def test_regress_covar():
""" test regress_covar"""
_make_tmp_dir()
extract_mean_wm_ts = ExtractMeanTS()
extract_mean_wm_ts.inputs.file_4D = img_file
extract_mean_wm_ts.inputs.mask_file = wm_mask_file
extract_mean_wm_ts.inputs.suffix = "wm"
mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file
extract_mean_csf_ts = ExtractMeanTS()
extract_mean_csf_ts.inputs.file_4D = img_file
extract_mean_csf_ts.inputs.mask_file = csf_mask_file
extract_mean_csf_ts.inputs.suffix = "csf"
mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file
extra_ts = ExtractTS()
extra_ts.inputs.indexed_rois_file = indexed_mask_file
extra_ts.inputs.file_4D = img_file
masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file
regress_covar = RegressCovar()
regress_covar.inputs.masked_ts_file = masked_ts_file
regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file
regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file
val = regress_covar.run().outputs
print(val)
os.remove(val.resid_ts_file)
os.remove(masked_ts_file)
os.remove(mean_csf_ts_file)
os.remove(mean_wm_ts_file)
def test_return_net_list():
"""
test transforming matrix as np.array to list of edges
"""
_make_tmp_dir()
conmat = np.load(conmat_file)
list_conmat = return_net_list(conmat, int_factor=1000)
assert list_conmat.shape[1] == 3
def test_export_Louvain_net_from_list():
"""testing Louvain Traag file building (for sake of compatibility of older
codes)"""
_make_tmp_dir()
coords = np.loadtxt(coords_file)
Z_list = np.loadtxt(Z_list_file)
Z_Louvain_file = os.path.abspath("Z_Louvain.txt")
export_Louvain_net_from_list(Z_Louvain_file, Z_list, coords)
assert os.path.exists(Z_Louvain_file)
def test_compute_net_list():
""" test ComputeNetList"""
_make_tmp_dir()
compute_net_list = ComputeNetList()
compute_net_list.inputs.Z_cor_mat_file = conmat_file
val = compute_net_list.run().outputs
print(val)
assert os.path.exists(val.net_List_file)
os.remove(val.net_List_file)
def test_prep_rada():
"""test PrepRada"""
_make_tmp_dir()
prep_rada = PrepRada()
prep_rada.inputs.net_List_file = Z_list_file
prep_rada.inputs.network_type = "U"
val = prep_rada.run().outputs
assert os.path.exists(val.Pajek_net_file)
def test_extract_ts():
""" test ExtractTS"""
_make_tmp_dir()
extra_ts = ExtractTS()
extra_ts.inputs.indexed_rois_file = indexed_mask_file
extra_ts.inputs.file_4D = img_file
val = extra_ts.run().outputs
print(val)
assert os.path.exists(val.mean_masked_ts_file)
os.remove(val.mean_masked_ts_file)
def test_KCore_undirected():
_make_tmp_dir()
np_mat_file = os.path.abspath("rand_bin_mat.npy")
np.save(np_mat_file, np_mat)
kcore = KCore()
kcore.inputs.np_mat_file = os.path.abspath("rand_bin_mat.npy")
kcore.inputs.is_directed = False
kcore.run()
assert os.path.exists(os.path.abspath("coreness.npy"))
assert os.path.exists(os.path.abspath("distrib_k.npy"))
def test_extract_mean_ts():
"""test ExtractMeanTS"""
_make_tmp_dir()
extract_mean_ts = ExtractMeanTS()
extract_mean_ts.inputs.file_4D = img_file
extract_mean_ts.inputs.mask_file = wm_mask_file
extract_mean_ts.inputs.suffix = "wm"
val = extract_mean_ts.run().outputs
print(val)
assert os.path.exists(val.mean_masked_ts_file)
os.remove(val.mean_masked_ts_file)
def test_comm_rada():
"""test CommRada"""
_make_tmp_dir()
comm_rada = CommRada()
comm_rada.inputs.Pajek_net_file = Pajek_net_file
comm_rada.inputs.optim_seq = "WS trfr 1"
val = comm_rada.run().outputs
assert os.path.exists(val.rada_lol_file)
assert os.path.exists(val.rada_log_file)
assert os.path.exists(val.lol_log_file)
def test_compute_module_mat_prop():
""" test ComputeModuleMatProp"""
_make_tmp_dir()
compute_module_graph_prop = ComputeModuleMatProp()
compute_module_graph_prop.inputs.rada_lol_file = lol_file
compute_module_graph_prop.inputs.Pajek_net_file = Pajek_net_file
compute_module_graph_prop.inputs.conmat_file = conmat_file
val = compute_module_graph_prop.run().outputs
print(val)
assert os.path.exists(val.df_avgmat_file)
def test_compute_conf_cor_mat():
"""test ComputeConfCorMat"""
_make_tmp_dir()
extract_mean_wm_ts = ExtractMeanTS()
extract_mean_wm_ts.inputs.file_4D = img_file
extract_mean_wm_ts.inputs.filter_thr = 0.9
extract_mean_wm_ts.inputs.filter_mask_file = csf_mask_file
extract_mean_wm_ts.inputs.suffix = "wm"
mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file
extract_mean_csf_ts = ExtractMeanTS()
extract_mean_csf_ts.inputs.file_4D = img_file
extract_mean_csf_ts.inputs.filter_thr = 0.9
extract_mean_csf_ts.inputs.filter_mask_file = csf_mask_file
extract_mean_csf_ts.inputs.suffix = "csf"
mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file
extra_ts = ExtractTS()
extra_ts.inputs.indexed_rois_file = indexed_mask_file
extra_ts.inputs.file_4D = img_file
masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file
regress_covar = RegressCovar()
regress_covar.inputs.masked_ts_file = masked_ts_file
regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file
regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file
resid_ts_file = regress_covar.run().outputs.resid_ts_file
compute_conf_cor_mat = ComputeConfCorMat()
compute_conf_cor_mat.inputs.ts_file = resid_ts_file
val = compute_conf_cor_mat.run().outputs
print(val)
assert os.path.exists(val.cor_mat_file)
assert os.path.exists(val.Z_cor_mat_file)
assert os.path.exists(val.conf_cor_mat_file)
assert os.path.exists(val.Z_conf_cor_mat_file)
os.remove(val.cor_mat_file)
os.remove(val.conf_cor_mat_file)
os.remove(val.Z_cor_mat_file)
os.remove(val.Z_conf_cor_mat_file)
os.remove(resid_ts_file)
os.remove(masked_ts_file)
os.remove(mean_csf_ts_file)
os.remove(mean_wm_ts_file)
def test_net_prop_rada():
"""test NetPropRada"""
_make_tmp_dir()
net_prop_rada = NetPropRada()
net_prop_rada.inputs.Pajek_net_file = Pajek_net_file
val = net_prop_rada.run().outputs
assert os.path.exists(val.global_file)
assert os.path.exists(val.dists_file)
assert os.path.exists(val.degrees_file)
assert os.path.exists(val.nodes_file)
assert os.path.exists(val.edges_betw_file)
assert os.path.exists(val.rada_log_file)
def test_compute_node_roles():
""" test ComputeNodeRoles"""
_make_tmp_dir()
compute_node_roles = ComputeNodeRoles()
compute_node_roles.inputs.rada_lol_file = lol_file
compute_node_roles.inputs.Pajek_net_file = Pajek_net_file
val = compute_node_roles.run().outputs
print(val)
assert os.path.exists(val.node_roles_file)
assert os.path.exists(val.all_Z_com_degree_file)
assert os.path.exists(val.all_participation_coeff_file)
os.remove(val.node_roles_file)
os.remove(val.all_Z_com_degree_file)
os.remove(val.all_participation_coeff_file)
def test_split_ts():
""" test SplitTS"""
_make_tmp_dir()
extract_mean_wm_ts = ExtractMeanTS()
extract_mean_wm_ts.inputs.file_4D = img_file
extract_mean_wm_ts.inputs.filter_thr = 0.9
extract_mean_wm_ts.inputs.filter_mask_file = csf_mask_file
extract_mean_wm_ts.inputs.suffix = "wm"
mean_wm_ts_file = extract_mean_wm_ts.run().outputs.mean_masked_ts_file
extract_mean_csf_ts = ExtractMeanTS()
extract_mean_csf_ts.inputs.file_4D = img_file
extract_mean_csf_ts.inputs.filter_thr = 0.9
extract_mean_csf_ts.inputs.filter_mask_file = csf_mask_file
extract_mean_csf_ts.inputs.suffix = "csf"
mean_csf_ts_file = extract_mean_csf_ts.run().outputs.mean_masked_ts_file
extra_ts = ExtractTS()
extra_ts.inputs.indexed_rois_file = indexed_mask_file
extra_ts.inputs.file_4D = img_file
masked_ts_file = extra_ts.run().outputs.mean_masked_ts_file
regress_covar = RegressCovar()
regress_covar.inputs.masked_ts_file = masked_ts_file
regress_covar.inputs.mean_wm_ts_file = mean_wm_ts_file
regress_covar.inputs.mean_csf_ts_file = mean_csf_ts_file
resid_ts_file = regress_covar.run().outputs.resid_ts_file
split_ts = SplitTS()
split_ts.inputs.ts_file = resid_ts_file
split_ts.inputs.win_length = 10
split_ts.inputs.offset = 5
splitted_ts_files = split_ts.run().outputs.splitted_ts_files
assert len(splitted_ts_files) != 0
assert os.path.exists(splitted_ts_files[0])
for splitted_ts_file in splitted_ts_files:
os.remove(splitted_ts_file)
def test_create_pipeline_nii_to_conmat():
tmp_dir = _make_tmp_dir()
wf = create_pipeline_nii_to_conmat(main_path=tmp_dir,
pipeline_name="nii_to_conmat_full")
wf.inputs.inputnode.nii_4D_file = nii_4D_file
wf.inputs.inputnode.ROI_mask_file = indexed_mask_file
wf.inputs.inputnode.gm_anat_file = gm_anat_file
wf.inputs.inputnode.wm_anat_file = wm_anat_file
wf.inputs.inputnode.csf_anat_file = csf_anat_file
# Warning, is necessary, otherwise Figures are removed!
wf.config['execution'] = {"remove_unnecessary_outputs": False}
wf.run()
def test_read_Pajek_corres_nodes():
"""Test reading corres node vector given a Pajek .net file"""
_make_tmp_dir()
corres = read_Pajek_corres_nodes(Pajek_net_file)
print(corres)
def test_read_Pajek_corres_nodes_and_sparse_matrix():
"""Test reading corres node vector and sparse graph representation
given a Pajek .net file"""
_make_tmp_dir()
corres, sp = read_Pajek_corres_nodes_and_sparse_matrix(Pajek_net_file)
assert len(corres) == sp.todense().shape[0]
