Write PEP 3119 section

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@ -807,41 +807,142 @@ constraints upon the memory returned. Some examples are:
PEP 3119: Abstract Base Classes PEP 3119: Abstract Base Classes
===================================================== =====================================================
XXX write this -- this section is currently just brief notes. Some object-oriented languages such as Java support interfaces: declarations
that a class has a given set of methods or supports a given access protocol.
Abstract Base Classes (or ABCs) are an equivalent feature for Python. The ABC
support consists of an :mod:`abc` module containing a metaclass called
:class:`ABCMeta`, special handling
of this metaclass by the :func:`isinstance` and :func:`issubclass` built-ins,
and a collection of basic ABCs that the Python developers think will be widely
useful.
How to identify a file object? Let's say you have a particular class and wish to know whether it supports
dictionary-style access. The phrase "dictionary-style" is vague, however.
It probably means that accessing items with ``obj[1]`` works.
Does it imply that setting items with ``obj[2] = value`` works?
Or that the object will have :meth:`keys`, :meth:`values`, and :meth:`items`
methods? What about the iterative variants such as :meth:`iterkeys`? :meth:`copy`
and :meth:`update`? Iterating over the object with :func:`iter`?
ABCs are a collection of classes describing various interfaces. Python 2.6 includes a number of different ABCs in the :mod:`collections`
Classes can derive from an ABC to indicate they support that ABC's module. :class:`Iterable` indicates that a class defines :meth:`__iter__`,
interface. Concrete classes should obey the semantics specified by and :class:`Container` means the class supports ``x in y`` expressions
an ABC, but Python can't check this; it's up to the implementor. by defining a :meth:`__contains__` method. The basic dictionary interface of
getting items, setting items, and
:meth:`keys`, :meth:`values`, and :meth:`items`, is defined by the
:class:`MutableMapping` ABC.
A metaclass lets you declare that an existing class or type You can derive your own classes from a particular ABC
derives from a particular ABC. You can even to indicate they support that ABC's interface::
class AppendableSequence: import collections
class Storage(collections.MutableMapping):
...
Alternatively, you could write the class without deriving from
the desired ABC and instead register the class by
calling the ABC's :meth:`register` method::
import collections
class Storage:
...
collections.MutableMapping.register(Storage)
For classes that you write, deriving from the ABC is probably clearer.
The :meth:`register` method is useful when you've written a new
ABC that can describe an existing type or class, or if you want
to declare that some third-party class implements an ABC.
For example, if you defined a :class:`PrintableType` ABC,
it's legal to do:
# Register Python's types
PrintableType.register(int)
PrintableType.register(float)
PrintableType.register(str)
Classes should obey the semantics specified by an ABC, but
Python can't check this; it's up to the class author to
understand the ABC's requirements and to implement the code accordingly.
To check whether an object supports a particular interface, you can
now write::
def func(d):
if not isinstance(d, collections.MutableMapping):
raise ValueError("Mapping object expected, not %r" % d)
(Don't feel that you must now begin writing lots of checks as in the
above example. Python has a strong tradition of duck-typing, where
explicit type-checking isn't done and code simply calls methods on
an object, trusting that those methods will be there and raising an
exception if they aren't. Be judicious in checking for ABCs
and only do it where it helps.)
You can write your own ABCs by using ``abc.ABCMeta`` as the
metaclass in a class definition::
from abc import ABCMeta
class Drawable():
__metaclass__ = ABCMeta __metaclass__ = ABCMeta
AppendableSequence.register(list) def draw(self, x, y, scale=1.0):
assert issubclass(list, AppendableSequence) pass
assert isinstance([], AppendableSequence)
@abstractmethod decorator -- you can't instantiate classes w/ def draw_doubled(self, x, y):
an abstract method. self.draw(x, y, scale=2.0)
::
@abstractproperty decorator class Square(Drawable):
def draw(self, x, y, scale):
...
In the :class:`Drawable` ABC above, the :meth:`draw_doubled` method
renders the object at twice its size and can be implemented in terms
of other methods described in :class:`Drawable`. Classes implementing
this ABC therefore don't need to provide their own implementation
of :meth:`draw_doubled`, though they can do so. An implementation
of :meth:`draw` is necessary, though; the ABC can't provide
a useful generic implementation. You
can apply the ``@abstractmethod`` decorator to methods such as
:meth:`draw` that must be implemented; Python will
then raise an exception for classes that
don't define the method::
class Drawable():
__metaclass__ = ABCMeta
@abstractmethod
def draw(self, x, y, scale):
pass
Note that the exception is only raised when you actually
try to create an instance of a subclass without the method::
>>> s=Square()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class Square with abstract methods draw
>>>
Abstract data attributes can be declared using the ``@abstractproperty`` decorator::
@abstractproperty @abstractproperty
def readonly(self): def readonly(self):
return self._x return self._x
Subclasses must then define a :meth:`readonly` property
.. seealso:: .. seealso::
:pep:`3119` - Introducing Abstract Base Classes :pep:`3119` - Introducing Abstract Base Classes
PEP written by Guido van Rossum and Talin. PEP written by Guido van Rossum and Talin.
Implemented by XXX. Implemented by Guido van Rossum.
Backported to 2.6 by Benjamin Aranguren, with Alex Martelli. Backported to 2.6 by Benjamin Aranguren, with Alex Martelli.
.. ====================================================================== .. ======================================================================