def _stc_to_label(stc, src, smooth, subjects_dir=None):
"""Compute a label from the non-zero sources in an stc object.
Parameters
----------
stc : SourceEstimate
The source estimates.
src : SourceSpaces | str | None
The source space over which the source estimates are defined.
If it's a string it should the subject name (e.g. fsaverage).
Can be None if stc.subject is not None.
smooth : int
Number of smoothing iterations.
subjects_dir : str | None
Path to SUBJECTS_DIR if it is not set in the environment.
Returns
-------
labels : list of Labels | list of list of Labels
The generated labels. If connected is False, it returns
a list of Labels (one per hemisphere). If no Label is available
in a hemisphere, None is returned. If connected is True,
it returns for each hemisphere a list of connected labels
ordered in decreasing order depending of the maximum value in the stc.
If no Label is available in an hemisphere, an empty list is returned.
"""
src = stc.subject if src is None else src
if isinstance(src, string_types):
subject = src
else:
subject = stc.subject
if isinstance(src, string_types):
subjects_dir = get_subjects_dir(subjects_dir)
surf_path_from = op.join(subjects_dir, src, 'surf')
rr_lh, tris_lh = read_surface(op.join(surf_path_from, 'lh.white'))
rr_rh, tris_rh = read_surface(op.join(surf_path_from, 'rh.white'))
rr = [rr_lh, rr_rh]
tris = [tris_lh, tris_rh]
else:
if not isinstance(src, SourceSpaces):
raise TypeError('src must be a string or a set of source spaces')
if len(src) != 2:
raise ValueError('source space should contain the 2 hemispheres')
rr = [1e3 * src[0]['rr'], 1e3 * src[1]['rr']]
tris = [src[0]['tris'], src[1]['tris']]
labels = []
cnt = 0
for hemi_idx, (hemi, this_vertno, this_tris, this_rr) in enumerate(
zip(['lh', 'rh'], stc.vertices, tris, rr)):
this_data = stc.data[cnt:cnt + len(this_vertno)]
e = mesh_edges(this_tris)
e.data[e.data == 2] = 1
n_vertices = e.shape[0]
e = e + sparse.eye(n_vertices, n_vertices)
clusters = [this_vertno[np.any(this_data, axis=1)]]
cnt += len(this_vertno)
clusters = [c for c in clusters if len(c) > 0]
if len(clusters) == 0:
this_labels = None
else:
this_labels = []
colors = _n_colors(len(clusters))
for c, color in zip(clusters, colors):
idx_use = c
for k in range(smooth):
e_use = e[:, idx_use]
data1 = e_use * np.ones(len(idx_use))
idx_use = np.where(data1)[0]
label = Label(idx_use,
this_rr[idx_use],
None,
hemi,
'Label from stc',
subject=subject,
color=color)
this_labels.append(label)
this_labels = this_labels[0]
labels.append(this_labels)
return labels
def _stc_to_label(stc, src, smooth, subjects_dir=None):
"""Compute a label from the non-zero sources in an stc object.
Parameters
----------
stc : SourceEstimate
The source estimates.
src : SourceSpaces | str | None
The source space over which the source estimates are defined.
If it's a string it should the subject name (e.g. fsaverage).
Can be None if stc.subject is not None.
smooth : int
Number of smoothing iterations.
subjects_dir : str | None
Path to SUBJECTS_DIR if it is not set in the environment.
Returns
-------
labels : list of Labels | list of list of Labels
The generated labels. If connected is False, it returns
a list of Labels (one per hemisphere). If no Label is available
in a hemisphere, None is returned. If connected is True,
it returns for each hemisphere a list of connected labels
ordered in decreasing order depending of the maximum value in the stc.
If no Label is available in an hemisphere, an empty list is returned.
"""
src = stc.subject if src is None else src
if isinstance(src, string_types):
subject = src
else:
subject = stc.subject
if isinstance(src, string_types):
subjects_dir = get_subjects_dir(subjects_dir)
surf_path_from = op.join(subjects_dir, src, 'surf')
rr_lh, tris_lh = read_surface(op.join(surf_path_from,
'lh.white'))
rr_rh, tris_rh = read_surface(op.join(surf_path_from,
'rh.white'))
rr = [rr_lh, rr_rh]
tris = [tris_lh, tris_rh]
else:
if not isinstance(src, SourceSpaces):
raise TypeError('src must be a string or a set of source spaces')
if len(src) != 2:
raise ValueError('source space should contain the 2 hemispheres')
rr = [1e3 * src[0]['rr'], 1e3 * src[1]['rr']]
tris = [src[0]['tris'], src[1]['tris']]
labels = []
cnt = 0
for hemi_idx, (hemi, this_vertno, this_tris, this_rr) in enumerate(
zip(['lh', 'rh'], stc.vertices, tris, rr)):
this_data = stc.data[cnt:cnt + len(this_vertno)]
e = mesh_edges(this_tris)
e.data[e.data == 2] = 1
n_vertices = e.shape[0]
e = e + sparse.eye(n_vertices, n_vertices)
clusters = [this_vertno[np.any(this_data, axis=1)]]
cnt += len(this_vertno)
clusters = [c for c in clusters if len(c) > 0]
if len(clusters) == 0:
this_labels = None
else:
this_labels = []
colors = _n_colors(len(clusters))
for c, color in zip(clusters, colors):
idx_use = c
for k in range(smooth):
e_use = e[:, idx_use]
data1 = e_use * np.ones(len(idx_use))
idx_use = np.where(data1)[0]
label = Label(idx_use, this_rr[idx_use], None, hemi,
'Label from stc', subject=subject,
color=color)
this_labels.append(label)
this_labels = this_labels[0]
labels.append(this_labels)
return labels
