././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/0000755000175100001710000000000000000000000012132 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/.gitattributes0000644000175100001710000000010200000000000015016 0ustar00runnerdockerdocument/* linguist-documentation Makefile linguist-vendored=true ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731802.9895055 emcee-3.1.1/.github/0000755000175100001710000000000000000000000013472 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/.github/ISSUE_TEMPLATE.md0000644000175100001710000000110200000000000016171 0ustar00runnerdocker **General information:** - emcee version: - platform: - installation method (pip/conda/source/other?): **Problem description:** ### Expected behavior: ### Actual behavior: ### What have you tried so far?: ### Minimal example: ```python import emcee # sample code goes here... ``` ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731802.9895055 emcee-3.1.1/.github/workflows/0000755000175100001710000000000000000000000015527 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/.github/workflows/tests.yml0000644000175100001710000000560000000000000017415 0ustar00runnerdockername: Tests on: push: branches: - main tags: - "*" paths-ignore: - "joss/**" - "docs/**" pull_request: jobs: tests: runs-on: ${{ matrix.os }} strategy: matrix: python-version: ["3.7", "3.8", "3.9"] os: ["ubuntu-latest"] include: - python-version: "3.8" os: "macos-latest" - python-version: "3.8" os: "windows-latest" steps: - name: Checkout uses: actions/checkout@v2 with: fetch-depth: 0 - name: Setup Python uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install -U pip python -m pip install -U coveralls coverage[toml] tox tox-gh-actions - name: Run tests run: python -m tox - name: Combine and upload coverage run: | python -m coverage combine python -m coverage xml -i python -m coveralls --service=github env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} COVERALLS_PARALLEL: true COVERALLS_FLAG_NAME: ${{ matrix.python-version }}-${{ matrix.os }} coverage: needs: tests runs-on: ubuntu-latest steps: - name: Setup Python uses: actions/setup-python@v2 with: python-version: "3.9" - name: Finish coverage collection run: | python -m pip install -U pip python -m pip install -U coveralls python -m coveralls --finish env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} lint: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 with: fetch-depth: 0 - name: Setup Python uses: actions/setup-python@v2 with: python-version: "3.9" - name: Install dependencies run: | python -m pip install -U pip python -m pip install tox - name: Lint the code run: python -m tox -e lint build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 with: fetch-depth: 0 - uses: actions/setup-python@v2 name: Install Python with: python-version: "3.9" - name: Build sdist and wheel run: | python -m pip install -U pip python -m pip install -U build python -m build . - uses: actions/upload-artifact@v2 with: path: dist/* upload_pypi: needs: [tests, lint, build] runs-on: ubuntu-latest if: startsWith(github.ref, 'refs/tags/') steps: - uses: actions/download-artifact@v2 with: name: artifact path: dist - uses: pypa/gh-action-pypi-publish@v1.4.2 with: user: __token__ password: ${{ secrets.pypi_password }} ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/.gitignore0000644000175100001710000000023500000000000014122 0ustar00runnerdocker*~ *.swp .DS_Store *.pyc *.so venv* build/* dist emcee.egg-info MANIFEST docs.tar *.pdf .coverage .pytest_cache htmlcov emcee_version.py .tox env .eggs ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/.pre-commit-config.yaml0000644000175100001710000000075400000000000016421 0ustar00runnerdocker repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.0.1 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: debug-statements - repo: https://github.com/PyCQA/isort rev: "5.9.3" hooks: - id: isort args: [] additional_dependencies: [toml] exclude: docs/tutorials - repo: https://github.com/psf/black rev: "21.7b0" hooks: - id: black - repo: https://github.com/dfm/black_nbconvert rev: v0.3.0 hooks: - id: black_nbconvert ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/AUTHORS.rst0000644000175100001710000000014700000000000014013 0ustar00runnerdockerThe list of contributors can be found `on GitHub `_. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/CODE_OF_CONDUCT.md0000644000175100001710000000622500000000000014736 0ustar00runnerdocker# Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at foreman.mackey@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version] [homepage]: http://contributor-covenant.org [version]: http://contributor-covenant.org/version/1/4/ ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/CONTRIBUTING.md0000644000175100001710000000344300000000000014367 0ustar00runnerdocker## How to contribute to emcee ### Expectations emcee is developed and maintained in my spare time and, while I try to be responsive, I don't always get to every issue immediately. If it has been more than a week or two, feel free to ping me to try to get my attention. Do not email me directly; all discussion should happen on [the mailing list](https://groups.google.com/forum/#!forum/emcee-users). ### Did you find a bug? **Ensure the bug was not already reported** by searching on GitHub under [Issues](https://github.com/dfm/emcee/issues). If you're unable to find an open issue addressing the problem, [open a new one](https://github.com/dfm/emcee/issues/new). Be sure to include a **title and clear description**, as much relevant information as possible, and the simplest possible **code sample** demonstrating the expected behavior that is not occurring. ### Did you write a patch that fixes a bug? Open a new GitHub pull request with the patch. Ensure the PR description clearly describes the problem and solution. Include the relevant issue number if applicable. ### Do you intend to add a new feature or change an existing one? First, read the [VISION](https://github.com/dfm/emcee/blob/main/VISION.md) notes and make sure that your feature is consistent with those. In particular, modifications of the core algorithm or additions of new algorithms are unlikely to be approved. If your feature seems to be consistent, suggest it on the [emcee-users mailing list](https://groups.google.com/forum/#!forum/emcee-users) for some discussion before opening a pull request. ### Do you have questions about the code or about MCMC in general? **Do not open an issue.** Ask any questions on the [emcee-users mailing list](https://groups.google.com/forum/#!forum/emcee-users). Thanks! [Dan](https://github.com/dfm) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/HISTORY.rst0000644000175100001710000000707400000000000014035 0ustar00runnerdocker.. :changelog: 3.1.1 (2021-08-23) ++++++++++++++++++ - Added support for a progress bar description `#401 `_ 3.1.0 (2021-06-25) ++++++++++++++++++ - Added preliminary support for named parameters `#386 `_ - Improved handling of blob dtypes `#363 `_ - Fixed various small bugs and documentation issues 3.0.2 (2019-11-15) ++++++++++++++++++ - Added tutorial for moves interface - Added information about contributions to documentation - Improved documentation for installation and testing - Fixed dtype issues and instability in linear dependence test - Final release for `JOSS `_ submission 3.0.1 (2019-10-28) ++++++++++++++++++ - Added support for long double dtypes - Prepared manuscript to submit to `JOSS `_ - Improved packaging and release infrastructure - Fixed bug in initial linear dependence test 3.0.0 (2019-09-30) ++++++++++++++++++ - Added progress bars using `tqdm `_. - Added HDF5 backend using `h5py `_. - Added new ``Move`` interface for more flexible specification of proposals. - Improved autocorrelation time estimation algorithm. - Switched documentation to using Jupyter notebooks for tutorials. - More details can be found `on the docs `_. 2.2.0 (2016-07-12) ++++++++++++++++++ - Improved autocorrelation time computation. - Numpy compatibility issues. - Fixed deprecated integer division behavior in PTSampler. 2.1.0 (2014-05-22) ++++++++++++++++++ - Removing dependence on ``acor`` extension. - Added arguments to ``PTSampler`` function. - Added automatic load-balancing for MPI runs. - Added custom load-balancing for MPI and multiprocessing. - New default multiprocessing pool that supports ``^C``. 2.0.0 (2013-11-17) ++++++++++++++++++ - **Re-licensed under the MIT license!** - Clearer less verbose documentation. - Added checks for parameters becoming infinite or NaN. - Added checks for log-probability becoming NaN. - Improved parallelization and various other tweaks in ``PTSampler``. 1.2.0 (2013-01-30) ++++++++++++++++++ - Added a parallel tempering sampler ``PTSampler``. - Added instructions and utilities for using ``emcee`` with ``MPI``. - Added ``flatlnprobability`` property to the ``EnsembleSampler`` object to be consistent with the ``flatchain`` property. - Updated document for publication in PASP. - Various bug fixes. 1.1.3 (2012-11-22) ++++++++++++++++++ - Made the packaging system more robust even when numpy is not installed. 1.1.2 (2012-08-06) ++++++++++++++++++ - Another bug fix related to metadata blobs: the shape of the final ``blobs`` object was incorrect and all of the entries would generally be identical because we needed to copy the list that was appended at each step. Thanks goes to Jacqueline Chen (MIT) for catching this problem. 1.1.1 (2012-07-30) ++++++++++++++++++ - Fixed bug related to metadata blobs. The sample function was yielding the ``blobs`` object even when it wasn't expected. 1.1.0 (2012-07-28) ++++++++++++++++++ - Allow the ``lnprobfn`` to return arbitrary "blobs" of data as well as the log-probability. - Python 3 compatible (thanks Alex Conley)! - Various speed ups and clean ups in the core code base. - New documentation with better examples and more discussion. 1.0.1 (2012-03-31) ++++++++++++++++++ - Fixed transpose bug in the usage of ``acor`` in ``EnsembleSampler``. 1.0.0 (2012-02-15) ++++++++++++++++++ - Initial release. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/LICENSE0000644000175100001710000000212500000000000013137 0ustar00runnerdockerThe MIT License (MIT) Copyright (c) 2010-2021 Daniel Foreman-Mackey & contributors. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/MANIFEST.in0000644000175100001710000000002600000000000013666 0ustar00runnerdockerinclude LICENSE *.rst ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/PKG-INFO0000644000175100001710000000527600000000000013241 0ustar00runnerdockerMetadata-Version: 2.1 Name: emcee Version: 3.1.1 Summary: The Python ensemble sampling toolkit for MCMC Home-page: https://emcee.readthedocs.io Author: Daniel Foreman-Mackey Author-email: foreman.mackey@gmail.com Maintainer: Daniel Foreman-Mackey Maintainer-email: foreman.mackey@gmail.com License: MIT Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Description-Content-Type: text/x-rst Provides-Extra: extras Provides-Extra: tests License-File: LICENSE License-File: AUTHORS.rst emcee ===== **The Python ensemble sampling toolkit for affine-invariant MCMC** .. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat :target: https://github.com/dfm/emcee .. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg :target: https://github.com/dfm/emcee/actions?query=workflow%3ATests .. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat :target: https://github.com/dfm/emcee/blob/main/LICENSE .. image:: http://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat :target: https://arxiv.org/abs/1202.3665 .. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main&style=flat&v=2 :target: https://coveralls.io/github/dfm/emcee?branch=main .. image:: https://readthedocs.org/projects/emcee/badge/?version=latest :target: http://emcee.readthedocs.io/en/latest/?badge=latest emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by `Goodman & Weare (2010) `_. The code is open source and has already been used in several published projects in the Astrophysics literature. Documentation ------------- Read the docs at `emcee.readthedocs.io `_. Attribution ----------- Please cite `Foreman-Mackey, Hogg, Lang & Goodman (2012) `_ if you find this code useful in your research. The BibTeX entry for the paper is:: @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {PASP}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } License ------- Copyright 2010-2021 Dan Foreman-Mackey and contributors. emcee is free software made available under the MIT License. For details see the LICENSE file. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/README.rst0000644000175100001710000000372500000000000013630 0ustar00runnerdockeremcee ===== **The Python ensemble sampling toolkit for affine-invariant MCMC** .. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat :target: https://github.com/dfm/emcee .. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg :target: https://github.com/dfm/emcee/actions?query=workflow%3ATests .. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat :target: https://github.com/dfm/emcee/blob/main/LICENSE .. image:: http://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat :target: https://arxiv.org/abs/1202.3665 .. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main&style=flat&v=2 :target: https://coveralls.io/github/dfm/emcee?branch=main .. image:: https://readthedocs.org/projects/emcee/badge/?version=latest :target: http://emcee.readthedocs.io/en/latest/?badge=latest emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by `Goodman & Weare (2010) `_. The code is open source and has already been used in several published projects in the Astrophysics literature. Documentation ------------- Read the docs at `emcee.readthedocs.io `_. Attribution ----------- Please cite `Foreman-Mackey, Hogg, Lang & Goodman (2012) `_ if you find this code useful in your research. The BibTeX entry for the paper is:: @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {PASP}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } License ------- Copyright 2010-2021 Dan Foreman-Mackey and contributors. emcee is free software made available under the MIT License. For details see the LICENSE file. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/VISION.md0000644000175100001710000000036000000000000013462 0ustar00runnerdocker- Easy to use gradient-free MCMC sampling of black box log probability functions - Few bells and whistles: no plotting, no modeling, no marginal likelihood calculation, etc. - Any sampling algorithm must have published proof of correctness ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731802.9895055 emcee-3.1.1/binder/0000755000175100001710000000000000000000000013375 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/binder/environment.yml0000644000175100001710000000032100000000000016460 0ustar00runnerdockername: emcee channels: - conda-forge dependencies: - python - numpy - scipy - h5py - matplotlib - corner - tqdm - mpi4py - schwimmbad - pip - pip: - celerite - autograd - .. ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731802.9935057 emcee-3.1.1/docs/0000755000175100001710000000000000000000000013062 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/.gitignore0000644000175100001710000000004500000000000015051 0ustar00runnerdocker_build *.h5 _static/*/*.py tutorials ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/Makefile0000644000175100001710000001272700000000000014533 0ustar00runnerdocker# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext default: dirhtml help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" clean: -rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(TUTORIALS) $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/emcee.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/emcee.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/emcee" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/emcee" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. 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This documentation won't teach you too much about MCMC but there are a lot of resources available for that (try `this one `_). We also `published a paper `_ explaining the emcee algorithm and implementation in detail. emcee has been used in quite a few projects in the astrophysical literature and it is being actively developed on `GitHub `_. .. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat :target: https://github.com/dfm/emcee .. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg :target: https://github.com/dfm/emcee/actions?query=workflow%3ATests .. image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat :target: https://github.com/dfm/emcee/blob/main/LICENSE .. image:: https://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat :target: https://arxiv.org/abs/1202.3665 .. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main&style=flat :target: https://coveralls.io/github/dfm/emcee?branch=main Basic Usage ----------- If you wanted to draw samples from a 5 dimensional Gaussian, you would do something like: .. code-block:: python import numpy as np import emcee def log_prob(x, ivar): return -0.5 * np.sum(ivar * x ** 2) ndim, nwalkers = 5, 100 ivar = 1. / np.random.rand(ndim) p0 = np.random.randn(nwalkers, ndim) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc(p0, 10000) A more complete example is available in the :ref:`quickstart` tutorial. How to Use This Guide --------------------- To start, you're probably going to need to follow the :ref:`install` guide to get emcee installed on your computer. After you finish that, you can probably learn most of what you need from the tutorials listed below (you might want to start with :ref:`quickstart` and go from there). If you need more details about specific functionality, the User Guide below should have what you need. We welcome bug reports, patches, feature requests, and other comments via `the GitHub issue tracker `_, but you should check out the `contribution guidelines `_ first. If you have a question about the use of emcee, please post it to `the users list `_ instead of the issue tracker. .. toctree:: :maxdepth: 2 :caption: User Guide user/install user/sampler user/moves user/blobs user/backends user/autocorr user/upgrade user/faq .. toctree:: :maxdepth: 1 :caption: Tutorials tutorials/quickstart tutorials/line tutorials/parallel tutorials/autocorr tutorials/monitor tutorials/moves License & Attribution --------------------- Copyright 2010-2021 Dan Foreman-Mackey and `contributors `_. emcee is free software made available under the MIT License. For details see the ``LICENSE``. If you make use of emcee in your work, please cite our paper (`arXiv `_, `ADS `_, `BibTeX `_). Changelog --------- .. include:: ../HISTORY.rst ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/requirements.txt0000644000175100001710000000017600000000000016352 0ustar00runnerdockersphinx-book-theme @ git+https://github.com/dfm/sphinx-book-theme.git@fix-outdir myst-nb matplotlib scipy h5py celerite corner ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731802.997506 emcee-3.1.1/docs/tutorials/0000755000175100001710000000000000000000000015110 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/autocorr.ipynb0000644000175100001710000175755600000000000020043 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(autocorr)=\n", "\n", "# Autocorrelation analysis & convergence\n", "\n", "In this tutorial, we will discuss a method for convincing yourself that your chains are sufficiently converged.\n", "This can be a difficult subject to discuss because it isn't formally possible to guarantee convergence for any but the simplest models, and therefore any argument that you make will be circular and heuristic.\n", "However, some discussion of autocorrelation analysis is (or should be!) a necessary part of any publication using MCMC.\n", "\n", "With emcee, we follow [Goodman & Weare (2010)](https://msp.org/camcos/2010/5-1/p04.xhtml) and recommend using the *integrated autocorrelation time* to quantify the effects of sampling error on your results.\n", "The basic idea is that the samples in your chain are not independent and you must estimate the effective number of independent samples.\n", "There are other convergence diagnostics like the [Gelman–Rubin statistic](http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/305.pdf) (*Note: you should not compute the G–R statistic using multiple chains in the same emcee ensemble because the chains are not independent!*) but, since the integrated autocorrelation time directly quantifies the Monte Carlo error (and hence the efficiency of the sampler) on any integrals computed using the MCMC results, it is the natural quantity of interest when judging the robustness of an MCMC analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monte Carlo error\n", "\n", "The goal of every MCMC analysis is to evaluate integrals of the form\n", "\n", "$$\n", "\\mathrm{E}_{p(\\theta)}[f(\\theta)] = \\int f(\\theta)\\,p(\\theta)\\,\\mathrm{d}\\theta \\quad.\n", "$$\n", "\n", "If you had some way of generating $N$ samples $\\theta^{(n)}$ from the probability density $p(\\theta)$, then you could approximate this integral as\n", "\n", "$$\n", "\\mathrm{E}_{p(\\theta)}[f(\\theta)] \\approx \\frac{1}{N} \\sum_{n=1}^N f(\\theta^{(n)})\n", "$$\n", "\n", "where the sum is over the samples from $p(\\theta)$.\n", "If these samples are independent, then the sampling variance on this estimator is\n", "\n", "$$\n", "\\sigma^2 = \\frac{1}{N}\\,\\mathrm{Var}_{p(\\theta)}[f(\\theta)]\n", "$$\n", "\n", "and the error decreases as $1/\\sqrt{N}$ as you generate more samples.\n", "In the case of MCMC, the samples are not independent and the error is actually given by\n", "\n", "$$\n", "\\sigma^2 = \\frac{\\tau_f}{N}\\,\\mathrm{Var}_{p(\\theta)}[f(\\theta)]\n", "$$\n", "\n", "where $\\tau_f$ is the *integrated autocorrelation time* for the chain $f(\\theta^{(n)})$.\n", "In other words, $N/\\tau_f$ is the effective number of samples and $\\tau_f$ is the number of steps that are needed before the chain \"forgets\" where it started.\n", "This means that, if you can estimate $\\tau_f$, then you can estimate the number of samples that you need to generate to reduce the relative error on your target integral to (say) a few percent.\n", "\n", "**Note:** It is important to remember that $\\tau_f$ depends on the specific function $f(\\theta)$.\n", "This means that there isn't just *one* integrated autocorrelation time for a given Markov chain.\n", "Instead, you must compute a different $\\tau_f$ for any integral you estimate using the samples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing autocorrelation times\n", "\n", "There is a great discussion of methods for autocorrelation estimation in [a set of lecture notes by Alan Sokal](https://pdfs.semanticscholar.org/0bfe/9e3db30605fe2d4d26e1a288a5e2997e7225.pdf) and the interested reader should take a look at that for a more formal discussion, but I'll include a summary of some of the relevant points here.\n", "The integrated autocorrelation time is defined as\n", "\n", "$$\n", "\\tau_f = \\sum_{\\tau=-\\infty}^\\infty \\rho_f(\\tau)\n", "$$\n", "\n", "where $\\rho_f(\\tau)$ is the normalized autocorrelation function of the stochastic process that generated the chain for $f$.\n", "You can estimate $\\rho_f(\\tau)$ using a finite chain $\\{f_n\\}_{n=1}^N$ as\n", "\n", "$$\n", "\\hat{\\rho}_f(\\tau) = \\hat{c}_f(\\tau) / \\hat{c}_f(0)\n", "$$\n", "\n", "where\n", "\n", "$$\n", "\\hat{c}_f(\\tau) = \\frac{1}{N - \\tau} \\sum_{n=1}^{N-\\tau} (f_n - \\mu_f)\\,(f_{n+\\tau}-\\mu_f)\n", "$$\n", "\n", "and\n", "\n", "$$\n", "\\mu_f = \\frac{1}{N}\\sum_{n=1}^N f_n \\quad.\n", "$$\n", "\n", "(Note: In practice, it is actually more computationally efficient to compute $\\hat{c}_f(\\tau)$ using a fast Fourier transform than summing it directly.)\n", "\n", "Now, you might expect that you can estimate $\\tau_f$ using this estimator for $\\rho_f(\\tau)$ as\n", "\n", "$$\n", "\\hat{\\tau}_f \\stackrel{?}{=} \\sum_{\\tau=-N}^{N} \\hat{\\rho}_f(\\tau) = 1 + 2\\,\\sum_{\\tau=1}^N \\hat{\\rho}_f(\\tau)\n", "$$\n", "\n", "but this isn't actually a very good idea.\n", "At longer lags, $\\hat{\\rho}_f(\\tau)$ starts to contain more noise than signal and summing all the way out to $N$ will result in a very noisy estimate of $\\tau_f$.\n", "Instead, we want to estimate $\\tau_f$ as\n", "\n", "$$\n", "\\hat{\\tau}_f (M) = 1 + 2\\,\\sum_{\\tau=1}^M \\hat{\\rho}_f(\\tau)\n", "$$\n", "\n", "for some $M \\ll N$.\n", "As discussed by Sokal in the notes linked above, the introduction of $M$ decreases the variance of the estimator at the cost of some added bias and he suggests choosing the smallest value of $M$ where $M \\ge C\\,\\hat{\\tau}_f (M)$ for a constant $C \\sim 5$.\n", "Sokal says that he finds this procedure to work well for chains longer than $1000\\,\\tau_f$, but the situation is a bit better with emcee because we can use the parallel chains to reduce the variance and we've found that chains longer than about $50\\,\\tau$ are often sufficient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A toy problem\n", "\n", "To demonstrate this method, we'll start by generating a set of \"chains\" from a process with known autocorrelation structure.\n", "To generate a large enough dataset, we'll use [celerite](http://celerite.readthedocs.io):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 279, "width": 394 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(1234)\n", "\n", "# Build the celerite model:\n", "import celerite\n", "from celerite import terms\n", "\n", "kernel = terms.RealTerm(log_a=0.0, log_c=-6.0)\n", "kernel += terms.RealTerm(log_a=0.0, log_c=-2.0)\n", "\n", "# The true autocorrelation time can be calculated analytically:\n", "true_tau = sum(2 * np.exp(t.log_a - t.log_c) for t in kernel.terms)\n", "true_tau /= sum(np.exp(t.log_a) for t in kernel.terms)\n", "true_tau\n", "\n", "# Simulate a set of chains:\n", "gp = celerite.GP(kernel)\n", "t = np.arange(2000000)\n", "gp.compute(t)\n", "y = gp.sample(size=32)\n", "\n", "# Let's plot a little segment with a few samples:\n", "plt.plot(y[:3, :300].T)\n", "plt.xlim(0, 300)\n", "plt.xlabel(\"step number\")\n", "plt.ylabel(\"$f$\")\n", "plt.title(\"$\\\\tau_\\mathrm{{true}} = {0:.0f}$\".format(true_tau), fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll estimate the empirical autocorrelation function for each of these parallel chains and compare this to the true function." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 280, "width": 722 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def next_pow_two(n):\n", " i = 1\n", " while i < n:\n", " i = i << 1\n", " return i\n", "\n", "\n", "def autocorr_func_1d(x, norm=True):\n", " x = np.atleast_1d(x)\n", " if len(x.shape) != 1:\n", " raise ValueError(\"invalid dimensions for 1D autocorrelation function\")\n", " n = next_pow_two(len(x))\n", "\n", " # Compute the FFT and then (from that) the auto-correlation function\n", " f = np.fft.fft(x - np.mean(x), n=2 * n)\n", " acf = np.fft.ifft(f * np.conjugate(f))[: len(x)].real\n", " acf /= 4 * n\n", "\n", " # Optionally normalize\n", " if norm:\n", " acf /= acf[0]\n", "\n", " return acf\n", "\n", "\n", "# Make plots of ACF estimate for a few different chain lengths\n", "window = int(2 * true_tau)\n", "tau = np.arange(window + 1)\n", "f0 = kernel.get_value(tau) / kernel.get_value(0.0)\n", "\n", "# Loop over chain lengths:\n", "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharex=True, sharey=True)\n", "for n, ax in zip([10, 100, 1000], axes):\n", " nn = int(true_tau * n)\n", " ax.plot(tau / true_tau, f0, \"k\", label=\"true\")\n", " ax.plot(\n", " tau / true_tau,\n", " autocorr_func_1d(y[0, :nn])[: window + 1],\n", " label=\"estimate\",\n", " )\n", " ax.set_title(r\"$N = {0}\\,\\tau_\\mathrm{{true}}$\".format(n), fontsize=14)\n", " ax.set_xlabel(r\"$\\tau / \\tau_\\mathrm{true}$\")\n", "\n", "axes[0].set_ylabel(r\"$\\rho_f(\\tau)$\")\n", "axes[-1].set_xlim(0, window / true_tau)\n", "axes[-1].set_ylim(-0.05, 1.05)\n", "axes[-1].legend(fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows how the empirical estimate of the normalized autocorrelation function changes as more samples are generated.\n", "In each panel, the true autocorrelation function is shown as a black curve and the empirical estimator is shown as a blue line.\n", "\n", "Instead of estimating the autocorrelation function using a single chain, we can assume that each chain is sampled from the same stochastic process and average the estimate over ensemble members to reduce the variance.\n", "It turns out that we'll actually do this averaging later in the process below, but it can be useful to show the mean autocorrelation function for visualization purposes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 280, "width": 722 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharex=True, sharey=True)\n", "for n, ax in zip([10, 100, 1000], axes):\n", " nn = int(true_tau * n)\n", " ax.plot(tau / true_tau, f0, \"k\", label=\"true\")\n", " f = np.mean(\n", " [\n", " autocorr_func_1d(y[i, :nn], norm=False)[: window + 1]\n", " for i in range(len(y))\n", " ],\n", " axis=0,\n", " )\n", " f /= f[0]\n", " ax.plot(tau / true_tau, f, label=\"estimate\")\n", " ax.set_title(r\"$N = {0}\\,\\tau_\\mathrm{{true}}$\".format(n), fontsize=14)\n", " ax.set_xlabel(r\"$\\tau / \\tau_\\mathrm{true}$\")\n", "\n", "axes[0].set_ylabel(r\"$\\rho_f(\\tau)$\")\n", "axes[-1].set_xlim(0, window / true_tau)\n", "axes[-1].set_ylim(-0.05, 1.05)\n", "axes[-1].legend(fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's estimate the autocorrelation time using these estimated autocorrelation functions.\n", "Goodman & Weare (2010) suggested averaging the ensemble over walkers and computing the autocorrelation function of the mean chain to lower the variance of the estimator and that was what was originally implemented in emcee.\n", "Since then, @fardal on GitHub [suggested that other estimators might have lower variance](https://github.com/dfm/emcee/issues/209).\n", "This is absolutely correct and, instead of the Goodman & Weare method, we now recommend computing the autocorrelation time for each walker (it's actually possible to still use the ensemble to choose the appropriate window) and then average these estimates.\n", "\n", "Here is an implementation of each of these methods and a plot showing the convergence as a function of the chain length:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 265, "width": 387 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Automated windowing procedure following Sokal (1989)\n", "def auto_window(taus, c):\n", " m = np.arange(len(taus)) < c * taus\n", " if np.any(m):\n", " return np.argmin(m)\n", " return len(taus) - 1\n", "\n", "\n", "# Following the suggestion from Goodman & Weare (2010)\n", "def autocorr_gw2010(y, c=5.0):\n", " f = autocorr_func_1d(np.mean(y, axis=0))\n", " taus = 2.0 * np.cumsum(f) - 1.0\n", " window = auto_window(taus, c)\n", " return taus[window]\n", "\n", "\n", "def autocorr_new(y, c=5.0):\n", " f = np.zeros(y.shape[1])\n", " for yy in y:\n", " f += autocorr_func_1d(yy)\n", " f /= len(y)\n", " taus = 2.0 * np.cumsum(f) - 1.0\n", " window = auto_window(taus, c)\n", " return taus[window]\n", "\n", "\n", "# Compute the estimators for a few different chain lengths\n", "N = np.exp(np.linspace(np.log(100), np.log(y.shape[1]), 10)).astype(int)\n", "gw2010 = np.empty(len(N))\n", "new = np.empty(len(N))\n", "for i, n in enumerate(N):\n", " gw2010[i] = autocorr_gw2010(y[:, :n])\n", " new[i] = autocorr_new(y[:, :n])\n", "\n", "# Plot the comparisons\n", "plt.loglog(N, gw2010, \"o-\", label=\"G&W 2010\")\n", "plt.loglog(N, new, \"o-\", label=\"new\")\n", "ylim = plt.gca().get_ylim()\n", "plt.plot(N, N / 50.0, \"--k\", label=r\"$\\tau = N/50$\")\n", "plt.axhline(true_tau, color=\"k\", label=\"truth\", zorder=-100)\n", "plt.ylim(ylim)\n", "plt.xlabel(\"number of samples, $N$\")\n", "plt.ylabel(r\"$\\tau$ estimates\")\n", "plt.legend(fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this figure, the true autocorrelation time is shown as a horizontal line and it should be clear that both estimators give outrageous results for the short chains.\n", "It should also be clear that the new algorithm has lower variance than the original method based on Goodman & Weare.\n", "In fact, even for moderately long chains, the old method can give dangerously over-confident estimates.\n", "For comparison, we have also plotted the $\\tau = N/50$ line to show that, once the estimate crosses that line, The estimates are starting to get more reasonable.\n", "This suggests that you probably shouldn't trust any estimate of $\\tau$ unless you have more than $F\\times\\tau$ samples for some $F \\ge 50$.\n", "Larger values of $F$ will be more conservative, but they will also (obviously) require longer chains." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A more realistic example\n", "\n", "Now, let's run an actual Markov chain and test these methods using those samples.\n", "So that the sampling isn't completely trivial, we'll sample a multimodal density in three dimensions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 500000/500000 [10:51<00:00, 767.17it/s] \n" ] } ], "source": [ "import emcee\n", "\n", "\n", "def log_prob(p):\n", " return np.logaddexp(-0.5 * np.sum(p ** 2), -0.5 * np.sum((p - 4.0) ** 2))\n", "\n", "\n", "sampler = emcee.EnsembleSampler(32, 3, log_prob)\n", "sampler.run_mcmc(\n", " np.concatenate(\n", " (np.random.randn(16, 3), 4.0 + np.random.randn(16, 3)), axis=0\n", " ),\n", " 500000,\n", " progress=True,\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the marginalized density in the first dimension." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 261, "width": 364 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "chain = sampler.get_chain()[:, :, 0].T\n", "\n", "plt.hist(chain.flatten(), 100)\n", "plt.gca().set_yticks([])\n", "plt.xlabel(r\"$\\theta$\")\n", "plt.ylabel(r\"$p(\\theta)$\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here's the comparison plot showing how the autocorrelation time estimates converge with longer chains." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 265, "width": 387 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Compute the estimators for a few different chain lengths\n", "N = np.exp(np.linspace(np.log(100), np.log(chain.shape[1]), 10)).astype(int)\n", "gw2010 = np.empty(len(N))\n", "new = np.empty(len(N))\n", "for i, n in enumerate(N):\n", " gw2010[i] = autocorr_gw2010(chain[:, :n])\n", " new[i] = autocorr_new(chain[:, :n])\n", "\n", "# Plot the comparisons\n", "plt.loglog(N, gw2010, \"o-\", label=\"G&W 2010\")\n", "plt.loglog(N, new, \"o-\", label=\"new\")\n", "ylim = plt.gca().get_ylim()\n", "plt.plot(N, N / 50.0, \"--k\", label=r\"$\\tau = N/50$\")\n", "plt.ylim(ylim)\n", "plt.xlabel(\"number of samples, $N$\")\n", "plt.ylabel(r\"$\\tau$ estimates\")\n", "plt.legend(fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, the short chains give absurd estimates of $\\tau$, but the new method converges faster and with lower variance than the old method.\n", "The $\\tau = N/50$ line is also included as above as an indication of where we might start trusting the estimates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What about shorter chains?\n", "\n", "Sometimes it just might not be possible to run chains that are long enough to get a reliable estimate of $\\tau$ using the methods described above.\n", "In these cases, you might be able to get an estimate using parametric models for the autocorrelation.\n", "One example would be to fit an [autoregressive model](https://en.wikipedia.org/wiki/Autoregressive_model) to the chain and using that to estimate the autocorrelation time.\n", "\n", "As an example, we'll use [celerite](http://celerite.readthedocs.io) to fit for the maximum likelihood autocorrelation function and then compute an estimate of $\\tau$ based on that model.\n", "The celerite model that we're using is equivalent to a second-order ARMA model and it appears to be a good choice for this example, but we're not going to promise anything here about the general applicability and we caution care whenever estimating autocorrelation times using short chains.\n", "\n", ":::{note}\n", "To run this part of the tutorial, you'll need to install [celerite](https://celerite.readthedocs.io) and [autograd](https://github.com/HIPS/autograd).\n", ":::" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "\n", "\n", "def autocorr_ml(y, thin=1, c=5.0):\n", " # Compute the initial estimate of tau using the standard method\n", " init = autocorr_new(y, c=c)\n", " z = y[:, ::thin]\n", " N = z.shape[1]\n", "\n", " # Build the GP model\n", " tau = max(1.0, init / thin)\n", " kernel = terms.RealTerm(\n", " np.log(0.9 * np.var(z)),\n", " -np.log(tau),\n", " bounds=[(-5.0, 5.0), (-np.log(N), 0.0)],\n", " )\n", " kernel += terms.RealTerm(\n", " np.log(0.1 * np.var(z)),\n", " -np.log(0.5 * tau),\n", " bounds=[(-5.0, 5.0), (-np.log(N), 0.0)],\n", " )\n", " gp = celerite.GP(kernel, mean=np.mean(z))\n", " gp.compute(np.arange(z.shape[1]))\n", "\n", " # Define the objective\n", " def nll(p):\n", " # Update the GP model\n", " gp.set_parameter_vector(p)\n", "\n", " # Loop over the chains and compute likelihoods\n", " v, g = zip(*(gp.grad_log_likelihood(z0, quiet=True) for z0 in z))\n", "\n", " # Combine the datasets\n", " return -np.sum(v), -np.sum(g, axis=0)\n", "\n", " # Optimize the model\n", " p0 = gp.get_parameter_vector()\n", " bounds = gp.get_parameter_bounds()\n", " soln = minimize(nll, p0, jac=True, bounds=bounds)\n", " gp.set_parameter_vector(soln.x)\n", "\n", " # Compute the maximum likelihood tau\n", " a, c = kernel.coefficients[:2]\n", " tau = thin * 2 * np.sum(a / c) / np.sum(a)\n", " return tau\n", "\n", "\n", "# Calculate the estimate for a set of different chain lengths\n", "ml = np.empty(len(N))\n", "ml[:] = np.nan\n", "for j, n in enumerate(N[1:8]):\n", " i = j + 1\n", " thin = max(1, int(0.05 * new[i]))\n", " ml[i] = autocorr_ml(chain[:, :n], thin=thin)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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ejS/oTn3my8XBxfBVakuiPFThKQstW7bkzTff5NVXX6VDhw7MnDnT3iHZTFq6hR2noqzJwLno/BeGu75uNfq39qN/az9a16mW6//JHs18WexfnbRjEXzrXY1dri45O9A0OicmMTn6Ko4tvLnRlpNLlYLkq5AYDQlRkBgFCdH61xzboqzbtIRoXJNjcLVdVKI4ko2tgGUESQyEEKKKOX31NGtC17Dm5BpOXT1Voj7GthrLcP/h1gt7V7NrubiwL62KXIWnOEwmEy+++CI33XQTfn5+ODkVvM5BRROXnMamo5dYdyiMDUcvEZOY96Rds0mja9Ma+pOB1n7Uq17IJXPoRrocnA7KQvewJEIcHQl2dSbOZMLDYiEgMZlmqRnnOjAN2t8ATfsUHnBqkn4xn/1CPiEq27bo3NsSo6GEq0mXiIMTuPqAmw8pSfE4XT1d7C6i29yPd+d79EaO3xfaNduyfVaUbTl+9ZS2r4Liyqf//cth60cUm7NMPhZCCGEHlxIusfbUWtaEruHg5YOl7m+4//AKOYSmKKpSFZ7u3bvbOwTDhF9Nsj4V2HbiMinpeSd2ns5m+rSsRb9WtejTohZero5FP8mm9yFbwtgsNTUrEbiWssDal6D74/newbde8KeWfDG24tPAtbp+ke/qDW4+1gt+fVv1a7Z56++d3K0Xw04Rh+HLrsWu0OR900PlbuiMIdrdU7LEoGlv42MpJUkMhBCikopNieWv03+x5uQadoTtyPMOuJvZDVezK5eTLhe534o6rr44KmMVngsXLlCrVq08qw5VREopjoXHse5QGOsOhbP/XP7zBep4uViHCAU0qVG8Wv5K6RfzZ/6B00HFCzLiEPzycPGOKQ5H94yL9+o5L+6zX9BbL/gzkgAXLzCVcsG0Wq2IqdUFr4gdRdpd0yCmVgBelTEpAD3ZadSjeP9/NOpZLpOkyvHbQQghBKCvB7D13FZWn1zNprOb8qzvbzaZ6VWvF4OaDqJP/T7su7SvSoyrL4nKUoUnISGBW2+9FS8vL5YtW0bDhg3tHVKJpKVb2HU62vpk4ExU/nfaW9XR5wvc2tqP6+vmni+AUpB0BWLDIPZixtew3O24MEi38ToZJnMeF/TeBdzVz9hmLt1k5NLwGvAKauFQNAr/vaEw4TXg5TKIyo56Pw+LhuZ4opQvzQS9p9o+phKQxEAIISq4dEs6u8J3sebkGtadWkdsau4JbRoanWt3ZlCTQfRv1B8vZy/rZ1VlXH1V9tRTT/Hff/8B+vChkJAQXFxcCjkqDxGH9UoqybH6+OimvW1+1zM+OY3NGesL/H00giv5LPJlNmkENPWhX8ta9L/Ojfrmq/oFfuReOHkx64I/Ljzrwj+tDMrs+rWBxj2vuYPvnTMJcPYseomf8qJpH7Q7ZqFWPomGpYACTSa0O2YXba5FRda0DwyZBaueLDg50EwwpPz+PCQxEEKICkgpxaGoQ6wJXcPak2uJSIzIc7+WPi0Z3GQwA5oMoLZ77Xz7q0rj6quawMBAvv32W2v7jTfeKH5SELpRH1+f11CJRj30u6UGXuhEXE3ir8MRrDsURtCJy6Sk6RdabiTRRIvGT4umFtE0cLxKR58kWrjFU1uLxpwQDpvC4C+Dx+w7e+l35+Pz/ndWoA7joOsUY+MpLzqOR6veEDZ9gHZ6a46PNA1o1BOt99RyexFsuI7jIePnwTU/D0AfPlTOfx6ayrH4g7AVTdN2d+zYsePu3cVbSEgIIbI7c/UMq0+uZk1o/hWF6nvUZ1DTQQxqMojrql9X7HNUpnH1VV1oaCgdOnTg6tWrAIwYMYJly5YVr4LUnoVFvwvacVyJ4lQp8Zw+Fcqe/45wIvQ4SVHnqWVNAK7oX7UreGr5lxotEUd3qFYHPOuAZ2395ZHxNfs2J3f9acmXJUiMH9leLseSG84OT5PKNTv+PDp16sSePXv2KKU6FfdYeWIghBDlXGRiJGtPrmXNyTUciDyQ5z4+Lj7c1vg2BjcdTDvfdqUqHVpZxtVXdSkpKYwYMcKaFDRt2pQ5c+YU7/+N0I2FJwWgf77qCajeIOfd0NQkfYx+HuP3VexFkqLOo8WF4ZIeR2OgceZxxSgUlCez6zUX99d+zXgVp1xkJZpgahO1WlWd77UoKujPQxIDIYQoh+JS4vjrzF+sCV1DcFhwvhWFbml4C4ObDiagTgBmk/xKF1leeukldu3Sh4U5OjoSGBhItWrVitfJNeU5C6Qs8NNkqN0mKwFIjM53dw2Kv9CWg3MeF/p+uS/8navZZsx+JZlgKkR+5K+IEEKUEynpKWw5v4XVoQVXFOpZryeDmwymd4PeuJplDVOR2+rVq/n444+t7ZkzZ9K5c+fidXLxQPHLc8ZHwIm/i3cMkKIciMSbJNdaOHvXxbdOI5y962Vd6GcO73H1tu8k3UoywVSI/EhiIIQQdpRuSWd3+G7WnFzDn6f/JDYld0Uh0Cf/Dmo6iFsb3ZqjopAQ1zp//jz33XeftT148GCefvrpvHdWSq/SE3kcLofor8z3UaGljiUdE+GqOhHKm3DlTYSqTrjyJhxvIpQ3qW61aN2iBT3a+tPtupq4OJayvn5ZqAQTTIXIjyQGQghRxpRSHI46zJrQNfx+6nciEvKvKDSoySAGNhlYYEUhIbJ76623uHxZX7CuXr16LFiwAC0lDi6fyH3xf/kE5JOMltSf5j7Mir+VcOXNZTxR5FxMrIWfJ/1b+zGmtR/t6nlhMlWwMp2gX/Q37SMTbkWlI4mBEEKUkbNXz+oVhU6u4WTMyTz3qedRj0FNBjG46eASVRQSVVh6Glw5zSePDMIUcYhvft3KkrEN8P2+pz7ev4z8k9iQ/1Rja9ukwY2NfawrDzeq4V5msdhcBZ1gKkR+JDEQQggbikyM5I9Tf7AmdA3/Rv6b5z6ZFYUGNRnEDTVvKFVFIVHJKQXxl66565/xijoJllRcgS/bwRN13WjpcggKeyDg7AW+zaBGM6jhDzWuA19/Tl+KodFPg3MtXFVQaJoGQZY2uDk5cJN/Tfq39qNvy1p4uzsZ8d0LIWxMEgMhhDBYXEoc68+sZ83JNWy/uD3PikKuZtccFYUcTaWtzyhswl5DRVISIOpExsX/Cbh8POt9ckyRumjpm228vskRfJpkXPw3A1//rETA3TfHlX9iSjohEXHM+y+UUZaWBJiOFOl8mgbbLa3ofGM3Vg25vmLMFxBC5CCJgRBCGCAlPYWt57fqFYXObSI5PTnXPmZNryg0qOkg+jToIxWFyrOyWOnXkg5XzmRd+FufApyAq+eK3o1SrDmexmB/M1q1unlc/DeD6o3AIeef/JQ0Cycj4zl64iLHwmI5Fq6/TkclkLn2aYRpGIsc38VBK3wx1HSlMTttKN28XCUpEKKCksTAhjRN8wQyV09xtFiKWAtaCFGmSrrSr0VZ2B2+m9Whq1l3eh1XU67muV8nv04MaqJXFKruUt3g6IXhClvp93SQXsu+qCv9xl/OGO5zPOfwn6hQSM9dkrZQTh45Lv4/+e0wzy1byD3DhzJn7ndUr149x+7pFsXpy/EZF/5xHA2P5VhYLCcj40mzFHzB/4+lDS+lTeJd81wcNJVrWFFmO11pvJg2mX8sbejvIpcWQlRU8q/Xtp4F3shshIeH2zEUIcS1tl/cztf7v2Z3+O5cn3Xy68SUG6bQtU7XHNuVUhyJOsKak2tYc3JNvhWFWni3YFDTQQxsPJA6HnVsEr+wgZKu9JuaqF/oZ7/rn/kUoIBFvvKlOYB349x3/n39wcPPenW+Y8cOXpz1GgA//vQzjZq3ZeD4RzgaFmd9AhASEUdyWtFvTJk0aFzDnTpeLgSduMwP6TdzTtXkCfPPdDUdzhlmxvCh2WlD+cfSBoAezXyL//0KIcoFTanCHw+KkrnmicHa9u3bt927d689QxICKPkd8spkxfEVTN82Pc/x/5lMmolp3aYx1H8oZ6+etSYDoTF513fPrCg0qMmgKvfzrDTmDyreol4u1fVVdmPOAiX4e+peK+PC/7qMib8ZF//VG4E57wm7SikuxSazO+Q842+/mciLZ/VQ6rag1uiZaA5Fn69S39uVFn6e+Pt50qK2B839PLmupod1KNC932xjx8ko6/7+2jl6mA7iQSJxuBJkacNxVd/6eUATHwIf6lb8n4MQwjCdOnViz549e5RSnYp7rDwxsCGlVCwZ9SA0TUs1mUyFHCGEbZXkDnlltP3i9kKTAtCHCr3xzxvM/29+vuVFvZ29ua3xbQxuOlgqClV0EYeLv9Jv0hX9VRBHt4wL/2bZLv6bgc914Fq9wEOj41M4Gh7L8fDYjCFA+lCgKwkpRK58n4SMpEBzcsNnyNR8k4Jans60qO1Jcz/PjETAA38/TzycC74MePIWf8bNCyZzxNFxVZ/j6fXz3NekwRO3+Bf8sxBClGuSGAhRRRR2h3x3+G4eWveQ9Q55Zfb1/q8LTQoyKVSupMDV7Erfhn0Z3GQwXet2lYpClUXoppIfq5n01XAzL/6zl//0rAOF3BiKTUrleEQcx8JiMxIBPQG4FJt7EjtA3P4/SDiyxdquMfAJHKvXxtvNUb/4r53xFMDPk+Z+HlR3K1m50B7NfHl3WFteWnGAgqYjmDSYOaydDCMSooKTxECIKqA4d8inbZtGHY86FfbJgVKKNEsaKZYUUtL1V6ollRRLCqnpqYReCc3ziUlhHHCgZ/2eDG46mN71e+Pm6GaD6IVdJZdwBeAuD8Gtb4HZudBdk1L1UqBHw2I5FhGbUQ0ojvNXEot8upRLp4heP8fa7n3HKGZ8+CzN/Tzx9XAy/KnViBsbUt/bjdnrjxOcbVhRpoAmPjxxi78kBUJUApIYCFEFFOcOuUVZ+Gb/N0VKDNIsaVkX3tm+Zl6EZ/+afbt1v8xtllR9v4x2Zl8FHpvPfqmW1NL+uPL0WIfHmNRukk36FuWEs0fJjvNpmispyCwFmjkB+GhY7lKgReHiaKJZLQ/rEKCGXg48NWoqF9P0akZt2rTh9+XzcHW1benbHs186dHMl2PhsQSFRBKXlIaHi5kezXxp7udZeAdCiApBEgMhKrmQ6JBi3yHfFb6L4SuH46A55LjjnpKe7YLfklrkZKMySFfp9g5B2NKVM/DfL8U6JLNU5znvLhw8GKYnABnzAUIvFV4KNDtHB42mvh40r+1Ji4zx/y38PGng44aDKesJwKRJkzh2RK8M5OrqSmBgoM2Tguya+3lKIiBEJSaJgRCVgFKKqylXuRB3gYvxF/VX3EUuxF/gYOTBEvV5LPqYwVGWHbNmxtHBEScHJ5xMTjg5OOFocsTRwZHY5FjCEsKK3aeHUwnvJovyzZIOwd/A329DanyxDs0s1Tly/kXgYpGOySwF2jxj7L+eCHjS2NcdR4eC5yFERkby+++/W9uff/45rVu3LlbMQghREEkMhKgA0ixpXEq4xIX4C9aL/ovx+oV/WFwYF+MvkpCWUOZxmTQTTiYn/SI82wV4Xl+t+2Vuy9w/41jrfgUcm72d5/aMY0xa/hdYIdEhDF1Z/MnVAbUDSvOjEuVR2EFY+Thc2GPdlHmT31SEYfqZK/3mp763q/UOe16lQIvL19eX/fv3M2HCBLy8vLj//vtL1I8QQuRHEgNRZZTn2v0JqQn6hX72O/7ZEoCIhIgyH8oyttVYbr/u9nwv+J1MTjiYSnaBY0/NvJvRya9TsYZXdfbrXG7+XxEGSE2Cze9D0CywpFk3R7tfxwNR4/E3nSvWSr8ezg50aOid9RQgY12AwkqBloSvry+rVq0iOTlZSuMKIQwniYGo9Oxdu9+iLEQlRWVd9MddzHnxH3+RmOSYUp/H1exKXfe61PGoQx33OtT1qEtt99pYLBZeCXql2P0N9x9eaS+Gp9wwhYfWPVSkORImzcRDNzxUBlGJMnFqK6x8AqJOZG1zcIKbnmdpymD2rj/J3nT/Yq30+9BN1/F4Gdbv1zQNFxeXMjufEKLqkMRAVGplUbs/JT2FsPgwfZhPXO67/WHxYaRYUkrzbQDg6+pLHfc6Wa9sCUAd9zpUc6qW7x3EFSEr5A55Nl3rdOWNbm8UeeXjilq6VWSTGA3rXoc9C3Nub9gdhsyCms1xC8par+IfSxv+SWlT6Eq/AB4utvtTunbtWpRSDBw40GbnEEKITJIYiErLiNr9mZN6cwzzueaOf2RiZKljdTQ55rrgz3xf170ufu5+ODsUXiM9P3KHPLdh/sOo61GXb/Z/w67wXbk+7+zXmYdueEiSgopOKTj0K/z+PMSFZ213rgb9p0PHCdbFx/Kqw1/QSr+ZbFW//9y5c4wZM4aoqCieffZZ3nnnHZycSrZQmRBCFIUkBqLSKm7t/neC3+GO6+7IlQAYMam3mlM169Ceuu51c93x93HxKXDCbGnJHfK8da3Tla51upbr+SeiFK5egNXPwdHVObe3vB0GfQDV6ubY3NzPky5NfNiRxyJe+Qlo4mOT8p1paWmMHj2aqCg9lsDAQF566SVq1Khh+LmEECKTJAaiUipJ7f6TMSeZtWdWsc9l0kzUcqtFXfeMC/+MoT3Z7/q7O7oXu1+jyR3y/DXzbiaJQGViscDu7+Cv6ZB8NWu7R209IWh9R76HPnmLP2PnBRdpETKTBk/YaG7BW2+9xZYtW/TzmEwsXbpUkgIhhM1JYiAqpeCwYMP6cjW75rzDf00CUMutFmZTxfinJHfIRaV36ag+ufjs9pzbO02AftPBtXqBh3drWoO6Xq6cv5JY4H4mDWYOa2eTYUQbNmzgrbfesranT59Or169DD+PEEJcq2JczQhRTHEpcSU6rn3N9gxoMiDHkB8vZ69KVxZQ7pCLSictBbZ+Als+hPRsk/1rNIMhs6FxjyJ1s2LveWtSoAF5PTgIaOLDE7f42yQpiIiIYMyYMaiMRxZ9+/blpZdeMvw8QgiRF0kMRKVU0lVqBzQZwJhWYwyORghhU2d36E8JLmUrLWoyQ8+noddz4Fi00p7xyWm8v/aItf3ozc24o31dgkIiiUtKw8PFTI9mvjaZUwBgsViYMGECFy/qqyjXrFmTxYsX4+BQ8dYLEUJUTJIYiEqppKvUyuq2QlQgSVdh/Zuwcy457u3X6wx3zAa/64vV3TebThARmwxATU9nHu5zHe7OZpslAtf6+OOP+f33363thQsXUqdOnTI5txBCgCQGopKS1W2FqOSOroXVz8DV81nbHN3hltehy2Qo5qrc568k8s3mUGv7+dta4G6DlYvzExwcnGPI0NSpUxkwYECZnV8IIQBsVx9RCDubcsOUIpcArSq1+4Wo8OIi4McJsGxEzqTA/1Z4dDt0nVLspADgvd+PkJyml/JtU68awzsWvHaB0cLCwqyrGQcEBDBjxowyPb8QQoAkBqISy6zdX1hyUNVq9wtRISkFexbB5zfCfz9nbXfzheHzYPQPUL1hibrefTqalfsvWNuvDW6NyVS2BQfuvPNO9uzZQ9++fVm2bBmOjo5len4hhAAZSiQqOandL0QlcPkE/PYUnNycc/sNo+G2GeDmU+KuLRbFW78dsrYHta1NQFP7rBfg7+/P+vXr7XJuIYQASQxEFSC1+4WooNJTYdvnsHEmpCVlba/eCIZ8Ctf1LfUpVu6/wL6zVwBwcjDx4oBWpe5TCCEqKkkMRJUhtfuFqEAu7IWVj0PYgaxtmgm6PQZ9XgInt1KfIjElnfeylSd9oGcTGtYofb9FER8fz+jRo5k2bRodOnQok3MKIURhZI6BEEKI8iMlHv54Bb7tmzMpqN0OJv8Nt75lSFIAMGdzKBdj9CcRvh5OPHrzdYb0WxSPP/44K1eupGvXrnzzzTdldl4hhCiIPDEQQghRPoSsh9+ehiuns7aZXeDml6Hro+Bg3J+sizGJfL3phLX93K0t8HQpmwm/S5YsYf78+QCkpKTg7OxcJucVQojCSGJgQ5qmeQKZK+M4WiwWe4YjhBDlU/xl+ONl+Hd5zu1NeutzCXyaGn7KD9YeJTE1HYBWdapxT+cGhp8jL8ePH2fKlCnW9tixY7nvvvvK5NxCCFEYSQxs61ngjcxGeHi4HUMRQohyRik48D9Y+wIkXM7a7lIdbnsH2o8GzfiyofvPXmHF3qw1EF67vRUOZVCeNDk5mREjRhAXFwfoVYi+/PJLNBt8j0IIURKSGNjWR8CcjPdr/fz82tozGCGEKDeunNGHDYX8lXN7m+EwYCZ41LLJaZVSvJmtPOmtrf3ofp2vTc51reeff569e/cC4OTkRGBgIJ6enoUcJYQQZUcSAxtSSsUCsQCapqWaTDLXWwhRxVnSIfgb+PttSI3P2l6tPtz+MTS/zaan/+3fi+w+HQ2Ao4PGy4PKpjzpr7/+yuzZs63tDz/8UKoRCSHKHUkMhBBClI2wg3oJ0gt7sm3UoMuDcMtr4Gzbu+dJqenM/D2rPOmE7o1p7Otu03MCnDlzhvvvv9/avvPOO3nsscdsfl4hhCguSQyEEELYVmoSbH4fgmaBJS1re81WcMdn0ODGMglj3taTnL+SCICPuxOP9fW3+TnT0tIYPXo00dH6U4oGDRrw3XffybwCIUS5JImBEEII2zm1FVY+AVFZpUFxcIKbnoceT4LZqUzCiLiaxBcbQqztZ/o3x8vV9uVJTSYTt99+O9u3bwdg2bJl+Pj42Py8QghREpIYCCGEMF7iFVj3Ouz5Puf2ht1hyCyo2bxMw/ngj6MkpOjlSZv7eTDyxrIpT2oymXjxxRe56aab2L9/Pz169CiT8wohRElIYiCEEMI4SsHhlbBmKsRlK9HsXA36T4eOE6CMCzEcPB/D//acs7Zfu701ZoeyjaF79+507969TM8phBDFJYmBEEIIY1y9AKufg6Orc25veTsM+gCq1S3zkDLLkyqlt29pWYte/jVtek6LxYLFYsFslj+xQoiKRepnCiGEKB2LBXbOgy8CciYFHrXh3kUwcoldkgKAtQfD2HEyCgCzSePlwbYvT/rBBx/Qp08fzpw5Y/NzCSGEkeR2hhBCiJK7dBRWPQlntuXc3mkC9JsOrtXtERUAyWnpvPP7YWt7XLdGXFfTw6bn3LZtG6+88grp6em0b9+eTZs20batrG0phKgYJDEQQghRfGkpsPUT2PIhpKdkba/RDIbMhsb2n2Q7P+gUZ6P08qTV3Rx58hbblieNjo5m5MiRpKfrk5xbtmxJy5YtbXpOIYQwkiQGQgghcoo4DKGbIDlWX3SsaW+olW0IztkdegnSS1l34zGZoefT0Os5cHQp+5ivcSk2mc//zipP+nS/5lR3s11pVKUUkyZNsg4fql69OsuWLcPR0fYlUYUQwiiSGAghhNCFboRN78PpoNyfNeoB3R+HE3/Djm8BlfVZvc5wx2zwu76sIi3Ux+uOEpesL6Z2XU13Rgc0tOn5vvrqK1asWGFtz5s3j0aNGtn0nEIIYTRJDIQQQsCehfpcAWXJ+/PTQbkTBkd3uOV16DIZTA62j7GIDl24SuDOs9b2q7e3xtGG5Un37dvHM888Y20/+uijDBs2zGbnE0IIW5HEQAghqrrQjQUnBXnxvxUGfwTVbXsnvriUUrz12yEsGQ80ejevyc0tatnsfHFxcYwYMYLk5GQAbrjhBj788EObnU8IIWxJEgMhhKjqNr1fvKTAtwWM/gE0zXYxldC6Q+FsC70MgINJ41Ublyd99NFHOXbsGADu7u4EBgbi4mL/ORZCCFESso6BEEJUZRGH855TUJDIo3DpiG3iKYWUNAvvrMmaED0moCH+fp42O9+KFStYuHChtf3ll1/SokULm51PCCFsTRIDIYSoykI3le1xNrRw2ylOXU4AoJqLmaf6Nbfp+QYOHMjDDz8MwLhx4xg/frxNzyeEELYmQ4mEEKIqS44t2+Ns5HJcMrPWH7e2n+zXHB9325UnBXB1deXLL79k4MCB3HzzzTY9lxBClAVJDIQQoipzLuFQm5IeZyOf/HWM2CS9PGkTX3fGdS27UqFDhgwps3MJIYQtyVAiIYSoypr2LtvjbOBoWCxLg89Y268MaoWT2TZ/3k6dOoVSqvAdhRCiApLEQAghqrJarcDnuuId06hnzpWQ7Ugpxdurs8qT9mzmyy2tbFOe9PTp03To0IERI0YQExNjk3MIIYQ9SWIghBBV2fG/IOpk0ffXTNB7qu3iKaYNRyPYcjwSAJMGr97eCs0GZVRTU1MZNWoUV65c4ccff2To0KGGn0MIIexNEgMhhKiqwg7CjxOAIq5hoJlgyGxo2seGQRVdarqFt1dnlScd2aUhLWtXs8m53njjDbZt2waAg4MDM2bMsMl5hBDCniQxEEKIqujqRVh6L6RkVBfyagB3L9CHCeWlUU8Y9zN0HFdmIRZm8fbThF6KB8DT2cwz/W1TnnTdunXMnDnT2p4xYwbdunWzybmEEMKepCqREEJUNclxelJw9bzedq6mr2Ts1xraDNUXPQvdpJckdfbUJxqXkzkFmaLjU/j0r6zypI/f0gxfD2fDzxMWFsa4ceOsE45vvfVWpk4tP0OphBDCSJIYCCFEVWJJh58mQti/eltzgHsW6ElBplqtyl0icK1Z648Tk5gKQKMabtzXvbHh57BYLIwbN47w8HAAateuzcKFCzGZ5GG7EKJykt9uQghRlax9CY6tzWrf/jE0u8V+8ZRASEQsi7aftrZfGtgKZ7OD4ed57733+OuvvwDQNI1Fixbh5+dn+HmEEKK8kMRACCGqiu1fwY5vsto9noJOE+wVTYnNWH2Y9Iz6pF2b+nDb9cZfrAcFBfHaa69Z2y+99BL9+vUz/DxCCFGeSGIghBBVwZHV+tOCTNcPhVvesF88JbTp2CU2HL0EgKbBa7e3Nrw8qVKKqVOnkp6eDkCPHj2YPn26oecQQojySBIDIYSo7C7shZ8mARmrgNXvAnd9BRVsrHxauoW3fztkbd/bqQHX1/Uy/DyaprFy5UoGDx6Mt7c3S5cuxWyWKXlCiMpPftMJIURlduUsLB0BqQl627sxjFoGjq52Dasklu04w/GIOADcnRx49jbblCcF8PX1ZdWqVZw4cYKGDRva7DxCCFGeVKzbRUIIIYouKUYvSxqnV9XBpTqM+R+4+9o1rJKISUjl43XHrO1H+zajlqeLTc+paRrNmjWz6TmEEKI8kcRACCEqo/RU+OE+iMgYemNyhJFLwNffvnGV0Gd/Hyc6QS9PWt/blQd6NDG0/9jYWGsFIiGEqKokMRBCiMpGKVj9DIRuyNp25xfQOJ9Vjcu50EtxLPjnlLX90sBWuDgaV55UKcUjjzxC//79ee6550hJSTGsbyGEqEgkMRBCiMom6FPYszCr3ecluGGE3cIprXfWHCEtozzpjY29GdS2tqH9L1y4kMWLFwPw0Ucf8ccffxjavxBCVBSSGAghRGXy38/w17SsdruR0PsFu4VTWkEhkfx1WJ8joWnw+u3XG1qe9MiRIzzyyCPW9oQJExgyZIhh/QshREUiiYEQQlQWZ3fAioey2o16wh2z9SvqCijdongrW3nSYR3q07a+ceVJExMTGTFiBAkJesWmli1b8vnnnxvWvxBCVDRSrtSGNE3zBDwzmo4Wi8We4QghKrOoUFg2EtKT9XYNfxixCMzO9o2rFAJ3nuVIWCwAro4OPD+ghaH9P/vss/z7778AODs7ExgYiLu7u6HnEEKIikSeGNjWs8D5jFfb8PBwO4cjhKiUEqJgyb2QcFlvu9WAMT+Am4994yqFq0mpfPTnUWv7kT7X4VfNuPKkP/30E1999ZW1/emnn9KuXTvD+hdCiIpIEgPb+giol/E64OfnZ+dwhBCVTloyBI6Dy8f1toMzjFwGPk3tG1cpfbEhhMvxenWgul4uTL7JuO/n5MmTTJw40doePnw4Dz30UAFHCCFE1SBDiWxIKRULxAJompZqMkkeJoQwkFKw8gk4vTVr27BvoGGA/WIywOnL8czfesrafmFgS8PKk6ampjJq1ChiYmIAaNy4MXPnzjV0QrMQQlRUcqUqhBAV1ab34N/lWe1+0+D6oXYLxyjvrjlCSro+J6tDw+rccUNdw/o+cOAA//33HwBms5nly5dTvXp1w/oXQoiKTBIDIYSoiPYHwsZ3s9odx0OPp+wWjlG2nbjM2v/CrO3Xb29t6N38jh07smfPHjp27Mg777xDQEDFfroihBBGkqFEQghR0ZzaCr8+mtVuejMM/rjCliXNlG5RvL06qzzpXe3r0qGht+Hn8ff3559//sHR0dHwvoUQoiKTxEAIISqSyOOwfAxYUvV2rdZw7/fgUPEvcn/afY7/LlwFwMXRxPMDWtrsXM7OFbeMqxBC2IoMJRJCiIoiPhKW3A1JV/S2hx+M/gFcjFv0y17iktN4/4+s8qQP3XQddau7GtL3nDlz2Lt3ryF9CSFEZSaJgRBCVASpibBsFESf0tuObjA6EKo3sGtYRvlqYwiRcfribH7VnHmotzHlSbds2cLDDz9M165d+eyzz1BKGdKvEEJURpIYCCFEeWexwC8Pw7kdGRs0GD4X6nawa1hGORuVwLdbTlrbLwxoiZtT6Ue6Xr58mdGjR2OxWEhJSeHHH38kPT291P0KIURlJYmBEEKUd3+/Cf/9nNUe8C60HGy/eAw2c+0RUtL08qQ31Pfirvb1St2nUooHHniAc+fOAeDj48PSpUsxm2VqnRBC5EcSAyGEKM92L4Ctn2S1uzwIAVPsFo7Rdp6KYvW/F63t14e0xmQqfXWl2bNns3LlSmt7wYIF1K9fv9T9CiFEZSaJgRBClFcn/obfnslqNx8AA2ZW+LKkmSwWxVu/ZZUnvb1dHTo18il1v7t372bq1KnW9lNPPcWQIUNK3a8QQlR2khgIIUR5FH4IfrgPVMaY+NrtYPg8MDnYNy4D/bz3PP+eiwHAyWzixYGlL08aGxvLyJEjSU3Vy7l26tSJmTNnlrpfIYSoCiQxEEKI8iY2DJbeC8l6TX+q1dPLkjp72DcuAyWkpPH+H0es7Qd7NaW+t1up+lRKMWXKFEJCQgDw9PRk+fLlsmaBEEIUkSQGQghRnqTEw9IREHNWbzt56ElBtTr2jctgX28KJfyqXp60pqczD/e5rtR9LliwgKVLl1rb33zzDc2aNSt1v0IIUVVIYiCEEOWFJR1+mgQX9+ltzQHu+R5qt7FrWEY7fyWRbzadsLan3tYCd+fSVwu6cuUKDg76UKuJEycyatSoUvcphBBViSQGQghRXvz5Khxdk9Ue/CH497NfPDby/tojJGeUJ72+bjXu7mhMtaCnn36azZs3M2DAAGbNmmVIn0IIUZVIQWchhCgPgufA9i+z2t2fgM4P2C8eG9lzJppf912wtl+/3ZjypJm6d+/O77//blh/QghRlcgTAyGEsLeja2HtC1ntVndAv+n2i8dGlFK8uSqrPOnANrUJaFrDjhEJIYTIThIDIYSwpwv74H8PgNKH1lCvMwybA6bK9+t55f4L7Dt7BQAnBxMvDWxVqv5CQ0MZPHgwZ86cMSA6IYQQle8vjxBCVBQx5/QKRKnxert6Qxi1DBxd7RuXDSSmpDPz96zypA/0bELDGiUvT5qSksKoUaNYs2YN7du3Z+3atUaEKYQQVZokBkIIYQ9JV2HJvRAXprddvGDM/8Cjln3jspFvt4RyMSYJAF8PJx69uXTlSV955RV27NgB6Iua+fiUfsVkIYSo6iQxEEKIspaeBv+7HyL+09smM4xYDDVb2DcuGwmLSeKrjVnlSZ+9tQWeLo4l7u/333/nww8/tLZnzpxJly5dShWjEEIISQyEEKJsKQVrnoOQv7K23fEZNLnJfjHZ2Pt/HCExNR2AlrU9ubdzgxL3df78ecaPH29tDxo0iKeffrrUMQohhJDEQAghytY/n8Hu+Vntm56H9qPtF4+N7T97hRV7zlvbrw9pjUMJy5Omp6czduxYIiMjAahbty4LFizAVAknagshhD3Ib1MhhCgrh36Fda9ltdveAze/bL94bEwpxVu/ZZUn7d/aj+7X+Za4vxkzZrBx40YATCYTS5YsoWbNmqUNUwghRAZJDIQQoiyc2wUrHsxqN+wOd34BmnGLe5U3qw9cZNfpaAAcHTReHlTy8qSbNm1i+vSstR1ee+01+vTpU9oQhRBCZCOJgRBC2Fr0Kb0saZpelQef62DkEjA72zUsW0pKTefdNVnlSSd0b0wTX/cS9RUZGcmYMWOwWPS1Hnr37s1rr71WyFFCCCGKSxIDIYSwpcRoWHIPJOjj4nH1gTE/glvlLq85b+tJzl9JBMDH3YnH+vqXuC93d3fuuOMOAHx9fVmyZAkODg6GxCmEECKL2d4BCCFEpZWWAoHjIPKY3nZw0hcwq1G6Gv7lXcTVJL7cEGJtP92/OV6uJS9P6urqypdffknfvn3x8PCgXr16RoQphBDiGpIYCCGELSgFvz0Fp7ZkbbvrK2jY1W4hlZUP/zxKfIpenrS5nwejbix5edLs7r77bkP6EUIIkTcZSiSEELaw+UPYtySr3fc1aFv5L2wPno/hx93nrO3Xbm+N2aH4f2qSk5NRShkZmhBCiEJIYiCEEEb790fY8HZWu8NY6PWs/eIpI5nlSTOv5/u2rEUv/+KXE1VKMWHCBEaOHElMTIzBUQohhMiPDCUSQggjnf4Hfn0kq92kN9z+aaUuS5rpj//CCD4ZBYDZVPLypPPmzWP58uUA7Ny5k927d+Pt7W1YnEIIIfImTwyEEMIokSGwfDSkp+jtmi3h3oXgUPKJtxVFclo672QrTzquWyOa1fIodj///fcfTzzxhLXdr18/SQqEEKKMSGIghBBGiL8MS+/Ry5MCuNeC0T+Aa3W7hlVWFgSd4kxUAgDV3Rx58pbilydNSEhgxIgRJCbqZU6vv/56Pv30UyPDFEIIUQBJDIQQorRSk/QnBVGhetvsCqOXg3cj+8ZVRi7FJvPZ31nlSZ+6xZ/qbk7F7uepp57iv//+A/QSpYGBgbi5uRkWpxBCiIJJYiCEEKVhsehzCs5uz9igwfBvoV4nu4ZVlj5ed4y45DQArqvpzpiuxU+IAgMD+fbbb63t2bNnc/311xsWoxBCiMJJYiCEEKWxYQYc/Cmrfevb0GqI/eIpY4cvXiVw5xlr+9XbW+NYzPKkoaGhTJ482doeOXIkEydONCxGIYQQRSOJgRBClNSeRbDlw6z2jZOg26P2i6eMKaV4e/UhLBnlSW9qXpObW9QqVh8pKSmMGDGC2NhYAJo2bco333yDVgWqOAkhRHkjiYEQQpRE6EZ9ZeNM/rfCgPeqRFnSTH8djiAo5DIADiaNVwcXvzzpO++8w65duwBwdHQkMDCQatWqGRqnEEKIopHEQAghiiviCASOB4s+rp7abeHu78Ch6iwNk5JmYcbqQ9b2mICGNPfzLHY/jz76KIMGDQLgvffeo3PnzobFKIQQoniqzl8xIYQwQmw4LLkHkjNW5PWsq5cldS7+RXFFtnDbKU5d1suTVnMx81S/5iXqp2bNmqxatYqff/6ZYcOGGRmiEEKIYpLEQAghiiolAZaNhJiMybZOHjA6EKrVtW9cZSwqPoVZ649b20/c4o+Pe/HLk2YymUwMHz7ciNCEEEKUgk2HEmmaVkPTtKGapt2maZqDLc8lhBA2ZUmHFZPhwh69rZng7vlQp51947KDT9YdIzZJH0bVxNed8d0aF+v40NBQG0QlhBCitAxJDDRNe1jTtGBN03yybesEHAH+B6wB/tE0zd2I8wkhRJlb9zoc+S2rPfB9aH6r/eKxk2PhsSzdkVWe9JVBrXAyF/1PyYYNG2jevDlTp04lJSXFFiEKIYQoIaOeGIwAlFIqKtu2DwBvYD56YnAjMMWg8wkhRNnZORe2fZ7V7vYYdJmc//6VlFKKt347RHpGfdIezWpwS6uilye9dOkSY8aMIT09nQ8//JCpU6faKlQhhBAlYFRi4A/8m9nQNM0X6A3MU0pNUkoNAXYCow06nxBClI1jf8KabBewLW+H/m/ZLx472nj0EluORwJg0uDVwa2LvN6AxWLhvvvu4+LFi4A+6fiFF16wWaxCCCGKz6jEoAYQka3dI+Prz9m2bQEaGXQ+IYSwvYv/wv/uB2XR23U7wrBvwVT1Kj2nplt4K1t50pFdGtKqTtHXG/jkk0/4/fffre3vv/+eunWr1qRtIYQo74yqShQF+GZr9wYswD/ZtinAxaDzCSGEcSIOQ+gmSI7Vy4427Q3O1WDpCEiJ0/fxagijloOTm31jtZMl208TeikeAE9nM8/0L3p50h07dvDiiy9a28899xwDBw40PEYhhBClY1RicBgYomnaK0A6MBLYqZS6mm2fxkCYQecTQojSC90Im96H00G5P3N0h1T9QhhnLxjzA3j6lWl45cWVhBQ++SurPOljfZvh6+FcpGNjYmIYOXIkaWl6FaMuXbowY8YMm8QphBCidIxKDGYBvwDngDTADXj+mn26AjsMOp8QQpTOnoWw6smsYULXykwKNBOMWAi1WpVdbOXMp38dJyYxFYCGPm5M6NG4SMcppZg8eTInT54EoFq1aixfvhwnp5KveSCEEMJ2DEkMlFIrNU2bAjyYsWmJUmpx5ueapvUBPIA/jDifEEKUSujGgpOC7JSyeTjlzbHwWIJCIolLSiMpNZ1F205ZP3t5UCuczUVblubbb7/lxx9/tLbnzp1LkyZNjA5XCCGEQQxb+VgpNQeYk89nG9FLlwohhP1ter9oSQEACjZ9AE372DKiciEoJJJZ64+z42RUnp+3rO3JbdcXbThVQkICr732mrX90EMPcc899xgSpxBCCNswvLSGpmnumqZ10DStl9F9CyFEqUUczntOQUFOb9WPq8QCd55h3LzgfJMCgKPhsfy461yR+nNzc2Pr1q107NiRtm3b8sknnxgVqhBCCBsxLDHQNK2+pmk/AdHALmBDts96app2KGNIkRBC2E/oprI9rgIIConkpRUHsBQyakopeHHFvwSFRBapX39/f/755x/WrFmDq6urAZEKIYSwJUMSA03T6gDBwJ3Ab8A2IPuqN8FALfQVkoUQwn6SY8v2uApg1vrjhSYFmSwKZq8/XviOGZydnalfv34JIxNCCFGWjHpi8Ab6hX9/pdQwYF32D5VSqegLnPXI49hyR9O0lzRN26lp2lVN0y5pmrZK07Q29o5LCGEAZ8+yPa6cOxYeW+DwobwEn4ziWHjuRCkkJIS9e/caFZoQQogyZlRiMAhYqZTaUMA+Z4CKssxlH+BLoDvQF70E61+apvnYMyghhAGa9i7b48q5og4LKuy45ORkRowYQdeuXfnss89QVbCakxBCVHRGJQZ+QGHPllMBd4POZ1NKqduUUvOVUgeVUgeAcUBNKsgTDyFEAWq1ggYBxTumUc9Ku45BXFKaIce98MIL7Nmzh5SUFJ599llCQkKMCE8IIUQZMioxiAIaFLJPcwxa+VjTtLs1TftM07QtGcN9lKZpiws5pr6mad9pmnZB07RkTdNOaZr2qaZpRSmj6on+s4o2In4hhB2lJBRvvoBmgt5TbRePnXm4lKxqdfbjVq5cyaxZs6ztDz74AH9//1LHJoQQomwZtY5BEHCHpmm1lVK5Lv41TfMHBgAFXrwXw6vADUAc+mrLLQvaWdO064B/0OdB/AocAboATwIDNE3roZS6XEAXs4B96JOqhRAVVXoq/DgBIg4VbX/NBENmV+o1DHo08y3VcWfPnuX++++3br/jjjt44oknDIlNCCFE2TLqicEHgAuwSdO0gYAbWNc0GAisAizARwad72n0JxDVgIeLsP+X6EnBE0qpu5RSLyql+gKfAC2AGfkdqGnax0BPYLhSKr3UkQsh7MNigV8ehuPZFmDv/IA+TCgvjXrCuJ+h47iyic9Omvt5Uq968UqJBjTxobmfJ2lpaYwePZqoKH3ycv369fnuu+/QNK2QHoQQQpRHhjwxUEoFa5r2EPAVernSTFczvqYBDyil/jPofNnXSChw34ynBbcCp4Avrvn4DeBBYJymac8qpeKvOfYTYCRws1IqtPSRCyHsQilY+wIc+DFrW6/n4JaMlXkjDuvrFCTH6tWHmvautHMKrvX538c5fyWxyPubNHjiFn2Y0PTp09m6dau+3WRi6dKl1KhRwyZxCiGEsD2jhhKhlPpO07QtwCNAV6AGEANsBz5XSh016lzFdHPG1z+VUpbsHyilYjVNC0JPHLoC6zM/0zRtFvq6CzcrpY6UVbBCCBvYOBN2zMlqd34A+r6a1a7VqsokAtl9uTGED/88VuT9TRrMHNaOHs18Wb9+PTNmZD1snT59Or16yYL3QghRkRmWGAAopY6jD/MpT1pkfM3vr99x9MSgORmJgaZpX6BXIroLiNY0rXbGvnFKqbiCTqZp2u58PipwHoQQwka2fw2bZma1rx8Ggz6EKj7c5ZtNJ3h/bdb9ml7+vjzQowlfbzpBcB7rGgQ08eGJW/zp0cyX8PBwxo4day1J2rdvX1566aUyi10IIYRtGJIYaJo2HtinlPq3gH3aAh2UUguNOGcxeGV8jcnn88zt1bNteyTj6/qcuzIdmGZIVEII29u/XB9ClKlZPxj6DZgc7BdTOTB3Syjv/p71ILT7dTWYM64zrk4O3NyyFsfCYwkKiSQuKQ0PFzM9mvnS3C9rgbfNmzdz6dIlAGrWrMnixYtxcKjaP1MhhKgMjHpisAD9gjnfxAC4A3gTKOvEoNiUUiW+laiU6pTX9ownCR1LHJQQoniO/g6/PJLVbhAA9y4Es5P9YioH5m09ydurD1vbXZv6MO++G3F1yrqwb+7nmSMRuNY999xDvXr1GD16NF9//TV16tSxacxCCCHKhqFDiQrhANhjKczMJwJe+Xyeuf2K7UMRQpSJU0F6WdLMQmK1rofRgeBUIdZYtJkFQSd567esUq1dmvjw3YScSUFRde/enaNHj+Ls7GxkiEIIIezIqHKlRdEc+ywQljmItnk+n2euwlP0GXhCiPLr4n5YNhLSkvS2d2MYtwJci7KWYeW1cNsppq3KSgpubOzN/Ak34uZU8vtDkhQIIUTlUuK/CJqmfXfNprs0TWucx64OQEOgF7C6pOcrhczSprdqmmbKXplI0zRPoAeQgF49SQhRkUWGwKJhkJxRKdmjNoz7BTxrF3hYZbd4+2le/zWrWnSnRt7Mv78L7s5F+xOglOLNN9/k/vvvp2HDhrYKUwghhJ2VZijRhGzvFdA+45UXBQRjh4pFSqkTmqb9iV556FHgs2wfTwfcgW+uXcNACFHBxJyHRXdBQqTedvHSnxT4NLFrWPa2NPgMr/5y0Nru0LA6C+6/EY8iJgUAX3/9NdOmTWPWrFnMnz+fO++80xahCiGEsLPSJAaZf201IBT4FJiVx37pQLSRF96apt2FXkoUIPNWYDdN0xZkvI9USj2X7ZBHgH+A2Zqm3QIcBgLQ1zg4BrxiVGxCCDuIvwyLhkLMWb3t6AZj/gd+19s3LjsL3HmGl38+YG3f0KA63z/QBU8XxyL3sX//fp5+Wr+nEx0dzZ9//imJgRBCVFIlTgyUUqcz32uaNh3YkH2bjbUH7rtmW9OMF8BpwJoYZDw16IxeFWkAMAi4iJ7ITFdK2WPugxDCCMmxsORuiMyYTmRyhBGLoEEX+8ZlZz/uOsuLK7KSgnb1vVj4QBeqFSMpiI+PZ8SIESQnJ+t9tGvHRx99ZHisQgghygdDqhIppaYb0U8xzjeNYq4noJQ6C9xvi3iEEHaSlgzLx8CFPRkbNBj2jb5eQRX20+5zPP/Tv2SsP0abetVY9EAAXq5FTwoAHnvsMY4e1RMuNzc3AgMDcXFxMTpcIYQQ5YSh5Uo1TasD3ALUA/IqV6GUUm8Zec7yLGNyc2YxcEeLxVLQ7kKI4khPg58mwslNWdtu/xjaDLdfTOXAL3vP89z/9luTgtZ1qrF4YgBebsVLChYvXsyCBQus7S+//JKWLWUBdyGEqMwMSwwyhhO9eE2fGllrF2S+rzKJAfAs8EZmIzw83I6hCFGJKAW/PQWHV2Vt6/sadH7AbiGVB7/uO88zP+yzJgUta3uyZFIA1d2Kt6jbsWPHmDJlirU9btw47rvv2tGbQgghKhtD1jHQNG0M8BqwBbgbPQn4HhgNfAtYgOVAXyPOV4F8hP70pB5wwM/Pz87hCFEJKAXrXoO9i7K2dXsMej1rv5jKgd/+vcDTgfuwZCQFLfz0pMDbvXhJQXJyMiNHjiQ+Xq8X4e/vz5dffml0uEIIIcoho54YPAycAwYopdI0TQM4pZRaDizXNO1n9DUMlhl0vgpBKRULxAJompZqMpXlenJCVFJbP4F/slUdbj8Gbn0b9N87VdKaAxd5cnlWUuBfy4MlkwOo4VH8BcimTp3K3r17AXByciIwMBAPDw8jwxVCCFFOGXWl2hZYo5RKy7bNIfONUuoP4A9gqkHnE0JURbvmw/pstQ5a3g5DZlfppGDtwTCeWLaX9IysoFktD5ZO7opvCZICi8VCenq6tf3RRx/RoUMHw2IVQghRvhn1xMARuJytnQh4XbPPQWAKQghREv/9DL9lWyOxcS8YPg8cDK2hUKH8+V8Yjy3dQ1pGUtC0pjtLJwdQ07P4SQGAyWTiiy++4Oabb2b16tU8+uijRoYrhBCinDPqL+pFoE629hmg3TX71AXSEEKI4gpZDz9NxlrLoE57GLkUHKtu6cy/DoXzaLakoImvO8smd6WWZ+l/JnfffTd33313qfsRQghRsRg1lGgv0CZb+2+gl6Zp4zRNc9c0bTD6pOS9Bp1PCFFVnN0JgWPBkqq3fZvD2J/ApZp947KjDUcieGTJHlLT9aSgcQ03lk3uil+1qpsoCSGEKD2jEoPfgDaapjXJaM8EYoAFwFVgJXqlolcNOp8QoioIP6SvapyaoLer1YdxP4O7r33jsqONRyN4aNFuUtL1dVEa+rix7MGu1PYqWVLw119/MXLkSGJiYowMUwghRAVkSGKglFqglHJTSp3MaJ8FbgS+Av4E5gA3KqW2G3E+IUQVEH0KFg2FpCt6280Xxv8CXvXtGJR9bT52iQezJQUNfFxZ9mBX6ni5lqi/8PBwxo4dS2BgIB06dODAgQNGhiuEEKKCsdmsvYwk4TFb9S+EqMRiw2HhXRAXpredPPXhQ77+dg3LnrYej2Tywl2kpOlJQX1vV5ZN7kq96iVLCiwWC+PGjbMuvBgfH0/NmjUNi1cIIUTFU3XLeZQBTdM8Ac+MpqPFYrFnOEJUDIlXYPFwiD6ptx2cYfRyqNvenlHZ1T8hkUxauJPkjKSgXnU9Kajv7VbiPt9//33WrVsHgKZpLF68mNq1axsSrxBCiIpJVtyyrWeB8xmvtpl35oQQ+UhJgKUjIDxjSIvmAPcsgMY97RqWPW0PvcwD3+8kKVVPCup6ubBsclca+JQ8Kfjnn3949dWsKV8vvvgi/fv3L3WsQgghKjbDnhhomlYfeBpoD9RHX9vgWkopdZ1R56wAPkKfXwGw1s/Pr609gxGiXEtPhR/vg7PZpiLd+QW0HGS/mOxsx8ko7p+flRTUrubCsge70rBGyZOCqKgoRo0aZV3IrHv37rz55puGxCuEEKJiMyQx0DStD7AGcEFfqyCcvNcsqFLLkyqlYoFYAE3TUk0meUAjRJ4sFvh5Chz/M2vbbe9C+1H2i8nOdp2KYsL8HSSm6hfwftWcWfZgVxrVcC9xn0opJk6cyJkzZwCoXr06y5Ytw2yWUaVCCCGMe2LwPuAAjAeWKqVkML0QomiUgt+nwsH/ZW27aSp0e8R+MdnZ7tPR3PfdDhJS9KSgpqczSyd3pYlvyZMCgC+++IJffvnF2p4/fz4NGzYsVZ9CCCEqD6MSg7bAMqXUYoP6E0JUFRvegZ1zs9o3ToKbX7FfPHa294yeFMRnJAW+Hs4sm9yV62p6lKrfffv28eyzz1rbjz32GHfddVep+hRCCFG5GDW2JRqIMqgvIURVsf0r2Px+VrvNcBj4AWhVatSh1f6zVxg/bwdxyfpITF8PJ5ZNDqBZrdIlBQD16tWjX79+ALRv354PPvig1H0KIYSoXIx6YvAb0NugvoQQVcG+ZbD2xax2s35w19dQRefi/HvuCmPnBRObkRT4uDuxZFJX/P08CzmyaGrWrMmqVauYPXs2gwYNwsWlZCslCyGEqLyM+gv8MuCladoXmqaVbhCsEKLyO7IGfn00q90gAO5dBGYn+8VkRwfPxzB2bjCxSXpS4O3myNLJAbSobUxSkMlkMvHUU0/RvHlzQ/sVQghRORjyxEApFalp2gAgGBivadoxICbvXdUtRpxTCFFBndoKP04ApY+hx68NjA4Ep5KX4KzI/rsQw5i5wVzNSAqquzmyZFJXWtauVuq+k5KS5MmAEEKIIjOqXOn1wAbAO2NTh3x2VUacTwhRQV3YB0tHQnqy3vZuAmNXgKt3gYdVVocvXmXs3GBiElMB8HJ1ZPHEAFrXNSYp6NatG7fccgvvvPMOTk5V82mMEEKIojNqKNHHQA3gdaAR4KiUMuXxcjDofEKIiiYyBBYPh5RYve1RG8b/Ap5+dg3LXo6GxTJmbjDRCXpSUM3FzOKJAbSp52VI/8899xz79u3jo48+on///lgsUkVaCCFEwYyafNwNWKGUetug/oQQlUnMOVh0FyRE6m2X6jDuZ/BubMeg7OdYeCyjv91OVHwKAJ4uZhZNDKBtfWOSghUrVvDFF19Y2yNGjEAWWBRCCFEYoxKDFOCUQX1VGpqmeQKZswcd5Y6dqJLiL8OioRBzVm87usGYH8GvtX3jspOQCD0puJyZFDibWfhAF25oUN2Q/k+dOsXEiROt7eHDh/Pwww8b0rcQQojKzahbSBuBLgb1VZk8C5zPeLUNDw+3czhClLHkWFgyHCKP6W2TI4xYDA2q5q+LE5fiGPVtMJFxelLg7uTAgge60KGhMXMsUlNTGT16NFeuXAGgUaNGzJ07F62KrgshhBCieIxKDJ4HWmua9qImf4Gy+wiol/E64OdXNcdSiyoqNQmWj4YLezM2aDBsDjSrmoXJQi/FMWrOdi7F6hOv3Zwc+P6BLnRqZNzE69dff51t27YB4ODgwPLly6levbph/QshhKjcjBpK9CpwEJgBTNY0bR/5lyudmMf2SkkpFQvEAmialipjfEWVkZ4GP02Ek5uztt3+CbQZZr+Y7OhUZDyjvt1ORLakYMH9Xejc2Mewc/z555/MnDnT2p4xYwZdu3Y1rH8hhBCVn1GJwYRs75tkvPKigCqTGAhRJSkFq56EI79lbbvldeh8v/1isqPTl/WkIPyqnhS4Ojrw3YQb6dLEuKQgLCyMcePGWdu33XYbU6dONax/IYQQVYNRiUF+iYAQoipRCv58FfYtztrW7THo+Yz9YrKjs1EJjJqznYsxSQC4OJqYN6EzXZvWMPQ8jzzyCBEREQDUrl2bhQsXShUiIYQQxWbUysenjehHCFHBbf0Ytn2e1W4/Fm59G6rg1KOzUQmMnLOdCxlJgbPZxLz7bqT7db6Gn+u9997j9OnT7N27l8WLF1OrVi3DzyGEEKLyM+qJgRCiqtv1Hax/M6vd8nYYMqtKJgXnryQy6tvtnL+SCICT2cS34zvTo5nxSQGAv78///zzD+vXr+eWW6rm5G4hhBClJ8+ahRCld3AF/JZtuFCTm2D4PHCoevceLlxJZNSc7ZyLzkgKHEzMGdeJm5rXtOl5nZ2dGTRokE3PIYQQonIr0V9tTdO+Q59I/LJSKjyjXRRVqiqREFVCyF+w4kH0XwlA3Q4wcik4utg1LHsIi0li1LfbOROVAOhJwTfjOtGnhbFDe5RSnDhxgmbNmhnarxBCiKqtpLfzJqBfBbwHhJOzKlFBpCqREJXJ2R0QOA4sqXrbtzmM+QmcPQs+rhIKv6onBacv60mBo4PGV2M7cnNL48f7f/755zz33HN8+OGHPPbYY7KAmRBCCEOUNDHIrEJ0/pq2EKKqCD8ES+6BVP1CGK8GMO4XcDe24k5FEJGRFJyMjAfAbNL4YnRHbmll/KKGe/bs4bnnniMlJYUnnngCV1dXJk2aZPh5hBBCVD0lSgzyqELUG9inlPo3v2M0TWsDdAQWluScQohyJOokLBoKSVf0tpuvnhR41bNnVHZxKTaZUd9uJ/RSVlLw+eiO3Hp9bcPPFRsby8iRI0lJSQGgY8eOOdYvEEIIIUrDqMnHC4C7CtnnTmC+QecTQthLbLieFMSF6W0nTxj7E/hWvfHukXHJjP52OycykgIHk8ZnozowoI3xSYFSikceeYTjx48D4OHhwfLly3F2djb8XEIIIaqmsqxK5IB1dqIQokJKjIbFwyD6pN42u8Do5VC3vV3DsofLccmM+TaY4xFxgJ4UzBrZnoFt69jkfN9//z2LF2ctHPf111/j7+9vk3MJIYSomsqylmBzILoMz2d3mqZ5ApmzMB0tFos9wxGidFLiYekICD+otzUHuGcBNO5p17DsISo+hTFzgzkaHguASYNPRrTn9nZ1bXK+w4cP8+ijj1rb999/P2PGjLHJuYQQQlRdJU4M8ihRepemaY3z2NUBaAj0AlaX9HwV1LPAG5mN8PBwO4YiRCmkpcAP4+FscNa2u76EFgPtF5OdXElIYezcYI6EZSUFH9/bnjtusE1SkJiYyIgRI0hI0Cd5t2rVis8++8wm5xJCCFG1leaJwYRs7xXQPuOVFwUEA0+X4nwV0UfAnIz3a/38/NraMxghSsSSDr9M0dcryDRgJtww0n4x2UlMQipj5gZz6OJVQF/U+cN7buCuDrabdP3MM89w4MABQF/ELDAwEHd3d5udTwghRNVVmsQgs0SpBoQCnwKz8tgvHYhWSsWX4lwVklIqFogF0DQt1WSShaZFBaMUrJkKB3/K2nbT89D1YfvFZCcxiamMnRfMfxeykoL3h7djWMf6NjvnsWPH+Pbbb63tTz/9lLZt5f6CEEII2yhxYpC9ZKmmadOBDXmUMRVCVGQbZsCueVntGyfDzS/bLx47uZqUyvh5wRw4H2Pd9t6wdtzTuYFNz9u8eXM2bdrEqFGjCAgI4KGHHrLp+YQQQlRthkw+VkpNN6IfIUQ5su1L2PxBVrvN3TDwff1WeRUSm5TK+Hk72H8uKyl4d1hb7r3RtklBph49erBv3z4cHBxkhWMhhBA2ZdOqRJqm1QBuAhKAv5RS6bY8nxDCIPuWwR8vZbWb9YehX0MVGw4Xl5zGfd/tYN/ZK9Ztb9/VhlFdGpZpHD4+PmV6PiGEEFWTIYmBpmkPo09GHqiUisrY1glYC2T+RdulaVrfqjjXQIgK5cga+DWrNCYNusK9C8HB0X4x2dix8FiCQiKJS0rDw8VMj2a+1K3uyoTvdrDnzBXrfm/eeT1juzayaSxBQUE0bNiQBg3K5omEEEIIkcmoJwYjAJWZFGT4APBGX+3YDxgMTEGv1COEKI9OboEfJ0Dmwz2/tjA6EJzc7BqWrQSFRDJr/XF2nIzK9Zmns5nY5DRre9qQ1ozv1tim8Vy4cIGhQ4eSlpbGggULuOOOO2x6PiGEECI7oxIDf7KtUaBpmi/QG5irlHooY1swMBpJDISwv4jDELoJkmPB2ROa9oa0JFg2CtKT9X28m8DYn8C1ul1DtZXAnWd4acUBLPmsx549KXjt9tZM6NEk7x0Nkp6eztixY7l06RIAU6ZM4ZZbbpHSpEIIIcqMUYlBDSAiW7tHxtefs23bQs61D4QQZS10I2x6H04H5f7MZAZLxsWwZx0Y/wt4+pVldGUmKCSywKQgOw1oWduz0P1K691332XDhg36OTWNxYsXS1IghBCiTBk1kzAK8M3W7g1YgH+ybVOAi0HnE0IU156FqIVD4XQQ6poLYqXISgrMrjB2BXg3LusIy8ys9ceLlBSA/otr9vrjNo1ny5YtvPGGdZF0Xn31Vfr27WvTcwohhBDXMuqJwWFgiKZpr6AvaDYS2KmUupptn8ZAmEHnE0IUR+hG1Mon0bAAuSuOZm+rtCS0+AigddnFZyOp6Rbik9OIS04jPjmduOQ0joZdzXNOQUGCT0ZxLDyW5n7GPzm4fPkyo0ePxmLR/9v06tWL119/3fDziMpFKUVCQgJXr14lLi6O9PR01LUZvxCiQtE0DScnJ7y8vPDy8sJstmnx0DwZdcZZwC/AOSANcAOev2afrsAOg84nhCiGmLUz8MpICgqjoYhZ+w5ej/SxbVB5sFgU8SlZF/HxGa+45DTiU9KIS07PuS05576Z+2VuS0kr2vdcFEEhkYYnBkop7r//fs6dOwfoZUmXLl1qlz8GouKwWCycP3+euLg4e4cihDCQUork5GQiIiK4fPkyjRs3xsnJqUxjMGqBs5Wapk0BHszYtEQptTjzc03T+gAewB9GnE8IUQwRh/GK2IFSRVubTCnwigjWJyjXalXIvoqkVEvOC/MCLuJzbbsmCUhIKb9LncQlpRW+UzHNnj2bVatWWdsLFiygfv36hp9HVB5KKWtS4ODggLe3N56enjg5OWGqYuuMCFHZWCwW4uPjuXz5MomJiZw9e5bGjRvj4OBQZjEYdltKKTUHmJPPZxvRS5cKIcpYxP4/qEXRFyzO3O/Xn5exy+/eQu/YF3Wsvj2YNHB3NuPhbMY943U1MYWTkQnF7svDxdi7+Lt372bq1KnW9lNPPcWQIUMMPYeofBISEqxJQcOGDXFxkal7QlQWJpMJT09P3NzcOHXqFCkpKcTGxlK9evUyi8Hw59WaprkDzQEPpdQWo/sXQhTPubAIapXguONnLrDo5GnD4ymMm5NDtot5B9ydcl7Yezg75LrY98jYL/t2D2czLo4mtGsyomPhsdz6yeZix9WjmW/hOxXD77//TmpqKgCdOnVi5syZhvYvKqerV/Wpe97e3pIUCFFJZT4NDA8PJz4+vmImBpqm1UefazAEcEAv5mHO+Kwn+tOERzKeHgghykisci3RcXEU7ThnsynPC3d3ZzPuTvlfxOfclpUEmExFfLRRQs39POnSxKdYE5ADmvgYPr/g1VdfpUWLFjz77LMsX74cZ2dnQ/sXlVPmvAJPT9uX0BVC2E9mueqEhOI/4S4NQxIDTdPqAMHoKxyvBGoB3bLtEpyxbQSw0YhzVgSapnkCmb+9HTOrjghRlqL8ukIoxZpjoGmQ1ugmpre5PtfFvvWOvJMZN2cHHB0q3rjmJ2/xZ9y84CINgzJp8MQt/jaJ45577uHOO+8s88llouJKT9fn4cj/M0JUbo6OjkDWv/myYtQTgzfQL/z7K6U2aJr2BtkSA6VUqqZpW8ha+KyqeBb9ZwNAeHi4HUMRVdX17bsSHNSSANORIu2vabDd0orxdw6wSXnO8qBHM1/eHda20EXOTBrMHNbO8GFE2ckFniiOzJKkMtFYiMotcxhsWZchNuo3yyBgpVJqQwH7nAHqGnS+iuIjoF7G64CfX+VcRVaUb839PNnqPTTXomb5SVca63zHV9qkINOIGxuyaGIAAU188vw8oIkPiyYGcO+NDQw5X2JiIi+88AIxMTGG9CeEEKLyunZ+XFkx6omBH1DY0qCpgLtB56sQlFKxQCyApmmpcodH2IMl3UK/pD9zLmJ2zbCizHa60ng5bTJ3DLq37AO1gx7NfOnRzJdj4bEEhUQSl5SGh4uZHs18DU+MnnrqKebMmcOPP/7I8uXL6dKli6H9CyGEEKVlVGIQBRR2W605svKxEGVKKcWa+W9ye/JuACwKjqiGtDadybFf5vChz9OGcsfQ0TYdOlMeNffztOkTkh9++IE5c/RqzidPnmTfvn2SGAghhCh3jEoMgoA7NE2rrZTKdfGvaZo/MABYnOtIIYTNLPh1LaPOfg4ZTwf+8LqHBe4TiTr1Lz1MB/EgkThcCbK0wadxO564xb/KJQW2FhoayuTJk63te++9N0dbCCGEKC+MSgw+AO4ENmma9hTgBtY1DW4CPgEs6GPuhRBl4LtNRwnY8wIuJr1W/jnn67j1sc8Z6OTCsfA21qEzNVzMjLbB0BkBKSkpjBw50lp7vmnTpsyZM8duY0eFEEKIghiSGCilgjVNewj4Cvgt20dXM76mAQ8opf4z4nxCiIL9sPMsKeve4nqzvkBZKo74TViEg5O+IJKth84I3csvv8zOnTsBMJvNLF++HC8vLztHJYQQQuTNsNmwSqnvgDbAbGAHcALYA3wJtFNKLTHqXEKI/K05cJGff17Ogw5ZObrq/yaOda63Y1RVz+rVq/noo6yHpDNnzuTGG2+0Y0RCCKNs2rSJqVOnEhAQQL169XBxccHd3Z169epx880389xzz7Fly5Yil5pMS0vjq6++olevXlSvXh1HR0dq1qxJr169ePXVVzlw4EChffz8889omoamabz//vsF7rt7927rvpqmERoaWuD+L730knXfv/76q0jfU3aHDx/mo48+4o477qBJkya4urri6upKw4YNGTZsGD/++GOx6vUfOXKERx99FH9/f9zc3KhRowZdu3blk08+ISkpqdDjz549y08//cSLL75I37598fLysn5/06ZNK/b3t27dOu655x4aNGiAs7MzdevWZdCgQfz444/F7svulFLyKoMXsLtjx45KCFvadDRCdXj5B3Xu9SZKvVFNqTeqqdQFdyqVnm7v0KqUc+fOqRo1aij0FeDVoEGDVLr8NxAGOHTokDp06JC9w6iydu3apbp37279t13Yq2nTpmrBggUF9hkTE6O6detWYD9eXl6Fxnbp0iWlaZoC1ODBgwvc9+OPP87R//z58wvcP/N7dnR0VHFxcYXGkt348eOL9LPq2rWrOnv2bKH9zZ8/X7m4uOTbT6tWrVRoaGi+x586darAON54440if28Wi0U9/PDDBfZ35513qqSkpCL3mV1J/7137NhRAbtVCa5XpX6mEJXE7tNRPLRoN6+bvqOedhkAi4s35qFfgZTKLTPp6emMGTOGy5f1/wZ169bl+++/lwWphKjgli1bRs+ePfnnn38AaNu2La+//jqrVq1i+/btBAcHs2rVKqZPn0737t0BvfjAQw89VGC/jz76KNu2bQOgffv2LF68mB07dvDXX3/x5ptv0rJlyyLF5+vrS6tWrQAICgrCYrHku+/mzZsBcHBwyNHOS2JiIrt27QKgU6dOuLsXr/L8+fPnAahevToPPPAAixYtIigoiF27djF//nw6duwIwPbt2+nXrx/x8fH59vXnn38yadIkkpKS8PX15eOPP2bbtm2sW7eO++67D9CfTgwePJi4uLg8+1DZnuJomkazZs246aabivU9ZXr99df56quvAP3/h8WLF7Nz507+97//0bNnTwB+/fXXilVwoiTZhLzkiYEoXw5diFFt31irHn/pJeuTAvVGNaX++9XeoVU5V69eVYMHD1aAMplMauPGjfYOSVQi8sTAPjZu3KjMZrMClKurq1q4cKGyWCwFHrN79241cOBA5ezsnO8+kZGRysHBQQHqhhtuUImJiXnut27duiLFmf3u9d69e/Pdz9fXVwFq1KhRClDXXXddvvtu2LDB2ufzzz9fpDiyu++++9RXX32lEhIS8vw8NTVVDR8+3HqOt956K9/9/P39FaA8PDzUkSNHcu3z1ltvWfuZPn16nv1ERkaqt99+W/35558qKioq1/dY1CcGISEhytHRUQGqffv2uZ6kpKamqgEDBlj73bx5c5H6zU6eGAghiu1kZDzj5u3AIymMtx3nZ33Qfiy0vsN+gVVRnp6erFy5ko8++oi33nqL3r172zskIWzuWHgs84NO8tn648wPOsmx8Fh7h2SYxMRERo0aRVpaGiaTiTVr1jBu3LhCq4t17NiRNWvWWNcwycuhQ4esY+vHjh2Li4tLnvv169evSLFmv/Od31OAQ4cOERkZiaZpvPjiiwCcOHGCCxcu5Ll/9n5K8vtswYIFTJkyBVdX1zw/N5vNfPPNNzg5OQHkOy7/119/5fhxfS3dF154gRYtWuTa5+WXX8bf3x+ATz/9lLS0tFz71KhRg1deeYX+/fvj7e1d7O8n06effkpqql7177PPPsv1JMVsNvP1119bnxYXNu+jvJDEQIgK7GJMImPnBhMVl8jHTl9RTUvQP/BuDANn2jW2qsxkMvHMM8/w8ssv2zsUIWwqKCSSe7/Zxq2fbGb6qkN8tO4Y01cd4tZPNnPvN9sICom0d4ilNm/ePC5evAjA448/Tp8+fYp1/Pjx4/P9zGzOKg6ZmJhYoviyK0pikLm9devWtGvXjsaNGxdpf5PJZB0eY7QaNWrQtm1bAEJCQvLcZ8WKFdb3DzzwQJ77mEwm65Ci6OhoNm7caGygGZRS/PLLLwA0b948359Lo0aN6Nu3LwB//fVXvsObyhNJDISooC7HJTN2bjDnryQyyWE1XU2H9Q80EwydA85SjlQIYTuBO88wbl4wO05G5fn5jpNRjJsXzA87z5ZxZMZasGABoI9Hf+KJJwztu2PHjtYSxnPmzCEmJqZU/dWtW5dmzZoBsGXLljz3ybzQ79WrV46veSUGqampbN++HdDnP1SrVq1U8RUkJSUFyJr3cK2tW7cC4O/vT926dfPt5+abb851jNFOnTrFuXPngMKfomTGk5SUZJ2rUZ4ZkhhomuZkRD9CiKKJTUplwvydnLgUT2vtFM+Zf8j6sNdz0DDAfsFVQZ988glTp061/mETorILConkpRUHsBRSjdOi4MUV/1bYJwdXr15l7969ALRo0YKmTZsa2r+zszNTp04F4Ny5c4wcOdI6PKWkMp8aREREcOTIkVyfZyYMmQlB5t3uvBKDPXv2WCcDl3SCblFERERw+LB+cytzAnV2cXFxnD2rJ5itW7cusK/sk7UPHTpkYJRZsvdbHuIxklFPDGI0TZtiUF9CiAIkpaYz8ftdHDgfgzMpzHL8Aicto/5zvU7Q+3n7BljF7Ny5kxdeeIEPP/yQXr16ER4ebu+QhLC5WeuPF5oUZLIomL3+uG0DspGDBw9aq/t06tTJJucYO3Yszs7OAKxdu5Zx48YVq6b/tQoaTnTy5Enrne5rnxgcOnTIWk0tr+NtOV/qvffes84HGDFiRK7Pz58/b60mVL9+/QL78vHxwc3NDcCaTBgt82dYlHgaNGhgfW+reIxkVGLgDNQyqC8hRD5S0y08smSP9dH9i+Zl+Jv0UnA4usGwb8HB0Y4RVi0xMTGMGDHCeodPKVWqyWxCVATHwmPzHT6Un+CTURVyQnJkZNaTjpo1axa4b2hoKAcPHszzFRERkecx+/fvp0ePHiQnJ3P99foilIGBgdx3330FlhstSEGJQWa7UaNG1gvWli1b4uvri1Iq1/CjzP01TbMmEEYLCgpi9uzZgH4R/fDDD+faJzY26/8dDw+PQvvM3MdWY/qLE0/2zyvCHANz4bsIIcoDi0Xx3I/7+fuI/gemt2k/95v/yNphwLtQ4zo7RVf1KKV48MEHOXnyJADVqlVj+fLl1soaQthb4xdX2zuEHG79JP9a+aV1auZgm/RbnAvA0aNHExwcnOdnb7zxRq4VdcPCwhg0aBAXLlxg6NCh/Pjjjzz33HN8+umnLFmyBAcHB+bPn5/nGijNmzfn+PHjDB06NMekXIAmTZrQoEEDzp49m++FfvaLfE3T6NGjB7/++iubN2/mrrvuAsBisRAUFATA9ddfT40aNQr8/kvi/Pnz3H333daKTwsXLsyzelH2idlF+R2b+QTGiAndeSlOPJmx2DIeI5XZ5GNN00ZqmvZBWZ1PiMpEKcXrKw/y6z69nJw3V/nCfW7WDi0GQcf77BRd1TR37lx++CFrbse3335r+PhjIYR9eXpmFXEw+m7vww8/zIULF6hTpw7fffcdDg4OfPLJJ9bFsBYuXMikSZNyLMgF+gV7WFgYAO3atcuz78ynBmfOnOHUqVPW7dfOL8iU1wTkgwcPEh0dnaM/I0VHRzNgwADr9/Lhhx/mW/Epe7JQlLlcycnJuY4zUnHiyYzFlvEYycjE4HZN057RNK2vpml5pZU3Ak8beD4hqowP/zzK4u1nMlqKJX5L8EjNGAvqXgvu+AwKqaktjHPw4MEc1UkefPBB7r33XjtGJISwhex3yS9dulTgvtu3b8+xUNSGDRvy3ffEiRP8+uuvgL7ycfXq1a2fff3114wZMwaA+fPn8+CDD+ZIDvbv3299kjFo0KA8+89rOFFYWJh1HYBrE4PMCcj79u2z9m3L+QVxcXEMHDiQgwcPAvDqq6/y9NP5XyIWN0HL3Kcow45KojjxZP/cVvEYycihRJ0zXgpA07QLwH7gIHAVGA/IrDwhimnO5hN8seGEtf1O4/20Dsv2ePjOL8Dd1w6RVU0JCQmMGDGCpKQkQH/E/sknn9g5KiFys9XwmmPhsSUaFvTn0zfR3K9ilVFu06YNJpMJi8XCnj17DOs3KCjIerF/7UW3yWRiwYIFJCQk8PPPPzN37lwcHBz46quv0DSNRYsWAXqVpC5duuTZf/Y+N2/ezPjx460X+r6+vjkq5YBeNtXNzY2EhASCgoIYMGBAjsTAyCcGiYmJDBkyxDrs6sknn+Stt94q8Jh69eqhaRpKqRwTf/MSFRVFQoK+pk/2ib9Gyj7huLB4sk84tlU8RjLyicECYDLwJRAEeAKDgOeBt4EawOcGnq/c0zTNU9O0upqm1QUcSzqRSFRdy3ec4Z01WeXmRlyXxqjLX2TtcOMkaH6rHSKrup544glryTlXV1d++OEHawUMIaqC5n6edGniU6xjApr4VLikAMDLy4sOHToAcOTIEeucotI6f/689X1ed5HNZjPLly9nwIABAHzzzTc89thjXLhwgblz9WGkr7zySr79t2jRAj8/PyDrzn/m1549e+ZatdnR0ZGAgIAc+2UOO2revDm1a9cu/jeZh5SUFIYNG2ZdeGzSpElFurHi4eFhvagurORn9hKthZUSLans/ZaHeIxkZGJwSik1Tyn1uFLqJqVUdeA6YDAwCuiklHrXwPNVBM8C5zNebaWMoSiO1f9e5KWfD1jbXRt78S6z0VL1mtLU8If+Bd9lEcZavnw58+bNs7Y/++yzCvGLXgijPXmLP6Yijl40afDELf62DciGMlfSVUoxa9YsQ/rMPkQps37/tZycnFixYoX17v+XX37JjTfeSGxsLF26dGH06NEFniNzuNDx48cJCwvLd37Btftv3rzZegwY97QgLS2NESNGsHbtWkCfrP3NN9/kSlLykznc6fjx41y4cCHf/bKvdmyrlZobN25MvXr1ANi0aVOB+2bG4+zsTOfOnW0Sj5FsOvlYKXVSKfW7UipQKbXXlucqpz4C6mW8DmRm70IUZuPRCJ4K3EvmsNI29arxfbPNmM5nrJpoMsPwb8FJ7lSXpU6dOtGxY0cARo4cyQMPPGDniISwjx7NfHl3WNtCkwOTBjOHtaNHs4o73HHSpEnWO+aff/55jgvPksp+cf7ZZ5/lmmCcydXVld9++816N//ChQuYTCbmz5+f7wrBmbJf0K9cudI6nj+/xCDzInrnzp38+eef1u1GzC+wWCyMHz+eX375BYChQ4fy/fff51lxKT/Dhg2zvv/uu+/yPc/3338PgLe3d76TmUtL0zSGDh0KwLFjx/JdYfn06dP8/fffAPTv379CzDEwKjEo4jInVYtSKlYpdUEpdQFILc4/AFF17ToVxZTFu0lN1/9ZXVfTnSUDHHAO+jBrp5tfhrod7BRh1eXv788///zD9OnTi3WnS4jKaMSNDVk0MYCAfIYVBTTxYdHEAO69sfyPqy6Iq6urtXxoeno6gwcPZvHixflezGeKisp/rYdWrVrRv39/ALZt28bDDz9sXeDrWhaLhUaNGuVoz58/v9C4s1/Qv/fee1gsFtzd3a1Do67VrVs3HBwcSElJ4eOPP7ZuL+0Tg8zSzsuWLQNgwIABLF++HLO5eNNc77zzTvz99SdP7733HkePHs21z7vvvsuxY8cAeOqpp4p9juJ48sknrf0/8cQT1hWiM6WlpTFlyhTrYnWZK1yXd1ph/2MXqRO9CpGHUup06UOqnDRN292xY8eOu3fvtncoohz770IMI+dsJzZJ/wNRr7orP01sR+1l/SEqVN+pYXeY8BuYCr5bJISofDKHnbRq1crOkeR0LDyWoJBI4pLS8HAx06OZb4WcU1CQRYsWMXnyZGv5ybZt2zJs2DC6dOlCzZo1MZvNREdHc+TIEdauXcuaNWusF4Xvvfcezz+fc1X6U6dO0b17dy5evAjo488feughOnfujIuLCxcuXGDjxo189913REdHYzKZaNSokXWewwcffMBzzz2Xb7xKKWrUqGEtOQrQr18/1q1bl+8xnTt3Jvt1SuPGjUs9r+K5557jo48+AvSbK0uWLCm0bGeLFi1wdMy9WOeff/7JoEGDSE9Px9fXl1deeYVu3boRFxfH4sWLWbBgAaD/+9ixY0e+d+jXrl1rHSoF+jyA9957D9ATkMy1HDJNmDAhz35ee+013n77bUD//+HFF1+kefPmnD17lo8//tj6JGHcuHEsXLiwwO85LyX9996pUyf27NmzRylV/OW6s5fWkpftXsDujh07KiHycyIiVnV660/V6IXfVKMXflOd3lqnQi/FKfXrY0q9UU1/vVNfqahT9g61SklISLB3CEJYHTp0SB06dMjeYVRZwcHBKiAgQKGPlCj01bRpU/X9998ri8WSZ3/Hjx9XnTp1KrSfFi1aqM2bN6uIiAjVqFEjBShN09T3339fYLxDhgzJ0c/06dML3P+pp57Ksf/48eNL/LPKlBlvcV4nT57Mt7/58+crFxeXfI9t1aqVCg0NLTCm3r17Fyue/FgsFjVlypQCj73zzjtVUlJSiX52Jf333rFjRwXsViW4XpWxLUKUAxeuJDJu3g4i4/SFUqq5mFn4QBeaXNoAe7LdZRj0IXg3yqcXYbRz587RrFmzAscACyGqji5durB9+3b+/vtvnnnmGTp37kydOnVwcnLCzc2NevXqcdNNN/Hss8+yYcMGQkJCGD9+fL7DDps1a8aOHTv43//+x913303Dhg1xcXHBzc2NJk2aMGrUKH744QcOHTpEr169qFmzJitXrsTDwwOlFBMnTmT16vxXuL52fkBhk3GvnX9gi4XNSmvChAns3buXhx9+mOuuuw4XFxe8vb0JCAjgo48+Yvfu3TRp0qRMYtE0ja+++oo///yT4cOHU69ePZycnKhduzYDBgzghx9+4Jdffsmx+nF5Z8hQIlE4GUok8nM5Lpl7vtlG6CV9fKKrowOLJ3Whk08KfNkNEjPGqV4/DO7+ThYyKyNpaWn07dvXWsnjkUce4YsvvijkKCFsq7wOJRJCGM8eQ4lsNytDCFGoq0mp3Dd/hzUpcHTQ+HpcJzo19IYld2clBdXqwe0fS1JQht58801rUmAymRg5cqSdIxJCCCFsS4YSCWEnSanpTPp+FwfPXwX0sn6zRnagd/OasHMuhPyVtfNdX4Grt50irXo2bNhgnVAGMG3atHxL/AkhhBCVhSQGQthBarqFR5bsYcfJrHJ27w5ry6C2deDSUfjz1ayduz0GTUtfR1oUTUREBGPGjLHOKejbty8vv/yynaMSQgghbE8SAyHKWLpF8ewP+/n7SIR12yuDWjHixoaQlgI/TYK0JP2DWtfDLa/bKdKqx2KxcN9991nLB9asWZPFixcXupCQEEIIURlIYiBEGVJK8fqvB1m5P2s598dubsbkm5rqjY3vQNi/+nsHZ311Y3PFqWZQ0X388cesXbvW2l64cCF16tSxY0RCCCFE2ZHEQIgy9MEfR1kSfMbaHt+tEc/e2lxvnNoKWz/N2rnfNPC7vkzjq8qCg4N56aWXrO2pU6cyYMAAO0YkhBBClC1JDIQoI19vOsGXG09Y23e1r8u0Idfr9a0Tr8DPU9DXQwGa3gwBU+wSZ1WUlpbGuHHjSEvTV5wOCAhgxowZdo5KCCGEKFuSGAhRBpYGn2Hm70es7X6tavHBPTdgMmWUH10zFWLO6u9dvfUqRCb551lWzGYzCxYsoGHDhnh5ebF8+XIcHR3tHZYQQghRpmQdAyFsbNX+C7zyywFrO6CJD5+P7oijQ8aF/4H/wYEfsg64/VOoJuPay1r37t3Zu3cvhw4donHjxvYORwghhChzcktSCBvacDSCpwP3kbnAeLv6Xsy9rzMujhlVbq6chd+eyTqg/Ri4/q4yj1PofHx86Nmzp73DEEIIIexCEgMhbGTHySgeXrybNIueFTSr5cGC+7vg6ZIxRMVigV8ehuQYvV29EQyYaadoq574+HjOnj1r7zCEEEKIckMSAyFs4OD5GCYu2ElSqgWAetVdWTSxCz7uTlk7bfscTm3R32smGDYHXKrZIdqq6fHHH+eGG27g119/tXcoQgghRLkgiYEQBjtxKY77vttBbLJe4cbXw5klkwKo4+WatVPYAVj/Zla717PQsGsZR1p1LVmyhPnz5xMdHc1dd91FcHCwvUMSQggh7E4SAyEMdP5KIuPmBnM5PgWAai5mFk3sQmNf96ydUhPhp8lgSdXbdTtA7xfsEG3VdPz4caZMySoFO2bMGLp06WLHiIQQQojyQRIDIQwSGZfMuLnBXIhJAsDV0YH593ehVZ1rhgf9NR0uHdbfO7rBsLngIKUxy0JycjIjR44kLi4OAH9/f7766it9LQkhhBCiipPEQAgDXE1K5b7vdhAaGQ+Ak4OJOeM70amRd84dQ9ZD8FdZ7dtmgG+zMoy0anv++efZs2cPAE5OTgQGBuLp6WnnqIQQQojyQRIDIUopMSWdiQt28t+FqwCYNJg9qj29/Gvm3DH+sl6FKFPzgdDp/jKMtGpbuXIls2fPtrY//PBDOnToYMeIhBBCiPJFEgMhSiElzcLDS3az81S0ddvM4e0Y0OaaBcqUgt+ehLhwve1eE+74DGQIS5k4e/Ys99+flYTdeeedPPbYY3aMSAghhCh/JDEQooTSLYpnftjHxqOXrNteHdyKezs3yL3zviVweFVW+84vwKNm7v2E4dLS0hg1ahRRUVEANGjQgO+++07mFQghhBDXMNs7gMpM0zRPIHMAs6PFYrFnOMJASile/eUgv/170brtib7NmNSrae6do0Lh92xVhzpPhOa3lUGUAmD16tUEBQUB4ODgwLJly/Dx8bFzVEIIIUT5I08MbOtZ4HzGq214eLidwxFGeW/tUZbtOGNtT+jemKf7N8+9Y3oarHgIUvQqONTwh1vfLqMoBejDhn744QeqVavGm2++SY8ePewdkhBCCFEuyRMD2/oImJPxfq2fn19bewYjjPHVxhN8vemEtT2sQz1ev7113kNTtn4M53bo701mfXVjJ7cyilRkuueee+jWrRt169a1dyhCCCFEuSWJgQ0ppWKBWABN01JNJnlAU9EtCT7Ne2uPWNv9Wvnx3t3tMJnySArO7YaNM7PafV6Ceh3LIEqRl/r169s7BCGEEKJckytVIYpo5f4LvPrLQWu7W9MafD66A44OefwzSo6DFZNApevtBl2h59NlFKlYtWoVMTEx9g5DCCGEqFDkiYEQRbDhSATPBO5DKb19Q30vvr2vMy6ODnkf8MfL+qRjACdPGPYNmPLZVxhq+/btDBs2jAYNGrB8+XK6dOli75CEqPwiDkPoJkiOBWdPaNobarWyd1RCiGKSJwZCFGLHySimLN5NmkXPCvxrebDg/i54OOeTVx9ZDXu+z2oP+gC8G9s+UEF0dDQjR44kLS2NkydPMnXqVFRmNieEMF7oRpg/CL7sCmtfgA1v61+/7KpvD91o7whLbdq0aWiaZp1HlpyczMcff0znzp3x8vLCw8ODDh068P7775OUlFRgXxaLhaVLl3LXXXdRv359nJ2d8fb2JiAggBkzZuT7pLNNmzZomsZdd92V5+dr1661xujj40NeVRBjYmIwm81omsbMmTPz6EUIeWIgRIEOno9h4oKdJKfpv2Tre7uyaGIA3u5OeR8QGw4rH89qXz8UbhhZBpEKpRSTJk3i9OnTAFSvXp2FCxfKegVC2MqehbDqSVD5lOI+HQSLhsKQ2dBxXNnGZiPh4eEMHDiQvXv35ti+b98+9u3bx6pVq1i3bh0uLi65jj137hx33XUXu3fvzrE9JSWFHTt2sGPHDj7//HNWrlzJjTfemGOfPn368N9//7F582YsFgvXzlncsGGD9X10dDT//vsv7du3z7HP5s2bSU9Pt/YnRF7kiYEQ+ThxKY77vttBbHIaADU9nVkyKYDaXrl/4QP66sa/PgoJl/W2Z10Y/LGsblxGvv76a1asWGFtz5s3j0aNGtkxIiEqsdCNBScFmZQFVj1RKZ4cAAwbNoyDBw/yyCOP8Mcff7Bnzx7+97//WYcsbt26lRkzZuQ6Lioqil69erF7924cHR2ZPHkygYGBBAcHs3nzZt588018fHwICwtj4MCBnDlzJsfxmRfy0dHR7N+/P1f/GzduLLCdfZuHhwedO3cu/jcvqgRJDITIw/kriYybG8zl+BQAvFwdWTSxC41quOd/0M65ELIuqz30K3CThbTKwv79+3n66azJ3Y8++ijDhg2zY0RCVHKb3i88KcikLLDpA9vGU0Z27NjBmjVr+OKLL7j11lvp0KEDw4cPZ+PGjbRu3RrQb1KkpaXlOO7JJ5/k1KlT1K1blwMHDjBnzhzuvfdeunTpQq9evXjttdfYtWsXfn5+XL58mVdeeSXH8X369LE+/cz+dAAgNjaWPXv2AHDHHXfkuQ9kJQY9evTAbJYBIyJvkhgIcY1LscmMmxvMhRh9rKibkwPz77+RlrWrFXDQUfjz1ax2t8egaR/bBioAiIuLY8SIESQnJwNwww038OGHH9o5KiEqsYjD+jCh4ji9VT+ugnvsscfo169fru2urq48/rg+jDQyMpJDhw5ZPzt9+jTLli0D4NNPP6VFixZ59t2kSRNef/11AAIDA0lISLB+5uvry/XXXw/kfhqwdetW0tLSqFmzJo8++igAW7ZsyTHPICYmhn379gEyjEgUTFJGIbKJSUxl/Hc7CI2MB8DJwcSccZ3p2NA7/4PSUmDFZEjLmHRW63ro+1oZRCsAHn/8cY4ePQqAu7s7gYGBeY7vFaLKmeZl7why+rKr7fqeVjbliceOHZvvZ9mH54SGhtKuXTsAfvvtN9LT03F0dOTOO+8ssP+bbroJgNTUVHbt2mVtg35Bf/DgQetcAQcHvdJdZqLQu3dvevbsiZOTE9HR0ezbt4+OHfW1czLnJmT2I0R+5ImBEBkSU9KZuGAnhy9eBcCkwexR7enp71vwgRvfhYsZYz4dnGD4t+AoF6ZlYfHixSxYsMDa/vLLL/O9GyeEEKXVqlX+JVh9fLKGjsbGxlrf79q1C9Av9p2dna3Vg/J6tW3b1npcWFhYjv4zL+hjYmJyTH7OTAz69OmDm5ubdeJy9icLMr9AFJUkBkIAKWkWpizeza7T0dZt7w1vx4A2dQo+8PQ/sPWTrHa/aeB3vW2CFLn89ttv1vfjx49n/PjxdoxGCFHZubm55ftZ9kpBmdV/ACIiIkp0ruxDiUB/InDtPIPs8wsyE4fMr3klBjK/QBRG/u8QVV66RfF04D42Hbtk3fba7a25p3ODgg9MioEVDwEZdfKb9IaAh20XqMhl6dKldOnShfnz5/PFF1/YOxwhyhdbDa+JOFyyYUGPbK+Si55lJgnVqlUjKKjoczPq16+fo+3r60ubNm04cOAAGzduZOrUqdb5BbVq1bLOQbj55puZMWOGdfhQbGyszC8QRSaJgajSlFK88vMBVh+4aN325C3+TOzZpPCD10yFmIySci7V4a6vwCQP4cqSyWTimWee4fHHH8fR0dHe4QhRNdRqBY16FG8CcqOeVTIpAP2CHvRCCf7+/jg7O5e4rz59+nDgwAG2bNlCWlpajvkFmbp3746Tk5N1yNGFCxdkfoEoMrmKEVXGsfBY5ged5LP1x5kfdJKjYVeZ+fsRlu88a91nQvfGPNXPv/DODvwP/g3Mag/5FLzqGR+0KBJJCoQoY72fB62IlxCaCXpPtW085ViHDh0AfdXjbdu2laqvzAv72NhYdu/ebU0Mbr75Zus+rq6uBAQEAPqQI5lfIIpDnhiISi8oJJJZ64+z42RUgfsN71if129vXfhKuTHnYPUzWe0bRukrHAubO3PmDHPmzOH111/HySmf1aeFELbXtA8MmVX4ImeaSV/5uAqXbx4yZAhTp05FKcUnn3xSqrv2mfMMlFKsWrUq1/yCTH369GHLli1s3LiRixf1J+Iyv0AUhTwxEJVa4M4zjJsXXGhScH2darw3vC0mUyFJgcUCP0/R5xcAVG8IA983KFpRkNTUVEaNGsWMGTPo1asXoaGh9g5JiKqt43gY97M+TCgvjXrqn3ccV7ZxlTPNmzdnxIgRAKxcuZK33nqrwP3DwsKYO3dunp/VqFHDWrno888/Jy0tDT8/v1zVkjIThU2bNsn8AlEskjqKSisoJJKXVhzAogrf93DYVYJPRtGjWSGlSbd/Aae26O81EwydAy4FLHwmDDNt2jT++ecfAHbv3k1YWBhNmza1c1RCVHFN++iviMMQugmSY8HZE5r2rrJzCvLy5ZdfsmvXLkJCQnj99ddZvXo1999/P23btsXV1ZXo6GgOHjzIunXr+OOPP2jXrh2TJk3Ks68+ffrw77//EhMTY21fq3v37jg7OxMXF5fjOCEKI4mBqLRmrT9epKQAwKJg9vrjBScGYQdg/ZtZ7Z7PQKNupQtSFMlff/3Fu+++a22//fbbdO/e3Y4RCSFyqNVKEoECeHt7ExQUxKhRo/j7778JDg4mODg43/2rVcv/hlOfPn2YPXt2jva1XFxcCAgIYPPmzYDMLxBFJ0OJRKV0LDy20OFD1wo+GcWx8Ni8P0xNgp8mQ3qK3q7bAfq8WMooRVGEh4czduxYlNKzvP79+/P888/bOSohhCieWrVqsX79etauXcv48eNp1qwZHh4emM1matSowY033shjjz3GmjVrWLduXb79ZF/PAPJ/EpB9QrLMLxBFpWX+sRW2pWna7o4dO3bcvXu3vUOpEuYHnWT6qkPFPu6NIa25v0cepUrXvgTbv9Tfm11hyhbwLUL1IlEqFouFAQMGWP9I+vn5sX//fvz8/OwcmRD2cfjwYaDgFXiFEJVDSf+9d+rUiT179uxRSnUq7jnliYGolOKS0ow77sTfWUkBwG0zJCkoI++99541KdA0jcWLF0tSIIQQQtiIJAaiUvJwKdkj01zHJUTBL49ktf1vg84PlCIyUVRBQUG89tpr1vZLL71Ev3797BiREEIIUblJYiAqpUKrCxXlOKVg1RMQm7Eqspsv3Pk5FLbOgSi1qKgoRo8eTXp6OqCPj50+fbqdoxJCCCEqN0kMRKXU3M+T6+sWr4xoQBMfmvt5Zm3YtxQOr8pq3/k5eNQyKEJRkNjYWGrUqAHo1TyWLl0qE+eEEEIIG5O/tKJSio5PIfxqUpH3N2nwxC3Z5g1EnYTfs1W+6XQ/tBhoYISiII0aNWLbtm08//zz3HzzzTRs2NDeIQkhhBCVniQGotJJtyieCtxHZFxKkfY3aTBzWLusYUTpafDzQ5CSsTBMjWb6hGNRppydnZk1a5a9wxBCCCGqDBlKJCqd2euPs+nYJWv76X7+BDTxyXPfgCY+LJoYwL03NsjauPUTOJux8IzJDMO+BSd3W4YshBBCCGF38sRAVCobjkQw++/j1vbDfa7jyX7NebKfvuhZUEgkcUlpeLiY6dHMN+ecAoBzu2Fj1gq79HkR6nUso+irtgcffJB27drx6KOP5li8RwghhBBlQxIDUWmcjUrgqcB9ZK7Z1/26Gjzbv7n18+Z+nrkTgexS4mHFZFB6JRwadIWez9gwYpHp+++/59tvvwVg/fr1BAYG4uTkZOeohBBCiKpFhhKJSiEpNZ0pi3cTk5gKQB0vF2aP6oDZoRj/i//x//buOzyqKv/j+PuEQCCEXgVEQHoRBEQEkeKq6CJNpRkU0VWsuCoqawN3dXctq8APFddCFxAExMqKiAJiQZEI0qRJCwJBQklCkvP7485MJmEmmYQpKZ/X88wzOfeee+93Zi7D/c495TE48qvzd5kKMHAKRJUKQbTibfPmzdx1V9ZcEZUrV1ZSICIiEgFKDKRYeHLxz2zYdwyA0qUMr9zYnupxMYHvYNNHsPbtrPI1z0GVBsENUs6QkpLCoEGDOHnyJADNmjVj0qRJEY5KRESkZFJiIEXenG93M+/7PZ7yk31acmH9KoHvIDkR3r8nq9yyH7QdGsQIxZ8HH3yQ9evXA84oRPPmzSMuLi7CUYmIiJRMSgykSFu/5yhPLt7gKQ+8sC7xnc8LfAfWOknBycNOucI50OdlzW4cBgsWLOCVV17xlF966SUuuOCCCEYkIiJSsikxkCIr6UQad878gbSMTACa167AMwPa5G9Em+/fhK1Ls8r9X4FY30ObSvDs3LmTW2+91VO+7rrrGDVqVAQjEhERESUGUiRlZFpGz13H3qOnAKhQNprX4jtQrkw+Ogv/vgU+fTyr3PkuOL9XkCOVnE6fPs3QoUP5448/AGeW4zfeeENDlIqIiESYhiuVImnCZ1v40msSs/8MakeD6nlMQnbwF9i+AlKToXQ5+GE6pDuJBTVbwuVPhTBicXvyySdZs2YNANHR0cyZM4fKlStHNigRERFRYiBFz+ebEpn4+TZP+e6e53NFy1r+N9j+Bax4Dnat8r3ePbtx6bLBDVR86tOnD7NmzeK3337jmWeeoXPnzpEOSURERFBiEFLGmAqAe0at0pmZmZEMp1jYffgk989Z5ylf2rg6D1zRzP8GP0yHJaPB5vLeZ2bAvh+hduvgBSp+de3alXXr1vHqq6/y0EMPRTocERERcVEfg9B6ENjrerRJTEyMcDhFm3sSs2Mp6QDUqVSWCUPaUSrKT9v07V/knRQAYGHJfU59CYuqVavy2GOPERWlryAREZHCQv8rh9aLQF3XI6FWrVyau0iurLU8vuhnNu53JjErUyqKV+I7UC23ScxWPBdAUuA+QCaseD4IkYov7gnMREREpPBSYhBC1tpka+0+a+0+4LR+HS24d779jflrvSYxu7Yl7c6t7H+Dg7/471Pgz66VznYSVF999RUNGjRg8eLFkQ5FRCTfvvjiC4wxnke5cuU8o6rlpWnTptm2fe2117Kt37lzp2fdiBEjQhC9SP7oSlUKvZ9+O8q4970mMWtflxsvrp/7RttXFOxgBd1OfDp8+DDDhg3j999/p3///kyePDnSIYmInJWUlBTmz5+fZ73Vq1ezdevWMEQkEjxKDKRQO3IijTtnrvVMYtbinIo80z+AScxSkwt2wIJuJ2ew1jJy5Ej27HHu9FStWpW+fftGOCoRkYIrW9YZvW769Ol51nXXKVeuXEhjEgkmJQZSaGVkWkbP+ZF9f6QA7knM2gc2iVlMhbzrBHM7OcPEiRN5//33PeW3336bc889N4IRiYicnX79+gFOE8ldu3b5rZeamsq8efOybSNSFCgxkELr5c+28NXWQ1nlwe04r1oek5i5NepesIMWdDvJZu3atYwZM8ZTHj16tO4WiBRj25K2MeuXWUz5aQqzfpnFtqRteW9UBHXv3p369etjrWXGjBl+673//vskJSURExPDoEGDwhihyNnRPAZSKH22MZFJXpOY3dOzMZe3yMeoTrHVIbpc1szGgTjvUqjZIh9Rii/JyckMGTKE06dPA9C+fXv+/e9/RzgqEQmFNfvX8NpPr7E2ce0Z6zrU6sCotqPofE7xmcTQGEN8fDzPPvssM2bM4PHHH/dZz92MqE+fPlSpUiWcIYqcFd0xkEJn1+ET/HXeOk+5W5Pq/PWKpoHvIOUPmDkwf0mBiYLuY/KuJ7my1jJq1Ci2bXOSuri4OObMmUNMTC7DyopIkfTe1ve44393+EwKANYmruWO/93Bwq0LwxxZaN10000AbNmyhW+++eaM9QcPHuSTTz7JVlekqFBiIIXKqbQMRs38gWTXJGZ1K5djwpAL/U9illPaSZg9GA6sdy0wrkcuTBRcOxEa9Sho2OIydepUZs+e7SlPmTKFJk2aRDAiEQmFNfvXMP7r8WTmMVdMps1k3NfjWLN/TZgiC71mzZrRqVMnAJ/NiWbPnk16ejo1atTg6quvDnd4ImdFiYEUGtZaHluUwC/ek5jd2J6q5csEtoP0NJh3E+z+OmtZ34lw0yKnmZAv510KwxdC++FnF7yQmJjIPffc4ymPHDmSYcOGRTAiEQmV1356Lc+kwC3TZjLlpykhjii83HcC5syZ42k26eZuRjRkyBBKly4d9thEzob6GEihMeub3bz3w15PeVzfVrTNbRIzb5kZsPB22Pa/rGVXPgPtXbdxG/VwJi/bvsIZkjSmgtPRWH0KgqZWrVpMnTqV2267jbp16zJx4sRIhyQiIbAtaZvf5kP+fJ/4PduSttG4SuMQRRVeQ4YM4a9//SuHDx/mo48+8ow8tGHDBn788UdAzYikaFJiIIXCj7uTGL8kaxKz6zvUY2inAIe2tBY++Cts8GrHetkY6HJP9no1WygRCLEbbriBjh07kpKSQvnyAY4gJSIh0WZam0iHkM2A9weEbN8JNyeEbN++VKtWjWuuuYbFixczY8YMT2Iwbdo0AFq0aEHHjh3DGpNIMKgpkUTc4eOp3DXrB05nWABanlORf/RvnfckZuAkBf97En6YlrWs0+3Q87EQRSt5adiwIS1aKAETkeLNfUfggw8+ICkpiYyMDGbNmgXA8OFqnipFk+4YSEQ5k5itY79rErOKZaN5Lb4DZUsHMIkZwMr/wGqvJisXDIHe/4ZAkgo5a3v27KFChQpUqlQp0qGIiIRVnz59qFq1KkeOHGHevHk0aNCAffv2ERUVRXx8fKTDEykQJQYSUf/532ZWbvOaxGxIO+pXiw1s4+/egGVPZ5Wb/Rn6TYYo3QgLh7S0NK677joOHTrEnDlzuOiiiyIdkoh4CVXzmm1J2wrULGhh34XFpo8BQJkyZRg8eDCvvvoq06dPp0GDBgD06NFDs7xLkaUrKImY/21MZPLyXz3l+3o1plfzACcxWz8PPnwoq9zwMrj+LSilXDdcHnvsMb799lu2b99Ojx49OHToUN4biUiR17hKYzrU6pCvbTrW6liskgI3d3Oi1atXs2DBgmzLRIoiJQYSETsPneCBues85cua1mD0nwKcxGzzx7BwFOD0SaBuBxgyG0qXDXqc4tvHH3/MCy+84CmPHz+e6tWrRzAiEQmnUW1HEWUCu4SIMlHc0faOEEcUGZ07d6ZpU+f/rtTUVMqXL891110X4ahECk6JgYSdM4nZWpJTvSYxG9wusEnMdnwF824Gm+GUa7aEG+c7w49KWOzbty/bL2JXX301DzzwQAQjEpFw63xOZ5665Kk8k4MoE8W4S8bR+ZzOYYos/EaMGEFMTAwxMTHccMMNxMXFRTokkQJTuwsJK2stf1uYwKYDyYAzidmr8e2pEsgkZnvXwjtDICPVKVdp4ExOFls1dAFLNhkZGcTHx3uaDZ1zzjlMmzaNKPXrEClxBjYZSJ24Okz5aQrfJ35/xvqOtTpyR9s7inVSADB27FjGjh0b6TBEgkKJgYTVzDW7WPhj1iRm4/u14oJ6lfPe8OAvMPM6SDvulCucAzcthgq1QxOo+PTMM8+wfPlyAIwxzJo1ixo1akQ4KhGJlM7ndKbzOZ3ZlrSNbw58w/G048SViePi2hcXyz4FobRt2zamTp2aZ72WLVvSqVOn0AckJZISAwmbH3Yn8fQHGz3lQR3rMeSiAEZuSNoJMwbAqSSnXK6Kc6egSoOQxCm+rVixgvHjx3vKTzzxBD179oxgRCJSWDSu0liJwFlatWoVq1atyrPe6NGjlRhIyOj+v4TFoeOp3O01iVmrOhV5ul8Ak5glH4Dp/SB5v1MuEwfxCzSDcZgdOnSIYcOGkZmZCcBll13GE088EeGoREREJJh0x0BCLj0jk/ve+dEziVmlcqUDm8Ts5BHnTkHSTqdcKgaGznFGIZKwmjJlCvv27QOgWrVqzJo1i+hofX2ISPHXo0cPrLUh2b5BgwZntW+RYNMdAwm5F/+3hdW/HgacCYlfHtKOc6vmMYlZajLMuh4OupoemVIwaBo07BbiaMWXsWPH8uKLL1KmTBmmTp1KvXr1Ih2SiIiIBJkSAwmppRsO8OoX3pOYNaFns5q5b3Q6BeYMc0YhAsDAgNeg2dWhC1RyFRUVxQMPPMCvv/5Knz59Ih2OiIiIhIASAwmZHYdO8OC8nzzl7k1rMPryJrlvlJEO80fCji+zll3zPFwwKERRSn7oToGIiEjxpcRAQuJkWjqjZmRNYlavSjkmDGlHVG6TmGVmwuK7YfOHWcsufxI6/SXE0UpO1lpmzZpFWlpapEMRERGRMFFiIEFnreVv7yWwOdE1iVl0FK/Fd6BybC6TmFkLnzwC6+dkLetyH1yqGXUj4c033yQ+Pp5u3bqxY8eOSIcjIiIiYaDEQIJuxppdLFq3z1P+e79WtK5bKfeNlj8L376eVW5/M1zxtNNbWcJqw4YN3HfffQB8++23vPzyy5ENSERERMJCiYEE1dpdSfzdaxKzwR3PZfBF9XPfaPX/wZfPZZVbDYQ+LykpiICTJ08yePBgTp06BUCrVq345z//GeGoREREJByUGEjQ5JzErE3dSozv1yr3jX6YDksfyyo3vgIGTIGoPOY4kJC4//772bBhAwDlypVj7ty5xMbmMbSsiIiIFAtKDCQo0jMyuXf2jxw45kxiVjm2NK/c2D73Scw2LIIlo7PK9bvAoOkQnUtfBAmZuXPn8t///tdTnjhxIq1a5ZHYiYiISLGhxECC4vmlm/l6u9ckZoPzmMRs22ew4DawmU659gUwbA6U0a/TkfDrr7/yl79kjf40ZMgQbr311ghGJCIiIuGmxEDO2ic/H2DKiu2e8v2XN6VHbpOY7V4Dc+Ih87RTrtYEhi+Esnl0UJaQSEtLY8iQISQnO6NINWrUiClTpmDUx0NERKREUWIgZ2X778d56N2sScx6NqvBvb0a+99g/3qYNQjSnc6tVDoXbloE5auHNlDxa+zYsXz//fcAlC5dmrlz51KxYsUIRyUiIiLhpsRACuxkWjqjZq7luNckZi8NzmUSs0PbYMYASP3DKZevATcthkqaTTdSUlJSWLFihaf873//m44dO0YwIhEREYmU6EgHIEWTtZZHFySwJfE4ADF5TWJ29DeY3g9OHnLKMZWc5kPVzg9TxOJL2bJlWbVqFQ8//DDbt2/n/vvvj3RIIiIiEiFKDKRApq3eyfs/eU1i1r+1/0nMjv8OM/rDsT1OuXQs3Pgu1G4T+kAlTzExMUyYMIH09HT1KxARESnB1JRI8m3triP848NfPOWhnc5lUMdzfVc+dRRmDoDD25xyVGkYPBPqXxz6QCVfoqP1O4GIiEhJpsRA8uX35FTumvUD6ZnOJGYX1KvEU9f6Ges+7SS8MwQOJDhlEwXXvwmNLw9TtOLLF198waRJk7DWRjoUERERKUT0E6EELD0jk3vf+YHEY6lAHpOYpafBvOGw++usZX0nQct+YYpWfPn999+58cYb2bdvH59//jlvvvkmVatWjXRYIiIiUgjojoEE7PlPN7Nm+xHAmcRs4pALqVfFx4RkmRnw3l+cSczcrvonXBgfpkjFl8zMTEaMGMG+fU7fkJUrV5KSkhLhqERERKSwUGIgAfk4YT9TvsyaxOyvf2rKZU1rnFnRWvjgfti4KGtZ90fgkrtCHqPk7qWXXuKjjz7ylKdPn06dOnUiGJGIiIgUJkoMJE+//n6cMfPXe8q9mtfknp4+JjGzFv73BPwwPWvZxaOgx9gwRCm5+fbbb3n00Uc95Yceeoirr746ghGJiIhIYaPEQHJ1IjWdUTOyJjGrXzWWlwb5mcTsqxdh9aSsctthThMiDYEZUX/88QdDhgwhPd35DDt16sQzzzwT4ahERCSSZs6ciTHG87jtttvy3GbUqFGe+omJiX7rHT16lDJlymCM4eOPP/Ys9z5ebo8GDRoE9Br27NnDI488QsuWLYmLi6Ny5cpceOGFPP300yQlJQW0D8lOnY/FL2stjyxYz9aDWZOYvRrfnkqxpc+s/O1/4fO/Z5Wb93E6G0cp94wkay233347O3bsAKBixYrMmTOHMmX8TEQnIiIlwo8//pitvGTJEjIzM4nK5f9t9za1a9emVq1afut98sknnD59mvLly9OrV6/gBOzjGEOHDuXo0aPZlq9bt45169bx+uuvs3jxYjp06BCS4xdXSgzEr7dX7eSD9fs95WcGtKFVHR+TmP00Fz56KKvcsDtc9yaU0ukVaf/973+ZN2+ep/zGG2/QsGHDCEYkIiKFQc7E4ODBg6xevZpLL73UZ/2MjAwSEpzhxy+88MJc9/3+++8DcOWVVxITE3PG+jvvvJO77vLf9zCvH6/Wr1/P9ddfz4kTJ4iNjeWRRx7h8ssvJz09ncWLFzNx4kT27t1Lnz59WLt2rfrT5YOu3MSn73ce4dmPsiYxG3Zxfa7vUO/Mips+gkV3ZpXrdoQhs6F02TBEKblJSEhg9OjRnvIdd9zBDTfcEMGIRESksPjpp58A6NatGwkJCRw9epRFixb5TQw2b97MqVOnAGjXrp3f/aanp3uaD/Xt29dnnZo1a9K6desCx37//fdz4sQJSpUqxccff8xll13mWde9e3fat2/P8OHDOXDgAI8//jhvvfVWgY9V0qidh5zhYHJKtknM2tarxFPXtjyz4o4v4d0RYDOccs2WcOO7EBMXvmDFr1OnTlGjhjNyVJs2bXjppZciHJGIiBQGu3bt4sgRZ/jxjh078uc//xmAxYsX+93G+w5DbonBV199xdGjR4mKivLsN5jWrl3L8uXLARgxYkS2pMAtPj7e04Rp+vTpHDx4MOhxFFdKDCSb0xmZ3DP7Rw4mO5OYVYktzSvxHYiJzjGJ2Z618M5QyHDqUaUhDF8IsZosq7Do1KkT69atY+jQocydO5dy5cpFOiQRkSJp+fLlAXec9X40atQo0qH7tG7dOs/f7dq1o18/Z/LRbdu28fPPP/vcxjsxyK0p0ZIlSwDo3Lmz58epYHrvvfc8f996661+640cORJwmkC5mzZJ3pQYSDbPfbKJb3d4TWI29ELqVs5xQZm4EWZdB2lOp2QqnAM3LYYKtcMcreSlatWqzJ49mxYtWkQ6FBGRImvVqlUF2q5r165BjiQ4cv76f/XVV3v6AixatMjnNu5kIi4ujsaNfQxZ7uJODK699trgBJvDypUrAYiNjeWiiy7yW69nz55nbCN5U2IgHh8l7Oe/X+3wlB+8oindmuTI9o/sgBkD4JRrGLByVWH4IqhyXvgCFRERCaNbbrmFhISEMx6xsbEADBgwwOf6F198McKR++a+yI+JifEM9elueuMvMXAnE23btsX4GYb8l19+Ydu2bYD//gUA7777Lq1ataJ8+fLExcVx/vnnM2zYMD788MM8Y9+4cSMATZo0ITraf1fZOnXqUKFChWzbSN6UGAgA2w4mM+bdnzzlP7WoyV09cvwicGw/TO8Hxw845TIVIH4B1GwexkjFn9TUVK666irdMhURCbK6devSunXrbI+KFSty8uRJwLkzkHN969atqVmzZp77LkgTpZyPcePG5ev1uC/yW7Vq5bm47t+/P+C04d+zZ0+2+rt37/b0Scitf4H7/59GjRrRsqWPvokuGzduZOPGjZw8eZITJ06wfft23nnnHfr06UOvXr38zpGQmprKoUOHAKhXz8eAKDmce+65APz222951hWHEgNxJjGb+QMn0pxOxOdVi+XFnJOYnTzi3Ck4usspR5eFYXOgbvsIRCy+PProoyxdupR+/foxZsyYSIcjIoXAuHHjAr64vP3228/Y/vbbbz+ri9Nrr7024O1ff/31M7bv0KFDUC+Ig2n9+vWevy+44IKIxZFfR44cYffu3UD2i/y+fft67gTkvGuQs0+CP+5mRP7uFsTGxjJ48GBef/11vvzyS3788UeWLVvG3//+d+rWrQs4/TmuuOIKkpOTz9jee1lcXN4DnbjrHD9+PM+64tBwpSWctZaHF6xnm2sSs7Klo3j1xg5UKuc1iVlqMsy8Dn53DV8aFQ2DpkMD30OaSfgtWbKEl19+2VN2/0oiIiKh4Z0YtGnTpsD7cc8NcDYCuTPh5u8iv3bt2lx88cWsWbOGRYsWcc8993jWBdLx+NChQ3z99deA//4Fe/fupXLlymcs79WrF/fddx8DBw5k2bJlJCQk8PTTT/P8889nq+ceLhXynusA8PSb8N5OcqfEoIR7c+UOPvSexKx/G1rWqZhV4XSKM/rQvh9cCwwMmAJNrwpvoOLXnj17GDFihKfct29f7r333sgFJCJSArgTgxo1alC7dsEH3zib8fwLIrdhR/v168eaNWtYsWIFR48e9VzEu7eJjo72G++HH35IZmYmlStXplu3bj7r+EoK3CpWrMi7775L48aNOXLkCK+99hrPPPNMtgTAe3S9tLS03F4m4DQ9yrmd5E5NiUqwb3cc4Z8fb/KU4zvX5zrvScwyTsP8W2DnV1nL/vwitLk+jFFKbtLT0xk2bJin7We9evV46623/HYME5GSZdy4cVhrA3r4asrz+uuvB7y9r2Y9S5YsCXh7X02Z1q5dm+9jhos7MTibuwWR4L5jYIyhbdu22da5+xmkp6fzwQcfnLFN8+bNfc5kDFnNiHr37k3p0qV91slLlSpVGDx4MOA0/1m7dm229e7OxO71eXHXCaTZkTh0x6CEOngshbtn/0CGexKzcyvzRB+vjkKZmbD4btj8Udayy5+Ci/yPGSzh9/TTT/PVV07iFhUVxezZs6lWrVqEoxIRKd5SU1PZsmULcPb9C/zNG5AfNWvWDLg5kfvX/4YNG1KxYsVs65o3b07Tpk3ZsmULixYtIj4+nqSkJHbtcvoX+mtGlJaWxtKlS4GzH6a0VatWnr9zdoKOiYmhevXqHDp06Ix1vrjrqHlt4JQY5GCMuQx4COgA1AFusdZOjWhQQXY6I5O7Z//A765JzKqWL8OrN7bPmsTMWvj4YVg/N2ujrvdDtwfCH6z49fnnn/OPf/zDUx4/frzf27ciIhI8GzduJCPDGbDjbBODYNxxeOqppwK6e5KSksLmzZsB/xf5/fv357nnnuPTTz8lJSUloBmPly9fTnJyMtHR0VxzzTX5jt9bXne8W7ZsyZdffsnWrVtJT0/3O2Tpvn37OHbsmGcbCYyaEp0pDvgZGA0Uy94q//p4E9/tdOYhiDIwaeiF1PGexGz5M/Ddf7PKHW6BP40Lb5CSq4MHD3LjjTdirXPHp1evXowdOzbCUYmIlAybNmU1w/X+hbuwS0hIID09HfB/ke+eBfn48eN89tln2Tor+0sm3MOUXnrppbn2IwjEhg0bPH/XqVPnjPWXXuoMfHLy5Em+++47v/v54osvzthG8qbEIAdr7UfW2r9Za+cDmZGOJ9g+WL+PN1d6TWJ2ZTO6Nq6eVWH1JPjSaxSA1tc5/QrUZr3QsNYyYsQIDhxw5pOoUaMGM2fOpFSpUhGOTESkZHA3rQFnzP6zEWgfjGD0tQjk1//OnTtTq1YtwBm2NJBt3P0RcpvULBBHjx5lzpw5gDO0aceOHc+oM3DgQM/fb775pt99vfXWWwCUKlXqrOMqSYpcYmCMud4YM8kY85Ux5pgxxhpjZuaxTT1jzFvGmH3GmFRjzE5jzMvGmCrhijsStiQm8/aqHUxatpW3V+3gs40HeHh+1vBqf2pRizu7n5+1wdppsPTxrHKTK50RiKJ0wVmYGGO45ZZbPG1Dp0+fzjnnnBPhqERESo7Tp097/i5Kgz0EMh9BVFSUp5/AkiVLPB2A69evT5UqZ142rVu3zjMvQm79C5YsWeK5W+HLsWPHuOGGGzyDadx2220+Ozp36NCBHj16ADB16lRPPztvs2bNYtmyZQDcdNNN+RrOtaQrin0MHgfaAseBPUCu0+4aY84HVgM1gcXAJqATTlOh3saYrtbawyGNOMxWbTvEhGVb+XbHEb91GlSL5cVBbbMmMduwEJaMzqpwXle4YRqUKtjIAhJaN9xwAx07duTTTz+ld+/ekQ5HRKREqV+/vufvMWPGcPvttxMXF0ejRo2IjY2NYGS5c//6X7169VxnDu7fvz9vvPEGBw8e5ODBg4D/ZkTu0YhatGhB48aN/e7z3nvvJS0tjYEDB3LJJZfQsGFDYmNjSUpKYuXKlUyZMoW9e/cCTifo3O6CTJgwgS5dunDixAl69+7No48+yuWXX056ejqLFy9mwoQJgDM3g3dfPMlbUUwM/oqTEGwDugPL86j/Ck5ScJ+1dpJ7oTHmP659PQOMCk2o4Tf3u92MfS8B12BDfvW/sG7WJGZbP4MFfwFcG53TFobOgTKF98tNnBElRo0qNqeuiEiR0bdvX6pVq8bhw4d5++23efvttwHYuXMn5513XoSj8y0zM9MzmVrOYUpzuvzyy4mLi8s2JKi/OwzuxCCQ0Yj279/P5MmTmTx5st86vXr1YsaMGT7vTrhdcMEFzJ8/n6FDh3L06FGefPJJnnzyyWx16taty+LFi332UxD/ilxTImvtcmvtVuvudZkL192CK4GdQM6z8CngBDDcGFM+6IFGwKpthwJKCgAmLtvKqm2HYNfXMDceMl23Ras3hfj3oGzF3HcgYWWt5cSJE5EOQ0REcMbbX758OX369KFKlSoYY6hcuXKhTQoAtmzZ4vl/xN9FvlvZsmW56qrsE5n62mb//v18//33QN6JwbRp0xg/fjzXXHMNzZo1o1q1akRHR1OpUiVatmzJyJEj+eyzz1i2bFlAF/O9e/cmISGBMWPG0KJFC8qXL0/FihVp27Yt48aNIyEhgQ4dOuS5H8muKN4xyI+eruel1tpsHYmttcnGmFU4iUNnYFkwDmiMWetnVa5NnoJhwrKtASUFAJkW3v/kY7om/w3SXYMvVaoPwxdB+eq5bivhN2XKFJ5//nnmzJnDRRddFOlwRERKvDZt2nh+LS8KmjdvTgC/qXrMnz8/zzoffPAB1lqqV69Oly5dcq3bvXt3unfvHvDxA1GvXj2ee+45nnvuuaDutyQrcncM8qmZ63mLn/VbXc9N3QuMMXHGmHbGmHY47099V7m+rx0UFlsSk3PtU5BTI7OPMb//DVKdMX4pXxNuWgSV6oYmQCmw9evXc//997N9+3a6dOnCZ599FumQREREPMOUXnPNNURFFfdLypKhuN8xqOR6/sPPevfyyl7LOpK938J412MaMCKvA1prfd63ct1JaJ/X9gW1atuhgOvW4RAzyvyT6saVFJStBMMXQrXzc99Qwu7EiRMMHjyY1FRnMrqWLVvStWvXCEclIiIC3bp1o0OHDmc927EUHsU9Mcg3a+0XQNEZe8zleMqZQ4A1MXvoGvUzcZziOOVYldmaI7YCM8r8k7rGGYjpdFRZSt84H2q3DnfIEoB7773XM5FObGwsc+fOpVy5cnlsJSIiEnoPP/xwpEOQICvuiYH7jkAlP+vdy4+GPpTQiiub9VF2ifqZ0dHvcXHUpjPqnbAxlDfOr8+pNpov2r3EVed2ClucErhZs2Z5RroAmDx5Ms2bh7yrioiIiJRQxT0x2Ox6bupnfRPXs78+CEWGe/biQaWW88/oNyhlLNZmn7DYWjxJQaaF+07fw4MX6/ZfYbR169ZsQ5HGx8dz8803RzAiERERKe6Ke08Rd1+BK40x2V6rMaYC0BU4CawJd2DB1rRWBUbW2eVJCiB7UnBm2VCvdi2a1qoQthglMKmpqQwePNgzfnSTJk145ZVXitTsmiIiIlL0FOvEwFr7K7AUaADcnWP1eKA8MMNaWywGiB8dvdCTFOQlyljui14U2oCkQMaMGeOZnbJMmTLMnTuXChWUwImIiEhoFbmmRMaY/kB/V7G26/kSY8xU19+HrLUPeW1yF7AamGiMuRz4BbgYZ46DLcBjIQ45PA7+QqWD32IJrOe0BSod/AYO/gI1W4Q4OAnUypUrmTTJM0E3L7zwgt9p6EVERESCqSjeMWgH3Ox6uKfla+S17Hrvyq67Bh2BqTgJwYPA+cAEoLO19nA4gg657SuAwIdT8tRzbSeFQ5cuXXjhhReIjo6mf//+3HPPPZEOSURERMIsP5PRBVORu2NgrR0HjMvnNr8Bt4Qinty4+jG424CUzszMzK362UlNDu92EhJRUVE8+OCDXHbZZZx//vnqVyAi2RhjsNaSmZmpCaVEijF3YhDu6wB9q4TWg8Be16NNYmJi6I4UU8A26AXdTkLqoosuomrVqpEOQ0QKmVKlSgGQlpYW4UhEJJROnz4NZP2bDxclBqH1IlDX9UioVatW6I7UqHt4t5Og2bRpk+cLQEQkN3FxcQAkJ+tur0hxduKEMy5ObGxsWI+rxCCErLXJ1tp91tp9wOmQ3vat2QLO65q/bc67VB2PIywxMZEePXrQrVs3duzYEelwRKSQq1ixIgBJSUmkpKREOBoRCYWMjAySkpIAKF++fFiPrcSgOOn+MJgAP1ITBd3HhDYeyVVmZiY33XQTiYmJfPPNN/Tu3ZuMjIxIhyUihVhsbCxxcXFkZGSwe/duDh48SEpKCpmZmRHrrCgiZ8/ddyg5OZnffvuNtLQ0ypQpE/bhyotc52PJRaMecO0EWDIabC4dnU0UXDvRqS8R8/zzz7N06VJPedKkSWFvSygiRYsxhrp167J3716OHz/O4cOHOXy4eAyuJyJZSpUqxbnnnhv26wIlBsVN+5ugcn1Y8TzsWnnm+vMude4UKCmIqK+//prHHsuaQuPRRx/lyiuvjGBEIlJUREVFUa9ePU6ePMmxY8c4fvw4GRkZumMgUsQZYyhTpgyVKlWiUqVKREeH/zJdiUFx1KiH8zj4izNPQWqyM/pQo+7qU1AIJCUlMWTIEE+zoUsuuYSnn346wlGJSFFijKF8+fJhb38sIsWbEoPirGYLJQKFjLWWW2+9ld27dwNQuXJl3nnnHUqXLh3hyERERKSkU+djkTB69dVXWbhwoaf85ptvct5550UwIhERERGHEgORMFm3bh0PPPCAp3z33XczcODACEYkIiIikkWJgUiY/Otf/yI1NRWAdu3a8cILL0Q4IhEREZEs6mMQQsaYCoB7ANrSmZm5DCEqxd60adOoVasWb731FnPmzKFs2bKRDklERETEQ3cMQutBYK/r0SYxMTHC4UgkxcTEMGHCBDZv3kyzZs0iHY6IiIhINkoMQutFoK7rkVCrVq0IhyOFQZ06dSIdgoiIiMgZlBiEkLU22Vq7z1q7DzgdFaW3uyRJSUlh5syZmnRIREREigRdqYqEyEMPPcTw4cMZOHAgR44ciXQ4IiIiIrlSYiASAu+99x6TJ08GYNGiRdnmLhAREREpjJQYiATZzp07ufXWWz3lAQMGMHLkyAhGJCIiIpI3o/bP4WGMOVyuXLmqLVq0iHQoEkLWWrZs2cKJEycAKF26NC1btqRUqVIRjkxERERKgl9++YVTp04dsdZWy++2SgzCxBizA6gI7PRaHAXUAhIBf5McnG2d5q7nTfkOunAJ5H0o7Mc82/0VZPv8bBNo3bzq5bW+OJyTxeF8DMY+C8M5qe9IR3E4J4vD+RhIPX1HFp1jFtVzsgFwzFrbMD/BghKDiDLG1MGZ46Cua+SioNcxxqwFsNZ2CGbs4RbI+1DYj3m2+yvI9vnZJtC6edULYH2RPyeLw/kYjH0WhnNS35GO4nBOFofzMZB6+o4sOscsLudkfqiPgYiIiIiIKDEQERERERElBpGWDIx3PYe6TlEXidcY7GOe7f4Ksn1+tgm0bl71dD4WnWMWh3NS35GO4nBOFofzMZB6Oh+LzjGLyzkZMPUxKOaKQ1tFKV50TkphovNRChudkxJJumMgIiIiIiK6YyAiIiIiIrpjICIiIiIiKDEQERERERGUGIiIiIiICEoMREREREQEJQYiIiIiIoISAxERERERQYmBeDHGjDXGfGeMOWaM+d0Ys8QY0zrScUnJZIy52xiz3nU+HjPGfG2M+XOk4xJxfVdaY8z/RToWKbmMMeNc56H340Ck45KiTYmBeOsBvAJ0AXoB6cBnxpiqkQxKSqw9wCNAe6Aj8DmwyBhzQUSjkhLNGNMZuB1YH+lYRIDNwDlejzaRDUeKuuhIByCFh7X2Ku+yMWY48AfQFVgSkaCkxLLWLs6x6DFjzJ3AJeiiTCLAGFMJmAWMBJ6KcDgiAOnWWt0lkKDRHYMixBhzvTFmkjHmK1fTCmuMmZnHNvWMMW8ZY/YZY1KNMTuNMS8bY6oEcMgKOOdIUlBegBQr4TwfjTGljDFDgDhgdTBfhxQPYTofXwfmW2uXB/8VSHETpnOykavuDmPMHGNMoxC8FClBdMegaHkcaAscx2lm0Ty3ysaY83EuomoCi4FNQCdgNNDbGNPVWns4l11MANYBX5915FIchfx8NMa0wTn/yrqOM8BamxDk1yHFQ0jPR2PMX4DGQHxIopfiKNTfkd8AI1z1arqOt9oY0yqP/9tF/NIdg6Llr0BToCJwZwD1X8H5srjPWtvfWvuotbYX8BLQDHjG34bGmP8AlwLXWWszzjpyKY7CcT5uBtoBFwOvAtPUIV78CNn5aIxpBjwLDLPWng565FJchfQ70lr7sbV2nrV2vbX2M6APznXdzcF8EVKyGGttpGOQAjDG9ACWA7OstWf8guX65WEbsBM431qb6bWuArAfMEBNa+2JHNu+BAwBelprN4XoJUgxEsrzMcd+PgN2WWtvDWb8UrwE+3w0xowA3ga8fyQpBVggEyhvrU0NxWuR4iGM35HLgU3W2kASEZEz6I5B8dXT9bzU+wsGwFqbDKwCYoHO3uuMMROAoUAvJQUSRAU6H32IAmKCH56UMPk9HxfhjPbSzuvxPTDH9XdaaMOVEuCsvyONMWVxmivtD1WQUvwpMSi+mrmet/hZv9X13NS9wBgzGbgFGAYkGWNqux5xoQtTSoiCnI//MsZ0M8Y0MMa0Mcb8E2dI3VmhC1NKiHydj9bao9ban70fwAngiKusW+9ytgryHfmCMaa7MaahMeZiYD5QHpgWujCluFPn4+Krkuv5Dz/r3csrey27y/W8LEfd8cC4oEQlJVVBzsfawEzX8x84Q5Reba39NBQBSolSkPNRJJQKck7WA94BqgO/A2uAztbaXaEIUEoGJQbiYa01kY5BxM1aOyLSMYj4Y63tEekYpGSz1g6JdAxS/KgpUfHl/nWhkp/17uVHQx+KiM5HKVR0Pkpho3NSCgUlBsXXZtdzUz/rm7ie/bVnFAkmnY9SmOh8lMJG56QUCkoMii/3zJxXGmOyfc6uoc+6Aidx2iSKhJrORylMdD5KYaNzUgoFJQbFlLX2V2Ap0AC4O8fq8TgjF8zIbTxkkWDR+SiFic5HKWx0TkphoQnOihBjTH+gv6tYG7gK2A585Vp2yFr7kFf9nNOr/4Izg2xPnNuRXTRtuhSUzkcpTHQ+SmGjc1KKIiUGRYgxZhzwVC5VdllrG+TY5lzgaaA3UA1n4pOFwHhrbVJoIpWSQOejFCY6H6Ww0TkpRZESAxERERERUR8DERERERFRYiAiIiIiIigxEBERERERlBiIiIiIiAhKDEREREREBCUGIiIiIiKCEgMREREREUGJgYiIiIiIoMRARERERERQYiAiIiIiIigxEBERERERlBiIiIiIiAhKDEREREREBCUGIiIiIiKCEgMRkULLGNPAGGONMVMjHUswGWPuM8ZsNMaccr2++yMdUyQU189XRIouJQYiIhI2xpghwAQgBXgZGA+siWRMkjdjzFJXEmONMR381HndtX5guOMTkeCIjnQAIiJSovRxP1tr90U0EskP72RgELDWR52OrufvQx+OiISC7hiIiEg41QFQUlB0GGMaAVVxLvj3ATf4qBMDtAYOWmt3hzdCEQkWJQYiUmJ4t+l2/T3HGHPIGJNijPneGNMnR/0ervrj/OxvpzFmZy7HON8YM98Yc9gYk+xqjtHaVa+Gq+nFftfxvzPG9Mwl9ubGmEXGmCPGmBPGmJXGmCtzqX+x69gHjDFpxpjfjDFTjDF1com3qTFmrjHmoDEm0xjTI6/31LWPQcaYL40xf7j6DSQYY8a6LhbddcYZYyzQ01V2N0uxAR6jrzFmmev9SjXG7DPGrDDG3JWj3ghjzAJjzHZXLMeMMauMMfE+9hnUzyrH/vL1efl5zQF9hvl5fwrIfSfgO2AB0NAYc1GOOm2B0vi+kyAiRYSaEolISXQe8C2wHZiB82voYGCxMeZP1trlQThGA+Ab4Bdgqqs8APjCGHMJ8AlwDJjrOv4Q4GNjTFMfv7g2BL4GEoApwDmueD82xgyz1s71rmyMGQm8DqQC7wO/AU2A24BrjTGdfRzjfFe8W4BZQDlXfLkyxjwLjAUOAbOB48DVwLPAVcaYK621acAXrk1G4Lz/4/Pat9cxbne97gPAEtexagIXALcAr3hVfxXYAHwJ7AeqAdcAM4wxzay1T/g4RAOC91lBPj8vP6854M8wn+9PQXg3EdoK3IvTnOg7P3VEpKiy1uqhhx56lIgHzgWfdT2eyrHuKtfyj7yW9XAtG+dnfzuBnbkc47Ec655wLT8CvAZEea0b7lr3kp99PZ9jXx2B00ASUNFreVMgDdgG1M2xzeVABrDQzzGezef7eYlru91Aba/l0TgXqBb4W45tvnD+68nXcdbiXCDX9LGueo7y+T7qlAGWud6vul7Lg/ZZFeTz8qo/NUfd/H6GAb8/Bfx387krzgtwWhrs9XHev+Wq0/dsj6eHHnpE7qGmRCJSEu0C/uG9wFr7Kc4FbqcgHWMn8K8cy6a5nmOAMdbaTK91s4F0oJ2Pff0BPO29wFr7Pc4v+5Vxft12uxOnScdoa+3eHNssw/n1+VpjTIUcx0gkH7/iu4x0Pf/DWnvA6zjpwINAJs4v3MGQjnNhnY219lCO8q8+6qQBk3ESlst97HsnwfusIH+fly8F+QwDen/yyxhjgPbAKWCj631YAJxnjLnYq6ruGIgUA2pKJCIl0TprbYaP5b/h/AoeqmO4O9xusdYme6+w1mYYYxKBej729UPO+i5fADcDF5J1IeuOv7uPduDgNDEphfOrtHd78J+stan+Xowf7V3Pn+dcYa3dYozZg9MevZK19o987tvbLOBFYKMxZg6wAlhlrf09Z0VjTH3gEZwEoD5OkyhvdX3sP5ifFeTv8/Ilv59hwO9PATQBKgFrXAkfwLtkNSf6xhhTDmgJ7LfqVC5SpCkxEJGS6Kif5ekEb1CGMy6ErbXpzg+wZ67zOn5pH8sT/dR3/0pfyWtZNdfzmDzii/Ozr/xwH3e/n/X7cS7OK+P/NefJWvsfY8wh4C7gPuB+wBpjVuD8mv89eEbP+RaoAnwFLHUdNwOn2c7NOHcAcgrmZwX5+7x8yddnGOj7U0C+7gSswvlsrzfGPIST6JRCdwtEijwlBiIi/rmbj/j7rqyM/yQjmGr5WV7b9ex98er+u5K1Ns/Ow14CGh0oB/exagNnNOHB6XTrXa/ArLXTgenGmMpAF5zmOCOBT40xzV2/jj+Ac1F9i7V2qvf2xpihOIlBOOTn8/Il359hgO9PQbgTA8/dJWttpjFmAXAP0Bk1IxIpNtTHQETEvyTX87k5VxhjGpP3L7/B0t5HnwBwOkcD/Oi1zD2LcLeQRpT9uD1yrnC9P/WAHdbao8E6oLX2qLX2I2vtX3BGEKoKXOZa3dj1vMDHpt2DFUMA8vN5+VLgzzCP96cg/F30v+t6HpRLHREpYpQYiIj4twlnmMp+xpia7oWuNtUTwxhHJeBJ7wXGmI7AjTi/Li/0WvV/OJ1QXzLGNM25I2NMGWNMsJKGt1zPjxtjangdoxTwAs7/MW+e7UGMMT1dnWBzcn8mJ13PO13PPXJsfxXB6wQdiPx8Xr7k6zPMx/vjrj/VNd/CiNyCMMZE4TQTOokzlKu3lThNo64H3P0glBiIFHFqSiQi4oe19rQxZgLO0JU/GmMW4nxvXoHTOTVcHS2/BG5zjQKziqxx8aOAO7ybm1hrN7nGwH8L2GCM+QRnboLSOO39uwG/A83PNihr7WpjzHPAw8DPxpj5wAmceQxa41w8Pn+2x8G5kD5ujFmDc/FvcF7HRThNXD5z1XsFZ9z+d12x7HPF0RuYh/OehUPAn5cvBfgMA31/3Nw/CqaTu+Y4/RhW5+yc7dWc6G7Xot+stQfz2J+IFHK6YyAikruncCbwSgFux5ksawHOvAdnDA8ZIjtw2o0nAaNwmm/8AFxjfUyWZa2dCXTAGa3mApy24PE4TW3m43RSDQpr7SPAUJyJr27C6fwaBTwOXOEaKvRsPYozmVZ7nNhvwblIfgToaa097YplPc7MyquBP+MM+1kRGIgzF0G45Ovz8iWfn2FA74+XNkAy8GEeYeTVROhdr791t0CkGDDWFqS/mYiIiHgzxjTASQqmWWtHRDYa31ydkw8DL1prH45wOCJSyOiOgYiISMnRDedO138iHYiIFD7qYyAiIlJCWGuXAGUjHYeIFE66YyAiIiIiIupjICIiIiIiumMgIiIiIiIoMRAREREREZQYiIiIiIgISgxERERERAQlBiIiIiIighIDERERERFBiYGIiIiIiKDEQEREREREUGIgIiIiIiIoMRAREREREZQYiIiIiIgISgxERERERAQlBiIiIiIighIDERERERFBiYGIiIiIiAD/DznxkaQg04ySAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "image/png": { "height": 265, "width": 387 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the comparisons\n", "plt.loglog(N, gw2010, \"o-\", label=\"G&W 2010\")\n", "plt.loglog(N, new, \"o-\", label=\"new\")\n", "plt.loglog(N, ml, \"o-\", label=\"ML\")\n", "ylim = plt.gca().get_ylim()\n", "plt.plot(N, N / 50.0, \"--k\", label=r\"$\\tau = N/50$\")\n", "plt.ylim(ylim)\n", "plt.xlabel(\"number of samples, $N$\")\n", "plt.ylabel(r\"$\\tau$ estimates\")\n", "plt.legend(fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure is the same as the previous one, but we've added the maximum likelihood estimates for $\\tau$ in green.\n", "In this case, this estimate seems to be robust even for very short chains with $N \\sim \\tau$." ] } ], "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/line.ipynb0000644000175100001710000323175000000000000017115 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(line)=\n", "\n", "# Fitting a model to data\n", "\n", "If you're reading this right now then you're probably interested in using\n", "emcee to fit a model to some noisy data.\n", "On this page, I'll demonstrate how you might do this in the simplest\n", "non-trivial model that I could think of: fitting a line to data when you\n", "don't believe the error bars on your data.\n", "The interested reader should check out [Hogg, Bovy & Lang (2010)](https://arxiv.org/abs/1008.4686) for a much more complete discussion of how\n", "to fit a line to data in The Real Worldâ„¢ and why MCMC might come in handy." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The generative probabilistic model\n", "\n", "When you approach a new problem, the first step is generally to write down the\n", "*likelihood function* (the probability of a dataset given the model\n", "parameters).\n", "This is equivalent to describing the generative procedure for the data.\n", "In this case, we're going to consider a linear model where the quoted\n", "uncertainties are underestimated by a constant fractional amount.\n", "You can generate a synthetic dataset from this model:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 261, "width": 390 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(123)\n", "\n", "# Choose the \"true\" parameters.\n", "m_true = -0.9594\n", "b_true = 4.294\n", "f_true = 0.534\n", "\n", "# Generate some synthetic data from the model.\n", "N = 50\n", "x = np.sort(10 * np.random.rand(N))\n", "yerr = 0.1 + 0.5 * np.random.rand(N)\n", "y = m_true * x + b_true\n", "y += np.abs(f_true * y) * np.random.randn(N)\n", "y += yerr * np.random.randn(N)\n", "\n", "plt.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "x0 = np.linspace(0, 10, 500)\n", "plt.plot(x0, m_true * x0 + b_true, \"k\", alpha=0.3, lw=3)\n", "plt.xlim(0, 10)\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The true model is shown as the thick grey line and the effect of the\n", "underestimated uncertainties is obvious when you look at this figure.\n", "The standard way to fit a line to these data (assuming independent Gaussian\n", "error bars) is linear least squares.\n", "Linear least squares is appealing because solving for the parameters—and\n", "their associated uncertainties—is simply a linear algebraic operation.\n", "Following the notation in [Hogg, Bovy & Lang (2010)](https://arxiv.org/abs/1008.4686), the linear least squares solution to these\n", "data is" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Least-squares estimates:\n", "m = -1.104 ± 0.016\n", "b = 5.441 ± 0.091\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 261, "width": 390 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "A = np.vander(x, 2)\n", "C = np.diag(yerr * yerr)\n", "ATA = np.dot(A.T, A / (yerr ** 2)[:, None])\n", "cov = np.linalg.inv(ATA)\n", "w = np.linalg.solve(ATA, np.dot(A.T, y / yerr ** 2))\n", "print(\"Least-squares estimates:\")\n", "print(\"m = {0:.3f} ± {1:.3f}\".format(w[0], np.sqrt(cov[0, 0])))\n", "print(\"b = {0:.3f} ± {1:.3f}\".format(w[1], np.sqrt(cov[1, 1])))\n", "\n", "plt.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "plt.plot(x0, m_true * x0 + b_true, \"k\", alpha=0.3, lw=3, label=\"truth\")\n", "plt.plot(x0, np.dot(np.vander(x0, 2), w), \"--k\", label=\"LS\")\n", "plt.legend(fontsize=14)\n", "plt.xlim(0, 10)\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows the least-squares estimate of the line parameters as a dashed line.\n", "This isn't an unreasonable result but the uncertainties on the slope and\n", "intercept seem a little small (because of the small error bars on most of the\n", "data points)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maximum likelihood estimation\n", "\n", "The least squares solution found in the previous section is the maximum\n", "likelihood result for a model where the error bars are assumed correct,\n", "Gaussian and independent.\n", "We know, of course, that this isn't the right model.\n", "Unfortunately, there isn't a generalization of least squares that supports a\n", "model like the one that we know to be true.\n", "Instead, we need to write down the likelihood function and numerically\n", "optimize it.\n", "In mathematical notation, the correct likelihood function is:\n", "\n", "$$\n", " \\ln\\,p(y\\,|\\,x,\\sigma,m,b,f) =\n", " -\\frac{1}{2} \\sum_n \\left[\n", " \\frac{(y_n-m\\,x_n-b)^2}{s_n^2}\n", " + \\ln \\left ( 2\\pi\\,s_n^2 \\right )\n", " \\right]\n", "$$\n", "\n", "where\n", "\n", "$$\n", " s_n^2 = \\sigma_n^2+f^2\\,(m\\,x_n+b)^2 \\quad .\n", "$$\n", "\n", "This likelihood function is simply a Gaussian where the variance is\n", "underestimated by some fractional amount: $f$.\n", "In Python, you would code this up as:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def log_likelihood(theta, x, y, yerr):\n", " m, b, log_f = theta\n", " model = m * x + b\n", " sigma2 = yerr ** 2 + model ** 2 * np.exp(2 * log_f)\n", " return -0.5 * np.sum((y - model) ** 2 / sigma2 + np.log(sigma2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this code snippet, you'll notice that we're using the logarithm of $f$\n", "instead of $f$ itself for reasons that will become clear in the next section.\n", "For now, it should at least be clear that this isn't a bad idea because it\n", "will force $f$ to be always positive.\n", "A good way of finding this numerical optimum of this likelihood function is to\n", "use the [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/optimize.html) module:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum likelihood estimates:\n", "m = -1.003\n", "b = 4.528\n", "f = 0.454\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 261, "width": 390 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.optimize import minimize\n", "\n", "np.random.seed(42)\n", "nll = lambda *args: -log_likelihood(*args)\n", "initial = np.array([m_true, b_true, np.log(f_true)]) + 0.1 * np.random.randn(3)\n", "soln = minimize(nll, initial, args=(x, y, yerr))\n", "m_ml, b_ml, log_f_ml = soln.x\n", "\n", "print(\"Maximum likelihood estimates:\")\n", "print(\"m = {0:.3f}\".format(m_ml))\n", "print(\"b = {0:.3f}\".format(b_ml))\n", "print(\"f = {0:.3f}\".format(np.exp(log_f_ml)))\n", "\n", "plt.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "plt.plot(x0, m_true * x0 + b_true, \"k\", alpha=0.3, lw=3, label=\"truth\")\n", "plt.plot(x0, np.dot(np.vander(x0, 2), w), \"--k\", label=\"LS\")\n", "plt.plot(x0, np.dot(np.vander(x0, 2), [m_ml, b_ml]), \":k\", label=\"ML\")\n", "plt.legend(fontsize=14)\n", "plt.xlim(0, 10)\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's worth noting that the optimize module *minimizes* functions whereas we\n", "would like to maximize the likelihood.\n", "This goal is equivalent to minimizing the *negative* likelihood (or in this\n", "case, the negative *log* likelihood).\n", "In this figure, the maximum likelihood (ML) result is plotted as a dotted black line—compared to\n", "the true model (grey line) and linear least-squares (LS; dashed line).\n", "That looks better!\n", "\n", "The problem now: how do we estimate the uncertainties on *m* and *b*?\n", "What's more, we probably don't really care too much about the value of *f* but\n", "it seems worthwhile to propagate any uncertainties about its value to our\n", "final estimates of *m* and *b*.\n", "This is where MCMC comes in." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Marginalization & uncertainty estimation\n", "\n", "This isn't the place to get into the details of why you might want to use MCMC\n", "in your research but it is worth commenting that a common reason is that you\n", "would like to marginalize over some \"nuisance parameters\" and find an estimate\n", "of the posterior probability function (the distribution of parameters that is\n", "consistent with your dataset) for others.\n", "MCMC lets you do both of these things in one fell swoop!\n", "You need to start by writing down the posterior probability function (up to a\n", "constant):\n", "\n", "$$\n", " p (m,b,f\\,|\\,x,y,\\sigma) \\propto p(m,b,f)\\,p(y\\,|\\,x,\\sigma,m,b,f) \\quad .\n", "$$\n", "\n", "We have already, in the previous section, written down the likelihood function\n", "\n", "$$\n", "p(y\\,|\\,x,\\sigma,m,b,f)\n", "$$\n", "\n", "so the missing component is the \"prior\" function\n", "\n", "$$\n", "p(m,b,f) \\quad .\n", "$$\n", "\n", "This function encodes any previous knowledge that we have about the\n", "parameters: results from other experiments, physically acceptable ranges, etc.\n", "It is necessary that you write down priors if you're going to use MCMC because\n", "all that MCMC does is draw samples from a probability distribution and you\n", "want that to be a probability distribution for your parameters.\n", "This is important: **you cannot draw parameter samples from your likelihood\n", "function**.\n", "This is because a likelihood function is a probability distribution **over\n", "datasets** so, conditioned on model parameters, you can draw representative\n", "datasets (as demonstrated at the beginning of this exercise) but you cannot\n", "draw parameter samples.\n", "\n", "In this example, we'll use uniform (so-called \"uninformative\") priors on $m$,\n", "$b$ and the logarithm of $f$.\n", "For example, we'll use the following conservative prior on $m$:\n", "\n", "$$\n", "p(m) = \\left \\{\\begin{array}{ll}\n", " 1 / 5.5 \\,, & \\mbox{if}\\,-5 < m < 1/2 \\\\\n", " 0 \\,, & \\mbox{otherwise}\n", " \\end{array}\n", " \\right .\n", "$$\n", "\n", "In code, the log-prior is (up to a constant):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def log_prior(theta):\n", " m, b, log_f = theta\n", " if -5.0 < m < 0.5 and 0.0 < b < 10.0 and -10.0 < log_f < 1.0:\n", " return 0.0\n", " return -np.inf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, combining this with the definition of ``log_likelihood`` from above, the full\n", "log-probability function is:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def log_probability(theta, x, y, yerr):\n", " lp = log_prior(theta)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", " return lp + log_likelihood(theta, x, y, yerr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After all this setup, it's easy to sample this distribution using emcee.\n", "We'll start by initializing the walkers in a tiny Gaussian ball around the\n", "maximum likelihood result (I've found that this tends to be a pretty good\n", "initialization in most cases) and then run 5,000 steps of MCMC." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5000/5000 [00:07<00:00, 712.03it/s]\n" ] } ], "source": [ "import emcee\n", "\n", "pos = soln.x + 1e-4 * np.random.randn(32, 3)\n", "nwalkers, ndim = pos.shape\n", "\n", "sampler = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_probability, args=(x, y, yerr)\n", ")\n", "sampler.run_mcmc(pos, 5000, progress=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at what the sampler has done.\n", "A good first step is to look at the time series of the parameters in\n", "the chain.\n", "The samples can be accessed using the {func}`EnsembleSampler.get_chain` method.\n", "This will return an array\n", "with the shape `(5000, 32, 3)` giving the parameter values for each walker\n", "at each step in the chain.\n", "The figure below shows the positions of each walker as a function of the\n", "number of steps in the chain:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 424, "width": 650 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, figsize=(10, 7), sharex=True)\n", "samples = sampler.get_chain()\n", "labels = [\"m\", \"b\", \"log(f)\"]\n", "for i in range(ndim):\n", " ax = axes[i]\n", " ax.plot(samples[:, :, i], \"k\", alpha=0.3)\n", " ax.set_xlim(0, len(samples))\n", " ax.set_ylabel(labels[i])\n", " ax.yaxis.set_label_coords(-0.1, 0.5)\n", "\n", "axes[-1].set_xlabel(\"step number\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned above, the walkers start in small distributions around the\n", "maximum likelihood values and then they quickly wander and start exploring the\n", "full posterior distribution.\n", "In fact, after fewer than 50 steps, the samples seem pretty well \"burnt-in\".\n", "That is a hard statement to make quantitatively, but we can look at an estimate\n", "of the integrated autocorrelation time (see the {ref}`autocorr` tutorial for more details):" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[39.16329084 39.96660169 35.8864348 ]\n" ] } ], "source": [ "tau = sampler.get_autocorr_time()\n", "print(tau)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This suggests that only about 40 steps are needed for the chain to \"forget\" where it started.\n", "It's not unreasonable to throw away a few times this number of steps as \"burn-in\".\n", "Let's discard the initial 100 steps, thin by about half the autocorrelation time (15 steps), and flatten the chain so that we have a flat list of samples:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10432, 3)\n" ] } ], "source": [ "flat_samples = sampler.get_chain(discard=100, thin=15, flat=True)\n", "print(flat_samples.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results\n", "\n", "Now that we have this list of samples, let's make one of the most useful plots\n", "you can make with your MCMC results: *a corner plot*.\n", "You'll need the [corner.py module](http://corner.readthedocs.io) but\n", "once you have it, generating a corner plot is as simple as:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 513, "width": 513 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import corner\n", "\n", "fig = corner.corner(\n", " flat_samples, labels=labels, truths=[m_true, b_true, np.log(f_true)]\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The corner plot shows all the one and two dimensional projections of the\n", "posterior probability distributions of your parameters.\n", "This is useful because it quickly demonstrates all of the covariances between\n", "parameters.\n", "Also, the way that you find the marginalized distribution for a parameter or\n", "set of parameters using the results of the MCMC chain is to project the\n", "samples into that plane and then make an N-dimensional histogram.\n", "That means that the corner plot shows the marginalized distribution for each\n", "parameter independently in the histograms along the diagonal and then the\n", "marginalized two dimensional distributions in the other panels.\n", "\n", "Another diagnostic plot is the projection of your results into the space of\n", "the observed data.\n", "To do this, you can choose a few (say 100 in this case) samples from the chain\n", "and plot them on top of the data points:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 261, "width": 390 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "inds = np.random.randint(len(flat_samples), size=100)\n", "for ind in inds:\n", " sample = flat_samples[ind]\n", " plt.plot(x0, np.dot(np.vander(x0, 2), sample[:2]), \"C1\", alpha=0.1)\n", "plt.errorbar(x, y, yerr=yerr, fmt=\".k\", capsize=0)\n", "plt.plot(x0, m_true * x0 + b_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": [ "This leaves us with one question: which numbers should go in the abstract?\n", "There are a few different options for this but my favorite is to quote the\n", "uncertainties based on the 16th, 50th, and 84th percentiles of the samples in\n", "the marginalized distributions.\n", "To compute these numbers for this example, you would run:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\mathrm{m} = -1.007_{-0.081}^{0.077}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\mathrm{b} = 4.550_{-0.358}^{0.366}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/latex": [ "$\\displaystyle \\mathrm{log(f)} = -0.772_{-0.148}^{0.161}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Math\n", "\n", "for i in range(ndim):\n", " mcmc = np.percentile(flat_samples[:, 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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/monitor.ipynb0000644000175100001710000414533500000000000017661 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(monitor)=\n", "\n", "# Saving & monitoring progress\n", "\n", "It is often useful to incrementally save the state of the chain to a file.\n", "This makes it easier to monitor the chain’s progress and it makes things a little less disastrous if your code/computer crashes somewhere in the middle of an expensive MCMC run.\n", "\n", "In this demo, we will demonstrate how you can use the new {class}`backends.HDFBackend` to save your results to a [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) file as the chain runs.\n", "To execute this, you'll first need to install the [h5py library](http://www.h5py.org).\n", "\n", "We'll also monitor the autocorrelation time at regular intervals (see {ref}`autocorr`) to judge convergence." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will set up the problem as usual with one small change:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import emcee\n", "import numpy as np\n", "\n", "np.random.seed(42)\n", "\n", "# The definition of the log probability function\n", "# We'll also use the \"blobs\" feature to track the \"log prior\" for each step\n", "def log_prob(theta):\n", " log_prior = -0.5 * np.sum((theta - 1.0) ** 2 / 100.0)\n", " log_prob = -0.5 * np.sum(theta ** 2) + log_prior\n", " return log_prob, log_prior\n", "\n", "\n", "# Initialize the walkers\n", "coords = np.random.randn(32, 5)\n", "nwalkers, ndim = coords.shape\n", "\n", "# Set up the backend\n", "# Don't forget to clear it in case the file already exists\n", "filename = \"tutorial.h5\"\n", "backend = emcee.backends.HDFBackend(filename)\n", "backend.reset(nwalkers, ndim)\n", "\n", "# Initialize the sampler\n", "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, backend=backend)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference here was the addition of a \"backend\".\n", "This choice will save the samples to a file called `tutorial.h5` in the current directory.\n", "Now, we'll run the chain for up to 10,000 steps and check the autocorrelation time every 100 steps.\n", "If the chain is longer than 100 times the estimated autocorrelation time and if this estimate changed by less than 1%, we'll consider things converged." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 6%|▌ | 5900/100000 [00:55<14:41, 106.81it/s]\n" ] } ], "source": [ "max_n = 100000\n", "\n", "# We'll track how the average autocorrelation time estimate changes\n", "index = 0\n", "autocorr = np.empty(max_n)\n", "\n", "# This will be useful to testing convergence\n", "old_tau = np.inf\n", "\n", "# Now we'll sample for up to max_n steps\n", "for sample in sampler.sample(coords, iterations=max_n, progress=True):\n", " # Only check convergence every 100 steps\n", " if sampler.iteration % 100:\n", " continue\n", "\n", " # Compute the autocorrelation time so far\n", " # Using tol=0 means that we'll always get an estimate even\n", " # if it isn't trustworthy\n", " tau = sampler.get_autocorr_time(tol=0)\n", " autocorr[index] = np.mean(tau)\n", " index += 1\n", "\n", " # Check convergence\n", " converged = np.all(tau * 100 < sampler.iteration)\n", " converged &= np.all(np.abs(old_tau - tau) / tau < 0.01)\n", " if converged:\n", " break\n", " old_tau = tau" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's take a look at how the autocorrelation time estimate (averaged across dimensions) changed over the course of this run.\n", "In this plot, the $\\tau$ estimate is plotted (in blue) as a function of chain length and, for comparison, the $N > 100\\,\\tau$ threshold is plotted as a dashed line." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 261, "width": 384 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "n = 100 * np.arange(1, index + 1)\n", "y = autocorr[:index]\n", "plt.plot(n, n / 100.0, \"--k\")\n", "plt.plot(n, y)\n", "plt.xlim(0, n.max())\n", "plt.ylim(0, y.max() + 0.1 * (y.max() - y.min()))\n", "plt.xlabel(\"number of steps\")\n", "plt.ylabel(r\"mean $\\hat{\\tau}$\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, we can also access all the properties of the chain:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "burn-in: 117\n", "thin: 24\n", "flat chain shape: (7680, 5)\n", "flat log prob shape: (7680,)\n", "flat log prior shape: (7680,)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 1119, "width": 1119 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import corner\n", "\n", "tau = sampler.get_autocorr_time()\n", "burnin = int(2 * np.max(tau))\n", "thin = int(0.5 * np.min(tau))\n", "samples = sampler.get_chain(discard=burnin, flat=True, thin=thin)\n", "log_prob_samples = sampler.get_log_prob(discard=burnin, flat=True, thin=thin)\n", "log_prior_samples = sampler.get_blobs(discard=burnin, flat=True, thin=thin)\n", "\n", "print(\"burn-in: {0}\".format(burnin))\n", "print(\"thin: {0}\".format(thin))\n", "print(\"flat chain shape: {0}\".format(samples.shape))\n", "print(\"flat log prob shape: {0}\".format(log_prob_samples.shape))\n", "print(\"flat log prior shape: {0}\".format(log_prior_samples.shape))\n", "\n", "all_samples = np.concatenate(\n", " (samples, log_prob_samples[:, None], log_prior_samples[:, None]), axis=1\n", ")\n", "\n", "labels = list(map(r\"$\\theta_{{{0}}}$\".format, range(1, ndim + 1)))\n", "labels += [\"log prob\", \"log prior\"]\n", "\n", "corner.corner(all_samples, labels=labels);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But, since you saved your samples to a file, you can also open them after the fact using the {class}`backends.HDFBackend`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "burn-in: 117\n", "thin: 24\n", "flat chain shape: (7680, 5)\n", "flat log prob shape: (7680,)\n", "flat log prior shape: (7680,)\n" ] } ], "source": [ "reader = emcee.backends.HDFBackend(filename)\n", "\n", "tau = reader.get_autocorr_time()\n", "burnin = int(2 * np.max(tau))\n", "thin = int(0.5 * np.min(tau))\n", "samples = reader.get_chain(discard=burnin, flat=True, thin=thin)\n", "log_prob_samples = reader.get_log_prob(discard=burnin, flat=True, thin=thin)\n", "log_prior_samples = reader.get_blobs(discard=burnin, flat=True, thin=thin)\n", "\n", "print(\"burn-in: {0}\".format(burnin))\n", "print(\"thin: {0}\".format(thin))\n", "print(\"flat chain shape: {0}\".format(samples.shape))\n", "print(\"flat log prob shape: {0}\".format(log_prob_samples.shape))\n", "print(\"flat log prior shape: {0}\".format(log_prior_samples.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This should give the same output as the previous code block, but you'll notice that there was no reference to `sampler` here at all.\n", "\n", "If you want to restart from the last sample, you can just leave out the call to {func}`backends.HDFBackend.reset`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial size: 5900\n", "Final size: 6000\n" ] } ], "source": [ "new_backend = emcee.backends.HDFBackend(filename)\n", "print(\"Initial size: {0}\".format(new_backend.iteration))\n", "new_sampler = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_prob, backend=new_backend\n", ")\n", "new_sampler.run_mcmc(None, 100)\n", "print(\"Final size: {0}\".format(new_backend.iteration))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to save *additional* ``emcee`` runs, you can do so on the same file\n", "as long as you set the ``name`` of the backend object to something other than\n", "the default:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 6000/6000 [00:42<00:00, 139.66it/s]\n" ] } ], "source": [ "run2_backend = emcee.backends.HDFBackend(filename, name=\"mcmc_second_prior\")\n", "\n", "# this time, with a subtly different prior\n", "def log_prob2(theta):\n", " log_prior = -0.5 * np.sum((theta - 2.0) ** 2 / 100.0)\n", " log_prob = -0.5 * np.sum(theta ** 2) + log_prior\n", " return log_prob, log_prior\n", "\n", "\n", "# Rinse, Wash, and Repeat as above\n", "coords = np.random.randn(32, 5)\n", "nwalkers, ndim = coords.shape\n", "sampler2 = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_prob2, backend=run2_backend\n", ")\n", "\n", "# note: this is *not* necessarily the right number of iterations for this\n", "# new prior. But it will suffice to demonstrate the second backend.\n", "sampler2.run_mcmc(coords, new_backend.iteration, progress=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now you can see *both* runs are in the file:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['mcmc', 'mcmc_second_prior']\n" ] } ], "source": [ "import h5py\n", "\n", "with h5py.File(filename, \"r\") as f:\n", " print(list(f.keys()))" ] } ], "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/moves.ipynb0000644000175100001710000073722000000000000017317 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(moves)=\n", "\n", "# Using different moves\n", "\n", "One of the most important new features included in the version 3 release of emcee is the interface for using different \"moves\" (see {ref}`moves-user` for the API docs).\n", "To demonstrate this interface, we'll set up a slightly contrived example where we're sampling from a mixture of two Gaussians in 1D:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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Dw4mA4ESpBgwbO3asc2WrXbt2atCggeFE/lGyZEmvG7R42RjhbsWKFVq3bp0kzw27/fr1M5zIf9xPEN5//31eeQLOgVINGOa+6uOeXQwH7qvuX3zxhTIyMgymAQrXqFGjnMf9+vUL6RsUc7v++usVHx8vSdqwYYO+//57w4mA4EOpBgxau3atVq5cKclzQ9CNN95oOJF/tWrVSg0bNpQkZWRkaOLEiYYTAYXj1KlTGjt2rLMeNGiQuTCFoESJErruuuuc9UcffWQwDRCcKNWAQe6r1FdddZVKly5tMI3/WZblddQxu4AgXH311Vc6dOiQJKlWrVrq3Lmz4UT+d+uttzqPx40bp8zMTINpgOBDqQYMycrK0scff+ysw23043c333yzc5rcrFmzlJ6ebjgR4H/uGxQHDhyoqKjw+/GanJysqlWrSpIOHDig6dOnG04EBJfw+6oHQsTMmTO1Z88eSVLFihXVvXt3w4kKR/Xq1ZWSkiJJysnJ0ZgxYwwnAvxr9+7dmjZtmrN2vzoTTqKiojRgwABn7b4oAIBSDRjj/oF08803KyYmxmCawuUuGR988AE7ByCsjB8/Xjk5OZKkpKQk1apVy3CiwuMeAZk0aZKOHDliMA0QXCjVgAEnT57UpEmTnPUtt9xiME3hu/baa1W8eHFJ0rp16/Tjjz+aDQT40bhx45zH7m0kw1HTpk3VrFkzSZ6bM92nRwKRjlINGDBlyhQdP35cklS/fn1dfvnlZgMVsvj4eK+j19k5AOFiy5YtWrp0qSQpJibGa4eMcOW+Ws3XMvA/lGrAAPeVrRtvvNG5kS+cua/Gf/rpp87L5UAoc38td+/eXWXKlDGYJjAGDBjg3Ig5d+5c594QINJRqoEAO3bsmNex5P379zeYJnBSU1NVtmxZSVJ6eroWL15sOBHgO/fe1DfddJPBJIFTuXJlZ8tA27b1xRdfGE4EBAdKNRBgX331lU6ePClJuvTSS9W4cWPDiQIjNjbW66Xx8ePHG0wD+G7t2rVau3atJKlYsWK6+uqrDScKnBtuuMF5/OmnnxpMAgQPSjUQYO4yGW4nKF6M+3/v559/ruzsbINpAN+4r1L36dMnrI4lv5hrr73WGQFZsGCBdu/ebTgRYB6lGgigw4cPa+rUqc460kp1UlKSKlasKEnas2eP5s+fbzgRUDC2bXvNU0fK6MfvKlasqKSkJEmMgAC/o1QDAfTll186R/u2bNlSdevWNZwosKKjo9WvXz9nzQgIQtWyZcu0ZcsWSVJCQoJ69uxpOFHgMQICeKNUAwHkLpGRcoNibu6r81988YWysrIMpgEKxv21fM011yguLs5gGjPcIyALFy5Uenq64USAWZRqIEAOHz6smTNnOuvrr7/eYBpzOnbsqEsuuUSSdODAAc2ePdtwIiB/co87RNoY1+8qVKiglJQUSYyAABKlGgiYb775xrkq26pVK9WoUcNwIjOioqK8XjZmBAShZsWKFdqxY4ckKTExUampqYYTmeO+OMAICCIdpRoIEPdxvu7TBSOR+8repEmTGAFBSHFfke3Tp4+KFCliMI1Z7hGQRYsWMQKCiEapBgLgxIkTmjZtmrO+5pprDKYxr3Xr1s4IyMGDB7VgwQLDiYC8yT3mEAnHkl9I+fLllZyc7KwnT55sLgxgGKUaCIAZM2boxIkTkqRGjRqpYcOGhhOZFRUV5fXEwn0VHwhm69at08aNGyVJJUqUULdu3QwnMs/9tfzll1+aCwIYRqkGAmDixInO40i/Sv0799/DxIkTlZOTYzANkDfuq9S9evVSsWLFDKYJDu6TJGfPnq3Dhw+bCwMYRKkGCllmZqbXS6KRPk/9u86dO6tMmTKSpPT0dC1fvtxwIuDi3K+qRProx++qVaumli1bSpKysrK8DrgCIgmlGihk8+bNc67cVK9eXS1atDAbKEjExMR4XeFiBATBbtOmTVq9erUkKS4uTr169TKcKHj07dvXecwICCIVpRooZO6yeM0118iyLINpgkvuuWrbtg2mAS7MPfrRvXt3xcfHG0wTXNylesqUKTp9+rS5MIAhlGqgEOXk5HhdtWGe2lvXrl1VokQJSdLGjRu1fv16w4mA82NbzPNr0qSJ6tSpI0k6fvw4hzohIlGqgUK0bNky7d69W5Jn66lOnToZThRcihYt6vUSOiMgCFY7d+7UsmXLJHlGl6666irDiYKLZVmMgCDiUaqBQuS+QbFPnz6Kjo42mCY4ua/4uXdJAYLJV1995TxOSUlR6dKlDaYJTu5SPWnSJHb0QcShVAOF6Ouvv3Ye9+nTx2CS4NWrVy/nRLoffvhBW7duNZwI+CP3E2SuUp9b+/btVb58eUnS3r17tXTpUsOJgMCiVAOFZPv27VqzZo0kqUiRIurSpYvhRMGpVKlSSktLc9buK4JAMDh27JjXjDBPkM8tOjra6wkHIyCINJRqoJC4r1KnpqaqZMmSBtMEN/cPYko1gs23336rM2fOSJIuu+wy1ahRw3Ci4OXeJtP9PRCIBJRqoJC4f6BceeWVBpMEP/ffz7x583T06FGDaQBvjH7kXVpamooWLSpJWr9+vbZt22Y2EBBAlGqgEOTeUopSfWFVq1ZV8+bNJXlOoJw+fbrhRIBHdna2vvnmG2fN6MeFFS9eXCkpKc7a/XcHhDtKNVAIZs6c6bxcfOmll/JycR64ywojIAgW3333nQ4cOCBJqlSpklq1amU4UfBzX0RgBASRhFINFAJ3KeTKVt64/56mTJmi7Oxsg2kAD/fX8pVXXqmoKH5sXkzv3r2dx3PmzFFGRobBNEDg8N0B8LOcnByvlzwZ/cibFi1aqHLlypKkgwcPasmSJYYTAcxTF0SNGjXUpEkTSdLp06c5XRERg1IN+Nny5cu1d+9eSVK5cuXUpk0bw4lCQ1RUlNcTEEZAYNqmTZv0008/SfKc/une+hEX5r5azQgIIgWlGvAz9w+Q3r17c4piPrhHQPhBDNPcT+y6du2q4sWLG0wTWtxPkKdMmSLbtg2mAQKDUg34GVvpFVzu7bi2bNliOBEimXv0g3sj8qd9+/bOUe47d+7U6tWrDScCCh+lGvCj9PR0/fDDD5Kk2NhYdevWzXCi0FK8eHFOV0RQOHTokBYsWOCseYKcPzExMerevbuzZms9RAJKNeBH06ZNcx537txZpUqVMpgmNLG1HoLBtGnTnB1o2rRp49xEi7xjaz1EGko14EfuUt2zZ0+DSUJX7tMVjxw5YjANItXUqVOdx+6b7pB3PXr0cLYgdO/3DYQrSjXgJ1lZWfr222+ddY8ePQymCV2XXHKJWrRoIcnzdzpz5kzDiRBpcnJyeILsB2XLllW7du0kSbZte/2dAuGIUg34ydKlS52rqlWrVlXjxo0NJwpd7hLjvmIIBMKKFSu0f/9+SVL58uXVsmVLw4lCl/sqP1/LCHeUasBP3FdhevToIcuyDKYJbe5SPW3aNLbjQkC5y597hAH5537FbsaMGcrJyTGYBihcfKcA/CR3qUbBtW3b1tmOKz09XWvWrDGcCJHEXaoZ/fDN5ZdfrgoVKkiSDhw44OyOBIQjSjXgB/v27dPy5cslSdHR0Zy85qOYmBiv7Qh52RiBcuDAAS1dulSS55RPtsX0Te6/w+nTpxtMAxQuSjXgB+4bFNu3b6/ExERzYcIEc9UwYcaMGc64Udu2bVW2bFnDiUKfe79qblZEOKNUA37A6If/uf8eFy1apKNHjxpMg0jB6If/ua9UL1myhK9lhC1KNeCjnJwcr5c0KdX+UbFiRbbWQ0Dl/lqmVPtHhQoVvL6WZ8+ebTgRUDgo1YCPVq5c6bX9VvPmzQ0nCh/uUjNlyhSDSRAJ3FvpuYsgfMcICCIBpRrwkfsHRPfu3dl+y4/YWg+B5H7ixlZ6/uUu1dOnT+drGWGJ7xiAj5inLjxsrYdAYp668LRv317x8fGSpG3btmnjxo2GEwH+R6kGfHDo0CEtWbJEkmRZFttv+Rlb6yFQDhw4oGXLlkliK73CUKRIEaWmpjprRkAQjijVgA9mzZrlnBDWqlUrlS9f3nCi8MPWeggE91Z67dq1U5kyZQwnCj+5R0CAcEOpBnzArh+Fj631EAjueWpGPwqHu1TPnTtXp0+fNpgG8D9KNVBAtm17HfrStWtXg2nCF1vrobCxlV5g1K5dW/Xq1ZMknThxQgsXLjScCPAvSjVQQJs3b9b27dslSSVLllS7du0MJwpfjICgMC1fvlwHDhyQ5NlKj20xCw9b6yGcUaqBAnJfpU5OTlZsbKzBNOHNXards6+AP8yYMcN5zLaYhYu5aoQzvnMABeQeQ2D0o3C1adNGpUqVkiTt2LFDGzZsMJwI4cT9BJldPwqX+wLEmjVrtGfPHsOJAP+hVAMFkJ2d7XXUbpcuXQymCX+xsbFKS0tz1u4ri4Avjh8/7myLKfG1XNhKliypDh06OOtZs2YZTAP4F6UaKIAVK1bo8OHDkqQqVaqoUaNGZgNFAPcVRF42hr/MmzdPmZmZkqTLLrtMlSpVMpwo/LmfuHDjMcIJpRooAPfLxV26dJFlWQbTRAZ3qZ4zZw7bccEv2MEn8HKXau6RQLigVAMFwA/iwKtdu7bq1KkjybMdl/sle6Cg+FoOvFatWjn3SOzcuZN7JBA2KNVAPmVkZGjx4sXO2j3ri8LlvlrNXDV8lZ6ervXr10vyHKN9xRVXGE4UGWJiYpSSkuKsGQFBuKBUA/k0f/58ZwazadOmqly5suFEkYNSDX9yl7lOnTqpePHiBtNEFuaqEY4o1UA+uX8AsFNAYKWkpCg6OlqStHLlSu3fv99wIoQy9xMzRj8Cy/33PWfOHGVlZRlMA/gHpRrIJ2YwzUlISHBOrrRtm+24UGA5OTnsNW9Q/fr1VbVqVUnSkSNHtGLFCsOJAN9RqoF82LNnj9asWSPJs3dy586dDSeKPGytB39Ys2aN9u3bJ0kqW7YsR5MHmGVZXq/0uS9WAKGKUg3kg/vKaPv27VWyZEmDaSKT+5hjjixHQeXeFpOjyQOPuWqEG76LAPnAy8XmtWrVSomJiZKkXbt2Obs3APnBGJd57p2TFi9erIyMDINpAN9RqoE8sm37D1e3EHjR0dFef/fsAoL8OnXqlObPn++sKdVmVKpUSU2bNpUkZWZmasGCBYYTAb6hVAN59Msvvyg9PV2S54a5Vq1aGU4UudhaD75YtGiRTp06Jclzw1z16tUNJ4pcjIAgnFCqgTxyX6VOSUlRTEyMwTSRzX1lcd68eU5BAvKC0Y/gQalGOKFUA3nEPHXwqFmzpurXry9JOnnypBYtWmQ4EUIJpTp4dO7c2blAsWrVKmdHFiAUUaqBPMjKytKcOXOcNfPU5jECgoLYv3+/Vq5cKckzn5+cnGw2UISLj49X+/btnfXs2bMNpgF8Q6kG8mD58uU6duyYJKlatWqqV6+e4URgv2oUhHtbzLZt2yohIcFgGkiMgCB8UKqBPHBfPUlLS5NlWQbTQJKSk5O9Xjbes2eP4UQIBe7RD/cTM5iT+xAY9p5HqKJUA3ngHv1ISUkxmAS/i4+PV4cOHZw1J7LhYnJvi8k8dXBo3bq14uPjJUk7duzQpk2bDCcCCoZSDVzE6dOntXDhQmdNqQ4e7iuNlGpczIYNG/Trr79KkkqVKqU2bdoYTgRJio2NVVJSkrN2X8QAQgmlGriIpUuXOlu21a1bV9WqVTOcCL/LXap52RgXwraYwSs1NdV5zM2KCFWUauAi3N/g3d/4YV6LFi1UunRpSdKePXu0du1aw4kQzBj9CF7uVwDnzJnDE2SEJEo1cBHMUwev3EeWMwKC88nMzPT6WqZUB5fLLrtMZcqUkSTt27dP69evN5wIyD9KNXABJ06c0JIlS5w1pTr4uMsRpRrns3TpUmdbzOrVq7MtZpCJiory+v7KCAhCEaUauIDFixcrMzNTktS4cWNVrFjRcCLkxpHlyIvcox9sixl8mKtGqKNUAxfAPHXwq1mzpnPV8eTJk1q8eLHhRAhG7E8d/NxXqufNm6fs7GyDaYD8o1QDF8A8dWhwX63myHLkduTIES1btkySZFmW0tLSDCfCuTRs2FCVKlWSJB06dEirVq0ynAjIH0o1cB7Hjh3T999/L8nzg9i9jyqCC/tV40LmzJnjXPVs0aKFypYtazgRzsWyLEZAENIo1cB5LFiwwPlB3KxZM34QB7Hk5GRFR0dLklauXKn9+/cbToRgwlZ6oYObFRHKKNXAeTBPHToSEhLUrl07Zz1r1iyDaRBsKNWhw/29dsGCBc6N4kAooFQD58E8dWhhrhrnsn37dm3cuFGSVKxYMXXs2NFwIlxIrVq1VKNGDUnS8ePHtXz5csOJgLyjVAPn8Ntvv+mHH36Q5DlgpHPnzoYT4WI4shzn4r5K3blzZ8XFxRlMg4thrhqhjFINnMP8+fOdUtayZUuVKlXKcCJcTOvWrZWQkCBJ2rlzp37++WfDiRAM3K9aMPoRGnIfWQ6ECko1cA7MU4eemJgYr38rdgFBdna213w9+1OHBnepXrRoEQc6IWRQqoFzYJ46NHFkOdx++OEH/fbbb5KkSpUqqWnTpoYTIS+qVq2q+vXrS5JOnTql7777znAiIG8o1UAu+/bt09q1ayVJsbGx3NgUQtxXIufMmaMzZ84YTAPT3E+sunTpwtHkIYSt9RCKKNVALnPnznUet23bViVKlDAXBvlSp04d1apVS5KUkZHBFa4Ix1Z6ocs9ysVcNUIFpRrIhXnq0MbWepCkEydOaNGiRc66S5cuBtMgv5KTk53H3333nTIyMsyFAfKIUg3kwjx1aOPIckieHXx+H/9p0qSJqlSpYjgR8qNChQrODHxWVpYWLlxoOBFwcZRqwCU9PV0bNmyQJBUtWtTrlD6EhtTUVEVFeb61ff/9986NaogsjH6EPkZAEGoo1YCL+xt3hw4dVLRoUYNpUBClS5dW69atJUm2bXOTU4SiVIc+DoFBqKFUAy7MU4cH5qoj2+7du7VmzRpJnh18kpKSDCdCQSQlJTmvOq1YsUKHDx82Gwi4CEo14MI8dXjgyPLINnPmTOdxx44d2cEnRCUmJqp58+aSpJycHM2fP99wIuDCKNXAWVu3btW2bdskSSVKlHBGCBB62rVrp5IlS0qStm3bps2bNxtOhEBi9CN8MFeNUEKpBs5yf8O+4oorFBsbazANfBEbG+v1SgMjIJHDtm2vK9WU6tDGXDVCCaUaOIt56vDCkeWRad26ddq9e7ckz02rLVq0MJwIvujUqZNiYmIkSatXr9b+/fsNJwLOj1INyHN1i3nq8OKeq549e7aysrIMpkGguJ9ApaWlKTo62mAa+KpkyZJq06aNs3afeAsEG0o1IGnDhg3atWuXJCkhIcG5OQahq379+qpWrZok6ejRo1q2bJnhRAgE5qnDD3PVCBWUakDe36iTkpK4uhUGLMtia70Ic/r0ac2bN89ZU6rDg/uVQ0o1ghmlGhDz1OGKI8sjy+LFi3XixAlJUt26dVWrVi3DieAP7du3V1xcnCTp559/dl5VBIINpRoRLycnx2tOj3nq8JGWlibLsiRJS5cu1ZEjRwwnQmFi9CM8FStWTO3bt3fWXK1GsKJUI+KtW7fOuaO8XLlyatq0qeFE8Jdy5co5uz9kZ2fzwzjMUarDF3PVCAWUakQ89+hHcnKycywuwgNz1ZHh4MGDWrFihSQpKiqKV5zCjPvfk/2qEaxoD4h47qsezFOHH+aqI8Ps2bOd4+jbtGmjxMREs4HgV23atFHx4sUleZ9+CwQTSjUiWnZ2NvPUYa5Dhw7OD+NNmzZp69athhOhMLifMLmfSCE8FClSRJ06dXLWjIAgGFGqEdF+/PFH5+a1ypUrq0GDBoYTwd/i4uKUlJTkrLlaHX5s2/Ya7WGeOjwxV41gR6lGRHPP5qWkpDg7RSC8cGR5eNu0aZO2b98uSYqPj1fbtm0NJ0JhyD1X/fu4DxAsKNWIaMxTRwb3OMCsWbOUnZ1tMA38zX2VOiUlRbGxsQbToLC0aNFCpUqVkiSlp6dr06ZNhhMB3ijViFiZmZmaP3++s2aeOnw1btxYVapUkSQdOnTI2SUC4YF56sgQExOjzp07O2t2AUGwoVQjYi1fvlwZGRmSpBo1anD6WhizLEtdunRx1mytFz4yMzO9yhXz1OGNuWoEM0o1Ihbz1JGFrfXC09KlS3Xs2DFJnifH9erVM5wIhcn9iuKcOXOYq0ZQoVQjYjFPHVncV6oXL17sFDGEttynKPLkOLxddtllKlOmjCRp3759Wr9+veFEwP9QqhGRTp8+rUWLFjlr5qnDX8WKFdWsWTNJUlZWlubNm2c4EfzBPcrDPHX4y31aJnPVCCaUakSk7777TqdOnZIk1atXT1WrVjWcCIHAkeXh5fDhw1q2bJkkz9w8rzhFhtwjIECwoFQjIuWep0ZkYK46vMyePVs5OTmSpFatWqls2bKGEyEQ3E+e5s6d6/x/ADCNUo2IxDx1ZOrUqZPi4uIkST///LN+/fVXw4ngi9zz1IgMDRs2VKVKlSR5tshctWqV4USAB6UaEefEiRP67rvvnHVycrK5MAioYsWKee1zy9Xq0MY8dWSyLIu5agQlSjUizqJFi5SZmSlJatKkiSpWrGg4EQKJI8vDw+bNm7VlyxZJUokSJdS+fXvDiRBIzFUjGFGqEXHc34CZp4487iuaM2fOZB4zRLmfECUnJ6tIkSIG0yDQ3GN78+fPV1ZWlsE0gAelGhHH/VIh89SR59JLL1WFChUkSQcOHNCPP/5oNhAKhHnqyFa7dm1Vq1ZNknTs2DGtWLHCcCKAUo0Ic/ToUS1fvlySZy4vKSnJcCIEWlRUFEeWh7isrCzNmjXLWTNPHXlyb6HIXDWCAaUaEWXBggXKzs6WJF1++eXOyVyILGytF9qWL1+uI0eOSJIuueQSNWzY0HAimMBcNYINpRoRhXlqSN5Hli9cuFAnTpwwmAb5lXvXD44mj0zu7+ELFy7U6dOnDaYBKNWIMMxTQ/Jc3WzSpIkk6cyZM5o/f77hRMgP5qkhSdWrV1edOnUkSSdPnnRO1wRMoVQjYvz222/OTWnR0dG64oorzAaCURxZHpqOHj2qJUuWOGv3qw6IPMxVI5hQqhEx5s+fL9u2JUktW7ZUqVKlDCeCScxVh6a5c+c690U0b95c5cuXN5wIJjFXjWBCqUbEYPQDbp07d3b2Nl67dq12795tOBHyglMU4eYu1UuWLNHJkycNpkGko1QjYnCTItxKlCihjh07OmuuVocGd6lmnhqVKlVSo0aNJHnuj1i8eLHhRIhklGpEhH379mnt2rWSpNjYWK8yhcjFkeWhZfPmzdq4caMkqXjx4nwdQxJz1QgelGpEhLlz5zqP27ZtqxIlSpgLg6CRe67695l7BKfp06c7j1NSUlS0aFGDaRAsmKtGsKBUIyIwT41zad68ucqWLStJ2rt3r9asWWM4ES5k2rRpzuMePXoYTIJgkpyc7DxetmyZjh07Zi4MIhqlGhGBeWqcS1RUlNLS0pw1W+sFr9OnT3s9Oe7Zs6fBNAgmZcuW1eWXXy5Jys7O1sKFC80GQsSiVCPspaena8OGDZKkokWLql27doYTIZiwtV5oWLRokTIyMiRJdevWdQ79ACTviyXMVcMUSjXCnvsqdYcOHZjDhBf3zYrz58/XqVOnDKbB+TD6gQtxj/UxVw1TKNUIe+5vsMxTI7fq1aurQYMGkqRTp07x0nGQolTjQq644gpFRXkqzcqVK3Xo0CHDiRCJ/F6qLcuKtyyrh2VZgy3LesyyrKGWZd1sWVYTf38uIC/cLwUyT41zYWu94LZz507nJtIiRYp43ZgGSFJCQoJatWolSbJtW/PnzzecCJHIL6XasqyilmXdYVnWIkkHJX0jaZik5yS9IulDSasty9prWdY7lmVd6o/PC1zM1q1btW3bNkmewz5at25tNhCCknuumpsVg497K73OnTuzJSbOiblqmOZTqbYsK8ayrD9LSpf0vqTmkpZKel3S3yUNlvSQpGclfSbppKR7Jf1oWdZ0y7Ka+vL5gYtxj35cccUVio2NNZgGwSo5OVkxMTGSpB9//FH79u0znAhujH4gL5irhmkxPn78z5JqSZom6QNJk2zbPn2hD7Asq76kQZJuk/SDZVl32rb9oY85gHNiKz3kRXx8vNq3b68FCxZIkmbOnKkBAwYYTgVJysrK8hrJYSs9nE/Hjh0VGxurzMxMrVmzRvv371f58uVNx0IE8XX8Y52kFrZt97Zt+9OLFWpJsm17g23bj0mqLelvkor5mAE4J9u2OfQFecZcdXBaunSpjhw5IkmqVq2aGjVqZDgRglWJEiXUtm1bZ+0+SRcIBJ9KtW3bV9u2vaqAH3vGtu3XbNse7ksG4Hw2btyoXbt2SfLcxNK8eXPDiRDMcs9Vc2R5cMg9+mFZlsE0CHbMVcMkttRD2HJ/Q01KSlJ0dLTBNAh2rVq1UunSpSVJu3bt0urVqw0ngsQ8NfKHuWqY5NdSbVlW5zy+30P+/LzAuTBPjfyIjo5W9+7dnfWUKVMMpoEk7du3T8uXL5fk+fdxHykPnEu7du0UFxcnSfrll1+cVyuBQPD3lepZlmU9fr7ftCwr0bKsSZJe9fPnBbzYts2hL8i3Xr16OY+/+eYbg0kgeW9v2KFDByUkJBhMg1BQtGhRdezY0VlztRqB5O9SvUnSM5ZlfWtZVkX3b1iW1UHSj5L6SPrSz58X8LJu3Trt379fklS2bFk1bcrujbg498zukiVLdPDgQcOJIhujHygI5qphir9LdUtJH0tKk2cv6q6SZFnW3yXNlVRR0v+zbfs6P39ewEvuUxR/P74WuJDy5cs7uwfk5ORwEIxB2dnZlGoUCHPVMMWvTcO27RO2bQ+UdLukkpKmWpa1Wp6TFTdLamvb9jv+/JzAucyaNct5zDw18qN3797OY0ZAzFm6dKnzSkHlypXZvQd51rp1a+fUTfepukBhK5TLd7ZtfyDp0bN/flNJByR1tm2b2+lR6LKysrz2J+3SpYu5MAg57rnqadOmKTs722CayOV+QtO7d2+20kOexcbG6oorrnDWXK1GoPi9VFuWFWVZ1r/kOar8uKTFkspLmmtZ1qX+/nxAbsuXL9fRo0cleQ6LqFevnuFECCXNmzdX5cqVJUkHDx7U0qVLDSeKTF9//bXz2P3qAZAXzFXDBH9vqVdN0jxJf5e0RlIr27Y7SfqHpHqSllqWdb8/PyeQ28yZM53HaWlpXOFCvliW5XW1mq31Am/Hjh3OPuFFihTh1SbkW+65ag5zQiD4+0r1j5I6ShomqZ1t2xskybbtFyQlS9ov6U3Lsib4+fMCDvc8NT+MURDMVZvlfiKTkpKikiVLGkyDUNS8eXNnC8b09HRt3LjRcCJEAn+X6ihJ/WzbfsC27dPu37Bte7GkZpImS7raz58XkCSdOHFCixcvdtbsT42C6NKli2JjYyVJP/74o9LT0w0niiyMfsBX0dHRSkpKctaMgCAQ/F2qm9u2fd6r0LZtH7Zt+xpJnKiIQrFw4UKdOXNGktSkSRNnNhbIj/j4eHXu/L8DYhkBCZwTJ054vdpEqUZBsbUeAs3fW+pty+P7veXPzwv8Lvc8NVBQ7jJHqQ6cOXPm6NSpU5KkRo0aqXbt2oYTIVS5b1ZkrhqBwIkYCCvMU8Nf3KX622+/1enTpy/w3vAX9+jHlVdeaTAJQl3Tpk1Vrlw5SdL+/fu1du1aw4kQ7nwq1ZZlfW1ZVrMCfmycZVlDLcu6z5cMwO8OHjyoH374QdIf5+mA/KpXr57q1KkjScrIyND8+fMNJwp/tm0zTw2/iYqK8rpa7b7oAhQGX69UN5C00rKsqZZl3WhZVtGLfYBlWY0sy3pB0hZJL0k65mMGQJL3y3tt2rRRqVKlDCdCKLMsi11AAmzNmjXauXOnJCkxMVEdOnQwnAihzv2K5bfffmswCSKBr6W6saRHJLWVNEbSIcuyFlmW9bplWY9alnWPZVkPWZb1T8uyPrUsa4ektWc/Zo2kFrZtf+xjBkAS89TwP/f4weTJk5nJLGTuq9Tdu3d3dmABCqpr167O43nz5jk3sgOFIcaXD7ZtO1PSy5ZlvSPpZkl3Smojqf3v7yLJffLGfkkjJL3DkeXwN+ap4W9JSUkqVaqUjh49qq1bt2rt2rW69FIOhi0s7lcDmKeGP9SqVUu1a9fWli1blJGRoe+++85rZx/An/xyo6Jt2yds2x5h23Y7SWUl9ZR0jzwnKQ6Vp3Bfatt2Rdu276VQw9+2b9+uTZs2SZKKFSumdu3aGU6EcFCkSBH17NnTWU+aNMlgmvC2f/9+LVmyRJJnFrZHjx6GEyFcuK9WMwKCwuT33T9s2z5q2/b0syX7Rdu2X7dte6xt2+v8/bmA37mvUnfu3FlxcXEG0yCcXH31/86qolQXnq+//toZr2nfvr2zawPgK+aqESg+jX+cj2VZJSVdI6m5pARJRyT9IGmibdvHC+NzIrIxT43C0rNnT8XExCgrK0vLly9Xenq6LrnkEtOxws6XX37pPO7bt6+xHAg/qampsixLtm3r+++/1+HDh5WYmGg6FsKQ369UW5Z1vaTtkkZL+pOk28/+OlrSdsuy+vn7cyKy2bbNPDUKTWJiopKTk5315MmTzYUJUxkZGZoxY4azdr86APiqTJkyatmypSQpJyeH0xVRaPxaqi3L6ipprKR4SR/KU6h7nv31o7NvH2tZFq0HfrN27Vrt27dPklS2bFk1a1agrdOB82IEpHDNmDHDOUWxSZMmqlevnuFECDfuuWr3K5uAP/n7SvWTkk5Lamfb9u22bX9wdr76A9u2B0nqICnz7PsBfuH+BpmamqqoKA4KhX9dddVVzuPZs2fr6NGjBtOEH0Y/UNi4WRGB4O/20VzSeNu2V57rN23bXi7pU0kt/Px5EcHcox/MU6MwVK9eXc2bN5ckZWZmaurUqYYThY+srCx99dVXzppSjcLQoUMHFStWTJK0ceNGbd++3XAihCN/l+rTknZf5H12nX0/wGeZmZmaN2+es2aeGoWFEZDCsWDBAh06dEiSdMkllzizr4A/xcXFee1PzQgICoO/S/UCSR0v8j4dJc338+dFhFq2bJmOH/dsKFOjRg3Vrl3bcCKEK/cV1ClTpigzM9NcmDCSe/TDsqzzvzPgA7bWQ2Hzd6l+RNJllmW9aFlWCfdvWJZVwrKs/5PUVNKjfv68iFDuHQPS0tL4gYxCc9lll6lGjRqSpCNHjni9QoKCsW2beWoEjHuuetasWcrJyTGYBuGoMEr1akl/k/SrZVkzLcv6xLKsmZJ+lfSXs7//iGVZI13//dfPORAh3KW6e/fuBpMg3FmWxQiIn61atUo7duyQJCUkJCgpKclwIoSzSy+9VBUqVJAkHThwQKtWrTKcCOHG36V6kKTOkixJiZJSJd109tfEs29POvt+uf8D8uXQoUNatmyZJE/h4SZFFDZ3qf7yyy+50uUj91XqK6+8UrGxsebCIOxFRUV5/ZxgBAT+5u9SXauA/zEIi3xzv3zXunVrlS1b1nAihLsrrrjC+f/Zzp07nSd1KBhGPxBobK2HwuTXUm3b9vaC/ufPHIgM7tGPbt26GUyCSBEbG+tV/j7//HNzYULcpk2bnJff4+LiGN9CQLhvVlywYIFOnjxpMA3CDadkICTZtk2phhH9+vVzHn/++eeybdtgmtD12WefOY+7d++u+Ph4g2kQKapVq6YGDRpIkk6fPq1FixYZToRwQqlGSNqwYYOzeX98fLzatWtnOBEiRWpqqhITEyVJ27dv1/Lly80GClHuUn399dcbTIJIw9Z6KCyUaoQk91Xq1NRUbnBCwBQpUsTrhkVGQPJv8+bN+uGHHyR5/j779OljOBEiiXuumkNg4E+UaoQkRj9gkvvKKiMg+ed+ItKtWzclJCQYTINIk5ycrOjoaEnSDz/8oP379xtOhHBBqUbIOXPmjObMmeOsKdUItC5duqhUqVKSpC1btjhXXZE3jH7ApISEBLVt21aS5/4crlbDXyjVCDmLFy9WRkaGJKl27dqqW7eu4USINHFxcbrqqqucNSMgebdlyxatWLFCkmc3FfffIxAoPXr0cB5PmzbNYBKEE0o1Qg6jHwgG7l1APvvsM0ZA8ij36MfvN30CgeQu1dOnT+cgJ/gFpRohh1KNYNC9e3eVLFlSkmfP5TVr1hhOFBoY/UAwaNGihXOQ0969ezmyHH5BqUZI2b9/v1auXClJio6OVmpqquFEiFRFixb12rXi008/NZgmNGzdutXZgpDRD5gUHR3tdVGGERD4A6UaIWXmzJnOy+zt2rVj1wAY5b7SOnbsWEZALsJ9lbpLly4qXbq0wTSIdLlHQABfUaoRUhj9QDDp2bOn88Ruy5YtWrp0qeFEwW3MmDHO4xtuuMFgEsD7Z8iiRYt09OhRg2kQDijVCBm5jybv3r27wTSAZwTkuuuuc9bu0ghv69atc+ZWixYtqmuvvdZwIkS6SpUqqXnz5pKkrKwszZ4923AihDpKNULGunXrtGvXLklSYmKiWrVqZTgRIA0YMMB5PH78eGVlZRlME7zcTzj69Onj7PMNmMTWevAnSjVChnvmrUuXLs6JWIBJycnJqly5siRp3759mjVrluFEwce2ba9S7X4iApiUu1RzXwR8QalGyJgyZYrzmNEPBIvo6Gj179/fWTMC8keLFy/Wtm3bJHleZerZs6fZQMBZ7du3V3x8vCRp+/bt+uWXXwwnQiijVCMkHDt2TAsWLHDWvXr1MpgG8HbzzTc7jydMmKATJ04YTBN83E80rr/+esXFxRlMA/xPbGysunTp4qwZAYEvKNUICbNmzVJmZqYkqVmzZqpSpYrhRMD/tGjRQvXr15ckHT9+XF9//bXhRMEjMzPTaw9vRj8QbJirhr9QqhES3KMfXKVGsLEsy6ssMgLyP99++60OHDggSapatao6d+5sOBHgzT1OOG/ePJ08edJgGoQySjWCnm3bmjp1qrOmVCMYuUv1lClTnCIZ6T766CPn8U033aSoKH7sILjUqFFDDRs2lCSdOnVK8+fPN5wIoYrvbgh6a9eu1c6dOyV5bnJq166d4UTAH9WrV09t27aV5Bl54Gq1dOjQIU2cONFZu2fPgWDCCAj8gVKNoOe+St2tWzfFxMQYTAOc3+233+48HjVqlMEkwWHcuHE6ffq0JM/cebNmzQwnAs6NUg1/oFQj6DFPjVDRv39/FS1aVJL0448/6ocffjCcyKyRI0c6j91POIBg07lzZ+dr9+eff3a2gATyg1KNoHbkyBEtXLjQWbuvJgDBJiEhwevY8ki+Wr1mzRotX75cklSkSBF2/UBQK1asmJKTk521+2IOkFeUagS1mTNnKjs7W5LUsmVLVaxY0XAi4MLcV2Q/+eQTZ/wh0rifUPTt21dlypQxmAa4uN69ezuPv/nmG4NJEKoo1QhqjH4g1KSkpKhGjRqSpN9++02TJ082nCjwzpw5o48//thZM/qBUOAu1bNmzVJGRobBNAhFlGoErdxb6XG0MUJBVFSUBg0a5KwjcQTkm2++0f79+yVJl1xyibp27Wo4EXBxtWrVUpMmTSRJp0+f1uzZsw0nQqihVCNorVq1Srt375YklSlTRm3atDGcCMgbd6mePn26syVkpHDfoDhw4EBFR0cbTAPkHSMg8AWlGkHLPfrRvXt3fjAjZNSsWVOpqamSpJycHL3//vuGEwXO9u3bvcqI+wkGEOyuvPJK5/E333wj27YNpkGooVQjaHGKIkLZPffc4zweMWKEMjMzDaYJnPfee88pIl27dlW9evUMJwLyrn379ipdurQkaefOnVq9erXhRAgllGoEpYMHD2rx4sWSJMuy1L17d8OJgPzp27evKlWqJEnatWtXRNyweObMGa+r8vfdd5/BNED+xcTEeG3d+vXXXxtMg1BDqUZQmjp1qnJyciRJ7dq1U/ny5Q0nAvKnSJEiuvvuu531O++8YzBNYEyYMEH79u2T5LlBsU+fPoYTAfnnHgGhVCM/KNUISu6revxgRqgaPHiwcy/A7Nmz9dNPPxlOVLiGDRvmPB48eLBiYmIMpgEKpkePHoqK8tSjpUuXOjvZABdDqUbQOXPmjKZNm+asr7rqKoNpgIKrWrWq1/9/3333XYNpCte6des0f/58SVJ0dLTuuusuw4mAgilTpow6dOgg6Y9buwIXQqlG0Jk3b56OHTsmybNvaOPGjQ0nAgrOPVc8evTosD1Q4u2333Ye9+3bV1WqVDGYBvANW+uhICjVCDpfffWV8/iqq66SZVkG0wC+SUtLc3bAOHr0qEaPHm02UCE4ePCg1/+uBx54wFwYwA/cc9XTpk2LmN174BtKNYKKbdvMUyOsREVFaciQIc761VdfVXZ2tsFE/jd8+HCdPHlSknT55ZcrOTnZbCDAR02aNFH16tUleZ4ML1y40HAihAJKNYLK2rVrtX37dklSQkKCOnfubDgR4Lvbb7/d2ft2y5YtmjRpkuFE/nP69Gm9+eabzvrPf/4zry4h5FmW9YeDYICLoVQjqLivUvfo0UOxsbEG0wD+UaJECd17773O+uWXXzaYxr/Gjx+vPXv2SJIqV66sG2+80XAiwD/cpXry5MmcroiLolQjqOSepwbCxYMPPug8SVyyZIlzuFEos21br776qrN+8MEHVaRIEYOJAP9JSUlRiRIlJEkbN27Uzz//bDgRgh2lGkFjz549Wrp0qSTPllw9e/Y0nAjwn8qVK+vmm2921q+88orBNP7x7bffatWqVZKk4sWLex3NDoS6okWLev0c+vLLL82FQUigVCNouGfWOnXq5MygAuHiL3/5i/N44sSJWr9+vcE0vrFtW88++6yzvuOOO1SmTBmDiQD/u/rqq53HlGpcDKUaQYPRD4S7pk2bqlevXpI8pfS5554znKjg5s2b5+yIEBsbq4cffthwIsD/evfu7ZyKumzZMqWnpxtOhGBGqUZQOHnypGbMmOGs2UoP4erJJ590Ho8bNy5k5zTdV6kHDRqkatWqGUwDFI7SpUt7bRHpvpkeyI1SjaAwc+ZMZ5/bhg0bOodlAOGmbdu26tGjh6TQvVq9ePFizZ49W5Ln/odHH33UcCKg8PTt29d5zAgILoRSjaAwceJE57F7hg0IR0899ZTzeOzYsdqwYYPBNPn39NNPO49vueUW1a5d21wYoJC5fybNnj1bhw8fNhcGQY1SDeOysrK8DsO49tprDaYBCl+7du3UrVs3SVJOTo6eeeYZw4nybtasWfr2228leU6LfOyxxwwnAgpXtWrV1LJlS0men1dTp041nAjBilIN4+bPn6/ffvtNklS1alW1atXKcCKg8Lmv9o4ZM0YrVqwwFyaPcnJy9Mgjjzjr22+/XfXr1zeYCAgMRkCQF5RqGDdhwgTn8TXXXKOoKP5vifDXvn17r5eV//a3vwX9iW2ff/65U/6LFi3q9cQACGfuUj1lyhSdPn3aXBgELdoLjMrJyfGap77mmmsMpgEC66WXXnK265ozZ46mTJliONH5ZWZm6vHHH3fWQ4YMUdWqVQ0mAgKnSZMmqlOnjiTp+PHjzo26gBulGkYtW7ZMu3btkiSVLVtWV1xxheFEQOA0aNBAgwcPdtYPP/ywsrKyDCY6vzfeeEMbN26UJCUmJrLjByKKZVmMgOCiKNUwyj36cfXVVysmJsZgGiDwnnrqKZUsWVKStH79er3zzjuGE/3Rrl27vEY9Hn/8cU48RcRxl+pJkyYpJyfHXBgEJUo1jLFt26tUs+sHIlHFihW9dtB4/PHHg+7UtocffljHjx+XJDVu3FhDhgwxnAgIvPbt26t8+fKSpL1792rx4sWGEyHYUKphzNq1a7V582ZJUnx8vNLS0gwnAsz485//rIYNG0qSjh07pqFDhxpO9D/z5s3TJ5984qzffPNNxcbGGkwEmBEdHe11389nn31mMA2CEaUaxrivUvfq1UtFixY1mAYwJy4uTsOGDXPWn332WVDshZuRkaE777zTWd9www1KTU01mAgwq1+/fs7jzz//nBEQeKFUwxhGP4D/SU5O1m233eas7777bmf/dlP+8Y9/OK8mJSQk6NVXXzWaBzAtJSVFZcuWleS512DJkiWGEyGYUKphxKZNm7R69WpJnqt0PXv2NJwIMO/ll192ZjbT09N1//33G9u7ev78+XrjjTec9WuvvaZLLrnESBYgWMTExHiNgHz++ecG0yDYUKphhHsWrWvXroqPjzeYBggO5cuX14gRI5z1+PHjNWbMmIDnOHDggAYMGOAU+p49e2rgwIEBzwEEo+uvv955zAgI3CjVMGL8+PHO4xtuuMFgEiC4XH311V5zzPfee69++umngH3+nJwc3Xrrrc4OJGXKlNF7770ny7IClgEIZikpKSpTpowkaefOnVq6dKnhRAgWlGoE3C+//KJVq1ZJ8ox+uI9qBiD95z//Ue3atSV5Tm/r27evjhw5EpDP/fzzz2vatGnO+sMPP+TkRMAlNjbWa89qdgHB7yjVCLhPP/3UedyzZ0+VKlXKYBog+MTHx2vChAkqVqyYJGnDhg0aMGBAoZ+2OHbsWD3xxBPO+uGHH1bv3r0L9XMCoSj3CIipex8QXCjVCDj36MeNN95oMAkQvJo1a6aRI0c66ylTpujuu+8utPnNBQsWaNCgQc46JSVFzz33XKF8LiDUpaWlOaeK/vrrr1q2bJnhRAgGlGoE1Lp167Ru3TpJUrFixXTllVcaTgQEr/79++vRRx911qNHj9Zf/vIXv18VW7hwoXr16qUzZ85Ikho2bKgvvviCQ16A82AEBOdCqUZAua9S9+7dWyVLljSYBgh+zz//vO644w5n/dprr2nw4MF+GwWZPXu2evbs6RxDXrFiRU2ZMsW5Cgfg3BgBQW6UagSMbdte89SMfgAXZ1mWhg8fruuuu8552/vvv6+rr77a58Nhhg8fru7du3sV6tmzZ6tWrVo+/blAJEhLS1NiYqIkafv27ewCAko1Amf16tX65ZdfJEklSpRQr169DCcCQkNMTIzGjh3rtVf0lClT1Lx5c82fPz/ff97Bgwd100036d5773WueFepUkVz5sxR48aN/ZYbCGdFihTxerJrYk95BBdKNQLGPfpx1VVXqXjx4gbTAKElNjZWo0aN8pqx3rFjh5KSknTrrbdq48aNF/0zTp8+rTfeeEONGjXSuHHjnLe3aNFCy5YtU6NGjQolOxCuBgwY4DweP358oe/Qg+BGqUZA2LbNrh+AjyzL0gsvvKCJEycqISHBefvHH3+sBg0aqFevXhoxYoRWr16to0eP6uTJk9q+fbu++uorDRkyRJdccokeeugh7d+/3/nY22+/XfPnz+cIcqAAkpKSVLlyZUnSvn37NGvWLMOJYFKM6QCIDMuXL9eWLVskSaVKlVL37t0NJwJCV9++fbV27VoNHTpUn3/+uSTPE9epU6dq6tSpefozqlevrjfffFNXXXVVYUYFwlp0dLT69++v//znP5I8IyD8fItcXKlGQHz88cfO42uuuUZFixY1mAYIfVWrVtVnn32muXPn5uv+hBo1aug///mPNmzYQKEG/MA9AjJhwgSdPHnSYBqYxJVqFLrMzEyNHTvWWd9yyy0G0wDhJSkpSUlJSdqyZYsmTZqkOXPmaN26ddq3b5/OnDmjsmXLqkaNGurUqZO6d++u1NRURUVxPQXwl5YtW6p+/frasGGDjh8/rq+//tpruz1EDko1Ct3MmTOdGc4qVaooJSXFcCIg/NSuXVtDhw7V0KFDTUcBIoplWRowYICefvppSZ4REEp1ZOJyBQrdRx995DweMGCAoqOjDaYBAMC/brrpJufxlClTdOjQIYNpYAqlGoXq2LFj+vLLL501ox8AgHBTv359tWrVSpJ05swZffHFF4YTwQRKNQrVxIkTnZs2mjZtqssuu8xwIgAA/M99w+Inn3xiMAlMoVSjULl3/bjllltkWZbBNAAAFI7+/fs7P+PmzZunHTt2GE6EQKNUo9Ds2rXL2Qj/9xs5AAAIR5UrV1bXrl0lefaN//DDDw0nQqBRqlFoxo0bp5ycHElScnKyqlWrZjgRAACFZ9CgQc7j0aNHy7Ztc2EQcJRqFArbtvXBBx84a25QBACEu759+yohIUGStHnzZi1atMhwIgQSpRqFYuXKlVq9erUkqVixYrruuusMJwIAoHAVK1ZM/fv3d9ajRo0ymAaBRqlGoRg5cqTzuF+/fs4zdwAAwpl7BOTTTz9VRkaGuTAIKEo1/O7kyZNe2wndcccdBtMAABA4bdu2VYMGDSRJx48f14QJEwwnQqBQquF3EydO1JEjRyRJderUUVJSkuFEAAAEhmVZuv322501IyCRg1INv3OPftx+++3sTQ0AiCi33HKLoqI8FWvOnDnatm2b2UAICEo1/Grr1q3O3tRRUVEaOHCg4UQAAATWJZdcom7dujlr925YCF+UavjV6NGjncfdu3dX1apVzYUBAMAQ9w2LI0eOVHZ2trkwCAhKNfwmOzvba3aMGxQBAJGqb9++Klu2rCRpx44dmj59uuFEKGyUavjNrFmz9Ouvv0qSypYtqz59+hhOBACAGXFxcV5Xq4cPH24uDAKCUg2/effdd53Ht9xyi+Li4gymAQDArMGDBzuPv/76a+3cudNgGhQ2SjX8YufOnZo0aZKzvueeewymAQDAvPr16ys1NVWSlJOTo//+97+GE6EwUarhFyNGjFBOTo4kKTk5WY0aNTKcCAAA89wXmUaMGKGsrCyDaVCYKNXwWWZmpkaMGOGs77vvPoNpAAAIHn379lWFChUkSenp6ZoyZYrhRCgslGr4bPLkydq9e7ckqWLFiurbt6/ZQAAABIkiRYp4nbDIDYvhi1INnw0bNsx5fNddd6lIkSIG0wAAEFzcNyxOnTpV27dvN5gGhYVSDZ9s2LDB6wRF9zcOAAAg1a5d2zlh0bZtr4tRCB+UavjEvY3elVdeqerVqxtMAwBAcHrggQecx++9955OnDhhMA0KA6UaBXb8+HGvExS5QREAgHPr3bu3atWqJUk6dOiQPvnkE8OJ4G+UahTYBx98oMOHD0uS6tat67y0BQAAvEVHR+vBBx901q+//rps2zaYCP5GqUaB5OTk6PXXX3fWDz30kKKi+L8TAADnc8cdd6hEiRKSpHXr1mn27NmGE8GfaEEokClTpmjjxo2SpISEBA0aNMhsIAAAglzun5dvvPGGuTDwO0o1CuQ///mP8/juu+9WyZIlDaYBACA0uEdAvvrqK23evNlgGvgTpRr5tmrVKuclq9wzYgAA4PwaNGigHj16SPJsr/fWW28ZTgR/oVQj39yz1Ndddx3b6AEAkA8PPfSQ8/j999/XoUOHDKaBv1CqkS979+712gZo6NChBtMAABB6unXrpsaNG0vybE/LYTDhgVKNfHnttdd05swZSVLbtm3Vrl07w4kAAAgtUVFRevjhh53166+/rpMnTxpMBH+gVCPPjhw5onfeecdZu78hAACAvLvppptUtWpVSdK+ffv04YcfGk4EX1GqkWfvvPOOjh49Kklq2LCh+vbtazYQAAAhqkiRIl4jlC+//LKys7MNJoKvKNXIk5MnT+q1115z1o888giHvQAA4IO7775biYmJkqRNmzZpwoQJZgPBJ7Qi5MnIkSO1b98+SVK1atU0YMAAw4kAAAht8fHxeuCBB5z1Sy+9xNHlIYxSjYvKzMzUv//9b2f917/+VUWKFDGYCACA8DBkyBAVLVpUkrRixQpNnz7dcCIUFKUaFzVu3Dht375dklSuXDndddddhhMBABAeKlSooDvvvNNZP/PMM1ytDlGUalxQVlaWnn32WWc9ZMgQFS9e3GAiAADCy6OPPuq8Avzdd99pxowZhhOhICjVuKCPP/5YGzdulCQlJiZyJDkAAH5WtWpV3X333c766aef5mp1CKJU47wyMzP1z3/+01n/5S9/ce5SBgAA/sPV6tBHqcZ5jR49Wlu3bpUklSlTRkOGDDGcCACA8MTV6tBHqcY5nT59Ws8995yzfvjhh1WqVCmDiQAACG+5r1ZPmzbNcCLkB6Ua5zRy5Ejt2LFDklS+fHmvfTQBAID/5b5a/dhjjyknJ8dgIuQHpRp/kJGR4TVL/cgjj6hkyZIGEwEAEBkee+wxFStWTJL0448/auzYsYYTIa8o1fiDV155RXv27JEkValSRffdd5/hRAAARIYqVapo6NChzvrxxx/X6dOnDSZCXlGq4WXPnj36v//7P2f97LPPsi81AAAB9PDDD6ts2bKSpG3btmnYsGGGEyEvKNXw8swzzygjI0OS1LRpUw0cONBwIgAAIktCQoIef/xxZ/3cc8/pyJEjBhMhLyjVcPz8888aMWKEs/6///s/RUdHG0wEAEBkuu+++1SzZk1J0sGDB/XSSy+ZDYSLolTD8cgjjyg7O1uSlJqaqh49ehhOBABAZIqLi/Pa2vbVV191zo5AcKJUQ5I0Y8YMTZ482Vn/+9//lmVZBhMBABDZbrrpJrVq1UqS5/yIv/71r4YT4UIo1dCZM2e8TkscOHCgWrRoYTARAACIiorSG2+84awnTJigmTNnGkyEC6FUQ6+99pp++eUXSVKpUqX04osvGk4EAAAkqX379rrtttuc9ZAhQ5SZmWkwEc6HUh3h0tPTvQ56eeaZZ1SpUiWDiQAAgNuLL77oHML2008/6e233zacCOdCqY5wf/vb35wt9Jo0acJx5AAABJnKlSvrySefdNZPPfWUdu3aZTARzoVSHcFmzpzpdfzpm2++qdjYWIOJAADAuTz00EOqX7++JOno0aNe90IhOFCqI1RGRoYGDx7srG+88UalpKQYTAQAAM6nSJEievfdd531F198oUmTJhlMhNwo1RHqySefdPa7LF26tF577TWzgQAAwAWlpKTojjvucNYPPPCAjh49ajAR3CjVEej777/3KtGvvPIKNycCABAC/v3vf6tChQqSPJsN/P3vfzecCL+jVEeYM2fO6M4771ROTo4kqUuXLho0aJDZUAAAIE/KlCmj119/3VkPGzZM8+fPN5gIv6NUR5inn35aa9askSQVL15cw4cP5+REAABCyI033qjevXtLkmzb1m233cYYSBCgVEeQhQsX6qWXXnLW//rXv1S7dm2DiQAAQH5ZlqXhw4erdOnSkqTt27frT3/6k9lQoFRHiqNHj+rWW291xj5SU1PZjgcAgBB1ySWXaNiwYc561KhRmjhxosFEoFRHiIceekjbtm2TJCUmJmr06NGKiuKfHwCAUHXjjTfqpptuctaDBw/Wnj17DCaKbLSqCDB+/HiNHj3aWQ8bNkzVqlUzFwgAAPjF22+/rUsuuUSSdODAAd1yyy3Kzs42nCoyUarD3M8//6y77rrLWQ8YMED9+/c3mAgAAPhL6dKl9cEHHzibDsyaNUvPPvus4VSRiVIdxjIyMtSvXz8dP35cklS3bl298847hlMBAAB/SktL0+OPP+6s//nPf2rGjBkGE0UmSnWYsm1b999/v9atWydJKlq0qD7//HMlJCQYTgYAAPztqaeeUmpqqiRPB7j55puVnp5uOFVkoVSHqWHDhunDDz901m+99ZaaNWtmMBEAACgs0dHRGjNmjHNC8oEDB3Tttdfq1KlThpNFDkp1GJo5c6bXdnmDBg3SHXfcYTARAAAobBUrVtS4ceMUHR0tSVq2bJnuvPNO2bZtOFlkoFSHmQ0bNuj666937vxt1aqV3n77bU5NBAAgAiQlJek///mPsx4zZoxeeOEFg4kiB6U6jBw6dEh9+vTR4cOHJUlVqlTRpEmTVLx4cbPBAABAwPy///f/NHjwYGf9j3/8QxMmTDCYKDJQqsPEiRMn1KdPH23YsEGSVKxYMU2aNElVqlQxnAwAAASSZVl66623lJyc7Lzt5ptv1sKFC82FigCU6jCQmZmpG2+8UYsWLXLeNnr0aLVq1cpgKgAAYEpsbKw+//xz1alTR5J06tQpXXnllVq9erXhZOGLUh3icnJydPfdd+vrr7923vbqq6/qhhtuMJgKAACYVrZsWU2fPl0VK1aUJB05ckQ9evTQ1q1bDScLT5TqEGbbth566CF98MEHztseffRRDR061GAqAAAQLOrUqaNp06apVKlSkqTdu3era9eu2rlzp+Fk4YdSHaJs29aDDz6ot956y3nbHXfcoeeff95gKgAAEGwuv/xyTZ48WXFxcZKkzZs3KykpSTt27DCcLLxQqkNQTk6OHnjgAb399tvO22644QYNHz6crfMAAMAfJCUl6bPPPlNsbKwkacuWLUpOTtb27dsNJwsflOoQk5WVpcGDB2vYsGHO2/r3769PPvlEMTExBpMBAIBg1qdPH33xxRcqUqSIJGnr1q1KSkrSxo0bDScLD5TqEHLixAlde+21+u9//+u8bcCAAfroo48o1AAA4KL69OmjiRMnOsV6+/bt6tChg5YtW2Y4WeijVIeIgwcPKi0tTV999ZXztoEDB+rDDz+kUAMAgDzr1auXJk2apGLFikmSDhw4oJSUFH3zzTeGk4U2SnUI+Omnn9S+fXt99913ztseffRRjRo1StHR0QaTAQCAUNSjRw/Nnj1bZcuWleR5Nfzqq6/WW2+9Jdu2DacLTZTqIDd58mS1bdvWmXeyLEtvvPGGXnjhBW5KBAAABdauXTstWrRINWvWlCRlZ2frwQcf1J133qlTp06ZDReCKNVBKjs7W88884yuvvpqHTt2TJJUvHhxffbZZ3rwwQcNpwMAAOGgQYMGWrJkidcpzKNGjVJSUpJ+/fVXg8lCD6U6CKWnp6tLly56+umnnbfVrFlTixcv1nXXXWcuGAAACDuVKlXSggULNHDgQOdty5YtU7NmzTRhwgSDyUILpTrIfPnll7rssss0d+5c522pqan6/vvv1axZM3PBAABA2CpatKhGjRqlN954w7lf69ChQ7ruuus0ePBgZWRkGE4Y/CjVQeLgwYMaOHCgrrnmGv3222+SpKioKD3xxBOaPn26ypUrZzghAAAIZ5Zl6cEHH9TcuXNVvXp15+0jRoxQ8+bNvS744Y8o1YbZtq2xY8eqUaNG+vDDD523V6tWTXPmzNE///lPtswDAAAB06lTJ61atUo33HCD87aNGzcqJSVFd999tw4dOmQwXfCiVBu0du1a9ejRQwMGDND+/fudt/fv31+rVq1S586dDaYDAACRKjExUePGjdOoUaNUqlQp5+3vv/++GjVqpNGjRysnJ8dgwuBDqTZg//79uv/++9WsWTPNmDHDeXvVqlX11VdfaezYsSpdurTBhAAAINJZlqVBgwbpp59+0rXXXuu8fe/evbr99tvVqlUrzZkzx2DC4EKpDqDffvtNTzzxhOrWrathw4Y5z/CioqL0wAMPaN26dbryyisNpwQAAPifKlWq6IsvvtCECRNUpUoV5+0//PCDUlNT1bt3b445F6U6IA4ePKjHH39cNWvW1HPPPaejR486v9elSxf9+OOPeuutt7xeXgEAAAgm11xzjX755Rc9+eSTzhHnkjRlyhS1bdtWPXv21JIlSwwmNItSXYjWrFmju+++W1WrVtW//vUv5xAXSWrYsKG++uorzZgxQ5deeqnBlAAAAHlTsmRJPfPMM9qwYYNuvfVWr9Odp02bpg4dOqhjx44aP368MjMzDSYNPEq1n508eVLjx49XamqqLrvsMr3//vteR302atRIY8eO1dq1a3XllVdy1DgAAAg5VatW1Ycffqi1a9dqwIABior6X6VcvHix+vfvr5o1a+rZZ5/V9u3bDSYNHEq1H9i2rUWLFmnw4MGqXLmy+vfv/4fB/ebNm2vcuHFas2aN+vfv72ysDgAAEKoaN26sTz75ROvXr9fAgQMVGxvr/N6uXbv05JNPqmbNmkpJSdGoUaO8RmDDjWXbtukMF2RZ1ooWLVq0WLFihekoXs6cOaN58+bpyy+/1KRJk5Senv6H94mOjta1116rIUOGqGPHjlyVBgAAYW3Pnj0aPny43n33Xe3Zs+cPvx8XF6cuXbqob9++6tOnjypWrGgg5fm1bNlSK1euXGnbdsv8fiylOo9s29bmzZs1a9YszZo1SzNmzNCRI0fO+b516tTRbbfdpkGDBnmdSAQAABAJzpw5owkTJuiDDz7QjBkzzrmntWVZateunbp166bU1FS1bdtWcXFxBtL+D6W6EGRmZmrt2rVatmyZlixZotmzZ+vXX3897/uXLl1a/fr108CBA9WhQweuSgMAAEjavXu3xowZo48++kirVq067/sVK1ZMnTp1UufOndWmTRu1atVKZcqUCWBSSrXPjhw5ovXr12v9+vVas2aNvv/+e61cudLrBsNzqVGjhvr27au+ffuqU6dOHCcOAABwAVu2bNGkSZM0adIkLViw4KKnMtatW1dt2rRR8+bN1aRJEzVu3FjVq1cvtIuXlOo8OHz4sLZu3aqtW7dq27Zt2rp1qzZs2KD169dr586defoz4uPjlZSUpNTUVKWlpenSSy/lijQAAEABHDhwQLNmzdLs2bM1e/Zsbdq0KU8fV7JkSTVq1EiNGjVS7dq1VbNmTdWqVUu1atVSlSpVfNoMIiJLtW3bOnbsmA4fPqzDhw9r//792rNnj/bs2aO9e/c6j/fs2aNff/1Vhw8fzvfnrlGjhtq0aaM2bdqoY8eOat26NVejAQAACsGOHTs0d+5cLV26VMuWLdOqVavyvdd1bGysqlWrpsqVK6tSpUrOf7+vy5cvr9KlSysxMVGJiYleu5VIEVCqExMTWzRv3twp0IcPH9aRI0cu+pJBXhUpUkQNGjRwXla4/PLL1aZNm6C7IxUAACBSnD59WqtWrdL333+vtWvXat26dVq3bp1+++03v32OkiVLKjExUaVLl1bp0qW1atUqHTlyJHxLtaQWvv45xYoVc14e+P3X2rVrq3Hjxqpbty5XoAEAAIKcbdvat2+f1q1bp40bNzqjvb//d+DAAX98msgr1b8/u0hMTFSZMmW8LvVXrFjReVylShVVqFCB+WcAAIAwdvz4cf36669/GAXes2ePdu/erYMHD+rw4cM6dOiQDh8+fL6ph/At1bVq1WoxYsQIp0AnJiYqISGBq8sAAAAokJycHB0/flyHDh1y/rvrrru0ZcuWApXqkGilpUuXVlpamukYAAAACBNRUVEqVaqUSpUqpRo1akiSEhMTC/7n+SkXAAAAELEo1QAAAICPKNUAAACAjyjVAAAAgI8o1QAAAICPKNUAAACAjyjVAAAAgI8o1QAAAICPKNUAAACAjyjVAAAAgI8o1QAAAICPKNUAAACAjyjVAAAAgI8o1QAAAICPKNUAAACAjyjVAAAAgI8s27ZNZ7ggy7IOFitWrEyjRo1MRwEAAEAY++mnn3Ty5MnfbNsum9+PDYVSvVVSKUnbDEeJFA3P/vqz0RQoTPwbRwb+nSMD/86RgX/nwKkp6aht27Xy+4FBX6oRWJZlrZAk27Zbms6CwsG/cWTg3zky8O8cGfh3Dg3MVAMAAAA+olQDAAAAPqJUAwAAAD6iVAMAAAA+olQDAAAAPmL3DwAAAMBHXKkGAAAAfESpBgAAAHxEqQYAAAB8RKkGAAAAfESpBgAAAHxEqQYAAAB8RKkGAAAAfESpRr5YlvW+ZVn22f/qms4D31mWVc+yrEcsy5ptWdavlmWdsSxrr2VZkyzLSjGdD/ljWVZVy7JGWpa1y7Ks05ZlbbMs6zXLskqbzgbfWZZV1rKsuyzLmmhZ1ibLsk5alnXEsqyFlmXdaVkWP9fDlGVZt7h+/t5lOg/+iMNfkGeWZfWRNFnScUklJdWzbXuT2VTwlWVZ4yTdKGm9pIWSfpPUQNJVkqIlPWTb9hvmEiKvLMuqI2mxpAqSJkn6WVIbSSmSfpHU0bbtg+YSwleWZd0raZik3ZLmSNohqaKkayUlSPpC0vU2P9zDimVZ1SStked7cklJd9u2/b7ZVMiNUo08sSyrvDxf0HMlVZKUJEp1WLAsa5CkVbZt/5Dr7UmSvpVkS6pp2/ZuA/GQD5ZlTZfUTdIQ27bfdL39VUlDJQ23bfteU/ngO8uyUiWVkPSNbds5rrdXkrRMUjVJ/Wzb/sJQRPiZZVmWPN+La0maIOmvolQHJV4mQl69d/bXB4ymgN/Ztj06d6E++/Z58jyJKiKpQ6BzIX/OXqXuJmmbpLdz/fZTkjIk3WpZVokAR4Mf2bY927btr9yF+uzb90h69+wyOeDBUJiGSEqVdLs8X8cIUpRqXNTZK5l9Jd3DS8cRJ/Psr1lGUyAvfp9/n3GOwnVM0iJJxSW1C3QwBAxfr2HGsqxGkl6U9Lpt2/NN58GFUapxQZZl1ZD0uqSPbdueZDoPAufsv32apBOS+GYe/Bqc/XXDeX5/49lf6wcgCwLMsqwYSbedXU4zmQX+cfbf9CN55uYfMxwHeRBjOgCC19m7yD+Q58bEIYbjIIAsy4qT9ImkOEkP27Z9yHAkXFzC2V+PnOf3f397YuFHgQEvSmoqaYpt29NNh4FfPCmpuaROtm2fNB0GF8eV6jB3djstOx//fez68KHy3JB4N6UquPn475z7z4qW5+pIR0njJb0cqP8dAPLPsqwhkv4iz24vtxqOAz+wLKutPFenX7Fte4npPMgbrlSHv82STuXj/XdJkmVZ9SX9S9Io27anFEYw+FWB/p1zO1uoP5Z0vaRPJd3C1lwh4/cr0Qnn+f3f33648KMgUCzL+n/yjOitl5Rm2/ZvhiPBR2fHPj6UZ5TrCcNxkA+U6jBn23ZaAT+0sTwv/d9uWdbt53mfjZ6dfnSNbdtfFvDzwA98+Hd2WJYVK8/Ix/WSxki6zbbtbF//XATML2d/Pd/MdL2zv55v5hohxrKsP0n6j6S18hTqfWYTwU9K6n9fx6fO/pzNbYRlWSPkuYHxT4EKhgujVON8tkn673l+r7c8e1V/Juno2fdFCLMsq4g8V6avlucKye25d5BA0Jtz9tdulmVF5drDOF6ecZ4Tkr4zEQ7+ZVnWI/LMUf8oqatt2wfMJoIfndb5f/62kGfOeqE8T6QZDQkiHP6CfLMsa644/CVsnL0pcYKkXvJ8Ix9MoQ5NHP4SGSzLekLSPyWtkNSNkY/IYVnW0/LsO8/hL0GIK9UA3pWnUB+QlC7pyXO83DjXtu25Ac6F/LtfnmPK37AsK03ST5LayrOH9QZJ/zCYDX5gWdZAeQp1tqQFkoac4+t1m23bowMcDYh4lGoAtc7+Wk6eLZzOZ27hR4EvbNvebFlWK3lKVw95niztludGtmfYxScs/P71Gi3pT+d5n3mSRgciDID/YfwDAAAA8BH7VAMAAAA+olQDAAAAPqJUAwAAAD6iVAMAAAA+olQDAAAAPqJUAwAAAD6iVAMAAAA+olQDAAAAPqJUAwAAAD6iVAMAAAA+olQDAAAAPqJUAwAAAD6iVAMAAAA+olQDAAAAPqJUAwAAAD6iVANAmLAs60vLsmzLsoac4/eePft7/zWRDQDCnWXbtukMAAA/sCyrjKQfJFWU1N627R/Ovj1N0gxJP0tqbdv2CXMpASA8UaoBIIxYltVB0jxJWyW1kFRC0o+SEuQp1OvMpQOA8MX4BwCEEdu2F0t6QlI9ScMlfSSpkqQhFGoAKDxcqQaAMGNZliVpmqRuZ9801rbtAQYjAUDY40o1AIQZ23O1ZILrTa8ZigIAEYMr1QAQZizLqidppaRMeWap10lqY9v2KaPBACCMcaUaAMKIZVlxksbLc4PijZJekHSpuFoNAIWKUg0A4eVlSc0l/Z9t299KekrSIkn3WJZ1vdFkABDGGP8AgDBhWdY18sxSL5XUybbtrLNvrybPtnoxkprbtr3FWEgACFOUagAIA5ZlVZenOEdJuty27W25fv9qSV9K+l6ewn0mwBEBIKxRqgEAAAAfMVMNAAAA+IhSDQAAAPiIUg0AAAD4iFINAAAA+IhSDQAAAPiIUg0AAAD4iFINAAAA+IhSDQAAAPiIUg0AAAD4iFINAAAA+IhSDQAAAPiIUg0AAAD4iFINAAAA+IhSDQAAAPiIUg0AAAD4iFINAAAA+IhSDQAAAPjo/wPzvFaWplbpNQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "image/png": { "height": 261, "width": 362 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def logprob(x):\n", " return np.sum(\n", " np.logaddexp(\n", " -0.5 * (x - 2) ** 2,\n", " -0.5 * (x + 2) ** 2,\n", " )\n", " - 0.5 * np.log(2 * np.pi)\n", " - np.log(2)\n", " )\n", "\n", "\n", "x = np.linspace(-5.5, 5.5, 5000)\n", "plt.plot(x, np.exp(list(map(logprob, x))), \"k\")\n", "plt.yticks([])\n", "plt.xlim(-5.5, 5.5)\n", "plt.ylabel(\"p(x)\")\n", "plt.xlabel(\"x\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can sample this using emcee and the default {class}`moves.StretchMove`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Autocorrelation time: 40.03 steps\n" ] } ], "source": [ "import emcee\n", "\n", "np.random.seed(589403)\n", "\n", "init = np.random.randn(32, 1)\n", "nwalkers, ndim = init.shape\n", "\n", "sampler0 = emcee.EnsembleSampler(nwalkers, ndim, logprob)\n", "sampler0.run_mcmc(init, 5000)\n", "\n", "print(\n", " \"Autocorrelation time: {0:.2f} steps\".format(\n", " sampler0.get_autocorr_time()[0]\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This autocorrelation time seems long for a 1D problem!\n", "We can also see this effect qualitatively by looking at the trace for one of the walkers:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 278, "width": 397 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(sampler0.get_chain()[:, 0, 0], \"k\", lw=0.5)\n", "plt.xlim(0, 5000)\n", "plt.ylim(-5.5, 5.5)\n", "plt.title(\"move: StretchMove\", fontsize=14)\n", "plt.xlabel(\"step number\")\n", "plt.ylabel(\"x\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For \"lightly\" multimodal problems like these, some combination of the {class}`moves.DEMove` and {class}`moves.DESnookerMove` can often perform better than the default.\n", "In this case, let's use a weighted mixture of the two moves.\n", "In deatil, this means that, at each step, we'll randomly select either a :class:`moves.DEMove` (with 80% probability) or a {class}`moves.DESnookerMove` (with 20% probability)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Autocorrelation time: 6.49 steps\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 278, "width": 397 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "np.random.seed(93284)\n", "\n", "sampler = emcee.EnsembleSampler(\n", " nwalkers,\n", " ndim,\n", " logprob,\n", " moves=[\n", " (emcee.moves.DEMove(), 0.8),\n", " (emcee.moves.DESnookerMove(), 0.2),\n", " ],\n", ")\n", "sampler.run_mcmc(init, 5000)\n", "\n", "print(\n", " \"Autocorrelation time: {0:.2f} steps\".format(\n", " sampler.get_autocorr_time()[0]\n", " )\n", ")\n", "\n", "plt.plot(sampler.get_chain()[:, 0, 0], \"k\", lw=0.5)\n", "plt.xlim(0, 5000)\n", "plt.ylim(-5.5, 5.5)\n", "plt.title(\"move: [(DEMove, 0.8), (DESnookerMove, 0.2)]\", fontsize=14)\n", "plt.xlabel(\"step number\")\n", "plt.ylabel(\"x\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks a lot better!\n", "\n", "The idea with the {ref}`moves-user` interface is that it should be easy for users to try out several different moves to find the combination that works best for their problem so you should head over to {ref}`moves-user` to see all the details!" ] } ], "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/parallel.ipynb0000644000175100001710000003436300000000000017760 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(parallel)=\n", "\n", "# Parallelization" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20\n", "\n", "import multiprocessing\n", "\n", "multiprocessing.set_start_method(\"fork\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ":::{note}\n", "Some builds of NumPy (including the version included with Anaconda) will automatically parallelize some operations using something like the MKL linear algebra. This can cause problems when used with the parallelization methods described here so it can be good to turn that off (by setting the environment variable `OMP_NUM_THREADS=1`, for example).\n", ":::" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"OMP_NUM_THREADS\"] = \"1\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With emcee, it's easy to make use of multiple CPUs to speed up slow sampling.\n", "There will always be some computational overhead introduced by parallelization so it will only be beneficial in the case where the model is expensive, but this is often true for real research problems.\n", "All parallelization techniques are accessed using the `pool` keyword argument in the :class:`EnsembleSampler` class but, depending on your system and your model, there are a few pool options that you can choose from.\n", "In general, a `pool` is any Python object with a `map` method that can be used to apply a function to a list of numpy arrays.\n", "Below, we will discuss a few options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In all of the following examples, we'll test the code with the following convoluted model:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "\n", "\n", "def log_prob(theta):\n", " t = time.time() + np.random.uniform(0.005, 0.008)\n", " while True:\n", " if time.time() >= t:\n", " break\n", " return -0.5 * np.sum(theta ** 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This probability function will randomly sleep for a fraction of a second every time it is called.\n", "This is meant to emulate a more realistic situation where the model is computationally expensive to compute.\n", "\n", "To start, let's sample the usual (serial) way:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:21<00:00, 4.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Serial took 21.5 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import emcee\n", "\n", "np.random.seed(42)\n", "initial = np.random.randn(32, 5)\n", "nwalkers, ndim = initial.shape\n", "nsteps = 100\n", "\n", "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob)\n", "start = time.time()\n", "sampler.run_mcmc(initial, nsteps, progress=True)\n", "end = time.time()\n", "serial_time = end - start\n", "print(\"Serial took {0:.1f} seconds\".format(serial_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiprocessing\n", "\n", "The simplest method of parallelizing emcee is to use the [multiprocessing module from the standard library](https://docs.python.org/3/library/multiprocessing.html).\n", "To parallelize the above sampling, you could update the code as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:06<00:00, 15.65it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multiprocessing took 6.5 seconds\n", "3.3 times faster than serial\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from multiprocessing import Pool\n", "\n", "with Pool() as pool:\n", " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, pool=pool)\n", " start = time.time()\n", " sampler.run_mcmc(initial, nsteps, progress=True)\n", " end = time.time()\n", " multi_time = end - start\n", " print(\"Multiprocessing took {0:.1f} seconds\".format(multi_time))\n", " print(\"{0:.1f} times faster than serial\".format(serial_time / multi_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have 4 cores on the machine where this is being tested:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 CPUs\n" ] } ], "source": [ "from multiprocessing import cpu_count\n", "\n", "ncpu = cpu_count()\n", "print(\"{0} CPUs\".format(ncpu))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We don't quite get the factor of 4 runtime decrease that you might expect because there is some overhead in the parallelization, but we're getting pretty close with this example and this will get even closer for more expensive models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MPI\n", "\n", "Multiprocessing can only be used for distributing calculations across processors on one machine.\n", "If you want to take advantage of a bigger cluster, you'll need to use MPI.\n", "In that case, you need to execute the code using the `mpiexec` executable, so this demo is slightly more convoluted.\n", "For this example, we'll write the code to a file called `script.py` and then execute it using MPI, but when you really use the MPI pool, you'll probably just want to edit the script directly.\n", "To run this example, you'll first need to install [the schwimmbad library](https://github.com/adrn/schwimmbad) because emcee no longer includes its own `MPIPool`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MPI took 8.9 seconds\n", "2.4 times faster than serial\n" ] } ], "source": [ "with open(\"script.py\", \"w\") as f:\n", " f.write(\"\"\"\n", "import sys\n", "import time\n", "import emcee\n", "import numpy as np\n", "from schwimmbad import MPIPool\n", "\n", "def log_prob(theta):\n", " t = time.time() + np.random.uniform(0.005, 0.008)\n", " while True:\n", " if time.time() >= t:\n", " break\n", " return -0.5*np.sum(theta**2)\n", "\n", "with MPIPool() as pool:\n", " if not pool.is_master():\n", " pool.wait()\n", " sys.exit(0)\n", " \n", " np.random.seed(42)\n", " initial = np.random.randn(32, 5)\n", " nwalkers, ndim = initial.shape\n", " nsteps = 100\n", "\n", " sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, pool=pool)\n", " start = time.time()\n", " sampler.run_mcmc(initial, nsteps)\n", " end = time.time()\n", " print(end - start)\n", "\"\"\")\n", "\n", "mpi_time = !mpiexec -n {ncpu} python script.py\n", "mpi_time = float(mpi_time[0])\n", "print(\"MPI took {0:.1f} seconds\".format(mpi_time))\n", "print(\"{0:.1f} times faster than serial\".format(serial_time / mpi_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is often more overhead introduced by MPI than multiprocessing so we get less of a gain this time.\n", "That being said, MPI is much more flexible and it can be used to scale to huge systems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pickling, data transfer & arguments\n", "\n", "All parallel Python implementations work by spinning up multiple `python` processes with identical environments then and passing information between the processes using `pickle`.\n", "This means that the probability function [must be picklable](https://docs.python.org/3/library/pickle.html#pickle-picklable).\n", "\n", "Some users might hit issues when they use `args` to pass data to their model.\n", "These args must be pickled and passed every time the model is called.\n", "This can be a problem if you have a large dataset, as you can see here:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:21<00:00, 4.70it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Serial took 21.5 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def log_prob_data(theta, data):\n", " a = data[0] # Use the data somehow...\n", " t = time.time() + np.random.uniform(0.005, 0.008)\n", " while True:\n", " if time.time() >= t:\n", " break\n", " return -0.5 * np.sum(theta ** 2)\n", "\n", "\n", "data = np.random.randn(5000, 200)\n", "\n", "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob_data, args=(data,))\n", "start = time.time()\n", "sampler.run_mcmc(initial, nsteps, progress=True)\n", "end = time.time()\n", "serial_data_time = end - start\n", "print(\"Serial took {0:.1f} seconds\".format(serial_data_time))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We basically get no change in performance when we include the `data` argument here.\n", "Now let's try including this naively using multiprocessing:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [01:05<00:00, 1.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multiprocessing took 66.0 seconds\n", "0.3 times faster(?) than serial\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "with Pool() as pool:\n", " sampler = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_prob_data, pool=pool, args=(data,)\n", " )\n", " start = time.time()\n", " sampler.run_mcmc(initial, nsteps, progress=True)\n", " end = time.time()\n", " multi_data_time = end - start\n", " print(\"Multiprocessing took {0:.1f} seconds\".format(multi_data_time))\n", " print(\n", " \"{0:.1f} times faster(?) than serial\".format(\n", " serial_data_time / multi_data_time\n", " )\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Brutal.\n", "\n", "We can do better than that though.\n", "It's a bit ugly, but if we just make `data` a global variable and use that variable within the model calculation, then we take no hit at all." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 100/100 [00:06<00:00, 14.60it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multiprocessing took 6.9 seconds\n", "3.1 times faster than serial\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def log_prob_data_global(theta):\n", " a = data[0] # Use the data somehow...\n", " t = time.time() + np.random.uniform(0.005, 0.008)\n", " while True:\n", " if time.time() >= t:\n", " break\n", " return -0.5 * np.sum(theta ** 2)\n", "\n", "\n", "with Pool() as pool:\n", " sampler = emcee.EnsembleSampler(\n", " nwalkers, ndim, log_prob_data_global, pool=pool\n", " )\n", " start = time.time()\n", " sampler.run_mcmc(initial, nsteps, progress=True)\n", " end = time.time()\n", " multi_data_global_time = end - start\n", " print(\n", " \"Multiprocessing took {0:.1f} seconds\".format(multi_data_global_time)\n", " )\n", " print(\n", " \"{0:.1f} times faster than serial\".format(\n", " serial_data_time / multi_data_global_time\n", " )\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's better!\n", "This works because, in the global variable case, the dataset is only pickled and passed between processes once (when the pool is created) instead of once for every model evaluation." ] } ], "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/quickstart.ipynb0000644000175100001710000004506100000000000020353 0ustar00runnerdocker{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(quickstart)=\n", "\n", "# Quickstart " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "%config InlineBackend.figure_format = \"retina\"\n", "\n", "from matplotlib import rcParams\n", "\n", "rcParams[\"savefig.dpi\"] = 100\n", "rcParams[\"figure.dpi\"] = 100\n", "rcParams[\"font.size\"] = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The easiest way to get started with using emcee is to use it for a project. To get you started, here’s an annotated, fully-functional example that demonstrates a standard usage pattern.\n", "\n", "## How to sample a multi-dimensional Gaussian\n", "\n", "We’re going to demonstrate how you might draw samples from the multivariate Gaussian density given by:\n", "\n", "$$\n", "p(\\vec{x}) \\propto \\exp \\left [ - \\frac{1}{2} (\\vec{x} -\n", " \\vec{\\mu})^\\mathrm{T} \\, \\Sigma ^{-1} \\, (\\vec{x} - \\vec{\\mu})\n", " \\right ]\n", "$$\n", "\n", "where $\\vec{\\mu}$ is an $N$-dimensional vector position of the mean of the density and $\\Sigma$ is the square N-by-N covariance matrix.\n", "\n", "The first thing that we need to do is import the necessary modules:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we’ll code up a Python function that returns the density $p(\\vec{x})$ for specific values of $\\vec{x}$, $\\vec{\\mu}$ and $\\Sigma^{-1}$. In fact, emcee actually requires the logarithm of $p$. We’ll call it `log_prob`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def log_prob(x, mu, cov):\n", " diff = x - mu\n", " return -0.5 * np.dot(diff, np.linalg.solve(cov, diff))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important that the first argument of the probability function is\n", "the position of a single \"walker\" (a *N* dimensional\n", "`numpy` array). The following arguments are going to be constant every\n", "time the function is called and the values come from the `args` parameter\n", "of our {class}`EnsembleSampler` that we'll see soon.\n", "\n", "Now, we'll set up the specific values of those \"hyperparameters\" in 5\n", "dimensions:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ndim = 5\n", "\n", "np.random.seed(42)\n", "means = np.random.rand(ndim)\n", "\n", "cov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))\n", "cov = np.triu(cov)\n", "cov += cov.T - np.diag(cov.diagonal())\n", "cov = np.dot(cov, cov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and where `cov` is $\\Sigma$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How about we use 32 walkers? Before we go on, we need to guess a starting point for each\n", "of the 32 walkers. This position will be a 5-dimensional vector so the\n", "initial guess should be a 32-by-5 array.\n", "It's not a very good guess but we'll just guess a\n", "random number between 0 and 1 for each component:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "nwalkers = 32\n", "p0 = np.random.rand(nwalkers, ndim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've gotten past all the bookkeeping stuff, we can move on to\n", "the fun stuff. The main interface provided by `emcee` is the\n", "{class}`EnsembleSampler` object so let's get ourselves one of those:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import emcee\n", "\n", "sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[means, cov])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remember how our function `log_prob` required two extra arguments when it\n", "was called? By setting up our sampler with the `args` argument, we're\n", "saying that the probability function should be called as:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2.596094589085444" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_prob(p0[0], means, cov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we didn't provide any\n", "`args` parameter, the calling sequence would be `log_prob(p0[0])` instead.\n", "\n", "It's generally a good idea to run a few \"burn-in\" steps in your MCMC\n", "chain to let the walkers explore the parameter space a bit and get\n", "settled into the maximum of the density. We'll run a burn-in of 100\n", "steps (yep, I just made that number up... it's hard to really know\n", "how many steps of burn-in you'll need before you start) starting from\n", "our initial guess ``p0``:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "state = sampler.run_mcmc(p0, 100)\n", "sampler.reset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that I saved the final position of the walkers (after the\n", "100 steps) to a variable called `state`. You can check out what will be\n", "contained in the other output variables by looking at the documentation for\n", "the {func}`EnsembleSampler.run_mcmc` function. The call to the\n", "{func}`EnsembleSampler.reset` method clears all of the important bookkeeping\n", "parameters in the sampler so that we get a fresh start. It also clears the\n", "current positions of the walkers so it's a good thing that we saved them\n", "first.\n", "\n", "Now, we can do our production run of 10000 steps:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "sampler.run_mcmc(state, 10000);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The samples can be accessed using the {func}`EnsembleSampler.get_chain` method.\n", "This will return an array\n", "with the shape `(10000, 32, 5)` giving the parameter values for each walker\n", "at each step in the chain.\n", "Take note of that shape and make sure that you know where each of those numbers come from.\n", "You can make histograms of these samples to get an estimate of the density that you were sampling:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "image/png": { "height": 263, "width": 364 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "samples = sampler.get_chain(flat=True)\n", "plt.hist(samples[:, 0], 100, color=\"k\", histtype=\"step\")\n", "plt.xlabel(r\"$\\theta_1$\")\n", "plt.ylabel(r\"$p(\\theta_1)$\")\n", "plt.gca().set_yticks([]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another good test of whether or not the sampling went well is to check\n", "the mean acceptance fraction of the ensemble using the\n", "{func}`EnsembleSampler.acceptance_fraction` property:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean acceptance fraction: 0.552\n" ] } ], "source": [ "print(\n", " \"Mean acceptance fraction: {0:.3f}\".format(\n", " np.mean(sampler.acceptance_fraction)\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the integrated autocorrelation time (see the {ref}`autocorr` tutorial for more details)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean autocorrelation time: 57.112 steps\n" ] } ], "source": [ "print(\n", " \"Mean autocorrelation time: {0:.3f} steps\".format(\n", " np.mean(sampler.get_autocorr_time())\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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 } ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/tutorials/tutorial_rst.tpl0000644000175100001710000000406200000000000020366 0ustar00runnerdocker{%- extends 'display_priority.tpl' -%} {% block header %} .. module:: emcee **Note:** This tutorial was generated from an IPython notebook that can be downloaded `here <../../_static/notebooks/{{ resources.metadata.name }}.ipynb>`_. .. _{{resources.metadata.name}}: {% endblock %} {% block in_prompt %} {% endblock in_prompt %} {% block output_prompt %} {% endblock output_prompt %} {% block input %} {%- if cell.source.strip() and not cell.source.startswith("%") -%} .. code:: python {{ cell.source | indent}} {% endif -%} {% endblock input %} {% block error %} :: {{ super() }} {% endblock error %} {% block traceback_line %} {{ line | indent | strip_ansi }} {% endblock traceback_line %} {% block execute_result %} {% block data_priority scoped %} {{ super() }} {% endblock %} {% endblock execute_result %} {% block stream %} .. parsed-literal:: {{ output.text | indent }} {% endblock stream %} {% block data_svg %} .. image:: {{ output.metadata.filenames['image/svg+xml'] | urlencode }} {% endblock data_svg %} {% block data_png %} .. image:: {{ output.metadata.filenames['image/png'] | urlencode }} {% endblock data_png %} {% block data_jpg %} .. image:: {{ output.metadata.filenames['image/jpeg'] | urlencode }} {% endblock data_jpg %} {% block data_latex %} .. math:: {{ output.data['text/latex'] | strip_dollars | indent }} {% endblock data_latex %} {% block data_text scoped %} .. parsed-literal:: {{ output.data['text/plain'] | indent }} {% endblock data_text %} {% block data_html scoped %} .. raw:: html {{ output.data['text/html'] | indent }} {% endblock data_html %} {% block markdowncell scoped %} {{ cell.source | markdown2rst }} {% endblock markdowncell %} {%- block rawcell scoped -%} {%- if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %} {{cell.source}} {% endif -%} {%- endblock rawcell -%} {% block headingcell scoped %} {{ ("#" * cell.level + cell.source) | replace('\n', ' ') | markdown2rst }} {% endblock headingcell %} {% block unknowncell scoped %} unknown type {{cell.type}} {% endblock unknowncell %} ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731802.997506 emcee-3.1.1/docs/user/0000755000175100001710000000000000000000000014040 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/autocorr.rst0000644000175100001710000000061200000000000016427 0ustar00runnerdocker.. _autocorr-user: Autocorrelation Analysis ======================== A good heuristic for assessing convergence of samplings is the integrated autocorrelation time. ``emcee`` includes tools for computing this and the autocorrelation function itself. More details can be found in :ref:`autocorr`. .. autofunction:: emcee.autocorr.integrated_time .. autofunction:: emcee.autocorr.function_1d ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/backends.rst0000644000175100001710000000210000000000000016335 0ustar00runnerdocker.. _backends: Backends ======== Starting with version 3, emcee has an interface for serializing the sampler output. This can be useful in any scenario where you want to share the results of sampling or when sampling with an expensive model because, even if the sampler crashes, the current state of the chain will always be saved. There is currently one backend that can be used to serialize the chain to a file: :class:`emcee.backends.HDFBackend`. The methods and options for this backend are documented below. It can also be used as a reader for existing samplings. For example, if a chain was saved using the :class:`backends.HDFBackend`, the results can be accessed as follows: .. code-block:: python reader = emcee.backends.HDFBackend("chain_filename.h5", read_only=True) flatchain = reader.get_chain(flat=True) The ``read_only`` argument is not required, but it will make sure that you don't inadvertently overwrite the samples in the file. .. autoclass:: emcee.backends.Backend :inherited-members: .. autoclass:: emcee.backends.HDFBackend :inherited-members: ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/blobs.rst0000644000175100001710000000626100000000000015700 0ustar00runnerdocker.. _blobs: Blobs ===== Way back in version 1.1 of emcee, the concept of blobs was introduced. This allows a user to track arbitrary metadata associated with every sample in the chain. The interface to access these blobs was previously a little clunky because it was stored as a list of lists of blobs. In version 3, this interface has been updated to use NumPy arrays instead and the sampler will do type inference to save the simplest possible representation of the blobs. Using blobs to track the value of the prior ------------------------------------------- A common pattern is to save the value of the log prior at every step in the chain. To do this, you could do something like: .. code-block:: python import emcee import numpy as np def log_prior(params): return -0.5 * np.sum(params**2) def log_like(params): return -0.5 * np.sum((params / 0.1)**2) def log_prob(params): lp = log_prior(params) if not np.isfinite(lp): return -np.inf, -np.inf ll = log_like(params) if not np.isfinite(ll): return lp, -np.inf return lp + ll, lp coords = np.random.randn(32, 3) nwalkers, ndim = coords.shape sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) sampler.run_mcmc(coords, 100) log_prior_samps = sampler.get_blobs() flat_log_prior_samps = sampler.get_blobs(flat=True) print(log_prior_samps.shape) # (100, 32) print(flat_log_prior_samps.shape) # (3200,) After running this, the "blobs" stored by the sampler will be a ``(nsteps, nwalkers)`` NumPy array with the value of the log prior at every sample. Named blobs & custom dtypes --------------------------- If you want to save multiple pieces of metadata, it can be useful to name them. To implement this, we use the ``blobs_dtype`` argument in :class:`EnsembleSampler`. For example, let's say that, for some reason, we wanted to save the mean of the parameters as well as the log prior. To do this, we would update the above example as follows: .. code-block:: python def log_prob(params): lp = log_prior(params) if not np.isfinite(lp): return -np.inf, -np.inf, -np.inf ll = log_like(params) if not np.isfinite(ll): return lp, -np.inf, -np.inf return lp + ll, lp, np.mean(params) coords = np.random.randn(32, 3) nwalkers, ndim = coords.shape # Here are the important lines dtype = [("log_prior", float), ("mean", float)] sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, blobs_dtype=dtype) sampler.run_mcmc(coords, 100) blobs = sampler.get_blobs() log_prior_samps = blobs["log_prior"] mean_samps = blobs["mean"] print(log_prior_samps.shape) print(mean_samps.shape) flat_blobs = sampler.get_blobs(flat=True) flat_log_prior_samps = flat_blobs["log_prior"] flat_mean_samps = flat_blobs["mean"] print(flat_log_prior_samps.shape) print(flat_mean_samps.shape) This will print .. code-block:: python (100, 32) (100, 32) (3200,) (3200,) and the ``blobs`` object will be a structured NumPy array with two columns called ``log_prior`` and ``mean``. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/faq.rst0000644000175100001710000000173500000000000015347 0ustar00runnerdocker.. _faq: FAQ === **The not-so-frequently asked questions that still have useful answers** What are "walkers"? ------------------- Walkers are the members of the ensemble. They are almost like separate Metropolis-Hastings chains but, of course, the proposal distribution for a given walker depends on the positions of all the other walkers in the ensemble. See `Goodman & Weare (2010) `_ for more details. How should I initialize the walkers? ------------------------------------ The best technique seems to be to start in a small ball around the a priori preferred position. Don't worry, the walkers quickly branch out and explore the rest of the space. Parameter limits ---------------- In order to confine the walkers to a finite volume of the parameter space, have your function return negative infinity outside of the volume corresponding to the logarithm of 0 prior probability using .. code-block:: python return -numpy.inf ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/install.rst0000644000175100001710000000434100000000000016242 0ustar00runnerdocker.. _install: Installation ============ Since emcee is a pure Python module, it should be pretty easy to install. All you'll need `numpy `_. .. note:: For pre-release versions of emcee, you need to follow the instructions in :ref:`source`. Package managers ---------------- The recommended way to install the stable version of emcee is using `pip `_ .. code-block:: bash python -m pip install -U pip pip install -U setuptools setuptools_scm pep517 pip install -U emcee or `conda `_ .. code-block:: bash conda update conda conda install -c conda-forge emcee Distribution packages --------------------- Some distributions contain `emcee` packages that can be installed with the system package manager as listed in the `Repology packaging status `_. Note that the packages in some of these distributions may be out-of-date. You can always get the current stable version via `pip` or `conda`, or the latest development version as described in :ref:`source` below. .. image:: https://repology.org/badge/vertical-allrepos/python:emcee.svg?header=emcee%20packaging%20status :target: https://repology.org/project/python:emcee/versions .. _source: From source ----------- emcee is developed on `GitHub `_ so if you feel like hacking or if you like all the most recent shininess, you can clone the source repository and install from there .. code-block:: bash python -m pip install -U pip python -m pip install -U setuptools setuptools_scm pep517 git clone https://github.com/dfm/emcee.git cd emcee python -m pip install -e . Test the installation --------------------- To make sure that the installation went alright, you can execute some unit and integration tests. To do this, you'll need the source (see :ref:`source` above) and `py.test `_. You'll execute the tests by running the following command in the root directory of the source code: .. code-block:: bash python -m pip install -U pytest h5py python -m pytest -v src/emcee/tests This might take a few minutes but you shouldn't get any errors if all went as planned. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/moves.rst0000644000175100001710000000502100000000000015721 0ustar00runnerdocker.. _moves-user: Moves ===== emcee was originally built on the "stretch move" ensemble method from `Goodman & Weare (2010) `_, but starting with version 3, emcee nows allows proposals generated from a mixture of "moves". This can be used to get a more efficient sampler for models where the stretch move is not well suited, such as high dimensional or multi-modal probability surfaces. A "move" is an algorithm for updating the coordinates of walkers in an ensemble sampler based on the current set of coordinates in a manner that satisfies detailed balance. In most cases, the update for each walker is based on the coordinates in some other set of walkers, the complementary ensemble. These moves have been designed to update the ensemble in parallel following the prescription from `Foreman-Mackey et al. (2013) `_. This means that computationally expensive models can take advantage of multiple CPUs to accelerate sampling (see the :ref:`parallel` tutorial for more information). The moves are selected using the ``moves`` keyword for the :class:`EnsembleSampler` and the mixture can optionally be a weighted mixture of moves. During sampling, at each step, a move is randomly selected from the mixture and used as the proposal. The default move is still the :class:`moves.StretchMove`, but the others are described below. Many standard ensemble moves are available with parallelization provided by the :class:`moves.RedBlueMove` abstract base class that implements the parallelization method described by `Foreman-Mackey et al. (2013) `_. In addition to these moves, there is also a framework for building Metropolis–Hastings proposals that update the walkers using independent proposals. :class:`moves.MHMove` is the base class for this type of move and a concrete implementation of a Gaussian Metropolis proposal is found in :class:`moves.GaussianMove`. .. note:: The :ref:`moves` tutorial shows a concrete example of how to use this interface. Ensemble moves -------------- .. autoclass:: emcee.moves.RedBlueMove :members: .. autoclass:: emcee.moves.StretchMove :members: .. autoclass:: emcee.moves.WalkMove :members: .. autoclass:: emcee.moves.KDEMove :members: .. autoclass:: emcee.moves.DEMove :members: .. autoclass:: emcee.moves.DESnookerMove :members: Metropolis–Hastings moves ------------------------- .. autoclass:: emcee.moves.MHMove :members: .. autoclass:: emcee.moves.GaussianMove :members: ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/sampler.rst0000644000175100001710000000053600000000000016241 0ustar00runnerdocker.. _sampler: The Ensemble Sampler ==================== Standard usage of ``emcee`` involves instantiating an :class:`EnsembleSampler`. .. autoclass:: emcee.EnsembleSampler :inherited-members: Note that several of the :class:`EnsembleSampler` methods return or consume :class:`State` objects: .. autoclass:: emcee.State :inherited-members: ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/docs/user/upgrade.rst0000644000175100001710000000405300000000000016223 0ustar00runnerdocker.. _upgrade: Upgrading From Pre-3.0 Versions =============================== The version 3 release of emcee is the biggest update in years. That being said, we've made every attempt to maintain backwards compatibility while still offering new features. The main new features include: 1. A :ref:`moves-user` interface that allows the use of a variety of ensemble proposals, 2. A more self consistent and user-friendly :ref:`blobs` interface, 3. A :ref:`backends` interface that simplifies the process of serializing the sampling results, and 4. The long requested progress bar (implemented using `tqdm `_) so that users can watch the grass grow while the sampler does its thing (this is as simple as installing tqdm and setting ``progress=True`` in :class:`EnsembleSampler`). To improve the stability and supportability of emcee, we also removed some features. The main removals are as follows: 1. The ``threads`` keyword argument has been removed in favor of the ``pool`` interface (see the :ref:`parallel` tutorial for more information). The old interface had issues with memory consumption and hanging processes when the sampler object wasn't explicitly deleted. The ``pool`` interface has been supported since the first release of emcee and existing code should be easy to update following the :ref:`parallel` tutorial. 2. The ``MPIPool`` has been removed and forked to the `schwimmbad `_ project. There was a longstanding issue with memory leaks and random crashes of the emcee implementation of the ``MPIPool`` that have been fixed in schwimmbad. schwimmbad also supports several other ``pool`` interfaces that can be used for parallel sampling. See the :ref:`parallel` tutorial for more details. 3. The ``PTSampler`` has been removed and forked to the `ptemcee `_ project. The existing implementation had been gathering dust and there aren't enough resources to maintain the sampler within the emcee project. ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731802.997506 emcee-3.1.1/document/0000755000175100001710000000000000000000000013750 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/document/.gitignore0000644000175100001710000000004300000000000015735 0ustar00runnerdocker*.aux *.brf *.log *.out plots/*.h5 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/document/Makefile0000644000175100001710000000071600000000000015414 0ustar00runnerdockerLATEX = pdflatex BASH = bash -c ECHO = echo RM = rm -rf RM_TMP = ${RM} $(foreach suff, ${TMP_SUFFS}, ${NAME}.${suff}) TMP_SUFFS = pdf aux bbl blg log dvi ps eps out SUFF = pdf CHECK_RERUN = grep Rerun $*.log NAME = ms DOC_OUT = ${NAME}.${SUFF} default: ${DOC_OUT} %.pdf: %.tex ${LATEX} $< ( ${CHECK_RERUN} && ${LATEX} $< ) || echo "Done." 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the public domain.}% \def\cpr@Crown{Crown copyright \cpr@year\space by the \cpr@holder.}% \def\cpr@none{No copyright is claimed for this article.}% \def\cpr@ASP{Copyright \cpr@year\space by the ASP.}% \def\cpr@year{\number\year}% \def\@slug{% \par \noindent\cpr@type\par \noindent Manuscript number \@paperid.\par \noindent\CCC@code\par }% \newcommand\lefthead{\substitute@command\lefthead\shorttitle}% \newcommand\righthead{\substitute@command\righthead\shortauthors}% \newcommand\shorttitle[1]{\gdef\@versohead{#1}}% \newcommand\shortauthors[1]{\gdef\@rectohead{#1}}% \shorttitle{\relax}% \shortauthors{\relax}% \def\@runheads{% \@tempcnta\c@page \@whilenum\@tempcnta>\z@\do{% \vskip 3ex \hbox\@to30pc{% \small \expandafter\uppercase\expandafter{\@versohead}: \expandafter\uppercase\expandafter{\@rectohead}\hfil }% \advance\@tempcnta\m@ne }% }% \newcommand\msid[1]{\gdef\ms@id{#1}}% mss ID \def\ms@id{}% \newcommand\slugcomment[1]{\gdef\slug@comment{#1}}% \slugcomment{}% \newdimen\@slugcmmntwidth \long\def\@makeslugcmmnt@mss{% \@ifx@empty\slug@comment{\relax}{% \vskip 5ex \sbox\@tempboxa{\slug@comment}% \@ifdim{\wd\@tempboxa >\@slugcmmntwidth}{% \hbox\@to\textwidth{% \hss \parbox\@slugcmmntwidth\slug@comment }% }{% \hbox\@to\textwidth{\hfil\box\@tempboxa\hfil}% }% }% }% \long\def\@makeslugcmmnt@ppt{% \@ifx@empty\slug@comment{\relax}{% \sbox\@tempboxa{\slug@comment}% \@ifdim{\wd\@tempboxa >\@slugcmmntwidth}{% \hbox\@to\textwidth{% \hss \parbox\@slugcmmntwidth\slug@comment }% }{% \hbox\@to\textwidth{\hfil\box\@tempboxa}% }% \vskip 2ex }% }% \def\@rcvaccrule{\vrule\@width1.75in\@height0.5pt\@depth\z@}% \def\@dates{\ifx\@title\@empty\else {Received}\space% \ifx\@empty\@recvdate\@rcvaccrule\else\@recvdate\fi;% \hspace{1.5em}{accepted}\space% \ifx\@empty\@accptdate\@rcvaccrule\else\@accptdate\fi% \fi}% % \def\@dates@pptt{% \@ifx@empty\@recvdate{}{% \@ifx@empty\@accptdate{}{% {\center \@ifnum{\col@number=\tw@}{\small}{\normalsize}% {\itshape Received}\space \@recvdate \@ifnum{\col@number=\tw@}{\\[.5ex]}{\hspace{1.5em}}% {\itshape accepted}\space \@accptdate \endcenter }% }% }% }% \def\sluginfo@mss{% {% \addpenalty{\@M}% \addvspace{6\p@}% \centering \@dates \@makeslugcmmnt \par \addvspace{6\p@}% }% }% \newcommand\sluginfo{% {% \addpenalty{\@M}% \addvspace{6\p@}% \centering \@dates \par \addvspace{6\p@}% }% }% \let\dates=\sluginfo \renewenvironment{abstract}{% \iflong@abstract% Abstract in one-column mode \onecolumn \fi \global\setbox\abstract@box\vtop\bgroup \columnwidth\textwidth\hsize\columnwidth\linewidth\hsize \normalsize \color@begingroup \@parboxrestore \@setminipage \quotation }{% \par \endquotation \removelastskip \vskip-\prevdepth \color@endgroup \egroup }% \newbox\abstract@box \def\abstractname{ABSTRACT}% \def\@abstract@mss{% \sluginfo \clearpage \@ifvoid\abstract@box{}{% \begin{center}\bfseries\abstractname\end{center}% \contribute@box\abstract@box }% }% \def\contribute@box#1{% \dimen@\ht#1% \leavevmode\hbox{\vrule\@height\ht#1\@width\z@}\par \nointerlineskip\nobreak\kern-\ht#1\unvbox#1\prevdepth\z@ }% \def\@abstract@ppt{% \@ifvoid\abstract@box{}{% \begin{center}\bfseries\abstractname\end{center}% \contribute@box\abstract@box }% }% \def\@abstract@pptt{% \@ifvoid\abstract@box{}{% \@ifnum{\col@number=\tw@}{% \section*{Abstract}% }{% \vspace{3em}% \begin{center}% \large \bfseries\abstractname\vspace{-.5em}\vspace{\z@}% \end{center}% }% \contribute@box\abstract@box }% }% \def\title#1{\appdef\@title{\make@title{#1}}}% \def\author#1{\appdef\@author{\make@author{#1}}}% %%% \def\authoraddr{\substitute@command\authoraddr\affil}% \def\email#1{\appdef\@author{\make@authoremail{#1}}}% \def\affil#1{\appdef\@author{\make@affil{#1}}}% \def\altaffilmark#1{\appdef\@author{\make@altaffilmark{#1}}}% \def\altaffiltext#1#2{\appdef\@author{\make@altaffiltext{#1}{#2}}}% \def\and{\appdef\@author{\make@and}}% \let\authoraddr\@gobble \let\@title\@empty \let\@author\@empty \let\@date\@empty \def\@maketitle{% \newpage \begingroup \let\footnote\thanks \let\email\make@authoremail \let\affil\make@affil \let\altaffilmark\make@altaffilmark \let\altaffiltext\make@altaffiltext \let\and\make@and \@title \@author \@date \par \@abstract \@ifxundefined\keyword@list{}{% \expandafter\@keywords \expandafter{\keyword@list}% }% \endgroup }% \def\make@title@mss#1{% {% \def\baselinestretch{\@tightleading}% \center\large\bfseries{#1}\endcenter %% }% \thispagestyle{msstitle}% mss ID }% \def\make@title@ppt#1{% \@makeslugcmmnt {\center\large\bfseries{#1}\endcenter}% \thispagestyle{empty}% }% \def\make@title@pptt#1{% {% \center \@ifnum{\col@number=\tw@}{\large}{\Large}% \bfseries #1% \endcenter }% }% \def\make@author#1{% {\topsep\z@\center\normalsize#1\endcenter}% }% \def\make@author@pptt#1{% {\topsep\z@ \center \@ifnum{\col@number=\tw@}{\normalsize}{\vspace{4ex}\large}% #1% \endcenter }% }% \let\make@authoraddr=\@gobble \def\make@authoremail#1{% {\topsep\z@\center\normalsize\tt#1\endcenter}% }% \newcommand\make@affil[1]{% \vspace*{-2.5ex}% {\topsep\z@\center#1\endcenter}% }% \def\make@affil@ppt#1{% \vspace*{-0.8ex}% {% \topsep\z@ \center \@ifnum{\col@number=\tw@}{\small}{\normalsize}% \itshape #1% \endcenter }% }% \def\make@affil@pptt#1{% \vspace*{-0.8ex}% {% \topsep\z@ \center \@ifnum{\col@number=\tw@}{\small}{\normalsize}% \itshape #1% \endcenter }% }% \def\thefootnote{\@arabic\c@footnote}% initial style \newcommand\make@altaffilmark[1]{$^{#1}$}% \newcommand\make@altaffiltext[2]{% \iflong@abstract% \footnotetext@ass[#1]{#2} \else \footnotetext@ass[#1]{\hsize\columnwidth #2} \fi }% %% \def\thanks#1{\footnotemark \protected@xdef\@thanks{\@thanks \iflong@abstract% \protect\footnotetext[\the\c@footnote]{\hsize18pc #1} \else \protect\footnotetext[\the\c@footnote]{#1} \fi }% } %% \def\footnotetext@ass{% \@ifnextchar [\@xfootnotenext@ass {%\protected@xdef\@thefnmark{\thempfn}% \@footnotetext}} \def\@xfootnotenext@ass[#1]{% \begingroup %\csname c@\@mpfn\endcsname #1\relax \unrestored@protected@xdef\@thefnmark{#1}% \endgroup \@footnotetext} \def\make@and{\vspace*{-0.5ex}{\topsep\z@\center and\endcenter}}% \newcommand\keywords{\appdef\keyword@list}% \let\keyword@list\undefined \def\@keywords#1{% \vspace*{-.7ex}% \@ifnum{\col@number=\tw@}{% \noindent {{\itshape\@keywordtext:\/}\space\@kwds{#1}}% }{% {% \quote {\itshape\@keywordtext:\/}\space \@kwds{#1}% \endquote }% }% }% \def\@keywords@pptt#1{% \vspace*{-.7ex}% \@ifnum{\col@number=\tw@}{% \noindent {% \small {\itshape\@keywordtext:\/}\space \@kwds{#1}% }% }{% {% \quote \small {\itshape\@keywordtext:\/}\space \@kwds{#1}% \endquote }% }% }% \def\@keywords@mss#1{%%% \vspace*{-.7ex}% \@ifnum{\col@number=\tw@}{% \noindent {% \small {\itshape\@keywordtext:\/}\space \@kwds{#1}% }% }{% {% \quote \small {\itshape\@keywordtext:\/}\space \@kwds{#1}% \endquote% \clearpage }% }% }% \let\subjectheadings=\keywords \def\@keywordtext{Subject headings}% \def\@keyworddelim{---}% \def\@kwds@jnl#1{% \def\@kwddlm{}% \@for\@kwd:=#1\do{% \@kwddlm \def\@kwddlm{\space\@keyworddelim\penalty\@m\space}% {\@kwd}% }% }% \def\@kwds#1{#1\relax}% \AtBeginDocument{% \everypar{% \everypar@hook }% }% \def\everypar@hook{% \setbox\z@\lastbox\par\removelastskip \everypar{}% \maketitle@trigger \leavevmode }% \def\@startsection@hook{\maketitle@trigger}% \prepdef\tableofcontents{\listof@hook}% \prepdef\listoffigures{\listof@hook}% \prepdef\listoftables{\listof@hook}% \def\listof@hook{\maketitle@trigger}% \def\maketitle@disarm{% \global\let\maketitle@trigger\relax \global\let\@startsection@hook\@empty \global\let\listof@hook\@empty \global\let\everypar@hook\@empty }% \def\maketitle@trigger{% \maketitle }% \setlength{\skip\footins}{4ex\@plus1ex\@minus.5ex}% \setlength\footnotesep{3ex}% \long\def\@makefntext@pptt#1{% \noindent \hbox\@to\z@{\hss$^{\@thefnmark}$}% #1% }% \newcounter{editornote}% \def\theeditornote{{\rmfamily E}\arabic{editornote}}% \newcommand\notetoeditor[1]{% \stepcounter{editornote}% \begingroup \def\protect{\noexpand\protect\noexpand}% \xdef\@thefnmark{\theeditornote}% \endgroup \@footnotemark\@footnotetext{NOTE TO EDITOR: #1}% }% \renewcommand\section{% \@startsection{section}{1}% {\z@}{5ex\@plus.5ex}{1ex\@plus.2ex}{\normalsize\bfseries}% }% \def\thesection{\@arabic{\c@section}}% \def\clear@section@page{}% \renewcommand\subsection{% \@startsection{subsection}{2}% {\z@}{5ex\@plus.5ex}{1ex\@plus.2ex}{\normalsize\bfseries}% }% \def\thesubsection{\thesection.\@arabic{\c@subsection}}% \renewcommand\subsubsection{% \@startsection{subsubsection}{3}% {\z@}{5ex\@plus.5ex}{1ex\@plus.2ex}{\normalsize\itshape}% }% \def\thesubsubsection{\thesubsection.\@arabic{\c@subsubsection}}% \newcommand\subsubsubsection{% \@startsection{subsubsection}{4}% {\z@}{-15\p@\@plus-5\p@\@minus-2\p@}{5\p@}{\normalfont\normalsize\itshape}% }% \def\thesubsubsubsection{\thesubsubsection.\@arabic{\c@subsubsubsection}}% \def\theparagraph{\thesubsubsection.\@arabic{\c@paragraph}}% \newcommand\acknowledgments{\vskip 3ex\@plus.8ex\@minus.4ex}% \let\acknowledgements=\acknowledgments \renewcommand\appendix{% \par \if@twocolumn\@restonecoltrue\onecolumn\fi \setcounter{section}{\z@}% \setcounter{subsection}{\z@}% \setcounter{equation}{\z@}% \def\thesection{\Alph{section}}% \def\theequation{% %\hbox{\normalsize\Alph{section}\arabic{equation}}% \thesection\arabic{equation}% }% \@addtoreset{equation}{section}% \appendix@figtab@defs \def\section{% \@startsection {section}{1}{\z@}% {5ex\@plus.5ex}{1ex\@plus.2ex}{\normalsize\bfseries}% }% }% \let\appendix@figtab@defs\@empty \def\appendix@figtab@defs@pptt{}% \newcounter{cureqno}% \newenvironment{mathletters}{% \refstepcounter{equation}% \setcounter{cureqno}{\value{equation}}% \let\@curtheeqn\theequation \edef\@tempa{\theequation}% \expandafter\def \expandafter\theequation \expandafter{\@tempa\alph{equation}}% \setcounter{equation}{0}% }{% \let\theequation\@curtheeqn \setcounter{equation}{\value{cureqno}}% }% \newcommand\eqnum[1]{% \def\theequation{#1}% \let\@currentlabel\theequation \addtocounter{equation}{\m@ne}% }% \renewenvironment{thebibliography}[1]{% \clear@thebibliography@page \subsection*{REFERENCES}% \thebib@list \def\refpar{\relax}% \def\newblock{\hskip .11em\@plus.33em\@minus.07em}% \sloppy \clubpenalty4000 \widowpenalty4000 \sfcode`\.=1000\relax }{% \endlist \revtex@pageid }% \def\clear@thebibliography@page{}% \def\thebib@list{% \list{\null}{% \leftmargin 3em\labelwidth\z@\labelsep\z@\itemindent-\leftmargin \usecounter{enumi}% }% }% \def\thebib@list@pptt{% \list{\null}{% \leftmargin 1.2em\labelwidth\z@\labelsep\z@\itemindent-\leftmargin \usecounter{enumi}% }% }% \newenvironment{references}{% \clear@thebibliography@page \subsection*{REFERENCES}% \bgroup \setlength\parindent\z@ \setlength\parskip\itemsep \let\refpar\references@refpar }{% \refpar \egroup \revtex@pageid }% \def\references@refpar@mss{% \par\setlength\hangindent{3em}\hangafter\@ne }% \def\references@refpar@pptt{% \par\setlength\hangindent{1.2em}\hangafter\@ne }% \let\references@refpar\references@refpar@mss \newcommand\reference{% \@ifnextchar\bgroup{\@reference}{% \@latexerr{Missing key on reference command}{% Each reference command should have a key corresponding to a \protect\markcite\space somewhere in the text }% }% }% \def\@reference#1{\relax\refpar}% \newcommand\markcite[1]{\remove@command\markcite}% \def\@biblabel#1{\relax}% \def\@cite#1#2{#1\if@tempswa , #2\fi}% \setcounter{topnumber}{7}% \newskip\tnotemarkskip \tnotemarkskip7pt \newdimen\@abovenoteskip% \newcommand\tablenotemark[1]{% \rlap{$^{\mathrm #1}$}\hskip\tnotemarkskip\ignorespaces% Fixed: the space after notemark }% \def\@tablenotetext#1#2{% \vspace{.5ex}% {\noindent\llap{$^{#1}$}#2\par}% }% \def\@tablenotes#1{% \par \vspace{4.5ex}\footnoterule\vspace{.5ex}% {\footnotesize#1}% }% \def\@tablenotes@pptt#1{% \par \vspace{3.2ex}\footnoterule\vspace{.4ex}% {\footnotesize#1}% }% \AtBeginDocument{% \let\tblnote@list\@empty }% \newcommand\tablenotetext[2]{\ifdim\@abovenoteskip=0pt\global\@abovenoteskip=20pt\fi% \appgdef\tblnote@list{\hsize\pt@width\leftskip\z@\rightskip\z@% \@tablenotetext{#1}{\parfillskip\z@ plus1fil#2\endgraf}}% }% \def\spew@tblnotes{% \@ifx@empty\tblnote@list{}{% \@tablenotes{\tblnote@list}% \global\let\tblnote@list\@empty }% }% \prepdef\endtable{\spew@tblnotes}% \expandafter\prepdef\csname endtable*\endcsname{\spew@tblnotes}% \let\tableline=\hline \long\def\@makecaption#1#2{\vskip 2ex\noindent#1\ #2\par}% \newcommand\tablenum[1]{% \def\thetable{#1}% \let\@currentlabel\thetable \addtocounter{table}{\m@ne}% }% \newcommand\figurenum[1]{% \def\thefigure{#1}% \let\@currentlabel\thefigure \addtocounter{figure}{\m@ne}% }% \newcommand\placetable{\@place@float{TABLE}}% \newcommand\placefigure{\@place@float{FIGURE}}% \newcommand\placeplate{\@place@float{PLATE}}% \def\@place@float#1#2{% \vspace{0.5ex}% \begin{center}EDITOR: PLACE #1 \ref{#2} HERE.\end{center}% \vspace{0.5ex}% }% \newcommand\figcaption{\@testopt{\@xfigcaption}{}}% \def\@xfigcaption[#1]#2{{\def\@captype{figure}\caption{#2}}}% \newcommand\dummytable{\refstepcounter{table}}% \newbox\pt@box \newdimen\pt@width \newcount\pt@line \newcount\pt@column \newcount\pt@nlines \newcount\pt@ncol \newcount\pt@page \newcommand\colhead[1]{\multicolumn{1}{c}{#1}\pt@addcol}% \gdef\pt@footnotesize{\string\footnotesize} \gdef\pt@scriptsize{\string\scriptsize} \newcommand\tabletypesize[1]{\def\pt@typesize{#1}\gdef\@typesize{\string#1} \ifx\@typesize\pt@footnotesize \def\pt@headfrac{\pt@headfrac@ass@footnotesize} \else \ifx\@typesize\pt@scriptsize \def\pt@headfrac{\pt@headfrac@ass@scriptsize} \else \def\pt@headfrac{\pt@headfrac@ass@normalsize}% \fi \fi }% Access to different type sizes in deluxetable \def\pt@typesize{}% \newcommand\tablecolumns[1]{% \pt@column=#1\relax% \pt@ncol=#1\relax% \global\let\pt@addcol\@empty% }% \newcommand\tablecaption[1]{\gdef\pt@caption{\tnotemarkskip8pt#1}}%Fixed: space after mark \newcommand\tablehead[1]{% \gdef\pt@head{% \hline\hline \relax\\[-1.7ex]% #1\hskip\tabcolsep \\[.7ex]% \hline \relax\\[-1.5ex]% }% }% \def\tablehead@aj#1{% \gdef\pt@head{% #1\hskip\tabcolsep \\[.7ex]% \hline \relax\\[-1.5ex]% }% }% \newif\if@pt@rot \newcommand\rotate{\@pt@rottrue}% \newcommand\tabletail[1]{\gdef\pt@tail{#1}}% \newcommand\tablewidth[1]{\pt@width=#1\relax}% \newcommand\tableheadfrac[1]{\gdef\pt@headfrac{#1}}% \AtBeginDocument{% \let\pt@caption\@empty% \let\pt@head\@empty% \let\pt@tail\@empty% \pt@width\textwidth% %\def\pt@headfrac{.1}% \def\pt@headfrac{\pt@headfrac@ass@normalsize}% initialize typesize to consider }% % while calculating rows \newdimen\tabbaseskip% \def\pt@calcnlines{% \begingroup% \if@pt@rot\textheight\textwidth\fi% rotate tables \pt@typesize% Type sizes in deluxetable \@tempdima\pt@headfrac\textheight \@tempdimb\textheight\advance\@tempdimb by -\@tempdima \@tempdima\arraystretch\baselineskip \global\tabbaseskip\baselineskip \divide\@tempdimb by\@tempdima \global\pt@nlines\@tempdimb \endgroup }% \def\pt@tabacol{% \edef\@preamble{\@preamble\hskip\tabcolsep\tabskip\fill}% }% \newdimen\pt@tmpcapwidth \long\def\@makecaption@plano#1#2{% \@ifdim{\pt@width>\z@}{% \pt@tmpcapwidth\pt@width }{% \pt@tmpcapwidth\hsize }% \parbox{\pt@tmpcapwidth}{% \@make@caption@text{#1}{#2}% }% \endcenter }% \def\fnum@ptable{Table \thetable}% \def\fnum@ptablecont{Table \thetable---{\rmfamily Continued}}% \long\def\@make@caption@text#1#2{% \center\rmfamily#1.\quad#2\endcenter }% \long\def\@makecaption@plano@cont#1#2{% \center\rmfamily#1\endcenter \vskip 2.5ex }% \def\fnum@ptablecont@pptt{Table \thetable---{\itshape Continued}}% \long\def\@make@caption@text@pptt#1#2{% \center\sc#1\\[.5ex]#2\endcenter }% \long\def\@makecaption@plano@cont@pptt#1#2{% \center\sc#1\endcenter \vskip 1.5ex }% \def\fnum@ptable@apj{TABLE \thetable}% \def\fnum@ptablecont@apj{TABLE \thetable---{\itshape Continued}}% \long\def\@make@caption@text@apj#1#2{% \center\Large\sc#1\\[.5ex]#2\endcenter }% \long\def\@makecaption@plano@cont@apj#1#2{% \center\Large\sc#1\endcenter \vskip 1.5ex }% \def\fnum@ptable@aj{Table \thetable.}% \def\fnum@ptablecont@aj{Table \thetable.\space{\rmfamily (continued)}}% \long\def\@make@caption@text@aj#1#2{% \center\large{\sc#1}\space#2\endcenter }% \long\def\@makecaption@plano@cont@aj#1#2{% \center\large{\sc#1}\endcenter \vskip 1.5ex }% \newbox\spew@tblnotesbox% \newbox\restof@tblnotesbox% \global\newdimen\noteheight% \global\newdimen\pt@tabnoteminht% \global\pt@tabnoteminht=0pt% \newenvironment{deluxetable}[1]{% \maketitle \def\pt@format{\string#1}% \let\@acol\pt@tabacol% \let\@tabularcr\@ptabularcr% \let\@tablenotetext\@ptablenotetext% \let\@tablenotes\@ptablenotes% \global\pt@ncol\z@% \global\pt@column\z@% \global\pt@page\@ne% \gdef\pt@addcol{\global\advance\pt@ncol\@ne}% \if@pt@rot\leavevmode\fi% }{% \ifx\@typesize\pt@footnotesize%%% considering the table typesize in calculation \global\pt@tabnoteminht=\pt@notemin@footnote\baselineskip \else \ifx\@typesize\pt@scriptsize \global\pt@tabnoteminht=\pt@notemin@script\baselineskip \else \global\pt@tabnoteminht=\pt@notemin@normal\baselineskip \fi \fi \global\advance\pt@tabnoteminht-\@abovenoteskip \global\@abovenoteskip=0pt \setbox\spew@tblnotesbox\vbox{\spew@tblnotes}% %% %% Finding the available space for tablenotes in current page \if@pt@rot\global\noteheight\textwidth\else\global\noteheight\textheight\fi% \global\advance\noteheight-\ht\captionbox% \global\advance\noteheight-\pt@line\tabbaseskip \if@twocolumn %% tables in preprint2 style \ifx\@typesize\pt@scriptsize \global\advance\noteheight-6\baselineskip% \else \global\advance\noteheight-3\baselineskip% \fi \else \global\advance\noteheight-2\baselineskip% \fi %% %% Print the maximum lines of notes below table within current page \ifdim\noteheight<\pt@tabnoteminht% if not possible to keep the minimum two lines \global\noteheight=0pt% % the whole notes move to next page. \global\setbox\restof@tblnotesbox\vbox{\unvbox\spew@tblnotesbox}% \else %% %% if possible two lines, check the length of notes \ifdim\ht\spew@tblnotesbox>\noteheight% % Print maximum notes in available space and store the remaining part \hbox\@to\hsize{\hfil\vsplit\spew@tblnotesbox to \noteheight\hfil}% \global\setbox\restof@tblnotesbox\vbox{\unvbox\spew@tblnotesbox}% \else% % Print the whole notes in current page. \vbox{\hbox\@to\hsize{\hfil\box\spew@tblnotesbox\hfil}}% \fi \fi \typeout@deluxetable% \endcenter% \end@plano@float% %% %% Print the remaining tablenotes into new page(s). %% print each page until the remaining box empty. \loop\ifvoid\restof@tblnotesbox\else% \clearpage %% %% check whether the remaining box is a full/partial page \ifdim\ht\restof@tblnotesbox<\if@pt@rot\hsize\else\vsize\fi% \vbox to \textheight{\if@pt@rot\vfill\fi\hbox to \textwidth{\if@pt@rot\else\hfil\fi \if@pt@rot\rotatebox{90}{\box\restof@tblnotesbox} \else{\box\restof@tblnotesbox}\fi\hfil}\vfill}% \else% \vbox to \textheight{\vfill\hbox to \textwidth{\hfil% \if@pt@rot\rotatebox{90}{\vsplit\restof@tblnotesbox to \textwidth} \else{\vsplit\restof@tblnotesbox to \textheight}\fi\hfil}\vfill}% \fi% \clearpage \repeat% \addtocounter{table}{\m@ne}% \tabletypesize{\normalsize}% }% \let@environment{planotable}{deluxetable}% \def\@plano@float{% Invoked by \startdata \begingroup% \if@pt@rot\columnwidth\textheight\fi% Deluxetable table \rotate \@plano@float@{table}% }% \def\end@plano@float{% S/B invoked by \enddata; instead by \enddeluxetable \end@plano@float@\endgroup}% \def\@plano@float@{\@float}% Extra layer of abstraction for float processing \def\end@plano@float@{% \end@float}% \newdimen\firsttabskip \firsttabskip-\hsize \appdef\@endfloatbox{% \if@pt@rot \global\setbox\@currbox\vbox{\hskip\firsttabskip\global\firsttabskip\z@% \rotatebox{90}{\box\@currbox}}\else\global\firsttabskip\z@\fi% }% \def\typeout@deluxetable@mss{% \typeout{% Table \thetable\space has been set to width \the\pt@width }% }% \def\typeout@deluxetable@ppt{% \typeout{% Page \the\pt@page \space of table \thetable\space has been set to width \the\pt@width\space with \the\pt@nlines\space lines per page }% }% \let\typeout@deluxetable\typeout@deluxetable@mss \newcommand\startdata{% \pt@calcnlines \@ifdim{\pt@width>\z@}{% \def\@halignto{\@to\pt@width}% }{% \def\@halignto{}% }% \let\fnum@table=\fnum@ptable \let\@makecaption\@makecaption@plano \global\pt@line\z@ \start@pt@tabular \after@startdata }% \global\newbox\captionbox \def\start@pt@tabular{\par% \@plano@float \center \expandafter\caption\expandafter{\pt@caption}% \global\setbox\captionbox\vbox{\expandafter\caption\expandafter{\pt@caption}} \pt@typesize% Type sizes in deluxetable \expandafter\@tabular\expandafter{\pt@format}% }% \def\set@pt@box#1{\setbox\pt@box}% \def\after@startdata{\pt@head}% \def\after@startdata@aj{\hline\hline\relax\\[-1.7ex]\pt@head}% \def\enddata{% \crcr \noalign{\vskip .7ex}% \before@enddata% \endtabular% \setbox\pt@box\lastbox% \pt@width\wd\pt@box \hbox \@to \hsize{\hfil\box\pt@box\hfil}% \ignorespaces}% \def\before@enddata{\hline}% \def\before@enddata@aj{\hline\hline}% \def\nl{\substitute@command\nl\\}% \def\nextline{\substitute@command\nextline\\}% \def\@ptabularcr{% {\ifnum0=`}\fi% A klootch just in case the next token is & or \cr \@ifstar{% \@testopt{\@argptabularcr\@empty}\z@skip% }{% \@testopt{\@argptabularcr\ptable@@split}\z@skip% }% }% \newdimen\pt@reduceline \newdimen\extra@vspace \extra@vspace\z@ \def\@xargptarraycr#1{\@tempdima #1% \global\advance\extra@vspace\@tempdima% % adding the optional spaces \advance\@tempdima\dp \@arstrutbox% \vrule \@height\z@ \@depth\@tempdima \@width\z@% \global\pt@reduceline\arraystretch\tabbaseskip% total lines to be reduced \@whilenum\pt@reduceline<\extra@vspace% % reducing the excess lines% \do{% \global\advance\extra@vspace-\pt@reduceline% \global\advance\pt@line1 }% \cr}% \def\@argptabularcr#1[#2]{% \ifnum0=`{\fi}% To undo the effect of the klootch. \@ifdim{#2>\z@}{% \unskip\@xargptarraycr{#2}% }{% \@yargarraycr{#2}% }\ptable@split#1% }% \def\ptable@split#1{% \noalign{% \global\advance\pt@line\@ne% \@ifnum{\pt@line<\pt@nlines}{}{% \aftergroup#1% }% }% }% \def\ptable@@split{% \before@suspendpt% \endtabular% \setbox\pt@box\lastbox% \pt@width\wd\pt@box\box\pt@box% \typeout@pt@nl% \global\advance\pt@page\@ne% \endcenter% \end@plano@float% \clearpage \global\extra@vspace\z@ \addtocounter{table}{-2}% \let\fnum@table=\fnum@ptablecont% \let\@makecaption\@makecaption@plano@cont% \global\pt@ncol=\pt@column% Either 0 or value of \tablecolumns \global\pt@line\z@% \start@pt@tabular% \before@resumept% \pt@head% }% \def\before@suspendpt{}% \def\before@resumept{}% \def\before@suspendpt@aj{\@tabularcr\noalign{\vskip .7ex}\hline}% \def\before@resumept@aj{\hline\relax\@tabularcr[-1.7ex]}% \def\typeout@pt@nl@ppt{% \typeout{% Page \the\pt@page\space of table \thetable\space has been set to width \the\pt@width }% }% \def\typeout@pt@nl@mss{% \typeout{% Page \the\pt@page\space of table \thetable\space has been set to width \the\pt@width\space with \the\pt@nlines\space lines per page }% }% \def\typeout@pt@nl@aj{% \typeout{% Table \thetable\space has been set to width \the\pt@width\space with \the\pt@nlines\space lines per page }% }% \let\typeout@pt@nl\typeout@pt@nl@mss \newcommand\tablevspace[1]{\substitute@command\tablevspace\\[#1]}% \newcommand\tablebreak{\cr\ptable@@split}%\\{\cr\ptable@@split}% \newcommand\cutinhead[1]{% \noalign{\vskip 1.5ex}% \hline \@ptabularcr \noalign{\vskip -4ex}% \multicolumn{\pt@ncol}{c}{#1}% \@ptabularcr \noalign{\vskip .8ex}% \hline \@ptabularcr \noalign{\vskip -2ex}% }% \def\cutinhead@ppt#1{% \noalign{\vskip 1.5ex}% \hline \@ptabularcr \noalign{\vskip -2ex}% Style Note: in apj, it is -1.5ex \multicolumn{\pt@ncol}{c}{#1}% \@ptabularcr \noalign{\vskip .8ex}% \hline \@ptabularcr \noalign{\vskip -2ex}% }% \newcommand\sidehead[1]{% \noalign{\vskip 1.5ex}% \multicolumn{\pt@ncol}{@{\hskip\z@}l}{#1}% \@ptabularcr \noalign{\vskip .5ex}% }% \def\@ptablenotetext#1#2{% \vspace{0ex}% Style Note: in ppt, it is gone {%\parbox{\pt@width}% {\hskip1em$^{\mathrm#1}$#2}\par}% }% \def\@ptablenotes#1{% \par \vspace{2ex}% {\setlength\parskip{1.5ex}#1}% }% \def\@ptablenotes@apj#1{% \par \vspace{2ex}% {#1}% }% \newcommand\tablerefs[1]{\ifdim\@abovenoteskip=0pt\global\@abovenoteskip=10pt\fi \appgdef\tblnote@list{\hsize\pt@width\leftskip\z@\rightskip\z@% \@tableref{\parfillskip\z@ plus1fil#1\endgraf}}}% \def\@tableref#1{% \par \vspace*{3ex}% {%\parbox{\pt@width} %%%% {\hskip1em\rmfamily References. --- #1}\par}% }% \newcommand\tablecomments[1]{\ifdim\@abovenoteskip=0pt\global\@abovenoteskip=10pt\fi \appgdef\tblnote@list{\hsize\pt@width\leftskip\z@\rightskip\z@% \@tablecom{\parfillskip\z@ plus1fil#1\endgraf}}}% \def\@tablecom#1{% \par \vspace*{3ex}% {%\parbox{\pt@width} %%% {\hskip1em\rmfamily Note. --- #1}\par}% }% \def\@tableref@pptt#1{% \par \vspace*{3ex}% {%\parbox{\pt@width} %%% {\hskip1em{\sc References.---}#1}\par}% }% \def\@tablecom@pptt#1{% \vspace*{3ex}{% %\parbox{\pt@width} %%% {\hskip1em{\sc Note.---}#1}\par}% }% \newcounter{plate}% \def\ftype@plate{4}% \def\ext@plate{lof}% \newenvironment{plate}{\@float{plate}}{\end@float}% \newenvironment{plate*}{\@dblfloat{plate}}{\end@dblfloat}% \let\platewidth=\tablewidth \newcommand\platenum[1]{% \def\theplate{#1}% \let\@currentlabel\theplate \addtocounter{plate}{\m@ne}% }% \def\thefigure{\@arabic\c@figure}% \def\thetable{\@arabic\c@table}% \def\theplate{\@arabic\c@plate}% \def\fnum@figure{{\rmfamily Fig.\space\thefigure.---}}% \def\fnum@table{{\rmfamily Table \thetable:}}% \def\fnum@plate{{\bfseries Plate \theplate.}}% \def\fps@figure{bp}% \def\fps@table{bp}% \def\fps@plate{bp}% \def\eps@scaling{1.0}% \newcommand\epsscale[1]{\gdef\eps@scaling{#1}}% \newcommand\plotone[1]{% \typeout{Plotone included the file #1} \centering \leavevmode \includegraphics[width={\eps@scaling\columnwidth}]{#1}% }% \newcommand\plottwo[2]{{% \typeout{Plottwo included the files #1 #2} \centering \leavevmode \columnwidth=.45\columnwidth \includegraphics[width={\eps@scaling\columnwidth}]{#1}% \hfil \includegraphics[width={\eps@scaling\columnwidth}]{#2}% }}% \def\plotfiddle#1#2#3#4#5#6#7{{ \typeout{Plotfiddle included the file #1} \centering\leavevmode%% Re-implement from v4.0 \vbox to #2{\rule{0pt}{#2}} \hspace*{#6pt}\includegraphics[width=#4pt, height=#5pt, angle=#3, origin=c]{#1}}% } \let\jnl@style=\rmfamily \def\ref@jnl#1{{\jnl@style#1}}% \newcommand\aj{\ref@jnl{AJ}}% % Astronomical Journal \newcommand\actaa{\ref@jnl{Acta Astron.}}% % Acta Astronomica \newcommand\araa{\ref@jnl{ARA\&A}}% % Annual Review of Astron and Astrophys \newcommand\apj{\ref@jnl{ApJ}}% % Astrophysical Journal \newcommand\apjl{\ref@jnl{ApJ}}% % Astrophysical Journal, Letters \newcommand\apjs{\ref@jnl{ApJS}}% % Astrophysical Journal, Supplement \newcommand\ao{\ref@jnl{Appl.~Opt.}}% % Applied Optics \newcommand\apss{\ref@jnl{Ap\&SS}}% % Astrophysics and Space Science \newcommand\aap{\ref@jnl{A\&A}}% % Astronomy and Astrophysics \newcommand\aapr{\ref@jnl{A\&A~Rev.}}% % Astronomy and Astrophysics Reviews \newcommand\aaps{\ref@jnl{A\&AS}}% % Astronomy and Astrophysics, Supplement \newcommand\azh{\ref@jnl{AZh}}% % Astronomicheskii Zhurnal \newcommand\baas{\ref@jnl{BAAS}}% % Bulletin of the AAS \newcommand\caa{\ref@jnl{Chinese Astron. Astrophys.}}% % Chinese Astronomy and Astrophysics \newcommand\cjaa{\ref@jnl{Chinese J. Astron. Astrophys.}}% % Chinese Journal of Astronomy and Astrophysics \newcommand\icarus{\ref@jnl{Icarus}}% % Icarus \newcommand\jcap{\ref@jnl{J. Cosmology Astropart. Phys.}}% % Journal of Cosmology and Astroparticle Physics \newcommand\jrasc{\ref@jnl{JRASC}}% % Journal of the RAS of Canada \newcommand\memras{\ref@jnl{MmRAS}}% % Memoirs of the RAS \newcommand\mnras{\ref@jnl{MNRAS}}% % Monthly Notices of the RAS \newcommand\na{\ref@jnl{New A}}% % New Astronomy \newcommand\nar{\ref@jnl{New A Rev.}}% % New Astronomy Review \newcommand\pra{\ref@jnl{Phys.~Rev.~A}}% % Physical Review A: General Physics \newcommand\prb{\ref@jnl{Phys.~Rev.~B}}% % Physical Review B: Solid State \newcommand\prc{\ref@jnl{Phys.~Rev.~C}}% % Physical Review C \newcommand\prd{\ref@jnl{Phys.~Rev.~D}}% % Physical Review D \newcommand\pre{\ref@jnl{Phys.~Rev.~E}}% % Physical Review E \newcommand\prl{\ref@jnl{Phys.~Rev.~Lett.}}% % Physical Review Letters \newcommand\pasa{\ref@jnl{PASA}}% % Publications of the Astron. Soc. of Australia \newcommand\pasp{\ref@jnl{PASP}}% % Publications of the ASP \newcommand\pasj{\ref@jnl{PASJ}}% % Publications of the ASJ \newcommand\qjras{\ref@jnl{QJRAS}}% % Quarterly Journal of the RAS \newcommand\rmxaa{\ref@jnl{Rev. Mexicana Astron. Astrofis.}}% % Revista Mexicana de Astronomia y Astrofisica \newcommand\skytel{\ref@jnl{S\&T}}% % Sky and Telescope \newcommand\solphys{\ref@jnl{Sol.~Phys.}}% % Solar Physics \newcommand\sovast{\ref@jnl{Soviet~Ast.}}% % Soviet Astronomy \newcommand\ssr{\ref@jnl{Space~Sci.~Rev.}}% % Space Science Reviews \newcommand\zap{\ref@jnl{ZAp}}% % Zeitschrift fuer Astrophysik \newcommand\nat{\ref@jnl{Nature}}% % Nature \newcommand\iaucirc{\ref@jnl{IAU~Circ.}}% % IAU Cirulars \newcommand\aplett{\ref@jnl{Astrophys.~Lett.}}% % Astrophysics Letters and Communications \newcommand\apspr{\ref@jnl{Astrophys.~Space~Phys.~Res.}}% % Astrophysics Space Physics Research \newcommand\bain{\ref@jnl{Bull.~Astron.~Inst.~Netherlands}}% % Bulletin Astronomical Institute of the Netherlands \newcommand\fcp{\ref@jnl{Fund.~Cosmic~Phys.}}% % Fundamental Cosmic Physics \newcommand\gca{\ref@jnl{Geochim.~Cosmochim.~Acta}}% % Geochimica Cosmochimica Acta \newcommand\grl{\ref@jnl{Geophys.~Res.~Lett.}}% % Geophysics Research Letters \newcommand\jcp{\ref@jnl{J.~Chem.~Phys.}}% % Journal of Chemical Physics \newcommand\jgr{\ref@jnl{J.~Geophys.~Res.}}% % Journal of Geophysical Research \newcommand\jqsrt{\ref@jnl{J.~Quant.~Spec.~Radiat.~Transf.}}% % Journal of Quantitiative Spectroscopy and Radiative Trasfer \newcommand\memsai{\ref@jnl{Mem.~Soc.~Astron.~Italiana}}% % Mem. Societa Astronomica Italiana \newcommand\nphysa{\ref@jnl{Nucl.~Phys.~A}}% % Nuclear Physics A \newcommand\physrep{\ref@jnl{Phys.~Rep.}}% % Physics Reports \newcommand\physscr{\ref@jnl{Phys.~Scr}}% % Physica Scripta \newcommand\planss{\ref@jnl{Planet.~Space~Sci.}}% % Planetary Space Science \newcommand\procspie{\ref@jnl{Proc.~SPIE}}% % Proceedings of the SPIE \let\astap=\aap \let\apjlett=\apjl \let\apjsupp=\apjs \let\applopt=\ao \newcommand\phn{\phantom{0}}% \newcommand\phd{\phantom{.}}% \newcommand\phs{\phantom{$-$}}% \newcommand\phm[1]{\phantom{#1}}% \let\la=\lesssim % For Springer A&A compliance... \let\ga=\gtrsim \newcommand\sq{\mbox{\rlap{$\sqcap$}$\sqcup$}}% \newcommand\arcdeg{\mbox{$^\circ$}}% \newcommand\arcmin{\mbox{$^\prime$}}% \newcommand\arcsec{\mbox{$^{\prime\prime}$}}% \newcommand\fd{\mbox{$.\!\!^{\mathrm d}$}}% \newcommand\fh{\mbox{$.\!\!^{\mathrm h}$}}% \newcommand\fm{\mbox{$.\!\!^{\mathrm m}$}}% \newcommand\fs{\mbox{$.\!\!^{\mathrm s}$}}% \newcommand\fdg{\mbox{$.\!\!^\circ$}}% \newcommand\farcm@mss{\mbox{$.\mkern-4mu^\prime$}}% \let\farcm\farcm@mss \newcommand\farcs@mss{\mbox{$.\!\!^{\prime\prime}$}}% \let\farcs\farcs@mss \newcommand\fp{\mbox{$.\!\!^{\scriptscriptstyle\mathrm p}$}}% \newcommand\micron{\mbox{$\mu$m}}% \def\farcm@apj{% \mbox{.\kern -0.7ex\raisebox{.9ex}{\scriptsize$\prime$}}% }% \def\farcs@apj{% \mbox{% \kern 0.13ex.% \kern -0.95ex\raisebox{.9ex}{\scriptsize$\prime\prime$}% \kern -0.1ex% }% }% \newcommand\case[2]{\mbox{$\frac{#1}{#2}$}}% \newcommand\slantfrac{\case}% \newcommand\onehalf{\slantfrac{1}{2}}% \newcommand\onethird{\slantfrac{1}{3}}% \newcommand\twothirds{\slantfrac{2}{3}}% \newcommand\onequarter{\slantfrac{1}{4}}% \newcommand\threequarters{\slantfrac{3}{4}}% \newcommand\ubvr{\mbox{$U\!BV\!R$}}%% UBVR system \newcommand\ub{\mbox{$U\!-\!B$}}% % U-B \newcommand\bv{\mbox{$B\!-\!V$}}% % B-V \newcommand\vr{\mbox{$V\!-\!R$}}% % V-R \newcommand\ur{\mbox{$U\!-\!R$}}% % U-R \newcommand\ion[2]{#1$\;${\small\rmfamily\@Roman{#2}}\relax}% \newcommand\nodata{ ~$\cdots$~ }% \newcommand\diameter{\ooalign{\hfil/\hfil\crcr\mathhexbox20D}}% \newcommand\degr{\arcdeg}% \newcommand\Sun{\sun}% Sun symbol, "S" \newcommand\Sol{\sun}% \newcommand\sun{\odot}% \newcommand\Mercury{\astro{\char1}}% Mercury symbol, "1" \newcommand\Venus{\astro{\char2}}% Venus symbol, "2" \newcommand\Earth{\earth}% Earth symbol, "3" \newcommand\Terra{\earth}% \newcommand\earth{\oplus}% \newcommand\Mars{\astro{\char4}}% Mars symbol, "4" \newcommand\Jupiter{\astro{\char5}}% Jupiter symbol, "5" \newcommand\Saturn{\astro{\char6}}% Saturn symbol, "6" \newcommand\Uranus{\astro{\char7}}% Uranus symbol, "7" \newcommand\Neptune{\astro{\char8}}% Neptune symbol, "8" \newcommand\Pluto{\astro{\char9}}% Pluo symbol, "9" \newcommand\Moon{\astro{\char10}}% Moon symbol, "M" \newcommand\Luna{\Moon}% \newcommand\Aries{\astro{\char11}}% \newcommand\VEq{\Aries}% vernal equinox (Aries) \newcommand\Taurus{\astro{\char12}}% \newcommand\Gemini{\astro{\char13}}% \newcommand\Cancer{\astro{\char14}}% \newcommand\Leo{\astro{\char15}}% \newcommand\Virgo{\astro{\char16}}% \newcommand\Libra{\astro{\char17}}% \newcommand\AEq{\Libra}% autumnal equinox (Libra) \newcommand\Scorpius{\astro{\char18}}% \newcommand\Sagittarius{\astro{\char19}}% \newcommand\Capricornus{\astro{\char20}}% \newcommand\Aquarius{\astro{\char21}}% \newcommand\Pisces{\astro{\char22}}% \def\load@astro{% \dimen@=1\aas@ptsize\p@ \font\astro@font=Astrosym at\dimen@ }% \def\astro#1{\leavevmode\hbox{\astro@font#1}}% \def\astro@font{% \ClassWarning{aastex}{% Please use class option `astro', since you are using the astro font.% }% }% \newcommand\sbond{\chem@bnd{\@sbnd}}% \newcommand\dbond{\chem@bnd{\@dbnd}}% \newcommand\tbond{\chem@bnd{\@tbnd}}% \def\chem@bnd#1{% {% \kern.1em\relax \setbox\z@\hbox{M}% \dimen@ii.8em\relax \p@=.1em\relax \dimen@.5\ht\z@\dimen@i-\dimen@ \advance\dimen@1.5\p@\advance\dimen@i-1.0\p@ #1% \kern.1em\relax }% }% \def\@sbnd{% \advance\dimen@-1.5\p@\advance\dimen@i1.5\p@ \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak }% \def\@dbnd{% \advance\dimen@-0.5\p@\advance\dimen@i0.5\p@ \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak \advance\dimen@-1.5\p@\advance\dimen@i1.5\p@ \hskip-\dimen@ii \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak }% \def\@tbnd{% \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak \advance\dimen@-1.5\p@\advance\dimen@i1.5\p@ \hskip-\dimen@ii \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak \advance\dimen@-1.5\p@\advance\dimen@i1.5\p@ \hskip-\dimen@ii \vrule\@height\dimen@\@depth\dimen@i\@width\dimen@ii\nobreak }% \renewcommand\LaTeX{% \leavevmode L% \raise.42ex\hbox{% \count@=\the\fam $\fam\count@\scriptstyle\kern-.3em A$% }% \kern-.15em\TeX }% \newcommand\anchor[2]{#2}% \newcommand\url{\@dblarg\@url}% \def\@url[#1]{\anchor{#1}}% \def\@text@email#1{#1}% \def\authoremail{\substitute@command\authoremail\email}% \newcommand\objectname{\@testopt\@objectname{[}} \def\@objectname[#1]#2{#2}% \newcommand\object{\@testopt\@object{[}}% \def\@object[#1]#2{#2}% %% macro for facility \newcommand\facility{\@testopt\@facility{[}}% \def\@facility[#1]#2{#2}% %% macro for supportfrom \newcommand\supportfrom{\@testopt\@supportfrom{[}}% \def\@supportfrom[#1]#2{#2}% %% macro for dataset \newcommand\dataset{\@testopt\@dataset{[}}% \def\@dataset[#1]#2{#2}% \newcommand\sizrpt{% (\fontname\the\font): em=\the\fontdimen6\font, ex=\the\fontdimen5\font \typeout{% (\fontname\the\font): em=\the\fontdimen6\font, ex=\the\fontdimen5\font }% }% \def\aas@preprint{% \def\revtex@genre{preprint}% \let\ptlandscape\@empty \setlength\@slugcmmntwidth{.67\textwidth}% \let\@makeslugcmmnt\@makeslugcmmnt@ppt \let\@abstract\@abstract@ppt \let\make@title\make@title@ppt \let\make@affil\make@affil@ppt \let\notetoeditor\@gobble \let\clear@section@page\@empty \let\placetable\@gobble \let\placefigure\@gobble \let\placeplate\@gobble \let\typeout@deluxetable\typeout@deluxetable@ppt \let\typeout@pt@nl\typeout@pt@nl@mss \ps@plaintop \let\references@refpar\references@refpar@mss \let\cutinhead\cutinhead@ppt \def\pt@headfrac@ass@normalsize{.1} \def\pt@headfrac@ass@footnotesize{.12} \def\pt@headfrac@ass@scriptsize{.15} \def\pt@notemin@normal{4.5} \def\pt@notemin@footnote{4.5} \def\pt@notemin@script{5} }% \@namedef{aas@preprint2}{% \aas@preprint \def\revtex@genre{2-column preprint}% \ps@plain \@twocolumntrue \@rightskip=\z@\@plus4em \rightskip\@rightskip \opt@just \just@just % -SZ restored preprint2 to full justification \setlength\parindent{1.2em}% \setlength\parskip{0.5ex}% \setlength\columnsep{0.5in}% \setlength\hoffset{-0.2in}% \tolerance=600 \setlength\emergencystretch{6\p@}% \def\baselinestretch{1.0}% \def\@tightleading{1.0}% \let\singlespace\@empty \let\doublespace\@empty \let\@dates\@dates@pptt \let\@abstract\@abstract@pptt \let\make@title\make@title@pptt \let\make@author\make@author@pptt \let\make@affil\make@affil@pptt \let\@keywords\@keywords@pptt \setlength{\skip\footins}{3ex\@plus1ex\@minus.5ex}% \setlength\footnotesep{2ex}% \let\@makefntext\@makefntext@pptt \let\section\section@pptt \let\subsection\subsection@pptt \let\subsubsection\subsubsection@pptt \let\section@centering\@empty \let\appendix@figtab@defs\appendix@figtab@defs@pptt \def\clear@thebibliography@page{% \if@restonecol\if@twocolumn\else\twocolumn\fi\fi }% \let\references@refpar\references@refpar@pptt \let\thebib@list\thebib@list@pptt \let\@tablenotes\@tablenotes@pptt \let\fnum@ptablecont\fnum@ptablecont@pptt \let\@make@caption@text\@make@caption@text@pptt \let\@makecaption@plano@cont\@makecaption@plano@cont@pptt \let\cutinhead\cutinhead@ppt \def\@plano@float@{\@dblfloat}% Extra layer of abstraction for float processing \def\end@plano@float@{\end@dblfloat}% %\let\@tableref\@tableref@pptt \let\@tablecom\@tablecom@pptt \def\pt@headfrac@ass@normalsize{.12} 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%\global\advance\c@colht by -\ht\my@footins \fi \setbox\@outputbox \vbox{% \global\setbox\footins\vbox{\unvbox\my@footins\unvbox\footins} \boxmaxdepth \@maxdepth \unvbox\@cclv \if@printfoot \vskip\skip\footins \color@begingroup \normalcolor \footnoterule \unvbox \footins \color@endgroup \else \global\@printfoottrue \fi }% \fi \xdef\@freelist{\@freelist\@midlist}% \global \let \@midlist \@empty \@combinefloats \ifvbox\@kludgeins \@makespecialcolbox \else \setbox\@outputbox \vbox to \@colht{% \@texttop \dimen@ \dp\@outputbox \unvbox \@outputbox \ifbotragg% \vskip -\dimen@ \@textbottom \fi }% \fi \global \maxdepth \@maxdepth } %% %% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% macro to switch from one-column long abstract to two-column normal text \iflong@abstract \newtoks\juo@sar \juo@sar={} \def\@ca#1#2#3#4{} \@ifundefined{stripsep}{\newskip\stripsep\stripsep 15pt}{} \newskip\m@addvipersep \m@addvipersep\z@ \newskip\c@addvipersep \c@addvipersep\z@ \newdimen\ht@strip \newdimen\right@cor \newdimen\cor@height \cor@height=0pt \newdimen\juo@pr \newdimen\juo@ht \newbox\@viper \newcount\juo@sk \newdimen\c@colht \newbox\my@outputbox %% macro for adding strip \def\add@strip#1#2#3#4{\begingroup% \xdef\ex@{\global\noexpand\juo@sar{\the\juo@sar\noexpand\@ca{#1}{#2}{#3}{#4}}}\ex@% \endgroup} %% macro for remove strip \def\remove@strip#1{\ifx#1\@empty\global\juo@sk=0\else \global\advance\juo@sk by-1\expandafter\next@item\the #1\@@#1\fi} \def\next@item \@ca#1#2#3#4#5\@@#6{\global #6={#5}\global\juo@pr=#1\global\juo@ht=#2% \global\cor@height=#3\global\m@addvipersep=#4} %% macro for strip command \def\strip{\@ifnextchar[{\@strip}{\@strip[0pt/0pt]}} \def\@strip[#1/#2]{\global\@tempdima=#1\global\@tempdimb=#2% \global \setbox\@viper\vbox\bgroup% \hsize\textwidth \@parboxrestore \col@number \@ne \vrule height\topskip width\z@ depth\z@} \def\endstrip{% \egroup \if@firstcolumn \ifdim\pagetotal>\z@ \global\ht@strip\pagegoal \global\advance\ht@strip by-\pagetotal \global\advance\ht@strip 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\ifvoid\my@outputbox% \global\botraggtrue \fi% \global \@printfoottrue \global\c@addvipersep\m@addvipersep% \else% \fi% \ifnum\juo@sk>0% \else% \global\@colht\c@colht% \fi% \else% \global \@firstcolumntrue% \global\setbox\@outputbox\vbox to \ht\@leftcolumn{\unvbox\@outputbox}% \setbox\@outputbox \vbox{\hb@xt@\textwidth {% \hb@xt@\columnwidth {\box\@leftcolumn \hss}% \hfil \vrule \@width\columnseprule \hfil \hb@xt@\columnwidth{\box\@outputbox \hss}}% \vss}% \@combinedblfloats \@outputpage \begingroup \@dblfloatplacement \@startdblcolumn \@whilesw\if@fcolmade \fi {\@outputpage\@startdblcolumn}% \endgroup \global\juo@sk=0\global\juo@sar={}% \global\cor@height\z@\global\c@addvipersep\z@ \fi \fi } \fi \endinput %% %% End of file `aastex.cls'. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/document/ms.tex0000644000175100001710000012110400000000000015110 0ustar00runnerdocker\documentclass[12pt,preprint]{aastex} % has to be before amssymb it seems \usepackage{color,hyperref} \definecolor{linkcolor}{rgb}{0,0,0.5} \hypersetup{colorlinks=true,linkcolor=linkcolor,citecolor=linkcolor, filecolor=linkcolor,urlcolor=linkcolor} \usepackage{url} \usepackage{algorithmic,algorithm} \usepackage{amssymb,amsmath} \newcommand{\arxiv}[1]{\href{http://arxiv.org/abs/#1}{arXiv:#1}} \usepackage{listings} \definecolor{lbcolor}{rgb}{0.9,0.9,0.9} \lstset{language=Python, basicstyle=\footnotesize\ttfamily, showspaces=false, showstringspaces=false, tabsize=2, breaklines=false, breakatwhitespace=true, identifierstyle=\ttfamily, keywordstyle=\bfseries\color[rgb]{0.133,0.545,0.133}, commentstyle=\color[rgb]{0.133,0.545,0.133}, stringstyle=\color[rgb]{0.627,0.126,0.941}, } \newcommand{\project}[1]{{\sffamily #1}} \newcommand{\Python}{\project{Python}} \newcommand{\numpy}{\project{numpy}} \newcommand{\Ubuntu}{\project{Ubuntu}} \newcommand{\github}{\project{GitHub}} \newcommand{\pip}{\project{pip}} \newcommand{\acor}{\project{acor}} \newcommand{\thisplain}{emcee} \newcommand{\this}{\project{\thisplain}} \newcommand{\paper}{document} \newcommand{\license}{MIT License} \newcommand{\foreign}[1]{\emph{#1}} \newcommand{\etal}{\foreign{et\,al.}} \newcommand{\etc}{\foreign{etc.}} \newcommand{\Fig}[1]{Figure~\ref{fig:#1}} \newcommand{\fig}[1]{\Fig{#1}} \newcommand{\figlabel}[1]{\label{fig:#1}} \newcommand{\Tab}[1]{Table~\ref{tab:#1}} \newcommand{\tab}[1]{\Tab{#1}} \newcommand{\tablabel}[1]{\label{tab:#1}} \newcommand{\Eq}[1]{Equation~(\ref{eq:#1})} \newcommand{\eq}[1]{\Eq{#1}} \newcommand{\eqlabel}[1]{\label{eq:#1}} \newcommand{\Sect}[1]{Section~\ref{sect:#1}} \newcommand{\sect}[1]{\Sect{#1}} \newcommand{\App}[1]{Appendix~\ref{sect:#1}} \newcommand{\app}[1]{\App{#1}} \newcommand{\sectlabel}[1]{\label{sect:#1}} \newcommand{\Algo}[1]{Algorithm~\ref{algo:#1}} \newcommand{\algo}[1]{\Algo{#1}} \newcommand{\algolabel}[1]{\label{algo:#1}} % math symbols \newcommand{\dd}{\mathrm{d}} \newcommand{\like}{\mathscr{L}} \newcommand{\bvec}[1]{\boldsymbol{#1}} \newcommand{\paramvector}[1]{\bvec{#1}} \newcommand{\normal}[2]{\mathcal{N} (#1, #2)} \newcommand{\ensemble}{S} \newcommand{\colorens}[1]{\ensemble^{(#1)}} \newcommand{\red}{\colorens{0}} \newcommand{\blue}{\colorens{1}} \renewcommand{\vector}[1]{#1} \renewcommand{\matrix}[1]{#1} \newcommand{\pr}[1]{\ensuremath{p(#1)}} \newcommand{\af}{\ensuremath{a_f}} \newcommand{\expect}[1]{\left<#1\right>} % model parameters \newcommand{\model}{\ensuremath{\vector{\Theta}}} \newcommand{\data}{\ensuremath{\vector{D}}} \newcommand{\nuisance}{\ensuremath{\vector{\alpha}}} \newcommand{\link}{\ensuremath{X}} % units \newcommand{\unit}[1]{\mathrm{#1}} % Citation alias \defcitealias{Goodman:2010}{GW10} \begin{document} \title{\this: The MCMC Hammer} \newcommand{\nyu}{2} \newcommand{\mpia}{3} \newcommand{\cmu}{4} \newcommand{\princeton}{5} \newcommand{\courant}{6} \author{Daniel~Foreman-Mackey\altaffilmark{1,\nyu}, David~W.~Hogg\altaffilmark{\nyu,\mpia}, Dustin~Lang\altaffilmark{\cmu,\princeton}, Jonathan~Goodman\altaffilmark{\courant}} \altaffiltext{1}{To whom correspondence should be addressed: \url{danfm@nyu.edu}} \altaffiltext{\nyu}{Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY, 10003, USA} \altaffiltext{\mpia}{Max-Planck-Institut f\"ur Astronomie, K\"onigstuhl 17, D-69117 Heidelberg, Germany} \altaffiltext{\cmu}{McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213} \altaffiltext{\princeton}{Princeton University Observatory, Princeton, NJ, 08544, USA} \altaffiltext{\courant}{Courant Institute, New York University, 251 Mercer St., New York, NY 10012, United States} \begin{abstract} We introduce a stable, well tested Python implementation of the affine-% invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman \& Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind \this\ has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\sim N^2$ for a traditional algorithm in an $N$-dimensional parameter space. In this \paper, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, \this\ permits \emph{any} user to take advantage of multiple CPU cores without extra effort. The code is available online at \url{http://dan.iel.fm/\thisplain} under the \license. \end{abstract} \keywords{ methods: data analysis --- methods: numerical --- methods: statistical } ~\clearpage \noindent \emph{Note: If you want to get started immediately with the \this\ package, start at \app{install} on page~\pageref{sect:install} or visit the online documentation at \url{http://dan.iel.fm/emcee}. If you are sampling with \this\ and having low-acceptance-rate or other issues, there is some advice in \sect{advice} starting on page~\pageref{sect:advice}.} \section{Introduction} Probabilistic data analysis---including Bayesian inference---has transformed scientific research in the past decade. Many of the most significant gains have come from numerical methods for approximate inference, especially Markov chain Monte Carlo (MCMC). For example, many problems in cosmology and astrophysics\footnote{The methods and discussion in this \paper\ have general applicability, but we will mostly present examples from astrophysics and cosmology, the fields in which we have most experience} have directly benefited from MCMC because the models are often expensive to compute, there are many free parameters, and the observations are usually low in signal-to-noise. Probabilistic data analysis procedures involve computing and using either the posterior probability density function (PDF) for the parameters of the model or the likelihood function. In some cases it is sufficient to find the maximum of one of these, but it is often necessary to understand the posterior PDF in detail. MCMC methods are designed to sample from---and thereby provide sampling approximations to---the posterior PDF efficiently even in parameter spaces with large numbers of dimensions. This has proven useful in too many research applications to list here but the results from the NASA Wilkinson Microwave Anisotropy Probe (WMAP) cosmology mission provide a dramatic example \citep[for example,][]{Dunkley:2005}. Arguably the most important advantage of Bayesian data analysis is that it is possible to \emph{marginalize} over nuisance parameters. A nuisance parameter is one that is required in order to model the process that generates the data, but is otherwise of little interest. Marginalization is the process of integrating over all possible values of the parameter and hence propagating the effects of uncertainty about its value into the final result. Often we wish to marginalize over all nuisance parameters in a model. The exact result of marginalization is the marginalized probability function \pr{\model | \data} of the set (list or vector) of model parameters \model\ given the set of observations \data \begin{equation} \eqlabel{marginalization} \pr{\model | \data} = \int \pr{ \model, \nuisance | \data} \, \dd \nuisance \quad, \end{equation} where \nuisance\ is the set (list or vector) of nuisance parameters. Because the nuisance parameter set \nuisance\ can be very large, this integral is often extremely daunting. However, a MCMC-generated sampling of values $(\model_t,\nuisance_t)$ of the model and nuisance parameters from the joint distribution $\pr{\model, \nuisance | \data}$ automatically provides a sampling of values $\model_t$ from the marginalized PDF $\pr{\model | \data}$. In addition to the problem of marginalization, in many problems of interest the likelihood or the prior is the result of an expensive simulation or computation. In this regime, MCMC sampling is very valuable, but it is even \emph{more} valuable if the MCMC algorithm is efficient, in the sense that it does not require many function evaluations to generate a statistically independent sample from the posterior PDF. The methods presented here are designed for efficiency. Most uses of MCMC in the astrophysics literature are based on slight modifications to the Metropolis-Hastings (M--H) method (introduced below in \sect{algo}). Each step in a M--H chain is proposed using a compact proposal distribution centered on the current position of the chain (normally a multivariate Gaussian or something similar). Since each term in the covariance matrix of this proposal distribution is an unspecified parameter, this method has $N\,[N+1]/2$ tuning parameters (where $N$ is the dimension of the parameter space). To make matters worse, the performance of this sampler is very sensitive to these tuning parameters and there is no fool-proof method for choosing the values correctly. As a result, many heuristic methods have been developed to attempt to determine the optimal parameters in a data-driven way \citep[for example,][]{Gregory:2005,Dunkley:2005,Widrow:2008}. Unfortunately, these methods all require a lengthy ``burn-in'' phase where shorter Markov chains are sampled and the results are used to tune the hyperparameters. This extra cost is unacceptable when the likelihood calls are computationally expensive. The problem with traditional sampling methods can be visualized by looking at the simple but highly anisotropic density \begin{equation} \eqlabel{anisotropic} p(\mathbf{x}) \propto f \left (-\frac{(x_1-x_2)^2}{2\,\epsilon} - \frac{(x_1+x_2)^2}{2} \right ) \end{equation} which would be considered difficult (in the small-$\epsilon$ regime) for standard MCMC algorithms. In principle, it is possible to tune the hyperparameters of a M--H sampler to make this sampling converge quickly, but if the dimension is large and calculating the density is computationally expensive the tuning procedure becomes intractable. Also, since the number of parameters scales as $\sim N^2$, this problem gets much worse in higher dimensions. \Eq{anisotropic} can, however, be transformed into the much easier problem of sampling an isotropic density by an \emph{affine transformation} of the form \begin{equation} y_1 = \frac{x_1-x_2}{\sqrt{\epsilon}} \, , \hspace{1cm} y_2 = x_1 + x_2 \quad . \end{equation} This motivates affine invariance: an algorithm that is \emph{affine invariant} performs equally well under all linear transformations; it will therefore be insensitive to covariances among parameters. Extending earlier work by \citet{Christen:2007}, \citet[][hereafter \citetalias{Goodman:2010}]{Goodman:2010} proposed an affine invariant sampling algorithm (\sect{algo}) with only two hyperparameters to be tuned for performance. \citet{Hou:2011} were the first group to implement this algorithm in astrophysics. The implementation presented here is an independent effort that has already proved effective in several projects \citep{sanders2013,reis2013,weisz2013,cieza2013,akeret2012,huppenkothen2012, monnier2012,morton2012,crossfield2012,roskar2012,bovy2012b,brown2012, brammer2012,bussmann2012,bovy2012a,lang2012,bovy2012,olofsson2012,dorman2012}. In what follows, we summarize the algorithm from \citetalias{Goodman:2010} and the implementation decisions made in \this. We also describe the small changes that must be made to the algorithm to parallelize it. Finally, in the Appendices, we outline the installation, usage and troubleshooting of the package. \section{The Algorithm}\sectlabel{algo} A complete discussion of MCMC methods is beyond the scope of this \paper. Instead, the interested reader is directed to a classic reference like \citet{MacKay:2003} and we will summarize some key concepts below. The general goal of MCMC algorithms is to draw $M$ samples $\{ \model_i \}$ from the posterior probability density \begin{equation} \pr{\model, \nuisance | \data} = \frac{1}{Z}\,\pr{\model, \nuisance} \, \pr{\data | \model, \nuisance} \quad, \end{equation} where the prior distribution $\pr{\model, \nuisance}$ and the likelihood function $\pr{\data|\model,\nuisance}$ can be relatively easily (but not necessarily quickly) computed for any particular value of $(\model_i, \nuisance_i)$. The normalization $Z=\pr{\data}$ is independent of $\model$ and $\nuisance$ once we have chosen the form of the generative model. This means that it is possible to sample from \pr{\model, \nuisance | \data} without computing $Z$ --- unless one would like to compare the validity of two different generative models. This is important because $Z$ is generally very expensive to compute. Once the samples produced by MCMC are available, the marginalized constraints on $\model$ can be approximated by the histogram of the samples projected into the parameter subspace spanned by $\model$. In particular, this implies that the expectation value of a function of the model parameters $f(\model)$ is \begin{equation} \expect{f(\model)} = \int \pr{\model|\data} \, f(\model) \, \dd\model \,\approx\, \frac{1}{M} \sum_{i=1} ^M f(\model_i) \quad. \end{equation} Generating the samples $\model_i$ is a non-trivial process unless $\pr{\model, \nuisance, \data}$ is a very specific analytic distribution (for example, a Gaussian). MCMC is a procedure for generating a random walk in the parameter space that, over time, draws a representative set of samples from the distribution. Each point in a Markov chain $\link (t_i) = [\model_i, \nuisance_i]$ depends only on the position of the previous step $\link (t_{i-1})$. \paragraph{The Metropolis-Hastings (M--H) Algorithm} The simplest and most commonly used MCMC algorithm is the M--H method \citep[\algo{mh};][]{MacKay:2003,Gregory:2005,Press:2007,Hogg:2010}. The iterative procedure is as follows: (1) given a position $X(t)$ sample a proposal position $Y$ from the transition distribution $Q(Y; X(t))$, (2) accept this proposal with probability \begin{equation} \mathrm{min} \left( 1,\, \frac{\pr{\vector{Y} | \data}}{\pr{\vector{X}(t) | \data}} \, \frac{Q(X(t); Y)}{ Q(Y;X(t))} \right) \quad. \end{equation} The transition distribution $Q(Y; X(t))$ is an easy-to-sample probability distribution for the proposal $Y$ given a position $X(t)$. A common parameterization of $Q(Y; X(t))$ is a multivariate Gaussian distribution centered on $X(t)$ with a general covariance tensor that has been tuned for performance. It is worth emphasizing that if this step is accepted $X(t+1) = Y$; Otherwise, the new position is set to the previous one $X(t+1) = X(t)$ (in other words, the position $X(t)$ is \emph{repeated in the chain}). The M--H algorithm converges (as $t \to \infty$) to a stationary set of samples from the distribution but there are many algorithms with faster convergence and varying levels of implementation difficulty. Faster convergence is preferred because of the reduction of computational cost due to the smaller number of likelihood computations necessary to obtain the equivalent level of accuracy. The inverse convergence rate can be measured by the autocorrelation function and more specifically, the integrated autocorrelation time (see \sect{tests}). This quantity is an estimate of the number of steps needed in the chain in order to draw independent samples from the target density. A more efficient chain has a shorter autocorrelation time. \begin{algorithm} \caption{The procedure for a single Metropolis-Hastings MCMC step. \algolabel{mh}} \begin{algorithmic}[1] \STATE Draw a proposal $Y \sim Q (Y; X(t))$ \STATE $q \gets [\pr{\vector{Y}} \, Q(X(t); Y)] / [\pr{\vector{X}(t)} \, Q(Y;X(t))]$% \hspace{1cm}{\footnotesize\it // This line is generally expensive} \STATE $r \gets R \sim [0, 1]$ \IF{$r \le q$} \STATE $\vector{X}(t+1) \gets \vector{Y}$ \ELSE \STATE $\vector{X}(t+1) \gets \vector{X}(t)$ \ENDIF \end{algorithmic} \end{algorithm} \paragraph{The stretch move} \citetalias{Goodman:2010} proposed an affine-invariant ensemble sampling algorithm informally called the ``stretch move.'' This algorithm significantly outperforms standard M--H methods producing independent samples with a much shorter autocorrelation time (see \sect{acor} for a discussion of the autocorrelation time). For completeness and for clarity of notation, we summarize the algorithm here and refer the interested reader to the original paper for more details. This method involves simultaneously evolving an ensemble of $K$ \emph{walkers} $\ensemble = \{ \vector{X_k} \}$ where the proposal distribution for one walker $k$ is based on the current positions of the $K-1$ walkers in the \emph{complementary ensemble} $\ensemble_{[k]} = \{ \vector{X_j}, \, \forall j \ne k \}$. Here, ``position'' refers to a vector in the $N$-dimensional, real-valued parameter space. To update the position of a walker at position $\vector{X_k}$, a walker $X_j$ is drawn randomly from the remaining walkers $\ensemble_{[k]}$ and a new position is proposed: \begin{equation} \eqlabel{proposal} \vector{X_k} (t) \to \vector{Y} = \vector{X_j} + Z \, [\vector{X_k} (t) - \vector{X_j}] \end{equation} where $Z$ is a random variable drawn from a distribution $g(Z = z)$. It is clear that if $g$ satisfies \begin{equation} g(z^{-1}) = z \, g(z), \end{equation} the proposal of \eq{proposal} is symmetric. In this case, the chain will satisfy detailed balance if the proposal is accepted with probability \begin{equation} \eqlabel{acceptance} q = \min \left( 1,\, Z^{N-1} \, \frac{\pr{\vector{Y}}}{\pr{\vector{X_k} (t)}} \right) \quad, \end{equation} where $N$ is the dimension of the parameter space. This procedure is then repeated for each walker in the ensemble \emph{in series} following the procedure shown in \algo{goodman}. \citetalias{Goodman:2010} advocate a particular form of $g(z)$, namely \begin{equation} \eqlabel{goodmanprop} g(z) \propto \left \{ \begin{array}{ll} \displaystyle\frac{1}{\sqrt{z}} & \mathrm{if}\, z\in \left [ \displaystyle\frac{1}{a}, a \right ], \\ 0 & \mathrm{otherwise} \end{array} \right . \end{equation} where $a$ is an adjustable scale parameter that \citetalias{Goodman:2010} set to 2. \begin{algorithm} \caption{A single stretch move update step from \citetalias{Goodman:2010} \algolabel{goodman}} \begin{algorithmic}[1] \FOR{$k = 1, \ldots, K$} \STATE Draw a walker $X_j$ at random from the complementary ensemble % $\ensemble_{[k]}(t)$ \STATE $z \gets Z \sim g(z)$, \Eq{goodmanprop} \STATE $\vector{Y} \gets \vector{X_j} % + z \, [ \vector{X_k} (t) - \vector{X_j}]$ \STATE $q \gets z^{N-1} \, p(Y)/p(X_k(t))$ \label{line:hard}% \hspace{1cm}{\footnotesize\it // This line is generally expensive} \STATE $r \gets R \sim [0, 1]$ \IF{$r \le q$, \eq{acceptance}} \STATE $X_k(t+1) \gets Y$ \ELSE \STATE $X_k(t+1) \gets X_k(t)$ \ENDIF \ENDFOR \end{algorithmic} \end{algorithm} \paragraph{The parallel stretch move} It is tempting to parallelize the stretch move algorithm by simultaneously advancing each walker based on the state of the ensemble instead of evolving the walkers in series. Unfortunately, this subtly violates detailed balance. Instead, we must split the full ensemble into two subsets ($\red = \{ \vector{X_k}, \, \forall k = 1, \ldots, K/2 \}$ and $\blue = \{ \vector{X_k}, \, \forall k = K/2+1, \ldots, K \}$) and simultaneously update all the walkers in $\red$ --- using the stretch move procedure from \algo{goodman} --- based \emph{only} on the positions of the walkers in the other set ($\blue$). Then, using the new positions $\red$, we can update $\blue$. In this case, the outcome is a valid step for all of the walkers. The pseudocode for this procedure is shown in \algo{parallel}. This code is similar to \algo{goodman} but now the computationally expensive inner loop (starting at line~\ref{line:parallelloop} in \algo{parallel}) can be run in parallel. The performance of this method --- quantified by the autocorrelation time --- is comparable to the serial stretch move algorithm but the fact that one can now take advantage of generic parallelization makes it extremely powerful. \begin{algorithm} \caption{The parallel stretch move update step \algolabel{parallel}} \begin{algorithmic}[1] \FOR{$i \in \{0, 1\}$} \FOR{$k = 1, \ldots, K/2$} \label{line:parallelloop} \STATE {\footnotesize\it // This loop can now be done in parallel % for all $k$} \STATE Draw a walker $\vector{X_j}$ at random from the complementary % ensemble $\colorens{\sim i} (t)$ \STATE $\vector{X_k} \gets \colorens{i}_k$ \STATE $z \gets Z \sim g(z)$, \Eq{goodmanprop} \STATE $\vector{Y} \gets \vector{X_j} + z \, [ \vector{X_k} (t) - \vector{X_j}]$ \STATE $q \gets z^{n-1} \, p(\vector{Y})/p(\vector{X}_k(t))$ \STATE $r \gets R \sim [0, 1]$ \IF{$r \le q$, \eq{acceptance}} \STATE $\vector{X_k} (t+\frac{1}{2}) \gets \vector{Y}$ \ELSE \STATE $\vector{X_k} (t+\frac{1}{2}) \gets \vector{X_k}(t)$ \ENDIF \ENDFOR \STATE $t \gets t+\frac{1}{2}$ \ENDFOR \end{algorithmic} \end{algorithm} \section{Tests} \sectlabel{tests} Judging the convergence and performance of an algorithm is a non-trival problem and there is a huge associated literature \citep[see, for example,][for a review]{Cowles:1996}. In astrophysics, spectral methods have been used extensively \citep[for example][]{Dunkley:2005}. Below, we advocate for one such method: the autocorrelation time. The autocorrelation time is especially applicable because it is an affine invariant measure of the performance. First, however, we should take note of another extremely important measurement: the acceptance fraction \af. This is the fraction of proposed steps that are accepted. There appears to be no agreement on the optimal acceptance rate but it is clear that both extrema are unacceptable. If $\af \sim 0$, then nearly all proposed steps are rejected, so the chain will have very few independent samples and the sampling will not be representative of the target density. Conversely, if $\af \sim 1$ then nearly all steps are accepted and the chain is performing a random walk with no regard for the target density so this will also not produce representative samples. As a rule of thumb, the acceptance fraction should be between $0.2$ and $0.5$ \citep[for example,][]{Gelman:1996}. For the M--H algorithm, these effects can generally be counterbalanced by decreasing (or increasing, respectively) the eigenvalues of the proposal distribution covariance. For the stretch move, the parameter $a$ effectively controls the step size so it can be used to similar effect. In our tests, it has never been necessary to use a value of $a$ other than $2$, but we make no guarantee that this is the optimal value. \paragraph{Autocorrelation time} \sectlabel{acor} The autocorrelation time is a direct measure of the number of evaluations of the posterior PDF required to produce independent samples of the target density. \citetalias{Goodman:2010} show that the stretch-move algorithm has a significantly shorter autocorrelation time on several non-trivial densities. This means that fewer PDF computations are required---compared to a M--H sampler---to produce the same number of independent samples. The autocovariance function of a time series $\vector{X} (t)$ is \begin{equation} C_f (T) = \lim_{t \to \infty} \mathrm{cov} \left [ f\left (\vector{X}(t+T) \right ), f\left (\vector{X}(t) \right ) \right ]. \end{equation} This measures the covariances between samples at a time lag $T$. The value of $T$ where $C_f(T) \to 0$ measures the number of samples that must be taken in order to ensure independence. In particular, the relevant measure of sampler efficiency is the integrated autocorrelation time \begin{equation} \tau_f = \sum_{T=-\infty} ^{\infty} \frac{C_f(T)}{C_f(0)} = 1+2\sum_{T=1} ^{\infty} \frac{C_f(T)}{C_f(0)}. \end{equation} In practice, one can estimate $C_f (T)$ for a Markov chain of $M$ samples as \begin{equation} C_f (T) \approx \frac{1}{M-T} \sum_{m=1}^{M-T} \left [ f(X(T+m)) - \expect{f} \right ] \, \left [ f(X(m)) - \expect{f} \right ]. \end{equation} We advocate for the autocorrelation time as a measure of sampler performance for two main reasons. First, it measures a quantity that \emph{we are actually interested in} when sampling in practice. The longer the autocorrelation time, the more samples that we must generate to produce a representative sampling of the target density. Second, the autocorrelation time is affine invariant. Therefore, it is reasonable to measure the performance and diagnose the convergence of the sampler on densities with different levels of anisotropy. \this\ can optionally calculate the autocorrelation time using the Python module \project{acor}\footnote{\url{http://github.com/dfm/acor}} to estimate the autocorrelation time. This module is a direct port of the original algorithm \citepalias[described by][]{Goodman:2010} and implemented by those authors in C++.\footnote{\url{http://www.math.nyu.edu/faculty/goodman/software/acor}} \section{Discussion \& Tips}\sectlabel{advice} The goal of this project has been to make a sampler that is a useful tool for a large class of data analysis problems---a ``hammer'' if you will. If development of statistical and data-analysis understanding is the key goal, a user who is new to MCMC benefits enormously by writing her or his own Metropolis--Hastings code (\algo{mh}) from scratch before downloading \this. For typical problems, the \this\ package will perform better than any home-built M--H code (for all the reasons given above), but the intuitions developed by writing and tuning a self-built MCMC code cannot be replaced by reading this document and running this pre-built package. That said, once those intuitions are developed, it makes sense to switch to \this\ or a similarly well engineered piece of code for performance on large problems. Ensemble samplers like \this\ require some thought for initialization. One general approach is to start the walkers at a sampling of the prior or spread out over a reasonable range in parameter space. Another general approach is to start the walkers in a very tight $N$-dimensional ball in parameter space around one point that is expected to be close to the maximum probability point. The first is more objective but, in practice, we find that the latter is much more effective if there is any chance of walkers getting stuck in low probability modes of a multi-modal probability landscape. The walkers initialized in the small ball will expand out to fill the relevant parts of parameter space in just a few autocorrelation times. A third approach would be to start from a sampling of the prior, and go through a ``burn-in'' phase in which the prior is transformed continuously into the posterior by increasing the ``temperature.'' Discussion of this kind of annealing is beyond the scope of this document. It is our present view that autocorrelation time is the best indicator of MCMC performance (the shorter the better), but there are several proxies. The easiest and simplest indicator that things are going well is the acceptance fraction; it should be in the 0.2 to 0.5 range \citep[there are theorems about this for specific problems; for example][]{Gelman:1996}. In principle, if the acceptance fraction is too low, you can raise it by decreasing the $a$ parameter; and if it is too high, you can reduce it by increasing the $a$ parameter. However, in practice, we find that $a=2$ is good in essentially all situations. That means that when using \this\ \emph{if the acceptance fraction is getting very low, something is going very wrong}. Typically a low acceptance fraction means that the posterior probability is multi-modal, with the modes separated by wide, low probability ``valleys.'' In situations like these, the best idea (though expensive of human time) is to split the space into disjoint single-mode regions and sample each one independently, combining the independently sampled regions ``properly'' (also expensive, and beyond the scope of this document) at the end. In previous work, we have advocated clustering methods to remove multiple modes \citep{Hou:2011}. These work well when the different modes have \emph{very} different posterior probabilities. Another proxy for short autocorrelation time is large expected or mean squared jump distance (ESJD; \citealt{Pasarica:2010}). The higher the ESJD the better; if walkers move (in the mean) a large distance per chain step then the autocorrelation time will tend to be shorter. The ESJD is not an affine-invariant measure of performance, and it doesn't have a trivial interpretation in terms of independent samples, so we prefer the autocorrelation time in principle. In practice, however, because the ESJD is a simple expectation value it can be more robustly evaluated on short chains. With \this\ you want (in general) to \emph{run with a large number of walkers}, like hundreds. In principle, there is no reason not to go large when it comes to walker number, until you hit performance issues. Although each step takes twice as much compute time if you double the number of walkers, it also returns to you twice as many independent samples per autocorrelation time. So go large. In particular, we have found that---in almost all cases of low acceptance fraction---increasing the number of walkers improves the acceptance fraction. The one disadvantage of having large numbers of walkers is that the burn-in phase (from initial conditions to reasonable sampling) can be slow; burn-in time is a few autocorrelation times; the total run time for burn-in scales with the number of walkers. These considerations, all taken together, suggest using the smallest number of walkers for which the acceptance fraction during burn-in is good, or the number of samples you want out at the end (see below), whichever is \emph{greater}. A more ambitious project would be to increase the number of walkers after burn-in; this requires thought beyond the scope of this document; it can be accomplished by burning in a set of small ensembles and then merging them into a big ensemble for the final run. One mistake many users of MCMC methods make is to take \emph{too many} samples! If all you want your MCMC to do is produce one- or two-dimensional error bars on two or three parameters, then you only need dozens of independent samples. With ensemble sampling, you get this from a \emph{single snapshot} or single timestep, provided that you are using dozens of walkers (and we would recommend that you use hundreds in most applications). The key point is that \emph{you should run the sampler for a few (say 10) autocorrelation times.} Once you have run that long, no matter how you initialized the walkers, the set of walkers you obtain at the end should be an independent set of samples from the distribution, of which you rarely need many. Another common mistake, of course, is to run the sampler for \emph{too few} steps. You can identify that you haven't run for enough steps in a couple of ways: If you plot the parameter values in the ensemble as a function of step number, you will see large-scale variations over the full run length if you have gone less than an autocorrelation time. You will also see that if you try to measure the autocorrelation time (with, say, \acor), it will give you a time that is always a significant fraction of your run time; it is only when the correlation time is much shorter (say by a factor of 10) than your run time that you are sure to have run long enough. The danger of both of these methods---an unavoidable danger at present---is that you can have a huge dynamic range in contributions to the autocorrelation time; you might think it is 30 when in fact it is 30\,000, but you don't ``see'' the 30\,000 in a run that is only 300 steps long. There is not much you can do about this; it is generic when the posterior is multi-modal: The autocorrelation time within each mode can be short but the mode--mode migration time can be long. See above on low acceptance ratio; in general when your acceptance ratio gets low your autocorrelation time is very, very long. There are some cases where \this\ won't perform as well as some more specialized sampling techniques. In particular, when the target density is multi-modal, walkers can become ``stuck'' in different modes. When this happens, the vector between walkers is no longer a good proposal direction. In these cases, the acceptance fraction and autocorrelation time can deteriorate quickly. While this is a fairly general problem, we find that in many applications the effect isn't actually very important. That being said, there are some problems where higher-end machinery \citep[such as][Hou et al.\ forthcoming]{dnest} is necessary \citep[see, for example,][]{brewer2012,vh2013}. Another limitation to the stretch move and moves like it is that they implicitly assume that the parameters can be assembled into a vector-like object on which linear operations can be performed. This is not (trivially) true for parameters that have non-trivial constraints, like parameters that must be integer-valued or equivalent, or parameters that are subject to deterministic non-linear constraints. Sometimes these issues can be avoided by reparameterization, but in some cases, samplers like \this\ will not be useful, or might require clever or interesting improvements. The \this\ package is open-source software; please push us changes! \acknowledgments It is a pleasure to thank Eric Agol (UWash), Jo Bovy (IAS), Brendon Brewer (Auckland), Jacqueline Chen (MIT), Alex Conley (Colorado), Will Meierjurgen Farr (Northwestern), Andrew Gelman (Columbia), John Gizis (Delaware), Fengji Hou (NYU), Jennifer Piscionere (Vanderbilt), Adrian Price-Whelan (Columbia), Hans-Walter Rix (MPIA), Jeremy Sanders (Cambridge), Larry Widrow (Queen's), and Joe Zuntz (Oxford) for helpful contributions to the ideas and code presented here. This project was partially supported by the NSF (grant AST-0908357), NASA (grant NNX08AJ48G), and DOE (grant DE-FG02-88ER25053). \this\ makes use of the open-source Python \numpy\ package. \begin{thebibliography}{}\raggedright \bibitem[Akeret et al.(2012)]{akeret2012} Akeret, J., Seehars, S., Amara, A., Refregier, A., \& Csillaghy, A.\ 2012, \arxiv{1212.1721} \bibitem[Bovy et al.(2012)]{bovy2012} Bovy, J., Rix, H.-W., Liu, C., et al.\ 2012, \apj, 753, 148 \bibitem[Bovy et al.(2012)]{bovy2012a} Bovy, J., Rix, H.-W., Hogg, D.~W., et al.\ 2012, \apj, 755, 115 \bibitem[Bovy et al.(2012)]{bovy2012b} Bovy, J., Allende Prieto, C., Beers, T.~C., et al.\ 2012, \apj, 759, 131 \bibitem[Brammer et al.(2012)]{brammer2012} Brammer, G.~B., S{\'a}nchez-Janssen, R., Labb{\'e}, I., et al.\ 2012, \apjl, 758, L17 \bibitem[Brewer et al.(2011)]{dnest} Brewer B.~J., P{\'a}rtay L.~B., Cs{\'a}nyi G., 2011, Statistics and Computing, 21, 4, 649-656, \arxiv{0912.2380} \bibitem[Brewer et al.(2012)]{brewer2012} Brewer, B.~J., Foreman-Mackey, D., \& Hogg, D.~W.\ 2012, \arxiv{1211.5805} \bibitem[Brown et al.(2012)]{brown2012} Brown, J.~M., Rosenfeld, K.~A., Andrews, S.~M., Wilner, D.~J., \& van Dishoeck, E.~F.\ 2012, \apjl, 758, L30 \bibitem[Bussmann et al.(2012)]{bussmann2012} Bussmann, R.~S., Gurwell, M.~A., Fu, H., et al.\ 2012, \apj, 756, 134 \bibitem[Christen(2007)]{Christen:2007} {Christen}, J., \emph{A general purpose scale-independent MCMC algorithm}, technical report I-07-16, CIMAT, Guanajuato, 2007 \bibitem[Cieza et al.(2013)]{cieza2013} Cieza, L.~A., Olofsson, J., Harvey, P.~M., et al.\ 2013, \apj, 762, 100 \bibitem[Cowles \& Carlin(1996))]{Cowles:1996} Cowles, M.~K. \& Carlin, B.~P., 1996, Journal of the American Statistical Association, 91, 883 \bibitem[Crossfield et al.(2012)]{crossfield2012} Crossfield, I.~J.~M., Barman, T., Hansen, B.~M.~S., Tanaka, I., \& Kodama, T.\ 2012, \apj, 760, 140 \bibitem[Dorman et al.(2012)]{dorman2012} Dorman, C.~E., Guhathakurta, P., Fardal, M.~A., et al.\ 2012, \apj, 752, 147 \bibitem[Dunkley\ \etal(2005)]{Dunkley:2005} {Dunkley}, J., {Bucher}, M., {Ferreira}, P.~G., {Moodley}, K., \& {Skordis}, C., 2005, \mnras, 356, 925--936 % http://adsabs.harvard.edu/abs/2005MNRAS.356..925D \bibitem[{Gelman}, {Roberts}, \& {Gilks}(1996)]{Gelman:1996} {Gelman}, A., {Roberts}, G.~O., \& {Gilks}, W.~R., in {\em Bayesian Statistics 5}, ed.\ J. Bernardo et al., Oxford University Press, 599--607 \bibitem[Goodman~\&\ Weare(2010)]{Goodman:2010} Goodman,~J. \& Weare,\ J., 2010, Comm.\ App.\ Math.\ Comp.\ Sci., 5, 65 \bibitem[Gregory(2005))]{Gregory:2005} {Gregory}, P.~C., \emph{Bayesian Logical Data Analysis for the Physical Sciences}, Cambridge University Press, 2005 % http://adsabs.harvard.edu/abs/2005blda.book.....G \bibitem[Hogg, Bovy \& Lang(2010))]{Hogg:2010} {Hogg}, D.~W., {Bovy}, J., \& {Lang}, D., 2010, \arxiv{1008.4686} [astro-ph.IM] % http://adsabs.harvard.edu/abs/2010arXiv1008.4686H \bibitem[Hou\ \etal(2012))]{Hou:2011} Hou, F., Goodman, J., Hogg, D.~W., Weare, J., \& Schwab, C.\ 2012, \apj, 745, 198 \bibitem[Huppenkothen et al.(2012)]{huppenkothen2012} Huppenkothen, D., Watts, A.~L., Uttley, P., et al.\ 2012, \arxiv{1212.1011} \bibitem[Lang \& Hogg(2012)]{lang2012} Lang, D., \& Hogg, D.~W.\ 2012, \aj, 144, 46 \bibitem[MacKay(2003))]{MacKay:2003} {MacKay}, D., \emph{Information Theory, Inference, and Learning Algorithms}, Cambridge University Press, 2003 \bibitem[Monnier et al.(2012)]{monnier2012} Monnier, J.~D., Che, X., Zhao, M., et al.\ 2012, \apjl, 761, L3 \bibitem[Morton(2012)]{morton2012} Morton, T.~D.\ 2012, \apj, 761, 6 \bibitem[Olofsson et al.(2012)]{olofsson2012} Olofsson, J., Juh{\'a}sz, A., Henning, T., et al.\ 2012, \aap, 542, A90 \bibitem[Pasarica~\&\ Gelman(2010)]{Pasarica:2010} Pasarica, C. \& Gelman, A., 2010, Statistica Sinica, 20, 343--364 \bibitem[Press \etal(2007)]{Press:2007} {Press}, W.~H., {Teukolsky}, S.~A., {Vetterling}, W.~T., \& {Flannery}, B.~P., \emph{Numerical Recipes: The Art of Scientific Computing}, Cambridge University Press, 2007 \bibitem[Reis et al.(2013)]{reis2013} Reis, R.~C., Miller, J.~M., Reynolds, M.~T., et al.\ 2013, \apj, 763, 48 \bibitem[Ro{\v s}kar et al.(2012)]{roskar2012} Ro{\v s}kar, R., Debattista, V.~P., \& Loebman, S.~R.\ 2012, \arxiv{1211.1982} \bibitem[Sanders \& Fabian(2013)]{sanders2013} Sanders, J.~S., \& Fabian, A.~C.\ 2013, \mnras, 453 \bibitem[van Haasteren et al.(2013)]{vh2013} van Haasteren, R., Mingarelli, C.~M.~F., Vecchio, A., \& Lassus, A.\ 2013, \arxiv{1301.6673} \bibitem[Weisz et al.(2013)]{weisz2013} Weisz, D.~R., Fouesneau, M., Hogg, D.~W., et al.\ 2013, \apj, 762, 123 \bibitem[Widrow\ \etal(2008)]{Widrow:2008} {Widrow}, L.~M. and {Pym}, B. and {Dubinski}, J., 2008, \apj, 679, 1239 \end{thebibliography} \clearpage \appendix \section{Installation}\sectlabel{install} The easiest way to install \this\ is using \pip\footnote{\url{http://pypi.python.org/pypi/pip}}. Running the command \begin{lstlisting} % pip install emcee \end{lstlisting} at the command line of a UNIX-based system will install the package in your \Python\ path. If you would like to install for all users, you might need to run the above command with superuser permissions. In order to use \this, you must also have \numpy\footnote{\url{https://numpy.org}} installed (this can also be achieved using \pip\ on most systems). \this\ has been tested with \Python\ 2.7 and \numpy\ 1.6 but it is likely to work with earlier versions of both of these as well. An alternative installation method is to download the source code from \url{http://dan.iel.fm/emcee} and run \begin{lstlisting} % python setup.py install \end{lstlisting} in the unzipped directory. Make sure that you have \numpy\ installed in this case as well. \section{Issues \& Contributions} The development of \this\ is being coordinated on \github\ at \url{http://github.com/dfm/emcee} and contributions are welcome. If you encounter any problems with the code, please report them at \url{http://github.com/dfm/emcee/issues} and consider contributing a patch. \section{Online Documentation} To learn more about how to use \this\ in practice, it is best to check out the documentation on the website \url{http://dan.iel.fm/emcee}. This page includes the API documentation and many examples of possible work flows. \end{document} ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731803.001506 emcee-3.1.1/document/plots/0000755000175100001710000000000000000000000015111 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/document/plots/oned.py0000644000175100001710000000412300000000000016410 0ustar00runnerdockerimport os import sys import time from multiprocessing import Pool import h5py import matplotlib.pyplot as pl import numpy as np import emcee sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..", ".."))) # import acor def lnprobfn(p, icov): return -0.5 * np.dot(p, np.dot(icov, p)) def random_cov(ndim, dof=1): v = np.random.randn(ndim * (ndim + dof)).reshape((ndim + dof, ndim)) return sum([np.outer(v[i], v[i]) for i in range(ndim + dof)]) / ( ndim + dof ) _rngs = {} def _worker(args): i, outfn, nsteps = args pid = os.getpid() _random = _rngs.get( pid, np.random.RandomState(int(int(pid) + time.time())) ) _rngs[pid] = _random ndim = int(np.ceil(2 ** (7 * _random.rand()))) nwalkers = 2 * ndim + 2 # nwalkers += nwalkers % 2 print(ndim, nwalkers) cov = random_cov(ndim) icov = np.linalg.inv(cov) ens_samp = emcee.EnsembleSampler(nwalkers, ndim, lnprobfn, args=[icov]) ens_samp.random_state = _random.get_state() pos, lnprob, state = ens_samp.run_mcmc( np.random.randn(nwalkers * ndim).reshape([nwalkers, ndim]), nsteps ) proposal = np.diag(cov.diagonal()) mh_samp = emcee.MHSampler(proposal, ndim, lnprobfn, args=[icov]) mh_samp.random_state = state mh_samp.run_mcmc(np.random.randn(ndim), nsteps) f = h5py.File(outfn) f["data"][i, :] = np.array( [ndim, np.mean(ens_samp.acor), np.mean(mh_samp.acor)] ) f.close() def oned(): nsteps = 10000 niter = 10 nthreads = 2 outfn = os.path.join(os.path.split(__file__)[0], "gauss_scaling.h5") print(outfn) f = h5py.File(outfn, "w") f.create_dataset("data", (niter, 3), "f") f.close() pool = Pool(nthreads) pool.map(_worker, [(i, outfn, nsteps) for i in range(niter)]) f = h5py.File(outfn) data = f["data"][...] f.close() pl.clf() pl.plot(data[:, 0], data[:, 1], "ks", alpha=0.5) pl.plot(data[:, 0], data[:, 2], ".k", alpha=0.5) pl.savefig(os.path.join(os.path.split(__file__)[0], "gauss_scaling.png")) if __name__ == "__main__": oned() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/document/plots/plot_acor.py0000644000175100001710000000031000000000000017437 0ustar00runnerdockerimport os import sys import matplotlib.pyplot as pl import numpy as np # sys.path.prepend(os.path.abspath(os.path.join(__file__, "..", "..", ".."))) # import emcee def plot_acor(acorfn): pass ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731803.001506 emcee-3.1.1/joss/0000755000175100001710000000000000000000000013110 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/.gitignore0000644000175100001710000000021300000000000015074 0ustar00runnerdockerpaper.aux paper.bbl paper.bcf paper.blg paper.fdb_latexmk paper.fls paper.log paper.out paper.pdf paper.run.xml *.png latex.template *.csl ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/make_latex.sh0000755000175100001710000000232200000000000015560 0ustar00runnerdocker#!/usr/bin/env bash echo "Downloading..." rm -rf latex.template logo.png aas-logo.png apa.csl wget -q https://raw.githubusercontent.com/openjournals/whedon/editor-and-reviewers-on-papers/resources/joss/latex.template wget -q https://raw.githubusercontent.com/openjournals/whedon/editor-and-reviewers-on-papers/resources/joss/logo.png wget -q https://raw.githubusercontent.com/openjournals/whedon/editor-and-reviewers-on-papers/resources/joss/aas-logo.png wget -q https://raw.githubusercontent.com/openjournals/whedon/editor-and-reviewers-on-papers/resources/joss/apa.csl echo "Done" pandoc \ -s paper.md \ -o paper.tex \ --template latex.template \ --csl=apa.csl \ --bibliography=paper.bib \ --filter pandoc-citeproc \ -V repository="https://github.com/dfm/emcee" \ -V archive_doi="https://doi.org/10.5281/zenodo.3543502" \ -V review_issue_url="https://github.com/openjournals/joss-reviews/issues/1864" \ -V editor_url="http://juanjobazan.com" \ -V graphics="true" \ --metadata-file=metadata.yaml # -o paper.pdf -V geometry:margin=1in \ # --pdf-engine=xelatex \ # --filter pandoc-citeproc \ # -t latex \ # -o paper.tex \ # --bibliography=paper.bib # --from markdown+autolink_bare_uris \ # --template latex.template \ # -s paper.md ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/metadata.yaml0000644000175100001710000000104000000000000015547 0ustar00runnerdocker repository: https://github.com/dfm/emcee archive_doi: https://doi.org/10.5281/zenodo.3543502 paper_url: https://doi.org/10.21105/joss.01864 journal_name: Journal of Open Source Software review_issue_url: https://github.com/openjournals/joss-reviews/issues/1864 issue: 4 volume: 43 page: 1864 logo_path: logo.png aas_logo_path: aas-logo.png year: 2019 submitted: 28 October 2019 published: 17 November 2019 formatted_doi: 10.21105/joss.01864 citation_author: Foreman-Mackey editor_name: Juanjo Bazán reviewers: - benjaminrose - mattpitkin ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/paper.bib0000644000175100001710000000645600000000000014710 0ustar00runnerdocker%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Dan Foreman-Mackey at 2019-10-17 16:15:45 -0400 %% Saved with string encoding Unicode (UTF-8) @article{Farr:2015, Author = {{Farr}, B. and {Farr}, W.~M.}, Date-Added = {2019-10-17 16:15:45 -0400}, Date-Modified = {2019-10-17 16:15:45 -0400}, Note = {in prep}, Title = {kombine: a kernel-density-based, embarrassingly parallel ensemble sampler}, Year = 2015, Url = {https://github.com/bfarr/kombine}} @article{Ter-Braak:2008, Author = {{ter Braak}, Cajo J.~F. and Vrugt, Jasper A}, Date-Added = {2019-10-17 16:07:14 -0400}, Date-Modified = {2019-10-17 16:15:43 -0400}, Journal = {Statistics and Computing}, Number = {4}, Pages = {435--446}, Publisher = {Springer}, Title = {{Differential evolution Markov chain with snooker updater and fewer chains}}, Volume = {18}, Year = {2008}, Doi = {10.1007/s11222-008-9104-9}} @article{Ter-Braak:2006, Author = {{ter Braak}, Cajo J.~F.}, Date-Added = {2019-10-17 16:06:50 -0400}, Date-Modified = {2019-10-17 16:15:43 -0400}, Journal = {Statistics and Computing}, Number = {3}, Pages = {239--249}, Publisher = {Springer}, Title = {{A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces}}, Volume = {16}, Year = {2006}, Doi = {10.1007/s11222-006-8769-1}} @article{Speagle:2019, Adsnote = {Provided by the SAO/NASA Astrophysics Data System}, Adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190402180S}, Archiveprefix = {arXiv}, Author = {{Speagle}, Joshua S}, Date-Added = {2019-10-17 15:43:02 -0400}, Date-Modified = {2019-10-17 15:43:03 -0400}, Eid = {arXiv:1904.02180}, Eprint = {1904.02180}, Journal = {arXiv e-prints}, Keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Computation}, Month = {Apr}, Pages = {arXiv:1904.02180}, Primaryclass = {astro-ph.IM}, Title = {{dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences}}, Year = {2019}} @article{Goodman:2010, Author = {Goodman, Jonathan and Weare, Jonathan}, Date-Added = {2019-10-17 14:38:39 -0400}, Date-Modified = {2019-10-17 14:38:40 -0400}, Journal = {Communications in applied mathematics and computational science}, Number = {1}, Pages = {65--80}, Publisher = {Mathematical Sciences Publishers}, Title = {Ensemble samplers with affine invariance}, Volume = {5}, Year = {2010}, Doi = {10.2140/camcos.2010.5.65}} @article{Foreman-Mackey:2013, Adsnote = {Provided by the SAO/NASA Astrophysics Data System}, Adsurl = {https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F}, Archiveprefix = {arXiv}, Author = {{Foreman-Mackey}, Daniel and {Hogg}, David W. and {Lang}, Dustin and {Goodman}, Jonathan}, Date-Added = {2019-10-17 14:36:34 -0400}, Date-Modified = {2019-10-17 14:36:36 -0400}, Doi = {10.1086/670067}, Eprint = {1202.3665}, Journal = {Publications of the Astronomical Society of the Pacific}, Keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics, Statistics - Computation}, Month = {Mar}, Number = {925}, Pages = {306}, Primaryclass = {astro-ph.IM}, Title = {{emcee: The MCMC Hammer}}, Volume = {125}, Year = {2013}, Bdsk-Url-1 = {https://doi.org/10.1086/670067}} ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/paper.md0000644000175100001710000001234500000000000014546 0ustar00runnerdocker--- title: 'emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC' tags: - Python - astronomy authors: - name: Daniel Foreman-Mackey orcid: 0000-0003-0872-7098 affiliation: 1 - name: Will M. Farr orcid: 0000-0003-1540-8562 affiliation: "1, 2" - name: Manodeep Sinha orcid: 0000-0002-4845-1228 affiliation: "3, 4" - name: Anne M. Archibald orcid: 0000-0003-0638-3340 affiliation: 5 - name: David W. Hogg orcid: 0000-0003-2866-9403 affiliation: "1, 6" - name: Jeremy S. Sanders orcid: 0000-0003-2189-4501 affiliation: 7 - name: Joe Zuntz orcid: 0000-0001-9789-9646 affiliation: 8 - name: Peter K. G. Williams orcid: 0000-0003-3734-3587 affiliation: "9, 10" - name: Andrew R. J. Nelson orcid: 0000-0002-4548-3558 affiliation: 11 - name: Miguel de Val-Borro orcid: 0000-0002-0455-9384 affiliation: 12 - name: Tobias Erhardt orcid: 0000-0002-6683-6746 affiliation: 13 - name: Ilya Pashchenko orcid: 0000-0002-9404-7023 affiliation: 14 - name: Oriol Abril Pla orcid: 0000-0002-1847-9481 affiliation: 15 affiliations: - name: Center for Computational Astrophysics, Flatiron Institute index: 1 - name: Department of Physics and Astronomy, Stony Brook University, United States index: 2 - name: Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Australia index: 3 - name: ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D) index: 4 - name: University of Newcastle index: 5 - name: Center for Cosmology and Particle Physics, Department of Physics, New York University index: 6 - name: Max Planck Institute for Extraterrestrial Physics index: 7 - name: Institute for Astronomy, University of Edinburgh, Edinburgh, EH9 3HJ, UK index: 8 - name: "Center for Astrophysics | Harvard & Smithsonian" index: 9 - name: American Astronomical Society index: 10 - name: Australian Nuclear Science and Technology Organisation, NSW, Australia index: 11 - name: Planetary Science Institute, 1700 East Fort Lowell Rd., Suite 106, Tucson, AZ 85719, USA index: 12 - name: Climate and Environmental Physics and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland index: 13 - name: P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia index: 14 - name: Universitat Pompeu Fabra, Barcelona index: 15 date: 17 October 2019 bibliography: paper.bib --- # Summary ``emcee`` is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published [@Foreman-Mackey:2013], with some applications in other fields. When it was first released in 2012, the interface implemented in ``emcee`` was fundamentally different from the MCMC libraries that were popular at the time, such as ``PyMC``, because it was specifically designed to work with "black box" models instead of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since ``emcee``'s release, other libraries have been developed with similar interfaces, such as ``dynesty`` [@Speagle:2019]. The version 3.0 release of ``emcee`` is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new "move" implementations. This new release includes both small quality of life improvements—like a progress bar using [``tqdm``](https://tqdm.github.io)—and larger features. For example, the new ``backends`` interface implements real time serialization of sampling results. By default ``emcee`` saves its results in memory (as in the original implementation), but it now also includes a ``HDFBackend`` class that serializes the chain to disk using [h5py](https://www.h5py.org). The most important new feature included in the version 3.0 release of ``emcee`` is the new ``moves`` interface. Originally, ``emcee`` implemented the affine-invariant "stretch move" proposed by @Goodman:2010, but there are other ensemble proposals that can get better performance for certain applications. ``emcee`` now includes implementations of several other ensemble moves and an interface for defining custom proposals. The implemented moves include: - The "stretch move" proposed by @Goodman:2010, - The "differential evolution" and "differential evolution snooker update" moves [@Ter-Braak:2006; @Ter-Braak:2008], and - A "kernel density proposal" based on the implementation in [the ``kombine`` library](https://github.com/bfarr/kombine) [@Farr:2015]. ``emcee`` has been widely used and the original paper has been highly cited, but there have been many contributions from members of the community. This paper is meant to highlight these contributions and provide citation credit to the academic contributors. A full up-to-date list of contributors can always be found [on GitHub](https://github.com/dfm/emcee/graphs/contributors). # References ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/joss/paper.tex0000644000175100001710000004072000000000000014744 0ustar00runnerdocker\documentclass[10pt,a4paper,onecolumn]{article} \usepackage{marginnote} \usepackage{graphicx} \usepackage{xcolor} \usepackage{authblk,etoolbox} \usepackage{titlesec} \usepackage{calc} \usepackage{tikz} \usepackage{hyperref} \hypersetup{colorlinks,breaklinks=true, urlcolor=[rgb]{0.0, 0.5, 1.0}, linkcolor=[rgb]{0.0, 0.5, 1.0}} \usepackage{caption} \usepackage{tcolorbox} \usepackage{amssymb,amsmath} \usepackage{ifxetex,ifluatex} \usepackage{seqsplit} \usepackage{xstring} \usepackage{float} \let\origfigure\figure \let\endorigfigure\endfigure \renewenvironment{figure}[1][2] { \expandafter\origfigure\expandafter[H] } { \endorigfigure } \usepackage{fixltx2e} % provides \textsubscript \usepackage[ backend=biber, % style=alphabetic, % citestyle=numeric ]{biblatex} \bibliography{paper.bib} % --- Splitting \texttt -------------------------------------------------- \let\textttOrig=\texttt \def\texttt#1{\expandafter\textttOrig{\seqsplit{#1}}} \renewcommand{\seqinsert}{\ifmmode \allowbreak \else\penalty6000\hspace{0pt plus 0.02em}\fi} % --- Pandoc does not distinguish between links like [foo](bar) and % --- [foo](foo) -- a simplistic Markdown model. However, this is % --- wrong: in links like [foo](foo) the text is the url, and must % --- be split correspondingly. % --- Here we detect links \href{foo}{foo}, and also links starting % --- with https://doi.org, and use path-like splitting (but not % --- escaping!) with these links. % --- Another vile thing pandoc does is the different escaping of % --- foo and bar. This may confound our detection. % --- This problem we do not try to solve at present, with the exception % --- of doi-like urls, which we detect correctly. \makeatletter \let\href@Orig=\href \def\href@Urllike#1#2{\href@Orig{#1}{\begingroup \def\Url@String{#2}\Url@FormatString \endgroup}} \def\href@Notdoi#1#2{\def\tempa{#1}\def\tempb{#2}% \ifx\tempa\tempb\relax\href@Urllike{#1}{#2}\else \href@Orig{#1}{#2}\fi} \def\href#1#2{% \IfBeginWith{#1}{https://doi.org}% {\href@Urllike{#1}{#2}}{\href@Notdoi{#1}{#2}}} \makeatother % --- Page layout ------------------------------------------------------------- \usepackage[top=3.5cm, bottom=3cm, right=1.5cm, left=1.0cm, headheight=2.2cm, reversemp, includemp, marginparwidth=4.5cm]{geometry} % --- Default font ------------------------------------------------------------ \renewcommand\familydefault{\sfdefault} % --- Style ------------------------------------------------------------------- \renewcommand{\bibfont}{\small \sffamily} \renewcommand{\captionfont}{\small\sffamily} \renewcommand{\captionlabelfont}{\bfseries} % --- Section/SubSection/SubSubSection ---------------------------------------- \titleformat{\section} {\normalfont\sffamily\Large\bfseries} {}{0pt}{} \titleformat{\subsection} {\normalfont\sffamily\large\bfseries} {}{0pt}{} \titleformat{\subsubsection} {\normalfont\sffamily\bfseries} {}{0pt}{} \titleformat*{\paragraph} {\sffamily\normalsize} % --- Header / Footer --------------------------------------------------------- \usepackage{fancyhdr} \pagestyle{fancy} \fancyhf{} %\renewcommand{\headrulewidth}{0.50pt} \renewcommand{\headrulewidth}{0pt} \fancyhead[L]{\hspace{-0.75cm}\includegraphics[width=5.5cm]{logo.png}} \fancyhead[C]{} \fancyhead[R]{} \renewcommand{\footrulewidth}{0.25pt} \fancyfoot[L]{\parbox[t]{0.98\headwidth}{\footnotesize{\sffamily Foreman-Mackey, (2019). emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC. \textit{Journal of Open Source Software}, 43(4), 1864. \url{https://doi.org/10.21105/joss.01864}}}} \fancyfoot[R]{\sffamily \thepage} \makeatletter \let\ps@plain\ps@fancy \fancyheadoffset[L]{4.5cm} \fancyfootoffset[L]{4.5cm} % --- Macros --------- \definecolor{linky}{rgb}{0.0, 0.5, 1.0} \newtcolorbox{repobox} {colback=red, colframe=red!75!black, boxrule=0.5pt, arc=2pt, left=6pt, right=6pt, top=3pt, bottom=3pt} \newcommand{\ExternalLink}{% \tikz[x=1.2ex, y=1.2ex, baseline=-0.05ex]{% \begin{scope}[x=1ex, y=1ex] \clip (-0.1,-0.1) --++ (-0, 1.2) --++ (0.6, 0) --++ (0, -0.6) --++ (0.6, 0) --++ (0, -1); \path[draw, line width = 0.5, rounded corners=0.5] (0,0) rectangle (1,1); \end{scope} \path[draw, line width = 0.5] (0.5, 0.5) -- (1, 1); \path[draw, line width = 0.5] (0.6, 1) -- (1, 1) -- (1, 0.6); } } % --- Title / Authors --------------------------------------------------------- % patch \maketitle so that it doesn't center \patchcmd{\@maketitle}{center}{flushleft}{}{} \patchcmd{\@maketitle}{center}{flushleft}{}{} % patch \maketitle so that the font size for the title is normal \patchcmd{\@maketitle}{\LARGE}{\LARGE\sffamily}{}{} % patch the patch by authblk so that the author block is flush left \def\maketitle{{% \renewenvironment{tabular}[2][] {\begin{flushleft}} {\end{flushleft}} \AB@maketitle}} \makeatletter \renewcommand\AB@affilsepx{ \protect\Affilfont} %\renewcommand\AB@affilnote[1]{{\bfseries #1}\hspace{2pt}} \renewcommand\AB@affilnote[1]{{\bfseries #1}\hspace{3pt}} \renewcommand{\affil}[2][]% {\newaffiltrue\let\AB@blk@and\AB@pand \if\relax#1\relax\def\AB@note{\AB@thenote}\else\def\AB@note{#1}% \setcounter{Maxaffil}{0}\fi \begingroup \let\href=\href@Orig \let\texttt=\textttOrig \let\protect\@unexpandable@protect \def\thanks{\protect\thanks}\def\footnote{\protect\footnote}% \@temptokena=\expandafter{\AB@authors}% {\def\\{\protect\\\protect\Affilfont}\xdef\AB@temp{#2}}% \xdef\AB@authors{\the\@temptokena\AB@las\AB@au@str \protect\\[\affilsep]\protect\Affilfont\AB@temp}% \gdef\AB@las{}\gdef\AB@au@str{}% {\def\\{, \ignorespaces}\xdef\AB@temp{#2}}% \@temptokena=\expandafter{\AB@affillist}% \xdef\AB@affillist{\the\@temptokena \AB@affilsep \AB@affilnote{\AB@note}\protect\Affilfont\AB@temp}% \endgroup \let\AB@affilsep\AB@affilsepx } \makeatother \renewcommand\Authfont{\sffamily\bfseries} \renewcommand\Affilfont{\sffamily\small\mdseries} \setlength{\affilsep}{1em} \ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \else % if luatex or xelatex \ifxetex \usepackage{mathspec} \usepackage{fontspec} \else \usepackage{fontspec} \fi \defaultfontfeatures{Ligatures=TeX,Scale=MatchLowercase} \fi % use upquote if available, for straight quotes in verbatim environments \IfFileExists{upquote.sty}{\usepackage{upquote}}{} % use microtype if available \IfFileExists{microtype.sty}{% \usepackage{microtype} \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts }{} \usepackage{hyperref} \hypersetup{unicode=true, pdftitle={emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC}, pdfborder={0 0 0}, breaklinks=true} \urlstyle{same} % don't use monospace font for urls % --- We redefined \texttt, but in sections and captions we want the % --- old definition \let\addcontentslineOrig=\addcontentsline \def\addcontentsline#1#2#3{\bgroup \let\texttt=\textttOrig\addcontentslineOrig{#1}{#2}{#3}\egroup} \let\markbothOrig\markboth \def\markboth#1#2{\bgroup \let\texttt=\textttOrig\markbothOrig{#1}{#2}\egroup} \let\markrightOrig\markright \def\markright#1{\bgroup \let\texttt=\textttOrig\markrightOrig{#1}\egroup} \usepackage{graphicx,grffile} \makeatletter \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi} \def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi} \makeatother % Scale images if necessary, so that they will not overflow the page % margins by default, and it is still possible to overwrite the defaults % using explicit options in \includegraphics[width, height, ...]{} \setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio} \IfFileExists{parskip.sty}{% \usepackage{parskip} }{% else \setlength{\parindent}{0pt} \setlength{\parskip}{6pt plus 2pt minus 1pt} } \setlength{\emergencystretch}{3em} % prevent overfull lines \providecommand{\tightlist}{% \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} \setcounter{secnumdepth}{0} % Redefines (sub)paragraphs to behave more like sections \ifx\paragraph\undefined\else \let\oldparagraph\paragraph \renewcommand{\paragraph}[1]{\oldparagraph{#1}\mbox{}} \fi \ifx\subparagraph\undefined\else \let\oldsubparagraph\subparagraph \renewcommand{\subparagraph}[1]{\oldsubparagraph{#1}\mbox{}} \fi \title{emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC} \author[1]{Daniel Foreman-Mackey} \author[1, 2]{Will M. Farr} \author[3, 4]{Manodeep Sinha} \author[5]{Anne M. Archibald} \author[1, 6]{David W. Hogg} \author[7]{Jeremy S. Sanders} \author[8]{Joe Zuntz} \author[9, 10]{Peter K. G. Williams} \author[11]{Andrew R. J. Nelson} \author[12]{Miguel de Val-Borro} \author[13]{Tobias Erhardt} \author[14]{Ilya Pashchenko} \author[15]{Oriol Abril Pla} \affil[1]{Center for Computational Astrophysics, Flatiron Institute} \affil[2]{Department of Physics and Astronomy, Stony Brook University, United States} \affil[3]{Centre for Astrophysics \& Supercomputing, Swinburne University of Technology, Australia} \affil[4]{ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)} \affil[5]{University of Newcastle} \affil[6]{Center for Cosmology and Particle Physics, Department of Physics, New York University} \affil[7]{Max Planck Institute for Extraterrestrial Physics} \affil[8]{Institute for Astronomy, University of Edinburgh, Edinburgh, EH9 3HJ, UK} \affil[9]{Center for Astrophysics \textbar{} Harvard \& Smithsonian} \affil[10]{American Astronomical Society} \affil[11]{Australian Nuclear Science and Technology Organisation, NSW, Australia} \affil[12]{Planetary Science Institute, 1700 East Fort Lowell Rd., Suite 106, Tucson, AZ 85719, USA} \affil[13]{Climate and Environmental Physics and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland} \affil[14]{P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia} \affil[15]{Universitat Pompeu Fabra, Barcelona} \date{\vspace{-7ex}} \begin{document} \maketitle \marginpar{ \begin{flushleft} %\hrule \sffamily\small {\bfseries DOI:} \href{https://doi.org/10.21105/joss.01864}{\color{linky}{10.21105/joss.01864}} \vspace{2mm} {\bfseries Software} \begin{itemize} \setlength\itemsep{0em} \item \href{https://github.com/openjournals/joss-reviews/issues/1864}{\color{linky}{Review}} \ExternalLink \item \href{https://github.com/dfm/emcee}{\color{linky}{Repository}} \ExternalLink \item \href{https://doi.org/10.5281/zenodo.3543502}{\color{linky}{Archive}} \ExternalLink \end{itemize} \vspace{2mm} \par\noindent\hrulefill\par \vspace{2mm} {\bfseries Editor:} \href{http://juanjobazan.com}{Juanjo Bazán} \ExternalLink \\ \vspace{1mm} {\bfseries Reviewers:} \begin{itemize} \setlength\itemsep{0em} \item \href{https://github.com/benjaminrose}{@benjaminrose} \item \href{https://github.com/mattpitkin}{@mattpitkin} \end{itemize} \vspace{2mm} {\bfseries Submitted:} 28 October 2019\\ {\bfseries Published:} 17 November 2019 \vspace{2mm} {\bfseries License}\\ Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (\href{http://creativecommons.org/licenses/by/4.0/}{\color{linky}{CC-BY}}). \end{flushleft} } \hypertarget{summary}{% \section{Summary}\label{summary}} \texttt{emcee} is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published (Foreman-Mackey, Hogg, Lang, \& Goodman, 2013), with some applications in other fields. When it was first released in 2012, the interface implemented in \texttt{emcee} was fundamentally different from the MCMC libraries that were popular at the time, such as \texttt{PyMC}, because it was specifically designed to work with ``black box'' models instead of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since \texttt{emcee}'s release, other libraries have been developed with similar interfaces, such as \texttt{dynesty} (Speagle, 2019). The version 3.0 release of \texttt{emcee} is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new ``move'' implementations. This new release includes both small quality of life improvements---like a progress bar using \href{https://tqdm.github.io}{\texttt{tqdm}}---and larger features. For example, the new \texttt{backends} interface implements real time serialization of sampling results. By default \texttt{emcee} saves its results in memory (as in the original implementation), but it now also includes a \texttt{HDFBackend} class that serializes the chain to disk using \href{https://www.h5py.org}{h5py}. The most important new feature included in the version 3.0 release of \texttt{emcee} is the new \texttt{moves} interface. Originally, \texttt{emcee} implemented the affine-invariant ``stretch move'' proposed by Goodman \& Weare (2010), but there are other ensemble proposals that can get better performance for certain applications. \texttt{emcee} now includes implementations of several other ensemble moves and an interface for defining custom proposals. The implemented moves include: \begin{itemize} \tightlist \item The ``stretch move'' proposed by Goodman \& Weare (2010), \item The ``differential evolution'' and ``differential evolution snooker update'' moves (ter Braak, 2006; ter Braak \& Vrugt, 2008), and \item A ``kernel density proposal'' based on the implementation in \href{https://github.com/bfarr/kombine}{the \texttt{kombine} library} (Farr \& Farr, 2015). \end{itemize} \texttt{emcee} has been widely used and the original paper has been highly cited, but there have been many contributions from members of the community. This paper is meant to highlight these contributions and provide citation credit to the academic contributors. A full up-to-date list of contributors can always be found \href{https://github.com/dfm/emcee/graphs/contributors}{on GitHub}. \hypertarget{references}{% \section*{References}\label{references}} \addcontentsline{toc}{section}{References} \hypertarget{refs}{} \leavevmode\hypertarget{ref-Farr:2015}{}% Farr, B., \& Farr, W. M. (2015). Kombine: A kernel-density-based, embarrassingly parallel ensemble sampler. Retrieved from \url{https://github.com/bfarr/kombine} \leavevmode\hypertarget{ref-Foreman-Mackey:2013}{}% Foreman-Mackey, D., Hogg, D. W., Lang, D., \& Goodman, J. (2013). emcee: The MCMC Hammer. \emph{Publications of the Astronomical Society of the Pacific}, \emph{125}(925), 306. doi:\href{https://doi.org/10.1086/670067}{10.1086/670067} \leavevmode\hypertarget{ref-Goodman:2010}{}% Goodman, J., \& Weare, J. (2010). Ensemble samplers with affine invariance. \emph{Communications in applied mathematics and computational science}, \emph{5}(1), 65--80. doi:\href{https://doi.org/10.2140/camcos.2010.5.65}{10.2140/camcos.2010.5.65} \leavevmode\hypertarget{ref-Speagle:2019}{}% Speagle, J. S. (2019). dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences. \emph{arXiv e-prints}, arXiv:1904.02180. Retrieved from \url{http://arxiv.org/abs/1904.02180} \leavevmode\hypertarget{ref-Ter-Braak:2006}{}% ter Braak, C. J. F. (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. \emph{Statistics and Computing}, \emph{16}(3), 239--249. doi:\href{https://doi.org/10.1007/s11222-006-8769-1}{10.1007/s11222-006-8769-1} \leavevmode\hypertarget{ref-Ter-Braak:2008}{}% ter Braak, C. J. F., \& Vrugt, J. A. (2008). Differential evolution Markov chain with snooker updater and fewer chains. \emph{Statistics and Computing}, \emph{18}(4), 435--446. doi:\href{https://doi.org/10.1007/s11222-008-9104-9}{10.1007/s11222-008-9104-9} \end{document} ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/pyproject.toml0000644000175100001710000000211400000000000015044 0ustar00runnerdocker[build-system] requires = ["setuptools>=40.6.0", "wheel", "setuptools_scm"] build-backend = "setuptools.build_meta" [tool.black] line-length = 79 target-version = ['py35'] exclude = ''' /( \.eggs | \.git | \.hg | \.mypy_cache | \.tox | \.venv | _build | buck-out | build | dist )/ ''' [tool.isort] line_length = 79 multi_line_output = 3 include_trailing_comma = true force_grid_wrap = 0 use_parentheses = true known_third_party = ["h5py", "matplotlib", "numpy", "pkg_resources", "pytest", "setuptools"] [tool.coverage.run] parallel = true branch = true source = ["emcee"] omit = [ "emcee/interruptible_pool.py", "emcee/mpi_pool.py", "emcee/ptsampler.py", "docs/*", "tests/*", "*__init__*" ] [tool.coverage.paths] source = ["src", "*/site-packages"] [tool.coverage.report] show_missing = true exclude_lines = [ "pragma: no cover", "raise NotImplementedError", "raise ImportError", "except ImportError", "def __len__", "def __repr__", "logging.warning", "deprecation_warning", "deprecated", "if tqdm is None" ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/readthedocs.yml0000644000175100001710000000031200000000000015136 0ustar00runnerdockerversion: 2 python: version: 3.8 install: - requirements: docs/requirements.txt - method: pip path: . sphinx: configuration: docs/conf.py fail_on_warning: true builder: dirhtml ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/setup.cfg0000644000175100001710000000004600000000000013753 0ustar00runnerdocker[egg_info] tag_build = tag_date = 0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/setup.py0000755000175100001710000000440300000000000013650 0ustar00runnerdocker#!/usr/bin/env python # Inspired by: # https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/ import codecs import os import re from setuptools import find_packages, setup # PROJECT SPECIFIC NAME = "emcee" PACKAGES = find_packages(where="src") META_PATH = os.path.join("src", "emcee", "__init__.py") CLASSIFIERS = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", ] INSTALL_REQUIRES = ["numpy"] SETUP_REQUIRES = INSTALL_REQUIRES + [ "setuptools>=40.6.0", "setuptools_scm", "wheel", ] EXTRA_REQUIRE = { "extras": ["h5py", "scipy"], "tests": ["pytest", "pytest-cov", "coverage[toml]"], } # END PROJECT SPECIFIC HERE = os.path.dirname(os.path.realpath(__file__)) def read(*parts): with codecs.open(os.path.join(HERE, *parts), "rb", "utf-8") as f: return f.read() def find_meta(meta, meta_file=read(META_PATH)): meta_match = re.search( r"^__{meta}__ = ['\"]([^'\"]*)['\"]".format(meta=meta), meta_file, re.M ) if meta_match: return meta_match.group(1) raise RuntimeError("Unable to find __{meta}__ string.".format(meta=meta)) if __name__ == "__main__": setup( name=NAME, use_scm_version={ "write_to": os.path.join( "src", NAME, "{0}_version.py".format(NAME) ), "write_to_template": '__version__ = "{version}"\n', }, author=find_meta("author"), author_email=find_meta("email"), maintainer=find_meta("author"), maintainer_email=find_meta("email"), url=find_meta("uri"), license=find_meta("license"), description=find_meta("description"), long_description=read("README.rst"), long_description_content_type="text/x-rst", packages=PACKAGES, package_dir={"": "src"}, include_package_data=True, install_requires=INSTALL_REQUIRES, setup_requires=SETUP_REQUIRES, extras_require=EXTRA_REQUIRE, classifiers=CLASSIFIERS, zip_safe=False, options={"bdist_wheel": {"universal": "1"}}, ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731802.9895055 emcee-3.1.1/src/0000755000175100001710000000000000000000000012721 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731803.001506 emcee-3.1.1/src/emcee/0000755000175100001710000000000000000000000013777 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/__init__.py0000644000175100001710000000151600000000000016113 0ustar00runnerdocker# -*- coding: utf-8 -*- __bibtex__ = """ @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {PASP}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } """ __uri__ = "https://emcee.readthedocs.io" __author__ = "Daniel Foreman-Mackey" __email__ = "foreman.mackey@gmail.com" __license__ = "MIT" __description__ = "The Python ensemble sampling toolkit for MCMC" from .emcee_version import __version__ # isort:skip from . import autocorr, backends, moves from .ensemble import EnsembleSampler, walkers_independent from .state import State __all__ = [ "EnsembleSampler", "walkers_independent", "State", "moves", "autocorr", "backends", "__version__", ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/autocorr.py0000644000175100001710000000765300000000000016222 0ustar00runnerdocker# -*- coding: utf-8 -*- import logging import numpy as np __all__ = ["function_1d", "integrated_time", "AutocorrError"] logger = logging.getLogger(__name__) def next_pow_two(n): """Returns the next power of two greater than or equal to `n`""" i = 1 while i < n: i = i << 1 return i def function_1d(x): """Estimate the normalized autocorrelation function of a 1-D series Args: x: The series as a 1-D numpy array. Returns: array: The autocorrelation function of the time series. """ x = np.atleast_1d(x) if len(x.shape) != 1: raise ValueError("invalid dimensions for 1D autocorrelation function") n = next_pow_two(len(x)) # Compute the FFT and then (from that) the auto-correlation function f = np.fft.fft(x - np.mean(x), n=2 * n) acf = np.fft.ifft(f * np.conjugate(f))[: len(x)].real acf /= acf[0] return acf def auto_window(taus, c): m = np.arange(len(taus)) < c * taus if np.any(m): return np.argmin(m) return len(taus) - 1 def integrated_time(x, c=5, tol=50, quiet=False): """Estimate the integrated autocorrelation time of a time series. This estimate uses the iterative procedure described on page 16 of `Sokal's notes `_ to determine a reasonable window size. Args: x: The time series. If multidimensional, set the time axis using the ``axis`` keyword argument and the function will be computed for every other axis. c (Optional[float]): The step size for the window search. (default: ``5``) tol (Optional[float]): The minimum number of autocorrelation times needed to trust the estimate. (default: ``50``) quiet (Optional[bool]): This argument controls the behavior when the chain is too short. If ``True``, give a warning instead of raising an :class:`AutocorrError`. (default: ``False``) Returns: float or array: An estimate of the integrated autocorrelation time of the time series ``x`` computed along the axis ``axis``. Raises AutocorrError: If the autocorrelation time can't be reliably estimated from the chain and ``quiet`` is ``False``. This normally means that the chain is too short. """ x = np.atleast_1d(x) if len(x.shape) == 1: x = x[:, np.newaxis, np.newaxis] if len(x.shape) == 2: x = x[:, :, np.newaxis] if len(x.shape) != 3: raise ValueError("invalid dimensions") n_t, n_w, n_d = x.shape tau_est = np.empty(n_d) windows = np.empty(n_d, dtype=int) # Loop over parameters for d in range(n_d): f = np.zeros(n_t) for k in range(n_w): f += function_1d(x[:, k, d]) f /= n_w taus = 2.0 * np.cumsum(f) - 1.0 windows[d] = auto_window(taus, c) tau_est[d] = taus[windows[d]] # Check convergence flag = tol * tau_est > n_t # Warn or raise in the case of non-convergence if np.any(flag): msg = ( "The chain is shorter than {0} times the integrated " "autocorrelation time for {1} parameter(s). Use this estimate " "with caution and run a longer chain!\n" ).format(tol, np.sum(flag)) msg += "N/{0} = {1:.0f};\ntau: {2}".format(tol, n_t / tol, tau_est) if not quiet: raise AutocorrError(tau_est, msg) logger.warning(msg) return tau_est class AutocorrError(Exception): """Raised if the chain is too short to estimate an autocorrelation time. The current estimate of the autocorrelation time can be accessed via the ``tau`` attribute of this exception. """ def __init__(self, tau, *args, **kwargs): self.tau = tau super(AutocorrError, self).__init__(*args, **kwargs) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731803.001506 emcee-3.1.1/src/emcee/backends/0000755000175100001710000000000000000000000015551 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/backends/__init__.py0000644000175100001710000000056400000000000017667 0ustar00runnerdocker# -*- coding: utf-8 -*- from .backend import Backend from .hdf import HDFBackend, TempHDFBackend __all__ = ["Backend", "HDFBackend", "TempHDFBackend", "get_test_backends"] def get_test_backends(): backends = [Backend] try: import h5py # NOQA except ImportError: pass else: backends.append(TempHDFBackend) return backends ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/backends/backend.py0000644000175100001710000002027000000000000017513 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .. import autocorr from ..state import State __all__ = ["Backend"] class Backend(object): """A simple default backend that stores the chain in memory""" def __init__(self, dtype=None): self.initialized = False if dtype is None: dtype = np.float64 self.dtype = dtype def reset(self, nwalkers, ndim): """Clear the state of the chain and empty the backend Args: nwakers (int): The size of the ensemble ndim (int): The number of dimensions """ self.nwalkers = int(nwalkers) self.ndim = int(ndim) self.iteration = 0 self.accepted = np.zeros(self.nwalkers, dtype=self.dtype) self.chain = np.empty((0, self.nwalkers, self.ndim), dtype=self.dtype) self.log_prob = np.empty((0, self.nwalkers), dtype=self.dtype) self.blobs = None self.random_state = None self.initialized = True def has_blobs(self): """Returns ``True`` if the model includes blobs""" return self.blobs is not None def get_value(self, name, flat=False, thin=1, discard=0): if self.iteration <= 0: raise AttributeError( "you must run the sampler with " "'store == True' before accessing the " "results" ) if name == "blobs" and not self.has_blobs(): return None v = getattr(self, name)[discard + thin - 1 : self.iteration : thin] if flat: s = list(v.shape[1:]) s[0] = np.prod(v.shape[:2]) return v.reshape(s) return v def get_chain(self, **kwargs): """Get the stored chain of MCMC samples Args: flat (Optional[bool]): Flatten the chain across the ensemble. (default: ``False``) thin (Optional[int]): Take only every ``thin`` steps from the chain. (default: ``1``) discard (Optional[int]): Discard the first ``discard`` steps in the chain as burn-in. (default: ``0``) Returns: array[..., nwalkers, ndim]: The MCMC samples. """ return self.get_value("chain", **kwargs) def get_blobs(self, **kwargs): """Get the chain of blobs for each sample in the chain Args: flat (Optional[bool]): Flatten the chain across the ensemble. (default: ``False``) thin (Optional[int]): Take only every ``thin`` steps from the chain. (default: ``1``) discard (Optional[int]): Discard the first ``discard`` steps in the chain as burn-in. (default: ``0``) Returns: array[..., nwalkers]: The chain of blobs. """ return self.get_value("blobs", **kwargs) def get_log_prob(self, **kwargs): """Get the chain of log probabilities evaluated at the MCMC samples Args: flat (Optional[bool]): Flatten the chain across the ensemble. (default: ``False``) thin (Optional[int]): Take only every ``thin`` steps from the chain. (default: ``1``) discard (Optional[int]): Discard the first ``discard`` steps in the chain as burn-in. (default: ``0``) Returns: array[..., nwalkers]: The chain of log probabilities. """ return self.get_value("log_prob", **kwargs) def get_last_sample(self): """Access the most recent sample in the chain""" if (not self.initialized) or self.iteration <= 0: raise AttributeError( "you must run the sampler with " "'store == True' before accessing the " "results" ) it = self.iteration blobs = self.get_blobs(discard=it - 1) if blobs is not None: blobs = blobs[0] return State( self.get_chain(discard=it - 1)[0], log_prob=self.get_log_prob(discard=it - 1)[0], blobs=blobs, random_state=self.random_state, ) def get_autocorr_time(self, discard=0, thin=1, **kwargs): """Compute an estimate of the autocorrelation time for each parameter Args: thin (Optional[int]): Use only every ``thin`` steps from the chain. The returned estimate is multiplied by ``thin`` so the estimated time is in units of steps, not thinned steps. (default: ``1``) discard (Optional[int]): Discard the first ``discard`` steps in the chain as burn-in. (default: ``0``) Other arguments are passed directly to :func:`emcee.autocorr.integrated_time`. Returns: array[ndim]: The integrated autocorrelation time estimate for the chain for each parameter. """ x = self.get_chain(discard=discard, thin=thin) return thin * autocorr.integrated_time(x, **kwargs) @property def shape(self): """The dimensions of the ensemble ``(nwalkers, ndim)``""" return self.nwalkers, self.ndim def _check_blobs(self, blobs): has_blobs = self.has_blobs() if has_blobs and blobs is None: raise ValueError("inconsistent use of blobs") if self.iteration > 0 and blobs is not None and not has_blobs: raise ValueError("inconsistent use of blobs") def grow(self, ngrow, blobs): """Expand the storage space by some number of samples Args: ngrow (int): The number of steps to grow the chain. blobs: The current array of blobs. This is used to compute the dtype for the blobs array. """ self._check_blobs(blobs) i = ngrow - (len(self.chain) - self.iteration) a = np.empty((i, self.nwalkers, self.ndim), dtype=self.dtype) self.chain = np.concatenate((self.chain, a), axis=0) a = np.empty((i, self.nwalkers), dtype=self.dtype) self.log_prob = np.concatenate((self.log_prob, a), axis=0) if blobs is not None: dt = np.dtype((blobs.dtype, blobs.shape[1:])) a = np.empty((i, self.nwalkers), dtype=dt) if self.blobs is None: self.blobs = a else: self.blobs = np.concatenate((self.blobs, a), axis=0) def _check(self, state, accepted): self._check_blobs(state.blobs) nwalkers, ndim = self.shape has_blobs = self.has_blobs() if state.coords.shape != (nwalkers, ndim): raise ValueError( "invalid coordinate dimensions; expected {0}".format( (nwalkers, ndim) ) ) if state.log_prob.shape != (nwalkers,): raise ValueError( "invalid log probability size; expected {0}".format(nwalkers) ) if state.blobs is not None and not has_blobs: raise ValueError("unexpected blobs") if state.blobs is None and has_blobs: raise ValueError("expected blobs, but none were given") if state.blobs is not None and len(state.blobs) != nwalkers: raise ValueError( "invalid blobs size; expected {0}".format(nwalkers) ) if accepted.shape != (nwalkers,): raise ValueError( "invalid acceptance size; expected {0}".format(nwalkers) ) def save_step(self, state, accepted): """Save a step to the backend Args: state (State): The :class:`State` of the ensemble. accepted (ndarray): An array of boolean flags indicating whether or not the proposal for each walker was accepted. """ self._check(state, accepted) self.chain[self.iteration, :, :] = state.coords self.log_prob[self.iteration, :] = state.log_prob if state.blobs is not None: self.blobs[self.iteration, :] = state.blobs self.accepted += accepted self.random_state = state.random_state self.iteration += 1 def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): pass ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/backends/hdf.py0000644000175100001710000002174700000000000016677 0ustar00runnerdocker# -*- coding: utf-8 -*- from __future__ import division, print_function import os from tempfile import NamedTemporaryFile import numpy as np from .. import __version__ from .backend import Backend __all__ = ["HDFBackend", "TempHDFBackend", "does_hdf5_support_longdouble"] try: import h5py except ImportError: h5py = None def does_hdf5_support_longdouble(): if h5py is None: return False with NamedTemporaryFile( prefix="emcee-temporary-hdf5", suffix=".hdf5", delete=False ) as f: f.close() with h5py.File(f.name, "w") as hf: g = hf.create_group("group") g.create_dataset("data", data=np.ones(1, dtype=np.longdouble)) if g["data"].dtype != np.longdouble: return False with h5py.File(f.name, "r") as hf: if hf["group"]["data"].dtype != np.longdouble: return False return True class HDFBackend(Backend): """A backend that stores the chain in an HDF5 file using h5py .. note:: You must install `h5py `_ to use this backend. Args: filename (str): The name of the HDF5 file where the chain will be saved. name (str; optional): The name of the group where the chain will be saved. read_only (bool; optional): If ``True``, the backend will throw a ``RuntimeError`` if the file is opened with write access. """ def __init__( self, filename, name="mcmc", read_only=False, dtype=None, compression=None, compression_opts=None, ): if h5py is None: raise ImportError("you must install 'h5py' to use the HDFBackend") self.filename = filename self.name = name self.read_only = read_only self.compression = compression self.compression_opts = compression_opts if dtype is None: self.dtype_set = False self.dtype = np.float64 else: self.dtype_set = True self.dtype = dtype @property def initialized(self): if not os.path.exists(self.filename): return False try: with self.open() as f: return self.name in f except (OSError, IOError): return False def open(self, mode="r"): if self.read_only and mode != "r": raise RuntimeError( "The backend has been loaded in read-only " "mode. Set `read_only = False` to make " "changes." ) f = h5py.File(self.filename, mode) if not self.dtype_set and self.name in f: g = f[self.name] if "chain" in g: self.dtype = g["chain"].dtype self.dtype_set = True return f def reset(self, nwalkers, ndim): """Clear the state of the chain and empty the backend Args: nwakers (int): The size of the ensemble ndim (int): The number of dimensions """ with self.open("a") as f: if self.name in f: del f[self.name] g = f.create_group(self.name) g.attrs["version"] = __version__ g.attrs["nwalkers"] = nwalkers g.attrs["ndim"] = ndim g.attrs["has_blobs"] = False g.attrs["iteration"] = 0 g.create_dataset( "accepted", data=np.zeros(nwalkers), compression=self.compression, compression_opts=self.compression_opts, ) g.create_dataset( "chain", (0, nwalkers, ndim), maxshape=(None, nwalkers, ndim), dtype=self.dtype, compression=self.compression, compression_opts=self.compression_opts, ) g.create_dataset( "log_prob", (0, nwalkers), maxshape=(None, nwalkers), dtype=self.dtype, compression=self.compression, compression_opts=self.compression_opts, ) def has_blobs(self): with self.open() as f: return f[self.name].attrs["has_blobs"] def get_value(self, name, flat=False, thin=1, discard=0): if not self.initialized: raise AttributeError( "You must run the sampler with " "'store == True' before accessing the " "results" ) with self.open() as f: g = f[self.name] iteration = g.attrs["iteration"] if iteration <= 0: raise AttributeError( "You must run the sampler with " "'store == True' before accessing the " "results" ) if name == "blobs" and not g.attrs["has_blobs"]: return None v = g[name][discard + thin - 1 : self.iteration : thin] if flat: s = list(v.shape[1:]) s[0] = np.prod(v.shape[:2]) return v.reshape(s) return v @property def shape(self): with self.open() as f: g = f[self.name] return g.attrs["nwalkers"], g.attrs["ndim"] @property def iteration(self): with self.open() as f: return f[self.name].attrs["iteration"] @property def accepted(self): with self.open() as f: return f[self.name]["accepted"][...] @property def random_state(self): with self.open() as f: elements = [ v for k, v in sorted(f[self.name].attrs.items()) if k.startswith("random_state_") ] return elements if len(elements) else None def grow(self, ngrow, blobs): """Expand the storage space by some number of samples Args: ngrow (int): The number of steps to grow the chain. blobs: The current array of blobs. This is used to compute the dtype for the blobs array. """ self._check_blobs(blobs) with self.open("a") as f: g = f[self.name] ntot = g.attrs["iteration"] + ngrow g["chain"].resize(ntot, axis=0) g["log_prob"].resize(ntot, axis=0) if blobs is not None: has_blobs = g.attrs["has_blobs"] if not has_blobs: nwalkers = g.attrs["nwalkers"] dt = np.dtype((blobs.dtype, blobs.shape[1:])) g.create_dataset( "blobs", (ntot, nwalkers), maxshape=(None, nwalkers), dtype=dt, compression=self.compression, compression_opts=self.compression_opts, ) else: g["blobs"].resize(ntot, axis=0) if g["blobs"].dtype.shape != blobs.shape[1:]: raise ValueError( "Existing blobs have shape {} but new blobs " "requested with shape {}".format( g["blobs"].dtype.shape, blobs.shape[1:] ) ) g.attrs["has_blobs"] = True def save_step(self, state, accepted): """Save a step to the backend Args: state (State): The :class:`State` of the ensemble. accepted (ndarray): An array of boolean flags indicating whether or not the proposal for each walker was accepted. """ self._check(state, accepted) with self.open("a") as f: g = f[self.name] iteration = g.attrs["iteration"] g["chain"][iteration, :, :] = state.coords g["log_prob"][iteration, :] = state.log_prob if state.blobs is not None: g["blobs"][iteration, :] = state.blobs g["accepted"][:] += accepted for i, v in enumerate(state.random_state): g.attrs["random_state_{0}".format(i)] = v g.attrs["iteration"] = iteration + 1 class TempHDFBackend(object): def __init__(self, dtype=None, compression=None, compression_opts=None): self.dtype = dtype self.filename = None self.compression = compression self.compression_opts = compression_opts def __enter__(self): f = NamedTemporaryFile( prefix="emcee-temporary-hdf5", suffix=".hdf5", delete=False ) f.close() self.filename = f.name return HDFBackend( f.name, "test", dtype=self.dtype, compression=self.compression, compression_opts=self.compression_opts, ) def __exit__(self, exception_type, exception_value, traceback): os.remove(self.filename) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee/emcee_version.py0000644000175100001710000000002600000000000017172 0ustar00runnerdocker__version__ = "3.1.1" ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/ensemble.py0000644000175100001710000006101300000000000016144 0ustar00runnerdocker# -*- coding: utf-8 -*- import warnings from itertools import count from typing import Dict, List, Optional, Union import numpy as np from .backends import Backend from .model import Model from .moves import StretchMove from .pbar import get_progress_bar from .state import State from .utils import deprecated, deprecation_warning __all__ = ["EnsembleSampler", "walkers_independent"] try: from collections.abc import Iterable except ImportError: # for py2.7, will be an Exception in 3.8 from collections import Iterable class EnsembleSampler(object): """An ensemble MCMC sampler If you are upgrading from an earlier version of emcee, you might notice that some arguments are now deprecated. The parameters that control the proposals have been moved to the :ref:`moves-user` interface (``a`` and ``live_dangerously``), and the parameters related to parallelization can now be controlled via the ``pool`` argument (:ref:`parallel`). Args: nwalkers (int): The number of walkers in the ensemble. ndim (int): Number of dimensions in the parameter space. log_prob_fn (callable): A function that takes a vector in the parameter space as input and returns the natural logarithm of the posterior probability (up to an additive constant) for that position. moves (Optional): This can be a single move object, a list of moves, or a "weighted" list of the form ``[(emcee.moves.StretchMove(), 0.1), ...]``. When running, the sampler will randomly select a move from this list (optionally with weights) for each proposal. (default: :class:`StretchMove`) args (Optional): A list of extra positional arguments for ``log_prob_fn``. ``log_prob_fn`` will be called with the sequence ``log_pprob_fn(p, *args, **kwargs)``. kwargs (Optional): A dict of extra keyword arguments for ``log_prob_fn``. ``log_prob_fn`` will be called with the sequence ``log_pprob_fn(p, *args, **kwargs)``. pool (Optional): An object with a ``map`` method that follows the same calling sequence as the built-in ``map`` function. This is generally used to compute the log-probabilities for the ensemble in parallel. backend (Optional): Either a :class:`backends.Backend` or a subclass (like :class:`backends.HDFBackend`) that is used to store and serialize the state of the chain. By default, the chain is stored as a set of numpy arrays in memory, but new backends can be written to support other mediums. vectorize (Optional[bool]): If ``True``, ``log_prob_fn`` is expected to accept a list of position vectors instead of just one. Note that ``pool`` will be ignored if this is ``True``. (default: ``False``) parameter_names (Optional[Union[List[str], Dict[str, List[int]]]]): names of individual parameters or groups of parameters. If specified, the ``log_prob_fn`` will recieve a dictionary of parameters, rather than a ``np.ndarray``. """ def __init__( self, nwalkers, ndim, log_prob_fn, pool=None, moves=None, args=None, kwargs=None, backend=None, vectorize=False, blobs_dtype=None, parameter_names: Optional[Union[Dict[str, int], List[str]]] = None, # Deprecated... a=None, postargs=None, threads=None, live_dangerously=None, runtime_sortingfn=None, ): # Warn about deprecated arguments if a is not None: deprecation_warning( "The 'a' argument is deprecated, use 'moves' instead" ) if threads is not None: deprecation_warning("The 'threads' argument is deprecated") if runtime_sortingfn is not None: deprecation_warning( "The 'runtime_sortingfn' argument is deprecated" ) if live_dangerously is not None: deprecation_warning( "The 'live_dangerously' argument is deprecated" ) # Parse the move schedule if moves is None: self._moves = [StretchMove()] 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) self.pool = pool self.vectorize = vectorize self.blobs_dtype = blobs_dtype self.ndim = ndim self.nwalkers = nwalkers self.backend = Backend() if backend is None else backend # Deal with re-used backends if not self.backend.initialized: self._previous_state = None self.reset() state = np.random.get_state() else: # Check the backend shape if self.backend.shape != (self.nwalkers, self.ndim): raise ValueError( ( "the shape of the backend ({0}) is incompatible with the " "shape of the sampler ({1})" ).format(self.backend.shape, (self.nwalkers, self.ndim)) ) # Get the last random state state = self.backend.random_state if state is None: state = np.random.get_state() # Grab the last step so that we can restart it = self.backend.iteration if it > 0: self._previous_state = self.get_last_sample() # This is a random number generator that we can easily set the state # of without affecting the numpy-wide generator self._random = np.random.mtrand.RandomState() self._random.set_state(state) # Do a little bit of _magic_ to make the likelihood call with # ``args`` and ``kwargs`` pickleable. self.log_prob_fn = _FunctionWrapper(log_prob_fn, args, kwargs) # Save the parameter names self.params_are_named: bool = parameter_names is not None if self.params_are_named: assert isinstance(parameter_names, (list, dict)) # Don't support vectorizing yet msg = "named parameters with vectorization unsupported for now" assert not self.vectorize, msg # Check for duplicate names dupes = set() uniq = [] for name in parameter_names: if name not in dupes: uniq.append(name) dupes.add(name) msg = f"duplicate paramters: {dupes}" assert len(uniq) == len(parameter_names), msg if isinstance(parameter_names, list): # Check for all named msg = "name all parameters or set `parameter_names` to `None`" assert len(parameter_names) == ndim, msg # Convert a list to a dict parameter_names: Dict[str, int] = { name: i for i, name in enumerate(parameter_names) } # Check not too many names msg = "too many names" assert len(parameter_names) <= ndim, msg # Check all indices appear values = [ v if isinstance(v, list) else [v] for v in parameter_names.values() ] values = [item for sublist in values for item in sublist] values = set(values) msg = f"not all values appear -- set should be 0 to {ndim-1}" assert values == set(np.arange(ndim)), msg self.parameter_names = parameter_names @property def random_state(self): """ The state of the internal random number generator. In practice, it's the result of calling ``get_state()`` on a ``numpy.random.mtrand.RandomState`` object. You can try to set this property but be warned that if you do this and it fails, it will do so silently. """ return self._random.get_state() @random_state.setter # NOQA def random_state(self, state): """ Try to set the state of the random number generator but fail silently if it doesn't work. Don't say I didn't warn you... """ try: self._random.set_state(state) except: pass @property def iteration(self): return self.backend.iteration def reset(self): """ Reset the bookkeeping parameters """ self.backend.reset(self.nwalkers, self.ndim) def __getstate__(self): # In order to be generally picklable, we need to discard the pool # object before trying. d = self.__dict__ d["pool"] = None return d def sample( self, initial_state, log_prob0=None, # Deprecated rstate0=None, # Deprecated blobs0=None, # Deprecated iterations=1, tune=False, skip_initial_state_check=False, thin_by=1, thin=None, store=True, progress=False, progress_kwargs=None, ): """Advance the chain as a generator Args: initial_state (State or ndarray[nwalkers, ndim]): The initial :class:`State` or positions of the walkers in the parameter space. iterations (Optional[int or NoneType]): The number of steps to generate. ``None`` generates an infinite stream (requires ``store=False``). tune (Optional[bool]): If ``True``, the parameters of some moves will be automatically tuned. thin_by (Optional[int]): 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. store (Optional[bool]): By default, the sampler stores (in memory) the positions and log-probabilities of the samples in the chain. If you are using another method to store the samples to a file or if you don't need to analyze the samples after the fact (for burn-in for example) set ``store`` to ``False``. progress (Optional[bool or str]): If ``True``, a progress bar will be shown as the sampler progresses. If a string, will select a specific ``tqdm`` progress bar - most notable is ``'notebook'``, which shows a progress bar suitable for Jupyter notebooks. If ``False``, no progress bar will be shown. progress_kwargs (Optional[dict]): A ``dict`` of keyword arguments to be passed to the tqdm call. skip_initial_state_check (Optional[bool]): If ``True``, a check that the initial_state can fully explore the space will be skipped. (default: ``False``) Every ``thin_by`` steps, this generator yields the :class:`State` of the ensemble. """ if iterations is None and store: raise ValueError("'store' must be False when 'iterations' is None") # Interpret the input as a walker state and check the dimensions. state = State(initial_state, copy=True) state_shape = np.shape(state.coords) if state_shape != (self.nwalkers, self.ndim): raise ValueError(f"incompatible input dimensions {state_shape}") if (not skip_initial_state_check) and ( not walkers_independent(state.coords) ): raise ValueError( "Initial state has a large condition number. " "Make sure that your walkers are linearly independent for the " "best performance" ) # Try to set the initial value of the random number generator. This # fails silently if it doesn't work but that's what we want because # we'll just interpret any garbage as letting the generator stay in # it's current state. if rstate0 is not None: deprecation_warning( "The 'rstate0' argument is deprecated, use a 'State' " "instead" ) state.random_state = rstate0 self.random_state = state.random_state # If the initial log-probabilities were not provided, calculate them # now. if log_prob0 is not None: deprecation_warning( "The 'log_prob0' argument is deprecated, use a 'State' " "instead" ) state.log_prob = log_prob0 if blobs0 is not None: deprecation_warning( "The 'blobs0' argument is deprecated, use a 'State' instead" ) state.blobs = blobs0 if state.log_prob is None: state.log_prob, state.blobs = self.compute_log_prob(state.coords) if np.shape(state.log_prob) != (self.nwalkers,): raise ValueError("incompatible input dimensions") # Check to make sure that the probability function didn't return # ``np.nan``. if np.any(np.isnan(state.log_prob)): raise ValueError("The initial log_prob was NaN") # Deal with deprecated thin argument if thin is not None: deprecation_warning( "The 'thin' argument is deprecated. " "Use 'thin_by' instead." ) # Check that the thin keyword is reasonable. thin = int(thin) if thin <= 0: raise ValueError("Invalid thinning argument") yield_step = 1 checkpoint_step = thin if store: nsaves = iterations // checkpoint_step self.backend.grow(nsaves, state.blobs) else: # Check that the thin keyword is reasonable. thin_by = int(thin_by) if thin_by <= 0: raise ValueError("Invalid thinning argument") yield_step = thin_by checkpoint_step = thin_by if store: self.backend.grow(iterations, state.blobs) # Set up a wrapper around the relevant model functions if self.pool is not None: map_fn = self.pool.map else: map_fn = map model = Model( self.log_prob_fn, self.compute_log_prob, map_fn, self._random ) if progress_kwargs is None: progress_kwargs = {} # Inject the progress bar total = None if iterations is None else iterations * yield_step with get_progress_bar(progress, total, **progress_kwargs) as pbar: i = 0 for _ in count() if iterations is None else range(iterations): for _ in range(yield_step): # Choose a random move move = self._random.choice(self._moves, p=self._weights) # Propose state, accepted = move.propose(model, state) state.random_state = self.random_state if tune: move.tune(state, accepted) # Save the new step if store and (i + 1) % checkpoint_step == 0: self.backend.save_step(state, accepted) pbar.update(1) i += 1 # Yield the result as an iterator so that the user can do all # sorts of fun stuff with the results so far. yield state def run_mcmc(self, initial_state, nsteps, **kwargs): """ Iterate :func:`sample` for ``nsteps`` iterations and return the result Args: initial_state: The initial state or position vector. Can also be ``None`` to resume from where :func:``run_mcmc`` left off the last time it executed. nsteps: The number of steps to run. Other parameters are directly passed to :func:`sample`. This method returns the most recent result from :func:`sample`. """ if initial_state is None: if self._previous_state is None: raise ValueError( "Cannot have `initial_state=None` if run_mcmc has never " "been called." ) initial_state = self._previous_state results = None for results in self.sample(initial_state, iterations=nsteps, **kwargs): pass # Store so that the ``initial_state=None`` case will work self._previous_state = results return results 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. This method 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") # If the parmaeters are named, then switch to dictionaries if self.params_are_named: p = ndarray_to_list_of_dicts(p, self.parameter_names) # Run the log-probability calculations (optionally in parallel). if self.vectorize: results = self.log_prob_fn(p) else: # If the `pool` property of the sampler has been set (i.e. we want # to use `multiprocessing`), use the `pool`'s map method. # Otherwise, just use the built-in `map` function. if self.pool is not None: map_func = self.pool.map else: map_func = map results = list(map_func(self.log_prob_fn, 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: with warnings.catch_warnings(record=True): warnings.simplefilter( "error", np.VisibleDeprecationWarning ) try: dt = np.atleast_1d(blob[0]).dtype except Warning: deprecation_warning( "You have provided blobs that are not all the " "same shape or size. This means they must be " "placed in an object array. Numpy has " "deprecated this automatic detection, so " "please specify " "blobs_dtype=np.dtype('object')" ) dt = np.dtype("object") except ValueError: dt = np.dtype("object") if dt.kind in "US": # Strings need to be object arrays or we risk truncation 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 @property def acceptance_fraction(self): """The fraction of proposed steps that were accepted""" return self.backend.accepted / float(self.backend.iteration) @property @deprecated("get_chain()") def chain(self): # pragma: no cover chain = self.get_chain() return np.swapaxes(chain, 0, 1) @property @deprecated("get_chain(flat=True)") def flatchain(self): # pragma: no cover return self.get_chain(flat=True) @property @deprecated("get_log_prob()") def lnprobability(self): # pragma: no cover log_prob = self.get_log_prob() return np.swapaxes(log_prob, 0, 1) @property @deprecated("get_log_prob(flat=True)") def flatlnprobability(self): # pragma: no cover return self.get_log_prob(flat=True) @property @deprecated("get_blobs()") def blobs(self): # pragma: no cover return self.get_blobs() @property @deprecated("get_blobs(flat=True)") def flatblobs(self): # pragma: no cover return self.get_blobs(flat=True) @property @deprecated("get_autocorr_time") def acor(self): # pragma: no cover return self.get_autocorr_time() def get_chain(self, **kwargs): return self.get_value("chain", **kwargs) get_chain.__doc__ = Backend.get_chain.__doc__ def get_blobs(self, **kwargs): return self.get_value("blobs", **kwargs) get_blobs.__doc__ = Backend.get_blobs.__doc__ def get_log_prob(self, **kwargs): return self.get_value("log_prob", **kwargs) get_log_prob.__doc__ = Backend.get_log_prob.__doc__ def get_last_sample(self, **kwargs): return self.backend.get_last_sample() get_last_sample.__doc__ = Backend.get_last_sample.__doc__ def get_value(self, name, **kwargs): return self.backend.get_value(name, **kwargs) def get_autocorr_time(self, **kwargs): return self.backend.get_autocorr_time(**kwargs) get_autocorr_time.__doc__ = Backend.get_autocorr_time.__doc__ class _FunctionWrapper(object): """ This is a hack to make the likelihood function pickleable when ``args`` or ``kwargs`` are also included. """ def __init__(self, f, args, kwargs): self.f = f self.args = args or [] self.kwargs = kwargs or {} def __call__(self, x): try: return self.f(x, *self.args, **self.kwargs) except: # pragma: no cover import traceback print("emcee: Exception while calling your likelihood function:") print(" params:", x) print(" args:", self.args) print(" kwargs:", self.kwargs) print(" exception:") traceback.print_exc() raise def walkers_independent(coords): if not np.all(np.isfinite(coords)): return False C = coords - np.mean(coords, axis=0)[None, :] C_colmax = np.amax(np.abs(C), axis=0) if np.any(C_colmax == 0): return False C /= C_colmax C_colsum = np.sqrt(np.sum(C ** 2, axis=0)) C /= C_colsum return np.linalg.cond(C.astype(float)) <= 1e8 def walkers_independent_cov(coords): C = np.cov(coords, rowvar=False) if np.any(np.isnan(C)): return False return _scaled_cond(np.atleast_2d(C)) <= 1e8 def _scaled_cond(a): asum = np.sqrt((a ** 2).sum(axis=0))[None, :] if np.any(asum == 0): return np.inf b = a / asum bsum = np.sqrt((b ** 2).sum(axis=1))[:, None] if np.any(bsum == 0): return np.inf c = b / bsum return np.linalg.cond(c.astype(float)) def ndarray_to_list_of_dicts( x: np.ndarray, key_map: Dict[str, Union[int, List[int]]] ) -> List[Dict[str, Union[np.number, np.ndarray]]]: """ A helper function to convert a ``np.ndarray`` into a list of dictionaries of parameters. Used when parameters are named. Args: x (np.ndarray): parameter array of shape ``(N, n_dim)``, where ``N`` is an integer key_map (Dict[str, Union[int, List[int]]): Returns: list of dictionaries of parameters """ return [{key: xi[val] for key, val in key_map.items()} for xi in x] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/interruptible_pool.py0000644000175100001710000000025200000000000020271 0ustar00runnerdocker# -*- coding: utf-8 -*- # The standard library now has an interruptible pool from multiprocessing.pool import Pool as InterruptiblePool __all__ = ["InterruptiblePool"] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/model.py0000644000175100001710000000026100000000000015450 0ustar00runnerdocker# -*- coding: utf-8 -*- from collections import namedtuple __all__ = ["Model"] Model = namedtuple( "Model", ("log_prob_fn", "compute_log_prob_fn", "map_fn", "random") ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/src/emcee/moves/0000755000175100001710000000000000000000000015130 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/__init__.py0000644000175100001710000000070300000000000017241 0ustar00runnerdocker# -*- coding: utf-8 -*- from .de import DEMove from .de_snooker import DESnookerMove from .gaussian import GaussianMove from .kde import KDEMove from .mh import MHMove from .move import Move from .red_blue import RedBlueMove from .stretch import StretchMove from .walk import WalkMove __all__ = [ "Move", "MHMove", "GaussianMove", "RedBlueMove", "StretchMove", "WalkMove", "KDEMove", "DEMove", "DESnookerMove", ] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/de.py0000644000175100001710000000305500000000000016075 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .red_blue import RedBlueMove __all__ = ["DEMove"] class DEMove(RedBlueMove): r"""A proposal using differential evolution. This `Differential evolution proposal `_ is implemented following `Nelson et al. (2013) `_. Args: sigma (float): The standard deviation of the Gaussian used to stretch the proposal vector. gamma0 (Optional[float]): The mean stretch factor for the proposal vector. By default, it is :math:`2.38 / \sqrt{2\,\mathrm{ndim}}` as recommended by the two references. """ def __init__(self, sigma=1.0e-5, gamma0=None, **kwargs): self.sigma = sigma self.gamma0 = gamma0 kwargs["nsplits"] = 3 super(DEMove, self).__init__(**kwargs) def setup(self, coords): self.g0 = self.gamma0 if self.g0 is None: # Pure MAGIC: ndim = coords.shape[1] self.g0 = 2.38 / np.sqrt(2 * ndim) def get_proposal(self, s, c, random): Ns = len(s) Nc = list(map(len, c)) ndim = s.shape[1] q = np.empty((Ns, ndim), dtype=np.float64) f = self.sigma * random.randn(Ns) for i in range(Ns): w = np.array([c[j][random.randint(Nc[j])] for j in range(2)]) random.shuffle(w) g = np.diff(w, axis=0) * self.g0 + f[i] q[i] = s[i] + g return q, np.zeros(Ns, dtype=np.float64) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/de_snooker.py0000644000175100001710000000273200000000000017636 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .red_blue import RedBlueMove __all__ = ["DESnookerMove"] class DESnookerMove(RedBlueMove): """A snooker proposal using differential evolution. Based on `Ter Braak & Vrugt (2008) `_. Credit goes to GitHub user `mdanthony17 `_ for proposing this as an addition to the original emcee package. Args: gammas (Optional[float]): The mean stretch factor for the proposal vector. By default, it is :math:`1.7` as recommended by the reference. """ def __init__(self, gammas=1.7, **kwargs): self.gammas = gammas kwargs["nsplits"] = 4 super(DESnookerMove, self).__init__(**kwargs) def get_proposal(self, s, c, random): Ns = len(s) Nc = list(map(len, c)) ndim = s.shape[1] q = np.empty((Ns, ndim), dtype=np.float64) metropolis = np.empty(Ns, dtype=np.float64) for i in range(Ns): w = np.array([c[j][random.randint(Nc[j])] for j in range(3)]) random.shuffle(w) z, z1, z2 = w delta = s[i] - z norm = np.linalg.norm(delta) u = delta / np.sqrt(norm) q[i] = s[i] + u * self.gammas * (np.dot(u, z1) - np.dot(u, z2)) metropolis[i] = np.log(np.linalg.norm(q[i] - z)) - np.log(norm) return q, 0.5 * (ndim - 1.0) * metropolis ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/gaussian.py0000644000175100001710000000765000000000000017324 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .mh import MHMove __all__ = ["GaussianMove"] class GaussianMove(MHMove): """A Metropolis step with a Gaussian proposal function. Args: cov: The covariance of the proposal function. This can be a scalar, vector, or matrix and the proposal will be assumed isotropic, axis-aligned, or general respectively. mode (Optional): Select the method used for updating parameters. This can be one of ``"vector"``, ``"random"``, or ``"sequential"``. The ``"vector"`` mode updates all dimensions simultaneously, ``"random"`` randomly selects a dimension and only updates that one, and ``"sequential"`` loops over dimensions and updates each one in turn. factor (Optional[float]): If provided the proposal will be made with a standard deviation uniformly selected from the range ``exp(U(-log(factor), log(factor))) * cov``. This is invalid for the ``"vector"`` mode. Raises: ValueError: If the proposal dimensions are invalid or if any of any of the other arguments are inconsistent. """ def __init__(self, cov, mode="vector", factor=None): # Parse the proposal type. try: float(cov) except TypeError: cov = np.atleast_1d(cov) if len(cov.shape) == 1: # A diagonal proposal was given. ndim = len(cov) proposal = _diagonal_proposal(np.sqrt(cov), factor, mode) elif len(cov.shape) == 2 and cov.shape[0] == cov.shape[1]: # The full, square covariance matrix was given. ndim = cov.shape[0] proposal = _proposal(cov, factor, mode) else: raise ValueError("Invalid proposal scale dimensions") else: # This was a scalar proposal. ndim = None proposal = _isotropic_proposal(np.sqrt(cov), factor, mode) super(GaussianMove, self).__init__(proposal, ndim=ndim) class _isotropic_proposal(object): allowed_modes = ["vector", "random", "sequential"] def __init__(self, scale, factor, mode): self.index = 0 self.scale = scale if factor is None: self._log_factor = None else: if factor < 1.0: raise ValueError("'factor' must be >= 1.0") self._log_factor = np.log(factor) if mode not in self.allowed_modes: raise ValueError( ( "'{0}' is not a recognized mode. " "Please select from: {1}" ).format(mode, self.allowed_modes) ) self.mode = mode def get_factor(self, rng): if self._log_factor is None: return 1.0 return np.exp(rng.uniform(-self._log_factor, self._log_factor)) def get_updated_vector(self, rng, x0): return x0 + self.get_factor(rng) * self.scale * rng.randn(*(x0.shape)) def __call__(self, x0, rng): nw, nd = x0.shape xnew = self.get_updated_vector(rng, x0) if self.mode == "random": m = (range(nw), rng.randint(x0.shape[-1], size=nw)) elif self.mode == "sequential": m = (range(nw), self.index % nd + np.zeros(nw, dtype=int)) self.index = (self.index + 1) % nd else: return xnew, np.zeros(nw) x = np.array(x0) x[m] = xnew[m] return x, np.zeros(nw) class _diagonal_proposal(_isotropic_proposal): def get_updated_vector(self, rng, x0): return x0 + self.get_factor(rng) * self.scale * rng.randn(*(x0.shape)) class _proposal(_isotropic_proposal): allowed_modes = ["vector"] def get_updated_vector(self, rng, x0): return x0 + self.get_factor(rng) * rng.multivariate_normal( np.zeros(len(self.scale)), self.scale ) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/kde.py0000644000175100001710000000231300000000000016244 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .red_blue import RedBlueMove try: from scipy.stats import gaussian_kde except ImportError: gaussian_kde = None __all__ = ["KDEMove"] class KDEMove(RedBlueMove): """A proposal using a KDE of the complementary ensemble This is a simplified version of the method used in `kombine `_. If you use this proposal, you should use *a lot* of walkers in your ensemble. Args: bw_method: The bandwidth estimation method. See `the scipy docs `_ for allowed values. """ def __init__(self, bw_method=None, **kwargs): if gaussian_kde is None: raise ImportError( "you need scipy.stats.gaussian_kde to use the KDEMove" ) self.bw_method = bw_method super(KDEMove, self).__init__(**kwargs) def get_proposal(self, s, c, random): c = np.concatenate(c, axis=0) kde = gaussian_kde(c.T, bw_method=self.bw_method) q = kde.resample(len(s)) factor = kde.logpdf(s.T) - kde.logpdf(q) return q.T, factor ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/mh.py0000644000175100001710000000453100000000000016111 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from ..state import State from .move import Move __all__ = ["MHMove"] class MHMove(Move): r"""A general Metropolis-Hastings proposal Concrete implementations can be made by providing a ``proposal_function`` argument that implements the proposal as described below. For standard Gaussian Metropolis moves, :class:`moves.GaussianMove` can be used. Args: proposal_function: The proposal function. It should take 2 arguments: a numpy-compatible random number generator and a ``(K, ndim)`` list of coordinate vectors. This function should return the proposed position and the log-ratio of the proposal probabilities (:math:`\ln q(x;\,x^\prime) - \ln q(x^\prime;\,x)` where :math:`x^\prime` is the proposed coordinate). ndim (Optional[int]): If this proposal is only valid for a specific dimension of parameter space, set that here. """ def __init__(self, proposal_function, ndim=None): self.ndim = ndim self.get_proposal = proposal_function def propose(self, model, state): """Use the move to generate a proposal and compute the acceptance Args: coords: The initial coordinates of the walkers. log_probs: The initial log probabilities of the walkers. log_prob_fn: A function that computes the log probabilities for a subset of walkers. random: A numpy-compatible random number state. """ # Check to make sure that the dimensions match. nwalkers, ndim = state.coords.shape if self.ndim is not None and self.ndim != ndim: raise ValueError("Dimension mismatch in proposal") # Get the move-specific proposal. q, factors = self.get_proposal(state.coords, model.random) # Compute the lnprobs of the proposed position. new_log_probs, new_blobs = model.compute_log_prob_fn(q) # Loop over the walkers and update them accordingly. lnpdiff = new_log_probs - state.log_prob + factors accepted = np.log(model.random.rand(nwalkers)) < lnpdiff # Update the parameters new_state = State(q, log_prob=new_log_probs, blobs=new_blobs) state = self.update(state, new_state, accepted) return state, accepted ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/move.py0000644000175100001710000000310700000000000016451 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np __all__ = ["Move"] class Move(object): def tune(self, state, accepted): pass def update(self, old_state, new_state, accepted, subset=None): """Update a given subset of the ensemble with an accepted proposal Args: coords: The original ensemble coordinates. log_probs: The original log probabilities of the walkers. blobs: The original blobs. new_coords: The proposed coordinates. new_log_probs: The proposed log probabilities. new_blobs: The proposed blobs. accepted: A vector of booleans indicating which walkers were accepted. subset (Optional): A boolean mask indicating which walkers were included in the subset. This can be used, for example, when updating only the primary ensemble in a :class:`RedBlueMove`. """ if subset is None: subset = np.ones(len(old_state.coords), dtype=bool) m1 = subset & accepted m2 = accepted[subset] old_state.coords[m1] = new_state.coords[m2] old_state.log_prob[m1] = new_state.log_prob[m2] if new_state.blobs is not None: if old_state.blobs is None: raise ValueError( "If you start sampling with a given log_prob, " "you also need to provide the current list of " "blobs at that position." ) old_state.blobs[m1] = new_state.blobs[m2] return old_state ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/red_blue.py0000644000175100001710000000765000000000000017273 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from ..state import State from .move import Move __all__ = ["RedBlueMove"] class RedBlueMove(Move): """ An abstract red-blue ensemble move with parallelization as described in `Foreman-Mackey et al. (2013) `_. Args: nsplits (Optional[int]): The number of sub-ensembles to use. Each sub-ensemble is updated in parallel using the other sets as the complementary ensemble. The default value is ``2`` and you probably won't need to change that. randomize_split (Optional[bool]): Randomly shuffle walkers between sub-ensembles. The same number of walkers will be assigned to each sub-ensemble on each iteration. By default, this is ``True``. live_dangerously (Optional[bool]): By default, an update will fail with a ``RuntimeError`` if the number of walkers is smaller than twice the dimension of the problem because the walkers would then be stuck on a low dimensional subspace. This can be avoided by switching between the stretch move and, for example, a Metropolis-Hastings step. If you want to do this and suppress the error, set ``live_dangerously = True``. Thanks goes (once again) to @dstndstn for this wonderful terminology. """ def __init__( self, nsplits=2, randomize_split=True, live_dangerously=False ): self.nsplits = int(nsplits) self.live_dangerously = live_dangerously self.randomize_split = randomize_split def setup(self, coords): pass def get_proposal(self, sample, complement, random): raise NotImplementedError( "The proposal must be implemented by " "subclasses" ) def propose(self, model, state): """Use the move to generate a proposal and compute the acceptance Args: coords: The initial coordinates of the walkers. log_probs: The initial log probabilities of the walkers. log_prob_fn: A function that computes the log probabilities for a subset of walkers. random: A numpy-compatible random number state. """ # Check that the dimensions are compatible. nwalkers, ndim = state.coords.shape if nwalkers < 2 * ndim and not self.live_dangerously: raise RuntimeError( "It is unadvisable to use a red-blue move " "with fewer walkers than twice the number of " "dimensions." ) # Run any move-specific setup. self.setup(state.coords) # Split the ensemble in half and iterate over these two halves. accepted = np.zeros(nwalkers, dtype=bool) all_inds = np.arange(nwalkers) inds = all_inds % self.nsplits if self.randomize_split: model.random.shuffle(inds) for split in range(self.nsplits): S1 = inds == split # Get the two halves of the ensemble. sets = [state.coords[inds == j] for j in range(self.nsplits)] s = sets[split] c = sets[:split] + sets[split + 1 :] # Get the move-specific proposal. q, factors = self.get_proposal(s, c, model.random) # Compute the lnprobs of the proposed position. new_log_probs, new_blobs = model.compute_log_prob_fn(q) # Loop over the walkers and update them accordingly. for i, (j, f, nlp) in enumerate( zip(all_inds[S1], factors, new_log_probs) ): lnpdiff = f + nlp - state.log_prob[j] if lnpdiff > np.log(model.random.rand()): accepted[j] = True new_state = State(q, log_prob=new_log_probs, blobs=new_blobs) state = self.update(state, new_state, accepted, S1) return state, accepted ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/stretch.py0000644000175100001710000000162600000000000017163 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .red_blue import RedBlueMove __all__ = ["StretchMove"] class StretchMove(RedBlueMove): """ A `Goodman & Weare (2010) `_ "stretch move" with parallelization as described in `Foreman-Mackey et al. (2013) `_. :param a: (optional) The stretch scale parameter. (default: ``2.0``) """ def __init__(self, a=2.0, **kwargs): self.a = a super(StretchMove, self).__init__(**kwargs) def get_proposal(self, s, c, random): c = np.concatenate(c, axis=0) Ns, Nc = len(s), len(c) ndim = s.shape[1] zz = ((self.a - 1.0) * random.rand(Ns) + 1) ** 2.0 / self.a factors = (ndim - 1.0) * np.log(zz) rint = random.randint(Nc, size=(Ns,)) return c[rint] - (c[rint] - s) * zz[:, None], factors ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/moves/walk.py0000644000175100001710000000210000000000000016431 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from .red_blue import RedBlueMove __all__ = ["WalkMove"] class WalkMove(RedBlueMove): """ A `Goodman & Weare (2010) `_ "walk move" with parallelization as described in `Foreman-Mackey et al. (2013) `_. :param s: (optional) The number of helper walkers to use. By default it will use all the walkers in the complement. """ def __init__(self, s=None, **kwargs): self.s = s super(WalkMove, self).__init__(**kwargs) def get_proposal(self, s, c, random): c = np.concatenate(c, axis=0) Ns, Nc = len(s), len(c) ndim = s.shape[1] q = np.empty((Ns, ndim), dtype=np.float64) s0 = Nc if self.s is None else self.s for i in range(Ns): inds = random.choice(Nc, s0, replace=False) cov = np.atleast_2d(np.cov(c[inds], rowvar=0)) q[i] = random.multivariate_normal(s[i], cov) return q, np.zeros(Ns, dtype=np.float64) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/mpi_pool.py0000644000175100001710000000064400000000000016173 0ustar00runnerdocker# -*- coding: utf-8 -*- try: from schwimmbad import MPIPool except ImportError: class MPIPool(object): def __init__(self, *args, **kwargs): raise ImportError( "The MPIPool from emcee has been forked to " "https://github.com/adrn/schwimmbad, " "please install that package to continue using the MPIPool" ) __all__ = ["MPIPool"] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/pbar.py0000644000175100001710000000264100000000000015300 0ustar00runnerdocker# -*- coding: utf-8 -*- import logging __all__ = ["get_progress_bar"] logger = logging.getLogger(__name__) try: import tqdm except ImportError: tqdm = None class _NoOpPBar(object): """This class implements the progress bar interface but does nothing""" def __init__(self): pass def __enter__(self, *args, **kwargs): return self def __exit__(self, *args, **kwargs): pass def update(self, count): pass def get_progress_bar(display, total, **kwargs): """Get a progress bar interface with given properties If the tqdm library is not installed, this will always return a "progress bar" that does nothing. Args: display (bool or str): Should the bar actually show the progress? Or a string to indicate which tqdm bar to use. total (int): The total size of the progress bar. kwargs (dict): Optional keyword arguments to be passed to the tqdm call. """ if display: if tqdm is None: logger.warning( "You must install the tqdm library to use progress " "indicators with emcee" ) return _NoOpPBar() else: if display is True: return tqdm.tqdm(total=total, **kwargs) else: return getattr(tqdm, "tqdm_" + display)(total=total, **kwargs) return _NoOpPBar() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/ptsampler.py0000644000175100001710000000067200000000000016365 0ustar00runnerdocker# -*- coding: utf-8 -*- try: from ptemcee import Sampler as PTSampler except ImportError: class PTSampler(object): def __init__(self, *args, **kwargs): raise ImportError( "The PTSampler from emcee has been forked to " "https://github.com/willvousden/ptemcee, " "please install that package to continue using the PTSampler" ) __all__ = ["PTSampler"] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/state.py0000644000175100001710000000362600000000000015500 0ustar00runnerdocker# -*- coding: utf-8 -*- from copy import deepcopy import numpy as np __all__ = ["State"] class State(object): """The state of the ensemble during an MCMC run For backwards compatibility, this will unpack into ``coords, log_prob, (blobs), random_state`` when iterated over (where ``blobs`` will only be included if it exists and is not ``None``). Args: coords (ndarray[nwalkers, ndim]): The current positions of the walkers in the parameter space. log_prob (ndarray[nwalkers, ndim], Optional): Log posterior probabilities for the walkers at positions given by ``coords``. blobs (Optional): The metadata “blobs†associated with the current position. The value is only returned if lnpostfn returns blobs too. random_state (Optional): The current state of the random number generator. """ __slots__ = "coords", "log_prob", "blobs", "random_state" def __init__( self, coords, log_prob=None, blobs=None, random_state=None, copy=False ): dc = deepcopy if copy else lambda x: x if hasattr(coords, "coords"): self.coords = dc(coords.coords) self.log_prob = dc(coords.log_prob) self.blobs = dc(coords.blobs) self.random_state = dc(coords.random_state) return self.coords = dc(np.atleast_2d(coords)) self.log_prob = dc(log_prob) self.blobs = dc(blobs) self.random_state = dc(random_state) def __repr__(self): return "State({0}, log_prob={1}, blobs={2}, random_state={3})".format( self.coords, self.log_prob, self.blobs, self.random_state ) def __iter__(self): if self.blobs is None: return iter((self.coords, self.log_prob, self.random_state)) return iter( (self.coords, self.log_prob, self.random_state, self.blobs) ) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/src/emcee/tests/0000755000175100001710000000000000000000000015141 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/__init__.py0000644000175100001710000000000000000000000017240 0ustar00runnerdocker././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/src/emcee/tests/integration/0000755000175100001710000000000000000000000017464 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/__init__.py0000644000175100001710000000000000000000000021563 0ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_de.py0000644000175100001710000000065300000000000021471 0ustar00runnerdocker# -*- coding: utf-8 -*- from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = ["test_normal_de", "test_normal_de_no_gamma", "test_uniform_de"] def test_normal_de(**kwargs): _test_normal(moves.DEMove(), **kwargs) def test_normal_de_no_gamma(**kwargs): _test_normal(moves.DEMove(gamma0=1.0), **kwargs) def test_uniform_de(**kwargs): _test_uniform(moves.DEMove(), **kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_de_snooker.py0000644000175100001710000000057400000000000023233 0ustar00runnerdocker# -*- coding: utf-8 -*- from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = ["test_normal_de_snooker", "test_uniform_de_snooker"] def test_normal_de_snooker(**kwargs): kwargs["nsteps"] = 4000 _test_normal(moves.DESnookerMove(), **kwargs) def test_uniform_de_snooker(**kwargs): _test_uniform(moves.DESnookerMove(), **kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_gaussian.py0000644000175100001710000000410100000000000022703 0ustar00runnerdocker# -*- coding: utf-8 -*- from itertools import product import numpy as np import pytest from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = [ "test_normal_gaussian", "test_uniform_gaussian", "test_normal_gaussian_nd", ] @pytest.mark.parametrize("mode,factor", product(["vector"], [None, 2.0, 5.0])) def test_normal_gaussian(mode, factor, **kwargs): _test_normal(moves.GaussianMove(0.5, mode=mode, factor=factor), **kwargs) @pytest.mark.parametrize( "mode,factor", product(["vector", "random", "sequential"], [None, 2.0]) ) def test_normal_gaussian_nd(mode, factor, **kwargs): ndim = 3 kwargs["nsteps"] = 8000 # Isotropic. _test_normal( moves.GaussianMove(0.5, factor=factor, mode=mode), ndim=ndim, **kwargs ) # Axis-aligned. _test_normal( moves.GaussianMove(0.5 * np.ones(ndim), factor=factor, mode=mode), ndim=ndim, **kwargs, ) with pytest.raises(ValueError): _test_normal( moves.GaussianMove( 0.5 * np.ones(ndim - 1), factor=factor, mode=mode ), ndim=ndim, **kwargs, ) # Full matrix. if mode == "vector": _test_normal( moves.GaussianMove( np.diag(0.5 * np.ones(ndim)), factor=factor, mode=mode ), ndim=ndim, **kwargs, ) with pytest.raises(ValueError): _test_normal( moves.GaussianMove(np.diag(0.5 * np.ones(ndim - 1))), ndim=ndim, **kwargs, ) else: with pytest.raises(ValueError): _test_normal( moves.GaussianMove( np.diag(0.5 * np.ones(ndim)), factor=factor, mode=mode ), ndim=ndim, **kwargs, ) @pytest.mark.parametrize("mode,factor", product(["vector"], [None, 2.0, 5.0])) def test_uniform_gaussian(mode, factor, **kwargs): _test_uniform(moves.GaussianMove(0.5, factor=factor, mode=mode), **kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_kde.py0000644000175100001710000000127100000000000021641 0ustar00runnerdocker# -*- coding: utf-8 -*- try: import scipy except ImportError: scipy = None import pytest from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = ["test_normal_kde", "test_uniform_kde", "test_nsplits_kde"] @pytest.mark.skipif(scipy is None, reason="scipy is not available") def test_normal_kde(**kwargs): _test_normal(moves.KDEMove(), **kwargs) @pytest.mark.skipif(scipy is None, reason="scipy is not available") def test_uniform_kde(**kwargs): _test_uniform(moves.KDEMove(), **kwargs) @pytest.mark.skipif(scipy is None, reason="scipy is not available") def test_nsplits_kde(**kwargs): _test_normal(moves.KDEMove(nsplits=5), **kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_longdouble.py0000644000175100001710000000311400000000000023226 0ustar00runnerdockerimport numpy as np import pytest import emcee from emcee.backends.hdf import TempHDFBackend, does_hdf5_support_longdouble def test_longdouble_doesnt_crash_bug_312(): def log_prob(x, ivar): return -0.5 * np.sum(ivar * x ** 2) ndim, nwalkers = 5, 20 ivar = 1.0 / np.random.rand(ndim).astype(np.longdouble) p0 = np.random.randn(nwalkers, ndim).astype(np.longdouble) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar]) sampler.run_mcmc(p0, 100) @pytest.mark.parametrize("cls", emcee.backends.get_test_backends()) def test_longdouble_actually_needed(cls): if issubclass(cls, TempHDFBackend) and not does_hdf5_support_longdouble(): pytest.xfail("HDF5 does not support long double on this platform") mjd = np.longdouble(58000.0) sigma = 100 * np.finfo(np.longdouble).eps * mjd def log_prob(x): assert x.dtype == np.longdouble return -0.5 * np.sum(((x - mjd) / sigma) ** 2) ndim, nwalkers = 1, 20 steps = 1000 p0 = sigma * np.random.randn(nwalkers, ndim).astype(np.longdouble) + mjd assert not all(p0 == mjd) with cls(dtype=np.longdouble) as backend: sampler = emcee.EnsembleSampler( nwalkers, ndim, log_prob, backend=backend ) sampler.run_mcmc(p0, steps) samples = sampler.get_chain().reshape((-1,)) assert samples.dtype == np.longdouble assert not np.all(samples == mjd) assert np.abs(np.mean(samples) - mjd) < 10 * sigma / np.sqrt( len(samples) ) assert 0.1 * sigma < np.std(samples) < 10 * sigma ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_proposal.py0000644000175100001710000000513500000000000022740 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np import pytest import emcee try: from scipy import stats except ImportError: stats = None __all__ = ["_test_normal", "_test_uniform"] def normal_log_prob_blobs(params): return -0.5 * np.sum(params ** 2), params def normal_log_prob(params): return -0.5 * np.sum(params ** 2) def uniform_log_prob(params): if np.any(params > 1) or np.any(params < 0): return -np.inf return 0.0 def _test_normal( proposal, ndim=1, nwalkers=32, nsteps=2000, seed=1234, check_acceptance=True, pool=None, blobs=False, ): # Set up the random number generator. np.random.seed(seed) # Initialize the ensemble and proposal. coords = np.random.randn(nwalkers, ndim) if blobs: lp = normal_log_prob_blobs else: lp = normal_log_prob sampler = emcee.EnsembleSampler( nwalkers, ndim, lp, moves=proposal, pool=pool ) if hasattr(proposal, "ntune") and proposal.ntune > 0: coords = sampler.run_mcmc(coords, proposal.ntune, tune=True) sampler.reset() sampler.run_mcmc(coords, nsteps) # Check the acceptance fraction. if check_acceptance: acc = sampler.acceptance_fraction assert np.all( (acc < 0.9) * (acc > 0.1) ), "Invalid acceptance fraction\n{0}".format(acc) # Check the resulting chain using a K-S test and compare to the mean and # standard deviation. samps = sampler.get_chain(flat=True) mu, sig = np.mean(samps, axis=0), np.std(samps, axis=0) assert np.all(np.abs(mu) < 0.08), "Incorrect mean" assert np.all(np.abs(sig - 1) < 0.05), "Incorrect standard deviation" if ndim == 1 and stats is not None: ks, _ = stats.kstest(samps[:, 0], "norm") assert ks < 0.05, "The K-S test failed" def _test_uniform(proposal, nwalkers=32, nsteps=2000, seed=1234): # Set up the random number generator. np.random.seed(seed) # Initialize the ensemble and proposal. coords = np.random.rand(nwalkers, 1) sampler = emcee.EnsembleSampler( nwalkers, 1, normal_log_prob, moves=proposal ) sampler.run_mcmc(coords, nsteps) # Check the acceptance fraction. acc = sampler.acceptance_fraction assert np.all( (acc < 0.9) * (acc > 0.1) ), "Invalid acceptance fraction\n{0}".format(acc) if stats is not None: # Check that the resulting chain "fails" the K-S test. samps = sampler.get_chain(flat=True) np.random.shuffle(samps) ks, _ = stats.kstest(samps[::100], "uniform") assert ks > 0.1, "The K-S test failed" ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_stretch.py0000644000175100001710000000124500000000000022553 0ustar00runnerdocker# -*- coding: utf-8 -*- import pytest from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = [ "test_normal_stretch", "test_uniform_stretch", "test_nsplits_stretch", ] @pytest.mark.parametrize("blobs", [True, False]) def test_normal_stretch(blobs, **kwargs): kwargs["blobs"] = blobs _test_normal(moves.StretchMove(), **kwargs) def test_uniform_stretch(**kwargs): _test_uniform(moves.StretchMove(), **kwargs) def test_nsplits_stretch(**kwargs): _test_normal(moves.StretchMove(nsplits=5), **kwargs) def test_randomize_stretch(**kwargs): _test_normal(moves.StretchMove(randomize_split=True), **kwargs) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/integration/test_walk.py0000644000175100001710000000050400000000000022032 0ustar00runnerdocker# -*- coding: utf-8 -*- from emcee import moves from .test_proposal import _test_normal, _test_uniform __all__ = ["test_normal_walk", "test_uniform_walk"] def test_normal_walk(**kwargs): _test_normal(moves.WalkMove(s=3), **kwargs) def test_uniform_walk(**kwargs): _test_uniform(moves.WalkMove(s=3), **kwargs) ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1629731803.0055063 emcee-3.1.1/src/emcee/tests/unit/0000755000175100001710000000000000000000000016120 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/__init__.py0000644000175100001710000000000000000000000020217 0ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_autocorr.py0000644000175100001710000000232400000000000021370 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np import pytest from emcee.autocorr import AutocorrError, integrated_time def get_chain(seed=1234, ndim=3, 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=1234, ndim=1, N=250000): x = get_chain(seed=seed, ndim=ndim, N=N) tau = integrated_time(x) assert np.all(np.abs(tau - 19.0) / 19.0 < 0.2) def test_nd(seed=1234, ndim=3, N=150000): x = get_chain(seed=seed, ndim=ndim, N=N) tau = integrated_time(x) assert np.all(np.abs(tau - 19.0) / 19.0 < 0.2) def test_too_short(seed=1234, ndim=3, N=100): x = get_chain(seed=seed, ndim=ndim, N=N) with pytest.raises(AutocorrError): integrated_time(x) tau = integrated_time(x, quiet=True) # NOQA def test_autocorr_multi_works(): np.random.seed(42) xs = np.random.randn(16384, 2) # This throws exception unconditionally in buggy impl's acls_multi = integrated_time(xs) acls_single = np.array( [integrated_time(xs[:, i]) for i in range(xs.shape[1])] ) assert np.all(np.abs(acls_multi - acls_single) < 2) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_backends.py0000644000175100001710000002045100000000000021305 0ustar00runnerdocker# -*- coding: utf-8 -*- import os from itertools import product from os.path import join import numpy as np import pytest from emcee import EnsembleSampler, State, backends from emcee.backends.hdf import does_hdf5_support_longdouble try: import h5py except ImportError: h5py = None __all__ = ["test_backend", "test_reload"] all_backends = backends.get_test_backends() other_backends = all_backends[1:] dtypes = [None, [("log_prior", float), ("mean", int)]] def normal_log_prob(params): return -0.5 * np.sum(params ** 2) def normal_log_prob_blobs(params): return normal_log_prob(params), 0.1, int(5) def run_sampler( backend, nwalkers=32, ndim=3, nsteps=25, seed=1234, thin_by=1, dtype=None, blobs=True, lp=None, ): if lp is None: lp = normal_log_prob_blobs if blobs else normal_log_prob if seed is not None: np.random.seed(seed) coords = np.random.randn(nwalkers, ndim) sampler = EnsembleSampler( nwalkers, ndim, lp, backend=backend, blobs_dtype=dtype ) sampler.run_mcmc(coords, nsteps, thin_by=thin_by) return sampler def _custom_allclose(a, b): if a.dtype.fields is None: assert np.allclose(a, b) else: for n in a.dtype.names: assert np.allclose(a[n], b[n]) @pytest.mark.skipif(h5py is None, reason="HDF5 not available") def test_uninit(tmpdir): fn = str(tmpdir.join("EMCEE_TEST_FILE_DO_NOT_USE.h5")) if os.path.exists(fn): os.remove(fn) with backends.HDFBackend(fn) as be: run_sampler(be) assert os.path.exists(fn) os.remove(fn) @pytest.mark.parametrize("backend", all_backends) def test_uninit_errors(backend): with backend() as be: with pytest.raises(AttributeError): be.get_last_sample() for k in ["chain", "log_prob", "blobs"]: with pytest.raises(AttributeError): getattr(be, "get_" + k)() @pytest.mark.parametrize("backend", all_backends) def test_blob_usage_errors(backend): with backend() as be: run_sampler(be, blobs=True) with pytest.raises(ValueError): run_sampler(be, blobs=False) with backend() as be: run_sampler(be, blobs=False) with pytest.raises(ValueError): run_sampler(be, blobs=True) @pytest.mark.parametrize( "backend,dtype,blobs", product(other_backends, dtypes, [True, False]) ) def test_backend(backend, dtype, blobs): # Run a sampler with the default backend. sampler1 = run_sampler(backends.Backend(), dtype=dtype, blobs=blobs) with backend() as be: sampler2 = run_sampler(be, dtype=dtype, blobs=blobs) values = ["chain", "log_prob"] if blobs: values += ["blobs"] else: assert sampler1.get_blobs() is None assert sampler2.get_blobs() is None # Check all of the components. for k in values: a = getattr(sampler1, "get_" + k)() b = getattr(sampler2, "get_" + k)() _custom_allclose(a, b) last1 = sampler1.get_last_sample() last2 = sampler2.get_last_sample() assert np.allclose(last1.coords, last2.coords) assert np.allclose(last1.log_prob, last2.log_prob) assert all( np.allclose(l1, l2) for l1, l2 in zip(last1.random_state[1:], last2.random_state[1:]) ) if blobs: _custom_allclose(last1.blobs, last2.blobs) else: assert last1.blobs is None and last2.blobs is None a = sampler1.acceptance_fraction b = sampler2.acceptance_fraction assert np.allclose(a, b), "inconsistent acceptance fraction" @pytest.mark.parametrize("backend,dtype", product(other_backends, dtypes)) def test_reload(backend, dtype): with backend() as backend1: run_sampler(backend1, dtype=dtype) # Test the state state = backend1.random_state np.random.set_state(state) # Load the file using a new backend object. backend2 = backends.HDFBackend( backend1.filename, backend1.name, read_only=True ) with pytest.raises(RuntimeError): backend2.reset(32, 3) assert state[0] == backend2.random_state[0] assert all( np.allclose(a, b) for a, b in zip(state[1:], backend2.random_state[1:]) ) # Check all of the components. for k in ["chain", "log_prob", "blobs"]: a = backend1.get_value(k) b = backend2.get_value(k) _custom_allclose(a, b) last1 = backend1.get_last_sample() last2 = backend2.get_last_sample() assert np.allclose(last1.coords, last2.coords) assert np.allclose(last1.log_prob, last2.log_prob) assert all( np.allclose(l1, l2) for l1, l2 in zip(last1.random_state[1:], last2.random_state[1:]) ) _custom_allclose(last1.blobs, last2.blobs) a = backend1.accepted b = backend2.accepted assert np.allclose(a, b), "inconsistent accepted" @pytest.mark.parametrize("backend,dtype", product(other_backends, dtypes)) def test_restart(backend, dtype): # Run a sampler with the default backend. b = backends.Backend() run_sampler(b, dtype=dtype) sampler1 = run_sampler(b, seed=None, dtype=dtype) with backend() as be: run_sampler(be, dtype=dtype) sampler2 = run_sampler(be, seed=None, dtype=dtype) # Check all of the components. for k in ["chain", "log_prob", "blobs"]: a = getattr(sampler1, "get_" + k)() b = getattr(sampler2, "get_" + k)() _custom_allclose(a, b) last1 = sampler1.get_last_sample() last2 = sampler2.get_last_sample() assert np.allclose(last1.coords, last2.coords) assert np.allclose(last1.log_prob, last2.log_prob) assert all( np.allclose(l1, l2) for l1, l2 in zip(last1.random_state[1:], last2.random_state[1:]) ) _custom_allclose(last1.blobs, last2.blobs) a = sampler1.acceptance_fraction b = sampler2.acceptance_fraction assert np.allclose(a, b), "inconsistent acceptance fraction" @pytest.mark.skipif(h5py is None, reason="HDF5 not available") def test_multi_hdf5(): with backends.TempHDFBackend() as backend1: run_sampler(backend1) backend2 = backends.HDFBackend(backend1.filename, name="mcmc2") run_sampler(backend2) chain2 = backend2.get_chain() with h5py.File(backend1.filename, "r") as f: assert set(f.keys()) == {backend1.name, "mcmc2"} backend1.reset(10, 2) assert np.allclose(backend2.get_chain(), chain2) with pytest.raises(AttributeError): backend1.get_chain() @pytest.mark.parametrize("backend", all_backends) def test_longdouble_preserved(backend): if ( issubclass(backend, backends.TempHDFBackend) and not does_hdf5_support_longdouble() ): pytest.xfail("HDF5 does not support long double on this platform") nwalkers = 10 ndim = 2 nsteps = 5 with backend(dtype=np.longdouble) as b: b.reset(nwalkers, ndim) b.grow(nsteps, None) for i in range(nsteps): coords = np.zeros((nwalkers, ndim), dtype=np.longdouble) coords += i + 1 coords += np.arange(nwalkers)[:, None] coords[:, 1] += coords[:, 0] * 2 * np.finfo(np.longdouble).eps assert not np.any(coords[:, 1] == coords[:, 0]) lp = 1 + np.arange(nwalkers) * np.finfo(np.longdouble).eps state = State(coords, log_prob=lp, random_state=()) b.save_step(state, np.ones((nwalkers,), dtype=bool)) s = b.get_last_sample() # check s has adequate precision and equals state assert s.coords.dtype == np.longdouble assert not np.any(s.coords[:, 1] == s.coords[:, 0]) assert np.all(s.coords == coords) assert s.log_prob.dtype == np.longdouble assert np.all(s.log_prob == lp) @pytest.mark.skipif(h5py is None, reason="HDF5 not available") def test_hdf5_compression(): with backends.TempHDFBackend(compression="gzip") as b: run_sampler(b, blobs=True) # re-open and read b.get_chain() b.get_blobs() b.get_log_prob() b.accepted ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_blobs.py0000644000175100001710000000537000000000000020637 0ustar00runnerdocker# -*- coding: utf-8 -*- import warnings import numpy as np import pytest from emcee import EnsembleSampler, backends __all__ = ["test_blob_shape"] class BlobLogProb(object): def __init__(self, blob_function): self.blob_function = blob_function def __call__(self, params): return -0.5 * np.sum(params ** 2), self.blob_function(params) @pytest.mark.parametrize("backend", backends.get_test_backends()) @pytest.mark.parametrize( "blob_spec", [ (True, False, 5, lambda x: np.random.randn(5)), (True, False, (5, 3), lambda x: np.random.randn(5, 3)), (True, False, (5, 3), lambda x: np.random.randn(1, 5, 1, 3, 1)), (True, False, 0, lambda x: np.random.randn()), (False, True, 2, lambda x: (1.0, np.random.randn(3))), (False, False, 0, lambda x: "face"), (False, False, 0, lambda x: object()), (False, False, 2, lambda x: ("face", "surface")), (False, True, 2, lambda x: (np.random.randn(5), "face")), ], ) def test_blob_shape(backend, blob_spec): # HDF backends don't support the object type hdf_able, ragged, blob_shape, func = blob_spec if backend in (backends.TempHDFBackend,) and not hdf_able: return with backend() as be: np.random.seed(42) model = BlobLogProb(func) coords = np.random.randn(32, 3) nwalkers, ndim = coords.shape sampler = EnsembleSampler(nwalkers, ndim, model, backend=be) nsteps = 10 if ragged: with warnings.catch_warnings(): warnings.simplefilter("ignore", DeprecationWarning) sampler.run_mcmc(coords, nsteps) else: sampler.run_mcmc(coords, nsteps) shape = [nsteps, nwalkers] if isinstance(blob_shape, tuple): shape += blob_shape elif blob_shape > 0: shape += [blob_shape] assert sampler.get_blobs().shape == tuple(shape) if not hdf_able: assert sampler.get_blobs().dtype == np.dtype("object") class VariableLogProb: def __init__(self): self.i = 3 def __call__(self, *args): return 0, np.zeros(self.i) @pytest.mark.parametrize("backend", backends.get_test_backends()) def test_blob_mismatch(backend): with backend() as be: np.random.seed(42) model = VariableLogProb() coords = np.random.randn(32, 3) nwalkers, ndim = coords.shape sampler = EnsembleSampler(nwalkers, ndim, model, backend=be) model.i += 1 # We don't save blobs from the initial points # so blob shapes are taken from the first round of moves sampler.run_mcmc(coords, 1) model.i += 1 with pytest.raises(ValueError): sampler.run_mcmc(coords, 1) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_ensemble.py0000644000175100001710000001355400000000000021333 0ustar00runnerdocker""" Unit tests of some functionality in ensemble.py when the parameters are named """ import string from unittest import TestCase import numpy as np import pytest from emcee.ensemble import EnsembleSampler, ndarray_to_list_of_dicts class TestNP2ListOfDicts(TestCase): def test_ndarray_to_list_of_dicts(self): # Try different numbers of keys for n_keys in [1, 2, 10, 26]: keys = list(string.ascii_lowercase[:n_keys]) key_set = set(keys) key_dict = {key: i for i, key in enumerate(keys)} # Try different number of walker/procs for N in [1, 2, 3, 10, 100]: x = np.random.rand(N, n_keys) LOD = ndarray_to_list_of_dicts(x, key_dict) assert len(LOD) == N, "need 1 dict per row" for i, dct in enumerate(LOD): assert dct.keys() == key_set, "keys are missing" for j, key in enumerate(keys): assert dct[key] == x[i, j], f"wrong value at {(i, j)}" class TestNamedParameters(TestCase): """ Test that a keyword-based log-probability function instead of a positional. """ # Keyword based lnpdf def lnpdf(self, pars) -> np.float64: mean = pars["mean"] var = pars["var"] if var <= 0: return -np.inf return ( -0.5 * ((mean - self.x) ** 2 / var + np.log(2 * np.pi * var)).sum() ) def lnpdf_mixture(self, pars) -> np.float64: mean1 = pars["mean1"] var1 = pars["var1"] mean2 = pars["mean2"] var2 = pars["var2"] if var1 <= 0 or var2 <= 0: return -np.inf return ( -0.5 * ( (mean1 - self.x) ** 2 / var1 + np.log(2 * np.pi * var1) + (mean2 - self.x - 3) ** 2 / var2 + np.log(2 * np.pi * var2) ).sum() ) def lnpdf_mixture_grouped(self, pars) -> np.float64: mean1, mean2 = pars["means"] var1, var2 = pars["vars"] const = pars["constant"] if var1 <= 0 or var2 <= 0: return -np.inf return ( -0.5 * ( (mean1 - self.x) ** 2 / var1 + np.log(2 * np.pi * var1) + (mean2 - self.x - 3) ** 2 / var2 + np.log(2 * np.pi * var2) ).sum() + const ) def setUp(self): # Draw some data from a unit Gaussian self.x = np.random.randn(100) self.names = ["mean", "var"] def test_named_parameters(self): sampler = EnsembleSampler( nwalkers=10, ndim=len(self.names), log_prob_fn=self.lnpdf, parameter_names=self.names, ) assert sampler.params_are_named assert list(sampler.parameter_names.keys()) == self.names def test_asserts(self): # ndim name mismatch with pytest.raises(AssertionError): _ = EnsembleSampler( nwalkers=10, ndim=len(self.names) - 1, log_prob_fn=self.lnpdf, parameter_names=self.names, ) # duplicate names with pytest.raises(AssertionError): _ = EnsembleSampler( nwalkers=10, ndim=3, log_prob_fn=self.lnpdf, parameter_names=["a", "b", "a"], ) # vectorize turned on with pytest.raises(AssertionError): _ = EnsembleSampler( nwalkers=10, ndim=len(self.names), log_prob_fn=self.lnpdf, parameter_names=self.names, vectorize=True, ) def test_compute_log_prob(self): # Try different numbers of walkers for N in [4, 8, 10]: sampler = EnsembleSampler( nwalkers=N, ndim=len(self.names), log_prob_fn=self.lnpdf, parameter_names=self.names, ) coords = np.random.rand(N, len(self.names)) lnps, _ = sampler.compute_log_prob(coords) assert len(lnps) == N assert lnps.dtype == np.float64 def test_compute_log_prob_mixture(self): names = ["mean1", "var1", "mean2", "var2"] # Try different numbers of walkers for N in [8, 10, 20]: sampler = EnsembleSampler( nwalkers=N, ndim=len(names), log_prob_fn=self.lnpdf_mixture, parameter_names=names, ) coords = np.random.rand(N, len(names)) lnps, _ = sampler.compute_log_prob(coords) assert len(lnps) == N assert lnps.dtype == np.float64 def test_compute_log_prob_mixture_grouped(self): names = {"means": [0, 1], "vars": [2, 3], "constant": 4} # Try different numbers of walkers for N in [8, 10, 20]: sampler = EnsembleSampler( nwalkers=N, ndim=5, log_prob_fn=self.lnpdf_mixture_grouped, parameter_names=names, ) coords = np.random.rand(N, 5) lnps, _ = sampler.compute_log_prob(coords) assert len(lnps) == N assert lnps.dtype == np.float64 def test_run_mcmc(self): # Sort of an integration test n_walkers = 4 sampler = EnsembleSampler( nwalkers=n_walkers, ndim=len(self.names), log_prob_fn=self.lnpdf, parameter_names=self.names, ) guess = np.random.rand(n_walkers, len(self.names)) n_steps = 50 results = sampler.run_mcmc(guess, n_steps) assert results.coords.shape == (n_walkers, len(self.names)) chain = sampler.chain assert chain.shape == (n_walkers, n_steps, len(self.names)) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_sampler.py0000644000175100001710000002541600000000000021204 0ustar00runnerdocker# -*- coding: utf-8 -*- import pickle from itertools import islice, product import numpy as np import pytest from emcee import EnsembleSampler, backends, moves, walkers_independent __all__ = ["test_shapes", "test_errors", "test_thin", "test_vectorize"] all_backends = backends.get_test_backends() def normal_log_prob(params): return -0.5 * np.sum(params ** 2) @pytest.mark.parametrize( "backend, moves", product( all_backends, [ None, moves.GaussianMove(0.5), [moves.StretchMove(), moves.GaussianMove(0.5)], [(moves.StretchMove(), 0.3), (moves.GaussianMove(0.5), 0.1)], ], ), ) def test_shapes(backend, moves, nwalkers=32, ndim=3, nsteps=10, seed=1234): # Set up the random number generator. np.random.seed(seed) with backend() as be: # Initialize the ensemble, moves and sampler. coords = np.random.randn(nwalkers, ndim) sampler = EnsembleSampler( nwalkers, ndim, normal_log_prob, moves=moves, backend=be ) # Run the sampler. sampler.run_mcmc(coords, nsteps) chain = sampler.get_chain() assert len(chain) == nsteps, "wrong number of steps" tau = sampler.get_autocorr_time(quiet=True) assert tau.shape == (ndim,) # Check the shapes. with pytest.warns(DeprecationWarning): assert sampler.chain.shape == ( nwalkers, nsteps, ndim, ), "incorrect coordinate dimensions" with pytest.warns(DeprecationWarning): assert sampler.lnprobability.shape == ( nwalkers, nsteps, ), "incorrect probability dimensions" assert sampler.get_chain().shape == ( nsteps, nwalkers, ndim, ), "incorrect coordinate dimensions" assert sampler.get_log_prob().shape == ( nsteps, nwalkers, ), "incorrect probability dimensions" assert sampler.acceptance_fraction.shape == ( nwalkers, ), "incorrect acceptance fraction dimensions" # Check the shape of the flattened coords. assert sampler.get_chain(flat=True).shape == ( nsteps * nwalkers, ndim, ), "incorrect coordinate dimensions" assert sampler.get_log_prob(flat=True).shape == ( nsteps * nwalkers, ), "incorrect probability dimensions" @pytest.mark.parametrize("backend", all_backends) def test_errors(backend, nwalkers=32, ndim=3, nsteps=5, seed=1234): # Set up the random number generator. np.random.seed(seed) with backend() as be: # Initialize the ensemble, proposal, and sampler. coords = np.random.randn(nwalkers, ndim) sampler = EnsembleSampler(nwalkers, ndim, normal_log_prob, backend=be) # Test for not running. with pytest.raises(AttributeError): sampler.get_chain() with pytest.raises(AttributeError): sampler.get_log_prob() # What about not storing the chain. sampler.run_mcmc(coords, nsteps, store=False) with pytest.raises(AttributeError): sampler.get_chain() # Now what about if we try to continue using the sampler with an # ensemble of a different shape. sampler.run_mcmc(coords, nsteps, store=False) coords2 = np.random.randn(nwalkers, ndim + 1) with pytest.raises(ValueError): list(sampler.run_mcmc(coords2, nsteps)) # Ensure that a warning is logged if the inital coords don't allow # the chain to explore all of parameter space, and that one is not # if we explicitly disable it, or the initial coords can. with pytest.raises(ValueError): sampler.run_mcmc(np.ones((nwalkers, ndim)), nsteps) sampler.run_mcmc( np.ones((nwalkers, ndim)), nsteps, skip_initial_state_check=True ) sampler.run_mcmc(np.random.randn(nwalkers, ndim), nsteps) def run_sampler( backend, nwalkers=32, ndim=3, nsteps=25, seed=1234, thin=None, thin_by=1, progress=False, store=True, ): np.random.seed(seed) coords = np.random.randn(nwalkers, ndim) sampler = EnsembleSampler(nwalkers, ndim, normal_log_prob, backend=backend) sampler.run_mcmc( coords, nsteps, thin=thin, thin_by=thin_by, progress=progress, store=store, ) return sampler @pytest.mark.parametrize("backend", all_backends) def test_thin(backend): with backend() as be: with pytest.raises(ValueError): with pytest.warns(DeprecationWarning): run_sampler(be, thin=-1) with pytest.raises(ValueError): with pytest.warns(DeprecationWarning): run_sampler(be, thin=0.1) thinby = 3 sampler1 = run_sampler(None) with pytest.warns(DeprecationWarning): sampler2 = run_sampler(be, thin=thinby) for k in ["get_chain", "get_log_prob"]: a = getattr(sampler1, k)()[thinby - 1 :: thinby] b = getattr(sampler2, k)() c = getattr(sampler1, k)(thin=thinby) assert np.allclose(a, b), "inconsistent {0}".format(k) assert np.allclose(a, c), "inconsistent {0}".format(k) @pytest.mark.parametrize( "backend,progress", product(all_backends, [True, False]) ) def test_thin_by(backend, progress): with backend() as be: with pytest.raises(ValueError): run_sampler(be, thin_by=-1) with pytest.raises(ValueError): run_sampler(be, thin_by=0.1) nsteps = 25 thinby = 3 sampler1 = run_sampler(None, nsteps=nsteps * thinby, progress=progress) sampler2 = run_sampler( be, thin_by=thinby, progress=progress, nsteps=nsteps ) for k in ["get_chain", "get_log_prob"]: a = getattr(sampler1, k)()[thinby - 1 :: thinby] b = getattr(sampler2, k)() c = getattr(sampler1, k)(thin=thinby) assert np.allclose(a, b), "inconsistent {0}".format(k) assert np.allclose(a, c), "inconsistent {0}".format(k) assert sampler1.iteration == sampler2.iteration * thinby @pytest.mark.parametrize("backend", all_backends) def test_restart(backend): with backend() as be: sampler = run_sampler(be, nsteps=0) with pytest.raises(ValueError): sampler.run_mcmc(None, 10) sampler = run_sampler(be) sampler.run_mcmc(None, 10) with backend() as be: sampler = run_sampler(be, store=False) sampler.run_mcmc(None, 10) def test_vectorize(): def lp_vec(p): return -0.5 * np.sum(p ** 2, axis=1) np.random.seed(42) nwalkers, ndim = 32, 3 coords = np.random.randn(nwalkers, ndim) sampler = EnsembleSampler(nwalkers, ndim, lp_vec, vectorize=True) sampler.run_mcmc(coords, 10) assert sampler.get_chain().shape == (10, nwalkers, ndim) @pytest.mark.parametrize("backend", all_backends) def test_pickle(backend): with backend() as be: sampler1 = run_sampler(be) s = pickle.dumps(sampler1, -1) sampler2 = pickle.loads(s) for k in ["get_chain", "get_log_prob"]: a = getattr(sampler1, k)() b = getattr(sampler2, k)() assert np.allclose(a, b), "inconsistent {0}".format(k) @pytest.mark.parametrize("nwalkers, ndim", [(10, 2), (20, 5)]) def test_walkers_dependent_ones(nwalkers, ndim): assert not walkers_independent(np.ones((nwalkers, ndim))) @pytest.mark.parametrize("nwalkers, ndim", [(10, 11), (2, 3)]) def test_walkers_dependent_toofew(nwalkers, ndim): assert not walkers_independent(np.random.randn(nwalkers, ndim)) @pytest.mark.parametrize("nwalkers, ndim", [(10, 2), (20, 5)]) def test_walkers_independent_randn(nwalkers, ndim): assert walkers_independent(np.random.randn(nwalkers, ndim)) @pytest.mark.parametrize( "nwalkers, ndim, offset", [(10, 2, 1e5), (20, 5, 1e10), (30, 10, 1e14)] ) def test_walkers_independent_randn_offset(nwalkers, ndim, offset): assert walkers_independent( np.random.randn(nwalkers, ndim) + np.ones((nwalkers, ndim)) * offset ) def test_walkers_dependent_big_offset(): nwalkers, ndim = 30, 10 offset = 10 / np.finfo(float).eps assert not walkers_independent( np.random.randn(nwalkers, ndim) + np.ones((nwalkers, ndim)) * offset ) def test_walkers_dependent_subtle(): nwalkers, ndim = 30, 10 w = np.random.randn(nwalkers, ndim) assert walkers_independent(w) # random unit vector p = np.random.randn(ndim) p /= np.sqrt(np.dot(p, p)) # project away the direction of p w -= np.sum(p[None, :] * w, axis=1)[:, None] * p[None, :] assert not walkers_independent(w) # shift away from the origin w += p[None, :] assert not walkers_independent(w) def test_walkers_almost_dependent(): nwalkers, ndim = 30, 10 squash = 1e-8 w = np.random.randn(nwalkers, ndim) assert walkers_independent(w) # random unit vector p = np.random.randn(ndim) p /= np.sqrt(np.dot(p, p)) # project away the direction of p proj = np.sum(p[None, :] * w, axis=1)[:, None] * p[None, :] w -= proj w += squash * proj assert not walkers_independent(w) def test_walkers_independent_scaled(): # Some of these scales will overflow if squared, hee hee scales = np.array([1, 1e10, 1e100, 1e200, 1e-10, 1e-100, 1e-200]) ndim = len(scales) nwalkers = 5 * ndim w = np.random.randn(nwalkers, ndim) * scales[None, :] assert walkers_independent(w) @pytest.mark.parametrize( "nwalkers, ndim, offset", [ (10, 2, 1e5), (20, 5, 1e10), (30, 10, 1e14), (40, 15, 0.1 / np.finfo(np.longdouble).eps), ], ) def test_walkers_independent_randn_offset_longdouble(nwalkers, ndim, offset): assert walkers_independent( np.random.randn(nwalkers, ndim) + np.ones((nwalkers, ndim), dtype=np.longdouble) * offset ) @pytest.mark.parametrize("backend", all_backends) def test_infinite_iterations_store(backend, nwalkers=32, ndim=3): with backend() as be: coords = np.random.randn(nwalkers, ndim) with pytest.raises(ValueError): next( EnsembleSampler( nwalkers, ndim, normal_log_prob, backend=be ).sample(coords, iterations=None, store=True) ) @pytest.mark.parametrize("backend", all_backends) def test_infinite_iterations(backend, nwalkers=32, ndim=3): with backend() as be: coords = np.random.randn(nwalkers, ndim) for state in islice( EnsembleSampler( nwalkers, ndim, normal_log_prob, backend=be ).sample(coords, iterations=None, store=False), 10, ): pass ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_state.py0000644000175100001710000000210300000000000020645 0ustar00runnerdocker# -*- coding: utf-8 -*- import numpy as np from emcee import EnsembleSampler from emcee.state import State def test_back_compat(seed=1234): np.random.seed(seed) coords = np.random.randn(16, 3) log_prob = np.random.randn(len(coords)) blobs = np.random.randn(len(coords)) rstate = np.random.get_state() state = State(coords, log_prob, blobs, rstate) c, l, r, b = state assert np.allclose(coords, c) assert np.allclose(log_prob, l) assert np.allclose(blobs, b) assert all(np.allclose(a, b) for a, b in zip(rstate[1:], r[1:])) state = State(coords, log_prob, None, rstate) c, l, r = state assert np.allclose(coords, c) assert np.allclose(log_prob, l) assert all(np.allclose(a, b) for a, b in zip(rstate[1:], r[1:])) def test_overwrite(seed=1234): np.random.seed(seed) def ll(x): return -0.5 * np.sum(x ** 2) nwalkers = 64 p0 = np.random.normal(size=(nwalkers, 1)) init = np.copy(p0) sampler = EnsembleSampler(nwalkers, 1, ll) sampler.run_mcmc(p0, 10) assert np.allclose(init, p0) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/tests/unit/test_stretch.py0000644000175100001710000000153700000000000021213 0ustar00runnerdocker# -*- coding: utf-8 -*- import warnings import numpy as np import pytest from emcee import moves from emcee.model import Model from emcee.state import State __all__ = ["test_live_dangerously"] def test_live_dangerously(nwalkers=32, nsteps=3000, seed=1234): warnings.filterwarnings("error") # Set up the random number generator. np.random.seed(seed) state = State( np.random.randn(nwalkers, 2 * nwalkers), log_prob=np.random.randn(nwalkers), ) model = Model(None, lambda x: (np.zeros(len(x)), None), map, np.random) proposal = moves.StretchMove() # Test to make sure that the error is thrown if there aren't enough # walkers. with pytest.raises(RuntimeError): proposal.propose(model, state) # Living dangerously... proposal.live_dangerously = True proposal.propose(model, state) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/src/emcee/utils.py0000644000175100001710000000310600000000000015511 0ustar00runnerdocker# -*- coding: utf-8 -*- import warnings from functools import wraps import numpy as np __all__ = ["sample_ball", "deprecated", "deprecation_warning"] def deprecation_warning(msg): warnings.warn(msg, category=DeprecationWarning, stacklevel=2) def deprecated(alternate): def wrapper(func, alternate=alternate): msg = "'{0}' is deprecated.".format(func.__name__) if alternate is not None: msg += " Use '{0}' instead.".format(alternate) @wraps(func) def f(*args, **kwargs): deprecation_warning(msg) return func(*args, **kwargs) return f return wrapper @deprecated(None) def sample_ball(p0, std, size=1): """ Produce a ball of walkers around an initial parameter value. :param p0: The initial parameter value. :param std: The axis-aligned standard deviation. :param size: The number of samples to produce. """ assert len(p0) == len(std) return np.vstack( [p0 + std * np.random.normal(size=len(p0)) for i in range(size)] ) @deprecated(None) def sample_ellipsoid(p0, covmat, size=1): """ Produce an ellipsoid of walkers around an initial parameter value, according to a covariance matrix. :param p0: The initial parameter value. :param covmat: The covariance matrix. Must be symmetric-positive definite or it will raise the exception numpy.linalg.LinAlgError :param size: The number of samples to produce. """ return np.random.multivariate_normal( np.atleast_1d(p0), np.atleast_2d(covmat), size=size ) ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1629731803.001506 emcee-3.1.1/src/emcee.egg-info/0000755000175100001710000000000000000000000015471 5ustar00runnerdocker././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/PKG-INFO0000644000175100001710000000527600000000000016600 0ustar00runnerdockerMetadata-Version: 2.1 Name: emcee Version: 3.1.1 Summary: The Python ensemble sampling toolkit for MCMC Home-page: https://emcee.readthedocs.io Author: Daniel Foreman-Mackey Author-email: foreman.mackey@gmail.com Maintainer: Daniel Foreman-Mackey Maintainer-email: foreman.mackey@gmail.com License: MIT Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python Description-Content-Type: text/x-rst Provides-Extra: extras Provides-Extra: tests License-File: LICENSE License-File: AUTHORS.rst emcee ===== **The Python ensemble sampling toolkit for affine-invariant MCMC** .. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat :target: https://github.com/dfm/emcee .. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg :target: https://github.com/dfm/emcee/actions?query=workflow%3ATests .. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat :target: https://github.com/dfm/emcee/blob/main/LICENSE .. image:: http://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat :target: https://arxiv.org/abs/1202.3665 .. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main&style=flat&v=2 :target: https://coveralls.io/github/dfm/emcee?branch=main .. image:: https://readthedocs.org/projects/emcee/badge/?version=latest :target: http://emcee.readthedocs.io/en/latest/?badge=latest emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by `Goodman & Weare (2010) `_. The code is open source and has already been used in several published projects in the Astrophysics literature. Documentation ------------- Read the docs at `emcee.readthedocs.io `_. Attribution ----------- Please cite `Foreman-Mackey, Hogg, Lang & Goodman (2012) `_ if you find this code useful in your research. The BibTeX entry for the paper is:: @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {PASP}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } License ------- Copyright 2010-2021 Dan Foreman-Mackey and contributors. emcee is free software made available under the MIT License. For details see the LICENSE file. ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/SOURCES.txt0000644000175100001710000000537100000000000017363 0ustar00runnerdocker.gitattributes .gitignore .pre-commit-config.yaml AUTHORS.rst CODE_OF_CONDUCT.md CONTRIBUTING.md HISTORY.rst LICENSE MANIFEST.in README.rst VISION.md pyproject.toml readthedocs.yml setup.py tox.ini .github/ISSUE_TEMPLATE.md .github/workflows/tests.yml binder/environment.yml docs/.gitignore docs/Makefile docs/conf.py docs/fix_internal_links.py docs/index.rst docs/requirements.txt docs/_static/favicon.png docs/_static/logo-sidebar.png docs/_static/logo.pxm docs/_static/logo2.png docs/_static/logo2.pxm docs/_static/line/line-data.png docs/_static/line/line-least-squares.png docs/_static/line/line-max-likelihood.png docs/_static/line/line-mcmc.png docs/_static/line/line-time.png docs/_static/line/line-triangle.png docs/tutorials/autocorr.ipynb docs/tutorials/line.ipynb docs/tutorials/monitor.ipynb docs/tutorials/moves.ipynb docs/tutorials/parallel.ipynb docs/tutorials/quickstart.ipynb docs/tutorials/tutorial_rst.tpl docs/user/autocorr.rst docs/user/backends.rst docs/user/blobs.rst docs/user/faq.rst docs/user/install.rst docs/user/moves.rst docs/user/sampler.rst docs/user/upgrade.rst document/.gitignore document/Makefile document/aastex.cls document/ms.tex document/plots/oned.py document/plots/plot_acor.py joss/.gitignore joss/make_latex.sh joss/metadata.yaml joss/paper.bib joss/paper.md joss/paper.tex src/emcee/__init__.py src/emcee/autocorr.py src/emcee/emcee_version.py src/emcee/ensemble.py src/emcee/interruptible_pool.py src/emcee/model.py src/emcee/mpi_pool.py src/emcee/pbar.py src/emcee/ptsampler.py src/emcee/state.py src/emcee/utils.py src/emcee.egg-info/PKG-INFO src/emcee.egg-info/SOURCES.txt src/emcee.egg-info/dependency_links.txt src/emcee.egg-info/not-zip-safe src/emcee.egg-info/requires.txt src/emcee.egg-info/top_level.txt src/emcee/backends/__init__.py src/emcee/backends/backend.py src/emcee/backends/hdf.py src/emcee/moves/__init__.py src/emcee/moves/de.py src/emcee/moves/de_snooker.py src/emcee/moves/gaussian.py src/emcee/moves/kde.py src/emcee/moves/mh.py src/emcee/moves/move.py src/emcee/moves/red_blue.py src/emcee/moves/stretch.py src/emcee/moves/walk.py src/emcee/tests/__init__.py src/emcee/tests/integration/__init__.py src/emcee/tests/integration/test_de.py src/emcee/tests/integration/test_de_snooker.py src/emcee/tests/integration/test_gaussian.py src/emcee/tests/integration/test_kde.py src/emcee/tests/integration/test_longdouble.py src/emcee/tests/integration/test_proposal.py src/emcee/tests/integration/test_stretch.py src/emcee/tests/integration/test_walk.py src/emcee/tests/unit/__init__.py src/emcee/tests/unit/test_autocorr.py src/emcee/tests/unit/test_backends.py src/emcee/tests/unit/test_blobs.py src/emcee/tests/unit/test_ensemble.py src/emcee/tests/unit/test_sampler.py src/emcee/tests/unit/test_state.py src/emcee/tests/unit/test_stretch.py././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/dependency_links.txt0000644000175100001710000000000100000000000021537 0ustar00runnerdocker ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/not-zip-safe0000644000175100001710000000000100000000000017717 0ustar00runnerdocker ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/requires.txt0000644000175100001710000000010500000000000020065 0ustar00runnerdockernumpy [extras] h5py scipy [tests] pytest pytest-cov coverage[toml] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731802.0 emcee-3.1.1/src/emcee.egg-info/top_level.txt0000644000175100001710000000000600000000000020217 0ustar00runnerdockeremcee ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1629731790.0 emcee-3.1.1/tox.ini0000644000175100001710000000054700000000000013453 0ustar00runnerdocker[tox] envlist = py{37,38,39}{,-extras},lint [gh-actions] python = 3.7: py37 3.8: py38 3.9: py39-extras [testenv] deps = coverage[toml] extras = tests extras: extras commands = pip freeze python -m coverage run -m pytest -v {posargs} [testenv:lint] skip_install = true deps = pre-commit commands = pre-commit run --all-files