Metadata-Version: 2.1
Name: sim-tools
Version: 0.2.1
Summary: Simulation Tools for Education and Practice
Home-page: https://github.com/TomMonks/sim-tools
Author: Thomas Monks
Author-email: t.m.w.monks@exeter.ac.uk
License: The MIT License (MIT)
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib>=3.1.3
Requires-Dist: numpy>=1.18.1
Requires-Dist: pandas>=2.0.0
Requires-Dist: scipy>=1.4.1
Requires-Dist: scikit-learn>=1.0.0

# sim-tools: tools to support the simulation process in python.

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TomMonks/sim-tools/HEAD)
[![DOI](https://zenodo.org/badge/225608065.svg)](https://zenodo.org/badge/latestdoi/225608065)
[![PyPI version fury.io](https://badge.fury.io/py/sim-tools.svg)](https://pypi.python.org/pypi/sim-tools/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-360+/)
[![License: MIT](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481)

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.

## 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

```tex
@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 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/TomMonks/sim-tools/blob/master/examples/sw21_tutorial.ipynb)


## 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!**
