def looping((sub, hemi)):
highres_file = '/scr/ilz3/myelinconnect/struct/surf_%s/prep_t1/profiles/%s_%s_mid_proflies.vtk'%(hemi, sub, hemi)
label_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.npy'%(sub, hemi)
old_lowres_file = '/scr/ilz3/myelinconnect/groupavg/indv_space/%s/lowres_%s_d_def.vtk'%(sub, hemi)
new_lowres_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/raw/%s_%s_profiles_raw.vtk'%(sub, hemi)
data_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/raw/%s_%s_profiles_raw.npy'%(sub, hemi)
cheb_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/raw/%s_%s_coeff_raw.npy'%(sub, hemi)
cheb_vtk_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/raw/%s_%s_coeff_raw.vtk'%(sub, hemi)
# load data
labels = np.load(label_file)[:,1]
highres_v, highres_f, highres_d = read_vtk(highres_file)
lowres_v, lowres_f, lowres_d = read_vtk(old_lowres_file)
# call to sampling function
new_lowres_d = sample_simple(highres_d, labels)
# save lowres vtk and txt
write_vtk(new_lowres_file, lowres_v, lowres_f, data=new_lowres_d)
np.save(data_file, new_lowres_d)
# calculate chebychev coefficients
t1_3_7 = new_lowres_d[:,3:8]
coeff, poly = chebapprox(t1_3_7, degree=4)
np.save(cheb_file, coeff)
write_vtk(cheb_vtk_file, lowres_v, lowres_f, data=coeff)
if os.path.isfile(data_file):
print sub+' '+hemi+' finished'
return
def create_mapping((sub, hemi)):
print sub, hemi
complex_file = '/scr/ilz3/myelinconnect/struct/surf_%s/orig/mid_surface/%s_%s_mid.vtk'
simple_file = '/scr/ilz3/myelinconnect/groupavg/indv_space/%s/lowres_%s_d_def.vtk'# version d
log_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/logs/log_worker_%s.txt'%(str(os.getpid()))
seed_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/seeds/%s_%s_highres2lowres_seeds.npy'
label_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.npy'
surf_label_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.vtk'
# load the meshes
log(log_file, 'Processing %s %s'%(sub, hemi))
log(log_file, '...loading data', logtime=False)
complex_v, complex_f, complex_d = read_vtk(complex_file%(hemi, sub, hemi))
simple_v, simple_f, simple_d = read_vtk(simple_file%(sub, hemi))
# find those points on the individuals complex mesh that correspond best
# to the simplified group mesh in subject space
log(log_file, '...finding unique voronoi seeds')
try:
voronoi_seed_idx = np.load(seed_file%(sub, hemi))
voronoi_seed_coord = complex_v[voronoi_seed_idx]
except IOError:
voronoi_seed_idx, inaccuracy = find_voronoi_seeds(simple_v, complex_v)
np.save(seed_file%(sub, hemi), voronoi_seed_idx)
# find coordinates of those points in the highres mesh
voronoi_seed_coord = complex_v[voronoi_seed_idx]
# double check differences
log(log_file, '...checking unique vs nearest mapping')
dist = np.linalg.norm((voronoi_seed_coord - simple_v), axis=1)
if ((np.mean(dist)-np.mean(inaccuracy[:,0])>0.1)):
log(log_file, 'Unique seeds very far from nearest seeds!')
return dist
sys.exit("Unique seeds very far from nearest seeds %s %s"%(sub,hemi))
# convert highres mesh into graph containing edge length
log(log_file, '...creating graph')
complex_graph = graph_from_mesh(complex_v, complex_f, edge_length=True)
# find the actual labels
log(log_file, '...competetive fast marching')
labels = competetive_fast_marching(complex_v, complex_graph, voronoi_seed_idx)
# write out labelling file and surface with labels
log(log_file, '...saving data')
np.save(label_file%(sub, hemi), labels)
write_vtk(surf_label_file%(sub, hemi), complex_v, complex_f,
data=labels[:,1, np.newaxis])
log(log_file, 'Finished %s %s'%(sub, hemi))
return log_file
print 'processing '+sub+' '+hemi
# load data
complex_v,complex_f, complex_d = read_vtk('/scr/ilz3/myelinconnect/struct/surf_%s/orig/mid_surface/%s_%s_mid.vtk'%(hemi, sub, hemi))
simple_v, simple_f, simple_d = read_vtk('/scr/ilz3/myelinconnect/groupavg/indv_space/%s/lowres_%s_d_def.vtk'%(sub, hemi))
labelling=np.load('/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.npy'%(sub, hemi))
seeds=np.load('/scr/ilz3/myelinconnect/all_data_on_simple_surf/seeds/%s_%s_highres2lowres_seeds.npy'%(sub, hemi))
# make an edge label surface and save it
G = graph_from_mesh(complex_v, complex_f)
edge_labelling = np.zeros_like(labelling[:,1])
for v in range(edge_labelling.shape[0]):
if any(labelling[G.neighbors(v),1] != labelling[v,1]):
edge_labelling[v] = 1
write_vtk(edge_file%(sub, hemi), complex_v, complex_f,
data=edge_labelling[:,np.newaxis])
# make a no label surface and save it
if np.where(labelling[:,1]==-1)[0].shape[0] >0:
no_labelling = np.zeros_like(labelling[:,1])
for n in list(np.where(labelling[:,1]==-1)[0]):
no_labelling[n] = 1
write_vtk(no_file%(sub, hemi), complex_v, complex_f,
data=no_labelling[:,np.newaxis])
# get actual and ideal seed distances
actual_seed_dist = np.linalg.norm((complex_v[seeds] - simple_v), axis=1)
ideal_seed_dist, mapping = spatial.KDTree(complex_v).query(simple_v)
# count how many of each label
try:
mean_3_7 = np.load(mean_pro_file%(sub, hemi))
except IOError:
mean_3_7= np.mean(profiles[:,layer_low:layer_high], axis=1)
np.save(mean_pro_file%(sub, hemi), mean_3_7)
avg_pro += profiles
avg_mean += mean_3_7
count += 1
print 'Finished '+sub
avg_pro = avg_pro / count
avg_mean = avg_mean / count
np.save(avg_pro_file%(hemi), avg_pro)
np.save(avg_mean_pro_file%(hemi), avg_mean)
#pdb.set_trace()
write_vtk(avg_mean_surf%(hemi), v, f, data=avg_mean[:,np.newaxis])
for p in range(profiles.shape[1]):
write_vtk(avg_pro_surf%(hemi, str(p)), v, f, data=avg_pro[:,p, np.newaxis])
for hemi in hemis:
v, f, d = read_vtk(mesh_file % (hemi))
for sub in subjects:
# print hemi, sub
# data = np.load(data_file%(sub, hemi))
# write_vtk(vtk_data_file%(sub, hemi), v,f,data=data)
for sess in sessions:
print hemi, sub, sess
data = np.load(data_file % (sub, hemi, sess))
write_vtk(vtk_data_file % (sub, hemi, sess), v, f, data=data)
elif mode == 'vtk2npy':
print 'vtk to npy'
for hemi in hemis:
for sub in subjects:
#print hemi, sub
#v,f,d = read_vtk(vtk_data_file%(sub, hemi))
#np.save(data_file%(sub, hemi), d)
for sess in sessions:
import numpy as np
from vtk_rw import read_vtk, write_vtk
iterations = 1000
hemis = ['rh', 'lh']
mesh_file = "/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk"
t1_file = '/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_3/%s_t1_avg_smooth_3.npy'
mask_file = '/scr/ilz3/myelinconnect/new_groupavg/masks/%s_fullmask_new.npy'
random_npy_file = '/scr/ilz3/myelinconnect/new_groupavg/model/random_data/raw/%s_random_normal_%s.npy'
random_vtk_file = '/scr/ilz3/myelinconnect/new_groupavg/model/random_data/raw/%s_random_normal_%s.vtk'
'''
Create random datasets by drawing from a normal distribution and write them to
group average surface for subsequent smoothing with cbstools.
'''
for hemi in hemis:
print hemi
v, f, d = read_vtk(mesh_file % hemi)
for r in range(iterations):
print r
random_data = np.random.randn(v.shape[0])
np.save(random_npy_file % (hemi, str(r)), random_data)
write_vtk(random_vtk_file % (hemi, str(r)),
v,
f,
data=random_data[:, np.newaxis])
import numpy as np
from vtk_rw import read_vtk, write_vtk
iterations = 100
hemis = ['rh', 'lh']
mesh_file="/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk"
random_npy_file = '/scr/ilz3/myelinconnect/new_groupavg/model/random_data/raw/%s_random_normal_%s.npy'
random_vtk_file = '/scr/ilz3/myelinconnect/new_groupavg/model/random_data/raw/%s_random_normal_%s.vtk'
for hemi in hemis:
print hemi
v,f,d = read_vtk(mesh_file%hemi)
data_shape = v.shape[0]
for r in range(iterations):
print r
random_data = np.random.randn(data_shape)
np.save(random_npy_file%(hemi, str(r)), random_data)
write_vtk(random_vtk_file%(hemi, str(r)), v, f, data=random_data[:,np.newaxis])
for hemi in hemis:
v,f,d = read_vtk(mesh_file%(hemi))
for sub in subjects:
# print hemi, sub
# data = np.load(data_file%(sub, hemi))
# write_vtk(vtk_data_file%(sub, hemi), v,f,data=data)
for sess in sessions:
print hemi, sub, sess
data = np.load(data_file%(sub, hemi, sess))
write_vtk(vtk_data_file%(sub, hemi, sess), v,f,data=data)
elif mode == 'vtk2npy':
print 'vtk to npy'
for hemi in hemis:
for sub in subjects:
#print hemi, sub
#v,f,d = read_vtk(vtk_data_file%(sub, hemi))
#np.save(data_file%(sub, hemi), d)
from ply_rw import read_ply
from vtk_rw import write_vtk
'''
Meta script to convert ply to vtk file
'''
in_ply = raw_input("input ply file: ")
out_vtk = raw_input("output vtk file: ")
comment = raw_input("comment (optional): ")
if comment == "":
comment = 'vtk format converted from ply'
v, f = read_ply(in_ply)
write_vtk(out_vtk, v, f, comment)
