def get_partitions(sparsity, level):
try:
path = f'/data01/ayagoz/sparse_32_concon_HCP/connectomes/agreement_graphs/level{level}/agreement_adj_{level}_{sparsity}.npy'
adj = np.load(path)
partition = compute_partition(adj)
parcellation_path = f'/data01/ayagoz/sparse_32_concon_HCP/parcellations/ensemble_parcellation/CSPA/level{level}/CSPA_{level}_{sparsity}.npy'
np.save(parcellation_path, partition)
except BaseException as e:
print(e)
def do_job(r=None):
level = 3
sparsity = 10
p = '/data01/ayagoz/sparse_32_concon_HCP/'
random = np.random.RandomState(r)
files = glob(
f'{p}parcellations/connectivity_parcellation_level{level}/{sparsity}/*.npy'
)
random.shuffle(files)
partitions = []
for file in files[:50]:
labels = np.load(file)
partitions.append(labels)
adj = compute_agreements(np.array(partitions))
labels = compute_partition(adj)
np.save(f'/home/kurmukov/subject_stability/CSPA/CSPA_3_10_{r}.npy', labels)
def get_partitions_at_levels(subject_id, labels_to_drop):
for sparsity in tqdm(range(10, 101, 10)):
try:
path = f'/data01/ayagoz/sparse_32_concon_HCP/ConCon_resolution/{sparsity}/{subject_id}.npz'
adj = load_concon(path, labels_to_drop=labels_to_drop)
partition_level_1 = compute_partition(adj)
partition_level_2 = cluster_subgraphs(adj, partition_level_1, 5,
200)
partition_level_3 = cluster_subgraphs(adj, partition_level_2, 5,
360)
parcellation_path = '/data01/ayagoz/sparse_32_concon_HCP/parcellations'
np.save(
f'{parcellation_path}/connectivity_parcellation_level1/{sparsity}/{subject_id}.npy',
partition_level_1)
np.save(
f'{parcellation_path}/connectivity_parcellation_level2/{sparsity}/{subject_id}.npy',
partition_level_2)
np.save(
f'{parcellation_path}/connectivity_parcellation_level3/{sparsity}/{subject_id}.npy',
partition_level_3)
except BaseException as e:
print(e, subject_id)
for r in tqdm(range(random_state, random_state + 10)):
random = np.random.RandomState(random_state)
sparsity = 10
average_desikan = np.load(
'/data01/ayagoz/sparse_32_concon_HCP/parcellations/desikan_aparc_average_1113.npy',
allow_pickle=True)
paths = glob(
f'/data01/ayagoz/sparse_32_concon_HCP/connectomes/ConCon_resolution/{sparsity}/*.npz'
)
random.shuffle(paths)
adj = load_concon(paths[0], labels_to_drop=average_desikan)
for file in tqdm(paths[1:50]):
adj_temp = load_concon(file, labels_to_drop=average_desikan)
adj += adj_temp
partition_level_1 = compute_partition(adj)
partition_level_2 = cluster_subgraphs(adj, partition_level_1, 5, 200)
partition_level_3 = cluster_subgraphs(adj, partition_level_2, 5, 360)
np.save(
f'/home/kurmukov/subject_stability/aver_50_level1_{sparsity}_{r}.npy',
partition_level_1)
np.save(
f'/home/kurmukov/subject_stability/aver_50_level2_{sparsity}_{r}.npy',
partition_level_2)
np.save(
f'/home/kurmukov/subject_stability/aver_50_level3_{sparsity}_{r}.npy',
partition_level_3)