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.. _basic_concepts:
.. currentmodule:: simpy.core
==============
Basic Concepts
==============
SimPy is a discrete-event simulation library. The behavior of active components
(like vehicles, customers or messages) is modeled with *processes*. All
processes live in an *environment*. They interact with the environment and with
each other via *events*.
Processes are described by simple Python `generators
<http://docs.python.org/3/reference/expressions.html#yieldexpr>`_. You can call
them *process function* or *process method*, depending on whether it is
a normal function or method of a class. During their lifetime, they create
events and ``yield`` them in order to wait for them to be triggered.
When a process yields an event, the process gets *suspended*. SimPy *resumes*
the process, when the event occurs (we say that the event is *triggered*).
Multiple processes can wait for the same event. SimPy resumes them in the same
order in which they yielded that event.
An important event type is the :class:`~simpy.events.Timeout`. Events of this
type are triggered after a certain amount of (simulated) time has passed. They
allow a process to sleep (or hold its state) for the given time.
A :class:`~simpy.events.Timeout` and all other events can be created by calling
the appropriate method of the :class:`Environment` that the process lives in
(:meth:`Environment.timeout()` for example).
Our First Process
=================
Our first example will be a *car* process. The car will alternately drive and
park for a while. When it starts driving (or parking), it will print the
current simulation time.
So let's start::
>>> def car(env):
... while True:
... print('Start parking at %d' % env.now)
... parking_duration = 5
... yield env.timeout(parking_duration)
...
... print('Start driving at %d' % env.now)
... trip_duration = 2
... yield env.timeout(trip_duration)
Our *car* process requires a reference to an :class:`Environment` (``env``) in
order to create new events. The *car*'s behavior is described in an infinite
loop. Remember, this function is a generator. Though it will never terminate,
it will pass the control flow back to the simulation once a ``yield`` statement
is reached. Once the yielded event is triggered ("it occurs"), the simulation
will resume the function at this statement.
As I said before, our car switches between the states *parking* and *driving*.
It announces its new state by printing a message and the current simulation
time (as returned by the :attr:`Environment.now` property). It then calls the
:meth:`Environment.timeout()` factory function to create
a :class:`~simpy.events.Timeout` event. This event describes the point in time
the car is done *parking* (or *driving*, respectively). By yielding the event,
it signals the simulation that it wants to wait for the event to occur.
Now that the behavior of our car has been modeled, lets create an instance of
it and see how it behaves::
>>> import simpy
>>> env = simpy.Environment()
>>> env.process(car(env))
<Process(car) object at 0x...>
>>> env.run(until=15)
Start parking at 0
Start driving at 5
Start parking at 7
Start driving at 12
Start parking at 14
The first thing we need to do is to create an instance of :class:`Environment`.
This instance is passed into our *car* process function. Calling it creates
a *process generator* that needs to be started and added to the environment via
:meth:`Environment.process()`.
Note, that at this time, none of the code of our process function is being
executed. Its execution is merely scheduled at the current simulation time.
The :class:`~simpy.events.Process` returned by :meth:`~Environment.process()`
can be used for process interactions (we will cover that in the next section,
so we will ignore it for now).
Finally, we start the simulation by calling :meth:`~Environment.run()` and
passing an end time to it.
What's Next?
============
You should now be familiar with SimPy's terminology and basic concepts. In the
:doc:`next section <process_interaction>`, we will cover process interaction.
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