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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_stats_oneWay_Manova_ERP.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_stats_oneWay_Manova_ERP.py:


====================================================================
Manova for ERP data
====================================================================

.. GENERATED FROM PYTHON SOURCE LINES 7-31

.. code-block:: default

    # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
    #
    # License: BSD (3-clause)

    import seaborn as sns

    from time import time
    from matplotlib import pyplot as plt

    import mne
    from mne import io
    from mne.datasets import sample

    from pyriemann.stats import PermutationDistance, PermutationModel
    from pyriemann.estimation import XdawnCovariances
    from pyriemann.tangentspace import TangentSpace

    from sklearn.pipeline import make_pipeline
    from sklearn.linear_model import LogisticRegression

    print(__doc__)
    sns.set_style('whitegrid')
    data_path = sample.data_path()


.. GENERATED FROM PYTHON SOURCE LINES 32-34

Set parameters and read data
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 34-58

.. code-block:: default

    raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
    event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
    tmin, tmax = -0., 1
    event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)

    # Setup for reading the raw data
    raw = io.Raw(raw_fname, preload=True)
    raw.filter(2, None, method='iir')  # replace baselining with high-pass
    events = mne.read_events(event_fname)

    raw.info['bads'] = ['MEG 2443']  # set bad channels
    picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=False,
                           exclude='bads')

    # Read epochs
    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
                        picks=picks, baseline=None, preload=True, verbose=False)

    labels = epochs.events[::5, -1]

    # get epochs
    epochs_data = epochs.get_data()[::5]

    n_perms = 100

.. GENERATED FROM PYTHON SOURCE LINES 59-61

Pairwise distance based permutation test
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 61-75

.. code-block:: default

    t_init = time()
    p_test = PermutationDistance(n_perms, metric='riemann', mode='pairwise',
                                 estimator=XdawnCovariances(2))
    p, F = p_test.test(epochs_data, labels)
    duration = time() - t_init

    fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
    p_test.plot(nbins=10, axes=axes)
    plt.title('Pairwise distance - %.2f sec.' % duration)
    print('p-value: %.3f' % p)
    sns.despine()
    plt.tight_layout()
    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 76-78

t-test distance based permutation test
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 78-93

.. code-block:: default


    t_init = time()
    p_test = PermutationDistance(n_perms, metric='riemann', mode='ttest',
                                 estimator=XdawnCovariances(2))
    p, F = p_test.test(epochs_data, labels)
    duration = time() - t_init

    fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
    p_test.plot(nbins=10, axes=axes)
    plt.title('Pairwise distance - %.2f sec.' % duration)
    print('p-value: %.3f' % p)
    sns.despine()
    plt.tight_layout()
    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 94-96

F-test distance based permutation test
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 96-111

.. code-block:: default


    t_init = time()
    p_test = PermutationDistance(n_perms, metric='riemann', mode='ftest',
                                 estimator=XdawnCovariances(2))
    p, F = p_test.test(epochs_data, labels)
    duration = time() - t_init

    fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
    p_test.plot(nbins=10, axes=axes)
    plt.title('Pairwise distance - %.2f sec.' % duration)
    print('p-value: %.3f' % p)
    sns.despine()
    plt.tight_layout()
    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 112-114

Classification based permutation test
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 114-130

.. code-block:: default


    clf = make_pipeline(XdawnCovariances(2), TangentSpace('logeuclid'),
                        LogisticRegression())

    t_init = time()
    p_test = PermutationModel(n_perms, model=clf, cv=3)
    p, F = p_test.test(epochs_data, labels)
    duration = time() - t_init

    fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
    p_test.plot(nbins=10, axes=axes)
    plt.title('Classification - %.2f sec.' % duration)
    print('p-value: %.3f' % p)
    sns.despine()
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_stats_oneWay_Manova_ERP.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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