text: """
  =========================================
  Compute source power using DICS beamfomer
  =========================================
  
  Compute a Dynamic Imaging of Coherent Sources (DICS) [1]_ filter from
  single-trial activity to estimate source power across a frequency band.
  
  References
  ----------
  .. [1] Gross et al. Dynamic imaging of coherent sources: Studying neural
         interactions in the human brain. PNAS (2001) vol. 98 (2) pp. 694-699
  """
  # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
  #         Roman Goj <roman.goj@gmail.com>
  #         Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  import numpy as np
  import mne
  from mne.datasets import sample
  from mne.time_frequency import csd_morlet
  from mne.beamformer import make_dics, apply_dics_csd
  
  print(__doc__)
  
  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'
  fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
  subjects_dir = data_path + '/subjects'
  
  ###############################################################################
  # Reading the raw data:
  raw = mne.io.read_raw_fif(raw_fname)
  raw.info['bads'] = ['MEG 2443']  # 1 bad MEG channel
  
  # Set picks
  picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
                         stim=False, exclude='bads')
  
  # Read epochs
  event_id, tmin, tmax = 1, -0.2, 0.5
  events = mne.read_events(event_fname)
  epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                      picks=picks, baseline=(None, 0), preload=True,
                      reject=dict(grad=4000e-13, mag=4e-12))
  evoked = epochs.average()
  
  # Read forward operator
  forward = mne.read_forward_solution(fname_fwd)
  
  ###############################################################################
  # Computing the cross-spectral density matrix at 4 evenly spaced frequencies
  # from 6 to 10 Hz. We use a decim value of 20 to speed up the computation in
  # this example at the loss of accuracy.
  #
  # .. warning:: The use of several sensor types with the DICS beamformer is
  #              not heavily tested yet. Here we use verbose='error' to
  #              suppress a warning along these lines.
  csd = csd_morlet(epochs, tmin=0, tmax=0.5, decim=20,
                   frequencies=np.linspace(6, 10, 4),
                   n_cycles=2.5)  # short signals, must live with few cycles
  
  # Compute DICS spatial filter and estimate source power.
  filters = make_dics(epochs.info, forward, csd, reg=0.5, verbose='error')
  print(filters)
  stc, freqs = apply_dics_csd(csd, filters)
  
  message = 'DICS source power in the 8-12 Hz frequency band'
  brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir,
                   time_label=message)
  