如何制作函数装饰器并将它们链接在一起？

Question

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>"

我并不想在真实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的工作原理。

Answer 1

查看the documentation 了解装饰器的工作原理。这是您要求的：

from functools import wraps

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

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

@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>"

Answer 2

Python 装饰器为另一个函数添加额外的功能

斜体装饰器可以像

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

请注意，函数是在函数内部定义的。 它基本上所做的就是用新定义的函数替换一个函数。比如我有这个类

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

现在说，我希望这两个函数在完成之后和之前都打印“---”。 我可以在每个打印语句之前和之后添加一个打印“---”。 但是因为我不喜欢重复自己，所以我会做一个装饰器

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

所以现在我可以把我的班级改成

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，请查看 http://www.ibm.com/developerworks/linux/library/l-cpdecor.html

Answer 3

或者，您可以编写一个工厂函数，该函数返回一个装饰器，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签中。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

这使您可以编写：

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

或

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

这会产生：

@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)))

Answer 4

看起来其他人已经告诉过你如何解决这个问题。我希望这将帮助您了解装饰器是什么。

装饰器只是语法糖。

这个

@decorator
def func():
    ...

扩展到

def func():
    ...
func = decorator(func)

Answer 5

如果您不喜欢冗长的解释，请参阅Paolo Bergantino’s answer。

装饰器基础知识

Python 的函数是对象

要了解装饰器，您必须首先了解函数是 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!'

请记住这一点。我们很快就会回到它。

Python 函数的另一个有趣特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

函数参考

好的，还在吗？现在有趣的部分...

您已经看到函数是对象。因此，函数：

• 可以赋值给变量
• 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，你已经具备了理解装饰器所需的一切。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，而无需修改函数本身。

手工装饰器

如何手动操作：

# 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，您可能希望每次调用 a_stand_alone_function 时都调用 a_stand_alone_function_decorated。很简单，只要用my_shiny_new_decorator返回的函数覆盖a_stand_alone_function：

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!

装饰器揭秘

前面的例子，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 只是一个捷径：

another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)

装饰器只是decorator design pattern 的pythonic 变体。 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~
#<\______/>

你设置装饰器的顺序很重要：

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

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

---

现在：回答问题...

作为结论，你可以很容易地看到如何回答这个问题：

# 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>

您现在可以开心地离开，或者多花点时间看看装饰器的高级用途。

---

让装饰器更上一层楼

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

Python 的一个妙处是方法和函数实际上是相同的。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)。

这意味着您可以以同样的方式为方法构建装饰器！请记住将self 考虑在内：

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——您将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

太好了，现在你对将参数传递给装饰器本身有什么看法？

这可能有点扭曲，因为装饰器必须接受一个函数作为参数。因此，您不能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

完全一样。 “my_decorator”被调用。因此，当您@my_decorator 时，您是在告诉 Python 调用“由变量“my_decorator”标记的函数。

这很重要！你给的标签可以直接指向装饰器——或者不。

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.

这并不奇怪。

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.

让我们把它更短：

@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“@”语法的函数调用！ :-)

所以，回到带参数的装饰器。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

这里是：带参数的装饰器。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

如您所见，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，您甚至可以使用*args, **kwargs。但请记住，装饰器只被调用一次。就在 Python 导入脚本时。之后您不能动态设置参数。当你执行“import x”时，函数已经被修饰了，所以你不能 改变任何东西。

---

让我们练习：装饰一个装饰器

好的，作为奖励，我会给你一个 sn-p 让任何装饰器普遍接受任何参数。毕竟，为了接受参数，我们使用另一个函数创建了我们的装饰器。

我们包装了装饰器。

我们最近看到的其他任何包装函数？

哦，是的，装饰师！

让我们玩得开心，为装饰器写一个装饰器：

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
       

可以这样使用：

# 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，上一次你有这种感觉，是在听一个人说：“在理解递归之前，你必须先理解递归”之后。但是现在，掌握这个感觉不好吗？

---

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 >= 2.4 上运行。
• 装饰器减慢了函数调用。请记住这一点。
• 你不能取消装饰函数。（有个技巧可以创建可以删除的装饰器，但没有人使用它们。）所以一旦一个函数被装饰了，它就被装饰了所有代码。
• 装饰器包装函数，这会使它们难以调试。 （这从 Python >= 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()，它将修饰函数的名称、模块和文档字符串复制到其包装器中。

（有趣的事实：functools.wraps() 是一个装饰器！☺）

# 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

---

装饰器有什么用处？

现在有个大问题：我可以使用装饰器做什么？

看起来很酷很强大，但是一个实际的例子会很棒。好吧，有1000种可能性。经典用途是从外部库扩展函数行为（您不能修改它），或用于调试（您不想修改它，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，装饰器的好处是您可以立即在几乎任何东西上使用它们而无需重写。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。
• 扭曲为伪造的内联异步函数调用。

这真是一个大操场。

Answer 6

当然，您也可以从装饰器函数中返回 lambda：

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

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

print say()

Answer 7

做同样事情的另一种方式：

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'

Answer 8

说到计数器示例 - 如上所述，计数器将在所有使用装饰器的函数之间共享：

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功能（或用于多次装饰同一个功能：func_counter1 = counter(func); func_counter2 = counter(func)），并且计数器变量将保持为每个功能私有。

Answer 9

装饰器接受函数定义并创建一个新函数来执行该函数并转换结果。

@deco
def do():
    ...

相当于：

do = deco(do)

示例：

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

这个

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

等价于这个

def do2(number):
    return chr(number)

do2 = deco(do2)

65 '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，它在内部执行函数并转换结果。

Answer 10

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  

Answer 11

这是一个链接装饰器的简单示例。注意最后一行 - 它显示了幕后发生的事情。

############################################################
#
#    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>

Answer 12

#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")

Answer 13

你可以制作两个独立的装饰器来做你想做的事，如下图所示。请注意 *args, **kwargs 在 wrapped() 函数的声明中的使用，该函数支持具有多个参数的修饰函数（这对于示例 say() 函数实际上不是必需的，但为了通用性而包含在内）。

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被装饰函数的元属性。这使得错误消息和嵌入式函数文档 (func.__doc__) 成为修饰函数的那些，而不是 wrapped() 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 say():
    return 'Hello'

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

改进

如您所见，这两个装饰器中有很多重复的代码。鉴于这种相似性，您最好改为创建一个实际上是 decorator factory 的通用函数——换句话说，创建其他装饰器的装饰器函数。这样会减少代码重复，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指定一个更具描述性的名称：

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>

甚至像这样组合它们：

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

虽然上面的示例可以完成所有工作，但当同时应用多个装饰器时，生成的代码会以无关函数调用的形式产生相当多的开销。这可能无关紧要，具体取决于具体用法（例如，可能是 I/O 绑定的）。

如果装饰函数的速度很重要，则可以通过编写一个稍微不同的装饰器工厂函数来将开销保持在单个额外的函数调用上，该函数实现一次添加所有标签，因此它可以生成避免额外的代码为每个标签使用单独的装饰器所引发的函数调用。

这需要装饰器本身中的更多代码，但这仅在应用于函数定义时运行，而不是稍后调用它们本身时。这也适用于使用如前所述的 lambda 函数创建更具可读性的名称。示例：

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>

Answer 14

如何在 Python 中创建两个可以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"

返回：

<b><i>Hello</i></b>

简单的解决方案

为了最简单地做到这一点，制作返回 lambdas（匿名函数）的装饰器，关闭函数（闭包）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来的功能。

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

要找到它，我们需要深入研究每个 lambda 的闭包，其中一个埋在另一个中：

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

因此，如果我们在此函数上添加文档，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查看的是什么函数，我们需要做一些事情使用我们的包装器进行更多操作。

功能齐全的解决方案 - 克服大部分问题

我们有来自标准库中functools 模块的装饰器wraps！

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

不幸的是，仍然有一些样板，但这已经尽可能简单了。

在 Python 3 中，您还可以默认分配 __qualname__ 和 __annotations__。

那么现在：

@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'

结论

所以我们看到wraps 使包装函数几乎可以做所有事情，除了告诉我们函数将什么作为参数。

还有其他模块可能会尝试解决这个问题，但标准库中还没有解决方案。

Answer 15

这个答案早就有了答案，但我想我会分享我的装饰器类，它使编写新的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)

一方面，我认为这使得装饰器的行为非常清晰，但它也可以非常简洁地定义新的装饰器。对于上面列出的示例，您可以将其解决为：

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"

您还可以使用它来执行更复杂的任务，例如一个装饰器，它自动使函数递归地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]))

哪些打印：

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，此示例未在装饰器的实例化中包含 list 类型，因此在最后的 print 语句中，该方法将应用于列表本身，而不是列表的元素。

Answer 16

Paolo Bergantino's answer 具有仅使用 stdlib 的巨大优势，并且适用于没有 decorator 参数或 装饰函数 参数的这个简单示例。

但是，如果您想处理更一般的情况，它有 3 个主要限制：

• 正如在几个答案中已经指出的那样，您不能轻易地修改代码以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参数名称访问参数。实际上，该参数可以出现在 *args、**kwargs 中，或者根本不出现（如果它是可选的）。

我写decopatch 来解决第一个问题，写makefun.wraps 来解决另外两个问题。请注意，makefun 使用的技巧与著名的 decorator 库相同。

这就是你将如何创建一个带参数的装饰器，返回真正保留签名的包装器：

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 为您提供了另外两种开发风格，可以根据您的喜好隐藏或显示各种 Python 概念。最紧凑的样式如下：

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>'    

详情请参考documentation。

Answer 17

当你需要在装饰器中添加自定义参数时，我添加一个案例，将其传递给最终函数，然后使用它。

装饰者：

def jwt_or_redirect(fn):
  @wraps(fn)
  def decorator(*args, **kwargs):
    ...
    return fn(*args, **kwargs)
  return decorator

def jwt_refresh(fn):
  @wraps(fn)
  def decorator(*args, **kwargs):
    ...
    new_kwargs = {'refreshed_jwt': 'xxxxx-xxxxxx'}
    new_kwargs.update(kwargs)
    return fn(*args, **new_kwargs)
  return decorator

以及最终的功能：

@app.route('/')
@jwt_or_redirect
@jwt_refresh
def home_page(*args, **kwargs):
  return kwargs['refreched_jwt']