parameter-checks

For more info, please visit the GitHub page of this project.

Basic example

import parameter_checks as pc


@pc.hints.cleanup   # be left with only type-annotations
@pc.hints.enforce   # enforce the lambda but not the types
def div(a: int, b: pc.annotations.Checks[int, lambda b: b != 0]):
    return a / b 

div(1, 1)   # returns 1.0
div(1, 0)   # raises ValueError

As can be seen in this example, this package provides a new type-annotation: pc.annotations.Checks (it also provides pc.annotations.Hooks, see below). Using @pc.hints.enforce on a function will enforce the checks given to those annotations (but not the types). @pc.hints.cleanup would produce the div.__annotations__ of {"a": int, "b": int} in the example above.

Complex example

import parameter_checks as pc


def decision_boundary(fct, parameter, parameter_name, typehint):
    if type(parameter) is not typehint:
        err_str = f"In function {fct}, parameter {parameter_name}={parameter} " \
                  f"is not of type {typehint}!"
        raise TypeError(err_str)
    
    return 3 + 4 * parameter - parameter ** 2


@pc.hints.enforce
def classify(
        x: pc.annotations.Hooks[float, decision_boundary],
        additional_offset: pc.annotations.Checks[float, lambda b: 0 <= b <= 100] = 0.
) -> bool:
    return (x + additional_offset) >= 0
    

if __name__ == '__main__':
    assert classify(1.) is True   # 6.
    assert classify(2.) is True   # 7.
    assert classify(5.) is False   # -2.
    assert classify(5., 2.) is True   # 0.
    
    classify("not a float!")  # raises TypeError
    classify(1., -1.)   # raises ValueError

For additional comments on this example (and much more), visit the GitHub page of this project.