Metadata-Version: 1.1
Name: molml
Version: 0.5.1
Summary: An interface between molecules and machine learning
Home-page: https://github.com/crcollins/molml/
Author: Chris Collins
Author-email: chris@crcollins.com
License: MIT
Description: MolML
        =====
        
        |Build Status| |Coverage Status| |PyPI version| |License|
        
        A library to interface molecules and machine learning. The goal of this
        library is to be a simple way to convert molecules into a vector
        representation for later use with libraries such as
        `scikit-learn <http://scikit-learn.org/>`__. This is done using a
        similar API scheme.
        
        All of the coordinates are assumed to be in angstroms.
        
        Example Usage
        =============
        
        .. code:: python
        
                >>> from molml.features import CoulombMatrix
                >>> feat = CoulombMatrix()
                >>> H2 = (
                ...         ['H', 'H'],
                ...         [
                ...             [0.0, 0.0, 0.0],
                ...             [1.0, 0.0, 0.0],
                ...         ]
                ... )
                >>> HCN = (
                ...         ['H', 'C', 'N'],
                ...         [
                ...             [-1.0, 0.0, 0.0],
                ...             [ 0.0, 0.0, 0.0],
                ...             [ 1.0, 0.0, 0.0],
                ...         ]
                ... )
                >>> feat.fit([H2, HCN])
                CoulombMatrix(input_type='list', n_jobs=1)
                >>> feat.transform([H2])
                array([[ 0.5,  1. ,  0. ,  1. ,  0.5,  0. ,  0. ,  0. ,  0. ]])
                >>> feat.transform([H2, HCN])
                array([[  0.5      ,   1.       ,   0.       ,   1.       ,   0.5      ,
                        0.       ,   0.       ,   0.       ,   0.       ],
                        [  0.5      ,   6.       ,   3.5      ,   6.       ,  36.8581052,
                        42.       ,   3.5      ,  42.       ,  53.3587074]])
        
        For more examples, look in the
        `examples <https://github.com/crcollins/molml/tree/master/examples>`__.
        Note: To run some of the examples scikit-learn>=0.16.0 is required.
        
        Dependencies
        ============
        
        MolML works with both Python 2 and Python 3 and depends on numpy, scipy,
        and pathos (and future for Python 2). The specific versions that have
        been tested are Python 2.7/3.4/3.5, numpy 1.9.1, scipy 0.15.1, and
        pathos 0.2.0, but newer versions should work.
        
        NOTE: Due to an issue with multiprocess (a pathos dependency), the
        minimum version of python that will work is 2.7.4. For full details see
        `this link <https://github.com/uqfoundation/multiprocess/issues/11>`__.
        Without this, the parallel computation of features will fail.
        
        Install
        =======
        
        Once the dependencies are installed, the package can be installed with
        pip.
        
        ::
        
            $ pip install molml
        
        Or for the bleeding edge version, you can use
        
        ::
        
            $ pip install git+git://github.com/crcollins/molml
        
        Development
        ===========
        
        To install a development version, just clone the git repo.
        
        ::
        
            $ git clone https://github.com/crcollins/molml
            $ # cd to molml and setup some virtualenv
            $ pip install -r requirements.txt
            $ pip install -r requirements-dev.txt
        
        Pull requests and bug reports are welcomed!
        
        Testing
        =======
        
        To run the tests, make sure that ``nose`` is installed and then run:
        
        ::
        
            $ nosetests
        
        To include coverage information, make sure that ``coverage`` is
        installed and then run:
        
        ::
        
            $ nosetests --with-coverage --cover-package=molml --cover-erase
        
        .. |Build Status| image:: https://travis-ci.org/crcollins/molml.svg?branch=master
           :target: https://travis-ci.org/crcollins/molml
        .. |Coverage Status| image:: https://coveralls.io/repos/github/crcollins/molml/badge.svg?branch=master
           :target: https://coveralls.io/github/crcollins/molml?branch=master
        .. |PyPI version| image:: https://img.shields.io/pypi/v/MolML.svg?style=flat
           :target: http://pypi.python.org/pypi/MolML
        .. |License| image:: https://img.shields.io/pypi/l/MolML.svg?style=flat
           :target: https://github.com/crcollins/molml/blob/master/LICENSE.txt
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
