def order_parameter (G):
if nx.is_connected(G):
# Simulate from random initial conditions
netevo.rnd_uniform_node_states(G, [(0.0, 10.0), (0.0, 10.0), (0.0,
10.0)])
# Simulate the system
res, nmap, emap = netevo.simulate_ode_fixed(G, [0.0, 20.0],
node_dim=3)
# Calculate the order_parameter and return value
mu = 0.0
for i in G.nodes():
for j in G.nodes():
if i != j:
dist = np.linalg.norm(res[-1][nmap[i]:nmap[i]+2] -
res[-1][nmap[j]:nmap[j]+2])
# Heaviside function (allow for some numerical error:0.01)
if dist - 0.01 >= 0.0:
mu += 100.0
return mu * (1.0 / (G.number_of_nodes() * (G.number_of_nodes() -
1.0)));
# If the network is not connected it is not valid
return float('inf')
if nx.is_connected(G):
break
# Only node dynamics are required - should all be chaotic Rossler oscillators
G.graph['node_dyn'] = True
G.graph['edge_dyn'] = False
netevo.set_all_node_dynamics(G, rossler_node_dyn)
#=========================================
# SIMULATE THE NETWORK
#=========================================
# Simulate from random initial conditions
netevo.rnd_uniform_node_states(G, [(0.0, 5.0), (0.0, 5.0), (0.0, 5.0)])
# Simulate the system
res, nmap, emap = netevo.simulate_ode_fixed(G, [0.0, 10.0, 20.0], node_dim=3)
# Print the results
print "Simulation Results:"
print '================================='
print "For t = 0:"
for n in G.nodes():
print "Node ", n, " = [", res[0][nmap[n]], ', ', res[0][nmap[n]+1], ', ' \
, res[0][nmap[n]+2], ']'
print '================================='
print "For t = 20:"
for n in G.nodes():
print "Node ", n, " = [", res[2][nmap[n]], ', ', res[2][nmap[n]+1], ', ' \
, res[2][nmap[n]+2], ']'
