========= Tensorpac

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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

Cite Tensorpac

Tensorpac software has been published in PLoS Computational Biology <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008302>_

Use the following Bibtex entry to cite it :

.. code-block:: latex

@article{combrisson_tensorpac_2020,
    title = {Tensorpac: {An} open-source {Python} toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals},
    volume = {16},
    issn = {1553-7358},
    shorttitle = {Tensorpac},
    doi = {10.1371/journal.pcbi.1008302},
    language = {eng},
    number = {10},
    journal = {PLoS computational biology},
    author = {Combrisson, Etienne and Nest, Timothy and Brovelli, Andrea and Ince, Robin A. A. and Soto, Juan L. P. and Guillot, Aymeric and Jerbi, Karim},
    month = oct,
    year = {2020},
    pmid = {33119593},
    pmcid = {PMC7654762},
    pages = {e1008302},
}