https://github.com/gjbex/best-practices-for-scientific-computing

Material for a training on best practices for scientific computing.
https://github.com/gjbex/best-practices-for-scientific-computing

c code coverage cpp documentation fortran functionaltests python training unittesting version-control

Last synced: about 1 month ago
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Material for a training on best practices for scientific computing.

• Host: GitHub
• URL: https://github.com/gjbex/best-practices-for-scientific-computing
• Owner: gjbex
• License: cc-by-4.0
• Created: 2024-01-19T10:55:26.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-09-10T13:08:19.000Z (3 months ago)
• Last Synced: 2024-09-11T16:18:10.237Z (3 months ago)
• Topics: c, code, coverage, cpp, documentation, fortran, functionaltests, python, training, unittesting, version-control
• Homepage: https://gjbex.github.io/Best-practices-for-scientific-computing/
• Size: 859 KB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
# Best practices in programming

[![DOI](https://zenodo.org/badge/745445839.svg)](https://zenodo.org/doi/10.5281/zenodo.10665372)

![Render and test

workflow](https://github.com/gjbex/Best-practices-in-programming/actions/workflows/build_and_deploy.yml/badge.svg)

Material for a training on best practices for programming and software

development, specifically for those working in the context of scientific

computing.

The material is available as a

[website](https://gjbex.github.io/Best-practices-in-programming/).

## Table of contents

1. Syntax versus semantics

1. Code style and conventions

1. Version control with git and collaboration

1. Code documentation

1. Testing

   * Testing as experiments

   * Unit testing

   * Functional testing

   * Code coverage

1. Optimization

1. Deployment

1. Continuous integration

1. Reproducibility

1. Tools

1. Training

## Programming languages covered

Although this training aims to be programming language-agnostic, the repository

also list a number of tools that are programming language-specific. Obviously,

this can not be exhaustive, so feel free to suggest additional tools if you are

aware of any.

Programming languages covered:

* C

* C++

* Fortran

* Python

* R

Of course, there are many programming language-agnostic tools as well.

## What is it?

1. `docs`: directory with the markdown source for the site.

1. `mkdocs.yml`: mkdocs configuration file.

1. `environment.yml`: conda environment for building the site.

1. `linkcheck_skip_file.txt`: skip file for

[linkcheck](https://github.com/filiph/linkcheck)

1. `LICENSE`: license for this repository and materials.

1. `CONTRIBUTING.md`: how to contribute to this repository.

1. `CODE_OF_CONDUCT.md`: code of conduct.

## About

This material is [licensed under Creative Commons](LICENSE).

You can [contribute](CONTRIBUTING.md) to this project in various ways.

Both contributing to this material and participating in the training are

subject to the [code of conduct](CODE_OF_CONDUCT.md).

## Acknowledgments

I've "borrowed" much of the table of contents from a

[training](https://www.esciencecenter.nl/event/good-practices-in-research-software-development-2/)

given by the Netherlands eScience Center, although no actual contents of that

training was used for the development of this material.