Question

This is a bank simulation that takes into account 20 different serving lines with a single queue, customers arrive following an exponential rate and they are served during a time that follows a normal probability distribution with mean 40 and standard deviation 20.

Things were working just fine till I decided to exclude the negative values given by the normal distribution using this method:

def getNormal(self):

    normal = normalvariate(40,20)

    if (normal>=1):
        return normal
    else:
        getNormal(self)

Am I screwing up the recursive call? I don’t get why it wouldn’t work. I have changed the getNormal() method to:

def getNormal(self):

    normal = normalvariate(40,20)

    while (normal <=1):
        normal = normalvariate (40,20)

    return normal

But I’m curious on why the previous recursive statement gets busted.

This is the complete source code, in case you’re interested.

""" bank21: One counter with impatient customers """
from SimPy.SimulationTrace import *
from random import *

## Model components ------------------------



class Source(Process):
    """ Source generates customers randomly """

    def generate(self,number):       
        for i in range(number):

            c = Customer(name = "Customer%02d"%(i,))

            activate(c,c.visit(tiempoDeUso=15.0))


            validateTime=now()
            if validateTime<=600:
                interval = getLambda(self)
                t = expovariate(interval)

                yield hold,self,t #esta es la rata de generación
            else:
                detenerGeneracion=999
                yield hold,self,detenerGeneracion



class Customer(Process):
    """ Customer arrives, is served and  leaves """

    def visit(self,tiempoDeUso=0):       
        arrive = now()       # arrival time                     
        print "%8.3f %s: Here I am     "%(now(),self.name)

        yield (request,self,counter),(hold,self,maxWaitTime)    
        wait = now()-arrive  # waiting time                     
        if self.acquired(counter):                              
            print "%8.3f %s: Waited %6.3f"%(now(),self.name,wait)

            tiempoDeUso=getNormal(self)
            yield hold,self,tiempoDeUso
            yield release,self,counter                          
            print "%8.3f %s: Completed"%(now(),self.name)
        else:
            print "%8.3f %s: Waited %6.3f. I am off"%(now(),self.name,wait)

## Experiment data -------------------------

maxTime = 60*10.5  # minutes                                 
maxWaitTime = 12.0 # minutes. maximum time to wait              

## Model  ----------------------------------

def model():                                                    
    global counter                                              
    #seed(98989)
    counter = Resource(name="Las maquinas",capacity=20)                            
    initialize()
    source = Source('Source')
    firstArrival= expovariate(20.0/60.0) #chequear el expovariate
    activate(source,
             source.generate(number=99999),at=firstArrival)   
    simulate(until=maxTime)                                     


def getNormal(self):

    normal = normalvariate(40,20)

    if (normal>=1):
        return normal
    else:
        getNormal(self)

def getLambda (self):

        actualTime=now()
        if (actualTime <=60):
            return 20.0/60.0
        if (actualTime>60)and (actualTime<=120):
            return 25.0/60.0
        if (actualTime>120)and (actualTime<=180):
            return 40.0/60.0
        if (actualTime>180)and (actualTime<=240):
            return 30.0/60.0
        if (actualTime>240)and (actualTime<=300):
            return 35.0/60.0
        if (actualTime>300)and (actualTime<=360):
            return 42.0/60.0
        if (actualTime>360)and (actualTime<=420):
            return 50.0/60.0
        if (actualTime>420)and (actualTime<=480):
            return 55.0/60.0
        if (actualTime>480)and (actualTime<=540):
            return 45.0/60.0
        if (actualTime>540)and (actualTime<=600):
            return 10.0/60.0
## Experiment  ----------------------------------
model()

Answer 1

I think you want

return getnormal(self)

instead of

getnormal(self)

If the function exits without hitting a return statement, then it returns the special value None, which is a NoneType object – that’s why Python complains about a ‘NoneType.’ The abs() function wants a number, and it doesn’t know what to do with a None.

Also, you could avoid recursion (and the cost of creating a new stack frame) by using

def getNormal(self):
    normal = 0
    while normal < 1:
        normal = normalvariate(40,20)
    return normal