def test_undirected_pure_geometric_graph_yields_right_adjacency_matrix(
self):
G, A, D = random_graph_bu.pure_geometric(N=426,
N_edges=7804,
L=.75,
brain_size=[7., 7., 7.])
self.assertEqual(len(G.nodes()), 426)
np.testing.assert_array_equal(
nx.adjacency_matrix(G).todense().astype(int), A.astype(int))
def test_undirected_pure_geometric_graph_yields_right_adjacency_matrix(self):
G, A, D = random_graph_bu.pure_geometric(N=426, N_edges=7804, L=.75, brain_size=[7., 7., 7.])
self.assertEqual(len(G.nodes()), 426)
np.testing.assert_array_equal(nx.adjacency_matrix(G).todense().astype(int), A.astype(int))
Gbrain,_,_ = binary_directed()
deg_brain = Gbrain.to_undirected().degree().values()
N = 100
axs[0].hist(deg_brain,deg_bins,facecolor=config.COLORS['brain'], alpha=0.75,normed=True)
leg.append(mlines.Line2D([],[],color=config.COLORS['brain'], linestyle='-',markersize=13,\
label='Connectome',lw=3,alpha=0.75))
for i,L in enumerate(Ls):
print 'L = {}'.format(L)
degs=[]
for k in range(N):
print 'repeat {}'.format(k)
G,_,_ = pure_geometric(N=bc.num_brain_nodes,N_edges=bc.num_brain_edges_undirected,
L=L)
G_un = G.to_undirected()
cc_dict = nx.clustering(G_un)
deg_dict = G_un.degree()
cc = [cc_dict[k] for k in range(bc.num_brain_nodes)]
deg = [deg_dict[k] for k in range(bc.num_brain_nodes)]
degs.extend(deg)
[slope,intercept,r,_,_] = stats.linregress(deg,cc)
slopes.append(slope)
rs.append(r)
axs[1].scatter(deg,cc,color=cols[i],marker='o',\
s=markersize,alpha=alpha,lw=0)
