Descriptor HowTo Guide¶
- Автор
Raymond Hettinger
- контакт
<python at rcn dot com>
Зміст
Анотація¶
Defines descriptors, summarizes the protocol, and shows how descriptors are called. Examines a custom descriptor and several built-in Python descriptors including functions, properties, static methods, and class methods. Shows how each works by giving a pure Python equivalent and a sample application.
Learning about descriptors not only provides access to a larger toolset, it creates a deeper understanding of how Python works and an appreciation for the elegance of its design.
Definition and Introduction¶
In general, a descriptor is an object attribute with «binding behavior», one
whose attribute access has been overridden by methods in the descriptor
protocol. Those methods are __get__()
, __set__()
, and
__delete__()
. If any of those methods are defined for an object, it is
said to be a descriptor.
The default behavior for attribute access is to get, set, or delete the
attribute from an object’s dictionary. For instance, a.x
has a lookup chain
starting with a.__dict__['x']
, then type(a).__dict__['x']
, and
continuing through the base classes of type(a)
excluding metaclasses. If the
looked-up value is an object defining one of the descriptor methods, then Python
may override the default behavior and invoke the descriptor method instead.
Where this occurs in the precedence chain depends on which descriptor methods
were defined.
Descriptors are a powerful, general purpose protocol. They are the mechanism
behind properties, methods, static methods, class methods, and super()
.
They are used throughout Python itself to implement the new style classes
introduced in version 2.2. Descriptors simplify the underlying C-code and offer
a flexible set of new tools for everyday Python programs.
Descriptor Protocol¶
descr.__get__(self, obj, type=None) -> value
descr.__set__(self, obj, value) -> None
descr.__delete__(self, obj) -> None
Це все. Визначте будь-який із цих методів, і об’єкт вважатиметься дескриптором і зможе замінити поведінку за замовчуванням, якщо його шукати як атрибут.
If an object defines __set__()
or __delete__()
, it is considered
a data descriptor. Descriptors that only define __get__()
are called
non-data descriptors (they are typically used for methods but other uses are
possible).
Дескриптори даних і не даних відрізняються тим, як обчислюються перевизначення щодо записів у словнику екземпляра. Якщо в словнику екземпляра є запис із таким самим іменем, як і дескриптор даних, дескриптор даних має пріоритет. Якщо в словнику екземпляра є запис із таким же ім’ям, що й дескриптор, що не є даними, пріоритет має словниковий запис.
Щоб створити дескриптор даних лише для читання, визначте __get__()
і __set__()
з __set__()
, що викликає AttributeError
під час виклику. Щоб зробити його дескриптором даних, достатньо визначити метод __set__()
із заповнювачем, що викликає винятки.
Invoking Descriptors¶
A descriptor can be called directly by its method name. For example,
d.__get__(obj)
.
Alternatively, it is more common for a descriptor to be invoked automatically
upon attribute access. For example, obj.d
looks up d
in the dictionary
of obj
. If d
defines the method __get__()
, then d.__get__(obj)
is invoked according to the precedence rules listed below.
The details of invocation depend on whether obj
is an object or a class.
For objects, the machinery is in object.__getattribute__()
which
transforms b.x
into type(b).__dict__['x'].__get__(b, type(b))
. The
implementation works through a precedence chain that gives data descriptors
priority over instance variables, instance variables priority over non-data
descriptors, and assigns lowest priority to __getattr__()
if provided.
The full C implementation can be found in PyObject_GenericGetAttr()
in
Objects/object.c.
For classes, the machinery is in type.__getattribute__()
which transforms
B.x
into B.__dict__['x'].__get__(None, B)
. In pure Python, it looks
like:
def __getattribute__(self, key):
"Emulate type_getattro() in Objects/typeobject.c"
v = object.__getattribute__(self, key)
if hasattr(v, '__get__'):
return v.__get__(None, self)
return v
Важливо пам’ятати:
descriptors are invoked by the
__getattribute__()
methodoverriding
__getattribute__()
prevents automatic descriptor callsobject.__getattribute__()
andtype.__getattribute__()
make different calls to__get__()
.data descriptors always override instance dictionaries.
non-data descriptors may be overridden by instance dictionaries.
The object returned by super()
also has a custom __getattribute__()
method for invoking descriptors. The attribute lookup super(B, obj).m
searches
obj.__class__.__mro__
for the base class A
immediately following B
and then returns A.__dict__['m'].__get__(obj, B)
. If not a descriptor,
m
is returned unchanged. If not in the dictionary, m
reverts to a
search using object.__getattribute__()
.
The implementation details are in super_getattro()
in
Objects/typeobject.c. and a pure Python equivalent can be found in
Guido’s Tutorial.
The details above show that the mechanism for descriptors is embedded in the
__getattribute__()
methods for object
, type
, and
super()
. Classes inherit this machinery when they derive from
object
or if they have a meta-class providing similar functionality.
Likewise, classes can turn-off descriptor invocation by overriding
__getattribute__()
.
Descriptor Example¶
The following code creates a class whose objects are data descriptors which
print a message for each get or set. Overriding __getattribute__()
is
alternate approach that could do this for every attribute. However, this
descriptor is useful for monitoring just a few chosen attributes:
class RevealAccess(object):
"""A data descriptor that sets and returns values
normally and prints a message logging their access.
"""
def __init__(self, initval=None, name='var'):
self.val = initval
self.name = name
def __get__(self, obj, objtype):
print('Retrieving', self.name)
return self.val
def __set__(self, obj, val):
print('Updating', self.name)
self.val = val
>>> class MyClass(object):
... x = RevealAccess(10, 'var "x"')
... y = 5
...
>>> m = MyClass()
>>> m.x
Retrieving var "x"
10
>>> m.x = 20
Updating var "x"
>>> m.x
Retrieving var "x"
20
>>> m.y
5
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 methods, static methods, and class methods are all based on the descriptor protocol.
Властивості¶
Calling property()
is a succinct way of building a data descriptor that
triggers function calls upon access to an attribute. Its signature is:
property(fget=None, fset=None, fdel=None, doc=None) -> property attribute
The documentation shows a typical use to define a managed attribute x
:
class C(object):
def getx(self): return self.__x
def setx(self, value): self.__x = value
def delx(self): del self.__x
x = property(getx, setx, delx, "I'm the 'x' property.")
To see how property()
is implemented in terms of the descriptor protocol,
here is a pure Python equivalent:
class Property(object):
"Emulate PyProperty_Type() in Objects/descrobject.c"
def __init__(self, fget=None, fset=None, fdel=None, doc=None):
self.fget = fget
self.fset = fset
self.fdel = fdel
if doc is None and fget is not None:
doc = fget.__doc__
self.__doc__ = doc
def __get__(self, obj, objtype=None):
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
return self.fget(obj)
def __set__(self, obj, value):
if self.fset is None:
raise AttributeError("can't set attribute")
self.fset(obj, value)
def __delete__(self, obj):
if self.fdel is None:
raise AttributeError("can't delete attribute")
self.fdel(obj)
def getter(self, fget):
return type(self)(fget, self.fset, self.fdel, self.__doc__)
def setter(self, fset):
return type(self)(self.fget, fset, self.fdel, self.__doc__)
def deleter(self, fdel):
return type(self)(self.fget, self.fset, fdel, self.__doc__)
Вбудована функція property()
допомагає щоразу, коли інтерфейс користувача надає доступ до атрибутів, а подальші зміни вимагають втручання методу.
For instance, a spreadsheet class may grant access to a cell value through
Cell('b10').value
. Subsequent improvements to the program require the cell
to be recalculated on every access; however, the programmer does not want to
affect existing client code accessing the attribute directly. The solution is
to wrap access to the value attribute in a property data descriptor:
class Cell(object):
. . .
def getvalue(self):
"Recalculate the cell before returning value"
self.recalc()
return self._value
value = property(getvalue)
Functions and Methods¶
Об’єктно-орієнтовані функції Python побудовані на функціональному середовищі. Використовуючи дескриптори, не пов’язані з даними, ці два об’єднуються без проблем.
Class dictionaries store methods as functions. In a class definition, methods
are written using def
or 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 __get__()
method for
binding methods during attribute access. This means that all functions are
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:
>>> class D(object):
... def f(self, x):
... return x
...
>>> d = D()
# 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>>
# Internally, the bound method stores the underlying function and
# the bound instance.
>>> d.f.__func__
<function D.f at 0x1012e5ae8>
>>> d.f.__self__
<__main__.D object at 0x1012e1f98>
Static Methods and Class Methods¶
Дескриптори, не пов’язані з даними, надають простий механізм для варіацій звичайних шаблонів зв’язування функцій у методи.
To recap, functions have a __get__()
method so that they can be converted
to a method when accessed as attributes. The non-data descriptor transforms an
obj.f(*args)
call into f(obj, *args)
. Calling klass.f(*args)
becomes f(*args)
.
Ця діаграма підсумовує прив’язку та два її найкорисніші варіанти:
Трансформація
Called from an Object
Called from a Class
функція
f(obj, *args)
f(*args)
статичний метод
f(*args)
f(*args)
метод класу
f(type(obj), *args)
f(klass, *args)
Статичні методи повертають базову функцію без змін. Виклик c.f
або C.f
є еквівалентом прямого пошуку object.__getattribute__(c, "f")
або object.__getattribute__(C, "f")
. У результаті функція стає однаково доступною з об’єкта або класу.
Хорошими кандидатами на статичні методи є методи, які не посилаються на змінну self
.
Наприклад, пакет статистики може містити клас контейнера для експериментальних даних. Клас надає звичайні методи для обчислення середнього, середнього, медіани та іншої описової статистики, яка залежить від даних. Однак можуть існувати корисні функції, які концептуально пов’язані, але не залежать від даних. Наприклад, erf(x)
— це зручна процедура перетворення, яка виникає в статистичній роботі, але безпосередньо не залежить від конкретного набору даних. Його можна викликати або з об’єкта, або з класу: s.erf(1.5) --> .9332
або Sample.erf(1.5) --> .9332
.
Since staticmethods return the underlying function with no changes, the example calls are unexciting:
>>> class E(object):
... def f(x):
... print(x)
... f = staticmethod(f)
...
>>> E.f(3)
3
>>> E().f(3)
3
Using the non-data descriptor protocol, a pure Python version of
staticmethod()
would look like this:
class StaticMethod(object):
"Emulate PyStaticMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
def __get__(self, obj, objtype=None):
return self.f
Unlike static methods, class methods prepend the class reference to the argument list before calling the function. This format is the same for whether the caller is an object or a class:
>>> class E(object):
... def f(klass, x):
... return klass.__name__, x
... f = classmethod(f)
...
>>> print(E.f(3))
('E', 3)
>>> print(E().f(3))
('E', 3)
This behavior is useful whenever the function only needs to have a class
reference and does not care about any underlying data. One use for classmethods
is to create alternate class constructors. In Python 2.3, the classmethod
dict.fromkeys()
creates a new dictionary from a list of keys. The pure
Python equivalent is:
class Dict(object):
. . .
def fromkeys(klass, iterable, value=None):
"Emulate dict_fromkeys() in Objects/dictobject.c"
d = klass()
for key in iterable:
d[key] = value
return d
fromkeys = classmethod(fromkeys)
Now a new dictionary of unique keys can be constructed like this:
>>> Dict.fromkeys('abracadabra')
{'a': None, 'r': None, 'b': None, 'c': None, 'd': None}
Using the non-data descriptor protocol, a pure Python version of
classmethod()
would look like this:
class ClassMethod(object):
"Emulate PyClassMethod_Type() in Objects/funcobject.c"
def __init__(self, f):
self.f = f
def __get__(self, obj, klass=None):
if klass is None:
klass = type(obj)
def newfunc(*args):
return self.f(klass, *args)
return newfunc