tensorpac 0.5.0

.. -*- mode: rst -*-

.. image:: https://travis-ci.org/EtienneCmb/tensorpac.svg?branch=master
:target: https://travis-ci.org/EtienneCmb/tensorpac

.. image:: https://codecov.io/gh/EtienneCmb/tensorpac/branch/master/graph/badge.svg
:target: https://codecov.io/gh/EtienneCmb/tensorpac

Tensorpac
#########

.. figure:: https://github.com/EtienneCmb/tensorpac/blob/master/docs/source/picture/tp.png
:align: center

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

Tensorpac is an Python open-source toolbox for computing Phase-Amplitude Coupling (PAC) using tensors and parallel computing. On top of that, we designed a modular implementation with a relatively large amount of parameters. Checkout the `documentation <http://etiennecmb.github.io/tensorpac/>`_for further details.

Installation
============

In a terminal, run :

.. code-block:: shell

git clone https://github.com/EtienneCmb/tensorpac.git tensorpac
cd tensorpac
pip setup.py install

Code snippet & illustration
===========================

.. code-block:: python

import matplotlib.pyplot as plt
from tensorpac.utils import PacSignals
from tensorpac import Pac

# Dataset of signals artificially coupled between 10hz and 100hz :
n = 100 # number of datasets
data, time = PacSignals(fpha=10, famp=100, noise=3, ndatasets=n, dpha=10, damp=10)

# Extract PAC :
p = Pac(idpac=(4, 0, 0), fpha=(2, 30, 1, 1), famp=(60, 150, 5, 5),
dcomplex='wavelet', width=12)
xpac, pval = p.filterfit(1024, data, data, axis=1, nperm=100)

# Plot your Phase-Amplitude Coupling :
p.comodulogram(xpac.mean(-1), title='Contour plot with 5 regions',
cmap='Spectral_r', plotas='contour', ncontours=5, vmin=60, vmax=300)

plt.show()


.. figure:: https://github.com/EtienneCmb/tensorpac/blob/master/docs/source/picture/readme.png
:align: center

Contributors
============

* `Etienne Combrisson <http://etiennecmb.github.io>`_
* Juan L.P. Soto
* `Karim Jerbi <www.karimjerbi.com>`_