Metadata-Version: 1.2
Name: pythia-learn
Version: 0.2.5
Summary: Machine learning fingerprints for particle environments
Home-page: UNKNOWN
Author: Matthew Spellings
Author-email: mspells@umich.edu
License: BSD
Project-URL: Documentation, https://pythia-learn.readthedocs.io/
Project-URL: Source, https://github.com/glotzerlab/pythia
Description: ==================================
        Welcome to pythia's documentation!
        ==================================
        
        Pythia is a library to generate numerical descriptions of particle
        systems. Most methods rely heavily on `freud
        <https://github.com/glotzerlab/freud>`_ for efficient neighbor search
        and other accelerated calculations.
        
        Installation
        ============
        
        Pythia is available on PyPI as `pythia-learn`::
        
          $ pip install pythia-learn freud-analysis
        
        You can install pythia from source like this::
        
           $ git clone https://github.com/glotzerlab/pythia.git
           $ # now install
           $ cd pythia && python setup.py install --user
        
        .. note::
        
           If using conda or a virtualenv, the `--user` argument in the pip
           command above is unnecessary.
        
        Citation
        ========
        
        In addition to the citations referenced in the docstring of each
        function, we encourage users to cite the pythia project itself.
        
        Documentation
        =============
        
        The documentation is available as standard sphinx documentation::
        
          $ cd doc
          $ make html
        
        Automatically-built documentation is available at
        https://pythia-learn.readthedocs.io .
        
        Usage
        =====
        
        In general, data types follow the `hoomd-blue schema
        <http://hoomd-blue.readthedocs.io/en/stable/box.html>`_:
        
        - Positions are an Nx3 array of particle coordinates, with `(0, 0, 0)` being the center of the box
        - Boxes are specified as an object with `Lx`, `Ly`, `Lz`, `xy`, `xz`, and `yz` elements
        - Orientations are specified as orientation quaternions: an Nx4 array of `(r, i, j, k)` elements
        
        Examples
        ========
        
        Example notebooks are available in the `examples` directory:
        
        - `Unsupervised learning <https://github.com/glotzerlab/pythia/blob/master/examples/Unsupervised%20Learning.ipynb>`_
        - `Supervised learning <https://github.com/glotzerlab/pythia/blob/master/examples/Supervised%20Learning.ipynb>`_
        - `Steinhardt and Pythia order parameter comparison (FCC and HCP) <https://github.com/glotzerlab/pythia/blob/master/examples/Steinhardt%20FCC%20HCP%20comparison.ipynb>`_
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3
