seed = 10000
map = full_dist[seed]
map[fullmask] = 0
vmin = 0
vmax = 160
cropped_img = []
sns.set_style('white')
for (elev, azim) in [(180, 0), (180, 180)]:
plot = plot_surf_stat_map(lv,
lf,
stat_map=map[:lv.shape[0]],
bg_map=lh_sulc,
bg_on_stat=True,
darkness=0.4,
elev=elev,
azim=azim,
figsize=(10, 9),
threshold=1e-50,
cmap='Reds_r',
symmetric_cbar=False,
vmin=vmin,
vmax=vmax)
cropped_img.append(crop_img(plot))
for (elev, azim) in [(180, 0), (180, 180)]:
plot = plot_surf_stat_map(rv,
rf,
stat_map=map[lv.shape[0]:],
bg_map=rh_sulc,
bg_on_stat=True,
darkness=0.4,
# load aligned
A_init = h5py.File('/nobackup/kocher1/bayrak/tmp/realigned_full_n10_468.h5', 'r')
A = np.array(A_init.get('aligned'))
# load sulc
D_in = h5py.File('/nobackup/kocher1/bayrak/tmp/sulc_full_468.h5', 'r')
D = np.array(D_in.get('sulc'))
# get first component
A = A[:,:,0]
# Correlate two datasets
C = []
for i in range(np.shape(A)[1]):
# covariance of one region (over subjects) in two datasets
C.append(np.cov(A[:,i],D[:,i])[0][1])
print i
C = np.array(C)
# Vizualize data:
data = np.zeros(len(vertices))
data[ind] = C
plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=0)
plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=90)
plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=180)
plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=270)
plt.show()
#embedding_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/profiles/%s_smooth_3_embedding_10_%s.npy' % (hemi, method)
#fig_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/figs/profiles_%s_%s_comp%s.png'
embedding_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/coefficients/%s_smooth_3_embedding_10_%s.npy' % (hemi, method)
fig_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/figs/coefficients_%s_%s_comp%s.png'
v, f, d = read_vtk(mesh_file)
data = np.load(embedding_file)
sulc = np.load(sulc_file)
for e in range(10):
print method, hemi, e
if hemi == 'lh':
sns.set_style('white')
lat=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True,
elev=180,azim=180, figsize=(14,10), darkness=0.3)
sns.set_style('white')
med=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True,
elev=180,azim=0, figsize=(14,10), darkness=0.3)
elif hemi == 'rh':
sns.set_style('white')
lat=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True,
elev=180,azim=0, figsize=(11,10), darkness=0.4)
sns.set_style('white')
med=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True,
elev=180,azim=180, figsize=(11,10), darkness=0.4)
print "OVERLAP"
x[n_lh[index]] = L1[n_lh[index]]
x[n_lh[index_neg]] = -1 * L2[n_lh[index_neg]]
print np.shape(index)[1], np.shape(index_neg)[1]
if np.shape(index)[1] != 0 and np.shape(index_neg)[1] != 0:
x[np.where(x==1)] = x[n_lh[index]].max() + 0.001
plotting.plot_surf_stat_map(vertices_lh, triangles_lh, fig_number,
'221', stat_map = x, cmap='jet', azim=180,
threshold=x[n_lh[index]].max(),
figsize=(14, 10))
plotting.plot_surf_stat_map(vertices_lh, triangles_lh, fig_number,
'223', stat_map = x, cmap='jet', azim=0,
threshold=x[n_lh[index]].max(),
figsize=(14, 10))
del index, index_neg
y = np.ones(len(vertices_rh))
index = np.where(R1[n_rh] < 0.05)
index_neg = np.where(R2[n_rh] < 0.05)
if set(index[0]).intersection(index_neg[0]).__len__() != 0:
print "OVERLAP"
#DATA = h5py.File(path + '468_embeddings.h5', 'r')
#mode = 'embedding'
# plot subjects individually
for subject_id in subject_list[args.begin : args.end]:
## chose subject_id randomly
#subject_id = choose_random_subject(subject_list)
#subject_id = '100307'
subject_id = ''.join(subject_id)
print subject_id
component = None
subject_component = choose_component(DATA, subject_id, mode, component)
data = np.zeros(len(vertices))
data[n] = subject_component
plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, cmap='jet', azim=0)
plt.title(subject_id + ' , component 1' )
plt.savefig(path_out + subject_id + '_comp_01' + '_000.png')
plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, cmap='jet', azim=180)
plt.title(subject_id + ' , component 1' )
plt.savefig(path_out + subject_id + '_comp_01' + '_180.png')
#plt.show()
# save out group level results...
#tmp_list = []
# plot a component over all subjects
#components = np.arange(0, 10, 1)
#for component in components:
#tmp = get_mean(DATA, subject_list, mode, component)
