第430页 | Learning Python | 阅读 ‧ 电子书库

Function Introspection

Because they are objects, we can also process functions with normal object tools. In fact, functions are more flexible than you might expect. For instance, once we make a function, we can call it as usual:

>>> def func(a):
...     b = 'spam'
...     return b * a
...
>>> func(8)
'spamspamspamspamspamspamspamspam'

But the call expression is just one operation defined to work on function objects. We can also inspect their attributes generically (the following is run in Python 3.0, but 2.6 results are similar):

>>> func.__name__
'func'
>>> dir(func)
['__annotations__', '__call__', '__class__', '__closure__', '__code__',
...more omitted...
'__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']

Introspection tools allow us to explore implementation details too—functions have attached code objects, for example, which provide details on aspects such as the functions’ local variables and arguments:

>>> func.__code__
<code object func at 0x0257C9B0, file "<stdin>", line 1>

>>> dir(func.__code__)
['__class__', '__delattr__', '__doc__', '__eq__', '__format__', '__ge__',
...more omitted...
'co_argcount', 'co_cellvars', 'co_code', 'co_consts', 'co_filename',
'co_firstlineno', 'co_flags', 'co_freevars', 'co_kwonlyargcount', 'co_lnotab',
'co_name', 'co_names', 'co_nlocals', 'co_stacksize', 'co_varnames']

>>> func.__code__.co_varnames
('a', 'b')
>>> func.__code__.co_argcount
1

Tool writers can make use of such information to manage functions (in fact, we will too in Chapter 38, to implement validation of function arguments in decorators).