def plot_lv(lv):
import matplotlib.pyplot as plt
fig1 = mlab.figure()
cor = surfer.Brain('mni', 'rh', 'cortex', curv=False, figure=fig1)
cor.add_data(saliences[:, lv], vertices=my_cor_vertices)
useTrans = len(my_cor_vertices) != saliences.shape[0]
cor.scale_data_colormap(np.min(saliences[:, lv]), 0,
np.max(saliences[:, lv]), useTrans)
fig2 = mlab.figure()
cor = surfer.Brain('mni', 'rh', 'cortex', curv=False, figure=fig2)
cor.add_data(saliences[:, lv], vertices=my_cor_vertices)
useTrans = len(my_cor_vertices) != saliences.shape[0]
cor.scale_data_colormap(np.min(saliences[:, lv]), 0,
np.max(saliences[:, lv]), useTrans)
ind = np.arange(num_comps) # the x locations for the groups
width = 0.15 # the width of the bars: can also be len(x) sequence
fig = plt.figure()
ax = plt.subplot(111)
# colors = ['r', 'g', 'b', 'y', 'k']
from random import random
colors = [
'red', 'orange', 'yellow', 'green', 'cyan', 'blue', 'purple', 'white',
'black', 'grey', 'brown'
]
# legend = ['mediodorsal', 'pulvinar', 'LGB', 'MGB', 'LP', 'LD', 'VPLc', 'VPL', 'VLO', 'VA', 'anterior']
# colors = [(1,1,1)] + [(random(),random(),random()) for i in xrange(patterns.shape[0])]
rects = [
plt.bar(i, patterns[i, lv], color=colors[i], label=label_names[i])
for i in range(patterns.shape[0] / 2)
]
rects = rects + [
plt.bar(i + 1, patterns[i, lv], color=colors[i - len(label_names)])
for i in range(len(label_names), patterns.shape[0])
]
plt.ylim([-1, 1])
# rects = []
# for c, color in enumerate(colors):
# rects.append(pl.bar(ind + c * width, patterns[c, lv], width, color=color))
# plt.ylabel('Saliences')
# plt.title('Saliences by seed-voxel')
# plt.xticks(ind + width / 2., ['LV' + str(i + 1) for i in range(num_comps)])
# plt.plot(plt.xlim(), [0, 0], 'k')
# plt.legend(rects, [str(i + 1) for i in my_sub_vertices], loc=0)
# Shink current axis by 20%
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# Put a legend to the right of the current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# plt.legend(rects, legend, loc=0)
plt.show(block=False)
def plot_distance(stc_est, stc_signal, D, surface='inflated'):
"""Plots the distance to the peak estimated signal, along with the true signal location"""
peak = stc_est.get_peak(vert_as_index=True)[0]
peak_hemi = 0 if peak < len(stc_est.vertices[0]) else 1
true_hemi = config.signal_hemi
est_vert = np.hstack(stc_est.vertices)[peak]
true_vert = stc_signal.vertices[true_hemi][0]
brain = surfer.Brain('sample', hemi='both', surf=surface)
brain.add_data(D[peak, :len(stc_est.vertices[0])],
vertices=stc_est.vertices[0],
hemi='lh',
transparent=True)
brain.add_data(D[peak, len(stc_est.vertices[0]):],
vertices=stc_est.vertices[1],
hemi='rh',
transparent=True)
brain.add_foci([est_vert],
coords_as_verts=True,
hemi='lh' if peak_hemi == 0 else 'rh',
color='red')
brain.add_foci([true_vert],
coords_as_verts=True,
hemi='lh' if true_hemi == 0 else 'rh',
color='green')
return brain
def surfplot_2x2(nodeData,atlas,savedir,ttl='',clim_type='all',cmap='plasma'):
# INPUTS:
# nodeData: Nx1 data for N nodes
# atlas: string specifiying atlas of node data
# ttl: title to go above entire plot
# savedir: where to output temporary files which will be deleted
# clim_type: optional limit for color axis, symmetric about 0.
# 'all' (default) means set color scales based on all values in nodeData
# a numeric input will be set as the color limit
if clim_type == 'all':
clim = np.round(np.max(np.abs(nodeData)),decimals=2)
print('color limits:' +str(clim))
if clim ==0:
clim = 0.2
print('setting clim to 0.2 by default')
else:
clim = clim_type
hemis = ['lh','rh']
views = ['lat','med']
thrsh = -10
subject_id = 'fsaverage'
surf = 'pial'
cbar = False
for hemi in hemis:
ntv = node_to_vertex(annot_path='data/annot/',node_data=nodeData)
vtx_data = ntv.getvertdata(atlas,hemi=hemi)
for view in views:
fig = my.mlab.figure(size=(340,340))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id, hemi, surf,figure=fig,views=view,background='white', alpha = 1)
brain.add_data(vtx_data, min = -clim, max = clim, thresh = thrsh, colormap=cmap, alpha=.8,colorbar=cbar,time_label=None)
fname = 'nodeData' + view + hemi + '.png'
my.mlab.savefig(figure=fig,filename=opj(savedir,fname),magnification=1)
my.mlab.close(all=True)
#Arrange brains into grid
arial = {'fontname':'Arial'}
plt.figure(figsize = [1.7,1.7])
for H,hemi in enumerate(hemis):
for V, view in enumerate(views): #Arrange brains into grid
fname = opj(savedir,'nodeData' + view + hemi + '.png')
img = mpimg.imread(fname)
plt.subplot(2,2,(H + 2*V+1))
imgplot = plt.imshow(img,aspect='auto')
plt.axis('off')
plt.subplots_adjust(hspace=0, wspace=0.05)
plt.suptitle(ttl,family='arial',size=8,weight='bold')
#delete intermediate files
[os.remove(f) for f in glob(opj(savedir,'nodeData*.png'))]
return(plt.gcf())
def build_surface(self, subjects_dir=None, subject=None):
'''
creates a pysurfer surface and plots it
specify subject, subjects_dir or these are taken from the environment
Returns
-------
brain | surfer.viz.Brain
Pysurfer brain object
figure | mlab.scene
Mayavi scene object
'''
if subjects_dir == None:
subjects_dir = os.environ['SUBJECTS_DIR']
if subject == None:
subject = os.environ['SUBJECT']
self.brain = surfer.Brain(subject,
hemi='both',
surf='pial',
figure=self.scene.mayavi_scene,
subjects_dir=subjects_dir,
curv=False)
#self.scene = self.brain._figures[0][0]
self.brain.toggle_toolbars(True)
#set the surface unpickable
for srf in self.brain.brains:
srf._geo_surf.actor.actor.pickable = False
srf._geo_surf.actor.property.opacity = self.opacity
return self.brain
''' Script to show the picked point in the surface on the screen'''
import surfer
from mayavi import mlab
import numpy as np
fig = mlab.figure()
tha = surfer.Brain('mni', 'rh', 'thalamus', curv=False, figure=fig)
Y = np.array(range(3108))
tha.add_data(Y)
def picker_callback(picker_obj):
print picker_obj.point_id
fig.on_mouse_pick(picker_callback)
mlab.show()
continue
if sensor_type == 'joint' and use_noise_cov == False:
continue
# Add row to the HTML table
html_table += '<tr><td>' + '</td><td>'.join([str(s)
for s in setting]) + '</td>'
try:
if len(sel) < 1000:
raise RuntimeError('not enough vertices')
# Create the brain plots
mlab.figure(1, size=(600, 500))
vertices = fwd['src'][1]['vertno'][sel['vertex']]
brain = surfer.Brain('sample', hemi='rh', surf='white', figure=1)
brain.add_data(sel['dist'], vertices=vertices, smoothing_steps=5)
brain.scale_data_colormap(0, 0.075, 0.15, transparent=False)
mlab.view(0, 90, 250, [33, -10, 35])
mlab.savefig('html/lcmv/%03d_dist_out.png' % i)
mlab.view(-180, 90, 300, [33, -10, 35])
mlab.savefig('html/lcmv/%03d_dist_in.png' % i)
mlab.close(1)
mlab.figure(1, size=(600, 500))
vertices = fwd['src'][1]['vertno'][sel['vertex']]
brain = surfer.Brain('sample', hemi='rh', surf='white', figure=1)
brain.add_data(sel['eval'], vertices=vertices, smoothing_steps=5)
brain.scale_data_colormap(0, 0.001, 0.002, transparent=False)
mlab.view(0, 90, 250, [33, -10, 35])
mlab.savefig('html/lcmv/%03d_eval_out.png' % i)
m + '-sig.mgh')
# p = nib.freesurfer.mghformat.load('data/Sunetal2018_Fig1Maps/left-CT-sig.mgh')
b = np.squeeze(np.asarray(
b.get_data()[:, :, :, 1])) # select 22q-control predictor betas
p = np.squeeze(np.asarray(p.get_data()))
data_plot = -b
clim = np.abs(b).max()
cmax = b.max()
cmin = b.min()
fig = my.mlab.figure(size=(340, 340))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
brain.add_data(data_plot,
min=cmin,
max=cmax,
mid=0,
thresh=thrsh,
colormap=cmap,
alpha=1,
colorbar=cbar)
my.mlab.savefig(figure=fig,
filename=opj('data', 'Sunetal2018_Fig1Maps',
view + m + subject_id + 'Betas.png'),
magnification=1)
def brain_plot(roi_data,
parcel_names,
parc_file,
fig_str,
subject_id='fsaverage',
hemi='lh',
surf='inflated',
color='coolwarm',
center_anchor=0,
showcolorbar=False):
vtx_data, plot_min, plot_max = roi_to_vtx(roi_data, parcel_names,
parc_file)
if np.all(vtx_data == -1000) == False:
if color == 'coolwarm':
if center_anchor == 0:
if abs(plot_max) > abs(plot_min): center_anchor = abs(plot_max)
elif abs(plot_max) < abs(plot_min):
center_anchor = abs(plot_min)
else:
center_anchor = abs(plot_max)
if center_anchor == -1000: center_anchor = 1
print(center_anchor)
if center_anchor != 0:
view = 'lat'
fname1 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
max=center_anchor,
center=0,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname1)
my.mlab.close()
view = 'med'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
max=center_anchor,
center=0,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
view = 'ventral'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
max=center_anchor,
center=0,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
else:
print('There' 's nothing to plot...')
elif color == 'viridis' or color == 'viridis_r':
view = 'lat'
fname1 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
max=plot_max,
min=plot_min,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname1)
my.mlab.close()
view = 'med'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
max=plot_max,
min=plot_min,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
elif color == 'hot':
if center_anchor != 0:
plot_max = center_anchor
view = 'lat'
fname1 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
min=0,
max=plot_max,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname1)
my.mlab.close()
view = 'med'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
min=0,
max=plot_max,
thresh=-999,
colormap=color,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
else:
my_cmap = list()
for i in np.arange(0, int(plot_max)):
my_cmap.append(get_cmap(color)(i))
view = 'lat'
fname1 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
min=plot_min,
max=plot_max,
thresh=-999,
colormap=my_cmap,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname1)
my.mlab.close()
view = 'med'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_data(vtx_data,
min=plot_min,
max=plot_max,
thresh=-999,
colormap=my_cmap,
alpha=1,
colorbar=showcolorbar)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
else:
view = 'lat'
fname1 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname1)
my.mlab.close()
view = 'med'
fname2 = view + '_' + fig_str + '.png'
fig = my.mlab.figure(size=(1000, 1000))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id,
hemi,
surf,
figure=fig,
views=view,
background='white',
alpha=1)
if subject_id == 'fsaverage':
brain.add_morphometry("avg_sulc",
colormap="binary",
min=-3,
max=3,
colorbar=False)
elif subject_id == 'lausanne125':
brain.add_morphometry("avg_curv",
colormap="binary",
min=-.5,
max=.5,
colorbar=False)
brain.add_annotation(parc_file,
hemi=hemi,
borders=True,
alpha=.25,
color='lightsteelblue')
my.mlab.savefig(figure=fig, filename=fname2)
my.mlab.close()
import glob
from os.path import join
#Enter directory with the mgh files
General_Directory = ''
Hemisphere = 'lh'
Viewpoint = ["lateral", "medial", "parietal", "ventral"]
C1_Col = 'Reds'
C2_Col = 'Greens'
C3_Col = 'Wistia'
brain = surfer.Brain('fsaverage',
'{}'.format(Hemisphere),
'inflated',
config_opts={
'cortex': 'bone',
'background': 'white'
})
light_settings = [{
'elevation': 0,
'azimuth': 0,
'intensity': 0.7
}, {
'elevation': 0,
'azimuth': -90,
'intensity': 0.7
}, {
'elevation': 0,
'azimuth': 90,
'intensity': 0.7
def surfplot_2xK(nodeData,hemi,atlas,ttls,savedir,clim_type='each',cbar_yn=True,cmap = 'plasma'):
# INPUTS:
# nodeData: NxK data for N nodes and K activation maps
# hemi: hemisphere to plot
# atlas: string specifiying atlas of node data
# ttls: length K list of plot titles
# savedir: where to output temporary files which will be deleted
# clim_type: optional limit for color axis, symmetric about 0.
# 'each' (default) means each map is scaled independently
# 'all' means set color scales based on all values in nodeData
# a numeric input will be set as the color limit
# cbar_yn: if True, put a colorbar on the bottom row of plots
views=['lat','med']
thrsh = -10
subject_id = 'fsaverage'
surf = 'pial'
tmp = 'TEMP_BRAIN'
N,K = nodeData.shape
ntv = node_to_vertex(annot_path='../data/annot/',node_data=nodeData)
vtx_data = ntv.getvertdata(atlas,hemi=hemi)
img=dict()
for act_map in range(K):
data_plot = vtx_data[:,act_map]
if clim_type == 'each':
clim = np.max(np.abs(data_plot[data_plot>-999]))
elif clim_type == 'all':
clim = np.max(np.abs(vtx_data[vtx_data > -999]))
else:
clim = clim_type
print('color limits:' +str(clim))
for view in views:
cbar = False
if view == 'med':
cbar = cbar_yn
fig = my.mlab.figure(size=(340,340))
fig = my.mlab.gcf()
brain = surfer.Brain(subject_id, hemi, surf,figure=fig,views=view,background='white', alpha = 1)
brain.add_data(data_plot, min = -clim, max = clim, thresh = thrsh, colormap= cmap,alpha=1,colorbar=cbar)
fname = tmp + str(act_map) + view + hemi + '.png'
#img[str(act_map)+view] = my.mlab.screenshot(figure=fig, mode='rgba', antialiased=True)
my.mlab.savefig(figure=fig,filename=opj(savedir,fname),magnification=1)
my.mlab.close(all=True)
fig = plt.figure(figsize = [K,2])
arial = {'fontname':'Arial'}
for view in np.arange(1,len(views)+1):
for act_map in np.arange(K):
fname = tmp + str(act_map) + views[view-1] + hemi + '.png'
img = mpimg.imread(opj(savedir,fname))
plt.subplot(2,K,1+act_map + K*(view-1))
#imgplot = plt.imshow(img[str(act_map)+views[view-1]],aspect='auto')
imgplot = plt.imshow(img,aspect='auto')
if view == 1:
ttl = ttls[act_map]
plt.title(ttl,fontsize=8,fontweight='bold',**arial)
plt.axis('off')
plt.subplots_adjust(hspace=0, wspace=0.05)
[os.remove(f) for f in glob(opj(savedir,tmp + '*' + hemi + '.png'))]
return(plt.gcf())
