outfile.write(initial_head) for ctr in range(arg.number_of_runs): if arg.graph_generation == "guyondata": G = Datasets.get_guyon_graph(ctr + 1) else: G = Datasets.get_scale_free_graph_edge(arg.network_size, initial_module, nb_modules, arg.module_size, arg.prob_p, arg.prob_q, arg.removed_edges, rng) rate_conection = len(G.edges) / len(G.nodes) average_shortest_paths = [] for _, cluster in Datasets.get_groups(G).items(): nodes = list(cluster) average_shortest_paths.append( Scores.average_shortest_path(G, nodes)) result = str(ctr) + "," + str(arg.network_size) + "," + str( arg.module_size) + "," + str(rate_conection) + "," + str( average_shortest_paths[0]) # save the network and the simulation transcripts in the file network_path, weight_path = write_network_edge_list( G, arg.outfile, "sim" + str(ctr + 1)) truehits = Datasets.get_groups(G) th = set([j for i in truehits.values() for j in i]) # geting the result of others tools (verify set_variables.json file) if 'bionet' in data['tools']: print("\nExecuting Bionet\n")