.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_auto_repair.py: =========================== Automatically repair epochs =========================== This example demonstrates how to use :mod:`autoreject` to automatically repair epochs. .. code-block:: default # Author: Mainak Jas # Denis A. Engemann # License: BSD (3-clause) Let us first define the parameters. `n_interpolates` are the :math:`\rho` values that we would like :mod:`autoreject` to try and `consensus_percs` are the :math:`\kappa` values that :mod:`autoreject` will try (see the `autoreject paper `_) for more information on these parameters). Epochs with more than :math:`\kappa * N` sensors (:math:`N` total sensors) bad are dropped. For the rest of the epochs, the worst :math:`\rho` bad sensors (as determined by channel-level thresholds) are interpolated. The exact values of these parameters are not preselected but learned from the data. If the number of bad sensors for a particular trial is less than :math:`\rho`, all the bad sensors are interpolated. .. code-block:: default import numpy as np n_interpolates = np.array([1, 4, 32]) consensus_percs = np.linspace(0, 1.0, 11) For the purposes of this example, we shall use the MNE sample dataset. Therefore, let us make some MNE related imports. .. code-block:: default import mne # noqa from mne.utils import check_random_state # noqa from mne.datasets import sample # noqa Now, we can import the class required for rejecting and repairing bad epochs. :func:`autoreject.compute_thresholds` is a callable which must be provided to the :class:`autoreject.AutoReject` class for computing the channel-level thresholds. .. code-block:: default from autoreject import (AutoReject, set_matplotlib_defaults) # noqa Let us now read in the raw `fif` file for MNE sample dataset. .. code-block:: default check_random_state(42) data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' raw = mne.io.read_raw_fif(raw_fname, preload=True) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Opening raw data file /Users/mainak/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Range : 6450 ... 48149 = 42.956 ... 320.665 secs Ready. Current compensation grade : 0 Reading 0 ... 41699 = 0.000 ... 277.709 secs... We can then read in the events .. code-block:: default event_fname = data_path + ('/MEG/sample/sample_audvis_filt-0-40_raw-' 'eve.fif') event_id = {'Auditory/Left': 1, 'Auditory/Right': 2} tmin, tmax = -0.2, 0.5 events = mne.read_events(event_fname) And pick MEG channels for repairing. Currently, :mod:`autoreject` can repair only one channel type at a time. .. code-block:: default raw.info['bads'] = [] picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=False, include=[], exclude=[]) Now, we can create epochs. The ``reject`` params will be set to ``None`` because we do not want epochs to be dropped when instantiating :class:`mne.Epochs`. .. code-block:: default raw.info['projs'] = list() # remove proj, don't proj while interpolating epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, 0), reject=None, verbose=False, detrend=0, preload=True) :class:`autoreject.AutoReject` internally does cross-validation to determine the optimal values :math:`\rho^{*}` and :math:`\kappa^{*}` Note that :class:`autoreject.AutoReject` by design supports multiple channels. If no picks are passed, separate solutions will be computed for each channel type and internally combined. This then readily supports cleaning unseen epochs from the different channel types used during fit. Here we only use a subset of channels to save time. .. code-block:: default ar = AutoReject(n_interpolates, consensus_percs, picks=picks, thresh_method='random_search', random_state=42) # Note that fitting and transforming can be done on different compatible # portions of data if needed. ar.fit(epochs['Auditory/Left']) epochs_clean = ar.transform(epochs['Auditory/Left']) evoked_clean = epochs_clean.average() evoked = epochs['Auditory/Left'].average() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 86 coil definitions read Running autoreject on ch_type=grad [ ] 0.00% Creating augmented epochs | [ ] 0.49% Creating augmented epochs / [ ] 0.98% Creating augmented epochs - [ ] 1.47% Creating augmented epochs \ [ ] 1.96% Creating augmented epochs | [ ] 2.45% Creating augmented epochs / [. ] 2.94% Creating augmented epochs - [. ] 3.43% Creating augmented epochs \ [. ] 3.92% Creating augmented epochs | [. ] 4.41% Creating augmented epochs / [. ] 4.90% Creating augmented epochs - [.. ] 5.39% Creating augmented epochs \ [.. ] 5.88% Creating augmented epochs | [.. ] 6.37% Creating augmented epochs / [.. ] 6.86% Creating augmented epochs - 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[....................................... ] 98.61% Repairing epochs \ [........................................] 100.00% Repairing epochs | [ ] 0.00% n_interp | [ ] 0.00% Repairing epochs | [ ] 1.39% Repairing epochs / [. ] 2.78% Repairing epochs - [. ] 4.17% Repairing epochs \ [.. ] 5.56% Repairing epochs | [.. ] 6.94% Repairing epochs / [... ] 8.33% Repairing epochs - [... ] 9.72% Repairing epochs \ [.... ] 11.11% Repairing epochs | [..... ] 12.50% Repairing epochs / [..... ] 13.89% Repairing epochs - [...... ] 15.28% Repairing epochs \ [...... ] 16.67% Repairing epochs | [....... ] 18.06% Repairing epochs / [....... ] 19.44% Repairing epochs - [........ ] 20.83% Repairing epochs \ [........ ] 22.22% Repairing epochs | [......... ] 23.61% Repairing epochs / [.......... ] 25.00% Repairing epochs - [.......... ] 26.39% Repairing epochs \ [........... ] 27.78% Repairing epochs | [........... ] 29.17% Repairing epochs / [............ ] 30.56% Repairing epochs - [............ ] 31.94% Repairing epochs \ [............. ] 33.33% Repairing epochs | [............. ] 34.72% Repairing epochs / [.............. ] 36.11% Repairing epochs - 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[............................ ] 70.83% Repairing epochs \ [............................ ] 72.22% Repairing epochs | [............................. ] 73.61% Repairing epochs / [.............................. ] 75.00% Repairing epochs - [.............................. ] 76.39% Repairing epochs \ [............................... ] 77.78% Repairing epochs | [............................... ] 79.17% Repairing epochs / [................................ ] 80.56% Repairing epochs - [................................ ] 81.94% Repairing epochs \ [................................. ] 83.33% Repairing epochs | [................................. ] 84.72% Repairing epochs / [.................................. ] 86.11% Repairing epochs - [................................... ] 87.50% Repairing epochs \ [................................... ] 88.89% Repairing epochs | [.................................... ] 90.28% Repairing epochs / [.................................... ] 91.67% Repairing epochs - [..................................... ] 93.06% Repairing epochs \ [..................................... ] 94.44% Repairing epochs | [...................................... ] 95.83% Repairing epochs / [...................................... ] 97.22% Repairing epochs - [....................................... ] 98.61% Repairing epochs \ [........................................] 100.00% Repairing epochs | [ ] 0.00% Fold | [.... ] 10.00% Fold / [........ ] 20.00% Fold - [............ ] 30.00% Fold \ [................ ] 40.00% Fold | [.................... ] 50.00% Fold / [........................ ] 60.00% Fold - [............................ ] 70.00% Fold \ [................................ ] 80.00% Fold | [.................................... ] 90.00% Fold / [........................................] 100.00% Fold - [............. ] 33.33% n_interp / [ ] 0.00% Repairing epochs | [ ] 1.39% Repairing epochs / [. ] 2.78% Repairing epochs - [. ] 4.17% Repairing epochs \ [.. ] 5.56% Repairing epochs | [.. ] 6.94% Repairing epochs / [... ] 8.33% Repairing epochs - [... ] 9.72% Repairing epochs \ [.... ] 11.11% Repairing epochs | [..... ] 12.50% Repairing epochs / [..... ] 13.89% Repairing epochs - [...... ] 15.28% Repairing epochs \ [...... ] 16.67% Repairing epochs | [....... ] 18.06% Repairing epochs / [....... ] 19.44% Repairing epochs - [........ ] 20.83% Repairing epochs \ [........ ] 22.22% Repairing epochs | [......... ] 23.61% Repairing epochs / [.......... ] 25.00% Repairing epochs - [.......... ] 26.39% Repairing epochs \ [........... ] 27.78% Repairing epochs | [........... ] 29.17% Repairing epochs / [............ ] 30.56% Repairing epochs - [............ ] 31.94% Repairing epochs \ [............. ] 33.33% Repairing epochs | [............. ] 34.72% Repairing epochs / [.............. ] 36.11% Repairing epochs - [............... ] 37.50% Repairing epochs \ [............... ] 38.89% Repairing epochs | [................ ] 40.28% Repairing epochs / [................ ] 41.67% Repairing epochs - [................. ] 43.06% Repairing epochs \ [................. ] 44.44% Repairing epochs | [.................. ] 45.83% Repairing epochs / [.................. ] 47.22% Repairing epochs - [................... ] 48.61% Repairing epochs \ [.................... ] 50.00% Repairing epochs | [.................... ] 51.39% Repairing epochs / [..................... ] 52.78% Repairing epochs - [..................... ] 54.17% Repairing epochs \ [...................... ] 55.56% Repairing epochs | [...................... ] 56.94% Repairing epochs / [....................... ] 58.33% Repairing epochs - [....................... ] 59.72% Repairing epochs \ [........................ ] 61.11% Repairing epochs | [......................... ] 62.50% Repairing epochs / [......................... ] 63.89% Repairing epochs - [.......................... ] 65.28% Repairing epochs \ [.......................... ] 66.67% Repairing epochs | [........................... ] 68.06% Repairing epochs / [........................... ] 69.44% Repairing epochs - [............................ ] 70.83% Repairing epochs \ [............................ ] 72.22% Repairing epochs | [............................. ] 73.61% Repairing epochs / [.............................. ] 75.00% Repairing epochs - [.............................. ] 76.39% Repairing epochs \ [............................... ] 77.78% Repairing epochs | [............................... ] 79.17% Repairing epochs / [................................ ] 80.56% Repairing epochs - [................................ ] 81.94% Repairing epochs \ [................................. ] 83.33% Repairing epochs | [................................. ] 84.72% Repairing epochs / [.................................. ] 86.11% Repairing epochs - [................................... ] 87.50% Repairing epochs \ [................................... ] 88.89% Repairing epochs | [.................................... ] 90.28% Repairing epochs / [.................................... ] 91.67% Repairing epochs - [..................................... ] 93.06% Repairing epochs \ [..................................... ] 94.44% Repairing epochs | [...................................... ] 95.83% Repairing epochs / [...................................... ] 97.22% Repairing epochs - [....................................... ] 98.61% Repairing epochs \ [........................................] 100.00% Repairing epochs | [ ] 0.00% Fold | [.... ] 10.00% Fold / [........ ] 20.00% Fold - [............ ] 30.00% Fold \ [................ ] 40.00% Fold | [.................... ] 50.00% Fold / [........................ ] 60.00% Fold - [............................ ] 70.00% Fold \ [................................ ] 80.00% Fold | [.................................... ] 90.00% Fold / [........................................] 100.00% Fold - [.......................... ] 66.67% n_interp - [ ] 0.00% Repairing epochs | [ ] 1.39% Repairing epochs / [. ] 2.78% Repairing epochs - [. ] 4.17% Repairing epochs \ [.. ] 5.56% Repairing epochs | [.. ] 6.94% Repairing epochs / [... ] 8.33% Repairing epochs - [... ] 9.72% Repairing epochs \ [.... ] 11.11% Repairing epochs | [..... ] 12.50% Repairing epochs / [..... ] 13.89% Repairing epochs - [...... ] 15.28% Repairing epochs \ [...... ] 16.67% Repairing epochs | [....... ] 18.06% Repairing epochs / [....... ] 19.44% Repairing epochs - [........ ] 20.83% Repairing epochs \ [........ ] 22.22% Repairing epochs | [......... ] 23.61% Repairing epochs / [.......... ] 25.00% Repairing epochs - [.......... ] 26.39% Repairing epochs \ [........... ] 27.78% Repairing epochs | [........... ] 29.17% Repairing epochs / [............ ] 30.56% Repairing epochs - [............ ] 31.94% Repairing epochs \ [............. ] 33.33% Repairing epochs | [............. ] 34.72% Repairing epochs / [.............. ] 36.11% Repairing epochs - [............... ] 37.50% Repairing epochs \ [............... ] 38.89% Repairing epochs | [................ ] 40.28% Repairing epochs / [................ ] 41.67% Repairing epochs - [................. ] 43.06% Repairing epochs \ [................. ] 44.44% Repairing epochs | [.................. ] 45.83% Repairing epochs / [.................. ] 47.22% Repairing epochs - [................... ] 48.61% Repairing epochs \ [.................... ] 50.00% Repairing epochs | [.................... ] 51.39% Repairing epochs / [..................... ] 52.78% Repairing epochs - [..................... ] 54.17% Repairing epochs \ [...................... ] 55.56% Repairing epochs | [...................... ] 56.94% Repairing epochs / [....................... ] 58.33% Repairing epochs - [....................... ] 59.72% Repairing epochs \ [........................ ] 61.11% Repairing epochs | [......................... ] 62.50% Repairing epochs / [......................... ] 63.89% Repairing epochs - [.......................... ] 65.28% Repairing epochs \ [.......................... ] 66.67% Repairing epochs | [........................... ] 68.06% Repairing epochs / [........................... ] 69.44% Repairing epochs - [............................ ] 70.83% Repairing epochs \ [............................ ] 72.22% Repairing epochs | [............................. ] 73.61% Repairing epochs / [.............................. ] 75.00% Repairing epochs - [.............................. ] 76.39% Repairing epochs \ [............................... ] 77.78% Repairing epochs | [............................... ] 79.17% Repairing epochs / [................................ ] 80.56% Repairing epochs - [................................ ] 81.94% Repairing epochs \ [................................. ] 83.33% Repairing epochs | [................................. ] 84.72% Repairing epochs / [.................................. ] 86.11% Repairing epochs - [................................... ] 87.50% Repairing epochs \ [................................... ] 88.89% Repairing epochs | [.................................... ] 90.28% Repairing epochs / [.................................... ] 91.67% Repairing epochs - [..................................... ] 93.06% Repairing epochs \ [..................................... ] 94.44% Repairing epochs | [...................................... ] 95.83% Repairing epochs / [...................................... ] 97.22% Repairing epochs - [....................................... ] 98.61% Repairing epochs \ [........................................] 100.00% Repairing epochs | [ ] 0.00% Fold | [.... ] 10.00% Fold / [........ ] 20.00% Fold - [............ ] 30.00% Fold \ [................ ] 40.00% Fold | [.................... ] 50.00% Fold / [........................ ] 60.00% Fold - [............................ ] 70.00% Fold \ [................................ ] 80.00% Fold | [.................................... ] 90.00% Fold / [........................................] 100.00% Fold - [........................................] 100.00% n_interp \ Estimated consensus=0.50 and n_interpolate=4 [ ] 0.00% Repairing epochs | [ ] 1.39% Repairing epochs / [. ] 2.78% Repairing epochs - [. ] 4.17% Repairing epochs \ [.. ] 5.56% Repairing epochs | [.. ] 6.94% Repairing epochs / [... ] 8.33% Repairing epochs - [... ] 9.72% Repairing epochs \ [.... ] 11.11% Repairing epochs | [..... ] 12.50% Repairing epochs / [..... ] 13.89% Repairing epochs - [...... ] 15.28% Repairing epochs \ [...... ] 16.67% Repairing epochs | [....... ] 18.06% Repairing epochs / [....... ] 19.44% Repairing epochs - [........ ] 20.83% Repairing epochs \ [........ ] 22.22% Repairing epochs | [......... ] 23.61% Repairing epochs / [.......... ] 25.00% Repairing epochs - [.......... ] 26.39% Repairing epochs \ [........... ] 27.78% Repairing epochs | [........... ] 29.17% Repairing epochs / [............ ] 30.56% Repairing epochs - [............ ] 31.94% Repairing epochs \ [............. ] 33.33% Repairing epochs | [............. ] 34.72% Repairing epochs / [.............. ] 36.11% Repairing epochs - [............... ] 37.50% Repairing epochs \ [............... ] 38.89% Repairing epochs | [................ ] 40.28% Repairing epochs / [................ ] 41.67% Repairing epochs - [................. ] 43.06% Repairing epochs \ [................. ] 44.44% Repairing epochs | [.................. ] 45.83% Repairing epochs / [.................. ] 47.22% Repairing epochs - [................... ] 48.61% Repairing epochs \ [.................... ] 50.00% Repairing epochs | [.................... ] 51.39% Repairing epochs / [..................... ] 52.78% Repairing epochs - [..................... ] 54.17% Repairing epochs \ [...................... ] 55.56% Repairing epochs | [...................... ] 56.94% Repairing epochs / [....................... ] 58.33% Repairing epochs - [....................... ] 59.72% Repairing epochs \ [........................ ] 61.11% Repairing epochs | [......................... ] 62.50% Repairing epochs / [......................... ] 63.89% Repairing epochs - [.......................... ] 65.28% Repairing epochs \ [.......................... ] 66.67% Repairing epochs | [........................... ] 68.06% Repairing epochs / [........................... ] 69.44% Repairing epochs - [............................ ] 70.83% Repairing epochs \ [............................ ] 72.22% Repairing epochs | [............................. ] 73.61% Repairing epochs / [.............................. ] 75.00% Repairing epochs - [.............................. ] 76.39% Repairing epochs \ [............................... ] 77.78% Repairing epochs | [............................... ] 79.17% Repairing epochs / [................................ ] 80.56% Repairing epochs - [................................ ] 81.94% Repairing epochs \ [................................. ] 83.33% Repairing epochs | [................................. ] 84.72% Repairing epochs / [.................................. ] 86.11% Repairing epochs - [................................... ] 87.50% Repairing epochs \ [................................... ] 88.89% Repairing epochs | [.................................... ] 90.28% Repairing epochs / [.................................... ] 91.67% Repairing epochs - [..................................... ] 93.06% Repairing epochs \ [..................................... ] 94.44% Repairing epochs | [...................................... ] 95.83% Repairing epochs / [...................................... ] 97.22% Repairing epochs - [....................................... ] 98.61% Repairing epochs \ [........................................] 100.00% Repairing epochs |No bad epochs were found for your data. Returning a copy of the data you wanted to clean. Interpolation may have been done. Now, we will manually mark the bad channels just for plotting. .. code-block:: default evoked.info['bads'] = ['MEG 2443'] evoked_clean.info['bads'] = ['MEG 2443'] Let us plot the results. .. code-block:: default import matplotlib.pyplot as plt # noqa set_matplotlib_defaults(plt) fig, axes = plt.subplots(2, 1, figsize=(6, 6)) for ax in axes: ax.tick_params(axis='x', which='both', bottom='off', top='off') ax.tick_params(axis='y', which='both', left='off', right='off') ylim = dict(grad=(-170, 200)) evoked.pick_types(meg='grad', exclude=[]) evoked.plot(exclude=[], axes=axes[0], ylim=ylim, show=False) axes[0].set_title('Before autoreject') evoked_clean.pick_types(meg='grad', exclude=[]) evoked_clean.plot(exclude=[], axes=axes[1], ylim=ylim) axes[1].set_title('After autoreject') plt.tight_layout() .. image:: /auto_examples/images/sphx_glr_plot_auto_repair_001.png :class: sphx-glr-single-img To top things up, we can also visualize the bad sensors for each trial using a heatmap. .. code-block:: default ar.get_reject_log(epochs['Auditory/Left']).plot() .. image:: /auto_examples/images/sphx_glr_plot_auto_repair_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /Users/mainak/Documents/github_repos/autoreject/autoreject/autoreject.py:1268: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 44.328 seconds) .. _sphx_glr_download_auto_examples_plot_auto_repair.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_auto_repair.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_auto_repair.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_