type-enforced 1.6.0

A pure python type enforcer for python type annotations

Type Enforced

A pure python (no special compiler required) type enforcer for type annotations. Enforce types in python functions and methods.

Setup

Make sure you have Python 3.9.x (or higher) installed on your system. You can download it here. For older python versions (3.7 | 3.8), you should use type_enforced==0.0.16.

• Note: Certain features are only available on newer python versions:
  • EG: Staticmethod typechecking requires python>=3.10
  • EG: Union types with | require python>=3.10

Installation

pip install type_enforced

Basic Usage

import type_enforced

@type_enforced.Enforcer(enabled=True)
def my_fn(a: int , b: [int, str] =2, c: int =3) -> None:
    pass

• Note: enabled=True by default if not specified. You can set enabled=False to disable type checking for a specific function, method, or class. This is useful for a production vs debugging environment or for undecorating a single method in a larger wrapped class.

Getting Started

type_enforcer contains a basic Enforcer wrapper that can be used to enforce many basic python typing hints. Technical Docs Here.

type_enforcer currently supports many single and multi level python types. This includes class instances and classes themselves. For example, you can force an input to be an int, a number [int, float], an instance of the self defined MyClass, or a even a vector with list[int]. Items like typing.List, typing.Dict, typing.Union and typing.Optional are supported.

You can pass union types to validate one of multiple types. For example, you could validate an input was an int or a float with [int, float], [int | float] or even typing.Union[int, float].

Nesting is allowed as long as the nested items are iterables (e.g. typing.List, dict, ...). For examle, you could validate that a list is a vector with list[int] or possibly typing.List[int].

Variables without an annotation for type are not enforced.

Note: Type Enforced does not support __future__.annotations. If you call from __future__ import annotations in your file, type enforced will not work as expected.

Supported Type Checking Features:

• Function/Method Input Typing
• Function/Method Return Typing
• All standard python types (str, list, int, dict, ...)
• Union types
  • typing.Union
  • , separated list (e.g. [int, float])
  • | separated list (e.g. [int | float])
• Nested types (e.g. dict[str] or list[int,float])
  • Note: Each parent level must be an iterable
    • Specifically a variant of list, set, tuple or dict
  • Note: dict keys are not validated, only values
  • Deeply nested types are supported too:
    • dict[dict[int]]
    • list[set[str]]
• Many of the typing (package) functions and methods including:
  • Standard typing functions:
    • List, Set, Dict, Tuple
  • Union
  • Optional
  • Sized
    • Essentially creates a union of:
      • list, tuple, dict, set, str, bytes, bytearray, memoryview, range
    • Note: Can not have a nested type
      • Because this does not always meet the criteria for Nested types above
  • Literal
    • Only allow certain values to be passed. Operates slightly differently than other checks.
    • e.g. Literal['a', 'b'] will require any passed values that are equal (==) to 'a' or 'b'.
      • This compares the value of the passed input and not the type of the passed input.
    • Note: Multiple types can be passed in the same Literal.
    • Note: Literals are evaluated after type checking occurs.
  • Callable
    • Essentially creates a union of:
      • staticmethod, classmethod, types.FunctionType, types.BuiltinFunctionType, types.MethodType, types.BuiltinMethodType, types.GeneratorType
  • Note: Other functions might have support, but there are not currently tests to validate them
    • Feel free to create an issue (or better yet a PR) if you want to add tests/support
• Constraint validation.
  • This is a special type of validation that allows passed input to be validated.
    • Standard and custom constraints are supported.
  • This is useful for validating that a passed input is within a certain range or meets a certain criteria.
  • Note: The constraint is checked after type checking occurs.
  • Note: See the example below or technical constraint and generic constraint docs for more information.

Interactive Example

>>> import type_enforced
>>> @type_enforced.Enforcer
... def my_fn(a: int , b: [int, str] =2, c: int =3) -> None:
...     pass
...
>>> my_fn(a=1, b=2, c=3)
>>> my_fn(a=1, b='2', c=3)
>>> my_fn(a='a', b=2, c=3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/conmak/development/personal/type_enforced/type_enforced/enforcer.py", line 85, in __call__
    self.__check_type__(assigned_vars.get(key), value, key)
  File "/home/conmak/development/personal/type_enforced/type_enforced/enforcer.py", line 107, in __check_type__
    self.__exception__(
  File "/home/conmak/development/personal/type_enforced/type_enforced/enforcer.py", line 34, in __exception__
    raise TypeError(f"({self.__fn__.__qualname__}): {message}")
TypeError: (my_fn): Type mismatch for typed variable `a`. Expected one of the following `[<class 'int'>]` but got `<class 'str'>` instead.

Nested Examples

import type_enforced
import typing

@type_enforced.Enforcer
def my_fn(
    a: dict[dict[int, float]], # Note: dict keys are not validated, only values
    b: list[typing.Set[str]] # Could also just use set
) -> None:
    return None

my_fn(a={'i':{'j':1}}, b=[{'x'}]) # Success

my_fn(a={'i':{'j':'k'}}, b=[{'x'}]) # Error:
# TypeError: (my_fn): Type mismatch for typed variable `a[i][j]`. Expected one of the following `[<class 'int'>]` but got `<class 'str'>` instead. 

Class and Method Use

Type enforcer can be applied to methods individually:

import type_enforced

class my_class:
    @type_enforced.Enforcer
    def my_fn(self, b:int):
        pass

You can also enforce all typing for all methods in a class by decorating the class itself.

import type_enforced

@type_enforced.Enforcer
class my_class:
    def my_fn(self, b:int):
        pass

    def my_other_fn(self, a: int, b: [int, str]):
      pass

You can also enforce types on staticmethods and classmethods if you are using python >= 3.10. If you are using a python version less than this, classmethods and staticmethods methods will not have their types enforced.

import type_enforced

@type_enforced.Enforcer
class my_class:
    @classmethod
    def my_fn(self, b:int):
        pass

    @staticmethod
    def my_other_fn(a: int, b: [int, str]):
      pass

You can skip enforcement if you add the argument enabled=False in the Enforcer call.

• This is useful for a production vs debugging environment.
• This is also useful for undecorating a single method in a larger wrapped class.
• Note: You can set enabled=False for an entire class or simply disable a specific method in a larger wrapped class.
• Note: Method level wrapper enabled values take precedence over class level wrappers.

import type_enforced
@type_enforced.Enforcer
class my_class:
    def my_fn(self, a: int) -> None:
        pass
        
    @type_enforced.Enforcer(enabled=False)
    def my_other_fn(self, a: int) -> None:
        pass

Validate with Constraints

Type enforcer can enforce constraints for passed variables. These constraints are vaildated after any type checks are made.

To enforce basic input values are integers greater than or equal to zero, you can use the Constraint class like so:

import type_enforced
from type_enforced.utils import Constraint

@type_enforced.Enforcer()
def positive_int_test(value: [int, Constraint(ge=0)]) -> bool:
    return True

positive_int_test(1) # Passes
positive_int_test(-1) # Fails
positive_int_test(1.0) # Fails

To enforce a GenericConstraint:

import type_enforced
from type_enforced.utils import GenericConstraint

CustomConstraint = GenericConstraint(
    {
        'in_rgb': lambda x: x in ['red', 'green', 'blue'],
    }
)

@type_enforced.Enforcer()
def rgb_test(value: [str, CustomConstraint]) -> bool:
    return True

rgb_test('red') # Passes
rgb_test('yellow') # Fails

Validate class instances and classes

Type enforcer can enforce class instances and classes. There are a few caveats between the two.

To enforce a class instance, simply pass the class itself as a type hint:

import type_enforced

class Foo():
    def __init__(self) -> None:
        pass

@type_enforced.Enforcer
class my_class():
    def __init__(self, object: Foo) -> None:
        self.object = object

x=my_class(Foo()) # Works great!
y=my_class(Foo) # Fails!

Notice how an initialized class instance Foo() must be passed for the enforcer to not raise an exception.

To enforce an uninitialized class object use typing.Type[classHere] on the class to enforce inputs to be an uninitialized class:

import type_enforced
import typing

class Foo():
    def __init__(self) -> None:
        pass

@type_enforced.Enforcer
class my_class():
    def __init__(self, object_class: typing.Type[Foo]) -> None:
        self.object = object_class()

y=my_class(Foo) # Works great!
x=my_class(Foo()) # Fails

Validate classes with inheritance

import type_enforced
from type_enforced.utils import WithSubclasses

class Foo:
    pass

class Bar(Foo):
    pass

class Baz:
    pass

@type_enforced.Enforcer
def my_fn(custom_class: WithSubclasses(Foo)):
    pass

print(WithSubclasses(Foo)) # Prints: [<class '__main__.Foo'>, <class '__main__.Bar'>]
my_fn(Foo()) # Passes as expected
my_fn(Bar()) # Passes as expected
my_fn(Baz()) # Raises TypeError as expected