content: """
  ==============================================================
  Compute real-time power spectrum density with FieldTrip client
  ==============================================================
  
  Please refer to `ftclient_rt_average.py` for instructions on
  how to get the FieldTrip connector working in MNE-Python.
  
  This example demonstrates how to use it for continuous
  computation of power spectra in real-time using the
  get_data_as_epoch function.
  
  """
  # Author: Mainak Jas <mainak@neuro.hut.fi>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  import mne
  from mne.realtime import FieldTripClient
  from mne.time_frequency import compute_epochs_psd
  
  print(__doc__)
  
  # user must provide list of bad channels because
  # FieldTrip header object does not provide that
  bads = ['MEG 2443', 'EEG 053']
  
  fig, ax = plt.subplots(1)
  with FieldTripClient(host='localhost', port=1972,
                       tmax=150, wait_max=10) as rt_client:
  
      # get measurement info guessed by MNE-Python
      raw_info = rt_client.get_measurement_info()
  
      # select gradiometers
      picks = mne.pick_types(raw_info, meg='grad', eeg=False, eog=True,
                             stim=False, include=[], exclude=bads)
  
      n_fft = 256  # the FFT size. Ideally a power of 2
      n_samples = 2048  # time window on which to compute FFT
      for ii in range(20):
          epoch = rt_client.get_data_as_epoch(n_samples=n_samples, picks=picks)
          psd, freqs = compute_epochs_psd(epoch, fmin=2, fmax=200, n_fft=n_fft)
  
          cmap = 'RdBu_r'
          freq_mask = freqs < 150
          freqs = freqs[freq_mask]
          log_psd = 10 * np.log10(psd[0])
  
          tmin = epoch.events[0][0] / raw_info['sfreq']
          tmax = (epoch.events[0][0] + n_samples) / raw_info['sfreq']
  
          if ii == 0:
              im = ax.imshow(log_psd[:, freq_mask].T, aspect='auto',
                             origin='lower', cmap=cmap)
  
              ax.set_yticks(np.arange(0, len(freqs), 10))
              ax.set_yticklabels(freqs[::10].round(1))
              ax.set_xlabel('Frequency (Hz)')
              ax.set_xticks(np.arange(0, len(picks), 30))
              ax.set_xticklabels(picks[::30])
              ax.set_xlabel('MEG channel index')
              im.set_clim()
          else:
              im.set_data(log_psd[:, freq_mask].T)
  
          plt.title('continuous power spectrum (t = %0.2f sec to %0.2f sec)'
                    % (tmin, tmax), fontsize=10)
  
          plt.pause(0.5)
  plt.close()
  