import csv

import networkx as nx

import matplotlib.pyplot as plt

# Creating the adjacency matrix

data = list(csv.reader(open("Final_Inverted.csv")))

votes= [[0 for x in range(100)] for y in range(100)]

for row in xrange(1,100):

for row2 in xrange(row+1,101):

for col in xrange(1,340):

if data[row][col] == data[row2][col]:

votes[row-1][row2-1] += 1

votes[row2-1][row-1] += 1

#print votes

# Creating the Network graph

sen_Names = []

node_color = {}

for row in data:

if row[0][-3:] == '[D]':

node_color[row[0][5:-3]]='blue'

elif row[0][-3:] == '[R]':

node_color[row[0][5:-3]]='red'

elif row[0][-3:] == '[I]':

node_color[row[0][5:-3]]='yellow'

sen_Names.append('%s' %row[0][5:-3])

sen_Names = sen_Names[1:]

#print sen_Names

g = nx.Graph()

gn = nx.Graph()

g.add_nodes_from(sen_Names)

gn.add_nodes_from(sen_Names)

for row in xrange(100):

for col in xrange(row, 100):

if row != col:

g.add_edge(sen_Names[row], sen_Names[col], weight=abs(votes[row][col]-1000))

mst = nx.minimum_spanning_edges(g, data = True)

edgelist = list(mst)

gn.add_edges_from(edgelist)

#print sorted(edgelist)

labels={}

for name in sen_Names:

labels[name] = r'%s' %name

#print labels

positions = nx.spring_layout(gn, k = 0.40)

nx.draw_networkx_labels(gn, positions, labels, font_size = 11)

nx.draw(gn, positions,node_size = 55, node_color=node_color.values())

# centrality

cen_dict = nx.degree_centrality(gn)

for k,v in cen_dict.iteritems():

if v >= .05: #prints senators with 5+ degrees

print k, v

#print average

# The average is 2 degrees!

plt.savefig("Min_span_tree.png")

plt.show()