Metadata-Version: 2.1
Name: tensorpac
Version: 0.6.3
Summary: Tensor-based Phase-Amplitude Coupling
Home-page: http://etiennecmb.github.io/tensorpac/
Author: Etienne Combrisson
Author-email: e.combrisson@gmail.com
Maintainer: Etienne Combrisson
License: BSD 3-Clause License
Download-URL: https://github.com/EtienneCmb/tensorpac/archive/v0.6.3.tar.gz
Keywords: phase-amplitude coupling pac tensor oscillation meg eeg python
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: joblib

=========
Tensorpac
=========

.. 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

.. image:: https://pepy.tech/badge/tensorpac
    :target: https://pepy.tech/project/tensorpac

.. image:: https://badges.gitter.im/EtienneCmb/tensorpac.svg
    :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
  n_times = 4000
  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, 3, 0), f_pha=(2, 20, 1, 1), f_amp=(60, 150, 5, 5))
  # Filter the data and extract PAC :
  xpac = p.filterfit(sf, data, n_perm=20)

  # 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


