def test_combination_label():
"Test combination label creation"
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', subjects_dir=subjects_dir)}
# standard
l = combination_label('temporal', "superiortemporal + middletemporal + inferiortemporal", labels)
lh = labels['superiortemporal-lh'] + labels['middletemporal-lh'] + labels['inferiortemporal-lh']
rh = labels['superiortemporal-rh'] + labels['middletemporal-rh'] + labels['inferiortemporal-rh']
eq_(len(l), 2)
eq_(l[0].name, 'temporal-lh')
eq_(l[1].name, 'temporal-rh')
assert_array_equal(l[0].vertices, lh.vertices)
assert_array_equal(l[1].vertices, rh.vertices)
# only rh
l = combination_label('temporal-rh', "superiortemporal + middletemporal + inferiortemporal", labels)
eq_(len(l), 1)
eq_(l[0].name, 'temporal-rh')
assert_array_equal(l[0].vertices, rh.vertices)
# names with .
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', 'PALS_B12_Brodmann', subjects_dir=subjects_dir)}
l = combination_label('Ba38-lh', "Brodmann.38", labels)[0]
assert_array_equal(l.vertices, labels['Brodmann.38-lh'].vertices)
def test_combination_label():
"Test combination label creation"
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', subjects_dir=subjects_dir)}
# standard
l = combination_label('temporal', "superiortemporal + middletemporal + inferiortemporal",
labels, subjects_dir)
lh = labels['superiortemporal-lh'] + labels['middletemporal-lh'] + labels['inferiortemporal-lh']
lh.name = 'temporal-lh'
rh = labels['superiortemporal-rh'] + labels['middletemporal-rh'] + labels['inferiortemporal-rh']
rh.name = 'temporal-rh'
eq_(len(l), 2)
assert_labels_equal(l[0], lh)
assert_labels_equal(l[1], rh)
# only rh
l = combination_label('temporal-rh', "superiortemporal + middletemporal + inferiortemporal",
labels, subjects_dir)
eq_(len(l), 1)
eq_(l[0].name, 'temporal-rh')
assert_array_equal(l[0].vertices, rh.vertices)
# with split_label
l2 = combination_label('temporal-rh', "superiortemporal + middletemporal +"
"split(inferiortemporal, 2)[0] +"
"split(inferiortemporal, 2)[1]",
labels, subjects_dir)
assert_labels_equal(l2[0], l[0], comment=False, color=False)
# names with .
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', 'PALS_B12_Lobes', subjects_dir=subjects_dir)}
l = combination_label('frontal-lh', "LOBE.FRONTAL", labels, subjects_dir)[0]
assert_array_equal(l.vertices, labels['LOBE.FRONTAL-lh'].vertices)
def test_morph_labels():
"""Test morph_labels."""
# Just process the first 5 labels for speed
parc_fsaverage = read_labels_from_annot('fsaverage',
'aparc',
subjects_dir=subjects_dir)[:5]
parc_sample = read_labels_from_annot('sample',
'aparc',
subjects_dir=subjects_dir)[:5]
parc_fssamp = morph_labels(parc_fsaverage,
'sample',
subjects_dir=subjects_dir)
for lf, ls, lfs in zip(parc_fsaverage, parc_sample, parc_fssamp):
assert lf.hemi == ls.hemi == lfs.hemi
assert lf.name == ls.name == lfs.name
perc_1 = np.in1d(lfs.vertices, ls.vertices).mean() * 100
perc_2 = np.in1d(ls.vertices, lfs.vertices).mean() * 100
# Ideally this would be 100%, but we do not use the same algorithm
# as FreeSurfer ...
assert perc_1 > 92
assert perc_2 > 88
with pytest.raises(ValueError, match='wrong and fsaverage'):
morph_labels(parc_fsaverage,
'sample',
subjects_dir=subjects_dir,
subject_from='wrong')
with pytest.raises(RuntimeError, match='Number of surface vertices'):
_load_vert_pos('sample', subjects_dir, 'white', 'lh', 1)
for label in parc_fsaverage:
label.subject = None
with pytest.raises(ValueError, match='subject_from must be provided'):
morph_labels(parc_fsaverage, 'sample', subjects_dir=subjects_dir)
def test_combination_label():
"Test combination label creation"
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', subjects_dir=subjects_dir)}
# standard
l = combination_label('temporal', "superiortemporal + middletemporal + inferiortemporal",
labels, subjects_dir)
lh = labels['superiortemporal-lh'] + labels['middletemporal-lh'] + labels['inferiortemporal-lh']
lh.name = 'temporal-lh'
rh = labels['superiortemporal-rh'] + labels['middletemporal-rh'] + labels['inferiortemporal-rh']
rh.name = 'temporal-rh'
eq_(len(l), 2)
assert_labels_equal(l[0], lh)
assert_labels_equal(l[1], rh)
# only rh
l = combination_label('temporal-rh', "superiortemporal + middletemporal + inferiortemporal",
labels, subjects_dir)
eq_(len(l), 1)
eq_(l[0].name, 'temporal-rh')
assert_array_equal(l[0].vertices, rh.vertices)
# with split_label
l2 = combination_label('temporal-rh', "superiortemporal + middletemporal +"
"split(inferiortemporal, 2)[0] +"
"split(inferiortemporal, 2)[1]",
labels, subjects_dir)
assert_labels_equal(l2[0], l[0], comment=False, color=False)
# names with .
labels = {l.name: l for l in
mne.read_labels_from_annot('fsaverage', 'PALS_B12_Lobes', subjects_dir=subjects_dir)}
l = combination_label('frontal-lh', "LOBE.FRONTAL", labels, subjects_dir)[0]
assert_array_equal(l.vertices, labels['LOBE.FRONTAL-lh'].vertices)
def test_morph_labels():
"""Test morph_labels."""
# Just process the first 5 labels for speed
parc_fsaverage = read_labels_from_annot(
'fsaverage', 'aparc', subjects_dir=subjects_dir)[:5]
parc_sample = read_labels_from_annot(
'sample', 'aparc', subjects_dir=subjects_dir)[:5]
parc_fssamp = morph_labels(
parc_fsaverage, 'sample', subjects_dir=subjects_dir)
for lf, ls, lfs in zip(parc_fsaverage, parc_sample, parc_fssamp):
assert lf.hemi == ls.hemi == lfs.hemi
assert lf.name == ls.name == lfs.name
perc_1 = np.in1d(lfs.vertices, ls.vertices).mean() * 100
perc_2 = np.in1d(ls.vertices, lfs.vertices).mean() * 100
# Ideally this would be 100%, but we do not use the same algorithm
# as FreeSurfer ...
assert perc_1 > 92
assert perc_2 > 88
with pytest.raises(ValueError, match='wrong and fsaverage'):
morph_labels(parc_fsaverage, 'sample', subjects_dir=subjects_dir,
subject_from='wrong')
with pytest.raises(RuntimeError, match='Number of surface vertices'):
_load_vert_pos('sample', subjects_dir, 'white', 'lh', 1)
for label in parc_fsaverage:
label.subject = None
with pytest.raises(ValueError, match='subject_from must be provided'):
morph_labels(parc_fsaverage, 'sample', subjects_dir=subjects_dir)
def add_motor_labels(brain, src_df):
try:
lnames = src_df.index.levels[0].map(lambda l: l[:-7]).unique()
except AttributeError:
lnames = src_df.index.map(lambda l: l[:-7]).unique()
premotor = ss.Glasser_areas[
ss.Glasser_areas['main section']==8]['area name'].values
for hemi in brain.geo.keys():
labels = mne.read_labels_from_annot('fsaverage', parc='HCPMMP1_5_8',
hemi=hemi)
hemilnames = lnames[list(lnames.map(
lambda s: s[0] == hemi[0].upper()).values)]
for label in labels:
if label.name in hemilnames:
if label.name[2:-7] in premotor:
col = 'k'
else:
col = 'w'
brain.add_label(label, borders=True, hemi=hemi, alpha=0.6,
color=col)
for lname, col in zip(['UpperExtremity', 'Ocular'],
['#225500', '#44aa00']):
mlabel = mne.read_labels_from_annot(
'fsaverage', parc='HCPMMP1_motor', hemi=hemi, regexp=lname)
brain.add_label(mlabel[0], borders=True, hemi=hemi, alpha=1,
color=col)
def test_read_labels_from_annot():
"""Test reading labels from FreeSurfer parcellation
"""
# test some invalid inputs
assert_raises(ValueError, read_labels_from_annot, 'sample', hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError, read_labels_from_annot, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh = read_labels_from_annot('sample', hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
# XXX fails on 2.6 for some reason...
if sys.version_info[:2] > (2, 6):
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_labels_from_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')
# XXX doesn't work on py26 for some reason
if sys.version_info[:2] > (2, 6):
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_labels_from_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_labels_from_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_labels_from_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_labels_from_annot, 'sample', parc='aparc',
annot_fname=annot_fname, regexp='JackTheRipper',
subjects_dir=subjects_dir)
def test_read_labels_from_annot():
"""Test reading labels from FreeSurfer parcellation
"""
# test some invalid inputs
assert_raises(ValueError, read_labels_from_annot, 'sample', hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError, read_labels_from_annot, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh = read_labels_from_annot('sample', hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
# XXX fails on 2.6 for some reason...
if sys.version_info[:2] > (2, 6):
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_labels_from_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')
# XXX doesn't work on py26 for some reason
if sys.version_info[:2] > (2, 6):
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_labels_from_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_labels_from_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_labels_from_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_labels_from_annot, 'sample', parc='aparc',
annot_fname=annot_fname, regexp='JackTheRipper',
subjects_dir=subjects_dir)
def test_read_labels_from_annot(tmpdir):
"""Test reading labels from FreeSurfer parcellation."""
# test some invalid inputs
pytest.raises(ValueError, read_labels_from_annot, 'sample', hemi='bla',
subjects_dir=subjects_dir)
pytest.raises(ValueError, read_labels_from_annot, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
with pytest.raises(IOError, match='does not exist'):
_read_annot_cands('foo')
with pytest.raises(IOError, match='no candidate'):
_read_annot(str(tmpdir))
# read labels using hemi specification
labels_lh = read_labels_from_annot('sample', hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert label.name.endswith('-lh')
assert label.hemi == 'lh'
assert label.color is not None
# read labels using annot_fname
annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot')
labels_rh = read_labels_from_annot('sample', annot_fname=annot_fname,
subjects_dir=subjects_dir)
for label in labels_rh:
assert label.name.endswith('-rh')
assert label.hemi == 'rh'
assert label.color is not 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_labels_from_annot('sample', subjects_dir=subjects_dir)
# we have the same result
_assert_labels_equal(labels_lhrh, labels_both)
# aparc has 68 cortical labels
assert (len(labels_both) == 68)
# test regexp
label = read_labels_from_annot('sample', parc='aparc.a2009s',
regexp='Angu', subjects_dir=subjects_dir)[0]
assert (label.name == 'G_pariet_inf-Angular-lh')
# silly, but real regexp:
label = read_labels_from_annot('sample', 'aparc.a2009s',
regexp='.*-.{4,}_.{3,3}-L',
subjects_dir=subjects_dir)[0]
assert (label.name == 'G_oc-temp_med-Lingual-lh')
pytest.raises(RuntimeError, read_labels_from_annot, 'sample', parc='aparc',
annot_fname=annot_fname, regexp='JackTheRipper',
subjects_dir=subjects_dir)
def test_read_labels_from_annot():
"""Test reading labels from FreeSurfer parcellation
"""
# test some invalid inputs
assert_raises(ValueError, read_labels_from_annot, "sample", hemi="bla", subjects_dir=subjects_dir)
assert_raises(ValueError, read_labels_from_annot, "sample", annot_fname="bla.annot", subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh = read_labels_from_annot("sample", hemi="lh", subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith("-lh"))
assert_true(label.hemi == "lh")
# XXX fails on 2.6 for some reason...
if sys.version_info[:2] > (2, 6):
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_labels_from_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_labels_from_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_labels_from_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_labels_from_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_labels_from_annot,
"sample",
parc="aparc",
annot_fname=annot_fname,
regexp="JackTheRipper",
subjects_dir=subjects_dir,
)
def test_annot_io():
"""Test I/O from and to *.annot files."""
# copy necessary files from fsaverage to tempdir
tempdir = _TempDir()
subject = 'fsaverage'
label_src = os.path.join(subjects_dir, 'fsaverage', 'label')
surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf')
label_dir = os.path.join(tempdir, subject, 'label')
surf_dir = os.path.join(tempdir, subject, 'surf')
os.makedirs(label_dir)
os.mkdir(surf_dir)
shutil.copy(os.path.join(label_src, 'lh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(label_src, 'rh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(surf_src, 'lh.white'), surf_dir)
shutil.copy(os.path.join(surf_src, 'rh.white'), surf_dir)
# read original labels
with pytest.raises(IOError, match='\nPALS_B12_Lobes$'):
read_labels_from_annot(subject,
'PALS_B12_Lobesey',
subjects_dir=tempdir)
labels = read_labels_from_annot(subject,
'PALS_B12_Lobes',
subjects_dir=tempdir)
# test saving parcellation only covering one hemisphere
parc = [l for l in labels if l.name == 'LOBE.TEMPORAL-lh']
write_labels_to_annot(parc, subject, 'myparc', subjects_dir=tempdir)
parc1 = read_labels_from_annot(subject, 'myparc', subjects_dir=tempdir)
parc1 = [l for l in parc1 if not l.name.startswith('unknown')]
assert_equal(len(parc1), len(parc))
for l1, l in zip(parc1, parc):
assert_labels_equal(l1, l)
# test saving only one hemisphere
parc = [l for l in labels if l.name.startswith('LOBE')]
write_labels_to_annot(parc,
subject,
'myparc2',
hemi='lh',
subjects_dir=tempdir)
annot_fname = os.path.join(tempdir, subject, 'label', '%sh.myparc2.annot')
assert os.path.isfile(annot_fname % 'l')
assert not os.path.isfile(annot_fname % 'r')
parc1 = read_labels_from_annot(subject,
'myparc2',
annot_fname=annot_fname % 'l',
subjects_dir=tempdir)
parc_lh = [l for l in parc if l.name.endswith('lh')]
for l1, l in zip(parc1, parc_lh):
assert_labels_equal(l1, l)
# test that the annotation is complete (test Label() support)
rr = read_surface(op.join(surf_dir, 'lh.white'))[0]
label = sum(labels, Label(hemi='lh', subject='fsaverage')).lh
assert_array_equal(label.vertices, np.arange(len(rr)))
def prepare_parcels(subject, subjects_dir, hemi, n_parcels, random_state):
if ((hemi == 'both') or (hemi == 'lh')):
annot_fname_lh = 'lh.random' + str(n_parcels) + '.annot'
annot_fname_lh = os.path.join(subjects_dir, subject, 'label',
annot_fname_lh)
if ((hemi == 'both') or (hemi == 'rh')):
annot_fname_rh = 'rh.random' + str(n_parcels) + '.annot'
annot_fname_rh = os.path.join(subjects_dir, subject, 'label',
annot_fname_rh)
make_random_parcellation(annot_fname_lh,
n_parcels,
'lh',
subjects_dir,
random_state,
subject,
remove_corpus_callosum=True)
make_random_parcellation(annot_fname_rh,
n_parcels,
'rh',
subjects_dir,
random_state,
subject,
remove_corpus_callosum=True)
# read the labels from annot
if ((hemi == 'both') or (hemi == 'lh')):
parcels_lh = mne.read_labels_from_annot(subject=subject,
annot_fname=annot_fname_lh,
hemi='lh',
subjects_dir=subjects_dir)
# remove the last, unknown label which is corpus callosum
assert parcels_lh[-1].name[:7] == 'unknown'
parcels_lh = parcels_lh[:-1]
cm_lh = find_centers_of_mass(parcels_lh, subjects_dir)
if ((hemi == 'both') or (hemi == 'rh')):
parcels_rh = mne.read_labels_from_annot(subject=subject,
annot_fname=annot_fname_rh,
hemi='rh',
subjects_dir=subjects_dir)
# remove the last, unknown label which is corpus callosum
assert parcels_rh[-1].name[:7] == 'unknown'
parcels_rh = parcels_rh[:-1]
cm_rh = find_centers_of_mass(parcels_rh, subjects_dir)
if hemi == 'both':
return [parcels_lh, parcels_rh], [cm_lh, cm_rh]
elif hemi == 'rh':
return [parcels_rh], [cm_rh]
elif hemi == 'lh':
return [parcels_lh], [cm_lh]
def test_annot_io():
"""Test I/O from and to *.annot files"""
# copy necessary files from fsaverage to tempdir
tempdir = _TempDir()
subject = 'fsaverage'
label_src = os.path.join(subjects_dir, 'fsaverage', 'label')
surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf')
label_dir = os.path.join(tempdir, subject, 'label')
surf_dir = os.path.join(tempdir, subject, 'surf')
os.makedirs(label_dir)
os.mkdir(surf_dir)
shutil.copy(os.path.join(label_src, 'lh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(label_src, 'rh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(surf_src, 'lh.white'), surf_dir)
shutil.copy(os.path.join(surf_src, 'rh.white'), surf_dir)
# read original labels
assert_raises(IOError,
read_labels_from_annot,
subject,
'PALS_B12_Lobesey',
subjects_dir=tempdir)
labels = read_labels_from_annot(subject,
'PALS_B12_Lobes',
subjects_dir=tempdir)
# test saving parcellation only covering one hemisphere
parc = [l for l in labels if l.name == 'LOBE.TEMPORAL-lh']
write_labels_to_annot(parc, subject, 'myparc', subjects_dir=tempdir)
parc1 = read_labels_from_annot(subject, 'myparc', subjects_dir=tempdir)
parc1 = [l for l in parc1 if not l.name.startswith('unknown')]
assert_equal(len(parc1), len(parc))
for l1, l in zip(parc1, parc):
assert_labels_equal(l1, l)
# test saving only one hemisphere
parc = [l for l in labels if l.name.startswith('LOBE')]
write_labels_to_annot(parc,
subject,
'myparc2',
hemi='lh',
subjects_dir=tempdir)
annot_fname = os.path.join(tempdir, subject, 'label', '%sh.myparc2.annot')
assert_true(os.path.isfile(annot_fname % 'l'))
assert_false(os.path.isfile(annot_fname % 'r'))
parc1 = read_labels_from_annot(subject,
'myparc2',
annot_fname=annot_fname % 'l',
subjects_dir=tempdir)
parc_lh = [l for l in parc if l.name.endswith('lh')]
for l1, l in zip(parc1, parc_lh):
assert_labels_equal(l1, l)
def test_annot_io():
"""Test I/O from and to *.annot files."""
# copy necessary files from fsaverage to tempdir
tempdir = _TempDir()
subject = 'fsaverage'
label_src = os.path.join(subjects_dir, 'fsaverage', 'label')
surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf')
label_dir = os.path.join(tempdir, subject, 'label')
surf_dir = os.path.join(tempdir, subject, 'surf')
os.makedirs(label_dir)
os.mkdir(surf_dir)
shutil.copy(os.path.join(label_src, 'lh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(label_src, 'rh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(surf_src, 'lh.white'), surf_dir)
shutil.copy(os.path.join(surf_src, 'rh.white'), surf_dir)
# read original labels
with pytest.raises(IOError, match='\nPALS_B12_Lobes$'):
read_labels_from_annot(subject, 'PALS_B12_Lobesey',
subjects_dir=tempdir)
labels = read_labels_from_annot(subject, 'PALS_B12_Lobes',
subjects_dir=tempdir)
# test saving parcellation only covering one hemisphere
parc = [l for l in labels if l.name == 'LOBE.TEMPORAL-lh']
write_labels_to_annot(parc, subject, 'myparc', subjects_dir=tempdir)
parc1 = read_labels_from_annot(subject, 'myparc', subjects_dir=tempdir)
parc1 = [l for l in parc1 if not l.name.startswith('unknown')]
assert_equal(len(parc1), len(parc))
for l1, l in zip(parc1, parc):
assert_labels_equal(l1, l)
# test saving only one hemisphere
parc = [l for l in labels if l.name.startswith('LOBE')]
write_labels_to_annot(parc, subject, 'myparc2', hemi='lh',
subjects_dir=tempdir)
annot_fname = os.path.join(tempdir, subject, 'label', '%sh.myparc2.annot')
assert os.path.isfile(annot_fname % 'l')
assert not os.path.isfile(annot_fname % 'r')
parc1 = read_labels_from_annot(subject, 'myparc2',
annot_fname=annot_fname % 'l',
subjects_dir=tempdir)
parc_lh = [l for l in parc if l.name.endswith('lh')]
for l1, l in zip(parc1, parc_lh):
assert_labels_equal(l1, l)
# test that the annotation is complete (test Label() support)
rr = read_surface(op.join(surf_dir, 'lh.white'))[0]
label = sum(labels, Label(hemi='lh', subject='fsaverage')).lh
assert_array_equal(label.vertices, np.arange(len(rr)))
def test_read_labels_from_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_labels_from_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 morph_labels_from_fsaverage(subject,
subjects_dir,
atlas='laus125',
fsaverage='fsaverage',
hemi='both',
surf_name='pial',
overwrite=False,
n_jobs=6):
subject_dir = op.join(subjects_dir, subject)
labels_fol = op.join(subjects_dir, fsaverage, 'label', atlas)
morphed_labels_fol = op.join(subject_dir, 'label', atlas)
if not op.isdir(morphed_labels_fol):
os.makedirs(morphed_labels_fol)
labels = mne.read_labels_from_annot(fsaverage,
atlas,
subjects_dir=subjects_dir,
surf_name=surf_name,
hemi=hemi)
if len(labels) == 0:
raise Exception(
'morph_labels_from_fsaverage: No labels for {}, {}'.format(
fsaverage, atlas))
# Make sure we have a morph map, and if not, create it here, and not in the parallel function
mne.surface.read_morph_map(subject, fsaverage, subjects_dir=subjects_dir)
verts = load_surf(subject, subjects_dir)
indices = np.array_split(np.arange(len(labels)), n_jobs)
chunks = [([labels[ind] for ind in chunk_indices], subject, fsaverage,
labels_fol, morphed_labels_fol, verts, subjects_dir, overwrite)
for chunk_indices in indices]
results = run_parallel(_morph_labels_parallel, chunks, n_jobs)
morphed_labels = []
for chunk_morphed_labels in results:
morphed_labels.extend(chunk_morphed_labels)
return morphed_labels
def load_parc_labels(self):
if self._labels is None:
self._labels = mne.read_labels_from_annot(
self.name,
parc=self.p['parcellation'],
subjects_dir=self.subjects_dir)
return self._labels
def plot_aparc_parcels(matrix, ax, fig, title):
"""
:param matrix:
The connectivity matrix (real or simulated) representing aparc parcels
:param ax:
axes to plot on
:return:
"""
# get labels and coordinates
labels = mne.read_labels_from_annot('fsaverage_1', parc='aparc', subjects_dir=join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS','FS_SUBDIR'))
labels = labels[0:-1]
label_names = [label.name for label in labels]
coords = []
# TODO: Find a better way to get centre of parcel
#get coords of centre of mass
for i in range(len(labels)):
if 'lh' in label_names[i]:
hem = 1
else:
hem = 0
coord = mne.vertex_to_mni(labels[i].center_of_mass(subjects_dir=join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS','FS_SUBDIR')), subject='fsaverage_1',hemis=hem,subjects_dir=join(MAINDIR,'FS_SUBDIR'))
coords.append(coord[0])
plotting.plot_connectome_strength(matrix,
node_coords=coords,
title=title,
figure=fig,
axes=ax,
cmap=plt.cm.YlOrRd)
def adaptive_parcellation(fwd, inv, subject, subjects_dir, hemi='both'):
"""
"""
# Magic happens
# res = mne.minimum_norm.make_inverse_resolution_matrix(fwd, inv)
# ...
# However, here's a magicless example
labels_aparc = mne.read_labels_from_annot(subject, parc='aparc', hemi='lh',
subjects_dir=subjects_dir)
# add frontal pole label to lh
vertices = labels_aparc[5].vertices
label1 = mne.Label(vertices, hemi='lh', name='frontalpole',
subject=subject, color=(1,0,0,1))
# add label of rightmost vertices to rh
vertices = inv['src'][1]['vertno']
vertices = [vv for vv in vertices if inv['src'][1]['rr'][vv, 0] > 0.05]
label2 = mne.Label(vertices, hemi='rh', name='lateral',
subject=subject, color=(0,1,0,1))
label2 = label2.fill(inv['src'])
labels = [label1, label2]
return labels
def load_hcpmmp1():
# os.environ["SUBJECTS_DIR"] = VALUE
labels = mne.read_labels_from_annot('fsaverage', parc='HCPMMP1_combined')
labels = sorted(labels, key=lambda x: x.name)
return labels
def plot_V1V2(measure, hemi, clim):
data, vert = extract_hemi_data(src_df, measure, time, hemi)
brain = Brain('fsaverage',
hemi,
'inflated',
cortex='low_contrast',
subjects_dir='mne_subjects',
background='w',
foreground='k')
brain.add_data(data,
vertices=vert,
min=-clim[2],
max=clim[2],
time=[time],
time_label=lambda t: '%d ms' % (t * 1000),
colormap=morph_divergent_cmap(cmap, clim),
hemi=hemi,
smoothing_steps=5)
labels = mne.read_labels_from_annot('fsaverage',
parc='HCPMMP1',
hemi=hemi,
regexp='[LR]_((V1)|(V2)).*')
for label in labels:
brain.add_label(label, borders=True)
mlab.view(*views[hemi])
return brain
def load_labsn_hcpmmp1_av_rois_small():
hcp_mmp1_labels = mne.read_labels_from_annot('fsaverage',
parc='HCPMMP1_combined')
hcp_mmp1_labels = combine_medial_labels(hcp_mmp1_labels)
label_names = [l.name for l in hcp_mmp1_labels]
#prim_visual_lh = label_names.index("Primary Visual Cortex (V1)-lh")
#prim_visual_rh = label_names.index("Primary Visual Cortex (V1)-rh")
#prim_visual_lh = hcp_mmp1_labels[prim_visual_lh]
#prim_visual_rh = hcp_mmp1_labels[prim_visual_rh]
prim_visual = [
l for l in hcp_mmp1_labels if 'Primary Visual Cortex' in l.name
]
# there should be only one b/c of medial merge
prim_visual = prim_visual[0]
early_visual_lh = label_names.index("Early Visual Cortex-lh")
early_visual_rh = label_names.index("Early Visual Cortex-rh")
early_visual_lh = hcp_mmp1_labels[early_visual_lh]
early_visual_rh = hcp_mmp1_labels[early_visual_rh]
#visual_lh = prim_visual_lh + early_visual_lh
#visual_rh = prim_visual_rh + early_visual_rh
visual = prim_visual + early_visual_lh + early_visual_rh
labels = [visual]
#labels = [visual_lh, visual_rh]
eac_labs = [
l for l in hcp_mmp1_labels if 'Early Auditory Cortex' in l.name
]
labels.extend(eac_labs)
tpo_labs = [
l for l in hcp_mmp1_labels
if 'Temporo-Parieto-Occipital Junction' in l.name
]
labels.extend(tpo_labs)
dpc_labs = [
l for l in hcp_mmp1_labels
if 'DorsoLateral Prefrontal Cortex' in l.name
]
labels.extend(dpc_labs)
## extra labels KC wanted
#pmc_labs = [l for l in hcp_mmp1_labels if 'Premotor Cortex' in l.name]
#labels.extend(pmc_labs)
#ips_str = glob.glob(os.path.join(subjects_dir, "fsaverage/label/*IPS*labsn*"))
#ips_labs = [mne.read_label(fn, subject='fsaverage') for fn in ips_str]
#labels.extend(ips_labs)
#rtpj_labs = [l for l in hcp_mmp1_labels if 'Inferior Parietal Cortex-rh' in l.name]
#labels.extend(rtpj_labs)
return labels
def morph_labels_from_fsaverage(mri_sub):
parcellations = ['aparc_sub', 'HCPMMP1_combined', 'HCPMMP1']
if not isfile(join(mri_sub.subjects_dir, 'fsaverage/label',
'lh.' + parcellations[0] + '.annot')):
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=mri_sub.subjects_dir,
verbose=True)
mne.datasets.fetch_aparc_sub_parcellation(subjects_dir=mri_sub.subjects_dir,
verbose=True)
else:
print('You\'ve already downloaded the parcellations, splendid!')
if not isfile(join(mri_sub.subjects_dir, mri_sub.name, 'label',
'lh.' + parcellations[0] + '.annot')):
for pc in parcellations:
labels = mne.read_labels_from_annot('fsaverage', pc, hemi='both')
m_labels = mne.morph_labels(labels, mri_sub.name, 'fsaverage', mri_sub.subjects_dir,
surf_name='pial')
mne.write_labels_to_annot(m_labels, subject=mri_sub.name, parc=pc,
subjects_dir=mri_sub.subjects_dir, overwrite=True)
else:
print(f'{parcellations} already exist')
def annotation_to_labels():
fol = os.path.join(subjects_dir, subject, 'label', aparc_name)
if not(os.path.isdir(fol)):
os.mkdir(fol)
labels = mne.read_labels_from_annot(subject, parc=aparc_name, hemi='both', surf_name='pial')
for label in labels:
label.save(os.path.join(fol, label.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_label_center_of_mass():
"""Test computing the center of mass of a label"""
stc = read_source_estimate(stc_fname)
stc.lh_data[:] = 0
vertex_stc = stc.center_of_mass('sample', subjects_dir=subjects_dir)[0]
assert_equal(vertex_stc, 124791)
label = Label(stc.vertices[1],
pos=None,
values=stc.rh_data.mean(axis=1),
hemi='rh',
subject='sample')
vertex_label = label.center_of_mass(subjects_dir=subjects_dir)
assert_equal(vertex_label, vertex_stc)
labels = read_labels_from_annot('sample',
parc='aparc.a2009s',
subjects_dir=subjects_dir)
src = read_source_spaces(src_fname)
# Try a couple of random ones, one from left and one from right
# Visually verified in about the right place using mne_analyze
for label, expected in zip([labels[2], labels[3], labels[-5]],
[141162, 145221, 55979]):
label.values[:] = -1
assert_raises(ValueError,
label.center_of_mass,
subjects_dir=subjects_dir)
label.values[:] = 1
assert_equal(label.center_of_mass(subjects_dir=subjects_dir), expected)
assert_equal(
label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=label.vertices), expected)
# restrict to source space
idx = 0 if label.hemi == 'lh' else 1
# this simple nearest version is not equivalent, but is probably
# close enough for many labels (including the test ones):
pos = label.pos[np.where(label.vertices == expected)[0][0]]
pos = (src[idx]['rr'][src[idx]['vertno']] - pos)
pos = np.argmin(np.sum(pos * pos, axis=1))
src_expected = src[idx]['vertno'][pos]
# see if we actually get the same one
src_restrict = np.intersect1d(label.vertices, src[idx]['vertno'])
assert_equal(
label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=src_restrict), src_expected)
assert_equal(
label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=src), src_expected)
# degenerate cases
assert_raises(ValueError,
label.center_of_mass,
subjects_dir=subjects_dir,
restrict_vertices='foo')
assert_raises(TypeError,
label.center_of_mass,
subjects_dir=subjects_dir,
surf=1)
assert_raises(IOError,
label.center_of_mass,
subjects_dir=subjects_dir,
surf='foo')
def test_fetch_parcellations(tmpdir):
"""Test fetching parcellations."""
this_subjects_dir = str(tmpdir)
os.mkdir(op.join(this_subjects_dir, 'fsaverage'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'label'))
os.mkdir(op.join(this_subjects_dir, 'fsaverage', 'surf'))
for hemi in ('lh', 'rh'):
shutil.copyfile(
op.join(subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi),
op.join(this_subjects_dir, 'fsaverage', 'surf', '%s.white' % hemi))
# speed up by prenteding we have one of them
with open(op.join(this_subjects_dir, 'fsaverage', 'label',
'lh.aparc_sub.annot'), 'wb'):
pass
datasets.fetch_aparc_sub_parcellation(subjects_dir=this_subjects_dir)
with ArgvSetter(('--accept-hcpmmp-license',)):
datasets.fetch_hcp_mmp_parcellation(subjects_dir=this_subjects_dir)
for hemi in ('lh', 'rh'):
assert op.isfile(op.join(this_subjects_dir, 'fsaverage', 'label',
'%s.aparc_sub.annot' % hemi))
# test our annot round-trips here
kwargs = dict(subject='fsaverage', hemi='both', sort=False,
subjects_dir=this_subjects_dir)
labels = read_labels_from_annot(parc='HCPMMP1', **kwargs)
write_labels_to_annot(
labels, parc='HCPMMP1_round',
table_name='./left.fsaverage164.label.gii', **kwargs)
orig = op.join(this_subjects_dir, 'fsaverage', 'label', 'lh.HCPMMP1.annot')
first = hashfunc(orig)
new = orig[:-6] + '_round.annot'
second = hashfunc(new)
assert first == second
def apply_cortical_parcellation_event_stcs(self,
stcs,
src,
save=True,
gen_mode=True):
labels = mne.read_labels_from_annot(self.subject)
self.labels = [lbl for lbl in labels if lbl.name != 'unknown-lh']
stc_path = 'stcs/'
self.stc_cp = dict()
for key, event_stcs in stcs.items():
stc_sub_path = stc_path + key + '/'
event_stcs_cp = np.zeros((68, 500, 5))
for event_id, event_stc in event_stcs.items():
event_stc_path = stc_sub_path + event_id + '.csv'
if gen_mode:
label_tc = mne.extract_label_time_course(event_stc,
self.labels,
src,
mode='pca_flip')
else:
label_tc = np.genfromtxt(event_stc_path, delimiter=',')
event_stcs_cp[:, :, int(event_id) - 1] = label_tc
if save:
np.savetxt(event_stc_path, label_tc, delimiter=',')
self.stc_cp[key] = event_stcs_cp
return self.stc_cp
def morph_labels(subject_from,
subject_to,
parc,
surf_name='pial',
smooth=2,
overwrite=True):
'''
mne_morph_labels --from fsaverage --to mg79 --labeldir /homes/5/npeled/space3/subjects/fsaverage/label/laus500_labels --smooth 5
'''
# brain = Brain(subject_from, 'both', surf_name, curv=False)
labels = mne.read_labels_from_annot(subject_from, parc, 'both', surf_name)
morphed_labels = []
for ind, label in enumerate(labels):
try:
print('label {}/{}'.format(ind, len(labels)))
label.values.fill(1.0)
morphed_label = label.morph(subject_from, subject_to, smooth)
morphed_labels.append(morphed_label)
except:
print('cant morph label {}'.format(label.name))
print(sys.exc_info()[1])
print('{} labels were morphed succefully.'.format(len(morphed_labels)))
mne.write_labels_to_annot(morphed_labels,
subject_to,
parc,
overwrite,
hemi='both')
def test_read_labels_from_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_labels_from_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 _simulate_data(fwd, idx): # Somewhere on the frontal lobe by default
"""Simulate an oscillator on the cortex."""
source_vertno = fwd['src'][0]['vertno'][idx]
sfreq = 50. # Hz.
times = np.arange(10 * sfreq) / sfreq # 10 seconds of data
signal = np.sin(20 * 2 * np.pi * times) # 20 Hz oscillator
signal[:len(times) // 2] *= 2 # Make signal louder at the beginning
signal *= 1e-9 # Scale to be in the ballpark of MEG data
# Construct a SourceEstimate object that describes the signal at the
# cortical level.
stc = mne.SourceEstimate(
signal[np.newaxis, :],
vertices=[[source_vertno], []],
tmin=0,
tstep=1 / sfreq,
subject='sample',
)
# Create an info object that holds information about the sensors
info = mne.create_info(fwd['info']['ch_names'], sfreq, ch_types='grad')
info.update(fwd['info']) # Merge in sensor position information
# heavily decimate sensors to make it much faster
info = mne.pick_info(info, np.arange(info['nchan'])[::5])
fwd = mne.pick_channels_forward(fwd, info['ch_names'])
# Run the simulated signal through the forward model, obtaining
# simulated sensor data.
raw = mne.apply_forward_raw(fwd, stc, info)
# Add a little noise
random = np.random.RandomState(42)
noise = random.randn(*raw._data.shape) * 1e-14
raw._data += noise
# Define a single epoch (weird baseline but shouldn't matter)
epochs = mne.Epochs(raw, [[0, 0, 1]],
event_id=1,
tmin=0,
tmax=raw.times[-1],
baseline=(0., 0.),
preload=True)
evoked = epochs.average()
# Compute the cross-spectral density matrix
csd = csd_morlet(epochs, frequencies=[10, 20], n_cycles=[5, 10], decim=10)
labels = mne.read_labels_from_annot('sample',
hemi='lh',
subjects_dir=subjects_dir)
label = [
label for label in labels if np.in1d(source_vertno, label.vertices)[0]
]
assert len(label) == 1
label = label[0]
vertices = np.intersect1d(label.vertices, fwd['src'][0]['vertno'])
source_ind = vertices.tolist().index(source_vertno)
assert vertices[source_ind] == source_vertno
return epochs, evoked, csd, source_vertno, label, vertices, source_ind
def sources_to_labels(stcs,
age=None,
template=None,
parc='aparc',
mode='mean_flip',
allow_empty=True,
return_generator=False,
subjects_dir=None,
include_vol_src=True):
template = __validate_template__(age, template, subjects_dir)
montage, trans, bem_model, bem_solution, src = get_bem_artifacts(
template, subjects_dir=subjects_dir, include_vol_src=include_vol_src)
labels_parc = mne.read_labels_from_annot(template,
subjects_dir=subjects_dir,
parc=parc)
labels_ts = mne.extract_label_time_course(
stcs,
labels_parc,
src,
mode=mode,
allow_empty=allow_empty,
return_generator=return_generator)
if include_vol_src:
labels_aseg = mne.get_volume_labels_from_src(src, template,
subjects_dir)
labels = labels_parc + labels_aseg
else:
labels = labels_parc
return labels_ts, labels
def region_centers_of_masse(age=None,
template=None,
parc="aparc",
surf_name="pial",
subjects_dir=None,
include_vol_src=True):
template = __validate_template__(age, template, subjects_dir)
montage, trans, bem_model, bem_solution, src = get_bem_artifacts(
template, subjects_dir=subjects_dir, include_vol_src=include_vol_src)
center_of_masses_dict = {}
if include_vol_src:
for src_obj in src[2:]:
roi_str = src_obj["seg_name"]
if 'left' in roi_str.lower():
roi_str = roi_str.replace('Left-', '') + '-lh'
elif 'right' in roi_str.lower():
roi_str = roi_str.replace('Right-', '') + '-rh'
center_of_masses_dict[roi_str] = np.average(
src_obj['rr'][src_obj["vertno"]], axis=0)
for label in mne.read_labels_from_annot(template,
subjects_dir=subjects_dir,
parc=parc):
ind_com = np.where(label.vertices == label.center_of_mass(
subject=template, subjects_dir=subjects_dir))[0]
if len(label.pos[ind_com, :]):
center_of_masses_dict[label.name] = label.pos[ind_com, :][0]
center_of_masses_df = pd.DataFrame(center_of_masses_dict).T.reset_index()
center_of_masses_df.columns = ["region", "x", "y", "z"]
center_of_masses_df["template"] = template
return center_of_masses_df
def find_clusters_overlapped_labeles(subject, clusters, contrast, atlas, hemi, verts, load_from_annotation=True,
n_jobs=1):
cluster_labels = []
annot_fname = op.join(SUBJECTS_DIR, subject, 'label', '{}.{}.annot'.format(hemi, atlas))
if load_from_annotation and op.isfile(annot_fname):
labels = mne.read_labels_from_annot(subject, annot_fname=annot_fname, surf_name='pial')
else:
# todo: read only the labels from the current hemi
labels = utils.read_labels_parallel(subject, SUBJECTS_DIR, atlas, n_jobs)
labels = [l for l in labels if l.hemi == hemi]
if len(labels) == 0:
print('No labels!')
return None
for cluster in clusters:
x = contrast[cluster]
cluster_max = np.min(x) if abs(np.min(x)) > abs(np.max(x)) else np.max(x)
inter_labels, inter_labels_tups = [], []
for label in labels:
overlapped_vertices = np.intersect1d(cluster, label.vertices)
if len(overlapped_vertices) > 0:
if 'unknown' not in label.name:
inter_labels_tups.append((len(overlapped_vertices), label.name))
# inter_labels.append(dict(name=label.name, num=len(overlapped_vertices)))
inter_labels_tups = sorted(inter_labels_tups)[::-1]
for inter_labels_tup in inter_labels_tups:
inter_labels.append(dict(name=inter_labels_tup[1], num=inter_labels_tup[0]))
if len(inter_labels) > 0:
# max_inter = max([(il['num'], il['name']) for il in inter_labels])
cluster_labels.append(dict(vertices=cluster, intersects=inter_labels, name=inter_labels[0]['name'],
coordinates=verts[cluster], max=cluster_max, hemi=hemi, size=len(cluster)))
else:
print('No intersected labels!')
return cluster_labels
def load_hcpmmp1_combined():
labels = mne.read_labels_from_annot('fsaverage', parc='HCPMMP1_combined')
labels = sorted(labels, key=lambda x: x.name)
labels = combine_medial_labels(labels)
return labels
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)
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 = datasets._mne_source_space(subject, 'ico-5', mri_sdir)
source = SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir)
source_v1 = source[source.dimindex(label_v1)]
eq_(source_v1, SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir,
label=label_v1))
source_ba1_v1 = source[source.dimindex(label_ba1_v1)]
source_v1_mt = source[source.dimindex(label_v1_mt)]
source_v1_intersection = source_ba1_v1.intersect(source_v1_mt)
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
parc = mne.read_labels_from_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):
eq_(ds[i, 'location'], parc[i].name)
# multiple labels
lingual_index = source.dimindex('lingual-lh')
cuneus_index = source.dimindex('cuneus-lh')
assert_array_equal(source.dimindex(('cuneus-lh', 'lingual-lh')),
np.logical_or(cuneus_index, lingual_index))
lingual_source = source[lingual_index]
cuneus_source = source[cuneus_index]
assert_raises(IndexError, lingual_source.dimindex, cuneus_source)
sub_source = source[source.dimindex(('cuneus-lh', 'lingual-lh'))]
eq_(sub_source[sub_source.dimindex('lingual-lh')], lingual_source)
eq_(sub_source[sub_source.dimindex('cuneus-lh')], cuneus_source)
eq_(len(sub_source), len(lingual_source) + len(cuneus_source))
# indexing
tgt = np.hstack(sub_source.vertno)
assert_array_equal([i for i in sub_source], tgt)
assert_array_equal([sub_source[i] for i in xrange(len(sub_source))], tgt)
# hemisphere indexing
lh = source.dimindex('lh')
source_lh = source[lh]
eq_(source_lh.dimindex('rh'), slice(0, 0))
eq_(source_lh.dimindex('lh'), slice(0, len(source_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)
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 = datasets._mne_source_space(subject, 'ico-5', mri_sdir)
source = SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir)
source_v1 = source[source.dimindex(label_v1)]
eq_(source_v1, SourceSpace.from_mne_source_spaces(src, 'ico-5', mri_sdir,
label=label_v1))
source_ba1_v1 = source[source.dimindex(label_ba1_v1)]
source_v1_mt = source[source.dimindex(label_v1_mt)]
source_v1_intersection = source_ba1_v1.intersect(source_v1_mt)
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
parc = mne.read_labels_from_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 range(ds.n_cases):
eq_(ds[i, 'location'], parc[i].name)
# multiple labels
lingual_index = source.dimindex('lingual-lh')
cuneus_index = source.dimindex('cuneus-lh')
assert_array_equal(source.dimindex(('cuneus-lh', 'lingual-lh')),
np.logical_or(cuneus_index, lingual_index))
lingual_source = source[lingual_index]
cuneus_source = source[cuneus_index]
assert_raises(IndexError, lingual_source.dimindex, cuneus_source)
sub_source = source[source.dimindex(('cuneus-lh', 'lingual-lh'))]
eq_(sub_source[sub_source.dimindex('lingual-lh')], lingual_source)
eq_(sub_source[sub_source.dimindex('cuneus-lh')], cuneus_source)
eq_(len(sub_source), len(lingual_source) + len(cuneus_source))
# indexing
tgt = np.hstack(sub_source.vertno)
assert_array_equal([i for i in sub_source], tgt)
assert_array_equal([sub_source[i] for i in range(len(sub_source))], tgt)
# hemisphere indexing
lh = source.dimindex('lh')
source_lh = source[lh]
eq_(source_lh.dimindex('rh'), slice(0, 0))
eq_(source_lh.dimindex('lh'), slice(len(source_lh)))
def mne_connec(matrix, this_title, fig, *labels):
MAINDIR = join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS')
if labels is None:
labels = mne.read_labels_from_annot('fsaverage_1', parc='aparc',
subjects_dir=join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS', 'FS_SUBDIR'))
labels = labels[0:-1]
label_colors = [label.color for label in labels]
label_names = [label.name for label in labels]
coords = []
# TODO: Find a better way to get centre of parcel
# get coords of centre of mass
for i in range(len(labels)):
if 'lh' in label_names[i]:
hem = 1
else:
hem = 0
coord = mne.vertex_to_mni(
labels[i].center_of_mass(subjects_dir=join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS', 'FS_SUBDIR')),
subject='fsaverage_1', hemis=hem, subjects_dir=join(MAINDIR, 'FS_SUBDIR'))
coords.append(coord[0])
# First, we reorder the labels based on their location in the left hemi
lh_labels = [name for name in label_names if name.endswith('lh')]
rh_labels = [name for name in label_names if name.endswith('rh')]
# Get the y-location of the label
label_ypos = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ypos.append(ypos)
lh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))]
# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels]
# make a list with circular plot order
node_order = list()
node_order.extend(lh_labels[::-1]) # reverse the order
node_order.extend(rh_labels)
node_order = node_order[::-1] # reverse the whole thing
# get a mapping to the original list
re_order_ind = [label_names.index(x) for x in node_order]
node_angles = mne.viz.circular_layout(label_names, node_order, start_pos=90,
group_boundaries=[0, len(label_names) / 2])
fig2, ax = mne.viz.plot_connectivity_circle(matrix, label_names, n_lines=300,
node_angles=node_angles, node_colors=label_colors,
title=this_title, fig=fig,
facecolor='white',
textcolor='black')
return fig2, ax
def calc_fsaverage_labels_indices(surf_name='pial', labels_from_annot=False, labels_fol='', parc='aparc250', subjects_dir=''):
labels_fol = os.path.join(subjects_dir, 'fsaverage', 'label', parc) if labels_fol=='' else labels_fol
if (labels_from_annot):
labels = mne.read_labels_from_annot('fsaverage', parc=parc, hemi='both', surf_name=surf_name)
else:
labels = utils.read_labels(labels_fol)
fsave_vertices = [np.arange(10242), np.arange(10242)]
labels_vertices, labels_names = utils.get_labels_vertices(labels, fsave_vertices)
np.savez(op.join(LOCAL_ROOT_DIR, 'fsaverage_labels_indices'), labels_vertices=labels_vertices, labels_names=labels_names)
def test_read_labels_from_annot_annot2labels():
"""Test reading labels from parc. by comparing with mne_annot2labels."""
label_fnames = glob.glob(label_dir + '/*.label')
label_fnames.sort()
labels_mne = [read_label(fname) for fname in label_fnames]
labels = read_labels_from_annot('sample', subjects_dir=subjects_dir)
# we have the same result, mne does not fill pos, so ignore it
_assert_labels_equal(labels, labels_mne, ignore_pos=True)
def generate_combined_simulation(raw_fname,
fwd,
subject=None,
subjects_dir=None,
topdir=None,
label_index=None,
sine_amplitude=None,
sine_frequency=None):
"""Create a combined dataset of simulated plus real data and save
to the topdir/(subjid)_(AMP)_nAm_(HZ)_hz folder"""
os.chdir(topdir)
if label_index == None:
print('Must provide a label index for simulation')
exit(1)
raw = mne.io.read_raw_fif(raw_fname)
rng = np.random.RandomState(0) # random state (make reproducible)
#Labels for simulation
labels = mne.read_labels_from_annot(subject, subjects_dir=subjects_dir)
labels_sim = [labels[label_index]]
times = raw.times #[:int(raw.info['sfreq'] * epoch_duration)]
src = fwd['src']
sig_generator = partial(data_fun,
amplitude=sine_amplitude,
freq=sine_frequency)
stc = simulate_sparse_stc(src,
n_dipoles=1,
times=times,
data_fun=sig_generator,
labels=labels_sim,
location='center',
subjects_dir=subjects_dir)
# Simulate raw data
raw_sim = simulate_raw(raw.info, [stc] * 1, forward=fwd, verbose=True)
#Load raw and save to outfolder
raw.load_data()
#Combine simulation w/raw
comb_out_fname = '{}_{}_label_{}_nAm_{}_hz_meg.fif'.format(
subject, str(label_index), sine_amplitude, sine_frequency)
# comb_out_fname = op.join(outfolder, outfolder+'_meg.fif')
combined = raw.copy()
combined._data += raw_sim.get_data()
combined.save(comb_out_fname)
print('Saved {}'.format(comb_out_fname))
#Save stc for later use
stc_out_fname = op.join('{}_{}_label_{}_nAm_{}_hz-stc.fif'.format(
subject, str(label_index), sine_amplitude, sine_frequency))
stc.save(stc_out_fname)
def test_read_labels_from_annot_annot2labels():
"""Test reading labels from parc. by comparing with mne_annot2labels."""
label_fnames = glob.glob(label_dir + '/*.label')
label_fnames.sort()
labels_mne = [read_label(fname) for fname in label_fnames]
labels = read_labels_from_annot('sample', subjects_dir=subjects_dir)
# we have the same result, mne does not fill pos, so ignore it
_assert_labels_equal(labels, labels_mne, ignore_pos=True)
def create_annot_csv(subject, parc, hemi, source_file, surf_name):
labels = mne.read_labels_from_annot(subject, parc, hemi, surf_name)
old, brain = get_hemi_data(source_file, hemi, surf_name)
colors = np.zeros((old.mlab_data.shape[0], 3)) # arrToColors(old.mlab_data, colorsMap='RdBu_r')[:, :3]
brain.toggle_toolbars(True)
for label_ind, label in enumerate(labels):
# label = labels[46]
brain.add_label(label)
print(label)
def test_annot_io():
"""Test I/O from and to *.annot files"""
# copy necessary files from fsaverage to tempdir
tempdir = _TempDir()
subject = 'fsaverage'
label_src = os.path.join(subjects_dir, 'fsaverage', 'label')
surf_src = os.path.join(subjects_dir, 'fsaverage', 'surf')
label_dir = os.path.join(tempdir, subject, 'label')
surf_dir = os.path.join(tempdir, subject, 'surf')
os.makedirs(label_dir)
os.mkdir(surf_dir)
shutil.copy(os.path.join(label_src, 'lh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(label_src, 'rh.PALS_B12_Lobes.annot'), label_dir)
shutil.copy(os.path.join(surf_src, 'lh.white'), surf_dir)
shutil.copy(os.path.join(surf_src, 'rh.white'), surf_dir)
# read original labels
assert_raises(IOError, read_labels_from_annot, subject, 'PALS_B12_Lobesey',
subjects_dir=tempdir)
labels = read_labels_from_annot(subject, 'PALS_B12_Lobes',
subjects_dir=tempdir)
# test saving parcellation only covering one hemisphere
parc = [l for l in labels if l.name == 'LOBE.TEMPORAL-lh']
write_labels_to_annot(parc, subject, 'myparc', subjects_dir=tempdir)
parc1 = read_labels_from_annot(subject, 'myparc', subjects_dir=tempdir)
parc1 = [l for l in parc1 if not l.name.startswith('unknown')]
assert_equal(len(parc1), len(parc))
for l1, l in zip(parc1, parc):
assert_labels_equal(l1, l)
# test saving only one hemisphere
parc = [l for l in labels if l.name.startswith('LOBE')]
write_labels_to_annot(parc, subject, 'myparc2', hemi='lh',
subjects_dir=tempdir)
annot_fname = os.path.join(tempdir, subject, 'label', '%sh.myparc2.annot')
assert_true(os.path.isfile(annot_fname % 'l'))
assert_false(os.path.isfile(annot_fname % 'r'))
parc1 = read_labels_from_annot(subject, 'myparc2',
annot_fname=annot_fname % 'l',
subjects_dir=tempdir)
parc_lh = [l for l in parc if l.name.endswith('lh')]
for l1, l in zip(parc1, parc_lh):
assert_labels_equal(l1, l)
def save_labels_from_annotation(subject, parc, surf_name, fol=""):
brain = Brain(subject, "both", surf_name, curv=False)
labels = mne.read_labels_from_annot(subject, parc, "both", surf_name)
if fol == "":
fol = os.path.join(os.environ["SUBJECTS_DIR"], os.environ["SUBJECT"], "label", "{}_labels".format(parc))
if not os.path.isdir(fol):
os.mkdir(fol)
for ind, label in enumerate(labels):
print("label {}/{}".format(ind, len(labels)))
label.save(os.path.join(fol, label.name))
def create_annot_dic(subject, parc, hemi, surf_name, obj_positions):
labels = mne.read_labels_from_annot(subject, parc, hemi, surf_name)
for label in [labels[161]]:
print(len(label.pos), len(obj_positions))
for label_pos, obj_pos in zip(label.pos, obj_positions):
label_pos = round_arr(label_pos * 1000)
obj_pos = round_arr(obj_pos)
eq = np.all(label_pos == obj_pos)
if not eq:
print(label_pos, obj_pos)
def test_label_center_of_mass():
"""Test computing the center of mass of a label."""
stc = read_source_estimate(stc_fname)
stc.lh_data[:] = 0
vertex_stc = stc.center_of_mass('sample', subjects_dir=subjects_dir)[0]
assert_equal(vertex_stc, 124791)
label = Label(stc.vertices[1], pos=None, values=stc.rh_data.mean(axis=1),
hemi='rh', subject='sample')
vertex_label = label.center_of_mass(subjects_dir=subjects_dir)
assert_equal(vertex_label, vertex_stc)
labels = read_labels_from_annot('sample', parc='aparc.a2009s',
subjects_dir=subjects_dir)
src = read_source_spaces(src_fname)
# Try a couple of random ones, one from left and one from right
# Visually verified in about the right place using mne_analyze
for label, expected in zip([labels[2], labels[3], labels[-5]],
[141162, 145221, 55979]):
label.values[:] = -1
pytest.raises(ValueError, label.center_of_mass,
subjects_dir=subjects_dir)
label.values[:] = 0
pytest.raises(ValueError, label.center_of_mass,
subjects_dir=subjects_dir)
label.values[:] = 1
assert_equal(label.center_of_mass(subjects_dir=subjects_dir), expected)
assert_equal(label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=label.vertices),
expected)
# restrict to source space
idx = 0 if label.hemi == 'lh' else 1
# this simple nearest version is not equivalent, but is probably
# close enough for many labels (including the test ones):
pos = label.pos[np.where(label.vertices == expected)[0][0]]
pos = (src[idx]['rr'][src[idx]['vertno']] - pos)
pos = np.argmin(np.sum(pos * pos, axis=1))
src_expected = src[idx]['vertno'][pos]
# see if we actually get the same one
src_restrict = np.intersect1d(label.vertices, src[idx]['vertno'])
assert_equal(label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=src_restrict),
src_expected)
assert_equal(label.center_of_mass(subjects_dir=subjects_dir,
restrict_vertices=src),
src_expected)
# degenerate cases
pytest.raises(ValueError, label.center_of_mass, subjects_dir=subjects_dir,
restrict_vertices='foo')
pytest.raises(TypeError, label.center_of_mass, subjects_dir=subjects_dir,
surf=1)
pytest.raises(IOError, label.center_of_mass, subjects_dir=subjects_dir,
surf='foo')
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
parc = mne.read_labels_from_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):
eq_(ds[i, 'location'], parc[i].name)
# multiple labels
lingual_index = source.dimindex('lingual-lh')
cuneus_index = source.dimindex('cuneus-lh')
assert_array_equal(source.dimindex(('cuneus-lh', 'lingual-lh')),
np.logical_or(cuneus_index, lingual_index))
lingual_source = source[lingual_index]
cuneus_source = source[cuneus_index]
sub_source = source[source.dimindex(('cuneus-lh', 'lingual-lh'))]
eq_(sub_source[sub_source.dimindex('lingual-lh')], lingual_source)
eq_(sub_source[sub_source.dimindex('cuneus-lh')], cuneus_source)
eq_(len(sub_source), len(lingual_source) + len(cuneus_source))
def test_annot_io():
"""Test I/O from and to *.annot files"""
# copy necessary files from fsaverage to tempdir
tempdir = _TempDir()
subject = "fsaverage"
label_src = os.path.join(subjects_dir, "fsaverage", "label")
surf_src = os.path.join(subjects_dir, "fsaverage", "surf")
label_dir = os.path.join(tempdir, subject, "label")
surf_dir = os.path.join(tempdir, subject, "surf")
os.makedirs(label_dir)
os.mkdir(surf_dir)
shutil.copy(os.path.join(label_src, "lh.PALS_B12_Lobes.annot"), label_dir)
shutil.copy(os.path.join(label_src, "rh.PALS_B12_Lobes.annot"), label_dir)
shutil.copy(os.path.join(surf_src, "lh.white"), surf_dir)
shutil.copy(os.path.join(surf_src, "rh.white"), surf_dir)
# read original labels
assert_raises(IOError, read_labels_from_annot, subject, "PALS_B12_Lobesey", subjects_dir=tempdir)
labels = read_labels_from_annot(subject, "PALS_B12_Lobes", subjects_dir=tempdir)
# test saving parcellation only covering one hemisphere
parc = [l for l in labels if l.name == "LOBE.TEMPORAL-lh"]
write_labels_to_annot(parc, subject, "myparc", subjects_dir=tempdir)
parc1 = read_labels_from_annot(subject, "myparc", subjects_dir=tempdir)
parc1 = [l for l in parc1 if not l.name.startswith("unknown")]
assert_equal(len(parc1), len(parc))
for l1, l in zip(parc1, parc):
assert_labels_equal(l1, l)
# test saving only one hemisphere
parc = [l for l in labels if l.name.startswith("LOBE")]
write_labels_to_annot(parc, subject, "myparc2", hemi="lh", subjects_dir=tempdir)
annot_fname = os.path.join(tempdir, subject, "label", "%sh.myparc2.annot")
assert_true(os.path.isfile(annot_fname % "l"))
assert_false(os.path.isfile(annot_fname % "r"))
parc1 = read_labels_from_annot(subject, "myparc2", annot_fname=annot_fname % "l", subjects_dir=tempdir)
parc_lh = [l for l in parc if l.name.endswith("lh")]
for l1, l in zip(parc1, parc_lh):
assert_labels_equal(l1, l)
def test_labels_to_stc():
"""Test labels_to_stc."""
labels = read_labels_from_annot(
'sample', 'aparc', subjects_dir=subjects_dir)
values = np.random.RandomState(0).randn(len(labels))
with pytest.raises(ValueError, match='1 or 2 dim'):
labels_to_stc(labels, values[:, np.newaxis, np.newaxis])
with pytest.raises(ValueError, match=r'values\.shape'):
labels_to_stc(labels, values[np.newaxis])
stc = labels_to_stc(labels, values)
for value, label in zip(values, labels):
stc_label = stc.in_label(label)
assert (stc_label.data == value).all()
stc = read_source_estimate(stc_fname, 'sample')
def test_split_label():
"""Test splitting labels"""
aparc = read_labels_from_annot('fsaverage', 'aparc', 'lh',
regexp='lingual', subjects_dir=subjects_dir)
lingual = aparc[0]
# Test input error
assert_raises(ValueError, lingual.split, 'bad_input_string')
# 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")
# Apply contiguous splitting to DMN label from parcellation in Yeo, 2011
label_default_mode = read_label(op.join(subjects_dir, 'fsaverage', 'label',
'lh.7Networks_7.label'))
DMN_sublabels = label_default_mode.split(parts='contiguous',
subject='fsaverage',
subjects_dir=subjects_dir)
assert_equal([len(label.vertices) for label in DMN_sublabels],
[16181, 7022, 5965, 5300, 823] + [1] * 23)
def morph_labels(subject_from, subject_to, parc, surf_name="pial", smooth=2, overwrite=True):
"""
mne_morph_labels --from fsaverage --to mg79 --labeldir /homes/5/npeled/space3/subjects/fsaverage/label/laus500_labels --smooth 5
"""
# brain = Brain(subject_from, 'both', surf_name, curv=False)
labels = mne.read_labels_from_annot(subject_from, parc, "both", surf_name)
morphed_labels = []
for ind, label in enumerate(labels):
try:
print("label {}/{}".format(ind, len(labels)))
label.values.fill(1.0)
morphed_label = label.morph(subject_from, subject_to, smooth)
morphed_labels.append(morphed_label)
except:
print("cant morph label {}".format(label.name))
print(sys.exc_info()[1])
print("{} labels were morphed succefully.".format(len(morphed_labels)))
mne.write_labels_to_annot(morphed_labels, subject_to, parc, overwrite, hemi="both")
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_labels_from_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")
# Compute inverse solution and for each epoch
snr = 3.0 # use smaller SNR for raw data
inv_method = 'dSPM' # sLORETA, MNE, dSPM
parc = 'aparc' # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
lambda2 = 1.0 / snr ** 2
# Compute inverse operator
inverse_operator = make_inverse_operator(evoked.info, fwd, noise_cov,
depth=None, fixed=False)
stc = apply_inverse(evoked, inverse_operator, lambda2, inv_method,
pick_ori=None)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels_parc = mne.read_labels_from_annot(
subject, parc=parc, subjects_dir=subjects_dir)
###############################################################################
# Average the source estimates within each label of the cortical parcellation
# and each sub structure contained in the src space
src = inverse_operator['src']
label_ts = mne.extract_label_time_course(
[stc], labels_parc, src, mode='mean', allow_empty=True)
# plot the times series of 2 labels
fig, axes = plt.subplots(1)
axes.plot(1e3 * stc.times, label_ts[0][0, :], 'k', label='bankssts-lh')
axes.plot(1e3 * stc.times, label_ts[0][71, :].T, 'r', label='Brain-stem')
axes.set(xlabel='Time (ms)', ylabel='MNE current (nAm)')
import mne
import sys
import numpy as np
import pandas as pd
from mne.minimum_norm import read_inverse_operator, source_induced_power
subject = sys.argv[1]
epochs = mne.read_epochs(epochs_folder + "%s_target-epo.fif" % subject)
inv = read_inverse_operator(mne_folder + "%s-inv.fif" % subject)
labels = mne.read_labels_from_annot(
subject,
parc='PALS_B12_Lobes',
# regexp="Bro",
subjects_dir=subjects_dir)
labels_selc = labels[9], labels[10]
frequencies = np.arange(8, 13, 1) # define frequencies of interest
n_cycles = frequencies / 3. # different number of cycle per frequency
method = "dSPM"
sides = ["left", "right"]
conditions = ["ctl", "ent"]
cor = ["correct", "incorrect"]
phase = ["in_phase", "out_phase"]
congrunet = ["cong", "incong"]
columns_keys = ["subject", "side", "condition", "phase", "ROI"]
----------
.. [1] Glasser MF et al. (2016) A multi-modal parcellation of human
cerebral cortex. Nature 536:171-178.
"""
# Author: Eric Larson <*****@*****.**>
#
# License: BSD (3-clause)
from surfer import Brain
import mne
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
verbose=True)
labels = mne.read_labels_from_annot(
'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1')
aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
brain.add_label(aud_label, borders=False)
###############################################################################
# We can also plot a combined set of labels (23 per hemisphere).
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1_combined')
def annot(annot, subject='fsaverage', surf='smoothwm', borders=False, alpha=0.7,
hemi=None, views=('lat', 'med'), w=None, h=None, axw=None, axh=None,
background=None, parallel=True, subjects_dir=None):
"""Plot the parcellation in an annotation file
Parameters
----------
annot : str
Name of the annotation (e.g., "PALS_B12_LOBES").
subject : str
Name of the subject (default 'fsaverage').
surf : 'inflated' | 'pial' | 'smoothwm' | 'sphere' | 'white'
Freesurfer surface to use as brain geometry.
borders : bool | int
Show only label borders (PySurfer Brain.add_annotation() argument).
alpha : scalar
Alpha of the annotation (1=opaque, 0=transparent, default 0.7).
hemi : 'lh' | 'rh' | 'both' | 'split'
Which hemispheres to plot (default includes hemisphere with more than one
label in the annot file).
views : str | iterator of str
View or views to show in the figure.
w, h, axw, axh : scalar
Layout parameters (figure width/height, subplot width/height).
background : mayavi color
Figure background color.
parallel : bool
Set views to parallel projection (default ``True``).
smoothing_steps : None | int
Number of smoothing steps if data is spatially undersampled (pysurfer
``Brain.add_data()`` argument).
subjects_dir : None | str
Override the subjects_dir associated with the source space dimension.
Returns
-------
brain : surfer.Brain
PySurfer Brain instance.
"""
if hemi is None:
annot_lh = mne.read_labels_from_annot(subject, annot, 'lh',
subjects_dir=subjects_dir)
use_lh = len(annot_lh) > 1
annot_rh = mne.read_labels_from_annot(subject, annot, 'rh',
subjects_dir=subjects_dir)
use_rh = len(annot_rh) > 1
if use_lh and use_rh:
hemi = 'split'
elif use_lh:
hemi = 'lh'
elif use_rh:
hemi = 'rh'
else:
raise ValueError("Neither hemisphere contains more than one label")
brain = _surfer_brain(subject, surf, hemi, views, w, h, axw, axh, background,
subjects_dir)
brain.add_annotation(annot, borders, alpha)
if parallel:
_set_parallel(brain, surf)
return brain
import sys
import numpy as np
import mne
from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs
from my_settings import (mne_folder, epochs_folder, source_folder)
subject = sys.argv[1]
method = "dSPM"
snr = 1.
lambda2 = 1. / snr**2
labels = mne.read_labels_from_annot(
subject=subject, parc="PALS_B12_Brodmann", regexp="Brodmann")
condition = "interupt"
inv = read_inverse_operator(mne_folder + "%s_%s-inv.fif" % (subject, condition
))
epochs = mne.read_epochs(epochs_folder + "%s_%s-epo.fif" % (subject, condition
))
# epochs.resample(500)
stcs = apply_inverse_epochs(
epochs["press"], inv, lambda2, method=method, pick_ori=None)
ts = [
mne.extract_label_time_course(
stc, labels, inv["src"], mode="mean_flip") for stc in stcs
]
def test_write_labels_to_annot():
"""Test writing FreeSurfer parcellation from labels"""
tempdir = _TempDir()
labels = read_labels_from_annot('sample', subjects_dir=subjects_dir)
# create temporary subjects-dir skeleton
surf_dir = op.join(subjects_dir, 'sample', 'surf')
temp_surf_dir = op.join(tempdir, 'sample', 'surf')
os.makedirs(temp_surf_dir)
shutil.copy(op.join(surf_dir, 'lh.white'), temp_surf_dir)
shutil.copy(op.join(surf_dir, 'rh.white'), temp_surf_dir)
os.makedirs(op.join(tempdir, 'sample', 'label'))
# test automatic filenames
dst = op.join(tempdir, 'sample', 'label', '%s.%s.annot')
write_labels_to_annot(labels, 'sample', 'test1', subjects_dir=tempdir)
assert_true(op.exists(dst % ('lh', 'test1')))
assert_true(op.exists(dst % ('rh', 'test1')))
# lh only
for label in labels:
if label.hemi == 'lh':
break
write_labels_to_annot([label], 'sample', 'test2', subjects_dir=tempdir)
assert_true(op.exists(dst % ('lh', 'test2')))
assert_true(op.exists(dst % ('rh', 'test2')))
# rh only
for label in labels:
if label.hemi == 'rh':
break
write_labels_to_annot([label], 'sample', 'test3', subjects_dir=tempdir)
assert_true(op.exists(dst % ('lh', 'test3')))
assert_true(op.exists(dst % ('rh', 'test3')))
# label alone
assert_raises(TypeError, write_labels_to_annot, labels[0], 'sample',
'test4', subjects_dir=tempdir)
# write left and right hemi labels with filenames:
fnames = [op.join(tempdir, hemi + '-myparc') for hemi in ['lh', 'rh']]
with warnings.catch_warnings(record=True): # specify subject_dir param
for fname in fnames:
write_labels_to_annot(labels, annot_fname=fname)
# read it back
labels2 = read_labels_from_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels22 = read_labels_from_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_labels_to_annot(labels, 'sample', annot_fname=fname,
overwrite=True, subjects_dir=subjects_dir)
labels3 = read_labels_from_annot('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels33 = read_labels_from_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_labels_to_annot, labels, 'sample',
annot_fname=fnames[0], subjects_dir=subjects_dir)
# however, this works
write_labels_to_annot(labels, 'sample', annot_fname=fnames[0],
overwrite=True, subjects_dir=subjects_dir)
# label without color
labels_ = labels[:]
labels_[0] = labels_[0].copy()
labels_[0].color = None
write_labels_to_annot(labels_, 'sample', annot_fname=fnames[0],
overwrite=True, subjects_dir=subjects_dir)
# duplicate color
labels_[0].color = labels_[2].color
assert_raises(ValueError, write_labels_to_annot, labels_, 'sample',
annot_fname=fnames[0], overwrite=True,
subjects_dir=subjects_dir)
# invalid color inputs
labels_[0].color = (1.1, 1., 1., 1.)
assert_raises(ValueError, write_labels_to_annot, labels_, 'sample',
annot_fname=fnames[0], overwrite=True,
subjects_dir=subjects_dir)
# overlapping labels
labels_ = labels[:]
cuneus_lh = labels[6]
precuneus_lh = labels[50]
labels_.append(precuneus_lh + cuneus_lh)
assert_raises(ValueError, write_labels_to_annot, labels_, 'sample',
annot_fname=fnames[0], overwrite=True,
subjects_dir=subjects_dir)
# unlabeled vertices
labels_lh = [label for label in labels if label.name.endswith('lh')]
write_labels_to_annot(labels_lh[1:], 'sample', annot_fname=fnames[0],
overwrite=True, subjects_dir=subjects_dir)
labels_reloaded = read_labels_from_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(in1d(label0.vertices, label1.vertices)))
# unnamed labels
labels4 = labels[:]
labels4[0].name = None
assert_raises(ValueError, write_labels_to_annot, labels4,
annot_fname=fnames[0])