degree_cut_off = 0.3 """ curs.execute("select vertex_set, recurrence_array from %s where id=%s"%(pattern_table, pattern_id)) rows = curs.fetchall() vertex_list, recurrence_array = rows[0] vertex_list = vertex_list[1:-1].split(',') vertex_list = map(int, vertex_list) recurrence_array = recurrence_array[1:-1].split(',') recurrence_array = map(float, recurrence_array) fuzzyDense_instance = fuzzyDense(edge2encodedOccurrence, debug) core_vertex_ls, recurrent_and_on_datasets_ls = fuzzyDense_instance.get_core_vertex_set(vertex_list, recurrence_array, degree_cut_off) from MpiClusterBsStat import MpiClusterBsStat MpiClusterBsStat_instance = MpiClusterBsStat() gene_no2bs_no_block = MpiClusterBsStat_instance.get_gene_no2bs_no_block(curs) gene_no2bs_no_set, bs_no2gene_no_set = MpiClusterBsStat_instance.construct_two_dicts(0, gene_no2bs_no_block) from TF_functions import cluster_bs_analysis ls_to_return = cluster_bs_analysis(core_vertex_ls, gene_no2bs_no_set, bs_no2gene_no_set, ratio_cutoff, \ top_number, p_value_cut_off) gene_id2symbol = get_gene_id2gene_symbol(curs, tax_id) dataset_no2desc = get_dataset_no2desc(curs) dataset_no_desc_ls = [] for dataset_index in recurrent_and_on_datasets_ls: dataset_no = dataset_index +1 dataset_no_desc_ls.append([dataset_no, dataset_no2desc[dataset_no]])