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]])
