generic-preserver 0.1.3

Extracting Generic Type References in Python

generic-preserver

Extracting Generic Type References in Python

Introduction

In Python, generic types are a powerful feature for writing reusable and type-safe code. However, one limitation is that generic type arguments are typically not preserved at runtime, making it challenging to access or utilize these types dynamically. generic-preserver is a Python package that overcomes this limitation by capturing and preserving generic type arguments, allowing you to access them at runtime.

This package is particularly useful when you need to perform operations based on the specific types used in your generic classes, such as serialization, deserialization, or dynamic type checking.

Features

• Preserve Generic Types at Runtime: Capture and retain generic type arguments for classes and instances.
• Runtime Access to Type Parameters: Easily access the type parameters passed to generic classes from their instances.
• Supports Inheritance and Nested Generics: Works seamlessly with class hierarchies and nested generic types.
• Simple and Intuitive API: Use either a metaclass or a decorator to enable functionality with minimal code changes.
• Python 3.9+ Support: Leverages modern Python features for type hinting and annotations.

Installation

Install generic-preserver via pip:

pip install generic-preserver

Or install using Poetry:

poetry add generic-preserver

Requirements

• Python 3.9 or higher

Usage

Using the GenericMeta Metaclass

To enable capturing generic type arguments, use the GenericMeta metaclass in your base class definition.

from typing import TypeVar, Generic
from generic_preserver.metaclass import GenericMeta

# Define type variables
A = TypeVar("A")
B = TypeVar("B")
C = TypeVar("C")

# Example classes to use as type arguments
class ExampleA:
    pass

class ExampleB:
    pass

class ExampleC:
    pass

# Base class with GenericMeta metaclass
class Parent(Generic[A, B], metaclass=GenericMeta):
    pass

# Child classes specifying some generic type arguments
class Child(Parent[ExampleA, B], Generic[B, C]):
    pass

class GrandChild(Child[ExampleB, C], Generic[C]):
    pass

# Create an instance of the generic class with type arguments
instance = GrandChild[ExampleC]()

# Access the preserved generic type arguments
print(instance[A])  # Output: <class '__main__.ExampleA'>
print(instance[B])  # Output: <class '__main__.ExampleB'>
print(instance[C])  # Output: <class '__main__.ExampleC'>

# View the internal generic map
print(instance.__generic_map__)
# Output:
# {
#     ~A: <class '__main__.ExampleA'>,
#     ~B: <class '__main__.ExampleB'>,
#     ~C: <class '__main__.ExampleC'>,
# }

Using the @generic_preserver Decorator

Alternatively, use the @generic_preserver decorator to enable capturing generic arguments without explicitly specifying the metaclass.

from typing import TypeVar, Generic
from generic_preserver.wrapper import generic_preserver

# Define type variables
A = TypeVar("A")
B = TypeVar("B")
C = TypeVar("C")

# Example classes to use as type arguments
class ExampleA:
    pass

class ExampleB:
    pass

class ExampleC:
    pass

# Use the decorator to enable generic preservation
@generic_preserver
class Parent(Generic[A, B]):
    pass

# Child classes specifying some generic type arguments
class Child(Parent[ExampleA, B], Generic[B, C]):
    pass

class GrandChild(Child[ExampleB, C], Generic[C]):
    pass

# Create an instance of the generic class with type arguments
instance = GrandChild[ExampleC]()

# Access the preserved generic type arguments
print(instance[A])  # Output: <class '__main__.ExampleA'>
print(instance[B])  # Output: <class '__main__.ExampleB'>
print(instance[C])  # Output: <class '__main__.ExampleC'>

# View the internal generic map
print(instance.__generic_map__)
# Output:
# {
#     ~A: <class '__main__.ExampleA'>,
#     ~B: <class '__main__.ExampleB'>,
#     ~C: <class '__main__.ExampleC'>,
# }

Accessing Type Variables

You can access the type arguments by indexing the instance with the corresponding TypeVar.

print(instance[A])  # Output: <class '__main__.ExampleA'>

If you attempt to access a type variable that was not defined or is not in the generic map, a KeyError will be raised.

D = TypeVar("D")
try:
    print(instance[D])
except KeyError as e:
    print(e)  # Output: No generic type found for generic arg ~D

Accessing Multiple Type Variables

You can retrieve multiple type variables at once by passing an iterable of TypeVar instances.

types = instance[A, B, C]
print(types)
# Output: (<class '__main__.ExampleA'>, <class '__main__.ExampleB'>, <class '__main__.ExampleC'>)

How It Works

The generic-preserver package uses a custom metaclass GenericMeta to intercept class creation and capture generic type arguments when a generic class is subscripted (e.g., MyClass[int, str]). Here's a brief overview:

• Metaclass (GenericMeta): Overrides the __getitem__ method to capture the type arguments and store them in a __generic_map__.
• Class Wrapper: Creates a wrapper class that inherits from the original class and includes the __generic_map__.
• Instance Access: Allows instances to access the type arguments via the __getitem__ method.
• Decorator (@generic_preserver): Provides a convenient way to apply GenericMeta without altering the class definition directly.

By preserving the generic type arguments in __generic_map__, you can access them at runtime, enabling more dynamic and type-aware programming patterns.

Testing

The package includes a test suite to verify its functionality. To run the tests, first install the development dependencies:

poetry install --with dev

Then, run the tests using pytest:

pytest

An example test case is provided in tests/test_wrapper.py:

def test_template():
    A = TypeVar("A")
    B = TypeVar("B")
    C = TypeVar("C")

    class ExampleA: pass
    class ExampleB: pass
    class ExampleC: pass

    @generic_preserver
    class Parent(Generic[A, B]): pass

    class Child(Parent[ExampleA, B], Generic[B, C]): pass

    class GrandChild(Child[ExampleB, C], Generic[C]): pass

    instance = GrandChild[ExampleC]()

    assert instance[A] is ExampleA
    assert instance[B] is ExampleB
    assert instance[C] is ExampleC

    D = TypeVar("D")
    with pytest.raises(KeyError):
        instance[D]

Limitations

• Python Version: Requires Python 3.9 or higher due to the use of internal structures from the typing module.
• Compatibility: May not be compatible with other metaclass-based libraries or complex metaclass hierarchies.
• TypeVar Constraints: Does not enforce TypeVar constraints or bounds at runtime; it only captures the types provided.

Contributing

Contributions are welcome! If you find a bug or have an idea for a new feature, please open an issue or submit a pull request.

To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/my-feature).
3. Commit your changes (git commit -am 'Add my feature').
4. Push to your branch (git push origin feature/my-feature).
5. Open a Pull Request.

Please ensure that your code passes all tests and follows the existing coding style.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Inspired by the need to access and utilize generic type parameters at runtime in Python applications.
• Special thanks to the Python community for their contributions and support.

To learn more about how I came up with this solution, please read my blog post: Extracting Generic Type References in Python

Contact

For questions, suggestions, or feedback, please contact:

Matthew Coulter
Email: [e-mail]

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Thank you for using generic-preserver! If you find this package helpful, consider giving it a star on GitHub.