[
  {
    "path": ".github/workflows/release_to_pypi.yml",
    "content": "name: Publish zeus to PyPI / GitHub\n\non:\n  push:\n    branches:\n      - main\n    paths:\n      - 'zeus/_version.py'  # Only run workflow on pushes where _version.py was changed\n\njobs:\n  build-n-publish:\n    name: Build and publish to PyPI\n    runs-on: ubuntu-latest\n\n    steps:\n      - name: Checkout source\n        uses: actions/checkout@v2\n\n      - name: Set up Python\n        uses: actions/setup-python@v2\n        with:\n          python-version: \"3.x\"\n\n      - name: Build source and wheel distributions\n        run: |\n          python -m pip install --upgrade build twine\n          python -m build\n          twine check --strict dist/*\n          \n      - name: Install zeus from the wheel file\n        run: |\n          pip install dist/*.whl\n          \n      - name: List the installed packages\n        run: |\n          pip freeze\n      \n      - name: Run zeus unittests\n        run: |\n          python -m unittest discover tests\n          \n      - name: Publish distribution to PyPI\n        uses: pypa/gh-action-pypi-publish@master\n        with:\n          user: __token__\n          password: ${{ secrets.PYPI_API_TOKEN }}\n\n      - name: Get the version\n        id: get_version\n        run: echo ::set-output name=VERSION::$(cat zeus/_version.py | grep version | cut -d'\"' -f 2)\n\n      - name: Create GitHub Release\n        id: create_release\n        uses: actions/create-release@v1\n        env:\n          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}  # This token is provided by Actions, no need to create our own\n        with:\n          tag_name: ${{ steps.get_version.outputs.VERSION }}\n          release_name: ${{ steps.get_version.outputs.VERSION }}\n          draft: false\n          prerelease: false\n\n      - name: Get Asset name\n        run: |\n          export PKG=$(ls dist/ | grep tar)\n          set -- $PKG\n          echo \"name=$1\" >> $GITHUB_ENV\n      - name: Upload Release Asset (sdist) to GitHub\n        id: upload-release-asset\n        uses: actions/upload-release-asset@v1\n        env:\n          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n        with:\n          upload_url: ${{ steps.create_release.outputs.upload_url }}\n          asset_path: dist/${{ env.name }}\n          asset_name: ${{ env.name }}\n          asset_content_type: application/zip"
  },
  {
    "path": ".github/workflows/setup_and_run_tests.yml",
    "content": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\nname: Setup zeus and run tests\n\non:\n  push:\n    branches: [ \"main\"]\n  pull_request:\n    branches: [ \"main\"]\n  workflow_dispatch:\n\njobs:\n  build:\n    name: Setup and Run Tests\n    runs-on: ubuntu-latest\n    strategy:\n      fail-fast: false\n      matrix:\n        python-version: [\"3.7\", \"3.8\", \"3.9\", \"3.10\"]\n\n    steps:\n    - uses: actions/checkout@v3\n    - name: Set up Python ${{ matrix.python-version }}\n      uses: actions/setup-python@v3\n      with:\n        python-version: ${{ matrix.python-version }}\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip\n        python -m pip install flake8\n        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi\n    - name: Lint with flake8\n      run: |\n        # stop the build if there are Python syntax errors or undefined names\n        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics\n        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide\n        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics\n    - name: Run tests\n      run: |\n        python -m unittest discover tests"
  },
  {
    "path": ".gitignore",
    "content": "__pycache__/\nzeus/_pycache__/\n*.py[cod]\n.ipynb_checkpoints/\nexamples/.ipynb_checkpoints/\nzeus_mcmc.egg-info/\nbuild/\ndist/"
  },
  {
    "path": ".readthedocs.yaml",
    "content": "# .readthedocs.yaml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\n# Set the version of Python and other tools you might need\nbuild:\n  os: ubuntu-20.04\n  tools:\n    python: \"3.9\"\n\n# Build documentation in the docs/ directory with Sphinx\nsphinx:\n   configuration: docs/conf.py\n\n# If using Sphinx, optionally build your docs in additional formats such as PDF\n# formats:\n#    - pdf\n\n# Optionally declare the Python requirements required to build your docs\npython:\n   install:\n   - requirements: docs/requirements.txt\n   - requirements: requirements.txt\n   system_packages: true"
  },
  {
    "path": "LICENSE",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  You can apply it to\nyour programs, too.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n  To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights.  Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n  For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received.  You must make sure that they, too, receive\nor can get the source code.  And you must show them these terms so they\nknow their rights.\n\n  Developers that use the GNU GPL protect your rights with two steps:\n(1) assert copyright on the software, and (2) offer you this License\ngiving you legal permission to copy, distribute and/or modify it.\n\n  For the developers' and authors' protection, the GPL clearly explains\nthat there is no warranty for this free software.  For both users' and\nauthors' sake, the GPL requires that modified versions be marked as\nchanged, so that their problems will not be attributed erroneously to\nauthors of previous versions.\n\n  Some devices are designed to deny users access to install or run\nmodified versions of the software inside them, although the manufacturer\ncan do so.  This is fundamentally incompatible with the aim of\nprotecting users' freedom to change the software.  The systematic\npattern of such abuse occurs in the area of products for individuals to\nuse, which is precisely where it is most unacceptable.  Therefore, we\nhave designed this version of the GPL to prohibit the practice for those\nproducts.  If such problems arise substantially in other domains, we\nstand ready to extend this provision to those domains in future versions\nof the GPL, as needed to protect the freedom of users.\n\n  Finally, every program is threatened constantly by software patents.\nStates should not allow patents to restrict development and use of\nsoftware on general-purpose computers, but in those that do, we wish to\navoid the special danger that patents applied to a free program could\nmake it effectively proprietary.  To prevent this, the GPL assures that\npatents cannot be used to render the program non-free.\n\n  The precise terms and conditions for copying, distribution and\nmodification follow.\n\n                       TERMS AND CONDITIONS\n\n  0. Definitions.\n\n  \"This License\" refers to version 3 of the GNU General Public License.\n\n  \"Copyright\" also means copyright-like laws that apply to other kinds of\nworks, such as semiconductor masks.\n\n  \"The Program\" refers to any copyrightable work licensed under this\nLicense.  Each licensee is addressed as \"you\".  \"Licensees\" and\n\"recipients\" may be individuals or organizations.\n\n  To \"modify\" a work means to copy from or adapt all or part of the work\nin a fashion requiring copyright permission, other than the making of an\nexact copy.  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Mere interaction with a user through\na computer network, with no transfer of a copy, is not conveying.\n\n  An interactive user interface displays \"Appropriate Legal Notices\"\nto the extent that it includes a convenient and prominently visible\nfeature that (1) displays an appropriate copyright notice, and (2)\ntells the user that there is no warranty for the work (except to the\nextent that warranties are provided), that licensees may convey the\nwork under this License, and how to view a copy of this License.  If\nthe interface presents a list of user commands or options, such as a\nmenu, a prominent item in the list meets this criterion.\n\n  1. 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For example, Corresponding Source\nincludes interface definition files associated with source files for\nthe work, and the source code for shared libraries and dynamically\nlinked subprograms that the work is specifically designed to require,\nsuch as by intimate data communication or control flow between those\nsubprograms and other parts of the work.\n\n  The Corresponding Source need not include anything that users\ncan regenerate automatically from other parts of the Corresponding\nSource.\n\n  The Corresponding Source for a work in source code form is that\nsame work.\n\n  2. Basic Permissions.\n\n  All rights granted under this License are granted for the term of\ncopyright on the Program, and are irrevocable provided the stated\nconditions are met.  This License explicitly affirms your unlimited\npermission to run the unmodified Program.  The output from running a\ncovered work is covered by this License only if the output, given its\ncontent, constitutes a covered work.  This License acknowledges your\nrights of fair use or other equivalent, as provided by copyright law.\n\n  You may make, run and propagate covered works that you do not\nconvey, without conditions so long as your license otherwise remains\nin force.  You may convey covered works to others for the sole purpose\nof having them make modifications exclusively for you, or provide you\nwith facilities for running those works, provided that you comply with\nthe terms of this License in conveying all material for which you do\nnot control copyright.  Those thus making or running the covered works\nfor you must do so exclusively on your behalf, under your direction\nand control, on terms that prohibit them from making any copies of\nyour copyrighted material outside their relationship with you.\n\n  Conveying under any other circumstances is permitted solely under\nthe conditions stated below.  Sublicensing is not allowed; section 10\nmakes it unnecessary.\n\n  3. Protecting Users' Legal Rights From Anti-Circumvention Law.\n\n  No covered work shall be deemed part of an effective technological\nmeasure under any applicable law fulfilling obligations under article\n11 of the WIPO copyright treaty adopted on 20 December 1996, or\nsimilar laws prohibiting or restricting circumvention of such\nmeasures.\n\n  When you convey a covered work, you waive any legal power to forbid\ncircumvention of technological measures to the extent such circumvention\nis effected by exercising rights under this License with respect to\nthe covered work, and you disclaim any intention to limit operation or\nmodification of the work as a means of enforcing, against the work's\nusers, your or third parties' legal rights to forbid circumvention of\ntechnological measures.\n\n  4. 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This License gives no\n    permission to license the work in any other way, but it does not\n    invalidate such permission if you have separately received it.\n\n    d) If the work has interactive user interfaces, each must display\n    Appropriate Legal Notices; however, if the Program has interactive\n    interfaces that do not display Appropriate Legal Notices, your\n    work need not make them do so.\n\n  A compilation of a covered work with other separate and independent\nworks, which are not by their nature extensions of the covered work,\nand which are not combined with it such as to form a larger program,\nin or on a volume of a storage or distribution medium, is called an\n\"aggregate\" if the compilation and its resulting copyright are not\nused to limit the access or legal rights of the compilation's users\nbeyond what the individual works permit.  Inclusion of a covered work\nin an aggregate does not cause this License to apply to the other\nparts of the aggregate.\n\n  6. 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But this requirement does not apply\nif neither you nor any third party retains the ability to install\nmodified object code on the User Product (for example, the work has\nbeen installed in ROM).\n\n  The requirement to provide Installation Information does not include a\nrequirement to continue to provide support service, warranty, or updates\nfor a work that has been modified or installed by the recipient, or for\nthe User Product in which it has been modified or installed.  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If additional permissions\napply only to part of the Program, that part may be used separately\nunder those permissions, but the entire Program remains governed by\nthis License without regard to the additional permissions.\n\n  When you convey a copy of a covered work, you may at your option\nremove any additional permissions from that copy, or from any part of\nit.  (Additional permissions may be written to require their own\nremoval in certain cases when you modify the work.)  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If the Program as you\nreceived it, or any part of it, contains a notice stating that it is\ngoverned by this License along with a term that is a further\nrestriction, you may remove that term.  If a license document contains\na further restriction but permits relicensing or conveying under this\nLicense, you may add to a covered work material governed by the terms\nof that license document, provided that the further restriction does\nnot survive such relicensing or conveying.\n\n  If you add terms to a covered work in accord with this section, you\nmust place, in the relevant source files, a statement of the\nadditional terms that apply to those files, or a notice indicating\nwhere to find the applicable terms.\n\n  Additional terms, permissive or non-permissive, may be stated in the\nform of a separately written license, or stated as exceptions;\nthe above requirements apply either way.\n\n  8. Termination.\n\n  You may not propagate or modify a covered work except as expressly\nprovided under this License.  Any attempt otherwise to propagate or\nmodify it is void, and will automatically terminate your rights under\nthis License (including any patent licenses granted under the third\nparagraph of section 11).\n\n  However, if you cease all violation of this License, then your\nlicense from a particular copyright holder is reinstated (a)\nprovisionally, unless and until the copyright holder explicitly and\nfinally terminates your license, and (b) permanently, if the copyright\nholder fails to notify you of the violation by some reasonable means\nprior to 60 days after the cessation.\n\n  Moreover, your license from a particular copyright holder is\nreinstated permanently if the copyright holder notifies you of the\nviolation by some reasonable means, this is the first time you have\nreceived notice of violation of this License (for any work) from that\ncopyright holder, and you cure the violation prior to 30 days after\nyour receipt of the notice.\n\n  Termination of your rights under this section does not terminate the\nlicenses of parties who have received copies or rights from you under\nthis License.  If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n  You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License.  For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n  11. Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  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To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  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Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License\n    along with this program.  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Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>.\n"
  },
  {
    "path": "MANIFEST.in",
    "content": "include LICENSE README.md requirements.txt\n"
  },
  {
    "path": "README.md",
    "content": "![logo](logo.png)\n\n**zeus is a Python implementation of the Ensemble Slice Sampling method.**\n\n- Fast & Robust *Bayesian Inference*,\n- Efficient *Markov Chain Monte Carlo (MCMC)*,\n- Black-box inference, no hand-tuning,\n- Excellent performance in terms of autocorrelation time and convergence rate,\n- Scale to multiple CPUs without any extra effort,\n- Automated Convergence diagnostics.\n\n[![GitHub](https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue)](https://github.com/minaskar/zeus)\n[![arXiv](https://img.shields.io/badge/arXiv-2002.06212-red)](https://arxiv.org/abs/2002.06212)\n[![arXiv](https://img.shields.io/badge/arXiv-2105.03468-brightgreen)](https://arxiv.org/abs/2105.03468)\n[![ascl](https://img.shields.io/badge/ascl-2008.010-blue.svg?colorB=262255)](https://ascl.net/2008.010)\n[![Build Status](https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master)](https://travis-ci.com/minaskar/zeus)\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/minaskar/zeus/blob/master/LICENSE)\n[![Documentation Status](https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9)](https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest)\n[![Downloads](https://pepy.tech/badge/zeus-mcmc)](https://pepy.tech/project/zeus-mcmc)\n\n\n## Example\n\nFor instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like:\n\n```python\nimport zeus\nimport numpy as np\n\ndef log_prob(x, ivar):\n    return - 0.5 * np.sum(ivar * x**2.0)\n\nnsteps, nwalkers, ndim = 1000, 100, 10\nivar = 1.0 / np.random.rand(ndim)\nstart = np.random.randn(nwalkers,ndim)\n\nsampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])\nsampler.run_mcmc(start, nsteps)\nchain = sampler.get_chain(flat=True)\n```\n\n## Documentation\n\nRead the docs at [zeus-mcmc.readthedocs.io](https://zeus-mcmc.readthedocs.io)\n\n\n## Installation\n\nTo install ``zeus`` using ``pip`` run:\n\n```bash\npip install zeus-mcmc\n```\n\nTo install ``zeus`` in a [[Ana]Conda](https://conda.io/projects/conda/en/latest/index.html) environment use:\n\n```bash\nconda install -c conda-forge zeus-mcmc\n```\n\n## Attribution\n\nPlease cite the following papers if you found this code useful in your research:\n\n```bash\n@article{karamanis2021zeus,\n  title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},\n  author={Karamanis, Minas and Beutler, Florian and Peacock, John A},\n  journal={arXiv preprint arXiv:2105.03468},\n  year={2021}\n}\n\n@article{karamanis2020ensemble,\n    title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},\n    author = {Karamanis, Minas and Beutler, Florian},\n    journal = {arXiv preprint arXiv: 2002.06212},\n    year = {2020}\n}\n```\n\n## Licence\n\nCopyright 2019-2021 Minas Karamanis and contributors.\n\nzeus is free software made available under the GPL-3.0 License. For details see the `LICENSE` file.\n"
  },
  {
    "path": "docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS    ?=\nSPHINXBUILD   ?= sphinx-build\nSOURCEDIR     = .\nBUILDDIR      = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n"
  },
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    "path": "docs/_static/copybutton.js",
    "content": "// originally taken from scikit-learn's Sphinx theme\n$(document).ready(function() {\n    /* Add a [>>>] button on the top-right corner of code samples to hide\n     * the >>> and ... prompts and the output and thus make the code\n     * copyable.\n     * Note: This JS snippet was taken from the official python.org\n     * documentation site.*/\n    var div = $('.highlight-python .highlight,' +\n                '.highlight-python3 .highlight,' +\n                '.highlight-pycon .highlight')\n    var pre = div.find('pre');\n\n    // get the styles from the current theme\n    pre.parent().parent().css('position', 'relative');\n    var hide_text = 'Hide the prompts and output';\n    var show_text = 'Show the prompts and output';\n    var border_width = pre.css('border-top-width');\n    var border_style = pre.css('border-top-style');\n    var border_color = pre.css('border-top-color');\n    var button_styles = {\n        'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0',\n        'border-color': border_color, 'border-style': border_style,\n        'border-width': border_width, 'color': border_color, 'text-size': '75%',\n        'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em'\n    }\n\n    // create and add the button to all the code blocks that contain >>>\n    div.each(function(index) {\n        var jthis = $(this);\n        if (jthis.find('.gp').length > 0) {\n            var button = $('<span class=\"copybutton\">&gt;&gt;&gt;</span>');\n            button.css(button_styles)\n            button.attr('title', hide_text);\n            jthis.prepend(button);\n        }\n        // tracebacks (.gt) contain bare text elements that need to be\n        // wrapped in a span to work with .nextUntil() (see later)\n        jthis.find('pre:has(.gt)').contents().filter(function() {\n            return ((this.nodeType == 3) && (this.data.trim().length > 0));\n        }).wrap('<span>');\n    });\n\n    // define the behavior of the button when it's clicked\n    $('.copybutton').toggle(\n        function() {\n            var button = $(this);\n            button.parent().find('.go, .gp, .gt').hide();\n            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');\n            button.css('text-decoration', 'line-through');\n            button.attr('title', show_text);\n        },\n        function() {\n            var button = $(this);\n            button.parent().find('.go, .gp, .gt').show();\n            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');\n            button.css('text-decoration', 'none');\n            button.attr('title', hide_text);\n        });\n});\n"
  },
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    "path": "docs/_static/default.css",
    "content": "body { color: #444444 !important; }\n\nh1 { font-size: 40px !important; }\nh2 { font-size: 32px !important; }\nh3 { font-size: 24px !important; }\nh4 { font-size: 18px !important; }\nh5 { font-size: 14px !important; }\nh6 { font-size: 10px !important; }\n\nfooter a{\n\n    color: #4c72b0 !important;\n}\na.reference {\n    color: #4c72b0 !important;\n}\n\nblockquote p {\n    font-size: 14px !important;\n}\n\nblockquote {\n    padding-top: 4px !important;\n    padding-bottom: 4px !important;\n    margin: 0 0 0px !important;\n}\n\npre {\n    background-color: #f6f6f9 !important;\n}\n\ncode {\n    color: #49759c !important;\n    background-color: #ffffff !important;\n}\n\ncode.descclassname {\n    padding-right: 0px !important;\n}\n\ncode.descname {\n    padding-left: 0px !important;\n}\n\ndt:target, span.highlighted {\n    background-color: #ffffff !important;\n}\n\nul {\n    padding-left: 20px !important;\n}\n\nul.dropdown-menu {\n    padding-left: 0px !important;\n}\n\n.alert-info {\n    background-color: #adb8cb !important;\n    border-color: #adb8cb !important;\n    color: #2c3e50 !important;\n}\n\n/* From https://github.com/twbs/bootstrap/issues/1768 */\n*[id]:before {\n  display: block;\n  content: \" \";\n  margin-top: -60px;\n  height: 60px;\n  visibility: hidden;\n}\n\ntable {\n    /*Uncomment to center tables horizontally*/\n    /* margin-left: auto; */\n    /* margin-right: auto; */\n    border: none;\n    border-collapse: collapse;\n    border-spacing: 0;\n    font-size: 12px;\n    table-layout: fixed;\n}\n\nthead {\n    border-bottom: 1px solid;\n    vertical-align: bottom;\n}\n\ntr, th, td {\n    text-align: right;\n    vertical-align: middle;\n    padding: 0.5em 0.5em;\n    line-height: normal;\n    white-space: normal;\n    max-width: none;\n    border: none;\n}\n\nth {\n    font-weight: bold;\n}\n\ntbody tr:nth-child(odd) {\n    background: #f5f5f5;\n}\n\ntbody tr:hover {\n    background: rgba(66, 165, 245, 0.2);\n}"
  },
  {
    "path": "docs/api/autocorr.rst",
    "content": "===============================\nAutocorrelation Time Estimation\n===============================\n\n.. autofunction:: zeus.AutoCorrTime"
  },
  {
    "path": "docs/api/callbacks.rst",
    "content": "=============\nThe Callbacks\n=============\n\nStarting from version 2.4.0, ``zeus`` supports callback functions. Those are functions that are \ncalled in every iteration of a run. Among other things, these can be used to monitor useful quantities,\nassess convergence, and save the chains to disk. Custom callback functions can also be used. Sampling\nterminates if a callback function returns ``True`` and continues running while ``False`` or ``None`` is\nreturned.\n\nAutocorrelation Callback\n========================\n\n.. autoclass:: zeus.callbacks.AutocorrelationCallback\n    :members:\n\n\nSplit-R Callback\n================\n\n.. autoclass:: zeus.callbacks.SplitRCallback\n    :members:\n\n\nParallel Split-R Callback\n=========================\n\n.. autoclass:: zeus.callbacks.ParallelSplitRCallback\n    :members:\n\n\nMinimum Iterations Callback\n===========================\n\n.. autoclass:: zeus.callbacks.MinIterCallback\n    :members:\n\n\nSave Progress Callback\n======================\n\n.. autoclass:: zeus.callbacks.SaveProgressCallback\n    :members:"
  },
  {
    "path": "docs/api/moves.rst",
    "content": "==================\nThe Ensemble Moves\n==================\n\n``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version\n2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers\nfollow to cross the parameter space. The ``Differential Move`` remains the default choice but we also\nprovide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from\nchallenging target distributions (e.g. highly dimensional multimodal distributions).\n\nDifferential Move\n=================\n\n.. autoclass:: zeus.moves.DifferentialMove\n    :members:\n\nGaussian Move\n=============\n\n.. autoclass:: zeus.moves.GaussianMove\n    :members:\n\nGlobal Move\n===========\n\n.. autoclass:: zeus.moves.GlobalMove\n    :members:\n\nKDE Move\n========\n\n.. autoclass:: zeus.moves.KDEMove\n    :members:\n\nRandom Move\n===========\n\n.. autoclass:: zeus.moves.RandomMove\n    :members:"
  },
  {
    "path": "docs/api/parallel.rst",
    "content": "=============================\nThe Chain Manager & MPI Tools\n=============================\n\nThe ``Chain Manager`` can be used to parallelize ``zeus``. The benefits of this appoach is that\nthe ``Chain Manager`` can parallelize many chains and walkers simultaneously. See the Cookbook\nfor more information.\n\n.. autoclass:: zeus.ChainManager\n    :members:"
  },
  {
    "path": "docs/api/plotting.rst",
    "content": "================\nPlotting Results\n================\n\n\nCornerplot\n==========\n\n.. currentmodule:: zeus\n\n.. autofunction:: zeus.cornerplot\n"
  },
  {
    "path": "docs/api/sampler.rst",
    "content": "==========================\nThe Ensemble Slice Sampler\n==========================\n\n.. autoclass:: zeus.EnsembleSampler\n    :members:"
  },
  {
    "path": "docs/api.rst",
    "content": "=============\nAPI Reference\n=============\n\n**zeus** consists mainly of six parts:\n\n.. toctree::\n   :maxdepth: 2\n\n   api/sampler\n   api/callbacks\n   api/moves\n   api/autocorr\n   api/parallel\n   api/plotting"
  },
  {
    "path": "docs/conf.py",
    "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# http://www.sphinx-doc.org/en/master/config\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\n\nsys.path.insert(0, os.path.abspath('../../zeus/'))\n\nimport zeus\n\nimport sphinx_bootstrap_theme\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'zeus'\ncopyright = '2019-2022, Minas Karamanis'\nauthor = 'Minas Karamanis'\n\n# The full version, including alpha/beta/rc tags\nrelease = zeus.__version__\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = ['sphinx.ext.autodoc',\n    'sphinx.ext.autosummary',\n    'sphinx.ext.intersphinx',\n    'sphinx.ext.mathjax',\n    'sphinx.ext.napoleon',\n    #'numpydoc',\n    'nbsphinx',\n    'sphinx.ext.coverage',\n    'IPython.sphinxext.ipython_console_highlighting',\n]\n\n# Generate the API documentation when building\nautosummary_generate = True\nnumpydoc_show_class_members = False\n\nnapoleon_google_docstring = True\nnapoleon_numpy_docstring = True\nnapoleon_include_init_with_doc = False\nnapoleon_include_private_with_doc = False\nnapoleon_include_special_with_doc = False\nnapoleon_use_admonition_for_examples = False\nnapoleon_use_admonition_for_notes = False\nnapoleon_use_admonition_for_references = False\nnapoleon_use_ivar = False\nnapoleon_use_param = True\nnapoleon_use_rtype = True\nnapoleon_use_keyword = True\nnapoleon_custom_sections = None\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\nsource_suffix = \".rst\"\nmaster_doc = \"index\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']\n#exclude_patterns = ['_build']\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'bootstrap'\nhtml_favicon = \"_static/favicon.png\"\n\n\n# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24).\n# Path should be relative to the ``_static`` files directory.\n#html_logo = \"my_logo.png\"\n\n# Theme options are theme-specific and customize the look and feel of a\n# theme further.\nhtml_theme_options = {\n    'navbar_title': \"zeus\",\n    'navbar_site_name': \"Contents\",\n\n    'navbar_links': [\n\n    (\"Cookbook\", \"cookbook\"),\n    (\"FAQ\", \"faq\"),\n    (\"API\", \"api\"),\n\n    ],\n\n    'navbar_sidebarrel': False, # Render the next and previous page links in navbar. (Default: true)\n\n    'navbar_pagenav': False, # Render the current pages TOC in the navbar. (Default: true)\n\n    'navbar_pagenav_name': \"Page\", # Tab name for the current pages TOC. (Default: \"Page\")\n\n    # Global TOC depth for \"site\" navbar tab. (Default: 1)\n    # Switching to -1 shows all levels.\n    'globaltoc_depth': 2,\n\n    # Include hidden TOCs in Site navbar?\n    #\n    # Note: If this is \"false\", you cannot have mixed ``:hidden:`` and\n    # non-hidden ``toctree`` directives in the same page, or else the build\n    # will break.\n    #\n    # Values: \"true\" (default) or \"false\"\n    'globaltoc_includehidden': \"true\",\n\n    # HTML navbar class (Default: \"navbar\") to attach to <div> element.\n    # For black navbar, do \"navbar navbar-inverse\"\n    #'navbar_class': \"navbar navbar-inverse\",\n\n    # Fix navigation bar to top of page?\n    # Values: \"true\" (default) or \"false\"\n    'navbar_fixed_top': \"true\",\n\n    #'bootswatch_theme': \"united\",\n    #'bootswatch_theme': \"paper\",\n    #'bootswatch_theme': \"cosmo\",\n    'bootswatch_theme': \"readable\",\n    #'bootswatch_theme': \"flatly\",\n    #'bootswatch_theme': \"Yeti\",\n\n    'bootstrap_version': \"3\",\n\n    'body_max_width' : '100%',\n    #'body_min_width' : '70%',\n\n}\n\nhtml_theme_path = sphinx_bootstrap_theme.get_html_theme_path()\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# If true, links to the reST sources are added to the pages.\nhtml_show_sourcelink = False\n\n# Add the 'copybutton' javascript, to hide/show the prompt in code\n# examples, originally taken from scikit-learn's doc/conf.py\n#def setup(app):\n#    app.add_javascript('copybutton.js')\n#    app.add_stylesheet('default.css')\n\n#html_css_files = ['_static',]\n\n#html_context = {'css_files': ['_static/default.css',  # override wide tables in RTD theme\n#],}\n\n#autodoc_default_options = {\n#    'exclude-members': '__init__'\n#}\n\n#autoclass_content = [\"class\"]"
  },
  {
    "path": "docs/cookbook.rst",
    "content": "========\nCookbook\n========\n\nMCMC Sampling recipes\n=====================\n\n- `Sampling from a multivariate Normal distribution`_\n    Demonstrates how to sample from a correlated multivariate Gaussian distribution and how to perform\n    the post-processing of the samples.\n\n- `Fitting a model to data`_\n    In this recipe we are going to produce some mock data and use them to illustrate how *zeus* works in\n    realistic scenarios.\n\n- `Sampling from multimodal distributions`_\n    In this recipe we will demonstrate how one can use ``zeus`` with the ``Moves`` interface to sample\n    efficiently from challenging high-dimensional multimodal distributions.\n\n.. _Sampling from a multivariate Normal distribution: notebooks/normal_distribution.ipynb\n\n.. _Fitting a model to data: notebooks/datafit.ipynb\n\n.. _Sampling from multimodal distributions: notebooks/multimodal.ipynb\n\n\nParallelisation recipes\n=======================\n\n- `Multiprocessing`_\n    Use many CPUs to sample from an expensive-to-evaluate probability distribution even faster.\n\n- `MPI and ChainManager`_\n    Distribute calculation to huge computer clusters.\n\n.. _Multiprocessing: notebooks/multiprocessing.ipynb\n\n.. _MPI and ChainManager: notebooks/MPI.ipynb\n\n.. raw:: html\n\n    <style>\n        .red {color:red; font-weight:bold;}\n        .b {color:#0000FF; background-color:white;}\n    </style>\n\n.. role:: red\n\nConvergence Diagnostics and Saving Progress recipes :red:`NEW`\n==============================================================\n\n- `Automated Convergence Diagnostics using the callback interface`_ :red:`NEW`\n    In this recipe we are going to use the callback interface to monitor convergence and stop sampling automatically.\n\n- `Saving progress to disk using h5py`_ :red:`NEW`\n    In this recipe we are going to use the callback interface to save the samples and their corresponding log-probability values in a ``.h5`` file.\n\n- `Parallel sampling using MPI and Gelman-Rubin convergence diagnostics`_ :red:`NEW`\n    In this recipe we are going to use the ChainManager to run zeus in parallel using MPI and terminate sampling automatically using Gelman-Rubin diagnostics.\n\n- `Tracking metadata using the blobs interface`_\n    We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain.\n\n.. _Automated Convergence Diagnostics using the callback interface: notebooks/convergence.ipynb\n\n.. _Saving progress to disk using h5py: notebooks/progress.ipynb\n\n.. _Parallel sampling using MPI and Gelman-Rubin convergence diagnostics: notebooks/GR.ipynb\n\n.. _Tracking metadata using the blobs interface: notebooks/blobs.ipynb\n\n\n\n.. toctree::\n    :maxdepth: 2\n    :hidden:\n\n    notebooks/normal_distribution.ipynb\n    notebooks/datafit.ipynb\n    notebooks/multimodal.ipynb\n    notebooks/multiprocessing.ipynb\n    notebooks/MPI.ipynb\n    notebooks/blobs.ipynb\n    notebooks/progress.ipynb\n    notebooks/convergence.ipynb\n    notebooks/GR.ipynb"
  },
  {
    "path": "docs/faq.rst",
    "content": "==========================\nFrequently Asked Questions\n==========================\n\nWhat is the acceptance rate of ``zeus``?\n========================================\n\nUnlike most MCMC methods, ``zeus`` acceptance rate isn't varying during a run. As a matter of fact,\nits acceptance rate is identically 1, always. This is because of the Slice Sampler at its core.\n\n\nWhy should I use zeus instead of other MCMC samplers?\n=====================================================\n\nThe first reason you should think of using ``zeus`` is due to the fact that it doesn't require\nany hand tuning at all. There is no need to adjust any hyperparameters or provide a proposal\ndistribution.\n\nMoreover, unlike other black-box MCMC methods ``zeus`` is more robust to the curse of\ndimensionality and handle challenging distributions better.\n\n\nWhat are the walkers?\n=====================\n\nWalkers are the members of the ensemble. They are interacting parallel chains which collectively explore \nthe posterior mass.\n\n\nHow many walkers should I use?\n==============================\n\nAt least twice the number of parameters of your problem. A good rule of thump is to use between 2 and 4\ntimes the number of parameters. If your distribution has multiple modes/peaks you may want to increase\nthe number of walkers.\n\n\nHow should I initialize the positions of the walkers?\n=====================================================\n\nA good practice seems to be to initialize the walkers from a small ball close to the *Maximum a Posteriori*\nestimate. After a few autocorrelation times the walkers would have explored the rest of the usefull regions\nof the parameter space (i.e. the typical set), producing a great number of independent samples.\n\n\nHow long should I run ``zeus``?\n===============================\n\nYou don't have to run ``zeus`` for very long. If your goal is to produce 2D/1D contours and/or 1-sigma/2-sigma\nconstraints for your parameters, running ``zeus`` for a few autocorrelation times (e.g. 10) is more than enough.\nYou can also use the implemented callback functions (see Cookbook and API) to automate the termination of a run.\n\n\nWhat can I do if the first few iterations take too long to complete?\n====================================================================\n\nThis usually occurs when the walkers are initialised closed to each other. During the  first ``10-100`` iterations \n``zeus`` is tuning its proposal scale ``mu``. During that time ``zeus`` may do more model evaluations than usual. \nTuning of ``mu`` is faster if initialised from a large value. We thus recommend to set ``mu`` to an large value \n(e.g. ``mu=1e3``) initially in the ``EnsembleSampler``.\n\n\nIs there any way to reduce the computational cost per iteration?\n================================================================\n\n``zeus``'s power originates in its flexibility. During each iteration, the walkers move along straight lines (i.e. slices)\nthat cross the posterior mass. The construction of a slice involves two steps, an initial expanding/stepping-out and a subsequent\nshrinking procedure. One can decrease the computational cost per iteration by forcing ``zeus`` to conduct no expansions. This is \nachieved by setting ``light_mode=True`` in the ``EnsembleSampler`` at the cost of reduced flexibility. If the target distribution\nis close to normal/Gaussian one then this procedure can cut the cost to half.\n\n\nWhat are the ``Moves`` and which one should I use?\n==================================================\n\n``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version\n2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers\nfollow to cross the parameter space. The ``Differential Move`` remains the default choice but we also\nprovide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from\nchallenging target distributions (e.g. highly dimensional multimodal distributions).\n\nThe move(s) you should use depends on the particular target distribution. The ``Differential Move`` \nseems to be a good choice for most distributions and 50-50 mixture of the ``Global Move`` and\n``Local Move`` seem to perform very well in highly dimensional multimodal distributions when used\nafter the burnin period is over."
  },
  {
    "path": "docs/index.rst",
    "content": ".. title:: zeus documentation\n\n.. figure:: ./../logo.png\n    :scale: 30 %\n    :align: center\n\n.. raw:: html\n\n    <style>\n        .red {color:red; font-weight:bold;}\n        .b {color:#0000FF; background-color:white;}\n    </style>\n\n.. role:: red\n\n**zeus is a Python implementation of the Ensemble Slice Sampling method.**\n\n- Fast & Robust *Bayesian Inference*,\n- Efficient *Markov Chain Monte Carlo (MCMC)*,\n- Black-box inference, no hand-tuning,\n- Excellent performance in terms of autocorrelation time and convergence rate,\n- Scale to multiple CPUs without any extra effort,\n- Automated Convergence diagnostics. :red:`NEW`\n\n\n.. image:: https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue\n    :target: https://github.com/minaskar/zeus\n.. image:: https://img.shields.io/badge/arXiv-2002.06212-red\n    :target: https://arxiv.org/abs/2002.06212\n.. image:: https://img.shields.io/badge/arXiv-2105.03468-brightgreen\n    :target: https://arxiv.org/abs/2105.03468\n.. image:: https://img.shields.io/badge/ascl-2008.010-blue.svg?colorB=262255\n    :target: https://ascl.net/2008.010\n.. image:: https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master\n    :target: https://travis-ci.com/minaskar/zeus\n.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg\n    :target: https://github.com/minaskar/zeus/blob/master/LICENSE\n.. image:: https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9\n    :target: https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest\n.. image:: https://pepy.tech/badge/zeus-mcmc\n    :target: https://pepy.tech/project/zeus-mcmc\n\nBasic use\n=========\n\nFor instance, if you wanted to draw samples from a *10-dimensional Normal distribution*, you would do something like:\n\n.. code:: Python\n\n    import zeus\n    import numpy as np\n\n    def log_prob(x, ivar):\n        return - 0.5 * np.sum(ivar * x**2.0)\n\n    nsteps, nwalkers, ndim = 1000, 100, 10\n    ivar = 1.0 / np.random.rand(ndim)\n    start = np.random.randn(nwalkers, ndim)\n\n    sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])\n    sampler.run_mcmc(start, nsteps)\n    chain = sampler.get_chain(flat=True)\n\n\nInstallation\n============\n\nTo install ``zeus`` using ``pip`` run:\n\n.. code:: bash\n\n    pip install zeus-mcmc\n\nTo install ``zeus`` in a `[Ana]Conda <https://conda.io/projects/conda/en/latest/index.html>`_ environment use:\n\n.. code:: bash\n\n    conda install -c conda-forge zeus-mcmc\n\n\nGetting Started\n===============\n- See the :doc:`cookbook` page to learn how to perform Bayesian Inference using ``zeus``.\n- See the :doc:`faq` page for frequently asked questions about ``zeus``' operation.\n- See the :doc:`api` page for detailed API documentation.\n\n\nCitation\n========\n\nPlease cite the following papers if you found this code useful in your\nresearch::\n\n    @article{karamanis2021zeus,\n             title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},\n             author={Karamanis, Minas and Beutler, Florian and Peacock, John A},\n             journal={arXiv preprint arXiv:2105.03468},\n             year={2021}\n            }\n\n    @article{karamanis2020ensemble,\n             title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},\n             author = {Karamanis, Minas and Beutler, Florian},\n             journal = {arXiv preprint arXiv: 2002.06212},\n             year = {2020}\n            }\n\nLicence\n=======\n\nCopyright 2019-2021 Minas Karamanis and contributors.\n\n``zeus`` is free software made available under the ``GPL-3.0 License``.\n\n\nChangelog\n=========\n\n**2.4.1 (17/11/21)**\n\n- Introduced ``ParallelSplitRCallback`` callback function for checking Gelman-Rubin statistics during ``MPI`` runs.\n\n**2.4.0 (01/11/21)**\n\n- Introduced callback interface.\n- Added convergence diagnostics.\n- Added ``H5DF`` support.\n\n**2.3.1 (03/08/21)**\n\n- Raise exception if model fails.\n\n**2.3.0 (25/02/21)**\n\n- Added ``sample`` method which advances the chain as a generator.\n- Added ``light_mode``. When used, ``light_mode`` can significantly reduce the number of log likelihood evaluations\n  and increase the general efficiency of the algorithm. ``light_mode`` works by performing no expansions after the\n  end of the tuning phase. The scale factor is set to its opttimal value. This works best for approximately Gaussian\n  distributions.\n- Added ``start=None`` support for ``run_mcmc``. When used, the sampler proceeds from the last known position of the walkers.\n- Added support for both ``thin`` and ``thin_by`` arguments.\n\n**2.2.2 (21/02/21)**\n\n- Added ``log_prob0`` and ``blobs0`` arguments in ``run``.\n- Added ``get_last_sample()``, ``get_last_log_prob()`` and ``get_last_blobs()`` methods.\n\n**2.2.0 (03/11/20)**\n\n- Improved vectorization.\n\n**2.1.1 (29/10/20)**\n\n- Added ``blobs`` interface to track arbitrary metadata.\n- Updated ``GlobalMove`` and multimodal example.\n- Fixed minor bugs.\n\n**2.0.0 (05/10/20)**\n\n- Added new ``Moves`` interface (e.g. ``DifferentialMove``, ``GlobalMove``, etc).\n- Plotting capabilities (i.e. ``cornerplot``).\n- Updated docs.\n- Fixed minor bugs.\n\n**1.2.2 (19/09/20)**\n\n- ``Sampler`` class is deprecated. New ``EnsembleSampler`` class in now available.\n- New estimator for the Integrated Autocorrelation Time. It's accurate even with short chains.\n- Updated ``ChainManager`` to handle thousands of CPUs.\n\n**1.2.1 (04/08/20)**\n\n- Changed to Flat-not-nested philosophy for diagnostics and ``ChainManager``.\n\n**1.2.0 (03/08/20)**\n\n- Extended ``ChainManager`` with ``gather``, ``scatter``, and ``bcast`` tools.\n\n**1.1.0 (02/08/20)**\n\n- Added ``ChainManager`` to deploy into supercomputing clusters, parallelizing both chains and walkers.\n- Added Convergence diagnostic tools (Gelman-Rubin, Geweke).\n\n**1.0.7 (11/05/20)**\n\n- Improved parallel distribution of tasks\n\n\n.. toctree::\n    :maxdepth: 1\n    :caption: Cookbook Recipes\n    :hidden:\n\n    Overview <cookbook>\n    notebooks/normal_distribution.ipynb\n    notebooks/datafit.ipynb\n    notebooks/multiprocessing.ipynb\n    notebooks/MPI.ipynb\n\n.. toctree::\n    :maxdepth: 3\n    :caption: Help & Reference\n    :hidden:\n\n    faq\n    api\n"
  },
  {
    "path": "docs/make.bat",
    "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=.\r\nset BUILDDIR=_build\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n"
  },
  {
    "path": "docs/notebooks/GR.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Parallel sampling using MPI and Gelman-Rubin convergence diagnostics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\\n\",\n    \"\\n\",\n    \"To do this we will use the ``ChainManager`` included in ``zeus``. We will also use the ``ParallelSplitRCallback`` function to check the Gelman-Rubing convergence diagnostic during the run and terminate sampling automatically.\\n\",\n    \"\\n\",\n    \"In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"mpiexec -n 8 python3 test_mpi_gr.py\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Save this as 'test_mpi_gr.py'\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import zeus\\n\",\n    \"from zeus import ChainManager\\n\",\n    \"\\n\",\n    \"ndim = 20\\n\",\n    \"nwalkers = 2 * ndim\\n\",\n    \"nsteps = 10000\\n\",\n    \"nchains = 2\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    return -0.5 * np.sum(x**2.0)\\n\",\n    \"\\n\",\n    \"start = 1e-2 * np.random.randn(nwalkers, ndim)  + 20.0\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"with ChainManager(nchains) as cm:\\n\",\n    \"    rank = cm.get_rank\\n\",\n    \"\\n\",\n    \"    cb = zeus.callbacks.ParallelSplitRCallback(epsilon=0.01, chainmanager=cm)\\n\",\n    \"    sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\\n\",\n    \"    sampler.run_mcmc(start, nsteps, callbacks=cb)\\n\",\n    \"    chain = sampler.get_chain(flat=True, discard=0.5)\\n\",\n    \"    \\n\",\n    \"    if rank == 0:\\n\",\n    \"        print('R =', cb.estimates, flush=True)\\n\",\n    \"    np.save('chain_'+str(rank)+'.npy', chain)\\n\",\n    \"\\n\",\n    \"```\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"288px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "docs/notebooks/MPI.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Parallelizing sampling using MPI\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\\n\",\n    \"\\n\",\n    \"To do this we will use the ``ChainManager`` included in ``zeus``.\\n\",\n    \"\\n\",\n    \"In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"mpiexec -n 8 python3 test_mpi.py\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Save this as 'test_mpi.py'\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import zeus\\n\",\n    \"from zeus import ChainManager\\n\",\n    \"\\n\",\n    \"ndim = 5\\n\",\n    \"nwalkers = 2 * ndim\\n\",\n    \"nsteps = 100\\n\",\n    \"nchains = 2\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    return -0.5 * np.sum(x**2.0)\\n\",\n    \"\\n\",\n    \"start = np.random.randn(nwalkers, ndim)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"with ChainManager(nchains) as cm:\\n\",\n    \"    rank = cm.get_rank\\n\",\n    \"\\n\",\n    \"    sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\\n\",\n    \"    sampler.run_mcmc(start, nsteps)\\n\",\n    \"    chain = sampler.get_chain(flat=True, discard=0.5)\\n\",\n    \"        \\n\",\n    \"    np.save('chain_'+str(rank)+'.npy', chain)\\n\",\n    \"\\n\",\n    \"```\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"288px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "docs/notebooks/blobs.ipynb",
    "content": "{\n \"metadata\": {\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6-final\"\n  },\n  \"orig_nbformat\": 2,\n  \"kernelspec\": {\n   \"name\": \"Python 3.7.6 64-bit ('nbodykit-env': conda)\",\n   \"display_name\": \"Python 3.7.6 64-bit ('nbodykit-env': conda)\",\n   \"metadata\": {\n    \"interpreter\": {\n     \"hash\": \"92a13e2fbe78b004d0fb7131bfe04f8cf6342cb1d93c749c200bfd2478bfd7dd\"\n    }\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2,\n \"cells\": [\n  {\n   \"source\": [\n    \"# Blobs and Metadata\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"source\": [\n    \"We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain.\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"source\": [\n    \"## Tracking the value of the log-prior\\n\",\n    \"\\n\",\n    \"We can easily use blobs to store the value of the log-prior at each step in the chain by doing something like:\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"stream\",\n     \"name\": \"stderr\",\n     \"text\": [\n      \"Initialising ensemble of 32 walkers...\\n\",\n      \"Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 160.45it/s](100, 32)\\n\",\n      \"(3200,)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import zeus\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def log_prior(x):\\n\",\n    \"    return -0.5 * np.dot(x,x)\\n\",\n    \"\\n\",\n    \"def log_like(x):\\n\",\n    \"    return -0.5 * np.dot(x,x) / 0.1**2.0\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    lp = log_prior(x)\\n\",\n    \"    if not np.isfinite(lp):\\n\",\n    \"        return -np.inf, -np.inf\\n\",\n    \"    ll = log_like(x)\\n\",\n    \"    if not np.isfinite(ll):\\n\",\n    \"        return lp, -np.inf\\n\",\n    \"    return lp + ll, lp\\n\",\n    \"\\n\",\n    \"nwalkers, ndim = 32, 3\\n\",\n    \"start = np.random.randn(nwalkers, ndim)\\n\",\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\\n\",\n    \"sampler.run_mcmc(start, 100)\\n\",\n    \"\\n\",\n    \"log_prior_samps = sampler.get_blobs()\\n\",\n    \"flat_log_prior_samps = sampler.get_blobs(flat=True)\\n\",\n    \"\\n\",\n    \"print(log_prior_samps.shape)  # (100, 32)\\n\",\n    \"print(flat_log_prior_samps.shape)  # (3200,)\"\n   ]\n  },\n  {\n   \"source\": [\n    \"Once this is done running, the “blobs” stored by the sampler will be a ``(nsteps, nwalkers)`` numpy array with the value of the log prior at every sample.\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"source\": [\n    \"## Tracking multiple species of metadata\\n\",\n    \"\\n\",\n    \"When handling multiple species of metadata, it can be useful to name them. This can be done using the ``blobs_dtype`` argument of the ``EnsembleSampler``. For instance, to save the mean of the parameters as well as the log-prior we could do something like:\"\n   ],\n   \"cell_type\": \"markdown\",\n   \"metadata\": {}\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"stream\",\n     \"name\": \"stderr\",\n     \"text\": [\n      \"Initialising ensemble of 32 walkers...\\n\",\n      \"Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 137.06it/s](100, 32)\\n\",\n      \"(100, 32)\\n\",\n      \"(3200,)\\n\",\n      \"(3200,)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def log_prob(params):\\n\",\n    \"    lp = log_prior(params)\\n\",\n    \"    if not np.isfinite(lp):\\n\",\n    \"        return -np.inf, -np.inf\\n\",\n    \"    ll = log_like(params)\\n\",\n    \"    if not np.isfinite(ll):\\n\",\n    \"        return lp, -np.inf\\n\",\n    \"    return lp + ll, lp, np.mean(params)\\n\",\n    \"\\n\",\n    \"nwalkers, ndim = 32, 3\\n\",\n    \"start = np.random.randn(nwalkers, ndim)\\n\",\n    \"\\n\",\n    \"# Here are the important lines\\n\",\n    \"dtype = [(\\\"log_prior\\\", float), (\\\"mean\\\", float)]\\n\",\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, blobs_dtype=dtype)\\n\",\n    \"\\n\",\n    \"sampler.run_mcmc(start, 100)\\n\",\n    \"\\n\",\n    \"blobs = sampler.get_blobs()\\n\",\n    \"log_prior_samps = blobs[\\\"log_prior\\\"]\\n\",\n    \"mean_samps = blobs[\\\"mean\\\"]\\n\",\n    \"print(log_prior_samps.shape)\\n\",\n    \"print(mean_samps.shape)\\n\",\n    \"\\n\",\n    \"flat_blobs = sampler.get_blobs(flat=True)\\n\",\n    \"flat_log_prior_samps = flat_blobs[\\\"log_prior\\\"]\\n\",\n    \"flat_mean_samps = flat_blobs[\\\"mean\\\"]\\n\",\n    \"print(flat_log_prior_samps.shape)\\n\",\n    \"print(flat_mean_samps.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ]\n}"
  },
  {
    "path": "docs/notebooks/convergence.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Automated Convergence Diagnostics using the callback interface\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Knowing when to stop sampling can be very useful when running expensive MCMC procedures. Ideally, if we want unbiased results, we want the sampler to stop after it has converged to the stationary phase (i.e. after the burn-in/warm-up period is over). To do this we can combine different ``Convergence Diagnostics`` offered as ``callback functions`` by zeus.\\n\",\n    \"\\n\",\n    \"We will start by setting the simple problem of sampling from a bimodal Gaussian mixture distribution:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 105,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import zeus\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"nsteps, nwalkers, ndim = 100000, 50, 5\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    return np.logaddexp(-0.5 * np.sum(x ** 2), -0.5 * np.sum((x - 4.0) ** 2))\\n\",\n    \"\\n\",\n    \"x0 = 1e-3*np.random.randn(nwalkers,ndim) + 5.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Where ``nsteps`` would be the maximum number of steps/iterations, ``ivar`` would be the inverse variance (precision) of the normal target distribution that we are going to sample from, and ``x0`` is the starting position of the walkers.\\n\",\n    \"\\n\",\n    \"We will then define all the convergence diagnostics that we will use as ``callback functions``.\\n\",\n    \"\\n\",\n    \"First of all, we would like check the integrated autocorrelation time (IAT) of the chain every ``ncheck=100`` steps and make sure that we don't stop running unless the length of the chain is longer than ``nact=50`` times the IAT and that the rate of change of IAT drops bellow 1 percent (i.e. ``dact=0.01``). We would also discard the first half of the chain (i.e. ``discard=0.5``) before computing the IAT.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 106,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cb0 = zeus.callbacks.AutocorrelationCallback(ncheck=100, dact=0.01, nact=50, discard=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will then use the **Split-R Gelman-Rubin statistic** computed using different segments (i.e. split into ``nsplits=2`` parts) of the same chain and decide that the sampler has converged if its value drops bellow ``(1+epsilon)=1.01``.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 107,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cb1 = zeus.callbacks.SplitRCallback(ncheck=100, epsilon=0.01, nsplits=2, discard=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, just to make sure that the sampler doesn't stop too early, we will set the minimum number of iterations to ``nmin=500``.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 108,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cb2 = zeus.callbacks.MinIterCallback(nmin=500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are now ready to start sampling and require that all three of the aforementioned criteria are satisfied before sampling terminates.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 109,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialising ensemble of 50 walkers...\\n\",\n      \"Sampling progress :   2%|▏         | 1898/100000 [00:13<13:42, 119.22it/s]\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\\n\",\n    \"sampler.run_mcmc(x0, nsteps, callbacks=[cb0, cb1, cb2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We noticed that the sampler automatically stopped running after approximately ``1900`` iterations. We can now have a look at the ``split-R`` statistics and the IAT estimate.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 110,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sampling progress :   2%|▏         | 1900/100000 [00:13<11:50, 138.00it/s]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tau = cb0.estimates\\n\",\n    \"R = cb1.estimates\\n\",\n    \"\\n\",\n    \"N = np.arange(len(tau)) * 100\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12,6))\\n\",\n    \"plt.subplot(121)\\n\",\n    \"\\n\",\n    \"plt.plot(N, tau, lw=2.5)\\n\",\n    \"plt.title('Integrated Autocorrelation Time', fontsize=14)\\n\",\n    \"plt.xlabel('Iterations', fontsize=14)\\n\",\n    \"plt.ylabel(r'$\\\\tau$', fontsize=14)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plt.subplot(122)\\n\",\n    \"\\n\",\n    \"plt.plot(N, R, lw=2.5)\\n\",\n    \"plt.title('Split-R Gelman-Rubin Statistic', fontsize=14)\\n\",\n    \"plt.xlabel('Iterations', fontsize=14)\\n\",\n    \"plt.ylabel(r'$R$', fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also have a look at the traces of the walkers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 111,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"samples = sampler.get_chain()\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12,5))\\n\",\n    \"plt.plot(samples[:,:,0],alpha=0.25)\\n\",\n    \"plt.xlabel('Iterations', fontsize=14)\\n\",\n    \"plt.ylabel(r'$x_{1}$', fontsize=14)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And also the 1-dimensional marginal distribution of the first parameter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 112,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 576x432 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"chain = sampler.get_chain(flat=True, discard=0.5)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8,6))\\n\",\n    \"plt.hist(chain[:,0], 50)\\n\",\n    \"plt.gca().set_yticks([])\\n\",\n    \"plt.xlabel(r\\\"$x_{1}$\\\", fontsize=14)\\n\",\n    \"plt.ylabel(r\\\"$p(x_{1})$\\\", fontsize=14)\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"interpreter\": {\n   \"hash\": \"42ef9c41c9809f9bfe38b73fa705c16bbb3d6fadc6a1917ff578a20446617baf\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.7.10 64-bit ('nbodykit-env': conda)\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/notebooks/datafit.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Fitting a model to data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this recipe we will demonstrate how to fit a simple model, namely a line, to some data. Although this example is simple, it illustrates what is the proper way of fitting our models to data and infering the parameters of the models.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let us first import the main packages that we will use:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# show plots inline in the notebook\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"from IPython.display import display, Math\\n\",\n    \"\\n\",\n    \"import zeus\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The generative probabilistic model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In order to create our *synthetic* data we need to construct a *generative probabilistic model*.\\n\",\n    \"\\n\",\n    \"We start by defining the *straight line* model and also setting the *true values* of the model parameters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# define the model function\\n\",\n    \"def straight_line(x, m, c):\\n\",\n    \"    ''' A straight line model: y = m*x + c '''\\n\",\n    \"    return m*x + c\\n\",\n    \"\\n\",\n    \"# set the true values of the model parameters for creating the data\\n\",\n    \"m_true = 3.5 # gradient of the line\\n\",\n    \"c_true = 1.2 # y-intercept of the line\\n\",\n    \"\\n\",\n    \"# Set the x-coordinates of the data points\\n\",\n    \"M = 70 # Number of data points\\n\",\n    \"x = np.sort(10.0 * np.random.rand(M)) # their x-coordinates\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are now ready to generate the synthetic data. To this end, we evaluate the model function at the *true values* of *m (slope)* and *c (y-intercept)* and we add some random *Gaussian* noise of known amplitude *sigma*.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# create the data - the model plus Gaussian noise\\n\",\n    \"sigma = 3.0 # standard deviation of the noise\\n\",\n    \"data = straight_line(x, m_true, c_true) + sigma * np.random.randn(M)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also plot the generative model and the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<Figure size 648x432 with 1 Axes>\",\n      \"image/svg+xml\": \"<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\" 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    },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     }\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(9,6))\\n\",\n    \"plt.errorbar(x, data, yerr=sigma, fmt=\\\"o\\\", label='data')\\n\",\n    \"plt.plot(x, straight_line(x, m_true, c_true), '-', lw=2, label='model')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlabel(r'$x$')\\n\",\n    \"plt.ylabel(r'$y$')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The likelihood, prior, and posterior distributions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first step to solve a problem is generally to write down the prior and likelihood functions. An important benefit of MCMC is that none of these probability densities need to be normalised.\\n\",\n    \"\\n\",\n    \"Here we'll start with the natural logarithm of the prior probability: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def logprior(theta):\\n\",\n    \"    ''' The natural logarithm of the prior probability. '''\\n\",\n    \"\\n\",\n    \"    lp = 0.\\n\",\n    \"\\n\",\n    \"    # unpack the model parameters from the tuple\\n\",\n    \"    m, c = theta\\n\",\n    \"\\n\",\n    \"    # uniform prior on c\\n\",\n    \"    cmin = -10. # lower range of prior\\n\",\n    \"    cmax = 10.  # upper range of prior\\n\",\n    \"\\n\",\n    \"    # set prior to 1 (log prior to 0) if in the range and zero (-inf) outside the range \\n\",\n    \"    lp = 0. if cmin < c < cmax else -np.inf\\n\",\n    \"\\n\",\n    \"    # Gaussian prior on m\\n\",\n    \"    mmu = 3.     # mean of the Gaussian prior\\n\",\n    \"    msigma = 10. # standard deviation of the Gaussian prior\\n\",\n    \"    lp -= 0.5*((m - mmu)/msigma)**2\\n\",\n    \"\\n\",\n    \"    return lp\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We assume that the likelihood is *Gaussian (Normal)*:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def loglike(theta, data, sigma, x):\\n\",\n    \"    '''The natural logarithm of the likelihood.'''\\n\",\n    \"    \\n\",\n    \"    # unpack the model parameters\\n\",\n    \"    m, c = theta\\n\",\n    \"\\n\",\n    \"    # evaluate the model\\n\",\n    \"    md = straight_line(x, m, c)\\n\",\n    \"\\n\",\n    \"    # return the log likelihood\\n\",\n    \"    return -0.5 * np.sum(((md - data)/sigma)**2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The log posterior is just the sum of the log prior and the log likelihood probability density functions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def logpost(theta, data, sigma, x):\\n\",\n    \"    '''The natural logarithm of the posterior.'''\\n\",\n    \"    \\n\",\n    \"    return logprior(theta) + loglike(theta, data, sigma, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Sampling the posterior using *zeus*\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We initialize and run zeus to sample from the posterior distribution. Thin only takes a few lines of code.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"output_type\": \"stream\",\n     \"name\": \"stderr\",\n     \"text\": \"Initialising ensemble of 10 walkers...\\nSampling progress : 100%|██████████| 2000/2000 [00:08<00:00, 237.71it/s, nexp=0.8, ncon=1.4]\\nSummary\\n-------\\nNumber of Generations: 2000\\nNumber of Parameters: 2\\nNumber of Walkers: 10\\nNumber of Tuning Generations: 24\\nScale Factor: 3.03521\\nMean Integrated Autocorrelation Time: 3.02\\nEffective Sample Size: 6629.56\\nNumber of Log Probability Evaluations: 104165\\nEffective Samples per Log Probability Evaluation: 0.063645\\n\"\n    }\n   ],\n   \"source\": [\n    \"ndim = 2 # Number of parameters/dimensions (e.g. m and c)\\n\",\n    \"nwalkers = 10 # Number of walkers to use. It should be at least twice the number of dimensions.\\n\",\n    \"nsteps = 2000 # Number of steps/iterations.\\n\",\n    \"\\n\",\n    \"start = 0.01 * np.random.randn(nwalkers, ndim) # Initial positions of the walkers.\\n\",\n    \"\\n\",\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, logpost, args=[data, sigma, x]) # Initialise the sampler\\n\",\n    \"sampler.run_mcmc(start, nsteps) # Run sampling\\n\",\n    \"sampler.summary # Print summary diagnostics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Lets plot the chains. 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\\n\"\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     }\n    }\n   ],\n   \"source\": [\n    \"# flatten the chains, thin them by a factor of 10, and remove the burn-in (first half of the chain)\\n\",\n    \"chain = sampler.get_chain(flat=True, discard=nsteps//2, thin=10)\\n\",\n    \"\\n\",\n    \"# plot marginal posterior distributions\\n\",\n    \"fig, axes = zeus.cornerplot(chain, labels=['m', 'c'], truth=[m_true, c_true]);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now lets plot the projection of our results into the space of the observed data. The easiest way to do this is to randomly select 100 samples from the chain and plot the respective models on top the data points.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<Figure size 648x432 with 1 Axes>\",\n      \"image/svg+xml\": \"<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\" standalone=\\\"no\\\"?>\\n<!DOCTYPE svg PUBLIC \\\"-//W3C//DTD SVG 1.1//EN\\\"\\n  \\\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\\\">\\n<!-- Created with matplotlib (https://matplotlib.org/) -->\\n<svg height=\\\"370.91625pt\\\" version=\\\"1.1\\\" viewBox=\\\"0 0 556.365625 370.91625\\\" width=\\\"556.365625pt\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\">\\n <metadata>\\n  <rdf:RDF xmlns:cc=\\\"http://creativecommons.org/ns#\\\" xmlns:dc=\\\"http://purl.org/dc/elements/1.1/\\\" 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ApiTm2nGPd9iK/6EtRsg1++7XNrLoyRufsoggL0wdmAEqjMHJQqir5EREMpkWp5PAfv36Oz92xjZ39eRY2Rfj6ey/g/dcvIewPSCVm5IAsMLSQCkggClaYbMYha6Vg9mXgH9/O3CP+lEAMUp6QibdDw2IZY7ZLUk0xTPk+lARcEU7lCpyaXlHBoiiKoiiTzUQR++OMb1+eULB4FZWWWg9BtwTVVtbtNVl93wgl2wCgO1Nm9b1leIWfzsUBEQfpLkh7Y8auTPyMjpX5xq+e5V/u3kF/tsJlcxP895+9jLdcOR/LsGS3z1i3TPxYBvh8ks0SjIlJNtHG9qEKPtOF0iCkxwAb/ClINUiGS908af34g1IJKo147apmET1OVXwrhiGR/6ZPKyyKoiiKMl0YFyN/+eNnJ/z581pGriuVjvwgVPL4qZK26qBhEWu/+xDFqnPU4UUb1j5RpjO8UbwrpaxE6mNwYDDHl36xnlsf2Eu+bPO68xv5+M3LeOX5szEMR9JoiyNQLoHPEhOtPyQiJZyCVLv4UypjtLCJoF2BUov4UmJN0tppWATJWTKFVC1CcXwHUZsIklpZfCumTyoxIB4apyDm21NABYuiKIqiTAGdF3cc2gl0LIe2Lx8jVDB9EGum15ch6OShfxM9mfKE5+8pGDC4XaaDTB8b946w9ufP8MPfHgTgXZe38rHXn8fKec1gl2VjcjUL1bJM/ATDIjKCcYg1yLRPMAXVDAzvgloJ03AYNeuh/QKItkPTIqms2GVpQQEEExBKSAuoVganKALI9IshuJr39hAFIORltpwCKlgURVEUZYqYXRdm73Aexz18W9hvseo1S6Qqkh8UY6rph1izxOiXs8yp7CHmZGHMoD0K3fnnn7s9UMR14YHnulj7i+e4c+MA0aDJh66fw1++/jzmNKekqpHpguqYiAkrIC0ff1i8JbFGCXTzheVa8jtE0ISikJrDa27ogNRsafsE49748rAIq0iD1/apSXXHtOR7DBE01dLhVpAVlKTbck5MuKeAChZFURRFmSIa4+LX6BotetuXQ6y6YS6dc0owOixCJd4qlY1SBvo2QDFNojaCYxqAy6rzxlj9VJiiYx06b9ioce3YvVz+8Z/w1L4MzXE//9i5mD+9aTl10ajkoQzvl4qK4Uh0fjgpiw4PtXbaAFsqJaP7wXTBH5dR5ESb50+ZJ6KjWoTisMT6R5tkX5BdPiyCAmERItWiN1rtE4FjGCJcKgX52heU408BFSyKoiiKMoU0xoM0xgKEnAL//s650iKxPaESqpPR477nZOeON06M4ZIqD8O+39JpDMHsBj59YDkjboyEnSH38L/z2cfvYXFzmG+9+zx+77olhIJBGUke3Scbmg1EEPkTUvmI1nvjyc1S7cgeFIFhGpKdkpzlhbwtkoh9u3L0WHK4HiyfVFPsslRODOvwceOCxAyIt6UyJq2gcfHij3DLvz5+yq+jChZFURRFmSpcl5BTIOWMytSP3e4JlZRsOe5ZD8W0vMFjylRN7iCfXDYqrZdMBSwfrwjt45LHv89tjx9gf6HG5fPifP5PL+HNVyzAMi0oj8hIc7UgQW1W0Gv7JKR1k2gBf1IEzcg+ESr+oFxL/RxIzoa6BSJcal41xTBFaARiIlpcRwRIIIaMKZc8/4zlGWlNrxWU9cRLSCoxvlOrqByLChZFURTltHBObW32zLSttW6Cbgnb8DFq1UPDQhEiPetlWsexpUrhOJA9AOmDUBiSnBPTYt/AGF/8+TN8+8E9FCoOr1ic5B/espxXrJiFAVAahlIaKlWwXDG7+iIQTvHrbocRq4G3XbZAqjf5XVCribhoWCCx+vXzZauy6Ze2TWFIWjbhehE8Tk0+fEE5ZrxyAnKbLyRVoWrRM9b6RST5wiKcJhEVLIqiKIoyWbiuJM2ODUC1gI8ao1Y9eaISq9/9jLSAHMcTKjVI74V0t8Tc2zUw/Ty7f5g1tz3NT57oxgDefXkzN13cRntzM9fOi0FuUCoZtbK0aQJhCEYhUCc7fCJt9HUfpJ40DO0EbJnmaZgjAqVxISTapWpSHgM3L+Ij3OgJE6+iEojK86p5fhXDFCED0hqqlQ8ba/0Rb3/R1KCCRVEURVFeKscIFTHTttFvjpBw0iy0u4m6OcibYoC1y17YW4+ErRkurmtx/4aD/PO69dyzeZBY0OTD13XwkdctZVZLE4/t7iFGPwz3ATUwAlLNCERl+WGkQSojuJDrZjbduLYJkXaoXwCpuVJZCTeCUxbhhCEVF39UKiLjnhPLEy3VwuHKiS8k5656GTJWQCaN/GERLVOMChZFURRlyli3vpv1B9JUbIeAZTK7LnymL2lycV2Z7skPekIlAPE28XnkB1lQ3U7UzWO4DrZrQbUCozsg56XSYlBzDH76mx2s/flzPHMgS2vCzz+9aT5/+pplpBIJqIiRtpUh/DhgxKVaEkxApBkiKfHEVAqyQ6hWAn+EIauZIV8rr1h+I6TmiTCpFKSNZFhy/0BEnsO458QwpXJSycttph/ZmOiNLo/H9vsjUtk5jahgURRFUaaEdeu7WX3bxkPBaRXbYe9w/uSW/01XHEcqKkcJlXapVIz1wcA2KGWIOxlqjkXQLdBu98O+/dKCMQ0KZZfv3reRz/9iI/uGiyxtDvHtdy/lPa9cSjAYgkqOrbs3EqXMnHgIB5McYZnkiTdDqEFERjkNI3uknRSMQHIxNC5hfX+RQbMJmpeJACmOiAAZ95iACBd/EKmclLwlipZUWTBkAgjErxKMe6Jm6qspE6GCRVEURZkS1t61/Xm7dByX4+/RORsYFypjAzJNYwUh0SECYKwPBjZDMXMofdZfKzLL6aWuNoLfLUGllaGxKl/75XN85a6tDOerXDU/xpfesZI3Xj4P0wxI5WW0Fyp5IpQp44NwPYM5g7yVZHnzcjCRKaPMfsCCcEJaPo2LxUgbb2L4iaeIuQWZQvKFIRL3KiZ4JlqfeGgqXiqdaXnptPbh6Z9gTKoppnWcF+T0oYJFURRFmRKety/nRW6f1hwpVKoFqTQkOkSw5HplQ3IxfdhMW5bx4WWVTfio4tpwMF3iQ//6IN95YDfFqsObLqjj4zct4JoL5gIu5DMyTlyrSjZKIEQmHyZDksVNi+gZzWPYDuT7Du/pibWKgbZxMaTmQCDh+VPSRJwCFTMIkUYRHIZ5OBbfrohhFkNuNwwRKdjebqGITBxNI1SwKIqiKFNCeypM9wTi5NAenbMBxznao+ILScCaFRChkj4oY8UuIiCKIzC6F/LDYFcwa1W292b430e28uvNA1imwXsva+Rjr1vE8nntsll5bFjaOuMTP6EIBJMQbaQ7VyVPDHwBmp29+KiC3SrVlKalMiZdN1taUtW8TBphgT9G1kyJkdYfFtOsY0tVyHU5JFTGs1WMw+Fukz2OPFmoYFEURVGmhFU3LWX1bRuPaguZhtw+7TkkVAZkKmZcqBi+w0KlkgVccH2Q74eR/ZKx4tZwDYN7Nnbzf37wOE/tTRMLmvz+VY18+s3LaW9tlipKtk92/NiVwybYcAqizd70TQjL3c0sDkKujqrhp9ffwZUrXiVTP7FWqYyUc+CMeXuCElIZMUzKZgjXkHh/aft4QgW8r5Fj/dFJC3ebSlSwKIqiKFPCuE9lfGPx+JTQtPavTCRUErOkpZLrkVHkSg5cEzBgrF98JGPD4LpUbZP/fnQ3a372DM915WiO+/joq1p406WzMUIx2usjkOkWoeI64Phkj1CkDpJeGu24PyXXTczNkrfi0HEJTw76GPK38o6518jETjkj1+sPQrDusC/Fy0LxU8V03cNZKeNixfJL1WUKwt2mEhUsiqIoypQwnmx78ZzUmb2QE+GFhEr6AGR7pJJhGmC7kDt4RDvIJV9x+M69m/nC7RvZP1xkeUuI7757Ab97zSKC0ThP7ukhxiBkAHt8OWAdxJsgVi9Cxa7CWO/hx4+3sTW4kN7AbG487zXs3bKVsFE+9Jj4QhCJilHWF5QqzaHUWQfLrclzG/eonIZwt6lEBYuiKIpy7uI4IgDyg95+nbAnVAxPqHh5KaYPXANGuyG9X24zTAYyRb76yw187Z7tjOSrvGJBlK++fRk3XzIPMxASX8noXloZlcczEhBPQKxFAtwCEWnXZLtEsPgj0LAIWldA0xLWd/fhYIFbI+nmcDHkmGBMBIpPtkFjV8Rs6xwWKa5hUTN8pzXcbSpRwaIoiqKce0woVDqkGjG6TyoqlbwXn+/KbZmDXpXFZFf/GJ9ft57vPbyHcs2h8/wkq149h6uWz5LJoXIWRvolxM2CMgHGSDC7eZGMIFshaemM9gGG+EialkPbBVA/DyL1AETYh+nagEvejFA1w5Joa/qkmlIrSf6KK1k3WP5D8fkZM4VjjC8mPPtRwaIoiqKcO4wLlbEBLxE2DKnZcvvoPsj0iIAxvcTX9F4ZWa7mwfTx5N5R1tz2JP/zVDd+0+B9l9fz0RvmsnR+m7SPCmko9YBTBCyZ+AnXcyDno2hFuShaL5NEpW6ZCIo2QN1iESp1syUh16lCKQuujY3FmBmDWDNjpiduXEfElFMDXBEoltcW8ocPhbs5xpnPTplMVLAoiqIoM5/nCZWITP3YNgzuFENtrSxm2lpZ2j65PqiVcF2DX63vZs3P1vPAtiGSIYtP3NjCh66bQ1tLs1RhSmlveWEFsDwjbQPEW8Ef54ZkUX4+uk+ERd1saDkfWpZLQq4vKKKoOAK4XtsnQc4aoIKIkQAVLNf2tiUbMtlj+g57U6ZBuNtUMmWCxTCMEPAQEPQe56eu6/6tYRjzgR8BDcDTwHtd161M1XUoiqIo5zATCpUOqZ4M7BChMi4yygXIdInx1a5QtQ1+9Mhu1v7sWTYezDIr5efzb+7gj18xm3iyQc5fHJapIccGDAg3iD8l2ihjxuUcZPdBzYFQGJq9tk/DIog1idip5KQ9ZPrFmxJMeCZaE8cwCTklKGb41u+cJ8cfqqZMv3C3F+LIvVL+pnkXnOz9p7LCUgZucF13zDAMP/AbwzDuBP4K+KLruj8yDOObwB8C35jC61AURVHONRxHthHnB48WKtUy9G+TGH2nBhiy22e0C/K9YLvkSjW+fd8Wvnj7JrpGS5zXGuLf3zWX37l6DoFwnSwCzA/KaLJji2cl2gCJFgl8MyxJva2OidE1mIC2xdC2UmLzQykJjCt7GSy+gGSv+MNeHoohVZ5amag9hoMhIsUKeNWU8FlXTTl2r5RhnXzwy5QJFtd1XWDM+9bvfbjADcDverf/O/ApVLAoiqLMaIZyZbpGi1Rsh2s+ez+rblo6NXksEwmVRIdsKe7bDLl+MatiiN8k7aXSGtA3UuArd27k6/dsJ12o8coFEb759oW87tJZGL442GWpyFS9EDYrAPEWiHZAKCZCpjgqLRtfUH7WtAzaLoRku1xLrSzbkh1HzLDRxsMCxHGgUpTrdm2wApTMEFUjIMeNTwSdAEdWM6b09T5BJtordbJMqYfFMAwLafssAr4G7AbSrjs+HM5BYMJX0DCMDwAfAJgzZ85UXqaiKIoyhQzlyuwdzuN44ard6SKrb9sIMHlvohMKlXYo56Fvk7SEXFsMq4WMeFTyQwDs6M3y+Z+v598f3kfFdnjr+QlW3djGFUs7wBeHak6Mt7UCOMhG5FCDCJJQFCrjQqYsJtuGhdCyEtpWQKQZ/AEo5eTaMCUCP5w8vNfHqYnJtlaRbDd/WKo2gQgZsw7XME9arBxZzZiS1/skmYz9UVMqWFzXtYGLDMNIAf8LLDuJ+94K3Apw2WWXuVNygYqiKMqU0zVaPCRWxilW7cnZ2jyRUIm3iXek+zmJyjdsMdcWRmD0gBwPPL5zkDXrnuV/n+kmYBm8/7I6PnpjO4vntEoKbDkL2b3ekkBTWjvRZkmkNfyy/ydzUILgggloXgztF/HJhyvkuqN85ZY2ie8vjnhtn0YRK5Zfgt9qRan8OLb8PBg7bKD1OiaucfJJtBNVMybt9T5FjrdX6mQ4LVNCruumDcP4NXAVkDIMw+dVWWYB3afjGhRFUZQzw/hf+sfykv7qdjwBUhj2hEpUzK7FLHSvF5HgIF6R3IBUSEppHMfgjmcOsPbnz/HQjmFSYZNP3tDIh66bTUtLk0zdlMZkz4/j7fgJ1YtBNtIIuNJKquTBsiRSv2EJtF8o+Sn+OCXzCeJOAQoDh70zgaj4WVxXrtEuyfPwh+Xax70pkxDuNh23ZE+0V+pkmcopoSag6omVMPBq4J+BXwNvRyaF3gf8bKquQVEURTnzBCxzQtFySlubx4VKfkg8Jf6oVD2KGehZL2ZXBxExYwNSAankqNgu//XQbtbevoEtPWPMTvn54pta+KNXzCaWqJeqx9go1LzWjBmAcBOkOsRr4rgiQGoVafEk26HlPDHSJtqlvVMtQL6fqJOjYEShbr5M8bguVEvifbFrXjUlIed9gaj8U/WhTMct2cfulXLt2klPB09lhaUN+HfPx2ICP3Fd93bDMLYAPzIM4x+A9cB3pvAaFEVRlGMY3/Hz4w9edVoeb3Zd+CgPC0DYb53c1uaJhEowLpWUoW0iWAwDyiWZAMp0QbVENl/h1vu386U7NtOdLnNBa5D/fNcsbrl8Fv5ICvB2CFWyMnrs80ulJtEhXhVPhFCrSpWkaSm0r4TmZRBtkvC3Ulo+TD9EGun1zZLQNtMv11X1xEMgItksR4S7HY+X4kOZqJpx0q/3FNB5cQc/fOIAAD8Z3LfxZO8/lVNCG4CLJ7h9D3D5VD2uoiiKMr1ojIthdHxKqCMVPvGplYmESiAqraChrWKgHR9NzvZIFaRSojdd4Mu3b+Ib9+8kW6xx/cII33nHXF5zfgdGJAG1mpeh4o0mByIQr4dYqwiNahbSMj1EMAWt86Wa0rRURpddL9/FqYkASXoptQb43G2EnJwIHSsA4ZRcsz8iAucEeCk+lGOrGSf1ek9jNOlWURRFmXIa48FDwuWEKjuOLePGheHDQsUfltvSXTK54xgiOLIHJZXWqbGtO8vnfvYc//noPmq2y9sviLPqhmYuW9Ampla7AmP9MvHjeomykWZZRGiaYqStFUW0JNqgeYkk0tbPB18U7KIYfF1HJn3CDV7bxxGTbrVA1B2jbATE/HuK4W4v1YdyZDXjdFXSphoVLIqiKMqUMv6GOd6KekEOCZUhERf+CPgTMDYM6YPiA3FtEQfpbigMQrXCozsHWfOzDfxsfS8hn8EfvSzJX93QxsK2JhEM5TIUeqXFgwWBOog1SPXDtaXa4lRlnLhunvhTWi/wovVD0jYq9XhtnwZvAaEX4z9e/bECEEoxaLVQMQKHFhieCtPRh3KmUcGiKIqinHmOFSq+MPhjYpzN9UpFxLahMApjPZAbwnEdbn/qAGt+sYFHdo5QH7H4f69q4M9f2UZzXYNnhM1L1H6tLKPIwQZItsn5q0XID4gRNpSA5GKZ9mk5T8SGgYxAF4elwpOcLWZZHBmbrhY5tPcn2iRtJV+Qsrn/Jb8c09WHciZRwaIoiqJMOhNNuEzIUULFq3D4wjA2KG2eakkqH8URyHRDYZRyrcoPHtzF2ts3sa0vz9w6P//ypib+4Jp2ovEGGU2ujMk4c82Lzo+1QrwJ8EMtL/4SXAg2QtMC2e/TvFy8JtWyVG4cr+2TnAP+oEwI5QePqKYkpULjC0u1ZRKZqT6Ul4IKFkVRFGVSOd6ES3sydMjHIkJlyPOoeELF9EvFI9cL1aqMJhdHIXcQChkyxSLf+tU2vnTXdnozZS5qD/Jf72rjHZd14IsnwDElUbaSFbHhD0GyTsaeXfdwVcTyQ6xNJn3axv0pIWkz5XpF8EQape1jIGm5R25RjjWLsDn5dTgnxUz0obwUVLAoiqIoJ80LjUYfb8Kla7RIU8wnlZP8sEzYWH4wTD71w/upc4b58Cvniuk1NyTjyaUs3YN5vvyrjXzz/t3kSjavWhTh398+h1dd0IIRSIDhQCErVRUc8EUk6C2YkuC4yqiXfxKCxoXQeqFUUxLtMrVTHJHrCUQhNVfGpWslL5TOq6aE6+RjksLdlJNHBYuiKIoyqRxvkqVi28yq7odcHAwfYIp4GRugodpHyC3C8B5ZUFjNs+VAms/94jm+/1gXtuPyzpUxVr2yhUsWepuNHRcqIxJvD2LOjTRIfopd9ZYM2jKG3DAX2i8Qf0q4XtpMpRGZ7gkmITkXfD45V7aHQ9WUVItMFx0n3E05fahgURRFUSaV4024RCwHw3WlPZPv9fb/VKGSo6XSRYo07lCM32ztZc3PN3H7hn7CfoMPXpHgr17ZzPy2Jm/jcUU8L05ZovPD9TKWbPnEZFsYltsj9dC4BFqXQ5PnT6nkpXJjej+PNosnpZyFQkmqKZF6raZMQ1SwKIqiKJOKTLhsoFg9HMcf9hmsjI2RdIahf7NXAcnIFFC+n6ZaD49s7edT/3aAx3aP0hCx+NSr6vnzlzfT2NQkQqJWhEKfiBwrKNWUkDc6XCtApSK+lbpZIlBaz4P6BSJeShnIZUS01M2RTJVaUSo8OGKcTc2RKaATDHdTTi/6X0VRFEWZPOwqnQtNuLGOT94zQME26YhZrLrEYf+23fiqJci7kOmBwjClYp7/fHAX//DzzRwYLjG/zs9X39TE71/dSiSeEmFSyUlFxXXFhxJvgkBY2j21gogff1SWD7Z6bZ9EhywYLI6KATeUEEGCK1WWUtYz19ZJhUarKdMeFSyKoijKS8euelM/Q1Cr0rnQxxPPDhAny+prW6Ba5j8qA6TsQejpYTSb55t3bebL9+yiP1vhgjY/X3tbIx+4cha+cEwyUypZqA3J+a2wjBBbITHSVgue/yQBLQuhZTm0ni+tnGpBsldMS6Z9QilPvHielfEo/XBKjlHOClSwKIqiKKfOkUKlWpbUWKcGhRHaat343BKkK5DvY25tL4PDOT76UDe3PriPsbLNTYsjfPyWZtrb4pSMIL5QXKZ97BJggT8O4Zh87TpQy3m+lUZoWiJCpWkp+IIS1z/WK1NCyTlyW2VMMlUMS8RMpEEC3pSzDhUsiqIoM4ip3MR85LlNp0bSScPgNqiURKjUqrKLp5SFSp76ai91zggMhNm0d5A1P32auzcO4AK/szLGquuauXBePfgjbBwYJUEeihUwDQgkZdoHS0aLqUh7KDHbM9GuEH8KjrSMKjkJcou2guHKtE91zMtNmSVi5TRUUyYKzDuXw94mExUsiqIoygljOjXIdDOrth+/U4ZSvXhESqNQGpMNxvkhyPYyq7aP53YP8vofHeCOTUOE/QbvuyzG37yqlbnNjRAIQrUC+WHqKFDFJ22a8ZFluwZOCcIJiHeIUGm5AOLNUs0pDYsPJZwSE61bE+FimHJbpPG0VlOOF5gHqGiZBFSwKIqiKC+OXaWuNkTcTkPaR9gdw3S9WP1iRhJmC5KpYheyrHtiD3972yY2d+dpilp8+lX1vP7iGOFIgrmtTWJ8LQyJMPEFSeOjhl+mgewyYIg/pXGxJNK2rhBjbbUggsgXgmiLROZXS141JSxm20j9GfGmHC8wb+1d21WweIxX/n7yJyd/XxUsiqIo5xAn3bKwqzJ6XBiiqdaLadhQLZFwsoTsAow6kO2DwgDFbIb/eHAHn7tzB7sGi8yrs/in19fxly/vIBxPio+klpdgONMAMwzBIJg+DDKEqcntoSZJpG06H1qWyfROtSDVm2BMpnoAnArUXKnAhBshGD0tr+HxOF5g3vFuV04OFSyKoijnCCfVsrCrIizyg1CWyPuAWybsFCB9gIZyL6naCPQcZGR4mG/cvZV/uW8PA7kqL5sV5L/f3coFC+JUzSjheETO4VQl4dYflSkgywDDBlxsfAxSx9J5V0P7SkjMASpSOTFM2dwcCIvx1qlIhSXWIeLFmh6TPscLzGtPhc/A1cy8/UMqWBRFUc4RTqhlcUioDEibBwdsG0pZ6u1BItUM9A4yr7qDkeE0H3mgm399qIt8xeHmpRFWXdvMK5e1YgQjbBkcJUoeShXAB4EYGEGwHGkFgUwBJWfzbC7CAd9crlvxSmnxVNKyDDFUJ0sGnZqIlVDSi9+Pnc6X7oSQwLyNR73GYb91/E3VykmhgkVRFOUc4QVbFuNCZaxfhIpry0d5DIppyA8zu7SLhJtlw/oSa//nWe7eOIxhwLsujLPqlQ1cMLfJ27ljQ3GEKEVq+ESUmJacjyq4lgS21c2RkLem5Wzr2yu7hIoZ8aWE60Sw4IixNtbsiZfpu9NnXPR9/KcbqNgOHamwTglNIipYFEVRzhGO27JI+KHnWdmn4zhSyShnvej8Qch045YyHNixmR8+uo9Hd2WJBgz+8IoY77uyjrpkguVN9RJ1X85IIq0/SAEXB69d49jS0gmmoHE+NJ8nhlrLhFqZCAUqBCCSFOMtpqTThuulmnKWpNB2XtzBD584AMy8lsyZRgWLoijKOcKELQsfrLrY8SLsbQlay49KpSXXR60wwm2P7mTN7dt5umuMpqjJP722gdeuTBAMR3DxEaAoY82GKWLD8IMBFlVMEMERaRCB0nY+JGZJtcUug21AIELWSGKYBgTiXsBbvW5IRkXPkahgURTlnGAqA9XOFjov7gC7yif+dxMlGzqiBqsuNemcb4hgKYzKRFC2m0J2hO/du5XP37WLPcNlljT6WfOGel5/YZIVza1sGckSooo13vYxQyJYcMB0wfRTIE7aSLJ00bXQvByiDWBXZKOy6ZdQt0AELD8FM07OjMt2ZdM80y+VMg1RwaIoinIuUC1DrofOpl62NO0Ex+CTNy+TysjwiCwjzA8wPDjI1+7axFfu289QvsaVs4N87uY23nRBC9vyNUxqrMst5DOl1zBAHU2keb/vl/yZuROwZXonnIBkB+vHovQEZnPdvEvFI1PJSVptuE4qMf4QhFOs217i7t4QFbvMNWseUN+HMiEqWBRFUWYy1TLkuiHbI5H5LmC7RN0sDO6EXC+M9bO3q5cv3L6R7z7aQ6Hi8IZlEf76ujauWdiAEYiAXSRGkXtql/D50lspEgBggDq+XHsn7eW76GzqlY3IrRdAw1wOdO0kQE1i8v3e8sLxqkqkHgIx1j3Xx+qfbdN0WOVFUcGiKIoyE6mWIdMlQqU8JrcZQGGU1uoB6uwh6PbzzLb9rL19Ez95ZhDLhPdcFOdj1zWzYlYd+HwSnV8ZBcNHBR+31t54SKyMUybA2vyr6bx+DJIdgAt2iaBboWBEINEuIiWYgHBSqjCeiVbTYZUTRQWLoijKDMLnVGi0++BAWeLvXVc8IYVhyPRDvo+FtR08t/0Ar/7Pg9y7PUM8aPDRV6T48LVNdDQmwTXBLUOpKOPIZgAMC5Myg9RN+Lg95RAkIlAriOk21MCgL0fZDEL9PMlgsZ7/lqPpsMqJooJFURRlJlApQXofS8qbGEyPsObnB/j4a5dBYUSi83O91DL9/PdvtvD3v9jB1r4SbXGLf35tPR+8poVkPAY44JQlKM7wizHW9MaJDZciYRrIMETqeQ/fHnW9RYTN4mEJxvj8By+UFtALjCRPt3RYZfqigkVRFOVsplKC0b2Q64Fynogj6Sex2ij0bIRsL/nhXr5772a+cM8e9o1UWNToY+0b6/nQK9oJBsNgIyPGriviwrAkNt8BMMV/EkrQnQ3y8nAXd1WSFO3DIsTEYdXLG6G+VVo+gZik054Amg6rnCgqWBRFOaeYMePN5TyM7hOhUil4I8UucWeUJfSwsJxjcFuVr96xga8+cJCRgs01c0N8+Y3tLJ0bwzF8BH1+icF38SophlRJXEd2/oQ8c2zdHNaVL+H/FhOMOX5SfhfXsSm5Fn5sUr4KnVefLzuCTnIkWdNhlRNFBYuiKMrZRDknQiXbKx4Vw5I9O/lh1u0ocuvwSkacqwmUhhn88fdIb9rPm5dHWXV9PdcsaADLx550BoMK2OZhgeF6GSqGJdM80Qaomw/NS1nXU8/q53wUHamqpKsGlmGwosGkP+9SIgHB+Ck/JU2HVU4EFSyKoihnA6WMV1Hpl6kf0y8x+mO9kOll3X6Tj++7mAqSMlsJN1L32g/xD69r5s+bt4vh1amCXcLAxQBP7Ljgt8AISBR+rBkaFkHDfAhEAZe1G3xHtYAAbNdgV9og6D+x1o+ivFRUsCiKokxniqOeUBmQhFjDkpZNej9kenDTXdz9xFY+Zv8FtejRUfaOFeS/zDfy5+ZW8ah4uCCSxTLBCkt1JNkKjYsgOUs8K4YpwW6RRnrywxNeWsV2CPo1lVY5PahgURRFmW64LhRHYGQf5Adlvw8+qFUh1wXZg1RHuvjJgxtZc+ceNvSWmPPxBiaaxelxUrIjyDXBZ4HrUsNPmQDE2iQ3pWExJJokH8XyS15KvEX2/wSitKeepjtdet65A5aKFeX0oYJFURRluuC6kB+S6kmuX7YfY0oIXP4AjHYxNrCfb//qOb54XxcH0lVWNPv5t3e0cKuVocd5fkZKu5mWLBXXlFHlYIR0DgasFi5YdDXEUuCTfT5E6yHWDpGUVFn8ETBNVt207HmTPKYBs+vCDIyVn/eYijIVqGBRlHOQ6TQpM52u5Yzh2J5QOSBCxS6Ba0CtLObakQP0d+3kK3du5OsP9TJadHjFvBBf62zh5vMaMC0fqcJdrM699agU2jAVVkXvASsEoTiEUlA3h405yFsJqOuQBNpYK8SaZKtyIAq+4FGXN9EkT8hn0hgPqmCZgHP63/IUooJFURTlTGHXpOWT8YRKrSSVkEoe8gMwuo+d27fw+Z9v5ntPDlGxXTqXR1l1fSNXzU8iWfsu2GU6A89A1OYz+dd5SwlH+WTqATrrD0JkHqTmQd0cCNeR3n2APFFoWeHt9InKh2kd91KPneQZF5qKcrpQwaIoinK6qVU8odIlZtpaWYy0lbwIl/Rennh6A2t+uY3bNmbwm/C+ixN89PomlrZ4ybGuKx+OA9TACtAZ28J59noyGPiJceHsRVB/BSRny4bkQBQiDezzBSlaUWhYcNRen5NlKFdmrFTDBa757P2an6JMKSpYFEVRThfVkgiVbPfhiorjiqk204s7soc7H36aNb/azYN7CqRCJp+4to7/75XNtCZCYLgibGwHsEWwWD6Z9DEsMIPkCHGQOOXgHC5ceDmE66UdFGuRbJVQkmG/jW34xKdyigzlyuwdzuN63+uWZWWqUcGiKMqMZ936btYfSFOxHQKWyey607inxnV577ceIGGP8rXXt8LYeOsHKGcg20elbzs/uudp1t57gE19ZWYlLL7w+ib+6MpG4mG/1/mxoeaAUQPHAisg+SmmCBWCCUi2sDUfZGspQirUAo1LZdonXCeixdvr819/8oqX/LS6Ros47tG36ZZlZSpRwaIoyoxm3fpuVt+2kYrtAJIdsnc4z7r13VP7xuq6nhdlkLmlHaTcDKRLUiEp5iG9n1zXFv71V+v54q97OJipcX6Ln/+4pZXfuagRv8/w4vIdqNlgeKPJVtCrqvjAH4RQEuKtIk6S7XT19LGn5KPB3wHtK09qr8/JMP56HotuWVamChUsiqLMaNbetf2ocVyQLsyUVQIcR1o8+SHIdENhiGa7D9N1YGwE0gfo272Bf/nFs3z9kUEyJYfr5oe59W1tvHZZAsMwwXAAF6qO97UJRkgqKlYAAl7YW91cL+ytQ+L0I40c8MfYjYvjaxRD7RQRsMwJRYtuWVamChUsiqLMaI73F/+kVwLsmgiVwjCku6E4LAsKHZtINU1zrZftD/yaz9++mX9/aoSqA287L8aq6xu4fG7Mi5+1AFvO5Trg80m7x/BL8mwgDOEGMcs2eKm04QREG+UjmGDYV6RAFtc4/sTPZDC7Lsze4fxRbSHdsqxMJSpYFEWZdkxmNkt7Kkz3BOJk0ioBtYpXURmGbA8Uh0So1CpQzMLIbkI7f803Ht3PXdsLBCyDP7g0yUevb2JRYxAwRKzYNTCq4OC1eyKAJXksgQjEm6FhCTQthkSrV1FpECNtIC734fDkzuN7R6Z0cqcxHiRXqjIwVgHAMgzedmmH+leUKUMFi6IoM5pVNy2dMKX1JVcCqiXxqBRGvNbPCFSyUK1AOYfTt5lfPvhb1vxqH7/ZVyQVMvg/19fzF9c00ZIY/9VriFChBlhgBsDnB8Mno8b+CMTboeU8aF4kVZRwCqLN0u7xh48aSV63vvu0Te4M5coM5SuHvrddl/95upvL5taraFGmBBUsiqLMaI5NaR2fEjqlN1XXhWpRhEox7QmVQSjlpKJSSFPu2cB/3fU4a+/tYutAlTlJi/93Ux23XJJiRSoEpgkYcryJ9xGW7cumKymz/igk50L7eVC/UCop4TrZpBxKHtdEu/au7adtckenhJTTjQoWRVFmPOMprUO5Ml2jRXYP5U+uXTI+8VMtQDEjOSr5AShlwa5AbojM7qe59Y6n+NJD/fRkbS5sDfCD32nlHRel6C5WMXBkPNmuyWfTFAPteDXF54NInQiUtpWQnCM7faINUlEJxj2xc3xOm18HqE7ylJDG2SsvhgoWRVHOCcaDzsarAifULhmf+KkWRahkDnrbk7OykDDTS8/WJ/jy7c/xzceGyJZdblwY5t/e2cirF8cxRKFg4GDigFMDy8tNMSwRLv6QtHealkLbRTLxc9S25BP32ky5X+cMPZaigAoWRVHOEU6qhTE+8VMrQXEU0r2Q74XymMTojx5g6zOP8rlfbuU/n0ljO/CO82Osur6RS2dHxChLFWwDDBcLBxtTWj24MvETDEvlpOUCaL8IYg2SShtvEQHzAnt9jseqm5byVz959rRM7kzkDdIpIWUqUcGiKMo5wfGCzrrTRW751mPSkhif+KmVoZSGTK9sSy7nRLwM7+KRR37Dmjt38/MtY4T9Bh+4LMlfXd/Egjq/ZKa4NtRcsJAofcuiRBAXV6oq4Rgk2qH1Qmg9T8RJrFmESiB2ynt9QCpFX7lvJ3uGxHjbkQpP2ZTQ+Dk/8uNnp/yxFAVUsCiKco5wvKCzgGXidysylmxXRKikuyHXIx6VcgGnbwu/+PVvWXPPfh7dX6I+bPK3N9bz59c00hQeD3qzZSTZMMU8a3pTP7g4WJQJQ/v50HEpNC+FcBLibRBrEqPtJNEYDzIwVmZFW2LKfSGdF3fwNz/bBMAjn7hhSh9LUVSwKMo5xpF7dc6lDbsTB52ZLIg7xJycmGhHD0CmRyoq5THKB9bz/bufYO39vWwfrDKvzsdX3tTC778sSdRnSgUFVyLzDc9Ua1lAAPCC3kIJdhdi7DIXc8HLXittn2Q7hOpe1ESrKMphVLAoyjnEsXt1zvSG3dMpnhrjUsXoGi1SsR06kgFWvbKNh57eQEN5APYOQCUDYyOkdz/Dt361ni89NEBfzubi9gA//J1W3r6yDp9RkxMa7uHPhgGmz5v48UEoDMGkVFLaX8Zjv8kxatVLdSUYm5LndzrQSR7lTKKCRVHOISbaq3Os8XQyU2ZfiBcST1OB4TrMjtZYGHEwcfnmW9shvZ/+8mbC1RwMBDm49Qm+fMcWvvXbYXJll1cvCvOf72zkxsUxDFygigSnADjiSTEtwAf+gHhQwnUS9Db3ckmkjbez84ndOKb/tIiVH3/wqkP/DRVlJqGCRVHOIU5nTseL8ULiaVbdJI7GehM/SWcUw3WJODkaqz2wrxsKGdrzO6j0b+X3bzvID9ZncFx45/kxVl3XyMVtITHRUpP4fMNCpnwML+jNACsqFZVoi2xHnnUZJNog0QGhFJgmjnlg8p6PopyjqGBRlHOI6ZSd8ULiaVIEy5ETP7hE7Swt1YM02sMEa1ncPSV+89RT/Msvd3PvjiJhv8GfXJ7kI9c2MD/hB8MTKoa368f0pndMvwS+WSERKvEOESntF0qGSrwNgtGXfv2KohyFChZFOYeYTtkZUyaeamXJS7ErklBbGIbh3VxQWk+4MkpL+QAbt2/n6m8M8NuuEvURk49el+AT1zbRGDTBdMCtHa6mgDfx4xOhEghJ6yc1C2ZdBR0rZWtyrAks//Mu51w1OSvKZKOCRVFmGC/kQTl2r86ZzM54IfH0wydOsoXiupKTUh6TNFlcGBuEoV2STpvtZV7mcX7z3E4++egQe0dqLKjz8fU3N3HDeSGifmgMOaJPHBP8JjjeaLLhVVRCUfGg1C+AeS+H5mWQnC1x+sfJTpluJmdFOZuZMsFiGMZs4D+AFuTXwK2u637ZMIx64MfAPGAf8E7XdUen6joURTma8b06cGanPl5IPB0pWF7QBOy6st+nkgfHlu/zAzCwXRYTjuxn9MAWvnH3Zr70YD+DeYeVbX6+8fYG/vjieizXprdawsECTBlJdpGkWn8IjACEoxBKQNNymPcKaFkmHpVA5EWf44mYnBVFOTGmssJSAz7quu4zhmHEgacNw7gHeD9wn+u6nzUM4xPAJ4C/nsLrUBRlmnLK4slxoJqHSgFcR4RKrh+GtsHofhjazYGdW/jSr3Zw6+PD5Csu1y0K8idXJ7lynh+fYWAZNXBNali4jI8lW+DzgxWUikq4Tvb7LLwOGhYet+1zPKaTyVlRznamTLC4rtsL9Hpf5wzD2Ap0AG8GrvMO+3fgAVSwKMqksqU3ezhu/izjWM9HyGceylDBrolQqRZFpLguZHtgcBsM74GhXWzcuIm1d+3hh89mcIF3rYzxsWsbaKgTaQLg4HhTPi5G1ZQKSyAmoiUQlQrKrMtg3jXQuAhCyVMKeZtOJuepYkVb4kxfgnKOcFo8LIZhzAMuBh4HWjwxA9CHtIwmus8HgA8AzJkz5zRcpaIoZ5qJPB+mASa2LCGslrwjXRjtgv4tMLwbd2AbDzy1hbX3HODO7XmiAYO/uDLBX16TZG4qADj0V1xqQA3L+8UnbZ9C0aJGEGJ1EJ8N86+FuVdC3WzwR17Sbp/pZHJWlLOdKRcshmHEgP8B/tJ13axxxP/8ruu6hmG4E93Pdd1bgVsBLrvssgmPURRlZjGR58NxJZ2WqownM3oAejfCwHbswe387yPbWHPPQZ48WKI5avIPr6rnT69MUB+2JEPFcMA1aAlHwTA4WChTJAChOPjC5KnR45/FisvfBbMvkbFkX2BSns90MjkrytnOlAoWwzD8iFj5geu6t3k39xuG0ea6bq9hGG3AwFReg6IoZw/H83aUbGTip/c5GNhCsXc73/v1Tj5/fy+7R6osqvfxzTc38nsXxwn7PE8LVa/t4wMc8Z5YQXJY2ISk7dO8jJ8VOzgYWszrz7/JS62dXKaLyVlRznamckrIAL4DbHVd9wtH/OjnwPuAz3qffzZV16AoylmE69KeDNKdKT/vR0mjBE//G8MHd/H1e3bylYcGGMzbvKwjwGff1cJbloVk5yA24KXQ+nwiXKyATP8YfghFyOZT7DSXcv4NfwAtS9jatdlLrp18saIoyuQxlRWWa4D3AhsNw3jWu+2TiFD5iWEYfwjsB945hdegKIrH6doRdLIYrkPQLcHYAKte2crqOw5QrB3uAgeo8tby//Lhr9zFt387QqHqcvOSMB9/RZJr5wYxXBssLy7fCgGGtzU5IFM/ll9Gk6OtMPcq/mt9EyO+Zt4z92Vi8O3KaKibopwFTOWU0G+QXxsTceNUPa6iKKefUxJD3sRP0hn1flG4dC5PwKjLp36TJ+1GaCJNeP33+ft77sIw4HdXRln18gTntwTEn2K5sh3Z9Dwnpg8sH7KMMCgR+Q3zYd51sOBqiLfRt/kZYGaHup1OUTrdBLAyc9GkW0VRTi92VXb8VEtgGDgYWHYF9j0G+x7hzUPbyXRv5DuP9vPw7hLRgMEfXRHl/1ydYHZdwMtd8TwpvqB0gSxDslPwQSAgY8hNy2DhDTDrEog1e0LmMBrqpihnFypYFOU0Ml3bMqeFWlkSaWtlpG1jQinH8sIzrChvoPZolv95dAdr7uvlme4yTVGTz7w6SeclAVIhg1bLB7jgD0glxUV8J36vohIIQaQR2i6Axa+G1hUQrj9ufoqGuinK2YUKFkVRDjHpi/rGd/xU8lJZMUzxmhTSsP8x2PsQ14w+x53ru/mz346wd7TGkgYfa9+Y5B0rA8z1BxlwK7iY0uIxfYip1jpsrA1EINoMs14Gi26UFlA4ddz8lHGxeM1n75/xoW6KMpNQwaIoCjDJno5jd/wYpnzkBmD3A3Dwtwwd3MfX7t3Dv/xmgJGCw5WzAnzhtQ28abGfQcPGwAK/RbXix8YnLR/LANcn8fnhBMTb+VpXO9tLF/Iv73yDpNWeYNCbhropytmFChZFUYBJ8nQcu+PHsAAD0l2w8z44+DR79+zmC/fu4ztPjFKsurx6SZAPXRPmDfPCGC7g82FXLREpvhB2pUoNU/wqlh8iCUgtgoWvhHlX8tsf7aZihCAYP6nnq6FuinJ2oYJFURTgpXk6TNeW0eT8gFRXDFM+D22DHfdA/yae3rSLtfd08d8bMlgmvOfCKB+7KkJjo4GLi2H6vIRZH9VqCQcf+EOUAJsApNqgcTksuh7aVkK0AfxhKmbPKT9nDXVTlLMHFSyKogCnuKjPm/hJOmlZK2iYYFckkXbXPbj9W7nn6V2subeb+3YViAcNPnp1jA9fEaUjYYFl0VerYuMHf1gqMiZUCXiCJc6IkWJ3YAnLX/leaFoiE0D+0JS9DoqiTE9UsCjKDOJI0+z6A2nWre+esMUxUTXhpDwdR078GAY2Jn67AHsehl33UevfwU8e28Oae3t4rrdMW9zks6+K8yeXRUgGTZnsMUwwA1RqJsa4qdb1gc+kiMOI2cySC1/Lfz9ZRybUzLvnXC5tIUVRzklUsCjKDOFY02zFdk7KNDt+zEd+/CwuPN/TMdHEj+mD/DArs49ynv0c+fszfOfBPXzhgX72p2ssa/TxnTclefcFIYI+H1im5KGYlpdM62dOIgyY4k8JxaFuHg+VmtkeWslo4BJuH95N2Xa45nOPqMdEUc5hVLAoyhRwJvJWJsM023lxB3/zs00APPKJG+TG5038WCJU0l2w9xHoepwLhzfw3090846n0owUHK6ZE+Arr6vj9YuDmMaRFRW/5KKYXmS+YYlvJZSChkUw/+XQcRlPD++mK29xz+17KdsS0z+TkmgVRTl5VLAoygtwNgW9Hc8cO5Ev5YQ4duJnPANlaKe0fnqfZdeunXzhnr1894k05ZpL57Igq66OcfWcILgm+A25n+ED0/vaCoJrQCAoQW8tyyU6v+08qbAE4uTNAfaPpg9Vi8bRJFpFOXdRwaIoJ8h0Fy/HM80aSLvoRDFxiHDExI/p84y0G2HPb2BgM09u3MHau/fzP5ty+Ex458oQf351kCubIuCKmVbWJ3ufTb9kp2DKfp9YE7SslNHkxkUS/haIHRWff6xYGUeTaBXl3EQFi6LMEFbdtPSQ/+RIXKRdNKvuRRJcvYmfBtK4eNWQUg66n4F9j+AObueup3ax5p6D/Hp3gWTI4OPXxPj/Lo9ixW0cDGnvWJZUUEy/CBCft5wwEIVkB7RdCoteDslZMhkUiEmL6BgCljmhaDkbk2inq8hVlLOJFxUshmF8CPi+67qjp+F6FEU5QY6t+HRe3MFf/vjZCY/tSRePL1iOmfipYFFHBrbcAQefpNq3gx89sp219/Wysa9CR8Lkc69J8MeXhEkETTBNBhxDgt5Mv9f28XtVFW8RYXI2zL4M5r8CovXg9yoqx9nzAzC7LkxPpqRJtIqiACdWYWkBnjQM4xngu8Bdruse+0ecoijTgI6TyVKpFg9P/JiWGGDTB3gND7OMveQezfHt+3fyxQcH6crUOK/Zx/fekuRd54UIWIDhh4AFhkmxZOPiEw+KaYIVluj8+gUw/xpovwzCcWn9+KMvKFTGaYwH+dCNizWJVlEU4AQEi+u6/9cwjL8BXgP8PvBVwzB+AnzHdd3dU32BijKdmW6+llU3LeWvfvIszhF/UoT9Ftcva+InTx6UpYafuZdV17fTuaJOqiGOCwPb4ODj0LeFy8ae4GePd/PBp3KkSw6vnBfgG6+v43WLApiGT/b5mNbhBYRWgCoVCXoLRiHaAk2LYd7V0HIBBMLSDgpET3jPzziaRKsoyjgn5GFxXdc1DKMP6ANqQB3wU8Mw7nFd9+NTeYGKopw4nRd38JX7drJnKH8oS+X6ZU38z9MHDy81zJRZ/cv9UKvQ2dgDXU9C/3Z27NzB5+/azfeeSlO14S3LQ3z86ihXzA6J+dYyDxtoDR/45fO60kX8Y+lGBknS0V9h1RUROq8+T0LeAlFp/5ykUFFxoijKsZyIh+XDwO8BQ8C3gVWu61YNwzCBnYAKFkU5AY5Mob3ms/dPWXujMR5kYKzMirYEP/7jy7nmn39NsXrseLDD2nt20zn/v3h84w7W3L2f/92cI2DBOy8I8YHLA7yiLQq4XovH9EaTx420AfAFWVe+mNXZ11D0fpV0l4OsfsyFpiqdl806JFSmWyVKUZSzjxOpsNQDb3Vdd/+RN7qu6xiG8YapuSxFmVkcm0I71SFoPmpEnRyMDdCTKU94THcpyHWfvpMH9xZJhQw++Yo4H3pZGNvvYOCISPH5Ab9M+hiWTPX4Q+CPQWo2a7e8hqJ79K+RYtVh7X376HzZgkl/XoqinLuciIflb1/gZ1sn93IUZWYyGSm0J0StTMzJUkcWvxsHoD1m0j32/PHgWnaQPSMVvvi6FH94UZh4wAFMesouFYIiTnzBw2FvwTAEElA/H2ZfAu2X0vP0/uedFzQrRVGUyUdzWJRzijPVmjjeG/ikvbEfMfFjuTYGNm3lPfDUFlbVZfnE2GWU8B8+vlbidwo/5TMfbsFvOIAr4sTykS9XZOInEJcKSyAK4RQ0LoHZL4Pm8yCSBCtAe7Kf7kzpeZdzurJSTlebTVGUM48KFkU5DRwvhfYlvbG7LkG3DGMDsuPHtKA0xvzCJi5mExdke+l9Is/j9+xieOgRglf/Hr5EI/X2CH8T/l/eMv9pb5ePD4yAiBPLRwE/VSyI1EO0EVqXQtsV0LZUDLTjZlpfkFWvXXbiG54nmclqs6mvRlHODlSwKMppYNVNSyfvjd3b8ZN0RjFxZc9Pfgh61kPPRq4fe4quoW6+8mg/6zZkqTnw9vMe5w/sDVwatmgEzzx7RLvH9EEgBGaQNGUGSXHZ0hsl7K1u/uH2UCAmhluPcWFwJrJSTlubTVGUaYEKFkU5DUzKG7tjQ2VM2j+uC06N+uoAbLoN+rbAaBePbtzDp+7cwz3bC4R88EeXRvmrK2MsrDcZcj3zrT98WKSYPm/0OAjBGCQ7uKfbYivzefuF7xRx4g95e378E17WmcpKmfI2m6Io0woVLIpymjjlN3Zvxw/VkowJV4swvJuX5++lo7ofZ1OV25/cw5p7unhkn0z8/H/XRvnA5SHOi5qACwbU3BBV/BLkZgW9aZ8whOqgfi40n8+6scX80E1jY3LNt3ay6jVL6Ly0bWpekJfIlLTZFEWZtqhgUY5C8zKmEUfu+HEcKGVhcBv0bYCh3SzJbee+jV38xWPDbBusMDdl8eXXpXjDRT7iAQcLR4SJLwhWgEqhShk/hBogGIFwvQiVjougaRnr9pqsfqAbG1lE2J0ps3rdFjCtadlimdQ2m6Io0x4VLIoyyZzs5MrzROKRO34cG3L90L8JBrfCSBeZoR6+de9OvvBgH/05mwtb/fzX2+p5xwo/PtNlhCoV/FQJQigMBMAfIFeoUSICTQvFl9K2UiL0IxLRv/bhTRRrR68Jm86ekDPpn1EU5fSjgkVRjsOxwiPkM2mMB1/0Pqc0ueK6IlIqeREp1QJku6F3MwztgPRBuru7+fK9u/nmI0Pkyg4vnx/gi2+K8zsLgxiGAzjgC1KoBShjYhIAf0ryU/wJurMGA8FZXLry9dAw31tU6Du0Ofl4AXPT2ROiu4YU5dxBBYuiTMBEwgNg91D+BasmJz254jiEnAJBtwTFNJQyMLIPhrbD4C7I9bJl1wE+d/devv/0CLYD7zwvwqpr4sxptTGpYQBYIbACEAxQyVXJYWEShVQbxNqhbTmP5QxGfI28p+NCz2x79OZk9YQoijKdUcGiKBMwkfAY58iqybGc8OTKERM/0VqGhJOBvY/CyG4Y3os71ssjG/ax5u79/GJLlrDf4IMvi/GRy6MsqHMBlyEMSaQNJyRDxQiAL0QWmx7C1AKzWLn8emhcCokW+p7dhm1YEEpOuDlZPSGKokxnVLAoygS8WBtkvGoyq+7o6sOLVSkst0bILcJYP5RzkBtgWWkDrbUu2GnijPbw8yd3s+aeLh7bX6AhYvKpG1L8+aUhGiM2YIPpZ517NZ8pvokB6mmvpFmVeIDOhi6ItXDR4g4e3hSgJ9DBmxa8UsSJ5SNvxikb3vjyBIxXgD7y42cPbXpWT4iiKNMFFSwzDJ3ymRyOJzyOpDtdZDBXPspce9wqxasWQGGElD1EzM5BD5A5ACN7Ob/4LMHKIN++bZC19/WwY7DC/DofX725jt+/KEjEXwVq0sbxhVhnX8nqwjspIgFu3U4dqzNvhPb9dK5og/p57Ni9n6oRlEj9QBT8Ycpm6EWfd+fFHfzNzzYB8MgnbjjVl29S0X/LiqIAmGf6ApRzk1u+9dghcTUdWXXTUsJ+6wWPMWBCc+1n3noBAUv+1+pIBvnMzXPonFeDgW3MLe9kUXED7LoPdj9AetvD3PnQU7zly8/wxz/aR8wPP3p7Azv+ooE/f5lJxG8DAQjGJSo/GGNt8Q2HxMo4RdfH2q7FMPtSSHRQMqOMWXGINYlX5Zj2j6IoytmGVlgUZQKOHZk1gCMHfo/9Hg63iR756+u57bc7iDoZvvG2eZAfgJ5BSPewrLSB+kofXc+N8aV793DrY8OMlR2uXRDkh29r4Ia57uGJH8PbkOwLeNWVsEzzDCYnvOaenAPBBARi/OufvW7yXxRFUZQziAoW5YQ519pNR47MvuvyOUflfRyvXdSTLsLIXtqr+4nZGejJi19leB9kuzEObuJLj/bx841ZXOCW86P84VUBVrYaNOIAPrDCYqL1BWTyxx+BUBQiTZCcRXu/TXfx+f/rtidDsqzwJbBufTdjpRouTNr2Y92orCjKZKCCRVFOgGPzPq757P0Tm2tjJgxuo6O8h5QzDHt34A7v5aFN+1hz70Hu2Joj7Df4s8vjfOSKIPNSMEoNFxOsCPgDspjQFwB/DCJxiDRD3WyoXwCNC1kVC7H63uGjQt7CfotVr132kp7j+Cj3+FlPdfvxROd8qRuVFUVRVLAoyikg5toNFKvOodvClsuqFWno2sPSwnrq7X7+55cjrLmvlycOFGiMWqy6LsYfvyzA4oiJWMhMSljYBKgPx7wdPzGIpCDaBHXzoHER1M8TA60/TGejmGn/+ue7KdvupE3zTMX2Y92orCjKZKGCRTnjnK5W06S0JlwXimk655Tguhj/575h8o6PjrDDqnl76Sw9RbFrH088uZNvPzbC/pEqCxv8fP0N9bz/Qj8lXwUHFwyfLB/0BakUapQISssnXAeJNmicB6n5UDcXQglvw/LhzcmdV7Txw2dHgMl73aZi+7FuVFYUZbJQwaKcE7zk1oRdI2GPEHeyMOiDUprOxhy9iSdpcXp566wCI737+McHD/IvDw8xMFZjZXuA/76libcsAcuUsLc8PmpYEElK6ycQY6RQo2QkoP0CqJ8DDQsgMUcWFB4SKmK4xTr1/2VfTNi8lKTb451b03MVRZksVLAoM5Jjqzan1JpwXQJOkbibhYEtNFd7iDg5SAcg1wtDe7mk9DiFzCAfuWuUf/3tMPmKw+uWRvijK+JcO8+g0XAAEww/+AOUKlDBB+EGaf3E6tmb9jEcaOOqpddDqt0z3QbBHzwsVMwXHrGeDKYi6VbTcxVFmSxUsCjnBCfVmrCrEptfGKa9dpCgU4B0gLrqIA21fti1E4b3sWFnF5+5q4vbN2YxDHjXyhgfuyrCymaXEWpyLiMIfj/4xFA7Kx6TKP14AyTnQ/0cNg0UyVh10LQIzIAnVCIS+HYahMo4U5F0qxuVFUWZLFSwKOcEL9qacByoFaE8JgsIy2NQyVNXHSBYK0D3KBfnH6HR6ef++7Ksub+fu7ZlifgNfv/yKH9zdZQ5iXEDrkUVcPBDrAEsPwTish051gh1C6F+rnxEGhh6Ygu24ZdwuED0qIWEp5upSLrVjcqKokwGKliUQ8zkvIzjR+YvhOLoIYFCJQ/lrHxfGGF+aQttlYPUNufZsmkvtz4yyqbeEi1xH//4qjrecYlFQ9ilHgC/CA1fiGIFKgRpizaKaTbeJt6U1FxIzZIFhL4QWD5KZoSyEYJo8xkTKmczKoIU5dxABYsCzPy8jOe1JpJBVl3fQecCINcHlaIIl0re86fshqFdLM3v4OfPDvH7j6fZM1xmXr2fb725nt8730fIB8PUcDCOmOAJgS/KyIhDxYxDy3kyklw/H5KzxUhreYFwviDrto7xQLdJxS5xzZoHZpRIVBRFmUxUsCjAuZGX0XlBIz//rUXAtfnmLQvBLklsfmEUilkY64PB7TC6j+GBbr72YB9femiA0YLNFXPC/J8bU7xhqUWzaSHB/CYOfkqEJDclEBGDbKyOA1kfg/42rlx6IyRbpZri80Lh/CEIRFm3aZTVv9g5Y0WioijKZKKCRQFmcF6GY0O1ANUi2DUiTg6fU4P8oHyUMjDaA8M7YGQP+7oH+cIDvXzn8WEKFYcblkT48NUh3jg3wAhlDBwgIOPFoQiZvEuRMLMTrRBvhvgsaFzIxsECWasOGheIcdYXFNHibU7GMFh79+MzXiQqiqJMFipYFGDq8jLOyP4h18Xvlgm6ZRgbANfxhEuRVG2YpJOGwbLs9xnaAeku1u8dYO39A/zk2RFMw+DdF8X52FURWhuqABi4uJjYBCCaFGNsMEo6b5O1kjD3chEnyVkQaWL4yU24WFJ18YcPC5UjmLEiUVEUZQpQwaIAMyQvw64dqqbEnDFcFxEq5Zz4VIqjtJf20WofhI2P4+b7uG9TP2t+PcQ92zPEgyYfuTrFh6+KMCviADYjIB6VQIixio8iIZrjLRCMQqyZ7aN+BgId3LDkBonONywwLcpGhKrhl3h9f2jCy52uoWor2hJn9PEVRVEmQgXLDOKlTPlMVl7Gaa+ouK60e6pFsCtym2HiuC4JOwP9m0SwZAdgaCeXFp8m5ozyo1/nWXP/IOsP5mmN+/jsTQ188NIQqYADbhWwAB8lDEqEaYw2kanYFAhDyzKoWwAN89jYnaZkRmVcGZkQwh8mayWpGYHjihWYISJRURTlNKGCZYYwGVM+pysv41hhFfKZNMaDJ3cSu3rYm+K6YPpk8qZaglw3iyrbJD+lrw8G98DILvKZYe54oodvPzZCT7rC0uYg335rM++5wE8QG6iAa8qen1AEAnHafREIRSGYomvMYMDfwVVLboBkBwQiFK3nwDAkGG7cdOsL8IM/eeWLPgUNVVMURTlxVLDMEM6WKZ+JhJVpnOCdx8PdqkURLIYhZlYMMc9mumGsH4oZOgo7aXe6YUuewZEMX314gK/9ZoDhfI2LZ4X4+htbeeNiE9N2gPHKTABCYQl480e9sLckJGZB03yeGa6RMeqgeQnggmFRMUOUzRDEWyQg7iTRUDVFUZQTQwXLDGGqDZyTFSo3kbByXOgafYHrrFWkmlIrSTXF8lJhHVtMtemDUBqGsVFI74ehXVxY3c7QaI4/v3uM7z4+SKnq8KYVcf7wihBXzjFpxhXRgymiJxCGYMIz08YgUg8pbxFh/UKIN/H/FvkRoWJ6RtoIaasex7BOSawoiqIoJ44KlhnCVBo4JzNU7ngCavzch3Ccwy0fpybVFH9YPCKVAozuhcxBCXsbG4LhPTDaBYU+nt49wv+9q4+7t6TxmQbvvSTFR68Js7zBYLBWxEcVCMqSwVBUlhCG49LWiTVK0FvdfGhYBNEGSZ91XS/F1pv48TYnO8bp2/VzLFqRURTlXEIFywxhKg2ck9luOp6wClheJH2tLGmztbJ8bwWkLWMFoDACgzsh2wOlNGT7Ib0XRvbj5oe5Z9soa+4f4L7taWJBkz++Ksn/uzZGe8QF24aaWGnLRCCegkBCKjW+CCSaJTa/YZFUVcJJqaS43vMOxg4LldO4kFBRFEURVLDMEKbSwDmZ7aaJhBVIheWaz9zDqle20XlBk5dbEpEMlbF+GN0vnwtpaQFl9kO6i2ohw3+vH2bNff08152nPRlgzc1NdK60SAQdWqiCDWBAIMJwxaRCjNZ4k4iVeBvUzZMMlYaFIkgwvKoO0iYKRI+7OVmrHIqiKKcHFSwziKkycE5mu+lYYWUgIfcA3ZkKq+/sglCKzvPj4kcZPXAoQ4X0QUjvg2wfY7kM33l8hC/+uo/9IyVWtIb43jvbeNd5AQJOmSHKSM0mIG0fr5qSHaxSNsPQev7hikr9PPGwgGS5mJZUdYJndnOyoiiKchgVLOcA49ko45ysmJnUdpNdo3N5nDs6HB7qdinZR48IFasOa+/cRGfElT0/+VFId0H6ABQGGRjK8JVHhvnaQ72MFmpcuzDOV99Qx82LA5i1Cjgl1tUu559rb6OPBtrNDKuiD9OZOAChBAfTPgYD7Vy99NVQNxd8fvGnOFUZjQ4lpf3jj6hQmUS0EqUoyktlygSLYRjfBd4ADLiue753Wz3wY2AesA94p+u6o1N1Dcrk8JLbTePhbpXCoXC3muGnZFcnPLwnV4PuDTByAMZ6oTjMrv48n/91P9/7bT/lmsNbzk+x6hUxrmw3oFqFWgEwWedexera71EkAEC3k2L1yOugeQ+di6I8M1RjxGqApkXSbqqVZcInlDosVIwTnbOemai4UBRlOjKVFZbvAV8F/uOI2z4B3Oe67mcNw/iE9/1fT+E1KJPEKbWb7CphJ0/ALUMxLRWMYBz8YfJmnIA1SsV2n3e39kAB9v4GKhme3JNmza8H+Z/1gwQsk/ddVsdHr46wJOWNJVdrgA/8QQgkWJt++yGxMk7R9bG2axmdr1/M0NMbMV3bEyoBCNepUFEURTkLmDLB4rruQ4ZhzDvm5jcD13lf/zvwACpYZhauK0sH80NgVwm6JapGQHJNfF6aba1CY6Wbq2I5nhhronhEWyhsVPlY9G7ufGYfa+7r44Edo6TCPlbf0MyHrgjRGqyBU4FxoROIyUcwCf4QPcPJCS+rZ8wBw+Brb18OvoAIpyM2JyuKoijTm9PtYWlxXbfX+7oPaDnegYZhfAD4AMCcOXNOw6UpLwkv3C3ljHDo7T+UIGPW4RpeOFs5L36U0b2cX15PnFHeMm8Wf797PiPEaTMzXDX8C/7ff/ySTT1jzEoF+cKb2vijS4LEzRrYZclnwQeBkEzwhOKHQtxIzqK9r0p3KfC8y2uP+wBTclYm2JysKIqiTG/OmOnWdV3XMIzn9wMO//xW4FaAyy677LjHKWeQCcLdKkaQshGEaCMALgZhOwfd62F0n4wmp3uZV9xKnT3KlbUtzDKG+fEzo/zoyWEeGy1xfnuE/3hXB7es8BNwxEiLbYPh95Jo4xBOSBsomITkLKibA/WLWJVqYfV9wxRrh//JhH0Gq26cC4m2F1xGqCiKokxfTrdg6TcMo8113V7DMNqAgdP8+JPOad9OPB2olb2o/LIXle+Fu/nDFMy9cozjwNggi8qbabQHYHcMcr0wNgjFIWbbXaTHynzy8TG+8utuxso21y1O8K3OBl63yMKo2Z6R1gDLJ2m0gaQsJfRHJNgtORvq50LjIqhfBP4wnR0OWD5W39VD0TboSAZZ9erFdF4270y+YoqiKMpL5HQLlp8D7wM+633+2Wl+fOUkOEqMOTYhpyD+lMLI4X06/qgICg/TqZGyB2HPQ6zb0MftfSkybhs/vafAqoZ9dAZ3sb0vz9/e3sUvnhum5ri8enmCD18d4eb5fqjZUCnJyXxByUKxYhCOyeNF6yA+Gxq96Py6eVI1cb3FiHaNzvMb+cXmIYpGmP/6k2vPwCunKIqiTDZTOdb8Q8Rg22gYxkHgbxGh8hPDMP4Q2A+8c6oeX5kEXBc/VREotTJht0jV8EM4JTt9jjSrVosw2sUFxSeps4dY92gLq7fNo+jKP7HuapSP917LFzfs5uG7HsJvGrz1oiT/cH2SeLSMgQ0Vb1+PPyw7e0IpCXTzhyDeBMk5kp3StEQ+mz4RKpW8tKT8UYg0QDDOmNlzRl4yRVEUZWqYyimhdx3nRzdO1WMqL50ff/AqSXstZUk6o5i4IgYCUdJmCtewDhtWXRdKWfGmjOxm3cZR/ntwFqPuEqxhF5ujg9cqRoC989/C/7nxSW650KA55tBsVBl2q7hYIk4CEREq/pDs+Ik3SRWlYT40LIZEh1R0nBpUxkSw+CMQaoJA/Khqj6IoijJz0N/uiuC6UCtJpcRbPFgz/FSMIMSa5ZDxzcSOA8URGNoJI3tgpIt1+2D1ruWHKio2E48KW4km/v5aH4N2CQMXLB/lWpA8YRoTLVK5CUa9HT9zZL9P4xK5BtMSoVLKylLCYMzb9RM7q4XKOeV/UhRFOUXO3t/yyuRgV71Jn5JUK0zrqHC3I7HcGvHaCOx9SITK8D5pFxWzrN178yGx8kK0G8NgV3DxUcEHoXpGx6BGkN8O+Rmx6rn52iu8HT8LZQzZMCU6v5QRYRWMQSihm5MVRVHOIVSwnIuMR+VXixKVbxhicPVHDoe7HUmlCJkezi88RVOtF7YbIlRKBajloVqkpxZ70YcNU2ZV4BcQTjBatKgRoiPRQKbgMmw0MRxso9/Xwc3L3yAJtCDVnkpevg7GZBopENM9P4qiKOcYKljOISy3KlM+Y/0iWkyfVCp84ecLANclVMvTaPfBjh4Y3M2FxfVEycJoSkREtcjW3jyfu2cf1QsG8SWbn/eYJjYuJu3GCKtCd9KZ3A/+VorFMnkzBe0XsiXt0u/roN/fQdGKiXG2WpTHMAy5xlBCNycriqKcw6hgmek4DkGnRNAtYWHjgvhE/BGJqH/e8TYUR2FkN5cWfkOrfRC2B6BSIF4dwk8NyjaP7Bhlza/28/Pn+gn5TW5q+l+2Jt4voXEeYcp83Pcf3ODbwNxUAwQj4GuAaD27M36G/G1cs+x1PLtviJIZAcZj/QelDRRKeBWVqMbnK4qinOOoYJlhHDJwHhHuFnHz1AwfeSNKhaCMJR9LrSL7f4Z2wOB2GNnLeeUtWE4NSkmoFQm5YzyyfYSP/WCQR3eN0hDz87evaeXPXxaiKbyedSWTz1bfQj8NtBujrArfxVJnN1lSEG+EaLOXSLuA9f0VRvwt0HYeRfMpwm6RoFvEwZLrCyV1IaGiKIpyCBUsMwnH9rwpBfnaC3fLmkls4zj/qSsFyPbB0Dbo2wq5LihmwK7iq5XxU6U85vL9R7v4+1/uYf9wkXkNIf6ls4M/uMhP1FcV427NpTPyLJeV11PCx6KGZvAFebo/zqjRwAVzroCGuTLxk+zgH1aEAAPKWVL2CLbhY8xIULC8CaGXIFRu+dZjbOnNsqItccrnUBRFUaYXKlhmAtXS4ah88BJi49zynfUMjVXoGi1SsZ1Dh1/z2ftYdcNcOmeXYGCrfGR7JQrfdrwwtjGcUoYfPdXL257qpy9TZmlrhC+/rZk/WxnEZzgyuVMzIOgH1w+BELmyQ40gxJog3sbWtMFAcDavWv46iLd6qbSuTPxUi2D5yZlJClaU6ng7SasqiqIoyjHMeMEyY3f9OLaYUmulw9WUQFTaKF4mydBYhb3DeZxjVkd2p0us/tk2WLydzuhWcCrgcGiZ4cGhDF++dy9f//UBChWb1yyv4/vvbOGGuS6G7SAHmxAMgmEdnjAKhMnlbUaNRi6YfxU0LGR9b5GcmZDgN9sRf0y1CJa3IDGUIOMbPt2vnqIoinKWMeMFy2RzRgXQBOFuUk1JyOdjKhNdo8XniZVxirbB2t2z6Txvo2xCrpXYfGCIz/1qNz94vAfHhdesSPKhq8O8bo5PjrFNsPzg84Hh8yL0IxDwtianZrMpa9Hv6+Cm5a+HaD0jDz+H4dqQH5br9oUg1uJlvejmZEVRFOXEUMFyNmDXoJo/JtwtJlWN4wWnue5RbaCJ6KlGcCsZfrO5lzV37eX25waIBCz+9OUtfOSKELFwEQdb2j2+AJh+8AdkO7MvdDhGPzUHGhdCwyKe7crIaHKiDZwqCSdNwC0DC+W2YHzirBdFURRFeQFUsExXTjbcbRzHgUoORvYRN8vknOMfm3LSXP13D/DbPWkaY34+/bp2/uyyAA3BGtgVehwTF79XSQl6QiUgQiVcD/XzoGkhn/pNlaH9LXz1Dy4h43sWn1uTrBdbqkAjViOk5k48Rs3hatV49UpRFEVRjkUFy3SjVvEMtKXD4W7BuFdNeYHQNLsKxTQM7Yb+Z6F/O+/223y3fAMV/BM8Tokdd3yb1myZr79tFu9baRGxHDmP64NQhGqhQg3L29cTlIC5aBPUL4DGBdC4FJIddD29jSoWuC519gh+twLGIkh0MOhLy4TSccSKoiiKopwIKlheAuvWd7P+QJqK7XDNZ+9n1U1L6by44+RP5DhQG6+mVL1qyguEux1JtQRjgzLp0/uc7Pgp58D0cYGZ493+h/l57TKG3TiG6+BgYmcHSW3+MWsv28tblzRjuTYYLhh+CPkBC0wfBSwcQhCrE99J/UJoWgR1CyHVISZfK4Dh2tQ7GRjrw8Vk2PI2LFu+449TTzEr2hIzz2itKIpyDqOC5RRZt76b1bdtPOQT6U4XWX3bRoATFy1HhLvhumJoDSVePDDNdWVCKNMN/Zug5znIHoRqWaoggSTgEKWPS/IPs/OxH3LbMwMUKzZXL0zwD6+u57qbahiOK+ZZX8ibLPJL28cyWVe5mH8sXcsgSToOVll1sUPnivmeYTYqfpbKGIwNUueMUjKCkJhFvy8jW53P4u3JiqIoyvRD31VOkbV3badYtY+6rVi1WXvX9hcWLIfC3Yrg1A6Fu8k48gStm2PvW8rC8B7o3QADmyE3ID/zRyCSFN9IaZSNe3r43G1b+NWmIXBdXnt+HX9xdZhLW6HJV/OmfDwzLZZM/ljy/brSxaweuYGi98+juxRg9ZMGtMXpvCgl11Dqk2pQMMqg2UzJDEOkTsSKoiiKokwyKlhOkZ508aRuf164mxWQCHpf6MWD0moVKI5Av9f2GdoOxaycI5gEfxDKY7jZbh7c2MWaX+3mzk2DhP0m73pZHX9xZYSFKRubGuVxE63pBzMIlnVIqBCKQd0s1j53PUX36H8axZrL2nv30jm3JkIrGJNE2mCcotV/kq+eoiiKopwcKlhOkfZUmO4JxEl7Knz4G8cWkVItHjfc7QWpFiHbA31boOdZyOyTto8/LCPCGFDOYI/08b+P72HNr3bz5N4MTfEA//C6dt58AbSEHRwqlAngEMDGD/4Y+PyHqyyhhIwmN6+AxsX0/LYy4eX0ZKvy2JF6CMR0c7KiKIpy2lDBcoqsumkpq2/beFRbKOy3WPWaJYdbPicQ7vY8HAfKYzCyF/o2Qf8G2fWDC/64pMMa0hoqpgf43oO7+Pzde9k9UGBRc5hvvn02v7fSImw69FVrlPDjYGEToIqFjQGBsFRVIvWQmgcty6BhCdTNglCS9uRzdGfKz7u09mQQ6uZOSXT+pBmYFUVRlBmJCpZTZPzN9OM/3UDFduhIhWQ/z2I/f/Kv9+FgcusfvuKFw92OxK5JbP3gds+fsk3aQIYfAimpgtSKUBpgeHCAr9+zk6/cv5/BXIWXzY3zz++dS+dSE8utgWmAFaFYrQB+Spg4+DAxqRGQ6kzdImhZCg2LoG724SA6YNW1zay+8yDF2uGY3LDfZNVrl0+ZWHnJBmZFURRlRqOC5SXQeVE7//P4DoJumW+/e4U3jhwgZ8apGQHJT3kxahXI9cLgNuhaD+k9UClKbH28DaywpNxmutjX3cMXf7WTbz/cRaFic/N59Xz8FUmuneVK/L1pyvFGAHx+ikUfYOAiAXCjZoIuczYrz3s1NC2GWKv4VvwRSdDND0E5S+dCC143i7++4yBl26UjFT7liseJjBafsoFZURRFOWdQwXIKWG7t0LbhqJPHxjoq3K1mvEh2iutCpQCj+6B3E/RtENHi2BCIQ6oJ8EE5DZlent19kLW/3MGPn+rDMOB3L23kY9ckuKDRBmzPQBuSNo/lAyMEPguXMWoEyJlJDlhz6Q3NY9DXxi2LrvMmk6KycTnXL+m4hilR+9EGOlvD/HBTHpj6vUknbWBWFEVRzjlUsJwoXrhb3E7jwxtN9gXJmglswy8tlRM5R3EUhnZA32aZ+ikMiuAIJKXtY5hQGMQtDnH/s12suWMHd28eIha0+MvrWvjwlRFmRx2gJgLF8LJTTD9YITABywAryLAZZa9/If3BOQz6WslZ9ZTNEESbwS6JSKrkZONyuA4iDS95IeGpiJsTMjAriqIo5zQqWF6MWkVaMuPhbkDBiMqbvmmKWHkx7JrX9tkO3c/C6G4o5yXkLd4iVZVaFcYGqOWH+J/f7mXNHTt55kCWlkSAz7xhFn9yWZBUwGubmEERGT4/GEFvt5ArYsUfgngHtJ7HY2MG/b428laCshmmRJCAW4FMl4S+mT4IN0C04YwuJDyugfmmpSd9LjXvKoqizExUsEyE4xwxjlwTb4oX7pazUnLMiYz0VoqQ3u9N+2wRoWBXIZCA5FzJYKkWIN1FITPMvz2wk8/ftZu9Q0WWNEe49R2zee+FfkKmC5hgeVNGPp9UU6wgmA5ggC8CiXbouBCalrOur45/Gx6gZEPAMliYrLE4kibolqDWBrFmqapMg83Jzzcwn5pnRs27iqIoM5cZLVhO+q/tWlki748MdwslRayc6HSM6xKuZWmt9cCzW6SqUhgELPGHBGIS0lbMwlgvQwMDfO3ubXzl/n0Mj1W5cl6cL7xhDm9a4sPEkUqKFZBdP76Q+E4MC3C82yJQNx86LhIjbXIu63ZXWH3vfkpewaJiu+wYrRF1oS5eJ5NB02wZYefFHfzwiQPAqXtm1LyrKIoyc5mxguWE/9o+iXC3iQTQUefJ9sLgVl6Z/xXt1W7YF5X2TawVAhFwTBlVTo+yt6ubL9y5je88fJBi1eGN59fz8VcmuabDxDBcT6iEvcmfoAgV0wK3CtjyfeNiaLtQPqe80eRAlLUPPEax6hx17bZrsDEb4aJU3bQTK5OFmncVRVFmLjNWsLzgX9sXtR+9eBBeNNzteAJoVtzHisgwbOqWakrmIHOr3eSNCMRnyaLAWhXyw5BP88yO/ay5fRv//XQflmnwnpc18bGXJ1hRDxiIKDH9nlAJiL/FNGWax61KeFzzUmi/CBoWQtJ7jEBUhE05Q0+6NOFrUrbdCW+fKah5V1EUZeYyYwXLC/61PTYguSOmdTgw7UXC3Y4ngPrTeT5YuxN2+AETwkn6jDYc05JzZg7iFtPc8/Ru1t6xk3u3DRMPWXz0hjY+fGWUjqjhTfYEwEWqOv6QxOebpmf2tSGchKYVnlBZ4O3x8YSK6YNiBgoHoFqkPe6jO1d73nMIWJMfpT+dTK6Tad5VFEVRphczVrAc96/thF/Ggf2RkxrhPZ4AyjpBYk4eoktE/LhgOYPUOWPUBkf5ycM7WHPHDp47OEZbMsCaN83iA5dESAaR9pPPFKFi+kR8BBLSXrJL4BqyILFtpQiV+vkyVeQPe7t8LCimoTAkLS1/BFJzWPXaBlav23zUG7dpwOy6ya00TDeT62SZdxVFUZTpx4wVLKtevZDV67Yc5eUI+01W3bRcduicDNXycasWKaPIkL9ZPCXlHBRGqSse4J5n9vKnX+pl/3CJZa0RvnvLXH73wiBBC3BNsEwRJIa3JTmQkEpKLS/qIt4MrSs9j8o8iDTJDqBAXFpWhREoDEsFxh+G1FwxCJsmnZfWg2lO+Rv3dDS5ToZ5V1EURZl+zCzB4oW7US3SuTgAN8/hr2/fR9nm1N60ixkY3gP9z7FqVobV2+dRdA6/ZH5qvCq4lUhtDNL7Gejr4at3beFf7ttPpmjz8oUJvvLmdl6/OIjpcwEDXB9YrvhUwilp/dgVqOZEqCTaof1iaF8JyTkQrRfDbiAmOTDFEfHD2BWpyNS1ivfmmDHr0/HGrSZXRVEU5XQxMwRLreIZaEvypm75IZSg84oWfrghC5zEm7bjQK4PBrbKR6YLymN0pvywFP52WzsZN0zKKPLGwDO8wtpMtn+QP7vnIP/2aA+lqsMNyxL8ydVx3rE0AYbjmWbHvSo+qZb4Q+CUJX7ftMQ823ExtF0g1ZJI3eHWj+vA2KCIFbsqQiXR7iXjTv4ywhNFTa6KoijK6WJmCJbKmFQcvHA3rBNInz2WahnSByTkbWQXZHq9cwYh3gpWkM5YgWL33UTdMSL2GAcPdvP5R/dx75ZRfJbB713ezEdfniIUrmHgShKtYwMGBEIQaZRrrOWhNCxVlrr5MOsyaDsfkrMhFPe8LFEJrRsbgOKwpOUGYzK+HIidUaEyjppcFUVRlNPFzBAsoaQYWE/iTfyWbz0GwI9/b4XX9tkII/vFF+I64hWJNMteHtdLvi2maa50s2nXAX7wyAGe3JcjFjT5/asa+fSrmmmPA5jsGXOwcKTaE4hCtEVEVCUH+UHwB6BxGcy+DFpWiFAZn/jxR0So5HrFp+LYslgx1Sw/nwZCZRw1uSqKoiini5khWF5kJPl5OA6p6iBttQOw/lnIHhTDrBmAUJ2YW13EBFsuQWGUanaQHz+8mU/fvpOdA0Wa435WvbqV3700Qipo0R63REw4VcBljCg0LgTDB6U0lEalWtN+AXRcCi3LITHL86dEvcpLGbI90vpxXREqMU+oTFPU5KooiqKcDmaGYDlRqiWv7bOVawr3k7BHYahBkl+jzV4CrAvVKtQKUMkyNtjDt+/exBfu3U/XaJlFTSH+6c3tvOWCMEEL5scj3skdcCzWOa/g70vXMUycjh05ViV/TWcqK9H5sy6HpiWQ7BCBEoxJUF2tDOkuETauK96UaLOIGUVRFEVRzgHB4rqEnRwcfBoGt8HwfiiNkqoNUcYvAWyG4WWfVKBchGKG/oP7+Mpdm/n6gwcZLdS4dlGCb7xtNkvbwTRMHMDFBbcmCbORZtZVX8bq7ispei9rt51gdfoNsLRK56XzINZyeOLHFxABNXpAhArIrqFY80nlw0wlWjFRFEVRpgszV7DYNcj1say4ng67CzYHoVKQ9lEgSdosYRieP6Vmi3G3mGbnrl18/s4tfO/RXiq2y1tWNrDqumaunBcE12FXpoxBDT8OVUKydTlcD26NtdsuoOge/ZIWHYu1m0N0vmaBCBXLL1ucR/dDKSMHheukouI/85uTFUVRFGU6MvMES7UII/tgaCeM7GFleQOmUwVnHkTrZKGgU8aihuU4YmwdG+SJjdtZc+dObnt2iIBl8P4rmvmrVzSxpCkoW5EB7Bo+qqTxM0qKGo0sCoZhrA/cMj12YsJL6slWRZRUCpDphnIGifGvl4rKDF1GqCiKoiiTxcwQLI4jbZXhPTC0A9IHpXrhQM0xqVpJyTVxbAmWc6rE7CyxyjB33D3Aml/t4cGdGVJhH598TQcfuqaBltj4S2NL9gkmhOL0jDVykCAJinTQD/kwRBug+QLah6A7//zLa08GYWSPGHsxZbw52qRCRVEURVFOkJkhWLqehP4NkOsXXwiGJMj6AhSsMfzUpPLiVqGYpTLaxXNPPsN/PtrDrsESs+uCfPEtc/jDKxuJB00xvro1aSthyth0sgOCcdyhPcyjHwvImfWw6AYZT25cwKpkldV37D96HYDPYNWVEamuRJtFrPhePCfm0Nj1OeQjOZeeq6IoinJyzAzBMrBdpmwwJD/FDIDpQrVCwK1gOTUY6yfbv49/vXcrX7z3AN3pCoubQ/xT5yzecH6KC5q8dNZqBfCESqQeEh0QSEJpAEb2kGSMA6TYzCLy8ZVcfcnrZdonEKXzygiEkvz1T5+lbLt0xH2suqZODLfRxpMfv1YURVEUBZgpgiUY9SZvglCrgVMCB6jmqa8MYGe6Wf3NXr7xUDeZos31S5L8zevbuXZhFNMAF1sqM64NZhAiLSJUfGEoDEC+F7Ag0c4T6WYeLHXQQzPzgk2QaJMRZdeFcpbOWXkeax3BNkw+975XqVBRFEVRlElgZggWMwCY0nZxkETZsQG279zJd9Zt4ecbhrEdl7dd1MiqG9p42YI4WwdyWNTwU8XAASsi7Zpku0zy5AYgcwBMHyTnw6xLoOMSnhweZE+pSoEQ9VYKfCEopmGsX9pOVoBhq5ExMwHxljP7uiiKoijKDGFmCJZaScLXSlnI9vLYxh2suXMXP9swTMAyeMtFDfzjzbNZ1BKVEeZamTBFwKFKgDQNzGtbDpYlSbPlnIiWpsXQfgnMuggScyAQJuO3SVMEXKJ2Vky+1aJksSQ7INxAzsqc4RdEURRFUWYWM0OwjPXi9Gzil49vZc1de/nN7ix1ER//97VzuOG8OI1RP4vqQzIhVJWNziWCjNBAmgYsw/YqJFlpCTUtk4rKrEtl8WEgfChDxcZHgiwNZGiyg+DOlV1AkfpptefnXEbNu4qiKDOPGSFY/vPH/8tnfvo4W/uKzKkL8uW3L+QPrmojFjLYMpAlSBGqNXANib2Pt7NzPwSp0ugMEnELQAu0XCAipeMSiDd725+jYPnEo5IfoqO6n4Faha21JjZ0+/jNd7tZdVOczounn1jRN25FURRlpjAjBMuvN/US8Jn84PeX8Y7LWvAbjmSuFAusSNjgBmU0OdEB0XqoFGlxdhOhQNkMsc9YyPkXvxlaL4R4o2xMDkTFLOsJFcYGwK6wvxBgby0OiEDpTpdYfdtGAN1SrCiKoihTxIwQLF/5wI1EMtswXEcmhEolcMpghSDYBKl2admUcpA9AOU8lgn7rMXsCK5gf2Ahb1h8g7c1OQKmKYIn1w+FIQmOC0Qh2UGfOwIUj3r8YtVm7V3bVbAoiqIoyhQxIwRL1KpBLS/+FLsmywOjs8RbEopDOQ3Du8WYG0jA7Mu5L5egK7CYsi9KyQhLRP74EsRcP+QHwalJrktyjpwH6EkXJ7yG492uKIqiKMpLZ0YIFoqDUB6T3JR4OyRmi2ippGF4JzgV2YTcdiHMvpx16bl8d7SHkg0By2B2nbcEMTcg7R/XFqESb5FQuCNoT4XpnkCctKfCp+e5TkPUK6MoiqJMNTNDsMTapG2T7JA2UHEUsl0SxR9ugKYlMOdKaFnOuh1lVt+9l5Itd63YDnuHx1j34BN0Lg2L1yXWAoHIhA+16qal/NVPnsVxD98W9lusumnpaXiiiqIoinJuMjMEy6xLJeCtOAL5/bJdOdoIDUtg3pXQuBxCkoa79oHfHLXrB8BxDdY+lqXz5RfJZNAL0HlxB1+5byd7hvK4QEcqzKqblqp/RVEURVGmkJkhWEb3w8hOcFwZR25aDPOuhrrFEI4dylCBF/CgZGsvKlbGaYwHGRgrs6Itoe0QRVEURTkNzAzBYlch1grNy6T107RYxEcgJhkqIIbcsX7a4xbdOft5pziXPSiKoiiKMt2ZGYLlgrdIPH/jIhk/Hs9QAU+o9Mm+H8Nk1XXtrP5V91FtIdPghD0o69Z3s/5AmortsP5AmnXru7UdpCiKoihTjHkmHtQwjNcahrHdMIxdhmF84qWe7/d+Ocb77qqIWTaUELFSLcLIXhjcJjuGok3QtIzOa1bymbeuJGDJUw9YJvMboickOtat72b1bRup2CJ2KrbD6ts2sm5990t9Cs97nPX/f3v3F1r3Wcdx/P1t0rVp15m5dLVNO1ZkVMOGROpYVxDZhG4otuxCNlCHCO7C6RTpWL3xRlihInoxhDGnA2c7qWUWGVZZBS+EbnWVbe0Mbq22TVfXrtaVmq3/vl6ck6yJLknXJM9zOO/XTc556PnlCw9NPnme7+/5HTzJrgMnWL1x55RfX5KkVjPjgSUiOoBHgDuBPuCeiOi7nGu+M2sub89qHvj2zml4cz8cG2jc6nzltXBtX+MOos4rgEbjbP913Xy4Zz4Arx0/PalgsGnHAENnR28nDR8aN1XGhqLBk0PTEookSWolJbaEbgZezcz9ABGxBVgL7Luci8658B84/hqcOQXR0ehpmd/zbg/LGMdPvcOBN0+P3J48HAzgvY/Yn+yhcZfTiDteKHLrSZLUrkpsCfUChy56f7g5NkpEfDUidkfE7mPHjo17wZ5zR1l87gicfxsWLIZFfXDVh94zrAAc+tfQqLNUYOLVkvdqzJ3Khl1P0pUk6X8V6WGZjMx8NDNXZubKhQsXjvtvh2bN48Ssa2DhRxun0w433I5jeMtlrPGCwfo1K+iaPfraU31o3EyEIkmSWk2JwDIILLvo/dLm2Pt2etZVvNV5daOHZZKGm27HGi8YrOvv5eG7bhrVsPvwXTdN6VbNTIQiSZJaTYnA8jxwQ0Qsj4grgLuB7TNdxLKru5gVo8cmEwyGG3YXzO2k/7ruKe8rGRuKeru7pjwUSZLUama86TYzz0XE/cAOoAN4PDP3znQdPQvmAI1eljPnL1R1xP66/l42P3cQ8MGCkiRBoYPjMvMZ4JkS3/tiPQvmjAQXg4EkSfWqtulWkiRpmIFFkiRVz8AiSZKqZ2CRJEnVM7C8T32Lr7JRV5KkGWJgkSRJ1TOwSJKk6hlYJElS9Vo+sDy9Z5A9B0+y68AJVm/cydN7JvdYoqfuW2UPiiRJLaKlA8vTewbZsO2lkScvD54cYsO2lyYdWiRJUmto6cCyaccAQ2fPjxobOnueTTsGClUkSZKmQ0sHliMnhy5pXJIktaYiDz+cKku6uxj8P+FkSXfXtH5fe18kSZpZLb3Csn7NCrpmd4wa65rdwfo1KwpVJEmSpkNLr7Cs6+8F4MGtL3Lm/AV6u7tYv2bFyHgrcxVHkqR3tXRggUZo2fzcQeD9/ZI3GEiSVL+W3hKSJEntwcAiSZKqZ2CRJEnVM7BIkqTqGVgkSVL1DCySJKl6BhZJklQ9A4skSaqegUWSJFXPwCJJkqpnYJEkSdUzsEiSpOoZWCRJUvUMLJIkqXoGFkmSVL3O0gVMhafuW1W6BEmSNI1cYZEkSdUzsEiSpOoZWCRJUvUMLJIkqXoGFkmSVD0DiyRJqp6BRZIkVc/AIkmSqmdgkSRJ1TOwSJKk6hlYJElS9QwskiSpegYWSZJUPQOLJEmqXmRm6RomFBGngIHSdWhED3C8dBEaxTmpi/NRH+ekLisyc8GlfKBzuiqZYgOZubJ0EWqIiN3OR12ck7o4H/VxTuoSEbsv9TNuCUmSpOoZWCRJUvVaJbA8WroAjeJ81Mc5qYvzUR/npC6XPB8t0XQrSZLaW6ussEiSpDZWdWCJiDsiYiAiXo2Ih0rX0+4iYllE/CEi9kXE3oh4oHRNgojoiIg9EfGb0rUIIqI7IrZGxF8j4pWIWFW6pnYWEd9q/rx6OSI2R8Tc0jW1m4h4PCLeiIiXLxr7YET8PiL+1vx69UTXqTawREQH8AhwJ9AH3BMRfWWranvngG9nZh9wC/A156QKDwCvlC5CI34E/DYzPwJ8DOemmIjoBb4BrMzMG4EO4O6yVbWlnwF3jBl7CHg2M28Anm2+H1e1gQW4GXg1M/dn5hlgC7C2cE1tLTNfz8wXmq9P0fhB3Fu2qvYWEUuBzwCPla5FEBEfAD4J/AQgM89k5smiRakT6IqITmAecKRwPW0nM/8InBgzvBZ4ovn6CWDdRNepObD0Aocuen8YfzlWIyKuB/qBXYVLaXc/BB4ELhSuQw3LgWPAT5vbdI9FxPzSRbWrzBwEvg8cBF4H/p2ZvytblZoWZebrzddHgUUTfaDmwKJKRcSVwK+Ab2bmW6XraVcR8Vngjcz8c+laNKIT+Djw48zsB04ziaVuTY9mX8RaGkFyCTA/Ir5QtiqNlY3blSe8ZbnmwDIILLvo/dLmmAqKiNk0wsqTmbmtdD1tbjXwuYj4O40t09si4udlS2p7h4HDmTm88riVRoBRGZ8GDmTmscw8C2wDbi1ckxr+GRGLAZpf35joAzUHlueBGyJieURcQaNRanvhmtpaRASNvflXMvMHpetpd5m5ITOXZub1NP5/7MxM/3osKDOPAociYkVz6HZgX8GS2t1B4JaImNf8+XU7NkHXYjtwb/P1vcCvJ/pAtQ8/zMxzEXE/sINGZ/fjmbm3cFntbjXwReCliPhLc+w7mflMuZKk6nwdeLL5h9Z+4MuF62lbmbkrIrYCL9C4y3EPnng74yJiM/ApoCciDgPfBTYCv4yIrwD/AD4/4XU86VaSJNWu5i0hSZIkwMAiSZJagIFFkiRVz8AiSZKqZ2CRJEnVM7BIkqTqGVgkSVL1DCySioqIT0TEixExNyLmR8TeiLixdF2S6uLBcZKKi4jvAXOBLhrP4nm4cEmSKmNgkVRc8xj754G3gVsz83zhkiRVxi0hSTW4BrgSWEBjpUWSRnGFRVJxEbEd2AIsBxZn5v2FS5JUmWqf1iypPUTEl4CzmfmLiOgA/hQRt2XmztK1SaqHKyySJKl69rBIkqTqGVgkSVL1DCySJKl6BhZJklQ9A4skSaqegUWSJFXPwCJJkqpnYJEkSdX7L4tftUOQSEaGAAAAAElFTkSuQmCC\\n\"\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     }\n    }\n   ],\n   \"source\": [\n    \"inds = np.random.randint(len(chain), size=100)\\n\",\n    \"plt.figure(figsize=(9,6))\\n\",\n    \"for ind in inds:\\n\",\n    \"    sample = chain[ind]\\n\",\n    \"    plt.plot(x, np.dot(np.vander(x, 2), sample[:2]), \\\"C1\\\", alpha=0.1)\\n\",\n    \"plt.errorbar(x, data, yerr=sigma, fmt=\\\"o\\\")\\n\",\n    \"plt.plot(x, straight_line(x,m_true,c_true), 'k', label=\\\"truth\\\")\\n\",\n    \"plt.legend(fontsize=14)\\n\",\n    \"plt.xlim(0, 10)\\n\",\n    \"plt.xlabel(\\\"x\\\")\\n\",\n    \"plt.ylabel(\\\"y\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And finally we will print the *maximum a posteriori (MAP)* estimate along with the *1-sigma* uncertainty for the model parameters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<IPython.core.display.Math object>\",\n      \"text/latex\": \"$\\\\displaystyle \\\\mathrm{m} = 3.467_{-0.138}^{0.122}$\"\n     },\n     \"metadata\": {}\n    },\n    {\n     \"output_type\": \"display_data\",\n     \"data\": {\n      \"text/plain\": \"<IPython.core.display.Math object>\",\n      \"text/latex\": \"$\\\\displaystyle \\\\mathrm{c} = 1.032_{-0.643}^{0.672}$\"\n     },\n     \"metadata\": {}\n    }\n   ],\n   \"source\": [\n    \"labels=['m','c']\\n\",\n    \"for i in range(ndim):\\n\",\n    \"    mcmc = np.percentile(chain[:, i], [16, 50, 84])\\n\",\n    \"    q = np.diff(mcmc)\\n\",\n    \"    txt = \\\"\\\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}\\\"\\n\",\n    \"    txt = txt.format(mcmc[1], q[0], q[1], labels[i])\\n\",\n    \"    display(Math(txt))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6-final\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "docs/notebooks/multiprocessing.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Parallelizing sampling using multiprocessing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are going to use the multiprocessing Pool to parallelize and accelerate sampling.\\n\",\n    \"\\n\",\n    \"This approach is ideal for personal computers, laptops, or small clusters and should work even in Jupyter notebooks. \\n\",\n    \"\\n\",\n    \"In order to simulate a computationaly expensive log probability density function we will use the time package.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import zeus\\n\",\n    \"import numpy as np\\n\",\n    \"import time\\n\",\n    \"from multiprocessing import Pool\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We define an uncorrelated normal distribution as our target distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"ndim = 5\\n\",\n    \"nwalkers = 2 * ndim\\n\",\n    \"nsteps = 100\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    time.sleep(0.003)\\n\",\n    \"    return -0.5 * np.sum(x**2.0)\\n\",\n    \"\\n\",\n    \"start = np.random.randn(nwalkers, ndim)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We first run the sampler without in serial, without multiprocessing:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialising ensemble of 10 walkers...\\n\",\n      \"Sampling progress : 100%|██████████| 100/100 [00:19<00:00,  5.18it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Serial took 19.3 seconds\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t0 = time.time()\\n\",\n    \"\\n\",\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\\n\",\n    \"sampler.run_mcmc(start, nsteps)\\n\",\n    \"\\n\",\n    \"print(\\\"Serial took {0:.1f} seconds\\\".format(time.time()-t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then run the sampler with multiprocessing:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialising ensemble of 10 walkers...\\n\",\n      \"Sampling progress : 100%|██████████| 100/100 [00:07<00:00, 12.93it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Multiprocessing took 7.8 seconds\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t0 = time.time()\\n\",\n    \"\\n\",\n    \"with Pool() as pool:\\n\",\n    \"    sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=pool)\\n\",\n    \"    sampler.run_mcmc(start, nsteps)\\n\",\n    \"\\n\",\n    \"print(\\\"Multiprocessing took {0:.1f} seconds\\\".format(time.time()-t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc\": {\n   \"base_numbering\": 1,\n   \"nav_menu\": {},\n   \"number_sections\": false,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "docs/notebooks/progress.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Incrementally saving progress to a file\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In many cases it is useful to save the chain to a file. This makes iit easier to post-process a long chain and makes things less disastrous if the computer crashes somewhere in the midle of an expensive MCMC run.\\n\",\n    \"\\n\",\n    \"In this recipe we are going to use the callback interface to save the samples and their corresponding log-probability values in a `.h5` file. To do this you need to have [``h5py``](https://docs.h5py.org/en/latest/build.html#pre-built-installation-recommended) installed.\\n\",\n    \"\\n\",\n    \"We will set up a simple problem of sampling from a normal/Gaussian distribution as an example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import zeus\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"ndim = 2\\n\",\n    \"nwalkers = 10\\n\",\n    \"nsteps = 1000\\n\",\n    \"\\n\",\n    \"def log_prob(x):\\n\",\n    \"    return -0.5*np.dot(x,x)\\n\",\n    \"\\n\",\n    \"x0 = 1e-3 * np.random.randn(nwalkers, ndim)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Where ``x0`` is the initial positions of the walkers.\\n\",\n    \"\\n\",\n    \"We will then initialise the sampler and start the MCMC run by providing the ``zeus.callbacks.SaveProgressCallback`` callback function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initialising ensemble of 10 walkers...\\n\",\n      \"Sampling progress : 100%|██████████| 1000/1000 [00:01<00:00, 656.62it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\\n\",\n    \"sampler.run_mcmc(x0, nsteps, callbacks=zeus.callbacks.SaveProgressCallback(\\\"saved_chains.h5\\\", ncheck=100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The above piece of code saved the chain incrementally every ``ncheck=100`` steps to a file named ``saved_chains.h5``. We can now access the chains using the ``h5py`` package as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1000, 10, 2)\\n\",\n      \"(1000, 10)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import h5py \\n\",\n    \"\\n\",\n    \"with h5py.File('saved_chains.h5', \\\"r\\\") as hf:\\n\",\n    \"    samples = np.copy(hf['samples'])\\n\",\n    \"    logprob_samples = np.copy(hf['logprob'])\\n\",\n    \"\\n\",\n    \"print(samples.shape)\\n\",\n    \"print(logprob_samples.shape)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"interpreter\": {\n   \"hash\": \"42ef9c41c9809f9bfe38b73fa705c16bbb3d6fadc6a1917ff578a20446617baf\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3.7.10 64-bit ('nbodykit-env': conda)\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.10\"\n  },\n  \"orig_nbformat\": 4\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "docs/requirements.txt",
    "content": "sphinx_bootstrap_theme\nnbsphinx\nIPython\ndocutils==0.17.1"
  },
  {
    "path": "requirements.txt",
    "content": "numpy\nscipy>=1.5.0\ntqdm\nsetuptools\npytest\nmatplotlib\nseaborn\nscikit-learn"
  },
  {
    "path": "setup.cfg",
    "content": "[metadata]\nname = zeus-mcmc\nversion = attr: zeus._version.version\nauthor = Minas Karamanis\nauthor_email = minaskar@gmail.com\nurl = https://github.com/minaskar/zeus\ndescription = zeus: Lightning Fast MCMC\nlong_description = file: README.md\nlong_description_content_type = text/markdown\nlicense = GPLv3\nlicense_file = LICENCE\nplatform = any\nclassifiers =\n    Programming Language :: Python :: 3\n    License :: OSI Approved :: GNU General Public License v3 (GPLv3)\n    Operating System :: OS Independent\n    Intended Audience :: Science/Research\n    Topic :: Scientific/Engineering\n    Topic :: Scientific/Engineering :: Mathematics\n\n[options]\nzip_safe = false\ninclude_package_data = true\npython_requires = >= 3.7\npackages =\n    zeus\ntest_suite = tests\nsetup_requires =\n    setuptools >=46.4.0\ninstall_requires =\n    numpy\n    scipy\n    tqdm\n    setuptools\n    pytest\n    matplotlib\n    seaborn\n    scikit-learn"
  },
  {
    "path": "setup.py",
    "content": "import setuptools\n\nsetuptools.setup()"
  },
  {
    "path": "tests/test_autocorr.py",
    "content": "import numpy as np\nimport pytest\n\nfrom zeus.autocorr import _autocorr_time_1d\n\ndef get_chain(seed=42, ndim=5, N=100000):\n    np.random.seed(seed)\n    a = 0.9\n    x = np.empty((N, ndim))\n    x[0] = np.zeros(ndim)\n    for i in range(1, N):\n        x[i] = x[i - 1] * a + np.random.rand(ndim)\n    return x\n\ndef test_1d(seed=42):\n    walker0 = get_chain(seed, ndim=1)\n    walker1 = get_chain(seed+1, ndim=1)\n    chain = np.hstack((walker0,walker1)).T\n    tau = _autocorr_time_1d(chain)\n    assert tau < 20.0\n"
  },
  {
    "path": "tests/test_fwrapper.py",
    "content": "import numpy as np\nimport pytest\n\n#from zeus import fwrapper\nfrom zeus.fwrapper import _FunctionWrapper\n\ndef func0(x):\n    return - 0.5 * np.sum(x**2.0)\n\n\ndef func1(x, mu):\n    return - 0.5 * np.sum((x-mu)**2.0)\n\n\ndef func2(x, mu, ivar):\n    return - 0.5 * np.sum(ivar*(x-mu)**2.0)\n\n\ndef test_none(func=func0,seed=42):\n    np.random.seed(seed)\n    args = None\n    kwargs = None\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    ndim = np.random.randint(2,200)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x))\n\n\ndef test_args1(func=func1,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,200)\n    mu = np.random.rand(ndim)\n    args = [mu]\n    kwargs = None\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x,mu))\n\n\ndef test_args2(func=func2,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,200)\n    mu = np.random.rand(ndim)\n    ivar = 1.0 / np.random.rand(ndim)\n    args = [mu, ivar]\n    kwargs = None\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x,mu,ivar))\n\n\ndef test_kwargs1(func=func1,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,200)\n    mu = np.random.rand(ndim)\n    args = None\n    kwargs = {'mu' : mu}\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x,mu))\n\n\ndef test_kwargs2(func=func2,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,200)\n    mu = np.random.rand(ndim)\n    ivar = 1.0 / np.random.rand(ndim)\n    args = None\n    kwargs = {'mu' : mu, 'ivar' : ivar}\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x,mu,ivar))\n\n\ndef test_argskwargs(func=func2,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,200)\n    mu = np.random.rand(ndim)\n    ivar = 1.0 / np.random.rand(ndim)\n    args = [mu]\n    kwargs = {'ivar' : ivar}\n    wrapped = _FunctionWrapper(func, args, kwargs)\n    x = np.random.rand(ndim)\n    assert np.allclose(wrapped(x),func(x,mu,ivar))\n"
  },
  {
    "path": "tests/test_sampler.py",
    "content": "import numpy as np\nimport pytest\n\nimport zeus\n\ndef logp(x):\n    return -0.5 * np.sum((x-1.0)**2.0)\n\n\ndef test_mean(logp=logp,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,5)\n    nwalkers = 2 * ndim\n    nsteps = np.random.randint(3000,5000)\n    sampler = zeus.EnsembleSampler(nwalkers,ndim,logp,verbose=False)\n    start = np.random.rand(nwalkers,ndim)\n    sampler.run_mcmc(start,nsteps)\n    assert np.all(np.abs(np.mean(sampler.get_chain(flat=True),axis=0)-1.0) < 0.1)\n    assert np.all(np.isfinite(sampler.get_log_prob(flat=True)))\n    assert np.all(np.isfinite(sampler.get_log_prob()))\n\n\ndef test_std(logp=logp,seed=42):\n    np.random.seed(seed)\n    ndim = np.random.randint(2,5)\n    nwalkers = 2 * ndim\n    nsteps = np.random.randint(3000,5000)\n    sampler = zeus.EnsembleSampler(nwalkers,ndim,logp,verbose=False)\n    start = np.random.rand(nwalkers,ndim)\n    sampler.run_mcmc(start,nsteps)\n    assert np.all(np.abs(np.std(sampler.get_chain(flat=True),axis=0)-1.0) < 0.1)\n\n\ndef test_ncall(seed=42):\n    np.random.seed(seed)\n    def loglike(theta):\n        assert len(theta) == 5\n        a = theta[:-1]\n        b = theta[1:]\n        loglike.ncalls += 1\n        return -2 * (100 * (b - a**2)**2 + (1 - a)**2).sum()\n    loglike.ncalls = 0\n\n    ndim = 5\n    nsteps = 100\n    nwalkers = 2 * ndim\n    sampler = zeus.EnsembleSampler(nwalkers,ndim,loglike,verbose=False)\n    start = np.random.rand(nwalkers,ndim)\n    sampler.run_mcmc(start,nsteps)\n    \n    assert loglike.ncalls == sampler.ncall + nwalkers\n\n\n\n\n"
  },
  {
    "path": "tests/test_samples.py",
    "content": "import pytest\nimport numpy as np\n\nfrom zeus import samples\n\n\ndef test_chain(seed=42):\n    np.random.seed(seed)\n    nsteps =np.random.randint(200,400)\n    ndim = np.random.randint(2,200)\n    nwalkers = 2 * ndim\n    s = samples(ndim, nwalkers)\n    s.extend(nsteps, None)\n    for i in range(nsteps):\n        x = np.random.rand(nwalkers,ndim)\n        z = np.random.rand(nwalkers)\n        s.save(x, z, None)\n    assert np.shape(s.chain) == (nsteps,nwalkers,ndim)\n    assert np.shape(s.logprob) == (nsteps,nwalkers)\n\n\ndef test_flatten(seed=42):\n    np.random.seed(seed)\n    nsteps =np.random.randint(200,400)\n    ndim = np.random.randint(2,200)\n    nwalkers = 2 * ndim\n    s = samples(ndim,nwalkers)\n    s.extend(nsteps, None)\n    for i in range(nsteps):\n        x = np.random.rand(nwalkers,ndim)\n        z = np.random.rand(nwalkers)\n        s.save(x, z, None)\n    assert np.shape(s.flatten()) == (nsteps*nwalkers,ndim)\n    burn = np.random.randint(2,100)\n    thin = np.random.randint(1,10)\n    assert np.shape(s.flatten(burn,thin)) == (np.ceil((nsteps-burn)/thin)*nwalkers,ndim)\n    assert np.shape(s.flatten_logprob(burn,thin)) == (np.ceil((nsteps-burn)/thin)*nwalkers)\n\n\ndef test_multiple():\n    for seed in range(10):\n        test_chain(seed=seed)\n        test_flatten(seed=seed)\n"
  },
  {
    "path": "zeus/__init__.py",
    "content": "__bibtex__ = \"\"\"\n@article{karamanis2021zeus,\n             title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},\n             author={Karamanis, Minas and Beutler, Florian and Peacock, John A},\n             journal={arXiv preprint arXiv:2105.03468},\n             year={2021}\n            }\n\n    @article{karamanis2020ensemble,\n             title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},\n             author = {Karamanis, Minas and Beutler, Florian},\n             journal = {arXiv preprint arXiv: 2002.06212},\n             year = {2020}\n            }\n\"\"\"\n\nfrom ._version import version\n\n__version__ = version\n__url__ = \"https://zeus-mcmc.readthedocs.io\"\n__author__ = \"Minas Karamanis\"\n__email__ = \"minaskar@gmail.com\"\n__license__ = \"GPL-3.0\"\n__description__ = \"Lightning Fast MCMC\"\n\n\nfrom .ensemble import *\nfrom .parallel import ChainManager\nfrom .autocorr import AutoCorrTime\nfrom .plotting import cornerplot\nfrom . import moves, callbacks"
  },
  {
    "path": "zeus/_version.py",
    "content": "version = \"2.5.4\""
  },
  {
    "path": "zeus/autocorr.py",
    "content": "import numpy as np\nfrom scipy.fft import fft, ifft\n\ndef _autocorr_func_1d(x, norm=True):\n    \"\"\"\n    Autocorrelation Function of 1-dimensional chain.\n\n    Args:\n        x (array) : 1-dimensional chain.\n        norm (bool) : By default norm=True and the autocorrelation function will be normalized.\n\n    Returns:\n        The (normalised if norm=True) autocorrelation function of the chain x.\n    \"\"\"\n    x = np.atleast_1d(x)\n    if len(x.shape) != 1:\n        raise ValueError(\"invalid dimensions for 1D autocorrelation function\")\n\n    # Next largest power of 2\n    n = 1\n    while n < len(x):\n        n = n << 1\n\n    # Compute the auto-correlation function using FFT\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    f = fft(x - np.mean(x), n=2 * n)\n    acf = ifft(f * np.conjugate(f))[: len(x)].real\n    acf /= 4 * n\n\n    # Normalize\n    if norm:\n        acf /= acf[0]\n\n    return acf\n\n\ndef _autocorr_time_1d(y, c=5.0, method='mk'):\n    \"\"\"\n    Integrated Autocorrelation Time (IAT) for 1-dimensional chain.\n\n    Args:\n        y (array) : (nsteps, nwalkers) array for one parameter.\n        c (float) : Truncation parameter of automated windowing procedure of Sokal (1989), default is 5.0.\n        method (str) : Method to use to compute the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``.\n\n    Returns:\n        The IAT of the chain y.\n    \"\"\"\n\n    if method not in ['mk', 'dfm', 'gw']:\n        raise ValueError('Please select one of the supported methods i.e. mk (Recommended), dfm, gw.')\n\n    if method == 'mk':\n        # Minas Karamanis method\n        f = _autocorr_func_1d(y.reshape((-1), order='C'))\n    elif method == 'dfm':\n        # Daniel Forman-Mackey method\n        f = np.zeros(y.shape[1])\n        for yy in y:\n            f += _autocorr_func_1d(yy)\n        f /= len(y)\n    else:\n        # Goodman-Weary method\n        f = _autocorr_func_1d(np.mean(y, axis=0))\n    \n    taus = 2.0 * np.cumsum(f) - 1.0\n\n    # 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    window = auto_window(taus, c)\n    return taus[window]\n\n\ndef AutoCorrTime(samples, c=5.0, method='mk'):\n    \"\"\"\n    Integrated Autocorrelation Time (IAT) for all the chains.\n\n    Parameters\n    ----------\n        samples : array\n            3-dimensional array of shape (nsteps, nwalkers, ndim)\n        c : float\n            Truncation parameter of automated windowing procedure of Sokal (1989), default is 5.0\n        method : str\n            Method to use to compute the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``.\n\n    Returns\n    -------\n    taus : array\n        Array with the IAT of all the chains.\n    \"\"\"\n\n    if method not in ['mk', 'dfm', 'gw']:\n        raise ValueError('Please select one of the supported methods i.e. mk (Recommended), dfm, gw.')\n\n    _, _, ndim = np.shape(samples)\n\n    taus = np.empty(ndim)\n    for i in range(ndim):\n        taus[i] = _autocorr_time_1d(samples[:,:,i].T, c, method)\n\n    return taus\n"
  },
  {
    "path": "zeus/callbacks.py",
    "content": "import numpy as np\nfrom .autocorr import AutoCorrTime\n\ntry:\n    import h5py\nexcept ImportError:\n    h5py = None\n\n\nclass AutocorrelationCallback:\n    \"\"\"\n    The Autocorrelation Time Callback class checks the integrated autocorrelation time (IAT)\n    of the chain during the run and terminates sampling if the rate of change of IAT is below\n    some threshold and the length of the chain is greater than some multiple of the IAT estimate.\n\n    Args:\n        ncheck (int): The number of steps after which the IAT is estimated and the tests are performed.\n            Default is ``ncheck=100``.\n        dact (float): Threshold of the rate of change of IAT. Sampling terminates once this threshold is\n            reached along with the other criteria. Default is ``dact=0.01``.\n        nact (float): Minimum lenght of the chain as a mutiple of the IAT. Sampling terminates once this threshold is\n            reached along with the other criteria. Default is ``nact=10``.\n        discard (float): Percentage of chain to discard prior to estimating the IAT. Default is ``discard=0.5``.\n        trigger (bool): If ``True`` (default) then terminatate sampling once converged, else just monitor statistics.\n        method (str): Method to use for the estimation of the IAT. Available options are ``mk`` (Default), ``dfm``, and ``gw``.\n    \"\"\"\n\n    def __init__(self, ncheck=100, dact=0.01, nact=10, discard=0.5, trigger=True, method='mk'):\n        self.ncheck = ncheck\n        self.dact = dact \n        self.nact = nact\n\n        self.discard = discard\n        self.trigger = trigger\n        self.method = method\n\n        self.estimates = []\n        self.old_tau = np.inf\n\n    def __call__(self, i, x, y):\n        \"\"\"\n        Method that calls the callback function.\n\n        Args:\n            i (int): Current iteration of the run.\n            x (array): Numpy array containing the chain elements up to iteration i for every walker.\n            y (array): Numpy array containing the log-probability values of all chain elements up to\n                iteration i for every walker.\n        Returns:\n            True if the criteria are satisfied and sampling terminates or False if the criteria are\n                not satisfied and sampling continues.\n        \n        \"\"\"\n        converged = False\n\n        if i % self.ncheck == 0:\n        \n            tau = np.mean(AutoCorrTime(x[int(i * self.discard):], method=self.method))\n            self.estimates.append(tau)\n\n            # Check convergence\n            converged = tau * self.nact < i\n            converged &= np.abs(self.old_tau - tau) / tau < self.dact\n        \n            self.old_tau = tau\n\n        if self.trigger:\n            return converged\n        else:\n            return None\n\n\nclass SplitRCallback:\n    \"\"\"\n    The Split-R Callback class checks the Gelman-Rubin criterion during the run by splitting the chain\n    into multiple parts and terminates sampling if the Split-R coefficient is close to unity.\n\n    Args:\n        ncheck (int): The number of steps after which the Gelman-Rubin statistics is estimated and the tests are performed.\n            Default is ``ncheck=100``.\n        epsilon (float): Threshold of the Split-R value. Sampling terminates when ``|R-1|<epsilon``. Default is ``0.05``\n        nsplits (int): Split each chain into this many pieces. Default is ``2``.\n        discard (float): Percentage of chain to discard prior to estimating the IAT. Default is ``discard=0.5``.\n        trigger (bool): If ``True`` (default) then terminatate sampling once converged, else just monitor statistics.\n    \"\"\"\n\n    def __init__(self, ncheck=100, epsilon=0.05, nsplits=2, discard=0.5, trigger=True):\n        self.ncheck = ncheck\n        self.epsilon = epsilon\n        self.nsplits = nsplits\n\n        self.trigger = trigger\n\n        self.discard = discard\n\n        self.estimates = []\n\n    def __call__(self, i, x, y):\n        \"\"\"\n        Method that calls the callback function.\n\n        Args:\n            i (int): Current iteration of the run.\n            x (array): Numpy array containing the chain elements up to iteration i for every walker.\n            y (array): Numpy array containing the log-probability values of all chain elements up to\n                iteration i for every walker.\n        Returns:\n            True if the criteria are satisfied and sampling terminates or False if the criteria are\n                not satisfied and sampling continues.\n        \n        \"\"\"\n        converged = False\n\n        if i % self.ncheck == 0:\n            ndim = x.shape[-1]\n            samples = np.array_split(x[int(i * self.discard):], self.nsplits)\n            mean = []\n            var = []\n            for sample in samples:\n                mean.append(np.mean(sample.reshape(-1,ndim),axis=0))\n                var.append(np.var(sample.reshape(-1,ndim),axis=0))\n                N = len(sample.reshape(-1,ndim))\n            \n            Rhat = np.mean(self.estimate_Rhat(mean, var, N))\n\n            self.estimates.append(Rhat)\n\n            # Check convergence\n            converged = (Rhat - 1.0) <= self.epsilon\n\n        if self.trigger:\n            return converged\n        else:\n            return None\n\n\n    def estimate_Rhat(self, means, vars, N):\n\n        _means = [item for sublist in means for item in sublist]\n        _vars = [item for sublist in vars for item in sublist]\n\n        # Between chain variance\n        B = N * np.var(_means, ddof=1, axis=0)\n\n        # Within chain variance\n        W = np.mean(_vars)\n\n        # Weighted variance\n        Var_hat = (1.0 - 1.0 / N) * W + B / N\n\n        # Return R_hat statistic\n        R_hat = np.sqrt(Var_hat / W)\n    \n        return R_hat\n\n\nclass MinIterCallback:\n    \"\"\"\n    The Minimum Iteration Callback class ensure that sampling does not terminate early prior to a\n    prespecified number of steps.\n\n    Args:\n        nmin (int): The number of minimum steps before other callbacks can terminate the run.\n    \"\"\"\n    def __init__(self, nmin=1000):\n        self.nmin = nmin \n\n    def __call__(self, i, x, y):\n        \"\"\"\n        Method that calls the callback function.\n\n        Args:\n            i (int): Current iteration of the run.\n            x (array): Numpy array containing the chain elements up to iteration i for every walker.\n            y (array): Numpy array containing the log-probability values of all chain elements up to\n                iteration i for every walker.\n        Returns:\n            True if the criteria are satisfied and sampling terminates or False if the criteria are\n                not satisfied and sampling continues.\n        \n        \"\"\"\n        if i >= self.nmin:\n            return True\n        else:\n            return False\n\n\nclass ParallelSplitRCallback:\n    \"\"\"\n    The Parallel Split-R Callback class extends the functionality of the Split-R Callback to more than one CPUs by \n    checking the Gelman-Rubin criterion during the run by splitting the chain into multiple parts and combining different \n    parts from parallel chains and terminates sampling if the Split-R coefficient is close to unity.\n\n    Args:\n        ncheck (int): The number of steps after which the Gelman-Rubin statistics is estimated and the tests are performed.\n            Default is ``ncheck=100``.\n        epsilon (float): Threshold of the Split-R value. Sampling terminates when ``|R-1|<epsilon``. Default is ``0.05``\n        nsplits (int): Split each chain into this many pieces. Default is ``2``.\n        discard (float): Percentage of chain to discard prior to estimating the IAT. Default is ``discard=0.5``.\n        trigger (bool): If ``True`` (default) then terminatate sampling once converged, else just monitor statistics.\n        chainmanager (ChainManager instance): The ``ChainManager`` used to parallelise the sampling process.\n    \"\"\"\n\n    def __init__(self, ncheck=100, epsilon=0.01, nsplits=2, discard=0.5, trigger=True, chainmanager=None):\n        self.ncheck = ncheck\n        self.epsilon = epsilon\n        self.nsplits = nsplits\n\n        self.trigger = trigger\n\n        self.discard = discard\n\n        self.estimates = []\n\n        if chainmanager is None:\n            raise ValueError(\"Please provide a ChainManager instance for this method to work.\")\n        self.cm = chainmanager\n\n\n    def __call__(self, i, x, y):\n        \"\"\"\n        Method that calls the callback function.\n\n        Args:\n            i (int): Current iteration of the run.\n            x (array): Numpy array containing the chain elements up to iteration i for every walker.\n            y (array): Numpy array containing the log-probability values of all chain elements up to\n                iteration i for every walker.\n        Returns:\n            True if the criteria are satisfied and sampling terminates or False if the criteria are\n                not satisfied and sampling continues.\n        \n        \"\"\"\n        converged = False\n\n        if i % self.ncheck == 0:\n            ndim = x.shape[-1]\n            samples = np.array_split(x[int(i * self.discard):], self.nsplits)\n            mean = []\n            var = []\n            for sample in samples:\n                mean.append(np.mean(sample.reshape(-1,ndim),axis=0))\n                var.append(np.var(sample.reshape(-1,ndim),axis=0))\n                N = len(sample.reshape(-1,ndim))\n\n            mean_all = self.cm.gather(mean, root=0)\n            var_all = self.cm.gather(var, root=0)\n            N_all = np.sum(self.cm.gather(N, root=0))\n\n            if self.cm.get_rank == 0:\n                Rhat = np.mean(self.estimate_Rhat(mean_all, var_all, N_all))\n                self.estimates.append(Rhat)\n                # Check convergence\n                converged = (Rhat - 1.0) <= self.epsilon\n\n            converged = self.cm.bcast(converged, root=0)\n            self.estimates  = self.cm.bcast(self.estimates, root=0)\n\n        if self.trigger:\n            return converged\n        else:\n            return None\n\n\n    def estimate_Rhat(self, means, vars, N):\n\n        _means = [item for sublist in means for item in sublist]\n        _vars = [item for sublist in vars for item in sublist]\n\n        # Between chain variance\n        B = N * np.var(_means, ddof=1, axis=0)\n\n        # Within chain variance\n        W = np.mean(_vars)\n\n        # Weighted variance\n        Var_hat = (1.0 - 1.0 / N) * W + B / N\n\n        # Return R_hat statistic\n        R_hat = np.sqrt(Var_hat / W)\n    \n        return R_hat\n\n\nclass SaveProgressCallback:\n    \"\"\"\n    The Save Progress Callback class iteratively saves the collected samples and log-probability values to a HDF5 file.\n\n    Args:\n        filename (str): Name of the directory and file to save samples. Default is ``./chains.h5``.\n        ncheck (int): The number of steps after which the samples are saved. Default is ``ncheck=100``.\n    \"\"\"\n\n    def __init__(self, filename='./chains.h5', ncheck=100):\n\n        if h5py is None:\n            raise ImportError(\"You must install 'h5py' to use the SaveProgressCallback\")\n\n        self.directory = filename\n        self.initialised = False\n        self.ncheck = ncheck\n\n\n    def __call__(self, i, x, y):\n        \"\"\"\n        Method that calls the callback function.\n\n        Args:\n            i (int): Current iteration of the run.\n            x (array): Numpy array containing the chain elements up to iteration i for every walker.\n            y (array): Numpy array containing the log-probability values of all chain elements up to\n                iteration i for every walker.\n        Returns:\n            True if the criteria are satisfied and sampling terminates or False if the criteria are\n                not satisfied and sampling continues.\n        \n        \"\"\"\n        if i % self.ncheck == 0:\n            if self.initialised:\n                self.__save(x[i-self.ncheck:], y[i-self.ncheck:])\n            else:\n                self.__initialize_and_save(x[i-self.ncheck:], y[i-self.ncheck:])\n\n        return None\n\n\n    def __save(self, x, y):\n        with h5py.File(self.directory, 'a') as hf:\n            hf['samples'].resize((hf['samples'].shape[0] + x.shape[0]), axis = 0)\n            hf['samples'][-x.shape[0]:] = x\n            hf['logprob'].resize((hf['logprob'].shape[0] + y.shape[0]), axis = 0)\n            hf['logprob'][-y.shape[0]:] = y\n\n\n    def __initialize_and_save(self, x, y):\n        with h5py.File(self.directory, 'w') as hf:\n            hf.create_dataset('samples', data=x, compression=\"gzip\", chunks=True, maxshape=(None,)+x.shape[1:])\n            hf.create_dataset('logprob', data=y, compression=\"gzip\", chunks=True, maxshape=(None,)+y.shape[1:]) \n        self.initialised  = True"
  },
  {
    "path": "zeus/ensemble.py",
    "content": "import numpy as np\nfrom tqdm import tqdm\nimport logging\n\ntry:\n    from collections.abc import Iterable\nexcept ImportError:\n    # for py2.7, will be an Exception in 3.8\n    from collections import Iterable\n\nfrom .samples import samples\nfrom .fwrapper import _FunctionWrapper\nfrom .autocorr import AutoCorrTime\nfrom .moves import DifferentialMove\n\n\nclass EnsembleSampler:\n    \"\"\"\n    An Ensemble Slice Sampler.\n\n    Args:\n        nwalkers (int): The number of walkers in the ensemble.\n        ndim (int): The number of dimensions/parameters.\n        logprob_fn (callable): A python function that takes a vector in the\n            parameter space as input and returns the natural logarithm of the\n            unnormalised posterior probability at that position.\n        args (list): Extra arguments to be passed into the logp.\n        kwargs (list): Extra arguments to be passed into the logp.\n        moves (list): This can be a single move object, a list of moves, or a “weighted” list of the form ``[(zeus.moves.DifferentialMove(), 0.1), ...]``.\n            When running, the sampler will randomly select a move from this list (optionally with weights) for each proposal. (default: DifferentialMove)\n        tune (bool): Tune the scale factor to optimize performance (Default is True.)\n        tolerance (float): Tuning optimization tolerance (Default is 0.05).\n        patience (int): Number of tuning steps to wait to make sure that tuning is done (Default is 5).\n        maxsteps (int): Number of maximum stepping-out steps (Default is 10^4).\n        mu (float): Scale factor (Default value is 1.0), this will be tuned if tune=True.\n        maxiter (int): Number of maximum Expansions/Contractions (Default is 10^4).\n        pool (bool): External pool of workers to distribute workload to multiple CPUs (default is None).\n        vectorize (bool): If true (default is False), logprob_fn receives not just one point but an array of points, and returns an array of log-probabilities.\n        blobs_dtype (list): List containing names and dtypes of blobs metadata e.g. ``[(\"log_prior\", float), (\"mean\", float)]``. It's useful when you want to save multiple species of metadata. Default is None.\n        verbose (bool): If True (default) print log statements.\n        check_walkers (bool): If True (default) then check that ``nwalkers >= 2*ndim`` and even.\n        shuffle_ensemble (bool): If True (default) then shuffle the ensemble of walkers in every iteration before splitting it.\n        light_mode (bool): If True (default is False) then no expansions are performed after the tuning phase. This can significantly reduce the number of log likelihood evaluations but works best in target distributions that are apprroximately Gaussian.\n    \"\"\"\n    def __init__(self,\n                 nwalkers,\n                 ndim,\n                 logprob_fn,\n                 args=None,\n                 kwargs=None,\n                 moves=None,\n                 tune=True,\n                 tolerance=0.05,\n                 patience=5,\n                 maxsteps=10000,\n                 mu=1.0,\n                 maxiter=10000,\n                 pool=None,\n                 vectorize=False,\n                 blobs_dtype=None,\n                 verbose=True,\n                 check_walkers=True,\n                 shuffle_ensemble=True,\n                 light_mode=False,\n                 ):\n\n        # Set up logger\n        self.logger = logging.getLogger()\n        for handler in self.logger.handlers[:]:\n            self.logger.removeHandler(handler)\n        handler = logging.StreamHandler()\n        self.logger.addHandler(handler)\n        if verbose:\n            self.logger.setLevel(logging.INFO)\n        else:\n            self.logger.setLevel(logging.WARNING)\n\n        # Parse the move schedule\n        if moves is None:\n            self._moves = [DifferentialMove()]\n            self._weights = [1.0]\n        elif isinstance(moves, Iterable):\n            try:\n                self._moves, self._weights = zip(*moves)\n            except TypeError:\n                self._moves = moves\n                self._weights = np.ones(len(moves))\n        else:\n            self._moves = [moves]\n            self._weights = [1.0]\n        self._weights = np.atleast_1d(self._weights).astype(float)\n        self._weights /= np.sum(self._weights)\n\n        # Set up Log Probability\n        self.logprob_fn = _FunctionWrapper(logprob_fn, args, kwargs)\n\n        # Set up walkers\n        self.nwalkers = int(nwalkers)\n        self.ndim = int(ndim)\n        self.check_walkers = check_walkers\n        if self.check_walkers:\n            if self.nwalkers < 2 * self.ndim:\n                raise ValueError(\"Please provide at least (2 * ndim) walkers.\")\n            elif self.nwalkers % 2 == 1:\n                raise ValueError(\"Please provide an even number of walkers.\")\n        self.shuffle_ensemble = shuffle_ensemble\n\n        # Set up Slice parameters\n        self.mu = mu\n        self.mus = []\n        self.mus.append(self.mu)\n        self.tune = tune\n        self.maxsteps = maxsteps\n        self.patience = patience\n        self.tolerance = tolerance\n        self.nexps = []\n        self.ncons =  []\n\n        # Set up maximum number of Expansions/Contractions\n        self.maxiter = maxiter\n\n        # Set up pool of workers\n        self.pool = pool\n        self.vectorize = vectorize\n\n        # Set up blobs dtype\n        self.blobs_dtype = blobs_dtype\n\n        # Initialise Saving space for samples\n        self.samples = samples(self.ndim, self.nwalkers)\n\n        # Initialise iteration counter and state\n        self.iteration = 0\n        self.state_X = None\n        self.state_Z = None\n        self.state_blobs = None\n\n        # Light mode\n        self.light_mode = light_mode\n\n\n    def run(self, *args, **kwargs):\n        logging.warning('The run method has been deprecated and it will be removed. Please use the new run_mcmc method.')\n        return self.run_mcmc(*args, **kwargs)\n\n\n    def reset(self):\n        \"\"\"\n        Reset the state of the sampler. Delete any samples stored in memory.\n        \"\"\"\n        self.samples = samples(self.ndim, self.nwalkers)\n    \n\n    def get_chain(self, flat=False, thin=1, discard=0):\n        \"\"\"\n        Get the Markov chain containing the samples.\n\n        Args:\n            flat (bool) : If True then flatten the chain into a 2D array by combining all walkers (default is False).\n            thin (int) : Thinning parameter (the default value is 1).\n            discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between\n                0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin.\n\n        Returns:\n            Array object containg the Markov chain samples (2D if flat=True, 3D if flat=False).\n        \"\"\"\n\n        if discard < 1.0:\n            discard  = int(discard * np.shape(self.chain)[0])\n\n        if flat:\n            return self.samples.flatten(discard=discard, thin=thin)\n        else:\n            return self.chain[discard::thin,:,:]\n\n    \n    def get_log_prob(self, flat=False, thin=1, discard=0):\n        \"\"\"\n        Get the value of the log probability function evalutated at the samples of the Markov chain.\n\n        Args:\n            flat (bool) : If True then flatten the chain into a 1D array by combining all walkers (default is False).\n            thin (int) : Thinning parameter (the default value is 1).\n            discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between\n                0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin.\n\n        Returns:\n            Array containing the value of the log probability at the samples of the Markov chain (1D if flat=True, 2D otherwise).\n        \"\"\"\n        if discard < 1.0:\n            discard  = int(discard * np.shape(self.chain)[0])\n\n        if flat:\n            return self.samples.flatten_logprob(discard=discard, thin=thin)\n        else:\n            return self.samples.logprob[discard::thin,:]\n\n\n    def get_blobs(self, flat=False, thin=1, discard=0):\n        \"\"\"\n        Get the values of the blobs at each step of the chain.\n\n        Args:\n            flat (bool) : If True then flatten the chain into a 1D array by combining all walkers (default is False).\n            thin (int) : Thinning parameter (the default value is 1).\n            discard (int) : Number of burn-in steps to be removed from each walker (default is 0). A float number between 0.0 and 1.0 can be used to indicate what percentage of the chain to be discarded as burnin.\n\n        Returns:\n            (structured) numpy array containing the values of the blobs at each step of the chain.\n        \"\"\"\n        if discard < 1.0:\n            discard  = int(discard * np.shape(self.chain)[0])\n\n        if flat:\n            return self.samples.flatten_blobs(discard=discard, thin=thin)\n        else:\n            return self.samples.blobs[discard::thin,:]\n\n\n    @property\n    def chain(self):\n        \"\"\"\n        Returns the chains.\n\n        Returns:\n            Returns the chains of shape (nsteps, nwalkers, ndim).\n        \"\"\"\n        return self.samples.chain\n\n\n    @property\n    def act(self):\n        \"\"\"\n        Integrated Autocorrelation Time (IAT) of the Markov Chain.\n\n        Returns:\n            Array with the IAT of each parameter.\n        \"\"\"\n        return AutoCorrTime(self.chain[int(self.nsteps/(self.thin*2.0)):,:,:])\n\n\n    @property\n    def ess(self):\n        \"\"\"\n        Effective Sampling Size (ESS) of the Markov Chain.\n\n        Returns:\n            ESS\n        \"\"\"\n        return self.nwalkers * self.samples.length / np.mean(self.act)\n\n\n    @property\n    def ncall(self):\n        \"\"\"\n        Number of Log Prob calls.\n\n        Returns:\n            ncall\n        \"\"\"\n        return np.sum(self.neval)\n\n\n    @property\n    def efficiency(self):\n        \"\"\"\n        Effective Samples per Log Probability Evaluation.\n\n        Returns:\n            efficiency\n        \"\"\"\n        return self.ess / self.ncall\n\n\n    @property\n    def scale_factor(self):\n        \"\"\"\n        Scale factor values during tuning.\n\n        Returns:\n            scale factor mu\n        \"\"\"\n        return np.asarray(self.mus)\n\n    @property\n    def get_last_sample(self):\n        logging.warning('The ``get_last_sample`` property is deprecated and it will be removed in a future release.\\n' + 'Please use the method ``get_last_sample()`` instead.')\n\n        return self.chain[-1]\n\n    \n    def get_last_sample(self):\n        \"\"\"\n        Return the last position of the walkers.\n        \"\"\"\n        return self.chain[-1]\n\n    \n    def get_last_log_prob(self):\n        \"\"\"\n        Return the log probability values for the last position of the walkers.\n        \"\"\"\n        return self.samples.logprob[-1]\n\n    \n    def get_last_blobs(self):\n        \"\"\"\n        Return the blobs for the last position of the walkers.\n        \"\"\"\n        return self.samples.blobs[-1]\n\n\n    @property\n    def summary(self):\n        \"\"\"\n        Summary of the MCMC run.\n        \"\"\"\n        logging.info('Summary')\n        logging.info('-------')\n        logging.info('Number of Generations: ' + str(self.samples.length))\n        logging.info('Number of Parameters: ' + str(self.ndim))\n        logging.info('Number of Walkers: ' + str(self.nwalkers))\n        logging.info('Number of Tuning Generations: ' + str(len(self.mus)))\n        logging.info('Scale Factor: ' + str(round(self.mu,6)))\n        logging.info('Mean Integrated Autocorrelation Time: ' + str(round(np.mean(self.act),2)))\n        logging.info('Effective Sample Size: ' + str(round(self.ess,2)))\n        logging.info('Number of Log Probability Evaluations: ' + str(self.ncall))\n        logging.info('Effective Samples per Log Probability Evaluation: ' + str(round(self.efficiency,6)))\n        if self.thin > 1:\n            logging.info('Thinning rate: ' + str(self.thin))\n    \n\n    def compute_log_prob(self, coords):\n        \"\"\"\n        Calculate the vector of log-probability for the walkers\n\n        Args:\n            coords: (ndarray[..., ndim]) The position vector in parameter space where the probability should be calculated.\n        Returns:\n            log_prob: A vector of log-probabilities with one entry for each walker in this sub-ensemble.\n            blob: The list of meta data returned by the ``log_post_fn`` at this position or ``None`` if nothing was returned.\n        \"\"\"\n        p = coords\n\n        # Check that the parameters are in physical ranges.\n        if np.any(np.isinf(p)):\n            raise ValueError(\"At least one parameter value was infinite\")\n        if np.any(np.isnan(p)):\n            raise ValueError(\"At least one parameter value was NaN\")\n\n        # Run the log-probability calculations (optionally in parallel).\n        if self.vectorize:\n            results = self.logprob_fn(p)\n        else:\n            results = list(self.distribute(self.logprob_fn, (p[i] for i in range(len(p)))))\n\n        try:\n            log_prob = np.array([float(l[0]) for l in results])\n            blob = [l[1:] for l in results]\n        except (IndexError, TypeError):\n            log_prob = np.array([float(l) for l in results])\n            blob = None\n        else:\n            # Get the blobs dtype\n            if self.blobs_dtype is not None:\n                dt = self.blobs_dtype\n            else:\n                try:\n                    dt = np.atleast_1d(blob[0]).dtype\n                except ValueError:\n                    dt = np.dtype(\"object\")\n            blob = np.array(blob, dtype=dt)\n\n            # Deal with single blobs properly\n            shape = blob.shape[1:]\n            if len(shape):\n                axes = np.arange(len(shape))[np.array(shape) == 1] + 1\n                if len(axes):\n                    blob = np.squeeze(blob, tuple(axes))\n\n        # Check for log_prob returning NaN.\n        if np.any(np.isnan(log_prob)):\n            raise ValueError(\"Probability function returned NaN\")\n\n        return log_prob, blob\n\n\n    def run_mcmc(self,\n                 start,\n                 nsteps=1000,\n                 thin=1,\n                 progress=True,\n                 log_prob0=None,\n                 blobs0=None,\n                 thin_by=1,\n                 callbacks=None):\n        '''\n        Run MCMC.\n\n        Args:\n            start (float) : Starting point for the walkers. If ``None`` then the sampler proceeds\n                from the last known position of the walkers.\n            nsteps (int): Number of steps/generations (default is 1000).\n            thin (float): Thin the chain by this number (default is 1, no thinning).\n            progress (bool): If True (default), show progress bar.\n            log_prob0 (float) : Log probability values of the walkers. Default is ``None``.\n            blobs0 (float) : Blob value of the walkers. Default is ``None``.\n            thin_by (float): If you only want to store and yield every\n                ``thin_by`` samples in the chain, set ``thin_by`` to an\n                integer greater than 1. When this is set, ``iterations *\n                thin_by`` proposals will be made.\n            callbacks (function): Callback function or list with multiple callback actions\n                (e.g. ``[callback_0, callback_1, ...]``) to be evaluated during the run.\n                Sampling terminates when all of the callback functions return ``True``.\n                This option is useful in cases in which sampling needs to terminate once\n                convergence is reached. Examples of callback functions can be found in the API docs.\n        '''\n        \n        for _ in self.sample(start,\n                             log_prob0=log_prob0,\n                             blobs0=blobs0,\n                             iterations=nsteps,\n                             thin=thin,\n                             thin_by=thin_by,\n                             progress=progress):\n\n            if callbacks is None:\n                pass\n            else:\n                if isinstance(callbacks, list):\n                    # Compute all callbacks\n                    cb_values = [cb(self.iteration, self.get_chain(), self.get_log_prob()) for cb in callbacks]\n                    # Keep only the non-None callbacks\n                    cb_notnan_values = [cb for cb in cb_values if cb != None]\n                    # Check them\n                    if len(cb_notnan_values) < 1:\n                        pass\n                    elif np.all(cb_notnan_values):\n                        break\n                else:\n                    if callbacks(self.iteration, self.get_chain(), self.get_log_prob()):\n                        break\n\n\n    def sample(self,\n            start,\n            log_prob0=None,\n            blobs0=None,\n            iterations=1,\n            thin=1,\n            thin_by=1,\n            progress=True):\n        '''\n        Advance the chain as a generator. The current iteration index of the generator is given by the ``sampler.iteration`` property.\n\n        Args:\n            start (float) : Starting point for the walkers.\n            log_prob0 (float) : Log probability values of the walkers. Default is ``None``.\n            blobs0 (float) : Blob value of the walkers. Default is ``None``.\n            iterations (int): Number of steps to generate (default is 1).\n            thin (float): Thin the chain by this number (default is 1, no thinning).\n            thin_by (float): If you only want to store and yield every\n                ``thin_by`` samples in the chain, set ``thin_by`` to an\n                integer greater than 1. When this is set, ``iterations *\n                thin_by`` proposals will be made.\n            progress (bool): If True (default), show progress bar.\n        '''\n        # Define task distributer\n        if self.pool is None:\n            self.distribute = map\n        else:\n            self.distribute = self.pool.map\n\n        # Initialise ensemble of walkers\n        logging.info('Initialising ensemble of %d walkers...', self.nwalkers)\n        if start is not None:\n            if np.shape(start) != (self.nwalkers, self.ndim):\n                raise ValueError('Incompatible input dimensions! \\n' +\n                                 'Please provide array of shape (nwalkers, ndim) as the starting position.')\n            X = np.copy(start)\n            if log_prob0 is None:\n                Z, blobs = self.compute_log_prob(X)\n            else:\n                Z = np.copy(log_prob0)\n                blobs = blobs0\n        elif (self.state_X is not None) and (self.state_Z is not None):\n            X = np.copy(self.state_X)\n            Z = np.copy(self.state_Z)\n            blobs = self.state_blobs\n        else:\n            raise ValueError(\"Cannot have `start=None` if run_mcmc has never been called before.\")\n\n\n        if not np.all(np.isfinite(Z)):\n            raise ValueError('Invalid walker initial positions! \\n' +\n                             'Initialise walkers from positions of finite log probability.')\n        batch = list(np.arange(self.nwalkers))\n\n        # Extend saving space\n        self.thin = int(thin)\n        self.thin_by = int(thin_by)\n        \n        if self.thin_by < 0:\n            raise ValueError('Invalid `thin_by` argument.')\n        elif self.thin < 0:\n            raise ValueError('Invalid `thin` argument.')\n        elif self.thin > 1 and self.thin_by == 1:\n            self.nsteps = int(iterations)\n            self.samples.extend(self.nsteps//self.thin, blobs)\n            self.ncheckpoint = self.thin\n        elif self.thin_by > 1 and self.thin == 1:\n            self.nsteps = int(iterations*self.thin_by)\n            self.samples.extend(self.nsteps//self.thin_by, blobs)\n            self.ncheckpoint = self.thin_by\n        elif self.thin == 1 and self.thin_by == 1:\n            self.nsteps = int(iterations)\n            self.samples.extend(self.nsteps, blobs)\n            self.ncheckpoint = 1\n        else:\n            raise ValueError('Only one of `thin` and `thin_by` arguments can be used.')\n        \n\n        # Define Number of Log Prob Evaluations vector\n        self.neval = np.zeros(self.nsteps, dtype=int)\n\n        # Define tuning count\n        ncount = 0\n\n        # Initialise progress bar\n        if progress:\n            t = tqdm(total=self.nsteps, desc='Sampling progress : ')\n\n        # Main Loop\n        for i in range(self.nsteps):\n\n            # Initialise number of expansions & contractions\n            nexp = 0\n            ncon = 0\n\n            move = np.random.choice(self._moves, p=self._weights)\n\n            # Shuffle and split ensemble\n            if self.shuffle_ensemble:\n                np.random.shuffle(batch)\n            batch0 = batch[:int(self.nwalkers/2)]\n            batch1 = batch[int(self.nwalkers/2):]\n            sets = [[batch0,batch1],[batch1,batch0]]\n\n            # Loop over two sets\n            for ensembles in sets:\n                indeces = np.arange(int(self.nwalkers/2))\n                # Define active-inactive ensembles\n                active, inactive = ensembles\n\n                # Compute directions\n                directions, tune_once = move.get_direction(X[inactive], self.mu)\n\n                # Get Z0 = LogP(x0)\n                Z0 = Z[active] - np.random.exponential(size=int(self.nwalkers/2))\n\n                # Set Initial Interval Boundaries\n                L = - np.random.uniform(0.0,1.0,size=int(self.nwalkers/2))\n                R = L + 1.0\n\n                # Parallel stepping-out\n                J = np.floor(self.maxsteps * np.random.uniform(0.0,1.0,size=int(self.nwalkers/2)))\n                K = (self.maxsteps - 1) - J\n\n                # Initialise number of Log prob calls\n                ncall = 0\n\n                # Left stepping-out initialisation\n                mask_J = np.full(int(self.nwalkers/2),True)\n                Z_L = np.empty(int(self.nwalkers/2))\n                X_L = np.empty((int(self.nwalkers/2),self.ndim))\n\n                # Right stepping-out initialisation\n                mask_K = np.full(int(self.nwalkers/2),True)\n                Z_R = np.empty(int(self.nwalkers/2))\n                X_R = np.empty((int(self.nwalkers/2),self.ndim))\n\n                cnt = 0\n                # Stepping-Out procedure\n                while len(mask_J[mask_J])>0 or len(mask_K[mask_K])>0:\n                    if len(mask_J[mask_J])>0:\n                        cnt += 1\n                    if len(mask_K[mask_K])>0:\n                        cnt += 1\n                    if cnt > self.maxiter:\n                        raise RuntimeError('Number of expansions exceeded maximum limit! \\n' +\n                                           'Make sure that the pdf is well-defined. \\n' +\n                                           'Otherwise increase the maximum limit (maxiter=10^4 by default).')\n\n                    for j in indeces[mask_J]:\n                        if J[j] < 1:\n                            mask_J[j] = False\n\n                    for j in indeces[mask_K]:\n                        if K[j] < 1:\n                            mask_K[j] = False\n\n                    X_L[mask_J] = directions[mask_J] * L[mask_J][:,np.newaxis] + X[active][mask_J]\n                    X_R[mask_K] = directions[mask_K] * R[mask_K][:,np.newaxis] + X[active][mask_K]\n\n                    if len(X_L[mask_J]) + len(X_R[mask_K]) < 1:\n                        Z_L[mask_J] = np.array([])\n                        Z_R[mask_K] = np.array([])\n                        cnt -= 1\n                    else:\n                        Z_LR_masked, _ = self.compute_log_prob(np.concatenate([X_L[mask_J],X_R[mask_K]]))\n                        #Z_LR_masked = np.array(list(self.distribute(self.logprob_fn, np.concatenate([X_L[mask_J],X_R[mask_K]]))))\n                        Z_L[mask_J] = Z_LR_masked[:X_L[mask_J].shape[0]]\n                        Z_R[mask_K] = Z_LR_masked[X_L[mask_J].shape[0]:]\n\n                    for j in indeces[mask_J]:\n                        ncall += 1\n                        if Z0[j] < Z_L[j]:\n                            L[j] = L[j] - 1.0\n                            J[j] = J[j] - 1\n                            nexp += 1\n                        else:\n                            mask_J[j] = False\n\n                    for j in indeces[mask_K]:\n                        ncall += 1\n                        if Z0[j] < Z_R[j]:\n                            R[j] = R[j] + 1.0\n                            K[j] = K[j] - 1\n                            nexp += 1\n                        else:\n                            mask_K[j] = False\n\n\n                # Shrinking procedure\n                Widths = np.empty(int(self.nwalkers/2))\n                Z_prime = np.empty(int(self.nwalkers/2))\n                X_prime = np.empty((int(self.nwalkers/2),self.ndim))\n                if blobs is not None:\n                    blobs_prime = np.empty(int(self.nwalkers/2), dtype=np.dtype((blobs[0].dtype, blobs[0].shape)))\n                mask = np.full(int(self.nwalkers/2),True)\n\n                cnt = 0\n                while len(mask[mask])>0:\n                    # Update Widths of intervals\n                    Widths[mask] = L[mask] + np.random.uniform(0.0,1.0,size=len(mask[mask])) * (R[mask] - L[mask])\n\n                    # Compute New Positions\n                    X_prime[mask] = directions[mask] * Widths[mask][:,np.newaxis] + X[active][mask]\n                    \n\n                    # Calculate LogP of New Positions\n                    if blobs is None:\n                        Z_prime[mask], _ = self.compute_log_prob(X_prime[mask])\n                        #Z_prime[mask] = np.array(list(self.distribute(self.logprob_fn, X_prime[mask])))\n                    else:\n                        Z_prime[mask], blobs_prime[mask] = self.compute_log_prob(X_prime[mask])\n\n                    # Count LogProb calls\n                    ncall += len(mask[mask])\n\n                    # Shrink slices\n                    for j in indeces[mask]:\n                        if Z0[j] < Z_prime[j]:\n                            mask[j] = False\n                        else:\n                            if Widths[j] < 0.0:\n                                L[j] = Widths[j]\n                                ncon += 1\n                            elif Widths[j] > 0.0:\n                                R[j] = Widths[j]\n                                ncon += 1\n\n                    cnt += 1\n                    if cnt > self.maxiter:\n                        raise RuntimeError('Number of contractions exceeded maximum limit! \\n' +\n                                           'Make sure that the pdf is well-defined. \\n' +\n                                           'Otherwise increase the maximum limit (maxiter=10^4 by default).')\n\n                # Update Positions\n                X[active] = X_prime\n                Z[active] = Z_prime\n                if blobs is not None:\n                    blobs[active] = blobs_prime\n                self.neval[i] += ncall\n\n            # Tune scale factor using Robbins-Monro optimization\n            if self.tune and tune_once:\n                self.nexps.append(nexp)\n                self.ncons.append(ncon)\n                nexp = max(1, nexp) # This is to prevent the optimizer from getting stuck\n                self.mu *= 2.0 * nexp / (nexp + ncon)\n                self.mus.append(self.mu)\n                if np.abs(nexp / (nexp + ncon) - 0.5) < self.tolerance:\n                    ncount += 1\n                if ncount > self.patience:\n                    self.tune = False\n                    if self.light_mode:\n                        self.mu *= (1.0 + nexp/self.nwalkers)\n                        self.maxsteps = 1\n\n            # Save samples\n            if (i+1) % self.ncheckpoint == 0:\n                self.samples.save(X, Z, blobs)\n\n            # Update progress bar\n            if progress:\n                t.update()\n\n            # Update iteration counter and state variables\n            self.iteration = i + 1\n            self.state_X = np.copy(X)\n            self.state_Z = np.copy(Z)\n            self.state_blobs = blobs\n\n            # Yield current state\n            if (i+1) % self.ncheckpoint == 0:\n                yield (X, Z, blobs)\n\n        # Close progress bar\n        if progress:\n            t.close()\n\n\nclass sampler(EnsembleSampler):\n    def __init__(self, *args, **kwargs):\n        logging.warning('The sampler class has been deprecated. Please use the new EnsembleSampler class.')\n        super().__init__(*args, **kwargs)"
  },
  {
    "path": "zeus/fwrapper.py",
    "content": "import numpy as np\n\nclass _FunctionWrapper(object):\n    \"\"\"\n    This is a hack to make the likelihood function pickleable when ``args``\n    or ``kwargs`` are also included.\n\n    Args:\n        f (callable) : Log Probability function.\n        args (list): Extra arguments to be passed into the logprob.\n        kwargs (dict): Extra arguments to be passed into the logprob.\n\n    Returns:\n        Log Probability function.\n    \"\"\"\n\n    def __init__(self, f, args, kwargs):\n        self.f = f\n        self.args = [] if args is None else args\n        self.kwargs = {} if kwargs is None else kwargs\n\n    def __call__(self, x):\n        try:\n            return self.f(x, *self.args, **self.kwargs)\n        except:\n            import traceback\n\n            print(\"zeus: Exception while calling your likelihood function:\")\n            print(\"  params:\", x)\n            print(\"  args:\", self.args)\n            print(\"  kwargs:\", self.kwargs)\n            print(\"  exception:\")\n            traceback.print_exc()\n            raise\n"
  },
  {
    "path": "zeus/moves.py",
    "content": "import numpy as np \nfrom itertools import permutations\nimport random\n\ntry:\n    from scipy.stats import gaussian_kde\nexcept ImportError:\n    gaussian_kde = None\n\ntry:\n    from sklearn.mixture import BayesianGaussianMixture\nexcept ImportError:\n    BayesianGaussianMixture = None\n\n\n\nclass DifferentialMove:\n    r\"\"\"\n    The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ \"Differential Move\" with parallelization.\n    When this Move is used the walkers move along directions defined by random pairs of walkers sampled (with no\n    replacement) from the complementary ensemble. This is the default choice and performs well along a wide range\n    of target distributions.\n\n    Parameters\n    ----------\n        tune : bool\n            If True then tune this move. Default is True.\n        mu0 : float\n            Default value of ``mu`` if ``tune=False``.\n\n    \"\"\"\n\n    def __init__(self, tune=True, mu0=1.0):\n        self.tune = tune\n        self.mu0 = mu0\n\n\n    def get_direction(self, X, mu):\n        r\"\"\"\n        Generate direction vectors.\n\n        Parameters\n        ----------\n            X : array\n                Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble.\n            mu : float\n                The value of the scale factor ``mu``.\n        \n        Returns\n        -------\n            directions : array\n                Array of direction vectors of shape ``(nwalkers//2, ndim)``.\n        \"\"\"\n\n        nsamples = X.shape[0]\n\n        perms = list(permutations(np.arange(nsamples), 2))\n        pairs = np.asarray(random.sample(perms,nsamples)).T\n\n        if not self.tune:\n            mu = self.mu0\n        \n        return 2.0 * mu * (X[pairs[0]]-X[pairs[1]]), self.tune\n\n\nclass GaussianMove:\n    r\"\"\"\n    The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ \"Gaussian Move\" with parallelization.\n    When this Move is used the walkers move along directions defined by random vectors sampled from the Gaussian\n    approximation of the walkers of the complementary ensemble.\n\n    Parameters\n    ----------\n        tune : bool\n            If True then tune this move. Default is True.\n        mu0 : float\n            Default value of ``mu`` if ``tune=False``.\n\n    \"\"\"\n\n    def __init__(self, tune=False, mu0=1.0, cov=None):\n        self.tune = tune \n        self.mu0 = mu0\n        self.cov = cov\n\n\n    def get_direction(self, X, mu):\n        r\"\"\"\n        Generate direction vectors.\n\n        Parameters\n        ----------\n            X : array\n                Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble.\n            mu : float\n                The value of the scale factor ``mu``.\n        \n        Returns\n        -------\n            directions : array\n                Array of direction vectors of shape ``(nwalkers//2, ndim)``.\n        \"\"\"\n\n        nsamples = X.shape[0]\n\n        mean = np.mean(X, axis=0)\n        if self.cov is None:\n            cov = np.cov(X, rowvar=False)\n        else:\n            cov = self.cov\n\n        if not self.tune:\n            mu = self.mu0\n\n        return 2.0 * mu * np.random.multivariate_normal(np.zeros_like(mean),cov,size=nsamples), self.tune\n\n\nclass GlobalMove:\n    r\"\"\"\n    The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ \"Global Move\" with parallelization.\n    When this Move is used a Bayesian Gaussian Mixture (BGM) is fitted to the walkers of complementary ensemble.\n    The walkers move along random directions which connect different components of the BGM in an attempt to\n    facilitate mode jumping. This Move should be used when the target distribution is multimodal. This move should\n    be used after any burnin period.\n\n    Parameters\n    ----------\n        tune : bool\n            If True then tune this move. Default is True.\n        mu0 : float\n            Default value of ``mu`` if ``tune=False``.\n        rescale_cov : float\n            Rescale the covariance matrices of the BGM components by this factor. This promotes mode jumping.\n            Default value is 0.001.\n        n_components : int\n            The number of mixture components. Depending on the distribution of the walkers the model can\n            decide not to use all of them.\n    \"\"\"\n\n    def __init__(self, tune=True, mu0=1.0, rescale_cov=0.001, n_components=5):\n        \n        if BayesianGaussianMixture is None:\n            raise ImportError(\"you need sklearn.mixture.BayesianGaussianMixture to use the GlobalMove\")\n\n        self.tune = tune\n        self.mu0 = mu0\n        self.rescale_cov = rescale_cov\n        self.n_components = n_components\n\n\n    def get_direction(self, X, mu):\n        r\"\"\"\n        Generate direction vectors.\n\n        Parameters\n        ----------\n            X : array\n                Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble.\n            mu : float\n                The value of the scale factor ``mu``.\n        \n        Returns\n        -------\n            directions : array\n                Array of direction vectors of shape ``(nwalkers//2, ndim)``.\n        \"\"\"\n        \n        if not self.tune:\n            mu = self.mu0\n\n        n = X.shape[0]\n\n        mixture = BayesianGaussianMixture(n_components=self.n_components)\n        labels = mixture.fit_predict(X)\n        means = mixture.means_\n        covariances = mixture.covariances_\n\n        i, j = np.random.choice(labels, 2, replace=False)\n        if i != j:\n            directions = np.random.multivariate_normal(means[i], covariances[i]*self.rescale_cov, size=n) - np.random.multivariate_normal(means[j], covariances[j]*self.rescale_cov, size=n)\n            tune_once = False\n        else:\n            directions = mu * np.random.multivariate_normal(np.zeros_like(means[i]), covariances[i], size=n)\n            if self.tune:\n                tune_once = True\n            else:\n                tune_once = False\n\n        return 2.0*directions, tune_once\n\n\nclass KDEMove:\n    r\"\"\"\n    The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ \"KDE Move\" with parallelization.\n    When this Move is used the distribution of the walkers of the complementary ensemble is traced using\n    a Gaussian Kernel Density Estimation methods. The walkers then move along random direction vectos\n    sampled from this distribution.\n\n    Parameters\n    ----------\n        tune : bool\n            If True then tune this move. Default is True.\n        mu0 : float\n            Default value of ``mu`` if ``tune=False``.\n        bw_method :\n            The bandwidth estimation method. See the scipy docs for allowed values.\n\n    \"\"\"\n\n    def __init__(self, tune=False, mu0=1.0, bw_method=None):\n\n        if gaussian_kde is None:\n            raise ImportError(\"you need scipy.stats.gaussian_kde to use the KDEMove\")\n\n        self.tune = tune\n        self.mu0 = mu0\n        self.bw_method = bw_method\n\n\n    def get_direction(self, X, mu):\n        r\"\"\"\n        Generate direction vectors.\n\n        Parameters\n        ----------\n            X : array\n                Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble.\n            mu : float\n                The value of the scale factor ``mu``.\n        \n        Returns\n        -------\n            directions : array\n                Array of direction vectors of shape ``(nwalkers//2, ndim)``.\n        \"\"\"\n\n        n = X.shape[0]\n\n        kde = gaussian_kde(X.T, bw_method=self.bw_method)\n\n        vectors = kde.resample(2*n).T\n        directions = vectors[:n] - vectors[n:]\n\n        if not self.tune:\n            mu = self.mu0\n\n        return 2.0 * mu * directions, self.tune\n\n\nclass RandomMove:\n    r\"\"\"\n    The `Karamanis & Beutler (2020) <https://arxiv.org/abs/2002.06212>`_ \"Random Move\" with parallelization.\n    When this move is used the walkers move along random directions. There is no communication between the\n    walkers and this Move corresponds to the vanilla Slice Sampling method. This Move should be used for\n    debugging purposes only.\n\n    Parameters\n    ----------\n        tune : bool\n            If True then tune this move. Default is True.\n        mu0 : float\n            Default value of ``mu`` if ``tune=False``.\n\n    \"\"\"\n\n    def __init__(self, tune=True, mu0=1.0):\n        self.tune = tune\n        self.mu0 = mu0\n\n\n    def get_direction(self, X, mu):\n        r\"\"\"\n        Generate direction vectors.\n\n        Parameters\n        ----------\n            X : array\n                Array of shape ``(nwalkers//2, ndim)`` with the walker positions of the complementary ensemble.\n            mu : float\n                The value of the scale factor ``mu``.\n        \n        Returns\n        -------\n            directions : array\n                Array of direction vectors of shape ``(nwalkers//2, ndim)``.\n        \"\"\"\n\n        directions = np.random.normal(0.0, 1.0, size=X.shape)\n        directions /= np.linalg.norm(directions, axis=0)\n\n        if not self.tune:\n            mu = self.mu0\n\n        return 2.0 * mu * directions, self.tune"
  },
  {
    "path": "zeus/parallel.py",
    "content": "import sys\nimport atexit\n\n\nMPI = None\n\ndef _import_mpi(use_dill=False):\n    global MPI\n    try:\n        from mpi4py import MPI as _MPI\n        if use_dill:\n            import dill\n            _MPI.pickle.__init__(dill.dumps, dill.loads, dill.HIGHEST_PROTOCOL)\n        MPI = _MPI\n    except:\n        raise ImportError(\"Please install mpi4py\")\n\n    return MPI\n\n\nclass MPIPool:\n    \"\"\"A processing pool that distributes tasks using MPI.\n    With this pool class, the master process distributes tasks to worker\n    processes using an MPI communicator.\n    This implementation is inspired by @juliohm in `this module\n    <https://github.com/juliohm/HUM/blob/master/pyhum/utils.py#L24>`_\n    and was adapted from schwimmbad.\n    Parameters\n    ----------\n    comm : :class:`mpi4py.MPI.Comm`, optional\n        An MPI communicator to distribute tasks with. If ``None``, this uses\n        ``MPI.COMM_WORLD`` by default.\n    \"\"\"\n\n    def __init__(self, comm=None):\n\n        self.comm = MPI.COMM_WORLD if comm is None else comm\n\n        self.master = 0\n        self.rank = self.comm.Get_rank()\n\n        atexit.register(lambda: MPIPool.close(self))\n\n        if not self.is_master():\n            # workers branch here and wait for work\n            self.wait()\n            sys.exit(0)\n\n        self.workers = set(range(self.comm.size))\n        self.workers.discard(self.master)\n        self.size = self.comm.Get_size() - 1\n\n        if self.size == 0:\n            raise ValueError(\"Tried to create an MPI pool, but there \"\n                             \"was only one MPI process available. \"\n                             \"Need at least two.\")\n\n\n    def wait(self):\n        \"\"\"Tell the workers to wait and listen for the master process. This is\n        called automatically when using :meth:`MPIPool.map` and doesn't need to\n        be called by the user.\n        \"\"\"\n        if self.is_master():\n            return\n\n        status = MPI.Status()\n        while True:\n            task = self.comm.recv(source=self.master, tag=MPI.ANY_TAG, status=status)\n\n            if task is None:\n                # Worker told to quit work\n                break\n\n            func, arg = task\n            result = func(arg)\n            # Worker is sending answer with tag\n            self.comm.ssend(result, self.master, status.tag)\n\n\n    def map(self, worker, tasks):\n        \"\"\"Evaluate a function or callable on each task in parallel using MPI.\n        The callable, ``worker``, is called on each element of the ``tasks``\n        iterable. The results are returned in the expected order.\n        Parameters\n        ----------\n        worker : callable\n            A function or callable object that is executed on each element of\n            the specified ``tasks`` iterable. This object must be picklable\n            (i.e. it can't be a function scoped within a function or a\n            ``lambda`` function). This should accept a single positional\n            argument and return a single object.\n        tasks : iterable\n            A list or iterable of tasks. Each task can be itself an iterable\n            (e.g., tuple) of values or data to pass in to the worker function.\n\n        Returns\n        -------\n        results : list\n            A list of results from the output of each ``worker()`` call.\n        \"\"\"\n\n        # If not the master just wait for instructions.\n        if not self.is_master():\n            self.wait()\n            return\n\n\n        workerset = self.workers.copy()\n        tasklist = [(tid, (worker, arg)) for tid, arg in enumerate(tasks)]\n        resultlist = [None] * len(tasklist)\n        pending = len(tasklist)\n\n        while pending:\n            if workerset and tasklist:\n                worker = workerset.pop()\n                taskid, task = tasklist.pop()\n                # \"Sent task %s to worker %s with tag %s\"\n                self.comm.send(task, dest=worker, tag=taskid)\n\n            if tasklist:\n                flag = self.comm.Iprobe(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG)\n                if not flag:\n                    continue\n            else:\n                self.comm.Probe(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG)\n\n            status = MPI.Status()\n            result = self.comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG,\n                                    status=status)\n            worker = status.source\n            taskid = status.tag\n\n            # \"Master received from worker %s with tag %s\"\n\n            workerset.add(worker)\n            resultlist[taskid] = result\n            pending -= 1\n\n        return resultlist\n\n\n    def close(self):\n        \"\"\" Tell all the workers to quit.\"\"\"\n        if self.is_worker():\n            return\n\n        for worker in self.workers:\n            self.comm.send(None, worker, 0)\n\n\n    def is_master(self):\n        return self.rank == 0\n\n\n    def is_worker(self):\n        return self.rank != 0\n\n\n    def __enter__(self):\n        return self\n\n\n    def __exit__(self, *args):\n        self.close()\n\n\n\ndef split_ranks(N_ranks, N_chunks):\n    \"\"\"\n    Divide the ranks into N chunks\n    \"\"\"\n    seq = range(N_ranks)\n    avg = int(N_ranks // N_chunks)\n    remainder = N_ranks % N_chunks\n\n    start = 0\n    end = avg\n    for i in range(N_chunks):\n        if remainder:\n            end += 1\n            remainder -= 1\n        yield i, seq[start:end]\n        start = end\n        end += avg\n\n\n\nclass ChainManager:\n    \"\"\"\n    Class to serve as context manager to handle to MPI-related issues,\n    specifically, the managing of ``MPIPool`` and splitting of communicators.\n    This class can be used to run ``nchains`` in parallel with each chain\n    having its own ``MPIPool`` of parallel walkers.\n    \n    Parameters\n    ----------\n    nchains : int\n        the number of independent chains to run concurrently\n    comm : MPI.Communicator\n        the global communicator to split\n    \"\"\"\n\n    def __init__(self, nchains=1, comm=None):\n        global MPI\n        MPI = _import_mpi(use_dill=False)\n\n        self.comm  = MPI.COMM_WORLD if comm is None else comm\n        self.nchains = nchains\n\n        # initialize comm for parallel chains\n        self.chains_group = None\n        self.chains_comm  = None\n\n        # intiialize comm for pool of workers for each parallel chain\n        self.pool_comm = None\n        self.pool      = None\n\n\n    def __enter__(self):\n        \"\"\"\n        Setup the MPIPool, such that only the ``pool`` master returns,\n        while the other processes wait for tasks\n        \"\"\"\n        # split ranks if we need to\n        if self.comm.size > 1:\n\n            ranges = []\n            for i, ranks in split_ranks(self.comm.size, self.nchains):\n                ranges.append(ranks[0])\n                if self.comm.rank in ranks:\n                    color = i\n\n            # split the global comm into pools of workers\n            self.pool_comm = self.comm.Split(color, 0)\n\n            # make the comm to communicate b/w parallel runs\n            if self.nchains >= 1:\n                self.chains_group = self.comm.group.Incl(ranges)\n                self.chains_comm = self.comm.Create(self.chains_group)\n\n        # initialize the MPI pool, if the comm has more than 1 process\n        if self.pool_comm is not None and self.pool_comm.size > 1:\n            self.pool = MPIPool(comm=self.pool_comm)\n\n        # explicitly force non-master ranks in pool to wait\n        if self.pool is not None and not self.pool.is_master():\n            self.pool.wait()\n            sys.exit(0)\n\n        self.rank = 0\n        if self.chains_comm is not None:\n            self.rank = self.chains_comm.rank\n\n        return self\n\n\n    def __exit__(self, exc_type, exc_value, exc_traceback):\n        \"\"\"\n        Exit gracefully by closing and freeing the MPI-related variables\n        \"\"\"\n        # wait for all the processes, if we more than one\n        if self.chains_comm is not None and self.chains_comm.size > 1:\n            self.chains_comm.Barrier()\n\n        # close and free the MPI stuff\n        if self.chains_group is not None:\n            self.chains_group.Free()\n        if self.chains_comm is not None:\n            self.chains_comm.Free()\n        if self.pool is not None:\n            self.pool.close()\n\n\n    @property\n    def get_rank(self):\n        '''\n        Get ``rank`` of current ``chain``. The minimum ``rank`` is ``0`` and the maximum is ``nchains-1``.\n        '''\n        return self.rank\n\n\n    @property\n    def get_pool(self):\n        '''\n        Get parallel ``pool`` of workers that correspond to a specific chain. This should be used to\n        parallelize the walkers of each ``chain`` (not the chains themselves). This includes the ``map``\n        method that ``zeus`` requires.\n        '''\n        return self.pool\n\n    \n    def gather(self, x, root):\n        '''\n        Gather method to gather ``x`` in ``rank = root`` chain.\n\n        Parameters\n        ----------\n        x : Python object\n            The python object to be gathered.\n        root : int\n            The rank  of the chain that x is gathered.\n\n        Returns\n        -------\n        x : Python object\n            The input object x gathered in ``rank = root``.\n        '''\n        return self.chains_comm.gather(x, root=root)\n\n    \n    def scatter(self, x, root):\n        '''\n        Scatter method to scatter ``x`` from ``rank = root`` chain to the rest. \n\n        Parameters\n        ----------\n        x : Python object\n            The python object to be scattered.\n        root : int\n            The rank of the origin chain from which the x is scattered.\n\n        Returns\n        -------\n        x : Pythonn object\n            Part of the input object x that was scattered along the ranks.\n        '''\n        return self.chains_comm.scatter(x, root=root)\n\n\n    def allgather(self, x):\n        '''\n        Allgather method to gather ``x`` in all chains. This is equivalent to first ``scatter`` and then ``bcast``.\n\n        Parameters\n        ----------\n        x : Python object\n            The python object to be gathered.\n\n        Returns\n        -------\n        x : Python object\n            The python object, gathered in all ranks.\n        '''\n        return self.chains_comm.allgather(x)\n\n\n    def bcast(self, x, root):\n        '''\n        Broadcast method to send ``x`` from ``rank = root`` to all chains.\n\n        Parameters\n        ----------\n        x : Python object\n            The python object to be send.\n        root : int\n            The rank of the origin chain from which the object x is sent.\n        \n        Returns\n        -------\n        x : Python object\n            The input object x in all ranks.\n        '''\n        return self.chains_comm.bcast(x, root=root)\n"
  },
  {
    "path": "zeus/plotting.py",
    "content": "import numpy as np\n\ndef cornerplot(samples,\n               labels=None,\n               weights=None,\n               levels=None,\n               span=None,\n               quantiles=[0.025, 0.5, 0.975],\n               truth=None,\n               color=None,\n               alpha=0.5,\n               linewidth=1.5,\n               fill=True,\n               fontsize=10,\n               show_titles=True,\n               title_fmt='.2f',\n               title_fontsize=12,\n               cut=3,\n               fig=None,\n               size=(10,10)):\n    r\"\"\"\n    Plot corner-plot of samples.\n\n    Parameters\n    ----------\n    samples : array\n        Array of shape (nsamples, ndim) containing the samples.\n    labels : list\n        List of names of for the parameters.\n    weights : array\n        Array with weights (useful if different samples have different weights e.g. as in Nested Sampling).\n    levels : list\n        The quantiles used for plotting the smoothed 2-D distributions. If not provided, these default to 0.5, 1, 1.5, and 2-sigma contours.\n    quantiles : list\n        A list of fractional quantiles to overplot on the 1-D marginalized posteriors as titles. Default is ``[0.025, 0.5, 0.975]`` (spanning the 95%/2-sigma credible interval).\n    truth : array\n        Array specifying a point to be highlighted in the plot. It can be the true values of the parameters, the mean, median etc. By default this is None.\n    color : str\n        Matplotlib color to be used in the plot.\n    alpha : float\n        Transparency value of figure (Default is 0.5).\n    linewidth : float\n        Linewidth of plot (Default is 1.5).\n    fill : bool\n        If True (Default) the fill the 1D and 2D contours with color.\n    fontsize : float\n        Fontsize of axes labels. Default is 10.\n    show_titles : bool\n        Whether to display a title above each 1-D marginalized posterior showing the quantiles. Default is True.\n    title_fmt : str\n        Format of the titles. Default is ``.2f``.\n    title_fontsize : float\n        Fontsize of titles. Default is 12.\n    cut : float\n        Factor, multiplied by the smoothing bandwidth, that determines how far the evaluation grid extends past the extreme datapoints.\n        When set to 0, truncate the curve at the data limits. Default is ``cut=3``.\n    fig : (figure, axes)\n        Pre-existing Figure and Axes for the plot. Otherwise create new internally. Default is None.\n    size : (int, int)\n        Size of the plot. Default is (10, 10).\n    \n    Returns\n    -------\n    Figure, Axes\n        The matplotlib figure and axes.\n    \"\"\"\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n\n    ndim = samples.shape[1]\n\n    if labels is None:\n        labels = [r\"$x_{\"+str(i+1)+\"}$\" for i in range(ndim)]\n\n    if levels is None:\n        levels = list(1.0 - np.exp(-0.5 * np.arange(0.5, 2.1, 0.5) ** 2))\n    levels.append(1.0)\n    \n    if color is None:\n        color = \"tab:blue\"\n    \n    # Determine plotting bounds.\n    if span is None:\n        span = [0.999999426697 for i in range(ndim)]\n    span = list(span)\n    if len(span) != ndim:\n        raise ValueError(\"Dimension mismatch between samples and span.\")\n    for i, _ in enumerate(span):\n        try:\n            xmin, xmax = span[i]\n        except:\n            q = [0.5 - 0.5 * span[i], 0.5 + 0.5 * span[i]]\n            span[i] = _quantile(samples[:,i], q, weights=weights)\n\n    idxs = np.arange(ndim**2).reshape(ndim, ndim)\n    tril = np.tril_indices(ndim)\n    triu = np.triu_indices(ndim)\n    lower = list(set(idxs[tril])-set(idxs[triu]))\n    upper = list(set(idxs[triu])-set(idxs[tril]))\n    \n    if fig is None:\n        figure, axes = plt.subplots(ndim, ndim, figsize=size, sharex=False)\n    else:\n        figure = fig[0]\n        axes = fig[1]\n\n    for idx, ax in enumerate(axes.flat):\n\n        i = idx // ndim\n        j = idx % ndim        \n        \n        if idx in lower:\n            \n            ax.set_ylim(span[i])\n            ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n\n            if fill:\n                ax = sns.kdeplot(x=samples[:,j], y=samples[:,i], weights=weights,\n                                fill=True, color=color,\n                                clip=None, cut=cut,\n                                thresh=levels[0], levels=levels,\n                                ax=ax, alpha=alpha, linewidth=0.0,\n                                )\n            ax = sns.kdeplot(x=samples[:,j], y=samples[:,i], weights=weights,\n                             fill=False, color=color,\n                             clip=None, cut=cut,\n                             thresh=levels[0], levels=levels,\n                             ax=ax, alpha=alpha, linewidth=linewidth,\n                             )\n\n            if truth is not None:\n                ax.axvline(truth[j], color='k', lw=1.0)\n                ax.axhline(truth[i], color='k', lw=1.0)\n\n            if j == 0:\n                ax.set_ylabel(labels[i], fontsize=fontsize)\n                [l.set_rotation(45) for l in ax.get_yticklabels()]\n            else:\n                ax.yaxis.set_ticklabels([])\n\n            if i == ndim - 1:\n                ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n                ax.set_xlabel(labels[j], fontsize=fontsize)\n                [l.set_rotation(45) for l in ax.get_xticklabels()]\n            else:\n                ax.set_xticklabels([])\n\n                        \n        elif idx in upper:\n            ax.set_axis_off()\n        else:\n\n            ax.yaxis.set_major_locator(plt.NullLocator())\n\n            if fill:\n                ax = sns.kdeplot(x=samples[:,j],\n                                fill=True, color=color, weights=weights,\n                                clip=None, cut=cut,\n                                ax=ax, linewidth=0.0, alpha=alpha,\n                                )\n            ax = sns.kdeplot(x=samples[:,j],\n                             fill=None, color=color, weights=weights,\n                             clip=None, cut=cut,\n                             ax=ax, linewidth=linewidth, alpha=alpha,\n                             )\n\n            if truth is not None:\n                ax.axvline(truth[j], color='k', lw=1.0)\n\n            if i == ndim - 1:\n                ax.set_xlabel(labels[j], fontsize=fontsize)\n                [l.set_rotation(45) for l in ax.get_xticklabels()]\n            else:\n                ax.set_xticklabels([])\n            \n            if show_titles:\n                ql, qm, qh = _quantile(samples[:,i], quantiles, weights=weights)\n                q_minus, q_plus = qm - ql, qh - qm\n                fmt = \"{{0:{0}}}\".format(title_fmt).format\n                title = r\"${{{0}}}_{{-{1}}}^{{+{2}}}$\"\n                title = title.format(fmt(qm), fmt(q_minus), fmt(q_plus))\n                title = \"{0} = {1}\".format(labels[i], title)\n                ax.set_title(title, fontsize=title_fontsize)\n            \n\n        ax.set_xlim(span[j])\n        ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n    \n    figure.subplots_adjust(top=0.95, right=0.95, wspace=.05, hspace=.05)\n\n    return figure, axes\n\n\ndef _quantile(x, q, weights=None):\n    \"\"\"\n    Compute (weighted) quantiles from an input set of samples.\n    Parameters\n    ----------\n    x : `~numpy.ndarray` with shape (nsamps,)\n        Input samples.\n    q : `~numpy.ndarray` with shape (nquantiles,)\n       The list of quantiles to compute from `[0., 1.]`.\n    weights : `~numpy.ndarray` with shape (nsamps,), optional\n        The associated weight from each sample.\n    Returns\n    -------\n    quantiles : `~numpy.ndarray` with shape (nquantiles,)\n        The weighted sample quantiles computed at `q`.\n    \"\"\"\n\n    # Initial check.\n    x = np.atleast_1d(x)\n    q = np.atleast_1d(q)\n\n    # Quantile check.\n    if np.any(q < 0.0) or np.any(q > 1.0):\n        raise ValueError(\"Quantiles must be between 0. and 1.\")\n\n    if weights is None:\n        # If no weights provided, this simply calls `np.percentile`.\n        return np.percentile(x, list(100.0 * q))\n    else:\n        # If weights are provided, compute the weighted quantiles.\n        weights = np.atleast_1d(weights)\n        if len(x) != len(weights):\n            raise ValueError(\"Dimension mismatch: len(weights) != len(x).\")\n        idx = np.argsort(x)  # sort samples\n        sw = weights[idx]  # sort weights\n        cdf = np.cumsum(sw)[:-1]  # compute CDF\n        cdf /= cdf[-1]  # normalize CDF\n        cdf = np.append(0, cdf)  # ensure proper span\n        quantiles = np.interp(q, cdf, x[idx]).tolist()\n        return quantiles"
  },
  {
    "path": "zeus/samples.py",
    "content": "import numpy as np\n\nclass samples:\n    '''\n    Creates object that stores the samples.\n    Args:\n        ndim (int): Number of dimensions/parameters\n        nwalkers (int): Number of walkers.\n\n    '''\n\n    def __init__(self, ndim, nwalkers):\n        self.initialised = False\n        self.index = 0\n        self.ndim = ndim\n        self.nwalkers = nwalkers\n\n\n    def extend(self, n, blobs):\n        \"\"\"\n        Method to extend saving space.\n        Args:\n            n (int) : Extend space by n slots.\n        \"\"\"\n        if self.initialised:\n            ext = np.empty((n,self.nwalkers,self.ndim))\n            self.samples = np.concatenate((self.samples,ext),axis=0)\n            ext = np.empty((n,self.nwalkers))\n            self.logp = np.concatenate((self.logp,ext),axis=0)\n            if blobs is not None:\n                dt = np.dtype((blobs[0].dtype, blobs[0].shape))\n                ext = np.empty((n,self.nwalkers), dtype=dt)\n                self.blobs = np.concatenate((self.blobs, ext),axis=0)\n        else:\n            self.samples = np.empty((n,self.nwalkers,self.ndim))\n            self.logp = np.empty((n,self.nwalkers))\n            if blobs is not None:\n                dt = np.dtype((blobs[0].dtype, blobs[0].shape))\n                self.blobs = np.empty((n,self.nwalkers), dtype=dt)\n            self.initialised = True\n\n\n    def save(self, x, logp, blobs):\n        \"\"\"\n        Save sample into the storage.\n        Args:\n            x (ndarray): Samples to be appended into the storage.\n            logp (ndarray): Logprob values to be appended into the storage.\n        \"\"\"\n        self.samples[self.index] = x\n        self.logp[self.index] = logp\n        if blobs is not None:\n            self.blobs[self.index] = blobs\n        self.index += 1\n\n\n    @property\n    def chain(self):\n        \"\"\"\n        Chain property.\n\n        Returns:\n            3D array of shape (nsteps,nwalkers,ndim) containing the samples.\n        \"\"\"\n        return self.samples[:self.index]\n\n\n    @property\n    def length(self):\n        \"\"\"\n        Number of samples per walker.\n\n        Returns:\n            The total number of samples per walker.\n        \"\"\"\n        return self.index\n\n\n    def flatten(self, discard=0, thin=1):\n        \"\"\"\n        Flatten samples by thinning them, removing the burn in phase, and combining all the walkers.\n\n        Args:\n            discard (int): Number of burn-in steps to be removed from each walker (default is 0).\n            thin (int): Thinning parameter (the default value is 1).\n\n        Returns:\n            2D object containing the ndim flattened chains.\n        \"\"\"\n        return self.chain[discard:self.index:thin,:,:].reshape((-1,self.ndim), order='F')\n\n\n    @property\n    def logprob(self):\n        \"\"\"\n        Chain property.\n\n        Returns:\n            2D array of shape (nwalkers,nsteps) containing the log-probabilities.\n        \"\"\"\n        return self.logp[:self.index]\n\n\n    def flatten_logprob(self, discard=0, thin=1):\n        \"\"\"\n        Flatten log probability by thinning the chain, removing the burn in phase, and combining all the walkers.\n\n        Args:\n            discard (int): Number of burn-in steps to be removed from each walker (default is 0).\n            thin (int): Thinning parameter (the default value is 1).\n\n        Returns:\n            1D object containing the logprob of the flattened chains.\n        \"\"\"\n        return self.logprob[discard:self.index:thin,:].reshape((-1,), order='F')\n\n\n    def flatten_blobs(self, discard=0, thin=1):\n        \"\"\"\n        Flatten blobs by thinning the chain, removing the burn in phase, and combining all the walkers.\n\n        Args:\n            discard (int): Number of burn-in steps to be removed from each walker (default is 0).\n            thin (int): Thinning parameter (the default value is 1).\n\n        Returns:\n            (structured) NumPy array containing the blobs metadata.\n        \"\"\"\n        return self.blobs[discard:self.index:thin,:].reshape((-1,), order='F')"
  }
]