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()