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>"
查看文档以了解装饰器是如何工作的。以下是您的要求：
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>"
Python装饰器为另一个函数添加了额外的功能
斜体装饰符可以如下所示
def makeitalic(fn):
def newFunc():
return "<i>" + fn() + "</i>"
return newFunc
注意，函数是在函数内部定义的。它基本上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，我希望两个函数在完成后和完成前都打印“---”。我可以在每个打印语句前后添加一个打印“---”。但因为我不喜欢重复自己，我会做一个装饰师
def addDashes(fn): # notice it takes a function as an argument
def newFunction(self): # define a new function
print "---"
fn(self) # call the original function
print "---"
return newFunction
# Return the newly defined function - it will "replace" the original
所以现在我可以把我的班级改成
class foo:
@addDashes
def bar(self):
print "hi"
@addDashes
def foobar(self):
print "hi again"
有关装饰器的详细信息，请查看http://www.ibm.com/developerworks/linux/library/l-cpdecor.html
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
这使您能够编写：
@wrap_in_tag('b')
@wrap_in_tag('i')
def say():
return 'hello'
or
makebold = wrap_in_tag('b')
makeitalic = wrap_in_tag('i')
@makebold
@makeitalic
def say():
return 'hello'
就我个人而言，我会用不同的方式来编写装饰器：
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
这将产生：
@wrap_in_tag('b')
@wrap_in_tag('i')
def say(val):
return val
say('hello')
不要忘了decorator语法是一种简写的构造：
say = wrap_in_tag('b')(wrap_in_tag('i')(say)))
装饰只是语法上的糖。
This
@decorator
def func():
...
扩展到
def func():
...
func = decorator(func)
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。
当然，您也可以从decorator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()
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，更灵活地说：
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'
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。
装饰器接受函数定义并创建一个新函数，该函数执行该函数并转换结果。
@deco
def do():
...
相当于：
do = deco(do)
例子：
def deco(func):
def inner(letter):
return func(letter).upper() #upper
return inner
This
@deco
def do(number):
return chr(number) # number to letter
相当于这个
def do2(number):
return chr(number)
do2 = deco(do2)
65<=>“a”
print(do(65))
print(do2(65))
>>> B
>>> B
要理解decorator，需要注意的是，decorator创建了一个新的函数do，它在内部执行函数并转换结果。
用不同数量的参数修饰函数：
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
这里是一个链接装饰器的简单示例。注意最后一行-它显示了封面下的情况。
############################################################
#
# 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))()
输出如下所示：
17/6/2013
<b>17/6/2013</b>
6/17/2013
<b>6/17/2013</b>
<b>6/17/2013</b>
#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")
您可以制作两个独立的装饰器，如下图所示。请注意，在wrapped（）函数的声明中使用了*args，**kwargs，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，这并不是真正必要的，但为了通用性，它包含在内）。
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特性。这使得错误消息和嵌入函数文档（func.__doc__）成为修饰函数的错误消息，而不是wrapped（）的错误消息。
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>
精炼
正如您所看到的，这两个装饰器中有很多重复的代码。考虑到这种相似性，您最好创建一个实际上是装饰器工厂的通用函数，换句话说，一个生成其他装饰器的装饰器函数。这样可以减少代码重复，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>
为了使代码更可读，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>
甚至可以这样组合：
makebolditalic = lambda fn: makebold(makeitalic(fn))
@makebolditalic
def greet(whom=''):
return 'Hello' + (' ' + whom) if whom else ''
print(greet('world')) # -> <b><i>Hello world</i></b>
效率
虽然上面的示例都能完成所有工作，但当同时应用多个修饰符时，生成的代码会以外部函数调用的形式产生大量开销。这可能无关紧要，具体取决于确切的用法（例如，可能是I/O绑定的）。
如果修饰函数的速度很重要，那么可以通过编写一个稍微不同的修饰器工厂函数来保持单个额外函数调用的开销，该函数实现一次添加所有标记，从而可以生成代码，避免对每个标记使用单独的修饰器而导致的额外函数调用。
这需要在decorator本身中有更多的代码，但这只在将其应用于函数定义时运行，而不是在稍后调用它们本身时运行。这也适用于通过使用前面所示的lambda函数创建更可读的名称时。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>
我如何在Python中制作两个装饰器来完成以下操作？
调用时需要以下函数：
@马克博尔德@使倾斜def say（）：return“您好”
要返回：
<b><i> 你好</i></b>
简单的解决方案
为了最简单地做到这一点，请制作返回lambdas（匿名函数）的装饰器，这些函数在函数（闭包）上关闭并调用它：
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最初的功能。
>>> say
<function <lambda> at 0x4ACFA070>
要找到它，我们需要深入研究每个lambda的闭包，其中一个被另一个所掩埋：
>>> say.__closure__[0].cell_contents
<function <lambda> at 0x4ACFA030>
>>> say.__closure__[0].cell_contents.__closure__[0].cell_contents
<function say at 0x4ACFA730>
因此，如果我们将文档放在这个函数上，或者希望能够修饰包含多个参数的函数，或者我们只是想知道在调试会话中看到的是什么函数，那么我们需要对包装器做更多的工作。
全功能解决方案-克服大多数这些问题
我们在标准库中有来自functools模块的修饰符包装！
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，但这是我们所能做到的最简单的。
在Python3中，默认情况下还会分配__qualiname__和__annotations__。
现在：
@makebold
@makeitalic
def say():
"""This function returns a bolded, italicized 'hello'"""
return 'Hello'
现在：
>>> say
<function say at 0x14BB8F70>
>>> help(say)
Help on function say in module __main__:
say(*args, **kwargs)
This function returns a bolded, italicized 'hello'
结论
所以我们看到，包装使包装函数几乎可以做所有的事情，除了告诉我们该函数将什么作为参数。
还有其他模块可以尝试解决这个问题，但标准库中还没有解决方案。
这个答案早就有了答案，但我想我会分享我的Decorator类，这使编写新的Decorator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)
首先，我认为这使装饰器的行为非常清晰，但也使定义新的装饰器变得非常简洁。对于上面列出的示例，您可以将其解为：
class MakeBold(Decorator):
def call():
return "<b>" + self.method() + "</b>"
class MakeItalic(Decorator):
def call():
return "<i>" + self.method() + "</i>"
@MakeBold()
@MakeItalic()
def say():
return "Hello"
您也可以使用它来执行更复杂的任务，例如，一个装饰器，它会自动将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]
注意，这个示例没有在decorator的实例化中包含列表类型，因此在最终的print语句中，该方法应用于列表本身，而不是列表的元素。
Paolo Bergan蒂诺的答案具有只使用stdlib的巨大优势，适用于这个简单的示例，其中既没有修饰器参数，也没有修饰函数参数。
然而，如果您想处理更一般的情况，它有三个主要限制：
正如在几个答案中已经指出的，您不能轻易地修改代码以添加可选的修饰符参数。例如，创建makestyle（style='bold'）装饰器非常简单。此外，使用@functools.wraps创建的包装器不保留签名，因此如果提供了错误的参数，它们将开始执行，并且可能引发与通常的TypeError不同的错误。最后，在使用@functools.wraps创建的包装器中，很难根据其名称访问参数。事实上，参数可以出现在*args、**kwargs中，也可以根本不出现（如果是可选的）。
我写了decopatch来解决第一个问题，写了makefun.wraps来解决另外两个问题。注意，makefun利用了与著名的decorator lib相同的技巧。
这是如何创建带有参数的装饰器，返回真正的签名保护包装器：
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为您提供了其他两种开发样式，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>'
有关详细信息，请参阅文档。
当您需要在decorator中添加自定义参数时，我会添加一个案例，将其传递给最终函数，然后使用它。
装饰师：
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
以及最终功能：
@app.route('/')
@jwt_or_redirect
@jwt_refresh
def home_page(*args, **kwargs):
return kwargs['refreched_jwt']
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)
下面有make_bold（）和make_italic（）：
def make_bold(func):
def core(*args, **kwargs):
result = func(*args, **kwargs)
return "<b>" + result + "</b>"
return core
def make_italic(func):
def core(*args, **kwargs):
result = func(*args, **kwargs)
return "<i>" + result + "</i>"
return core
您可以使用say（）将它们用作装饰器，如下所示：
@make_bold
@make_italic
def say():
return "Hello"
print(say())
输出：
<b><i>Hello</i></b>
当然，您可以直接使用make_bold（）和make_italic（）而不使用修饰符，如下所示：
def say():
return "Hello"
f1 = make_italic(say)
f2 = make_bold(f1)
result = f2()
print(result)
简而言之：
def say():
return "Hello"
result = make_bold(make_italic(say))()
print(result)
输出：
<b><i>Hello</i></b>
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
