def get_activations_one_img(img, threshold, verbose=False):
"""
Retrieve the xyz activation coordinates from an image.
Args:
img (Nifti1Image): Nifti1Image from which to extract coordinates.
threshold (float): value below threshold are ignored. Used for
peak detection.
Returns:
(tuple): Size 3 tuple of lists storing respectively the X, Y and
Z coordinates.
"""
if verbose:
print('Extracting')
X, Y, Z = [], [], []
img = image.resample_to_img(img, template)
peaks = get_3d_peaks(img, mask=gray_mask, threshold=threshold)
if not peaks:
return X, Y, Z
for peak in peaks:
X.append(peak['pos'][0])
Y.append(peak['pos'][1])
Z.append(peak['pos'][2])
del peaks
return X, Y, Z
def get_activations(path, threshold):
"""
Retrieve the xyz activation coordinates from an image.
Args:
path (string or Nifti1Image): Path to or object of a
nibabel.Nifti1Image from which to extract coordinates.
threshold (float): Same as the extract_from_paths function.
Returns:
(tuple): Size 3 tuple of lists storing respectively the X, Y and
Z coordinates
"""
X, Y, Z = [], [], []
try:
img = nilearn.image.load_img(path)
except ValueError: # File path not found
print(f'File {path} not found. Ignored.')
return None
if np.isnan(img.get_fdata()).any():
print(f'Img {path} contains Nan. Ignored.')
return None
img = image.resample_to_img(img, template)
peaks = get_3d_peaks(img, mask=gray_mask, threshold=threshold)
if not peaks:
return X, Y, Z
for peak in peaks:
X.append(peak['pos'][0])
Y.append(peak['pos'][1])
Z.append(peak['pos'][2])
del peaks
return X, Y, Z
def compute_labelled_mask_from_HO_and_merged_thr_spm_mask():
write_dir = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name)
print spm_contrasts_path
if not os.path.exists(write_dir):
os.makedirs(write_dir)
spm_contrast_indexes = [3,4,5,8,9,10]
spm_mask_files = [os.path.join(spm_contrasts_path,"_contrast_index_"+str(index)+"_group_contrast_index_0/spmT_0001_thr.img") for index in spm_contrast_indexes]
#spm_mask_files.sort()
print len(spm_mask_files)
# prepare the data
img = nib.load(spm_mask_files[0])
img_header = img.get_header()
img_affine = img.get_affine()
img_shape = img.shape
img_data = img.get_data()
########################## Computing combined HO areas
resliced_full_HO_data,HO_labels,HO_abbrev_labels = compute_recombined_HO_template(img_header,img_affine,img_shape)
########################## Creating peak activation mask contrained by HO areas
#print len(HO_abbrev_labels)
#print len(HO_labels)
#0/0
np_HO_abbrev_labels = np.array(HO_abbrev_labels,dtype = 'string')
np_HO_labels = np.array(HO_labels,dtype = 'string')
template_indexes = np.unique(resliced_full_HO_data)[1:]
#print template_indexes
print np_HO_labels.shape,np_HO_abbrev_labels.shape,template_indexes.shape
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_template = np.hstack((template_indexes.reshape(len(HO_labels),1),np_HO_labels.reshape(len(HO_labels),1),np_HO_abbrev_labels.reshape(len(HO_labels),1)))
#,rois_MNI_coords))
print info_template
np.savetxt(info_template_file,info_template, fmt = '%s %s %s')
#np.savetxt(info_template_file,info_rois, fmt = '%s %s %s %s %s %s')
merged_mask_data = np.zeros(shape = img_shape,dtype = float)
print merged_mask_data.shape
list_orig_ROI_spm_index = []
### list for all info about peaks after merging between different contrasts
list_orig_peak_coords = []
list_orig_peak_MNI_coords = []
list_orig_peak_vals = []
for i,spm_mask_file in enumerate(spm_mask_files):
print spm_mask_file
spm_mask_img = nib.load(spm_mask_file)
spm_mask_data = spm_mask_img.get_data()
#### get peaks (avec la fonction stat_map.get_3d_peaks)
peaks = stat_map.get_3d_peaks(image=spm_mask_img,mask=None)
#print len(peaks)
if peaks != None :
print len(peaks)
list_orig_peak_vals = list_orig_peak_vals + [peak['val'] for peak in peaks]
list_orig_peak_coords = list_orig_peak_coords + [peak['ijk'] for peak in peaks]
list_orig_peak_MNI_coords = list_orig_peak_MNI_coords + [peak['pos'] for peak in peaks]
#print list_orig_peak_vals
#print np.where(np.isnan(spm_mask_data))
#print spm_mask_data[]
#merged_mask_data[np.logical_and(spm_mask_data != 0.0, np.logical_not(np.isnan(spm_mask_data)))] = 1.0
merged_mask_data[np.logical_and(spm_mask_data != 0.0, np.logical_not(np.isnan(spm_mask_data)))] += i+1
#print np.sum(np.logical_and(merged_mask_data != 0.0, np.logical_not(np.isnan(merged_mask_data))))
list_orig_ROI_spm_index = list_orig_ROI_spm_index + [i+1] * len(peaks)
print len(list_orig_peak_coords)
print len(list_orig_ROI_spm_index)
#### selectionne les pics sur leur distance entre eux et sur leur appatenance au template HO
list_selected_peaks_coords,indexed_mask_rois_data,list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(list_orig_peak_coords,resliced_full_HO_data,min_dist_between_ROIs)
#list_selected_peaks_coords,indexed_mask_rois_data,list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(sorded_merged_peaks_coords,resliced_full_HO_data,min_dist_between_ROIs)
nib.save(nib.Nifti1Image(data = merged_mask_data,header = img_header,affine = img_affine),merged_mask_img_file)
print list_selected_peaks_indexes
print len(list_selected_peaks_indexes)
#for coord in list_selected_peaks_coords:
#print coord
##template_indexes =
#print resliced_full_HO_data[coord[0],coord[1],coord[2]]
template_indexes = np.array([resliced_full_HO_data[coord[0],coord[1],coord[2]] for coord in list_selected_peaks_coords],dtype = 'int64')
print template_indexes
np_HO_abbrev_labels = np.array(HO_abbrev_labels,dtype = 'string')
np_HO_labels = np.array(HO_labels,dtype = 'string')
print template_indexes-1
label_rois = np_HO_abbrev_labels[template_indexes-1]
full_label_rois = np_HO_labels[template_indexes-1]
#print label_rois2
print label_rois
#### exporting Rois image with different indexes
print np.unique(indexed_mask_rois_data)[1:].shape
nib.save(nib.Nifti1Image(data = indexed_mask_rois_data,header = img_header,affine = img_affine),indexed_mask_rois_file)
#### saving ROI coords as textfile
np.savetxt(coord_rois_file,np.array(list_selected_peaks_coords,dtype = int), fmt = '%d')
#### saving MNI coords as textfile
list_rois_MNI_coords = [list_orig_peak_MNI_coords[index] for index in list_selected_peaks_indexes]
print list_rois_MNI_coords
rois_MNI_coords = np.array(list_rois_MNI_coords,dtype = int)
np.savetxt(MNI_coord_rois_file,rois_MNI_coords, fmt = '%d')
### orig index of peaks
list_rois_orig_indexes = [list_orig_ROI_spm_index[index] for index in list_selected_peaks_indexes]
print list_rois_orig_indexes
rois_orig_indexes = np.array(list_rois_orig_indexes,dtype = int).reshape(len(list_rois_orig_indexes),1)
print rois_orig_indexes.shape
np.savetxt(rois_orig_indexes_file,rois_orig_indexes, fmt = '%d')
#### mask with orig spm index
orig_spm_index_mask_data = np.zeros(shape = img_shape,dtype = int)
print np.unique(indexed_mask_rois_data)
for i in np.unique(indexed_mask_rois_data)[1:]:
print i,np.sum(indexed_mask_rois_data == i),rois_orig_indexes[i]
orig_spm_index_mask_data[indexed_mask_rois_data == i] = rois_orig_indexes[i]
nib.save(nib.Nifti1Image(data = orig_spm_index_mask_data,header = img_header,affine = img_affine),orig_spm_index_mask_file)
#### saving labels
np.savetxt(label_rois_file,label_rois, fmt = '%s')
### saving all together for infosource
np_label_rois = np.array(label_rois,dtype = 'string').reshape(len(label_rois),1)
np_full_label_rois = np.array(full_label_rois,dtype = 'string').reshape(len(full_label_rois),1)
print np_label_rois.shape
print rois_MNI_coords.shape
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords,rois_orig_indexes))
print info_rois
np.savetxt(info_rois_file,info_rois, fmt = '%s %s %s %s %s %s %s')
return indexed_mask_rois_file,coord_rois_file
def compute_labelled_mask_from_HO_all_signif_contrasts():
write_dir = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name)
print spm_contrasts_path
if not os.path.exists(write_dir):
os.makedirs(write_dir)
#spm_mask_files = glob.glob(os.path.join(spm_contrasts_path,rel_spm_mask_path,"_contrast_index_[1-6]_group_contrast_index_0/spmT_*.img"))
spm_mask_files = glob.glob(os.path.join(spm_contrasts_path,contrast_pattern))
print spm_mask_files
print spm_mask_files.sort()
# prepare the data
img = nib.load(spm_mask_files[0])
img_header = img.get_header()
img_affine = img.get_affine()
img_shape = img.shape
img_data = img.get_data()
########################## Computing combined HO areas
resliced_full_HO_data,HO_labels,HO_abbrev_labels = compute_recombined_HO_template(img_header,img_affine,img_shape)
########################## Creating peak activation mask contrained by HO areas
#print len(HO_abbrev_labels)
#print len(HO_labels)
#0/0
np_HO_abbrev_labels = np.array(HO_abbrev_labels,dtype = 'string')
np_HO_labels = np.array(HO_labels,dtype = 'string')
template_indexes = np.unique(resliced_full_HO_data)[1:]
#print template_indexes
print np_HO_labels.shape,np_HO_abbrev_labels.shape,template_indexes.shape
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_template = np.hstack((template_indexes.reshape(len(HO_labels),1),np_HO_labels.reshape(len(HO_labels),1),np_HO_abbrev_labels.reshape(len(HO_labels),1)))
#,rois_MNI_coords))
print info_template
np.savetxt(info_template_file,info_template, fmt = '%s %s %s')
#np.savetxt(info_template_file,info_rois, fmt = '%s %s %s %s %s %s')
indexed_mask_rois_files = []
coord_rois_files = []
for i,spm_mask_file in enumerate(spm_mask_files):
print spm_mask_file
spm_mask_img = nib.load(spm_mask_file)
spm_mask_data = spm_mask_img.get_data()
#### get peaks (avec la fonction stat_map.get_3d_peaks)
peaks = stat_map.get_3d_peaks(image=spm_mask_img,mask=None, threshold = threshold,nn = cluster_nbvoxels)
#print len(peaks)
list_orig_ROI_spm_index = []
if peaks != None :
print len(peaks)
list_orig_peak_vals = [peak['val'] for peak in peaks]
list_orig_peak_coords = [peak['ijk'] for peak in peaks]
list_orig_peak_MNI_coords = [peak['pos'] for peak in peaks]
merged_mask_data = spm_mask_data[np.logical_and(spm_mask_data != 0.0, np.logical_not(np.isnan(spm_mask_data)))]
list_orig_ROI_spm_index = list_orig_ROI_spm_index + [i+1] * len(peaks)
print len(list_orig_peak_coords)
print len(list_orig_ROI_spm_index)
list_selected_peaks_coords,indexed_mask_rois_data,list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(list_orig_peak_coords,resliced_full_HO_data,min_dist_between_ROIs)
print list_selected_peaks_indexes
print len(list_selected_peaks_indexes)
merged_mask_data[indexed_mask_rois_data != 0] += i+1
template_indexes = np.array([resliced_full_HO_data[coord[0],coord[1],coord[2]] for coord in list_selected_peaks_coords],dtype = 'int64')
print template_indexes
np_HO_abbrev_labels = np.array(HO_abbrev_labels,dtype = 'string')
np_HO_labels = np.array(HO_labels,dtype = 'string')
print template_indexes-1
label_rois = np_HO_abbrev_labels[template_indexes-1]
full_label_rois = np_HO_labels[template_indexes-1]
#print label_rois2
print label_rois
#### indexed_mask
indexed_mask_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "indexed_mask-" + ROI_mask_prefix + "_spm_contrast" + str(i+1) + ".nii")
#### saving ROI coords as textfile
### ijk coords
coord_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "coords-" + ROI_mask_prefix + "_spm_contrast" + str(i+1) + ".txt")
### coords in MNI space
MNI_coord_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "coords-MNI-" + ROI_mask_prefix + "_spm_contrast" + str(i+1) + ".txt")
#### saving ROI coords as textfile
label_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "labels-" + ROI_mask_prefix + "_spm_contrast" + str(i+1) + ".txt")
#label_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "labels-" + ROI_mask_prefix + "_jane.txt")
#### all info in a text file
info_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "info-" + ROI_mask_prefix + "_spm_contrast" + str(i+1) + ".txt")
#### exporting Rois image with different indexes
print np.unique(indexed_mask_rois_data)[1:].shape
nib.save(nib.Nifti1Image(data = indexed_mask_rois_data,header = img_header,affine = img_affine),indexed_mask_rois_file)
#### saving ROI coords as textfile
np.savetxt(coord_rois_file,np.array(list_selected_peaks_coords,dtype = int), fmt = '%d')
#### saving MNI coords as textfile
list_rois_MNI_coords = [list_orig_peak_MNI_coords[index] for index in list_selected_peaks_indexes]
print list_rois_MNI_coords
rois_MNI_coords = np.array(list_rois_MNI_coords,dtype = int)
np.savetxt(MNI_coord_rois_file,rois_MNI_coords, fmt = '%d')
### orig index of peaks
list_rois_orig_indexes = [list_orig_ROI_spm_index[index] for index in list_selected_peaks_indexes]
print list_rois_orig_indexes
rois_orig_indexes = np.array(list_rois_orig_indexes,dtype = int).reshape(len(list_rois_orig_indexes),1)
print rois_orig_indexes.shape
#### saving labels
np.savetxt(label_rois_file,label_rois, fmt = '%s')
### saving all together for infosource
np_label_rois = np.array(label_rois,dtype = 'string').reshape(len(label_rois),1)
np_full_label_rois = np.array(full_label_rois,dtype = 'string').reshape(len(full_label_rois),1)
print np_label_rois.shape
print rois_MNI_coords.shape
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords,rois_orig_indexes))
print info_rois
np.savetxt(info_rois_file,info_rois, fmt = '%s %s %s %s %s %s %s')
indexed_mask_rois_files.append(indexed_mask_rois_file)
coord_rois_files.append(coord_rois_file)
return indexed_mask_rois_files,coord_rois_files
def compute_labelled_mask_from_HO_and_merged_thr_spm_mask():
write_dir = os.path.join(nipype_analyses_path,
peak_activation_mask_analysis_name)
print(spm_contrasts_path)
if not os.path.exists(write_dir):
os.makedirs(write_dir)
spm_contrast_indexes = [3, 4, 5, 8, 9, 10]
spm_mask_files = [
os.path.join(
spm_contrasts_path, "_contrast_index_" + str(index) +
"_group_contrast_index_0/spmT_0001_thr.img")
for index in spm_contrast_indexes
]
#spm_mask_files.sort()
print(len(spm_mask_files))
# prepare the data
img = nib.load(spm_mask_files[0])
img_header = img.get_header()
img_affine = img.get_affine()
img_shape = img.shape
img_data = img.get_data()
########################## Computing combined HO areas
resliced_full_HO_data, HO_labels, HO_abbrev_labels = compute_recombined_HO_template(
img_header, img_affine, img_shape)
########################## Creating peak activation mask contrained by HO areas
#print len(HO_abbrev_labels)
#print len(HO_labels)
#0/0
np_HO_abbrev_labels = np.array(HO_abbrev_labels, dtype='string')
np_HO_labels = np.array(HO_labels, dtype='string')
template_indexes = np.unique(resliced_full_HO_data)[1:]
#print template_indexes
print(np_HO_labels.shape, np_HO_abbrev_labels.shape,
template_indexes.shape)
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_template = np.hstack(
(template_indexes.reshape(len(HO_labels),
1), np_HO_labels.reshape(len(HO_labels), 1),
np_HO_abbrev_labels.reshape(len(HO_labels), 1)))
#,rois_MNI_coords))
print(info_template)
np.savetxt(info_template_file, info_template, fmt='%s %s %s')
#np.savetxt(info_template_file,info_rois, fmt = '%s %s %s %s %s %s')
merged_mask_data = np.zeros(shape=img_shape, dtype=float)
print(merged_mask_data.shape)
list_orig_ROI_spm_index = []
### list for all info about peaks after merging between different contrasts
list_orig_peak_coords = []
list_orig_peak_MNI_coords = []
list_orig_peak_vals = []
for i, spm_mask_file in enumerate(spm_mask_files):
print(spm_mask_file)
spm_mask_img = nib.load(spm_mask_file)
spm_mask_data = spm_mask_img.get_data()
#### get peaks (avec la fonction stat_map.get_3d_peaks)
peaks = stat_map.get_3d_peaks(image=spm_mask_img, mask=None)
#print len(peaks)
if peaks != None:
print(len(peaks))
list_orig_peak_vals = list_orig_peak_vals + [
peak['val'] for peak in peaks
]
list_orig_peak_coords = list_orig_peak_coords + [
peak['ijk'] for peak in peaks
]
list_orig_peak_MNI_coords = list_orig_peak_MNI_coords + [
peak['pos'] for peak in peaks
]
#print list_orig_peak_vals
#print np.where(np.isnan(spm_mask_data))
#print spm_mask_data[]
#merged_mask_data[np.logical_and(spm_mask_data != 0.0, np.logical_not(np.isnan(spm_mask_data)))] = 1.0
merged_mask_data[np.logical_and(
spm_mask_data != 0.0,
np.logical_not(np.isnan(spm_mask_data)))] += i + 1
#print np.sum(np.logical_and(merged_mask_data != 0.0, np.logical_not(np.isnan(merged_mask_data))))
list_orig_ROI_spm_index = list_orig_ROI_spm_index + [i + 1
] * len(peaks)
print(len(list_orig_peak_coords))
print(len(list_orig_ROI_spm_index))
#### selectionne les pics sur leur distance entre eux et sur leur appatenance au template HO
list_selected_peaks_coords, indexed_mask_rois_data, list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(
list_orig_peak_coords, resliced_full_HO_data, min_dist_between_ROIs)
#list_selected_peaks_coords,indexed_mask_rois_data,list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(sorded_merged_peaks_coords,resliced_full_HO_data,min_dist_between_ROIs)
nib.save(
nib.Nifti1Image(data=merged_mask_data,
header=img_header,
affine=img_affine), merged_mask_img_file)
print(list_selected_peaks_indexes)
print(len(list_selected_peaks_indexes))
#for coord in list_selected_peaks_coords:
#print coord
##template_indexes =
#print resliced_full_HO_data[coord[0],coord[1],coord[2]]
template_indexes = np.array([
resliced_full_HO_data[coord[0], coord[1], coord[2]]
for coord in list_selected_peaks_coords
],
dtype='int64')
print(template_indexes)
np_HO_abbrev_labels = np.array(HO_abbrev_labels, dtype='string')
np_HO_labels = np.array(HO_labels, dtype='string')
print(template_indexes - 1)
label_rois = np_HO_abbrev_labels[template_indexes - 1]
full_label_rois = np_HO_labels[template_indexes - 1]
#print label_rois2
print(label_rois)
#### exporting Rois image with different indexes
print(np.unique(indexed_mask_rois_data)[1:].shape)
nib.save(
nib.Nifti1Image(data=indexed_mask_rois_data,
header=img_header,
affine=img_affine), indexed_mask_rois_file)
#### saving ROI coords as textfile
np.savetxt(coord_rois_file,
np.array(list_selected_peaks_coords, dtype=int),
fmt='%d')
#### saving MNI coords as textfile
list_rois_MNI_coords = [
list_orig_peak_MNI_coords[index]
for index in list_selected_peaks_indexes
]
print(list_rois_MNI_coords)
rois_MNI_coords = np.array(list_rois_MNI_coords, dtype=int)
np.savetxt(MNI_coord_rois_file, rois_MNI_coords, fmt='%d')
### orig index of peaks
list_rois_orig_indexes = [
list_orig_ROI_spm_index[index] for index in list_selected_peaks_indexes
]
print(list_rois_orig_indexes)
rois_orig_indexes = np.array(list_rois_orig_indexes, dtype=int).reshape(
len(list_rois_orig_indexes), 1)
print(rois_orig_indexes.shape)
np.savetxt(rois_orig_indexes_file, rois_orig_indexes, fmt='%d')
#### mask with orig spm index
orig_spm_index_mask_data = np.zeros(shape=img_shape, dtype=int)
print(np.unique(indexed_mask_rois_data))
for i in np.unique(indexed_mask_rois_data)[1:]:
print(i, np.sum(indexed_mask_rois_data == i), rois_orig_indexes[i])
orig_spm_index_mask_data[indexed_mask_rois_data ==
i] = rois_orig_indexes[i]
nib.save(
nib.Nifti1Image(data=orig_spm_index_mask_data,
header=img_header,
affine=img_affine), orig_spm_index_mask_file)
#### saving labels
np.savetxt(label_rois_file, label_rois, fmt='%s')
### saving all together for infosource
np_label_rois = np.array(label_rois,
dtype='string').reshape(len(label_rois), 1)
np_full_label_rois = np.array(full_label_rois, dtype='string').reshape(
len(full_label_rois), 1)
print(np_label_rois.shape)
print(rois_MNI_coords.shape)
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_rois = np.hstack(
(np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),
1), np_full_label_rois,
np_label_rois, rois_MNI_coords, rois_orig_indexes))
print(info_rois)
np.savetxt(info_rois_file, info_rois, fmt='%s %s %s %s %s %s %s')
return indexed_mask_rois_file, coord_rois_file
def compute_labelled_mask_from_HO_all_signif_contrasts():
write_dir = os.path.join(nipype_analyses_path,
peak_activation_mask_analysis_name)
print(spm_contrasts_path)
if not os.path.exists(write_dir):
os.makedirs(write_dir)
#spm_mask_files = glob.glob(os.path.join(spm_contrasts_path,rel_spm_mask_path,"_contrast_index_[1-6]_group_contrast_index_0/spmT_*.img"))
spm_mask_files = glob.glob(
os.path.join(spm_contrasts_path, contrast_pattern))
print(spm_mask_files)
print(spm_mask_files.sort())
# prepare the data
img = nib.load(spm_mask_files[0])
img_header = img.get_header()
img_affine = img.get_affine()
img_shape = img.shape
img_data = img.get_data()
########################## Computing combined HO areas
resliced_full_HO_data, HO_labels, HO_abbrev_labels = compute_recombined_HO_template(
img_header, img_affine, img_shape)
########################## Creating peak activation mask contrained by HO areas
#print len(HO_abbrev_labels)
#print len(HO_labels)
#0/0
np_HO_abbrev_labels = np.array(HO_abbrev_labels, dtype='string')
np_HO_labels = np.array(HO_labels, dtype='string')
template_indexes = np.unique(resliced_full_HO_data)[1:]
#print template_indexes
print(np_HO_labels.shape, np_HO_abbrev_labels.shape,
template_indexes.shape)
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_template = np.hstack(
(template_indexes.reshape(len(HO_labels),
1), np_HO_labels.reshape(len(HO_labels), 1),
np_HO_abbrev_labels.reshape(len(HO_labels), 1)))
#,rois_MNI_coords))
print(info_template)
np.savetxt(info_template_file, info_template, fmt='%s %s %s')
#np.savetxt(info_template_file,info_rois, fmt = '%s %s %s %s %s %s')
indexed_mask_rois_files = []
coord_rois_files = []
for i, spm_mask_file in enumerate(spm_mask_files):
print(spm_mask_file)
spm_mask_img = nib.load(spm_mask_file)
spm_mask_data = spm_mask_img.get_data()
#### get peaks (avec la fonction stat_map.get_3d_peaks)
peaks = stat_map.get_3d_peaks(image=spm_mask_img,
mask=None,
threshold=threshold,
nn=cluster_nbvoxels)
#print len(peaks)
list_orig_ROI_spm_index = []
if peaks != None:
print(len(peaks))
list_orig_peak_vals = [peak['val'] for peak in peaks]
list_orig_peak_coords = [peak['ijk'] for peak in peaks]
list_orig_peak_MNI_coords = [peak['pos'] for peak in peaks]
merged_mask_data = spm_mask_data[np.logical_and(
spm_mask_data != 0.0, np.logical_not(np.isnan(spm_mask_data)))]
list_orig_ROI_spm_index = list_orig_ROI_spm_index + [i + 1
] * len(peaks)
print(len(list_orig_peak_coords))
print(len(list_orig_ROI_spm_index))
list_selected_peaks_coords, indexed_mask_rois_data, list_selected_peaks_indexes = remove_close_peaks_neigh_in_binary_template(
list_orig_peak_coords, resliced_full_HO_data,
min_dist_between_ROIs)
print(list_selected_peaks_indexes)
print(len(list_selected_peaks_indexes))
merged_mask_data[indexed_mask_rois_data != 0] += i + 1
template_indexes = np.array([
resliced_full_HO_data[coord[0], coord[1], coord[2]]
for coord in list_selected_peaks_coords
],
dtype='int64')
print(template_indexes)
np_HO_abbrev_labels = np.array(HO_abbrev_labels, dtype='string')
np_HO_labels = np.array(HO_labels, dtype='string')
print(template_indexes - 1)
label_rois = np_HO_abbrev_labels[template_indexes - 1]
full_label_rois = np_HO_labels[template_indexes - 1]
#print label_rois2
print(label_rois)
#### indexed_mask
indexed_mask_rois_file = os.path.join(
nipype_analyses_path, peak_activation_mask_analysis_name,
"indexed_mask-" + ROI_mask_prefix + "_spm_contrast" +
str(i + 1) + ".nii")
#### saving ROI coords as textfile
### ijk coords
coord_rois_file = os.path.join(
nipype_analyses_path, peak_activation_mask_analysis_name,
"coords-" + ROI_mask_prefix + "_spm_contrast" + str(i + 1) +
".txt")
### coords in MNI space
MNI_coord_rois_file = os.path.join(
nipype_analyses_path, peak_activation_mask_analysis_name,
"coords-MNI-" + ROI_mask_prefix + "_spm_contrast" +
str(i + 1) + ".txt")
#### saving ROI coords as textfile
label_rois_file = os.path.join(
nipype_analyses_path, peak_activation_mask_analysis_name,
"labels-" + ROI_mask_prefix + "_spm_contrast" + str(i + 1) +
".txt")
#label_rois_file = os.path.join(nipype_analyses_path,peak_activation_mask_analysis_name, "labels-" + ROI_mask_prefix + "_jane.txt")
#### all info in a text file
info_rois_file = os.path.join(
nipype_analyses_path, peak_activation_mask_analysis_name,
"info-" + ROI_mask_prefix + "_spm_contrast" + str(i + 1) +
".txt")
#### exporting Rois image with different indexes
print(np.unique(indexed_mask_rois_data)[1:].shape)
nib.save(
nib.Nifti1Image(data=indexed_mask_rois_data,
header=img_header,
affine=img_affine), indexed_mask_rois_file)
#### saving ROI coords as textfile
np.savetxt(coord_rois_file,
np.array(list_selected_peaks_coords, dtype=int),
fmt='%d')
#### saving MNI coords as textfile
list_rois_MNI_coords = [
list_orig_peak_MNI_coords[index]
for index in list_selected_peaks_indexes
]
print(list_rois_MNI_coords)
rois_MNI_coords = np.array(list_rois_MNI_coords, dtype=int)
np.savetxt(MNI_coord_rois_file, rois_MNI_coords, fmt='%d')
### orig index of peaks
list_rois_orig_indexes = [
list_orig_ROI_spm_index[index]
for index in list_selected_peaks_indexes
]
print(list_rois_orig_indexes)
rois_orig_indexes = np.array(list_rois_orig_indexes,
dtype=int).reshape(
len(list_rois_orig_indexes), 1)
print(rois_orig_indexes.shape)
#### saving labels
np.savetxt(label_rois_file, label_rois, fmt='%s')
### saving all together for infosource
np_label_rois = np.array(label_rois, dtype='string').reshape(
len(label_rois), 1)
np_full_label_rois = np.array(full_label_rois,
dtype='string').reshape(
len(full_label_rois), 1)
print(np_label_rois.shape)
print(rois_MNI_coords.shape)
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),np_full_label_rois,np_label_rois,rois_MNI_coords))
#info_rois = np.hstack((np.unique(indexed_mask_rois_data)[1:].reshape(len(label_rois),1),rois_MNI_coords))
info_rois = np.hstack(
(np.unique(indexed_mask_rois_data)[1:].reshape(
len(label_rois), 1), np_full_label_rois, np_label_rois,
rois_MNI_coords, rois_orig_indexes))
print(info_rois)
np.savetxt(info_rois_file, info_rois, fmt='%s %s %s %s %s %s %s')
indexed_mask_rois_files.append(indexed_mask_rois_file)
coord_rois_files.append(coord_rois_file)
return indexed_mask_rois_files, coord_rois_files