def test_cov_ctf():
"""Test basic cov computation on ctf data with/without compensation."""
raw = read_raw_ctf(ctf_fname).crop(0., 2.).load_data()
events = make_fixed_length_events(raw, 99999)
assert len(events) == 2
ch_names = [raw.info['ch_names'][pick]
for pick in pick_types(raw.info, meg=True, eeg=False,
ref_meg=False)]
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0.,
method=['empirical'])
prepare_noise_cov(noise_cov, raw.info, ch_names)
raw.apply_gradient_compensation(0)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0., method=['empirical'])
raw.apply_gradient_compensation(1)
# TODO This next call in principle should fail.
prepare_noise_cov(noise_cov, raw.info, ch_names)
# make sure comps matrices was not removed from raw
assert raw.info['comps'], 'Comps matrices removed'
def _get_data():
"""Read in data used in tests."""
# read forward model
forward = mne.read_forward_solution(fname_fwd)
# read data
raw = mne.io.read_raw_fif(fname_raw, preload=True)
events = mne.read_events(fname_event)
event_id, tmin, tmax = 1, -0.1, 0.15
# decimate for speed
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, selection=left_temporal_channels)
picks = picks[::2]
raw.pick_channels([raw.ch_names[ii] for ii in picks])
del picks
raw.info.normalize_proj() # avoid projection warnings
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=(None, 0.), preload=True, reject=reject)
noise_cov = mne.compute_covariance(epochs, tmin=None, tmax=0.)
data_cov = mne.compute_covariance(epochs, tmin=0.01, tmax=0.15)
return epochs, data_cov, noise_cov, forward
def test_compute_covariance_auto_reg():
"""Test automated regularization"""
raw = Raw(raw_fname, preload=False)
events = find_events(raw, stim_channel='STI 014')
event_ids = [1, 2, 3, 4]
reject = dict(mag=4e-12)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
picks = pick_types(raw.info, meg='mag', eeg=False)
epochs = Epochs(raw, events_merged, 1234, tmin=-0.2, tmax=0,
picks=picks[:5], baseline=(-0.2, -0.1), proj=True,
reject=reject, preload=True)
epochs.crop(None, 0)[:10]
method_params = dict(factor_analysis=dict(iter_n_components=[30]),
pca=dict(iter_n_components=[30]))
with warnings.catch_warnings(record=True) as w:
covs = compute_covariance(epochs, method='auto',
method_params=method_params,
projs=True,
return_estimators=True)
warnings.simplefilter('always')
assert_equal(len(w), 1)
logliks = [c['loglik'] for c in covs]
assert_true(np.diff(logliks).max() <= 0) # descending order
methods = ['empirical',
'factor_analysis',
'ledoit_wolf',
# 'pca', XXX FAILS
]
with warnings.catch_warnings(record=True) as w:
cov3 = compute_covariance(epochs, method=methods,
method_params=method_params, projs=False,
return_estimators=True)
warnings.simplefilter('always')
assert_equal(len(w), 1)
assert_equal(set([c['method'] for c in cov3]),
set(methods))
# projs not allowed with FA or PCA
assert_raises(ValueError, compute_covariance, epochs, method='pca',
projs=True)
# invalid prespecified method
assert_raises(ValueError, compute_covariance, epochs, method='pizza')
# invalid scalings
assert_raises(ValueError, compute_covariance, epochs, method='shrunk',
scalings=dict(misc=123))
def test_cov_mismatch():
"""Test estimation with MEG<->Head mismatch."""
raw = read_raw_fif(raw_fname).crop(0, 5).load_data()
events = find_events(raw, stim_channel='STI 014')
raw.pick_channels(raw.ch_names[:5])
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw, events, None, tmin=-0.2, tmax=0., preload=True)
for kind in ('shift', 'None'):
epochs_2 = epochs.copy()
# This should be fine
with warnings.catch_warnings(record=True) as w:
compute_covariance([epochs, epochs_2])
assert_equal(len(w), 0)
if kind == 'shift':
epochs_2.info['dev_head_t']['trans'][:3, 3] += 0.001
else: # None
epochs_2.info['dev_head_t'] = None
assert_raises(ValueError, compute_covariance, [epochs, epochs_2])
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch='ignore')
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch='warn')
assert_raises(ValueError, compute_covariance, epochs,
on_mismatch='x')
assert_true(any('transform mismatch' in str(ww.message) for ww in w))
# This should work
epochs.info['dev_head_t'] = None
epochs_2.info['dev_head_t'] = None
compute_covariance([epochs, epochs_2], method=None)
def test_cov_mismatch():
"""Test estimation with MEG<->Head mismatch."""
raw = read_raw_fif(raw_fname, add_eeg_ref=False).crop(0, 5).load_data()
events = find_events(raw, stim_channel="STI 014")
raw.pick_channels(raw.ch_names[:5])
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw, events, None, tmin=-0.2, tmax=0.0, preload=True, add_eeg_ref=False)
for kind in ("shift", "None"):
epochs_2 = epochs.copy()
# This should be fine
with warnings.catch_warnings(record=True) as w:
compute_covariance([epochs, epochs_2])
assert_equal(len(w), 0)
if kind == "shift":
epochs_2.info["dev_head_t"]["trans"][:3, 3] += 0.001
else: # None
epochs_2.info["dev_head_t"] = None
assert_raises(ValueError, compute_covariance, [epochs, epochs_2])
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch="ignore")
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch="warn")
assert_raises(ValueError, compute_covariance, epochs, on_mismatch="x")
assert_true(any("transform mismatch" in str(ww.message) for ww in w))
# This should work
epochs.info["dev_head_t"] = None
epochs_2.info["dev_head_t"] = None
compute_covariance([epochs, epochs_2], method=None)
def test_cov_mismatch():
"""Test estimation with MEG<->Head mismatch."""
raw = read_raw_fif(raw_fname).crop(0, 5).load_data()
events = find_events(raw, stim_channel='STI 014')
raw.pick_channels(raw.ch_names[:5])
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw, events, None, tmin=-0.2, tmax=0., preload=True)
for kind in ('shift', 'None'):
epochs_2 = epochs.copy()
# This should be fine
compute_covariance([epochs, epochs_2])
if kind == 'shift':
epochs_2.info['dev_head_t']['trans'][:3, 3] += 0.001
else: # None
epochs_2.info['dev_head_t'] = None
pytest.raises(ValueError, compute_covariance, [epochs, epochs_2])
compute_covariance([epochs, epochs_2], on_mismatch='ignore')
with pytest.raises(RuntimeWarning, match='transform mismatch'):
compute_covariance([epochs, epochs_2], on_mismatch='warn')
pytest.raises(ValueError, compute_covariance, epochs,
on_mismatch='x')
# This should work
epochs.info['dev_head_t'] = None
epochs_2.info['dev_head_t'] = None
compute_covariance([epochs, epochs_2], method=None)
def test_cov_estimation_with_triggers():
"""Estimate raw with triggers
"""
raw = Raw(raw_fname)
events = find_events(raw)
event_ids = [1, 2, 3, 4]
reject = dict(grad=10000e-13, mag=4e-12, eeg=80e-6, eog=150e-6)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
epochs = Epochs(raw, events_merged, 1234, tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True,
reject=reject, preload=True)
cov = compute_covariance(epochs, keep_sample_mean=True)
cov_mne = read_cov(cov_km_fname)
assert_true(cov_mne.ch_names == cov.ch_names)
assert_true((linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 0.005)
# Test with tmin and tmax (different but not too much)
cov_tmin_tmax = compute_covariance(epochs, tmin=-0.19, tmax=-0.01)
assert_true(np.all(cov.data != cov_tmin_tmax.data))
assert_true((linalg.norm(cov.data - cov_tmin_tmax.data, ord='fro')
/ linalg.norm(cov_tmin_tmax.data, ord='fro')) < 0.05)
# cov using a list of epochs and keep_sample_mean=True
epochs = [Epochs(raw, events, ev_id, tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True, reject=reject)
for ev_id in event_ids]
cov2 = compute_covariance(epochs, keep_sample_mean=True)
assert_array_almost_equal(cov.data, cov2.data)
assert_true(cov.ch_names == cov2.ch_names)
# cov with keep_sample_mean=False using a list of epochs
cov = compute_covariance(epochs, keep_sample_mean=False)
cov_mne = read_cov(cov_fname)
assert_true(cov_mne.ch_names == cov.ch_names)
assert_true((linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 0.005)
# test IO when computation done in Python
cov.save('test-cov.fif') # test saving
cov_read = read_cov('test-cov.fif')
assert_true(cov_read.ch_names == cov.ch_names)
assert_true(cov_read.nfree == cov.nfree)
assert_true((linalg.norm(cov.data - cov_read.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 1e-5)
def test_compute_covariance_auto_reg():
"""Test automated regularization"""
raw = read_raw_fif(raw_fname, preload=True)
raw.resample(100, npad='auto') # much faster estimation
events = find_events(raw, stim_channel='STI 014')
event_ids = [1, 2, 3, 4]
reject = dict(mag=4e-12)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
# we need a few channels for numerical reasons in PCA/FA
picks = pick_types(raw.info, meg='mag', eeg=False)[:10]
raw.pick_channels([raw.ch_names[pick] for pick in picks])
raw.info.normalize_proj()
epochs = Epochs(
raw, events_merged, 1234, tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True, reject=reject, preload=True)
epochs = epochs.crop(None, 0)[:10]
method_params = dict(factor_analysis=dict(iter_n_components=[3]),
pca=dict(iter_n_components=[3]))
covs = compute_covariance(epochs, method='auto',
method_params=method_params,
projs=True,
return_estimators=True)
logliks = [c['loglik'] for c in covs]
assert_true(np.diff(logliks).max() <= 0) # descending order
methods = ['empirical',
'factor_analysis',
'ledoit_wolf',
'pca']
cov3 = compute_covariance(epochs, method=methods,
method_params=method_params, projs=None,
return_estimators=True)
assert_equal(set([c['method'] for c in cov3]),
set(methods))
# invalid prespecified method
assert_raises(ValueError, compute_covariance, epochs, method='pizza')
# invalid scalings
assert_raises(ValueError, compute_covariance, epochs, method='shrunk',
scalings=dict(misc=123))
def _get_bf_data(save_fieldtrip=False):
raw, epochs, evoked, data_cov, _, _, _, _, _, fwd = _get_data(proj=False)
if save_fieldtrip is True:
# raw needs to be saved with all channels and picked in FieldTrip
raw.save(op.join(ft_data_path, 'raw.fif'), overwrite=True)
# src (tris are not available in fwd['src'] once imported into MATLAB)
src = fwd['src'].copy()
mne.write_source_spaces(op.join(ft_data_path, 'src.fif'), src)
# pick gradiometers only:
epochs.pick_types(meg='grad')
evoked.pick_types(meg='grad')
# compute covariance matrix
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.145,
method='empirical')
if save_fieldtrip is True:
# if the covariance matrix and epochs need resaving:
# data covariance:
cov_savepath = op.join(ft_data_path, 'sample_cov')
sample_cov = {'sample_cov': data_cov['data']}
savemat(cov_savepath, sample_cov)
# evoked data:
ev_savepath = op.join(ft_data_path, 'sample_evoked')
data_ev = {'sample_evoked': evoked.data}
savemat(ev_savepath, data_ev)
return evoked, data_cov, fwd
def test_lcmv():
"""Test LCMV
"""
event_id, tmin, tmax = 1, -0.2, 0.2
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
exclude=raw.info['bads'], selection=left_temporal_channels)
# Read epochs
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, eog=150e-6))
evoked = epochs.average()
noise_cov = mne.read_cov(fname_cov)
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, eeg=0.1, proj=True)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
assert_true(0.09 < tmax < 0.1)
assert_true(2. < np.max(max_stc) < 3.)
def run_evoked(subject_id):
subject = "sub%03d" % subject_id
print("processing subject: %s" % subject)
data_path = op.join(meg_dir, subject)
epochs = mne.read_epochs(op.join(data_path, '%s-epo.fif' % subject),
preload=False)
evoked_famous = epochs['face/famous'].average()
evoked_scrambled = epochs['scrambled'].average()
evoked_unfamiliar = epochs['face/unfamiliar'].average()
# Simplify comment
evoked_famous.comment = 'famous'
evoked_scrambled.comment = 'scrambled'
evoked_unfamiliar.comment = 'unfamiliar'
contrast = mne.combine_evoked([evoked_famous, evoked_unfamiliar,
evoked_scrambled], weights=[0.5, 0.5, -1.])
contrast.comment = 'contrast'
faces = mne.combine_evoked([evoked_famous, evoked_unfamiliar], 'nave')
faces.comment = 'faces'
mne.evoked.write_evokeds(op.join(data_path, '%s-ave.fif' % subject),
[evoked_famous, evoked_scrambled,
evoked_unfamiliar, contrast, faces])
# take care of noise cov
cov = mne.compute_covariance(epochs, tmax=0, method='shrunk')
cov.save(op.join(data_path, '%s-cov.fif' % subject))
def test_lcmv_reg_proj(proj):
"""Test LCMV with and without proj."""
raw = mne.io.read_raw_fif(fname_raw, preload=True)
events = mne.find_events(raw)
raw.pick_types()
assert len(raw.ch_names) == 305
epochs = mne.Epochs(raw, events, None, preload=True, proj=proj)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = mne.compute_covariance(epochs, tmax=0)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
forward = mne.read_forward_solution(fname_fwd)
filters = make_lcmv(epochs.info, forward, data_cov, reg=0.05,
noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai', rank=None, verbose=True)
want_rank = 302 # 305 good channels - 3 MEG projs
assert filters['rank'] == want_rank
def test_low_rank_methods(rank, raw_epochs_events):
"""Test low-rank covariance matrix estimation."""
epochs = raw_epochs_events[1]
sss_proj_rank = 139 # 80 MEG + 60 EEG - 1 proj
n_ch = 366
methods = ('empirical', 'diagonal_fixed', 'oas')
bounds = {
'None': dict(empirical=(-15000, -5000),
diagonal_fixed=(-1500, -500),
oas=(-700, -600)),
'full': dict(empirical=(-18000, -8000),
diagonal_fixed=(-2000, -1600),
oas=(-1600, -1000)),
'info': dict(empirical=(-15000, -5000),
diagonal_fixed=(-700, -600),
oas=(-700, -600)),
}
with pytest.warns(RuntimeWarning, match='Too few samples'):
covs = compute_covariance(
epochs, method=methods, return_estimators=True, rank=rank,
verbose=True)
for cov in covs:
method = cov['method']
these_bounds = bounds[str(rank)][method]
this_rank = _cov_rank(cov, epochs.info, proj=(rank != 'full'))
if rank == 'full' and method != 'empirical':
assert this_rank == n_ch
else:
assert this_rank == sss_proj_rank
assert these_bounds[0] < cov['loglik'] < these_bounds[1], \
(rank, method)
def _get_data(tmin=-0.1, tmax=0.15, all_forward=True, epochs=True,
epochs_preload=True, data_cov=True):
"""Read in data used in tests."""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)
raw = mne.io.read_raw_fif(fname_raw, preload=True)
forward = mne.read_forward_solution(fname_fwd)
if all_forward:
forward_surf_ori = _read_forward_solution_meg(
fname_fwd, surf_ori=True)
forward_fixed = _read_forward_solution_meg(
fname_fwd, force_fixed=True, surf_ori=True, use_cps=False)
forward_vol = _read_forward_solution_meg(fname_fwd_vol)
else:
forward_surf_ori = None
forward_fixed = None
forward_vol = None
event_id, tmin, tmax = 1, tmin, tmax
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bad channels
# Set up pick list: MEG - bad channels
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True,
eog=True, ref_meg=False, exclude='bads',
selection=left_temporal_channels)
raw.pick_channels([raw.ch_names[ii] for ii in picks])
raw.info.normalize_proj() # avoid projection warnings
if epochs:
# Read epochs
epochs = mne.Epochs(
raw, events, event_id, tmin, tmax, proj=True,
baseline=(None, 0), preload=epochs_preload,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
if epochs_preload:
epochs.resample(200, npad=0, n_jobs=2)
epochs.crop(0, None)
evoked = epochs.average()
info = evoked.info
else:
epochs = None
evoked = None
info = raw.info
noise_cov = mne.read_cov(fname_cov)
noise_cov['projs'] = [] # avoid warning
with warnings.catch_warnings(record=True): # bad proj
noise_cov = mne.cov.regularize(noise_cov, info, mag=0.05, grad=0.05,
eeg=0.1, proj=True)
if data_cov:
with warnings.catch_warnings(record=True): # too few samples
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.145)
else:
data_cov = None
return raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol
def calc_inverse_operator(events_id, epochs_fn, fwd_sub_fn, inv_fn, min_crop_t=None, max_crop_t=0):
for cond in events_id.keys():
epochs = mne.read_epochs(epochs_fn.format(cond=cond))
noise_cov = mne.compute_covariance(epochs.crop(min_crop_t, max_crop_t, copy=True))
forward_sub = mne.read_forward_solution(fwd_sub_fn.format(cond=cond))
inverse_operator_sub = make_inverse_operator(epochs.info, forward_sub, noise_cov,
loose=None, depth=None)
write_inverse_operator(inv_fn.format(cond=cond), inverse_operator_sub)
def compute_epochs_cov_evokeds(subject):
"""Epoch, compute noise covariance and average.
params:
subject : str
the subject id to be loaded
"""
raw = Raw(save_folder + "%s_filtered_ica_mc_raw_tsss.fif" % subject,
preload=True)
# Select events to extract epochs from.
event_id = {'ent_left': 1,
'ent_right': 2,
'ctl_left': 4,
'ctl_right': 8}
# Setup for reading the raw data
events = mne.find_events(raw, min_duration=0.01)
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=False, eog=False,
include=include, exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=reject,
preload=True)
epochs.save(epochs_folder + "%s_filtered_ica_mc_tsss-epo.fif" % subject)
# Plot epochs.
# epochs.plot(trellis=False)
# Look at channels that caused dropped events, showing that the subject's
# blinks were likely to blame for most epochs being dropped
epochs.drop_bad_epochs()
fig = epochs.plot_drop_log(subject=subject, show=False)
fig.savefig(epochs_folder + "pics/%s_drop_log.png" % subject)
# Make noise cov
cov = compute_covariance(epochs, tmin=None, tmax=0, method="auto")
mne.write_cov(epochs_folder + "%s-cov.fif" % subject, cov)
# Average epochs and get evoked data corresponding to the left stimulation
###########################################################################
# Save evoked responses for different conditions to disk
# average epochs and get Evoked datasets
evokeds = [epochs[cond].average() for cond in ['ent_left', 'ent_right',
'ctl_left', 'ctl_right']]
evokeds = [epochs[cond].average() for cond in epochs.event_id.keys()]
# save evoked data to disk
mne.write_evokeds(epochs_folder +
'%s_filtered_ica_mc_raw_tsss-ave.fif' % subject, evokeds)
plt.close("all")
def _get_data(tmin=-0.1, tmax=0.15, all_forward=True, epochs=True,
epochs_preload=True, data_cov=True):
"""Read in data used in tests
"""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)
raw = mne.fiff.Raw(fname_raw, preload=True)
forward = mne.read_forward_solution(fname_fwd)
if all_forward:
forward_surf_ori = mne.read_forward_solution(fname_fwd, surf_ori=True)
forward_fixed = mne.read_forward_solution(fname_fwd, force_fixed=True,
surf_ori=True)
forward_vol = mne.read_forward_solution(fname_fwd_vol, surf_ori=True)
else:
forward_surf_ori = None
forward_fixed = None
forward_vol = None
event_id, tmin, tmax = 1, tmin, tmax
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
if epochs:
# Set up pick list: MEG - bad channels
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False,
stim=True, eog=True, ref_meg=False,
exclude='bads',
selection=left_temporal_channels)
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0),
preload=epochs_preload,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
if epochs_preload:
epochs.resample(200, npad=0, n_jobs=2)
evoked = epochs.average()
info = evoked.info
else:
epochs = None
evoked = None
info = raw.info
noise_cov = mne.read_cov(fname_cov)
noise_cov = mne.cov.regularize(noise_cov, info,
mag=0.05, grad=0.05, eeg=0.1, proj=True)
if data_cov:
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
else:
data_cov = None
return raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol
def test_lcmv():
"""Test LCMV with evoked data and single trials
"""
event_id, tmin, tmax = 1, -0.1, 0.15
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False,
stim=True, eog=True, exclude='bads',
selection=left_temporal_channels)
# Read epochs
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, eog=150e-6))
epochs.resample(200, npad=0, n_jobs=2)
evoked = epochs.average()
noise_cov = mne.read_cov(fname_cov)
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, eeg=0.1, proj=True)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
assert_true(0.09 < tmax < 0.1)
assert_true(2. < np.max(max_stc) < 3.)
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
forward_fixed = mne.read_forward_solution(fname_fwd, force_fixed=True,
surf_ori=True)
stcs = lcmv_epochs(epochs, forward_fixed, noise_cov, data_cov, reg=0.01)
epochs.drop_bad_epochs()
assert_true(len(epochs.events) == len(stcs))
# average the single trial estimates
stc_avg = np.zeros_like(stc.data)
for this_stc in stcs:
stc_avg += this_stc.data
stc_avg /= len(stcs)
# compare it to the solution using evoked with fixed orientation
stc_fixed = lcmv(evoked, forward_fixed, noise_cov, data_cov, reg=0.01)
assert_array_almost_equal(stc_avg, stc_fixed.data)
def test_cov_estimation_with_triggers():
"""Test estimation from raw with triggers
"""
events = find_events(raw)
event_ids = [1, 2, 3, 4]
reject = dict(grad=10000e-13, mag=4e-12, eeg=80e-6, eog=150e-6)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
epochs = Epochs(raw, events_merged, 1234, tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True,
reject=reject, preload=True)
cov = compute_covariance(epochs, keep_sample_mean=True)
cov_mne = read_cov(cov_km_fname)
assert_true(cov_mne.ch_names == cov.ch_names)
assert_true((linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 0.005)
# Test with tmin and tmax (different but not too much)
cov_tmin_tmax = compute_covariance(epochs, tmin=-0.19, tmax=-0.01)
assert_true(np.all(cov.data != cov_tmin_tmax.data))
assert_true((linalg.norm(cov.data - cov_tmin_tmax.data, ord='fro')
/ linalg.norm(cov_tmin_tmax.data, ord='fro')) < 0.05)
# cov using a list of epochs and keep_sample_mean=True
epochs = [Epochs(raw, events, ev_id, tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True, reject=reject)
for ev_id in event_ids]
cov2 = compute_covariance(epochs, keep_sample_mean=True)
assert_array_almost_equal(cov.data, cov2.data)
assert_true(cov.ch_names == cov2.ch_names)
# cov with keep_sample_mean=False using a list of epochs
cov = compute_covariance(epochs, keep_sample_mean=False)
cov_mne = read_cov(cov_fname)
assert_true(cov_mne.ch_names == cov.ch_names)
assert_true((linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 0.005)
# test IO when computation done in Python
cov.save('test-cov.fif') # test saving
cov_read = read_cov('test-cov.fif')
assert_true(cov_read.ch_names == cov.ch_names)
assert_true(cov_read.nfree == cov.nfree)
assert_true((linalg.norm(cov.data - cov_read.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 1e-5)
# cov with list of epochs with different projectors
epochs = [Epochs(raw, events[:4], event_ids[0], tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=True, reject=reject),
Epochs(raw, events[:4], event_ids[0], tmin=-0.2, tmax=0,
baseline=(-0.2, -0.1), proj=False, reject=reject)]
# these should fail
assert_raises(ValueError, compute_covariance, epochs)
assert_raises(ValueError, compute_covariance, epochs, projs=None)
# these should work, but won't be equal to above
cov = compute_covariance(epochs, projs=epochs[0].info['projs'])
cov = compute_covariance(epochs, projs=[])
def localize_epochs(epochs, fwd, reg=0):
''' Returns a list of Sourceestimates, one per Epoch '''
cov = mne.compute_covariance(epochs)
weights = calculate_weights(fwd, cov, reg=reg)
stcs = []
print 'Multiplying data by beamformer weights...'
for epoch in epochs:
sol = np.dot(weights, epoch)
src = mne.SourceEstimate(sol, [fwd['src'][0]['vertno'], fwd['src'][1]['vertno']], epochs.tmin, epochs.times[1] - epochs.times[0])
stcs.append(src)
return stcs
def test_cov_estimation_with_triggers():
"""Estimate raw with triggers
"""
raw = Raw(raw_fname)
events = mne.find_events(raw)
event_ids = [1, 2, 3, 4]
cov = mne.compute_covariance(raw, events, event_ids, tmin=-0.2, tmax=0,
reject=dict(grad=10000e-13, mag=4e-12,
eeg=80e-6, eog=150e-6),
keep_sample_mean=True)
cov_mne = mne.Covariance(cov_fname)
assert cov_mne.ch_names == cov.ch_names
assert (linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro')) < 0.05
def compute_bias(raw):
events = find_events(raw, 'STI201', verbose=False)
events = events[1:] # first one has an artifact
tmin, tmax = -0.2, 0.1
epochs = mne.Epochs(raw, events, dipole_number, tmin, tmax,
baseline=(None, -0.01), preload=True, verbose=False)
sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None,
verbose=False)
cov = mne.compute_covariance(epochs, tmax=0, method='oas',
rank=None, verbose=False)
idx = epochs.time_as_index(0.036)[0]
data = epochs.get_data()[:, :, idx].T
evoked = mne.EvokedArray(data, epochs.info, tmin=0.)
dip = fit_dipole(evoked, cov, sphere, n_jobs=1, verbose=False)[0]
actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1]
misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)
return misses
def test_add_noise():
"""Test noise addition."""
rng = np.random.RandomState(0)
data_path = testing.data_path()
raw = read_raw_fif(data_path + '/MEG/sample/sample_audvis_trunc_raw.fif')
raw.del_proj()
picks = pick_types(raw.info, eeg=True, exclude=())
cov = compute_raw_covariance(raw, picks=picks)
with pytest.raises(RuntimeError, match='to be loaded'):
add_noise(raw, cov)
raw.crop(0, 1).load_data()
with pytest.raises(TypeError, match='Raw, Epochs, or Evoked'):
add_noise(0., cov)
with pytest.raises(TypeError, match='Covariance'):
add_noise(raw, 0.)
# test a no-op (data preserved)
orig_data = raw[:][0]
zero_cov = cov.copy()
zero_cov['data'].fill(0)
add_noise(raw, zero_cov)
new_data = raw[:][0]
assert_allclose(orig_data, new_data, atol=1e-30)
# set to zero to make comparisons easier
raw._data[:] = 0.
epochs = EpochsArray(np.zeros((1, len(raw.ch_names), 100)),
raw.info.copy())
epochs.info['bads'] = []
evoked = epochs.average(picks=np.arange(len(raw.ch_names)))
for inst in (raw, epochs, evoked):
with catch_logging() as log:
add_noise(inst, cov, random_state=rng, verbose=True)
log = log.getvalue()
want = ('to {0}/{1} channels ({0}'
.format(len(cov['names']), len(raw.ch_names)))
assert want in log
if inst is evoked:
inst = EpochsArray(inst.data[np.newaxis], inst.info)
if inst is raw:
cov_new = compute_raw_covariance(inst, picks=picks,
verbose='error') # samples
else:
cov_new = compute_covariance(inst, verbose='error') # avg ref
assert cov['names'] == cov_new['names']
r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
assert r > 0.99
def _run_interface(self, runtime):
raw_filename = self.inputs.raw_filename
cov_fname_in = self.inputs.cov_fname_in
is_epoched = self.inputs.is_epoched
is_evoked = self.inputs.is_evoked
events_id = self.inputs.events_id
t_min = self.inputs.t_min
t_max = self.inputs.t_max
if cov_fname_in == '' or not op.exists(cov_fname_in):
if is_epoched and is_evoked:
raw = Raw(raw_filename)
events = find_events(raw)
data_path, basename, ext = split_f(raw.info['filename'])
self.cov_fname_out = op.join(data_path, '%s-cov.fif' % basename)
if not op.exists(self.cov_fname_out):
print '\n*** COMPUTE COV FROM EPOCHS ***\n' + self.cov_fname_out
reject = create_reject_dict(raw.info)
picks = pick_types(raw.info, meg=True, ref_meg=False,
exclude='bads')
epochs = Epochs(raw, events, events_id, t_min, t_max,
picks=picks, baseline=(None, 0),
reject=reject)
# TODO method='auto'? too long!!!
noise_cov = compute_covariance(epochs, tmax=0,
method='diagonal_fixed')
write_cov(self.cov_fname_out, noise_cov)
else:
print '\n *** NOISE cov file %s exists!!! \n' % self.cov_fname_out
else:
'\n *** NO EPOCH DATA \n'
else:
print '\n *** NOISE cov file %s exists!!! \n' % cov_fname_in
self.cov_fname_out = cov_fname_in
return runtime
def test_lcmv_ctf_comp():
"""Test interpolation with compensated CTF data."""
ctf_dir = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
raw = mne.io.read_raw_ctf(raw_fname, preload=True)
events = mne.make_fixed_length_events(raw, duration=0.2)[:2]
epochs = mne.Epochs(raw, events, tmin=0., tmax=0.2)
evoked = epochs.average()
with pytest.warns(RuntimeWarning,
match='Too few samples .* estimate may be unreliable'):
data_cov = mne.compute_covariance(epochs)
fwd = mne.make_forward_solution(evoked.info, None,
mne.setup_volume_source_space(pos=15.0),
mne.make_sphere_model())
filters = mne.beamformer.make_lcmv(evoked.info, fwd, data_cov)
assert 'weights' in filters
def test_plot_evoked_cov():
"""Test plot_evoked with noise_cov."""
evoked = _get_epochs().average()
cov = read_cov(cov_fname)
cov['projs'] = [] # avoid warnings
evoked.plot(noise_cov=cov, time_unit='s')
with pytest.raises(TypeError, match='Covariance'):
evoked.plot(noise_cov=1., time_unit='s')
with pytest.raises(IOError, match='No such file'):
evoked.plot(noise_cov='nonexistent-cov.fif', time_unit='s')
raw = read_raw_fif(raw_sss_fname)
events = make_fixed_length_events(raw)
epochs = Epochs(raw, events, picks=picks)
cov = compute_covariance(epochs)
evoked_sss = epochs.average()
with pytest.warns(RuntimeWarning, match='relative scaling'):
evoked_sss.plot(noise_cov=cov, time_unit='s')
plt.close('all')
def test_plot_evoked_cov():
"""Test plot_evoked with noise_cov."""
import matplotlib.pyplot as plt
evoked = _get_epochs().average()
cov = read_cov(cov_fname)
cov['projs'] = [] # avoid warnings
evoked.plot(noise_cov=cov, time_unit='s')
with pytest.raises(TypeError, match='Covariance'):
evoked.plot(noise_cov=1., time_unit='s')
with pytest.raises(IOError, match='No such file'):
evoked.plot(noise_cov='nonexistent-cov.fif', time_unit='s')
raw = read_raw_fif(raw_sss_fname)
events = make_fixed_length_events(raw)
epochs = Epochs(raw, events)
cov = compute_covariance(epochs)
evoked_sss = epochs.average()
with warnings.catch_warnings(record=True) as w:
evoked_sss.plot(noise_cov=cov, time_unit='s')
plt.close('all')
assert any('relative scal' in str(ww.message) for ww in w)
def test_rank_deficiency():
"""Test adding noise from M/EEG float32 (I/O) cov with projectors."""
# See gh-5940
evoked = read_evokeds(ave_fname, 0, baseline=(None, 0))
evoked.info['bads'] = ['MEG 2443']
evoked.info['lowpass'] = 20 # fake for decim
picks = pick_types(evoked.info, meg=True, eeg=False)
picks = picks[::16]
evoked.pick_channels([evoked.ch_names[pick] for pick in picks])
evoked.info.normalize_proj()
cov = read_cov(cov_fname)
cov['projs'] = []
cov = regularize(cov, evoked.info, rank=None)
cov = pick_channels_cov(cov, evoked.ch_names)
evoked.data[:] = 0
add_noise(evoked, cov)
cov_new = compute_covariance(
EpochsArray(evoked.data[np.newaxis], evoked.info), verbose='error')
assert cov['names'] == cov_new['names']
r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
assert r > 0.98
def _calc_inverse(params):
subject, epochs, overwrite = params
epo = op.join(REMOTE_ROOT_DIR, 'ave', '{}_ecr_nTSSS_conflict-epo.fif'.format(subject))
fwd = op.join(REMOTE_ROOT_DIR, 'fwd', '{}_ecr-fwd.fif'.format(subject))
local_inv_file_name = op.join(LOCAL_ROOT_DIR, 'inv', '{}_ecr_nTSSS_conflict-inv.fif'.format(subject))
if os.path.isfile(local_inv_file_name) and not overwrite:
inverse_operator = read_inverse_operator(local_inv_file_name)
print('inv already calculated for {}'.format(subject))
else:
if epochs is None:
epochs = mne.read_epochs(epo)
noise_cov = mne.compute_covariance(epochs.crop(None, 0, copy=True))
inverse_operator = None
if not os.path.isfile(fwd):
print('no fwd for {}'.format(subject))
else:
forward = mne.read_forward_solution(fwd)
inverse_operator = make_inverse_operator(epochs.info, forward, noise_cov,
loose=None, depth=None)
write_inverse_operator(local_inv_file_name, inverse_operator)
return inverse_operator
true_ori = fwd_disc_true['src'][0]['nn'][config.vertex] # del info, fwd_disc_true, er_raw epochs = create_epochs(raw) ############################################################################### # Sensor-level analysis ############################################################################### epochs_grad = epochs.copy().pick_types(meg='grad') epochs_mag = epochs.copy().pick_types(meg='mag') epochs_joint = epochs.copy().pick_types(meg=True) # Make cov matrices cov = mne.compute_covariance(epochs, tmin=0, tmax=1, method='empirical') noise_cov = mne.compute_covariance(epochs, tmin=-1, tmax=0, method='empirical') # Compute evokeds evoked_grad = epochs_grad.average() evoked_mag = epochs_mag.average() evoked_joint = epochs_joint.average() ############################################################################### # Compute LCMV beamformer results ############################################################################### # Read in forward solution fwd_disc_man = mne.read_forward_solution(fname.fwd_discrete_man) dists = []
event_id,
tmin,
tmax,
baseline=(None, 0),
preload=True,
proj=True,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average()
forward = mne.read_forward_solution(fname_fwd)
forward = mne.convert_forward_solution(forward, surf_ori=True)
# Compute regularized noise and data covariances
noise_cov = mne.compute_covariance(epochs,
tmin=tmin,
tmax=0,
method='shrunk',
rank=None)
data_cov = mne.compute_covariance(epochs,
tmin=0.04,
tmax=0.15,
method='shrunk',
rank=None)
evoked.plot(time_unit='s')
###############################################################################
# Run beamformers and look at maximum outputs
pick_oris = [None, 'normal', 'max-power', None]
descriptions = ['Free', 'Normal', 'Max-power', 'Fixed']
def test_low_rank():
"""Test low-rank covariance matrix estimation."""
raw = read_raw_fif(raw_fname).set_eeg_reference(projection=True).crop(0, 3)
raw = maxwell_filter(raw, regularize=None) # heavily reduce the rank
sss_proj_rank = 139 # 80 MEG + 60 EEG - 1 proj
n_ch = 366
proj_rank = 365 # one EEG proj
events = make_fixed_length_events(raw)
methods = ('empirical', 'diagonal_fixed', 'oas')
epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
bounds = {
'None':
dict(empirical=(-6000, -5000),
diagonal_fixed=(-1500, -500),
oas=(-700, -600)),
'full':
dict(empirical=(-9000, -8000),
diagonal_fixed=(-2000, -1600),
oas=(-1600, -1000)),
}
for rank in ('full', None):
covs = compute_covariance(epochs,
method=methods,
return_estimators=True,
verbose='error',
rank=rank)
for cov in covs:
method = cov['method']
these_bounds = bounds[str(rank)][method]
this_rank = _cov_rank(cov, epochs.info)
if rank is None or method == 'empirical':
assert this_rank == sss_proj_rank
else:
assert this_rank == proj_rank
assert these_bounds[0] < cov['loglik'] < these_bounds[1], \
(rank, method)
if method == 'empirical':
emp_cov = cov # save for later, rank param does not matter
# Test equivalence with mne.cov.regularize subspace
with pytest.raises(ValueError, match='are dependent.*must equal'):
regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == proj_rank
del reg_cov
with catch_logging() as log:
reg_r_cov = regularize(emp_cov,
epochs.info,
proj=True,
rank=None,
verbose=True)
log = log.getvalue()
assert 'jointly' in log
assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
del reg_r_only_cov, reg_r_cov
# test that rank=306 is same as rank='full'
epochs_meg = epochs.copy().pick_types()
assert len(epochs_meg.ch_names) == 306
epochs_meg.info.update(bads=[], projs=[])
cov_full = compute_covariance(epochs_meg,
method='oas',
rank='full',
verbose='error')
assert _cov_rank(cov_full, epochs_meg.info) == 306
cov_dict = compute_covariance(epochs_meg,
method='oas',
rank=306,
verbose='error')
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
# Work with just EEG data to simplify projection / rank reduction
raw.pick_types(meg=False, eeg=True)
n_proj = 2
raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
n_ch = len(raw.ch_names)
rank = n_ch - n_proj - 1 # plus avg proj
assert len(raw.info['projs']) == 3
epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
assert len(raw.ch_names) == n_ch
emp_cov = compute_covariance(epochs, rank='full', verbose='error')
assert _cov_rank(emp_cov, epochs.info) == rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == rank
reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_cov, epochs.info) == rank
dia_cov = compute_covariance(epochs,
rank=None,
method='diagonal_fixed',
verbose='error')
assert _cov_rank(dia_cov, epochs.info) == rank
assert_allclose(dia_cov['data'], reg_cov['data'])
# test our deprecation: can simply remove later
epochs.pick_channels(epochs.ch_names[:103])
# degenerate
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='pca')
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='factor_analysis')
def test_compute_covariance_auto_reg(rank):
"""Test automated regularization."""
raw = read_raw_fif(raw_fname, preload=True)
raw.resample(100, npad='auto') # much faster estimation
events = find_events(raw, stim_channel='STI 014')
event_ids = [1, 2, 3, 4]
reject = dict(mag=4e-12)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
# we need a few channels for numerical reasons in PCA/FA
picks = pick_types(raw.info, meg='mag', eeg=False)[:10]
raw.pick_channels([raw.ch_names[pick] for pick in picks])
raw.info.normalize_proj()
epochs = Epochs(raw,
events_merged,
1234,
tmin=-0.2,
tmax=0,
baseline=(-0.2, -0.1),
proj=True,
reject=reject,
preload=True)
epochs = epochs.crop(None, 0)[:5]
method_params = dict(factor_analysis=dict(iter_n_components=[3]),
pca=dict(iter_n_components=[3]))
covs = compute_covariance(epochs,
method='auto',
method_params=method_params,
return_estimators=True,
rank=rank)
# make sure regularization produces structured differencess
diag_mask = np.eye(len(epochs.ch_names)).astype(bool)
off_diag_mask = np.invert(diag_mask)
for cov_a, cov_b in itt.combinations(covs, 2):
if (cov_a['method'] == 'diagonal_fixed' and
# here we have diagnoal or no regularization.
cov_b['method'] == 'empirical' and rank == 'full'):
assert not np.any(
cov_a['data'][diag_mask] == cov_b['data'][diag_mask])
# but the rest is the same
assert_array_equal(cov_a['data'][off_diag_mask],
cov_b['data'][off_diag_mask])
else:
# and here we have shrinkage everywhere.
assert not np.any(
cov_a['data'][diag_mask] == cov_b['data'][diag_mask])
assert not np.any(
cov_a['data'][diag_mask] == cov_b['data'][diag_mask])
logliks = [c['loglik'] for c in covs]
assert np.diff(logliks).max() <= 0 # descending order
methods = ['empirical', 'ledoit_wolf', 'oas', 'shrunk', 'shrinkage']
if rank == 'full':
methods.extend(['factor_analysis', 'pca'])
cov3 = compute_covariance(epochs,
method=methods,
method_params=method_params,
projs=None,
return_estimators=True,
rank=rank)
method_names = [cov['method'] for cov in cov3]
best_bounds = [-45, -35]
bounds = [-55, -45] if rank == 'full' else best_bounds
for method in set(methods) - set(['empirical', 'shrunk']):
this_lik = cov3[method_names.index(method)]['loglik']
assert bounds[0] < this_lik < bounds[1]
this_lik = cov3[method_names.index('shrunk')]['loglik']
assert best_bounds[0] < this_lik < best_bounds[1]
this_lik = cov3[method_names.index('empirical')]['loglik']
bounds = [-110, -100] if rank == 'full' else best_bounds
assert bounds[0] < this_lik < bounds[1]
assert_equal(set([c['method'] for c in cov3]), set(methods))
cov4 = compute_covariance(epochs,
method=methods,
method_params=method_params,
projs=None,
return_estimators=False,
rank=rank)
assert cov3[0]['method'] == cov4['method'] # ordering
# invalid prespecified method
pytest.raises(ValueError, compute_covariance, epochs, method='pizza')
# invalid scalings
pytest.raises(ValueError,
compute_covariance,
epochs,
method='shrunk',
scalings=dict(misc=123))
events = mne.make_fixed_length_events(raw, id=1, duration=.250)
# Epoch length is 1.5 second
meg_epochs = Epochs(raw,
events,
tmin=0.,
tmax=1.500,
baseline=None,
detrend=1,
decim=1,
preload=True)
emg_epochs = Epochs(emg, events, tmin=0., tmax=1.500, baseline=None)
# Prepare data
X = np.array([
mne.compute_covariance(meg_epochs[ii], method='oas')['data'][None]
for ii in range(len(meg_epochs))
])
y = emg_epochs.get_data().var(axis=2)[:, 0] # target is EMG power
n_sub, n_fb, n_ch, _ = X.shape
# Define models
identity = ProjIdentitySpace()
lw = ProjLWSpace(shrink=shrink)
commoneucl = ProjCommonSpace(scale=scale, n_compo=n_compo, reg=reg)
spoc = ProjSPoCSpace(shrink=shrink, scale=scale, n_compo=n_compo, reg=reg)
logdiag = LogDiag()
naivevec = NaiveVec(method='upper')
def test_tf_lcmv():
"""Test TF beamforming based on LCMV."""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)
raw = mne.io.read_raw_fif(fname_raw, preload=True)
forward = mne.read_forward_solution(fname_fwd)
event_id, tmin, tmax = 1, -0.2, 0.2
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
# Set up pick list: MEG - bad channels
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, selection=left_temporal_channels)
picks = picks[::2] # decimate for speed
raw.pick_channels([raw.ch_names[ii] for ii in picks])
raw.info.normalize_proj() # avoid projection warnings
del picks
# Read epochs
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
baseline=None,
preload=False,
reject=reject)
epochs.load_data()
freq_bins = [(4, 12), (15, 40)]
time_windows = [(-0.1, 0.1), (0.0, 0.2)]
win_lengths = [0.2, 0.2]
tstep = 0.1
reg = 0.05
source_power = []
noise_covs = []
for (l_freq, h_freq), win_length in zip(freq_bins, win_lengths):
raw_band = raw.copy()
raw_band.filter(l_freq,
h_freq,
method='iir',
n_jobs=1,
iir_params=dict(output='ba'))
epochs_band = mne.Epochs(raw_band,
epochs.events,
epochs.event_id,
tmin=tmin,
tmax=tmax,
baseline=None,
proj=True)
noise_cov = mne.compute_covariance(epochs_band,
tmin=tmin,
tmax=tmin + win_length)
noise_cov = mne.cov.regularize(noise_cov,
epochs_band.info,
mag=reg,
grad=reg,
eeg=reg,
proj=True,
rank=None)
noise_covs.append(noise_cov)
del raw_band # to save memory
# Manually calculating source power in on frequency band and several
# time windows to compare to tf_lcmv results and test overlapping
if (l_freq, h_freq) == freq_bins[0]:
for time_window in time_windows:
data_cov = mne.compute_covariance(epochs_band,
tmin=time_window[0],
tmax=time_window[1])
stc_source_power = _lcmv_source_power(
epochs.info,
forward,
noise_cov,
data_cov,
reg=reg,
label=label,
weight_norm='unit-noise-gain')
source_power.append(stc_source_power.data)
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
reg=reg,
label=label)
stcs = tf_lcmv(epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
reg=reg,
label=label,
raw=raw)
assert (len(stcs) == len(freq_bins))
assert (stcs[0].shape[1] == 4)
# 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_lcmv results
stc = stcs[0]
# Comparing tf_lcmv results with _lcmv_source_power results
assert_array_almost_equal(stc.data[:, 2], source_power[:, 0])
# Test if using unsupported max-power orientation is detected
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins=freq_bins,
pick_ori='max-power')
# Test if incorrect number of noise CSDs is detected
# Test if incorrect number of noise covariances is detected
pytest.raises(ValueError, tf_lcmv, epochs, forward, [noise_covs[0]], tmin,
tmax, tstep, win_lengths, freq_bins)
# Test if freq_bins and win_lengths incompatibility is detected
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths=[0, 1, 2],
freq_bins=freq_bins)
# Test if time step exceeding window lengths is detected
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs,
tmin,
tmax,
tstep=0.15,
win_lengths=[0.2, 0.1],
freq_bins=freq_bins)
# Test if missing of noise covariance matrix is detected when more than
# one channel type is present in the data
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs=None,
tmin=tmin,
tmax=tmax,
tstep=tstep,
win_lengths=win_lengths,
freq_bins=freq_bins)
# Test if unsupported weight normalization specification is detected
pytest.raises(ValueError,
tf_lcmv,
epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
weight_norm='nai')
# Test unsupported pick_ori (vector not supported here)
with pytest.raises(ValueError, match='pick_ori must be one of'):
tf_lcmv(epochs,
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
pick_ori='vector')
# Test correct detection of preloaded epochs objects that do not contain
# the underlying raw object
epochs_preloaded = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
baseline=(None, 0),
preload=True)
epochs_preloaded._raw = None
pytest.raises(ValueError, tf_lcmv, epochs_preloaded, forward, noise_covs,
tmin, tmax, tstep, win_lengths, freq_bins)
# Pass only one epoch to test if subtracting evoked
# responses yields zeros
with pytest.warns(RuntimeWarning,
match='Too few samples .* estimate may be unreliable'):
stcs = tf_lcmv(epochs[0],
forward,
noise_covs,
tmin,
tmax,
tstep,
win_lengths,
freq_bins,
subtract_evoked=True,
reg=reg,
label=label,
raw=raw)
assert_array_almost_equal(stcs[0].data, np.zeros_like(stcs[0].data))
def test_channel_name_limit(tmpdir, monkeypatch, fname):
"""Test that our remapping works properly."""
#
# raw
#
if fname.endswith('fif'):
raw = read_raw_fif(fname)
raw.pick_channels(raw.ch_names[:3])
ref_names = []
data_names = raw.ch_names
else:
assert fname.endswith('.ds')
raw = read_raw_ctf(fname)
ref_names = [
raw.ch_names[pick]
for pick in pick_types(raw.info, meg=False, ref_meg=True)
]
data_names = raw.ch_names[32:35]
proj = dict(data=np.ones((1, len(data_names))),
col_names=data_names[:2].copy(),
row_names=None,
nrow=1)
proj = Projection(data=proj,
active=False,
desc='test',
kind=0,
explained_var=0.)
raw.add_proj(proj, remove_existing=True)
raw.info.normalize_proj()
raw.pick_channels(data_names + ref_names).crop(0, 2)
long_names = ['123456789abcdefg' + name for name in raw.ch_names]
fname = tmpdir.join('test-raw.fif')
with catch_logging() as log:
raw.save(fname)
log = log.getvalue()
assert 'truncated' not in log
rename = dict(zip(raw.ch_names, long_names))
long_data_names = [rename[name] for name in data_names]
long_proj_names = long_data_names[:2]
raw.rename_channels(rename)
for comp in raw.info['comps']:
for key in ('row_names', 'col_names'):
for name in comp['data'][key]:
assert name in raw.ch_names
if raw.info['comps']:
assert raw.compensation_grade == 0
raw.apply_gradient_compensation(3)
assert raw.compensation_grade == 3
assert len(raw.info['projs']) == 1
assert raw.info['projs'][0]['data']['col_names'] == long_proj_names
raw.info['bads'] = bads = long_data_names[2:3]
good_long_data_names = [
name for name in long_data_names if name not in bads
]
with catch_logging() as log:
raw.save(fname, overwrite=True, verbose=True)
log = log.getvalue()
assert 'truncated to 15' in log
for name in raw.ch_names:
assert len(name) > 15
# first read the full way
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'Reading extended channel information' in log
for ra in (raw, raw_read):
assert ra.ch_names == long_names
assert raw_read.info['projs'][0]['data']['col_names'] == long_proj_names
del raw_read
# next read as if no longer names could be read
monkeypatch.setattr(meas_info, '_read_extended_ch_info',
lambda x, y, z: None)
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'extended' not in log
if raw.info['comps']:
assert raw_read.compensation_grade == 3
raw_read.apply_gradient_compensation(0)
assert raw_read.compensation_grade == 0
monkeypatch.setattr( # restore
meas_info, '_read_extended_ch_info', _read_extended_ch_info)
short_proj_names = [
f'{name[:13 - bool(len(ref_names))]}-{len(ref_names) + ni}'
for ni, name in enumerate(long_data_names[:2])
]
assert raw_read.info['projs'][0]['data']['col_names'] == short_proj_names
#
# epochs
#
epochs = Epochs(raw, make_fixed_length_events(raw))
fname = tmpdir.join('test-epo.fif')
epochs.save(fname)
epochs_read = read_epochs(fname)
for ep in (epochs, epochs_read):
assert ep.info['ch_names'] == long_names
assert ep.ch_names == long_names
del raw, epochs_read
# cov
epochs.info['bads'] = []
cov = compute_covariance(epochs, verbose='error')
fname = tmpdir.join('test-cov.fif')
write_cov(fname, cov)
cov_read = read_cov(fname)
for co in (cov, cov_read):
assert co['names'] == long_data_names
assert co['bads'] == []
del cov_read
#
# evoked
#
evoked = epochs.average()
evoked.info['bads'] = bads
assert evoked.nave == 1
fname = tmpdir.join('test-ave.fif')
evoked.save(fname)
evoked_read = read_evokeds(fname)[0]
for ev in (evoked, evoked_read):
assert ev.ch_names == long_names
assert ev.info['bads'] == bads
del evoked_read, epochs
#
# forward
#
with pytest.warns(None): # not enough points for CTF
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(
pos=dict(rr=[[0, 0, 0.04]], nn=[[0, 1., 0.]]))
fwd = make_forward_solution(evoked.info, None, src, sphere)
fname = tmpdir.join('temp-fwd.fif')
write_forward_solution(fname, fwd)
fwd_read = read_forward_solution(fname)
for fw in (fwd, fwd_read):
assert fw['sol']['row_names'] == long_data_names
assert fw['info']['ch_names'] == long_data_names
assert fw['info']['bads'] == bads
del fwd_read
#
# inv
#
inv = make_inverse_operator(evoked.info, fwd, cov)
fname = tmpdir.join('test-inv.fif')
write_inverse_operator(fname, inv)
inv_read = read_inverse_operator(fname)
for iv in (inv, inv_read):
assert iv['info']['ch_names'] == good_long_data_names
apply_inverse(evoked, inv) # smoke test
def get_data(base_path,
dipole_idx,
dipole_amplitude,
use_maxwell_filter,
bads=[],
show=False):
if "phantom_aston" in base_path:
data_path = base_path + '/tSSS mc Data'
data_path = data_path + '/Amp%d_IASoff_movement/' % dipole_amplitude
fname = 'Amp%d_Dip%d_IASoff_movement_tsss_mc.fif' % (dipole_amplitude,
dipole_idx)
# fname = 'Amp%d_Dip%d_IASoff.fif' % (dipole_amplitude, dipole_idx)
stim_channel = 'SYS201'
# assert use_maxwell_filter in ['mne', False]
else:
data_path = base_path + '/%dnAm/' % dipole_amplitude
if use_maxwell_filter is True:
fname = 'dip%02d_%dnAm_sss.fif' % (dipole_idx, dipole_amplitude)
else:
fname = 'dip%02d_%dnAm.fif' % (dipole_idx, dipole_amplitude)
stim_channel = 'STI201'
raw_fname = op.join(data_path, fname)
raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose='error')
raw.info['bads'] = bads
if "phantom_aston" in base_path:
raw.crop(20, None)
events = mne.find_events(raw, stim_channel=stim_channel)
if show:
raw.plot(events=events)
if show:
raw.plot_psd(tmax=np.inf, fmax=60, average=False)
raw.fix_mag_coil_types()
if use_maxwell_filter == 'mne':
# Use Maxwell filtering from MNE
raw = mne.preprocessing.maxwell_filter(raw, origin=(0., 0., 0.))
if show:
raw.plot(events=events)
#######################################################################
# We know our phantom produces sinusoidal bursts below 25 Hz, so let's
# filter.
raw.filter(None,
40.,
h_trans_bandwidth='auto',
filter_length='auto',
phase='zero')
if show:
raw.plot(events=events)
#######################################################################
# Now we epoch our data, average it
tmin, tmax = -0.15, 0.1
event_id = events[0, 2]
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=(None, -0.05),
preload=True)
evoked = epochs.average()
if show:
evoked.plot(spatial_colors=True)
if show:
evoked.plot_joint()
evoked.crop(0, None)
sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=0.08)
cov = mne.compute_covariance(epochs, tmax=-0.05)
print(fname + " nave=%d" % evoked.nave, end='')
return epochs, evoked, cov, sphere
def run_evoked(subject_id):
subject = "sub%03d" % subject_id
print("processing subject: %s" % subject)
in_path = op.join(data_path, "EEG_Process")
evo_path = op.join(data_path, "EEG_Evoked")
for run in range(1, 2):
fname = op.join(in_path, 'sub_%03d_raw-epo.fif' % (subject_id, ))
epochs = mne.read_epochs(fname, preload=True)
#compute covariance for later on (inverse solution)
fname_cov = op.join(evo_path,
"sub_%03d_LSF_HSF-cov.fif" % (subject_id, ))
cv = KFold(3, random_state=97) # make sure cv is deterministic
cov = mne.compute_covariance(epochs,
tmax=-0.01,
method='shrunk',
cv=cv)
cov.save(fname_cov)
mne.viz.plot_cov(cov, epochs.info)
#general: HSF vs LSF
evoked_LSF = epochs['LSF'].average()
evoked_HSF = epochs['HSF'].average()
contrast = mne.combine_evoked([evoked_HSF, evoked_LSF],
weights=[1, -1])
#name the conditions
# Simplify comment
evoked_LSF.comment = 'evoked_LSF'
evoked_HSF.comment = 'evoked_HSF'
contrast.comment = 'contrast'
#contrast.plot(picks=('Oz'), window_title='CONTRAST')
#plot
#evoked_LSF.plot(picks=['Oz'], window_title='evoked, condition LSF, electrode Oz')
#evoked_HSF.plot(picks=['Oz'], window_title='evoked, condition HSF, electrode Oz')
fname_evo = op.join(evo_path,
"sub_%03d_LSF_HSF-ave.fif" % (subject_id, ))
mne.evoked.write_evokeds(fname_evo, [evoked_LSF, evoked_HSF, contrast])
#compute forward solution for later on (inverse solution)
fname_fwd = op.join(evo_path,
"sub_%03d_LSF_HSF-fwd.fif" % (subject_id, ))
info = mne.io.read_info(fname_evo)
fwd = mne.make_forward_solution(info=info,
trans=trans,
src=src,
bem=bem,
eeg=True,
mindist=5.0,
n_jobs=1)
print(fwd)
leadfield = fwd['sol']['data']
print("Leadfield size : %d sensors x %d dipoles" % leadfield.shape)
mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
# for illustration purposes use fwd to compute the sensitivity map
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')
eeg_map.plot(time_label='EEG sensitivity LSF',
clim=dict(lims=[5, 50, 100]))
n_dipoles=n_dipoles,
times=times,
data_fun=data_fun,
random_state=rng)
# look at our source data
fig, ax = plt.subplots(1)
ax.plot(times, 1e9 * stc.data.T)
ax.set(ylabel='Amplitude (nAm)', xlabel='Time (sec)')
mne.viz.utils.plt_show()
##############################################################################
# Simulate raw data
raw_sim = simulate_raw(raw.info, [stc] * 10,
forward=fwd,
cov=None,
verbose=True)
cov = make_ad_hoc_cov(raw_sim.info)
add_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], random_state=rng)
add_ecg(raw_sim, random_state=rng)
add_eog(raw_sim, random_state=rng)
raw_sim.plot()
##############################################################################
# Plot evoked data
events = find_events(raw_sim) # only 1 pos, so event number == 1
epochs = Epochs(raw_sim, events, 1, tmin=-0.2, tmax=epoch_duration)
cov = compute_covariance(epochs, tmax=0., method='empirical',
verbose='error') # quick calc
evoked = epochs.average()
evoked.plot_white(cov, time_unit='s')
# should use baseline correction when constructing the epochs. Otherwise the
# covariance matrix will be inaccurate. In MNE this is done by default, but
# just to be sure, we define it here manually.
events = mne.find_events(raw)
epochs = mne.Epochs(raw,
events,
event_id=1,
tmin=-0.2,
tmax=0.5,
baseline=(-0.2, 0.0),
decim=3) # we'll decimate for speed
###############################################################################
# Note that this method also attenuates any activity in your
# source estimates that resemble the baseline, if you like it or not.
noise_cov_baseline = mne.compute_covariance(epochs, tmax=0)
###############################################################################
# Plot the covariance matrices
# ----------------------------
#
# Try setting proj to False to see the effect. Notice that the projectors in
# epochs are already applied, so ``proj`` parameter has no effect.
noise_cov.plot(raw_empty_room.info, proj=True)
noise_cov_baseline.plot(epochs.info, proj=True)
###############################################################################
# How should I regularize the covariance matrix?
# ----------------------------------------------
#
# The estimated covariance can be numerically
epochs = mne.read_epochs(fname.epochs)
trans = mne.transforms.read_trans(fname.trans)
fwd = mne.read_forward_solution(fname.fwd)
###############################################################################
# Sensor-level analysis for beamformer
###############################################################################
epochs_grad = epochs.copy().pick_types(meg='grad')
epochs_mag = epochs.copy().pick_types(meg='mag')
epochs_joint = epochs.copy().pick_types(meg=True)
# Make cov matrices
noise_cov = mne.compute_covariance(epochs,
tmin=-0.2,
tmax=0,
method='shrunk',
rank='info')
data_cov = mne.compute_covariance(epochs,
tmin=0,
tmax=0.4,
method='empirical',
rank='info')
# Compute evokeds
tmin = 0.03
tmax = 0.05
evoked_grad = epochs_grad.average().crop(tmin, tmax)
evoked_mag = epochs_mag.average().crop(tmin, tmax)
evoked_joint = epochs_joint.average().crop(tmin, tmax)
events = mne.find_events(raw, stim_channel='STI 014')
event_id, tmin, tmax = 1, -.2, .15
baseline = None
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=baseline,
reject=dict(grad=4000e-13, eog=350e-6),
preload=True)
evoked = epochs.average()
fwd = mne.read_forward_solution(fwd_fname)
cov = mne.compute_covariance(epochs)
inv = make_inverse_operator(epochs.info, fwd, cov)
method = "MNE"
snr = 3.
lambda2 = 1. / snr**2
stc = apply_inverse(evoked,
inv,
lambda2,
method=method,
pick_ori="normal",
return_residual=False,
verbose=True)
pick_vertex = np.argmax(np.linalg.norm(stc.data, axis=1))
par['actiwin']=[0.010, 0.100]
dfname_stimcat = dfname + '_' + stimcat
epochs_stimcat = epochs[stimcat]
#% % Find trial variance > index outliers> remove beyond plow and phigh percentile
badtrls, plow, phigh = [], 2.0, 98.0
bad_trials=my_var_cut_fn(epochs_stimcat, plow, phigh, to_plot=False)
print('\n%d trial to remove from total %d trials...\nNo. of remaining trials = %d\n'%(len(bad_trials),
len(epochs_stimcat),
len(epochs_stimcat)-len(bad_trials)))
epochs_stimcat.drop(bad_trials, reason='variance based rejection', verbose=True)
bad_trials=[]
# Compute covariance
noise_cov = mne.compute_covariance(epochs_stimcat, tmin=par['ctrlwin'][0], tmax=par['ctrlwin'][1], method='empirical', verbose=True)
data_cov = mne.compute_covariance(epochs_stimcat, tmin=par['actiwin'][0], tmax=par['actiwin'][1], method='empirical', verbose=True)
evoked = epochs_stimcat.average()
evoked = evoked.crop(par['actiwin'][0], par['actiwin'][1])
# Pull rank from data preprocessing history
cov_rank = None if epochs_stimcat.info['proc_history']==[] else int(epochs_stimcat.info['proc_history'][0]['max_info']['sss_info']['nfree'])
# Compute SNR
inverse_operator=mne.minimum_norm.make_inverse_operator(evoked.info, fwd, noise_cov,
rank=cov_rank, loose=1, depth=0.199, verbose=True)
snr, _ = mne.minimum_norm.estimate_snr(evoked, inverse_operator, verbose=True)
peak_ch, peak_time = evoked.get_peak(ch_type='grad')
tstep=1000/(evoked.info['sfreq']*1000)
tp = int(peak_time//tstep - evoked.times[0]//tstep)
SNR=snr[tp]
raw.set_eeg_reference(projection=True)
events = mne.find_events(raw)
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2}
epochs = mne.Epochs(raw, events, event_id, baseline=(None, 0), preload=True)
epochs.info['bads'] = []
evoked = epochs.average()
labels = mne.read_labels_from_annot('sample', subjects_dir=subjects_dir)
label_names = [label.name for label in labels]
n_labels = len(labels)
# %%
# Estimate the background noise covariance from the baseline period
# -----------------------------------------------------------------
cov = mne.compute_covariance(epochs, tmin=None, tmax=0.)
# %%
# Generate sinusoids in two spatially distant labels
# --------------------------------------------------
# The known signal is all zero-s off of the two labels of interest
signal = np.zeros((n_labels, T))
idx = label_names.index('inferiorparietal-lh')
signal[idx, :] = 1e-7 * np.sin(5 * 2 * np.pi * times)
idx = label_names.index('rostralmiddlefrontal-rh')
signal[idx, :] = 1e-7 * np.sin(7 * 2 * np.pi * times)
# %%
# Find the center vertices in source space of each label
# ------------------------------------------------------
# check for M100
evoked.plot()
# set metadata: allows you to specify more complex info about events,
# can use pandas-style queries to access subsets of data
epochs.metadata = trial_info
epochs.save(epochs_fname)
# SANITY CHECK!!:
assert (len(epochs.events) == len(trial_info))
#-------------------------------------------------------------------------------
# step 7- make noise covariance matrix
if not op.isfile(cov_fname):
noise_cov = compute_covariance(epochs, tmax=0., method=['shrunk'])
write_cov(cov_fname, noise_cov)
else:
noise_cov = read_cov(cov_fname)
# if using native MRI, need to make_bem_model
if not op.isfile(bem_fname):
surfaces = make_bem_model(subject,
ico=4,
conductivity=(0.3, ),
subjects_dir=mri_dir,
verbose=None)
bem = make_bem_solution(surfaces)
write_bem_solution(bem_fname, bem)
# step 8- make forward solution
tmin, tmax = -.2, .25 # epoch duration
epochs = mne.Epochs(raw,
events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
picks=picks,
baseline=(-.2, 0.),
preload=True)
del raw
# covariance matrix for pre-stimulus interval
tmin, tmax = -.2, 0.
cov_pre = mne.compute_covariance(epochs,
tmin=tmin,
tmax=tmax,
method='empirical')
# covariance matrix for post-stimulus interval (around main evoked responses)
tmin, tmax = 0.05, .25
cov_post = mne.compute_covariance(epochs,
tmin=tmin,
tmax=tmax,
method='empirical')
info = epochs.info
del epochs
# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# use forward operator with fixed source orientations
mne.convert_forward_solution(forward,
def _get_data(tmin=-0.1,
tmax=0.15,
all_forward=True,
epochs=True,
epochs_preload=True,
data_cov=True):
"""Read in data used in tests."""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)
raw = mne.io.read_raw_fif(fname_raw, preload=True)
forward = mne.read_forward_solution(fname_fwd)
if all_forward:
forward_surf_ori = _read_forward_solution_meg(fname_fwd, surf_ori=True)
forward_fixed = _read_forward_solution_meg(fname_fwd,
force_fixed=True,
surf_ori=True,
use_cps=False)
forward_vol = _read_forward_solution_meg(fname_fwd_vol)
else:
forward_surf_ori = None
forward_fixed = None
forward_vol = None
event_id, tmin, tmax = 1, tmin, tmax
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bad channels
# Set up pick list: MEG - bad channels
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, selection=left_temporal_channels)
picks = picks[::2] # decimate for speed
raw.pick_channels([raw.ch_names[ii] for ii in picks])
del picks
raw.info.normalize_proj() # avoid projection warnings
if epochs:
# Read epochs
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
baseline=(None, 0),
preload=epochs_preload,
reject=reject)
if epochs_preload:
epochs.resample(200, npad=0, n_jobs=2)
epochs.crop(0, None)
evoked = epochs.average()
info = evoked.info
else:
epochs = None
evoked = None
info = raw.info
noise_cov = mne.read_cov(fname_cov)
noise_cov['projs'] = [] # avoid warning
noise_cov = mne.cov.regularize(noise_cov,
info,
mag=0.05,
grad=0.05,
eeg=0.1,
proj=True,
rank=None)
if data_cov:
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.145)
else:
data_cov = None
return raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol
###############################################################################
# Power mapping
# -------------
# With our simulated dataset ready, we can now pretend to be researchers that
# have just recorded this from a real subject and are going to study what parts
# of the brain communicate with each other.
#
# First, we'll create a source estimate of the MEG data. We'll use both a
# straightforward MNE-dSPM inverse solution for this, and the DICS beamformer
# which is specifically designed to work with oscillatory data.
###############################################################################
# Computing the inverse using MNE-dSPM:
# Estimating the noise covariance on the trial that only contains noise.
cov = mne.compute_covariance(epochs['noise'])
inv = make_inverse_operator(epochs.info, fwd, cov)
# Apply the inverse model to the trial that also contains the signal.
s = apply_inverse(epochs['signal'].average(), inv)
# Take the root-mean square along the time dimension and plot the result.
s_rms = (s**2).mean()
brain = s_rms.plot('sample',
subjects_dir=subjects_dir,
hemi='both',
figure=1,
size=400)
# Indicate the true locations of the source activity on the plot.
brain.add_foci(source_vert1, coords_as_verts=True, hemi='lh')
if isMEG:
epochs1 = mne.epochs.concatenate_epochs(
[epochs, copy.deepcopy(epochs)])
epochs = epochs1[0:n_im]
epochs._data = data[:, :, 0:n_times1].copy()
del (epochs1)
else:
epochs = epochs[0:n_im]
epochs._data = data[:, :, 0:n_times1].copy()
# interplolate bad channels
epochs.interpolate_bads(reset_bads=True)
evoked = epochs.average()
evoked.save(evoked_path)
t_cov_baseline = -0.05
noise_cov = mne.compute_covariance(epochs, tmin=None, tmax=t_cov_baseline)
noise_cov.save(noise_cov_path)
#offset = 0.04 if isMEG else 0.00
time_ind = np.all(np.vstack([times >= -0.05, times <= 0.9]), axis=0)
M = data[:, :, time_ind]
#times_in_ms = (times[time_ind]-offset)*1000.0
#print len(times_in_ms)
#========= actual computation including bootstrap===============================
prior_Q0, prior_Q, prior_sigma_J_list = None, None, None
prior_A = dict(lambda0=0.0, lambda1=1.0)
depth = None
force_fixed = True if depth is None else False
MaxIter0, MaxIter = 100, 30
tol0, tol = 1E-4, 2E-2
def _get_data(tmin=-0.1, tmax=0.15, all_forward=True, epochs=True,
epochs_preload=True, data_cov=True, proj=True):
"""Read in data used in tests."""
label = mne.read_label(fname_label)
events = mne.read_events(fname_event)
raw = mne.io.read_raw_fif(fname_raw, preload=True)
forward = mne.read_forward_solution(fname_fwd)
if all_forward:
forward_surf_ori = _read_forward_solution_meg(
fname_fwd, surf_ori=True)
forward_fixed = _read_forward_solution_meg(
fname_fwd, force_fixed=True, surf_ori=True, use_cps=False)
forward_vol = _read_forward_solution_meg(fname_fwd_vol)
else:
forward_surf_ori = None
forward_fixed = None
forward_vol = None
event_id, tmin, tmax = 1, tmin, tmax
# Setup for reading the raw data
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bad channels
# Set up pick list: MEG - bad channels
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg=True,
selection=left_temporal_channels)
picks = picks[::2] # decimate for speed
# add a couple channels we will consider bad
bad_picks = [100, 101]
bads = [raw.ch_names[pick] for pick in bad_picks]
assert not any(pick in picks for pick in bad_picks)
picks = np.concatenate([picks, bad_picks])
raw.pick_channels([raw.ch_names[ii] for ii in picks])
del picks
raw.info['bads'] = bads # add more bads
if proj:
raw.info.normalize_proj() # avoid projection warnings
else:
raw.del_proj()
if epochs:
# Read epochs
epochs = mne.Epochs(
raw, events, event_id, tmin, tmax, proj=True,
baseline=(None, 0), preload=epochs_preload, reject=reject)
if epochs_preload:
epochs.resample(200, npad=0)
with pytest.warns(RuntimeWarning, match='baseline = None'):
epochs.crop(0, None)
evoked = epochs.average()
info = evoked.info
else:
epochs = None
evoked = None
info = raw.info
noise_cov = mne.read_cov(fname_cov)
noise_cov['projs'] = [] # avoid warning
noise_cov = mne.cov.regularize(noise_cov, info, mag=0.05, grad=0.05,
eeg=0.1, proj=True, rank=None)
if data_cov:
data_cov = mne.compute_covariance(
epochs, tmin=0.04, tmax=0.145, verbose='error') # baseline warning
else:
data_cov = None
return raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol
# Set up pick list: EEG + MEG - bad channels (modify to your needs)
left_temporal_channels = mne.read_selection('Left-temporal')
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,
exclude='bads', selection=left_temporal_channels)
# Read epochs
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, eog=150e-6))
evoked = epochs.average()
forward = mne.read_forward_solution(fname_fwd, surf_ori=True)
# Read regularized noise covariance and compute regularized data covariance
noise_cov = mne.read_cov(fname_cov)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15,
method='shrunk')
plt.close('all')
pick_oris = [None, 'normal', 'max-power']
names = ['free', 'normal', 'max-power']
descriptions = ['Free orientation', 'Normal orientation', 'Max-power '
'orientation']
colors = ['b', 'k', 'r']
for pick_ori, name, desc, color in zip(pick_oris, names, descriptions, colors):
stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01,
pick_ori=pick_ori)
# View activation time-series
label = mne.read_label(fname_label)
tmin = -0.2 # start of each epoch (200ms before the trigger)
tmax = 0.5 # end of each epoch (500ms after the trigger)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
baseline = (None, 0) # means from the first instant to t = 0
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=('meg', 'eog'), baseline=baseline, reject=reject)
###############################################################################
# Compute regularized noise covariance
# ------------------------------------
#
# For more details see :ref:`tut_compute_covariance`.
noise_cov = mne.compute_covariance(
epochs, tmax=0., method=['shrunk', 'empirical'], rank=None, verbose=True)
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)
###############################################################################
# Compute the evoked response
# ---------------------------
# Let's just use the MEG channels for simplicity.
evoked = epochs.average().pick('meg')
evoked.plot(time_unit='s')
evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',
time_unit='s')
###############################################################################
# It's also a good idea to look at whitened data:
def test_low_rank_cov(raw_epochs_events):
"""Test additional properties of low rank computations."""
raw, epochs, events = raw_epochs_events
sss_proj_rank = 139 # 80 MEG + 60 EEG - 1 proj
n_ch = 366
proj_rank = 365 # one EEG proj
with pytest.warns(RuntimeWarning, match='Too few samples'):
emp_cov = compute_covariance(epochs)
# Test equivalence with mne.cov.regularize subspace
with pytest.raises(ValueError, match='are dependent.*must equal'):
regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == proj_rank
with pytest.warns(RuntimeWarning, match='exceeds the theoretical'):
_compute_rank_int(reg_cov, info=epochs.info)
del reg_cov
with catch_logging() as log:
reg_r_cov = regularize(emp_cov,
epochs.info,
proj=True,
rank=None,
verbose=True)
log = log.getvalue()
assert 'jointly' in log
assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
del reg_r_only_cov, reg_r_cov
# test that rank=306 is same as rank='full'
epochs_meg = epochs.copy().pick_types(meg=True)
assert len(epochs_meg.ch_names) == 306
with epochs_meg.info._unlock():
epochs_meg.info.update(bads=[], projs=[])
cov_full = compute_covariance(epochs_meg,
method='oas',
rank='full',
verbose='error')
assert _cov_rank(cov_full, epochs_meg.info) == 306
with pytest.warns(RuntimeWarning, match='few samples'):
cov_dict = compute_covariance(epochs_meg,
method='oas',
rank=dict(meg=306))
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
cov_dict = compute_covariance(epochs_meg,
method='oas',
rank=dict(meg=306),
verbose='error')
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
# Work with just EEG data to simplify projection / rank reduction
raw = raw.copy().pick_types(meg=False, eeg=True)
n_proj = 2
raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
n_ch = len(raw.ch_names)
rank = n_ch - n_proj - 1 # plus avg proj
assert len(raw.info['projs']) == 3
epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
assert len(raw.ch_names) == n_ch
emp_cov = compute_covariance(epochs, rank='full', verbose='error')
assert _cov_rank(emp_cov, epochs.info) == rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == rank
reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_cov, epochs.info) == rank
dia_cov = compute_covariance(epochs,
rank=None,
method='diagonal_fixed',
verbose='error')
assert _cov_rank(dia_cov, epochs.info) == rank
assert_allclose(dia_cov['data'], reg_cov['data'])
epochs.pick_channels(epochs.ch_names[:103])
# degenerate
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='pca')
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='factor_analysis')
# Create epochs
###############################################################################
title = 'Simulated evoked for two signal vertices'
epochs = create_epochs(raw2,
title=title,
fn_simulated_epochs=None,
fn_report_h5=fn_report_h5)
epochs_grad = epochs.copy().pick_types(meg='grad')
epochs_mag = epochs.copy().pick_types(meg='mag')
epochs_joint = epochs.copy().pick_types(meg=True)
# Make cov matrix
data_cov = mne.compute_covariance(epochs,
tmin=0,
tmax=None,
method='empirical')
noise_cov = mne.compute_covariance(epochs,
tmin=None,
tmax=0,
method='empirical')
evoked_grad = epochs_grad.average()
evoked_mag = epochs_mag.average()
evoked_joint = epochs_joint.average()
###############################################################################
# Compute LCMV beamformer results
###############################################################################
# Speed things up by restricting the forward solution to only the two
# Also, because we want to combine different channel types (magnetometers and
# gradiometers), we need to account for the different amplitude scales of these
# channel types. To do this we will supply a noise covariance matrix to the
# beamformer, which will be used for whitening.
# The data covariance matrix should be estimated from a time window that
# includes the brain signal of interest,
# and incorporate enough samples for a stable estimate. A rule of thumb is to
# use more samples than there are channels in the data set; see
# :footcite:`BrookesEtAl2008` for more detailed advice on covariance estimation
# for beamformers. Here, we use a time
# window incorporating the expected auditory response at around 100 ms post
# stimulus and extend the period to account for a low number of trials (72) and
# low sampling rate of 150 Hz.
data_cov = mne.compute_covariance(epochs,
tmin=0.01,
tmax=0.25,
method='empirical')
noise_cov = mne.compute_covariance(epochs,
tmin=tmin,
tmax=0,
method='empirical')
data_cov.plot(epochs.info)
del epochs
# %%
# When looking at the covariance matrix plots, we can see that our data is
# slightly rank-deficient as the rank is not equal to the number of channels.
# Thus, we will have to regularize the covariance matrix before inverting it
# in the beamformer calculation. This can be achieved by setting the parameter
# ``reg=0.05`` when calculating the spatial filter with
# :func:`~mne.beamformer.make_lcmv`. This corresponds to loading the diagonal
def test_cov_estimation_with_triggers(rank):
"""Test estimation from raw with triggers."""
tempdir = _TempDir()
raw = read_raw_fif(raw_fname)
raw.set_eeg_reference(projection=True).load_data()
events = find_events(raw, stim_channel='STI 014')
event_ids = [1, 2, 3, 4]
reject = dict(grad=10000e-13, mag=4e-12, eeg=80e-6, eog=150e-6)
# cov with merged events and keep_sample_mean=True
events_merged = merge_events(events, event_ids, 1234)
epochs = Epochs(raw,
events_merged,
1234,
tmin=-0.2,
tmax=0,
baseline=(-0.2, -0.1),
proj=True,
reject=reject,
preload=True)
cov = compute_covariance(epochs, keep_sample_mean=True)
_assert_cov(cov, read_cov(cov_km_fname))
# Test with tmin and tmax (different but not too much)
cov_tmin_tmax = compute_covariance(epochs, tmin=-0.19, tmax=-0.01)
assert np.all(cov.data != cov_tmin_tmax.data)
err = (linalg.norm(cov.data - cov_tmin_tmax.data, ord='fro') /
linalg.norm(cov_tmin_tmax.data, ord='fro'))
assert err < 0.05
# cov using a list of epochs and keep_sample_mean=True
epochs = [
Epochs(raw,
events,
ev_id,
tmin=-0.2,
tmax=0,
baseline=(-0.2, -0.1),
proj=True,
reject=reject) for ev_id in event_ids
]
cov2 = compute_covariance(epochs, keep_sample_mean=True)
assert_array_almost_equal(cov.data, cov2.data)
assert cov.ch_names == cov2.ch_names
# cov with keep_sample_mean=False using a list of epochs
cov = compute_covariance(epochs, keep_sample_mean=False)
_assert_cov(cov, read_cov(cov_fname), nfree=False)
method_params = {'empirical': {'assume_centered': False}}
pytest.raises(ValueError,
compute_covariance,
epochs,
keep_sample_mean=False,
method_params=method_params)
pytest.raises(ValueError,
compute_covariance,
epochs,
keep_sample_mean=False,
method='shrunk',
rank=rank)
# test IO when computation done in Python
cov.save(op.join(tempdir, 'test-cov.fif')) # test saving
cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
_assert_cov(cov, cov_read, 1e-5)
# cov with list of epochs with different projectors
epochs = [
Epochs(raw,
events[:1],
None,
tmin=-0.2,
tmax=0,
baseline=(-0.2, -0.1),
proj=True),
Epochs(raw,
events[:1],
None,
tmin=-0.2,
tmax=0,
baseline=(-0.2, -0.1),
proj=False)
]
# these should fail
pytest.raises(ValueError, compute_covariance, epochs)
pytest.raises(ValueError, compute_covariance, epochs, projs=None)
# these should work, but won't be equal to above
with pytest.warns(RuntimeWarning, match='Too few samples'):
cov = compute_covariance(epochs, projs=epochs[0].info['projs'])
with pytest.warns(RuntimeWarning, match='Too few samples'):
cov = compute_covariance(epochs, projs=[])
# test new dict support
epochs = Epochs(raw,
events,
dict(a=1, b=2, c=3, d=4),
tmin=-0.01,
tmax=0,
proj=True,
reject=reject,
preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
compute_covariance(epochs)
with pytest.warns(RuntimeWarning, match='Too few samples'):
compute_covariance(epochs, projs=[])
pytest.raises(TypeError, compute_covariance, epochs, projs='foo')
pytest.raises(TypeError, compute_covariance, epochs, projs=['foo'])
def test_lcmv_reg_proj(proj, weight_norm):
"""Test LCMV with and without proj."""
raw = mne.io.read_raw_fif(fname_raw, preload=True)
events = mne.find_events(raw)
raw.pick_types(meg=True)
assert len(raw.ch_names) == 305
epochs = mne.Epochs(raw, events, None, preload=True, proj=proj)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = mne.compute_covariance(epochs, tmax=0)
data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15)
forward = mne.read_forward_solution(fname_fwd)
filters = make_lcmv(epochs.info, forward, data_cov, reg=0.05,
noise_cov=noise_cov, pick_ori='max-power',
weight_norm='nai', rank=None, verbose=True)
want_rank = 302 # 305 good channels - 3 MEG projs
assert filters['rank'] == want_rank
# And also with and without noise_cov
with pytest.raises(ValueError, match='several sensor types'):
make_lcmv(epochs.info, forward, data_cov, reg=0.05,
noise_cov=None)
epochs.pick_types(meg='grad')
kwargs = dict(reg=0.05, pick_ori=None, weight_norm=weight_norm)
filters_cov = make_lcmv(epochs.info, forward, data_cov,
noise_cov=noise_cov, **kwargs)
filters_nocov = make_lcmv(epochs.info, forward, data_cov,
noise_cov=None, **kwargs)
ad_hoc = mne.make_ad_hoc_cov(epochs.info)
filters_adhoc = make_lcmv(epochs.info, forward, data_cov,
noise_cov=ad_hoc, **kwargs)
evoked = epochs.average()
stc_cov = apply_lcmv(evoked, filters_cov)
stc_nocov = apply_lcmv(evoked, filters_nocov)
stc_adhoc = apply_lcmv(evoked, filters_adhoc)
# Compare adhoc and nocov: scale difference is necessitated by using std=1.
if weight_norm == 'unit-noise-gain':
scale = np.sqrt(ad_hoc['data'][0])
else:
scale = 1.
assert_allclose(stc_nocov.data, stc_adhoc.data * scale)
a = np.dot(filters_nocov['weights'], filters_nocov['whitener'])
b = np.dot(filters_adhoc['weights'], filters_adhoc['whitener']) * scale
atol = np.mean(np.sqrt(a * a)) * 1e-7
assert_allclose(a, b, atol=atol, rtol=1e-7)
# Compare adhoc and cov: locs might not be equivalent, but the same
# general profile should persist, so look at the std and be lenient:
if weight_norm == 'unit-noise-gain':
adhoc_scale = 0.12
else:
adhoc_scale = 1.
assert_allclose(
np.linalg.norm(stc_adhoc.data, axis=0) * adhoc_scale,
np.linalg.norm(stc_cov.data, axis=0), rtol=0.3)
assert_allclose(
np.linalg.norm(stc_nocov.data, axis=0) / scale * adhoc_scale,
np.linalg.norm(stc_cov.data, axis=0), rtol=0.3)
if weight_norm == 'nai':
# NAI is always normalized by noise-level (based on eigenvalues)
for stc in (stc_nocov, stc_cov):
assert_allclose(stc.data.std(), 0.584, rtol=0.2)
elif weight_norm is None:
# None always represents something not normalized, reflecting channel
# weights
for stc in (stc_nocov, stc_cov):
assert_allclose(stc.data.std(), 2.8e-8, rtol=0.1)
else:
assert weight_norm == 'unit-noise-gain'
# Channel scalings depend on presence of noise_cov
assert_allclose(stc_nocov.data.std(), 7.8e-13, rtol=0.1)
assert_allclose(stc_cov.data.std(), 0.187, rtol=0.2)
fwd = mne.make_forward_solution(epochs.info, trans=transfile, src=src_vol, bem=bem,
meg=True, eeg=False, mindist=2.5, n_jobs=1)
if 'fwd' in locals() and not len(epochs.ch_names)==fwd['nchan']:
fwd = mne.make_forward_solution(epochs.info, trans=transfile, src=src_vol, bem=bem,
meg=True, eeg=False, mindist=2.5, n_jobs=1)
print("Leadfield size : %d sensors x %d dipoles" % fwd['sol']['data'].shape)
#%% Average post-stim data and find input SNR
evoked = epochs.average()
evoked_pst = evoked.copy().crop(tmin=0.050, tmax=0.500)
if more_plots:
evoked.comment=dfname
evoked.plot(spatial_colors=True, gfp=True, time_unit='ms')
evoked_pst.plot(spatial_colors=True, gfp=True, time_unit='ms')
noise_cov = mne.compute_covariance(epochs, tmin=-0.500, tmax=-0.050, method='empirical')
data_cov = mne.compute_covariance(epochs, tmin=0.050, tmax=0.500, method='empirical')
cov_rank = None if raw.info['proc_history']==[] else int(raw.info['proc_history'][0]['max_info']['sss_info']['nfree'])
inverse_operator = mne.minimum_norm.make_inverse_operator(epochs.info, fwd, noise_cov,
rank=cov_rank, loose=1.0, depth=0.199)
snr_mnep, kk = mne.minimum_norm.estimate_snr(evoked_pst, inverse_operator, verbose=True)
peak_ch, peak_time = evoked_pst.get_peak(ch_type='mag')
tp = int((peak_time - 0.050)*evoked_pst.info['sfreq'])
snr = snr_mnep[tp]
snr_10log10 = 10*np.log10(snr)
#%% Compute spatial filter and apply on post-stim data
filters = mne.beamformer.make_lcmv(evoked.info, fwd, data_cov, reg=0.05, noise_cov=noise_cov,
pick_ori='max-power', rank=cov_rank, weight_norm='nai',
subtract_evoked = False
# Calculating covariance from empty room noise. To use baseline data as noise
# substitute raw for raw_noise, epochs.events for epochs_noise.events, tmin for
# 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
event_id, tmin, tmax = 1, -1., 3.
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
reject=reject,
preload=True)
evoked = epochs.filter(1, None).average()
evoked = evoked.pick_types(meg=True)
evoked.crop(tmin=0.008, tmax=0.2, verbose='error') # ignore baseline
# Compute noise covariance matrix
cov = mne.compute_covariance(epochs, rank='info', tmax=0.)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname)
###############################################################################
# Run iterative reweighted multidict TF-MxNE solver
alpha, l1_ratio = 20, 0.05
loose, depth = 1, 0.95
# Use a multiscale time-frequency dictionary
wsize, tstep = [4, 16], [2, 4]
n_tfmxne_iter = 10
# Compute TF-MxNE inverse solution with dipole output
dipoles, residual = tf_mixed_norm(evoked,