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sim-tools is being developed to support simulation education and applied simulation research. It is MIT licensed and freely available to practitioners, students and researchers via PyPi. There is a longer term plan to make sim-tools available via conda-forge.

Vision for sim-tools

1. Deliver high quality reliable code for simulation education and practice with full documentation.

2. Provide a simple to use pythonic interface.

3. To improve the quality of simulation education and encourage the use of best practice.

Features:

1. Implementation of classic optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-m

2. Distributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.

3. Implementation of Thinning to sample from Non-stationary poisson processes in a discrete-event simulation

Two simple ways to explore sim-tools

1. pip install sim-tools

2. Click on the launch-binder at the top of this readme. This will open example Jupyter notebooks in the cloud via Binder.

Citation

If you use sim0tools for research, a practical report, education or any reason please include the following citation.

Monks, Thomas. (2021). sim-tools: tools to support the forecasting process in python. Zenodo. http://doi.org/10.5281/zenodo.4553642

@software{sim_tools,
  author       = {Thomas Monks},
  title        = {sim-tools: fundamental tools to support the simulation process in python},
  year         = {2021},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4553642},
  url          = {http://doi.org/10.5281/zenodo.4553642}
}

Examples

• Optimisation Via Simulation

Contributing to sim-tools

Please fork Dev, make your modifications, run the unit tests and submit a pull request for review.

Development environment:

• conda env create -f binder/environment.yml

• conda activate sim_tools

All contributions are welcome!