def main(script, n='1000', k='10', *args):
n = int(n) #number of vertices
k = int(k) #number of edges in regular graph
vertices = [Vertex(c) for c in range(n)]
g = SmallWorldGraph(vertices)
g.add_regular_edges(k)
#regular graph's clustering coefficient and avg path length
c_zero = g.clustering_coefficient()
l_zero = g.average_path_length()
print c_zero, l_zero
f = open("plots/output.csv", "wb")
print 'p\tC\tL'
f.write('p,C(p)/C(0),L(p)/L(0)\n')
#begin rewiring to tease out small-world network characteristics
for log_exp in numpy.arange(-40, 1, 1): #incrementation scheme for logarithmic exponents
g = SmallWorldGraph(vertices)
g.add_regular_edges(k)
p = numpy.power(10, log_exp/10.0)
g.rewire(p)
print '%s\t%s\t%s' % (p, g.clustering_coefficient()/c_zero, g.average_path_length()/l_zero)
f.write('%s,%s,%s\n' % (p, g.clustering_coefficient()/c_zero, g.average_path_length()/l_zero))
f.close()
