old_contents: """
  ====================================================================
  Extract epochs for multiple conditions, save evoked response to disk
  ====================================================================
  
  This script shows how to read the epochs for multiple conditions from
  a raw file given a list of events. The epochs are averaged to produce
  evoked data and then saved to disk.
  
  """
  # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
  #          Eric Larson <larson.eric.d@gmail.com>
  #          Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  print(__doc__)
  
  import mne
  from mne import io
  from mne.datasets import sample
  from mne.epochs import combine_event_ids
  data_path = sample.data_path()
  
  ###############################################################################
  # Set parameters
  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'
  event_ids = {'AudL': 1, 'AudR': 2, 'VisL': 3, 'VisR': 4}
  tmin = -0.2
  tmax = 0.5
  
  #   Setup for reading the raw data
  raw = io.Raw(raw_fname)
  events = mne.read_events(event_fname)
  
  #   Set up pick list: EEG + STI 014 - bad channels (modify to your needs)
  include = []  # or stim channels ['STI 014']
  raw.info['bads'] += ['EEG 053']  # bads + 1 more
  
  # pick EEG channels
  picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True,
                         include=include, exclude='bads')
  # Read epochs
  epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks,
                      baseline=(None, 0), reject=dict(eeg=80e-6, eog=150e-6))
  # Let's equalize the trial counts in each condition
  epochs.equalize_event_counts(['AudL', 'AudR', 'VisL', 'VisR'], copy=False)
  # Now let's combine some conditions
  combine_event_ids(epochs, ['AudL', 'AudR'], {'Auditory': 12}, copy=False)
  combine_event_ids(epochs, ['VisL', 'VisR'], {'Visual': 34}, copy=False)
  
  # average epochs and get Evoked datasets
  evokeds = [epochs[cond].average() for cond in ['Auditory', 'Visual']]
  
  # save evoked data to disk
  io.write_evokeds('sample_auditory_and_visual_eeg-ave.fif', evokeds)
  
  ###############################################################################
  # View evoked response
  import matplotlib.pyplot as plt
  plt.clf()
  ax = plt.subplot(2, 1, 1)
  evokeds[0].plot(axes=ax)
  plt.title('EEG evoked potential, auditory trials')
  plt.ylabel('Potential (uV)')
  ax = plt.subplot(2, 1, 2)
  evokeds[1].plot(axes=ax)
  plt.title('EEG evoked potential, visual trials')
  plt.ylabel('Potential (uV)')
  plt.show()
  
new_contents: """
  ====================================================================
  Extract epochs for multiple conditions, save evoked response to disk
  ====================================================================
  
  This script shows how to read the epochs for multiple conditions from
  a raw file given a list of events. The epochs are averaged to produce
  evoked data and then saved to disk.
  
  """
  # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
  #          Eric Larson <larson.eric.d@gmail.com>
  #          Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  print(__doc__)
  
  import mne
  from mne import io
  from mne.datasets import sample
  from mne.epochs import combine_event_ids
  data_path = sample.data_path()
  
  ###############################################################################
  # Set parameters
  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'
  event_ids = {'AudL': 1, 'AudR': 2, 'VisL': 3, 'VisR': 4}
  tmin = -0.2
  tmax = 0.5
  
  #   Setup for reading the raw data
  raw = io.Raw(raw_fname)
  events = mne.read_events(event_fname)
  
  #   Set up pick list: EEG + STI 014 - bad channels (modify to your needs)
  include = []  # or stim channels ['STI 014']
  raw.info['bads'] += ['EEG 053']  # bads + 1 more
  
  # pick EEG channels
  picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=True,
                         include=include, exclude='bads')
  # Read epochs
  epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks,
                      baseline=(None, 0), reject=dict(eeg=80e-6, eog=150e-6))
  # Let's equalize the trial counts in each condition
  epochs.equalize_event_counts(['AudL', 'AudR', 'VisL', 'VisR'], copy=False)
  # Now let's combine some conditions
  combine_event_ids(epochs, ['AudL', 'AudR'], {'Auditory': 12}, copy=False)
  combine_event_ids(epochs, ['VisL', 'VisR'], {'Visual': 34}, copy=False)
  
  # average epochs and get Evoked datasets
  evokeds = [epochs[cond].average() for cond in ['Auditory', 'Visual']]
  
  # save evoked data to disk
  mne.write_evokeds('sample_auditory_and_visual_eeg-ave.fif', evokeds)
  
  ###############################################################################
  # View evoked response
  import matplotlib.pyplot as plt
  plt.clf()
  ax = plt.subplot(2, 1, 1)
  evokeds[0].plot(axes=ax)
  plt.title('EEG evoked potential, auditory trials')
  plt.ylabel('Potential (uV)')
  ax = plt.subplot(2, 1, 2)
  evokeds[1].plot(axes=ax)
  plt.title('EEG evoked potential, visual trials')
  plt.ylabel('Potential (uV)')
  plt.show()
  
subject: FIX : plot_from_raw_to_multiple_epochs_to_evoked example