def test_labels_from_parc():
"""Test reading labels from FreeSurfer parcellation
"""
# test some invalid inputs
assert_raises(ValueError, labels_from_parc, 'sample', hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError, labels_from_parc, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh, colors_lh = labels_from_parc('sample', hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
assert_true(len(labels_lh) == len(colors_lh))
# read labels using annot_fname
annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot')
labels_rh, colors_rh = labels_from_parc('sample', annot_fname=annot_fname,
subjects_dir=subjects_dir)
assert_true(len(labels_rh) == len(colors_rh))
for label in labels_rh:
assert_true(label.name.endswith('-rh'))
assert_true(label.hemi == 'rh')
# 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, colors = labels_from_parc('sample', subjects_dir=subjects_dir)
assert_true(len(labels_both) == len(colors))
# 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 = labels_from_parc('sample', parc='aparc.a2009s', regexp='Angu',
subjects_dir=subjects_dir)[0][0]
assert_true(label.name == 'G_pariet_inf-Angular-lh')
# silly, but real regexp:
label = labels_from_parc('sample', parc='aparc.a2009s',
regexp='.*-.{4,}_.{3,3}-L',
subjects_dir=subjects_dir)[0][0]
assert_true(label.name == 'G_oc-temp_med-Lingual-lh')
assert_raises(RuntimeError, labels_from_parc, 'sample', parc='aparc',
annot_fname=annot_fname, regexp='JackTheRipper',
subjects_dir=subjects_dir)
def test_parc_from_labels():
"""Test writing FreeSurfer parcellation from labels"""
labels, colors = labels_from_parc('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:
parc_from_labels(labels, colors, annot_fname=fname)
# read it back
labels2, colors2 = labels_from_parc('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels22, colors22 = labels_from_parc('sample', subjects_dir=subjects_dir,
annot_fname=fnames[1])
labels2.extend(labels22)
colors2.extend(colors22)
names = [label.name for label in labels2]
for label, color in zip(labels, colors):
idx = names.index(label.name)
assert_labels_equal(label, labels2[idx])
assert_array_almost_equal(np.array(color), np.array(colors2[idx]))
# same with label-internal colors
for fname in fnames:
parc_from_labels(labels, annot_fname=fname, overwrite=True)
labels3, _ = labels_from_parc('sample', subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels33, _ = labels_from_parc('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, parc_from_labels, labels, colors,
annot_fname=fnames[0])
# however, this works
parc_from_labels(labels, colors=None, annot_fname=fnames[0],
overwrite=True)
# test some other invalid inputs
assert_raises(ValueError, parc_from_labels, labels[:-1], colors,
annot_fname=fnames[0], overwrite=True)
colors2 = np.asarray(colors)
assert_raises(ValueError, parc_from_labels, labels, colors2[:, :3],
annot_fname=fnames[0], overwrite=True)
colors2[0] = 1.1
assert_raises(ValueError, parc_from_labels, labels, colors2,
annot_fname=fnames[0], overwrite=True)
def test_labels_from_parc_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, _ = labels_from_parc('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 test_labels_from_parc_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)
cmd = 'mne_annot2labels --subject %s --parc %s' % (subject, parc)
st, output = commands.getstatusoutput(cmd)
if st != 0:
raise RuntimeError('mne_annot2labels non-zero exit status %d'
% st)
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, _ = labels_from_parc('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 test_split_label():
aparc = labels_from_parc('fsaverage', 'aparc', 'lh', regexp='lingual',
subjects_dir=subjects_dir)[0]
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
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_labels_from_parc_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, _ = labels_from_parc('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 test_labels_from_parc():
"""Test reading labels from parcellation
"""
# test some invalid inputs
assert_raises(ValueError, labels_from_parc, 'sample', hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError, labels_from_parc, 'sample',
annot_fname='bla.annot', subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh, colors_lh = labels_from_parc('sample', hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
assert_true(len(labels_lh) == len(colors_lh))
# read labels using annot_fname
annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot')
labels_rh, colors_rh = labels_from_parc('sample', annot_fname=annot_fname,
subjects_dir=subjects_dir)
assert_true(len(labels_rh) == len(colors_rh))
for label in labels_rh:
assert_true(label.name.endswith('-rh'))
assert_true(label.hemi == 'rh')
# 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, colors = labels_from_parc('sample', subjects_dir=subjects_dir)
assert_true(len(labels_both) == len(colors))
# we have the same result
_assert_labels_equal(labels_lhrh, labels_both)
# aparc has 68 cortical labels
assert_true(len(labels_both) == 68)
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 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():
aparc = labels_from_parc('fsaverage',
'aparc',
'lh',
regexp='lingual',
subjects_dir=subjects_dir)[0]
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
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 loadannot(p,subj,subjdir,surf_type='pial'): import mne annot=mne.labels_from_parc(parc=p,subject=subj,surf_name=surf_type, subjects_dir=subjdir,verbose=False) return annot
empty_room_dir = home+'/data/meg/empty_room/'
res = np.recfromtxt(empty_room_dir + 'closest_empty_room_data.csv',skip_header=0,delimiter=',')
window_length=13.65 #s
closest_empty_room = {}
for rec in res:
closest_empty_room[rec[0]] = str(rec[2])
subjs_fname = home+'/data/meg/usable_subjects_5segs13p654.txt'
markers_fname = home+'/data/meg/marker_data_clean.npy'
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
markers = np.load(markers_fname)[()]
labels = mne.labels_from_parc('fsaverage', parc='Yeo2011_7Networks_N1000')[0]
net_labels = labels[:-2] # the last two are the medial wall
# fill them in so we can morph them later
for label in net_labels:
label.values.fill(1.0)
for subj in subjs[:1]:
er_fname = empty_room_dir+'empty_room_'+closest_empty_room[subj]+'_raw.fif'
raw_fname = data_dir + 'fifs/rest/%s_rest_LP100_CP3_DS300_raw.fif'%subj
fwd_fname = data_dir + 'analysis/rest/%s_rest_LP100_CP3_DS300_raw-5-fwd.fif'%subj
forward = mne.read_forward_solution(fwd_fname, surf_ori=True)
raw = mne.fiff.Raw(raw_fname, preload=True, compensation=3)
er_raw = mne.fiff.Raw(er_fname, preload=True, compensation=3)
picks = mne.fiff.pick_channels_regexp(raw.info['ch_names'], 'M..-*')
raw.filter(l_freq=1, h_freq=50, picks=picks)
er_raw.filter(l_freq=1, h_freq=50, picks=picks)
skip_header=0,
delimiter=',')
window_length = 13.65 #s
closest_empty_room = {}
for rec in res:
closest_empty_room[rec[0]] = str(rec[2])
subjs_fname = home + '/data/meg/usable_subjects_5segs13p654.txt'
markers_fname = home + '/data/meg/marker_data_clean.npy'
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
markers = np.load(markers_fname)[()]
labels = mne.labels_from_parc('fsaverage', parc='Yeo2011_7Networks_N1000')[0]
net_labels = labels[:-2] # the last two are the medial wall
# fill them in so we can morph them later
for label in net_labels:
label.values.fill(1.0)
for subj in subjs[:1]:
er_fname = empty_room_dir + 'empty_room_' + closest_empty_room[
subj] + '_raw.fif'
raw_fname = data_dir + 'fifs/rest/%s_rest_LP100_CP3_DS300_raw.fif' % subj
fwd_fname = data_dir + 'analysis/rest/%s_rest_LP100_CP3_DS300_raw-5-fwd.fif' % subj
forward = mne.read_forward_solution(fwd_fname, surf_ori=True)
raw = mne.fiff.Raw(raw_fname, preload=True, compensation=3)
er_raw = mne.fiff.Raw(er_fname, preload=True, compensation=3)
picks = mne.fiff.pick_channels_regexp(raw.info['ch_names'], 'M..-*')
raw.filter(l_freq=1, h_freq=50, picks=picks)
def test_labels_from_parc():
"""Test reading labels from parcellation
"""
# test some invalid inputs
assert_raises(ValueError,
labels_from_parc,
'sample',
hemi='bla',
subjects_dir=subjects_dir)
assert_raises(ValueError,
labels_from_parc,
'sample',
annot_fname='bla.annot',
subjects_dir=subjects_dir)
# read labels using hemi specification
labels_lh, colors_lh = labels_from_parc('sample',
hemi='lh',
subjects_dir=subjects_dir)
for label in labels_lh:
assert_true(label.name.endswith('-lh'))
assert_true(label.hemi == 'lh')
assert_true(len(labels_lh) == len(colors_lh))
# read labels using annot_fname
annot_fname = op.join(subjects_dir, 'sample', 'label', 'rh.aparc.annot')
labels_rh, colors_rh = labels_from_parc('sample',
annot_fname=annot_fname,
subjects_dir=subjects_dir)
assert_true(len(labels_rh) == len(colors_rh))
for label in labels_rh:
assert_true(label.name.endswith('-rh'))
assert_true(label.hemi == 'rh')
# 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, colors = labels_from_parc('sample', subjects_dir=subjects_dir)
assert_true(len(labels_both) == len(colors))
# 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 = labels_from_parc('sample',
parc='aparc.a2009s',
regexp='Angu',
subjects_dir=subjects_dir)[0][0]
assert_true(label.name == 'G_pariet_inf-Angular-lh')
label = labels_from_parc(
'sample',
parc='aparc.a2009s',
regexp='.*-.{4,}_.{3,3}-L', # silly, but real regexp
subjects_dir=subjects_dir)[0][0]
assert_true(label.name == 'G_oc-temp_med-Lingual-lh')
assert_raises(RuntimeError,
labels_from_parc,
'sample',
parc='aparc',
annot_fname=annot_fname,
regexp='JackTheRipper',
subjects_dir=subjects_dir)
# selected_labels = ['isthmuscingulate-rh', 'superiorfrontal-rh', 'inferiorparietal-rh', 'isthmuscingulate-lh', 'superiorfrontal-lh', 'inferiorparietal-lh']
# selected_labels = ['isthmuscingulate-rh', 'superiorfrontal-rh', 'inferiorparietal-rh']
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
fid1 = open(g1_fname, 'r')
fid2 = open(g2_fname, 'r')
g1 = [line.rstrip() for line in fid1]
g2 = [line.rstrip() for line in fid2]
print 'g1 =', g1_fname
print 'g2 =', g2_fname
m = ['pli', 'imcoh', 'plv', 'wpli', 'pli2_unbiased', 'wpli2_debiased']
print lmethod, '-', m[cmethod]
labels, label_colors = mne.labels_from_parc(subjs[0], parc='aparc')
nlabels = len(labels)
il = np.tril_indices(nlabels, k=-1)
if len(selected_labels) > 0:
label_names = [l.name for l in labels]
idx = [
l for s in selected_labels for l, label in enumerate(label_names)
if label == s
]
keep = [False] * len(il[0])
for i in idx:
for j in idx:
keep = keep | ((il[0] == i) & (il[1] == j))
il = [il[0][keep], il[1][keep]]
g1_data = [[] for b in range(len(bands))]
er_raw = mne.fiff.Raw(er_fname, preload=True, compensation=3)
picks = mne.fiff.pick_channels_regexp(raw.info["ch_names"], "M..-*")
raw.filter(l_freq=1, h_freq=50, picks=picks)
er_raw.filter(l_freq=1, h_freq=50, picks=picks)
noise_cov = mne.compute_raw_data_covariance(er_raw)
# note that MNE reads CTF data as magnetometers!
noise_cov = mne.cov.regularize(noise_cov, raw.info, mag=noise_reg)
inverse_operator = mne.minimum_norm.make_inverse_operator(raw.info, forward, noise_cov, loose=0.2, depth=0.8)
data, time = raw[0, :] #
events = fg.get_good_events(markers[subj], time, window_length)
epochs = mne.Epochs(raw, events, None, 0, window_length, preload=True, baseline=None, detrend=0, picks=picks)
stcs = mne.minimum_norm.apply_inverse_epochs(epochs, inverse_operator, lambda2, "MNE", return_generator=False)
labels, label_colors = mne.labels_from_parc(subj, parc="aparc")
label_ts = mne.extract_label_time_course(stcs, labels, forward["src"], mode=label_mode)
# label_data is nlabels by time, so here we can use whatever connectivity method we fancy
con, freqs, times, n_epochs, n_tapers = mne.connectivity.spectral_connectivity(
label_ts,
method=method,
mode="multitaper",
sfreq=raw.info["sfreq"],
fmin=[1, 4, 8, 13, 30],
fmax=[4, 8, 13, 30, 50],
faverage=True,
n_jobs=3,
mt_adaptive=False,
)
np.save(dir_out + subj + "-" + label_mode + "-" + "-".join(method), con)
# stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method,
# pick_ori="normal", return_generator=True)
#
##vertices_to = mne.grade_to_vertices('fsaverage', grade = 5)
##stcs = mne.morph_data(subj, 'fsaverage', stcs_orig, grade = vertices_to)
##teststc_fname = data_path + 'ave_projon/stc/'+ subj + '_run1-spm-test-lh.stc'
###stcs.save(teststc_fname)
##
########################################################################################3
########Connectivity Circle Plotting############
# print inverse_operator['src']
from mne.viz import circular_layout, plot_connectivity_circle
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels per hemi
labels, label_colors = mne.labels_from_parc(
subj, parc="aparc", subjects_dir=subjects_dir
) ##or use read_labels_from_annot()
print labels
label_names = [label.name for label in labels]
# for label in label_names:
# print label
lh_labels = [name for name in label_names if name.endswith("lh")]
###############################Labels Temporal
import numpy as np
# temporals = ['lh.superiortemporal', 'lh.inferiortemporal' , 'lh.middletemporal', 'lh.transversetemporal', 'lh.entorhinal','lh.temporalpole', 'lh.parahippocampal']
num_plots = 20
if subj == "EP1":
selected_labels = []
# selected_labels = ['isthmuscingulate-rh', 'superiorfrontal-rh', 'inferiorparietal-rh', 'isthmuscingulate-lh', 'superiorfrontal-lh', 'inferiorparietal-lh']
# selected_labels = ['isthmuscingulate-rh', 'superiorfrontal-rh', 'inferiorparietal-rh']
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
res = np.recfromtxt(sx_fname, delimiter='\t')
sx = {}
for rec in res:
sx[rec[0]] = rec[1]
print 'sx =',sx_fname
m = ['pli','imcoh','plv','wpli','pli2_unbiased','wpli2_debiased']
print lmethod, '-', m[cmethod]
labels, label_colors = mne.labels_from_parc(subjs[0], parc='aparc')
nlabels=len(labels)
il = np.tril_indices(nlabels, k=-1)
if len(selected_labels)>0:
label_names = [l.name for l in labels]
idx = [l for s in selected_labels for l, label in enumerate(label_names) if label == s]
keep = [False]*len(il[0])
for i in idx:
for j in idx:
keep = keep | ((il[0]==i) & (il[1]==j))
il = [il[0][keep], il[1][keep]]
subj_data = [[] for b in range(len(bands))]
sx_data = []
for s in subjs:
# 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.labels_from_parc(subject,
parc='aparc',
subjects_dir=subjects_dir,
regexp=aparc_label_name)[0][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
# 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, label_colors = mne.labels_from_parc('sample',
parc='aparc',
subjects_dir=subjects_dir)
# 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
else:
con_diff_temp[i,:].fill(0)
con_t_temp[i,:].fill(0)
####################################################################################################
########################## 2 sample T test - Hotelling T squared statistic ##########################################################
#tsq_matrix_fname = '/home/custine/MEG/results/source_level/ConnectivityPlots/textfiles/Hotel-Tsq_GrandAvgConnectivityMatrix_' + freq + '_' + gp1 + '-' + gp2 + '.txt'
#tsq_plot_fname = '/home/custine/MEG/results/source_level/ConnectivityPlots/figures/Hotel-Tsq_GrandAvgConnectivityPlot_' + freq + '_' + gp1 + '-' + gp2 + '.png'
#
#tsq = np.loadtxt(tsq_matrix_fname, delimiter = ' ')
#print tsq
#
##############################################################################################
##############################################################################################
###############Plotting the Conn - Diff or T test stats##################
labels, label_colors = mne.labels_from_parc('fsaverage', parc='aparc', subjects_dir=subjects_dir) ##or use read_labels_from_annot()
print
#### Now, we visualize the connectivity using a circular graph layout
# First, we reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
#print label_names[:-1] #### TO GET RID OF UNKNOWN LABEL.LH
label_names = label_names[:-1]
for name in label_names:
print name
#np.savetxt('/home/custine/MEG/results/source_level/ConnectivityPlots/label_names.txt', label_names)
lh_labels = [name for name in label_names if name.endswith('lh')]
#print len(lh_labels)
# Get the y-location of the label
events,
None,
0,
window_length,
preload=True,
baseline=None,
detrend=0,
picks=picks)
stcs = mne.beamformer.lcmv_epochs(epochs,
forward,
noise_cov.as_diag(),
data_cov,
reg=data_reg,
pick_ori='max-power')
labels, label_colors = mne.labels_from_parc(subj,
parc='Yeo2011_7Networks_N1000')
label_ts = mne.extract_label_time_course(stcs,
labels,
forward['src'],
mode=label_mode)
# label_data is nlabels by time, so here we can use whatever connectivity method we fancy
con, freqs, times, n_epochs, n_tapers = mne.connectivity.spectral_connectivity(
label_ts,
method=method,
mode='multitaper',
sfreq=raw.info['sfreq'],
fmin=[1, 4, 8, 13, 30],
fmax=[4, 8, 13, 30, 50],
faverage=True,
n_jobs=3,
# 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.labels_from_parc(subject, parc='aparc', subjects_dir=subjects_dir,
regexp=aparc_label_name)[0][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.labels_from_parc(subject, parc='aparc',
subjects_dir=subjects_dir,
regexp=aparc_label_name)[0][0]
in_network = ['superiorfrontal-rh', 'inferiorparietal-rh']
bands = [[1, 4], [4, 8], [8, 13], [13, 30], [30, 50]]
subjs_fname = '/Users/sudregp/data/meg/usable_subjects_pm2std_withFamily.txt'
data_dir = '/Users/sudregp/data/meg_diagNoise_noiseRegp03_dataRegp001/'
g1_fname = '/Users/sudregp/data/meg/nv_subjs.txt'
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
fid1 = open(g1_fname, 'r')
g1 = [line.rstrip() for line in fid1]
in_conn = [[] for b in range(5)]
out_conn = [[] for b in range(5)]
for s in subjs:
if s in g1:
labels, label_colors = mne.labels_from_parc(s, parc='aparc')
label_names = [l.name for l in labels]
fname = data_dir + 'connectivity/pli-%s.npy' % (s)
conn = np.load(fname)[()]
seed_idx = label_names.index(seed)
hemi_labels = [l for l in label_names if l.find('-' + hemi) > 0]
out_network = list(
np.setdiff1d(set(hemi_labels), set(in_network + [seed])))
for b in range(5):
in_conn[b].append(
np.mean([
conn[seed_idx, label_names.index(i), b] for i in in_network
]))
out_conn[b].append(
np.mean([
conn[seed_idx, label_names.index(i), b]
import mne
import numpy as np
bands = [[1, 4], [4, 8], [8, 13], [13, 30], [30, 50]]
subjs_fname = '/Users/sudregp/data/meg/usable_subjects_pm2std.txt'
data_dir = '/Users/sudregp/data/meg_diagNoise_noiseRegp03_dataRegp001/'
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
dir_out = data_dir + '/connectivity/'
for cnt, s in enumerate(subjs[100:]):
print cnt+1, '/', len(subjs), ':', s
labels, label_colors = mne.labels_from_parc(s, parc='aparc')
fwd_fname = '/mnt/neuro/MEG_data/analysis/rest/%s_rest_LP100_CP3_DS300_raw-5-fwd.fif'%s
forward = mne.read_forward_solution(fwd_fname, surf_ori=True)
for l_freq, h_freq in bands:
print 'Band %d to %d Hz'%(l_freq, h_freq)
fname = data_dir + 'lcmv-%dto%d-'%(l_freq,h_freq) + s
stc = mne.read_source_estimate(fname)
label_data = stc.extract_label_time_course(labels=labels,src=forward['src'],mode='pca_flip',allow_empty=True)
# label_data is nlabels by time, so here we can use whatever connectivity method we fancy
conn = np.corrcoef(label_data)
np.save(dir_out + 'labelPCACorrelation-%dto%d-'%(l_freq,h_freq) + s, conn)
# 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, label_colors = mne.labels_from_parc('sample', parc='aparc',
subjects_dir=subjects_dir)
# 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
# compute connectivity in computationally efficient manner where the amount
def test_parc_from_labels():
"""Test writing FreeSurfer parcellation from labels"""
labels, colors = labels_from_parc('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:
parc_from_labels(labels, colors, annot_fname=fname)
# read it back
labels2, colors2 = labels_from_parc('sample',
subjects_dir=subjects_dir,
annot_fname=fnames[0])
labels22, colors22 = labels_from_parc('sample',
subjects_dir=subjects_dir,
annot_fname=fnames[1])
labels2.extend(labels22)
colors2.extend(colors22)
names = [label.name for label in labels2]
for label, color in zip(labels, colors):
idx = names.index(label.name)
assert_labels_equal(label, labels2[idx])
assert_array_almost_equal(np.array(color), np.array(colors2[idx]))
# make sure we can't overwrite things
assert_raises(ValueError,
parc_from_labels,
labels,
colors,
annot_fname=fnames[0])
# however, this works
parc_from_labels(labels,
colors=None,
annot_fname=fnames[0],
overwrite=True)
# test some other invalid inputs
assert_raises(ValueError,
parc_from_labels,
labels[:-1],
colors,
annot_fname=fnames[0],
overwrite=True)
colors2 = np.asarray(colors)
assert_raises(ValueError,
parc_from_labels,
labels,
colors2[:, :3],
annot_fname=fnames[0],
overwrite=True)
colors2[0] = 1.1
assert_raises(ValueError,
parc_from_labels,
labels,
colors2,
annot_fname=fnames[0],
overwrite=True)