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')
#ims = []
plt.show()
def visual_reporter (G, t):
# Clear the figure
plt.clf()
n_sizes = []
# Calculate the sizes of the nodes (their state)
for i in G.nodes():
new_size = 100.0 * G.node[i]['state']
if new_size < 1.0: new_size = 1
n_sizes.append(new_size)
# Draw the network and update the canvas
nx.draw(G, pos, node_size=n_sizes, node_color='#A0CBE2', width=4,
with_labels=False)
im = fig.canvas.draw()
#im = plt.imshow()
#im = plt.draw()
#ims.append([im])
#=========================================
# SIMULATE THE DYNAMICS
#=========================================
# Simulate the dynamics (discrete-time) using the visual reporter
netevo.simulate_steps(G, 200, visual_reporter)
#ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True, repeat_delay=2000)
# Close the visualization
plt.close()
# 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)
