Refactor Dynamic Float Types Creation for Pydantic Compatibility #2
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eksdsoto
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Nov 15, 2024
numpy_pydantic_types/__init__.py
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| from .floats import NumpyFloat # Adjust import path as needed | ||
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| __all__ = ["float16", "float32", "float64"] |
eksdsoto
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Nov 15, 2024
tests/.placeholder
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You may delete this file now that you have some tests, right
eksdsoto
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Nov 15, 2024
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Overview: This update introduces dynamic creation of numpy float types (float16, float32, and float64) as subclasses of NumpyFloat, improving maintainability and avoiding redundant code.
Key Changes:
Dynamic Class Creation:
Using types.new_class() to dynamically create float16, float32, and float64 classes as subclasses of NumpyFloat.
This approach handles numpy types that may interfere with type() due to method resolution order (MRO) issues.
Static Type Checker Compatibility:
Added a conditional TYPE_CHECKING block at the top of init.py to define each class (float16, float32, and float64) for static analysis tools like Pylance.
This ensures Pylance and similar tools recognize these classes, improving code completion and static type checking without affecting runtime.
Benefits:
Reduces Code Duplication: The dynamic approach removes the need to manually define each float type class.
Enhances Static Analysis: Explicit type declarations make classes visible to static type checkers, which is useful for both development and IDE support.