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.
Some quick instructions for using our template are below. We also have a detailed tutorial that has been given in a couple of workshops geared towards researchers at UCL. The tutorial goes into much more pedagogical detail, it both describes using the template to create a package and how to use the newly created package with some of the tools included.
-
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
-
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
-
A series of questions will pop up to configure the project. Type the answer or hit return to use the default option (shown in parenthesis).
Note that these project variables are defined in the
cookiecutter.json
file. -
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 package_name [python_template]: PACKAGE_NAME
we will get a project folder named
PROJECT_SLUG
, structured like this:PROJECT_SLUG ├── ... ├── README.md ├── pyproject.toml ├── src │ └── PACKAGE_NAME │ └── __init__.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 ...
-
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]"
-
Build your package
python -m build
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
-
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
Pages all live in the docs/pages sub-directory, and are written in markdown.
To build the webpage locally (for testing)
- Install jekyll
- Run
bundle install
from thedocs/
directory of this repository to install dependencies. - 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.