def test_tf_dics(_load_forward, idx, mat_tol, vol_tol):
"""Test 5D time-frequency beamforming based on DICS."""
fwd_free, fwd_surf, fwd_fixed, _ = _load_forward
epochs, _, _, source_vertno, label, vertices, source_ind = \
_simulate_data(fwd_fixed, idx)
reg = 1 # Lots of regularization for our toy dataset
tmin = 0
tmax = 9
tstep = 4
win_lengths = [5, 5]
frequencies = [10, 20]
freq_bins = [(8, 12), (18, 22)]
with pytest.raises(RuntimeError, match='several sensor types'):
stcs = tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
frequencies=frequencies,
decim=10,
reg=reg,
label=label)
epochs.pick_types(meg='grad')
# Compute DICS for two time windows and two frequencies
for mode in ['fourier', 'multitaper', 'cwt_morlet']:
stcs = tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
mode=mode,
freq_bins=freq_bins,
frequencies=frequencies,
decim=10,
reg=reg,
label=label)
# Did we find the true source at 20 Hz?
dist = _fwd_dist(stcs[1], fwd_surf, vertices, source_ind, tidx=0)
assert dist == 0
dist = _fwd_dist(stcs[1], fwd_surf, vertices, source_ind, tidx=1)
assert dist == 0
# 20 Hz power should decrease over time
assert stcs[1].data[source_ind, 0] > stcs[1].data[source_ind, 1]
# 20 Hz power should be more than 10 Hz power at the true source
assert stcs[1].data[source_ind, 0] > stcs[0].data[source_ind, 0]
# Manually compute source power and compare with the last tf_dics result.
source_power = []
time_windows = [(0, 5), (4, 9)]
for time_window in time_windows:
csd = csd_morlet(epochs,
frequencies=[frequencies[1]],
tmin=time_window[0],
tmax=time_window[1],
decim=10)
csd = csd.sum()
csd._data /= csd.n_fft
filters = make_dics(epochs.info, fwd_surf, csd, reg=reg, label=label)
stc_source_power, _ = apply_dics_csd(csd, filters)
source_power.append(stc_source_power.data)
# Comparing tf_dics results with dics_source_power results
assert_allclose(stcs[1].data, np.array(source_power).squeeze().T, atol=0)
# Test using noise csds. We're going to use identity matrices. That way,
# since we're using unit-noise-gain weight normalization, there should be
# no effect.
stcs = tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
mode='cwt_morlet',
frequencies=frequencies,
decim=10,
reg=reg,
label=label,
normalize_fwd=False,
weight_norm='unit-noise-gain')
noise_csd = csd.copy()
inds = np.triu_indices(csd.n_channels)
# Using [:, :] syntax for in-place broadcasting
noise_csd._data[:, :] = 2 * np.eye(csd.n_channels)[inds][:, np.newaxis]
noise_csd.n_fft = 2 # Dividing by n_fft should yield an identity CSD
noise_csds = [noise_csd, noise_csd] # Two frequency bins
stcs_norm = tf_dics(epochs,
fwd_surf,
noise_csds,
tmin,
tmax,
tstep,
win_lengths,
mode='cwt_morlet',
frequencies=frequencies,
decim=10,
reg=reg,
label=label,
normalize_fwd=False,
weight_norm='unit-noise-gain')
assert_allclose(stcs_norm[0].data, stcs[0].data, atol=0)
assert_allclose(stcs_norm[1].data, stcs[1].data, atol=0)
# Test invalid parameter combinations
with pytest.raises(ValueError, match='fourier.*freq_bins" parameter'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
mode='fourier',
freq_bins=None)
with pytest.raises(ValueError, match='cwt_morlet.*frequencies" param'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
mode='cwt_morlet',
frequencies=None)
# Test if incorrect number of noise CSDs is detected
with pytest.raises(ValueError, match='One noise CSD object expected per'):
tf_dics(epochs,
fwd_surf, [noise_csds[0]],
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
with pytest.raises(ValueError, match='One time window length expected'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths=[0, 1, 2],
freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
with pytest.raises(ValueError, match='Time step should not be larger'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep=0.15,
win_lengths=[0.2, 0.1],
freq_bins=freq_bins)
# Test if incorrect number of n_ffts is detected
with pytest.raises(ValueError, match='When specifying number of FFT'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
n_ffts=[1])
# Test if incorrect number of mt_bandwidths is detected
with pytest.raises(ValueError, match='When using multitaper mode and'):
tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths=win_lengths,
freq_bins=freq_bins,
mode='multitaper',
mt_bandwidths=[20])
# Test if subtracting evoked responses yields NaN's, since we only have one
# epoch. Suppress division warnings.
assert len(epochs) == 1, len(epochs)
with np.errstate(invalid='ignore'):
stcs = tf_dics(epochs,
fwd_surf,
None,
tmin,
tmax,
tstep,
win_lengths,
mode='cwt_morlet',
frequencies=frequencies,
subtract_evoked=True,
reg=reg,
label=label,
decim=20)
assert np.all(np.isnan(stcs[0].data))
def test_tf_dics():
"""Test 5D time-frequency beamforming based on DICS."""
fwd_free, fwd_surf, fwd_fixed, fwd_vol, label = _load_forward()
epochs, evoked, _, source_vertno = _simulate_data(fwd_fixed)
vertices = np.intersect1d(label.vertices, fwd_free['src'][0]['vertno'])
source_ind = vertices.tolist().index(source_vertno)
reg = 1 # Lots of regularization for our toy dataset
tmin = 0
tmax = 9
tstep = 4
win_lengths = [5, 5]
frequencies = [10, 20]
freq_bins = [(8, 12), (18, 22)]
# Compute DICS for two time windows and two frequencies
for mode in ['fourier', 'multitaper', 'cwt_morlet']:
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode=mode, freq_bins=freq_bins, frequencies=frequencies,
decim=10, reg=reg, label=label)
# Did we find the true source at 20 Hz?
assert np.argmax(stcs[1].data[:, 0]) == source_ind
assert np.argmax(stcs[1].data[:, 1]) == source_ind
# 20 Hz power should decrease over time
assert stcs[1].data[source_ind, 0] > stcs[1].data[source_ind, 1]
# 20 Hz power should be more than 10 Hz power at the true source
assert stcs[1].data[source_ind, 0] > stcs[0].data[source_ind, 0]
# Manually compute source power and compare with the last tf_dics result.
source_power = []
time_windows = [(0, 5), (4, 9)]
for time_window in time_windows:
csd = csd_morlet(epochs, frequencies=[frequencies[1]],
tmin=time_window[0], tmax=time_window[1], decim=10)
csd = csd.sum()
csd._data /= csd.n_fft
filters = make_dics(epochs.info, fwd_surf, csd, reg=reg, label=label)
stc_source_power, _ = apply_dics_csd(csd, filters)
source_power.append(stc_source_power.data)
# Comparing tf_dics results with dics_source_power results
assert_allclose(stcs[1].data, np.array(source_power).squeeze().T, atol=0)
# Test using noise csds. We're going to use identity matrices. That way,
# since we're using unit-noise-gain weight normalization, there should be
# no effect.
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode='cwt_morlet', frequencies=frequencies, decim=10,
reg=reg, label=label, normalize_fwd=False,
weight_norm='unit-noise-gain')
noise_csd = csd.copy()
inds = np.triu_indices(csd.n_channels)
# Using [:, :] syntax for in-place broadcasting
noise_csd._data[:, :] = 2 * np.eye(csd.n_channels)[inds][:, np.newaxis]
noise_csd.n_fft = 2 # Dividing by n_fft should yield an identity CSD
noise_csds = [noise_csd, noise_csd] # Two frequency bins
stcs_norm = tf_dics(epochs, fwd_surf, noise_csds, tmin, tmax, tstep,
win_lengths, mode='cwt_morlet',
frequencies=frequencies, decim=10, reg=reg,
label=label, normalize_fwd=False,
weight_norm='unit-noise-gain')
assert_allclose(stcs_norm[0].data, stcs[0].data, atol=0)
assert_allclose(stcs_norm[1].data, stcs[1].data, atol=0)
# Test invalid parameter combinations
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, mode='fourier', freq_bins=None)
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, mode='cwt_morlet', frequencies=None)
# Test if incorrect number of noise CSDs is detected
raises(ValueError, tf_dics, epochs, fwd_surf, [noise_csds[0]], tmin, tmax,
tstep, win_lengths, freq_bins=freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths=[0, 1, 2], freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep=0.15,
win_lengths=[0.2, 0.1], freq_bins=freq_bins)
# Test if incorrent number of n_ffts is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, freq_bins=freq_bins, n_ffts=[1])
# Test if incorrect number of mt_bandwidths is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths=win_lengths, freq_bins=freq_bins, mode='multitaper',
mt_bandwidths=[20])
# Test if subtracting evoked responses yields NaN's, since we only have one
# epoch. Suppress division warnings.
with pytest.warns(RuntimeWarning, match='[invalid|empty]'):
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode='cwt_morlet', frequencies=frequencies,
subtract_evoked=True, reg=reg, label=label, decim=20)
assert np.all(np.isnan(stcs[0].data))
def test_tf_dics():
"""Test 5D time-frequency beamforming based on DICS."""
fwd_free, fwd_surf, fwd_fixed, fwd_vol, label = _load_forward()
epochs, evoked, _, source_vertno = _simulate_data(fwd_fixed)
vertices = np.intersect1d(label.vertices, fwd_free['src'][0]['vertno'])
source_ind = vertices.tolist().index(source_vertno)
reg = 1 # Lots of regularization for our toy dataset
tmin = 0
tmax = 9
tstep = 4
win_lengths = [5, 5]
frequencies = [10, 20]
freq_bins = [(8, 12), (18, 22)]
# Compute DICS for two time windows and two frequencies
for mode in ['fourier', 'multitaper', 'cwt_morlet']:
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode=mode, freq_bins=freq_bins, frequencies=frequencies,
decim=10, reg=reg, label=label)
# Did we find the true source at 20 Hz?
assert np.argmax(stcs[1].data[:, 0]) == source_ind
assert np.argmax(stcs[1].data[:, 1]) == source_ind
# 20 Hz power should decrease over time
assert stcs[1].data[source_ind, 0] > stcs[1].data[source_ind, 1]
# 20 Hz power should be more than 10 Hz power at the true source
assert stcs[1].data[source_ind, 0] > stcs[0].data[source_ind, 0]
# Manually compute source power and compare with the last tf_dics result.
source_power = []
time_windows = [(0, 5), (4, 9)]
for time_window in time_windows:
csd = csd_morlet(epochs, frequencies=[frequencies[1]],
tmin=time_window[0], tmax=time_window[1], decim=10)
csd = csd.sum()
csd._data /= csd.n_fft
filters = make_dics(epochs.info, fwd_surf, csd, reg=reg, label=label)
stc_source_power, _ = apply_dics_csd(csd, filters)
source_power.append(stc_source_power.data)
# Comparing tf_dics results with dics_source_power results
assert_allclose(stcs[1].data, np.array(source_power).squeeze().T, atol=0)
# Test using noise csds. We're going to use identity matrices. That way,
# since we're using unit-noise-gain weight normalization, there should be
# no effect.
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode='cwt_morlet', frequencies=frequencies, decim=10,
reg=reg, label=label, normalize_fwd=False,
weight_norm='unit-noise-gain')
noise_csd = csd.copy()
inds = np.triu_indices(csd.n_channels)
# Using [:, :] syntax for in-place broadcasting
noise_csd._data[:, :] = 2 * np.eye(csd.n_channels)[inds][:, np.newaxis]
noise_csd.n_fft = 2 # Dividing by n_fft should yield an identity CSD
noise_csds = [noise_csd, noise_csd] # Two frequency bins
stcs_norm = tf_dics(epochs, fwd_surf, noise_csds, tmin, tmax, tstep,
win_lengths, mode='cwt_morlet',
frequencies=frequencies, decim=10, reg=reg,
label=label, normalize_fwd=False,
weight_norm='unit-noise-gain')
assert_allclose(stcs_norm[0].data, stcs[0].data, atol=0)
assert_allclose(stcs_norm[1].data, stcs[1].data, atol=0)
# Test invalid parameter combinations
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, mode='fourier', freq_bins=None)
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, mode='cwt_morlet', frequencies=None)
# Test if incorrect number of noise CSDs is detected
raises(ValueError, tf_dics, epochs, fwd_surf, [noise_csds[0]], tmin, tmax,
tstep, win_lengths, freq_bins=freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths=[0, 1, 2], freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep=0.15,
win_lengths=[0.2, 0.1], freq_bins=freq_bins)
# Test if incorrent number of n_ffts is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths, freq_bins=freq_bins, n_ffts=[1])
# Test if incorrect number of mt_bandwidths is detected
raises(ValueError, tf_dics, epochs, fwd_surf, None, tmin, tmax, tstep,
win_lengths=win_lengths, freq_bins=freq_bins, mode='multitaper',
mt_bandwidths=[20])
# Test if subtracting evoked responses yields NaN's, since we only have one
# epoch. Suppress division warnings.
with pytest.warns(RuntimeWarning, match='[invalid|empty]'):
stcs = tf_dics(epochs, fwd_surf, None, tmin, tmax, tstep, win_lengths,
mode='cwt_morlet', frequencies=frequencies,
subtract_evoked=True, reg=reg, label=label, decim=20)
assert np.all(np.isnan(stcs[0].data))
def test_tf_dics():
"""Test TF beamforming based on DICS
"""
tmin, tmax, tstep = -0.2, 0.2, 0.1
raw, epochs, _, _, _, label, forward, _, _, _ =\
_get_data(tmin, tmax, read_all_forward=False, compute_csds=False)
freq_bins = [(4, 20), (30, 55)]
win_lengths = [0.2, 0.2]
reg = 0.001
noise_csds = []
for freq_bin, win_length in zip(freq_bins, win_lengths):
noise_csd = csd_epochs(epochs, mode='fourier',
fmin=freq_bin[0], fmax=freq_bin[1],
fsum=True, tmin=tmin,
tmax=tmin + win_length)
noise_csds.append(noise_csd)
stcs = tf_dics(epochs, forward, noise_csds, tmin, tmax, tstep, win_lengths,
freq_bins, reg=reg, label=label)
assert_true(len(stcs) == len(freq_bins))
assert_true(stcs[0].shape[1] == 4)
# Manually calculating source power in several time windows to compare
# results and test overlapping
source_power = []
time_windows = [(-0.1, 0.1), (0.0, 0.2)]
for time_window in time_windows:
data_csd = csd_epochs(epochs, mode='fourier',
fmin=freq_bins[0][0],
fmax=freq_bins[0][1], fsum=True,
tmin=time_window[0], tmax=time_window[1])
noise_csd = csd_epochs(epochs, mode='fourier',
fmin=freq_bins[0][0],
fmax=freq_bins[0][1], fsum=True,
tmin=-0.2, tmax=0.0)
data_csd.data /= data_csd.n_fft
noise_csd.data /= noise_csd.n_fft
stc_source_power = dics_source_power(epochs.info, forward, noise_csd,
data_csd, reg=reg, label=label)
source_power.append(stc_source_power.data)
# Averaging all time windows that overlap the time period 0 to 100 ms
source_power = np.mean(source_power, axis=0)
# Selecting the first frequency bin in tf_dics results
stc = stcs[0]
# Comparing tf_dics results with dics_source_power results
assert_array_almost_equal(stc.data[:, 2], source_power[:, 0])
# Test if using unsupported max-power orientation is detected
assert_raises(ValueError, tf_dics, epochs, forward, noise_csds, tmin, tmax,
tstep, win_lengths, freq_bins=freq_bins,
pick_ori='max-power')
# Test if incorrect number of noise CSDs is detected
assert_raises(ValueError, tf_dics, epochs, forward, [noise_csds[0]], tmin,
tmax, tstep, win_lengths, freq_bins=freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
assert_raises(ValueError, tf_dics, epochs, forward, noise_csds, tmin, tmax,
tstep, win_lengths=[0, 1, 2], freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
assert_raises(ValueError, tf_dics, epochs, forward, noise_csds, tmin, tmax,
tstep=0.15, win_lengths=[0.2, 0.1], freq_bins=freq_bins)
# Test if incorrect number of mt_bandwidths is detected
assert_raises(ValueError, tf_dics, epochs, forward, noise_csds, tmin, tmax,
tstep, win_lengths, freq_bins, mode='multitaper',
mt_bandwidths=[20])
# Pass only one epoch to test if subtracting evoked responses yields zeros
stcs = tf_dics(epochs[0], forward, noise_csds, tmin, tmax, tstep,
win_lengths, freq_bins, subtract_evoked=True, reg=reg,
label=label)
assert_array_almost_equal(stcs[0].data, np.zeros_like(stcs[0].data))
fmin=freq_bin[0],
fmax=freq_bin[1],
fsum=True,
tmin=-win_length,
tmax=0,
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
# 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,
def test_tf_dics():
"""Test TF beamforming based on DICS
"""
tmin, tmax, tstep = -0.2, 0.2, 0.1
raw, epochs, _, _, _, label, forward, _, _, _ =\
_get_data(tmin, tmax, read_all_forward=False, compute_csds=False)
freq_bins = [(4, 20), (30, 55)]
win_lengths = [0.2, 0.2]
reg = 0.001
noise_csds = []
for freq_bin, win_length in zip(freq_bins, win_lengths):
noise_csd = compute_epochs_csd(epochs,
mode='fourier',
fmin=freq_bin[0],
fmax=freq_bin[1],
fsum=True,
tmin=tmin,
tmax=tmin + win_length)
noise_csds.append(noise_csd)
stcs = tf_dics(epochs,
forward,
noise_csds,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
reg=reg,
label=label)
assert_true(len(stcs) == len(freq_bins))
assert_true(stcs[0].shape[1] == 4)
# Manually calculating source power in several time windows to compare
# results and test overlapping
source_power = []
time_windows = [(-0.1, 0.1), (0.0, 0.2)]
for time_window in time_windows:
data_csd = compute_epochs_csd(epochs,
mode='fourier',
fmin=freq_bins[0][0],
fmax=freq_bins[0][1],
fsum=True,
tmin=time_window[0],
tmax=time_window[1])
noise_csd = compute_epochs_csd(epochs,
mode='fourier',
fmin=freq_bins[0][0],
fmax=freq_bins[0][1],
fsum=True,
tmin=-0.2,
tmax=0.0)
data_csd.data /= data_csd.n_fft
noise_csd.data /= noise_csd.n_fft
stc_source_power = dics_source_power(epochs.info,
forward,
noise_csd,
data_csd,
reg=reg,
label=label)
source_power.append(stc_source_power.data)
# Averaging all time windows that overlap the time period 0 to 100 ms
source_power = np.mean(source_power, axis=0)
# Selecting the first frequency bin in tf_dics results
stc = stcs[0]
# Comparing tf_dics results with dics_source_power results
assert_array_almost_equal(stc.data[:, 2], source_power[:, 0])
# Test if using unsupported max-power orientation is detected
assert_raises(ValueError,
tf_dics,
epochs,
forward,
noise_csds,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
pick_ori='max-power')
# Test if incorrect number of noise CSDs is detected
assert_raises(ValueError,
tf_dics,
epochs,
forward, [noise_csds[0]],
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
assert_raises(ValueError,
tf_dics,
epochs,
forward,
noise_csds,
tmin,
tmax,
tstep,
win_lengths=[0, 1, 2],
freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
assert_raises(ValueError,
tf_dics,
epochs,
forward,
noise_csds,
tmin,
tmax,
tstep=0.15,
win_lengths=[0.2, 0.1],
freq_bins=freq_bins)
# Test if incorrect number of mt_bandwidths is detected
assert_raises(ValueError,
tf_dics,
epochs,
forward,
noise_csds,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
mode='multitaper',
mt_bandwidths=[20])
# Pass only one epoch to test if subtracting evoked responses yields zeros
stcs = tf_dics(epochs[0],
forward,
noise_csds,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
subtract_evoked=True,
reg=reg,
label=label)
assert_array_almost_equal(stcs[0].data, np.zeros_like(stcs[0].data))
win_lengths = [0.3, 0.2, 0.15, 0.1] # s
# Then set FFTs length for each frequency range.
# Should be a power of 2 to be faster.
n_ffts = [256, 128, 128, 128]
# 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 = csd_fourier(epochs_noise, fmin=freq_bin[0], fmax=freq_bin[1],
tmin=-win_length, tmax=0, n_fft=n_fft)
noise_csds.append(noise_csd.sum())
# 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=0.05, label=label, inversion='matrix')
# 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)
annot_fname=None,
regexp=None,
subjects_dir=mri_dir,
sort=False,
verbose=None)
label = [l for l in labels if l.name == loi][0]
print("Calculating TF_DICS negative for {}".format(meg))
stcs_neg = tf_dics(epo['negative'],
fwd,
noise_csds=None,
tmin=tmin,
tmax=tmax,
tstep=tstep,
win_lengths=win_lengths,
subtract_evoked=False,
mode='cwt_morlet',
frequencies=frequencies,
cwt_n_cycles=cwt_n_cycles,
reg=0.05,
label=label,
pick_ori='max-power',
inversion='single',
depth=1.0,
n_jobs=n_jobs)
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))
print("Calculating TF_DICS positive for {}".format(meg))
stcs_pos = tf_dics(epo['positive'],
fwd,
noise_csds=None,
reject = dict(mag=4e-12)
# Re-normalize our empty-room projectors, which should be fine after subselection
raw.info.normalize_proj()
# Setting time windows.
tmin, tmax, tstep = -0.5, 0.75, 0.05
# Read epochs
event_id = 1
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
baseline=None, preload=True, proj=True, reject=reject)
# Read forward operator
forward = mne.read_forward_solution(fname_fwd)
# Read label
label = mne.read_label(fname_label)
# CWT_MORLET exemplary PARAMETERS for TF-DICS beamformer to re-produce bug/error
frequencies = [[4.,5.,6.,7.,8.,9.,10.,11.,12.],[12.,13.,14.,15.,16.,17.,18.,19.,20.,21.,22.,23.,24.,25.,26.,27.,28.,29.,30.]]
win_lengths = [0.3, 0.2]
# use this line for cwt_cycles as list of float
cwt_n_cycles = [5.,7.]
# use this line to test single value
# cwt_n_cycles = 7.
# TF_DICS CALCULATION
stcs = tf_dics(epochs, forward, noise_csds = None, tmin=tmin, tmax=tmax, tstep=tstep, win_lengths=win_lengths, subtract_evoked=False, mode='cwt_morlet', frequencies=frequencies,
reg = 0.05, label=label, pick_ori='max-power', inversion='single', depth=1.0)