window[start:stop] = 1.
n += 1
data = 25e-9 * np.sin(2. * np.pi * 10. * n * times)
data *= window
return data
times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
src = read_source_spaces(src_fname)
stc = simulate_sparse_stc(src, n_dipoles=n_dipoles, times=times,
data_fun=data_fun, random_state=0)
# 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)')
fig.show()
##############################################################################
# Simulate raw data
raw_sim = simulate_raw(raw, stc, trans_fname, src, bem_fname, cov='simple',
iir_filter=[0.2, -0.2, 0.04], ecg=True, blink=True,
n_jobs=2, verbose=True)
raw_sim.plot()
##############################################################################
# Plot evoked data
events = find_events(raw_sim) # only 1 pos, so event number == 1
epochs = Epochs(raw_sim, events, 1, -0.2, epoch_duration)
cov = compute_covariance(epochs, tmax=0., method='empirical') # quick calc
evoked = epochs.average()
evoked.plot_white(cov)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
add_eeg_ref=False)
epochs_train = epochs.copy().crop(tmin=1., tmax=2.)
labels = epochs.events[:, -1] - 2
###############################################################################
# Classification with linear discrimant analysis
from sklearn.lda import LDA # noqa
from sklearn.cross_validation import ShuffleSplit # noqa
# Assemble a classifier
svc = LDA()
def test_render_report():
"""Test rendering -*.fif files for mne report.
"""
tempdir = _TempDir()
raw_fname_new = op.join(tempdir, 'temp_raw.fif')
event_fname_new = op.join(tempdir, 'temp_raw-eve.fif')
cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif')
fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif')
inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif')
for a, b in [[raw_fname, raw_fname_new], [event_fname, event_fname_new],
[cov_fname, cov_fname_new], [fwd_fname, fwd_fname_new],
[inv_fname, inv_fname_new]]:
shutil.copyfile(a, b)
# create and add -epo.fif and -ave.fif files
epochs_fname = op.join(tempdir, 'temp-epo.fif')
evoked_fname = op.join(tempdir, 'temp-ave.fif')
raw = Raw(raw_fname_new)
picks = pick_types(raw.info, meg='mag', eeg=False) # faster with one type
epochs = Epochs(raw, read_events(event_fname), 1, -0.2, 0.2, picks=picks)
epochs.save(epochs_fname)
epochs.average().save(evoked_fname)
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir)
assert_true(len(w) >= 1)
# Check correct paths and filenames
fnames = glob.glob(op.join(tempdir, '*.fif'))
for fname in fnames:
assert_true(
op.basename(fname) in [op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
assert_equal(len(report.fnames), len(fnames))
assert_equal(len(report.html), len(report.fnames))
# Check saving functionality
report.data_path = tempdir
report.save(fname=op.join(tempdir, 'report.html'), open_browser=False)
assert_true(op.isfile(op.join(tempdir, 'report.html')))
assert_equal(len(report.html), len(fnames))
assert_equal(len(report.html), len(report.fnames))
# Check saving same report to new filename
report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False)
assert_true(op.isfile(op.join(tempdir, 'report2.html')))
# Check overwriting file
report.save(fname=op.join(tempdir, 'report.html'),
open_browser=False,
overwrite=True)
assert_true(op.isfile(op.join(tempdir, 'report.html')))
# Check pattern matching with multiple patterns
pattern = ['*raw.fif', '*eve.fif']
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, pattern=pattern)
assert_true(len(w) >= 1)
fnames = glob.glob(op.join(tempdir, '*.raw')) + \
glob.glob(op.join(tempdir, '*.raw'))
for fname in fnames:
assert_true(
op.basename(fname) in [op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
def save_epochs(p, subjects, in_names, in_numbers, analyses, out_names,
out_numbers, must_match, decim, run_indices):
"""Generate epochs from raw data based on events
Can only complete after preprocessing is complete.
Parameters
----------
p : instance of Parameters
Analysis parameters.
subjects : list of str
Subject names to analyze (e.g., ['Eric_SoP_001', ...]).
in_names : list of str
Names of input events.
in_numbers : list of list of int
Event numbers (in scored event files) associated with each name.
analyses : list of str
Lists of analyses of interest.
out_names : list of list of str
Event types to make out of old ones.
out_numbers : list of list of int
Event numbers to convert to (e.g., [[1, 1, 2, 3, 3], ...] would create
three event types, where the first two and last two event types from
the original list get collapsed over).
must_match : list of int
Indices from the original in_names that must match in event counts
before collapsing. Should eventually be expanded to allow for
ratio-based collapsing.
decim : int | list of int
Amount to decimate.
run_indices : array-like | None
Run indices to include.
"""
in_names = np.asanyarray(in_names)
old_dict = dict()
for n, e in zip(in_names, in_numbers):
old_dict[n] = e
# let's do some sanity checks
if len(in_names) != len(in_numbers):
raise RuntimeError('in_names (%d) must have same length as '
'in_numbers (%d)' %
(len(in_names), len(in_numbers)))
if np.any(np.array(in_numbers) <= 0):
raise ValueError('in_numbers must all be > 0')
if len(out_names) != len(out_numbers):
raise RuntimeError('out_names must have same length as out_numbers')
for name, num in zip(out_names, out_numbers):
num = np.array(num)
if len(name) != len(np.unique(num[num > 0])):
raise RuntimeError('each entry in out_names must have length '
'equal to the number of unique elements in the '
'corresponding entry in out_numbers:\n%s\n%s' %
(name, np.unique(num[num > 0])))
if len(num) != len(in_names):
raise RuntimeError('each entry in out_numbers must have the same '
'length as in_names')
if (np.array(num) == 0).any():
raise ValueError('no element of out_numbers can be zero')
ch_namess = list()
drop_logs = list()
sfreqs = set()
for si, subj in enumerate(subjects):
if p.disp_files:
print(' Loading raw files for subject %s.' % subj)
epochs_dir = op.join(p.work_dir, subj, p.epochs_dir)
if not op.isdir(epochs_dir):
os.mkdir(epochs_dir)
evoked_dir = op.join(p.work_dir, subj, p.inverse_dir)
if not op.isdir(evoked_dir):
os.mkdir(evoked_dir)
# read in raw files
raw_names = get_raw_fnames(p, subj, 'pca', False, False,
run_indices[si])
first_samps = []
last_samps = []
for raw_fname in raw_names:
raw = read_raw_fif(raw_fname, preload=False)
first_samps.append(raw._first_samps[0])
last_samps.append(raw._last_samps[-1])
raw = [read_raw_fif(fname, preload=False) for fname in raw_names]
_fix_raw_eog_cals(raw) # EOG epoch scales might be bad!
raw = concatenate_raws(raw)
# read in events
events = _read_events(p, subj, run_indices[si], raw)
new_sfreq = raw.info['sfreq'] / decim[si]
if p.disp_files:
print(' Epoching data (decim=%s -> sfreq=%0.1f Hz).' %
(decim[si], new_sfreq))
if new_sfreq not in sfreqs:
if len(sfreqs) > 0:
warnings.warn('resulting new sampling frequency %s not equal '
'to previous values %s' % (new_sfreq, sfreqs))
sfreqs.add(new_sfreq)
epochs_fnames, evoked_fnames = get_epochs_evokeds_fnames(
p, subj, analyses)
mat_file, fif_file = epochs_fnames
if p.autoreject_thresholds:
assert len(p.autoreject_types) > 0
assert all(a in ('mag', 'grad', 'eeg', 'ecg', 'eog')
for a in p.autoreject_types)
from autoreject import get_rejection_threshold
print(' Computing autoreject thresholds', end='')
rtmin = p.reject_tmin if p.reject_tmin is not None else p.tmin
rtmax = p.reject_tmax if p.reject_tmax is not None else p.tmax
temp_epochs = Epochs(raw,
events,
event_id=None,
tmin=rtmin,
tmax=rtmax,
baseline=_get_baseline(p),
proj=True,
reject=None,
flat=None,
preload=True,
decim=decim[si],
reject_by_annotation=p.reject_epochs_by_annot)
kwargs = dict()
if 'verbose' in get_args(get_rejection_threshold):
kwargs['verbose'] = False
new_dict = get_rejection_threshold(temp_epochs, **kwargs)
use_reject = dict()
msgs = list()
for k in p.autoreject_types:
msgs.append('%s=%d %s' % (k, DEFAULTS['scalings'][k] *
new_dict[k], DEFAULTS['units'][k]))
use_reject[k] = new_dict[k]
print(': ' + ', '.join(msgs))
hdf5_file = fif_file.replace('-epo.fif', '-reject.h5')
assert hdf5_file.endswith('.h5')
write_hdf5(hdf5_file, use_reject, overwrite=True)
else:
use_reject = _handle_dict(p.reject, subj)
# create epochs
flat = _handle_dict(p.flat, subj)
use_reject, use_flat = _restrict_reject_flat(use_reject, flat, raw)
epochs = Epochs(raw,
events,
event_id=old_dict,
tmin=p.tmin,
tmax=p.tmax,
baseline=_get_baseline(p),
reject=use_reject,
flat=use_flat,
proj=p.epochs_proj,
preload=True,
decim=decim[si],
on_missing=p.on_missing,
reject_tmin=p.reject_tmin,
reject_tmax=p.reject_tmax,
reject_by_annotation=p.reject_epochs_by_annot)
del raw
if epochs.events.shape[0] < 1:
epochs.plot_drop_log()
raise ValueError('No valid epochs')
drop_logs.append(epochs.drop_log)
ch_namess.append(epochs.ch_names)
# only kept trials that were not dropped
sfreq = epochs.info['sfreq']
# now deal with conditions to save evoked
if p.disp_files:
print(' Matching trial counts and saving data to disk.')
for var, name in ((out_names, 'out_names'), (out_numbers,
'out_numbers'),
(must_match, 'must_match'), (evoked_fnames,
'evoked_fnames')):
if len(var) != len(analyses):
raise ValueError('len(%s) (%s) != len(analyses) (%s)' %
(name, len(var), len(analyses)))
for analysis, names, numbers, match, fn in zip(analyses, out_names,
out_numbers, must_match,
evoked_fnames):
# do matching
numbers = np.asanyarray(numbers)
nn = numbers[numbers >= 0]
new_numbers = []
for num in numbers:
if num > 0 and num not in new_numbers:
# Eventually we could relax this requirement, but not
# having it in place is likely to cause people pain...
if any(num < n for n in new_numbers):
raise RuntimeError('each list of new_numbers must be '
' monotonically increasing')
new_numbers.append(num)
new_numbers = np.array(new_numbers)
in_names_match = in_names[match]
# use some variables to allow safe name re-use
offset = max(epochs.events[:, 2].max(), new_numbers.max()) + 1
safety_str = '__mnefun_copy__'
assert len(new_numbers) == len(names) # checked above
if p.match_fun is None:
# first, equalize trial counts (this will make a copy)
e = epochs[list(in_names[numbers > 0])]
if len(in_names_match) > 1:
e.equalize_event_counts(in_names_match)
# second, collapse relevant types
for num, name in zip(new_numbers, names):
collapse = [
x for x in in_names[num == numbers] if x in e.event_id
]
combine_event_ids(e,
collapse,
{name + safety_str: num + offset},
copy=False)
for num, name in zip(new_numbers, names):
e.events[e.events[:, 2] == num + offset, 2] -= offset
e.event_id[name] = num
del e.event_id[name + safety_str]
else: # custom matching
e = p.match_fun(epochs.copy(), analysis, nn, in_names_match,
names)
# now make evoked for each out type
evokeds = list()
n_standard = 0
kinds = ['standard']
if p.every_other:
kinds += ['even', 'odd']
for kind in kinds:
for name in names:
this_e = e[name]
if kind == 'even':
this_e = this_e[::2]
elif kind == 'odd':
this_e = this_e[1::2]
else:
assert kind == 'standard'
if len(this_e) > 0:
ave = this_e.average(picks='all')
stde = this_e.standard_error(picks='all')
if kind != 'standard':
ave.comment += ' %s' % (kind, )
stde.comment += ' %s' % (kind, )
evokeds.append(ave)
evokeds.append(stde)
if kind == 'standard':
n_standard += 2
write_evokeds(fn, evokeds)
naves = [
str(n) for n in sorted(
set([evoked.nave for evoked in evokeds[:n_standard]]))
]
naves = ', '.join(naves)
if p.disp_files:
print(' Analysis "%s": %s epochs / condition' %
(analysis, naves))
if p.disp_files:
print(' Saving epochs to disk.')
if 'mat' in p.epochs_type:
spio.savemat(mat_file,
dict(epochs=epochs.get_data(),
events=epochs.events,
sfreq=sfreq,
drop_log=epochs.drop_log),
do_compression=True,
oned_as='column')
if 'fif' in p.epochs_type:
epochs.save(fif_file, **_get_epo_kwargs())
if p.plot_drop_logs:
for subj, drop_log in zip(subjects, drop_logs):
plot_drop_log(drop_log, threshold=p.drop_thresh, subject=subj)
def test_csd_degenerate(evoked_csd_sphere):
"""Test degenerate conditions."""
evoked, csd, sphere = evoked_csd_sphere
warn_evoked = evoked.copy()
warn_evoked.info['bads'].append(warn_evoked.ch_names[3])
with pytest.raises(ValueError, match='Either drop.*or interpolate'):
compute_current_source_density(warn_evoked)
with pytest.raises(TypeError, match='must be an instance of'):
compute_current_source_density(None)
fail_evoked = evoked.copy()
with pytest.raises(ValueError, match='Zero or infinite position'):
for ch in fail_evoked.info['chs']:
ch['loc'][:3] = np.array([0, 0, 0])
compute_current_source_density(fail_evoked, sphere=sphere)
with pytest.raises(ValueError, match='Zero or infinite position'):
fail_evoked.info['chs'][3]['loc'][:3] = np.inf
compute_current_source_density(fail_evoked, sphere=sphere)
with pytest.raises(ValueError, match='No EEG channels found.'):
fail_evoked = evoked.copy()
fail_evoked.set_channel_types(
{ch_name: 'ecog'
for ch_name in fail_evoked.ch_names})
compute_current_source_density(fail_evoked, sphere=sphere)
with pytest.raises(TypeError, match='lambda2'):
compute_current_source_density(evoked, lambda2='0', sphere=sphere)
with pytest.raises(ValueError, match='lambda2 must be between 0 and 1'):
compute_current_source_density(evoked, lambda2=2, sphere=sphere)
with pytest.raises(TypeError, match='stiffness must be'):
compute_current_source_density(evoked, stiffness='0', sphere=sphere)
with pytest.raises(ValueError, match='stiffness must be non-negative'):
compute_current_source_density(evoked, stiffness=-2, sphere=sphere)
with pytest.raises(TypeError, match='n_legendre_terms must be'):
compute_current_source_density(evoked,
n_legendre_terms=0.1,
sphere=sphere)
with pytest.raises(ValueError,
match=('n_legendre_terms must be '
'greater than 0')):
compute_current_source_density(evoked,
n_legendre_terms=0,
sphere=sphere)
with pytest.raises(ValueError, match='sphere must be'):
compute_current_source_density(evoked, sphere=-0.1)
with pytest.raises(ValueError,
match=('sphere radius must be '
'greater than 0')):
compute_current_source_density(evoked, sphere=(-0.1, 0., 0., -1.))
with pytest.raises(TypeError):
compute_current_source_density(evoked, copy=2, sphere=sphere)
# gh-7859
raw = RawArray(evoked.data, evoked.info)
epochs = Epochs(raw, [[0, 0, 1]],
tmin=0,
tmax=evoked.times[-1] - evoked.times[0],
baseline=None,
preload=False,
proj=False)
epochs.drop_bad()
assert len(epochs) == 1
assert_allclose(epochs.get_data()[0], evoked.data)
with pytest.raises(RuntimeError, match='Computing CSD requires.*preload'):
compute_current_source_density(epochs)
epochs.load_data()
raw = compute_current_source_density(raw)
assert not np.allclose(raw.get_data(), evoked.data)
evoked = compute_current_source_density(evoked)
assert_allclose(raw.get_data(), evoked.data)
epochs = compute_current_source_density(epochs)
assert_allclose(epochs.get_data()[0], evoked.data)
def test_ica_core(method):
"""Test ICA on raw and epochs."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
# XXX. The None cases helped revealing bugs but are time consuming.
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
noise_cov = [None, test_cov]
# removed None cases to speed up...
n_components = [2, 1.0] # for future dbg add cases
max_pca_components = [3]
picks_ = [picks]
methods = [method]
iter_ica_params = product(noise_cov, n_components, max_pca_components,
picks_, methods)
# # test init catchers
pytest.raises(ValueError, ICA, n_components=3, max_pca_components=2)
pytest.raises(ValueError, ICA, n_components=2.3, max_pca_components=2)
# test essential core functionality
for n_cov, n_comp, max_n, pcks, method in iter_ica_params:
# Test ICA raw
ica = ICA(noise_cov=n_cov, n_components=n_comp,
max_pca_components=max_n, n_pca_components=max_n,
random_state=0, method=method, max_iter=1)
pytest.raises(ValueError, ica.__contains__, 'mag')
print(ica) # to test repr
# test fit checker
pytest.raises(RuntimeError, ica.get_sources, raw)
pytest.raises(RuntimeError, ica.get_sources, epochs)
# Test error upon empty epochs fitting
with pytest.raises(RuntimeError, match='none were found'):
ica.fit(epochs[0:0])
# test decomposition
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=pcks, start=start, stop=stop)
repr(ica) # to test repr
assert ('mag' in ica) # should now work without error
# test re-fit
unmixing1 = ica.unmixing_matrix_
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=pcks, start=start, stop=stop)
assert_array_almost_equal(unmixing1, ica.unmixing_matrix_)
raw_sources = ica.get_sources(raw)
# test for #3804
assert_equal(raw_sources._filenames, [None])
print(raw_sources)
# test for gh-6271 (scaling of ICA traces)
fig = raw_sources.plot()
assert len(fig.axes[0].lines) in (4, 5, 6)
for line in fig.axes[0].lines:
y = line.get_ydata()
if len(y) > 2: # actual data, not markers
assert np.ptp(y) < 15
plt.close('all')
sources = raw_sources[:, :][0]
assert (sources.shape[0] == ica.n_components_)
# test preload filter
raw3 = raw.copy()
raw3.preload = False
pytest.raises(RuntimeError, ica.apply, raw3,
include=[1, 2])
#######################################################################
# test epochs decomposition
ica = ICA(noise_cov=n_cov, n_components=n_comp,
max_pca_components=max_n, n_pca_components=max_n,
random_state=0, method=method)
with pytest.warns(None): # sometimes warns
ica.fit(epochs, picks=picks)
data = epochs.get_data()[:, 0, :]
n_samples = np.prod(data.shape)
assert_equal(ica.n_samples_, n_samples)
print(ica) # to test repr
sources = ica.get_sources(epochs).get_data()
assert (sources.shape[1] == ica.n_components_)
pytest.raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# test preload filter
epochs3 = epochs.copy()
epochs3.preload = False
pytest.raises(RuntimeError, ica.apply, epochs3,
include=[1, 2])
# test for bug with whitener updating
_pre_whitener = ica.pre_whitener_.copy()
epochs._data[:, 0, 10:15] *= 1e12
ica.apply(epochs.copy())
assert_array_equal(_pre_whitener, ica.pre_whitener_)
# test expl. var threshold leading to empty sel
ica.n_components = 0.1
pytest.raises(RuntimeError, ica.fit, epochs)
offender = 1, 2, 3,
pytest.raises(ValueError, ica.get_sources, offender)
pytest.raises(TypeError, ica.fit, offender)
pytest.raises(TypeError, ica.apply, offender)
def test_render_report(renderer, tmpdir):
"""Test rendering *.fif files for mne report."""
tempdir = str(tmpdir)
raw_fname_new = op.join(tempdir, 'temp_raw.fif')
raw_fname_new_bids = op.join(tempdir, 'temp_meg.fif')
ms_fname_new = op.join(tempdir, 'temp_ms_raw.fif')
event_fname_new = op.join(tempdir, 'temp_raw-eve.fif')
cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif')
proj_fname_new = op.join(tempdir, 'temp_ecg-proj.fif')
fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif')
inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif')
nirs_fname_new = op.join(tempdir, 'temp_raw-nirs.snirf')
for a, b in [[raw_fname, raw_fname_new],
[raw_fname, raw_fname_new_bids],
[ms_fname, ms_fname_new],
[event_fname, event_fname_new],
[cov_fname, cov_fname_new],
[proj_fname, proj_fname_new],
[fwd_fname, fwd_fname_new],
[inv_fname, inv_fname_new],
[nirs_fname, nirs_fname_new]]:
shutil.copyfile(a, b)
# create and add -epo.fif and -ave.fif files
epochs_fname = op.join(tempdir, 'temp-epo.fif')
evoked_fname = op.join(tempdir, 'temp-ave.fif')
# Speed it up by picking channels
raw = read_raw_fif(raw_fname_new, preload=True)
raw.pick_channels(['MEG 0111', 'MEG 0121', 'EEG 001', 'EEG 002'])
raw.del_proj()
raw.set_eeg_reference(projection=True)
epochs = Epochs(raw, read_events(event_fname), 1, -0.2, 0.2)
epochs.save(epochs_fname, overwrite=True)
# This can take forever, so let's make it fast
# Also, make sure crop range is wide enough to avoid rendering bug
evoked = epochs.average()
with pytest.warns(RuntimeWarning, match='tmax is not in Evoked'):
evoked.crop(0.1, 0.2)
evoked.save(evoked_fname)
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir,
projs=True)
with pytest.warns(RuntimeWarning, match='Cannot render MRI'):
report.parse_folder(data_path=tempdir, on_error='raise')
assert repr(report)
# Check correct paths and filenames
fnames = glob.glob(op.join(tempdir, '*.fif'))
fnames.extend(glob.glob(op.join(tempdir, '*.snirf')))
for fname in fnames:
assert (op.basename(fname) in
[op.basename(x) for x in report.fnames])
assert (''.join(report.html).find(op.basename(fname)) != -1)
assert len(report.fnames) == len(fnames)
assert len(report.html) == len(report.fnames)
assert len(report.fnames) == len(report)
# Check saving functionality
report.data_path = tempdir
fname = op.join(tempdir, 'report.html')
report.save(fname=fname, open_browser=False)
assert (op.isfile(fname))
with open(fname, 'rb') as fid:
html = fid.read().decode('utf-8')
assert '(MaxShield on)' in html
# Projectors in Raw.info
assert '<h4>SSP Projectors</h4>' in html
# Projectors in `proj_fname_new`
assert f'SSP Projectors: {op.basename(proj_fname_new)}' in html
# Evoked in `evoked_fname`
assert f'Evoked: {op.basename(evoked_fname)} ({evoked.comment})' in html
assert 'Topomap (ch_type =' in html
assert f'Evoked: {op.basename(evoked_fname)} (GFPs)' in html
assert len(report.html) == len(fnames)
assert len(report.html) == len(report.fnames)
# Check saving same report to new filename
report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False)
assert (op.isfile(op.join(tempdir, 'report2.html')))
# Check overwriting file
report.save(fname=op.join(tempdir, 'report.html'), open_browser=False,
overwrite=True)
assert (op.isfile(op.join(tempdir, 'report.html')))
# Check pattern matching with multiple patterns
pattern = ['*raw.fif', '*eve.fif']
with pytest.warns(RuntimeWarning, match='Cannot render MRI'):
report.parse_folder(data_path=tempdir, pattern=pattern)
assert (repr(report))
fnames = glob.glob(op.join(tempdir, '*.raw')) + \
glob.glob(op.join(tempdir, '*.raw'))
for fname in fnames:
assert (op.basename(fname) in
[op.basename(x) for x in report.fnames])
assert (''.join(report.html).find(op.basename(fname)) != -1)
pytest.raises(ValueError, Report, image_format='foo')
pytest.raises(ValueError, Report, image_format=None)
# SVG rendering
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir,
image_format='svg')
tempdir = pathlib.Path(tempdir) # test using pathlib.Path
with pytest.warns(RuntimeWarning, match='Cannot render MRI'):
report.parse_folder(data_path=tempdir, on_error='raise')
# ndarray support smoke test
report.add_figs_to_section(np.zeros((2, 3, 3)), 'caption', 'section')
with pytest.raises(TypeError, match='figure must be a'):
report.add_figs_to_section('foo', 'caption', 'section')
with pytest.raises(TypeError, match='figure must be a'):
report.add_figs_to_section(['foo'], 'caption', 'section')
def PreProcess(raw,
event_id,
plot_psd=False,
filter_data=True,
filter_range=(1, 30),
plot_events=False,
epoch_time=(-.2, 1),
baseline=(-.2, 0),
rej_thresh_uV=200,
rereference=False,
emcp_raw=False,
emcp_epochs=False,
epoch_decim=1,
plot_electrodes=False,
plot_erp=False):
sfreq = raw.info['sfreq']
#create new output freq for after epoch or wavelet decim
nsfreq = sfreq / epoch_decim
tmin = epoch_time[0]
tmax = epoch_time[1]
if filter_range[1] > nsfreq:
filter_range[
1] = nsfreq / 2.5 #lower than 2 to avoid aliasing from decim??
#pull event names in order of trigger number
event_names = ['A_error', 'B_error']
i = 0
for key, value in sorted(event_id.items(), key=lambda x: (x[1], x[0])):
event_names[i] = key
i += 1
#Filtering
if rereference:
print('Rerefering to average mastoid')
raw = mastoidReref(raw)
if filter_data:
print('Filtering Data Between ' + str(filter_range[0]) + ' and ' +
str(filter_range[1]) + ' Hz.')
raw.filter(filter_range[0],
filter_range[1],
method='iir',
verbose='WARNING')
if plot_psd:
raw.plot_psd(fmin=filter_range[0], fmax=nsfreq / 2)
#Eye Correction
if emcp_raw:
print('Raw Eye Movement Correction')
raw = GrattonEmcpRaw(raw)
#Epoching
events = find_events(raw, shortest_event=1)
color = {1: 'red', 2: 'black'}
#artifact rejection
rej_thresh = rej_thresh_uV * 1e-6
#plot event timing
if plot_events:
viz.plot_events(events,
sfreq,
raw.first_samp,
color=color,
event_id=event_id)
#Construct events - Main function from MNE
epochs = Epochs(raw,
events=events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
baseline=baseline,
preload=True,
reject={'eeg': rej_thresh},
verbose=False,
decim=epoch_decim)
print('Remaining Trials: ' + str(len(epochs)))
#Gratton eye movement correction procedure on epochs
if emcp_epochs:
print('Epochs Eye Movement Correct')
epochs = GrattonEmcpEpochs(epochs)
## plot ERP at each electrode
evoked_dict = {
event_names[0]: epochs[event_names[0]].average(),
event_names[1]: epochs[event_names[1]].average()
}
# butterfly plot
if plot_electrodes:
picks = pick_types(evoked_dict[event_names[0]].info,
meg=False,
eeg=True,
eog=False)
fig_zero = evoked_dict[event_names[0]].plot(spatial_colors=True,
picks=picks)
fig_zero = evoked_dict[event_names[1]].plot(spatial_colors=True,
picks=picks)
# plot ERP in each condition on same plot
if plot_erp:
#find the electrode most miximal on the head (highest in z)
picks = np.argmax([
evoked_dict[event_names[0]].info['chs'][i]['loc'][2]
for i in range(len(evoked_dict[event_names[0]].info['chs']))
])
colors = {event_names[0]: "Red", event_names[1]: "Blue"}
viz.plot_compare_evokeds(evoked_dict,
colors=colors,
picks=picks,
split_legend=True)
return epochs
def test_add_reference():
"""Test adding a reference."""
raw = read_raw_fif(fif_fname, preload=True)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# check if channel already exists
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add reference channel to Raw
raw_ref = add_reference_channels(raw, 'Ref', copy=True)
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 1)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
_check_channel_names(raw_ref, 'Ref')
orig_nchan = raw.info['nchan']
raw = add_reference_channels(raw, 'Ref', copy=False)
assert_array_equal(raw._data, raw_ref._data)
assert_equal(raw.info['nchan'], orig_nchan + 1)
_check_channel_names(raw, 'Ref')
# for Neuromag fif's, the reference electrode location is placed in
# elements [3:6] of each "data" electrode location
assert_allclose(raw.info['chs'][-1]['loc'][:3],
raw.info['chs'][picks_eeg[0]]['loc'][3:6], 1e-6)
ref_idx = raw.ch_names.index('Ref')
ref_data, _ = raw[ref_idx]
assert_array_equal(ref_data, 0)
# add reference channel to Raw when no digitization points exist
raw = read_raw_fif(fif_fname).crop(0, 1).load_data()
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
del raw.info['dig']
raw_ref = add_reference_channels(raw, 'Ref', copy=True)
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 1)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
_check_channel_names(raw_ref, 'Ref')
orig_nchan = raw.info['nchan']
raw = add_reference_channels(raw, 'Ref', copy=False)
assert_array_equal(raw._data, raw_ref._data)
assert_equal(raw.info['nchan'], orig_nchan + 1)
_check_channel_names(raw, 'Ref')
# Test adding an existing channel as reference channel
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add two reference channels to Raw
raw_ref = add_reference_channels(raw, ['M1', 'M2'], copy=True)
_check_channel_names(raw_ref, ['M1', 'M2'])
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 2)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
assert_array_equal(raw_ref._data[-2:, :], 0)
raw = add_reference_channels(raw, ['M1', 'M2'], copy=False)
_check_channel_names(raw, ['M1', 'M2'])
ref_idx = raw.ch_names.index('M1')
ref_idy = raw.ch_names.index('M2')
ref_data, _ = raw[[ref_idx, ref_idy]]
assert_array_equal(ref_data, 0)
# add reference channel to epochs
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
# default: proj=True, after which adding a Ref channel is prohibited
assert_raises(RuntimeError, add_reference_channels, epochs, 'Ref')
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
epochs_ref = add_reference_channels(epochs, 'Ref', copy=True)
# CAR after custom reference is an Error
assert_raises(RuntimeError, epochs_ref.set_eeg_reference)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 1)
_check_channel_names(epochs_ref, 'Ref')
ref_idx = epochs_ref.ch_names.index('Ref')
ref_data = epochs_ref.get_data()[:, ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add two reference channels to epochs
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
with warnings.catch_warnings(record=True): # multiple set zero
epochs_ref = add_reference_channels(epochs, ['M1', 'M2'], copy=True)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 2)
_check_channel_names(epochs_ref, ['M1', 'M2'])
ref_idx = epochs_ref.ch_names.index('M1')
ref_idy = epochs_ref.ch_names.index('M2')
assert_equal(epochs_ref.info['chs'][ref_idx]['ch_name'], 'M1')
assert_equal(epochs_ref.info['chs'][ref_idy]['ch_name'], 'M2')
ref_data = epochs_ref.get_data()[:, [ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add reference channel to evoked
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
evoked = epochs.average()
evoked_ref = add_reference_channels(evoked, 'Ref', copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 1)
_check_channel_names(evoked_ref, 'Ref')
ref_idx = evoked_ref.ch_names.index('Ref')
ref_data = evoked_ref.data[ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# add two reference channels to evoked
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
evoked = epochs.average()
with warnings.catch_warnings(record=True): # multiple set zero
evoked_ref = add_reference_channels(evoked, ['M1', 'M2'], copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 2)
_check_channel_names(evoked_ref, ['M1', 'M2'])
ref_idx = evoked_ref.ch_names.index('M1')
ref_idy = evoked_ref.ch_names.index('M2')
ref_data = evoked_ref.data[[ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# Test invalid inputs
raw_np = read_raw_fif(fif_fname, preload=False)
assert_raises(RuntimeError, add_reference_channels, raw_np, ['Ref'])
assert_raises(ValueError, add_reference_channels, raw, 1)
raw_fname = op.join(base_dir, 'test_raw.fif')
event_name = op.join(base_dir, 'test-eve.fif')
evoked_nf_name = op.join(base_dir, 'test-nf-ave.fif')
event_id, tmin, tmax = 1, -0.2, 0.5
event_id_2 = 2
raw = io.Raw(raw_fname, add_eeg_ref=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg=False, eeg=True, exclude=[])
reject = dict(eeg=80e-6)
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
preload=True,
reject=reject)
# make sure it works if MEG channels are present:
picks2 = np.concatenate([[0, 1, 2], picks])
n_meg = 3
epochs2 = Epochs(raw,
events[epochs.selection],
event_id,
tmin,
tmax,
picks=picks2,
preload=True)
def test_cov_estimation_with_triggers():
"""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_true(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_true(err < 0.05, msg=err)
# 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)
_assert_cov(cov, read_cov(cov_fname), nfree=False)
method_params = {'empirical': {'assume_centered': False}}
assert_raises(ValueError,
compute_covariance,
epochs,
keep_sample_mean=False,
method_params=method_params)
assert_raises(ValueError,
compute_covariance,
epochs,
keep_sample_mean=False,
method='factor_analysis')
# 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
assert_raises(ValueError, compute_covariance, epochs)
assert_raises(ValueError, compute_covariance, epochs, projs=None)
# these should work, but won't be equal to above
with warnings.catch_warnings(record=True) as w: # too few samples warning
warnings.simplefilter('always')
cov = compute_covariance(epochs, projs=epochs[0].info['projs'])
cov = compute_covariance(epochs, projs=[])
assert_equal(len(w), 2)
# 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 warnings.catch_warnings(record=True): # samples
compute_covariance(epochs)
# projs checking
compute_covariance(epochs, projs=[])
assert_raises(TypeError, compute_covariance, epochs, projs='foo')
assert_raises(TypeError, compute_covariance, epochs, projs=['foo'])
def test_plot_ica_properties():
"""Test plotting of ICA properties."""
res = 8
raw = _get_raw(preload=True)
raw.add_proj([], remove_existing=True)
events = _get_events()
picks = _get_picks(raw)[:6]
pick_names = [raw.ch_names[k] for k in picks]
raw.pick_channels(pick_names)
reject = dict(grad=4000e-13, mag=4e-12)
epochs = Epochs(raw,
events[:10],
event_id,
tmin,
tmax,
baseline=(None, 0),
preload=True)
ica = ICA(noise_cov=read_cov(cov_fname),
n_components=2,
max_iter=1,
max_pca_components=2,
n_pca_components=2,
random_state=0)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw)
# test _create_properties_layout
fig, ax = _create_properties_layout()
assert_equal(len(ax), 5)
topoargs = dict(topomap_args={'res': res, 'contours': 0, "sensors": False})
ica.plot_properties(raw, picks=0, **topoargs)
ica.plot_properties(epochs, picks=1, dB=False, plot_std=1.5, **topoargs)
ica.plot_properties(epochs,
picks=1,
image_args={'sigma': 1.5},
topomap_args={
'res': 10,
'colorbar': True
},
psd_args={'fmax': 65.},
plot_std=False,
figsize=[4.5, 4.5],
reject=reject)
plt.close('all')
pytest.raises(TypeError, ica.plot_properties, epochs, dB=list('abc'))
pytest.raises(TypeError, ica.plot_properties, ica)
pytest.raises(TypeError, ica.plot_properties, [0.2])
pytest.raises(TypeError, plot_ica_properties, epochs, epochs)
pytest.raises(TypeError, ica.plot_properties, epochs, psd_args='not dict')
pytest.raises(ValueError, ica.plot_properties, epochs, plot_std=[])
fig, ax = plt.subplots(2, 3)
ax = ax.ravel()[:-1]
ica.plot_properties(epochs, picks=1, axes=ax, **topoargs)
fig = ica.plot_properties(raw, picks=[0, 1], **topoargs)
assert_equal(len(fig), 2)
pytest.raises(TypeError,
plot_ica_properties,
epochs,
ica,
picks=[0, 1],
axes=ax)
pytest.raises(ValueError, ica.plot_properties, epochs, axes='not axes')
plt.close('all')
# Test merging grads.
pick_names = raw.ch_names[:15:2] + raw.ch_names[1:15:2]
raw = _get_raw(preload=True).pick_channels(pick_names)
raw.info.normalize_proj()
ica = ICA(random_state=0, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
ica.plot_properties(raw)
plt.close('all')
# Test handling of zeros
raw._data[:] = 0
with pytest.warns(None): # Usually UserWarning: Infinite value .* for epo
ica.plot_properties(raw)
ica = ICA(random_state=0, max_iter=1)
epochs.pick_channels(pick_names)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
epochs._data[0] = 0
with pytest.warns(None): # Usually UserWarning: Infinite value .* for epo
ica.plot_properties(epochs)
plt.close('all')
evoked_fnames = load_data(condition=condition,
data_format='evoked',
data_type='simulation',
verbose=True)
raw = Raw(raw_fnames[0])
events = find_events(raw, stim_channel="STI 014", shortest_event=1)
# Visualize raw file
raw.plot()
# Make an evoked file from the experimental data
picks = pick_types(raw.info, meg=True, eog=True, exclude='bads')
# Read epochs
event_id, tmin, tmax = 9, -0.2, 0.5
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=(None, 0),
picks=picks,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
evoked = epochs.average() # average epochs and get an Evoked dataset.
evoked.plot()
# Compare to the simulated data
evoked_sim = read_evokeds(evoked_fnames[0], condition=0)
evoked_sim.plot()
def test_apply_reference():
"""Test base function for rereferencing."""
raw = read_raw_fif(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = _apply_reference(raw.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert (reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# The CAR reference projection should have been removed by the function
assert (not _has_eeg_average_ref_proj(reref.info['projs']))
# Test that data is modified in place when copy=False
reref, ref_data = _apply_reference(raw, ['EEG 001', 'EEG 002'])
assert (raw is reref)
# Test that disabling the reference does not change anything
reref, ref_data = _apply_reference(raw.copy(), [])
assert_array_equal(raw._data, reref._data)
# Test re-referencing Epochs object
raw = read_raw_fif(fif_fname, preload=False)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
reref, ref_data = _apply_reference(epochs.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert (reref.info['custom_ref_applied'])
_test_reference(epochs, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test re-referencing Evoked object
evoked = epochs.average()
reref, ref_data = _apply_reference(evoked.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert (reref.info['custom_ref_applied'])
_test_reference(evoked, reref, ref_data, ['EEG 001', 'EEG 002'])
# Referencing needs data to be preloaded
raw_np = read_raw_fif(fif_fname, preload=False)
pytest.raises(RuntimeError, _apply_reference, raw_np, ['EEG 001'])
# Test having inactive SSP projections that deal with channels involved
# during re-referencing
raw = read_raw_fif(fif_fname, preload=True)
raw.add_proj(
Projection(
active=False,
data=dict(col_names=['EEG 001', 'EEG 002'],
row_names=None,
data=np.array([[1, 1]]),
ncol=2,
nrow=1),
desc='test',
kind=1,
))
# Projection concerns channels mentioned in projector
pytest.raises(RuntimeError, _apply_reference, raw, ['EEG 001'])
# Projection does not concern channels mentioned in projector, no error
_apply_reference(raw, ['EEG 003'], ['EEG 004'])
def test_ica_eeg():
"""Test ICA on EEG."""
method = 'fastica'
raw_fif = read_raw_fif(fif_fname, preload=True)
with warnings.catch_warnings(record=True): # events etc.
raw_eeglab = read_raw_eeglab(input_fname=eeglab_fname,
montage=eeglab_montage,
preload=True)
for raw in [raw_fif, raw_eeglab]:
events = make_fixed_length_events(raw,
99999,
start=0,
stop=0.3,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = []
picks_all.extend(picks_meg)
picks_all.extend(picks_eeg)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2,
random_state=0,
max_iter=2,
method=method)
with warnings.catch_warnings(record=True): # convergence
ica.fit(inst, picks=picks)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
with warnings.catch_warnings(record=True): # misc channel
raw = read_raw_ctf(ctf_fname2, preload=True)
events = make_fixed_length_events(raw,
99999,
start=0,
stop=0.2,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = picks_meg + picks_eeg
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2,
random_state=0,
max_iter=2,
method=method)
with warnings.catch_warnings(record=True): # convergence
ica.fit(inst)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
def test_psd():
"""Tests the welch and multitaper PSD
"""
raw = io.read_raw_fif(raw_fname)
picks_psd = [0, 1]
# Populate raw with sinusoids
rng = np.random.RandomState(40)
data = 0.1 * rng.randn(len(raw.ch_names), raw.n_times)
freqs_sig = [8., 50.]
for ix, freq in zip(picks_psd, freqs_sig):
data[ix, :] += 2 * np.sin(np.pi * 2. * freq * raw.times)
first_samp = raw._first_samps[0]
raw = RawArray(data, raw.info)
tmin, tmax = 0, 20 # use a few seconds of data
fmin, fmax = 2, 70 # look at frequencies between 2 and 70Hz
n_fft = 128
# -- Raw --
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
kws_welch = dict(n_fft=n_fft)
kws_mt = dict(low_bias=True)
funcs = [(psd_welch, kws_welch), (psd_multitaper, kws_mt),
(compute_raw_psd, kws_welch)]
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(raw, proj=False, **kws)
psds_proj, freqs_proj = func(raw, proj=True, **kws)
assert_true(psds.shape == (len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Is power found where it should be
ixs_max = np.argmax(psds, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj)
# Array input shouldn't work
assert_raises(ValueError, func, raw[:3, :20][0])
assert_true(len(w), 3)
# -- Epochs/Evoked --
events = read_events(event_fname)
events[:, 0] -= first_samp
tmin, tmax, event_id = -0.5, 0.5, 1
epochs = Epochs(raw,
events[:10],
event_id,
tmin,
tmax,
picks=picks_psd,
proj=False,
preload=True,
baseline=None)
evoked = epochs.average()
tmin_full, tmax_full = -1, 1
epochs_full = Epochs(raw,
events[:10],
event_id,
tmin_full,
tmax_full,
picks=picks_psd,
proj=False,
preload=True,
baseline=None)
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
funcs = [(psd_welch, kws_welch), (psd_multitaper, kws_mt),
(compute_epochs_psd, kws_welch)]
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(epochs[:1], proj=False, **kws)
psds_proj, freqs_proj = func(epochs[:1], proj=True, **kws)
psds_f, freqs_f = func(epochs_full[:1], proj=False, **kws)
# this one will fail if you add for example 0.1 to tmin
assert_array_almost_equal(psds, psds_f, 27)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj, 27)
# Is power found where it should be
ixs_max = np.argmax(psds.mean(0), axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
assert_true(psds.shape == (1, len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Array input shouldn't work
assert_raises(ValueError, func, epochs.get_data())
if func is not compute_epochs_psd:
# Testing evoked (doesn't work w/ compute_epochs_psd)
psds_ev, freqs_ev = func(evoked, proj=False, **kws)
psds_ev_proj, freqs_ev_proj = func(evoked, proj=True, **kws)
# Is power found where it should be
ixs_max = np.argmax(psds_ev, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs_ev))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds_ev, psds_ev_proj, 27)
assert_true(psds_ev.shape == (len(kws['picks']), len(freqs)))
assert_true(len(w), 3)
def test_ica_full_data_recovery(method):
"""Test recovery of full data when no source is rejected."""
# Most basic recovery
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[:10]
with warnings.catch_warnings(record=True): # bad proj
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
evoked = epochs.average()
n_channels = 5
data = raw._data[:n_channels].copy()
data_epochs = epochs.get_data()
data_evoked = evoked.data
raw.annotations = Annotations([0.5], [0.5], ['BAD'])
methods = [method]
for method in methods:
stuff = [(2, n_channels, True), (2, n_channels // 2, False)]
for n_components, n_pca_components, ok in stuff:
ica = ICA(n_components=n_components,
max_pca_components=n_pca_components,
n_pca_components=n_pca_components,
method=method,
max_iter=1)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=list(range(n_channels)))
raw2 = ica.apply(raw.copy(), exclude=[])
if ok:
assert_allclose(data[:n_channels],
raw2._data[:n_channels],
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(data[:n_channels] - raw2._data[:n_channels])
assert (np.max(diff) > 1e-14)
ica = ICA(n_components=n_components,
method=method,
max_pca_components=n_pca_components,
n_pca_components=n_pca_components)
with warnings.catch_warnings(record=True):
ica.fit(epochs, picks=list(range(n_channels)))
epochs2 = ica.apply(epochs.copy(), exclude=[])
data2 = epochs2.get_data()[:, :n_channels]
if ok:
assert_allclose(data_epochs[:, :n_channels],
data2,
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(data_epochs[:, :n_channels] - data2)
assert (np.max(diff) > 1e-14)
evoked2 = ica.apply(evoked.copy(), exclude=[])
data2 = evoked2.data[:n_channels]
if ok:
assert_allclose(data_evoked[:n_channels],
data2,
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(evoked.data[:n_channels] - data2)
assert (np.max(diff) > 1e-14)
pytest.raises(ValueError, ICA, method='pizza-decomposision')
def test_time_frequency():
"""Test the to-be-deprecated time-frequency transform (PSD and ITC)"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
include=include,
exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0))
data = epochs.get_data()
times = epochs.times
nave = len(data)
epochs_nopicks = Epochs(raw,
events,
event_id,
tmin,
tmax,
baseline=(None, 0))
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0],
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
# Now compute evoked
evoked = epochs.average()
power_evoked = tfr_morlet(evoked,
freqs,
n_cycles,
use_fft=True,
return_itc=False)
assert_raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
power_, itc_ = tfr_morlet(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True,
decim=slice(0, 2))
# Test picks argument and average parameter
assert_raises(ValueError,
tfr_morlet,
epochs,
freqs=freqs,
n_cycles=n_cycles,
return_itc=True,
average=False)
power_picks, itc_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=True, picks=picks, average=True)
epochs_power_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=False, picks=picks, average=False)
power_picks_avg = epochs_power_picks.average()
# the actual data arrays here are equivalent, too...
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(power.data, power_picks_avg.data)
assert_array_almost_equal(itc.data, itc_picks.data)
assert_array_almost_equal(power.data, power_evoked.data)
print(itc) # test repr
print(itc.ch_names) # test property
itc += power # test add
itc -= power # test sub
power = power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert_true('meg' in power)
assert_true('grad' in power)
assert_false('mag' in power)
assert_false('eeg' in power)
assert_equal(power.nave, nave)
assert_equal(itc.nave, nave)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(power_.data.shape == (len(picks), len(freqs), 2))
assert_true(power_.data.shape == itc_.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
# grand average
itc2 = itc.copy()
itc2.info['bads'] = [itc2.ch_names[0]] # test channel drop
gave = grand_average([itc2, itc])
assert_equal(
gave.data.shape,
(itc2.data.shape[0] - 1, itc2.data.shape[1], itc2.data.shape[2]))
assert_equal(itc2.ch_names[1:], gave.ch_names)
assert_equal(gave.nave, 2)
itc2.drop_channels(itc2.info["bads"])
assert_array_almost_equal(gave.data, itc2.data)
itc2.data = np.ones(itc2.data.shape)
itc.data = np.zeros(itc.data.shape)
itc2.nave = 2
itc.nave = 1
itc.drop_channels([itc.ch_names[0]])
combined_itc = combine_tfr([itc2, itc])
assert_array_almost_equal(combined_itc.data,
np.ones(combined_itc.data.shape) * 2 / 3)
# more tests
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=2,
use_fft=False,
return_itc=True)
assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
assert_true(power.data.shape == itc.data.shape)
assert_true(np.sum(itc.data >= 1) == 0)
assert_true(np.sum(itc.data <= 0) == 0)
Fs = raw.info['sfreq'] # sampling in Hz
tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
assert_true(tfr.shape == (len(picks), len(freqs), len(times)))
tfr2 = cwt_morlet(data[0],
Fs,
freqs,
use_fft=True,
n_cycles=2,
decim=slice(0, 2))
assert_true(tfr2.shape == (len(picks), len(freqs), 2))
single_power = single_trial_power(data,
Fs,
freqs,
use_fft=False,
n_cycles=2)
single_power2 = single_trial_power(data,
Fs,
freqs,
use_fft=False,
n_cycles=2,
decim=slice(0, 2))
single_power3 = single_trial_power(data,
Fs,
freqs,
use_fft=False,
n_cycles=2,
decim=slice(1, 3))
single_power4 = single_trial_power(data,
Fs,
freqs,
use_fft=False,
n_cycles=2,
decim=slice(2, 4))
assert_array_almost_equal(np.mean(single_power, axis=0), power.data)
assert_array_almost_equal(np.mean(single_power2, axis=0),
power.data[:, :, :2])
assert_array_almost_equal(np.mean(single_power3, axis=0), power.data[:, :,
1:3])
assert_array_almost_equal(np.mean(single_power4, axis=0), power.data[:, :,
2:4])
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
# Test decimation:
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in [2, 3, 8, 9]:
for use_fft in [True, False]:
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=2,
use_fft=use_fft,
return_itc=True,
decim=decim)
assert_equal(power.data.shape[2],
np.ceil(float(len(times)) / decim))
freqs = range(50, 55)
decim = 2
_, n_chan, n_time = data.shape
tfr = cwt_morlet(data[0, :, :],
sfreq=epochs.info['sfreq'],
freqs=freqs,
decim=decim)
assert_equal(tfr.shape, (n_chan, len(freqs), n_time // decim))
# Test cwt modes
Ws = morlet(512, [10, 20], n_cycles=2)
assert_raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
for use_fft in [True, False]:
for mode in ['same', 'valid', 'full']:
# XXX JRK: full wavelet decomposition needs to be implemented
if (not use_fft) and mode == 'full':
assert_raises(ValueError,
cwt,
data[0, :, :],
Ws,
use_fft=use_fft,
mode=mode)
continue
cwt(data[0, :, :], Ws, use_fft=use_fft, mode=mode)
# Test decim parameter checks
assert_raises(TypeError,
single_trial_power,
data,
Fs,
freqs,
use_fft=False,
n_cycles=2,
decim=None)
assert_raises(TypeError,
tfr_morlet,
epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True,
decim='decim')
def test_ica_additional(method):
"""Test additional ICA functionality."""
_skip_check_picard(method)
tempdir = _TempDir()
stop2 = 500
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
raw.del_proj() # avoid warnings
raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
# XXX This breaks the tests :(
# raw.info['bads'] = [raw.ch_names[1]]
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[1::2]
epochs = Epochs(raw, events, None, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, proj=False)
epochs.decimate(3, verbose='error')
assert len(epochs) == 4
# test if n_components=None works
ica = ICA(n_components=None, max_pca_components=None,
n_pca_components=None, random_state=0, method=method, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
# for testing eog functionality
picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
baseline=(None, 0), preload=True)
del picks2
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
n_pca_components=4, method=method)
assert (ica.info is None)
with pytest.warns(RuntimeWarning, match='normalize_proj'):
ica.fit(raw, picks[:5])
assert (isinstance(ica.info, Info))
assert (ica.n_components_ < 5)
ica = ICA(n_components=3, max_pca_components=4, method=method,
n_pca_components=4, random_state=0)
pytest.raises(RuntimeError, ica.save, '')
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# check passing a ch_name to find_bads_ecg
with pytest.warns(RuntimeWarning, match='longer'):
_, scores_1 = ica.find_bads_ecg(raw)
_, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
assert scores_1[0] != scores_2[0]
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True,
ch_type="mag")
with pytest.raises(RuntimeError, match='No component detected'):
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False,)
corrmap([ica, ica2], (0, 0), threshold=0.5, plot=False, show=False)
assert (ica.labels_["blinks"] == ica2.labels_["blinks"])
assert (0 in ica.labels_["blinks"])
# test retrieval of component maps as arrays
components = ica.get_components()
template = components[:, 0]
EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')
corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True,
ch_type="mag")
assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
# No match
bad_ica = ica2.copy()
bad_ica.mixing_matrix_[:] = 0.
with pytest.warns(RuntimeWarning, match='divide'):
with catch_logging() as log:
corrmap([ica, bad_ica], (0, 0), threshold=0.5, plot=False,
show=False, verbose=True)
log = log.getvalue()
assert 'No maps selected' in log
# make sure a single threshold in a list works
corrmap([ica, ica3], template, threshold=[0.5], label='blinks', plot=True,
ch_type="mag")
ica_different_channels = ICA(n_components=2, random_state=0).fit(
raw, picks=[2, 3, 4, 5])
pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0))
# test warnings on bad filenames
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-ica.fif'):
ica.save(ica_badname)
with pytest.warns(RuntimeWarning, match='-ica.fif'):
read_ica(ica_badname)
# test decim
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=picks[:5], decim=3)
assert raw_._data.shape[1] == n_samples
# test expl var
ica = ICA(n_components=1.0, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=None, decim=3)
assert (ica.n_components_ == 4)
ica_var = _ica_explained_variance(ica, raw, normalize=True)
assert (np.all(ica_var[:-1] >= ica_var[1:]))
# test ica sorting
ica.exclude = [0]
ica.labels_ = dict(blink=[0], think=[1])
ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True)
assert_equal(ica_sorted.exclude, [3])
assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2]))
# epochs extraction from raw fit
pytest.raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4,
n_pca_components=4, method=method, max_iter=1)
with pytest.warns(None): # ICA does not converge
ica.fit(raw, picks=picks[:10], start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert (ica.mixing_matrix_.shape == (2, 2))
assert (ica.unmixing_matrix_.shape == (2, 2))
assert (ica.pca_components_.shape == (4, 10))
assert (sources.shape[1] == ica.n_components_)
for exclude in [[], [0], np.array([1, 2, 3])]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (list(ica.exclude) == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.apply(raw)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert (ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert (ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert (ica.exclude == [ica_raw.ch_names.index(e) for e in
ica_raw.info['bads']])
# test filtering
d1 = ica_raw._data[0].copy()
ica_raw.filter(4, 20, fir_design='firwin2')
assert_equal(ica_raw.info['lowpass'], 20.)
assert_equal(ica_raw.info['highpass'], 4.)
assert ((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin')
assert ((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ pre_whitener_')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'pre_whitener_']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert (ica.ch_names == ica_read.ch_names)
assert (isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
# Make sure detect_artifacts marks the right components.
# For int criterion, the doc says "E.g. range(2) would return the two
# sources with the highest score". Assert that's what it does.
# Only test for skew, since it's always the same code.
ica.exclude = []
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None,
eog_ch=None, skew_criterion=0,
var_criterion=None, kurt_criterion=None)
assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores))
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
pytest.raises(ValueError, ica.find_bads_ecg, epochs.average(),
method='ctps')
pytest.raises(ValueError, ica.find_bads_ecg, raw,
method='crazy-coupling')
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert (isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(evoked, method='correlation')
assert_equal(len(scores), ica.n_components_)
assert_array_equal(raw_data, raw[:][0])
assert_array_equal(epochs_data, epochs.get_data())
assert_array_equal(evoked_data, evoked.data)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog, target='EOG 061',
score_func=func)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw, target='MEG 1531',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
ecg_events = ica_find_ecg_events(
raw, sources[np.abs(ecg_scores).argmax()])
assert (ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw, target='EOG 061',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
eog_events = ica_find_eog_events(
raw, sources[np.abs(eog_scores).argmax()])
assert (eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert (ica_raw.last_samp - ica_raw.first_samp == 100)
assert_equal(len(ica_raw._filenames), 1) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = read_raw_fif(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert (ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
assert (ica.n_components_ == ica_epochs.get_data().shape[1])
assert (ica_epochs._raw is None)
assert (ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert (ncomps_ == expected)
ica = ICA(method=method)
with pytest.warns(None): # sometimes does not converge
ica.fit(raw, picks=picks[:5])
with pytest.warns(RuntimeWarning, match='longer'):
ica.find_bads_ecg(raw)
ica.find_bads_eog(epochs, ch_name='MEG 0121')
assert_array_equal(raw_data, raw[:][0])
raw.drop_channels(['MEG 0122'])
pytest.raises(RuntimeError, ica.find_bads_eog, raw)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
def test_compute_tfr():
"""Test _compute_tfr function"""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = read_events(event_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
include=[],
exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0))
data = epochs.get_data()
sfreq = epochs.info['sfreq']
freqs = np.arange(10, 20, 3).astype(float)
# Check all combination of options
for method, use_fft, zero_mean, output in product(
('multitaper', 'morlet'), (False, True), (False, True),
('complex', 'power', 'phase', 'avg_power_itc', 'avg_power', 'itc')):
# Check exception
if (method == 'multitaper') and (output == 'phase'):
assert_raises(NotImplementedError,
_compute_tfr,
data,
freqs,
sfreq,
method=method,
output=output)
continue
# Check runs
out = _compute_tfr(data,
freqs,
sfreq,
method=method,
use_fft=use_fft,
zero_mean=zero_mean,
n_cycles=2.,
output=output)
# Check shapes
shape = np.r_[data.shape[:2], len(freqs), data.shape[2]]
if ('avg' in output) or ('itc' in output):
assert_array_equal(shape[1:], out.shape)
else:
assert_array_equal(shape, out.shape)
# Check types
if output in ('complex', 'avg_power_itc'):
assert_equal(np.complex, out.dtype)
else:
assert_equal(np.float, out.dtype)
assert_true(np.all(np.isfinite(out)))
# Check that functions are equivalent to
# i) single_trial_power: X, shape (n_signals, n_chans, n_times)
old_power = single_trial_power(data, sfreq, freqs, n_cycles=2.)
new_power = _compute_tfr(data,
freqs,
sfreq,
n_cycles=2.,
method='morlet',
output='power')
assert_array_almost_equal(old_power, new_power)
old_power = single_trial_power(data,
sfreq,
freqs,
n_cycles=2.,
times=epochs.times,
baseline=(-.100, 0),
baseline_mode='ratio')
new_power = rescale(new_power, epochs.times, (-.100, 0), 'ratio')
# ii) cwt_morlet: X, shape (n_signals, n_times)
old_complex = cwt_morlet(data[0], sfreq, freqs, n_cycles=2.)
new_complex = _compute_tfr(data[[0]],
freqs,
sfreq,
n_cycles=2.,
method='morlet',
output='complex')
assert_array_almost_equal(old_complex, new_complex[0])
# Check errors params
for _data in (None, 'foo', data[0]):
assert_raises(ValueError, _compute_tfr, _data, freqs, sfreq)
for _freqs in (None, 'foo', [[0]]):
assert_raises(ValueError, _compute_tfr, data, _freqs, sfreq)
for _sfreq in (None, 'foo'):
assert_raises(ValueError, _compute_tfr, data, freqs, _sfreq)
for key in ('output', 'method', 'use_fft', 'decim', 'n_jobs'):
for value in (None, 'foo'):
kwargs = {key: value} # FIXME pep8
assert_raises(ValueError, _compute_tfr, data, freqs, sfreq,
**kwargs)
# No time_bandwidth param in morlet
assert_raises(ValueError,
_compute_tfr,
data,
freqs,
sfreq,
method='morlet',
time_bandwidth=1)
# No phase in multitaper XXX Check ?
assert_raises(NotImplementedError,
_compute_tfr,
data,
freqs,
sfreq,
method='multitaper',
output='phase')
# Inter-trial coherence tests
out = _compute_tfr(data, freqs, sfreq, output='itc', n_cycles=2.)
assert_true(np.sum(out >= 1) == 0)
assert_true(np.sum(out <= 0) == 0)
# Check decim shapes
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in (2, 3, 8, 9, slice(0, 2), slice(1, 3), slice(2, 4)):
_decim = slice(None, None, decim) if isinstance(decim, int) else decim
n_time = len(np.arange(data.shape[2])[_decim])
shape = np.r_[data.shape[:2], len(freqs), n_time]
for method in ('multitaper', 'morlet'):
# Single trials
out = _compute_tfr(data,
freqs,
sfreq,
method=method,
decim=decim,
n_cycles=2.)
assert_array_equal(shape, out.shape)
# Averages
out = _compute_tfr(data,
freqs,
sfreq,
method=method,
decim=decim,
output='avg_power',
n_cycles=2.)
assert_array_equal(shape[1:], out.shape)
picks = pick_types(raw.info, eeg=True)
events = mne.find_events(raw)
###############################################################################
# Create Epochs
# define target events:
# 1. find response times: distance between "square" and "rt" events
# 2. extract A. "square" events B. followed by a button press within 700 msec
tmax = .7
sfreq = raw.info["sfreq"]
reference_id, target_id = 2, 1
new_events, rts = define_target_events(events, reference_id, target_id, sfreq,
tmin=0., tmax=tmax, new_id=2)
epochs = Epochs(raw, events=new_events, tmax=tmax + .1,
event_id={"square": 2}, picks=picks)
###############################################################################
# Plot
# Parameters for plotting
order = rts.argsort() # sorting from fast to slow trials
rois = dict()
for pick, channel in enumerate(epochs.ch_names):
last_char = channel[-1] # for 10/20, last letter codes the hemisphere
roi = ("Midline" if last_char in "z12" else
("Left" if int(last_char) % 2 else "Right"))
rois[roi] = rois.get(roi, list()) + [pick]
# The actual plots
def test_simulate_raw_bem(raw_data):
"""Test simulation of raw data with BEM."""
raw, src_ss, stc, trans, sphere = raw_data
src = setup_source_space('sample', 'oct1', subjects_dir=subjects_dir)
for s in src:
s['nuse'] = 3
s['vertno'] = src[1]['vertno'][:3]
s['inuse'].fill(0)
s['inuse'][s['vertno']] = 1
# use different / more complete STC here
vertices = [s['vertno'] for s in src]
stc = SourceEstimate(np.eye(sum(len(v) for v in vertices)), vertices, 0,
1. / raw.info['sfreq'])
stcs = [stc] * 15
raw_sim_sph = simulate_raw(raw.info, stcs, trans, src, sphere)
raw_sim_bem = simulate_raw(raw.info, stcs, trans, src, bem_fname)
# some components (especially radial) might not match that well,
# so just make sure that most components have high correlation
assert_array_equal(raw_sim_sph.ch_names, raw_sim_bem.ch_names)
picks = pick_types(raw.info, meg=True, eeg=True)
n_ch = len(picks)
corr = np.corrcoef(raw_sim_sph[picks][0], raw_sim_bem[picks][0])
assert_array_equal(corr.shape, (2 * n_ch, 2 * n_ch))
med_corr = np.median(np.diag(corr[:n_ch, -n_ch:]))
assert med_corr > 0.65
# do some round-trip localization
for s in src:
transform_surface_to(s, 'head', trans)
locs = np.concatenate([s['rr'][s['vertno']] for s in src])
tmax = (len(locs) - 1) / raw.info['sfreq']
cov = make_ad_hoc_cov(raw.info)
# The tolerance for the BEM is surprisingly high (28) but I get the same
# result when using MNE-C and Xfit, even when using a proper 5120 BEM :(
for use_raw, bem, tol in ((raw_sim_sph, sphere, 2), (raw_sim_bem,
bem_fname, 31)):
events = find_events(use_raw, 'STI 014')
assert len(locs) == 6
evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None).average()
assert len(evoked.times) == len(locs)
fits = fit_dipole(evoked, cov, bem, trans, min_dist=1.)[0].pos
diffs = np.sqrt(np.sum((locs - fits)**2, axis=-1)) * 1000
med_diff = np.median(diffs)
assert med_diff < tol, '%s: %s' % (bem, med_diff)
# also test event timings with SourceSimulator
first_samp = raw.first_samp
events = find_events(raw, initial_event=True, verbose=False)
evt_times = events[:, 0]
assert len(events) == 3
labels_sim = [[], [], []] # random l+r hemisphere points
labels_sim[0] = Label([src_ss[0]['vertno'][1]], hemi='lh')
labels_sim[1] = Label([src_ss[0]['vertno'][4]], hemi='lh')
labels_sim[2] = Label([src_ss[1]['vertno'][2]], hemi='rh')
wf_sim = np.array([2, 1, 0])
for this_fs in (0, first_samp):
ss = SourceSimulator(src_ss,
1. / raw.info['sfreq'],
first_samp=this_fs)
for i in range(3):
ss.add_data(labels_sim[i], wf_sim, events[np.newaxis, i])
assert ss.n_times == evt_times[-1] + len(wf_sim) - this_fs
raw_sim = simulate_raw(raw.info,
ss,
src=src_ss,
bem=bem_fname,
first_samp=first_samp)
data = raw_sim.get_data()
amp0 = data[:, evt_times - first_samp].max()
amp1 = data[:, evt_times + 1 - first_samp].max()
amp2 = data[:, evt_times + 2 - first_samp].max()
assert_allclose(amp0 / amp1, wf_sim[0] / wf_sim[1], rtol=1e-5)
assert amp2 == 0
assert raw_sim.n_times == ss.n_times
fwd = mne.read_forward_solution(fwd_fname)
src = fwd['src']
stc = simulate_sparse_stc(src,
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, 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')
def test_render_report():
"""Test rendering -*.fif files for mne report."""
tempdir = _TempDir()
raw_fname_new = op.join(tempdir, 'temp_raw.fif')
ms_fname_new = op.join(tempdir, 'temp_ms_raw.fif')
event_fname_new = op.join(tempdir, 'temp_raw-eve.fif')
cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif')
fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif')
inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif')
for a, b in [[raw_fname, raw_fname_new],
[ms_fname, ms_fname_new],
[event_fname, event_fname_new],
[cov_fname, cov_fname_new],
[fwd_fname, fwd_fname_new],
[inv_fname, inv_fname_new]]:
shutil.copyfile(a, b)
# create and add -epo.fif and -ave.fif files
epochs_fname = op.join(tempdir, 'temp-epo.fif')
evoked_fname = op.join(tempdir, 'temp-ave.fif')
# Speed it up by picking channels
raw = read_raw_fif(raw_fname_new, preload=True)
raw.pick_channels(['MEG 0111', 'MEG 0121'])
raw.del_proj()
epochs = Epochs(raw, read_events(event_fname), 1, -0.2, 0.2)
epochs.save(epochs_fname)
# This can take forever (stall Travis), so let's make it fast
# Also, make sure crop range is wide enough to avoid rendering bug
epochs.average().crop(0.1, 0.2).save(evoked_fname)
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, on_error='raise')
assert_true(len(w) >= 1)
assert_true(repr(report))
# Check correct paths and filenames
fnames = glob.glob(op.join(tempdir, '*.fif'))
for fname in fnames:
assert_true(op.basename(fname) in
[op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
assert_equal(len(report.fnames), len(fnames))
assert_equal(len(report.html), len(report.fnames))
assert_equal(len(report.fnames), len(report))
# Check saving functionality
report.data_path = tempdir
fname = op.join(tempdir, 'report.html')
report.save(fname=fname, open_browser=False)
assert_true(op.isfile(fname))
with open(fname, 'rb') as fid:
html = fid.read().decode('utf-8')
assert '(MaxShield on)' in html
assert_equal(len(report.html), len(fnames))
assert_equal(len(report.html), len(report.fnames))
# Check saving same report to new filename
report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False)
assert_true(op.isfile(op.join(tempdir, 'report2.html')))
# Check overwriting file
report.save(fname=op.join(tempdir, 'report.html'), open_browser=False,
overwrite=True)
assert_true(op.isfile(op.join(tempdir, 'report.html')))
# Check pattern matching with multiple patterns
pattern = ['*raw.fif', '*eve.fif']
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, pattern=pattern)
assert_true(len(w) >= 1)
assert_true(repr(report))
fnames = glob.glob(op.join(tempdir, '*.raw')) + \
glob.glob(op.join(tempdir, '*.raw'))
for fname in fnames:
assert_true(op.basename(fname) in
[op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
assert_raises(ValueError, Report, image_format='foo')
assert_raises(ValueError, Report, image_format=None)
# SVG rendering
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir,
image_format='svg')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, on_error='raise')
# ndarray support smoke test
report.add_figs_to_section(np.zeros((2, 3, 3)), 'caption', 'section')
def test_io_set_raw(fnames, tmpdir):
"""Test importing EEGLAB .set files."""
tmpdir = str(tmpdir)
raw_fname, raw_fname_onefile = fnames
with pytest.warns(RuntimeWarning) as w:
_test_raw_reader(read_raw_eeglab,
input_fname=raw_fname,
montage=montage)
_test_raw_reader(read_raw_eeglab,
input_fname=raw_fname_onefile,
montage=montage)
for want in ('Events like', 'consist entirely', 'could not be mapped',
'string preload is not supported'):
assert (any(want in str(ww.message) for ww in w))
with pytest.warns(RuntimeWarning) as w:
# test finding events in continuous data
event_id = {'rt': 1, 'square': 2}
raw0 = read_raw_eeglab(input_fname=raw_fname,
montage=montage,
event_id=event_id,
preload=True)
raw1 = read_raw_eeglab(input_fname=raw_fname,
montage=montage,
event_id=event_id,
preload=False)
raw2 = read_raw_eeglab(input_fname=raw_fname_onefile,
montage=montage,
event_id=event_id)
raw3 = read_raw_eeglab(input_fname=raw_fname,
montage=montage,
event_id=event_id)
raw4 = read_raw_eeglab(input_fname=raw_fname, montage=montage)
assert raw0.filenames[0].endswith('.fdt') # .set with additional .fdt
assert raw2.filenames[0].endswith('.set') # standalone .set
Epochs(raw0, find_events(raw0), event_id)
epochs = Epochs(raw1, find_events(raw1), event_id)
assert_equal(len(find_events(raw4)), 0) # no events without event_id
assert_equal(epochs["square"].average().nave, 80) # 80 with
assert_array_equal(raw0[:][0], raw1[:][0], raw2[:][0], raw3[:][0])
assert_array_equal(raw0[:][-1], raw1[:][-1], raw2[:][-1], raw3[:][-1])
assert_equal(len(w), 4)
# 1 for preload=False / str with fname_onefile, 3 for dropped events
raw0.filter(1,
None,
l_trans_bandwidth='auto',
filter_length='auto',
phase='zero') # test that preloading works
# test that using uint16_codec does not break stuff
raw0 = read_raw_eeglab(input_fname=raw_fname,
montage=montage,
event_id=event_id,
preload=False,
uint16_codec='ascii')
# test old EEGLAB version event import (read old version)
eeg = io.loadmat(raw_fname_mat, struct_as_record=False,
squeeze_me=True)['EEG']
for event in eeg.event: # old version allows integer events
event.type = 1
assert_equal(read_events_eeglab(eeg)[-1, -1], 1)
eeg.event = eeg.event[0] # single event
eeg.event.latency = float(eeg.event.latency) - .1 # test rounding
assert_equal(read_events_eeglab(eeg)[-1, -1], 1)
# test reading file with one event (read old version)
eeg = io.loadmat(raw_fname_mat, struct_as_record=False,
squeeze_me=True)['EEG']
one_event_fname = op.join(tmpdir, 'test_one_event.set')
io.savemat(one_event_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': eeg.nbchan,
'data': 'test_one_event.fdt',
'epoch': eeg.epoch,
'event': eeg.event[0],
'chanlocs': eeg.chanlocs,
'pnts': eeg.pnts
}
},
appendmat=False,
oned_as='row')
shutil.copyfile(op.join(base_dir, 'test_raw.fdt'),
one_event_fname.replace('.set', '.fdt'))
event_id = {eeg.event[0].type: 1}
test_raw = read_raw_eeglab(input_fname=one_event_fname,
montage=montage,
event_id=event_id,
preload=True)
# test that sample indices are read python-wise (zero-based)
assert find_events(test_raw)[0, 0] == round(eeg.event[0].latency) - 1
# test negative event latencies
negative_latency_fname = op.join(tmpdir, 'test_negative_latency.set')
evnts = deepcopy(eeg.event[0])
evnts.latency = 0
io.savemat(negative_latency_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': eeg.nbchan,
'data': 'test_one_event.fdt',
'epoch': eeg.epoch,
'event': evnts,
'chanlocs': eeg.chanlocs,
'pnts': eeg.pnts
}
},
appendmat=False,
oned_as='row')
shutil.copyfile(op.join(base_dir, 'test_raw.fdt'),
negative_latency_fname.replace('.set', '.fdt'))
event_id = {eeg.event[0].type: 1}
with pytest.warns(RuntimeWarning, match="has a sample index of -1."):
read_raw_eeglab(input_fname=negative_latency_fname,
preload=True,
event_id=event_id,
montage=montage)
# test overlapping events
overlap_fname = op.join(tmpdir, 'test_overlap_event.set')
io.savemat(overlap_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': eeg.nbchan,
'data': 'test_overlap_event.fdt',
'epoch': eeg.epoch,
'event': [eeg.event[0], eeg.event[0]],
'chanlocs': eeg.chanlocs,
'pnts': eeg.pnts
}
},
appendmat=False,
oned_as='row')
shutil.copyfile(op.join(base_dir, 'test_raw.fdt'),
overlap_fname.replace('.set', '.fdt'))
event_id = {'rt': 1, 'square': 2}
with pytest.warns(RuntimeWarning, match='will be dropped'):
raw = read_raw_eeglab(input_fname=overlap_fname,
montage=montage,
event_id=event_id,
preload=True)
events_stimchan = find_events(raw)
events_read_events_eeglab = read_events_eeglab(overlap_fname, event_id)
assert (len(events_stimchan) == 1)
assert (len(events_read_events_eeglab) == 2)
# test reading file with one channel
one_chan_fname = op.join(tmpdir, 'test_one_channel.set')
io.savemat(one_chan_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': 1,
'data': np.random.random((1, 3)),
'epoch': eeg.epoch,
'event': eeg.epoch,
'chanlocs': {
'labels': 'E1',
'Y': -6.6069,
'X': 6.3023,
'Z': -2.9423
},
'times': eeg.times[:3],
'pnts': 3
}
},
appendmat=False,
oned_as='row')
with pytest.warns(None) as w:
read_raw_eeglab(input_fname=one_chan_fname, preload=True)
# no warning for 'no events found'
assert len(w) == 0
# test reading file with 3 channels - one without position information
# first, create chanlocs structured array
ch_names = ['F3', 'unknown', 'FPz']
x, y, z = [1., 2., np.nan], [4., 5., np.nan], [7., 8., np.nan]
dt = [('labels', 'S10'), ('X', 'f8'), ('Y', 'f8'), ('Z', 'f8')]
chanlocs = np.zeros((3, ), dtype=dt)
for ind, vals in enumerate(zip(ch_names, x, y, z)):
for fld in range(4):
chanlocs[ind][dt[fld][0]] = vals[fld]
if LooseVersion(np.__version__) == '1.14.0':
# There is a bug in 1.14.0 (or maybe with SciPy 1.0.0?) that causes
# this write to fail!
raise SkipTest('Need to fix bug in NumPy 1.14.0!')
# save set file
one_chanpos_fname = op.join(tmpdir, 'test_chanpos.set')
io.savemat(one_chanpos_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': 3,
'data': np.random.random((3, 3)),
'epoch': eeg.epoch,
'event': eeg.epoch,
'chanlocs': chanlocs,
'times': eeg.times[:3],
'pnts': 3
}
},
appendmat=False,
oned_as='row')
# load it
with pytest.warns(RuntimeWarning, match='did not have a position'):
raw = read_raw_eeglab(input_fname=one_chanpos_fname, preload=True)
# position should be present for first two channels
for i in range(2):
assert_array_equal(
raw.info['chs'][i]['loc'][:3],
np.array([-chanlocs[i]['Y'], chanlocs[i]['X'], chanlocs[i]['Z']]))
# position of the last channel should be zero
assert_array_equal(raw.info['chs'][-1]['loc'][:3], [np.nan] * 3)
# test reading channel names from set and positions from montage
with pytest.warns(RuntimeWarning, match='did not have a position'):
raw = read_raw_eeglab(input_fname=one_chanpos_fname,
preload=True,
montage=montage)
# when montage was passed - channel positions should be taken from there
correct_pos = [[-0.56705965, 0.67706631, 0.46906776], [np.nan] * 3,
[0., 0.99977915, -0.02101571]]
for ch_ind in range(3):
assert_array_almost_equal(raw.info['chs'][ch_ind]['loc'][:3],
np.array(correct_pos[ch_ind]))
# test reading channel names but not positions when there is no X (only Z)
# field in the EEG.chanlocs structure
nopos_chanlocs = chanlocs[['labels', 'Z']]
nopos_fname = op.join(tmpdir, 'test_no_chanpos.set')
io.savemat(nopos_fname, {
'EEG': {
'trials': eeg.trials,
'srate': eeg.srate,
'nbchan': 3,
'data': np.random.random((3, 2)),
'epoch': eeg.epoch,
'event': eeg.epoch,
'chanlocs': nopos_chanlocs,
'times': eeg.times[:2],
'pnts': 2
}
},
appendmat=False,
oned_as='row')
# load the file
raw = read_raw_eeglab(input_fname=nopos_fname, preload=True)
# test that channel names have been loaded but not channel positions
for i in range(3):
assert_equal(raw.info['chs'][i]['ch_name'], ch_names[i])
assert_array_equal(raw.info['chs'][i]['loc'][:3],
np.array([np.nan, np.nan, np.nan]))
# find events and generate epochs
event_fname = './data/record-[2014.03.10-19.17.37]-eve.fif'
events = read_events(event_fname)
event_id = {'13 Hz': 2, '17 Hz': 4, '21 Hz': 3, 'resting-state': 1}
## Modificação de parâmetros Epoch e adicionado "Picks"
# Define time range (1 to labeled)
# Only works up to 151s in record-[2012.07.19-11.24.02]
tmin, tmax = -0., 1
# epochs = Epochs(raw, events, event_id, tmin=0, tmax=360.9, baseline=None)
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=False,
baseline=None,
preload=True,
verbose=False)
# Extract data from Epochs object in (Trial, Channel, Sample) format
epochs_data = epochs.get_data()
# Make a pipeline with riemannian geometry models
clf = make_pipeline(Covariances(), TangentSpace(metric='riemann'),
LogisticRegression())
# Get labels
def test_add_reference():
raw = Raw(fif_fname, preload=True)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# check if channel already exists
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add reference channel to Raw
raw_ref = add_reference_channels(raw, 'Ref', copy=True)
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 1)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
_check_channel_names(raw_ref, 'Ref')
orig_nchan = raw.info['nchan']
raw = add_reference_channels(raw, 'Ref', copy=False)
assert_array_equal(raw._data, raw_ref._data)
assert_equal(raw.info['nchan'], orig_nchan + 1)
_check_channel_names(raw, 'Ref')
ref_idx = raw.ch_names.index('Ref')
ref_data, _ = raw[ref_idx]
assert_array_equal(ref_data, 0)
raw = Raw(fif_fname, preload=True)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# Test adding an existing channel as reference channel
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add two reference channels to Raw
raw_ref = add_reference_channels(raw, ['M1', 'M2'], copy=True)
_check_channel_names(raw_ref, ['M1', 'M2'])
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 2)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
assert_array_equal(raw_ref._data[-2:, :], 0)
raw = add_reference_channels(raw, ['M1', 'M2'], copy=False)
_check_channel_names(raw, ['M1', 'M2'])
ref_idx = raw.ch_names.index('M1')
ref_idy = raw.ch_names.index('M2')
ref_data, _ = raw[[ref_idx, ref_idy]]
assert_array_equal(ref_data, 0)
# add reference channel to epochs
raw = Raw(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
epochs_ref = add_reference_channels(epochs, 'Ref', copy=True)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 1)
_check_channel_names(epochs_ref, 'Ref')
ref_idx = epochs_ref.ch_names.index('Ref')
ref_data = epochs_ref.get_data()[:, ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add two reference channels to epochs
raw = Raw(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
epochs_ref = add_reference_channels(epochs, ['M1', 'M2'], copy=True)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 2)
_check_channel_names(epochs_ref, ['M1', 'M2'])
ref_idx = epochs_ref.ch_names.index('M1')
ref_idy = epochs_ref.ch_names.index('M2')
assert_equal(epochs_ref.info['chs'][ref_idx]['ch_name'], 'M1')
assert_equal(epochs_ref.info['chs'][ref_idy]['ch_name'], 'M2')
ref_data = epochs_ref.get_data()[:, [ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add reference channel to evoked
raw = Raw(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
evoked = epochs.average()
evoked_ref = add_reference_channels(evoked, 'Ref', copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 1)
_check_channel_names(evoked_ref, 'Ref')
ref_idx = evoked_ref.ch_names.index('Ref')
ref_data = evoked_ref.data[ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# add two reference channels to evoked
raw = Raw(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
evoked = epochs.average()
evoked_ref = add_reference_channels(evoked, ['M1', 'M2'], copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 2)
_check_channel_names(evoked_ref, ['M1', 'M2'])
ref_idx = evoked_ref.ch_names.index('M1')
ref_idy = evoked_ref.ch_names.index('M2')
ref_data = evoked_ref.data[[ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# Test invalid inputs
raw_np = Raw(fif_fname, preload=False)
assert_raises(RuntimeError, add_reference_channels, raw_np, ['Ref'])
assert_raises(ValueError, add_reference_channels, raw, 1)
def test_array_raw():
"""Test creating raw from array."""
# creating
raw = read_raw_fif(fif_fname).crop(2, 5)
data, times = raw[:, :]
sfreq = raw.info['sfreq']
ch_names = [(ch[4:] if 'STI' not in ch else ch)
for ch in raw.info['ch_names']] # change them, why not
types = list()
for ci in range(101):
types.extend(('grad', 'grad', 'mag'))
types.extend(['ecog', 'seeg', 'hbo']) # really 3 meg channels
types.extend(['stim'] * 9)
types.extend(['eeg'] * 60)
picks = np.concatenate([
pick_types(raw.info)[::20],
pick_types(raw.info, meg=False, stim=True),
pick_types(raw.info, meg=False, eeg=True)[::20]
])
del raw
data = data[picks]
ch_names = np.array(ch_names)[picks].tolist()
types = np.array(types)[picks].tolist()
types.pop(-1)
# wrong length
pytest.raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
pytest.raises(KeyError, create_info, ch_names, sfreq, types)
types[-1] = 'eog'
# default type
info = create_info(ch_names, sfreq)
assert_equal(info['chs'][0]['kind'], _kind_dict['misc'][0])
# use real types
info = create_info(ch_names, sfreq, types)
raw2 = _test_raw_reader(RawArray,
test_preloading=False,
data=data,
info=info,
first_samp=2 * data.shape[1])
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
assert ('RawArray' in repr(raw2))
pytest.raises(TypeError, RawArray, info, data)
# filtering
picks = pick_types(raw2.info, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
kwargs = dict(fir_design='firwin', picks=picks)
raw_lp.filter(None, 4.0, h_trans_bandwidth=4., **kwargs)
raw_hp = raw2.copy()
raw_hp.filter(16.0, None, l_trans_bandwidth=4., **kwargs)
raw_bp = raw2.copy()
raw_bp.filter(8.0,
12.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
**kwargs)
raw_bs = raw2.copy()
raw_bs.filter(16.0,
4.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
**kwargs)
data, _ = raw2[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
sig_dec = 15
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
# plotting
raw2.plot()
raw2.plot_psd(tmax=2., average=True, n_fft=1024, spatial_colors=False)
plt.close('all')
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert len(events) > 2
epochs = Epochs(raw2, events, 1, -0.2, 0.4, preload=True)
evoked = epochs.average()
assert_equal(evoked.nave, len(events) - 1)
# complex data
rng = np.random.RandomState(0)
data = rng.randn(1, 100) + 1j * rng.randn(1, 100)
raw = RawArray(data, create_info(1, 1000., 'eeg'))
assert_allclose(raw._data, data)
# Using digital montage to give MNI electrode coordinates
n_elec = 10
ts_size = 10000
Fs = 512.
ch_names = [str(i) for i in range(n_elec)]
ch_pos_loc = np.random.randint(60, size=(n_elec, 3)).tolist()
data = np.random.rand(n_elec, ts_size)
montage = make_dig_montage(ch_pos=dict(zip(ch_names, ch_pos_loc)))
info = create_info(ch_names, Fs, 'ecog', montage=montage)
raw = RawArray(data, info)
raw.plot_psd(average=False) # looking for inexistent layout
raw.plot_psd_topo()
def test_epochs_proj_mixin():
"""Test SSP proj methods from ProjMixin class
"""
for proj in [True, False]:
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
proj=proj)
assert_true(all(p['active'] == proj for p in epochs.info['projs']))
# test adding / deleting proj
if proj:
epochs.get_data()
assert_true(all(p['active'] == proj for p in epochs.info['projs']))
assert_raises(ValueError, epochs.add_proj, epochs.info['projs'][0],
{'remove_existing': True})
assert_raises(ValueError, epochs.add_proj, 'spam')
assert_raises(ValueError, epochs.del_proj, 0)
else:
projs = deepcopy(epochs.info['projs'])
n_proj = len(epochs.info['projs'])
epochs.del_proj(0)
assert_true(len(epochs.info['projs']) == n_proj - 1)
epochs.add_proj(projs, remove_existing=False)
assert_true(len(epochs.info['projs']) == 2 * n_proj - 1)
epochs.add_proj(projs, remove_existing=True)
assert_true(len(epochs.info['projs']) == n_proj)
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
proj=False,
add_eeg_ref=True)
epochs2 = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
proj=True,
add_eeg_ref=True)
assert_allclose(epochs.copy().apply_proj().get_data()[0],
epochs2.get_data()[0])
data = epochs.get_data().copy()
data2 = np.array([e for e in epochs])
assert_array_equal(data, data2)
epochs.apply_proj()
assert_array_equal(epochs._projector, epochs2._projector)
assert_allclose(epochs._data, epochs2.get_data())
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=None,
proj=False,
add_eeg_ref=True)
data = epochs.get_data().copy()
epochs.apply_proj()
assert_allclose(np.dot(epochs._projector, data[0]), epochs._data[0])
raw.filter(l_freq=.05, h_freq=25.0, fir_design='firwin')
channel_trigger = np.where(np.array(raw.ch_names) == 'USPT001')[0][0]
# trigger baseline is 255
# Replace 255 values with 0 for easier reading of events
trigger_baseline = np.where(raw._data[channel_trigger, :] == 255)[0]
raw._data[channel_trigger, trigger_baseline] = 0.
# find triggers
events_meg = mne.find_events(raw)
# Add 48ms to the trigger events (according to the photodiod)
events_meg = np.array(events_meg, float)
events_meg[:, 0] += round(.048 * raw.info['sfreq'])
events_meg = np.array(events_meg, int)
# Select only events corresponding to stimulus onset
events_meg = events_meg[np.where(events_meg[:, 2] <= 125)]
epochs = Epochs(raw, events_meg,
tmin=-0.35, tmax=0, preload=True,
baseline=(-0.35, 0.0), decim=10)
# Copy first run dev_head_t to following runs
if run_number == 0:
dev_head_t = epochs.info['dev_head_t']
else:
epochs.info['dev_head_t'] = dev_head_t
# no button 2 recording during acquisition of the 1st session for sub01
if (subject == 'sub01_YFAALKWR_JA') & (epochs.info['nchan'] == 308):
epochs.drop_channels(['UADC007-2104'])
epochs_list.append(epochs)
epochs = concatenate_epochs(epochs_list)
# compute noise covariance and save it
cov = mne.compute_covariance(epochs)
cov_fname = op.join(subject_path, '%s-cov.fif' % subject)
cov.save(cov_fname)
