Parallelqueue

Base models now fully rewritten using new components interface (general arrivals and service).
For distributions with multiple parameters, in order to get around the fact that tuple unpacking is currently not implemented in Python 3.x, one can write in their distributions as lambdas:
python
sim = RedundancyQueueSystem(seed=42, Arrival=random.expovariate, AArgs=0.1, Service=lambda w : random.uniform(0,w), SArgs=2,
parallelism=1, d=1, doPrint=True, df=True, infiniteJobs = False, numberJobs = 5)
sim.RunSim()

Hotfix: Fixed naming for GeneralArrivals
Hotfix 2: Fixed error in JSQd - registers properly with environment

Base models now fully rewritten using new components interface (general arrivals and service).
For distributions with multiple parameters, in order to get around the fact that tuple unpacking is currently not implemented in Python 3.x, one can write in their distributions as lambdas:
python
sim = RedundancyQueueSystem(seed=42, Arrival=random.expovariate, AArgs=0.1, Service=lambda w : random.uniform(0,w), SArgs=2,
parallelism=1, d=1, doPrint=True, df=True, infiniteJobs = False, numberJobs = 5)
sim.RunSim()

Adds general distributions as a base_model. Previously one would need to use the components and build such a model from scratch should they wanted non-Exponential based inter-arrival/service distributions. Thus, for example, one can achieve the prior functionality by writing:

python
Specifies a SimPy environment consisting of a Redundancy-2
queueing system and a Poisson arrival process.
sim = RedundancyQueueSystem(doPrint=True,maxTime=100.0,
parallelism=100, seed=1234, d=2,
Arrival=random.expovariate, AArgs=0.5,
Service=random.expovariate, SArgs=1)
sim.RunSim

Refactorization implemented to better organize code into components.