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]