CODING_GUIDELINES.md

Coding Guidelines

Software design

Designing a clean architecture for a software product is a difficult task and developers can easily overlook it under pressure or when they are eager to deliver exciting new features. However, choosing the quick solution instead of a clean one will likely lead to a large amount of additional work in the future (technical debt).

To keep technical debt at acceptable levels, design and implementation of new features should follow best practices:

1. Make sure your code is always covered by automatic testing to ensure its correctness. Where unit tests are impractical, use integration tests.
2. Adhere to the SOLID principles of software design.
3. Do not reinvent the wheel: if someone solved your problem within the project or in a third party library, consider using their solution or extending it before writing a new component for the same purpose.
4. You ain't gonna need it (YAGNI): do not design solutions for problems that might come up in the future, as chances are that you will never need that code. Focus on current problems and prepare for future requirements by writing clean code.
5. Do not repeat yourself (DRY): if you are writing the same code snippet in several places, extract it into a function.
6. Use meaningful names: the purpose of an object should be clear from its name. Usually, class names are nouns and function names are verbs.

Remember that important design decisions should be properly documented for future reference and to share the knowledge with other developers. We decided to use lightweight Architecture Decision Records (ADRs) for this purpose. The full list of ADRs and documentation for writing new ones can be found in docs/functional/architecture/Index.md.

Code Style

We follow the Google Python Style Guide with a few minor changes (mentioned below). Since the best way to remember something is to understand the reasons behind it, make sure you go through the style guide at least once, paying special attention to the discussions in the Pros, Cons, and Decision subsections.

We deviate from the Google Python Style Guide only in the following points:

• We use flake8 with some plugins instead of pylint.
• We use black and isort for source code and imports formatting, which may work differently than indicated by the guidelines in section 3. Python Style Rules. For example, maximum line length is set to 100 instead of 79 (although docstring lines should still be limited to 79).
• According to subsection 2.19 Power Features, direct use of power features (e.g. custom metaclasses, import hacks, reflection) should be avoided, but standard library classes that internally use these power features are accepted. Following the same spirit, we allow the use of power features in infrastructure code with similar functionality and scope as the Python standard library.
• According to subsection 3.19.12 Imports For Typing, symbols from typing and collections.abc modules used in type annotations "can be imported directly to keep common annotations concise and match standard typing practices". Following the same spirit, we allow symbols to be imported directly from third-party or internal modules when they only contain a collection of frequently used typying definitions.

Common questions

• pass vs ... (Ellipsis)

  pass is the no-op statement in Python and ... is a literal value (called Ellipsis) introduced for slicing collections of unknown number of dimensions. Although they are very different in nature, both of them are used in places where a statement is required purely for syntactic reasons, and there is not yet a clear standard practice in the community about when to use one or the other. We decided to align with the common pattern of using ... in the body of empty function definitions working as placeholders for actual implementations defined somewhere else (e.g. type stubs, abstract methods and methods appearing in Protocol classes) and pass in any other place where its usage is mixed with actual statements.

  # Correct use of `...` as the empty body of an abstract method
  class AbstractFoo:
     @abstractmethod
     def bar(self) -> Bar:
        ...
  
  # Correct use of `pass` when mixed with other statements
  try:
     resource.load(id=42)
  except ResourceException:
     pass

• where to import from gt4py.next or import gt4py.next as gtx?

  • Client code (like tests, doctests and examples) should use the above style for public FieldView API
  • Library code should always import the defining module and use qualified names.

Docstrings

We generate the API documentation automatically from the docstrings using Sphinx and some extensions such as Sphinx-autodoc and Sphinx-napoleon. These follow the Google Python Style Guide docstring conventions to automatically format the generated documentation. A complete overview can be found here: Example Google Style Python Docstrings.

Sphinx supports the reStructuredText (reST) markup language for defining additional formatting options in the generated documentation, however section 3.8 Comments and Docstrings of the Google Python Style Guide does not specify how to use markups in docstrings. As a result, we decided to forbid reST markup in docstrings, except for the following cases:

• Cross-referencing other objects using Sphinx text roles for the Python domain (as explained here).
• Very basic formatting markup to improve readability of the generated documentation without obscuring the source docstring (e.g. ``literal`` strings, bulleted lists).

We highly encourage the doctest format for code examples in docstrings. In fact, doctest runs code examples and makes sure they are in sync with the codebase.

Module structure

In general, you should structure new Python modules in the following way:

1. shebang line: #! /usr/bin/env python3 (only for executable scripts!).
2. License header (see LICENSE_HEADER.txt).
3. Module docstring.
4. Imports, alphabetically ordered within each block (fixed automatically by isort):
   1. Block of imports from the standard library.
   2. Block of imports from general third party libraries using standard shortcuts when customary (e.g. numpy as np).
   3. Block of imports from specific modules of the project.
5. Definition of exported symbols (optional, mainly for re-exporting symbols from other modules):

__all__ = ["func_a", "CONST_B"]

6. Public constants and typing definitions.
7. Module contents organized in a convenient way for understanding how the pieces of code fit together, usually defining functions before classes.

Try to keep sections and items logically ordered, add section separator comments to make section boundaries explicit when needed. If there is not a single evident logical order, pick the order you consider best or use alphabetical order.

Consider configuration files as another type of source code and apply the same criteria, using comments when possible for better readability.

Ignoring QA errors

You may occasionally need to disable checks from quality assurance (QA) tools (e.g. linters, type checkers, etc.) on specific lines as some tool might not be able to fully understand why a certain piece of code is needed. This is usually done with special comments, e.g. # type: ignore. However, you should only ignore QA errors when you fully understand their source and rewriting your code to pass QA checks would make it less readable. Additionally, you should add a brief comment for future reference, e.g.:

f = lambda: 'empty'  # noqa: E731  # assign lambda expression for testing

Testing

Testing components is a critical part of a software development project. We follow standard practices in software development and write unit, integration, and regression tests. Note that even though doctests are great for documentation purposes, they lack many features and are difficult to debug. Hence, they should not be used as replacement for proper unit tests except in trivial cases.

Test suite folder structure

There are separate test suites (each living in a subfolder) for the separate subpackages of GT4Py. This is so that not all subpackages have to be tested on CI all the time (see [ci-docs][the ci docs] for details).

The tests folder should not be a package but the contained test suites are python packages.

Each test suite should follow the following structure:

<subpackage>_tests/
  __init__.py  # each subpackage test should be a python package
  integration_tests/
    __init__.py
    feature_test/
      __init__.py
      <starting_end>_tests/
        __init__.py
        test_<feature>.py  # test <feature> in bigger context, starting from <starting_end>
    multi_feature_tests/
      __init__.py
      <starting_end>_tests/
        test_<feature_combination>.py  # test <feature_combination>, which can be user code snipets etc
        <feature_group>_tests/  # grouped feature combination tests with common data
          __init__.py
          <test_data>.py
          test_<feature_combination>.py
          test_<other_feature_combination>.py
  unit_tests/
    __init__.py
    ...  # mirror subpackage folder structure
    <subsubpackage>_tests/
      test_<module>.py  # unit tests for gt4py.<subpackage>.<subsubpackage>.<module>
  regression_tests/
    __init__.py
    test_<bug_description>.py

Temporarily it may be allowed to split unit tests for a module into multiple test_<module>_<feature>.py test modules. This should be taken as a reason to think about splitting the source module though.

Temporarily, tests for testing utilities can be placed next to the module containing them, with the name test_util_<module>.py. This should be taken as a hint that the tested utils should be moved into the library.

TODO: add missing test conventions.