Netherlands eScience Center Python Template

Spend less time setting up and configuring your new Python packages and comply with the Netherlands eScience Center Software Development Guide from the start.

Use this Cookiecutter template to generate an empty Python package. Features include:

Boilerplate tests and documentation,
Python setup configuration,
Open source software license,
Default Github actions for building, testing and deployment
Code style checking,
Editorconfig,
Miscellaneous files, such as Change log, Code of Conduct, and Contributing guidelines,
A README and a separate document with extensive documentation about project setup.

The file structure of the generated package looks like:

path/to/package/
├── .editorconfig
└── .github/
    └── workflows
        ├── build.yml
        └── pypi_deploy.yml
├── .gitignore
├── .prospector.yml
├── CHANGELOG.rst
├── CODE_OF_CONDUCT.rst
├── CONTRIBUTING.rst
├── docs
│   ├── conf.py
│   ├── index.rst
│   └── ...
├── LICENSE
├── MANIFEST.in
├── NOTICE
├── package
│   ├── __init__.py
│   ├── __version__.py
│   └── package.py
├── README.rst
├── project_setup.rst
├── requirements.txt
├── setup.cfg
├── setup.py
└── tests
    ├── __init__.py
    ├── test_lint.py
    └── test_package.py

Code (existing or new) should be placed in path/to/package/package/ (please choose a better name for your software!).
Add documentation by editing path/to/package/docs/index.rst
Tests go in the path/to/package/tests/ directory
The generated project setup document contains extensive documentation about the project setup and provides further instructions on what to do.

How to use

We recommend developing your software in an isolated Python environment and assume you are familiar with either virtualenv + pip or conda (check the guide if you are not).

Step 1: Install `cookiecutter`

We recommend installing cookiecutter outside the virtual environment you will be using for developing your software. This way, you don't have to install cookiecutter for every new project.

If you are using virtualenv + pip:
```
pip install --user cookiecutter
```

If you are using conda:

conda install -c conda-forge cookiecutter

Step 2: Generate the files and directory structure

To create a new package, type:

cookiecutter https://github.com/nlesc/python-template.git

You will be asked to supply the following information:

Name	Default value	Explanation
project_name	My Python Project	Full project/package name.
project_slug	my_python_project	This will be the name of the directory to be created and the git repository. It is safest not to use dashes (-) or spaces in this name.
project_short_description		The information that you enter here will end up in the README, documentation, license, and setup.py, so it may be a good idea to prepare something in advance.
version	0.1.0
github_organization		GitHub organization that will contain this project's repository. This can also be your github user name.
open_source_license	Apache 2.0 (1)	The software license under which the code is made available.
apidoc	no (1)	Add support for automatically generating a module index from the `docstrings` in your Python package (look at the scriptcwl package for an example).
full_name	John Smith	Your full name, e.g. John Smith.
email	yourname@esciencecenter.nl	Your (work) email address
copyright_holder		Name(s) of the organization(s) or person(s) who hold the copyright of the software (e.g., Netherlands eScience Center).
code_of_conduct_email	yourname@esciencecenter.nl	Email address of the person who should be contacted in case of violations of the Code of Conduct.

Step 3: Create and activate a Python environment

If you are using virtualenv + pip, do:

 $ virtualenv -p python3 env
 $ . env/bin/activate

If you are using conda, type:
```
$ conda create -n env python=3
$ source activate env
```
(On windows use activate env to activate the conda environment.)

Continuous integration with Github Actions

The template has two Ci workflows. They can be found in .github/workflows folder.

build.yml

This workflow install the dependencies, builds the package and runs tests.

pypi.yml

This workflow pushes the package to PYPI. This action will require PYPI token to be stored as Github secret. The workflow uses secret with a name of PYPI_TOKEN.

You can learn more about Python packaging at this link.

How to contribute

Suggestions/improvements/edits are most welcome. Please read the contribution guidelines before creating an issue or a pull request.