Repository: minaskar/zeus Branch: main Commit: 1abdf08252a9 Files: 49 Total size: 89.1 MB Directory structure: gitextract_8ypbapnn/ ├── .github/ │ └── workflows/ │ ├── release_to_pypi.yml │ └── setup_and_run_tests.yml ├── .gitignore ├── .readthedocs.yaml ├── LICENSE ├── MANIFEST.in ├── README.md ├── docs/ │ ├── Makefile │ ├── _static/ │ │ ├── copybutton.js │ │ └── default.css │ ├── api/ │ │ ├── autocorr.rst │ │ ├── callbacks.rst │ │ ├── moves.rst │ │ ├── parallel.rst │ │ ├── plotting.rst │ │ └── sampler.rst │ ├── api.rst │ ├── conf.py │ ├── cookbook.rst │ ├── faq.rst │ ├── index.rst │ ├── make.bat │ ├── notebooks/ │ │ ├── GR.ipynb │ │ ├── MPI.ipynb │ │ ├── blobs.ipynb │ │ ├── convergence.ipynb │ │ ├── datafit.ipynb │ │ ├── multimodal.ipynb │ │ ├── multiprocessing.ipynb │ │ ├── normal_distribution.ipynb │ │ └── progress.ipynb │ └── requirements.txt ├── requirements.txt ├── setup.cfg ├── setup.py ├── tests/ │ ├── test_autocorr.py │ ├── test_fwrapper.py │ ├── test_sampler.py │ └── test_samples.py └── zeus/ ├── __init__.py ├── _version.py ├── autocorr.py ├── callbacks.py ├── ensemble.py ├── fwrapper.py ├── moves.py ├── parallel.py ├── plotting.py └── samples.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/release_to_pypi.yml ================================================ name: Publish zeus to PyPI / GitHub on: push: branches: - main paths: - 'zeus/_version.py' # Only run workflow on pushes where _version.py was changed jobs: build-n-publish: name: Build and publish to PyPI runs-on: ubuntu-latest steps: - name: Checkout source uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: "3.x" - name: Build source and wheel distributions run: | python -m pip install --upgrade build twine python -m build twine check --strict dist/* - name: Install zeus from the wheel file run: | pip install dist/*.whl - name: List the installed packages run: | pip freeze - name: Run zeus unittests run: | python -m unittest discover tests - name: Publish distribution to PyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} - name: Get the version id: get_version run: echo ::set-output name=VERSION::$(cat zeus/_version.py | grep version | cut -d'"' -f 2) - name: Create GitHub Release id: create_release uses: actions/create-release@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, no need to create our own with: tag_name: ${{ steps.get_version.outputs.VERSION }} release_name: ${{ steps.get_version.outputs.VERSION }} draft: false prerelease: false - name: Get Asset name run: | export PKG=$(ls dist/ | grep tar) set -- $PKG echo "name=$1" >> $GITHUB_ENV - name: Upload Release Asset (sdist) to GitHub id: upload-release-asset uses: actions/upload-release-asset@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: upload_url: ${{ steps.create_release.outputs.upload_url }} asset_path: dist/${{ env.name }} asset_name: ${{ env.name }} asset_content_type: application/zip ================================================ FILE: .github/workflows/setup_and_run_tests.yml ================================================ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions name: Setup zeus and run tests on: push: branches: [ "main"] pull_request: branches: [ "main"] workflow_dispatch: jobs: build: name: Setup and Run Tests runs-on: ubuntu-latest strategy: fail-fast: false matrix: python-version: ["3.7", "3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip python -m pip install flake8 if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Run tests run: | python -m unittest discover tests ================================================ FILE: .gitignore ================================================ __pycache__/ zeus/_pycache__/ *.py[cod] .ipynb_checkpoints/ examples/.ipynb_checkpoints/ zeus_mcmc.egg-info/ build/ dist/ ================================================ FILE: .readthedocs.yaml ================================================ # .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-20.04 tools: python: "3.9" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf # Optionally declare the Python requirements required to build your docs python: install: - requirements: docs/requirements.txt - requirements: requirements.txt system_packages: true ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . ================================================ FILE: MANIFEST.in ================================================ include LICENSE README.md requirements.txt ================================================ FILE: README.md ================================================ ![logo](logo.png) **zeus is a Python implementation of the Ensemble Slice Sampling method.** - Fast & Robust *Bayesian Inference*, - Efficient *Markov Chain Monte Carlo (MCMC)*, - Black-box inference, no hand-tuning, - Excellent performance in terms of autocorrelation time and convergence rate, - Scale to multiple CPUs without any extra effort, - Automated Convergence diagnostics. [![GitHub](https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue)](https://github.com/minaskar/zeus) [![arXiv](https://img.shields.io/badge/arXiv-2002.06212-red)](https://arxiv.org/abs/2002.06212) [![arXiv](https://img.shields.io/badge/arXiv-2105.03468-brightgreen)](https://arxiv.org/abs/2105.03468) [![ascl](https://img.shields.io/badge/ascl-2008.010-blue.svg?colorB=262255)](https://ascl.net/2008.010) [![Build Status](https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master)](https://travis-ci.com/minaskar/zeus) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/minaskar/zeus/blob/master/LICENSE) [![Documentation Status](https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9)](https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/zeus-mcmc)](https://pepy.tech/project/zeus-mcmc) ## Example For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like: ```python import zeus import numpy as np def log_prob(x, ivar): return - 0.5 * np.sum(ivar * x**2.0) nsteps, nwalkers, ndim = 1000, 100, 10 ivar = 1.0 / np.random.rand(ndim) start = np.random.randn(nwalkers,ndim) sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc(start, nsteps) chain = sampler.get_chain(flat=True) ``` ## Documentation Read the docs at [zeus-mcmc.readthedocs.io](https://zeus-mcmc.readthedocs.io) ## Installation To install ``zeus`` using ``pip`` run: ```bash pip install zeus-mcmc ``` To install ``zeus`` in a [[Ana]Conda](https://conda.io/projects/conda/en/latest/index.html) environment use: ```bash conda install -c conda-forge zeus-mcmc ``` ## Attribution Please cite the following papers if you found this code useful in your research: ```bash @article{karamanis2021zeus, title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference}, author={Karamanis, Minas and Beutler, Florian and Peacock, John A}, journal={arXiv preprint arXiv:2105.03468}, year={2021} } @article{karamanis2020ensemble, title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions}, author = {Karamanis, Minas and Beutler, Florian}, journal = {arXiv preprint arXiv: 2002.06212}, year = {2020} } ``` ## Licence Copyright 2019-2021 Minas Karamanis and contributors. zeus is free software made available under the GPL-3.0 License. For details see the `LICENSE` file. ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/_static/copybutton.js ================================================ // originally taken from scikit-learn's Sphinx theme $(document).ready(function() { /* Add a [>>>] button on the top-right corner of code samples to hide * the >>> and ... prompts and the output and thus make the code * copyable. * Note: This JS snippet was taken from the official python.org * documentation site.*/ var div = $('.highlight-python .highlight,' + '.highlight-python3 .highlight,' + '.highlight-pycon .highlight') var pre = div.find('pre'); // get the styles from the current theme pre.parent().parent().css('position', 'relative'); var hide_text = 'Hide the prompts and output'; var show_text = 'Show the prompts and output'; var border_width = pre.css('border-top-width'); var border_style = pre.css('border-top-style'); var border_color = pre.css('border-top-color'); var button_styles = { 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', 'border-color': border_color, 'border-style': border_style, 'border-width': border_width, 'color': border_color, 'text-size': '75%', 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em' } // create and add the button to all the code blocks that contain >>> div.each(function(index) { var jthis = $(this); if (jthis.find('.gp').length > 0) { var button = $('>>>'); button.css(button_styles) button.attr('title', hide_text); jthis.prepend(button); } // tracebacks (.gt) contain bare text elements that need to be // wrapped in a span to work with .nextUntil() (see later) jthis.find('pre:has(.gt)').contents().filter(function() { return ((this.nodeType == 3) && (this.data.trim().length > 0)); }).wrap(''); }); // define the behavior of the button when it's clicked $('.copybutton').toggle( function() { var button = $(this); button.parent().find('.go, .gp, .gt').hide(); button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); button.css('text-decoration', 'line-through'); button.attr('title', show_text); }, function() { var button = $(this); button.parent().find('.go, .gp, .gt').show(); button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); button.css('text-decoration', 'none'); button.attr('title', hide_text); }); }); ================================================ FILE: docs/_static/default.css ================================================ body { color: #444444 !important; } h1 { font-size: 40px !important; } h2 { font-size: 32px !important; } h3 { font-size: 24px !important; } h4 { font-size: 18px !important; } h5 { font-size: 14px !important; } h6 { font-size: 10px !important; } footer a{ color: #4c72b0 !important; } a.reference { color: #4c72b0 !important; } blockquote p { font-size: 14px !important; } blockquote { padding-top: 4px !important; padding-bottom: 4px !important; margin: 0 0 0px !important; } pre { background-color: #f6f6f9 !important; } code { color: #49759c !important; background-color: #ffffff !important; } code.descclassname { padding-right: 0px !important; } code.descname { padding-left: 0px !important; } dt:target, span.highlighted { background-color: #ffffff !important; } ul { padding-left: 20px !important; } ul.dropdown-menu { padding-left: 0px !important; } .alert-info { background-color: #adb8cb !important; border-color: #adb8cb !important; color: #2c3e50 !important; } /* From https://github.com/twbs/bootstrap/issues/1768 */ *[id]:before { display: block; content: " "; margin-top: -60px; height: 60px; visibility: hidden; } table { /*Uncomment to center tables horizontally*/ /* margin-left: auto; */ /* margin-right: auto; */ border: none; border-collapse: collapse; border-spacing: 0; font-size: 12px; table-layout: fixed; } thead { border-bottom: 1px solid; vertical-align: bottom; } tr, th, td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } th { font-weight: bold; } tbody tr:nth-child(odd) { background: #f5f5f5; } tbody tr:hover { background: rgba(66, 165, 245, 0.2); } ================================================ FILE: docs/api/autocorr.rst ================================================ =============================== Autocorrelation Time Estimation =============================== .. autofunction:: zeus.AutoCorrTime ================================================ FILE: docs/api/callbacks.rst ================================================ ============= The Callbacks ============= Starting from version 2.4.0, ``zeus`` supports callback functions. Those are functions that are called in every iteration of a run. Among other things, these can be used to monitor useful quantities, assess convergence, and save the chains to disk. Custom callback functions can also be used. Sampling terminates if a callback function returns ``True`` and continues running while ``False`` or ``None`` is returned. Autocorrelation Callback ======================== .. autoclass:: zeus.callbacks.AutocorrelationCallback :members: Split-R Callback ================ .. autoclass:: zeus.callbacks.SplitRCallback :members: Parallel Split-R Callback ========================= .. autoclass:: zeus.callbacks.ParallelSplitRCallback :members: Minimum Iterations Callback =========================== .. autoclass:: zeus.callbacks.MinIterCallback :members: Save Progress Callback ====================== .. autoclass:: zeus.callbacks.SaveProgressCallback :members: ================================================ FILE: docs/api/moves.rst ================================================ ================== The Ensemble Moves ================== ``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version 2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers follow to cross the parameter space. The ``Differential Move`` remains the default choice but we also provide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from challenging target distributions (e.g. highly dimensional multimodal distributions). Differential Move ================= .. autoclass:: zeus.moves.DifferentialMove :members: Gaussian Move ============= .. autoclass:: zeus.moves.GaussianMove :members: Global Move =========== .. autoclass:: zeus.moves.GlobalMove :members: KDE Move ======== .. autoclass:: zeus.moves.KDEMove :members: Random Move =========== .. autoclass:: zeus.moves.RandomMove :members: ================================================ FILE: docs/api/parallel.rst ================================================ ============================= The Chain Manager & MPI Tools ============================= The ``Chain Manager`` can be used to parallelize ``zeus``. The benefits of this appoach is that the ``Chain Manager`` can parallelize many chains and walkers simultaneously. See the Cookbook for more information. .. autoclass:: zeus.ChainManager :members: ================================================ FILE: docs/api/plotting.rst ================================================ ================ Plotting Results ================ Cornerplot ========== .. currentmodule:: zeus .. autofunction:: zeus.cornerplot ================================================ FILE: docs/api/sampler.rst ================================================ ========================== The Ensemble Slice Sampler ========================== .. autoclass:: zeus.EnsembleSampler :members: ================================================ FILE: docs/api.rst ================================================ ============= API Reference ============= **zeus** consists mainly of six parts: .. toctree:: :maxdepth: 2 api/sampler api/callbacks api/moves api/autocorr api/parallel api/plotting ================================================ FILE: docs/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # http://www.sphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- # 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. # import os import sys sys.path.insert(0, os.path.abspath('../../zeus/')) import zeus import sphinx_bootstrap_theme # -- Project information ----------------------------------------------------- project = 'zeus' copyright = '2019-2022, Minas Karamanis' author = 'Minas Karamanis' # The full version, including alpha/beta/rc tags release = zeus.__version__ # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', #'numpydoc', 'nbsphinx', 'sphinx.ext.coverage', 'IPython.sphinxext.ipython_console_highlighting', ] # Generate the API documentation when building autosummary_generate = True numpydoc_show_class_members = False napoleon_google_docstring = True napoleon_numpy_docstring = True napoleon_include_init_with_doc = False napoleon_include_private_with_doc = False napoleon_include_special_with_doc = False napoleon_use_admonition_for_examples = False napoleon_use_admonition_for_notes = False napoleon_use_admonition_for_references = False napoleon_use_ivar = False napoleon_use_param = True napoleon_use_rtype = True napoleon_use_keyword = True napoleon_custom_sections = None # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] source_suffix = ".rst" master_doc = "index" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints'] #exclude_patterns = ['_build'] # -- 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 = 'bootstrap' html_favicon = "_static/favicon.png" # (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24). # Path should be relative to the ``_static`` files directory. #html_logo = "my_logo.png" # Theme options are theme-specific and customize the look and feel of a # theme further. html_theme_options = { 'navbar_title': "zeus", 'navbar_site_name': "Contents", 'navbar_links': [ ("Cookbook", "cookbook"), ("FAQ", "faq"), ("API", "api"), ], 'navbar_sidebarrel': False, # Render the next and previous page links in navbar. (Default: true) 'navbar_pagenav': False, # Render the current pages TOC in the navbar. (Default: true) 'navbar_pagenav_name': "Page", # Tab name for the current pages TOC. (Default: "Page") # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. 'globaltoc_depth': 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and # non-hidden ``toctree`` directives in the same page, or else the build # will break. # # Values: "true" (default) or "false" 'globaltoc_includehidden': "true", # HTML navbar class (Default: "navbar") to attach to
element. # For black navbar, do "navbar navbar-inverse" #'navbar_class': "navbar navbar-inverse", # Fix navigation bar to top of page? # Values: "true" (default) or "false" 'navbar_fixed_top': "true", #'bootswatch_theme': "united", #'bootswatch_theme': "paper", #'bootswatch_theme': "cosmo", 'bootswatch_theme': "readable", #'bootswatch_theme': "flatly", #'bootswatch_theme': "Yeti", 'bootstrap_version': "3", 'body_max_width' : '100%', #'body_min_width' : '70%', } html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() # 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'] # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # Add the 'copybutton' javascript, to hide/show the prompt in code # examples, originally taken from scikit-learn's doc/conf.py #def setup(app): # app.add_javascript('copybutton.js') # app.add_stylesheet('default.css') #html_css_files = ['_static',] #html_context = {'css_files': ['_static/default.css', # override wide tables in RTD theme #],} #autodoc_default_options = { # 'exclude-members': '__init__' #} #autoclass_content = ["class"] ================================================ FILE: docs/cookbook.rst ================================================ ======== Cookbook ======== MCMC Sampling recipes ===================== - `Sampling from a multivariate Normal distribution`_ Demonstrates how to sample from a correlated multivariate Gaussian distribution and how to perform the post-processing of the samples. - `Fitting a model to data`_ In this recipe we are going to produce some mock data and use them to illustrate how *zeus* works in realistic scenarios. - `Sampling from multimodal distributions`_ In this recipe we will demonstrate how one can use ``zeus`` with the ``Moves`` interface to sample efficiently from challenging high-dimensional multimodal distributions. .. _Sampling from a multivariate Normal distribution: notebooks/normal_distribution.ipynb .. _Fitting a model to data: notebooks/datafit.ipynb .. _Sampling from multimodal distributions: notebooks/multimodal.ipynb Parallelisation recipes ======================= - `Multiprocessing`_ Use many CPUs to sample from an expensive-to-evaluate probability distribution even faster. - `MPI and ChainManager`_ Distribute calculation to huge computer clusters. .. _Multiprocessing: notebooks/multiprocessing.ipynb .. _MPI and ChainManager: notebooks/MPI.ipynb .. raw:: html .. role:: red Convergence Diagnostics and Saving Progress recipes :red:`NEW` ============================================================== - `Automated Convergence Diagnostics using the callback interface`_ :red:`NEW` In this recipe we are going to use the callback interface to monitor convergence and stop sampling automatically. - `Saving progress to disk using h5py`_ :red:`NEW` In this recipe we are going to use the callback interface to save the samples and their corresponding log-probability values in a ``.h5`` file. - `Parallel sampling using MPI and Gelman-Rubin convergence diagnostics`_ :red:`NEW` In this recipe we are going to use the ChainManager to run zeus in parallel using MPI and terminate sampling automatically using Gelman-Rubin diagnostics. - `Tracking metadata using the blobs interface`_ We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain. .. _Automated Convergence Diagnostics using the callback interface: notebooks/convergence.ipynb .. _Saving progress to disk using h5py: notebooks/progress.ipynb .. _Parallel sampling using MPI and Gelman-Rubin convergence diagnostics: notebooks/GR.ipynb .. _Tracking metadata using the blobs interface: notebooks/blobs.ipynb .. toctree:: :maxdepth: 2 :hidden: notebooks/normal_distribution.ipynb notebooks/datafit.ipynb notebooks/multimodal.ipynb notebooks/multiprocessing.ipynb notebooks/MPI.ipynb notebooks/blobs.ipynb notebooks/progress.ipynb notebooks/convergence.ipynb notebooks/GR.ipynb ================================================ FILE: docs/faq.rst ================================================ ========================== Frequently Asked Questions ========================== What is the acceptance rate of ``zeus``? ======================================== Unlike most MCMC methods, ``zeus`` acceptance rate isn't varying during a run. As a matter of fact, its acceptance rate is identically 1, always. This is because of the Slice Sampler at its core. Why should I use zeus instead of other MCMC samplers? ===================================================== The first reason you should think of using ``zeus`` is due to the fact that it doesn't require any hand tuning at all. There is no need to adjust any hyperparameters or provide a proposal distribution. Moreover, unlike other black-box MCMC methods ``zeus`` is more robust to the curse of dimensionality and handle challenging distributions better. What are the walkers? ===================== Walkers are the members of the ensemble. They are interacting parallel chains which collectively explore the posterior mass. How many walkers should I use? ============================== At least twice the number of parameters of your problem. A good rule of thump is to use between 2 and 4 times the number of parameters. If your distribution has multiple modes/peaks you may want to increase the number of walkers. How should I initialize the positions of the walkers? ===================================================== A good practice seems to be to initialize the walkers from a small ball close to the *Maximum a Posteriori* estimate. After a few autocorrelation times the walkers would have explored the rest of the usefull regions of the parameter space (i.e. the typical set), producing a great number of independent samples. How long should I run ``zeus``? =============================== You don't have to run ``zeus`` for very long. If your goal is to produce 2D/1D contours and/or 1-sigma/2-sigma constraints for your parameters, running ``zeus`` for a few autocorrelation times (e.g. 10) is more than enough. You can also use the implemented callback functions (see Cookbook and API) to automate the termination of a run. What can I do if the first few iterations take too long to complete? ==================================================================== This usually occurs when the walkers are initialised closed to each other. During the first ``10-100`` iterations ``zeus`` is tuning its proposal scale ``mu``. During that time ``zeus`` may do more model evaluations than usual. Tuning of ``mu`` is faster if initialised from a large value. We thus recommend to set ``mu`` to an large value (e.g. ``mu=1e3``) initially in the ``EnsembleSampler``. Is there any way to reduce the computational cost per iteration? ================================================================ ``zeus``'s power originates in its flexibility. During each iteration, the walkers move along straight lines (i.e. slices) that cross the posterior mass. The construction of a slice involves two steps, an initial expanding/stepping-out and a subsequent shrinking procedure. One can decrease the computational cost per iteration by forcing ``zeus`` to conduct no expansions. This is achieved by setting ``light_mode=True`` in the ``EnsembleSampler`` at the cost of reduced flexibility. If the target distribution is close to normal/Gaussian one then this procedure can cut the cost to half. What are the ``Moves`` and which one should I use? ================================================== ``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version 2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers follow to cross the parameter space. The ``Differential Move`` remains the default choice but we also provide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from challenging target distributions (e.g. highly dimensional multimodal distributions). The move(s) you should use depends on the particular target distribution. The ``Differential Move`` seems to be a good choice for most distributions and 50-50 mixture of the ``Global Move`` and ``Local Move`` seem to perform very well in highly dimensional multimodal distributions when used after the burnin period is over. ================================================ FILE: docs/index.rst ================================================ .. title:: zeus documentation .. figure:: ./../logo.png :scale: 30 % :align: center .. raw:: html .. role:: red **zeus is a Python implementation of the Ensemble Slice Sampling method.** - Fast & Robust *Bayesian Inference*, - Efficient *Markov Chain Monte Carlo (MCMC)*, - Black-box inference, no hand-tuning, - Excellent performance in terms of autocorrelation time and convergence rate, - Scale to multiple CPUs without any extra effort, - Automated Convergence diagnostics. :red:`NEW` .. image:: https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue :target: https://github.com/minaskar/zeus .. image:: https://img.shields.io/badge/arXiv-2002.06212-red :target: https://arxiv.org/abs/2002.06212 .. image:: https://img.shields.io/badge/arXiv-2105.03468-brightgreen :target: https://arxiv.org/abs/2105.03468 .. image:: https://img.shields.io/badge/ascl-2008.010-blue.svg?colorB=262255 :target: https://ascl.net/2008.010 .. image:: https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master :target: https://travis-ci.com/minaskar/zeus .. image:: https://img.shields.io/badge/License-GPLv3-blue.svg :target: https://github.com/minaskar/zeus/blob/master/LICENSE .. image:: https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9 :target: https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest .. image:: https://pepy.tech/badge/zeus-mcmc :target: https://pepy.tech/project/zeus-mcmc Basic use ========= For instance, if you wanted to draw samples from a *10-dimensional Normal distribution*, you would do something like: .. code:: Python import zeus import numpy as np def log_prob(x, ivar): return - 0.5 * np.sum(ivar * x**2.0) nsteps, nwalkers, ndim = 1000, 100, 10 ivar = 1.0 / np.random.rand(ndim) start = np.random.randn(nwalkers, ndim) sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc(start, nsteps) chain = sampler.get_chain(flat=True) Installation ============ To install ``zeus`` using ``pip`` run: .. code:: bash pip install zeus-mcmc To install ``zeus`` in a `[Ana]Conda `_ environment use: .. code:: bash conda install -c conda-forge zeus-mcmc Getting Started =============== - See the :doc:`cookbook` page to learn how to perform Bayesian Inference using ``zeus``. - See the :doc:`faq` page for frequently asked questions about ``zeus``' operation. - See the :doc:`api` page for detailed API documentation. Citation ======== Please cite the following papers if you found this code useful in your research:: @article{karamanis2021zeus, title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference}, author={Karamanis, Minas and Beutler, Florian and Peacock, John A}, journal={arXiv preprint arXiv:2105.03468}, year={2021} } @article{karamanis2020ensemble, title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions}, author = {Karamanis, Minas and Beutler, Florian}, journal = {arXiv preprint arXiv: 2002.06212}, year = {2020} } Licence ======= Copyright 2019-2021 Minas Karamanis and contributors. ``zeus`` is free software made available under the ``GPL-3.0 License``. Changelog ========= **2.4.1 (17/11/21)** - Introduced ``ParallelSplitRCallback`` callback function for checking Gelman-Rubin statistics during ``MPI`` runs. **2.4.0 (01/11/21)** - Introduced callback interface. - Added convergence diagnostics. - Added ``H5DF`` support. **2.3.1 (03/08/21)** - Raise exception if model fails. **2.3.0 (25/02/21)** - Added ``sample`` method which advances the chain as a generator. - Added ``light_mode``. When used, ``light_mode`` can significantly reduce the number of log likelihood evaluations and increase the general efficiency of the algorithm. ``light_mode`` works by performing no expansions after the end of the tuning phase. The scale factor is set to its opttimal value. This works best for approximately Gaussian distributions. - Added ``start=None`` support for ``run_mcmc``. When used, the sampler proceeds from the last known position of the walkers. - Added support for both ``thin`` and ``thin_by`` arguments. **2.2.2 (21/02/21)** - Added ``log_prob0`` and ``blobs0`` arguments in ``run``. - Added ``get_last_sample()``, ``get_last_log_prob()`` and ``get_last_blobs()`` methods. **2.2.0 (03/11/20)** - Improved vectorization. **2.1.1 (29/10/20)** - Added ``blobs`` interface to track arbitrary metadata. - Updated ``GlobalMove`` and multimodal example. - Fixed minor bugs. **2.0.0 (05/10/20)** - Added new ``Moves`` interface (e.g. ``DifferentialMove``, ``GlobalMove``, etc). - Plotting capabilities (i.e. ``cornerplot``). - Updated docs. - Fixed minor bugs. **1.2.2 (19/09/20)** - ``Sampler`` class is deprecated. New ``EnsembleSampler`` class in now available. - New estimator for the Integrated Autocorrelation Time. It's accurate even with short chains. - Updated ``ChainManager`` to handle thousands of CPUs. **1.2.1 (04/08/20)** - Changed to Flat-not-nested philosophy for diagnostics and ``ChainManager``. **1.2.0 (03/08/20)** - Extended ``ChainManager`` with ``gather``, ``scatter``, and ``bcast`` tools. **1.1.0 (02/08/20)** - Added ``ChainManager`` to deploy into supercomputing clusters, parallelizing both chains and walkers. - Added Convergence diagnostic tools (Gelman-Rubin, Geweke). **1.0.7 (11/05/20)** - Improved parallel distribution of tasks .. toctree:: :maxdepth: 1 :caption: Cookbook Recipes :hidden: Overview notebooks/normal_distribution.ipynb notebooks/datafit.ipynb notebooks/multiprocessing.ipynb notebooks/MPI.ipynb .. toctree:: :maxdepth: 3 :caption: Help & Reference :hidden: faq api ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build if "%1" == "" goto help %SPHINXBUILD% >NUL 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 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docs/notebooks/GR.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallel sampling using MPI and Gelman-Rubin convergence diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\n", "\n", "To do this we will use the ``ChainManager`` included in ``zeus``. We will also use the ``ParallelSplitRCallback`` function to check the Gelman-Rubing convergence diagnostic during the run and terminate sampling automatically.\n", "\n", "In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\n", "\n", "```\n", "mpiexec -n 8 python3 test_mpi_gr.py\n", "```\n", "\n", "This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save this as 'test_mpi_gr.py'\n", "\n", "```python\n", "\n", "import numpy as np\n", "import zeus\n", "from zeus import ChainManager\n", "\n", "ndim = 20\n", "nwalkers = 2 * ndim\n", "nsteps = 10000\n", "nchains = 2\n", "\n", "def log_prob(x):\n", " return -0.5 * np.sum(x**2.0)\n", "\n", "start = 1e-2 * np.random.randn(nwalkers, ndim) + 20.0\n", "\n", "\n", "with ChainManager(nchains) as cm:\n", " rank = cm.get_rank\n", "\n", " cb = zeus.callbacks.ParallelSplitRCallback(epsilon=0.01, chainmanager=cm)\n", " sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\n", " sampler.run_mcmc(start, nsteps, callbacks=cb)\n", " chain = sampler.get_chain(flat=True, discard=0.5)\n", " \n", " if rank == 0:\n", " print('R =', cb.estimates, flush=True)\n", " np.save('chain_'+str(rank)+'.npy', chain)\n", "\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "288px" }, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: docs/notebooks/MPI.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallelizing sampling using MPI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\n", "\n", "To do this we will use the ``ChainManager`` included in ``zeus``.\n", "\n", "In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\n", "\n", "```\n", "mpiexec -n 8 python3 test_mpi.py\n", "```\n", "\n", "This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save this as 'test_mpi.py'\n", "\n", "```python\n", "\n", "import numpy as np\n", "import zeus\n", "from zeus import ChainManager\n", "\n", "ndim = 5\n", "nwalkers = 2 * ndim\n", "nsteps = 100\n", "nchains = 2\n", "\n", "def log_prob(x):\n", " return -0.5 * np.sum(x**2.0)\n", "\n", "start = np.random.randn(nwalkers, ndim)\n", "\n", "\n", "with ChainManager(nchains) as cm:\n", " rank = cm.get_rank\n", "\n", " sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\n", " sampler.run_mcmc(start, nsteps)\n", " chain = sampler.get_chain(flat=True, discard=0.5)\n", " \n", " np.save('chain_'+str(rank)+'.npy', chain)\n", "\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "288px" }, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: docs/notebooks/blobs.ipynb ================================================ { "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6-final" }, "orig_nbformat": 2, "kernelspec": { "name": "Python 3.7.6 64-bit ('nbodykit-env': conda)", "display_name": "Python 3.7.6 64-bit ('nbodykit-env': conda)", "metadata": { "interpreter": { "hash": "92a13e2fbe78b004d0fb7131bfe04f8cf6342cb1d93c749c200bfd2478bfd7dd" } } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# Blobs and Metadata" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## Tracking the value of the log-prior\n", "\n", "We can easily use blobs to store the value of the log-prior at each step in the chain by doing something like:" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Initialising ensemble of 32 walkers...\n", "Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 160.45it/s](100, 32)\n", "(3200,)\n", "\n" ] } ], "source": [ "import zeus\n", "\n", "import numpy as np\n", "\n", "def log_prior(x):\n", " return -0.5 * np.dot(x,x)\n", "\n", "def log_like(x):\n", " return -0.5 * np.dot(x,x) / 0.1**2.0\n", "\n", "def log_prob(x):\n", " lp = log_prior(x)\n", " if not np.isfinite(lp):\n", " return -np.inf, -np.inf\n", " ll = log_like(x)\n", " if not np.isfinite(ll):\n", " return lp, -np.inf\n", " return lp + ll, lp\n", "\n", "nwalkers, ndim = 32, 3\n", "start = np.random.randn(nwalkers, ndim)\n", "sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n", "sampler.run_mcmc(start, 100)\n", "\n", "log_prior_samps = sampler.get_blobs()\n", "flat_log_prior_samps = sampler.get_blobs(flat=True)\n", "\n", "print(log_prior_samps.shape) # (100, 32)\n", "print(flat_log_prior_samps.shape) # (3200,)" ] }, { "source": [ "Once this is done running, the “blobs” stored by the sampler will be a ``(nsteps, nwalkers)`` numpy array with the value of the log prior at every sample." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## Tracking multiple species of metadata\n", "\n", "When handling multiple species of metadata, it can be useful to name them. This can be done using the ``blobs_dtype`` argument of the ``EnsembleSampler``. For instance, to save the mean of the parameters as well as the log-prior we could do something like:" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Initialising ensemble of 32 walkers...\n", "Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 137.06it/s](100, 32)\n", "(100, 32)\n", "(3200,)\n", "(3200,)\n", "\n" ] } ], "source": [ "def log_prob(params):\n", " lp = log_prior(params)\n", " if not np.isfinite(lp):\n", " return -np.inf, -np.inf\n", " ll = log_like(params)\n", " if not np.isfinite(ll):\n", " return lp, -np.inf\n", " return lp + ll, lp, np.mean(params)\n", "\n", "nwalkers, ndim = 32, 3\n", "start = np.random.randn(nwalkers, ndim)\n", "\n", "# Here are the important lines\n", "dtype = [(\"log_prior\", float), (\"mean\", float)]\n", "sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, blobs_dtype=dtype)\n", "\n", "sampler.run_mcmc(start, 100)\n", "\n", "blobs = sampler.get_blobs()\n", "log_prior_samps = blobs[\"log_prior\"]\n", "mean_samps = blobs[\"mean\"]\n", "print(log_prior_samps.shape)\n", "print(mean_samps.shape)\n", "\n", "flat_blobs = sampler.get_blobs(flat=True)\n", "flat_log_prior_samps = flat_blobs[\"log_prior\"]\n", "flat_mean_samps = flat_blobs[\"mean\"]\n", "print(flat_log_prior_samps.shape)\n", "print(flat_mean_samps.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] } ================================================ FILE: docs/notebooks/convergence.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automated Convergence Diagnostics using the callback interface" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Knowing when to stop sampling can be very useful when running expensive MCMC procedures. Ideally, if we want unbiased results, we want the sampler to stop after it has converged to the stationary phase (i.e. after the burn-in/warm-up period is over). To do this we can combine different ``Convergence Diagnostics`` offered as ``callback functions`` by zeus.\n", "\n", "We will start by setting the simple problem of sampling from a bimodal Gaussian mixture distribution:" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "import zeus\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "nsteps, nwalkers, ndim = 100000, 50, 5\n", "\n", "def log_prob(x):\n", " return np.logaddexp(-0.5 * np.sum(x ** 2), -0.5 * np.sum((x - 4.0) ** 2))\n", "\n", "x0 = 1e-3*np.random.randn(nwalkers,ndim) + 5.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where ``nsteps`` would be the maximum number of steps/iterations, ``ivar`` would be the inverse variance (precision) of the normal target distribution that we are going to sample from, and ``x0`` is the starting position of the walkers.\n", "\n", "We will then define all the convergence diagnostics that we will use as ``callback functions``.\n", "\n", "First of all, we would like check the integrated autocorrelation time (IAT) of the chain every ``ncheck=100`` steps and make sure that we don't stop running unless the length of the chain is longer than ``nact=50`` times the IAT and that the rate of change of IAT drops bellow 1 percent (i.e. ``dact=0.01``). We would also discard the first half of the chain (i.e. ``discard=0.5``) before computing the IAT." ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "cb0 = zeus.callbacks.AutocorrelationCallback(ncheck=100, dact=0.01, nact=50, discard=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will then use the **Split-R Gelman-Rubin statistic** computed using different segments (i.e. split into ``nsplits=2`` parts) of the same chain and decide that the sampler has converged if its value drops bellow ``(1+epsilon)=1.01``." ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "cb1 = zeus.callbacks.SplitRCallback(ncheck=100, epsilon=0.01, nsplits=2, discard=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, just to make sure that the sampler doesn't stop too early, we will set the minimum number of iterations to ``nmin=500``." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "cb2 = zeus.callbacks.MinIterCallback(nmin=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to start sampling and require that all three of the aforementioned criteria are satisfied before sampling terminates." ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initialising ensemble of 50 walkers...\n", "Sampling progress : 2%|▏ | 1898/100000 [00:13<13:42, 119.22it/s]" ] } ], "source": [ "sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n", "sampler.run_mcmc(x0, nsteps, callbacks=[cb0, cb1, cb2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We noticed that the sampler automatically stopped running after approximately ``1900`` iterations. We can now have a look at the ``split-R`` statistics and the IAT estimate." ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling progress : 2%|▏ | 1900/100000 [00:13<11:50, 138.00it/s]\n" ] }, { "data": { "image/png": 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NW0ZdMreanb87LQemm9nRfny/xhsSGW41MNn/XfYzs52qdOatBXqhmU0xs9F4TTKq8b4/NfXcDbX0vUIiTAmWNPRXvLMhS/HOII1wzgUrJgHgVbx2rf/Gm6RaGXa/h/A62czxtz2LN/m4Sc65RXhDEK7yn/NCGgxF9Mdl34l3lm8r3gEUvA+Z/8P7Qr4Mr432AXiJDc65tXhjxo/Ca7V9Jd549uZcAPyvwTy1oCfxkoZxfiXvdLxx2QvxOtDVG2fvnFuPdzD8I14SEzyLdS3ewfT/8IbnTQWOcs5tDLvfAXiVvXfwzjz9CK9TE8AteEnWX/A6O54InOycW9jCa9uJP9zxALymHk/ivY8P4M3BKmjiblfhteN9H2/ewxz/erjf4M0V+Jq6Cl/D556PdzA92X8dN/qXuFrouinOuf/gNQk5G+9v4H28joPfxDIukR6iFO+z5yfAe3jHpT/hjQRo2MXu73ifs5/jVZR+45x7qg3P9VO8tuZ57FxBaY2/AUeb2T6N7fQrNmcAPwaOxBvuuBxvtERw2HzwttH63PkD3jzqV/C6CO/AOyHXKZxzW/C+Q/zWTxD+jJdkPYdXpfmAxt/7jv5uW+M1vPf7Nbxj5O147/sRYd8VdvruhLe8yBN4f5Of4Z3w/VuDx74HL0mb69+v4agO8KqGF/jPuQTvmHmScy74O2/suetpxfcKiTCrP/9fJHLM7HO8SZU/inUsIiLS85jZarwOa+2p8IuItIvmYElEmNes4ki8M4nJeGuGTPX/FRERERHpEZRgSaQE8MY+34w39HQpcLRzrrFW4SIiIiIi3ZKGCIqIiIiIiESImlyIiIiIiIhESJccItivXz83atSoWIchIiLtNG/evG3Ouf6xjiNWdBwTEen6mjqWdckEa9SoUcydq6k9IiJdlZmtiXUMsaTjmIhI19fUsUxDBEVERERERCJECZaIiIiIiEiEKMESERERERGJECVYIiIiIiIiEaIES0REREREJEKUYImIiIiIiESIEiwREREREZEIUYIlIiIiIiISIUqwREREREREIkQJloiIiIiISIQowRIREREREYkQJVgiIiIiIiIRogRLREREREQkQpRgicQR5xxPfJbHG0s3xzoUEekBnHM8OTePV5dsjHUoIiLdhhIskTjy7vKt/OzpRfzgwbk8t2B9rMMRkW5u/tpCrnlqET98eD7rCspiHY6ISLegBEskjnyyKj90/bqnF7Nic0kMoxGR7m71th0AOAd5+eUxjkZEpHtQgiUSRxatKwxdL6+u5ZL/zqO0siZ2AYlIt1ZQVhW6XlxRHcNIRES6j6gmWGa22swWm9kCM5vrb+tjZm+Y2Qr/39xoxiQSLwIBx+J1RQBkpycD8PXWHVz79CKcc7EMTUS6qaLyuqSqpEInc0REIiEWFayDnXO7O+dm+j9fB7zlnBsLvOX/LNLjrN6+gxK/WvXTI8ax/9h+ALy0aCP3f7Q6hpGJSHdVr4JVrgqWiEgkxMMQwROAB/zrDwDfiV0oIrGzyK9eAew+PId/nb4HQ7LTAPjjS18yb01BrEITkW6qsKwuqdIQQRGRyIh2guWA181snpld5G8b6JwL9ofdBAxs7I5mdpGZzTWzuVu3bo1GrCJRFUywUhITGD8okz69Urj9rBkkJxo1AcdlD89nW2lljKMUke6kXoJVriGCIiKREO0Eaz/n3HTgaOAyMzsgfKfzJpo0OtnEOXe3c26mc25m//79oxCqSHQFG1xMGJxJalIi4FWyfnPcRAA2FVfwk8c+pzag+VgiEhmF5WpyISISaVFNsJxz6/1/twDPAnsBm81sMID/75ZoxiQSD2pqA3yxoRiAqcOy6+07a9ZITth9CAAfrtzOP95YHvX4RKR7KtgRXsFSgiUiEglRS7DMrJeZZQavA0cAS4DngXP9m50LPBetmETixcqtpZRX1wIwdVhOvX1mxp9PmsK4gb0BuO2dlby9bHO0QxSRbii8i6AqWCIikRHNCtZA4AMzWwh8CrzknHsVuBE43MxWAIf5P4v0KOENLhpWsAAyUpK446wZ9Erxhg5e8dgC8vLLohafiHQ/1bWBeuvsqU27iEhkRC3Bcs6tcs5N8y+TnHN/9Ldvd84d6pwb65w7zDmXH62YROJFcP5VenIiu/bv3ehtxvTvzV9OmQZAcUUNP3x4HhV+1UtEpK3CG1yAKlgiIpESD23aRXq8YAVr0pAskhKb/m957NTBnL/vaACWrC/mdy98EZX4RKT7KQprcAHqIigiEilKsERirLKmli83Bhtc5LR4+58fM4GZI3MBePTTPJ6cm9eZ4YlIN1XQoIJVUlFNQF1KRUQ6TAmWSIx9tamE6lrvS01j868aSk5M4LYzp9OvdwoAv/rfEpb6HQhFRFqr4RDBgIMdVapiiYh0lBIskRhrqcFFYwZlp3HL6XuQYFBZE+CHD8+r1w0sksqrann007X8+eUvtdCxSDdSUFa107ZiNboQEekwJVgiMRZscJGZlsSovr1afb99du3HT48YD8Ca7WVc8+RCvLW6I2NLSQV/e/0r9rnxLX7+zGLumr2KCx+YS2WNGmuIdAdFZTuflNFaWCIiHacESyTGghWsKUOzSUiwNt33hweO4dAJAwB4felm7p69qsPxLN1QzE+fWMh+N77DrW+vrDdPY0FeIb9/cWmHn0NEYq/RCpYSLBGRDlOCJRJD5VW1rNhSCrSuwUVDCQnG30/bneF90gG46dVlzFm1vc2PEwg43lm2he/9Zw7H3PI+T89fR1VtAIDdh+dw6xl7sPtwL77/zlnLU/PWtfk5RCS+FDaSTGktLBGRjkuKdQAiPdnSjUXUBlrf4KIx2RnJ3PG9GZx0x0dU1QS4/JHPefnH+zEgK63F+5ZX1fLM5+u494NvWLV1R2h7gsFRkwdxwX67MMPvWDhzVC7H3fIB23dU8ctnF7Pb4EwmDWlfzCISe4V+BSs50UKNdrQWlohIx6mCJRJDC/Pa3uCiMZOHZvP7EyYBsK20kssf+ZxqvwLVmPD5Vb98dkkoueqVksj5+47mvWsO5vbvzQglVwCDs9O59Yy6xhqX/Hde6AuaiHQ9wS6Cw3IzQts0RFBEpOOUYInE0OL1XoLVp1cKQ3PSO/RY391zBKfOGAbAp6vzufm1r3a6zZcbi7n6yZ3nVw3NSedXx+7Gx784lN8cP5HhfTJ2ui94jTWuPWoCAHn55Vzx+AKtmyPSRQUTrPD/7+oiKCLScUqwRGJood9BcOqwbMza1uCiMb//zmR2G5wFwN2zV/Hqko315lcd/a/3eWpe3fyqacNzuO3MPXjvmoO4cP9dyEpLbvE5LjpgF46aNAiAd7/ayi1vr+hw3CLRYmb3mdkWM1vSxH4zs1vMbKWZLTKz6WH7XjWzQjN7sYn73mJmpZ0Ve6QFK9D9e6eSnpwIqIIlIhIJmoMlEiPFFdWhoXlTh0ZmLlNaciJ3njWd4279gJKKGq5+chEDs77i6wbzq46cNIgL9x/N9BG5bU7szIybT53K8i0lrNq6g3+9tYJpw3I42O9mKBLn7gduAx5sYv/RwFj/sjdwh/8vwM1ABnBxwzuZ2Uwgt+H2eBZscpGTkUxWehLl1bWagyUiEgGqYInEyJL14fOvciL2uCP79uJvp04DoLSyJpRchc+vuuOsGcwY2afdVbPMtGTuOmsGGSmJOAc/eexz1m4vi9hrEOkszrnZQH4zNzkBeNB55gA5ZjbYv+9bQEnDO5hZIl7y9bNOCLlTVNbUUlblrWmXm5Ecql4Xl2uIoIhIRynBEomR4PpX0LEGF405YtIgrjhsLABDstP45TEtz69qq7EDM7n5FC+RK66o4eL/zqO8SosQS5c3FMgL+3mdv605lwPPO+c2NncjM7vIzOaa2dytW7d2MMyOCV9kODsjhax0L8EqqVQFS0SkozREUCRGFvsJ1qCstFa1VG+rKw4bx/f2HkluRjJJiZ1zLuXYqYNZkDeae97/hi83FvPL/y3mb6dOi8h8MpGuwMyGAKcCB7V0W+fc3cDdADNnzoxpd5jwBcRzM5LJTPO+DqiCJSLScapgicRIeIOLztI/M7XTkquga4+awN6j+wDwzPz1PPzJ2k59PpFOth4YHvbzMH9bU/YAdgVWmtlqIMPMVnZeeJERvsRCTnpK3RBBzcESEekwJVgiMZC/o4p1BeVA5yZY0ZCUmMBtZ05nYFYqAL974Qvmry2IcVQi7fY8cI7fTXAWUNTc0D/n3EvOuUHOuVHOuVFAmXNu12gF217hFaxgkwtQF0ERkUhQgiUSA4v86hVEtsFFrPTPTOX2780gOdGornVc+t/5bCutjHVYIjsxs0eBj4HxZrbOzC4ws0vM7BL/Ji8Dq4CVwD3ApWH3fR94EjjUv++RUQ4/YorKwypY4U0uKmpwTmvbiYh0hOZgicTA4rAGF1Mi1KI91maMzOXXx03kN899wabiCn70yOc8dMFenT5EUaQtnHNntLDfAZc1sW//Vjx+73aGFlX152DVNbmoDTjKqmrplaqvByIi7aVvPiIxsNBPsEb0ySC3V0qMo4mcs2eN5MQ9vIZrH6/azs2vfxXjiESkMYV+gpWcaGSkJNZbZFzzsEREOkYJlkgMBIcITuni868aMjP+dOIUJgzKBOCu91bxyuJmO1eLSAwEhwhmp6dgZqE5WKBOgiIiHaUESyTKNhdXsKXEm580rZslWADpKYncdfaMUNvnq59cyMotpTGOSkTCFezwqlS5GV7lKryCVaIKlohIhyjBEomyhXmFoevdocFFY0b27cU/v7s7ADuqarnkv/MordRZcZF4UehXsHL8BCt4QgQ0RFBEpKOUYIlE2eL13vwrM5jcTRpcNObQ3Qby40O8btUrt5Ry7VOL1J1MJE4E52DlZHhzQINNLkBDBEVEOkoJlkiUBRtcjOnfm97dvFPXTw4bx4Hj+gPw0uKN3PvBNzGOSEQgLMFK33mIoCpYIiIdowRLJIqccyz2G1xM7cbVq6DEBONfp+/OsNx0AP78yjI+/np7jKMSkYIyb4hgsItpvSGCWmxYRKRDlGCJRNG6gvLQ+jNTu2GDi8bkZKRw51kzSElKoDbg+NGj89lUVBHrsER6rIrqWiprAgBk+xWstOREUpO8rwTFFRoiKCLSEUqwRKJoUfgCw920wUVjJg/N5g/fmQzAttIqfvjwPA1DEomRYPUKvEWGg4LzsFTBEhHpGCVYIlEUXP8qMcGYNCQrtsFE2Wkzh3Pm3iMA+HxtISfc9iHLNhXHOCqRnic4/wrquggCZPnDBEtUwRIR6RAlWCJRtNBPsMYNzCQtOTG2wcTA9cdP5LDdBgLwzbYdfOffH/Ls5+tiHJVIzxJewQpPsDL9RheqLouIdIwSLJEoCQQcS9Z7FZvuuMBwa6QmJXL32TO45sjxJBhUVAe48vGF/Pp/S6isqY11eCI9QlF4BStdQwRFRCJNCZZIlKzatiO02G53XWC4NRISjMsO3pUHz9+bPn4Hs4fmrOG7d81hQ2F5jKMT6f4KwhKs3F47DxFUkwsRkY5RgiUSJYvXF4au95QOgs3Zb2w/XvzRfkwbngPAgrxCjrv1Az5YsS22gYl0c4XlYUMEVcESEYk4JVgiUbIwz+sgmJKUwLiBmTGOJj4MyUnniYtncfaskQDk76ji7Ps+4ba3VxAIuBhHJ9I9BYcIpiQlkJZc9zUgK2wOlnP6/yci0l5KsESiZPF6L8HabXAWKUn6rxeUmpTI778zmX98dxppyQk4B399fTkXPTS33lwREYmM0CLDGcmYWWh7Vro3RLC61lFRHYhJbCIi3YG+5YlEQU1tgC82eAlWT21w0ZIT9xjG/y7bl1F9MwB488stHH/bB6H3TUQiI9imPXx4INRVsECdBEVEOkIJlkgUrNhSGjojPGWoEqymTBiUxfM/2o8jJnqt3Nfml3HS7R/x5Ny8GEcm0n2EEqywFu1QNwcLoEQJlohIuynBEomC4ALDQKipgzQuKy2Zu86ewXVHTyDBoLImwDVPLeLnzyyiolqt3EU6KtjkomGClel3EQQoKlcnQRGR9lKCJRIFC9d5w9wyUhIZ0793jKOJf2bGJQeO4eELZ9GvtzeM6dFP8zj1zo/Jyy+LcXQiXVuwTXtuhoYIioh0BiVYIlGw2E+wJg/JJjHBWri1BH1rTF9e/NH+zBiZC3iNQo679QPe+WpLjCMT6Zqcc6HmMdkNKljZ6XUVLLVqFxFpPyVYIp2ssqaWZZuKAa1/1R6DstN47KJZnLfvKACKyqs5//7P+Mcby9XKXaSNyqpqqar15oM2X8HSEEERkfZSgiXSyZZtLKG61ksEpijBapfkxASuP34St5yxBxkpiTgH/3prBefd/xkFO6pafgARAaAwrDKVk950kwtVsERE2k8Jlkgnq9fgYlhOzOLoDr49bQj/u2xfdunfC4D3lm/lu3d/TGmlzraLtEb4CYmcBhWs1KQEUhK9rwWagyUi0n5KsEQ62SJ//lVWWhIj/TWepP3GDczk+cv345gpgwBYvrmUnz21EOc0XFCkJUXhFawGc7DMLLTYcImGCIqItJsSLJFOFkywpg7LwUwNLiKhd2oSt54xnQPG9Qfg5cWbuHv2qhhHJRL/gmtgwc4JFkCmPw9LQwRFRNpPCZZIJyqrqmHFlhJA868iLTHBuOX03RneJx2Am15dxocrt8U4KpH4VlBWN0SwYZML8CrtoCYXIiIdoQRLpBN9saGYYKO7aUqwIi4nI4U7z5pBalICAQeXPzKfdQVaJ0ukKeFDBLPTd65gBRtdqIIlItJ+SrBEOtHCvMLQ9SlqcNEpJg3J5saTpwDeAqo//O98KqprYxyVSHwKNrlIT04kLTlxp/3BVu1qciEi0n5KsEQ60eL13vyrfr1TGJKdFuNouq8T9xjG9/cZBXjv+W+eW6KmFyKNCLZpb2z+FRBqclFcriGCIiLtpQRLpBOpwUX0/OKY3Zg5MheAJ+au49FP82IckUj8KfTnYDVs0R6kCpaISMcpwRLpJEXl1XyzbQcAU4Zq/lVnS0lK4PbvTad/ZioA1z+/hPlrC2IclUh8CXYRbLjIcFBwDlZVTUBDbUVE2kkJlkgnWeIPDwSYNlwJVjQMyErjju9NJynBqK51XPrf+WwtqYx1WCJxI9hFMLdXEwmW30UQtBaWiEh7KcES6STB4YEAU4bmxC6QHmbmqD785viJAGwqruDyR+ZTXRuIcVQi8SHYRTA7vfEhgsF1sEDDBEVE2ksJlkgnWbSuEIAh2WmhYWsSHWfPGslJ04cC8Mk3+dz4yrIYRyTxwszuM7MtZrakif1mZreY2UozW2Rm08P2vWpmhWb2YoP7PGxmX5nZEv/xGy8PxZhzLjREMLeFJhegVu0iIu2lBEukkwQrWFpgOPrMjD+dOIWJg7MAuPeDb3huwfoYRyVx4n7gqGb2Hw2M9S8XAXeE7bsZOLuR+zwMTACmAOnAhZEINNJKK2uo8Rfma7KLYL0KloYIioi0hxIskU6wvbSS9YXlgNdBUKIvLTmRu86eEVpM9bqnF7NsU3GMo5JYc87NBvKbuckJwIPOMwfIMbPB/n3fAkoaecyX/ds74FNgWCeE3mHB6hU000UwrPmFKlgiIu2jBEukE4TPv5qmBCtmhvfJ4JYz9sAMyqtrufiheaE5KCJNGAqE9/hf529rkT808Gzg1Sb2X2Rmc81s7tatWzscaFvVS7Ca6iKoOVgiIh2mBEukE9RvcKEhgrF04Lj+XH3EeADWbC/jyscXEAhoEWLpFLcDs51z7ze20zl3t3NupnNuZv/+/aMcGhSWV4WuN13BCp+DpSGCIiLtoQRLpBMEG1yM6ptBdhNzHSR6fnjgGI6YOBCAt5dt4Za3V8Q4Iolj64HhYT8P87c1y8yuB/oDV3VSXB1WEFbBaqrJRXpyIkkJ3qLoJapgiYi0ixIskQhzzrFofbDBRU5sgxEAEhKMv502jV369QLgn2+u4K0vN8c4KolTzwPn+N0EZwFFzrmNzd3BzC4EjgTOcM7F7ZoARWV1FaymTvyYWWgeloYIioi0jxIskQjbVFwRWtx2mjoIxo3MtGTuOnsGvVISAbji8QWs3rYjxlFJtJnZo8DHwHgzW2dmF5jZJWZ2iX+Tl4FVwErgHuDSsPu+DzwJHOrf90h/153AQOBjM1tgZr+J1utpi4J6c7AaHyIIkOkvNqwhgiIi7ZPU8k1EpC00/yp+jR2Yyc2nTuPSh+dTUlHDxQ/N49nL9iEjRR+FPYVz7owW9jvgsib27d/E9i7xBxRsctErJZGUpKbPrwYbXaiCJSLSPqpgiURYcP6VGUxWghV3jpkymIsP3AWArzaXcO3Ti/G+U4t0b4X+EMGmGlwEBRtdqE27iEj7KMESibBgBWvX/r3pldolTmz3ONccMZ59d+0LwAsLN3Dfh6tjG5BIFBT6CVNTiwwH1VWwNERQRKQ9op5gmVmimX1uZi/6P482s0/MbKWZPW5mzZ9aE4ljzjkW+w0utMBw/EpKTOCW0/dgaE46AH96+UvmrNoe46hEOleBX8HKbamCFUywVMESEWmXWFSwfgJ8GfbzTcA/nHO7AgXABTGISSQi1uaXheY5TBuu4YHxrG/vVO44azopSQnUBhyXPzKfFZtLYh2WSKcp8j+bWlo6IjREUHOwRETaJaoJlpkNA44F/uP/bMAhwFP+TR4AvhPNmEQiSQ0uupapw3L4w3cmA7CttIoj/jmbix+ay/y1BTGOTCTy6ipYrRsiWFEdoKombrvOi4jErWhXsP4J/AwIfmL3BQqdc8GB3uuAoVGOSSRigg0ukhKM3QZnxTYYaZXTZg7nhweNAcA5eO2LzZx0+0ecdufHvLl0M4GAGmBI1xcIOIqCc7CaadEOdW3aQYsNi4i0R9QSLDM7DtjinJvXzvtfZGZzzWzu1q1bIxydSGQEK1jjB2WSlpwY42ikta49agJvXnUA3505nOREA+DT1flc+OBcjvznbJ6cm6cz+dKllVTWEDxX0GKTi/S6/Wp0ISLSdtGsYO0LfNvMVgOP4Q0N/BeQY2bB02XDgPWN3dk5d7dzbqZzbmb//v2jEa9Im9QGHEvU4KLL2nVAJjedMpUPrj2ESw4cQ6bfAXLFllKueWoR+//lbe6e/bXO6EuXFGzRDq1o054WlmCp0YWISJtFLcFyzv3cOTfMOTcKOB142zn3PeAd4BT/ZucCz0UrJpFI+mZbKTuqagGYOkzzr7qqgVlpXHf0BD76+SH84pgJDMxKBWBzcSV/enkZ+/z5bW58ZRmbiytiHKlI6wWb7wDkpLelgqUES0SkreJhHaxrgavMbCXenKx7YxyPSLsszKtrcKEEq+vLTEvmogPG8P7PDuHmU6ay64DegDfU6s73vmb/m97h2qcWsXJLaYwjFWlZQVgFK7dX67oIAhSXa4igiEhbxWQVVOfcu8C7/vVVwF6xiEMkkoLrX6UmJTBuYGaMo5FISUlK4NSZwzl5+jDe+WoLd723ik9X51NVG+DxuXk8PjePwycO5JIDd2HGyD6xDlekUUVhQ/2yW2hyUW+IoCpYIiJtFpMES6Q7Wuh3EJw4JIvkxHgoDkskJSQYh+42kEN3G8i8NQXcPftrXl+6GefgjaWbeWPpZmaOzOXiA8dw6IQBJCRYrEMWCSnYEVbBakOTC805FBFpO30LFImA6toASzcUAzBNDS66vRkjc7nr7Jm8edWBnLHXcFL8hHrumgJ+8OBcjv7X+3y9VUMHJX4U1qtgNZ9g9UpJJHh+QEMERUTaTgmWSAQs31xCpd/GWwsM9xxj+vfmzydN5YNrD+bSg8aE1g/6anMJJ9/xEfPWaMFiiQ/BJheZaUkktVBhNzMy/WGCGiIoItJ2SrBEIiC4/hXAtOFKsHqaAVlp/OyoCXz880NDixYXllVz5j1zeP2LTTGOTqSuTXtLa2AFBRtdqE27iEjbKcESiYBggtUrJZHR/XrHOBqJld6pSVx71ARuOnkKiQlGZU2AS/47j4c/WRPr0KSHK/ArWLktrIEVlBWqYGmIoIhIWynBEomARX6Di8lDs0lUc4Me77t7juCec2aQnpxIwMEvn13C317/CudcrEOTHio4B6ul+VdBoQRLFSwRkTZTgiXSQcUV1Xy1qQTQ+ldS55AJA3n0oln06eVVDG59eyXXPLWI6tpAjCOTnqjIHyLY6gpWcIig5mCJiLSZEiyRDnpp0UZqAl5lYv+x/WMcjcST3Yfn8MwP92FEnwwAnpq3jgsfmMuOSg27kugKDhFs9RysUAVLf6siIm2lBEukg56atw6AQVlp7LtrvxhHI/FmVL9ePP3DfULVzfeWb+X0u+ewtaQyxpFJT1EbcKFKVE5rhwj6t9M6WCIibacES6QDvtm2I9SK+8TpQzX/ShrVPzOVR38wi4PGexXOxeuLOPmOj/hm244YRyY9QXF5NcHpfzmtHCIYXHJgR1UtNRrWKiLSJkqwRDrgab96BXDy9GExjETiXa/UJO45ZyanzvD+Ttbml3HyHR+xIK8wtoFJtxe+yHBbhwgClKiToIhImyjBEmmnQMDx7OfrAW+uza4D1J5dmpecmMBfTpnKjw/ZFYD8HVWccfcc3l62OcaRSXdW4De4gLY0uahLsNToQkSkbZRgibTTnFXbWV9YDsDJM1S9ktYxM646Yjx/PHEyCQbl1bX84MF5PPbp2liHJt1UUVldgpTd6gpWUui6Gl2IiLSNEiyRdgo2t0hJTODbU4fEOBrpar6390juPGsGqUkJ1AYc1z2zmH++uVxrZUnEqYIlIhJdSrBE2qG0soZXlmwC4PCJA1t9Vlgk3BGTBvHID2aR6//9/PPNFfz8mcVqKiARVRhWwWp1F8GwOVhabFhEpG2UYIm0wyuLN1JeXQvAyTOGxjga6cpmjMzlqR/uw7DcdAAe+yyPix+aR1mVhmVJZBT6FSyz+pWp5gQXGgY1uRARaSslWN1MIOB4c+lmNvhzg6RzBIcH9uudygFaXFg6aEz/3jxz6T5MGpIFwFvLtnDmPZ+wvVRrZUnHBbsIZqUlt3opCQ0RFBFpPyVY3cxDc9Zw4YNzOfKfs/lqU0msw+mW8vLL+OSbfABO3GMISYn6byQdNyAzjccv/hb7j/UWq16QV8jJd3zEkvVF1AY0L0var8AfIpjbhqHMvVOSMD8X0xBBEZG2SWr5JtKVPP5ZHuAN6fj+/33K0z/chyE56TGOqnt5en7Y2lfqHigR1Ds1iXvP3ZNrn17Es5+vZ/X2Mo679QNSkxIYO7A34wdmMWFQJhMGZzJ+UCb9e6dipsWtpXnBIYLZrWxwAZCQYPROTaKkooZiDREUEWkTJVjdyKqtpSzdWBz6eWNRBd//v0958pJ9yG7luHtpnnOOZ+Z7a19NHprFhEFZMY5IupuUpAT+fto0BmWncce7XwNQWRNgyfpilqwvrnfbPr1SGD/QS7YmDPL+HTcwk16p+miXOkV+Baq1DS6CstKSvQRLFSwRkTbRUbgbeXHRxtD1IycN5LUvNrN8cyk/eHAuD56/F2nJiTGMrnv4bHUBa/PLADh5uqpX0jnMjGuPmsCJewzl87UFLNtUwrKNJXy1uYT8HXUtt/N3VPHxqu18vGp7vfuP6JPB+EGZ7DYok/GDshg/KJNRfTM0nLWHCrZpb8sQQfDmYa0vLNccLBGRNlKC1Y28uGgDABMGZXL792Zw+SPzeWXJJj79Jp+rnljAbWdMJ6GVE5ylcU/N84ZgJicaJ+yu7oHSucYN9CpSQc45tpZW8tWmEr7aVOIlXpuKWbG5lMqautbua/PLWJtfxhtLN4e2pSQmMDQ3ncHZaQzJSWdITjpDc+quD8lOJz1FJ2G6o2Cb9pw2DBGEusWGtdCwiEjbKMHqJpZvLmH55lIAjp82hMQE4x/f3Z3tpZ/y6ep8Xl68iRsyl3L98RM1Z6OdyqtqeXmxt/bVweMH0KdX276siHSUmTEgM40BmWnsH9a9sjbgWL19Ryjp+mpTMcs2lbA2v4zgusVVtQG+2baDb7btaPLx+/RKYUhOGkOy/aQrJzwZS6d/71SdpOliamoDoTbrOe2oYIG6CIqItJUSrG7ixYUbQtePmzoYgLTkRO45Zyan3PkRK7aUcv9HqxmcncbFB46JVZhd2mtfbKK00vuiouYWEk8SE4wx/Xszpn9vjpkyOLS9rKqG5ZtL+cqvcm0oKmdDYQUbCsvZUrJzC/j8HVXk76jaaa5XUHKiMSjbS8DO3HtEl6zimtl9wHHAFufc5Eb2G/Av4BigDPi+c26+v+9VYBbwgXPuuLD7jAYeA/oC84CznXNVDR87ForK277IcFBwsWGtgyUi0jZKsLoB51xo/tWUodmM7NsrtC87I5kHzt+Lk27/iE3FFfz5lWUMyErlxD2UILRVcO2rPr1SOHj8gBhHI9KyjJQkdh+ew+7Dc3baV1lTy+aiStYXlrMheCkqZ72fgG0oLKesqrbefaprHXn55eTll3P05EFRehURdz9wG/BgE/uPBsb6l72BO/x/AW4GMoCLG9znJuAfzrnHzOxO4AL/fjEXbNEOkNvGqntmaIigKlgiIm2hBKsbWLqxmFX+sJ/jpw3eaf+QnHTuP39PTr3zY0oqarjmyUX0651ab4iRNG9DYTkffr0NgG9PG0JKkpoFSNeWmpTIiL4ZjOib0eh+5xzF5TV1CVhRuX/dS8DGDOgd5Ygjwzk328xGNXOTE4AHnXMOmGNmOWY22Dm30Tn3lpkdFH5jv+J1CHCmv+kB4LfESYJVVF5XSGtrN9ngEMGSyhpqA67VixSLiPR0SrC6gfDugcdOHdLobSYMyuLus2dy7n2fUlUb4JKH5vH4xd9i8tDsaIUZMSUV1SQnJkS1K+Kzn68PzWU5RcMDpQcwM7IzksnOSGbikB61HMFQIC/s53X+to2N35y+QKFzrqbB7XdiZhcBFwGMGDEiIsG2pGBHWAWrnU0uAEorashu4xwuEZGeSqfhuzhveKA3/2r6iByGNrOo8LfG9OXv352GGeyoquW8+z8jz2853hVU1tTy55e/ZNrvXuf4Wz+I2sRr5xxP+8MDJwzKZFLP+rIpIhHinLvbOTfTOTezf//ojCAoDJ+D1c4mF6BGFyIibaEEq4tbtK6IvPxyAI5ronoV7ripQ/j1sRMB2FpSybn3fVpvXZ149dWmEr7z74+4a/YqAg5WbCnlxleWReW5P88rDA3BPHn6MHVhFOne1gPDw34e5m9rynYgx8ySWnn7qCosq/t8b3ub9roEq0jzsEREWk0JVhcXrF6ZwbFTd55/1Zjz9xvNRQfsAsCqbTu44IHPKG8wmT1eBAKOez/4huNv+4AvN3qdzVL8xVIf+WQtH3+9vbm7R0SwuUVignHCHi0nsSLSpT0PnGOeWUCRc66p4YH4c7XeAU7xN50LPNf5YbZOcA2sBIPM1LbNCshKr7u9KlgiIq2nBKsLCwQcL/nzr/Yc1YeBWWmtvu91R03ghN29ZOHztYX86NH51NQGWrhXdG0sKufs+z7h9y8upaomQILB5QfvyitX7E9asven+/NnFlFR3XnJYUV1bagF/oHj+jMgs/XvsYjEHzN7FPgYGG9m68zsAjO7xMwu8W/yMrAKWAncA1wadt/3gSeBQ/37Hunvuha4ysxW4s3JujdKL6dFhX6Ti5yMlDavYRZewVKrdhGR1lOTiy7s87wCNhRVAHB8K6tXQQkJxs2nTGNbaSUfrtzOm19u4dfPLeFPJ06JiyFwLy7awC+fXRIaljK8Tzr/OG13Zo7qA8DVR4znDy99yertZfzjzeX8/OjdOiWON7/cTLH/xeLk6WpuIdLVOefOaGG/Ay5rYt/+TWxfBezV8egiL9imva1rYEH9roNq1S4i0nqqYHVhLyz0qlcJBkdNbluCBZCSlMCdZ81gt8Fe04ZHP83j1rdXRjTGtiquqObKxxdw+SOfh5KrU2cM4+Uf7x9KrgDO23c00/y1fe6ZvYpF6wo7JZ7g8MDs9GQO3U1rX4lI11LkJ1jt6QCYmRY+RFAVLBGR1lKC1UXVBhwvL/YSrG+N6Uv/zNR2PU5mWjIPnLdnqPvg399YzuOfrY1YnG0xZ9V2jv7n+zz7uTc/PDcjmTvPms7Np04jM63+l4PEBOMvJ08lOdEIOPjZU4uojvAQxy3FFcxevhXw1heLZlt4EZFIKPCbXLS1RTtA77A5W6pgiYi0nhKsLuqz1flsKakEWtc9sDkDstJ44Py9Qi18f/HsEt5etrnDMbZWZU0tf37lS864Zw7rC72OiAeO689rVxzQbGVu/KBMLj1oVwCWbSrhrve+jmhcz36+noC/9pWGB4pIV1TYgSGCSYkJoSRLTS5ERFpPCVYXFewemJRgHDVpUIcfb9cBvbn33JmkJiVQG3Bc9vDnLMgr7PDjtmT5Zr/9+nurcA5SkxK44YRJ3H/engxoRdOOSw8ew7iBvQG45a2VrNxSEpG4nHM8Pd8bHjimfy9294cjioh0JcE27W1t0R4UXGy4uFxDBEVEWksJVhdUUxvglcWbANh3137k9mrfgbOhGSP7cOsZe5BgUF5dy/n3f8Y3/vpPkRYIOO774BuOu7Wu/frkoVm89OP9OOdbo1rdaCM1KZGbTp6KGVTVBrj26cUEgmWnDliyvpjlm0sBOHmG1r4Ska6nqibADn8JjrYuMhwUXGxYFSwRkdZTgtUFfbxqO9v9xYGPa2P3wJYcMWkQv//OZADyd1Rxzn2f8OKiDSzMK2R7aSVeg62O2VRUwTn3fcoNYe3XLzt4DM/8cF92HZDZ5sfbY0Qu5+0zGoB5awp4aM6aDsf41Lw8wGsgctIeGh4oIl1PsEU7eHNa2yPYql1zsEREWk9t2rugF/3ugSmJCRwRgeGBDX1v75FsKqrg1rdXkpdfzuWPfB7al5GSyLDcdIbnZjAsN51h/r/D+3j/ZqcnN1vtaan9entdfeQ4Xl+6iXUF5dz06jIO3W0Aw3Iz2vVYVTUBnvfXvtp3134MytbaVyLS9QQ7CAJkt3eIoL/YsNbBEhFpPSVYXUxVTYBXv/CGBx4wrl+9dUoi6arDx1FWVct9H35DeNGqrKqW5ZtLQ8PnGspMTWJoI4nX4Ow07v9wNc/4HQIBTpkxjOuPn7hTh8D2yEhJ4saTpnLWvZ9QVlXLL55dwgPn7dmuoX1vL9sSWjvmlBmqXolI11QQlmB1uIKlIYIiIq2mBKuL+XDltlD1p6PdA5tjZvz6uIn8+NCxrCsoIy+/nHUFZawrqPs3L78sNL4/qKSyhmWbSli2qelmEzkZydx40pR2rd3VnP3G9uO0mcN4Yu46Zi/fyrOfr+ekdnT/C659lZmaxBETI18hFBGJhmCDC4Cc9PZVsDJDTS6UYImItJYSrC7mBb97YGpSAodNHNjpz5ednkx2ejaThmTvtM85R2FZtZdsFZTVS7y8RKyc8ur6CdgB4/pz8ylTGdiKDoHt8ctjJvLOV1vZWlLJDS8uZf+x/du0Rti20kre/WoLAMdOHUx6ita+EpGuqTAsKepok4uSyhoCAUdCghr+iIi0RAlWF1JRXcsbX3jrUx08fkC9RSBjwczI7ZVCbq8UpgxrPAHbvqMqlHT16ZXCPmP6dmpHvuyMZH5/wiQu+e98Csuq+e0LX/DvM6e3+v7PLdhAjd+F8GQNDxSRLqxeBauDQwSdg9KqmtDPIiLSNHUR7EJmL99KSaU30fi4aZEdXtcZzIx+vVPZfXgOx08bwr679otKu/OjJg/m6Mne0L6XFm3kdX/OWms87Q8PHNk3g5kjczslPhGRaAguMpyUYO0+IRdscgEaJigi0lpKsLqQFxd53QPTkxM5ZMKAGEcT3353wqTQApm/fq6ua2Fzlm4oZqm/JtfJ07X2lYh0bcEmFzkZzXd3bU54xUqLDYuItI4SrC6ivKqWN7/0hgceutsAMlI0urM5AzLT+NVxEwHYXFzJja982eJ9np6/LnT9pOlDOy02EZFoKPLXwepIt9mssPuqk6CISOsoweoi3vlqC2V+x77O7B7YnZw6Yxj77doPgEc/zeOjlduavG11bYDnFngt5L+1S992r6ElIhIvCnZ4CVFuO9fAgvoVLK2FJSLSOkqwuogX/e6BvVOTOGh8/xhH0zWYGX8+aQrpyV4nwOueWUx5g7byQbOXb2VbqXe2V80tRKQ7CHYRbG+DC6hr0w6agyUi0lpKsLqAHZU1vL3Max1++MSBpCWrdXhrDe+TwTVHjgdgbX4Z/3hzeaO3C659lZGSGGqQISLSlQW7COZ0pIKlIYIiIm2mBKsLePPLzVRUBwA4bmr8dw+MN+fuM4rdh+cA8J/3V7Ewr7De/oIdVbz1pZfAHj15ML1i3P5eRCQSgl0EczowB6t+BUtDBEVEWkMJVhcQ7B6YlZbE/mM1PLCtEhOMv5wyleREI+Dg2qcXUVUTCO1/YdEGqmq9n0+eoeYWItL1VVTXhhZ6z+3V/gpWcmICGf6C66pgiYi0jhKsOFdcUc17X20F4MhJg0hJ0q+sPcYNzOTyg8cCsGxTCXe+93VoX3Dtq6E56cwa3Tcm8YmIRFL40hQd6SIIdY0uNAdLRKR19G09zr3xxeZQdeW4aeoe2BE/PGgM4wdmAnDb2ytZsbmEFZtLWLiuCICTpw8lIUFrX4lI11fgz7+CjnURhLrFhlXBEhFpHSVYcS7YPTA3I5l9xqi60hEpSQncdMpUEgyqagNc+/QinpxXt/aVugeKSHcRnH8FHesiCHUVLLVpFxFpHSVYcaywrIr3V3hrNx01eTDJifp1ddTuw3M4f9/RAMxfW8h/3l8FwJ6jchnZt1csQxMRiZiIJlj+EENVsEREWkff2OPYa19soibgADhe3QMj5qojxjGij7eQsP/2cvJ0Va9EpPsoDBsi2JE27VDXSVBdBEVEWkcJVhwLdg/s1zuVvXfR8MBIyUhJ4saTpoR+TktO4BglsCLSjRSGNaToSJt2CGtyoQqWiEirKMGKU9tLK/no6+0AHDNlEIlqvhBR++zajwv284YKnj1rZOgLhIhIdxBscpES1ma9vUJNLsqrcc51ODYRke5OK6rGqVeWbKLWH7923FR1D+wMvzp2Ny46YBf6906NdSgiIhFV5M/Bys5IxqxjJ+iCJ6ACDnZU1dJbi7GLiDRLFaw4FeweOCgrjZkjc2McTfdkZgzMSlNrdhHpdoIVrNwONriAuiYXoLWwRERaQwlWHNpcXMEn3+QDcMyUwUoARESkTYJdBHPSO9bgAqg3hFrzsEREWqYEKw69vHgjwWHux01T8wUREWmbUIIVkQpW3ZBArYUlItIyJVhxKNg9cGhOOnsMz4ltMCIi0uUUlntDBCORYGWmaYigiEhbKMGKMxsKy5m3pgDwqlcdnZwsIiI9i3OOAr+CldvBNbAAstLqKlgaIigi0jIlWHHmJb96BXC8ugeKiEgbVVQHqKoJAF4XwY6q3+RCQwRFRFoStQTLzNLM7FMzW2hmX5jZ7/zto83sEzNbaWaPm1nHT7d1YcHugaP6ZjBpSFaMoxERka4mODwQIlPBygyvYGmIoIhIi6JZwaoEDnHOTQN2B44ys1nATcA/nHO7AgXABVGMKa6s3V7GwnVFgLf2lYYHiohElpndZ2ZbzGxJE/vNzG7xT/otMrPpYfvONbMV/uXcsO1nmNli//avmlm/aLyWphTsqEuCctI7XsFKTUokLdn7uqAhgiIiLYtaguU8pf6Pyf7FAYcAT/nbHwC+E62Y4s2LizeErqt7oIhIp7gfOKqZ/UcDY/3LRcAdAGbWB7ge2BvYC7jezHLNLAn4F3Cwc24qsAi4vNOib4XwClYkhghCXat2DREUEWlZVOdgmVmimS0AtgBvAF8Dhc654Cf2OmBoE/e9yMzmmtncrVu3RiXeaHtxoTf/atcBvRk/MDPG0YiIdD/OudlAfjM3OQF40D8pOAfIMbPBwJHAG865fOdcAd4x7CjA/Esv84YdZAEbmnjsqAi2aIfIDBGEunlYJZWqYImItCSqCZZzrtY5tzswDO8M4IQ23Pdu59xM59zM/v37d1aIMbNqaylLNxYDcNxUdQ8UEYmRoUBe2M/BE3+NbnfOVQM/BBbjJVYTgXsbe+BonSgMT7Ai0aYd6joJqoIlItKymHQRdM4VAu8A38I7OxicQTsMWB+LmGLtxbDugcepe6CISJdgZsl4CdYewBC8IYI/b+y20TpRWFAW2SYXULcWluZgiYi0LJpdBPubWY5/PR04HPgSL9E6xb/ZucBz0YopngS7B04YlMmuA3rHOBoRkR5rPTA87Ofgib+mtu8O4Jz72jnngCeAfaISaROK/E5/qUkJpCUnRuQxg0ME1UVQRKRl0axgDQbeMbNFwGd4Y9lfBK4FrjKzlUBfmhha0Z19tHIbyzd7/T+On6bqlYhIDD0PnON3E5wFFDnnNgKvAUf4jS1ygSP8beuBiWYWLEkFTx7GTMEOr4IVqeoVhA0RrNAQQRGRliS1fJPIcM4twhtC0XD7Krz5WD1SSUU11zy1CID05ERO3KPRHh8iIhIBZvYocBDQz8zW4XUGTAZwzt0JvAwcA6wEyoDz/H35ZvZ7vBOEADc45/L9x/wdMNvMqoE1wPej9XoaU+hXmSI1/wrqV7Ccc5onLCLSjKglWNK4G15YyvrCcgB+fswEhuSkxzgiEZHuyzl3Rgv7HXBZE/vuA+5rZPudwJ0RCTACCv05WBFNsPw5WDUBR3l1LRkp+vogItKUmDS5EM+bSzfz5Lx1AOw/th9n7T0yxhGJiEhXF+wimJMewSGC6XUJlToJiog0r1UJlpmdE9bpTyIgf0cV1z2zGIDMtCT+cspUEhI05EJEpDFmNs3MzjSzRpf3MLPdoh1TvAoOEcztFfkKFnhD20VEpGmtrWD9H9CnMwPpSZxz/Op/i9lWWgnA7749icHZGhooItIYM7sQmA/8F1hsZj/yt08ysxvNbBmwJJYxxgvnXGiIYHZEK1h1CZZatYuINK+1CVajpRW/y1LkTpH1EM8v3MDLizcBcOSkgWpsISLSvJ8Bf8Rrk/408BczuwVvzanDgGfxuvf1eGVVtVTXOgByIzgHKzNNQwRFRFqrLcP+9jWzD5xz4cvPDwW+BlIjG1b3tamogl//zzvR2rdXCn86cYq6MYmING8UcI9zbr2ZXQmcBuwK7OKcWxPTyOJM+CLDndHkAlTBEhFpSVsSrCfxilZb8c4afoV3NnFjZwTWHTnn+NnTi0LriPz5pCn07a3cVESkBUlAJYBzbqOZlQO/UHK1s2CDC4j0EMHwCpYSLBGR5rQ2wXLALngLAU8BpuKdUQwA53dKZN3Qw5+sZfZyrwB48vRhHDFpUIwjEhHpMr5vZq8Ci/GOPUUxjicuhSdYkRwiWL+CpSGCIiLNaW2CZUCFc+5z4PNOjKfbWrN9B396+UsAhmSncf23J8Y4IhGRLuMd4Drgz0AxkA78zMw+wku4ljrnqpq5f49RWB4+RDByFay05ERSkhKoqgmogiUi0oLWJljPAPpEbafagOOnTyykrKoWgJtPnVbvbKCIiDTNOXcogJntAszwL9OBU/E63Naa2UrnXI9v1V7QSRUs8KpY20orVcESEWlBqxIs59wpnR1Id/af91cxd00BAOd+ayT77tovxhGJiHQ9zrlVwCq8OcEAmNkoYCZewtXjFYU1uciOdIKVnuQnWDrfKiLSHC0e3Mm+2lTC315fDsDofr247ugef4JVRCRinHOrgdXAU7GNJD4EK1gZKYmkJiVG9LEz/ZEXGiIoItK81q6DJe1QVRPgyscXUFUbIMHgb6dNIz0lsgc8ERGRoGCTi5z0yA9Dz/LXwtIQQRGR5inB6kS3vr2CpRuLAfjhQWOYPiI3xhGJiEh3VugPEYxkg4ugLD9pK1EFS0SkWUqwOsmCvEJuf/drAHYbnMVPDh0X44hERKS7K/STn0guMhwUbM6kOVgiIs1TgtUJKqprueqJBdQGHMmJxt9Pm0ZKkt5qERHpXMEKVm6nVLD8IYLlNTjnIv74IiLdhb71d4KbXl3Gqq07ALjy8HHsNjgrxhGJiEhPEJyDFekOglBXwaqqDVBZE4j444uIdBdKsCLso6+38X8frgZgxshcLj5gTGwDEhGRHsE5VzdEsDOaXIQ9poYJiog0TQlWBBVXVHPNk4sASE9O5G+nTiMxwWIclYiI9AQllTXUBryhe50yRDCtbmWX4nJ1EhQRaYoSrAj6/QtLWV9YDsAvjpnAqH69YhyRiIj0FEVldVWlzhwiCKpgiYg0RwlWhLy5dDNPzlsHwP5j+3HWrJExjkhERHqSAr/BBXRukwvQYsMiIs1RghUB+TuquO6ZxQBkpiXxl1OmYqahgSIiEj2FYRWszmzTDlpsWESkOUqwOsg5x6/+t5htpZUA3HDCJAZnp8c4KhER6WnqV7A6ucmFKlgiIk1SgtVBzy/cwMuLNwFw1KRBfGf3oTGOSEREeqKisKQnO70zmlxoDpaISGsoweqATUUV/Pp/SwDo1zuFP544WUMDRUQkJgp2dO4QwbTkBJITvWNciYYIiog0SQlWB/z2+S9C49D/dOIU+vZOjXFEIiLSUxWWe0MEe6cmkZwY+cO7mYWqWBoiKCLSNCVY7eSc493lWwA4Zsogjpg0KMYRiYhITxZs094Z1augTH8tLDW5EBFpmhKsdtpaWklFdQCAPUf1iXE0IiLS0wWbXHRmghVsdKEKlohI05RgtVNeflno+og+GTGMREREBAr9pKcz1sAKCg0RVJMLEZEmKcFqp7z88tB1JVgiIhJrwXWwstM7s4LlDxFUBUtEpElKsNppbVgFa1iuEiwREYmtwmgMEQxVsDQHS0SkKUqw2imYYA3ITCU9JTHG0YiISE8WCLjQOlidOkRQc7BERFqkBKudggmWhgeKiEislVTUEHDe9U4dIuh3EaysCVBZU9tpzyMi0pUpwWqnPCVYIiISJ4IdBCE6FSzQYsMiIk1RgtUOFdW1bCquAGCYEiwREYmxwrAhe9FYBws0TFBEpClKsNphfWE5zh+KoQqWiEjXYWb3mdkWM1vSxH4zs1vMbKWZLTKz6WH7zjWzFf7l3LDtKWZ2t5ktN7NlZnZyNF5LuPAKVk4U2rSDGl2IiDRFCVY7rNUaWCIiXdX9wFHN7D8aGOtfLgLuADCzPsD1wN7AXsD1Zpbr3+eXwBbn3DhgIvBep0TejKKy6FSwwocIqoIlItK4pJZvIg2tU4IlItIlOedmm9moZm5yAvCgc84Bc8wsx8wGAwcBbzjn8gHM7A28RO1R4Hxggv/4AWBb572CxhVGaw5WvQqWEiwRkcaogtUOwQpWSlICAzJTYxyNiIhE0FAgL+zndf62RrebWY7/8+/NbL6ZPWlmA6MSaZiCsApWVlrnnTsNLjQMUFyuIYIiIo1RgtUOwQRreG46CQkW42hERCSGkoBhwEfOuenAx8BfG7uhmV1kZnPNbO7WrVsjGkRwDaystCSSEjvv0B5ewSpRBUtEpFFKsNphbX45oOGBIiLd0HpgeNjPw/xtTW3fDpQBz/jbnwSm0wjn3N3OuZnOuZn9+/ePaNDBJhed2eACICMlkUT/xKKGCIqINE4JVhs550JrYA1XgiUi0t08D5zjdxOcBRQ55zYCrwFHmFmu39ziCOA1f67WC3hztAAOBZZGO+hCf4hgZza4ADCzUKt2DREUEWmcmly0UUFZNaWV3kFFFSwRka7FzB7FS4b6mdk6vM6AyQDOuTuBl4FjgJV4lanz/H35ZvZ74DP/oW4INrwArgUeMrN/AluD94mmwihVsMAbJlhYVq0KlohIE5RgtVF4i3ZVsEREuhbn3Bkt7HfAZU3suw+4r5Hta4ADIhJgOwUXGs5J79wKFtQ1ulCbdhGRxmmIYBvlqUW7iIjEmYIdXgUrt5OHCEJdowstNCwi0jglWG2kCpaIiMST2oALJTvZURoiCKpgiYg0RQlWGwUrWH17pdA7VSMsRUQktorCEp2oVLCCQwQ1B0tEpFFKsNporToIiohIHAk2uIDO7yIIdRWsEg0RFBFplBKsNlKCJSIi8aSgrK6SFJUugn4jjbKqWqprA53+fCIiXY0SrDaorg2woTC4yHB6jKMRERGBovKwClYUuggG18ECVbFERBqjBKsNNhSWE3DedXUQFBGReFBYFj4HK3pNLkCNLkREGqMEqw3UQVBEROJN/SGC0WhyEZZgqdGFiMhOlGC1QV5+eei6KlgiIhIPivwmF2b1q0udJStsiGBxuYYIiog0pASrDYIVrKQEY3C25mCJiEjsBStY2enJJCRYpz+fKlgiIs1TgtUGwTWwhuWmkxiFg5iIiEhLCv15UNFocAH1E6wSJVgiIjtRgtUGatEuIiLxJrgOVjRatIOGCIqItEQJVhsowRIRkXgT7CIYjQYXAL1SkggO4tAQQRGRnSnBaqWismqK/GEYanAhIiLxosCvYEWjRTtAQoLRO9WrYqlNu4jIzpRgtVJeQV2LdiVYIiISL4rCmlxES3AeVrEWGhYR2YkSrFbKy1eCJSIi8aW6NkBJpZfkRKuCBXXt4FXBEhHZmRKsVtIiwyIiEm+KyqO7yHBQVro/RFBzsEREdqIEq5WCCVZ2enJUh2GIiIg0JdjgAqKcYIUqWBoiKCLSkBKsVqrrIKgFhkVEJD4EW7RD9Nq0Q90cLK2DJSKyMyVYrRScg6X5VyIiEi/CK1i5sahgqcmFiMhOopZgmdlwM3vHzJaa2Rdm9hN/ex8ze8PMVvj/5kYrptaqDTjWFZQDmn8lIiLxoyC8gpUevQpWpr/YcGllDTW1gag9r4hIVxDNClYN8FPn3ERgFnCZmU0ErgPecs6NBd7yf44rG4vKqQk4QBUsERGJH+FNLrKj2uSi7rlKK1XFEhEJF7UEyzm30Tk3379eAnwJDAVOAB7wb/YA8J1oxdRaefnloetKsEREJF4EK1iJCUaWX1WKhvDnUqMLEZH6YjIHy8xGAXsAnwADnXMb/V2bgIGxiKk5WgNLRETiUWHYIsNmFrXnDa9gqVW7iEh9UU+wzKw38DRwhXOuOHyfc84Bron7XWRmc81s7tatW6MQaZ1gB8EEgyE56iIoIiLxIZhgRbNFO9Q1uQAtNiwi0lBUEywzS8ZLrh52zj3jb95sZoP9/YOBLY3d1zl3t3NupnNuZv/+/aMTsC+YYA3OTic5UY0XRUQkPhSWe0MEc6K8PmNwoWFQJ0ERkYai2UXQgHuBL51zfw/b9Txwrn/9XOC5aMXUWmvVol1EROJQwQ6vepQbxTWwoEEFS0MERUTqid6MWNgXOBtYbGYL/G2/AG4EnjCzC4A1wGlRjKlVtAaWiIjEo2AXwWh2EIQGc7A0RFBEpJ6oJVjOuQ+ApmbgHhqtONqqtLKG7Tu8IRgj+irBEhGR+FHodxGMdgWrd6qGCIqINEUTiloQ3kFQiwyLiEi8qKoJsKOqFoj+HKzEBCPTT7JUwRIRqU8JVgvUol1EROJRsMEFQE6v6FawoG6YoOZgiYjUpwSrBWuVYImISBwKtmiH6FewADLTghUsDREUEQmnBKsFwQpWr5REcqM8iVhERKQp4QlWtOdggSpYIiJNUYLVgmAFa3ifDLxO8yIiIrFXUBY2RDAGJwCDrdpL1ORCRKQeJVgt0BpYIiISj4rCKljZMRgiGFxsWE0uRETqU4LVjEDAkVdQDijBEhHpDszsPjPbYmZLmthvZnaLma00s0VmNj1s37lmtsK/nNvIfZ9v6nE7Q3gFKzcWTS7SNERQRKQxSrCasaWkkqqaAKA1sEREuon7gaOa2X80MNa/XATcAWBmfYDrgb2BvYDrzSw3eCczOwko7ZyQG1foV46SEoxeKYnRfGoAsvwmF6WVNQQCLurPLyISr5RgNSOvQGtgiYh0J8652UB+Mzc5AXjQeeYAOWY2GDgSeMM5l++cKwDewE/UzKw3cBXwh86Nvr7gIsM5GSkxmSMcbHLhHJRUah6WiEiQEqxmrN2uFu0iIj3MUCAv7Od1/ramtgP8HvgbUEYzzOwiM5trZnO3bt3a4UCDXQRj0eAC6oYIguZhiYiEU4LVjPA1sIbmpMcwEhERiUdmtjswxjn3bEu3dc7d7Zyb6Zyb2b9//w4/d3AOVqyWEAk2uQDNwxIRCacEqxnBNbAGZaWRlhz98e0iIhJ164HhYT8P87c1tf1bwEwzWw18AIwzs3ejEWiwgpWdHv0GF1C/gqVW7SIidZRgNUMt2kVEepzngXP8boKzgCLn3EbgNeAIM8v1m1scAbzmnLvDOTfEOTcK2A9Y7pw7KBqBFvnD8mJXwdIQQRGRxiS1fJOeK3yRYRER6frM7FHgIKCfma3D6wyYDOCcuxN4GTgGWIk3p+o8f1++mf0e+Mx/qBucc801y+h0BaEmF3EwB0sVLBGRECVYTaiormVLSSWgCpaISHfhnDujhf0OuKyJffcB9zVz39XA5I7E11oV1bVUVHvLiORkxGaIYGZa2BwsVbBEREI0RLAJ68JatI/oqwYXIiISP4LzryB2Fax6CZaaXIiIhCjBakJ4B0FVsEREJJ4UlleFrufEqMlFUmJCaIHj4nINERQRCVKC1YTwNbCG5yrBEhGR+FGwo65iFKsmF1DX6EIVLBGROkqwmrA2vxyA1KQE+memxjgaERGROkVhFazsWCZYfqMLzcESEamjBKsJ4S3azSzG0YiIiNQpKAuvYMVmiCDULTasdbBEROoowWpCntbAEhGROBUPTS4grIKlIYIiIiFKsBrhnNMaWCIiErcK/TWwUpISSE9OjFkcmoMlIrIzJViN2FZaRXl1LaAKloiIxJ9gBSsnPTmmw9iDrdrVRVBEpI4SrEbkFahFu4iIxK9gm/ZYzr+CuiGCJRXVBAIuprGIiMQLJViNyAtfA6uvEiwREYkvwSYXsewgCHVNLgIOdlSpiiUiAkqwGhW+Btaw3PQYRiIiIrKzIj/BiuUaWFBXwQIoVidBERFACVajgg0u+vVOJSMlKcbRiIiI1FfgN7nISY/xEMH0sARLa2GJiABKsBpVtwaWqlciIhJfnHMU+slMTq/4qWBpLSwREY8SrEZoDSwREYlX5dW1VNUEgHioYNWN8lAFS0TEo/FvDVTW1LKxuAJQgiUiIvEnJTGBF3+0H0Xl1QzPje1xKrPeHCwlWCIioARrJxsKK3B+p1ktMiwiIvEmKTGByUOzYx0GAFlpqmCJiDSkIYINrM3XGlgiIiKtkakugiIiO1GC1UB4gqUKloiISNNSkhJIT04EVMESEQlSgtVAsMFFSmICA7PSYhyNiIhIfAs2utAcLBERjxKsBoKLDA/LTScxwWIcjYiISHwLtmpXm3YREY8SrAaCQwQ1PFBERKRlwcWGVcESEfEowQrjnNMaWCIiIm0Q7CRYXK4KlogIKMGqp7CsmpJK7wChBEtERKRlwU6CqmCJiHiUYIXJK1AHQRERkbYINblQF0EREUAJVj31W7SnxzASERGRriErVMGqwTkX42hERGJPCVYYrYElIiLSNsEmF7UBR1lVbYyjERGJPSVYYYINLnIzkkNn5ERERKRp4cdLzcMSEVGCVc9adRAUERFpk+AcLNBaWCIioASrHq2BJSIi0jb1KlhqdCEiogQrqKY2wIbCCkAVLBERkdbKTKurYGmIoIiIEqyQjUUV1Aa87kdKsERERFon2OQCtNiwiAgowQpRB0EREZG2U5MLEZH6lGD5whMsVbBERERap94QQc3BEhFRghUUTLASE4zB2WkxjkZERKRrSEtOJDXJ+zpRrC6CIiJKsIKCCdbQnHSSEvW2iIh0R2Z2n5ltMbMlTew3M7vFzFaa2SIzmx6271wzW+FfzvW3ZZjZS2a2zMy+MLMbo/Va4klwHlaJhgiKiCjBCsrTGlgiIj3B/cBRzew/GhjrXy4C7gAwsz7A9cDewF7A9WaW69/nr865CcAewL5mdnTnhB6/svxhgmpyISKiBCtEa2CJiHR/zrnZQH4zNzkBeNB55gA5ZjYYOBJ4wzmX75wrAN4AjnLOlTnn3vEfuwqYDwzr3FcRf4IVLDW5EBFRggV4B4TCMu+goAqWiEiPNhTIC/t5nb+tqe0hZpYDHA+81dgDm9lFZjbXzOZu3bo1kjHHXKbfSVBNLkRElGABdcMDAYb3SY9hJCIi0hWZWRLwKHCLc25VY7dxzt3tnJvpnJvZv3//6AbYyUJDBNXkosv6YkMRM37/Bmff+wk1tYFYhyPSpSnBon6CpQqWiEiPth4YHvbzMH9bU9uD7gZWOOf+2dkBxqPQEEFVsLok5xy/e34p23dU8f6KbfxvwYZYhyTSpSnBQmtgiYhIyPPAOX43wVlAkXNuI/AacISZ5frNLY7wt2FmfwCygStiFHPMBRcbLq6oxjkX42ikrWav2Manq+umJt7y1gqqVcUSaTclWNQlWJlpSWSnJ7dwaxER6arM7FHgY2C8ma0zswvM7BIzu8S/ycvAKmAlcA9wKYBzLh/4PfCZf7nBOZdvZsOAXwITgflmtsDMLozuq4q9rHRviGB1raOiWl/MuxLnHH97/at629bml/H0vHUxikik60tq+Sbd39r8csCrXplZjKMREZHO4pw7o4X9DrisiX33Afc12LYO6PEHjmAFC7y1sNJTEmMYjbTFa19sZtG6IgAuOXAMzy9Yz4aiCm59eyUnTR9GSpLOxYu0lf7XoDWwREREOiIrbPSHWrV3HbWBuupVdnoylx48hssPGQvA+sJynpib19zdeyTnHE/MzeO0Oz/W+yNN6vEJVm3Asa5ACZaIiEh7ZabVDYgp0mLDXcbzC9ezYkspAD88aAxZacmcOnNYqKPybW+vpKK6NpYhxpW128s4695P+NlTi/h0dT4/e2oRry7ZFOuwJA71+ARrc3EF1bXehNxhSrBERETaLHyIoCpYXUN1bYB/vLECgP6ZqZz7rVEAJCcm8CO/irWpuILHPl0bqxDjRk1tgHtmr+KIf77Hhyu319t35eMLWLK+KEaRSbzq8QmWOgiKiIh0THZ6XQVLrdq7hifm5oW+A/3okF3rzZs7aY+hjOrrfSf697tf9+gq1tINxZx0x0f88eUvQw1cztx7BP86fXfMoLy6lh88OJctJRUxjlTiSY9vcqEES0REpGPqV7A0RDDeVVTXcstbXvVqaE46p+85ot7+pMQEfnLYWK58fCFbSyr575w1XLj/LrEINWYqqmu59e0V3PXeKmoC3kin0f168eeTpjBrl74AbC2p5A8vfcnGogouenAej100i7RkNXjpDM45Hpqzhi/WF9M7LYmstGSy0pPITEsmKy2JrPRkMkPbk+mdmkRiQuz6D/X4BCvY4MLM+5ARERGRtqnX5EIVrLj33zlr2FxcCcAVh41ttFPgt6cN5da3V7Jq6w7ufO9rztx7BBkpPeNr46ff5HPd04tYtW0HAIkJxsUH7MKPDx1bL4G6YL/RrNhcyuNz81iQV8i1Ty/in9/dXR2pO8FDc9bwm+e+aNN9MlOTvKQrPZmstOSw635ilp7EOd8a1SlJcc/4n9KMYAVrSHa6WpGKiIi0Q2pSAimJCVTVBihRBSuulVbWcPu7XwOwS/9enLjH0EZvl5hgXHHYOH786OdsK63iwY/XcMmBY6IZatSVVFRz4yvLePiTunlnk4dmcdPJU5k0JHun25sZv//OZL7ZvoNPv8nnuQUbGDugd6gTo0TG/LUF/P7FpQCkJCWQlGCUVbU8bLWksoaSyho2FDU9fPPsWaMiFWY9UUuwzOw+4Dhgi3Nusr+tD/A4MApYDZzmnCuIVkxQl2AFO+aIiIhI25gZWelJbCutUpOLOHffB9+Qv6MKgJ8ePp6kxKZPLh83ZTC3vb2C5ZtLueu9rzlr1kh6p3bPc/NvLt3Mr/63hE3F3pfx1KQErjp8HBfsN7rZ9yglKYE7z5rBCf/+gLz8cv76+nLG9O/N0VMGRyv0bm1baSWX/nc+1bWO5ETjsYtmMX1ELtW1AUoraiiuqKakoobi8mqK/Z+Ly/1tFdUUl9dQUlFdd7uwbQlmpCV3TnElmv9L7gduAx4M23Yd8JZz7kYzu87/+dooxqQ1sERERCIgKy3ZS7A0RLBFLy7awH/nrOGKw8aF5vNEQ2FZFffMXgXAxMFZHD15ULO3T0gwrjxsHD98eD4FZdXc/+E33a46s7Wkkt++8AUvLdoY2vatXfry55OmMKpfr1Y9Rp9eKdx37p6cdPtHlFTWcNUTCxneJ4PJQ3eueknr1dQG+PGjn4eS3l8fN5HpI3IBr9tlbq8UcnultOuxnXOUVdV22nDOqI2Jc87NBvIbbD4BeMC//gDwnWjFA1BWVcO2Uu8szvBcJVgiIiLtFVwLS00umvfVphKuenwhc1bl84MH5rJic0nUnvvO91ZRUun9fq4+chwJrWgCcOSkQew2OAuAu2ev6jYVSuccT81bx2F/fy+UXGWmJXHTyVN45Ad7tzq5Cho7MJNbztyDBL+z4IUPzGVLsToLdsTf31jOR197bfFP2H0IZ88aGbHHNjN6dWI1NtaTjgY654KnDDYBA5u6oZldZGZzzWzu1q1bI/Lkefnloesj+irBEhERaa9gowtVsJpWVRPgiscXUFXrtfsuqazhwgfnUuAP2etMW4oruP+jbwCYMTKXg8cPaNX9vCqWV7Uqrqjhvg++6bQYoyUvv4xz7vuUq59cSJH/93rUpEG8ddWBfHfPEe2uahw8fgC/PHYi4K0h9oMH5/boFvcd8foXm0JzBccPzOTPJ03pUs1DYp1ghTjnHOCa2X+3c26mc25m//79I/Kc4S3ah2uIoIiISLsFW7Xn76jCO6RLQ/98czlfbiwGYMKgTADWbC/jskfmU+0nXZ3l3++sDK3jdPUR49v0ZfXwiQOZ4g93u/f9bygs6/yEsDPUBhz/eX8VR/xjNu+v2AZ4iyzfedZ07jx7BgOy0jr8HOfvO4rT9xwOwMJ1RVzz1CL9f2ij1dt28NMnFgLQOzWJO86a3uU6WMY6wdpsZoMB/H+3RPPJtQaWiIhIZAzzm0WtzS/jH28sj3E08WfemnzufM87Iz9pSBbPXb4vx071GiF89PV2bnhhaac9d15+GY986nXG239sP741pm3zvsyMqw4fB3hVt/+83/WqWFuKKzj5jo/4w0tfUu5XlU7fczhvXnUgR02OXEMKM+OGEyaz9+g+ALywcAO3vr0yYo/f3ZVX1XLJf+eFhrL+9dSp7NK/d4yjartYJ1jPA+f6188FnovmkwcbXGSkJNK3nZPkREREBC7Yd3RoPclb3l7ZLYaSRcoOv/FBwHld5/7x3d1JTUrkr6dMY/JQb37TQ3PW8NCcNZ3y/Le8tYLqWq+K8tMjxrfrMQ4a35/dh+cA8H8f1nUi7Aq2FFdw+j1zWJBXCMDIvhk88oO9ufHkqWSHreEWKcHOgiP96Sd/f2N5vSYa0jjnHL/832KWbfLmJV58wC4RTX6jKWoJlpk9CnwMjDezdWZ2AXAjcLiZrQAO83+OmrVhHQS70rhOERGReDMgK43/Xrg3/Xp7JyxveHEpz8xfF+Oo4sMfX/6SNdu97xw/O3I84wZ6wwPTUxK555yZ9M9MBeC3z3/BR19vi+hzr9xSytP+7+GIiQNDSVJbhVexdlTVcrffjTDebS6u4PS757Bqq7do8KkzhvHaFQewz5h+nfq8ub1SuPfcmWT6jRR++uQCFq8r6tTn7Ooe/mQtz8xfD8CsXfpwzZHtOxkQD6LZRfAM59xg51yyc26Yc+5e59x259yhzrmxzrnDnHMNuwx2qro1sDQ8UEREpKNG9+vF/eftFfpSec1Ti3hz6eYYRxVb7yzbwiP+wrV7j+7D+fuOrrd/cHY6d509g5SkBGoDjksfns+a7Tsi9vz/eHM5AQdm7a9eBe0/th97jvLaZD/w0Wq2llRGIsROs6nIT662ee/nmXuP4KaTp5KWnBiV5991QCa3+p0FK6oDXPjgZ2zuhM6C1bUBXvtiE5c8NI8rH1/AttL4/r00ZkFeYWiY7IDMVG49Y3qz64/Fu64beQc550JDBNWiXUREJDImD83mP+fOJNVPGC57ZD6ffhPV86dxI39HFT97ehHgTdb/22nTGm2NPn1ELjeeNAWAwrJqLnhgLiURaIf+xYai0NC0E6YNYbzfWKO9zIwr/SpWeXUtd/lzyuLRxqJyTr/7Y77xk6uzZo3gDydMblVr+kg6aPwAfn2c11lwc3ElP3hwLuVVkeksuGb7Dm56dRn73Pg2Fz80j1e/2MSzn6/nmH+9z5xV2yPyHNGQv6OKS/87j6raAEkJxu3fmx6q6nZVPTbB2lpSSWWN101nhD8xV0RERDpu71368u8zp5OYYFTWBLjg/s/4YkPPGh7lnONX/1scqvJcf/xEhjVzQvek6cO4+MBdAG9Y308eW0BtoGPd5/72utdsJDHBuOKwcR16rKB9xvRj1i5eA4eH5qyJy7WevORqDqv9YZnnfGskv49BchX0/X1GccZeIwBYtK6Iq59a2O7OghXVtTy3YD1n3jOHA29+lzve/Tr0N5aS5H2t31JSyZn3zOHf76wk0MG/oc5WG3D85LHP2VDk/R394pjdmDmqT4yj6rgem2DV6yCoNbBEREQi6rCJA/nLyVMBr/Pcufd9Gqom9ATPLdjAy4s3Ad7cp1NmDGvxPj87cgKHTvDWp3p72Rb+8uqydj//vDX5vL3Ma8582szhbV44tzlXHe4NNaysCYTWKooXGwq95Co45+3cb43kd9+eFNO59l5nwUmhxPSlRRv511sr2vQYyzeXcMMLS5n157f4yWMLQgvwgreu2c2nTGXBbw7nTydOISUpgYCDm1/7iu/f/xnb43jI4D/fXB5qmX/c1MGct++o2AYUIUqwUIt2ERGRznDyjGGh4VHbSqs4+95POmUOSrzZUFjOr59bAkC/3imtXiQ1McH45+m7M26g15b6rtmreHpe2xuFOOe4+bWvAK+q8eNDd23zYzRnr9F92H+s1yTikU/WsrGoPKKP317rGyRX399nFL+NcXIVlJyYwB3fm8Eo/6T+P99cwYuLNjR7n7KqGp6Ym8dJt3/IEf+YzX0ffkNhmTd0NDcjmQv2G83rVx7A0z/ch1NnDicjJYkz9x7B/y7dl9F+Qj17+VaOveUDPlsdf8N03162OdTCftcBvbnp5Klx8buKBCVY0GzJXkRERNrvgv1G86NDvC/46wrKOfveT7rsQrWtEQg4rnlqISUV3jo+fz5pKn17t34+SWZaMv85Z09yM7z24T9/ZjHz1hS0KYYPV25nzirvC/VZe49kcHbkp0IEhxxW1Qb49zuxX+dpXUEZp9/9cej73Xn7juL64yfG1Rf23F4p/OfcPclM8zsLPrGQhX7r+HCL1xXxi2cXs9cf3+JnTy1i/tq62+wzpi+3nLEHc35xKL8+bmKoI2W4iUOyeP7yfTnOX2dtk99J8Y53v46bIYNrt5dxxWMLAOiVksidZ82gV2rXWky4OT0+wRqYlRq1bjIiIiI90VWHj+N7e3tzUJZvLuW8+z+jrKomxlF1jgc+Xs2HK73hW6fNHMbhEwe2+TFG9M3g9u/NICnBqKoNcPFD89hQ2LoqkVe98oYWZqQkcunBY9r8/K0xY2QuB43vD8Djn+WxrqCshXt0nrz8Mk6/ew55+d57dMF+o/nNcfGVXAXtOqB3vfmJP3hwLpuKKiiuqOahj1dz7C3vc/xtH/DIJ2sp9Rfb7Z+ZyqUHjeG9aw7ikR/M4tvThpCa1Px318y0ZG49Yw9+/53JpCR6DWduenUZFzzwGQUxXsOsotpbTLjYPwnxl1OmseuArreYcHN6bIKVF7YGloiIiHQebw7K5NAZ9c/XFnLJf+dT5Teb6i5Wbinlxle85GZYbnpoeGR7fGtMX353wiQAtpV63edak5S+sXQzC/31ls7fdzT92lA9a6vguljVtY7b3o5NFSuYXK0r8JKrC/cbza+O3S0uk6ugA8b15zf+38aWkkpOuv1D9vrjm/z6uS/4YkMxAAkGh0wYwN1nz+Cj6w7hZ0dNYGTfts2jMzPOnjWSZy7dJ7To8TtfbeWYW95n3prYDRn8zXNLWLrRe50X7DeaY6d2zcWEm9NjE6y1atEuIiISNYkJxt9P2z00d2f28q1c9UTHO+XFi+raAFc9sYDKmgBm8PfTdiczLblDj/m9vUdy7rdGAvDFhmKufnJhs0O8agMu1DkwKy2JHxywS4eevyVTh+Vw2G5ehe7Jeesiun5XawSTq/V+de+iA3bhl3GeXAWd862RoaruhqIKKqq9kw1Dc9K56vBxfHjdIdz3/T05YtIgkju4HtTkodm88KP9OGbKIAA2FlXw3bvmcPfsr9vdzbC9Hvt0LU/M9eYV7jkql+uOnhDV54+WHplgVVTXsrnY66iiRYZFRESiIyUpgbvOnsEeI3IAeHHRRn7z3JKof8nrDLe9vZJFfuXoov13Ya/RkWk1/evjJrLvrn0BeHnxJm55u+nucy8u2sBXm0sAuPjAMWSndyzBa40rDx8LeMndrVGsYq3dXj+5uvjAXfj50RO6RHIFXnXpt9+exJGTBpKSmMDRkwfxwPl7MftnB/PjQ8dGfN5cVloy/z5zOr/79iRSEhOoCTj+9PIyfvDg3KjNiVy8rojfPP8F4A17/PeZ0zucPMar7vmqWhAsI4OGCIqIiERTRkoS//f9PUOd8h7+ZC3/eGN5jKPqmAV5hdzmN3oYPzAztBhvJCQlJvDvM6fX6z738uKNO92uujbA3/33sV/vlKi1u540JJujJ3uVkWfmr2PV1tJOf84123dw+t0fh5KrSw4cw3VHdZ3kKig5MYE7z5rBV384ijvOmsGB4/qT2IlrdZkZ5+4ziqd++C2G+2vAvvnlFo695QPmr21bI5W2Kiyr4pL/zqOqJkBignHbGXswICutU58zlnpkgpWnNbBERERiJicjhQfP35thud6XvFveXsl9H3wT8eepqQ2weF0RK7eURPyxg8qrakNDHZMTjX98d/eIN8/KyfC7z/ld1q56YgFL1tdfuPmpeetC7ckvO3hXMlKi15HtisPGYQYBB7e0cX2ntlq9bQen3z0ntDDtpQeN4dqjxne55CrIzKIe+9RhObz4o/05cpI3vHN9YTmn3fkx/3l/VadUkwMBxxWPLwglxNcdNYG9d+kb8eeJJz0ywdIaWCIiIrE1KDuNhy7Ym369UwC44cWlPDO/7Ws+hauuDTBvTQG3v7uSc+77lGm/e53jb/uAw/4+m9Pu/Ji3vtwc8TbVN726jFVbvblHVx4+jolDsiL6+EG7DujNLWfuQYJBRXWAix6cy5YSL8moqK4NJTZDstM405/bEy3jB2Vy7BSvUcFzCzd0WkIbTK42+snV5QfvyjVHdt3kKpay05O586wZXH/8RJITjZqA4w8vfclFD82jyF9rK1JueXsF7361FYBjpgziwv1HR/Tx41H3aTjfBsEEKzUpgf6d2F1HREREmja6Xy8eOH8vTr9rDiWVNVzz1CKy0pI5rJWtzStralm0rohPVm3nk2/ymbu6gPLq2kZv++nqfD5dnc+uA3pz0f67cMIeLbe6bsn7K7Zy/0erAZg5MpeLD+icluhBB48fwC+O2Y0/vPQlG4oquOSheTx60Swe/mRtKOn4yWFjO/y62uOKw8by8uKNBJw3jPG2M6dH9PG/2eYNCwzOof/RIbty1eHjlFx1gJlx3r6j2WNELpc/Mp91BeW8sXQzx976Pv8+czrThufsdB/nHJU1AcqratlRVUN5VS1lDa4H95VV1ZK/o4r7PvSq07v079WtFhNujnXFiaUzZ850c+fObff9f/DgXN5YupldB/TmzasOjGBkIiLSGmY2zzk3M9ZxxEpHj2Pdzaff5HP2vZ9QWRMgNSmBB8/fq9EhRBXVtSzIK+STVfl88s125q8tCHVfa2hEnwz2Ht2HvUb3YeWWUh75ZC0llXVtzgdkpnLevqM5c+8R7WoGUVRWzZH/nM2m4goyUhJ55Sf7t7mNdns45/jZU4t4cp5X7fv2tCF8uHIb23dUMbpfL9648gCSYtQ44MrHF/Ds5+sBePWK/ZkwKDLVvFVbSzn97jlsKfGSqx8fOpYrDxvbI76oR0tRWTVXP7WQN5ZuBiA50Zg6LMdPmLxkybvU0J4icHpyIs9dvm+jCyN3ZU0dy3pkBSsv1KI98iubi4iISNvsNboPt39vOhc9NI/KmgAXPjCXxy6exZj+vZm/toA5q/L5ZNV2Ps8rbHLtrNH9erH36D7M2qUve43uw5Cc+sf4yw7ZlUc/Wcv/fbiaTcUVbCmp5KZXl3Hb2ys4Y68RnL/f6J3u05zrn1/CpmKvavSrYydGJbkCr+rwhxMns2rbDuatKeD5hRtC+648fFzMkivwEp/nF26gNuD45xsruPPsGW1+jJraADuqatlRWUNZVQ2biyu58vEFoeTqJ4eOjWgTEfFkZyRz99kzuPeDb7jxlWVU1zrmrel444vEBCM3I5k/fGdKt0uumtPjKljOOSZf/xo7qmo591sj+d0JkyMcnYiItEQVLFWwGvPM/HVc9cRCAHqlJFJd66iqbTyhGtO/F7N26cveu/Rl79F9GNjKjmRVNQGeX7iBe2avCrU0B0hKMI6fNoSLDtiF3QY3X3l5adFGLntkPuAtBnvvuTOjXk3ZWlLJd/79YahxwIRBmbz84/1J6MQudK1x9ZMLecqvrv3q2N1ITkygtLLGT5hqKfUTp9LKWsoqa7x9VTWUVXr7KptZfPqKw8ZyxWFKrjrb52sLuOPdr9lRVUN6chIZKYn0Sk0MXU9PSaRXSiIZKUne9bB9GSmJZKQmkZHs3S41KaFbVxpVwfLl76hiR5U3PltrYImIiMSPk6YPo7CsmhteXBo6VgeNG9jbS6hGexWq/pntm0OdkpTAKTOGcfL0oby7fCt3v7eKj1dtpybgePbz9Tz7+XoOGNefiw/YhX3G9N3py+GW4gp++b/FAORmJHPjyVNi8gWyf2Yq95wzk1Pv/Iiy6lp+fsxuMU+uAH58yFj+9/n6UNOESPnp4eP40aFjI/Z40rQ9RuRy9zk99vxXRPS4BEsdBEVEROLX+fuNxgxeXryRSUOymbVLH/Yc1Ye+EW5KZWYcPH4AB48fwKJ1hdw1exWv+E0aZi/fyuzlW5k8NIuLDhjDMZMHkZSY4M1/enoRhX6XtT+dOIUBmbFby2fikCze+ulBlFRUMzZOhl+N6JvBefuO4p7367fdT0wweqUk0js1iYzUJHqlJtErJZFeqUnetuC+lCR6pXrbvX2JjOrbi136947RKxJpu56dYGkNLBGRHsXM7gOOA7Y453YaI25eKeJfwDFAGfB959x8f9+5wK/8m/7BOfeAv30GcD+QDrwM/MR1xfH3ceS8fUdz3r7Ra+U8dVgO/z5zOmu3l3HvB6t4fG4eFdUBlqwv5sePfs5fctO5YL/R1AZcqN30iXsM5Wi/NXksDcpOY1B2fC3Y+otjduPsWaMwwx9eltTth4qJhOtxCVb4IsPDc5VgiYj0MPcDtwEPNrH/aGCsf9kbuAPY28z6ANcDMwEHzDOz551zBf5tfgB8gpdgHQW80omvQTrJiL4Z/O6EyVxx2DgemrOGBz5azfYdVawrKOd3LywN3W5wdhq//fakGEYa38xMJ7GlR+txCw0HK1j9eqfQK7XH5ZciIj2ac242kN/MTU4AHnSeOUCOmQ0GjgTecM7l+0nVG8BR/r4s59wcv2r1IPCdzn0V0tlye6Xw40PH8uF1h/DHEyczqkGy8NdTp7WrtbuI9Aw9LsMY0783e4/uow9GERFpzFAgL+zndf625rava2T7TszsIuAigBEjRkQuYuk0acmJfG/vkZy+5wjeWLqJFxZt5ICx/dh3136xDk1E4liPS7AuPnAMFx/YuSuti4iINOScuxu4G7w27TEOR9ogMcE4avJgjpoc+zlXIhL/etwQQRERkWasB4aH/TzM39bc9mGNbBcRkR5KCZaIiEid54FzzDMLKHLObQReA44ws1wzywWOAF7z9xWb2Sy/A+E5wHMxi15ERGKuxw0RFBGRnsvMHgUOAvqZ2Tq8zoDJAM65O/G6AB4DrMRr036evy/fzH4PfOY/1A3OuWCzjEupa9P+CuogKCLSoynBEhGRHsM5d0YL+x1wWRP77gPua2T7XGCnNbVERKRn0hBBERERERGRCFGCJSIiIiIiEiFKsERERERERCJECZaIiIiIiEiEKMESERERERGJECVYIiIiIiIiEaIES0REREREJEKUYImIiIiIiESIEiwREREREZEIUYIlIiIiIiISIeaci3UMbWZmW4E1HXyYfsC2CIQTbV01blDssdJVY++qcYNib42Rzrn+UXieuNTDj2Og2GOhq8YNij1Wumrs0Yy70WNZl0ywIsHM5jrnZsY6jrbqqnGDYo+Vrhp7V40bFLtER1f+XSn26OuqcYNij5WuGns8xK0hgiIiIiIiIhGiBEtERERERCRCenKCdXesA2inrho3KPZY6aqxd9W4QbFLdHTl35Vij76uGjco9ljpqrHHPO4eOwdLREREREQk0npyBUtERERERCSilGCJiIiIiIhESI9LsMzsKDP7ysxWmtl1sY6nITMbbmbvmNlSM/vCzH7ib/+tma03swX+5Ziw+/zcfz1fmdmRsYsezGy1mS32Y5zrb+tjZm+Y2Qr/31x/u5nZLX7si8xseoxiHh/2vi4ws2IzuyJe33Mzu8/MtpjZkrBtbX6Pzexc//YrzOzcGMZ+s5kt8+N71sxy/O2jzKw87P2/M+w+M/y/s5X+67MYxd7mv5FofwY1EffjYTGvNrMF/va4es+lafF8LNNxLGZx61gWhWOZjmOx+fzpcscy51yPuQCJwNfALkAKsBCYGOu4GsQ4GJjuX88ElgMTgd8CVzdy+4n+60gFRvuvLzGG8a8G+jXY9hfgOv/6dcBN/vVjgFcAA2YBn8TB+58IbAJGxut7DhwATAeWtPc9BvoAq/x/c/3ruTGK/Qggyb9+U1jso8Jv1+BxPvVfj/mv7+gYxd6mv5FYfAY1FneD/X8DfhOP77kuTf5O4/pYho5j8fA70LEsunHrOBaD2Bvsj6tjWU+rYO0FrHTOrXLOVQGPASfEOKZ6nHMbnXPz/eslwJfA0GbucgLwmHOu0jn3DbAS73XGkxOAB/zrDwDfCdv+oPPMAXLMbHAM4gt3KPC1c25NM7eJ6XvunJsN5DcSU1ve4yOBN5xz+c65AuAN4KhYxO6ce905V+P/OAcY1txj+PFnOefmOO/T8kHqXm+naeJ9b0pTfyNR/wxqLm7/zN1pwKPNPUas3nNpUlwfy3Qci/lxDHQsi2rcOo51/udPVzuW9bQEayiQF/bzOpr/0I8pMxsF7AF84m+63C8/3xcsmxN/r8kBr5vZPDO7yN820Dm30b++CRjoX4+32AFOp/5/0K7wnkPb3+N4fA0A5+OdUQoabWafm9l7Zra/v20oXrxBsY69LX8j8fa+7w9sds6tCNvWFd7zni7e/o6apONYzOhYFjs6jkVf3B3LelqC1WWYWW/gaeAK51wxcAcwBtgd2IhXCo1H+znnpgNHA5eZ2QHhO/0zBnG5NoCZpQDfBp70N3WV97yeeH6Pm2NmvwRqgIf9TRuBEc65PYCrgEfMLCtW8TWhS/6NhDmD+l/CusJ7Ll2EjmOxoWNZ7Og4FjNxdyzraQnWemB42M/D/G1xxcyS8Q5KDzvnngFwzm12ztU65wLAPdSV8ePqNTnn1vv/bgGexYtzc3DIhP/vFv/mcRU73sF0vnNuM3Sd99zX1vc4rl6DmX0fOA74nn9QxR+WsN2/Pg9vzPc4P87w4Rcxi70dfyNx876bWRJwEvB4cFtXeM8FiKO/o6boOBZTOpbFgI5jsRGvx7KelmB9Bow1s9H+GZ7TgedjHFM9/jjSe4EvnXN/D9sePqb7RCDYReV54HQzSzWz0cBYvAl8UWdmvcwsM3gdb9LnEj/GYGefc4Hn/OvPA+eYZxZQFDY0IBbqnQHpCu95mLa+x68BR5hZrj8c4Ah/W9SZ2VHAz4BvO+fKwrb3N7NE//oueO/zKj/+YjOb5f9/OYe61xtV7fgbiafPoMOAZc650HCJrvCeCxBff0c70XEspscx0LEs6scyHcdi+vkTn8cy18ldP+LtgteJZjleNvvLWMfTSHz74ZXEFwEL/MsxwEPAYn/788DgsPv80n89XxHDzl54HWUW+pcvgu8v0Bd4C1gBvAn08bcb8G8/9sXAzBjG3gvYDmSHbYvL9xzvwLkRqMYbP3xBe95jvHHiK/3LeTGMfSXeeO7g3/ud/m1P9v+OFgDzgePDHmcm3kHga+A2wGIUe5v/RqL9GdRY3P72+4FLGtw2rt5zXZr9vcbtsQwdx2L53utYFpu4dRyLQez+9vuJw2OZ+U8mIiIiIiIiHdTThgiKiIiIiIh0GiVYIiIiIiIiEaIES0REREREJEKUYImIiIiIiESIEiwREREREZEIUYIl0kWY2W/NbEnLtxQREYk/Oo5JT6E27SKNMLP7gX7OuePCr0fpuUcB3wB7Oufmhm3vDaQ6f3VyERGRpug4JhI7SbEOQKSnMLMkoNa186yGc64UKI1sVCIiIq2j45hI62iIoEgzzOy3wLnAsWbm/MtB/r6hZvaYmRX4l5fMbGz4fc1siZl938y+BiqBXmZ2lJm9798n38xeM7Pdwp72G//fz/znezf88cIeP8HMfm1meWZWaWaLzeyEsP2j/PufbGZvmFmZmS01s8PDbpNsZreY2Qb/MfLM7MZIv48iIhIbOo6JRJ8SLJHm/RV4AngTGOxfPjKzDOAdoAI4EPgWsBF4098XNBo4EzgVmObfvhfwT2Av4CCgCHjBzFL8++zl/3uU/3wnNRHbT4BrgGuBKcCzwDNmtnuD2/0RuMV//s+Ax/xhGgA/Bk4ETgfGAt8FvmrpTRERkS5DxzGRKNMQQZFmOOdKzawcqHTObQpuN7OzAAPOCw6VMLOLgS3AcXgHM4AU4Gzn3Oawh306/DnM7DygGO+A9AGw1d+1Pfw5G3E18Ffn3CP+z78xswP87WeF3e4fzrkX/Of6BXAOsLv/XCOB5cD7/utYC3zU7JsiIiJdho5jItGnCpZI+8zAO6tXYmalZlaKdwYvFxgTdrt1DQ5KmNkYM3vEzL42s2JgM97/xRGtfXIzywKGAB822PUBMLHBtkVh1zf4/w7w/70f7yC13Mz+bWbHmpk+F0REuj8dx0Q6iSpYIu2TACzAG5LQUH7Y9R2N7H8RWAdcDKwHaoCleGcJI6Hh5OPq0A7nnJmBf3LFOTffvG5PRwKHAg8AC83scOdcIELxiIhI/NFxTKSTKMESaVkVkNhg23zgDGCbc66wtQ9kZn2BCcClzrl3/G3Tqf9/scr/t+Fzhjjnis1sA7Av8FbYrv3wDnKt5pwrAZ4CnjKvle8cYFe8IRciItL16TgmEkVKsERatho42szGA9vxhlA8jDdG/Dkz+w3emO/hwAnAnc65FU08VgGwDfiBmeUBQ4Gb8c7+BW0ByoEjzWw1UOGcK2rksW4GbjCzFcA8vPHq+wPTW/vCzOwqvEnNC/DOEJ6JN45+XWsfQ0RE4t5qdBwTiRqNURVp2T3Al8BcvIm7+zrnyoADgFXAk8AyvGEJuXgHn0b5wxW+C0wFlgD/Bn6N1/o2eJsavK5IF+KNNX+uiYe7Be/g9Bf/sU4ETnbOLWzDayvB6+D0Kd7ZzN2Bo/3XJyIi3YOOYyJRZO1cK05EREREREQaUAVLREREREQkQpRgiYiIiIiIRIgSLBERERERkQhRgiUiIiIiIhIhSrBEREREREQiRAmWiIiIiIhIhCjBEhERERERiRAlWCIiIiIiIhHy/0Wu5Pjk93BWAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tau = cb0.estimates\n", "R = cb1.estimates\n", "\n", "N = np.arange(len(tau)) * 100\n", "\n", "\n", "plt.figure(figsize=(12,6))\n", "plt.subplot(121)\n", "\n", "plt.plot(N, tau, lw=2.5)\n", "plt.title('Integrated Autocorrelation Time', fontsize=14)\n", "plt.xlabel('Iterations', fontsize=14)\n", "plt.ylabel(r'$\\tau$', fontsize=14)\n", "\n", "\n", "plt.subplot(122)\n", "\n", "plt.plot(N, R, lw=2.5)\n", "plt.title('Split-R Gelman-Rubin Statistic', fontsize=14)\n", "plt.xlabel('Iterations', fontsize=14)\n", "plt.ylabel(r'$R$', fontsize=14)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also have a look at the traces of the walkers." ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "samples = sampler.get_chain()\n", "\n", "plt.figure(figsize=(12,5))\n", "plt.plot(samples[:,:,0],alpha=0.25)\n", "plt.xlabel('Iterations', fontsize=14)\n", "plt.ylabel(r'$x_{1}$', fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also the 1-dimensional marginal distribution of the first parameter." ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "chain = sampler.get_chain(flat=True, discard=0.5)\n", "\n", "plt.figure(figsize=(8,6))\n", "plt.hist(chain[:,0], 50)\n", "plt.gca().set_yticks([])\n", "plt.xlabel(r\"$x_{1}$\", fontsize=14)\n", "plt.ylabel(r\"$p(x_{1})$\", fontsize=14)\n", "plt.show()" ] } ], "metadata": { "interpreter": { "hash": "42ef9c41c9809f9bfe38b73fa705c16bbb3d6fadc6a1917ff578a20446617baf" }, "kernelspec": { "display_name": "Python 3.7.10 64-bit ('nbodykit-env': conda)", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: docs/notebooks/datafit.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting a model to data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this recipe we will demonstrate how to fit a simple model, namely a line, to some data. Although this example is simple, it illustrates what is the proper way of fitting our models to data and infering the parameters of the models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us first import the main packages that we will use:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# show plots inline in the notebook\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from IPython.display import display, Math\n", "\n", "import zeus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The generative probabilistic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to create our *synthetic* data we need to construct a *generative probabilistic model*.\n", "\n", "We start by defining the *straight line* model and also setting the *true values* of the model parameters:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# define the model function\n", "def straight_line(x, m, c):\n", " ''' A straight line model: y = m*x + c '''\n", " return m*x + c\n", "\n", "# set the true values of the model parameters for creating the data\n", "m_true = 3.5 # gradient of the line\n", "c_true = 1.2 # y-intercept of the line\n", "\n", "# Set the x-coordinates of the data points\n", "M = 70 # Number of data points\n", "x = np.sort(10.0 * np.random.rand(M)) # their x-coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to generate the synthetic data. To this end, we evaluate the model function at the *true values* of *m (slope)* and *c (y-intercept)* and we add some random *Gaussian* noise of known amplitude *sigma*." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# create the data - the model plus Gaussian noise\n", "sigma = 3.0 # standard deviation of the noise\n", "data = straight_line(x, m_true, c_true) + sigma * np.random.randn(M)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the generative model and the data:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": { "needs_background": "light" } } ], "source": [ "plt.figure(figsize=(9,6))\n", "plt.errorbar(x, data, yerr=sigma, fmt=\"o\", label='data')\n", "plt.plot(x, straight_line(x, m_true, c_true), '-', lw=2, label='model')\n", "plt.legend()\n", "plt.xlabel(r'$x$')\n", "plt.ylabel(r'$y$')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The likelihood, prior, and posterior distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step to solve a problem is generally to write down the prior and likelihood functions. An important benefit of MCMC is that none of these probability densities need to be normalised.\n", "\n", "Here we'll start with the natural logarithm of the prior probability: " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def logprior(theta):\n", " ''' The natural logarithm of the prior probability. '''\n", "\n", " lp = 0.\n", "\n", " # unpack the model parameters from the tuple\n", " m, c = theta\n", "\n", " # uniform prior on c\n", " cmin = -10. # lower range of prior\n", " cmax = 10. # upper range of prior\n", "\n", " # set prior to 1 (log prior to 0) if in the range and zero (-inf) outside the range \n", " lp = 0. if cmin < c < cmax else -np.inf\n", "\n", " # Gaussian prior on m\n", " mmu = 3. # mean of the Gaussian prior\n", " msigma = 10. # standard deviation of the Gaussian prior\n", " lp -= 0.5*((m - mmu)/msigma)**2\n", "\n", " return lp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We assume that the likelihood is *Gaussian (Normal)*:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def loglike(theta, data, sigma, x):\n", " '''The natural logarithm of the likelihood.'''\n", " \n", " # unpack the model parameters\n", " m, c = theta\n", "\n", " # evaluate the model\n", " md = straight_line(x, m, c)\n", "\n", " # return the log likelihood\n", " return -0.5 * np.sum(((md - data)/sigma)**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The log posterior is just the sum of the log prior and the log likelihood probability density functions:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def logpost(theta, data, sigma, x):\n", " '''The natural logarithm of the posterior.'''\n", " \n", " return logprior(theta) + loglike(theta, data, sigma, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling the posterior using *zeus*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We initialize and run zeus to sample from the posterior distribution. Thin only takes a few lines of code." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": "Initialising ensemble of 10 walkers...\nSampling progress : 100%|██████████| 2000/2000 [00:08<00:00, 237.71it/s, nexp=0.8, ncon=1.4]\nSummary\n-------\nNumber of Generations: 2000\nNumber of Parameters: 2\nNumber of Walkers: 10\nNumber of Tuning Generations: 24\nScale Factor: 3.03521\nMean Integrated Autocorrelation Time: 3.02\nEffective Sample Size: 6629.56\nNumber of Log Probability Evaluations: 104165\nEffective Samples per Log Probability Evaluation: 0.063645\n" } ], "source": [ "ndim = 2 # Number of parameters/dimensions (e.g. m and c)\n", "nwalkers = 10 # Number of walkers to use. It should be at least twice the number of dimensions.\n", "nsteps = 2000 # Number of steps/iterations.\n", "\n", "start = 0.01 * np.random.randn(nwalkers, ndim) # Initial positions of the walkers.\n", "\n", "sampler = zeus.EnsembleSampler(nwalkers, ndim, logpost, args=[data, sigma, x]) # Initialise the sampler\n", "sampler.run_mcmc(start, nsteps) # Run sampling\n", "sampler.summary # Print summary diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets plot the chains. We can see that the burn-in phase is very brief." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "plt.figure(figsize=(16,1.5*ndim))\n", "for n in range(ndim):\n", " plt.subplot2grid((ndim, 1), (n, 0))\n", " plt.plot(sampler.get_chain()[:,:,n], alpha=0.5)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We discard the first half of the chain elements, thin the samples by a factor of 10, and flatten the resulted chain. We then proceed to plot the marginal posterior distributions:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# flatten the chains, thin them by a factor of 10, and remove the burn-in (first half of the chain)\n", "chain = sampler.get_chain(flat=True, discard=nsteps//2, thin=10)\n", "\n", "# plot marginal posterior distributions\n", "fig, axes = zeus.cornerplot(chain, labels=['m', 'c'], truth=[m_true, c_true]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets plot the projection of our results into the space of the observed data. The easiest way to do this is to randomly select 100 samples from the chain and plot the respective models on top the data points." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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piTm2nGPd9iK/6EtRsg1++7XNrLoyRufsoggL0wdmAEqjMHJQqir5EREMpkWp5PAfv36Oz92xjZ39eRY2Rfj6ey/g/dcvIewPSCVm5IAsMLSQCkggClaYbMYha6Vg9mXgH9/O3CP+lEAMUp6QibdDw2IZY7ZLUk0xTPk+lARcEU7lCpyaXlHBoiiKoiiTzUQR++OMb1+eULB4FZWWWg9BtwTVVtbtNVl93wgl2wCgO1Nm9b1leIWfzsUBEQfpLkh7Y8auTPyMjpX5xq+e5V/u3kF/tsJlcxP895+9jLdcOR/LsGS3z1i3TPxYBvh8ks0SjIlJNtHG9qEKPtOF0iCkxwAb/ClINUiGS908af34g1IJKo147apmET1OVXwrhiGR/6ZPKyyKoiiKMl0YFyN/+eNnJ/z581pGriuVjvwgVPL4qZK26qBhEWu/+xDFqnPU4UUb1j5RpjO8UbwrpaxE6mNwYDDHl36xnlsf2Eu+bPO68xv5+M3LeOX5szEMR9JoiyNQLoHPEhOtPyQiJZyCVLv4UypjtLCJoF2BUov4UmJN0tppWATJWTKFVC1CcXwHUZsIklpZfCumTyoxIB4apyDm21NABYuiKIqiTAGdF3cc2gl0LIe2Lx8jVDB9EGum15ch6OShfxM9mfKE5+8pGDC4XaaDTB8b946w9ufP8MPfHgTgXZe38rHXn8fKec1gl2VjcjUL1bJM/ATDIjKCcYg1yLRPMAXVDAzvgloJ03AYNeuh/QKItkPTIqms2GVpQQEEExBKSAuoVganKALI9IshuJr39hAFIORltpwCKlgURVEUZYqYXRdm73Aexz18W9hvseo1S6Qqkh8UY6rph1izxOiXs8yp7CHmZGHMoD0K3fnnn7s9UMR14YHnulj7i+e4c+MA0aDJh66fw1++/jzmNKekqpHpguqYiAkrIC0ff1i8JbFGCXTzheVa8jtE0ISikJrDa27ogNRsafsE49748rAIq0iD1/apSXXHtOR7DBE01dLhVpAVlKTbck5MuKeAChZFURRFmSIa4+LX6BotetuXQ6y6YS6dc0owOixCJd4qlY1SBvo2QDFNojaCYxqAy6rzxlj9VJiiYx06b9ioce3YvVz+8Z/w1L4MzXE//9i5mD+9aTl10ajkoQzvl4qK4Uh0fjgpiw4PtXbaAFsqJaP7wXTBH5dR5ESb50+ZJ6KjWoTisMT6R5tkX5BdPiyCAmERItWiN1rtE4FjGCJcKgX52heU408BFSyKoiiKMoU0xoM0xgKEnAL//s650iKxPaESqpPR477nZOeON06M4ZIqD8O+39JpDMHsBj59YDkjboyEnSH38L/z2cfvYXFzmG+9+zx+77olhIJBGUke3Scbmg1EEPkTUvmI1nvjyc1S7cgeFIFhGpKdkpzlhbwtkoh9u3L0WHK4HiyfVFPsslRODOvwceOCxAyIt6UyJq2gcfHij3DLvz5+yq+jChZFURRFmSpcl5BTIOWMytSP3e4JlZRsOe5ZD8W0vMFjylRN7iCfXDYqrZdMBSwfrwjt45LHv89tjx9gf6HG5fPifP5PL+HNVyzAMi0oj8hIc7UgQW1W0Gv7JKR1k2gBf1IEzcg+ESr+oFxL/RxIzoa6BSJcal41xTBFaARiIlpcRwRIIIaMKZc8/4zlGWlNrxWU9cRLSCoxvlOrqByLChZFURTltHBObW32zLSttW6Cbgnb8DFq1UPDQhEiPetlWsexpUrhOJA9AOmDUBiSnBPTYt/AGF/8+TN8+8E9FCoOr1ic5B/espxXrJiFAVAahlIaKlWwXDG7+iIQTvHrbocRq4G3XbZAqjf5XVCribhoWCCx+vXzZauy6Ze2TWFIWjbhehE8Tk0+fEE5ZrxyAnKbLyRVoWrRM9b6RST5wiKcJhEVLIqiKIoyWbiuJM2ODUC1gI8ao1Y9eaISq9/9jLSAHMcTKjVI74V0t8Tc2zUw/Ty7f5g1tz3NT57oxgDefXkzN13cRntzM9fOi0FuUCoZtbK0aQJhCEYhUCc7fCJt9HUfpJ40DO0EbJnmaZgjAqVxISTapWpSHgM3L+Ij3OgJE6+iEojK86p5fhXDFCED0hqqlQ8ba/0Rb3/R1KCCRVEURVFeKscIFTHTttFvjpBw0iy0u4m6OcibYoC1y17YW4+ErRkurmtx/4aD/PO69dyzeZBY0OTD13XwkdctZVZLE4/t7iFGPwz3ATUwAlLNCERl+WGkQSojuJDrZjbduLYJkXaoXwCpuVJZCTeCUxbhhCEVF39UKiLjnhPLEy3VwuHKiS8k5656GTJWQCaN/GERLVOMChZFURRlyli3vpv1B9JUbIeAZTK7LnymL2lycV2Z7skPekIlAPE28XnkB1lQ3U7UzWO4DrZrQbUCozsg56XSYlBzDH76mx2s/flzPHMgS2vCzz+9aT5/+pplpBIJqIiRtpUh/DhgxKVaEkxApBkiKfHEVAqyQ6hWAn+EIauZIV8rr1h+I6TmiTCpFKSNZFhy/0BEnsO458QwpXJSycttph/ZmOiNLo/H9vsjUtk5jahgURRFUaaEdeu7WX3bxkPBaRXbYe9w/uSW/01XHEcqKkcJlXapVIz1wcA2KGWIOxlqjkXQLdBu98O+/dKCMQ0KZZfv3reRz/9iI/uGiyxtDvHtdy/lPa9cSjAYgkqOrbs3EqXMnHgIB5McYZnkiTdDqEFERjkNI3uknRSMQHIxNC5hfX+RQbMJmpeJACmOiAAZ95iACBd/EKmclLwlipZUWTBkAgjErxKMe6Jm6qspE6GCRVEURZkS1t61/Xm7dByX4+/RORsYFypjAzJNYwUh0SECYKwPBjZDMXMofdZfKzLL6aWuNoLfLUGllaGxKl/75XN85a6tDOerXDU/xpfesZI3Xj4P0wxI5WW0Fyp5IpQp44NwPYM5g7yVZHnzcjCRKaPMfsCCcEJaPo2LxUgbb2L4iaeIuQWZQvKFIRL3KiZ4JlqfeGgqXiqdaXnptPbh6Z9gTKoppnWcF+T0oYJFURRFmRKety/nRW6f1hwpVKoFqTQkOkSw5HplQ3IxfdhMW5bx4WWVTfio4tpwMF3iQ//6IN95YDfFqsObLqjj4zct4JoL5gIu5DMyTlyrSjZKIEQmHyZDksVNi+gZzWPYDuT7Du/pibWKgbZxMaTmQCDh+VPSRJwCFTMIkUYRHIZ5OBbfrohhFkNuNwwRKdjebqGITBxNI1SwKIqiKFNCeypM9wTi5NAenbMBxznao+ILScCaFRChkj4oY8UuIiCKIzC6F/LDYFcwa1W292b430e28uvNA1imwXsva+Rjr1vE8nntsll5bFjaOuMTP6EIBJMQbaQ7VyVPDHwBmp29+KiC3SrVlKalMiZdN1taUtW8TBphgT9G1kyJkdYfFtOsY0tVyHU5JFTGs1WMw+Fukz2OPFmoYFEURVGmhFU3LWX1bRuPaguZhtw+7TkkVAZkKmZcqBi+w0KlkgVccH2Q74eR/ZKx4tZwDYN7Nnbzf37wOE/tTRMLmvz+VY18+s3LaW9tlipKtk92/NiVwybYcAqizd70TQjL3c0sDkKujqrhp9ffwZUrXiVTP7FWqYyUc+CMeXuCElIZMUzKZgjXkHh/aft4QgW8r5Fj/dFJC3ebSlSwKIqiKFPCuE9lfGPx+JTQtPavTCRUErOkpZLrkVHkSg5cEzBgrF98JGPD4LpUbZP/fnQ3a372DM915WiO+/joq1p406WzMUIx2usjkOkWoeI64Phkj1CkDpJeGu24PyXXTczNkrfi0HEJTw76GPK38o6518jETjkj1+sPQrDusC/Fy0LxU8V03cNZKeNixfJL1WUKwt2mEhUsiqIoypQwnmx78ZzUmb2QE+GFhEr6AGR7pJJhGmC7kDt4RDvIJV9x+M69m/nC7RvZP1xkeUuI7757Ab97zSKC0ThP7ukhxiBkAHt8OWAdxJsgVi9Cxa7CWO/hx4+3sTW4kN7AbG487zXs3bKVsFE+9Jj4QhCJilHWF5QqzaHUWQfLrclzG/eonIZwt6lEBYuiKIpy7uI4IgDyg95+nbAnVAxPqHh5KaYPXANGuyG9X24zTAYyRb76yw187Z7tjOSrvGJBlK++fRk3XzIPMxASX8noXloZlcczEhBPQKxFAtwCEWnXZLtEsPgj0LAIWldA0xLWd/fhYIFbI+nmcDHkmGBMBIpPtkFjV8Rs6xwWKa5hUTN8pzXcbSpRwaIoiqKce0woVDqkGjG6TyoqlbwXn+/KbZmDXpXFZFf/GJ9ft57vPbyHcs2h8/wkq149h6uWz5LJoXIWRvolxM2CMgHGSDC7eZGMIFshaemM9gGG+EialkPbBVA/DyL1AETYh+nagEvejFA1w5Joa/qkmlIrSf6KK1k3WP5D8fkZM4VjjC8mPPtRwaIoiqKcO4wLlbEBLxE2DKnZcvvoPsj0iIAxvcTX9F4ZWa7mwfTx5N5R1tz2JP/zVDd+0+B9l9fz0RvmsnR+m7SPCmko9YBTBCyZ+AnXcyDno2hFuShaL5NEpW6ZCIo2QN1iESp1syUh16lCKQuujY3FmBmDWDNjpiduXEfElFMDXBEoltcW8ocPhbs5xpnPTplMVLAoiqIoM5/nCZWITP3YNgzuFENtrSxm2lpZ2j65PqiVcF2DX63vZs3P1vPAtiGSIYtP3NjCh66bQ1tLs1RhSmlveWEFsDwjbQPEW8Ef54ZkUX4+uk+ERd1saDkfWpZLQq4vKKKoOAK4XtsnQc4aoIKIkQAVLNf2tiUbMtlj+g57U6ZBuNtUMmWCxTCMEPAQEPQe56eu6/6tYRjzgR8BDcDTwHtd161M1XUoiqIo5zATCpUOqZ4M7BChMi4yygXIdInx1a5QtQ1+9Mhu1v7sWTYezDIr5efzb+7gj18xm3iyQc5fHJapIccGDAg3iD8l2ihjxuUcZPdBzYFQGJq9tk/DIog1idip5KQ9ZPrFmxJMeCZaE8cwCTklKGb41u+cJ8cfqqZMv3C3F+LIvVL+pnkXnOz9p7LCUgZucF13zDAMP/AbwzDuBP4K+KLruj8yDOObwB8C35jC61AURVHONRxHthHnB48WKtUy9G+TGH2nBhiy22e0C/K9YLvkSjW+fd8Wvnj7JrpGS5zXGuLf3zWX37l6DoFwnSwCzA/KaLJji2cl2gCJFgl8MyxJva2OidE1mIC2xdC2UmLzQykJjCt7GSy+gGSv+MNeHoohVZ5amag9hoMhIsUKeNWU8FlXTTl2r5RhnXzwy5QJFtd1XWDM+9bvfbjADcDverf/O/ApVLAoiqLMaIZyZbpGi1Rsh2s+ez+rblo6NXksEwmVRIdsKe7bDLl+MatiiN8k7aXSGtA3UuArd27k6/dsJ12o8coFEb759oW87tJZGL442GWpyFS9EDYrAPEWiHZAKCZCpjgqLRtfUH7WtAzaLoRku1xLrSzbkh1HzLDRxsMCxHGgUpTrdm2wApTMEFUjIMeNTwSdAEdWM6b09T5BJtordbJMqYfFMAwLafssAr4G7AbSrjs+HM5BYMJX0DCMDwAfAJgzZ85UXqaiKIoyhQzlyuwdzuN44ard6SKrb9sIMHlvohMKlXYo56Fvk7SEXFsMq4WMeFTyQwDs6M3y+Z+v598f3kfFdnjr+QlW3djGFUs7wBeHak6Mt7UCOMhG5FCDCJJQFCrjQqYsJtuGhdCyEtpWQKQZ/AEo5eTaMCUCP5w8vNfHqYnJtlaRbDd/WKo2gQgZsw7XME9arBxZzZiS1/skmYz9UVMqWFzXtYGLDMNIAf8LLDuJ+94K3Apw2WWXuVNygYqiKMqU0zVaPCRWxilW7cnZ2jyRUIm3iXek+zmJyjdsMdcWRmD0gBwPPL5zkDXrnuV/n+kmYBm8/7I6PnpjO4vntEoKbDkL2b3ekkBTWjvRZkmkNfyy/ydzUILgggloXgztF/HJhyvkuqN85ZY2ie8vjnhtn0YRK5Zfgt9qRan8OLb8PBg7bKD1OiaucfJJtBNVMybt9T5FjrdX6mQ4LVNCruumDcP4NXAVkDIMw+dVWWYB3afjGhRFUZQzw/hf+sfykv7qdjwBUhj2hEpUzK7FLHSvF5HgIF6R3IBUSEppHMfgjmcOsPbnz/HQjmFSYZNP3tDIh66bTUtLk0zdlMZkz4/j7fgJ1YtBNtIIuNJKquTBsiRSv2EJtF8o+Sn+OCXzCeJOAQoDh70zgaj4WVxXrtEuyfPwh+Xax70pkxDuNh23ZE+0V+pkmcopoSag6omVMPBq4J+BXwNvRyaF3gf8bKquQVEURTnzBCxzQtFySlubx4VKfkg8Jf6oVD2KGehZL2ZXBxExYwNSAankqNgu//XQbtbevoEtPWPMTvn54pta+KNXzCaWqJeqx9go1LzWjBmAcBOkOsRr4rgiQGoVafEk26HlPDHSJtqlvVMtQL6fqJOjYEShbr5M8bguVEvifbFrXjUlIed9gaj8U/WhTMct2cfulXLt2klPB09lhaUN+HfPx2ICP3Fd93bDMLYAPzIM4x+A9cB3pvAaFEVRlGMY3/Hz4w9edVoeb3Zd+CgPC0DYb53c1uaJhEowLpWUoW0iWAwDyiWZAMp0QbVENl/h1vu386U7NtOdLnNBa5D/fNcsbrl8Fv5ICvB2CFWyMnrs80ulJtEhXhVPhFCrSpWkaSm0r4TmZRBtkvC3Ulo+TD9EGun1zZLQNtMv11X1xEMgItksR4S7HY+X4kOZqJpx0q/3FNB5cQc/fOIAAD8Z3LfxZO8/lVNCG4CLJ7h9D3D5VD2uoiiKMr1ojIthdHxKqCMVPvGplYmESiAqraChrWKgHR9NzvZIFaRSojdd4Mu3b+Ib9+8kW6xx/cII33nHXF5zfgdGJAG1mpeh4o0mByIQr4dYqwiNahbSMj1EMAWt86Wa0rRURpddL9/FqYkASXoptQb43G2EnJwIHSsA4ZRcsz8iAucEeCk+lGOrGSf1ek9jNOlWURRFmXIa48FDwuWEKjuOLePGheHDQsUfltvSXTK54xgiOLIHJZXWqbGtO8vnfvYc//noPmq2y9sviLPqhmYuW9Ampla7AmP9MvHjeomykWZZRGiaYqStFUW0JNqgeYkk0tbPB18U7KIYfF1HJn3CDV7bxxGTbrVA1B2jbATE/HuK4W4v1YdyZDXjdFXSphoVLIqiKMqUMv6GOd6KekEOCZUhERf+CPgTMDYM6YPiA3FtEQfpbigMQrXCozsHWfOzDfxsfS8hn8EfvSzJX93QxsK2JhEM5TIUeqXFgwWBOog1SPXDtaXa4lRlnLhunvhTWi/wovVD0jYq9XhtnwZvAaEX4z9e/bECEEoxaLVQMQKHFhieCtPRh3KmUcGiKIqinHmOFSq+MPhjYpzN9UpFxLahMApjPZAbwnEdbn/qAGt+sYFHdo5QH7H4f69q4M9f2UZzXYNnhM1L1H6tLKPIwQZItsn5q0XID4gRNpSA5GKZ9mk5T8SGgYxAF4elwpOcLWZZHBmbrhY5tPcn2iRtJV+Qsrn/Jb8c09WHciZRwaIoiqJMOhNNuEzIUULFq3D4wjA2KG2eakkqH8URyHRDYZRyrcoPHtzF2ts3sa0vz9w6P//ypib+4Jp2ovEGGU2ujMk4c82Lzo+1QrwJ8EMtL/4SXAg2QtMC2e/TvFy8JtWyVG4cr+2TnAP+oEwI5QePqKYkpULjC0u1ZRKZqT6Ul4IKFkVRFGVSOd6ES3sydMjHIkJlyPOoeELF9EvFI9cL1aqMJhdHIXcQChkyxSLf+tU2vnTXdnozZS5qD/Jf72rjHZd14IsnwDElUbaSFbHhD0GyTsaeXfdwVcTyQ6xNJn3axv0pIWkz5XpF8EQape1jIGm5R25RjjWLsDn5dTgnxUz0obwUVLAoiqIoJ80LjUYfb8Kla7RIU8wnlZP8sEzYWH4wTD71w/upc4b58Cvniuk1NyTjyaUs3YN5vvyrjXzz/t3kSjavWhTh398+h1dd0IIRSIDhQCErVRUc8EUk6C2YkuC4yqiXfxKCxoXQeqFUUxLtMrVTHJHrCUQhNVfGpWslL5TOq6aE6+RjksLdlJNHBYuiKIoyqRxvkqVi28yq7odcHAwfYIp4GRugodpHyC3C8B5ZUFjNs+VAms/94jm+/1gXtuPyzpUxVr2yhUsWepuNHRcqIxJvD2LOjTRIfopd9ZYM2jKG3DAX2i8Qf0q4XtpMpRGZ7gkmITkXfD45V7aHQ9WUVItMFx0n3E05fahgURRFUSaV4024RCwHw3WlPZPv9fb/VKGSo6XSRYo07lCM32ztZc3PN3H7hn7CfoMPXpHgr17ZzPy2Jm/jcUU8L05ZovPD9TKWbPnEZFsYltsj9dC4BFqXQ5PnT6nkpXJjej+PNosnpZyFQkmqKZF6raZMQ1SwKIqiKJOKTLhsoFg9HMcf9hmsjI2RdIahf7NXAcnIFFC+n6ZaD49s7edT/3aAx3aP0hCx+NSr6vnzlzfT2NQkQqJWhEKfiBwrKNWUkDc6XCtApSK+lbpZIlBaz4P6BSJeShnIZUS01M2RTJVaUSo8OGKcTc2RKaATDHdTTi/6X0VRFEWZPOwqnQtNuLGOT94zQME26YhZrLrEYf+23fiqJci7kOmBwjClYp7/fHAX//DzzRwYLjG/zs9X39TE71/dSiSeEmFSyUlFxXXFhxJvgkBY2j21gogff1SWD7Z6bZ9EhywYLI6KATeUEEGCK1WWUtYz19ZJhUarKdMeFSyKoijKS8euelM/Q1Cr0rnQxxPPDhAny+prW6Ba5j8qA6TsQejpYTSb55t3bebL9+yiP1vhgjY/X3tbIx+4cha+cEwyUypZqA3J+a2wjBBbITHSVgue/yQBLQuhZTm0ni+tnGpBsldMS6Z9QilPvHielfEo/XBKjlHOClSwKIqiKKfOkUKlWpbUWKcGhRHaat343BKkK5DvY25tL4PDOT76UDe3PriPsbLNTYsjfPyWZtrb4pSMIL5QXKZ97BJggT8O4Zh87TpQy3m+lUZoWiJCpWkp+IIS1z/WK1NCyTlyW2VMMlUMS8RMpEEC3pSzDhUsiqIoM4ip3MR85LlNp0bSScPgNqiURKjUqrKLp5SFSp76ai91zggMhNm0d5A1P32auzcO4AK/szLGquuauXBePfgjbBwYJUEeihUwDQgkZdoHS0aLqUh7KDHbM9GuEH8KjrSMKjkJcou2guHKtE91zMtNmSVi5TRUUyYKzDuXw94mExUsiqIoygljOjXIdDOrth+/U4ZSvXhESqNQGpMNxvkhyPYyq7aP53YP8vofHeCOTUOE/QbvuyzG37yqlbnNjRAIQrUC+WHqKFDFJ22a8ZFluwZOCcIJiHeIUGm5AOLNUs0pDYsPJZwSE61bE+FimHJbpPG0VlOOF5gHqGiZBFSwKIqiKC+OXaWuNkTcTkPaR9gdw3S9WP1iRhJmC5KpYheyrHtiD3972yY2d+dpilp8+lX1vP7iGOFIgrmtTWJ8LQyJMPEFSeOjhl+mgewyYIg/pXGxJNK2rhBjbbUggsgXgmiLROZXS141JSxm20j9GfGmHC8wb+1d21WweIxX/n7yJyd/XxUsiqIo5xAn3bKwqzJ6XBiiqdaLadhQLZFwsoTsAow6kO2DwgDFbIb/eHAHn7tzB7sGi8yrs/in19fxly/vIBxPio+klpdgONMAMwzBIJg+DDKEqcntoSZJpG06H1qWyfROtSDVm2BMpnoAnArUXKnAhBshGD0tr+HxOF5g3vFuV04OFSyKoijnCCfVsrCrIizyg1CWyPuAWybsFCB9gIZyL6naCPQcZGR4mG/cvZV/uW8PA7kqL5sV5L/f3coFC+JUzSjheETO4VQl4dYflSkgywDDBlxsfAxSx9J5V0P7SkjMASpSOTFM2dwcCIvx1qlIhSXWIeLFmh6TPscLzGtPhc/A1cy8/UMqWBRFUc4RTqhlcUioDEibBwdsG0pZ6u1BItUM9A4yr7qDkeE0H3mgm399qIt8xeHmpRFWXdvMK5e1YgQjbBkcJUoeShXAB4EYGEGwHGkFgUwBJWfzbC7CAd9crlvxSmnxVNKyDDFUJ0sGnZqIlVDSi9+Pnc6X7oSQwLyNR73GYb91/E3VykmhgkVRFOUc4QVbFuNCZaxfhIpry0d5DIppyA8zu7SLhJtlw/oSa//nWe7eOIxhwLsujLPqlQ1cMLfJ27ljQ3GEKEVq+ESUmJacjyq4lgS21c2RkLem5Wzr2yu7hIoZ8aWE60Sw4IixNtbsiZfpu9NnXPR9/KcbqNgOHamwTglNIipYFEVRzhGO27JI+KHnWdmn4zhSyShnvej8Qch045YyHNixmR8+uo9Hd2WJBgz+8IoY77uyjrpkguVN9RJ1X85IIq0/SAEXB69d49jS0gmmoHE+NJ8nhlrLhFqZCAUqBCCSFOMtpqTThuulmnKWpNB2XtzBD584AMy8lsyZRgWLoijKOcKELQsfrLrY8SLsbQlay49KpSXXR60wwm2P7mTN7dt5umuMpqjJP722gdeuTBAMR3DxEaAoY82GKWLD8IMBFlVMEMERaRCB0nY+JGZJtcUug21AIELWSGKYBgTiXsBbvW5IRkXPkahgURTlnGAqA9XOFjov7gC7yif+dxMlGzqiBqsuNemcb4hgKYzKRFC2m0J2hO/du5XP37WLPcNlljT6WfOGel5/YZIVza1sGckSooo13vYxQyJYcMB0wfRTIE7aSLJ00bXQvByiDWBXZKOy6ZdQt0AELD8FM07OjMt2ZdM80y+VMg1RwaIoinIuUC1DrofOpl62NO0Ex+CTNy+TysjwiCwjzA8wPDjI1+7axFfu289QvsaVs4N87uY23nRBC9vyNUxqrMst5DOl1zBAHU2keb/vl/yZuROwZXonnIBkB+vHovQEZnPdvEvFI1PJSVptuE4qMf4QhFOs217i7t4QFbvMNWseUN+HMiEqWBRFUWYy1TLkuiHbI5H5LmC7RN0sDO6EXC+M9bO3q5cv3L6R7z7aQ6Hi8IZlEf76ujauWdiAEYiAXSRGkXtql/D50lspEgBggDq+XHsn7eW76GzqlY3IrRdAw1wOdO0kQE1i8v3e8sLxqkqkHgIx1j3Xx+qfbdN0WOVFUcGiKIoyE6mWIdMlQqU8JrcZQGGU1uoB6uwh6PbzzLb9rL19Ez95ZhDLhPdcFOdj1zWzYlYd+HwSnV8ZBcNHBR+31t54SKyMUybA2vyr6bx+DJIdgAt2iaBboWBEINEuIiWYgHBSqjCeiVbTYZUTRQWLoijKDMLnVGi0++BAWeLvXVc8IYVhyPRDvo+FtR08t/0Ar/7Pg9y7PUM8aPDRV6T48LVNdDQmwTXBLUOpKOPIZgAMC5Myg9RN+Lg95RAkIlAriOk21MCgL0fZDEL9PMlgsZ7/lqPpsMqJooJFURRlJlApQXofS8qbGEyPsObnB/j4a5dBYUSi83O91DL9/PdvtvD3v9jB1r4SbXGLf35tPR+8poVkPAY44JQlKM7wizHW9MaJDZciYRrIMETqeQ/fHnW9RYTN4mEJxvj8By+UFtALjCRPt3RYZfqigkVRFOVsplKC0b2Q64Fynogj6Sex2ij0bIRsL/nhXr5772a+cM8e9o1UWNToY+0b6/nQK9oJBsNgIyPGriviwrAkNt8BMMV/EkrQnQ3y8nAXd1WSFO3DIsTEYdXLG6G+VVo+gZik054Amg6rnCgqWBRFOaeYMePN5TyM7hOhUil4I8UucWeUJfSwsJxjcFuVr96xga8+cJCRgs01c0N8+Y3tLJ0bwzF8BH1+icF38SophlRJXEd2/oQ8c2zdHNaVL+H/FhOMOX5SfhfXsSm5Fn5sUr4KnVefLzuCTnIkWdNhlRNFBYuiKMrZRDknQiXbKx4Vw5I9O/lh1u0ocuvwSkacqwmUhhn88fdIb9rPm5dHWXV9PdcsaADLx550BoMK2OZhgeF6GSqGJdM80Qaomw/NS1nXU8/q53wUHamqpKsGlmGwosGkP+9SIgHB+Ck/JU2HVU4EFSyKoihnA6WMV1Hpl6kf0y8x+mO9kOll3X6Tj++7mAqSMlsJN1L32g/xD69r5s+bt4vh1amCXcLAxQBP7Ljgt8AISBR+rBkaFkHDfAhEAZe1G3xHtYAAbNdgV9og6D+x1o+ivFRUsCiKokxniqOeUBmQhFjDkpZNej9kenDTXdz9xFY+Zv8FtejRUfaOFeS/zDfy5+ZW8ah4uCCSxTLBCkt1JNkKjYsgOUs8K4YpwW6RRnrywxNeWsV2CPo1lVY5PahgURRFmW64LhRHYGQf5Adlvw8+qFUh1wXZg1RHuvjJgxtZc+ceNvSWmPPxBiaaxelxUrIjyDXBZ4HrUsNPmQDE2iQ3pWExJJokH8XyS15KvEX2/wSitKeepjtdet65A5aKFeX0oYJFURRluuC6kB+S6kmuX7YfY0oIXP4AjHYxNrCfb//qOb54XxcH0lVWNPv5t3e0cKuVocd5fkZKu5mWLBXXlFHlYIR0DgasFi5YdDXEUuCTfT5E6yHWDpGUVFn8ETBNVt207HmTPKYBs+vCDIyVn/eYijIVqGBRlHOQ6TQpM52u5Yzh2J5QOSBCxS6Ba0CtLObakQP0d+3kK3du5OsP9TJadHjFvBBf62zh5vMaMC0fqcJdrM699agU2jAVVkXvASsEoTiEUlA3h405yFsJqOuQBNpYK8SaZKtyIAq+4FGXN9EkT8hn0hgPqmCZgHP63/IUooJFURTlTGHXpOWT8YRKrSSVkEoe8gMwuo+d27fw+Z9v5ntPDlGxXTqXR1l1fSNXzU8iWfsu2GU6A89A1OYz+dd5SwlH+WTqATrrD0JkHqTmQd0cCNeR3n2APFFoWeHt9InKh2kd91KPneQZF5qKcrpQwaIoinK6qVU8odIlZtpaWYy0lbwIl/Rennh6A2t+uY3bNmbwm/C+ixN89PomlrZ4ybGuKx+OA9TACtAZ28J59noyGPiJceHsRVB/BSRny4bkQBQiDezzBSlaUWhYcNRen5NlKFdmrFTDBa757P2an6JMKSpYFEVRThfVkgiVbPfhiorjiqk204s7soc7H36aNb/azYN7CqRCJp+4to7/75XNtCZCYLgibGwHsEWwWD6Z9DEsMIPkCHGQOOXgHC5ceDmE66UdFGuRbJVQkmG/jW34xKdyigzlyuwdzuN63+uWZWWqUcGiKMqMZ936btYfSFOxHQKWyey607inxnV577ceIGGP8rXXt8LYeOsHKGcg20elbzs/uudp1t57gE19ZWYlLL7w+ib+6MpG4mG/1/mxoeaAUQPHAisg+SmmCBWCCUi2sDUfZGspQirUAo1LZdonXCeixdvr819/8oqX/LS6Ros47tG36ZZlZSpRwaIoyoxm3fpuVt+2kYrtAJIdsnc4z7r13VP7xuq6nhdlkLmlHaTcDKRLUiEp5iG9n1zXFv71V+v54q97OJipcX6Ln/+4pZXfuagRv8/w4vIdqNlgeKPJVtCrqvjAH4RQEuKtIk6S7XT19LGn5KPB3wHtK09qr8/JMP56HotuWVamChUsiqLMaNbetf2ocVyQLsyUVQIcR1o8+SHIdENhiGa7D9N1YGwE0gfo272Bf/nFs3z9kUEyJYfr5oe59W1tvHZZAsMwwXAAF6qO97UJRkgqKlYAAl7YW91cL+ytQ+L0I40c8MfYjYvjaxRD7RQRsMwJRYtuWVamChUsiqLMaI73F/+kVwLsmgiVwjCku6E4LAsKHZtINU1zrZftD/yaz9++mX9/aoSqA287L8aq6xu4fG7Mi5+1AFvO5Trg80m7x/BL8mwgDOEGMcs2eKm04QREG+UjmGDYV6RAFtc4/sTPZDC7Lsze4fxRbSHdsqxMJSpYFEWZdkxmNkt7Kkz3BOJk0ioBtYpXURmGbA8Uh0So1CpQzMLIbkI7f803Ht3PXdsLBCyDP7g0yUevb2JRYxAwRKzYNTCq4OC1eyKAJXksgQjEm6FhCTQthkSrV1FpECNtIC734fDkzuN7R6Z0cqcxHiRXqjIwVgHAMgzedmmH+leUKUMFi6IoM5pVNy2dMKX1JVcCqiXxqBRGvNbPCFSyUK1AOYfTt5lfPvhb1vxqH7/ZVyQVMvg/19fzF9c00ZIY/9VriFChBlhgBsDnB8Mno8b+CMTboeU8aF4kVZRwCqLN0u7xh48aSV63vvu0Te4M5coM5SuHvrddl/95upvL5taraFGmBBUsiqLMaI5NaR2fEjqlN1XXhWpRhEox7QmVQSjlpKJSSFPu2cB/3fU4a+/tYutAlTlJi/93Ux23XJJiRSoEpgkYcryJ9xGW7cumKymz/igk50L7eVC/UCop4TrZpBxKHtdEu/au7adtckenhJTTjQoWRVFmPOMprUO5Ml2jRXYP5U+uXTI+8VMtQDEjOSr5AShlwa5AbojM7qe59Y6n+NJD/fRkbS5sDfCD32nlHRel6C5WMXBkPNmuyWfTFAPteDXF54NInQiUtpWQnCM7faINUlEJxj2xc3xOm18HqE7ylJDG2SsvhgoWRVHOCcaDzsarAifULhmf+KkWRahkDnrbk7OykDDTS8/WJ/jy7c/xzceGyJZdblwY5t/e2cirF8cxRKFg4GDigFMDy8tNMSwRLv6QtHealkLbRTLxc9S25BP32ky5X+cMPZaigAoWRVHOEU6qhTE+8VMrQXEU0r2Q74XymMTojx5g6zOP8rlfbuU/n0ljO/CO82Osur6RS2dHxChLFWwDDBcLBxtTWj24MvETDEvlpOUCaL8IYg2SShtvEQHzAnt9jseqm5byVz959rRM7kzkDdIpIWUqUcGiKMo5wfGCzrrTRW751mPSkhif+KmVoZSGTK9sSy7nRLwM7+KRR37Dmjt38/MtY4T9Bh+4LMlfXd/Egjq/ZKa4NtRcsJAofcuiRBAXV6oq4Rgk2qH1Qmg9T8RJrFmESiB2ynt9QCpFX7lvJ3uGxHjbkQpP2ZTQ+Dk/8uNnp/yxFAVUsCiKco5wvKCzgGXidysylmxXRKikuyHXIx6VcgGnbwu/+PVvWXPPfh7dX6I+bPK3N9bz59c00hQeD3qzZSTZMMU8a3pTP7g4WJQJQ/v50HEpNC+FcBLibRBrEqPtJNEYDzIwVmZFW2LKfSGdF3fwNz/bBMAjn7hhSh9LUVSwKMo5xpF7dc6lDbsTB52ZLIg7xJycmGhHD0CmRyoq5THKB9bz/bufYO39vWwfrDKvzsdX3tTC778sSdRnSgUFVyLzDc9Ua1lAAPCC3kIJdhdi7DIXc8HLXittn2Q7hOpe1ESrKMphVLAoyjnEsXt1zvSG3dMpnhrjUsXoGi1SsR06kgFWvbKNh57eQEN5APYOQCUDYyOkdz/Dt361ni89NEBfzubi9gA//J1W3r6yDp9RkxMa7uHPhgGmz5v48UEoDMGkVFLaX8Zjv8kxatVLdSUYm5LndzrQSR7lTKKCRVHOISbaq3Os8XQyU2ZfiBcST1OB4TrMjtZYGHEwcfnmW9shvZ/+8mbC1RwMBDm49Qm+fMcWvvXbYXJll1cvCvOf72zkxsUxDFygigSnADjiSTEtwAf+gHhQwnUS9Db3ckmkjbez84ndOKb/tIiVH3/wqkP/DRVlJqGCRVHOIU5nTseL8ULiaVbdJI7GehM/SWcUw3WJODkaqz2wrxsKGdrzO6j0b+X3bzvID9ZncFx45/kxVl3XyMVtITHRUpP4fMNCpnwML+jNACsqFZVoi2xHnnUZJNog0QGhFJgmjnlg8p6PopyjqGBRlHOI6ZSd8ULiaVIEy5ETP7hE7Swt1YM02sMEa1ncPSV+89RT/Msvd3PvjiJhv8GfXJ7kI9c2MD/hB8MTKoa368f0pndMvwS+WSERKvEOESntF0qGSrwNgtGXfv2KohyFChZFOYeYTtkZUyaeamXJS7ErklBbGIbh3VxQWk+4MkpL+QAbt2/n6m8M8NuuEvURk49el+AT1zbRGDTBdMCtHa6mgDfx4xOhEghJ6yc1C2ZdBR0rZWtyrAks//Mu51w1OSvKZKOCRVFmGC/kQTl2r86ZzM54IfH0wydOsoXiupKTUh6TNFlcGBuEoV2STpvtZV7mcX7z3E4++egQe0dqLKjz8fU3N3HDeSGifmgMOaJPHBP8JjjeaLLhVVRCUfGg1C+AeS+H5mWQnC1x+sfJTpluJmdFOZuZMsFiGMZs4D+AFuTXwK2u637ZMIx64MfAPGAf8E7XdUen6joURTma8b06cGanPl5IPB0pWF7QBOy6st+nkgfHlu/zAzCwXRYTjuxn9MAWvnH3Zr70YD+DeYeVbX6+8fYG/vjieizXprdawsECTBlJdpGkWn8IjACEoxBKQNNymPcKaFkmHpVA5EWf44mYnBVFOTGmssJSAz7quu4zhmHEgacNw7gHeD9wn+u6nzUM4xPAJ4C/nsLrUBRlmnLK4slxoJqHSgFcR4RKrh+GtsHofhjazYGdW/jSr3Zw6+PD5Csu1y0K8idXJ7lynh+fYWAZNXBNali4jI8lW+DzgxWUikq4Tvb7LLwOGhYet+1zPKaTyVlRznamTLC4rtsL9Hpf5wzD2Ap0AG8GrvMO+3fgAVSwKMqksqU3ezhu/izjWM9HyGceylDBrolQqRZFpLguZHtgcBsM74GhXWzcuIm1d+3hh89mcIF3rYzxsWsbaKgTaQLg4HhTPi5G1ZQKSyAmoiUQlQrKrMtg3jXQuAhCyVMKeZtOJuepYkVb4kxfgnKOcFo8LIZhzAMuBh4HWjwxA9CHtIwmus8HgA8AzJkz5zRcpaIoZ5qJPB+mASa2LCGslrwjXRjtgv4tMLwbd2AbDzy1hbX3HODO7XmiAYO/uDLBX16TZG4qADj0V1xqQA3L+8UnbZ9C0aJGEGJ1EJ8N86+FuVdC3WzwR17Sbp/pZHJWlLOdKRcshmHEgP8B/tJ13axxxP/8ruu6hmG4E93Pdd1bgVsBLrvssgmPURRlZjGR58NxJZ2WqownM3oAejfCwHbswe387yPbWHPPQZ48WKI5avIPr6rnT69MUB+2JEPFcMA1aAlHwTA4WChTJAChOPjC5KnR45/FisvfBbMvkbFkX2BSns90MjkrytnOlAoWwzD8iFj5geu6t3k39xuG0ea6bq9hGG3AwFReg6IoZw/H83aUbGTip/c5GNhCsXc73/v1Tj5/fy+7R6osqvfxzTc38nsXxwn7PE8LVa/t4wMc8Z5YQXJY2ISk7dO8jJ8VOzgYWszrz7/JS62dXKaLyVlRznamckrIAL4DbHVd9wtH/OjnwPuAz3qffzZV16AoylmE69KeDNKdKT/vR0mjBE//G8MHd/H1e3bylYcGGMzbvKwjwGff1cJbloVk5yA24KXQ+nwiXKyATP8YfghFyOZT7DSXcv4NfwAtS9jatdlLrp18saIoyuQxlRWWa4D3AhsNw3jWu+2TiFD5iWEYfwjsB945hdegKIrH6doRdLIYrkPQLcHYAKte2crqOw5QrB3uAgeo8tby//Lhr9zFt387QqHqcvOSMB9/RZJr5wYxXBssLy7fCgGGtzU5IFM/ll9Gk6OtMPcq/mt9EyO+Zt4z92Vi8O3KaKibopwFTOWU0G+QXxsTceNUPa6iKKefUxJD3sRP0hn1flG4dC5PwKjLp36TJ+1GaCJNeP33+ft77sIw4HdXRln18gTntwTEn2K5sh3Z9Dwnpg8sH7KMMCgR+Q3zYd51sOBqiLfRt/kZYGaHup1OUTrdBLAyc9GkW0VRTi92VXb8VEtgGDgYWHYF9j0G+x7hzUPbyXRv5DuP9vPw7hLRgMEfXRHl/1ydYHZdwMtd8TwpvqB0gSxDslPwQSAgY8hNy2DhDTDrEog1e0LmMBrqpihnFypYFOU0Ml3bMqeFWlkSaWtlpG1jQinH8sIzrChvoPZolv95dAdr7uvlme4yTVGTz7w6SeclAVIhg1bLB7jgD0glxUV8J36vohIIQaQR2i6Axa+G1hUQrj9ufoqGuinK2YUKFkVRDjHpi/rGd/xU8lJZMUzxmhTSsP8x2PsQ14w+x53ru/mz346wd7TGkgYfa9+Y5B0rA8z1BxlwK7iY0uIxfYip1jpsrA1EINoMs14Gi26UFlA4ddz8lHGxeM1n75/xoW6KMpNQwaIoCjDJno5jd/wYpnzkBmD3A3Dwtwwd3MfX7t3Dv/xmgJGCw5WzAnzhtQ28abGfQcPGwAK/RbXix8YnLR/LANcn8fnhBMTb+VpXO9tLF/Iv73yDpNWeYNCbhropytmFChZFUYBJ8nQcu+PHsAAD0l2w8z44+DR79+zmC/fu4ztPjFKsurx6SZAPXRPmDfPCGC7g82FXLREpvhB2pUoNU/wqlh8iCUgtgoWvhHlX8tsf7aZihCAYP6nnq6FuinJ2oYJFURTgpXk6TNeW0eT8gFRXDFM+D22DHfdA/yae3rSLtfd08d8bMlgmvOfCKB+7KkJjo4GLi2H6vIRZH9VqCQcf+EOUAJsApNqgcTksuh7aVkK0AfxhKmbPKT9nDXVTlLMHFSyKogCnuKjPm/hJOmlZK2iYYFckkXbXPbj9W7nn6V2subeb+3YViAcNPnp1jA9fEaUjYYFl0VerYuMHf1gqMiZUCXiCJc6IkWJ3YAnLX/leaFoiE0D+0JS9DoqiTE9UsCjKDOJI0+z6A2nWre+esMUxUTXhpDwdR078GAY2Jn67AHsehl33UevfwU8e28Oae3t4rrdMW9zks6+K8yeXRUgGTZnsMUwwA1RqJsa4qdb1gc+kiMOI2cySC1/Lfz9ZRybUzLvnXC5tIUVRzklUsCjKDOFY02zFdk7KNDt+zEd+/CwuPN/TMdHEj+mD/DArs49ynv0c+fszfOfBPXzhgX72p2ssa/TxnTclefcFIYI+H1im5KGYlpdM62dOIgyY4k8JxaFuHg+VmtkeWslo4BJuH95N2Xa45nOPqMdEUc5hVLAoyhRwJvJWJsM023lxB3/zs00APPKJG+TG5038WCJU0l2w9xHoepwLhzfw3090846n0owUHK6ZE+Arr6vj9YuDmMaRFRW/5KKYXmS+YYlvJZSChkUw/+XQcRlPD++mK29xz+17KdsS0z+TkmgVRTl5VLAoygtwNgW9Hc8cO5Ev5YQ4duJnPANlaKe0fnqfZdeunXzhnr1894k05ZpL57Igq66OcfWcILgm+A25n+ED0/vaCoJrQCAoQW8tyyU6v+08qbAE4uTNAfaPpg9Vi8bRJFpFOXdRwaIoJ8h0Fy/HM80aSLvoRDFxiHDExI/p84y0G2HPb2BgM09u3MHau/fzP5ty+Ex458oQf351kCubIuCKmVbWJ3ufTb9kp2DKfp9YE7SslNHkxkUS/haIHRWff6xYGUeTaBXl3EQFi6LMEFbdtPSQ/+RIXKRdNKvuRRJcvYmfBtK4eNWQUg66n4F9j+AObueup3ax5p6D/Hp3gWTI4OPXxPj/Lo9ixW0cDGnvWJZUUEy/CBCft5wwEIVkB7RdCoteDslZMhkUiEmL6BgCljmhaDkbk2inq8hVlLOJFxUshmF8CPi+67qjp+F6FEU5QY6t+HRe3MFf/vjZCY/tSRePL1iOmfipYFFHBrbcAQefpNq3gx89sp219/Wysa9CR8Lkc69J8MeXhEkETTBNBhxDgt5Mv9f28XtVFW8RYXI2zL4M5r8CovXg9yoqx9nzAzC7LkxPpqRJtIqiACdWYWkBnjQM4xngu8Bdruse+0ecoijTgI6TyVKpFg9P/JiWGGDTB3gND7OMveQezfHt+3fyxQcH6crUOK/Zx/fekuRd54UIWIDhh4AFhkmxZOPiEw+KaYIVluj8+gUw/xpovwzCcWn9+KMvKFTGaYwH+dCNizWJVlEU4AQEi+u6/9cwjL8BXgP8PvBVwzB+AnzHdd3dU32BijKdmW6+llU3LeWvfvIszhF/UoT9Ftcva+InTx6UpYafuZdV17fTuaJOqiGOCwPb4ODj0LeFy8ae4GePd/PBp3KkSw6vnBfgG6+v43WLApiGT/b5mNbhBYRWgCoVCXoLRiHaAk2LYd7V0HIBBMLSDgpET3jPzziaRKsoyjgn5GFxXdc1DKMP6ANqQB3wU8Mw7nFd9+NTeYGKopw4nRd38JX7drJnKH8oS+X6ZU38z9MHDy81zJRZ/cv9UKvQ2dgDXU9C/3Z27NzB5+/azfeeSlO14S3LQ3z86ihXzA6J+dYyDxtoDR/45fO60kX8Y+lGBknS0V9h1RUROq8+T0LeAlFp/5ykUFFxoijKsZyIh+XDwO8BQ8C3gVWu61YNwzCBnYAKFkU5AY5Mob3ms/dPWXujMR5kYKzMirYEP/7jy7nmn39NsXrseLDD2nt20zn/v3h84w7W3L2f/92cI2DBOy8I8YHLA7yiLQq4XovH9EaTx420AfAFWVe+mNXZ11D0fpV0l4OsfsyFpiqdl806JFSmWyVKUZSzjxOpsNQDb3Vdd/+RN7qu6xiG8YapuSxFmVkcm0I71SFoPmpEnRyMDdCTKU94THcpyHWfvpMH9xZJhQw++Yo4H3pZGNvvYOCISPH5Ab9M+hiWTPX4Q+CPQWo2a7e8hqJ79K+RYtVh7X376HzZgkl/XoqinLuciIflb1/gZ1sn93IUZWYyGSm0J0StTMzJUkcWvxsHoD1m0j32/PHgWnaQPSMVvvi6FH94UZh4wAFMesouFYIiTnzBw2FvwTAEElA/H2ZfAu2X0vP0/uedFzQrRVGUyUdzWJRzijPVmjjeG/ikvbEfMfFjuTYGNm3lPfDUFlbVZfnE2GWU8B8+vlbidwo/5TMfbsFvOIAr4sTykS9XZOInEJcKSyAK4RQ0LoHZL4Pm8yCSBCtAe7Kf7kzpeZdzurJSTlebTVGUM48KFkU5DRwvhfYlvbG7LkG3DGMDsuPHtKA0xvzCJi5mExdke+l9Is/j9+xieOgRglf/Hr5EI/X2CH8T/l/eMv9pb5ePD4yAiBPLRwE/VSyI1EO0EVqXQtsV0LZUDLTjZlpfkFWvXXbiG54nmclqs6mvRlHODlSwKMppYNVNSyfvjd3b8ZN0RjFxZc9Pfgh61kPPRq4fe4quoW6+8mg/6zZkqTnw9vMe5w/sDVwatmgEzzx7RLvH9EEgBGaQNGUGSXHZ0hsl7K1u/uH2UCAmhluPcWFwJrJSTlubTVGUaYEKFkU5DUzKG7tjQ2VM2j+uC06N+uoAbLoN+rbAaBePbtzDp+7cwz3bC4R88EeXRvmrK2MsrDcZcj3zrT98WKSYPm/0OAjBGCQ7uKfbYivzefuF7xRx4g95e378E17WmcpKmfI2m6Io0woVLIpymjjlN3Zvxw/VkowJV4swvJuX5++lo7ofZ1OV25/cw5p7unhkn0z8/H/XRvnA5SHOi5qACwbU3BBV/BLkZgW9aZ8whOqgfi40n8+6scX80E1jY3LNt3ay6jVL6Ly0bWpekJfIlLTZFEWZtqhgUY5C8zKmEUfu+HEcKGVhcBv0bYCh3SzJbee+jV38xWPDbBusMDdl8eXXpXjDRT7iAQcLR4SJLwhWgEqhShk/hBogGIFwvQiVjougaRnr9pqsfqAbG1lE2J0ps3rdFjCtadlimdQ2m6Io0x4VLIoyyZzs5MrzROKRO34cG3L90L8JBrfCSBeZoR6+de9OvvBgH/05mwtb/fzX2+p5xwo/PtNlhCoV/FQJQigMBMAfIFeoUSICTQvFl9K2UiL0IxLRv/bhTRRrR68Jm86ekDPpn1EU5fSjgkVRjsOxwiPkM2mMB1/0Pqc0ueK6IlIqeREp1QJku6F3MwztgPRBuru7+fK9u/nmI0Pkyg4vnx/gi2+K8zsLgxiGAzjgC1KoBShjYhIAf0ryU/wJurMGA8FZXLry9dAw31tU6Du0Ofl4AXPT2ROiu4YU5dxBBYuiTMBEwgNg91D+BasmJz254jiEnAJBtwTFNJQyMLIPhrbD4C7I9bJl1wE+d/devv/0CLYD7zwvwqpr4sxptTGpYQBYIbACEAxQyVXJYWEShVQbxNqhbTmP5QxGfI28p+NCz2x79OZk9YQoijKdUcGiKBMwkfAY58iqybGc8OTKERM/0VqGhJOBvY/CyG4Y3os71ssjG/ax5u79/GJLlrDf4IMvi/GRy6MsqHMBlyEMSaQNJyRDxQiAL0QWmx7C1AKzWLn8emhcCokW+p7dhm1YEEpOuDlZPSGKokxnVLAoygS8WBtkvGoyq+7o6sOLVSkst0bILcJYP5RzkBtgWWkDrbUu2GnijPbw8yd3s+aeLh7bX6AhYvKpG1L8+aUhGiM2YIPpZ517NZ8pvokB6mmvpFmVeIDOhi6ItXDR4g4e3hSgJ9DBmxa8UsSJ5SNvxikb3vjyBIxXgD7y42cPbXpWT4iiKNMFFSwzDJ3ymRyOJzyOpDtdZDBXPspce9wqxasWQGGElD1EzM5BD5A5ACN7Ob/4LMHKIN++bZC19/WwY7DC/DofX725jt+/KEjEXwVq0sbxhVhnX8nqwjspIgFu3U4dqzNvhPb9dK5og/p57Ni9n6oRlEj9QBT8Ycpm6EWfd+fFHfzNzzYB8MgnbjjVl29S0X/LiqIAmGf6ApRzk1u+9dghcTUdWXXTUsJ+6wWPMWBCc+1n3noBAUv+1+pIBvnMzXPonFeDgW3MLe9kUXED7LoPdj9AetvD3PnQU7zly8/wxz/aR8wPP3p7Azv+ooE/f5lJxG8DAQjGJSo/GGNt8Q2HxMo4RdfH2q7FMPtSSHRQMqOMWXGINYlX5Zj2j6IoytmGVlgUZQKOHZk1gCMHfo/9Hg63iR756+u57bc7iDoZvvG2eZAfgJ5BSPewrLSB+kofXc+N8aV793DrY8OMlR2uXRDkh29r4Ia57uGJH8PbkOwLeNWVsEzzDCYnvOaenAPBBARi/OufvW7yXxRFUZQziAoW5YQ519pNR47MvuvyOUflfRyvXdSTLsLIXtqr+4nZGejJi19leB9kuzEObuJLj/bx841ZXOCW86P84VUBVrYaNOIAPrDCYqL1BWTyxx+BUBQiTZCcRXu/TXfx+f/rtidDsqzwJbBufTdjpRouTNr2Y92orCjKZKCCRVFOgGPzPq757P0Tm2tjJgxuo6O8h5QzDHt34A7v5aFN+1hz70Hu2Joj7Df4s8vjfOSKIPNSMEoNFxOsCPgDspjQFwB/DCJxiDRD3WyoXwCNC1kVC7H63uGjQt7CfotVr132kp7j+Cj3+FlPdfvxROd8qRuVFUVRVLAoyikg5toNFKvOodvClsuqFWno2sPSwnrq7X7+55cjrLmvlycOFGiMWqy6LsYfvyzA4oiJWMhMSljYBKgPx7wdPzGIpCDaBHXzoHER1M8TA60/TGejmGn/+ue7KdvupE3zTMX2Y92orCjKZKGCRTnjnK5W06S0JlwXimk655Tguhj/575h8o6PjrDDqnl76Sw9RbFrH088uZNvPzbC/pEqCxv8fP0N9bz/Qj8lXwUHFwyfLB/0BakUapQISssnXAeJNmicB6n5UDcXQglvw/LhzcmdV7Txw2dHgMl73aZi+7FuVFYUZbJQwaKcE7zk1oRdI2GPEHeyMOiDUprOxhy9iSdpcXp566wCI737+McHD/IvDw8xMFZjZXuA/76libcsAcuUsLc8PmpYEElK6ycQY6RQo2QkoP0CqJ8DDQsgMUcWFB4SKmK4xTr1/2VfTNi8lKTb451b03MVRZksVLAoM5Jjqzan1JpwXQJOkbibhYEtNFd7iDg5SAcg1wtDe7mk9DiFzCAfuWuUf/3tMPmKw+uWRvijK+JcO8+g0XAAEww/+AOUKlDBB+EGaf3E6tmb9jEcaOOqpddDqt0z3QbBHzwsVMwXHrGeDKYi6VbTcxVFmSxUsCjnBCfVmrCrEptfGKa9dpCgU4B0gLrqIA21fti1E4b3sWFnF5+5q4vbN2YxDHjXyhgfuyrCymaXEWpyLiMIfj/4xFA7Kx6TKP14AyTnQ/0cNg0UyVh10LQIzIAnVCIS+HYahMo4U5F0qxuVFUWZLFSwKOcEL9qacByoFaE8JgsIy2NQyVNXHSBYK0D3KBfnH6HR6ef++7Ksub+fu7ZlifgNfv/yKH9zdZQ5iXEDrkUVcPBDrAEsPwTish051gh1C6F+rnxEGhh6Ygu24ZdwuED0qIWEp5upSLrVjcqKokwGKliUQ8zkvIzjR+YvhOLoIYFCJQ/lrHxfGGF+aQttlYPUNufZsmkvtz4yyqbeEi1xH//4qjrecYlFQ9ilHgC/CA1fiGIFKgRpizaKaTbeJt6U1FxIzZIFhL4QWD5KZoSyEYJo8xkTKmczKoIU5dxABYsCzPy8jOe1JpJBVl3fQecCINcHlaIIl0re86fshqFdLM3v4OfPDvH7j6fZM1xmXr2fb725nt8730fIB8PUcDCOmOAJgS/KyIhDxYxDy3kyklw/H5KzxUhreYFwviDrto7xQLdJxS5xzZoHZpRIVBRFmUxUsCjAuZGX0XlBIz//rUXAtfnmLQvBLklsfmEUilkY64PB7TC6j+GBbr72YB9femiA0YLNFXPC/J8bU7xhqUWzaSHB/CYOfkqEJDclEBGDbKyOA1kfg/42rlx6IyRbpZri80Lh/CEIRFm3aZTVv9g5Y0WioijKZKKCRQFmcF6GY0O1ANUi2DUiTg6fU4P8oHyUMjDaA8M7YGQP+7oH+cIDvXzn8WEKFYcblkT48NUh3jg3wAhlDBwgIOPFoQiZvEuRMLMTrRBvhvgsaFzIxsECWasOGheIcdYXFNHibU7GMFh79+MzXiQqiqJMFipYFGDq8jLOyP4h18Xvlgm6ZRgbANfxhEuRVG2YpJOGwbLs9xnaAeku1u8dYO39A/zk2RFMw+DdF8X52FURWhuqABi4uJjYBCCaFGNsMEo6b5O1kjD3chEnyVkQaWL4yU24WFJ18YcPC5UjmLEiUVEUZQpQwaIAMyQvw64dqqbEnDFcFxEq5Zz4VIqjtJf20WofhI2P4+b7uG9TP2t+PcQ92zPEgyYfuTrFh6+KMCviADYjIB6VQIixio8iIZrjLRCMQqyZ7aN+BgId3LDkBonONywwLcpGhKrhl3h9f2jCy52uoWor2hJn9PEVRVEmQgXLDOKlTPlMVl7Gaa+ouK60e6pFsCtym2HiuC4JOwP9m0SwZAdgaCeXFp8m5ozyo1/nWXP/IOsP5mmN+/jsTQ188NIQqYADbhWwAB8lDEqEaYw2kanYFAhDyzKoWwAN89jYnaZkRmVcGZkQwh8mayWpGYHjihWYISJRURTlNKGCZYYwGVM+pysv41hhFfKZNMaDJ3cSu3rYm+K6YPpk8qZaglw3iyrbJD+lrw8G98DILvKZYe54oodvPzZCT7rC0uYg335rM++5wE8QG6iAa8qen1AEAnHafREIRSGYomvMYMDfwVVLboBkBwQiFK3nwDAkGG7cdOsL8IM/eeWLPgUNVVMURTlxVLDMEM6WKZ+JhJVpnOCdx8PdqkURLIYhZlYMMc9mumGsH4oZOgo7aXe6YUuewZEMX314gK/9ZoDhfI2LZ4X4+htbeeNiE9N2gPHKTABCYQl480e9sLckJGZB03yeGa6RMeqgeQnggmFRMUOUzRDEWyQg7iTRUDVFUZQTQwXLDGGqDZyTFSo3kbByXOgafYHrrFWkmlIrSTXF8lJhHVtMtemDUBqGsVFI74ehXVxY3c7QaI4/v3uM7z4+SKnq8KYVcf7wihBXzjFpxhXRgymiJxCGYMIz08YgUg8pbxFh/UKIN/H/FvkRoWJ6RtoIaasex7BOSawoiqIoJ44KlhnCVBo4JzNU7ngCavzch3Ccwy0fpybVFH9YPCKVAozuhcxBCXsbG4LhPTDaBYU+nt49wv+9q4+7t6TxmQbvvSTFR68Js7zBYLBWxEcVCMqSwVBUlhCG49LWiTVK0FvdfGhYBNEGSZ91XS/F1pv48TYnO8bp2/VzLFqRURTlXEIFywxhKg2ck9luOp6wClheJH2tLGmztbJ8bwWkLWMFoDACgzsh2wOlNGT7Ib0XRvbj5oe5Z9soa+4f4L7taWJBkz++Ksn/uzZGe8QF24aaWGnLRCCegkBCKjW+CCSaJTa/YZFUVcJJqaS43vMOxg4LldO4kFBRFEURVLDMEKbSwDmZ7aaJhBVIheWaz9zDqle20XlBk5dbEpEMlbF+GN0vnwtpaQFl9kO6i2ohw3+vH2bNff08152nPRlgzc1NdK60SAQdWqiCDWBAIMJwxaRCjNZ4k4iVeBvUzZMMlYaFIkgwvKoO0iYKRI+7OVmrHIqiKKcHFSwziKkycE5mu+lYYWUgIfcA3ZkKq+/sglCKzvPj4kcZPXAoQ4X0QUjvg2wfY7kM33l8hC/+uo/9IyVWtIb43jvbeNd5AQJOmSHKSM0mIG0fr5qSHaxSNsPQev7hikr9PPGwgGS5mJZUdYJndnOyoiiKchgVLOcA49ko45ysmJnUdpNdo3N5nDs6HB7qdinZR48IFasOa+/cRGfElT0/+VFId0H6ABQGGRjK8JVHhvnaQ72MFmpcuzDOV99Qx82LA5i1Cjgl1tUu559rb6OPBtrNDKuiD9OZOAChBAfTPgYD7Vy99NVQNxd8fvGnOFUZjQ4lpf3jj6hQmUS0EqUoyktlygSLYRjfBd4ADLiue753Wz3wY2AesA94p+u6o1N1Dcrk8JLbTePhbpXCoXC3muGnZFcnPLwnV4PuDTByAMZ6oTjMrv48n/91P9/7bT/lmsNbzk+x6hUxrmw3oFqFWgEwWedexera71EkAEC3k2L1yOugeQ+di6I8M1RjxGqApkXSbqqVZcInlDosVIwTnbOemai4UBRlOjKVFZbvAV8F/uOI2z4B3Oe67mcNw/iE9/1fT+E1KJPEKbWb7CphJ0/ALUMxLRWMYBz8YfJmnIA1SsV2n3e39kAB9v4GKhme3JNmza8H+Z/1gwQsk/ddVsdHr46wJOWNJVdrgA/8QQgkWJt++yGxMk7R9bG2axmdr1/M0NMbMV3bEyoBCNepUFEURTkLmDLB4rruQ4ZhzDvm5jcD13lf/zvwACpYZhauK0sH80NgVwm6JapGQHJNfF6aba1CY6Wbq2I5nhhronhEWyhsVPlY9G7ufGYfa+7r44Edo6TCPlbf0MyHrgjRGqyBU4FxoROIyUcwCf4QPcPJCS+rZ8wBw+Brb18OvoAIpyM2JyuKoijTm9PtYWlxXbfX+7oPaDnegYZhfAD4AMCcOXNOw6UpLwkv3C3ljHDo7T+UIGPW4RpeOFs5L36U0b2cX15PnFHeMm8Wf797PiPEaTMzXDX8C/7ff/ySTT1jzEoF+cKb2vijS4LEzRrYZclnwQeBkEzwhOKHQtxIzqK9r0p3KfC8y2uP+wBTclYm2JysKIqiTG/OmOnWdV3XMIzn9wMO//xW4FaAyy677LjHKWeQCcLdKkaQshGEaCMALgZhOwfd62F0n4wmp3uZV9xKnT3KlbUtzDKG+fEzo/zoyWEeGy1xfnuE/3hXB7es8BNwxEiLbYPh95Jo4xBOSBsomITkLKibA/WLWJVqYfV9wxRrh//JhH0Gq26cC4m2F1xGqCiKokxfTrdg6TcMo8113V7DMNqAgdP8+JPOad9OPB2olb2o/LIXle+Fu/nDFMy9cozjwNggi8qbabQHYHcMcr0wNgjFIWbbXaTHynzy8TG+8utuxso21y1O8K3OBl63yMKo2Z6R1gDLJ2m0gaQsJfRHJNgtORvq50LjIqhfBP4wnR0OWD5W39VD0TboSAZZ9erFdF4270y+YoqiKMpL5HQLlp8D7wM+633+2Wl+fOUkOEqMOTYhpyD+lMLI4X06/qgICg/TqZGyB2HPQ6zb0MftfSkybhs/vafAqoZ9dAZ3sb0vz9/e3sUvnhum5ri8enmCD18d4eb5fqjZUCnJyXxByUKxYhCOyeNF6yA+Gxq96Py6eVI1cb3FiHaNzvMb+cXmIYpGmP/6k2vPwCunKIqiTDZTOdb8Q8Rg22gYxkHgbxGh8hPDMP4Q2A+8c6oeX5kEXBc/VREotTJht0jV8EM4JTt9jjSrVosw2sUFxSeps4dY92gLq7fNo+jKP7HuapSP917LFzfs5uG7HsJvGrz1oiT/cH2SeLSMgQ0Vb1+PPyw7e0IpCXTzhyDeBMk5kp3StEQ+mz4RKpW8tKT8UYg0QDDOmNlzRl4yRVEUZWqYyimhdx3nRzdO1WMqL50ff/AqSXstZUk6o5i4IgYCUdJmCtewDhtWXRdKWfGmjOxm3cZR/ntwFqPuEqxhF5ujg9cqRoC989/C/7nxSW650KA55tBsVBl2q7hYIk4CEREq/pDs+Ik3SRWlYT40LIZEh1R0nBpUxkSw+CMQaoJA/Khqj6IoijJz0N/uiuC6UCtJpcRbPFgz/FSMIMSa5ZDxzcSOA8URGNoJI3tgpIt1+2D1ruWHKio2E48KW4km/v5aH4N2CQMXLB/lWpA8YRoTLVK5CUa9HT9zZL9P4xK5BtMSoVLKylLCYMzb9RM7q4XKOeV/UhRFOUXO3t/yyuRgV71Jn5JUK0zrqHC3I7HcGvHaCOx9SITK8D5pFxWzrN178yGx8kK0G8NgV3DxUcEHoXpGx6BGkN8O+Rmx6rn52iu8HT8LZQzZMCU6v5QRYRWMQSihm5MVRVHOIVSwnIuMR+VXixKVbxhicPVHDoe7HUmlCJkezi88RVOtF7YbIlRKBajloVqkpxZ70YcNU2ZV4BcQTjBatKgRoiPRQKbgMmw0MRxso9/Xwc3L3yAJtCDVnkpevg7GZBopENM9P4qiKOcYKljOISy3KlM+Y/0iWkyfVCp84ecLANclVMvTaPfBjh4Y3M2FxfVEycJoSkREtcjW3jyfu2cf1QsG8SWbn/eYJjYuJu3GCKtCd9KZ3A/+VorFMnkzBe0XsiXt0u/roN/fQdGKiXG2WpTHMAy5xlBCNycriqKcw6hgmek4DkGnRNAtYWHjgvhE/BGJqH/e8TYUR2FkN5cWfkOrfRC2B6BSIF4dwk8NyjaP7Bhlza/28/Pn+gn5TW5q+l+2Jt4voXEeYcp83Pcf3ODbwNxUAwQj4GuAaD27M36G/G1cs+x1PLtviJIZAcZj/QelDRRKeBWVqMbnK4qinOOoYJlhHDJwHhHuFnHz1AwfeSNKhaCMJR9LrSL7f4Z2wOB2GNnLeeUtWE4NSkmoFQm5YzyyfYSP/WCQR3eN0hDz87evaeXPXxaiKbyedSWTz1bfQj8NtBujrArfxVJnN1lSEG+EaLOXSLuA9f0VRvwt0HYeRfMpwm6RoFvEwZLrCyV1IaGiKIpyCBUsMwnH9rwpBfnaC3fLmkls4zj/qSsFyPbB0Dbo2wq5LihmwK7iq5XxU6U85vL9R7v4+1/uYf9wkXkNIf6ls4M/uMhP1FcV427NpTPyLJeV11PCx6KGZvAFebo/zqjRwAVzroCGuTLxk+zgH1aEAAPKWVL2CLbhY8xIULC8CaGXIFRu+dZjbOnNsqItccrnUBRFUaYXKlhmAtXS4ah88BJi49zynfUMjVXoGi1SsZ1Dh1/z2ftYdcNcOmeXYGCrfGR7JQrfdrwwtjGcUoYfPdXL257qpy9TZmlrhC+/rZk/WxnEZzgyuVMzIOgH1w+BELmyQ40gxJog3sbWtMFAcDavWv46iLd6qbSuTPxUi2D5yZlJClaU6ng7SasqiqIoyjHMeMEyY3f9OLaYUmulw9WUQFTaKF4mydBYhb3DeZxjVkd2p0us/tk2WLydzuhWcCrgcGiZ4cGhDF++dy9f//UBChWb1yyv4/vvbOGGuS6G7SAHmxAMgmEdnjAKhMnlbUaNRi6YfxU0LGR9b5GcmZDgN9sRf0y1CJa3IDGUIOMbPt2vnqIoinKWMeMFy2RzRgXQBOFuUk1JyOdjKhNdo8XniZVxirbB2t2z6Txvo2xCrpXYfGCIz/1qNz94vAfHhdesSPKhq8O8bo5PjrFNsPzg84Hh8yL0IxDwtianZrMpa9Hv6+Cm5a+HaD0jDz+H4dqQH5br9oUg1uJlvejmZEVRFOXEUMFyNmDXoJo/JtwtJlWN4wWnue5RbaCJ6KlGcCsZfrO5lzV37eX25waIBCz+9OUtfOSKELFwEQdb2j2+AJh+8AdkO7MvdDhGPzUHGhdCwyKe7crIaHKiDZwqCSdNwC0DC+W2YHzirBdFURRFeQFUsExXTjbcbRzHgUoORvYRN8vknOMfm3LSXP13D/DbPWkaY34+/bp2/uyyAA3BGtgVehwTF79XSQl6QiUgQiVcD/XzoGkhn/pNlaH9LXz1Dy4h43sWn1uTrBdbqkAjViOk5k48Rs3hatV49UpRFEVRjkUFy3SjVvEMtKXD4W7BuFdNeYHQNLsKxTQM7Yb+Z6F/O+/223y3fAMV/BM8Tokdd3yb1myZr79tFu9baRGxHDmP64NQhGqhQg3L29cTlIC5aBPUL4DGBdC4FJIddD29jSoWuC519gh+twLGIkh0MOhLy4TSccSKoiiKopwIKlheAuvWd7P+QJqK7XDNZ+9n1U1L6by44+RP5DhQG6+mVL1qyguEux1JtQRjgzLp0/uc7Pgp58D0cYGZ493+h/l57TKG3TiG6+BgYmcHSW3+MWsv28tblzRjuTYYLhh+CPkBC0wfBSwcQhCrE99J/UJoWgR1CyHVISZfK4Dh2tQ7GRjrw8Vk2PI2LFu+449TTzEr2hIzz2itKIpyDqOC5RRZt76b1bdtPOQT6U4XWX3bRoATFy1HhLvhumJoDSVePDDNdWVCKNMN/Zug5znIHoRqWaoggSTgEKWPS/IPs/OxH3LbMwMUKzZXL0zwD6+u57qbahiOK+ZZX8ibLPJL28cyWVe5mH8sXcsgSToOVll1sUPnivmeYTYqfpbKGIwNUueMUjKCkJhFvy8jW53P4u3JiqIoyvRD31VOkbV3badYtY+6rVi1WXvX9hcWLIfC3Yrg1A6Fu8k48gStm2PvW8rC8B7o3QADmyE3ID/zRyCSFN9IaZSNe3r43G1b+NWmIXBdXnt+HX9xdZhLW6HJV/OmfDwzLZZM/ljy/brSxaweuYGi98+juxRg9ZMGtMXpvCgl11Dqk2pQMMqg2UzJDEOkTsSKoiiKokwyKlhOkZ508aRuf164mxWQCHpf6MWD0moVKI5Av9f2GdoOxaycI5gEfxDKY7jZbh7c2MWaX+3mzk2DhP0m73pZHX9xZYSFKRubGuVxE63pBzMIlnVIqBCKQd0s1j53PUX36H8axZrL2nv30jm3JkIrGJNE2mCcotV/kq+eoiiKopwcKlhOkfZUmO4JxEl7Knz4G8cWkVItHjfc7QWpFiHbA31boOdZyOyTto8/LCPCGFDOYI/08b+P72HNr3bz5N4MTfEA//C6dt58AbSEHRwqlAngEMDGD/4Y+PyHqyyhhIwmN6+AxsX0/LYy4eX0ZKvy2JF6CMR0c7KiKIpy2lDBcoqsumkpq2/beFRbKOy3WPWaJYdbPicQ7vY8HAfKYzCyF/o2Qf8G2fWDC/64pMMa0hoqpgf43oO7+Pzde9k9UGBRc5hvvn02v7fSImw69FVrlPDjYGEToIqFjQGBsFRVIvWQmgcty6BhCdTNglCS9uRzdGfKz7u09mQQ6uZOSXT+pBmYFUVRlBmJCpZTZPzN9OM/3UDFduhIhWQ/z2I/f/Kv9+FgcusfvuKFw92OxK5JbP3gds+fsk3aQIYfAimpgtSKUBpgeHCAr9+zk6/cv5/BXIWXzY3zz++dS+dSE8utgWmAFaFYrQB+Spg4+DAxqRGQ6kzdImhZCg2LoG724SA6YNW1zay+8yDF2uGY3LDfZNVrl0+ZWHnJBmZFURRlRqOC5SXQeVE7//P4DoJumW+/e4U3jhwgZ8apGQHJT3kxahXI9cLgNuhaD+k9UClKbH28DaywpNxmutjX3cMXf7WTbz/cRaFic/N59Xz8FUmuneVK/L1pyvFGAHx+ikUfYOAiAXCjZoIuczYrz3s1NC2GWKv4VvwRSdDND0E5S+dCC143i7++4yBl26UjFT7liseJjBafsoFZURRFOWdQwXIKWG7t0LbhqJPHxjoq3K1mvEh2iutCpQCj+6B3E/RtENHi2BCIQ6oJ8EE5DZlent19kLW/3MGPn+rDMOB3L23kY9ckuKDRBmzPQBuSNo/lAyMEPguXMWoEyJlJDlhz6Q3NY9DXxi2LrvMmk6KycTnXL+m4hilR+9EGOlvD/HBTHpj6vUknbWBWFEVRzjlUsJwoXrhb3E7jwxtN9gXJmglswy8tlRM5R3EUhnZA32aZ+ikMiuAIJKXtY5hQGMQtDnH/s12suWMHd28eIha0+MvrWvjwlRFmRx2gJgLF8LJTTD9YITABywAryLAZZa9/If3BOQz6WslZ9ZTNEESbwS6JSKrkZONyuA4iDS95IeGpiJsTMjAriqIo5zQqWF6MWkVaMuPhbkDBiMqbvmmKWHkx7JrX9tkO3c/C6G4o5yXkLd4iVZVaFcYGqOWH+J/f7mXNHTt55kCWlkSAz7xhFn9yWZBUwGubmEERGT4/GEFvt5ArYsUfgngHtJ7HY2MG/b428laCshmmRJCAW4FMl4S+mT4IN0C04YwuJDyugfmmpSd9LjXvKoqizExUsEyE4xwxjlwTb4oX7pazUnLMiYz0VoqQ3u9N+2wRoWBXIZCA5FzJYKkWIN1FITPMvz2wk8/ftZu9Q0WWNEe49R2zee+FfkKmC5hgeVNGPp9UU6wgmA5ggC8CiXbouBCalrOur45/Gx6gZEPAMliYrLE4kibolqDWBrFmqapMg83Jzzcwn5pnRs27iqIoM5cZLVhO+q/tWlki748MdwslRayc6HSM6xKuZWmt9cCzW6SqUhgELPGHBGIS0lbMwlgvQwMDfO3ubXzl/n0Mj1W5cl6cL7xhDm9a4sPEkUqKFZBdP76Q+E4MC3C82yJQNx86LhIjbXIu63ZXWH3vfkpewaJiu+wYrRF1oS5eJ5NB02wZYefFHfzwiQPAqXtm1LyrKIoyc5mxguWE/9o+iXC3iQTQUefJ9sLgVl6Z/xXt1W7YF5X2TawVAhFwTBlVTo+yt6ubL9y5je88fJBi1eGN59fz8VcmuabDxDBcT6iEvcmfoAgV0wK3CtjyfeNiaLtQPqe80eRAlLUPPEax6hx17bZrsDEb4aJU3bQTK5OFmncVRVFmLjNWsLzgX9sXtR+9eBBeNNzteAJoVtzHisgwbOqWakrmIHOr3eSNCMRnyaLAWhXyw5BP88yO/ay5fRv//XQflmnwnpc18bGXJ1hRDxiIKDH9nlAJiL/FNGWax61KeFzzUmi/CBoWQtJ7jEBUhE05Q0+6NOFrUrbdCW+fKah5V1EUZeYyYwXLC/61PTYguSOmdTgw7UXC3Y4ngPrTeT5YuxN2+AETwkn6jDYc05JzZg7iFtPc8/Ru1t6xk3u3DRMPWXz0hjY+fGWUjqjhTfYEwEWqOv6QxOebpmf2tSGchKYVnlBZ4O3x8YSK6YNiBgoHoFqkPe6jO1d73nMIWJMfpT+dTK6Tad5VFEVRphczVrAc96/thF/Ggf2RkxrhPZ4AyjpBYk4eoktE/LhgOYPUOWPUBkf5ycM7WHPHDp47OEZbMsCaN83iA5dESAaR9pPPFKFi+kR8BBLSXrJL4BqyILFtpQiV+vkyVeQPe7t8LCimoTAkLS1/BFJzWPXaBlav23zUG7dpwOy6ya00TDeT62SZdxVFUZTpx4wVLKtevZDV67Yc5eUI+01W3bRcduicDNXycasWKaPIkL9ZPCXlHBRGqSse4J5n9vKnX+pl/3CJZa0RvnvLXH73wiBBC3BNsEwRJIa3JTmQkEpKLS/qIt4MrSs9j8o8iDTJDqBAXFpWhREoDEsFxh+G1FwxCJsmnZfWg2lO+Rv3dDS5ToZ5V1EURZl+zCzB4oW7US3SuTgAN8/hr2/fR9nm1N60ixkY3gP9z7FqVobV2+dRdA6/ZH5qvCq4lUhtDNL7Gejr4at3beFf7ttPpmjz8oUJvvLmdl6/OIjpcwEDXB9YrvhUwilp/dgVqOZEqCTaof1iaF8JyTkQrRfDbiAmOTDFEfHD2BWpyNS1ivfmmDHr0/HGrSZXRVEU5XQxMwRLreIZaEvypm75IZSg84oWfrghC5zEm7bjQK4PBrbKR6YLymN0pvywFP52WzsZN0zKKPLGwDO8wtpMtn+QP7vnIP/2aA+lqsMNyxL8ydVx3rE0AYbjmWbHvSo+qZb4Q+CUJX7ftMQ823ExtF0g1ZJI3eHWj+vA2KCIFbsqQiXR7iXjTv4ywhNFTa6KoijK6WJmCJbKmFQcvHA3rBNInz2WahnSByTkbWQXZHq9cwYh3gpWkM5YgWL33UTdMSL2GAcPdvP5R/dx75ZRfJbB713ezEdfniIUrmHgShKtYwMGBEIQaZRrrOWhNCxVlrr5MOsyaDsfkrMhFPe8LFEJrRsbgOKwpOUGYzK+HIidUaEyjppcFUVRlNPFzBAsoaQYWE/iTfyWbz0GwI9/b4XX9tkII/vFF+I64hWJNMteHtdLvi2maa50s2nXAX7wyAGe3JcjFjT5/asa+fSrmmmPA5jsGXOwcKTaE4hCtEVEVCUH+UHwB6BxGcy+DFpWiFAZn/jxR0So5HrFp+LYslgx1Sw/nwZCZRw1uSqKoiini5khWF5kJPl5OA6p6iBttQOw/lnIHhTDrBmAUJ2YW13EBFsuQWGUanaQHz+8mU/fvpOdA0Wa435WvbqV3700Qipo0R63REw4VcBljCg0LgTDB6U0lEalWtN+AXRcCi3LITHL86dEvcpLGbI90vpxXREqMU+oTFPU5KooiqKcDmaGYDlRqiWv7bOVawr3k7BHYahBkl+jzV4CrAvVKtQKUMkyNtjDt+/exBfu3U/XaJlFTSH+6c3tvOWCMEEL5scj3skdcCzWOa/g70vXMUycjh05ViV/TWcqK9H5sy6HpiWQ7BCBEoxJUF2tDOkuETauK96UaLOIGUVRFEVRzgHB4rqEnRwcfBoGt8HwfiiNkqoNUcYvAWyG4WWfVKBchGKG/oP7+Mpdm/n6gwcZLdS4dlGCb7xtNkvbwTRMHMDFBbcmCbORZtZVX8bq7ispei9rt51gdfoNsLRK56XzINZyeOLHFxABNXpAhArIrqFY80nlw0wlWjFRFEVRpgszV7DYNcj1say4ng67CzYHoVKQ9lEgSdosYRieP6Vmi3G3mGbnrl18/s4tfO/RXiq2y1tWNrDqumaunBcE12FXpoxBDT8OVUKydTlcD26NtdsuoOge/ZIWHYu1m0N0vmaBCBXLL1ucR/dDKSMHheukouI/85uTFUVRFGU6MvMES7UII/tgaCeM7GFleQOmUwVnHkTrZKGgU8aihuU4YmwdG+SJjdtZc+dObnt2iIBl8P4rmvmrVzSxpCkoW5EB7Bo+qqTxM0qKGo0sCoZhrA/cMj12YsJL6slWRZRUCpDphnIGifGvl4rKDF1GqCiKoiiTxcwQLI4jbZXhPTC0A9IHpXrhQM0xqVpJyTVxbAmWc6rE7CyxyjB33D3Aml/t4cGdGVJhH598TQcfuqaBltj4S2NL9gkmhOL0jDVykCAJinTQD/kwRBug+QLah6A7//zLa08GYWSPGHsxZbw52qRCRVEURVFOkJkhWLqehP4NkOsXXwiGJMj6AhSsMfzUpPLiVqGYpTLaxXNPPsN/PtrDrsESs+uCfPEtc/jDKxuJB00xvro1aSthyth0sgOCcdyhPcyjHwvImfWw6AYZT25cwKpkldV37D96HYDPYNWVEamuRJtFrPhePCfm0Nj1OeQjOZeeq6IoinJyzAzBMrBdpmwwJD/FDIDpQrVCwK1gOTUY6yfbv49/vXcrX7z3AN3pCoubQ/xT5yzecH6KC5q8dNZqBfCESqQeEh0QSEJpAEb2kGSMA6TYzCLy8ZVcfcnrZdonEKXzygiEkvz1T5+lbLt0xH2suqZODLfRxpMfv1YURVEUBZgpgiUY9SZvglCrgVMCB6jmqa8MYGe6Wf3NXr7xUDeZos31S5L8zevbuXZhFNMAF1sqM64NZhAiLSJUfGEoDEC+F7Ag0c4T6WYeLHXQQzPzgk2QaJMRZdeFcpbOWXkeax3BNkw+975XqVBRFEVRlElgZggWMwCY0nZxkETZsQG279zJd9Zt4ecbhrEdl7dd1MiqG9p42YI4WwdyWNTwU8XAASsi7Zpku0zy5AYgcwBMHyTnw6xLoOMSnhweZE+pSoEQ9VYKfCEopmGsX9pOVoBhq5ExMwHxljP7uiiKoijKDGFmCJZaScLXSlnI9vLYxh2suXMXP9swTMAyeMtFDfzjzbNZ1BKVEeZamTBFwKFKgDQNzGtbDpYlSbPlnIiWpsXQfgnMuggScyAQJuO3SVMEXKJ2Vky+1aJksSQ7INxAzsqc4RdEURRFUWYWM0OwjPXi9Gzil49vZc1de/nN7ix1ER//97VzuOG8OI1RP4vqQzIhVJWNziWCjNBAmgYsw/YqJFlpCTUtk4rKrEtl8WEgfChDxcZHgiwNZGiyg+DOlV1AkfpptefnXEbNu4qiKDOPGSFY/vPH/8tnfvo4W/uKzKkL8uW3L+QPrmojFjLYMpAlSBGqNXANib2Pt7NzPwSp0ugMEnELQAu0XCAipeMSiDd725+jYPnEo5IfoqO6n4Faha21JjZ0+/jNd7tZdVOczounn1jRN25FURRlpjAjBMuvN/US8Jn84PeX8Y7LWvAbjmSuFAusSNjgBmU0OdEB0XqoFGlxdhOhQNkMsc9YyPkXvxlaL4R4o2xMDkTFLOsJFcYGwK6wvxBgby0OiEDpTpdYfdtGAN1SrCiKoihTxIwQLF/5wI1EMtswXEcmhEolcMpghSDYBKl2admUcpA9AOU8lgn7rMXsCK5gf2Ahb1h8g7c1OQKmKYIn1w+FIQmOC0Qh2UGfOwIUj3r8YtVm7V3bVbAoiqIoyhQxIwRL1KpBLS/+FLsmywOjs8RbEopDOQ3Du8WYG0jA7Mu5L5egK7CYsi9KyQhLRP74EsRcP+QHwalJrktyjpwH6EkXJ7yG492uKIqiKMpLZ0YIFoqDUB6T3JR4OyRmi2ippGF4JzgV2YTcdiHMvpx16bl8d7SHkg0By2B2nbcEMTcg7R/XFqESb5FQuCNoT4XpnkCctKfCp+e5TkPUK6MoiqJMNTNDsMTapG2T7JA2UHEUsl0SxR9ugKYlMOdKaFnOuh1lVt+9l5Itd63YDnuHx1j34BN0Lg2L1yXWAoHIhA+16qal/NVPnsVxD98W9lusumnpaXiiiqIoinJuMjMEy6xLJeCtOAL5/bJdOdoIDUtg3pXQuBxCkoa79oHfHLXrB8BxDdY+lqXz5RfJZNAL0HlxB1+5byd7hvK4QEcqzKqblqp/RVEURVGmkJkhWEb3w8hOcFwZR25aDPOuhrrFEI4dylCBF/CgZGsvKlbGaYwHGRgrs6Itoe0QRVEURTkNzAzBYlch1grNy6T107RYxEcgJhkqIIbcsX7a4xbdOft5pziXPSiKoiiKMt2ZGYLlgrdIPH/jIhk/Hs9QAU+o9Mm+H8Nk1XXtrP5V91FtIdPghD0o69Z3s/5AmortsP5AmnXru7UdpCiKoihTjHkmHtQwjNcahrHdMIxdhmF84qWe7/d+Ocb77qqIWTaUELFSLcLIXhjcJjuGok3QtIzOa1bymbeuJGDJUw9YJvMboickOtat72b1bRup2CJ2KrbD6ts2sm5990t9Cs97nPX/f3v3F1r3Wcdx/P1t0rVp15m5dLVNO1ZkVMOGROpYVxDZhG4otuxCNlCHCO7C6RTpWL3xRlihInoxhDGnA2c7qWUWGVZZBS+EbnWVbe0Mbq22TVfXrtaVmq3/vl6ck6yJLknXJM9zOO/XTc556PnlCw9NPnme7+/5HTzJrgMnWL1x55RfX5KkVjPjgSUiOoBHgDuBPuCeiOi7nGu+M2sub89qHvj2zml4cz8cG2jc6nzltXBtX+MOos4rgEbjbP913Xy4Zz4Arx0/PalgsGnHAENnR28nDR8aN1XGhqLBk0PTEookSWolJbaEbgZezcz9ABGxBVgL7Luci8658B84/hqcOQXR0ehpmd/zbg/LGMdPvcOBN0+P3J48HAzgvY/Yn+yhcZfTiDteKHLrSZLUrkpsCfUChy56f7g5NkpEfDUidkfE7mPHjo17wZ5zR1l87gicfxsWLIZFfXDVh94zrAAc+tfQqLNUYOLVkvdqzJ3Khl1P0pUk6X8V6WGZjMx8NDNXZubKhQsXjvtvh2bN48Ssa2DhRxun0w433I5jeMtlrPGCwfo1K+iaPfraU31o3EyEIkmSWk2JwDIILLvo/dLm2Pt2etZVvNV5daOHZZKGm27HGi8YrOvv5eG7bhrVsPvwXTdN6VbNTIQiSZJaTYnA8jxwQ0Qsj4grgLuB7TNdxLKru5gVo8cmEwyGG3YXzO2k/7ruKe8rGRuKeru7pjwUSZLUama86TYzz0XE/cAOoAN4PDP3znQdPQvmAI1eljPnL1R1xP66/l42P3cQ8MGCkiRBoYPjMvMZ4JkS3/tiPQvmjAQXg4EkSfWqtulWkiRpmIFFkiRVz8AiSZKqZ2CRJEnVM7C8T32Lr7JRV5KkGWJgkSRJ1TOwSJKk6hlYJElS9Vo+sDy9Z5A9B0+y68AJVm/cydN7JvdYoqfuW2UPiiRJLaKlA8vTewbZsO2lkScvD54cYsO2lyYdWiRJUmto6cCyaccAQ2fPjxobOnueTTsGClUkSZKmQ0sHliMnhy5pXJIktaYiDz+cKku6uxj8P+FkSXfXtH5fe18kSZpZLb3Csn7NCrpmd4wa65rdwfo1KwpVJEmSpkNLr7Cs6+8F4MGtL3Lm/AV6u7tYv2bFyHgrcxVHkqR3tXRggUZo2fzcQeD9/ZI3GEiSVL+W3hKSJEntwcAiSZKqZ2CRJEnVM7BIkqTqGVgkSVL1DCySJKl6BhZJklQ9A4skSaqegUWSJFXPwCJJkqpnYJEkSdUzsEiSpOoZWCRJUvUMLJIkqXoGFkmSVL3O0gVMhafuW1W6BEmSNI1cYZEkSdUzsEiSpOoZWCRJUvUMLJIkqXoGFkmSVD0DiyRJqp6BRZIkVc/AIkmSqmdgkSRJ1TOwSJKk6hlYJElS9QwskiSpegYWSZJUPQOLJEmqXmRm6RomFBGngIHSdWhED3C8dBEaxTmpi/NRH+ekLisyc8GlfKBzuiqZYgOZubJ0EWqIiN3OR12ck7o4H/VxTuoSEbsv9TNuCUmSpOoZWCRJUvVaJbA8WroAjeJ81Mc5qYvzUR/npC6XPB8t0XQrSZLaW6ussEiSpDZWdWCJiDsiYiAiXo2Ih0rX0+4iYllE/CEi9kXE3oh4oHRNgojoiIg9EfGb0rUIIqI7IrZGxF8j4pWIWFW6pnYWEd9q/rx6OSI2R8Tc0jW1m4h4PCLeiIiXLxr7YET8PiL+1vx69UTXqTawREQH8AhwJ9AH3BMRfWWranvngG9nZh9wC/A156QKDwCvlC5CI34E/DYzPwJ8DOemmIjoBb4BrMzMG4EO4O6yVbWlnwF3jBl7CHg2M28Anm2+H1e1gQW4GXg1M/dn5hlgC7C2cE1tLTNfz8wXmq9P0fhB3Fu2qvYWEUuBzwCPla5FEBEfAD4J/AQgM89k5smiRakT6IqITmAecKRwPW0nM/8InBgzvBZ4ovn6CWDdRNepObD0Aocuen8YfzlWIyKuB/qBXYVLaXc/BB4ELhSuQw3LgWPAT5vbdI9FxPzSRbWrzBwEvg8cBF4H/p2ZvytblZoWZebrzddHgUUTfaDmwKJKRcSVwK+Ab2bmW6XraVcR8Vngjcz8c+laNKIT+Djw48zsB04ziaVuTY9mX8RaGkFyCTA/Ir5QtiqNlY3blSe8ZbnmwDIILLvo/dLmmAqKiNk0wsqTmbmtdD1tbjXwuYj4O40t09si4udlS2p7h4HDmTm88riVRoBRGZ8GDmTmscw8C2wDbi1ckxr+GRGLAZpf35joAzUHlueBGyJieURcQaNRanvhmtpaRASNvflXMvMHpetpd5m5ITOXZub1NP5/7MxM/3osKDOPAociYkVz6HZgX8GS2t1B4JaImNf8+XU7NkHXYjtwb/P1vcCvJ/pAtQ8/zMxzEXE/sINGZ/fjmbm3cFntbjXwReCliPhLc+w7mflMuZKk6nwdeLL5h9Z+4MuF62lbmbkrIrYCL9C4y3EPnng74yJiM/ApoCciDgPfBTYCv4yIrwD/AD4/4XU86VaSJNWu5i0hSZIkwMAiSZJagIFFkiRVz8AiSZKqZ2CRJEnVM7BIkqTqGVgkSVL1DCySioqIT0TEixExNyLmR8TeiLixdF2S6uLBcZKKi4jvAXOBLhrP4nm4cEmSKmNgkVRc8xj754G3gVsz83zhkiRVxi0hSTW4BrgSWEBjpUWSRnGFRVJxEbEd2AIsBxZn5v2FS5JUmWqf1iypPUTEl4CzmfmLiOgA/hQRt2XmztK1SaqHKyySJKl69rBIkqTqGVgkSVL1DCySJKl6BhZJklQ9A4skSaqegUWSJFXPwCJJkqpnYJEkSdX7L4tftUOQSEaGAAAAAElFTkSuQmCC\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "inds = np.random.randint(len(chain), size=100)\n", "plt.figure(figsize=(9,6))\n", "for ind in inds:\n", " sample = chain[ind]\n", " plt.plot(x, np.dot(np.vander(x, 2), sample[:2]), \"C1\", alpha=0.1)\n", "plt.errorbar(x, data, yerr=sigma, fmt=\"o\")\n", "plt.plot(x, straight_line(x,m_true,c_true), 'k', label=\"truth\")\n", "plt.legend(fontsize=14)\n", "plt.xlim(0, 10)\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally we will print the *maximum a posteriori (MAP)* estimate along with the *1-sigma* uncertainty for the model parameters:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "", "text/latex": "$\\displaystyle \\mathrm{m} = 3.467_{-0.138}^{0.122}$" }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": "", "text/latex": "$\\displaystyle \\mathrm{c} = 1.032_{-0.643}^{0.672}$" }, "metadata": {} } ], "source": [ "labels=['m','c']\n", "for i in range(ndim):\n", " mcmc = np.percentile(chain[:, i], [16, 50, 84])\n", " q = np.diff(mcmc)\n", " txt = \"\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}\"\n", " txt = txt.format(mcmc[1], q[0], q[1], labels[i])\n", " display(Math(txt))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6-final" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: docs/notebooks/multimodal.ipynb ================================================ [File too large to display: 50.6 MB] ================================================ FILE: docs/notebooks/multiprocessing.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallelizing sampling using multiprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to use the multiprocessing Pool to parallelize and accelerate sampling.\n", "\n", "This approach is ideal for personal computers, laptops, or small clusters and should work even in Jupyter notebooks. \n", "\n", "In order to simulate a computationaly expensive log probability density function we will use the time package." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import zeus\n", "import numpy as np\n", "import time\n", "from multiprocessing import Pool" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define an uncorrelated normal distribution as our target distribution." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ndim = 5\n", "nwalkers = 2 * ndim\n", "nsteps = 100\n", "\n", "def log_prob(x):\n", " time.sleep(0.003)\n", " return -0.5 * np.sum(x**2.0)\n", "\n", "start = np.random.randn(nwalkers, ndim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first run the sampler without in serial, without multiprocessing:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initialising ensemble of 10 walkers...\n", "Sampling progress : 100%|██████████| 100/100 [00:19<00:00, 5.18it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Serial took 19.3 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "t0 = time.time()\n", "\n", "sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n", "sampler.run_mcmc(start, nsteps)\n", "\n", "print(\"Serial took {0:.1f} seconds\".format(time.time()-t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then run the sampler with multiprocessing:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initialising ensemble of 10 walkers...\n", "Sampling progress : 100%|██████████| 100/100 [00:07<00:00, 12.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multiprocessing took 7.8 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "t0 = time.time()\n", "\n", "with Pool() as pool:\n", " sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=pool)\n", " sampler.run_mcmc(start, nsteps)\n", "\n", "print(\"Multiprocessing took {0:.1f} seconds\".format(time.time()-t0))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: docs/notebooks/normal_distribution.ipynb ================================================ [File too large to display: 36.6 MB] ================================================ FILE: docs/notebooks/progress.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Incrementally saving progress to a file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In many cases it is useful to save the chain to a file. This makes iit easier to post-process a long chain and makes things less disastrous if the computer crashes somewhere in the midle of an expensive MCMC run.\n", "\n", "In this recipe we are going to use the callback interface to save the samples and their corresponding log-probability values in a `.h5` file. To do this you need to have [``h5py``](https://docs.h5py.org/en/latest/build.html#pre-built-installation-recommended) installed.\n", "\n", "We will set up a simple problem of sampling from a normal/Gaussian distribution as an example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import zeus\n", "import numpy as np\n", "\n", "ndim = 2\n", "nwalkers = 10\n", "nsteps = 1000\n", "\n", "def log_prob(x):\n", " return -0.5*np.dot(x,x)\n", "\n", "x0 = 1e-3 * np.random.randn(nwalkers, ndim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where ``x0`` is the initial positions of the walkers.\n", "\n", "We will then initialise the sampler and start the MCMC run by providing the ``zeus.callbacks.SaveProgressCallback`` callback function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Initialising ensemble of 10 walkers...\n", "Sampling progress : 100%|██████████| 1000/1000 [00:01<00:00, 656.62it/s]\n" ] } ], "source": [ "sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n", "sampler.run_mcmc(x0, nsteps, callbacks=zeus.callbacks.SaveProgressCallback(\"saved_chains.h5\", ncheck=100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above piece of code saved the chain incrementally every ``ncheck=100`` steps to a file named ``saved_chains.h5``. We can now access the chains using the ``h5py`` package as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 10, 2)\n", "(1000, 10)\n" ] } ], "source": [ "import h5py \n", "\n", "with h5py.File('saved_chains.h5', \"r\") as hf:\n", " samples = np.copy(hf['samples'])\n", " logprob_samples = np.copy(hf['logprob'])\n", "\n", "print(samples.shape)\n", "print(logprob_samples.shape)" ] } ], "metadata": { "interpreter": { "hash": "42ef9c41c9809f9bfe38b73fa705c16bbb3d6fadc6a1917ff578a20446617baf" }, "kernelspec": { "display_name": "Python 3.7.10 64-bit ('nbodykit-env': conda)", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: docs/requirements.txt ================================================ sphinx_bootstrap_theme nbsphinx IPython docutils==0.17.1 ================================================ FILE: requirements.txt ================================================ numpy scipy>=1.5.0 tqdm setuptools pytest matplotlib seaborn scikit-learn ================================================ FILE: setup.cfg ================================================ [metadata] name = zeus-mcmc version = attr: zeus._version.version author = Minas Karamanis author_email = minaskar@gmail.com url = https://github.com/minaskar/zeus description = zeus: Lightning Fast MCMC long_description = file: README.md long_description_content_type = text/markdown license = GPLv3 license_file = LICENCE platform = any classifiers = Programming Language :: Python :: 3 License :: OSI Approved :: GNU General Public License v3 (GPLv3) Operating System :: OS Independent Intended Audience :: Science/Research Topic :: Scientific/Engineering Topic :: Scientific/Engineering :: Mathematics [options] zip_safe = false include_package_data = true python_requires = >= 3.7 packages = zeus test_suite = tests setup_requires = setuptools >=46.4.0 install_requires = numpy scipy tqdm setuptools pytest matplotlib seaborn scikit-learn ================================================ FILE: setup.py ================================================ import setuptools setuptools.setup() ================================================ FILE: tests/test_autocorr.py ================================================ import numpy as np import pytest from zeus.autocorr import _autocorr_time_1d def get_chain(seed=42, ndim=5, N=100000): np.random.seed(seed) a = 0.9 x = np.empty((N, ndim)) x[0] = np.zeros(ndim) for i in range(1, N): x[i] = x[i - 1] * a + np.random.rand(ndim) return x def test_1d(seed=42): walker0 = get_chain(seed, ndim=1) walker1 = get_chain(seed+1, ndim=1) chain = np.hstack((walker0,walker1)).T tau = _autocorr_time_1d(chain) assert tau < 20.0 ================================================ FILE: tests/test_fwrapper.py ================================================ import numpy as np import pytest #from zeus import fwrapper from zeus.fwrapper import _FunctionWrapper def func0(x): return - 0.5 * np.sum(x**2.0) def func1(x, mu): return - 0.5 * np.sum((x-mu)**2.0) def func2(x, mu, ivar): return - 0.5 * np.sum(ivar*(x-mu)**2.0) def test_none(func=func0,seed=42): np.random.seed(seed) args = None kwargs = None wrapped = _FunctionWrapper(func, args, kwargs) ndim = np.random.randint(2,200) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x)) def test_args1(func=func1,seed=42): np.random.seed(seed) ndim = np.random.randint(2,200) mu = np.random.rand(ndim) args = [mu] kwargs = None wrapped = _FunctionWrapper(func, args, kwargs) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x,mu)) def test_args2(func=func2,seed=42): np.random.seed(seed) ndim = np.random.randint(2,200) mu = np.random.rand(ndim) ivar = 1.0 / np.random.rand(ndim) args = [mu, ivar] kwargs = None wrapped = _FunctionWrapper(func, args, kwargs) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x,mu,ivar)) def test_kwargs1(func=func1,seed=42): np.random.seed(seed) ndim = np.random.randint(2,200) mu = np.random.rand(ndim) args = None kwargs = {'mu' : mu} wrapped = _FunctionWrapper(func, args, kwargs) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x,mu)) def test_kwargs2(func=func2,seed=42): np.random.seed(seed) ndim = np.random.randint(2,200) mu = np.random.rand(ndim) ivar = 1.0 / np.random.rand(ndim) args = None kwargs = {'mu' : mu, 'ivar' : ivar} wrapped = _FunctionWrapper(func, args, kwargs) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x,mu,ivar)) def test_argskwargs(func=func2,seed=42): np.random.seed(seed) ndim = np.random.randint(2,200) mu = np.random.rand(ndim) ivar = 1.0 / np.random.rand(ndim) args = [mu] kwargs = {'ivar' : ivar} wrapped = _FunctionWrapper(func, args, kwargs) x = np.random.rand(ndim) assert np.allclose(wrapped(x),func(x,mu,ivar)) ================================================ FILE: tests/test_sampler.py ================================================ import numpy as np import pytest import zeus def logp(x): return -0.5 * np.sum((x-1.0)**2.0) def test_mean(logp=logp,seed=42): np.random.seed(seed) ndim = np.random.randint(2,5) nwalkers = 2 * ndim nsteps = np.random.randint(3000,5000) sampler = zeus.EnsembleSampler(nwalkers,ndim,logp,verbose=False) start = np.random.rand(nwalkers,ndim) sampler.run_mcmc(start,nsteps) assert np.all(np.abs(np.mean(sampler.get_chain(flat=True),axis=0)-1.0) < 0.1) assert np.all(np.isfinite(sampler.get_log_prob(flat=True))) assert np.all(np.isfinite(sampler.get_log_prob())) def test_std(logp=logp,seed=42): np.random.seed(seed) ndim = np.random.randint(2,5) nwalkers = 2 * ndim nsteps = np.random.randint(3000,5000) sampler = zeus.EnsembleSampler(nwalkers,ndim,logp,verbose=False) start = np.random.rand(nwalkers,ndim) sampler.run_mcmc(start,nsteps) assert np.all(np.abs(np.std(sampler.get_chain(flat=True),axis=0)-1.0) < 0.1) def test_ncall(seed=42): np.random.seed(seed) def loglike(theta): assert len(theta) == 5 a = theta[:-1] b = theta[1:] loglike.ncalls += 1 return -2 * (100 * (b - a**2)**2 + (1 - a)**2).sum() loglike.ncalls = 0 ndim = 5 nsteps = 100 nwalkers = 2 * ndim sampler = zeus.EnsembleSampler(nwalkers,ndim,loglike,verbose=False) start = np.random.rand(nwalkers,ndim) sampler.run_mcmc(start,nsteps) assert loglike.ncalls == sampler.ncall + nwalkers ================================================ FILE: tests/test_samples.py ================================================ import pytest import numpy as np from zeus import samples def test_chain(seed=42): np.random.seed(seed) nsteps =np.random.randint(200,400) ndim = np.random.randint(2,200) nwalkers = 2 * ndim s = samples(ndim, nwalkers) s.extend(nsteps, None) for i in range(nsteps): x = np.random.rand(nwalkers,ndim) z = np.random.rand(nwalkers) s.save(x, z, None) assert np.shape(s.chain) == (nsteps,nwalkers,ndim) assert np.shape(s.logprob) == (nsteps,nwalkers) def test_flatten(seed=42): np.random.seed(seed) nsteps =np.random.randint(200,400) ndim = np.random.randint(2,200) nwalkers = 2 * ndim s = samples(ndim,nwalkers) s.extend(nsteps, None) for i in range(nsteps): x = np.random.rand(nwalkers,ndim) z = np.random.rand(nwalkers) s.save(x, z, None) assert np.shape(s.flatten()) == (nsteps*nwalkers,ndim) burn = np.random.randint(2,100) thin = np.random.randint(1,10) assert np.shape(s.flatten(burn,thin)) == (np.ceil((nsteps-burn)/thin)*nwalkers,ndim) assert np.shape(s.flatten_logprob(burn,thin)) == (np.ceil((nsteps-burn)/thin)*nwalkers) def test_multiple(): for seed in range(10): test_chain(seed=seed) test_flatten(seed=seed) ================================================ FILE: zeus/__init__.py ================================================ __bibtex__ = """ @article{karamanis2021zeus, title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference}, author={Karamanis, Minas and Beutler, Florian and Peacock, John A}, journal={arXiv preprint arXiv:2105.03468}, year={2021} } @article{karamanis2020ensemble, title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions}, author = {Karamanis, Minas and Beutler, Florian}, journal = {arXiv preprint arXiv: 2002.06212}, year = {2020} } """ from ._version import version __version__ = version __url__ = "https://zeus-mcmc.readthedocs.io" __author__ = "Minas Karamanis" __email__ = "minaskar@gmail.com" __license__ = "GPL-3.0" __description__ = "Lightning Fast MCMC" from .ensemble import * from .parallel import ChainManager from .autocorr import AutoCorrTime from .plotting import cornerplot from . import moves, callbacks ================================================ FILE: zeus/_version.py ================================================ version = "2.5.4" ================================================ FILE: zeus/autocorr.py ================================================ import numpy as np from scipy.fft import fft, ifft def _autocorr_func_1d(x, norm=True): """ Autocorrelation Function of 1-dimensional chain. Args: x (array) : 1-dimensional chain. norm (bool) : By default norm=True and the autocorrelation function will be normalized. Returns: The (normalised if norm=True) autocorrelation function of the chain x. """ x = np.atleast_1d(x) if len(x.shape) != 1: raise ValueError("invalid dimensions for 1D autocorrelation function") # Next largest power of 2 n = 1 while n < len(x): n = n << 1 # Compute the auto-correlation function using FFT #f = np.fft.fft(x - np.mean(x), n=2 * n) #acf = np.fft.ifft(f * np.conjugate(f))[: len(x)].real f = fft(x - np.mean(x), n=2 * n) acf = ifft(f * np.conjugate(f))[: len(x)].real acf /= 4 * n # Normalize if norm: acf /= acf[0] return acf def _autocorr_time_1d(y, c=5.0, method='mk'): """ Integrated Autocorrelation Time (IAT) for 1-dimensional chain. Args: y (array) : (nsteps, nwalkers) array for one parameter. c (float) : Truncation parameter of automated windowing procedure of Sokal (1989), default is 5.0. method (str) : Method to use to compute the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``. Returns: The IAT of the chain y. """ if method not in ['mk', 'dfm', 'gw']: raise ValueError('Please select one of the supported methods i.e. mk (Recommended), dfm, gw.') if method == 'mk': # Minas Karamanis method f = _autocorr_func_1d(y.reshape((-1), order='C')) elif method == 'dfm': # Daniel Forman-Mackey method f = np.zeros(y.shape[1]) for yy in y: f += _autocorr_func_1d(yy) f /= len(y) else: # Goodman-Weary method f = _autocorr_func_1d(np.mean(y, axis=0)) taus = 2.0 * np.cumsum(f) - 1.0 # Automated windowing procedure following Sokal (1989) def auto_window(taus, c): m = np.arange(len(taus)) < c * taus if np.any(m): return np.argmin(m) return len(taus) - 1 window = auto_window(taus, c) return taus[window] def AutoCorrTime(samples, c=5.0, method='mk'): """ Integrated Autocorrelation Time (IAT) for all the chains. Parameters ---------- samples : array 3-dimensional array of shape (nsteps, nwalkers, ndim) c : float Truncation parameter of automated windowing procedure of Sokal (1989), default is 5.0 method : str Method to use to compute the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``. Returns ------- taus : array Array with the IAT of all the chains. """ if method not in ['mk', 'dfm', 'gw']: raise ValueError('Please select one of the supported methods i.e. mk (Recommended), dfm, gw.') _, _, ndim = np.shape(samples) taus = np.empty(ndim) for i in range(ndim): taus[i] = _autocorr_time_1d(samples[:,:,i].T, c, method) return taus ================================================ FILE: zeus/callbacks.py ================================================ import numpy as np from .autocorr import AutoCorrTime try: import h5py except ImportError: h5py = None class AutocorrelationCallback: """ The Autocorrelation Time Callback class checks the integrated autocorrelation time (IAT) of the chain during the run and terminates sampling if the rate of change of IAT is below some threshold and the length of the chain is greater than some multiple of the IAT estimate. Args: ncheck (int): The number of steps after which the IAT is estimated and the tests are performed. Default is ``ncheck=100``. dact (float): Threshold of the rate of change of IAT. Sampling terminates once this threshold is reached along with the other criteria. Default is ``dact=0.01``. nact (float): Minimum lenght of the chain as a mutiple of the IAT. Sampling terminates once this threshold is reached along with the other criteria. Default is ``nact=10``. discard (float): Percentage of chain to discard prior to estimating the IAT. Default is ``discard=0.5``. trigger (bool): If ``True`` (default) then terminatate sampling once converged, else just monitor statistics. method (str): Method to use for the estimation of the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``. """ def __init__(self, ncheck=100, dact=0.01, nact=10, discard=0.5, trigger=True, method='mk'): self.ncheck = ncheck self.dact = dact self.nact = nact self.discard = discard self.trigger = trigger self.method = method self.estimates = [] self.old_tau = np.inf def __call__(self, i, x, y): """ Method that calls the callback function. Args: i (int): Current iteration of the run. x (array): Numpy array containing the chain elements up to iteration i for every walker. y (array): Numpy array containing the log-probability values of all chain elements up to iteration i for every walker. Returns: True if the criteria are satisfied and sampling terminates or False if the criteria are not satisfied and sampling continues. """ converged = False if i % self.ncheck == 0: tau = np.mean(AutoCorrTime(x[int(i * self.discard):], method=self.method)) self.estimates.append(tau) # Check convergence converged = tau * self.nact < i converged &= np.abs(self.old_tau - tau) / tau < self.dact self.old_tau = tau if self.trigger: return converged else: return None class SplitRCallback: """ The Split-R Callback class checks the Gelman-Rubin criterion during the run by splitting the chain into multiple parts and terminates sampling if the Split-R coefficient is close to unity. Args: ncheck (int): The number of steps after which the Gelman-Rubin statistics is estimated and the tests are performed. Default is ``ncheck=100``. epsilon (float): Threshold of the Split-R value. Sampling terminates when ``|R-1|= self.nmin: return True else: return False class ParallelSplitRCallback: """ The Parallel Split-R Callback class extends the functionality of the Split-R Callback to more than one CPUs by checking the Gelman-Rubin criterion during the run by splitting the chain into multiple parts and combining different parts from parallel chains and terminates sampling if the Split-R coefficient is close to unity. Args: ncheck (int): The number of steps after which the Gelman-Rubin statistics is estimated and the tests are performed. Default is ``ncheck=100``. epsilon (float): Threshold of the Split-R value. Sampling terminates when ``|R-1|= 2*ndim`` and even. shuffle_ensemble (bool): If True (default) then shuffle the ensemble of walkers in every iteration before splitting it. light_mode (bool): If True (default is False) then no expansions are performed after the tuning phase. This can significantly reduce the number of log likelihood evaluations but works best in target distributions that are apprroximately Gaussian. """ def __init__(self, nwalkers, ndim, logprob_fn, args=None, kwargs=None, moves=None, tune=True, tolerance=0.05, patience=5, maxsteps=10000, mu=1.0, maxiter=10000, pool=None, vectorize=False, blobs_dtype=None, verbose=True, check_walkers=True, shuffle_ensemble=True, light_mode=False, ): # Set up logger self.logger = logging.getLogger() for handler in self.logger.handlers[:]: self.logger.removeHandler(handler) handler = logging.StreamHandler() self.logger.addHandler(handler) if verbose: self.logger.setLevel(logging.INFO) else: self.logger.setLevel(logging.WARNING) # Parse the move schedule if moves is None: self._moves = [DifferentialMove()] self._weights = [1.0] elif isinstance(moves, Iterable): try: self._moves, self._weights = zip(*moves) except TypeError: self._moves = moves self._weights = np.ones(len(moves)) else: self._moves = [moves] self._weights = [1.0] self._weights = np.atleast_1d(self._weights).astype(float) self._weights /= np.sum(self._weights) # Set up Log Probability self.logprob_fn = _FunctionWrapper(logprob_fn, args, kwargs) # Set up walkers self.nwalkers = int(nwalkers) self.ndim = int(ndim) self.check_walkers = check_walkers if self.check_walkers: if self.nwalkers < 2 * self.ndim: raise ValueError("Please provide at least (2 * ndim) walkers.") elif self.nwalkers % 2 == 1: raise ValueError("Please provide an even number of walkers.") self.shuffle_ensemble = shuffle_ensemble # Set up Slice parameters self.mu = mu self.mus = [] self.mus.append(self.mu) self.tune = tune self.maxsteps = maxsteps self.patience = patience self.tolerance = tolerance self.nexps = [] self.ncons = [] # Set up maximum number of Expansions/Contractions self.maxiter = maxiter # Set up pool of workers self.pool = pool self.vectorize = vectorize # Set up blobs dtype self.blobs_dtype = blobs_dtype # Initialise Saving space for samples self.samples = samples(self.ndim, self.nwalkers) # Initialise iteration counter and state self.iteration = 0 self.state_X = None self.state_Z = None self.state_blobs = None # Light mode self.light_mode = light_mode def run(self, *args, **kwargs): logging.warning('The run method has been deprecated and it will be removed. Please use the new run_mcmc method.') return self.run_mcmc(*args, **kwargs) def reset(self): """ Reset the state of the sampler. Delete any samples stored in memory. """ self.samples = samples(self.ndim, self.nwalkers) def get_chain(self, flat=False, thin=1, discard=0): """ Get the Markov chain containing the samples. Args: flat (bool) : If True then flatten the chain into a 2D array by combining all walkers (default is False). thin (int) : Thinning parameter (the default value is 1). discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between 0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin. Returns: Array object containg the Markov chain samples (2D if flat=True, 3D if flat=False). """ if discard < 1.0: discard = int(discard * np.shape(self.chain)[0]) if flat: return self.samples.flatten(discard=discard, thin=thin) else: return self.chain[discard::thin,:,:] def get_log_prob(self, flat=False, thin=1, discard=0): """ Get the value of the log probability function evalutated at the samples of the Markov chain. Args: flat (bool) : If True then flatten the chain into a 1D array by combining all walkers (default is False). thin (int) : Thinning parameter (the default value is 1). discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between 0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin. Returns: Array containing the value of the log probability at the samples of the Markov chain (1D if flat=True, 2D otherwise). """ if discard < 1.0: discard = int(discard * np.shape(self.chain)[0]) if flat: return self.samples.flatten_logprob(discard=discard, thin=thin) else: return self.samples.logprob[discard::thin,:] def get_blobs(self, flat=False, thin=1, discard=0): """ Get the values of the blobs at each step of the chain. Args: flat (bool) : If True then flatten the chain into a 1D array by combining all walkers (default is False). thin (int) : Thinning parameter (the default value is 1). discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between 0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin. Returns: (structured) numpy array containing the values of the blobs at each step of the chain. """ if discard < 1.0: discard = int(discard * np.shape(self.chain)[0]) if flat: return self.samples.flatten_blobs(discard=discard, thin=thin) else: return self.samples.blobs[discard::thin,:] @property def chain(self): """ Returns the chains. Returns: Returns the chains of shape (nsteps, nwalkers, ndim). """ return self.samples.chain @property def act(self): """ Integrated Autocorrelation Time (IAT) of the Markov Chain. Returns: Array with the IAT of each parameter. """ return AutoCorrTime(self.chain[int(self.nsteps/(self.thin*2.0)):,:,:]) @property def ess(self): """ Effective Sampling Size (ESS) of the Markov Chain. Returns: ESS """ return self.nwalkers * self.samples.length / np.mean(self.act) @property def ncall(self): """ Number of Log Prob calls. Returns: ncall """ return np.sum(self.neval) @property def efficiency(self): """ Effective Samples per Log Probability Evaluation. Returns: efficiency """ return self.ess / self.ncall @property def scale_factor(self): """ Scale factor values during tuning. Returns: scale factor mu """ return np.asarray(self.mus) @property def get_last_sample(self): logging.warning('The ``get_last_sample`` property is deprecated and it will be removed in a future release.\n' + 'Please use the method ``get_last_sample()`` instead.') return self.chain[-1] def get_last_sample(self): """ Return the last position of the walkers. """ return self.chain[-1] def get_last_log_prob(self): """ Return the log probability values for the last position of the walkers. """ return self.samples.logprob[-1] def get_last_blobs(self): """ Return the blobs for the last position of the walkers. """ return self.samples.blobs[-1] @property def summary(self): """ Summary of the MCMC run. """ logging.info('Summary') logging.info('-------') logging.info('Number of Generations: ' + str(self.samples.length)) logging.info('Number of Parameters: ' + str(self.ndim)) logging.info('Number of Walkers: ' + str(self.nwalkers)) logging.info('Number of Tuning Generations: ' + str(len(self.mus))) logging.info('Scale Factor: ' + str(round(self.mu,6))) logging.info('Mean Integrated Autocorrelation Time: ' + str(round(np.mean(self.act),2))) logging.info('Effective Sample Size: ' + str(round(self.ess,2))) logging.info('Number of Log Probability Evaluations: ' + str(self.ncall)) logging.info('Effective Samples per Log Probability Evaluation: ' + str(round(self.efficiency,6))) if self.thin > 1: logging.info('Thinning rate: ' + str(self.thin)) def compute_log_prob(self, coords): """ Calculate the vector of log-probability for the walkers Args: coords: (ndarray[..., ndim]) The position vector in parameter space where the probability should be calculated. Returns: log_prob: A vector of log-probabilities with one entry for each walker in this sub-ensemble. blob: The list of meta data returned by the ``log_post_fn`` at this position or ``None`` if nothing was returned. """ p = coords # Check that the parameters are in physical ranges. if np.any(np.isinf(p)): raise ValueError("At least one parameter value was infinite") if np.any(np.isnan(p)): raise ValueError("At least one parameter value was NaN") # Run the log-probability calculations (optionally in parallel). if self.vectorize: results = self.logprob_fn(p) else: results = list(self.distribute(self.logprob_fn, (p[i] for i in range(len(p))))) try: log_prob = np.array([float(l[0]) for l in results]) blob = [l[1:] for l in results] except (IndexError, TypeError): log_prob = np.array([float(l) for l in results]) blob = None else: # Get the blobs dtype if self.blobs_dtype is not None: dt = self.blobs_dtype else: try: dt = np.atleast_1d(blob[0]).dtype except ValueError: dt = np.dtype("object") blob = np.array(blob, dtype=dt) # Deal with single blobs properly shape = blob.shape[1:] if len(shape): axes = np.arange(len(shape))[np.array(shape) == 1] + 1 if len(axes): blob = np.squeeze(blob, tuple(axes)) # Check for log_prob returning NaN. if np.any(np.isnan(log_prob)): raise ValueError("Probability function returned NaN") return log_prob, blob def run_mcmc(self, start, nsteps=1000, thin=1, progress=True, log_prob0=None, blobs0=None, thin_by=1, callbacks=None): ''' Run MCMC. Args: start (float) : Starting point for the walkers. If ``None`` then the sampler proceeds from the last known position of the walkers. nsteps (int): Number of steps/generations (default is 1000). thin (float): Thin the chain by this number (default is 1, no thinning). progress (bool): If True (default), show progress bar. log_prob0 (float) : Log probability values of the walkers. Default is ``None``. blobs0 (float) : Blob value of the walkers. Default is ``None``. thin_by (float): If you only want to store and yield every ``thin_by`` samples in the chain, set ``thin_by`` to an integer greater than 1. When this is set, ``iterations * thin_by`` proposals will be made. callbacks (function): Callback function or list with multiple callback actions (e.g. ``[callback_0, callback_1, ...]``) to be evaluated during the run. Sampling terminates when all of the callback functions return ``True``. This option is useful in cases in which sampling needs to terminate once convergence is reached. Examples of callback functions can be found in the API docs. ''' for _ in self.sample(start, log_prob0=log_prob0, blobs0=blobs0, iterations=nsteps, thin=thin, thin_by=thin_by, progress=progress): if callbacks is None: pass else: if isinstance(callbacks, list): # Compute all callbacks cb_values = [cb(self.iteration, self.get_chain(), self.get_log_prob()) for cb in callbacks] # Keep only the non-None callbacks cb_notnan_values = [cb for cb in cb_values if cb != None] # Check them if len(cb_notnan_values) < 1: pass elif np.all(cb_notnan_values): break else: if callbacks(self.iteration, self.get_chain(), self.get_log_prob()): break def sample(self, start, log_prob0=None, blobs0=None, iterations=1, thin=1, thin_by=1, progress=True): ''' Advance the chain as a generator. The current iteration index of the generator is given by the ``sampler.iteration`` property. Args: start (float) : Starting point for the walkers. log_prob0 (float) : Log probability values of the walkers. Default is ``None``. blobs0 (float) : Blob value of the walkers. Default is ``None``. iterations (int): Number of steps to generate (default is 1). thin (float): Thin the chain by this number (default is 1, no thinning). thin_by (float): If you only want to store and yield every ``thin_by`` samples in the chain, set ``thin_by`` to an integer greater than 1. When this is set, ``iterations * thin_by`` proposals will be made. progress (bool): If True (default), show progress bar. ''' # Define task distributer if self.pool is None: self.distribute = map else: self.distribute = self.pool.map # Initialise ensemble of walkers logging.info('Initialising ensemble of %d walkers...', self.nwalkers) if start is not None: if np.shape(start) != (self.nwalkers, self.ndim): raise ValueError('Incompatible input dimensions! \n' + 'Please provide array of shape (nwalkers, ndim) as the starting position.') X = np.copy(start) if log_prob0 is None: Z, blobs = self.compute_log_prob(X) else: Z = np.copy(log_prob0) blobs = blobs0 elif (self.state_X is not None) and (self.state_Z is not None): X = np.copy(self.state_X) Z = np.copy(self.state_Z) blobs = self.state_blobs else: raise ValueError("Cannot have `start=None` if run_mcmc has never been called before.") if not np.all(np.isfinite(Z)): raise ValueError('Invalid walker initial positions! \n' + 'Initialise walkers from positions of finite log probability.') batch = list(np.arange(self.nwalkers)) # Extend saving space self.thin = int(thin) self.thin_by = int(thin_by) if self.thin_by < 0: raise ValueError('Invalid `thin_by` argument.') elif self.thin < 0: raise ValueError('Invalid `thin` argument.') elif self.thin > 1 and self.thin_by == 1: self.nsteps = int(iterations) self.samples.extend(self.nsteps//self.thin, blobs) self.ncheckpoint = self.thin elif self.thin_by > 1 and self.thin == 1: self.nsteps = int(iterations*self.thin_by) self.samples.extend(self.nsteps//self.thin_by, blobs) self.ncheckpoint = self.thin_by elif self.thin == 1 and self.thin_by == 1: self.nsteps = int(iterations) self.samples.extend(self.nsteps, blobs) self.ncheckpoint = 1 else: raise ValueError('Only one of `thin` and `thin_by` arguments can be used.') # Define Number of Log Prob Evaluations vector self.neval = np.zeros(self.nsteps, dtype=int) # Define tuning count ncount = 0 # Initialise progress bar if progress: t = tqdm(total=self.nsteps, desc='Sampling progress : ') # Main Loop for i in range(self.nsteps): # Initialise number of expansions & contractions nexp = 0 ncon = 0 move = np.random.choice(self._moves, p=self._weights) # Shuffle and split ensemble if self.shuffle_ensemble: np.random.shuffle(batch) batch0 = batch[:int(self.nwalkers/2)] batch1 = batch[int(self.nwalkers/2):] sets = [[batch0,batch1],[batch1,batch0]] # Loop over two sets for ensembles in sets: indeces = np.arange(int(self.nwalkers/2)) # Define active-inactive ensembles active, inactive = ensembles # Compute directions directions, tune_once = move.get_direction(X[inactive], self.mu) # Get Z0 = LogP(x0) Z0 = Z[active] - np.random.exponential(size=int(self.nwalkers/2)) # Set Initial Interval Boundaries L = - np.random.uniform(0.0,1.0,size=int(self.nwalkers/2)) R = L + 1.0 # Parallel stepping-out J = np.floor(self.maxsteps * np.random.uniform(0.0,1.0,size=int(self.nwalkers/2))) K = (self.maxsteps - 1) - J # Initialise number of Log prob calls ncall = 0 # Left stepping-out initialisation mask_J = np.full(int(self.nwalkers/2),True) Z_L = np.empty(int(self.nwalkers/2)) X_L = np.empty((int(self.nwalkers/2),self.ndim)) # Right stepping-out initialisation mask_K = np.full(int(self.nwalkers/2),True) Z_R = np.empty(int(self.nwalkers/2)) X_R = np.empty((int(self.nwalkers/2),self.ndim)) cnt = 0 # Stepping-Out procedure while len(mask_J[mask_J])>0 or len(mask_K[mask_K])>0: if len(mask_J[mask_J])>0: cnt += 1 if len(mask_K[mask_K])>0: cnt += 1 if cnt > self.maxiter: raise RuntimeError('Number of expansions exceeded maximum limit! \n' + 'Make sure that the pdf is well-defined. \n' + 'Otherwise increase the maximum limit (maxiter=10^4 by default).') for j in indeces[mask_J]: if J[j] < 1: mask_J[j] = False for j in indeces[mask_K]: if K[j] < 1: mask_K[j] = False X_L[mask_J] = directions[mask_J] * L[mask_J][:,np.newaxis] + X[active][mask_J] X_R[mask_K] = directions[mask_K] * R[mask_K][:,np.newaxis] + X[active][mask_K] if len(X_L[mask_J]) + len(X_R[mask_K]) < 1: Z_L[mask_J] = np.array([]) Z_R[mask_K] = np.array([]) cnt -= 1 else: Z_LR_masked, _ = self.compute_log_prob(np.concatenate([X_L[mask_J],X_R[mask_K]])) #Z_LR_masked = np.array(list(self.distribute(self.logprob_fn, np.concatenate([X_L[mask_J],X_R[mask_K]])))) Z_L[mask_J] = Z_LR_masked[:X_L[mask_J].shape[0]] Z_R[mask_K] = Z_LR_masked[X_L[mask_J].shape[0]:] for j in indeces[mask_J]: ncall += 1 if Z0[j] < Z_L[j]: L[j] = L[j] - 1.0 J[j] = J[j] - 1 nexp += 1 else: mask_J[j] = False for j in indeces[mask_K]: ncall += 1 if Z0[j] < Z_R[j]: R[j] = R[j] + 1.0 K[j] = K[j] - 1 nexp += 1 else: mask_K[j] = False # Shrinking procedure Widths = np.empty(int(self.nwalkers/2)) Z_prime = np.empty(int(self.nwalkers/2)) X_prime = np.empty((int(self.nwalkers/2),self.ndim)) if blobs is not None: blobs_prime = np.empty(int(self.nwalkers/2), dtype=np.dtype((blobs[0].dtype, blobs[0].shape))) mask = np.full(int(self.nwalkers/2),True) cnt = 0 while len(mask[mask])>0: # Update Widths of intervals Widths[mask] = L[mask] + np.random.uniform(0.0,1.0,size=len(mask[mask])) * (R[mask] - L[mask]) # Compute New Positions X_prime[mask] = directions[mask] * Widths[mask][:,np.newaxis] + X[active][mask] # Calculate LogP of New Positions if blobs is None: Z_prime[mask], _ = self.compute_log_prob(X_prime[mask]) #Z_prime[mask] = np.array(list(self.distribute(self.logprob_fn, X_prime[mask]))) else: Z_prime[mask], blobs_prime[mask] = self.compute_log_prob(X_prime[mask]) # Count LogProb calls ncall += len(mask[mask]) # Shrink slices for j in indeces[mask]: if Z0[j] < Z_prime[j]: mask[j] = False else: if Widths[j] < 0.0: L[j] = Widths[j] ncon += 1 elif Widths[j] > 0.0: R[j] = Widths[j] ncon += 1 cnt += 1 if cnt > self.maxiter: raise RuntimeError('Number of contractions exceeded maximum limit! \n' + 'Make sure that the pdf is well-defined. \n' + 'Otherwise increase the maximum limit (maxiter=10^4 by default).') # Update Positions X[active] = X_prime Z[active] = Z_prime if blobs is not None: blobs[active] = blobs_prime self.neval[i] += ncall # Tune scale factor using Robbins-Monro optimization if self.tune and tune_once: self.nexps.append(nexp) self.ncons.append(ncon) nexp = max(1, nexp) # This is to prevent the optimizer from getting stuck self.mu *= 2.0 * nexp / (nexp + ncon) self.mus.append(self.mu) if np.abs(nexp / (nexp + ncon) - 0.5) < self.tolerance: ncount += 1 if ncount > self.patience: self.tune = False if self.light_mode: self.mu *= (1.0 + nexp/self.nwalkers) self.maxsteps = 1 # Save samples if (i+1) % self.ncheckpoint == 0: self.samples.save(X, Z, blobs) # Update progress bar if progress: t.update() # Update iteration counter and state variables self.iteration = i + 1 self.state_X = np.copy(X) self.state_Z = np.copy(Z) self.state_blobs = blobs # Yield current state if (i+1) % self.ncheckpoint == 0: yield (X, Z, blobs) # Close progress bar if progress: t.close() class sampler(EnsembleSampler): def __init__(self, *args, **kwargs): logging.warning('The sampler class has been deprecated. Please use the new EnsembleSampler class.') super().__init__(*args, **kwargs) ================================================ FILE: zeus/fwrapper.py ================================================ import numpy as np class _FunctionWrapper(object): """ This is a hack to make the likelihood function pickleable when ``args`` or ``kwargs`` are also included. Args: f (callable) : Log Probability function. args (list): Extra arguments to be passed into the logprob. kwargs (dict): Extra arguments to be passed into the logprob. Returns: Log Probability function. """ def __init__(self, f, args, kwargs): self.f = f self.args = [] if args is None else args self.kwargs = {} if kwargs is None else kwargs def __call__(self, x): try: return self.f(x, *self.args, **self.kwargs) except: import traceback print("zeus: Exception while calling your likelihood function:") print(" params:", x) print(" args:", self.args) print(" kwargs:", self.kwargs) print(" exception:") traceback.print_exc() raise ================================================ FILE: zeus/moves.py ================================================ import numpy as np from itertools import permutations import random try: from scipy.stats import gaussian_kde except ImportError: gaussian_kde = None try: from sklearn.mixture import BayesianGaussianMixture except ImportError: BayesianGaussianMixture = None class DifferentialMove: r""" The `Karamanis & Beutler (2020) `_ "Differential Move" with parallelization. When this Move is used the walkers move along directions defined by random pairs of walkers sampled (with no replacement) from the complementary ensemble. This is the default choice and performs well along a wide range of target distributions. Parameters ---------- tune : bool If True then tune this move. Default is True. mu0 : float Default value of ``mu`` if ``tune=False``. """ def __init__(self, tune=True, mu0=1.0): self.tune = tune self.mu0 = mu0 def get_direction(self, X, mu): r""" Generate direction vectors. Parameters ---------- X : array Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble. mu : float The value of the scale factor ``mu``. Returns ------- directions : array Array of direction vectors of shape ``(nwalkers//2, ndim)``. """ nsamples = X.shape[0] perms = list(permutations(np.arange(nsamples), 2)) pairs = np.asarray(random.sample(perms,nsamples)).T if not self.tune: mu = self.mu0 return 2.0 * mu * (X[pairs[0]]-X[pairs[1]]), self.tune class GaussianMove: r""" The `Karamanis & Beutler (2020) `_ "Gaussian Move" with parallelization. When this Move is used the walkers move along directions defined by random vectors sampled from the Gaussian approximation of the walkers of the complementary ensemble. Parameters ---------- tune : bool If True then tune this move. Default is True. mu0 : float Default value of ``mu`` if ``tune=False``. """ def __init__(self, tune=False, mu0=1.0, cov=None): self.tune = tune self.mu0 = mu0 self.cov = cov def get_direction(self, X, mu): r""" Generate direction vectors. Parameters ---------- X : array Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble. mu : float The value of the scale factor ``mu``. Returns ------- directions : array Array of direction vectors of shape ``(nwalkers//2, ndim)``. """ nsamples = X.shape[0] mean = np.mean(X, axis=0) if self.cov is None: cov = np.cov(X, rowvar=False) else: cov = self.cov if not self.tune: mu = self.mu0 return 2.0 * mu * np.random.multivariate_normal(np.zeros_like(mean),cov,size=nsamples), self.tune class GlobalMove: r""" The `Karamanis & Beutler (2020) `_ "Global Move" with parallelization. When this Move is used a Bayesian Gaussian Mixture (BGM) is fitted to the walkers of complementary ensemble. The walkers move along random directions which connect different components of the BGM in an attempt to facilitate mode jumping. This Move should be used when the target distribution is multimodal. This move should be used after any burnin period. Parameters ---------- tune : bool If True then tune this move. Default is True. mu0 : float Default value of ``mu`` if ``tune=False``. rescale_cov : float Rescale the covariance matrices of the BGM components by this factor. This promotes mode jumping. Default value is 0.001. n_components : int The number of mixture components. Depending on the distribution of the walkers the model can decide not to use all of them. """ def __init__(self, tune=True, mu0=1.0, rescale_cov=0.001, n_components=5): if BayesianGaussianMixture is None: raise ImportError("you need sklearn.mixture.BayesianGaussianMixture to use the GlobalMove") self.tune = tune self.mu0 = mu0 self.rescale_cov = rescale_cov self.n_components = n_components def get_direction(self, X, mu): r""" Generate direction vectors. Parameters ---------- X : array Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble. mu : float The value of the scale factor ``mu``. Returns ------- directions : array Array of direction vectors of shape ``(nwalkers//2, ndim)``. """ if not self.tune: mu = self.mu0 n = X.shape[0] mixture = BayesianGaussianMixture(n_components=self.n_components) labels = mixture.fit_predict(X) means = mixture.means_ covariances = mixture.covariances_ i, j = np.random.choice(labels, 2, replace=False) if i != j: directions = np.random.multivariate_normal(means[i], covariances[i]*self.rescale_cov, size=n) - np.random.multivariate_normal(means[j], covariances[j]*self.rescale_cov, size=n) tune_once = False else: directions = mu * np.random.multivariate_normal(np.zeros_like(means[i]), covariances[i], size=n) if self.tune: tune_once = True else: tune_once = False return 2.0*directions, tune_once class KDEMove: r""" The `Karamanis & Beutler (2020) `_ "KDE Move" with parallelization. When this Move is used the distribution of the walkers of the complementary ensemble is traced using a Gaussian Kernel Density Estimation methods. The walkers then move along random direction vectos sampled from this distribution. Parameters ---------- tune : bool If True then tune this move. Default is True. mu0 : float Default value of ``mu`` if ``tune=False``. bw_method : The bandwidth estimation method. See the scipy docs for allowed values. """ def __init__(self, tune=False, mu0=1.0, bw_method=None): if gaussian_kde is None: raise ImportError("you need scipy.stats.gaussian_kde to use the KDEMove") self.tune = tune self.mu0 = mu0 self.bw_method = bw_method def get_direction(self, X, mu): r""" Generate direction vectors. Parameters ---------- X : array Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble. mu : float The value of the scale factor ``mu``. Returns ------- directions : array Array of direction vectors of shape ``(nwalkers//2, ndim)``. """ n = X.shape[0] kde = gaussian_kde(X.T, bw_method=self.bw_method) vectors = kde.resample(2*n).T directions = vectors[:n] - vectors[n:] if not self.tune: mu = self.mu0 return 2.0 * mu * directions, self.tune class RandomMove: r""" The `Karamanis & Beutler (2020) `_ "Random Move" with parallelization. When this move is used the walkers move along random directions. There is no communication between the walkers and this Move corresponds to the vanilla Slice Sampling method. This Move should be used for debugging purposes only. Parameters ---------- tune : bool If True then tune this move. Default is True. mu0 : float Default value of ``mu`` if ``tune=False``. """ def __init__(self, tune=True, mu0=1.0): self.tune = tune self.mu0 = mu0 def get_direction(self, X, mu): r""" Generate direction vectors. Parameters ---------- X : array Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble. mu : float The value of the scale factor ``mu``. Returns ------- directions : array Array of direction vectors of shape ``(nwalkers//2, ndim)``. """ directions = np.random.normal(0.0, 1.0, size=X.shape) directions /= np.linalg.norm(directions, axis=0) if not self.tune: mu = self.mu0 return 2.0 * mu * directions, self.tune ================================================ FILE: zeus/parallel.py ================================================ import sys import atexit MPI = None def _import_mpi(use_dill=False): global MPI try: from mpi4py import MPI as _MPI if use_dill: import dill _MPI.pickle.__init__(dill.dumps, dill.loads, dill.HIGHEST_PROTOCOL) MPI = _MPI except: raise ImportError("Please install mpi4py") return MPI class MPIPool: """A processing pool that distributes tasks using MPI. With this pool class, the master process distributes tasks to worker processes using an MPI communicator. This implementation is inspired by @juliohm in `this module `_ and was adapted from schwimmbad. Parameters ---------- comm : :class:`mpi4py.MPI.Comm`, optional An MPI communicator to distribute tasks with. If ``None``, this uses ``MPI.COMM_WORLD`` by default. """ def __init__(self, comm=None): self.comm = MPI.COMM_WORLD if comm is None else comm self.master = 0 self.rank = self.comm.Get_rank() atexit.register(lambda: MPIPool.close(self)) if not self.is_master(): # workers branch here and wait for work self.wait() sys.exit(0) self.workers = set(range(self.comm.size)) self.workers.discard(self.master) self.size = self.comm.Get_size() - 1 if self.size == 0: raise ValueError("Tried to create an MPI pool, but there " "was only one MPI process available. " "Need at least two.") def wait(self): """Tell the workers to wait and listen for the master process. This is called automatically when using :meth:`MPIPool.map` and doesn't need to be called by the user. """ if self.is_master(): return status = MPI.Status() while True: task = self.comm.recv(source=self.master, tag=MPI.ANY_TAG, status=status) if task is None: # Worker told to quit work break func, arg = task result = func(arg) # Worker is sending answer with tag self.comm.ssend(result, self.master, status.tag) def map(self, worker, tasks): """Evaluate a function or callable on each task in parallel using MPI. The callable, ``worker``, is called on each element of the ``tasks`` iterable. The results are returned in the expected order. Parameters ---------- worker : callable A function or callable object that is executed on each element of the specified ``tasks`` iterable. This object must be picklable (i.e. it can't be a function scoped within a function or a ``lambda`` function). This should accept a single positional argument and return a single object. tasks : iterable A list or iterable of tasks. Each task can be itself an iterable (e.g., tuple) of values or data to pass in to the worker function. Returns ------- results : list A list of results from the output of each ``worker()`` call. """ # If not the master just wait for instructions. if not self.is_master(): self.wait() return workerset = self.workers.copy() tasklist = [(tid, (worker, arg)) for tid, arg in enumerate(tasks)] resultlist = [None] * len(tasklist) pending = len(tasklist) while pending: if workerset and tasklist: worker = workerset.pop() taskid, task = tasklist.pop() # "Sent task %s to worker %s with tag %s" self.comm.send(task, dest=worker, tag=taskid) if tasklist: flag = self.comm.Iprobe(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG) if not flag: continue else: self.comm.Probe(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG) status = MPI.Status() result = self.comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status) worker = status.source taskid = status.tag # "Master received from worker %s with tag %s" workerset.add(worker) resultlist[taskid] = result pending -= 1 return resultlist def close(self): """ Tell all the workers to quit.""" if self.is_worker(): return for worker in self.workers: self.comm.send(None, worker, 0) def is_master(self): return self.rank == 0 def is_worker(self): return self.rank != 0 def __enter__(self): return self def __exit__(self, *args): self.close() def split_ranks(N_ranks, N_chunks): """ Divide the ranks into N chunks """ seq = range(N_ranks) avg = int(N_ranks // N_chunks) remainder = N_ranks % N_chunks start = 0 end = avg for i in range(N_chunks): if remainder: end += 1 remainder -= 1 yield i, seq[start:end] start = end end += avg class ChainManager: """ Class to serve as context manager to handle to MPI-related issues, specifically, the managing of ``MPIPool`` and splitting of communicators. This class can be used to run ``nchains`` in parallel with each chain having its own ``MPIPool`` of parallel walkers. Parameters ---------- nchains : int the number of independent chains to run concurrently comm : MPI.Communicator the global communicator to split """ def __init__(self, nchains=1, comm=None): global MPI MPI = _import_mpi(use_dill=False) self.comm = MPI.COMM_WORLD if comm is None else comm self.nchains = nchains # initialize comm for parallel chains self.chains_group = None self.chains_comm = None # intiialize comm for pool of workers for each parallel chain self.pool_comm = None self.pool = None def __enter__(self): """ Setup the MPIPool, such that only the ``pool`` master returns, while the other processes wait for tasks """ # split ranks if we need to if self.comm.size > 1: ranges = [] for i, ranks in split_ranks(self.comm.size, self.nchains): ranges.append(ranks[0]) if self.comm.rank in ranks: color = i # split the global comm into pools of workers self.pool_comm = self.comm.Split(color, 0) # make the comm to communicate b/w parallel runs if self.nchains >= 1: self.chains_group = self.comm.group.Incl(ranges) self.chains_comm = self.comm.Create(self.chains_group) # initialize the MPI pool, if the comm has more than 1 process if self.pool_comm is not None and self.pool_comm.size > 1: self.pool = MPIPool(comm=self.pool_comm) # explicitly force non-master ranks in pool to wait if self.pool is not None and not self.pool.is_master(): self.pool.wait() sys.exit(0) self.rank = 0 if self.chains_comm is not None: self.rank = self.chains_comm.rank return self def __exit__(self, exc_type, exc_value, exc_traceback): """ Exit gracefully by closing and freeing the MPI-related variables """ # wait for all the processes, if we more than one if self.chains_comm is not None and self.chains_comm.size > 1: self.chains_comm.Barrier() # close and free the MPI stuff if self.chains_group is not None: self.chains_group.Free() if self.chains_comm is not None: self.chains_comm.Free() if self.pool is not None: self.pool.close() @property def get_rank(self): ''' Get ``rank`` of current ``chain``. The minimum ``rank`` is ``0`` and the maximum is ``nchains-1``. ''' return self.rank @property def get_pool(self): ''' Get parallel ``pool`` of workers that correspond to a specific chain. This should be used to parallelize the walkers of each ``chain`` (not the chains themselves). This includes the ``map`` method that ``zeus`` requires. ''' return self.pool def gather(self, x, root): ''' Gather method to gather ``x`` in ``rank = root`` chain. Parameters ---------- x : Python object The python object to be gathered. root : int The rank of the chain that x is gathered. Returns ------- x : Python object The input object x gathered in ``rank = root``. ''' return self.chains_comm.gather(x, root=root) def scatter(self, x, root): ''' Scatter method to scatter ``x`` from ``rank = root`` chain to the rest. Parameters ---------- x : Python object The python object to be scattered. root : int The rank of the origin chain from which the x is scattered. Returns ------- x : Pythonn object Part of the input object x that was scattered along the ranks. ''' return self.chains_comm.scatter(x, root=root) def allgather(self, x): ''' Allgather method to gather ``x`` in all chains. This is equivalent to first ``scatter`` and then ``bcast``. Parameters ---------- x : Python object The python object to be gathered. Returns ------- x : Python object The python object, gathered in all ranks. ''' return self.chains_comm.allgather(x) def bcast(self, x, root): ''' Broadcast method to send ``x`` from ``rank = root`` to all chains. Parameters ---------- x : Python object The python object to be send. root : int The rank of the origin chain from which the object x is sent. Returns ------- x : Python object The input object x in all ranks. ''' return self.chains_comm.bcast(x, root=root) ================================================ FILE: zeus/plotting.py ================================================ import numpy as np def cornerplot(samples, labels=None, weights=None, levels=None, span=None, quantiles=[0.025, 0.5, 0.975], truth=None, color=None, alpha=0.5, linewidth=1.5, fill=True, fontsize=10, show_titles=True, title_fmt='.2f', title_fontsize=12, cut=3, fig=None, size=(10,10)): r""" Plot corner-plot of samples. Parameters ---------- samples : array Array of shape (nsamples, ndim) containing the samples. labels : list List of names of for the parameters. weights : array Array with weights (useful if different samples have different weights e.g. as in Nested Sampling). levels : list The quantiles used for plotting the smoothed 2-D distributions. If not provided, these default to 0.5, 1, 1.5, and 2-sigma contours. quantiles : list A list of fractional quantiles to overplot on the 1-D marginalized posteriors as titles. Default is ``[0.025, 0.5, 0.975]`` (spanning the 95%/2-sigma credible interval). truth : array Array specifying a point to be highlighted in the plot. It can be the true values of the parameters, the mean, median etc. By default this is None. color : str Matplotlib color to be used in the plot. alpha : float Transparency value of figure (Default is 0.5). linewidth : float Linewidth of plot (Default is 1.5). fill : bool If True (Default) the fill the 1D and 2D contours with color. fontsize : float Fontsize of axes labels. Default is 10. show_titles : bool Whether to display a title above each 1-D marginalized posterior showing the quantiles. Default is True. title_fmt : str Format of the titles. Default is ``.2f``. title_fontsize : float Fontsize of titles. Default is 12. cut : float Factor, multiplied by the smoothing bandwidth, that determines how far the evaluation grid extends past the extreme datapoints. When set to 0, truncate the curve at the data limits. Default is ``cut=3``. fig : (figure, axes) Pre-existing Figure and Axes for the plot. Otherwise create new internally. Default is None. size : (int, int) Size of the plot. Default is (10, 10). Returns ------- Figure, Axes The matplotlib figure and axes. """ import matplotlib.pyplot as plt import seaborn as sns ndim = samples.shape[1] if labels is None: labels = [r"$x_{"+str(i+1)+"}$" for i in range(ndim)] if levels is None: levels = list(1.0 - np.exp(-0.5 * np.arange(0.5, 2.1, 0.5) ** 2)) levels.append(1.0) if color is None: color = "tab:blue" # Determine plotting bounds. if span is None: span = [0.999999426697 for i in range(ndim)] span = list(span) if len(span) != ndim: raise ValueError("Dimension mismatch between samples and span.") for i, _ in enumerate(span): try: xmin, xmax = span[i] except: q = [0.5 - 0.5 * span[i], 0.5 + 0.5 * span[i]] span[i] = _quantile(samples[:,i], q, weights=weights) idxs = np.arange(ndim**2).reshape(ndim, ndim) tril = np.tril_indices(ndim) triu = np.triu_indices(ndim) lower = list(set(idxs[tril])-set(idxs[triu])) upper = list(set(idxs[triu])-set(idxs[tril])) if fig is None: figure, axes = plt.subplots(ndim, ndim, figsize=size, sharex=False) else: figure = fig[0] axes = fig[1] for idx, ax in enumerate(axes.flat): i = idx // ndim j = idx % ndim if idx in lower: ax.set_ylim(span[i]) ax.yaxis.set_major_locator(plt.MaxNLocator(5)) if fill: ax = sns.kdeplot(x=samples[:,j], y=samples[:,i], weights=weights, fill=True, color=color, clip=None, cut=cut, thresh=levels[0], levels=levels, ax=ax, alpha=alpha, linewidth=0.0, ) ax = sns.kdeplot(x=samples[:,j], y=samples[:,i], weights=weights, fill=False, color=color, clip=None, cut=cut, thresh=levels[0], levels=levels, ax=ax, alpha=alpha, linewidth=linewidth, ) if truth is not None: ax.axvline(truth[j], color='k', lw=1.0) ax.axhline(truth[i], color='k', lw=1.0) if j == 0: ax.set_ylabel(labels[i], fontsize=fontsize) [l.set_rotation(45) for l in ax.get_yticklabels()] else: ax.yaxis.set_ticklabels([]) if i == ndim - 1: ax.xaxis.set_major_locator(plt.MaxNLocator(5)) ax.set_xlabel(labels[j], fontsize=fontsize) [l.set_rotation(45) for l in ax.get_xticklabels()] else: ax.set_xticklabels([]) elif idx in upper: ax.set_axis_off() else: ax.yaxis.set_major_locator(plt.NullLocator()) if fill: ax = sns.kdeplot(x=samples[:,j], fill=True, color=color, weights=weights, clip=None, cut=cut, ax=ax, linewidth=0.0, alpha=alpha, ) ax = sns.kdeplot(x=samples[:,j], fill=None, color=color, weights=weights, clip=None, cut=cut, ax=ax, linewidth=linewidth, alpha=alpha, ) if truth is not None: ax.axvline(truth[j], color='k', lw=1.0) if i == ndim - 1: ax.set_xlabel(labels[j], fontsize=fontsize) [l.set_rotation(45) for l in ax.get_xticklabels()] else: ax.set_xticklabels([]) if show_titles: ql, qm, qh = _quantile(samples[:,i], quantiles, weights=weights) q_minus, q_plus = qm - ql, qh - qm fmt = "{{0:{0}}}".format(title_fmt).format title = r"${{{0}}}_{{-{1}}}^{{+{2}}}$" title = title.format(fmt(qm), fmt(q_minus), fmt(q_plus)) title = "{0} = {1}".format(labels[i], title) ax.set_title(title, fontsize=title_fontsize) ax.set_xlim(span[j]) ax.xaxis.set_major_locator(plt.MaxNLocator(5)) figure.subplots_adjust(top=0.95, right=0.95, wspace=.05, hspace=.05) return figure, axes def _quantile(x, q, weights=None): """ Compute (weighted) quantiles from an input set of samples. Parameters ---------- x : `~numpy.ndarray` with shape (nsamps,) Input samples. q : `~numpy.ndarray` with shape (nquantiles,) The list of quantiles to compute from `[0., 1.]`. weights : `~numpy.ndarray` with shape (nsamps,), optional The associated weight from each sample. Returns ------- quantiles : `~numpy.ndarray` with shape (nquantiles,) The weighted sample quantiles computed at `q`. """ # Initial check. x = np.atleast_1d(x) q = np.atleast_1d(q) # Quantile check. if np.any(q < 0.0) or np.any(q > 1.0): raise ValueError("Quantiles must be between 0. and 1.") if weights is None: # If no weights provided, this simply calls `np.percentile`. return np.percentile(x, list(100.0 * q)) else: # If weights are provided, compute the weighted quantiles. weights = np.atleast_1d(weights) if len(x) != len(weights): raise ValueError("Dimension mismatch: len(weights) != len(x).") idx = np.argsort(x) # sort samples sw = weights[idx] # sort weights cdf = np.cumsum(sw)[:-1] # compute CDF cdf /= cdf[-1] # normalize CDF cdf = np.append(0, cdf) # ensure proper span quantiles = np.interp(q, cdf, x[idx]).tolist() return quantiles ================================================ FILE: zeus/samples.py ================================================ import numpy as np class samples: ''' Creates object that stores the samples. Args: ndim (int): Number of dimensions/parameters nwalkers (int): Number of walkers. ''' def __init__(self, ndim, nwalkers): self.initialised = False self.index = 0 self.ndim = ndim self.nwalkers = nwalkers def extend(self, n, blobs): """ Method to extend saving space. Args: n (int) : Extend space by n slots. """ if self.initialised: ext = np.empty((n,self.nwalkers,self.ndim)) self.samples = np.concatenate((self.samples,ext),axis=0) ext = np.empty((n,self.nwalkers)) self.logp = np.concatenate((self.logp,ext),axis=0) if blobs is not None: dt = np.dtype((blobs[0].dtype, blobs[0].shape)) ext = np.empty((n,self.nwalkers), dtype=dt) self.blobs = np.concatenate((self.blobs, ext),axis=0) else: self.samples = np.empty((n,self.nwalkers,self.ndim)) self.logp = np.empty((n,self.nwalkers)) if blobs is not None: dt = np.dtype((blobs[0].dtype, blobs[0].shape)) self.blobs = np.empty((n,self.nwalkers), dtype=dt) self.initialised = True def save(self, x, logp, blobs): """ Save sample into the storage. Args: x (ndarray): Samples to be appended into the storage. logp (ndarray): Logprob values to be appended into the storage. """ self.samples[self.index] = x self.logp[self.index] = logp if blobs is not None: self.blobs[self.index] = blobs self.index += 1 @property def chain(self): """ Chain property. Returns: 3D array of shape (nsteps,nwalkers,ndim) containing the samples. """ return self.samples[:self.index] @property def length(self): """ Number of samples per walker. Returns: The total number of samples per walker. """ return self.index def flatten(self, discard=0, thin=1): """ Flatten samples by thinning them, removing the burn in phase, and combining all the walkers. Args: discard (int): Number of burn-in steps to be removed from each walker (default is 0). thin (int): Thinning parameter (the default value is 1). Returns: 2D object containing the ndim flattened chains. """ return self.chain[discard:self.index:thin,:,:].reshape((-1,self.ndim), order='F') @property def logprob(self): """ Chain property. Returns: 2D array of shape (nwalkers,nsteps) containing the log-probabilities. """ return self.logp[:self.index] def flatten_logprob(self, discard=0, thin=1): """ Flatten log probability by thinning the chain, removing the burn in phase, and combining all the walkers. Args: discard (int): Number of burn-in steps to be removed from each walker (default is 0). thin (int): Thinning parameter (the default value is 1). Returns: 1D object containing the logprob of the flattened chains. """ return self.logprob[discard:self.index:thin,:].reshape((-1,), order='F') def flatten_blobs(self, discard=0, thin=1): """ Flatten blobs by thinning the chain, removing the burn in phase, and combining all the walkers. Args: discard (int): Number of burn-in steps to be removed from each walker (default is 0). thin (int): Thinning parameter (the default value is 1). Returns: (structured) NumPy array containing the blobs metadata. """ return self.blobs[discard:self.index:thin,:].reshape((-1,), order='F')