๐ฒ โ๏ธ ๐ ๐งช Markov Chain Monte Carlo for fun and profit
Using random numbers to do all the things.
This is an exemplar project designed to showcase best practices in developing scientific software as part of the ReCode Project at Imperial College London.
**You do not need to know or care about Markov Chain Monte Carlo for this to be useful to you.**
Rather this project is primarily designed to showcase the tools and practices available to you when developing scientific softare projects. Maybe you are a PhD student just starting or a researcher just about to embark on a larger scale softare project there should be something intersting here for you.
## Table of contents
1. [A short introduction][intro]
1. [Organising code and python packaging][packaging]
1. [Testing your code][testing]
1. Python development environnments: Pip, Conda, setup.py and all that.
1. Planning out a larger software project
1. Using Jupyter Notebooks during development
1. Documentation
1. Software Reproducability
1. Citing software in a publication: CITATION.cff
## How to use this repo
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`
When you're ready to dive in you have three options:
### 1. Launch them in Binder (easiest but a bit slow)
[](https://mybinder.org/v2/gh/TomHodson/ReCoDE_MCMCFF/HEAD?labpath=learning%2F01%20Introduction.ipynb)
### 2. Clone the repo and run the jupyter notebooks locally. (Faster but requires you have python installed)
```
git clone
cd
pip install -r requirements.txt
jupyter lab
```
### 3. View them non-interactively with the links in the table of contents
## The map
``` bash
.
โโโ CITATION.cff #This file describes how to cite the work contained in this repository.
โโโ LICENSE # Outlines what legal rights you have to use this software.
โโโ README.md # You are here!
โโโ code
โย ย โโโ README.md # Human readable information about the little python package in here
โย ย โโโ pyproject.toml # Machine readable information about that same package
โย ย โโโ setup.cfg # Tells Pip how to install this package
โย ย โโโ src
โย ย โย ย โโโ MCFF # The actual code lives in here!
โย ย โโโ tests # automated tests for the code
โโโ learning # Supporting documentation
```
## External Resources
- [The Turing Way](https://the-turing-way.netlify.app/) has tons of great resources on the topics discussed here.
- [Intermediate Research Software Development in Python](https://carpentries-incubator.github.io/python-intermediate-development/index.html)
[TDD]: learning/01%20Introduction.ipynb
[intro]: learning/01%20Introduction.ipynb
[packaging]: learning/02%20Packaging%20it%20up.ipynb
[testing]: learning/02%20Testing.ipynb
