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Se genera un JSON global dinamico segun el numero de empresas

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JesusMSM 2016-03-04 13:02:11 +01:00
parent 745b68776f
commit 8e45fe9044
7 changed files with 784 additions and 18599 deletions
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__pycache__/settings.cpython-34.pyc
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settings.py
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@ -21,8 +21,8 @@ def init():
global tweet_probability_enterprises
network_type=1
number_of_nodes=50
max_time=10000
number_of_nodes=20
max_time=1000
num_trials=1
timeout=10

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soil.py
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@ -8,6 +8,7 @@ import numpy as np
import networkx as nx
import settings
import math
import json
settings.init() # Loads all the data from settings
@ -33,12 +34,20 @@ networkStatus=[] # This list will contain the status of every node of the networ
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
@ -53,30 +62,35 @@ init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys
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 = ""
number_of_enterprises = len(settings.tweet_probability_about)
self.number_of_enterprises = len(settings.tweet_probability_about)
if self.id < number_of_enterprises: #Empresas
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']=number_of_enterprises
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 < number_of_enterprises): # Empresa
if(self.id < self.number_of_enterprises): # Empresa
self.enterpriseBehaviour()
else: # Usuario
self.userBehaviour()
@ -88,7 +102,7 @@ class BigMarketModel(BaseNetworkAgent):
def enterpriseBehaviour(self):
if random.random()< self.tweet_probability: #Twittea
aware_neighbors = self.get_neighboring_agents(state_id=number_of_enterprises) #Nodos vecinos usuarios
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
@ -103,12 +117,23 @@ class BigMarketModel(BaseNetworkAgent):
#Visualización
#if self.id < number_of_enterprises:
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]
#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]
@ -151,10 +176,21 @@ class BigMarketModel(BaseNetworkAgent):
x.sentiment_about[enterprise] = -1
#Visualización
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]
# 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):
@ -408,24 +444,26 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
# 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):
emotionStatusAux=[]
for tiempo in enterprise1Status[x]:
if tiempo != 'id':
prec = 2
output = math.floor(enterprise1Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
emotionStatusAux.append((output,tiempo,None))
G.add_node(x, enterprise1emotion= 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)
# 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!")
@ -439,6 +477,9 @@ print("Done!")
# 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)

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