# SimPy Cheatsheet

Authors: | Tony Vignaux <Vignaux@users.sourceforge.net>, Klaus Muller <Muller@users.sourceforge.net>
Date: | 2003-November-12
SimPy version: | 1.4
Web-site: | http://simpy.sourceforge.net/
Python-Version: | 2.2, 2.3

Contents

- SimPy Cheatsheet
  - Changes from Version 1.3
  - SimPy
  - Processes
  - Resources
  - Random variates
  - Monitors
  - SimPlot
  - Error Messages
  - Acknowledgments

## Changes from Version 1.3

There have been changes to Monitor. Old code should work but some old methods, such as tally and accum are now deprecated and replaced by observe(y,t)

## SimPy

This document briefly outlines the commands available in SimPy. It refers to SimPy version 1.4 or later. The facilities described require Python 2.2 or later. (When using Python 2.2, the following import statement must be used at the head of SimPy scripts: from __future__ import generators)

A SimPy model is made up of Processes, Resources and Monitors and operations on them.

Basic structure of a SimPy simulation:

- from SimPy.Simulation import * which imports all facilities for the simulation program.
- initialize() which sets up the simulation model
- ... the activation of at least one process....
- simulate(until=endtime) starts the simulation which will run until one of the following:
  - there are no more events to execute. now()==last event time
  - the simulation time reaches endtime. now()==endtime
  - the stopSimulation() command is executed. now()==stop time
- stopSimulation() will stop all simulation activity.
- now() always returns the current simulation time.

## Processes

Processes inherit from class Process, imported from SimPy.Simulation.

- class Pclass(Process): defines a new Process class (here, Pclass). Such a class must have at least these two methods:
  - __init__(self,..), the first line of which must be a call to the Class __init__ in the form: Process.__init__(self,name='a_process'). Other commands can be used to initialize attributes of the object.
  - An execution method, which may have arguments, describes the actions of a process object and must contain at least one of the yield statements to make it a Python generator function. The yield statements are:
    - yield hold,self,t to execute a time delay of length t (unless the process is interrupted, see below). The process continues at the statement following after a delay in simulated time.
    - yield passivate,self to suspend operations indefinitely.
    - yield request,self,r (see Resources, below)
    - yield request,self,rp,priority (see Resources, below)
    - yield release,self,r (see Resources, below)
- p = Pclass(..), constructs a new Pclass object, called, p, where the arguments are those specified in the Class's __init__ method.

### Starting and stopping SimPy Processes

By the process itself:

- yield passivate,self suspends the process itself.

By other processes:

- activate(p,p.execute(args),at=t,delay=period,prior=boolean) activates the execution method p.execute()* of Process p with arguments args. The default action is to activate at the current time, otherwise one of the optional timing clauses operate. If prior==True, the process will be activated before any others in the event list at the specified time.
- reactivate(p,at=t,delay=period,prior=boolean) will reactivate p after it has been passivated. The optional timing clauses work as for activate.
- self.cancel(p) deletes all scheduled future events for process p. Note: This new format replaces the p.cancel() form of earlier SimPy versions.

### Asynchronous interruptions

- self.interrupt(victim) interrupts another process. The interrupt is just a signal. After this statement, the interrupting process immediately continues its current method.

  The victim must be active to be interrupted (that is executing a yield hold,self,t) otherwise the interruption has no effect.

  The introduction of interrupts changes the semantics of yield hold. After before=now(); yield hold,self,T, we have the post-condition now()== before+T OR (self.interrupted() AND now()< before+T). The program must allow for this, i.e., for interrupted, incomplete activities.

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

- self.interrupted() which returns True if it has been interrupted. If so:

  - self.interruptCause gives the interruptor instance.
  - self.interruptLeft is the time remaining in the interrupted yield hold,

  The interruption is reset at the victims next call to a yield hold,. Alternatively it can be reset by calling

- self.interruptReset()

## Resources

The modeller may define Resources. These inherit from class Resource which is imported at the start of the program: from SimPy.Simulation import Resource

A Resource, r, is established using the command:

- r = Resource(capacity=1, name='a_resource', unitName='units', qType=FIFO, preemptable=0, monitored=False)

- capacity is the number of identical units of the resource available. Its default setting is 1 but can be any positive integer.
- name is the name by which the resource is known (eg gasStation)
- unitName is the name of a unit of the resource (eg pump)
- qType describes the queue discipling of the waiting queue of processes; typically, this is FIFO (First-in, First-out). and this is the default. An alternative is PriorityQ (see below)
- preemptable indicates, if it has a non-zero value, that a process being put into the PriorityQ may also pre-empt a lower-priority process already using a unit of the resource. This only has an effect when qType == PriorityQ (see below)
- monitored indicates if the number of processes in the resource's queues (see below) are to be monitored (see Monitors, below)

A Resource, r, has the following attributes:

- r.n The number of currently free units
- r.waitQ, a waiting queue (list) of processes (FIFO by default) The number of Proceeses waiting is len(r.waitQ)
- r.activeQ, a queue (list) of processes holding units,. The number of Proceeses in the active queue is len(r.activeQ)
- r.waitMon A Monitor recording the number in r.waitQ
- r.actMon A Monitor recording the number in r.activeQ

A unit of resource, r, can be requested and later released by a process using the following yield commands:

- yield request,self,r to request a unit of resource, r. The process may be temporarily queued and suspended until a unit is available.
- yield release,self,r releases a unit of r. This may have the side-effect of allocating the released unit to the next process in the Resource's waiting queue.

### 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, where 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.

### Requesting a resource with preemptive priority

If a Resource, r, is defined with priority queueing (that is qType=PriorityQ) and also preemption (that is preemptable=1) a request can be made for a unit by:

- yield request,self,r,priority, where priority is real or integer. Larger values of priority represent higher priorities and if there are not enough units available immediately, one of the active processes may be preempted.

If there are several lower priority processes, that with the lowest priority is suspended, put at the front of the waitQ and the higher priority, 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.

## Random variates

SimPy uses the standard random variate routines in the Python random module. To use them, import the random module:

- from random import Random
- g = Random([seed]) defines a random variable object g using a large integer, seed to initialize the sequence.

A good range of distributions is available. For example:

- g.random(), returns the next (uniform) random number between 0 and 1
- g.expovariate(lambd), returns a sample from the exponential distribution with mean 1.0/lambd.
- g.normalvariate(mu,sigma), returns a sample from the normal (Gaussian) distribution. mu is the mean, and sigma is the standard deviation.

## Monitors

Monitors are part of the SimPy package and can be imported by from SimPy.Simulation import Monitor

To define a new Monitor object:

- m=Monitor([name]), where name is the name of the monitor

    object, set to '' if it is missing.

Methods:

- m.observe(y [,t]) record the current value of y and time t (the current time, now(), if t is missing).
- m.reset([t]) reset the observations. The recorded time series is set to the empty list, [] and the starting time to t or, if it is missing, to the current simulation time, now().

Simple data summaries:

- m.series(), the recorded time series as a list of data pairs. Each pair, [t,y], records one observation and its time.
- m.yseries(), a list of the recorded data values.
- m.tseries(), a list of the recorded times.
- m.count() returns the current number of observations.
- m.total(), the sum of the y values
- m.mean(), the simple average of the observations.
- m.var(), the sample variance of the observations.
- m.timeAverage([t]), the time-average of the y values, calculated from time 0 (or the last time m.reset([t]) was called) to time t (the current simulation time if t is missing). It is assumed that y is continuous in time. See the right hand figure below)

- m.histogram(low=0.0,high=100.0,nbins=10) is a histogram object (a derived class of list) which contains the number of y values in each of its bins. It is calculated from the monitored y values.
- m.__str__(), a string that briefly describes the current state of the monitor.

Deprecated methods:

The following methods are retained for backwards compatibility but are not recommended. They mey be removed in future releases of SimPy:

- m.tally(y), records the current value of y and the current time, now().
- m.accum(y [,t]) records the current value of y and time t (the current time, now(), if t is missing).

## SimPlot

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

## Error Messages

### Advisory messages

These messages are returned by simulate(), as in message=simulate(until=123).

Upon a normal end of a simulation, simulate() returns the message:

- SimPy: Normal exit. This means that no errors have occurred and the simulation has run to the time specified by the until parameter.

The following messages, returned by simulate(), are produced at a premature termination of the simulation but allow continuation of the program.

- SimPy: No more events at time x. All processes were completed prior to the endtime given in simulate(until=endtime).
- SimPy: No activities scheduled. No activities were scheduled when simulate() was called.

### Fatal error messages

These messages are generated when SimPy-related fatal exceptions occur. They end the SimPy program. Fatal SimPy error messages are output to sysout.

- Fatal SimPy error: activating function which is not a generator (contains no 'yield'). A process tried to (re)activate a function which is not a SimPy process (=Python generator). SimPy processes must contain at least one yield . . . statement.
- Fatal SimPy error: Simulation not initialized. The SimPy program called simulate() before calling initialize().

### Monitor error messages

- SimPy: No observations for mean. No observations were made by the monitor before attempting to calculate the mean.
- SimPy: No observations for sample variance. No observations were made by the monitor before attempting to calculate the sample variance.
- SimPy: No observations for timeAverage, No observations were made by the monitor before attempting to calculate the time-average.
- SimPy: No elapsed time for timeAverage. No simulation time has elapsed before attempting to calculate the time-average.

## Acknowledgments

We will be grateful for any corrections or suggestions for improvements to the document.

Version: | $Revision: 1.0 $ :Date: $Date: 2004-05-15 11:09:11+02 $ gav
Python-Version: | 2.2, 2.3
Created: | 2002-December-10