在Python中,如果不使用traceback模块,是否有一种方法可以从函数内部确定函数的名称?
假设我有一个模块foo和一个功能栏。当执行foo.bar()时,是否有一种方法让bar知道bar的名字?或者更好的是,foo。酒吧的名字吗?
#foo.py
def bar():
print "my name is", __myname__ # <== how do I calculate this at runtime?
在Python中,如果不使用traceback模块,是否有一种方法可以从函数内部确定函数的名称?
假设我有一个模块foo和一个功能栏。当执行foo.bar()时,是否有一种方法让bar知道bar的名字?或者更好的是,foo。酒吧的名字吗?
#foo.py
def bar():
print "my name is", __myname__ # <== how do I calculate this at runtime?
当前回答
@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。
其他回答
functionNameAsString = sys._getframe().f_code.co_name
我想要一个非常相似的东西,因为我想把函数名放在一个log字符串中,在我的代码中有很多地方。可能不是最好的方法,但这里有一种获取当前函数名称的方法。
你可以使用@Andreas Jung显示的方法获得它定义时的名称,但这可能不是调用函数时的名称:
import inspect
def Foo():
print inspect.stack()[0][3]
Foo2 = Foo
>>> Foo()
Foo
>>> Foo2()
Foo
这种区别对你来说是否重要,我不能说。
我想检查是最好的方法。例如:
import inspect
def bar():
print("My name is", inspect.stack()[0][3])
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。
我喜欢使用装饰器的想法,但我更喜欢避免触及函数参数。因此,我提供了另一种选择:
import functools
def withname(f):
@functools.wraps(f)
def wrapper(*args, **kwargs):
global __name
__saved_name = globals().get("__name")
__name = f.__name__
ret = f(*args, **kwargs)
__name = __saved_name
return ret
return wrapper
@withname
def f():
print(f"in f: __name=={__name}")
g()
print(f"back in f: __name=={__name}")
@withname
def g():
print(f"in g: __name=={__name}")
由于__name是一个全局变量,所以在调用函数时需要保存和恢复__name。调用上面的f()会产生:
in f: __name==f
in g: __name==g
back in f: __name==f
不幸的是,如果我们不改变函数参数,就没有全局变量的替代品。引用一个不是在函数上下文中创建的变量,将生成寻找全局变量的代码:
>>> def f(): print(__function__)
>>> from dis import dis
>>> dis(f)
1 0 LOAD_GLOBAL 0 (print)
2 LOAD_GLOBAL 1 (__function__)
4 CALL_FUNCTION 1
6 POP_TOP
8 LOAD_CONST 0 (None)
10 RETURN_VALUE