Simulation Tools for Education and Practice
Project description
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
- Deliver high quality reliable code for DES education and practice with full documentation.
- Provide a simple to use pythonic interface.
- To improve the quality of DES education using FOSS tools and encourage the use of best practice.
Features:
- Implementation of classic Optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-m
- Distributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.
- Implementation of Thinning to sample from Non-stationary poisson processes in a DES.
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
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!
Project details
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