magic_constrains provides following decorators for parameter and return type declaration:

• function_constraints
• method_constraints
• class_initialization_constraints

function_constraints is a function decorator supporting three forms of invocations:

1. function_constraints(function)
2. function_constraints(*type_objects, return_type=None)
3. function_constraints(*contraints)

Example:

# py3 annotation.
@function_constraints
def func1(foo: str, bar: Sequence[int]) -> Mapping[str, Sequence[int]]:
    return {foo: bar}
    
    
# py2 annotation hack.
# NOT RECOMMENDED. Use the forms described later instead.
def func2(foo, bar):
    return {foo: bar}

func2.__annotations__ = {
    'foo': str,
    'bar': Sequence[int],
    'return': Mapping[str, Sequence[int]],
}

func2 = function_constraints(func2)

Each parameter should be bound with a type annotation. If missing, a SyntaxError would be raised. Return type can be omitted. If return type is omitted, it defaults to Any.

# func1 is equivalent to func2.


@function_constraints
def func1():
    pass

    
@function_constraints
def func2() -> Any:
    pass

Type checking on the default value happens during function inspection. If default value is not an instance of corresponding type annotation, a TypeError will be raised.

Example:

@function_constraints(
    str, Sequence[int],
    # return value could be None or a sequence of ints.
    return_type=Optional[Mapping[str, Sequence[int]]],
)
def func2(foo, bar):
    return {foo: bar}

In this case, type_objects should be an n-tuple of type objects, n equals to the number of parameters in the decorated function. Keyword-only parameter return_type accepts a type object to indicate the type of return value. If omitted, return_type defaults to Any, meaning that there's no restriction on the return value.

There are rules should be followed:

• Only parameters with the the kind of POSITIONAL_ONLY or POSITIONAL_OR_KEYWORD are accepted, see inspect.Parameter.kind for more information.
• If default value exists and the default value is not an instance of corresponding type, a TypeError will be raised.

As a special case, type_objects could be Ellipsis to indicate the function accept arbitrary arguments, in other words, there's no checking on the function's parameters. When type_objects is Ellipsis, there's no limitation on the kind of function's parameters.

@function_constraints(
    ...,
    # return value could be None or a sequence of ints.
    return_type=Optional[Mapping[str, Sequence[int]]],
)
def func2(foo, bar):
    return {foo: bar}
    
@function_constraints(...)
def func3(*args, **kwargs):
    return 'whatever you want.'

Notice that:

• In Python 2, ... is only supported in slicing. Passing ... as the argument would cause a SyntaxError. For Python 2 user, use Ellipsis instead of ...:

  @function_constraints(Ellipsis, return_type=int)
  def func3(*args, **kwargs):
      return 42

• function_constraints(...)(decorated_function) makes no sense.

Example:

# explicitly declare Parameter and ReturnType.
@function_constraints(
    Parameter('foo', str),
    # bar accepts None or a sequence of ints.
    Parameter('bar', Optional[Sequence[int]], default=[1, 2, 3]),
    ReturnType(Mapping[str, Sequence[int]]),
)
def func3(args):
    return {args.foo: args.bar}

In this case, contraints accepts one or more instances of Parameter and ReturnType, with following restrictions:

• contraints should not be empty.
• contraints could only contains instances of Parameter and ReturnType, otherwise a TypeError will be raised.
• Instance of ReturnType can be omitted. If omitted, there's no restriction on the return value. If not omitted, instance of ReturnType must be placed as the last element of contraints, otherwise a SyntaxError will be raised.

After checking the input arguments in runtime, those arguments will be bound to a single object as its attributes. Hence, user-defined function, that is, the one decorated by function_constraints should accept only one POSITIONAL_ONLY argument.

• name is name of parameter. name must follows the rule of defining identifier of Python.
• type_ defines the type valid argument, should be a type object.
• (optional) default defines the default value of parameter. If omitted and there is no argument could be bound to the parameter in the runtime, a SyntaxError will be raised.
• (optional) validator accepts a callable with a single positional argument and returns a boolean value. If defined, validator will be invoked after the type introspection. If validator returns False, a TypeError will be raised.

• ReturnType accepts less arguments than Parameter. The meaning of ReturnType's parameter is identical to Parameter, see Parameter for the details.

Due to the side effect of touching Iterable, Iterator and Callable, type checking on such argument is deferred to the time that evaluation really happens. For example:

# f should be a callable accepting an int argument.
@function_constraints
def function(f: Callable[[int], Any]):
    # ok.
    f(42)
    # type error.
    f(42.0)

Actually, there's no way to check f when calling function. In other to solve the problem, magic-constraints defer the type checking by tranforming f to Callable[[int], Any](f). Similar strategy is applied to Iterable and Iterator.

method_constraints is a method decorator supporting three forms of invocations:

1. method_constraints(method)
2. method_constraints(*type_objects, return_type=None)
3. method_constraints(*contraints)

method_constraints is almost identical to function_constraints, except that method_constraints decorates method instead of function. Make sure you understand what the method is. See function_constraints for more details.

Here's the example of usage:

from magic_constraints import method_constraints, Parameter

class Example(object):

    @method_constraints
    def method1(self, foo: int, bar: float) -> float:
        return foo + bar

    @classmethod
    @method_constraints(
        int, float, int, Optional[str],
    )
    def method2(cls, a, b, c=42, d=None):
        return a, b, c, d

    @method_constraints(
        Parameter('a', int),
        Parameter('b', float),
        Parameter('c', int, default=42),
        Parameter('d', Optional[str], default=None),
    )
    def method3(self, args):
        return args.a, args.b, args.c, args.d

class_initialization_constraints is a class decorator requires a class with INIT_PARAMETERS attribute. INIT_PARAMETERS should be a sequence contains one or more instances of Parameter and ReturnType. Restriction of INIT_PARAMETERS is identical to the contraints introduced in function_constraints(*contraints) section.

After decoration, class_initialization_constraints will inject a __init__ for argument processing. After type/value checking, accepted arguments will be bound to self as its attributes. User-defined __init__, within the decorated class or the superclass, will be invoked with a single argument self within the injected __init__. As a consequence, user-defined __init__ should not define any parameter except for self.

Example:

from magic_constraints import class_initialization_constraints, Parameter

@class_initialization_constraints
class Example(object):
                                  
    INIT_PARAMETERS = [
        Parameter('a', int),
    ]
                                  
    def __init__(self):
        assert self.a == 1