Metadata-Version: 2.1
Name: powerdataclass
Version: 1.1.0
Summary: Power Dataclass: dataclasses with auto typecasting and other power features
Home-page: http://pypi.org/simple/powerdataclass
Author: Arish Pyne
Author-email: arishpyne@gmail.com
License: MIT
Description: # ⚡ Power Dataclass ⚡
        [![Build Status](https://api.travis-ci.org/arishpyne/powerdataclass.svg?branch=master)](https://api.travis-ci.org/arishpyne/powerdataclass.svg?branch=master)
        
        >### DISCLAIMER
        **This library is leveraging the inner mechanics of Python 3.7's `typing` module, and thus only works with Python 3.7**
        **You may experience real shock when it breaks. Wear protective gloves.**
        
        ## Installation
        `pip install powerdataclass`
        
        ## Usage
        Python 3.7 have introduced a spiritual successor of `NamedTuple`: the `dataclass`.
        While being nice, the `dataclass` type hinting is only, well, _hinting_.
        
        This library gives you an ability to create dataclasses with field values automatically casted to 
        the types defined in the `dataclass`'s type hints:
        
        ```python
        @dataclasses.dataclass
        class Coordinates(powerdataclass.PowerDataclass):
            x: int
            y: int
        
        c1 = Coordinates(1,2)
        c2 = Coordinates('1', '2')
        c3 = Coordinates(**{'x': 1.1, 'y': 2.2})
        
        
        >>> c1
        Coordinates(x=1, y=2)
        >>> c1 == c2 == c3 
        True
         ```  
           
        This also works with every generic type that has a Python primitive type as it's origin. This applies to subscriptable types of any level of nestedness as well:
         
        ```python
        @dataclasses.dataclass
        class Vector(powerdataclass.PowerDataclass):
            items: List[int]
        
        v1 = Vector(['1', '2', '3'])
        v2 = Vector({1.1, 2.2, 3.3})
        v3 = Vector(range(1, 4))
        
        >>> v1
        Vector(items=[1, 2, 3])
        >>> v1 == v2 == v3 
        True
        ```
        
        The typecasting also respects other dataclasses (and Power Dataclasses) declared in type hints.
        If you pass a mapping in place of actual dataclass instance, Power Dataclass will attempt to unpack it to a corresponding dataclass:
        
        ```python
        @dataclasses.dataclass
        class Vector(powerdataclass.PowerDataclass):
            items: List[int]
           
        @dataclasses.dataclass
        class Tensor(powerdataclass.PowerDataclass):
            vectors: List[Vector]
        
        t1 = Tensor(**{
            'vectors': [
                {'items': [1, 2, 3]},
                {'items': [4, 5, 6]},
                {'items': [7, 8, 9]},
            ]
        })
        
        >>> t1
        Tensor(vectors=[Vector(items=[1, 2, 3]), Vector(items=[4, 5, 6]), Vector(items=[7, 8, 9])])
        ```
        
        You can modify the behaviour of type casting by registering two types of handlers on your fancy PowerDataclass:
        * **type handlers**: an unary method marked as a _type handler_ will be applied to any value that has a matching type declared in your dataclass typehints.
        * **field handlers**: an unary method marked as a _field handler_ will be applied to a value of a specific PDC field.
        
        Those functions must _always_ return a value.
        
        You can do this by marking your methods with special decorators:
        
        ```python
        @dataclasses.dataclass
        class CoolBool(powerdataclass.PowerDataclass):
            string_bool: bool
            negated_bool: bool
        
            @powerdataclass.register_pdc_type_handler(bool)
            def handle_bools(self, v):
                if type(v) is str:
                    return v.lower() in ['y', 'yes', '1', 'True']
                else:
                    return bool(v)
                    
            @powerdataclass.register_pdc_field_handler('negated_bool')
            def handle_xored_bools(self, v):
                return not self.handle_bools(v)
        
        >>> CoolBool('yes', 'no')
        CoolBool(string_bool=True, negated_bool=True)
        ```   
        
        Field handlers take precedence over the type handlers.
        Field handlers and type handlers are scoped to a particular Power Dataclass. Inheritance is respected.
        
        If you want to accept `None` as a valid value but also want non-null values to be typecasted you can mark your field as nullable by either setting the corresponding flag in the fields's `metadata` dictionary or using a premade partial:
        
        ```python
        @dataclasses.dataclass
        class Nihilus(powerdataclass.PowerDataclass):
            x: int = dataclasses.field(metadata={powerdataclass.FieldMeta.NULLABLE: True})
            y: int = powerdataclass.nullable_field()
        
        >>> Nihilus(None, None)
        Nihilus(x=None, y=None) 
        >>> Nihilus('1', None)
        Nihilus(x=1, y=None)
        ```
        
        If you want to disable type checking for a specific field you can mark your field as nullable by either setting the corresponding flag in the fields's `metadata` dictionary or using a premade partial:
        
        ```python
        @dataclasses.dataclass
        class Noncasted(powerdataclass.PowerDataclass):
            x: int = field(metadata={powerdataclass.FieldMeta.SKIP_TYPECASTING: True})
            y: int = powerdataclass.noncasted_field()
            
        >>> Noncasted('1', 2.2)
        Noncasted(x='1', y=2.2)
        ```
            
        If some of your field processing requires other fields typecasted before you can declare this field dependencies by name by setting the corresponding value in the fields's `metadata`:
        
        ```python
        @dataclasses.dataclass
        class Dependent(powerdataclass.PowerDataclass):
            a: int
            b: int = field(metadata={powerdataclass.FieldMeta.DEPENDS_ON_FIELDS: ['a']})
            c: int = field(metadata={powerdataclass.FieldMeta.DEPENDS_ON_FIELDS: ['d', 'b']})
            d: int = field(metadata={powerdataclass.FieldMeta.DEPENDS_ON_FIELDS: ['a']})
        ```
               
        Fields will be topologically sorted by their dependencies and type casting will be done in this order. For this example, the order will be:
        1) a
        2) b
        3) d
        4) c
        
        You can use a combination of field handlers and dependent fields to declare calculated fields:
        
        ```python  
        @dataclasses.dataclass
        class CubeSquarer(PowerDataclass):
            n: int
            n_square: int = field(default=None, metadata={FieldMeta.DEPENDS_ON_FIELDS: ['n']})
            n_cube: int = powerdataclass.calculated_field(depends_on=['n'])
        
            @register_pdc_field_handler('n_square')
            def handle_n_square(self, v):
                return self.n ** 2
            
            @register_pdc_field_handler('n_cube')
            def handle_n_cube(self, v):
                return self.n ** 3
         
        >>> CubeSquarer(4)
        CubeSquarer(n=4, n_square=16, n_cube=256)
        ```
        
        It is an error to declare a field as `calculatable` without registering a corresponding `field_handler`
        
        ## Other features
        * Automatic recursive conversion to dict with the `.as_dict()` method.
        * Automatic recursive conversion to and from JSON strings with the `.as_json()` and `.from_json()`  methods.
        
        
        ---
        Made with ⚡ by Arish Pyne (https://github.com/arishpyne/powerdataclass)
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.7
Description-Content-Type: text/markdown
