Metadata-Version: 2.1
Name: frites
Version: 0.4.1
Summary: Framework of Information Theory for Electrophysiological data and Statistics
Home-page: https://github.com/brainets/frites
Author: BraiNets
Author-email: e.combrisson@gmail.com
Maintainer: Etienne Combrisson
License: BSD 3-Clause License
Download-URL: https://github.com/brainets/frites/archive/v0.4.1.tar.gz
Keywords: information-theory statistics
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Provides-Extra: all
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: flake
Provides-Extra: full
License-File: LICENSE

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======
Frites
======

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.

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Documentation
-------------

Frites documentation is available online at https://brainets.github.io/frites/

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


