text: """
  ==============================
  Generate simulated evoked data
  ==============================
  
  """
  # Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
  #         Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from mne import (read_proj, read_forward_solution, read_cov, read_label,
                   pick_types_evoked, pick_types_forward, pick_types,
                   read_evokeds)
  from mne.io import Raw
  from mne.datasets import sample
  from mne.time_frequency import iir_filter_raw, morlet
  from mne.viz import plot_sparse_source_estimates
  from mne.simulation import generate_sparse_stc, generate_evoked
  
  print(__doc__)
  
  ###############################################################################
  # Load real data as templates
  data_path = sample.data_path()
  
  raw = Raw(data_path + '/MEG/sample/sample_audvis_raw.fif')
  proj = read_proj(data_path + '/MEG/sample/sample_audvis_ecg_proj.fif')
  raw.info['projs'] += proj
  raw.info['bads'] = ['MEG 2443', 'EEG 053']  # mark bad channels
  
  fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
  ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
  cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
  
  fwd = read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
  fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
  
  cov = read_cov(cov_fname)
  
  condition = 'Left Auditory'
  evoked_template = read_evokeds(ave_fname, condition=condition, baseline=None)
  evoked_template = pick_types_evoked(evoked_template, meg=True, eeg=True,
                                      exclude=raw.info['bads'])
  
  label_names = ['Aud-lh', 'Aud-rh']
  labels = [read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
            for ln in label_names]
  
  ###############################################################################
  # Generate source time courses and the correspond evoked data
  snr = 6  # dB
  tmin = -0.1
  sfreq = 1000.  # Hz
  tstep = 1. / sfreq
  n_samples = 600
  times = np.linspace(tmin, tmin + n_samples * tstep, n_samples)
  
  # Generate times series from 2 Morlet wavelets
  stc_data = np.zeros((len(labels), len(times)))
  Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5])
  stc_data[0][:len(Ws[0])] = np.real(Ws[0])
  stc_data[1][:len(Ws[1])] = np.real(Ws[1])
  stc_data *= 100 * 1e-9  # use nAm as unit
  
  # time translation
  stc_data[1] = np.roll(stc_data[1], 80)
  stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep,
                            random_state=0)
  
  ###############################################################################
  # Generate noisy evoked data
  picks = pick_types(raw.info, meg=True, exclude='bads')
  iir_filter = iir_filter_raw(raw, order=5, picks=picks, tmin=60, tmax=180)
  evoked = generate_evoked(fwd, stc, evoked_template, cov, snr,
                           tmin=0.0, tmax=0.2, iir_filter=iir_filter)
  
  ###############################################################################
  # Plot
  plot_sparse_source_estimates(fwd['src'], stc, bgcolor=(1, 1, 1),
                               opacity=0.5, high_resolution=True)
  
  plt.figure()
  plt.psd(evoked.data[0])
  
  evoked.plot()
  