Cookiecutter Python

Cookiecutter template for Python packages.

• GitHub repo: https://github.com/tomtom-international/cookiecutter-python/
• Free software: Apache Software License 2.0

Features

• Supports projects hosted in GitHub and BitBucket Server.
• Ready for Azure Pipelines for GitHub projects and Jenkins Declarative Pipelines for internally hosted projects in BitBucket Server.
• Validation of your code
  • Testing based on py.test
  • Linting based on pylint
  • Formatting based on black (optional)
  • Spellchecking based on pyenchant (optional)
• Bumpversion: Pre-configured version bumping with a single command
• Auto-release to PyPI when PR gets merged into master via Azure Pipelines or Jenkins Pipelines
• Dependabot integration for Github based projects

Experimental Features

• Tox: Setup to easily test for different Python version (currently only for 3.6)
• Makefile support for building, testing, releasing, etc.

TODO

• Sphinx docs: Documentation ready for generation with, for example, ReadTheDocs

Getting Started

Prerequisites

Install the following minimum requirements

• python3
• pip3
• virtualenv >= 16.6.0

Installing

Install the latest Cookiecutter if you haven't installed it yet (this requires Cookiecutter 1.4.0 or higher):

pip install cookiecutter

Generate the Python package project:

cookiecutter gh:tomtom-international/cookiecutter-python

Then:

• Create a repo and push the generated project to master.
• Install the dev requirements into a virtualenv. (pip install -r requirements_dev.txt) and start coding.

Variables explained

Cookiecutter will prompt you for various settings which we are explained here:

• full_name - The authors name (default: John Doe)
• email - The authors email (default: john.doe@example.com)
• vs - Select between github (1) or bitbucket (2) (default: 1)
• vs_url - Provide the domain name of the versioning system without http(s):// (default: github.com)
• vs_account - Account/organization on Github or project name on BitBucket Server (default: tomtom-international)
• open_source_project - Generate additional files for OSS project (AUTHORS.md, LICENSE, etc) (default: y for github, n for bitbucket)
• project_name - The name of your Python project. This is also the name of the repository in Github/BitBucket (default: python-skeleton)
• project_slug - Python friendly module name. By default this is the normalized version of project_name (default: python_skeleton)
• project_short_description - A short description of your Python package. This will end up in the README.md of your project
• version - The current in-development version. The project makes use of SemVer (default: 0.0.1-dev)
• use_black_formatter - Use black for formatting your code (default: n)
• use_spellcheck - Use spellchecking and user configurable dictionary in .pylintrc (default: n)
• use_tox - EXPERIMENTAL: Adds support for tox (default: n)
• use_makefile - EXPERIMENTAL: Adds support for Makefiles (default: n)
• ci - Select the CI system you want to use. By default the CI system is chosen based on the selected versioning system (vs). If you chose Github as VS the default CI system will be Azure Pipelines. In case of Bitbucket you will get Jenkins.
• ci_url - URL to your CI system (eg. https://dev.azure.com, https://jenkins.acme.org)
• ci_org_name - For Azure Pipelines this is the organization name. In case of Jenkins you can leave this either empty or in case you have defined your Jenkins projects under a folder use the folder name.
• ci_project_name - For Azure Pipelines this is the name of your project. In case of Jenkins this is by default vs_account which usually is a BitBucket Team Project
• pypi_repo - PyPI repository the package should be deployed to. In case you select Github for vs the default value is the official PyPI index whereas in case of BitBucket you get a hint to provide a URL to an Artifactory PyPI repository.
• pypi_credentials_id - For Azure Pipelines this is the service connection that allows you to push Python packages to the official PyPI index. For Jenkins this is the credentials ID used for authentication with an Artifactory PyPI repository.
• docker_registry_credentials_id - For Azure Pipelines this is the service connection that allows you to push Docker images to a public Docker registry. For Jenkins this is the credentials ID used for authentication with for example an Artifactory Docker repository.
• jenkins_scm_credentials_id - Skip this for Azure Pipelines. For Jenkins this is the credentials ID for a user that has write access to a git repository. This is required to allow git commits/pushes during the versioning process.
• azure_build_definition_id - Skip this for Jenkins. For Azure Pipelines this is the ID identifiying the pipeline (eg. https://dev.azure.com/tomtomweb/GitHub-TomTom-International/_build?definitionId=26)

Tips

Azure

In order to use Azure DevOps/Pipelines, first create an Azure DevOps account, create an organization and project.

For detailed instructions please read the Azure DevOps documentation.

Creating Azure Pipelines

A detailed description on how to create Azure Pipelines can be found as well here.

Remark:

In case you want to host a project in the tomtom-international organization and you chose Azure for the CI contact the @nav-pipeline team to request a build pipeline in the TomTom Azure DevOps account.

Jenkins

In case of hosting on Jenkins/Bitbucket Server we recommend using the BitBucket Branch Source Plugin that allows you to configure a folder in Jenkins that will scan the whole BitBucket project for repositories with a Jenkinsfile.

The following Jenkins plugins are required by the pythonSetupPyPipeline used in the Jenkinsfile:

• Warnings Next Generation Plugin >=4.0.0
• Cobertura Plugin >=1.13
• Pipeline: GitHub Groovy Libraries >=1.0

Credits

This package is inspired by Cookiecutter PyPackage project template.