Python带参数的装饰器

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.\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