def test_add_patch_info():
"""Test adding patch info to source space."""
# let's setup a small source space
src = read_source_spaces(fname_small)
src_new = read_source_spaces(fname_small)
for s in src_new:
s['nearest'] = None
s['nearest_dist'] = None
s['pinfo'] = None
# test that no patch info is added for small dist_limit
try:
add_source_space_distances(src_new, dist_limit=0.00001)
except RuntimeError: # what we throw when scipy version is wrong
pass
else:
assert all(s['nearest'] is None for s in src_new)
assert all(s['nearest_dist'] is None for s in src_new)
assert all(s['pinfo'] is None for s in src_new)
# now let's use one that works
add_source_space_distances(src_new)
for s1, s2 in zip(src, src_new):
assert_array_equal(s1['nearest'], s2['nearest'])
assert_allclose(s1['nearest_dist'], s2['nearest_dist'], atol=1e-7)
assert_equal(len(s1['pinfo']), len(s2['pinfo']))
for p1, p2 in zip(s1['pinfo'], s2['pinfo']):
assert_array_equal(p1, p2)
def test_add_patch_info():
"""Test adding patch info to source space"""
# let's setup a small source space
src = read_source_spaces(fname_small)
src_new = read_source_spaces(fname_small)
for s in src_new:
s['nearest'] = None
s['nearest_dist'] = None
s['pinfo'] = None
# test that no patch info is added for small dist_limit
try:
add_source_space_distances(src_new, dist_limit=0.00001)
except RuntimeError: # what we throw when scipy version is wrong
pass
else:
assert_true(all(s['nearest'] is None for s in src_new))
assert_true(all(s['nearest_dist'] is None for s in src_new))
assert_true(all(s['pinfo'] is None for s in src_new))
# now let's use one that works
add_source_space_distances(src_new)
for s1, s2 in zip(src, src_new):
assert_array_equal(s1['nearest'], s2['nearest'])
assert_allclose(s1['nearest_dist'], s2['nearest_dist'], atol=1e-7)
assert_equal(len(s1['pinfo']), len(s2['pinfo']))
for p1, p2 in zip(s1['pinfo'], s2['pinfo']):
assert_array_equal(p1, p2)
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=False)
split_size = 10. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp + 1)
# do an annoying case (off-by-one splitting)
tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=False)
assert_equal(raw.first_samp, all_raw_2.first_samp)
assert_equal(raw.last_samp, all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=False)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_equal(raw.first_samp, ar.first_samp)
assert_equal(raw.last_samp, ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
# test shape consistency of cropped raw
data = np.zeros((1, 1002001))
info = create_info(1, 1000)
raw = RawArray(data, info)
for tmin in range(0, 1001, 100):
raw1 = raw.crop(tmin=tmin, tmax=tmin + 2, copy=True)
assert_equal(raw1[:][0].shape, (1, 2001))
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=False)
split_size = 10. # in seconds
sfreq = raw.info['sfreq']
nsamp = (raw.last_samp - raw.first_samp + 1)
# do an annoying case (off-by-one splitting)
tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=False)
assert_equal(raw.first_samp, all_raw_2.first_samp)
assert_equal(raw.last_samp, all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=False)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_equal(raw.first_samp, ar.first_samp)
assert_equal(raw.last_samp, ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
# test shape consistency of cropped raw
data = np.zeros((1, 1002001))
info = create_info(1, 1000)
raw = RawArray(data, info)
for tmin in range(0, 1001, 100):
raw1 = raw.crop(tmin=tmin, tmax=tmin + 2, copy=True)
assert_equal(raw1[:][0].shape, (1, 2001))
def test_drop_epochs_mult():
"""Test that subselecting epochs or making less epochs is equivalent"""
for preload in [True, False]:
epochs1 = Epochs(raw, events, {'a': 1, 'b': 2},
tmin, tmax, picks=picks, reject=reject,
preload=preload)['a']
epochs2 = Epochs(raw, events, {'a': 1},
tmin, tmax, picks=picks, reject=reject,
preload=preload)
if preload:
# In the preload case you cannot know the bads if already ignored
assert_equal(len(epochs1.drop_log), len(epochs2.drop_log))
for d1, d2 in zip(epochs1.drop_log, epochs2.drop_log):
if d1 == ['IGNORED']:
assert_true(d2 == ['IGNORED'])
if d1 != ['IGNORED'] and d1 != []:
assert_true((d2 == d1) or (d2 == ['IGNORED']))
if d1 == []:
assert_true(d2 == [])
assert_array_equal(epochs1.events, epochs2.events)
assert_array_equal(epochs1.selection, epochs2.selection)
else:
# In the non preload is should be exactly the same
assert_equal(epochs1.drop_log, epochs2.drop_log)
assert_array_equal(epochs1.events, epochs2.events)
assert_array_equal(epochs1.selection, epochs2.selection)
def test_add_source_space_distances_limited():
"""Test adding distances to source space with a dist_limit."""
tempdir = _TempDir()
src = read_source_spaces(fname)
src_new = read_source_spaces(fname)
del src_new[0]['dist']
del src_new[1]['dist']
n_do = 200 # limit this for speed
src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy()
src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy()
out_name = op.join(tempdir, 'temp-src.fif')
try:
add_source_space_distances(src_new, dist_limit=0.007)
except RuntimeError: # what we throw when scipy version is wrong
raise SkipTest('dist_limit requires scipy > 0.13')
write_source_spaces(out_name, src_new)
src_new = read_source_spaces(out_name)
for so, sn in zip(src, src_new):
assert_array_equal(so['dist_limit'], np.array([-0.007], np.float32))
assert_array_equal(sn['dist_limit'], np.array([0.007], np.float32))
do = so['dist']
dn = sn['dist']
# clean out distances > 0.007 in C code
do.data[do.data > 0.007] = 0
do.eliminate_zeros()
# make sure we have some comparable distances
assert np.sum(do.data < 0.007) > 400
# do comparison over the region computed
d = (do - dn)[:sn['vertno'][n_do - 1]][:, :sn['vertno'][n_do - 1]]
assert_allclose(np.zeros_like(d.data), d.data, rtol=0, atol=1e-6)
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
'hand']
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
assert_equal(raw.info['meas_date'], raw_read.info['meas_date'])
raw.anonymize()
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for this_raw in (raw, raw_read):
assert_true(this_raw.info.get('subject_info') is None)
assert_equal(this_raw.info['meas_date'], [0, 0])
assert_equal(raw.info['file_id']['secs'], 0)
assert_equal(raw.info['meas_id']['secs'], 0)
# When we write out with raw.save, these get overwritten with the
# new save time
assert_true(raw_read.info['file_id']['secs'] > 0)
assert_true(raw_read.info['meas_id']['secs'] > 0)
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
'hand']
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
raw_read.anonymize()
assert_true(raw_read.info.get('subject_info') is None)
out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif')
raw_read.save(out_fname_anon, overwrite=True)
raw_read = Raw(out_fname_anon)
assert_true(raw_read.info.get('subject_info') is None)
def test_evoked_standard_error():
"""Test calculation and read/write of standard error
"""
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
evoked = [epochs.average(), epochs.standard_error()]
io.write_evokeds(op.join(tempdir, 'evoked.fif'), evoked)
evoked2 = read_evokeds(op.join(tempdir, 'evoked.fif'), [0, 1])
evoked3 = [read_evokeds(op.join(tempdir, 'evoked.fif'), 'Unknown'),
read_evokeds(op.join(tempdir, 'evoked.fif'), 'Unknown',
kind='standard_error')]
for evoked_new in [evoked2, evoked3]:
assert_true(evoked_new[0]._aspect_kind ==
FIFF.FIFFV_ASPECT_AVERAGE)
assert_true(evoked_new[0].kind == 'average')
assert_true(evoked_new[1]._aspect_kind ==
FIFF.FIFFV_ASPECT_STD_ERR)
assert_true(evoked_new[1].kind == 'standard_error')
for ave, ave2 in zip(evoked, evoked_new):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, copy=False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = [
'id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex', 'hand'
]
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
assert_equal(raw.info['meas_date'], raw_read.info['meas_date'])
raw.anonymize()
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for this_raw in (raw, raw_read):
assert_true(this_raw.info.get('subject_info') is None)
assert_equal(this_raw.info['meas_date'], [0, 0])
assert_equal(raw.info['file_id']['secs'], 0)
assert_equal(raw.info['meas_id']['secs'], 0)
# When we write out with raw.save, these get overwritten with the
# new save time
assert_true(raw_read.info['file_id']['secs'] > 0)
assert_true(raw_read.info['meas_id']['secs'] > 0)
def test_volume_source_space():
"""Test setting up volume source spaces
"""
tempdir = _TempDir()
src = read_source_spaces(fname_vol)
temp_name = op.join(tempdir, 'temp-src.fif')
surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN)
surf['rr'] *= 1e3 # convert to mm
# The one in the testing dataset (uses bem as bounds)
for bem, surf in zip((fname_bem, None), (None, surf)):
src_new = setup_volume_source_space('sample',
temp_name,
pos=7.0,
bem=bem,
surface=surf,
mri='T1.mgz',
subjects_dir=subjects_dir)
src[0]['subject_his_id'] = 'sample' # XXX: to make comparison pass
_compare_source_spaces(src, src_new, mode='approx')
del src_new
src_new = read_source_spaces(temp_name)
_compare_source_spaces(src, src_new, mode='approx')
assert_raises(
IOError,
setup_volume_source_space,
'sample',
temp_name,
pos=7.0,
bem=None,
surface='foo', # bad surf
mri=fname_mri,
subjects_dir=subjects_dir)
assert_equal(repr(src), repr(src_new))
assert_equal(src.kind, 'volume')
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname)
raw.crop(0, 1, False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
'hand']
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
raw_read.anonymize()
assert_true(raw_read.info.get('subject_info') is None)
out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif')
raw_read.save(out_fname_anon, overwrite=True)
raw_read = Raw(out_fname_anon)
assert_true(raw_read.info.get('subject_info') is None)
def test_add_source_space_distances_limited():
"""Test adding distances to source space with a dist_limit"""
tempdir = _TempDir()
src = read_source_spaces(fname)
src_new = read_source_spaces(fname)
del src_new[0]['dist']
del src_new[1]['dist']
n_do = 200 # limit this for speed
src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy()
src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy()
out_name = op.join(tempdir, 'temp-src.fif')
try:
add_source_space_distances(src_new, dist_limit=0.007)
except RuntimeError: # what we throw when scipy version is wrong
raise SkipTest('dist_limit requires scipy > 0.13')
write_source_spaces(out_name, src_new)
src_new = read_source_spaces(out_name)
for so, sn in zip(src, src_new):
assert_array_equal(so['dist_limit'], np.array([-0.007], np.float32))
assert_array_equal(sn['dist_limit'], np.array([0.007], np.float32))
do = so['dist']
dn = sn['dist']
# clean out distances > 0.007 in C code
do.data[do.data > 0.007] = 0
do.eliminate_zeros()
# make sure we have some comparable distances
assert_true(np.sum(do.data < 0.007) > 400)
# do comparison over the region computed
d = (do - dn)[:sn['vertno'][n_do - 1]][:, :sn['vertno'][n_do - 1]]
assert_allclose(np.zeros_like(d.data), d.data, rtol=0, atol=1e-6)
def test_volume_source_space():
"""Test setting up volume source spaces."""
tempdir = _TempDir()
src = read_source_spaces(fname_vol)
temp_name = op.join(tempdir, 'temp-src.fif')
surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN)
surf['rr'] *= 1e3 # convert to mm
# The one in the testing dataset (uses bem as bounds)
for bem, surf in zip((fname_bem, None), (None, surf)):
src_new = setup_volume_source_space(
'sample', pos=7.0, bem=bem, surface=surf, mri='T1.mgz',
subjects_dir=subjects_dir)
write_source_spaces(temp_name, src_new, overwrite=True)
src[0]['subject_his_id'] = 'sample' # XXX: to make comparison pass
_compare_source_spaces(src, src_new, mode='approx')
del src_new
src_new = read_source_spaces(temp_name)
_compare_source_spaces(src, src_new, mode='approx')
pytest.raises(IOError, setup_volume_source_space, 'sample',
pos=7.0, bem=None, surface='foo', # bad surf
mri=fname_mri, subjects_dir=subjects_dir)
assert repr(src) == repr(src_new)
assert src.kind == 'volume'
# Spheres
sphere = make_sphere_model(r0=(0., 0., 0.), head_radius=0.1,
relative_radii=(0.9, 1.0), sigmas=(0.33, 1.0))
src = setup_volume_source_space(pos=10)
src_new = setup_volume_source_space(pos=10, sphere=sphere)
_compare_source_spaces(src, src_new, mode='exact')
pytest.raises(ValueError, setup_volume_source_space, sphere='foo')
# Need a radius
sphere = make_sphere_model(head_radius=None)
pytest.raises(ValueError, setup_volume_source_space, sphere=sphere)
def test_volume_source_space():
"""Test setting up volume source spaces."""
tempdir = _TempDir()
src = read_source_spaces(fname_vol)
temp_name = op.join(tempdir, 'temp-src.fif')
surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN)
surf['rr'] *= 1e3 # convert to mm
# The one in the testing dataset (uses bem as bounds)
for bem, surf in zip((fname_bem, None), (None, surf)):
src_new = setup_volume_source_space(
'sample', pos=7.0, bem=bem, surface=surf, mri='T1.mgz',
subjects_dir=subjects_dir)
write_source_spaces(temp_name, src_new, overwrite=True)
src[0]['subject_his_id'] = 'sample' # XXX: to make comparison pass
_compare_source_spaces(src, src_new, mode='approx')
del src_new
src_new = read_source_spaces(temp_name)
_compare_source_spaces(src, src_new, mode='approx')
pytest.raises(IOError, setup_volume_source_space, 'sample',
pos=7.0, bem=None, surface='foo', # bad surf
mri=fname_mri, subjects_dir=subjects_dir)
assert repr(src) == repr(src_new)
assert src.kind == 'volume'
# Spheres
sphere = make_sphere_model(r0=(0., 0., 0.), head_radius=0.1,
relative_radii=(0.9, 1.0), sigmas=(0.33, 1.0))
src = setup_volume_source_space(pos=10)
src_new = setup_volume_source_space(pos=10, sphere=sphere)
_compare_source_spaces(src, src_new, mode='exact')
pytest.raises(ValueError, setup_volume_source_space, sphere='foo')
# Need a radius
sphere = make_sphere_model(head_radius=None)
pytest.raises(ValueError, setup_volume_source_space, sphere=sphere)
def test_evoked_standard_error():
"""Test calculation and read/write of standard error
"""
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0))
evoked = [epochs.average(), epochs.standard_error()]
fiff.write_evoked(op.join(tempdir, 'evoked.fif'), evoked)
evoked2 = read_evoked(op.join(tempdir, 'evoked.fif'), [0, 1])
evoked3 = [
read_evoked(op.join(tempdir, 'evoked.fif'), 'Unknown'),
read_evoked(op.join(tempdir, 'evoked.fif'),
'Unknown',
kind='standard_error')
]
for evoked_new in [evoked2, evoked3]:
assert_true(
evoked_new[0]._aspect_kind == fiff.FIFF.FIFFV_ASPECT_AVERAGE)
assert_true(evoked_new[0].kind == 'average')
assert_true(
evoked_new[1]._aspect_kind == fiff.FIFF.FIFFV_ASPECT_STD_ERR)
assert_true(evoked_new[1].kind == 'standard_error')
for ave, ave2 in zip(evoked, evoked_new):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_make_field_map_meeg():
"""Test making a M/EEG field map onto helmet & head."""
evoked = read_evokeds(evoked_fname, baseline=(-0.2, 0.0))[0]
picks = pick_types(evoked.info, meg=True, eeg=True)
picks = picks[::10]
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.info.normalize_proj()
maps = make_field_map(evoked,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
n_jobs=1,
verbose='debug')
assert_equal(maps[0]['data'].shape, (642, 6)) # EEG->Head
assert_equal(maps[1]['data'].shape, (304, 31)) # MEG->Helmet
# reasonable ranges
maxs = (1.2, 2.0) # before #4418, was (1.1, 2.0)
mins = (-0.8, -1.3) # before #4418, was (-0.6, -1.2)
assert_equal(len(maxs), len(maps))
for map_, max_, min_ in zip(maps, maxs, mins):
assert_allclose(map_['data'].max(), max_, rtol=5e-2)
assert_allclose(map_['data'].min(), min_, rtol=5e-2)
# calculated from correct looking mapping on 2015/12/26
assert_allclose(
np.sqrt(np.sum(maps[0]['data']**2)),
19.0903, # 16.6088,
atol=1e-3,
rtol=1e-3)
assert_allclose(
np.sqrt(np.sum(maps[1]['data']**2)),
19.4748, # 20.1245,
atol=1e-3,
rtol=1e-3)
def test_drop_epochs_mult():
"""Test that subselecting epochs or making less epochs is equivalent"""
for preload in [True, False]:
epochs1 = Epochs(raw, events, {'a': 1, 'b': 2},
tmin, tmax, picks=picks, reject=reject,
preload=preload)['a']
epochs2 = Epochs(raw, events, {'a': 1},
tmin, tmax, picks=picks, reject=reject,
preload=preload)
if preload:
# In the preload case you cannot know the bads if already ignored
assert_equal(len(epochs1.drop_log), len(epochs2.drop_log))
for d1, d2 in zip(epochs1.drop_log, epochs2.drop_log):
if d1 == ['IGNORED']:
assert_true(d2 == ['IGNORED'])
if d1 != ['IGNORED'] and d1 != []:
assert_true((d2 == d1) or (d2 == ['IGNORED']))
if d1 == []:
assert_true(d2 == [])
assert_array_equal(epochs1.events, epochs2.events)
assert_array_equal(epochs1.selection, epochs2.selection)
else:
# In the non preload is should be exactly the same
assert_equal(epochs1.drop_log, epochs2.drop_log)
assert_array_equal(epochs1.events, epochs2.events)
assert_array_equal(epochs1.selection, epochs2.selection)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject"""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
# Morph the data over using standard methods
stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph],
smooth=1, subjects_dir=subjects_dir)
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# Compare to the original data
stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert_true(corr > 0.99, corr)
# Degenerate cases
stc_morph.subject = None # no .subject provided
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
assert_raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject"""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
# Morph the data over using standard methods
stc_morph = stc_fs.morph('sample', [s['vertno'] for s in src_morph],
smooth=1, subjects_dir=subjects_dir)
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# Compare to the original data
stc_morph_morph = stc_morph.morph('fsaverage', stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert_true(corr > 0.99, corr)
# Degenerate cases
stc_morph.subject = None # no .subject provided
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
assert_raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
assert_raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def test_crop():
"""Test cropping raw files
"""
# split a concatenated file to test a difficult case
raw = Raw([fif_fname, fif_fname], preload=False)
split_size = 10.0 # in seconds
sfreq = raw.info["sfreq"]
nsamp = raw.last_samp - raw.first_samp + 1
# do an annoying case (off-by-one splitting)
tmins = np.r_[1.0, np.round(np.arange(0.0, nsamp - 1, split_size * sfreq))]
tmins = np.sort(tmins)
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.crop(tmin, tmax, True)
all_raw_2 = concatenate_raws(raws, preload=False)
assert_equal(raw.first_samp, all_raw_2.first_samp)
assert_equal(raw.last_samp, all_raw_2.last_samp)
assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])
tmins = np.round(np.arange(0.0, nsamp - 1, split_size * sfreq))
tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
tmaxs /= sfreq
tmins /= sfreq
# going in revere order so the last fname is the first file (need it later)
raws = [None] * len(tmins)
for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
raws[ri] = raw.copy()
raws[ri].crop(tmin, tmax, False)
# test concatenation of split file
all_raw_1 = concatenate_raws(raws, preload=False)
all_raw_2 = raw.crop(0, None, True)
for ar in [all_raw_1, all_raw_2]:
assert_equal(raw.first_samp, ar.first_samp)
assert_equal(raw.last_samp, ar.last_samp)
assert_array_equal(raw[:, :][0], ar[:, :][0])
def test_legendre_val():
"""Test Legendre polynomial (derivative) equivalence."""
rng = np.random.RandomState(0)
# check table equiv
xs = np.linspace(-1., 1., 1000)
n_terms = 100
# True, numpy
vals_np = legendre.legvander(xs, n_terms - 1)
# Table approximation
for nc, interp in zip([100, 50], ['nearest', 'linear']):
lut, n_fact = _get_legen_table('eeg', n_coeff=nc, force_calc=True)
lut_fun = interp1d(np.linspace(-1, 1, lut.shape[0]),
lut,
interp,
axis=0)
vals_i = lut_fun(xs)
# Need a "1:" here because we omit the first coefficient in our table!
assert_allclose(vals_np[:, 1:vals_i.shape[1] + 1],
vals_i,
rtol=1e-2,
atol=5e-3)
# Now let's look at our sums
ctheta = rng.rand(20, 30) * 2.0 - 1.0
beta = rng.rand(20, 30) * 0.8
c1 = _comp_sum_eeg(beta.flatten(), ctheta.flatten(), lut_fun, n_fact)
c1.shape = beta.shape
# compare to numpy
n = np.arange(1, n_terms, dtype=float)[:, np.newaxis, np.newaxis]
coeffs = np.zeros((n_terms, ) + beta.shape)
coeffs[1:] = (np.cumprod([beta] * (n_terms - 1), axis=0) *
(2.0 * n + 1.0) * (2.0 * n + 1.0) / n)
# can't use tensor=False here b/c it isn't in old numpy
c2 = np.empty((20, 30))
for ci1 in range(20):
for ci2 in range(30):
c2[ci1, ci2] = legendre.legval(ctheta[ci1, ci2], coeffs[:, ci1,
ci2])
assert_allclose(c1, c2, 1e-2, 1e-3) # close enough...
# compare fast and slow for MEG
ctheta = rng.rand(20 * 30) * 2.0 - 1.0
beta = rng.rand(20 * 30) * 0.8
lut, n_fact = _get_legen_table('meg', n_coeff=10, force_calc=True)
fun = interp1d(np.linspace(-1, 1, lut.shape[0]), lut, 'nearest', axis=0)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
lut, n_fact = _get_legen_table('meg', n_coeff=20, force_calc=True)
fun = interp1d(np.linspace(-1, 1, lut.shape[0]), lut, 'linear', axis=0)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
def test_triangle_neighbors():
"""Test efficient vertex neighboring triangles for surfaces"""
this = read_source_spaces(fname)[0]
this["neighbor_tri"] = [list() for _ in range(this["np"])]
for p in range(this["ntri"]):
verts = this["tris"][p]
this["neighbor_tri"][verts[0]].append(p)
this["neighbor_tri"][verts[1]].append(p)
this["neighbor_tri"][verts[2]].append(p)
this["neighbor_tri"] = [np.array(nb, int) for nb in this["neighbor_tri"]]
neighbor_tri = _triangle_neighbors(this["tris"], this["np"])
assert_true(np.array_equal(nt1, nt2) for nt1, nt2 in zip(neighbor_tri, this["neighbor_tri"]))
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates
"""
# obtained using "tksurfer (sample/fsaverage) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords_s = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
coords_f = np.array([[-41.28, -40.04, 18.20], [-6.05, 49.74, -18.15],
[-61.71, -14.55, 20.52], [21.70, -60.84, 25.02]])
hemis = [0, 0, 0, 1]
for coords, subject in zip([coords_s, coords_f], ['sample', 'fsaverage']):
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_triangle_neighbors():
"""Test efficient vertex neighboring triangles for surfaces."""
this = read_source_spaces(fname)[0]
this['neighbor_tri'] = [list() for _ in range(this['np'])]
for p in range(this['ntri']):
verts = this['tris'][p]
this['neighbor_tri'][verts[0]].append(p)
this['neighbor_tri'][verts[1]].append(p)
this['neighbor_tri'][verts[2]].append(p)
this['neighbor_tri'] = [np.array(nb, int) for nb in this['neighbor_tri']]
neighbor_tri = _triangle_neighbors(this['tris'], this['np'])
assert all(np.array_equal(nt1, nt2)
for nt1, nt2 in zip(neighbor_tri, this['neighbor_tri']))
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates
"""
# obtained using "tksurfer (sample/fsaverage) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords_s = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
coords_f = np.array([[-41.28, -40.04, 18.20], [-6.05, 49.74, -18.15],
[-61.71, -14.55, 20.52], [21.70, -60.84, 25.02]])
hemis = [0, 0, 0, 1]
for coords, subject in zip([coords_s, coords_f], ['sample', 'fsaverage']):
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_legendre_val():
"""Test Legendre polynomial (derivative) equivalence
"""
rng = np.random.RandomState(0)
# check table equiv
xs = np.linspace(-1., 1., 1000)
n_terms = 100
# True, numpy
vals_np = legendre.legvander(xs, n_terms - 1)
# Table approximation
for fun, nc in zip([_get_legen_lut_fast, _get_legen_lut_accurate],
[100, 50]):
lut, n_fact = _get_legen_table('eeg', n_coeff=nc, force_calc=True)
vals_i = fun(xs, lut)
# Need a "1:" here because we omit the first coefficient in our table!
assert_allclose(vals_np[:, 1:vals_i.shape[1] + 1], vals_i,
rtol=1e-2, atol=5e-3)
# Now let's look at our sums
ctheta = rng.rand(20, 30) * 2.0 - 1.0
beta = rng.rand(20, 30) * 0.8
lut_fun = partial(fun, lut=lut)
c1 = _comp_sum_eeg(beta.flatten(), ctheta.flatten(), lut_fun, n_fact)
c1.shape = beta.shape
# compare to numpy
n = np.arange(1, n_terms, dtype=float)[:, np.newaxis, np.newaxis]
coeffs = np.zeros((n_terms,) + beta.shape)
coeffs[1:] = (np.cumprod([beta] * (n_terms - 1), axis=0) *
(2.0 * n + 1.0) * (2.0 * n + 1.0) / n)
# can't use tensor=False here b/c it isn't in old numpy
c2 = np.empty((20, 30))
for ci1 in range(20):
for ci2 in range(30):
c2[ci1, ci2] = legendre.legval(ctheta[ci1, ci2],
coeffs[:, ci1, ci2])
assert_allclose(c1, c2, 1e-2, 1e-3) # close enough...
# compare fast and slow for MEG
ctheta = rng.rand(20 * 30) * 2.0 - 1.0
beta = rng.rand(20 * 30) * 0.8
lut, n_fact = _get_legen_table('meg', n_coeff=10, force_calc=True)
fun = partial(_get_legen_lut_fast, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
lut, n_fact = _get_legen_table('meg', n_coeff=20, force_calc=True)
fun = partial(_get_legen_lut_accurate, lut=lut)
coeffs = _comp_sums_meg(beta, ctheta, fun, n_fact, False)
def test_output_formats():
"""Test saving and loading raw data using multiple formats
"""
formats = ["short", "int", "single", "double"]
tols = [1e-4, 1e-7, 1e-7, 1e-15]
# let's fake a raw file with different formats
raw = Raw(fif_fname, preload=True)
raw.crop(0, 1, copy=False)
temp_file = op.join(tempdir, "raw.fif")
for ii, (format, tol) in enumerate(zip(formats, tols)):
# Let's test the overwriting error throwing while we're at it
if ii > 0:
assert_raises(IOError, raw.save, temp_file, format=format)
raw.save(temp_file, format=format, overwrite=True)
raw2 = Raw(temp_file)
raw2_data = raw2[:, :][0]
assert_allclose(raw2_data, raw._data, rtol=tol, atol=1e-25)
assert_true(raw2.orig_format == format)
def test_output_formats():
"""Test saving and loading raw data using multiple formats
"""
tempdir = _TempDir()
formats = ['short', 'int', 'single', 'double']
tols = [1e-4, 1e-7, 1e-7, 1e-15]
# let's fake a raw file with different formats
raw = Raw(test_fif_fname).crop(0, 1, copy=False)
temp_file = op.join(tempdir, 'raw.fif')
for ii, (fmt, tol) in enumerate(zip(formats, tols)):
# Let's test the overwriting error throwing while we're at it
if ii > 0:
assert_raises(IOError, raw.save, temp_file, fmt=fmt)
raw.save(temp_file, fmt=fmt, overwrite=True)
raw2 = Raw(temp_file)
raw2_data = raw2[:, :][0]
assert_allclose(raw2_data, raw[:, :][0], rtol=tol, atol=1e-25)
assert_equal(raw2.orig_format, fmt)
def test_output_formats():
"""Test saving and loading raw data using multiple formats
"""
tempdir = _TempDir()
formats = ['short', 'int', 'single', 'double']
tols = [1e-4, 1e-7, 1e-7, 1e-15]
# let's fake a raw file with different formats
raw = Raw(test_fif_fname).crop(0, 1, copy=False)
temp_file = op.join(tempdir, 'raw.fif')
for ii, (fmt, tol) in enumerate(zip(formats, tols)):
# Let's test the overwriting error throwing while we're at it
if ii > 0:
assert_raises(IOError, raw.save, temp_file, fmt=fmt)
raw.save(temp_file, fmt=fmt, overwrite=True)
raw2 = Raw(temp_file)
raw2_data = raw2[:, :][0]
assert_allclose(raw2_data, raw[:, :][0], rtol=tol, atol=1e-25)
assert_equal(raw2.orig_format, fmt)
def test_volume_source_space():
"""Test setting up volume source spaces
"""
tempdir = _TempDir()
src = read_source_spaces(fname_vol)
temp_name = op.join(tempdir, 'temp-src.fif')
surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN)
surf['rr'] *= 1e3 # convert to mm
# The one in the testing dataset (uses bem as bounds)
for bem, surf in zip((fname_bem, None), (None, surf)):
src_new = setup_volume_source_space('sample', temp_name, pos=7.0,
bem=bem, surface=surf,
mri=fname_mri,
subjects_dir=subjects_dir)
_compare_source_spaces(src, src_new, mode='approx')
del src_new
src_new = read_source_spaces(temp_name)
_compare_source_spaces(src, src_new, mode='approx')
assert_raises(IOError, setup_volume_source_space, 'sample', temp_name,
pos=7.0, bem=None, surface='foo', # bad surf
mri=fname_mri, subjects_dir=subjects_dir)
def test_add_source_space_distances():
"""Test adding distances to source space."""
tempdir = _TempDir()
src = read_source_spaces(fname)
src_new = read_source_spaces(fname)
del src_new[0]['dist']
del src_new[1]['dist']
n_do = 19 # limit this for speed
src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy()
src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy()
out_name = op.join(tempdir, 'temp-src.fif')
n_jobs = 2
assert n_do % n_jobs != 0
add_source_space_distances(src_new, n_jobs=n_jobs)
write_source_spaces(out_name, src_new)
src_new = read_source_spaces(out_name)
# iterate over both hemispheres
for so, sn in zip(src, src_new):
v = so['vertno'][:n_do]
assert_array_equal(so['dist_limit'], np.array([-0.007], np.float32))
assert_array_equal(sn['dist_limit'], np.array([np.inf], np.float32))
do = so['dist']
dn = sn['dist']
# clean out distances > 0.007 in C code (some residual), and Python
ds = list()
for d in [do, dn]:
d.data[d.data > 0.007] = 0
d = d[v][:, v]
d.eliminate_zeros()
ds.append(d)
# make sure we actually calculated some comparable distances
assert np.sum(ds[0].data < 0.007) > 10
# do comparison
d = ds[0] - ds[1]
assert_allclose(np.zeros_like(d.data), d.data, rtol=0, atol=1e-9)
def test_add_source_space_distances():
"""Test adding distances to source space"""
tempdir = _TempDir()
src = read_source_spaces(fname)
src_new = read_source_spaces(fname)
del src_new[0]['dist']
del src_new[1]['dist']
n_do = 19 # limit this for speed
src_new[0]['vertno'] = src_new[0]['vertno'][:n_do].copy()
src_new[1]['vertno'] = src_new[1]['vertno'][:n_do].copy()
out_name = op.join(tempdir, 'temp-src.fif')
n_jobs = 2
assert_true(n_do % n_jobs != 0)
add_source_space_distances(src_new, n_jobs=n_jobs)
write_source_spaces(out_name, src_new)
src_new = read_source_spaces(out_name)
# iterate over both hemispheres
for so, sn in zip(src, src_new):
v = so['vertno'][:n_do]
assert_array_equal(so['dist_limit'], np.array([-0.007], np.float32))
assert_array_equal(sn['dist_limit'], np.array([np.inf], np.float32))
do = so['dist']
dn = sn['dist']
# clean out distances > 0.007 in C code (some residual), and Python
ds = list()
for d in [do, dn]:
d.data[d.data > 0.007] = 0
d = d[v][:, v]
d.eliminate_zeros()
ds.append(d)
# make sure we actually calculated some comparable distances
assert_true(np.sum(ds[0].data < 0.007) > 10)
# do comparison
d = ds[0] - ds[1]
assert_allclose(np.zeros_like(d.data), d.data, rtol=0, atol=1e-9)
def test_volume_source_space():
"""Test setting up volume source spaces."""
tempdir = _TempDir()
src = read_source_spaces(fname_vol)
temp_name = op.join(tempdir, 'temp-src.fif')
surf = read_bem_surfaces(fname_bem, s_id=FIFF.FIFFV_BEM_SURF_ID_BRAIN)
surf['rr'] *= 1e3 # convert to mm
# The one in the testing dataset (uses bem as bounds)
for bem, surf in zip((fname_bem, None), (None, surf)):
src_new = setup_volume_source_space(
'sample', pos=7.0, bem=bem, surface=surf, mri='T1.mgz',
subjects_dir=subjects_dir)
write_source_spaces(temp_name, src_new, overwrite=True)
src[0]['subject_his_id'] = 'sample' # XXX: to make comparison pass
_compare_source_spaces(src, src_new, mode='approx')
del src_new
src_new = read_source_spaces(temp_name)
_compare_source_spaces(src, src_new, mode='approx')
assert_raises(IOError, setup_volume_source_space, 'sample',
pos=7.0, bem=None, surface='foo', # bad surf
mri=fname_mri, subjects_dir=subjects_dir)
assert_equal(repr(src), repr(src_new))
assert_equal(src.kind, 'volume')
def test_compensation_raw_mne():
"""Test Raw compensation by comparing with MNE
"""
tempdir = _TempDir()
def compensate_mne(fname, grad):
tmp_fname = op.join(tempdir, 'mne_ctf_test_raw.fif')
cmd = ['mne_process_raw', '--raw', fname, '--save', tmp_fname,
'--grad', str(grad), '--projoff', '--filteroff']
run_subprocess(cmd)
return Raw(tmp_fname, preload=True)
for grad in [0, 2, 3]:
raw_py = Raw(ctf_comp_fname, preload=True, compensation=grad)
raw_c = compensate_mne(ctf_comp_fname, grad)
assert_allclose(raw_py._data, raw_c._data, rtol=1e-6, atol=1e-17)
assert_equal(raw_py.info['nchan'], raw_c.info['nchan'])
for ch_py, ch_c in zip(raw_py.info['chs'], raw_c.info['chs']):
for key in ('ch_name', 'coil_type', 'scanno', 'logno', 'unit',
'coord_frame', 'kind'):
assert_equal(ch_py[key], ch_c[key])
for key in ('loc', 'unit_mul', 'range', 'cal'):
assert_allclose(ch_py[key], ch_c[key])
def test_make_field_map_meeg():
"""Test making a M/EEG field map onto helmet & head."""
evoked = read_evokeds(evoked_fname, baseline=(-0.2, 0.0))[0]
picks = pick_types(evoked.info, meg=True, eeg=True)
picks = picks[::10]
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.info.normalize_proj()
maps = make_field_map(evoked, trans_fname, subject='sample',
subjects_dir=subjects_dir, n_jobs=1, verbose='debug')
assert_equal(maps[0]['data'].shape, (642, 6)) # EEG->Head
assert_equal(maps[1]['data'].shape, (304, 31)) # MEG->Helmet
# reasonable ranges
maxs = (1.2, 2.0) # before #4418, was (1.1, 2.0)
mins = (-0.8, -1.3) # before #4418, was (-0.6, -1.2)
assert_equal(len(maxs), len(maps))
for map_, max_, min_ in zip(maps, maxs, mins):
assert_allclose(map_['data'].max(), max_, rtol=5e-2)
assert_allclose(map_['data'].min(), min_, rtol=5e-2)
# calculated from correct looking mapping on 2015/12/26
assert_allclose(np.sqrt(np.sum(maps[0]['data'] ** 2)), 19.0903, # 16.6088,
atol=1e-3, rtol=1e-3)
assert_allclose(np.sqrt(np.sum(maps[1]['data'] ** 2)), 19.4748, # 20.1245,
atol=1e-3, rtol=1e-3)
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, False)
assert_true(raw.info["subject_info"] is None)
# fake some subject data
keys = ["id", "his_id", "last_name", "first_name", "birthday", "sex", "hand"]
vals = [1, "foobar", "bar", "foo", (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info["subject_info"] = subject_info
out_fname = op.join(tempdir, "test_subj_info_raw.fif")
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info["subject_info"][key])
raw_read.anonymize()
assert_true(raw_read.info.get("subject_info") is None)
out_fname_anon = op.join(tempdir, "test_subj_info_anon_raw.fif")
raw_read.save(out_fname_anon, overwrite=True)
raw_read = Raw(out_fname_anon)
assert_true(raw_read.info.get("subject_info") is None)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject."""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
# Morph the data over using standard methods
with pytest.warns(RuntimeWarning, match='vertices not included'):
stc_morph = SourceMorph(subject_to='sample', subject_from='fsaverage',
spacing=[s['vertno'] for s in src_morph],
smooth=1, subjects_dir=subjects_dir)(stc_fs)
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# Compare to the original data
with pytest.warns(RuntimeWarning, match='vertices not included'):
stc_morph_morph = SourceMorph(subject_to='fsaverage',
spacing=stc_morph_return.vertices,
smooth=1,
subjects_dir=subjects_dir)(stc_morph)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Explicitly test having two vertices map to the same target vertex. We
# simulate this by having two vertices be at the same position.
src_fs2 = src_fs.copy()
vert1, vert2 = src_fs2[0]['vertno'][:2]
src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2]
stc_morph_return = stc_morph.to_original_src(
src_fs2, subjects_dir=subjects_dir)
# test to_original_src method result equality
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Degenerate cases
stc_morph.subject = None # no .subject provided
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
pytest.raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def _compare_source_spaces(src0, src1, mode='exact'):
"""Compare two source spaces
Note: this function is also used by forward/tests/test_make_forward.py
"""
for s0, s1 in zip(src0, src1):
for name in ['nuse', 'ntri', 'np', 'type', 'id']:
print(name)
assert_equal(s0[name], s1[name])
for name in ['subject_his_id']:
if name in s0 or name in s1:
print(name)
assert_equal(s0[name], s1[name])
for name in ['interpolator']:
if name in s0 or name in s1:
print(name)
diffs = (s0['interpolator'] - s1['interpolator']).data
if len(diffs) > 0:
assert_true(np.sqrt(np.mean(diffs ** 2)) < 0.05) # 5%
for name in ['nn', 'rr', 'nuse_tri', 'coord_frame', 'tris']:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
if mode == 'exact':
assert_array_equal(s0[name], s1[name])
elif mode == 'approx':
assert_allclose(s0[name], s1[name], rtol=1e-3, atol=1e-4)
else:
raise RuntimeError('unknown mode')
if mode == 'exact':
for name in ['inuse', 'vertno', 'use_tris']:
assert_array_equal(s0[name], s1[name])
# these fields will exist if patch info was added, these are
# not tested in mode == 'approx'
for name in ['nearest', 'nearest_dist']:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
assert_array_equal(s0[name], s1[name])
for name in ['dist_limit']:
print(name)
assert_true(s0[name] == s1[name])
for name in ['dist']:
if s0[name] is not None:
assert_equal(s1[name].shape, s0[name].shape)
assert_true(len((s0['dist'] - s1['dist']).data) == 0)
for name in ['pinfo']:
if s0[name] is not None:
assert_true(len(s0[name]) == len(s1[name]))
for p1, p2 in zip(s0[name], s1[name]):
assert_true(all(p1 == p2))
elif mode == 'approx':
# deal with vertno, inuse, and use_tris carefully
assert_array_equal(s0['vertno'], np.where(s0['inuse'])[0])
assert_array_equal(s1['vertno'], np.where(s1['inuse'])[0])
assert_equal(len(s0['vertno']), len(s1['vertno']))
agreement = np.mean(s0['inuse'] == s1['inuse'])
assert_true(agreement > 0.99)
if agreement < 1.0:
# make sure mismatched vertno are within 1.5mm
v0 = np.setdiff1d(s0['vertno'], s1['vertno'])
v1 = np.setdiff1d(s1['vertno'], s0['vertno'])
dists = cdist(s0['rr'][v0], s1['rr'][v1])
assert_allclose(np.min(dists, axis=1), np.zeros(len(v0)),
atol=1.5e-3)
if s0['use_tris'] is not None: # for "spacing"
assert_array_equal(s0['use_tris'].shape, s1['use_tris'].shape)
else:
assert_true(s1['use_tris'] is None)
assert_true(np.mean(s0['use_tris'] == s1['use_tris']) > 0.99)
# The above "if s0[name] is not None" can be removed once the sample
# dataset is updated to have a source space with distance info
for name in ['working_dir', 'command_line']:
if mode == 'exact':
assert_equal(src0.info[name], src1.info[name])
elif mode == 'approx':
print(name)
if name in src0.info:
assert_true(name in src1.info)
else:
assert_true(name not in src1.info)
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF + gz)
"""
# test unicode io
for chars in [b"\xc3\xa4\xc3\xb6\xc3\xa9", b"a"]:
with Raw(fif_fname) as r:
desc1 = r.info["description"] = chars.decode("utf-8")
temp_file = op.join(tempdir, "raw.fif")
r.save(temp_file, overwrite=True)
with Raw(temp_file) as r2:
desc2 = r2.info["description"]
assert_equal(desc1, desc2)
# Let's construct a simple test for IO first
raw = Raw(fif_fname, preload=True)
raw.crop(0, 3.5)
# put in some data that we know the values of
data = np.random.randn(raw._data.shape[0], raw._data.shape[1])
raw._data[:, :] = data
# save it somewhere
fname = op.join(tempdir, "test_copy_raw.fif")
raw.save(fname, buffer_size_sec=1.0)
# read it in, make sure the whole thing matches
raw = Raw(fname)
assert_true(np.allclose(data, raw[:, :][0], 1e-6, 1e-20))
# let's read portions across the 1-sec tag boundary, too
inds = raw.time_as_index([1.75, 2.25])
sl = slice(inds[0], inds[1])
assert_true(np.allclose(data[:, sl], raw[:, sl][0], 1e-6, 1e-20))
# now let's do some real I/O
fnames_in = [fif_fname, fif_gz_fname, ctf_fname]
fnames_out = ["raw.fif", "raw.fif.gz", "raw.fif"]
for fname_in, fname_out in zip(fnames_in, fnames_out):
fname_out = op.join(tempdir, fname_out)
raw = Raw(fname_in)
nchan = raw.info["nchan"]
ch_names = raw.info["ch_names"]
meg_channels_idx = [k for k in range(nchan) if ch_names[k][0] == "M"]
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_as_index([0, 5])
data, times = raw[meg_channels_idx, start : (stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ["STI 014"]
include += meg_ch_names
picks = pick_types(
raw.info, meg=True, eeg=False, stim=True, misc=True, ref_meg=True, include=include, exclude="bads"
)
# Writing with drop_small_buffer True
raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3, drop_small_buffer=True, overwrite=True)
raw2 = Raw(fname_out, preload=True)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True)
if fname_in == fif_fname or fname_in == fif_fname + ".gz":
assert_true(len(raw.info["dig"]) == 146)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(np.allclose(data, data2, 1e-6, 1e-20))
assert_allclose(times, times2)
assert_allclose(raw.info["sfreq"], raw2.info["sfreq"], rtol=1e-5)
# check transformations
for trans in ["dev_head_t", "dev_ctf_t", "ctf_head_t"]:
if raw.info[trans] is None:
assert_true(raw2.info[trans] is None)
else:
assert_array_equal(raw.info[trans]["trans"], raw2.info[trans]["trans"])
# check transformation 'from' and 'to'
if trans.startswith("dev"):
from_id = FIFF.FIFFV_COORD_DEVICE
else:
from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
if trans[4:8] == "head":
to_id = FIFF.FIFFV_COORD_HEAD
else:
to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
for raw_ in [raw, raw2]:
assert_true(raw_.info[trans]["from"] == from_id)
assert_true(raw_.info[trans]["to"] == to_id)
if fname_in == fif_fname or fname_in == fif_fname + ".gz":
assert_allclose(raw.info["dig"][0]["r"], raw2.info["dig"][0]["r"])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_badname = op.join(tempdir, "test-bad-name.fif.gz")
raw.save(raw_badname)
Raw(raw_badname)
assert_true(len(w) > 0) # len(w) should be 2 but Travis sometimes has more
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF + gz)
"""
tempdir = _TempDir()
# test unicode io
for chars in [b'\xc3\xa4\xc3\xb6\xc3\xa9', b'a']:
with Raw(fif_fname) as r:
assert_true('Raw' in repr(r))
assert_true(op.basename(fif_fname) in repr(r))
desc1 = r.info['description'] = chars.decode('utf-8')
temp_file = op.join(tempdir, 'raw.fif')
r.save(temp_file, overwrite=True)
with Raw(temp_file) as r2:
desc2 = r2.info['description']
assert_equal(desc1, desc2)
# Let's construct a simple test for IO first
raw = Raw(fif_fname).crop(0, 3.5, False)
raw.load_data()
# put in some data that we know the values of
data = rng.randn(raw._data.shape[0], raw._data.shape[1])
raw._data[:, :] = data
# save it somewhere
fname = op.join(tempdir, 'test_copy_raw.fif')
raw.save(fname, buffer_size_sec=1.0)
# read it in, make sure the whole thing matches
raw = Raw(fname)
assert_allclose(data, raw[:, :][0], rtol=1e-6, atol=1e-20)
# let's read portions across the 1-sec tag boundary, too
inds = raw.time_as_index([1.75, 2.25])
sl = slice(inds[0], inds[1])
assert_allclose(data[:, sl], raw[:, sl][0], rtol=1e-6, atol=1e-20)
# now let's do some real I/O
fnames_in = [fif_fname, test_fif_gz_fname, ctf_fname]
fnames_out = ['raw.fif', 'raw.fif.gz', 'raw.fif']
for fname_in, fname_out in zip(fnames_in, fnames_out):
fname_out = op.join(tempdir, fname_out)
raw = Raw(fname_in)
nchan = raw.info['nchan']
ch_names = raw.info['ch_names']
meg_channels_idx = [k for k in range(nchan)
if ch_names[k][0] == 'M']
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_as_index([0, 5])
data, times = raw[meg_channels_idx, start:(stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ['STI 014']
include += meg_ch_names
picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
misc=True, ref_meg=True, include=include,
exclude='bads')
# Writing with drop_small_buffer True
raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3,
drop_small_buffer=True, overwrite=True)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_equal(len(raw.info['dig']), 146)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_allclose(data, data2, rtol=1e-6, atol=1e-20)
assert_allclose(times, times2)
assert_allclose(raw.info['sfreq'], raw2.info['sfreq'], rtol=1e-5)
# check transformations
for trans in ['dev_head_t', 'dev_ctf_t', 'ctf_head_t']:
if raw.info[trans] is None:
assert_true(raw2.info[trans] is None)
else:
assert_array_equal(raw.info[trans]['trans'],
raw2.info[trans]['trans'])
# check transformation 'from' and 'to'
if trans.startswith('dev'):
from_id = FIFF.FIFFV_COORD_DEVICE
else:
from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
if trans[4:8] == 'head':
to_id = FIFF.FIFFV_COORD_HEAD
else:
to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
for raw_ in [raw, raw2]:
assert_equal(raw_.info[trans]['from'], from_id)
assert_equal(raw_.info[trans]['to'], to_id)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r'])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_badname = op.join(tempdir, 'test-bad-name.fif.gz')
raw.save(raw_badname)
Raw(raw_badname)
assert_naming(w, 'test_raw_fiff.py', 2)
def test_morphed_source_space_return():
"""Test returning a morphed source space to the original subject."""
# let's create some random data on fsaverage
data = rng.randn(20484, 1)
tmin, tstep = 0, 1.
src_fs = read_source_spaces(fname_fs)
stc_fs = SourceEstimate(data, [s['vertno'] for s in src_fs],
tmin, tstep, 'fsaverage')
n_verts_fs = sum(len(s['vertno']) for s in src_fs)
# Create our morph source space
src_morph = morph_source_spaces(src_fs, 'sample',
subjects_dir=subjects_dir)
n_verts_sample = sum(len(s['vertno']) for s in src_morph)
assert n_verts_fs == n_verts_sample
# Morph the data over using standard methods
stc_morph = compute_source_morph(
src_fs, 'fsaverage', 'sample',
spacing=[s['vertno'] for s in src_morph], smooth=1,
subjects_dir=subjects_dir, warn=False).apply(stc_fs)
assert stc_morph.data.shape[0] == n_verts_sample
# We can now pretend like this was real data we got e.g. from an inverse.
# To be complete, let's remove some vertices
keeps = [np.sort(rng.permutation(np.arange(len(v)))[:len(v) - 10])
for v in stc_morph.vertices]
stc_morph = SourceEstimate(
np.concatenate([stc_morph.lh_data[keeps[0]],
stc_morph.rh_data[keeps[1]]]),
[v[k] for v, k in zip(stc_morph.vertices, keeps)], tmin, tstep,
'sample')
# Return it to the original subject
stc_morph_return = stc_morph.to_original_src(
src_fs, subjects_dir=subjects_dir)
# This should fail (has too many verts in SourceMorph)
with pytest.warns(RuntimeWarning, match='vertices not included'):
morph = compute_source_morph(
src_morph, subject_from='sample',
spacing=stc_morph_return.vertices, smooth=1,
subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='vertices do not match'):
morph.apply(stc_morph)
# Compare to the original data
with pytest.warns(RuntimeWarning, match='vertices not included'):
stc_morph_morph = compute_source_morph(
src=stc_morph, subject_from='sample',
spacing=stc_morph_return.vertices, smooth=1,
subjects_dir=subjects_dir).apply(stc_morph)
assert_equal(stc_morph_return.subject, stc_morph_morph.subject)
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Explicitly test having two vertices map to the same target vertex. We
# simulate this by having two vertices be at the same position.
src_fs2 = src_fs.copy()
vert1, vert2 = src_fs2[0]['vertno'][:2]
src_fs2[0]['rr'][vert1] = src_fs2[0]['rr'][vert2]
stc_morph_return = stc_morph.to_original_src(
src_fs2, subjects_dir=subjects_dir)
# test to_original_src method result equality
for ii in range(2):
assert_array_equal(stc_morph_return.vertices[ii],
stc_morph_morph.vertices[ii])
# These will not match perfectly because morphing pushes data around
corr = np.corrcoef(stc_morph_return.data[:, 0],
stc_morph_morph.data[:, 0])[0, 1]
assert corr > 0.99, corr
# Degenerate cases
stc_morph.subject = None # no .subject provided
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='fsaverage', subjects_dir=subjects_dir)
stc_morph.subject = 'sample'
del src_fs[0]['subject_his_id'] # no name in src_fsaverage
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'fsaverage' # name mismatch
pytest.raises(ValueError, stc_morph.to_original_src,
src_fs, subject_orig='foo', subjects_dir=subjects_dir)
src_fs[0]['subject_his_id'] = 'sample'
src = read_source_spaces(fname) # wrong source space
pytest.raises(RuntimeError, stc_morph.to_original_src,
src, subjects_dir=subjects_dir)
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF + gz)
"""
tempdir = _TempDir()
# test unicode io
for chars in [b'\xc3\xa4\xc3\xb6\xc3\xa9', b'a']:
with Raw(fif_fname) as r:
assert_true('Raw' in repr(r))
assert_true(op.basename(fif_fname) in repr(r))
desc1 = r.info['description'] = chars.decode('utf-8')
temp_file = op.join(tempdir, 'raw.fif')
r.save(temp_file, overwrite=True)
with Raw(temp_file) as r2:
desc2 = r2.info['description']
assert_equal(desc1, desc2)
# Let's construct a simple test for IO first
raw = Raw(fif_fname).crop(0, 3.5)
raw.load_data()
# put in some data that we know the values of
data = rng.randn(raw._data.shape[0], raw._data.shape[1])
raw._data[:, :] = data
# save it somewhere
fname = op.join(tempdir, 'test_copy_raw.fif')
raw.save(fname, buffer_size_sec=1.0)
# read it in, make sure the whole thing matches
raw = Raw(fname)
assert_allclose(data, raw[:, :][0], rtol=1e-6, atol=1e-20)
# let's read portions across the 1-sec tag boundary, too
inds = raw.time_as_index([1.75, 2.25])
sl = slice(inds[0], inds[1])
assert_allclose(data[:, sl], raw[:, sl][0], rtol=1e-6, atol=1e-20)
# now let's do some real I/O
fnames_in = [fif_fname, test_fif_gz_fname, ctf_fname]
fnames_out = ['raw.fif', 'raw.fif.gz', 'raw.fif']
for fname_in, fname_out in zip(fnames_in, fnames_out):
fname_out = op.join(tempdir, fname_out)
raw = Raw(fname_in)
nchan = raw.info['nchan']
ch_names = raw.info['ch_names']
meg_channels_idx = [k for k in range(nchan) if ch_names[k][0] == 'M']
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_as_index([0, 5])
data, times = raw[meg_channels_idx, start:(stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ['STI 014']
include += meg_ch_names
picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=True,
misc=True,
ref_meg=True,
include=include,
exclude='bads')
# Writing with drop_small_buffer True
raw.save(fname_out,
picks,
tmin=0,
tmax=4,
buffer_size_sec=3,
drop_small_buffer=True,
overwrite=True)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_equal(len(raw.info['dig']), 146)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_allclose(data, data2, rtol=1e-6, atol=1e-20)
assert_allclose(times, times2)
assert_allclose(raw.info['sfreq'], raw2.info['sfreq'], rtol=1e-5)
# check transformations
for trans in ['dev_head_t', 'dev_ctf_t', 'ctf_head_t']:
if raw.info[trans] is None:
assert_true(raw2.info[trans] is None)
else:
assert_array_equal(raw.info[trans]['trans'],
raw2.info[trans]['trans'])
# check transformation 'from' and 'to'
if trans.startswith('dev'):
from_id = FIFF.FIFFV_COORD_DEVICE
else:
from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
if trans[4:8] == 'head':
to_id = FIFF.FIFFV_COORD_HEAD
else:
to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
for raw_ in [raw, raw2]:
assert_equal(raw_.info[trans]['from'], from_id)
assert_equal(raw_.info[trans]['to'], to_id)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r'])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_badname = op.join(tempdir, 'test-bad-name.fif.gz')
raw.save(raw_badname)
Raw(raw_badname)
assert_naming(w, 'test_raw_fiff.py', 2)
def _compare_source_spaces(src0, src1, mode='exact'):
"""Compare two source spaces
Note: this function is also used by forward/tests/test_make_forward.py
"""
for s0, s1 in zip(src0, src1):
for name in ['nuse', 'ntri', 'np', 'type', 'id']:
print(name)
assert_equal(s0[name], s1[name])
for name in ['subject_his_id']:
if name in s0 or name in s1:
print(name)
assert_equal(s0[name], s1[name])
for name in ['interpolator']:
if name in s0 or name in s1:
print(name)
diffs = (s0['interpolator'] - s1['interpolator']).data
if len(diffs) > 0:
assert_true(np.sqrt(np.mean(diffs**2)) < 0.05) # 5%
for name in ['nn', 'rr', 'nuse_tri', 'coord_frame', 'tris']:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
if mode == 'exact':
assert_array_equal(s0[name], s1[name])
elif mode == 'approx':
assert_allclose(s0[name], s1[name], rtol=1e-3, atol=1e-4)
else:
raise RuntimeError('unknown mode')
if mode == 'exact':
for name in ['inuse', 'vertno', 'use_tris']:
assert_array_equal(s0[name], s1[name])
# these fields will exist if patch info was added, these are
# not tested in mode == 'approx'
for name in ['nearest', 'nearest_dist']:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
assert_array_equal(s0[name], s1[name])
for name in ['dist_limit']:
print(name)
assert_true(s0[name] == s1[name])
for name in ['dist']:
if s0[name] is not None:
assert_equal(s1[name].shape, s0[name].shape)
assert_true(len((s0['dist'] - s1['dist']).data) == 0)
for name in ['pinfo']:
if s0[name] is not None:
assert_true(len(s0[name]) == len(s1[name]))
for p1, p2 in zip(s0[name], s1[name]):
assert_true(all(p1 == p2))
elif mode == 'approx':
# deal with vertno, inuse, and use_tris carefully
assert_array_equal(s0['vertno'], np.where(s0['inuse'])[0])
assert_array_equal(s1['vertno'], np.where(s1['inuse'])[0])
assert_equal(len(s0['vertno']), len(s1['vertno']))
agreement = np.mean(s0['inuse'] == s1['inuse'])
assert_true(agreement > 0.99)
if agreement < 1.0:
# make sure mismatched vertno are within 1.5mm
v0 = np.setdiff1d(s0['vertno'], s1['vertno'])
v1 = np.setdiff1d(s1['vertno'], s0['vertno'])
dists = cdist(s0['rr'][v0], s1['rr'][v1])
assert_allclose(np.min(dists, axis=1),
np.zeros(len(v0)),
atol=1.5e-3)
if s0['use_tris'] is not None: # for "spacing"
assert_array_equal(s0['use_tris'].shape, s1['use_tris'].shape)
else:
assert_true(s1['use_tris'] is None)
assert_true(np.mean(s0['use_tris'] == s1['use_tris']) > 0.99)
# The above "if s0[name] is not None" can be removed once the sample
# dataset is updated to have a source space with distance info
for name in ['working_dir', 'command_line']:
if mode == 'exact':
assert_equal(src0.info[name], src1.info[name])
elif mode == 'approx':
print(name)
if name in src0.info:
assert_true(name in src1.info)
else:
assert_true(name not in src1.info)
def _compare_source_spaces(src0, src1, mode="exact"):
"""Compare two source spaces
Note: this function is also used by forward/tests/test_make_forward.py
"""
for s0, s1 in zip(src0, src1):
for name in ["nuse", "ntri", "np", "type", "id"]:
print(name)
assert_equal(s0[name], s1[name])
for name in ["subject_his_id"]:
if name in s0 or name in s1:
print(name)
assert_equal(s0[name], s1[name])
for name in ["interpolator"]:
if name in s0 or name in s1:
print(name)
diffs = (s0["interpolator"] - s1["interpolator"]).data
assert_true(np.sqrt(np.mean(diffs ** 2)) < 0.05) # 5%
for name in ["nn", "rr", "nuse_tri", "coord_frame", "tris"]:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
if mode == "exact":
assert_array_equal(s0[name], s1[name])
elif mode == "approx":
assert_allclose(s0[name], s1[name], rtol=1e-3, atol=1e-4)
else:
raise RuntimeError("unknown mode")
if mode == "exact":
for name in ["inuse", "vertno", "use_tris"]:
assert_array_equal(s0[name], s1[name])
# these fields will exist if patch info was added, these are
# not tested in mode == 'approx'
for name in ["nearest", "nearest_dist"]:
print(name)
if s0[name] is None:
assert_true(s1[name] is None)
else:
assert_array_equal(s0[name], s1[name])
for name in ["dist_limit"]:
print(name)
assert_true(s0[name] == s1[name])
for name in ["dist"]:
if s0[name] is not None:
assert_equal(s1[name].shape, s0[name].shape)
assert_true(len((s0["dist"] - s1["dist"]).data) == 0)
for name in ["pinfo"]:
if s0[name] is not None:
assert_true(len(s0[name]) == len(s1[name]))
for p1, p2 in zip(s0[name], s1[name]):
assert_true(all(p1 == p2))
elif mode == "approx":
# deal with vertno, inuse, and use_tris carefully
assert_array_equal(s0["vertno"], np.where(s0["inuse"])[0])
assert_array_equal(s1["vertno"], np.where(s1["inuse"])[0])
assert_equal(len(s0["vertno"]), len(s1["vertno"]))
agreement = np.mean(s0["inuse"] == s1["inuse"])
assert_true(agreement > 0.99)
if agreement < 1.0:
# make sure mismatched vertno are within 1.5mm
v0 = np.setdiff1d(s0["vertno"], s1["vertno"])
v1 = np.setdiff1d(s1["vertno"], s0["vertno"])
dists = cdist(s0["rr"][v0], s1["rr"][v1])
assert_allclose(np.min(dists, axis=1), np.zeros(len(v0)), atol=1.5e-3)
if s0["use_tris"] is not None: # for "spacing"
assert_array_equal(s0["use_tris"].shape, s1["use_tris"].shape)
else:
assert_true(s1["use_tris"] is None)
assert_true(np.mean(s0["use_tris"] == s1["use_tris"]) > 0.99)
# The above "if s0[name] is not None" can be removed once the sample
# dataset is updated to have a source space with distance info
for name in ["working_dir", "command_line"]:
if mode == "exact":
assert_equal(src0.info[name], src1.info[name])
elif mode == "approx":
print(name)
if name in src0.info:
assert_true(name in src1.info)
else:
assert_true(name not in src1.info)
