old_contents: """
  ======================================================
  Compute source power spectral density (PSD) in a label
  ======================================================
  
  Returns an STC file containing the PSD (in dB) of each of the sources
  within a label.
  """
  # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #
  # License: BSD (3-clause)
  
  import matplotlib.pyplot as plt
  
  import mne
  from mne import io
  from mne.datasets import sample
  from mne.minimum_norm import read_inverse_operator, compute_source_psd
  
  print(__doc__)
  
  ###############################################################################
  # Set parameters
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
  fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
  fname_label = data_path + '/MEG/sample/labels/Aud-lh.label'
  
  # Setup for reading the raw data
  raw = io.read_raw_fif(raw_fname, verbose=False)
  events = mne.find_events(raw, stim_channel='STI 014')
  inverse_operator = read_inverse_operator(fname_inv)
  raw.info['bads'] = ['MEG 2443', 'EEG 053']
  
  # picks MEG gradiometers
  picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                         stim=False, exclude='bads')
  
  tmin, tmax = 0, 120  # use the first 120s of data
  fmin, fmax = 4, 100  # look at frequencies between 4 and 100Hz
  n_fft = 2048  # the FFT size (n_fft). Ideally a power of 2
  label = mne.read_label(fname_label)
  
  stc = compute_source_psd(raw, inverse_operator, lambda2=1. / 9., method="dSPM",
                           tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
                           pick_ori="normal", n_fft=n_fft, label=label,
                           dB=True)
  
  stc.save('psd_dSPM')
  
  ###############################################################################
  # View PSD of sources in label
  plt.plot(1e3 * stc.times, stc.data.T)
  plt.xlabel('Frequency (Hz)')
  plt.ylabel('PSD (dB)')
  plt.title('Source Power Spectrum (PSD)')
  plt.show()
  
new_contents: """
  ======================================================
  Compute source power spectral density (PSD) in a label
  ======================================================
  
  Returns an STC file containing the PSD (in dB) of each of the sources
  within a label.
  """
  # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #
  # License: BSD (3-clause)
  
  import matplotlib.pyplot as plt
  
  import mne
  from mne import io
  from mne.datasets import sample
  from mne.minimum_norm import read_inverse_operator, compute_source_psd
  
  print(__doc__)
  
  ###############################################################################
  # Set parameters
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
  fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
  fname_label = data_path + '/MEG/sample/labels/Aud-lh.label'
  
  # Setup for reading the raw data
  raw = io.read_raw_fif(raw_fname, verbose=False)
  events = mne.find_events(raw, stim_channel='STI 014')
  inverse_operator = read_inverse_operator(fname_inv)
  raw.info['bads'] = ['MEG 2443', 'EEG 053']
  
  # picks MEG gradiometers
  picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                         stim=False, exclude='bads')
  
  tmin, tmax = 0, 120  # use the first 120s of data
  fmin, fmax = 4, 100  # look at frequencies between 4 and 100Hz
  n_fft = 2048  # the FFT size (n_fft). Ideally a power of 2
  label = mne.read_label(fname_label)
  
  stc = compute_source_psd(raw, inverse_operator, lambda2=1. / 9., method="dSPM",
                           tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
                           pick_ori="normal", n_fft=n_fft, label=label,
                           dB=True)
  
  stc.save('psd_dSPM')
  
  ###############################################################################
  # View PSD of sources in label
  plt.plot(stc.times, stc.data.T)
  plt.xlabel('Frequency (Hz)')
  plt.ylabel('PSD (dB)')
  plt.title('Source Power Spectrum (PSD)')
  plt.show()
  
subject: fix Hz scale