A package to describe workflow using semantic technologies.

📦️ Installation

This package requires Python >=3.7, simply install it with:

shell pip install fair-workflow

🪄 Usage

⌨️ Use as a command-line interface

You can easily use your package from your terminal after installing fair-workflow with pip:

bash fair-workflow

Get a full rundown of the available options with:

bash fair-workflow --help

🐍 Use with python

Use this package in python scripts:

```python import fair_workflow

TODO: add example to use your package

```

🧑‍💻 Development setup

The final section of the README is for if you want to run the package in development, and get involved by making a code contribution.

📥️ Clone

Clone the repository:

bash git clone https://github.com/MaastrichtU-IDS/fair-workflow cd fair-workflow

🐣 Install dependencies

Install Hatch, this will automatically handle virtual environments and make sure all dependencies are installed when you run a script in the project:

bash pip install --upgrade hatch

Install the dependencies in a local virtual environment:

bash hatch -v env create

☑️ Run tests

Make sure the existing tests still work by running pytest. Note that any pull requests to the fairworkflows repository on github will automatically trigger running of the test suite;

bash hatch run test

To display all print():

bash hatch run test -s

🧹 Code formatting

The code will be automatically formatted when you commit your changes using pre-commit. But you can also run the script to format the code yourself:

hatch run fmt

Check the code for errors, and if it is in accordance with the PEP8 style guide, by running flake8 and mypy:

hatch run check

📖 Generate documentation

The documentation is automatically generated from the markdown files in the docs folder and python docstring comments, and published by a GitHub Actions workflow.

Start the docs on http://localhost:8001{:target="_blank"}

bash hatch run docs

♻️ Reset the environment

In case you are facing issues with dependencies not updating properly you can easily reset the virtual environment with:

bash hatch env prune

🏷️ New release process

The deployment of new releases is done automatically by a GitHub Action workflow when a new release is created on GitHub. To release a new version:

1. Make sure the PYPI_TOKEN secret has been defined in the GitHub repository (in Settings > Secrets > Actions). You can get an API token from PyPI at pypi.org/manage/account.
2. Increment the version number in the pyproject.toml file in the root folder of the repository.
3. Create a new release on GitHub, which will automatically trigger the publish workflow, and publish the new release to PyPI.

You can also manually trigger the workflow from the Actions tab in your GitHub repository webpage.