我用自己的方法在多重继承场景中安全地调用super(我把所有代码都放在这里)

def safe_super(_class, _inst):
    """safe super call"""
    try:
        return getattr(super(_class, _inst), _inst.__fname__)
    except:
        return (lambda *x,**kx: None)


def with_name(function):
    def wrap(self, *args, **kwargs):
        self.__fname__ = function.__name__
        return function(self, *args, **kwargs)
return wrap

示例用法:

class A(object):

    def __init__():
        super(A, self).__init__()

    @with_name
    def test(self):
        print 'called from A\n'
        safe_super(A, self)()

class B(object):

    def __init__():
        super(B, self).__init__()

    @with_name
    def test(self):
        print 'called from B\n'
        safe_super(B, self)()

class C(A, B):

    def __init__():
        super(C, self).__init__()

    @with_name
    def test(self):
        print 'called from C\n'
        safe_super(C, self)()

测试:

a = C()
a.test()

输出:

called from C
called from A
called from B

在每个@with_name修饰的方法中，您可以访问self。__fname__作为当前函数名。

我把这个实用工具放在附近:

import inspect
myself = lambda: inspect.stack()[1][3]

用法:

myself()

有几种方法可以达到相同的效果:

import sys
import inspect

def what_is_my_name():
    print(inspect.stack()[0][0].f_code.co_name)
    print(inspect.stack()[0][3])
    print(inspect.currentframe().f_code.co_name)
    print(sys._getframe().f_code.co_name)

注意inspect。堆栈调用比替代方法慢数千倍:

$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
1000 loops, best of 3: 499 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
1000 loops, best of 3: 497 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
10000000 loops, best of 3: 0.1 usec per loop
$ python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
10000000 loops, best of 3: 0.135 usec per loop

2021年8月更新(原文章为Python2.7编写)

Python 3.9.1 (default, Dec 11 2020, 14:32:07)
[GCC 7.3.0] :: Anaconda, Inc. on linux

python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
500 loops, best of 5: 390 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
500 loops, best of 5: 398 usec per loop
python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
2000000 loops, best of 5: 176 nsec per loop
python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
5000000 loops, best of 5: 62.8 nsec per loop

这很容易用装饰器完成。

>>> from functools import wraps

>>> def named(func):
...     @wraps(func)
...     def _(*args, **kwargs):
...         return func(func.__name__, *args, **kwargs)
...     return _
... 

>>> @named
... def my_func(name, something_else):
...     return name, something_else
... 

>>> my_func('hello, world')
('my_func', 'hello, world')

@jeff-laughlin的回答很漂亮。我对它进行了轻微的修改，以达到我认为的目的:跟踪函数的执行，并捕获参数列表以及关键字参数。谢谢你@jeff-laughlin!

from functools import wraps                                                                                                                                                                                                     
import time                                                                                                                                                                                                                     
                                                                                                                                                                                                                                
def named(func):                                                                                                                                                                                                                
    @wraps(func)                                                                                                                                                                                                                
    def _(*args, **kwargs):                                                                                                                                                                                                     
        print(f"From wrapper function: Executing function named: {func.__name__}, with arguments: {args}, and keyword arguments: {kwargs}.")                                                                                    
        print(f"From wrapper function: {func}")                                                                                                                                                                                 
        start_time = time.time()                                                                                                                                                                                                
        return_value = func(*args, **kwargs)                                                                                                                                                                                    
        end_time = time.time()                                                                                                                                                                                                  
        elapsed_time = end_time - start_time                                                                                                                                                                                    
        print(f"From wrapper function: Execution of {func.__name__} took {elapsed_time} seconds.")                                                                                                                              
        return return_value                                                                                                                                                                                                     
    return _                                                                                                                                                                                                                    
                                                                                                                                                                                                                                
@named                                                                                                                                                                                                                          
def thanks(message, concepts, username='@jeff-laughlin'):                                                                                                                                                                       
    print(f"From inner function: {message} {username} for teaching me about the {concepts} concepts of closures and decorators!")                                                                                               
                                                                                                                                                                                                                                
thanks('Thank you', 'two', username='@jeff-laughlin')                                                                                                                                                                           
print('-'*80)                                                                                                                                                                                                                   
thanks('Thank you', 'two', username='stackoverflow')
print(thanks) 

From wrapper function: Executing function named: thanks, with arguments: ('Thank you', 'two'), and keyword arguments: {'username': '@jeff-laughlin'}. From wrapper function: <function thanks at 0x7f13e6ceaa60> From inner function: Thank you @jeff-laughlin for teaching me about the two concepts of closures and decorators! From wrapper function: Execution of thanks took 2.193450927734375e-05 seconds. -------------------------------------------------------------------------------- From wrapper function: Executing function named: thanks, with arguments: ('Thank you', 'two'), and keyword arguments: {'username': 'stackoverflow'}. From wrapper function: <function thanks at 0x7f13e6ceaa60> From inner function: Thank you stackoverflow for teaching me about the two concepts of closures and decorators! From wrapper function: Execution of thanks took 7.152557373046875e-06 seconds. <function thanks at 0x7f13e6ceaca0>

最让我惊讶的是，有一种方法可以在运行时拦截函数，检查它们，并基于此采取一些操作。另一件令人惊讶的事情是内部函数的内存地址两次都是相同的。有人知道这是为什么吗?在我能够理解这个装饰器/闭包魔法之前，我还有一段路要走。

你可以使用装饰器:

def my_function(name=None):
    return name

def get_function_name(function):
    return function(name=function.__name__)

>>> get_function_name(my_function)
'my_function'