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https://github.com/gsi-upm/soil
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Se genera un JSON global dinamico segun el numero de empresas
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@ -21,8 +21,8 @@ def init():
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global tweet_probability_enterprises
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network_type=1
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number_of_nodes=50
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max_time=10000
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number_of_nodes=20
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max_time=1000
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num_trials=1
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timeout=10
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103
soil.py
103
soil.py
@ -8,6 +8,7 @@ import numpy as np
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import networkx as nx
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import settings
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import math
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import json
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settings.init() # Loads all the data from settings
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@ -33,12 +34,20 @@ networkStatus=[] # This list will contain the status of every node of the networ
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emotionStatus=[]
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enterprise1Status=[]
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enterprise2Status=[]
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allEnterprisesEmotionList = {}
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for x in range(0, settings.number_of_nodes):
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networkStatus.append({'id':x})
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emotionStatus.append({'id':x})
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enterprise1Status.append({'id':x})
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enterprise2Status.append({'id':x})
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for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
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allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)] = enterprise1Status
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# for node in range(0, settings.number_of_nodes):
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# allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)].update({'id':node})
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#print(allEnterprisesEmotionList)
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# Initialize agent states. Let's assume everyone is normal.
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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
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@ -53,30 +62,35 @@ init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys
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class BigMarketModel(BaseNetworkAgent):
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number_of_enterprises = 0
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.time_awareness = 0
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self.type = ""
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number_of_enterprises = len(settings.tweet_probability_about)
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self.number_of_enterprises = len(settings.tweet_probability_about)
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if self.id < number_of_enterprises: #Empresas
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if self.id < self.number_of_enterprises: #Empresas
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self.state['id']=self.id
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self.type="Enterprise"
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self.tweet_probability = settings.tweet_probability_enterprises[self.id]
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else: #Usuarios normales
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self.state['id']=number_of_enterprises
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self.state['id']=self.number_of_enterprises
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self.type="User"
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self.tweet_probability = settings.tweet_probability_users
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self.tweet_relevant_probability = settings.tweet_relevant_probability
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self.tweet_probability_about = settings.tweet_probability_about #Lista
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self.sentiment_about = settings.sentiment_about #Lista
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#Inicializacion de visualizacion
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for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
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allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)][self.id].update({0:self.sentiment_about[enterpriseIndex]})
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#print(allEnterprisesEmotionList)
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#networkStatus[self.id][self.env.now]=self.state['id']
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#emotionStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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if(self.id < number_of_enterprises): # Empresa
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if(self.id < self.number_of_enterprises): # Empresa
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self.enterpriseBehaviour()
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else: # Usuario
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self.userBehaviour()
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@ -88,7 +102,7 @@ class BigMarketModel(BaseNetworkAgent):
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: #Twittea
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aware_neighbors = self.get_neighboring_agents(state_id=number_of_enterprises) #Nodos vecinos usuarios
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
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for x in aware_neighbors:
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if random.uniform(0,10) < 5:
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x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
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@ -103,12 +117,23 @@ class BigMarketModel(BaseNetworkAgent):
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#Visualización
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#if self.id < number_of_enterprises:
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enterpriseEmotion=[]
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if self.id < self.number_of_enterprises:
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#try:
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#enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
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#print (enterpriseEmotion)
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#except IndexError: # Si no existe la inicializamos
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# for y in range(0, settings.number_of_nodes):
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# enterpriseEmotion.append({'id':y})
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#enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[self.id]
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#allEnterprisesEmotionList.insert(self.id,enterpriseEmotion) #Guardamos el valor
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#enterpriseEmotion[:] = [] #Vaciamos la lista
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allEnterprisesEmotionList['enterprise'+str(self.id)][x.id].update({self.env.now:x.sentiment_about[self.id]})
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if self.id == 0:
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enterprise1Status[x.id][self.env.now]=x.sentiment_about[self.id]
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if self.id == 1:
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enterprise2Status[x.id][self.env.now]=x.sentiment_about[self.id]
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#if self.id == 0:
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# enterprise1Status[x.id][self.env.now]=x.sentiment_about[self.id]
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#if self.id == 1:
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# enterprise2Status[x.id][self.env.now]=x.sentiment_about[self.id]
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@ -151,10 +176,21 @@ class BigMarketModel(BaseNetworkAgent):
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x.sentiment_about[enterprise] = -1
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#Visualización
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if enterprise == 0:
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enterprise1Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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if enterprise == 1:
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enterprise2Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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# enterpriseEmotion=[]
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# try:
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# enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
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# except IndexError: # Si no existe la inicializamos
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# for y in range(0, settings.number_of_nodes):
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# enterpriseEmotion.append({'id':y})
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# enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[enterprise]
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# allEnterprisesEmotionList.insert(enterprise,enterpriseEmotion) #Guardamos el valor
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# enterpriseEmotion[:] = [] #Vaciamos la lista
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#if enterprise == 0:
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# enterprise1Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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#if enterprise == 1:
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# enterprise2Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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allEnterprisesEmotionList['enterprise'+str(enterprise)][x.id].update({self.env.now:x.sentiment_about[enterprise]})
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def checkLimits(sentimentValue):
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@ -408,24 +444,26 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
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# print (enterprise1Status)
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# print("Empresa2")
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# print (enterprise2Status)
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for y in allEnterprisesEmotionList:
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for x in range(0, settings.number_of_nodes):
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emotionStatusAux=[]
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enterpriseStatus = allEnterprisesEmotionList[y]
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for tiempo in enterpriseStatus[x]:
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if tiempo != 'id':
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prec = 2
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output = math.floor(enterpriseStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux.append((output,tiempo,None))
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keyword = 'enterprise'+str(y)+'Emotion'
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G.add_node(x, keyword = emotionStatusAux)
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for x in range(0, settings.number_of_nodes):
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emotionStatusAux=[]
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for tiempo in enterprise1Status[x]:
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if tiempo != 'id':
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prec = 2
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output = math.floor(enterprise1Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux.append((output,tiempo,None))
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G.add_node(x, enterprise1emotion= emotionStatusAux)
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for x in range(0, settings.number_of_nodes):
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emotionStatusAux2=[]
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for tiempo in enterprise2Status[x]:
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if tiempo != 'id':
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prec = 2
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output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux2.append((output,tiempo,None))
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G.add_node(x, enterprise2emotion= emotionStatusAux2)
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# for x in range(0, settings.number_of_nodes):
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# emotionStatusAux2=[]
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# for tiempo in enterprise2Status[x]:
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# if tiempo != 'id':
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# prec = 2
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# output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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# emotionStatusAux2.append((output,tiempo,None))
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# G.add_node(x, enterprise2emotion= emotionStatusAux2)
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print("Done!")
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@ -439,6 +477,9 @@ print("Done!")
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# G.add_node(x, status= networkStatusAux)
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#print(networkStatus)
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print(allEnterprisesEmotionList)
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with open('data.txt', 'w') as outfile:
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json.dump(allEnterprisesEmotionList, outfile)
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nx.write_gexf(G,"test.gexf", version="1.2draft")
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plt.plot(status_census)
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