Five steps to add the 'bling' factor your Python package

Introduction

In previous posts I have shown how to create a Python package.

We started by using Cookicutter to generate a basic structure for our project. We then looked at how to setup and use clean development environments. This was followed by an outline of Python tools for testing and the implementation of the Python package using test-driven development. Finally we looked at how to generate beautiful technical documentation using Sphnix.

Now it is time to show off our hard work. In this post I will show you how to make use of cloud services to host your documentation, run continuous integration tests and distribute your package. Furthermore, we will add neat looking badges to the README file.

Step 1: Host the documentation on readthedocs

You have spent hours documenting your package using Sphinx. It is time to share it with the world. Register with readthedocs and sync your GitHub account with it. Then you can simply select the project that you want readthedocs to host documentation for.

If you are using Sphinx’s autodoc functionality and your package depends on numpy / scipy / matplotlib you may run into trouble as Readthedocs’ server may not be able to compile the C extensions. The first thing to try is to go into the advanced settings section of your project in Readthedocs’ web interface and make sure that the project is set to install into a virtual environment and that the option to “Give the virtual environment access to the global site-packages dir” is selected. The system packages now appear to include numpy , scipy , and matplotlib so this should go a long way. However, if you are still running into trouble you may need to mock out the dependencies.

Step 2: Set up continuous integration testing on Travis Ci

You have spent hours using test-driven development to create a solid Python package. It is time to automate the running of the test suite and to get automatic testing of the code on different versions of Python.

Sign into Travis CI using your GitHub account. Select the project that you want to test and add a .travis.yml file to the root of your code repository.

Below is a simple setup for testing a Python package with no dependencies on Python versions 2.7, 3.2, and 3.4 using the nose test runner.

language : python python : - " 2.7" - " 3.2" - " 3.3" - " 3.4" script : nosetests

If your code includes dependencies on numpy and scipy things get a little bit trickier as Travis CI can time out trying to install these from source. The solution is to make use of Miniconda.

The .travis.yml file below is based on the template from the conda documentation and Dan Blanchard’s post Quicker Travis builds that rely on numpy and scipy using Miniconda. It installs Miniconda with numpy , scipy and nose and runs the test suite on Python 2.7, 3.3. and 3.4.

python : # We don't actually use the Travis Python, but this keeps it organized. - " 2.7" - " 3.3" - " 3.4" install : - sudo apt-get update # We do this conditionally because it saves us some downloading if the # version is the same. - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh; else wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; fi - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - hash -r - conda config --set always_yes yes --set changeps1 no - conda update -q conda # Useful for debugging any issues with conda - conda info -a - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy scipy nose - source activate test-environment - python setup.py install # command to run tests script : nosetests

Step 3: Calculate your code coverage using Codecov

As you have developed your code using test-driven development you have a high degree of code coverage. It is time to integrate the code coverage calculation into the Travis CI testing. We will use Codecov to do this.

Sign in using your GitHub account, sync your repos and add the project that you want to measure the code coverage for. Then edit the .travis.yml file to look like the below.

language : python python : - " 2.7" - " 3.2" - " 3.3" - " 3.4" script : nosetests before_install : pip install codecov after_success : codecov

Step 4: Upload your Package to PyPi

You have developed a great Python package, it is time to share it with the world. This is done, most effectively, by uploading it to PyPi.

Peter Down has written a great post explaining how to submit a package to PyPi.

Hosting your package on PyPi makes it easy for people to install using pip .

Step 5: Add badges to your project’s README file

Finally the part that we have all been waiting for: cool looking badges!

Readthedocs, Travis CI and Codecov all provide badges as part of their service. For the PyPi package we will make use of Version Badge.

Below is part of the reStructuredText markup I use for my tinyfasta package.

.. image:: https://badge.fury.io/py/tinyfasta.svg :target: http://badge.fury.io/py/tinyfasta :alt: PyPI package .. image:: https://travis-ci.org/tjelvar-olsson/tinyfasta.svg?branch=master :target: https://travis-ci.org/tjelvar-olsson/tinyfasta :alt: Travis CI build status (Linux) .. image:: https://codecov.io/github/tjelvar-olsson/tinyfasta/coverage.svg?branch=master :target: https://codecov.io/github/tjelvar-olsson/tinyfasta?branch=master :alt: Code Coverage .. image:: https://readthedocs.org/projects/tinyfasta/badge/?version=latest :target: https://readthedocs.org/projects/tinyfasta/?badge=latest :alt: Documentation Status

The images in the README.rst file gets rendered by GitHub into a neat looking header with the badges below.

Conclusion

You should now have a Python package that looks loved and cared for.