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        "author": "Matthew Spellings",
        "author_email": "mspells@umich.edu",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "==================================\nWelcome to pythia's documentation!\n==================================\n\nPythia is a library to generate numerical descriptions of particle\nsystems. Most methods rely heavily on `freud\n<https://github.com/glotzerlab/freud>`_ for efficient neighbor search\nand other accelerated calculations.\n\nInstallation\n============\n\nPythia is available on PyPI as `pythia-learn`::\n\n  $ pip install pythia-learn freud-analysis\n\nYou can install pythia from source like this::\n\n   $ git clone https://github.com/glotzerlab/pythia.git\n   $ # now install\n   $ cd pythia && python setup.py install --user\n\n.. note::\n\n   If using conda or a virtualenv, the `--user` argument in the pip\n   command above is unnecessary.\n\nCitation\n========\n\nIn addition to the citations referenced in the docstring of each\nfunction, we encourage users to cite the pythia project itself.\n\nDocumentation\n=============\n\nThe documentation is available as standard sphinx documentation::\n\n  $ cd doc\n  $ make html\n\nAutomatically-built documentation is available at\nhttps://pythia-learn.readthedocs.io .\n\nUsage\n=====\n\nIn general, data types follow the `hoomd-blue schema\n<http://hoomd-blue.readthedocs.io/en/stable/box.html>`_:\n\n- Positions are an Nx3 array of particle coordinates, with `(0, 0, 0)` being the center of the box\n- Boxes are specified as an object with `Lx`, `Ly`, `Lz`, `xy`, `xz`, and `yz` elements\n- Orientations are specified as orientation quaternions: an Nx4 array of `(r, i, j, k)` elements\n\nExamples\n========\n\nExample notebooks are available in the `examples` directory:\n\n- `Unsupervised learning <https://github.com/glotzerlab/pythia/blob/master/examples/Unsupervised%20Learning.ipynb>`_\n- `Supervised learning <https://github.com/glotzerlab/pythia/blob/master/examples/Supervised%20Learning.ipynb>`_\n- `Steinhardt and Pythia order parameter comparison (FCC and HCP) <https://github.com/glotzerlab/pythia/blob/master/examples/Steinhardt%20FCC%20HCP%20comparison.ipynb>`_\n\n\n\n",
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        "name": "pythia-learn",
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