def simulate_evokeds(self, data):
iir_filter = fit_iir_model_raw(data,
order=5,
picks=self.picks,
tmin=60,
tmax=180)[1]
snr = SNR # dB
evoked = simulate_evoked(self.fwd,
self.stc,
self.info,
self.cov,
snr,
iir_filter=iir_filter)
return evoked
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 = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]
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,
# Generate source time courses from 2 dipoles and the correspond evoked data
times = np.arange(300, dtype=np.float) / raw.info['sfreq'] - 0.1
rng = np.random.RandomState(42)
def data_fun(times):
"""Function to generate random source time courses"""
return (1e-9 * np.sin(30. * times) *
np.exp(- (times - 0.15 + 0.05 * rng.randn(1)) ** 2 / 0.01))
stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
random_state=42, labels=labels, data_fun=data_fun)
###############################################################################
# Generate noisy evoked data
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
iir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]
snr = 6. # dB
evoked = simulate_evoked(fwd, stc, info, cov, snr, 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()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
proj_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'
raw = mne.io.read_raw_fif(raw_fname)
proj = mne.read_proj(proj_fname)
raw.add_proj(proj)
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
# Set up pick list: Gradiometers - bad channels
picks = mne.pick_types(raw.info, meg='grad', exclude='bads')
order = 5 # define model order
picks = picks[:1]
# Estimate AR models on raw data
b, a = fit_iir_model_raw(raw, order=order, picks=picks, tmin=60, tmax=180)
d, times = raw[0, 10000:20000] # look at one channel from now on
d = d.ravel() # make flat vector
innovation = signal.convolve(d, a, 'valid')
d_ = signal.lfilter(b, a, innovation) # regenerate the signal
d_ = np.r_[d_[0] * np.ones(order), d_] # dummy samples to keep signal length
# %%
# Plot the different time series and PSDs
plt.close('all')
plt.figure()
plt.plot(d[:100], label='signal')
plt.plot(d_[:100], label='regenerated signal')
plt.legend()
plt.figure()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
proj_fname = data_path + '/MEG/sample/sample_audvis_ecg-proj.fif'
raw = mne.io.read_raw_fif(raw_fname)
proj = mne.read_proj(proj_fname)
raw.info['projs'] += proj
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
# Set up pick list: Gradiometers - bad channels
picks = mne.pick_types(raw.info, meg='grad', exclude='bads')
order = 5 # define model order
picks = picks[:1]
# Estimate AR models on raw data
b, a = fit_iir_model_raw(raw, order=order, picks=picks, tmin=60, tmax=180)
d, times = raw[0, 10000:20000] # look at one channel from now on
d = d.ravel() # make flat vector
innovation = signal.convolve(d, a, 'valid')
d_ = signal.lfilter(b, a, innovation) # regenerate the signal
d_ = np.r_[d_[0] * np.ones(order), d_] # dummy samples to keep signal length
###############################################################################
# Plot the different time series and PSDs
plt.close('all')
plt.figure()
plt.plot(d[:100], label='signal')
plt.plot(d_[:100], label='regenerated signal')
plt.legend()
plt.figure()