def test_vmin_none_in_dataview2d():
data = np.arange(np.product(volshape)).reshape(volshape, order='C')
view = cortex.Volume2D(data, data + 1, subject=subj, xfmname=xfmname)
cortex.quickshow(view)
data = np.arange(nverts)
view = cortex.Vertex2D(data, data + 1, subject=subj)
cortex.quickshow(view)
def alphaplot(sub, dat, R2, thresh, fig, save, fname):
light = cortex.Vertex2D(dat,
R2,
subject=sub,
vmin=np.nanmin(dat),
vmax=np.nanmax(dat),
vmin2=thresh,
vmax2=1,
cmap='plasma_alpha')
mfig = cortex.quickshow(light, with_curvature=True, fig=fig)
sp.savefig(fname,
dpi=300,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format='png',
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None)
figure_out,
'flatmap_space-fsaverage_rsq-%0.2f_type-polar_angle_noalpha.svg' %
rsq_threshold)
print('saving %s' % filename)
_ = cortex.quickflat.make_png(filename,
images['polar_noalpha'],
recache=True,
with_colorbar=False,
with_curvature=True,
with_sulci=True)
# vertex for rsq
images['rsq'] = cortex.Vertex2D(masked_rsq.T,
alpha_ones,
'fsaverage_gross',
vmin=0,
vmax=1.0,
vmin2=0,
vmax2=1.0,
cmap='Reds_cov')
#cortex.quickshow(images['rsq'],with_curvature=True,with_sulci=True)
filename = os.path.join(
figure_out,
'flatmap_space-fsaverage_rsq-%0.2f_type-rsquared.svg' % rsq_threshold)
print('saving %s' % filename)
_ = cortex.quickflat.make_png(filename,
images['rsq'],
recache=True,
with_colorbar=True,
with_curvature=True,
with_sulci=True)
# plot rsq values in cortex
alpha_ones = np.ones(alpha_mask.shape)
alpha_ones[alpha_mask] = np.nan
# seems stupid, but necessary to take ou the dtype object info (pycortex doesnt work otherwise)
rsq = estimates['r2']
rsq = np.vstack(rsq)
rsq = rsq[..., 0]
# vertex for rsq
images = {}
images['rsq'] = cortex.Vertex2D(rsq,
alpha_ones,
'fsaverage',
vmin=0,
vmax=1.0,
vmin2=0,
vmax2=1.0,
cmap='Reds_cov')
#images['rsq'] = cortex.Vertex(rsq,'fsaverage',
# vmin=0, vmax=1.0,
# cmap='Reds')
#cortex.quickshow(images['rsq'],with_curvature=True,with_sulci=True)
filename = os.path.join(
figure_out,
'flatmap_space-fsaverage_rsq-%0.2f_type-rsquared.svg' % rsq_threshold)
print('saving %s' % filename)
_ = cortex.quickflat.make_png(filename,
images['rsq'],
recache=False,
with_colorbar=True,
filename = os.path.join(figure_out,'flatmap_space-fsaverage_type-rsquared-normalized_visual.svg')
print('saving %s' %filename)
_ = cortex.quickflat.make_png(filename, images['rsq_visual_norm'], recache=False,with_colorbar=True,with_curvature=True,with_sulci=True)
images['rsq_motor_norm'] = cortex.Vertex(rsq_motor_norm,'fsaverage_gross',
vmin=0, vmax=1,
cmap='Blues')
cortex.quickshow(images['rsq_motor_norm'],with_curvature=True,with_sulci=True)
filename = os.path.join(figure_out,'flatmap_space-fsaverage_type-rsquared-normalized_motor.svg')
print('saving %s' %filename)
_ = cortex.quickflat.make_png(filename, images['rsq_motor_norm'], recache=False,with_colorbar=True,with_curvature=True,with_sulci=True)
images['rsq_combined'] = cortex.Vertex2D(rsq_visual_norm,rsq_motor_norm,
subject='fsaverage_gross',
vmin=0, vmax=1,
vmin2=0,vmax2=1,
cmap=col2D_name)#'PU_RdBu_covar')
cortex.quickshow(images['rsq_combined'],with_curvature=True,with_sulci=True)
filename = os.path.join(figure_out,'flatmap_space-fsaverage_type-rsquared-normalized_combined_bins-%d.svg'%n_bins)
print('saving %s' %filename)
_ = cortex.quickflat.make_png(filename, images['rsq_combined'], recache=False,with_colorbar=True,with_curvature=True,with_sulci=True)
# correlation coeficient for each task
images['R_visual_norm'] = cortex.Vertex(r_visual_norm,'fsaverage_gross',
vmin=0, vmax=1,
cmap='Reds')
cortex.quickshow(images['R_visual_norm'],with_curvature=True,with_sulci=True)
filename = os.path.join(figure_out,'flatmap_space-fsaverage_type-R-normalized_visual.svg')
import cortex
import cortex.polyutils
import numpy as np
import matplotlib.pyplot as plt
subject = 'S1'
# In order to get the number of vertices in this subject's cortical surface
# we have to load in their surfaces and get the number of points in each
surfs = [cortex.polyutils.Surface(*d)
for d in cortex.db.get_surf(subject, "fiducial")]
# This is the total number of vertices in the left and right hemispheres
num_verts = [s.pts.shape[0] for s in surfs]
# Creating one random dataset that is basically a gradient across each
# hemisphere based on vertex number
test_data1 = np.hstack((np.arange(num_verts[0]), np.arange(num_verts[1])))
# Picking a different vertex in each hemisphere to create another fake
# gradient away from that vertex
second_verts = [n / 4 for n in num_verts]
test_data2 = np.hstack((np.abs(np.arange(num_verts[0]) - second_verts[0]),
np.abs(np.arange(num_verts[1]) - second_verts[1])))
# This creates a 2D Vertex object with both of our test datasets for the
# given subject
vertex_data = cortex.Vertex2D(test_data1, test_data2, subject)
cortex.quickshow(vertex_data, with_colorbar=False)
plt.show()
# define Vertex images
#contains RGBA colors for each voxel in a volumetric dataset
# vertex for polar angles
images['polar'] = cortex.VertexRGB(rgb[..., 0].T,
rgb[..., 1].T,
rgb[..., 2].T,
subject='fsaverage',
alpha=alpha)
# vertex for ecc
images['ecc'] = cortex.Vertex2D(eccentricity.T,
alpha * 10,
'fsaverage',
vmin=0,
vmax=10,
vmin2=0,
vmax2=1.0,
cmap='BROYG_2D')
#images['ecc'] = cortex.Vertex2D(eccentricity.T, rsq.T, 'fsaverage',
# vmin=0, vmax=10,
# vmin2=rsq_threshold, vmax2=1.0, cmap='BROYG_2D')
# vertex for size
images['size'] = cortex.dataset.Vertex2D(size.T,
alpha * 10,
'fsaverage',
vmin=0,
vmax=10,
vmin2=0,
vmax2=1.0,