Contributing code

Structure of the package

The functionality of the droplets package is split into multiple modules. In particular, we distinguish classes that deal with single droplets from those classes that represent collections (emulsions). The functions analyzing images are collected in a separate module.

Extending functionality

All code is build on a modular basis, making it easy to introduce new classes that integrate with the rest of the package. For instance, it is simple to define a droplet class that stores additional information by subclassing SphericalDroplet.

Coding style

The coding style is enforced using isort and black. Moreover, we use Google Style docstrings, which might be best learned by example. The documentation, including the docstrings, are written using reStructuredText, with examples in the following cheatsheet. To ensure the integrity of the code, we also try to provide many test functions, which are typically contained in separate modules in sub-packages called tests. These tests can be ran using scripts in the tests subfolder in the root folder. This folder also contain a script, which uses mypy to check the consistency of the python type annotations. We use these type annotations for additional documentation and they have also already been useful for finding some bugs.

Running unit tests

The droplets package contains several unit tests, typically contained in sub-module tests in the folder of a given module. These tests ensure that basic functions work as expected, in particular when code is changed in future versions. To run all tests, there are a few convenience scripts in the root directory tests. The most basic script is, which uses pytest to run the tests in the sub-modules of the droplets package. Clearly, the python package pytest needs to be installed. There are also additional scripts that for instance run tests in parallel (need the python package pytest-xdist installed), measure test coverage (need package pytest-cov installed), and make simple performance measurements. Moreover, there is a script, which uses mypy to check the consistency of the python type annotations and there is a script, which checks the coding style.

Before committing a change to the code repository, it is good practice to run the tests, check the type annotations, and the coding style with the scripts described above.