.. meta::
   :description: SimPy Python Simulation Language
   :keywords: simulation python stochastic

.. highlight:: python
   :linenothreshold: 5 

=======================================
Simulation with SimPy - In Depth Manual
=======================================


:Authors: - Tony Vignaux <Vignaux@users.sourceforge.net>
          - Klaus Muller <Muller@users.sourceforge.net>
          - Bob Helmbold
:SimPy release: |release|
:SimPy Web-site: http://simpy.sourceforge.net/
:Python-Version: 2.3 and later (not 3.0)
:Date: |today|



.. REPLACEMENTS ==============================================
.. |yrequest| replace:: ``yield request,self,``
.. |rrequest| replace:: ``yield (request,self,``\ *r [,P]*\ ``),``
.. |init| replace:: ``__init__``
.. ===========================================================

.. contents:: Contents
   :depth: 2



  
This document describes SimPy version 2.0.  Changes from version 1.9.1
are listed in `Appendix A0`_.


.. note:: 

   This document does **not** describe the object oriented (OO) API
   which has been added to SimPy with version 2.0. SimPy 2.0 
   is fully backward compatible with previous versions. The
   procedural API and the OO API co-exist happily in SimPy 2.0.  


Introduction
-------------------

SimPy is a Python-based discrete-event simulation system that models
active components such as messages, customers, trucks, planes by
parallel processes. It provides a number of tools for the simulation
programmer including Processes_ to model active entities, three kinds
of resource facilities (Resources_, Levels_, and Stores_) and ways of
recording results by using Monitors_ and Tallys_. 

The basic active elements of a SimPy model are process objects (i.e.,
objects of a Process class -- see Processes_).  As a general practice
and for brevity we will often refer to both process objects and their
classes as "processes."  Thus, "process" may refer to a Process class
or to a process object, depending on context. To avoid ambiguity or
for added emphasis we often explicitly state whether a class or an
object is intended. In addition we will use "entity" to refer to
process objects as this is frequently used in the simulation
literature. Here, though, we restrict it to process objects and it
will not be used for any other elements in the simulation.


During the simulation, Process objects may be delayed for fixed or
random times, queued at resource facilities, and may be interrupted by
or interact in other ways with other processes and components. For
example, Automobiles in a model of a gas station may have to queue
while waiting for a pump to become available . One obtaining a pump it
takes some time to fill before releasing the pump.


A SimPy script contains the declaration of one or more Process classes
and the creation of process objects (entities) from them.  Each
process object executes its *Process Execution Method* (referred to
later as a PEM_), a method that determines its actions. Each PEM runs
in parallel with (and may interact with) the PEMs of other process
objects.

There are three types of resource facilities (Resources_, Levels_, and
Stores_). Each type models a congestion point where process objects
may have to queue while waiting to acquire or, in some cases to
deposit, a resource.

Resources_ have several *resource units*, each of which may be used by
process objects. Extending the example above, the gas station might be
modeled as a resource with its pumps as resource units. On receiving
a request for a pump from a car, the gas station resource
automatically queues waiting cars until one becomes available. The
pump resource unit is held by the car until it is released for
possible use by another car.

Levels_ model the supply and consumption of a homogeneous
undifferentiated "material." The Level at any time holds an amount of
material that is fully described by a scalar (real or integer). This
can be increased or decreased by process objects. For example, a gas
(petrol) station stores gas in large storage tanks. The tanks can be
increased by Tanker deliveries and reduced by cars refuelling. A car
need not return the gas to the Level in contrast to the requirement
for Resource units.

.. Note that though a Resource must be  released by an entity that has
   finished using it, a Level resource need not be  released ever though it may
   be put back if that is wanted in the model. On the other hand it
   might never be released at all. It might be replenished by another entity.

Stores_ model the production and consumption of individual items. A
store hold a list of items.  Process objects can insert or remove
items from the list.  For example, surgical procedures (treated as
process objects) require specific lists of personnel and equipment
that may be treated as the items in a Store facility such as a
clinic or hospital. The items held in a Store can be of any Python
type. In particular they can be process objects, and this may be
exploited to facilitate modeling Master/Slave relationships.

In addition to the number of free units or quantities, resource
facilities all hold queues of waiting process objects which are
operated automatically by SimPy. They also operate a reneging
mechanism so that a process object can abandon the wait.

Monitors_ and Tallys_ are used to compile statistics as a function of
time on variables such as waiting times and queue lengths. These
statistics consist of simple averages and variances, time-weighted
averages, or histograms. They can be gathered on the queues associated
with Resources, Levels and Stores. For example we may collect
data on the average number of cars waiting at a gas station and the
distribution of their waiting times. Tallys update the current
statistics as the simulation progresses, but cannot preserve complete
time-series records. Monitors can preserve complete time-series
records that may later be used for more advanced post-simulation
analyses.


Before attempting to use SimPy, you should be able to write Python
code. In particular, you should be able to define and use classes and
their objects. Python is free and usable on most platforms.  We do not
expound it here. You can find out more about it and download it from
the Python_ web-site (http://www.Python.org). SimPy requires *Python*
2.3 or later.


Simulation with SimPy
-------------------------

To use the SimPy simulation system you must import its ``Simulation``
module (or one of the `alternatives`_):

   ``from SimPy.Simulation import *``

All discrete-event simulation programs automatically maintain the
current simulation time in a software clock. This cannot be changed by
the user directly.  In SimPy the current clock value is returned by the
``now()`` function.

At the start of the simulation the software clock is set to 0.0.
While the simulation program runs, simulation time steps forward from
one *event* to the next. An event occurs whenever the state of the
simulated system changes. For example, an event might be the arrival
or departure of a car from the gas station.

The following statement initializes global simulation variables and
sets the software clock to zero. It must appear in the script before
any SimPy process objects are activated.

   ``initialize( )``

This is followed by SimPy statements creating and activating process
objects. Activation of process objects adds events to the simulation
schedule. Execution of the simulation itself starts with the following
statement:

   ``simulate(until=``\ *endtime*\ ``)``

The simulation starts, and SimPy seeks and executes the first
scheduled event. Having executed that event, the simulation seeks and
executes the next event, and so on. 

Typically a simulation is terminated when *endtime* is reached but it
can be  stopped at any time by the command:

   ``stopSimulation( )``
 
``now( )`` will then equal the time when this was called. The
simulation will also stop if there are no more events to execute (so
``now()`` equals the time the last scheduled event occurred)

After the simulation has stopped, further statements can be executed.
``now()`` will retain the time of stopping and data held in Monitors
will be available for display or further analysis.


The following fragment shows only the *main* block in a simulation
program.  (Complete, runnable examples are shown in `Example 1`_ and
`Example 2`_). Here ``Message`` is a (previously defined) Process
class and ``m`` is defined as an object of that class, that is, a
particular message. Activating ``m`` has the effect of scheduling at
least one event by starting the PEM of ``m`` (here called ``go``).
The ``simulate(until=1000.0)`` statement starts the simulation itself,
which immediately jumps to the first scheduled event. It will continue
until it runs out of events to execute or the simulation time reaches
``1000.0``. When the simulation stops the (previously written) ``Report``
function is called to display the results::

  initialize()
  m = Message()
  activate(m,m.go(),at=0.0)
  simulate(until=1000.0)

  Report()  #  report results when the simulation finishes
  

The object-oriented programmers interface
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

An object-oriented API interface was added in SimPy 2.0.  It is
described more fully in ``SimPyOO_API``\. It defines a class of
*Simulation* objects and makes running multiple simulations cleaner
and easier. It is compatible with the procedural version described in
this Manual. Using the object-oriented API, the program fragment listed
at the end of the previous subsection would look like this::


  s=Simulation()
  s.initialize()
  m = Message(sim=s)
  s.activate(m,m.go(),at=0.0)
  s.simulate(until=1000.0)

  Report()  #  report results when the simulation finishes

Further examples of the OO style exist in the *SimPyModels* directory.


Alternative SimPy simulation libraries
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In addition to *SimPy.Simulation*, SimPy provides four alternative
simulation libraries which have the basic ``SimPy.Simulation``
capabilities, plus additional facilities:

*  *SimPy.SimulationTrace* for program tracing:
   With ``from SimPy.SimulationTrace import *``, any SimPy program automatically
   generates detailed event-by-event tracing output. This makes the library 
   ideal for program development/testing and for teaching SimPy.

*  *SimPy.SimulationRT* for real time synchronization:
   ``from SimPy.SimulationRT import *`` facilitates synchronizing simulation
   time and real (wall-clock) time. This capability can be used to implement,
   e.g., interactive game applications or to demonstrate a model's execution
   in real time.

*  *SimPy.SimulationStep* for event-stepping through a simulation:
   The import ``from SimPy.SimulationStep import *`` provides an API for
   stepping through a simulation event by event. This can assist with 
   debugging models, interacting with them on an event-by-event basis, 
   getting event-by-event output from a model (e.g. for plotting purposes),
   etc.

*  *SimPy.SimulationGUIDebug* for event-stepping through a simulation
   with a GUI: ``from SimPy.SimulationGUIDebug import *`` provides an
   API for stepping through a simulation event-by-event, with a GUI
   for user control. The event list, Process and Resource objects are
   shown in windows. This is useful for debugging models and for teaching
   discrete event simulation with SimPy.

.. ==================================================================


Processes
-------------------

The active objects for discrete-event simulation in SimPy are process
objects -- instances of some class that inherits from SimPy's Process
class.

For example, if we are simulating a computing network we might model
each message as an object of the class ``Message``.  When message
objects arrive at the computing network they make transitions between
nodes, wait for service at each one, are served for some time, and
eventually leave the system. The ``Message`` class specifies all the
actions of each message in its Process Execution Method (PEM).
Individual message objects are created as the simulation runs, and
their evolutions are directed by the ``Message`` class's PEM.


Defining a process
~~~~~~~~~~~~~~~~~~~~~~~~~

Each Process class inherits from SimPy's ``Process`` class. For example
the header of the definition of a new ``Message`` Process class would
be:

   ``class Message(Process):``


At least one Process Execution Method (PEM) must be defined in each
Process class [#]_.  A PEM may have arguments in addition to the required
``self`` argument that all methods must have. Naturally, other
methods and, in particular, an ``__init__`` method, may be defined.

.. [#] More than one can be defined but only one can be executed by
   any process object.

.. Add a paragraph about the standard PEM name ACTIONS

.. _PEM:

* ``A Process Execution Method (PEM)`` defines the actions that are
  performed by its process objects. Each PEM must contain at least one
  of the ``yield`` statements, described later. This makes it a Python
  generator function so that it has resumable execution -- it can be
  restarted again after the yield statement without losing its current
  state.  A PEM may have any name of your choice. For example it may
  be called ``execute( )`` or ``run( )``.


  "The ``yield`` statements are simulation commands which affect an
  ongoing life-cycle of Process objects. These statements control the
  execution and synchronization of multiple processes. They can delay
  a process, put it to sleep, request a shared resource or provide a
  resource. They can add new events on the simulation event schedule,
  cancel existing ones, or cause processes to wait for a state
  change."


  For example, here is a the Process Execution Method, ``go(self)``,
  for the ``Message`` class. Upon activation it prints out the current
  time, the message object's identification number and the word
  "Starting". After a simulated delay of 100.0 time units (in the
  ``yield hold, ...`` statement) it announces that this message object
  has "Arrived"::

      def go(self):
          print now(), self.i, 'Starting'
          yield hold,self,100.0
          print now(), self.i, 'Arrived'

  A process object's PEM starts execution when the object is
  activated, provided the ``simulate(until= ...)`` statement has been
  executed.


* **__init__(self, ...)**, where *...* indicates method
  arguments. This method initializes the process object, setting
  values for some or all of its attributes.  As for any sub-class in
  Python, the first line of this method must call the ``Process`` class's
  ``__init__( )`` method in the form:

      ``Process.__init__(self)``

  You can then use additional commands to initialize attributes of the
  Process class's objects. You can also override the standard ``name``
  attribute of the object. 

  The ``__init__( )`` method is always called whenever you create a
  new process object. If you do not wish to provide for any attributes
  other than a ``name``, the ``__init__`` method may be dispensed
  with. An example of an ``__init__( )`` method is shown in the
  example below.



Creating a process object 
~~~~~~~~~~~~~~~~~~~~~~~~~~~

An entity (process object) is created in the usual Python manner by
calling the Class. Process classes have a single argument, ``name``
which can be specified if no |init| method is defined. It defaults to
``'a_process'``. It can be over-ridden if an |init| method is defined.

For example to create a new ``Message`` object with a name
``Message23``:

   ``m = Message(name="Message23")``


--------

.. _`Example 1`:

  **Example 1**: This is is a complete, runnable, SimPy script. We
  declare a ``Message`` class and define an ``__init__( )`` method and
  a PEM called ``go( )``.  The ``__init__( )`` method provide an
  instance variables of an identification number and message length.
  We do not actually use the ``len`` attribute in this example.

  Two ``messages``, ``p1`` and ``p2`` are created. ``p1`` and ``p2``
  are activated to start at simulation times 0.0 and 6.0,
  respectively. Nothing happens until the ``simulate(until=200)``
  statement. When both messages have finished (at time
  6.0+100.0=106.0) there will be no more events so the simulation will
  stop at that time:

  .. include:: programs/message.py
     :literal:

  Running this program gives the following output::

      0 1 Starting
      6.0 2 Starting
      100.0 1 Arrived
      106.0 2 Arrived
      Current time is  106.0

------------

Elapsing time in a Process
~~~~~~~~~~~~~~~~~~~~~~~~~~

A PEM_ uses the ``yield hold`` command to temporarily delay a process
object's operations.

yield hold
++++++++++++

    ``yield hold,self,``\ *t* 

    Causes the process object to delay *t* time units [#]_.  After the
    delay, it continues with the next statement in its PEM.  During
    the ``hold`` the object's operations are suspended.

.. [#] unless it is further delayed by being interrupted_.

    This is used to model any elapsed time  an entity might be involved
    in. For example while it is passively being provided with service.

..
    * ``yield passivate,self`` suspends the process object's operations
      until reactivated by explicit command (which must be issued by a
      different process object).

------

.. _`Example 2`:

   **Example 2:** In this example the Process Execution Method,
   ``buy``, has an extra argument, ``budget``:

   .. include:: programs/shopping.py
      :literal:

------

.. =================================================================

Starting and stopping SimPy Process Objects
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A process object is "passive" when first created, i.e., it has no
scheduled events. It must be *activated* to start its Process
Execution Method. To activate an instance of a Process class you can
use either the ``activate`` function or the ``start`` method of the
Process. (see the Glossary_ for an explanation of the modified Backus-Naur Form (BNF)
notation used).


activate
+++++++++

.. |arguments| replace:: \ ``[,{at=now()|delay=0}][,prior=False])``

.. -    ``activate(``\ *p, p.pemname([args])*\ ``[,{at=`` *t* ``|delay=``\ *period*\ ``}][,prior=False])``

-    ``activate(``\ *p, p.pemname([args])*\ |arguments|

     activates process object *p*, provides its Process Execution Method
     *p.pemname( )* with arguments *args* and possibly assigns values to the
     other optional parameters. The default is to activate at the current
     time (``at=now( )``) with no delay (``delay=0.0``) and ``prior`` set to
     ``False``.  You may assign other values to ``at``, ``delay``, and ``prior``.

     Example: to activate a process object, ``cust`` with name
     ``cust001`` at time 10.0 using a PEM called ``lifetime``::

       activate(cust,cust.lifetime(name='cust001'),at=10.0)


     However, ``delay`` overrides ``at``, in the sense that when a
     ``delay=``\ *period* clause is included, then activation occurs at
     ``now( )`` or ``now( )+``\ *period* (whichever is larger), irrespective of
     what value of *t* is assigned in the ``at=``\ *t* clause. This is true
     even when the value of *period* in the delay clause is zero, or
     even negative.  So it is better and clearer to choose one (or
     neither) of ``at=``\ *t* and ``delay=``\ *period*, but not both.

     If you set ``prior=True``, then process object *p* will be
     activated *before* any others that happen to be scheduled for
     activation at the same time. So, if several process objects are
     scheduled for activation at the same time and all have
     ``prior=True``, then the last one scheduled will actually be the
     first to be activated, the next-to-last of those scheduled, the
     second to be activated, and so forth.

     Retroactive activations that attempt to activate a process
     object before the current simulation time terminate the
     simulation with an error report.


start
+++++++++

An alternative to ``activate()`` function is the ``start``
method. There are a number of ways of using it:

-    *p.*\ ``start(``\ *p.pemname([args])*\ |arguments|

     is an alternative to the ``activate`` statement. *p* is a Process
     object.  The generator function, *pemname*, can have any
     identifier (such as ``run``, ``life-cycle``, etc). It can have
     parameters.

     For example, to activate the process object ``cust`` using the PEM
     with identifier, ``lifetime`` at time 10.0 we would use::

       cust.start(cust.lifetime(name='cust001'),at=10.0)

-    *p.*\ ``start([``\ *p.*\ ``ACTIONS()]`` |arguments|

     if ``p`` is a Process object and the generator function is given
     the *standard identifier*, ``ACTIONS``.  ``ACTIONS``, is
     recognized as a Process Execution Method. It may *not* have
     parameters. The call *p.*\ ``ACTIONS()`` is optional.

     For example, to activate the process object ``cust`` with the standard
     PEM identifier ``ACTIONS`` at time 10.0, the following are
     equivalent (and the second version is more convenient)::

       cust.start(cust.ACTIONS(), at=10.0)
       cust.start(at=10.0)


-    An *anonymous* instance of Process class *PR* can be created and
     activated in one command using ``start`` with the standard PEM
     identifier, ``ACTIONS``.

     *PR.*\ ``([args]).start(`` |arguments|

     Here, *PR* is the identifier for the Process class and not for a
     Process object as was *p*, in the statements above. The generator
     method ``ACTIONS`` may *not* have parameters.

     For example, if ``Customer`` is a SimPy Process class we can create
     and activate an anonymous instance at time 10.0::

        Customer(name='cust001').start(at=10.0)


You can use the ``passivate``, ``reactivate``, or ``cancel`` commands to
control Process objects.

passivate
++++++++++++++

-    ``yield passivate,self`` 

     suspends the process object itself. It becomes "passive". To get
     it going again another process must ``reactivate`` it.

reactivate
++++++++++++

-    ``reactivate(``\ *p*\ |arguments|

     reactivates a passive process object, *p*.  It becomes
     "active". The optional parameters work as for ``activate``. A
     process object cannot reactivate itself.  To temporarily suspend
     itself it must use ``yield hold,self,t`` instead.

cancel
+++++++++++

-    ``self.cancel(``\ *p*\ ``)`` 
 
     deletes all scheduled future events for process object *p*.  A
     process cannot ``cancel`` itself.  If that is required, use
     ``yield passivate,self`` instead. Only "active" process objects
     can be canceled. 

A process object is "terminated" after all statements in its process
execution method have been completed. If the object is still
referenced by a variable, it becomes just a data container. This can be useful for
extracting information.  Otherwise, it is automatically destroyed.

Even activated process objects will not start operating until the
``simulate(until=``\ *endtime*\ ``)`` statement is executed. This starts the
simulation going and it will continue until time *endtime* (unless it runs
out of events to execute or the command ``stopSimulation( )`` is
executed).

------------

.. an example to illustrate yield hold, perhaps. 


.. _`Example 3`:

**Example 3** This simulates a firework with a time fuse.  We have put
in a few extra ``yield hold`` commands for added suspense.

.. include:: programs/firework.py
   :literal:


Here is the output. No formatting was attempted so it looks a bit
ragged::

   0.0  firework launched
   11.0  tick
   12.0  tick
   13.0  tick
   14.0  tick
   15.0  tick
   16.0  tick
   17.0  tick
   18.0  tick
   19.0  tick
   20.0  tick
   30.0  Boom!!

------------

A source fragment
+++++++++++++++++++++

.. Perhaps move this earlier and use it as a further example of yield
   hold

One useful program pattern is the *source*. This is a process object
with a Process Execution Method (PEM) that sequentially generates and activates
other process objects -- it is a source of other process
objects. Random arrivals can be modeled using random intervals
between activations.

------------

.. _`Example 4`:

**Example 4: A source**.  Here a source creates and activates a series of
customers who arrive at regular intervals of 10.0 units of time. This
continues until the simulation time exceeds the specified ``finishTime``
of 33.0.  (Of course, to model customers with random inter-arrival
times the ``yield hold`` statement would use a random variate, such as
``expovariate( )``, instead of the constant 10.0 inter-arrival time used
here.)  The following example assumes that the ``Customer`` class has
previously been defined with a PEM called ``run`` that does not require
any arguments::

   class Source(Process):

       def execute(self, finish):
          while now() < finish:
             c = Customer()         # create a new customer object, and
                 # activate it (using default parameters)
             activate(c,c.run())    
             print now(), ' customer'
             yield hold,self,10.0

   initialize()
   g = Source()                     # create the Source object, g,
                                    # and activate it
   activate(g,g.execute(finish=33.0),at=0.0) 
   simulate(until=100)

------------


Asynchronous interruptions
~~~~~~~~~~~~~~~~~~~~~~~~~~

An active process object can be interrupted by another but cannot
interrupt itself.

interrupt
+++++++++

-    ``self.interrupt(victim)``

     The *interrupter* process object uses its ``interrupt`` method to
     interrupt the *victim* process object. The interrupt is just a
     *signal*. After this statement, the *interrupter* process object
     continues its PEM.

     For the interrupt to have an immediate effect, the *victim* process
     object must be *active* -- that is it must have an event scheduled
     for it (that is, it is "executing" a ``yield hold`` ). If the
     *victim* is not active (that is, it is either *passive* or
     *terminated*) the interrupt has no effect. For example, process
     objects queuing for resource facilities cannot be interrupted
     because they are *passive* during their queuing phase. 

If interrupted, the *victim* returns from its ``yield hold`` statement
prematurely. It must then check to see if it has been interrupted by
calling:

interrupted
+++++++++++++

-   ``self.interrupted( )`` 

    which returns ``True`` if it has been interrupted. The *victim* can
    then either continue in the current activity or switch to an
    alternative, making sure it tidies up the current state, such as
    releasing any resources it owns. 

interruptCause
++++++++++++++++

-   ``self.interruptCause`` 

    when the *victim* has been interrupted, ``self.interruptCause`` is a
    reference to the *interrupter* object.

interruptLeft
+++++++++++++

-   ``self.interruptLeft`` 

    gives the time remaining in the interrupted ``yield hold``. The
    interruption is reset (that is, "turned off") at the *victim's*
    next call to a ``yield hold``.

interruptReset
++++++++++++++++

-   ``self.interruptReset( )``

    will reset the interruption.

It may be helpful to think of an interruption signal as instructing
the *victim* to determine whether it should interrupt itself. If the
*victim* determines that it should interrupt itself, it then becomes
responsible for making any necessary readjustments -- not only to
itself but also to any other simulation components that are
affected. (The *victim* must take responsibility for these
adjustments, because it is the only simulation component that "knows"
such details as whether or not it is interrupting itself, when, and
why.)

-----------

.. _`Example 5`:

**Example 5**. A simulation with interrupts.  A bus is subject to
breakdowns that are modeled as interrupts caused by a ``Breakdown``
process.  Notice that the ``yield hold,self,tripleft`` statement may
be interrupted, so if the ``self.interrupted()`` test returns ``True``
a reaction to it is required. Here, in addition to delaying the bus
for repairs, the reaction includes scheduling the next breakdown. In
this example the ``Bus`` Process class does not require an
``__init__()`` method::

    from SimPy.Simulation import *

    class Bus(Process):

      def operate(self,repairduration,triplength):    # PEM
         tripleft = triplength   
            # "tripleft" is the driving time to finish trip 
            # if there are no further breakdowns
         while tripleft > 0:
            yield hold,self,tripleft      # try to finish the trip
                # if a breakdown intervenes
            if self.interrupted():        
                  print self.interruptCause.name, 'at %s' %now()
                  tripleft=self.interruptLeft
                    # update driving time to finish
                    # the trip if no more breakdowns                           
                  self.interruptReset()        # end self-interrupted state
                    # update next breakdown time
                  reactivate(br,delay=repairduration)
                    # impose delay for repairs on self
                  yield hold,self,repairduration  
                  print 'Bus repaired at %s' %now()
            else:   # no breakdowns intervened, so bus finished trip
                  break   
         print 'Bus has arrived at %s' %now()

    class Breakdown(Process):
       def __init__(self,myBus):
           Process.__init__(self,name='Breakdown '+myBus.name)
           self.bus=myBus

       def breakBus(self,interval):      # Process Execution Method
           while True:
              yield hold,self,interval   # driving time between breakdowns
              if self.bus.terminated(): break
                # signal "self.bus" to break itself down
              self.interrupt(self.bus)   

    initialize()
    b=Bus('Bus')                   # create a Bus object "b" called "Bus"
    activate(b,b.operate(repairduration=20,triplength=1000))
        # create a Breakdown object "br" for bus "b", and
    br=Breakdown(b)
        # activate it with driving time between
        # breakdowns equal to 300
    activate(br,br.breakBus(300))  
                                   
    simulate(until=4000)
    print 'SimPy: No more events at time %s' %now()

The output from this example::

    Breakdown Bus at 300
    Bus repaired at 320
    Breakdown Bus at 620
    Bus repaired at 640
    Breakdown Bus at 940
    Bus repaired at 960
    Bus has arrived at 1060
    SimPy: No more events at time 1260

The bus finishes at 1060 but the simulation finished at 1260. Why? The
``breakdown``\ s PEM consists of a loop, one breakdown following
another at ``300`` intervals. The last breakdown finishes at 960 and
then a breakdown event is scheduled for 1260. But the bus finished at
1060 and is not affected by the breakdown.  These details can easily
be checked by importing from ``SimPy.SimulationTrace`` and re-running
the program.

------------

Where interrupts can occur, the victim of interrupts must test for
interrupt occurrence after every appropriate ``yield hold`` and react
appropriately to it. A victim holding a resource facility when it gets
interrupted continues to hold it.


Advanced synchronization/scheduling capabilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The preceding scheduling constructs all depend on specified time
values. That is, they delay processes for a specific time, or use
given time parameters when reactivating them. For a wide range of
applications this is all that is needed.

However, some applications either require or can profit from an
ability to activate processes that must wait for other processes to
complete. For example, models of real-time systems or operating
systems often use this kind of approach. Event_ Signalling is
particularly helpful in such situations. Furthermore, some
applications need to activate processes when certain conditions occur,
even though when (or if) they will occur may be unknown. SimPy has a
general `wait until`_ to support clean implementation of this
approach.

This section describes how SimPy provides event_ Signalling and `wait
until`_ capabilities.

.. _Event:
.. .. _SimEvent:

Creating and Signalling SimEvents
+++++++++++++++++++++++++++++++++++

As mentioned in the Introduction, for ease of expression when no
confusion can arise we often refer to both process objects and their
classes as "processes", and mention their object or class status only
for added clarity or emphasis. Analogously, we will refer to objects
of SimPy's ``SimEvent`` class as "SimEvents" [#]_ (or, if no confusion
can arise, simply as "events"). However, we sometimes mention their
object or class character for clarity or emphasis.

.. [#] The name SimEvent was chosen because "event" is already
  used in Python's standard library. See Python Library Reference
  section *7.5 threading -- Higher-level threading interface*, specifically
  subsection 7.5.5.

SimEvent objects must be created before they can be fired by a
``signal``. You create the SimEvent object, ``sE``, from SimPy's
``SimEvent`` class by a statement like the following:

   ``sE = SimEvent(name='I just had a great new idea!')``

A SimEvent's ``name`` attribute defaults to ``a_SimEvent`` unless you
provide your own, as shown here. Its ``occurred`` attribute,
``sE.occurred``, is a Boolean that defaults to ``False``. It indicates
whether the event ``sE`` has occurred.

You program a SimEvent to "occur" or "fire" by "signaling" it like this:

   ``sE.signal(``\ *<payload parameter>*\ ``)``

This "signal" is "received" by all processes that are either "waiting"
or "queueing" for this event to occur. What happens when they receive
this signal is explained in the next section. The *<payload
parameter>* is optional -- it defaults to ``None``. It can be of any
Python type. Any process can retrieve it from the event's
``signalparam`` attribute, for example by:

   ``message = sE.signalparam``


Waiting or Queueing for SimEvents
''''''''''''''''''''''''''''''''''

You can program a process either to "wait" or to "queue" for the
occurrence of SimEvents. The difference is that *all* processes
"waiting" for some event are reactivated as soon as it occurs. For
example, all firemen go into action when the alarm sounds. In
contrast, only the *first* process in the "queue" for some event is
reactivated when it occurs. That is, the "queue" is FIFO [#FIFO]_. An example
might be royal succession -- when the present ruler dies: "The king is
dead. Long live the (new) king!" (And all others in the line of
succession move up one step.)

.. [#FIFO] "First-in-First-Out" or FCFS, "First-Come-First-Served"

You program a process to ``wait`` for SimEvents by including in its PEM:

yield waitevent
++++++++++++++++

-  ``yield waitevent,self,``\ *<events part>*

   where *<events part>* can be either:

   - one SimEvent object, e.g. ``myEvent``, or

   - a tuple of SimEvent objects, e.g. ``(myEvent,myOtherEvent,TimeOut)``, or

   - a list of SimEvent objects, e.g. ``[myEvent,myOtherEvent,TimeOut]``

   If none of the events in the *<events part>* have occurred, the process
   is passivated and joined to the list of processes waiting for some
   event in *<events part>* to occur (or to recur).

   On the other hand, when *any* of the events in the *<events part>*
   occur, then *all* of the processes "waiting" for those particular
   events are reactivated at the current time. Then the ``occurred`` flag
   of those particular events is reset to ``False``. Resetting their
   ``occurred`` flag prevents the waiting processes from being constantly
   reactivated. (For instance, we do not want firemen to keep responding
   to any such "false alarms.")  For example, suppose the *<events part>*
   lists events ``a, b`` and ``c``  in that order. If events ``a`` and ``c``
   occur, then all of the processes waiting for event ``a`` are
   reactivated. So are all processes waiting for event ``c`` but not
   ``a``. Then the ``occurred`` flags of events ``a`` and ``c`` are toggled to
   ``False``. No direct changes are made to event ``b`` or to any processes
   waiting for it to occur.

You program a process to "queue" for events by including in its PEM:

yield queueevent
+++++++++++++++++++

-  ``yield queueevent,self,``\ *<events part>*

   where the  *<events part>* is as described above.

   If none of the events in the *<events part>* has occurred, the process
   is passivated and appended to the FIFO queue of processes queuing for
   some event in *<events part>* to occur (or recur).

   But when any of the events in *<events part>* occur, the process at
   the head of the "queue" is taken off the queue and reactivated at the
   current time. Then the ``occurred`` flag of those events that occurred
   is reset to ``False`` as in the "waiting" case.



Finding Which Processes Are Waiting/Queueing for an Event, and Which Events Fired
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''

SimPy automatically keeps current lists of what processes are
"waiting" or "queueing" for SimEvents. They are kept in the ``waits``
and ``queues`` attributes of the SimEvent object and can be read by
commands like the following::

   TheProcessesWaitingFor_myEvent = myEvent.waits
   TheProcessesQueuedFor_myEvent = myEvent.queues

However, you should not attempt to change these attributes yourself.

Whenever ``myEvent`` occurs, i.e., whenever a ``myEvent.signal(...)``
statement is executed, SimPy does the following:

- If there are any processes waiting or queued for that event, it
  reactivates them as described in the preceding section.

- If there are no processes waiting or queued (i.e., ``myEvent.waits``
  and ``myEvent.queues`` are both empty), it toggles ``myEvent.occurred``
  to ``True``.


SimPy also automatically keeps track of which events were fired when a
process object was reactivated. For example, you can get a list of the
events that were fired when the object ``Godzilla`` was reactivated with
a statement like this::

   GodzillaRevivedBy = Godzilla.eventsFired


------------

.. _`example 6`:

**Example 6**. This complete SimPy script illustrates these
constructs. (It also illustrates that a Process class may have more
than one PEM. Here the ``Wait_Or_Queue`` class has two PEMs --
``waitup`` and ``queueup``.)::

   from SimPy.Simulation import *

   class Wait_Or_Queue(Process):
       def waitup(self,myEvent):      # PEM illustrating "waitevent"
            # wait for "myEvent" to occur
           yield waitevent, self, myEvent      
           print 'At %s, some SimEvent(s) occurred that \
                activated object %s.' %(now(), self.name)
           print '   The activating event(s) were %s' \
               %([x.name for x in self.eventsFired])

       def queueup(self, myEvent):    # PEM illustrating "queueevent"
            # queue up for "myEvent" to occur
           yield queueevent, self, myEvent     
           print 'At %s, some SimEvent(s) occurred that \
               activated object %s.' %(now(), self.name)
           print '   The activating event(s) were %s' \
               %([x.name for x in self.eventsFired])

   class Signaller(Process):
            # here we just schedule some events to fire
       def sendSignals(self):
           yield hold, self, 2
           event1.signal()        # fire "event1" at time 2
           yield hold, self, 8
           event2.signal()        # fire "event2" at time 10
           yield hold, self, 5
           event1.signal()        # fire all four events at time 15
           event2.signal()
           event3.signal()
           event4.signal()
           yield hold, self, 5
           event4.signal()        # event4 recurs at time 20

   initialize()

            # Now create each SimEvent and give it a name
   event1 = SimEvent('Event-1')   
   event2 = SimEvent('Event-2')
   event3 = SimEvent('Event-3')
   event4 = SimEvent('Event-4')
   Event_list = [event3,event4]   # define an event list

   s = Signaller()
            # Activate Signaller "s" *after* events created
   activate (s,s.sendSignals())   

   w0 = Wait_Or_Queue('W-0')      
            # create object named "W-0", and set it to
            # "waitup" for SimEvent "event1" to occur
   activate (w0, w0.waitup(event1))
   w1 = Wait_Or_Queue('W-1')
   activate (w1, w1.waitup(event2))
   w2 = Wait_Or_Queue('W-2')
   activate(w2, w2.waitup(Event_list))
   q1 = Wait_Or_Queue('Q-1')  
            # create object named "Q-1", and put it to be first
            # in the queue for Event_list to occur
   activate(q1, q1.queueup(Event_list))
   q2 = Wait_Or_Queue('Q-2')  
            # create object named "Q-2", and append it to
            # the queue for Event_list to occur
   activate(q2, q2.queueup(Event_list))    

   simulate(until=50)

This program outputs::

   At 2, some SimEvent(s) occurred that activated object W-0.
      The activating event(s) were ['Event-1']
   At 10, some SimEvent(s) occurred that activated object W-1.
      The activating event(s) were ['Event-2']
   At 15, some SimEvent(s) occurred that activated object W-2.
      The activating event(s) were ['Event-3']
   At 15, some SimEvent(s) occurred that activated object Q-1.
      The activating event(s) were ['Event-3', 'Event-4']
   At 20, some SimEvent(s) occurred that activated object Q-2.
      The activating event(s) were ['Event-4']

Each output line, ``The activating event(s) were ...``, lists the
contents of the named object's ``eventsFired`` attribute. One of those
events "caused" the object to reactivate at the indicated time. Note
that at time 15 objects ``W-0`` and ``W-1`` were not affected by the
recurrence of ``event1`` and ``event2`` because they already were
active. Also at time 15, even though objects ``W-2``, ``Q-1`` and ``Q-2``
were all waiting for ``event3``, only ``W-2`` and ``Q-1`` were
reactivated. Process object ``Q-2`` was not reactivated at that time
because it was not first in the queue.  Finally, ``Q-2`` was reactivated
at time 20, when ``event4`` fired again.

------------

.. _`wait until`:

"waituntil" synchronization -- waiting for any condition
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

SimPy provides the ``waituntil`` feature that makes a process's
progress depend on the state of the simulation. This is useful if, for
example, you need to reactivate a process when (if ever) the
simulation enters the state ``goodWeather OR (nrCustomers>50 AND
price<22.50)``.  Doing that requires *interrogative* scheduling,
while all other SimPy synchronization constructs are *imperative* --
i.e., the condition must be tested after every change in state until
it becomes ``True``. 

This requires that after every change in system state SimPy must run a
special (hidden) process that tests and responds appropriately to the
condition's truth-value. This clearly takes more run time than SimPy's
imperative scheduling constructs. So SimPy activates its interrogative
testing process only so long as at least one process is executing a
``waituntil`` statement. When this is not the case, the run time overhead
is minimal (about 1 percent extra run time).

yield waituntil
++++++++++++++++

You program a process to wait for a condition to be satisfied by
including in its PEM a statement of the form:

   ``yield waituntil, self,``\ *<cond>*

where *<cond>* is a reference to a function, without parameters, that
returns a Boolean value indicating whether the simulation state or
condition to be waited for has occurred.

------------

.. _Romulans:
.. _`example 7`:

**Example 7**. This program using the ``yield waituntil ...``
statement. Here the function ``killed()``, in the ``life()`` PEM of
the ``Player`` process, defines the condition to be waited for ::

  from SimPy.Simulation import *
  import random

  class Player(Process):

    def __init__(self,lives=1,name='ImaTarget'):
      Process.__init__(self,name)
      self.lives=lives
        # provide Player objects with a "damage" property
      self.damage=0

    def life(self):
      self.message='Drat! Some %s survived Federation attack!' %(target.name)

      def killed():     # function testing for "damage > 5"
        return self.damage>5

      while True:
        yield waituntil,self,killed
        self.lives-=1; self.damage=0
        if self.lives==0:
          self.message= '%s wiped out by Federation at \
            time %s!' %(target.name,now())
          stopSimulation()

  class Federation(Process):

    def fight(self):                # simulate Federation operations
      print 'Three %s attempting to escape!' %(target.name)
      while True:
        if random.randint(0,10)<2:  # check for hit on player
          target.damage+=1          # hit! increment damage to player
          if target.damage <= 5:    # target survives
            print 'Ha! %s hit! Damage = %i'%(target.name, target.damage)
          else:
            if (target.lives-1)==0:
              print 'No more %s left!' %(target.name)
            else:
              print 'Now only %i %s left!' %(target.lives-1,target.name)

        yield hold,self,1

  initialize()
  gameOver=100
    # create a Player object named "Romulans"
  target=Player(lives=3,name='Romulans')  
  activate(target,target.life())
    # create a Federation object
  shooter=Federation()                    
  activate(shooter,shooter.fight())
  simulate(until=gameOver)
  print target.message


One possible output from this program is shown below. Whether the
Romulans are wiped out or some escape depends on what simulation
states the randomization feature produces::

      Three Romulans attempting to escape!
      Ha! Romulans hit! Damage = 1
      Ha! Romulans hit! Damage = 2
      Ha! Romulans hit! Damage = 3
      Ha! Romulans hit! Damage = 4
      Ha! Romulans hit! Damage = 5
        Now only 2 Romulans left!
      Ha! Romulans hit! Damage = 1
      Ha! Romulans hit! Damage = 2
      Ha! Romulans hit! Damage = 3
      Ha! Romulans hit! Damage = 4
      Ha! Romulans hit! Damage = 5
        Now only 1 Romulans left!
      Ha! Romulans hit! Damage = 1
      Ha! Romulans hit! Damage = 2
      Ha! Romulans hit! Damage = 3
      Ha! Romulans hit! Damage = 4
      Ha! Romulans hit! Damage = 5
        No more Romulans left!
      Romulans wiped out by Federation at time 73!

------------


The ``waituntil`` construct is so general that in principle it could
replace all the other synchronization approaches (but at a run time
cost).

[Return to Top_ ]

.. ==================================================================


Resources
-------------------

The three resource facilities provided by SimPy are Resources_,
Levels_ and Stores_. Each models a congestion point where process
objects may have to queue up to obtain resources. This section
describes the Resource type of resource facility.

An example of queueing for a Resource might be a manufacturing plant
in which a ``Task`` (modeled as a *process object*) needs work done
by a ``Machine`` (modeled as a Resource object). If all of the
``Machines`` are currently being used, the ``Task`` must wait until
one becomes free. A SimPy Resource can have a number of identical
``units``, such as a number of identical ``machine`` units. A
process obtains a unit of the Resource by ``requesting`` it and, when
it is finished, ``releasing`` it. A Resource maintains a list of
process objects that have requested but not yet received one of the
Resource's units (called the ``waitQ``), and another list of processes
that are currently using a unit (the ``activeQ``).  SimPy creates and
updates these queues itself -- the user can access them, but should
not change them.

Defining a Resource object
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A Resource object, ``r``,  is established by the following statement::

 r = Resource(capacity=1, name='a_resource', unitName='units',
            qType=FIFO, preemptable=False,
            monitored=False, monitorType=Monitor)

where

  - ``capacity`` is a positive real or integer value that specifies the total
    number of identical units in Resource object ``r``.

  - ``name`` is a descriptive name for this Resource object (e.g.,
    ``'gasStation'``).

  - ``unitName`` is a descriptive name for a unit of the resource
    (e.g., ``'pump'``).

  - ``qType`` is either ``FIFO`` [#FIFO]_ or ``PriorityQ``. It
    specifies the queue discipline of the resource's ``waitQ``;
    typically, this is ``FIFO`` and that is the default value. If
    ``PriorityQ`` is specified, then higher-priority requests waiting
    for a unit of Resource ``r`` are inserted into the ``waitQ`` ahead
    of lower priority requests.  See `Priority requests for a Resource
    unit`_ for details.

  - ``preemptable`` is a Boolean (``False`` or ``True``); typically,
    this is ``False`` and that is the default value. If it is
    ``True``, then a process requesting a unit of this resource may
    preempt a lower-priority process in the ``activeQ``, i.e., one that
    is already using a unit of the resource.  See `Preemptive requests
    for a Resource unit`_ for details.

  - ``monitored`` is a boolean (``False`` or ``True``). If set to
    ``True``, then information is gathered on the sizes of ``r``'s
    ``waitQ`` and ``activeQ``, otherwise not.

  - ``monitorType`` is either ``Monitor`` or ``Tally`` and indicates the
    type of Recorder_ to be used (see `Recording
    Resource queue lengths`_ for an example and additional
    discussion).

Each Resource object, ``r``,  has the following additional attributes:

  - ``r.n``,  the number of units that are currently free.

  - ``r.waitQ``, a queue (list) of processes that have requested but
    not yet received a unit of ``r``, so ``len(r.waitQ)`` is the
    number of process objects currently waiting.

  - ``r.activeQ``, a queue (list) of process objects currently using
    one of the Resource's units, so ``len(r.activeQ)`` is the number of
    units that are currently in use.

  - ``r.waitMon``, the record (made by a ``Monitor`` or a ``Tally``
    whenever ``monitored==True``) of the activity in ``r.waitQ``. So,
    for example, ``r.waitMon.timeaverage()`` is the average number of
    processes in ``r.waitQ``.  See `Recording Resource queue lengths`_
    for an example.
 
  - ``r.actMon``, the record (made by a ``Monitor`` or a ``Tally`` whenever
    ``monitored==True``) of the activity in ``r.activeQ``.


Requesting and releasing a unit of a Resource
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A process can request and later release a unit of the Resource object,
``r``, by using the following yield commands in a Process Execution
Method:

yield request
+++++++++++++++


- |yrequest|\ *r [,P=0]*

  requests a unit of Resource *r* with (optional) real or integer
  priority value *P*. If no priority is specified, it defaults to 0.
  Larger values of *P* represent higher priorities. See the following
  sections on `Queue Order`_ for more information on how priority
  values are used.  Although this form of request can be used for
  either ``FIFO`` or ``PriorityQ`` priority types, these values are
  *ignored* when ``qType==FIFO``.

yield release
+++++++++++++++

  ``yield release,self,r``

  releases the  unit of *r*.




.. ==================================================================

Queue Order
~~~~~~~~~~~~

If a requesting process must wait it is placed into the resource's
``waitQ`` in an order determined by settings of the resource's
``qType`` and ``preemptable`` attributes and of the priority value it
uses in the ``request`` call.



Non-priority queueing
++++++++++++++++++++++++

If the ``qType`` is not specified it takes the presumed value of
``FIFO`` [#FIFO]_. In that case processes wait in the usual
first-come-first-served order.

If a Resource unit is free when the request is made, the requesting
process takes it and moves on to the next statement in its PEM. If no
Resource unit is available when the request is made, then the
requesting process is appended to the Resource's ``waitQ`` and
suspended.  The next time a unit becomes available the first process
in the ``r.waitQ`` takes it and continues its execution. All priority
assignments are ignored. Moreover, in the FIFO case no preemption is
possible, for preemption requires that priority assignments be
recognized. (However, see the `Note on preemptive requests with waitQ
in FIFO order`_ for one way of simulating such situations.)

------------

**Example** In this complete script, the ``server`` Resource object is
given two resource units (``capacity=2``). By not specifying its
``Qtype`` it takes the default value, ``FIFO``.  Here six clients
arrive in the order specified by the program. They all request a
resource unit from the ``server`` Resource object at the same
time. Even though they all specify a priority value in their requests,
it is ignored and they get their Resource units in the same order as
their requests:

.. include:: programs/resource.py
   :literal:


This program results in the following output::

   c1 requests 1 unit at t = 0
   c2 requests 1 unit at t = 0
   c3 requests 1 unit at t = 0
   c4 requests 1 unit at t = 0
   c5 requests 1 unit at t = 0
   c6 requests 1 unit at t = 0
   c1 done at time = 100
   c2 done at time = 100
   c3 done at time = 200
   c4 done at time = 200
   c5 done at time = 300
   c6 done at time = 300

   Request order:  ['c1', 'c2', 'c3', 'c4', 'c5', 'c6']
   Service order:  ['c1', 'c2', 'c3', 'c4', 'c5', 'c6']

As illustrated, the clients are served in FIFO order. Clients ``c1``
and ``c2`` each take one Resource unit right away, but the others must
wait. When ``c1`` and ``c2`` finish with their resources, clients
``c3`` and ``c4`` can each take a unit, and so forth.



Priority requests for a Resource unit
++++++++++++++++++++++++++++++++++++++++

If the Resource ``r`` is defined with ``qType==PriorityQ``, priority
values in requests are recognized.  If a Resource unit is available
when the request is made, the requesting process takes it. If no
Resource unit is available when the request is made, the requesting
process is inserted into the Resource's ``waitQ`` in order of priority
(from high to low) and suspended. For an example where priorities are
used, we simply change the preceding example's specification of the
``server`` Resource object to::

   server=Resource(capacity=2, qType=PriorityQ)


where, by not specifying it, we allow ``preemptable`` to take its
default value, ``False``.

------------

**Example** After this change the program's output becomes::

   c1 requests 1 unit at t = 0
   c2 requests 1 unit at t = 0
   c3 requests 1 unit at t = 0
   c4 requests 1 unit at t = 0
   c5 requests 1 unit at t = 0
   c6 requests 1 unit at t = 0
   c1 done at time = 100
   c2 done at time = 100
   c6 done at time = 200
   c5 done at time = 200
   c4 done at time = 300
   c3 done at time = 300

   Request order:  ['c1', 'c2', 'c3', 'c4', 'c5', 'c6']
   Service order:  ['c1', 'c2', 'c6', 'c5', 'c4', 'c3']

Although ``c1`` and ``c2`` have the lowest priority values, each requested
and got a ``server`` unit immediately. That was because at the time they
made those requests a ``server`` unit was available and the
``server.waitQ`` was empty -- it did not start to fill until ``c3`` made
its request and found all of the ``server`` units busy. When ``c1`` and
``c2`` completed service, ``c6`` and ``c5`` (with the highest priority
values of all processes in the ``waitQ``) each got a Resource unit,
etc.

------------

When some processes in the ``waitQ`` have the same priority level as a
process making a priority request, SimPy inserts the requesting
process immediately *behind* them. Thus for a given priority value,
processes are placed in FIFO order. For example, suppose that when a
"priority 3" process makes its priority request the current ``waitQ``
consists of processes with priorities ``[5,4,3a,3b,3c,2a,2b,1]``,
where the letters indicate the order in which the equal-priority
processes were placed in the queue.  Then SimPy inserts this
requesting process into the current ``waitQ`` immediately behind its
last "priority 3" process. Thus, the new ``waitQ`` will be
``[5,4,3a,3b,3c,3d,2a,2b,1]``, where the inserted process is ``3d``.

One consequence of this is that, if all priority requests are assigned
the same priority value, then the ``waitQ`` will in fact be maintained
in ``FIFO`` order. In that case, using a ``FIFO`` instead of a
``PriorityQ`` discipline provides some saving in execution time which
may be important in simulations where the ``waitQ`` may be long.



Preemptive requests for a Resource unit
++++++++++++++++++++++++++++++++++++++++

In some models, higher priority processes can actually *preempt* lower
priority processes, i.e., they can take over and use a Resource unit
currently being used by a lower priority process whenever no free
Resource units are available. A Resource object that allows its units
to be preempted is created by setting its properties to
``qType==PriorityQ`` and ``preemptable==True``. 

Whenever a ``preemptable`` Resource unit is free when a request is
made, then the requesting process takes it and continues its
execution. On the other hand, when a higher priority request finds all
the units in a ``preemptable`` Resource in use, then SimPy adopts the
following procedure regarding the Resource's ``activeQ`` and
``waitQ``:

-  The process with the lowest priority is removed from the ``activeQ``,
   suspended, and put at the front of the ``waitQ`` -- so (barring
   additional preemptions) it will be the next one to get a resource
   unit.
 
-  The preempting process gets the vacated resource unit and is
   inserted into the ``activeQ`` in order of its priority value.
 
-  The time for which the preempted process had the resource unit is
   taken into account when the process gets into the ``activeQ`` again.
   Thus, its *total hold time* is always the same, regardless of how
   many times it has been preempted.

Warning: SimPy only supports preemption of processes which are
implemented in the following pattern::

  yield request (one or more request statements)
  <some code>
  yield hold (one or more hold statements)
  <some code>
  yield release (one or more release statements)

Modeling the preemption of a process in any other pattern may lead to
errors or exceptions.



We emphasize that a process making a ``preemptive`` request to a
fully-occupied Resource gets a resource unit if -- but only if -- some
process in the current ``activeQ`` has a lower priority. Otherwise, it
will be inserted into the ``waitQ`` at a location determined by its
priority value and the current contents of the ``waitQ``, using a
procedure analogous to that described for priority requests near the
end of the preceding section on `Priority requests for a Resource
unit`_. This may have the effect of advancing the preempting process
ahead of any lower-priority processes that had earlier been preempted
and put at the head of the ``waitQ``. In fact, if several preemptions
occur before a unit of resource is freed up, then the head of the
``waitQ`` will consist of the processes that have been preempted -- in
order from the last process preempted to the first of them.

------------

.. .. literalinclude:: programs/diffpriority.py
      :language: python


**Example** In this example two clients of different priority compete
for the same resource unit::

   from SimPy.Simulation import *

   class Client(Process):
      def __init__(self,name):
         Process.__init__(self,name)

      def getserved(self,servtime,priority,myServer):
          print self.name, 'requests 1 unit at t=',now()
          yield request, self, myServer, priority
          yield hold, self, servtime
          yield release, self,myServer
          print self.name,'done at t= ',now()

   initialize()
   # create the *server* Resource object
   server=Resource(capacity=1,qType=PriorityQ,preemptable=1)
   # create some Client process objects
   c1=Client(name='c1')
   c2=Client(name='c2')
   activate(c1,c1.getserved(servtime=100,priority=1,myServer=server),at=0)
   activate(c2,c2.getserved(servtime=100,priority=9,myServer=server),at=50)
   simulate(until=500)


The output from this program is::

   c1 requests 1 unit at t= 0
   c2 requests 1 unit at t= 50
   c2 done at t= 150
   c1 done at t= 200

Here, ``c1`` is preempted by ``c2`` at ``t=50``. At that time, ``c1``
had held the resource for 50 of its total of 100 time units. When
``c2`` finished and released the resource unit at 150, ``c1`` got the
resource back and finished the last 50 time units of its service at
``t=200``.

------------

If preemption occurs when the last few processes in the current
``activeQ`` have the same priority value, then the last process in the
current ``activeQ`` is the one that will be preempted and inserted
into the ``waitQ`` ahead of all others.  To describe this, it will be
convenient to indicate by an added letter the order in which
equal-priority processes have been inserted into a queue. Now, suppose
that a "priority 4" process makes a preemptive request when the
current ``activeQ`` priorities are ``[5,3a,3b]`` and the current
``waitQ`` priorities are ``[2,1,0a,0b]``. Then process ``3b`` will be
preempted.  After the preemption the ``activeQ`` will be ``[5,4,3a]``
and the ``waitQ`` will be ``[3b,2,1,0a,0b]``.

..  To give another example of how this works, consider the following sequence of 
    events, where several preemptive requests are received by a Resource with 
    two Resource units. Throughout this example, we give the ``activeQ`` 
    before the ``waitQ``:
    
    1. The current ``activeQ`` and ``waitQ`` are [1a,1b] and [0a,0b],
       respectively.
    
    2. A "priority 5" process makes a preemptive request. Then the queues
       become: [5,1a] and [1b,0a,0b].
    
    3. A "priority 3" process makes a preemptive request. Then the queues
       become: [5,3a] and [1a,1b,0a,0b].
    
    4. Another "priority 3" process makes a preemptive request. Then the
       queues become: [5,3a] and [3b,1a,1b,0a,0b].
    
    5. A "priority 7" process makes a preemptive request. Then the queues
       become: [7,5] and [3a,3b,1a,1b,0a,0b].
    
    6. The "priority 7" process finishes using its resource unit. Then the
       queues become: [5,3a] and [3b,1a,1b,0a,0b].


Note on preemptive requests with waitQ in FIFO order
+++++++++++++++++++++++++++++++++++++++++++++++++++++

You may consider doing the following to model a system whose queue of
items waiting for a resource is to be maintained in FIFO order, but in
which preemption is to be possible. It uses SimPy's ``preemptable``
Resource objects, and uses priorities in a way that allows for
preempts while maintaining a FIFO ``waitQ`` order.

-  Set ``qType=PriorityQ`` and ``preemptable=True`` (so that SimPy
   will process preemptive requests correctly).
 
-  Model "system requests that are to be considered as non-preemptive"
   in SimPy as process objects each of which has exactly the same
   (low) priority value -- for example, either assign all of them a
   priority value of 0 (zero) or let it default to that value.  (This
   has the effect of maintaining all of these process objects in the
   ``waitQ`` in FIFO order, as explained at the end of the section on
   `Priority requests for a Resource unit`_, above.)

-  Model "system requests that are to be considered as preemptive" in
   SimPy as process objects each of which is assigned a uniform
   priority value, but give them a higher value than the one used to
   model the "non-preemptive system requests" -- for example, assign
   all of them a priority value of 1 (one). Then they will have a higher
   priority value than any of the non-preemptive requests.

------------

**Example** Here is an example of how this works for a Resource with
two Resource units -- we give the ``activeQ`` before the ``waitQ``
throughout this example:

1. Suppose that the current ``activeQ`` and ``waitQ`` are ``[0a,0b]``
   and ``[0c],`` respectively.

2. A "priority 1" process makes a preemptive request. Then the queues
   become: ``[1a,0a]`` and`` [0b,0c]``.

3. Another "priority 1" process makes a preemptive request. Then the
   queues become: ``[1a,1b]`` and ``[0a,0b,0c]``.

4. A third "priority 1" process makes a preemptive request. Then the
   queues become: ``[1a,1b]`` and ``[1c,0a,0b,0c]``.

5. Process ``1a`` finishes using its resource unit. Then the queues
   become: ``[1b,1c]`` and ``[0a,0b,0c]``.

------------

.. ---------------------------------------------------------------------


Reneging -- leaving a queue before acquiring a resource
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In most real world situations, people and other items do not wait
forever for a requested resource facility to become
available. Instead, they leave its queue when their patience is
exhausted or when some other condition occurs. This behavior is
called *reneging*, and the reneging person or thing is said to
*renege*.

SimPy provides an extended (i.e., compound) ``yield request`` statement
to handle reneging.

Reneging yield request
++++++++++++++++++++++++

There are two types of reneging clause, one for reneging after a
certain time and one for reneging when an event has happened. 
Their general form is

  |rrequest|\ ``(``\ *<reneging clause>*\ ``)``

to request a unit of Resource *r* (with optional priority *P*,
assuming the Resource has been defined as a ``priorityQ``) but with
reneging.

A SimPy program that models Resource requests with reneging must use
the following pattern of statements::

 yield (request,self,r),(<reneging clause>)
 if self.acquired(resource):
    ## process got resource and so did NOT renege
    . . . .
    yield release,self,resource
 else:
    ## process reneged before acquiring resource
    . . . . .

A call to the ``self.acquired(resource)`` method is mandatory after a
compound ``yield request`` statement.  It not only indicates whether or
not the process has acquired the resource, it also removes the
reneging process from the resource's ``waitQ``.

Reneging after a time limit
+++++++++++++++++++++++++++

To make a process give up (renege) after a certain time, use a
reneging clause of the following form:

 |rrequest|\ ``(hold,self,``\ *waittime*\ ``)``


Here the process requests one unit of the resource *r* with optional
priority *P*. If a resource unit is available it takes it and continues
its PEM. Otherwise, as usual, it is passivated and inserted into *r*\
's ``waitQ``.

The process takes a unit if it becomes available before *waittime*
expires and continues executing its PEM. If, however, the process has
not acquired a unit before the *waittime* has expired it abandons the
request (reneges) and leaves the ``waitQ``.

------------

**Example**: part of a parking lot simulation::

    . . . .
    parking_lot=Resource(capacity=10)
    patience=5   # wait no longer than "patience" time units
                 # for a parking space
    park_time=60 # park for "park_time" time units if get a parking space
    . . . .
    yield (request,self,parking_lot),(hold,self,patience)
    if self.acquired(parking_lot):
       # park the car
       yield hold,self,park_time
       yield release,self,parking_lot
    else:
       # patience exhausted, so give up
       print 'I'm not waiting any longer. I am going home now.'

------------


Reneging when an event has happened
+++++++++++++++++++++++++++++++++++

To make a process renege at the occurrence of an event, use a reneging
clause having a pattern like the one used for a ``yield waitevent``
statement, namely ``waitevent,self,``\ *events* (see `yield waitevent`_). 
For example:

 |rrequest|\ ``(waitevent,self,``\ *events*\ ``)``

Here the process requests one unit of the resource *r* with optional
priority *P*. If a resource unit is available it takes it and continues
its PEM. Otherwise, as usual, it is passivated and inserted into *r*\
's ``waitQ``.

The process takes a unit if it becomes available before any of the
*events* occur, and continues executing its PEM. If, however, any of
the SimEvents in *events* occur first, it abandons the
request (reneges) and leaves the ``waitQ``. (Recall that *events* can be
either one event, a list, or a tuple of several SimEvents.)




------------

**Example** Queuing for movie tickets (part)::

    . . . .
    seats=Resource(capacity=100)
    sold_out=SimEvent() # signals "out of seats"
    too_late=SimEvent() # signals "too late for this show"
    . . . .
    # Leave the ticket counter queue when movie sold out 
    # or it is too late for the show
    yield (request,self,seats),(waitevent,self,[sold_out,too_late])
    if self.acquired(seats):
       # watch the movie
       yield hold,self,120
       yield release,self,seats
    else:
       # did not get a seat
       print 'Who needs to see this silly movie anyhow?'

------------


Exiting conventions and preemptive queues
++++++++++++++++++++++++++++++++++++++++++++++++++++

Many discrete event simulations (including SimPy) adopt the normal
"exiting convention", according to which processes that have once
started using a Resource unit stay in some Resource queue until their
``hold`` time has completed. This is of course automatically the case
for FIFO and non-preemptable ``PriorityQ`` disciplines. The point is
that the exiting convention is also applied in the ``preemptable`` queue
discipline case. Thus, processes remain in some Resource queue until
their ``hold`` time has completed, even if they are preempted by higher
priority processes.

Some real-world situations conform to this convention and some do
not. An example of one that does conform can be described as
follows. Suppose that at work you are assigned tasks of varying levels
of priority. You are to set aside lower priority tasks in order to
work on higher priority ones. But you are eventually to complete all
of your assigned tasks. So you are operating like a SimPy resource
that obeys a ``preemptable`` queue discipline and has one resource
unit. With this convention, half-finished low-priority tasks may be
postponed indefinitely if they are continually preempted by
higher-priority tasks.

An example that does not conform to the exiting convention can
be described as follows. Suppose again that you are assigned tasks of
varying levels of priority and are to set aside lower priority tasks
to work on higher priority ones. But you are instructed that any tasks
not completed within 24 hours after being assigned are to
be sent to another department for completion. Now, suppose
that you are assigned Task-A that has a priority level of 3 and will
take 10 hours to complete. After working on Task-A for an hour, you
are assigned Task-B, which has a priority level of 5 and will take 20
hours to complete. Then, at 11 hours, after working on Task-B for 10
hours, you are assigned Task-C, which has a priority level of 1 and
will take 4 hours to complete. (At this point Task-B needs 10 hours to
complete, Task-A needs 9 hours to complete, and Task-C needs 4 hours
to complete.) At 21 hours you complete Task-B and resume working on
Task-A, which at that point needs 9 hours to complete. At 24 hours
Task-A still needs another 6 hours to complete, but it has reached the
24-hour deadline and so is sent to another department for
completion. At the same time, Task-C has been in the waitQ for 13
hours, so you take it up and complete it at hour 28. This queue
discipline does not conform to the exiting convention, for under that
convention at 24 hours you would continue work on Task-A, 
complete it at hour 30, and then start on Task-C.

Recording Resource queue lengths
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Many discrete event models are used mainly to explore the
statistical properties of the ``waitQ`` and ``activeQ`` associated with
some or all of their simulated resources. SimPy's support for this
includes the `Monitor`_ and the `Tally`_. For more information on 
these and other recording
methods, see the section on `Recording Simulation Results`_. 


If a Resource, ``r``, is defined with ``monitored=True`` SimPy
automatically records the length of its associated ``waitQ`` and
``activeQ``. These records are kept in the recorder objects called
``r.waitMon`` and ``r.actMon``, respectively. This solves a problem,
particularly for the ``waitQ`` which cannot easily be recorded
externally to the resource.

The property ``monitorType`` indicates which variety of recorder is to
be used, either Monitor_ or Tally_. The default is ``Monitor``. If
this is chosen, complete time series for both queue lengths are
maintained and can be used for advanced post-simulation statistical
analyses as well as for displaying summary statistics (such as
averages, standard deviations, and histograms). If ``Tally`` is chosen
summary statistics can be displayed, but complete time series
cannot. For more information on these and SimPy's other recording
methods, see the section on `Recording Simulation Results`_.

------------

**Example** The following program uses a ``Monitor`` to record the
``server`` resource's queues.  After the simulation ends, it displays
some summary statistics for each queue, and then their complete time
series:

.. include:: programs/resourcemonitor.py
   :literal:




The output from this program is::

   c1 requests 1 unit at t = 0
   c2 requests 1 unit at t = 0
   c3 requests 1 unit at t = 0
   c4 requests 1 unit at t = 0
   c1 done at t = 100
   c2 done at t = 200
   c3 done at t = 300
   c4 done at t = 400

   (Time) Average no. waiting: 1.5
   (Number) Average no. waiting: 1.5
   (Number) Var of no. waiting: 0.916666666667
   (Number) SD of no. waiting: 0.957427107756
   (Time) Average no. in service: 1.0
   (Number) Average no. in service: 0.5
   (Number) Var of no. in service: 0.25
   (Number) SD of no. in service: 0.5
   ========================================
   Time history for the 'server' waitQ:
   [time, waitQ]
   [0, 1]
   [0, 2]
   [0, 3]
   [100, 2]
   [200, 1]
   [300, 0]
   ========================================
   Time history for the 'server' activeQ:
   [time, activeQ]
   [0, 1]
   [100, 0]
   [100, 1]
   [200, 0]
   [200, 1]
   [300, 0]
   [300, 1]
   [400, 0]

This output illustrates the difference between the *(Time) Average*
and the *number statistics*. Here process ``c1`` was in the ``waitQ``
for zero time units, process ``c2`` for 100 time units, and so
forth. The total wait time accumulated by all four processes during
the entire simulation run, which ended at time 400, amounts to 0 + 100
+ 200 + 300 = 600 time units. Dividing the 600 accumulated time units
by the simulation run time of 400 gives 1.5 for the *(Time) Average*
number of processes in the ``waitQ``. It is the time-weighted average
length of the ``waitQ``, but is almost always called simply the average
length of the ``waitQ`` or the average number of items waiting for a
resource.

It is also the expected number of processes you would find in the
``waitQ`` if you took a snapshot of it at a random time during the
simulation. The ``activeQ``\ 's time average computation is similar,
although in this example the resource is held by some process
throughout the simulation. Even though the number in the ``activeQ``
momentarily drops to zero as one process releases the resource and
immediately rises to one as the next process acquires it, that occurs
instantaneously and so contributes nothing to the *(Time) Average*
computation.

------------

*Number statistics* such as the Average, Variance, and SD are computed
differently. At time zero the number of processes in the ``waitQ``
starts at 1, then rises to 2, and then to 3. At time 100 it drops back
to two processes, and so forth. The average and standard deviation of
the six values [1, 2, 3, 2, 1, 0] is 1.5 and 0.9574...,
respectively. Number statistics for the ``activeQ`` are computed using
the eight values [1, 0, 1, 0, 1, 0, 1, 0] and are as shown in the
output.

When the ``monitorType`` is changed to ``Tally``, all the output up to
and including the lines::

   Time history for the 'server' waitQ:
   [time, waitQ]

is displayed. Then the output concludes with an error message
indicating a problem with the reference to ``server.waitMon``. Of
course, this is because ``Tally`` does not generate complete time
series.


[Return to Top_ ]



.. ==========================================================================




Levels
-----------

The three resource facilities provided by the SimPy system are
Resources_, Levels_ and Stores_. Each models a congestion point where
process objects may have to queue up to obtain resources. This section
describes the Level type of resource facility.

Levels model the production and consumption of a homogeneous
undifferentiated "material." Thus, the currently-available amount of
material in a Level resource facility can be fully described by a
scalar (real or integer). Process objects may increase or decrease the
currently-available amount of material in a Level facility. 

For example, a gasoline station stores gas (petrol) in large
tanks. Tankers increase, and refueled cars decrease, the amount of gas
in the station's storage tanks. Both getting amounts and putting
amounts may be subjected to reneging_ like requesting amounts from a
Resource.

Defining a Level
~~~~~~~~~~~~~~~~~~

You define the Level resource facility *lev* by a statement like this::

 lev = Level(name='a_level', unitName='units',
             capacity='unbounded', initialBuffered=0,
             putQType=FIFO, getQType=FIFO,
             monitored=False, monitorType=Monitor)

where

 - ``name`` (string type) is a descriptive name for the Level object *lev* is
   known (e.g., ``'inventory'``).
 - ``unitName`` (string type) is a descriptive name for the units in which the
   amount of material in *lev* is measured (e.g., ``'kilograms'``).
 - ``capacity`` (positive real or integer) is the capacity of the Level
   object *lev*.  The default value is set to ``'unbounded'`` which is
   interpreted as ``sys.maxint``.
 - ``initialBuffered`` (positive real or integer) is the initial amount of 
   material in the Level object *lev*.
 - ``putQType`` (``FIFO`` or ``PriorityQ``) is the (producer) queue
   discipline.
 - ``getQType`` (``FIFO`` or ``PriorityQ``) is the (consumer) queue
   discipline.
 - ``monitored`` (boolean) specifies whether the queues and the amount
   of material in *lev* will be recorded.
 - ``monitorType`` (``Monitor`` or ``Tally``) specifies which type of 
   Recorder_ to use. Defaults to ``Monitor``.

Every Level resource object, such as ``lev``, also has the following
additional attributes:

 - ``lev.amount`` is the amount currently held in *lev*.

 - ``lev.putQ`` is the queue of processes waiting to add amounts to *lev*,
   so ``len(lev.putQ)`` is the number of processes waiting to add
   amounts.

 - ``lev.getQ`` is the queue of processes waiting to get amounts from
   *lev*, so ``len(lev.getQ)`` is the number of processes waiting to
   get amounts.

 - ``lev.monitored`` is ``True`` if the queues are to be recorded. In
   this case ``lev.putQMon``, ``lev.getQMon``, and ``lev.bufferMon``
   exist.

 - ``lev.putQMon`` is a Recorder_ observing ``lev.putQ``.

 - ``lev.getQMon`` is a Recorder_ observing ``lev.getQ``.

 - ``lev.bufferMon`` is a Recorder_ observing ``lev.amount``.


Getting amounts from a Level
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


.. |yget| replace:: ``yield get,self,``\ *lev,ask[,P]* 

Processes can request amounts from a Level and the same or other
processes can offer amounts to it.

A process, the *requester*, can request an amount *ask* from the
Level resource object *lev* by a ``yield get`` statement.:

*  ``yield get,self,``\ *lev,ask[,P]* 

Here *ask* must be a positive real or integer (the amount) and *P* is
an optional priority value (real or integer). If *lev* does not hold
enough to satisfy the request (that is, *ask > lev*\ ``.amount``) the
requesting process is passivated and queued (in *lev*\ ``.getQ``) in order
of its priority. Subject to the priority order, it will be reactivated
when there is enough to satisfy the request.

``self.got`` holds the amount actually received by the requester.



Putting amounts into  a Level
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


.. |yput| replace:: ``yield put,self,``\ *lev,give[,P]* 


A process, the *offerer*, which is usually but not necessarily
different from the *requester*, can offer an amount *give* to a Level,
*lev*, by a ``yield put`` statement:

*  ``yield put,self,``\ *lev,give[,P]*

Here *give* must be a positive real or integer, and *P* is an optional
priority value (real or integer). If the amount offered would lead to
an overflow (that is, *lev.*\ ``amount`` + *give > lev.*\
``capacity``)  the offering process is passivated and queued (in
``lev.putQ``). Subject to the priority order, it will be reactivated
when there is enough space to hold the amount offered.

The orderings of processes in a Level's ``getQ`` and ``putQ`` behave like
those described for the ``waitQ`` under Resources_, except that they are
not preemptable.  Thus, priority values are ignored when the queue
type is FIFO. Otherwise higher priority values have higher priority,
etc.

------------

**Example**. Suppose that a random demand on an inventory is made each
day.  Each requested amount is distributed normally with a mean of 1.2
units and a standard deviation of 0.2 units.  The inventory (modeled
as an object of the Level class) is refilled by 10 units at fixed
intervals of 10 days. There are no back-orders, but a accumulated sum
of the total stock-out quantities is to be maintained.  A trace is to
be printed out each day and whenever there is a stock-out:

.. include:: programs/levelinventory.py
   :literal:


Here is the last ten day's output from one run of this program::

 at 40.0000, add 10.0000 units, now amount = 10.0000
 day 40.0000, supplied  0.7490, now amount = 9.2510
 day 41.0000, supplied  1.1651, now amount = 8.0858
 day 42.0000, supplied  1.1117, now amount = 6.9741
 day 43.0000, supplied  1.1535, now amount = 5.8206
 day 44.0000, supplied  0.9202, now amount = 4.9004
 day 45.0000, supplied  0.8990, now amount = 4.0014
 day 46.0000, supplied  1.1448, now amount = 2.8566
 day 47.0000, supplied  1.7287, now amount = 1.1279
 day 48.0000, supplied  0.9608, now amount = 0.1670
 day 49.0000, demand =  0.9837, shortfall = -0.8167

 Summary of results through end of day 49.0000:
 average stock =  4.2720, cumulative stockout =  9.7484                
    
------------

[Return to Top_ ]

Reneging
~~~~~~~~~~~~~~

.. |ryput| replace:: ``yield (put,self,``\ *lev,ask[,P]*\ ``),``

The ``yield put`` can be subject to reneging_ using one of the compound
statements:

*  |ryput|\ ``(hold,self,``\ *waittime*\ ``)``

where if the process does not acquire the amount before *waittime* is
elapsed, the offerer leaves the ``waitQ`` and its execution continues or

*  |ryput|\ ``(waitevent,self,``\ *events*\ ``)``

where if one of the SimEvents in *events* occurs before enough becomes
available, the offerer leaves the ``waitQ`` and its execution
continues.

In either case if reneging has *not* occurred the quantity will have
been put into the Level and ``self.stored(``\ *lev*\ ``)`` will be
``True``. This must be tested immediately after the ``yield``::

 yield (put,self,lev,ask[,P]),(<reneging clause>)
 if self.stored(lev):
    ## process  did not renege
    . . . .
 else:
    ## process reneged before being able to put into the resource


The ``yield get`` can also be subject to reneging_ using one of the compound
statements:

*  ``yield (get,self,lev,ask[,P]),(hold,self,waittime)``

where if the process does not acquire the amount before *waittime* is
elapsed, the offerer leaves the ``waitQ`` and its execution continues.

*  ``yield (get,self,lev,ask[,P]),(waitevent,self,events)``

where if one of the SimEvents in *events* occurs before enough becomes
available, reneging occurs, the offerer leaves the ``waitQ`` and its execution
continues.

In either case if reneging has *not* occurred ``self.got == ask`` and
``self.acquired(lev)`` will be ``True``. ``self.acquired(lev)`` must be called
immediately after the ``yield``::

 yield (get,self,lev,ask[,P]),(<reneging clause>)
 if self.acquired(lev):
    ## process  did not renege, self.got == ask
    . . . .
 else:
    ## process reneged before being able to put into the resource

This test removes the reneging process from the ``getQ``.

[Return to Top_ ]

.. =================================================================


Stores
-----------

The three resource facilities provided by the SimPy system are
Resources_, Levels_ and Stores_. Each models a congestion point where
process objects may have to queue up to obtain resources. This section
describes the Store type of resource facility.

Stores model the production and consumption of individual items of any
Python type.  Process objects can insert or remove specific items from
the list of items available in a Store.  For example, surgical
procedures (treated as process objects) require specific lists of
personnel and equipment that may be treated as the items available in
a Store type of resource facility such as a clinic or hospital. As the
items held in a Store may be of any Python type, they may in
particular be process objects, and this can be exploited to facilitate
modeling Master/Slave relationships. *putting* and *getting* may also
be subjected to reneging.

Defining a Store
~~~~~~~~~~~~~~~~~~~

The Store object ``sObj`` is established by a statement
like the following::

 sObj = Store(name='a_store', 
              unitName='units',
              capacity='unbounded',
              initialBuffered=None,
              putQType=FIFO, 
              getQType=FIFO,
              monitored=False, 
              monitorType=Monitor)

where

 - ``name`` (string type) is a descriptive name for *sObj* (e.g.,
   ``'Inventory'``).
 - ``unitName`` (string type) is a descriptive name for the items 
   in *sObj* (e.g., ``'widgets'``).
 - ``capacity`` (positive integer) is the maximum number of 
   individual items that can be held in *sObj*.
   The default value is set to ``'unbounded'`` which is
   interpreted as ``sys.maxint``.
 - ``initialBuffered`` (a list of individual items) is *sObj*'s initial content.
 - ``putQType`` (``FIFO`` or ``PriorityQ``) is the (producer) queue
   discipline.
 - ``getQType`` (``FIFO`` or ``PriorityQ``) is the (consumer) queue discipline.
 - ``monitored`` (boolean) specifies whether *sObj*'s queues and contents 
   are to be recorded.
 - ``monitorType`` (``Monitor`` or ``Tally``) specifies the type of Recorder_
   to be used. Defaults to ``Monitor``.

The Store object *sObj* also has the following additional attributes:

 - ``sObj.theBuffer`` is a queue (list) of the individual items in *sObj*.
   This list is in FIFO order unless the user stores them in a particular
   order (see `Storing objects in an order`_ , below). It is
   read-only and not directly changeable by the user.
   
 - ``sObj.nrBuffered`` is the current number of objects in *sObj*.
   This is read-only and not directly changeable by the user.
   
 - ``sObj.putQ`` is the queue of processes waiting to add items to *sObj*,
   so that ``len(sObj.putQ)`` is the number of processes waiting to add items.
   
 - ``sObj.getQ`` is the queue of processes waiting to get items from
   *sObj*, so that ``len(sObj.getQ)`` is the number of processes waiting to
   get items.

 - If ``sObj.monitored`` is ``True`` then the queues are to be recorded.
   In this case ``sObj.putQMon``,
   ``sObj.getQMon``, and ``sObj.bufferMon`` exist.

 - ``sObj.putQMon`` is a Recorder_ observing ``sObj.putQ``.
 
 - ``sObj.getQMon`` is a Recorder_ observing ``sObj.getQ``.
 
 - ``sObj.bufferMon``  is a Recorder_ observing ``sObj.nrBuffered``.


Putting objects into a Store
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Processes can request items from a Store and the same or 
other processes can offer items to it. First look at the simpler of
these operations, the ``yield put``.

A process, the *offerer*, which is usually but not necessarily
different from the *requester*, can offer a list of
items to *sObj* by a ``yield put`` statement:

* ``yield put,self,sObj,give[,P]``

Here ``give`` is a list of any Python objects. If this statement would
lead to an overflow (that is, ``sObj.nrBuffered + len(give) >
sObj.capacity``) the putting process is passivated and queued (in
``sObj.putQ``) until there is sufficient room. *P* is an optional
priority value (real or integer).

The ordering of processes in a Store's putQ and getQ behave like those
described for the waitQ under Resources_ , except that they are not
preemptable.  Thus, priority values are ignored when the queue type is
FIFO.  Otherwise higher priority values indicate higher priority, etc.

The items in *sObj* are stored in the form of a queue called
``sObj.theBuffer``, which is in FIFO order unless the user has
arranged to sort them into a particular order (see `Storing objects in
an order`_ below).



Getting objects from  a Store
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

There are two ways of getting objects from a Store. A process, the
*requester*, can either extract the first ``n`` objects from *sObj* or a list
of items chosen by a *filter function*.  

Getting *n* items is achieved by the following statement:

* ``yield get,self,sObj,n [,P]``

Here *n* must be a positive integer and *P* is an optional priority
value (real or integer).  If *sObj* does not currently hold enough
objects to satisfy this request (that is, ``n > sObj.nrBuffered``)
then the requesting process is passivated and queued (in
``sObj.getQ``). Subject to the priority ordering, it will be
reactivated when the request can be satisfied.

The retrieved objects are returned in the list attribute ``got`` of
the requesting process.

``yield get`` requests with a numerical parameter are honored in 
priority/FIFO order. Thus, if 
there are two processes in the Store's ``getQ``, with the first requesting
two items and the second one, the second process gets the requested item
only after the first process has been given its two items.

Using the get filter function
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The second method is to get a list of items chosen by a *filter
function*, written by the user.

The command, using filter function *ffn* is as follows:

* ``yield get,self,sObj,ffn [,P]``


The user provides a filter function that has a single list argument and
returns a list. The argument represents the buffer of the Store. The
function must search through the objects in the buffer and return a
sub-list of those that satisfy the requirement. 

------------

**Example** The filter function ``allweight``, shown below, is an example
of such a filter. The argument, ``buff``, will be automatically 
replaced in the execution of 
``yield get,self,store,allweight`` by the buffer of the Store. In this
example the objects in the Store are assumed to have ``weight``
attributes. The function ``allweight`` selects all those that have a
weight attribute over a value ``W`` and returns these as a list. The
list appears to the calling process as ``self.got``::

        def allweight(buff):
            """filter: get all items with .weight >=W from store"""
            result=[]
            for i in buff:
                if i.weight>=W:
                    result.append(i)
            return result

This might be used as follows::

    yield get,self,sObj,allweight [,P]

The retrieved objects are returned in the list attribute ``got`` of
the requesting process.

------------

**Note:** *``yield get``* requests with a filter function parameter
are not necessarily honored in priority/FIFO order, but rather
according to the filter function. An example: There are two processes
in the Store's ``getQ``, with the first requesting an item with a
*weight* attribute less than 2 kilograms and the second one requesting
one with a ``weight`` attribute less than 3 kilograms. If there is an
item in the Store's buffer with a ``weight`` attribute between 2 and 3
and none with an attribute of less than 2, the second ``get`` requester
gets unblocked before the first one. Effectively, the SimPy run time
system runs through all processes in the ``getQ`` in sequence and
tests their filter functions as long as there are still items in the
Store's buffer.

------------

**Example** The following program illustrates the use of a Store to
model the production and consumption of "widgets". The widgets are
distinguished by their weight:

.. include:: programs/storewidget.py
   :literal:

   
This program produces the following outputs 
(some lines may be formatted differently)::

    0 Got widget weights [5, 5, 5]
    0 Got widget weights [5, 5, 5]
    0 Got widget weights [5, 5, 5]
    11 Got widget weights [5, 9, 7]
    11 Got widget weights [9, 7, 9]
    11 Got widget weights [7, 9, 7]
    22 Got widget weights [9, 7, 9]
    22 Got widget weights [7, 9, 7]
    22 Got widget weights [9, 7, 9]
    33 Got widget weights [7, 9, 7]
    33 Got widget weights [9, 7, 9]
    40 Got widget weights [7, 9, 7]
    44 Got widget weights [9, 7, 9]
    50 Got widget weights [7, 9, 7]
    LenBuffer: [[0, 10], [0, 7], [0, 9], [0, 11], [0, 8], [0, 10], [0, 7], 
        [10, 9], [10, 11], [11, 8], [11, 10], [11, 7], [11, 4], 
        [20, 6], [20, 8], [21, 10], [22, 7], [22, 4], [22, 1], 
        [30, 3], [30, 5], [31, 7], [33, 4], [33, 1], 
        [40, 3], [40, 0], [40, 2], [41, 4], [44, 1], [50, 3], [50, 0], [50, 2]]
    getQ: [[0, 0], [33, 1], [40, 0], [44, 1], [50, 0]]
    putQ [[0, 0], [0, 1], [0, 2], [0, 3], [0, 2], [0, 1], [0, 0], [10, 1],\
        [11, 0]]   

--------------
       
[Return to Top_ ]


Reneging
~~~~~~~~~~~~~~

The ``yield put`` can be subject to reneging_ using one of the compound
statements:

*  ``yield (put,self,sObj,give [,P]),(hold,self,waittime)``

where if the process cannot put the list of objects in *give* before *waittime* is
elapsed, the offerer leaves the ``putQ`` and its execution continues or

*  ``yield (put,self,sObj,give [,P]),(waitevent,self,events)``

where if one of the SimEvents in *events* occurs before it can put the
list of objects in *give* the offerer leaves the ``putQ`` and its
execution continues.

In either case if reneging has *not* occurred the list of objects in
*give* will have been put into the Store and ``self.stored(Sobj)`` will
be ``True``. 

The mandatory pattern for a ``put`` with reneging is::

 yield (put,self,sObj,give [,P]),(<reneging clause>)
 if self.stored(sObj):
    ## process  did not renege
    . . . .
 else:
    ## process reneged before being able to put into the resource
    
This is so because ``self.stored( )`` not only tests for reneging, but
it also cleanly removes a reneging process from the ``putQ``.

The ``yield get`` can be subject to similar reneging_ using one of the compound
statements:

*  ``yield (get,self,sObj,n [,P]),(hold,self,waittime)``
*  ``yield (get,self,sObj,ffn [,P]),(hold,self,waittime)``

where if the process does not acquire the amount before *waittime* is
elapsed, the offerer leaves the ``waitQ`` and its execution continues.

*  ``yield (get,self,sObj,n [,P]),(waitevent,self,events)``
*  ``yield (get,self,sObj,ffn [,P]),(waitevent,self,events)``

where if one of the SimEvents in *events* occurs before enough becomes
available, reneging occurs, the offerer leaves the ``waitQ`` and its execution
continues.

In either case if reneging has *not* occurred ``self.got`` contains
the list of retrieved objects and ``self.acquired(Sobj)`` will be
``True``. 

The mandatory pattern for a ``get`` with reneging is::

 yield (get,self,lev,sObj,<n or ffn> [,P]),(<reneging clause>)
 if self.acquired(sObj):
    ## process  did not renege,
    . . . .
 else:
    ## process reneged before being able to put into the resource

This is so because ``self.acquired( )`` not only tests for reneging, but
it also cleanly removes a reneging process from the ``getQ``.

[Return to Top_ ]

Storing objects in an order
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The contents of a Store instance are listed in a queue.  By default,
this list is kept in FIFO order.  However, the list can be kept in a
user-defined order. You do this by defining a function for reordering
the list and adding it to the Store instance for which you want to
change the list order. Subsequently, the SimPy system will
automatically call that function after any addition (``put``) to the
queue.

------------

**Example** ::

   class Parcel:
        def __init__(self,weight):
            self.weight=weight

   lightFirst=Store()

   def getLightFirst(self,par):
        """Lighter parcels to front of queue"""
        tmplist=[(x.weight,x) for x in par]
        tmplist.sort()
        return [x for (key,x) in tmplist]

   lightFirst.addSort(getLightFirst)
   
Now any ``yield get`` will get the lightest parcel in ``lightFirst``'s
queue.

------------

The ``par`` parameter is automatically given the Store's buffer list as value 
when the SimPy run time system calls the re-ordering function.

*<aStore>.addSort(<reorderFunction>)* adds a re-order function to <aStore>.

Note that such function only changes the sorting order of the Store instance,
NOT of the Store class.




Master/Slave modeling with a Store
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The items in a ``Store`` can be of any Python type. In particular, they
may be SimPy processes. This can be used to model
a Master/Slave situation -- an asymmetrical cooperation between two or
more processes, with one process (the Master) being in charge of the
cooperation.

The consumer (Master) requests one or more Slaves to be added to the
Store's contents by the Producer (which may be the same process as the
Slave).  For Master/Slave cooperation, the Slave has to be passivated
(by a ``yield passivate`` or ``yield waitevent`` statement) after it is
``put`` and reactivated when it is retrieved and finished with. As this
is NOT done automatically by the ``Store``, the Master has to signal the
end of the cooperation. This Master/Slave pattern results in the slave process'
life-cycle having a hole between the slave process arrival and its departure
after having been served.

------------

**Example** Cars arrive randomly at a car wash and add themselves to
the ``waitingCars`` queue. They wait (passively) for a ``doneSignal``.
There are two ``Carwash`` washers. These ``get`` a car, if one is
available, wash it, and then send the ``doneSignal`` to reactivate
it. We elect to model the ``Carwash`` as Master and the ``Cars`` as
slaves.

Four cars are put into the ``waiting`` list and these make up the
initial set of cars waiting for service. Additional cars are generated
randomly by the ``CarGenerator`` process. Each car ``yield puts`` itself
onto the ``waitingCars`` ``Store`` and immediately passivates itself by
waiting for a ``doneSignal`` from a car washer. The car washers cycle
round ``getting`` the next car on the queue, washing it and then sending
a ``doneSignal`` to it when it has finished:

.. include:: programs/carwash.py
   :literal:

The output of this program, running to time 30, is::

   5 car -1 done by Carwash 0
   5 car -2 done by Carwash 1
   10 car -3 done by Carwash 0
   10 car -4 done by Carwash 1
   15 car 0 done by Carwash 0
   15 car 1 done by Carwash 1
   20 car 2 done by Carwash 0
   20 car 3 done by Carwash 1
   25 car 4 done by Carwash 0
   25 car 5 done by Carwash 1
   30 car 6 done by Carwash 0
   30 car 7 done by Carwash 1
   waitingCars [10, 11, 12, 13, 14]

It is also possible to model this car wash with the cars as Master
and the ``Carwash`` as Slaves.

------------


[Return to Top_ ]

.. ==========================================================================

Random Number Generation
-------------------------

Simulations usually need random numbers. As SimPy does not supply
random number generators of its own, users need to import them from
some other source.  Perhaps the most convenient source is the standard
`Python random module`_.  It can generate random variates from the
following continuous distributions: uniform, beta, exponential, gamma,
normal, log-normal, weibull, and vonMises.  It can also generate random
variates from some discrete distributions. Consult the module's
documentation for details.  (Excellent brief descriptions of these
distributions, and many others, can be found in the `Wikipedia
<http://www.wikipedia.com/>`_.)

Python's ``random`` module can be used in two ways: you can import the
methods directly or you can import the ``Random`` class and make your
own random objects. In the second method, each object gives a
different random number sequence, thus providing multiple random
streams as in Simscript and ModSim.

Here the first method is described (and minimally at that). A single
pseudo-random sequence is used for all calls. You ``import`` the
methods you need from the ``random`` module. For example::

 from random import seed, random, expovariate, normalvariate

In simulation it is good practice to set the initial ``seed`` for the
pseudo-random sequence at the start of each run. Then you have control
over the random numbers used. Replications and comparisons are
easier and, together with variance reduction techniques, can provide more
accurate estimates. In the following code snippet we set the initial
seed to 333555.  ``X`` and ``Y`` are pseudo-random variates from the
two distributions. Both distributions have the same mean::

   from random import seed, expovariate, normalvariate

   seed(333555)
   X = expovariate(0.1)
   Y = normalvariate(10.0, 1.0)


[Return to Top_ ]

.. ============================================================================

Recording Simulation Results
-----------------------------

The ``Tally`` and ``Monitor`` class objects  enable us to observe a single
variable of interest and to return a simple data summary either during
or at the completion of a simulation run.

Both use the ``observe`` method to record data on one variable. For
example we might use a Monitor object to record the waiting times for
a sequence of customers and another to record the total number of
customers in the shop. In a discrete-event system the number of
customers changes only at arrival or departure events and it is at
those events that the waiting times and number in the shop must be
observed. Monitors and Tallys provide elementary statistics useful
either alone or as the start of a more sophisticated statistical
analysis and have proved invaluable in many simulations.

A few more tools associated with recording results are:

- All Monitors are registered automatically in the global list
  variable ``allMonitors`` and all Tallys in variable
  ``allTallies``. When a simulation is completed results can easily be
  tabulated and summarized using these lists.

- The function ``startCollection()`` can be called to initialize Monitors
  and Tallys at a certain simulation time. This is helpful when a
  simulation needs a 'warmup' period to achieve steady state before
  measurements are started.


Defining Tallys and Monitors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ''Tally'' class records enough information (such as sums and sums of
squares) while the simulation runs to return simple data summaries.
This has the advantage of speed and low memory use. Tallys can also
furnish data for a histogram. However, they do not preserve a
time-series usable in more advanced statistical analysis. When a Tally
is defined it is automatically added  to the global list ``allTallies``.

To define a new Tally object:

* ``m=Tally(name='a_Tally', ylab='y', tlab='t')``

 - ``name`` is a descriptive name for the tally object (default='``a_Tally``' ).

 - ``ylab`` and ``tlab`` are descriptive labels used by the ``SimPy.SimPlot``
   package when plotting graphs of the recorded data. They
   default to ``'y'`` and ``'t'``, respectively. (If a histogram_ is
   required the method ``setHistogram`` must be called before recording
   starts).

The ``Monitor`` class preserves a complete time-series of the observed
data values, *y*, and their associated times, *t*. It calculates the
data summaries using these series only when they are needed. It is
slower and uses more memory than ``Tally``. In long simulations its
memory demands may be a disadvantage. When a Monitor is defined it is
automatically added to the global list ``allMonitors``.

To define a new Monitor object:

* ``m=Monitor(name='a_Monitor', ylab='y', tlab='t')``

 - ``name`` is a descriptive name for the Monitor object (default='``a_Monitor``').

 - ``ylab`` and ``tlab`` are descriptive labels used by the ``SimPy.SimPlot``
   package when plotting graphs of the recorded data. They
   default to ``'y'`` and ``'t'``, respectively. 
   (A histogram_ can be
   requested at any time).

.. _histogram: Histograms_


Observing data
~~~~~~~~~~~~~~~~~

Both Tallys and Monitors use the ``observe`` method to record data.
Here and in the next section, *r* is either a Tally or a Monitor object:

* ``r.observe(y [,t])`` records the current value of the variable, ``y``
  and time *t* (or the current time, ``now( )``, if *t* is missing). A
  Monitor retains the two values as a sub-list ``[t,y]``. A Tally
  uses them to update the accumulated statistics.

  To assure that time averages are calculated correctly ``observe``
  should be called immediately *after* a change in the variable. For
  example, if we are using Monitor *r* to record the number *N* 
  of jobs in a system, the correct sequence of commands on an arrival
  is::

     N = N+1      # FIRST, increment the number of jobs
     r.observe(N) # THEN observe the new value of N using r


The recording of data can be ``reset`` to start at any time in the
simulation:

* ``r.reset([t])`` resets the observations. The recorded data is
  re-initialized, and the observation starting time is set to *t*,
  or to the current simulation time, ``now( )``, if *t*
  is missing.

Data summaries
~~~~~~~~~~~~~~~~~

The following simple data summaries can be obtained from either
Monitors or Tallys at any time during or after the simulation run:

* ``r.count( )``, the current number of observations. (If *r* is a
  Monitor this is the same as ``len(r)``).

* ``r.total( )``, the sum of the ``y`` values

* ``r.mean( )``, the simple average of the observed ``y`` values,
  ignoring the times at which they were made.  This is ``r.total( )/N``
  where ``N=r.count( )``. (If there are no observations, the message:
  "SimPy: No observations for mean" is printed). See
  `Recording Resource queue lengths`_ for the difference between
  the simple or numerical average and the time-average.

  .. figure:: images/Mon004.png
     :alt: Standard mean value
     :align: center
     :figwidth: 400

     ``r.mean`` is the simple average of the ``y`` values observed.

* ``r.var( )`` the *sample* variance of the observations, ignoring the
  times at which they were made. If an unbiased estimate of the
  *population* variance is desired, the sample variance should be
  multiplied by *n/(n-1)*, where *n = r.count( )*.  In either case the
  standard deviation is, of course, the square-root of the variance
  (If there are no observations, the message: "SimPy: No observations
  for sample variance" is printed).

* ``r.timeAverage([t])`` the time-weighted average of ``y``,
  calculated from time 0 (or the last time ``r.reset([t])`` was
  called) to time *t* (or to the current simulation time, ``now( )``,
  if *t* is missing).  This is determined from the area under the
  graph shown in the figure, divided by the total time of observation.
  For accurate time-average results ``y`` most be piecewise constant
  and ``observed`` just after each change in its value. (If there are
  no observations, the message "SimPy: No observations for
  timeAverage" is printed. If no time has elapsed, the message "SimPy:
  No elapsed time for timeAverage" is printed).

  .. figure:: images/Mon005.png
     :alt: Time Average
     :align:  center
     :figwidth: 400

     ``r.timeAverage( )`` is the time-weighted average of the observed
     ``y`` values. Each ``y`` value is weighted by the time for which it
     exists. The average is the area under the above curve divided by the total
     time, *t*.

* ``r.timeVariance([t])`` the time-weighted variance of the ``y``
  values calculated from time 0 (or the last time ``r.reset([t])`` was
  called) to time *t* (or to the current simulation time, ``now()``,
  if *t* is missing). 

* ``r.__str__( )`` is a string that briefly describes the current state
  of the monitor. This can be used in a print statement.


Special methods for Monitor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``Monitor`` variety of Recorder is a sub-class of ``List`` 
and has a few extra methods:

* ``m[i]`` holds the observation ``i`` as a two-item list, *[ti, yi]*
* ``m.yseries( )`` is a list of the recorded data values, *yi*
* ``m.tseries( )`` is a list of the recorded times, *ti*



Histograms
~~~~~~~~~~~~~~~~~

A ``Histogram`` is a derived class of ``list`` that counts the
observations that fall into a number of specified ranges, called bins.
A histogram object can be displayed either by printing it out in text
form using ``printHistogram`` method or using the ``plotHistogram`` method in the
``SimPy.SimPlot`` package.


* ``h = Histogram(low=<float>,high=<float>,nbins=<integer>)`` is a
  histogram object that counts the
  number of ``y`` values in each of its bins, based on the
  recorded ``y`` values.

  - ``low`` is the nominal lowest value of the histogram (default=0.0)
  - ``high`` is the nominal highest value of the histogram (default=100.0)

  - ``nbins`` is the number of bins between ``low`` and ``high`` into which
    the histogram is to be divided (default=10). 
    SimPy automatically constructs an additional two bins 
    to count the
    number of ``y`` values ``under`` the ``low`` value and the number
    ``over`` the ``high`` value. Thus, the total number of bins 
    actually used is ``nbins + 2``. The number of ``y``
    values in each of these bins is counted and assigned 
    to the appropriate bin. 

  .. figure:: images/Mon006.png
     :align: center
     :alt: Histogram
     :figwidth: 500

     A Histogram contains the number of observed ``y`` values falling
     into each of its ``nbins+2`` bins.

A Histogram, *h*, can be printed out in text form using 

* ``h.printHistogram(fmt="%s")`` prints out a histogram in a standard
  format. 

  - ``fmt`` is a python string format for the bin range values.
    
.. THIS MUST BE CLARIFIED. hOW IS IT USED WITH MONITOR?

------------

**Example** Printing a histogram from a Tally::

   from SimPy.Simulation import *
   import random as r

   print version

   t=Tally(name="myTally",ylab="wait time (sec)")
   t.setHistogram(low=0.0,high=1.0,nbins=10)
   for i in range(100000):
       t.observe(y=r.random())
   print t.printHistogram(fmt="%6.4f")

This gives a printed histogram like this::

   Histogram for myTally:
   Number of observations: 100000
             wait time (sec) < 0.0000:      0 (cum:      0/  0.0%)
   0.0000 <= wait time (sec) < 0.1000:   9983 (cum:   9983/ 10.0%)
   0.1000 <= wait time (sec) < 0.2000:  10121 (cum:  20104/ 20.1%)
   0.2000 <= wait time (sec) < 0.3000:   9800 (cum:  29904/ 29.9%)
   0.3000 <= wait time (sec) < 0.4000:   9911 (cum:  39815/ 39.8%)
   0.4000 <= wait time (sec) < 0.5000:   9996 (cum:  49811/ 49.8%)
   0.5000 <= wait time (sec) < 0.6000:   9881 (cum:  59692/ 59.7%)
   0.6000 <= wait time (sec) < 0.7000:  10144 (cum:  69836/ 69.8%)
   0.7000 <= wait time (sec) < 0.8000:  10029 (cum:  79865/ 79.9%)
   0.8000 <= wait time (sec) < 0.9000:  10088 (cum:  89953/ 90.0%)
   0.9000 <= wait time (sec) < 1.0000:  10047 (cum: 100000/100.0%)
   1.0000 <= wait time (sec)         :      0 (cum: 100000/100.0%)

---------


Although both Tallys and Monitors can return a histogram of the data, they
furnish histogram data in different ways.

* The Tally object accumulates the histogram's bin counts as each
  value is observed during the simulation run. Since none of the
  individual values are preserved, the ``setHistogram`` method must be
  called to provide a histogram object to hold the accumulated bin
  counts before any values are actually observed.

* The Monitor object stores all its data, so the accumulated bin
  counts can be computed whenever they are desired. Thus, the
  histogram need not be set up until it is needed and this can be done
  after the data has been gathered.


Setting up a Histogram for a Tally object
++++++++++++++++++++++++++++++++++++++++++++

To establish a histogram for a Tally object, ``r``, we call the
``setHistogram`` method with appropriate arguments before we observe any
data, e.g.,

* ``r.setHistogram(name = '',low=0.0,high=100.0,nbins=10)``

As usual, ``name`` is a descriptive title for the histogram (defaults to blank). 
Then, after ``observing`` the data:

* ``h=r.getHistogram( )`` returns a completed histogram using the
  histogram parameters as set up.

---------

**Example** In the following example we establish a ``Tally`` recorder
to observe values of an exponential random variate. It uses a
histogram with 30 bins (plus the under- and over-count bins)::

   from SimPy.Simulation import *
   from random import expovariate

   r = Tally('Tally')                          # define a tally object, r
   r.setHistogram(name='exponential',
                  low=0.0,high=20.0,nbins=30)  # set before observations

   for i in range(1000):    # make the observations
      y = expovariate(0.1)
      r.observe(y)

   h = r.getHistogram()     # return the completed histogram

---------

Setting up a Histogram for a Monitor object
++++++++++++++++++++++++++++++++++++++++++++++

For Monitor objects, a histogram can be set up and returned in
a single call, e.g.,

* ``h = r.histogram(low=0.0,high=100.0,nbins=10)``

This  call is  equivalent to the following pair:

* ``r.setHistogram(name = '',low=0.0,high=100.0,nbins=10)``
* ``h = r.getHistogram( )``, which returns the completed histogram.

---------

**Example** Here we establish a ``Monitor`` to observe values of an
exponential random variate. It uses a histogram with 30 bins (plus the
under- and over-count bins)::

   from SimPy.Simulation import *
   from random import expovariate

   m = Monitor()        # define the Monitor object, m

   for i in range(1000):    # make the observations
      y = expovariate(0.1)
      m.observe(y)

      # set up and return the completed histogram
   h = m.histogram(low=0.0, high=20, nbins=30)

---------

.. -------------------------------------------------------------------------

.. Note: The following methods of the Monitor class are
   retained for backwards compatibility
   but are not recommended. They may be removed in future releases of
   SimPy.

   * ``r.tally(y)`` records the current value of ``y`` and the current
     time, ``now( )``. (DO NOT USE)
   * ``r.accum(y [,t])`` records the current value of ``y`` and time ``t``
     (the current time, ``now( )``, if ``t`` is missing). (DO NOT USE)  


.. -------------------------------------------------------------------------

Other Links
-------------------

Several example SimPy models are included with the SimPy code
distribution in the file ``SimPyModels``.

Klaus Muller and Tony Vignaux, *SimPy: Simulating Systems in Python*,
O'Reilly ONLamp.com, 2003-Feb-27,  http://www.onlamp.com/pub/a/python/2003/02/27/simpy.html

Norman Matloff, *Introduction to the SimPy Discrete-Event Simulation
Package*, U Cal: Davis, 2003,
http://heather.cs.ucdavis.edu/~matloff/simpy.html

David Mertz, *Charming Python: SimPy simplifies complex models*, IBM
Developer Works, Dec 2002,
http://www-106.ibm.com/developerworks/linux/library/l-simpy.html


Acknowledgments
-------------------

We thank those users who have sent comments to correct or improve this
text. These include: F. Benichu, Bob Helmbold, M. Matti. We will be
grateful for further corrections or suggestions.



.. ===================================================================

Appendices
-------------


A0. Changes from the previous  version of SimPy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

SimPy 2.0 differs from version 1.9.1 in the following ways:

**Additions:**

- New (object oriented) API added.
- Documentation on running SimPy in parallel processes on multiple CPUs, 
  cores or computerswith Parallel Pythonf

**Changes:**

- The documentation is now formattted using the Sphinx documentation generator.




A1. SimPy Error Messages
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. include:: messages.txt

A2. SimPy Process States
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From the viewpoint of the model builder a SimPy process, *p*,
can at any time be in one of the following states:

- **Active**: Waiting for a scheduled event. This state simulates an
  activity in the model.  Simulated time passes in this
  state. The process state *p.active( )* returns *True*.

- **Passive**: Not active or terminated. Awaiting *(re-)activation* by
  another process.  This state simulates a real world process which
  has not finished and is waiting for some trigger to continue. Does
  not change simulation time.  *p.passive( )* returns *True*.

- **Terminated**: The process has executed all its action statements.
  If referenced, it serves as a data instance. *p.terminated( )*
  returns *True*

Initially (upon creation of the Process instance), a process returns *passive*.

In addition, a SimPy process, *p*,  can be in the following (sub)states:

- **Interrupted**: Active process has been interrupted by another
  process. It can immediately respond to the interrupt. This
  simulates an interruption of a simulated activity before its
  scheduled completion time.  *p.interrupted( )* returns *True*.

- **Queuing**: Active process has requested a busy resource and is
  waiting (passive) to be reactivated upon resource
  availability. *p.queuing(a_resource)* returns *True*.


.. -------------------------------------------------------------------------


A3. SimPlot, The SimPy plotting utility
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``SimPlot`` provides an easy way to graph the results of simulation runs.




A4. SimGUI, The SimPy Graphical User Interface
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``SimGUI``  provides a  way for users to interact with a SimPy program,
changing its parameters and examining the output.




A5. SimulationTrace, the SimPy tracing utility
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``SimulationTrace`` has been developed to give users insight into the
dynamics of the execution of SimPy simulation programs. It can help
developers with testing and users with explaining SimPy models to themselves
and others (e.g., for documentation or teaching purposes).



A6. SimulationStep, the SimPy event stepping utility
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``SimulationStep`` can assist with debugging models, interacting with them on
an event-by-event basis, getting event-by-event output from a model (e.g.
for plotting purposes), etc.

It caters for:

   - running a simulation model, while calling a user-defined procedure after every event,
   - running a simulation model one event at a time by repeated calls,
   - starting and stopping the event-stepping mode under program control.


A7. SimulationRT, a real-time synchronizing utility
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``SimulationRT`` allows synchronizing simulation time and real (wall-clock) time.
This capability can be used to implement, e.g., interactive game applications or
to demonstrate a model's execution in real time.



.. _glossary:




Glossary
-----------
 

.. include:: glossary.txt


.. ------------------------------------------------------------------------
.. some useful stuff used above


.. |simpylogo| image:: images/sm_SimPy_Logo.png
.. _`simpydownload`: http://sourceforge.net/projects/simpy/

.. _`Python random module`: http://www.python.org/doc/current/lib/module-random.html



.. _Top: Contents_
.. _alternatives: `Alternative SimPy simulation libraries`_
.. _Monitor: `Defining Tallys and Monitors`_
.. _Monitors: `Defining Tallys and Monitors`_
.. _Tally: `Defining Tallys and Monitors`_
.. _Tallys: `Defining Tallys and Monitors`_
.. _Recorder: `Recording Simulation Results`_
.. _Recorders: `Recording Simulation Results`_
.. _reneging: `Reneging -- leaving a queue before acquiring a resource`_
.. _interrupted: `Asynchronous interruptions`_
.. _Level: `Levels`_
.. _Store: `Stores`_
.. _Resource: `Resources`_
.. _`Appendix A0`: `A0. Changes from the previous  version of SimPy`_
.. _Python: http://www.Python.org


..
 .. image:: http://sourceforge.net/sflogo.php?group_id=62366&type=4
    :width: 125
    :height: 37
    :alt:  SourceForge Logo

.. ----------------------------------------------------------------------------
.. $Revision: 313 $ $Date: 2009-04-05 01:25:11 +0200 (So, 05 Apr 2009) $
.. ----------------------------------------------------------------------------



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