def plot_brain_color_legend(palette):
from surfer import Brain
from pymeg import atlas_glasser as ag
from pymeg import source_reconstruction as sr
labels = sr.get_labels(subject='S04',
filters=['*wang*.label', '*JWDG*.label'],
annotations=['HCPMMP1'])
labels = sr.labels_exclude(labels=labels,
exclude_filters=[
'wang2015atlas.IPS4', 'wang2015atlas.IPS5',
'wang2015atlas.SPL', 'JWDG_lat_Unknown'
])
labels = sr.labels_remove_overlap(labels=labels,
priority_filters=['wang', 'JWDG'])
lc = ag.labels2clusters(labels)
brain = Brain('S04', 'lh', 'inflated', views=['lat'], background='w')
for cluster, labelobjects in lc.items():
if cluster in palette.keys():
color = palette[cluster]
for l0 in labelobjects:
if l0.hemi == 'lh':
brain.add_label(l0, color=color, alpha=1)
brain.save_montage(
'/Users/nwilming/Dropbox/UKE/confidence_study/brain_colorbar.png',
[['par', 'fro'], ['lat', 'med']])
return brain
def plot_V1V2(measure, hemi, clim):
data, vert = extract_hemi_data(src_df, measure, time, hemi)
brain = Brain('fsaverage',
hemi,
'inflated',
cortex='low_contrast',
subjects_dir='mne_subjects',
background='w',
foreground='k')
brain.add_data(data,
vertices=vert,
min=-clim[2],
max=clim[2],
time=[time],
time_label=lambda t: '%d ms' % (t * 1000),
colormap=morph_divergent_cmap(cmap, clim),
hemi=hemi,
smoothing_steps=5)
labels = mne.read_labels_from_annot('fsaverage',
parc='HCPMMP1',
hemi=hemi,
regexp='[LR]_((V1)|(V2)).*')
for label in labels:
brain.add_label(label, borders=True)
mlab.view(*views[hemi])
return brain
def plot_distance(subject, data_dir, parcels, parcel_name='1-lh'):
print('displaying brain of subject {}, distances from label {}'.format(
subject, parcel_name))
data_path = 'mne_data/MNE-sample-data' # Maja
subjects_dir = os.path.join(data_path, 'subjects')
hemi = 'both'
brain = Brain(subject,
hemi,
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white')
dist_path_lh = os.path.join(data_dir, subject + '_dist_matrix_lh.csv')
dist_matrix_lh = pd.read_csv(dist_path_lh, index_col=0)
dist_matrix_lh_norm = dist_matrix_lh.divide(dist_matrix_lh.max().max())
if 'lh' in parcel_name:
hemi = 'lh'
elif 'rh' in parcel_name:
hemi = 'rh'
for parcel in parcels:
if parcel_name == parcel.name:
brain.add_label(parcel, alpha=1, color='blue')
elif hemi in parcel.name:
dist = dist_matrix_lh_norm.loc[parcel_name][parcel.name]
brain.add_label(parcel, alpha=1, color=str(1 - dist))
import pdb
pdb.set_trace()
cm = parcel.center_of_mass(subject, subjects_dir=subjects_dir)
brain.add_foci(cm, coords_as_verts=True, hemi='lh', color='blue')
def drawROI():
for hemi in ["lh"]:
# load data
roivol = io.project_volume_data(roifile,
hemi,
subject_id=surfsubj,
smooth_fwhm=4.0,
projmeth="dist",
projsum="avg",
projarg=[0,6,0.1],
surf="white")
# create label
roivol = abs(roivol)
roivol[roivol < 0.33] = 0
#if max(roivol) < 1:
# brain.close()
# continue
#else:
write_label(np.asarray(np.nonzero(roivol)),"/gablab/p/bps/zqi_ytang/scripts/roi/surf-IFG.label")
# load brain
my_fig = mlab.figure(figure="new_fig1", size=(800,800))
brain = Brain("fsaverage",hemi,"inflated",curv=True,size=[800,800],background="white",cortex=(("gist_yarg",-1.5,3.5,False)),figure=my_fig)
set_mylights(my_fig,lights[hemi])
#add label
brain.add_label("/gablab/p/bps/zqi_ytang/scripts/roi/surf-IFG.label",borders=False,color="#ffff00",alpha=1)
brain.add_label("/gablab/p/bps/zqi_ytang/scripts/roi/surf-IFG.label",borders=1,color="black",alpha=0.5)
brain.show_view('lat')
brain.save_image("/gablab/p/bps/zqi_ytang/scripts/roi/surf-IFG.tiff")
brain.close()
def img2disc(data, foci_all=False, foci_dmn=False, labelfile=False, hemi='lh', filename='temp.png'):
brain = Brain('fsaverage5', hemi, 'inflated', curv=False)
brain.add_data(data, data.min(), data.max(), colormap="spectral", alpha=0.6)
if labelfile:
brain.add_label(labelfile, borders=True, color='grey')
if foci_all:
brain.add_foci(foci_all, coords_as_verts=True, scale_factor=.5, color='black')
if foci_dmn:
brain.add_foci(foci_dmn, coords_as_verts=True, scale_factor=.7, color='blue')
brain.save_montage(filename, order=['lat', 'med'], orientation='h', border_size=10)
def make_brain_figures(srcdf, measure, cinfo, filepat_base, top=5):
views = {'rh': ['medial', 'lateral'],
'lh': ['lateral', 'medial']}
toplabels = {}
for hemi in ['lh', 'rh']:
brain = Brain('fsaverage', hemi, 'inflated', cortex='low_contrast',
subjects_dir=sv.subjects_dir, background='w',
foreground='k')
sv.show_labels_as_data(srcdf, measure, brain,
time_label=None, parc=parc, **cinfo)
if top:
ind = srcdf.index.get_level_values('label').map(
lambda x: x.startswith(hemi[0].upper()))
topl = srcdf[ind].abs().sort_values(
measure, ascending=False).head(top)
topl = (
topl[(topl[measure] != 0)
& topl[measure].notna()]
.index.droplevel('time'))
toplabels[hemi] = topl
alllabels = mne.read_labels_from_annot(
'fsaverage', parc=parc, hemi=hemi)
brain.remove_labels()
for label in alllabels:
if label.name in topl:
brain.add_label(label, borders=1, hemi=hemi, alpha=0.8,
color='k')
# increase font size of colorbar - this only works by increasing the
# colorbar itself and setting the ratio of colorbar to text
brain.data['colorbar'].scalar_bar.bar_ratio = 0.35
brain.data['colorbar'].scalar_bar_representation.position = [0.075, 0.01]
brain.data['colorbar'].scalar_bar_representation.position2 = [0.85, 0.12]
filepat = os.path.join(figdir, filepat_base + '_{}.png'.format(hemi))
brain.save_montage(filepat, views[hemi], colorbar=0)
brain.close()
infiles = [
os.path.join(figdir, filepat_base + '_{}.png'.format(hemi))
for hemi in ['lh', 'rh']]
outfile = os.path.join(figdir, filepat_base + '.png')
os.system("montage -tile 2x1 -geometry +0+0 {} {}".format(
' '.join(infiles), outfile))
return toplabels
def test_probabilistic_labels():
"""Test plotting of probabilistic labels
"""
mlab.options.backend = 'test'
brain = Brain("fsaverage",
"lh",
"inflated",
config_opts=dict(cortex="low_contrast"))
brain.add_label("BA1", color="darkblue")
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
label_file = pjoin(subj_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = nib.freesurfer.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
brain.close()
def test_probabilistic_labels():
"""Test plotting of probabilistic labels."""
_set_backend()
brain = Brain("fsaverage", "lh", "inflated", cortex="low_contrast")
extra, subj_dir = _get_extra()
brain.add_label("BA1" + extra, color="darkblue")
brain.add_label("BA1" + extra, color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45" + extra, color="firebrick", borders=True)
brain.add_label("BA45" + extra,
color="salmon",
borders=True,
scalar_thresh=.5)
label_file = pjoin(subj_dir, "fsaverage", "label",
"lh.BA6%s.label" % (extra, ))
prob_field = np.zeros_like(brain.geo['lh'].x)
ids, probs = nib.freesurfer.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
with warnings.catch_warnings(record=True):
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
brain.close()
def inflated_surfaces(out_dir, subj, close=True):
"""Native inflated surfaces with cortical label."""
for hemi in ["lh", "rh"]:
b = Brain(subj, hemi, "inflated", curv=False,
config_opts=dict(background="white",
width=800, height=500))
b.add_label("cortex", color="#6B6B6B")
for view in ["lat", "med"]:
b.show_view(view)
mlab.view(distance=400)
png = op.join(out_dir, "%s.surface_%s.png" % (hemi, view))
b.save_image(png)
if close:
b.close()
def plot_roi(hemi, labels, colors, view='parietal',
fs_dir=os.environ['SUBJECTS_DIR']):
import os
subject_id = "fsaverage"
surf = "inflated"
brain = Brain(subject_id, hemi, surf, offscreen=True)
for label, color in zip(labels, colors):
label_file = os.path.join(fs_dir, subject_id, 'label',
(label.replace('-rh', '.label')
.replace('-lh', '.label')
.replace('&', '_and_')
.replace('_Havg', '')
.replace('_Lateralized', '')))
brain.add_label(label_file, color=color)
brain.show_view(view)
return brain.screenshot()
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 save_groups_labels_figures():
labels, groups = get_groups()
for group in groups:
print(group)
if len(get_group_labels(group)) < 4:
group_label = [l for l in labels if group in l]
colors = get_spaced_colors(len(group_label))
brain = Brain(subject, hemi, surf, offscreen=False)
for label_id, label in enumerate(group_label):
print(label)
brain.add_label(label, color=colors[label_id])
fol = os.path.join(subjects_dir, subject, 'label', '{}_figures'.format(aparc_name))
if not os.path.isdir(fol):
os.mkdir(fol)
brain.save_imageset(os.path.join(fol, group), get_views(), 'jpg')
brain.remove_labels()
brain.close()
def plot_src_ROI(subjects_dir=None, subject=None, parc=None):
'''Plot selected frontal regions in source space.'''
from surfer import Brain
subject = 'fsaverage' if subject is None else subject
brain = Brain(subject,
'lh',
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white',
size=(800, 600),
views='l')
# brain.add_annotation('HCPMMP1_combined')
labels, _ = get_frontal_labels(subjects_dir=subjects_dir, parc=parc)
for lab in labels:
brain.add_label(lab, borders=False, color='b')
return brain
def plot_y_pred_true_parcels(subject_name,
labels_pred,
labels_true,
colors={
'pred': 'y',
'true': 'b',
'overlap': 'g'
}):
# fig_name = (subject_name + '_' + str(len(parcels_subject)) + '_' +
# str(n_parcels_max))
# TODO: make the switch automatic depending on who/where it runs
data_path = 'mne_data/MNE-sample-data' # Maja
# mne_dir = mne.get_config('MNE_DATASETS_SAMPLE_PATH') # Alex
# data_path = mne_dir + '/MNE-sample-data'
subjects_dir = os.path.join(data_path, 'subjects')
hemi = 'both'
brain = Brain(subject_name,
hemi,
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white')
for parcel in labels_pred:
brain.add_label(parcel, alpha=1, color=colors['pred'])
for parcel in labels_true:
brain.add_label(parcel, alpha=1, color=colors['true'])
for parcel in set(labels_pred) & set(labels_true):
brain.add_label(parcel, alpha=1, color=colors['overlap'])
import pdb
pdb.set_trace()
def plot_rois(
labels,
clusters,
view="lat",
fs_dir=os.environ["SUBJECTS_DIR"],
subject_id="S04",
surf="inflated",
):
hemi = "lh"
from surfer import Brain
import seaborn as sns
colors = sns.color_palette("husl", len(clusters))
brain = Brain(subject_id, hemi, surf, offscreen=False)
for color, cluster in zip(colors, clusters):
for label in labels[cluster]:
if "rh" in label.name:
continue
# print(cluster, label)
brain.add_label(label, color=color)
brain.show_view(view)
return brain, brain.screenshot()
def visualize_brain(subject,
hemi,
annot_name,
subjects_dir,
parcels_selected,
fig_dir='figs',
ext='.png'):
# visualize the brain with the parcellations and the source of the signal
if not os.path.isdir(fig_dir):
os.mkdir(fig_dir)
brain = Brain(subject,
hemi,
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white',
size=(800, 600))
for parcel in parcels_selected:
brain.add_label(parcel, alpha=1, color=parcel.color)
save_fig = os.path.join(fig_dir, 'brain_' + annot_name + ext)
brain.save_image(save_fig)
print('brain image saved in {}'.format(save_fig))
def plot_brains(axes, palette):
lat_ax, med_ax = axes
lat_color, med_color = palette
b = Brain("fsaverage", "lh", "pial",
background="white", size=(1200, 1200))
b.add_morphometry("curv", grayscale=True, min=-.5, max=.5,
colormap="Greys", colorbar=False)
b.add_label("roi_labels/lh.ifs.label", alpha=.9, color="#feb308")
b.add_label("roi_labels/lh.mfc.label", alpha=.9, color="#cf6275")
mlab.view(160, 70)
lat_ax.imshow(crop(b.screenshot()), rasterized=True)
mlab.view(15, 90)
med_ax.imshow(crop(b.screenshot()), rasterized=True)
b.close()
for ax in axes:
ax.set_axis_off()
def brain_labels(labels,
names,
subjects_dir,
stc=None,
title=None,
hemi='both',
view=['med'],
save=False,
fname='',
dataset='sample_LAud'):
from surfer import Brain
n_support = names.shape[0]
label_names = [label.name for label in labels]
# Plot the selected labels in a Brain
brain = Brain('sample',
hemi=hemi,
surf='inflated',
subjects_dir=subjects_dir,
title=title,
views=view,
background='white')
for label in np.unique(names[:n_support // 2]):
if hemi == 'both':
# Left hemi
idx = label_names.index(label)
brain.add_label(labels[idx], color=labels[idx].color)
# Right hemi
idx = label_names.index(label[:-3] + '-rh')
brain.add_label(labels[idx], color=labels[idx].color)
elif hemi == 'lh':
# Left hemi
idx = label_names.index(label)
brain.add_label(labels[idx], color=labels[idx].color)
elif hemi == 'rh':
# Right hemi
idx = label_names.index(label[:-3] + '-rh')
brain.add_label(labels[idx], color=labels[idx].color)
if save:
brain.save_montage('paper_figures/images/' + fname + '_' + view[0] +
'_' + hemi + '.png',
order=view,
border_size=1)
def test_probabilistic_labels():
"""Test plotting of probabilistic labels."""
mlab.options.backend = 'test'
brain = Brain("fsaverage", "lh", "inflated",
cortex="low_contrast")
brain.add_label("BA1", color="darkblue")
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
label_file = pjoin(subj_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = nib.freesurfer.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
brain.close()
def test_probabilistic_labels():
"""Test plotting of probabilistic labels."""
_set_backend()
brain = Brain("fsaverage", "lh", "inflated",
cortex="low_contrast")
brain.add_label("BA1", color="darkblue")
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
subj_dir = utils._get_subjects_dir()
label_file = pjoin(subj_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain.geo['lh'].x)
ids, probs = nib.freesurfer.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
with warnings.catch_warnings(record=True):
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
brain.close()
'''
import os
import glob
import mne
from surfer import Brain
subjects_dir = '/home/uais_common/dong/freesurfer/subjects/'
stcs_path = subjects_dir + '/fsaverage/conf_stc/'
labels_dir = stcs_path + 'STC_ROI/merge/'
subject_id = 'fsaverage'
hemi = "lh"
# surf = "smoothwm"
surf = 'inflated'
fn_list = glob.glob(labels_dir + '*')
brain = Brain(subject_id, hemi, surf)
color = ['#990033', '#9900CC', '#FF6600', '#FF3333', '#00CC33']
i = 0
for fn_label in fn_list:
label_name = os.path.split(fn_label)[-1]
# if label_name.split('_')[0] == 'sti,RRst':
i = i + 1
ind = i % 5
label = mne.read_label(fn_label)
if label.hemi == 'lh':
brain.add_label(label)
brain.add_annotation(annot='aparc', borders=True)
def drawContrast(infile,outname):
for hemi in ["rh"]:
# load data
volpos = io.project_volume_data(infile,
hemi,
subject_id=surfsubj,
smooth_fwhm=4.0,
projmeth="dist",
projsum="max",
projarg=[-6,6,0.1],
surf="pial")
"""
volneg = io.project_volume_data(rsfcfile_neg,
hemi,
subject_id=surfsubj,
smooth_fwhm=4.0,
projmeth="dist",
projsum="max",
projarg=[-6,6,0.1],
surf="white")
volneg=volneg*-1
ccvol=volpos+volneg
"""
# load brain
my_fig = mlab.figure(figure="new_fig1", size=(800,800))
brain = Brain("fsaverage",hemi,"inflated",curv=True,size=[800,800],background="white",cortex=(("gist_yarg",-1.5,3.5,False)),figure=my_fig)
set_mylights(my_fig,lights[hemi])
if outname == "fig6b":
# create label
labIFG = volpos
labIFG[labIFG < 2.0739] = 0
write_label(np.asarray(np.nonzero(labIFG)),"fig6prep/roi_IFG.label")
#brain.add_label("fig6prep/roi_IFG.label",borders=False,color="red",alpha=0.125)
brain.add_label("fig6prep/roi_IFG.label",borders=2,color="#e7298a",alpha=1)
if outname == "fig6d":
# create label
labAG = io.project_volume_data("/gablab/p/CASL/Results/Imaging/resting/zqi/conn_CASL_pre_post_training13o/results/secondlevel/ANALYSIS_01/3mohsk_all(0).3mohsk(1)/pre-training(-1).post-training(1)/anat_func_lifg_1_1/roi_AG.nii.gz",
hemi,
subject_id=surfsubj,
smooth_fwhm=4.0,
projmeth="dist",
projsum="max",
projarg=[-6,6,0.1],
surf="pial")
labAG[labAG < 0.33] = 0
write_label(np.asarray(np.nonzero(labAG)),"fig6prep/roi_AG.label")
labSTGMTG = io.project_volume_data("/gablab/p/CASL/Results/Imaging/resting/zqi/conn_CASL_pre_post_training13o/results/secondlevel/ANALYSIS_01/3mohsk_all(0).3mohsk(1)/pre-training(-1).post-training(1)/anat_func_lifg_1_1/roi_STGMTG.nii.gz",
hemi,
subject_id=surfsubj,
smooth_fwhm=4.0,
projmeth="dist",
projsum="max",
projarg=[-6,6,0.1],
surf="pial")
labSTGMTG[labSTGMTG < 0.66] = 0
write_label(np.asarray(np.nonzero(labSTGMTG)),"fig6prep/roi_STGMTG.label")
#brain.add_label("fig6prep/roi_AG.label",borders=False,color="black",alpha=0.125)
brain.add_label("fig6prep/roi_AG.label",borders=2,color="#1b9e77",alpha=1)
#brain.add_label("fig6prep/roi_STGMTG.label",borders=False,color="blue",alpha=0.125)
brain.add_label("fig6prep/roi_STGMTG.label",borders=2,color="#7570b3",alpha=1)
brain.add_overlay(volpos,
min=2.0739, #p = 0.025 2-tailed
max=5.0216, #p = 0.000025, 2-tailed
sign="abs",
name=outname,
hemi=hemi)
brain.show_view('lat')
brain.save_image("fig6prep/%s.tiff"%(outname))
brain.close()
temp_frange = list(np.arange(frange[fs[0]], frange[fs[1]]) + 1)
XX = X[:, :, fs[0]:fs[1], ]
XX = XX.mean(axis=-1) # average over tone
XX_t = XX / sem(XX, axis=0) # t by subject
XXX_t = XX_t.mean(axis=0)
these_data = XXX_t
for cl_idx, cl in enumerate(clust_labels):
hemi = cl.hemi
fig, axes = plt.subplots(4, 1)
mfig = mlab.figure()
brain = Brain("fsaverage",
hemi,
"inflated",
subjects_dir=subjects_dir,
figure=mfig)
brain.add_label(cl, color="blue", alpha=0.7)
axes[0].imshow(mlab.screenshot(figure=mfig))
axes[0].axis("off")
mlab.close()
mfig = mlab.figure()
brain = Brain("fsaverage",
hemi,
"inflated",
subjects_dir=subjects_dir,
figure=mfig,
views=["med"])
brain.add_label(cl, color="blue", alpha=0.7)
axes[1].imshow(mlab.screenshot(figure=mfig))
axes[1].axis("off")
mlab.close()
temp_mean = these_data.mean(axis=0)
continue
this_con = cons["{}-rest".format(block)]
con = np.hstack((this_con[bh_reg_inds], 0))
alphas = abs(con) / vmax
node_n = len(node_names)
con_mat = np.zeros((node_n, node_n))
con_mat[-1, ] = con
pcc(con_mat,
node_names,
node_angles=angles,
colormap="seismic",
node_colors=colors,
vmin=vmin,
vmax=vmax,
title=block,
alphas=None,
facecolor="white",
textcolor="black")
fig = mlab.figure()
subjects_dir = "/home/jeff/freesurfer/subjects"
brain = Brain('fsaverage',
'lh',
"inflated",
alpha=1,
subjects_dir=subjects_dir,
figure=fig)
for bon_idx, bon in enumerate(bh_ordered_names):
this_label = [l for l in labels if l.name == bon + "-lh"][0]
brain.add_label(this_label, color=this_cm(gradient[bon_idx]))
def show(self, figure=None, color=None, opacity=1, bgcolor=(1, 1, 1), size=(600, 600),
show_brain=False, brain=None, get_brain=False, fileformat='white'):
"""show surface
Parameters
----------
figure : None | Figure object
color : list(3) | None
Color of the surface
opacity : float | None
Opacity of the surface. It's must be between 0 and 1
bgcolor : list(3)
Color of the background color
size : list(2)
Size of the window that displays figures
show_brain : Boolean
If True, display regions on brain hemispheres
brain : Brain surfer instance
The basic hemisphere
get_brain : Boolean
If True, return the last Brain surfer instance
fileformat : str
The format of file surface hemisphere. It's used only if show_brain is True
Returns
-------
figure : Figure object
brain : Brain surfer object
It returned if show_brain is True
-------
Author : Alexandre Fabre
"""
if figure is None:
figure = mlab.figure(size=size, bgcolor=bgcolor)
if show_brain:
if brain is None:
# NB: there is an error in sucjects_dir setting
brain = Brain(self.label.subject, self.label.hemi, fileformat, subjects_dir='/hpc/comco/brovelli.a/db_mne/meg_te/',
figure=figure, background='gii', curv=False, show_toolbar=True)
if color is None:
color = self.label.color
if self.label is not None:
brain.add_label(self.label, color=color)
else:
raise Exception('instance of Surface has not associate a label')
else:
if color is None:
color = self.color
x, y, z = np.transpose(self.pos)
mesh = mlab.pipeline.triangular_mesh_source(x, y, z, self.triangles, opacity=opacity)
mlab.pipeline.surface(mesh, color=color, opacity=opacity)
if get_brain:
return figure, brain
else:
return figure
to help characterize the location of your data.
"""
from os import environ
from os.path import join
import numpy as np
from surfer import Brain, io
brain = Brain("fsaverage", "lh", "inflated",
config_opts=dict(cortex="low_contrast"))
"""
The easiest way to label any vertex that could be in the region is with
add_label.
"""
brain.add_label("BA1", color="#A6BDDB")
"""
You can also threshold based on the probability of that region being at each
vertex.
"""
brain.add_label("BA1", color="#2B8CBE", scalar_thresh=.5)
"""
It's also possible to plot just the label boundary, in case you wanted to
overlay the label on an activation plot to asses whether it falls within that
region.
"""
brain.add_label("BA45", color="#F0F8FF", borders=True, scalar_thresh=.5)
brain.add_label("BA45", color="#F0F8FF", alpha=.3, scalar_thresh=.5)
def test_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
view = get_view(brain)
extra, subj_dir = _get_extra()
brain.add_label("BA1" + extra)
check_view(brain, view)
brain.add_label("BA1" + extra, color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT%s.label" % (hemi, extra))
brain.add_label(label_file)
brain.add_label("BA44" + extra, borders=True)
brain.add_label("BA6" + extra, alpha=.7)
brain.show_view("medial")
brain.add_label("V1" + extra, color="steelblue", alpha=.6)
brain.add_label("V2" + extra, color="#FF6347", alpha=.6)
brain.add_label("entorhinal" + extra, color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
brain.show_view(dict(elevation=40, distance=430), distance=430)
with pytest.raises(ValueError, match='!='):
brain.show_view(dict(elevation=40, distance=430), distance=431)
# remove labels
brain.remove_labels('V1' + extra)
assert 'V2' + extra in brain.labels_dict
assert 'V1' + extra not in brain.labels_dict
brain.remove_labels()
assert 'V2' + extra not in brain.labels_dict
brain.close()
----------
.. [1] Glasser MF et al. (2016) A multi-modal parcellation of human
cerebral cortex. Nature 536:171-178.
"""
# Author: Eric Larson <*****@*****.**>
#
# License: BSD (3-clause)
from surfer import Brain
import mne
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
verbose=True)
labels = mne.read_labels_from_annot(
'fsaverage', 'HCPMMP1', 'lh', subjects_dir=subjects_dir)
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1')
aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
brain.add_label(aud_label, borders=False)
###############################################################################
# We can also plot a combined set of labels (23 per hemisphere).
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
cortex='low_contrast', background='white', size=(800, 600))
brain.add_annotation('HCPMMP1_combined')
def test_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
view = get_view(brain)
extra, subj_dir = _get_extra()
brain.add_label("BA1" + extra)
check_view(brain, view)
brain.add_label("BA1" + extra, color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id, "label",
"%s.MT%s.label" % (hemi, extra))
brain.add_label(label_file)
brain.add_label("BA44" + extra, borders=True)
brain.add_label("BA6" + extra, alpha=.7)
brain.show_view("medial")
brain.add_label("V1" + extra, color="steelblue", alpha=.6)
brain.add_label("V2" + extra, color="#FF6347", alpha=.6)
brain.add_label("entorhinal" + extra, color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
brain.show_view(dict(elevation=40, distance=430), distance=430)
with pytest.raises(ValueError, match='!='):
brain.show_view(dict(elevation=40, distance=430), distance=431)
# remove labels
brain.remove_labels('V1' + extra)
assert 'V2' + extra in brain.labels_dict
assert 'V1' + extra not in brain.labels_dict
brain.remove_labels()
assert 'V2' + extra not in brain.labels_dict
brain.close()
if m == 0:
ATL = my_ATL[m]
else:
ATL = ATL + my_ATL[m]
brain = Brain('fsaverage',
'lh',
'inflated',
subjects_dir=C.data_path,
cortex='low_contrast',
background='white',
size=(400, 400))
for m in np.arange(0, len(my_ATL)):
brain.add_label(my_ATL[m], borders=False)
#label_ATL = ['L_MT_ROI-lh','L_MST_ROI-lh','L_V4t_ROI-lh','L_FST_ROI-lh',
# 'L_LO1_ROI-lh','L_LO2_ROI-lh','L_LO3_ROI-lh','L_PH_ROI-lh']
#label_ATL = ['L_A4_ROI-lh','L_A5_ROI-lh','L_STSda_ROI-lh','L_STSdp_ROI-lh',
# 'L_STSva_ROI-lh','L_STSvp_ROI-lh','L_STGa_ROI-lh','L_TA2_ROI-lh']
label_ATL = [
'L_STSvp_ROI-lh', 'L_TPOJ1_ROI-lh', 'L_PH_ROI-lh', 'L_TE1p_ROI-lh',
'L_TE2p_ROI-lh'
]
my_ATL = []
for j in np.arange(0, len(label_ATL)):
my_ATL.append([label for label in labels if label.name == label_ATL[j]][0])
for m in np.arange(0, len(my_ATL)):
if m == 0:
subject_id = "fsaverage"
hemi = "both"
#surf = "smoothwm"
surf = 'inflated'
brain = Brain(subject_id, hemi, surf)
list_dirs = os.walk(subject_path + '/func_labels/')
color = ['#990033', '#9900CC', '#FF6600', '#FF3333', '#00CC33']
for root, dirs, files in list_dirs:
for f in files:
label_fname = os.path.join(root, f)
label = mne.read_label(label_fname)
#label.values.fill(1.0)
#label_morph = label.morph(subject_from='fsaverage', subject_to=subject, smooth=5,
# n_jobs=1, copy=True)
if label.hemi == 'lh':
brain.add_label(label, color=random.choice(color))
elif label.hemi == 'rh':
brain.add_label(label, color=random.choice(color))
#brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue', scale_factor=0.6)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
#ref_ROI_fname = '/home/qdong/freesurfer/subjects/fsaverage/label/lh.Auditory_82.label'
#ref_label = mne.read_label(ref_ROI_fname)
#brain.add_label(ref_label, color="blue")
#brain.add_label("ROI1", color="blue")
#brain.add_label("ROI2", color="red")
#brain.add_label("ROI3", color="green")
#brain.add_label("ROI4", color="blue")
#brain.add_label("ROI5", color="blue")
#brain.add_label("ROI6", color="blue")
#brain.add_label("ROI7", color="blue")
and visual areas. Here we show several ways to visualize these labels
to help characterize the location of your data.
"""
from os import environ
from os.path import join
import numpy as np
from surfer import Brain, io
brain = Brain("fsaverage", "lh", "inflated",
config_opts=dict(cortex="low_contrast"))
"""
The simplest way is to use add_label.
"""
brain.add_label("BA1", color="darkblue")
"""
You can also threshold based on the probability of that
region being at each vertex.
"""
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
"""
It's also possible to plot just the label boundary, in case
you wanted to overlay the label on an activation plot to
asses whether it falls within that region.
"""
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
subject_id = "fsaverage"
hemi = "both"
#surf = "smoothwm"
surf = 'inflated'
brain = Brain(subject_id, hemi, surf)
list_dirs = os.walk(subject_path + '/func_labels/')
color = ['#990033', '#9900CC', '#FF6600', '#FF3333', '#00CC33']
for root, dirs, files in list_dirs:
for f in files:
label_fname = os.path.join(root, f)
label = mne.read_label(label_fname)
#label.values.fill(1.0)
#label_morph = label.morph(subject_from='fsaverage', subject_to=subject, smooth=5,
# n_jobs=1, copy=True)
if label.hemi == 'lh':
brain.add_label(label, color=random.choice(color))
elif label.hemi == 'rh':
brain.add_label(label, color=random.choice(color))
#brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue', scale_factor=0.6)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
#ref_ROI_fname = '/home/qdong/freesurfer/subjects/fsaverage/label/lh.Auditory_82.label'
#ref_label = mne.read_label(ref_ROI_fname)
#brain.add_label(ref_label, color="blue")
#brain.add_label("ROI1", color="blue")
#brain.add_label("ROI2", color="red")
#brain.add_label("ROI3", color="green")
#brain.add_label("ROI4", color="blue")
#brain.add_label("ROI5", color="blue")
#brain.add_label("ROI6", color="blue")
#brain.add_label("ROI7", color="blue")
"""
brain = Brain(subject_id, "lh", "inflated")
"""
First we'll get a set of stereotaxic foci in the MNI
coordinate system. These might be peak activations from
a volume based analysis.
"""
coord = [-43, 25, 24]
utils.coord_to_label(subject_id,
coord,
label='example_data/coord',
hemi='lh',
n_steps=50,
map_surface="white")
brain.add_label('example_data/coord-lh.label')
"""
Now we plot the foci on the inflated surface. We will map
the foci onto the surface by finding the vertex on the "white"
mesh that is closest to the coordinate of the point we want
to display.
"""
brain.add_foci([coord], map_surface="white", color="gold")
"""
or using a vertex index
"""
coord = 0
utils.coord_to_label(subject_id,
coord,
label='example_data/coord',
if doMorph:
print 'Morphing %i labels to %i subjects...\n' % (len(labels_to_morph),
len(subject_list))
for i, label_orig in enumerate(labels_to_morph):
for si, subject in enumerate(subject_list):
label = deepcopy(label_orig) # Important to prevent double morphing
label.values.fill(1.0)
morphedLabel = label.morph(subject_from=modelSubj,
subject_to=subject, smooth=n_smooth,
grade=src_vert_list[si], n_jobs=6,
verbose=False)
print(morphedLabel)
labelSavePath = op.join(subjectDir, subject, 'label',
morphedLabel.name[:-3] + '.label')
morphedLabel.save(labelSavePath)
print 'Morphing Complete'
###########
# Plot
###########
colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
color_hexes = ['4C0000', 'FF4D4D', 'FF0000', 'FF8080', 'FFCCCC']
if doPlot:
brain = Brain(modelSubj, hemi='lh', surf='white', views=views,
show_toolbar=False)
for li, label in enumerate([l for l in orig_label_list if l.hemi == 'lh']):
brain.add_label(label=label, color=(colors * 2)[li], alpha=0.7,
hemi=label.hemi)
with a large amount of control over the visual representation.
"""
import os
from surfer import Brain
print(__doc__)
subject_id = "fsaverage"
hemi = "lh"
surf = "smoothwm"
brain = Brain(subject_id, hemi, surf)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
brain.add_label("BA1_exvivo")
# Some labels have an associated scalar value at each ID in the label.
# For example, they may be probabilistically defined. You can threshold
# what vertices show up in the label using this scalar data
brain.add_label("BA1_exvivo", color="blue", scalar_thresh=.5)
# Or you can give a path to a label in an arbitrary location
subj_dir = brain.subjects_dir
label_file = os.path.join(subj_dir, subject_id,
"label", "%s.MT_exvivo.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44_exvivo", borders=True)
"""
Bring up the visualization.
"""
brain = Brain(subject_id, "lh", "inflated")
"""
First we'll get a set of stereotaxic foci in the MNI
coordinate system. These might be peak activations from
a volume based analysis.
"""
coord = [-43, 25, 24]
utils.coord_to_label(subject_id, coord, label='examples/example_data/coord',
hemi='lh', n_steps=50, map_surface="white")
brain.add_label('examples/example_data/coord-lh.label')
"""
Now we plot the foci on the inflated surface. We will map
the foci onto the surface by finding the vertex on the "white"
mesh that is closest to the coordinate of the point we want
to display.
"""
brain.add_foci([coord], map_surface="white", color="gold")
"""
or using a vertex index
"""
coord = 0
utils.coord_to_label(subject_id, coord, label='examples/example_data/coord',
def test_label():
"""Test plotting of label
"""
mlab.options.backend = 'test'
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.close()
with a large amount of control over the visual representation.
"""
import os
from surfer import Brain
print(__doc__)
subject_id = "fsaverage"
hemi = "lh"
surf = "smoothwm"
brain = Brain(subject_id, hemi, surf)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
brain.add_label("BA1")
# Some labels have an associated scalar value at each ID in the label.
# For example, they may be probabilistically defined. You can threshold
# what vertices show up in the label using this scalar data
brain.add_label("BA1", color="blue", scalar_thresh=.5)
# Or you can give a path to a label in an arbitrary location
subj_dir = os.environ["SUBJECTS_DIR"]
label_file = os.path.join(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44", borders=True)
# Author: Jean-Remi King <*****@*****.**>
#
# Licence: BSD 3-clause
"""Make a plot of the selected region of interest typically associated with
visual perception (i.e. ventral and dorsal stream + PFC)
Used to generate Figure 2.a
"""
import numpy as np
import matplotlib.pyplot as plt
import mne
from surfer import Brain
rois = ['lingual', 'inferiortemporal', 'superiorparietal',
'supramarginal', 'rostralmiddlefrontal', 'precentral']
brain = Brain('fsaverage', 'split', 'inflated', background='w')
labels = mne.read_labels_from_annot('fsaverage', parc='aparc')
cmap = plt.get_cmap('rainbow')
colors = cmap(np.linspace(0., 1., len(rois)))
for roi, color in zip(rois, colors):
for hemi in ['lh', 'rh']:
label = [lbl for lbl in labels if lbl.name == (roi + '-' + hemi)][0]
brain.add_label(label, color=color, alpha=.9, hemi=hemi)
units='m')
"""
First we'll identify a stereotaxic focus in the MNI coordinate system. This
might be a peak activations from a volume based analysis.
"""
coord = [-43, 25, 24]
"""
Next we grow a label along the surface around the neareset vertex to this
coordinate in the white surface mesh. The `n_steps` argument controls the size
of the resulting label.
"""
utils.coord_to_label(subject_id, coord, label='example_data/coord',
hemi='lh', n_steps=25, map_surface="white")
brain.add_label('example_data/coord-lh.label', color="darkseagreen", alpha=.8)
"""
Now we plot the focus on the inflated surface at the vertex identified in the
previous step.
"""
brain.add_foci([coord], map_surface="white", color="mediumseagreen")
"""
We can also do this using a vertex index, perhaps defined as the peak
activation in a surface analysis. This will be more accurate than using a
volume-based focus.
"""
coord = 0
utils.coord_to_label(subject_id, coord, label='example_data/coord',
# Load labels
fslabels = mne.read_labels_from_annot('fsaverage',
'aparc_sub', 'both', surf_name='white',
subjects_dir=defaults.subjects_dir)
wernicke_mni = np.array([-54, -47.5, 7.5]) / 1000
dists = [np.min(np.linalg.norm(l.pos - wernicke_mni, axis=-1)) for l in
fslabels]
print([fslabels[idx] for idx in np.argsort(dists)[:10]])
wernicke_roi = np.array([fslabels[idx] for idx in np.argsort(dists)[:4]]).sum()
broca_mni = np.array([-51, 21.7, 7.5]) / 1000
dists = [np.min(np.linalg.norm(l.pos - broca_mni, axis=-1)) for l in fslabels]
print([fslabels[idx] for idx in np.argsort(dists)[:10]])
broca_roi = np.array([fslabels[idx] for idx in np.argsort(dists)[:6]]).sum()
brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=defaults.subjects_dir,
cortex='low_contrast', background='white', size=(800, 600))
brain.add_label(wernicke_roi + broca_roi, borders=False)
picks = pd.read_csv(op.join(defaults.static, 'picks.tsv'), sep='\t')
picks.drop(picks[picks.id.isin(defaults.exclude)].index, inplace=True)
picks.sort_values(by='id', inplace=True)
for aix, age in enumerate(defaults.ages):
subjects = ['genz%s' % ss for ss in picks[picks.ag == age].id]
for ix, (kk, vv) in enumerate(defaults.bands.items()):
hp, lp = vv
for si, subject in enumerate(subjects):
bem_dir = os.path.join(defaults.subjects_dir, subject, 'bem')
bem_fname = os.path.join(bem_dir, '%s-5120-bem-sol.fif' % subject)
src_fname = os.path.join(bem_dir, '%s-oct-6-src.fif' % subject)
subj_dir = os.path.join(defaults.megdata, subject)
raw_fname = os.path.join(subj_dir, 'sss_pca_fif',
'%s_rest_01_allclean_fil100_raw_sss.fif' %
b.add_morphometry(args.morphometry)
# Maybe load an overlay
if args.overlay is not None:
if args.range is not None:
args.min, args.max = args.range
b.add_overlay(args.overlay, args.min, args.max, args.sign)
# Maybe load an annot
if args.annotation is not None:
if not args.borders:
args.borders = any([args.overlay, args.morphometry])
b.add_annotation(args.annotation, args.borders)
# Maybe load a label
if args.label is not None:
if not args.borders:
args.borders = any([args.overlay, args.morphometry])
b.add_label(args.label, args.borders)
# Also point brain at the Brain() object
brain = b
# It's nice to have mlab in the namespace, but we'll import it
# after the other stuff so getting usage is not interminable
from enthought.mayavi import mlab
# Now clean up the namespace a bit
del parser, args
[0.91, 0.79, sq_size, sq_size]]
print '\nPlotting...\n'
for ri, roi_name in enumerate(rois):
###################################
# Plot source space with roi
###################################
width = 500 if ri == 2 else 1000
height = 400 if ri == 2 else 800
brain = Brain(subject_id='fsaverage', hemi=hemi,
surf='inflated_pre', size=(height, width),
offscreen=True, background='white')
# Load corresponding label from fsaverage
if roi_name == 'G_temp_sup-G_T_transv-rh.label':
brain.add_label(aud_label_fsaverage, color=label_color, alpha=0.75)
else:
fname_load_label = op.join(struct_dir, model_subj, 'label', roi_name)
temp_roi = mne.read_label(fname_load_label, subject=model_subj)
brain.add_label(temp_roi, color=label_color, alpha=0.75)
# Save montage as an image
montage = brain.save_montage(None, order=view_dict[roi_name],
orientation='v', border_size=15,
colorbar=None)
axes[0, ri].imshow(montage, interpolation='nearest', origin='upper',
aspect='equal')
brain.close()
ax_coord = plt.axes(coord_ax_dims[ri], axisbg='none', projection='3d',
aspect='equal')
def test_label():
"""Test plotting of label
"""
mlab.options.backend = 'test'
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id, "label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.close()
def test_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
subj_dir = utils._get_subjects_dir()
label_file = pjoin(subj_dir, subject_id, "label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
# remove labels
brain.remove_labels('V1')
assert_in('V2', brain.labels_dict)
assert_not_in('V1', brain.labels_dict)
brain.remove_labels()
assert_not_in('V2', brain.labels_dict)
brain.close()
subjects_dir = '/home/qdong/freesurfer/subjects/'
subject_path = subjects_dir + 'fsaverage'
labels_dir = subject_path + '/func_labels/common/'
subject_id = 'fsaverage'
hemi = "split"
#surf = "smoothwm"
surf = 'inflated'
#label_fname='/home/qdong/freesurfer/subjects/101611/func_labels/new_func_temporal-lh.label'
brain = Brain(subject_id, hemi, surf)
list_dirs = os.walk(labels_dir)
for root, dirs, files in list_dirs:
for f in files:
label_fname = os.path.join(root, f)
label = mne.read_label(label_fname)
brain.add_label(label, color='red')
#if f[0:6]=='101611':
# brain.add_label(label, color='green', alpha=0.5)
#elif f[0:6]=='108815':
# brain.add_label(label, color='yellow', alpha=0.5)
#elif f[0:6]=='109925':
# brain.add_label(label, color='blue', alpha=0.5)
#elif f[0:6]=='110061':
# brain.add_label(label, color='cyan', alpha=0.5)
#elif f[0:6]=='201394':
# brain.add_label(label, color='red', alpha=0.5)
#elif f[0:6]=='202825':
# brain.add_label(label, color='magenta', alpha=0.5)
#else:
# brain.add_label(label, color='red', subdir=root)
#mne.gui.coregistration()
def show_labels(srcfile,
measure,
brain=None,
r_name=None,
src_df=None,
transform=None,
surface='white',
time=0.17,
labels=None):
parc = re.match('source_(\w+)_allsubs_\d+_slabs_\w+.h5',
srcfile).group(1)
if labels is None:
labels = mne.read_labels_from_annot('fsaverage',
parc=parc,
hemi='both')
labels = {l.name: l for l in labels}
file = os.path.join(bem_dir, srcfile)
if srcfile.find('_slabs_') >= 0:
slabsi = file.find('slabs_')
r_n = file[slabsi + 6:-3]
if r_name is None:
r_name = r_n
elif r_name != r_n:
raise ValueError(
"Provided source file name and name of desired "
"regressor are not compatible!")
if src_df is None:
src_df = pd.read_hdf(file, 'second_level_src')
if brain is None:
brain = Brain('fsaverage',
'both',
surface,
cortex='low_contrast',
subjects_dir=subjects_dir)
else:
brain.remove_labels(hemi='rh')
brain.remove_labels(hemi='lh')
data = src_df.loc[(slice(None), time), measure]
if transform is not None:
data = transform(data)
vmin = 0.9
norm = matplotlib.colors.Normalize(vmin, 1, clip=True)
alpha = matplotlib.colors.Normalize(vmin, (1 - vmin) / 2 + vmin,
clip=True)
data = data[data > vmin]
cmap = matplotlib.cm.hot
print(data)
for index, value in data.iteritems():
if index[0].startswith('L'):
hemi = 'left'
else:
hemi = 'right'
print(index[0])
brain.add_label(labels[index[0]],
color=cmap(norm(value)),
alpha=alpha(value),
hemi=hemi)
return brain
print(__doc__)
brain = Brain("fsaverage", "lh", "inflated")
"""
Show the morphometry with a continuous grayscale colormap.
"""
brain.add_morphometry("curv", colormap="binary",
min=-.8, max=.8, colorbar=False)
"""
The easiest way to label any vertex that could be in the region is with
add_label.
"""
brain.add_label("BA1_exvivo", color="#A6BDDB")
"""
You can also threshold based on the probability of that region being at each
vertex.
"""
brain.add_label("BA1_exvivo", color="#2B8CBE", scalar_thresh=.5)
"""
It's also possible to plot just the label boundary, in case you wanted to
overlay the label on an activation plot to asses whether it falls within that
region.
"""
brain.add_label("BA45_exvivo", color="#F0F8FF", borders=3, scalar_thresh=.5)
brain.add_label("BA45_exvivo", color="#F0F8FF", alpha=.3, scalar_thresh=.5)
def test_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
subj_dir = utils._get_subjects_dir()
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
# remove labels
brain.remove_labels('V1')
assert_in('V2', brain.labels_dict)
assert_not_in('V1', brain.labels_dict)
brain.remove_labels()
assert_not_in('V2', brain.labels_dict)
brain.close()
"""
Bring up the visualization.
"""
brain = Brain(subject_id, "lh", "inflated")
"""
First we'll get a set of stereotaxic foci in the MNI
coordinate system. These might be peak activations from
a volume based analysis.
"""
coord = [-43, 25, 24]
utils.coord_to_label(subject_id, coord, label='coord', hemi='lh', n_steps=50,
map_surface="white")
brain.add_label('coord-lh.label')
"""
Now we plot the foci on the inflated surface. We will map
the foci onto the surface by finding the vertex on the "white"
mesh that is closest to the coordinate of the point we want
to display.
"""
brain.add_foci([coord], map_surface="white", color="gold")
"""
or using a vertex index
"""
coord = 0
utils.coord_to_label(subject_id, coord, label='coord', hemi='lh', n_steps=50,
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
verbose=True)
labels = mne.read_labels_from_annot('fsaverage',
'HCPMMP1',
'lh',
subjects_dir=subjects_dir)
brain = Brain('fsaverage',
'lh',
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white',
size=(800, 600))
brain.add_annotation('HCPMMP1')
aud_label = [label for label in labels if label.name == 'L_A1_ROI-lh'][0]
brain.add_label(aud_label, borders=False)
###############################################################################
# We can also plot a combined set of labels (23 per hemisphere).
brain = Brain('fsaverage',
'lh',
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
background='white',
size=(800, 600))
brain.add_annotation('HCPMMP1_combined')
"""
print __doc__
import os
from surfer import Brain
subject_id = "fsaverage"
hemi = "lh"
surf = "smoothwm"
brain = Brain(subject_id, hemi, surf)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
brain.add_label("BA1")
# Or you can give a path to a label in an arbitrary location
subj_dir = os.environ["SUBJECTS_DIR"]
label_file = os.path.join(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44", borders=True)
# You can also control the opacity of the label color
brain.add_label("BA6", alpha=.7)
# Author: Jean-Remi King <*****@*****.**>
#
# Licence: BSD 3-clause
"""Make a plot of the selected region of interest typically associated with
visual perception (i.e. ventral and dorsal stream + PFC)
Used to generate Figure 2.a
"""
import numpy as np
import matplotlib.pyplot as plt
import mne
from surfer import Brain
rois = [
'lingual', 'inferiortemporal', 'superiorparietal', 'supramarginal',
'rostralmiddlefrontal', 'precentral'
]
brain = Brain('fsaverage', 'split', 'inflated', background='w')
labels = mne.read_labels_from_annot('fsaverage', parc='aparc')
cmap = plt.get_cmap('rainbow')
colors = cmap(np.linspace(0., 1., len(rois)))
for roi, color in zip(rois, colors):
for hemi in ['lh', 'rh']:
label = [lbl for lbl in labels if lbl.name == (roi + '-' + hemi)][0]
brain.add_label(label, color=color, alpha=.9, hemi=hemi)
cont_result = np.expand_dims(cont_result, 0)
XXX = np.vstack((XXX, cont_result))
fig, axes = plt.subplots(len(these_labels),
len(temp_conds) + 1,
figsize=(12, 8))
for tl_idx, this_label in enumerate(these_labels):
mfig = mlab.figure()
hemi = "lh" if "lh" in this_label.name else "rh"
brain = Brain('fsaverage',
hemi,
'inflated',
subjects_dir=subjects_dir,
cortex='low_contrast',
size=(800, 600),
figure=mfig)
brain.add_label(this_label)
axes[tl_idx, 0].imshow(mlab.screenshot(figure=mfig))
axes[tl_idx, 0].axis("off")
mlab.close()
for ax_idx, ax in enumerate(axes[tl_idx, 1:]):
ax.imshow(XXX[ax_idx, :, tl_idx, v[0]:v[1]],
vmin=vmin,
vmax=vmax,
cmap="seismic")
ax.set_title(temp_conds[ax_idx])
plt.sca(ax)
if ax_idx == len(temp_conds) - 1:
plt.yticks(ticks=np.arange(len(subjs_t)), labels=subjs_t)
ax.yaxis.tick_right()
else:
ax.yaxis.set_visible(False)
Display ROIs based on MNE estimates and select dipoles for causality analysis.
'''
import os
import glob
import mne
from surfer import Brain
subjects_dir = os.environ['SUBJECTS_DIR']
stcs_path = subjects_dir + '/fsaverage/conf_stc/'
labels_dir = stcs_path + 'STC_ROI/func/'
subject_id = 'fsaverage'
hemi = "split"
# surf = "smoothwm"
surf = 'inflated'
fn_list = glob.glob(labels_dir + '*')
brain = Brain(subject_id, hemi, surf)
color = ['#990033', '#9900CC', '#FF6600', '#FF3333', '#00CC33']
i = 0
for fn_label in fn_list:
label_name = os.path.split(fn_label)[-1]
# if label_name.split('_')[0] == 'sti,RRst':
i = i + 1
ind = i % 5
label = mne.read_label(fn_label)
brain.add_label(label, color=color[ind], alpha=0.8)
brain.add_annotation(annot='aparc', borders=True)
