tensorpac's Introduction

Tensorpac

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 for details.

Installation

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

$ pip install tensorpac

Code snippet & illustration

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()

Cite Tensorpac

Tensorpac software has been published in PLoS Computational Biology

Use the following Bibtex entry to cite it :

@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},
}

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