17 题: 如何制作一系列功能装饰器?

在...创建的问题 Wed, Feb 8, 2017 12:00 AM

如何在Python中创建两个可以执行以下操作的装饰器?

 
@makebold
@makeitalic
def say():
   return "Hello"

......应该返回:

 
"<b><i>Hello</i></b>"

我并不是想在真正的应用程序中以这种方式制作HTML - 只是想了解装饰器和装饰器链是如何工作的。

    
2572
17答案                              17 跨度>                         

查看文档,了解装饰器的工作原理。这是你要求的:

 
from functools import wraps

def makebold(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<b>" + fn(*args, **kwargs) + "</b>"
    return wrapped

def makeitalic(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<i>" + fn(*args, **kwargs) + "</i>"
    return wrapped

@makebold
@makeitalic
def hello():
    return "hello world"

@makebold
@makeitalic
def log(s):
    return s

print hello()        # returns "<b><i>hello world</i></b>"
print hello.__name__ # with functools.wraps() this returns "hello"
print log('hello')   # returns "<b><i>hello</i></b>"
    
2798
2019-04-03 16:23:00Z
  1. 考虑使用 functools .wraps 或者更好的是,来自PyPI的装饰模块:它们保留了某些重要的元数据(如__name__,并谈到装饰包,函数签名)。
    2011-03-11 02:30:48Z
  2. 应该在答案中添加
    *args**kwargs。装饰函数可以有参数,如果没有指定,它们将丢失。
    2017-04-02 11:47:29Z
  3. 虽然这个答案具有仅使用stdlib的巨大优势,但适用于这个简单的例子,其中没有装饰器参数,也没有装饰函数参数,它有3个主要限制:(1)没有对可选装饰器参数的简单支持(2)不签名保留(3)没有从*args,**kwargs中提取命名参数的简单方法。一次解决这三个问题的简单方法是使用decopatch,如此处所述。您也可以使用Marius Gedminas已经提到的decorator来解决第2点和第3点。
    2019-03-11 15:28:59Z
  4. 醇>

如果您不想进行长篇解释,请参阅 Paolo Bergantino的回答

装饰器基础知识

Python的函数是对象

要理解装饰器,首先必须了解函数是Python中的对象。这具有重要的后果。让我们用一个简单的例子来看看为什么:

 
def shout(word="yes"):
    return word.capitalize()+"!"

print(shout())
# outputs : 'Yes!'

# As an object, you can assign the function to a variable like any other object 
scream = shout

# Notice we don't use parentheses: we are not calling the function,
# we are putting the function "shout" into the variable "scream".
# It means you can then call "shout" from "scream":

print(scream())
# outputs : 'Yes!'

# More than that, it means you can remove the old name 'shout',
# and the function will still be accessible from 'scream'

del shout
try:
    print(shout())
except NameError as e:
    print(e)
    #outputs: "name 'shout' is not defined"

print(scream())
# outputs: 'Yes!'

记住这一点。我们很快就会回过头来。

Python函数的另一个有趣的属性是它们可以在另一个函数中定义!

 
def talk():

    # You can define a function on the fly in "talk" ...
    def whisper(word="yes"):
        return word.lower()+"..."

    # ... and use it right away!
    print(whisper())

# You call "talk", that defines "whisper" EVERY TIME you call it, then
# "whisper" is called in "talk". 
talk()
# outputs: 
# "yes..."

# But "whisper" DOES NOT EXIST outside "talk":

try:
    print(whisper())
except NameError as e:
    print(e)
    #outputs : "name 'whisper' is not defined"*
    #Python's functions are objects

函数引用

好的,还在吗?现在有趣的部分......

您已经看到函数是对象。因此,功能:

  • 可以分配给变量
  • 可以在另一个函数中定义

这意味着一个函数可以return另一个函数

 
def getTalk(kind="shout"):

    # We define functions on the fly
    def shout(word="yes"):
        return word.capitalize()+"!"

    def whisper(word="yes") :
        return word.lower()+"...";

    # Then we return one of them
    if kind == "shout":
        # We don't use "()", we are not calling the function,
        # we are returning the function object
        return shout  
    else:
        return whisper

# How do you use this strange beast?

# Get the function and assign it to a variable
talk = getTalk()      

# You can see that "talk" is here a function object:
print(talk)
#outputs : <function shout at 0xb7ea817c>

# The object is the one returned by the function:
print(talk())
#outputs : Yes!

# And you can even use it directly if you feel wild:
print(getTalk("whisper")())
#outputs : yes...

还有更多!

如果你可以return一个函数,你可以传递一个作为参数:

 
def doSomethingBefore(func): 
    print("I do something before then I call the function you gave me")
    print(func())

doSomethingBefore(scream)
#outputs: 
#I do something before then I call the function you gave me
#Yes!

嗯,你只需要了解修饰器所需的一切。你看,装饰器是“包装器”,这意味着它们允许你在它们装饰的函数之前和之后执行代码而不修改函数本身。

手工装饰

如何手动完成:

 
# A decorator is a function that expects ANOTHER function as parameter
def my_shiny_new_decorator(a_function_to_decorate):

    # Inside, the decorator defines a function on the fly: the wrapper.
    # This function is going to be wrapped around the original function
    # so it can execute code before and after it.
    def the_wrapper_around_the_original_function():

        # Put here the code you want to be executed BEFORE the original function is called
        print("Before the function runs")

        # Call the function here (using parentheses)
        a_function_to_decorate()

        # Put here the code you want to be executed AFTER the original function is called
        print("After the function runs")

    # At this point, "a_function_to_decorate" HAS NEVER BEEN EXECUTED.
    # We return the wrapper function we have just created.
    # The wrapper contains the function and the code to execute before and after. It’s ready to use!
    return the_wrapper_around_the_original_function

# Now imagine you create a function you don't want to ever touch again.
def a_stand_alone_function():
    print("I am a stand alone function, don't you dare modify me")

a_stand_alone_function() 
#outputs: I am a stand alone function, don't you dare modify me

# Well, you can decorate it to extend its behavior.
# Just pass it to the decorator, it will wrap it dynamically in 
# any code you want and return you a new function ready to be used:

a_stand_alone_function_decorated = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function_decorated()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

现在,您可能希望每次拨打a_stand_alone_function时,都会调用a_stand_alone_function_decorated。这很简单,只需用a_stand_alone_function返回的函数覆盖my_shiny_new_decorator

 
a_stand_alone_function = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

# That’s EXACTLY what decorators do!

装饰者揭秘

上一个示例,使用装饰器语法:

 
@my_shiny_new_decorator
def another_stand_alone_function():
    print("Leave me alone")

another_stand_alone_function()  
#outputs:  
#Before the function runs
#Leave me alone
#After the function runs

是的,就是这样,就这么简单。 @decorator只是一个快捷方式:

 
another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)

装饰器只是装饰设计模式。 Python中嵌入了几种经典设计模式以简化开发(如迭代器)。

当然,你可以积累装饰器:

 
def bread(func):
    def wrapper():
        print("</''''''\>")
        func()
        print("<\______/>")
    return wrapper

def ingredients(func):
    def wrapper():
        print("#tomatoes#")
        func()
        print("~salad~")
    return wrapper

def sandwich(food="--ham--"):
    print(food)

sandwich()
#outputs: --ham--
sandwich = bread(ingredients(sandwich))
sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

使用Python装饰器语法:

 
@bread
@ingredients
def sandwich(food="--ham--"):
    print(food)

sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

您设置装饰器MATTERS的顺序:

 
@ingredients
@bread
def strange_sandwich(food="--ham--"):
    print(food)

strange_sandwich()
#outputs:
##tomatoes#
#</''''''\>
# --ham--
#<\______/>
# ~salad~

现在:回答这个问题......

作为结论,您可以轻松地了解如何回答这个问题:

 
# The decorator to make it bold
def makebold(fn):
    # The new function the decorator returns
    def wrapper():
        # Insertion of some code before and after
        return "<b>" + fn() + "</b>"
    return wrapper

# The decorator to make it italic
def makeitalic(fn):
    # The new function the decorator returns
    def wrapper():
        # Insertion of some code before and after
        return "<i>" + fn() + "</i>"
    return wrapper

@makebold
@makeitalic
def say():
    return "hello"

print(say())
#outputs: <b><i>hello</i></b>

# This is the exact equivalent to 
def say():
    return "hello"
say = makebold(makeitalic(say))

print(say())
#outputs: <b><i>hello</i></b>

你现在可以离开快乐,或者更多地燃烧你的大脑并看到装饰者的高级用途。


将装饰器提升到新的水平

将参数传递给修饰函数

 
# It’s not black magic, you just have to let the wrapper 
# pass the argument:

def a_decorator_passing_arguments(function_to_decorate):
    def a_wrapper_accepting_arguments(arg1, arg2):
        print("I got args! Look: {0}, {1}".format(arg1, arg2))
        function_to_decorate(arg1, arg2)
    return a_wrapper_accepting_arguments

# Since when you are calling the function returned by the decorator, you are
# calling the wrapper, passing arguments to the wrapper will let it pass them to 
# the decorated function

@a_decorator_passing_arguments
def print_full_name(first_name, last_name):
    print("My name is {0} {1}".format(first_name, last_name))

print_full_name("Peter", "Venkman")
# outputs:
#I got args! Look: Peter Venkman
#My name is Peter Venkman

装饰方法

关于Python的一个很好的事情是方法和函数真的是一样的。唯一的区别是方法期望它们的第一个参数是对当前对象的引用(self)。

这意味着您可以以相同的方式为方法构建装饰器!请记住考虑self

 
def method_friendly_decorator(method_to_decorate):
    def wrapper(self, lie):
        lie = lie - 3 # very friendly, decrease age even more :-)
        return method_to_decorate(self, lie)
    return wrapper


class Lucy(object):

    def __init__(self):
        self.age = 32

    @method_friendly_decorator
    def sayYourAge(self, lie):
        print("I am {0}, what did you think?".format(self.age + lie))

l = Lucy()
l.sayYourAge(-3)
#outputs: I am 26, what did you think?

如果您正在制作通用装饰器 - 您可以将其应用于任何函数或方法,无论其参数如何 - 那么只需使用*args, **kwargs

 
def a_decorator_passing_arbitrary_arguments(function_to_decorate):
    # The wrapper accepts any arguments
    def a_wrapper_accepting_arbitrary_arguments(*args, **kwargs):
        print("Do I have args?:")
        print(args)
        print(kwargs)
        # Then you unpack the arguments, here *args, **kwargs
        # If you are not familiar with unpacking, check:
        # http://www.saltycrane.com/blog/2008/01/how-to-use-args-and-kwargs-in-python/
        function_to_decorate(*args, **kwargs)
    return a_wrapper_accepting_arbitrary_arguments

@a_decorator_passing_arbitrary_arguments
def function_with_no_argument():
    print("Python is cool, no argument here.")

function_with_no_argument()
#outputs
#Do I have args?:
#()
#{}
#Python is cool, no argument here.

@a_decorator_passing_arbitrary_arguments
def function_with_arguments(a, b, c):
    print(a, b, c)

function_with_arguments(1,2,3)
#outputs
#Do I have args?:
#(1, 2, 3)
#{}
#1 2 3 

@a_decorator_passing_arbitrary_arguments
def function_with_named_arguments(a, b, c, platypus="Why not ?"):
    print("Do {0}, {1} and {2} like platypus? {3}".format(a, b, c, platypus))

function_with_named_arguments("Bill", "Linus", "Steve", platypus="Indeed!")
#outputs
#Do I have args ? :
#('Bill', 'Linus', 'Steve')
#{'platypus': 'Indeed!'}
#Do Bill, Linus and Steve like platypus? Indeed!

class Mary(object):

    def __init__(self):
        self.age = 31

    @a_decorator_passing_arbitrary_arguments
    def sayYourAge(self, lie=-3): # You can now add a default value
        print("I am {0}, what did you think?".format(self.age + lie))

m = Mary()
m.sayYourAge()
#outputs
# Do I have args?:
#(<__main__.Mary object at 0xb7d303ac>,)
#{}
#I am 28, what did you think?

将参数传递给装饰器

很好,现在您对将参数传递给装饰器本身有什么看法?

这可能会有点扭曲,因为装饰器必须接受函数作为参数。因此,您无法将装饰函数的参数直接传递给装饰器。

在急于解决之前,让我们写一点提醒:

 
# Decorators are ORDINARY functions
def my_decorator(func):
    print("I am an ordinary function")
    def wrapper():
        print("I am function returned by the decorator")
        func()
    return wrapper

# Therefore, you can call it without any "@"

def lazy_function():
    print("zzzzzzzz")

decorated_function = my_decorator(lazy_function)
#outputs: I am an ordinary function

# It outputs "I am an ordinary function", because that’s just what you do:
# calling a function. Nothing magic.

@my_decorator
def lazy_function():
    print("zzzzzzzz")

#outputs: I am an ordinary function

它完全一样。 “my_decorator”被称为。因此,当您@my_decorator时,您告诉Python将函数称为“由变量标记”my_decorator“'。

这很重要!您提供的标签可以直接指向装饰者 - 或不是

让我们变得邪恶。 ☺

 
def decorator_maker():

    print("I make decorators! I am executed only once: "
          "when you make me create a decorator.")

    def my_decorator(func):

        print("I am a decorator! I am executed only when you decorate a function.")

        def wrapped():
            print("I am the wrapper around the decorated function. "
                  "I am called when you call the decorated function. "
                  "As the wrapper, I return the RESULT of the decorated function.")
            return func()

        print("As the decorator, I return the wrapped function.")

        return wrapped

    print("As a decorator maker, I return a decorator")
    return my_decorator

# Let’s create a decorator. It’s just a new function after all.
new_decorator = decorator_maker()       
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator

# Then we decorate the function

def decorated_function():
    print("I am the decorated function.")

decorated_function = new_decorator(decorated_function)
#outputs:
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function

# Let’s call the function:
decorated_function()
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

这里不足为奇。

让我们做同样的事情,但跳过所有讨厌的中间变量:

 
def decorated_function():
    print("I am the decorated function.")
decorated_function = decorator_maker()(decorated_function)
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

# Finally:
decorated_function()    
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

让它更短

 
@decorator_maker()
def decorated_function():
    print("I am the decorated function.")
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

#Eventually: 
decorated_function()    
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.
嘿,你看到了吗?我们使用了一个带有“@”语法的函数调用! : - )

所以,回到带有参数的装饰器。如果我们可以使用函数动态生成装饰器,我们可以将参数传递给该函数,对吗?

 
def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print("I make decorators! And I accept arguments: {0}, {1}".format(decorator_arg1, decorator_arg2))

    def my_decorator(func):
        # The ability to pass arguments here is a gift from closures.
        # If you are not comfortable with closures, you can assume it’s ok,
        # or read: https://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
        print("I am the decorator. Somehow you passed me arguments: {0}, {1}".format(decorator_arg1, decorator_arg2))

        # Don't confuse decorator arguments and function arguments!
        def wrapped(function_arg1, function_arg2) :
            print("I am the wrapper around the decorated function.\n"
                  "I can access all the variables\n"
                  "\t- from the decorator: {0} {1}\n"
                  "\t- from the function call: {2} {3}\n"
                  "Then I can pass them to the decorated function"
                  .format(decorator_arg1, decorator_arg2,
                          function_arg1, function_arg2))
            return func(function_arg1, function_arg2)

        return wrapped

    return my_decorator

@decorator_maker_with_arguments("Leonard", "Sheldon")
def decorated_function_with_arguments(function_arg1, function_arg2):
    print("I am the decorated function and only knows about my arguments: {0}"
           " {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments("Rajesh", "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Sheldon
#I am the decorator. Somehow you passed me arguments: Leonard Sheldon
#I am the wrapper around the decorated function. 
#I can access all the variables 
#   - from the decorator: Leonard Sheldon 
#   - from the function call: Rajesh Howard 
#Then I can pass them to the decorated function
#I am the decorated function and only knows about my arguments: Rajesh Howard

这是:带有参数的装饰器。参数可以设置为变量:

 
c1 = "Penny"
c2 = "Leslie"

@decorator_maker_with_arguments("Leonard", c1)
def decorated_function_with_arguments(function_arg1, function_arg2):
    print("I am the decorated function and only knows about my arguments:"
           " {0} {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments(c2, "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Penny
#I am the decorator. Somehow you passed me arguments: Leonard Penny
#I am the wrapper around the decorated function. 
#I can access all the variables 
#   - from the decorator: Leonard Penny 
#   - from the function call: Leslie Howard 
#Then I can pass them to the decorated function
#I am the decorated function and only know about my arguments: Leslie Howard

正如您所看到的,您可以像使用此技巧的任何函数一样将参数传递给装饰器。如果您愿意,甚至可以使用*args, **kwargs。但请记住装饰者只被称为一次。就在Python导入脚本的时候。之后您无法动态设置参数。当你执行“导入x”时,功能已经装饰,所以你不能 改变一切。


让我们练习:装饰装饰

好的,作为奖励,我会给你一个片段,让任何装饰者一般都接受任何争论。毕竟,为了接受参数,我们使用另一个函数创建了装饰器。

我们包装了装饰师。

我们最近看到的包装功能还有什么?

哦,是的,装饰者!

让我们玩得开心,为装饰者写一个装饰器:

 
def decorator_with_args(decorator_to_enhance):
    """ 
    This function is supposed to be used as a decorator.
    It must decorate an other function, that is intended to be used as a decorator.
    Take a cup of coffee.
    It will allow any decorator to accept an arbitrary number of arguments,
    saving you the headache to remember how to do that every time.
    """

    # We use the same trick we did to pass arguments
    def decorator_maker(*args, **kwargs):

        # We create on the fly a decorator that accepts only a function
        # but keeps the passed arguments from the maker.
        def decorator_wrapper(func):

            # We return the result of the original decorator, which, after all, 
            # IS JUST AN ORDINARY FUNCTION (which returns a function).
            # Only pitfall: the decorator must have this specific signature or it won't work:
            return decorator_to_enhance(func, *args, **kwargs)

        return decorator_wrapper

    return decorator_maker

可以按如下方式使用:

 
# You create the function you will use as a decorator. And stick a decorator on it :-)
# Don't forget, the signature is "decorator(func, *args, **kwargs)"
@decorator_with_args 
def decorated_decorator(func, *args, **kwargs): 
    def wrapper(function_arg1, function_arg2):
        print("Decorated with {0} {1}".format(args, kwargs))
        return func(function_arg1, function_arg2)
    return wrapper

# Then you decorate the functions you wish with your brand new decorated decorator.

@decorated_decorator(42, 404, 1024)
def decorated_function(function_arg1, function_arg2):
    print("Hello {0} {1}".format(function_arg1, function_arg2))

decorated_function("Universe and", "everything")
#outputs:
#Decorated with (42, 404, 1024) {}
#Hello Universe and everything

# Whoooot!

我知道,你最后一次有这种感觉,是在听了一个人说:“在理解递归之前,你必须先了解递归”。但是现在,掌握这个对你感觉不好吗?


最佳实践:装饰器

  • 装饰器是在Python 2.4中引入的,因此请确保您的代码将在&gt; = 2.4上运行。
  • 装饰器减慢了函数调用。记住这一点。
  • 你不能解开一个函数。 hacks来创建可以删除的装饰器,但是没有人使用它们。)所以一旦一个函数被装饰,它就会被装饰代码。
  • 装饰器包装函数,这使得它们难以调试。 (这从Python&gt; = 2.5变得更好;见下文。)

functools模块是在Python 2.5中引入的。它包括函数functools.wraps(),它将装饰函数的名称,模块和文档字符串复制到其包装器。

(有趣的事实:functools.wraps()是装饰师!☺)

 
# For debugging, the stacktrace prints you the function __name__
def foo():
    print("foo")

print(foo.__name__)
#outputs: foo

# With a decorator, it gets messy    
def bar(func):
    def wrapper():
        print("bar")
        return func()
    return wrapper

@bar
def foo():
    print("foo")

print(foo.__name__)
#outputs: wrapper

# "functools" can help for that

import functools

def bar(func):
    # We say that "wrapper", is wrapping "func"
    # and the magic begins
    @functools.wraps(func)
    def wrapper():
        print("bar")
        return func()
    return wrapper

@bar
def foo():
    print("foo")

print(foo.__name__)
#outputs: foo

装饰器如何有用?

现在这个大问题:我可以使用装饰器做什么?

看起来酷而有力,但一个实际的例子会很棒。嗯,有1000种可能性。经典用法是从外部lib扩展函数行为(你不能修改它),或者用于调试(你不想修改它,因为它是临时的)。

您可以使用它们以DRY的方式扩展多个功能,如下所示:

 
def benchmark(func):
    """
    A decorator that prints the time a function takes
    to execute.
    """
    import time
    def wrapper(*args, **kwargs):
        t = time.clock()
        res = func(*args, **kwargs)
        print("{0} {1}".format(func.__name__, time.clock()-t))
        return res
    return wrapper


def logging(func):
    """
    A decorator that logs the activity of the script.
    (it actually just prints it, but it could be logging!)
    """
    def wrapper(*args, **kwargs):
        res = func(*args, **kwargs)
        print("{0} {1} {2}".format(func.__name__, args, kwargs))
        return res
    return wrapper


def counter(func):
    """
    A decorator that counts and prints the number of times a function has been executed
    """
    def wrapper(*args, **kwargs):
        wrapper.count = wrapper.count + 1
        res = func(*args, **kwargs)
        print("{0} has been used: {1}x".format(func.__name__, wrapper.count))
        return res
    wrapper.count = 0
    return wrapper

@counter
@benchmark
@logging
def reverse_string(string):
    return str(reversed(string))

print(reverse_string("Able was I ere I saw Elba"))
print(reverse_string("A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!"))

#outputs:
#reverse_string ('Able was I ere I saw Elba',) {}
#wrapper 0.0
#wrapper has been used: 1x 
#ablE was I ere I saw elbA
#reverse_string ('A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!',) {}
#wrapper 0.0
#wrapper has been used: 2x
#!amanaP :lanac a ,noep a ,stah eros ,raj a ,hsac ,oloR a ,tur a ,mapS ,snip ,eperc a ,)lemac a ro( niaga gab ananab a ,gat a ,nat a ,gab ananab a ,gag a ,inoracam ,elacrep ,epins ,spam ,arutaroloc a ,shajar ,soreh ,atsap ,eonac a ,nalp a ,nam A

当然好机智装饰器是你几乎可以在没有重写的情况下立即使用它们。干,我说:

 
@counter
@benchmark
@logging
def get_random_futurama_quote():
    from urllib import urlopen
    result = urlopen("http://subfusion.net/cgi-bin/quote.pl?quote=futurama").read()
    try:
        value = result.split("<br><b><hr><br>")[1].split("<br><br><hr>")[0]
        return value.strip()
    except:
        return "No, I'm ... doesn't!"


print(get_random_futurama_quote())
print(get_random_futurama_quote())

#outputs:
#get_random_futurama_quote () {}
#wrapper 0.02
#wrapper has been used: 1x
#The laws of science be a harsh mistress.
#get_random_futurama_quote () {}
#wrapper 0.01
#wrapper has been used: 2x
#Curse you, merciful Poseidon!

Python本身提供了几个装饰器:property,staticmethod等。

  • Django使用装饰器来管理缓存和查看权限。
  • 扭曲假冒内联异步函数调用。

这真的是一个很大的游乐场。

    
4015
2019-04-29 00:06:42Z
  1. “你不能解开一个函数。” - 虽然通常是正确的,但是可以通过装饰器(即通过其__closure__属性)在函数返回中到达闭包内部以拉出原始的未修饰函数。 此答案中记录了一个示例用法,其中介绍了如何在有限的情况下在较低级别注入装饰器功能
    2015-10-22 00:04:23Z
  2. 虽然这是一个很好的答案,但我认为它在某些方面有点误导。 Python的@decorator语法可能最常用于用包装器闭包替换函数(如答案所述)。但它也可以用其他东西取代功能。例如,内置property,classmethodstaticmethod装饰器用描述符替换该函数。装饰器也可以对某个函数执行某些操作,例如在某种类型的注册表中保存对它的引用,然后在没有任何包装的情况下返回它,不进行任何修改。
    2016-04-11 13:04:10Z
  3. “函数是对象”这一事实虽然在Python中完全正确,但有点误导。将函数存储在变量中,将它们作为参数传递,并将它们作为结果返回都是可能的,而函数实际上不是对象,并且有各种语言具有第一类函数但没有对象。
    2016-12-15 02:18:43Z
  4. 这是一个史诗般的答案......谢谢你!为什么函数的默认参数不会在decorator的包装器中显示为args /kwargs?
    2019-01-06 01:13:57Z
  5. 我不喜欢模式!为什么不说“让函数前后执行代码而不更改函数”,我会轻松地做到这一点
    2019-06-18 18:01:35Z
  6. 醇>

或者,您可以编写一个返回装饰器的工厂函数,该装饰器将装饰函数的返回值包装在传递给工厂函数的标记中。例如:

 
from functools import wraps

def wrap_in_tag(tag):
    def factory(func):
        @wraps(func)
        def decorator():
            return '<%(tag)s>%(rv)s</%(tag)s>' % (
                {'tag': tag, 'rv': func()})
        return decorator
    return factory

这使您可以写:

 
@wrap_in_tag('b')
@wrap_in_tag('i')
def say():
    return 'hello'

 
makebold = wrap_in_tag('b')
makeitalic = wrap_in_tag('i')

@makebold
@makeitalic
def say():
    return 'hello'

就我个人而言,我会以不同的方式编写装饰器:

 
from functools import wraps

def wrap_in_tag(tag):
    def factory(func):
        @wraps(func)
        def decorator(val):
            return func('<%(tag)s>%(val)s</%(tag)s>' %
                        {'tag': tag, 'val': val})
        return decorator
    return factory

会产生:

 
@wrap_in_tag('b')
@wrap_in_tag('i')
def say(val):
    return val
say('hello')

不要忘记装饰器语法是简写的构造:

 
say = wrap_in_tag('b')(wrap_in_tag('i')(say)))
    
139
2009-04-11 09:29:28Z
  1. 在我看来,最好尽量避免使用多个装饰器。如果我必须写一个工厂函数,我会用* kwargs编码,如def wrap_in_tag(*kwargs)然后@wrap_in_tag('b','i')
    2013-10-29 22:29:45Z
  2. 醇>

看起来其他人已经告诉过你如何解决这个问题。我希望这能帮助你理解装饰器是什么。

装饰者只是语法糖。

 
@decorator
def func():
    ...

扩展为

 
def func():
    ...
func = decorator(func)
    
110
2009-04-11 08:00:42Z
  1. 这是如此优雅,简单,易于理解。奥克姆爵士,万人为你投票。
    2017-09-02 17:01:13Z
  2. 简单明了的答案。想补充一点,当使用@decorator()(而不是@decorator)时,它是func = decorator()(func)的语法糖。当您需要“动态”
    生成装饰器时,这也是常见的做法
    2017-09-28 11:00:59Z
  3. 醇>

当然,您也可以从装饰器函数返回lambdas:

 
def makebold(f): 
    return lambda: "<b>" + f() + "</b>"
def makeitalic(f): 
    return lambda: "<i>" + f() + "</i>"

@makebold
@makeitalic
def say():
    return "Hello"

print say()
    
60
2010-10-25 06:18:12Z
  1. 更进了一步:makebold = lambda f : lambda "<b>" + f() + "</b>"
    2013-03-04 22:26:00Z
  2. @Robᵩ:语法正确:makebold = lambda f: lambda: "<b>" + f() + "</b>"
    2013-12-20 16:01:44Z
  3. 晚会,但我真的建议makebold = lambda f: lambda *a, **k: "<b>" + f(*a, **k) + "</b>"
    2015-02-06 18:19:40Z
  4. 这需要functools.wraps才能不丢弃文档字符串/签名/名称say
    2018-09-17 06:58:45Z
  5. 嗯,重要的是你的答案中是否提到了它。当我打印@wraps并获得“帮助函数&lt; lambda&gt;`而不是”帮助函数说“”时,在此页面上的其他位置help(say)将无法​​帮助我>
    2018-09-18 18:02:11Z
  6. 醇>

Python装饰器为另一个函数添加额外的功能

斜体装饰器可能就像

 
def makeitalic(fn):
    def newFunc():
        return "<i>" + fn() + "</i>"
    return newFunc

请注意,函数是在函数内定义的。 它基本上做的是用新定义的函数替换函数。例如,我有这个类

 
class foo:
    def bar(self):
        print "hi"
    def foobar(self):
        print "hi again"

现在说,我希望两个函数在完成之后和之前打印“---”。 我可以在每个print语句之前和之后添加一个打印“---”。 但因为我不喜欢重复自己,我会做一个装饰师

 
def addDashes(fn): # notice it takes a function as an argument
    def newFunction(self): # define a new function
        print "---"
        fn(self) # call the original function
        print "---"
    return newFunction
    # Return the newly defined function - it will "replace" the original

所以现在我可以将课程改为

 
class foo:
    @addDashes
    def bar(self):
        print "hi"

    @addDashes
    def foobar(self):
        print "hi again"

有关装饰器的更多信息,请检查 http://www.ibm.com/developerworks/linux/library/l -cpdecor.html

    
58
2009-04-11 07:19:12Z
  1. 注意与@Rune Kaagaard提出的lambda函数一样优雅
    2011-08-19 10:46:49Z
  2. 这里有self关键字的工作是什么?
    2013-05-16 14:38:33Z
  3. @ Phoenix:需要self参数,因为newFunction()中定义的addDashes()是专门设计为方法装饰器而不是通用函数装饰器。 self参数表示类实例,无论是否使用它们都会传递给类方法 - 请参阅@ e-satisf's answer中标题为装饰方法的部分。
    2013-07-12 15:46:27Z
  4. 请打印输出。
    2015-05-26 17:38:44Z
  5. 缺少functools.wraps
    2018-09-17 06:58:18Z
  6. 醇>

可以制作两个独立的装饰器来做你想要的,如下图所示。请注意在*args, **kwargs函数的声明中使用wrapped(),该函数支持具有多个参数的修饰函数(对于示例say()函数来说,这不是必需的,但是为了通用性而包括在内)。

出于类似的原因,functools.wraps装饰器用于将包装函数的元属性更改为正在装饰的元属性。这使得错误消息和嵌入式函数文档(func.__doc__)成为装饰函数而不是wrapped()的函数。

 
from functools import wraps

def makebold(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<b>" + fn(*args, **kwargs) + "</b>"
    return wrapped

def makeitalic(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<i>" + fn(*args, **kwargs) + "</i>"
    return wrapped

@makebold
@makeitalic
def say():
    return 'Hello'

print(say())  # -> <b><i>Hello</i></b>

加细

正如您所看到的,这两个装饰器中有很多重复的代码。鉴于这种相似性,你最好制作一个实际上是装饰工厂的通用工具 - 换句话说,就是制作其他装饰器的装饰器。这样可以减少代码重复次数,并允许遵循 DRY 原则。

 
def html_deco(tag):
    def decorator(fn):
        @wraps(fn)
        def wrapped(*args, **kwargs):
            return '<%s>' % tag + fn(*args, **kwargs) + '</%s>' % tag
        return wrapped
    return decorator

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

print(greet('world'))  # -> <b><i>Hello world</i></b>

为了使代码更具可读性,您可以为工厂生成的装饰器分配更具描述性的名称:

 
makebold = html_deco('b')
makeitalic = html_deco('i')

@makebold
@makeitalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

甚至将它们组合起来:

 
makebolditalic = lambda fn: makebold(makeitalic(fn))

@makebolditalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

效率

虽然上面的示例都可以正常工作,但是当一次应用多个装饰器时,生成的代码会以无关函数调用的形式涉及相当大的开销。这可能无关紧要,具体取决于具体用法(例如,可能是I /O绑定)。

如果装饰函数的速度很重要,可以通过编写稍微不同的装饰工厂函数来保持一个额外的函数调用,该函数实现一次添加所有标签,因此它可以生成避免附加的代码通过为每个标记使用单独的装饰器而产生的函数调用。

这需要装饰器本身有更多的代码,但这仅在它被应用于函数定义时运行,而不是在它们自身被调用时运行。当使用如前所述的lambda函数创建更易读的名称时,这也适用。样品:

 
def multi_html_deco(*tags):
    start_tags, end_tags = [], []
    for tag in tags:
        start_tags.append('<%s>' % tag)
        end_tags.append('</%s>' % tag)
    start_tags = ''.join(start_tags)
    end_tags = ''.join(reversed(end_tags))

    def decorator(fn):
        @wraps(fn)
        def wrapped(*args, **kwargs):
            return start_tags + fn(*args, **kwargs) + end_tags
        return wrapped
    return decorator

makebolditalic = multi_html_deco('b', 'i')

@makebolditalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>
    
28
2019-04-28 15:08:19Z

另一种做同样事情的方法:

 
class bol(object):
  def __init__(self, f):
    self.f = f
  def __call__(self):
    return "<b>{}</b>".format(self.f())

class ita(object):
  def __init__(self, f):
    self.f = f
  def __call__(self):
    return "<i>{}</i>".format(self.f())

@bol
@ita
def sayhi():
  return 'hi'

或者,更灵活:

 
class sty(object):
  def __init__(self, tag):
    self.tag = tag
  def __call__(self, f):
    def newf():
      return "<{tag}>{res}</{tag}>".format(res=f(), tag=self.tag)
    return newf

@sty('b')
@sty('i')
def sayhi():
  return 'hi'
    
17
2014-10-19 18:47:01Z
  1. 需要functools.update_wrapper才能保留sayhi.__name__ == "sayhi"
    2018-09-17 06:57:56Z
  2. 醇>
  

如何在Python中创建两个执行以下操作的装饰器?

调用时需要以下函数:

 
@makebold
@makeitalic
def say():
    return "Hello"

要返回:

 
<b><i>Hello</i></b>

简单解决方案

最简单的做法是,使装饰器返回关闭函数(闭包)的lambdas(匿名函数)并调用它:

 
def makeitalic(fn):
    return lambda: '<i>' + fn() + '</i>'

def makebold(fn):
    return lambda: '<b>' + fn() + '</b>'

现在根据需要使用它们:

 
@makebold
@makeitalic
def say():
    return 'Hello'

现在:

 
>>> say()
'<b><i>Hello</i></b>'

简单解决方案的问题

但我们似乎几乎失去了原有的功能。

 
>>> say
<function <lambda> at 0x4ACFA070>

为了找到它,我们需要挖掘每个lambda的闭合,其中一个被埋在另一个中:

 
>>> say.__closure__[0].cell_contents
<function <lambda> at 0x4ACFA030>
>>> say.__closure__[0].cell_contents.__closure__[0].cell_contents
<function say at 0x4ACFA730>

因此,如果我们将文档放在这个函数上,或者希望能够装饰带有多个参数的函数,或者我们只是想知道我们在调试会话中看到了什么函数,我们需要做一些更多与我们的包装。

全功能解决方案 - 克服大多数这些问题

我们在标准库中有wraps模块的装饰器functools

 
from functools import wraps

def makeitalic(fn):
    # must assign/update attributes from wrapped function to wrapper
    # __module__, __name__, __doc__, and __dict__ by default
    @wraps(fn) # explicitly give function whose attributes it is applying
    def wrapped(*args, **kwargs):
        return '<i>' + fn(*args, **kwargs) + '</i>'
    return wrapped

def makebold(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return '<b>' + fn(*args, **kwargs) + '</b>'
    return wrapped

遗憾的是还有一些样板,但这就像我们能做到的那样简单。

在Python 3中,默认情况下还会分配__qualname____annotations__

现在:

 
@makebold
@makeitalic
def say():
    """This function returns a bolded, italicized 'hello'"""
    return 'Hello'

现在:

 
>>> say
<function say at 0x14BB8F70>
>>> help(say)
Help on function say in module __main__:

say(*args, **kwargs)
    This function returns a bolded, italicized 'hello'

Çonclusion

所以我们看到wraps使包装函数几乎完成所有操作,除了告诉我们函数作为参数的确切内容。

还有其他模块可能会尝试解决此问题,但该解决方案尚未出现在标准库中。

    
16
2016-12-05 17:33:41Z

装饰器接受函数定义并创建一个执行此函数并转换结果的新函数。

 
@deco
def do():
    ...

完全符合:

 
do = deco(do)

实施例

 
def deco(func):
    def inner(letter):
        return func(letter).upper()  #upper
    return inner

 
@deco
def do(number):
    return chr(number)  # number to letter

与此等效     def do2(数字):         return chr(number)

 
do2 = deco(do2)

65&lt; =&gt; 'A'

 
print(do(65))
print(do2(65))
>>> B
>>> B

要理解装饰器,重要的是要注意,装饰器创建了一个新的函数do,它执行func并转换结果。

    
10
2012-07-26 16:11:42Z
  1. print(do(65))print(do2(65))的输出不应该是AA吗?
    2017-01-31 19:59:20Z
  2. 醇>

以更简单的方式解释装饰器:

使用:

 
@decor1
@decor2
def func(*args, **kwargs):
    pass

何时:

 
func(*args, **kwargs)

你真的这样做:

 
decor1(decor2(func))(*args, **kwargs)
    
8
2015-03-20 09:48:56Z
 
#decorator.py
def makeHtmlTag(tag, *args, **kwds):
    def real_decorator(fn):
        css_class = " class='{0}'".format(kwds["css_class"]) \
                                 if "css_class" in kwds else ""
        def wrapped(*args, **kwds):
            return "<"+tag+css_class+">" + fn(*args, **kwds) + "</"+tag+">"
        return wrapped
    # return decorator dont call it
    return real_decorator

@makeHtmlTag(tag="b", css_class="bold_css")
@makeHtmlTag(tag="i", css_class="italic_css")
def hello():
    return "hello world"

print hello()

你也可以在Class

中编写装饰器  
#class.py
class makeHtmlTagClass(object):
    def __init__(self, tag, css_class=""):
        self._tag = tag
        self._css_class = " class='{0}'".format(css_class) \
                                       if css_class != "" else ""

    def __call__(self, fn):
        def wrapped(*args, **kwargs):
            return "<" + self._tag + self._css_class+">"  \
                       + fn(*args, **kwargs) + "</" + self._tag + ">"
        return wrapped

@makeHtmlTagClass(tag="b", css_class="bold_css")
@makeHtmlTagClass(tag="i", css_class="italic_css")
def hello(name):
    return "Hello, {}".format(name)

print hello("Your name")
    
5
2017-07-24 14:14:06Z
  1. 这里喜欢一个类的原因是有明显相关的行为,有两个实例。实际上,您可以通过将构造的类分配给所需的名称来获得两个装饰器,而不是重新迭代参数。这对函数来说更难。将它添加到示例中将指出为什么这不仅仅是多余的。
    2014-08-28 15:46:58Z
  2. 醇>

这是一个链接装饰器的简单示例。请注意最后一行 - 它显示了幕后的内容。

 
############################################################
#
#    decorators
#
############################################################

def bold(fn):
    def decorate():
        # surround with bold tags before calling original function
        return "<b>" + fn() + "</b>"
    return decorate


def uk(fn):
    def decorate():
        # swap month and day
        fields = fn().split('/')
        date = fields[1] + "/" + fields[0] + "/" + fields[2]
        return date
    return decorate

import datetime
def getDate():
    now = datetime.datetime.now()
    return "%d/%d/%d" % (now.day, now.month, now.year)

@bold
def getBoldDate(): 
    return getDate()

@uk
def getUkDate():
    return getDate()

@bold
@uk
def getBoldUkDate():
    return getDate()


print getDate()
print getBoldDate()
print getUkDate()
print getBoldUkDate()
# what is happening under the covers
print bold(uk(getDate))()

输出如下:

 
17/6/2013
<b>17/6/2013</b>
6/17/2013
<b>6/17/2013</b>
<b>6/17/2013</b>
    
4
2013-06-17 04:43:25Z

这个答案早已得到解答,但我想我会分享我的Decorator类,这使得编写新装饰器变得简单而紧凑。

 
from abc import ABCMeta, abstractclassmethod

class Decorator(metaclass=ABCMeta):
    """ Acts as a base class for all decorators """

    def __init__(self):
        self.method = None

    def __call__(self, method):
        self.method = method
        return self.call

    @abstractclassmethod
    def call(self, *args, **kwargs):
        return self.method(*args, **kwargs)

对于我认为这使得装饰器的行为非常清晰,但它也使得很容易非常简洁地定义新的装饰器。对于上面列出的示例,您可以将其解析为:

 
class MakeBold(Decorator):
    def call():
        return "<b>" + self.method() + "</b>"

class MakeItalic(Decorator):
    def call():
        return "<i>" + self.method() + "</i>"

@MakeBold()
@MakeItalic()
def say():
   return "Hello"

您也可以使用它来执行更复杂的任务,例如装饰器自动使函数以递归方式应用于迭代器中的所有参数:

 
class ApplyRecursive(Decorator):
    def __init__(self, *types):
        super().__init__()
        if not len(types):
            types = (dict, list, tuple, set)
        self._types = types

    def call(self, arg):
        if dict in self._types and isinstance(arg, dict):
            return {key: self.call(value) for key, value in arg.items()}

        if set in self._types and isinstance(arg, set):
            return set(self.call(value) for value in arg)

        if tuple in self._types and isinstance(arg, tuple):
            return tuple(self.call(value) for value in arg)

        if list in self._types and isinstance(arg, list):
            return list(self.call(value) for value in arg)

        return self.method(arg)


@ApplyRecursive(tuple, set, dict)
def double(arg):
    return 2*arg

print(double(1))
print(double({'a': 1, 'b': 2}))
print(double({1, 2, 3}))
print(double((1, 2, 3, 4)))
print(double([1, 2, 3, 4, 5]))

打印哪些:

 
2
{'a': 2, 'b': 4}
{2, 4, 6}
(2, 4, 6, 8)
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]

请注意,此示例在装饰器的实例化中不包含list类型,因此在最终的print语句中,该方法将应用于列表本身,而不是列表的元素。

    
4
2018-11-11 15:02:23Z

说到反例 - 如上所述,计数器将在使用装饰器的所有函数之间共享:

 
def counter(func):
    def wrapped(*args, **kws):
        print 'Called #%i' % wrapped.count
        wrapped.count += 1
        return func(*args, **kws)
    wrapped.count = 0
    return wrapped

这样,您的装饰器可以重复用于不同的函数(或用于多次装饰相同的函数:func_counter1 = counter(func); func_counter2 = counter(func)),并且计数器变量将保持对每个函数都是私有的。

    
3
2012-03-02 21:47:17Z

用不同数量的参数装饰函数:

 
def frame_tests(fn):
    def wrapper(*args):
        print "\nStart: %s" %(fn.__name__)
        fn(*args)
        print "End: %s\n" %(fn.__name__)
    return wrapper

@frame_tests
def test_fn1():
    print "This is only a test!"

@frame_tests
def test_fn2(s1):
    print "This is only a test! %s" %(s1)

@frame_tests
def test_fn3(s1, s2):
    print "This is only a test! %s %s" %(s1, s2)

if __name__ == "__main__":
    test_fn1()
    test_fn2('OK!')
    test_fn3('OK!', 'Just a test!')

结果:

 
Start: test_fn1  
This is only a test!  
End: test_fn1  


Start: test_fn2  
This is only a test! OK!  
End: test_fn2  


Start: test_fn3  
This is only a test! OK! Just a test!  
End: test_fn3  
    
2
2014-02-15 19:09:04Z
  1. 通过def wrapper(*args, **kwargs):fn(*args, **kwargs)提供对关键字参数的支持,可以很容易地实现这一点。
    2015-05-17 02:33:50Z
  2. 醇>

Paolo Bergantino的答案具有仅使用stdlib的巨大优势,适用于这个简单的例子,其中没有 decorator 参数或修饰函数参数。

但是,如果您想解决更多一般情况,它有三个主要限制:

  • 正如在几个答案中已经提到的,你不能轻易地将代码修改为添加可选的装饰器参数。例如,创建makestyle(style='bold')装饰器并非易事。
  • 此外,使用@functools.wraps 创建的包装器不保留签名,因此如果提供了错误的参数,它们将开始执行,并且可能引发与通常的TypeError不同的错误。
  • 最后,使用@functools.wraps创建的包装器很难根据其名称访问参数。实际上,该论点可以出现在*args中,在**kwargs中,或者可能根本不出现(如果它是可选的)。

我写了 decopatch 来解决第一个问题,写了 makefun.wraps 解决另外两个问题。请注意,makefun利用了与着名的 decorator 库相同的技巧。

这是如何使用参数创建装饰器,返回真正的签名保留包装器:

 
from decopatch import function_decorator, DECORATED
from makefun import wraps

@function_decorator
def makestyle(st='b', fn=DECORATED):
    open_tag = "<%s>" % st
    close_tag = "</%s>" % st

    @wraps(fn)
    def wrapped(*args, **kwargs):
        return open_tag + fn(*args, **kwargs) + close_tag

    return wrapped

decopatch为您提供了两种隐藏或显示各种python概念的开发样式,具体取决于您的偏好。最紧凑的风格如下:

 
from decopatch import function_decorator, WRAPPED, F_ARGS, F_KWARGS

@function_decorator
def makestyle(st='b', fn=WRAPPED, f_args=F_ARGS, f_kwargs=F_KWARGS):
    open_tag = "<%s>" % st
    close_tag = "</%s>" % st
    return open_tag + fn(*f_args, **f_kwargs) + close_tag

在这两种情况下,您都可以检查装饰器是否按预期工作:

 
@makestyle
@makestyle('i')
def hello(who):
    return "hello %s" % who

assert hello('world') == '<b><i>hello world</i></b>'    

有关详细信息,请参阅文档

    
2
2019-03-11 15:24:57Z
来源放置 这里