from nxsim import NetworkSimulation from nxsim import BaseNetworkAgent from nxsim import BaseLoggingAgent from random import randint from matplotlib import pyplot as plt import random import numpy as np import networkx as nx import settings import math import json settings.init() # Loads all the data from settings #################### # 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,3) if settings.network_type == 2: G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None) # More types of networks can be added here ############################## # Variables initializitation # ############################## myList=[] # List just for debugging networkStatus=[] # This list will contain the status of every node of the network emotionStatus=[] enterprise1Status=[] enterprise2Status=[] allEnterprisesEmotionList = {} for x in range(0, settings.number_of_nodes): networkStatus.append({'id':x}) emotionStatus.append({'id':x}) enterprise1Status.append({'id':x}) enterprise2Status.append({'id':x}) for enterpriseIndex in range(0,len(settings.tweet_probability_about)): allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)] = enterprise1Status # for node in range(0, settings.number_of_nodes): # allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)].update({'id':node}) #print(allEnterprisesEmotionList) # Initialize agent states. Let's assume everyone is normal. init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category # Seed a zombie, just for zombie model #init_states[5] = {'id': 1} #init_states[3] = {'id': 1} #################### # Available models # #################### class BigMarketModel(BaseNetworkAgent): number_of_enterprises = 0 def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.time_awareness = 0 self.type = "" self.number_of_enterprises = len(settings.tweet_probability_about) if self.id < self.number_of_enterprises: #Empresas self.state['id']=self.id self.type="Enterprise" self.tweet_probability = settings.tweet_probability_enterprises[self.id] else: #Usuarios normales self.state['id']=self.number_of_enterprises self.type="User" self.tweet_probability = settings.tweet_probability_users self.tweet_relevant_probability = settings.tweet_relevant_probability self.tweet_probability_about = settings.tweet_probability_about #Lista self.sentiment_about = settings.sentiment_about #Lista #Inicializacion de visualizacion for enterpriseIndex in range(0,len(settings.tweet_probability_about)): allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)][self.id].update({0:self.sentiment_about[enterpriseIndex]}) #print(allEnterprisesEmotionList) #networkStatus[self.id][self.env.now]=self.state['id'] #emotionStatus[self.id][self.env.now]=0 def run(self): while True: if(self.id < self.number_of_enterprises): # Empresa self.enterpriseBehaviour() else: # Usuario self.userBehaviour() yield self.env.timeout(settings.timeout) def enterpriseBehaviour(self): if random.random()< self.tweet_probability: #Twittea aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios for x in aware_neighbors: if random.uniform(0,10) < 5: x.sentiment_about[self.id] += 0.1 #Aumenta para empresa else: x.sentiment_about[self.id] -= 0.1 #Reduce para empresa # Establecemos limites if x.sentiment_about[self.id] > 1: x.sentiment_about[self.id] = 1 if x.sentiment_about[self.id] < -1: x.sentiment_about[self.id] = -1 #Visualización enterpriseEmotion=[] if self.id < self.number_of_enterprises: #try: #enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada #print (enterpriseEmotion) #except IndexError: # Si no existe la inicializamos # for y in range(0, settings.number_of_nodes): # enterpriseEmotion.append({'id':y}) #enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[self.id] #allEnterprisesEmotionList.insert(self.id,enterpriseEmotion) #Guardamos el valor #enterpriseEmotion[:] = [] #Vaciamos la lista allEnterprisesEmotionList['enterprise'+str(self.id)][x.id].update({self.env.now:x.sentiment_about[self.id]}) #if self.id == 0: # enterprise1Status[x.id][self.env.now]=x.sentiment_about[self.id] #if self.id == 1: # enterprise2Status[x.id][self.env.now]=x.sentiment_about[self.id] def userBehaviour(self): if random.random() < self.tweet_probability: #Twittea if random.random() < self.tweet_relevant_probability: #Twittea algo relevante #Probabilidad de tweet para cada empresa for i in range(len(self.tweet_probability_about)): random_num = random.random() if random_num < self.tweet_probability_about[i]: #Se ha cumplido la condicion, evaluo los sentimientos hacia esa empresa if self.sentiment_about[i] < 0: #NEGATIVO self.userTweets("negative",i) elif self.sentiment_about[i] == 0: #NEUTRO pass else: #POSITIVO self.userTweets("positive",i) def userTweets(self,sentiment,enterprise): aware_neighbors = self.get_neighboring_agents(state_id=2) #Nodos vecinos usuarios for x in aware_neighbors: if sentiment == "positive": x.sentiment_about[enterprise] +=0.003 elif sentiment == "negative": x.sentiment_about[enterprise] -=0.003 else: pass # Establecemos limites if x.sentiment_about[enterprise] > 1: x.sentiment_about[enterprise] = 1 if x.sentiment_about[enterprise] < -1: x.sentiment_about[enterprise] = -1 #Visualización # enterpriseEmotion=[] # try: # enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada # except IndexError: # Si no existe la inicializamos # for y in range(0, settings.number_of_nodes): # enterpriseEmotion.append({'id':y}) # enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[enterprise] # allEnterprisesEmotionList.insert(enterprise,enterpriseEmotion) #Guardamos el valor # enterpriseEmotion[:] = [] #Vaciamos la lista #if enterprise == 0: # enterprise1Status[x.id][self.env.now]=x.sentiment_about[enterprise] #if enterprise == 1: # enterprise2Status[x.id][self.env.now]=x.sentiment_about[enterprise] allEnterprisesEmotionList['enterprise'+str(enterprise)][x.id].update({self.env.now:x.sentiment_about[enterprise]}) def checkLimits(sentimentValue): if sentimentValue > 1: return 1 if sentimentValue < -1: return -1 ################################################ class SentimentCorrelationModel(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.outside_effects_prob = settings.outside_effects_prob self.anger_prob = settings.anger_prob self.joy_prob = settings.joy_prob self.sadness_prob = settings.sadness_prob self.disgust_prob = settings.disgust_prob self.time_awareness=[] for i in range(4): #En este modelo tenemos 4 sentimientos self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust networkStatus[self.id][self.env.now]=0 def run(self): while True: angry_neighbors_1_time_step=[] joyful_neighbors_1_time_step=[] sad_neighbors_1_time_step=[] disgusted_neighbors_1_time_step=[] angry_neighbors = self.get_neighboring_agents(state_id=1) for x in angry_neighbors: if x.time_awareness[0] > (self.env.now-500): angry_neighbors_1_time_step.append(x) num_neighbors_angry = len(angry_neighbors_1_time_step) joyful_neighbors = self.get_neighboring_agents(state_id=2) for x in joyful_neighbors: if x.time_awareness[1] > (self.env.now-500): joyful_neighbors_1_time_step.append(x) num_neighbors_joyful = len(joyful_neighbors_1_time_step) sad_neighbors = self.get_neighboring_agents(state_id=3) for x in sad_neighbors: if x.time_awareness[2] > (self.env.now-500): sad_neighbors_1_time_step.append(x) num_neighbors_sad = len(sad_neighbors_1_time_step) disgusted_neighbors = self.get_neighboring_agents(state_id=4) for x in disgusted_neighbors: if x.time_awareness[3] > (self.env.now-500): disgusted_neighbors_1_time_step.append(x) num_neighbors_disgusted = len(disgusted_neighbors_1_time_step) anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob) joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob) sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob) disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob) outside_effects_prob= settings.outside_effects_prob num = random.random() if(numanger_prob): myList.append(self.id) self.state['id'] = 2 networkStatus[self.id][self.env.now]=2 self.time_awareness[self.state['id']-1] = self.env.now elif (numjoy_prob+anger_prob): myList.append(self.id) self.state['id'] = 3 networkStatus[self.id][self.env.now]=3 self.time_awareness[self.state['id']-1] = self.env.now elif (numsadness_prob+anger_prob+joy_prob): myList.append(self.id) self.state['id'] = 4 networkStatus[self.id][self.env.now]=4 self.time_awareness[self.state['id']-1] = self.env.now yield self.env.timeout(settings.timeout) class BassModel(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.innovation_prob = settings.innovation_prob self.imitation_prob = settings.imitation_prob networkStatus[self.id][self.env.now]=0 def run(self): while True: #Outside effects if random.random() < settings.innovation_prob: if self.state['id'] == 0: self.state['id'] = 1 myList.append(self.id) networkStatus[self.id][self.env.now]=1 yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) #Imitation effects if self.state['id'] == 0: aware_neighbors = self.get_neighboring_agents(state_id=1) num_neighbors_aware = len(aware_neighbors) if random.random() < (settings.imitation_prob*num_neighbors_aware): myList.append(self.id) self.state['id'] = 1 networkStatus[self.id][self.env.now]=1 yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) class IndependentCascadeModel(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.innovation_prob = settings.innovation_prob self.imitation_prob = settings.imitation_prob self.time_awareness = 0 networkStatus[self.id][self.env.now]=0 def run(self): while True: aware_neighbors_1_time_step=[] #Outside effects if random.random() < settings.innovation_prob: if self.state['id'] == 0: self.state['id'] = 1 myList.append(self.id) networkStatus[self.id][self.env.now]=1 self.time_awareness = self.env.now #Para saber cuando se han contagiado yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) #Imitation effects if self.state['id'] == 0: aware_neighbors = self.get_neighboring_agents(state_id=1) for x in aware_neighbors: if x.time_awareness == (self.env.now-1): aware_neighbors_1_time_step.append(x) num_neighbors_aware = len(aware_neighbors_1_time_step) if random.random() < (settings.imitation_prob*num_neighbors_aware): myList.append(self.id) self.state['id'] = 1 networkStatus[self.id][self.env.now]=1 yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) class ZombieOutbreak(BaseNetworkAgent): def __init__(self, environment=None, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) self.bite_prob = settings.bite_prob networkStatus[self.id][self.env.now]=0 def run(self): while True: if random.random() < settings.heal_prob: if self.state['id'] == 1: self.zombify() yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) else: if self.state['id'] == 1: print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num)) networkStatus[self.id][self.env.now]=0 if self.id in myList: myList.remove(self.id) self.state['id'] = 0 yield self.env.timeout(settings.timeout) else: yield self.env.timeout(settings.timeout) def zombify(self): normal_neighbors = self.get_neighboring_agents(state_id=0) for neighbor in normal_neighbors: if random.random() < self.bite_prob: print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id)) neighbor.state['id'] = 1 # zombie myList.append(neighbor.id) networkStatus[self.id][self.env.now]=1 networkStatus[neighbor.id][self.env.now]=1 print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t') break ############## # Simulation # ############## sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel, max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0) sim.run_simulation() ########### # Results # ########### myList = sorted(myList, key=int) #print("Los zombies son: " + str(myList)) trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0) status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()] ################# # Visualization # ################# # print("Empresa1") # print (enterprise1Status) # print("Empresa2") # print (enterprise2Status) for y in allEnterprisesEmotionList: for x in range(0, settings.number_of_nodes): emotionStatusAux=[] enterpriseStatus = allEnterprisesEmotionList[y] for tiempo in enterpriseStatus[x]: if tiempo != 'id': prec = 2 output = math.floor(enterpriseStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo emotionStatusAux.append((output,tiempo,None)) keyword = 'enterprise'+str(y)+'Emotion' G.add_node(x, keyword = emotionStatusAux) # for x in range(0, settings.number_of_nodes): # emotionStatusAux2=[] # for tiempo in enterprise2Status[x]: # if tiempo != 'id': # prec = 2 # output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo # emotionStatusAux2.append((output,tiempo,None)) # G.add_node(x, enterprise2emotion= emotionStatusAux2) print("Done!") #lista = nx.nodes(G) #print('Nodos: ' + str(lista)) # for x in range(0, settings.number_of_nodes): # networkStatusAux=[] # for tiempo in networkStatus[x]: # if tiempo != 'id': # networkStatusAux.append((networkStatus[x][tiempo],tiempo,None)) # G.add_node(x, status= networkStatusAux) #print(networkStatus) print(allEnterprisesEmotionList) with open('data.txt', 'w') as outfile: json.dump(allEnterprisesEmotionList, outfile) nx.write_gexf(G,"test.gexf", version="1.2draft") plt.plot(status_census) plt.draw() # pyplot draw() plt.savefig("status.png") #print(networkStatus) #nx.draw(G) #plt.show() #plt.savefig("path.png")