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Tensorpac

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.. 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:: console

$ pip install tensorpac

Code snippet & illustration

.. code-block:: python

from tensorpac import Pac from tensorpac.signals import pac_signals_tort

Dataset of signals artificially coupled between 10hz and 100hz :

n_epochs = 20 # number of trials n_times = 4000 # number of time points sf = 512. # sampling frequency

Create artificially coupled signals using Tort method :

data, time = pac_signals_tort(f_pha=10, f_amp=100, noise=2, n_epochs=n_epochs, dpha=10, damp=10, sf=sf, n_times=n_times)

Define a Pac object

p = Pac(idpac=(6, 0, 0), f_pha='hres', f_amp='hres')

Filter the data and extract pac

xpac = p.filterfit(sf, data)

plot your Phase-Amplitude Coupling :

p.comodulogram(xpac.mean(-1), cmap='Spectral_r', plotas='contour', ncontours=5, title=r'10hz phase$\Leftrightarrow$100Hz amplitude coupling', fz_title=14, fz_labels=13)

p.show()

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