tensorpac 0.5.6

Copyright (c) 2017, Etienne Combrisson
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Download-URL: https://github.com/EtienneCmb/tensorpac/archive/v0.5.6.tar.gz
Description: .. -*- mode: rst -*-

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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 for an efficient, and highly flexible modular implementation of PAC metrics both known and novel. Check out our `documentation <http://etiennecmb.github.io/tensorpac/>`_ for details.

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

Tensorpac uses NumPy, SciPy and joblib for parallel computing. To get started, just open your terminal and run :

.. code-block:: shell

pip install tensorpac

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

.. code-block:: python

from tensorpac.utils import pac_signals_tort
from tensorpac import Pac

# Dataset of signals artificially coupled between 10hz and 100hz :
n = 20 # number of datasets
sf = 512. # sampling frequency

# Create artificially coupled signals using Tort method :
data, time = pac_signals_tort(fpha=10, famp=100, noise=2, ntrials=n,
dpha=10, damp=10, sf=sf)

# Define a PAC object :
p = Pac(idpac=(4, 0, 0), fpha=(2, 20, 1, 1), famp=(60, 150, 5, 5),
dcomplex='wavelet', width=12)
# Filter the data and extract PAC :
xpac = p.filterfit(sf, data, axis=1)

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

p.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
* Timothy C. Nest
* `Karim Jerbi <www.karimjerbi.com>`_


Keywords: phase-amplitude-coupling pac tensor
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Programming Language :: Python :: 3.5