def plot_overlays_diff_group_window(condition,method,modality,hemi,window,azimuth,elevation):
subject_id, surface = 'fsaverage', 'inflated'
hemi = hemi
brain = Brain(subject_id, hemi, surface, size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/BrainMaps/IcaCorr_Normalized'
+ modality + '_' + condition[0] + '-' + condition[1] + '_pick_oriNone_' + method
+ '_ico-5-fwd-fsaverage.stc-'+ hemi +'.stc')
stcl_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/BrainMaps/IcaCorr_Normalized'
+ modality + '_' + condition[0] + '-' + condition[1] + '_pick_oriNone_' + method
+ '_ico-5-fwd-fsaverage.stc-lh.stc')
stcr_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/BrainMaps/IcaCorr_Normalized'
+ modality + '_' + condition[0] + '-' + condition[1] + '_pick_oriNone_' + method
+ '_ico-5-fwd-fsaverage.stc-rh.stc')
stcl = read_stc(stcl_fname)
stcr = read_stc(stcr_fname)
datal = stcl['data']
datar = stcr['data']
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
winstart = np.where(time < window[0])[0][-1]
winend = np.where(time >= window[1])[0][0]
meanval = np.mean(data[:,winstart:winend],1)
meanvalr = np.mean(datar[:,winstart:winend],1)
meanvall = np.mean(datal[:,winstart:winend],1)
maxval = np.max([np.max(meanvalr),np.max(meanvall)])
minval = np.min([np.min(meanvalr),np.min(meanvall)])
fmin = -np.max(np.abs([maxval,minval]))*0.8
fmax = np.max(np.abs([maxval,minval]))*0.8
colormap = mne.viz.mne_analyze_colormap(limits=[fmin, fmin/3, fmin/3.1, fmax/3.1, fmax/3, fmax], format='mayavi')
#colormap = 'jet'
time_label = lambda t: 'time=%0.2f ms' % (0)
brain.add_data(meanval, colormap=colormap, vertices=vertices,
smoothing_steps=15, time=time, time_label=time_label,
hemi=hemi)
brain.scale_data_colormap(fmin=fmin, fmid=0, fmax=fmax, transparent=False)
brain.show_view(dict(azimuth=azimuth,elevation=elevation, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plots/IcaCorr_Window_'
+ condition[0] + '-' + condition[1] + str(window[0]) + '-' + str(window[1]))
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/IcaCorr_' + modality + '_' + method + '_'
+ '_Normalized' + condition[0] + '-' + condition[1] + '_'
+ str(window[0]) + '-' + str(window[1])
+ hemi + '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'+'.png')
def plot_overlays_Fgroup(condition,modality,hemi,azimuth):
brain = Brain(subject_id='fsaverage', hemi=hemi,surf='pial',cortex = 'low_contrast', size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plot_STATS/'
+"_vs_".join(condition) +'/fmap'+ modality+ '_'
+"_vs_".join(condition)+ '-' + hemi+'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
brain.add_data(data, thresh = 3.259,colormap='hot',alpha=1, vertices=vertices,
smoothing_steps=3,hemi=hemi)
brain.set_data_time_index(0)
brain.scale_data_colormap(fmin=3.26, fmid=5.84, fmax= 8.42, transparent=False)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plot_STATS/'
+ "_vs_".join(condition))
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/Fmap_IcaCorr_' + modality + '_' + 'dSPM' + '_'
+ '_' + "_vs_".join(condition) + '_'
+ hemi + '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'+'.png')
def plot_ctx():
# Visualize the left or right hemisphere activation map per run with Yeo 2011 overlay of functional connectivity maps
# State path to statistical mask files generated by freesurfer processes
path = "/home/lauri/Documents/temp/"
statpath = os.listdir(path)
statfile = []
hemispheres = ['lh','rh']
# Creates a function to grab all cortical statistical files
for h in hemispheres:
for s in statpath:
if s.startswith(h) and s.endswith("gz"):
statfile.append(s)
print('Making images for:')
statfile.sort()
print(statfile)
# Create the image using pysurfer with backend function initialized
for h in hemispheres:
for f in statfile:
sig = os.path.join(path,f)
sub = "fsaverage"
hemi = h
surf = "inflated"
view_types = ['lat','med']
brain = Brain(sub, hemi, surf, cortex=None, background="midnightblue", views=view_types)
overlay_file = sig
brain.add_overlay(sig, min=1, max=3, name="ovrlay")
# To change colour of activations: note replace pos or neg: brain.overlays["ovrlay"].neg_bar.lut_mode = "winter"
subjects_dir = os.environ["SUBJECTS_DIR"]
annot_path = os.path.join(subjects_dir, sub, "label", "lh.Yeo2011_7Networks_N1000.annot")
brain.add_annotation(annot_path, hemi=h, borders=False, alpha=.33)
brain.save_image(os.path.join(path,f + ".jpeg"))
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 curvature_normalization(data_dir, subj, close=True):
"""Normalize the curvature map and plot contour over fsaverage."""
surf_dir = op.join(data_dir, subj, "surf")
snap_dir = op.join(data_dir, subj, "snapshots")
for hemi in ["lh", "rh"]:
cmd = ["mri_surf2surf",
"--srcsubject", subj,
"--trgsubject", "fsaverage",
"--hemi", hemi,
"--sval", op.join(surf_dir, "%s.curv" % hemi),
"--tval", op.join(surf_dir, "%s.curv.fsaverage.mgz" % hemi)]
sub.check_output(cmd)
b = Brain("fsaverage", hemi, "inflated",
config_opts=dict(background="white",
width=700, height=500))
curv = nib.load(op.join(surf_dir, "%s.curv.fsaverage.mgz" % hemi))
curv = (curv.get_data() > 0).squeeze()
b.add_contour_overlay(curv, min=0, max=1.5, n_contours=2, line_width=4)
b.contour["colorbar"].visible = False
for view in ["lat", "med"]:
b.show_view(view)
mlab.view(distance=330)
png = op.join(snap_dir, "%s.surf_warp_%s.png" % (hemi, view))
b.save_image(png)
if close:
b.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 pysurfer_plot_perm_ttest_results(vertices, vertives_values, max_vals, fol):
T = max(vertices.keys())
for t in range(T+1):
print(t)
brain = Brain('fsaverage', 'split', 'pial', curv=False, offscreen=False, views=['lat', 'med'], title='{} ms'.format(t))
for hemi in ['rh', 'lh']:
if t in vertices:
brain.add_data(np.array(vertives_values[t][hemi]), hemi=hemi, min=1, max=max_vals, remove_existing=True,
colormap="YlOrRd", alpha=1, vertices=np.array(vertices[t][hemi]))
brain.save_image(os.path.join(fol, '{}.jpg'.format(t)))
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 make_pysurfer_images(folder,suffix='cope1',threshold=0.9499,coords=(),surface='inflated',fwhm=0,filename='',saveFolder=[]):
from surfer import Brain, io
TFCEposImg,posImg,TFCEnegImg,negImg=getFileNamesfromFolder(folder,suffix)
pos=image.math_img("np.multiply(img1,img2)",
img1=image.threshold_img(TFCEposImg,threshold=threshold),img2=posImg)
neg=image.math_img("np.multiply(img1,img2)",
img1=image.threshold_img(TFCEnegImg,threshold=threshold),img2=negImg)
fw=image.math_img("img1-img2",img1=pos,img2=neg)
if fwhm==0:
smin=np.min(np.abs(fw.get_data()[fw.get_data()!=0]))
else:
smin=2
mri_file = "%s/thresholded_posneg.nii.gz" % folder
fw.to_filename(mri_file)
"""Bring up the visualization"""
brain = Brain("fsaverage", "split", surface ,views=['lat', 'med'], offscreen=True , background="white")
"""Project the volume file and return as an array"""
reg_file = os.path.join("/opt/freesurfer","average/mni152.register.dat")
surf_data_lh = io.project_volume_data(mri_file, "lh", reg_file,smooth_fwhm=fwhm)
surf_data_rh = io.project_volume_data(mri_file, "rh", reg_file,smooth_fwhm=fwhm)
"""
You can pass this array to the add_overlay method for a typical activation
overlay (with thresholding, etc.).
"""
brain.add_overlay(surf_data_lh, min=smin, max=5, name="ang_corr_lh", hemi='lh')
brain.add_overlay(surf_data_rh, min=smin, max=5, name="ang_corr_rh", hemi='rh')
if len(coords)>0:
if coords[0]>0:
hemi='rh'
else:
hemi='lh'
brain.add_foci(coords, map_surface="pial", color="gold",hemi=hemi)
if len(saveFolder)>0:
folder=saveFolder
brain.save_image('%s/%s.png' % (folder,filename))
else:
brain.save_image('%s/surfaceplot.jpg' % folder)
brain.close()
def plot_data_surf_bh(in_file, colormap='jet', thr_list=[(None, None, None)],roi_coords=(), fwhm=0):
'''
allows more flexible visualization than plot_rs_surf_bh
thr_list = [(min, max, thresh)]
colormap: matplotlib colormap (http://matplotlib.org/examples/color/colormaps_reference.html)
'''
# in_file .nii to be projected on surface
import os
from surfer import Brain, io
out_file_list = []
in_file_name = os.path.basename(in_file)
reg_file = os.path.join(os.environ["FREESURFER_HOME"],"average/mni152.register.dat")
for thr in thr_list:
min_thr = thr[0]
max_thr = thr[1]
thr_thr = thr[2]
brain = Brain("fsaverage", "split", "inflated", views=['lat', 'med'], config_opts=dict(background="white"))
surf_data_lh = io.project_volume_data(in_file, "lh", reg_file, smooth_fwhm=fwhm)
surf_data_rh = io.project_volume_data(in_file, "rh", reg_file, smooth_fwhm=fwhm)
brain.add_data(surf_data_lh, min=min_thr, max=max_thr, thresh=thr_thr, colormap=colormap, hemi='lh')
brain.add_data(surf_data_rh, min=min_thr, max=max_thr, thresh=thr_thr, colormap=colormap, hemi='rh')
roi_str = ''
if not(roi_coords == ()):
if roi_coords[0] <0: #lh
hemi_str = 'lh'
else:
hemi_str = 'rh'
roi_str = '_roi_%s.%s.%s' % roi_coords
brain.add_foci(roi_coords, map_surface="white", hemi=hemi_str, color='red', scale_factor=2)
out_filename = os.path.join(os.getcwd(), in_file_name + roi_str + '_thr_%s' % min_thr + '.png')
out_file_list += [out_filename]
brain.save_image(out_filename)
brain.close()
return out_file_list
def plot_rs_surf(in_file, thr_list=[(.2,1)],roi_coords=(), fwhm=0):
# in_file .nii to be projected on surface
# list of tuples defining min and max thr_list=[(.2,1)]
import os
import subprocess
from surfer import Brain, io
arch = subprocess.check_output('arch')
if arch.startswith('x86_'): # set offscrene rendering to avoid intereference on linux
from mayavi import mlab
mlab.options.offscreen = True
out_file_list = []
in_file_name = os.path.basename(in_file)
reg_file = os.path.join(os.environ["FREESURFER_HOME"],"average/mni152.register.dat")
for thr in thr_list:
min_thr = thr[0]
max_thr = thr[1]
print(min_thr)
print(max_thr)
for hemi in ['lh', 'rh']:
brain = Brain("fsaverage", hemi, "inflated", views=['lat', 'med'], config_opts=dict(background="white"))
surf_data = io.project_volume_data(in_file, hemi, reg_file, smooth_fwhm=fwhm)
brain.add_overlay(surf_data, min=min_thr, max=max_thr, name="ang_corr", hemi=hemi)
roi_str = ''
if not(roi_coords == ()):
if roi_coords[0] <0: #lh
hemi_str = 'lh'
else:
hemi_str = 'rh'
roi_str = '_roi_%s.%s.%s' % roi_coords
if hemi_str == hemi:
brain.add_foci(roi_coords, map_surface="white", hemi=hemi_str, color='red', scale_factor=2)
out_filename = os.path.join(os.getcwd(), in_file_name + roi_str + '_thr_%s' % min_thr + '_' + hemi + '.png')
out_file_list += [out_filename]
brain.save_image(out_filename)
brain.close()
return out_file_list
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 vis_ers_comp(group=None,phase=None,surf=None,cmap=None,split='lh'):
mri_file = f'/mnt/c/Users/ACH/Desktop/ers_comps/{group}_{phase}/{group}_{phase}_ClusterEffEst.nii.gz' #find the file containing stats
surf_data_lh = project_volume_data(mri_file, "lh", reg_file, projarg=[0, 1, .01], smooth_fwhm=1) #project to lh
_max = .55 if phase == 'acquisition' else .3
for view in ['med','lat']: #lateral and medial views
brain = Brain('MNI2009c', split, surf, cortex='low_contrast',size=1000,
views=view, background='white', foreground=None) #initialize the brain object
brain.add_data(surf_data_lh, 0, _max, center=None, hemi='lh', thresh=None,
colorbar=False, colormap=cmap, transparent=True) #add lh data
for vert, color in zip([115262,135014],['white','black']): #add focal ROIs
brain.add_foci(vert,coords_as_verts=True,color=color,alpha=1)
fname = f'/mnt/c/Users/ACH/Documents/gPPI/paper/pysurfer/{group}_{phase}_{view}.png'
os.system(f'rm {fname}')
brain.save_image(fname,antialiased=True)
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 plot_surface_vertices(common_space, morph_data, vertex_idx, aug_data, hemi,
surf, view, cmap, save_path):
b = Brain(common_space, hemi, surf, background="white", views=view)
x, y, z = b.geo[hemi].coords.T
coords = np.array([x, y, z]).T
print('Number of vertices to be plotted {}'.format(len(vertex_idx)))
if aug_data:
aug_vertex_idx = get_nbrs(coords, vertex_idx)
print('Number of vertices to be plotted after augmentation {}'.format(
len(aug_vertex_idx)))
morph_data[aug_vertex_idx] = 1
else:
morph_data[vertex_idx] = 1
print(np.sum(morph_data))
b.add_data(morph_data, colormap=cmap, alpha=.9, colorbar=True)
print(save_path)
b.save_image(save_path)
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.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 plot_rs_surf_bh(in_file, thr_list=[(.2,1)],roi_coords=(), fwhm=0):
# in_file .nii to be projected on surface
# list of tuples defining min and max thr_list=[(.2,1)]
import os
from surfer import Brain, io
out_file_list = []
in_file_name = os.path.basename(in_file)
reg_file = os.path.join(os.environ["FREESURFER_HOME"],"average/mni152.register.dat")
for thr in thr_list:
min_thr = thr[0]
max_thr = thr[1]
print(min_thr)
print(max_thr)
brain = Brain("fsaverage", "split", "inflated", views=['lat', 'med'], config_opts=dict(background="white"))
surf_data_lh = io.project_volume_data(in_file, "lh", reg_file, smooth_fwhm=fwhm)
surf_data_rh = io.project_volume_data(in_file, "rh", reg_file, smooth_fwhm=fwhm)
brain.add_overlay(surf_data_lh, min=min_thr, max=max_thr, name="ang_corr_lh", hemi='lh')
brain.add_overlay(surf_data_rh, min=min_thr, max=max_thr, name="ang_corr_rh", hemi='rh')
roi_str = ''
if not(roi_coords == ()):
if roi_coords[0] <0: #lh
hemi_str = 'lh'
else:
hemi_str = 'rh'
roi_str = '_roi_%s.%s.%s' % roi_coords
brain.add_foci(roi_coords, map_surface="white", hemi=hemi_str, color='red', scale_factor=2)
out_filename = os.path.join(os.getcwd(), in_file_name + roi_str + '_thr_%s' % min_thr + '.png')
out_file_list += [out_filename]
brain.save_image(out_filename)
brain.close()
return out_file_list
def plot_overlays_diff_singlesubj(subject,condition,method,modality,hemi,indextime, azimuth):
subject_id, surface = 'fsaverage', 'inflated'
hemi = hemi
brain = Brain(subject_id, hemi, surface, size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/' + subject + '/mne_python/STCS_diff/IcaCorr_'
+ condition[0] + '-' + condition[1]
+ '/' + modality + '_' + method + '_' + subject
+ '_' + condition[0] + '-' + condition[1]
+ '_' + '_ico-5-fwd-fsaverage-.stc-'+hemi+'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
colormap = 'hot'
time_label = lambda t: 'time=%0.2f ms' % (t * 1e3)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=4, time=time, time_label=time_label,
hemi=hemi)
brain.set_data_time_index(indextime)
brain.scale_data_colormap(fmin=0, fmid=2.5, fmax=5, transparent=True)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
realtime = stc['tmin'] + stc['tstep']*indextime
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/' + subject + '/mne_python/BrainMaps/IcaCorr_' +
+ condition[0] + '-' + condition[1])
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/IcaCorr_' + modality + '_' + method + '_' + subject
+ '_' + condition[0] + '-' + condition[1] + '_' + str(realtime) + hemi
+ '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'
+'.png')
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 plot_overlays_diff_group(condition,method,modality,hemi,indextime,azimuth):
hemi = hemi
brain = Brain(subject_id='fsaverage', hemi=hemi, surface='pial', size=(600, 600))
stc_fname = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/BrainMaps/IcaCorr_'
+ modality + '_' + condition[0] + '-' + condition[1] + '_pick_oriNone_' + method
+ '_ico-5-fwd-fsaverage.stc-'+ hemi +'.stc')
stc = read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1])
# colormap = 'seismic'
colormap = mne.viz.mne_analyze_colormap(limits=[-3,-1.81,-1.80, 1.80,1.81, 3], format='mayavi')
time_label = lambda t: 'time=%0.2f ms' % (t * 1e3)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=20, time=time, time_label=time_label,
hemi=hemi)
brain.set_data_time_index(indextime)
brain.scale_data_colormap(fmin=-1.82, fmid=0, fmax= 1.82, transparent=False)
brain.show_view(dict(azimuth=azimuth,elevation=None, distance=None))
# mayavi.mlab.view(azimuth=0, elevation=None, distance=None, focalpoint=None,
# roll=None, reset_roll=True, figure=None)
realtime = stc['tmin'] + stc['tstep']*indextime
PlotDir = []
PlotDir = ('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/mne_python/Plots/IcaCorr_'
+ condition[0] + '-' + condition[1] )
if not os.path.exists(PlotDir):
os.makedirs(PlotDir)
brain.save_image(PlotDir + '/IcaCorr_' + modality + '_' + method + '_'
+ '_' + condition[0] + '-' + condition[1] + '_' + str(realtime)
+ hemi + '_'+ str(azimuth)+ '_ico-5-fwd-fsaverage-'+'.png')
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 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()
right_df = pd.DataFrame({
'col': [
key.replace('rh_', '').replace('.', '-').replace('_thickness_D', '')
for key, val in right_count.items()
],
'val': [val for key, val in right_count.items()]
})
label_dir = '/cm/shared/openmind/freesurfer/5.3.0/subjects/fsaverage/label'
left_label_file = 'lh.aparc.a2009s.annot'
right_label_file = 'rh.aparc.a2009s.annot'
lh_aparc_file = os.path.join(label_dir, left_label_file)
rh_aparc_file = os.path.join(label_dir, right_label_file)
lh_labels, lh_ctab, lh_names = nb.freesurfer.read_annot(lh_aparc_file)
rh_labels, rh_ctab, rh_names = nb.freesurfer.read_annot(rh_aparc_file)
left_df = left_df.set_index('col').loc[lh_names].reset_index().fillna(0)
right_df = right_df.set_index('col').loc[rh_names].reset_index().fillna(0)
vtx_lh = left_df.val.values[lh_labels]
vtx_lh[lh_labels == -1] = 0
vtx_rh = right_df.val.values[rh_labels]
vtx_rh[rh_labels == -1] = 0
brain.add_data(vtx_lh, 0, 400, colormap="Reds", alpha=.8, hemi='lh')
brain.add_annotation(lh_aparc_file, hemi='lh')
brain.add_data(vtx_rh, 0, 400, colormap="Reds", alpha=.8, hemi='rh')
brain.add_annotation(rh_aparc_file, hemi='rh', remove_existing=False)
save_name = "../images/{}_brain.png".format(save_name)
brain.save_image(save_name)
hemi='lh')
brain.add_overlay(surf_data_rh,
min=minval,
max=maxval,
name="thirty_sec_rh",
hemi='rh')
for overlay in brain.overlays_dict["thirty_sec_lh"]:
overlay.remove()
for overlay in brain.overlays_dict["thirty_sec_rh"]:
overlay.remove()
brain.add_data(surf_data_lh,
0,
maxval,
colormap="rainbow",
alpha=.65,
hemi='lh')
brain.add_data(surf_data_rh,
0,
maxval,
colormap="rainbow",
alpha=.65,
hemi='rh')
brain.save_image(datadir + 'plots/avg_raw_zthresh_' + str(z) + ".png")
mlab.show()
brain.show_view(view)
"""
===================
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')
grade = 3
annot = "aparc_sub"
subject = "fsaverage"
subjects_dir = config.get_subjects_dir_subj("sample")
ground_metric = np.load("data/ground_metric.npy")
hemi = "lh"
label_index = 0
labels = mne.read_labels_from_annot(subject, annot, hemi,
subjects_dir=subjects_dir)
labels = [label.morph(subject_to=subject, subject_from=subject,
grade=grade, subjects_dir=subjects_dir)
for label in labels]
distances = np.zeros(642)
label_vertices = labels[label_index].vertices
for ii, label_i in enumerate(labels):
v_i = label_i.vertices
distances[v_i] = ground_metric[0][ii]
f = mlab.figure(size=(700, 600))
brain = Brain(subject, hemi, "inflated", subjects_dir=subjects_dir, figure=f,
background="white", foreground='black')
vertices = np.arange(642)
brain.add_annotation('aparc_sub', alpha=0.2)
brain.add_data(distances, vertices=vertices,
hemi="lh", transparent=True, smoothing_steps=5,
colormap="hot", alpha=0.95, mid=40)
brain.save_image("data/ground_metric.png")
from surfer import Brain
DST = '../images/brain_%s.png'
subjects_dir = '/Volumes/Seagate/refpred/mri'
surfaces = ('inflated',
'inflated_avg',
'inflated_pre',
# 'orig',
# 'pial',
'smoothwm',
'sphere',
'white')
for surf in surfaces:
brain = Brain('fsaverage', 'lh', surf, size=400, background='white',
subjects_dir=subjects_dir)
brain.screenshot()
brain.save_image(DST % surf, 'rgba', True)
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')
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.add_label("ROI8", color="blue")
#brain.add_label("ROI9", color="blue")
#brain.add_label("ROI10", color="blue")
#brain.add_label("ROI11", color="blue")
#brain.add_label("RefROI1", color="yellow")
#
brain.show_view("lateral")
brain.save_image('/home/qdong/freesurfer/subjects/101611/lh_ROI2.tiff')
from surfer import Brain
DST = '../images/brain_%s.png'
subjects_dir = '/Volumes/Seagate/refpred/mri'
surfaces = ('inflated',
'inflated_avg',
'inflated_pre',
# 'orig',
# 'pial',
'smoothwm',
'sphere',
'white')
for surf in surfaces:
brain = Brain('fsaverage', 'lh', surf, size=400, background='white',
subjects_dir=subjects_dir)
brain.save_image(DST % surf, 'rgba', True)
time=str(time), subid=subid))
############################################
# Visualize accuracy (for given subject)
############################################
from surfer import Brain, project_volume_data
tr_shift_test_list = [0, 2, 4, 6, 8, 10,
12] # seconds to shift onset forward by
# for subid in subj_info.subid:
subid = 'ap101'
for time in tr_shift_test_list:
brain = Brain(subid,
"split",
"inflated",
views=['lat', 'med', 'ven'],
background="white")
volume_file = '/Volumes/group/awagner/sgagnon/AP/analysis/mvpa_raw/searchlight_test/sourcehit_time{time}_acc_{subid}.nii.gz'.format(
time=str(time), subid=subid)
for hemi in ['lh', 'rh']:
zstat = project_volume_data(volume_file,
hemi,
subject_id=subid,
smooth_fwhm=0.5)
brain.add_overlay(zstat, hemi=hemi, min=.333)
brain.save_image(
'/Volumes/group/awagner/sgagnon/AP/analysis/mvpa_raw/searchlight_test/sourcehit_time{time}_acc_{subid}.png'
.format(time=str(time), subid=subid))
#Plot grid locations on brain, save to source_peripheral
#NEED TO DO - FIGURE OUT HOW TO GET CHILD WINDOW
brain = Brain(subj_id, "both", "inflated", views="frontal")
for b in brain.brains:
surf = b._geo
if surf.hemi == 'lh':
vertidx = np.where(src[0]['inuse'])[0]
elif surf.hemi == 'rh':
vertidx = np.where(src[1]['inuse'])[0]
mayavi.mlab.points3d(surf.x[vertidx],
surf.y[vertidx],
surf.z[vertidx],
color=(1, 1, 0),
scale_factor=1.5)
brain.save_image(sourceP_dir + subj_id + '_vertex.png')
brain.close()
#Compute BEM / forward model (aka electromagnetic current runs thusly...)
conductivity = (0.3, ) # for single layer
model = mne.make_bem_model(subject=subj_id,
ico=4,
conductivity=conductivity)
mne.write_bem_surfaces(sourceP_dir + subj_id[:4] + '_bem_model.fif', model)
bem_sol = mne.make_bem_solution(model)
mne.write_bem_solution(sourceP_dir + subj_id[:4] + '_bem_sol.fif', bem_sol)
infosrc = sensor_dir + subj_id[:4] + '_conds_freqs-ave.fif'
fname = sourceP_dir + subj_id[:4] + '-fwd.fif'
fwd = mne.make_forward_solution(info=infosrc,
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.add_label("ROI8", color="blue")
#brain.add_label("ROI9", color="blue")
#brain.add_label("ROI10", color="blue")
#brain.add_label("ROI11", color="blue")
#brain.add_label("RefROI1", color="yellow")
#
brain.show_view("lateral")
brain.save_image('/home/qdong/freesurfer/subjects/101611/lh_ROI2.tiff')
surf_data_lh = project_volume_data(mri_file,"lh",reg_file)
surf_data_rh = project_volume_data(mri_file,"rh",reg_file)
qmri_file = '/Users/jamalw/Desktop/PNI/music_event_structures/qstats_map.nii.gz'
qdata_lh = project_volume_data(qmri_file,"lh",reg_file)
surf_data_lh[np.logical_or(qdata_lh>0.005,surf_data_lh<0)] = 0
qdata_rh = project_volume_data(qmri_file,"rh",reg_file)
surf_data_rh[np.logical_or(qdata_rh>0.005, surf_data_rh<0)] = 0
brain.add_overlay(surf_data_lh,min=minval,max=maxval,name="thirty_sec_lh", hemi='lh',sign="pos")
brain.add_overlay(surf_data_rh,min=minval,max=maxval,name="thirty_sec_rh", hemi='rh',sign="pos")
#
#for overlay in brain.overlays_dict["thirty_sec_lh"]:
# overlay.remove()
#for overlay in brain.overlays_dict["thirty_sec_rh"]:
# overlay.remove()
#
#brain.add_data(surf_data_lh, minval,maxval, colormap="CMRmap", alpha=1,
# hemi='lh')
#brain.add_data(surf_data_rh, minval,maxval, colormap="CMRmap", alpha=1,
# hemi='rh')
#
brain.save_image("zstats_human_bounds_srm_k30.png")
#
mlab.show()
brain.show_view(view)
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()
from mayavi import mlab
from surfer import Brain, io
subject = 'fsaverage' # SAD_017
surface = 'pial'
br1 = Brain(subject, 'rh', surface, title='lgroup')
l_surf_data = io.project_volume_data('/mindhive/scratch/satra/sadfigures/nipype_mem/nipype-interfaces-spm-model-EstimateContrast/cf535c8b3e6380c2c8512307f3c294ad/spmT_0001.img',
'rh', subject_id='fsaverage',
target_subject=subject)
br1.add_overlay(l_surf_data, min=2, max=3.7, sign='pos', name='lgroup')
br2 = Brain(subject, 'rh', surface, title='hgroup')
h_surf_data = io.project_volume_data('/mindhive/scratch/satra/sadfigures/nipype_mem/nipype-interfaces-spm-model-EstimateContrast/d1fbe9c9d24038d7d1e16b16936f52ed/spmT_0001.img',
'rh', subject_id='fsaverage',
target_subject=subject)
br2.add_overlay(h_surf_data, min=2, max=3.7, sign='pos', name='hgroup')
mlab.sync_camera(br1._f, br2._f)
mlab.sync_camera(br2._f, br1._f)
"""
br1.save_image('lgroup.png')
br2.save_image('hgroup.png')
"""
def plot_4_view(data1,
parcel_names,
parcellation,
style='linear',
alpha=0.95,
zmin=None,
zmax=None,
zmid=None,
cmap='auto',
show=True,
filename=None,
surface='inflated',
null_val=0,
transparent=True,
subj='fsaverage',
sub_dir='K:\\palva\\resting_state\\_fsaverage\\'):
'''
Plots 1d array of data. Plotted views are lateral and medial on both HS.
Used brain is fsaverage.
INPUT:
data1: 1-dimensional data array, len = # parcels.
1st half must be left HS, 2nd half right.
parcel_names: Parcel_names, in the same order as the data.
parcellation: Abbreviation, e.g. 'parc2018yeo7_100' or "parc2009'
style: 'linear': pos. values only, 'divergent': both pos & neg
alpha: Transparency value; transparency might look weird.
zmin: The minimum value of a linear z-axis, or center of a
divergent axis (thus should be 0)
zmax: Maximum value of linear z-axis, or max/-min of div.
zmid: Midpoint of z-axis.
cmap: Colormap by name. Default is 'rocket' for linear, and
'icefire' for divergent; other recommended options:
'YlOrRd' for linear, or 'bwr' for divergent.
show: If False, plot is closed after creation.
filename: File to save plot as, e.g. 'plot_13.png'
surface: Surface type.
null_val: Value for unassigned vertices
transparent: Whether parcels with minimum value should be transparent.
OUTPUT:
instance of surfer.Brain, if show==True
'''
N_parc = len(data1) # the number of actually used parcels
if len(parcel_names) != N_parc:
raise ValueError('Number of parcels != len(data1) ')
if parcel_names[0][-3:] != '-lh':
parcel_names[:N_parc //
2] = [p + '-lh' for p in parcel_names[:N_parc // 2]]
parcel_names[N_parc //
2:] = [p + '-rh' for p in parcel_names[N_parc // 2:]]
hemi = 'split'
#### load parcels
if parcellation == 'parc2009':
aparc_lh_file = sub_dir + '\\' + subj + '\\label\\lh.aparc.a2009s.annot'
aparc_rh_file = sub_dir + '\\' + subj + '\\label\\rh.aparc.a2009s.annot'
else:
aparc_lh_file = sub_dir + '\\' + subj + '\\label\\lh.' + parcellation + '.annot'
aparc_rh_file = sub_dir + '\\' + subj + '\\label\\rh.' + parcellation + '.annot'
labels_lh, ctab, names_lh = nib.freesurfer.read_annot(aparc_lh_file)
labels_rh, ctab, names_rh = nib.freesurfer.read_annot(aparc_rh_file)
names_lh = [str(n)[2:-1] + '-lh' for n in names_lh]
names_rh = [str(n)[2:-1] + '-rh' for n in names_rh]
N_label_lh = len(
names_lh
) # number of labels/parcels with unkown and med. wall included
N_label_rh = len(names_rh)
#### map parcels in data to loaded parcels
indicesL = np.full(N_label_lh, -1)
indicesR = np.full(N_label_rh, -1)
for i in range(N_parc):
for j in range(N_label_lh):
if names_lh[j] == parcel_names[i]:
indicesL[j] = i
for j in range(N_label_rh):
if names_rh[j] == parcel_names[i]:
indicesR[j] = i - N_parc // 2
indicesL += 1
indicesR += 1
## assign values to loaded parcels
data1L = np.concatenate(([null_val], data1[:N_parc // 2]))
data1R = np.concatenate(([null_val], data1[N_parc // 2:]))
data_left = data1L[indicesL]
data_right = data1R[indicesR]
## map parcel values to vertices
vtx_data_left = data_left[labels_lh]
vtx_data_left[labels_lh == -1] = null_val
vtx_data_right = data_right[labels_rh]
vtx_data_right[labels_rh == -1] = null_val
if zmin == None:
zmin = 0
if zmax == None:
zmax = np.nanmax(abs(data1))
if zmid == None:
zmid = zmax / 2
if style == 'linear': # shows only positive values
center = None
elif style == 'divergent': # shows positive and negative values
center = 0
#### plot to 4-view Brain
hemi = 'split'
brain = Brain(subj,
hemi,
background='white',
surf=surface,
size=[900, 800],
cortex='classic',
subjects_dir=sub_dir,
views=['lat', 'med'])
brain.add_data(vtx_data_left,
zmin,
zmax,
colormap=cmap,
center=center,
alpha=alpha,
hemi='lh')
brain.add_data(vtx_data_right,
zmin,
zmax,
colormap=cmap,
center=center,
alpha=alpha,
hemi='rh')
# adjust colorbar
brain.scale_data_colormap(zmin,
zmid,
zmax,
transparent=transparent,
center=center,
alpha=alpha,
verbose=None) #data=None, hemi=None,
if filename != None:
brain.save_image(filename)
if show:
return brain
CIs = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_consensus_CI')
coords = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_coordinates')
PC = np.loadtxt('//Volumes/neuro/Rest/Thalamic_parcel/roiPC')
PC = PC/100
PC[PC>0.8] = 1
#PC[PC<0.3] = 0
PC = PC/0.9
from matplotlib import cm
for i, coord in enumerate(coords):
if CIs[i] != 0:
if coord[0] < 0:
brain.add_foci(coord, map_surface="white", color=cm.inferno(int(PC[i]*255))[0:3])
# save 1 image
brain.save_image("pc_lat.tiff")
###### to visualize cortical ROI wmd values
subject_id = "fsaverage"
subjects_dir = os.environ["SUBJECTS_DIR"]
brain = Brain("fsaverage", "lh", "inflated", views=['lat', 'med'], background="white")
CIs = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_consensus_CI')
coords = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_coordinates')
WMD = np.loadtxt('//Volumes/neuro/Rest/Thalamic_parcel/roiWMD')
WMD = WMD/100
WMD = WMD+1.5
WMD[WMD<0] =0
WMD = WMD/3
WMD[WMD>1] = 1
from surfer import Brain
subj_dir = '/usr/local/freesurfer/subjects'
subject = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
brain = Brain(subject, hemi, surf, subjects_dir=subj_dir)
brain.show_view('lat')
brain.save_image("pysurfer_test.png")
for hem,h in zip(['lh','rh'],[0,1]):
vtx, _, t = stc_avg_cropped.center_of_mass(subject = 'fsaverage',hemi=h, restrict_vertices=False)
# vtx2,_2,t2 = stc_avg_cropped.center_of_mass(subject = 'fsaverage',hemi=hem, restrict_vertices=False)
print(vtx)
np.save(meg_dir + '_STCS/%s/%s/%s/_CM_%s_%s_%s_%s_cropped.npy' % (tonetype,key,freq,tonetype,key,freq,hem), (vtx, _, t))
tonetype = 'pure'
hemi = 'rh'
hem = 1
cmap = cm.rainbow(np.linspace(0,1,len(frequencies)))
t_bin = []
b = Brain('fsaverage', hemi = hemi , surf = 'pial',background='white',alpha=0.5)
for f,freq in enumerate(frequencies):
vtx, _, t = np.load(meg_dir + '_STCS/%s/%s/%s/_CM_%s_%s_%s_%s_cropped.npy' % (tonetype,key,freq,tonetype,key,freq,hemi))
# vtx, _, t = np.load(meg_dir + '_STCS/%s/%s/_CM_%s_%s_%s_cropped.npy' % (tonetype,freq,tonetype,freq,hemi))
# get the mni co-ordinates of the center of mass
coords = vertex_to_mni(int(vtx), hemis=hem, subject='fsaverage')
print (coords)
# plot result
b.add_foci(coords, color=cmap[f], map_surface='pial', scale_factor=0.5)
t_bin.append(t)
b.save_image(meg_dir + '_STCS/%s/%s/_CM_%s.png'%(tonetype,key,key))
brain = stc_avg.plot(subject='fsaverage',hemi='rh',colormap='rainbow',smoothing_steps=1)
brain.scale_data_colormap(fmin=220, fmid=370, fmax=624,transparent=True)
rh_aparc_file = os.path.join(label_dir, right_label_file)
lh_labels, lh_ctab, lh_names = nb.freesurfer.read_annot(lh_aparc_file)
rh_labels, rh_ctab, rh_names = nb.freesurfer.read_annot(rh_aparc_file)
left_df = left_df.set_index('col').loc[lh_names].reset_index().fillna(0)
right_df = right_df.set_index('col').loc[rh_names].reset_index().fillna(0)
vtx_lh = left_df.val.values[lh_labels]
vtx_lh[lh_labels == -1] = 0
vtx_rh = right_df.val.values[rh_labels]
vtx_rh[rh_labels == -1] = 0
brain.add_data(vtx_lh,
0,
400,
colormap="Reds",
alpha=.8,
hemi='lh')
brain.add_annotation(lh_aparc_file, hemi='lh')
brain.add_data(vtx_rh,
0,
400,
colormap="Reds",
alpha=.8,
hemi='rh')
brain.add_annotation(rh_aparc_file, hemi='rh', remove_existing=False)
save_name = "../images/{}_brain.png".format(save_name)
brain.save_image(save_name)
def drawBrainSurface(args):
import pandas as pd
subject_id = args.subject_id
hemi = args.hemi
surface = args.surface
"""
Read in the Buckner resting state network annotation. (This requires a
relatively recent version of Freesurfer, or it can be downloaded separately).
"""
aparc_file = os.path.join(os.environ["SUBJECTS_DIR"], subject_id, "label",
hemi + ".aparc.annot")
labels, ctab, names = nib.freesurfer.read_annot(aparc_file)
"""
Make a random vector of scalar data corresponding to a value for each region in
the parcellation.
"""
matData = pd.read_csv(args.input)
ePhenLabel = matData['ePhenLabel'].as_matrix()
values = matData['value'].as_matrix()
if hemi == "lh":
prefixStr = 'Left-'
prefixStrLen = len(prefixStr)
else:
prefixStr = 'Right-'
prefixStrLen = len(prefixStr)
# match the name of values with ROI's
idx = []
roi_data = np.zeros((len(names), )) # fill in with default
for i in range(0, len(ePhenLabel)):
if (str(ePhenLabel[i]))[prefixStrLen:] in names:
#print "name of structure on MATLAB side :" , ePhenLabel[i]
#print "name of structure on freesurfer side :", names[ names.index( (str(ePhenLabel[i]))[prefixStrLen:] ) ]
#print "---------------------------"
idx.append(i)
roi_data[names.index(
(str(ePhenLabel[i]))[prefixStrLen:])] = values[i]
if (args.interval == [np.inf, -np.inf]):
minVal = roi_data.min()
maxVal = roi_data.max()
else:
minVal = args.interval[0]
maxVal = args.interval[1]
#minVal = 0
#maxVal = 1
"""
Make a vector containing the data point at each vertex.
"""
vtx_data = roi_data[labels]
"""
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.
"""
"""
Bring up the visualization.
"""
brain = Brain(subject_id,
hemi,
surface,
config_opts=dict(background="white"))
brain.add_data(vtx_data,
minVal,
maxVal,
colormap=args.colormap,
alpha=args.alpha)
brain.save_image(args.output)
brain.show_view(view=args.view)
brain.save_image(args.output)
from surfer import Brain
print(__doc__)
brain = Brain("fsaverage", "both", "inflated", views="lateral",
background="white")
"""
You can also use a custom colormap and tweak its range.
"""
brain.add_morphometry("thickness",
colormap="Blues", min=1, max=4,
colorbar=False)
brain.save_image('./figures/elements_fig_concept_anatomy.png')
"""
Get a path to the overlay file.
"""
mri_file = '/Volumes/scratch/jamalw/HMM_searchlight_K_sweep_srm_bound_match/zstats_map_srmK=' + srmK + '.nii.gz'
reg_file = '/Applications/freesurfer/average/mni152.register.dat'
data = nib.load(mri_file)
minval = 0.2
maxval = np.max(data.get_data())
surf_data_lh = project_volume_data(mri_file,"lh",reg_file)
surf_data_rh = project_volume_data(mri_file,"rh",reg_file)
qmri_file = '/Volumes/scratch/jamalw/HMM_searchlight_K_sweep_srm_bound_match/qstats_map_srmK=' + srmK + '.nii.gz'
qdata_lh = project_volume_data(qmri_file,"lh",reg_file)
surf_data_lh[np.logical_or(qdata_lh>0.005,surf_data_lh<0)] = 0
qdata_rh = project_volume_data(qmri_file,"rh",reg_file)
surf_data_rh[np.logical_or(qdata_rh>0.005, surf_data_rh<0)] = 0
brain.add_overlay(surf_data_lh,min=minval,max=maxval,name="thirty_sec_lh", hemi='lh',sign="pos")
brain.add_overlay(surf_data_rh,min=minval,max=maxval,name="thirty_sec_rh", hemi='rh',sign="pos")
brain.save_image("zstats_human_bounds_srm_k" + srmK + ".png")
mlab.show()
brain.show_view(view)
