def importAgents(self):
'''
Function to import agents from a file previously prepared
'''
print '\n|- AGENTS IMPORT PROCESS...'
# file name
tmpFileName = os.path.join(self.simPath, 'initAgents.csv')
try:
fileFID = open(tmpFileName, 'r')
except:
print "Technology file has not been found: ", tmpFileName;
sys.exit(1)
# read file
agents = fileFID.readlines()
# for each Agent
for a in agents:
if a[0] != '#':
tmpID, tmpX, tmpY, tmpEN, tmpHA, tmpSolPot, tmpCO2, tmpSocialLobby, tmpintCap, \
tmpEquity, tmpBalance, tmpMbalance, tmpInvL, tmpHealth, tmpAge = a.split()
# Insert Agent
self.allAgents.append(
ag.agents(self.debugLevel, int(tmpID), float(tmpX), float(tmpY), float(tmpEN), float(tmpHA),
float(tmpSocialLobby), \
float(tmpSolPot), float(tmpEquity), float(tmpintCap), int(tmpInvL), float(tmpHealth), 55,
int(tmpAge), None, None, None, None, \
float(tmpBalance), float(tmpMbalance), None, None, None, None, None))
fileFID.close()
print ' |- done...'
def createPopulation(self):
'''
Function to create the population
To assign the social lobby parameter (1-sociallobby is the committiment) the beta distribution is used.
'''
# if agentCreationMethod is equal to 0 random population is created otherwise it is uploaded from file
if self.agentCreationMethod == 0:
sl = []
for i in range(0, self.Nagents):
if self.H == 1:
tmprndHealth = (0.1 * np.random.randn(1)) + 0.5
if tmprndHealth > 1: tmprndHealth = 1
if tmprndHealth < 0: tmprndHealth = 0
else:
tmprndHealth = 0.5
# Accordind to parameters alpha and beta, the social lobby is provided
socialLobby = np.random.beta(self.socialLobby_betadist_alpha, self.socialLobby_betadist_beta)
self.allAgents.append(
ag.agents(self.debugLevel, i, ran.uniform(0, self.xMaxPos), ran.uniform(0, self.yMaxPos), \
ran.randint(self.minNrgDim, self.maxNrgDim), ran.uniform(0, self.agroMaxDim), \
socialLobby, ran.uniform(self.minIrradiation, 1), self.intRiskRate, \
self.ratioInternalCapital, self.invLength, tmprndHealth))
else:
self.importAgents()
self.genDistanceLists()
# self.promptAgents()
# Compute the overall energy need of the system
self.computeTotEnergyNeed()
