.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_pac_plot_pac_methods.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_pac_plot_pac_methods.py:


=========================================
Comparison of the implemented PAC methods
=========================================

This script offers a comparison between all of the implemented PAC methods, in
particular the methods that are computed across times-points.


.. code-block:: default

    from tensorpac import Pac
    from tensorpac.signals import pac_signals_wavelet

    import matplotlib.pyplot as plt







Simulate artificial coupling
##############################################################################
 first, we generate several trials that contains a coupling between a 10hz
 phase and a 100hz amplitude. By default, the returned dataset is organized as
 (n_epochs, n_times) where n_times is the number of time points and n_epochs
 is the number of trials


.. code-block:: default


    f_pha = 10      # frequency phase for the coupling
    f_amp = 100     # frequency amplitude for the coupling
    n_epochs = 20   # number of trials
    n_times = 4000  # number of time points
    sf = 512.       # sampling frequency
    data, time = pac_signals_wavelet(sf=sf, f_pha=f_pha, f_amp=f_amp, noise=.8,
                                  n_epochs=n_epochs, n_times=n_times)








Extract phases and amplitudes
##############################################################################
 Since we're going to compute PAC using several methods, we're first going
 to extract all of the phases and amplitudes only once


.. code-block:: default


    # define a pac object and extract high-resolution phases and amplitudes using
    # Morlet's wavelets
    p = Pac(f_pha='hres', f_amp='hres', dcomplex='wavelet')
    # etract all of the phases and amplitudes
    phases = p.filter(sf, data, ftype='phase', n_jobs=1)
    amplitudes = p.filter(sf, data, ftype='amplitude', n_jobs=1)








Compute, plot and compare PAC
##############################################################################
 Once all of the phases and amplitudes extracted we can compute PAC by
 ietrating over the implemented methods.


.. code-block:: default


    plt.figure(figsize=(14, 8))
    for i, k in enumerate([1, 2, 3, 4, 5, 6]):
        # switch method of PAC
        p.idpac = (k, 0, 0)
        # compute only the pac without filtering
        xpac = p.fit(phases, amplitudes)
        # plot
        plt.subplot(2, 3, k)
        title = p.method.replace(' (', f' ({k})\n(')
        p.comodulogram(xpac.mean(-1), title=title, cmap='viridis')
        if k <= 3:
            plt.xlabel('')

    plt.tight_layout()
    plt.show()



.. image:: /auto_examples/pac/images/sphx_glr_plot_pac_methods_001.png
    :class: sphx-glr-single-img





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  18.072 seconds)


.. _sphx_glr_download_auto_examples_pac_plot_pac_methods.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_pac_methods.py <plot_pac_methods.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_pac_methods.ipynb <plot_pac_methods.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_