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network3.py
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network3.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import os
import codecs
stdin = sys.stdin
sys.stdout = codecs.getwriter('utf_8')(sys.stdout)
sys.stdin = codecs.getreader('euc_jp')(sys.stdin)
#sys.stdout = codecs.getwriter('shift_jis')(sys.stdout)
sys.path.insert(0,'lib')
reload(sys)
sys.setdefaultencoding('utf-8')
sys.path.append(os.pardir + '/lib/')
import StringIO
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from pylab import show
import math
import json as json
def jsonloader(time="day",centrality="eigenvector",percent="100"):
print "test"
#-------------------------Define Edges-------------------------:
#allLines = open('merged.csv').read().encode('utf-8')
allLines = open('edges5.Csv').read().encode('utf-8')
#allLines = open(/csv).read().encode('utf-8')
data = StringIO.StringIO(allLines)
G = nx.Graph()
edges = nx.read_edgelist(data, delimiter=',', nodetype=unicode)
for e in edges.edges():
G.add_edge(*e)
N,K = edges.order(), edges.size()
print "Nodes: ", N
print "Edges: ", K
avg_deg = int(math.ceil(float(K)/N))
print "Average degree: ", avg_deg
degree = G.degree()
degreelist = []
for n in degree:
degreelist.append(degree[n])
degreelist = filter(lambda x: x>1, degreelist)
#set min %
degreelist.sort()
topp = 90
topn = 50
toplist = degreelist[int(len(degreelist) * topp/100) : int(len(degreelist))]
median=sorted(degreelist)[len(degreelist)/2]
mediantopp=sorted(toplist)[len(toplist)/2]
print "Degree Median: ", median
print "Degree Median(top %): ", mediantopp
Range = (max(degreelist)-min(degreelist))
Rangetopp = (max(toplist)-min(toplist))
print "Degree Range: ", Range
print "Degree Range(top %): ", Rangetopp
#'Fan-boy' trimmer
def remove_edges(g, in_degree):
g2=g.copy()
#d_in=g2.in_degree(g2)
#d_out=g2.out_degree(g2)
#print(d_in)
#print(d_out)
d = g2.degree(g2)
for n in g2.nodes():
#if d_in[n]==in_degree and d_out[n] == out_degree:
if d[n] <= in_degree:
g2.remove_node(n)
return g2
def remove_minoredges(g, topn):
import heapq
g3=g.copy()
d = g3.degree(g3)
#d.most_common()
a = sorted(d, key=d.get, reverse=False)[:int(len(d)-topn)]
for item in a:
g3.remove_node(item)
return g3
#G = remove_edges(G,mediantopp)
G = remove_minoredges(G,topn)
#-------------------------Finding Community-------------------------
import community
Gc = community.best_partition(G)
for n, m in Gc.items():
Gc[n] = int(m)
color = []
for nodes in nx.nodes_iter(G):
value = int(Gc[nodes]*100)
color.append(value)
#print "color: ",color
nx.set_node_attributes(G,'group',Gc)
#-------------------------Finding Centrality-------------------------
#大きさを定義するリストを作成
#bb=nx.degree_centrality(G)
#bb=nx.betweenness_centrality(G)
#bb=nx.closeness_centrality(G)
bb=nx.eigenvector_centrality(G)
for n, m in bb.items():
bb[n] = int(math.ceil(m*25))
size = []
for nodes in nx.nodes_iter(G):
value = int(bb[nodes]*100)
#print value
size.append(value)
#print len(size)
#print bb
#for n in len(size):
#nx.set_node_attributes(G, 'betweenness',
nx.set_node_attributes(G,'betweenness',bb)
#-------------------------Finding edge Centrality-------------------------
#線分の大きさを定義するリストを作成
cc=nx.edge_betweenness_centrality(G)
for n, m in cc.items():
cc[n] = int(math.ceil(m*500))
edgesize = []
for edges in nx.edges_iter(G):
value = int(cc[edges]*500)
edgesize.append(value)
nx.set_edge_attributes(G,'length',cc)
#--------------------------------------------------
G = remove_minoredges(G,topn)
size = np.asarray(size)
pos = nx.spring_layout(G)
#nx.draw_networkx_nodes(G,pos,node_color=color,alpha=0.8,node_size=size)
nx.draw_networkx_nodes(G,pos,alpha=0.8,node_size=size)
nx.draw_networkx_edges(G,pos,alpha=0.2,edge_size=edgesize)
nx.draw_networkx_labels(G,pos,font_size=10,font_color='black')
def save(G, fname):
from networkx.readwrite import json_graph
data = json_graph.dumps(G, sort_keys=True,indent=2)
f = open(fname, 'w')
f.write(data)
save(G, "./d3/graph.json")
plt.savefig('./d3/graph_merged.png')
#plt.show()