The materials and methods in this repository support work towards developing the S.T.A.R.S healthcare framework version 1.5 (Sharing Tools and Artifacts for Reproducible Simulations in healthcare). The code and written materials here are a work in progress to demonstrate the application of S.T.A.R.S' version to sharing a simpy discrete-event simuilation model and associated research artifacts.
The model will run on a users browser without the need to install any components. This is achieved using Web Assembly technology i.e. JupterLite and xeus-python. A model notebook is downloaded to the users local machine and all dependencies are pre-installed via conda-forge. The model then lives in the browsers cache. The user can make changes to the model or create new files and these are persisted (until the browser cache is cleared).
Try the example DES in your browser: https://pythonhealthdatascience.github.io/stars-simpy-jupterlite
Use case
- A researcher wishes to share a runnable version of a simulation model with their publication (e.g. written in
simpy). The code allows others to replicate the simulation results, tables and charts in a paper and allows others to reuse the model. - The researcher wants the model to be immediately usable. Users should not need to install python,
simpyor any dependencies. - The researcher either wants to reduce load on online open science compute infrastructure (e.g. mybinder.org) or does not want to rely on it.
- Users may want to use a version of their own data due to governance, ethics or other reasons cannot upload the data to a remote instance of the model.
- Loading the model is as simple as clicking a URL.
Changes
- PATCH: Trauma patient treatment uses correct distribution and parameters
- PATCH: Pathway classes modified to use
self.argsinstead of notebook level scopeargsvariable.
Credits ✨
We would like to thank the JupterLite and xeus-python developers for making this work possible. This discrete-event simulation focussed repository was based on the learning materials and template provided by Jupyterlite xeus-python demo and tutorial given at PyData 2023 by Jeremy Tuloup.