如何在Python中创建两个装饰器来执行以下操作？
@makebold
@makeitalic
def say():
return "Hello"
...应该返回：
"<b><i>Hello</i></b>"
我不是想在一个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是如何工作的。
查看the documentation以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>"
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():
...
相当于：
do = deco(do)
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
相当于这个def do2（数字）：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，它执行func并转换结果。
以更简单的方式解释装饰器：
附：
@decor1
@decor2
def func(*args, **kwargs):
pass
什么时候：
func(*args, **kwargs)
你真的这样做：
decor1(decor2(func))(*args, **kwargs)
#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")
这是一个链接装饰器的简单示例。注意最后一行 - 它显示了封底下发生的事情。
############################################################
#
# 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>
这个答案早已得到回答，但我想我会分享我的Decorator类，这使得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)
对于我认为这使得装饰器的行为非常清楚，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]))
哪个印刷品：
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。
说到计数器示例 - 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功能（或用于多次装饰相同的功能：func_counter1 = counter(func); func_counter2 = counter(func)），并且计数器变量将保持对每个功能都是私有的。
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
Paolo Bergantino's answer具有仅使用stdlib的巨大优势，并且适用于这个简单的示例，其中没有装饰器参数或装饰函数参数。
但是，如果您想解决更多一般情况，它有三个主要限制：
makestyle(style='bold')装饰器是非常重要的。@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。@functools.wraps创建的包装器很难根据其名称访问参数。事实上，这个论点可以出现在*args，**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，并写了makefun.wraps来解决另外两个问题。请注意，makefun利用与着名的decorator lib相同的技巧。
这是你如何使用参数创建一个装饰器，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概念的开发方式，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documentation。
如果您没有详细解释，请参阅Paolo Bergantino’s answer。
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。让我们看一个简单的例子：
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
好的，还在吗？现在有趣的部分......
你已经看到函数是对象。因此，功能：
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!
好吧，你只需要了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不修改函数本身。
你是如何手动完成的：
# 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!
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)
装饰者只是decorator design pattern的pythonic变种。 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~
总之，您可以轻松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>
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器的高级用途。
# 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
关于Python的一个好消息是方法和函数真的是一样的。唯一的区别是方法期望他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?
如果你正在制作通用装饰 - 一个你将适用于任何函数或方法，无论它的论点 - 然后只使用*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?
好的，现在您对将参数传递给装饰器本身有什么看法？
这可能会有些扭曲，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。因此当你@my_decorator时，你告诉Python将函数称为“由变量标记”my_decorator“'。
这个很重要！你给的标签可以直接指向装饰者 - 或者不是。
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，对吧？
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，甚至可以使用*args, **kwargs。但请记住装饰器只被调用一次。就在Python导入脚本的时候。之后您无法动态设置参数。当您执行“导入x”时，该功能已经过装饰，因此您无法更改任何内容。
好的，作为奖励，我会给你一个片段，让任何装饰师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。
我们包裹了装饰者。
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
它可以使用如下：
# 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，你掌握这个并不是很好吗？
functools模块是在Python 2.5中引入的。它包含函数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中。
（有趣的事实：functools.wraps()是装饰者！☺）
# 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
现在最大的问题是：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，装饰器的好处在于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。
这真的是一个大型游乐场。
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'
要么
makebold = wrap_in_tag('b')
makeitalic = wrap_in_tag('i')
@makebold
@makeitalic
def say():
return 'hello'
就个人而言，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)))
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。
这个
@decorator
def func():
...
扩展到
def func():
...
func = decorator(func)
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()
Python装饰器为另一个函数添加了额外的功能
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，我有这门课
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
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
所以现在我可以改变我的课程
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
您可以制作两个单独的装饰器，它们可以执行您想要的操作，如下图所示。请注意*args, **kwargs在wrapped()函数声明中的使用，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。
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>
正如您所看到的，这两个装饰器中存在大量重复代码。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工。这样就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>
甚至将它们组合成这样：
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>
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）。
如果修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开销到一个额外的函数调用，该函数实现一次添加所有标记，因此它可以生成代码以避免发生的附加函数调用通过为每个标签使用单独的装饰器。
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函数创建更可读的名称时，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>
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'
How can I make two decorators in Python that would do the following?
调用时，您需要以下函数：
@makebold @makeitalic def say(): return "Hello"
回来：
<b><i>Hello</i></b>
为了最简单地做到这一点，让make decorators返回关闭函数（闭包）并调用它的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>'
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的闭包，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，或者想要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，我们需要对我们的函数做更多的事情。包装。
我们有来自标准库中的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
不幸的是，仍然有一些样板，但这很简单，我们可以做到。
在Python 3中，您还可以获得默认分配的__qualname__和__annotations__。
所以现在：
@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'
因此，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。
还有其他模块可能尝试解决该问题，但该解决方案尚未出现在标准库中。