def order_parameter (G):
if nx.is_connected(G):
# Simulate from random initial conditions
netevo.rnd_uniform_node_states(G, [(0.0, 6.2)])
netevo.simulate_steps(G, 100, None)
# 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(G.node[i]['state'] -
G.node[j]['state'])
# Heaviside function (allow for some numerical error:0.01)
if dist - 0.001 >= 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')
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')
#=========================================
# 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
pos = nx.circular_layout(G)
# Function to generate the visualisation of the network
def visual_reporter (G, t):
plt.clf()
n_sizes = []
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
# as it is being simulated
#ims = []
plt.show()
for n in range(12):
G.node[n]['color'] = 'b'
G.node[12]['color'] = 'r'
# 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, netevo.no_edge_dyn)
# Randomly assign node states
netevo.rnd_uniform_node_states (G, [(3.0, 10.0), (3.0, 10.0), (3.0, 10.0)])
# Edges all start with a very weak strength
netevo.rnd_uniform_edge_states (G, [(0.3, 0.3)])
# Our pinning node has no dynamics just fixed states that change
G.node[12]['state'] = [30.0, 30.0, 30.0]
G.node[12]['dyn'] = pinner_node_dyn
#=========================================
# DEFINE THE VISUAL REPORTER
#=========================================
# Turn on animation in pylab
# http://stackoverflow.com/questions/8965055/basic-animation-with-matplotlibs-pyplot
pylab.ion()
# Create the figure to display the visualization
# Create an empty graph (undirected)
G2 = nx.Graph()
# We only need node dynamics
G2.graph["node_dyn"] = True
G2.graph["edge_dyn"] = False
# Create the network of n nodes connected in a ring
n_nodes = 4
G2.add_node(0)
G2.node[0]["dyn"] = kuramoto_node_dyn
for i in range(1, n_nodes):
# Create the node
G2.add_node(i)
# Set the dynamics of the new node
G2.node[i]["dyn"] = kuramoto_node_dyn
# Connect it to the previous node in the ring
G2.add_edge(i - 1, i)
# Finish the ring
G2.add_edge(i, 0)
# Set the initial state to a random number in the range (0, 6.0)
netevo.rnd_uniform_node_states(G2, [(0.0, 6.0)])
# =========================================
# SIMULATE THE NETWORK DYNAMICS
# =========================================
# Simulate the dynamics and print the state to the screen
netevo.simulate_steps(G2, 100, netevo.state_reporter)
