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/%s_%s_profiles.vtk'%(sub, hemi)
data_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/%s_%s_profiles.npy'%(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)
if os.path.isfile(data_file):
print sub+' '+hemi+' finished'
return
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
def looping((sub, hemi, sess)):
print 'running', sub, hemi, sess
label_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.npy'%(sub, hemi)
rest_file = '/scr/ilz3/myelinconnect/resting/final/%s_rest%s_denoised.nii.gz'%(sub, sess)
highres_file = '/scr/ilz3/myelinconnect/struct/surf_%s/orig2func/rest%s/%s_%s_mid_groupavgsurf.vtk'%(hemi, sess, sub, hemi)
data_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/rest/%s_%s_rest%s_smooth_3.npy'%(sub, hemi, sess)
# load data
labels = np.load(label_file)[:,1]
highres_v, highres_f, highres_d = read_vtk(highres_file)
# sample resting state time series on highres mesh
rest_highres = sample_volume(rest_file, highres_v)
# average across highres vertices that map to the same lowres vertex
rest_lowres = sample_simple(rest_highres, labels)
# save data
np.save(data_file, rest_lowres)
if os.path.isfile(data_file):
print sub+' '+hemi+' ' +sess+' finished'
return data_file
def fit_iteration((smooth, iteration)):
print smooth, iteration, hemi
_,_,t1 = read_vtk(random_file%(smooth, hemi, str(iteration)))
masked_t1 = np.delete(t1, mask)
masked_orig = np.delete(t1_orig, mask)
# histogramm matching to original t1
masked_t1 = hist_match(masked_t1, masked_orig)
clf = linear_model.LinearRegression()
clf.fit(masked_embed[:,maps], masked_t1)
modelled_fit = clf.predict(masked_embed[:,maps])
residuals = masked_t1 - clf.predict(masked_embed[:,maps])
# write data back into cortex dimensions to avoid confusion
modelled_fit_cortex = np.zeros((t1.shape[0]))
modelled_fit_cortex[nonmask_small]=modelled_fit
residuals_cortex = np.zeros((t1.shape[0]))
residuals_cortex[nonmask_small]=residuals
t1_cortex = np.zeros((t1.shape[0]))
t1_cortex[nonmask_small]=masked_t1
model_dict = dict()
model_dict['maps']=maps
model_dict['coef']=clf.coef_
model_dict['modelled_fit']=modelled_fit_cortex
model_dict['residuals']=residuals_cortex
model_dict['t1']=t1_cortex
model_dict['score'] = clf.score(masked_embed[:,maps], masked_t1)
model_dict['corr'] = stats.pearsonr(modelled_fit, masked_t1)
pkl_out = open(model_file%(smooth, hemi, iteration, maps_str), 'wb')
pickle.dump(model_dict, pkl_out)
pkl_out.close()
return
if __name__ == "__main__":
### load data
print 'loading'
rh_mesh_file = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/rh_lowres_new.vtk'
lh_mesh_file = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/lh_lowres_new.vtk'
full_mask_file = '/scr/ilz3/myelinconnect/new_groupavg/masks/fullmask_lh_rh_new.npy'
rh_t1_file = '/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_1.5/rh_t1_avg_smooth_1.5.npy'
lh_t1_file = '/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_1.5/lh_t1_avg_smooth_1.5.npy'
embed_file = '/scr/ilz3/myelinconnect/new_groupavg/embed/both_smooth_3_embed.npy'
embed_dict_file = '/scr/ilz3/myelinconnect/new_groupavg/embed/both_smooth_3_embed_dict.pkl'
model_file_template = '/scr/ilz3/myelinconnect/new_groupavg/model/linear_combination/t1avg/smooth_1.5/model_comparison/model_%s.pkl'
lv, lf, _ = read_vtk(lh_mesh_file)
lh_t1 = np.load(lh_t1_file)
rv, rf, _ = read_vtk(rh_mesh_file)
rh_t1 = np.load(rh_t1_file)
mask = np.load(full_mask_file)
# prepare embedding (normalized from entry in dict)
pkl_in = open(embed_dict_file, 'r')
embed_dict = pickle.load(pkl_in)
pkl_in.close()
embed_masked = np.zeros(
(embed_dict['vectors'].shape[0], embed_dict['vectors'].shape[1] - 1))
for comp in range(100):
embed_masked[:, comp] = (embed_dict['vectors'][:, comp + 1] /
from vtk_rw import read_vtk
import numpy as np
import gdist
mesh_file="/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk"
hemis=['rh', 'lh']
for hemi in hemis:
v, f, _ = read_vtk(mesh_file %hemi)
v = v.astype(np.float64)
f = f.astype(np.int32)
dist_matrix=gdist.local_gdist_matrix(v, f)
hemis = ['rh', 'lh']
sessions = ['1_1', '1_2', '2_1', '2_2']
mesh_file = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk'
data_file = '/scr/ilz3/myelinconnect/new_groupavg/rest/smooth_3/%s_%s_rest%s_smooth_3.npy'
vtk_data_file = '/scr/ilz3/myelinconnect/new_groupavg/rest/smooth_3/%s_%s_rest%s_smoothdata.vtk'
mode = 'vtk2npy'
if mode == 'npy2vtk':
print 'npy to vtk'
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':
from vtk_rw import read_vtk
from ply_rw import write_ply
'''
Meta script to convert vtk to ply file
'''
in_vtk = raw_input("input vtk file: ")
out_ply = raw_input("output ply file: ")
comment = raw_input("comment (optional): ")
if comment == "":
comment = 'ply format converted from vtk'
v, f, d = read_vtk(in_vtk)
write_ply(out_ply, v, f, comment)
inv2prob_full_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/masks/%s_inv2prob_full.npy'
inv2prob_min_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/masks/%s_inv2prob_min.npy'
for hemi in hemis:
n_vertices = np.load(label_file%(subjects[0], hemi))[:,1].max()+1
tsnr = np.zeros((n_vertices, len(subjects)*len(sessions)))
inv2prob = np.zeros((n_vertices, len(subjects)))
inv2prob_count = 0
tsnr_count = 0
for sub in subjects:
labels = np.load(label_file%(sub, hemi))[:,1]
inv2prob_highres_v, inv2prob_highres_f, inv2prob_highres_d = read_vtk(inv2prob_highres_file%(sub, hemi))
# average across highres vertices that map to the same lowres vertex
inv2prob[:,inv2prob_count] = np.squeeze(sample_simple(inv2prob_highres_d, labels))
inv2prob_count += 1
for sess in sessions:
print sub, sess
highres_func_v, highres_func_f, highres_func_d = read_vtk(highres_func_file%(hemi, sess, sub, hemi))
# sample resting state time series on highres mesh
tsnr_highres = sample_volume(tsnr_file%(sub, sess, sub, sess), highres_func_v)
subjects = pd.read_csv('/scr/ilz3/myelinconnect/subjects.csv')
subjects=list(subjects['DB'])
subjects.remove('KSMT')
hemis = ['rh', 'lh']
sessions = ['1_1', '1_2', '2_1', '2_2']
for hemi in hemis:
for sess in sessions:
mapping_x='/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/x/%s_lowres_new_avgsurf_groupdata.vtk'%(sess, hemi)
mapping_y='/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/y/%s_lowres_new_avgsurf_groupdata.vtk'%(sess, hemi)
mapping_z='/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/z/%s_lowres_new_avgsurf_groupdata.vtk'%(sess, hemi)
v, f, dx = read_vtk(mapping_x)
_, _, dy = read_vtk(mapping_y)
_, _, dz = read_vtk(mapping_z)
for sub in range(len(subjects)):
tsnr_file='/scr/ilz3/myelinconnect/resting/preprocessed/%s/rest%s/realignment/corr_%s_rest%s_roi_tsnr.nii.gz'%(subjects[sub], sess, subjects[sub], sess)
out_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.npy'%(subjects[sub], hemi, sess)
#out_vtk = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.vtk'%(subjects[sub], hemi, sess)
tsnr_vol = nb.load(tsnr_file).get_data()
tsnr_mesh = np.zeros(dx.shape[0],)
coords = np.vstack((dx[:,sub],dy[:,sub],dz[:,sub]))
coords = coords.T
from vtk_rw import read_vtk import numpy as np filename = '/Users/ghfc/Desktop/P8_F10_test.vtk' #filename = '/Users/ghfc/Desktop/P8_F10_DTI.vtk' vertex_array, face_array, data_array = read_vtk(filename) #print vertex_array
subjects.remove('KSMT')
hemis = ['rh', 'lh']
sessions = ['1_1', '1_2', '2_1', '2_2']
for hemi in hemis:
for sess in sessions:
mapping_x = '/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/x/%s_lowres_new_avgsurf_groupdata.vtk' % (
sess, hemi)
mapping_y = '/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/y/%s_lowres_new_avgsurf_groupdata.vtk' % (
sess, hemi)
mapping_z = '/scr/ilz3/myelinconnect/mappings/rest2groupavg_surf/rest%s/z/%s_lowres_new_avgsurf_groupdata.vtk' % (
sess, hemi)
v, f, dx = read_vtk(mapping_x)
_, _, dy = read_vtk(mapping_y)
_, _, dz = read_vtk(mapping_z)
for sub in range(len(subjects)):
tsnr_file = '/scr/ilz3/myelinconnect/resting/preprocessed/%s/rest%s/realignment/corr_%s_rest%s_roi_tsnr.nii.gz' % (
subjects[sub], sess, subjects[sub], sess)
out_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.npy' % (
subjects[sub], hemi, sess)
#out_vtk = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.vtk'%(subjects[sub], hemi, sess)
tsnr_vol = nb.load(tsnr_file).get_data()
tsnr_mesh = np.zeros(dx.shape[0], )
coords = np.vstack((dx[:, sub], dy[:, sub], dz[:, sub]))
sessions = ['1_1', '1_2', '2_1', '2_2']
tsnr_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.npy'
inv2prob_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_lowres_new_groupavgsurf.vtk'
tsnr_full_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_tsnr_full.npy'
tsnr_min_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_tsnr_min.npy'
inv2prob_full_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_inv2prob_full.npy'
inv2prob_min_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_inv2prob_min.npy'
for hemi in hemis:
n_vertices = np.load(tsnr_file % (subjects[0], hemi, sessions[0])).shape[0]
tsnr = np.zeros((n_vertices, len(subjects) * len(sessions)))
_, _, inv2prob = read_vtk(inv2prob_file % (hemi))
tsnr_count = 0
for sub in range(len(subjects)):
for sess in sessions:
print subjects[sub], sess
tsnr_d = np.load(tsnr_file % (subjects[sub], hemi, sess))
tsnr[:, tsnr_count] = tsnr_d
tsnr_count += 1
print 'saving'
import numpy as np
from vtk_rw import read_vtk
vtk_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s/%s_lowres_new_avgsurf_groupdata.vtk'
pro_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s_lowres_new_avgsurf_groupdata.npy'
pro_mean_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s_lowres_new_avgsurf_groupdata_mean.npy'
for hemi in ['lh', 'rh']:
_, _, d = read_vtk(vtk_file % (0, hemi))
pro = np.zeros((d.shape[0], d.shape[1], 11))
for layer in range(11):
_, _, d = read_vtk(vtk_file % (layer, hemi))
pro[:, :, layer] = d
pro_mean = np.mean(pro, axis=1)
np.save(pro_file % (hemi), pro)
np.save(pro_mean_file % (hemi), pro_mean)
'''clustering'''
if calc_cluster:
if not calc_embed:
embedding_recort = np.load(embed_file%(smooth, masktype, hemi, str(n_embedding)))
mask = np.load(mask_file%(hemi, masktype))
for nk in n_kmeans:
print 'clustering %s'%str(nk)
kmeans_recort = kmeans(embedding_recort, nk, mask)
np.save(kmeans_file%(smooth, masktype, hemi, str(n_embedding), str(nk)),
kmeans_recort)
if calc_subcluster:
print 'subclustering %s'%str(nk)
v, f, d = read_vtk(mesh_file%hemi)
subclust_arr=subcluster(kmeans_recort, f)
np.save(subclust_file%(smooth, masktype, hemi, str(n_embedding), str(nk)), subclust_arr)
'''subclustering'''
if not calc_cluster:
if calc_subcluster:
v, f, d = read_vtk(mesh_file%hemi)
for nk in n_kmeans:
print 'subclustering %s'%str(nk)
kmeans_recort = np.load(kmeans_file%(smooth, masktype, hemi, str(n_embedding), str(nk)))
subclust_arr=subcluster(kmeans_recort, f)
hemis = ['rh', 'lh']
sessions = ['1_1', '1_2', '2_1', '2_2']
mesh_file = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk'
data_file = '/scr/ilz3/myelinconnect/new_groupavg/rest/smooth_3/%s_%s_rest%s_smooth_3.npy'
vtk_data_file = '/scr/ilz3/myelinconnect/new_groupavg/rest/smooth_3/%s_%s_rest%s_smoothdata.vtk'
mode = 'vtk2npy'
if mode == 'npy2vtk' :
print 'npy to vtk'
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)
(FC1 and FC1,5,6) for each hemisphere separately for comparison to random data.
'''
mesh_file = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/%s_lowres_new.vtk'
mask_file="/scr/ilz3/myelinconnect/new_groupavg/masks/%s_fullmask_new.npy"
fullmask_file = '/scr/ilz3/myelinconnect/new_groupavg/masks/fullmask_lh_rh_new.npy'
t1_file = '/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_1.5/%s_t1_avg_smooth_1.5.npy'
embed_dict_file="/scr/ilz3/myelinconnect/new_groupavg/embed/both_smooth_3_embed_dict.pkl"
model_file = '/scr/ilz3/myelinconnect/new_groupavg/model/linear_combination/t1avg/smooth_1.5/%s_t1avg_by_fc_maps_%s.pkl'
all_maps = [[0],[0,4,5]]
all_maps_str = ['0', 'best']
# load one file for each hemishpere once to get dimensions
lv,_,_ = read_vtk(mesh_file%('lh'))
rv,_,_ = read_vtk(mesh_file%('rh'))
# prepare embedding (normalized from entry in dict)
pkl_in = open(embed_dict_file, 'r')
embed_dict=pickle.load(pkl_in)
pkl_in.close()
embed_masked = np.zeros((embed_dict['vectors'].shape[0], embed_dict['vectors'].shape[1]-1))
for comp in range(100):
embed_masked[:,comp]=(embed_dict['vectors'][:,comp+1]/embed_dict['vectors'][:,0])
fullmask = np.load(fullmask_file)
# unmask embed which has been saved in masked form
idcs_full=np.arange(0,(lv.shape[0]+rv.shape[0]))
nonmask_full=np.delete(idcs_full, fullmask)
import numpy as np
import gdist
from vtk_rw import read_vtk
import time
sub = 'BP4T'
hemi = 'lh'
complex_file = '/scr/ilz3/myelinconnect/struct/surf_%s/orig/mid_surface/%s_%s_mid.vtk'%(hemi, sub, hemi)
simple_file = '/scr/ilz3/myelinconnect/groupavg/indv_space/%s/lowres_%s_d_def.vtk'%(sub, hemi)# version d
complex_v, complex_f, complex_d = read_vtk(complex_file)
complex_vertices = complex_v.astype(np.float64)
complex_faces = complex_f.astype(np.int32)
simple_v, simple_f, simple_d = read_vtk(simple_file)
simple_vertices = simple_v.astype(np.float64)
simple_faces = simple_f.astype(np.int32)
complex_radius = 2
print time.ctime()
complex_matrix=gdist.local_gdist_matrix(complex_vertices, complex_faces, max_distance=complex_radius)
for sv in range(len(simple_v.shape[0])):
dist = 1000000
for cv in range(len(complex_v.shape[0])):
if sv in complex_matrix[:,cv].indices:
dist_now = complex_matrix[:,cv][sv].data
if dist_now < dist:
dist = dist_now
# TO DO: allow transformation input, find a better way to get pixdims?
vol_file = raw_input('volume file: ')
surf = raw_input('surface file: ')
out = raw_input('output file: ')
# load information from volume
vol = nb.load(vol_file)
vol_data = vol.get_data()
vol_hdr = vol.get_header()
vol_affine = vol.get_affine()
# read vertices from surface
if surf[-3:] == 'vtk':
v, f = read_vtk(surf)
elif surf[-3:] == 'ply':
v, f = read_ply(surf)
else:
print "only vtk and ply format supported yet"
# scale and translate surface into volume space
pixdims = list(vol_hdr['pixdim'][1:4])
v_scaled = np.empty_like(v)
v_scaled[:, 0] = ((1. / pixdims[0]) * v[:, 0])
v_scaled[:, 1] = (-(1. / pixdims[1]) * v[:, 1])
v_scaled[:, 2] = (-(1. / pixdims[2]) * v[:, 2])
trans = [0, (vol.shape[1] - 1), (vol.shape[2] - 1)]
'''
# TO DO: allow transformation input, find a better way to get pixdims?
vol_file = raw_input('volume file: ')
surf = raw_input('surface file: ')
out = raw_input('output file: ')
# load information from volume
vol = nb.load(vol_file)
vol_data = vol.get_data()
vol_hdr = vol.get_header()
vol_affine = vol.get_affine()
# read vertices from surface
if surf[-3:] == 'vtk':
v, f = read_vtk(surf)
elif surf[-3:] == 'ply':
v, f = read_ply(surf)
else:
print "only vtk and ply format supported yet"
# scale and translate surface into volume space
pixdims = list(vol_hdr['pixdim'][1:4])
v_scaled = np.empty_like(v)
v_scaled[:, 0] = ((1. / pixdims[0]) * v[:, 0])
v_scaled[:, 1] = (-(1. / pixdims[1]) * v[:, 1])
v_scaled[:, 2] = (-(1. / pixdims[2]) * v[:, 2])
trans = [0, (vol.shape[1] - 1), (vol.shape[2] - 1)]
if __name__ == "__main__":
### load data
print 'loading'
rh_mesh_file='/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/rh_lowres_new.vtk'
lh_mesh_file='/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/lh_lowres_new.vtk'
full_mask_file='/scr/ilz3/myelinconnect/new_groupavg/masks/fullmask_lh_rh_new.npy'
rh_t1_file='/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_1.5/rh_t1_avg_smooth_1.5.npy'
lh_t1_file='/scr/ilz3/myelinconnect/new_groupavg/t1/smooth_1.5/lh_t1_avg_smooth_1.5.npy'
embed_file='/scr/ilz3/myelinconnect/new_groupavg/embed/both_smooth_3_embed.npy'
embed_dict_file='/scr/ilz3/myelinconnect/new_groupavg/embed/both_smooth_3_embed_dict.pkl'
lv,lf,_ = read_vtk(lh_mesh_file)
lh_t1 = np.load(lh_t1_file)
rv,rf,_ = read_vtk(rh_mesh_file)
rh_t1 = np.load(rh_t1_file)
mask = np.load(full_mask_file)
# prepare embedding (normalized from entry in dict)
pkl_in = open(embed_dict_file, 'r')
embed_dict=pickle.load(pkl_in)
pkl_in.close()
embed_masked = np.zeros((embed_dict['vectors'].shape[0], embed_dict['vectors'].shape[1]-1))
for comp in range(100):
embed_masked[:,comp]=(embed_dict['vectors'][:,comp+1]/embed_dict['vectors'][:,0])
tsnr_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_%s_rest%s.npy'
inv2prob_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_lowres_new_groupavgsurf.vtk'
tsnr_full_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_tsnr_full.npy'
tsnr_min_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/tsnr/%s_tsnr_min.npy'
inv2prob_full_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_inv2prob_full.npy'
inv2prob_min_file = '/scr/ilz3/myelinconnect/new_groupavg/snr/inv2prob/%s_inv2prob_min.npy'
for hemi in hemis:
n_vertices = np.load(tsnr_file%(subjects[0], hemi, sessions[0])).shape[0]
tsnr = np.zeros((n_vertices, len(subjects)*len(sessions)))
_, _, inv2prob = read_vtk(inv2prob_file%(hemi))
tsnr_count = 0
for sub in range(len(subjects)):
for sess in sessions:
print subjects[sub], sess
tsnr_d = np.load(tsnr_file%(subjects[sub], hemi, sess))
tsnr[:,tsnr_count] = tsnr_d
tsnr_count += 1
print 'saving'
from __future__ import division
import numpy as np
import h5py
from scipy.spatial.distance import pdist
from vtk_rw import read_vtk
lh_mesh = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/lh_lowres_new.vtk'
rh_mesh = '/scr/ilz3/myelinconnect/new_groupavg/surfs/lowres/rh_lowres_new.vtk'
mat_file = '/scr/ilz3/myelinconnect/new_groupavg/corr/both_euclid_dist.hdf5'
# load t1 data
print 'load data'
v_left, _, _ = read_vtk(lh_mesh)
v_right, _, _ = read_vtk(rh_mesh)
v = np.concatenate((v_left, v_right))
size = v.shape[0]
# calculate t1 distances for each subject and average
print 'calc mat'
dist_upper = pdist(v,'euclidean')
# save
print 'saving matrix'
f = h5py.File(mat_file, 'w')
f.create_dataset('upper', data=dist_upper)
f.create_dataset('shape', data=(size, size))
f.close()
mean_pro_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/%s_%s_profiles_mean_3_7.npy'
avg_pro_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/avg_%s_profiles.npy'
avg_mean_pro_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/avg_%s_mean_3_7.npy'
avg_pro_surf = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/avg_%s_profiles_%s.vtk'
avg_mean_surf = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/avg_%s_mean_3_7.vtk'
surf_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/surfs/lowres_%s_d.vtk'
for hemi in hemis:
get_size = np.load('/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/%s_%s_profiles.npy'%(subjects[0], hemi)).shape
avg_pro = np.zeros((get_size[0], get_size[1]))
avg_mean = np.zeros((get_size[0]))
v,f,d = read_vtk(surf_file%(hemi))
count = 0
for sub in subjects:
profiles = np.load(pro_file%(sub, hemi))
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
from vtk_rw import read_vtk
from ply_rw import write_ply
'''
Meta script to convert vtk to ply file
'''
in_vtk = raw_input("input vtk file: ")
out_ply = raw_input("output ply file: ")
comment = raw_input("comment (optional): ")
if comment == "":
comment = 'ply format converted from vtk'
v, f, d= read_vtk(in_vtk)
write_ply(out_ply, v, f, comment)
'labels min',
'labels max',
'labels mean',
'labels sdv',
'vertex no label',
'label no vertex'])
for sub in subjects:
for hemi in hemis:
if os.path.isfile('/scr/ilz3/myelinconnect/all_data_on_simple_surf/labels/%s_%s_highres2lowres_labels.npy'%(sub, hemi)):
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:
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])
mask_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/masks/%s_fullmask.npy'%hemi
t1_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/%s/avg_%s_profiles_%s.npy'%(smooth, hemi, smooth)
#t1_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/%s/avg_%s_coeffs_%s.npy'%(smooth, hemi, smooth)
#euclid_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/profile_embedding/%s_euclidian_dist_%s.hdf5'
#corr_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/t1/profile_embedding/%s_profile_corr_%s.hdf5'
affinity_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/profiles/%s_%s_cheb_affinity_%s.hdf5'%(hemi, smooth, affine_method)
embed_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/profiles/%s_%s_embedding_%s_%s.npy'%(hemi, smooth, str(n_embedding), affine_method)
embed_dict_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/profiles/%s_%s_embedding_dict_%s_%s.pkl'%(hemi, smooth, str(n_embedding), affine_method)
#affinity_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/coefficients/%s_%s_cheb_affinity_%s.hdf5'%(hemi, smooth, affine_method)
#embed_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/coefficients/%s_%s_embedding_%s_%s.npy'%(hemi, smooth, str(n_embedding), affine_method)
#embed_dict_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/coefficients/%s_%s_embedding_dict_%s_%s.pkl'%(hemi, smooth, str(n_embedding), affine_method)
print 'loading data'
v, f, d = read_vtk(mesh_file)
t1=np.load(t1_file)
full_shape=tuple((t1.shape[0], t1.shape[0]))
if euclid:
print 'euclid'
t1_3_7_diff = sp.spatial.distance.pdist(t1[:,3:8], 'euclidean')
f = h5py.File(euclid_file%('rh', '3_7'), 'w')
f.create_dataset('upper', data=t1_3_7_diff)
f.create_dataset('shape', data=full_shape)
f.close()
del t1_3_7_diff
if corr:
print 'corr'
import numpy as np
from vtk_rw import read_vtk
vtk_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s/%s_lowres_new_avgsurf_groupdata.vtk'
pro_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s_lowres_new_avgsurf_groupdata.npy'
pro_mean_file = '/nobackup/ilz3/myelinconnect/new_groupavg/profiles/smooth_1.5/%s_lowres_new_avgsurf_groupdata_mean.npy'
for hemi in ['lh', 'rh']:
_, _, d = read_vtk(vtk_file%(0, hemi))
pro = np.zeros((d.shape[0], d.shape[1], 11))
for layer in range(11):
_, _, d = read_vtk(vtk_file%(layer, hemi))
pro[:,:,layer] = d
pro_mean = np.mean(pro, axis=1)
np.save(pro_file%(hemi), pro)
np.save(pro_mean_file%(hemi), pro_mean)
