import numpy as np
import matplotlib.pyplot as plt
plt.xkcd()
import networkx as nx
%matplotlib inline

G = nx.karate_club_graph()
pos = nx.spring_layout(G) # Fix node positions on all pictures

# Original network
nx.draw_networkx(G, pos)

# Degree centrality
dc = nx.degree_centrality(G)
plt.figure(1, figsize=(10,7))
coord = nx.spring_layout(G)
nx.draw(G,
          pos,
          nodelist=dc.keys(),
          node_size = [d*7000 for d in dc.values()],
          node_color=dc.values(),
          font_size=8,
          cmap=plt.cm.Reds,
          )

# Closeness centrality
cl = nx.closeness_centrality(G)
plt.figure(1, figsize=(10,7))
coord = nx.spring_layout(G)
nx.draw(G,
          pos,
          nodelist=cl.keys(),
          node_size = [d*3000 for d in cl.values()],
          node_color=cl.values(),
          font_size=8,
          cmap=plt.cm.Reds,
          )

# Plot degree-closeness 
xdata = dc.values()
ydata = cl.values()
plt.figure(1, figsize=(7,7))
plt.plot(xdata,ydata, '+')
plt.xlabel('Degree Centrality')
plt.ylabel('Closeness Centrality')

# Not Clear. Lets add node ids:
fig = plt.figure(1, figsize=(14,7))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

for v in xrange(len(dc)):
   ax1.text(x = xdata[v], y = ydata[v], s=str(v))
ax1.set_xlim(0, 0.6)
ax1.set_ylim(0.25, 0.6)
ax1.set_xlabel('Degree Centrality')
ax1.set_ylabel('Closeness Centrality')

ax2 = nx.draw_networkx(G, pos)