a[mask_left__mesh] = 1
a[mask_right__mesh] = 2
mask_left__beta = a[mask__mesh] == 1 # project mesh to mesh masked
mask_right__beta = a[mask__mesh] == 2
assert (
mask_left__beta.sum() + mask_right__beta.sum()
) == mask_left__beta.shape[0] == mask_right__beta.shape[0] == mask__mesh.sum()
assert mask_left__mesh.sum() == mask_left__beta.sum()
assert mask_right__mesh.sum() == mask_right__beta.sum()
# Check mapping from beta left part to left_mesh
assert mask_left__beta.sum() == mask_left__left_mesh.sum()
assert mask_right__beta.sum() == mask_right__right_mesh.sum()
#############################################################################
#Save loading vectors
#############################################################################
# left
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = beta[mask_left__beta]
print("left", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_beta_left.gii"),
data=tex)
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = beta[mask_right__beta]
print("right", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_beta_right.gii"),
data=tex)
############################################################################# # Some basic stat before centering/scaling means = np.mean(X, axis=0) stds = np.std(X, axis=0) mins = np.min(X, axis=0) maxs = np.max(X, axis=0) print "Means:", means.min(), means.max(), means.mean() print "Std:", stds.min(), stds.max(), stds.mean() print "Mins:", mins.min(), mins.max(), mins.mean() print "Maxs:", maxs.min(), maxs.max(), maxs.mean(), (maxs == 0).sum() arr = np.zeros(mask.shape); arr[mask] = means mesh_utils.save_texture(filename=os.path.join(OUTPUT, "mean.gii"), data=arr)#, intent='NIFTI_INTENT_NONE') arr = np.zeros(mask.shape); arr[mask] = stds mesh_utils.save_texture(filename=os.path.join(OUTPUT, "std.gii"), data=arr)#, intent='NIFTI_INTENT_NONE') arr = np.zeros(mask.shape); arr[mask] = maxs mesh_utils.save_texture(filename=os.path.join(OUTPUT, "max.gii"), data=arr)#, intent='NIFTI_INTENT_NONE') ############################################################################# X = np.hstack([Z, X]) assert X.shape == (34, 314785) # Center/scale X -= X.mean(axis=0) X /= X.std(axis=0)
# Check mapping from beta left part to left_mesh
assert mask_left__beta.sum() == mask_left__left_mesh.sum()
assert mask_right__beta.sum() == mask_right__right_mesh.sum()
#############################################################################
#SVM weight map
svm_weight_map = "/neurospin/brainomics/2016_schizConnect/analysis/all_studies/Freesurfer/all_subjects_less_than_30years/results/svm/svm_model_selection_5folds_all_subjects_less_than_30years/model_selectionCV/all/all/1e-09/beta.npz"
svm = np.load(svm_weight_map)['arr_0'].T
svm_t,t = array_utils.arr_threshold_from_norm2_ratio(svm,0.99)
# left
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = svm_t[mask_left__beta]
print ("left", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "svm_weight_map_left.gii"), data=tex)
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = svm_t[mask_right__beta]
print ("right", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "svm_weight_map_right.gii"), data=tex)
#############################################################################
#Enet weight map
enet_weight_map = "/neurospin/brainomics/2016_schizConnect/analysis/all_studies/Freesurfer/all_subjects_less_than_30years/results/enettv/model_selection_5folds_all_subjects_less_than_30years/model_selectionCV/refit/refit/0.1_0.36_0.04_0.6/beta.npz"
enet = np.load(enet_weight_map)['arr_0']
enet_t,t = array_utils.arr_threshold_from_norm2_ratio(enet,0.99)
# left
tex = np.zeros(mask_left__left_mesh.shape)
enet_weight_map = "/neurospin/brainomics/2017_asd_charles/Freesurfer/results/\
enettv_asd_charles_10000ite/model_selectionCV/refit/refit/0.1_0.0_0.0_1.0"
param = os.path.basename(enet_weight_map)
beta_path = os.path.join(enet_weight_map, "beta.npz")
penalty_start = 5
enet = np.load(beta_path)['arr_0'][penalty_start:, :]
enet_t, t = array_utils.arr_threshold_from_norm2_ratio(enet, 0.99)
# left
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = enet_t[mask_left__beta]
print("left", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT,
param + "_weight_map_left.gii"),
data=tex)
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = enet_t[mask_right__beta]
print("right", np.sum(tex != 0), tex.max(), tex.min())
mesh_utils.save_texture(filename=os.path.join(OUTPUT,
param + "_weight_map_right.gii"),
data=tex)
#######################################################################################
OUTPUT = "/neurospin/brainomics/2017_asd_charles/Freesurfer/results/svm/svm_asd/weight_map"
shutil.copyfile(os.path.join(TEMPLATE_PATH, "lh.pial.gii"),
os.path.join(OUTPUT, "lh.pial.gii"))
shutil.copyfile(os.path.join(TEMPLATE_PATH, "rh.pial.gii"),
print "Mins:", mins.min(), mins.max(), mins.mean()
print "Maxs:", maxs.min(), maxs.max(), maxs.mean(), (maxs == 0).sum()
#In [93]: Means: 0.00120785424662 4.70434341146 2.20050826134
#In [94]: Std: 0.0170816385694 2.18101036803 0.593103047434
#In [95]: Mins: 0.0 3.02653193474 0.936884414047
#In [96]: Maxs: 0.24277870357 5.0 3.94083362002 0
# Smoothed
#Means: 0.000738471496825 4.4246063956 2.17232778568
#Std: 0.010000705246 1.16976456436 0.386480145866
#Mins: 0.0 3.22148299217 1.24649752094
#Maxs: 0.0534101724625 4.97702121735 3.30445936329 0
arr = np.zeros(mask.shape)
arr[mask] = means
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "mean.gii"),
data=arr) #, intent='NIFTI_INTENT_NONE')
arr = np.zeros(mask.shape)
arr[mask] = stds
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "std.gii"),
data=arr) #, intent='NIFTI_INTENT_NONE')
arr = np.zeros(mask.shape)
arr[mask] = maxs
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "max.gii"),
data=arr) #, intent='NIFTI_INTENT_NONE')
# anatomist mean.gii std.gii max.gii ../freesurfer_template/lrh.pial.gii
#############################################################################
# Center/scale
X -= X.mean(axis=0)
X /= X.std(axis=0)
n, p = X.shape
np.vstack(tnsr_inv_pc12__lacunes__perfo_to_yaxis_all)
mesh_processing.mesh_from_arrays(
tnsr_inv_pc12__lacunes__perfo_to_yaxis_all,
tri_lacunes_all,
path=os.path.join(OUTPUT, "tnsr_inv_pc12__lacunes__perfo_to_yaxis.gii"))
### =======================================================================
### brain__lacunes__native & brain__perfo__native
### =======================================================================
aims.write(brain_mesh_lacunes__native_all,
os.path.join(OUTPUT, "brain__lacunes__native.gii"))
aims.write(brain_mesh_perfo__native_all,
os.path.join(OUTPUT, "brain__perforators__native.gii"))
for k in textures:
textures[k]["lacunes"] = np.hstack(textures[k]["lacunes"])
#print textures[k]
mesh_processing.save_texture(
filename=os.path.join(OUTPUT, "tex__lacunes__%s.gii" % k),
data=textures[k]["lacunes"]) #, intent='NIFTI_INTENT_NONE')
textures[k]["perforators"] = np.hstack(textures[k]["perforators"])
#print textures[k]
mesh_processing.save_texture(
filename=os.path.join(OUTPUT, "tex__perforators__%s.gii" % k),
data=textures[k]["perforators"]) #, intent='NIFTI_INTENT_NONE')
"""
anatomist /tmp/mesh_*.gii
freeview -f mnts-inv_pc12_scaled.gii:overlay=tex_tensor_invariant_fa.gii
"""
comp2_all = comp2
comp3_all = comp3
comp4_all = comp4
comp5_all = comp5
comp6_all = comp6
comp7_all = comp7
comp8_all = comp8
comp9_all = comp9
#Save loading vectors
#############################################################################
# left
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = comp0[mask_left__beta]
print "left", np.sum(tex != 0), tex.max(), tex.min()
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_0_left.gii"),
data=tex)
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = comp0[mask_right__beta]
print "right", np.sum(tex != 0), tex.max(), tex.min()
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_0_right.gii"),
data=tex)
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = comp1[mask_left__beta]
print "left", np.sum(tex != 0), tex.max(), tex.min()
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_1_left.gii"),
data=tex)
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = comp1[mask_right__beta]
for k in todo:
todo[k]["path"] = os.path.join("5cv", "*", "_".join([str(p) for p in todo[k]["param"]]))
#############################################################################
## CV 0
for k in todo:
#k = "l1l2"
Betas = np.vstack([array_utils.arr_threshold_from_norm2_ratio(
np.load(os.path.join(filename, "beta.npz"))['arr_0'][config["penalty_start"]:, :].ravel(), .99)[0]
for filename in glob.glob(todo[k]["path"])])
# left
tex = np.zeros(mask_left__left_mesh.shape)
tex[mask_left__left_mesh] = Betas[0, mask_left__beta]
print k, "left", np.sum(tex != 0), tex.max(), tex.min()
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_%s_left.gii" % k), data=tex)#, intent='NIFTI_INTENT_TTEST')
tex[mask_left__left_mesh] = np.sum(Betas[1:, mask_left__beta] != 0, axis=0)
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_%s_left_cvcountnonnull.gii" % k), data=tex)#, intent='NIFTI_INTENT_TTEST')
# right
tex = np.zeros(mask_right__right_mesh.shape)
tex[mask_right__right_mesh] = Betas[0, mask_right__beta]
print k, "right", np.sum(tex != 0), tex.max(), tex.min()
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_%s_right.gii" % k), data=tex)#, intent='NIFTI_INTENT_TTEST')
tex[mask_right__right_mesh] = np.sum(Betas[1:, mask_right__beta] != 0, axis=0)
mesh_utils.save_texture(filename=os.path.join(OUTPUT, "tex_%s_right_cvcountnonnull.gii" % k), data=tex)#, intent='NIFTI_INTENT_TTEST')
"""
l2tv left 76582 0.00314405576456 -0.00234570367821
l2tv left 97835 0.0256844622603 -0.0057810300272
l1tv left 2514 0.00345520687922 -0.00792580028994
l1tv left 2420 0.0253011691567 -0.00683345243997
