thesis/model.py at master · riccardoscheda/thesis · GitHub

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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from random import choice
from functools import reduce
import matplotlib.animation as animation # animation plot
import pandas as pd
import networkx as nx
import  random_network  as rn

fig, ax = plt.subplots(1,1)

#iterations
frames = 500

N = 5
K = 2
number_of_clusters = 2

#################################################################

#creation of the subnetworks
gr = [rn.Random_Network(N,K) for i in range(number_of_clusters)]

control_nodes = [gr[i].control_nodes[0]+i*N for i in range(number_of_clusters)]
env_control_nodes = [gr[i].control_nodes[1]+i*N for i in range(number_of_clusters)]
tot = rn.create_net(gr, control_nodes,env_control_nodes, N,N)
negedges = list(zip(list(np.where(tot.T<0)[0]),list(np.where(tot.T<0)[1])))
#print(negedges)
Net = rn.Network(tot,number_of_clusters)
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 #######################
tot1 = rn.create_clusters(gr, control_nodes,env_control_nodes, N,number_of_clusters,visual=True)
abs_tot = abs(tot1)
graph1 = nx.from_numpy_matrix(abs_tot.T, create_using=nx.DiGraph)
npos = nx.kamada_kawai_layout(graph1)
#################################################################


def init():
    ax = nx.draw(graph,pos = npos)
    active_nodes = []
    non_active_nodes = []
    for i in range(number_of_clusters):
        Net.nodes[np.random.randint(N*i, N*(i+1))] = 1

    # for i in range(N):
    #     Net.nodes[i] = 1


    for i in range(len(Net.nodes)):
        if Net.nodes[i] == 1 :
            active_nodes.append(i)
        else:
            non_active_nodes.append(i)
    ax = nx.draw_networkx(graph,npos, with_labels= False)
    ax = nx.draw_networkx_nodes(graph,npos,
                        nodelist=active_nodes,
                        node_color='y')

    return ax,


def evo(frames):
    plt.ion()
    plt.cla()

    up = Net.adj_matrix.dot(Net.nodes)
    Net.nodes = (up >0).astype(int)
    rn.env(Net,env_control_nodes,p=0.1)
    #rn.env(Net,control_nodes,p=0.1)
    rn.noise(Net,p=0.2)
    active_nodes = []
    non_active_nodes = []

    for i in range(len(Net.nodes)):
        if Net.nodes[i] == 1 :
            active_nodes.append(i)
        else:
            non_active_nodes.append(i)

    ax = nx.draw_networkx(graph,npos, with_labels= True)
    ax = nx.draw_networkx_nodes(graph,npos,
                        nodelist=control_nodes,
                        node_size=800)
    ax = nx.draw_networkx_nodes(graph,npos,
                        nodelist=active_nodes,
                        node_color='y')

    ax = nx.draw_networkx_edges(graph, npos,
                       edgelist=negedges,
                       width=3, alpha=0.4, edge_color='r')
    plt.title("frame " +str(frames))
    return  ax

#print(control_nodes)
ani = FuncAnimation(fig, evo, frames = np.arange(0,500), interval = 200,init_func = init, blit = False)
#ani.save('network.gif',dpi = 100,writer = "imagemagick")
plt.savefig("network.png")

# #%%
# import numpy as np
# import matplotlib.pyplot as plt
# from matplotlib.animation import FuncAnimation
# from random import choice
# from functools import reduce
# import matplotlib.animation as animation # animation plot
# import pandas as pd
# import networkx as nx
# import  random_network  as rn

# fig, ax = plt.subplots(1,1)

# #iterations
# frames = 500

# N = 10
# K = 2
# number_of_clusters = 2

# ######################################

# #creation of the subnetworks
# gr = [rn.Random_Network(N,K)]
# gr.append(rn.Random_Network(1, 1))
# gr[1].adj_matrix
# single_cluster_control_nodes = [rn.outgoing_links(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)
# #################################################################


# def init():
#     ax = nx.draw(graph,pos = npos)
#     active_nodes = []
#     non_active_nodes = []
#     for i in range(number_of_clusters):
#         Net.nodes[control_nodes[i]] = 1

#     for i in range(N):
#         Net.nodes[i] = 1


#     for i in range(len(Net.nodes)):
#         if Net.nodes[i] == 1 :
#             active_nodes.append(i)
#         else:
#             non_active_nodes.append(i)
#     ax = nx.draw_networkx(graph,npos, with_labels= False)
#     ax = nx.draw_networkx_nodes(graph,npos,
#                         nodelist=active_nodes,
#                         node_color='y')

#     return ax,


# def evo(frames):
#     plt.ion()
#     plt.cla()

#     up = Net.adj_matrix.dot(Net.nodes)
#     Net.nodes = (up >0).astype(int)
#     rn.noise(Net,p=0.2)
#     active_nodes = []
#     non_active_nodes = []

#     for i in range(len(Net.nodes)):
#         if Net.nodes[i] == 1 :
#             active_nodes.append(i)
#         else:
#             non_active_nodes.append(i)

#     ax = nx.draw_networkx(graph,npos, with_labels= True)
#     ax = nx.draw_networkx_nodes(graph,npos,
#                         nodelist=control_nodes,
#                         node_size=800)
#     ax = nx.draw_networkx_nodes(graph,npos,
#                         nodelist=active_nodes,
#                         node_color='y')

#     ax = nx.draw_networkx_edges(graph, npos,
#                        edgelist=negedges,
#                        width=3, alpha=0.4, edge_color='r')
#     plt.title("frame " +str(frames))
#     return  ax

# print(rn.outgoing_links(gr[0], N))
# print(rn.outgoing_links(Net, N))
# ani = FuncAnimation(fig, evo, frames = np.arange(0,500), interval = 200,init_func = init, blit = False)
# #ani.save('network.gif',dpi = 100,writer = "imagemagick")