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 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_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
subj_dir = utils._get_subjects_dir()
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
# remove labels
brain.remove_labels('V1')
assert_in('V2', brain.labels_dict)
assert_not_in('V1', brain.labels_dict)
brain.remove_labels()
assert_not_in('V2', brain.labels_dict)
brain.close()
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 main(subid, overlay):
_, fname = os.path.split(overlay)
hemi = fname[:2]
brain = Brain(subid, hemi, "pial",
config_opts=dict(cortex="low_contrast"))
brain.add_overlay(overlay, min=0.4, max=4, sign="pos")
brain.show_view('m')
def curvature_normalization(data_dir, subj):
"""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")
panels = []
for hemi in ["lh", "rh"]:
# Load the curv values and apply registration to fsaverage
curv_fname = op.join(surf_dir, "{}.curv".format(hemi))
curv_vals = nib.freesurfer.read_morph_data(curv_fname)
subj_curv_vals = apply_surface_warp(data_dir, subj,
hemi, curv_vals)
subj_curv_binary = (subj_curv_vals > 0)
# Load the template curvature
norm_fname = op.join(data_dir, "fsaverage", "surf",
"{}.curv".format(hemi))
norm_curv_vals = nib.freesurfer.read_morph_data(norm_fname)
norm_curv_binary = (norm_curv_vals > 0)
# Compute the curvature overlap image
curv_overlap = np.zeros_like(norm_curv_binary, np.int)
curv_overlap[norm_curv_binary & subj_curv_binary] = 1
curv_overlap[norm_curv_binary ^ subj_curv_binary] = 2
# Mask out the medial wall
cortex_fname = op.join(data_dir, "fsaverage", "label",
"{}.cortex.label".format(hemi))
cortex = nib.freesurfer.read_label(cortex_fname)
medial_wall = ~np.in1d(np.arange(curv_overlap.size), cortex)
curv_overlap[medial_wall] = 1
# Plot the curvature overlap image
try:
b = Brain("fsaverage", hemi, "inflated", background="white")
except TypeError:
# PySurfer <= 0.5
b = Brain("fsaverage", hemi, "inflated",
config_opts=dict(background="white"))
b.add_data(curv_overlap, min=0, max=2,
colormap=[".9", ".45", "indianred"], colorbar=False)
for view in ["lat", "med", "ven"]:
b.show_view(view, distance="auto")
panels.append(crop(b.screenshot()))
b.close()
# Make and save a figure
f = multi_panel_brain_figure(panels)
fname = op.join(snap_dir, "surface_registration.png")
f.savefig(fname, bbox_inches="tight")
plt.close(f)
def contrast_loop(subj, contrasts, stat_temp, mask_temp, png_temp,
args, z_thresh, sign):
"""Iterate over contrasts and make surface images."""
for contrast in contrasts:
# Calculate where the overlay should saturate
z_max = calculate_sat_point(stat_temp, contrast, sign, subj)
panels = []
for hemi in ["lh", "rh"]:
# Initialize the brain object
b_subj = subj if args.regspace == "epi" else "fsaverage"
try:
b = Brain(b_subj, hemi, args.geometry, background="white")
except TypeError:
# PySurfer <= v0.5
b = Brain(b_subj, hemi, args.geometry,
config_opts={"background": "white"})
# Plot the mask
mask_file = mask_temp.format(contrast=contrast,
hemi=hemi, subj=subj)
add_mask_overlay(b, mask_file)
# Plot the overlay
stat_file = stat_temp.format(contrast=contrast,
hemi=hemi, subj=subj)
add_stat_overlay(b, stat_file, z_thresh, z_max, sign,
sig_to_z=args.regspace == "fsaverage")
# Take screenshots
for view in ["lat", "med", "ven"]:
b.show_view(view, distance="auto")
sleep(.1)
panels.append(crop(b.screenshot()))
b.close()
# Make a single figure with all the panels
f = multi_panel_brain_figure(panels)
kwargs = {}
if sign in ["pos", "abs"]:
kwargs["pos_cmap"] = "Reds_r"
if sign in ["neg", "abs"]:
kwargs["neg_cmap"] = "Blues"
add_colorbars(f, z_thresh, z_max, **kwargs)
# Save the figure in both hemisphere outputs
for hemi in ["lh", "rh"]:
png_file = png_temp.format(hemi=hemi, contrast=contrast, subj=subj)
f.savefig(png_file, bbox_inches="tight")
plt.close(f)
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 test_label():
"""Test plotting of label."""
mlab.options.backend = 'test'
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
brain.add_label("BA1")
brain.add_label("BA1", color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
brain.add_label("BA44", borders=True)
brain.add_label("BA6", alpha=.7)
brain.show_view("medial")
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
brain.close()
def test_label():
"""Test plotting of label."""
_set_backend()
subject_id = "fsaverage"
hemi = "lh"
surf = "inflated"
brain = Brain(subject_id, hemi, surf)
view = get_view(brain)
extra, subj_dir = _get_extra()
brain.add_label("BA1" + extra)
check_view(brain, view)
brain.add_label("BA1" + extra, color="blue", scalar_thresh=.5)
label_file = pjoin(subj_dir, subject_id,
"label", "%s.MT%s.label" % (hemi, extra))
brain.add_label(label_file)
brain.add_label("BA44" + extra, borders=True)
brain.add_label("BA6" + extra, alpha=.7)
brain.show_view("medial")
brain.add_label("V1" + extra, color="steelblue", alpha=.6)
brain.add_label("V2" + extra, color="#FF6347", alpha=.6)
brain.add_label("entorhinal" + extra, color=(.2, 1, .5), alpha=.6)
brain.set_surf('white')
brain.show_view(dict(elevation=40, distance=430), distance=430)
with pytest.raises(ValueError, match='!='):
brain.show_view(dict(elevation=40, distance=430), distance=431)
# remove labels
brain.remove_labels('V1' + extra)
assert 'V2' + extra in brain.labels_dict
assert 'V1' + extra not in brain.labels_dict
brain.remove_labels()
assert 'V2' + extra not in brain.labels_dict
brain.close()
def surface_images(out_dir, subj):
"""Plot the white, pial, and inflated surfaces to look for defects."""
for surf in ["white", "pial", "inflated"]:
panels = []
for hemi in ["lh", "rh"]:
try:
b = Brain(subj, hemi, surf, curv=False, background="white")
except TypeError:
# PySurfer <= 0.5
b = Brain(subj, hemi, surf, curv=False,
config_opts=dict(background="white"))
for view in ["lat", "med", "ven"]:
b.show_view(view, distance="auto")
panels.append(crop(b.screenshot()))
b.close()
# Make and save a figure
f = multi_panel_brain_figure(panels)
fname = op.join(out_dir, "{}_surface.png".format(surf))
f.savefig(fname, bbox_inches="tight")
plt.close(f)
def test_views():
"""Test showing different views."""
mlab.options.backend = 'test'
brain = Brain(*std_args)
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.show_view({'distance': 432})
brain.show_view({'azimuth': 135, 'elevation': 79}, roll=107)
brain.close()
import os
from surfer import Brain, project_volume_data
import numpy as np
### script to visualize cognitive components
subject_id = "fsaverage"
subjects_dir = os.environ["SUBJECTS_DIR"]
brain = Brain("fsaverage", "lh", "inflated", views=['lat'], background="white")
overlay_file = "/Volumes/neuro/Rest/ROIs/Yeo_12.nii.gz"
reg_file = os.path.join(os.environ["FREESURFER_HOME"], "average/mni152.register.dat")
zstat = project_volume_data(overlay_file, "lh", reg_file)
zstat = project_volume_data(overlay_file, "lh", subject_id="fsaverage", smooth_fwhm=4)
brain.add_data(zstat, min=1, max=5, thresh=1, colormap="hot", colorbar=False)
brain.show_view("medial")
#load data to visualize
#mask_file = "Cortical_CI.nii.gz"
### to visualize ROI's CI assignment
#load coordinates
subject_id = "fsaverage"
subjects_dir = os.environ["SUBJECTS_DIR"]
brain = Brain("fsaverage", "rh", "inflated", views=['lat', 'med'], background="white")
#brain = Brain("fsaverage", "rh", "inflated", background="white")
coords = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_coordinates')
CIs = np.loadtxt('/Volumes/neuro/Rest/ROIs/Gordon_consensus_CI')
colors = {1:'red', 2:'purple', 3:'green', 4:'yellow', 5:'cyan', 6:'blue', 7:'brown', 8:'pink', 9:'teal', 12:'pink'}
Among the views available are lateral, rostral, caudal, frontal etc.
"""
print __doc__
from surfer import Brain
sub = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
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('ve')
brain.show_view('frontal')
brain.show_view('par')
brain.show_view('dor')
###############################################################################
# More advanced parameters
brain.show_view({'distance': 432})
# with great power comes great responsibility
brain.show_view({'azimuth': 135, 'elevation': 79}, roll=107)
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 some images
"""
odir = "/home/data/Projects/CWAS/%s/viz" % study
brain.save_imageset(
path.join(odir, "zpics_surface_%s_lh" % onames[i][j]),
['med', 'lat', 'ros', 'caud'], 'jpg')
brain.close()
def test_views():
"""Test showing different views
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
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.show_view({'distance': 432})
brain.show_view({'azimuth': 135, 'elevation': 79}, roll=107)
brain.close()
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')
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
brain.add_label("BA1")
# Some labels have an associated scalar value at each ID in the label.
# For example, they may be probabilistically defined. You can threshold
# what vertices show up in the label using this scalar data
brain.add_label("BA1", color="blue", scalar_thresh=.5)
# Or you can give a path to a label in an arbitrary location
subj_dir = os.environ["SUBJECTS_DIR"]
label_file = os.path.join(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44", borders=True)
# You can also control the opacity of the label color
brain.add_label("BA6", alpha=.7)
# Finally, you can plot the label in any color you want.
brain.show_view(dict(azimuth=-42, elevation=105, distance=225,
focalpoint=[-30, -20, 15]))
# Use any valid matplotlib color.
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
We are going to plot the contour of the subject's curvature estimate after
transforming that map into the common space (this step is performed outside
of PySurfer using the Freesurfer program ``mri_surf2surf``).
With a perfect transformation, the contour lines should follow the light/dark
gray boundary on the fsaverage surface. Large deviations may reflect problems
with the underlying data that you should investigate.
"""
import nibabel as nib
from surfer import Brain
print(__doc__)
brain = Brain("fsaverage", "both", "inflated")
for hemi in ["lh", "rh"]:
# This file was created with mri_surf2surf
curv = nib.load("example_data/%s.curv.fsaverage.mgz" % hemi)
# Binarize the curvature at 0
curv_bin = (curv.get_data() > 0).squeeze()
# Add the data as a contour overlay, but turn off the colorbar
brain.add_contour_overlay(curv_bin, min=0, max=1.5, n_contours=2,
line_width=3, hemi=hemi)
brain.contour_list[-1]["colorbar"].visible = False
brain.show_view("dorsal")
Now we plot the focus on the inflated surface at the vertex identified in the
previous step.
"""
brain.add_foci([coord], map_surface="white", color="mediumseagreen")
"""
We can also do this using a vertex index, perhaps defined as the peak
activation in a surface analysis. This will be more accurate than using a
volume-based focus.
"""
coord = 0
utils.coord_to_label(subject_id, coord, label='example_data/coord',
hemi='lh', n_steps=40, map_surface="white",
coord_as_vert=True)
brain.add_label('example_data/coord-lh.label', color='royalblue', alpha=.8)
"""
Now we plot the foci on the inflated surface. We will map the foci onto the
surface by finding the vertex on the "white" mesh that is closest to the
coordinate of the point we want to display.
"""
brain.add_foci([coord], map_surface="white", coords_as_verts=True,
color="mediumblue")
"""
Set the camera position to show the extent of the labels.
"""
if not brain.patch_mode:
brain.show_view(dict(elevation=40, distance=0.430))
mne.write_forward_solution(shared_dir+'\\'+mri_partic+'\\2020-fwd.fif',fwd)
#leadfield = fwd['sol']['data']
#print("Leadfield size : %d sensors x %d dipoles" % leadfield.shape)
# SENSITIVITY/FIELD MAP
#--------------------------------
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')
if plot_steps_source==True:
clim = dict(kind='percent', lims=(0.0, 50, 95), smoothing_steps=3) # let's see single dipoles
brain = eeg_map.plot(subject=mri_partic, time_label='EEG sensitivity', surface='inflated',
subjects_dir=shared_dir, clim=clim, smoothing_steps=8, alpha=0.9);
view = 'lat'
brain.show_view(view)
# FIELD MAP
#---------------
# Visualizing field lines based on coregistration
from mne import make_field_map
#make_field_map?
maps = mne.make_field_map(evoked, trans=trans, subject=mri_partic,
subjects_dir=shared_dir, n_jobs=1)
# explore several points in time
field_map = evoked.plot_field(maps, time=.07);
else:
brain = Brain(subject_id=mri_partic,subjects_dir=shared_dir,surf='orig',hemi='both', background='white', size=(800, 600))
brain.add_annotation(parc)
brain = stc.plot(surface='inflated', hemi='lh', subjects_dir=shared_dir)
brain.set_data_time_index(300) # 221 for S2
brain.scale_data_colormap(fmin=-1e-12, fmid=1e-12, fmax=50e-12, transparent=True)
brain.show_view('lateral')
vertno_max, time_max = stc.get_peak(hemi='rh')
surfer_kwargs = dict(
subjects_dir=shared_dir,
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)
brain = stc.plot(**surfer_kwargs)
def bnsurf(data=None,
val=None,
cmap=None,
min_val=None,
max_val=None,
mid_val=None,
tail='greater',
out=None):
subjects_dir = '/Users/ACH/Documents/freesurfer/subjects'
subject_id = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
brain = Brain(subject_id,
hemi,
surf,
size=(1200, 1200),
background='w',
subjects_dir=subjects_dir,
interaction='terrain',
cortex='low_contrast',
units='mm',
title=out)
aparc_file = os.path.join(subjects_dir, subject_id, "label",
hemi + 'BN_pfc.annot')
# hemi + ".BN_Atlas.annot")
labels, ctab, names = nib.freesurfer.read_annot(aparc_file)
# [187,179,183,177,41,47,49,13,11,1,9]
# pfc_names = [b'Unknown',b'A32sg_L',b'A32p_L',b'A24cd_L',b'A24rv_L',b'A14m_L',b'A11m_L',b'A13_L',b'A10m_L',b'A9m_L',b'A8m_L',b'A6m_L']
# roi_data = rs.uniform(.5, .8, size=len(names))
rois = {
'A32sg': {
'name': b'A32sg_L',
'label': 187
},
'A32p': {
'name': b'A32p_L',
'label': 179
},
'A24cd': {
'name': b'A24cd_L',
'label': 183
},
'A24rv': {
'name': b'A24rv_L',
'label': 177
},
'A14m': {
'name': b'A14m_L',
'label': 41
},
'A11m': {
'name': b'A11m_L',
'label': 47
},
'A13': {
'name': b'A13_L',
'label': 49
},
'A10m': {
'name': b'A10m_L',
'label': 13
},
'A9m': {
'name': b'A9m_L',
'label': 11
},
'A8m': {
'name': b'A8m_L',
'label': 1
},
'A6m': {
'name': b'A6m_L',
'label': 9
}
}
#initialize the data with -1 so ROIs we don't care about are transparent
if tail == 'greater':
roi_data = np.repeat([-1.], len(names))
elif tail == 'less':
roi_data = np.ones(len(names))
elif tail == 'two':
roi_data = np.zeros(len(names))
#this is where we apply the value we want
for roi in rois:
roi_data[rois[roi]['label']] = data.loc[roi, val]
#this maps the roi values onto the vertex data using repeated sampling
vtx_data = roi_data[labels]
#again cancelling out ROIs we don't care about
if tail == 'greater':
vtx_data[labels == -1] = -1
if tail == 'less':
vtx_data[labels == -1] = -100
#this is where we actually put it on the brain
if tail == 'greater':
#get min/max values if not specified
if min_val is None:
min_val = data[val].astype(float).drop_duplicates().nsmallest(2)[1]
if max_val is None: max_val = data[val].max()
brain.add_data(vtx_data,
min=min_val,
max=max_val,
mid=mid_val,
colormap=cmap,
thresh=0.00001,
alpha=1)
elif tail == 'less':
brain.add_data(vtx_data,
data[val].min(),
0,
colormap=cmap,
thresh=data[val].min(),
alpha=1)
elif tail == 'two':
brain.add_data(
vtx_data,
data[val].min(),
data[val].max(),
center=0,
colormap=cmap,
)
# brain.show_view('m')
brain.show_view({'azimuth': 40, 'elevation': 100})
if out is not None:
brain.save_image('brainnetome_maps/%s.png' % (out), antialiased=True)
=======================
"""
print __doc__
from surfer import Brain
sub = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
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')
"""
===================
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_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()
#%% save figures
if make_figures is not None:
if len(make_figures) == 0:
times = src_df.index.levels[1]
else:
times = make_figures
outfiles = []
for time in times:
brain.set_time(time)
files = {}
for hemi, view in views.iteritems():
brain.show_view(*view)
file = os.path.join(figdir, 'motor_vertices_%s_%s_%s_%d%s.png'
% (r_name, measure, hemi, time, datatype))
files[hemi] = file
brain.save_image(file, antialiased=True)
# stitch them together
outfiles.append(os.path.join(figdir, 'motor_vertices_%s_%s_%d%s.png' %
(r_name, measure, time, datatype)))
os.system("montage -tile 2x1 -geometry +0+0 %s %s" % (
' '.join([files['lh'], files['rh']]), outfiles[-1]))
if len(times) > 1:
import shutil
for ind, file in enumerate(outfiles):
shutil.copyfile(file, 'tmp_%d.png' % (ind+1))
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()
brain.add_label("BA1")
# Some labels have an associated scalar value at each ID in the label.
# For example, they may be probabilistically defined. You can threshold
# what vertices show up in the label using this scalar data
brain.add_label("BA1", color="blue", scalar_thresh=.5)
# Or you can give a path to a label in an arbitrary location
subj_dir = os.environ["SUBJECTS_DIR"]
label_file = os.path.join(subj_dir, subject_id, "label", "%s.MT.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44", borders=True)
# You can also control the opacity of the label color
brain.add_label("BA6", alpha=.7)
# Finally, you can plot the label in any color you want.
brain.show_view(
dict(azimuth=-42, elevation=105, distance=225, focalpoint=[-30, -20, 15]))
# Use any valid matplotlib color.
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
# Finally, show everything on screen
mlab.show(stop=True)
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()
"""
Now we plot the focus on the inflated surface at the vertex identified in the
previous step.
"""
brain.add_foci([coord], map_surface="white", color="mediumseagreen")
"""
We can also do this using a vertex index, perhaps defined as the peak
activation in a surface analysis. This will be more accurate than using a
volume-based focus.
"""
coord = 0
utils.coord_to_label(subject_id, coord, label='example_data/coord',
hemi='lh', n_steps=40, map_surface="white",
coord_as_vert=True)
brain.add_label('example_data/coord-lh.label', color='royalblue', alpha=.8)
"""
Now we plot the foci on the inflated surface. We will map the foci onto the
surface by finding the vertex on the "white" mesh that is closest to the
coordinate of the point we want to display.
"""
brain.add_foci([coord], map_surface="white", coords_as_verts=True,
color="mediumblue")
"""
Set the camera position to show the extent of the labels.
"""
brain.show_view(dict(elevation=40, distance=0.430))
Freesurfer's curvature-driven normalization to a common template.
We are going to plot the contour of the subject's curvature estimate after
transforming that map into the common space (this step is performed outside
of PySurfer using the Freesurfer program ``mri_surf2surf``).
With a perfect transformation, the contour lines should follow the light/dark
gray boundary on the fsaverage surface. Large deviations may reflect problems
with the underlying data that you should investigate.
"""
import nibabel as nib
from surfer import Brain
brain = Brain("fsaverage", "both", "inflated")
for hemi in ["lh", "rh"]:
# This file was created with mri_surf2surf
curv = nib.load("example_data/%s.curv.fsaverage.mgz" % hemi)
# Binarize the curvature at 0
curv_bin = (curv.get_data() > 0).squeeze()
# Add the data as a contour overlay, but turn off the colorbar
brain.add_contour_overlay(curv_bin, min=0, max=1.5, n_contours=2,
line_width=3, hemi=hemi)
brain.contour_list[-1]["colorbar"].visible = False
brain.show_view("dorsal")
==============================
Among the views available are lateral, rostral, caudal, frontal etc.
"""
print __doc__
from surfer import Brain
sub = 'fsaverage'
hemi = 'lh'
surf = 'inflated'
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('ve')
brain.show_view('frontal')
brain.show_view('par')
brain.show_view('dor')
###############################################################################
# More advanced parameters
brain.show_view({'distance': 432})
# with great power comes great responsibility
brain.show_view({'azimuth': 135, 'elevation': 79}, roll=107)
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")
surf = "smoothwm"
brain = Brain(subject_id, hemi, surf)
# If the label lives in the normal place in the subjects directory,
# you can plot it by just using the name
brain.add_label("BA1")
# Or you can give a path to a label in an arbitrary location
subj_dir = os.environ["SUBJECTS_DIR"]
label_file = os.path.join(subj_dir, subject_id,
"label", "%s.MT.label" % hemi)
brain.add_label(label_file)
# By default the label is 'filled-in', but you can
# plot just the label boundaries
brain.add_label("BA44", borders=True)
# You can also control the opacity of the label color
brain.add_label("BA6", alpha=.7)
# Finally, you can plot the label in any color you want.
brain.show_view("medial")
# Use any valid matplotlib color.
brain.add_label("V1", color="steelblue", alpha=.6)
brain.add_label("V2", color="#FF6347", alpha=.6)
brain.add_label("entorhinal", color=(.2, 1, .5), alpha=.6)
import source_visualisations as sv
from surfer import Brain
import mne
figdir = os.path.expanduser('~/ZIH/texts/BeeMEG/figures')
hemi = 'rh'
parc = 'HCPMMP1_5_8'
views = {'rh': ({'azimuth': -146.2, 'elevation': 76.8,
'focalpoint': (-4.3, -5.0, 0.0)}, 64.7, 300.),
'lh': ({'azimuth': 168.8, 'elevation': 60.3,
'focalpoint': (-0.60, 0., 0.)}, 90, 295.)}
brain = Brain('fsaverage', hemi, 'inflated', cortex='low_contrast',
subjects_dir=sv.subjects_dir, background='w',
foreground='k')
labels = mne.read_labels_from_annot('fsaverage', parc=parc, hemi=hemi)
pcc = sv.Glasser_areas[sv.Glasser_areas['main section'] == 18]['area name']
pcc = list(pcc.map(lambda s: hemi[0].upper() + '_' + s + '_ROI-' + hemi))
for label in labels:
if label.name in pcc:
brain.add_label(label, borders=1, hemi=hemi, alpha=0.6,
color='k')
brain.show_view(*views[hemi])
brain.save_image(os.path.join(figdir, 'pcc_map.png'), antialiased=True)