#!/usr/bin/python

# coding: UTF-8

# CSVファイルの読み込み

import csv

import networkx as nx

import matplotlib.pyplot as plt

from networkx.algorithms import bipartite

G=nx.DiGraph()

filename = "TFnew_1000.csv"

csvfile = open(filename) #1~300

# print G.node

for row in csv.reader(csvfile):

G.add_edges_from([(row[0],row[1])],weight=row[2])

print "From HERE

"

print G.number_of_nodes()

print G.number_of_edges()

print "average_neighbor_degree"

dict = nx.average_neighbor_degree(G)

list1 = dict.keys()

list2 = dict.values()

print list1

print list2

print "degree_assortativity_coefficient"

print nx.degree_assortativity_coefficient(G)

print "degree_pearson_correlation_coefficient"

print nx.degree_pearson_correlation_coefficient(G)

#print nx.k_nearest_neighbors(G)

print "STOP HERE"

print "bipartite.closeness_centrality(G,G.node)"

dict2 = bipartite.closeness_centrality(G,G.node)

list3 = dict2.values()

print list3

print "nx.degree_centrality(G)"

dict3 = nx.degree_centrality(G)

list4 = dict3.values()

print list4

print "nx.betweenness_centrality(G)"

dict4 = nx.betweenness_centrality(G)

list5 = dict4.values()

print list5

#print nx.current_flow_closeness_centrality(G, normalized=True, weight='weight', dtype='float', solver='lu')

#centrality=nx.eigenvector_centrality(G)

#print(['%s %0.2f'%(node,centrality[node]) for node in centrality])

#print nx.eigenvector_centrality(G, max_iter=100, tol=1e-02, nstart=None)

#print nx.communicability(G)

#print nx.triangles(G)

#directed Graphでなくてはだめ

#print(nx.clustering(G,0))

#print nx.average_clustering(G)

# print nx.diameter(G, e=None)

#print nx.center(G, e=None)

#print "nx.max_flow(G, 'Fosl2.48h', 'Hoxa9.48h')"

#print nx.max_flow(G, 'Fosl2.48h', 'Hoxa9.48h')

#print nx.network_simplex(G)

#print nx.pagerank(G,alpha=0.9)

#print "nx.pagerank_numpy"

#print nx.pagerank_numpy(G,alpha=0.9)

print "hits_numpy"

dict5 = nx.hits_numpy(G)

print dict5

# print(nx.shortest_path(G,source=0,target=4))

# print(nx.average_shortest_path_length(G))

#paths = nx.all_simple_paths(G, source=0, target=3, cutoff=2)

#print(list(paths))

csvfile.close()