pythonhealthdatascience / stress_update

A review and update of the Strengthening the Reporting of Empirical Simulation Studies guidelines for DES, SD, and ABS.

The STRESS guidelines: a 1st review and update

🏗 WORK IN PROGRESS (STRESS review and update 2024/25) 🏗️

Overview

A review and update of the Strengthening the Reporting of Empirical Simulation Studies guidelines for DES, SD, and ABS. It aims to improve the STRESS reporting guidelines and provide additional support for Hybrid Simulation.

Authors

To - do add FA, et al ORCID badges

Funding

TODO: Add in statement about FM funding from BS.

Instructions to run the code

To download code and run locally

Downloading the code

Either clone the repository using git or click on the green "code" button and select "Download Zip".

git clone https://github.com/pythonhealthdatascience/stress_update

this assumes you have installed git and setup SSH

Installing dependencies

All dependencies can be found in environment.yml and are pulled from conda-forge. To run the code locally, we recommend installing miniforge.

miniforge is FOSS alternative to Anaconda and miniconda that uses conda-forge as the default channel for packages. It installs both conda and mamba (a drop in replacement for conda) package managers. We recommend mamba for faster resolving of dependencies and installation of packages.

navigating your terminal (or cmd prompt) to the directory containing the repo and issuing the following command:

if you are using conda then replace mamba with conda.

mamba env create -f environment.yml

Activate the mamba environment using the following command:

mamba activate stress

Auto-formatting code in notebooks

The stress environment contains code linting and formatting tools: nbqa, flake8 and black. Use nbqa and black to autoformat jupyter notebooks from a terminal or cmd prompt:

nbqa black notebooks/<insert-notebook-name>.ipynb

Use flake8 to issue a report about code quality:

nbqa flake8 notebooks/<insert-notebook-name>.ipynb

Some notes on linting code: https://www.pythonhealthdatascience.com/content/001_setup/prereq/05_pep8.html