https://github.com/etiennecmb/tensorpac

Phase-Amplitude Coupling under Python
https://github.com/etiennecmb/tensorpac

amplitude coupling event-related filtering neuroscience oscillations pac parallel-computing phase phase-amplitude-coupling tensor

Last synced: about 1 year ago
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Phase-Amplitude Coupling under Python

• Host: GitHub
• URL: https://github.com/etiennecmb/tensorpac
• Owner: EtienneCmb
• License: bsd-3-clause
• Created: 2017-06-04T13:47:18.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2024-07-30T12:42:12.000Z (almost 2 years ago)
• Last Synced: 2025-03-30T07:08:23.364Z (about 1 year ago)
• Topics: amplitude, coupling, event-related, filtering, neuroscience, oscillations, pac, parallel-computing, phase, phase-amplitude-coupling, tensor
• Language: Python
• Homepage: https://etiennecmb.github.io/tensorpac/
• Size: 117 MB
• Stars: 106
• Watchers: 9
• Forks: 29
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE
  • Citation: CITATION.cff

Awesome Lists containing this project

README

          
=========

Tensorpac

=========

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.. 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 `_  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

Cite Tensorpac

--------------

Tensorpac software has been published in `PLoS Computational Biology `_

Use the following Bibtex entry to cite it :

.. code-block:: latex

    @article{combrisson_tensorpac_2020,

        title = {Tensorpac: {An} open-source {Python} toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals},

        volume = {16},

        issn = {1553-7358},

        shorttitle = {Tensorpac},

        doi = {10.1371/journal.pcbi.1008302},

        language = {eng},

        number = {10},

        journal = {PLoS computational biology},

        author = {Combrisson, Etienne and Nest, Timothy and Brovelli, Andrea and Ince, Robin A. A. and Soto, Juan L. P. and Guillot, Aymeric and Jerbi, Karim},

        month = oct,

        year = {2020},

        pmid = {33119593},

        pmcid = {PMC7654762},

        pages = {e1008302},

    }