def make_frame(self):
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
graph = self.graph.handle
dst = degrees_to_hist(graph.degree())
ccdf = sna.ccdf(dst)
ax.loglog(ccdf)
return fig
def make_distributions(arg):
if isinstance(arg, nx.Graph):
values = arg.degree().values()
elif isinstance(arg, collections.Sequence):
values = arg
elif isinstance(arg, ndarray):
values = arg.flatten()
else:
raise TypeError("Don't know what to do with %s" % arg)
bins = np.bincount(values)
ccdf = sna.ccdf(bins)
pdf = np.asfarray(bins) / np.sum(bins)
return ccdf, pdf
def make_distributions(arg):
if isinstance(arg, nx.Graph):
values = arg.degree().values()
elif isinstance(arg, collections.Sequence):
values = arg
elif isinstance(arg, ndarray):
values = arg.flatten()
else:
raise TypeError("Don't know what to do with %s" % arg)
bins = np.bincount(values)
ccdf = sna.ccdf(bins)
pdf = np.asfarray(bins) / np.sum(bins)
return ccdf, pdf
for _iteration in xrange(ITERATIONS):
sim = barabasi_albert.BA()
sim.setup_parameters(starting_edges=11, starting_network_size=11, steps=STEPS)
sim.run()
label = '%d starting size' % sim.starting_network_size
steps = sim.steps
starting_edges = sim.starting_edges
starting_networks_size = sim.starting_network_size
graph = sim.graph.handle
del sim
bins = np.bincount(graph.degree().values())
ccdf = sna.ccdf(bins)
pdf = np.asfarray(bins) / len(bins)
ba_ccdfs.append(ccdf)
ba_pdfs.append(pdf)
ccdf_axes = F_ccdf.gca()
pdf_axes = F_pdf.gca()
for dst, color in zip(ba_ccdfs, colors):
ccdf_axes.loglog(dst, color=color)
#ccdf_axes.loglog(ba_avg_ccdf, color='red')
ccdf_axes.set_title('CCDF BA(%d, %d)' % (steps, starting_edges))
ccdf_axes.set_xlabel('Degree $x$')
def make_distributions(graph):
bins = np.bincount(graph.degree().values())
ccdf = sna.ccdf(bins)
pdf = np.asfarray(bins) / np.sum(bins)
return ccdf, pdf
def make_distributions(graph):
bins = np.bincount(graph.degree().values())
ccdf = sna.ccdf(bins)
pdf = np.asfarray(bins) / np.sum(bins)
return ccdf, pdf
