Metadata-Version: 1.2
Name: tensorpac
Version: 0.6.1
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/v0.6.1.tar.gz
Description: =========
        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
        
        
        .. 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, 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
        
        Contributors
        ------------
        
        * `Etienne Combrisson <http://etiennecmb.github.io>`_
        * Juan L.P. Soto
        * Timothy C. Nest
        * `Karim Jerbi <www.karimjerbi.com>`_
        
        
Keywords: phase-amplitude-coupling pac tensor
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Programming Language :: Python :: 3.5
