Visualizacion aparte, codigo reestructurado

clase_base.py

@@ -0,0 +1,74 @@
+import random
+import time
+
+settings = {
+    "empresas": ["BBVA", "Santander"]
+}
+
+class BaseNetworkAgent:
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.id = random.random()
+
+    @property
+    def env(self):
+        class T(object):
+            pass
+
+        temp = T()
+        temp.now = time.time()
+        return temp
+
+
+def agentes_a_json(agentes):
+    final = {}
+    for agente in agentes:
+        for stamp, attrs in self._attrs.items():
+            for a in attrs:
+                if a not in final:
+                   final[a] = {}
+                final[a][stamp] = attrs[a]
+    return final
+
+class ComportamientoBase(BaseNetworkAgent):
+    def __init__(self, *args, **kwargs):
+        self._attrs = {}
+
+    @property
+    def attrs(self):
+        now = self.env.now
+        if now not in self._attrs:
+            self._attrs[now] = {}
+        return self._attrs[now]
+
+    @attrs.setter
+    def attrs(self, value):
+        self._attrs[self.env.now] = value
+
+    def run(self):
+        while True:
+            self.step(self.env.now)
+            #yield self.env.timeout(settings.timeout)
+
+    def step(self, now):
+        pass
+
+    def a_json(self):
+        final = {}
+        for stamp, attrs in self._attrs.items():
+            for a in attrs:
+                if a not in final:
+                   final[a] = {}
+                final[a][stamp] = attrs[a]
+        return final
+
+class NuevoComportamiento(ComportamientoBase):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.empresas = settings["empresas"]
+
+    def step(self, now):
+        for i in self.empresas:
+            self.attrs['sentimiento_empresa_%s' % i] = random.random()

models.py

@@ -0,0 +1,342 @@
+from nxsim import NetworkSimulation
+from nxsim import BaseNetworkAgent
+from nxsim import BaseLoggingAgent
+from random import randint
+from pprint import pprint
+from matplotlib import pyplot as plt
+import random
+import numpy as np
+import networkx as nx
+import settings
+
+settings.init()
+
+####################
+# 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 #
+##############################
+def init():
+
+    global networkStatus
+    networkStatus={} # Dict that will contain the status of every agent in the network
+
+sentimentCorrelationNodeArray=[]
+for x in range(0, settings.number_of_nodes):
+    sentimentCorrelationNodeArray.append({'id':x})
+# 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
+
+
+####################
+# Available models #
+####################
+
+class ComportamientoBase(BaseNetworkAgent):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self._attrs = {}
+
+    @property
+    def attrs(self):
+        now = self.env.now
+        if now not in self._attrs:
+            self._attrs[now] = {}
+        return self._attrs[now]
+
+    @attrs.setter
+    def attrs(self, value):
+        self._attrs[self.env.now] = value
+
+    def run(self):
+        while True:
+            self.step(self.env.now)
+            yield self.env.timeout(settings.timeout)
+
+    def step(self, now):
+        networkStatus['agente_%s'% self.id] = self.a_json()
+
+    def a_json(self):
+        final = {}
+        for stamp, attrs in self._attrs.items():
+            for a in attrs:
+                if a not in final:
+                   final[a] = {}
+                final[a][stamp] = attrs[a]
+        return final
+
+class BigMarketModel(ComportamientoBase):
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.enterprises = settings.enterprises
+        self.type = ""
+        self.number_of_enterprises = len(settings.enterprises)
+
+        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
+
+    def step(self, now):
+
+        if(self.id < self.number_of_enterprises): # Empresa
+            self.enterpriseBehaviour()
+        else:  # Usuario
+            self.userBehaviour()
+
+        super().step(now)
+
+    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
+
+                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = 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(self.number_of_enterprises):
+                    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=self.number_of_enterprises) #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
+
+            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
+
+class SentimentCorrelationModel(ComportamientoBase):
+
+    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
+        sentimentCorrelationNodeArray[self.id][self.env.now]=0
+
+
+    def step(self, now):
+        self.behaviour()
+        super().step(now)
+
+    def behaviour(self):
+
+        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(num<outside_effects_prob):
+            self.state['id'] = random.randint(1,4)
+
+            sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
+            self.time_awareness[self.state['id']-1] = self.env.now
+            self.attrs['sentiment'] = self.state['id']
+
+
+
+        if(num<anger_prob):
+
+
+            self.state['id'] = 1
+            sentimentCorrelationNodeArray[self.id][self.env.now]=1
+            self.time_awareness[self.state['id']-1] = self.env.now
+        elif (num<joy_prob+anger_prob and num>anger_prob):
+
+
+            self.state['id'] = 2
+            sentimentCorrelationNodeArray[self.id][self.env.now]=2
+            self.time_awareness[self.state['id']-1] = self.env.now
+        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
+
+
+            self.state['id'] = 3
+            sentimentCorrelationNodeArray[self.id][self.env.now]=3
+            self.time_awareness[self.state['id']-1] = self.env.now
+        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
+
+
+            self.state['id'] = 4
+            sentimentCorrelationNodeArray[self.id][self.env.now]=4
+            self.time_awareness[self.state['id']-1] = self.env.now
+
+        self.attrs['sentiment'] = self.state['id']
+
+
+class BassModel(ComportamientoBase):
+    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
+        sentimentCorrelationNodeArray[self.id][self.env.now]=0
+
+    def step(self, now):
+        self.behaviour()
+        super().step(now)
+
+    def behaviour(self):
+        #Outside effects
+        if random.random() < settings.innovation_prob:
+            if self.state['id'] == 0:
+                self.state['id'] = 1
+                sentimentCorrelationNodeArray[self.id][self.env.now]=1
+            else:
+                pass
+
+            self.attrs['status'] = self.state['id']
+            return
+
+        #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):
+                self.state['id'] = 1
+                sentimentCorrelationNodeArray[self.id][self.env.now]=1
+
+            else:
+                pass
+            self.attrs['status'] = self.state['id']
+
+
+class IndependentCascadeModel(ComportamientoBase):
+    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
+        sentimentCorrelationNodeArray[self.id][self.env.now]=0
+
+    def step(self,now):
+        self.behaviour()
+        super().step(now)
+
+    def behaviour(self):
+        aware_neighbors_1_time_step=[]
+            #Outside effects
+        if random.random() < settings.innovation_prob:
+            if self.state['id'] == 0:
+                self.state['id'] = 1
+                sentimentCorrelationNodeArray[self.id][self.env.now]=1
+                self.time_awareness = self.env.now #Para saber cuando se han contagiado
+
+            else:
+                pass
+
+            self.attrs['status'] = self.state['id']
+            return
+
+        #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):
+                self.state['id'] = 1
+                sentimentCorrelationNodeArray[self.id][self.env.now]=1
+            else:
+                pass
+
+            self.attrs['status'] = self.state['id']
+            return

settings.py

@@ -19,34 +19,37 @@ def init():
     global tweet_probability_about
     global sentiment_about
     global tweet_probability_enterprises
+    global enterprises
 
     network_type=1
-    number_of_nodes=20
-    max_time=1000
+    number_of_nodes=50
+    max_time=500
     num_trials=1
-    timeout=10
+    timeout=1
 
     #Zombie model
     bite_prob=0.01 # 0-1
     heal_prob=0.01 # 0-1
 
     #Bass model
-    innovation_prob=0.01
-    imitation_prob=0.01
+    innovation_prob=0.001
+    imitation_prob=0.005
 
     #Sentiment Correlation model
     outside_effects_prob = 0.2
-    anger_prob = 0.08
+    anger_prob = 0.06
     joy_prob = 0.05
     sadness_prob = 0.02
     disgust_prob = 0.02
 
     #Big Market model
+    ##Names
+    enterprises = ["BBVA","Santander", "Bankia"]
     ##Users
     tweet_probability_users = 0.44
     tweet_relevant_probability = 0.25
-    tweet_probability_about = [0.25, 0.25]
-    sentiment_about = [0, 0] #Valores por defecto
+    tweet_probability_about = [0.15, 0.15, 0.15]
+    sentiment_about = [0, 0, 0] #Valores por defecto
     ##Enterprises
-    tweet_probability_enterprises = [0.3, 0.3]
+    tweet_probability_enterprises = [0.3, 0.3, 0.3]
 

soil.py

@@ -1,16 +1,18 @@
+#from clase_base import *
+from models import *
 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 models
 import math
 import json
 
 settings.init() # Loads all the data from settings
+models.init() # Loads the models and network variables
 
 ####################
 # Network creation #
@@ -24,397 +26,6 @@ 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(num<outside_effects_prob):
-                self.state['id'] = random.randint(1,4)
-                myList.append(self.id)
-                networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
-                self.time_awareness[self.state['id']-1] = self.env.now
-                yield self.env.timeout(settings.timeout)
-
-
-            if(num<anger_prob):
-
-                myList.append(self.id)
-                self.state['id'] = 1
-                networkStatus[self.id][self.env.now]=1
-                self.time_awareness[self.state['id']-1] = self.env.now
-            elif (num<joy_prob+anger_prob and num>anger_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 (num<sadness_prob+anger_prob+joy_prob and num>joy_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 (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_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 #
 ##############
@@ -429,8 +40,6 @@ 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()]
@@ -440,53 +49,23 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
 # Visualization #
 #################
 
-# print("Empresa1")
-# print (enterprise1Status)
-# print("Empresa2")
-# print (enterprise2Status)
-for y in allEnterprisesEmotionList:
-    for x in range(0, settings.number_of_nodes):
+
+for x in range(0, settings.number_of_nodes):
+    for empresa in models.networkStatus["agente_%s"%x]:
         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 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)
 
-# 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)
+    json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
 
 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")