Kaydet (Commit) 0d4497b9 authored tarafından Raymond Hettinger's avatar Raymond Hettinger Kaydeden (comit) GitHub

bpo-23702: Update Descriptor-HOWTO to reflect the removal of unbound methods (#3739)

üst 0bf287b6
......@@ -180,7 +180,7 @@ descriptor is useful for monitoring just a few chosen attributes::
The protocol is simple and offers exciting possibilities. Several use cases are
so common that they have been packaged into individual function calls.
Properties, bound and unbound methods, static methods, and class methods are all
Properties, bound methods, static methods, and class methods are all
based on the descriptor protocol.
......@@ -266,22 +266,23 @@ Python's object oriented features are built upon a function based environment.
Using non-data descriptors, the two are merged seamlessly.
Class dictionaries store methods as functions. In a class definition, methods
are written using :keyword:`def` and :keyword:`lambda`, the usual tools for
creating functions. The only difference from regular functions is that the
are written using :keyword:`def` or :keyword:`lambda`, the usual tools for
creating functions. Methods only differ from regular functions in that the
first argument is reserved for the object instance. By Python convention, the
instance reference is called *self* but may be called *this* or any other
variable name.
To support method calls, functions include the :meth:`__get__` method for
binding methods during attribute access. This means that all functions are
non-data descriptors which return bound or unbound methods depending whether
they are invoked from an object or a class. In pure python, it works like
this::
non-data descriptors which return bound methods when they are invoked from an
object. In pure python, it works like this::
class Function(object):
. . .
def __get__(self, obj, objtype=None):
"Simulate func_descr_get() in Objects/funcobject.c"
if obj is None:
return self
return types.MethodType(self, obj)
Running the interpreter shows how the function descriptor works in practice::
......@@ -291,25 +292,34 @@ Running the interpreter shows how the function descriptor works in practice::
... return x
...
>>> d = D()
>>> D.__dict__['f'] # Stored internally as a function
<function f at 0x00C45070>
>>> D.f # Get from a class becomes an unbound method
<unbound method D.f>
>>> d.f # Get from an instance becomes a bound method
# Access through the class dictionary does not invoke __get__.
# It just returns the underlying function object.
>>> D.__dict__['f']
<function D.f at 0x00C45070>
# Dotted access from a class calls __get__() which just returns
# the underlying function unchanged.
>>> D.f
<function D.f at 0x00C45070>
# The function has a __qualname__ attribute to support introspection
>>> D.f.__qualname__
'D.f'
# Dotted access from an instance calls __get__() which returns the
# function wrapped in a bound method object
>>> d.f
<bound method D.f of <__main__.D object at 0x00B18C90>>
The output suggests that bound and unbound methods are two different types.
While they could have been implemented that way, the actual C implementation of
:c:type:`PyMethod_Type` in :source:`Objects/classobject.c` is a single object
with two different representations depending on whether the :attr:`im_self`
field is set or is *NULL* (the C equivalent of ``None``).
Likewise, the effects of calling a method object depend on the :attr:`im_self`
field. If set (meaning bound), the original function (stored in the
:attr:`im_func` field) is called as expected with the first argument set to the
instance. If unbound, all of the arguments are passed unchanged to the original
function. The actual C implementation of :func:`instancemethod_call()` is only
slightly more complex in that it includes some type checking.
# Internally, the bound method stores the underlying function,
# the bound instance, and the class of the bound instance.
>>> d.f.__func__
<function D.f at 0x1012e5ae8>
>>> d.f.__self__
<__main__.D object at 0x1012e1f98>
>>> d.f.__class__
<class 'method'>
Static Methods and Class Methods
......
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