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Change log

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Change log#

v0.3.0#

  • Distributions classes now have python type hints.

  • Added distributions and time dependent arrivals via thinning example notebooks.

  • Added datasets module and function to load example NSPP dataset.

  • Distributions added

    • Erlang (mean and stdev parameters)

    • ErlangK (k and theta parameters)

    • Poisson

    • Beta

    • Gamma

    • Weibull

    • PearsonV

    • PearsonVI

    • Discrete (values and observed frequency parameters)

    • ContinuousEmpirical (linear interpolation between groups)

    • RawEmpirical (resample with replacement from individual X’s)

    • TruncatedDistribution (arbitrary truncation of any distribution)

  • Added sim_tools.time_dependent module that contains NSPPThinning class for modelling time dependent arrival processes.

  • Updated test suite for distributions and thinning

  • Basic Jupyterbook of documentation.

v0.2.0#

  • Added sim_tools.distribution module. This contains classes representing popular sampling distributions for Discrete-event simulation. All classes encapsulate a numpy.random.Generator object, a random seed, and the parameters of a sampling distribution.

  • Python has been updated, tested, and patched for 3.10 and 3.11 as well as numpy 1.20+

  • Minor linting and code formatting improvement.

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