||This article contains content that is written like an advertisement. (April 2014)|
|This article relies too much on references to primary sources. (April 2014)|
|Original author(s)||Klaus G. Müller, Tony Vignaux|
|Developer(s)||Ontje Lünsdorf, Stefan Scherfke|
|Initial release||September 17, 2002|
|Stable release||3.0.7 / March 1, 2015|
|Type||Discrete event simulation|
SimPy is a process-based discrete-event simulation framework based on standard Python. Its event dispatcher is based on Python’s generators and can also be used for asynchronous networking or to implement multi-agent systems (with both, simulated and real communication).
Processes in SimPy are simple Python generator functions and are used to model active components like customers, vehicles or agents. SimPy also provides various types of shared resources to model limited capacity congestion points (like servers, checkout counters and tunnels). From version 3.1, it will also provide monitoring capabilities to aid in gathering statistics about resources and processes.
Simulations can be performed “as fast as possible”, in real time (wall clock time) or by manually stepping through the events.
Though it is theoretically possible to do continuous simulations with SimPy, it has no features that help you with that. On the other hand, SimPy is overkill for simulations with a fixed step size where your processes don’t interact with each other or with shared resources — use a simple
while loop in this case.
The SimPy distribution contains tutorials, in-depth documentation, and a large number of examples.
One of SimPy's main goals is to be easy to use. Here is an example for a simple SimPy simulation: a clock process that prints the current simulation time at each step:
>>> import simpy >>> >>> def clock(env, name, tick): ... while True: ... print(name, env.now) ... yield env.timeout(tick) ... >>> env = simpy.Environment() >>> env.process(clock(env, 'fast', 0.5)) <Process(clock) object at 0x...> >>> env.process(clock(env, 'slow', 1)) <Process(clock) object at 0x...> >>> env.run(until=2) fast 0 slow 0 fast 0.5 slow 1 fast 1.0 fast 1.5