def run(file_path, inv_name, firm_id, inv_amt, degree_run, eigen_run, between_run, close_run, load_run, subgraph_run, harmonic_run): inv_name = inv_name.get() firm_id = firm_id.get() inv_amt = inv_amt.get() df = gg.read_file(file_path, inv_name, firm_id, inv_amt) edgelist = gg.generate_edgelist(df) graph = gg.generate_graph(edgelist) adj_mat = gg.generate_matrix(graph) cent_df_list = [] degree_df = C.get_degree(graph, degree_run.get()) eigen_df = C.get_eigenvector(graph, eigen_run.get()) between_df = C.get_betweenness(graph, between_run.get()) close_df = C.get_closeness(graph, close_run.get()) load_df = C.get_load(graph, load_run.get()) subgraph_df = C.get_subgraph(graph, subgraph_run.get()) harmonic_df = C.get_harmonic(graph, harmonic_run.get()) cent_df_list.append(degree_df) cent_df_list.append(eigen_df) cent_df_list.append(between_df) cent_df_list.append(close_df) cent_df_list.append(load_df) cent_df_list.append(subgraph_df) cent_df_list.append(harmonic_df) cent_df = pd.concat(cent_df_list) cent_df = cent_df.transpose() gg.export_graph(cent_df, 'centralitymeasures.csv') gg.export_graph(edgelist, 'edgelist.csv') gg.export_graph(adj_mat, 'adjacencymatrix.csv')
def main(): edges = [5000, 10000, 50000, 100000, 200000, 300000, 400000, 500000, 600000, 700000] nodes = 1000 max_weight = 1000000 source = 0 for test_no in range(len(edges)): for graph_no in range(20): file_id = "{}{}".format(test_no, graph_no) print("Running test number {}, iteration {}".format(test_no, graph_no)) if graph_no < 10: GraphGenerator.generate_graph("{}/graph{}.txt".format(DATA_FOLDER_NAME, file_id), nodes, edges[test_no], source, max_weight) else: GraphGenerator.generate_graph_probability("{}/graph{}.txt".format(DATA_FOLDER_NAME, file_id), nodes, edges[test_no]/(nodes*nodes), source, max_weight) graph = Graph() graph.read_from_file("{}/graph{}.txt".format(DATA_FOLDER_NAME, file_id), True) run_algorithms(graph, file_id)
import GraphGenerator as GG import Graph as G import RandomWalk graph = GG.generate_graph("twenty_nodes.brite") #graph = G.generateGraph(100,5) src = 1 dest = 4 print graph print "src : " + str(src) print "dest : " + str(dest) print "random walk path is :", RandomWalk.randomWalk(graph, src, dest) print "shortest path: ", G.shortest_path(graph, src, dest)