.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/stats/plot_oneWay_Manova_time.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_stats_plot_oneWay_Manova_time.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_stats_plot_oneWay_Manova_time.py:


====================================================================
One Way manova time
====================================================================

One way manova to compare Left vs Right in time.

.. GENERATED FROM PYTHON SOURCE LINES 8-24

.. code-block:: Python

    from time import time

    import numpy as np
    from pylab import plt
    import seaborn as sns

    from mne import Epochs, pick_types, events_from_annotations
    from mne.io import concatenate_raws
    from mne.io.edf import read_raw_edf
    from mne.datasets import eegbci

    from pyriemann.stats import PermutationDistance
    from pyriemann.estimation import Covariances

    sns.set_style('whitegrid')








.. GENERATED FROM PYTHON SOURCE LINES 25-27

Set parameters and read data
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 27-63

.. code-block:: Python


    # avoid classification of evoked responses by using epochs that start 1s after
    # cue onset.
    tmin, tmax = -2., 6.
    event_id = dict(hands=2, feet=3)
    subject = 1
    runs = [6, 10, 14]  # motor imagery: hands vs feet

    raw_files = [
        read_raw_edf(f, preload=True, verbose=False)
        for f in eegbci.load_data(subject, runs)
    ]
    raw = concatenate_raws(raw_files)

    events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))
    picks = pick_types(
        raw.info, meg=False, eeg=True, stim=False, eog=False, exclude='bads')

    raw.filter(7., 35., method='iir', picks=picks)

    epochs = Epochs(
        raw,
        events,
        event_id,
        tmin,
        tmax,
        proj=True,
        picks=picks,
        baseline=None,
        preload=True,
        verbose=False)
    labels = epochs.events[:, -1] - 2

    # get epochs
    epochs_data = epochs.get_data(copy=False)





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

 .. code-block:: none

    Used Annotations descriptions: ['T1', 'T2']
    Filtering raw data in 3 contiguous segments
    Setting up band-pass filter from 7 - 35 Hz

    IIR filter parameters
    ---------------------
    Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:
    - Filter order 16 (effective, after forward-backward)
    - Cutoffs at 7.00, 35.00 Hz: -6.02, -6.02 dB





.. GENERATED FROM PYTHON SOURCE LINES 64-66

Pairwise distance based permutation test
----------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 66-107

.. code-block:: Python


    covest = Covariances()

    Fs = 160
    window = 2 * Fs
    Nwindow = 20
    Ns = epochs_data.shape[2]
    step = int((Ns - window) / Nwindow)
    time_bins = range(0, Ns - window, step)

    pv = []
    Fv = []
    # For each frequency bin, estimate the stats
    t_init = time()
    for t in time_bins:
        covmats = covest.fit_transform(epochs_data[:, ::1, t:(t + window)])
        p_test = PermutationDistance(1000, metric='riemann', mode='pairwise')
        p, F = p_test.test(covmats, labels, verbose=False)
        pv.append(p)
        Fv.append(F[0])
    duration = time() - t_init
    # plot result
    fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
    sig = 0.05
    times = np.array(time_bins) / float(Fs) + tmin

    axes.plot(times, Fv, lw=2, c='k')
    plt.xlabel('Time (sec)')
    plt.ylabel('Score')

    a = np.where(np.diff(np.array(pv) < sig))[0]
    a = a.reshape(int(len(a) / 2), 2)
    st = (times[1] - times[0]) / 2.0
    for p in a:
        axes.axvspan(times[p[0]] - st, times[p[1]] + st, facecolor='g', alpha=0.5)
    axes.legend(['Score', 'p<%.2f' % sig])
    axes.set_title('Pairwise distance - %.1f sec.' % duration)

    sns.despine()
    plt.tight_layout()
    plt.show()



.. image-sg:: /auto_examples/stats/images/sphx_glr_plot_oneWay_Manova_time_001.png
   :alt: Pairwise distance - 10.7 sec.
   :srcset: /auto_examples/stats/images/sphx_glr_plot_oneWay_Manova_time_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 11.704 seconds)


.. _sphx_glr_download_auto_examples_stats_plot_oneWay_Manova_time.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

    .. container:: sphx-glr-download sphx-glr-download-python

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


.. only:: html

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

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