Tools to support Discrete-Event Simulation (DES) and Monte-Carlo Simulation education and practice
sim-tools is being developed to support Discrete-Event Simulation (DES) and Monte-Carlo Simulation 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 and Monte-Carlo Simulation education and practice with full documentation.
- Provide a simple to use pythonic interface.
- To improve the quality of simulation education using FOSS tools and encourage the use of best practice.
👥 Authors
-
Thomas Monks
-
Amy Heather
-
Alison Harper
Features:
- Implementation of classic Optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-m
- Theoretical and empirical distributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.
- An extendable Distribution registry that provides a quick reproduible way to parameterise simulation models.
- Implementation of Thinning to sample from Non-stationary Poisson Processes (time-dependent) in a DES.
- Automatic selection of the number of replications to run via the Replications Algorithm.
- 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
Mamba
mamba is a FOSS alternative to conda that is also quicker at resolving and installing environments.
mamba install sim-tools
Binder
Learn how to use sim-tools
- Online documentation: https://sim-tools.github.io/sim-tools
- Introduction to DES in python: https://health-data-science-or.github.io/simpy-streamlit-tutorial/
Citation
If you use sim-tools for research, a practical report, education or any reason please include the following citation.
Monks, T., Heather, A., Harper, A. (2025). sim-tools: fundamental tools to support the simulation process in python. Zenodo. https://doi.org/10.5281/zenodo.4553641.
@software{sim_tools,
author = {Thomas Monks and Amy Heather and Alison Harper},
title = {sim-tools: fundamental tools to support the simulation process in python},
year = {2025},
publisher = {Zenodo},
doi = {10.5281/zenodo.4553641},
url = {https://doi.org/10.5281/zenodo.4553641}
}
Online Tutorials
- Optimisation Via Simulation
Contributing to sim-tools
All contributions are welcome! Please see CONTRIBUTING.md for instructions on how to contribute.
2 maintainers
Release files for sim-tools 1.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sim_tools-1.3.0.tar.gz | 47.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sim_tools-1.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 99.4 kB
Release files / sim_tools-1.3.0.tar.gz
| Download URL | sim_tools-1.3.0.tar.gz |
|---|---|
| Size | 47.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / sim_tools-1.3.0-py3-none-any.whl
| Download URL | sim_tools-1.3.0-py3-none-any.whl |
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| Size | 52.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 24, 2026.
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