def test_invalid_nbins():
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, num_bins=np.NaN)
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, num_bins=np.Inf)
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, num_bins=2)
def test_atlas_parcel_subdivision():
wm = 'manhattan'
# much slower: zip(cfg.allowed_mvpp, cfg.mvpp_to_total_num_patches)
for mvpp, tot_patch_count in zip((1000, 10000), (273, 68)):
edge_weights_all = extract(subject_id_list,
example_dir,
base_feature=base_feature,
weight_method_list=[
wm,
],
atlas='fsaverage',
node_size=mvpp,
smoothing_param=fwhm,
out_dir=out_dir,
return_results=True,
num_procs=1)
num_combinations = len(list(edge_weights_all))
if num_combinations != len(subject_id_list):
raise ValueError('mvpp: invalid count : # subjects')
num_links = tot_patch_count * (tot_patch_count - 1) / 2
for sub in subject_id_list:
if edge_weights_all[(wm, sub)].size != num_links:
raise ValueError('mvpp: invalid count : # links')
def test_run_API_on_original_features():
for base_feature in base_feature_list:
for atlas in feature_to_atlas_list[base_feature]:
sud_id_list = feature_to_subject_id_list[base_feature]
edge_weights_all = extract(
sud_id_list,
feature_to_in_dir[base_feature],
base_feature=base_feature,
weight_method_list=weight_methods_orig_feat_subset,
atlas=atlas,
smoothing_param=fwhm,
out_dir=out_dir,
return_results=True,
num_procs=1)
num_combinations = len(list(edge_weights_all))
if num_combinations != len(sud_id_list) * len(
weight_methods_orig_feat_subset):
raise ValueError('invalid results : # subjects')
num_roi_wholebrain = num_roi_atlas[atlas]
num_links = num_roi_wholebrain * (num_roi_wholebrain - 1) / 2
for wm in weight_methods_orig_feat_subset:
for sub in sud_id_list:
if edge_weights_all[(wm, sub)].size != num_links:
raise ValueError('invalid results : # links')
def test_run_no_IO():
for base_feature in base_feature_list:
for atlas in feature_to_atlas_list[base_feature]:
try:
sud_id_list = feature_to_subject_id_list[base_feature]
edge_weights_all = graynet.extract(
sud_id_list,
feature_to_in_dir[base_feature],
base_feature=base_feature,
weight_method_list=weight_methods,
atlas=atlas,
smoothing_param=fwhm,
out_dir=out_dir,
return_results=True,
num_procs=1)
num_combinations = len(list(edge_weights_all))
if num_combinations != len(sud_id_list) * len(weight_methods):
raise ValueError('invalid results : # subjects')
num_roi_wholebrain = num_roi_atlas[atlas]
num_links = num_roi_wholebrain * (num_roi_wholebrain - 1) / 2
for wm in weight_methods:
for sub in sud_id_list:
if edge_weights_all[(wm, sub)].size != num_links:
raise ValueError('invalid results : # links')
except:
traceback.print_exc()
raise
def test_empty_subject_list():
# API
with raises(ValueError):
ew = graynet.extract([], fs_dir)
# in CLI, only non-Freesurfer lead to an error
for feat in cfg.features_volumetric: # invalid list
with raises(ValueError):
sys.argv = shlex.split('graynet -i {} -f {}'.format(fs_dir, feat))
run_cli()
def test_run_no_IO():
edge_weights_all = graynet.extract(subject_id_list,
fs_dir,
base_feature=base_feature,
weight_method_list=weight_methods,
atlas=atlas,
smoothing_param=fwhm,
out_dir=out_dir,
return_results=True,
num_procs=4)
num_combinations = len(list(edge_weights_all))
if num_combinations != len(subject_id_list) * len(weight_methods):
raise ValueError('invalid results : # subjects')
for wm in weight_methods:
for sub in subject_id_list:
if edge_weights_all[(wm, sub)].size != num_links:
raise ValueError('invalid results : # links')
def test_invalid_edge_range():
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=-1)
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=[])
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=[
1,
])
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=[1, 2, 3])
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=(1, np.NaN))
with raises(ValueError):
ew = graynet.extract(subject_id_list, fs_dir, edge_range=(2, 1))
def test_empty_subject_list():
with raises(ValueError):
ew = graynet.extract([], fs_dir)