# Create an undirected graph with only node dynamics
G = nx.Graph()
G.graph['node_dyn'] = True
G.graph['edge_dyn'] = False
# Graph should have a ring topology
n_nodes = 30
G.add_node(0)
for i in range(1, n_nodes):
G.add_node(i)
G.add_edge(i-1, i)
G.add_edge(i, 0)
# All nodes are Kuramoto oscillators (discrete-time)
netevo.set_all_node_dynamics(G, kuramoto_node_dyn)
# Set random initial conditions for each node
netevo.rnd_uniform_node_states (G, [(0.0, 6.2)])
#=========================================
# DEFINE THE VISUAL REPORTER
#=========================================
# Create the figure to display the visualization
fig = plt.figure(figsize=(6,6))
# Node positions to use for the visualization
pos=nx.circular_layout(G)
# A visual reporter that will display the current state of the network
dist = np.linalg.norm(s1 - s2)
return 0.08 * dist
#=========================================
# CREATE THE NETWORK
#=========================================
# Create a fully connected graph
G = nx.complete_graph(12)
# Both node and edge dynamics are required
G.graph['node_dyn'] = True
G.graph['edge_dyn'] = True
# All nodes are chaotic Lorenz oscillators
netevo.set_all_node_dynamics(G, lorenz_node_dyn)
# All edges follow the adaptive rule
netevo.set_all_edge_dynamics(G, adaptive_law_edge_dyn)
# Randomly assign node states
netevo.rnd_uniform_node_states(G, [(0.1, 20.0), (0.1, 20.0), (0.1, 20.0)])
# Edges all start with a very weak strength
netevo.rnd_uniform_edge_states(G, [(0.00000001, 0.00000001)])
#=========================================
# DEFINE THE VISUAL REPORTER
#=========================================
# Create the figure to display the visualization
fig = plt.figure(figsize=(6.5,6.5))
# Node positions to use for the visualization
#=========================================
# CREATE THE NETWORK
#=========================================
# Create a random undirected graph and check valid (n=50, m=100)
n = 20
G = []
while True:
G = nx.gnm_random_graph(n, 2*n)
if nx.is_connected(G):
break
# No dynamics are required
G.graph['node_dyn'] = True
G.graph['edge_dyn'] = False
netevo.set_all_node_dynamics(G, rossler_node_dyn)
#=========================================
# EVOLVE THE NETWORK
#=========================================
# Perform the evolution (using simulated dynamics as part of the performance measure)
iteration, G_final = netevo.evolve_sa(G, order_parameter, rewire,
initial_temp=100.0, min_temp=0.0001,
reporter=netevo.evo_sa_reporter)
# Output GML files containing the initial and final toplogies (viewable in Cytoscape/yEd)
netevo.write_to_file(G, 'evolution_sa_dyn_initial.gml')
netevo.write_to_file(G_final, 'evolution_sa_dyn_final.gml', node_keys=['state'])
