def test_vertex_to_mni_fs_nibabel(monkeypatch):
"""Test equivalence of vert_to_mni for nibabel and freesurfer."""
n_check = 1000
subject = 'sample'
vertices = rng.randint(0, 100000, n_check)
hemis = rng.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, subjects_dir)
monkeypatch.setattr(mne.source_space, 'has_nibabel', lambda: False)
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir)
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def test_vertex_to_mni_fs_nibabel():
"""Test equivalence of vert_to_mni for nibabel and freesurfer
"""
n_check = 1000
for subject in ['sample', 'fsaverage']:
vertices = np.random.randint(0, 100000, n_check)
hemis = np.random.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, mode='nibabel')
coords_2 = vertex_to_mni(vertices, hemis, subject, mode='freesurfer')
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def test_vertex_to_mni_fs_nibabel():
"""Test equivalence of vert_to_mni for nibabel and freesurfer
"""
n_check = 1000
for subject in ['sample', 'fsaverage']:
vertices = np.random.randint(0, 100000, n_check)
hemis = np.random.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, mode='nibabel')
coords_2 = vertex_to_mni(vertices, hemis, subject, mode='freesurfer')
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def test_vertex_to_mni_fs_nibabel():
"""Test equivalence of vert_to_mni for nibabel and freesurfer."""
n_check = 1000
subject = 'sample'
vertices = rng.randint(0, 100000, n_check)
hemis = rng.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, subjects_dir, 'nibabel')
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir,
'freesurfer')
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def test_vertex_to_mni_fs_nibabel():
"""Test equivalence of vert_to_mni for nibabel and freesurfer."""
n_check = 1000
subject = 'sample'
vertices = rng.randint(0, 100000, n_check)
hemis = rng.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, subjects_dir,
'nibabel')
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir,
'freesurfer')
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def location(self):
if self.locate is True: self.locate=1
print "Locate ",self.locate," dipoles"
if self.selection is 'all':
nd=self.stc[0].data.shape[0]
ns=self.stc[0].data.shape[1]
loc=np.zeros((len(self.stc),ns,3*self.locate))
vtx = self.stc[0].vertices
vtx_long = np.hstack((self.stc[0].vertices[0],self.stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
for s in range(0,len(self.stc)):
#max location (index)
mxloca = np.argsort(np.abs(self.stc[s].data),axis=0)
mxloc=mxloca[-1-self.locate:-1,:]
assert mxloc.shape[0]==self.locate and mxloc.shape[1]==ns
hemi = np.where(mxloc<nd/2,0,1).reshape([-1])
mxvtx_long =vtx_long[mxloc].reshape([-1])
if self.subject is 'sample':
loc[s,: ,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,subjects_dir='/home/jcasa/mne_data/MNE-sample-data/subjects',verbose=False).reshape([-1,self.locate*3])
else:
loc[s,: ,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,verbose=False).reshape([-1,self.locate*3])
self.qtrue_all = loc
self.p=loc.shape[2]
else:
nd=self.stc[0].data.shape[0]
ns=self.stc[0].data.shape[1]
loc=np.zeros((len(self.selection),ns,3*self.locate))
vtx = self.stc[0].vertices
vtx_long = np.hstack((self.stc[0].vertices[0],self.stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
ind_s = 0
for s in self.selection:
#max location (index)
mxloca = np.argsort(np.abs(self.stc[s].data),axis=0)
mxloc=mxloca[-1-self.locate:-1,:]
assert mxloc.shape[0]==self.locate and mxloc.shape[1]==ns
hemi = np.where(mxloc<nd/2,0,1).reshape([-1])
mxvtx_long =vtx_long[mxloc].reshape([-1])
if self.subject is 'sample':
loc[ind_s,: ,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,subjects_dir='/home/jcasa/mne_data/MNE-sample-data/subjects',verbose=False).reshape([-1,self.locate*3])
else:
loc[ind_s,: ,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,verbose=False).reshape([-1,self.locate*3])
ind_s+=1
self.qtrue_all = loc
self.p=loc.shape[2]
def test_vertex_to_mni_fs_nibabel(monkeypatch):
"""Test equivalence of vert_to_mni for nibabel and freesurfer."""
n_check = 1000
subject = 'sample'
vertices = rng.randint(0, 100000, n_check)
hemis = rng.randint(0, 1, n_check)
coords = vertex_to_mni(vertices, hemis, subject, subjects_dir)
read_mri = mne._freesurfer._read_mri_info
monkeypatch.setattr(
mne._freesurfer, '_read_mri_info',
lambda *args, **kwargs: read_mri(*args, use_nibabel=True, **kwargs))
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir)
# less than 0.1 mm error
assert_allclose(coords, coords_2, atol=0.1)
def locationXYZT(self):
if self.locate is True: self.locate=1
print "Locate ",self.locate," dipoles"
if self.selection is 'all':
nd=self.stc[0].data.shape[0]
ns=self.stc[0].data.shape[1]
loc=np.zeros((len(self.stc),ns,3*self.locate))
vtx = self.stc[0].vertices
vtx_long = np.hstack((self.stc[0].vertices[0],self.stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
for s in range(0,len(self.stc)):
#max location (index)
[i,j] = np.unravel_index(np.argsort(np.ravel(np.abs(self.stc[s].data))),self.stc[s].data.shape)
I,J=i[-1-self.locate:-1],j[-1-self.locate:-1]#I is neuron index,J is temporal index
hemi = np.where(I<nd/2,0,1).reshape([-1])
mxvtx_long =vtx_long[I].reshape([-1])
if self.subject is 'sample':
loc[s,-1,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,subjects_dir='/home/jcasa/mne_data/MNE-sample-data/subjects',verbose=False).reshape([-1,self.locate*3])
else:
loc[s,-1,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,verbose=False).reshape([-1,self.locate*3])
self.qtrue_all = loc
self.p=loc.shape[2]
else:
nd=self.stc[0].data.shape[0]
ns=self.stc[0].data.shape[1]
loc=np.zeros((len(self.selection),ns,3*self.locate))
vtx = self.stc[0].vertices
vtx_long = np.hstack((self.stc[0].vertices[0],self.stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
ind_s = 0
for s in self.selection:
#max location (index)
[i,j] = np.unravel_index(np.argsort(np.ravel(np.abs(self.stc[s].data))),self.stc[s].data.shape)
I,J=i[-1-self.locate:-1],j[-1-self.locate:-1]#I is neuron index,J is temporal index
hemi = np.where(I<nd/2,0,1).reshape([-1])
mxvtx_long =vtx_long[I].reshape([-1])
if self.subject is 'sample':
loc[ind_s,-1,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,subjects_dir='/home/jcasa/mne_data/MNE-sample-data/subjects',verbose=False).reshape([-1,self.locate*3])
else:
loc[ind_s,-1,:] = mne.vertex_to_mni(mxvtx_long,hemi,self.subject,verbose=False).reshape([-1,self.locate*3])
ind_s+=1
self.qtrue_all = loc
self.p=loc.shape[2]
def convert_cortex_mri_to_mni(labels_cortex, vertno_left, vertno_right, sbj,
sbj_dir):
"""Convert the coordinates of the ROIs cortex to MNI space.
Parameters
----------
labels_cortex : list
List of labels.
"""
import mne
import numpy as np
roi_mni_coords = list()
roi_name = list()
roi_color = list()
# labels_cortex is in MRI (surface RAS) space
for label in labels_cortex:
if label.hemi == 'lh':
# from MRI (surface RAS) -> MNI
roi_coo_mni = mne.vertex_to_mni(label.vertices, 0, sbj, sbj_dir)
# get the vertices of the ROI used in the src space (index points
# to dense src space of FS segmentation)
this_vertno = np.intersect1d(vertno_left, label.vertices)
elif label.hemi == 'rh':
roi_coo_mni = mne.vertex_to_mni(label.vertices, 1, sbj, sbj_dir)
this_vertno = np.intersect1d(vertno_right, label.vertices)
# find
vertidx_roi = np.searchsorted(label.vertices, this_vertno)
roi_mni_coords.append(roi_coo_mni[vertidx_roi, :])
roi_name.append(label.name)
roi_color.append(label.color)
# TODO check!
# nvert_ROI = [len(vn) for vn in roi_mni_coords]
# if np.sum(nvert_ROI) != (len(vertno_left) + len(vertno_right)):
# raise RuntimeError('number of src space vertices must be equal to \
# the total number of ROI vertices')
roi_mni = dict(ROI_name=roi_name,
ROI_MNI_coords=roi_mni_coords,
ROI_color=roi_color)
return roi_mni
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 plot_perm_ttest_results(events_id, inverse_method='dSPM', plot_type='scatter_plot'):
print('plot_perm_ttest_results')
all_data = defaultdict(dict)
fsave_vertices = [np.arange(10242), np.arange(10242)]
fs_pts = mne.vertex_to_mni(fsave_vertices, [0, 1], 'fsaverage', LOCAL_SUBJECTS_DIR) # 0 for lh
for cond_id, cond_name, patient, hc, data in patients_hcs_conds_gen(events_id, True, inverse_method):
all_data[patient][hc] = data[()]
print(all_data.keys())
for patient, pat_data in all_data.iteritems():
print(patient)
fol = op.join(LOCAL_ROOT_DIR, 'permutation_ttest_results', patient)
utils.make_dir(fol)
if op.isfile(op.join(fol, 'perm_ttest_points.npz')):
d = np.load(op.join(fol, 'perm_ttest_points.npz'))
if plot_type == 'scatter_plot':
points, values = d['points'][()], d['values'][()]
elif plot_type == 'pysurfer':
vertices, vertives_values = d['vertices'][()], d['vertives_values'][()]
else:
points, values, vertices, vertives_values = calc_points(pat_data, fs_pts)
np.savez(op.join(fol, 'perm_ttest_points'), points=points, values=values, vertices=vertices, vertives_values=vertives_values)
max_vals = 8 # int(np.percentile([max(v) for v in values.values()], 70))
print(max_vals)
fol = op.join(fol, '{}_figures'.format(plot_type))
utils.make_dir(fol)
if plot_type == 'scatter_plot':
scatter_plot_perm_ttest_results(points, values, fs_pts, max_vals, fol)
elif plot_type == 'pysurfer':
pysurfer_plot_perm_ttest_results(vertices, vertives_values, max_vals, fol)
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 test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates."""
# obtained using "tksurfer (sample) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
hemis = [0, 0, 0, 1]
coords_2 = vertex_to_mni(vertices, hemis, 'sample', subjects_dir)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates."""
# obtained using "tksurfer (sample) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
hemis = [0, 0, 0, 1]
coords_2 = vertex_to_mni(vertices, hemis, 'sample', subjects_dir)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates
"""
# obtained using "tksurfer (sample/fsaverage) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords_s = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
coords_f = np.array([[-41.28, -40.04, 18.20], [-6.05, 49.74, -18.15],
[-61.71, -14.55, 20.52], [21.70, -60.84, 25.02]])
hemis = [0, 0, 0, 1]
for coords, subj in zip([coords_s, coords_f], ['sample', 'fsaverage']):
coords_2 = vertex_to_mni(vertices, hemis, subj)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates
"""
# obtained using "tksurfer (sample/fsaverage) (l/r)h white"
vertices = [100960, 7620, 150549, 96761]
coords_s = np.array([[-60.86, -11.18, -3.19], [-36.46, -93.18, -2.36],
[-38.00, 50.08, -10.61], [47.14, 8.01, 46.93]])
coords_f = np.array([[-41.28, -40.04, 18.20], [-6.05, 49.74, -18.15],
[-61.71, -14.55, 20.52], [21.70, -60.84, 25.02]])
hemis = [0, 0, 0, 1]
for coords, subject in zip([coords_s, coords_f], ['sample', 'fsaverage']):
coords_2 = vertex_to_mni(vertices, hemis, subject, subjects_dir)
# less than 1mm error
assert_allclose(coords, coords_2, atol=1.0)
def test_vertex_to_mni():
"""Test conversion of vertices to MNI coordinates
"""
# these were random vertices pulled from "sample" in mne_analyze
# but mne_analyze won't load the xfm file! So we must use fsaverage,
# which is sily because the xfm is the identity matrix..
#vertices = [109445, 82962, 137444]
#coords = [[-33.3, 11.5, 50.7], [51.8, -15.4, 30.5], [37.6, 38.4, 57.1]]
#hemi = [0, 1, 1]
#coords_2 = vertex_to_mni(vertices, hemis, 'sample')
vertices = [148611, 157229, 95466]
coords = [[-55.7, -36.6, -9.6], [-48.5, -35.7, -1.1], [44.0, -34.9, -0.9]]
hemis = [0, 0, 1] # , 1]
coords_2 = np.round(vertex_to_mni(vertices, hemis, 'fsaverage'), 1)
for vi in range(len(vertices)):
assert_true(coords[vi] == coords_2[vi].tolist())
def get_labels():
MAINDIR = join('/imaging/ai05/RED/RED_MEG/resting/STRUCTURALS')
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])
# 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]
return labels, label_names, re_order_ind
def find_mni_coordinates(label, parc='HCPMMP1_5_8'):
"""Returns MNI-coordinates of center of mass for selected labels."""
if type(label) is mne.label.Label:
labels = [label]
else:
labels = mne.read_labels_from_annot('fsaverage',
parc=parc,
regexp=label)
mnicoords = {}
for label in labels:
if label.name.startswith('L'):
hemi = 0
else:
hemi = 1
vertno = label.center_of_mass()
mnicoords[label.name] = mne.vertex_to_mni(vertno, hemi, 'fsaverage')
return mnicoords
def coordinates_computation(
subject: str,
subjects_dir: str,
labels: List[mne.Label],
_subject_tree: Optional[SubjectTree] = None,
_priority: Optional[int] = None) -> Dict[str, np.ndarray]:
"""computes central coordinates for given brain regions, uses :func:`nodestimation.project.read_or_write` decorator
:param subject: patient`s ID
:type subject: str
:param subjects_dir: path to directory with patient`s files
:type subjects_dir: str
:param labels: brain regions representation
:type labels: |ilist|_ *of* |imne.Label|_
:param _subject_tree: representation of patient`s files structure, default None
:type _subject_tree: *look for SubjectTree in* :mod:`nodestimation.project.annotations` *, optional*
:param _priority: if several files are read, which one to choose, if None, read all of them, default None
:type _priority: int, optional
:return: central coordinates relatively to brain region names
:rtype: |idict|_ *of* |istr|_ *to* |inp.ndarray|_
.. _labels:
.. _imne.Label:
.. _mne.Label: https://mne.tools/stable/generated/mne.Label.html?highlight=label#mne.Label
.. |imne.Label| replace:: *mne.Label*
"""
vertexes = [
mne.vertex_to_mni(label.vertices,
hemis=0 if label.hemi == 'lh' else 1,
subject=subject,
subjects_dir=subjects_dir) for label in labels
]
return {
label.name: np.mean(vertex, axis=0)
for label, vertex in zip(labels, vertexes)
}
#common indexes for vertices in label
vertices_max_list = ind[:10].tolist()
common_indexes_max = []
for ind in vertices_max_list:
# for left hemishere
#common_indexes_max.append(stc_in_label.vertices[0][ind])
# for left hemishere
common_indexes_max.append(stc_in_label.vertices[1][ind])
common_indexes_max = np.array(common_indexes_max)
#for left hemishere
#mni_coordinate = mne.vertex_to_mni(common_indexes_max, 0, 'avg_platon_27sub', subjects_dir=subjects_dir)
#for right hemishere
mni_coordinate = mne.vertex_to_mni(common_indexes_max, 1, 'avg_platon_27sub', subjects_dir=subjects_dir)
df = pd.DataFrame()
df['p_value'] = max_pval_10.tolist()
df['MNI_coordinates'] = mni_coordinate.tolist()
df.to_csv('/home/vtretyakova/Рабочий стол/speach_learn/Labels/p_val_mne_coordinate/{0}.csv'.format(l))
# load the stc
stc = mne.read_source_estimate(meg_path + '/sample_audvis-meg')
# load the labels
aud_lh = mne.read_label(meg_path + '/labels/Aud-lh.label')
aud_rh = mne.read_label(meg_path + '/labels/Aud-rh.label')
# extract the time course for different labels from the stc
stc_lh = stc.in_label(aud_lh)
stc_rh = stc.in_label(aud_rh)
stc_bh = stc.in_label(aud_lh + aud_rh)
# calculate center of mass and transform to mni coordinates
vtx, _, t_lh = stc_lh.center_of_mass('sample')
mni_lh = mne.vertex_to_mni(vtx, 0, 'sample')[0]
vtx, _, t_rh = stc_rh.center_of_mass('sample')
mni_rh = mne.vertex_to_mni(vtx, 1, 'sample')[0]
# plot the activation
plt.figure()
plt.axes([.1, .275, .85, .625])
hl = plt.plot(stc.times, stc_lh.data.mean(0), 'b')[0]
hr = plt.plot(stc.times, stc_rh.data.mean(0), 'g')[0]
hb = plt.plot(stc.times, stc_bh.data.mean(0), 'r')[0]
plt.xlabel('Time (s)')
plt.ylabel('Source amplitude (dSPM)')
plt.xlim(stc.times[0], stc.times[-1])
# add a legend including center-of-mass mni coordinates to the plot
labels = ['LH: center of mass = %s' % mni_lh.round(2),
def _cluster_sel(sel_path, label_list, stc, src, min_dist, weight, mni_subject='fsaverage'):
"""
subfunctions of sel_ROIs
----------
sel_path: string or list
The directory for storing selected ROIs.
label_list: list
Labels to be selected.
stc: the object of source estimates.
src: the object of the common source space
min_dist: int (mm)
Least distance between ROIs candidates.
weight: float
Euclidean_norms weight related to the larger candidate's standard deviation.
"""
class_list = []
class_list.append(label_list[0])
for test_fn in label_list[1:]:
test_label = mne.read_label(test_fn)
i = 0
belong = False
while (i < len(class_list)) and (belong is False):
class_label = mne.read_label(class_list[i])
if test_label.hemi != class_label.hemi:
i = i + 1
continue
else:
class_pca = stc.extract_label_time_course(class_label, src, mode='pca_flip')
test_pca = stc.extract_label_time_course(test_label, src, mode='pca_flip')
class_pow = np.sum(class_pca ** 2)
test_pow = np.sum(test_pca ** 2)
max_pca = class_pca
exch = False
if class_pow < test_pow:
max_pca = test_pca
exch = True
nearby = False
class_stc = stc.in_label(class_label)
test_stc = stc.in_label(test_label)
if class_label.hemi == 'lh':
class_vtx, _ = class_stc.get_peak(hemi='lh')
test_vtx, _ = test_stc.get_peak(hemi='lh')
class_mni = mne.vertex_to_mni(class_vtx, 0, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_vtx, 0, mni_subject)[0]
elif class_label.hemi == 'rh':
class_vtx, _ = class_stc.get_peak(hemi='rh')
test_vtx, _ = test_stc.get_peak(hemi='rh')
class_mni = mne.vertex_to_mni(class_vtx, 1, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_vtx, 1, mni_subject)[0]
if np.linalg.norm(class_mni - test_mni) < min_dist:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
nearby = True
belong = True
if nearby == False:
thre = max_pca.std() * weight
diff = np.abs(np.linalg.norm(class_pca) - np.linalg.norm(test_pca))
if diff < thre:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
belong = True
i = i + 1
if belong is False:
class_list.append(test_fn)
return len(class_list)
def create_mxne_summary(
subjects,
p,
morph_subject=None,
n_display=None,
pattern_in='',
pattern_out='_mxne',
path_out='./',
title='%s Dipoles',
):
'''Create a report and spreadsheet about mixed-norm dipoles.'''
src_file = os.path.join(p.subjects_dir, 'fsaverage', 'bem',
'fsaverage-ico-5-src.fif')
src_fsavg = mne.read_source_spaces(src_file)
src_file = os.path.join(p.subjects_dir, '14mo_surr', 'bem',
'14mo_surr-oct-6-src.fif')
src_14mo = mne.read_source_spaces(src_file)
mni_labels = mne.read_labels_from_annot('fsaverage',
'HCPMMP1',
subjects_dir=p.subjects_dir)
labels = mne.read_labels_from_annot('14mo_surr',
use_parc,
subjects_dir=p.subjects_dir)
for subject in subjects:
# Load STCs and other saved data #
cond = p.stc_params['condition']
stc_path = os.path.join(p.work_dir, subject, p.stc_dir)
stc_stem = subject + '_' + cond + pattern_in
stc_file = os.path.join(stc_path, stc_stem)
if not os.path.isfile(stc_file + '-lh.stc'):
print(f'** STC file matching {stc_stem} not found ********.\n')
continue
stc_mxne = mne.read_source_estimate(stc_file)
n_dipoles, n_times = stc_mxne.data.shape
meta_data = np.load(stc_file + '.npy', allow_pickle=True)
gof_mxne = meta_data[0]
residual_mxne = meta_data[1]
evk_path = os.path.join(p.work_dir, subject, p.inverse_dir)
evk_file = f'Locations_40-sss_eq_{subject}-ave.fif'
evk_file = os.path.join(evk_path, evk_file)
evoked = mne.read_evokeds(evk_file, condition=cond, kind='average')
evoked.pick_types(meg=True)
cov_path = os.path.join(p.work_dir, subject, p.cov_dir)
cov_file = f'{subject}-40-sss-cov.fif'
cov_file = os.path.join(cov_path, cov_file)
cov = mne.read_cov(cov_file)
trans_path = os.path.join(p.work_dir, subject, p.trans_dir)
trans_file = f'{subject}-trans.fif'
trans_file = os.path.join(trans_path, trans_file)
trans = mne.read_trans(trans_file, verbose=False)
fwd_path = os.path.join(p.work_dir, subject, p.forward_dir)
fwd_file = f'{subject}-sss-fwd.fif'
fwd_file = os.path.join(fwd_path, fwd_file)
fwd = mne.read_forward_solution(fwd_file, verbose=False)
assert fwd['src'][0]['nuse'] == src_14mo[0]['nuse']
assert fwd['src'][1]['nuse'] == src_14mo[1]['nuse']
# Run analysis on the dipoles, then sort then by goodness-of-fit #
results = analyze_dipoles(stc_mxne, gof_mxne, evoked, cov,
p.stc_params['gof_t_range'])
results, sort_idx = sort_dipoles(results) # stc still unsorted
gof_results, amp_results = results
assert len(gof_results) == n_dipoles
# Collect info for the top dipoles, in order #
n_show = n_dipoles
if n_display:
n_show = min(n_display, n_show)
n_left = len(stc_mxne.vertices[0]) # .data stacked lh then rh
postop, mnitop, wavtop = [], [], []
for i in range(n_dipoles):
di = sort_idx[i]
hemid = int(di >= n_left)
vidx = di - hemid * n_left
vert = stc_mxne.vertices[hemid][vidx]
pos = fwd['src'][hemid]['rr'][vert]
postop.append(pos)
mni = mne.vertex_to_mni(vert,
hemid,
subject,
subjects_dir=p.subjects_dir)
mnitop.append(mni)
wav = stc_mxne.data[di, :]
wavtop.append(wav)
assert wav[amp_results[i].pidx] == amp_results[i].peak # check last
# Make various figures #
figure_list, figure_info, figure_comment = [], [], []
# 1) Top dipoles in one set of surface maps.
if morph_subject:
src_subject = morph_subject
caption = 'Surface Plots | ' + morph_subject
else:
src_subject = subject
caption = 'Surface Plots | Coreg.'
fig_surface = make_surfaceplots(stc_mxne,
src_subject,
p.subjects_dir,
sort_idx,
parc=use_parc)
figure_list.append(fig_surface)
figure_info.append([caption, 'Surface Plots'])
figure_comment.append(color_comment)
# 2) Top dipoles in 3D slices (non-morphed and MNI).
mri_file = os.path.join(p.subjects_dir, subject, 'mri', 'T1.mgz')
postop_mri = mne.head_to_mri(postop,
mri_head_t=trans,
subject=subject,
subjects_dir=p.subjects_dir)
postop_mni = mne.head_to_mni(postop,
mri_head_t=trans,
subject=subject,
subjects_dir=p.subjects_dir)
assert_allclose(mnitop[0], postop_mni[0], atol=0.01)
assert_allclose(mnitop[-1], postop_mni[-1], atol=0.01)
fig_orthog1 = make_orthogplots(mri_file, postop_mri[:n_show])
fig_orthog2 = make_orthogplots(mni_template, postop_mni[:n_show])
figure_list.append(fig_orthog1)
figure_info.append(['Orthogonal Plots | Coreg.', 'Orthogonal Plots'])
figure_comment.append(None)
figure_list.append(fig_orthog2)
figure_info.append(['Orthogonal Plots | MNI)', 'Orthogonal Plots'])
figure_comment.append(f'Top {n_show} of {n_dipoles} dipoles '
'displayed.')
# 3) Top dipoles' time waveforms.
fig_wav = make_sourcewavs(wavtop, stc_mxne.times,
p.stc_params['gof_t_range'])
figure_list.append(fig_wav)
figure_info.append(['STC Time Course', 'Temporal Waveforms'])
figure_comment.append(None)
# 4) Evoked and residual waveforms (averages across sensors)
fig_sensor = make_sensorwavs(evoked, residual_mxne)
figure_list.append(fig_sensor)
figure_info.append(['Sensor Time Course', 'Temporal Waveforms'])
figure_comment.append(None)
# Determine 14-mo surrogate "aparc" label for each dipole #
labels_stc = [] # note these are not gof-ordered
for hh, hemi in enumerate(('lh', 'rh')):
for vert in stc_mxne.vertices[hh]:
label = which_label(vert, hemi, labels)
if label:
labels_stc.append(label.name)
else:
labels_stc.append('no_label')
# Expand the sparse STC so it can be morphed (X='expanded') #
# v_lh = fwd['src'][0]['vertno'] # the full source space
# v_rh = fwd['src'][1]['vertno']
# n_vtotal = vertices = len(v_lh) + len(v_rh)
# data_mxneX = np.zeros((n_vtotal, n_times))
# idx_vrts = np.isin(v_lh, stc_mxne.vertices[0])
# idx_vrts = np.where(idx_vrts)[0]
# data_mxneX[idx_vrts, :] = stc_mxne.data[:n_left, :]
# idx_vrts = np.isin(v_rh, stc_mxne.vertices[1])
# idx_vrts = np.where(idx_vrts)[0]
# data_mxneX[idx_vrts, :] = stc_mxne.data[n_left:, :]
# stc_mxneX = mne.SourceEstimate(data_mxneX, [v_lh, v_rh],
# tmin=stc_mxne.tmin, tstep=stc_mxne.tstep,
# subject=stc_mxne.subject)
# Determine fsaverage "HCPMMP1" labels for each dipole #
# Note: 'sparse' doesn't give a 1-to-1 mapping.
morph_fcn = mne.compute_source_morph(stc_mxne,
src_to=src_fsavg,
smooth='nearest',
spacing=None,
warn=False,
subjects_dir=p.subjects_dir,
niter_sdr=(),
sparse=True,
subject_from=subject,
subject_to='fsaverage')
mlabels_stc = [] # like above, but now for fsaverage:HCPMMP1
verts_mni = []
for di in range(n_dipoles):
stc_temp = stc_mxne.copy() # zero all but dipole of interest
stc_temp.data = np.zeros((n_dipoles, n_times))
stc_temp.data[di, :] = stc_mxne.data[di, :]
mstc_temp = morph_fcn.apply(stc_temp)
vidx = np.where(mstc_temp.data[:, 0] > 0)[0]
vidx_lh = [i for i in vidx if i < n_left] # don't assume hemi
vidx_rh = [i - n_left for i in vidx if i >= n_left]
verts_byhemi = [None, None]
verts_byhemi[0] = mstc_temp.vertices[0][vidx_lh]
verts_byhemi[1] = mstc_temp.vertices[1][vidx_rh]
verts_mni.append(verts_byhemi)
cnt = 0
for verts, hemi, prefix in zip(verts_byhemi, ['lh', 'rh'],
['L_', 'R_']):
if not verts:
continue
vert = verts[0] # should only be one with sparse arg.
lbl = which_label(vert, hemi, mni_labels)
if lbl:
lbl = lbl.name
else:
lbl = 'no_label'
lbl = re.sub(rf"^{prefix}", "", lbl)
lbl = re.sub(r"_ROI", "", lbl)
cnt += 1
assert cnt == 1 # only one hemisphere should be valid
mlabels_stc.append(lbl)
# Create formatted tables for a report section #
# SMB: Saving as string objects in case they can be added to report.
strobj1 = StringIO() # TABLE 1: sorted gof and amplitude info
sprint = lambda *x: print(*x, file=strobj1, end='')
ff = '<8.2f' # format: center on 8-char field, 2 decimal places
sprint(f'{"Dip #":^6} {"Peak/Mean Amp":<16} '
f'{"Peak/Mean GOF":<16} {"GOF Time":<8}\n')
for i in range(n_dipoles):
amp_m = 1e9 * amp_results[i].mean
amp_p = 1e9 * amp_results[i].peak
gof_m = gof_results[i].mean
gof_p = gof_results[i].peak
time_p = evoked.times[gof_results[i].pidx]
sprint(f'{i:^6} {amp_p:{ff}}{amp_m:{ff}} '
f'{gof_p:{ff}}{gof_m:{ff}} '
f'{time_p:{"<8.3f"}}\n')
sprint('\n')
strobj2 = StringIO() # TABLE 2: coordinate and label info
sprint = lambda *x: print(*x, file=strobj2, end='')
ff = '<20'
sprint(f'{"Dip #":^6} {"14mo Coord":{ff}} {"MNI Coord":{ff}} '
f'{"14mo Aparc | Fsavg HCPMMP1":{ff}}\n')
for i in range(n_dipoles):
di = sort_idx[i]
hemid = int(di >= n_left)
# hemi = 'rh' if hemid else 'lh'
vidx = di - hemid * n_left
vert = stc_mxne.vertices[hemid][vidx]
coord = src_14mo[hemid]['rr'][vert] * 1000
coord_str = ' '.join([f'{x:.1f}' for x in coord])
vert = verts_mni[di][hemid][0] # just the first one
coord = src_fsavg[hemid]['rr'][vert] * 1000
mcoord_str = ' '.join([f'{x:.1f}' for x in coord])
sprint(f'{i:^6} {coord_str:{ff}} {mcoord_str:{ff}} '
f'{labels_stc[di]:{ff}}\n {"":<47} '
f'{mlabels_stc[di]:{ff}}\n')
# Print out the tables #
print(f'\nGOF-sorted dipole info for {subject}:')
strobj1.seek(0)
print(strobj1.read())
strobj1.close()
print(f'\nGOF-sorted position info for {subject}:')
strobj2.seek(0)
print(strobj2.read())
strobj2.close()
# Compile all figures into a report #
print(f'Compiling report for {subject}.')
if not os.path.exists(path_out):
os.mkdir(path_out)
if '%s ' in title:
title_use = title.replace('%s ', 'Group')
else:
title_use = title
report = mne.Report(title=title_use, image_format='png')
for fig, info, cstr in zip(figure_list, figure_info, figure_comment):
report.add_figs_to_section(fig,
captions=info[0],
scale=1.0,
section=info[1],
comments=cstr)
report_file = os.path.join(path_out, subject + pattern_out + '.html')
report.save(report_file, open_browser=False, overwrite=True)
fid.close()
# prepare the fsaverage subject if we're dealing with AFNI mask
if roi.find('+tlrc') > 0:
mask_fname = '/lscratch/' + os.environ['SLURM_JOBID'] + '/mask.txt'
os.system('3dmaskdump -xyz -o ' + mask_fname + ' ' + roi)
a = np.genfromtxt(mask_fname)
os.system('rm ' + mask_fname)
gv = a[a[:, 6] > 0, 3:6] # separate good voxels in the mask
# change AFNI results from RAI to LPI
gv[:, 0] = gv[:, 0] * -1
gv[:, 1] = gv[:, 1] * -1
src = mne.setup_source_space(subject='fsaverage_mne', fname=None, spacing='ico5', surface='inflated', n_jobs=2)
# get left and right coordinates for all the sources
coord0 = mne.vertex_to_mni(vertices=src[0]['vertno'], hemis=0, subject='fsaverage_mne')
coord1 = mne.vertex_to_mni(vertices=src[1]['vertno'], hemis=1, subject='fsaverage_mne')
coord = np.vstack([coord0, coord1])
# store the index of the sources within min_dist of the mask voxels
b = []
for i in range(gv.shape[0]):
dist = np.sqrt((coord[:, 0] - gv[i, 0]) ** 2 + (coord[:, 1] - gv[i, 1]) ** 2 + (coord[:, 2] - gv[i, 2]) ** 2)
if min(dist) <= min_dist:
b.append(np.argmin(dist))
# create a stc with 1s for the near sources
d = np.zeros([coord.shape[0], 1])
d[b] = 1
stc = mne.SourceEstimate(d, vertices=[src[0]['vertno'], src[1]['vertno']],
tmin=0, tstep=1, subject='fsaverage_mne')
# convert the stc to a label so we can morph it per subject later
avg_label = mne.stc_to_label(stc, src=src, smooth=True, connected=False)
def test_scale_mri_xfm(tmp_path, few_surfaces):
"""Test scale_mri transforms and MRI scaling."""
# scale fsaverage
tempdir = str(tmp_path)
fake_home = testing.data_path()
# add fsaverage
create_default_subject(subjects_dir=tempdir,
fs_home=fake_home,
verbose=True)
# add sample (with few files)
sample_dir = op.join(tempdir, 'sample')
os.mkdir(sample_dir)
os.mkdir(op.join(sample_dir, 'bem'))
for dirname in ('mri', 'surf'):
copytree(op.join(fake_home, 'subjects', 'sample', dirname),
op.join(sample_dir, dirname))
subject_to = 'flachkopf'
spacing = 'oct2'
for subject_from in ('fsaverage', 'sample'):
if subject_from == 'fsaverage':
scale = 1. # single dim
else:
scale = [0.9, 2, .8] # separate
src_from_fname = op.join(tempdir, subject_from, 'bem',
'%s-%s-src.fif' % (subject_from, spacing))
src_from = mne.setup_source_space(subject_from,
spacing,
subjects_dir=tempdir,
add_dist=False)
write_source_spaces(src_from_fname, src_from)
vertices_from = np.concatenate([s['vertno'] for s in src_from])
assert len(vertices_from) == 36
hemis = ([0] * len(src_from[0]['vertno']) +
[1] * len(src_from[0]['vertno']))
mni_from = mne.vertex_to_mni(vertices_from,
hemis,
subject_from,
subjects_dir=tempdir)
if subject_from == 'fsaverage': # identity transform
source_rr = np.concatenate(
[s['rr'][s['vertno']] for s in src_from]) * 1e3
assert_allclose(mni_from, source_rr)
if subject_from == 'fsaverage':
overwrite = skip_fiducials = False
else:
with pytest.raises(IOError, match='No fiducials file'):
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir)
skip_fiducials = True
with pytest.raises(IOError, match='already exists'):
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir,
skip_fiducials=skip_fiducials)
overwrite = True
if subject_from == 'sample': # support for not needing all surf files
os.remove(op.join(sample_dir, 'surf', 'lh.curv'))
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir,
verbose='debug',
overwrite=overwrite,
skip_fiducials=skip_fiducials)
if subject_from == 'fsaverage':
assert _is_mri_subject(subject_to, tempdir), "Scaling failed"
src_to_fname = op.join(tempdir, subject_to, 'bem',
'%s-%s-src.fif' % (subject_to, spacing))
assert op.exists(src_to_fname), "Source space was not scaled"
# Check MRI scaling
fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz')
assert op.exists(fname_mri), "MRI was not scaled"
# Check MNI transform
src = mne.read_source_spaces(src_to_fname)
vertices = np.concatenate([s['vertno'] for s in src])
assert_array_equal(vertices, vertices_from)
mni = mne.vertex_to_mni(vertices,
hemis,
subject_to,
subjects_dir=tempdir)
assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm
# load the stc
stc = mne.read_source_estimate(meg_path + '/sample_audvis-meg')
# load the labels
aud_lh = mne.read_label(meg_path + '/labels/Aud-lh.label')
aud_rh = mne.read_label(meg_path + '/labels/Aud-rh.label')
# extract the time course for different labels from the stc
stc_lh = stc.in_label(aud_lh)
stc_rh = stc.in_label(aud_rh)
stc_bh = stc.in_label(aud_lh + aud_rh)
# calculate center of mass and transform to mni coordinates
vtx, _, t_lh = stc_lh.center_of_mass('sample')
mni_lh = mne.vertex_to_mni(vtx, 0, 'sample')[0]
vtx, _, t_rh = stc_rh.center_of_mass('sample')
mni_rh = mne.vertex_to_mni(vtx, 1, 'sample')[0]
# plot the activation
plt.figure()
plt.axes([.1, .275, .85, .625])
hl = plt.plot(stc.times, stc_lh.data.mean(0), 'b')
hr = plt.plot(stc.times, stc_rh.data.mean(0), 'g')
hb = plt.plot(stc.times, stc_bh.data.mean(0), 'r')
plt.xlabel('Time (s)')
plt.ylabel('Source amplitude (dSPM)')
plt.xlim(stc.times[0], stc.times[-1])
# add a legend including center-of-mass mni coordinates to the plot
labels = [
def source_loc(evoked, cov, fwd, subj, img_type, study_path, plot=False):
cond = evoked.info['description']
stim_coords = find_stim_coords(cond, subj, study_path)
stim_params = get_stim_params(cond)
inv = mne.minimum_norm.make_inverse_operator(evoked.info,
fwd,
cov,
loose=0.2,
depth=0.8)
method = "dSPM"
snr = 300.
lambda2 = 1. / snr**2
stc = mne.minimum_norm.apply_inverse(evoked,
inv,
lambda2,
method=method,
pick_ori=None)
stc.save(
op.join(study_path, 'source_stim', subj, 'source_files', img_type,
'stc', '%s_%s' % (subj, cond)))
# plot_source_space(subj, study_path, subjects_dir)
hemi = 'lh' if stim_params['is_left'] else 'rh'
contact_nr = int(re.findall('\d+', stim_params['ch'])[0])
view = 'medial' if contact_nr < 5 else 'lateral' # improve view selection
vertno_max, time_max = stc.get_peak(hemi=hemi)
stc_max = np.max(stc.data)
if plot:
brain_inf = stc.plot(surface='inflated',
hemi=hemi,
subjects_dir=subjects_dir,
subject=fs_subj,
clim=dict(kind='value',
lims=[0, stc_max * 0.75, stc_max]),
initial_time=time_max,
time_unit='s',
alpha=1,
background='w',
foreground='k')
brain_inf.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.8)
brain_inf.add_foci(stim_coords['surf_ori'],
map_surface='pial',
hemi=hemi,
color='green',
scale_factor=0.8)
brain_inf.show_view(view) # mlab.view(130, 90)
fig_fname = op.join(study_path, 'source_stim', subj, 'figures',
'distributed', '%s_inf_foci.eps' % cond)
#brain_inf.save_image(fig_fname)
mlab.show()
#mlab.clf()
max_mni = mne.vertex_to_mni(
vertno_max, hemis=0, subject=subj, subjects_dir=subjects_dir
) # mni coords of vertex (ojo vertex resolution ico-5)
stim_mni = stim_coords['mni'] # mni coords of stimulation
dist_mni = euclidean(max_mni, stim_mni)
print(dist_mni)
return dist_mni
# load the stc
stc = mne.read_source_estimate(meg_path / 'sample_audvis-meg')
# load the labels
aud_lh = mne.read_label(meg_path / 'labels' / 'Aud-lh.label')
aud_rh = mne.read_label(meg_path / 'labels' / 'Aud-rh.label')
# extract the time course for different labels from the stc
stc_lh = stc.in_label(aud_lh)
stc_rh = stc.in_label(aud_rh)
stc_bh = stc.in_label(aud_lh + aud_rh)
# calculate center of mass and transform to mni coordinates
vtx, _, t_lh = stc_lh.center_of_mass('sample', subjects_dir=subjects_dir)
mni_lh = mne.vertex_to_mni(vtx, 0, 'sample', subjects_dir=subjects_dir)[0]
vtx, _, t_rh = stc_rh.center_of_mass('sample', subjects_dir=subjects_dir)
mni_rh = mne.vertex_to_mni(vtx, 1, 'sample', subjects_dir=subjects_dir)[0]
# plot the activation
plt.figure()
plt.axes([.1, .275, .85, .625])
hl = plt.plot(stc.times, stc_lh.data.mean(0), 'b')[0]
hr = plt.plot(stc.times, stc_rh.data.mean(0), 'g')[0]
hb = plt.plot(stc.times, stc_bh.data.mean(0), 'r')[0]
plt.xlabel('Time (s)')
plt.ylabel('Source amplitude (dSPM)')
plt.xlim(stc.times[0], stc.times[-1])
# add a legend including center-of-mass mni coordinates to the plot
labels = [
tonetype = 'pure'
hemi = 'rh'
hem = 1
cmap = cm.rainbow(np.linspace(0,1,len(frequencies)))
t_bin = []
b = Brain('fsaverage', hemi = hemi , surf = 'pial',background='white')
for f,freq in enumerate(frequencies):
vtx, _, t = np.load(meg_dir + '_STCS/%s/%s/_CM_%s_%s_%s_cropped.npy' % (tonetype,freq,tonetype,freq,hemi))
# vtx, _, t = np.load(meg_dir + '_STCS/%s/%s/_CM_%s_%s_%s_cropped.npy' % (tonetype,freq,tonetype,freq,hemi))
# get the mni co-ordinates of the center of mass
coords = vertex_to_mni(int(vtx), hemis=hem, subject='fsaverage')
print (coords)
# plot result
b.add_foci(coords, color=cmap[f], map_surface='pial', scale_factor=0.5)
t_bin.append(t)
key = 'A'
if key == 'A':
frequencies = [220, 247, 277, 294, 330, 370, 415, 440]
elif key == 'C':
frequencies = [262, 294, 330, 349, 392, 440, 494, 523]
elif key == 'Eb':
frequencies = [312, 349, 392, 415, 466, 523, 587, 624]
def test_scale_mri_xfm(tmp_path, few_surfaces, subjects_dir_tmp_few):
"""Test scale_mri transforms and MRI scaling."""
# scale fsaverage
tempdir = str(subjects_dir_tmp_few)
sample_dir = subjects_dir_tmp_few / 'sample'
subject_to = 'flachkopf'
spacing = 'oct2'
for subject_from in ('fsaverage', 'sample'):
if subject_from == 'fsaverage':
scale = 1. # single dim
else:
scale = [0.9, 2, .8] # separate
src_from_fname = op.join(tempdir, subject_from, 'bem',
'%s-%s-src.fif' % (subject_from, spacing))
src_from = mne.setup_source_space(subject_from,
spacing,
subjects_dir=tempdir,
add_dist=False)
write_source_spaces(src_from_fname, src_from)
vertices_from = np.concatenate([s['vertno'] for s in src_from])
assert len(vertices_from) == 36
hemis = ([0] * len(src_from[0]['vertno']) +
[1] * len(src_from[0]['vertno']))
mni_from = mne.vertex_to_mni(vertices_from,
hemis,
subject_from,
subjects_dir=tempdir)
if subject_from == 'fsaverage': # identity transform
source_rr = np.concatenate(
[s['rr'][s['vertno']] for s in src_from]) * 1e3
assert_allclose(mni_from, source_rr)
if subject_from == 'fsaverage':
overwrite = skip_fiducials = False
else:
with pytest.raises(IOError, match='No fiducials file'):
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir)
skip_fiducials = True
with pytest.raises(IOError, match='already exists'):
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir,
skip_fiducials=skip_fiducials)
overwrite = True
if subject_from == 'sample': # support for not needing all surf files
os.remove(op.join(sample_dir, 'surf', 'lh.curv'))
scale_mri(subject_from,
subject_to,
scale,
subjects_dir=tempdir,
verbose='debug',
overwrite=overwrite,
skip_fiducials=skip_fiducials)
if subject_from == 'fsaverage':
assert _is_mri_subject(subject_to, tempdir), "Scaling failed"
src_to_fname = op.join(tempdir, subject_to, 'bem',
'%s-%s-src.fif' % (subject_to, spacing))
assert op.exists(src_to_fname), "Source space was not scaled"
# Check MRI scaling
fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz')
assert op.exists(fname_mri), "MRI was not scaled"
# Check MNI transform
src = mne.read_source_spaces(src_to_fname)
vertices = np.concatenate([s['vertno'] for s in src])
assert_array_equal(vertices, vertices_from)
mni = mne.vertex_to_mni(vertices,
hemis,
subject_to,
subjects_dir=tempdir)
assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm
# Check head_to_mni (the `trans` here does not really matter)
trans = rotation(0.001, 0.002, 0.003) @ translation(0.01, 0.02, 0.03)
trans = Transform('head', 'mri', trans)
pos_head_from = np.random.RandomState(0).randn(4, 3)
pos_mni_from = mne.head_to_mni(pos_head_from, subject_from, trans,
tempdir)
pos_mri_from = apply_trans(trans, pos_head_from)
pos_mri = pos_mri_from * scale
pos_head = apply_trans(invert_transform(trans), pos_mri)
pos_mni = mne.head_to_mni(pos_head, subject_to, trans, tempdir)
assert_allclose(pos_mni, pos_mni_from, atol=1e-3)
fid = open(subjs_fname, 'r')
subjs = [line.rstrip() for line in fid]
# find closest source to seed
if seed[0] < 0:
hemis = 0 # LH
else:
hemis = 1 # RH
# important to do this by source, because not every vertex has a source
fname = data_dir + 'morphed-lcmv-%dto%d-' % (bands[0][0],
bands[0][1]) + subjs[0]
stc = mne.read_source_estimate(fname)
# this is what we get when we read in the fsaverage subject
coord = mne.vertex_to_mni(vertices=stc.vertno[hemis],
hemis=hemis,
subject='fsaverage')
dist = np.sqrt((coord[:, 0] - seed[0])**2 + (coord[:, 1] - seed[1])**2 +
(coord[:, 2] - seed[2])**2)
seed_src = np.argmin(dist) + hemis * len(stc.lh_vertno)
print 'Distance to seed: %.2fmm' % np.min(dist)
# for each band, compute subject-based correlation map
for l_freq, h_freq in bands:
subj_corrs = []
print 'Band %d to %d Hz' % (l_freq, h_freq)
cnt = 0
for s in subjs:
print cnt + 1, '/', len(subjs)
fname = data_dir + 'morphed-lcmv-%dto%d-' % (l_freq, h_freq) + s
stc = mne.read_source_estimate(fname)
subject = 'fsaverage'
# Plot brain in 3D with PySurfer if available
brain = new_stc.plot(subject, hemi='lh', subjects_dir=subjects_dir)
brain.show_view('lateral')
# use peak getter to move vizualization to the time point of the peak
vertno_max, time_idx = new_stc.get_peak(hemi='lh', time_as_index=True)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue',
scale_factor=0.6, map_surface='white')
mni_coords = mne.vertex_to_mni(vertno_max, hemis=0, subject=subject,
subjects_dir=subjects_dir)
print 'The MNI coords are ', mni_coords
#my_trans = mne.read_trans(?)
#src_pts = apply_trans(trans, some_tgt_pts)
from surfer import utils
utils.coord_to_label(subject, mni_coords[0], label='mycoord',
hemi='lh', n_steps=25, map_surface="white")
brain.add_label('mycoord-lh.label', color="darkseagreen", alpha=.8)
# if the new mni_coords are computed
brain.add_foci(mni_coords[0], coords_as_verts=False, hemi='lh', color='red',
map_surface='white', scale_factor=0.6)
parc='aparc')
# nodes in one hemisphere can be plotted as well
aparc_lh = [lab for lab in aparc if lab.hemi == 'lh']
coords = []
# plot 10 nodes from left hemisphere only for better viz
for lab in aparc_lh[:10]:
if lab.name is 'unknown-lh':
continue
# get the center of mass
com = lab.center_of_mass('fsaverage')
# obtain mni coordinated to the vertex from left hemi
coords_ = mne.vertex_to_mni(com,
hemis=0,
subject=subject,
subjects_dir=subjects_dir)[0]
coords.append(coords_)
n_nodes = np.array(coords).shape[0]
# make a random connectivity matrix
con = np.random.random((n_nodes, n_nodes))
con[np.diag_indices(5)] = 0.
con[np.triu_indices(5, k=1)] = 0.
con += con.T
con[con < 0.6] = 0.
# plot the connectome on a glass brain background
plotting.plot_connectome(con, coords)
plt.show()
def _cluster_rois(sel_path, label_list, stc, src, min_dist, weight, mni_subject='fsaverage'):
"""
subfunctions of merge_ROIs
----------
mer_path: str
The directory for storing merged ROIs.
label_list: list
Labels to be merged
"""
class_list = []
class_list.append(label_list[0])
for test_fn in label_list[1:]:
test_label = mne.read_label(test_fn)
i = 0
belong = False
while (i < len(class_list)) and (belong is False):
class_label = mne.read_label(class_list[i])
if test_label.hemi != class_label.hemi:
i = i + 1
continue
else:
# Get the centroids
class_stc = stc.in_label(class_label)
test_stc = stc.in_label(test_label)
class_pow = np.sum(class_stc.data ** 2, axis=1)
test_pow = np.sum(test_stc.data ** 2, axis=1)
if class_label.hemi == 'lh':
h = 0
elif class_label.hemi == 'rh':
h = 1
class_seed = class_stc.vertices[h][np.argmax(class_pow)]
test_seed = test_stc.vertices[h][np.argmax(test_pow)]
class_vtx = np.searchsorted(stc.vertices[h], class_seed)
test_vtx = np.searchsorted(stc.vertices[h], test_seed)
class_mni = mne.vertex_to_mni(class_vtx, h, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_vtx, h, mni_subject)[0]
# Get the representative STCs for class label and test label
class_ts = stc.data[class_vtx, :]
test_ts = stc.data[test_vtx, :]
# Mark the more apparent ROI
max_ts = class_ts
exch = False
if np.max(class_pow) < np.max(test_pow):
max_ts = test_ts
exch = True
# Compute the centroids distance
if np.linalg.norm(class_mni - test_mni) < min_dist:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
belong = True
# Compute the similarity
else:
thre = max_ts.std() * weight
diff = np.abs(np.linalg.norm(class_ts) - np.linalg.norm(test_ts))
if diff < thre:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
belong = True
i = i + 1
if belong is False:
class_list.append(test_fn)
return len(class_list)
def test_brain_traces(renderer_interactive_pyvistaqt, hemi, src, tmpdir,
brain_gc):
"""Test brain traces."""
hemi_str = list()
if src in ('surface', 'vector', 'mixed'):
hemi_str.extend([hemi] if hemi in ('lh', 'rh') else ['lh', 'rh'])
if src in ('mixed', 'volume'):
hemi_str.extend(['vol'])
# label traces
brain = _create_testing_brain(
hemi=hemi,
surf='white',
src=src,
show_traces='label',
volume_options=None, # for speed, don't upsample
n_time=5,
initial_time=0,
)
if src == 'surface':
brain._data['src'] = None # test src=None
if src in ('surface', 'vector', 'mixed'):
assert brain.show_traces
assert brain.traces_mode == 'label'
brain.widgets["extract_mode"].set_value('max')
# test picking a cell at random
rng = np.random.RandomState(0)
for idx, current_hemi in enumerate(hemi_str):
if current_hemi == 'vol':
continue
current_mesh = brain._layered_meshes[current_hemi]._polydata
cell_id = rng.randint(0, current_mesh.n_cells)
test_picker = TstVTKPicker(current_mesh, cell_id, current_hemi,
brain)
assert len(brain.picked_patches[current_hemi]) == 0
brain._on_pick(test_picker, None)
assert len(brain.picked_patches[current_hemi]) == 1
for label_id in list(brain.picked_patches[current_hemi]):
label = brain._annotation_labels[current_hemi][label_id]
assert isinstance(label._line, Line2D)
brain.widgets["extract_mode"].set_value('mean')
brain.clear_glyphs()
assert len(brain.picked_patches[current_hemi]) == 0
brain._on_pick(test_picker, None) # picked and added
assert len(brain.picked_patches[current_hemi]) == 1
brain._on_pick(test_picker, None) # picked again so removed
assert len(brain.picked_patches[current_hemi]) == 0
# test switching from 'label' to 'vertex'
brain.widgets["annotation"].set_value('None')
brain.widgets["extract_mode"].set_value('max')
else: # volume
assert "annotation" not in brain.widgets
assert "extract_mode" not in brain.widgets
brain.close()
# test colormap
if src != 'vector':
brain = _create_testing_brain(
hemi=hemi,
surf='white',
src=src,
show_traces=0.5,
initial_time=0,
volume_options=None, # for speed, don't upsample
n_time=1 if src == 'mixed' else 5,
diverging=True,
add_data_kwargs=dict(colorbar_kwargs=dict(n_labels=3)),
)
# mne_analyze should be chosen
ctab = brain._data['ctable']
assert_array_equal(ctab[0], [0, 255, 255, 255]) # opaque cyan
assert_array_equal(ctab[-1], [255, 255, 0, 255]) # opaque yellow
assert_allclose(ctab[len(ctab) // 2], [128, 128, 128, 0], atol=3)
brain.close()
# vertex traces
brain = _create_testing_brain(
hemi=hemi,
surf='white',
src=src,
show_traces=0.5,
initial_time=0,
volume_options=None, # for speed, don't upsample
n_time=1 if src == 'mixed' else 5,
add_data_kwargs=dict(colorbar_kwargs=dict(n_labels=3)),
)
assert brain.show_traces
assert brain.traces_mode == 'vertex'
assert hasattr(brain, "picked_points")
assert hasattr(brain, "_spheres")
assert brain._scalar_bar.GetNumberOfLabels() == 3
# add foci should work for volumes
brain.add_foci([[0, 0, 0]], hemi='lh' if src == 'surface' else 'vol')
# test points picked by default
picked_points = brain.get_picked_points()
spheres = brain._spheres
for current_hemi in hemi_str:
assert len(picked_points[current_hemi]) == 1
n_spheres = len(hemi_str)
if hemi == 'split' and src in ('mixed', 'volume'):
n_spheres += 1
assert len(spheres) == n_spheres
# test switching from 'vertex' to 'label'
if src == 'surface':
brain.widgets["annotation"].set_value('aparc')
brain.widgets["annotation"].set_value('None')
# test removing points
brain.clear_glyphs()
assert len(spheres) == 0
for key in ('lh', 'rh', 'vol'):
assert len(picked_points[key]) == 0
# test picking a cell at random
rng = np.random.RandomState(0)
for idx, current_hemi in enumerate(hemi_str):
assert len(spheres) == 0
if current_hemi == 'vol':
current_mesh = brain._data['vol']['grid']
vertices = brain._data['vol']['vertices']
values = current_mesh.cell_arrays['values'][vertices]
cell_id = vertices[np.argmax(np.abs(values))]
else:
current_mesh = brain._layered_meshes[current_hemi]._polydata
cell_id = rng.randint(0, current_mesh.n_cells)
test_picker = TstVTKPicker(None, None, current_hemi, brain)
assert brain._on_pick(test_picker, None) is None
test_picker = TstVTKPicker(current_mesh, cell_id, current_hemi, brain)
assert cell_id == test_picker.cell_id
assert test_picker.point_id is None
brain._on_pick(test_picker, None)
brain._on_pick(test_picker, None)
assert test_picker.point_id is not None
assert len(picked_points[current_hemi]) == 1
assert picked_points[current_hemi][0] == test_picker.point_id
assert len(spheres) > 0
sphere = spheres[-1]
vertex_id = sphere._vertex_id
assert vertex_id == test_picker.point_id
line = sphere._line
hemi_prefix = current_hemi[0].upper()
if current_hemi == 'vol':
assert hemi_prefix + ':' in line.get_label()
assert 'MNI' in line.get_label()
continue # the MNI conversion is more complex
hemi_int = 0 if current_hemi == 'lh' else 1
mni = vertex_to_mni(vertices=vertex_id,
hemis=hemi_int,
subject=brain._subject_id,
subjects_dir=brain._subjects_dir)
label = "{}:{} MNI: {}".format(hemi_prefix,
str(vertex_id).ljust(6),
', '.join('%5.1f' % m for m in mni))
assert line.get_label() == label
# remove the sphere by clicking in its vicinity
old_len = len(spheres)
test_picker._actors = sum((s._actors for s in spheres), [])
brain._on_pick(test_picker, None)
assert len(spheres) < old_len
screenshot = brain.screenshot()
screenshot_all = brain.screenshot(time_viewer=True)
assert screenshot.shape[0] < screenshot_all.shape[0]
# and the scraper for it (will close the instance)
# only test one condition to save time
if not (hemi == 'rh' and src == 'surface'
and check_version('sphinx_gallery')):
brain.close()
return
fnames = [str(tmpdir.join(f'temp_{ii}.png')) for ii in range(2)]
block_vars = dict(image_path_iterator=iter(fnames),
example_globals=dict(brain=brain))
block = ('code', """
something
# brain.save_movie(time_dilation=1, framerate=1,
# interpolation='linear', time_viewer=True)
#
""", 1)
gallery_conf = dict(src_dir=str(tmpdir), compress_images=[])
scraper = _BrainScraper()
rst = scraper(block, block_vars, gallery_conf)
assert brain.plotter is None # closed
gif_0 = fnames[0][:-3] + 'gif'
for fname in (gif_0, fnames[1]):
assert op.basename(fname) in rst
assert op.isfile(fname)
img = image.imread(fname)
assert img.shape[1] == screenshot.shape[1] # same width
assert img.shape[0] > screenshot.shape[0] # larger height
assert img.shape[:2] == screenshot_all.shape[:2]
def _cluster_rois(sel_path, label_list, stc, src, min_dist, weight, mni_subject='fsaverage'):
"""
subfunctions of merge_ROIs
----------
mer_path: str
The directory for storing merged ROIs.
label_list: list
Labels to be merged
"""
class_list = []
class_list.append(label_list[0])
for test_fn in label_list[1:]:
test_label = mne.read_label(test_fn)
i = 0
belong = False
while (i < len(class_list)) and (belong is False):
class_label = mne.read_label(class_list[i])
if test_label.hemi != class_label.hemi:
i = i + 1
continue
else:
class_pca = stc.extract_label_time_course(class_label, src, mode='pca_flip')
test_pca = stc.extract_label_time_course(test_label, src, mode='pca_flip')
class_pow = np.sum(class_pca ** 2)
test_pow = np.sum(test_pca ** 2)
max_pca = class_pca
exch = False
if class_pow < test_pow:
max_pca = test_pca
exch = True
nearby = False
class_stc = stc.in_label(class_label)
test_stc = stc.in_label(test_label)
if class_label.hemi == 'lh':
class_vtx, _ = class_stc.get_peak(hemi='lh')
test_vtx, _ = test_stc.get_peak(hemi='lh')
class_mni = mne.vertex_to_mni(class_vtx, 0, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_vtx, 0, mni_subject)[0]
elif class_label.hemi == 'rh':
class_vtx, _ = class_stc.get_peak(hemi='rh')
test_vtx, _ = test_stc.get_peak(hemi='rh')
class_mni = mne.vertex_to_mni(class_vtx, 1, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_vtx, 1, mni_subject)[0]
if np.linalg.norm(class_mni - test_mni) < min_dist:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
nearby = True
belong = True
if nearby == False:
thre = max_pca.std() * weight
diff = np.abs(np.linalg.norm(class_pca) - np.linalg.norm(test_pca))
if diff < thre:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
elif exch == False:
os.remove(test_fn)
belong = True
i = i + 1
if belong is False:
class_list.append(test_fn)
return len(class_list)
def location(stc, subject, selection='all', locate=True):
if locate is True: locate = 1
print "Locate ", locate, " dipoles"
if selection is 'all':
nd = stc[0].data.shape[0]
ns = stc[0].data.shape[1]
loc = np.zeros((len(stc), ns, 3 * locate))
vtx = stc[0].vertices
vtx_long = np.hstack((stc[0].vertices[0], stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
for s in range(0, len(stc)):
#max location (index)
mxloca = np.argsort(np.abs(stc[s].data), axis=0)
mxloc = mxloca[-1 - locate:-1, :]
assert mxloc.shape[0] == locate and mxloc.shape[1] == ns
hemi = np.where(mxloc < nd / 2, 0, 1).reshape([-1])
mxvtx_long = vtx_long[mxloc].reshape([-1])
if subject is 'sample':
#ns*locatex3
tmp = mne.vertex_to_mni(
mxvtx_long,
hemi,
subject,
subjects_dir=
'/home/jcasa/mne_data/MNE-sample-data/subjects',
verbose=False)
else:
tmp = mne.vertex_to_mni(mxvtx_long,
hemi,
subject,
verbose=False)
assert tmp.shape[1] == 3 and tmp.shape[0] == ns * locate, tmp.shape
tmp = tmp.reshape([locate, ns, 3])
tmp = np.transpose(tmp, (1, 0, 2)).reshape([-1, locate * 3])
assert tmp.shape[1] == 3 * locate and tmp.shape[0] == ns, tmp.shape
loc[s, :, :] = tmp
qtrue_all = loc
p = loc.shape[2]
return qtrue_all, p
else:
nd = stc[0].data.shape[0]
ns = stc[0].data.shape[1]
loc = np.zeros((len(selection), ns, 3 * locate))
vtx = stc[0].vertices
vtx_long = np.hstack((stc[0].vertices[0], stc[0].vertices[1]))
hem0 = np.size(vtx[0])
hem1 = np.size(vtx[1])
ind_s = 0
for s in selection:
#max location (index)
mxloca = np.argsort(np.abs(stc[s].data), axis=0)
mxloc = mxloca[-1 - locate:-1, :]
assert mxloc.shape[0] == locate and mxloc.shape[1] == ns
hemi = np.where(mxloc < nd / 2, 0, 1).reshape([-1])
mxvtx_long = vtx_long[mxloc].reshape([-1])
if subject is 'sample':
#ns*locatex3
tmp = mne.vertex_to_mni(
mxvtx_long,
hemi,
subject,
subjects_dir=
'/home/jcasa/mne_data/MNE-sample-data/subjects',
verbose=False)
else:
tmp = mne.vertex_to_mni(mxvtx_long,
hemi,
subject,
verbose=False)
assert tmp.shape[1] == 3 and tmp.shape[0] == ns * locate, tmp.shape
tmp = tmp.reshape([locate, ns, 3])
tmp = np.transpose(tmp, (1, 0, 2)).reshape([-1, locate * 3])
assert tmp.shape[1] == 3 * locate and tmp.shape[0] == ns, tmp.shape
loc[ind_s, :, :] = tmp
ind_s += 1
qtrue_all = loc
p = loc.shape[2]
return qtrue_all, p
def _cluster_rois(sel_path, label_list, stc, src, min_dist, mni_subject='fsaverage'):
"""
subfunctions of merge_ROIs
----------
mer_path: str
The directory for storing merged ROIs.
label_list: list
Labels to be merged
"""
class_list = []
label_list = _sortlist(label_list, stc, src)
class_list.append(label_list[0])
for test_fn in label_list[1:]:
test_label = mne.read_label(test_fn)
i = 0
belong = False
while (i < len(class_list)) and (belong is False):
class_label = mne.read_label(class_list[i])
if test_label.hemi != class_label.hemi:
i = i + 1
continue
else:
# Get the centroids
class_stc = stc.in_label(class_label)
test_stc = stc.in_label(test_label)
class_pca = stc.extract_label_time_course(class_label, src, mode='pca_flip')
test_pca = stc.extract_label_time_course(test_label, src, mode='pca_flip')
class_pca = np.squeeze(class_pca)
test_pca = np.squeeze(test_pca)
class_pow = np.sum(class_pca ** 2)
test_pow = np.sum(test_pca ** 2)
if class_label.hemi == 'lh':
h = 0
elif class_label.hemi == 'rh':
h = 1
class_seed, _, _ = class_stc.center_of_mass(mni_subject, hemi=h)
test_seed, _, _ = test_stc.center_of_mass(mni_subject, hemi=h)
class_mni = mne.vertex_to_mni(class_seed, h, mni_subject)[0]
test_mni = mne.vertex_to_mni(test_seed, h, mni_subject)[0]
exch = False
if np.max(class_pow) < np.max(test_pow):
exch = True
# Compute the centroids distance
if np.linalg.norm(class_mni - test_mni) < min_dist:
if exch == True:
os.remove(class_list[i])
class_list[i] = test_fn
class_list = _sortlist(class_list, stc, src)
elif exch == False:
os.remove(test_fn)
belong = True
i = i + 1
if belong is False:
class_list.append(test_fn)
class_list = _sortlist(class_list, stc, src)
return len(class_list)
def get_label_distances(subject, subjects_dir, parc='aparc'):
"""Get Euclidean distance between label center of masses.
Get the Euclidean distance between label center of mass and return the
distance matrix. The distance are computed between vertices in the MNI
coordinates in the subject source space.
Parameters:
-----------
subject: str
Name of the subject.
subjects_dir: str
The subjects directory.
parc: str
Name of the parcellation. Default 'aparc'.
Return:
-------
rounded_com: ndarray | (N, N)
The distance between center of masses of different labels
coords_all: ndarray | (N, )
The MNI coordinates of the vertices in the source space.
coms_lh, coms_rh: list | (N, )
The centre of masses of labels in left and right hemispheres.
"""
import itertools
from scipy import linalg
# get the labels
aparc = mne.read_labels_from_annot(subject,
subjects_dir=subjects_dir,
parc=parc)
# get rid of the unknown label
aparc = [apa for apa in aparc if apa.name.find('unknown') == -1]
N = len(aparc) # get the number of labels
# get the center of mass of each of the labels and
coords_all, coms_lh, coms_rh = [], [], []
for mylab in aparc:
# now, split between hemispheres
if mylab.name.endswith('-lh'):
com_lh = mylab.center_of_mass(subject, subjects_dir=subjects_dir)
coords_ = mne.vertex_to_mni(com_lh,
hemis=0,
subject=subject,
subjects_dir=subjects_dir)
coms_lh.append(com_lh)
else:
com_rh = mylab.center_of_mass(subject, subjects_dir=subjects_dir)
coords_ = mne.vertex_to_mni(com_rh,
hemis=1,
subject=subject,
subjects_dir=subjects_dir)
coms_rh.append(com_rh)
coords_all.append(coords_)
# compute the distances
com_distances = np.zeros((N, N))
for (i, j) in itertools.combinations(list(range(N)), 2):
com_distances[i, j] = linalg.norm(coords_all[i] - coords_all[j])
# only one half matrix is created above, make it full
com_distances += com_distances.T
rounded_com = np.round(com_distances, 0)
# return the distance matrix rounded to nearest integer
return rounded_com, np.array(coords_all), coms_lh, coms_rh
subject = 'fsaverage'
# Plot brain in 3D with PySurfer if available
brain = new_stc.plot(subject, hemi='lh',
subjects_dir=subjects_dir, backend='mayavi')
brain.show_view('lateral')
# use peak getter to move vizualization to the time point of the peak
vertno_max, time_idx = new_stc.get_peak(hemi='lh', time_as_index=True)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue',
scale_factor=0.6, map_surface='white')
mni_coords = mne.vertex_to_mni(vertno_max, hemis=0, subject=subject,
subjects_dir=subjects_dir)
print('The MNI coords are ', mni_coords)
# my_trans = mne.read_trans(?)
# src_pts = apply_trans(trans, some_tgt_pts)
utils.coord_to_label(subject, mni_coords, label='mycoord',
hemi='lh', n_steps=25, map_surface="white")
brain.add_label('mycoord-lh.label', color="darkseagreen", alpha=.8)
# if the new mni_coords are computed
brain.add_foci(mni_coords, coords_as_verts=False, hemi='lh', color='red',
map_surface='white', scale_factor=0.6)
def test_scale_mri_xfm():
"""Test scale_mri transforms and MRI scaling."""
# scale fsaverage
tempdir = _TempDir()
os.environ['_MNE_FEW_SURFACES'] = 'true'
fake_home = testing.data_path()
# add fsaverage
create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
verbose=True)
# add sample (with few files)
sample_dir = op.join(tempdir, 'sample')
os.mkdir(sample_dir)
os.mkdir(op.join(sample_dir, 'bem'))
for dirname in ('mri', 'surf'):
copytree(op.join(fake_home, 'subjects', 'sample', dirname),
op.join(sample_dir, dirname))
subject_to = 'flachkopf'
spacing = 'oct2'
for subject_from in ('fsaverage', 'sample'):
if subject_from == 'fsaverage':
scale = 1. # single dim
else:
scale = [0.9, 2, .8] # separate
src_from_fname = op.join(tempdir, subject_from, 'bem',
'%s-%s-src.fif' % (subject_from, spacing))
src_from = mne.setup_source_space(
subject_from, spacing, subjects_dir=tempdir, add_dist=False)
write_source_spaces(src_from_fname, src_from)
print(src_from_fname)
vertices_from = np.concatenate([s['vertno'] for s in src_from])
assert len(vertices_from) == 36
hemis = ([0] * len(src_from[0]['vertno']) +
[1] * len(src_from[0]['vertno']))
mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from,
subjects_dir=tempdir)
if subject_from == 'fsaverage': # identity transform
source_rr = np.concatenate([s['rr'][s['vertno']]
for s in src_from]) * 1e3
assert_allclose(mni_from, source_rr)
if subject_from == 'fsaverage':
overwrite = skip_fiducials = False
else:
with pytest.raises(IOError, match='No fiducials file'):
scale_mri(subject_from, subject_to, scale,
subjects_dir=tempdir)
skip_fiducials = True
with pytest.raises(IOError, match='already exists'):
scale_mri(subject_from, subject_to, scale,
subjects_dir=tempdir, skip_fiducials=skip_fiducials)
overwrite = True
scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir,
verbose='debug', overwrite=overwrite,
skip_fiducials=skip_fiducials)
if subject_from == 'fsaverage':
assert _is_mri_subject(subject_to, tempdir), "Scaling failed"
src_to_fname = op.join(tempdir, subject_to, 'bem',
'%s-%s-src.fif' % (subject_to, spacing))
assert op.exists(src_to_fname), "Source space was not scaled"
# Check MRI scaling
fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz')
assert op.exists(fname_mri), "MRI was not scaled"
# Check MNI transform
src = mne.read_source_spaces(src_to_fname)
vertices = np.concatenate([s['vertno'] for s in src])
assert_array_equal(vertices, vertices_from)
mni = mne.vertex_to_mni(vertices, hemis, subject_to,
subjects_dir=tempdir)
assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm
del os.environ['_MNE_FEW_SURFACES']
doMorph = True
subjectDir = os.environ['SUBJECTS_DIR']
modelSubj = 'fsaverage'
# Distance from fMRI-based central point to include in hand motor label
labelRadii = [10]
subject_set = [str(subj) for subj in subj_nums_hcp]
# Load list of labels and pull out pre-central gyrus
labelList = mne.read_labels_from_annot(subject=modelSubj, parc='aparc.a2009s',
hemi='both')
primaryMotor = [l for l in labelList if 'G_precentral-' in l.name]
# Get MNI coords for vertices in L/R hemis
primaryMotorMNI_pos = [mne.vertex_to_mni(primaryMotor[0].vertices, 0, modelSubj),
mne.vertex_to_mni(primaryMotor[1].vertices, 1, modelSubj)]
# Find closest point in fsaverage brain space according to
# Witt 2008, Functional neuroimaging correlates of finger-tapping task
# variations coords in Talairach space
# list is [left hemi, right hemi]
hand_knob_pos = [np.atleast_2d(np.array([-38, -26, 50])),
np.atleast_2d(np.array([36, -22, 54]))]
SMA = [-4, -8, 52]
dists = []
for orig_label, label_pos, knob_pos in zip(primaryMotor, primaryMotorMNI_pos, hand_knob_pos):
# Find dist between MNI point and all vertices
dists.append(np.squeeze(cdist(knob_pos, label_pos, 'euclidean')))
# Find min dist and index
persistent = [line.rstrip() for line in pfid]
remitted = [line.rstrip() for line in rfid]
sx = np.recfromcsv(sx_fname)
hi = [rec[2] for s in subjs for rec in sx if rec[0]==s]
inatt = [rec[1] for s in subjs for rec in sx if rec[0]==s]
# find closest source to seed
if seed[0] < 0:
hemis = 0 # LH
else:
hemis = 1 # RH
# important to do this by source, because not every vertex has a source
fname = data_dir + 'morphed-lcmv-%dto%d-'%(bands[0][0],bands[0][1]) + subjs[0]
stc = mne.read_source_estimate(fname)
# this is what we get when we read in the fsaverage subject
coord = mne.vertex_to_mni(vertices=stc.vertno[hemis],hemis=hemis,subject='fsaverage')
dist = np.sqrt((coord[:,0] - seed[0])**2 + (coord[:,1] - seed[1])**2 + (coord[:,2] - seed[2])**2)
seed_src = np.argmin(dist) + hemis*len(stc.lh_vertno)
print 'Distance to seed: %.2fmm'%np.min(dist)
# for each band, compute subject-based correlation map
for l_freq, h_freq in bands:
nv_corrs = []
adhd_corrs = []
per_corrs = []
rem_corrs = []
print 'Band %d to %d Hz'%(l_freq, h_freq)
cnt=0
for s in subjs:
print cnt+1, '/', len(subjs)
fname = data_dir + 'morphed-lcmv-%dto%d-'%(l_freq,h_freq) + s
os.system('3dmaskdump -xyz -o ' + mask_fname + ' ' + roi)
a = np.genfromtxt(mask_fname)
os.system('rm ' + mask_fname)
gv = a[a[:, 6] > 0, 3:6] # separate good voxels in the mask
# change AFNI results from RAI to LPI
gv[:, 0] = gv[:, 0] * -1
gv[:, 1] = gv[:, 1] * -1
src = mne.setup_source_space(subject='fsaverage_mne',
fname=None,
spacing='ico5',
surface='inflated',
n_jobs=2)
# get left and right coordinates for all the sources
coord0 = mne.vertex_to_mni(vertices=src[0]['vertno'],
hemis=0,
subject='fsaverage_mne')
coord1 = mne.vertex_to_mni(vertices=src[1]['vertno'],
hemis=1,
subject='fsaverage_mne')
coord = np.vstack([coord0, coord1])
# store the index of the sources within min_dist of the mask voxels
b = []
for i in range(gv.shape[0]):
dist = np.sqrt((coord[:, 0] - gv[i, 0])**2 +
(coord[:, 1] - gv[i, 1])**2 +
(coord[:, 2] - gv[i, 2])**2)
if min(dist) <= min_dist:
b.append(np.argmin(dist))
# create a stc with 1s for the near sources
d = np.zeros([coord.shape[0], 1])
