🎲 ⛓️ 👉 🧪 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 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. ## Table of contents 1. [Introduction](docs/learning/01%20Introduction.ipynb) 1. [Packaging It Up](docs/learning/02%20Packaging%20It%20Up.ipynb) 1. [Writing a Markov Chain Monte Carlo Sampler](docs/learning/03%20Writing%20a%20Markov%20Chain%20Monte%20Carlo%20Sampler.ipynb) 1. [Testing](docs/learning/04%20Testing.ipynb) 1. [Adding Functionality](docs/learning/05%20Adding%20Functionality.ipynb) 1. [Speeding It Up](docs/learning/06%20Speeding%20It%20Up.ipynb) 1. [Producing Research Outputs](docs/learning/07%20Producing%20Research%20Outputs.ipynb) 1. [Doing Reproducible Science](docs/learning/08%20Doing%20Reproducible%20Science.ipynb) 1. [Adding Documentation](docs/learning/09%20Adding%20Documentation.ipynb) ## How to use this repository Take a look at the table of contents below and see if there are any topics that might be useful to you. The actual code lives in `src` and the documentation in `docs/learning` in the form of Jupyter notebooks. When you're ready to dive in you have three options: ### 1. Launch them in Binder (easiest but a bit slow) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ImperialCollegeLondon/ReCoDE_MCMCFF/HEAD?urlpath=lab%2Ftree%2Fdocs%2Flearning%2F01%20Introduction.ipynb) ### 2. Clone the repo and run the Jupyter notebooks locally. (Faster but requires you have python/jupyter installed) ```bash git clone https://github.com/ImperialCollegeLondon/ReCoDE_MCMCFF mcmc cd mcmc pip install .[dev] jupyter lab ``` ### 3. View them non-interactively in GitHub via the links in the table of contents ## The map ```bash . ├── CITATION.cff # This file describes how to cite the work contained in this repository. ├── LICENSE # Outlines what legal rights you have to use this software. ├── README.md # You are here! ├── docs │ ├── ... #Files to do with making the documentation │ └── learning │ └── #The Jupyter notebooks that form the main body of this project │ ├── pyproject.toml # Machine readable information about the MCFF package ├── readthedocs.yaml # Tells readthedocs.com how to build the documentation ├── requirements.txt # What packages MCFF requires ├── setup.cfg # Machine readable information about the MCFF package ├── src │ └── MCFF # The actual code! │ └── tests # automated tests for the code ``` ## 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