在装饰器函数里传入参数
# -*- 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?
把参数传递给装饰器
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. " "I can access all the variables " " - from the decorator: {0} {1} " " - from the function call: {2} {3} " "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