def vizify(self):
for hemi in self.hemis:
print "visualize %s" % hemi
# Bring up the beauty (the underlay)
brain = Brain(self.subject_id, hemi, self.surf, \
config_opts=self.config_opts, \
subjects_dir=self.subjects_dir)
surf_data = io.read_scalar_data(self.overlay_surf[hemi])
if (sum(abs(surf_data)) > 0):
# Overlay another hopeful beauty (functional overlay)
brain.add_overlay(self.overlay_surf[hemi], name=self.overlay_name,
min=self.min, max=self.max, sign=self.sign)
# Update colorbar
#brain.overlays[self.overlay_name].pos_bar.lut_mode = self.colorbar
tmp = brain.overlays[self.overlay_name]
lut = tmp.pos_bar.lut.table.to_array()
lut[:,0:3] = self.colorbar
tmp.pos_bar.lut.table = lut
# Refresh
brain.show_view("lat")
brain.hide_colorbar()
# Save the beauts
brain.save_imageset("%s_%s" % (self.outprefix, hemi), self.views,
'jpg', colorbar=None)
# End a great journey, till another life
brain.close()
return
def montage_plot(parameter, in_dir, task, fdr_correct=True, hemi='lh', input_hemisphere='', annot=None):
'''
Make plots for parameter on the cortical surface using pysurf module
- Arguments:
a) parameter
b) output directory
c) task (inference or instructed)
d) FDR correction (boolean)
'''
out_dir = join(in_dir, 'pysurf_plots')
slu.mkdir_p(out_dir)
fsaverage = "fsaverage"
surf = "inflated"
t_data, p_data, str_names, labels = get_data(task, in_dir, input_hemisphere, hemi)
if fdr_correct is True:
data = fdr_filter(t_data, p_data, parameter)
else:
data = t_data[parameter].values
data = data[labels]
brain = Brain(fsaverage, hemi, surf,
background="white", title=parameter + task)
brain.add_data(data, -10, 10, thresh=None, colormap="RdBu_r", alpha=.8)
if annot is not None:
brain.add_annotation(annot, color='white', alpha=1)
brain.save_imageset(join(out_dir, parameter + '_' + task + input_hemisphere),
['lateral', 'medial', 'par'], colorbar=None)
def plot_surface(vtx_data, subject_id, subjects_dir, hemi, surface, output_dir, prefix, l, u, cmap, center, thresh):
# Open up a brain in pysurfer
brain = Brain(
subject_id,
hemi,
surface,
subjects_dir=subjects_dir,
config_opts=dict(background="white", height=665, width=800),
)
if center:
# Make sure the colorbar is centered
if l ** 2 < u ** 2:
l = u * -1
else:
u = l * -1
# Create an empty brain if the values are all below threshold
if np.max(vtx_data) < thresh:
# Add your data to the brain
brain.add_data(vtx_data * 0, l, u, thresh=thresh, colormap=cmap, alpha=0.0)
# Otherwise, add the data appropriately!
else:
# Add your data to the brain
brain.add_data(vtx_data, l, u, thresh=thresh, colormap=cmap, alpha=0.8)
# Save the images for medial and lateral
# putting a color bar on all of them
brain.save_imageset(prefix=os.path.join(output_dir, prefix), views=views_list, colorbar=range(len(views_list)))
def plot_parcel(num_nodes=600,numbers=[1],hemi='lh'):
from surfer import Brain, io
for n in numbers:
brain = Brain("fsaverage", "%s" %(hemi), "pial",config_opts=dict(background="white"))
image = io.project_volume_data('/home/despo/mb3152/random_nodes/%s/parcel_%s.nii'%(num_nodes,n),hemi, subject_id="fsaverage", projsum = 'max', smooth_fwhm = 0)
brain.add_data(image,thresh=1,colormap = "spectral")
brain.save_imageset('/home/despo/mb3152/random_nodes/%s/parcel_%s' %(num_nodes,n),['med','lat'],'jpg',colorbar= None)
brain.close()
def test_image():
"""Test image saving
"""
mlab.options.backend = 'auto'
brain = Brain(*std_args, config_opts=small_brain)
tmp_name = mktemp() + '.png'
brain.save_image(tmp_name)
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.screenshot()
def test_image():
"""Test image saving
"""
mlab.options.backend = 'auto'
brain = Brain(*std_args, config_opts=small_brain)
tmp_name = mktemp() + '.png'
brain.save_image(tmp_name)
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.screenshot()
brain.close()
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 test_image(tmpdir):
"""Test image saving."""
tmp_name = tmpdir.join('temp.png')
tmp_name = str(tmp_name) # coerce to str to avoid PIL error
_set_backend()
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.save_image(tmp_name)
brain.save_image(tmp_name, 'rgba', True)
brain.screenshot()
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.close()
def test_image():
"""Test image saving
"""
tmp_name = mktemp() + '.png'
mlab.options.backend = 'auto'
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain = Brain(*std_args, size=100)
brain.save_image(tmp_name)
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.screenshot()
brain.close()
def test_image():
"""Test image saving."""
tmp_name = mktemp() + '.png'
_set_backend()
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain = Brain(*std_args, size=100)
brain.save_image(tmp_name)
brain.save_image(tmp_name, 'rgba', True)
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.screenshot()
brain.close()
def test_image():
"""Test image saving
"""
tmp_name = mktemp() + '.png'
mlab.options.backend = 'auto'
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, config_opts=small_brain)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain = Brain(*std_args, config_opts=small_brain)
brain.save_image(tmp_name)
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.screenshot()
brain.close()
def corr_image(resting_image,fwhm):
"""This function makes correlation image on brain surface"""
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
from surfer import Brain, Surface
import os
img = nb.load(resting_image)
corrmat = np.corrcoef(np.squeeze(img.get_data()))
corrmat[np.isnan(corrmat)] = 0
corrmat_npz = os.path.abspath('corrmat.npz')
np.savez(corrmat_npz,corrmat=corrmat)
br = Brain('fsaverage5', 'lh', 'smoothwm')
#br.add_overlay(corrmat[0,:], min=0.2, name=0, visible=True)
lh_aparc_annot_file = os.path.join(os.environ["FREESURFER_HOME"],'/subjects/label/lh.aparc.annot')
values = nb.freesurfer.read_annot(lh_aparc_annot_file)
#br.add_overlay(np.mean(corrmat[values[0]==5,:], axis=0), min=0.8, name='mean', visible=True)
data = img.get_data()
data = np.squeeze(img.get_data())
#
precuneus_signal = np.mean(data[values[0]==np.nonzero(np.array(values[2])=='precuneus')[0][0],:], axis=0)
precuneus = np.corrcoef(precuneus_signal, data)
#precuneus.shape
#br.add_overlay(precuneus[0,1:], min=0.3, sign='pos', name='mean', visible=True)
br.add_overlay(precuneus[0,1:], min=0.2, name='mean')#, visible=True)
#br.add_overlay(precuneus[0,1:], min=0.2, name='mean')#, visible=True)
plt.hist(precuneus[0,1:], 128)
plt.savefig(os.path.abspath("histogram.png"))
plt.close()
corr_image = os.path.abspath("corr_image%s.png"%fwhm)
br.save_montage(corr_image)
ims = br.save_imageset(prefix=os.path.abspath('fwhm_%s'%str(fwhm)),views=['medial','lateral','caudal','rostral','dorsal','ventral'])
br.close()
print ims
#precuneus[np.isnan(precuneus)] = 0
#plt.hist(precuneus[0,1:])
roitable = [['Region','Mean Correlation']]
for i, roi in enumerate(np.unique(values[2])):
roitable.append([roi,np.mean(precuneus[values[0]==np.nonzero(np.array(values[2])==roi)[0][0]])])
#images = [corr_fimage]+ims+[os.path.abspath("histogram.png"), roitable]
roitable=[roitable]
histogram = os.path.abspath("histogram.png")
return corr_image, ims, roitable, histogram, corrmat_npz
def corr_image(resting_image,fwhm):
"""This function makes correlation image on brain surface"""
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
from surfer import Brain, Surface
import os
img = nb.load(resting_image)
corrmat = np.corrcoef(np.squeeze(img.get_data()))
corrmat[np.isnan(corrmat)] = 0
corrmat_npz = os.path.abspath('corrmat.npz')
np.savez(corrmat_npz,corrmat=corrmat)
br = Brain('fsaverage5', 'lh', 'smoothwm')
#br.add_overlay(corrmat[0,:], min=0.2, name=0, visible=True)
lh_aparc_annot_file = os.path.join(os.environ["FREESURFER_HOME"],'/subjects/label/lh.aparc.annot')
values = nb.freesurfer.read_annot(lh_aparc_annot_file)
#br.add_overlay(np.mean(corrmat[values[0]==5,:], axis=0), min=0.8, name='mean', visible=True)
data = img.get_data()
data = np.squeeze(img.get_data())
#
precuneus_signal = np.mean(data[values[0]==np.nonzero(np.array(values[2])=='precuneus')[0][0],:], axis=0)
precuneus = np.corrcoef(precuneus_signal, data)
#precuneus.shape
#br.add_overlay(precuneus[0,1:], min=0.3, sign='pos', name='mean', visible=True)
br.add_overlay(precuneus[0,1:], min=0.2, name='mean')#, visible=True)
#br.add_overlay(precuneus[0,1:], min=0.2, name='mean')#, visible=True)
plt.hist(precuneus[0,1:], 128)
plt.savefig(os.path.abspath("histogram.png"))
plt.close()
corr_image = os.path.abspath("corr_image%s.png"%fwhm)
br.save_montage(corr_image)
ims = br.save_imageset(prefix=os.path.abspath('fwhm_%s'%str(fwhm)),views=['medial','lateral','caudal','rostral','dorsal','ventral'])
br.close()
print ims
#precuneus[np.isnan(precuneus)] = 0
#plt.hist(precuneus[0,1:])
roitable = [['Region','Mean Correlation']]
for i, roi in enumerate(np.unique(values[2])):
roitable.append([roi,np.mean(precuneus[values[0]==np.nonzero(np.array(values[2])==roi)[0][0]])])
#images = [corr_fimage]+ims+[os.path.abspath("histogram.png"), roitable]
roitable=[roitable]
histogram = os.path.abspath("histogram.png")
return corr_image, ims, roitable, histogram, corrmat_npz
def plot_group(hub,num_nodes,hemi='lh'):
from surfer import Brain, io
brain = Brain("fsaverage", "%s" %(hemi), "pial",config_opts=dict(background="white"))
if hub == 'pc' or hub =='wmd':
image = io.project_volume_data('/home/despo/mb3152/random_nodes/%s/final_group_%s.nii'%(num_nodes,hub),hemi, subject_id="fsaverage", projsum = 'max', smooth_fwhm = 20)
brain.add_data(image,thresh = np.nanmin(image[image>0]),colormap = "jet", colorbar= True)
brain.save_imageset('/home/despo/mb3152/random_nodes/%s/final_%s' %(num_nodes,hub),['med','lat'],'jpg',colorbar= None)
else:
pc_image = io.project_volume_data('/home/despo/mb3152/random_nodes/%s/final_group_pc.nii'%(num_nodes),hemi, subject_id="fsaverage", projsum = 'max', smooth_fwhm = 20)
wmd_image = io.project_volume_data('/home/despo/mb3152/random_nodes/%s/final_group_wmd.nii'%(num_nodes),hemi, subject_id="fsaverage", projsum = 'max', smooth_fwhm = 20)
wmd_thresh = np.nanmean(wmd_image[wmd_image>0])
pc_thresh = np.nanmean(pc_image[pc_image >0])
#find connetor hub activity
connector_hub_image = pc_image.copy()
connector_hub_image[pc_image < pc_thresh] = 0.
connector_hub_image[wmd_image < wmd_thresh] = 0.
#find sattelite connector activty
satellite_image = pc_image.copy()
satellite_image[pc_image < pc_thresh] = 0.
satellite_image[wmd_image > wmd_thresh] = 0.
# find provincial hub activity
provincial_hub_image = wmd_image.copy()
provincial_hub_image[pc_image > pc_thresh] = 0.
provincial_hub_image[wmd_image < wmd_thresh] = 0.
node_image = pc_image.copy()
node_image[provincial_hub_image > 0] = 0
node_image[connector_hub_image > 0] = 0
node_image[satellite_image > 0] = 0
node_image[node_image > 0] = 1
# brain.add_data(node_image,thresh= 0, max = max(wmd_image), colormap = 'Blues',alpha= .65,hemi=hemi,smoothing_steps = 0)
# brain.add_data(connector_hub_image,thresh=pc_thresh,max=np.nanmax(pc_image), colormap = 'Purples',alpha= .65,hemi=hemi,smoothing_steps = 0)
# brain.add_data(satellite_image,thresh= pc_thresh,max=np.nanmax(pc_image),colormap = 'Reds',alpha= .65,hemi=hemi,smoothing_steps = 0)
# brain.add_data(provincial_hub_image,thresh=wmd_thresh,max=np.nanmax(wmd_image),colormap = 'Blues',alpha= .65,hemi=hemi,smoothing_steps = 0)
brain.add_data(node_image,thresh= 0, max = max(wmd_image), colormap = 'Blues',hemi=hemi,smoothing_steps = 0)
brain.add_data(connector_hub_image,thresh=pc_thresh,max=np.nanmax(pc_image), colormap = 'Purples',hemi=hemi,smoothing_steps = 0)
brain.add_data(satellite_image,thresh= pc_thresh,max=np.nanmax(pc_image),colormap = 'Reds',hemi=hemi,smoothing_steps = 0)
brain.add_data(provincial_hub_image,thresh=wmd_thresh,max=np.nanmax(wmd_image),colormap = 'Blues',hemi=hemi,smoothing_steps = 0)
brain.save_imageset('/home/despo/mb3152/random_nodes/%s/final_combined' %(num_nodes),['med','lat'],'jpg',colorbar= None)
def plot_surface(vtx_data, subject_id, subjects_dir, hemi, surface, output_dir,
prefix, l, u, cmap, center, thresh):
# Open up a brain in pysurfer
# brain = Brain(subject_id, hemi, surface,
# subjects_dir = subjects_dir,
# config_opts=dict(background="white",
# height=665,
# width=800))
brain = Brain(subject_id,
hemi,
surface,
subjects_dir=subjects_dir,
background="white")
if center:
# Make sure the colorbar is centered
if l**2 < u**2:
l = u * -1
else:
u = l * -1
# Create an empty brain if the values are all below threshold
if np.max(vtx_data) < thresh:
# Add your data to the brain
brain.add_data(vtx_data * 0,
l,
u,
thresh=thresh,
colormap=cmap,
alpha=0.0)
# Otherwise, add the data appropriately!
else:
# Add your data to the brain
brain.add_data(vtx_data, l, u, thresh=thresh, colormap=cmap, alpha=.8)
# Save the images for medial and lateral
# putting a color bar on all of them
brain.save_imageset(prefix=os.path.join(output_dir, prefix),
views=views_list,
colorbar=range(len(views_list)))
def test_image():
"""Test image saving."""
tmp_name = mktemp() + '.png'
_set_backend()
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.close()
brain = Brain(*std_args, size=100)
brain.save_image(tmp_name)
brain.save_image(tmp_name, 'rgba', True)
brain.screenshot()
if not os.getenv('TRAVIS', 'false') == 'true':
# for some reason these fail on Travis sometimes
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.close()
def test_image(tmpdir):
"""Test image saving."""
tmp_name = tmpdir.join('temp.png')
tmp_name = str(tmp_name) # coerce to str to avoid PIL error
_set_backend()
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.close()
brain = Brain(*std_args, size=100)
brain.save_image(tmp_name)
brain.save_image(tmp_name, 'rgba', True)
brain.screenshot()
if os.getenv('TRAVIS', '') != 'true':
# for some reason these fail on Travis sometimes
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.close()
def vizify(self):
for hemi in self.hemis:
print "visualize %s" % hemi
# Bring up the beauty (the underlay)
brain = Brain(self.subject_id, hemi, self.surf, \
config_opts=self.config_opts, \
subjects_dir=self.subjects_dir)
surf_data = io.read_scalar_data(self.overlay_surf[hemi])
if (sum(abs(surf_data)) > 0):
# Overlay another hopeful beauty (functional overlay)
brain.add_overlay(self.overlay_surf[hemi],
name=self.overlay_name,
min=self.min,
max=self.max,
sign=self.sign)
# Update colorbar
#brain.overlays[self.overlay_name].pos_bar.lut_mode = self.colorbar
tmp = brain.overlays[self.overlay_name]
lut = tmp.pos_bar.lut.table.to_array()
lut[:, 0:3] = self.colorbar
tmp.pos_bar.lut.table = lut
# Refresh
brain.show_view("lat")
brain.hide_colorbar()
# Save the beauts
brain.save_imageset("%s_%s" % (self.outprefix, hemi),
self.views,
'jpg',
colorbar=None)
# End a great journey, till another life
brain.close()
return
def test_image(tmpdir):
"""Test image saving."""
tmp_name = tmpdir.join('temp.png')
tmp_name = str(tmp_name) # coerce to str to avoid PIL error
_set_backend()
subject_id, _, surf = std_args
brain = Brain(subject_id, 'both', surf=surf, size=100)
brain.add_overlay(overlay_fname, hemi='lh', min=5, max=20, sign="pos")
brain.save_imageset(tmp_name, ['med', 'lat'], 'jpg')
brain.close()
brain = Brain(*std_args, size=100)
if sys.platform == 'darwin' and os.getenv('TRAVIS', '') == 'true':
raise SkipTest('image saving on OSX travis is not supported')
brain.save_image(tmp_name)
brain.save_image(tmp_name, 'rgba', True)
brain.screenshot()
if os.getenv('TRAVIS', '') != 'true':
# for some reason these fail on Travis sometimes
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='v')
brain.save_montage(tmp_name, ['l', 'v', 'm'], orientation='h')
brain.save_montage(tmp_name, [['l', 'v'], ['m', 'f']])
brain.close()
data_max = data.max()
if data_max == 0:
data_min = 0
else:
data_min = 1.3
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(surf_data, min=data_min, max=data_max, name="ang_corr")
"""
Save some images
"""
odir = "/home/data/Projects/CWAS/development+motion/viz"
brain.save_imageset(
path.join(odir, "zpics_glm_regress_surface_%s_lh" % "only_age"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
## Age with motion as covariate
print "age with motion"
mdmr_dir = "/home2/data/Projects/CWAS/development+motion/cwas_regress_motion/rois_random_k3200/age+motion_sex+tr.mdmr"
"""Bring up the visualization"""
brain = Brain("fsaverage",
"lh",
"inflated",
config_opts=dict(background="white"))
"""Project the volume file and return as an array"""
projmeth=projmeth,
projsum=projsum,
projarg=projarg,
smooth_fwhm = smooth_fwhm,
subject_id='fsaverage')
# nothing to do, if all points in the projection are nul
if surf_data.max()!=0:
name_overlay = '{name_roi}_{hemi}'.format(name_roi=name_roi, hemi=hemi)
brain.add_overlay(surf_data, name=name_overlay, hemi=hemi)
overlay_roi = brain.overlays[name_overlay]
lut = overlay_roi.pos_bar.lut.table.to_array()
lut[0:255, 0:3] = color_list[cpt_color]
overlay_roi.pos_bar.lut.table = lut
# Save in png
#brain.save_imageset(save_png, ['lat'], 'png', colorbar=None)
# Color cpt
cpt_color += 1
# If you just want one roi by one
# Remove for the next roi
# for overlay in brain.overlays_dict[name_overlay]:
# overlay.remove()
# Save in png
save_png = os.path.join(datadir, "all_rois")
brain.save_imageset(save_png, ['lat'], 'png', colorbar=None)
brain.close()
tmp1 = brain.overlays["%s_rh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:,0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""
Save some images
"""
odir = "/home/data/Projects/CWAS/development+motion/viz"
brain.save_imageset(path.join(odir, "zpics_surface_%s_rh" % "only_age"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
## Age with motion as covariate
print "age with motion"
mdmr_dir = "/home2/data/Projects/CWAS/development+motion/cwas/rois_random_k3200/age+motion_sex+tr.mdmr"
factor = "age"
"""Bring up the visualization"""
brain = Brain("fsaverage_copy", "rh", "iter8_inflated",
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer")
def plot_surface(vtx_data, subject_id, hemi, surface, subjects_dir, output_dir, prefix, l, u, cmap, thresh, thresh2=None, cmap2='autumn', cortex_style='classic'):
"""
This function needs more documentation, but for now
it is sufficient to know this one important fact:
For the variable "cmap":
If you pass a word that defines a matplotlib
colormap (eg: jet, Rd_Bu etc) then the code
will use that for the color scheme.
If you pass a **list** of colors then you'll
just loop through those colors instead.
"""
if cortex_style.count('_') == 2:
cortex_style_list = cortex_style.split('_')
cortex_name = cortex_style_list[0]
cortex_min = np.float(cortex_style_list[1])
cortex_max = np.float(cortex_style_list[2])
cortex_style = ( cortex_name, cortex_min, cortex_max, False )
# Open up a brain in pysurfer
brain = Brain(subject_id, hemi, surface,
subjects_dir = subjects_dir,
background="white",
size=(800, 665),
cortex=cortex_style)
# Create an empty brain if the values are all below threshold
if np.max(vtx_data) < thresh:
# Add your data to the brain
brain.add_data(vtx_data*0,
l,
u,
thresh = thresh,
colormap=cmap,
alpha=0.0)
# If you only have one threshold
# then add the data!
elif not thresh2:
# Add your data to the brain
brain.add_data(vtx_data,
l,
u,
thresh = thresh,
colormap=cmap,
alpha=.8)
else:
# Plot the data twice for the two
# different settings
vtx_data1 = np.copy(vtx_data)
vtx_data1[vtx_data1>thresh2] = 0
brain.add_data(vtx_data1,
l,
u,
thresh = thresh,
colormap = cmap,
alpha = .8)
brain.add_data(vtx_data,
l,
u,
thresh = thresh2,
colormap = cmap2,
alpha = .8)
# Save the images for medial and lateral
# putting a color bar on all of them
brain.save_imageset(prefix = os.path.join(output_dir, prefix),
views = views_list,
colorbar = range(len(views_list)) )
## update color scheme
single = [251, 154, 143]
overlap = [227, 26, 28]
lut[0:85,0:3] = single
lut[85:170,0:3] = single
lut[170:256,0:3] = overlap
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""Save Pictures"""
odir = "/home/data/Projects/CWAS/age+gender/03_robustness/viz_cwas/pysurfer"
brain.save_imageset(path.join(odir, "zpics_overlap_age_surface_rh"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
## SEX
"""Bring up the visualization"""
brain = Brain("fsaverage_copy", "rh", "iter8_inflated",
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer")
"""Project the volume file and return as an array"""
cwas_file = path.join(overlap_dir, "surf_rh_fdr_logp_sex_thr2.nii.gz")
"""
"""
Handle vertices that are not defined in the annotation.
"""
vtx_data[labels == -1] = -1000
"""
Display these values on the brain. Use a sequential colormap (assuming
these data move from low to high values), and add an alpha channel so the
underlying anatomy is visible.
See: https://pysurfer.github.io/generated/surfer.Brain.html#surfer.Brain.add_data
"""
brain.add_data(vtx_data,
0,
thresh=0,
colormap="YlOrRd",
alpha=1,
transparent=True,
colorbar=True)
#Save images
brain.save_imageset(analysis_name, ['med', 'lat'], 'jpg')
# other views
#brain.show_view('lateral')
#brain.show_view('m')
#brain.show_view('rostral')
#brain.show_view('caudal')
#brain.show_view('ve')
#brain.show_view('frontal')
#brain.show_view('par')
#brain.show_view('dor')
brain = Brain(sub, hemi, surf)
###############################################################################
# show all views
brain.show_view('lateral')
brain.show_view('m')
brain.show_view('rostral')
brain.show_view('caudal')
brain.show_view('dor')
brain.show_view('ve')
brain.show_view('frontal')
brain.show_view('par')
###############################################################################
# save some images
brain.show_view('lat')
brain.save_image("%s_lat.png" % sub)
brain.save_imageset(sub, ['med', 'lat', 'ros', 'caud'], 'jpg')
###############################################################################
# More advanced parameters
brain.show_view({'distance': 375})
# with great power comes great responsibility
brain.show_view({'azimuth': 20, 'elevation': 30}, roll=20)
###############################################################################
# Save a set of images as a montage
brain.save_montage('fsaverage_h_montage.png', ['l', 'v', 'm'], orientation='h')
brain.save_montage('fsaverage_v_montage.png', ['l', 'v', 'm'], orientation='v')
class plotting_points(object):
def __init__(self,
points,
hemi='both',
surf='pial',
map_surface=None,
annot=None,
map_to_annot=None,
show_skin=True,
show_roi=False,
show_name=False,
name_scale=1,
name_color=(0, 0, 0),
show_directions=False,
background='white',
show_average=False,
opacity=1,
scale_factor=.5,
color=np.array([(1, 0, 0)]),
use_default=True):
self.points = copy.deepcopy(points)
self.subject = points.subject
self.freesurfer_dir = points.freesurfer_dir
self.hemi = hemi
self.surf = surf
self.background = background
self.map_surface = map_surface
self.annot = annot
self.map_to_annot = map_to_annot
self.show_skin = show_skin
self.show_roi = show_roi
self.scale_factor = scale_factor
self.show_directions = show_directions
self.show_name = show_name
self.show_average = show_average
self.opacity = opacity
self.color = color
self.scale_factor = scale_factor
self.use_default = use_default
self.name_scale = name_scale
self.name_color = name_color
self._set_config()
self._plot()
def _set_config(self):
if not hasattr(self.points, 'color'):
if self.color.shape[0] == 1:
self.points.add_trait(
'color', np.repeat(self.color, self.points.npoints,
axis=0))
elif self.color.shape[0] == self.points.npoints:
self.points.add_trait('color', self.color)
else:
raise 'Color size is incorrect'
if not hasattr(self.points, 'scale_factor'):
self.points.add_trait(
'scale_factor',
np.ones(self.points.npoints) * self.scale_factor)
if not hasattr(self.points, 'opacity'):
self.points.add_trait('opacity',
np.ones(self.points.npoints) * self.opacity)
def _plot(self):
## plot
self.brain = Brain(hemi=self.hemi,
surf=self.surf,
subject_id=self.subject,
subjects_dir=self.freesurfer_dir,
background=self.background,
offset=False)
if self.show_skin:
self._show_skin()
if self.annot:
self._show_annot()
self._add_points()
if self.show_average:
self._show_average()
def show(self):
mlab.show()
def _add_points(self):
if self.map_surface:
temp = self.points.map_to_surface(self.map_surface)
mapped_vertices, mapped_coords_ras_tkr = temp['vertices'], temp[
'ras_tkr_coord']
mapped_coords = mapped_coords_ras_tkr
### FreesurferCoords.map_to_annot requires map_surface to be set ( set to white by default). Here, we use map_surface if it has been set, otherwise use 'white' by default.
if self.map_to_annot:
if self.map_surface:
self.points.name, self.points.color = self.points.map_to_annot(
self.map_to_annot, map_surface=self.map_surface)
else:
self.points.name, self.points.color = self.points.map_to_annot(
self.map_to_annot, map_surface='white')
for i in range(self.points.npoints):
point = self.points[i]
if point.hemi == self.hemi or self.hemi == 'both':
if self.map_surface:
self.brain.add_foci(mapped_coords[i, :],
hemi=point.hemi,
color=point.color,
scale_factor=point.scale_factor,
alpha=point.opacity)
else:
self.brain.add_foci(point.ras_tkr_coord,
hemi=point.hemi,
color=point.color,
scale_factor=point.scale_factor,
alpha=point.opacity)
if self.show_roi and hasattr(point, 'roi'):
self.brain.add_label(point.roi,
hemi=point.hemi,
color=point.roi.color)
if self.show_name and hasattr(point, 'name'):
mlab.text3d(point.ras_tkr_coord[0],
point.ras_tkr_coord[1],
point.ras_tkr_coord[2],
point.name,
scale=self.name_scale,
color=self.name_color)
if self.show_directions and hasattr(point, 'direction'):
origin = point.ras_tkr_coord.flatten().tolist()
X, Y, Z = zip(origin, origin, origin)
p0 = point.direction[0:3]
p1 = point.direction[3:6]
p2 = point.direction[6:9]
U, W, V = zip(p0, p1, p2)
plot_directions(X, Y, Z, U, W, V)
def _show_skin(self):
## create head model under freesurfer_dir/{subject}/bem (if none existing!)
anat_img = os.path.join(self.points.freesurfer_dir,
self.points.subject, 'mri', 'rawavg.mgz')
out_dir = os.path.join(self.points.freesurfer_dir, self.points.subject,
'bem')
make_head_model(anat_img, out_dir)
skin_surf = Surf(
'{freesurfer_dir}/{subject}/bem/outer_skin_surface'.format(
freesurfer_dir=self.freesurfer_dir, subject=self.subject))
mlab.triangular_mesh(skin_surf.vertices[:, 0],
skin_surf.vertices[:, 1],
skin_surf.vertices[:, 2],
skin_surf.faces,
opacity=0.2,
color=(1, 1, 0))
def _show_annot(self):
if self.hemi in ['lh', 'rh']:
self.brain.add_annotation(self.annot,
hemi=self.hemi,
borders=False)
elif self.hemi == 'both':
self.brain.add_annotation(self.annot, hemi='lh', borders=False)
self.brain.add_annotation(self.annot,
hemi='rh',
borders=False,
remove_existing=False)
def _show_average(self, scale_factor=6, color=(0, 0, 1)):
avg_coord = np.mean(self.points.coordinates['ras_tkr_coord'], axis=0)
mlab.points3d(avg_coord[0],
avg_coord[1],
avg_coord[2],
color=color,
scale_factor=scale_factor)
def save_image(self,
out_dir,
views=['lat', 'med', 'dor', 'ros', 'caud'],
prefix='',
filetype='jpg'):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
prefix = out_dir + '/' + prefix
self.brain.save_imageset(prefix=prefix, views=views, filetype=filetype)
mlab.close()
fig, ax = plt.subplots(1, len(views), figsize=(15, 8))
for i, view in enumerate(views):
img_name = prefix + '_' + view + '.' + filetype
img = plt.imread(img_name)
ax[i].imshow(img)
ax[i].set_axis_off()
os.remove(img_name)
fig.savefig(prefix + '.png', bbox_inches='tight', dpi=600)
plt.close()
## return saved image path
return prefix + '.png'
data_min = data[data.nonzero()].min()
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file,
min=data_min,
max=data_max,
name="%s_rh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_rh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:, 0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""
Save some images
"""
odir = "/home/data/Projects/CWAS/%s/viz" % study
brain.save_imageset(
path.join(odir, "zpics_surface_%s_rh" % onames[i][j]),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 9 13:22:37 2019
@author: kwn500
"""
from surfer import Brain
import os
subject_id = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
cope_filepath = '/scratch/groups/Projects/P1361/fMRI/fsl_output/level3/freesurfer/'
cope_output = cope_filepath + 'output/'
cope_file = 'shape_versus_passive_zstat_lh'
os.environ['SUBJECTS_DIR'] = "/usr/share/freesurfer-data/subjects"
brain = Brain(subject_id, hemi, surf)
brain.add_overlay(cope_filepath + cope_file + '.mgh', 2.3, 8)
brain.save_imageset(
cope_output + cope_file,
['medial', 'lateral', 'rostral', 'caudal', 'dorsal', 'ventral'], 'png',
[1, 2, 3, 4, 5, 6])
brain.close()
projmeth=projmeth,
projsum=projsum,
projarg=projarg,
smooth_fwhm=smooth_fwhm,
subject_id='fsaverage')
# nothing to do, if all points in the projection are nul
if surf_data.max() != 0:
name_overlay = '{name_roi}_{hemi}'.format(name_roi=name_roi,
hemi=hemi)
brain.add_overlay(surf_data, name=name_overlay, hemi=hemi)
overlay_roi = brain.overlays[name_overlay]
lut = overlay_roi.pos_bar.lut.table.to_array()
lut[0:255, 0:3] = color_list[cpt_color]
overlay_roi.pos_bar.lut.table = lut
# Save in png
#brain.save_imageset(save_png, ['lat'], 'png', colorbar=None)
# Color cpt
cpt_color += 1
# If you just want one roi by one
# Remove for the next roi
# for overlay in brain.overlays_dict[name_overlay]:
# overlay.remove()
# Save in png
save_png = os.path.join(datadir, "all_rois")
brain.save_imageset(save_png, ['lat'], 'png', colorbar=None)
brain.close()
data_min = 0
else:
data_min = data[data.nonzero()].min()
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file, min=data_min, max=data_max, name="%s_lh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_lh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:,0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""
Save images
"""
odir = "/home/data/Projects/CWAS/ldopa/viz"
brain.save_imageset(path.join(odir, "zpics_surface_lh"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
def plot_surface(vtx_data,
subject_id,
hemi,
surface,
subjects_dir,
output_dir,
prefix,
l,
u,
cmap,
thresh,
thresh2=None,
cmap2='autumn',
cortex_style='classic'):
"""
This function needs more documentation, but for now
it is sufficient to know this one important fact:
For the variable "cmap":
If you pass a word that defines a matplotlib
colormap (eg: jet, Rd_Bu etc) then the code
will use that for the color scheme.
If you pass a **list** of colors then you'll
just loop through those colors instead.
"""
if cortex_style.count('_') == 2:
cortex_style_list = cortex_style.split('_')
cortex_name = cortex_style_list[0]
cortex_min = np.float(cortex_style_list[1])
cortex_max = np.float(cortex_style_list[2])
cortex_style = (cortex_name, cortex_min, cortex_max, False)
# Open up a brain in pysurfer
brain = Brain(subject_id,
hemi,
surface,
subjects_dir=subjects_dir,
background="white",
size=(800, 665),
cortex=cortex_style)
# Create an empty brain if the values are all below threshold
if np.max(vtx_data) < thresh:
# Add your data to the brain
brain.add_data(vtx_data * 0,
l,
u,
thresh=thresh,
colormap=cmap,
alpha=0.0)
# If you only have one threshold
# then add the data!
elif not thresh2:
# Add your data to the brain
brain.add_data(vtx_data, l, u, thresh=thresh, colormap=cmap, alpha=.8)
else:
# Plot the data twice for the two
# different settings
vtx_data1 = np.copy(vtx_data)
vtx_data1[vtx_data1 > thresh2] = 0
brain.add_data(vtx_data1, l, u, thresh=thresh, colormap=cmap, alpha=.8)
brain.add_data(vtx_data,
l,
u,
thresh=thresh2,
colormap=cmap2,
alpha=.8)
# Save the images for medial and lateral
# putting a color bar on all of them
brain.save_imageset(prefix=os.path.join(output_dir, prefix),
views=views_list,
colorbar=range(len(views_list)))
## update color scheme
single = [251, 154, 143]
overlap = [227, 26, 28]
lut[0:85,0:3] = single
lut[85:170,0:3] = single
lut[170:256,0:3] = overlap
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""Save Pictures"""
odir = "/home/data/Projects/CWAS/age+gender/03_robustness/viz_cwas/pysurfer"
brain.save_imageset(path.join(odir, "zpics_overlap_age_surface_lh"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
## SEX
"""Bring up the visualization"""
brain = Brain("fsaverage_copy", "lh", "iter8_inflated",
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer")
"""Project the volume file and return as an array"""
cwas_file = path.join(overlap_dir, "surf_lh_fdr_logp_sex_thr2.nii.gz")
"""
"""
===================
Save a set of views
===================
Save some views in png files.
"""
print(__doc__)
from surfer import Brain
sub = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
brain = Brain(sub, hemi, surf)
###############################################################################
# save 1 image
brain.show_view('lat')
brain.save_image("%s_lat.png" % sub)
###############################################################################
# save some images
brain.save_imageset(sub, ['med', 'lat', 'ros', 'caud'], 'jpg')
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer")
"""Get the volume => surface file"""
cwas_file = path.join(mdmr_dir, "surf_rh_fdr_logp_%s.nii.gz" % factor)
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file,
min=2,
max=max_zvals[i],
name="%s_rh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_rh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:, 0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""Save Pictures"""
brain.save_imageset(
path.join(odir, "zpics_%s_%s_surface_rh" % (sample, factor)),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
"lh",
"iter8_inflated",
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer",
)
"""Get the volume => surface file"""
cwas_file = path.join(mdmr_dir, "surf_lh_fdr_logp_%s.nii.gz" % factor)
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file, min=2, max=max_zvals[i], name="%s_lh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_lh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:, 0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""Save Pictures"""
brain.save_imageset(
path.join(odir, "zpics_%s_%s_surface_lh" % (sample, factor)), ["med", "lat", "ros", "caud"], "jpg"
)
brain.close()
l = u*-1
else:
cmap = "jet"
"""
Make a vector containing the data point at each vertex.
"""
vtx_data = roi_data[labels]
"""
Display these values on the brain.
"""
### MEAN
brain = Brain(subject_id, hemi, surface,
subjects_dir = subjects_dir,
config_opts=dict(background="white"))
brain.add_data(vtx_data,
l,
u,
thresh = -98,
colormap=cmap,
alpha=.8)
views_list = [ 'medial', 'lateral' ]
prefix = '_'.join([data_file.strip('.csv'), surface, hemi])
brain.save_imageset(prefix = os.path.join(fs_rois_dir, prefix),
views = views_list,
colorbar = range(len(views_list)) )
if l**2 < u**2:
l = u * -1
else:
u = l * -1
brain.add_data(vtx_data_r,
-0.35,
0.35,
thresh=-98,
colormap="RdBu_r",
alpha=.8)
views_list = ['medial', 'lateral']
prefix = '_'.join([measure1, hemi, surface, 'r', measure2])
brain.save_imageset(prefix=os.path.join(fs_rois_dir, parc, prefix),
views=views_list,
colorbar=range(len(views_list)))
### SIGNIFICANCE w AGE
brain = Brain(subject_id,
hemi,
surface,
subjects_dir=subjects_dir,
config_opts=dict(background="white"))
l = roi_data_p[roi_data_mean > -99].min()
u = roi_data_p[roi_data_mean > -99].max()
l = np.floor(l * 20) / 20.0
u = np.ceil(u * 20) / 20.0
brain.add_data(vtx_data_p,
data = img.get_data()
data_max = data.max()
if data_max == 0:
data_min = 0
else:
data_min = data[data.nonzero()].min()
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file, min=data_min, max=data_max, name="%s_rh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_rh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:, 0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""
Save images
"""
odir = "/home/data/Projects/CWAS/ldopa/viz"
brain.save_imageset(path.join(odir, "zpics_surface_rh"),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
def visOverlay(pathToSurface, overlay, outputPath, hemi, type='white'):
brain = Brain(pathToSurface, hemi, type)
brain.add_overlay(overlay, -1, 1)
brain.show_view('lateral')
brain.save_imageset(outputPath, ['med', 'lat'], 'png')
brain.close()
surf = "pial"
subjects_dir = os.environ.get('SUBJECTS_DIR', '/home/noam/subjects/mri')
# x=range(15)
# cm = plt.get_cmap('jet')
# cNorm = matplotlib.colors.Normalize(vmin=min(x) vmax=max(x))
# return cmx.ScalarMappable(norm=cNorm, cmap=cm)
def get_spaced_colors(n):
HSV_tuples = [(x * 1.0 / n, 0.5, 0.5) for x in range(n)]
RGB_tuples = map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples)
return RGB_tuples
labels = glob.glob(
os.path.join(subjects_dir, subject_id, 'label', 'aparc250',
'*{}.label'.format(hemi)))
groups = set([l.split('/')[-1].split('_')[0] for l in labels])
brain = Brain(subject_id, hemi, surf, offscreen=True)
for group in groups:
group_label = [l for l in labels if group in l]
colors = get_spaced_colors(len(group_label))
for label_id, label in enumerate(group_label):
print(label)
brain.add_label(label, color=colors[label_id])
brain.save_imageset('{}-{}'.format(subject_id, group),
['med', 'lat', 'ros', 'caud'], 'jpg')
break
brain.remove_labels()
print('sdf')
data_min = 0
else:
data_min = data[data.nonzero()].min()
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file, min=data_min, max=data_max, name="%s_rh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_rh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:,0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""
Save some images
"""
odir = "/home/data/Projects/CWAS/%s/viz" % study
brain.save_imageset(path.join(odir, "zpics_surface_%s_rh" % onames[i][j]),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
config_opts=dict(background="white"),
subjects_dir="/home2/data/PublicProgram/freesurfer")
"""Get the volume => surface file"""
cwas_file = path.join(mdmr_dir, "surf_lh_fdr_logp_%s.nii.gz" % factor)
"""
You can pass this array to the add_overlay method for
a typical activation overlay (with thresholding, etc.)
"""
brain.add_overlay(cwas_file, min=2, max=max_zvals[i], name="%s_lh" % factor)
## get overlay and color bar
tmp1 = brain.overlays["%s_lh" % factor]
lut = tmp1.pos_bar.lut.table.to_array()
## update color scheme
lut[:,0:3] = cols
tmp1.pos_bar.lut.table = lut
## refresh view
brain.show_view("lat")
brain.hide_colorbar()
"""Save Pictures"""
brain.save_imageset(path.join(odir, "zpics_%s_%s_surface_lh" % (sample, factor)),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
def plot_MMP(data,
save_file=None,
minmax=None,
thresh=None,
cmap='inferno',
alpha=1,
add_border=False,
bp=3,
title=None):
"""
Plots arbitrary array of data onto MMP parcellation
"""
## Folloing this tutorial
# https://github.com/nipy/PySurfer/blob/master/examples/plot_parc_values.py
# I assume I will always be using this parcellation, at least for now
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
annot_file = subjects_dir + '/fsaverage/label/lh.HCPMMP1.annot'
mmp_labels, ctab, names = ni.freesurfer.read_annot(annot_file)
if len(names) - len(data) == 1:
# short one label cuz 0 is unassigned in the data, fill with big negative number
data_app = np.hstack((-1e6, data))
vtx_data = data_app[mmp_labels]
else:
vtx_data = data[mmp_labels]
vtx_data[mmp_labels < 1] = -1e6
# plot brain
brain = Brain('fsaverage',
'lh',
'inflated',
subjects_dir=subjects_dir,
cortex=None,
background='white',
size=800,
show_toolbar=False,
offscreen=True)
if add_border:
brain.add_annotation(
(mmp_labels, np.array([[0, 0, 0, c[3], c[4]] for c in ctab])))
if minmax is None:
minmax = [np.min(data), np.max(data)]
# add data
if thresh is None:
thresh = minmax[0]
brain.add_data(vtx_data,
minmax[0],
minmax[1],
colormap=cmap,
alpha=alpha,
colorbar=False,
thresh=thresh)
# plot brain views
brainviews = brain.save_imageset(None, ['lat', 'med'])
# merge brainviews and plot horizontally
plt.imshow(np.concatenate(brainviews, axis=1), cmap=cmap)
despine(bottom=True, left=True)
plt.xticks([])
plt.yticks([])
cbaxes = inset_axes(plt.gca(),
width="50%",
height="4%",
loc=8,
borderpad=bp)
plt.colorbar(cax=cbaxes, orientation='horizontal')
plt.clim(minmax[0], minmax[1])
plt.tight_layout()
if title is not None:
plt.title(title)
if save_file:
plt.savefig(save_file, bbox_inches='tight')