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Osnove simulacije diskretnih događaja pomoću SimPyja

SimPy je moćan okvir za simulaciju diskretnih događaja napisan u Pythonu. Instalacija: To install SimPy use the following command - 
pip install simpy
Osnovni koncepti: The core idea behind SimPy is the generator function in Python. The difference between a normal function and a generator is that a normal function uses the 'return' statement while a generator uses 'yield' statement. If the function has a return statement then even on multiple function calls it returns the same value. For eg - Python 
def func(): return 1 return 2
When the func() is called during the runtime it will always return at the first instance of the return statement that is the function func() always returns 1 and the next return statement is never executed. However in discrete event simulation we may need to find the state of the system at a given time T. For that it is required to remember the state of the interval just before T and then perform the given simulation and return to state at time T. This is where generator functions are quite useful. For example consider the following function Python 
def func(): while True: yield 1 yield 2
Now when the first time this function is called it 'yields' 1. However on the very next call it will yield 2. In some sense it remembers what it returned upon the last call and moves on to the next yield statement. Events in SimPy are called procesima koji su definirani vlastitim generatorskim funkcijama. Ovi se procesi odvijaju unutar okoliš . (Imagine the environment to be a large box inside of which the processes are kept.) Consider a simple example involving the simulation of a traffic light - Python3 
# Python 3 code to demonstrate basics of SimPy package  # Simulation of a Traffic Light  # import the SimPy package  import simpy # Generator function that defines the working of the traffic light  # 'timeout()' function makes next yield statement wait for a  # given time passed as the argument  def Traffic_Light(env): while True: print ('Light turns GRN at ' + str(env.now)) # Light is green for 25 seconds  yield env.timeout(25) print ('Light turns YEL at ' + str(env.now)) # Light is yellow for 5 seconds  yield env.timeout(5) print ('Light turns RED at ' + str(env.now)) # Light is red for 60 seconds  yield env.timeout(60) # env is the environment variable  env = simpy.Environment() # The process defined by the function Traffic_Light(env)  # is added to the environment  env.process(Traffic_Light(env)) # The process is run for the first 180 seconds (180 is not included)  env.run(until = 180)
Izlaz: 
Light turns GRN at 0 Light turns YEL at 25 Light turns RED at 30 Light turns GRN at 90 Light turns YEL at 115 Light turns RED at 120
In this code the generator function Traffic_Light(env) takes the environment variable as the argument and simulates the operation of the traffic light for the time period passed as argument in the env.run() function. (Actually time in SimPy is unitless. Though it can be converted to hours minutes or seconds as per convenience). env.now returns the current value of the time elapsed. env.timeout() function is the base of this simulation as it waits for the time passed as the argument to be elapsed on the computer's simulation clock (it is not a real time clock) and then initiate the next yield statement till the time passed as argument in env.run() has finished. env.run() starts all the processes linked to the environment at the same time = 0.