Let’s talk about Python decorators

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Decorator

1. is essentially a function that accepts parameters as functions.
2. Function: Add additional general functions to an already implemented method, such as logging, running timing, etc.

Example

Decorator without parameters, without @

# 不带参数的装饰器def deco_test(func):
    def wrapper(*args, **kwargs):
        print("before function")
        f = func(*args, **kwargs)
        print("after function")
        return f    return wrapperdef do_something(a,b,c):
    print(a)
    time.sleep(1)
    print(b)
    time.sleep(1)
    print(c)
    return aif __name__ == '__main__':
    # 不用@
    f = deco_test(do_something)("1","2","3")

Output:

before function
1
2
3
after function

Personal understanding:

is equivalent to putting two outputs outside the do_something function: before function and after function.

Decorator without parameters, use @

# 不带参数的装饰器def deco_test(func):
    def wrapper(*args, **kwargs):
        print("before function")
        f = func(*args, **kwargs)
        print("after function")
        return f    return wrapper

@deco_testdef do_something(a,b,c):
    print(a)
    time.sleep(1)
    print(b)
    time.sleep(1)
    print(c)
    return aif __name__ == '__main__':
    # 使用@
    f = do_something("1","2","3")

to output:

before function
1
2
3
after function

Personal understanding:

Equivalent to when executing the do_something function, because of @ reasons, we already know that there is a layer of decorator deco_test, so there is no need to write it separately deco_test(do_something) is gone.

Decorator with parameters

# 带参数的装饰器def logging(level):
    def wrapper(func):
        def inner_wrapper(*args, **kwargs):
            print("[{level}]: enter function {func}()".format(level=level, func=func.__name__))
            f = func(*args, **kwargs)
            print("after function: [{level}]: enter function {func}()".format(level=level, func=func.__name__))
            return f        return inner_wrapper    return wrapper

@logging(level="debug")def do_something(a,b,c):
    print(a)
    time.sleep(1)
    print(b)
    time.sleep(1)
    print(c)
    return aif __name__ == '__main__':
    # 使用@
    f = do_something("1","2","3")

Output:

[debug]: enter function do_something()
1
2
3
after function: [debug]: enter function do_something()

Personal understanding:

Decorator With a parameter level = "debug".

The outermost function logging() accepts parameters and applies them to the inner decorator function. The inner function wrapper() accepts a function as a parameter, and then places a decorator on the function. The key point here is that the decorator can use the parameters passed to logging().

Class Decorator

# 类装饰器class deco_cls(object):
    def __init__(self, func):
        self._func = func    def __call__(self, *args, **kwargs):
        print("class decorator before function")
        f = self._func(*args, **kwargs)
        print("class decorator after function")
        return f

@deco_clsdef do_something(a,b,c):
    print(a)
    time.sleep(1)
    print(b)
    time.sleep(1)
    print(c)
    return aif __name__ == '__main__':
    # 使用@
    f = do_something("1","2","3")

Output:

class decorator before function
1
2
3
class decorator after function

Personal understanding:

Use a decorator To wrap a function, return a callable instance. Therefore a class decorator is defined.

Two-layer decorator

# 不带参数的装饰器def deco_test(func):
    def wrapper(*args, **kwargs):
        print("before function")
        f = func(*args, **kwargs)
        print("after function")
        return f    return wrapper# 带参数的装饰器def logging(level):
    def wrapper(func):
        def inner_wrapper(*args, **kwargs):
            print("[{level}]: enter function {func}()".format(level=level, func=func.__name__))
            f = func(*args, **kwargs)
            print("after function: [{level}]: enter function {func}()".format(level=level, func=func.__name__))
            return f        return inner_wrapper    return wrapper

@logging(level="debug")@deco_testdef do_something(a,b,c):
    print(a)
    time.sleep(1)
    print(b)
    time.sleep(1)
    print(c)
    return aif __name__ == '__main__':
    # 使用@
    f = do_something("1","2","3")

Output:

[debug]: enter function wrapper()
before function
1
2
3
after function
after function: [debug]: enter function wrapper()

Personal understanding:

In functiondo_something() First put a layer of deco_test() decorator on the outside, and then put a layer of logging() decorator on the outside.

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