frites 0.4.4
Framework of Information Theory for Electrophysiological data and Statistics
License: BSD 3-Clause License
Keywords: information-theory statistics

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Frites
======

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.. _Documentation: https://brainets.github.io/frites/
.. |Documentation| replace:: **Documentation**

.. _Installation: https://brainets.github.io/frites/install.html
.. |Installation| replace:: **Installation**

.. _Usage: https://brainets.github.io/frites/auto_examples/index.html
.. |Usage| replace:: **Usage example**

.. _API: https://brainets.github.io/frites/api/index.html
.. |API| replace:: **List of functions**

.. _Cite: https://brainets.github.io/frites/overview/ovw_cite.html
.. |Cite| replace:: **Cite Frites**

|Documentation|_ | |Installation|_ | |Usage|_ | |API|_ | |Cite|_


Description
===========

`Frites <https://brainets.github.io/frites/>`_ is a Python toolbox for assessing information-theorical measures on human and animal neurophysiological data (M/EEG, Intracranial). The aim of Frites is to extract task-related cognitive brain networks (i.e modulated by the task). The toolbox also includes directed and undirected connectivity metrics such as group-level statistics. Frites documentation is available online at https://brainets.github.io/frites/

.. figure::  https://github.com/brainets/frites/blob/master/docs/source/_static/network_framework.png
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Installation
============

Run the following command into your terminal to get the latest stable version :

.. code-block:: shell

    pip install -U frites


You can also install the latest version of the software directly from Github :

.. code-block:: shell

    pip install git+https://github.com/brainets/frites.git


For developers, you can install it in develop mode with the following commands :

.. code-block:: shell

    git clone https://github.com/brainets/frites.git
    cd frites
    python setup.py develop
    # or : pip install -e .

Dependencies
++++++++++++

The main dependencies of Frites are :

* `Numpy <https://numpy.org/>`_
* `Scipy <https://www.scipy.org/>`_
* `MNE Python <https://mne.tools/stable/index.html>`_
* `Xarray <http://xarray.pydata.org/en/stable/>`_
* `Joblib <https://joblib.readthedocs.io/en/latest/>`_

In addition to the main dependencies, here's the list of additional packages that you might need :

* `Numba <http://numba.pydata.org/>`_ : speed up the computations of some functions
* `Dcor <https://dcor.readthedocs.io/en/latest/>`_ for fast implementation of distance correlation
* `Matplotlib <https://matplotlib.org/>`_, `Seaborn <https://seaborn.pydata.org/>`_ and `Networkx <https://networkx.github.io/>`_ for plotting the examples
* Some example are using `scikit learn <https://scikit-learn.org/stable/index.html>`_ estimators

Acknowledgments
===============

See `acknowledgments <https://brainets.github.io/frites/overview/ovw_acknowledgments.html>`_