def test_plot_source_spectrogram():
"""Test plotting of source spectrogram
"""
sample_src = read_source_spaces(op.join(data_dir, 'subjects', 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts, n_time))
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]])
assert_raises(ValueError, plot_source_spectrogram, [], [])
def test_plot_source_spectrogram():
"""Test plotting of source spectrogram
"""
sample_src = read_source_spaces(
op.join(data_dir, 'subjects', 'sample', 'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts, n_time))
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]])
assert_raises(ValueError, plot_source_spectrogram, [], [])
def test_plot_source_spectrogram():
"""Test plotting of source spectrogram."""
sample_src = read_source_spaces(op.join(subjects_dir, 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_times = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts, n_times))
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]])
pytest.raises(ValueError, plot_source_spectrogram, [], [])
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmin=0)
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmax=7)
plt.close('all')
def test_plot_source_spectrogram():
"""Test plotting of source spectrogram."""
sample_src = read_source_spaces(op.join(subjects_dir, 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_times = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.ones((n_verts, n_times))
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]])
pytest.raises(ValueError, plot_source_spectrogram, [], [])
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmin=0)
pytest.raises(ValueError, plot_source_spectrogram, [stc, stc],
[[1, 2], [3, 4]], tmax=7)
plt.close('all')
# desired baseline length, and 0 for tmax_plot.
# Note, if using baseline data, the averaged evoked response in the baseline
# period should be flat.
noise_covs = []
for (l_freq, h_freq) in freq_bins:
raw_band = raw_noise.copy()
raw_band.filter(l_freq, h_freq, picks=epochs.picks, method='iir', n_jobs=1)
epochs_band = mne.Epochs(raw_band, epochs_noise.events, event_id,
tmin=tmin_plot, tmax=tmax_plot, baseline=None,
picks=epochs.picks, proj=True)
noise_cov = compute_covariance(epochs_band)
noise_cov = mne.cov.regularize(noise_cov, epochs_band.info, mag=noise_reg,
grad=noise_reg, eeg=noise_reg, proj=True)
noise_covs.append(noise_cov)
del raw_band # to save memory
# Computing LCMV solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_lcmv(epochs, forward, noise_covs, tmin, tmax, tstep, win_lengths,
freq_bins=freq_bins, subtract_evoked=subtract_evoked,
reg=data_reg, label=label)
# Plotting source spectrogram for source with maximum activity.
# Note that tmin and tmax are set to display a time range that is smaller than
# the one for which beamforming estimates were calculated. This ensures that
# all time bins shown are a result of smoothing across an identical number of
# time windows.
plot_source_spectrogram(stcs, freq_bins, tmin=tmin_plot, tmax=tmax_plot,
source_index=None, colorbar=True)
tmax=tmax,
picks=epochs.picks,
proj=True)
noise_cov = compute_covariance(epochs_band)
noise_cov = mne.cov.regularize(noise_cov,
epochs_band.info,
mag=noise_reg,
grad=noise_reg,
eeg=noise_reg,
proj=True)
noise_covs.append(noise_cov)
del raw_band # to save memory
# Computing LCMV solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_lcmv(epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
subtract_evoked=subtract_evoked,
reg=data_reg,
label=label)
# Plotting source spectrogram for source with maximum activity
plot_source_spectrogram(stcs, freq_bins, source_index=None, colorbar=True)
eeg=noise_reg,
proj=True)
noise_covs.append(noise_cov)
del raw_band # to save memory
# Computing LCMV solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_lcmv(epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
subtract_evoked=subtract_evoked,
reg=data_reg,
label=label)
# Plotting source spectrogram for source with maximum activity.
# Note that tmin and tmax are set to display a time range that is smaller than
# the one for which beamforming estimates were calculated. This ensures that
# all time bins shown are a result of smoothing across an identical number of
# time windows.
plot_source_spectrogram(stcs,
freq_bins,
tmin=tmin_plot,
tmax=tmax_plot,
source_index=None,
colorbar=True)
# Setting time windows, please note tmin stretches over the baseline, which is
# selected to be as long as the longest time window. This enables a smooth and
# accurate localization of activity in time
tmin = -0.3 # s
tmax = 0.5 # s
tstep = 0.05 # s
# Subtract evoked response prior to computation?
subtract_evoked = False
# Calculating noise cross-spectral density from empty room noise for each
# frequency bin and the corresponding time window length. To calculate noise
# from the baseline period in the data, change epochs_noise to epochs
noise_csds = []
for freq_bin, win_length, n_fft in zip(freq_bins, win_lengths, n_ffts):
noise_csd = compute_epochs_csd(epochs_noise, mode='fourier',
fmin=freq_bin[0], fmax=freq_bin[1],
fsum=True, tmin=tmin,
tmax=tmin + win_length, n_fft=n_fft)
noise_csds.append(noise_csd)
# Computing DICS solutions for time-frequency windows in a label in source
# space for faster computation, use label=None for full solution
stcs = tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
freq_bins=freq_bins, subtract_evoked=subtract_evoked,
n_ffts=n_ffts, reg=0.001, label=label)
# Plotting source spectrogram for source with maximum activity
plot_source_spectrogram(stcs, freq_bins, source_index=None, colorbar=True)
cwt_n_cycles=cwt_n_cycles,
reg=0.05,
label=label,
pick_ori='max-power',
inversion='single',
depth=1.0,
n_jobs=n_jobs)
coll_stcs_neg.append(stcs_neg)
for fb in range(len(freq_bins)):
stcs_neg[fb].save("{}{}_TF_dics_neg_{}-{}_{}-stc.h5".format(
save_dir, meg, freq_bins[fb][0], freq_bins[fb][-1], loi))
fig_n = plot_source_spectrogram(stcs_neg,
freq_bins,
tmin=tmin_plot,
tmax=tmax_plot,
source_index=None,
colorbar=True,
cmap='hot_r',
vmin=0,
vmax=6e-26,
show=False) # freq_bins! or error
fig_n.savefig("{}{}_neg_{}.png".format(plot_dir, meg, loi))
stcs_pos = tf_dics(epo['positive'],
fwd,
noise_csds=None,
tmin=tmin,
tmax=tmax,
tstep=tstep,
win_lengths=win_lengths,
subtract_evoked=False,
mode='cwt_morlet',
frequencies=frequencies,
