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Frites

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|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/

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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>_