old_contents: """
  =============================================
  Compute the power spectral density of epochs
  =============================================
  
  This script shows how to compute the power spectral density (PSD)
  of measurements on epochs. It also shows how to plot its spatial
  distribution.
  """
  # Authors: Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import mne
  from mne import io
  from mne.datasets import sample
  
  print(__doc__)
  
  ###############################################################################
  # Set parameters
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
  event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
  
  # Setup for reading the raw data
  raw = io.Raw(raw_fname)
  events = mne.read_events(event_fname)
  
  tmin, tmax, event_id = -1., 1., 1
  raw.info['bads'] += ['MEG 2443']  # bads
  
  epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                      proj=True, baseline=(None, 0), preload=True,
                      reject=dict(grad=4000e-13, eog=150e-6))
  
  # Let's first check out all channel types by averaging across epochs.
  epochs.plot_psd(fmin=2, fmax=200)
  
  # picks MEG gradiometers
  picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
                         stim=False, exclude='bads')
  
  # Now let's take a look at the spatial distributions of the psd.
  epochs.plot_psd_topomap(picks=picks, normalize=True)
  
new_contents: """
  =============================================
  Compute the power spectral density of epochs
  =============================================
  
  This script shows how to compute the power spectral density (PSD)
  of measurements on epochs. It also shows how to plot its spatial
  distribution.
  """
  # Authors: Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import mne
  from mne import io
  from mne.datasets import sample
  
  print(__doc__)
  
  ###############################################################################
  # Set parameters
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
  event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
  
  # Setup for reading the raw data
  raw = io.Raw(raw_fname)
  events = mne.read_events(event_fname)
  
  tmin, tmax, event_id = -1., 1., 1
  raw.info['bads'] += ['MEG 2443']  # bads
  
  epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
                      proj=True, baseline=(None, 0), preload=True,
                      reject=dict(grad=4000e-13, eog=150e-6))
  
  # Let's first check out all channel types by averaging across epochs.
  epochs.plot_psd(fmin=2, fmax=200)
  
  # picks MEG gradiometers
  picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=False,
                         stim=False, exclude='bads')
  
  # Now let's take a look at the spatial distributions of the psd.
  epochs.plot_psd_topomap(picks=picks, normalize=True)
  
subject: fix grads