UCL ARC Python Recommendations

This repository collects the UCL ARC recommendations for a research software project in Python. It contains a template for new Python packages and a website documenting our recommendations. We've turned on discussions for this repo, and we welcome questions there or in the #helpme channel on the UCL research programming hub Slack.

🍪 Our template is a cookiecutter template which automatically creates new Python packages with our recommended tooling set up and ready to go.

Note If you're making a package within a community that has an existing package template (e.g., scikit-hep), we recommend using their template instead of this one.

Using this template

1. Install cookiecutter in a Conda or venv environment (commented lines for Conda example).

   # conda create --channel conda-forge --name new-env-name
   # conda activate new-env-name
   # conda install pip
   pip install cookiecutter
   

2. Run cookiecutter in the desired directory

   cookiecutter gh:ucl-arc/python-tooling
   

   If you have this repo locally (this may be the case if you are developing), you can run the following:

   cookiecutter /path/to/your/checkout/of/python-tooling
   

3. A series of questions will pop up to configure the project. Type the answer or hit return to use the default option (shown in square brackets).

   Note that these project variables are defined in the cookiecutter.json file.

4. This will create a specific directory structure.

   For example, for a project with the following variables:

   project_name [Python Template]: PROJECT_NAME
   project_slug [python-template]: PROJECT_SLUG
   

   we will get a project folder named PROJECT_SLUG, structured like this:

   PROJECT_SLUG
   ├── ...
   ├── README.md
   ├── pyproject.toml
   ├── src
   │   └── PROJECT_SLUG
   │       └── PROJECT_SLUG.py
   └── tests
       └── test_dummy.py
   

   And the PROJECT_NAME will appear in the README.md as the human-readable name of the project.

   cat PROJECT_SLUG/README.md
   # PROJECT_NAME
   ...
   

5. To work on your project, initialise a git repository and install it in editable mode.

   cd PROJECT_SLUG
   git init
   python -m pip install -e ".[dev]"
   

6. Build your package

   python -m build
   

Notes for developers

Click to expand...

First, clone repository

• (Optional) Generate your SSH keys as suggested here
• (Optional) GitHub CLI as suggested here
• Clone the repository by typing (or copying) the following line in a terminal at your selected path in your machine:

git clone git@github.com:UCL-ARC/python-tooling.git
cd python-tooling

Developing the cookiecutter template

• To create and remove your virtual environment

  conda create -n ptoolingVE pip -c conda-forge
  conda activate ptoolingVE
  conda deactivate ptoolingVE
  conda remove -n ptoolingVE --all
  

• To run template in the same path of this repo. We do a test cookiecut of a dummy package and install it to ensure the template setup works.

  cookiecutter .
  cd python-template
  git init
  python -m pip install -e ".[dev]"
  

• To run cookiecutter using a specific branch of the template:

  cookiecutter https://github.com/UCL-ARC/python-tooling --checkout <branch-name>
  

• To run the tests locally:

  pytest -s
  

Developing the recommended tooling pages

Pages all live in the docs/pages sub-directory, and are written in markdown.

To build the webpage locally (for testing)

1. Install jekyll
2. Run bundle install from the docs/ directory of this repository to install dependencies.
3. Run bundle exec jekyll serve from the root directory of this repository. This should fire up a local web server and tell you its address. By default the server will automatically refresh the HTML pages if any changes are made to the markdown sources.

See the jekyll docs for more info.