I’ve been using the SimPy discrete-event simulation package lately, and I really like it.

As the SimPy home page says, “SimPy (= Simulation in Python) is an object-oriented, process-based discrete-event simulation language based on standard Python.” What does that mean? Well, first of all, it is a Python module, and you import and then use it like any other Python module.

(If you haven’t used Python, get yourself over to www.python.org and download it now! Or if you use Mac OS X or Linux, you already have it. Python is a powerful, high-level general-purpose language, and comes with excellent documentation.)

“Discrete-event simulations” are something a good nerd will often need to write. They are simulations where things happen at discrete times. There are three levels of sophistication in handling such simulations. Level zero is to just step forward in time by small increments, checking every time step for each possible event. Not too bright, because you waste a lot of computation on time steps when nothing happens. Level one is the “event-oriented” approach, where you keep events in some “agenda,” and you process events in the agenda one at a time, with each event in the agenda possibly creating new events in the future that are then added to the agenda.

Level one is as far as most people get, but it’s not where you should stop, because writing an event-oriented simulation is painful and error-prone. The right thing to do is to go to the next level of sophistication, the “process-oriented” approach.

In the process-oriented approach, you create special objects, called processes, which are like “living” objects. Processes have a special event method which functions as an event loop. To program the events in your simulation, you need to write one or more process event methods which describes how each process object reacts to the possible events in the simulation. It turns out that these process event methods are very natural and easy to write, because they properly correspond to how we think about what is happening in the simulation.

So how does it work? Well, SimPy sets up and handles the event agenda underlying the system, so you don’t have to do it yourself. When you call the SimPy “simulate()” method, it begins stepping through the events in the event agenda, calling the appropriate process event methods defined in your processes in the correct order. The Python feature that makes the whole thing work is the Python “yield” statement, which is like a return, except that the next time a function with a “yield” is called, it picks up after the yield rather than at the beginning of the function. All the process event methods you define when using SimPy will use yields to give back control to the SimPy run-time system.

Anyways, Professor Norm Matloff from UC Davis has written some excellent tutorials, or you can use the documentation that comes with SimPy.



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Tags: discrete event simulation, process-oriented simulation, Python, SimPy