Updates from Amy

CHANGELOG.md

@@ -0,0 +1,14 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). Dates formatted as YYYY-MM-DD as per [ISO standard](https://www.iso.org/iso-8601-date-and-time-format.html).
+
+## Unreleased - Date - DOI
+
+🌱 Initial release of tutorial materials.
+
+### Added
+
+* TBC

CITATION.cff

@@ -2,7 +2,7 @@
 # Visit https://bit.ly/cffinit to generate yours today!
 
 cff-version: 1.2.0
-title: Simpy JupyterLite Template
+title: An introduction to Discrete-Event Simulation using Free and Open Source Software
 message: >-
   If you use this software, please cite it using the
   metadata from this file.
@@ -16,8 +16,15 @@ authors:
     family-names: Harper
     affiliation: University of Exeter
     orcid: 'https://orcid.org/0000-0001-5274-5037'
+  - given-names: Amy
+    family-names: Heather
+    affiliation: University of Exeter
+    orcid: 'https://orcid.org/0000-0002-6596-3479'
 repository-code: >-
-  https://github.com/pythonhealthdatascience/stars-simpy-jupterlite
+  https://github.com/pythonhealthdatascience/intro-open-sim
+abstract: >-
+  This is a STARS project being prepared for the Operational Research Society
+  12th Simulation Workshop in 2025 (SW25).
 keywords:
   - simpy
   - jupyterlite

README.md

@@ -1,158 +1,60 @@
-[![lite-badge](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://pythonhealthdatascience.github.io/stars-simpy-jupterlite/notebooks/?path=01_urgent_care_model.ipynb)
+[![lite-badge](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://pythonhealthdatascience.github.io/intro-open-sim/lab/index.html)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10987817.svg)](https://doi.org/10.5281/zenodo.10987817)
-[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-3100/)
-[![License: MIT](https://img.shields.io/badge/ORCID-0000--0001--5274--5037-brightgreen)](https://orcid.org/0000-0001-5274-5037)
-[![License: MIT](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481)
+[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/release/python-3100/)
+[![ORCID: Alison Harper](https://img.shields.io/badge/ORCID:_Alison_Harper-0000--0001--5274--5037-brightgreen)](https://orcid.org/0000-0001-5274-5037)
+[![ORCID: Tom Monks](https://img.shields.io/badge/ORCID:_Tom_Monks-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481)
+[![ORCID: Amy Heather](https://img.shields.io/badge/ORCID:_Amy_Heather-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479)
 
-#  An introduction to Discrete-Event Simulation using Free and Open Source Software.
+#  An introduction to Discrete-Event Simulation (DES) using Free and Open Source Software
 
-> Work in progress.  This is a STARS project being prepared for [SW25](https://www.theorsociety.com/ORS/ORS/Events/2025/Simulation-Workshop/SW25.aspx)
+> Work in progress.  This is a STARS project being prepared for the [Operational Research Society 12th Simulation Workshop in 2025 (SW25)](https://www.theorsociety.com/ORS/ORS/Events/2025/Simulation-Workshop/SW25.aspx). It has been adapted from our [template repository](https://github.com/pythonhealthdatascience/stars-simpy-jupterlite) for sharing `simpy` models with JupyterLite.
 
-## 1. Overview
+## 🧑‍💻 1. Tutorial
 
-The materials and methods in this repository support work towards developing the S.T.A.R.S healthcare framework version 1.5 (**S**haring **T**ools and **A**rtifacts for **R**eproducible **S**imulations 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. 
+We being with two notebooks that introduce some basic concepts for creating DES in python:
 
-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](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/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).  
+* `01_sampling.ipynb`: Sampling from statistical distributions using `numpy`
+* `02_basic_simpy.ipynb`: Creating simple `simpy` DES models that make use of `numpy` sampling
 
-> Try it in your browser now: https://pythonhealthdatascience.github.io/stars-simpy-jupterlite
+Your understanding of these is then tested in:
 
-### 1.1. Use case
+* `03a_exercise1.ipynb`: Exercise to testing understanding of the basics of `simpy` and `numpy`
+* `03b_exercise1_solutions.ipynb`: Example solutions for the exercise
 
-* 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, `simpy` or 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.
+We then move on to some more advanced concepts, and create a full process model:
 
-### 1.2. Credits ✨
+* `04_111_model.ipynb`: Full `simpy` process model, creating a model for a 111 call centre 
+* `05_basic_results.ipynb`: Collecting results from a single run by storing process metrics during a run and performing calculations at the end
 
-> We would like to thank the [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/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](https://github.com/jupyterlite/xeus-python-demo) and [tutorial given at PyData 2023](https://www.youtube.com/watch?v=WXRslU9D3bo) by Jeremy Tuloup.
+## 🔧 2. Set-up
 
-### 1.3. Citation
+To run the notebooks in this tutorial, you can either run via your browser or locally...
 
-If you use the template in your work we would greatly appreciate a citation when you publish your work. 
+### 2.1 Running notebooks within your browser
 
-> Monks, T., & Harper, A. (2024). Simpy JupyterLite Template (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.10987817
+This tutorial has been set up to run on your browser without the need to install any components. This is achieved using Web Assembly technology i.e. [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python). A model notebook is downloaded to your local machine and all dependencies are pre-installed via conda-forge. The model then lives in the browsers cache. You can make changes to the model or create new files and these are persisted (until the browser cache is cleared).
 
-```
-@software{monks_harper_jupyterlite_template,
-  author       = {Monks, Thomas and
-                  Harper, Alison},
-  title        = {Simpy JupyterLite Template},
-  month        = apr,
-  year         = 2024,
-  publisher    = {Zenodo},
-  version      = {v0.1.0},
-  doi          = {10.5281/zenodo.10987817},
-  url          = {https://doi.org/10.5281/zenodo.10987817}
-}
-```
-
-
-## 2. The example model included
-
-The `simpy` model is adapted from [Monks and Harper (2023)](https://github.com/pythonhealthdatascience/stars-simpy-example-docs)
-
-> Monks, T., & Harper, A. (2023). Towards Sharing Tools and Artifacts for Reusable Simulation: example enhanced documentation for a simpy model. (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.10054063
-
-Full documentation of this model is available in our [JupyterBook](https://pythonhealthdatascience.github.io/stars-simpy-example-docs)
-
-In summary, we adapt a textbook example from Nelson (2013): a terminating discrete-event simulation model of a U.S based treatment centren summary the model. The example is based on exercise 13 from Nelson (2013) page 170.
-
-> *Nelson. B.L. (2013). [Foundations and methods of stochastic simulation](https://www.amazon.co.uk/Foundations-Methods-Stochastic-Simulation-International/dp/1461461596/ref=sr_1_1?dchild=1&keywords=foundations+and+methods+of+stochastic+simulation&qid=1617050801&sr=8-1). Springer.*
-
-## 3. Try the example DES in your browser
-
-* Jupyterlab: https://pythonhealthdatascience.github.io/stars-simpy-jupterlite
-* Classic notebook: https://pythonhealthdatascience.github.io/stars-simpy-jupterlite/notebooks/?path=01_urgent_care_model.ipynb
+> ✨ **To access this tutorial in your browser:** <https://pythonhealthdatascience.github.io/intro-open-sim/>.
 
-## 4. Using the template to create a new repo.
+### 2.2 Running notebooks locally on your machine
 
-> There are three steps: i.) create a new repo form the template; ii.) modify you repo settings so that GitHub pages are built from Actions.  iii.) Commit changes and trigger the GitHub Action and deployment. We recommend reading all instructions first.
+You can also run the notebooks in `content/` locally on your machine. You'll need to install the provided conda environment `environment.yml`.
 
-Let's assume you wanted to create a new discrete-event simulation model of cancer services.  
-
-1. The first step is to copy the template. Click on the green "use this template" button in the top right and select "create a new repository"
-2. You will be prompted to enter a name of the repository - e.g. `cancer_model` - and a short description
-3. Click on "Create Repository"
-
-![Deploy your own](img/use_template_1.gif)
-
-The JuypterLite interactive website is built from GitHub actions.  **In the newly created repo for your model** do the following
-
-4. Navigate to "Settings->Pages"
-5. Under "Build and Deployment" set the "Source" to "GitHub actions".
-
-![Deploy your own](img/actions.gif)
-
-By default the build is trigged on any commit to the `main` branch.  Push a small commit and it will trigger the build.  This will take a few minutes.  Your site will be published under https://{USERNAME}.github.io/{DEMO_REPO_NAME}
+```
+conda env create --name xeus-python-kernel --file environment.yml
+conda activate xeus-python-kernel
+```
 
-## 5. How to install extra packages supporting your DES model. 📦
+## 📝 3. Citation
 
-The repo contains two environment files. To install more dependencies for your DES model and analysis you need to edit the ``environment.yml`` file.
+TBC.
 
-The template ``environment.yml`` is as follows:
+## 🤝 4. Acknowledgements
 
+<!--TODO: Is this just relevant to the template repository, or likewise to this one?-->
 
-```yml
-name: xeus-python-kernel
-channels:
-  - https://repo.mamba.pm/emscripten-forge
-  - conda-forge
-dependencies:
-  - xeus-python
-  - ipycanvas
-  - simpy=4.1.1
-  - numpy
-  - pandas
-  - matplotlib
-```
+We would like to thank the [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python) developers for making this work possible. This discrete-event simulation focussed tutorial was based on the learning materials and template provided by [Jupyterlite xeus-python demo](https://github.com/jupyterlite/xeus-python-demo) and [tutorial given at PyData 2023](https://www.youtube.com/watch?v=WXRslU9D3bo) by Jeremy Tuloup.
 
-**Key points:**
-
-* There are two channels in use. 
-  * `encription-forge` contains specific versions of the packages for web assembly These include `numpy` `pandas`, and `matplotlib`. Other popular packages include `scipy`, `scikit-learn` and `pytest`.
-  * `conda-forge` for other installs you can use conda-forge.  Only ``no-arch`` packages from ``conda-forge`` can be installed (simpy qualifies)
-* Note that `numpy`, `pandas` and `matplotlib` have specific versions available on `enscription-forge`. For this reason we recommend not including the package version number.
-* `simpy` is installed from `conda-forge` we were therefore able to freeze the version to 4.1.1 to aid reproducibility.
-* At the time of writing the xeus-python kernal will use python 3.11.3
-
-As an example modification assume that you wanted to add two new packages: `plotly` and `scipy`.  The first `plotly` is available ``no-arch`` from conda-forge so it is safe to include and if you wanted to you could try to include a version number. There is a specific version of `scipy` is available on `encription-forge`
-
-Our modified environment looks like:
-
-```yml
-name: xeus-python-kernel
-channels:
-  - https://repo.mamba.pm/emscripten-forge
-  - conda-forge
-dependencies:
-  - xeus-python
-  - ipycanvas
-  - simpy=4.1.1
-  - numpy
-  - pandas
-  - matplotlib
-  - plotly
-  - scipy
-```
+## 💰 5. Funding
 
-If you wanted to use an alternative simulation package to `simpy` this would need to be available on `conda-forge` and be ``no-arch``.  An example package is `salabim`.  A modification of the enviroment is:
-
-
-```yml
-name: xeus-python-kernel
-channels:
-  - https://repo.mamba.pm/emscripten-forge
-  - conda-forge
-dependencies:
-  - xeus-python
-  - ipycanvas
-  - salabim
-  - numpy
-  - pandas
-  - matplotlib
-  - plotly
-  - scipy
-```
+STARS is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)].