>>> 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
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.
SimPy is not suited for continuous simulation. And it 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.
SimPy is released under the MIT License. Simulation model developers are encouraged to share their SimPy modeling techniques with the SimPy community. Please post a message to the SimPy-Users mailing list.