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)
