def main(args):
filename = sys.argv[1]
filepath = '/home/shridhar/Acads/5245/net-enlightenment/python/graphml-data/' + filename + '.graphml'
G1 = read_graphml_file(filepath)
print G1.nodes()
print "number of edges = ", len(G1.edges())
G_list = []
num_certain_graphs_generated = 100
#generate a bunch of certain graphs based on probability
for i in range(0,num_certain_graphs_generated):
G = nx.DiGraph()
for u,v in G1.edges():
if not G.has_edge(u,v):
edge_dict = G1[u][v]
if "weight" not in edge_dict:
edge_dict = edge_dict[0]
if edge_dict['weight']:
if random.random() < edge_dict['weight']:
G.add_edge(u, v, weight=edge_dict['weight'])
#M, clusters = networkx_mcl(G, expand_factor = 2,inflate_factor =2 , max_loop = 60,mult_factor = 2)
#print "output matrix = ", M
#print "cluster node mapping:", clusters
G_list.append(G)
for i in range(0,len(G_list)):
print_edgelist_graph(G_list[i], filename + "-certain-" + str(i) + ".edgelist")
print_graphml_graph(G_list[i], filename + "-certain-" + str(i) + ".graphml")
G_dedup = threshold_graph(G1, 0)
print G_dedup.nodes()
print "number of edges = ", len(G_dedup.edges())
for i in range(1,3):
thresh = i/10.0
G_thresh = threshold_graph(G1, thresh)
print_edgelist_graph(G_thresh,filename +"thresh-"+str(thresh) + ".edgelist")
print_graphml_graph(G_thresh,filename +"thresh-"+str(thresh) + ".graphml")
if(nodeClusterList[p][i] == nodeClusterList[p][j]):
weight = clust_rel_list[p][nodeClusterList[p][i]]
weighted_edge_list[index][2]['weight'] += weight
index+=1
return weighted_edge_list
#tests
path = "/home/shridhar/Acads/5245/net-enlightenment/python/intermediate_graphs/"
orig_graph_name = "127_session_2"
num_sets = 10
num_nodes = 116
Glist = []
n_c_map_list = []
for i in range(0,num_sets):
graph_file = path+orig_graph_name + "-certain-" + str(i) + ".graphml"
G = read_graphml_file(graph_file)
community_file = path+orig_graph_name+"-certain-" + str(i) + ".edgelist.txt.communities"
c_n_map = create_comm_node_mapping(G,community_file,False)
n_c_map = create_node_comm_mapping(c_n_map)
Glist.append(G)
n_c_map_list.append(n_c_map)
final_map = weighted_cons(Glist, n_c_map_list, num_nodes)
G = nx.from_edgelist(final_map)
out_f = orig_graph_name+"consensus_weighted_graph.gexf"
nx.write_gexf(G,out_f)
#for tup in final_map:
# out_f.write(str(tup)+"\n")
#print ("\n".join(final_map))
