#!/usr/bin/env python
"""
Origin: http://networkx.github.com/documentation/latest/examples/drawing/giant_component.html

This example illustrates the sudden appearance of a 
giant connected component in a binomial random graph.

Requires pygraphviz and matplotlib to draw.

"""
#    Copyright (C) 2006-2008
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

try:
    import matplotlib.pyplot as plt
except:
    raise 

import networkx as nx
import math
import os
import pickle

try:
    from networkx import graphviz_layout
    layout=nx.graphviz_layout
except ImportError:
    print "PyGraphviz not found; drawing with spring layout; will be slow."
    layout=nx.spring_layout


#n=150  # 150 nodes
n = int(os.environ['n'])
# p value at which giant component (of size log(n) nodes) is expected
p_giant=1.0/(n-1)     
# p value at which graph is expected to become completely connected
p_conn=math.log(n)/float(n) 
                       
# the following range of p values should be close to the threshold
#pvals=[0.003, 0.006, 0.008, 0.015] 

#region=220 # for pylab 2x2 subplot layout
#plt.subplots_adjust(left=0,right=1,bottom=0,top=0.95,wspace=0.01,hspace=0.01)
#for p in pvals:    
p = float(os.environ['p'])
if True:
    G=nx.binomial_graph(n,p)
    pos=layout(G)
    #region+=1
    #plt.subplot(region)
    plt.title("n = %d, p = %5.4f"%(n,p))
    nx.draw(G,pos,
            with_labels=False,
            node_size=10
            )
    # identify largest connected component
    Gcc=nx.connected_component_subgraphs(G)
    G0=Gcc[0] 
    nx.draw_networkx_edges(G0,pos,
                           with_labels=False,
                           edge_color='r',
                           width=6.0
                        )
    # show other connected components
    for Gi in Gcc[1:]:
       if len(Gi)>1:
          nx.draw_networkx_edges(Gi,pos,
                                 with_labels=False,
                                 edge_color='r',
                                 alpha=0.3,
                                 width=5.0
                                 )         

# dump graph in human-readable form
print "Generated binomial graph (n=%d, p=%5.4f):" % (n,p)
for n,ns in nx.to_dict_of_lists(G).iteritems():
    print " %s\t%s" % (n, ns)
print
# as well as a pickled form
pickle.dump(G, file("graph.pickle", "w"))
print "Created graph.pickle"

plt.savefig("giant_component.png")
#plt.show() # display
print "Created giant_component.png"