def comp_link_sim_matrices(cfg): """ Compute link similarity matrices for given network densities. specified in ``cfg['density_range']`` across ``cfg['all_fnames']`` Results are saved in ``cfg['outdata_dir'])`` Parameters ---------- cfg : dict brainnets config dict """ config.require(cfg, [ "all_fnames", "blacklist_fname", "density_range", "include_mst", "n_cpus" ]) densities = cfg['density_range'] arg_list = zip(densities, [cfg] * len(densities)) link_sim_mat_list = ch.run_in_parallel(_comp_link_sim_mat_worker, arg_list, cfg["n_cpus"], chunksize=1) out_dict = { settings.densities_tag: densities, settings.common_links_tag: link_sim_mat_list, settings.config_tag: cfg } dataio.save_pickle(fnc.get_fname(cfg, settings.common_links_tag), out_dict)
def comp_paired_common_and_differing_link_distances(cfg): """ For each pair, computes the distances of common and differing links. """ config.require(cfg, [ "group_1_mat_fnames", "group_2_mat_fnames", "blacklist_fname", "node_info_fname", "density_range", "n_cpus" ]) fnames_group_1 = cfg['group_1_mat_fnames'] fnames_group_2 = cfg['group_2_mat_fnames'] assert len(fnames_group_1) == len(fnames_group_2) n = len(fnames_group_1) arg_list = zip(fnames_group_1, fnames_group_2, [cfg] * n) ch.run_in_parallel(_paired_common_and_diff_link_distances_worker, arg_list, cfg['n_cpus'])
def comp_louvain_communities(cfg): """ Computes louvain communities for a certain network density for all correlation matrices in ``cfg['all_fnames']`` The results are saved to the output folder (``cfg['outdata_dir']``) Currently only the unweighted louvain method is used. Parameters ---------- cfg : a brainnets config dict Returns ------- coms : the communities as a membershiplist mods : numpy array the corresponding values of modularity """ config.require(cfg, ['all_fnames', 'blacklist_fname', 'density', 'n_it_comdet', 'include_mst', 'n_cpus']) argList = [(fname, cfg) for fname in cfg['all_fnames']] comsAndModularities = ch.run_in_parallel( _compute_louvain_coms_worker, argList, cfg['n_cpus']) coms = [comsAndModularities[i][0] for i in range(len(comsAndModularities))] mods = [comsAndModularities[i][1] for i in range(len(comsAndModularities))] return coms, mods
def comp_communities_igraph(cfg, com_det_method, com_det_options_dict=None): """ Computes communities for a certain network density for all correlation matrices in ``cfg['all_fnames']`` The results are saved to the output folder (``cfg['outdata_dir']``) Parameters ---------- cfg : a brainnets config dict com_det_method: str, or igraph function returning com_det_options_dict: dict Returns ------- coms : the communities as a membership list """ if isinstance(com_det_method, str): com_det_method = tag_to_igraph_comdet_method(com_det_method) if com_det_options_dict is None: com_det_options_dict = {} config.require(cfg, ['all_fnames', 'blacklist_fname', 'density', 'include_mst', 'n_cpus', 'n_it_comdet' ] ) arg_list = [(fname, cfg, com_det_method, com_det_options_dict) for fname in cfg['all_fnames']] coms = ch.run_in_parallel(_compute_coms_worker, arg_list, cfg['n_cpus']) return coms
def comp_link_distances(cfg): """ Computes all link distances (in MNI space) over a range of network densities. Parameters ---------- cfg : dict the brainnets config dictionary """ config.require(cfg, [ "all_fnames", "blacklist_fname", "density_range", "node_info_fname", "include_mst", "n_cpus" ]) all_fnames = cfg["all_fnames"] arg_list = zip(all_fnames, [cfg] * len(all_fnames)) ch.run_in_parallel(_link_dist_worker, arg_list, cfg['n_cpus'])
def comp_node_props(cfg): """ Computes node properties for ``cfg["all_fnames"]`` for a given network density Parameters ---------- cfg : dict brainnets config dictionary """ config.require(cfg, [ "all_fnames", "blacklist_fname", "density", "include_mst", "n_cpus", "node_props" ]) all_fnames = cfg["all_fnames"] n = len(all_fnames) arg_list = zip(all_fnames, [cfg] * n) ch.run_in_parallel(_node_prop_worker, arg_list, cfg["n_cpus"])
def compute_global_properties(cfg, weighted=False): """ Computes global network properties for ``cfg["all_fnames"]`` for a given network density Parameters ---------- cfg : dict brainnets config dictionary weighted : bool if True, the network is considered as weighted. if False, network is unweighted """ config.require(cfg, [ "all_fnames", "blacklist_fname", "density_range", "include_mst", "n_cpus", "global_w_props" ]) n = len(cfg['all_fnames']) arg_list = zip(cfg['all_fnames'], [cfg] * n) if weighted: worker = _global_w_prop_worker else: worker = _global_uw_prop_worker ch.run_in_parallel(worker, arg_list, cfg['n_cpus'])