Metadata-Version: 1.1
Name: Tensorpac
Version: 0.5.2
Summary: Tensor-based Phase-Amplitude Coupling
Home-page: http://etiennecmb.github.io/tensorpac/
Author: Etienne Combrisson
Author-email: e.combrisson@gmail.com
License: BSD 3-Clause License

Copyright (c) 2017, Etienne Combrisson
All rights reserved.

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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Download-URL: https://github.com/EtienneCmb/tensorpac/archive/v0.5.2.tar.gz
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
        
            import matplotlib.pyplot as plt
            from tensorpac.utils import PacSignals
            from tensorpac import Pac
        
            # Dataset of signals artificially coupled between 10hz and 100hz :
            n = 100  # number of datasets
            data, time = PacSignals(fpha=10, famp=100, noise=3, ndatasets=n, dpha=10, damp=10)
        
            # Extract PAC :
            p = Pac(idpac=(4, 0, 0), fpha=(2, 30, 1, 1), famp=(60, 150, 5, 5),
                    dcomplex='wavelet', width=12)
            xpac, pval = p.filterfit(1024, data, data, axis=1, nperm=100)
        
            # Plot your Phase-Amplitude Coupling :
            p.comodulogram(xpac.mean(-1), title='Contour plot with 5 regions',
                           cmap='Spectral_r', plotas='contour', ncontours=5, vmin=60, vmax=300)
        
            plt.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>`_
        
        
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
