.. 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

        :ref:`Go to the end <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 7-30

.. code-block:: Python

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

    from time import time

    from matplotlib import pyplot as plt
    import seaborn as sns

    import mne
    from mne import io
    from mne.datasets import sample
    from sklearn.pipeline import make_pipeline
    from sklearn.linear_model import LogisticRegression

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

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


.. GENERATED FROM PYTHON SOURCE LINES 31-33

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

.. GENERATED FROM PYTHON SOURCE LINES 33-58

.. code-block:: Python


    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(copy=False)[::5]

    n_perms = 100

.. GENERATED FROM PYTHON SOURCE LINES 59-61

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

.. GENERATED FROM PYTHON SOURCE LINES 61-76

.. code-block:: Python


    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 77-79

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

.. GENERATED FROM PYTHON SOURCE LINES 79-94

.. code-block:: Python


    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 95-97

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

.. GENERATED FROM PYTHON SOURCE LINES 97-112

.. code-block:: Python


    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 113-115

Classification based permutation test
-------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 115-131

.. code-block:: Python


    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()


.. _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-jupyter

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

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

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


.. only:: html

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

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