我如何在Python中制作两个装饰器来完成以下操作？
@make_bold
@make_italic
def say():
return "Hello"
调用say（）应返回：
"<b><i>Hello</i></b>"
当前回答
#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()
也可以在类中编写decorator
#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")
其他回答
如果你不喜欢长篇大论的解释，请看保罗·贝尔甘蒂诺的回答。
装饰师基础知识
Python的函数是对象
要理解修饰符，您必须首先了解函数是Python中的对象。这有重要的后果。让我们举一个简单的例子来看看原因：
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。我们很快就会回来讨论。
Python函数的另一个有趣的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
函数引用
好了，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返回另一个函数。
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！
如果可以返回函数，则可以将其作为参数传递：
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，你只需要了解装饰师所需的一切。你看，修饰器是“包装器”，这意味着它们允许你在修饰函数之前和之后执行代码，而不修改函数本身。
手工装饰师
如何手动执行：
# 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_Sstand_alone_function_decorated。这很简单，只需使用my_shiny_new_decorator返回的函数覆盖a_stand_alone_function即可：
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!
装饰者揭开神秘面纱
上一个示例使用decorator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)
装饰器只是装饰器设计模式的蟒蛇变体。Python中嵌入了几个经典的设计模式，以便于开发（如迭代器）。
当然，您可以累积装饰器：
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~
#<\______/>
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。。。
作为一个结论，您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>
你现在可以开心地离开，或者多烧点脑子，看看装饰器的高级用法。
将装饰师提升到下一个级别
向修饰函数传递参数
# 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（self）的引用。
这意味着你可以用同样的方式为方法构建一个装饰器！记住要考虑到自己：
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（无论其参数如何，都可以应用于任何函数或方法），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?
向装饰器传递参数
太好了，现在你觉得如何将参数传递给装饰器本身？
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，不能将修饰函数的参数直接传递给修饰器。
在匆忙找到解决方案之前，让我们写一个小提示：
# 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。调用“my_decorator”。因此，当您@my_decorator时，您告诉Python调用变量“my_decortor”标记的函数。
这很重要！您给出的标签可以直接指向或不指向装饰器。
让我们作恶吧。☺
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.
这一点也不奇怪。
让我们做完全相同的事情，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.
让我们把它做得更短：
@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？我们使用了带有“@”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来动态生成装饰器，那么我们可以向该函数传递参数，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
这就是：一个带参数的装饰器。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。如果愿意，您甚至可以使用*args、**kwargs。但请记住，装饰者只被调用一次。就在Python导入脚本时。之后不能动态设置参数。当您执行“import x”时，函数已经被修饰，因此您不能改变一切。
让我们练习一下：装饰一个装饰师
好的，作为奖励，我将给你一个代码片段，让任何装饰器都能接受任何参数。毕竟，为了接受参数，我们使用另一个函数创建了装饰器。
我们把装饰工包好了。
我们最近还看到了那个包装函数吗？
哦，是的，装修工人！
让我们玩得开心一点，为装饰者编写一个装饰器：
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
其用途如下：
# 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，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。但现在，你不觉得掌握这一点很好吗？
最佳实践：装饰
Python 2.4中引入了装饰器，因此请确保代码将在>=2.4上运行。装饰器减慢了函数调用的速度。记住这一点。不能取消修饰函数。（有一些黑客可以创建可以删除的修饰符，但没有人使用它们。）因此，一旦一个函数被修饰，它就会被所有代码修饰。装饰器包装函数，这会使它们很难调试。（Python>=2.5会更好；请参见下文。）
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docstring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
装饰器如何有用？
现在有一个大问题：我可以使用装饰器做什么？
看起来很酷，很强大，但一个实际的例子会很好。嗯，有1000种可能性。经典用法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的方式扩展多个函数，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事情上使用它们，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本身提供了几个修饰符：property、staticmethod等。
Django使用decorator来管理缓存和查看权限。扭曲到伪内联异步函数调用。
这真是一个大操场。
说到计数器示例-如上所述，计数器将在使用decorator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（或用于多次装饰同一个函数：func_counter1=counter（func）；func_counter2=counter（func）），并且计数器变量将对每个变量保持私有。
考虑下面的修饰符，注意我们将wrapper（）函数作为对象返回
def make_bold(func):
def wrapper():
return '<b>'+func()+'</b>'
return wrapper
所以这个
@make_bold
def say():
return "Hello"
计算结果为
x = make_bold(say)
注意，x不是say（），而是在内部调用say（（）的包装器对象。这就是装饰师的工作原理。它总是返回调用实际函数的包装器对象。如果链接此
@make_italic
@make_bold
def say():
return "Hello"
转换为此
x = make_bold(say)
y = make_italic(x)
以下是完整的代码
def make_italic(func):
def wrapper():
return '<i>'+func()+'</i>'
return wrapper
def make_bold(func):
def wrapper():
return '<b>'+func()+'</b>'
return wrapper
@make_italic
@make_bold
def say():
return "Hello"
if __name__ == '__main__':
# x = make_bold(say) When you wrap say with make_bold decorator
# y = make_italic(x) When you also add make_italic as part of chaining
# print(y())
print(say())
上述代码将返回
<i><b>Hello</b></i>
希望这有帮助
用于绘制图像的嵌套装饰器的又一示例：
import matplotlib.pylab as plt
def remove_axis(func):
def inner(img, alpha):
plt.axis('off')
func(img, alpha)
return inner
def plot_gray(func):
def inner(img, alpha):
plt.gray()
func(img, alpha)
return inner
@remove_axis
@plot_gray
def plot_image(img, alpha):
plt.imshow(img, alpha=alpha)
plt.show()
现在，让我们先使用嵌套的装饰器显示一个没有轴标签的彩色图像：
plot_image(plt.imread('lena_color.jpg'), 0.4)
接下来，让我们使用嵌套的装饰器remove_axis和plot_gray显示一个没有轴标签的灰度图像（我们需要cmap='gray'，否则默认的颜色映射是viridis，因此除非明确指定，否则默认情况下灰度图像不会以黑白色显示）
plot_image(plt.imread('lena_bw.jpg'), 0.8)
上述函数调用缩减为以下嵌套调用
remove_axis(plot_gray(plot_image))(img, alpha)
#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()
也可以在类中编写decorator
#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")
