def find_centroids(streamlines,th,max_clus, folder_name='', vol_weighted = True, weight_by = '1.5_2_AxPasi5'):
from dipy.segment.clustering import QuickBundles
''' parameters:
streamlines: ArraySequence of streamlines
th: float for max dist from a streamline to the centroids.
Above this value a new centroid is created as long as the number of centroids < max clus.
max_clus: int of max number of clusters
:return:
centroids: a list of streamilnes centroid representation
s_list: a list of lists.
Each inner list is the group of streamlines cooresponds to a single centroid.
'''
qb = QuickBundles(th,max_nb_clusters=max_clus)
qbmap = qb.cluster(streamlines)
centroids = qbmap.centroids
s_list = []
for i in qbmap.clusters:
s_list.append(list(i))
if vol_weighted:
bvec_file = load_dwi_files(folder_name)[6]
vec_vols = weight_clus_by_vol_img(weight_by,s_list, folder_name, bvec_file)
return centroids, s_list, vec_vols
else:
return centroids, s_list
m_weighted[pair[1] - 1, pair[0] - 1] = mean_path_vol
return m_clean, m_weighted
if __name__ == '__main__':
subj = all_subj_folders
names = all_subj_names
index_to_text_file = r'C:\Users\Admin\my_scripts\aal\megaatlas\megaatlascortex2nii.txt'
fig_types = ['cc_1d', 'genu_1d', 'body_1d', 'splenium_1d']
weight_by = '1.5_2_AxPasi5'
a = True
for s, n in zip(subj, names):
for fig_type in fig_types:
main_folder = r'C:\Users\Admin\my_scripts\Ax3D_Pack\V6\after_file_prep' + s
gtab, data, affine, labels, white_matter, nii_file, bvec_file = load_dwi_files(
main_folder)
labels_headers, idx, m, grouping, h = load_fibers(
main_folder, n, index_to_text_file, fig_type, nii_file)
#if os.path.exists(os.path.join(main_folder, 'non-weighted('+fig_type+', MegaAtlas).png')):
#if not os.path.exists(os.path.join(main_folder, 'non-weighted(' + fig_type + ', MegaAtlas).png')):
if a:
clean_grouping, m_clean, clean_streamlines, m_weighted = create_empty_vars(
)
weight_by_data, affine = extract_weighted_data(
bvec_file, weight_by)
clean_grouping, clean_streamlines = clean_non_cc(
grouping, idx, clean_grouping, clean_streamlines)
m_clean, m_weighted = cleaned_tracts_to_mat(
clean_grouping, m_clean, weight_by_data, affine,
m_weighted)
for i,s in enumerate(s_list):
mean_vol_per_tract = weighting_streamlines(folder_name,s,bvec_file,show=False,weight_by=weight_by)
vec_vols[i] = np.nanmean(mean_vol_per_tract)
return vec_vols
if __name__ == '__main__':
main_folder = subj_folder
for n,s in zip(all_subj_names[1::],all_subj_folders[1::]):
folder_name = main_folder+s
dir_name = folder_name + '\streamlines'
sft_target = load_trk(f'{dir_name}{n}_wholebrain_3d.trk', "same", bbox_valid_check=False)
streamlines = sft_target.streamlines
bvec_file = load_dwi_files(folder_name)[6]
index_to_text_file = r'C:\Users\hila\data\megaatlas\megaatlas2nii.txt'
idx = nodes_labels_mega(index_to_text_file)[1]
lab_labels_index, affine = nodes_by_index_mega(folder_name)
m, grouping = utils.connectivity_matrix(streamlines, affine, lab_labels_index,
return_mapping=True,
mapping_as_streamlines=True)
mat_file = f'{folder_name}\weighted_mega_wholebrain_4d_labmask.npy'
con_mat = np.load(mat_file)
id = np.argsort(idx)
con_mat = con_mat[id]
con_mat = con_mat[:, id]
vec_vols = []
s_list = []