Metadata-Version: 1.1
Name: magic_constraints
Version: 0.3.0
Summary: Magic Type Introspection And Runtime Parameter Type/Value Checking.
Home-page: https://github.com/huntzhan/magic-constraints
Author: huntzhan
Author-email: programmer.zhx@gmail.com
License: UNKNOWN
Description: # magic-constraints
        
        [![PyPI](https://img.shields.io/pypi/pyversions/magic_constraints.svg)](https://pypi.python.org/pypi/magic_constraints) [![Build
          Status](https://travis-ci.org/huntzhan/magic-constraints.svg?branch=master)](https://travis-ci.org/huntzhan/magic-constraints)
        [![Coverage Status](https://coveralls.io/repos/github/huntzhan/magic-constraints/badge.svg?branch=master)](https://coveralls.io/github/huntzhan/magic-constraints?branch=master) 
        
        # Introduction
        
        `magic-constraints`:
        
        1. Implemented (or hacked) several [abstract base classes][1] (ABC for short) to support [type introspection][2], that is, the `isinstance`/`issubclass` operatios in Python. Specialization of ABC is support, i.e. `Sequence -> Sequence[int]`.
        2. Provides several decorators to enable runtime type/value checking on the parameters and return value of function/method. Especially, thoses decorators fit well with the type annotation feature introduced in Python 3.x:
          ```python
          from magic_constraints import function_constraints
          
          @function_constraints
          def function(foobar: int=None) -> float:
              if foobar is None:
                  # should fail the return type checking.
                  return 42
              else:
                  # good case.
                  return 42.0
          
          >>> function(1)
          42.0
          >>> function(None)
          Traceback (most recent call last):
          ...
          magic_constraints.exception.MagicTypeError: 
          MagicTypeError: return value unmatched.
          ---------------------------------------
          ret: 42
          return_type: ReturnType(type_=float)
          ---------------------------------------
        
          ```
        
        
        
        [1]: https://docs.python.org/3/glossary.html#term-abstract-base-class
        [2]: https://en.wikipedia.org/wiki/Type_introspection
        
        # Quick Start
        
        ## Install
        
        ```
        pip install magic-constraints
        ```
        
        ## Usage Of ABCs
        
        You can invoke `isinstance`/`issubclass` operatios on `Sequence/MutableSequence/ImmutableSequence`:
        
        ```python
        from magic_constraints import Sequence, MutableSequence, ImmutableSequence
        
        # True.
        isinstance([1, 2, 3], Sequence)
        # True.
        isinstance([1, 2, 3], MutableSequence)
        # True.
        isinstance((1, 2, 3), ImmutableSequence)
        
        # True, Sequence with int.
        isinstance([1, 2, 3],   Sequence[int])
        # False, 2.0 is float.
        isinstance([1, 2.0, 3], Sequence[int])
        
        # True.
        isinstance([(1, 2), (3, 4)],   Sequence[ImmutableSequence[int]])
        # False, 3.0 is float.
        isinstance([(1, 2), (3.0, 4)], Sequence[ImmutableSequence[int]])
        # False, [3, 4] is MutableSequence.
        isinstance([(1, 2), [3, 4]],   Sequence[ImmutableSequence[int]])
        
        # True
        issubclass(MutableSequence, Sequence)
        # True
        issubclass(ImmutableSequence, Sequence)
        # False
        issubclass(MutableSequence, ImmutableSequence)
        # False
        issubclass(ImmutableSequence, MutableSequence)
        ```
        
        More avaliable ABCs:
        
        ```
        from magic_constraints import (
            Sequence,
            MutableSequence,
            ImmutableSequence,
            
            Set,
            MutableSet,
            ImmutableSet,
            
            Mapping,
            MutableMapping,
            ImmutableMapping,
            
            Iterable,
            Iterator,
            
            Any,
            Union,
            NoneType,
        )
        ```
        
        ## Usage Of Decorators
        
        Declaration on function parameters and return value:
        
        ```python
        from magic_constraints import (
            function_constraints,
            Sequence, Mapping,
        )
        
        @function_constraints
        def func1(foo: str, bar: Sequence[int]) -> Mapping[str, Sequence[int]]:
            return {foo: bar}
        ```
        
        More decorators:
        
        ```
        from magic_constraints.decorator import (
            function_constraints,
            method_constraints,
            class_initialization_constraints,
        )
        ```
        
        ## Runtime Type/Value Checking
        
        Exceptoin would be raised if there's something wrong in the invocation of decorated function, i.e. input argument is not an instance of declared type. 
        
        Only derived classes of `SyntaxError` and `TypeError` would be raised:
        
        1. anything related to types, such as failing to pass `isinstance`, would raise an exception with derived type of `TypeError`.
        2. besides (1), anything related to the promise of interface (function) invocation, would raise an exception with derived type of `SyntaxError`.
        
        Example:
        
        ```python
        >>> func1('key', [1, 2, 3])
        {'key': [1, 2, 3]}
        >>> 
        >>> func1('42 is not a sequence', 42)
        Traceback (most recent call last):
        ...
        magic_constraints.exception.MagicTypeError: 
        MagicTypeError: argument unmatched.
        -----------------------------------
        argument: 42
        parameter: Parameter(name='bar', type_=Sequence[int])
        -----------------------------------
        >>> 
        >>> func1('2.0 is not int', [1, 2.0, 3])
        Traceback (most recent call last):
        ...
        magic_constraints.exception.MagicTypeError: 
        MagicTypeError: argument unmatched.
        -----------------------------------
        argument: [1, 2.0, 3]
        parameter: Parameter(name='bar', type_=Sequence[int])
        -----------------------------------
        ```
        
        # `magic_constrains.types`
        
        Supported ABCs and avaliable forms of specialization:
        
        ```
        type     ::=    abc
                      | speical
                      | <any other type object>
        
        abc      ::=    sequence
                      | set
                      | mapping 
                      | iterable
                      | iterator
        
        sequence ::=    Sequence
                      | Sequence          [ type ]
                      | Sequence          [ type, ... ]
                      | MutableSequence
                      | MutableSequence   [ type ]
                      | MutableSequence   [ type, ... ]
                      | ImmutableSequence
                      | ImmutableSequence [ type ]
                      | ImmutableSequence [ type, ... ]
        
        set      ::=    Set
                      | Set               [ type ]
                      | MutableSet
                      | MutableSet        [ type ]
                      | ImmutableSet
                      | ImmutableSet      [ type ]
        
        mapping  ::=    Mapping
                      | Mapping           [ type, type ]
                      | MutableMapping
                      | MutableMapping    [ type, type ]
                      | ImmutableMapping
                      | ImmutableMapping  [ type, type ]
        
        iterable ::=    Iterable
                      | Iterable          [ type ]
                      | Iterable          [ type, ... ]
        
        iterator ::=    Iterator
                      | Iterator          [ type ]
                      | Iterator          [ type, ... ]
        
        speical  ::=  | Union             [ type, ... ]
                      | Any
                      | NoneType
                      
        ```
        
        Explanations are as follow.
        
        `type` means type object in Python. `abc` defines several supported ABCs. `speical` defines some type objects for some special purposes.
        
        ## `sequence`
        
        `Sequence` is equivalent to [collections.abc.Sequence][3]. `MutableSequence` is equivalent to [collections.abc.MutableSequence][4]. `ImmutableSequence` is a `Sequence` that is not a `MutableSequence`.
        
        `Sequence[ type ]` specializes `Sequence`, accepting a sequence with instances of `type`.
        
        `Sequence[ type, ... ]` specialized `Sequence`, accepting a sequence with instances of exactly mapping of `type, ...`. For example, `Sequence[int, float]` accepts `(1, 2.0)` or `[1, 2.0]`.
        
        ## `set`
        
        `Set` is equivalent to [collections.abc.Set][5]. `MutableSet` is equivalent to [collections.abc.MutableSet][6]. `ImmutableSet` is a `Set` that is not a `MutableSet`.
        
        `Set[ type ]` specializes `Sequence`, accepting a set with instances of `type`.
        
        ## `mapping`
        
        `Mapping` is equivalent to [collections.abc.Mapping][7]. `MutableMapping` is equivalent to [collections.abc. MutableMapping][8]. `ImmutableMapping` is equivalent to [types.MappingProxyType][9].
        
        `Mapping[ key_type, val_type ]` specializes `Mapping`, accepting items with key of `key_type` and value of `val_type`.
        
        ## `iterable`
        
        `Iterable` is equivalent to [collections.abc.Iterable][10].
        
        Dual to the side effect of iterating the iterable, `isinstance(instance, Iterable[ type ])` and `isinstance(instance, Iterable[ type, ... ])` always return `False`.
        
        `Iterable[ type ](iterable)` and `Iterable[ type, ... ](iterable)` creates a iterable proxy with lazy type instrospection on its elements. Example:
        	
        ```python
        for i in Iterable[int]([1, 2, 3]):
            print(i)
        ``` 
        
        ## `iterator`
        
        `Iterator` is equivalent to [collections.abc.Iterator][11].
        
        Dual to the side effect of iterating the iterator, `isinstance(instance, Iterator[ type ])` and `isinstance(instance, Iterator[ type, ... ])` always return `False`.
        
        `Iterator[ type ](iterator)` and `Iterator[ type, ... ](iterator)` creates a iterator proxy with lazy type instrospection on the elements. Example:
        	
        ```python
        for i in Iterator[int](iter([1, 2, 3])):
            print(i)
        ``` 
        
        ## `special`
        
        `Union[ type, ... ]` acceps instance that match one of `type, ...`. For example, `isinstance(42, Union[int, float]` returns `True`.
        
        `Any` accepts any object, including type and non-type objects. It's guaranteed that `isinstance(..., Any)` returns `True` and `issubclass(..., Any)` returns `True`.
        
        `NoneType` is an alias of `type(None)`.
        
        [3]: https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence
        [4]: https://docs.python.org/3/library/collections.abc.html#collections.abc.MutableSequence
        [5]: https://docs.python.org/3/library/collections.abc.html#collections.abc.Set
        [6]: https://docs.python.org/3/library/collections.abc.html#collections.abc.MutableSet
        [7]: https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping
        [8]: https://docs.python.org/3/library/collections.abc.html#collections.abc.MutableMapping
        [9]: https://docs.python.org/3.4/library/types.html#types.MappingProxyType
        [10]: https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterable
        [11]: https://docs.python.org/3/library/collections.abc.html#collections.abc.Iterator
        
        # `magic_constrains.decorator`
        
        `magic_constrains` provides following decorators for parameter declaration:
        
        * `function_constraints`
        * `method_constraints`
        * `class_initialization_constraints`
        
        ## `function_constraints`
        
        `function_constraints` is a function decorator supporting three forms of invocations:
        
        1. `function_constraints(function)`
        1. `function_constraints(*type_objects, return_type=None)`
        1. `function_constraints(*contraints)`
        
        ### `function_constraints(function)`
        
        Example:
        
        ```python
        # 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`.
        
        ```python
        # func1 is equivalent to func2.
        
        @function_constraints
        def func1():
            pass
            
        @function_constraints
        def func2() -> Any:
            pass
        ```
        
        ### `function_constraints(*type_objects, return_type=Any)`
        
        Example:
        
        ```python
        @function_constraints(
            str, Sequence[int],
            return_type=Mapping[str, Sequence[int]],
        )
        def func2(foo, bar=None):
            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][12] for more information.
        * Parameter without default value is treated as non-`nullable` and with no `default` value. `nullable` and `default` will be explained in the usage of `Parameter`.
        * Parameter with `None` as its default value is treated as `nullable` and with `default` bound to `None`.
        * Parameter with any default value other than `None` is treated as non-`nullable` and with `default` bound to such value.
        
        
        ### `function_constraints(*contraints)`
        
        Example:
        
        ```python
        # explicitly declare Parameter and ReturnType.
        @function_constraints(
            Parameter('foo', str),
            Parameter('bar', Sequence[int], nullable=True, 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, thoses 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.
        
        #### `Parameter(name, type_, nullable=False, default=None, validator=None)`
        
        * `name` is name of parameter. `name` must follows [the rule of defining identifier][13] of Python.
        * `type_` defines the type valid argument, should be a type object.
        * (optional) `nullable=True` means the parameter can accept `None` as its value. If omitted, `nullable=False`, meaning that `None` is not accepted. But there are some exceptional cases:
          * If `type_` is `Any`, `nullable` is ignored, since `Any` could accept any kinds of argument.
          * If `type_` is `NoneType`, `nullable` is ignored, since `NoneType` is the type of `None`.
        * (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(type_, nullable=False, validator=None)`
        
        `ReturnType` accepts less arguments than `Parameter`. The meaning of `ReturnType`'s parameter is identical to `Parameter`, see `Parameter` for the details.
        
        [12]: https://docs.python.org/3.5/library/inspect.html#inspect.Parameter.kind
        [13]: https://docs.python.org/2/reference/lexical_analysis.html#identifiers
        
        ## `method_constraints`
        
        `method_constraints` is a method decorator supporting three forms of invocations:
        
        1. `method_constraints(method)`
        1. `method_constraints(*type_objects, return_type=None)`
        1. `method_constraints(*contraints)`
        
        `method_constraints` is almost identical to `function_constraints`, except that `method_constraints` decorates [method][14] instead of [function][15]. Make sure you understand what the method is. See `function_constraints` for more details.
        
        Here's the example of usage:
        
        ```python
        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, 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', str, nullable=True, default=None),
            )
            def method3(self, args):
                return args.a, args.b, args.c, args.d
        ```
        
        [14]: https://docs.python.org/3/glossary.html#term-method
        [15]: https://docs.python.org/3/glossary.html#term-function
        
        ## `class_initialization_constraints`
        
        `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:
        
        ```python
        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
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
