Enhance Brainglobe-atlasapi With Type Annotations

by Alex Johnson 50 views

In the realm of scientific computing, particularly within projects like brainglobe-atlasapi, the clarity, maintainability, and robustness of code are paramount. Introducing type annotations is a crucial step toward achieving these goals. This article delves into the necessity of adding type annotations across core modules of the brainglobe-atlasapi, highlighting the benefits they bring in terms of code readability, IDE autocompletion, and static analysis.

The Case for Type Annotations

Type annotations, or type hints, are a way of specifying the expected type of a variable, function argument, or function return value in Python code. While Python is dynamically typed, meaning that the type of a variable is checked during runtime, type annotations allow for static type checking before runtime. This is particularly useful for catching type-related errors early on, preventing unexpected behavior in production code.

Improving Code Readability

One of the most immediate benefits of type annotations is improved code readability. By explicitly stating the expected types of variables and function parameters, developers can quickly understand the purpose and usage of a piece of code without having to delve into its implementation details. This is especially helpful when working with complex codebases or when onboarding new contributors to a project.

Consider the following example from the brainglobe-atlasapi codebase:

def get_structure_descendants(self, structure):
    # Returns a list of structure descendants
    pass

Without type annotations, it's not immediately clear what type of data structure should be or what type of data the function returns. However, with type annotations, the code becomes much more explicit:

def get_structure_descendants(self, structure: str | int) -> list[str]:
    # Returns a list of structure descendants
    pass

Now, it's clear that structure can be either a string or an integer, and the function returns a list of strings. This level of explicitness greatly enhances code readability and reduces the cognitive load on developers.

Enhancing IDE Autocompletion

Another significant advantage of type annotations is their ability to enhance IDE autocompletion. Modern IDEs like VS Code, PyCharm, and others can leverage type annotations to provide more accurate and relevant autocompletion suggestions. This can save developers a significant amount of time and effort, as they don't have to manually look up the available methods and properties of an object.

For example, if a variable is annotated as a numpy.ndarray, the IDE can suggest the available methods and properties of NumPy arrays, such as shape, dtype, and reshape. This level of autocompletion can greatly improve the development experience and reduce the likelihood of errors.

Facilitating Static Analysis with Mypy

Static analysis tools like Mypy can leverage type annotations to perform static type checking, identifying type-related errors before runtime. Mypy is already included as a development dependency in brainglobe-atlasapi, making it easy to integrate type checking into the development workflow. By running Mypy on the codebase, developers can catch type errors early on, preventing them from propagating into production code.

For example, if a function expects an integer argument but is called with a string argument, Mypy will flag this as an error. This can help prevent unexpected behavior and improve the overall reliability of the code. Embracing static type checking through tools like Mypy elevates code quality and fosters a more robust development process.

Proposed Solution: Adding Type Annotations to Core Modules

To address the lack of type annotations in the brainglobe-atlasapi codebase, I propose adding standard Python type annotations to the following modules and functions that currently have untyped parameters or return values.

1. brainglobe_atlasapi/core.py

The Atlas class, a cornerstone of the brainglobe-atlasapi, currently contains several methods lacking type annotations. Addressing this improves code clarity and maintainability.

  • get_structure_descendants(self, structure)

    • structure: str | int
    • return: list[str] (returns list of acronyms)

  • get_structure_ancestors(self, structure)

    • structure: str | int
    • return: list[str]

  • hemisphere_from_coords(self, coords, microns=False, as_string=False)

    • coords: tuple | list | np.ndarray
    • return: int | str

Adding type annotations to these methods will clarify the expected input types and return values, making the code easier to understand and use. For instance, explicitly defining coords as a tuple, list, or np.ndarray in hemisphere_from_coords removes ambiguity and enhances code usability.

2. brainglobe_atlasapi/utils.py

The utility functions in brainglobe_atlasapi/utils.py also benefit from type annotations, particularly for functions that handle file I/O.

  • read_json(path)

    • path: str | Path
    • return: dict

  • read_tiff(path)

    • path: str | Path
    • return: np.ndarray

By annotating read_json and read_tiff with type hints, we explicitly state that read_json should return a dictionary and read_tiff should return a NumPy array. This is crucial for ensuring that the data is processed correctly and that any errors related to data types are caught early on.

3. brainglobe_atlasapi/structure_tree_util.py

Functions related to structure tree manipulation also stand to gain from type annotations. The functions child_ids and get_structures_tree are prime examples where type annotations improve clarity.

  • child_ids(structure, structure_list)

    • structure: int
    • (Note: current docstring incorrectly states dict, but source code expects an integer ID.)
    • structure_list: list
    • return: list[int]

  • get_structures_tree(structures_list)

    • structures_list: list
    • return: Tree (from treelib)

Annotating child_ids with type hints clarifies that the structure parameter should be an integer ID, which corrects the current docstring discrepancy. Similarly, annotating get_structures_tree with a return type of Tree (from treelib) explicitly states the expected return type, enhancing code understanding and maintainability.

Alternatives Considered

The primary alternative to adding type annotations is to maintain the current untyped structure. However, this approach has several drawbacks. Without type annotations, the effectiveness of static type checking is reduced, cognitive load for contributors is increased, and ambiguity in function behavior is introduced. While Python's dynamic typing provides flexibility, it also requires more careful attention to detail to avoid type-related errors. Type annotations provide clearer documentation with no runtime overhead, making them a valuable addition to the codebase.

Additional Context

This proposal is fully non-breaking and only adds type hints plus matching docstring updates. I will submit the work as small, review-friendly PRs if approved.

Conclusion

In conclusion, adding type annotations to the core modules of brainglobe-atlasapi is a worthwhile investment that will improve code readability, enhance IDE autocompletion, and facilitate static analysis with Mypy. By explicitly stating the expected types of variables and function parameters, we can make the codebase easier to understand, use, and maintain. This will not only benefit existing contributors but also make it easier for new contributors to onboard and contribute to the project.

For more information on type annotations in Python, visit the Python documentation on typing.