decorator

参考
https://taizilongxu.gitbooks.io/stackoverflow-about-python/content/3/README.html

简单介绍下

• 装饰器就是把其他函数作为参数的函数

def my_shiny_new_decorator(a_function_to_decorate):
    def the_wrapper_around_the_original_function():

        # 把要在原始函数被调用前的代码放在这里
        print "Before the function ru先简单ns"

        # 调用原始函数(用括号)
        a_function_to_decorate()

        # 把要在原始函数调用后的代码放在这里
        print "After the function runs"
return the_wrapper_around_the_original_function

• 装饰器真实面纱

@my_shiny_new_decorator
def another_stand_alone_function():
    print "Leave me alone"

another_stand_alone_function()
#输出:
#Before the function runs
#Leave me alone
#After the function runs

就这么简单.@decorator就是下面的简写:

another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)

• 装饰器里传入参数

# 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

• 装饰方法
  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)

来点刺激的

把参数传递给装饰器

• 先来看个@decorator_maker()例子（相当于decorated_function = decorator_maker()(decorated_function)）

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

@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.

所以如果我们在函数运行过程中动态生成装饰器,就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

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

最后来一个屌爆的线程安全单实例装饰器

参考http://www.jianshu.com/p/ec6589e02e2f

def singleton(cls):
    instance = cls()
    instance.__call__ = lambda: instance
    return instance

#
# Sample use
#

@singleton
class Highlander:
    x = 100
    # Of course you can have any attributes or methods you like.

Highlander() is Highlander() is Highlander #=> True
id(Highlander()) == id(Highlander) #=> True
Highlander().x == Highlander.x == 100 #=> True
Highlander.x = 50
Highlander().x == Highlander.x == 50 #=> True

• 在定义class Highlander的时候已经执行完所有singleton装饰器中的代码，得到了一个instance，所以这之后所有对Highlander的调用实际上是在调用instance的call 方法。
• 我们通过lambda函数定义了call方法让它始终返回instance，因此Highlander()和Highlander都返回instance
• 同时由于在类定义代码执行时就已经创建了instance，所以后续不论是多线程还是单线程，在调用Highlander时都是在调用instance的call方法，也就无需同步了。
  最后我想说的是这种方法简直碉堡了～～～