Developer’s Guide

Requirements

  • pytest
  • flake8
  • sphinx >= 1.3
  • sphinx-rtd-theme >= 0.1.7
  • IPython >= 3.1
  • pandoc (not a python package)

Conda

Settings up a development environment in MetPy is as easy as (from the base of the repository):

conda env create
conda develop -n devel .

The environment.yml contains all of the configuration needed to easily set up the environment, called devel. The second line sets up conda to run directly out of the git repository.

Making Changes

The changes to the MetPy source (and documentation) should be made via GitHub pull requests against master, even for those with administration rights. While it’s tempting to make changes directly to master and push them up, it is better to make a pull request so that others can give feedback. If nothing else, this gives a chance for the automated tests to run on the PR. This can eliminate “brown paper bag” moments with buggy commits on the master branch.

During the Pull Request process, before the final merge, it’s a good idea to rebase the branch and squash together smaller commits. It’s not necessary to flatten the entire branch, but it can be nice to eliminate small fixes and get the merge down to logically arranged commit. This can also be used to hide sins from history–this is the only chance, since once it hits master, it’s there forever!

Versioning

To manage identifying the version of the code, MetPy relies upon versioneer. versioneer takes the current version of the source from git tags and any additional commits. For development, the version will have a string like 0.1.1+76.g136e37b.dirty, which comes from git describe. This version means that the current code is 76 commits past the 0.1.1 tag, on git hash 136e37b, with local changes on top (indicated by dirty). For a release, or non-git repo source dir, the version will just come from the most recent tag (i.e. v0.1.1).

To make a new version, simply add a new tag with a name like vMajor.Minor.Bugfix and push to GitHub. Github will add a new release with a source archive.zip file. Running

python setup.py sdist

will build a new source distribution with the appropriately generated version file as well. This will also create a new stable set of documentation.

versioneer is installed in the base of the repository. To update, install the latest copy using pip install versioneer. Then recreate the _version.py file using:

python setup.py versioneer

Testing

Unit tests are the lifeblood of the project, as it ensures that we can continue to add and change the code and stay confident that things have not broken. Running the tests requires pytest, which is easily available through conda or pip. Running the tests can be done via either:

python setup.py test

or

py.test

Using py.test also gives you the option of passing a path to the directory with tests to run, which can speed running only the tests of interest when doing development. For instance, to only run the tests in the metpy/calc directory, use:

py.test metpy/calc

Some tests (for matplotlib plotting code) are done through an image comparison, using the pytest-mpl plugin. To run these tests, use:

py.test --mpl

When adding new image comparison tests, start by creating the baseline images for the tests:

py.test --mpl-generate-path=baseline

That command runs the tests and saves the images in the baseline directory. Once the images are reviewed and determined to be correct, they should be moved to a baseline directory in the same directory as the test script (e.g. metpy/plots/tests) For more information, see the docs for mpl-test.

Code Style

MetPy uses the Python code style outlined in PEP8. For better or worse, this is what the majority of the Python world uses. The one deviation is that line length limit is 95 characters. 80 is a good target, but some times longer lines are needed.

While the authors are no fans of blind adherence to style and so-called project “clean-ups” that go through and correct code style, MetPy has adopted this style from the outset. Therefore, it makes sense to enforce this style as code is added to keep everything clean and uniform. To this end, part of the automated testing for MetPy checks style. To check style locally within the source directory you can use the flake8 tool. Running it from the root of the source directory is as easy as:

flake8 metpy

Documentation

MetPy’s documentation is built using sphinx >= 1.3. API documentation is automatically generated from docstrings, written using the NumPy docstring standard. There are also example IPython notebooks in the examples/notebooks directory. Using IPython’s API, these are automatically converted to restructured text for inclusion in the documentation. The examples can also be converted to standalone scripts using:

python setup.py examples

The documentation is hosted by Read the Docs. The docs are built automatically from master as well as for the tagged versions on github. master is used for the latest documentation, and the latest tagged version is used for the stable documentation. To see what the docs will look like on RTD, you also need to install the sphinx-rtd-theme package.

Other Tools

Continuous integration is performed by Travis CI. This service runs the unit tests on all support versions, as well as runs against the minimum package versions. flake8 is also run against the code to check formatting. Travis is also used to build the documentation and to run the examples to ensure they stay working.

Test coverage is monitored by codecov.io.

The following services are used to track code quality: * QuantifiedCode * Codacy * Landscape.io

Releasing

To create a new release:

  1. Go to the GitHub page and make a new release. The tag should be a sensible version number, like v1.0.0. Add a name (can just be the version) and add some notes on what the big changes are.
  2. Do a pull locally to grab the new tag. This will ensure that versioneer will give you the proper version.
  3. (optional) Perform a git clean -f -x -d from the root of the repository. This will delete everything not tracked by git, but will also ensure clean source distribution. MANIFEST.in is set to include/exclude mostly correctly, but could miss some things.
  4. Run python setup.py sdist bdist_wheel (this requires wheel is installed).
  5. Upload using twine: twine upload dist/*, assuming the dist/ directory contains only files for this release. This upload process will include any changes to the README as well as any updated flags from setup.py.
  6. Tagging a new version on GitHub should also update the stable docs on Read the Docs.