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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning. Dates formatted as YYYY-MM-DD as per ISO standard.

The following are in progress, and are a record of changes between various branches and release, to be used when creating the next release/s...

dev_amy and dev

Added

  • Created and back-dated a CHANGELOG.md
  • GitHub action to deploy book on push to main
  • Copy of environment as requirements.txt for GitHub action (as conda environment was very slow and had an error)

Changed

  • Add logo and fixed GitHub url in _config.yml
  • Minor alterations for clarity, spelling, grammar or corrections in README.md and content/

dev and main

Changed

  • Add rich to environment
  • Updated tracing in content/02_model_code/04_model.ipynb

main and release v1.1.2

Added

  • Notebooks/pages on testing (content/02_model_code/05_testing.ipynbg) and the test package (content/02_model_code/06_test_package.ipynb)
  • Example data for tests
  • Process flow diagram

Changed

  • Citation, links and minimum python version on various pages
  • Updated and altered environment.yml
  • Updated src/treat_sim/model.py, content/02_model_code/04_model.ipynb and src/full_model.ipynb

v1.1.2 - 2024-05-02

Changed

  • PATCH: Pathway sampling now correctly uses self.args to select distributions.

v1.1.1 - 2024-05-01

Changed

  • PATCH: Trauma patient treatment fixed to use the correct sampling distribution (mistakenly using stabilisation).

v1.1.0 - 2023-10-30

The materials and methods in this repository support work towards developing the S.T.A.R.S healthcare framework (Sharing Tools and Artifacts for Reusable Simulations in healthcare). The code and written materials here demonstrate the application of S.T.A.R.S' version 1 to sharing a simpy discrete-event simuilation model and associated research artifacts.

  • All artifacts in this repository are linked to study researchers via ORCIDs;
  • Model code is made available under a MIT license;
  • Python dependencies are managed through conda;`
  • The python code itself can be viewed and executed in Jupyter notebooks via Binder;
  • The model is documented and explained in a Jupyter book website served up by GitHub pages;
  • The materials are deposited and made citatable using Zenodo;
  • The models are sharable with other researchers and the NHS without the need to install software.