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

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

.. _sphx_glr_auto_examples_pac_plot_compare_filtering.py:


============================
Compare filtering properties
============================

Tensorpac provides two ways for extracting phase and amplitude :

* Using filtering followed by Hilbert transform.
* Using wavelets.



.. image:: /auto_examples/pac/images/sphx_glr_plot_compare_filtering_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Filtering with fir1 filter
    Filtering with wavelets





|


.. code-block:: default

    from __future__ import print_function
    import matplotlib.pyplot as plt
    from tensorpac import Pac
    from tensorpac.signals import pac_signals_wavelet
    plt.style.use('seaborn-paper')

    # First, we generate a dataset of signals artificially coupled between 10hz
    # and 100hz. By default, this dataset is organized as (ntrials, n_times) where
    # n_times is the number of time points.
    n_epochs = 5         # number of datasets
    n_times = 4000  # number of time points
    data, time = pac_signals_wavelet(f_pha=10, f_amp=100, noise=1.,
                                     n_epochs=n_epochs, n_times=n_times)

    # First, let's use the MVL, without any further correction by surrogates :
    p = Pac(idpac=(1, 0, 0), f_pha=(5, 14, 2, .3), f_amp=(80, 120, 2, 1),
            verbose=False)

    plt.figure(figsize=(18, 9))
    # Define several cycle options for the fir1 (eegfilt like) filter :
    print('Filtering with fir1 filter')
    for i, k in enumerate([(1, 3), (2, 4), (3, 6)]):
        p.cycle = k
        xpac = p.filterfit(1024, data, n_jobs=1)
        plt.subplot(2, 3, i + 1)
        p.comodulogram(xpac.mean(-1), title='Fir1 - cycle ' + str(k))

    # Define several wavelet width :
    p.dcomplex = 'wavelet'
    print('Filtering with wavelets')
    for i, k in enumerate([7, 9, 12]):
        p.width = k
        xpac = p.filterfit(1024, data)
        plt.subplot(2, 3, i + 4)
        p.comodulogram(xpac.mean(-1), title='Wavelet - width ' + str(k))

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  4.322 seconds)


.. _sphx_glr_download_auto_examples_pac_plot_compare_filtering.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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