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    "info": {
        "author": "Chris Collins",
        "author_email": "chris@crcollins.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "MolML\n=====\n\n|Build Status| |Coverage Status| |Documentation Status| |PyPI version|\n|License|\n\nA library to interface molecules and machine learning. The goal of this\nlibrary is to be a simple way to convert molecules into a vector\nrepresentation for later use with libraries such as\n`scikit-learn <http://scikit-learn.org/>`__. This is done using a\nsimilar API scheme.\n\nAll of the coordinates are assumed to be in angstroms.\n\nFeatures\n========\n\n::\n\n    - Simple interface to many common molecular descriptors and their variants\n        - Molecule\n            - Coulomb Matrix\n            - Bag of Bonds\n            - Encoded Bonds\n            - Encoded Angles\n            - Connectivity\n            - Connectivity Tree\n            - Autocorrelation\n        - Atom\n            - Shell\n            - Local Encoded Bonds\n            - Local Encoded Angles\n            - Local Coulomb Matrix\n            - Behler-Parrinello\n        - Kernel\n            - Atom/Summation Kernel\n        - Fragment\n            - FragmentMap\n        - Crystal\n            - Generallized Crystal\n            - Ewald Sum Matrix\n            - Sine Matrix\n    - Parallel feature generation\n    - Ability to save/load fit models\n    - Multiple input formats supported (and ability to define your own)\n    - Supports both Python 2 and Python 3\n\nExample Usage\n=============\n\n.. code:: python\n\n        >>> from molml.features import CoulombMatrix\n        >>> feat = CoulombMatrix()\n        >>> H2 = (\n        ...         ['H', 'H'],\n        ...         [\n        ...             [0.0, 0.0, 0.0],\n        ...             [1.0, 0.0, 0.0],\n        ...         ]\n        ... )\n        >>> HCN = (\n        ...         ['H', 'C', 'N'],\n        ...         [\n        ...             [-1.0, 0.0, 0.0],\n        ...             [ 0.0, 0.0, 0.0],\n        ...             [ 1.0, 0.0, 0.0],\n        ...         ]\n        ... )\n        >>> feat.fit([H2, HCN])\n        CoulombMatrix(input_type='list', n_jobs=1, sort=False, eigen=False, drop_values=False, only_lower_triangle=False)\n        >>> feat.transform([H2])\n        array([[ 0.5,  1. ,  0. ,  1. ,  0.5,  0. ,  0. ,  0. ,  0. ]])\n        >>> feat.transform([H2, HCN])\n        array([[  0.5      ,   1.       ,   0.       ,   1.       ,   0.5      ,\n                0.       ,   0.       ,   0.       ,   0.       ],\n                [  0.5      ,   6.       ,   3.5      ,   6.       ,  36.8581052,\n                42.       ,   3.5      ,  42.       ,  53.3587074]])\n        >>>\n        >>> # Example loading from files directly\n        >>> feat2 = CoulombMatrix(input_type='filename')\n        CoulombMatrix(input_type='filename', n_jobs=1, sort=False, eigen=False, drop_values=False, only_lower_triangle=False)\n        >>> paths = ['data/qm7/qm-%04d.out' % i for i in xrange(2)]\n        >>> feat2.fit_transform(paths)\n        array([[ 36.8581052 ,   5.49459021,   5.49462885,   5.4945    ,\n                  5.49031286,   0.        ,   0.        ,   0.        ,\n                  5.49459021,   0.5       ,   0.56071947,   0.56071656,\n                  0.56064037,   0.        ,   0.        ,   0.        ,\n                  5.49462885,   0.56071947,   0.5       ,   0.56071752,\n                  0.56064089,   0.        ,   0.        ,   0.        ,\n                  5.4945    ,   0.56071656,   0.56071752,   0.5       ,\n                  0.56063783,   0.        ,   0.        ,   0.        ,\n                  5.49031286,   0.56064037,   0.56064089,   0.56063783,\n                  0.5       ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ,\n                  0.        ,   0.        ,   0.        ,   0.        ],\n               [ 36.8581052 ,  23.81043959,   5.48396427,   5.48394941,\n                  5.4837656 ,   2.78378686,   2.78375582,   2.78376439,\n                  23.8104396,  36.8581052 ,   2.78378953,   2.78375777,\n                  2.78375823,   5.4839846 ,   5.48393324,   5.48376877,\n                  5.48396427,   2.78378953,   0.5       ,   0.56363019,\n                  0.56362464,   0.40019757,   0.39971446,   0.3261774 ,\n                  5.48394941,   2.78375777,   0.56363019,   0.5       ,\n                  0.56362305,   0.39971429,   0.32617621,   0.40019524,\n                  5.4837656 ,   2.78375823,   0.56362464,   0.56362305,\n                  0.5       ,   0.32617702,   0.40019469,   0.3997145 ,\n                  2.78378686,   5.4839846 ,   0.40019757,   0.39971429,\n                  0.32617702,   0.5       ,   0.56362996,   0.56362587,\n                  2.78375582,   5.48393324,   0.39971446,   0.32617621,\n                  0.40019469,   0.56362996,   0.5       ,   0.56362278,\n                  2.78376439,   5.48376877,   0.3261774 ,   0.40019524,\n                  0.3997145 ,   0.56362587,   0.56362278,   0.5       ]])\n\nFor more examples, look in the\n`examples <https://github.com/crcollins/molml/tree/master/examples>`__.\nNote: To run some of the examples scikit-learn>=0.16.0 is required.\n\nFor the full documentation, refer to the\n`docs <http://molml.readthedocs.io>`__ or the docstrings in the code.\n\nDependencies\n============\n\nMolML works with both Python 2 and Python 3. It has been tested with the\nversions listed below, but newer versions should work.\n\n::\n\n    python>=2.7/3.5/3.6\n    numpy>=1.9.1\n    scipy>=0.15.1\n    pathos>=0.2.0\n    bidict>=0.17.5\n    future  # For python 2\n\nNOTE: Due to an issue with multiprocess (a pathos dependency), the\nminimum version of Python that will work is 2.7.4. For full details see\n`this link <https://github.com/uqfoundation/multiprocess/issues/11>`__.\nWithout this, the parallel computation of features will fail.\n\nInstall\n=======\n\nOnce ``numpy`` and ``scipy`` are installed, the package can be installed\nwith pip.\n\n::\n\n    $ pip install molml\n\nOr for the bleeding edge version, you can use\n\n::\n\n    $ pip install git+git://github.com/crcollins/molml\n\nDevelopment\n===========\n\nTo install a development version, just clone the git repo.\n\n::\n\n    $ git clone https://github.com/crcollins/molml\n    $ # cd to molml and setup some virtualenv\n    $ pip install -r requirements-dev.txt\n\n`Pull requests <https://github.com/crcollins/molml/pulls>`__ and `bug\nreports <https://github.com/crcollins/molml/issues>`__ are welcomed!\n\nTo build the documentation, you just need to install the documentation\ndependencies. These are already included in the dev install.\n\n::\n\n    $ cd docs/\n    $ pip install -r requirements-docs.txt\n    $ make html\n\nTesting\n=======\n\nTo run the tests, make sure that ``nose`` is installed and then run:\n\n::\n\n    $ nosetests\n\nTo include coverage information, make sure that ``coverage`` is\ninstalled and then run:\n\n::\n\n    $ nosetests --with-coverage --cover-package=molml --cover-erase\n\nCitation\n========\n\nCurrently, there is not a dedicated publication for MolML. Instead, feel\nfree to cite the work that spawned this library.\n\n::\n\n    @article{collins2018constant,\n        title={Constant size descriptors for accurate machine learning models of molecular properties},\n        author={Collins, Christopher R and Gordon, Geoffrey J and von Lilienfeld, O Anatole and Yaron, David J},\n        journal={The Journal of Chemical Physics},\n        volume={148},\n        number={24},\n        pages={241718},\n        year={2018},\n        publisher={AIP Publishing}\n    }\n\nIn addition, each feature extraction method has its own main reference\nlisted in the docstring. These can also be accessed as follows:\n\n.. code:: python\n\n        >>> from molml.features import CoulombMatrix\n        >>> print(CoulombMatrix().get_citation())\n        Rupp, M.; Tkatchenko, A.; Muller, K.-R.; von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Phys. Rev. Lett. 2012, 108, 058301.\n        Hansen, K.; Montavon, G.; Biegler, F.; Fazli, S.; Rupp, M.; Scheffler, M.; von Lilienfeld, O. A.; Tkatchenko, A.; Muller, K.-R. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J. Chem. Theory Comput. 2013, 9, 3404-3419.\n\n.. |Build Status| image:: https://travis-ci.org/crcollins/molml.svg?branch=master\n   :target: https://travis-ci.org/crcollins/molml\n.. |Coverage Status| image:: https://coveralls.io/repos/github/crcollins/molml/badge.svg?branch=master\n   :target: https://coveralls.io/github/crcollins/molml?branch=master\n.. |Documentation Status| image:: https://readthedocs.org/projects/molml/badge/?version=latest\n   :target: http://molml.readthedocs.io/en/latest/?badge=latest\n.. |PyPI version| image:: https://img.shields.io/pypi/v/MolML.svg?style=flat\n   :target: http://pypi.python.org/pypi/MolML\n.. |License| image:: https://img.shields.io/pypi/l/MolML.svg?style=flat\n   :target: https://github.com/crcollins/molml/blob/master/LICENSE.txt\n",
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