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panda.py
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panda.py
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#!/usr/bin/python
import sys
import matplotlib.pyplot as plt
import networkx as nx
import json
import heapq as heap
from operator import itemgetter
import numpy as np
import sim
import betweenness_centrality
# Load data from file given by command line argument
filename = sys.argv[1]
N = int(filename.split('.')[-3])
f = open(filename)
graph_data = json.load(f)
f.close()
G = nx.from_dict_of_lists(graph_data)
def save_graph(graph, save_name):
'''
Saves networkx graph "graph" as pdf named "save_name"
Source: http://stackoverflow.com/a/17388676
'''
#initialze Figure
plt.figure(num=None, figsize=(20, 20), dpi=80)
plt.axis('off')
fig = plt.figure(1)
pos = nx.spring_layout(graph)
nx.draw_networkx_nodes(graph,pos)
nx.draw_networkx_edges(graph,pos)
nx.draw_networkx_labels(graph,pos)
cut = 1.00
xmax = cut * max(xx for xx, yy in pos.values())
ymax = cut * max(yy for xx, yy in pos.values())
plt.xlim(0, xmax)
plt.ylim(0, ymax)
plt.savefig(save_name, bbox_inches="tight")
del fig
#Assuming that the graph g has nodes and edges entered
# save_graph(G, filename + '-visualization.pdf')
# Remove lone nodes
G.remove_nodes_from(nx.isolates(G))
# Degree centrality
# d = G.degree()
# sorted_deg_nodes = sorted(d.keys(), key=lambda k: d[k], reverse=True)
# high_degree_nodes = sorted_deg_nodes[:N]
# spaced_high_degree_nodes = []
# for node in sorted_deg_nodes:
# is_a_neighbor = False
# for gc in spaced_high_degree_nodes:
# if node in G.neighbors(gc):
# is_a_neighbor = True
# break
# if not is_a_neighbor:
# spaced_high_degree_nodes.append(node)
# if len(spaced_high_degree_nodes) == N:
# break
# print "-" * 20
# print "spaced high degree nodes:"
# for choice in spaced_high_degree_nodes:
# print choice
# for choice in spaced_high_degree_nodes:
# print "degree: " + str(d[choice]) + ". node: " + choice
# print "-" * 20
# print "high degree nodes:"
# for node in high_degree_nodes:
# print node
# for node in high_degree_nodes:
# print "degree: " + str(d[node]) + ". node: " + node
# print "-" * 20
# print "strategies:"
d = nx.closeness_centrality(G)
sorted_centrality_nodes = sorted(d.keys(), key=lambda k: d[k], reverse=True)
deg_centrality_nodes = sorted_centrality_nodes[:N]
# d = nx.communicability_centrality(G)
# sorted_centrality_nodes = sorted(d.keys(), key=lambda k: d[k], reverse=True)
# comm_centrality_nodes = sorted_centrality_nodes[:N]
d = betweenness_centrality.betweenness_centrality_parallel(G)
sorted_centrality_nodes = sorted(d.keys(), key=lambda k: d[k], reverse=True)
btwn_centrality_nodes = sorted_centrality_nodes[:N]
# for node in btwn_centrality_nodes:
# print node
graph = nx.to_dict_of_lists(G)
nodes = {"btwn_centrality": btwn_centrality_nodes, "closeness_centrality": deg_centrality_nodes}
s = sim.run(graph, nodes)
print s
#d = nx.betweenness_centrality(G)
#btwn = heap.nlargest(N, d, key = lambda k: d[k])