sim-tools: tools to support the Discrete-Event Simulation process in python.

sim-tools is being developed to support Discrete-Event Simulation (DES) education and applied simulation research. It is MIT licensed and freely available to practitioners, students and researchers via PyPi and conda-forge

Vision for sim-tools

1. Deliver high quality reliable code for DES education and practice with full documentation.
2. Provide a simple to use pythonic interface.
3. To improve the quality of DES education using FOSS tools 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 DES.
4. Automatic selection of the number of replications to run via the Replications Algorithm.
5. EXPERIMENTAL: model trace functionality to support debugging of simulation models.

Installation

Pip and PyPi

pip install sim-tools

Conda-forge

conda install -c conda-forge sim-tools

Binder

Learn how to use sim-tools

• Online documentation: https://tommonks.github.io/sim-tools
• Introduction to DES in python: https://health-data-science-or.github.io/simpy-streamlit-tutorial/

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}
}

Online Tutorials

• 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!

Tips

Once in the sim_tools environment, you can run tests using the following command:

pytest

To view the documentation, navigate to the top level directory of the code repository in your terminal and issue the following command to build the Jupyter Book:

jb build docs/

To lint the repository, run:

bash lint.sh