SimPy Manual

Defining a process

A process is a class that that inherits from the class Process. For example here is the header of the definition of a new Message process class:

• class Message(Process):

The user must define two particular methods and may define any others.

• __init__(self,...), where ... indicates method arguments. This function initializes the Process object, setting values for any attributes. The first line of this method must be a call to the Class __init__() in the form: Process.__init__(self,name='a_process')

  Then other commands can be used to initialize attributes of the object. The __init__() method is called automatically when a new message is created.

  In this example of an __init__() method for a Message class we give each new message an integer identification number, i, and message length, len as instance variables:

  def __init__(self,i,len):
      Process.__init__(self,name='Message'+str(i))
      self.i = i
      self.len = len
  

• A process execution method (PEM) This describes the actions of the process object and must contain at least one of the yield statements, described later, to make it a Python generator function. It can have arguments. Typically this can be called execute() or run() but any name may be chosen.

  The execution method starts when the process is activated and the simulate(until=...) statement has been called.

  In this example of the process execution method for the same Message class, the message prints out the current time, its identification number and the word 'Starting'. After a simulated delay it then announces it has 'Arrived':

  def go(self):
      print now(), self.i, 'Starting'
      yield hold,self,100.0
      print now(), self.i, 'Arrived'
  

A Process must be activated in order to start it operating (see Starting and stopping SimPy Processes)

Following is a complete, runnable, SimPy script. We declare a Message class and define __init__() and go() methods for it. Two messages, p1 and p2 are created. We do not actually use the len attribute in this example. p1 and p2 are activated to start at simulation times 0.0 and 6.0, respectively. Nothing happens until the simulate(until=200) statement. When they have both finished (at time 6.0+100.0=106.0) there will be no more events so the simulation will stop at that time:

from __future__ import generators
from SimPy.Simulation import *

class Message(Process):
   """ a simple Process """
   def __init__(self,i,len):
       Process.__init__(self,name='Message'+str(i))
       self.i = i
       self.len = len

   def go(self):
       print now(), self.i, 'Starting'
       yield hold,self,100.0
       print now(), self.i, 'Arrived'

initialize()
p1  = Message(1,203)
activate(p1,p1.go())
p2  = Message(2,33)
activate(p2,p2.go(),at=6.0)
simulate(until=200)
print now() # will print 106.0

Elapsing time in a Process

An execution method can cause time to elapse for a process using the yield hold command:

• yield hold,self,t causes the object to wait for a delay of t time units (unless it is interrupted). It then continues its operation with the next statement. During the hold the object is suspended.
• yield passivate,self suspends the process's operations indefinitely.

This example of an execution method (buy) for a Customer class demonstrates that the method can have arguments which can be used in the activation. The Customer also has an identification attribute id. The yield hold is executed 4 times:

def buy(self,budget=0):
   print 'Here I am at the shops ',self.id
   t = 5.0
   for i in range(4):
       yield hold,self,t
       print 'I just bought something ',self.id
       budget -= 10.00
   print   'All I have left is ', budget,\
           ' I am going home ',self.id,

initialize()
C = Customer(1)
activate(C,C.buy(budget=100),at=10.0)
simulate(until=100.0)

Starting and stopping SimPy Processes

Once a Process object has been created, it is 'passive', i.e., it has no event scheduled. It must be activated to start the process execution method:

• activate(p,p.PEM(args)[,at=t][,delay=period][,prior=boolean]) will activate the execution method p.PEM() of Process instance, p with arguments args. The default action is to activate at the current time, otherwise one of the optional timing clauses operate. If prior is true, the process will be activated before any others at the specified time in the event list.

The process can be suspended and reactivated:

• yield passivate,self suspends the process itself. It becomes 'passive'.
• reactivate(p,at=t,delay=period,prior=boolean) will reactivate a passive process, p. It becomes 'active'. The optional timing clauses work as for activate. A process cannot reactivate itself. If that is required, use yield hold,self, . . . instead.
• self.cancel(p) deletes all scheduled future events for process p. Only 'active' processes can be cancelled. A process cannot cancel itself. If that is required, use yield passivate,self instead. Note: This new format replaces the p.cancel() form of earlier SimPy versions.

When all statements in a process execution method have been completed, a process becomes 'terminated'. If the instance is still referenced, it becomes just a data container. Otherwise, it is automatically destroyed.

And, finally,

• stopSimulation() stops all simulation activity, even if some processes still have events scheduled.

Asynchronous interruptions

One process (the interrupter) can interrupt another, active, process (the victim). A process cannot interrupt itself.

• self.interrupt(victim)

The interrupt is just a signal. After this statement, the interrupter continues its current method.

The victim must be active. An active process is one that has an event scheduled for it (that is, it is 'executing' a yield hold,self,t). If the victim is not active (that is it is either passive or terminated) the interrupt has no effect on it. As processes queuing for resources are passive, they cannot be interrupted. Active processes which have acquired a resource can be interrupted.

If interrupted, the victim returns from its yield hold prematurely. It can sense if it has been interrupted by calling

• self.interrupted() which returns True if it has been interrupted. It can then either continue in the current activity or switch to an alternative, making sure it tidies up the current state, such as releasing any resources it owns. When this is True:
  • self.interruptCause is a reference to the interrupter instance.
  • self.interruptLeft gives the time remaining in the interrupted yield hold,

The interruption is reset at the victim's next call to a yield hold,. It can also be reset by calling

• self.interruptReset()

Here is a complete example of a simulation with interrupts. A bus is subject to breakdowns which are modelled as interruptions. Notice that in the first yield hold, interrupts may occur, so a reaction to the interrupt (= repair) has been programmed:

class Bus(Process):
  def __init__(self,name):
     Process.__init__(self,name)

  def operate(self,repairduration,triplength):  # process execution method
     tripleft = triplength
     while tripleft > 0:
        yield hold,self,tripleft                # try to get through trip
        if self.interrupted():
              print self.interruptCause.name, "at %s" %now() # breakdown
              tripleft=self.interruptLeft       # yes; time to drive 
              self.interruptReset()             # end interrupt state
              reactivate(br,delay=repairduration) # delay any breakdowns 
              yield hold,self,repairduration
              print "Bus repaired at %s" %now()
        else:
              break                             # no breakdown, bus arrived
        print "Bus has arrived at %s" %now()

class Breakdown(Process):
   def __init__(self,myBus):
       Process.__init__(self,name="Breakdown "+myBus.name)
       self.bus=myBus

   def breakBus(self,interval):                 # process execution method 
       while True:
          yield hold,self,interval
          if self.bus.terminated(): break
          self.interrupt(self.bus)

initialize()
b=Bus("Bus")
activate(b,b.operate(repairduration=20,triplength=1000))
br=Breakdown(b)
activate(br,br.breakBus(300))
print simulate(until=4000)

The ouput from this example:

Breakdown Bus at 300
Bus repaired at 320
Breakdown Bus at 620
Bus repaired at 640
Breakdown Bus at 940
Bus repaired at 960
Bus has arrived at 1060
SimPy: No more events at time 1260

Where interrupts can occur, the process which may be the victim of interrupts must test for interrupt occurrence after every "yield hold" and react to it. If a process holds a resource when it gets interrupted, it continues holding the resource.

Starting the simulation

Even activated processes will not start until the following statement has been executed:

• simulate(until=T) starts the simulation going and it will continue until time T unless it runs out of events to execute or the command stopSimulation() is executed.

A complete SimPy script

This complete runnable script simulates a firework with a time fuse. I have put in a few extra yield hold commands for added suspense:

from __future__ import generators
from SimPy.Simulation import *

class Firework(Process):
   def __init__(self):
       Process.__init__(self)

   def execute(self):
       print now(), ' firework activated'
       yield hold,self, 10.0
       for i in range(10):
           yield hold,self,1.0
           print now(),  ' tick'
       yield hold,self,10.0
       print now(), ' Boom!!'

initialize()
f = Firework()
activate(f,f.execute(),at=0.0)
simulate(until=100)

The output from Example . No formatting of the output was attempted so it looks a bit ragged:

0.0  firework activated
11.0  tick
12.0  tick
13.0  tick
14.0  tick
15.0  tick
16.0  tick
17.0  tick
18.0  tick
19.0  tick
20.0  tick
30.0  Boom!!

A source fragment

One useful program pattern is the source. This is an process with an execution method that generates events or activates other processes as a sequence -- it is a source of other processes. Random arrivals can be modelled using random (exponential) intervals between activations.

The following example is of a source which activates a series of customers to arrive at regular intervals of 10.0 units of time. The sequence continues until the simulation time exceeds the specified finishTime. (Of course, to achieve random'' arrivals of *customer*s the *yield hold method should use an exponential random variate instead of, as here, a constant 10.0 value) The example assumes that the Customer class has been defined with a PEM called run:

class Source(Process):
   def __init__(self,finish): 
       Process.__init__(self)
       self.finishTime = finish

   def execute(self):
      while now() < self.finishTime:
         c = Customer()          ## new customer
         activate(c,c.run())     ## activate it now
         print now(), ' customer'
         yield hold,self,10.0

initialize()
g = Source(33.0)
activate(g,g.execute(),at=0.0)   ## start the source
simulate(until=100)

Requesting resources with priority

If a Resource, r is defined with priority queueing (that is qType==PriorityQ) a request can be made for a unit by:

• yield request,self,r,priority requests a unit with priority. priority is real or integer. Larger values of priority represent higher priorities and these will go to the head of the r.waitQ if there not enough units immediately.

An example of a complete script where priorities are used. Four clients with different priorities request a resource unit from a server at the same time. They get the resource in the order set by their relative priorities:

from __future__ import generators
from SimPy.Simulation import *
class Client(Process):
    inClients=[]
    outClients=[]

    def __init__(self,name):
       Process.__init__(self,name)

    def getserved(self,servtime,priority,myServer):
        Client.inClients.append(self.name)
        print self.name, 'requests 1 unit at t=',now()
        yield request, self, myServer, priority
        yield hold, self, servtime
        yield release, self,myServer
        print self.name,'done at t=',now()
        Client.outClients.append(self.name)

initialize()
server=Resource(capacity=1,qType=PriorityQ)
c1=Client(name='c1') ; c2=Client(name='c2')
c3=Client(name='c3') ; c4=Client(name='c4')
activate(c1,c1.getserved(servtime=100,priority=1,myServer=server))
activate(c2,c2.getserved(servtime=100,priority=2,myServer=server))
activate(c3,c3.getserved(servtime=100,priority=3,myServer=server))
activate(c4,c4.getserved(servtime=100,priority=4,myServer=server))
simulate(until=500)
print 'Request order: ',Client.inClients
print 'Service order: ',Client.outClients

This program results in the following output:

c1 requests 1 unit at t= 0
c2 requests 1 unit at t= 0
c3 requests 1 unit at t= 0
c4 requests 1 unit at t= 0
c1 done at t= 100
c4 done at t= 200
c3 done at t= 300
c2 done at t= 400
Request order:  ['c1', 'c2', 'c3', 'c4']
Service order:  ['c1', 'c4', 'c3', 'c2']

Requesting a resource with preemptive priority

In some models, higher priority processes can preempt lower priority processes when all resource units have been allocated. A resource with preemption can be created by setting arguments qType==PriorityQ and preemptable non-zero.

When a process requests a unit of resource and all units are in use it can preempt a lower priority process holding a resource unit. If there are several processes already active (that is, in the activeQ), the one with the lowest priority is suspended, put at the front of the waitQ and the preempting process gets its resource unit and is put into the activeQ. The preempted process is the next one to get a resource unit (unless another preemption occurs). The time for which the preempted process had the resource unit is taken into account when the process gets into the activeQ again. Thus, the total hold time is always the same, regardless of whether or not a process gets preempted.

An example of a complete script. Two clients of different priority compete for the same resource unit:

from __future__ import generators
from SimPy.Simulation import *
class Client(Process):
    def __init__(self,name):
       Process.__init__(self,name)

    def getserved(self,servtime,priority,myServer):
        print self.name, 'requests 1 unit at t=',now()
        yield request, self, myServer, priority
        yield hold, self, servtime
        yield release, self,myServer
        print self.name,'done at t=',now()

initialize()
server=Resource(capacity=1,qType=PriorityQ,preemptable=1)
c1=Client(name='c1')
c2=Client(name='c2')
activate(c1,c1.getserved(servtime=100,priority=1,myServer=server),at=0)
activate(c2,c2.getserved(servtime=100,priority=9,myServer=server),at=50)
simulate(until=500)

The output from this program is:

c1 requests 1 unit at t= 0
c2 requests 1 unit at t= 50
c2 done at t= 150
c1 done at t= 200

Here, c2 preempted c1 at t=50. At that time, c1 had held the resource for 50 of the total of 100 time units. c1 got the resource back when c2 completed at t=150.

Monitoring a resource

If monitored is set True for a resource, r, the length of the waiting queue, len(r.waitQ) and the active queue,*len(r.activeQ)* are Both monitored automatically (see Monitors, below). This solves a problem, particularly for the waiting queue which cannot be monitored externally to the resource. The monitors are called r.waitMon and r.actMon, respectively. Complete time series for both queue lengths are maintained so that statistics, such as the time average can be found.

In this example, the resource, server is monitored and the time-average of each is calculated:

from SimPy.Simulation import *
class Client(Process):
     inClients=[]
     outClients=[]

     def __init__(self,name):
        Process.__init__(self,name)

     def getserved(self,servtime,myServer):
         print self.name, 'requests 1 unit at t=',now()
         yield request, self, myServer
         yield hold, self, servtime
         yield release, self,myServer
         print self.name,'done at t=',now()

initialize()
server=Resource(capacity=1,monitored=True)
c1=Client(name='c1') ; c2=Client(name='c2')
c3=Client(name='c3') ; c4=Client(name='c4')
activate(c1,c1.getserved(servtime=100,myServer=server))
activate(c2,c2.getserved(servtime=100,myServer=server))
activate(c3,c3.getserved(servtime=100,myServer=server))
activate(c4,c4.getserved(servtime=100,myServer=server))
simulate(until=500)
print 'Average waiting',server.waitMon.timeAverage()
print 'Average in service',server.actMon.timeAverage()

The output from this program is:

c1 requests 1 unit at t= 0
c2 requests 1 unit at t= 0
c3 requests 1 unit at t= 0
c4 requests 1 unit at t= 0
c1 done at t= 100
c2 done at t= 200
c3 done at t= 300
c4 done at t= 400
Average waiting 1.5
Average in service 1.0

A1. SimPy Error Messages

A2. SimPy Process States

From the point of the model builder, at any time, a SimPy process, p, can be in one of the following states:

• Active: Waiting for a scheduled event. This state simulates an activity in the model. Simulated time passes in this state. The process state p.active() returns True.
• Passive: Not active or terminated. Awaiting (re-)activation by another process. This state simulates a real world process which has not finished and is waiting for some trigger to continue. Does not change simulation time. p.passive() returns True.
• Terminated: The process has executed all its action statements and continues as a data instance, if referenced. p.terminated() returns True

Initially (upon creation of the Process instance), a process returns passive.

In addition, a SimPy process, p, can be in the following (sub)states:

• Interrupted: Active process has been interrupted by another

  process. It can immediately respond to the interrupt. This simulates an interruption of a simulated activity before its scheduled completion time. p.interrupted() returns True.

• Queuing: Active process has requested a busy resource and is waiting (passive) to be reactivated upon resource availability. p.queuing(a_resource) returns True.

A3. SimPlot, The Simpy plotting utility

SimPlot provides an easy way to graph the results of simulation runs.

A4. SimGUI, The Simpy Graphical User Interface

SimGUI provides a way for users to interact with a SimPy program, changing its parameters and examining the output.

A5. SimulationTrace, the SimPy tracing utility

SimulationTrace has been developed to give users insight into the dynamics of the execution of SimPy simulation programs. It can help developers with testing and users with explaining SimPy models to themselves and others (e.g. for documentation or teaching purposes).

A6. SimulationStep, the SimPy event stepping utility

SimulationStep can assist with debugging models, interacting with them on an event-by-event basis, getting event-by-event output from a model (e.g. for plotting purposes), etc.

It caters for:

• running a simulation model, with calling a user-defined procedure after every event,
• running a simulation model one event at a time by repeated calls,
• starting and stopping the event stepping mode under program control.

A7. SimulationRT, a real-time synchronizing utility

SimulationRT allows synchronizing simulation time and real (wallclock) time. This capability can be used to implement e.g. interactive game applications or to demonstrate a model's execution in real time.