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This is looking great @TomHodson. Feel free to review and request changes to my updates where I've made minor changes to the text.
82 lines
3.4 KiB
Markdown
82 lines
3.4 KiB
Markdown
<h1 align="center">🎲 ⛓️ 👉 🧪 Markov Chain Monte Carlo for fun and profit</h1>
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<p align="center">
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<em>Using random numbers to do all the things.</em>
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</p>
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<p align="center">
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<img src="https://github.com/Imperial-CMTH/koala/actions/workflows/ci.yml/badge.svg"/>
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<a href="https://zenodo.org/badge/latestdoi/422218038">
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<img src="https://zenodo.org/badge/422218038.svg"/>
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</a>
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<a href="https://wfxr.mit-license.org/2017">
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<img src="https://img.shields.io/badge/License-MIT-brightgreen.svg"/>
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</a>
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<a href="https://mybinder.org/v2/gh/TomHodson/ReCoDE_MCMCFF/HEAD">
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<img src="https://mybinder.org/badge_logo.svg"/>
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</a>
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</p>
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This is an exemplar project designed to showcase best practices in developing scientific software as part of the ReCoDE Project at Imperial College London.
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**You do not need to know or care about Markov Chain Monte Carlo for this to be useful to you.**
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Rather this project is primarily designed to showcase the tools and practices available to you when developing scientific software projects. Maybe you are a PhD student just starting, or a researcher just about to embark on a larger scale software project - there should be something interesting here for you.
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## Table of contents
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1. [A short introduction][intro]
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1. [Organising code and python packaging][packaging]
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1. [Testing your code][testing]
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1. Python packages and environments: Pip, Conda, setup.py and all that.
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1. Planning out a larger software project
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1. Using Jupyter Notebooks during development
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1. Documentation
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1. Reproducibility of software outputs
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1. Citing software in a publication: CITATION.cff
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## How to use this repository
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Take a look at a the table of contents below and see if there are any topics that might be useful to you. The actual code lives in `./code` and the documentation in `./learning`
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When you're ready to dive in you have three options:
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### 1. Launch them in Binder (easiest but a bit slow)
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[](https://mybinder.org/v2/gh/TomHodson/ReCoDE_MCMCFF/HEAD?labpath=learning%2F01%20Introduction.ipynb)
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### 2. Clone the repo and run the jupyter notebooks locally. (Faster but requires you have python/jupyter installed)
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```
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git clone
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cd
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pip install -r requirements.txt
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jupyter lab
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```
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### 3. View them non-interactively in GitHub via the links in the table of contents
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## The map
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``` bash
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.
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├── CITATION.cff # This file describes how to cite the work contained in this repository.
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├── LICENSE # Outlines what legal rights you have to use this software.
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├── README.md # You are here!
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├── code
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│ ├── README.md # Human readable information about the little python package in here
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│ ├── pyproject.toml # Machine readable information about that same package
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│ ├── setup.cfg # Tells Pip how to install this package
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│ ├── src
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│ │ └── MCFF # The actual code lives in here!
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│ └── tests # automated tests for the code
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└── learning # Supporting documentation
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```
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## External Resources
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- [The Turing Way](https://the-turing-way.netlify.app/) has tons of great resources on the topics discussed here.
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- [Intermediate Research Software Development in Python](https://carpentries-incubator.github.io/python-intermediate-development/index.html)
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[TDD]: learning/01%20Introduction.ipynb
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[intro]: learning/01%20Introduction.ipynb
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[packaging]: learning/02%20Packaging%20it%20up.ipynb
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[testing]: learning/02%20Testing.ipynb
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