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 defined by Python generator functions and can, for example, be 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).
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. Also, SimPy is not really required for simulations with a fixed step size and where your processes don’t interact with each other or with shared resources.
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 mailing list.
A Simple Example
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
SimPy requires Python 2.7, 3.2, PyPy 2.0 or above.
You can install SimPy easily via pip:
$ pip install -U simpy
You can also download and install SimPy manually:
$ cd where/you/put/simpy/ $ python setup.py install
To run SimPy’s test suite on your installation, execute:
$ python -c "import simpy; simpy.test()"
Documentation and Help
You can find a tutorial, examples, topical guides and an API reference, as well as some information about SimPy and its history in our online documentation. For more help, contact the SimPy mailing list. SimPy users are pretty helpful. You can, of course, also dig through the source code.
If you find any bugs, please post them on our issue tracker.
Enjoy simulation programming in SimPy!
An almost feature-complete reimplementation of SimPy in C# was written by Andreas Beham and is available at github.com/abeham/SimSharp