此系列文档：
1. 我终于弄懂了Python的装饰器（一）
2. 我终于弄懂了Python的装饰器（二）
3. 我终于弄懂了Python的装饰器（三）
4. 我终于弄懂了Python的装饰器（四）
二、装饰器的高级用法
将参数传递给装饰函数
＃它不是黑魔法，只是给包装（wrapper）传递参数：
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
＃因为当您调用装饰器返回的函数时，调用的包装器（wrapper），将参数传递给被包装器包装的函数
@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")
# 输出:
#I got args! Look: Peter Venkman
#My name is Peter Venkman
将参数传递给装饰器
关于将参数传递给装饰器本身，您怎么认为？
因为装饰器必须接受一个函数作为参数，所以这可能会有些别扭。
因此，您不能将装饰函数的参数直接传递给装饰器。
在寻求解决方案之前，让我们写一些提醒：
＃装饰器是普通函数
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，你可以调用它，而不用 "@"
def lazy_function():
print("zzzzzzzz")
decorated_function = my_decorator(lazy_function)
#输出: I am an ordinary function
# 它输出了 "I am an ordinary function", 因为你只是调用了装饰器，而没有调用函数:
# 这里没有什么神奇的地方，使用'@'
@my_decorator
def lazy_function():
print("zzzzzzzz")
#outputs: I am an ordinary function
结果一样。
my_decorator”被调用了。
因此，当您使用时@my_decorator，您要告诉Python，通过变量来调用my_decorator标记了的函数。
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
#让我们新建一个装饰器
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
# 让我们装饰这个函数
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
# 让我们调用这个函数
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.
毫不奇怪，跟我们前边演示的内容一样。
让我们再做一次完全一样的事情，但是这次我们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)
#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.
# Finally:
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.
让我们把它变的更精简：
@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.
嘿，你看到了吗？我们使用了带有“ @”语法的函数调用！
因此，回到带有参数的装饰器。
如果我们可以使用函数即时生成装饰器，则可以将参数传递给该函数，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):
＃这里传递的参数是闭包的。
＃如果您对封包感到不舒服，可以忽略这点。
print("I am the decorator. Somehow you passed me arguments: {0}, {1}".format(decorator_arg1, decorator_arg2))
＃不要混淆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
记住它：带参数的装饰器，可以将变量作为参数：
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 know about my arguments: Leslie Howard
如您所见，您可以像任何函数传递参数一样传递参数给装饰器。
您甚至可以根据需要使用*args, **kwargs。
但是请记住，装饰器仅被调用一次，仅在Python导入脚本时。之后，您将无法动态设置参数。
当您执行“ import x”时，该函数已经被修饰，因此您无法进行任何更改。
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