def test_read_annot():
"""Test reading labels from FreeSurfer parcellation
"""
# test some invalid inputs
assert_raises(ValueError, read_annot, 'sample', hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError, read_annot, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh = read_annot('sample', hemi='lh', subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
assert_is_not(label.color, None)
# read labels using annot_fname
annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot')
labels_rh = read_annot('sample', annot_fname=annot_fname,
subjects_dir=subjects_dir)
for label in labels_rh:
assert_true(label.name.endswith('-rh'))
assert_true(label.hemi == 'rh')
assert_is_not(label.color, None)
# combine the lh, rh, labels and sort them
labels_lhrh = list()
labels_lhrh.extend(labels_lh)
labels_lhrh.extend(labels_rh)
names = [label.name for label in labels_lhrh]
labels_lhrh = [label for (name, label) in sorted(zip(names, labels_lhrh))]
# read all labels at once
labels_both = read_annot('sample', subjects_dir=subjects_dir)
# we have the same result
_assert_labels_equal(labels_lhrh, labels_both)
# aparc has 68 cortical labels
assert_true(len(labels_both) == 68)
# test regexp
label = read_annot('sample', parc='aparc.a2009s', regexp='Angu',
subjects_dir=subjects_dir)[0]
assert_true(label.name == 'G_pariet_inf-Angular-lh')
# silly, but real regexp:
label = read_annot('sample', 'aparc.a2009s', regexp='.*-.{4,}_.{3,3}-L',
subjects_dir=subjects_dir)[0]
assert_true(label.name == 'G_oc-temp_med-Lingual-lh')
assert_raises(RuntimeError, read_annot, 'sample', parc='aparc',
annot_fname=annot_fname, regexp='JackTheRipper',
subjects_dir=subjects_dir)
def test_read_annot_annot2labels():
"""Test reading labels from parc. by comparing with mne_annot2labels
"""
def _mne_annot2labels(subject, subjects_dir, parc):
"""Get labels using mne_annot2lables"""
label_dir = _TempDir()
cwd = os.getcwd()
try:
os.chdir(label_dir)
env = os.environ.copy()
env['SUBJECTS_DIR'] = subjects_dir
cmd = ['mne_annot2labels', '--subject', subject, '--parc', parc]
run_subprocess(cmd, env=env)
label_fnames = glob.glob(label_dir + '/*.label')
label_fnames.sort()
labels = [read_label(fname) for fname in label_fnames]
finally:
del label_dir
os.chdir(cwd)
return labels
labels = read_annot('sample', subjects_dir=subjects_dir)
labels_mne = _mne_annot2labels('sample', subjects_dir, 'aparc')
# we have the same result, mne does not fill pos, so ignore it
_assert_labels_equal(labels, labels_mne, ignore_pos=True)
def loadannot_mne(p,
subj,
subjdir,
labnam=None,
surf_type='pial',
surf_struct=None,
quiet=False):
verbosity = 'ERROR' if quiet else 'WARNING'
from distutils.version import LooseVersion
if LooseVersion(mne.__version__) >= LooseVersion('0.8'):
#MNE python changed the API to read an annotation twice in the same
#release cycle. Check for both versions.
try:
annot = mne.read_labels_from_annot(parc=p,
subject=subj,
surf_name=surf_type,
subjects_dir=subjdir,
verbose=verbosity)
except:
annot = mne.read_annot(parc=p,
subject=subj,
surf_name=surf_type,
subjects_dir=subjdir,
verbose=verbosity)
else:
annot = mne.labels_from_parc(parc=p,
subject=subj,
surf_name=surf_type,
subjects_dir=subjdir,
verbose=verbosity)
annot = annot[0] #discard the color table
return annot
def loadannot_mne(p,subj,subjdir,labnam=None,surf_type='pial',surf_struct=None, quiet=False): verbosity = 'ERROR' if quiet else 'WARNING' if float(mne.__version__[:3]) >= 0.8: annot = mne.read_annot(parc=p, subject=subj, surf_name=surf_type, subjects_dir=subjdir, verbose=verbosity) else: annot = mne.labels_from_parc(parc=p, subject=subj, surf_name=surf_type, subjects_dir=subjdir, verbose=verbosity) annot = annot[0] #discard the color table return annot
def test_split_label():
"""Test splitting labels"""
aparc = read_annot('fsaverage',
'aparc',
'lh',
regexp='lingual',
subjects_dir=subjects_dir)
lingual = aparc[0]
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
lingual_reconst.color = lingual.color
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [
210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716, 32719,
36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283, 71864, 71865,
71866, 71874, 71883, 79901, 79903, 79910, 103024, 107849, 107850,
122928, 139356, 139357, 139373, 139374, 139375, 139376, 139377, 139378,
139381, 149117, 149118, 149120, 149127
]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
def test_source_space():
"Test SourceSpace Dimension"
subject = 'fsaverage'
data_path = mne.datasets.sample.data_path()
mri_sdir = os.path.join(data_path, 'subjects')
mri_dir = os.path.join(mri_sdir, subject)
src_path = os.path.join(mri_dir, 'bem', subject + '-ico-5-src.fif')
label_dir = os.path.join(mri_dir, 'label')
label_ba1 = mne.read_label(os.path.join(label_dir, 'lh.BA1.label'))
label_v1 = mne.read_label(os.path.join(label_dir, 'lh.V1.label'))
label_mt = mne.read_label(os.path.join(label_dir, 'lh.MT.label'))
label_ba1_v1 = label_ba1 + label_v1
label_v1_mt = label_v1 + label_mt
src = mne.read_source_spaces(src_path)
source = SourceSpace((src[0]['vertno'], src[1]['vertno']), subject,
'ico-5', mri_sdir)
index = source.dimindex(label_v1)
source_v1 = source[index]
index = source.dimindex(label_ba1_v1)
source_ba1_v1 = source[index]
index = source.dimindex(label_v1_mt)
source_v1_mt = source[index]
index = source_ba1_v1.dimindex(source_v1_mt)
source_v1_intersection = source_ba1_v1[index]
assert_source_space_equal(source_v1, source_v1_intersection)
# index from label
index = source.index_for_label(label_v1)
assert_array_equal(index.source[index.x].vertno[0],
np.intersect1d(source.lh_vertno, label_v1.vertices, 1))
# parcellation and cluster localization
if mne.__version__ < '0.8':
return
parc = mne.read_annot(subject, parc='aparc', subjects_dir=mri_sdir)
indexes = [source.index_for_label(label) for label in parc
if len(label) > 10]
x = np.vstack([index.x for index in indexes])
ds = source._cluster_properties(x)
for i in xrange(ds.n_cases):
assert_equal(ds[i, 'location'], parc[i].name)
def loadannot_mne(p,subj,subjdir,labnam=None,surf_type='pial',surf_struct=None,
quiet=False):
verbosity = 'ERROR' if quiet else 'WARNING'
from distutils.version import LooseVersion
if LooseVersion(mne.__version__) >= LooseVersion('0.8'):
#MNE python changed the API to read an annotation twice in the same
#release cycle. Check for both versions.
try:
annot = mne.read_labels_from_annot(parc=p, subject=subj,
surf_name=surf_type, subjects_dir=subjdir, verbose=verbosity)
except:
annot = mne.read_annot(parc=p, subject=subj,
surf_name=surf_type, subjects_dir=subjdir, verbose=verbosity)
else:
annot = mne.labels_from_parc(parc=p, subject=subj, surf_name=surf_type,
subjects_dir=subjdir, verbose=verbosity)
annot = annot[0] #discard the color table
return annot
def test_split_label():
"""Test splitting labels"""
aparc = read_annot('fsaverage', 'aparc', 'lh', regexp='lingual',
subjects_dir=subjects_dir)
lingual = aparc[0]
# split with names
parts = ('lingual_post', 'lingual_ant')
post, ant = split_label(lingual, parts, subjects_dir=subjects_dir)
# check output names
assert_equal(post.name, parts[0])
assert_equal(ant.name, parts[1])
# check vertices add up
lingual_reconst = post + ant
lingual_reconst.name = lingual.name
lingual_reconst.comment = lingual.comment
lingual_reconst.color = lingual.color
assert_labels_equal(lingual_reconst, lingual)
# compare output of Label.split() method
post1, ant1 = lingual.split(parts, subjects_dir=subjects_dir)
assert_labels_equal(post1, post)
assert_labels_equal(ant1, ant)
# compare fs_like split with freesurfer split
antmost = split_label(lingual, 40, None, subjects_dir, True)[-1]
fs_vert = [210, 4401, 7405, 12079, 16276, 18956, 26356, 32713, 32716,
32719, 36047, 36050, 42797, 42798, 42799, 59281, 59282, 59283,
71864, 71865, 71866, 71874, 71883, 79901, 79903, 79910, 103024,
107849, 107850, 122928, 139356, 139357, 139373, 139374, 139375,
139376, 139377, 139378, 139381, 149117, 149118, 149120, 149127]
assert_array_equal(antmost.vertices, fs_vert)
# check default label name
assert_equal(antmost.name, "lingual_div40-lh")
def test_write_annot():
"""Test writing FreeSurfer parcellation from labels"""
labels = read_annot('sample', subjects_dir=subjects_dir)
# write left and right hemi labels:
fnames = ['%s/%s-myparc' % (tempdir, hemi) for hemi in ['lh', 'rh']]
for fname in fnames:
write_annot(labels, annot_fname=fname)
# read it back
labels2 = read_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels22 = read_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[1])
labels2.extend(labels22)
names = [label.name for label in labels2]
for label in labels:
idx = names.index(label.name)
assert_labels_equal(label, labels2[idx])
# same with label-internal colors
for fname in fnames:
write_annot(labels, annot_fname=fname, overwrite=True)
labels3 = read_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels33 = read_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[1])
labels3.extend(labels33)
names3 = [label.name for label in labels3]
for label in labels:
idx = names3.index(label.name)
assert_labels_equal(label, labels3[idx])
# make sure we can't overwrite things
assert_raises(ValueError, write_annot, labels, annot_fname=fnames[0])
# however, this works
write_annot(labels, annot_fname=fnames[0], overwrite=True)
# label without color
labels_ = labels[:]
labels_[0] = labels_[0].copy()
labels_[0].color = None
write_annot(labels_, annot_fname=fnames[0], overwrite=True)
# duplicate color
labels_[0].color = labels_[2].color
assert_raises(ValueError, write_annot, labels_, annot_fname=fnames[0],
overwrite=True)
# invalid color inputs
labels_[0].color = (1.1, 1., 1., 1.)
assert_raises(ValueError, write_annot, labels_, annot_fname=fnames[0],
overwrite=True)
# overlapping labels
labels_ = labels[:]
cuneus_lh = labels[6]
precuneus_lh = labels[50]
labels_.append(precuneus_lh + cuneus_lh)
assert_raises(ValueError, write_annot, labels_, annot_fname=fnames[0],
overwrite=True)
# unlabeled vertices
labels_lh = [label for label in labels if label.name.endswith('lh')]
write_annot(labels_lh[1:], 'sample', annot_fname=fnames[0], overwrite=True,
subjects_dir=subjects_dir)
labels_reloaded = read_annot('sample', annot_fname=fnames[0],
subjects_dir=subjects_dir)
assert_equal(len(labels_lh), len(labels_reloaded))
label0 = labels_lh[0]
label1 = labels_reloaded[-1]
assert_equal(label1.name, "unknown-lh")
assert_true(np.all(np.in1d(label0.vertices, label1.vertices)))
# Compute inverse solution
stc = apply_inverse(evoked,
inverse_operator,
lambda2,
method,
pick_normal=True)
# Make an STC in the time interval of interest and take the mean
stc_mean = stc.copy().crop(tmin, tmax).mean()
# use the stc_mean to generate a functional label
# region growing is halted at 60% of the peak value within the
# anatomical label / ROI specified by aparc_label_name
label = mne.read_annot(subject,
parc='aparc',
subjects_dir=subjects_dir,
regexp=aparc_label_name)[0]
stc_mean_label = stc_mean.in_label(label)
data = np.abs(stc_mean_label.data)
stc_mean_label.data[data < 0.6 * np.max(data)] = 0.
func_labels, _ = mne.stc_to_label(stc_mean_label,
src=src,
smooth=5,
subjects_dir=subjects_dir,
connected=True)
# take first as func_labels are ordered based on maximum values in stc
func_label = func_labels[0]
# load the anatomical ROI for comparison
# Define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,
eog=150e-6))
# Compute inverse solution and for each epoch. By using "return_generator=True"
# stcs will be a generator object instead of a list.
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels = mne.read_annot('sample', parc='aparc', subjects_dir=subjects_dir)
label_colors = [label.color for label in labels]
# Average the source estimates within each label using sign-flips to reduce
# signal cancellations, also here we return a generator
src = inverse_operator['src']
label_ts = mne.extract_label_time_course(stcs, labels, src, mode='mean_flip',
return_generator=True)
# Now we are ready to compute the connectivity in the alpha band. Notice
# from the status messages, how mne-python: 1) reads an epoch from the raw
# file, 2) applies SSP and baseline correction, 3) computes the inverse to
# obtain a source estimate, 4) averages the source estimate to obtain a
# time series for each label, 5) includes the label time series in the
# connectivity computation, and then moves to the next epoch. This
# behaviour is because we are using generators and allows us to
# Load data
evoked = Evoked(fname_evoked, setno=0, baseline=(None, 0))
inverse_operator = read_inverse_operator(fname_inv)
src = inverse_operator['src'] # get the source space
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator, lambda2, method,
pick_normal=True)
# Make an STC in the time interval of interest and take the mean
stc_mean = stc.copy().crop(tmin, tmax).mean()
# use the stc_mean to generate a functional label
# region growing is halted at 60% of the peak value within the
# anatomical label / ROI specified by aparc_label_name
label = mne.read_annot(subject, parc='aparc', subjects_dir=subjects_dir,
regexp=aparc_label_name)[0]
stc_mean_label = stc_mean.in_label(label)
data = np.abs(stc_mean_label.data)
stc_mean_label.data[data < 0.6 * np.max(data)] = 0.
func_labels, _ = mne.stc_to_label(stc_mean_label, src=src, smooth=5,
subjects_dir=subjects_dir, connected=True)
# take first as func_labels are ordered based on maximum values in stc
func_label = func_labels[0]
# load the anatomical ROI for comparison
anat_label = mne.read_annot(subject, parc='aparc', subjects_dir=subjects_dir,
regexp=aparc_label_name)[0]
# extract the anatomical time course for each label
# Define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,
eog=150e-6))
# Compute inverse solution and for each epoch. By using "return_generator=True"
# stcs will be a generator object instead of a list.
snr = 1.0 # use lower SNR for single epochs
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
pick_ori="normal", return_generator=True)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels = mne.read_annot('sample', parc='aparc', subjects_dir=subjects_dir)
label_colors = [label.color for label in labels]
# Average the source estimates within each label using sign-flips to reduce
# signal cancellations, also here we return a generator
src = inverse_operator['src']
label_ts = mne.extract_label_time_course(stcs, labels, src, mode='mean_flip',
return_generator=True)
# Now we are ready to compute the connectivity in the alpha band. Notice
# from the status messages, how mne-python: 1) reads an epoch from the raw
# file, 2) applies SSP and baseline correction, 3) computes the inverse to
# obtain a source estimate, 4) averages the source estimate to obtain a
# time series for each label, 5) includes the label time series in the
# connectivity computation, and then moves to the next epoch. This
# behaviour is because we are using generators and allows us to
