.. PyInform documentation master file, created by
   sphinx-quickstart on Tue Aug  2 13:21:59 2016.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to PyInform's documentation!
====================================

Contents:

.. toctree::
   :maxdepth: 2

   dist
   

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`


PyInform
========

PyInform is a python library of information-theoretic measures for time series
data. PyInform is backed by the `Inform <https://github.com/elife-asu/inform>`_
C library.

The library is built out of three primary components.

1. The :py:class:`pyinform.dist.Dist` class provides discrete, emperical
probability distributions. These form the basis for all of the
information-theoretic measures.

2. A collection of information measures built upon the distribution class
provide the core algorithms for the library and are implemented in the
:py:mod:`pyinform.shannon` submodule.

3. A host of measures of information dynamics on time series are built upon the
core information measures. Each measure is housed in its own submodule, but are
exposed for convenience by the root packge, :py:mod:`pyinform`.

In addition to the core components, a small collection of utilities are
provided by the :py:mod:`pyinform` module.

.. toctree::
   :maxdepth: 2

   dist
   

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`


Add a short description of pyinform
Add a short description of pyinform
