https://github.com/brainets/frites
Framework for Information Theoretical analysis of Electrophysiological data and Statistics
https://github.com/brainets/frites
brainhack brainhack-marseille brainweb cluster-based electrophysiological-data ffx functional-connectivity gaussian-copula group-level information-theory institut-neurosciences-timone neurophysiological-data rfx statistics
Last synced: 8 months ago
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Framework for Information Theoretical analysis of Electrophysiological data and Statistics
- Host: GitHub
- URL: https://github.com/brainets/frites
- Owner: brainets
- License: bsd-3-clause
- Created: 2019-10-09T08:56:55.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-17T14:40:25.000Z (over 1 year ago)
- Last Synced: 2024-09-17T18:19:44.398Z (over 1 year ago)
- Topics: brainhack, brainhack-marseille, brainweb, cluster-based, electrophysiological-data, ffx, functional-connectivity, gaussian-copula, group-level, information-theory, institut-neurosciences-timone, neurophysiological-data, rfx, statistics
- Language: Python
- Homepage: https://brainets.github.io/frites/
- Size: 412 MB
- Stars: 76
- Watchers: 11
- Forks: 20
- Open Issues: 8
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
.. figure:: https://github.com/brainets/frites/blob/master/docs/source/_static/logo_desc.png
:align: center
Frites
======
.. image:: https://github.com/brainets/frites/actions/workflows/test_doc.yml/badge.svg
:target: https://github.com/brainets/frites/actions/workflows/test_doc.yml
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:target: https://github.com/brainets/frites/actions/workflows/flake.yml
.. image:: https://travis-ci.org/brainets/frites.svg?branch=master
:target: https://travis-ci.org/brainets/frites
.. image:: https://codecov.io/gh/brainets/frites/branch/master/graph/badge.svg
:target: https://codecov.io/gh/brainets/frites
.. image:: https://badge.fury.io/py/frites.svg
:target: https://badge.fury.io/py/frites
.. image:: https://pepy.tech/badge/frites
:target: https://pepy.tech/project/frites
.. image:: https://zenodo.org/badge/213869364.svg
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:target: https://joss.theoj.org/papers/437a7362501b2ea984e1d4fed4646076
.. _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 `_ 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
:align: center
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 `_
* `Scipy `_
* `MNE Python `_
* `Xarray `_
* `Joblib `_
In addition to the main dependencies, here's the list of additional packages that you might need :
* `Numba `_ : speed up the computations of some functions
* `Dcor `_ for fast implementation of distance correlation
* `Matplotlib `_, `Seaborn `_ and `Networkx `_ for plotting the examples
* Some example are using `scikit learn `_ estimators
Acknowledgments
===============
See `acknowledgments `_