def simu_bimodal_meg(snr=6, white=True, seed=None, freqs=[3, 50], n_cycles=[1, 1.5],
phases=[0, 0], offsets=[0, 80]):
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, freqs, n_cycles=n_cycles)
stc_data[0][:len(Ws[0])] = np.real(Ws[0] * np.exp(1j * phases[0]))
stc_data[1][:len(Ws[1])] = np.real(Ws[1] * np.exp(1j * phases[1]))
stc_data *= 100 * 1e-9 # use nAm as unit
# time translation
stc_data[0] = np.roll(stc_data[0], offsets[0])
stc_data[1] = np.roll(stc_data[1], offsets[1])
stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep,
random_state=0)
###############################################################################
# Generate noisy evoked data
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
if white:
iir_filter = None # for white noise
else:
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, random_state=seed)
return evoked
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 = mne.fiff.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])
plot_evoked(evoked)
