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

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

.. _sphx_glr_auto_examples_misc_plot_itc.py:


================================================
Compute and plot the Inter-Trial Coherence (ITC)
================================================

This example illustrate how to compute and plot the Inter-Trial Coherence
(ITC). The ITC can be used to inspect if phases are aligned across trials or
said differently, it provides a measure of the consistency across trials.


.. code-block:: default

    import numpy as np

    from tensorpac.utils import ITC, PSD

    import matplotlib.pyplot as plt







Generate a random data of shape (n_epochs, n_times)
##############################################################################
 the dataset used in this example is composed of pure sines and noise. All
 sines across epochs are going to have a unique frequency so that there is no
 synchronization between them except around 0 second


.. code-block:: default


    # Let's start by creating a random dataset
    n_epochs = 100  # number of trials
    n_pts = 1000    # number of time points
    sf = 512.       # sampling frequency
    f_min = 10      # minimum sine frequency
    f_max = 15      # maximum sine frequency

    # create sines
    time = np.linspace(-n_pts / 2, n_pts / 2, n_pts) / sf
    freqs = np.linspace(f_min, f_max, n_epochs)
    data = np.sin(2 * np.pi * freqs.reshape(-1, 1) * time.reshape(1, -1))
    data += .1 * np.random.rand(n_epochs, n_pts)

    # plot some trials and see how sines are synchronized around 0
    trials = np.linspace(0, n_epochs - 1, 10).astype(int)
    plt.figure(0)
    plt.plot(time, data[trials, :].T, alpha=.5)
    plt.xlabel('Time (seconds)'), plt.ylabel('Amplitude (V)')




.. image:: /auto_examples/misc/images/sphx_glr_plot_itc_001.png
    :class: sphx-glr-single-img




Compute and plot the Power Spectrum Density (PSD)
##############################################################################
 the PSD can also be used to inspect how frequencies are distributed across
 trials


.. code-block:: default


    psd = PSD(data, sf)
    plt.figure(1)
    psd.plot(f_max=30, confidence=None)




.. image:: /auto_examples/misc/images/sphx_glr_plot_itc_002.png
    :class: sphx-glr-single-img




Compute and plot the Inter-Trial Coherence (ITC)
##############################################################################
 finally, compute and plot the ITC
 sphinx_gallery_thumbnail_number = 3


.. code-block:: default


    edges = 10  # remove 10 points to remove edge effects due to filtering
    cycle = 6   # number of cycles to use to extract the phase

    plt.figure(2, figsize=(14, 4))

    # compute ITC for phases between [2, 30]Hz
    itc = ITC(data, sf, f_pha=[f_min, f_max], edges=edges, cycle=cycle, n_jobs=1)
    plt.subplot(121)
    itc.plot(times=time)
    plt.axvline(0, linestyle='--', color='black', lw=2)

    # compute ITC for phases between [2, 30]Hz with frequency steps
    itc = ITC(data, sf, f_pha=(2, 30, 1, .5), edges=edges, cycle=cycle, n_jobs=1)
    plt.subplot(122)
    itc.plot(times=time, cmap='viridis')
    plt.axvline(0, linestyle='--', color='white', lw=2)
    itc.show()



.. image:: /auto_examples/misc/images/sphx_glr_plot_itc_003.png
    :class: sphx-glr-single-img





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

   **Total running time of the script:** ( 0 minutes  3.965 seconds)


.. _sphx_glr_download_auto_examples_misc_plot_itc.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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