Tensor-based Phase-Amplitude Coupling
Project description
.. -*- mode: rst -*-
.. image:: https://travis-ci.org/EtienneCmb/tensorpac.svg?branch=master
:target: https://travis-ci.org/EtienneCmb/tensorpac
.. image:: https://codecov.io/gh/EtienneCmb/tensorpac/branch/master/graph/badge.svg
:target: https://codecov.io/gh/EtienneCmb/tensorpac
.. image:: https://badge.fury.io/py/Tensorpac.svg
:target: https://badge.fury.io/py/Tensorpac
Tensorpac
#########
.. 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. On top of that, we designed a modular implementation with a relatively large amount of parameters. Checkout the `documentation <http://etiennecmb.github.io/tensorpac/>`_ for further details.
Installation
============
Tensorpac use NumPy, SciPy and joblib for parallel computing. In a terminal, run :
.. code-block:: shell
pip install tensorpac
Code snippet & illustration
===========================
.. code-block:: python
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()
.. figure:: https://github.com/EtienneCmb/tensorpac/blob/master/docs/source/picture/readme.png
:align: center
Contributors
============
* `Etienne Combrisson <http://etiennecmb.github.io>`_
* Juan L.P. Soto
* `Karim Jerbi <www.karimjerbi.com>`_
.. image:: https://travis-ci.org/EtienneCmb/tensorpac.svg?branch=master
:target: https://travis-ci.org/EtienneCmb/tensorpac
.. image:: https://codecov.io/gh/EtienneCmb/tensorpac/branch/master/graph/badge.svg
:target: https://codecov.io/gh/EtienneCmb/tensorpac
.. image:: https://badge.fury.io/py/Tensorpac.svg
:target: https://badge.fury.io/py/Tensorpac
Tensorpac
#########
.. 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. On top of that, we designed a modular implementation with a relatively large amount of parameters. Checkout the `documentation <http://etiennecmb.github.io/tensorpac/>`_ for further details.
Installation
============
Tensorpac use NumPy, SciPy and joblib for parallel computing. In a terminal, run :
.. code-block:: shell
pip install tensorpac
Code snippet & illustration
===========================
.. code-block:: python
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()
.. figure:: https://github.com/EtienneCmb/tensorpac/blob/master/docs/source/picture/readme.png
:align: center
Contributors
============
* `Etienne Combrisson <http://etiennecmb.github.io>`_
* Juan L.P. Soto
* `Karim Jerbi <www.karimjerbi.com>`_
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