import pandas as pd
time = 300
noise = 5
number_of_clusters = 1
N = 30
K = 2
graphs = [rn.Random_Network(N, 2) for i in range(number_of_clusters)]
single_cluster_control_nodes = [
rn.find_control_nodes(graphs[i], N) for i in range(number_of_clusters)
]
control_nodes = [
single_cluster_control_nodes[i] + i * N for i in range(number_of_clusters)
]
tot = rn.create_clusters(graphs, control_nodes, N, number_of_clusters)
Net = rn.Network(tot)
############# INITIAL CONDITIONS ########################
# for i in range(number_of_clusters):
# Net.nodes[control_nodes[i]] = 1
# Net.nodes[np.random.randint(N*i,N*(i+1))] = 1
Net.nodes = np.ones((N * number_of_clusters, 1))
for i in range(noise):
activities = []
Net = rn.Random_Network(N, K)
Net.nodes = np.ones((N * number_of_clusters, 1))
for j in range(time):
#for i in control_nodes:
##### PROBABILITÀ DI AZZERARE IL CONTROL NODE AD OGNI STEP
a = True
while a:
try:
graphs = [rn.Random_Network(N, K)]
graphs.append(rn.Random_Network(M, K1))
control_nodes.append([
graphs[i].control_nodes[0] + i * N
for i in range(number_of_clusters)
])
env_control_nodes.append([
graphs[i].control_nodes[1] + i * N
for i in range(number_of_clusters)
])
tot = rn.create_net(graphs, control_nodes[i], env_control_nodes[i],
N, M)
net = rn.Network(tot, number_of_clusters)
qx.append(net)
a = False
except:
pass
######################### INITIAL CONDITIONS ################################
# for i in range(realizations):
# #qx[i].nodes[np.random.randint(N+M)] = 1
for j in range(N, N + M):
qx[i].nodes[j] = 1
#############################################################################
act = np.zeros(realizations)
t = np.zeros(realizations)
times = []
number_of_clusters = 3
#creation of the subnetworks
gr = [rn.Random_Network(N, K) for i in range(number_of_clusters)]
single_cluster_control_nodes = [
rn.find_control_nodes(gr[i], N) for i in range(number_of_clusters)
]
control_nodes = [
single_cluster_control_nodes[i] + i * N for i in range(number_of_clusters)
]
tot = rn.create_clusters(gr, control_nodes, N, number_of_clusters)
negedges = list(zip(list(np.where(tot.T < 0)[0]),
list(np.where(tot.T < 0)[1])))
Net = rn.Network(tot)
graph = nx.from_numpy_matrix(tot.T, create_using=nx.DiGraph)
npos = nx.spring_layout(graph)
cycles = nx.cycle_basis(graph.to_undirected())
################## ONLY FOR VISUALIZATION #######################
abs_tot = abs(tot)
graph1 = nx.from_numpy_matrix(abs_tot.T, create_using=nx.DiGraph)
npos = nx.kamada_kawai_layout(graph1)
#################################################################
##########################################################################################################
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
f0 = 1