=========
Tensorpac
=========
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:target: https://circleci.com/gh/EtienneCmb/tensorpac/tree/master
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:target: https://codecov.io/gh/EtienneCmb/tensorpac
.. image:: https://badge.fury.io/py/tensorpac.svg
:target: https://badge.fury.io/py/tensorpac
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:target: https://pepy.tech/project/tensorpac
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:target: https://gitter.im/EtienneCmb/tensorpac?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
.. 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
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