def test_dics_source_power():
"""Test old DICS source power computation."""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
epochs.crop(0, None)
reg = 0.05
stc_source_power = dics_source_power(epochs.info, forward, noise_csd,
data_csd, label=label, reg=reg)
max_source_idx = np.argmax(stc_source_power.data)
max_source_power = np.max(stc_source_power.data)
# TODO: Maybe these could be more directly compared to dics() results?
assert max_source_idx == 1
assert 0.004 < max_source_power < 0.005
# Test picking normal orientation
stc_normal = dics_source_power(epochs.info, forward_surf_ori, noise_csd,
data_csd, pick_ori="normal", label=label,
reg=reg)
assert stc_normal.data.shape == stc_source_power.data.shape
# The normal orientation results should always be smaller than free
# orientation results
assert (np.abs(stc_normal.data) <= stc_source_power.data).all()
# Test if fixed forward operator is detected when picking normal
# orientation
raises(ValueError, dics_source_power, raw.info, forward_fixed, noise_csd,
data_csd, pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
raises(ValueError, dics_source_power, raw.info, forward, noise_csd,
data_csd, pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
raises(ValueError, dics_source_power, epochs.info, forward_vol, noise_csd,
data_csd, pick_ori="normal")
# Test detection of different frequencies in noise and data CSD objects
noise_csd.frequencies = [1, 2]
data_csd.frequencies = [1, 2, 3]
raises(ValueError, dics_source_power, epochs.info, forward, noise_csd,
data_csd)
# Test detection of uneven frequency spacing
data_csd.frequencies = [1, 3, 4]
data_csd._data = data_csd._data.repeat(3, axis=1)
noise_csd = data_csd
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
dics_source_power(epochs.info, forward, noise_csd, data_csd)
assert len(w) == 2 # Also deprecation warning
fname = out_dir + '%s_%s_LCMV_blank_clean' % (subj, conds[c])
stc.save(fname)
stc = lcmv(evoked_ds[c], forward, cov_base, data_cov, reg=0.01,
pick_ori='max-power')
stc = mne.morph_data_precomputed(subj, 'fsaverage', stc,
vertices_to, morph_mat)
fname = out_dir + '%s_%s_LCMV_base_clean' % (subj, conds[c])
stc.save(fname)
# finally, compute DICS per band
for band in bands:
data_csd = compute_epochs_csd(epochs[conds[c]], mode='multitaper',
tmin=tmin, tmax=tmax + btmax,
fmin=band[0], fmax=band[1])
noise_csd = compute_epochs_csd(epochs[conds[c]], mode='multitaper',
tmin=btmin, tmax=btmax,
fmin=band[0], fmax=band[1])
stc = dics(evoked[c], forward, noise_csd, data_csd)
stc = mne.morph_data_precomputed(subj, 'fsaverage', stc,
vertices_to, morph_mat)
stc.resample(20)
fname = out_dir + '%s_%s_DICSevoked_%dto%d_clean' % (subj, conds[c],
band[0], band[1])
stc.save(fname)
stc = dics_source_power(epochs.info, forward, noise_csd, data_csd)
stc = mne.morph_data_precomputed(subj, 'fsaverage', stc,
vertices_to, morph_mat)
fname = out_dir + '%s_%s_DICSepochs_%dto%d_clean' % (subj, conds[c],
band[0], band[1])
stc.save(fname)
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))
def test_dics_source_power():
"""Test DICS source power computation
"""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
stc_source_power = dics_source_power(epochs.info, forward, noise_csd,
data_csd, label=label)
max_source_idx = np.argmax(stc_source_power.data)
max_source_power = np.max(stc_source_power.data)
# TODO: Maybe these could be more directly compared to dics() results?
assert_true(max_source_idx == 0)
assert_true(0.5 < max_source_power < 1.15)
# Test picking normal orientation and using a list of CSD matrices
stc_normal = dics_source_power(epochs.info, forward_surf_ori,
[noise_csd] * 2, [data_csd] * 2,
pick_ori="normal", label=label)
assert_true(stc_normal.data.shape == (stc_source_power.data.shape[0], 2))
# The normal orientation results should always be smaller than free
# orientation results
assert_true((np.abs(stc_normal.data[:, 0]) <=
stc_source_power.data[:, 0]).all())
# Test if fixed forward operator is detected when picking normal
# orientation
assert_raises(ValueError, dics_source_power, raw.info, forward_fixed,
noise_csd, data_csd, pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
assert_raises(ValueError, dics_source_power, raw.info, forward, noise_csd,
data_csd, pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
assert_raises(ValueError, dics_source_power, epochs.info, forward_vol,
noise_csd, data_csd, pick_ori="normal")
# Test detection of different number of CSD matrices provided
assert_raises(ValueError, dics_source_power, epochs.info, forward,
[noise_csd] * 2, [data_csd] * 3)
# Test detection of different frequencies in noise and data CSD objects
noise_csd.frequencies = [1, 2]
data_csd.frequencies = [1, 2, 3]
assert_raises(ValueError, dics_source_power, epochs.info, forward,
noise_csd, data_csd)
# Test detection of uneven frequency spacing
data_csds = [cp.deepcopy(data_csd) for i in range(3)]
frequencies = [1, 3, 4]
for freq, data_csd in zip(frequencies, data_csds):
data_csd.frequencies = [freq]
noise_csds = data_csds
with warnings.catch_warnings(record=True) as w:
dics_source_power(epochs.info, forward, noise_csds, data_csds)
assert len(w) == 1
# Read epochs
event_id, tmin, tmax = 1, -0.2, 0.5
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0), preload=True,
reject=dict(grad=4000e-13, mag=4e-12))
evoked = epochs.average()
# Read forward operator
forward = mne.read_forward_solution(fname_fwd)
# Computing the data and noise cross-spectral density matrices
# The time-frequency window was chosen on the basis of spectrograms from
# example time_frequency/plot_time_frequency.py
# We use Morlet wavelets to estimate the CSD for two specific frequencies.
data_csds = csd_morlet(epochs, tmin=0.04, tmax=0.15, frequencies=[18, 27])
noise_csds = csd_morlet(epochs, tmin=-0.11, tmax=-0.001, frequencies=[18, 27])
# Compute DICS spatial filter and estimate source power
stc = dics_source_power(epochs.info, forward, noise_csds, data_csds)
# Plot the power maps at both frequencies
for i, freq in enumerate(data_csds.frequencies):
message = 'DICS source power at %0.1f Hz' % freq
brain = stc.plot(surface='inflated', hemi='rh', subjects_dir=subjects_dir,
time_label=message, figure=i)
brain.set_data_time_index(i)
brain.show_view('lateral')
# Uncomment line below to save images
# brain.save_image('DICS_source_power_freq_%d.png' % csd.freqs[0])
stc20.save(stc20_fname, ftype='stc')
##40 hz#######################################################
data_csd40 = csd_epochs(epochs40,
mode='multitaper',
tmin=0.01,
tmax=0.50,
fmin=35,
fmax=45)
noise_csd40 = csd_epochs(epochs40,
mode='multitaper',
tmin=-0.49,
tmax=0.00,
fmin=35,
fmax=45)
stc40_2 = dics_source_power(epochs40.info, fwd40, noise_csd40, data_csd40)
# Compute DICS spatial filter and estimate source power
stc40_3 = dics(evoked40, fwd40, noise_csd40, data_csd40, reg=0.05)
stc40_2.save(stc40_fname2, ftype='stc')
stc40_3.save(stc40_fname3, ftype='stc')
################################################################
####30 hz#######################################################
data_csd30 = csd_epochs(epochs30,
mode='multitaper',
tmin=0.01,
tmax=0.50,
fmin=25,
fmax=35)
noise_csd30 = csd_epochs(epochs30,
mode='multitaper',
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))
def test_dics_source_power():
"""Test DICS source power computation
"""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
stc_source_power = dics_source_power(epochs.info,
forward,
noise_csd,
data_csd,
label=label)
max_source_idx = np.argmax(stc_source_power.data)
max_source_power = np.max(stc_source_power.data)
# TODO: Maybe these could be more directly compared to dics() results?
assert_true(max_source_idx == 0)
assert_true(0.5 < max_source_power < 1.15)
# Test picking normal orientation and using a list of CSD matrices
stc_normal = dics_source_power(epochs.info,
forward_surf_ori, [noise_csd] * 2,
[data_csd] * 2,
pick_ori="normal",
label=label)
assert_true(stc_normal.data.shape == (stc_source_power.data.shape[0], 2))
# The normal orientation results should always be smaller than free
# orientation results
assert_true(
(np.abs(stc_normal.data[:, 0]) <= stc_source_power.data[:, 0]).all())
# Test if fixed forward operator is detected when picking normal
# orientation
assert_raises(ValueError,
dics_source_power,
raw.info,
forward_fixed,
noise_csd,
data_csd,
pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
assert_raises(ValueError,
dics_source_power,
raw.info,
forward,
noise_csd,
data_csd,
pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
assert_raises(ValueError,
dics_source_power,
epochs.info,
forward_vol,
noise_csd,
data_csd,
pick_ori="normal")
# Test detection of different number of CSD matrices provided
assert_raises(ValueError, dics_source_power, epochs.info, forward,
[noise_csd] * 2, [data_csd] * 3)
# Test detection of different frequencies in noise and data CSD objects
noise_csd.frequencies = [1, 2]
data_csd.frequencies = [1, 2, 3]
assert_raises(ValueError, dics_source_power, epochs.info, forward,
noise_csd, data_csd)
# Test detection of uneven frequency spacing
data_csds = [cp.deepcopy(data_csd) for i in range(3)]
frequencies = [1, 3, 4]
for freq, data_csd in zip(frequencies, data_csds):
data_csd.frequencies = [freq]
noise_csds = data_csds
with warnings.catch_warnings(record=True) as w:
dics_source_power(epochs.info, forward, noise_csds, data_csds)
assert len(w) == 1
else:
label = avg_label[1].morph(subject_to=s)
epochs_fname = home + '/data/meg/stop/parsed/%s_stop_parsed_matched_clean_BP1-100_DS300-epo.fif.gz' % s
epochs = mne.read_epochs(epochs_fname, proj=True)
fwd_fname = home + '/data/meg/stop/%s_task-5-fwd.fif' % s
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
# calculate source power estimates for the whole brain
# quick hack in tmax ot make it the same length as btmax
data_csds = compute_epochs_csd(epochs[cond], mode='multitaper',
tmin=tmin, tmax=tmax + btmax,
fmin=band[0], fmax=band[1],
fsum=False)
noise_csds = compute_epochs_csd(epochs[cond], mode='multitaper',
tmin=btmin, tmax=btmax,
fmin=band[0], fmax=band[1],
fsum=False)
stc = dics_source_power(epochs.info, fwd, noise_csds, data_csds)
ts = mne.extract_label_time_course(stc, label, fwd['src'])
data.append(ts)
# export one CSV file
fname = out_dir + '%s_%s_%02dto%02d_tmin%.2f.csv' % (cond, roi_pretty, band[0],
band[1], tmin)
fid = open(fname, 'w')
fid.write('subj,power\n')
for j, d in enumerate(data):
fid.write('%s,' % subjs[j] + ','.join(['%e' % t for t in d[0]]) + '\n')
fid.close()
def test_dics_source_power():
"""Test old DICS source power computation."""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
epochs.crop(0, None)
reg = 0.05
stc_source_power = dics_source_power(epochs.info,
forward,
noise_csd,
data_csd,
label=label,
reg=reg)
max_source_idx = np.argmax(stc_source_power.data)
max_source_power = np.max(stc_source_power.data)
# TODO: Maybe these could be more directly compared to dics() results?
assert max_source_idx == 1
assert 0.004 < max_source_power < 0.005
# Test picking normal orientation
stc_normal = dics_source_power(epochs.info,
forward_surf_ori,
noise_csd,
data_csd,
pick_ori="normal",
label=label,
reg=reg)
assert stc_normal.data.shape == stc_source_power.data.shape
# The normal orientation results should always be smaller than free
# orientation results
assert (np.abs(stc_normal.data) <= stc_source_power.data).all()
# Test if fixed forward operator is detected when picking normal
# orientation
raises(ValueError,
dics_source_power,
raw.info,
forward_fixed,
noise_csd,
data_csd,
pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
raises(ValueError,
dics_source_power,
raw.info,
forward,
noise_csd,
data_csd,
pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
raises(ValueError,
dics_source_power,
epochs.info,
forward_vol,
noise_csd,
data_csd,
pick_ori="normal")
# Test detection of different frequencies in noise and data CSD objects
noise_csd.frequencies = [1, 2]
data_csd.frequencies = [1, 2, 3]
raises(ValueError, dics_source_power, epochs.info, forward, noise_csd,
data_csd)
# Test detection of uneven frequency spacing
data_csd.frequencies = [1, 3, 4]
data_csd._data = data_csd._data.repeat(3, axis=1)
noise_csd = data_csd
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
dics_source_power(epochs.info, forward, noise_csd, data_csd)
assert len(w) == 1
