tensorpac 0.5.5

Tensor-based Phase-Amplitude Coupling

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.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()

Contributors

• Etienne Combrisson

• Juan L.P. Soto

• Timothy C. Nest

• Karim Jerbi