Cookiecutter PyPackage

AICS Cookiecutter template for a Python package.

About

Cookiecutter is a Python package to generate templated projects. This repository is a template for cookiecutter to generate a Python project which contains following:

• A directory structure for your project
• Prebuilt setup.py file to help you develop and install your package
• Includes examples of good Python practices, including tests
• Continuous integration
  • Preconfigured to generate project documentation
  • Preconfigured to automatically run tests every time you push to GitHub
  • Preconfigured to help you release your package publicly (PyPI)

We think that this template provides a good starting point for any Python project.

Features

• Uses tox (an environment manager) and pytest for local testing, simply run tox or make build from a terminal in the project home directory
• Runs tests on Windows, Mac, and Ubuntu on every branch and pull request commit using GitHub Actions
• Releases your Python Package to PyPI when you push to main after using bump2version
• Automatically builds documentation using Sphinx on every push to main and deploys to GitHub Pages
• Includes example code samples for objects, tests, and bin scripts

Example

• For an example of the base project that is built from this template, go to the example-build branch.

Quickstart

To use this template use the following commands and then follow the prompts from the terminal.

1. pip install cookiecutter
2. cookiecutter gh:AllenCellModeling/cookiecutter-pypackage

The Four Commands You Need To Know

1. pip install -e .[dev]

   This will install your package in editable mode with all the required development dependencies (i.e. tox).

2. make build

   This will run tox which will run all your tests in both Python 3.7 and Python 3.8 as well as linting your code.

3. make clean

   This will clean up various Python and build generated files so that you can ensure that you are working in a clean environment.

4. make docs

   This will generate and launch a web browser to view the most up-to-date documentation for your Python package.

Optional Steps:

• Turn your project into a GitHub repository:
  • Make an account on github.com
  • Go to make a new repository
  • Recommendations:
    • It is strongly recommended to make the repository name the same as the Python package name
    • A lot of the following optional steps are free if the repository is Public, plus open source is cool
  • After a GitHub repo has been created, run the commands listed under: "...or push an existing repository from the command line"
• Register your project with Codecov:
  • Make an account on codecov.io(Recommended to sign in with GitHub) everything else will be handled for you.
• Ensure that you have set GitHub pages to build the gh-pages branch by selecting the gh-pages branch in the dropdown in the "GitHub Pages" section of the repository settings.
• Register your project with PyPI:
  • Make an account on pypi.org
  • Go to your GitHub repository's settings and under the Secrets tab, add a secret called PYPI_TOKEN with your password for your PyPI account. Don't worry, no one will see this password because it will be encrypted.
  • Next time you push to the branch: main after using bump2version, GitHub actions will build and deploy your Python package to PyPI.

Suggested Git Branch Strategy

1. main is for the most up-to-date development, very rarely should you directly commit to this branch. GitHub Actions will run on every push and on a CRON to this branch but still recommended to commit to your development branches and make pull requests to main. If you push a tagged commit with bumpversion, this will also release to PyPI.
2. Your day-to-day work should exist on branches separate from main. Even if it is just yourself working on the repository, make a PR from your working branch to main so that you can ensure your commits don't break the development head. GitHub Actions will run on every push to any branch or any pull request from any branch to any other branch.
3. It is recommended to use "Squash and Merge" commits when committing PR's. It makes each set of changes to main atomic and as a side effect naturally encourages small well defined PR's.

Original repo: https://github.com/audreyr/cookiecutter-pypackage/