old_contents: # -*- coding: utf-8 -*-
  """
  .. _ex-brainstorm-raw:
  
  =====================================
  Brainstorm raw (median nerve) dataset
  =====================================
  
  Here we compute the evoked from raw for the Brainstorm
  tutorial dataset. For comparison, see :footcite:`TadelEtAl2011` and:
  
      https://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf
  """
  
  # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
  #
  # License: BSD-3-Clause
  
  # %%
  
  import numpy as np
  
  import mne
  from mne.datasets.brainstorm import bst_raw
  from mne.io import read_raw_ctf
  
  print(__doc__)
  
  tmin, tmax, event_id = -0.1, 0.3, 2  # take right-hand somato
  reject = dict(mag=4e-12, eog=250e-6)
  
  data_path = bst_raw.data_path()
  
  raw_path = (data_path / 'MEG' / 'bst_raw' /
              'subj001_somatosensory_20111109_01_AUX-f.ds')
  # Here we crop to half the length to save memory
  raw = read_raw_ctf(raw_path).crop(0, 180).load_data()
  raw.plot()
  
  # set EOG channel
  raw.set_channel_types({'EEG058': 'eog'})
  raw.set_eeg_reference('average', projection=True)
  
  # show power line interference and remove it
  raw.plot_psd(tmax=60., average=False)
  raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
  
  events = mne.find_events(raw, stim_channel='UPPT001')
  
  # pick MEG channels
  picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
                         exclude='bads')
  
  # Compute epochs
  epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), reject=reject, preload=False)
  
  # compute evoked
  evoked = epochs.average()
  
  # remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline
  evoked.add_proj(mne.compute_proj_evoked(evoked.copy().crop(tmax=0)))
  evoked.apply_proj()
  
  # fix stim artifact
  mne.preprocessing.fix_stim_artifact(evoked)
  
  # correct delays due to hardware (stim artifact is at 4 ms)
  evoked.shift_time(-0.004)
  
  # plot the result
  evoked.plot(time_unit='s')
  
  # show topomaps
  evoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]),
                      time_unit='s')
  
  # %%
  # References
  # ----------
  # .. footbibliography::
  
new_contents: # -*- coding: utf-8 -*-
  """
  .. _ex-brainstorm-raw:
  
  =====================================
  Brainstorm raw (median nerve) dataset
  =====================================
  
  Here we compute the evoked from raw for the Brainstorm
  tutorial dataset. For comparison, see :footcite:`TadelEtAl2011` and:
  
      https://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf
  """
  
  # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
  #
  # License: BSD-3-Clause
  
  # %%
  
  import numpy as np
  
  import mne
  from mne.datasets.brainstorm import bst_raw
  from mne.io import read_raw_ctf
  
  print(__doc__)
  
  tmin, tmax, event_id = -0.1, 0.3, 2  # take right-hand somato
  reject = dict(mag=4e-12, eog=250e-6)
  
  data_path = bst_raw.data_path()
  
  raw_path = (data_path / 'MEG' / 'bst_raw' /
              'subj001_somatosensory_20111109_01_AUX-f.ds')
  # Here we crop to half the length to save memory
  raw = read_raw_ctf(raw_path).crop(0, 120).load_data()
  raw.plot()
  
  # set EOG channel
  raw.set_channel_types({'EEG058': 'eog'})
  raw.set_eeg_reference('average', projection=True)
  
  # show power line interference and remove it
  raw.plot_psd(tmax=60., average=False)
  raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
  
  events = mne.find_events(raw, stim_channel='UPPT001')
  
  # pick MEG channels
  picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
                         exclude='bads')
  
  # Compute epochs
  epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                      baseline=(None, 0), reject=reject, preload=False)
  
  # compute evoked
  evoked = epochs.average()
  
  # remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline
  evoked.add_proj(mne.compute_proj_evoked(evoked.copy().crop(tmax=0)))
  evoked.apply_proj()
  
  # fix stim artifact
  mne.preprocessing.fix_stim_artifact(evoked)
  
  # correct delays due to hardware (stim artifact is at 4 ms)
  evoked.shift_time(-0.004)
  
  # plot the result
  evoked.plot(time_unit='s')
  
  # show topomaps
  evoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]),
                      time_unit='s')
  
  # %%
  # References
  # ----------
  # .. footbibliography::
  
subject: reduce memory [circle deploy] (#11199)