def _make_label_connectivity():
labels = [Label(vertices=np.arange(3), hemi='lh', name='Label1'),
Label(vertices=np.arange(3, 6), hemi='lh', name='Label2'),
Label(vertices=np.arange(6, 9), hemi='lh', name='Label3')]
pairs = [[0, 0, 1], [1, 2, 2]]
data = np.arange(len(pairs[1]))
return LabelConnectivity(data, pairs, labels)
def _generate_labels(vertices, n_labels):
vert_lh, vert_rh = vertices
n_lh_chunck = len(vert_lh)//(n_labels // 2)
n_rh_chunck = len(vert_rh)//(n_labels - n_labels // 2)
labels_lh = [Label(vertices=vert_lh[x:x+n_lh_chunck], hemi='lh',
name='Label' + str(x))
for x in range(0, len(vert_lh), n_lh_chunck)]
labels_rh = [Label(vertices=vert_rh[x:x+n_rh_chunck], hemi='rh',
name='Label' + str(x))
for x in range(0, len(vert_rh), n_lh_chunck)]
return labels_lh + labels_rh
def plot_roi(
hemi,
labels,
color,
annotation="HCPMMP1",
view="parietal",
fs_dir=os.environ["SUBJECTS_DIR"],
subject_id="S04",
surf="inflated",
):
import matplotlib
import os
import glob
from surfer import Brain
from mne import Label
color = np.array(matplotlib.colors.to_rgba(color))
brain = Brain(subject_id, hemi, surf, offscreen=False)
labels = [label.replace("-rh", "").replace("-lh", "") for label in labels]
# First select all label files
label_names = glob.glob(
os.path.join(fs_dir, subject_id, "label", "lh*.label"))
label_names = [
label for label in label_names if any([l in label for l in labels])
]
for label in label_names:
brain.add_label(label, color=color)
# Now go for annotations
from nibabel.freesurfer import io
ids, colors, annot_names = io.read_annot(
os.path.join(fs_dir, subject_id, "label", "lh.%s.annot" % annotation),
orig_ids=True,
)
for i, alabel in enumerate(annot_names):
if any([label in alabel.decode("utf-8") for label in labels]):
label_id = colors[i, -1]
vertices = np.where(ids == label_id)[0]
l = Label(np.sort(vertices), hemi="lh")
brain.add_label(l, color=color)
brain.show_view(view)
return brain.screenshot()
def extract_roi(stc, src, label=None, thresh=0.5):
"""Extract a functional ROI.
Parameters
----------
stc : instance of SourceEstimate
The source estimate data. The maximum positive peak will be selected.
If you want the maximum negative peak, consider passing
abs(stc) or -stc.
src : instance of SourceSpaces
The associated source space.
label : instance of Label | None
The label within which to select the peak.
Can be None to use the entire STC.
thresh : float
Threshold value (relative to the peak value) above which vertices
will be taken.
Returns
-------
roi : instance of Label
The functional ROI.
"""
assert isinstance(stc, SourceEstimate)
if label is None:
stc_label = stc.copy()
else:
stc_label = stc.in_label(label)
del label
max_vidx, max_tidx = np.unravel_index(np.argmax(stc_label.data),
stc_label.data.shape)
max_val = stc_label.data[max_vidx, max_tidx]
if max_vidx < len(stc_label.vertices[0]):
hemi = 'lh'
max_vert = stc_label.vertices[0][max_vidx]
max_vidx = list(stc.vertices[0]).index(max_vert)
else:
hemi = 'rh'
max_vert = stc_label.vertices[1][max_vidx - len(stc_label.vertices[0])]
max_vidx = list(stc.vertices[1]).index(max_vert)
max_vidx += len(stc.vertices[0])
del stc_label
assert max_val == stc.data[max_vidx, max_tidx]
# Get contiguous vertices within 50%
threshold = max_val * thresh
connectivity = spatial_src_adjacency(src, verbose='error') # holes
_, clusters, _, _ = spatio_temporal_cluster_1samp_test(
np.array([stc.data]), threshold, n_permutations=1,
stat_fun=lambda x: x.mean(0), tail=1,
connectivity=connectivity)
for cluster in clusters:
if max_vidx in cluster[0] and max_tidx in cluster[1]:
break # found our cluster
else: # in case we did not "break"
raise RuntimeError('Clustering failed somehow!')
if hemi == 'lh':
verts = stc.vertices[0][cluster]
else:
verts = stc.vertices[1][cluster - len(stc.vertices[0])]
func_label = Label(verts, hemi=hemi, subject=stc.subject)
func_label = func_label.fill(src)
return func_label, max_vert, max_vidx, max_tidx
def get_surface_labels(surface, texture, subject, hemi):
"""get areas on the surface
Parameters
----------
surface : instance of Surface
texture : str | array
Array to get areas or the filename to get this
subject : str
Name of the subject
hemi : 'lh' | 'rh'
Name of the hemisphere
fname_atlas : str | None
Filename for area atlas
fname_color : str | None
Filename for area color
Returns
-------
labels : instance of mne.Labels
MarsAtlas labels for surface sources
"""
labels = []
rr = surface['rr']
# normals = surface['nn']
# Get texture with gifti format (BainVisa)= labels of MarsAtlas
if isinstance(texture, str):
giftiImage = gifti.giftiio.read(texture)
base_values = giftiImage.darrays[0].data
else:
base_values = texture
values = base_values
# Get parcels and count the number of nodes in each parcel (count)
parcels, counts = np.unique(values, return_counts=True)
# Get parcels information
# info = read_texture_info(fname_atlas, hemi)
from bv2mne.marsatlas_parcels import ma_parcels
info = ma_parcels
# Get triangles for whole surface
triangles = surface['tris']
total_nodes = 0
for pos, val in enumerate(parcels):
name_process = info.get(parcels[pos], False)
if not name_process:
name = 'no_name'
# lobe = 'no_name'
else:
name = name_process[0]
# lobe = name_process[1]
# Find index for nodes of the parcel
ind = np.where(values == val)
# Keep only those nodes and pos of parcel that are associated with a
# face (triangle) in its parcel
# get triangles where points of the parcel are
ix = np.in1d(triangles.ravel(), ind).reshape(triangles.shape)
# Counting the number of True per lines --> True : 1 , False : 0
# to know how many points of the parcel are in each face
counts = ix.sum(1)
# Indices of each triangles that contains 3 points of the parcel
ind_all = np.where(counts == 3)
tris_cour = triangles[ind_all]
# Select nodes that are connected through triangles
nodes = np.unique(tris_cour)
iy = np.in1d(ind, nodes)
ind_n = np.where(iy)
ind_n = ind_n[0]
ind = ind[0]
# Positions and normals
rr_parcel = rr[ind[ind_n]]
# normals_parcel = normals[ind[ind_n]]
# Textures (values)
values_parcel = values[ind[ind_n]]
# values_parcel = ind ############check#############
# Locations in meters
# rr_parcel = rr_parcel * 1e-3
# Number of nodes
nodes, tmp = rr_parcel.shape
# vertex_ind = np.arange(total_nodes, total_nodes + nodes, 1)
vertex_ind = ind[ind_n]
total_nodes = total_nodes + nodes # (was =+???)
label = Label(vertices=vertex_ind,
pos=rr_parcel,
values=values_parcel,
hemi=hemi,
comment=name,
name=name,
filename=None,
subject=subject,
verbose=None)
labels.append(label)
return labels
def test_extract_label_time_course(kind, vector):
"""Test extraction of label time courses from (Mixed)SourceEstimate."""
n_stcs = 3
n_times = 50
src = read_inverse_operator(fname_inv)['src']
if kind == 'mixed':
label_names = ('Left-Cerebellum-Cortex', 'Right-Cerebellum-Cortex')
src += setup_volume_source_space('sample',
pos=20.,
volume_label=label_names,
subjects_dir=subjects_dir,
add_interpolator=False)
klass = MixedVectorSourceEstimate
else:
klass = VectorSourceEstimate
if not vector:
klass = klass._scalar_class
vertices = [s['vertno'] for s in src]
n_verts = np.array([len(v) for v in vertices])
vol_means = np.arange(-1, 1 - len(src), -1)
vol_means_t = np.repeat(vol_means[:, np.newaxis], n_times, axis=1)
# get some labels
labels_lh = read_labels_from_annot('sample',
hemi='lh',
subjects_dir=subjects_dir)
labels_rh = read_labels_from_annot('sample',
hemi='rh',
subjects_dir=subjects_dir)
labels = list()
labels.extend(labels_lh[:5])
labels.extend(labels_rh[:4])
n_labels = len(labels)
label_tcs = dict(mean=np.arange(n_labels)[:, None] *
np.ones((n_labels, n_times)))
label_tcs['max'] = label_tcs['mean']
# compute the mean with sign flip
label_tcs['mean_flip'] = np.zeros_like(label_tcs['mean'])
for i, label in enumerate(labels):
label_tcs['mean_flip'][i] = i * np.mean(label_sign_flip(
label, src[:2]))
# generate some stc's with known data
stcs = list()
pad = (((0, 0), (2, 0), (0, 0)), 'constant')
for i in range(n_stcs):
data = np.zeros((n_verts.sum(), n_times))
# set the value of the stc within each label
for j, label in enumerate(labels):
if label.hemi == 'lh':
idx = np.intersect1d(vertices[0], label.vertices)
idx = np.searchsorted(vertices[0], idx)
elif label.hemi == 'rh':
idx = np.intersect1d(vertices[1], label.vertices)
idx = len(vertices[0]) + np.searchsorted(vertices[1], idx)
data[idx] = label_tcs['mean'][j]
for j in range(len(vol_means)):
offset = n_verts[:2 + j].sum()
data[offset:offset + n_verts[j]] = vol_means[j]
if vector:
# the values it on the Z axis
data = np.pad(data[:, np.newaxis], *pad)
this_stc = klass(data, vertices, 0, 1)
stcs.append(this_stc)
if vector:
for key in label_tcs:
label_tcs[key] = np.pad(label_tcs[key][:, np.newaxis], *pad)
vol_means_t = np.pad(vol_means_t[:, np.newaxis], *pad)
# test some invalid inputs
with pytest.raises(ValueError, match="Invalid value for the 'mode'"):
extract_label_time_course(stcs, labels, src, mode='notamode')
# have an empty label
empty_label = labels[0].copy()
empty_label.vertices += 1000000
with pytest.raises(ValueError, match='does not contain any vertices'):
extract_label_time_course(stcs, empty_label, src)
# but this works:
with pytest.warns(RuntimeWarning, match='does not contain any vertices'):
tc = extract_label_time_course(stcs,
empty_label,
src,
allow_empty=True)
end_shape = (3, n_times) if vector else (n_times, )
for arr in tc:
assert arr.shape == (1 + len(vol_means), ) + end_shape
assert_array_equal(arr[:1], np.zeros((1, ) + end_shape))
if len(vol_means):
assert_array_equal(arr[1:], vol_means_t)
# test the different modes
modes = ['mean', 'mean_flip', 'pca_flip', 'max', 'auto']
for mode in modes:
if vector and mode not in ('mean', 'max', 'auto'):
with pytest.raises(ValueError, match='when using a vector'):
extract_label_time_course(stcs, labels, src, mode=mode)
continue
label_tc = extract_label_time_course(stcs, labels, src, mode=mode)
label_tc_method = [
stc.extract_label_time_course(labels, src, mode=mode)
for stc in stcs
]
assert (len(label_tc) == n_stcs)
assert (len(label_tc_method) == n_stcs)
for tc1, tc2 in zip(label_tc, label_tc_method):
assert tc1.shape == (n_labels + len(vol_means), ) + end_shape
assert tc2.shape == (n_labels + len(vol_means), ) + end_shape
assert_allclose(tc1, tc2, rtol=1e-8, atol=1e-16)
if mode == 'auto':
use_mode = 'mean' if vector else 'mean_flip'
else:
use_mode = mode
# XXX we don't check pca_flip, probably should someday...
if use_mode in ('mean', 'max', 'mean_flip'):
assert_array_almost_equal(tc1[:n_labels], label_tcs[use_mode])
assert_array_almost_equal(tc1[n_labels:], vol_means_t)
# test label with very few vertices (check SVD conditionals)
label = Label(vertices=src[0]['vertno'][:2], hemi='lh')
x = label_sign_flip(label, src[:2])
assert (len(x) == 2)
label = Label(vertices=[], hemi='lh')
x = label_sign_flip(label, src[:2])
assert (x.size == 0)
