python装饰器、描述符模拟源码实现

概要

本人python理论知识远达不到传授级别，写文章主要目的是自我总结，并不能照顾所有人，请见谅，文章结尾贴有相关链接可以作为补充

全文分为三个部分装饰器理论知识、装饰器应用、装饰器延申

装饰理基础：无参装饰器、有参装饰器、functiontools、装饰器链
装饰器进阶：property、staticmethod、classmethod源码分析（python代码实现）

装饰器基础

无参装饰器

'''
假定有一个需求是：打印程序函数运行顺序
此案例打印的结果为：
    foo1 function is starting
    foo2 function is starting
'''
from functools import wraps


def NoParamDec(func):
    #函数在被装饰器装时后，其函数属性也会改变，wraps作用就是保证被装饰函数属性不变
    @wraps(func)
    def warpper(*args, **kwargs):
        print('{} function is starting'.format(func.__name__))
        return func(*args, **kwargs)
    
    return warpper


#python黑魔法省略了NoParamDec=NoParamDec(foo1)
@NoParamDec
def foo1():
    foo2()

@NoParamDec
def foo2():
    pass


if __name__ == "__main__":

    foo1()

有参装饰器

'''
假定有一个需求是：检查函数参数的类型，只允许匹配正确的函数通过程序
此案例打印结果为：
('a', 'b', 'c')
-----------------------分割线------------------------
ERROS!!!!b must be <class 'str'> 
ERROS!!!!c must be <class 'str'> 
('a', 2, ['b', 'd'])

    
'''
from functools import wraps
from  inspect import signature


def typeAssert(*args, **kwargs):
    deco_args = args
    deco_kwargs = kwargs
    
    def factor(func):
        #python标准模块类，可以用来检查函数参数类型，只允许特定类型通过
        sig = signature(func)
        #将函数形式参数和规定类型进行绑定
        check_bind_args = sig.bind_partial(*deco_args, **deco_kwargs).arguments
        
        @wraps(func)
        def wrapper(*args, **kwargs):
            #将实际参数值和形式参数进行绑定
            wrapper_bind_args = sig.bind(*args, **kwargs).arguments.items()
            for name, obj in wrapper_bind_args:
                #遍历判断是否实际参数值是规定参数的实例
                if not isinstance(obj, check_bind_args[name]):
                    try:
                        raise TypeError('ERROS!!!!{arg} must be {obj} '.format(**{'arg': name, 'obj': check_bind_args[name]}))
                    except Exception as e:
                        print(e)
            return func(*args, **kwargs)
        
        return wrapper
    
    return factor


@typeAssert(str, str, str)
def inspect_type(a, b, c):
    return (a, b, c)


if __name__ == "__main__":
    print(inspect_type('a', 'b', 'c'))
    print('{:-^50}'.format('分割线'))
    print(inspect_type('a', 2, ['b', 'd']))

装饰器链

'''
假定有一个需求是：
输入类似代码：
@makebold
@makeitalic
def say():
   return "Hello"

输出：
<b><i>Hello</i></b>
'''
from functools import wraps


def html_deco(tag):
    def decorator(fn):
        @wraps(fn)
        def wrapped(*args, **kwargs):
            return '<{tag}>{fn_result}<{tag}>'.format(**{'tag': tag, 'fn_result': fn(*args, **kwargs)})
        
        return wrapped
    
    return decorator


@html_deco('b')
@html_deco('i')
def greet(whom=''):
    # 等价于 geet=html_deco('b')(html_deco('i)(geet))
    return 'Hello' + (' ' + whom) if whom else ''


if __name__ == "__main__":
    print(greet('world'))  # -> <b><i>Hello world</i></b>

装饰器进阶

property 原理

通常，描述符是具有“绑定行为”的对象属性，其属性访问已经被描述符协议中的方法覆盖。这些方法是__get__()、__set__()和__delete__()。如果一个对象定义这些方法中的任何一个，它被称为一个描述符。如果对象定义__get__()和__set__()，则它被认为是数据描述符。仅定义__get__()的描述器称为非数据描述符（它们通常用于方法，但是其他用途也是可能的）。

属性查找优先级为：

类属性
数据描述符
实例属性
非数据描述符
默认为__getattr__()

class Property(object):
    '''
    内部property是用c实现的，这里用python模拟实现property功能
    代码参考官方doc文档
    '''

    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
        self.fget = fget
        self.fset = fset
        self.fdel = fdel
        self.__doc__ = doc

    def __get__(self, obj, objtype=None):
        if obj is None:
            return self
        if self.fget is None:
            raise (AttributeError, "unreadable attribute")
        print('self={},obj={},objtype={}'.format(self,obj,objtype))
        return self.fget(obj)

    def __set__(self, obj, value):
        if self.fset is None:
            raise (AttributeError, "can't set attribute")
        self.fset(obj, value)

    def __delete__(self, obj):
        if self.fdel is None:
            raise (AttributeError, "can't delete attribute")
        self.fdel(obj)

    def getter(self, fget):
        return type(self)(fget, self.fset, self.fdel, self.__doc__)

    def setter(self, fset):
        return type(self)(self.fget, fset, self.fdel, self.__doc__)

    def deleter(self, fdel):
        return type(self)(self.fget, self.fset, fdel, self.__doc__)


class Student( object ):
    @Property
    def score( self ):
        return self._score
    @score.setter
    def score( self, val ):
        if not isinstance( val, int ):
            raise ValueError( 'score must be an integer!' )
        if val > 100 or val < 0:
            raise ValueError( 'score must between 0 ~ 100!' )
        self._score = val


if __name__ == "__main__":
    s = Student()
    s.score = 60   
    s.score

staticmethod 原理

@staticmethod means: when this method is called, we don't pass an instance of the class to it (as we normally do with methods). This means you can put a function inside a class but you can't access the instance of that class (this is useful when your method does not use the instance).

class StaticMethod(object):
    "python代码实现staticmethod原理"
    
    def __init__(self, f):
        self.f = f
    
    def __get__(self, obj, objtype=None):
        return self.f


class E(object):
    #StaticMethod=StaticMethod(f)
    @StaticMethod
    def f( x):
        return x

if __name__ == "__main__":
    print(E.f('staticMethod Test'))

classmethod

class ClassMethod(object):
    "python代码实现classmethod原理"
    
    def __init__(self, f):
        self.f = f
    
    def __get__(self, obj, klass=None):
        if klass is None:
            klass = type(obj)
        
        def newfunc(*args):
            return self.f(klass, *args)
        
        return newfunc
    
class E(object):
    #ClassMethod=ClassMethod(f)
    @ClassMethod
    def f(cls,x):
        return x
    
if __name__ == "__main__":
    print(E().f('classMethod Test'))