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你的第一个装饰器

在上一个例子里,其实我们已经创建了一个装饰器!现在我们修改下上一个装饰器,并编写一个稍微更有用点的程序:

def a_new_decorator(a_func):

    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")

        a_func()

        print("I am doing some boring work after executing a_func()")

    return wrapTheFunction

def a_function_requiring_decoration():
    print("I am the function which needs some decoration to remove my foul smell")

a_function_requiring_decoration()
#outputs: "I am the function which needs some decoration to remove my foul smell"

a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)
#now a_function_requiring_decoration is wrapped by wrapTheFunction()

a_function_requiring_decoration()
#outputs:I am doing some boring work before executing a_func()
#        I am the function which needs some decoration to remove my foul smell
#        I am doing some boring work after executing a_func()

你看明白了吗?我们刚刚应用了之前学习到的原理。这正是python中装饰器做的事情!它们封装一个函数,并且用这样或者那样的方式来修改它的行为。现在你也许疑惑,我们在代码里并没有使用 @ 符号?那只是一个简短的方式来生成一个被装饰的函数。这里是我们如何使用 @ 来运行之前的代码:

@a_new_decorator
def a_function_requiring_decoration():
    """Hey you! Decorate me!"""
    print("I am the function which needs some decoration to "
          "remove my foul smell")

a_function_requiring_decoration()
#outputs: I am doing some boring work before executing a_func()
#         I am the function which needs some decoration to remove my foul smell
#         I am doing some boring work after executing a_func()

#the @a_new_decorator is just a short way of saying:
a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)

希望你现在对 Python 装饰器的工作原理有一个基本的理解。如果我们运行如下代码会存在一个问题:

print(a_function_requiring_decoration.__name__)
# Output: wrapTheFunction

这并不是我们想要的!Ouput 输出应该是 “a_function_requiring_decoration”。这里的函数被 warpTheFunction 替代了。它重写了我们函数的名字和注释文档(docstring)。幸运的是 Python 提供给我们一个简单的函数来解决这个问题,那就是 functools.wraps。我们修改上一个例子来使用 functools.wraps

from functools import wraps

def a_new_decorator(a_func):
    @wraps(a_func)
    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")
        a_func()
        print("I am doing some boring work after executing a_func()")
    return wrapTheFunction

@a_new_decorator
def a_function_requiring_decoration():
    """Hey yo! Decorate me!"""
    print("I am the function which needs some decoration to "
          "remove my foul smell")

print(a_function_requiring_decoration.__name__)
# Output: a_function_requiring_decoration

现在好多了。我们接下来学习装饰器的一些常用场景。

蓝本规范:

from functools import wraps
def decorator_name(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        if not can_run:
            return "Function will not run"
        return f(*args, **kwargs)
    return decorated

@decorator_name
def func():
    return("Function is running")

can_run = True
print(func())
# Output: Function is running

can_run = False
print(func())
# Output: Function will not run

注意:@wraps 接受一个函数来进行装饰,并加入了复制函数名称、注释文档、参数列表等等的功能。这可以让我们在装饰器里面访问在装饰之前的函数的属性。

使用场景

现在我们来看一下装饰器在哪些地方特别耀眼,以及使用它可以让一些事情管理起来变得更简单。

授权(Authorization)

装饰器能有助于检查某个人是否被授权去使用一个 web 应用的端点(endpoint)。它们被大量使用于 Flask 和 Django web 框架中。这里是一个例子来使用基于装饰器的授权:

from functools import wraps

def requires_auth(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        auth = request.authorization
        if not auth or not check_auth(auth.username, auth.password):
            authenticate()
        return f(*args, **kwargs)
    return decorated

日志(Logging)

日志是装饰器运用的另一个亮点。这是个例子:

from functools import wraps

def logit(func):
    @wraps(func)
    def with_logging(*args, **kwargs):
        print(func.__name__ + " was called")
        return func(*args, **kwargs)
    return with_logging

@logit
def addition_func(x):
   """Do some math."""
   return x + x


result = addition_func(4)
# Output: addition_func was called

我敢肯定你已经在思考装饰器的一个其他聪明用法了。