from models import * from nxsim import NetworkSimulation import numpy from matplotlib import pyplot as plt import networkx as nx import settings import models import math import json settings.init() # Loads all the data from settings models.init() # Loads the models and network variables #################### # Network creation # #################### if settings.network_type == 0: G = nx.complete_graph(settings.number_of_nodes) if settings.network_type == 1: G = nx.barabasi_albert_graph(settings.number_of_nodes,10) if settings.network_type == 2: G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None) # More types of networks can be added here ############## # Simulation # ############## sim = NetworkSimulation(topology=G, states=init_states, agent_type=SpreadModelM2, max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0) sim.run_simulation() ########### # Results # ########### x_values = [] infected_values = [] neutral_values = [] cured_values = [] vaccinated_values = [] attribute_plot = 'status' for time in range(0, settings.max_time): value_infectados = 0 value_neutral = 0 value_cured = 0 value_vaccinated = 0 real_time = time * settings.timeout activity= False for x in range(0, settings.number_of_nodes): if attribute_plot in models.networkStatus["agente_%s" % x]: if real_time in models.networkStatus["agente_%s" % x][attribute_plot]: if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 1: ##Representar infectados value_infectados += 1 activity = True if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 0: ##Representar neutrales value_neutral += 1 activity = True if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 2: ##Representar cured value_cured += 1 activity = True if models.networkStatus["agente_%s" % x][attribute_plot][real_time] == 3: ##Representar vaccinated value_vaccinated += 1 activity = True if activity: x_values.append(real_time) infected_values.append(value_infectados) neutral_values.append(value_neutral) cured_values.append(value_cured) vaccinated_values.append(value_vaccinated) activity=False infected_line = plt.plot(x_values,infected_values,label='Infected') neutral_line = plt.plot(x_values,neutral_values, label='Neutral') cured_line = plt.plot(x_values,cured_values, label='Cured') vaccinated_line = plt.plot(x_values,vaccinated_values, label='Vaccinated') plt.legend() plt.savefig('spread_model.png') #plt.show() ################# # Visualization # ################# for x in range(0, settings.number_of_nodes): for empresa in models.networkStatus["agente_%s"%x]: emotionStatusAux=[] for tiempo in models.networkStatus["agente_%s"%x][empresa]: prec = 2 output = math.floor(models.networkStatus["agente_%s"%x][empresa][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo emotionStatusAux.append((output,tiempo,None)) attributes = {} attributes[empresa] = emotionStatusAux G.add_node(x, attributes) print("Done!") with open('data.txt', 'w') as outfile: json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': ')) nx.write_gexf(G,"test.gexf", version="1.2draft")