Tensorpac is an Python open-source toolbox for computing Phase-Amplitude Coupling (PAC) using tensors and parallel computing. On top of that, we designed a modular implementation with a relatively large amount of parameters. Checkout the documentation for further details.
Tensorpac use NumPy, SciPy and joblib for parallel computing. In a terminal, run :
pip install tensorpacimport matplotlib.pyplot as plt
from tensorpac.utils import PacSignals
from tensorpac import Pac
# Dataset of signals artificially coupled between 10hz and 100hz :
n = 100 # number of datasets
data, time = PacSignals(fpha=10, famp=100, noise=3, ndatasets=n, dpha=10, damp=10)
# Extract PAC :
p = Pac(idpac=(4, 0, 0), fpha=(2, 30, 1, 1), famp=(60, 150, 5, 5),
dcomplex='wavelet', width=12)
xpac, pval = p.filterfit(1024, data, data, axis=1, nperm=100)
# Plot your Phase-Amplitude Coupling :
p.comodulogram(xpac.mean(-1), title='Contour plot with 5 regions',
cmap='Spectral_r', plotas='contour', ncontours=5, vmin=60, vmax=300)
plt.show()
- Etienne Combrisson
- Juan L.P. Soto
- Karim Jerbi