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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.
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@ -16,24 +16,24 @@
</a>
</p>
This is an exemplar project designed to showcase best practices in developing scientific software as part of the ReCode Project at Imperial College London.
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.
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. [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. Python packages and environments: 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. Reproducibility of software outputs
1. Citing software in a publication: CITATION.cff
## How to use this repo
## How to use this repository
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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### 1. Launch them in Binder (easiest but a bit slow)
[![Binder](https://mybinder.org/badge_logo.svg)](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)
### 2. Clone the repo and run the jupyter notebooks locally. (Faster but requires you have python/jupyter installed)
```
git clone
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jupyter lab
```
### 3. View them non-interactively with the links in the table of contents
### 3. View them non-interactively in GitHub via the links in the table of contents
## The map