-y[ؕ;#wRll'û!xך$е;zuoJ*:p%ؿk9ܫE3K| 園sDRؖ6Ϭzoٲۻw3q?mKrr-i ˖ʔ)syo9~$Ngkc=RP."ߢg2p@BBBطooA.ݽ7?ɪU?Ԕt"N; UmV++Mixo3bV׽SM͵Ua'6#fv R_ꈠֽ =Bso72Ed}¬ Nco2%$_K/Mqmn_E&MOKw7|8i-XL $R33i ;y6~[W{B<|q8lܺ_X̾'0U7.X` Ww d܄ ( nZǰT(UMkX*D(3GDΝc|I8GnkmIT̼BNC򒟙q:3th֮X%I"Q-K&%$vsjd@Fx_|}E Bͨ7B]P*MmlsMf.\Iw7Zk5ULt'$) kYL?y3q0-[ROe+ޕ$.7uv}?d/YȔqt9n(T@;t =֫bu:N46J5! C5:٬p83x0k_~'O`^&?>/qחuSHMݴ͞Rв2S\)\=r}4v0.}\W6C]$N3EJM\AN'ax3е߼8^Oe}̘>KM+"ڍ1wL$PP ʌ>w܆>̢I5XoT]$Ib+fL=rucփc+wv1>sҒٴg~v>Gc)Pwsr@ˈ\M{FTѤ<2 tAO<VZܥS. vb$q'5}Ax H|B1t·7A@Dam̒>c]_D47Tjb~C#A}Gvun=l/a):f!!nZO}e^BtH[dSZ+R o݇^Ļ2|+{5_ndƜCFC hŴFnܪ; -cm4:qg.!^ϭ_GVR**;v=I1 |< <PLdF%/LKݯk@$fN&ŏJX,̶ۙ7w._.^\>V+s|Htt$~~S1,|џ\p3BԬY3VXEhȋu֗dnmۖ^W]ğ>MGMو~BA Yr֔k BSӜy2df;p8Wl$(!WRdd#I"{P1$o0c+(֝=ωߧ@QmtgeZOSyBBP Gr_J \22)Xiyη+f21WtM F;E:9mx W>?6-2VбEeAWj_֭'2AB-"ϟ/~ |YnouTZ -Jrt@G`[z-3kuř0Womf <7]+'\-zVb݇LNäG/ćPj6I^RTn}Y"Z;yNzN߻qx"|RRPH A@S.۽\P)WEhj[rIbDy3q׮W}uVI.A‡3%o"9ۗ :7ɕ-7`I£C@[5|h6Nɚ5k0<ZI|ySL E\DhSz\gV[<;r!S4At^$B: h{$M{^ vZrm y!o>o)O>kihW̓K1Yl4*]1]XC߇Z`5@ƹpdfKwuXuX}h_f{GO~Սŋh Lt=r۷w ]KlNK[ pSR9#4ieY᭷-Ҫ=2E$#v=+N Mz ĽM?vPUPҥTߴ*- ӉBvo;X(g2q~u%P ޾.a]5n4EHbMi?zoDz=zلAS*BwCJ~^.>uҐZ3l4|y۽V deqw;}WOj39l9xxtb($_Xܦnkx6jN;Fqa{?BL>~6d-y{Fyw]uYO@NLa}_S$I·Yn\Jv^8Yw5WRaދn֥͘u+CHn$I2֫4G:7P –t6ʲcP-)@Vj2^>Б8V%`QN+1WxwoVVRSG3o?W~2M:BYDh dXjEA iiideeQtɨUE?""(6ر4jT?#F #c0ǏGӦMzIX,Y8:w]wU:=dʕ ѫŷ$I"^^^s ׄ.EQdGT4GÒ+N ⺿תlJ0]q W=HA0WBwh+nc2p>:l?sp8fc.,>BTV`KR5ce6b/pKaK;ϒ Dz\+nCCg([|x$%B}ĶTL'dۀ^7 1_r)zuZW^B6mwFaZiסc׃(:QhJρCQxU! =RgQ>O`!H*Ju/?rX"[WRzd5(ϛTSDuy3;S @{XI8|`?ϗI#ݭvشBkO#>;O^}=Cqw> 熎o֨BGL./hPp1W~ESѨf#S~re̅6bi_|*<8S~6c>n(vĉl'B ꇯWx~tI6+I1)UJLQD޾~=eL=k&I"I'NNGr r"H?&QE5Ge ,9 q 7dcsPd&H.l1S(WQʶ?D@ ͽ&?I7`=;k% GmۃBwW-oswb 2'c?S^z TGLLLdڵH;&_7ĩs$e'f[u*;ogLFUضRZ޲9 O>gMV{ݤp~y,{&Pq O zdӺ vJֵmYy;v%1HRL3ʑTV*XU͠Tt{_E'{6(Z%Z˓`(`o%RMv;PgC9- aE^iw KQٴilʼ.)$$$ԋ T*ѣG @^0a4vA{e׮ _nh$&&NGyXLX$>pcL>8$RRPmK&M|jJxv - Vnx4~"{v)c,IyIgɌ>Ő6MlF RzYZdĽv! xy_;bR 2,_v;-Dd2 O7=,@Ӂ{35RٳcnVc >e' I; ~GJjQo}v&(EaD>[ӵcku"9YhőT[qDO2Lfe,VL<)oR+g Mލ0KQ e'xJuK2\_W :w$1u Vڀ6"?f7 $f6`u: ֺj.b DEg20#zD@_ՍϏTY 2uEX!;1`ȖTvԶĐU@EQ.BKccX(݇L(ص<ղ-$ NFGGwdg曊x/ot?D7<gj(gӚTԳU|XOnj i3l^Ƶƭ I5w jj}}w~u$i $UMJ$;ƁiA!*<'zNjl_ ~:v^ޮ`9R*}M ܼ$ 3 %+5a8f=;ƥW("< pe:f?5w0uC[UM&=+ _:;UR w$e̒$CKZPp>wZ»R"Y ߉'DY *U&ӿN )H1ۈR;rv6S!$Sx5zZux?󟚊=<ϪL4&5%oo:f9Ǐ2hĠP0Wyw6kngyZGF =r8pPvA= Pt8H8Գ(E./&0,EՃ WlTNzgOf{ۻc@Vs IDATy]ϋ S]`HBZC>:Qu9u1d'A@,:/?ܕ| Fb e Ɗ$$&;,|D2  E@W`gpk(ManBπǨд[f\Bؘ+>!eoL[ɗ`"Z5m]ٞyKB]f@rҝΫ]T˪Qܗ_{IU*Iـ\huccE~?dI$+`0;I6+)j-=kYZ4hÃ3{F>Py:gά9ʈ6l,{a4B&EV".@%IBRS'SxAn/I$ղfzX,OG9L<(zȠJQWq;>R">W <2N_VRxޫw#ZHęH?1 גvs R[?ŧoCZ8yO*mwY~Wmv@Fҝ8mRMEFfbnlƴA=%'ܔYǥyx@fuVgR*ׯ*e.s{"If/L>t|EЕTj'CSMInȰjg++mthzNyc'ra8gdqN!׹rC1wKck v!0zQԖ;D=XQ/T> Hh,{l:0V0xlSLL52$&s(rtzz p7b# XUc M`m~\$`֢5\8lix{?=[7}Lg7\;1/Wi\e5! L$"uTj4nQH 'M⮅ `6H ^0Q( s؄`-z#\ks2{b s#9xp}P J v[ϣ2 L9y qL+ȡ&a-p#ƍGs  nTv<+66f 0c5{SakAC'11/r(^z>о݈ 8duPϒDTA|zLǁGN1c5&M yCbyС9JGer9M[d%3XL[#Nrڏu?y(Rb[VZ"r}QTXg`l8OnƈwѧONvL#Dd$"1v;j#9E2 RA={ ={a[|71vx>l"-[ a'(5 ZI y#p|SXLˁ;Za_}kv(s?i**$ 2Gslv- ;| 2y#8zx(?,^kW<^$Ib΃ݲ@E8p7L՘lV|5Y֍x~rKY ]Vo_Rv Z8"jp֟[dY $5pX擗:*mzjmi.H"TO%n-JvI@Z"ݼԍ;`Dns,s8KZNxZ}ZIco7;;sg":]Z,Y̘f\$bo-iDIaTVBŝőA _|=F qZdZ8aMVϺuX+4//ٹ3Q`۶/ywHM@V-ZgN:1 ߿Q\Uj %-YöO>an@jb΢;hP:n0X h@f̘G+*qZo>;)附 d %6Ax<GUx SgaNYJ;sس5I?P$(Gzv.7ޮſ?p$~pYgl+3nn8M m{FtQ(8pЩeSμrc>z!y?m!*!vMy;لd>]a\1HR'|Iӈ&+'u?n[̅$N'yu|l${7!Xg)wn?tʥk1J/xGNlEЦv~X̓6m=f )-IoIoe1bZbrRm:2ɇtP*Iۑ3_J>4]F铸q4Yv+1 D`5,η [ȞY^?~)\RSEEӁ'$=ínl3i3ft2uWHӉa'].g{EE,+~_ZMCh7`4Y?4|Ajݺ 4nw[XLn֭Cx 95G+3˚93AERG|xM,= 漑h4C^:_ׯg¨̵ZЈ"1}ChXV e/0ڜ1c'TEy} IC\O: i\3V:'d׽5keݱ eMn}1cϿU* qžC(LSnM.i,$Lʛ߲LƢ`QKˠRYɜޥsny$Q$ߤ<ز=Br=\ܽKAk L'vCP둈NV[&1 czL\Vwv֭Pr9$ǗyXZ76|l% HpagCS%I-&ºt\.uzJr/inŝ#SBTQd&' vUb1o_T@>ٍyB øHe)4jՌ~*7}ziO}=G`9Zdr jՋ&ɺxû kNCJ2'/ـJC n;Aw.B\14t6 s =Sj 1V* _M+@珱nJ+$suw&~ۿ76LޞwKB10q%$9̡U?S A+fJ{p+cAz9I ?W_bժ^;{ I\}[2P&+:Q$JKf'Ck5j>{>914U*|(~9O<įv؛ 1[R6 ^@Cߘ3'pMR$`/8zAXׁR_Z8lfo$) %a]zѨmo#1T8,5&e Grցn!Љ(z;kLZ4Nz),.DRɲdǟB,FZ]@RV_2+M͝'LKɪhR/ʛ_/&F  Y8Ifvz? WʭǗkiwtgZ(OnیnY޴k\yfp k V&*>FE&:4R&ŘR:.ǐhX{TdeK?`(,ٓ[&!Q̭yP`0ƭ'Jtd'X F4Iaն wF\}N)dKQWf1~d6Jp=1^j_.'p[ 4>Y3f hNf?$̋r˃sd\8 L^\i) rnS%*ÔNM%#yP(VFx|zF}9Hi|(^9p3 ω%?2ibrr Wؼt!"[||ڍ&٭?c-Fw'zV$6UW2_Ü{F_D!nuА?~&% zo:5*x\cm Q+^*%Gl\V1fAWC[yw7-j=_M!0K Q;I<ؽ;H9 1WѸ|0=xzBx Jl"o"+J}^㸆Vr1u2Yx J8qJLr9*N<(}P\GRhVeVMwߤMZZIeG(u WU<(XҭV 4lڴW_}i#0ñq,X1\&_"I l΢uƏW j*t܇O>Iܹ?sSѣ/2祶JT0~JYp+7h޼9RNHSq'^~1d5Yuj~ٴ>e5Hҫ9D\\tuAE1hhE MY(:qqr3-Q?ps8rElwzP\Eu[L宛X%fd t%qx\˟L4'GVvNnmb~$= tAdX@V~pExݱ 3ntZ$ۛ9{NaxhD$䡒o+"5-QI="FJ`wmGj a{#߮-;I;8f4qjŰu;\ 2Ǚ),Y4$a3Òn@iҽᕼ{&琗kX "fwܸG~ n^JڴxjwV:Q!WPJgS*&ɬ?Υˈ9i4jA/[A݀qX7wW Ix .6D„dDٹ3ד2 cΗ$ֱɛ8J-*WK*f/Mv;؜N̤\%P~[29ogPغe ͟)sdIdMtl+vHW-h\:ӎ$z ) @?DQ +¯Yޡ7)fNǘ I'"طx.jo+|!SjsM=[[CMpidG^+n۷~oɓ6s&-[YL畉cض72|ٖ ú؃MG@+N".=P@Xfu"~ GV|JvxLH=Ӎ}K^ǐRF 5(&gO 'rϞ-CqX(|. /Oka@L{i<}ܺ g3Z ' R>~\ŢG`PB;@ks$mf|MxﱉĞPpWLZB깄2;Ɍ{v"8+a.3A 2$PI11e,HZ21/'sz4qۀ@_D/X Ȍ>$CR= |yx{м _6d'swI;Sbs/ȺX1آcWd\E1}Ƹ+/y`5q+7#[LFVm77:uU\GtmVLF |3=+b5oz":Rԃ]k 9ԉkÙ_O`˶op=_1^¹}Aىl98@`E4ڏxh|6VwE1C&e;MnzgYE ]+Ȏ?E5k%HO!iRVaH\t>jZEv?1/L_S:۷})TZ4-AV bM{^i;lBwP1)J+S \y_7;sVc|2v F,v/a[EVVRL٦P|о}+o95:lpVD&֠6x9**X^v:)F/L|`ryмL}4nݺ lϽABo;^,ꊂ6oÇ+oպxm|GTZ}p`.nnX9߻` *K*/'*g;qx<'?}W䟩% IDAT>D=8l&%mv ~CpSQ'}F>͔ӭ!Axn2FGDp !THfZJ%qJqmYõXě&Ae}o߱Yj@Vq8+3FcB"Y8|Y)M(pdqڷﱙ}he߳Q#Z=cT6_|plA>(2O+P(yPy0cfLJ8Kr8r02QHEA:_D' .W'Ժ({TI5Y(H!l&8VR6/@!KNڮ}O6Dv퍨lŸ#*{PV2xxS$lA/j-SjZl "z=M^z_~6_eelܣ SxS1 ':ϷH٩_ꜫ)<{ـY8v>c.a <ɛ.ioVOTOvihr^%U-<:-Ӵzu fߞy]+tpi*tr:Y'LcW%v= xv\.X_DUG^os4]0Cra.;)8UE۸~ Z/`b}?O/oޘ9v(Ϣu侩Яfǵ։Eo/<[EF!~: |=k/%IOI7m+O$˵Xp&5B;|nۈtp`,(,)[u7'$|N߃RٙMBT FׁZ3vBI&q?τh4ܯ1ȓ^|:!8[_7iv;JF!W  tCֽHupRlnֆL0>%Gc3߉a[Ş_.~ wa|~0 :;C6;.c<;|R.Пa*%c*nU)5q4?!\)J`/UYrJ^u(ܻ A !W"K)L3("r% X'y6SUj8(gӲonDo&cfm !mhԌ^AW!zLz1֝gͱZ85)ULG!ῡ+^Pb=-N7MelzJj0V^AgJp">z5ff2zk TyFP2YTYتZ^s0ETQݞ+a~4)""B [7@l 'Hvvm殻&x~ /WD\YXkllnXX<+㦛.} bccIK;… IKKK5jJe⤤$vŬݻIj)w8<|Q(|>f ~~xex+pGU \ujpיܣ1!j .a8 U Q"GkUU߂TPeUߎAFYi O}j_uHK9Etܝ> IYrr[R;K?wZ)IrrǃwQ,߼ zp~ gK&tpuD%f|<):B Nct}g};.7N/@$2Qxg|^'utl[6nѩӐ@FTAnډ4j{Iqaޟ7zC3泍<)5/vBlC&02m%T \k(1i TY,DSYqz 5x:#ȒD;ѿ/*m|ۿ%mϵ k7d)@^CAn>u9'vU7k*>j5g޾$Wc40t ]6 BN6Xx͠ķa49k OTT6ZN%74 T"9 P<{4W$(Tg&PBމ h Qlxx19 #84) uJya~-+b)OgS hH?QTT1N$ h EStv%,)@D P(}p.u,-h*eO`.Dk4Ź :,FqWt! Ĺ}(jAlA÷[3jOHL ))%( U(ϧ!؍ZTS4|TK-둜HN *cngTs`l*m 8v7&g3UTr$ʲ?{?11F%Ke[nZJŽ&"#pVA|eӳ9| ! #MYy5&"*tӘL4'w;f(߅Z$A֡PoBw˙D(fPēDQf yrv Gk";h,EP$"Kgxeh u6jFq5 d\=Ky~&ud~Q۸cD=%(ϣG۷1uC'<3.jUUs8xj0uZ@R<4jJO!k[+V(q%`3#;Ki+ќ)8r.,dWҶBTx`̧84! =Z<uYG2 AnӐ/4H N\Ԟ9s>g\.wѹ&:ہ9VSt  Bc< A5Z-HJoQĀ]TWTpr% 㾏q|oDQLT;xObnrL fGeQL`oq>Ib_M=ܵkWN;3WIgÆ'Z?Upzz&YY]kajFsu֭1[2&tӭr;ED#)K8g z{m>Ke9(JzUˉݻ{y!Cre>Zƒq4[]}B=T1wØ*3L@NԈ ڡP6A:sϵ8c+ 96.i}3h:CY酠80FHEqaC_wGyʡn8zJwMKF1]C0!& 2u"E6NC ~ʣ~ OvsØKpskۆG '3~B!EALT(^L2GY&Uy!c6-jzs2ǧ  BۂIMۿ AP}0hv&##4&߅XA 6iZm kޮ=i) (FƐ wRQ< nKq\dy GbPi*)coU֯qq8lVV<6ӧP9/gPTAhd<|]kb!?NVGD>}5xSF}vʳJL@DA`D|W$uǕxy :1^|Y@G\FD%CDTLYΏ`}=J]R~ v nEDGzSe}gbxW6 ;NXcFɄ\\@=>e+ږ_'i<폟VωsvB<}iA!thGfSգ<d2$s&c 38?.$8!yH2Sgz Q!˾͕-&. AM #(.2AmDvFo5^d10 c`e jiI?`*h"*UEwįkFǍduklvf=6o͸YLr"+Ӎm!PzD1@>{:!;w 9T@Fn7SuL.Zk066{Tu MV!0)=Bc"ح#H܎żx֙Tr?wsqE|BnC$6.|ɔխBq`Q+X*J.@7+6h3ۖR'&r_Gj Z͜ ;b05"($'|j`@_زsG gFx=R˴Q]^9P/O8)@wg>2JՕ |2r$votkx&wg;u00_?WDW>z$aVWm/th-ٴ*;Z"\{6q\>;AZrUsɻ:x ə IU]5xƭX(yFr R'S=izAHn+KgQF^}3]RMkERx9:#~cwq5/p=!ÀC`cTyPȳrf4^Ph4IQȒF~kX3^Dqǟ(D] ӻxF.C^4t֮@Gf֬<:kǜx ۷\$r޽ݻs-"ܔ2 Z @Pތ'yj,ZZOۈYĢT`oYz хHG=L`5y {gtQݢgG*5ᴙxΎMqx,HA^AʫkqDFóoAC};YDrݥ?7=W6mMRY™m"K:o9 KE"5@Z'w)-jc8$3d͍@6ŠQU7_9?݈ SFI߀]ZO8c¨Qps{uuVO`H>Gxspwa!wXx6t{?'VOBr yEh/"[Ztr#Q.^N'KWmd}UӥSSoQרU HlǀvXr2oM@3et4,D"8D8j%a/XEtIebB W"9nn;KIT ~WO}p:Il,H3UmȮ9/$Pj m6s%6S:Zۢ9,dÙ0WT@BQ$u'h}a~z>P3nέJ4V|:Y UYh/{p%MnZY+X*,9DfnFYdh^(? p{iױ# SqUȲ ?_Dd{zi4 -Gtcx皕B:g7ނk*!єsh,]۵l c1aГu{6;eW9=b2i$A7Oթ.,!"]2͘mowo>`V IDAT\1cg"90y{12///fm,aPVV_0G()~Rah~,dzXW(A8 7i/S ,|[9t.$ޡ{cn1:`늟aZk˦'+.Ax٧yi8eO_9[ ݒEuY. erMAs] lD+yH0,D13fÿSYAJ+1`׹2" g9YBw^.]vܲ3x Q l-%bҠ.!-b8w GtFϘl3w&"6wz.C8gI:Ot"O Y<|e0T, ށò^ nҪW°UK.@Ϩ,AA+hTWmfpj*6~5 2=ڇeX˦c² \1`c{lJ~ڷl}____6Q㉈8$  8AS]JuVރ,j=n ꒳tͿ9 AýD@ڲ@p/) EP|;1˭=ȱᴉR5('<,찙97QhʙcF87fDnMWd d,god[EYy|`J{ﾏNmN,߾tvfY=L~F6 WfȒc(խjM5YI**uBpXt4,2l<ܨo $晇';3 APg_̦SW}&2oDDY`o+I>6m3n1kd$F;0鎻|ށ%,HN;:Z_TAIVʛX,8Z/bނc`D5䰹ȡ`(#c_1ٙC@\O4`,ү*D< KbA͎ٺIC` F#$'jG%'mإerSx7HLoM~a)F}BR͡SU%[r| @r88y)P(@@ б-~FW jejl잓S_!Q@H yeIRf='BE41fO+iI%ˉ{x|3䏀>CFەj?T\;M9cFd s%AUQ<[7gtLZBvK[gP nSBAtxwڭ_Eg#@ CTrvpܰ6rtq#*%<}|x(2EEF;,[^20=wQo7݉AgQ]R֟! P}h>y-;YSY,~xK 7=[vPTPA\6ޅQ 1 ?+~j1\Q VƇ70u$D5~)U4(SXt=Ii4R (|1F]jԚ% Fc;wʮ{8 k l(k`Y ,GpEfiٝ=fr3Z)eEܱ"b9u8f2j1AQ"{I1a]׵/SQ/޿uz=aZR;aK`" "`܂M8^C$, Hd={Dq;PQn'42K)c`U[jn4yfd2l\sD};#W`blh*,9M>Ղ+`=!ߗ^{ٗSGDLM]xr3bD"oY5LnY Q <5$ҁ2[Kj$7sy! _2:oa[tL/?8.wstaAzs1"`ma'hpvJe{֊ dh)!1)?_-;[3 Qt5ǎ;9fTS-A`Q 9e_ |kV3ĝ}m]tCb ضdٷ^.E5z>c(@޻Z5vON'h;Em:`~c,#'Эk=}6梁D,^~7P*]@nezag .r5ʯ:Z٤RBS\ 4F0⊓C@\Ars%tnD`Tz6kpb͏ @rfŕnI:\PQM#XEu?:Q] GL9g]CqD%"=l'c'fCDE\IsHټcH3tq%GpxGjrzFt~|6y`V`PF T ݺc׮E@ͦ$f:vaѢx~ndAEɻﾉgƇJd5@yy9K,jM%ЖxO?~<[lw!#h|ٰaiiY ~%5ueE <ᘚ ȾbV Yц אBsaR Dy $c@ Dt xD4jOH/?~ղqE`1/1H9qf.BVwC%{_G!zgW.9R0[,)Y?cs ZUҹKw].Y?&^17LĦ" &ﰚE`|=(dο: GNΏ띇JJUڢ:x cN-iA 2gfOC4$"=I:SpX (5IDbFkǝ|K\1VZCE[*yoS^N?=38q5{*#v'U7{0Q3N3I#*Nq ĺZD~HD7s 0qhY&Fݿt!Kc=LN Y||}ˌ?^ד=8`{myF 2[ 𶈊pJQ#(Tɴ>پ QMpl($3 !*BKtϿ5;Nk$#=da&"\,j.e9 !yjKڠPFra.}AZ]v@; :{2&>wWMڏCr<r TP/^䖕H\ك d1lօXq֛ٷO $^YwAX KV !OKjA4Н'V";%UXhJ}l7+X3Hd$g?DuW~ HDƶq#ٍ,?ZqWV,^]Lf*H fO "X2s/\oגz7: *IYA(#m #_ KqH<$H#*?݈Ǜ Pu/._Z,J)3A4Ͽ̆_~ؒT,H=NTTh swձd7]Kg CjDَNZ'M?UՄe'WeM\|!DSob7OFriD$ R 80[uڼ4?þmwl⓯g/VX݋R9Rǎ K/2h8í|aЮA cP;*s%:2#/as=壑SDX-ByIj\dg&Y:{ABKމTޝ+Q뢩_vz?*W,up)u\"8n1%EЯ nQ풯]XPޘ`XڒٔlPHD˵J[ߡ zz$)ȰDcμa07Q͠IZ?00A\BbZ^֮!t^YSz_"8ku)mhX2h˹Uki>`W/-:͖JTLӿ?*2DYx\Q|:?ڛM+˶3pK'~h N}pq lz1nIo$_h=Dt}RkϹXy/_]Vt*-%'ka#R{,ht&4vyYzF`͕"~-id\J1'ovcW^ S^$ Sf0a-W+EO\Cqem)Kea2^AugI)gZJ$DS\Y9z#'0gPkI>NϺ1#`ﳤ5M Kď?.B(p8#<4ZfKBN=(/-H?(m\T2|MpX(zCUɨM2>Ysz]Vϫ%KoQZ#T_ُV'7tf6l}Bc8~=J(uWAdӠK1W"x2(ʧIL+zɊλTLO`lθ`Y)KAe4M>{Q#X_a?I^XwfANA+h"|{c(0 }|*Tz;o DA"QSH+Q+}#(3%KϸjG+ʛKS*Tߎ\'zueG鏤(lb`I֙}i;Ņ4%t2 ?Dk٫AQNTʂt 3z^ =pdb֧KV~ؽ0z@ _p*s]㧢(%ԥ$J$gTpl.d t0/_?f c6}MڿsS;nx6-ڲ": A݆3@k<ʩZ 0ZRO}L[Q":"!}ƞGmBqFw!-: @Oh'\<s[߸ˏ.#4%v.]P 7G E2&-/hbHPR?T˥F͚1 +F_-n߈"T7/Q 9լokֻ/^I™,G5e|W^qȡ.!t5v0JZ3"~ xS\vvS9U AM@G~n'dFK!a"DZ?U)8'দ-w1#4JGDT {CTj-*߇#Qetze rQŹq\Z+Z=~qXʛj%a]a*R+V=a*'R&\7M#?ƅvsI /Obi#V/^nr ,RET IDATO'b0 Ho=\0ok< 旁?嵈_܊\ PAA=7c $%qs r|q3pkZt׈q  Nl'c.d{p{듺u;VG0tn-Txxx;Y AP^>:"v"O5y% P3k@wR0qCrO fvAHv,Xvgssjz#+%K,^;S.̹s&v{<؍w?;ZNAtT90 LKI~$MHD*3#c>Gn8͍w#? tłZ[叽Jh1<ԭE$*sx_mf@=S\Ń+9A~<|}"mxo2 4|:fT^WD} L+=A°L?_ʲswH@ KfҹucfzY3{___4.aBk~?s,hrq7\,H{}: EY#yu5*bߒv_wH|QF|ƐXnGJѫclX4RODsըꍎPa::{L*CՌ x(̞oZ (AzC g;ya./d UB$.'ahmv4Ŕf_xxq1,H Q\ʳE%16}9 J4u:ҶȻO>Bveo8F)Lŧߐ҅\ȺS_O2{ιh8K0{z$t10fnge Ν0pPNddf^sz}G3ׅت)"<,K&Rq:CkV-_D T"B ` `"<Y|BYP:c/ T2Mbѻo:oRYien\43vRǚ&rs15Bo? s9_D@&|șElKqԚ,-I n5wxrO&aġ|vʍFPgzӣVKߥH(-.vSιXg]#(R:5Zz#'\Ȥ_飛Q#)[XRIs'~Y V5|Ȁk{T+{w#0(KE5Qt=v;-GMSZbJ\c_8K~G{ѷ{&V{eT2Ftj >xI K2X壏0oܳ·E>Ϸbٷafͺ+k9v#;]s5jslS'缦>}8zt5SQ\[k:pM܌IsUX<ع"'.Ei86lS{ܵYJFs{\v :y/NĞݹtJt~Y҉};y=BtTa^Mw]_6츳v8/o/݌U஽%|'/&:!n}!cyՏD"IV*˗14$ml1DZVNɃm5"rcO`!1uWn{'4z9ǣlo.`{Gr5xx0F# ȲY_s$n<ɹ;ωM?#W阜,IQ^"zpXMa>{`8&z ? y_mTG@P7fOk/~oȊDA#':/ EH}NCQl Cگ]D|&[q^G9z,ap c;0n${ wk@˱T(B}|mܜnfT1$zuH=^w=n( ,\~/(y,R7,U*^~?K@f֝CQ k1Yzh|/qA%<}ϠEf,m ;g is2b"GaS)b۟~Ē/?c"ӯwߋZfȐ|bKe0|}Cg^B񫒱W8&J2H+pqda݆Xy]P-*NN+p Ch9O-'V#FsK1xz߽'ޫ(*<8UxzGHִ4DaJb:N8auZCostխzs6v#urX<9HO㷰yGͭ;wbRQV)yLxLӨ[xۉG,E8:w mBV"w/cohd$9}4,98+]3U= ?ZiDʹ>7)(YgDA@rk}UGƷ݇\I vAc Vҹ?o~ma*{M`83\DpsgDhJrLs82ƒHg\Xtj5ݎ~Qo f+@MaP>ubϯuNI&1i$??a8zt+*U :]>zex RTBP5q]]\AkAanכb-H3[T(sJd+ UM۟ ^o`Zo9~Y!*+omzc~<qs//h1է_a=HbEؽc;Fly{Լ]lS\ϣRE(9G&ew:8;c9NX*ʀ3؅|r*jc'W2<)/;O`{o;g*3a1` 1H&/79wxgw Au}AX1H.M:c7Uϛ=CaN` u1P&V W>\Pz']_^A#E !?ga7U MAYLV]_4KI/d^rnǤۚ;EoH,31Ԋڟz2=5Q"+F*:mg|v>{]ge*rgJkWea4 {sey@В4}IӝEq BxxTTE"ZKPq_."P$ Gpj WKQkbp`,qmBTRvEZS<Aus4l ~|u Z[zYߟ97@Axq/z)ػ5cg[VR I<(K")Q ,E[- @dDMV^0GWs34Anntbܬ ځ(Z ɽfBꖎ"95Bz$NnɈ>kCska1p>ZͬRx{XGdg 2QM[K㦸BmJdɌ,[.# (fE!hZߛd*X1w*AK)C:6d]\8EVj\q)j%Q ޒc4׊=&NAsx3b4`DkYefFk:@EqJ412h 7^DϑuYS83$ZRdҲct\A~p.їcG!AOgKup@`$~O`v}mfP[ D ZM>ճ:h9F315T7S~㖂F=}֊\h5!¨U\Cv{CWcvYa``=ԅx7{ t ֌4 A%@jtt$2p638辧װ -mB[LT>4D`3U<9&tiy _gϞ|dffVo,yv;J-"9vwum@gf6nj@߬갘8μ]k601\;?;vBng%u96F7 50$|&Xkpf ?=Vt7Wcw?BA"z`Y7-3sܺjhBːnd,BZVd{K;q) Mɛfj<k$&JZK]DkXxRJ$/3r$,"$i*n 7yEH SN/Bߨu !!DE56kQ9|0DGG([={u]A;mƅ:}Eq5tyE/"-( (*t+B;\ M쁩3dPO|=fdY&=%38ZDQv([!4П&ǻ@ь6.\%:@᜕'LsbPρBj",ABЮn3UP] ߰/lFG7 Aa$xj5^-?+t>ij0n-'J^*&kf setʖ7{S_{n1NUQ&Æg`H/^Z7 *Z":u.8[RYzN.BgsrHTP ~G}a,lpnC;X IjNFQUE,Iy)ܥ+_1N=RrnXvRN JA/@Okzv?N%qX{t u'x0hkF#>;%AXFϞؽq'N0d//{1mUFf ڟ ;ռӻ8x2⮗ص$aCDw;Kܠ)>6ӵ REPive5]Z&>])j ֱ7#: =?}0jo!χ݋uYyBy1y69KFx1`1 9Gz@]|A!3p8k<2U|kGOhAۡ7x0m^N9<=9i άwhB5n+(Hh'q4diDz,/[6LP7__DZŠW͗ᖁxr}XtV\7FU5H7,)<1A}8{nSM85 EoB/?z0yxڱ:h}g飛98E՟~u'3Z>"a{,J=H΁ yGi9+ Aȿ3OpYDpA0HʎG[Ĵo \RB_O1(@, 4't<r筤xG@J6;o0QZSeP$nꆿ[KR"LN:ubݷSQZFCxx4ٿ-]*x͋EB{k+} ROkz!2dD_o"I3 <=~,SܙA}@2w8=&Kg#KAJGys Iۺі)l?~QPz`9=VFv ?G?㇢>-AnhirsQֽߋR;o"ۿw{YߑxTm43.w%Ϩ(E'8[| k2oyon <4Ib:-W|B:92v'B8>geWR*Y!TwI|kDXZ '!B]]v+t/9wDU_ l𿘴Wv+i[Q]⏢tE11MvΩÿo1EzR}.|}}1btJ``Zl(Ȇ _o$).E%;{(11xӏ}竮}{oMwڟZѸYT3[N_du0o:3wz܃)NǗA&q$Wv"Ј h6k֏$Vb''őՋfgtAhRۋ>{ i 0"ʲz9K/uϰ&N%$"{(KL$vӞ7tDP5unѡ5h oݝ{kI5jfwVŮњ}w _DuK_Ǣew.Y~>y>_B-/&q Y{15.dwc7J"0qE7kڝvCQdc 6Er\>.I%eiG6 2dqDQ#C?py\ѯ=:b/h[w ApdY+8.@)@ۅ?"CdQ--%F*q㰎 }lT<ߋĀ,D;ҞwLM Wd.۰( qr|qXu:ן\??mY| ׄ9h O}lU1/{Ag᳷nSgGxegܣf2@@hLlX IDATږmB7+o˩*@dR~dnV?H`Ps8EoMsϞO{Eu@Qڱ.#}'g$mDz0l/2/rod܅"߁YdW}m|S)k6Lv}g>KwݜA.9QH0ͯZBnLȦ=hy k~8cc=S|Bq%r)Օ  I18XU]s /? -ޣ;ccJE_]qAbk+-jQh xy5xܯ12=c 'fxԚwx֪|EBq;2W:wVzy&u~ԯ8'MkO.9 QUյ5cWlsj%6 ?i\( xyy;77[4!8ɚ5o n`^x9^x܏u8l|n҂HE?Tuj߭(#;1%hu5jlڂ.{}gD$!"נ5[) $? "*M˱w ݃ޣָJDˆ;oTx"kl 6 8ϣXx/[@L?2Z$QJ,0W1x5YNa *m(1춛08jl Ͼ ,%|;4m}f?v&,8u΅,2+2>> T=48L=:ڿ+07nq2r* 8AAMIIa?BSDA0fSs\91獈>*4 kU)'7lA'ud̥4k5]G +i3A[EsDD%j6 #p9UK\Q_ۦLE|uJɺt]Xro3ifŠDIFz 9PV򈥚ҽgm \ 뮇?ؙ2e: W`^ItXRR¢E?2o޳-}(//ѣh4틇G.96gq "ZP_8ͺ[,}1*38t/L6!|kmN`Xn)dĢ{ +0rbE 6Q& VAAې[sPлS'>{6c']ؗ7鬷Z苹>hjrv;ݒoY-Q=hzo‚ޱ Aߙ;^x}sOُGu,Yţ,%%b DVl[!"Ɉ6 ՛]C@VnA\%5v.Fq}/D8ژz/(h #,JsR9,rO݃̓OCؖc+E*AtA[ݧJ5(U.>Ȣ#|O- tt=Ke %iE+ .ι qQSIvK%`LhKpd Zȥ( -\＃$P%~QA`T<g{>/z M[.9Min.KDeTͤBXDm#]n?|ml>Y;Q}uuTjE&4htg?:};>vj#B4:C+͕ q#nX[ۅ<*+*;&` mYt5*-9G+ޅlӮ X1@գ pu%կSgs3-W1TGlh>B2p0)Af`qg}U֚uڗ19p3g>@^V*,}0S~ bJZ5QPdAQ>ubsaGڐOװJX:+v{O4qAA}HNḰ̙/1wI^~3g>Ǒ#G_1|x7 6nSJ4 ?z'%krر| 2I͘g|g<;J<n'Pd#\1rv/~E714g5hf˅-?ʑ+I.]ʕsSםp(^VA0j*p*?ߏItp*cw>žwpe87?& )F=v A.puvd 8{ xx_~z3Ob}k+HqE` J9 "27 ;(n/!crϝK{ətۙ.K nTֹ'Np g+g;$M-,*wyw·lU1}ld"_OM FV6?,@X)9Qجxl$ve''0x>\ Ɲ\lVna/0?UoP'^ճf>3Grjk6eg;gT9.FV=mkd1Õnńâky8=) EQ=kv˓-%$]1~!f+aOcZ+o?s-:߻9|Ut]x\EQH۹~ǡ&v?a\Ь?x/NXQd(T7hqxCfƄ]L;r~MVYE_ NQ3Y3yK"AΧ8#?v2=5t\Vz=(<αUح%}c~{J/2igx/Vsr[*IYkahqR^KQq1OAf8R1yɣa΃g yVAQLԩ\ ZM=Ƶ?Vr:.^`-osPGW^RYYNWf5j P]]GeU`p.*+3x饅|k:gϾyBdB 8yi&G8׳o~szwy:JppS֥Ju͘93ͭjW||x%0PݼҬ'En6KzEArXQit-v:&ҷ@).H2vDcYkRsnU EAp'zNfFNzQP9O,8NoAE :Arl^,(ʣ!lUe7MEH#Rع,ssNEb6jѵh٘Uq&n/^'{bb]KڱOH?GT|Q}Znt)jǩ[egRS(f iKwRP( Pׁw ?|Ǎ=o7XBpoR -EQ@.Fѵy[DZqFneV"o#aHM+"q9껏Gطu.PLp1z(,%Jŏ; uu9-&NB4>l:ݾi8gɧksyz!)M B1GpX;Hg G{v|seEr$lD K%ek( E {=?}( ZEaZcqn?>YQƼ# e@:p_ _J̽(RPH7Y|_1hP@)Z7Ά,:PV~vR]vgx"@x'o(Cj #0mG0I;qw/:ToC[¬}pNώ`ѧcjX- lˌb^(J 4pb AG Á,9=Ztl@X|BnFE(Nl1PB`7w<8d(d'}p4ڒ´(O]a]7]/o" ?% Y=2jM81Z(;m)YE;jk)N1J%̕+9ɓ/=#W+#y^#E$ d-fc. >EEbl6Nl 7-iZB@.))aʕ(W\ᖓ6%%Ql<;Čoѳg7Yf |E qZis[+). 7} /n: |wuxzz}vvATT3fp)L/^/'m6Bm6$੃M7b"# 2cCN,ɬ9jce=9R5jAI߾<4H+t5(kJBw6* 3[J-GD.o(Rں-6y0֒]e JI#8w FUkvry'$ or1<$M"d$sHGFްT3vx޿+VYLktY.(lhj?.?ž&t8b23Y 9Tg2Jha܄\b<ODD@#0=5+ kuznyp6٧NPt::'@7v0o˛_P0yi;zOM`srWngV3ϽkxKӲ(61 *aN"\ [dv~ݎH$G8suO CDފO?.fXKB.6ax`,G截Ә˽u:5hu8 Q{| Z͋@ʔ4Ne1q׸:ĩÇY*&&v"EFQ'~HWt5n[u܅u ax,) ˊȷVөŸ)B ;:KGZIҳs^ 8ك?z#ã6;gz&}{H!zb^ko׮WQQ,HQEDAzZ(^9cHd&w}sf=d>{wrgk؃!Ia}DvX9w%nHPL } km҉.`P5AРэR,I^{@q z>~ty/F&-_$I?tu5|r=uŌMO!!HK.^ܿy駯&f")<"7Yj!Ϋ(l3KΌ6]Kf8lbօEOmq9;/E2ul?o<6_]v ~T$]ԜD? hR^2@Tz?c8y2Aч Gm &qNd|>2EL,.'C#I0z$E}yaFOH &5w_;{Q;N54YpY@Q~ /<"фꩮX)b bnCLA78w剿ֽ6_MA I 8&^l~!A2O4b{FIFJ,GzO": &o8u7 t .Kl,a[pӛbK+ "P(8F|@0 (`EBBvv6[n%""I&ӝ=n*Yyp4d^ApT* vk)=8Drc!;UCut=>(:pqp6VcY= R{דޟ bkeHɨѣq8,]B˰sP/:<* _~˼(wAyݴxbI5("KZ@44@0A"Ku8m+H6eP}T+@rAl c}'WҞBhc]R蛰0bȘ8<\M]1G9#Y[ 3~fDPl'~]^|!46Ph03W䝗_GM? $l:ZT\UЪL҇1pGV'}L IDATA;ʇONLrI޽kP 4-e3y~js!E.osBvs=QhTFR#TSv*dtɘb?IdĤ)wWq4 71}xj56ma$'&0yPoDʾ7Ɲ;]v^Z'2XV}V9Omudm{='%m]iUrÂw:Ie>@`@J"_0;kc>E݃܅kdE#DX=_vX9#ưX}H<`oڄ;*c/{}Mee5x&HQ:!^q9ΩwNmX(x/,YQOeǩ":F$.PZa xzzjVݻ~\VoI&*#="ztNjf`9pnlցl]Af>P$"ϼB{{ȒNџi(?,CMYr'IIaҟ#{[#hh0;(5jvJD2U EVHLJ⮻jՏ3 $FfFpZLQ./: \(l*-`V? Zs֛\kԖT0\HE{ h%`D*0܃4 {S6A0R Pe+ae!tTE]I atһpbj ,_JISQk ^Y3~̥/'FuoP}V?3 T>yaQ\ޚ )z$W y CP|?dK۷trѓSD7{ EpF\C`` UUہ=a9!"cR^A0d;g` DY$+2;6eE3~c ZeK);1I@]!6|a]!ȻtӪe RGr' k|{}ٸ{Y] <lJSIk!Aqwj]_KʼnT"31zF9(9Zh !Em"x8MK;M*CyT'SUYPY(j-O|E+V/k/$sKX!Sxjeʼ:,F ů2Y01)RHQ4`/NB \ĄE٩J,M_|ab%c.@Ay6sZK +d )NbH\r|1JML@5&$MAy; h[lf[Y1h4L89כL<'pB}AQPVIdtNb᤹P&I>cƠ8lݺe("ptՖDt._JP /ʅr ~yπTIDk+v$^tʷ4瓷b\5X'"ͻHǽknY6zspK>Or9Xs3$[e/1/'8{RB^<%ۇ3\1cVJM866mSN'sfFIf, յ+5մ5&jvMrЦ$q>}أF)tބy;ntK0tjϑ>/JZk,-("Kf-$$n}.ãZa }'UK<6$w@D"YrNnXt&|OjNVmxsM˵mU#K(2k6 iu5 E B}$Yޥ@65xÏYѯ;m?:_wpE'xi =åW̓',4Sh{4\ Xm|\tu|ߙD ;p:GVR5Fbb`ϏRyE9Ni]Qf@op3dGѫoLJs;غ#4xvcbJ䘠bހ))xXM>+elv {O8wv@ Hg8 ֋f3xl2zK.kO@ճ* +~4oc.݂2FT3nSQ6M}P{"1q(J ԗ" )ưN(ݺVw _}%tr~֡DJiɤ$}f,P8{ >S^ü}GxܬB71gx>[1E x"{LBg0` }HI_/8+m&OsWpґQi6s|r]O Agy7vBt&pƊ|i_@'Qe#|[cNoRmO vGImnECEaht">rō KjY1+2E@90T 7:tk?S$6 a9>JPl]#Bsc8}P$4Du'6+HD1вu՟(NU*@+?tqX^Kߒ>_soײDs +<Ƌ" 0*xSs}5U\r8&TV~ms}92ԧkKOxSx Z_zϢRe2r儇CX"eezvϜgbوERy NVqٜ8R2.y y{ TUqAߌk$q"PQZI'x' &c=DDLmud-=ϩnfEQxvp9i."1뷮dˆ'd"+!5EIrj [f,ggwy𩇹㚛Ybge^/_4uXsȨ(>Mf}>wf^ZSʏqݿ(w!xl&y]8=~q9'sj_&FQ$kw) ׵i&njndtM`ZC,,q)D6*8?8~ڰpfG,} 8~xw=~?[䐁 6D -A|w\n/ԮM([C{]sD3>ec(8diǖ$C")ؽ.e '};<}]L(<+.H.n(8y}zѐ"K"+L2N[X. \T[6eҟtaW?GI([Y?u|X~E'=" 0 kbx[ JP 2ү?| E9>:F wb^t7Ebwɠ G_-0$L·6!Ef)Bk(&*ѳ-y J^!16"-'VF\ *ӣ u6W: p2IBReHldgp%3?}noPd,="_KNEIq.9[t=oBcUt%pt"J9}LK'cg~Ԛ Qk]:bʎDxj//KxJ"F$EHLײ-YBԱ}q\=y|6!VA@L:\JڄM{鏮>I.^?t G(FfҒ)vބ4W"G1]N@mp9ғP?W"Kfa'ބsGh˙3(+qǕZg=Õt~ ;7,,{0{vS1>{ >eZm@S"))C򸶶[nu N(SXdk׮[TTgVlVZ^swܙ/Π pUUs tX2o6ǧqy f@k4YCf@SNٰ4Oh~{ z>Vc IPsFeM&aA d`o(KN|;!5E.Jx[Pi<oh@QpٽM1I8̯`_'\Wɜ9(wPxPٽf;J򹯙<>i@PD8 iPr2tǀҮ_J1:]q.{EQd+wrLذYBbTӆjG- =()-'1>CXQ!j x&͹=fv,eǺϵM+y1IfKŷĦZS*B~;-ܣȜ]̵ PQf%vt ƛW'X4Z#Fu fP0_DLUX 9 e+>6=77n9bm=Du\p.8TWE 0յ`Ef\F*ˬ/e&!E;H4e9@7&=P}5mZYG|`xf)xr&O3E/!!=8t<۞~5X-p?jggD1KSqڿ-&ΜJxϔ̥l%`ZK1c_7NvZ7+2U$<m/dM{/Z5Z,<tn]TRPÇ۽O{ >_BBB|/#:mnPp7N(&($ШX*;qXOrӾzLXOdH,IQa$FQSFx>T59 N:NmV3thxQX]]͞Ux `gV+~\=z(Tb<",Z-m3p 45nӖѫ7#+AgLt}<&Dvcp).9ٕ <" KϜdɥaPi5zB .-Կoe1wplnjbJFդư cnW@crKb9< sAN'_mRH+.LNL`ǚLrSBBgutE: s|z?8{Q9[ٸtpNAW!c1nǭ. ilZ=)'.5T-j~ M/![|2n&O~YdG6ȧmW@)/l*9P:S\:C8 !{ Ć!zD ANk ~FFv"'ScVnpjDi=DXtk|^bNN˶_䚫DE]HPs%%!AT6Ǖ8&g,[iE{/$\Bsq%S~`5 pO* >gywXK,zEFJ ={իhF``6_…7n\nر#)'NPYYD.F9s.eذٳVàA$y#ܶ Ot8虗GD}]m6"o^\(#q'n b}0m3'عe3_ί^m& (A(`lGQ֖ Zd睔z#[Qq<&k] }R IDATwQBdZ#+qoM~yꮫ<7>cŦϗBF#h' oY͈#m̖%N/*Lo{)h8x3v_ͥb%Ѡg3u4g?Q,"$hyTT6Z]bݷ\Ǟ݇ CӶ? SJ {vfWINIA2"h|$]QxdFpFNOIWB7M w$᮰ )#eFnnf[n9ztZ(-UeyZ4;48m*HY]~=-W##Pà a4]/w湷q4k/VsiI蜎KR|ž}QX<ֳI /N.uIkWן.`X8Z] 5EQ䦋rS)6pg_TQivగȴAޱBRϋ=T=-D!0 A8"aZWht{v91$P[CY)B˜vijΦ>VCex֔i80P0T_ͽxKThWˍNm-dՔӷ;[AxlgWW{vDmDt3sH^^z~TBQ$V3rPDͮvgPM<M %0j>â;0C gH]FD i\; qap+Oֳ72bSF B$.z~RLL% >x9Vx&xK 3<:E)@lXv:P붢~6"4Uh25Z F.({_mn c5ܠȬ `\}5/DYZOuloWVF'q7&K͝+ouO5@Cd;}g=A@A`ljkOTau޺?z+~b7g`wlJUQ@M>k-l}7q! NL崶dIVjhlcY-+qII;F#o?g$n0ng4"\1ƶ艐.Lzyv.YH}iډZwv%cy^dK AO-VF(ELXjKq9\";&ENCXQz& |=xK#q~o`Z-tZV?Yh8 9(| Rp5\7u RR:G/QF>-=<j-v_]Z.Z# kA߻5h )-8IvQ#:]}}-"Ӯ>Cs?OOz[ 1}Z}vko 6+N5ZxSb$KV$>nF{*dIs7~Ànp^HF Ƿ%w ZܢcBX(6hjߩ? !5#yG”xh 8ֺ2V *U~Ͼ -rʳT"C9|n\s.']+JXD "h QF;Eaq%W"2*}Mj^z^z|/?!2*VPJgli!ꊏWL ,Z}p1gPbH•OSNe,s7 'GCnƍ@LM!EA.ozwE 8j=7K/ݾ/xm۲9zf9}}=VC39sF̘= ZoCpܕ".IQAWz6OF`,1/m@$vso$ҽw.!2&Evak\>a5dQXrD⏔<5R]|[q-E'a6A:"G%mĝ|h]DA ka]q^O;&(Yi׹ϕHdhe?#+"S^* =8z 1|)mʉ:?/g.ߟjRS]E]~tJk1`g[7"U d?wn>۰vOFbHFCp=g+mV|m+w %U;dى(Tggʢs~>̭u6ht k02;ay?SWbp9ǿorRWUILb"C'M!1o^ST-.),PhMV0}"Ʋ89e)4e [S-u҇W<>5& mތCtlBfd<v(憄3K9Aa^ +nz&jdw*%"3  pL Zj ^"gM2MUi(>V[c :c݃8(>Xׁm( is0߆Vp8S{Ͼ#шVަhX[yJŅ3)/ݭXJihYFƊ@"}"0(㻓8V}JM}TN0EjF5}(zgێmiXh ©R5w5}02pEjp]yvX;P% VӦOk9}k]bi}gnvo^MM&2U}66t <ǜʩ,M%t'&`zf|*>|dtG^Wp?KڮA#`7)9!ƫغbEA_fNg o1nn?w{N/i/@r5b]HbtTX z?5("=b{_PfRSASէgeNrSRW:cQ?z,9_;Vbˆq̹Do.-dJhO<&=5<<w˛rk݅7#FƎ|Oc\. % 62r}* E㨫˖or`!%y%wyc] {f&GG`Z`yUYB+݈+`@oމw7]{b:T!{d'WqIlވM؛( l\-$㦹smᶝ,ڬnҗ]@]I Syr=R$NDvBZqkDK9q%.Yb 9y<އ/Ma?}5t̕$j+}WR,Nj(ډ8]j^ǴK7􁿈<.xǭquW_+Riii$őRYeRZ@dd$=woOcto: Тs`UW-۩S'T2S( C'.p6c^FbҤ9/ŋF;!IC݌wss~SGng,#RC#?aW4I~96jj ]PjkeJXbۆ?]iܑZEao+sup {hHJڛ')9u>[QqEj~'TnֽK#M!ecX iQ-7`DTm'i@/B;^ J-9nf) Q碊zYu*` DX@z=Umq^| Khk nC4pM}δq%I`\e_?á?]wM 3w?Ll:y9/cNa$ym.އ"߀["X~zUT#霙ȎRW1QpL:~Qd, t[(2KDŲļV)or?mOF)@E}1Ikr3lcWSWCsa 3c\{JSr[/t OZu}P>r,Oq| LB#Y"Qrׇ'x<}u3ޚ?icDz᠗J# w8 n:ݻ lyM$MΆ ϟ̭E'g6 5bEVBXRzMptkj+C:d95}o nY(F:1686 [N";װy"XB)?cB2|HPCԸ߿8nFƤYw=OrU.'W2B҆Z^:vc+ 9ϼHnp!&_p˃_8z3qi| (2~|oV׮ 98&ɕ4/sʕMv: oB{cK.{fYmU|)ɌFAӪ5Wr Kx"`NK׮]y'Cn,Y]4A,(˹;{4?ۮZ0m+1={K7%TV@T$~S/ 'P6H AG%kul_%%ofjA>W;@y,8?SX2=6Rx74}QbHT|ė"l⾻綛m8E(ZKלK "ϼ*辰r,2QLPsn NUPsMh j3#):%B!}'僿3LA-uk֡wf=p o 1@7 xl4pÙ-"b%JQ=׉"Peu=uˇ ] ۍ(,F9W)gQ7)PAvP4]M12,S'Ϟ2%&S#P^O#YU„|nW4"xF;/&:x <Ͼď^C T"$)A˔{^dgYvd'z9/59C8 `[h:@LGTqS349b~^_ _FGPY~c(mnܦ*%2bǀd|r0鏀't} 8475^ӟO\~?NdИqjO9чRXP/Ǎppɽq ˼g12~4?Y41z $1L>\ y}KDxXg%qx⇎܄drq{Bb0u.yߏbM[ȅvv4V#;URè+J@g\ZOCٗX"a; SbABS]Te/Ac۟FHJIau^/-@h`?)e O,ҹ붛=O7 jLp8v 9vgOnQvIw"qiia?!9*.;\2 QXM[M)mw9ej@S%J'*:꫚h$XCi^* K@Уi YCsյGAyN8O~ 2< >3ٓ¤8O?b[|*Tl5yƢ 0:"gc}E1kkB|*LLMM&%)eF73vF(4ȳ(l!Çdk-˿^KiyS'&1 mG"$$dn{%dx\[[CA[;~m?q$C1zx,މ1*f]LЏtgW a:Cn :BTs3P}h:$w6)&ʃ="W{ $G%ߑa-+7",^bǪ {@6`5b JyNAonjPJo#_{eg~h.0M ql/DQ<F)pR{8> F.J`FQ6 qD}O$lYGDntѹ`I!GZ.")({½?PSCDbjծk %3E%b\ӄŸ!mQ_ꦩ:/} .$}oUqgGEpg~maž?Ƨܳ$qˣ <:=ISA0t3k 幘C&fZMZW4CeTIԀ?b _\+zi%p>:A@gL&7!]zbkDvs~ϙChlּE3H:>Gֆ/WSpь 1>K ZI{:BlLNe{p4֨mv!w&"' a/Rydnȴ㿉B%=Jˊq>&m”.ǑsL>: 맣Vr䰰06ff_eˈ_.褜Odd$zkpõ8E,j+E2+*d&k'3'}AXXw_LC/Ji,Vby =l *ˈT{QeAZ6`\vm}_5b8A,- cPeZOcyVhw\6~;Xc^b:$q4A+/fee/lه \p&7-'0L\qI7r%{v>Ͷ``2H 10wnCQ*[S-O#5_;C7:e>+1袹|?v)+9k&AMb+\>*$[ؕ,tLsZIߦ 4¬go{u.WB"bp]DŽ3fmdwN{2pO0 bH%%7Ȁ~ٗ+ 5$nFE'x Gހ$P+koūDd F؎.4B0}+rGf.!$'{~<3ie#nng% k^]Έ9~-qcئn\ܱ2x;'$EY48 /'[["Rҵ7d2 cb o풑 .HUvPR,k(rEu!FqՆE24O F!`jħqr>b'N1SL\xM"(PT".aݍ2}KzpcIcYj,Ʊ6lXzEn]홛l։$&!kΔXBi談$>K DwlE bHu#184+G_= Z`/Jahj"ڄDMUe8R*(ZFὌz oHNL=]:>$n[2m~1ry3$a !mrNўd=.^FLdϟPj%` 49CUǠ7߄3\<>}Wx>#/ pX#/:{'|6!"%j*"&:1g;ю^)e=K 1?w^{n?c@8;ec_ O/?MFohd348xVE^*'3]Gx<&j*ơPYnd-c,4M#4fk.tlf3`6/!$l,eKxW0HBywEwⱗ@?61n]o_b31g`fi sGϣ!)TƊZTe/Æ{e /pCa u}7Z.d k@D@H6WZUkS0{;x Ǿ^QtOGشi[laϞ=$'_oCS~ Fy:ƍ}f;(,f=]vr eI9h@k*L=CV65 OR$^$*v>O>)H$@@q%("@s[(­CE a6 Mvr??NA{)[6o"2j70Tu> P Ɓ/Q]i=9I "8x&uټj=?NpkeJJ0xQ}K;XH. ЮtڣT;o):JE6k&&OA稫Jnۂ-"8t,! ו|JkEs&?)y0;V*kƀEdsbW0x37 4Op&T%Q2v5#Ci˾M2> Fu3wY~ߧ}q?x:QTe@ST5CQ4D)ECq}N Gdw.:a.h%xQSLh@Ir>;TDي*@ԅ*Mퟒ2~(%/h*CSR Aw+;eG/t.䔌<7ֆZfrTb0š6>EgQx*క!,c0Y#zt[)8E$t̾=;iO`b9'ߋZ1wDq3$./Ɉ م*qCD'j`.Ԣǟu{p "K q tY}=o\O^N I4[ )3ilGA݁ݖ^w3#`.8̭[tZ#%=,^]!Jɝ-Je^j G6FS*1AT J#H4V.4 ۷t'ay!#INjru]ȾV֓B^ԲFl{9VYtL4VP1*vAOJ*2.cպ]CJ| iӘ6co8brs?DdƌWz`֬_idy:QQCCgh ;%߶P.@7zi(mT5ID b&nhy?P=HXS@m"M3& aaA4$iB M$N'a8k(%{JCgi8 W '}uv =#g?1YذYꫂ&&Ebc :A<JL*+~d![KE;_؈J y<Emo݆ ܅<hdE 'd_DQ왳9{lSRyw)/S4J;GD^]ND8$(r#Ci*̃.~ol e!.4_@SL aנ7Y(޷-M)d d@Q4g>}|,v!{ D@hg{X ZDuƠpju (/͆^Nc8rYYVL3!0`?QSS$IDDD/^ɀQeeNϱrb]D͘[3+Gg!{j֮%*&{97ס*aADN8 {4fAS< w^߅|l48lĎ83h#ETtL,IS蓿Ԁ݄ӕEJ|g.MNf",f,.`ꭐ ݋&KnWYqFF!}˭| E)@O$3d7ihMJ8N'd{;9P蕥531ۆaxt̛o*/`q9s p1b`c'cgBWU*$i49HhFoFN*yu֒;'A nxb3tbȘq1 \ Q%;:} yUU/.EEҒ{х }+"C;Oʐhnh hãc?E^OrZ\ fADvn}u/}qZM4 ]@!BbOLFs4J:R'.~/v#7A1c#Ow+ڊr*+'&ֿ >)q\%46^HpH2V,o6EhJ43UXekRthL0Ck8v!Jn(1uAf{/1:L8U vNgz nT q5#: 2V k IDATOp G g4!ftE$W" Wl } R:XEdh#&{e<A 6u1#J^~Ԕid؉$&}vWjj)Zn@ۖ44zM$#sa3wg-l#`]1y6z}KvHo8+ۂPd<Y&1} z69<}C侎DxT(RR2BYYI0oe_|t5z$ϙB#M!A405"2ލ m;ULVUv ":(mO"IӉ 2BLq{u usN[_!Jg475%BX35}VNu+@4fBb0u~ETR ahL!v4v}-{ (ķ:TDz,$k݊),c^ ns);<) \h\Ǣlm{3ͺq|9223Ur2 Ppϓgvb Rnq6W\qqsE^+|ԩù_VoTj.5 M1Z`Ui쩭n%-(Q,[͠TÛM\WD4ж5[ϫK9 QLDU?aւQ\x-ҹWٰjO=1ӅAг,(i[Yoe#e`MUy69h@@4M{'q̛4I:q:Rl.ASQ|f-wހ^qa郞Z!享 7O>}y,4(RUS* ,[VrB=8Go5tr.ЮdQXѧl_wQ1dL$r]KI,ZLLRY}Oiɷ$rյ2n„6uWt%ܟŝE'MuԔPQB47TA:$57Z@! tkki6`4ɋc h:pLHLc#Qq̿d&zL?N ]~Pbk" DKTMkA]uLfm&IDt^R2{7ŻiAΞ1c""i(S{MUk0L'l*|kГi|_k7s*=( ̳ڈ o? WNJ`d^֩{,?_oFAk@eUOG[-9;0M 2i:n-{#/WL|e$ǢixWc!]555, n7/\Hrrk!"{ו\wK'O$xt>M֮wzt^w:]mjqA@kpׅs 5XcuXVϔ!=ey|6DK=öA'vr 0f(f]t{~G4-8 &ALy0Cui+"w-Mv56ΪL#F7FnO'%f!I7߶Zgɶ~+|,+̜>h];†2>%" N:$<27a)'fb:GS=;}F}Aa bY3; ̸^r sM\ܦ[xQgwTxi%w#.;yGcBh 'i4Uba:&Z0j/E@l6~em2``×_$ 3soldKipr'g}$'/@\&|9Ne7 pU\MLȔUrɄw"\IUI@7'2ar9+A8}}V9?{읝{AP߬Ii0ۋȬgiK(dZnvBMؕ,Q2W$ 7۝\S3[Q@Sx(_G#OJ?M$%/A%TU5$!i%]ڱSf7̻6c8ń3slr4"Yx<+ŭ(< PNyZ!6>Lw_ǖ6b 4MЮl9 g:㲹pܖkCVu$BvF3UcO Mxp&?Ts1{⡱08yȁ*t%${ lʳrJE"-˙FP{3ӌ8A1KvsiF5&Ӆ|;UN~ %O #A(?RxYq?]ro# ႀCQ`} 늲9,U>WP^N%Ǜ\q.S!8ܷY||D$l:]»n7leJ*NS?ٶMji)./yyD;9"4 [nf·,p̣k|<tgaha1]  t+{鷯96iH})+ 72>yc <9)-̉(TršD#qnvAMgi&& o tokG]ֆ&) Q÷vty,k|Ϲ3|s?68u:umb$`Pa{;}?/kENjlPKM K<.~E}|Vjv>;~`yQ66? IIfgWNZb`K2;t*9\XJ=j- ?.am`l߱h?٧t\HKgHg ص?2w{:[Ueg*Ё6.`C˟(q5NnY+ի.fg9s,oO/ dW{|Ʃ7Q|T|@h&M'ٕ56C{+"w'${fM0#j&FX]r}U9NCa =1FZUek z}nZڼo_ik#f: BOP55v`;opC`)>:g?|sE _T&>n, Z-";NT-PS˟iZV[9|"4o׬珿į^~o^X@UީN gMc00[ o){pG_(SQTNBpO ùŷ/|s N$q &ϦiJL'Y}E#U58mvn A̗;=l{Y4˪..GnWhQuO;~AIL`D8]ǽy7{7ʦʢ6ϫӁ@r Qq TTj%;Čw_TOya~Ju9)^a.]eAtJD].ɏInqEq!=3~NGϵc9VvD#m2]ȾB?bo{1FQM&ڕoue#Mv=oәmx|*k%Zs}SO(V+qyu,HL˫'ĕE Pa6s/uBRR45xn 8眎JQQդݻg\{v33֭<A`b {L$qPQj0rALbkiOmʋggf'#06cW݇y ,# !9-ʽEiy{^l@)!?aןšt]ꏜ:عŚʚr(<Ÿ' FdcЩDжsǎo7Rd%M#-ɠ߭4JOVRqH3k9| 0~R{wm!6uNufњXiЈHk-؍Ŭz&u9.s_|67}~' 3y9e46ZQ(|#J,*A#t`v+ g$ AFOΕ3'"1Jzʴaj3Iz^ML弡}@6SP^ML| N[( .'O.7ݒ[w52>od3&:/q:쌒$*UUĔ!Z(ZkP~m!2IiM97Hn̋۫)BQc/>>H:Dp\褮 ZIM|&3#/_x@,#r?Axx)N:Q{! E+T/0"4Vd];Q*nN'IIIB->lvDέ{?s5\M( 5BvpP%IEpmFšP\jkp',ߒi(fREbX,+J'.~,y zkMhγp=gqKWm@guds rĊbQ*fAmn/26&j{љa쁵*V;^-|$75e/k~:7)kJK㵷W0J>C15qzmنDpwdhջ+0"* ƅ]c8\lMug sJA[\Q i t3+2HC!=}ddU:m ў,Yy\^[m{ 2)_Mg\)dL1062a̞^߷uhh(I||W|&/i4,YGNe^}ҋ <ㄿ*9r３6"RZA}}:*U 6[&Æ3=f3o<4ջvP$1|ٲm$I⦫c-]o6sea!/< ~AΧ256^ܣ&{( ^6Uv?ĀCvwk9oc\E$7Ky{cjѨzT8KZ% [H`%H.p >ן)(U8e[NuOخĊyf3ޢ(C"*;snW;+qOD}z~:; 9TWOP?EcyqV^j 0mhN/VoQn G)4\wlObTÏꇟP"]^U K8ܣRz&=?>.Z:xcP$61L rPP ɜnA˺i47p_gγ.o$>ht@e ٞ_Z~>\KaCyq{&S\Vɒ/(_rQ1/ԢMx|"}H_Jc?}y%wG$/^=/qĄFQް+]6G#dY߶eV IB fIrjju +?"Ma? IDAT[b[_/+?]RsnHV]S=Z!7DŽDR%7y3P*$t;P[ISq1hK7NyFpy7f!vMCw@lxh|@b7gg+/|zNgJFA!=XeWxD݊-ȟ Gu.Iǒ\R,f@,s#M]0(0uUJŸP"WywƸ(^*zɍgW;\XGcKGdLl*c^P$Y֖Rt27?% 8QC)c JQw88Y.BN{e@zfR>&E~LR!r͜)*(᷂T*sbI\Di'8]xV|Q"-vq^GJ,ˬ ;qLVf)m23*,bLtA'S Y/PMɆ$ʨ JxoQJf^|$J7UQTSξvI"InR MřAbHHaǡ<Qd wcg(-)@%K:\[VVƗO>f-ϺATDԸ\=5TUo=_q|!]ŴݸkXWw:񌶿LsvcѤ^۟iH Hg`cm%.*ђ/ӄ*KJ%wL:rݼ\G $W{ZY,SvM.Er;He^_מCfTxcog8'riAyQ^kv @ iÇ27%w3ylwx7{7%74ܿHnP^z:0=֏Mu)&̎Ϳa78"Eh&;( sxV"bRw_e}k*H3#v+ozk+0cy݋^E.՚D!*8k~ 5Q˹cD)ÖA?Y).e3%ݼ=ǒ$}ƕX-\VV^OӿNk؃ăEٌoc+qyw&|,<}eW]?0"A8ƥlhng-2ƪSL,q:9TZy,';[VYjv3SY/n7Zu[yOގ_\W~>zA}=z22wu ^*[èV!Y̬?vOkܹkzNWY4h B! &5--@֒Fd1! cduWJnLȵCFTrRO-ꪱDij2e|&>7U <>~΀ *-4 *bIdYb*RJ6rBIq: 0Ay_qҎ<HF kMLJ`JB Sb0l9Ɩ}G괌^a!\4)Qp˅ n5hjFN D@Uy6WTS_bCQ)oI(s1a^%.N aBV2 V(2et*gcYu=^ǤS;"ǠDh0}+Y.7:u:\u:Yؼi.Q-I 0%8ǏŁ7ba"~nr~#͞{RQ.Fd}IIqlTp8çJjHe =41Jhho"7/p/I [,s=Aˆ\DFq>ڸAMZDX4Ϟr,Dg̴I;ulkseJ|;d?#9f[fl #'oE]qҿsD's0c/.SK~R` f47>$_|yՕk!v^mmaf+DSW)G D|6"2 /:$Ij D!t) Y- x*^!\I+zZ?Ȯùs*nckʶn 0kb7~~L("Ykj wVY|o `vK|ٍe[O~ FL$y"$rXLEwFX 졩P"REM5ockm%ܳ@@YE <ЈKydSւo"JŖ={xnZu7iii>v>|:e ~f3~ݘU*L\ergK6 b!#rU<\y'@II /s&QVY9}z Q(X}7 Ycvq:`85r('mj{SFF TgI XQVsC98M' Bۅ7BÎ p9YWV@ۍ;ǡ%vKGwkHjPF2fy6Ykc@ sY|OEǞ\AlU QYcE;<50.J4)z6fP#Ph0i@i9d Rr vt+||acZt(/ϞWGjw%Fz'jXt9ۦ̙9g-^t,VEJd_ x >Mr(,mq T'Uj5uZS(.C5&m $qit_WYsEDzÈd:UˀWqS(l,.J| KmRgb39뺕Hf̙+} i~DJI"chFe~uw,>Iu6 ks0"AϵLINdiJQJTj2v266ȩfkN.WCLʻ_e6߯ Hh:qSA MNhb2Bu~LIFU&wtV:ra„FTbByӆ:A ێpxeO(p1石Ƕ7_Y'w?aSs0uF&qXYy[OFZ "c&SZl]V|FF#z԰sVν~mkW*QN ^ӿ,LE9K%ˬ+ 1 ,zVJÂYu(z_珎A*8:Bx7>FYbaՋOf#~ʥ=ȅ`D|Tq=~ƤMhWa`L+FM}7_(DۃJQ֔X@:60/:D`E$Ǣ%6.=V vkK))` -/B?7j|X]q]Cyo-~p8ȝm\B_ =м竧y%n *C"]?XաS*ٙu3{P@Ƨॵ;y79Xu,7 E'e WPq]q%?:lDoYͰnD{Ivra4VP ƙݔ1cU-*37@;7@an*= WR^cɣ-|]FM>EjJ2"c1D24љP3tZW|#긦U59J+oT(RCVkxǕӹdbXDUNJر};gt=txmkPPgR+2;l6N|ypA! 551-yMr8ؚIeq1>C(bi'(50]`Ϯ cB-½5'_dJU ΦDv:qv4 Cxe"`Y!2ؖ@FMy,tNՄZMy=3~^ztAd5ZHnKUt?lÆ\f9?txPu},%a9GA`ۜ(..7IdLNJI*L92OBxQ9e(f#A¯|S),LNdarߢ ƷʹZ he5%9sN227yLiU+nO7m|jJ+!4V![Asӆr(0-:~zGSQ̹TREΗLjLyM}V$-76Aҁ*"Ng'2vC };W#Cޟ;4#2220]ý,4[^e uˑ [ Alή052G'u_z{=ӱt-c$^"y${  0@^b"q\Nl* NH 3"scwT sm)AGs?h {ʻC7pzxUq$BtŶ"M\"≝V|dd(##{"UXur(U*P*D2 f.bYS Ǻ_jAH#9X}ѵ{V޲L@`X4> Y@暀?|{&u\폤Ϙѷg8& R͚!iilYrkI pvt4tvz\Cb@3[ckIMl+k'@G rsׯ-X-T`…sxtENu-Rݺ2ROoϒQ~uCh>'">LfIRZew+LOX;Tj̓3.wVpNOyRobe~|b6?ۀ_UJŷ zd=QmPSο4:T-c0IZ Z-~\9nm_$k{,4)!JjH<= "dY՛0 q8X^UCMv^Sto1@R[+'EXwC"TT_G0-D]/!gnRMm+BxG+4:xdYfӎ6~M0oTS_Z0(`|Jd0*9rRZpgM96OP"ۛ6"= $2adv\:=S.׶g2ys%E,PD~$5%53 ݓ=}[:f'k38gzVXtPSVI/ 8]]3+9:ǎ# Zch1hٳf 7 6W 1qD cW X pn'.Z, {29O͠Rn&u7tEG3ۛkb[ уQJ%US"#ydaOL6r`<5ȧyݟفpc53SN}K˯zo(\P#*9/&g(6k+y(A KOUohj$$ij k(ݱƀdHvTvmբKDCwOuɭKNZZ=c}j+6h#;:n*kjɫwNC Je CC6kh{ÓeBs-vUDYӶE";P[+=D@iV}}:dmpيKoOioB qJ%TUbs[vĈNJ5 \> 'U6+YNkmV޲q45[dT(HQ^ҥ?,[]0 IDAT:Ǹ?|Owm1]i%餾]pDP1HBE.T(PFǭ#' FM᪎AH AssFg cVJ#NmVt@@,91é,ɂJ$8,Ե{VA")c,P2IVFMEQEH뤨R&)oU1!>S~f^X3)_ebu 񒄡]`tl@9@ESc$2GQ.K8Qlt J1Ǫ?`ZacP**v] {B+Iw̘10a/W]EpHa~~. 1R#1@J&_?{4TvC#) [?sI;nRFPU,iXӐV-W~WGA4萴j-0]bQMI쫭fOMV7,C# `W6_]7g[-1@M %emysqIi|2蘭rNKJ?Q'g?p:Hh%y)''f( Gij]ʕxEp:eAQ6}:Po?rJ;\&=oX# ^\7YmL&J2ee..Li=n,_l&lgF&3s2v|W6 ;WrOFTKr T*jgMT\֬S:m*[N@97\!2,T@EP(p2W&%JJT9l^8{İ?/ 6PZZIHH=GNĄ[;j$ I/G_ ~k[|, DP27o|4TJ,B TZ n`מ0,.v5v'(=Ox9,)ٸEIYs %I]-u-*T*iQ^ZRM-3/v~R*,L%L%0g߷3)k(TsowHv1 Q~\055DzhzA=j>IF lS*ş-IJ"{*FN9Fj5N`VvM :\n6*D."t JQ?,"C啜Ȱ?lJMˉWy^+??CFsTQs#zy!u:c χjy\u1B(j! yԄE[Uli@Vl]cRgxD$^?}BUAYCȪaZpcШԘ6eAd ZRđ6U}S=ZH㲚2^$€U3jBs AT9w:Zc`F͛N;si_/K$w.K!=qN *%Ι}X nyŹ+8P(8d6 ^nldii)Qdݎ9 >~6U2/&o-}YȲG%O3QQS?c0@үXFٹA z4Rnξt|f2fJvGYt* ԈhhŠ2J2v*1f88n@:b(Y+_fmA,vF`/U蕪n` BޟQX%&J"Y5"*(-PAr].jWLT<5Q/csvsBp%EA`їw74rS`ڸ>;#@]%MN'OЍO陆u/% WRr1|Z:9϶`:1ox4L:lf4IH:QdW5 s[Uj"FL+edޛ -K(6;(>,ɣ0>o؃H^ۨQc껏3<ӓ+O!OيYQ(6cdh/=%#/ā<J*~J&) '9lVE|IbR7;Wr DD37<Qoh`JŝNgɃTRSSol4J%g@9\b;|8K-#9Բ_;#VKq#FFCv;W>(ŹRRxƌ-EYOw}`bFRtE0d:]xfXBH5+xzF >]NN>:b R>>3TKiQaQ1V:l`r;W_WHJ GmRGmD" @oZ=t*Iq|wP".N&  .]xyoow#E_J$ V+ U?1ebϒƃƆrs :,aͧq^Ĉ.>l4sЇhT|'>c ==NkЉ"n7_[muOjy!vI&JcϲRaYPQ=l2)SO,i*BT0DtH¦rLt!. -u(D "X}3Bucx. $L%ˬnh" 2w=AR2}HӻTt^V)u7fH 1n+?c_VɲLMq.[d@DogST*l"Pnk/&;crfD@-xL _uzcRj/odB< ZU+:؁/#U*ӦOgZ7i7z+$詧ml$ח3gYDGEX\];2= ĖjSA$˲LA^.6a(%Z&%NDZFi&sHy'%8vfMDZZoi9od 5V[kUcF^˒ޭ" Q2j,CFw/n1s S[gǠgu{mE4,Er: 3q__BNJV˖ ԙHkV2//t*+p4:2ɣPNlϠUYM/Uv;줵%.' grCzd,Bu쌏% }F ) ''/~qp8 眙4skuVe T-$WC?[k<;$cSv(X dŊ-q{(EQq=ȡq숎esISBF6*̞г}]OYkl؍԰1"wȝ>E(E%!C8l|&q4,M#L_)ҵUc'$lTkP {߬e]|/J2X‡n+DQdx?%1~\RH 4\gDbi4Ĺ_DZJR":jڮg}R} 9@~z&߬M8A=i 8a ƍ322|j~j@X;=@b;~17T"s쯭c[_z:}MfM7sfٸY;x|nzրM]]/> \#D̙p'< B,>FqkdCI.M PP0:2>vP?$7e`B0 $݌McIm8^#~vSRrjjx_U9KǞG,<Ӽ˨T8]OMu98vm#Y(eLq,|O|?Λeg18vhG}X~h1eƤ&1/uȠVv:V%Iyj^&2ȕC>X7Im$]iXy|V9Sˉ -y!{-QDENg)cƎ74"I} G~Z0A ۖX֥U N@ͽw]eYfge1y%@D`SBc;$qLbM$0U&5EH ]K]m?8|)>#-N=ǐ!F/Dpjʏ^%2Qey=~XY(溋D͞#uy 8*׫NmFA lۍI,Ο -IOes0QFj(=;y},˼ 03>9²2d؛or|M`YSјd-{'Ͻ 0KÛ}GK1 ̘5ѣG_֘MU(Kوj[<EV Jt\'-'. <|-7wU(Rb2Yt(-~_~_y4&/P576#-{J ;~Dw ir#:!"gVWY3}} B Sڻ'eF#~..c٤7<)I$$>&||E2زf_9X[a}MZ= (C(Pw +P*xMI^V^vn}wR,1{("-Ş32=c(qѢup`]<+D.w8ˬ"Wo/P帑ɐp^?vbldupxge~3_=pS-otoUL&;9e"Igݍ*6 kBK yA'aLF} U< 04 |)6qWeS WMۀ/'I">-Q:-sI} H9ĝlM:!S6- rBd_ y z&2_`(g-w5هҒ&#Z7Q э z&tûyI$q; IDATn.Z 7MIlU紽Q՗b ocn624-$ @|v.'S9jP^`Ei#C]td79Fr?*DI9\tF#foL1Z5@nOmA_Lǩʌ&f{ylDI{F[΋ e"{p# ))d2f6pSBryA6Z+D}Q1>L-g݁Mj80bɽ^i`kYUsFY\VΒ2EJFs+[բ_{lrA-W8!tbT}ƶh -=]2܊K)*%;U` 6GTm$q`ó>Ө~x?6@պ/$jį*#^B9[PĜ0lJ&Rxa84mgI{1ɫwXZ`(fWъ\P*H,iP@68V3vsy@;&zzG&l1؄Z$?1X(5.x٬ +&mTGRY4`L(V;'6XC Ӡl6yWZYYD(7x Yym'8y -'A)7VoUiu;3KE+e2K9}g;gWt6r24Xr{9f!K/`@')D$9|m _< Tɗ7) ޶ro$r ˭;~fa]FaȐg7}l2=J=J>^zI];v}| yN(;4gU5ed}F0g^cs/?hEO;& 1 ޿ٸ4lOn97Djj%[j@|}}o_~Ě_cɌ$9u&x J-ק:U+$ U)wё_XRL?+sPK+h$!=K]W`6?̏X('!:o* ̪ۛ[ *Qdi),8oZ\~j5cǎrqLȥjj^E7 g{ >a{Oe}jM"0l1CLA1Wج7O&3Rٳz'.fZnU!b2< r6` umU?{X28y|juF==ɱX`7)lKM;YZFpx} p-/MHL' gef8{ Ɣls(oTT6i vXV~FCHQϔ$is[HݚmcE$1x[HBSf_ Ur)Խc9~,1v#Nd Gʹ5*e ԯ[_Mf!ye|{FSm E€lcPWYZd]=ɰ gRT<k4lVZHe]ck)qAv~!MќWm`SU,4Yvq6dZ-r>@ Re %%2Os1ń1yx,ȭFZ5̼\*}dl˓i2{$QQ䳄,4!mtVͷm-1hG& F&ID)c59m62]XZwyf3l_ϹAu{3`M\:6͇C^GCZIC2 xJ@1c{/?bn֍lPylFӧ;wb܍Q1o||<_?5<= QkX#| ,GFtn:Ԕ݀++e zPP=T-l1n5Y!Jh<벋;;UJ5{n$to/=@OO{CCl?'Oc; PB lVN7KnDEGұY\|eM|Q1VTp݃6xPjS:\)ۇ>,xh1WglO_D᧜<\-mAawldh;npQEW M@ȕ3zS| DkZa;*J k梗]Xu>,y羪#,zDmꂩic_QIJq)5AȬ-8i 0>+ S^= _|}H5RϠA(\UDw aRKءTYd6'#'=ᓜe(Yʽ20 f A%6I:f_yID˚qU3W|-ZGf!C~ٸUkτtiiwws[TPQcN "$:XF?|K"(OG O2Hhu˹b-Htqտ{qC;=o }jJ6շww̥r4[_}AQ900G7ܣ B48pv=5%:?%Ƶr̊S+W{I3lh4ykxY% [+#Yv(`4_y{I[k̛Gru޺V ^|$nl)`F#|Q\Jd@(V9?-]rJIOL~d'ͅem-3oI}YRC<[/j]#!"i(+=v8ezZ/P<`LՅEt<3Dk6=pbbj`[vfw(լ4J`9Q}ofJZɝ=\ۨhXqO+QhQbDpX Z{`-@x;zEfUjo^X* ߛk%.!>VwQk%﬽t:V_MӧXG-kȑd"j@6>Gt//flk4L4^_[Zo;dY"Pl@^kTնh:Tdw΀L~#yv0$"<}+΍a]CΜd6[G(Q*Ml\Zu3g:t0!>JA&ۍF:gr}Pt.0}Vι,k'o>gl*]r \ٯ ̺zkg|hZK\A~$zR2x@WD:`BM+cܶ(oL_+S;q߱gd3+ȶ3*9);8RW@v ̙wZW(ezDe @)]#x\]b_e9]r#ȈjtD"h$*5¸!7D+j)*kZUR^[}?+S**H,e3݀ Բ% %́JAF\fC;M0KNOL)QS΃qhJd3` 'Z>_־YƂP2΅)eJ1B6Q Ḁg&$P㭳bBn h4yر,xy,ZDo4g^Okol&=ǝ?Η wt,ZB|@iI1k)Mo0cL]k]ju1(V+je)ۓ/^@\NhRbI=Fc{]CwCgpCc#F?|3T>0Oħs*7x;p5*kRyI{S7+Y,ҟ&T0K`tmq=,vCl: 3= V8IvhtQE @L=PkB܌&| ۛJĤcw<<gzӸyoܩ7`qw"c)+yG  7\͋ↂ"UJnԗUiX0T~}'3F~+L03JORN~,LIG~!i"Ed;p7Ml8Nt^k$_5P;&C|*wMDT}L^Po_sv*:Uß}u8jۓ!0,a8hDZT= % m%;%ZIm7&5ˏ)y2'Lƴ>%:LzQRVeDVAJ w\oNzy \Ze' rFqalڻՊL&ktJJA1n8z Ӧӓ˗3%EYWk& HL c#A+?!VE276ҕS'r?Uh5e'HJؗeAk<|n2%ZY'I@Ԉ?e#EdԬ,d-7Dxj5-F!3ԳSa?("ש+RL_cޢW´n6sʦok&@-Q|W  qwAxsb(-P!Y.ӣ`zQvrc(b  ֩ˬ@Vw_rKq.+4U``V^Eø{p3{)$[BDvnrGkP79gկnՑQ^oE.#݃{ F.E GVrY Ȏ)+%Is /0O+|UP䧞j~]uU_d2ԩ@' [ްȂLAfJKFwys qu~Qϫ^h/M'9Sˋb99^=7ww(*n~Gpͻvx;%L16ib3CCV'ٿ'|Pa2;C*%ݕ_id^?;LrMPjf$ Xr>l`sn#}H"E#l`xc;:-+$0d7gS:) 4i A? RkFguk$|r7SeE$0KBo/?ՒryQ K8ͪ#LD% 2dm/ @PΖpI*  p"24`m Hn.MA___"Ǎ?䆠 \JU#^E@+2?=waˮ}x)alSij ju:^=Vl?jU}6cPl؏VQḨ.K,-E&{YFsT2|erRZ$bCѸpq0Ax;V,ȍ59]Ete:*+w6b+ywA'7G{cpJ%",f" DLF"D<Çƾl*-J+q m#<4~%iih}|7ee  ,w|Tfe wu;ﱭE-WpΎS@ncF1c0jtr1)rCL8&&RL&CX,V?4jΜHڼ-'=~uF#JvmNMH|YfX7Ʋsq箞:_4FBq^p1Eiqg5"2РÂ{od6#d(MASvzlSٙuyqUص;0[Mv!:>-w ":oM6={P;#Qt̚;łZWץ5>ڻ L:&ͼ\#t@_ xbDBe\$ 3YZ%= vp&#JU18ˍ(wo"ݼ0" u.;Q'C4V~A>2$RL&Crs> cLl6t^juJS봅k MYA]GRaښ_oZݦqMZ]y4Z| _Svz*Iؾo[vKuٮb(M۩2?霠llH{!֋U4e_h/d2sI"*9]0tv1y!*ukf;APNpf wTc0FFzk'Nv6T1H}ffｔ=1C:}]x$g;B[077SGd4q#lO.zmA~A![6E֙d»2v|[t.Gg!w6j;f"۩)JMTT1N:b"h{"8g])__VZO I]ˁt{;YkpA!MeLF#$܉Ã'ңGRӭMDvi;KDI{[۱X,ڹFV3hx[ m'Nx~$I59]$RQBrQ.V _zx c!؞ǒńp\]皖pf#7FƙSڱKY)~};h*u뵒:x| 0р'*.O)N?d[o1IKbΝeġ +?.>?v?^D>ȫvģqwuܢⴲqpMϜbu&?<>-𜴌֊ȶ,6n CFjb59i%FW0A#aO{»?;cc;v+K֊v Jy핏PgRAFF6_%ɣ h`)&78'?_O "fӳK{6|tđM&V =$2Ņre60rʔ^$/6oDdh_!YE>~ Ŀf &6m"曹ۅയvr(Z+Zdl{''Mf=tf:ieP}2A`GIJ5s 6H ed&"2p ;'\)T>m”U"r[:/-Nr(%eė zS\~N,÷J,Ǐ}6&Lt:q,u3EQdEGm"Ýe2!ڳ"rm{v^q$˿fLNlH,[͸[زqGwݸXk'ya02:$8/OrXrZt~N΃]Dv eέ}QzUe~ym) _)ȡYsL2rB|rC@1g 5 g6|C%Gf!CNJ>Br||<;vT.v,c}AZj i~cQh(ʪn_Q_7:xI:W7㛢%ߣ`b{Жg2MCrckj9qyW֎+)4:WN.'2dF[+`KBrrr=T Ik]k$+Hv{2>APV N8yd~8qhN$U șS:gN.jlE]@ttMd;zfxGL'b6hi^bG);PM\,jNܒUrY iii~ LJgx^puuHk-N8.eJMF>Iǝ[a_<_ i4TfGٌ{9v33P^^S,kToc{deo)(J$ ϊMFTm3\8ɂ=lEV&瑝k/l}eWX͍:"]Ʉ͵^gS穋]nxǗ$&$ ƭՃ[9H[j4YE LJ!oƝt$1,\ܿVdfwH_T %b1h$%P˝cRfW^OܿVpϟ~=(1km$bEo/f٩eulbvqϘOϑ -߯!B͍:k3Eku;՝Csԉ e||GNGQ3E >x)^Q;GmyxZV$`_ T4$a{Wtr~)#2$MdBr~v#F[Li)`{W^-:#pW- }TIx~Bw~$qz[bq1G\t?}7T-\S)/f#'D uq'KaXNa]m\̴W6#2+Jyᦩ ;J6a]{|acfk^N7SbLǶp cLZB ZIhwhߠvKg[/Z^Β*"` j.|.Sv:V33dd\y h:H΄dg-6A޴=HO8z{Nk+%,[nZ^ʒ= @Y ٮpRMH|n.69C#٤"ڪmgD+2p]xz8ۚ<")|OBdD)nCwYS\cϘ6vn+&6d%sJWrE֦`~vP # K9Dp.0wIaE~>6Jlն[ ʗgTQڟpWCx%+`L!*7OƅEk?/+Y*ZWy& ,{k&{XPhaX9Ŕ"?dՁЧ}=d;5"-U \և9Zdͨ:>>>]xL=O>Rlq'“z=>*+q\Vcs͛ᚙr'*ZysF5qMPNId 9rҜNjіukRFI޲FTVR dHđI8J yv2*5ﺟzJ0[RRƑZ#Qd_i>dD͵XgøJ`LѣGU@$^{L***wxTṲFzAQX՟k׵v-uk廓T@PAH @H'=?„ɤgʝ}=O͝vBNn=٘L2?5ֱj6̷Z)n?b^WOA"øҳW]&2 +BBdMlڲ٪ 炔rb41˨_;151(As rۺe LWtuB7қl@ ^YϾ*qguDq2lEGS,{}w(=y 䱯ذP"Ta ކNU;c7עILNLeL\Um̈́ {>R\54i"0P~lJJJ %==]A,l$)7!20 ySAaf747eCD &0(..Ơד](-} pL EoQcl\gfej ?k8ߩFt:v77x5@7xdZl GH*o5Hv V woSUt:Jvlj WLME9m-L xlko '++q0crRgnΡ0LQ;`|5?*#Gj3^Qϗ8 bu;5z=iUjQo./sKRJx,5"7tz|ƈQZ4 s\ _[SLlXpI)(e<{854_Tv($GtQv}aèy=:M˟;Lm6c@jD O&#yI#fi0fzã$<:E!=Nۿr"FFAFFCZ ۼa|m+DGF.{y됪 yyydt郃}RrFdӶJΰt-N#;'\ @rD kzk~Um EQH gXxnu9{+sJ ,'}AA1F'sNG`w=JƉslFglT js% spNz:Zs&KgZ0 /ϡ|CGU^\/5C !)_/.oR:gފDBdQHx tM&%LYxE@y#;d%#vk۹k1Hxwf%.@NNNUÈ S/d ^my\ B2>[0ѥp IDAT,֟!Pt:~6G- ` V狙3"(=ȗ? C\;HBμMls{x,'珳/=L n ⪉1<*8׵wqgsހ Ma7f6`GZ˼bĄ©!&1G 2D',ÿu$qTt"m_ar_<վoڌCx׸qpBu:`ŗԺi"H 䚉WD4ahsJ=T p $CarxL<}q /LFNG!p3oz5+q GcswV"+(_7Vl60Ah(94&b8 3,,l:JYS3B蘅 ArXmo'&9͆([hz0䵐GQ3D.2qi?j#Du;Vy-侞'ƟB8kD1͆3NQ85w '猦fèɀVUptFG,}0h&3ձ8:ɧx4;(fLFBW{ϼ nF)-nTLN_@͊Nѡcq@Z'ٜC{KEѡ?؃Qh&38 ʝYpNN%0Zsΰed/^?hCW.dEȾ ߊ/ב/G"݂dr0s =ؑpr?pym͛})BS:E$}PhH>|!Dɡy3A(LݬC}, A"uɖ(+~[TgњcIC!@`\T@x5f`)*Ykun,Zw>===i dbȥ@B!B`JCld>Hv櫁8YBt'ŸgBMd/^u3rWS=~T^_d?w.SWW B!zM td㎟CW !Z64tYYx: 3{/D)`EW*˘!$"ۿJB!D0ɭARZ!`tW؃LpScX&  ",d!f!Kmkn_58((7[r!WCd;j{}"!2H %<J@"xts jb kk ѺA]k!GvYU SxO[!"'qu:ɮ>tm7\!|=0pke@{Ynkk$:Oq%8.(z "..N f8c(P|FsQ9=KJUU~u}#*j3<)i煝(jLdK)L6EbBuyąx(TUeoK6U%=<>NZ^ wZ^޵ZGrV8c@6(qZdgݞ3+9kyBea#Ik"ﮮiCޢ 1]C mmj*,BUavN7ya *+QYQIn^aa{NcZyuq+Iq\z L4tM 䄱\|4L mTA!xşug<l_z/6UeZ0.1#k&Qݾ\gLTg b23 d(qHRCF;_xY{#cX 9k8PC卬;lO! 㙒p޽:l6~?dWU-r:EO//K爃54b !F.w(4gnܸfne%y\5f:KJwp/+y${a_KoZm:3 Z7MvM`=W2De>Ϻ$4N=s%HveGW{Z:]S&7y_P\̛NQxhZg~FͶvXGM Y[\u߹+n&__dlJ+x5Է|e%uŲ]j?Xn(#Mcj;.(9enmwoZh0_ײK(ܻi wL㥖1DF6n{z~8o?Ymg]LǮ\n_xJy㶞y#+l͢;XV+΍9W$~ ~a|IJ5 ~Z7Oe|[6+Ášrߺ<0j\^6Z0s or͹qlU=o~ě7_ΊG1:&M 0neEMɋqc ][v 0h_={1~fچ^&<. j˺i(x#N"[.DBx-BӳHABYUY@;pϾJ.{}\r֐/]YXKo}?c3y,{jx:OBCK ogb";Fql/\||?PTʺ}V_x~~ !:}xk`8\^֭vOb副rClΡeGE5=u%fGo  5:OʝE366~- (.e%vϞЂspv33;.&$1 r)9&}Wl?%\ nZ]3KMAȀU|=a2>Z1[ 87IMdS ;_ ݱV4* J̶i_0x.-ㅓO`zzɑ+-L߼U7"Ҷ6"srԱk<߯9sx.8cD 54mŇM#ޡ̡ zrvRCX+(VmYm]2S8&?ug} fp8o_/clv 7qO+P[D_8&8ldR'g ϱ!+97y8.ko]8w9}jk8;]w$^sYy1_x_OEJ|Yƻ89k$d`JB M Y'$=~<܌}fds3O<j4$(ƽvQS6*liwg Kk[ӝ Ĵ] fNcm-ް:0b߿[®<n<_(*)դMZk*gG/qv{lŗގ1<3<GXLՅx>k03^KVf>IU45r~/jX:k.62s z=aC0/_vY/+KXlS9N\lP{cWcgԌ._ϰf]AGrg1f I &g#3.JYSIt?88Z2m#WF^όă3,N0#To@3E6Unz(\j=C}0m))#v=cb?\gn煥+}}kaA痵KK}; 10'yeI) a1dңXYrCU[[ò?屶!3#;ylf,VآBM]=,[26% &O6/XU_- t6+_nӰeceE&Q~GL3<Orh8? ~'FsF's@GSVTK /= ܽ}@Dx8w'[]{.r5@ͬו4b09})\SAk=U\^SHm9@]s3E`ScnE;E_՛VsɌ1.ƐՓ`nn 632*7Xk{%i 1P^Q6* Ԛ"$==<>=n`;](*v_gw\]՛ʍx_r#gn2ק^z{5"#8ej|FvoqOe{':aZ)gT֐} *kzLy6t0kkeTl6jZzxEpQUUe֥lIiz^(H:s+@dHG$ɬ52㯣QJ]kM)Ao])U Y* ɞ[I)ɀ&A;>[UU>۹y99\/֑}١qq}Uͽ!`yH|MiOXW&Jw5mbiw9:E!+.K- f j f$P>/wep:ii ={)(v77st;vkDS> x0U7_g J֠aQmkz[YIP>bt]}L5KOȻesضPz<ȹ/> EQq6x򏛶1#o Y2ذj:.b0:|m Ow,Əv1~i(۸^ϱ6b=$4: qtʀN 杢2ζX:o\V6Y>>63gKNύg}Zb O,ԄX>:bL\l~u 4/g=:6q9k{Yݓyq(ºxA㿾 jf* cGGDv/LQye.11 <(ܯ9 78VJVf%w;0|$wn\MCu+FEjc?`ݯocz!:Ǧgr|2a6Ud;M80Z~(i=!'_ۭE 1p褡 8ꩼ5xĵ;f{u{R1#Էe꾿6)|PfV6?1]_GU =v gnOzZ`Q$8UU} I7ik3G ejjj̭ 6w.e(`d_΂w,k _']\;lL2w|-+8bYIO➬{v-w/58(W?G {&YA)a(.7᭬$zcͣ6k;xǴn'rZN6~DADEps7"yt\q1il7r/{kU#Iht8wS~ho-< C@fZ{ g IF?sv}#YT8mXR?>xy<0< %{GGrykw<ԑ9ܼ[fTr"[+x5ĆrݞoZO 7p#5hSGNkfs!oʊR" !L sckε;A2t^շy'Hek+l6( w}x]v͐f`H7".*WW0[8lj ~$&!4+G1>.Iik/E_@fj7\t&w<2)K.g!366Ko*JY0,Gx&'%Ox'$''pܢ^hoٺ}MymM'6K;J[S!FBio~ڄAnVw M"O#F^9tv=l+ d9asq wxsy̚8ce.]hz#-V^ B nse۟k 1k;>, K>fMs|*4[ꑿ GL i٣yh NU;x:+ `ܸq[_}'Qld2{}Uw&;lw%LvGjgkKwS1#==[ IDATm/7sޖ`0kғYo,B)j)Ibq⣺<$Weѷذ6wwS]2&FLj !wԕm|m}.ZW+]o,=΢.}vjYɉl.*n|NͅeGp[3}[/x|3("ۼ]e{9Z4Wo*J32>pJi:۬4ՓŴWELWY&Xz_tڊ._}q|;㖒.>ޱpfw=++f۹mJ뙕-Хt۹y_e;O=SuELOpq=ħPgn"4Բ)lX 7uj}u00/_{xkRb=itxorĄ[wHx,\NVD4VU勽gV',[x^Y܌aWC |_zI)b+YR԰ CSNԠK.g'&5n6֑6v-EĦ$>{?}sv~$dl`b_y 3"qt_ǯ~K/OvJ"'1yVs;g|i >7j;v/|58?}Mlmf";fxgNæq\Np#qĔ̟9UE{zyhYY[ߤfX{AZq8&}8*]Ԛ[Y 7>z᯴X-I \*/̤4r"{_/85${CO!px5'^\f廊X<&>8yX*gfp˹(ë2uX*e|^!Eol }ɗCzL4 sUY,NNd˾J]=njABxNn_)ƐO;/+.yB HXҕv#$8|Mj#%4f>,݉NQ85@vnD(Ga0R\ѬDd!רjCEfqߋoQw18pD:\tL;' Kimm%#)Òm(B 6+^6?}+3S55CdwXm>s+Ēe쭮%-1 g-|\>׵\x{gb{.]=1ĵ?ŷ=MU9Cy?j#?gS;x]>-,Qq2,[~ ;"& ȵgg fG~Sf%1429-'[ke{J6÷%4={j^߮/Ʀٲ< VU4<{^*HܡŽk6nὍ[-ԉYeG&qErpOS>˱/cŝxbzTT&rmYZ;+D =ŹL5~fN;r]k_[^.=~),JJwwEs&QUSL>Cqq9,i7"* |O!f0f%:ӧOWG7B>EQ֩:gmDt߸IAa wEk!IsK?,E}Ӈ䱞 ~Bȫ0ݻkk]º?+\GvJ|YnF媫M8w 5?n Kh{:=z(w7TgJ.Eb1P㒓7O?]Mr z*_ =@>%n9O.9Ʈ:,,rz(A̕j1Te{湿Gbi>5#n>3-psN=o=XN)8vr~0ЯK D@BW.Ժ Bpǚ<8Xc!5Nr B=q \wid__+YKF;ws}>5@GwBGsNxbmk"Ge:ɞ{sֱ# B@Z'5X!ww$NqkYk;Ա5d@.$8@d!1^!DlEd7 \ B`$mYY!DwU[u{&{/4s2mr~Dd!,f<S3,.P7NjLtAf:'+J%Q٪&&  IZ>(&}PA"Bkr<Z>(&}PhMڀd!B!B!B!KuB!B!B!B B!B!B!B !B!B!B!8@d!B!B!B!B!B!B!B!Y!B!B!B! B!B!B!B$@B!B!B!BH,B!B!B! B!B!B!B !B!B!B!8@d!B!B!B!B!B!B!B!Y!B!B!B! B!B!B!B$@B!B!B!Ba0ONLLTsrr<֭[Wjc Vb\~0[lDFitTkKm4TԻA-}04*VL{Xl*. DS cl6KvlmlfuCچY鵅郉 jvfo#j}:wo}/cԌL:D?QmV?b:sihӺsBJ}T6oy?h؈ l6kގqFߦzaQjh-V#É0RdFZm*FL}3U% ^$Nm]YM }_Ѫ>"aHǥ x5TQpn=IbȀ":j~yAݍzDRa㎷fln<{q.bafk.*`ţ\@w#q_t=,>-G$>MeDBoLHUXk+h-tZJ,ltCf[ۢ3Y>/ X 7ysP\zlԌLKwUPS$LO>EmPk./-Cwʾ;7E5MܳCQ8'`8Et~Om}m>f-<6-73?;X[B ]kИ$=wU)nb8j[9uFGZb&9'tsgMjs*C9$V! x5TqmZ_#˵q`=$iq*K)xsBxHf\8#.G.ӳ&rbB!Ux6`U~ݠqKB syMBtGw>ao6G"3}3oHI,CdW#Bx$DLr|B!/TB̸p>LHN6ԘuY7"> jb B!z+m v2 Y!B1 yּ^ZY]p %@K!P5K>jJF !gRx+pOB!B')c?g `L͐!1d]m/Т9#^S\_PًeBZ_S᱔LC)eGxAB!M}ʒB~Hku#,epEzہt'^R>&k7!i]+|)zTB!8hzzMBȉe>X,d!z'^f?!J~m(,_Ǡjn_er`Y!pFfBYmP,Iڐ2BxLo !PJv)c-5پqOJ,G !52 `e`S)c[dd!K¹dڬ. $@B#W.\3z}~8J B K "BT2 Y!xP̸^CHldzu/:bֺZ4ǣzONimsg9go8 ɣ(vVB UXV6ISYبuSJ0;j>GкZ }!}^ Ι+.]Tg !B!|%{rgy)cd00o_s*Ѩ^2fHE1hMl鶽s>4!P+i]9(:3{BV< !DLhDzC1Yع@_` LOy}!B!ߋk;CïԨH&sxf׫y_ih0++aDb|llD'@4-]Sa@G7ew w=^OUkp9̝W]|\6FGDuD ܰpN>Ò3nUf|ffEeT46^C,:BcW]3=un|z-K[8t/<&;*dloce&4R-M<;~Һi"H[̻~fhK/oYwn! cQjUZ|>)ew3z/m/jݺzi ?MZ,&n_5gﯢBUYniuΟAVtWA]Bm} Ͼɫ~š*3.9T\(lCPmVlk9 Ћ?&nH/uz֭[uD!@Nm>nL,emk+eiATJu^o<-T9l ]UbngZ7O ෨xbe \ jƺ PSF8~dEڬz^Yw-L,_MO6l=@8[^ ոu"X,)lVaQs6+K L ۲ʉtgo٬,)7 E`xH`*n?['ZC cj St~GwVg;9fr  gC^*[C1lζ-;Y#SZu5=#b@J$@@-]O͛ipV.Mh5{}}OKkE`JYkB ֲrflrs- dU5Lp.D3j`oE,dk&=6JOe wexHNg*;pVBTNgr>(p@ܰ`(~K]'-V+-$l`}PWM+ijA:E!wNWY>|cM@^O!D& *S}@JF\̸p>+^:%*tms3Y1,mҐ3iYzK[k!1.B lځׁ zΟ3S6s\H~3`;V։`8w F:=K fKOKz=Y= rB ğ>X_ߙ\#yuen/>:su<1t)$ހ.Z;ݹz˹'2cYy9L7J6Y(!!s1:43d矣_ȚYȾTڨsy|J(lHƭnGx(?z z3y4!S%jX& Doe ㅳ˰hό2;sl^s4fG%1:5imb0NÔ1m0\%aBA r9v$4b3QSkE8ov E#syh`qYq| j1mp^<Xӏ~u8^S8_#yMMgxT4SZ.INZs?I>铈)6`:zo?/JĐ4-{H͊m~4A5j~5&dgk"C"F(G IDAT:ՆPًR2' 0x8>(4ΣW#WMKI1UEMZ7In8j.[4RQu@hzVϵnB;~h q\z n4[xqMob+:\` z=9j69j66.Hnߡ( 'fsbV>6U~PwB3Õt?,])c]l6t>~Z.h謪5FDqyɒ;$aʘ~AJk|?=Am'm/YCSo:cܗB6BkE!YǽѺBkZ>(&}PhMc:Ȏ.bk; wP,Z:ޅoA?S^^ʺ?\ Y! uWB6ɨi!}S!p6 #Av m :lBeeM^o}BqωcҦzjZ~B VKQVUY~Fo2[;>ZofJZUKd[)&$#B$@B( %$@{%Y%Yju,ɪ[|vsϹx8-R|I"6ѣ+-EQ'*UmV*m-x9(Դr u80633ןi{N8OP}?U[8r6+X B8X^IMDXuC頢P"V0ߍ#a+)ԡQPח^wȑe*=,[m&z> C ;Ly;{K'w o+9ݽ'?wm:SU*9VslNb$4F\NfV@GQQ0g֫.$).&ԡ F?OlNZhnLbAP,Ac(Dz]e ~$>$_wȨ D+ikemAꘓ;P&DGazY0|}{u-D,x:4!J9xQ$ /Kf֘ﺯΝ뤗`p+ k+Khl8E9"#DjaH(}>'GTFϚ-_NeueV}x=6nJrkdJD0Rwz<0#2Fr{Cϥx>nh;Fwt<|Vvm`dwe=Qk:CcӾ#MYu Ge-*Q_O F6y`@;0A'Sj-$dKMFi3O:g-qAh13Yfc^"7W"Ȇ/`< 2fdD(h&7 ҺVրO%u'T%V^:dI":Piӎ\I{gJxZt]Yd \DʱG7|E[]tmm$oʮC%pIǗ tWj=r:f) GB-ڬ$m2:R5ZG2OB7Xr؎s0ILMK|%ع:*=cZ.1!N }Ct_9TUXmW ΜI2cu'~|a :ȟ1j47Wr͈$6(r%BP,_ݴi`\H #$ ]tvrB [}nZq]I]/zS;& V ݪeI"IE%D63/'y@K}hMw6$rqe V+=T'mv4nId;CBx'!Θ-BىsZk5݋a N ڥ Z-Vщ!u~od)$ +Gqm"^|@cc(ϣYm$tGjq;=bZ>[ ٹ@Fesmjkə4I[ZDP\l+_X'[ƭ7|l;P;%ONNud$02!Ri'v0.Ws$SJEH>9llQ$ wvl)-?/޿h{[Yϱ,e%2ZSRF~S-m6&jdj< H=g@mn㇩ܱ̊>K|']xD z>QU.:niՓ3Q$:js}m b(9P|6‡VfM@*O7m쯬&31`ȿYoNaa!\SVdvI-"35ŧ׶SL\=#:~^ZP8ʧ %`"=5gj*X*h$=Tq>⃊;YgIhKȅHa3X&IH=-̊Q1v@'kYԬBhu_s.t:-_g_$i4lsHI9>ӣ(|ZUjy,Fu¹$IW./ms 9Ο>UU՛ۿ>pr9q2L+YNN2[Pmd>= g% ЙY{9 G=6ju,O}Rֺ*֜8d|Jf&F+B.|wZ ;+|OoQ]y)ܺ?"RSS}#?]l6oϜW_e]6x!rzN"wk@R>FVǗFOa)>h=Qo4R|W󻽛_0k*xxR2}"Qc南qQ-'&.NGA?܃5|mU VRo鑟OpWyQ6=hબdRZm)<ʛv񘢲TwUdt^`C"y%܊#;֒CB hKGk}o!Kt99KUz~SmCsV`u_#ei5K10UUU~g#:? k3{yb"Уܳ6Q978ɧ}& xV&DzMn Fdy3H(wC?Ϗ x{-6xaX>qfr,FΚ"7W"Zp8w'6l[tF/x׿9Ün^xJv|jY0y &^y5skN'?:R`ͽf=zSMD$KcJl*nS,2fb֬Yp80㾸v W9] 2`YN]Zضw&Iq ߺeLvFv*€,:ߺ;/;Bvm7՞lk}EXRCAq.)dY F$%3{˥ u"_ֈ$nd9rG.گ8,:^Ż당v 7z-V8?3_%KBg.?E}qpOqyh.,!.+.:p:yq6Wvs->0-_>ۮp 5'#;W"z 6۷{n\֟I b\.}Q@!FnR.,!V֝Bidek!Z8҄]N.z`_SmOa_Iv~nۮ?]Ou|IZ:n0pmqu1mۅ)[ mvnORGਪy!V5HNغN!G7UUikHs P}57vJdS$͍;647Pk\K 5t"t]#DF*xC{`긦S\IBW Vȉ>ba.e:Oɑk>߶ au GdBhs#[cBK`ꮳD%YB>nM^Z%+/|S6h[̖X]Nb4h4,JY̶je٢5r{xopFR?2_RYUҵ'pVCә{<}Gtҷx}QUU^k\dJIAK[)+9ӹO}Nu+RKp ǽ?eܗIb%kDrtdMo0UWeYyRְnHf bo-DSd43 FrRyYYw{x8.kȰzvNȣ|=9XbP]?_Y6D&%w;ZIb~BvßlgܙďRXX]ǫo~/9 $5`F!2TZ<:|h:eQD.Md4s$ӣd ˲Fx{RnZ {;k\By--ٳ曔̝K Yf3+~K/} ?Yl@}zؽ^i51hד0rhn?o.JFO%8)xJ&Ihi@aKڹ];eOfV l{A.?h7uoi޻%ikbZ.cle$1C0E%S7&{Bdp) U`8u:eU~%NcFD,Yc-|A/D*.Ϝ$q@i-\L1;q6juhɕ5y?ώLh`Mq1$7}D;B"Vxj_y |mc}S珩z=I6Zd$ISX Y-k•h]=8.$w$MڙZҦnEN!gPL@rȧBQ  UERUt=[ zOsI4Z=Ȼ q{<-`d<3HNwWPz!'&0]8b{^Ш*nwI<3ʬȋ?/D7Ak~Y0!pn<.7M؝.&Ao!tܹSі{;$$O+~݄<71=\L*Lb4qfv2Bdq$P`R^|՜odLWoXn\.'g9O7bw$Jt06qt6* N/#N'{%8yyy̞=[$#LOmIgֶD^Bs!'69P"Sj7II! gLFeN݊&UhTvd21%=-*@a* rs6r&,L<׈T,QU O3~>@ԗ%]f4i'<A!..W []NF&8:YC%I$'t:DFHNE$jkH?}7y:6F$L,I%P 9&s[[ GZm8c :Œ$1>/"yVmd>;^O\d6*Jvյ岆=ec)$ETx nB&&[b$QTe3kRyHV _ql$lf"=W"FXQQ9+V:EaIL;e')oso+k7smoO"W#Q-Og2TY{o-c‹c`$xHZĄP7tN gUSeAF3gBx)݋&"%_udYfw'a~U%cbt:i7燶taQ-fgͣ{; IDAT(%IT\:"3ԡ Q⼑9|LѲDMgHp ]r|}r [8(lit1@]m Ժ=|pӍ_BBvK+ա6&.dkg,Ӣ$z:NBqe 33=_ҥZ?E9d//mfVCUY\pe$Z&f!Hk/,) CggQ|(. 1)/S BUGȳgN''70/1ybV c#ٳsp73qDf͞mvg무ӄ'h沱8TGF9+S00DF./ulhb1rEB2&>#UQ5@nfb; XQCx>+zsl=x%ljE%Ƈ:4!ړ ږKnDe玣4 :$IpbuEH\$[='{:-MĨlQ`"T×-Ŗ-;\ s$1yd&OPah UȊs(';EPKK&/-<)QuL6{ UZ!С `ў<^_4[mejҽh'4ǝ${ӾF[PT[sǚf0Ahj$ ~"AZu%YBU.u0|YnPAUEhh4b-Swb6ls,Z,+F˗O.ڞ"]UUxCyHIƇx[<9:*,UUtUT,,$`$dž,;{_%YCq98U$s0D6[ǟ EY;B, ԡ ˅^scy扷p:g&$%f_\:W{"־pzLDN7}腋1&:!( n^FQqN}'kxUl I,Nc`9;%*e[6q݀2̶e4UJ}>FL qUfO$1/9[N'^ouhQ彿ƺIN TK9vu~2WΫEC GH3 M j6ښfa̖7Lb[,D ]vx'kOURXiŊxC^kFc1&nb*F/0ﯼ:Z]nfeoc{ @ζnz_W6s\$YPQU.Nqo)H1ps^֭"5'gr%g?FṒzu$o . uhQ%s%Ci8J"r;w$,|֑>|8o|~ЀSQxI~;:r qARP ԆzO$Y^uVVB{ 5{x'o88]NGǂГvxw$txeadILŘ?J_“p*kt 9Qpl]}AQc2;i&dy!۷yN 7hSUv߱hjs3\W7ޒHm:k"IZd"xֲ9}gcU}[^xƪUj*xjWb-\m-Ag>IdԗV0ksxhh 8LgעC/9(%im$-#{5̬FUi@S GLgmm_CrAv# ,6H% ON֑@3eN}ֱMUUV1{ѤF|'mQgV:7f:]U, _s`}-aE7^Κ~CI Iu7͡`AKe=nan^iBN$ɟclN ~aժH̤8eQD$i;i X BT[sbb6 br%$F@`͚Ǒocoމwlyr:J07p_m h6!!(.UTUC7I‹uhA5[xk ;?~ H^4֕:U*!&̉[{@}鼌CNWTyeπJN"%5>*㑤bɞ$ԡUaHܜ:`uobdrO DDPtY (61:.rC}-BU!-M{o\لIa+Xu'->_HȕjJ" !Ir=˖gLL~#111-$Ib”)\e /3OoF#z}xRL#>km9~ZG)v2s_37?q_DI͸хNMo^wi`Dn(iF Z[Z>&ԹHAwȀz0'ab Z@`85Z,P$Zcq#VD(=)<+_WU#ՈkP$ܙKHs [CZ Kڕ?'%Y.Fӊ|j:XTm5Q'Ih/z'LI%_fSvc|w\^>BW+++uVV˗/g…lP̵yK={Q<$ɜ22X OHdEYq$(Z ûxx{e 5kmaVoh]c؟XH4:뼽 8|uդqhZ»1K'a,WwnHGJHƺ'= ,Š#5+Z14[;WaE;Y#!c )SII8ql;k!xסdf Hj;q OQq4Z&'`0X"$\$ĥR0KZހǽ.̥.@É$I$g7Q:hPyE֐9(-g夹 i\.˼#kGpIho. ?:c Y-2k5%pmP&DFOF'VzYfFXn)Є(V0jJ ܨQo2ϤP&D%9f$kNQ4Z~sbBpI-5[o*k zR&0x1nB?ndu*;7 \ItL"$Op]wj*, ]y%qq]'SV,9N͢Yٔ8TRb+fg:u™,<V!QP 8,Ѥx-{OtfA.9C?SMĠ#\WngnMyf' GP9̻*vVZcn(BxH4՜hʭ O1ku<<|6Qz+MqLIJ1u145gOAK; $PY{,ʬT4;SFbh0A$Iw/_'c楥󈋉Bw"\xbPcƌa̘1>rt:?K])F3g|DT|זx9 A}ޣ(n:]؝?)ȟŸqD76S#q3ʏՊbs0ua?.#txƞfvnmhYd%UU{5Z4>MKH(զL'{z2Y4O#ILNkv$`DcI8I/x%"" $I*Ŭ"ߟNoQQT녝Uv?pTЗɋ'/ .EAQ Q~^1x0+W] }˟xÔBz͛Єu:%+/d3[ W.Eፒ=:YKU4p*3RTn/;vca4r`}u<JUU^߶?LE^⦙Sn2CND vtu.FYÊ\_4 JAՎrx'm$ Gѓщ$0DnOsւũ|p* [)?_ncossҹmtãbE'ǻIcyw!pT4ʖAO 'N/Φj<*LKq#-'w-4Ԭmvx}0>%~$a0B+ d9p ?W,_T[. IDATƝ;=zt$Kpc+aih^9שJ*6n޻L]Hah{(ߋ[ټc7n /lmܰz1zKB] .yl<}>Xv;qFt Oq,cfN@ph_魫{ռ~p;o(G%+fa*VM:'ILHmOӳH1*\Z<5a9&;H#-~߇厇ŲG=oT=wXy yɉO *A\=ȑ#ls^r8H;zN~: ou Wd\-.'jNWC *>(? B;_vux3n7\#RZW2yƺ/9ukϷ1T%>cR8jDa!`!W':38P /_ 4;!dY"V_}|8n9e$תܡ*|{q*p_B^| k\p`U}Mu; ⟟o#f'B {C.ďw!$yN/ p $6) νjcfm3R UU}jc6$piVg3[ھ:P檶s|@$` ́ն'YLÆeV˩f]IDu[3˻MڒJUկ5a %V}|}u|c-眃Y` #ʍKnÅ'HSlhJv|-c 9bEa%uXsPFe@4*ȤP%IB(sCEWy{CYC%|gBeF>'WFx0!W"Cj_.)WDuU%˲Lܡm>ȱ IԮ/s*+t`JK}װeY?FWchV}2?x?Ӕ{n^PՑ} pVP}p0\䑆-P*nnPQ6ON9Kosf" JV`AqRADw: '%ں!#ѲM/^iӊ?1(|u_|xPO~wVҝdU111Lǘ's"*qz#J (` ͓&;d>{}< i1S[fZ9<0^[;NMx`?<1Skڤb4v$͋G x-W٨ Z-3g~ED?d 7]Z/C{u!V+*ڡͳQH#o rB vS[ jBяP9 {믿i2w|f3f\4׷/;g$00MQIdY"~\Y,Ĝɾ{t޼CP85h.dlwGtlpW/kMsb4Ok$S9~/ۗF|i`D81 bVԴiƭkf_ee2_?qf^,rS~ܞIￕפr>&hrh]ְMuCs#ePMN˘ =ow1@3w/j۝cy ?;Gjν6GAʙ}D[dhMAֿ$nOO vrb4STq_ nTӦA,UE e;|?/z-di< 1ٹ[Mw}Xv1ٚ \*z7zMk4U@tt4}zDQΙ3Y~L.틝[QAbZ>,sf v fTF")1@;SΞ>Şc9g:CZT*ܷ ̚q%{wЦM}& I*ek*240SKu<\voަ+@J&IwPXk 09~SWiBck4ѽq}썿*R]G%5W$TB^^,ȖیU'5zYe.\(%K^7 Mx㍗ _T*S})[Ӧ*t9if#.{Cٲ?}h٨ㄷ~,A`@p#amABA^򡵁g 3 䙎ɃzլEش 6i3-_]Rf=*z:P5B<.dpzrDE|?عv%G IF}$݋^߄?{ӫۗ:dVue%;H.+#n>5m-3{lD Jzw)5mJNJRH}j%.oJޡH6{`!5jɓ ))Y//G-?"" ((xBúuxPjD?P Ph=Y|TvG![FQ{mq*&eI,C՛m)o#,~|w2qOR"{]"Q7ÞCǑ!Vhuv>{2FW/XkI>B,q8y*=!b UDKQghz[Vr;:aHɖeZVNG4㗜܄R(&Ѣ/~}Ph@x\h~KGcŝ.c%!u,Kۺb2zb(fِhB*cd-©&̲q]Cd1q(uI4M> +ɣ>6y9Tx4m|ԉ{ݝ9/YL#m=c3j#D?!.Rrojޜ5Zd\<©".r.A}9no0`ώG;c4*9y8AMw$꟱dل;Oŧv$ Ehц(Ɯd'E-v2ϳvhr%.v@ƍ{FQR+Qe<;l *Uk IZI3G)HCݾᨰ:޽::]q$&Nde=mH?*Ul2'x[FlQ6,qh,3"YRqK8 4)5mMrr޾lb?0}ku{Mud?{!۸woM"6S2Y/=uMi=QN`z|; ⵏl 6\C4_l8xWd /f(\U1 ϝޞ9－Qgbg_Ÿ*:j{쯾ݿ^-9ѱYm6TvNR`.E4Ծo?7=+>q?H3Hb,)t\عMiTȲ-I,<.=J&$&RDo(,x7,ZBDi \*I,&:\H{{;K8yi;&#WPӦ4]NX$DIi=5FlhZa%9ɒB~H.Se(>~~^uxh$aFp}cj8l6F$K279K$KwO; y*g"w+fJ(Q_&7u c"cڬl-r55mMcJJA\@P߱kmfl4B%s q[>c}|ϨJjj*vXf p,YdIw <3տl{S0oZ}7}4f+oq-7 AnD(XX E`YSNH,ݪMxcrǐh(.4-u>8_OSB xub zi~[̛ӯߓ;;9laߘ4y򻇒"!?{,s/f,8v [TD!*{ſI\-,a/^,Xm6;S;WNOlis_$lꥬU$Z޲'`dwzۯ_8y g`:+8+'G>6ZD~Yx"V$\ o$$\6jg:!(_ ED`2=H{wF'DIbOe:ogr1zwq!ge\I?Q_4BAAe"Px^bDDF =,LӇaNdf^l7͕#lrSbפiĤIqrz(=Llp.8믩}0?>đ:\+\> I|~1jrS.֤i <@aP}p0f9Wc;ᳱ|&~:"?mѻa>j0Ҕ5k-0i?N]9bZ`5aX4םRA?lKI,c9 Il0XslsȲ̼ӇXq 7?diLݪRW0MKC>/>f8; (ј-*gp%/^⁴DE3m"mrG_-<<c{ab4wT8+62Z.;͌_L|0 % RSںDףCkfR({Pdq'K(nVް*ǨՐ|j7RYD͵U.A- 28 Q m^*b/z?ͧ:~0ydbl~;y^ m̠k*CّͲeXjMx._f}BD|' W2Acѕ5TR(o0 bA-ZҜe %٠#6boX,K7`F|+>'o;/`x3oHrR/AV< |-fg#6P;8] |Qܼ-&/"J;UnCf$٣5jqhՙV8{7Ȓ${]s+DsG'syÚqr I7̏HBӰ_|DDk ^}( \YJ&9z'Y70UJ&}VҲoKS,'t$f?ILf %WJG$/{zmǻaŲGc\|U ,|8w!#\R(̜)qgx!89ݏUSh3c2Yf&3ɜ˫AnPRMœ|j7+(ꛯqiޝ.-U \\Zgگˈ4x_y4I;}#T6 o軶XGHx١J")j'Y 7X\{&35QoK~(۹clDcDBtZ6'ݰmwN4.(= 忔JT+i۹sv#$dz?ټys|KXTr eYf`+Ū tfWsh%Eׂ/M'!|SR O3IbIJwF+_{ҥ {&*m[? pۯ/2&/G \Eo_GxjzꅏO0@"G6Bqqqgg3&ϯ\fݲ]6JOP$dDD[k IDATPOo6m:N+(do*8v!z!; x`=MP(VK"_fHh JwMZڪQc1 P}w=W{ۯ/2o/D $2WѬYEVӣG}IK+nR$.)RZ [#l)ZIIAA?ې_Z+ rC(;(eln٦DAϿ믳EcQ JVXPնcРDUY#_} ?NVcƌUsvvF'( ŶA!ts,f#i=[ߤ(縸g# =^:Cܟ+Mfʸ Z7{7y/WJ8TV*ȓ${^MWgǜ͕Fq?<y|59ޅ{Ό"&&W^kb ОQ!.<7Dzc+ }pr|OZ]vSm2_D 7{wik.XmV&A[_q Jba˜,ͯdYC73k2gϺܐQ@No3puv(V@([uLjWWVova7\~/ֽ?~7d1sx׈Mh…]״R ȚǑɈ8 'yh } `TZ+A{`O}@ O[@$,sdL=@4¥6JLz!KzMES5U*j8VFi=[&_=h"^Z\ppN_R"yncS9{B'v%!:Jn⣘j3>RM RcF5X;{i_OVD\h1|o+2A*$F_Tyù_O6o)J#x⾷-]h&KڳقUշb9ӻ_U2lMԆz;'gZF?Ř,kg^ޯ|XBxt@{Egho_iZz@( "k=eNV8ZQ(t~b G6rM %!ҶgUUxtiґfXv4-9g"Acƿz.5OGohAV1owɏf rλvjuҶvӮ׭g*K莵%?^zΚ <,Qnۼ)Ὑ 85`e"8rrtݰef3vX]6﨔<ر5`G [&rܲ-?=ND7Y=qF'rj2ysXLF P;:}Ց—`p1 $R y|h?FX }" #6[:'f~ؗ!).v Ф5ӚMrɯ[!+! 5#0>!KOw Fq9$т!/ʎt}w4Ww} ۗw@<ݘH^\.Q\ ilI<frↃMD2\|Ci;tB_7?=V [+ރ,&fϚeID (pp)*5]z=I=; %|]x^ 5$D sDWB,Bp9 'Z rS`y.:@~hRc5bqLlx N 08j!vFD= ~CQ)9CW=L΁GgP(U}Q>pZT27ʼc0J.]Z@TTmڴdggPJ+5bt>KSh֐O_.mo]IMuitM+c>9; CmkZŨXjbw q/~D#?/Nٿ@P7e,]JP7S98uZ+y!!!苵 #0Y^ݻ+F#Ζ-gQ(Fc /vy}6`ȨGmve]+CD#$JQ6[hӜD6;{n/{[W]ŋXO;vU( ˲X:IDO f]f>e`'E)%=ӓr3fyGhpm+k.J9XQ]w'pv!D1YƵ3M>-XX&@Յq>pAz dR09}"־ bHCp1䂔 񤃀MqI8|,k\L|K3g!K2]uycJ-އ0 '4N HD88aL8YFb1%=VѤ4\O`.;>?=1}#u퉃[4 C|v]6sbp洊;M7AAtu ,JY/IMh|ob x ־` 1/sLqj Uд3ȧwt=d>2BB\ܭ=Be$9م~̀%X&Tjrgh$_{V/7Fj<;)ώ,6֔Z~YdwG}aZV$ YgĀ&6ub2sM,dRV~pA k>]^( Ǝ{C66٩U,-{g3Ev[,iڐ{0ɰB'm8oJi ˘X+G4!/-ʄs\ZM@..]*) X;U̢E?z@Pc6kY{vҷo2/2̔BԩSD]>sHjٹe zGFajnLǎ׏Q;qtw'7 dc/#>ZMpl|}E; 7M\vPcɾG@0-;t-JAEs8zgӓ?+՜פḣYioݕ6u~P%=@Z=O۲4?N$ g-L|5h6[8txxg`f/pR2Sx@y9}2?ԗH)gq Sdrى,`_PD@.ZFNE!B= Ow伬1R૧Ҁ..עb 7. xXq67V &)Tdgn C&<ָq]Gq Zd_ +ksqA8Ge \7- +3[Jet$kP ܼq(;H OtN*AAj"/ ƂGmڐtDI"@FAf#1eemRmvsl()j#(Bd+ ˲_ )~!^ܯ@eds0<:=գUÐ2Kv oe߱iiH 'SEz:YVlvvpG4\j5=抅K9u4BPb(Hc_AK Ĺ, f~ή4r(h+vH&|`}Ms2% B$yLHbJ^;lv@bi%d&%_JX4]}oc z?EfF6ز)vҙ~>_{")=~y"fohc0iǟ?|=-·QVzȒsv8J@kv=2ҁM$pJgSt;ݻڎML~)z-$Ue!ZM@.1cFr*$"GEq=/;l6vA>Ej<=r2䤤$2  "5/յUz6`8`t3h <0o_.$&2(-zv _qMp*ZHlf)xdƙe y9|*a)+I"xuڇXA;+iю)fIEtv*m%j Idɽ̌_9G 㫯!fCR7 S?pݧP v~3#LŲZzyx%R$u81{DzA,\~jN#Dc֜ DA~4=$! (Fk nպ±DRbunl:s%?W'ft|D(d2dKiVϩ!n [4e}Yz!p.1K ۹ޞZ&~9R"\- ЊYq灾(V (I"/= wbwtY3OWue'>NppXveOvX+Fa t(gr+~Ů9[9Awn!oKwZ˲._8AWΔѺqΞ~!&\MF:-$8.љ _ou`HW3lTX Cݎʹ>s 3d&m% Z^Qi%qgOJ@ kHi_@4Py g\.Gg1.$is 4~8ۂ *~Ik`a!P+qt*w\d{ d_g;!9]y=skMwYd`c-xs2EdG>AhSh$˜0O)nNdrUQnKܡMXH?Y$.]4`,OnN_vn͓O}$k?O˰YܳLW:=7RɎx@Iizs:b *!`T:I\%Vm^A{{Ov&Zm_jLB(4D6lD Y3z?*3SLg62.tڨzZSJ+Kg6ٰ0%agirRz&}+- U%6ƫpu.[0ulvuatXh%2)Z0-չ%1^&ؙgVv.;I k{AaPВ+e+i3q?i<и5v[@'anfREt֝\0vXO7WvvSСq}f3&FXo4mcjj},a{{sب<^~uO!@;jez`Ac ZJ0sYPappWf߽ y-l΢_~ KEgMߢ(!"\y1d IDAT,"E2WnCeH.ph0e,U(3i ǚ &kkPk6 A_WѣG}P25W3cv^(?sN$ԒD#8$OXE(TӴ+5LBYŠ7ӤP?0o;b>Y2)nfΉ=p vtE1wt?@jֱEݵ^n`sw7T $g][CEir3s@ `$.h>{45f}gＣ>KzAPwA@QutFQq28q1bCH*w{/s?.4 _"YE=g^EW|#?& lWhNǾz^Lw+ۤ}M?3L \7U*"Nh|a]K7;jpUn`kcӥXQd8BNƯKάrFj E\n 5;զL d}#Mx #$IkW-zOx/uR70w9M'% j U~sO?:U/@Sڏa!8D݆ #2:I\CnӉZG'Dp]t8Ǡ؂kGqsOaso(T]~QF)4.2:I:qAX$ c)) 7NddtY,q<'{Uj:/A ،+4XQisǝ(<aKP%|"" b?&IF^np~FvF cP& W*8<*|8dRZ@o V&Pbv3~k><#WDLLZ)SO ˂ɔMvx RB_j0L:9 0D${ @wV4^@7IRK(Z}zm[Ǎ7J/W~ӢV՟ZIY. |F 8כû48b旪jjxcrZIcx`3շ=z$C3Wi% f / <,~crsǠuh<.f~2 d2X&(-GN5Ǘ-ٳ-ǀu:?guφºwg̤I&vt=8a^D*w-jkM_^HqAZ]W,tTML3kJl66QDqnVvX\ Z ?o P uGT۬.+T^g@(ȮZEղ`_Y௭j7!l߷z]|duSgjƈHOa*c f+E0 ǒÆBBp^XPK6 bu96IBpE׊u3r WI(Ospt h&Rz@޼ٖdז t7.txJWSS_8,*(5:u2{r\t } lU$Zy 24?18 ׆MOo8NTPPUBBʰ8K>>ED69 చeTbFmn\AS b<[PH cqe)JX\5=@7 ENT]QLL2FLaM~_E%$),oXZ;u#wϴ!.;`T*Jx6Cn[ZٍŕXVz@\5^JJ5$q"DI$'>n;N@xϾyVcMTqf-ݻaBzn&*IM!u2ʝFZp9##D$2 U-+tOo*wv|?*I{hu:AL郣|/6aBƴUQ(Z#G*6`ɂ0>ٲrh4LZL<o(}g>w:֮=!忻s|jCxLM!a|PwKF/f@h4np+p .'"cmf|$ ݪ6 pyN9d֬/ pHR=&( /fazA$~,-eI-ӓ~xJ$ܭ5-#Gr|!-cJV^?LVVG':غC#x=P^|ЈDZ\$I(-؋pX8,V'kfv|ojy Q&BgpPV3?>Hlq.-&zrZ$py ʼǬ`򘓼46z~!TYCPvMI)]{'ka!+tƣr9{;J<7n& mَI;7[qk֨[)*̿VOof޽~Ŭ!eI?=侍?rtk&N'ҹmUxDդx Ѭ~)2Gqe;}'|%W2KVS*[ [z6#S(6|DDwyFHɹyTM!C`6bp\>jThuI~r\N;W f%EVeG9 dעvtAFq m:Wƭ+z/lSrݣ? % IXsꬓL10XEiq6ug'Fs0tOA|6|/=<`/ؔ8zۏIi,]Y&n.zkA&ǸPTS S: SZďa2q,I} O^8i$7 aid{.IH5J[~dqVVn##SYshZ~:vA&3S<]jlv?| aKfvsExGUO[c3@Pi] ZˀA0yv 8%c$]$ATMΞ5%5&"HP-f ]:(/_Du'Tɪrk*]=?Ƌ"+ʡ0^ʖtl6+b&.=,7VAFj-]{Y$\700Ѭ: UKtw=]Rhg+$^+/8 `mML7 ŌjBL&#OIrj/FڎdpYI=|ymX~:kUj!*[͔Bw4TttvՕxxyg BoVm-*]ZQ Ko@Sg#^v?f5%>~vfgsP򙩚e$kvvDـ(FDl7:44^B&qCw_X"NNN%:xzýsYk?JS^Oti!j R5jagF6c7 :XMTZ}]y{dv5-Sw?LM0z4].J%˪,*A)3$a -gEhk*TJTj;~FbfKpXoD$_ݷ$4mjjWvO[nOA"/,r-yٗ, Zt*Fcc$Q&Jx,D[sӛc3^3 a,ǩߓ8a r(,&~\ʢۉRq^s oFR$rA IgmPcJ,U˂/q'MQdgi> PiCV^go BI) Jc3sdՏ~\4aD.\xk7ld q3k|)$x_Y`Uch 9^h܁ycגSF7P(=03wohb|fJ7NCjRw4jb(3YڴZ2$$IV*$ԝ Wjyu9 >e*_"V?u dAUuXv?ȷ,n3udvL)m^R\E2]+Ej7!2I@ Z;=g?mh2g7F" D{\¤drE}"{ vf>ka]Z_/a slیL|SJK>$iT*A SfX'Cjӳy7IPrGDVbn-e/DG_A TrIҙ՛NwN7WfJIX,] +V``r2_'Oy?t(K.LjfUQsFKJqrO7W' (1 hvvfv;#u}%+ͫ2t|”yާrsmRzY(n:6mj_ #f lwLG!yJ6}$JMbNٹ]Xf4 ŻFKia$Lw4 h8f3oR>8v:Rn IDATuc@JvW(I1  س_F\j3"Q{E GlD GkxhH4)_.r3;N[p%'-,k Jrp{Y1Wa,ՠABi 4tҾr|/ 9ӻֳJAvP٬Vsg~vZLU(%0t6r"+IG9WW?oqˈλIXgo&>ڞϿZ^]gvncLN|;nv=FtSQ(TSee~1$O'lO|<ɫ^n} 0!vMn&ncAQAHHKqΞd,-ӟ1huW5?*1JEU 3/tG-2[xj/.}I~a?+@Hx7$CGR r)YE޶(z6xvk Ku 7=x攕ʹ ZZ1fF|A=Z[ŀ\>\6;gYWSFK=0V3 'c_re7 ?ʼSHWH4I/Jq-C2 agd`ftEK7INC!%Y#W026/OFcٰ ܲ[,FwLjI^jFo-5anݺgaN,:_t!҂"ĚǭA.1.6Y'lD Tjvo]+8V O,(vpЮ=M2y}6Ң?_\x׮]{-xHpJҧ0qcIFwgsi6 %阥::]?_,-!K,C&L"WC+ NEjmEM歷ڝL&z\˨Yd+aFjo#@Bȴ~t p\.4gc)CLp|?ZKфLQ_ PiAF+k Rw^\ULh@jmr K(bzc$rsI^=A`@pBYT):=nGfbբOq$ Hdx7Yj%A!جKbb6s8,%5fi](g.ûhC*9Y#sNl\ζh]a}Ɣ', FF_K]x8zÚsַGxD:VZm!ץODIѪeעu=XPt0g>I9O%wIKp$ #h}°A=j;Ң2[+c:Hь}6=џ2 GjZE*rOg?@ F ҷ 0oA$d00*)tx|A@$q]BuJCՌ1xgnA-;ɺ7GbVJXuCn._W_+1siF+ٿZʿVml7Y.sˌ0~$U׺-/jYj}Zs*L8 X*Jh%nN' WlؐŢ`<=9s71y O?}&^5 QDz5*n/6<ǎ!9b{HΧ0Um>vG`l"gWcVi-!J=35hQ+IvYS¢U-WtG~iV34` ,vS<ͫ|vǐ!Yt+Y_?9?^AiCVL@!aE꾾ګ`% o$;3"9lYQ*Zo* \Uh xV;lϛL}lڰ#\dCyΗh<#vu%x^?j־@Rb_# f_p\\NW'iʡ"9w $pڿ'~ka 'ٟԎ[ 3hU(Q5\tX"j  rb$I=π,:)zvV @ɏQhCivUH麟JIB%owBAcڵ4X5'?L%臼Hrq L]]P=SQ|܋~|{=yGZsjk]ҾȂ vo|0 X\H"t!]͍Ɛ$į?|DUͨTFlFB!qLVlVmEa^2'`G6-ͰIg@Aͺ}> ƚ `M͌Ac#05r9jeo"_1Y{P]$ztWESeQtj!{?’R(S.9S/d{j;2hWAVYH_kh'"HNZNbiW|6:ny|Idri%Oqei%OO}u0[j%  4jloFtk%-\w] f9f,Z#,,=V@b/ld|VX%^ϭ]vF߃~+pWtk-{ *Pj<>vF?&xmf7j5ۅ(kݕWK["8݁ݸ`W،LA8.EPѧȽHKJ*su=*wy&8{6_sl7NNYdWJ$P"<m:ʝvz4D~牻|o(I>c?DFM%1>״՜L.GHqzjKxyyq-dSf ;F$NSZU^G.tˢ,dA&˲1hGvC].Qzʌlڰ|&B!vb禐6<s!a#[ F/W< h\RI8SVbgH jEZd2\'jA#_X&WԵA]/lv4j_-/=;;#HΈXʮJ/JhoNh3\cIAd'2v*7|0NUDW8LgRw.zk6k\kK$ ڸ-,3#}u- {x&kkn6x4_ǰT8L#q%!&vB@ÝE*AsP ȯh@Lwy%K€'ۜ" ~tn%h Pk'dr\ Ǡ X_LZcqĺ\,| w?`2ڻWDƩRz^ہgwBȮB&o-*@ 亇f?V*7>gE?#;veau:0TPm].yDymqx;.|AXd ӯO&D5ȭ2ch`s89ZPF{H`_C+\+iG_Wgie=v$Z:ݮ7y0h6M$rAJwR7@n3ً~Y ^t:ιy}qv ܅1Ѵp^edJcz0˵!@S ̀4!WTڵM)I6c%(@A#7tXe]*7 k diIdaE13'^M؃])f#% 2NK"tMn(5x$P/w%AK@m~_SU9N oI?j&vn  iU D$2 [bt:؞&EeGiDaOsMְn): ҏc@;q`9}-5UF~mlHkvS>V$Gfn  IC$>? ٧Ϭ$u#f nHo`HLE}:'OfW{q7ⲰE4Tc?t;?z$XKgXmB`BξRnͿ&^.'$)4Ϝ 5Brfr7ȕoҴCⰙ t^lY P)lgVA9k~\<EVyaY8P^ď5{15'I&!?9H$ŒD΃ǒ]5Icƒ{3ZF:SZqVҳgO=ϋ?g$֪k:|WCӉ 2yDYǯ;~?AD(3te΢4C]lw;jVRK{Ü9s&+Wn`%X, k<:͛%.I|&Ipݷ Rɗgðcw<= ,d 25+xbv\R/8zW^?S.RE%qqÛ= :}@F؋{߹~5%Jsܽ $ -54ғޞ3>tҾL[ySsk*x()$WK?H2 0~FI >er%}f3` L!}f=&ٟX ܊&bD~E zm}\IǡT(.b^0U;d_zr%DYɘ6_sbYW`\&x`PN{(0`l~Z ]Aƣ>ף׿Hx{2kee8}|b/y|,: >)B>p\2|~4QJ`Zq.?h̊  5ů`Drd94meؿr'rzMFDbߖ7B\ͭƶ}婳}1mI,MiWrW*( ErO~m~Mc{&wL IDATum0СPGtBn%vRFMFhӓ_H q\rۯ [y )3o.k'ϲd9tq8+ekxo6_g 8JϞ<ۉ W2^mv/ItwxŧsA ]9ƠVf~Z$vO 2ph;']cs**:+q7mԱdPa״̝>Ta\?$I%?xN?ʹ~b w9犵I.΃ǘqS,Xy[Ucz??-ǵZ@YVSW8|T^ɝ PȯΠ\k9\辉D [J:'~=v?W&;;h1zjI`-,\xPq8,'K;u"EAA/XU'`֬Y^z)^35ΎL&n{In6Ͼ?NpGlfL0YGHxPO\Yӌ_ԽxŷJuQ&YQDGe.Cm':udmtdu+kxӯYz#XƄFzyyqw:xϱ@_ &DƵy֢\K,ӀO%%y nuFxf5e+.^cm:3yeV!˹馩<0&֍?mbƋkCj#nN5`cKf6ݜ"uÓ7K[գM$ /HCSYȱFЛ=OGGDrY_,LUݺAqwQUmiT+)zoEbC 6  *6lXE,;$BHBzu-7@&Lke-r9̙S<{clTC\2fhd9o|d1 ]d\xG~&=E;)EdT1[l2񄢜u<,I|8.uʼnư"|#0bևOuQ&]^ l77̢BҶ(xwkU<۾t-EQzbBF Cv߿ja"?2Lė:=XSh1ZUƮZ%Y⋬˒@ jÈYS|temeY"ifɸx@$})]ǎkrd%Qa7IQsqmR)UeT]ώŊUv 2镀YSK"sNeGW+D[& 6 OnfmĵHQv,[JYxB43FO*$8*7l>+UqkZ"CĞl2e3WyȕD6qCs:E4ֹ7,quveK Z1(N"7j?d?#֒{2 vk>V}[+ Mk߾}|+>@<6vz4Q1fg]v2 ̙Tg Ib])ψaKV2_Yi7cY]aw7΂?ql.Nw7/ɪ>#; FV+?77>ȻoDGa<_eEA9"=vᔉM1q^9idkLKn.4Zo~$JO`.GG@y6PKI\^DqE95zc<mO=}w_yûv_M0reĘߣwY⥔c|yKv O֒Ǡ=(|a6^SA/eṧb:~CWҾsW^k],]Hh輳\ɚKqw[n //,`_ͽㆫ69Ƈ_ȕXn;\t ,:?+ |=ݸuT?) -\I]#ϠGN'矷s z錄Leꗘ:A`۶mdddпti8A={2v2SSyqtxn޼&8kQQQŽ)^J'{VYzmmAbr:qRa-F)51s7(Q7O,ʌjw*`h[h6#$$X∎f! n $Ibt6Dw?e}&m&bOfV_^GBV R:DD+266TO#c H^Ȯ|dyFu.(+Ky^>am(M0n>E_xo0玩TKf3.G-eº듗]Z|*KA\PEӪ΃?.5K5NW1_Fei$[w^{,,H;BJлzbnPPWz O'^4X6q#(mtU$aLhk%Ԫrȕv+q$P_3 2{]Յj1z ,];`OyN2m{b.$=ވs,YwʼtFi r 0^Z^|Ycr T/VWUX49jp=WcpfAX"}7j8*&Wn߭b]+y&W_Yo^'|ZQBPJir@tyrF%]56t sU9yΒz`lkI;|1jٷm3Fa#Uu6nȽ7<灭y{՟EnO M"RVo E&aQ$Q[s-n~?|ҋ|t|5̖˒@u zJ3Pܟ@Q4Z nO=e8Nl soNI=}W\}55̔$rs^7dE!RC}kLUI/*zm Pywt¸>VA^b-۷I~ܹB0Iee,LNr:gItT*_kWUSQ]sm%Jj@:RQ(uW/s%!ۭyVF0STW”-\I]%(.ht|1Za2ɤq5SP "I(3\{(~kz| ޯ%bWufΙw㳣ˆn[*Cs=ˆ1cl* PlEmf)f:~ Ku)~* ϠNyp7vm=]#tgJ+Xj 6%f1!c\݉зN[P(x; z&d3u`F,4.@aQ.. hh(**{fwQ+LT/m\0Ngvv.f`,ލ?Dq49 uK(JD87oX() Up-#|=oM󥬝6A8jbC-'nz!m\vx}#ΩZ`9µA W7?7 P΍ADZ a<-J!uB3? ՄD׮(حE$n^IB A[7>ux]#bӪHX8Su$1Ět7/7՝-Kd8ҧ? !͋|P7]rY}h3P `7瑶o=;~(|篇WY`E~Jv}OF76p?/xx lYZ_ t=ZfA ht+I;V;$ӧ8^E^Z=ų.}$:56- EAB>:ǠFъ+k=^c-p\x%@gTi"+v~(Y~9^溩UGK3b_]e7Td@b|_u~B\p Ȓqop񧲼#̟q=t ]y}?^3@ScՍO6ԭœLA@wk6lcr.iE6D8 |+ VTs}sPpBy>OB\٬΄S((=+<+Yh+*.Wbuُr`aUuZe9X#;v#7]"3e;D`(?8WNYE%DqW2~z[mNNGQ q>Ǜ:cHiE܀4ZVd[/$eo3(J߳kE ErcS[n@5/ޘ ]#I=jIf+%qmtV9xyewxpNa$s+`^F@1ѱs+\ћa]$J%˃7P)>jw` EQe[EDTNl6;I#'SeqpZ &#݌ |>e--OE;U7Ƌ( 5Zf,}?pjSi=%UWkH=p/@'Tά;n7C;uh, 09'ɌQV,Dz_5uLٕ;bO(D=?7ԗ)'X $ :=Ѥc Ag/έ;6q8ӇMCHD8e5~]xXrLDeJS^I~y%"JK'Y=ܫ26ҹVg0: ssSbp,h1#BLrFCv$eÄ:r%~w#AQr+c:.|~ҡ[hC\^ϱTNna eUUth}Q9yjJ.̜:خ1u E%̰ToԷ+-4:˕E nZZF&eY/G˨b1̝$~~ N:mӳ#55N>{Ey 5P ާѫ>`>:pu$Pm@͆hdȑ޻*w>7l.~7SQZS3 5f=0ϿJ^qQ8?OzZ7= 7K^ofMGѫqc0}7J2R)5tAP ԕ߽ <* Ҏj%}W:~롎$ Cgc8TOFC1 7]}ֱSS⨶ۑΗϮDE1C",(G][6egѻ_f>QmW6|P6iiʫ?6[9zy5w=QxP3/7JL!d$' X7I=||[0\o'j:g+ د?pK3J #}Y oM&&QSUMǀ(D;"?)#VR_d[\;Qk 3pUѹ DḦ́t &i:di$0u0̔ {-9 gR]tѐ+L֑*=@q1D!v0kQc1}KΒ.a{j>;ORe083KNeXTkUn|G'ӗAWжq~FO_Ppj1Z(Ah݃wX%9 Cu<#*9鿼IJe&Z'{^6/iN>F7e]r^@"ӹvW+<{8_$baE1͓;K1aEa$ڟ0U۟oW[ ,U%޽ԓi2Յht.t:nA$S= %!F:QZQw⡮/H$?}0_&&-bt剎}wIKxf[ua>сP'_R\Eu&ţ5==%g] Z Ǣa3/,v@u X[R{:„;jvO^K'^TQ o/޽:6|~s?I:Jۂlzѡs%Yhi%6 u@|IT /KĚesݟhF]?OG߈ \3Z~ C1לk~ݱ}ଘmHgQYQ]0ʱ˄ItŅ ~?Ypsueu+GG AhW9#3sUdEa_F6ewG3sx%#/OЛޭx)BD2Iv~dY$KEgp^]ы?`*Gk@nĻuR끛QOdd뗟,yJ#ʩ,rL"~ I%x`tשDž.K LBIQ1( dTpkdVBIz oG/'[\eu?.e?`f5#Yiܺ5e812ALdɒJzM>p y1wޝGHDϕE.&{N\VÂgq븆%o=ʊ*/ZbCNeA0^u:F\ ؁ XO")$צ&ktg`@yZUNQ~!{'5~mv EQ" 1A YṞChj ~Y7u ixټi{׃1c[nMTw3r"Qdf=L"={7?3/FrꬹD8i0$)i ?lຯ (8Ə8Ne瓔 g vDgh}սU|x]ί )I*-7| qz`Oa6<ĕؤ(Lk׃Qa-MAMi{dT&ibFѫq^{# ԵȲ_dWs30!ݑ+\|2<[z2wwFd V&, u$?U^G\OJBI1nto0]}++c{( ߡ0)"]/u֔g Dqnr_jU9o(H^;% mf-{KHi9PJ)͢۸GD~zx? [[g l܉ܬ`;W-;c3>M&,a w4f>b=rגly\)E:n mCC:y kxvB$*osp.eH wNvj(v", n~^Xemtg8m{ ϰ۳E}MA~ d릏?j `΂XmRF&N˹GQU*~5c.IMSQGgÂC4ƋԘ_TWVZh߀u)CQLp!m{&/`44Z!tz)G1ϓvWa,BQũ#~kft# cf2u|``>  ɒfQ䭸TT2Lm`t^Cxsٸ&e_Y¶?&8d{aN&Jd?c>hȑ_?&>mwg:wjK|FI,mv+> ( ORW=Gڡ scvIVըRF10o^os%5%b^F?_&nZI<Ǖ6K WR,η.bIL`fGQ |2f~Q2}T6EQpuu&8ل4^ qcfF>Xg`Qjs5& : xSx3yb|IYLhh9VZȦrƵTdf*~l/ %XHvyU,cdf^Z3q8&N޶m{rPVu8o| kXQ ^g wqb9[laߞG NNgO0I%@BB KOg2s :o<˺c$JYZ`ں 2i׹ϐlX`wD7V,aҼՇF?ю.!rd:!A%\M?Wf7W#Od+~3X,;UAԙsrLUL=x R#Z2] ugJq)mQIMA`?4k:\r"i*d-ږbk7=H`>ZS4-~]t\"))@ VQ]Q+{{A0}֥%v-y j*gLdQ.Dax$:̬.GՙN+*YavdJ}qmk,Rvd ʋ-=]H6݇}M|ZQ7<ҌBPDTǹchT1BOY0ۓE-+[h4޾E9̭%A* aUekUi{rWv,*=]Xa$/G [P9v`"}yM&d$m =ԡ(|`7Z쉺TŅk^３eI2v=z=$ɵ GQbӌvnK+$W5z=VEtvm x_S̶Y8bj.-Tƅ7NUpxF2$֟Xf©N$&'G1(vzKPg*a5Z!c6gO?_.rMӭg}u*~~;Ĵ~[EaCxqEM'uj7suy%rm]+L-y|TٸK,+ YLZJYS@ q;ˏCڴ.n51Bu< Ns2!G^ϥ,S$L~7CXM`Wْ-eX 0?MULRSUw5oOPז"/;?Wz+(?}MkT?Lr%nJrYn3o97Ξ]˕޻;/Ϻ^? 1 dM 7A1:)u* ޯER?qdzlR``ף7(:}gx?+7LFΝ{m_'_fdkF_gsޯC<.Iu;RDuq6m{d1ܡ]'Stoӿ56$YHBf<#GX[$G+}x{5cOi B^]ɳz|kut:d|;:}oN*u6+5DT!7 v I=m:l6 [m rq?u¡- ̎mZ?z:9%e|ݯ|Z,vk艭Fԍ`]i9´W6EYkhY!| 퍪B6RPzu1;/وdQbMb]N>>''{׮C.)hlUjTJsSٗI7VAKmsVڂLBۨWgW?qiL:~4n~yx;Iu1KehCی~אj-[ F% !)8k}?n`DťWR`1Q_gr0/2 h |F}Eimlnw|.EgN(+b}cXd+@M:tEl9Y^L|A6JeM5w4ႆ}E$Ph6KG+vF׎PIt:zsR0.p<%veM@IFtOP4ڎTf3Sa/TG# u 0ܬ >b1 kEUA UENџ[@w.=*Z{H2#:^+Ȥ_*Rq %Mk!4dqjVOP*R(Q<=g;$:RO}V[4vç'PS3$>}~ٹ(ULϢv^1D|N&O7C')F׳nNOÊxWMi@GEa_M_&xi:=}" etC_;#:>tUSՑvg<^Q‹syPQ[pa:N?Ց+'qxzn~76 p=Y1\0_0fXҷoE-i ( sp16Ņ" ^Ҽv9ՃYT<xBQx;m[_JJs$~qjjL9p%p݄('QA?Ittzs>ʈEF57a7ݻwBQ6.6NǣBdԐV NSG|뉉T=z<>5yjQ_HT5l7-hFd)5ӥ.pm|ֽFSBQ+?nF K/_ < wpJ+-9Y8D;chD*ı~9@={jC0z6/3 1 娒P`"PRIݞ\/YyrZטY8r`,!#ieEYZV'W@1992niM7xVS=75ZϽ k \ Zw=tHqv:^|~ij䶪*ZgAO~sa'SXUYʹloFꐴe:S.첌wֲ7 ANԼ <0q÷'&I~>꺋.:`2tB:e{LfO!$tu"N' Yu~eęF@VyE.EAV鋠= jt1:!F3rAP@OGDQ%PW%ѰuS33lXJvѵk*ɻp!n6N#@D2-:Rs (7Fz,)g3n4ڤ{;c$5ȕ_1¦+iŴZ riOjUp%pI7DKgQ 'z̜yc4RQ[B=ۀ.`GINNr~$ݝ 77lۖJ`0KgHL;+-NPZZpyV+!'b7`pXN~"# q.UTecF<*u "nt#% wOE`=.Ǹ)-.І<H+͓I,E|HK"l@ (v e %W'eXApi l m.";3/<{+4i#'0>so5z=3gކK<x5&qs+Au]@yzc0pMp 3FNGug\ XL&[lFI]xn}Xx9sdX|9liNG^Γma6w]AuHG=&knLJ)l3T Xv  GfЩz=Cy<2wԎZ-բȝN xS8ed iFD^G]Z:X(hyNm3~htw{4м 8TA 7D&zwv+n5lsӌbvf{Jx| 4dCQj#ѢoE`V 羹RCh h6ݿ`p<)C 7sF]-+P#{PB3Fw3na .?4: Zƈ"|6!k5.@EGhxi̔e2P SnnF!q-B條9c D`FK uq[(<|_0O Wڝ]P% P+E9ԹI|} ?~<7oa`U5cÝcn00^cs흎p&JZ\<*+(>oNAF7 .Ӧh4q\ EQػq={7h[HAKDFcp; ZTٵWѼu'7r {]A׵h$Ý:u$U+FUOdNK^$QEa=j-BIf_y8/d[zB^G+~BQȌ݄,ơ*Eq=B x'ӏNm@@SEm}d>Coa7N%66ϔn $B;(3=eS6 I {*X@E,x,a IDATGrc9ޱ{DTEE-JzO6R_ŕl|}>yX0?h$Dm5K+I xB xW"H}^X:z{x*VѾôٳ5vDSk%ÿYxm=zڭ[M@AfDE0JmJh5|$:8]yBӍ~c{$$J sI+4woTpdO &n'XhJZ𗳰?v|]?p+jeK@>$&4 <;F,ߊjOQjk-z ҫW/V^M 4P]`:7mQ&~=ojמftQCe@4jj4}\/XVV<(׬!*&˄ \ x1|Ler~{ 2]Ƒu_ɨAPLJ/C9D4K=; @)iWMJPÿh <H喇$=aqU\wlV$XHv:8X^!TÑ,2OG/| Ⱦb:F$244ʫ14>Oc`] Wzk 0 Uy#B$N;?M7@uWb] :ƸN^(sM((pLD9Rt"hWl/ogGQP}y0#^t5NM)@k0Ս|=4uA d]y7Q^\Fhioza!L3V䓢ȧw?OQZIhBDj[wjADA´nꗆNj}"=qˍMxGe"s 6}F&*1$.Tl@cbR`6Gsb8>VR7eUu~VCQsE)頏ǃEe8tm2?d ,',15D}D({o#I;7ie* ݠ΃Qx>KANhi)|tAPi8#\#tis@^AUGW4s&n,O炐(M[7pHI]n',8u()eݱ =.,L늿Z&d̕PxW oo=8X}`.j2E uDvz:1HҰI, Pp0SZ":EQpsN1IЈNwx'ˁKP8n.tT~љ۴TZT;$pq|2no 8EcIB:/p;CثPیH .v10a?wh4Z&O_N#a&?Kzv,,{oC#Lۃ~3PWo( vzx_>DZ+K> z:8>Hwo֟vo[Y&UY*IM<>12.}{Q ;Q&hI;b4k=~jmr.3(3|D{f3ɠ3S'ILFf\,3{WvlJePTU3:P oe22),ս.([*$1aymqq\lr{vRAG9"(LGR< <h{=LҹG?^pTI !o$@Z'C cXkkI ·( nQGv)+kw;&iFܺ:@P/]+xndEuȿ3DzO'j%~f#:1~Z##F@QdZIZbz?ԦVrQLƑ`A*1t;6r򘟚j%uDnzld7Ǧ ausѰ^}s 2"]#\X4sRVV3'0aHIA kݡ4/% oذEu[YE@-55vF"##x-1؝MVc<'SHHx\`gվheeet`4![h0WP^QDF2l޼;++!{s}m-oy<msq\lLO>c/ҥ OΙChhi_{S_Yđ_D}P~b@)Fv h1E3uL QڀLJ=CMb56 6(&cBf%}Q7PW;O vɭ65.o]%.' <{NA]53;p:I#R^ڊu#ċ@NueNj.P~ 1յ߃E$^x){)v=(J*:|E~بUjdGF'^( Ԫ)AA >1uxdF=㦌O}%l'{J^t-(fKw^ɒ( ٘l|zKJ|`2~쯎bՋ(aNTsDT v?ljS^G\enn ]w++}EYNoV96YvNB\ww=Wx[ 185x ?(9"Ƞ.Ȋ}I@I)Ϲ=xĖl^.AF-5U$LM"@(%5 eCO/{ñzO8 =!L~<+\XEQ -*yZ`9c-<=ft0!(~1xZ%ZTՋ#ck [MzKH7u:K87;Yp=&Uڢ\㲇4`eO L60 aHHT?xu%ZuP^%z2s8NL\8J^|㥦1DEt5\@]SnjCƖ|>c5V~dž; f^|!+{>nkk>+W>ѧwI|5| SݺbԶ__C)@}r=BM u-~kk8;R;n@F,^!Y,aa\^9\ҫMGmwԕ l5aS#cZ91dZxlZ=\l`u97vπ1yvxZE? ŷ]oOI:k|oo^k$$tci%vyC`!j_:@B]_ e4|*FÚ;IH3JreࠞL aѱ2^)yw 'PQ8Rgc~ݡF=fHENZ0gB.|4Z B2r--gv_BŜ~,c1 kPQ3Ӏ{N&{wWLzt//[Lч1GEˈ^5"w!{pA yn7KXQr ޹BEqUTo4P5/eUA{a\ϬcwK>Obz3u2]ED_ʝ.>wEnKg ۭ7V (.CMKi;7z(rko%uDIpK[C__ZK06eIld8y 0( '^Q=MCTG[uvsI}͋~93lf9N/tМmT%ףY BMgg/05#G&TURF"n&~CX?3$"+ +* 鄤s 1v3zW&DV6ԟgCI_aޑDx7j4\a~^Z,?=sod鯿Vȣ2hp_^#\vE̝{]+{,盃{o T*2=rNKqDA͎hns<̵x_q?Ac{䴟K0ww>q9^t^hxi=Xv5eYrth%-K AͦYo EEɬ]`n رZMd^sʔ)L2牢>5k@QH8;pBI$u\v(營ŃMkV,IrrB@@ouZ#@$1 mхңq;0az7Aq k 2y#NӉlgLTfY{ nNjn N.(G*;/&:Ԏi_.>䢩v/W5%#G@Eׯ}G=ն:ٗ SϚAT1!Thv"Bmwlԟu!9^ EҋKuUꆼz1hEvB](VvSgkEUTWH 'ՙ7-rSq#g\7Z-O,WY9`f\>>e5M#g15OG%b,@Z JRvq,xdvy% wp4-kQ7ZJ7Q#k:ieUc3|vUht7N7sšГN 'Púvš_ȣ(nNOjl}D4τuA{[=TpJ\񰾲kB\ yj݀ y$ʲQ]Tޤ'$+>Ag7?}I}9|>󺚃q&ZQ A%9:NM@5:D" )њJ "A-fە%ी! 4GǠ)5;G憫1T嗠k иןA3o`)7rM.\v˵|wT У_.HMhfӹmKA`Txot##'*T6+|NݮGZ> ODSZ+(Kb ظf u56R&w8A4i&u(,^oޖBBdnt2}$",wS W~>'ܼ==$UTJ`wВsM+;sX* O#{y:BC+i_J@KywqF0 @,O>(~:7z<=& ܃;eFcĉv"@1R/"9 R5@EHHIh '+Q7('.P.R U!"j,XPc'~k/iR?R ?4ϋD':- >h TtD 3A <ϡVx(ӊEl\EC9rkW=k/ծV-j5\qT.6ǃ^:Ck8C,|ǵ 3z-&:$8!FbQgtΊa=ܞ7P+9.72=R_F߉ڂ][E#O:DQK&3y@ "ܦs zF"k({E!-*'VWwe6G+Ak>G-pAU%T+Wv G%QWZHY G2Xe)/ʯAN; A'JmDXi>/*  0K7Hg`mS8v5 sx7 Aގ/9Ԗ[я'#S,s3 $H83_^ڟTh4M\Wæ(6jsߗ3S(ʓu8#'K^}h &?k&_=wm]ދnGѠѨ!Z Iܙ.76QWcEɉ$SQ=SqLy AKUwB]_<# #S۝ c»L((E͒hE2k>UT?1P=%EnsnV| "u.ʗZ"_NEi$ZEhtfĶ _lkgj:"o5u°!˭%P Zk%n7UF%ɠ`3G"#[i|JMSx'x7`РAy͈zT_]=SMx]!(;qqs@oC]ն)*2/#2דj00﮻Xp<`2`^ɓ [^8mVlߒq5e04fi[ ՠ5C;!yvJ3!n1ԹEF=uK7 "DhXN`\lψ~w Q^y;ul]/^xW}7˯ J|ԍrs@PkU0>s2Ө˗KW!h0mnAמf?&&It밤vqCzFbF:*? A_Gll1Ňw#H8TuP(GHle(ʥM?5۪>>(ngEQĨob _S󦏉Dq'u&wI@j4]a^7e9y{A=}$QZr/q!h ] C!wNb9ؓ >4!42p}ɖ$^&W2QdTDXww"SkVpp݉vv hZԾ~ՉC n< ! 4ݞ,{Z_`[1=3|̤JnK鹡w{@ z"@kDq톢(TȺ\Ev6~z_=MMFr[U2+gv{A* B(7ABIý- Ry%Lt3[UjwckIWqo)ػ .C/܇m9QUQ܄;&s|oG*tAסѴ7@I$k5,Diyr`4^T$ #âQl8vK5egNAv_!xUKR]^fҷ+/~x y[`O,ᷲe!n0 鮑'I\0Vቩ3Aݯ'7i47h5\5}htb.%{ZהuQ~~u" XK433&c0_Gu?5Ƚu:ڶ`WuI(r(r(WFX7Q"2Qb|d'><9Y̹sk%:C:j uG_{j5m~Bu2虧Ӓq՗үw]}sDeb4O؁woC+SV qS_?y9<p^Ϸ\k7L-m[Vy4jwA!Ϣi0j;,EQڴch "i 5k01B~6a|i/КC"jZÝç~7 Y^D^I&?zO-4GM7o% QVUhQ-yH S=&0G)E毩BERYav4! uy32.[f&+H;L ,G5wZC-ZxB b;1Glw=ݙ>p6"Y"Ǹx;rcJdQa\%7W)),GaX rٱC=3Yu/V%r;Z]shm;3fj䒱#i=vu} MyqfEf<VUV[gO) rIDG2~~TVKx$''믳`F22qc2#z6n\3t:˱X?$$yZxM7̵s璙Ihhו.,4 :u]z̔ك6jqFpl D%b %y5ަ߀(% xԫQvW5>Lȩh^'vh?k f`H$:|ZEئDaFm+A3( A.|%4[W]w M $9w'.&i;E;]FcO>JQ=#dak 4u_(B^,$h; 713&ab{U`4kt{)zܝq Pћ8۹NĈ"=9u7=d7gNt0 Q<>ԈP5Ǫj_PPcvmG󝺲<*t!:S4a]1o1DZME"i)QMQr#>\܈AgIge$cQj-˄}^U8lԹ]Ls>c), ~A3}'j^$sxFc$|N\ Z\&ĆH/igݓ)Ho>#pذyD}NrڱN^G"Jj WFCX: "e(C=FIkZ&06MØSr$hL2ԢOjus(N5.F_j\:=ݧU.vGsF9hت=e#B!Kr (-IL} E܍#z$fh JJyL.uIN.-w(w:N,ˬXύ|yGY?Ą+/'`Rfy_S_k` EuZt3Xql `NٹH#e`DsJ;]X;y+ЛTATQK$9 =/>س.u7"64ٍ ػ'Rz "aS\ ->$(/|֯aB2 8gQqy<F#;Ih%'ҩtZɟ2cGW4;BVV6 I<у{7n:&Nz3<xU^fȐ֭;y(*ƏpPVVF@@>>r۷lݺXFqR3Aד;w㧟&bރy~.RSTV߼ nU_Ӵ32PS֏yUT-J@d"w{B uhtVksOd`u9 15(ձv( C~هv/Ⓣ2; 3kvL @)ϧsjd֧쩗PY1[ ٹZ YK.d݇,a28 P#ٹb!!>’2lچ B}~EQ$!R jNs6Ykrb{-?xc6F- 5ZWXK 06zuFOl5|XKS[Rqsl5.z@ذ|#ۇ5w4ɄcEQUprmrJ9VRGPYocC1Nqt mǍ@lY6  _ݯ'Ν B]U1$4+0٫%uFdgc-v?pI%ʼn51M8VY1XSb8$VT8lhqYQu`tA@Oq[H%c܂b I<ueû4I=a[ep*p;?Q'*k-WHRX.Yf{y V3NYǬ.edQZ^D4Q gxdv#pV"C'kikV16ad5Y~eOIt9:/sfBEՊ9n#[[oc,AYVؘKqM-b"l9䅖iuy\WŹ@! ~NZ|>_Z<ܓ}4;oz,~»O=GmC@.}G\C@=|ѥ z=5n'}A\.ݓ(l+ P )a{?d eox _;J(9;5;f|^`$G$(~2P\,$ͯ\tFq9pkg6O8].ɢ z|L/pq)!6J`\hU;/hYɗ1q^?'01_d pmzKr7>\]7pusE<ö o:%&alXرgVv&Zɉcþlut$"$lw؈P>ZNGJgͺ9ُ+F&?V~ T7lJzkVJ9$Lqqq3j /} G^@gӦk_ >xqqeb)?+*Nu]qʍ xE <*HhH|f \Β>zan!.* EQ^V<so}حeh@͊=g#O<%wFWN܀B9]{q;@ bzA_̷%[+oOl;j"9õ<Ɖ"x[q.s?7dp9y+s?RvT??b Ȟ2[/d~^kM/ܮ@$7N¢keUC2Ի;v䕗B3p`, n6"6;9B'e-v{:;wlRAQd/'nݲ̒M[rGsxivƌ<#? gk_ `A7;( \ƒ;Gv/`(a~ʼa}tH\w6U٤J ǎf߯Lf& 59 c_ DAFy-?,~g&NQ7m3Q[IYf:;hdT_C`/}OrUe=RDS_KX[Ҁ*$5Cl"/*o >j4hׁbk~:^T6Š;x}<f<n!Σ+\ݙ+C_V-:A(|\VvL(J%jL#K(lٽh.} E # RJvK?\Ž&:_=~ܸϽm5\$+ O 'eEu zۊⷰwg('l[AEDT\JV[VZkjUZ-jP\Y5@=X#pc H(-DI|Ao+ %{4 x@G'y 0D1rՄ|nm(1ݪ{:֮$>kĤR-yO+jc\"uI5KGʀOt!8v!I/,kQ]Nyc [mگmefͼPu@>xT +FLGLHbM@ xi(K[] ZI΄nPw3d-fY0zѯW;!&R6o&IXlbwjO3HcHTaIF3>.croA]#+,R\R_-:$ ,V:E -H>1I2sFE\}V#k&:cB̛~Cڂ5<SUʵ e]qI(V|JxYnScSz~G_Ld8솭~<>VQJ4f;Ȼ;bBgLd6Sw4M'#ܰoI`z1Ε}v^JRW<y^$'O>[niJcc$bq\~,|7Y,$I!<e]vr8Ok+q=Q99"3Ȝ|FVK\v#Ym6*v6!BhA?20Ώ᪪jw#;ˉ!cd>ѺCPb%VlxFǮ-D[/8V0a3?mHG4Gb%BgtL7 ,A~]%ZH `JBHk*Ija`5ȈMNfrsP>G_JPjɿpv+nCAa )q\>e1'HB!"9~D>tb}18JlqxmQ\v:f3 Ĩ--d5HbI rx (J~O_\Ы0wXp kv';#I21ө."sv|VAhy"v3G* F`N&#R.>|$NfY,]z>/-cci9av.6o`A"ۭvX|ѝA>#dTTD2jS~c'#h* JŤoLɨztu71ɓEE<) .EbN҅m.m(!>adx=Y2KN oPhlyqy,12#y_H偍;bBU-/hq'ITcJRu~/_֔WUbwҶTU$$ Qu| ]xm_އX.f_5ܡ#QC!EJbw@ q ]gT"[4Df_}CNul5b˪$C1T5Gl\!wy׾BޜXt*jZltt_Uwihk'쩮%UV8"t~;uWˣ=E4yL~'pcg&I" _H%'?-X@m(dԑYf 76H$<Ti.W9l|f|.'HHFTc Yi/ R±G^΁}44D9ϖ5ze/#! AV]%7KHxno;kɔdJ\.G FX%c:*ۯԮ8#<#MX!\-Ȳ; X}[+57oHN# !q?sM[K"/ƕ$'_Gc܋{JZ(,IMgǕܫ$KgXwI]3!}V /[́# ˥3`;I}?HO ]"o|W]JFU-2S\Iw|/ dI8466ŋb8}'>̊ס닁K4h<#HKIPUo<΅=lvK~{n0 N]'\UUb"H `,OUe0$2T1 k~q0j9kp*H RcfU#Muխ (ʃ8$zdiY5>kuXm!I2Okk+Q!jzG΃ cH@Χ[RMpGQ 7?. vY>yetΉKfFEG k/] Ψ8֪w8+m|kQDfqu7s5"+&lՉBm7`l\ q@i>Rz2᱿FVl!h*}+>#9ޏ̓$g8q"^! Eb W?]H@k.&!"B) sfwH_ң"Y(4*!DT]gkyUcLz2aA?LX{dVRgEPu<ЄgRM{6Dט}nܶ1 hj]IH!8I( L"-.E&!t,ڻ-tH#l돻އ X؀zdAN{^k%@}C++#$۠ ik8$j*P\+ʅ@Ӹa:'c}a~kx e 悓 v{~t q!(nk9觿3[|pF:ժo NnE pD"c~@;JM-k'3 3[*~9z5Pvnb%Y  H]BC! I VT;o?EV>[׻:Ga6;徹UELݞi=s%?7^5CDCWJZYj~&3I T|wφ]ڕ+tbpl9RneLzjG3\=g>B Oq%}{XVRRR:c Ȏ  33A.#59sbILLjjg`Pdp=zj8;6Y.#lJRNsc陙v2Ǝc 4Պy3P_b?nΫϿ#? ),زߩ*G#lxAXRw2Bډg&?~t{6beO/c/ޢޯ7$a FZOC+f2񹗠Xw!+HJI~.Ό}@QwÛ?B z721Im.- tUq6;%TG5GV Yal\s3w2p i!WGx/t9jMEl; xmz+K C=x媪ҏX&[ HJ<ֈߒ1!z?U[@sb/BTB N {e&oBcڨ HpT? +Oe}]3"wD5k*٭k KQ? ]IÛBc_(bf "cg 5}Rkp'a1@J᣻}.s؝&d% Œy%.ufy Hƺ7T0Fҷ o@k0m 7{9 y lo![׌StUyn]cCUI cXVXnK>>ǥw(^~#h2 R$1-'V7*kxT;(\k6NHeMPkYciuW;>Vy.bƘ<\,h97>|}?rPVl1#pDMFVA6")I4 IDAT>Kؤ{@V.U}1%I""!o_㾶]eݞ,77 sJq:8Π3 XTgudgGxLSy=FAuH>k_GeD$| g0,elW_+hgbA1\IrZ&9sڮjOla".n\Ic|fMѣij:+IK +I#1Osk'ϐ~vǕ%Ɠ }?\?-}~+9yO<}?93%mnF e=ȳ8Sp*N M0A@iܾD0W}!<<I*I'>iՕy:ef3K^7~ֵr4m\wJޒ%xߛ,ꮻ4u*[Of̠]w;~y9fG;V)԰9ioDnԠQ*Em عcb?bwa:">w8 w6 MmDW?!kPrﯵ 2WpÇ$%0Ѽg4J$W$_"˴6O1׿D@Ӱ`$ILIɢ8"-M61o*ZGܰs=ϞvIeaw.л9)2Ͻ/ n2);\^, v&i:7 zz1t>Y8K$*j" ~Y=hLcw)@17tL>e^y|ޝ84t΃:FMBt˄ EY}N<zznVQj&7CUerLܻwߔ"Փ)mV4Յt>!Y#v8m5M6m> $k3Q#`ӯ5<1b$YP1r0`MÈbvXE3ykxC:%E!&*qq,8RHNkn렛[:F+jfxfcS;^"N.I/B ҝќ~/m5cu%Y'_2Q{́#Ph*7ok+jdײbv~nv}=Fg0[E6EmG!}apvM?ĒU#yM5e<($2(NLǚ2̃O1lq1ƦJ;{W q6 c_c{}np΋Imkkm9u, _u%v ~P #ejԑKЧ)4U tOcaAKWs5"˻]d6s3yx=c%iNTl=Ԅ8(W?Rkf=e<[˪'I 51dQ؁[tEeL N doSK8f$qbZu˚xdIW@՚~QL} v#&4U<\  3Շl]3W_͕u/ڐ$Yg^"8={?6*&}]tԿGOLV/ٳ ,7j晧Z0jBpvkB*O)1'ngLHkq8`y! qDӈ4T: s cbqLP_G)$ WuYf|5hjIR6 4'i0;Xl<z S  ~}bpo/4MA3Iџ߻'tU#_Y*)8 b''\O:WԖa(̲yYC(liF,& cc(msCB'^сy6c8?a >x|g@qa=AF3*^6oچ2bΜ[9A,\q\ɥ˦OSYٕ+?9G@x[1לg^z<G|l5u<RbfIa_+itF L! YrgČ \>?-ZNU!C>S\IJ*[%+l1v^Zw+?89//аQHDKK! ?).^K~!1 ]?㹀kg˯pM7v#%%N,YΞ=0fL,\r399_ 4h/g֯HSSCK;eZsr~㍤)G֭p1EOC o` &O?Oܹh$O54PPP{֏a1"6po6ސGV}2:$B\wX t1!ٟ]9%>b˺a3s;֚x%gf.ރgB7.~=0B6+aPM8-vN$nd ݆jfbT=]|0/n$8YIuFwY֡\tn^ E \fbB'9!u?%skP%*iH+i@'nbcw zNLl\,Vi347DE(~2sdR-Yb杬kvqVN?.9L^ZiUJ{1\8Lf%bXՇ٦V ]g|Sr/6n,^͑/iksMYrh>$bz 0xQLhC^^4]t_+_aÎ4Kn3l [&-=?Mjs1(\1ב> "3_uw!ݸ>vVԐ~?t_p%0gjzW Gj塇nG^b JDb6z6t}.FHVwyx'uќݎxMxM}` ^/wa8)Oa8+~ܟ7IDa HBwd8@CaΜ {^aZ6iLcϚų^2뷱(aȆHw+`{OI$%F_`$xd];n[qAWCdyZcj܇$#8=hXn~ b/F-2D*Qn;WN dCDfIC3i(+h({_6j?۾I6R 0 KN8:'@D]gqWA?9yXzJj^B8v~0B?xWoGT" FN vhBoW޳t&ŧyAx\mCra$9٠W @g0(_(?>hѡ=guE"HjGz /n~|m D0]UuAƕ$d 8vIG˒Dfx$᝽+Gbdc Crqe O<9>M%Z1}O)B!VQtI3f+BOA:f3 ?DkX* ʂa Y@ 4۸$;\c^uU쎰գIA]FFpe>`RB:cki:{Y_«I}ǞDs<h1A5B6̶pdwU\̝kqw!! VXPʯmVw[ڶۭPwR((Nq@ݮ1BBӇg$'gyLA 5r (@dDɌ,7HuV/ƣ搶'̸w%&x|l5&6k.6?[2iVt$'SOuO[EuACÇwwu7* s"jJȾ9FO? >$MDgDT[EZF _Gs<44\UVˈqZX;ۑfWV͞<(*Z[CHXw\{?f@ v!} Aq+uw#@ s{Y :GDFp>^މ&E=p/Zmlx|f;LF,.@c;p0<:"˴v27ps^ˮQxh=h +飉ֻGmAT%B}hRģh]4*Iֺm 0,}aۭϫ_܇P֋w u4ϫ iPWW%Rq'k 3ZHt1!IHD$b@oOM3kJ ~h)U,;} }?sgu"G{$\8_kH'z1DtAem͡* tOJ:ACf{ Y[^ބFDqcR*s~Z';6*KF3[+7E=̕ϟV"IFb4R IDAT0wO"VSQRg]tVrjQ.L>p8Jq8XŒ@tt4!8Z@[,߁Tю,\4l04eЖ_\ì޲oof~YͼKk6ylI*fN`Os?fd.G^@Zp*p JO4>'pvd|vF3 XF2 "byfd -յ%JVW`(1#2᜶qтգdA!ѱݍ-_B(Tw6 k leRF ~ &<,G]AED-hX_ XMO$^ /@s+#1cN}%J< L% Ƈ}pw-Kq|%QuQFY}'tԕ`ⷱW/Bqcdҷ'w O_PP;ptY6yG[W!&,K -x6΀6{@8]wGW D¤d:= %'QR\,= IXd4HrmJvv01a!Y7^Gm>J8)#wx,)_r&i%Qpq(Tōa)'KZIY_|uЌ2D%mǮ]7̟?={ß(xxs*_,@g!ãcpadkxec =HNӱ\f9L:<}oc*Zt~^L|+?% PjRdd4(}sQ\Z1u7{z7ɀށ֗Ki獧 9Kuh{xm .8 Qe$kxG:噚C Ԛ17/4Z ƨPF7>㕏H&nE 4&O`i\ pyJoNlۮ#l[oL@U,Usfω:{͛弋 f?'т3A }Ɖj׳M7Ɩyך (T'S{_y5lέmEU)ln#yp V%U| OjH>'OOV*rQEK|[c|FUѻ8{[ < G((AI>^ϹCb. Dj~ya }PUU6͕F%87ЗCΚgkף6(i7{_T?RU=F ĂgD|$ϽI`pH$/ٶl%Ώ{6nfn;5'Ʊ94|C&b@vrPWm%~s|QU ͠E[#ǁpJDx̼]?Ðp%*; ,6sِ}oeca v'}x|DoD'ItR+uP(η9nY=0z`/y՘G/mCnev[kOYC&כ|Ƒxnc5]?]멯/shAo* FMHE)`ʥyd&x}=&d=F|T^k%ĘC( ><])uNF89]TV712r&Θ' ׋\w1\gVR?0_jtK }r]74dΝ|:T5}6s3&UVvEmm,DF'SYYɘ1o1r3"N̞ _>/d֭zzoj^7n~9zRr_믿M&yI^}v&oohZ \5QU폵Iv!kY_PwvBW3Me;t.1}'Em~ Jmbc},Ḷ[~Ea[A$qAloJSYJN&+Jq&ֳ:u~g@TTAdvT"{6ۓD!Q;S6ښd5U( %B$>q2N.Bm$LM"*+L6 xU<`Hg{qvtlD|r=e3)Jbn^[ ˅S(7@qLKcCc:1@uQ.ڏ1n~j^S+DZX ̦^܎ H=B!]YWDb8Pfm>w#M͊Թ753[Y ͒ LMI\d2PV^6Q6 -=ȳ+VQeTaV7eF{#c8x.W[+$W*)i*޹v2_Q[Km<VP[k*!~`Dt1:}׋&Sx0A5\v_  z=.'2V谦6^ZȢ[(pC6y?Do?NuCURwc-{I{"k:6k۩_EKAd?!I#-PΦ,r&M(ѣѲ[ee!~drTVTxsf5QuɃdkX-3.EQЄVUؤ*do_ja I>_@YrTp!J9`,zR$@H3,gJ?BHM5 0?xb&2sqZ9UofTkt7'!o/l᪞NuK5y@i?yjCKUU۳bSi"7ȓ_*.ŌN~#eAE}WЙQ2]faft3sFz07"2fA`cA&K+J߷ՄRĎD\<f.re%'UQ]Yo@C-Ⱦ"PX(lYUp91>Mr1xuEsm8F.+oEmQ$8ȟk$S}z|u[\n4V,+mv]#+C 3&ѠM~DmYMAO){U)sŲeTmAU!#[ŕ$%0^EގWH'ɔ8dQCk*{j4 *[a.D=,(aJBV˛nʪluzHGjPCqج]Bs&rMj4wueOtHd}{X #[8|McU(ʟYb,%""n@UU#Gƈ#:D+9r^{o̻|vhw,Sœ7]Ł1l!b=۲Wƻn$P˭_pvddYSpІ,s~\:91Z+iɓ%[TPȘ1el+q86ҷol@#,ƀ v{EEn/Z#G2IM3g"˭ͻÇa>x}r231n;/ԫSTXޝ- 8c}jVg35*7F _70d6{Z%@NM'ѹ7QYob|ޞفIX1BIc|6m0bp5Ym: Nt2RìJs73L*k` AU l x7M+:P̾W᧍A mE z ϙ( me=φDQ鴓KՒʋ) qkmYgp si> ʘ죽k_IyߠHK@dl4/)O+fxgžtiZż?c13 -ZIWph%R@xA8l/Ȇ ݝ;wb&ZA4)ngC@n䡣 G.ED -)."=JpȺcd1fϙgY]٠è$Jau`р\RNyM-^'޵ͼxT<usJM"c’wn#*=ďA[F\> e2;aԂDwJ/IՓV/,f[QT k= .N/-'N<>FoY滂bhA@>XTb~r+uY .7o_81}vx廈2I($9$]_gd 0Tar; nI̪V)wXGU; <,c?I}Gtݟ]̽*Xt._Q |195o/v[OCb(H21 k@?nb^ݿN'Ź$4\?v7ngQsNO\ ]t%V-k qe%L!.v{Bx&m].ZG3[׻,cڰDNM ~'8K IQhWH= dW?@D<)=/!6՗I[>(ʨՅ;f]mB^}pHҰXF[1*yN{~@cpQ7xQ^t!]{|dv@h/$zC%&t|ɵ5mUrS+xbӕS]Ӥ*ݤvxk$c3`VO<5cJkƆ FUUPU}S]mMx.GǧOO yΆs RxZΔ)hbv J"E5msɾ\Iq}1܀lGH&_P8UQFAFmcƖpY!u6&FsUUCJnI$AN܈Y2 n IDATM# _JUï\\q7%TE@}ǙE6Cy_.k>G*"}oex?l_ |šBȨYu#,LN3s[Zk4 p)cl V|_D6<D`&mEmp AУ^uKtE"UiR76WQ1R VK?Um2TJ|g=zw>x&&)n{L&mNB31BahHQq5y.^-Ȯ$6e_U--;=0z%lߝ$OKMjk}PfIFc+N vHѻ?Plc>>~&o2aQr3'u]ũ㠬aPho˱kjCRfS|UZPpq(TōaI٦t뭶ݻ76[ FfwrPu Ҹ6@B&\r ׆k0PP[L 6uQWdЛ5*zMvf$y.G.:VIU](5FFxncZ J7)kEHk/I|k@I\qA-= @Oejf; i>3̻.x'N.($ 7{ѬTUWRGuYcS lv\n/`Ј->'ۀ6Vqۻ1Nf0F3~>oRU[jzj>1|z8=VU"b"meqP/$^'(PT"}Z^\}| A<-} oT(k1zfuԬ?夻-JO dtq+6\oύgiE) X[-^A ,e,a)(DŽg &i>cmN~|'agGmM o,A?Kr" ®?8NP/:kbN>^{眩xx*".^-8OH:ve+2IV_to]ٻvaf*qf" o3`j*h0"{]J}OVYqYI?l[Oj>Y{(DI8E>"o3?Ш0>a&ArA1-& dc|'I$Gt, wK<(qТxu]w͡$B0 F,HbEa@PPU%Kz#pbyg[m$-a/Ao1n u$ޣZ{zXA7ue lv>*.aeo3F.w;Oعsi ]Dc"!(?agVҩrUU9)ooon}6?$np:$$tnc6]We̙8cEE==z Ѳ$I<̿v?Zg|lfY3Ig n{uɂl?ŴѩU58D z#p4L'ݲJD}cUQ,Ctxi4yr/FoˢhsLM lY R&Z7:Alw#OpYkp VS"zpotp ڎ{E!NB*PU^V08?˩)+DzlA-aw} #eJDV~cBX6nQUEgb.Cu0z#S2# &T濌 GU 73tT9t(257e}U`⼈C5Uܧ*& ހӎQGmZ$g%St'7io-OAuclGdr~ 0)&rKE1 Ƀ1x >ӇXfٷ$ \ߊҝ,+Z]oP0zC$F%0lIRA"I똵`*&-tv[TR Y#UŃ#P/ 3SO8]+2_<{3N[ NkztTkGxrv<Ua-cuQL.&Yn;yxH4GI.FE%DG:IcNTOeK:;mj+J1yb$Qi%f}qһrcw:0SƤtDz_ЗU60wv'pMIcX-YsNCESƴ^Y+9&O߬kܑ4SV h'|FT5(`֬A,\xiĉHNp8l t5]Hv\|'KZ<#"?1pPSE^9s _(R3_pu#ɠAX j}騪J-NCUQbc0$wh)(>ۙgPm'Kۅg*[_v!zRBcՎs ? j.L҉EUUQ.8?Cxx7VH9Kkl@qR3k~ֻ ynmLEY=z!)oESEUU~]?Wy%͙#56KX*jm 2kUUqj]M ╹xs(*&ӓ ĸؘSxauzyW>?FpE;| (8k! xwVGV>M{%T䔠:%'0!)]@Of(0.v* 1~>wE:^%̵3wfoLW>VY[$YT!0XZ Q^$OIy!0z6S'"scՠ1 jndNB?j\NdQlQ0q+ >7&㩓< j 8~-J3j↏bTM$NAe~e[,촀esY٘}*TA 7a1 nh43+.'}( |~dӊrы"bur`w^]-#aC :NN&aT* ө)ށl21I9Ena A L9]^zzauPQ10Um-# W{S nPj_EU O$8zA?AӑM’ԅ;K+oٻaalͬ;>}8W( vg:vk-K}E$]gͣ޳Y/ӳuv]Huu WF$½۾Ơ1op?{cs2ZpvߧM@o*FETZqT(n'Y[P|@ CHN$ ;$x uq0gWy8K+eڰ\9$QddX4CC"ݘu ߥܘ$|f<@N?Bs)TU+qh~ ($nH<'LEdQ`֭6'"&Ԯ)r_Xo6;@̟^m_ȬIc6nUxyx5k[*դhH_|x^|Wk۲Uo>S`梏t"iAD'vafL$s]1֒]ȞᨪBil] ])4h""eial EPrݬPn_UM A9+5;RU]}F|o[mt֪r~QwxTUdk?tc~tzZjxx7W" ժ7E٬v߮k ТrTj0D̍Zwmv+J Z?pswH>S\QZk..UUR Fʲ|VLmIK]VM%[s)Dӓ0&`avow@U\< .{WN4ND x8V/ r,ؘзmK 6#.0!At:.e}8UU9KFb ^*ɓ}0~MZG8/6óX` &L>aB`k$KV6SPQ(DA1/&y1I͟xGwK":$n~@XLb~TU<qTh0zQdcz&rô xAwrep(SԾ4rHҿ]ݵ` w59;BoikO&ӟD{2,OO *[ R)j_,J'pX\ To72>4][3|AAi}DP|Vu:\KRUU3ƪUشiOV _͂| Z&`7t:~{ỵ޼mx{(++;-w&Rx |DIˢeEi9(.g窪Bi namMY>ʲ4*ޑdݾZfw),.?&/S17܈y+^Nud3ơé*jZMY.j *4W F4F2J[~Ȓ#{)?Y*\{^U}ٽّa@/o { =WU5yit,aKF/*5p?Wa:rihD'pYVD w&܎_ɾH۟rp%N~ʍ<\հavyFC-?F+Ucr8ywtauK08UQv,oR\ɩ(-WzyRTdEUfUO;v`bGQ}_?\M7݉ $(V>}& YƯjyǎ#&^=a0"26_"D ~FQt(ج5݋DrG$FT=(F[WEH;g+֒ǟ>vu.fըHXCQZvW?Izyur/|[ʟrb^KN^h $Yf<seJƬ:>TwW7`z-N4zA@Є bⲙYT#h{H&~EqXkpT9aۘ?]LPU^EH'A:-F-bn_ۨ?sOau=;E} F5yWr9嵧):nk5vVUF"$UTvdFA $ɾT8TZ-y:f`~i>C#6 !jߛv@r7S j)M߅/,> hٻjhHw /y9Was:JK#t"վƇˋ t?>ôZfx:bjn?Bj*QA;xO~4(a» mEEE5泐HZUEL?/Oce7UbiN Bp m?%wv84wN6Ym*~ DP>l()oqrO ,>ʄߚuq>s%{l~!$ESvbOk?tr%WPYfvu8[㎇Xz5_JX),|qvAt`عs'-ؼ)sԟ|kjumOeeej'Z$6!0* Q?KrE̡VU|?)d!dz[QȭQ IDAT?}})?Gt&5"m y3&~}0Dz,?.~`"!^ gKn}ۿ΅Yt'ZtXR@X玸{]%M5APV8 #m%;M?mUKy[)=e6/l:"%3f7̿ɝX,JZW\v++ѱ|rk+E!ARjyw"Z :&HF~%TUb%PgSij;|Sš*{) "(y|H!_$w>n T2}?Z> EGr"}eYo޴,2|Ȕ꿋-B dhtI[kuxd F'3UUBR iP](ͰU(bҡZ%V}AuaɵE>B 3RpJHٷ^!}Ho0O]J a'},N1 M?U*5g?>Q('ς*{h~]/IFTWd1 G,.̍)1ȅH-(reYٛ_?dPL(;[QU20V zA Z0'3w`4[,#0*vUdo^"DvGvey=<"yTDuAiO`Bqq1;wd:+DLd8\zsPUUןm f /0|8-Em7ۮH{s+XMyR~b)vfΣ捇<ߦy*>BUe)M 6P ) m<*mv8&%U-hFH Q'l&-}ٽoe&c(.$d1[-^4/S";mfeV}j.C(EKDָC#> ^0wԟI18,]sѡs5`%=1n.܎ߐxgٱq/<(v*l֝8l{gB ]vJr eAa33oʾf'F# kRY}I)W5;9q%  s%O!o t%BMM:+h~ছlʕ+^eu@og_D1Ϛ5ku;;Ν`y7 ΋/f@׮L?Y{h^%sAi)t:P\:&^ߣ"W2JO=9r4俿-"Ey3e %5b6g#:[\A!gϨ%u>KI2#S:r1["W Ƣ>idCE\=+oϾ&QQ걤C'>߄$t5t $W¤q;Qd 4)GR)[7Pl%"|cZ.[v6D>]3Y] )Dijv>ͪ/?G3|6IO سYGө澄.lt :|8#!H^}N6*nTUeWjۈEIDNy9դ95;`y?K:a 5 i':fTfA(@mW-i89!rx/]lUew. n {vIq7;SN '.5Z-j,TKNcw2oFIWg A@IQrr*8lszC|WX?a9ݔ'|&u2du7ʯSeۇۑӴ*#mwtdyvP'VRo.P\Cަ`v"Kdm^oOڧ QUWҩ^})--Rŕ60HWb-KT\Z=sPU j t}\݈LEplflFjp ӋQ7Zˈ"MI&C&Lh[}UUUٻ;jvKkf~~fnbP<4e xg%p5)t9dFWb(.fZjt:VUB#sPcuY>|Vs:BQmM@]T'?|).\:dEPYrෟIn@7f:vn,I| pViS)J6YŀWc6 ʜv2`u9s(%X+WeaAƎnM^fT1W"st|dFs%@ٹv>l"At6=]bbx`dFՓ&Q]t>^DŽ'ae5*]n6UcFxZ8]WQTJ~LQ"<wU\ɉqWY~avlTZŔ&;EL{1*AOT<_JJO񴊋c dd ))ɓ!*͐lFQo%)jq[PNTV_ͧe˖q7v_Dn{/»<.΀ xpfn1%dOD@p0~R:^{ʿG[!9gˬVn7ˀ/u:^v޹ o 'La,eCx6( H-‘\N"KF[j; O W!ԋeʸHݗ'O=zM=37\66qPuYRRȈ{ f~AWv`dk+^ :P3^>&';lV4`p7$qج(JÍ$PY~5222>l vw8̟/22o@#:s&vF qNͼ-5Q{R7V/yD$ufe%^ VK]y럸&\9| i9q%kZAW_GqZ {3擉<54@.+H5Raj?坽>\8ny_} XM5uL>ۨMAXlqYN?B@(j%&<;ŊWpA\=Qgg]K+3%Q>:ڶ|T ZN%D$RKr[pɎ֐IՂ:Hn'[?{;؏MRv׿QWn7_7*YUC&F&&!_-xIM`gH/c(0dUEw-,y] FEeJP!4#A7(4xG-0ʲĎ_RKJ3yF2:pwW/UAku[>lLNziu\؛El@3ǐ?lf.z2@?Wu Q=v#kQ$HBb'9E"jjA@9IԢ[T9@'p1yS[oClC\u$ 3t҅ &xc_x9,[ VKB+QZΤ]: /頿"Q|5zsi?tHw`G6"9E;v 49Dh6B$W:`:e UUYmڏ*L-3c3t=ڵ;:1 pSX |tד9==k@Jn0[!Kuzǟ@ KOf]`IK1mN9GioMpņ| jWHίC 'xSy+a%^9((r="GhDG)%mbPnVÝewkLTaԤn@{E +0k>|;W֭ymM;Al&L5$ ˽/"C!E̿rۍRxp`v޹+Eua{UxՋM46n5Y7F}+wzO.K`(Ğ\ZH̙si?n8d!7~~_3mD{o!6=TT!I2ze۶mgΜpkxst:>z5? 4n76nuA`a 6Kݻڿ0F\taa(:NGbp \&I:{6{[n|ˏC'Aɨ@&'Or{?-&$3B$/%D#``;ӱb}/>!d/ v.j=}&+PQUr5[jkut # vbPU~VU|Ts`z`/qF7SD'pY:5m+5 \Kcjr5p܂'VѼb*2WQ<O@ ٝޕ?;cqAޔgv>? & O|[=0:$W_ 4]g,Ȩ! $?]Ƚb. Tt ë 5N;n(dd{[&'say&!gۻ(\d."K ~"M$1$0GkP{AyN~ pY:A h&kp}Q*@PA]dY3z5( 68$/I)K,:>>xU啃xwDG\A|B9vR7Gl!(4J}KưJdy <.ݾ}ؘ+yᅉqt:>}F\n7AIEa 0om>h/I2{R3o n?ܹsټy Mn 0vqqg.op8"kl Ê)RYYIDDe%%4|p& rx0K Y|Üy:ɹVUb'z3kj;`ݶ <4H$0xF .=PSr Q]Q]ZDUu0>q]z ѦPvb]%f d,;rkBqMDuOtN zO(މtAoLc!m+Fr< 0zo@B*+SPo`HjZqѷ5@gU{y-躍 %6_nMa%#׿uAyEYa cp@Uj.zwcg4f&{j' H4-A[dkHQh{Y!!#܏Qh P@בMgbP'% `YitseMKJt9)+?uݱSa]Z%xӲHٕIZ0j1N[w1:)gP?VuHNTDN&w̭ۑ4dqZ&T'd8J9qht{o&~smr Pz(S!ǯ~@th&FQUQN&?E涫VUH[?ʊ dlVϕJFLj)HA#\O'߀*pZȒҌXJ $ptwQWRq9$w!4Av/}Yrw{ KHl<d<WߠH0[f"NR'*ڑlS)G ̀&um:TI_xȵޭ8 BD i "Kٽmԥx\_93=v3H4YhusOɏ܃$fnD+3O)8 k'lTW"K)7"TË:eǎ @ r~ &ĝMVh!) b0)'Vx%*znFIځ] IDATE𲵆]&b|*Th*{^ѯeQo֪(hߦؙ%`.T*OB ޡ%˾B@n}Hy 9pHs"4WH9};dp;,Bfscp9jv6(86'}#IpX"h(VU0&ޭ~v 0A`ZXf,=Uз9s UdlUI:͗X$f/C\x|B1?ٿk&٩p:nxyԙ '57jAS\u+)/-m+j15I ;}1=~z-«+s7+D؆&.\m<>e,޶W;\3kiȕhpԔL@TD\ Pp_eJz=|r8E10}^}u^m}1cFb91ONcÆ_/כ;x)S˘>La/z}8nwEQ\C`!k |ǰ:~FҥK7l 0%Np{k҂hZTn'-@ҥ5.$Cr2}.Ņ1x#Oلn'Kz^Vg+X6|@})k sћF҈/8Q, "cMsxO*IvD)jmƧOodԪ^|\Yb3Tɣ;]YvgP+d:/-ϵQxx~{*KsHFLy/_?f)M϶K"rx` `Uw ޯu:&Y7x4<tzn>O<j!ŋ kc )|N eĭ/Qw=5Jy' _\j1 |Ay㵤LByA{tI% |3`Ll+(*>d`US%Jzb)hZL\oʀ-H-rQﲰfM(Et|&_TJ~6}5;I䇩Nߌ#~z#0T߀r-^ay}YNjaV%7`*W!hAWB2TT $ " ; kaTO!%C4m/b ˓1XUYh5s[]t:=fY7UD*ށK:'q>B%|b x-m.9[UjF<ɱ-d,CLOGGwp~ 3X: Uuysy9'ˉO '0(JK[" G z}\%Ƣz.+6RʊߪZ5LèUeL՝{¿ rϭwv6H|M.v)j4\+~^ʲ%cȋX|ɧl>;Er݋vwʮ=TU$!TՄG\Dlg }~fiyA6?OI>]9]N1>uB E&A` $ %/ёJp'uEA"w\/AضץsBŀJt+4Av@i||p !qHc.e팠 AU|$ѯӉ'+ xrW "<`hrEAg sܺmhR$ ?aCQ.C¢"z[ Y _y-vݭt՗CVt:s-t9:wUxz1uӌ7]ƜJQ?C8ؘ+jOr%ǍcѣMs%qmOw#(-ۢO^+ y?*ĨR)J/=U e&5a xee^az|Jja7U`#h@l̕(r^ծ3ߖpdbk!O\8M/s%guǟއ'Gx̘qi)njØ1F#~>V kn ??E؂ TC= Q? [lfM&։{Z7ri`}R sx|{bsJ j`<(rƍ̚:U7i*DC+>E1EY'YICR$zOn@4Z{$[޴EyN5(S=x^q@Z|cYs/]V n%Xv,Mm>_*y12$]1[Z# fg{cA9dTUe(<(cOz^| 'j4~GT zმ3~*dW1y 0HU,l5'9YHxj,ƳY l$,ܾ'Vy8־=můorڙ :DcW2[C3bfCۣbctřg6DT9\g(|8dN9JF'vF L6n 0A`rBXJ /CpGq P~]L?O2Aoq/]ddeA3i۟W4XzO] w=Ň:q u!ɲL>{Hk9DGZwl8J6hɤ 0\eM(61-'~I @pXQo_\Wisܺ|pW@U~TTK~7iC\Ԧ? '1`i.3"/cFxn)|xd !.xn'Iz4< I2lb$л$Q Pokʡrrp8tYDx| KQ.=r/ovhYv>`Zj (>@",<1p:}бO]8VQA튇|޸Yg<5f>pt:@UY7kzup%l>DodkM9@ʷzNzY) lTTZ*oȰzrV؍˹AAo{F3 zzLF`8@zaA9(Nrrk1>K]= S9e;*J$_՛9`bz?dhd:vsee%fBAAe$Mq% gHf3Wd/z{w`зО\S+XZAQY1oξ &8CDKJT{dr x|q6\ƕxbOb) Ɠ\ "z8Q>Xn n/ &}JZ<;;CGvOvA z/?8!>x˹[2}GGGA~u]}1FZ$ջ*NUm5)- 3wfjZb< c:5A=dggPpY\ .;EBm2iRr3;(=, DqS؋/hϻ7t3gww}= 5E65N=VpTxʊ\QɰF >wvˋ/)?onv${M}KQ(If`#GwOo4Yi.ZļKquwgsi44"c!MfP6ס:n`,)5/yPvY;* s^1s@H?]G@}m56€@ 5? W:OG, FHi3Xm թҳvcBf` Z:EaxnkfM 4 i)º 7aPQ9$|QHMhhk.|Jv Fk{GAX×XLr|+оwѴ )xyDVUĭ? Xr$s.@]ZVNnbN9k&L! b B_8MSn zAĢ UE{Ž Z˚ KsŋJd,wmt@xl3de Gi+ޫx'OߪU+LJ3wB۴a0"3y.&,rzҍW?cpcJa }TQYMN>iťD Ǖ"9g ;/`,\ 2Bc|<~uFW+c Q=阚yLNs̕*]Kqp'kE;ßPU\$%M"dʔJ^ 5;vך,-yytBpp0` Çy7Xx1 s8S]HNNeC ǭX|Ut&lͲXg@- ,d-75Pxy01r[UiZUsZv@6rY9(1ꁪ;4oIȜ^=;)V]U hJcwŎ/ߥ4'Hd-h}şo4;8 AX̓ONfO4jlf#M2j#cK4[Hv1rSmR\QC jr ̿m*KCdF 5T/5У2d;%27o8P?l3Ꙝ"ACz;THLcHW^ /<~:SZLCH'2n'Fcw_ukŊUponA ر-%_U #ɤW6Gߓ{[o aCMФT 4K,d=ZQ]:sq2*0_Q_^녛F'w^9 p 3܅NC/Aq؄w4zD=AbšF{?Uw/ōj7ڭ|R6*]y"ITKv& MA`L`y1R*JP$ k Xb>POΕgAT<&@̓Kт6{1z7ӿԦ[?|{1'< F\=}^Y0 `SUKmk;S5!ӇSPVag>D-LR]ߖڃcPՎpDTK#Ahd@W8|ip}ӧ}}k* w+m JZ o"Sna0:Uo 0$2W/+翏PZR7Fp _6)IGg嵒YD c:3ogT?pg0ݤfZ ͶXR6\uz2A#=y8\t"( EFw ~7ͅ"9[(xs!^Schb|4 A9M?svrs_eD.3tz 5cpJO&YU)[1:`Ww?i.?2eh1h9>@eB Ⰾ,b3;=g!?/o+|kL|Uv!!!^L|%Z5:E1SSXn1uTv` jߏ;?ߴE܎F=T:kD??[ Z8}GA)x \XBr&!4tm"m>OC}FD%Qc >vSVWAЦ਎mձ-Gry䛟Qg * gxW\پ-F_DD{!EՈ6R,U=dL%ESa "t`䏌J z# UjF[^.X }VdNjh:WhbW hO Z "X;Ь@ j(y|!##;<8V >:FɖrVUJE1irӇ'YrpOjz+ߵLd؊"=~m_XV/=}!*J]#D{p lU Lj]ѻ8 ՟EA% &8ѾSp`R7=9O`Fs0y*@PD4)5r_gʓiG'bUUe#;|6L9!zѳk GG֡D>]J:( ^.TWT7bhD8_2G%^OVt |^f jG;Fu|Zu"c-'n zHעK]m^/xUwJB.t2E14Ao$O<Q+ LQ|>' ٛ+;sHKba **1u|)* F#^7P/s`Ef\ZOI&]χQ:΢쫫so%J1ZI-@_`r ڨJ YNtrf}Ӈc ;CHjH~"{X^w_|C%hE+7nC0W 0IQpt:`sbC"L rh h{ m F@D{9]?häۂb!8*6=G c_c_AU6QQϷXV3 "B7r}^dUW`e&ya0=' S^T7AhUV2Q~N7pmD4WE&VXF0PBᫀ=M\1enʲOw#֊ v ؋"E`WhV;bUU2!">5cڎWmZr0  sd2SUp܍.,H҉"3:tgJB*2]vJ/3 &\)@<~?={Jsc8^DųZUy؃V荫#LFۜ-[?`xH"}ٻ ;=⊿ɕ"2E<.*f(2{p] @3}]T˧lyYv{ZLfyr ްOW׆kljJ2>X#t=Cdv;FwS\+p5ڞ?pÍgI`;^INݽщBRF&7̕$e`$-х%Ft׶ƿRȌFuP542v*Pot.L b,+w"3mpx234~ܯ՚K>%b)0[|6( jH;E6 Rq6* Ljץ~j1m "SѤBO:nla/EH634ƕ uRYFoH?tO?z]}=Q/N-cNfKJf„eYf}d-zwIc9ə9xyٚҊ*>}3xs2-/sxxhD'pfԽybfE!ks~xl,jQ3r%!z7ʕxD75vlYe λ?\I]ܿC@ p#ZE1>͜95hSPP@() w'oײh[rK+WY7ov-o^^cƵv; bbuFYTl5kPU,W >>Ν;#._V& 0{믿E_b%ZI+@XDՐ4Vv~֊.(3@.a(hZ<* ^Cqkت";&3Cm;Meپ97(*D5\x ⋊+Dm=}I,sdUF"wf3u{F^~aTD[j%}IvSUշ%ￓ$#v@%Id[UUpp}/?1)&׭˯~h"f3G8~cz=rwcOiޚ>]i $8@ddHNG=˜>e[D̷0d":A`Lu|Q #e!w@/Ѫ ,ŷM UUUxEXHCa|fC/g*"5uCnB ys.093g~Y&Kv3nt8M+-1*y@}[s -ݝYu;Ck4/8$绚}GGBʫAw`>27Q͟˱lO$DMgdA\hkm JLHrwEqD+@+~WO י~| )\ "_$XAt >+6nF ZG;lŬd,auʅ_i_ 1Qhu6 #Hٱ 4Bes[ O<FHE /i7p~Z2Xzɀ'Zq:PvsՌG20˗YNS#;Tmt浈h|ZsLii^pA<Đ;?pN9c"He2=Ǡ__E0ziUUDIhynt I:̍1u-iL*P1`7?f9M  '[-N9'; }WrxMTF>gWlӠMQ!<^T&s >?3)ȒGg;QPP=+#yYj$nKLW0LSU] 3fv0B]=qnNgHy1hǁ^^|SK6Mf!ע0Oig Ӈ#c<CGwfb1Vb0ȗ˾e ;fYÑCiٕ ]Ħ$I%s7Z;澂Nu-)\|e"Uy fښ@"<pA%h7`t/oi|VUJ'w"~]k)Eh\S_tٴ2Asi;?YUſF#m˻7 tt"Gz3ɳCi~SJɷD !#grtz"ܒJhi$LHJ͂9ei'X^V+wDDq퀪Ǚ\7Nrby6U#"OH u\y#N^\ɻС㨪<@u`ݷ;׵HyE%LQJ#IW'mV|&,W߂=v$_'bpw-(bspcu2_wWҗݜ?[;<˓+6,9xV*FWpkS|ӝk>*1g?2Q,/HBS‚dJQ@RnÀ  Y d>Ҋ@h>uHaR:žZåN夢:*l!Vfa۸Z\,ۊRiw)B#z%B1@d i:Fc؟PchYHWcDMDqΦ|PFVa,e]tjO['^B:Ų^2+XzÊr1~b?]}Y)-l53 TRej~y``nwGXd܎tt;hXL԰%ߠHȓ$ujGbZ]KTW 2`\ w#K9E݇OdYBձc{ ?gl7f g_veLт*XK$S{:H$_09ՕdU1uzK)PUY+q9zܡȎJthsB䱪[ 5Z/kig-.*/WwIDmYf y #njE׌ZBQf>. ,6j;f/AAOPdd$0-V3^Er8M {so\bp$2Cv<x3|Wh(a]5&a $CָըWH=8C {%{(IEEŷuf>Gqae{ OPT_Do- UsEI'O~Ǫ(=+Y*p% ѫ,Xm\{$fϞueoqɓ'y JJJ jr2s׻r. ۜ0ZlݺUU>|8..)m6t:^WR23)ESX 02?|8SǏ iӦ!1q !}&oמ6=^:V"=D(G8x9s^*٭8\=7aʌAa&g\=#qwt; U'Iڳtek6<UUBs_UlUUDE Uhu:EX줏wFt>rGHt/&]GO2Aƞ\mamx4zwD~m:pK8G>$ 2cq̜A X.Ȏ9Y"IT`pqåd+&`d{&ƽk+,1$;Ә10R=>v¼<NPMCpb:9@ x͇UAIǭI3m/ࣹ쫝WP+f5Hs"?>q<덤{V9 YQdl%zctuQLrl2;1yAv䑾["5bƩ>BR1r=cHk{R/1K!E5 -(}^ sJ eUH@dr}cЬq)Nyo/"z?WHVI(R"JA҂fȪ`,Au}h.!qK:㭘:Hd\NXB:t%9Ttyܦԝez[˕_`aՆK ,bc Øg3z)Cg01`3dB\ umw.AU%k*R|/ѣ[UUlU<9% ܄ɳ3\Iџ ~u=CrF{6 ?cRUv A= IDATP$A@q$& P7\YU-N*%-o6\VqgYbbxã2 G=KE ZA@ϷKb`|?7SY}\UQAZ+2d5AQ,$^l;Ԍ&Ӽ :Mw.).\m&84줛j>pAw2[7Nf2^xIƕwN<=/vpX;zO(2 sSˋqr[5{22$_6u,GN?"̸B;"؋3NZu#K%F7/vXfL UU!qz0yfd-]M9gۺ[dߏhĻC}+I.,:9)FEܴzr*INjmJ߻aAaգ}auXyd g8"}p^79ih%FCue%zn䝷ˮh\ѣx|9ydm(jF@or.1fթE,_G="ʨϕXTKݎgז_i]]+\I]B$n u;1HdO y p%-8:y$&@Y Q n?IqS쐤cPIzQ$jT!/)L{ֽ/ڇ:R㔍G@nr.bFoHA:C$g$AjA:nFvJD3j$9AJ-sy( Qaw ur1(&$Yw]'hAllFdT /.M#y n qSx g qiaՌgCZ?4xC9] V۪+IOhݶkNd3}Ӗw$EUHjÎ t&kk~ |¥}\ ;L݀5z*YC"fI۵ [ 'Ӷ͏?E[wuL7Q= ʒ>$# : 1-m=Ή*A RDhv&&(Fn13?$r tޫiARؠ޷3 ]X*qm|^(J>䩹Wzo\fPT7a*2GVNމ]dHG ouϽGà]]Bώv&1 ;&Um8Q G#<ш݀Urp{Z<6Ib% uNXMޫ3K)X[u9iQUbqX}W%0q [M:π&HeQ׹ݐTU駞磏a2 @ShƓU̲~4YA֠b€hфEd-չ_EWOeSpw2ӱ6L=GVVTG_r@{95~:ZC[M%59'ӥiw >W_`苪eaEtY! uU#YЄ5KNcy,TCpлxfu2(Nd)I/QWH+:ؾqavUa&?L^O?\_|Nڦߡ(3%&A |&{ H^G|aWUjjؙΉC])nȕ(r1&'H.#+d4S ##={ o)$8xYYl۶ɓ'Z_o?! TU9cCEQQUJP˕pvx'!?߉9D5y\ ([y#E\ҥp%upH!̹7дz3?0,͖.&(uZz_Y8M4zAAqjBIZZAr7QQ#juLAA]]]9p t \s$$$9ՓHݐ@֭hH~q)~Z]/- z?d. 0 !rČݶ] Vk@ؠ  :c 7Oѵo8,~ưyG!7} 6K5l|d-+rn=0nzbX73E'`xm g ake ɱqx>s.CI݈9`Dg30;P6um[<ՒJE*4~Y8g'8D:qJ7@֜Tf"?JCEfe1n)\ ~ q:!Ϣ=‚kHKMiB 5`جqTUٹ3{PaOt[ Du9D=P^{M;A"M4Zi`4ZUH(!" Ggx m5"&8zrfNb|$}_y xU%ߣ, Fmnތݲ[B=鹨=*|QwFV4 CFxEn@spPUm6 kTJDSt솻76Zu|^?xx?Ϫet !t縒gQYYÕTnz%&ۻ= 1eҳxE* -EɐoN8>&*94wƲaAߝ[L$oXR$C+\q1߈&t 6.s"܃}d#dâ-l(L ~/E F-jݴeHQNE6 G9^e2: xs0Z5AhEka1?T7Pڹ]5Дy W$4ekvfƉɗ$$اR =;p=uL.J%D`kt7|^M {z1l($u@:iB (tWmdML4u {7oh=951]Sд"l1om z/[PR%f5͒d3&%*`5L+N0۩h: d"՞7m޻2=)LL+Ul@RL}s9kX X͊vۤ麎NIɝ^ 44f] sF"D %"q 鬯bD6T3b$^2Z0CrOhݴ&m$R!JYqPNS/ PsL!(HTnSx4; ZK@UZEKA9gK%ǀL%i9l?b-gIMI1!\ (]vH~#ig}؀hƒVJ £e߷&99]NNN ~LgK;j֊'p;qJ^M2-g GYP+ൟ]h%j%-GoG+Qk" 0dJqB¸ﾻذa;~[!&lURR͗_>O|YrIH(`$@ۅZ_^RT[oE\:dwfﾛ>ee<EѬV^E^4I5e~l\BhEQP%z' i&w" 0lq!LJli=%;B[+DMzR 3HH%fMz߃T%GA;-ڒҧ_g#ia\E_, "]Ty\q$j/HER:w AE0Iߘ?fs"UM[9: Qо 7Ync'oтŬZai}4"yvL$:$GdǷӬjB,DzgunK:>E,]sJrMOOMVbt ozD;~M=dA{:~1[CRAt6x]HR6&G47.6dljw)q6+%7;UH l4ܩ*ǦPUBڴ^ dfR?#{m =!h7!H k?.9ٔ/EKz~_A8lc%'yhE<ͮD\Q)okY_WY64,a4TWD>ǐBA65$,5eVRuDuA/5x?֨F_'5{x]D& 9@z*P|y˄FOz~b 5Ek  U =^Ə5Ȃ iEPqj/*D[kaiόH5BSUdQp;~ˋ'ߙu?["D],Z>B* _Od܊ [\2}c@ b,vۤ̎/ KH4Ս)='f;lК騞Aں!y HۨM/+pq־>,I<XI `| O:XHv h*BwFy_TGqQ!Y$%w.lFmLXj6ۦAdd.`wѻN@jd` ={oޒftqP@hnvP& ?ȟ[ ϹdU.`;IS,󭣎ZlUj|AϤ9 #.z`6YtC~pSXt2ixcD$XD$tnj{Դx͡&sy륬pZd #bDwog7  FL E,. !]44t ]{8qޕ ~w`%:GA܁W%zdiX'0zNp-'$pP2RXk"v>8wҩMF{Eq-XL*I 'ųtk!ڡ\l4pvb%UC4̆c#(u_Vs9yJ2N%4g"#&]uiV & DEE1n\h\-1LdeŲf͓45ա:1Lq7_Ef|3\^fϜ9wBI&(4pb2ŶGDD]w]+VhX}8Q(2x9kznص_efx@w\uͼgĈ!h&: Of~dF6aopOr6QYo^8&b aҗ9Xfy3Ɲ=wb22!)UPm@ľ L$ړf9ziQs7RP46uY1nM#bœA F IDAT> _#Cm|x:أsԉ⣷n,.Z;A `.Vj}SCe0Ȝ;tΝ:aw?2SAk2;09674ׯ ,qhMyu:^_@T=9=tۆ"Z]A%;Fjo + sx֨HvmOo@%mvNbAC{ d`ҨǍƗrFh$QLNn=~ٽII"pU_hE<,ᇽnSg6q\~-Kn $ڻx-䈄X ӸUi`J@!Vv=7 K>D$f:{mbqT~|JKL1PCLfFۅ zqѺ~@<ȖDNS 6.ُwm?P`!p(qc'u?*]y$f0+Q ɿ@x=PXl"[*Q|Ȧh,#MXǴGıo^Hs;l{aO Qi2_y{Cɉ& u5PD,0Ȍ=[WMe=p(qv*ܱb(2)99=u (-I!,&rNٓ8*|UH}}pa Fk'Re%Mc0ILfhجTl}&xpGraVkH2`8a#%Y}rDh*3MęQt`NKH㒜C8YF%tI09Ξ~M< ʝDďDSV~lePl)e[" җ8̉7ѕndc甍;vQua٢ E•yùշh*ĻI8-1 u9xmZ;FRrrRRA/fۖаY66E-aO>e!ׅY>mi<94|,a™:ܜ/̂ 5FU EDœ|:^7ݱ,$gq!,D@s]As92t7 @Tv5$sPTFS"ThaV.Eqdþ>( >ePq!x?"&K8yQW;>^+Xw(: sYBt@;0E=BDrz4Q&`0?}o_ɧvy:5ο5$dĉ̛w!GZi%w4=shs|̝.zA )<]Ɏ ݙ ""yw5ft=5zel~ٌLN\ "WntbDC/Ui#.$Edb3ȉ .JJBܔdOP79X,WG8f^tQbvf_LW@ (Q_a [o11Q9=dee$ F(LIAx~/=-6/?XSW>opYIqn֑- K>>UI7IϹyRcxcJ {̤#fsqogG0J6>\ ʌKF;wm"d̾MThMrGSs="OhDÙJHqp9<_$gl$+CW@"/2'$]ZA372[\r`01-#))oTz\<~O*sZ]疪ʂWI@Bώ^,Q ~&`[n'RUu9HNLY>LY1p$k8r*/͎z첁ɩٜѱ^s.>%R1G.'.I休$>]lI꽉c M`5sILIEBFf$~j j b x.>$gOB~^Na0=]t(` vE<"nځL[3&`;n}#CQZIe W>r2bO_uJlI\|1呸A"?INЂɨj۷S\loeϞrq짦uK/ٳ?Wf̮1s䩧H52q+ :$cg_~g_NΝ;Yl%^h[~?SO>Y6Јq;>s;5g4MEWDxTùS2q=R˄蕐WWӰ">]g2[IڐrrYNhzsϒ534K mSBnlvm3&\$$G6io[kAUU>f^yWYYgÜٳ87QtaAk5mdb{ToEa8_XA-Ck&ɇ|P&s4;rtMCM}"Vqg1q7ߠ|-˜KmŻ1'Ptة \`CDM8{qO<^ (\6rln o_+،'ƔrҤrgo& ;>Wd]뺆#FO̫{1}PnbdQ=S~t"əB,#%rIbzLVLH9?-?52t'z})h)߼|͍t6u5C >4F}=kRU$ן='<2 *e[ޒ$@ ȸУ?/}ʙx[SQ 6 noq%ȾrjU uM#HrJV9"se/^Ŗ@ԗHNX (4#_:Ukt⃁ q61s~5,tԦ:ͤ&cΩݵ|68,Xϡ*Taa(v~\W_}~D f8Fbcɩ Шi|d;$t7E%Lf)]͖Z;c_q3*>H3x,A^Q1; ̇_}̇᳏>"&&[zC: l "<ϛ!ͺgJzBpb X[AO5C-Dgcm9XAE(m!hC=sUt硾dehn%mlG`OQjr[kd̂v$vc-7֒ӄעH ?wD #OM(js_ЩRZȌ&cpL>jkp67qzr5Fbr_:ٵaÆ1lX jB+tX&:/KVtdv_w?2R mtjV=( v&9h%$g|8xhYkkB DfOh}IDu6s5S[ݫXez`+'[evƀVc pyTܷz!)0Kfl,nvqG0B_T#3xgeحV@Td.MS#p~fNuTnE! 9/؞OẻqCINşˊ#߉,"lez>}Qqqh?Q#4>khAM2u=w|܍Tn݁ϩ tr3I03?S|sL'hbxV?&`(YX;F3!_f-cRZ[@>Jk0s QlO^F o-Atz7<$0Z‰rƁ͛cQ= 7r߇ kyw&ƥd1#WCU.GZtv/Tx\Q\݈l`,YIWU=g]^g;VnO=1'y&cv{sM7rMýKQk /Xht"$|VRcU&j0Lש8dR,!DEىjQʩ2r#"(//>Y%"EKd䑭'f3s.9\IsL5I4Eo%bUfX|*g5Dm=ȑB,Or u 0 ֿ^G?Z,qxu(~; h, 2NºYrN#w4~ہ|5z2LZ@LZ埒ĹYy!%*݆n7`mȊoqJ>>c.d$%%uz<|89X[5ņonk(3\uit9HSP\:}&vX[Î=] $35ղVGeUQh4MSŃûϻ{ 7TSS塶~ג2gP$;^ 26eyYl+(fKi%kV`B.|ixFF|QfYbҲ@N ]BO{BpKpBqgݸ0!4Յ(\$D%Tli-ƞr.I x㗑h{$Aʱ#سndK1Xlmߓ{=G)--r"Z?bs2ղ"SY-)L2S=kQ׊tlqsДZJּfZr_ZJ pw6*M\'5¦:nָuƜG9Ҋ8i>t=E,{](jKbfs?hxxs:ӯp#>=zF؉7Ք<|\/nW8͵eM)8*`Ǣț:눳; ЪNgD,\o[jnfiu1f5"g+p]1 KǾעDjYJ^š6a/SO}i%<,_,_x /,^IB$IJVQh%H"ik-m]ݚ㥕Zɉ5:  Djj-ee_i4MbE̟g~'~o1c\$$9Kov[/%K0,YlAMcſϐΗYgs: 0boy7`J$<և t͇k\ ?иw,wCYMh郦MmDgdD[ rbw)`0  MUdwhV%V5G_d':6Sҩ,E f265lFQRTʖ ?Ls%XOkrͅGFd狏#~ê՛X|v 2ky}NeI@Eʹ7@&;YSB~9sNq࿣=l2/W]еOPʶ&az" i*ۼ9 | W]o=^\;j^5.ҁ9˪S,׏ղ@=1p,si|D>Dcn|eν(1uwDa R$sx+ q,sؼڌ bMcc{jVM)p4qqp&nC2:⬬~!JAp-JӣR7Kے♏@ pCXKg;xEmRMxWUTr~UzM 8BSt'X IDAT.%{ћ% 9sIE | 5 D$)]߈lM Qz/wgR8-!uurEP_ڊb<~/~34ǁ^KM2LI|Ibu:LQdXl2fk'—49 HIFUq־E|M_nC6 (y Qb=.k+#Bu6԰fjU!WQxKP@Өp4!sKLclw^`p(En3n4fw׭CuMy;dKйYQ|5p67(IN>EfvlmBu (Y0 `_]#bm]hߟ!18f ""ZAh&?&'|^-9W*X9MQW"7v/\.cMS'DPlbey1E*w3_#\@%=!/;NBx "ۀRn`,sZb:#B ۟jjJꪗi/d5Z%pJ/_?IYR}Ζ1EXu' ZC:R"׋ k0pɨ.[uu[~nW@]S]GZɯ&Ye}V}M3t dĈ wNadWu9Xk3STIb]PC)sz .'/νI?&\z%g("iQL9<Ĥ|>4݀٘NaрK*x0` ͡SߗtθLoWot4=GOb.g!9갫듯Q!D@XLӇBGBU4dS:3i)QFǃSqun//X(F{N7O66:b's$?.^&"(&:s$Ǔrc,GE^i oow` PD9]8Wݿ;uXtOETNO2uG I&}&2 fV57p@l?DpۀQmfw9H49G.㓎L >n=5 !!T҇zGFY_$ہp't ɠy0YO 5|V^.2N]gG}51HIvDGZX9W 7;G1#z1-/ 39G:tDY%e@UF$r$߁(E!q7݃lCWe$ ǁ|bdk%ۉog,u^j%ꐵ *v6$G,14)c 5K2dqvFNv9xz:+_Y3*3Qlݍ(#pUߣV$لCuť0Ur,! VS=FuЖ77yE6ǡv&U`$&sGV_T9XUqڤ~Yص3Bfk.ظ+hDQܾjXwfA#g lmndXl{| r/̈`шTRs7G6?=k=ti1XnQB8{K4t݊Ǡs6VRZV4Q$BYPWIq McG=9G882,&?e)#2y[~O1Z94Jrq:aÇ(J`Ezga+%Dol$TTU婒ȹ3x~^FS{B`>n5C+|D:v퇯C U+91vx<t]jpB heEjJh@D|-LÀUUyg)*t[pJb5|F~KG#Ȳ==lodi*LS.h0UZ[\NpXYs2N"%nǿdw䣶Ɱ)+Q!he*Jim]ujPe3=* Lf3s.s.<߇5ƞ"$ю &d)zhר,߉O@ (.|S}1Ϙ0&.AO Kc->х3sssLxq$V?%,smWBTG\T$^G`Èlf|o X- z7N ɚ(e_템1u!W"/i@x2[ e_}A^#sh_ndB  gBV%( !~c" A-mCaS}5>Da\IC%yuB0Zߏ qzZȟonC{ &QlvdSP\5TJ]AD7?c ! lo#"5y_6{u.-n{&=5^~(Jԗ|$liBSc;`a[Njv6aijJ{*+VVo*Eיхd[:M rک9H#l7D4UՌ5!8q <B>9ݸ꫸l^nvOϮfuamcpCpn8>zfGr! L&^|5d98_E؀8*TWi)b%\_g9ࣴz/X ,UakE1wMDDèr5kDK›I8?gt͵Aljk%Q륨BUJEE6Q\#ŤɣmTU僷? >6F+Y' ƅn{,K"7Nō|Nظ'3?V?Pcw9gbXZ ǜ`j #55(4m*PEv?̥ cHFQZ^>m2q.:_Jn9']l@6hQD9ף?+o6|~dHFt+]no h.Qdʛk>Qb>K]P{xFJMG<0[8 lEEzLa-^rt}Ќ(s-vYcͮ-ɏ>( Դdlkߚ\޴[k.w<~~+c- .;:zϽӋF#z/lɖ\q7l1%H$_$! $@ $Rt0)Ƹ^e5KV#Mcky4 >cM9Cn܎@qs#~Ip Yz/溪޻5gΖ36p~lۭnӨej'jx/͚wxbl/<;F[HETLȲȜu ˋ`E_Tq}66k=]Xwg]XDU['F%EEKRў;-9=gAAQ6Bc#p7` 9O [@3')<8lnMI=6Ċ 7 "b ׿5. -rx;b]a"8nEzK8W OY{ߚa0)kn`@$4s(w "1PRm$/m㋳}ALνWƌ)<ƪ̀/Yڳ)]r!Kr}=;eZcDSI_?GA#7/86ŃxV>;Gs/mA0t5t\]ȲLxҒ/$i>Ix䡇w(s41E<6HI ǁ=*"ֲBwx;/§'i+\zT=1dYB31p68 λ9w@ҙRr/-1U'Ko eq)$gx? RV No@o'J'X(-։Vnw稬Gho,F<-m]ߟeϏoMV27(}{ph%Ŭ\rkXr,EEQY.ןSZcRU@a2h\^p3g@"U#\4^y]xWvxt4so?5Ρћ}hH3N]vUnRJATnXQC+ "7jܒ5Vڡ J sZ=Wٕw⦆B\wTMH+z`[Re]Ԉ'Uif*W[0>)(k54jd6򽛿WfM`ϩB־kyjF;o"" :8MS&Du>*p| X .nYqW޶0la\`Sհl4nğ_]˻lfEm=Mph.+=q0MlϑF҅DEGq1.$a#,| | X 鐌]FGu;-LghpZC +Ot Ը|v˚Vwqb{R}7OF~gGR *[;V+y٣#'hV~aD"Do=n֟:ȥ 5NGlKN7PhMTD4pԿ[Fw x*p)p >w)=gX@ ݨVc PA$^9m窉>"_k4L" )KaIʽKQ*_z_^_{ >8,Mj  !b0A J,,_LJdZyj~0,>$ 3S/NFJ#>8Fw~Y;c `P% fl@ BH?4J,|Rtb~F k sd"D7q;E^ǟCGTo-JM rTAȐ0!VAMR|TPV܆Z6(kGpEDG5rw|I+gdзJ&cFRRq_X(Ot}#o.*^Zu;No V2P\Zeawy'o磏I|~*+_,KToBECKLD#T9sy̤R t:ٴf cfDEXZʆ|g8EO̸i <LJr'͵% eaM3sM@̾@%FFY1FIbʌ(ԆŢѶĠ$.+Z-CFղϐRf͛GTL4oB}]3gjښl,;ѨGƣaފEyehxۮ]i944;3PB}q_723+Ǚ܄KngjF|! "o*IJᕭh2}}{iiGcmb7`ʍrC5Cm>sQu㧎MMۈhU$M,6LOÉECjI&ˌMi(NK/:{qᰯ0l;#<>w&Nw>'e y8At!߮N+:NMs-j+IyT2ﻜ$GsG8<.Lj-Vqu,$lcUjn։)q\gGb#kR*T Gt爰'%zHPkd.t>FJRpY^ŠN_uM9u XZCYKSu Л#yHYfr=n24Z}M)?MGE'm'jr\  +>ZBj)IgE T**{AUR@0ܲ2Xug}Vӧ_:bY >?ݹEüK2>ΟcжMkv`k4UV`h%7Hbj}}bJ. =?=lx ;wf-A9Zŋ/`0{v뉍mԫ4mDFbfg>Uǩ<DO"2jqؚx}WyeIE 4$qc꺭ZҊ*d t׋)C"sڋcAXJU gG֢(r͕Y:}Q=?X3Ia Qr`PhF[U4KUX4nxr#ג: q=-ad_BˁV{uU+!ى0||<YJ!vL2.ᵐ` qSv9P aZ}:@,e|dr!7V+5mWu$MHҫ}7rU$jN$YƦ3taZ=yqyD*18Sq+g *'d?gBVNb4V̳#͛WS>{";bB׍cըNiڈ.7yW`@M{ǚXѱ7z>]mƮ]HIYQք*`00gjsEjWޕWH t]4P{(:T"|oǂb92VZM  ŧV#Rs IbZt€Ek>>UŢ1>^{7\l)Y\v0t=: zz'- /o7!0ƨ'&KW,A@: ZDk}Qk$M6zsETOyqFVЮ+.eN:x 'wL-kp*A âx()ZvIbۅT@q_< Nx1G(D23CmUS:.ֻxBˆk݋Tn[{M%q%*-AZzD9}3vhuFR靬uظ86Ə7PTXpY#2(Bxe::A`A##T<5Wd[J0huآXaE]lRYayk݋^ٳgKXlYܞRXRFÔ<ךDjhv(rS֘~A>xJ+ .6d)C ml{Kcc#G$rssZot:yٰaj/Ʋe,h0$YC}I 鹹| 1+1ZD`N9>ƊJk_;eum3F{$=5>|0i =!A4LM&;EaIICJSr;n7n'#3ۮG$6nOfތdV-Io|S3Rv**k g\26cV;c"$md/c[;n-^ O"z~ ,wJLױ$#0c6 XRTj22cdft>#xb 1aqߪdI8GC f[ ؞X_ݶu7vPT̬^oV Kέ^^!#>}NkxpM,9,TSɊ)"~@645r@ fEZzѲ$}JMZL2 _zbDe8*̄D&! @&(Y siTƾT*簣uUHrT 1j5]*+9y8:9O7YN6Y3"tdQK.Kˮb.'IĆF$&_ Rr|p+u :|6r!UUP[r /Ha:ʾڗ?^+L7S[X;6%-HyJhWS#[^~dOPkZ0Z:ddBޚw C-_O gOiԯ ,"ͬL0 "<|y[6X2Әl{3g >JkN7|PaPEs#urkLoAz#Rg$( > (AoKƼgGv4|Fu"9^_K#-oVLD,KkI "!!;=ýPCyu촿I-aϨְ0!9> PbBCe}]^{e;<F淸g6&6u&~ANV.ƬɢY[ɌȾUmjEcR_Tx198SJ,#:=x}'rB44{V g,[BJD%G)-;v?۝ > ̿$7k|>;8F6d/m'RX% ư0k̀Ơ"\%OQa޳y`^&$^L&Yş{ƏGTTph n>V'---`T!4{@cJf$F}adeܶrWDorʜMrEZE{!=[wƅZ0gʐ#2?$${5(r2eJpO*kyk}IR:jغ .j:[/FCR"# IW"{]W;K:VCDF$6ؔY}cbp -GZh+6,q7 Q`WI#;VεB$^YA{AgQVx|aRvl e[AdY^C;I\b,ˬ?ۄNu;fnҩ©skX?K @y_T>@ua$Лq#EзJּ0[ 1РIM~v+yh~c?gA?8|{1~:Q:K *i0&NgK~Q_wy@->yK9: F6?ʤpoC gc5E~Fka,afΝ hzc1|r" \!T`PG¢ zb~_atW#b6Cd z6(YD|i %)ZV>4W11XW,$]1rЁt Z[h%mQsz޽{xr[c>g:v'O9mHUr6… MI~@ Aq`6 b' G3mlA@&f ^ႠR56j=b>$y,"isDyU;>ɾ Io5::J<:>C|&"?I~^pbw`F I``ZyE<T>ִAtETA(2NݾYJaU)(\T>ְE<Й/l=>#/skkR'Q [f&wl0 ŋNi7AY7C۾zU^ Mn.NmރJE<fQ_IA_P~f6EK[ٟQ8O]idi;1dQStel_mp*쬥@QF5gE WЋK; =h%.onvZBrqp~;XGSج`jf|SY$E+i" χ,w}p \6…IY}3<<]duQFC1H ؂T|>42A<n I6ݞRb;Eah/$6T?cMCATgW[:$ In;:UĘ.CpV}!=b87MMm6&dIEx}~% "U^4Q㑍,I팤Au9\Z-m[ ӎLiC O!Fqx|0*?ׇ 3we{IT]ׇ tl$|= #ΦiE =.dIjVYj]Un \'&˲D9f /k%σafK:|.PB}",dYse5~l(ZI[.o$TkqVӝ,Qv0yw _Gyfm~Jbb-9995|QNqm3kS8;61aD]m"jcHS(6Ij&! P9YLK`~-dY|XOE1&0rLQSϞ:@FVǤ(nHsOH5mZ%GsFͿ$&3>XK\2DQCMa ,u0x,{yQj\|Tu nʉNlF#S-V$N$L7בB@DX[Ddl/~[7Ye7$Ib{D,A%hzAW,<һ<*J׷}%zU2?pW뀌Ow4b2(?S*Xek7r׃5K̼w--_B27nZ5SLг\p=4QxMM Ťgl=M^#FU<nj\0"D3ɣ,Fq>:OE)R'0)| V818;2d+18ƦPrp43ADC#G`Ƞ׋NcܐEgE~?e>p&$Sl╂c5nѩ QPVޯx , cƌ=.UƖm""Y4w YV ܗh?v3.a\'N>̄ho{MªDLiho[s~ MY* )V6J"" _ OBəc@ p.]$ CgNg V)Iz?/`;윧G_^^|oIq>L؇NS`bw{1_ٙ9KP2@HA(,\~o&OjW!C՞#8]/yͧ ''IH#퀰v~k q,d#OZkpH ㉶)/o`I9}#j21Si\  Ku]AY݄A ')DPB7kET 5c&Rv=0(E;@ҴT_IEE wmędhᒈ{W(188}Gp/ q?6DQDnnhyֶ+V`Ml>AV&X30 ݡb|N./φB`:v4|t'^54;hlŎʝH"KL,|ڔ,RV>//Фi70U (18čGi\ xmLY5) yO",ѳ21zГyp%y@_+y޽[Lⶩ Kɫ;v*eפ&Ş_x\Bƺٳ"{!AhcWaYn*{v1 Ȟ un,7Twk_ > >z:Ꮷ՘.y;W凫Wpu+XrI. K ooVg%3s~i(ef2m0LV}NKTe͝*ilyI=[7K:u**tK`MIDɃ#"'$Hqn^TNVT珷W0-9if-$'~76h&,Ga!BckˏK x~^|L:{D`ovM hflNQd_NCy͵GMƭBCş?}/=^F ?IJ *CVsݷwڳF0cϱXzיm3e `\ʏE\cQ ڢCnF-8ɑCG1LLva'].-_:\S) ErbW&cpRx4gy崍8LM8B WɸSXd.~S@>%PQ|ToPҥȾ'$#t努N|TQɣU*.{wּ5_$3?3cťǕW ȵ^{5Rjky'xt| -(^-3'*"tQ+Lsi?Dv6,Vyl,aaLtvGÎmc09vP;SH P'xa~~ymO=Od m>_4uOnF_8;PZ 㧶&?gœ}Ns >fz-S[ 4y,1H/vЪ; FE&CTƏ:N,ü&ZknLăyb\ ATNhJys?_OfaL&2(182IE13-'ϭ _+@ `,1+2Zщ gd9 ۚus?/ޓ%RʋJj6kT~.sİ(֞Qbp$ $c]͝%gCi>K>~~mY&M>̃]cw{ 4bcHZ8k@M46عyg؜WĜ1V;Ǭ(?S4kYtv0[S,F:Zaj%9g"3(Ca?{?tDiuGqc] /E,Qhj$x$F;LFH%ksD*ڛl,p8: m/kT"I $x|>kS)=07"zR[ZNgX9kjRh̩A]znvsVUyqwTO z|CH~n7yJ*8Bٶ-BrO0MM_OOl.t|մ]H#?Ą&% nU|TzܴoF(p;;e@c7u{ ݸMSbpZriiYjc@%g|d"DaJjJBFټg2A_ ljY> {:C<#ȾfQE fDL6v 'j~s`3{k!Vϝ=vv OFj2;}>mNKƸAw%8T^V}[68]mul.L½K hlmkZD*1 ǭXn$k(ǽ>쀹Ȉ^ݻ=RcO3#]c7Nxw}&,%tAotPcJ.*`iocF %@b@jdG;]Ur{9p p+ϔ1%ξ3eKT?d9`,`@ܚ@b@=z*ߍ\u'_>E̺ < #]wInj5.\OuPfEg9}/hUjn&&щVxds(ˋ_ r}Șacz0eZ) >Ǐ%3{,<3 nzPӱ&C+m jT #bp{1zG,#OK*Zŋ3zz+^8G^z %[daDQ@؈"*2?t<ΡB

 ~pbj۸y[7(AR8n8}$MD_Kkpof$ ĪȷבjCasu<2 |]Tqއ[eV[ݱRS\Jr8pTt})1p׵WtƸ|8|[T1C065\H4ZH6EE1y8Lg18IV'Q`#EQI݉< aq\*bOV m2[eTX}~gi%"2Rޭߋ=nƆ.ez$%7+#\+yPFf3n.æRVSj~*e():@% b|bx?,.@?/%h}񋵼Ut&յ96h&341Iju|^Q1& G󐍵Ήeڊ2PNKsyx%Cϛx iq5i<[DEy*H.d+z4 nT*u?FK i8xr0%6Wn4:]E[vb"1,B $+>A`E'e4CׄB*njAg ƥh\:| TZ._~^ўw)+O=fGT N%!~Kuw]Ԡ=H+arȕkH7<:gb5ϯW1]wVH|rcȈ>.E*+#GviTDzR] #~6i|ƾ?_S*{7|k ko(VM=Wi^0O/lPT.~]حc׋“{_yW'Zb] 61 =`~˶*QGTuAx^=~~Sav↡r 5V/~0YK"*Wwu\V,Ptu^2& YrձBZ]"zN-3{tH}O^|W4emLsriۣWG.*֪}e*u/E_#ܘe_sj>1 g>qT5!+6c|gc4X;{Bx??uR͆EcZјF=u uO5>ٸ},].8OM`ضY+2\}л 9.p=7Du/ỉVl,yVxTg3Ω] # N^Nh TFc?2`8vz9I49Ȁ1% Sbg gDŽٱ~YٟxDlsɧ`MfiG~Cvr9Í^j;r\wyTIx ٻ|g({5 s}lgcgo yș #u6-@RjR zN8b˿ 9:cW.ϝȹ'P.d6=?^̸%c+F<&*dEFu^_ddfs# mVڮ@x~s)$cm{k+MZ:V0j}lfW;ū[tӶͺ:(󭃌w2+绍0cSHcŻ#PG6'P|< өMPP0wqhVW)""rP#"""r#Y~=o ,cRֆ/L2)J_`*#^ǍaܸqԩSLs*pDDDDnk׮ŗ'tbjnlMd 2:L &yg6^{Y5jdt4@Hyk.ȣYzkF}b{/J*LDDė)v;&Mb[lgt+v%EMR7^gFU*pDDDDʛ۷{8r'e,FǺ&Gpcc`ڍ#""k*pDDDDʓRQ1T7:W݀#i/h۶ёDDD|ɜUMSO?K B 8c{IDDħqGa2ARnGs`St7:3:wߘ5k!ob8b%x+0|}et*x_݈@x xEr7%LG~N71Q6mKn:=F`F){n'P;0[7dt"&""""n{0kw$F1ُ>v'N#""b(8"""">hڴi?y~/ |BdMoɹ}K: z<`۶mv_*Avgt*gp;RsӪXZY53fivQ| Ώr2 xp'X`gXkw"jfΜitC!7nȡtl͆0{Χ""r33:5k`%(4{,65bkq7Mc&~5R)vŇr~*Se߼AkR|b hMPkZ 'o>6muEDD!mƫk| ǶSyd[M~ad Zu'Uk{xc w'vŃO o?{mMBٟ~S^]F,aφ9(x_AUkAWr ~*[SDDP#"""C0TlX*']Z呿f*ŇV){[S1(>SO<}#""7-8"""">bWqB F1\Z=AH9Ê˰2:/FҮE6_K:u$""3T]tas4BRKG)c_/r<ɘG<5*UKDDħ)G*PB ˟þp4FǺfE?}CX-jbQXXht,GDDDgҤImۖl+۸&a.v7/0:+>v ?> }߰YDDJ<Ow+"""rKJJG%77{G}棏>Iϓ?am <Hіq{ڔWҥK+yxi۶-o6;v40O8"""">j߾} 8;３^zw^Kرc9to@ɞ}~q$ g?' @VX5/)o*WLBBiiil6tB\\gΜ1(oԩSyWhٲ%o֯roxfV~z ]1T*#W> Wbܸqhx<̚5S\\̳>c=٬ ED3GIJJG!//WRWɓ,\y/`ͪUn5S=۰5R&ueǙpچf gPRe ̟t 00ֿ0V;ЦMz-:ut]EDD8""""`߾}Kt3sLV\IV&iUƪ"##YvѱDDD.GDDD2lB׮]=z4Æ Ӹ ⱪ5kҳgOUOQ#""";Ξ=[2.eغu+ TXhRJnVk-ZĚ5kJNr:FG _pݿZb-[4:vO<ĉiժF l޼]2fqcU 4pQMHK.q)iԨ/櫯b׮]4k֌I&QTTdt4R91˗/׸ʅoꫴnݚcMBܴҥƥ2i$vIF0`ƪDDLNVVtԉӥ*T`t4)'6lHrrƪDD̨R ,(jѢѤXUVo%""7 8"""rSشi;wf̘11={gt,\ڵk7fp DN:C=DΝP۶mӸ DJJ /*,,4:T t@ӦMYx11˖-yFG\TT۷o__u,]X""RΩΚ5k;F*9]JϞ={ԩ~;111>|h""RNƩSW^}v 1:ܤj׮Mbb"8p͛kJDD ).K5i҄+V'Jf͌&@~.jժK,1:#*pDDD\[z5ڵc:]J|j%>>{ҹsg DLL 2:*pDDD\:y$qqqݛիk\JʍZjȲe8p-ZX\ )W.>]¸TJJ M65:U۷%cU-[믿6:(8"""RnZmSO?YKIwXU.];4V%""I'NG>}_>;wdM+.U-_ӪDDGU\\\2.~z+hРDJE>}ضm ӦMe˖,^X""TOZbEɸ_WvIttѱDJcU]v%::R$%%q-MP{f֭+lْdcHS#"""[bmڴd\;0:ϸ0Vgv!==h""RFTa?^2.ՠAvޭq)ⱪCѲeK&NH~~DD2wԆ Xx1IIIԯ_h"B^Jƪ}]6mJbbѱDD2|rn6~i|IvɠA%RVէO~޽{&""@cǎG~hذaɸf3:HVF Yr%vmǓgt4"8"""r233Yf}ͅqf͚qF,YBRR+"7={eL'|Bf.;VtR{2J(""x,XR#"Wⱪڵks)KZуKWy233$;;AQQϟk+VRJԪUR$"TCi&vɎرs7ǏU넆SN]ڵiE-hݺ5:uRJ^ɹ{n:vHAA%{fΜ9ԬY+6mO>o>gXxGHHHʵٽ{7{aǮ=?psgNt_~Q&ѺUK6mJVh۶b1BVVɤ.cي<~[:xBa mb-LAaXB"0Td 0S 0w32p?O|fZӯO/nvsM;uh׮'NtyL:uطou#⣊h֬r}]~^?;;o+WbZ~q>0U9 ,`d SxqSpϸs?HQ!i߾={ **=z"2GDD0w\>7ukրقͶjDW~0Y[| U8nbJ1cЪU+C2x Ҳm6%~[4wc h+%|=ܯ_~bl6S\\Lll,+"rz-^|E222l:>_|?aKlM!/(]S1:ȵP#""mYD@63z݌pӻDTʄ?)((( <<.]ЩS'ڷoOJIx6mbƍl޼^|pΞ=e,bf`k< 8nbd'" o8pCe]wx\>OAq矦`K8bѣݺu}oߞ#H)sر'ҥ+ +Pq\f.{ёDD.GZ`WHOZH͕})ܘ@[B6lht,)#;w䮻r1l'bkv'LFGӁ}8~C? $$X""GHH[ IDATHJ`,FG*,} ~5۷7:%K;\ B ##q>E[j$ԩct$2lt'OG`׿0WEАTlJ^YrёD}DGEoZ+weٱcёDD~v\iӦFH ؚ 1:NsvXzm۶5:x1c0bt2)cv*6mjt$iJDDj͛7{ｗ h38eULG϶]щDK,XС~,5:aE0p< U[jt$=pDDD#p$ۓF1D)lSL1:\BO+5$uL08""%T\9s搶iQ \BdMoٷەu_&:_x'NGDôi؟~SRvl̾^[R="!!۷{m]GDD 0aؚKحz-[?Nu:9OA~e F܇?}"r? /ј+2:5 lw?aįjmk><^_[DZ##3Ǎѷ3YlX?Lbb"O64\ӧV FGQ&lƳnj֮]kt8"""WbƇaksp9LT |a `FGTTT}ݘ,6,XQDDT\Ή'ذn-~M4:O1ٰ4HFGdϝ. oW-bgP|x-= )2UƕH^qFލEDv\ƍUk]Gș7gN* zб O߿+%o؏w~0*|zd%?7f7q!OzyW.M6y}m)=WSS)F&t*{Npo2L v~1+  w-%vxwb^^?Ϫ5Z+eF`X]֬YS H?xwz}C\{>nL ,aEǖ!ݿE9TRT!"SB/ڭsLfpaOaaSpjU *pDDD.#Z88Εz݁Sp׹߂Bm.8cPn[QM=UHyR9 Yc.sP8KG3rO,իrm T\F-0e->)'S|db;v5z2 osw=3?v9iٲ_,">mqetew~; rAqfuemX&M4"""roOC9 :NplmF}|Sq={pΜM3pgQTz&?GΓ[0*J_6ޘ:&T%"sQV-]8;! GsQf 0aHDD2{Bw)G62fFGԾ}{ZiGŷq<?AAAFGRŝOon̝(< ּcBc2ؽ{7|ǜ9s.]}ݧA1 b 9vݛ(LDD׮r*:6L#Q?<4&MbKDHQQ&MW`ו>0T7:Wۄc3 2dȯ^rXb3f`…q=0rHwn@jî]HNN&55UVviӦMXT׮]1KTqL2g,&z>_vpחm|!|:wN_qIO@䃘u]X:?~1|T~rɓ̛73g?ЬY3FE_D:)++˗Jrr2'NZjՋhbbb -\w?ukl0cTs\#)m3vȣEJ7}}D9‹/ ̓ZUwI[?hԫW7^'::ڼy3̞=øq2do^D THvٺu+rJ<mڴ))lڵk'x<gСؚ`k%VMѡom NL֭N&">ԩS|Ljwa7:M9j5m""%T:}4+W$55SNAa>Cr1w\Kiai(L@CsOQw= (9ɀ&ѱcGCs$!!y\lN{b E?.o!}4nڜ'g᥶Cرc̙33fNdd$#G#,:]DJ .m۶JRR6ll6өS'bbb]6W|ry=-Z& zkknfu E+ZF c;v,ꃻDħ9-ZćdٲT~6u:W7ֺ0W/yGsd%; aؽХK2vY~=fbΜ9\.bbb9r$ EZceffjժcϝ;-BbTX^si-Z|rb6XފJL3p܂ sOS94Ÿ(DfvIX+-[NQak@X"nK^Ξ=λ˶mhڴ)?F"" !$"_(""1cC%,,=z0|"##YhgϞ%%% &T¸qغu+iii 0SRvm)**2:?HTPPuHMM%55͛7L.]x2d3:HIdd$SL᫯"11aÆQbEbccyi۶1E@*pDD䊥6K,!// ɓٳN56Xbcc9~8g>`ƌ4oޜ8FMժ^8KDP2qD7oNÆ y')((_ȑ#8pI-""ejb„ ㏬Yݻ /PvmJRRN"RFGDD.qaMRR)))ҼysիV"""7 L޽;SNe…̚5;３5kr}1f5jdtT)E*pDDnrlذ$-ZÇ o߾L>;CGQ+G\\O??W_%22q1|pBBB*"^*Pzz: ߟ*U0p@֭[ǽKJJ Nb޼y7N午jҤ &MРA{1jժE\\x<chM //+V̒%K8z(UVO>PF c50DEEŹs?>q 6x@Cs*pDDnPv"99TVZM6 >hv٬"""7PƍǸqصkf^o߾9X*"WI? ?>=kצe˖L6P>CN>MZZ'O{*oDDDnp-Z`;v>FM͚5y衇زeE*hH9vٺu+rJ<mڴa̘1Ю];L&QEDD@6Xbcc9~8g?dƌ4oޜ8|AÍ*"~)GΜ9s.oPF Ν˙3gHKKcҤIDFFKԪU &O?FyS111̟?itL *pDD|bͼtޝի3|pvE||&33UVDrr2Ν[n3p@*VhtLAX̝;O>W_}HƍǰaèPQEnjځ#"b l&MD퉈 ..'O2aHOO'66V午ƍ3i$IIIy<T^C1:MI;pDD ҥKINN&%%l4h@TT&Lo׷\"""bLTTQQQL>y1c Oݺu6l?07:MCHp:lܸdRSSٲe t֍'2x`5kftL_\2ƍcܸqڵYf1sLNJ߾}9r$w}7AAAFGiJDbƌ4oޜ8xUftTv\]6իW̟?nݺR&>>^午ܐj֬I||ld;wx^zs==˗/'//d^u&N(oAA"00e˖qyoNLL =̝;;v`2,] t{  l6mDbb"{E1p@ϟĉ;v,Re  XVVVL8'b ֯_ի4i,XKbR[ 8 ܔjjjصklٲ\<<<?~<+V`ƌZLAA K,aɒ%={իW[o1|p-ZwߍKnCB%M#997xI&ٳIJJbݻb֯_ϒ%KD  B_m۶ǣ>K.e߾}.QGVyy9vbǎl޼|<==7n~)ӧO  tS Y~=}V"::ŋsmR"DAnF$s/___.x9z͢ED  B7O<ɓ'9z(&M7ߟI&at:.%zУ//]XX7'Of͚5L4 K)  S||<lڴUV1o<;w.?.S:hG1 8q`sAö IDAT:t(O<'N$..DbRAA$ɘ3gs!//5kְj*VXA||[2Dzeˈ7  7-[FFF{%>>Ν˦M0 .S:#BsͦMHLLرc1j(y&NH||AA,ʊQF1j(|M[V^̙3 `… ,,ҥ uL&A(..f۶m$&&}vJ%+$$$d2˽klذh/;;\>{=ƍg*ANЙRRR//(--e,Z "-] р#Ez:Dbb";vرcd2Fĉ1c111.S,nL>ϓd "߄`0_Xj7nޞYfh"&L Nzр#B׹p۷ogǎTUU{L>I&!,] t+z///*++[| saڵ]X &t ;>cN8A޽{Yx1>>>.OE4y4 cǎر$1bӧOg֬Ye Bgj}\"i&Mŕ \o$%%zj֬YRd,YYfakkk9Ginٺu+jPOΌ3=z4vvv.Sz={0vW( 8"KdlٲB9sx衇8p+AO]]gǎlڴg"?~<3f !! K)=dߟ&IR>> T&p}D I~~>_5~)ϟ'&&E4X[,D4vzlڴ;vhaƌL81cƈo=ӬX ~׫~-Н|ȁꫯXf z3gr=0elll,]ps 8p+))!//vOi&~Gpssc„ L8SЁU {ǎ#>>aeeeA7;STlܸ;ww̓>Hxxn.kD ,11h/_fff|r&Mz+g޼yl߾"֯_ϒ%KD t&mmmYx8G&tg E}vRRRxo`РAZjK)$D" z3gRYY͛Vg6mҥK &,,^{ WWWVXAnn.G_gĉH.ZA.{{Z˂ t oBO/̅ ؾ};#Fo9<!!!dffZ*A'M*++ٰa+WDFFr]wq}~n駟2dHV-P*AQF/_Û4ڲe***ذaK.%00};ʧ~JIIͨQD t3aaaDb>@F M\]]Yd ǎ̙3̞=>P&Mիk> TRd|^iDAdgg`:hlD"LJ$ #F !! D"@Ղ ǻSO=d"--HK$!D 7z~GV^֭[qttdܹ,]8T*^^^7zݲe?)> -Y#&h J@ףV=+աh9_;;;DKT;99/P(| 6pS__6L3k,JA9]Wfﳱө1_^`N<ɓ'ڶ8 Tj~-4KhmmBhb p&-cΜ9̙3\K VZE\\t&{뭷8_Hk;lr2G$֗;JeXY|e7+)6vr~8]^5Wod`k :&Lzt Bi1L& UcףUŨPWAC[SAWFZd8)x\\\puqA+in[pSvq=|n R777l"$mVIOO'##sΑKvN.YY9PS}Fj {'lTbE[BP_NF.^] ]u9UF^66R| $4$@zEDDQQQq 8ATRTTDII EEES^^nn)+oSYQA488bTkaqpB*sBj忥 N:`cH#t-Jtuꆛ] ]mmF~F *ԕuM l2yEEEUUUOaa!L-++VVVFYi jUJ+ܱslP{g흱upnLrl/tp6l.~0=V,hA^SVD[B[DWB[ ]} ]mr4VVVyၧx{y鉇~~~曯/2տ@Kvu"D-[{w/aeݺu̜9-//}???Kz!77bϬ\ (,GSWk~^Hݱu@N95<&zm÷nԫ˨*1 W.G.G.qʒF=5]\%0?_&00??? 7oB[|9z|}})++k/}Qe˖/((ȑ#?~cOt88;oξ(|q joU]U UiΣ*@]AuQ: zNˠ 0x]J 8p{jaa!PXX5ըw+r7l\9bꃝW_]|=G| 85Jj+ NYDmeaY6סQ,h|_ 576h@AhIee%n29I~^NS>]qpA_Cw1穽y*AKX,Ze WR_UDMEa![[;| %<,ԜyW^79B+|,I[ϗȌ3JXYYa0H$ k7na{8k !q #4{XhС*եTP]CuY6uԔf./4oa''$4H"wDFFf%:7ֽm߾{j]ҀrZ-TVVR]SbC7cg ბʜ,D7р2/f,U%HiSHHH >>R7Nh$;;Ν;Gff&Ν'y.P_0WPImMN(zaWN!8z+G` t muɅ˷ hʳ.@eQ& ԖаpCLL }!44}bgggEh|̙3$%%s$m8y A׀s:yrͧe~ %Y ?)T,d4 <"~}b'>>"˯=qÉ|ֵٰa} 3~;Y K.{' Ol0e?V8ia89uj݀S^^αcHJJĉIN!#= DW~Q8K@ ~{قpթP槡;KUA:UgQQU}a'"}0 Aҥ8t䷣I9?A}q 7B%TM"499MEn2ںjNX°aÈw0o9DulVZW_%7' HI;ptyBhk}G_e̙r-tݧ$8z4#G3X~Ѹٿ7.Q؉^ATO*?9'*i=?0!4(^4\'@rr2sH$p[P?\BgAH&u*OS}e)Fue R-5bÇgĈXC Xȷ3Ll߾?7'"w׈[pK't##: ScEPtX4L&Ξ=ݻٽgnXk n{@d Ϯ.Q Ш(p Ǩpuرc;v,}&7d2q)~g~޾RS ϰA3b(CsttpRgRqsG8wd=>~7['OfZnC ".|׼tbF9q AgޔYTd&uJGjc|x{{_碌h4r)vͯw{^*˰sp;jCEc ͺQaSP_՝1cF3~XƌCll,7nرm۶uvJKxz}~z,2ÏUJ\Wx{X+B*OQԽV]™qq̘۬1O1833u8}6R#w'+V .=}A񙝔`'g5v[GvVD FϷ}dä:j>;e d֑S&z /7|Ill<[6dff?8VV6oxG5xCLFy)CAa#vm[QXXYv9ŃAOhqATSpjy?++ ӦMcႻ:ujZ ܼn|S+?IHez-AF5NNR"'R *w͛ŋիKlNOVgnlerͤװ9/"7!m6^nr=G#C ty&Mv_@"t|K$tcU􏔞^|֎پƍ+]+r'Af=ZZЅqǷ*0덣=|3sts81s΅bvee(tOFM$j3f!fϞݞn]FT)^H[r РNYL%N7h˞ wy'VVݳ׳7AZڵk{ R\NhH궕],wq_~925րSWW'|»-'// ]L1v7P~^Hi"gT2q=F]Kc%i54lzv8 &>- x5F?s~$?<888XO~zyB_u{0Ix'P|aƛ.K3a21. 2+k2r{q7_xp>c Ko!"7oɏs){EB:.z{yG`c_qۛI%n| F._?Ckq O?Kuv3D^ÙN ¶LcS7#֏_ +k+8 r&RSC7I|bstޝg~wNp|&B}u+?E*s7:_s9['``+F^Ibe-.k3|0f>ժl>_2 Z P"G7hT#SxEب ? 6ͳcT ݃ɨǽʉ_1p~kwH? +)UIRb#8p y42N&m[^G2p l-w.O7v$VxF e袷3꯹m?[d2r8}VX>TTRcńnB'Yr־:xiM Y?*>hHb4Y}CncֶԜ@˟%i ?h3|0qs_+rxbn_c=}5 E|reO7o"ߺJW۫oϬO!sx%yɽóZs<\#/|ǫ>2v1}W~t/VHK=ܕСC=~-#;~O}>cБV.ܞ~'c[m?&R~^ɭmF=x ZF,#SEȈ?"r_j4̀;#l-8c^ {b Q#֩I>g'o[cyZjyk+=woAܼW=.XϑOg ZFWLj? QH7ʐ{$fʣ\[Z=Oh[<ϏF&gOJui6yUauʢ&Wji]?x, ~d?X[2οJo931묈/à0r'xIMY.>~#o[dA7/$0;ߞk` Vsm5D|^aΝ;Su=vvGn}Yhks3ЖYjr0D0#t5?>;UQ&A}rEUQUg7=2q}ۭ^Ֆry_;ＳS%M7o]h4א{ӜEK&Ӳ9=j~{+Qߡ]рKa3ܹ[n7?3˞'z;K9Xr~Z}m9IPxrs{>zu/R]ŐEo8p R{ <.>/U4o'fLC^`FN_q mwbЂ|b]97э-Sqi ]vú9aE;MvmulKhkmM@*s5~B]ӌ/!6z.>-?R#QѳyǷ5zmҺ.ao0V2G\/6"8œ80Ih\m5Df>E@$~毝:ϳ ups'ړ׳6:siKֶv'L&#?hVk]/7/Ghc^ˑO]lIGTwyA u/jG7o"ߺZW[rr2EԞh+JvڪecgpnMpj5&GiѕRpzube3|0'/Š#HùeՕCYmx,``BZK;Sۛ#|PN$玘ǫ]ںR}kP?ʼ}֍M?i7ҖyUɆ_wpEޮiznZ~4_GmearDbE`ܔFۻxMT73v_mT#(--yܹ;{'yt'ڛ׳6:sim mY?}+s{|M(V6W̿};yS\KEisz{gK:"wG ɧ),켬&Z|L[|KMMנ6;/Z8늬jZSk]EKש(..d2ߨz 9g߻ 3VTf{g/sЕle}1s{װAԄ 8-էUy99G<mmUM[vmeգӨ/q֍s%}H$HSu>^ZgkiqsMuh|+k[FӿmzS]?)(--^c/oNW͊n͹ڒm]?}='u'n8(uOeVsl/SxR[YH]U)rv9]޿;k__k<}D|Zm"DuȷjlleɷEs]Uߵ-cGwǚ4 ;=2ӋLstmhsGXvR,ޯh-AVNnͪM݂%F/ lEgpfrv7wN_Ԯ;(NEo2bnC1[>W`ak߻ڲvme-C*sBQT7iĹ[аΜpmض@I!llDDt^pT9WWbiںOtVV\k\g?mڶ^$i݋l[IOrfr"o<::`25٫Q5rwh]}:2;*p+kk:m"DuD #|Xh55:h]U~<<{qFYOW59Ɔ?<g7Dz%d؝TdaU?lLFǿ;uEAN;޸AWO_.6~PUP~ 4GèZ%]z^me2(Ikd0: zm]_^7ۚ<83[Zγ.uɽt*%u9Œ~[ AOI&hhߥ۲m5N[$7X|bd2ٵ_NӧOGșMv<-imuӚڳ~mIx䟖sw&24e&527B܂ae34)=w/k{6>"4i;VQv(|"/70>B9oSu/O.;:e1vފ7lxqȌ캱sr'z4h-Kk2|G6}ۖ7 0 :\cp$o31^4Y#}CM]ۖ4h5-44y3/JNm|)V['6\"Ҳ~ڶFBGg#]}jI سb!ؾ~u|>gXQwu<۫m3 h}V F[s%G>ϙclj~|v(u9>ȑ$0FϰA{W]ͭϖ2ܾz{U[޿+C?AulذYf]E@7o":%wqS_!>K'/t|V~_lZs|cyv1}g3tl~~8cEk)kZl(,,dwt3ߌ'ͧ  : goҿ6tM&_y뭷T Bgt3ö/\?e~ uueYvSL:7F'Ͳ,o'MXJSOa>Ϲ]8}dKc9iK|}}ٿoo:i[g_?ګL3t?<՟ķ^]x lܸST,!%hkU],F='҃&B/֪س|86:DƯ_pf|:UpLs,v 769S_]O?O?M珈ߒ3gr>#U쐆b5 4jNoz!W[:Sr-"A)غe3;̗8#1^x/^|^'%ht,¯-HpA`2)< |Eo?"cd8{,|[ADLXBĸ],] ڙ5~>Uil "???K o@[v)NF"gSl钄d4WϐJ^|E^~kc\MMM ׯơx7p*A3;ki]~WZ z! uy!CczɎ|_|IVG) "cAߓshEi=ߏBtyW%MIi38à{!t\K$ Tdg_xթiΕٰaoIG9; i B+;c)8m]5},̙C޽-]bT*֭[ka=;8h&ϸ ݗFIo?}hgvcmfpnVyv|"MKzrUWW3ϰrJr n.K膌=ɛ &:/Ӏs|6m_E1QDƫp1R BQSv}8(VV;gƌ3 t׳f:v;>&o~&`cAH&AљJԩSY.MqPopm޽w=qb>&}&~w*:+/SO=18;J*[k.vT =r>1c!{UU8e/i(<Ѡ'4< 0apvvt]"33lٺiw߆#ũiTIى7wO&OȴiS9sf;|͐ozyɊi&zC-]`!vp꿯Rr.YIddd{&չ 8W\\̞={ؽ{7;MZJ2H$%x !q[p MN__KE)/qSd$w Əe̘1;lmm-wf۶mg2R{0"1ҥ p% J3S~#e'cmmð#rd&OL\\\>}3|&ϷJ~m_Ԟq5.M]=YKgeiWvmo>:ѤcMJ++kP hh ihԑʺ &8Y':Ne^*F+ ǰa5jCcHOMd2kP<Ç9 1Ƅ t!FM$J3Sv0ePNF\\V~)eO%jR| 4!sTq<':thGL޲ 8)(( ))$M#?o\bp[Pd  W-Bkt3Q槠KA 4LF#N gA7bbb7V9y$g}*e6R\|q5h\ "rU:@me9*.$w^޾ Q9r$ B&7Az"N?UNsT4u8{덓oo\Qd<uTE*HE0 Ui.v2{""{ӯo4}!::boFd;ɓ'9qǏ@YYw05ξ(|ŘЌ T(SIF{UW"H !n`,W',"|OfڵYܜ," r'f_J;̯1s k>SNE*vl{FNsF#YYYLJJ 'O'JMk-^ȽB{ş^sRBd֡.B]LjJ/PSzlzDF&_C#MLL 111ܐ:9y$'O?q $VV8{w8~Q(|QF(>l74^< Q8tjUӗAq߿y&f9&Ç߲ oo hwiaOnϔfDjkGBBw͟nj3;}ߞۀs5dffrys ?K srѫ2{ ^p1))ϥ|jr,2uiaaDFhbpVܹsNzz:gΦnF[jgo8=qx3X\mPL&jETfQ]p+ˡ,8m4H$;*ֻ7DFF$NaD 7o=dlݺ?mი&W"@Q,%)M?@I~ꪕ1}j ?+mtc6\M]]a'33,rss'''Ғ"F#V6(}b⏃r@dN8"sDBnѨѨJШʨ,D*<ڊ4yԔQSYA@"C`@ AҫW/BCC 5'ͬ4RSS9wYYYd^…,Jh0ɝq ={`<{a⃃.>8bmko%ntozƲ-ER%T\/mf8 L_4O 4ft?A2g^*m\D(*6F?|N{!惿yPQ>I90W}I_1ڝm~_k>SIX,}.//z\\\{}Nz}f/5Tݤ41MtkQMLQvUmZZɐ53fɄ4OCi@LȤ1i8" ɢ1y4&FpvPTVCӡV-d{{{Ut:V\(zr~~)rv~ Z=Eը9-LQoeOHnamLl[ [.2ƛ9YM}/-`=ɐ|2$ W4M*ѻUnooov١Ւ&^&MEQ3>}ӧOXٴ\:pv?yut6߯Y yΞ1'@O_l>YD>Yv|]7hit67nWKasssm[o77Cuqq6[gi5DM}.FK}65wk0nardBI*ωGģ Z#~&C>|Ƚ{PU" *sFA2 0 ?| Ð,M ;}nZ(Qs(Zz๥Tr\WQ4ը˲Y{m fEwg2D"O)7':Iļg9$*EF,|y>e1+ϲ!$)lYϱSm ulƶmǡh`/:849!>xł`yxc|_m{>㱇q4yT@3k5E3Pr;t+ QQx |DEQUi֩(oQdSo'w"8K#泜b<)$yB2xYD>74$_fVkT5ץhhj6pۮ뾱h4hض :oB|7y=99\3VV մЪ.B1j^s˶k=_8ŋk]q@N()y2H,fNH!`H%kgU:[e?:`=}64T/EI| ,#"8&MS|'s MS8f29lFbN$Iy\'i-e] (b("+(]uO4W)z8˛k8v}YW^imTULq4MuU}˲d!>EQQ$ x<&I)$ q\>]>Wq 'dY|>}Unɋ yEU7 àʄ( .dݪae#(#]4M,qLӤ^c6ib6ea&+syqI|&y=j(V(&D=(HӔ|NW`گYuTCjsٶuFeQ[8vujiW=_ P7$pB!B!n)ȅB!B!n8I!B!BpB!B!T$B!B![1@'yIENDB`gplearn-0.4.2/doc/images/gp_ops_crossover.png000066400000000000000000001026531423420364700212750ustar00rootroot00000000000000PNG  IHDR`bKGD pHYs  tIME q IDATxwxTևsd&@( RI  i" T("6*EAwBRHLd҆dM䜳^I(@ ,@ @ਨE<_$&&Err26 WWW#VPPF+eʔ#8fJ*%*GUAn͛7iB083HR/* XL`Lջ-VYúhՆ *R\9QA&1 \~ .p!\۷pssC'l\\z*UPfMVիWGJQ@csuΟ=͑c9r nI N ?0ģthVX2*U!9Ş={;ܻF_EUBI?@%1^Z^2'2rH< $$aÆm6yWx3?NSQl6{eݺu9s~y$*X &Nb;piwADh1zu KQ4iFToe? R UAfIY3+*$Y%.gp敜:k.5k.saٲezłfT**Xضm<`KT@E&džM[ *lVPj$Y5>omID>|͛7.ĪYbFh.Fs3q  %NbTPO?VbhZBCC8q"TXQ@ vE7P*tĽx9Mtl8cHܦ1 ޔfRD a!VU*U3NC9q{FqY*Wv@nݺ&!!W^yq1zhahۦ5mF%ٽ[ڵk'%jgL[iQYQHZL<浉=|HKP`8s zbƍ96/2,MjVkc2h֬QQQxyy 9ِєq_j+*y#T%Ӎ]<\kbxsQ] _sTKNd>`ԨQYN믿J:Dtd UIr4?t`LY~b`br>M (6jhrzVgT/NLP`l9>OG>V ٜk޼90qŐybCS Ț,mfMlA!!V/ZkY)1XĖ5,X\HP,F,gQ,FS2si PR8w0@pZhj%44۷ogPBHHCRZMXX߷{>fC `{\ ʺ4a}x4ذD^Nfq[J4Մ%"(,'qPyU$dFb`bwmI,-!f#nH>%{i[b8Γ-b\-`a&Ũ]vQR,gCZ-*w%$$RH FQTXi'|۷ooo޻ϏǏ 7_ƩKY%]%F~I݇_c{TZ~$am&]jdyD s%VMF$GV*"iQi\]^yI;dh;hts7[)4^ T^6DbwulѨ<˂;"Дnb686+&x1tԉye)*Iaaa8q>LƍX,=zc6\2EeϞ=K"EZjrNF*.ƣ,` ?% iєoEmEіoLH^ .ŐE19Rdrbm}[n X\)b`VG5o I5E{0p`Aj_^^'IDDDyfNl'(QBa@qgL:a/;+a|BL#Kٿ Œ*Ř1cj5:0PYnKbZj!TX-|ѽ{ƒo$b5e}y";|'Kxx8qqqiӆ[n9z{Ҽysp0@4nܘa⺇إ%G:ÙIX܆} BUU8;;^6.Jsa =qΙ=d[B1?C;s6"1.5cADs)֮]k֭[j899e{If.]J׮]wQQQv% Fŋ9m%g|[@CRsIF8=U];q4k0"87od냸y7=pI j:%QLRH =*5kv**Y x #X~=m۶G/^WWWz}=U)I$aHII!)){fΞ=˨Q0ad 2`N:Mo'wE셤uER#6*S Jj""ƛLsd!VY%%%{l2~#8p- @^犂9KX#o ݺwPn]C <|Eh"pssN:T^ *>>,XDƌ#*R %~.]ԩSvwp?,؇Ѥ(6Z'N:JtiT'8!V+(]4v ooҨQ#QA&9}4CٳvdŅp+&*T aF#DEE忟}-ZdKjՊ7QA3rHk 0?G>O̪ҵkWK~}ڵk}AQ\\\r ʕvٳٿ\ 2e iӦeZ%J 88XP 2hΝ;9"T:tuD  60|;vDCQA/PXp!}Yu0oM&Mrz111-[Xj J*q֭ ۷//_,d?//[e4)Rxzz bUYnܸAƍѽ>s^|Eڷo/*Y xǣ( 3fkmۖI&ѢE Q$$$PT)r46.[7n0eQB 2K~0a5kQ)))TPA,Aʕsm H&M8whN+VEDչzdGYu"##~z Ugggz͜9sDE {u:UVeŢ;HNNf?^{5QȬ:5bڴi˹v5jpQ~r,*] "#GVZ[v+ryq4#2@||<*T_|!*\ >3N8'=z4-.dPXt)lFdV_~SF\hˋhE -7nܠk׮y6Ґ'111v)F @߿bťK(SxDdV'OB-X׋jʕ+~...X 2ATTwu Vٺu+ӧOj>өS'/_NѢE~,YhT*0q]ƏϪUʕڵk_ ~yv^xcǎ" `j>r% C U'''&N&M0u|_|A>}Axx8K,nݺyz?.]*HGQvʼyޣGfǎB޽{ bŊrW^yhF0@?b|K. X@Pd&MSN*}|__вeKa rm:t/d@?rcfy뭷Xn]1)8gϦM6h|}Mpa):vȧ~o … BnvM"E} 67 ?C<ˑ#G3f0@DDDбc|I& Z|0h42m4;v,fbk֬a„ \x>}+ '(T\rJŨQؾ};OηŏO3w\uƍY&5kb`~'|B߾})_CX,jVX믿NTT`瞨(z=6W5));ȁŊvY`pu+:wLڵINNZl6nܸAϞ=r VUdWX-b53g˶m홮^Jٲeҥ ~6Dxx0@az1b 4Aׯ9r$D1 GPv?____f͚o5"555TBl˷gX" 67n/_v۱c==4#~0C>g˖-9{,zI#+6鉑+ry<==z-fϞ1uF'Ԣ 2(X֌nܸABBfݩU^^^T*RRR04k֌cǎ9lپ+F# , 0ٌhdV׷4իWGVRP1PP0fC\Bbb"(>'2,B.]زe×^ƍ-[EQJbb"/]"6.T*+[UeT|P ?X,\BBB&S*NN:\]Y&%KBeZ-rU4Ck̘1z} j41\tPx{f͚t:T**!Vs7o`nJjj*,YecccIMMeÆ ܿU=zl6;Ctԉ[7u"%LzE+#SIXbxo% @/1pڴi#nnڵk9v,SD )Y$ʕCenoDDDIhh(6mӦMtiS7n;>p)))xxx8l9jQǯ{vR5?QW W%dOF؟p2G?uu.H#ܺuKqFZ-mۖ *`X2zisdY6>}}qA|||:t(={t9aY2 W L#$',(lVPHO($z2Rb0>H]R0GK ʋ$ű}vV\իWe˖4i҄%J<1a>wQQQ?~ÇDJxҥ Ŋsrfo‚?ۨ+4åJ[4^Y6+(GNxe$I d-0 7cub! L~*,Y`#諼xGX}Zq3P?R/veW}s={uq/Z QRN"l %jS2w|u7JT;gțoӺM[јXdt˗3gl6ތ$ߋ IDAThXjӧOgĉL<9˨( vcnkvGrFt9ҪTRH )ٹ} /PO S h4ҿnG}D^.BvƍL>.]?76&Fsd5;$oI"=hpla2x!^.3Tn&rBBHtc @SČ AA4m K8z𶫜2ŀTkСCXͼx$7xs1fjժ+Șzaϟϴi0`@588pŭu_Ev-;.wi &4rGNݻ7aaa9uhrמV/_RhQnݚqU^Dd7p1c#OI%ɿϥS-g6m; c„|ZfO+௒ 63hONccGcԾm N$Uћ<˦e$%Er~5Ueʗ+'2QiGիWVXA5lH1$uZKWJV³"XZSԷ>YH^nϜh^ַ݆9䯊 $/!fⲳ hOttLsݯkq+QKQtT>|3ry6 s1\@sP5(+Wh֬7oη9$qa̙Cppp l6wHo\L|[̇rgʔτفԭ[s=WާObcc3gNLe>ۇJʕ4kڔ }b_tDά]=ݻw$P\%t}֣r+{ߒ;WlP!߬9[ϟOuȫXth|zWٳmۖ+B֭1c^^^$''mr\iKM(T9;"jԨA:u/Fw&OLΝ\2P*Aރ 7 p^|ܹs d;ۿ?MBCB(S&-Ok+r~;h^_XueK J~ؾ};zN0[Vi޼yeWGzSɕ@[,; = Vʕ+3tPwgkf:v|@֭std\+&J9H#$/s]vmRD fΜɥKfU&dgY@fkE^ ݫo1bex&w 6+N~hB&M?:TZT^3bDo>11,6W|0!ŋ ;v,oڵl6;sf7oN˖-ԩS\׵k9ǥVUӎ~fы HHH(Xўב7???Ynb-qԿ/n]Grrw 9#4k,ۗuSQ,SNbgXƍٳ'UTj4[( ~!{Vp7ҕ"ݿ*uHjl\|B /,~Ujsѣf|M޽ulڴI8?pq6PiBKRڢB~١ "{Neİ!x_-bҶ76q0Yjի&ĉS[2cƌ[a02do"̻*_r1YLUt:ڞDFF O,;~!$&LU2e … iݺ5zI cҤI\Sfg?3k,߿}Ȉ#V٧Mb\ʾX_ g z^z%~f3f͢aÆ$&&r!wNcҤI G}~}h9Dܒ)o}_2. fH׷EmDU<U>:xP4*Vj׮ m6M4شoߞQFe:{YfY1.usgC**899QdL%>#7oN=hٲ%Æ {5kԨ1kѣO>/^[sN+Fӧ={\2ePxqΟ?owׯY[\w&P i})M,yqlj??Fqujj*'Onݺz~Q̟!恦D\䚭9kI8Btt}q8kBs!6*8UMF̴ՂkYlu$U={@V$;99yf֬YÉ'*Pٳg~-!!! :;vjoZiԨYǍM+[R3KH3fw|@(x"ƌ69HXco̲bC]"KW@)(ٽ^nl2ZnMjj:K,s^z%ׯϕ+WbFvؼy3֭# Պ7fk׮eY, ԩSv?聤o_BƷD_%~m?$a_è3Iݙ0}ڱ^gΜ9L&nܸqpvNF~^O-X|yO~NjN(vM[Q0 c*vk4zLd|ôSMoVYjy®rY N>/ؙ`I!>%5Ca5|೴QWI4 si(d>%R9~A*V]F"eKÐ!CTR -::cǎQF ʗ/$I(ByzYVԩCbbb6>>>mBILh|룫Eň-$g)+}[[OB߾>|ӧ3)]tn22F<)jWIyt(B% !""ŋ( NNNHfѴilcPd8FҾO7?%Дi%< T=𿲽t@ܚWyL)Q!ժUTW*U1c)))Oh4rQ\T=Pnm I96*RmX\YS#Σo0kwcިt#BLM%9).Z7Q)Ű? j'dXyH8KԕfX X3Rӱd^u(VXXMF0i$E!..o/Ĭd<==Z-6O7k5Lw"iF/X[- ]K`s"5^8dAYZؤ( $%%=ֆ Py?`׎i ))YKfCR۱ Z$MO]2;ꉺd]_xn=*%%U4G [Ⱥd%c4x"uTUVnݺ13fd+Xj+>ytָ؞\)Iin5dGъw ቏ذa?jCRR*T믿6DUT)Cxf.`|\t ???nܸARPTQ3^%JŊԩSkwwwNZl)Ѹ9/d6.|||&?@U>HezLFM&=<8*Rގ Kb8ne\FV,($;v%P@VQd!}B>hvJ"IOrCM/fau;Kb`00zhF9}4E!%%wr} Z)S~z\\\ jǏh]\W֑m6A[9kYj$US቏ѣAӥK-Gnؾ};M6͔)'|… 6l˗/g٨T*6lȮ]t ^J\\xzz2{{Z/JO͚59uf%S U*wiSeI%J Y]H9 @v-ҠЊčC* ׶r/9.Ke̷bن2;8ܼyŋ3o޼'Ft: 6䭷b(Rԯiʽ,vz#1=S .bX ?7|KUnF,-k^ވk;;9$,Qj֓A>I.}~AW'Q/%(6Y_HZdKYU^J!E~E:vb>.4O".uQ;-WOrKQcȬ 0\ӹ7f///bcc[nT 6dŊYqJEó2ڶm˱cǨ\]eQ=Վ`(I_1tO?'|¼y@t|WۅO*ՑG1'ny:>lUf~!ɮgΜ\Ɂ2>Yv;}SD\`T|:e|9~~~(g-`$}vƏoPxDL'my22音ZN5?gӼEK"EdyE=[;{|˔+Wn ;{p/g?$VZV̙Ë/3syb~ʹs0 ^B`?{o!'ۭ%o lA-[͛/d|VX1֮]K||<{9E22)'s`c_ү. ?gxFPidAv*IGe<|a&t:֯_O͚5yw}=OOO}ѫg<\01ߴbop ;3fvMd֬Y9r͚ͦߑ?;8b1+(Rxqf̘ŋwޅnT{j|&qɮs'f9mL_XDL̒,,mW(t[a[`VWK.|9z\=믿~zr:nxxoed:t$<0㴭c赣( ~-gǎfJ7.Gco{ul_6[8ؿ?жm[Ȇ?X,V(Mt/p*b[Y9wO|sT}w|0{5n];BQyF$͠ЪtI|򄆆2h ~|}O?ӧOs…̘>#fi$YhN%f?G7|#/g'd͚5Ջv8@X,:vH*UrmZuc1 kYmy%N/B_G;(e*Ȝ$c:3>͛9m|gp~5ZŔou( ˻aB l\\\ ü?t\~ݻϖ-[r^GȖEpiݛIS`n\ƍ ϳDEŋݳߟ{1yddY{R$ӧӧOoθqruxir4 ;c{IGC| *'bv}B3]/ދĬ-9TyTZl,Ɉf 7o tY[n\pǂShᕤa ZvS\EϞ=E*źux0`]tH"9$ JEpp0?wa߾},Y2ٳu&M KzԵMdX͘C?gP||||ҡCJ,j͘srYFRÊ+x뭷O)))tЎ#Ը6ڻj>)Íx\1b ѣ> ^p٭D |@VaK 'r$amԮ];;Xa7֦-d_GdRQDzt]_ƚF&jPUV1p@:w$!2ׯ_g„ $''sA*U/x@vޏKi8U6/)Ad5ӋI:2Ҿ{neV'cǎYfX,Ó'O2sLYr%:t7111nێ78u9%lR~O̘9ʗ q:t.3]-Ĝ–A"E_d4i4_0bg\ZGW5QB^D➉xq~hheX-4i҄zV\ѣG)[,-Z_jժ頚.JU*/rq;@NݻMX7E.W}Vh}^@r)6sZf"ig>*q`}[XZTkҾcÔ)SakA֭[ٶmϏf͚Qn]*U,WJݺu ;FPP%Kst҅^x} 6+7qN2-Д} MH:GukM,gH~ѷC=ةseK.t֍ڵXrzT ]5E]fB%lb`1w7IWгw M:۱u&V]FsVh}#{IKج %c#Xnz޿7]r%UbLz9sh?z(˗/ȑ#QD )RhX,X,&66___uF)_C?))5kVv*BolV)E8V ͆R1'jt7g-U&_?zzoݶmCŋ-ZT44bi&6lٳgOOO+k"jCLL xzz /ЫW/zdxoH2$J`zcZ-ia 97K$Φ0xh`tU4 A( ]te˖;;wV<Ơ&Aƨ-MI;d2AgIb%hӮ럧C`׵Z/h!ISE=P[qDnUc\Ūy5W_[͛ԯ_3gΈBfш{~wVkFfA5HNNl6#r:mٞ(Zfiӆjժ=^eUƂݻwQnG48AI7EQYMHOy#&&%J`02Dx=]1jm۶f-[& cA50aJw IDATtlT/j`HAklTQXGǣo; ><}^`4)UvxўLQiР[nWyz=$V ܰtg{V^tsy7ooF:uDd9eu֡h2|0tܙh4M6eʔ)'˾(JFQw.uONN:Z]1}W-Z_ ޳4IaXVʂ h֬hH*U8z(u O?e(Н;w2gQl6Ӽys)*bŊoڵkEwþ}hٲh y#ݻtؑ~MT@kիǦM*2e0a $*$bV%$$/iӦJb5иqcN|hp 55:pE-RIбcGBCCiҤ " 44vqmRYPԩ'Nǂ Ӻu2kܹRiiie@*jժUk!5D,]I˹sݻy8qDդIte-͛Z*Ѵnݚ#GXjeŋ0a( TWrMf,w%<<oors޽L:2pD\\ 599#G~ YL8p$<<"/dȐ![ĂJCnݺrSTb08xeDYfDDD`mmm,Z5kXje 5k Lƍ73g%=Ҵir{L<2 @ЦMqtt Q9u;wd֬Yj8r .}accÅ Xx1˗/  ݻwg޼yRwi۶-nݲL?7n`РA$&&Z A8to޳UsĦM:u%?7oٳ32T Rg͚Ev,"}%&&2xxxУG Y5',_UVqURiהP(3lٲLƸq,nj3طo_VZ 8GZ&Gpp0 jccHmccc63gΤk׮j.de֭9sB `ccC1"^+teĉckkK̬ж^^-f;w?qF,aL`ҥK ˄@8y$&L &&ˆj>}P("JET2mڴjO>L4 [[[pDӦMQZQXx1k׮0=z@RRtc0ӧ "??ZJ*9X׮]K6mR Ə'=z_;w45nܘw}www9{e^:tI&q޽ mGnh4j={PA~~>*H*m'Ofxzzg!a+̙É'"~g>>>0yG]I&1}t,ʔ222,;tZ-G%>>O? o׎;`͚5pu˄UBDEEyÆ L2k׮>(|O}@V^9 z곡Cbeee޽;[nE$%%.Za.]1$$DEQ\t8x`iN}}}E1cƈ/;V*6k׮-T*`0kE ,(oY/ x kӗ_~:qY&!##C_~EEQܾ}جY3jc޽YÆ _yuS)!!y J*brr(صkWJ*j͚0@DL&S1bٵq̘1E ~Rǎ;m;CBBD;;;CDTjÂrMȑ#~nٲSӧe*LRx>|dvm\fM9ۺu 0ZY3::ZT*bf ۻw^ Y-+՛7o]; * &sf"rӼysfA!;;)ݵ}vi_Ϟ=e˖IDHLL|Jov)l ;֭+ͺJ4l>S5lؐ۷oU;h޼y?틊W_}~РAO%|28YP. -5NbРAfӾ'i 552i-z>}]:];;;z]K.(J K{-Y\\˖-ld5++g͒XKR .nݺЦMjԨa h4>n,(dUpӫ`.4ֶ۞Hhh(VbÆ 3$$={+ !>ZNC*T*qQ5] %Ăg4"oѧy9jDFi3[ޞwy3fZ{7pObB{6pNW郈p"ÉK|\,8+Ѫs>d{xE4hА*UTxۯ^Jxx8+V`ݺusXr%3{72C<<O|wg&M^&YI> 7ʫYb%666W0iDlZK.6Uc0hKOΑI!Crժ +W6 Ϧ=sFtx,˿ |4yD'w(),[zGʏ(֛:T4>6!E֮]K^*؝8~F+8R+Cj2퓁x>9$<#SJ/mu>'Ofݯ?5 ;^ }  h2Per菤^ȀYju*WDDDJ~d`S5ڎ+ֶX:>6ｱV&/DNFڕiӖ~^k-sH>Cyk׮e…jՊѣGJzdq!P*rJDQdٲelٲ\SrA) c=R+#?qJ͗ A&|fddp0mQwZVhЗZ= +\j)ڵkgafP*ö2|>rק{fYTiNOp^E4<] !}_PUs '//}ʪ43=F#[̥iE XTNmoRc|BZoYďma_?zAWz R5Tlf6>}Y}BfϞ֭[Dח ZYYtROXXe>q)))tڕ_jKA8N~XdI\޽CO ^[ɬ>|HX5kV. Qӱ|r&OA׏azD*'?#shR:{vǎ{ 1hV>$2+bԪq8x/]t)ɿFÑ;'R5h, TRd B}:c/n W=CrЧYi~vh(S F/zѶ^UlT.7χҹ[mFį2KG9yvNg( F ~Vo~ISѩoo$Rt</z {} Go?'Ya 8TZDJ2i$ g}VfuXŋfJŊZ駟r2Mڶu 9nh1h&Rl7NMn:WkCgnCw?6M;]FfmSn}o$Ϙaaf^/w'JS6o\i_~kAs~ jIs`مTt2݃^ǻעT,CgVb$. U+MX;T-WW_3qBZ}ӣO#>]Lj }YBdH@ŁXT^ziK(w@۟& sPrdXȩgq*NWmjtBUz+n G6OV333?p`\O?Drr2[n-rk<Ȳeܵy޽;aaaԫWNRkػVئUH̥2\9"N9]ef Se蒰 #!oeVDH\|+ 6[kqiZaaoҚf?Sp?3 BT|(mܚ:tJo\iE)E}za|o]0R#lW_{TΠlѷmčYe8pfԯ z[WDʣ0iz&Y~0REѭ[7FUv… YvmYTyeeeѿK%lyU} ޥqfŪḲGiK1u'O4E)H ۿ7ؒU<ѷWwj v1UI'^?S:6oVRSSלK#)f-F#;&7a_ܹStĮ">]F{O/^DǗ0de$l^49NVF~Ǘn8A݅6BB/JQQQ1~OD61۽Q N(1G?裏1bf32e %*h42aVXa6D(9r~DjeV/H$tp[(̖W g6D\wF} J!}nNN. 5%h↗,;tġ4Ft]ШQ#Μ9SŨPRD*f@Q(XYYV ɢ.\m8zɃ駟B׮]OQQT 0TF|R) 0~N$feeѣٱc&QRQ~+9 :|dV6Hdr :5&HHd);]ԃ$%rwlz-޵smТшAIX%܂VBAC}UFqy'c!Y Afd^ܻsah(6 ZvS,2F&A"AjeD*àUakY)ȭk1HǡiEӟp/ԧׯ_g{'}0Ȭ $ 5ym~6>8>s+ed[;&MTdHV^~+D* : RR+d@v')t͚5|+o/*dRr- IDATc*wչE\oJb[rk{*?}2vڛϱ}[D*"7ndŊf˂ V0a+ f֯_J"22 &{ÇMΝ;f`AA.FHbb";wf@AtF#(CϞ=MN?j;v,}5'O$=M zsk'y I nίNН ^Noŧ)~7\yѽs)xܚGD~F"!?OxlJ/}BEӎh2Hu'!W(g1^@INUrzzM1BϝNUUql Oq뻿ƞԹ<̹'rj(6FVm(+n4+WϾ_IHD=`Ә\߽T^ tt[|DˉA1ur=5Y8ק>SO_c Y;ra9ns ig^S8{(hg~SHZ,z;4]w1.}bѠШ$y乌 J 2IVCxl]mnũUЩsNҶ9j.l*#٤T:V#ڵ~fgg-\DU*Wpy2W6(9/aqF#DʑN%'%"V}fQ'סdߧ>o3u_/ZX;wҬYbEQޞ7b4ٱc9992uT?E5k@XX>}i&B>}L.$JScӦMH$V^qwwG*"%hٲ%w-̭\\K<\ߢݧmɫ| F\< [?F>FX&=&]1j ͆|GtQ`|y4yT GY dƇu2M{llOٌM7D#[@gB~OjukUiW=X".^ o,le&Z _#}8D"#Jt$RmG.ΙOHYt LjѠï|L@4,~>nA"ѽ&Jġ,՛_cK \N뷾8Bb¡:nSdhlӄ$ OQ:IЌb[u&1==;vظ 8 h>bv$j[~U1~;ڏFLjM7ҲUDGG3gqI 1d [Z%>_He>#A`W͓?'s^Y}'2b2x=w<Uk͗*H8]}+qמF}6jt86.8ٻ1!HS`l]qc\NB*#zŃs&QVqc{MF[~\~(O_D4A%h^M#x^7 qzǠQרʕ8467?6O-N$ 4&4tݻw* / }:i}* &ONBtZݶ&"ʶ?11 Y):8|>Bw>Z z @je]p]l rE~Ajj\U>ɟV#c4$Y6$ȬlI^cZh3 oƚǎҿcwuZ6D\Y^fmˣPvMmO!^(^k;*;TC6ߑ#{ݻiSuv$;;ic,\X`-I&gggcbb_~FaС$^hN:LR+ӕc8yޤ{5txzK@HuA"5 Yq;2y0 8= ]@QE–1E6FחxmU@T9X:"bAR |TQBvJ3-Z6$&$Xe".QH@mQm^f'zD"BncOL D-w32pմDA :5{Kj+394m(qՌ;w*Y-5}{"ɘ^<@+MV}Oc%fkm q7hצ˪(RͷIZFӡZs¯d'ErzK^GD$s"60orRqFM~o~Hv"n ~^ ]}p.n.?ȬzVg4uAAL٫!R+%: zDTU$mK{-sk'oK)kh0)`џGPo7drɭM^"?O(ЧF_ͅ%>R}VȭKDB &&+NHedDSկdibrw%% $H<w˾ٝ 6FqvͻhMJ2):y6@{G8{7#Q4>8hDƃ[k:+]@ʡz֏ $jS&[yQI'.֭K6mo ^;99iժsrr3Ma`5h42ff͚?mڴaO'b,OZY[R K)Q,R~q{7x i:h6F|CkAOO"rDs\DHy$29TFZ:^W } z d^лĜہD&Ghbh2kUHdV]MAa|69wZUuB4q װwŽq-ʥ-IWQʈ:o/FfmK~at\I6Y爢C nJ<٬G PR tjG @vJ4^^F~JME>$RCvаGظxSY7Yz{{ɉ$A¹0n n~40s>֫6kKrX +N.UoF*0zMN6~Bi?v5׹wrCa$'5mYlLn5tޭ_#/=h@"&A^.MP?a4uAH~܇1\AΣH$RCR?hƑ3ZurǗ5adu6%yx Mõ|}vI#uv*>Df:;DHwϺIC*&牸P"D*'6w6j\itG>>/{qk\'Ŷl]=CgS}Lӧ5&>j};S7Pv}䦳6d'EH }!m H>F&ȐT*%AcM7I6udziODH~zM8=B*3q(;29M|mY t?!ߓq[ Y*sry"4++yY2XFJqV}]HjՊөP(&//^T*-$* FS[U|1(>͛7\'&&RNΜ9cJ*밶FP`4 -J\N~~W;$br$Ѩ[R52k zN "WX!H'Pf ʭu.keV&q1~c~ )R++@" PA^FP `4PKdVHR@."=C^g%#toae[hz- `zzD\)G@hT 5L~:C 3͒bg+Ew)vr5zQ4"R EkEr zD~[q&9)GGJ#C+V,gOKYj ,凢>ԙx#urC2ai[n7MhtmQ/,#S+R0 O<TVp[)PDG< VA\lʤI_֏NV3zhnر#'NN:fGD²eٳɹsr۬#}Z"gYh4=7$ A#̹lr׊JEk%5kִ02Ô||6䇀e BI$DG\ʕ+t݊/(,w) G+>3ލ$7g߅:2>  qUL1z}ns)F6Nteܥ+#6c<CTNonE,E4w6mPTܼyl;2f/^\2vܹs7>j2Qpu/1S#%d'|U.J5-jYMx+R-DPՕOf|Ber4paL^2{=b)~ݴqw}\4~:.4'L(Q9F ̏!HĜ͔?(q~\ÞiV= q7ɺQ}Buu̙f bCBBhҤ z*QYμK̟?JT!0d~k0FWK\\~r n^Seu y DS-L0ggdYJNJɃ*3U.[^²V\޹=k c#NO.1o}^¹;BH^<˗P(JT9s&%;9eeDmZPiɩ_:u{Oqxm۶1g6nX&1DMADD)))&:woM͂rf̘!C1bDy%v%XZ䦳u?Ko`0ЬYSlGQxJ̠LaDE19!99&ƤG$em^&HK/>-]cwl2TO_bZ3j$w=+C孳fɲKT=.!`w}uwwR+vs4Hh`8_Ig) Œ3:th8Dٳgψ/5 p-[FpppOj%ǏEFTZh#ڼ"P 1+6&7!:2TXHR_w~ǗevV̊9<=q-DųGY?u WGْk׮Q\9z,V6Kd %Ww/pq%7m2ԢE N=`V2Чk譜4beHn$StI+5 PjUn܊m BU>Щsm;ǃ*Qشy3C;xy F۬ɹIT,XÇs}Z$ 6m~֭[ٕjVVV\vǏ_[ Q.'55ӿ͛Wukߎ;7plN;F F]d-:wnƃ9[lލ5%% R: QAԏН_B<=+O M6#.ʃ+*P#Yp(gv%!ׯ_u,_-7ڼ妿%RApi#ڵ3/}sLX ,x^~DNˡ[?-:|}}x$waO̥T Ii͝kh׮}3o<LɾMyy :O/.l{ '3%0Qvmǻ[nd.###F7ߔy}+'NdƍxcݻwW_UbgNG&MWoAqJ¾tiV-[KYYYԫ[{i(!D؋{8 6ojrl` AA=HB7?h4r!Ԕs.l+Wآ9/MށG&$2 .rڵ=_~!"鱅}\NܹsԵ ]-#%M]*Orp~/NWnܠpfԐ=sRViӦdK]Vʊyh}_`9|Ɍ}@_`kkKbb";wW^^RY&V1b_~%'N( 0rH"""X~= ""R'RRΝ;֭666dgg9Q+nD-x$eCfD׸$WQ$ ɬmBDW N\>Z) Ww.`{uCrC Q5C>|M+fsmFCXrߏ̼@6.sbilִ)9_`8?mGPf\UIR)ֽuŽDB?t9ӶmH}{|=ʦ S( ֡aA23˜>?_Жc CCjU@>NY-ʍBrxx8;^ݟ'IQ0eHҥrkVJ OET9,3gsNZjEn( o0U" JH$sUكNcѢEtҥ”PDD&L ++}HZEܹ2HR4 o㄄жm[֬YS/c|~0jѠG4?Ⱦ AJ$2I{멦dڬ9 47`sb2$87G8{5@nX>9 Th4t.sn;.H^8saA/kײbb2q8kVl DD*#?3w/uz3ιW?#FSa}a^m4 ŧ P DD"Cdć _OSߪZsUG?v4#Mw٫cŐ!AIDAT AOz b!vrVZa\n5 isMR"EHQg?ὋDނ}zO߯pps>AD\&6zݴ.ލLǽF:u*;vaÆՋ:~OEDQŋ?~ ɘ?>ƍ3&۶m?&55 wNVꚖƙ3g8x aaaK,_cZZg͗ZE_x Fg\OER.{iq!3k,MVJǎcgO|ھNF/豓]Or̫2I ?E\N\٥*̘ΤIݿۂC^^seɒpWjg k]G 29%%\ cW ţXVy׫Hqwa7ެ\=zPӋ^[OgΚt3ԧǎ W`ZƽqkgzU.$.l;qӸIs/_k* /7_/ΉZij+TmsYOQ$-.A2a$+/3klSqoԃA<ި )4.eR>MV~LV@ͽ{p7n ))T** A wwwhݺ5QF W@ܻwHΞ=KLL ɤ`@PP(puuf͚ԯ_v닯/666fGHLL41\|KrA QqDqrϳ:kҪe 5ϯ\J *8b y+Wq?>HIBQ!j (lfUjTA}Zoo\\\ǵWp2QID?xHvn6R? lN&ͨ뇏1''hbbr 7#K!>9 ZQ -.J_ڵk_|}}qrrb433{܋˹nމ&&IT!ȭP(xJ~k_wbcȽ$ƥ6?Ă׽:~n6oF-U˗ZFvu:]y;Kp/6Th0 =6J^5 ƀlO-}puu-6Z`X`X`AYAb , , ve3IENDB`gplearn-0.4.2/doc/images/gp_ops_hoist.png000066400000000000000000000533671423420364700204050ustar00rootroot00000000000000PNG  IHDRNbKGD pHYs  tIME  | IDATxw|Tׇ}@B !D  RPzxQJQKGQHDI( 5Rғl#!?7p1ew3;3wA$ |B!@FFFF&?Q&(ZCZZ(qddVh$ Z,I\q;s#@(J,`LFD4Sf[W"*T)#Sq5B.]oG9~ WnMb\Pi (iPßмE +_)A-}qI~VnZT3*=)3HHtLWv"|c>}:jZ6L>Ɛp>mPVڻ.x6,0_ WyC૯lN0n ;v+87GJUHGBPaKya?Ӡt 2ƍc>)oy.Z *a%*$L1In7a ;tF.R{5d3dV;`cӡsţtiddrɉW!Z +5#H=V5X+W^ 8Y@X\ò/{I&rA䐶ڳR2ZEJO$~m;=#B#$rCR^?#Y `5{2^/¯ EF&$$$P綾?]e?1 |sZ{/{$n7 BST%?)ۼ*|3hȶ;‘ybccP eE(Kڰ*8g/F$I>Aa'G* 1% IF?HKKåN=ts/Tw{إdf#bym~2&Zq;/V¾{傒7t-gWā(7VZ- ̞͋ݿOj HT$YZ|g>5F.(gpeg;g$cbĹxHBB,4/*] F`x?Xϐ,={E-Rbras/ s ๪_,{Qz[̦ key9x`?X2D4eB&?9#\h22`XiAWWpl<:IDS)}H۶XgGQlai⮁$bX޽hC~CP'ByC!C- 10l"Cc9r7d,w1!뵔^BDtt,4/n\AWA''##C/R~7I?'CX0l MF8ƭ|6LW~&ik?+ ^%fǒ$yJN$.(({g *j>uRdǒٯ0nPmҥ*%QŖp9NxuNDZWyLFDEEJ֩4JGwTPhkђ>**yDp,Ž{1мhtR/[KqH_vI"#<9AiMEBynFю3gBhyH2sA 1)WmԾ/p[E@@螭~x[,gW]U. =~!:ޫ…yt fQ7IS%Ey'󿲯6,[ 9N~ }QM<RɐacH:4~3PayFS0V4@йYUb<:V1'ʅ%#/VFߠйF$Otڵ@̙34< 5<5@Y>s%WnݺݕM=Y-/tGFq)ddUToH(Ah$]8y,4̈#X:=6tG 4c(5CQ>K\^ GF]6?Gw- UP0>Dnm&/ǎ].^[ƣjro@2bcrA0ddrիWiڸFіi)8ՆH;:V򌦐hҤ DVĵo!ſbZMz,222 ۑ1+JܒW%E{\A|m7KGo"a+Yh FÆ 9m5 "f]o.oB  1_-)NJ6j$L.t,\aq~u7JY Kむ#LZAjo>û@@^:#bccӧ7/ڀP:"hGSȄKV$ɔeW7G?eed Ij:3J'0}>2۰dIC2b&1tX&L(dCF#cM|: R@ڻʒQfLF@2*d5X\l4oє )^ܹȌ|ddL&qqq|x1+[GSE@%T.d5aц.V|+T*=dc֬YCTTSL+ܾ}h0LHFE具pvvݝxgL!Ҷm[,YaaaIddqҍHRB鍗7*_|RdĈE)ŋ@bŲӧSD .SF?2e kgEZm! zjDQd9N# WƔ)`DQL2>}OOsA=B#xyy3f`03glPѣڵs:-)Yd,4vȤItL6-i.]۷od$뉈TRNoǎ,[_~E#00˗/IZ۷o֭[5J6LtRtyܹ3Gkey &PB{>E1hР_E2\L:U6L3yY{5k3oӣG:v옯ױZ(Q|}}e/^d?~@vUS;"**urѣGٴiK,/#K$Iߟ;wRJIHH`ذaENY!3{; M6ĉƗ%.\UV*2zb٘L&yF#DFFҰaCn߾]wΑ#GB䉫i&nʖ-[̿3x`֬YS1cz-IHH AF&lذΝ;у,,42O'--|9,,]wyG.2}|=7,̛7MB#tZjU1&jԨK$dҤIѣѴiSL&ݷcM!p5:wŋ 59[ʅ"#L& :vȾ}͋Σz>qDeʔ!..SVU.,`ԩ,]nje׮]@NkBB`τ*k^_Ү];oNPPr<7I&qIV^MV* cƌ_~ԨQ+VBdL ܹsvxlРA̟??kȅ&#`jY(S iii+W؉f/j=V+6 FLL W\!)9ŌNXbTRҥKP(h4F*T˗[G'FC ĉcX %&&d$IwwwT^GTT* gLvE1>AB [Ez?(a3cK]z (̉єpvbٲev/2t5xy"44AEqoJXi?JIS5)> R"o䨧k֭; ^~9]lټkp-FBW-Qhц$@z~zAP띳n9t5\tcwaq -[Ƒ#GTZAxxx`Xfp& ddT*Ǐs.\@Zxiٲ\d #fŲl 8Tj|S#+@E( *DCHu5{ޣaFvsaaa}+ňz25[U9j&6EIeɶ,(T % EƸ91&QqnѦazyZVe-tރ_|嚀䁙A|u{&{‰tyE4iBՙ1cnnnyvMP`27o{aÆ kn e$Iôl&77qI̛ JL@w;ӰaBג$N]K!jFʫ}x %ʅrY?9gge,4A>b~<453ļNf5@555MG~(SSg,yJO߿={/2yNtt4oj%} 쉢gC7^P(0YEu:kkHH(DjtOPl%?GJfC2qǏh׺˗/YAn9/ĩh$kt$Bk?k׮D?#GdΜ9ԭ[˨jnݺȑ#5j~3KY1sqk9$`J_$<VΧkz=1#Y mFTXM (P(qu]z&**gz"žA܎xyرc9zhzj̙3͛R&WH8tKS˙\ A#n` b )W=PED.@P(1)#ﳡ?8[EQ:nRVϞ=-t&ѣG`wo7Yns}lő8D@Sޠ}h47.<8胾F|z+P} cYyl||R{tppGl޼J*ʐܻwt|' 6 Z]dw>}OO?^PGW )BIkIZN=Lq/o#J#K3s`'\3sңF+ZO.(<˟F/NܲrΝ\ӿV^/"hs~s+Ȫs9F=1==cRX1^z%l6zNs.2 %Ɖ4XNмۛpj9:u:|Fpl}@ӣQ(UOyuzNztXMiPg|OGxibQ?艾.]PAAy<2A%RCPabDt}qiiiX,~&'00ggST)عs yBf͚|HիiҤ ZӧC"Ϝ\3୳ ٽ(cW9kjsƊ.N\ [gqv nFLJ)wop|PBv-#qyRj~f-47n\Z<*rPˇ[T8:3OU\rGʛ8q";v̗$ѦM׹~'lذAV|o߾=zȂ prrZ:[d 6DT2ydD<suAUJqbĥތs7y6JG,vl1I̟?r;b<}ZMiX9:?FljDߏ9-G>Fci~@or&nӨa-$l[o"PtJgZj$͆[hn޸J7$1Q$*L~~=曇1h'~_Aӵ=ҕRƿ%sƍ9wc4Yb_5||gXVfϞͬY*]XXÇ9Z+]S 0|pRRR/N>CRQV-Νkw(sw:-= =xb}o$bI9%'#1ξl@s3op @DDϠWi-ݻr߿e Gg&|s($Iġ7h\.YR{8{<ˡBv CbCѿAFA~$>hKIDS訨ld2gTt J" ={FgϞ_v"~e tA|1Axهf#|8 w]]NP oȃp+Wz?.Q(s.ŃBEuh8p17LџH;(e.9 *Ipp")J˞ 8s+4qzWLP0XN$ыfjB[iGghg\(nӲ=ł(tlf1~x.\G}Dҥ\H:~N 7dݺu$I]/tKH0 Pd%,aSCat.;֘`/ iA@LN JM'Tj2dZ-d\<lt#eŧfknAI: IYf1Q )-7 IDAT]:ur#6+4=<S=Y*dNvdz:}~p,BK~Cʫ5Ų%@Ɉ*  XSO*uLI”Pr7(_y3d5kcue6݈S)ttΈ8WSbLI (f6 :gTP`y? 9OOO"""?R*5jwޥcǎTT ݻǏ?Q(Ƒ#G~:Z޽{g{sJQV`ƌY3ʼ⫯k֬Y9s I+V ;8::s*hJwվ/t#tL"'aK G4ePm cuBʳ:.Ѭ^PzeWng{97c^?lb}n^F$}:NH?qX)Uh!a[ ŷ2͆%n_z쳻 JW^T^Ǒ9x6\Y!d3u]Ӎ9Q~gF:v옃[q?sٝ\zÇ3EIXx1'O&--r8q"3gδYȑ#Y}1R95#+<ڨ%&ۃDMQ7n`h:di'̺ޣvL+/WAy(Ki[ӝs*yo|TVʶmHMM˻T(tؑ 6rDw֬YêU8y\L޽iР݇㎋÷~g;A2gF&eEcB>>hl梳YFTszT52d/CI<=͟2 _}tU֭[Ո !L> "3AјL&4 cɒ%HuEDTs~Ns(`M_ʕ,S~]AYtaqjaYؗTmPg[ڱy j\ *L%8t;h4꫄EHu6sL^}%''QGr1 _j͈'ɽrC\O:>w# q qˏ[Rd7_%Ua+MZdvwve{GGGϼydgT*cƌɓFƎF)a/3|JDܷ-ټ138;7']=~\& ȼ3IHLL$R-GQpݍ+՘o_pڵJwLꒈZl$tXX֯ӣ;TRŮ_El jARprrB,iiiq=DQߟݻӳg'FGEEQJNA`߾}?cfysV((9X,xzzo߾<.#j׮_$IXVDQlOiRI||<Ν+{~=5N^z^֭[+! Gg?/"܍VEj5ׯ_q|gkNx2š5kvE=|Qzu.]DժUl;.l^x1 ԭ[Yf=.^H o߾QdQ7naaa9Ncʔ)4jԈX٠9>Gc0(]4+WCrm(ѕB֭[_ɓ'FW:ĸq2J*ѺukO٠dDjժeaȑ1dFǎ4hPZt)C * ͳD͚58(իLj*>\\\r#^^^l޼Y6,48J,ɞ={[\ -[Aec)9sɳ~gO.V&<<:upE<1m4GFX,Adrs?;Km۲~uv}7ṉc(ӭ2ǐ!CP4jԈ#Gh dWo{n=zhʕ+GZZ]lƎ|򄅅2TR]TPի/%mOOOƍǾ}x7ec?ly .иqce)4k֌#G͛7ed f̘ecBADDmڴ!88///uܹs4hЀp 2ŋYpa.*JZho&YhW^%((/,tˋk׮QfMBBBd<W~!n~|r[/;v֭[\E"##iӦ [n " 0>@|rsՋB (&+VHltU6F:v9tP\]]իKEќ:u͛seYd3_ΪUec<̙3 |vQX1VXM<7hvرc )IIIٻw/L8-I бcGYxze{.~~~$%%Jhe"##L`4yFC?ѣvZQf#޶m 9f ٳٺuk1c< ;}0`W\KaŊ4nܘƍxZ8p6mZNݻx^c„ \rPA/^ jv!CoӰaCk.]N0IdǎRZ 57t: jAh4,=OH^Z T*… իWK˗RRR$矤$)<<\$IJLLj֬iEQt҃$Ip)99Y.LfժU̝;PѲeK1 X, fTlY FɄ`jRRB[߾}%!!A9>,\+ҬY3 U"_ 믿SF d/EiFsʕ7oԴiSțf~$\F&N}vٮx B ͛7F$Ip9e˖nݺIϟ/|mwLHHʟ^n+ʦM޲><)]ҟn߾D0o<߸qȟ M֭:z9sE 1⹭(wܑ%.%K<|Grk/+%Axm$;uf2طoSv)SХKvӈ'Ŋ\rT^nnݺ7`0?EDQgĈv <>@qAr{./reDGy6C& ,鎷/eʖ'0 Uz^{RJ1p@Ə_e^Pn\hbb9k'}%*W ϯHUBCC %""j t邓HժUquu-?~0-ZĚ5kHLL >>3u'ƵkW ~1w`2?Vz=^޸Ϗ {`4 P"n":28F#$qtt۷ ^޾V!$$R;{j\r;EfʽT)}|)[/_|\iةfZZQ,\`0Y|*p+_e˜AƆq{Oq7qCZ=!ѵ۸rMUlՕS2jԨMRRv,C8+x4ĵl5\ʬV QݾLj+fC2ڿè^y0L$%%rJ֮]͛7 䥗^"((J,DnܸAhh(O&88%Jлwo}<<wGUkSNBcǎeʕ4mڔݻၳ3*gxYm Y?6dٽ{7۷oE=fh߾=DK})R{Nh\j@/'lDIqt@`?+VB 3XҚVRtyJPj2m.e62˰$0HOokJhβ+ }K=p*RQ7N%Pe DP ZIO'u[QN~ ?f/r([-D j}&ZɐD$^4ej73u ^y.H-i+*TZ57sL<ě̙3-[+SO?;еkW4 f9O<"k4A`,_ڵk_SD/OQY|Yڊb%+Tj"Z-yz-ZB o9R'M{/ 擙3j:EsjIwώUZ?qfgV4 ^W˗7z8ǮyGnFGzJ~^Hɍp&L,r $I,ZQq >Q4z3 G||<-Z!^C?!ZMy )YMޒʱ~t:]I˖-2e oKVٳߟ 6еk!IRW߷S)=_U(V,Cz)Mj^x 4ZTylS*-61,￟EQd|ts '2lKJ3p5=}__Gh*ŹHT`CUbLϑ/ަOF̙;/߮i >W'?$\nsl~;۵:tĉY;x Yt)ϟwu(R-(+hɌ4cL'S޽{kn_~H!+P;C*:w )sejU j, R44V0I11:A|oԨ 6_~BM^Ϟ={XlG=ϯqYzL7vT:=~޳NdzM"55c&6k1FG\8P\ <<v[Y:&7/'#PZ5Zhf;ƒ%K GGGS^ >m5 .DM 0nxǕ!|~N=;߻v%]7bYF'k1SUTtf1O,G6mVʬRc`>)qh<z=zf:_~1cưi&G^ٳi۶m f3=yu| "O)*/Fsq ;wl/"s)6=fmBŽ9#|8,Wi 2TjER͚e}4/4TR^+2jj2 ((5(*lf#6FBf6f>Mb>{.ǝܹsᒒo|D V I :  D՜q`5JcˇqV\ hfejhZf3h4Z-& I!ǣr@f0: =o\-GՔ(TOYfYұAHOGE$If2BSAP6!ktPi{fdEu+͚OєDVS$TkQ634j-*ff5e&ױYIwrTyejkDbf6"( %VjFq@R#6lJ!&(q'6rvE6;pAj6r.lJQ~υ󰤧u3[oseسZ3ZKx[!lIDATdbI67ۣTIHO~e8f *sڑ{)sh96mGҮc;PsԸf36mYfY'ܹɓ1 ܽ{ `6Yp!1119}'''z( ·$2J]_r ijusl3JbH1.ǗFun7}}v2ޝѡ{pӧuzq,=b,ENi+Q9,vmXCoz{L+ kvս[FF.C¶7ٿl}C`ז $E܆}m"Ll¶r"=eץSٿ|RC>#oN4J>_'IJ:k~=z),]8yϟ_$222jYt:] ZMAAͿ?rnY %EӪ=Bl1ART{j*Qsn $q&ܺB-&t 9*ɀ/2z ±[6zV}uih~<|NlWF=gFPQKP*$ T;1b>|my?cm$QdjSj,ˌ\ʡ,Q'˴ڋ-[:/xo0n]Xu ƅ#yX&*sj$'-|v>}:ܵj9|62~b6m9HQaaGzf3=]]]7nYYYk~  2֍lGܺܺAkD_܆?αe}G3*l?\b2plkPT su\PUqo$&8#c6 QR},^@oJ; -eWSjv6kdj V ]ھ+2nu$%';1 }% \ @rTD}^2/(?yI._پ(b0A DƾZYW0$b?y2rmՌņԗj>l Q… ԩS2e +V1cu?ab2 [-H|ܼn BeيbFq eJn+KA*7A0VWF жhP@+*T4ȲjECA(Iz#>uܙM,7RZ?m?],V/h<(OuxQHDEE˗vZ&OŋIOOg+44+V駟x⺾-mB{؝Z{bW`2T!*Td%0jLz%,#"WS *=G>GQIDQZz"O{'źHVN'a5ݺTsv8 $h)/cWpxKGTS=!JJS J+}'Qv5AUͥۂ RvyP8Mֹd%571V؆UVV2E6?*Hea*6D-@O_gBu&i: gtuU&O`}Ι+8b:Haj;Aq/ &qR|?x >/$78&J3֞B5{ޥ4}ѝ Jd;ƌ3[EA_:ab]";^+h\m{8V S6`ۖbTƒoضmۢl*;Jd[!)5Dյ\$o;IFMAMYߤ,_ǔɓR^nG SkS! Iڃ3AT*0d,F}ݙ:}`+ˇetܹKKKؙ[J>TZ lERjPdLz#lEԾ tƪ|'2޽78P(pvvä vT*f.^ȥK=zM=klQVpHOO!L|ֽNA@A. %j= Q,FC JhqVe, f$z -?2sɧ %QcmQo")j^P") hP)/sm ͜yH$j4|E")7cj[hnE+SMn#맒foIHHY<إ\eL>j#}ЩpǴ.;&+ĬyZXnlԓ./;v S{S@TގxϮ~{v P9k9f#~9s5ϔ)SXrCqs4ϬY22[>CB=H&}%7h#lDo]vGyy|x? #q6p=/AcY=3hC0+ȿ3L<ٮT*>;"f:rl <#o<2.ֶm{ֱ'n8wgb|G3^5?%&&^xhSr*3g0yd~~۲ܹ 8/WE ͨQ9 .%2z#ӓsg$.e=_F1eeD'}9s^`ٷdͣE?\tޮM?M[n0&/k2Kn*?:a8lt۷oBBvv5"!8[Z1r>&;1L,]"nb0`@0Zڹu /̌'o'ƭ Y-fNm{S.nzyLxbME1zfbd53yyy,z߾ҍ;PkƷ |,A'9qdwcǒIgl{^/Ơj=$ ̸6۟oJGqFz)x zm U*(j*&Nܹs܈VO:f}@RP9iokPhɈ__IH>_cdc;Gv~ι9Qjʋmw-*T$u1;_F5 W^嵱>lɟީh*&*l(۟tedn)- [}(ZײZ- -":LS<,[#G6ߡ[Nv6/y:ϠK!ujn-zm+JVkId>Deҷ ӝ? [nܶmxxx0~xzꅇfzd I$QZZٿ?)))̞=^{ܹs,Y4+kHܽ{m6B J\LB:8.b=m9Ұk.֭^A]{h=quj[Z}!aNaz"iߢ΋fj4ZڬϹp|<Ը [X[.Pg(БGVxiJ _СC[4@vv6,iۗBr\;"#noƿLd"+X_l*Ldѵcxc:~ IMe9HvO@Plz# pg4 ^ZjŸq:t(ĉ&e4;Ɓt̙35kЦM/;w~'ҹHw?WA꿭KIGNN,̙ŋСwԖJdd@ixXmd(O%~O|CaF23fdHڷoPϙμyڽ uT7w?֋V+u2opR)Xzg?`Mfhnh4#C#dϞ##Zz}u-u m[A7NGǎEӑv\.+lFTQk4t?/z3ׯEegg8{,'O$//<***m₳3tԉ@!/, 9dggz9D2ȺRLn5 FV]tNOo/-Bddp.x2SH Օ`rvIwyy؛㋯]MedХsdgΓ[Hz^Ԁ*'ڷk|:108w߃1nCswpwpw ywpwpwp;^IENDB`gplearn-0.4.2/doc/images/gp_ops_point.png000066400000000000000000000415411423420364700203770ustar00rootroot00000000000000PNG  IHDR% (bKGD pHYs  tIME 99ݠ IDATxy\U " iZZ-eokjLKM- }MDEEA{/jZe|^zsΙ|2s(@ r,@ ZdA";;$233l899䄻@ !z&9y( FăZ:G?T1CU:Ot~+~)oo^H˖-E " 7nᯡS7Q,FPlE_GrɌ3gƵjUQ{iԘHu3'TR/(&b6iӦ  rx íQuuF VF GSЬL}Q  ӧNѼu7Io _Ɩxs5^p hJ57j2}cƌ bMFe]G!eӜ͛%oo#y 5z?z)bz#\G‹/%oo@.xߜ8.W$+q*ΦUaÆEa " fMVi_T(6=[|&x2752uQhF9AVRuR]#3?˔D G.suDQU-g E@ !Qs%w#}XSFٻ8=Yd((@xCxCrpuu]d *Lw@aEA " ;2eFĄui1G$oo@.'O7PR"(?8uJ% G (K`>J%h7{゗bJ';#zԃ͛677D ؃db!l?x"*-Pli هc;Cмy QF9G ":͛~f,=Rֽ)0 A3MuĖLʞOP~dQ =rAQsj<Çˤ>)d}edhw- "6 Jn)cB(KS՚?/2Y \Pdgwk2bS:7@}򉊂bJǜ| %9 Kb8`ݙDZl5/D0zh.?O\\ JMl6( ZOj֤nj5>>>(oo@.( nݺE*U{N8v1`䂒O>VZ 4ȮV+DDD77D $6  uqQV-&M$2U \PR=@xBחX䂒aÆ?Hεdz=F+oo@.(n^uuƿ"9_FFUV%-- GGG"!!h4ƌ#2Y \P\L8Ӻu"?qqq" AZZ-Z 22X?gnܸ.2[ \PӇ#Gҿb .],r GQ2eFJJJVFs2c  Uҹsbٌ TVMd@ !!zΩS2hZ~'.]*2^ =rAQ /G]bYV-t #Kdd$O.0{l. @ \PW_}U;|p._b oo@.UFvJ?cyQF9G̑-Z *kpssĉ77D\8qT+#@HH"oo <䓬[ʕ+ԯ_ݻwbAVB77D\p#G ISLl6l2?ݻw&oo#D8}4}cǎ%;;R.ł$I#;@{QIxW9x &MqHwaQTF@q{C轁B1FéO=c2 oOy /nYYn5BQb VFC׮]ٲe .Ց] j6!"p>MCvr/` ;VǹRNK[Q4oٚo0x#R<@"4>2W^6LzWytt4͚5cӦM:tI}^N+#Y8^hfI-^Ų_'(h ېq!WP>Lغuk*Ժuk/_Njո~:Eپӷ/"]q*#kSYˆCE+FΜ9EFG^8PTEcãI"W4yپ};jN:]HXS:fl=tr1cQxzE*UEHZǼK?3?ЩFE0gܵ^86lP;wVO:fGٻ񴎤TZU"f۶mjʕYhCkbIF6:Yxz%OGG^غu+۷aÆv`0hz}]ߘ1cge{ YQ<JCR۟fŜSOhޢ@9|0/b1b*Ucǎ" N7* %#iIDZpA1tPf}&|G9t3gD.rrr0aÇɜȪH/ڽ8uꤨENM6|7(N6me,<Ȑ!CPTLDd]Cf.k˵Ӥo~˵3 pUQ]%%VeF9s&...y~Zyjk׎Jdm.^Cجv}XKcŭ#i1oN>ه>îUT:u)!#̅ hѢE{e͛7ǘ1chܸq~jo>֭K&Ml(={kaJ-HNN.٘LFd? "~4#Pr4z,g4HZ,{v+C07#WEMt$IEUvmfΜy_-77#G0f{-I2a08 o@ҴiS_y뜜&Oĉٺu+!!!NDD׮]#++ ܹseBBB,5z*Uaͺ^JKq،Z=45`I<&,7.f͂s!7:Kn5|ƎL ?QE);s^Xжm[-[S)&M1G0˵Ә.@W/o@Uɇmow ɍُJmð\"".xPTb!Qp DDDйsgsvIOɡC ”)SXޯ~FiUn7~Gl63w\ϟߴJO>'"##hԢ3NB{:uHMW旭7D U-ǧl$w^^ʂ :dR8ŜQ6 BVu.>]QŋvZ?~×,3i$HEF >xl ʾeUdG,o]G+_SOҧO u^=raZߎe{6YS"d2q/_7|sϿ9::ҴiSƏϐ!CDf=_ʿ=obYۓDፊ"&N:lܸ~]ɓy } PױbLJصk}kWTE1l0ֺ^]/wxፊHlǎ^$l)4 zYf|~ۼmzRê+L?jyr̚53g䯵.luգkFbE/xnިTE!)) _Sn]V{+ tR.^Xd|Vǥ/FY>e/#P}ިT VKtt4?"mz8}Y8|p 9S%} 'C~QHxrd4&eؼdu'kZ,)+2c? oX!_~W^yA3Pj"N[Rϊ+?VZ%ΑÇHlR ϙ{ ҿ[sV^#n2#ǽB8>Z풔˭+d)~&!(¦M>}g*eaoIBRi1'c,\+sj]S8vukUXoƐ=L8FN:oRjU֬YSNjՊ$Ij>0Yd2Ç9x f={2x`ZhQ&zunĚu{ M0ֳYrV (+$}jl`>5exO?Μ9/ŋ1 222hذ!~)]vwD1ӧ4h۶m"o^9x ͚5wC ֝iӦDFF2a1 ZhDe,֣ZMhh(v* +`e bÆ e5޲e ?"o@p$&& w@)̕+Wׯ @xC'""v'[˗/yF@xC !}$$$ЩS'8 *cdƌtޝz׷77e*Sԯ_<<<_Fywח! pI̕+W^8< ԨQ0\]]E7#=E>}8s挨=zBÆ IJJ"oT@ЬY3prrwy9# gRn]E7)޽@*U]Nqww'<<@~w! FEgϞ套^"""GGGqWs<<̪U3g@xQ !!!L:0dYl("I,g~w&N(2E !xM7nΝwm~CӫW/ AF˗/gܹVts888k;w.-[&777rʆ VZ5hB`P4h4?V%\\\E+z^qppPRٳg+m۶UL&"ooTDo˧֗,Y9vX^G=8ydϹjUV~Y6m45k7zooT(o{~裏ٷo_5z*dvݨQh4ezͷ~K͹~&rrrHNN"!QQ Ν;޽{EQJ߾}56iD G8q4JzzzSN)ժUSnܸ(tAqss ፊ2ȇ JRzGGGV .Oرc>7~~~"U77*7رc]f8{lNWWWڷoo93n,/0evyYt8ooTDo@EHHmezT<QFHDÆ /׹i&l6}_`r8(JQڵk\p/rbl2jnnxV-7hHFj]v9r$gF;＃Kf؅ p>%\%)1dv9} OxxyS^=4lDZUKKK\pq\!33ÇjhѢ%...xyUӇ ʲΝ/`ժU~ٲe3澿u.]q51Lf oQ GGGQuԡ^zԭ[WDP ˗.q+qq$%& H}ˋ}ҩS'\\\pvvFRqOZHt\%Ih4ARR6mbϞ=oߞի-!QνѷO9jE?Ω ZJyE(ek5`J#+*Vrf#۵g+C˗/oM oجVls$ F)+mun c^'x}f53fEJgţf-e^C~6k,XN;hSSS6}y?M#e5\*^Qi(KVe!=ՆGGǒ5L̛7.]RvmF#膶$ VJb߾}|Ȳ+.Vmj{%*b`ᘸJCRIĝɴ3x@( gLJ_ =~&{r1|}}n$'ӥ[]ZLѺ*e,l6O{5믖={}|Z)j d;wl'?aØ4i#G$;;DRb„ dddo9 !QiA]ō$5fs3ؒ ޱ<ߗJߢTbIM[]0u9sV|w?Av\E$GK`` =ѽWZPb$\cG}ښ+x ̤~ٓ_|gg>Z2iHHHߦCXBr 荠pl>&FPHFTM{Fr ?HﮭYdd]ݩpG m3(LAsϟEqr-Pnz9NCؾ"LPPٵq4MPylJnE&dfeg˧~ĉqtt, ~g͛h4tԩDGJ ]qMW7yhΥݩX']t[o}ߨ_@g7'k$J;˜®O%:2߫Iw"Ӵػhdii߿n(k}ًSgݰ:vCk E!+%uc|QFk"OcMz19C֎j67"Odr#VS6GMٷEyS WV/BPPCqNg}Ftt4iiiƝ7n .䣏>f!2*ƲtR [ne֭ddd0l0nݺPmXXj/q 药ͿCJwp SMv}} +'rٿdkGVB6=1e9"2|c'ql{Yry|2޽VQ uD !sadRh7tNUط fsUUHYv?n:ǂ#I t};.^be!-|5٪A\O ))ɮ'06cgP7~YVs'z'7oOosvNf1rGlX4o< ЂbΝ4jjԨ3g镯X-Z0j(/^7GLJZjQ~}lŋ>}:O<5j`Ĉ[JPGyBxCxyhĨFՋDـkԨc5og^=5ONNccޫ!P=dC_O~xCkeҠu:_rPs"6k.=>U}VwIpBwPǯC f}|Y>b*LHNSZhk,4Dv=wRɡDXˆ i3|s 2uUH<~{u;[Lh۷SfMEbڵk SNѿF``ʕ(xzz|r*Wfe˖ر@ t4i҄_~\r }v߿ٽ6( 6K.[s歑pO)#)YJ Z]㧏QK5n^joֻȣ1>j܎ʞ15'M\ѕo(6TZ6U =gױ5E_iF<1Sr=p_S |lFFZ=X&~m?d1Y> !I2ɻvcoݺUwβl8;;wz~oٹs'.\`ɒ%dff߂t PR% ]@@aaa6 ooOoRΘJM9p+1oq֢up!ߤ38v9C?g30z!H8^wm& Y*TJ3h9#s|J?9D~#9,?GVa2ڷllB(;ƹ̂[<씫Q#QX9?ӿѸ+>Îz!*l\_\*ƬT?1Pi+ Ve7opfbPjUڴiCdd$͛7NI&uG$_oL?,W(P Y rs2nQ"V2A ++^͈3JuТRӘ(6+6k.:u9`Yh4===1eg2ތ9C%zx=3 Os)܈<@҅9b5ԝ[.Y IQxgPa'a̗T @v~/v3*2"qr*օw6=% I<#?!*$I&; .a+Drsj3={rC?~<&)z*zbذav< 7 ɠr1WPm`e<-ZHL846ߊkh z7n\c&m/20h}K/6iڌC?4eۇOb5琓w3"5>:DnN'؈94*5Sc5 gW:oCF]nwsZ7@frSӃ 5'jq˖-ٻPT|',\ӡCؿ?ƍcԨQ,Ywy'o:to͛QTlٲ5kְ|rݻw>|0}-\xCx~XáG3Y5Bт͒Rk$9j17ĥٻx{ONw-HƠGQb4iu&Z=* ߮1'-_oʭ[ivfIn/uYEW.7#$U`KǹxjJYb2uqavӌ9+WRyl6cXPhZqttDe, F1?0j4M>w^w3 (fl~Z?#G, !Q޼qi]rQ~ՖҠҨ51gsFo@kجh :%oӊ{pcǎ̋xr2g6rbNs_]uڕ4Js~Ό5aڍƜ2NEv+W%;tؑ vEZf޼y <\ȅ77ʫ7wH1[Pk 妮* G-{>}{&|ZnCUU*f*$? >/in̉o'u4Q䐸gA*#@82+ e U,YBҥK8pq ꍥ_.wᾒf\(6 Gȗ#K} $CƮׯPrttd^<ֲy,]r22czr #){3cB+((_~ ^yVVo&ϟQAxCx>TfZL?OǸq#9~zI^3{)'`5^Y-sFEEѠAC|R&RYYJ-FU}7}ɉw}VFJٶm:tQBxCxFb;w.RK,aƌ\v-["!Q^1s~ԔvVdwmoa6ѫqZkopzcM~ޅ{ϋ~q/ ΓBݸZ~faq6F YVڶuōb6VkLDƍqssO?T*s3f 3gޣ !Q^o|FX*F vYlF:::r5*aB]NZg@V}K[7}3-ӻf(m/0p"#p &qQ4CI @䁵S.QC9~>Hf|1%XZ*jejwXe/_… 8q"#F 22''ڝ^Z-:={ri"""*T(޸t?|9 KIr>Lq]ѡ5֣~ODE^~hocL7nVuZfh\Pl6CύILFR Is[jj5uVjdkc9Aé٨%$;=IH*j1'%'g: d0Jome,"R]Qs¡{ $yeRDXK[D^zitÇ3uTnܸAi۶-գrX,l6Cg4KȲ,HD||<.\ 88!ڣIDAThw… tooWo̚3pj4>M7kӛ_,@HOF"s)= ww'aznonk =?~'bC'+CƍW%{شixkm/|Z3*>Ppr;̛\9+qGwf7w_,Yfe*̚5ŋPŃZӢ7Uk5͛۲Tqw{ү9sFe'N0uTv؁7O?4۷aÆH)EQHOOĉݻ={hx74i77=oL:;~:oogoŋ9}4q׈MAL&lFc޾NN8׳ աe˖֪OzЁ\ A!,[u͒IENDB`gplearn-0.4.2/doc/images/gp_ops_subtree.png000066400000000000000000000742601423420364700207230ustar00rootroot00000000000000PNG  IHDRu~QbKGD pHYs  tIME ,TDX IDATxw|NgYLAb$miRjϷ-PҾmU찊ޣvdA {=yRV"Ir?ON<纮\׹uI,@ 5 !@ J|ILL$;;ł- MRRR777! J4F^ܙ?9rOGpj (I f#fuv Y=<ՈPۯk a A%#99W8}N3^-hm!&3HiT:tL~֬)YĚ:L4K.Mw<  _6KxϘɜ9sPD /{2id]Ayt+$/W̡јyL>BAౘ45.ʭ>(T`1l) *$ sF< (mG6m Ab҄qX逪j=P(b BCcȋ?vJ|g N(mBߗǣ C7Můh-H:/vdqs& kѯa+صlm |cQw={NNNB"ܣeu Ԯkq(֭[ EA9bPϿ7]m=>Zb5?O$ @(Êe@TTyOƦۇ7D7m^z7^*"SNQI Qƻa{hs"P!FJe~$M:U:RvFfo$#Vi۩/ Ѕ+5$:e񒯄rDPW08TC7|&k@"Ɏ  % wͺ،8WJ+!a͘5kP*&zujqgCV 7/SNBY@`e 1>#*^C== ee-DP:lݲzlZ QB82#1q򿄢 ϗSR![6(UySMy%N#=K]~aP@ X3}s%C ]{L)Q53 o&%ͮ];ɳ]:d ~.WNᓅ fzXGLї ?? A]MoRgMŜ%H-n0ݼ%7ٔW𹨉'~l0Bi@`%||އ$)bƔt)3 0%G6 s?C7jBP*67.iU&Cik"禒~Y{AYśu/b?*Y{xEo@,&Vb!>& GB~S’C!"ޤ9 m2we:<#F"&MVVkr@©jV(쪡t%3$ IdHJ퓭e*X,4@ dY&OG$IE}uE®rn Rs7T |HJA]FA,|YH %XHJ_I72 )-ܓ.$T$k$IP5ӢE" O*d)?+"DPW)Rzt3ZH(ͦ'(kSU,9w0E*rQ*Bi@` NZɥ*E!6bFt[P:@i20%ˇ֗H*ӅCɒΖ;X7,SiT 霐(tUPػ!t(쪢tD[邀OS*d@B߼y8ſMG@ X7ndZ0%CUI猤Ԡ: ivS24}QUoҹJ1}l?e3ƌJ+#DE׉)˅#e+XŢE 5?\(,`g;ٜP@ X1O?݃kmZ锝| Q񿹆# 1}tzk8kF/J y~?k.jrOxD+׍+\-+i'D~S+Al(#< a%jFw;g)BR 9)عsP@ 3mʰQ{|Zۀ')ظqPaÆ8XdRS&7|iպP@ Srssy\LJ6 KЇ7q4 J2khذa=ZJsI^;G@[ߨ$-R~qs\UғE@'v;[W|DqϣIk!#1>讬U͇}΀EP'(,ܼȺuX ӓ gj P9WK Yn]F)10ӵ['ol՚*UE@PI!.6kV鿿!73Q{!>ȷ0%],Du'eŊoNL IMMƐ,[hhu6xxx퍿=pww'%%EpJg}/'44ؘőGe4Z-:xy{S^}LF#Ν;"A8ёt ~5k2vX!L@ dFVV:ߟ2e }{B"<)| >ʕ+B@Pɘ5km۶O>E^QF\zUSu'Ae|||{曨T*>!T@ $L&7nLxxgذaiӆI& NPTMFjx뭷\DGGV`0p@zŨQ\u%22RUuҠA'~úǺuHIIaB@P1Lqҥb9߂ _A>Zɉ;wPJ!`@ ;Cұcb9_ZZnnndddiÅ@u۷oSF rrru˹}6fB "--X}ȧ~7 BAqyw֭۷/sשSGb ̨QxhҤIh4KBBCFF 4 >>DοxbΟ?ϊ+qvʅ J"_W^ :ĮQvmBCC Dpp0o6z*kܧ֏!e%\rD:Oh}:bHпFM޽K:& GGG"## O?L```^'//GGGn޼"Fʘ7npT*۶mO? ٳgzjtZkײj*!x+Eԕ1ǏgƌԩSԮӧ rL߾}YlժU+Y,<==1RWDGG{nJ^ rLxx8w)@P{1qD g|Ś$qׯdee %A9DeyR9}P(R.eɒ%@ S iӦerٳg3l0+C+#Zj_|A6mʬ ۷ A9I&lٲZjY\]] ](J#ue@XXzL:#G A9b߾}ۗi@{nM#ue@>}XlnnneޖVZaj֬ STBI@`tЁ?NWe___N:U0Zg6Q*BIe+Ξ=$I̚5[TK,_~ԯ_ 0@ "8$I{lڴjժy@ I˗/g̘1['V!Rŋ|G9ʴ]fBhh( k7-@ N: eؙL& ;w~k 1RWA=V\K.-6ݹsڴi`(8X,Bi@`%3,Yg uz ¬}6l~؅ DPWq7籷M< m;vL(M $u-ZhZj|oY#GL&f3f._LFFhذ!nnn( J%zBCCV˴i8z(?k4 ͛ddd pFt:JRB!%,}l6s ]Fzz:f {TBÆ qrr*K[`6H320p׆]\\P*`4iݺ5Ǐ{[`fEq1&O^'//n߾MfF22NNNx{PA4!"+]bbboٲe YYYxyy၏N#55ۉ'11`^";;Ә/iP?v^#D@'SL&PF:b$}>/FPl|w߽\3*o25(}Q:ztșkRA%*DjC;np2!22e-eϮgIQDr ۰)SVYdF0  4gNyz~ZcC%y@Z}>$s̩ߠ!dԼ5FBϞ=QQrk֬aӦMȲLҥ ul6c2X,)zJٳ߿={+qvv.<~(+-eٯ5=h¾:X`1![ =`:P"IJP 8Dvȷ/2g3FO.]1 ˗Yj߮NRnt= _v)A{ $". 'ɽc:=x A J(֯YɷkEװ'6utS!lX'9{0^GL?>,[l;45Zu1CP I*P(2Ȼv?P_fs9j, cA]>|EѶm[F#!&IPl۶s2n82sѮ}'d:3ŔDR r1|}77W^Ezz:]v#$* gt .% d=H (̳?]ŨMHGEO%r^:yCs[6Jpp!"{|$S~fΜI&M$D*J8~8~)ӧOgԩrڞ?8'-0 KV9Q7s[wdȑlw MY7%]D6u p2rd͛7ݽ!84#P(dēs{s]{* NuhL`` ׿+K 9s I+WɩĮ~Z^=ʣ)I_:Vj0߹B),tǎ=;wL&Æ tD tAJ.%IOiLz|ZFЄGo`b\z`b\0i;f0,؝/vv%ofk p*:R*so}-bKCщҨQ#~2]vE^}UKd3)SX}DFcf&J֬^%z_x7 F;GkImSFOA C.~C+ҥK<զc k*]10Ngztloe~3Œ oNaR#ed&%=Jٳlْ۷Rmiڴ)VݝbudI#/,+@6q<_j1DY}Gc7$unZe4o.0LTw &- عcɷaMYnZQHZ%Ws!=#XmGϞRY@ 8?9+Ϊ;z0@ŭ[ܹ3۷o޾f___6nHn݊mAҥ[3JhCa{۠N,z`% :!)eŌƻ9>m۶zA~=ݻwg֭ ,8 Wv#tVaz]f3ܰi:z|H˱,˂y CA]>]veҥVb΀ѣʕ+]?(wFZ?!&&FJFnn.[4ù[{XviB՜P?3aϞ=zM||ٳgyt|:⊠k.4iBF|j5Ʀ 60Eea8 YWrbDRjh lƮ秴hVJ+z6*>E?]A:IARS$ق]X6￧vv܉7~~~?8a~DR(\݆նH|R}wxm7|flQ Kd>=ˆl͛ A݀>|8hBS(iܸ1?~ +ӧ3 gdd?U/@IBe;w>C}чT{=1q 4=̭SHH rO/#PLMI2p8ƏQ#G$**/Yt)>>>iӆ 6pMa㥡`۰C$~ŝ/R!|+恢xJ\-&1bĈ"#>.˚V(jQ;A)Q{6#.ǖK(+kP>>E%X,t%27fΜ97 Ǯ]eĮɀޒet籘q蹈7=ufT5>3`lZN*֜vƵި d͚5qE v ۷',,LLGB %2j6^ϸ3x f/?ySYobdP  z< MOZY+W0p@L¯PTڵuֱw^ u|$''3n8~t:WfϞ=E*(Vҥ . dڵhs)9>G"<}l#k{d>u;-1ƞ([d?EOR4Ypl)6/:L F0%_%BM1ƝoZ;ddk{) 7ܹso>uW s 6חÇsJOϝ]ETZ6c̉cC_9=Fw7$18\%4ZF;t6g)!禐{fE@o{{dC6YgVkS)2Lt+>" IDATDQ8ˎ}T֠.44͛RB?ի?M6Y,h4Cڵiժ)ѣ#>"!!s5}4lAa),JҮu[aض@Ưc0.Bp&'?ܥ?c[k6ڳY_%ńScPTȟne̷0G$h$2*:hjw)Pńѹ!\}hZtž={fll,W~jժRӨ[>^%7Qd ;nDU*W?@FeεQRX7##/&fJԕ=Q ŤP$=(Unղ`|$aNQ$*mPSru:1bydYFբP(g…fΝ;Wy]\\-w C~e[wMdmU{S2{hCƯkp DO,QkLˑs,Pj_\0dC6 ;}2( I&=6-'g.lِUd*EׯWHCQĒJG4¶{6_Dt8Ymؒ}٘Sԧ3Fc៯~GT=݀}9dn::ǿFeQzoޭPH#,!l7έ[-T`'I\tL{GԩƍO?}D ]N$Iܢ-Wp=sI0_H @{NCqS{6/ʍ׉xnYb7oSRנ$K''g,ٷ y_-F̷PVIڳJZdx l,*JVZ]]bJ/(+ 0%rk6+TUlJƖ8.Ah |&C} #wk&ί" =b:Y6Q5,RuquM6~cȑZWٳ9s&o&899ʕ+Wy&h4ϟ'|=pC-w5 nICT !BIƶ8Z. чQy{fy?aJٔH[U\Iwׯ >wڵݟr0z eS<ێoa daɈS22]sT6EaT'%PeD6. =ʂ SN[ԯ_RрFM0;B صIIv)O6.X XRn`Lc.R}Cցƾ;l%I"3 ][[[8cMEa[糤Ғu"C~Fsf<Ŷ3E9J`uKIEy`JdS_荏AyzA:E}(;,8V|ϢOGs/Rѩ<9Ck׮~-p9N#~/ZBRaِMd4<}t4Ch>D18?˸ BT5u^_T|b%LR`Μ9C"iҀLzC>[xK1/=СCG̷NQvXp<7f]/2l0iҤ]VX~=_+{@gKV۰D?>sV̝; T4}$g,zzV<\+kPw^/^l!j5~!_|ۚ6[$7ChӨ&M5=eӏ@i*pI̙3+cbbSNAjbܸqp  0ڿ{od_\U ׳Xy| ZTpm}n{ ___awk֬ׯo߾V׾>~=(k6wC;Se*/6jS#:JJFN'_cj9;Q)pj;W ,JP: ahO{wQd%viC߿=Æ z֭!99?9v$U.~%7ا._ܾޞ_,!y!^TjI'k׬(:Ç6l/_.jd~b;o|2O-4EG&| -qƑݻӧOAMA{va洘 Qjل~b; Τ='U\.Lϯ꛹<ȕlFL6nݺɎBB~˱c8}t6m!qp ?RwFۏ0`}E`ڴik-+BMJJV͚hz2j~ѷ?VPdffRs/Py,;>4`dv{\믿fҌq{v I]ك?~]@;}DeR$**W2qDKmNՒ@qww%v_ك~}Sː4QЇGQtO^"uիW'.>?mzO)I*L7/}oEqT"y <ҲaHWD[cNq"_Ҫ#usNƌC~߿?JPX$J% \'OSnR ǰ~6sQ{5yjVR,cJH<֟~F&| f6'aWg~^-I )1Dyl -ضk/UV <17ndWI}sH6%gid u[v˳sȠl;"x6j&w0蝤ل1#ygkV|R ,oߟ:;T*eΜ9DGG{n6mZ&sr4y*?No )$ I!/| gdO|\,:(&g, K&k23S<Ð@Ĕt̝3t8v (f͏ 1m>u)&V`I"Sn2 F2_lǾhTl>Dq-YB|\('ӹsg&L;k֬xxxL˖-W ȂJR` 44'Npz=O?4 EVql߶7(`[#Ϧ(}lIR(X21$\@0khԋW^}ɠA Yృ]va:Vm?N~ڳy~Wْ!@R"21%]sCVW2nȳ7  ! J-[2w\Z ǠJMl:U #fI7$1SRyG_9q{| {y&۶š rlJ l:l_lCG%(3b1ğGtm]C>:uSV%`0̸q3f;uk֬a߾}FժUquuEej5FYsN{fСV_+l6iF֭]õ0fԞdcR!˖7BIBetS"j8%4*QQQ4mڔ3gΔ4w^FJΤ SAbFeJ% لU,d\=?+nnnB%++ >3>̺WsѸA킥PbFg6TU}xԪU˪?77M7~Zb]!]Yi P6fag1u0! C#'OgϞ\t ///ah"gF#>>>ZIff3BZZ^^^ȲLnn.$P( F#,ߣٌْm߾XƏ_prz<<<8x M4'(*uaԩL'[,$Y]ʲLj8<5jh@Ɂ |l"[,$޼I h4=;.ܛɺWJ%WݝN;?'i 9%%.^0&E^ݻ{f0 >[kBGu ZFmϧ鈌}|Sfڵܾ};Uȵ5+o&?lyܹG{~CRUch4hu""""ܹ3Vm۶%wiҤ {NPt\]]x"cǎeB bɒ%^ر# a d֭iݺH ͙3gxٽ{##uT^}ѲeKa %$Ijbbbʕ+̝;WF_IMMe$''?yy:w̉'PFJJ ބPN!R!ѨQ#x Ə/sv.55f͚w^ЕA???N<ɹspEڵkԬY/MV _¨lAiѢԫWOhJ裏xر#9 *\ryo-,(!!!<\~OOO!+@$~ԩC޽̙*MPDpp0_+cڴi 4N:@*9g槟Vp:u$$$! ETT]vرc"`y*@bccɓ R&Mܹsqww/ZիWٴi7./\`!hcs4iBxxLbż[ >ڵk"wƎ;z+{ر@*!fbXlْ )-G:Đ!CHHHU8p | "M^8< m۶?Yf"ddeeXi/_ΨQ^z%Μ9H9wٳW4F6 QF\t*U+/gԨQ6lHTTH%!88įF^^J/8p]N)g4lؐsW #Y3GaƌDDDRV@o$$$0zh! µkh4ԨQL?b\!(_l߾%KTuWՇT*8Q%EPWرc.vZ6lHΝ0*;vd…ev}GGG*|w{>}Zw^, }A5b fϞMxxxU)XE[^uz-rNXX[&00LO?'T2>c֯_ϟR,k_l공*ڲh"7nL~DPg l޼gϖYڶm jj|HcQ1cAU0͜:u #Xti? ͦp9 ʕ+ٵk{-KDÆ YCRx #g#vvv :Ν;cggG*UʼyhԨ:t(x&{rҥK2oܹse࡟3gGg^^Uò! gʀ"OsLBBBʼmg~X͚50WT4kcǎ/˖-YfܹsdggjqˀgYeoiժo]vpo!!!e%&&Z];/_,WREeY{)ڊa +b0aB)##CVT$IYNIIJ+>|X˲, .nUXk~F]p|nnյ?UV[n-bу}~x^z=믿^t5jo>>>TZOhh( 4uڵ~G1\a%muLII)h_EZbE}8Ъ:pS YX zz@7m۶QBBB)gΜyHW\v&&&ѱ/7xêڽ{O=)'O~dmm;Ҝر#&Mo]t)HgZm;?쳂HHH(xc-uѣGmܸq"wYDe zquj{sVXݻ m̔)S3fxhGtTT˖-+rʠ.77={yUV|N\l41dfdpSL&ڶi^>xԠ~6lK,~ 33ȺYYY\rDt306EԮGݺԫ_:+qj$7HHGdFRˇui :u+b dY.(fm\v`\]]ٸ!?9ml ccHMMEEelll˧^>4hЀpuuU!'<,˗/Klt4 yHNg[xyPfm;(4iBHHرcy>WrEN:իWiܸ1Gf8;;sU4ihV IDATٙskb- ߿o{AVhڴ)P5i4"22ӧOs)y׿EƍE^^iii,[Vȍ1TDq@l7&DZL(#) V#0n8Ww| , 55[njcw߱m$&$Rfj8{ CN:iŅq;On^>NVR4A͛1dP?<G''lmm+Õet;ƒ|`GNooc=#)r0cB~}H\zƔIi)  V5&,2N7[6 ;}yn .NN&NãBēETT/ ?cBw@;:R3Qi_֔/̤ĄtIJ͙<~ 郣#ǎChѢ2m6d׮|2^{e(Fjjh2wy%Kt_ҥVVZ+gTB G^V*iƆr)nF'Nڶ4h0OQ.--^z .ЧOyppp(xeS`.I$a0HOO'66kr9j֬ɍ7Xz5۷/"۷ogҤI2lذ`{&==G~zt:W|v*?Zs~6;?{BB IM. MvAtzz %t!~?$dz_/jfUf:8! %eد?S }͊1%X\K앭.+WF(ZDFGѭ[7^`A&hbHH3{ RH{'g~ njլ q뿣SN1zǤl7U};ڿB|f?/A1%w# *RٰG4mZ>m߯^Iꝩn.ETj3xe! YlR^bHDo}N~pGzI(eZ ǫI/4N튦I @j6`L'[/IRR2[naÆvܹc&*Tl?:8PۥPV0%aH'A"N͙M?lr= ~8KcW4'Q=b`L}I(O&n~K-+=z֭Vc*Zg/\hyW?ِuդǘ@jSL_huqqq;PcƏOhh(G歷jJ h4زe gϞo߾L:5?ꫯ^:Æ ||c.) }6V"11EѺu|crr23gLǨSQj2ا*]>9[~٪˾@- ˖,b-W@6C(Qɂ͒1v Q!IwUӦN?4r߾L6whBYs~ZVE\=.S}̟O.]0站|ُFRpj;,flɂA {бWpq%B{q%wPDj \Q>x c+φ =osJa1r J}Y5j{eIx2z~NGX͹S[bYxtj3w.c`f|y:7 |`>sTl1Eza5ZYsц A!~0WYL)r:u=P'I+V`ڴil߾,KFal߾WKLZ\\ڵٙKb6ɥpÿNQh42x`jժƍFyZHӡŔ):WI=E%IСCtыzS zC֬VSK!GKLLS%<-oeADlV GuwQڙ/4{ u-JۏZy+ * 1A?-6mZ͇|Phc sV+͂9fK" *U\a wr%J*zo6(ݛ|Z7ߔ ޒ+VKcǎѥG/Z ܆(:BprN:[&ooi2dջ|9V >c {P˯㑅 <==۷/{FPrW+..ӡC,Xgkmݺ'vZs+u&p}ƎJnQ.yV&.FClG1;gp/zf>@Plj^]Wc35O\\\4k~cfWw$I6ܪeQ=O?'mۖRZ=r2/IJ5ߛKT\gϟO7oZΛOQ1DgC$]QW-=>4o:5|,s*rPڧ%g^ķm7N:iTR4hR6fCumpDu˱GpvHi8`*CWzٝKp9Z6#--A_ypwwɓJbǎo߾\wͬY 6d^)Hloߞ;wu\͸Ƈ)Sm ֑1wV'suS'OҦӻ P9AI|EyJ*4|Wtek5\k5jj%oH.%O>t/^\ ,ZFR͕өS$1;w.\]b6]#=r 5j̕9F#%K#8(x"* FQܺu+Wg Zk񁹽ǗCBCÃ2e͈7|Cs5jv*TOfwy;wJEwq۩@=V2+I;;KWoQYg"BEa%A eZ9K/ӺCOzS ~$7!.lٲ9n̘ܱաB ~w#x0> )㋤Bג{]1Bd\\ s}r<;?o=f*PO/ D~:OsIK}><Q8W{.{Ӭ__+1S 6{>3BWf1qD/^Z!4l06o\bYT* .7sbet(4.VKTlއ$>sT^S (M$Ξu:{?B Zu`Ϝ9]j^tN{}-?@C G+QP.fɐo_1p=v<&.<."+JO6ѳ{OvZn8Rŷ}V4k/t5*KԮۈv_LsJ|Z;Cy{BCC^:ϟӄl`0 :B^dBբV1HfvmVXeɂ HJJO>ڧ$I f3* NfCg[f3f!Z̙3/zi͌91ZBf1b1PiQuXMiHR}KSjۆc6SfqXeZ*C^u wAGW/$5%/Pk3nsSqQ!+qC&lY=;n\jxl+m1 ńPԦŘH(^'fbLA~aJÕX>iܰ>F6J6+c*$JbbLEҤBFL=9d5iF}s,ۀNlR5ڛskf* {0~ *k~y5;,eQeqiRc'E۷1R0&qedf#~Gbd,YPu~3//B9uWq쟁 `58:=~[WLѩAEoh啀*߰;gDcjj* .l/76+)ѡlZ{ɸ88ŤO$gnJ%~ItY>/^H6dx4mʶK#Y D=8Dž`6$u,w.bLQX ~c/<}q%|%?e@Pk$`L^zU8[ d!uGeJ[Sj7ǘ2kbs,\eϞ=ԭ[7$O?B`P(:u*EX,J.l^O>ҺukY}JEQlٲ,]}a6ٺu+#G;3=E2e UQu hЍ?N@TF}8n:FP*oĂ(WBط~ZeodrLR1iIt'\*6FhNʋ$|p*W'W ej%ܨ5VY `{+X9e /M SjOnzq0*<{cJ|ŵIH#Yg*-zvUjc?NDrg׼={gw?->@D.rrIT2MJD38~0d:v%+6Ⱦ-ٰ b } VλSQۥԸą#7ZLJɞ={d?i*R BG X#+D̡Kpp)mJ5e8'bVnJppHf{Ҵ_R~g6DB+[c6Zŗ+[&ǎ:ۓ~Qx6͉Ж1b~ܱ]6Λ;o2@Oxk(Y"++JK~K(Ӓ=&]8z+EVh/_G6zskKzq[E}.3^{P#c6Yz5/棏>bᤤ0b"""Xb|ײ֐$ OOO٠#G0ڶmˀ4hEq9%Aر#ߗ5(^Gi6b-{Le}Ղ BǘiRcO|xL!Z@٪gOClD"Ω f1R)VN?ƇeQޡbfPLMa$.Uy8 en͂{lݲ@ ppPŚ|ZR\i> g*GCR BEq9*6{KF<~R!4[/DP(to1E%IQO2\ːY,bfQE9vzO1ҳEgūI;HyfYoݸFɏS$*[Nʾ8{7=z,99}ѲmCgsgs*-Mz=oS3* ?XŨGT@jLX擣љǗe1N\;Ozo߬'<9EJU@l,fJxU]UZG 9<JKq(}In2cq$Ɋk\܅"HnUp?r, 6ۛ=˖-cѢEX,5utt޽{>~*UxFÃ0LyXi٨Z*qo::ۏ' ji,CBY )$䧎GGGSwov&}s$Ih슑"wE zc[}b b֧dčUiIzL/>!$N+]۷n |@5sDcf(/V@prJUmAn̋WmEfkm20 9Wjև$F>D2dlfɜ]9ʳ2V+S$ o@sZxW£h1_?7v»ŀ@8/EYc(^Vllj_j5eݧ®#->ۤĆw喔l6t/LB#%HDrR2nYRU$xC9K ƭbNKBeW$o*-ѡ:$ )qxpAhQ!?O.]q!I6mʌg޼y8pӧO|r( y@h4Vs$$vZ9s&ϞM]4F#+vfK/UJmp˷Lnz0IH_TT’j[6-Z$('$ٰY-٤qcR "ƴJz7HjԄ(J&*դ .`@TirXVZv;$CndpɄ_ۏ1%fRHJsA$12SijI3ybs.IG9:O?QhL6K pn8@jSnAT1%3sfM4U6*yrg2d*~wA @qtqg2.˦|RfZ9 K~יrrѻ 6F.ƽ^'.!%FQl/_+!|2#lجftE])]% OZCb (d؍\*6IjJO2M}+O&4 ?2ed>ʓE@hڴ)~~~Xw7(VF___BCCennn<}4G&NȬYG ><A <<\VVEELD :9;gQXK\-n:و5=&Cj6ܧH1G CrRlcZLJ5PXz k$A 1 U;jѼLϖ}~we]Q$7ՔHFŊy?ѡ^};ch6{KySTlűIDATդv4Fũo,>H Dɒ)j ;9r>FS٤GfĽҟF븹+< CnnL$G.S!lӞ&CVPܣ&ejҾ=͸VmNQxۏT@JL:]c3/^@7YMGT(:tHW*TA2ȳ6,ekC$RQf[ή՟>GՑYmh?pn dװ Av՘A ՟&SlU?1Bɩ}90wΤjx6ZWsljoCBsDE$GR/$"aw~_bk֨key;&=.~HKx 8,1%S}0C r vRn=YrSAe;Fǽ^$}b4qlj\X;Z1%ózntE7W _ JC\^6(T3#s#I^}B ^ŔS8gѧwoYckծGݲ>1óZq(19IQʧ%/sxVkL \.Z9Э ^VՔ>!Ϛ AvĵrS %& gzEӑ98$RjG @>֪ӀYOPE-lOi<+J\86 Gf45bT5pud.mOD%ޡo<,4|VCȭtJթ9r$={dɒ#ZFCZZfB(b00 AݷM0}tV\)E\\nnng{ d֞l6jufzV"55U[811],k={ct~ QFU`1r (5D%جtxfv s[^>&N5ÇnޕY㞤zJ6QDVaQiu@eɘy~7`12cє] \VkQFn4y2o1 Se;zыeٖyQBSb5ŤHf@_7ܧGA jϰX`;v[J1J z 69 Ao/ qbLJ-Sׅ;du\r nx.3t$XjJ5H6H{C%?tGXBH l_cbe߰~=?u;ٶ! Vkj3@cjG++t%wvg/y 4ׯ_݀ ty4˶LDy6ՂPP)1t:!]GXLL *TJeO۶ۆزuʝoqſuW\alڴ)goyH&}";A}Ջ =)^8#G=OYjtZrA_()I6~T'. жm;J^HI)$"7vaH gƌ~G>}x o߾ȀSe)Sɞǽj2RY>"7/LJ*§6 UߓYL\rC6t+SkE/slƣJ=,SxmƎ42gg79_wPqixz.]6lh$ p2 駟dɒe۷EQHBOķ~˰ar4ޝ8*a3~: qhZlw(H'DtwytlV Wǘr41C9r61O2o4x睞(Qh/[1oͱ/RhbHxgl@VhӸ:Zpt5_Μ#@2NGawH5@'Pp%M+r ׮]Օ^zh.VK0aBkV>|8Wq6_c >AjBנ8-)[G s<_No+nYd58:%wn(gΔO98{Zo>ewgyEcǍ>.q8+˖I낮T~u;5UV9Ņw)Q/u9kmoPgǤ^^ԩSr/"SP:֑a:u*4? H+f rslfredJq}tE] ŹJ5p˂ Zf  M#\/TֲYSåRrE~:`& h͕9߿O: ֡P|Ky%s/SD?`A-វ9__?_ ݻ7ӧOvb9yu:+NW ɃGxzz^ߐd;oZ!t,M2*9/k WHZ݊Rc_ *'g쯘<鿗eooOdd$mڴcǎ|wtݫ 9m%7žR$HZTJ7O`gX2@DԋhF.Q{`)*.X.VGrT~So$w@0lP> fząBzȋ *Pjt<:yҥ\ y23SkqnP$&!j6_ \_vo*< b/ ~3ڐ ]b(Tj-'gJTMKpГ,,yHӧOgϞ=4hЀ6mッV՚ njaܹ`@@ӧ`})Fy|犍2=$q W$ ?)_|A%__џ%#C|_/x⼡ѣG3:D*htৌӶYw{ E{gȐ!Ν;?~qqqoҋr {RzO!)7~i'ghٲVZjj̚5U+W\^ NӷD Eo'%6Ό3+{Ϙq*EygzYo_}[cr9dWd2 !((WgψDף3k999QLʕ+Gƍ[Vxy/^$(((bbbHKKbd sqqL2T\___Gxm "-\NPs ))SZ HjGrs۽աNzxzz?, ᄇa o (4DFa4 ;r(Lԯ_޸{xPt78!! &$2cbFPjrݕjh O/o)ZhgDD!ܻ{K/HHL #v:ʻ]֢n  B  Oh"0 X v:;B%r4iҘU|յTglL !<~ŋ 'i Q1RjEաhq/w9WjVLM)酗w9wڄ7vA/xErJf}j )W9*azԬUO//<@D";vxo H$?0v k1 梬 %%%`oB(vJ65119`ii SSSX[[ :::]=BHTWWl(++Cyy9*++Q__aա XXXmXXXt!Skk+ۛ:nD]]Ig mmm 氰`oޞv^ONzrܺu DVVଦՅ6@CCPVV())A]]n mmmhllDcc#؈z֢555@ `puu<==i-!}Pcc#ܹ;w)33c``FFF044Ā/YRVVf 0v[[[ H%YCuu5q}TUUA,444ggg8p`7!ݡuӑlC*11%v))=kkkc{T*H$X,0nܗjjjP__QaNnnn5ZZZk׮!)) nBuu5x<,,,`kk X[[VVVKRTVV(**Bqq1rss! '''`ذa;v,\]]9dAZZ.]HRVVVz CPB]ZZ a`jj x{{ceBzJ"!!IIIHNNFKK `ggǎacc b1:b塭 􄏏FcB__Ӛ 7I!Jh͛ŰĠAAťq=EEEӑL1qDZIHTXX . 66сۛ\]]abbuOVZZˡ{СC+BHЀh"&&YYYpuue [[[(**r]iooGnn.;vͺ3 cCMMr @pL" >>'N'PYY CCCxyy#F)eʅT*EVVܹ{Dpqqy0m4 :2 qiDFF">>2dgggg奦͛7J`ҤI;w.yBHř3gpqDGG2d0bĈ^9 HIIA||< cڴi9s&.c '?ĉhll+ޞ8ֆDprr믿@:nPVVF~~>1n8ۛ=Bll,.],⥗^ŋuyy---Ǐ?k׮AEE#F1fhkks]"'._$$$o`ڴit; פ '''̘1B*޽{8<Ξ=& Xd LggD"~/^6L^xQVV . 22pwwoW_} wb߾}8rZZZ0vXL:#F*("W^ETTnܸ===bɒ%prr'O &Mg}#Fp](\)..ƺupQ`…3gNK̐'SXX000UtRZD _FL>LD";v FCC^}Usf"/Oƺup]_@ R)bccq`ؼy3FuipMH$֭[sN᭷”)SPSSرc077Ǘ_~3fp]!=08~8VZZ,X ..ֆH|xV^?V"::x뭷R],)) CBB̙۷ƆHCt~ ˖-CKK ,Y_~|> ܹƏVVV\EH_>}:/_NgV \EH҂իWo V\I˗_~)>3"]5 x?_ć~7ݻ 6wF`` %#0 {?5i6ݿ_~%Ν;w}_|Dܺu .DUUV\iӦ KI{{;9ݻwc_uYpMYJJ O&cر\/",, ^z%߿$J5H ٳXx1.]J9<6mcccDEE {;wDPPmWPP> `K"kL/;vc{x\ Bdd$.nWUU)S_5-G QQQt&/IR|ob t?믿/͛7O?$ҋQ&OĉXp!&N7ұ=H~~>.] MMM\|#Nbv͞={P=X,FPPnܸpL26/2"##eL8ȟ=z۷oҥKk.!kT^'bΜ9 =o@GG׮]%"w}=T*ʕ+$B{ｇcϞ=:t(pE\!!!4M 5<̜9cǎŪU8"Ucc#% wߡ/R)%"WxQ__o`Mx<ӦMCii)%"w;wĞ={uV΃57aa͚58v^faGF}}=~'t'&&7ހLMMT(++ ---())A[[{!99KxRqg=555~:PUUÇ˭dkسg|Mm}pȑ.?a'pB899̙3r!\Ά;-[ŋwsSo-[ܹsΦ6'B3:t7n5kXjܷE פӤR)֬YyM.Xx11:::?>\x***Ot<زeW_u6 N7oƀ|VHz|xyyaʔ)Xzum?k֬57ɇ*>c"dggCKK AAApppr>aaa@MM ***fvɩX,ݻ ===H$t M:C\\Fu9t .@(br&qss-9Bg'}Jkk+N<_n۠$? 4i~lڴrH/A3פӒ{ѣXr%n݊|lݺ8q***wy#Fn݊qaƍ8vx<|M,K.Eff&>C ''AAA]rhjjºu|r̞=X666000@JJ ץҥaii KM3tPODHW+((H$ץ :t(8o75鴒L82ѻ hkk#;;2dLLLP\\sB[[~~~HIIɓ'qɿ=۷1f̟?]:FGGǏ#55!!!,,,`aa{Oh=>BFiii 8p ܹutzS瘚xHBtZ}}=.N񠫫"dM\[[/==vvv9܌GmmmH$7n;|/ @]]eҥB!455.QommmB KCQQ\7ԛ:G3,ZN:555\%QVVNYfovjyz}ۙ_{Suu5̸..ejj DZKyf7)@ȿpM: ݺMa666hmmŏ?|}1r0 իWR>uuu,RSS1i$?aaa8p |||p=v?gad.u 477cرݲ=B?VXd 2[I봶ذaClbii \t ݲMM]+66=mwS\~z mۆ!CBۻ~:8lD" R?bbb<yyyСC}<@rr2\R$cccBII ǏGYYnܸ7ok׮eݻw)S)))/ѣG#;;GEDD Ԅ#Gz,Qbb"~'ddd@$AYYjjj@aϞ=000'|"m###oØ1c=M]+66gϞ{Nꖝkc޼ypuu}쉒 O>/2'pAGGrH/A<ݻw###NI ð+-Zu9;'''l޼R8tJJJcK!D.lllGa׮]}ċAqq1:-[P&O5y" W_!//rH'۷w}h鳔{n\C:!55}֬YGGG!Dn֮] ===]\CCss3V\H<[{3om'S!+G͛cr]y ;v,>S!D444p)$''cƍ\CC*O?EMM ~W(**r]ekT0|pNeddqE.^իWc/.nd>|_}{Isw߅6$f8}4fΜ O>d=Çn:6l%^SÅ  "77ȟ:t| ,YB2~üy|rL2 \=x{c`\EHQ\\@cѢEx뭷(uTSNo:<1cp]!=Fkk+֭[/~~~Xj._HII;|Ǵҏt פK |ؿ?ܰrJ 2vػw/e,^vhR);[<˖-ٳi0%' /wy!!!,Bz8,_iii3g.] ]]]ꓪgbԨQؽ{7V.EEff&>#?Xx1ƎuY}B[[Ο?xwqF B۷oGhh(`̝;***\'ܹs˗Pq]!=T*/ 444`֬Yxallui}Byy9:'O6m¢EhBt9 DΟ?m۶ʕ+pwwkqAIIzZȑ#hnno+WʊucQYYv&&&0aƏ/// XDGG###JJJ055֭[`#W0f< `̘1ǨQ(hP(ĕ+WׯCAAJJJxwi&q]"'(\gcܻw(((|>DBBB0|p9r#ƍ#Fݽ_- HJJիWqg… qm]yyy={6BBBuلXϟGxx8 PPPr6{'pIdffBOOƍèQp][[RRRX3g… -[̙3g}9sqbܹyyy )hiiٳHOOqx:6RPP 6ɖ>QRRuuuxzzbذa IDATs:>F("99HLLDNNx<|}}/b֬Y=R'O\ ل<ҥKgϞN?111fq):u n8;;cذa6l[[[$$&&޽{ŰŬY0sL1o3ؼy35kP&\p?|555\|Ceﯫӧoŋhnn)|||0l0xyy\/RŸ}6@mmmL<f¤I6}|عs'TTTtR|Gj')^3  TxW_=qyyyt.]XTWWCII NNNpuuŠA kk^G  ??Wyy9x<qa̘1jn:`ظq#;ѱS W^} dcccL0 KKWDZZ222viܸqsR&yzz"55oAUU=| Ell, &7778::Դ"HPVV,###DPWWǨQ0~x7^^^:Z oΝ;e˖Q&rA<X KKKܿxX|9BCCx999uw 055amm000xڗD>|TWW (,,djhhv==ޮ,d#;;gƆ U/^^Ñ#G:,+>GGG\z߿ kkk0661a PSSǜ ///?m̓JIIMcڵ ̜9󱏑`̘1ObwПrrr J̎aiivۤH$BUUPSSb%%%hoo\\\UBvhh(|>-[?O@"ܢpM_UVXKb׮]]2H$Cff& A SVV --- 0/cɎgv'D&<| hlld :X033\\\611yOR)Μ9u!%%'OFHH "[5χ=]}}.fEEۛlTTT|+((0`ۗdIII 쒺:e; lODhllDmm-jjj:X022\\\ggg8;;Nn3Y Axx8S&0@FF?dܹs7n\ll_Aff& >NKK &&&KZZZPSS TTT<gZZZ~#ӃPYY8s ɉK:FwFhh({Q&]5yjP#''CsQTTĢEnLb%%%(//@ P(D}}=P__ MMMjHчltttv211`nnΖ[~HNNɓqFxzzrR!\JMM_|SNo|>~v|D"AUUJKKQ^^2TUUuMBb@jjjC[[oА]cjj sssv@puuʕ+p<& u_~%;3Y#""0iҤnOxI_PX H!DTUU٠uuuwK:::Ӄ9`aasssNEEE{YMcyBd xf***.IMMe-Z(**2nnn?̴s]!rw}&88f u1N uNmm-0zzzLpp0# .BII$㌋ s2 0LSS!Xϙ3MC!֭[\E\$''ݝ9~8#Ja 0|> ֖LYYoiiil4hlgB&88y0 !X+**2Wܿ=xٶmhii1AAApM,T;;;:33o۱cHuYt{ãCbx<sssj_!ՕB63eR)(((0<9y$GՒjhh`m!H/@+YvrrbCuVVֿ>^$1̌3vc.^x{{3 ٤׺{. ^yfLAAA7VI:B6+jjj F]]144dm45571k7VI:B6yRB#;uuu4 .^!;11xfԩDFF>6Tˈb*$"??Yd 2{WxPgߗsuY5662۶mcMMM&((,i: Ç8x \\\+8~8;lID\x 6l&LDK#cڴi>|8jkkי+#=-݋l`ٲeprr¾}Kғ`հn:#((ӗ200sYijj"((شi~'XYYaŊ<҃P&l4h|M"==t&S@@pE466&L@BBץqM#F}윜6d;::R&=,T[[[FQQuyDN455b aӦM8~8b TUUq](\cP7|~~~uy͛x"D"0aܼyH?uuL6 =B!"##Hacc{"77&Le@!p ?~B67Çޞk׮A"`9r$.]uiôi0rH6T#5'N{ ><bŊƑ#GuV l޼y3N8J#pݏT[>|8233)T9r$bccq5(++c\Q(T=}tppp@XXZ[[.A%%%Xbc۶m+uy\6d;88P(\ #F`C![EE ٱ\F8:::B5OVVV Cvv6f̘ 899Q&]FPM<_gٙ슊 #݀uֆ}o֖H/2rHڵkPUU?{!O#..5jb1bbb,Y3zj8::",, bH/T\\TGDDL5y*Xb sNo6>u$ նX|9&Oݻ\Gz#G"::׮].0rHDEEq]%0~x5 e#iYZZ33gիٙl ٤3d ؾ};;SuyRQQ%KPXX]v̙3or#r@immeCￏ)ŠH" qqq)dNJøq0j(ҥK}tYaC6d)**o {{{DFFb׮]ˣPM2,Y޽gϞegʸ.t! }j•{B6yhaԨQPRR͛7cr],,,",X~)}vp] o/^ķ~\,YJJJ\G(YgC>u/܌0?Ĝ9sPmjjuyׯ![vdz„ bui166ƶmPTT^{ 6lB#srr(Tn#::vvv DAAg@jjjBXXf̙3yyy PM85bDEEƍH ___6T'$$P&322bC믿7!H7(((`Cutt4j#(++#00q 00\G^䯡zܹEXXuy(C__3f̀'N8a.Att4  &`HLLŋ1l0K#E!D`` q5ΦPMzx6dq]y{H!Tϛ7PMz2???DEEݻppp)d! ** >>>8q"/LJWl^t)lkkk_ \G@FF1x`ܾ}Gjj*)T#00pM"00\G:uPvZ̛7 i8~8޽ GGG ٽܟC3`bb$DEEۛ4CCC_Xt)v ;;; ٽXzz:;w`HIIA`` .Nt6d4Bv/@Dؾ};vZ̟?666꺡ROd3fq-DEEaС\GS300`Ceʆ?|,T{xx:99B5d!;++ FBB1o;}oߎv,]AAAk"] BDhllD{{;@,w,6xG ;ա ]]]ERSS_puuʕ+pBw9s&`aaׯs455uuuhhhD"A[[PRR `hhuuu+oRgΜApp01ydlܸ\֧0 zBH"tXf4455כb "{=|aC$#""&MG p}D"5PQQa455add===.?n 4|>7ȳDI,wX!?0v2T'O?Gnn.fϞ899ux܌3p0 w}wGU?0L\'Dx!aΗcuKK RRR}}}ppp3ٝ;\IMMEHH?s<06lo븩uuu_)++C[[0339,,,vvvOH׭[̞=7n3. PԄddeeuuew<)===I֟z̎Auaa!bbb$***bӃ>RSS0hllY BvHII 4d{m---;r\NCCكm۶A*wի)uByy9nݺddff"++ lQSS `ddcccB[[ZZZ€UUU(++=~?Dcc#Euu5jjjPUU@6 ###򂧧'ɓ4hfΜõk```eۮ˗qu$%%Ν;hll"&333CWWzzz~jnnF{{;$ PWWT*6 9cƌVixsܹLzCCC `d=JYYʆe'3O֗d=ըQUUX Ѐx{{ =]]]BCCk.(**3A^] ϟǸqlHHH+W$xz۟tuu~Ommm{B$A$uuuCUUJKKQRRR Æ /aiieqRRRi&cXv-NhooGFFnݺtddd ;;ŐJ٘FFFfbLOR"bøI6vs_aJJJaNnnnHǎæMC ҥKx!8>_}Un?77l̄D"ۛdNXdw'''\zHNNѣGq1Ç P+ IDAT7o"!!񨨨`/?ΙEpbcc @յW^&紴4CMM =CRHss]~񠬬 .`ԨQ~ޚ;v ǎC||<444 ___ >VVV]Ikk+ݻ4hkkc֬Y?>i5{e шELL ~ 4eNYwa0x`v4~xq]n2{lDEEu3`͚5~ζ69sǎCTTZ[[???ãGA^^㑘D<|ƍ1gf0몪*oQZZ ;;;L>SNQWo˵">>HcXd :<| BCCl2 !H'Nĉ!#FԔ2B*"++ w{puD"`޼y6m]СC|><<<ӭ޽{SSS7'Ng… 8}4JKK具~PUUsBڵkٳgcӦMpttsKzz:N>HCUUC aw;;;s>-/555lpCee% 0i$̝;?|P6[lAppc{"TUUq%O>7MJZzʙ3gػw/o޼aر,^O׮]cxxxХK,,,Ak.3={dРAtҥ@d4'qi]FŊ166fEfSDDuEQQ1ضmSNK-[Ƒ#Ghذ!'NwޅjrY(rmz*]vjddGuJJ ֭c͚5TZ PAAAl߾___ҥKqrrbǎ-[333LR رM6!ׯiF iiipi\B:u9s&'O.{Ν;˗ '44pD﫨DzzWLMM3g=ccB?9ruuulBdd$V"**1c`ii9rkkkBBBر# .ЫIPP={Ə5k֔4aaa bbbD~HII 222Di TP6nŋEOteffή]cɒ%t+++֭K.EGGGRe֬Yٳg@OO===i7$ၛKKKt"mi%55m۶ʋ/ իWY~(]=666Ԯ]SһwBݿ{{{]!666%k___&L@tt4SLȨPh䆛7obmmMTT***,\3gʃ%KpqqAMM ### &~0@MMcjjZD~Frr?ϟ?'"">8p s-{Itt4k׮ӓ2eпVZE-M&HII֖7'رcD9 3Kښ9AddKDvUϟ& iР .,riii8::gJ*E͚5TK,! N:1nܸBJ@7_%mIRE(rQϟ5jFFF 1;w&>> Ν[l~ETTܼy'2yd+-^~˹sWfΜ9E~ţG={6ݛI&[u۷ok.n޼ɰaXvO222Xx1k׮[n,_m ܹtuuqppS'cpСCr seԨQy]Xy!>/Ǐgڴi|ɓ'3rHiVbӦM\p^zw^iӒX[[dòe mԼG/^?666̜9Sڒ7nׯ_g̘1W$8p={PR%vE~-K*\r}}}ʔ)_! qttΎLҥ-+3l۶&M`ffV26nț7oX`Er(66===ݻҖ$3ܹs J(VFnupGtt4vvvٴ8qBdfΜg2'(p}/_NLL vvvK[R!1c;wܜ#GJ[̑=e^Ȭ Bvܹs`ҥW>;6ls:u*ׯ/R[N<ȑ#ڵ+Ew-g֬Y4lؐgL;w`ddDtt4fffT\i3vvv4oޜC/_o߾$&&u"۩g|ss<<}%K0qD-G$&&2j(Ξ=155-籲jժ>}H b166fС,\P4pLLL(W~~~E"-uӦMӶm[ѽ!'yx={ܰ0vJ2eؾ}<'bnnεk8}t.2NLL[n| GG<EB!˗/ٳҾ}{iK룥źuԌruhڴ)TTIڒ$󱵵ޞ;J[N̞=e˖h"iˑ 86n(O޾}˜9sӅ&/}0r'&&cccԩW̞=[2k,ƍWd2d 6nȡCfҖ$1>|@.]PTTdϞ=)SFڒd@ ^Bs :V2331cO>ݻ-ɱc022o߾XBZ UUU||| eq&NȪU4h(>̪UpqqaĈҖ#v"""D4TRRRDWWW(mIb'44ӡC֭['_3Fȑ#qwwgժUWڒ| k׮Ԕ-[H[̤O>I[+Wf|CCC%PQWWgڴiҖS`;w.e˖eʔ)Җ"6222ǏlݺUꁵ7匢"K.u+iKƍXBX&;wfĈ̘1i6mޞիWK=ӧOYpOƳgXx1&&&7)WK.رc?~pĄsq? *JRJԭ[////k~P(ĉ\z:u{:vȀ~UVNj/(VϟBwǏٷom֭[L0ҥKSF J(Ç)V|/_1=UG^IMMeѣ ?~^|IZ022BOOO";{{{&N(7ϟg899Ѽys[|d=z*DGG$dܽzjqF,--qvv{,ooJLLMMMΜ9#7IIIԮ]}}}f͚%˂/INNFWWccc֯_/JOҢE MznoϪU8wO>-ӣFݻ=z%%%[|@HH[l޽{(((ЩS'ɓ'J^HHHM6[ZlIDD`U\n<{Waٳ'۷⛲y }%1cPWW]bȈSr$t=Ǝˉ'$rb,X .}sN^z%,?_~z &ݻw> QFX[[}QV|Shh([laԨQAǎٳgX9vXs(]HeW[ ɉVZfٳ'wnTT;vePlY͛XWJ/`wIJJrۗ:G pB.E(_<#F@(TŋZztt4ѬY#Gbaa͛8tĴM6 lll$vk׮%"""ן{ 3Fzd7}I~3f 2k``&V",,,ןlٲb_F$C6m8p F^pttr̘1k׮n"v؁F $v555LMMrJmˋ .H7I%J0w\8@ppĮ[Pѣ|9ןݽ{7UV&YMUaڵФIV\*<ԩ6۶msѭ[7r=f̘jԫWO(Ϝ9Cffw{:vHJJO޽{s]޽{'s畤$bccbر1tPN8Ajj?we"##=zĴuޝΝ;#FPNQQQߵeҥ *T ..Nb-]4zzz8p@&^̢E[.:ub?Ν;GrNV|4ڵ+%KŋҖd?{KR~}ڷoo߾gqqq@b\Ir='Op^'7DFFv_E͚5*_DDOb$+)mttܙϋܿׯ{{T^sss߿L2ӠA|||Ϗ'NvZ|}}\2VVV4m4߻wU~zjՀ1C(**-3o4N>͉'(Y$ >&B-Аvʕ+uﯥݻwۛ2|^~ŋѶ6ld 6oLZ!** r`ggGrr2+V$333?굛.> b׮]ӽ{w^+d?nݺŝ;w1c:t`ܸq2Kt:uHZH7IbŊѶm[.]Ą -GDmܽ{f͚Eǎ144d_mox">|H͌lIr4k֌z,ÕEQQQذzj4hѣ=zoͥK(]D ʊt҅ד.>IKKPڲn:ׯFCjj*nnn̜9SbIr(((п:D[?ѣ899ʐ!C066W^_޽{G`` &MYM"غu+ ;wLzz:~~~ߍ=؅3WF[[+Vp8q"IIIhiiqbbb8|0ӦM‚Nd+W׳):ҥKSV-={&stB!>| Beb...zjBCCYz5p1pwwgʔ)teeV,)̞=+W3ٰ́ǓĒ%K1cC̄Ό3|KݺuQSSH̟" D pmN $''Y. o5YߔmB۷o3w\W.$]hTѶm[XTHHVQFhjjvZ޼yduJ$e$iiԨDEEIJmbmm6xb϶rߔ;ڶmKDD V^IOOF9B>}Vf[BȌ7n{e׮]?~>gH$" IDATȮ]PWW1uTVʕӧ+VVZQZ5"""000\rtԉҶF!r/_^ѣ<|+WVZԪU+O{333ټy3+VI&b&~NժUxwYMJJf/]155eذayP&iQR%^~-u?oɒm6NJ͚5e7իsU&ϟ?g…XZZңG222TD4ɪɶsggg4h k_MLg ---6-MFٳZjIz?WuQ'z޽{=[l~t@"{e7o nݺ ˖-CYYY"3+U㖵뤤$J*%v$&&昛}SbE.\@9yTAAetFzw>}*M&;蔔,]4wѨdׯ0Y$;FQQ *.z/FTx/mؘd\\\HHH --M4v5k3ym aooON$%%KVGv=XIJJb(((j$-J.MBBÇ2~Dm$''ٳ\.Q\9bbbnJQ˗QRRej")?l${Rb )mXZZ.TR9.|=$ـh$Nn/ǏDFFO2կ_TX;8P( }IXrefΜIj$V" rc[>dȐ!ԪU OJ,ɔ)S~y%>>%KHsS [II EEE+СCqwwGMMP&iB(?1p@ T DQMo߾I&R9sR***(((о}{lmm122S޼ )l${@Fnmk{ӄ(ʾ Ui_n[eoMQ޽ o)蛾D[[@br\.NI&Qzulllr[.ݻCCCqvvmr֭^y& {L}Iv2e0b.]۷oټy3ޮG(> ȝmYZZA׮]" [.ʵk׈ԯ (BPRRBYY}H\\RZ5Tܗ$- oʶ EEEw#߿Ã>}k&+Mdž biiIhh(7od֬Yhhh&ō챨2{ $$$7oV;%QG}xyJJJ߭ٓ7jjjL:+Wɚ5kD)'K"H(o.-\CC+ ^ƥJb̜9CCCVXA-D:;w$&&; >|ƍc  E3#ŏ222vMr4iǎcذa.]D\]]111HQ(e Beeetuu3f nG[[۷#$ҫ1'oeJJkɔ*UJd#Vll,\~8Q1ҿΟ?͛^:۷"ks| K޽qqq722%KJ,d=ٳD(((@ m۶5jٴkN'ĉ/ɶX H ?EAAeee222D1j(*Wq:::̚5@?"M#55W|rO(ViiicddĸqrjӦ +W$..Nu!d?巍.***SF _ƎKƍ:v4lؐ˗/Ӻu|%MۛKTTTȠTR 2$.?ZZZ*U%tTUUӧeʔ!5573rHސU[ӧO9uJ˖-[ ĠA.r 6 Ξ=˾}8s gΜٳgL29s栨2z"22k׮q UƢE(W...={6QRq9rDԯ5LNVZQdI>LPP'ODWW7?rÅ s[n{Ć \FfkժŚ5khժUuz*<}Dbcc)Uۇ58kӦ O`` @V:JժU\/m zSH͚5yь9ݻS\\\8ujjj$%%ѵkWTB53e^|ɕ+WD?=z`Ϟ=߽cpN ޽?֭[L0k׮ѹsgi#?~L֭Yp!C9@(2qD7o,"ӦMgt߿?ǏgҖGd)S;vH[\ȶmxLdN .]o߾9rDԂId?~Lpppn-X[ݶ9IDDlڴ SSSi#RRR@OOӧK[N̚5Ҵi[0gxEfvvvٳM6eƌl[.9_k.ݻǮ] aDGGsQiK)*d899I[\Co… |` ЧOڴim+<,,, skѢETXEʑ]133y昘H[SD Os;ətmۆn5 ա&MhѢZFV8x AAAI[Xz5ڵcʔ)Tƹp۶m6ߊHzannmW/Lcoo ݻcmmͺu9?!**3gҳgO,X m9bޞ[nqqiK)dffdڶm刅ҥKs Yb  Ç M5k֔{n^|ap̞={xX b۶m,^XץIbpuu\rJm?ҥKXZZ2gM&m9beԭ[E*K=?ܜ-Z䪯oA‚ &`-GNDFFbbb:ǎeD;w.6l 44Tr $vvvo߾B@VÇκu W2Hzz: .={ H[(^8w߿@ ݻǞ={XfOmj_Rzu*Te˨[n22Iĉر#"KJ,qvvٙN:QR%i˒?<Ȳe˘2e 7nxFYY=zaBBBݻw%@ `Μ9sByߟ/^vZ*W,*C|rOӧZTT/1_$Ø3}t,Y"m9gϞ\~PnnnҖT$8880zhչyft۷ԩSt钴4OljԨڵk~:;w.Ϲ!##CpQʖ-+mYNÆ ;v,^ׯOjդ-P3s%""M6accS%r^z 0sA455Ue/_ԔRJq…BS5>//_1cPbEqqqt4iD>7odΜ9:uqq1455-+QUUeȑܿ#h۶ׯ_cjjʵkppp(eee郎lݺӼysV k׮QF8q}}"wO*((пʕ+'ܹ|qLuֱaƍǡC(>|8̜9aÆo ʊ0vرc-I&fΜ9?֭[3~xz)mY4Ο?ݻbʔ)X̸DFFb nݺE߾}Yxq^ذa3m۶d(][[[T¨Q000xҖV(wm;Ν;9p_&MH[T;;;4448qDx@C077'!!!C0n8V*miׯ_sAܨ\2VVVE.$ùx"| rmaϞ= >< ˖-c4i 7o;*aggiٲ%tܙ%KЯ_?i˓Ο?Ϛ5kE;mmmyaLrr2&L̬Hׄ~ ŋĐg*TfBOOLfdd֭[QUU%!!2e0|&O,_ !!![P\9F͐!Cr%IKKӓ}L>},2111DDD|BCC !22ԯZ*2eʗ//%Ͻ{Xz5aaa4lؐgϞ1~x,X@ڵ-O&HLLޞM6.FFF򂱿Ƀ8pԩS333Ə_$eaaa"Ƴgx?~ҥKBVqRR`kk `֭]Ml=z)W\G'>|ё#GP\96n܈!gժUҲeK,--6lX7nvZݩP bȐ!ԯ__dL8y$~~~)SiӦ1sL*W,myg֬Y;vYQQ222 !+idoNZ0116ڵo2k,.]Jrr2666QtiΝˌ3͛7ڲc>6C K.VӧO9qgϞ%!!aÆ1|ڴi#miԩSKQ𬠠 B>N̟?d uM`` ۷oڵkݛm۶Qn]\\\Xr%dyʡCX~=O>y1`_'Onsss VzDtt4|Ltt4cǎw}d2gΜ"Y"'quueϞ=SjUC^hӦMy)x{{IPP 4`35jH[^qi㕕ڵ+ΝDnIKKC__Çg@ nܸ_Ŋ+իWǥd/^PP(Ϗ{P(GЭ[B,S>|/^^^\z%K2rHƏO-/HHHf͚ܣL2e- BYt)>D[[#FЩSB<}ÇAJX`'N1ٜ={7퍺:Æ cT^]͍[nQ~}Oɓeݼys-Z$@ pe9u߿QFhkkӥKZhQR?|۷ח?ҢE otBCCiԨ*((rJ.\Ϟ=cÆ 899h޼y/ɓ;w7o/m%66[i&+ƴiA7<Fpp0+VD[[nݺѾ}"]&,,*UQ/ߪU+=zc͗ЦM<==s >}پ};^K.ӣGY 223g۷o۷/CGG租9ϑ#G8z((**ҩS'zIǎ\j}HH7nLJ;w č1CmP˗/I(Y$9f BN<ɦMr Я_)11ooo\]] q̜9Nj׊=ӧ۳~iٲ%}UUz*.\ʕ+dddпMF>}zAO2ɓ|RJѺuk:t@vhܸqunٳg(**ұcG2dՓLؘÇfK.eҥ<Ǐٿ?<}uuuC>}ḫ֬Pfffŋ۷4oޜSbdd*A-***L6ٳg!;ĉܹsƍӡC:t@- wJpp0o֭[ܿիǐ!Cӓ=Ns IDAT'O2dȐBV=ə3gؾ};(Q=zзo_Kx"=BMMqabb_777-ZDHHCʊFIH}$;\tdjԨAСmڴf͚Җ)6ܽ{[nqmbcc)W `Ȑ!_O$LZ~zD%UG}v:DRR[F[[[%$$p.^?fԩhkkK$XpMvGrIS;vsδnݚJ*IROIII!((ׯsM>|@Ϟ=>|8C AMM폯CV\+͚5c pe._7o߾EYYMMMhڴ)ZZZhhhYXBCCy(**ҬY3ttt֦GEjE,Ю]^+**2{l6lؐsrQ=JHH˗}tԉSN/^pmnܸ۷M4aдi?ﱳeeeO.@vڡ7^^^# ]WaÆ0Zrr2aaa#x9ԨQC䛴l̤Yf<y7n͛<~XTb?'KҸv\x{Ijhڴ)M4~ԩSڵk!˗Dpp0dff!ZիGn: ###wtٳgܹs۷osmHNNFAA5jPN444РzTZʕ+eR$7۷o&**0Q؄ьri׮۷bŊ G6o͛QSS#<}JPPO}w+_2\K||ƿ{.w!""BTZjTV *PB(]4"(Q$''Y7ϟ߿Ç)WP6mڈnv|vL&M033cٷ\IHH<}gϞDHH+F*UT)Seˊ(JRPP}iO$%%NBB>}$$$˻wDTQQA]]MMMQ@6#Kd?\v-2l0:v굢"ى]Czz:"tm(..H$BII ={lH$\\\nݺ:ujm6lݺ=yA!rrrx$$$ ''APRR1 %yohhhun=I"@,uNHh߾={{{:[2 Ν?駟b՘8q"k@v 9Q۷o_Cii){VVV044 uuuhjjxqeeeTTTވD"D"'x<lllnݺ5j+y^r%1rHYU.}bB/%&&B("55Nֆ+}I[[PUUeC|}zqyy+cR,dv%|9DS-BRRVBCCr:Ν;sO>&3BCCPRRb^bTTTur222B׮]++p:HNNFrr2͞ygRUUU2;Յ:abbb:88]vppphR?ƍ''',^B{Dff&JA#3())Aii)s/hiiA @WWdll̮177e):$$$`ȑXz53f @ jaa!<{ yyyl`O̔A&AI[;iddKKK888vvvMMWTTl?egg%"H$uBUUU :}IOO`iiI!۩VVVH$ϟ?REE;nJJJBNN H$B&:y(&7.aHcfĉ2Lmm-epEEE?0H$zuBx3eF*rPmSVV06s]! 'JG2NNN cF׾ё3yyyTHR&$$ҥ !C0,BE\\3zhFIIquue=dW^|r#P*m}(\71IkQXXȆjCC /;w.3}t MZ yvttdCu||~ٳg&;;$rݵkW6d߽{Qﳡs劊WWW̙3XeFẉJIIafΜ|Ύٽ{7SSSuY7yeCuII ezL@@c``hii1~~~LQQe%TC&1!!! 2se ݻdž.]1TšMvލx{{cΜ9pttĞ={xSxBBZ _7 %%V뭚---!==֭kkk,XD3 B(ѣ#u3ܾ}OFnn.;wp]!͛񁛛233qiܽ{Gn2{(\7q򐝘ȆS&IKK ,@rr2֭[#G]vIQSS@899aѣqQ("!!!sypL ???8::R&)((`CrJ\G}Zi(\7sغu+gQ&B5yd3999\GZj6TϘ1{fC=y{{֭[tb1z聁֭[\FZH 8Dtu !ىϰdo[nEee%(?? տ;Puy Sl޼ìY(dƞ={`oo3f )))+qM\t ={qMK#-Tdd$鉊 ϑu Ӷm[6d6B6ipX`lmm 6 55B5W9s&RSSm6;w5k./yO?EJJ v KKK#M7quHR }˗.򂧧'*++>GZ -K!H3 }8y$6l؀D,XB57̜9O>qyv&;++H3$vvvl~)j郈\~0`!"22 '>GZ& -M(!~CXX1i$<}H$vvv?> B(![MM ɿ CϞ= >7o"224H(\2&&&lȞ:u*֬YÆr#MJ! cG{\F8Bẕ366fCٳ?VBYYFrx"v؁$̜9H+%0i$}Yf} ïD դIx:tn݂ƌ7~ÇwE4W@}  >{Ammm#UKˡ>bccqs]ijBEaa!2?SRR&%%1ʌ)ȕ7IIIafΜ|Ύٽ{7SSSuYT=zqttdx<3zh&>>dLǎ:t+%o"b""ݻw=z4t҅9z(#^UV1/mJɿdLHH())1C aܹuYpMEؐ-fɌ@ `|>cnnΤqT-yǏ'2ʌ 8i1O:`0<9vGՒ1>M8gCvΝ_ %%%6QVVf&MHR+& s%$$ڵ+(TBᚼ"6d!;99xV PjҚCB!0 ӥKFYYO̩S8z]...,Bʆ5k0|>No2e l.T߽{H30oDؼy3mGGGܹs555k,,,$AZ]] HH$bJTTTbK A__zzzz16n܈ 899a?~<.J?~z$&&W\%%%(++ĉЗ0 D"D:H*TRCC5@ Ԕwعs' J1{l,Yzzz\FHs֬Y3gΠsHJJ뼆aʔ)ػw/8I$vTYYYg2HRߏꌝ455d8~8#Gbpvv4 Q&E$a͚5ضmd2+/`ii(:addd )) YYY@NN"Nx~_򁬙,--aaam666pttlG?"((PMZ-L#G`DPNO:+b$$$ -- 999DVVzmY_ׇ`eesssxQ\\m۶a֭x7o դպw&MPJo^sb֭ M7梸%%% њ tuuahh XZZʊ}loov5 By^r%1rHYNNNViy(\6}t8pάڶm(ױqmA(">> lpVWWLLL`bbSSSCWWֆ455χa],|9!!QVVX!??Caa!Aibbggg899nnnڵkC0a;NHku9 2… 0`{K,ݻ{.ۛB! 1FFFhӦ abbccc_#aݪ*TUUA"}Iޣ|<{ yyyhjjNNNpvvFΝ'@}vlݺʘ;w.jBb6_Ox<̟?7o~cɓ'}6?~'O !!줈;f255 tuuپ$SEE쌺{jd2H$TVV7N/x'mmmٱSǎGGC$9k׮c^{Ȁl 888ڵk044{WTT ::ׯ_Gll,n߾|xEZZӑHeee8::ݻwG~Nz!֮]`t=F$t < ~}Gpe7%&&B&ڵueff /HT"--} a`nno߾ѣmC-[1w\|UOEH?c'%%%|w駟޹[8TTT@]]zRee%t$''ZZZڵ+<<<лwoׯcɗQ&5y/3f/pqqիW_;ȒdAXXqMTVVm۶С\\\СC8;;׹ǏcB<|bwn@ IDAT€0hР7.|֭[`t +VPM\p~i^񠪪p뵯IMMŋBOO;vd{KX,Ɠ'OYG!;;ooot_Laa!v؁-[@ `Μ9 X ++z_իWߗ!,, G||<;ޒuvvv2Z$%%c';0ԩ;v0`}zŊHNNȑ#n:o߾ZP&L&A[[@Ν;#""ڐJƱcp1nnnٳ'z$6CSLxܽ{GTT$ 1f[n~z˗/PM??~<󡬬 ! 555\|^lxY ::jjjҥ z쉞={ɉhE)((qMDFF"77FFFO0zh|Glߗ͛7CEEs… ,g'p13]5#Ju]s@@Xr9=zaaat777ݻYNBGyy9u!-- _|{888pS5y/3RddffB"SUU0 :t8qb...vqq111Ǖ+WPXXGGG :CXXw+W*t&BZbܾ}]j(_bB?|>!Jɓ'㯿BJJ LMMѿxyyݝGDD._xc̘1H$8y$xb|Wu'U[[1R蔔dggA[e2:p„ ס޽{c[ B\ru8q1yd,[ WKZ DaJJJAmZZB!_TUUcƌvod2߿P;wbXbbgi UUUl_]~>Dii)4440rH|ԩ,\x!!!HII9[̘1 i2 999uzݻwq-ax{{cȐ!ݻ7Ը.IH$vΜ9(b„ Xh]WM kpyyy͛7C&?ܸ.Yƕ+Wp\~֘?>fΜB]UU9#99=z!CGqOsPRR´i KKKK#YD@@Ο? 6 Æ ץ5 8{,?,|'XlzuipM&==+WÇadd &`ԨQd=b8{,xb̞=fزe 6mXCbʔ)~D"ȑ#8t0yd=3Qgbʕwz쉩SR |2 ؿ?>>ai#EPPoߎN:tk (\ :ϟ??uIRUUn݊/}h'QҪI$7ϟǴi0{lHhh(֭[SSS9sۼy3Э[7 >}e˖ӧػw/ƍuIpMKXXFtb ___t!!!044$B]^^tlڴY6Xp!pڤJ2 ~-v؁ `ԩt?bӦM8x ֯_Kr]i(\wv1L0 š5k&$%%gφ\B'Jzz:f׮]t[&~~~q1x`K"HR;!!!0h K"/9|06n܈ٳgc۶m\C) \v  ¨QGg]gϞaסuI(\II ;#g dXj/\z\DH;w.ۇ]v[n\C^ҥKڵki5D 6 ŋ9X,1v܉ |d\DBbĈ());8ԛ^]v233.ۼy3vڅ 6p7˗/Ǒ#G.4C4sM 0۷/JJJCUUA?&&ӧO&͡BEE@FF1i$|GVȊz"?}BC8Ν;U!OIqannC)8es]i>> WWW(ob]111066ƺu;w ""8p/^l?~8tҐmmmlݺVVV(((@NN/_.9Q^^HRTTT+ɰc)CbȐ!سg"##ѷo_tED"| ;&ر#D; Ǐv MO>ĺuz,rP&d>/1glذ@ cPUU|Wp|>6l؀ɓ'0sL|8w0{ld2lڴ 􄟟N< TiӦ}233z@ܽ{#F(lu[[[Im̌R^A~uK4OB"ÃR^A~u놽{ ::: ;i9(\zA.㵾kxB&MզMܽ{WaO222I'M# o,I@[[2^KIǃ>ؿ7q]]]ǏN4iqaRp ey:::с=l2(CBGG {B uE~tuu!pດWPoH}цfQPPu YYYGÇ1|pXYYa֬Yu>iyRPP!͝={u |XXX(46sssHRq]{kͽ x!57kkkdff61Qڢ <#rٲeE>}0 {ѵk===^G&!''J?FVVPojXԛH#jcRojXPVVpMꍖz߿?v L{5 ieekD. ?W^^ TWWwFAAz聧OÇشi[" ::(++'|Plݺmڴ߿Ξegg,L`` 0p@hkk 6mG}cǾ?qѯ_?.xyyaC.]ԛNUUzj.m|2v(Ǥ԰"""@EEE!-UVq]i.]JNjDBhhh2 ;sիWXdjj ccc| 0YYYqn޼ 333XFSSCBB KKK|O?>}毽(** '݋SNUI]v-R1ႉ N8b| ?c߾}MyBUQQ0qDj PojhWu린㐖Eiut0`h%%%a̘1ػw/&Ou94;v`ѢE8yd# cƌ˿nDHs ''',ZHᳬamٲHKKfy+۷oǓ'OpIK!0 `ĉ\CB|Wptt. ##?#ץXp!mb6^l q[pMJ0o۷#::R>Ν;|roߞrQ+V+V@mm-7(///:uYfq]ifhMچ Ei/زeKmBWDDD_~Xt)P8y$f!B&aҥ((( eeeK" ]sMIqq1zFY&&&WDҥKXd ͛ri43gġC/!MGVVk"2261#ٳg1l07-dMLMM V\p\rݻw$ ŋPVVƄ uI%E0sL:;wĘ1c0o<?~rK=z'B__I2d8pGŋQUUuIb1k\z!!!;pMޙ5ɓ'… \ꕗ?36n܈۷әq|۷˗/ի@&ĉ6mq5q]!nر ͛71}tdffr]R' 1qDdff"22\D1u/bĉÒ%K .UØ1cpU8qgMZ5%%%Z ę3g_@(r]V$7`՘;w.Μ9C3֤Uׯ0 FM+l8"ɰo>?u\]].4st5i0.\i0  <JJJ\╖b׮]8r~iӆi21i$DGGcĉ1cF pIر ć~uY4UUUXr%~g/=e }EBIii)9Gիxٳ'=zm۶\بqM\rocȑPWWD Q&{yS tݻw;Z1"qqqALL УG1Çeɗ>q.]rݻw,--.d2Ν;All, O?Ç'|B3sI˗/ˈ@~~>|>:666daa!RRRc+;;<;v?U͈ȗFDD <<B àm۶uzC x---vvICC u 0 JK[#IDATKٞ$H QTT԰711Ѿ}{899 M~&~b1B!ۗ! u033{/ikkC]]PUUb%d7富JKKˎ@SSڵ#ۗ544_Gᚴ(DNN222BD"%%%(--Euu5?EH$.VRR|>_ VVV%hJyT*E^^233,MR"R?%ZZZ}7+{aii !eEEEfSvv6;^)PYY DR'DWUUؠ hhhKzzz000%,,,`eeKKo! !B!DSlB!B{pM!B!' ׄB!B{8uB!BHsfJLIENDB`gplearn-0.4.2/doc/images/syntax_tree.png000066400000000000000000000675011423420364700202500ustar00rootroot00000000000000PNG  IHDR}[9^zbKGD IDATxyXU ff^^ff喏Y[i{VK䊸 3v߿?,_Z3sp^\O3|Μ,2 BV: : # #ZҸvv|`0鉗UV%00F`Z݉/n">>'N8bNqUfSP5.[!$$P7n Je$&&eGoIOFoP o* 4%*)'w{%[rr@MAʺ&̹= Ӧu+y7oZ-fw#QXd Hl)%V G]hJc1׏J>1"eҭkGVڟDصks姟.Z;t5ڡj3\؉S e{K/,뉢/NbYr%L%>>P?]f?({%g6b;Jbл0j(&8(ǎwr<* CvG[ұItb9dIT* & 8Pvv6o }ZCPSX&1[27g*Kp QA"##ѫF-AH̞9bAQ! 8_`ȑ誶q ~JGrea(QBPCE_nApB<!ґΖrӶԬ\~bŊJG(V+{dx>=}P].biZ##QF # H}Ax$綾Ya] S:ɖ\SFPґ5jk֮뙙Ţ>xu!?'l)1JQl3c9<:_Ȉ Iht8W81:ͥDjժJ }Pլn^*#%r~SYx((pʕ+ 't 㸔F3rHutAx@ׯWx6磃tkQ~Dϳb2T.Mp<1ԣG,^"~zgW̢r~;yӽkg,^$ ~!&FvA=njJGrɎ)r.2|0&O, ~!' 8@\\={bum>C_ #=4)A9?~)Ip1#Pn]FK}y0ƟsΖ$1K]UZE/BH_,""7 !!]H#G:s_͛o~' (護bǎ={Ծ -[8wNKp_\APHll,K,O?uz7n999̚5} KA!]t!!!'Oh\رc9s&.\lYPLp>1۷͛73yd|#FŤI\֧^H_\Le7o7wvy_}~!qqqԨQ E_\lƍtޝ#GV+#<<+WAY YVBBBhРk֬Q,ڵkӧGqƊ\O}ApoOj!2-[LJ;v(Cp=QEUݻwgΜ9J!""֭[cz)." 6m.\p-;vȵk8~8jXWWY\ %%)S0l0)'OӬZJ( Y~=ϟw-^{5G||<._P ]p']N̛7O( 8 /S8uK#Gd…\x% !#}Ap(֬Yݶ|GjMtH_M6l6߯t_2fΞ=Kʕ#8($۶mguԡ},X@8mǎ<ٳ'xB8/_'pyʗ/tҾ}{8|0*J8%@L0!Cڂ0m4 B 8ȑ#YlϟW8o߾DEEqit:q#}Ap˗/3{lF] > HJJbJGHA^u~7xСY-7 >}˗0vX1cQ#}ApN:Bddd[2i$&Nȅ (Wq$ <={Oo>Znt3LԮ]]2{lI}Ax,ӬY3J./tYhqqqԬYS8CE_ӧ'N 44T8Nc #44TXȉ/jR^=ZhҥKt[lK.DFFҤIH}Ax@_5ÇܹsTXQ8.Ѷm[$Ib߾}JGX)h4bXv[vv6~)n)'O&""m۶ݹ-55e(zEPP˗/G$)S$1zhӹVӦM֭#F ''> ̙÷~t< @RRRPTԬY?'0l0܉'hٲ%jłj@c4,`A_eee ^y^y|||R$`}YV+yyyw >>nҥK D}AgϞ ˲,ܼyg}'xǏ+5hڴ) %%vǻ8pD O>qu4裏8v؝79sɄ% ZHtϑoQvZ7oIDll S B}A111+JJb̙L0\dɒݻN:V߽ll6]L_b ʕ+sܮVQT,]~)LvhѢNxyy[v-JH_d2qڵܮh0 l۶X|,X|ׯƻ~! ùs1j(Q}Ѯ];)KRg1gΜ;S\%>uo =mZRB=JxxàAذaZIp2߈/p̙;UZ-kԨQCdgƍt:Z-$nNtAp˗INNjugZ:Sre<==F\\VKXX۶mtҮ~*ncǎ8pۓ~<ܸqTv;999[兇 *P|.E_(n޼ѣG!66ѧr9jϿTaX!!wqdYm۶x{{;Y~7fiӆ8f͚ٳg;s LIlοv})_NSn]K6B%''[{nRc(YuԾQV@SO9T_P$ۭH)HH97rn@ETYIү"%ӻO=:tؽİk.9z% J_JWW x5oF1eqS rR.b@VrƜ Z!iѼ-Z})SƅϼE_p{&իWf:كndzR#rUj60 >e[>A 4 O>${wriغu+6m◭Iq R(܂M)]3;{cLţܺpHEb k؈g;} qZ(JLLdܹ,\\<CS)۠(,r~}H0_ڏ/|,Je,_݌OS)iJU ^e3I>NnF62Shب o*/"%KT,;E_p;'N_*S꽀G3>JG@b>9{L7/32~4h@he9k6'RTiїM} i3$VI>>|x>~/t>h4-BPM4te,a>X.V8aBYʲ̺ux짜?OOSPy\hn1q!b%f@׮3qԭ[WhE_p ˗/ah!Ood X}Vb֌KJz ;v`ȏ>AP=? (Ȳ壛0gb_Ə*U(M `˖04臡@Tl5k򣿣K.,^-J*[nCT iC>)]ұJ\S?nbI 8𞻆U {a|j 0#9F4 ѱe7nte^}M/A=T6s'7L nW4{9kWOJ2-Np7oU6dy{})a@Gu{?ґJe}9]vů<7D/Zo8Nsj 5Jr1\nżVxWJq ٘/futU8L&>ߝ_;!nk#)K9z,V:Swٿ?/GhbSs裏*j.8|vP"/|FowARѿS9 .EݐUѝҹY"o;sIJpx_cS{k>%ٳ/\%T1iijWTj<O,"VZŜٳi7C^PqGuɍ7b/8ݡChٲ%>@_x1nÇiڴy&j?B} 7eF6s?k5pp%C?D_|ْ벾ڣ/ƐO GyӰ4WZJGCM+_~Zo߮t#}rs{(_`,yYݜ 54: ܶ#/<$Q)Y$jkSw *5Ö|u}1m|6U^;BԖlJ*>˕68>јm w:- ܖJç$%kmG_$Lʔ)6&4>LGJndܗ;?mJ9sF?g9Q2ethU=JF쾿ݞ+j-@J-c 7ּG۾V~-dYlY\I}iBBB0JD8OcӉH @G<|Q罦ѣ)r>$򣿿5O IDATeƐTLpZnْV"NaÆL8j;wvι݋n%g5esv"žhNm+{3 &rdd]n1qh`5AHn;Gjuݔ ]͛7GRcUrbr~&Yk`ɔb8ޭ??=xMhJT@2g#K6ی&?{r~_ETjjxUGdYrmڴo$-8e;F=[* ;2 !Oz>:4ˌyo˩y}z z]8Bk___$TkV)ŭ:J?{}שj+RF(fa: {Ǝt;SnF+mV[x7\Cs1bö'(..X9/wyG(%T}E0oQ:0fo߾.o߾<:%B3٣GZ NVR%|k[dK.֣s ʗwz{e)<˧]gay:dMtsӥPFM:j9T82$^$I4m֜V*2IJL3 sa7 Fӕ+W_~A%XE)G1֫G?3g|@V^!.0Ͼž/{вEs )'1\{^=G]IqËtЖիW,͛41T,;= ;Jҥ .EqϮKQyF6gc 587|Ϛ5CҠ6Z,bfg&g.BBvQ!R׮]#Ys2Tz+T©49?O dq!}x_ϟwhw2jMN3e~M-E:݈݊9}*VȾ=(BJʾ釗(˾=ܦ 0իVݓŔt$E\/cZ V >/( 88GS/`|HHgM:qË<TÇYґW^D9ϣrNZF~7]S|g9| ժUS:K/(\r=#g/"xO7~u[cGݭ3fY/~MXNu~ݔ xyy)eĜիWѵZ#=K,ehzt/[IKK܇轊g.I1X3+'wdXx+WV:ˉ/d 5WajGAh9waG%Gc>2üb_b:Jz yyy̚5)San?O׹;SZIb#?P^(&SNJR([ٽ{7#G}cQxx]7^'}4 o)x'@FF3f`3RK|uJU-oĒ5WbѰQcFNϞ=Q([_8=g`]u{οu$ْ/Hq0%!Ys}::(YL&+V8G@:Tiփ͞sl[ͤzl3c;u⃡Chݺ܆([;~8 ,dʕxTmړZr͔doH0'i~}y4h رc\֐|Uߞa)W>=n/p#WGoF>,\7~/һwoʕ+LN}P0/^~sπe)t0˂FwVd+R&0%:u*͛7/VիW9rHN}:;v`ΝNG\IBE$5jDٺuKС?~د ?,*V"&&S]wvԩSa.SEBl6SNZnҥK]+BDDxxxoAp$1 T&L'NHJJ sy߂Hb/ ԨQ7|ӥS;5|p.]ʅ +:O ŋ ´iPՌܙ$I7ApQwfΜɨQW,?Fbƌ\rE0|MvE||<A,Ԯ]۳pBEƒ#}ƲtRƍx0 7%KtAob/.]p(IhҤ UTaӦMJEp{eL8m >ްm„ ߿_8p_H_pK,裏RD ]vr!$>'A7ŴiӔrO'O&22RL njBFXjqU>}8qOFa-FH_p;-"))'*?M4$/^tA(1JNN5k֤O>̚5K82x`֯_ű}#}̜9t;v,F3f(E(HIIaԩ1ҥK+J.Ù2e )))J%w{?rY<==s_L&<̞=[8pOb/70vBW<==;v,… J{#}-ӇӧOFQ:턅KMK>|k2qB[4 &L`͚5DEE)GJŵiZ͞={?8})EAEmݺ&Ot>}:l۶M(b/(nӰaCjժņ Pݺu… >I&)&OL||<}QoH_PdVZtܙy)) mGA ;w.|gJGqqƑܹs"w/\zz:&L`С-[V8NSlY „ HOOW: >>Jq:ƌٳ|qA|+8,dee_}U"##9uZVteZW-[dɒ%Zff&~~~]eH_p+WR\9>C8y$+V`ܸqŦt:&L9y$iii|ᇔ+W+W*P(NH_paÆ1sLT*=rrr8p\NeZl='Nb 2Cu n`Oj+uf0-[ݻZw`2;S>}oZL&,X@jX`7n9L|JIL/RHD&Mطo_=L`ժUzR^gժU :_X,b/(EGGcN_R1sLMV,>UTL>ε %˲X)X#8l* ZŋyN/_믿$Iw5 yyyxxx(N(H_pg)^ϦMD_~7|k9wɄ@\+aHII!)) шd"??K(FlٲTXCV``˖-iUOܹ3[n#??F||uss+5I*UOWDhH]BCzg_%KsNœ4 'x~sM~7l6%6qܸvT6j ˖vp0aC !44ƍ#sł$I5*1&ұ L6gc:e4n΢ﺌT(D/dYf 1mx>8|n+փ a4iDH$22aDr8Z #cK4h2YYY~/;wP _ /qXi`#ݗ/#UmדzzdLǗxOwx+WPD C IBի<3$^aڲu`2'ø:dnunApB<!ґΖrӶԬ\~bŊJG(yUe?<;E]VHNcI؋i:u|ukso~YV;|z:]MFGFJG(իWiּ%ir);/T>(ۍ؟d%n7,I^zu6e݄lŸ?et9> *(IxXX<ڵt_-gdʕ3F80j(֬]33EP}8y0JG(n!CIHgy ]l=I&{nܱcOW4W:K.&Ô#<16oL.]}K5+G1y[P"'gbR4##:uûfQV`˖-tQ8}#}7eXP -OI62q$dj3V(~};xVU8}EM͝;׮}ɘ6LG8Mo1slJHH9s7P;5l1+oڮ\!I:Ktz.QxD0E#g0ڶGPy3w\{?+ޥ1R,rk񫄾nwLE m߾0t)ULJ;mFN7|>lfђhP|M az%]v9DwCk׭P! QJ#<BVF:N^KvV&u:DS2[i}%ڽWTZ(KTijt5E~DD-^)rۋ7orr" ;mjrq7KbϹgWujrl3c<4Ui?Mۗ^Kޞq >l%z߹2K!hˇ4\ҧH$%%!˲S6Ti=b>{z*~VaK9MX/ [j,yS< _?տ j .OK0V^,+J(81w3iiiYR+]SHjrس k F6'Ry~˥}ddv…aE5Q{-׿.#}7~&*I=s2y"L.גZg} Z_?&^fJ*䆿@6g_#n$sϮDw3Ȧ lsl|l$ (풾TLߔMY軙`lqNi_[~(nd5~+-X5b=3-X,!o?fqJ~n0u7 [5,OjժExxxP~'޶dLth6kXšt{ug!g=R5LǗy?>]XAQh+4BSu:!g\x`rbe)1h[h==l7iР!:]R8[+1c0ET}kQdoxBBB\S ïro&]ޞC>S:P7xKv K)ŭXc8 >@ kKz,^S:P@軡jժhbV*mH9X.ו+.o*֋;rSnl1+yV)E( 1"""hݺ5}>qg1rxǡ`XUnzn"܉%a?ϡCh֬q#}7ժU+:<,C_m9^qdM٭L?NOȏ [Jb9܁l3c9<:_ȈKLL~ثwƫ((Bb\ۓVضT^J$8q^kQͣӯ\JLIVt>V. ?z% Y´S|V[\VY TFL{?U@mNfEB軹^z`1(㸔i4K )SF8w+WaO܍Jq)#wfȑtM8;,˼b_~':-eLfl16k׮Jӫwo@=Xx1)Hv#9v6{,YHBL I1b3f(aT̻F8˦?O*@vA=njJGrɎ)r.2|0&O, ~!'~!l2.:x<9uJGzh=X}JjXv uQ:}g\Lg?t&e_ü#H?7ѯ_?#  ~!u9z~3vȇ3/c>0_y睁|t g \&>7aFt&1E-rr !n*f"~!f٘={6c>G;)ɘFoĬVZ,f?ұ"""7 !!m8- IDAT]Hٳ'M6nӿ/_΂ޝt&Me'Gb' !vRZ[VmiѪWjתRk)EECIHldWZMB03gujyɹF&+F"ѢIxAnѣ666Jɫp~D߽CII1ZGnh\ 5///E9IhJP\YwnӪUJrΝ˪UXbsQ-X˗/ӿ:vȑ#G^<55۷oNtt4q+VfҤIB#?333&NHFF[nZTCoDM̝;5k/0zh^˸q011aȑdddo>ԩ#tXo L.3}t֮]˞={Ă/8u 8,CX$ɘ:u*۶m~[D"ѣ~~~DDDЫW/>}*tH@d2|vȑ# [nr9I$v\\\ps=CqĢKKKW!DՖ#AAA͛7FgϞ닩!D՞ϟI&ٓ .R!7pmqpp?DDĄ3gЫW/ɓ'Fk WWW|}}166:$5!C}vC4r\ ˗/ӿ:vȑ#G:$ݻ7w)wAAS̝;Yf:Zi|wϽS-{y={Vᩄ\.gܹZ+V0gCć^ѥK߿?8pCRω랪NfffSRR_CCF_mjhhb ,,,HNNyMb+8<{ӳF|#H^ƍSQD"444^D"a H@cƍXӧ#Ʉwٳ 2wy;v0VW4hРܙ3oߞ˗/8ڭcǎ\~"ALLLy ѣ=w^';;۷WxPo@Ν+P<Ƹq*=ԤK. :'OrY Dvv!UbK2qD&OYѣGb„ l߾Fg^T`FhD/={GXXN:ubE"yrr\OOO555ӦMd2C:::Yfe2aLrr\"ȁҒ{zz ^իW /D,tx;88ȝ山r\.JzK!ё e˖QRRL&cӦMӇ#G2c ֮]i5jd2cǎ(*رckҢO>XXX𜝝p2nݺqm˗`TFnBBNNN>mڴڵk?{nƏ=[^YYYXZZ>]/c 11~!J=MMMBCCqqq0BPW^]H\έ[#@T:ts+ikk3h nݺ 8ߋ.Æ 0*acc=zC/BK. |@d,]K82UV`8*@oQŋ}SQQǏ'88XK.ׯt<3r raE>|||ʾ4h\6GQQcƌ8"uV-Z‡/UzE?99uUz$-[QEE6L|F 1tP ׯ /77S[qq15v~Rwy 111!J/{nݺH$իF6%%%x{{3{lڷoT'cǎӧQHRHLL$??ׯ>{)~/666 jp9V\V's>7n TǏIJJ̲ MMMZ];\z0n߾MhX8Q$?MBVI!^pmጫ -[M64olĎ|̚5ƍϧοyR7oATTwFq.I dgetuIF4o֔͛ӬY3ڶm[WuKJJ⧟~bddddUKNNʕ+έpĐj SAZtWWWj/ԩSq 14ض)Mk}s{ 14w@QH3K'7-X}MZ|ER-ѽ{7zzxUkfΜ9BOO9s0m4RvO>ח`/pv(%5ʉ:0jHS Lmcb-ZO-+)FIœC^C'+%\C}GvLNӧOXPPY|9H$+{MMMlmm~I9uΝ? ;hhhblU#4Т>7CV/VzĒHrIr9M9OOOQǝ")g~X84âywc܍:&]<\.ARdwxDnV*-[aaL0===ׯO~~>ZZZ3sL,--1i~ c߾}}¯VY|9?l6=&Ш8m*boL.O5ą3eر(YT?ƃKٙys?1PV\ɾ}32ũXvСU 'GymמO>Ř1cjPPSM#2"34Ge!Hj 7KЦML&c֭r"N354UHʐaGѕttO7жm[z-bGxx8 Zȱccjӈ>$kkRD'{ ͚%ywt$iii|lݲSf=Pϓq;2.]oT˜49Jj$&-h1G ֭Z;vAۨ sQת!)=#%~n[iӪ_WL?::=zRgNώojAk'>}j1zA̿zرc|0}6m[Hw̕ݟ3bvtB۶mLBSp{oEn{F.+9Dmc۶{U\~\\v5v4gy1{;vllF̿fÆ |G|3y/1WY+1#o+tH:tFj| e]?8t`d|/\ܻ{UE/PC B10̱-~^HaA!_??GfBڎXX >Q=G9g9.3jHA.\!Ci9k6.H3ټk wa/E{\1g|})ރcڻ$Q^#2k z]bK (U7q]~%'o>Uqqi^ݚ5VUI3 7+ݮүBf}2]߭̑ߪM1"̘9 )]l 4|k$3mLRS_m7uEjmcbMkٽk'/_tJ *%D !sYn=111*iS߄?'oMXYk_SyU'?úyWtvՑ05qc+ݦLC}GLZ{+\Rdw|7(7̝ТL.l? Ҩh;耝:C]&H3b҃oTJϔW>0Nm/OMd9] gʃ\+R_EKi7j zF/xµ*9W6oŃ8{899)l 歷bskE)m%z[gMcsF7|2;v,~O>MbBgOQZ`X7FF]_ M .{Ń?75]Z:-xKP/:B3D[=ƳyO,YDz:o/ۤ}ńzdmF|O%2o~*9W6oʱpnZ̺uXvB"lҩ >ofKCi**źEw,[ wĜ:ʆ.д4۸i*1SUvYȉO X5늙ckMuJku(a91\f Z)-^Wdtܹ{νW_ [{ MRN"JڅꪼM1SUknеhJg)֏ 1p?ѸI3t=aWW!;>8&;ޫpINM zQ -o+:vRz_1U`ԩHRxx۪IO s>X%}0y"P\]IqA./ʔ+|¢DqJ :McL8^m/Ow!jd%E*i&8==+Eb/T\8VVV^'NTt\5_ ;NNNly7swr@G];)Q1@KKC`UjosTyx)A{u4QDL2w_'"H$!dG _̞= &Ժoѫ#IvHOVF̿'tOq”h"̓K 8PXnٌ}=SBa^?PCfp!8c/_q&3ƇOX5S+˹~`1XC:tWA#+)97mbB@||<:wEf`ǜ Rb:Ʉ\ ?UMCC;w5g\d%EoJɵܱC- ТE ²N 'UnB~Π3&7%V萔&'9Nq~TH-ZJJ$ >L\&^h2%v$w9~Z1̌pou5O>tbV{w.3zǏ"ZLȪa)T|YWqP*}(=۷/ صkB`բ *kp~0lMu='nn[F6-~^1$thJGut9oagg А>><~t#+R[hJM7Vs\ }01yJ}b1DDFbЧvDVE֓\e.ɴYr%zzzBRbCLL 'MYGQ`"tX SA豕݈۷nBJǔ7r {u=̣DΖ7ZyP\\Y60; hjm*|c+{v3M4aO?.tXD̿8~8_|i]\}ckzmY)9ёș?o.fB__y+)SRR~:}~žuoڌ:V$߻̓;Ϙ1>ZkkoTcٻ+'Z=P'yi 3?adh/`ƌhkW>Y1A&~}Л7ߺMLǮUo4T$H ##CfΘ'|]ܹs̛ׯ]AAx}i%"ѵtteҳg7ޯB3ow^tҰ45VkJH 'b’ys?cԩ̥"bYz NDOoeͻ@zl1ApviOf1vj*N<ɢKrq)8u)f 6a(:we ڥТӧOٹs'7mhƶwԾ< &Q'7+޽2g>ؿ?;wƵ+a]~XN]* ''wyP_ccQжm[$ׯg/صľطOz*A񄸛w~1nLB6mޞR3r<{LC,a 36:ڑIzl8)~?䇡Jqԅѣ>|8_OW/>ĉ~8olE7ꈩ ƶM1n!qd&FWIp{lAxyy9sI^^'Odq`_ inspired and compatible API. While Genetic Programming (GP) can be used to perform a `very wide variety of tasks `_, ``gplearn`` is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations. ``gplearn`` retains the familiar scikit-learn ``fit``/``predict`` API and works with the existing scikit-learn `pipeline `_ and `grid search `_ modules. You can get started with ``gplearn`` as simply as:: est = SymbolicRegressor() est.fit(X_train, y_train) y_pred = est.predict(X_test) However, don't let that stop you from exploring all the ways that the evolution can be tailored to your problem. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, reading the documentation here should make the more relevant ones clear for your problem. ``gplearn`` supports regression through the :class:`SymbolicRegressor`, binary classification with the :class:`SymbolicClassifier`, as well as transformation for automated feature engineering with the :class:`SymbolicTransformer`, which is designed to support regression problems, but should also work for binary classification. ``gplearn`` is built on scikit-learn and a fairly recent copy (0.22.1+) is required for installation. If you come across any issues in running or installing the package, `please submit a bug report `_. Next up, read some more details about :ref:`what Genetic Programming is `, and how it works... Contents: .. toctree:: :maxdepth: 2 intro examples reference advanced installation contributing changelog gplearn-0.4.2/doc/installation.rst000066400000000000000000000013601423420364700171520ustar00rootroot00000000000000.. _installation: Installation ============ ``gplearn`` requires a recent version of scikit-learn (which requires numpy and scipy). So first you will need to `follow their installation instructions `_ to get the dependencies. Now that you have scikit-learn installed, you can install ``gplearn`` using pip:: pip install gplearn Or if you wish to install to the home directory:: pip install --user gplearn For the latest development version, first get the source from github:: git clone https://github.com/trevorstephens/gplearn.git Then navigate into the local ``gplearn`` directory and simply run:: python setup.py install or:: python setup.py install --user and you're done! gplearn-0.4.2/doc/intro.rst000066400000000000000000000627041423420364700156150ustar00rootroot00000000000000.. _intro: Introduction to GP ================== | .. image:: logos/gplearn-wide.png :align: center | .. math:: Owing \,to \,this \,struggle \,for \,life, .. math:: any \,variation, \,however \,slight \,and \,from \,whatever \,cause \,proceeding, .. math:: if \,it \,be \,in \,any \,degree \,profitable \,to \,an \,individual \,of \,any \,species, .. math:: in \,its \,infinitely \,complex \,relations \,to \,other \,organic \,beings \,and \,to \,external \,nature, .. math:: will \,tend \,to \,the \,preservation \,of \,that \,individual, .. math:: and \,will \,generally \,be \,inherited \,by \,its \,offspring. .. math:: - \,Charles \,Darwin, \,On \,the \,Origin \,of \,Species \,(1859) | | .. currentmodule:: gplearn.genetic ``gplearn`` extends the `scikit-learn `_ machine learning library to perform Genetic Programming (GP) with symbolic regression. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations. Genetic programming is capable of taking a series of totally random programs, untrained and unaware of any given target function you might have had in mind, and making them breed, mutate and evolve their way towards the truth. Think of genetic programming as a stochastic optimization process. Every time an initial population is conceived, and with every selection and evolution step in the process, random individuals from the current generation are selected to undergo random changes in order to enter the next. You can control this randomness by using the ``random_state`` parameter of the estimator. So you're skeptical. I hope so. Read on and discover the ways that the fittest programs in the population interact with one another to yield an even better generation. .. _representation: Representation -------------- As mentioned already, GP seeks to find a mathematical formula to represent a relationship. Let's use an arbitrary relationship as an example for the different ways that this could be written. Say we have two variables X0 and X1 that interact as follows to define a dependent variable y: .. math:: y = X_0^{2} - 3 \times X_1 + 0.5 This could be re-written as: .. math:: y = X_0 \times X_0 - 3 \times X_1 + 0.5 Or as a LISP symbolic expression (S-expression) representation which uses prefix-notation, and happens to be very common in GP, as: .. math:: y = (+ (- (\times X_0 X_0) (\times 3 X_1)) 0.5) Or, since we're working in python here, let's express this as a numpy formula:: y = np.add(np.subtract(np.multiply(X0, X0), np.multiply(3., X1)), 0.5) In each of these representations, we have a mix of variables, constants and functions. In this case we have the functions addition, subtraction, and multiplication. We also have the variables :math:`X_0` and :math:`X_1` and constants 3.0 and 0.5. Collectively, the variables and constants are known as terminals. Combined with the functions, the collection of available variables, constants and functions are known as the primitive set. We could also represent this formula as a syntax tree, where the functions are interior nodes, shown in dark blue, and the variables and constants make up the leaves (or terminal nodes), shown in light blue: .. image:: images/syntax_tree.png :align: center Now you can see that the formula can be interpreted in a recursive manner. If we start with the left-hand leaves, we multiply :math:`X_0` and :math:`X_0` and that portion of the formula is evaluated by the subtraction operation (once the :math:`X_1 \times 3.0` portion is also evaluated). The result of the subtraction is then evaluated by the addition operation as we work up the syntax tree. Importantly for GP the :math:`X_0 \times X_0` sub-expression, or sub-tree, can be replaced by any other valid expression that evaluates to a numerical answer, even if that is a constant. That sub-expression, and any larger one such as everything below the subtraction function, all reside adjacent to one another in the list-style representation, making replacement of these segments simple to do programatically. A function has a property known as its arity. Arity, in a python functional sense, refers to the number of arguments that the function takes. In the cases above, all of the functions require two arguments, and thus have an arity of two. But other functions such as ``np.abs()``, only require a single argument, and have an arity of 1. Since we know the arity of all the available functions in our function set, we can actually simplify the S-expression and remove all of the parentheses: .. math:: y = + - \times X_0 X_0 \times 3 X_1 0.5 This could then be evaluated recursively, starting from the left and holding onto a stack which keeps track of how much cumulative arity needs to be satisfied by the terminal nodes. Under the hood, ``gplearn``'s representation is similar to this, and uses Python lists to store the functions and terminals. Constants are represented by floating point numbers, variables by integers and functions by a custom ``Function`` object. In ``gplearn``, the available function set is controlled by an argument that is set when initializing an estimator. The default set is the arithmetic operators: addition, subtraction, division and multiplication. But you can also add in some transformers, comparison functions or trigonometric functions that are all built-in. These strings are put into the ``function_set`` argument to include them in your programs. - 'add' : addition, arity=2. - 'sub' : subtraction, arity=2. - 'mul' : multiplication, arity=2. - 'div' : division, arity=2. - 'sqrt' : square root, arity=1. - 'log' : log, arity=1. - 'abs' : absolute value, arity=1. - 'neg' : negative, arity=1. - 'inv' : inverse, arity=1. - 'max' : maximum, arity=2. - 'min' : minimum, arity=2. - 'sin' : sine (radians), arity=1. - 'cos' : cosine (radians), arity=1. - 'tan' : tangent (radians), arity=1. You should choose whether these functions are valid for your program. .. currentmodule:: gplearn You can also set up your own functions by using the :func:`functions.make_function` factory function which will create a gp-compatible function node that can be incorporated into your programs. See :ref:`advanced use here `. .. currentmodule:: gplearn.genetic .. _fitness: Fitness ------- Now that we can represent a formula as an executable program, we need to determine how well it performs. In a throwback to Darwin, in GP this measure is called a program's fitness. If you have used machine learning before, you may be more familiar with terms such as “score”, “error” or “loss”. It's basically the same thing, and as with those other machine learning terms, in GP we have to know whether the metric needs to be maximized or minimized in order to be able to select the best program in a group. In ``gplearn``, several metrics are available by setting the ``metric`` parameter. For the :class:`SymbolicRegressor` several common error metrics are available and the evolution process seeks to minimize them. The default is the magnitude of the error, 'mean absolute error'. Other metrics available are: - 'mse' for mean squared error, and - 'rmse' for root mean squared error. For the :class:`SymbolicTransformer`, where indirect optimization is sought, the metrics are based on correlation between the program's output and the target, these are maximized by the evolution process: - 'pearson', for Pearson's product-moment correlation coefficient (the default), and - 'spearman' for Spearman's rank-order correlation coefficient. These two correlation metrics are also supported by the :class:`SymbolicRegressor`, though their output will not directly predict the target; they are better used as a value-added feature to a second-stage estimator. Both will equally prefer strongly positively or negatively correlated predictions. The :class:`SymbolicClassifier` currently uses the 'log loss' aka binary cross-entropy loss as its default metric to optimise. .. currentmodule:: gplearn You can also set up your own fitness measures by using the :func:`fitness.make_fitness` factory function which will create a gp-compatible fitness function that can be used to evaluate your programs. See :ref:`advanced use here `. Evaluating the fitness of all the programs in a population is probably the most expensive part of GP. In ``gplearn``, you can parallelize this computation by using the ``n_jobs`` parameter to choose how many cores should work on it at once. If your dataset is small, the overhead of splitting the work over several cores is probably more than the benefit of the reduced work per core. This is because the work is parallelized per generation, so use this only if your dataset is large and the fitness calculation takes a long time. .. _closure: Closure ------- We have already discussed that the measure of a program's fitness is through some function that evaluates the program's predictions compared to some ground truth. But with functions like division in the function set, what happens if your denominator happens to be close to zero? In the case of zero division, or near-zero division in a computer program, the result happens to be an infinite quantity. So there goes your error for the entire test set, even if all other fitness samples were evaluated almost perfectly! Thus, a critical component of rugged GP becomes apparent: we need to protect against such cases for functions that might break for certain arguments. Functions like division must be modified to be able to accept any input argument and still return a valid number at evaluation so that nodes higher up the tree can successfully evaluate their output. In ``gplearn``, several protected functions are used: - division, if the denominator lies between -0.001 and 0.001, returns 1.0. - square root returns the square root of the absolute value of the argument. - log returns the logarithm of the absolute value of the argument, or for very small values less than 0.001, it returns 0.0. - inverse, if the argument lies between -0.001 and 0.001, returns 0.0. In this way, no matter the value of the input data or structure of the evolved program, a valid numerical output can be guaranteed, even if we must sacrifice some interpretability to get there. If you define your own functions, you will need to guard for this as well. The :func:`functions.make_function` factory function will perform some basic checks on your function to ensure it will guard against the most common invalid operations with negative or near-zero operations. .. _sufficiency: Sufficiency ----------- Another requirement of a successful GP run is called sufficiency. Basically, can this problem be solved to an adequate degree with the functions and variables available (i.e., are the functions and inputs *sufficient* for the given problem). For toy symbolic regression tasks, like that solved in example 1, this is easy to ascertain. But in real life, things are less easy to quantify. It may be that there is a good solution lurking in the given multi-dimensional space, but there were insufficient generations evolved, or bad luck turned the evolution process in the wrong direction. It may also be possible that no good relationship can be found through symbolic combinations of your variables. In practice, try to set the constant range to a value that will be helpful to get close to the target. For example, if you are trying to regress on a target with values from 500 – 1000 using variables in a range of 0 – 1, a constant of 0.5 is unlikely to help, and the “best” solution is probably just going to be large amounts of irrelevant additions to try and get close to the lower bound. Similarly, `standardizing `_ or `scaling `_ your variables and targets can make the problem much easier to learn in some cases. If you are using trigonometric functions, make sure all angles are measured in radians and that these functions are useful for your problem. (Do you expect inputs to have a periodic or oscillatory effect on the target? Perhaps temporal variables have a seasonal effect?) If you think that the problem requires a very large formula to solve, start with a larger program depth. And if your dataset has many variables, perhaps the “full” initialization method (initializing the population with full-size programs) makes more sense than waiting for programs to grow large enough to make use of all variables. .. _initilization: Initialization -------------- When starting a GP run, the first generation is blissfully unaware that there is any fitness function that needs to be maximized. These naive programs are a random mix of the available functions and variables and will generally perform poorly. But the user might be able to "strengthen" the initial population by providing good initialization parameters. While these parameters may be of some help, bear in mind that one of the most significant factors impacting performance is the number of features in your dataset. The first parameter to look at is the ``init_depth`` of the programs in the first generation. ``init_depth`` is a tuple of two integers which specify the range of initial depths that the first generation of programs can have. (Though, depending on the ``init_method`` used, first generation programs may be smaller than this range specifies; see below for more information.) Each program in the first generation is randomly assigned a depth from this range, and this range *only applies to the first generation*. The default range of 2 – 6 is generally a good starting point, but if your dataset has many variables, you may want to shift the range to the right so that the first generation contains larger programs. Next, you should consider ``population_size``. This controls the number of programs competing in the first generation and every generation thereafter. If you have very few variables, and have a limited function set, a smaller population size may suffice. If you have a lot of variables, or expect a very large program is required, you may want to start with a larger population. More likely, the number of programs you wish to maintain will be constrained by the amount of time you want to spend evaluating them. Finally, you need to decide on the ``init_method`` appropriate for your data. This can be one of ``'grow'``, ``'full'``, or ``'half and half'``. For all options, the root node must be a function (as opposed to a variable or a constant). For the ``'grow'`` method, nodes are chosen at random from both functions and terminals, allowing for smaller trees than ``init_depth`` specifies. This tends to grow asymmetrical trees as terminals can be chosen before the max depth is reached. If your dataset has a lot of variables, this will likely result in *much smaller* programs than ``init_depth`` specifies. Similarly, if you have very few variables and have chosen a large function set, you will likely see programs approaching the maximum depth specified by ``init_depth``. The ``'full'`` method chooses nodes from the function set until the max depth is reached, and then terminals are chosen. This tends to grow "bushy", symmetrical trees. The default is the ``'half and half'`` method. Program trees are grown through a 50/50 mix of ``'full'`` and ``'grow'`` (i.e., half the population has ``init_method`` set to ``'full'``, and the other half is set to ``'grow'``). This makes for a mix of tree shapes in the initial population. .. _selection: Selection --------- Now that we have a population of programs, we need to decide which ones will get to evolve into the next generation. In ``gplearn`` this is done through tournaments. From the population, a smaller subset is selected at random to compete, the size of which is controlled by the ``tournament_size`` parameter. The fittest individual in this subset is then selected to move on to the next generation. Having a large tournament size will generally find fitter programs more quickly and the evolution process will tend to converge to a solution in less time. A smaller tournament size will likely maintain more diversity in the population as more programs are given a chance to evolve and the population may find a better solution at the expense of taking longer. This is known as selection pressure, and your choice here may be governed by the computation time. .. _evolution: Evolution --------- As discussed in the selection section, we use the fitness measure to find the fittest individual in the tournament to survive. But this individual does not just graduate unaltered to the next generation: first, genetic operations are performed on them. Several common genetic operations are supported by ``gplearn``. **Crossover** Crossover is the principle method of mixing genetic material between individuals and is controlled by the ``p_crossover`` parameter. Unlike other genetic operations, it requires two tournaments to be run in order to find a parent and a donor. Crossover takes the winner of a tournament and selects a random subtree from it to be replaced. A second tournament is performed to find a donor. The donor also has a subtree selected at random and this is inserted into the original parent to form an offspring in the next generation. .. image:: images/gp_ops_crossover.png :align: center **Subtree Mutation** Subtree mutation is one of the more aggressive mutation operations and is controlled by the ``p_subtree_mutation`` parameter. The reason it is more aggressive is that more genetic material can be replaced by totally naive random components. This can reintroduce extinct functions and operators into the population to maintain diversity. Subtree mutation takes the winner of a tournament and selects a random subtree from it to be replaced. A donor subtree is generated at random and this is inserted into the parent to form an offspring in the next generation. .. image:: images/gp_ops_subtree.png :align: center **Hoist Mutation** Hoist mutation is a bloat-fighting mutation operation. It is controlled by the ``p_hoist_mutation`` parameter. The sole purpose of this mutation is to remove genetic material from tournament winners. Hoist mutation takes the winner of a tournament and selects a random subtree from it. A random subtree of that subtree is then selected and this is "hoisted" into the original subtree's location to form an offspring in the next generation. .. image:: images/gp_ops_hoist.png :align: center **Point Mutation** Point mutation is probably the most common form of mutation in genetic programming. Like subtree mutation, it can also reintroduce extinct functions and operators into the population to maintain diversity. Point mutation takes the winner of a tournament and selects random nodes from it to be replaced. Terminals are replaced by other terminals and functions are replaced by other functions that require the same number of arguments as the original node. The resulting tree forms an offspring in the next generation. Functions and terminals are randomly chosen for replacement as controlled by the ``p_point_replace`` parameter which guides the average amount of replacement to perform. .. image:: images/gp_ops_point.png :align: center **Reproduction** Should the sum of the above genetic operations' probabilities be less than one, the balance of genetic operations shall fall back on reproduction. That is, a tournament winner is cloned and enters the next generation unmodified. .. _termination: Termination ----------- There are two ways that the evolution process will stop. The first is that the maximum number of generations, controlled by the parameter ``generations``, is reached. The second way is that at least one program in the population has a fitness that exceeds the parameter ``stopping_criteria``, which defaults to being a perfect score. You may need to do a couple of test runs to determine what metric is possible if you are working with real-life data in order to set this value appropriately. .. _bloat: Bloat ----- A program's size can be measured in two ways: its depth and length. The depth of a program is the distance from its root node to the furthest leaf node. A degenerative program with only a single value (i.e., y = X0) has a depth of zero. The length of a program is the number of elements in the formula which is equal to the total number of nodes. An interesting phenomenon is often encountered in GP where the program sizes grow larger and larger with no significant improvement in fitness. This is known as bloat and leads to longer and longer computation times with little benefit to the solution. Bloat can be fought in ``gplearn`` in several ways. The principle weapon is using a penalized fitness measure during selection where the fitness of an individual is made worse the larger it is. In this way, should there be two programs with identical fitness competing in a tournament, the smaller program will be selected and the larger one discarded. The ``parsimony_coefficient`` parameter controls this penalty and may need to be experimented with to get good performance. Too large a penalty and your smallest programs will tend to be selected regardless of their actual performance on the data, too small and bloat will continue unabated. The final winner of the evolution process is still chosen based on the unpenalized fitness, otherwise known as its raw fitness. A recent paper introduced the covariant parsimony method which can be used by setting ``parsimony_coefficient='auto'``. This method adapts the penalty depending on the relationship between program fitness and size in the population and will change from generation to generation. Another method to fight bloat is by using genetic operations that make programs smaller. ``gplearn`` provides hoist mutation which removes parts of programs during evolution. It can be controlled by the ``p_hoist_mutation`` parameter. Finally, you can increase the amount of subsampling performed on your data to get more diverse looks at individual programs from smaller portions of the data. ``max_samples`` controls this rate and defaults to no subsampling. As a bonus, if you choose to subsample, you also get to see the “out of bag” fitness of the best program in the verbose reporter (activated by setting ``verbose=1``). Hopefully this is pretty close to the in-sample fitness that is also reported. .. currentmodule:: gplearn.genetic .. _classification: Classification -------------- The :class:`SymbolicClassifier` works in exactly the same way as the :class:`SymbolicRegressor` in how the evolution takes place. The only difference is that the output of the program is transformed through a `sigmoid function `_ in order to transform the numeric output into probabilities of each class. In essence this means that a negative output of a function means that the program is predicting one class, and a positive output predicts the other. Note that the sigmoid function is not considered when evaluating the depth or length of the program, ie. the size of the programs and thus the behaviour of bloat reduction measures are equivalent to those in the regressor. .. _transformer: Transformer ----------- The :class:`SymbolicTransformer` works slightly differently to the :class:`SymbolicRegressor`. While the regressor seeks to minimize the error between the programs' outputs and the target variable based on an error metric, the transformer seeks an indirect relationship that can then be exploited by a second estimator. Essentially, this is automated feature engineering and can create powerful non-linear interactions that may be difficult to discover in conventional methods. Where the regressor looks to minimize the direct error, the transformer looks to maximize the correlation between the predicted value and the target. This is done through either the Pearson product-moment correlation coefficient (the default) or the Spearman rank-order correlation coefficient. In both cases the absolute value of the correlation is maximized in order to accept strongly negatively correlated programs. The Spearman correlation is appropriate if your next estimator is going to be tree-based, such as a Random Forest or Gradient Boosting Machine. If you plan to send the new transformed variables into a linear model, it is probably better to stick with the default Pearson correlation. The :class:`SymbolicTransformer` looks at the final generation of the evolution and picks the best programs to evaluate. The number of programs it will look at is controlled by the ``hall_of_fame`` parameter. From the hall of fame, it will then whittle down the best programs to the least correlated amongst them as controlled by the ``n_components`` parameter. You may have the top two programs being almost identical, so this step removes that issue. The correlation between individuals within the hall of fame uses the same correlation method, Pearson or Spearman, as used by the evolution process. Convinced? :ref:`See some examples `, :ref:`explore the full API reference ` and :ref:`install the package `! gplearn-0.4.2/doc/logos/000077500000000000000000000000001423420364700150425ustar00rootroot00000000000000gplearn-0.4.2/doc/logos/gplearn-wide.png000066400000000000000000000512771423420364700201420ustar00rootroot00000000000000PNG  IHDRbKGD pHYs  tIME/[c IDATxwՇ]mlc-z&@CS(!BB =Ћ@k-T\0Ɏe4{ѳEҔ;w`TE8^8oM^^$"?[kaaŚ*q:8 ذG.$"raa&P݀n82̳V4 0 O5A mԙ ܨ0 0 0 N2#Rf4 0 (, HUnj,E&dja(fA-|+Ysaa@C}֑xzkR0 0 հۻƚ0 0 j5,V GYaa@mڀRaa&P+1 kZ0 0 {h8Yaa@-[ּaa&P5̀'k^0 0 %3<,`a+O7 0 fM7G>,p Nε+aaÁswp5M-&JՁ_K>c2J;kP |8_f[aFe(Ɲ<\@7t``5hhK^a~ID^ia]&PK-R`WRL v6PF}TKS`ݎ0L0 \ 'FAi5I` L"0Oq8YLw `4KO~V]7xxD_oL Zgg0ףx,W Y`SFb aȒf>mzX*{l2%}<i_Sk#[u~n+`/:bhYwMas̀8cg{=4 P?e5}ԕƐ.NAd;,pudo~u|Qt+p!1u&y /EwJn u@屹ܩnF H:38dc\ X /G7~$}Ƒ:TbmXxز＂dhH#G[W0jLgs d6VBKH#U$Y5=ҵ >5H ϯܨ t2m$+ȲQ=e6H% h1 X*ۂDL |L,x& ~Ut[$q~m4UE73Ȅp<uy<7 c+1a҅*p1=׷wG|?''> kH25nH#ud44@ z4Wۙ?n|f4*a҅j,xvbTh@TvHd~ b W_u0УIjۅ^I Y׾rOvA{ё2ϧkC\j@"lu<'| l,/`bXoede nvO${V:,@*E<inL`,:2W~Kw`Sk6)nXںRC:rHvguMӢmlo6icQiBk-ǐ7z}:*iHrRF ÁoTkHO6 qY;UNn`X/p9:V{θw1;qF z@s<>Ѧ~⯿ tp`2Luyׇ`M=t_(t 3tݐ\?w%7Oh~k{PMx 0~TӧHZ1D#nܓ rt&.+,y g=ZGx)_Cz\q=*:O;pD,? =Q@AR@mD chj7IDI> e@Fg;RCrs0=_C:7 A"nʱF.%N X OA|I}+0quJZ)~QbQ}Pc167xv{#jc,"U> <}비u1DsUNp ~s^hwzajaDǀ9~Tۧ(Z1$b5 pXhu]m~Y { |l}tQX*7~|5R~uuF?PXc/D]Q[u]]g[Az\/W~1`>kɅ*6)`87\3sOMwls>Rp 4kw|1[_PwĿJH$*>(g&uj1 /|"P-"y=CKߛz qcyq إ9=ᾼ>>w~kͯÏ>F8z! ]TJy{Uub0@[ysY穰w]^ERgݦ}"k[hS9Rg~|,Lͻ z\nBwZLg; K,u) >n>boؤd:-v bY}_B6{*ĊeX^˟ ne:Gb|vΥ^ Ze|Z;=!d55zWȍ F=5~է\a REa:y"cu9^({ {1XW>XPG!>>k ۑeY:1 zYGa`${ée?tZ &yzoQ![0ڷ%TP'WJp>?zt'{er|[:|`KqϏt9I/R._#>gR $ AMv ^kO56r %)]fʳz dn< =եĐ_.t:Kx< ͩCv7j%}gc$21!VY뽝%."^RAy 5*I76 HZǗAV@ɻQN@$e7}}dije?زsA{dKW܇sdۜQX5#E#GP}ϵQƐ{UƐBRvuvP ǺG.Ms<_$GikȶY]4Cv7J!.x$x̜2CD(q|+"9>?>Z]\:kzLTSˢP`4ԧ\C~@/!PWøo ƺX*ۆA6bZPg ҵt"~3Tg?F?w<Ή:-ufNtQ]rދ:Ai NڤƊ 1 Uƨ/ҧ ƐAtXr蝑F'.R^x>b-# FsP9H͐(5sdxYcv*MtO7Qr|_S{4WR3h߂@qrHD)>S 3v b_=CNk!wjCB2\Frl b@mk %"d4AtΊ;֥1~l)$RxKj^ Nč?^36 >Ԧrqr#[?CP.FrQ^n"֙ȟ.DdN,hLݧ*?!Ag-Hmus@u"9>IFCGȅ*@ArCx?뫘s\DzS>չ ,¢YyDJǺ.#Pg!s7 f'! ӭH-%u%.g"'7ЎDIޫ7GA}K_h㛆ҕ,}oE%bly);x!(Fn:tE^}8R$p${]|@m#%و3?P9gy RAV7&:uAF>i;Qfy_>(i iH%ȣW'dg\#V9zl#s %U\qc]jP4Rbd4tcxz:E}Fפy.C3vdu}Xƃ*$|D[MCR"~#Uu ܂,GrStMiUһ0z)C|{{'dvǺ TSLR| H'  HiU.XJ_QFe%F3HmT c ]gVu#RArQ"%ˊS|F_R/]~9uNHvAO 4Grd99jSP>eH̉zbzbܮX$Kh_p\=<]11Rˀ$hN b;|#Z"UďOmn_%hynbw>kTߣ*$H]{d8•~qy$@]Ȫ·+e ѻK:nFZ tTsHv}X4EjcrOq\9w!O SjY9@ 48x~? r;9ү.>$'t_!bru2:&tVl2!6W\UK@<4@.n:,@,? _EP_]eXz}imQU{"B\3(=-~_֞Xe[{\`O0'3I3b,a(F͖f(]lPRmazƨ" ܡd ߦ-z'x.=u3D֍j,*~?gS8:jXSW3C}ʏ{Z1a[Q~Pq{2SAJ:l >%rjǺ:~Eԓ)4c)KFCv 5Xo<3t&v\ID Bң,^D}˺X Cu&9Xoz}>1|,8Zΰp|Kȩjys5)zCZ|X^/@\ f̪2mi;FW/ 4R%_].hZ׀#o8i$a⛉O]3}|IDw7„>DdO 4v?/8_F E,Z+QGp ik _LB#榑TWgwde z&11h\"+ !˷[֧')g-7S 'Rhӑ賃e}v _hTFщUTC?$DdV8~ɱG>9Yx&I7fFs,.aH wHz5=nYOfmh˖6uIh &"Cqt]Y}5a}l1r 6CSՔg;$"_JP\-aa~#z2N<ʶ 5{!]y6=*`c&yǺaaZPOTu`s\D-8!s{[iH8rʲZAv`ӆpnB"j 6v\ =s,U[EZ}lKeNFCg)8K8t~GaMuWPBd,.<tx`RvmFdX61:&ǁvs8%LQ{:W5"I և &P"gG*w6v}ݰmyw Nޡ^NFC3dp<}$pN 'PQ%]ki}Ȱ>`mR[^dQq*n`Nq 8u  l #po~*P餝ipx2r{nn$"VNz>@1aaa}ڤb)U@}[_nN@V~*{ˁ߳hj]SK)tN'Iahq5p{ ?81̶{7^t%[Zs5>dtgmҹ ܊6֭1|DULg.(I$31 ' #N#@F; RI +և 0dx"P/xb{6Уd448IjG:V58љDe 0 ӱz8փ+tRS*R_knTD~~!QmX2z6GFmx/G; 0 |'=u:&(J36h[B|1EUx:)f"tt ? \hxzP.sDd~'yGq\Sr7bgoݷ7n|m?~S:z iضt5LHS:9`+`=I@xcjl[_ЉdաԵ,Rʸ3؇b 6u\5s^se`[Drn{H=~*k߫1{.r3^_6VPΗFmzo{LppQγkdhjlsфKe7 f$Od49O{<$"_uH:Bg=u^D_l$:\ IDATCά=Ϋh>P($ȁ,Z;`M {i;Tzu3{g0=VTd%༆>CUOk;~r-)rmryE̎`}֢UCsgu{Q'UB\C`\} =z_{sڧ>E|G('K}܃G7l_DjЯ3`mh? M]oC//6Y$$=5 B}W#euNv߮pwd=ѦF3 |v- Φj|T~7wϢ> /Ё_Dƺ;{wtbM\rl>HNue#$BQT ~D'y>S-#LrM؍HG]˵,S_?:uCAK*8ns*UQ3C}P{ߛm7E3^7NnBV<.Qa.JU ^^X=xoT\"ս R-y/&^_:FNĝ!x GsGu8岾_:G ;@2Z:XwoR|$ 6D :/ TW~{`{vsBpOmw8 ocWU3hW:S@(M,C~M}Fwbqt V@:yDԵ,ȲZX>k܇B[X=Ȋja->=G"inD,+wk|Ľ*^^X=0uq,bPMk->WEz_#N{1̣:Nz:ZŸm s-9 YN2:8^> &zDKeR7mЪ>p\ޣg!!Kz]M=z^G|)תr; 3+HB+z#C ֻ(bn]>;T0L?SE;E>ӎXuקxYǠs/:;"/\oڈ:*<\"/8*h :LPJ{bͯIVLFC (: G,MRٲml X>d%\dIv$ȨP}7Ix<٫ej77=%\NB.l~o;=w\X=X-ck?d:_"Wӈ_p5VGE#~#An_YФX*'W{Zn'o |o5:Pؑ0Eg6Z_wIJ#<Ug;뀾]Z\/BҍAeD, t7; +Hs8m蕡-Y>gwVW~Q BPY b*{'SY-u0 /l7Gh(KP8 D lKeOKFCy?$ 01$"S1C (뾁& uTl?gF~ZG"s&i~2MGR\b$x +H 妯kp':!anVbqS==X-N ;|P>H ±srĊ3!P6 8Yz '"F*}Ψ9B5hvZG2ќG)8Rq{ ]wU;G'yhJ:GwX*X* 2]>+ 3;֍<ŢQ{\$B@/QHHƉ)uA#Ze's{ZB'6Y֭5qbEeym~u T/84=^Ke{L"r J'fz"%G|J,ۑ"G5p AkN,R!ޡւ\݈;NJ ~? 0CHzM J鍸݄dd2'~ n D(/kf=vHHIqiϫp$bM]˵DF:EgϨ،"~Ŗ^¾?qnsh$5ժ*ꫝȝ /,pA% N|w>tM A7-׎>uMoAX[\/B zmܤ` j(PUKFC"5;oA|]ތRٶ$""&yo:RQg{vڝR̔[:],:i=VEN)~}~I7F#~Pp?-H! Ttn[b"EAX>KU*WzjQYOMJF`UT% ]KeRKK?{"i6Η35ξz qzXxw >Vj)֢#5*sC:2L`q: нTYs!{)u| +/eznWQ>TNp/s_oD~Idࣈ{K."ѬIyH;2xiT^ۗy-gymc Y%9IoJ}J?Ky5*V)k) gPg+՞uP*#zM׳l*5}}ރjJ#oAV];5]\ M ]o?_s׽6=S_!o(f2۫Trҩ6D".Rǵ,S=s;O O+~`X/A(X*Z*pzs2Z^p8^_{hID==V^k<8.R/k@q1׍99ָ)",'U.5:K:hF\u,^d"\Zj >>V*}*H.=:A@,gr-?)ڸ)/KH]TrD>7-wҶP+՞,:yN{1>$_mRzocsͪsʹ,^@T#z}K \R2rݮ\F?v XTSv.3 yDj/dZ:^v$"3Lsv :vDj@ǸjWԁy,6vQ~ %3 o;U- oE+,ч) U`}OFk AGzR:DE'zM-f5֡;+D+5xL+]-vVs߇X*; Y\ }/pd2G6˩a Thzjy>D|:Kew}#<ĩaaFm[X,D]Ջ,Rd4de 0f, H Dד- 0 Q?\; C C}+l |Ke7naaQ?*Al,=I01Od44׺a`d `ёa64 %DH>=xHÓ&aFQ*o.xi&Pݑn알޴ba4eJ|jL0@u -"{2aa^F*aS!YΊaat#R'#G@Tve6aaKxz0b-} 'L\,]x]/OFCca j8,H3Ȃ;鰳aaF'x/p+W !L}#Xppm2c]0 0 j8<R/f7Qnuld4u'0 0&x 1$"#RgQp? 잌fZ2 0 h2# PoN$"=Hq8XXY2|b ,jnaF%4O\ *}3n<ԣL`d4u1JDVrnDk.0 wqnz L"zxآNU2zɺm_LaS\8]8^ZQоRaa4@ w|d}o\TJFC})5nmTv[jaauxz#^$#hfg)#?߫5nATvnaauxzuәDRLFC!yj@X*ߺaaFj8$Q_% hhv2:8 ey!ӱTu;0 0 p< جF\&/&Tu=0 0T9j8i$g_oհc 0 0x[8ش\ \ID[OSS-eTvu?0 0ii;g蘓iT Wo ?!|3_RˁɓgZ4 0 X+IL"S]%H@1+@u xM&y9X*{8p ڠ94 %N.aa5HxFǺByUs3ȿr߈w\+ d44˺a0 0xz$XJ-Un70&O!d4/$xiiT놆aaU p<2p fp<}y8#ʶ$AKehaQ@`N=P|8}CRsaat>Do bOpTVū%*Z~Z5:yEk32wHnJ-z¯ɿRў{cWz,<;aO$KI;XC.]<]C\z 6sfe=ׁ}3 ij8Rx bM4}M$"ђЏT'Rd4ISH$ޤǿI%׀M|^ 'btUȒ)v b5\譮2ߊ%!𥊮b>tXR'ػ]dPY{:2Я !J'ss߮<b]Fnd *OvS1hFS p<}q>Ԍ RKDf'58m6PYO.ӿ jA~p: QZ{1TeYyqqv-rC ɓ EV;L(^$#%ux15U`o}N(c`À(?>ЇG3G$ʶ4Y;,XL}H.DLfC~b+XCa#K>Xyn~@kTQuflubD8\Ⓢ7^^AG1zw]E*NG, .^ffp-:^+~ devA2׀7Mǯ}0}5S >!p*GwtKP7@9K|Xe_b N4F@3 HPv56EڷBB\'j*F,;o{;bp 8P1 q3]ᶦlԎaF# Tć3px&lmǞΪCs߻,Py$ZF'!)TUXb1 ([< 2ȋ9tcdlGEn ESRYiT|Ih,r* }[# @oQSGB}v NH=/u ^xJMMDoX4B 3NŴ |YZ_4?QsFe`C}o hO ~"@5 j8nߠ\$"w:q-.V,uё[uyW.< 3fX*ے=V4IDATamgx ~:q(vv|R~H5qenw,,{m{čH`é>o&K VzԩZsB<^y8u@dcaFZ=DaY\ڢ^zM>7>ˊz5xMGDq!ޠ|ƧJ : b),JHFH:Y7Hf>TV!cp~|IIU  5Qi0 %M h?BDN7#}' ~҇KQlόqJ?;ZsOp!>of0Ra4@E 5,Ke/cR0XvkeGmk!r~(]H tQ .:ٗ-j0)R gsH:[Zπ&iK $X# 6W4r8!pCGU0 &PJtDdqpM6>)P}5_F,<lyx[Bo+~ų tZ9 d4 0 G8\rT&󿛐@0D#˸^%5xJ}b[5u Ե |{QqD0B,]"}maF p< 4RR>HJP᳔RvYW^EHԳ^⿷A E}Pݾ[wyT@: I?RۆX50 T}`nIDf8-I,TІU.@:3WKߖ/Dr{*SL) [Z@(aFbO {LjuGqd>z3W 0loE|0 jw+"v5HFCl/dt!KK꫿>z^f\HYŻTdGR/m"Wb;WS|^3@S/\}zq!XL$ɧ1RgFIox3t$@چe0 ƪG_s? sA)9BQ&[ge 6_ػx;p#pS hp}Ƣn"9nat ڭI3wri`*^?A,2iBZ+|_ИXx8M"d#Հ&F3 LcU[̏x}^eXD/G8Nv7qjaOdQ5.p<50ik9m,S2 5j 8kjT6^ ܗU7 Y$ )\1%N֎d(0 0P"XY_n{)r8ąL"2'OdQ#ovZ݁#VF ݾ1rX(g5йBңjÝvkާs}T0 h:A+t?{ytf2*bl?fa]߶HUWHOW\Fa`4Hj*Ɲr\hk:p/R@aI kx ojzm.TІx0҂,oL/`?eA탷NQ|Vl/aݮĥ):|6*!@#*ni\h L7/3^Ip\/`'$o_b݂Uz3 h:Zb?X~ 梜wG,O~2. DmArS} 2oډ*N'9^ `O$= Wv@x-ƙtXQO`nG9ENsDs> W-0 l?q,$QtԱ_YzmL"kŻ}̦]7$%ϽL[I)5EwH~HkT/X@@2[ tb| 솸lZQV>HMۨ=-[ZMտ"~-y5rڮ|.77HF bXXG,/)hUInE` b^K`A/qH<5)ú 5ix,_*l:qY@mC^~Bm5 7ku%~ HY1bQGqA8> yfSkuxz:@J&;a'o OcJFCPh.'S0} trw{EoOW!-t N>NIgoW;-{ [gtv7 8ˆ_ۜၒIDp<ݍ}_D"WwF7ri K&Q$p)_ƏJlc ⾱ nx9$+S2tm ӆa]W~"u 9Y;DdV)zٞ*׵lrI+ߛ @w qq\7uoi$1Դbicaat"IDo$P$"6[Cd9O[ུe9$'o&eUl{\!aaw SibH H&YM$KGrאBnaatRIDE[WuDN(N h!F)k{ov~¿JVaa4@UW%vdzH4wgd?d)oځ'gM&H;!9J0 0j)PUTUZgp+0Z:#[PL%LG\+I%T)QRX<|i$ɾDܼ-*}0 0jD4gG"~ e$"2s,uh&mW)n7p% Z쉔E!T?ϳxRXDjbV+f6\aFm(gRS/C>$TWz IDfuUgм&mrTN !H}enf{nI,^"yZ 0 èE{kU 7& ]>{XS0 tZ$+x+UyA|Tߵia&Phh ReO{Q2 O_>1N0 0ڕ)^)'G:Q|,\,/ `s`q% 0 fM{3)^ $Rgrh`mUlo-B,pT/~aQ#Z Ke"G&@s F+ᮟ}4ThHx+usO#U,} 0 j#Կ ̕pI2aaFq<.B-N 0 0Jc CJj(y]dY0 0 jEj8 IA! UHaOZaa3m2@IENDB`gplearn-0.4.2/doc/make.bat000066400000000000000000000150571423420364700153340ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . set I18NSPHINXOPTS=%SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. xml to make Docutils-native XML files echo. pseudoxml to make pseudoxml-XML files for display purposes echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) %SPHINXBUILD% 2> nul if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\gplearn.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\gplearn.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdf" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf cd %BUILDDIR%/.. echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdfja" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf-ja cd %BUILDDIR%/.. echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) if "%1" == "xml" ( %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml if errorlevel 1 exit /b 1 echo. echo.Build finished. The XML files are in %BUILDDIR%/xml. goto end ) if "%1" == "pseudoxml" ( %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml if errorlevel 1 exit /b 1 echo. echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. goto end ) :end gplearn-0.4.2/doc/reference.rst000066400000000000000000000011711423420364700164070ustar00rootroot00000000000000.. _reference: API reference ============= Symbolic Regressor ------------------ .. autoclass:: gplearn.genetic.SymbolicRegressor :members: :inherited-members: Symbolic Classifier ------------------- .. autoclass:: gplearn.genetic.SymbolicClassifier :members: :inherited-members: Symbolic Transformer -------------------- .. autoclass:: gplearn.genetic.SymbolicTransformer :members: :inherited-members: User-Defined Functions ---------------------- .. autofunction:: gplearn.functions.make_function User-Defined Fitness Metrics ---------------------------- .. autofunction:: gplearn.fitness.make_fitness gplearn-0.4.2/doc/rtd-pip-requirements000066400000000000000000000001131423420364700177350ustar00rootroot00000000000000numpy>=1.8.1 numpydoc>=0.5 scipy>=0.13 scikit-learn>=0.22.1 joblib>=0.13.0 gplearn-0.4.2/gplearn/000077500000000000000000000000001423420364700146025ustar00rootroot00000000000000gplearn-0.4.2/gplearn/__init__.py000066400000000000000000000003321423420364700167110ustar00rootroot00000000000000"""Genetic Programming in Python, with a scikit-learn inspired API ``gplearn`` is a set of algorithms for learning genetic programming models. """ __version__ = '0.4.2' __all__ = ['genetic', 'functions', 'fitness'] gplearn-0.4.2/gplearn/_program.py000066400000000000000000000601371423420364700167710ustar00rootroot00000000000000"""The underlying data structure used in gplearn. The :mod:`gplearn._program` module contains the underlying representation of a computer program. It is used for creating and evolving programs used in the :mod:`gplearn.genetic` module. """ # Author: Trevor Stephens # # License: BSD 3 clause from copy import copy import numpy as np from sklearn.utils.random import sample_without_replacement from .functions import _Function from .utils import check_random_state class _Program(object): """A program-like representation of the evolved program. This is the underlying data-structure used by the public classes in the :mod:`gplearn.genetic` module. It should not be used directly by the user. Parameters ---------- function_set : list A list of valid functions to use in the program. arities : dict A dictionary of the form `{arity: [functions]}`. The arity is the number of arguments that the function takes, the functions must match those in the `function_set` parameter. init_depth : tuple of two ints The range of tree depths for the initial population of naive formulas. Individual trees will randomly choose a maximum depth from this range. When combined with `init_method='half and half'` this yields the well- known 'ramped half and half' initialization method. init_method : str - 'grow' : Nodes are chosen at random from both functions and terminals, allowing for smaller trees than `init_depth` allows. Tends to grow asymmetrical trees. - 'full' : Functions are chosen until the `init_depth` is reached, and then terminals are selected. Tends to grow 'bushy' trees. - 'half and half' : Trees are grown through a 50/50 mix of 'full' and 'grow', making for a mix of tree shapes in the initial population. n_features : int The number of features in `X`. const_range : tuple of two floats The range of constants to include in the formulas. metric : _Fitness object The raw fitness metric. p_point_replace : float The probability that any given node will be mutated during point mutation. parsimony_coefficient : float This constant penalizes large programs by adjusting their fitness to be less favorable for selection. Larger values penalize the program more which can control the phenomenon known as 'bloat'. Bloat is when evolution is increasing the size of programs without a significant increase in fitness, which is costly for computation time and makes for a less understandable final result. This parameter may need to be tuned over successive runs. random_state : RandomState instance The random number generator. Note that ints, or None are not allowed. The reason for this being passed is that during parallel evolution the same program object may be accessed by multiple parallel processes. transformer : _Function object, optional (default=None) The function to transform the output of the program to probabilities, only used for the SymbolicClassifier. feature_names : list, optional (default=None) Optional list of feature names, used purely for representations in the `print` operation or `export_graphviz`. If None, then X0, X1, etc will be used for representations. program : list, optional (default=None) The flattened tree representation of the program. If None, a new naive random tree will be grown. If provided, it will be validated. Attributes ---------- program : list The flattened tree representation of the program. raw_fitness_ : float The raw fitness of the individual program. fitness_ : float The penalized fitness of the individual program. oob_fitness_ : float The out-of-bag raw fitness of the individual program for the held-out samples. Only present when sub-sampling was used in the estimator by specifying `max_samples` < 1.0. parents : dict, or None If None, this is a naive random program from the initial population. Otherwise it includes meta-data about the program's parent(s) as well as the genetic operations performed to yield the current program. This is set outside this class by the controlling evolution loops. depth_ : int The maximum depth of the program tree. length_ : int The number of functions and terminals in the program. """ def __init__(self, function_set, arities, init_depth, init_method, n_features, const_range, metric, p_point_replace, parsimony_coefficient, random_state, transformer=None, feature_names=None, program=None): self.function_set = function_set self.arities = arities self.init_depth = (init_depth[0], init_depth[1] + 1) self.init_method = init_method self.n_features = n_features self.const_range = const_range self.metric = metric self.p_point_replace = p_point_replace self.parsimony_coefficient = parsimony_coefficient self.transformer = transformer self.feature_names = feature_names self.program = program if self.program is not None: if not self.validate_program(): raise ValueError('The supplied program is incomplete.') else: # Create a naive random program self.program = self.build_program(random_state) self.raw_fitness_ = None self.fitness_ = None self.parents = None self._n_samples = None self._max_samples = None self._indices_state = None def build_program(self, random_state): """Build a naive random program. Parameters ---------- random_state : RandomState instance The random number generator. Returns ------- program : list The flattened tree representation of the program. """ if self.init_method == 'half and half': method = ('full' if random_state.randint(2) else 'grow') else: method = self.init_method max_depth = random_state.randint(*self.init_depth) # Start a program with a function to avoid degenerative programs function = random_state.randint(len(self.function_set)) function = self.function_set[function] program = [function] terminal_stack = [function.arity] while terminal_stack: depth = len(terminal_stack) choice = self.n_features + len(self.function_set) choice = random_state.randint(choice) # Determine if we are adding a function or terminal if (depth < max_depth) and (method == 'full' or choice <= len(self.function_set)): function = random_state.randint(len(self.function_set)) function = self.function_set[function] program.append(function) terminal_stack.append(function.arity) else: # We need a terminal, add a variable or constant if self.const_range is not None: terminal = random_state.randint(self.n_features + 1) else: terminal = random_state.randint(self.n_features) if terminal == self.n_features: terminal = random_state.uniform(*self.const_range) if self.const_range is None: # We should never get here raise ValueError('A constant was produced with ' 'const_range=None.') program.append(terminal) terminal_stack[-1] -= 1 while terminal_stack[-1] == 0: terminal_stack.pop() if not terminal_stack: return program terminal_stack[-1] -= 1 # We should never get here return None def validate_program(self): """Rough check that the embedded program in the object is valid.""" terminals = [0] for node in self.program: if isinstance(node, _Function): terminals.append(node.arity) else: terminals[-1] -= 1 while terminals[-1] == 0: terminals.pop() terminals[-1] -= 1 return terminals == [-1] def __str__(self): """Overloads `print` output of the object to resemble a LISP tree.""" terminals = [0] output = '' for i, node in enumerate(self.program): if isinstance(node, _Function): terminals.append(node.arity) output += node.name + '(' else: if isinstance(node, int): if self.feature_names is None: output += 'X%s' % node else: output += self.feature_names[node] else: output += '%.3f' % node terminals[-1] -= 1 while terminals[-1] == 0: terminals.pop() terminals[-1] -= 1 output += ')' if i != len(self.program) - 1: output += ', ' return output def export_graphviz(self, fade_nodes=None): """Returns a string, Graphviz script for visualizing the program. Parameters ---------- fade_nodes : list, optional A list of node indices to fade out for showing which were removed during evolution. Returns ------- output : string The Graphviz script to plot the tree representation of the program. """ terminals = [] if fade_nodes is None: fade_nodes = [] output = 'digraph program {\nnode [style=filled]\n' for i, node in enumerate(self.program): fill = '#cecece' if isinstance(node, _Function): if i not in fade_nodes: fill = '#136ed4' terminals.append([node.arity, i]) output += ('%d [label="%s", fillcolor="%s"] ;\n' % (i, node.name, fill)) else: if i not in fade_nodes: fill = '#60a6f6' if isinstance(node, int): if self.feature_names is None: feature_name = 'X%s' % node else: feature_name = self.feature_names[node] output += ('%d [label="%s", fillcolor="%s"] ;\n' % (i, feature_name, fill)) else: output += ('%d [label="%.3f", fillcolor="%s"] ;\n' % (i, node, fill)) if i == 0: # A degenerative program of only one node return output + '}' terminals[-1][0] -= 1 terminals[-1].append(i) while terminals[-1][0] == 0: output += '%d -> %d ;\n' % (terminals[-1][1], terminals[-1][-1]) terminals[-1].pop() if len(terminals[-1]) == 2: parent = terminals[-1][-1] terminals.pop() if not terminals: return output + '}' terminals[-1].append(parent) terminals[-1][0] -= 1 # We should never get here return None def _depth(self): """Calculates the maximum depth of the program tree.""" terminals = [0] depth = 1 for node in self.program: if isinstance(node, _Function): terminals.append(node.arity) depth = max(len(terminals), depth) else: terminals[-1] -= 1 while terminals[-1] == 0: terminals.pop() terminals[-1] -= 1 return depth - 1 def _length(self): """Calculates the number of functions and terminals in the program.""" return len(self.program) def execute(self, X): """Execute the program according to X. Parameters ---------- X : {array-like}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y_hats : array-like, shape = [n_samples] The result of executing the program on X. """ # Check for single-node programs node = self.program[0] if isinstance(node, float): return np.repeat(node, X.shape[0]) if isinstance(node, int): return X[:, node] apply_stack = [] for node in self.program: if isinstance(node, _Function): apply_stack.append([node]) else: # Lazily evaluate later apply_stack[-1].append(node) while len(apply_stack[-1]) == apply_stack[-1][0].arity + 1: # Apply functions that have sufficient arguments function = apply_stack[-1][0] terminals = [np.repeat(t, X.shape[0]) if isinstance(t, float) else X[:, t] if isinstance(t, int) else t for t in apply_stack[-1][1:]] intermediate_result = function(*terminals) if len(apply_stack) != 1: apply_stack.pop() apply_stack[-1].append(intermediate_result) else: return intermediate_result # We should never get here return None def get_all_indices(self, n_samples=None, max_samples=None, random_state=None): """Get the indices on which to evaluate the fitness of a program. Parameters ---------- n_samples : int The number of samples. max_samples : int The maximum number of samples to use. random_state : RandomState instance The random number generator. Returns ------- indices : array-like, shape = [n_samples] The in-sample indices. not_indices : array-like, shape = [n_samples] The out-of-sample indices. """ if self._indices_state is None and random_state is None: raise ValueError('The program has not been evaluated for fitness ' 'yet, indices not available.') if n_samples is not None and self._n_samples is None: self._n_samples = n_samples if max_samples is not None and self._max_samples is None: self._max_samples = max_samples if random_state is not None and self._indices_state is None: self._indices_state = random_state.get_state() indices_state = check_random_state(None) indices_state.set_state(self._indices_state) not_indices = sample_without_replacement( self._n_samples, self._n_samples - self._max_samples, random_state=indices_state) sample_counts = np.bincount(not_indices, minlength=self._n_samples) indices = np.where(sample_counts == 0)[0] return indices, not_indices def _indices(self): """Get the indices used to measure the program's fitness.""" return self.get_all_indices()[0] def raw_fitness(self, X, y, sample_weight): """Evaluate the raw fitness of the program according to X, y. Parameters ---------- X : {array-like}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. sample_weight : array-like, shape = [n_samples] Weights applied to individual samples. Returns ------- raw_fitness : float The raw fitness of the program. """ y_pred = self.execute(X) if self.transformer: y_pred = self.transformer(y_pred) raw_fitness = self.metric(y, y_pred, sample_weight) return raw_fitness def fitness(self, parsimony_coefficient=None): """Evaluate the penalized fitness of the program according to X, y. Parameters ---------- parsimony_coefficient : float, optional If automatic parsimony is being used, the computed value according to the population. Otherwise the initialized value is used. Returns ------- fitness : float The penalized fitness of the program. """ if parsimony_coefficient is None: parsimony_coefficient = self.parsimony_coefficient penalty = parsimony_coefficient * len(self.program) * self.metric.sign return self.raw_fitness_ - penalty def get_subtree(self, random_state, program=None): """Get a random subtree from the program. Parameters ---------- random_state : RandomState instance The random number generator. program : list, optional (default=None) The flattened tree representation of the program. If None, the embedded tree in the object will be used. Returns ------- start, end : tuple of two ints The indices of the start and end of the random subtree. """ if program is None: program = self.program # Choice of crossover points follows Koza's (1992) widely used approach # of choosing functions 90% of the time and leaves 10% of the time. probs = np.array([0.9 if isinstance(node, _Function) else 0.1 for node in program]) probs = np.cumsum(probs / probs.sum()) start = np.searchsorted(probs, random_state.uniform()) stack = 1 end = start while stack > end - start: node = program[end] if isinstance(node, _Function): stack += node.arity end += 1 return start, end def reproduce(self): """Return a copy of the embedded program.""" return copy(self.program) def crossover(self, donor, random_state): """Perform the crossover genetic operation on the program. Crossover selects a random subtree from the embedded program to be replaced. A donor also has a subtree selected at random and this is inserted into the original parent to form an offspring. Parameters ---------- donor : list The flattened tree representation of the donor program. random_state : RandomState instance The random number generator. Returns ------- program : list The flattened tree representation of the program. """ # Get a subtree to replace start, end = self.get_subtree(random_state) removed = range(start, end) # Get a subtree to donate donor_start, donor_end = self.get_subtree(random_state, donor) donor_removed = list(set(range(len(donor))) - set(range(donor_start, donor_end))) # Insert genetic material from donor return (self.program[:start] + donor[donor_start:donor_end] + self.program[end:]), removed, donor_removed def subtree_mutation(self, random_state): """Perform the subtree mutation operation on the program. Subtree mutation selects a random subtree from the embedded program to be replaced. A donor subtree is generated at random and this is inserted into the original parent to form an offspring. This implementation uses the "headless chicken" method where the donor subtree is grown using the initialization methods and a subtree of it is selected to be donated to the parent. Parameters ---------- random_state : RandomState instance The random number generator. Returns ------- program : list The flattened tree representation of the program. """ # Build a new naive program chicken = self.build_program(random_state) # Do subtree mutation via the headless chicken method! return self.crossover(chicken, random_state) def hoist_mutation(self, random_state): """Perform the hoist mutation operation on the program. Hoist mutation selects a random subtree from the embedded program to be replaced. A random subtree of that subtree is then selected and this is 'hoisted' into the original subtrees location to form an offspring. This method helps to control bloat. Parameters ---------- random_state : RandomState instance The random number generator. Returns ------- program : list The flattened tree representation of the program. """ # Get a subtree to replace start, end = self.get_subtree(random_state) subtree = self.program[start:end] # Get a subtree of the subtree to hoist sub_start, sub_end = self.get_subtree(random_state, subtree) hoist = subtree[sub_start:sub_end] # Determine which nodes were removed for plotting removed = list(set(range(start, end)) - set(range(start + sub_start, start + sub_end))) return self.program[:start] + hoist + self.program[end:], removed def point_mutation(self, random_state): """Perform the point mutation operation on the program. Point mutation selects random nodes from the embedded program to be replaced. Terminals are replaced by other terminals and functions are replaced by other functions that require the same number of arguments as the original node. The resulting tree forms an offspring. Parameters ---------- random_state : RandomState instance The random number generator. Returns ------- program : list The flattened tree representation of the program. """ program = copy(self.program) # Get the nodes to modify mutate = np.where(random_state.uniform(size=len(program)) < self.p_point_replace)[0] for node in mutate: if isinstance(program[node], _Function): arity = program[node].arity # Find a valid replacement with same arity replacement = len(self.arities[arity]) replacement = random_state.randint(replacement) replacement = self.arities[arity][replacement] program[node] = replacement else: # We've got a terminal, add a const or variable if self.const_range is not None: terminal = random_state.randint(self.n_features + 1) else: terminal = random_state.randint(self.n_features) if terminal == self.n_features: terminal = random_state.uniform(*self.const_range) if self.const_range is None: # We should never get here raise ValueError('A constant was produced with ' 'const_range=None.') program[node] = terminal return program, list(mutate) depth_ = property(_depth) length_ = property(_length) indices_ = property(_indices) gplearn-0.4.2/gplearn/fitness.py000066400000000000000000000146331423420364700166360ustar00rootroot00000000000000"""Metrics to evaluate the fitness of a program. The :mod:`gplearn.fitness` module contains some metric with which to evaluate the computer programs created by the :mod:`gplearn.genetic` module. """ # Author: Trevor Stephens # # License: BSD 3 clause import numbers import numpy as np from joblib import wrap_non_picklable_objects from scipy.stats import rankdata __all__ = ['make_fitness'] class _Fitness(object): """A metric to measure the fitness of a program. This object is able to be called with NumPy vectorized arguments and return a resulting floating point score quantifying the quality of the program's representation of the true relationship. Parameters ---------- function : callable A function with signature function(y, y_pred, sample_weight) that returns a floating point number. Where `y` is the input target y vector, `y_pred` is the predicted values from the genetic program, and sample_weight is the sample_weight vector. greater_is_better : bool Whether a higher value from `function` indicates a better fit. In general this would be False for metrics indicating the magnitude of the error, and True for metrics indicating the quality of fit. """ def __init__(self, function, greater_is_better): self.function = function self.greater_is_better = greater_is_better self.sign = 1 if greater_is_better else -1 def __call__(self, *args): return self.function(*args) def make_fitness(*, function, greater_is_better, wrap=True): """Make a fitness measure, a metric scoring the quality of a program's fit. This factory function creates a fitness measure object which measures the quality of a program's fit and thus its likelihood to undergo genetic operations into the next generation. The resulting object is able to be called with NumPy vectorized arguments and return a resulting floating point score quantifying the quality of the program's representation of the true relationship. Parameters ---------- function : callable A function with signature function(y, y_pred, sample_weight) that returns a floating point number. Where `y` is the input target y vector, `y_pred` is the predicted values from the genetic program, and sample_weight is the sample_weight vector. greater_is_better : bool Whether a higher value from `function` indicates a better fit. In general this would be False for metrics indicating the magnitude of the error, and True for metrics indicating the quality of fit. wrap : bool, optional (default=True) When running in parallel, pickling of custom metrics is not supported by Python's default pickler. This option will wrap the function using cloudpickle allowing you to pickle your solution, but the evolution may run slightly more slowly. If you are running single-threaded in an interactive Python session or have no need to save the model, set to `False` for faster runs. """ if not isinstance(greater_is_better, bool): raise ValueError('greater_is_better must be bool, got %s' % type(greater_is_better)) if not isinstance(wrap, bool): raise ValueError('wrap must be an bool, got %s' % type(wrap)) if function.__code__.co_argcount != 3: raise ValueError('function requires 3 arguments (y, y_pred, w),' ' got %d.' % function.__code__.co_argcount) if not isinstance(function(np.array([1, 1]), np.array([2, 2]), np.array([1, 1])), numbers.Number): raise ValueError('function must return a numeric.') if wrap: return _Fitness(function=wrap_non_picklable_objects(function), greater_is_better=greater_is_better) return _Fitness(function=function, greater_is_better=greater_is_better) def _weighted_pearson(y, y_pred, w): """Calculate the weighted Pearson correlation coefficient.""" with np.errstate(divide='ignore', invalid='ignore'): y_pred_demean = y_pred - np.average(y_pred, weights=w) y_demean = y - np.average(y, weights=w) corr = ((np.sum(w * y_pred_demean * y_demean) / np.sum(w)) / np.sqrt((np.sum(w * y_pred_demean ** 2) * np.sum(w * y_demean ** 2)) / (np.sum(w) ** 2))) if np.isfinite(corr): return np.abs(corr) return 0. def _weighted_spearman(y, y_pred, w): """Calculate the weighted Spearman correlation coefficient.""" y_pred_ranked = np.apply_along_axis(rankdata, 0, y_pred) y_ranked = np.apply_along_axis(rankdata, 0, y) return _weighted_pearson(y_pred_ranked, y_ranked, w) def _mean_absolute_error(y, y_pred, w): """Calculate the mean absolute error.""" return np.average(np.abs(y_pred - y), weights=w) def _mean_square_error(y, y_pred, w): """Calculate the mean square error.""" return np.average(((y_pred - y) ** 2), weights=w) def _root_mean_square_error(y, y_pred, w): """Calculate the root mean square error.""" return np.sqrt(np.average(((y_pred - y) ** 2), weights=w)) def _log_loss(y, y_pred, w): """Calculate the log loss.""" eps = 1e-15 inv_y_pred = np.clip(1 - y_pred, eps, 1 - eps) y_pred = np.clip(y_pred, eps, 1 - eps) score = y * np.log(y_pred) + (1 - y) * np.log(inv_y_pred) return np.average(-score, weights=w) weighted_pearson = _Fitness(function=_weighted_pearson, greater_is_better=True) weighted_spearman = _Fitness(function=_weighted_spearman, greater_is_better=True) mean_absolute_error = _Fitness(function=_mean_absolute_error, greater_is_better=False) mean_square_error = _Fitness(function=_mean_square_error, greater_is_better=False) root_mean_square_error = _Fitness(function=_root_mean_square_error, greater_is_better=False) log_loss = _Fitness(function=_log_loss, greater_is_better=False) _fitness_map = {'pearson': weighted_pearson, 'spearman': weighted_spearman, 'mean absolute error': mean_absolute_error, 'mse': mean_square_error, 'rmse': root_mean_square_error, 'log loss': log_loss} gplearn-0.4.2/gplearn/functions.py000066400000000000000000000150721423420364700171710ustar00rootroot00000000000000"""The functions used to create programs. The :mod:`gplearn.functions` module contains all of the functions used by gplearn programs. It also contains helper methods for a user to define their own custom functions. """ # Author: Trevor Stephens # # License: BSD 3 clause import numpy as np from joblib import wrap_non_picklable_objects __all__ = ['make_function'] class _Function(object): """A representation of a mathematical relationship, a node in a program. This object is able to be called with NumPy vectorized arguments and return a resulting vector based on a mathematical relationship. Parameters ---------- function : callable A function with signature function(x1, *args) that returns a Numpy array of the same shape as its arguments. name : str The name for the function as it should be represented in the program and its visualizations. arity : int The number of arguments that the ``function`` takes. """ def __init__(self, function, name, arity): self.function = function self.name = name self.arity = arity def __call__(self, *args): return self.function(*args) def make_function(*, function, name, arity, wrap=True): """Make a function node, a representation of a mathematical relationship. This factory function creates a function node, one of the core nodes in any program. The resulting object is able to be called with NumPy vectorized arguments and return a resulting vector based on a mathematical relationship. Parameters ---------- function : callable A function with signature `function(x1, *args)` that returns a Numpy array of the same shape as its arguments. name : str The name for the function as it should be represented in the program and its visualizations. arity : int The number of arguments that the `function` takes. wrap : bool, optional (default=True) When running in parallel, pickling of custom functions is not supported by Python's default pickler. This option will wrap the function using cloudpickle allowing you to pickle your solution, but the evolution may run slightly more slowly. If you are running single-threaded in an interactive Python session or have no need to save the model, set to `False` for faster runs. """ if not isinstance(arity, int): raise ValueError('arity must be an int, got %s' % type(arity)) if not isinstance(function, np.ufunc): if function.__code__.co_argcount != arity: raise ValueError('arity %d does not match required number of ' 'function arguments of %d.' % (arity, function.__code__.co_argcount)) if not isinstance(name, str): raise ValueError('name must be a string, got %s' % type(name)) if not isinstance(wrap, bool): raise ValueError('wrap must be an bool, got %s' % type(wrap)) # Check output shape args = [np.ones(10) for _ in range(arity)] try: function(*args) except (ValueError, TypeError): raise ValueError('supplied function %s does not support arity of %d.' % (name, arity)) if not hasattr(function(*args), 'shape'): raise ValueError('supplied function %s does not return a numpy array.' % name) if function(*args).shape != (10,): raise ValueError('supplied function %s does not return same shape as ' 'input vectors.' % name) # Check closure for zero & negative input arguments args = [np.zeros(10) for _ in range(arity)] if not np.all(np.isfinite(function(*args))): raise ValueError('supplied function %s does not have closure against ' 'zeros in argument vectors.' % name) args = [-1 * np.ones(10) for _ in range(arity)] if not np.all(np.isfinite(function(*args))): raise ValueError('supplied function %s does not have closure against ' 'negatives in argument vectors.' % name) if wrap: return _Function(function=wrap_non_picklable_objects(function), name=name, arity=arity) return _Function(function=function, name=name, arity=arity) def _protected_division(x1, x2): """Closure of division (x1/x2) for zero denominator.""" with np.errstate(divide='ignore', invalid='ignore'): return np.where(np.abs(x2) > 0.001, np.divide(x1, x2), 1.) def _protected_sqrt(x1): """Closure of square root for negative arguments.""" return np.sqrt(np.abs(x1)) def _protected_log(x1): """Closure of log for zero and negative arguments.""" with np.errstate(divide='ignore', invalid='ignore'): return np.where(np.abs(x1) > 0.001, np.log(np.abs(x1)), 0.) def _protected_inverse(x1): """Closure of inverse for zero arguments.""" with np.errstate(divide='ignore', invalid='ignore'): return np.where(np.abs(x1) > 0.001, 1. / x1, 0.) def _sigmoid(x1): """Special case of logistic function to transform to probabilities.""" with np.errstate(over='ignore', under='ignore'): return 1 / (1 + np.exp(-x1)) add2 = _Function(function=np.add, name='add', arity=2) sub2 = _Function(function=np.subtract, name='sub', arity=2) mul2 = _Function(function=np.multiply, name='mul', arity=2) div2 = _Function(function=_protected_division, name='div', arity=2) sqrt1 = _Function(function=_protected_sqrt, name='sqrt', arity=1) log1 = _Function(function=_protected_log, name='log', arity=1) neg1 = _Function(function=np.negative, name='neg', arity=1) inv1 = _Function(function=_protected_inverse, name='inv', arity=1) abs1 = _Function(function=np.abs, name='abs', arity=1) max2 = _Function(function=np.maximum, name='max', arity=2) min2 = _Function(function=np.minimum, name='min', arity=2) sin1 = _Function(function=np.sin, name='sin', arity=1) cos1 = _Function(function=np.cos, name='cos', arity=1) tan1 = _Function(function=np.tan, name='tan', arity=1) sig1 = _Function(function=_sigmoid, name='sig', arity=1) _function_map = {'add': add2, 'sub': sub2, 'mul': mul2, 'div': div2, 'sqrt': sqrt1, 'log': log1, 'abs': abs1, 'neg': neg1, 'inv': inv1, 'max': max2, 'min': min2, 'sin': sin1, 'cos': cos1, 'tan': tan1} gplearn-0.4.2/gplearn/genetic.py000066400000000000000000002013641423420364700166000ustar00rootroot00000000000000"""Genetic Programming in Python, with a scikit-learn inspired API The :mod:`gplearn.genetic` module implements Genetic Programming. These are supervised learning methods based on applying evolutionary operations on computer programs. """ # Author: Trevor Stephens # # License: BSD 3 clause import itertools from abc import ABCMeta, abstractmethod from time import time from warnings import warn import numpy as np from joblib import Parallel, delayed from scipy.stats import rankdata from sklearn.base import BaseEstimator from sklearn.base import RegressorMixin, TransformerMixin, ClassifierMixin from sklearn.exceptions import NotFittedError from sklearn.utils import compute_sample_weight from sklearn.utils.validation import check_array, _check_sample_weight from sklearn.utils.multiclass import check_classification_targets from ._program import _Program from .fitness import _fitness_map, _Fitness from .functions import _function_map, _Function, sig1 as sigmoid from .utils import _partition_estimators from .utils import check_random_state __all__ = ['SymbolicRegressor', 'SymbolicClassifier', 'SymbolicTransformer'] MAX_INT = np.iinfo(np.int32).max def _parallel_evolve(n_programs, parents, X, y, sample_weight, seeds, params): """Private function used to build a batch of programs within a job.""" n_samples, n_features = X.shape # Unpack parameters tournament_size = params['tournament_size'] function_set = params['function_set'] arities = params['arities'] init_depth = params['init_depth'] init_method = params['init_method'] const_range = params['const_range'] metric = params['_metric'] transformer = params['_transformer'] parsimony_coefficient = params['parsimony_coefficient'] method_probs = params['method_probs'] p_point_replace = params['p_point_replace'] max_samples = params['max_samples'] feature_names = params['feature_names'] max_samples = int(max_samples * n_samples) def _tournament(): """Find the fittest individual from a sub-population.""" contenders = random_state.randint(0, len(parents), tournament_size) fitness = [parents[p].fitness_ for p in contenders] if metric.greater_is_better: parent_index = contenders[np.argmax(fitness)] else: parent_index = contenders[np.argmin(fitness)] return parents[parent_index], parent_index # Build programs programs = [] for i in range(n_programs): random_state = check_random_state(seeds[i]) if parents is None: program = None genome = None else: method = random_state.uniform() parent, parent_index = _tournament() if method < method_probs[0]: # crossover donor, donor_index = _tournament() program, removed, remains = parent.crossover(donor.program, random_state) genome = {'method': 'Crossover', 'parent_idx': parent_index, 'parent_nodes': removed, 'donor_idx': donor_index, 'donor_nodes': remains} elif method < method_probs[1]: # subtree_mutation program, removed, _ = parent.subtree_mutation(random_state) genome = {'method': 'Subtree Mutation', 'parent_idx': parent_index, 'parent_nodes': removed} elif method < method_probs[2]: # hoist_mutation program, removed = parent.hoist_mutation(random_state) genome = {'method': 'Hoist Mutation', 'parent_idx': parent_index, 'parent_nodes': removed} elif method < method_probs[3]: # point_mutation program, mutated = parent.point_mutation(random_state) genome = {'method': 'Point Mutation', 'parent_idx': parent_index, 'parent_nodes': mutated} else: # reproduction program = parent.reproduce() genome = {'method': 'Reproduction', 'parent_idx': parent_index, 'parent_nodes': []} program = _Program(function_set=function_set, arities=arities, init_depth=init_depth, init_method=init_method, n_features=n_features, metric=metric, transformer=transformer, const_range=const_range, p_point_replace=p_point_replace, parsimony_coefficient=parsimony_coefficient, feature_names=feature_names, random_state=random_state, program=program) program.parents = genome # Draw samples, using sample weights, and then fit if sample_weight is None: curr_sample_weight = np.ones((n_samples,)) else: curr_sample_weight = sample_weight.copy() oob_sample_weight = curr_sample_weight.copy() indices, not_indices = program.get_all_indices(n_samples, max_samples, random_state) curr_sample_weight[not_indices] = 0 oob_sample_weight[indices] = 0 program.raw_fitness_ = program.raw_fitness(X, y, curr_sample_weight) if max_samples < n_samples: # Calculate OOB fitness program.oob_fitness_ = program.raw_fitness(X, y, oob_sample_weight) programs.append(program) return programs class BaseSymbolic(BaseEstimator, metaclass=ABCMeta): """Base class for symbolic regression / classification estimators. Warning: This class should not be used directly. Use derived classes instead. """ @abstractmethod def __init__(self, *, population_size=1000, hall_of_fame=None, n_components=None, generations=20, tournament_size=20, stopping_criteria=0.0, const_range=(-1., 1.), init_depth=(2, 6), init_method='half and half', function_set=('add', 'sub', 'mul', 'div'), transformer=None, metric='mean absolute error', parsimony_coefficient=0.001, p_crossover=0.9, p_subtree_mutation=0.01, p_hoist_mutation=0.01, p_point_mutation=0.01, p_point_replace=0.05, max_samples=1.0, class_weight=None, feature_names=None, warm_start=False, low_memory=False, n_jobs=1, verbose=0, random_state=None): self.population_size = population_size self.hall_of_fame = hall_of_fame self.n_components = n_components self.generations = generations self.tournament_size = tournament_size self.stopping_criteria = stopping_criteria self.const_range = const_range self.init_depth = init_depth self.init_method = init_method self.function_set = function_set self.transformer = transformer self.metric = metric self.parsimony_coefficient = parsimony_coefficient self.p_crossover = p_crossover self.p_subtree_mutation = p_subtree_mutation self.p_hoist_mutation = p_hoist_mutation self.p_point_mutation = p_point_mutation self.p_point_replace = p_point_replace self.max_samples = max_samples self.class_weight = class_weight self.feature_names = feature_names self.warm_start = warm_start self.low_memory = low_memory self.n_jobs = n_jobs self.verbose = verbose self.random_state = random_state def _verbose_reporter(self, run_details=None): """A report of the progress of the evolution process. Parameters ---------- run_details : dict Information about the evolution. """ if run_details is None: print(' |{:^25}|{:^42}|'.format('Population Average', 'Best Individual')) print('-' * 4 + ' ' + '-' * 25 + ' ' + '-' * 42 + ' ' + '-' * 10) line_format = '{:>4} {:>8} {:>16} {:>8} {:>16} {:>16} {:>10}' print(line_format.format('Gen', 'Length', 'Fitness', 'Length', 'Fitness', 'OOB Fitness', 'Time Left')) else: # Estimate remaining time for run gen = run_details['generation'][-1] generation_time = run_details['generation_time'][-1] remaining_time = (self.generations - gen - 1) * generation_time if remaining_time > 60: remaining_time = '{0:.2f}m'.format(remaining_time / 60.0) else: remaining_time = '{0:.2f}s'.format(remaining_time) oob_fitness = 'N/A' line_format = '{:4d} {:8.2f} {:16g} {:8d} {:16g} {:>16} {:>10}' if self.max_samples < 1.0: oob_fitness = run_details['best_oob_fitness'][-1] line_format = '{:4d} {:8.2f} {:16g} {:8d} {:16g} {:16g} {:>10}' print(line_format.format(run_details['generation'][-1], run_details['average_length'][-1], run_details['average_fitness'][-1], run_details['best_length'][-1], run_details['best_fitness'][-1], oob_fitness, remaining_time)) def fit(self, X, y, sample_weight=None): """Fit the Genetic Program according to X, y. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. sample_weight : array-like, shape = [n_samples], optional Weights applied to individual samples. Returns ------- self : object Returns self. """ random_state = check_random_state(self.random_state) # Check arrays if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) if isinstance(self, ClassifierMixin): X, y = self._validate_data(X, y, y_numeric=False) check_classification_targets(y) if self.class_weight: if sample_weight is None: sample_weight = 1. # modify the sample weights with the corresponding class weight sample_weight = (sample_weight * compute_sample_weight(self.class_weight, y)) self.classes_, y = np.unique(y, return_inverse=True) n_trim_classes = np.count_nonzero(np.bincount(y, sample_weight)) if n_trim_classes != 2: raise ValueError("y contains %d class after sample_weight " "trimmed classes with zero weights, while 2 " "classes are required." % n_trim_classes) self.n_classes_ = len(self.classes_) else: X, y = self._validate_data(X, y, y_numeric=True) hall_of_fame = self.hall_of_fame if hall_of_fame is None: hall_of_fame = self.population_size if hall_of_fame > self.population_size or hall_of_fame < 1: raise ValueError('hall_of_fame (%d) must be less than or equal to ' 'population_size (%d).' % (self.hall_of_fame, self.population_size)) n_components = self.n_components if n_components is None: n_components = hall_of_fame if n_components > hall_of_fame or n_components < 1: raise ValueError('n_components (%d) must be less than or equal to ' 'hall_of_fame (%d).' % (self.n_components, self.hall_of_fame)) self._function_set = [] for function in self.function_set: if isinstance(function, str): if function not in _function_map: raise ValueError('invalid function name %s found in ' '`function_set`.' % function) self._function_set.append(_function_map[function]) elif isinstance(function, _Function): self._function_set.append(function) else: raise ValueError('invalid type %s found in `function_set`.' % type(function)) if not self._function_set: raise ValueError('No valid functions found in `function_set`.') # For point-mutation to find a compatible replacement node self._arities = {} for function in self._function_set: arity = function.arity self._arities[arity] = self._arities.get(arity, []) self._arities[arity].append(function) if isinstance(self.metric, _Fitness): self._metric = self.metric elif isinstance(self, RegressorMixin): if self.metric not in ('mean absolute error', 'mse', 'rmse', 'pearson', 'spearman'): raise ValueError('Unsupported metric: %s' % self.metric) self._metric = _fitness_map[self.metric] elif isinstance(self, ClassifierMixin): if self.metric != 'log loss': raise ValueError('Unsupported metric: %s' % self.metric) self._metric = _fitness_map[self.metric] elif isinstance(self, TransformerMixin): if self.metric not in ('pearson', 'spearman'): raise ValueError('Unsupported metric: %s' % self.metric) self._metric = _fitness_map[self.metric] self._method_probs = np.array([self.p_crossover, self.p_subtree_mutation, self.p_hoist_mutation, self.p_point_mutation]) self._method_probs = np.cumsum(self._method_probs) if self._method_probs[-1] > 1: raise ValueError('The sum of p_crossover, p_subtree_mutation, ' 'p_hoist_mutation and p_point_mutation should ' 'total to 1.0 or less.') if self.init_method not in ('half and half', 'grow', 'full'): raise ValueError('Valid program initializations methods include ' '"grow", "full" and "half and half". Given %s.' % self.init_method) if not((isinstance(self.const_range, tuple) and len(self.const_range) == 2) or self.const_range is None): raise ValueError('const_range should be a tuple with length two, ' 'or None.') if (not isinstance(self.init_depth, tuple) or len(self.init_depth) != 2): raise ValueError('init_depth should be a tuple with length two.') if self.init_depth[0] > self.init_depth[1]: raise ValueError('init_depth should be in increasing numerical ' 'order: (min_depth, max_depth).') if self.feature_names is not None: if self.n_features_in_ != len(self.feature_names): raise ValueError('The supplied `feature_names` has different ' 'length to n_features. Expected %d, got %d.' % (self.n_features_in_, len(self.feature_names))) for feature_name in self.feature_names: if not isinstance(feature_name, str): raise ValueError('invalid type %s found in ' '`feature_names`.' % type(feature_name)) if self.transformer is not None: if isinstance(self.transformer, _Function): self._transformer = self.transformer elif self.transformer == 'sigmoid': self._transformer = sigmoid else: raise ValueError('Invalid `transformer`. Expected either ' '"sigmoid" or _Function object, got %s' % type(self.transformer)) if self._transformer.arity != 1: raise ValueError('Invalid arity for `transformer`. Expected 1, ' 'got %d.' % (self._transformer.arity)) params = self.get_params() params['_metric'] = self._metric if hasattr(self, '_transformer'): params['_transformer'] = self._transformer else: params['_transformer'] = None params['function_set'] = self._function_set params['arities'] = self._arities params['method_probs'] = self._method_probs if not self.warm_start or not hasattr(self, '_programs'): # Free allocated memory, if any self._programs = [] self.run_details_ = {'generation': [], 'average_length': [], 'average_fitness': [], 'best_length': [], 'best_fitness': [], 'best_oob_fitness': [], 'generation_time': []} prior_generations = len(self._programs) n_more_generations = self.generations - prior_generations if n_more_generations < 0: raise ValueError('generations=%d must be larger or equal to ' 'len(_programs)=%d when warm_start==True' % (self.generations, len(self._programs))) elif n_more_generations == 0: fitness = [program.raw_fitness_ for program in self._programs[-1]] warn('Warm-start fitting without increasing n_estimators does not ' 'fit new programs.') if self.warm_start: # Generate and discard seeds that would have been produced on the # initial fit call. for i in range(len(self._programs)): _ = random_state.randint(MAX_INT, size=self.population_size) if self.verbose: # Print header fields self._verbose_reporter() for gen in range(prior_generations, self.generations): start_time = time() if gen == 0: parents = None else: parents = self._programs[gen - 1] # Parallel loop n_jobs, n_programs, starts = _partition_estimators( self.population_size, self.n_jobs) seeds = random_state.randint(MAX_INT, size=self.population_size) population = Parallel(n_jobs=n_jobs, verbose=int(self.verbose > 1))( delayed(_parallel_evolve)(n_programs[i], parents, X, y, sample_weight, seeds[starts[i]:starts[i + 1]], params) for i in range(n_jobs)) # Reduce, maintaining order across different n_jobs population = list(itertools.chain.from_iterable(population)) fitness = [program.raw_fitness_ for program in population] length = [program.length_ for program in population] parsimony_coefficient = None if self.parsimony_coefficient == 'auto': parsimony_coefficient = (np.cov(length, fitness)[1, 0] / np.var(length)) for program in population: program.fitness_ = program.fitness(parsimony_coefficient) self._programs.append(population) # Remove old programs that didn't make it into the new population. if not self.low_memory: for old_gen in np.arange(gen, 0, -1): indices = [] for program in self._programs[old_gen]: if program is not None: for idx in program.parents: if 'idx' in idx: indices.append(program.parents[idx]) indices = set(indices) for idx in range(self.population_size): if idx not in indices: self._programs[old_gen - 1][idx] = None elif gen > 0: # Remove old generations self._programs[gen - 1] = None # Record run details if self._metric.greater_is_better: best_program = population[np.argmax(fitness)] else: best_program = population[np.argmin(fitness)] self.run_details_['generation'].append(gen) self.run_details_['average_length'].append(np.mean(length)) self.run_details_['average_fitness'].append(np.mean(fitness)) self.run_details_['best_length'].append(best_program.length_) self.run_details_['best_fitness'].append(best_program.raw_fitness_) oob_fitness = np.nan if self.max_samples < 1.0: oob_fitness = best_program.oob_fitness_ self.run_details_['best_oob_fitness'].append(oob_fitness) generation_time = time() - start_time self.run_details_['generation_time'].append(generation_time) if self.verbose: self._verbose_reporter(self.run_details_) # Check for early stopping if self._metric.greater_is_better: best_fitness = fitness[np.argmax(fitness)] if best_fitness >= self.stopping_criteria: break else: best_fitness = fitness[np.argmin(fitness)] if best_fitness <= self.stopping_criteria: break if isinstance(self, TransformerMixin): # Find the best individuals in the final generation fitness = np.array(fitness) if self._metric.greater_is_better: hall_of_fame = fitness.argsort()[::-1][:self.hall_of_fame] else: hall_of_fame = fitness.argsort()[:self.hall_of_fame] evaluation = np.array([gp.execute(X) for gp in [self._programs[-1][i] for i in hall_of_fame]]) if self.metric == 'spearman': evaluation = np.apply_along_axis(rankdata, 1, evaluation) with np.errstate(divide='ignore', invalid='ignore'): correlations = np.abs(np.corrcoef(evaluation)) np.fill_diagonal(correlations, 0.) components = list(range(self.hall_of_fame)) indices = list(range(self.hall_of_fame)) # Iteratively remove least fit individual of most correlated pair while len(components) > self.n_components: most_correlated = np.unravel_index(np.argmax(correlations), correlations.shape) # The correlation matrix is sorted by fitness, so identifying # the least fit of the pair is simply getting the higher index worst = max(most_correlated) components.pop(worst) indices.remove(worst) correlations = correlations[:, indices][indices, :] indices = list(range(len(components))) self._best_programs = [self._programs[-1][i] for i in hall_of_fame[components]] else: # Find the best individual in the final generation if self._metric.greater_is_better: self._program = self._programs[-1][np.argmax(fitness)] else: self._program = self._programs[-1][np.argmin(fitness)] return self class SymbolicRegressor(BaseSymbolic, RegressorMixin): """A Genetic Programming symbolic regressor. A symbolic regressor is an estimator that begins by building a population of naive random formulas to represent a relationship. The formulas are represented as tree-like structures with mathematical functions being recursively applied to variables and constants. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations such as crossover, mutation or reproduction. Parameters ---------- population_size : integer, optional (default=1000) The number of programs in each generation. generations : integer, optional (default=20) The number of generations to evolve. tournament_size : integer, optional (default=20) The number of programs that will compete to become part of the next generation. stopping_criteria : float, optional (default=0.0) The required metric value required in order to stop evolution early. const_range : tuple of two floats, or None, optional (default=(-1., 1.)) The range of constants to include in the formulas. If None then no constants will be included in the candidate programs. init_depth : tuple of two ints, optional (default=(2, 6)) The range of tree depths for the initial population of naive formulas. Individual trees will randomly choose a maximum depth from this range. When combined with `init_method='half and half'` this yields the well- known 'ramped half and half' initialization method. init_method : str, optional (default='half and half') - 'grow' : Nodes are chosen at random from both functions and terminals, allowing for smaller trees than `init_depth` allows. Tends to grow asymmetrical trees. - 'full' : Functions are chosen until the `init_depth` is reached, and then terminals are selected. Tends to grow 'bushy' trees. - 'half and half' : Trees are grown through a 50/50 mix of 'full' and 'grow', making for a mix of tree shapes in the initial population. function_set : iterable, optional (default=('add', 'sub', 'mul', 'div')) The functions to use when building and evolving programs. This iterable can include strings to indicate either individual functions as outlined below, or you can also include your own functions as built using the ``make_function`` factory from the ``functions`` module. Available individual functions are: - 'add' : addition, arity=2. - 'sub' : subtraction, arity=2. - 'mul' : multiplication, arity=2. - 'div' : protected division where a denominator near-zero returns 1., arity=2. - 'sqrt' : protected square root where the absolute value of the argument is used, arity=1. - 'log' : protected log where the absolute value of the argument is used and a near-zero argument returns 0., arity=1. - 'abs' : absolute value, arity=1. - 'neg' : negative, arity=1. - 'inv' : protected inverse where a near-zero argument returns 0., arity=1. - 'max' : maximum, arity=2. - 'min' : minimum, arity=2. - 'sin' : sine (radians), arity=1. - 'cos' : cosine (radians), arity=1. - 'tan' : tangent (radians), arity=1. metric : str, optional (default='mean absolute error') The name of the raw fitness metric. Available options include: - 'mean absolute error'. - 'mse' for mean squared error. - 'rmse' for root mean squared error. - 'pearson', for Pearson's product-moment correlation coefficient. - 'spearman' for Spearman's rank-order correlation coefficient. Note that 'pearson' and 'spearman' will not directly predict the target but could be useful as value-added features in a second-step estimator. This would allow the user to generate one engineered feature at a time, using the SymbolicTransformer would allow creation of multiple features at once. parsimony_coefficient : float or "auto", optional (default=0.001) This constant penalizes large programs by adjusting their fitness to be less favorable for selection. Larger values penalize the program more which can control the phenomenon known as 'bloat'. Bloat is when evolution is increasing the size of programs without a significant increase in fitness, which is costly for computation time and makes for a less understandable final result. This parameter may need to be tuned over successive runs. If "auto" the parsimony coefficient is recalculated for each generation using c = Cov(l,f)/Var( l), where Cov(l,f) is the covariance between program size l and program fitness f in the population, and Var(l) is the variance of program sizes. p_crossover : float, optional (default=0.9) The probability of performing crossover on a tournament winner. Crossover takes the winner of a tournament and selects a random subtree from it to be replaced. A second tournament is performed to find a donor. The donor also has a subtree selected at random and this is inserted into the original parent to form an offspring in the next generation. p_subtree_mutation : float, optional (default=0.01) The probability of performing subtree mutation on a tournament winner. Subtree mutation takes the winner of a tournament and selects a random subtree from it to be replaced. A donor subtree is generated at random and this is inserted into the original parent to form an offspring in the next generation. p_hoist_mutation : float, optional (default=0.01) The probability of performing hoist mutation on a tournament winner. Hoist mutation takes the winner of a tournament and selects a random subtree from it. A random subtree of that subtree is then selected and this is 'hoisted' into the original subtrees location to form an offspring in the next generation. This method helps to control bloat. p_point_mutation : float, optional (default=0.01) The probability of performing point mutation on a tournament winner. Point mutation takes the winner of a tournament and selects random nodes from it to be replaced. Terminals are replaced by other terminals and functions are replaced by other functions that require the same number of arguments as the original node. The resulting tree forms an offspring in the next generation. Note : The above genetic operation probabilities must sum to less than one. The balance of probability is assigned to 'reproduction', where a tournament winner is cloned and enters the next generation unmodified. p_point_replace : float, optional (default=0.05) For point mutation only, the probability that any given node will be mutated. max_samples : float, optional (default=1.0) The fraction of samples to draw from X to evaluate each program on. feature_names : list, optional (default=None) Optional list of feature names, used purely for representations in the `print` operation or `export_graphviz`. If None, then X0, X1, etc will be used for representations. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more generations to the evolution, otherwise, just fit a new evolution. low_memory : bool, optional (default=False) When set to ``True``, only the current generation is retained. Parent information is discarded. For very large populations or runs with many generations, this can result in substantial memory use reduction. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for `fit`. If -1, then the number of jobs is set to the number of cores. verbose : int, optional (default=0) Controls the verbosity of the evolution building process. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Attributes ---------- run_details_ : dict Details of the evolution process. Includes the following elements: - 'generation' : The generation index. - 'average_length' : The average program length of the generation. - 'average_fitness' : The average program fitness of the generation. - 'best_length' : The length of the best program in the generation. - 'best_fitness' : The fitness of the best program in the generation. - 'best_oob_fitness' : The out of bag fitness of the best program in the generation (requires `max_samples` < 1.0). - 'generation_time' : The time it took for the generation to evolve. See Also -------- SymbolicTransformer References ---------- .. [1] J. Koza, "Genetic Programming", 1992. .. [2] R. Poli, et al. "A Field Guide to Genetic Programming", 2008. """ def __init__(self, *, population_size=1000, generations=20, tournament_size=20, stopping_criteria=0.0, const_range=(-1., 1.), init_depth=(2, 6), init_method='half and half', function_set=('add', 'sub', 'mul', 'div'), metric='mean absolute error', parsimony_coefficient=0.001, p_crossover=0.9, p_subtree_mutation=0.01, p_hoist_mutation=0.01, p_point_mutation=0.01, p_point_replace=0.05, max_samples=1.0, feature_names=None, warm_start=False, low_memory=False, n_jobs=1, verbose=0, random_state=None): super(SymbolicRegressor, self).__init__( population_size=population_size, generations=generations, tournament_size=tournament_size, stopping_criteria=stopping_criteria, const_range=const_range, init_depth=init_depth, init_method=init_method, function_set=function_set, metric=metric, parsimony_coefficient=parsimony_coefficient, p_crossover=p_crossover, p_subtree_mutation=p_subtree_mutation, p_hoist_mutation=p_hoist_mutation, p_point_mutation=p_point_mutation, p_point_replace=p_point_replace, max_samples=max_samples, feature_names=feature_names, warm_start=warm_start, low_memory=low_memory, n_jobs=n_jobs, verbose=verbose, random_state=random_state) def __str__(self): """Overloads `print` output of the object to resemble a LISP tree.""" if not hasattr(self, '_program'): return self.__repr__() return self._program.__str__() def predict(self, X): """Perform regression on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples] Predicted values for X. """ if not hasattr(self, '_program'): raise NotFittedError('SymbolicRegressor not fitted.') X = check_array(X) _, n_features = X.shape if self.n_features_in_ != n_features: raise ValueError('Number of features of the model must match the ' 'input. Model n_features is %s and input ' 'n_features is %s.' % (self.n_features_in_, n_features)) y = self._program.execute(X) return y class SymbolicClassifier(BaseSymbolic, ClassifierMixin): """A Genetic Programming symbolic classifier. A symbolic classifier is an estimator that begins by building a population of naive random formulas to represent a relationship. The formulas are represented as tree-like structures with mathematical functions being recursively applied to variables and constants. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations such as crossover, mutation or reproduction. Parameters ---------- population_size : integer, optional (default=500) The number of programs in each generation. generations : integer, optional (default=10) The number of generations to evolve. tournament_size : integer, optional (default=20) The number of programs that will compete to become part of the next generation. stopping_criteria : float, optional (default=0.0) The required metric value required in order to stop evolution early. const_range : tuple of two floats, or None, optional (default=(-1., 1.)) The range of constants to include in the formulas. If None then no constants will be included in the candidate programs. init_depth : tuple of two ints, optional (default=(2, 6)) The range of tree depths for the initial population of naive formulas. Individual trees will randomly choose a maximum depth from this range. When combined with `init_method='half and half'` this yields the well- known 'ramped half and half' initialization method. init_method : str, optional (default='half and half') - 'grow' : Nodes are chosen at random from both functions and terminals, allowing for smaller trees than `init_depth` allows. Tends to grow asymmetrical trees. - 'full' : Functions are chosen until the `init_depth` is reached, and then terminals are selected. Tends to grow 'bushy' trees. - 'half and half' : Trees are grown through a 50/50 mix of 'full' and 'grow', making for a mix of tree shapes in the initial population. function_set : iterable, optional (default=('add', 'sub', 'mul', 'div')) The functions to use when building and evolving programs. This iterable can include strings to indicate either individual functions as outlined below, or you can also include your own functions as built using the ``make_function`` factory from the ``functions`` module. Available individual functions are: - 'add' : addition, arity=2. - 'sub' : subtraction, arity=2. - 'mul' : multiplication, arity=2. - 'div' : protected division where a denominator near-zero returns 1., arity=2. - 'sqrt' : protected square root where the absolute value of the argument is used, arity=1. - 'log' : protected log where the absolute value of the argument is used and a near-zero argument returns 0., arity=1. - 'abs' : absolute value, arity=1. - 'neg' : negative, arity=1. - 'inv' : protected inverse where a near-zero argument returns 0., arity=1. - 'max' : maximum, arity=2. - 'min' : minimum, arity=2. - 'sin' : sine (radians), arity=1. - 'cos' : cosine (radians), arity=1. - 'tan' : tangent (radians), arity=1. transformer : str, optional (default='sigmoid') The name of the function through which the raw decision function is passed. This function will transform the raw decision function into probabilities of each class. This can also be replaced by your own functions as built using the ``make_function`` factory from the ``functions`` module. metric : str, optional (default='log loss') The name of the raw fitness metric. Available options include: - 'log loss' aka binary cross-entropy loss. parsimony_coefficient : float or "auto", optional (default=0.001) This constant penalizes large programs by adjusting their fitness to be less favorable for selection. Larger values penalize the program more which can control the phenomenon known as 'bloat'. Bloat is when evolution is increasing the size of programs without a significant increase in fitness, which is costly for computation time and makes for a less understandable final result. This parameter may need to be tuned over successive runs. If "auto" the parsimony coefficient is recalculated for each generation using c = Cov(l,f)/Var( l), where Cov(l,f) is the covariance between program size l and program fitness f in the population, and Var(l) is the variance of program sizes. p_crossover : float, optional (default=0.9) The probability of performing crossover on a tournament winner. Crossover takes the winner of a tournament and selects a random subtree from it to be replaced. A second tournament is performed to find a donor. The donor also has a subtree selected at random and this is inserted into the original parent to form an offspring in the next generation. p_subtree_mutation : float, optional (default=0.01) The probability of performing subtree mutation on a tournament winner. Subtree mutation takes the winner of a tournament and selects a random subtree from it to be replaced. A donor subtree is generated at random and this is inserted into the original parent to form an offspring in the next generation. p_hoist_mutation : float, optional (default=0.01) The probability of performing hoist mutation on a tournament winner. Hoist mutation takes the winner of a tournament and selects a random subtree from it. A random subtree of that subtree is then selected and this is 'hoisted' into the original subtrees location to form an offspring in the next generation. This method helps to control bloat. p_point_mutation : float, optional (default=0.01) The probability of performing point mutation on a tournament winner. Point mutation takes the winner of a tournament and selects random nodes from it to be replaced. Terminals are replaced by other terminals and functions are replaced by other functions that require the same number of arguments as the original node. The resulting tree forms an offspring in the next generation. Note : The above genetic operation probabilities must sum to less than one. The balance of probability is assigned to 'reproduction', where a tournament winner is cloned and enters the next generation unmodified. p_point_replace : float, optional (default=0.05) For point mutation only, the probability that any given node will be mutated. max_samples : float, optional (default=1.0) The fraction of samples to draw from X to evaluate each program on. class_weight : dict, 'balanced' or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` feature_names : list, optional (default=None) Optional list of feature names, used purely for representations in the `print` operation or `export_graphviz`. If None, then X0, X1, etc will be used for representations. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more generations to the evolution, otherwise, just fit a new evolution. low_memory : bool, optional (default=False) When set to ``True``, only the current generation is retained. Parent information is discarded. For very large populations or runs with many generations, this can result in substantial memory use reduction. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for `fit`. If -1, then the number of jobs is set to the number of cores. verbose : int, optional (default=0) Controls the verbosity of the evolution building process. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Attributes ---------- run_details_ : dict Details of the evolution process. Includes the following elements: - 'generation' : The generation index. - 'average_length' : The average program length of the generation. - 'average_fitness' : The average program fitness of the generation. - 'best_length' : The length of the best program in the generation. - 'best_fitness' : The fitness of the best program in the generation. - 'best_oob_fitness' : The out of bag fitness of the best program in the generation (requires `max_samples` < 1.0). - 'generation_time' : The time it took for the generation to evolve. See Also -------- SymbolicTransformer References ---------- .. [1] J. Koza, "Genetic Programming", 1992. .. [2] R. Poli, et al. "A Field Guide to Genetic Programming", 2008. """ def __init__(self, *, population_size=1000, generations=20, tournament_size=20, stopping_criteria=0.0, const_range=(-1., 1.), init_depth=(2, 6), init_method='half and half', function_set=('add', 'sub', 'mul', 'div'), transformer='sigmoid', metric='log loss', parsimony_coefficient=0.001, p_crossover=0.9, p_subtree_mutation=0.01, p_hoist_mutation=0.01, p_point_mutation=0.01, p_point_replace=0.05, max_samples=1.0, class_weight=None, feature_names=None, warm_start=False, low_memory=False, n_jobs=1, verbose=0, random_state=None): super(SymbolicClassifier, self).__init__( population_size=population_size, generations=generations, tournament_size=tournament_size, stopping_criteria=stopping_criteria, const_range=const_range, init_depth=init_depth, init_method=init_method, function_set=function_set, transformer=transformer, metric=metric, parsimony_coefficient=parsimony_coefficient, p_crossover=p_crossover, p_subtree_mutation=p_subtree_mutation, p_hoist_mutation=p_hoist_mutation, p_point_mutation=p_point_mutation, p_point_replace=p_point_replace, max_samples=max_samples, class_weight=class_weight, feature_names=feature_names, warm_start=warm_start, low_memory=low_memory, n_jobs=n_jobs, verbose=verbose, random_state=random_state) def __str__(self): """Overloads `print` output of the object to resemble a LISP tree.""" if not hasattr(self, '_program'): return self.__repr__() return self._program.__str__() def _more_tags(self): return {'binary_only': True} def predict_proba(self, X): """Predict probabilities on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- proba : array, shape = [n_samples, n_classes] The class probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """ if not hasattr(self, '_program'): raise NotFittedError('SymbolicClassifier not fitted.') X = check_array(X) _, n_features = X.shape if self.n_features_in_ != n_features: raise ValueError('Number of features of the model must match the ' 'input. Model n_features is %s and input ' 'n_features is %s.' % (self.n_features_in_, n_features)) scores = self._program.execute(X) proba = self._transformer(scores) proba = np.vstack([1 - proba, proba]).T return proba def predict(self, X): """Predict classes on test vectors X. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples,] The predicted classes of the input samples. """ proba = self.predict_proba(X) return self.classes_.take(np.argmax(proba, axis=1), axis=0) class SymbolicTransformer(BaseSymbolic, TransformerMixin): """A Genetic Programming symbolic transformer. A symbolic transformer is a supervised transformer that begins by building a population of naive random formulas to represent a relationship. The formulas are represented as tree-like structures with mathematical functions being recursively applied to variables and constants. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations such as crossover, mutation or reproduction. The final population is searched for the fittest individuals with the least correlation to one another. Parameters ---------- population_size : integer, optional (default=1000) The number of programs in each generation. hall_of_fame : integer, or None, optional (default=100) The number of fittest programs to compare from when finding the least-correlated individuals for the n_components. If `None`, the entire final generation will be used. n_components : integer, or None, optional (default=10) The number of best programs to return after searching the hall_of_fame for the least-correlated individuals. If `None`, the entire hall_of_fame will be used. generations : integer, optional (default=20) The number of generations to evolve. tournament_size : integer, optional (default=20) The number of programs that will compete to become part of the next generation. stopping_criteria : float, optional (default=1.0) The required metric value required in order to stop evolution early. const_range : tuple of two floats, or None, optional (default=(-1., 1.)) The range of constants to include in the formulas. If None then no constants will be included in the candidate programs. init_depth : tuple of two ints, optional (default=(2, 6)) The range of tree depths for the initial population of naive formulas. Individual trees will randomly choose a maximum depth from this range. When combined with `init_method='half and half'` this yields the well- known 'ramped half and half' initialization method. init_method : str, optional (default='half and half') - 'grow' : Nodes are chosen at random from both functions and terminals, allowing for smaller trees than `init_depth` allows. Tends to grow asymmetrical trees. - 'full' : Functions are chosen until the `init_depth` is reached, and then terminals are selected. Tends to grow 'bushy' trees. - 'half and half' : Trees are grown through a 50/50 mix of 'full' and 'grow', making for a mix of tree shapes in the initial population. function_set : iterable, optional (default=('add', 'sub', 'mul', 'div')) The functions to use when building and evolving programs. This iterable can include strings to indicate either individual functions as outlined below, or you can also include your own functions as built using the ``make_function`` factory from the ``functions`` module. Available individual functions are: - 'add' : addition, arity=2. - 'sub' : subtraction, arity=2. - 'mul' : multiplication, arity=2. - 'div' : protected division where a denominator near-zero returns 1., arity=2. - 'sqrt' : protected square root where the absolute value of the argument is used, arity=1. - 'log' : protected log where the absolute value of the argument is used and a near-zero argument returns 0., arity=1. - 'abs' : absolute value, arity=1. - 'neg' : negative, arity=1. - 'inv' : protected inverse where a near-zero argument returns 0., arity=1. - 'max' : maximum, arity=2. - 'min' : minimum, arity=2. - 'sin' : sine (radians), arity=1. - 'cos' : cosine (radians), arity=1. - 'tan' : tangent (radians), arity=1. metric : str, optional (default='pearson') The name of the raw fitness metric. Available options include: - 'pearson', for Pearson's product-moment correlation coefficient. - 'spearman' for Spearman's rank-order correlation coefficient. parsimony_coefficient : float or "auto", optional (default=0.001) This constant penalizes large programs by adjusting their fitness to be less favorable for selection. Larger values penalize the program more which can control the phenomenon known as 'bloat'. Bloat is when evolution is increasing the size of programs without a significant increase in fitness, which is costly for computation time and makes for a less understandable final result. This parameter may need to be tuned over successive runs. If "auto" the parsimony coefficient is recalculated for each generation using c = Cov(l,f)/Var( l), where Cov(l,f) is the covariance between program size l and program fitness f in the population, and Var(l) is the variance of program sizes. p_crossover : float, optional (default=0.9) The probability of performing crossover on a tournament winner. Crossover takes the winner of a tournament and selects a random subtree from it to be replaced. A second tournament is performed to find a donor. The donor also has a subtree selected at random and this is inserted into the original parent to form an offspring in the next generation. p_subtree_mutation : float, optional (default=0.01) The probability of performing subtree mutation on a tournament winner. Subtree mutation takes the winner of a tournament and selects a random subtree from it to be replaced. A donor subtree is generated at random and this is inserted into the original parent to form an offspring in the next generation. p_hoist_mutation : float, optional (default=0.01) The probability of performing hoist mutation on a tournament winner. Hoist mutation takes the winner of a tournament and selects a random subtree from it. A random subtree of that subtree is then selected and this is 'hoisted' into the original subtrees location to form an offspring in the next generation. This method helps to control bloat. p_point_mutation : float, optional (default=0.01) The probability of performing point mutation on a tournament winner. Point mutation takes the winner of a tournament and selects random nodes from it to be replaced. Terminals are replaced by other terminals and functions are replaced by other functions that require the same number of arguments as the original node. The resulting tree forms an offspring in the next generation. Note : The above genetic operation probabilities must sum to less than one. The balance of probability is assigned to 'reproduction', where a tournament winner is cloned and enters the next generation unmodified. p_point_replace : float, optional (default=0.05) For point mutation only, the probability that any given node will be mutated. max_samples : float, optional (default=1.0) The fraction of samples to draw from X to evaluate each program on. feature_names : list, optional (default=None) Optional list of feature names, used purely for representations in the `print` operation or `export_graphviz`. If None, then X0, X1, etc will be used for representations. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more generations to the evolution, otherwise, just fit a new evolution. low_memory : bool, optional (default=False) When set to ``True``, only the current generation is retained. Parent information is discarded. For very large populations or runs with many generations, this can result in substantial memory use reduction. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for `fit`. If -1, then the number of jobs is set to the number of cores. verbose : int, optional (default=0) Controls the verbosity of the evolution building process. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Attributes ---------- run_details_ : dict Details of the evolution process. Includes the following elements: - 'generation' : The generation index. - 'average_length' : The average program length of the generation. - 'average_fitness' : The average program fitness of the generation. - 'best_length' : The length of the best program in the generation. - 'best_fitness' : The fitness of the best program in the generation. - 'best_oob_fitness' : The out of bag fitness of the best program in the generation (requires `max_samples` < 1.0). - 'generation_time' : The time it took for the generation to evolve. See Also -------- SymbolicRegressor References ---------- .. [1] J. Koza, "Genetic Programming", 1992. .. [2] R. Poli, et al. "A Field Guide to Genetic Programming", 2008. """ def __init__(self, *, population_size=1000, hall_of_fame=100, n_components=10, generations=20, tournament_size=20, stopping_criteria=1.0, const_range=(-1., 1.), init_depth=(2, 6), init_method='half and half', function_set=('add', 'sub', 'mul', 'div'), metric='pearson', parsimony_coefficient=0.001, p_crossover=0.9, p_subtree_mutation=0.01, p_hoist_mutation=0.01, p_point_mutation=0.01, p_point_replace=0.05, max_samples=1.0, feature_names=None, warm_start=False, low_memory=False, n_jobs=1, verbose=0, random_state=None): super(SymbolicTransformer, self).__init__( population_size=population_size, hall_of_fame=hall_of_fame, n_components=n_components, generations=generations, tournament_size=tournament_size, stopping_criteria=stopping_criteria, const_range=const_range, init_depth=init_depth, init_method=init_method, function_set=function_set, metric=metric, parsimony_coefficient=parsimony_coefficient, p_crossover=p_crossover, p_subtree_mutation=p_subtree_mutation, p_hoist_mutation=p_hoist_mutation, p_point_mutation=p_point_mutation, p_point_replace=p_point_replace, max_samples=max_samples, feature_names=feature_names, warm_start=warm_start, low_memory=low_memory, n_jobs=n_jobs, verbose=verbose, random_state=random_state) def __len__(self): """Overloads `len` output to be the number of fitted components.""" if not hasattr(self, '_best_programs'): return 0 return self.n_components def __getitem__(self, item): """Return the ith item of the fitted components.""" if item >= len(self): raise IndexError return self._best_programs[item] def __str__(self): """Overloads `print` output of the object to resemble LISP trees.""" if not hasattr(self, '_best_programs'): return self.__repr__() output = str([gp.__str__() for gp in self]) return output.replace("',", ",\n").replace("'", "") def _more_tags(self): return { "_xfail_checks": { "check_sample_weights_invariance": ( "zero sample_weight is not equivalent to removing samples" ), } } def transform(self, X): """Transform X according to the fitted transformer. Parameters ---------- X : array-like, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- X_new : array-like, shape = [n_samples, n_components] Transformed array. """ if not hasattr(self, '_best_programs'): raise NotFittedError('SymbolicTransformer not fitted.') X = check_array(X) _, n_features = X.shape if self.n_features_in_ != n_features: raise ValueError('Number of features of the model must match the ' 'input. Model n_features is %s and input ' 'n_features is %s.' % (self.n_features_in_, n_features)) X_new = np.array([gp.execute(X) for gp in self._best_programs]).T return X_new def fit_transform(self, X, y, sample_weight=None): """Fit to data, then transform it. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. sample_weight : array-like, shape = [n_samples], optional Weights applied to individual samples. Returns ------- X_new : array-like, shape = [n_samples, n_components] Transformed array. """ return self.fit(X, y, sample_weight).transform(X) gplearn-0.4.2/gplearn/tests/000077500000000000000000000000001423420364700157445ustar00rootroot00000000000000gplearn-0.4.2/gplearn/tests/__init__.py000066400000000000000000000000001423420364700200430ustar00rootroot00000000000000gplearn-0.4.2/gplearn/tests/test_estimator_checks.py000066400000000000000000000022731423420364700227100ustar00rootroot00000000000000"""Testing the Genetic Programming module's underlying datastructure (gplearn.genetic._Program) as well as the classes that use it, gplearn.genetic.SymbolicRegressor and gplearn.genetic.SymbolicTransformer.""" # Author: Trevor Stephens # # License: BSD 3 clause from sklearn.utils.estimator_checks import check_estimator from gplearn.genetic import SymbolicClassifier, SymbolicRegressor from gplearn.genetic import SymbolicTransformer def test_sklearn_regressor_checks(): """Run the sklearn estimator validation checks on SymbolicRegressor""" check_estimator(SymbolicRegressor(population_size=1000, generations=5)) def test_sklearn_classifier_checks(): """Run the sklearn estimator validation checks on SymbolicClassifier""" check_estimator(SymbolicClassifier(population_size=50, generations=5)) def test_sklearn_transformer_checks(): """Run the sklearn estimator validation checks on SymbolicTransformer""" check_estimator(SymbolicTransformer(population_size=50, hall_of_fame=10, generations=5)) gplearn-0.4.2/gplearn/tests/test_examples.py000066400000000000000000000243021423420364700211740ustar00rootroot00000000000000"""Testing the examples from the documentation.""" # Author: Trevor Stephens # # License: BSD 3 clause import numpy as np from sklearn.datasets import load_diabetes, load_breast_cancer from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.linear_model import Ridge from sklearn.metrics import roc_auc_score from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.utils._testing import assert_almost_equal from sklearn.utils.validation import check_random_state from gplearn.genetic import SymbolicClassifier, SymbolicRegressor from gplearn.genetic import SymbolicTransformer from gplearn.functions import make_function def test_symbolic_regressor(): """Check that SymbolicRegressor example works""" rng = check_random_state(0) X_train = rng.uniform(-1, 1, 100).reshape(50, 2) y_train = X_train[:, 0] ** 2 - X_train[:, 1] ** 2 + X_train[:, 1] - 1 X_test = rng.uniform(-1, 1, 100).reshape(50, 2) y_test = X_test[:, 0] ** 2 - X_test[:, 1] ** 2 + X_test[:, 1] - 1 est_gp = SymbolicRegressor(population_size=5000, generations=20, stopping_criteria=0.01, p_crossover=0.7, p_subtree_mutation=0.1, p_hoist_mutation=0.05, p_point_mutation=0.1, max_samples=0.9, parsimony_coefficient=0.01, random_state=0) est_gp.fit(X_train, y_train) assert(len(est_gp._programs) == 7) expected = 'sub(add(-0.999, X1), mul(sub(X1, X0), add(X0, X1)))' assert(est_gp.__str__() == expected) assert_almost_equal(est_gp.score(X_test, y_test), 0.99999, decimal=5) dot_data = est_gp._program.export_graphviz() expected = ('digraph program {\nnode [style=filled]\n0 [label="sub", ' 'fillcolor="#136ed4"] ;\n1 [label="add", fillcolor="#136ed4"] ' ';\n2 [label="-0.999", fillcolor="#60a6f6"] ;\n3 [label="X1", ' 'fillcolor="#60a6f6"] ;\n1 -> 3 ;\n1 -> 2 ;\n4 [label="mul", ' 'fillcolor="#136ed4"] ;\n5 [label="sub", fillcolor="#136ed4"] ' ';\n6 [label="X1", fillcolor="#60a6f6"] ;\n7 [label="X0", ' 'fillcolor="#60a6f6"] ;\n5 -> 7 ;\n5 -> 6 ;\n8 [label="add", ' 'fillcolor="#136ed4"] ;\n9 [label="X0", fillcolor="#60a6f6"] ' ';\n10 [label="X1", fillcolor="#60a6f6"] ;\n8 -> 10 ;\n8 -> 9 ' ';\n4 -> 8 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}') assert(dot_data == expected) assert(est_gp._program.parents == {'method': 'Crossover', 'parent_idx': 1555, 'parent_nodes': range(1, 4), 'donor_idx': 78, 'donor_nodes': []}) idx = est_gp._program.parents['donor_idx'] fade_nodes = est_gp._program.parents['donor_nodes'] assert(est_gp._programs[-2][idx].__str__() == 'add(-0.999, X1)') assert_almost_equal(est_gp._programs[-2][idx].fitness_, 0.351803319075) dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes) expected = ('digraph program {\nnode [style=filled]\n0 [label="add", ' 'fillcolor="#136ed4"] ;\n1 [label="-0.999", ' 'fillcolor="#60a6f6"] ;\n2 [label="X1", fillcolor="#60a6f6"] ' ';\n0 -> 2 ;\n0 -> 1 ;\n}') assert(dot_data == expected) idx = est_gp._program.parents['parent_idx'] fade_nodes = est_gp._program.parents['parent_nodes'] expected = 'sub(sub(X1, 0.939), mul(sub(X1, X0), add(X0, X1)))' assert(est_gp._programs[-2][idx].__str__() == expected) assert_almost_equal(est_gp._programs[-2][idx].fitness_, 0.17080204042) dot_data = est_gp._programs[-2][idx].export_graphviz(fade_nodes=fade_nodes) expected = ('digraph program {\nnode [style=filled]\n0 [label="sub", ' 'fillcolor="#136ed4"] ;\n1 [label="sub", fillcolor="#cecece"] ' ';\n2 [label="X1", fillcolor="#cecece"] ;\n3 [label="0.939", ' 'fillcolor="#cecece"] ;\n1 -> 3 ;\n1 -> 2 ;\n4 [label="mul", ' 'fillcolor="#136ed4"] ;\n5 [label="sub", fillcolor="#136ed4"] ' ';\n6 [label="X1", fillcolor="#60a6f6"] ;\n7 [label="X0", ' 'fillcolor="#60a6f6"] ;\n5 -> 7 ;\n5 -> 6 ;\n8 [label="add", ' 'fillcolor="#136ed4"] ;\n9 [label="X0", fillcolor="#60a6f6"] ' ';\n10 [label="X1", fillcolor="#60a6f6"] ;\n8 -> 10 ;\n8 -> 9 ' ';\n4 -> 8 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}') assert(dot_data == expected) def test_symbolic_transformer(): """Check that SymbolicTransformer example works""" rng = check_random_state(0) diabetes = load_diabetes() perm = rng.permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] est = Ridge() est.fit(diabetes.data[:300, :], diabetes.target[:300]) assert_almost_equal(est.score(diabetes.data[300:, :], diabetes.target[300:]), desired=0.43406, decimal=5) function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max', 'min'] gp = SymbolicTransformer(generations=20, population_size=2000, hall_of_fame=100, n_components=10, function_set=function_set, parsimony_coefficient=0.0005, max_samples=0.9, random_state=0) gp.fit(diabetes.data[:300, :], diabetes.target[:300]) gp_features = gp.transform(diabetes.data) new_diabetes = np.hstack((diabetes.data, gp_features)) est = Ridge() est.fit(new_diabetes[:300, :], diabetes.target[:300]) assert_almost_equal(est.score(new_diabetes[300:, :], diabetes.target[300:]), desired=0.53368, decimal=5) def test_custom_functions(): """Test the custom programs example works""" rng = check_random_state(0) diabetes = load_diabetes() perm = rng.permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] def logic(x1, x2, x3, x4): return np.where(x1 > x2, x3, x4) logical = make_function(function=logic, name='logical', arity=4) function_set = ['add', 'sub', 'mul', 'div', logical] gp = SymbolicTransformer(generations=2, population_size=2000, hall_of_fame=100, n_components=10, function_set=function_set, parsimony_coefficient=0.0005, max_samples=0.9, random_state=0) gp.fit(diabetes.data[:300, :], diabetes.target[:300]) expected = ('add(X3, logical(div(X5, sub(X5, X5)), ' 'add(X9, -0.621), X8, X4))') assert(gp._programs[0][3].__str__() == expected) dot_data = gp._programs[0][3].export_graphviz() expected = ('digraph program {\nnode [style=filled]\n0 [label="add", ' 'fillcolor="#136ed4"] ;\n1 [label="X3", fillcolor="#60a6f6"] ;' '\n2 [label="logical", fillcolor="#136ed4"] ;\n3 [label="div",' ' fillcolor="#136ed4"] ;\n4 [label="X5", fillcolor="#60a6f6"] ' ';\n5 [label="sub", fillcolor="#136ed4"] ;\n6 [label="X5", ' 'fillcolor="#60a6f6"] ;\n7 [label="X5", fillcolor="#60a6f6"] ' ';\n5 -> 7 ;\n5 -> 6 ;\n3 -> 5 ;\n3 -> 4 ;\n8 [label="add", ' 'fillcolor="#136ed4"] ;\n9 [label="X9", fillcolor="#60a6f6"] ' ';\n10 [label="-0.621", fillcolor="#60a6f6"] ;\n8 -> 10 ;\n8 ' '-> 9 ;\n11 [label="X8", fillcolor="#60a6f6"] ;\n12 ' '[label="X4", fillcolor="#60a6f6"] ;\n2 -> 12 ;\n2 -> 11 ;\n2 ' '-> 8 ;\n2 -> 3 ;\n0 -> 2 ;\n0 -> 1 ;\n}') assert(dot_data == expected) def test_classifier_comparison(): """Test the classifier comparison example works""" X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) datasets = [make_moons(noise=0.3, random_state=0), make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable] scores = [] for ds in datasets: X, y = ds X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = \ train_test_split(X, y, test_size=.4, random_state=42) clf = SymbolicClassifier(random_state=0) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) scores.append(('%.2f' % score).lstrip('0')) assert(scores == ['.95', '.93', '.95']) def test_symbolic_classifier(): """Check that SymbolicClassifier example works""" rng = check_random_state(0) cancer = load_breast_cancer() perm = rng.permutation(cancer.target.size) cancer.data = cancer.data[perm] cancer.target = cancer.target[perm] est = SymbolicClassifier(parsimony_coefficient=.01, feature_names=cancer.feature_names, random_state=1) est.fit(cancer.data[:400], cancer.target[:400]) y_true = cancer.target[400:] y_score = est.predict_proba(cancer.data[400:])[:, 1] assert_almost_equal(roc_auc_score(y_true, y_score), 0.96937869822485212) dot_data = est._program.export_graphviz() expected = ('digraph program {\nnode [style=filled]\n0 [label="sub", ' 'fillcolor="#136ed4"] ;\n1 [label="div", fillcolor="#136ed4"] ' ';\n2 [label="worst fractal dimension", fillcolor="#60a6f6"] ' ';\n3 [label="mean concave points", fillcolor="#60a6f6"] ' ';\n1 -> 3 ;\n1 -> 2 ;\n4 [label="mul", fillcolor="#136ed4"] ' ';\n5 [label="mean concave points", fillcolor="#60a6f6"] ;\n6 ' '[label="area error", fillcolor="#60a6f6"] ;\n4 -> 6 ;\n4 -> ' '5 ;\n0 -> 4 ;\n0 -> 1 ;\n}') assert(dot_data == expected) gplearn-0.4.2/gplearn/tests/test_fitness.py000066400000000000000000000211251423420364700210310ustar00rootroot00000000000000"""Testing the Genetic Programming fitness module.""" # Author: Trevor Stephens # # License: BSD 3 clause import pickle import numpy as np from sklearn.datasets import load_diabetes, load_breast_cancer from sklearn.metrics import mean_absolute_error from sklearn.utils._testing import assert_raises from sklearn.utils.validation import check_random_state from gplearn.genetic import SymbolicRegressor, SymbolicClassifier from gplearn.genetic import SymbolicTransformer from gplearn.fitness import make_fitness, _mean_square_error # load the breast cancer dataset and randomly permute it cancer = load_breast_cancer() perm = check_random_state(0).permutation(cancer.target.size) cancer.data = cancer.data[perm] cancer.target = cancer.target[perm] # load the diabetes dataset and randomly permute it diabetes = load_diabetes() perm = check_random_state(0).permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] def test_validate_fitness(): """Check that valid fitness measures are accepted & invalid raise error""" # Check arg count checks _ = make_fitness(function=_mean_square_error, greater_is_better=True) # non-bool greater_is_better assert_raises(ValueError, make_fitness, function=_mean_square_error, greater_is_better='Sure') assert_raises(ValueError, make_fitness, function=_mean_square_error, greater_is_better=1) # non-bool wrap assert_raises(ValueError, make_fitness, function=_mean_square_error, greater_is_better=True, wrap='f') # Check arg count tests def bad_fun1(x1, x2): return 1.0 assert_raises(ValueError, make_fitness, function=bad_fun1, greater_is_better=True) # Check return type tests def bad_fun2(x1, x2, w): return 'ni' assert_raises(ValueError, make_fitness, function=bad_fun2, greater_is_better=True) def _custom_metric(y, y_pred, w): """Calculate the root mean square error.""" return np.sqrt(np.average(((y_pred - y) ** 2), weights=w)) custom_metric = make_fitness(function=_custom_metric, greater_is_better=True) for Symbolic in (SymbolicRegressor, SymbolicTransformer): # These should be fine est = Symbolic(generations=2, random_state=0, metric=custom_metric) est.fit(diabetes.data, diabetes.target) def test_custom_regressor_metrics(): """Check whether greater_is_better works for SymbolicRegressor.""" x_data = check_random_state(0).uniform(-1, 1, 100).reshape(50, 2) y_true = x_data[:, 0] ** 2 + x_data[:, 1] ** 2 est_gp = SymbolicRegressor(metric='mean absolute error', stopping_criteria=0.000001, random_state=415, parsimony_coefficient=0.001, init_method='full', init_depth=(2, 4)) est_gp.fit(x_data, y_true) formula = est_gp.__str__() assert('add(mul(X0, X0), mul(X1, X1))' == formula) def neg_mean_absolute_error(y, y_pred, sample_weight): return -1 * mean_absolute_error(y, y_pred, sample_weight=sample_weight) customized_fitness = make_fitness(function=neg_mean_absolute_error, greater_is_better=True) c_est_gp = SymbolicRegressor(metric=customized_fitness, stopping_criteria=-0.000001, random_state=415, parsimony_coefficient=0.001, verbose=0, init_method='full', init_depth=(2, 4)) c_est_gp.fit(x_data, y_true) c_formula = c_est_gp.__str__() assert('add(mul(X0, X0), mul(X1, X1))' == c_formula) def test_custom_transformer_metrics(): """Check whether greater_is_better works for SymbolicTransformer.""" est_gp = SymbolicTransformer(generations=2, population_size=100, hall_of_fame=10, n_components=1, metric='pearson', random_state=415) est_gp.fit(diabetes.data, diabetes.target) for program in est_gp: formula = program.__str__() expected_formula = 'mul(-0.111, add(add(X9, sub(X2, 0.606)), X3))' assert(expected_formula == formula) def _neg_weighted_pearson(y, y_pred, w): """Calculate the weighted Pearson correlation coefficient.""" with np.errstate(divide='ignore', invalid='ignore'): y_pred_demean = y_pred - np.average(y_pred, weights=w) y_demean = y - np.average(y, weights=w) corr = ((np.sum(w * y_pred_demean * y_demean) / np.sum(w)) / np.sqrt((np.sum(w * y_pred_demean ** 2) * np.sum(w * y_demean ** 2)) / (np.sum(w) ** 2))) if np.isfinite(corr): return -1 * np.abs(corr) return 0. neg_weighted_pearson = make_fitness(function=_neg_weighted_pearson, greater_is_better=False) c_est_gp = SymbolicTransformer(generations=2, population_size=100, hall_of_fame=10, n_components=1, stopping_criteria=-1, metric=neg_weighted_pearson, random_state=415) c_est_gp.fit(diabetes.data, diabetes.target) for program in c_est_gp: c_formula = program.__str__() assert(expected_formula == c_formula) def test_custom_classifier_metrics(): """Check whether greater_is_better works for SymbolicClassifier.""" x_data = check_random_state(0).uniform(-1, 1, 100).reshape(50, 2) y_true = x_data[:, 0] ** 2 + x_data[:, 1] ** 2 y_true = (y_true < y_true.mean()).astype(int) est_gp = SymbolicClassifier(metric='log loss', stopping_criteria=0.000001, random_state=415, parsimony_coefficient=0.01, init_method='full', init_depth=(2, 4)) est_gp.fit(x_data, y_true) formula = est_gp.__str__() expected_formula = 'sub(0.364, mul(add(X0, X0), add(X0, X0)))' assert(expected_formula == formula) def negative_log_loss(y, y_pred, w): """Calculate the log loss.""" eps = 1e-15 y_pred = np.clip(y_pred, eps, 1 - eps) score = y * np.log(y_pred) + (1 - y) * np.log(1 - y_pred) return np.average(score, weights=w) customized_fitness = make_fitness(function=negative_log_loss, greater_is_better=True) c_est_gp = SymbolicClassifier(metric=customized_fitness, stopping_criteria=0.000001, random_state=415, parsimony_coefficient=0.01, init_method='full', init_depth=(2, 4)) c_est_gp.fit(x_data, y_true) c_formula = c_est_gp.__str__() assert(expected_formula == c_formula) def test_parallel_custom_metric(): """Regression test for running parallel training with custom transformer""" def _custom_metric(y, y_pred, w): """Calculate the root mean square error.""" return np.sqrt(np.average(((y_pred - y) ** 2), weights=w)) custom_metric = make_fitness(function=_custom_metric, greater_is_better=True) est = SymbolicRegressor(generations=2, metric=custom_metric, random_state=0, n_jobs=2) est.fit(diabetes.data, diabetes.target) _ = pickle.dumps(est) # Unwrapped functions should fail custom_metric = make_fitness(function=_custom_metric, greater_is_better=True, wrap=False) est = SymbolicRegressor(generations=2, metric=custom_metric, random_state=0, n_jobs=2) est.fit(diabetes.data, diabetes.target) assert_raises(AttributeError, pickle.dumps, est) # Single threaded will also fail in non-interactive sessions est = SymbolicRegressor(generations=2, metric=custom_metric, random_state=0) est.fit(diabetes.data, diabetes.target) assert_raises(AttributeError, pickle.dumps, est) gplearn-0.4.2/gplearn/tests/test_functions.py000066400000000000000000000160021423420364700213640ustar00rootroot00000000000000"""Testing the Genetic Programming functions module.""" # Author: Trevor Stephens # # License: BSD 3 clause import pickle import numpy as np from numpy import maximum from sklearn.datasets import load_diabetes, load_breast_cancer from sklearn.utils._testing import assert_raises from sklearn.utils.validation import check_random_state from gplearn.functions import _protected_sqrt, make_function from gplearn.genetic import SymbolicRegressor, SymbolicTransformer from gplearn.genetic import SymbolicClassifier # load the diabetes dataset and randomly permute it rng = check_random_state(0) diabetes = load_diabetes() perm = rng.permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] # load the breast cancer dataset and randomly permute it cancer = load_breast_cancer() perm = check_random_state(0).permutation(cancer.target.size) cancer.data = cancer.data[perm] cancer.target = cancer.target[perm] def test_validate_function(): """Check that valid functions are accepted & invalid ones raise error""" # Check arity tests _ = make_function(function=_protected_sqrt, name='sqrt', arity=1) # non-integer arity assert_raises(ValueError, make_function, function=_protected_sqrt, name='sqrt', arity='1') assert_raises(ValueError, make_function, function=_protected_sqrt, name='sqrt', arity=1.0) # non-bool wrap assert_raises(ValueError, make_function, function=_protected_sqrt, name='sqrt', arity=1, wrap='f') # non-matching arity assert_raises(ValueError, make_function, function=_protected_sqrt, name='sqrt', arity=2) assert_raises(ValueError, make_function, function=maximum, name='max', arity=1) # Check name test assert_raises(ValueError, make_function, function=_protected_sqrt, name=2, arity=1) # Check return type tests def bad_fun1(x1, x2): return 'ni' assert_raises(ValueError, make_function, function=bad_fun1, name='ni', arity=2) # Check return shape tests def bad_fun2(x1): return np.ones((2, 1)) assert_raises(ValueError, make_function, function=bad_fun2, name='ni', arity=1) # Check closure for negatives test def _unprotected_sqrt(x1): with np.errstate(divide='ignore', invalid='ignore'): return np.sqrt(x1) assert_raises(ValueError, make_function, function=_unprotected_sqrt, name='sqrt', arity=1) # Check closure for zeros test def _unprotected_div(x1, x2): with np.errstate(divide='ignore', invalid='ignore'): return np.divide(x1, x2) assert_raises(ValueError, make_function, function=_unprotected_div, name='div', arity=2) def test_function_in_program(): """Check that using a custom function in a program works""" def logic(x1, x2, x3, x4): return np.where(x1 > x2, x3, x4) logical = make_function(function=logic, name='logical', arity=4) function_set = ['add', 'sub', 'mul', 'div', logical] est = SymbolicTransformer(generations=2, population_size=2000, hall_of_fame=100, n_components=10, function_set=function_set, parsimony_coefficient=0.0005, max_samples=0.9, random_state=0) est.fit(diabetes.data[:300, :], diabetes.target[:300]) formula = est._programs[0][3].__str__() expected_formula = ('add(X3, logical(div(X5, sub(X5, X5)), ' 'add(X9, -0.621), X8, X4))') assert(expected_formula == formula) def test_parallel_custom_function(): """Regression test for running parallel training with custom functions""" def _logical(x1, x2, x3, x4): return np.where(x1 > x2, x3, x4) logical = make_function(function=_logical, name='logical', arity=4) est = SymbolicRegressor(generations=2, function_set=['add', 'sub', 'mul', 'div', logical], random_state=0, n_jobs=2) est.fit(diabetes.data, diabetes.target) _ = pickle.dumps(est) # Unwrapped functions should fail logical = make_function(function=_logical, name='logical', arity=4, wrap=False) est = SymbolicRegressor(generations=2, function_set=['add', 'sub', 'mul', 'div', logical], random_state=0, n_jobs=2) est.fit(diabetes.data, diabetes.target) assert_raises(AttributeError, pickle.dumps, est) # Single threaded will also fail in non-interactive sessions est = SymbolicRegressor(generations=2, function_set=['add', 'sub', 'mul', 'div', logical], random_state=0) est.fit(diabetes.data, diabetes.target) assert_raises(AttributeError, pickle.dumps, est) def test_parallel_custom_transformer(): """Regression test for running parallel training with custom transformer""" def _sigmoid(x1): with np.errstate(over='ignore', under='ignore'): return 1 / (1 + np.exp(-x1)) sigmoid = make_function(function=_sigmoid, name='sig', arity=1) est = SymbolicClassifier(generations=2, transformer=sigmoid, random_state=0, n_jobs=2) est.fit(cancer.data, cancer.target) _ = pickle.dumps(est) # Unwrapped functions should fail sigmoid = make_function(function=_sigmoid, name='sig', arity=1, wrap=False) est = SymbolicClassifier(generations=2, transformer=sigmoid, random_state=0, n_jobs=2) est.fit(cancer.data, cancer.target) assert_raises(AttributeError, pickle.dumps, est) # Single threaded will also fail in non-interactive sessions est = SymbolicClassifier(generations=2, transformer=sigmoid, random_state=0) est.fit(cancer.data, cancer.target) assert_raises(AttributeError, pickle.dumps, est) gplearn-0.4.2/gplearn/tests/test_genetic.py000066400000000000000000001407401423420364700210010ustar00rootroot00000000000000"""Testing the Genetic Programming module's underlying datastructure (gplearn.genetic._Program) as well as the classes that use it, gplearn.genetic.SymbolicRegressor and gplearn.genetic.SymbolicTransformer.""" # Author: Trevor Stephens # # License: BSD 3 clause import pickle import pytest import sys from io import StringIO import numpy as np from scipy.stats import pearsonr, spearmanr from sklearn.datasets import load_diabetes, load_breast_cancer from sklearn.metrics import mean_absolute_error from sklearn.model_selection import GridSearchCV from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeRegressor from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_raises from sklearn.utils.validation import check_random_state from gplearn.genetic import SymbolicClassifier, SymbolicRegressor from gplearn.genetic import SymbolicTransformer from gplearn.fitness import weighted_pearson, weighted_spearman from gplearn._program import _Program from gplearn.fitness import _fitness_map from gplearn.functions import (add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2) from gplearn.functions import _Function # load the diabetes dataset and randomly permute it rng = check_random_state(0) diabetes = load_diabetes() perm = rng.permutation(diabetes.target.size) diabetes.data = diabetes.data[perm] diabetes.target = diabetes.target[perm] # load the breast cancer dataset and randomly permute it rng = check_random_state(0) cancer = load_breast_cancer() perm = rng.permutation(cancer.target.size) cancer.data = cancer.data[perm] cancer.target = cancer.target[perm] def test_weighted_correlations(): """Check weighted Pearson correlation coefficient matches scipy""" random_state = check_random_state(415) x1 = random_state.uniform(size=500) x2 = random_state.uniform(size=500) w1 = np.ones(500) w2 = random_state.uniform(size=500) # Pearson's correlation coefficient scipy_pearson = pearsonr(x1, x2)[0] # Check with constant weights (should be equal) gplearn_pearson = weighted_pearson(x1, x2, w1) assert_almost_equal(scipy_pearson, gplearn_pearson) # Check with irregular weights (should be different) gplearn_pearson = weighted_pearson(x1, x2, w2) assert(abs(scipy_pearson - gplearn_pearson) > 0.01) # Spearman's correlation coefficient scipy_spearman = spearmanr(x1, x2)[0] # Check with constant weights (should be equal) gplearn_spearman = weighted_spearman(x1, x2, w1) assert_almost_equal(scipy_spearman, gplearn_spearman) # Check with irregular weights (should be different) gplearn_spearman = weighted_pearson(x1, x2, w2) assert(abs(scipy_spearman - gplearn_spearman) > 0.01) def test_program_init_method(): """Check 'full' creates longer and deeper programs than other methods""" params = {'function_set': [add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2], 'arities': {1: [sqrt1, log1, abs1], 2: [add2, sub2, mul2, div2, max2, min2]}, 'init_depth': (2, 6), 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) programs = [] for _ in range(20): programs.append(_Program(init_method='full', random_state=random_state, **params)) full_length = np.mean([gp.length_ for gp in programs]) full_depth = np.mean([gp.depth_ for gp in programs]) programs = [] for _ in range(20): programs.append(_Program(init_method='half and half', random_state=random_state, **params)) hnh_length = np.mean([gp.length_ for gp in programs]) hnh_depth = np.mean([gp.depth_ for gp in programs]) programs = [] for _ in range(20): programs.append(_Program(init_method='grow', random_state=random_state, **params)) grow_length = np.mean([gp.length_ for gp in programs]) grow_depth = np.mean([gp.depth_ for gp in programs]) assert(full_length > hnh_length) assert(hnh_length > grow_length) assert(full_depth > hnh_depth) assert(hnh_depth > grow_depth) def test_program_init_depth(): """Check 'full' creates constant depth programs for single depth limit""" params = {'function_set': [add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2], 'arities': {1: [sqrt1, log1, abs1], 2: [add2, sub2, mul2, div2, max2, min2]}, 'init_depth': (6, 6), 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) programs = [] for _ in range(20): programs.append(_Program(init_method='full', random_state=random_state, **params)) full_depth = np.bincount([gp.depth_ for gp in programs]) programs = [] for _ in range(20): programs.append(_Program(init_method='half and half', random_state=random_state, **params)) hnh_depth = np.bincount([gp.depth_ for gp in programs]) programs = [] for _ in range(20): programs.append(_Program(init_method='grow', random_state=random_state, **params)) grow_depth = np.bincount([gp.depth_ for gp in programs]) assert(full_depth[-1] == 20) assert(hnh_depth[-1] != 20) assert(grow_depth[-1] != 20) def test_validate_program(): """Check that valid programs are accepted & invalid ones raise error""" function_set = [add2, sub2, mul2, div2, sqrt1, log1, abs1, max2, min2] arities = {1: [sqrt1, log1, abs1], 2: [add2, sub2, mul2, div2, max2, min2]}, init_depth = (2, 6) init_method = 'half and half' n_features = 10 const_range = (-1.0, 1.0) metric = 'mean absolute error' p_point_replace = 0.05 parsimony_coefficient = 0.1 random_state = check_random_state(415) test_gp = [sub2, abs1, sqrt1, log1, log1, sqrt1, 7, abs1, abs1, abs1, log1, sqrt1, 2] # This one should be fine _ = _Program(function_set, arities, init_depth, init_method, n_features, const_range, metric, p_point_replace, parsimony_coefficient, random_state, program=test_gp) # Now try a couple that shouldn't be assert_raises(ValueError, _Program, function_set, arities, init_depth, init_method, n_features, const_range, metric, p_point_replace, parsimony_coefficient, random_state, program=test_gp[:-1]) assert_raises(ValueError, _Program, function_set, arities, init_depth, init_method, n_features, const_range, metric, p_point_replace, parsimony_coefficient, random_state, program=test_gp + [1]) def test_print_overloading(): """Check that printing a program object results in 'pretty' output""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) test_gp = [mul2, div2, 8, 1, sub2, 9, .5] gp = _Program(random_state=random_state, program=test_gp, **params) orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(gp) output = out.getvalue().strip() finally: sys.stdout = orig_stdout lisp = "mul(div(X8, X1), sub(X9, 0.500))" assert(output == lisp) # Test with feature names params['feature_names'] = [str(n) for n in range(10)] gp = _Program(random_state=random_state, program=test_gp, **params) orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(gp) output = out.getvalue().strip() finally: sys.stdout = orig_stdout lisp = "mul(div(8, 1), sub(9, 0.500))" assert(output == lisp) def test_export_graphviz(): """Check output of a simple program to Graphviz""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) # Test for a small program test_gp = [mul2, div2, 8, 1, sub2, 9, .5] gp = _Program(random_state=random_state, program=test_gp, **params) output = gp.export_graphviz() tree = 'digraph program {\n' \ 'node [style=filled]\n' \ '0 [label="mul", fillcolor="#136ed4"] ;\n' \ '1 [label="div", fillcolor="#136ed4"] ;\n' \ '2 [label="X8", fillcolor="#60a6f6"] ;\n' \ '3 [label="X1", fillcolor="#60a6f6"] ;\n' \ '1 -> 3 ;\n1 -> 2 ;\n' \ '4 [label="sub", fillcolor="#136ed4"] ;\n' \ '5 [label="X9", fillcolor="#60a6f6"] ;\n' \ '6 [label="0.500", fillcolor="#60a6f6"] ;\n' \ '4 -> 6 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}' assert(output == tree) # Test with feature names params['feature_names'] = [str(n) for n in range(10)] gp = _Program(random_state=random_state, program=test_gp, **params) output = gp.export_graphviz() tree = tree.replace('X', '') assert(output == tree) # Test with fade_nodes params['feature_names'] = None gp = _Program(random_state=random_state, program=test_gp, **params) output = gp.export_graphviz(fade_nodes=[0, 1, 2, 3]) tree = 'digraph program {\n' \ 'node [style=filled]\n' \ '0 [label="mul", fillcolor="#cecece"] ;\n' \ '1 [label="div", fillcolor="#cecece"] ;\n' \ '2 [label="X8", fillcolor="#cecece"] ;\n' \ '3 [label="X1", fillcolor="#cecece"] ;\n' \ '1 -> 3 ;\n1 -> 2 ;\n' \ '4 [label="sub", fillcolor="#136ed4"] ;\n' \ '5 [label="X9", fillcolor="#60a6f6"] ;\n' \ '6 [label="0.500", fillcolor="#60a6f6"] ;\n' \ '4 -> 6 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}' assert(output == tree) # Test a degenerative single-node program test_gp = [1] gp = _Program(random_state=random_state, program=test_gp, **params) output = gp.export_graphviz() tree = 'digraph program {\n' \ 'node [style=filled]\n' \ '0 [label="X1", fillcolor="#60a6f6"] ;\n}' assert(output == tree) def test_invalid_feature_names(): """Check invalid feature names raise errors""" for Symbolic in (SymbolicRegressor, SymbolicTransformer): # Check invalid length feature_names est = Symbolic(feature_names=['foo', 'bar']) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check invalid type feature_name feature_names = [str(n) for n in range(12)] + [0] est = Symbolic(feature_names=feature_names) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) def test_execute(): """Check executing the program works""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) # Test for a small program test_gp = [mul2, div2, 8, 1, sub2, 9, .5] X = np.reshape(random_state.uniform(size=50), (5, 10)) gp = _Program(random_state=random_state, program=test_gp, **params) result = gp.execute(X) expected = [-0.19656208, 0.78197782, -1.70123845, -0.60175969, -0.01082618] assert_array_almost_equal(result, expected) def test_all_metrics(): """Check all supported metrics work""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) # Test for a small program test_gp = [mul2, div2, 8, 1, sub2, 9, .5] gp = _Program(random_state=random_state, program=test_gp, **params) X = np.reshape(random_state.uniform(size=50), (5, 10)) y = random_state.uniform(size=5) sample_weight = np.ones(5) expected = [1.48719809776, 1.82389179833, 1.76013763179, -0.2928200724, -0.5] result = [] for m in ['mean absolute error', 'mse', 'rmse', 'pearson', 'spearman']: gp.metric = _fitness_map[m] gp.raw_fitness_ = gp.raw_fitness(X, y, sample_weight) result.append(gp.fitness()) assert_array_almost_equal(result, expected) def test_get_subtree(): """Check that get subtree does the same thing for self and new programs""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) # Test for a small program test_gp = [mul2, div2, 8, 1, sub2, 9, .5] gp = _Program(random_state=random_state, program=test_gp, **params) self_test = gp.get_subtree(check_random_state(0)) external_test = gp.get_subtree(check_random_state(0), test_gp) assert(self_test == external_test) def test_genetic_operations(): """Check all genetic operations are stable and don't change programs""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) # Test for a small program test_gp = [mul2, div2, 8, 1, sub2, 9, .5] donor = [add2, 0.1, sub2, 2, 7] gp = _Program(random_state=random_state, program=test_gp, **params) expected = ['mul', 'div', 8, 1, 'sub', 9, 0.5] assert([f.name if isinstance(f, _Function) else f for f in gp.reproduce()] == expected) assert(gp.program == test_gp) assert([f.name if isinstance(f, _Function) else f for f in gp.crossover(donor, random_state)[0]] == ['sub', 2, 7]) assert(gp.program == test_gp) expected = ['mul', 'div', 8, 1, 'sub', 'sub', 3, 5, 'add', 6, 3] assert([f.name if isinstance(f, _Function) else f for f in gp.subtree_mutation(random_state)[0]] == expected) assert(gp.program == test_gp) assert([f.name if isinstance(f, _Function) else f for f in gp.hoist_mutation(random_state)[0]] == ['div', 8, 1]) assert(gp.program == test_gp) expected = ['mul', 'div', 8, 1, 'sub', 9, 0.5] assert([f.name if isinstance(f, _Function) else f for f in gp.point_mutation(random_state)[0]] == expected) assert(gp.program == test_gp) def test_input_validation(): """Check that guarded input validation raises errors""" for Symbolic in (SymbolicRegressor, SymbolicTransformer): # Check too much proba est = Symbolic(p_point_mutation=.5) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check invalid init_method est = Symbolic(init_method='ni') assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check invalid const_ranges est = Symbolic(const_range=2) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(const_range=[2, 2]) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(const_range=(2, 2, 2)) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(const_range='ni') assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # And check acceptable, but strange, representations of const_range est = Symbolic(population_size=100, generations=1, const_range=(2, 2)) est.fit(diabetes.data, diabetes.target) est = Symbolic(population_size=100, generations=1, const_range=None) est.fit(diabetes.data, diabetes.target) est = Symbolic(population_size=100, generations=1, const_range=(4, 2)) est.fit(diabetes.data, diabetes.target) # Check invalid init_depth est = Symbolic(init_depth=2) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(init_depth=2) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(init_depth=[2, 2]) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(init_depth=(2, 2, 2)) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(init_depth='ni') assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(init_depth=(4, 2)) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # And check acceptable, but strange, representations of init_depth est = Symbolic(population_size=100, generations=1, init_depth=(2, 2)) est.fit(diabetes.data, diabetes.target) # Check hall_of_fame and n_components for transformer est = SymbolicTransformer(hall_of_fame=2000) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = SymbolicTransformer(n_components=2000) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = SymbolicTransformer(hall_of_fame=0) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = SymbolicTransformer(n_components=0) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check regressor metrics for m in ['mean absolute error', 'mse', 'rmse', 'pearson', 'spearman']: est = SymbolicRegressor(population_size=100, generations=1, metric=m) est.fit(diabetes.data, diabetes.target) # And check a fake one est = SymbolicRegressor(metric='the larch') assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check transformer metrics for m in ['pearson', 'spearman']: est = SymbolicTransformer(population_size=100, generations=1, metric=m) est.fit(diabetes.data, diabetes.target) # And check the regressor metrics as well as a fake one for m in ['mean absolute error', 'mse', 'rmse', 'the larch']: est = SymbolicTransformer(metric=m) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) def test_input_validation_classifier(): """Check that guarded input validation raises errors""" # Check too much proba est = SymbolicClassifier(p_point_mutation=.5) assert_raises(ValueError, est.fit, cancer.data, cancer.target) # Check invalid init_method est = SymbolicClassifier(init_method='ni') assert_raises(ValueError, est.fit, cancer.data, cancer.target) # Check invalid const_ranges est = SymbolicClassifier(const_range=2) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(const_range=[2, 2]) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(const_range=(2, 2, 2)) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(const_range='ni') assert_raises(ValueError, est.fit, cancer.data, cancer.target) # And check acceptable, but strange, representations of const_range est = SymbolicClassifier(population_size=100, generations=1, const_range=(2, 2)) est.fit(cancer.data, cancer.target) est = SymbolicClassifier(population_size=100, generations=1, const_range=None) est.fit(cancer.data, cancer.target) est = SymbolicClassifier(population_size=100, generations=1, const_range=(4, 2)) est.fit(cancer.data, cancer.target) # Check invalid init_depth est = SymbolicClassifier(init_depth=2) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(init_depth=2) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(init_depth=[2, 2]) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(init_depth=(2, 2, 2)) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(init_depth='ni') assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(init_depth=(4, 2)) assert_raises(ValueError, est.fit, cancer.data, cancer.target) # And check acceptable, but strange, representations of init_depth est = SymbolicClassifier(population_size=100, generations=1, init_depth=(2, 2)) est.fit(cancer.data, cancer.target) # Check classifier metrics for m in ['log loss']: est = SymbolicClassifier(population_size=100, generations=1, metric=m) est.fit(cancer.data, cancer.target) # And check a fake one est = SymbolicClassifier(metric='the larch') assert_raises(ValueError, est.fit, cancer.data, cancer.target) # Check classifier transformers for t in ['sigmoid']: est = SymbolicClassifier(population_size=100, generations=1, transformer=t) est.fit(cancer.data, cancer.target) # And check an incompatible one with wrong arity est = SymbolicClassifier(transformer=sub2) assert_raises(ValueError, est.fit, cancer.data, cancer.target) # And check a fake one est = SymbolicClassifier(transformer='the larch') assert_raises(ValueError, est.fit, cancer.data, cancer.target) def test_none_const_range(): """Check that const_range=None produces no constants""" # Check with None as const_range est = SymbolicRegressor(population_size=100, generations=2, const_range=None) est.fit(diabetes.data, diabetes.target) float_count = 0 for generation in est._programs: for program in generation: if program is None: continue for element in program.program: if isinstance(element, float): float_count += 1 assert(float_count == 0) # Check with default const_range est = SymbolicRegressor(population_size=100, generations=2) est.fit(diabetes.data, diabetes.target) float_count = 0 for generation in est._programs: for program in generation: if program is None: continue for element in program.program: if isinstance(element, float): float_count += 1 assert(float_count > 1) def test_sample_weight_and_class_weight(): """Check sample_weight param works""" # Check constant sample_weight has no effect sample_weight = np.ones(diabetes.target.shape[0]) est1 = SymbolicRegressor(population_size=100, generations=2, random_state=0) est1.fit(diabetes.data, diabetes.target) est2 = SymbolicRegressor(population_size=100, generations=2, random_state=0) est2.fit(diabetes.data, diabetes.target, sample_weight=sample_weight) # And again with a scaled sample_weight est3 = SymbolicRegressor(population_size=100, generations=2, random_state=0) est3.fit(diabetes.data, diabetes.target, sample_weight=sample_weight * 1.1) assert_almost_equal(est1._program.fitness_, est2._program.fitness_) assert_almost_equal(est1._program.fitness_, est3._program.fitness_) # And again for the classifier sample_weight = np.ones(cancer.target.shape[0]) est1 = SymbolicClassifier(population_size=100, generations=2, random_state=0) est1.fit(cancer.data, cancer.target) est2 = SymbolicClassifier(population_size=100, generations=2, random_state=0) est2.fit(cancer.data, cancer.target, sample_weight=sample_weight) # And again with a scaled sample_weight est3 = SymbolicClassifier(population_size=100, generations=2, random_state=0) est3.fit(cancer.data, cancer.target, sample_weight=sample_weight * 1.1) # And then using class weight to do the same thing est4 = SymbolicClassifier(class_weight={0: 1, 1: 1}, population_size=100, generations=2, random_state=0) est4.fit(cancer.data, cancer.target) est5 = SymbolicClassifier(class_weight={0: 1.1, 1: 1.1}, population_size=100, generations=2, random_state=0) est5.fit(cancer.data, cancer.target) assert_almost_equal(est1._program.fitness_, est2._program.fitness_) assert_almost_equal(est1._program.fitness_, est3._program.fitness_) assert_almost_equal(est1._program.fitness_, est4._program.fitness_) assert_almost_equal(est1._program.fitness_, est5._program.fitness_) # And again for the transformer sample_weight = np.ones(diabetes.target.shape[0]) est1 = SymbolicTransformer(population_size=100, generations=2, random_state=0) est1 = est1.fit_transform(diabetes.data, diabetes.target) est2 = SymbolicTransformer(population_size=100, generations=2, random_state=0) est2 = est2.fit_transform(diabetes.data, diabetes.target, sample_weight=sample_weight) assert_array_almost_equal(est1, est2) def test_trigonometric(): """Check that using trig functions work and that results differ""" est1 = SymbolicRegressor(population_size=100, generations=2, random_state=0) est1.fit(diabetes.data[:400, :], diabetes.target[:400]) est1 = mean_absolute_error(est1.predict(diabetes.data[400:, :]), diabetes.target[400:]) est2 = SymbolicRegressor(population_size=100, generations=2, function_set=['add', 'sub', 'mul', 'div', 'sin', 'cos', 'tan'], random_state=0) est2.fit(diabetes.data[:400, :], diabetes.target[:400]) est2 = mean_absolute_error(est2.predict(diabetes.data[400:, :]), diabetes.target[400:]) assert(abs(est1 - est2) > 0.01) def test_subsample(): """Check that subsample work and that results differ""" est1 = SymbolicRegressor(population_size=100, generations=2, max_samples=1.0, random_state=0) est1.fit(diabetes.data[:400, :], diabetes.target[:400]) est1 = mean_absolute_error(est1.predict(diabetes.data[400:, :]), diabetes.target[400:]) est2 = SymbolicRegressor(population_size=100, generations=2, max_samples=0.1, random_state=0) est2.fit(diabetes.data[:400, :], diabetes.target[:400]) est2 = mean_absolute_error(est2.predict(diabetes.data[400:, :]), diabetes.target[400:]) assert(abs(est1 - est2) > 0.01) def test_parsimony_coefficient(): """Check that parsimony coefficients work and that results differ""" est1 = SymbolicRegressor(population_size=100, generations=2, parsimony_coefficient=0.001, random_state=0) est1.fit(diabetes.data[:400, :], diabetes.target[:400]) est1 = mean_absolute_error(est1.predict(diabetes.data[400:, :]), diabetes.target[400:]) est2 = SymbolicRegressor(population_size=100, generations=2, parsimony_coefficient='auto', random_state=0) est2.fit(diabetes.data[:400, :], diabetes.target[:400]) est2 = mean_absolute_error(est2.predict(diabetes.data[400:, :]), diabetes.target[400:]) assert(abs(est1 - est2) > 0.01) def test_early_stopping(): """Check that early stopping works""" est1 = SymbolicRegressor(population_size=100, generations=2, stopping_criteria=200, random_state=0) est1.fit(diabetes.data[:400, :], diabetes.target[:400]) assert(len(est1._programs) == 1) est1 = SymbolicTransformer(population_size=100, generations=2, stopping_criteria=100, random_state=0) est1.fit(cancer.data[:400, :], cancer.target[:400]) assert(len(est1._programs) == 2) def test_verbose_output(): """Check verbose=1 does not cause error""" old_stdout = sys.stdout sys.stdout = StringIO() est = SymbolicRegressor(population_size=100, generations=10, random_state=0, verbose=1) est.fit(diabetes.data, diabetes.target) verbose_output = sys.stdout sys.stdout = old_stdout # check output verbose_output.seek(0) header1 = verbose_output.readline().rstrip() true_header = ' |{:^25}|{:^42}|'.format('Population Average', 'Best Individual') assert(true_header == header1) header2 = verbose_output.readline().rstrip() true_header = '-' * 4 + ' ' + '-' * 25 + ' ' + '-' * 42 + ' ' + '-' * 10 assert(true_header == header2) header3 = verbose_output.readline().rstrip() line_format = '{:>4} {:>8} {:>16} {:>8} {:>16} {:>16} {:>10}' true_header = line_format.format('Gen', 'Length', 'Fitness', 'Length', 'Fitness', 'OOB Fitness', 'Time Left') assert(true_header == header3) n_lines = sum(1 for l in verbose_output.readlines()) assert(10 == n_lines) def test_verbose_with_oob(): """Check oob scoring for subsample does not cause error""" old_stdout = sys.stdout sys.stdout = StringIO() est = SymbolicRegressor(population_size=100, generations=10, max_samples=0.9, random_state=0, verbose=1) est.fit(diabetes.data, diabetes.target) verbose_output = sys.stdout sys.stdout = old_stdout # check output verbose_output.seek(0) # Ignore header rows _ = verbose_output.readline().rstrip() _ = verbose_output.readline().rstrip() _ = verbose_output.readline().rstrip() n_lines = sum(1 for l in verbose_output.readlines()) assert(10 == n_lines) def test_more_verbose_output(): """Check verbose=2 does not cause error""" old_stdout = sys.stdout old_stderr = sys.stderr sys.stdout = StringIO() sys.stderr = StringIO() est = SymbolicRegressor(population_size=100, generations=10, random_state=0, verbose=2) est.fit(diabetes.data, diabetes.target) verbose_output = sys.stdout joblib_output = sys.stderr sys.stdout = old_stdout sys.stderr = old_stderr # check output verbose_output.seek(0) # Ignore header rows _ = verbose_output.readline().rstrip() _ = verbose_output.readline().rstrip() _ = verbose_output.readline().rstrip() n_lines = sum(1 for l in verbose_output.readlines()) assert(10 == n_lines) joblib_output.seek(0) n_lines = sum(1 for l in joblib_output.readlines()) # New version of joblib appears to output sys.stderr assert(0 == n_lines % 10) def test_parallel_train(): """Check predictions are the same for different n_jobs""" # Check the regressor ests = [ SymbolicRegressor(population_size=100, generations=4, n_jobs=n_jobs, random_state=0).fit(diabetes.data[:100, :], diabetes.target[:100]) for n_jobs in [1, 2, 3, 8, 16] ] preds = [e.predict(diabetes.data[400:, :]) for e in ests] for pred1, pred2 in zip(preds, preds[1:]): assert_array_almost_equal(pred1, pred2) lengths = np.array([[gp.length_ for gp in e._programs[-1]] for e in ests]) for len1, len2 in zip(lengths, lengths[1:]): assert_array_almost_equal(len1, len2) # Check the transformer ests = [ SymbolicTransformer(population_size=100, hall_of_fame=50, generations=4, n_jobs=n_jobs, random_state=0).fit(diabetes.data[:100, :], diabetes.target[:100]) for n_jobs in [1, 2, 3, 8, 16] ] preds = [e.transform(diabetes.data[400:, :]) for e in ests] for pred1, pred2 in zip(preds, preds[1:]): assert_array_almost_equal(pred1, pred2) lengths = np.array([[gp.length_ for gp in e._programs[-1]] for e in ests]) for len1, len2 in zip(lengths, lengths[1:]): assert_array_almost_equal(len1, len2) # Check the classifier ests = [ SymbolicClassifier(population_size=100, generations=4, n_jobs=n_jobs, random_state=0).fit(cancer.data[:100, :], cancer.target[:100]) for n_jobs in [1, 2, 3, 8, 16] ] preds = [e.predict(cancer.data[400:, :]) for e in ests] for pred1, pred2 in zip(preds, preds[1:]): assert_array_almost_equal(pred1, pred2) lengths = np.array([[gp.length_ for gp in e._programs[-1]] for e in ests]) for len1, len2 in zip(lengths, lengths[1:]): assert_array_almost_equal(len1, len2) def test_pickle(): """Check pickability""" # Check the regressor est = SymbolicRegressor(population_size=100, generations=2, random_state=0) est.fit(diabetes.data[:100, :], diabetes.target[:100]) score = est.score(diabetes.data[400:, :], diabetes.target[400:]) pickle_object = pickle.dumps(est) est2 = pickle.loads(pickle_object) assert(type(est2) == est.__class__) score2 = est2.score(diabetes.data[400:, :], diabetes.target[400:]) assert(score == score2) # Check the transformer est = SymbolicTransformer(population_size=100, generations=2, random_state=0) est.fit(diabetes.data[:100, :], diabetes.target[:100]) X_new = est.transform(diabetes.data[400:, :]) pickle_object = pickle.dumps(est) est2 = pickle.loads(pickle_object) assert(type(est2) == est.__class__) X_new2 = est2.transform(diabetes.data[400:, :]) assert_array_almost_equal(X_new, X_new2) # Check the classifier est = SymbolicClassifier(population_size=100, generations=2, random_state=0) est.fit(cancer.data[:100, :], cancer.target[:100]) score = est.score(cancer.data[500:, :], cancer.target[500:]) pickle_object = pickle.dumps(est) est2 = pickle.loads(pickle_object) assert(type(est2) == est.__class__) score2 = est2.score(cancer.data[500:, :], cancer.target[500:]) assert(score == score2) def test_output_shape(): """Check output shape is as expected""" random_state = check_random_state(415) X = np.reshape(random_state.uniform(size=50), (5, 10)) y = random_state.uniform(size=5) # Check the transformer est = SymbolicTransformer(population_size=100, generations=2, n_components=5, random_state=0) est.fit(X, y) assert(est.transform(X).shape == (5, 5)) def test_gridsearch(): """Check that SymbolicRegressor can be grid-searched""" # Grid search parsimony_coefficient parameters = {'parsimony_coefficient': [0.001, 0.1, 'auto']} clf = SymbolicRegressor(population_size=50, generations=5, tournament_size=5, random_state=0) grid = GridSearchCV(clf, parameters, cv=3, scoring='neg_mean_absolute_error') grid.fit(diabetes.data, diabetes.target) expected = {'parsimony_coefficient': 0.001} assert(grid.best_params_ == expected) def test_pipeline(): """Check that SymbolicRegressor/Transformer can work in a pipeline""" # Check the regressor est = make_pipeline(StandardScaler(), SymbolicRegressor(population_size=50, generations=10, tournament_size=5, random_state=0)) est.fit(diabetes.data, diabetes.target) assert_almost_equal(est.score(diabetes.data, diabetes.target), -3.702070228336284, decimal=5) # Check the classifier est = make_pipeline(StandardScaler(), SymbolicClassifier(population_size=50, generations=5, tournament_size=5, random_state=0)) est.fit(cancer.data, cancer.target) assert_almost_equal(est.score(cancer.data, cancer.target), 0.934973637961) # Check the transformer est = make_pipeline(SymbolicTransformer(population_size=50, hall_of_fame=20, generations=5, tournament_size=5, random_state=0), DecisionTreeRegressor()) est.fit(diabetes.data, diabetes.target) assert_almost_equal(est.score(diabetes.data, diabetes.target), 1.0) def test_transformer_iterable(): """Check that the transformer is iterable""" random_state = check_random_state(415) X = np.reshape(random_state.uniform(size=50), (5, 10)) y = random_state.uniform(size=5) function_set = ['add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max', 'min'] est = SymbolicTransformer(population_size=500, generations=2, function_set=function_set, random_state=0) # Check unfitted unfitted_len = len(est) unfitted_iter = [gp.length_ for gp in est] expected_iter = [] assert(unfitted_len == 0) assert(unfitted_iter == expected_iter) # Check fitted est.fit(X, y) fitted_len = len(est) fitted_iter = [gp.length_ for gp in est] expected_iter = [8, 12, 2, 29, 9, 33, 9, 8, 4, 22] assert(fitted_len == 10) assert(fitted_iter == expected_iter) # Check IndexError assert_raises(IndexError, est.__getitem__, 10) def test_print_overloading_estimator(): """Check that printing a fitted estimator results in 'pretty' output""" random_state = check_random_state(415) X = np.reshape(random_state.uniform(size=50), (5, 10)) y = random_state.uniform(size=5) # Check the regressor est = SymbolicRegressor(population_size=100, generations=2, random_state=0) # Unfitted orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_unfitted = out.getvalue().strip() finally: sys.stdout = orig_stdout # Fitted est.fit(X, y) orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_fitted = out.getvalue().strip() finally: sys.stdout = orig_stdout orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est._program) output_program = out.getvalue().strip() finally: sys.stdout = orig_stdout assert(output_unfitted != output_fitted) assert(output_unfitted == est.__repr__()) assert(output_fitted == output_program) # Check the transformer est = SymbolicTransformer(population_size=100, generations=2, random_state=0) # Unfitted orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_unfitted = out.getvalue().strip() finally: sys.stdout = orig_stdout # Fitted est.fit(X, y) orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_fitted = out.getvalue().strip() finally: sys.stdout = orig_stdout orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out output = str([gp.__str__() for gp in est]) print(output.replace("',", ",\n").replace("'", "")) output_program = out.getvalue().strip() finally: sys.stdout = orig_stdout assert(output_unfitted != output_fitted) assert(output_unfitted == est.__repr__()) assert(output_fitted == output_program) # Check the classifier y = (y > .5).astype(int) est = SymbolicClassifier(population_size=100, generations=2, random_state=0) # Unfitted orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_unfitted = out.getvalue().strip() finally: sys.stdout = orig_stdout # Fitted est.fit(X, y) orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est) output_fitted = out.getvalue().strip() finally: sys.stdout = orig_stdout orig_stdout = sys.stdout try: out = StringIO() sys.stdout = out print(est._program) output_program = out.getvalue().strip() finally: sys.stdout = orig_stdout assert(output_unfitted != output_fitted) assert(output_unfitted == est.__repr__()) assert(output_fitted == output_program) def test_validate_functions(): """Check that valid functions are accepted & invalid ones raise error""" for Symbolic in (SymbolicRegressor, SymbolicTransformer): # These should be fine est = Symbolic(population_size=100, generations=2, random_state=0, function_set=(add2, sub2, mul2, div2)) est.fit(diabetes.data, diabetes.target) est = Symbolic(population_size=100, generations=2, random_state=0, function_set=('add', 'sub', 'mul', div2)) est.fit(diabetes.data, diabetes.target) # These should fail est = Symbolic(generations=2, random_state=0, function_set=('ni', 'sub', 'mul', div2)) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(generations=2, random_state=0, function_set=(7, 'sub', 'mul', div2)) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) est = Symbolic(generations=2, random_state=0, function_set=()) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Now for the classifier... These should be fine est = SymbolicClassifier(population_size=100, generations=2, random_state=0, function_set=(add2, sub2, mul2, div2)) est.fit(cancer.data, cancer.target) est = SymbolicClassifier(population_size=100, generations=2, random_state=0, function_set=('add', 'sub', 'mul', div2)) est.fit(cancer.data, cancer.target) # These should fail est = SymbolicClassifier(generations=2, random_state=0, function_set=('ni', 'sub', 'mul', div2)) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(generations=2, random_state=0, function_set=(7, 'sub', 'mul', div2)) assert_raises(ValueError, est.fit, cancer.data, cancer.target) est = SymbolicClassifier(generations=2, random_state=0, function_set=()) assert_raises(ValueError, est.fit, cancer.data, cancer.target) def test_indices(): """Check that indices are stable when generated on the fly.""" params = {'function_set': [add2, sub2, mul2, div2], 'arities': {2: [add2, sub2, mul2, div2]}, 'init_depth': (2, 6), 'init_method': 'half and half', 'n_features': 10, 'const_range': (-1.0, 1.0), 'metric': 'mean absolute error', 'p_point_replace': 0.05, 'parsimony_coefficient': 0.1} random_state = check_random_state(415) test_gp = [mul2, div2, 8, 1, sub2, 9, .5] gp = _Program(random_state=random_state, program=test_gp, **params) assert_raises(ValueError, gp.get_all_indices) assert_raises(ValueError, gp._indices) def get_indices_property(): return gp.indices_ assert_raises(ValueError, get_indices_property) indices, _ = gp.get_all_indices(10, 7, random_state) assert_array_equal(indices, gp.get_all_indices()[0]) assert_array_equal(indices, gp._indices()) assert_array_equal(indices, gp.indices_) def test_run_details(): """Check the run_details_ attribute works as expected.""" est = SymbolicRegressor(population_size=100, generations=5, random_state=0) est.fit(diabetes.data, diabetes.target) # Check generations are indexed as expected without warm_start assert(est.run_details_['generation'] == list(range(5))) est.set_params(generations=10, warm_start=True) est.fit(diabetes.data, diabetes.target) # Check generations are indexed as expected with warm_start assert(est.run_details_['generation'] == list(range(10))) # Check all details have expected number of elements for detail in est.run_details_: assert(len(est.run_details_[detail]) == 10) def test_warm_start(): """Check the warm_start functionality works as expected.""" est = SymbolicRegressor(population_size=50, generations=10, random_state=0) est.fit(diabetes.data, diabetes.target) cold_fitness = est._program.fitness_ cold_program = est._program.__str__() # Check fitting fewer generations raises error est.set_params(generations=5, warm_start=True) assert_raises(ValueError, est.fit, diabetes.data, diabetes.target) # Check fitting the same number of generations warns est.set_params(generations=10, warm_start=True) with pytest.warns(UserWarning): est.fit(diabetes.data, diabetes.target) # Check warm starts get the same result est = SymbolicRegressor(population_size=50, generations=5, random_state=0) est.fit(diabetes.data, diabetes.target) est.set_params(generations=10, warm_start=True) est.fit(diabetes.data, diabetes.target) warm_fitness = est._program.fitness_ warm_program = est._program.__str__() assert_almost_equal(cold_fitness, warm_fitness) assert(cold_program == warm_program) def test_low_memory(): """Check the low_memory functionality works as expected.""" est = SymbolicRegressor(population_size=50, generations=10, random_state=56, low_memory=True) # Check there are no parents est.fit(diabetes.data, diabetes.target) assert(est._programs[-2] is None) def test_low_memory_warm_start(): """Check the warm_start functionality works as expected with low_memory.""" est = SymbolicRegressor(population_size=50, generations=20, random_state=415, low_memory=True) est.fit(diabetes.data, diabetes.target) cold_fitness = est._program.fitness_ cold_program = est._program.__str__() # Check warm start with low memory gets the same result est = SymbolicRegressor(population_size=50, generations=10, random_state=415, low_memory=True) est.fit(diabetes.data, diabetes.target) est.set_params(generations=20, warm_start=True) est.fit(diabetes.data, diabetes.target) warm_fitness = est._program.fitness_ warm_program = est._program.__str__() assert_almost_equal(cold_fitness, warm_fitness) assert(cold_program == warm_program) gplearn-0.4.2/gplearn/tests/test_utils.py000066400000000000000000000021731423420364700205200ustar00rootroot00000000000000"""Testing the utils module.""" # Author: Trevor Stephens # # License: BSD 3 clause import numpy as np from sklearn.utils._testing import assert_raises from gplearn.utils import _get_n_jobs, check_random_state, cpu_count def test_check_random_state(): """Check the check_random_state utility function behavior""" assert(check_random_state(None) is np.random.mtrand._rand) assert(check_random_state(np.random) is np.random.mtrand._rand) rng_42 = np.random.RandomState(42) assert(check_random_state(42).randint(100) == rng_42.randint(100)) rng_42 = np.random.RandomState(42) assert(check_random_state(rng_42) is rng_42) rng_42 = np.random.RandomState(42) assert(check_random_state(43).randint(100) != rng_42.randint(100)) assert_raises(ValueError, check_random_state, "some invalid seed") def test_get_n_jobs(): """Check that _get_n_jobs returns expected values""" jobs = _get_n_jobs(4) assert(jobs == 4) jobs = -2 expected = cpu_count() + 1 + jobs jobs = _get_n_jobs(jobs) assert(jobs == expected) assert_raises(ValueError, _get_n_jobs, 0) gplearn-0.4.2/gplearn/utils.py000066400000000000000000000047211423420364700163200ustar00rootroot00000000000000"""Utilities that are required by gplearn. Most of these functions are slightly modified versions of some key utility functions from scikit-learn that gplearn depends upon. They reside here in order to maintain compatibility across different versions of scikit-learn. """ import numbers import numpy as np from joblib import cpu_count def check_random_state(seed): """Turn seed into a np.random.RandomState instance Parameters ---------- seed : None | int | instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError. """ if seed is None or seed is np.random: return np.random.mtrand._rand if isinstance(seed, (numbers.Integral, np.integer)): return np.random.RandomState(seed) if isinstance(seed, np.random.RandomState): return seed raise ValueError('%r cannot be used to seed a numpy.random.RandomState' ' instance' % seed) def _get_n_jobs(n_jobs): """Get number of jobs for the computation. This function reimplements the logic of joblib to determine the actual number of jobs depending on the cpu count. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. Parameters ---------- n_jobs : int Number of jobs stated in joblib convention. Returns ------- n_jobs : int The actual number of jobs as positive integer. """ if n_jobs < 0: return max(cpu_count() + 1 + n_jobs, 1) elif n_jobs == 0: raise ValueError('Parameter n_jobs == 0 has no meaning.') else: return n_jobs def _partition_estimators(n_estimators, n_jobs): """Private function used to partition estimators between jobs.""" # Compute the number of jobs n_jobs = min(_get_n_jobs(n_jobs), n_estimators) # Partition estimators between jobs n_estimators_per_job = (n_estimators // n_jobs) * np.ones(n_jobs, dtype=int) n_estimators_per_job[:n_estimators % n_jobs] += 1 starts = np.cumsum(n_estimators_per_job) return n_jobs, n_estimators_per_job.tolist(), [0] + starts.tolist() gplearn-0.4.2/setup.py000066400000000000000000000027521423420364700146720ustar00rootroot00000000000000#! /usr/bin/env python """Genetic Programming in Python, with a scikit-learn inspired API""" from setuptools import setup, find_packages import gplearn DESCRIPTION = __doc__ VERSION = gplearn.__version__ setup(name='gplearn', version=VERSION, description=DESCRIPTION, long_description=open("README.rst").read(), classifiers=['Development Status :: 3 - Alpha', 'Intended Audience :: Science/Research', 'Intended Audience :: Developers', 'License :: OSI Approved', 'Topic :: Software Development', 'Topic :: Scientific/Engineering', 'Operating System :: Microsoft :: Windows', 'Operating System :: Unix', 'Operating System :: MacOS', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10'], author='Trevor Stephens', author_email='trev.stephens@gmail.com', url='https://github.com/trevorstephens/gplearn', license='new BSD', packages=find_packages(exclude=['*.tests', '*.tests.*']), zip_safe=False, package_data={'': ['LICENSE']}, install_requires=['scikit-learn>=1.0.2', 'joblib>=1.0.0'])