# -*- coding: utf-8 -*-
# 2017/12/2 21:38
# 这不是什么黑魔法,你只需要让包装器传递参数:
def a_decorator_passing_arguments(function_to_decorate):
def a_wrapper_accepting_arguments(arg1, arg2):
print("I got args! Look:", arg1, arg2)
function_to_decorate(arg1, arg2)
return a_wrapper_accepting_arguments
# 当你调用装饰器返回的函数时,也就调用了包装器,把参数传入包装器里,
# 它将把参数传递给被装饰的函数里.
@a_decorator_passing_arguments
def print_full_name(first_name, last_name):
print("My name is", first_name, last_name)
print_full_name("Peter", "Venkman")
# 输出:
#I got args! Look: Peter Venkman
#My name is Peter Venkman
在Python里方法和函数几乎一样.唯一的区别就是方法的第一个参数是一个当前对象的(self)
也就是说你可以用同样的方式来装饰方法!只要记得把self加进去:
def method_friendly_decorator(method_to_decorate):
def wrapper(self, lie):
lie = lie - 3 # 女性福音 :-)
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 %s, what did you think?" % (self.age + lie))
l = Lucy()
l.sayYourAge(-3)
#输出: I am 26, what did you think?
如果你想造一个更通用的可以同时满足方法和函数的装饰器,用*args,**kwargs就可以了
def a_decorator_passing_arbitrary_arguments(function_to_decorate):
# 包装器接受所有参数
def a_wrapper_accepting_arbitrary_arguments(*args, **kwargs):
print("Do I have args?:")
print(args)
print(kwargs)
# 现在把*args,**kwargs解包
# 如果你不明白什么是解包的话,请查阅:
# 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()
#输出
#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)
#输出
#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 %s, %s and %s like platypus? %s" %(a, b, c, platypus))
function_with_named_arguments("Bill", "Linus", "Steve", platypus="Indeed!")
#输出
#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): # 可以加入一个默认值
print("I am %s, what did you think ?" % (self.age + lie))
m = Mary()
m.sayYourAge()
#输出
# Do I have args?:
#(<__main__.Mary object at 0xb7d303ac>,)
#{}
#I am 28, what did you think?
好了,如何把参数传递给装饰器自己?
因为装饰器必须接收一个函数当做参数,所以有点麻烦.好吧,你不可以直接把被装饰函数的参数传递给装饰器.
在我们考虑这个问题时,让我们重新回顾下:
# 装饰器就是一个‘平常不过‘的函数
def my_decorator(func):
print "I am an ordinary function"
def wrapper():
print "I am function returned by the decorator"
func()
return wrapper
# 因此你可以不用"@"也可以调用他
def lazy_function():
print "zzzzzzzz"
decorated_function = my_decorator(lazy_function)
#输出: I am an ordinary function
# 之所以输出 "I am an ordinary function"是因为你调用了函数,
# 并非什么魔法.
@my_decorator
def lazy_function():
print "zzzzzzzz"
#输出: I am an ordinary function
看见了吗,和"my_decorator"一样只是被调用.所以当你用@my_decorator你只是告诉Python去掉用被变量my_decorator标记的函数.
这非常重要!你的标记能直接指向装饰器.
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
# 让我们建一个装饰器.它只是一个新函数.
new_decorator = decorator_maker()
#输出:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
# 下面来装饰一个函数
def decorated_function():
print "I am the decorated function."
decorated_function = new_decorator(decorated_function)
#输出:
#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()
#输出:
#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.
下面让我们去掉所有可恶的中间变量:
def decorated_function():
print "I am the decorated function."
decorated_function = decorator_maker()(decorated_function)
#输出:
#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.
# 最后:
decorated_function()
#输出:
#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.
让我们简化一下:
@decorator_maker()
def decorated_function():
print "I am the decorated function."
#输出:
#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.
#最终:
decorated_function()
#输出:
#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.
看到了吗?我们用一个函数调用"@"语法!:-)
所以让我们回到装饰器的.如果我们在函数运行过程中动态生成装饰器,我们是不是可以把参数传递给函数?
def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):
print "I make decorators! And I accept arguments:", decorator_arg1, decorator_arg2
def my_decorator(func):
# 这里传递参数的能力是借鉴了 closures.
# 如果对closures感到困惑可以看看下面这个:
# http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
print "I am the decorator. Somehow you passed me arguments:", decorator_arg1, decorator_arg2
# 不要忘了装饰器参数和函数参数!
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")
#输出:
#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
上面就是带参数的装饰器.参数可以设置成变量:
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")
#输出:
#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 knows about my arguments: Leslie Howard
你可以用这个小技巧把任何函数的参数传递给装饰器.如果你愿意还可以用*args,**kwargs.但是一定要记住了装饰器只能被调用一次.当Python载入脚本后,你不可以动态的设置参数了.当你运行import x,函数已经被装饰,所以你什么都不能动了.
functools模块在2.5被引进.它包含了一个functools.wraps()函数,可以复制装饰器函数的名字,模块和文档给它的包装器.
如何为被装饰的函数保存元数据
解决方案:
使用标准库functools中的装饰器wraps 装饰内部包裹函数,
可以 制定将原函数的某些属性，更新到包裹函数的上面
其实也可以通过
wrapper.name = func.name
update_wrapper(wrapper, func, (‘name‘,’doc‘), (‘dict‘,))
f.__name__ 函数的名字
f.__doc__ 函数文档字符串
f.__module__ 函数所属模块名称
f.__dict__ 函数的属性字典
f.__defaults__ 默认参数元组
f.__closure__ 函数闭包
>>> def f(): ... a=2 ... return lambda k:a**k ... >>> g=f() >>> g.__closure__ (<cell at 0x000001888D17F2E8: int object at 0x0000000055F4C6D0>,) >>> c=g.__closure__[0] >>> c.cell_contents 2
from functools import wraps,update_wrapper
def log(level="low"):
def deco(func):
@wraps(func)
def wrapper(*args,**kwargs):
‘‘‘ I am wrapper function‘‘‘
print("log was in...")
if level == "low":
print("detailes was needed")
return func(*args,**kwargs)
#wrapper.__name__ = func.__name__
#update_wrapper(wrapper, func, (‘__name__‘,‘__doc__‘), (‘__dict__‘,))
return wrapper
return deco
@log()
def myFunc():
‘‘‘I am myFunc...‘‘‘
print("myFunc was called")
print(myFunc.__name__)
print(myFunc.__doc__)
myFunc()
"""
myFunc
I am myFunc...
log was in...
detailes was needed
myFunc was called
"""
如何定义带参数的装饰器
实现一个装饰器，它用来检查被装饰函数的参数类型，装饰器可以通过参数指明函数参数的类型，
调用时如果检测出类型不匹配则抛出异常。
提取函数签名python3 inspect.signature()
带参数的装饰器，也就是根据参数定制化一个装饰器可以看生成器的工厂
每次调用typeassert，返回一个特定的装饰器，然后用它去装饰其他函数
>>> from inspect import signature >>> def f(a,b,c=1):pass >>> sig=signature(f) >>> sig.parameters mappingproxy(OrderedDict([(‘a‘, <Parameter "a">), (‘b‘, <Parameter "b">), (‘c‘, <Parameter "c=1">)])) >>> a=sig.parameters[‘a‘] >>> a.name ‘a‘ >>> a <Parameter "a"> >>> dir(a) [‘KEYWORD_ONLY‘, ‘POSITIONAL_ONLY‘, ‘POSITIONAL_OR_KEYWORD‘, ‘VAR_KEYWORD‘, ‘VAR_POSITIONAL‘, ‘__class__‘, ‘__delattr__‘, ‘__dir__‘, ‘__doc__‘, ‘__eq__‘, ‘__format__‘, ‘__ge__‘, ‘__getattribute__‘, ‘__gt__‘, ‘__hash__‘, ‘__init__‘, ‘__init_subclass__‘, ‘__le__‘, ‘__lt__‘, ‘__module__‘, ‘__ne__‘, ‘__new__‘, ‘__reduce__‘, ‘__reduce_ex__‘, ‘__repr__‘, ‘__setattr__‘, ‘__setstate__‘, ‘__sizeof__‘, ‘__slots__‘, ‘__str__‘, ‘__subclasshook__‘, ‘_annotation‘, ‘_default‘, ‘_kind‘, ‘_name‘, ‘annotation‘, ‘default‘, ‘empty‘, ‘kind‘, ‘name‘, ‘replace‘] >>> a.kind <_ParameterKind.POSITIONAL_OR_KEYWORD: 1> >>> a.default <class ‘inspect._empty‘> >>> c=sig.parameters[‘c‘] >>> c.default 1 >>> sig.bind(str,int,int) <BoundArguments (a=<class ‘str‘>, b=<class ‘int‘>, c=<class ‘int‘>)> >>> bargs=sig.bind(str,int,int) >>> bargs.arguments OrderedDict([(‘a‘, <class ‘str‘>), (‘b‘, <class ‘int‘>), (‘c‘, <class ‘int‘>)]) >>> bargs.arguments[‘a‘] <class ‘str‘> >>> bargs.arguments[‘b‘] <class ‘int‘>
from inspect import signature
def typeassert(*ty_args,**ty_kargs):
def decorator(func):
#func ->a,b
#d = {‘a‘:int,‘b‘:str}
sig = signature(func)
btypes = sig.bind_partial(*ty_args,**ty_kargs).arguments
def wrapper(*args,**kargs):
#arg in d,instance(arg,d[arg])
for name, obj in sig.bind(*args,**kargs).arguments.items():
if name in btypes:
if not isinstance(obj,btypes[name]):
raise TypeError(‘"%s" must be "%s"‘ %(name,btypes[name]))
return func(*args,**kargs)
return wrapper
return decorator
@typeassert(int,str,list)
def f(a,b,c):
print(a,b,c)
f(1,‘abc‘,[1,2,3])
# f(1,2,[1,2,3])
如何实现属性可修改的函数装饰器
为分析程序内哪些函数执行时间开销较大，我们定义一个带timeout参数的函数装饰器，装饰功能如下：
1.统计被装饰函数单词调用运行时间
2.时间大于参数timeout的，将此次函数调用记录到log日志中
3.运行时可修改timeout的值。
解决方案:
python3 nolocal
为包裹函数添加一个函数，用来修改闭包中使用的自由变量.
python中，使用nonlocal访问嵌套作用域中的变量引用,或者在python2中列表方式,这样就不会在函数本地新建一个局部变量
from functools import wraps
import time
import logging
def warn(timeout):
# timeout = [timeout]
def deco(func):
def wrapper(*args,**kwargs):
start = time.time()
res = func(*args,**kwargs)
used = time.time() -start
if used > timeout:
msg = ‘"%s" : %s > %s‘%(func.__name__,used,timeout)
logging.warn(msg)
return res
def setTimeout(k):
nonlocal timeout
# timeout[0] = k
timeout=k
print("timeout was given....")
wrapper.setTimeout = setTimeout
return wrapper
return deco
from random import randint
@warn(1.5)
def test():
print("in test...")
while randint(0,1):
time.sleep(0.5)
for _ in range(30):
test()
test.setTimeout(1)
print("after set to 1....")
for _ in range(30):
test()
小练习：
#为了debug,堆栈跟踪将会返回函数的 __name__
def foo():
print("foo")
print(foo.__name__)
#输出: foo
########################################
# 如果加上装饰器,将变得有点复杂
def bar(func):
def wrapper():
print("bar")
return func()
return wrapper
@bar
def foo():
print("foo")
print(foo.__name__)
#输出: wrapper
#######################################
# "functools" 将有所帮助
import functools
def bar(func):
# 我们所说的"wrapper",正在包装 "func",
# 好戏开始了
@functools.wraps(func)
def wrapper():
print("bar")
return func()
return wrapper
@bar
def foo():
print("foo")
print(foo.__name__)
#输出: foo
原文：https://www.cnblogs.com/ExMan/p/10171142.html