描述器指南

作者:

Raymond Hettinger（译者：wh2099 at outlook dot com）

联系方式:

<python at rcn dot com>

描述器 让对象能够自定义属性查找、存储和删除的操作。

本指南主要分为四个部分：

1. “入门” 部分从简单的示例着手，逐步添加特性，从而给出基本的概述。如果你是刚接触到描述器，请从这里开始。

2. 第二部分展示了完整的、实用的描述器示例。如果您已经掌握了基础知识，请从此处开始。

3. 第三部分提供了更多技术教程，详细介绍了描述器如何工作。大多数人并不需要深入到这种程度。

4. 最后一部分有对内置描述器（用 C 编写）的纯 Python 等价实现。如果您想了解函数如何变成绑定方法或对 classmethod()， staticmethod()，property() 和 __slots__ 这类常见工具的实现感兴趣，请阅读此部分。

入门

现在，让我们从最基本的示例开始，然后逐步添加新功能。

简单示例：返回常量的描述器

Ten 类是一个描述器，其 __get__() 方法始终返回常量 10:

class Ten:
    def __get__(self, obj, objtype=None):
        return 10

要使用描述器，它必须作为一个类变量存储在另一个类中：

class A:
    x = 5                       # 常规类属性
    y = Ten()                   # 描述器实例

用交互式会话查看普通属性查找和描述器查找之间的区别：

>>> a = A()                     # 创建一个类 A 的实例
>>> a.x                         # 正常属性查找
5
>>> a.y                         # 描述器查找
10

在 a.x 属性查找中，点运算符会找到存储在类字典中的 'x': 5。 在 a.y 查找中，点运算符会根据描述器实例的 __get__ 方法将其识别出来，调用该方法并返回 10 。

请注意，值 10 既不存储在类字典中也不存储在实例字典中。相反，值 10 是在调用时才取到的。

这个简单的例子展示了一个描述器是如何工作的，但它不是很有用。在查找常量时，用常规属性查找会更好。

在下一节中，我们将创建更有用的东西，即动态查找。

动态查找

有趣的描述器通常运行计算而不是返回常量：

import os

class DirectorySize:

    def __get__(self, obj, objtype=None):
        return len(os.listdir(obj.dirname))

class Directory:

    size = DirectorySize()              # 描述器实例

    def __init__(self, dirname):
        self.dirname = dirname          # 常规实例属性

交互式会话显示查找是动态的，每次都会计算不同的，经过更新的返回值:

>>> s = Directory('songs')
>>> g = Directory('games')
>>> s.size                              # songs 目录有二十个文件
20
>>> g.size                              # games 目录有三个文件
3
>>> os.remove('games/chess')            # 删除一个 game
>>> g.size                              # 文件计数将自动更新
2

Besides showing how descriptors can run computations, this example also reveals the purpose of the parameters to __get__(). The self parameter is size, an instance of DirectorySize. The obj parameter is either g or s, an instance of Directory. It is the obj parameter that lets the __get__() method learn the target directory. The objtype parameter is the class Directory.

托管属性

A popular use for descriptors is managing access to instance data. The descriptor is assigned to a public attribute in the class dictionary while the actual data is stored as a private attribute in the instance dictionary. The descriptor's __get__() and __set__() methods are triggered when the public attribute is accessed.

在下面的例子中，age 是公开属性，_age 是私有属性。当访问公开属性时，描述器会记录下查找或更新的日志：

import logging

logging.basicConfig(level=logging.INFO)

class LoggedAgeAccess:

    def __get__(self, obj, objtype=None):
        value = obj._age
        logging.info('Accessing %r giving %r', 'age', value)
        return value

    def __set__(self, obj, value):
        logging.info('Updating %r to %r', 'age', value)
        obj._age = value

class Person:

    age = LoggedAgeAccess()             # 描述器实例

    def __init__(self, name, age):
        self.name = name                # 常规实例属性
        self.age = age                  # 调用 __set__()

    def birthday(self):
        self.age += 1                   # 调用 __get__() 和 __set__()

交互式会话展示中，对托管属性 age 的所有访问都被记录了下来，但常规属性 name 则未被记录：

>>> mary = Person('Mary M', 30)         # The initial age update is logged
INFO:root:Updating 'age' to 30
>>> dave = Person('David D', 40)
INFO:root:Updating 'age' to 40

>>> vars(mary)                          # The actual data is in a private attribute
{'name': 'Mary M', '_age': 30}
>>> vars(dave)
{'name': 'David D', '_age': 40}

>>> mary.age                            # Access the data and log the lookup
INFO:root:Accessing 'age' giving 30
30
>>> mary.birthday()                     # Updates are logged as well
INFO:root:Accessing 'age' giving 30
INFO:root:Updating 'age' to 31

>>> dave.name                           # Regular attribute lookup isn't logged
'David D'
>>> dave.age                            # Only the managed attribute is logged
INFO:root:Accessing 'age' giving 40
40

此示例的一个主要问题是私有名称 _age 在类 LoggedAgeAccess 中是硬耦合的。这意味着每个实例只能有一个用于记录的属性，并且其名称不可更改。

定制名称

当一个类使用描述器时，它可以告知每个描述器使用了什么变量名。

In this example, the Person class has two descriptor instances, name and age. When the Person class is defined, it makes a callback to __set_name__() in LoggedAccess so that the field names can be recorded, giving each descriptor its own public_name and private_name:

import logging

logging.basicConfig(level=logging.INFO)

class LoggedAccess:

    def __set_name__(self, owner, name):
        self.public_name = name
        self.private_name = '_' + name

    def __get__(self, obj, objtype=None):
        value = getattr(obj, self.private_name)
        logging.info('Accessing %r giving %r', self.public_name, value)
        return value

    def __set__(self, obj, value):
        logging.info('Updating %r to %r', self.public_name, value)
        setattr(obj, self.private_name, value)

class Person:

    name = LoggedAccess()                # 第一个描述器实例
    age = LoggedAccess()                 # 第二个描述器实例

    def __init__(self, name, age):
        self.name = name                 # 调用第一个描述器
        self.age = age                   # 调用第二个描述器

    def birthday(self):
        self.age += 1

An interactive session shows that the Person class has called __set_name__() so that the field names would be recorded. Here we call vars() to look up the descriptor without triggering it:

>>> vars(vars(Person)['name'])
{'public_name': 'name', 'private_name': '_name'}
>>> vars(vars(Person)['age'])
{'public_name': 'age', 'private_name': '_age'}

现在，新类会记录对 name 和 age 二者的访问：

>>> pete = Person('Peter P', 10)
INFO:root:Updating 'name' to 'Peter P'
INFO:root:Updating 'age' to 10
>>> kate = Person('Catherine C', 20)
INFO:root:Updating 'name' to 'Catherine C'
INFO:root:Updating 'age' to 20

这两个 Person 实例仅包含私有名称:

>>> vars(pete)
{'_name': 'Peter P', '_age': 10}
>>> vars(kate)
{'_name': 'Catherine C', '_age': 20}

结束语

A descriptor is what we call any object that defines __get__(), __set__(), or __delete__().

Optionally, descriptors can have a __set_name__() method. This is only used in cases where a descriptor needs to know either the class where it was created or the name of class variable it was assigned to. (This method, if present, is called even if the class is not a descriptor.)

在属性查找期间，描述器由点运算符调用。如果使用 vars(some_class)[descriptor_name] 间接访问描述器，则返回描述器实例而不调用它。

描述器仅在用作类变量时起作用。放入实例时，它们将失效。

描述器的主要目的是提供一个挂钩，允许存储在类变量中的对象控制在属性查找期间发生的情况。

传统上，调用类控制查找过程中发生的事情。描述器反转了这种关系，并允许正在被查询的数据对此进行干涉。

描述器的使用贯穿了整个语言。就是它让函数变成绑定方法。常见工具诸如 classmethod()， staticmethod()，property() 和 functools.cached_property() 都作为描述器实现。

完整的实际例子

在此示例中，我们创建了一个实用而强大的工具来查找难以发现的数据损坏错误。

验证器类

验证器是一个用于托管属性访问的描述器。在存储任何数据之前，它会验证新值是否满足各种类型和范围限制。如果不满足这些限制，它将引发异常，从源头上防止数据损坏。

This Validator class is both an abstract base class and a managed attribute descriptor:

from abc import ABC, abstractmethod

class Validator(ABC):

    def __set_name__(self, owner, name):
        self.private_name = '_' + name

    def __get__(self, obj, objtype=None):
        return getattr(obj, self.private_name)

    def __set__(self, obj, value):
        self.validate(value)
        setattr(obj, self.private_name, value)

    @abstractmethod
    def validate(self, value):
        pass

Custom validators need to inherit from Validator and must supply a validate() method to test various restrictions as needed.

自定义验证器

这是三个实用的数据验证工具：

1. OneOf 验证值是指定的受约束选项集合中的一项。

2. Number 验证值是否为 int 或 float。 作为可选项，它还能验证值在给定的最小值和最大值之间。

3. String 验证值是否为 str。 作为可选项，它还能验证给定的最小或最大长度。 它还能验证用户定义的 predicate。

class OneOf(Validator):

    def __init__(self, *options):
        self.options = set(options)

    def validate(self, value):
        if value not in self.options:
            raise ValueError(
                f'Expected {value!r} to be one of {self.options!r}'
            )

class Number(Validator):

    def __init__(self, minvalue=None, maxvalue=None):
        self.minvalue = minvalue
        self.maxvalue = maxvalue

    def validate(self, value):
        if not isinstance(value, (int, float)):
            raise TypeError(f'Expected {value!r} to be an int or float')
        if self.minvalue is not None and value < self.minvalue:
            raise ValueError(
                f'Expected {value!r} to be at least {self.minvalue!r}'
            )
        if self.maxvalue is not None and value > self.maxvalue:
            raise ValueError(
                f'Expected {value!r} to be no more than {self.maxvalue!r}'
            )

class String(Validator):

    def __init__(self, minsize=None, maxsize=None, predicate=None):
        self.minsize = minsize
        self.maxsize = maxsize
        self.predicate = predicate

    def validate(self, value):
        if not isinstance(value, str):
            raise TypeError(f'Expected {value!r} to be an str')
        if self.minsize is not None and len(value) < self.minsize:
            raise ValueError(
                f'Expected {value!r} to be no smaller than {self.minsize!r}'
            )
        if self.maxsize is not None and len(value) > self.maxsize:
            raise ValueError(
                f'Expected {value!r} to be no bigger than {self.maxsize!r}'
            )
        if self.predicate is not None and not self.predicate(value):
            raise ValueError(
                f'Expected {self.predicate} to be true for {value!r}'
            )

实际应用

这是在真实类中使用数据验证器的方法：

class Component:

    name = String(minsize=3, maxsize=10, predicate=str.isupper)
    kind = OneOf('wood', 'metal', 'plastic')
    quantity = Number(minvalue=0)

    def __init__(self, name, kind, quantity):
        self.name = name
        self.kind = kind
        self.quantity = quantity

描述器阻止无效实例的创建：

>>> Component('Widget', 'metal', 5)      # Blocked: 'Widget' is not all uppercase
Traceback (most recent call last):
    ...
ValueError: Expected <method 'isupper' of 'str' objects> to be true for 'Widget'

>>> Component('WIDGET', 'metle', 5)      # Blocked: 'metle' is misspelled
Traceback (most recent call last):
    ...
ValueError: Expected 'metle' to be one of {'metal', 'plastic', 'wood'}

>>> Component('WIDGET', 'metal', -5)     # Blocked: -5 is negative
Traceback (most recent call last):
    ...
ValueError: Expected -5 to be at least 0

>>> Component('WIDGET', 'metal', 'V')    # Blocked: 'V' isn't a number
Traceback (most recent call last):
    ...
TypeError: Expected 'V' to be an int or float

>>> c = Component('WIDGET', 'metal', 5)  # Allowed:  The inputs are valid

技术教程

接下来是专业性更强的技术教程，以及描述器工作原理的详细信息。

摘要

定义描述器，总结协议，并说明如何调用描述器。提供一个展示对象关系映射如何工作的示例。

学习描述器不仅能提供接触到更多工具集的途径，还能更深地理解 Python 工作的原理。

定义与介绍

In general, a descriptor is an attribute value that has one of the methods in the descriptor protocol. Those methods are __get__(), __set__(), and __delete__(). If any of those methods are defined for an attribute, it is said to be a descriptor.

属性访问的默认行为是从一个对象的字典中获取、设置或删除属性。对于实例来说，a.x 的查找顺序会从 a.__dict__['x'] 开始，然后是 type(a).__dict__['x']，接下来依次查找 type(a) 的方法解析顺序（MRO）。 如果找到的值是定义了某个描述器方法的对象，则 Python 可能会重写默认行为并转而发起调用描述器方法。这具体发生在优先级链的哪个环节则要根据所定义的描述器方法及其被调用的方式来决定。

描述器是一种强大的，通用的协议。 它们是属性、方法、静态方法、类方法和 super() 背后的机制。 它们在整个 Python 中都有使用。 描述器简化了底层的 C 代码并为日常的 Python 程序提供了一套灵活的新工具。

描述器协议

descr.__get__(self, obj, type=None)

descr.__set__(self, obj, value)

descr.__delete__(self, obj)

描述器的方法就这些。一个对象只要定义了以上方法中的任何一个，就被视为描述器，并在被作为属性时覆盖其默认行为。

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 often used for methods but other uses are possible).

数据和非数据描述器的不同之处在于，如何计算实例字典中条目的替代值。如果实例的字典具有与数据描述器同名的条目，则数据描述器优先。如果实例的字典具有与非数据描述器同名的条目，则该字典条目优先。

To make a read-only data descriptor, define both __get__() and __set__() with the __set__() raising an AttributeError when called. Defining the __set__() method with an exception raising placeholder is enough to make it a data descriptor.

描述器调用概述

描述器可以通过 d.__get__(obj) 或 desc.__get__(None, cls) 直接调用。

但更常见的是通过属性访问自动调用描述器。

表达式 obj.x 在 obj 的命名空间链中查找属性 x。 如果搜索发现了一个实例 __dict__ 以外的描述器，将根据下面列出的优先级规则调用其 __get__() 方法。

调用的细节取决于 obj 是对象、类还是超类的实例。

通过实例调用

Instance lookup scans through a chain of namespaces giving data descriptors the highest priority, followed by instance variables, then non-data descriptors, then class variables, and lastly __getattr__() if it is provided.

如果 a.x 找到了一个描述器，那么将通过 desc.__get__(a, type(a)) 调用它。

点运算符的查找逻辑在 object.__getattribute__() 中。这里是一个等价的纯 Python 实现：

def find_name_in_mro(cls, name, default):
    "Emulate _PyType_Lookup() in Objects/typeobject.c"
    for base in cls.__mro__:
        if name in vars(base):
            return vars(base)[name]
    return default

def object_getattribute(obj, name):
    "Emulate PyObject_GenericGetAttr() in Objects/object.c"
    null = object()
    objtype = type(obj)
    cls_var = find_name_in_mro(objtype, name, null)
    descr_get = getattr(type(cls_var), '__get__', null)
    if descr_get is not null:
        if (hasattr(type(cls_var), '__set__')
            or hasattr(type(cls_var), '__delete__')):
            return descr_get(cls_var, obj, objtype)     # data descriptor
    if hasattr(obj, '__dict__') and name in vars(obj):
        return vars(obj)[name]                          # instance variable
    if descr_get is not null:
        return descr_get(cls_var, obj, objtype)         # non-data descriptor
    if cls_var is not null:
        return cls_var                                  # class variable
    raise AttributeError(name)

Note, there is no __getattr__() hook in the __getattribute__() code. That is why calling __getattribute__() directly or with super().__getattribute__ will bypass __getattr__() entirely.

Instead, it is the dot operator and the getattr() function that are responsible for invoking __getattr__() whenever __getattribute__() raises an AttributeError. Their logic is encapsulated in a helper function:

def getattr_hook(obj, name):
    "Emulate slot_tp_getattr_hook() in Objects/typeobject.c"
    try:
        return obj.__getattribute__(name)
    except AttributeError:
        if not hasattr(type(obj), '__getattr__'):
            raise
    return type(obj).__getattr__(obj, name)             # __getattr__

通过类调用

The logic for a dotted lookup such as A.x is in type.__getattribute__(). The steps are similar to those for object.__getattribute__() but the instance dictionary lookup is replaced by a search through the class's method resolution order.

如果找到了一个描述器，那么将通过 desc.__get__(None, A) 调用它。

完整的 C 实现可在 Objects/typeobject.c 里的 type_getattro() 和 _PyType_Lookup() 中找到。

通过 super 调用

The logic for super's dotted lookup is in the __getattribute__() method for object returned by super().

类似 super(A, obj).m 形式的点分查找将在 obj.__class__.__mro__ 中搜索紧接在 A 之后的基类 B，然后返回 B.__dict__['m'].__get__(obj, A)。如果 m 不是描述器，则直接返回其值。

完整的 C 实现可在 Objects/typeobject.c 里的 super_getattro() 中找到。 纯 Python 的等价实现可在 Guido 的教程 中找到。

调用逻辑总结

The mechanism for descriptors is embedded in the __getattribute__() methods for object, type, and super().

要记住的重要点是：

• Descriptors are invoked by the __getattribute__() method.

• 类从 object，type 或 super() 继承此机制。

• Overriding __getattribute__() prevents automatic descriptor calls because all the descriptor logic is in that method.

• object.__getattribute__() and type.__getattribute__() make different calls to __get__(). The first includes the instance and may include the class. The second puts in None for the instance and always includes the class.

• 数据描述器始终会覆盖实例字典。

• 非数据描述器会被实例字典覆盖。

自动名称通知

Sometimes it is desirable for a descriptor to know what class variable name it was assigned to. When a new class is created, the type metaclass scans the dictionary of the new class. If any of the entries are descriptors and if they define __set_name__(), that method is called with two arguments. The owner is the class where the descriptor is used, and the name is the class variable the descriptor was assigned to.

实现的细节在 Objects/typeobject.c 里的 type_new() 和 set_names() 中。

Since the update logic is in type.__new__(), notifications only take place at the time of class creation. If descriptors are added to the class afterwards, __set_name__() will need to be called manually.

ORM （对象关系映射）示例

以下代码展示了如何使用数据描述器来实现简单的 对象关系映射 框架。

其核心思路是将数据存储在外部数据库中，Python 实例仅持有数据库表中对应的的键。描述器负责对值进行查找或更新：

class Field:

    def __set_name__(self, owner, name):
        self.fetch = f'SELECT {name} FROM {owner.table} WHERE {owner.key}=?;'
        self.store = f'UPDATE {owner.table} SET {name}=? WHERE {owner.key}=?;'

    def __get__(self, obj, objtype=None):
        return conn.execute(self.fetch, [obj.key]).fetchone()[0]

    def __set__(self, obj, value):
        conn.execute(self.store, [value, obj.key])
        conn.commit()

We can use the Field class to define models that describe the schema for each table in a database:

class Movie:
    table = 'Movies'                    # 表名
    key = 'title'                       # 主键
    director = Field()
    year = Field()

    def __init__(self, key):
        self.key = key

class Song:
    table = 'Music'
    key = 'title'
    artist = Field()
    year = Field()
    genre = Field()

    def __init__(self, key):
        self.key = key

要使用模型，首先要连接到数据库：

>>> import sqlite3
>>> conn = sqlite3.connect('entertainment.db')

交互式会话显示了如何从数据库中检索数据及如何对其进行更新：

>>> Movie('Star Wars').director
'George Lucas'
>>> jaws = Movie('Jaws')
>>> f'Released in {jaws.year} by {jaws.director}'
'Released in 1975 by Steven Spielberg'

>>> Song('Country Roads').artist
'John Denver'

>>> Movie('Star Wars').director = 'J.J. Abrams'
>>> Movie('Star Wars').director
'J.J. Abrams'

纯 Python 等价实现

描述器协议很简单，但它提供了令人兴奋的可能性。有几个用例非常通用，以至于它们已预先打包到内置工具中。属性、绑定方法、静态方法、类方法和 __slots__ 均基于描述器协议。

属性

调用 property() 是构建数据描述器的简洁方式，该数据描述器在访问属性时触发函数调用。它的签名是：

property(fget=None, fset=None, fdel=None, doc=None) -> property

该文档显示了定义托管属性 x 的典型用法：

class C:
    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.")

要了解 property() 是如何按描述器协议的方式来实现的，以下是一个实现了大部分核心功能的纯 Python 等价实现：

class Property:
    "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 __set_name__(self, owner, name):
        self.__name__ = name

    def __get__(self, obj, objtype=None):
        if obj is None:
            return self
        if self.fget is None:
            raise AttributeError
        return self.fget(obj)

    def __set__(self, obj, value):
        if self.fset is None:
            raise AttributeError
        self.fset(obj, value)

    def __delete__(self, obj):
        if self.fdel is None:
            raise AttributeError
        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__)

这个内置的 property() 每当用户访问属性时生效，随后的变化需要一个方法的参与。

例如，一个电子表格类可以通过 Cell('b10').value 授予对单元格值的访问权限。对程序的后续改进要求每次访问都要重新计算单元格；但是，程序员不希望影响直接访问该属性的现有客户端代码。解决方案是将对 value 属性的访问包装在属性数据描述器中:

class Cell:
    ...

    @property
    def value(self):
        "Recalculate the cell before returning value"
        self.recalc()
        return self._value

Either the built-in property() or our Property() equivalent would work in this example.

函数和方法

Python 的面向对象功能是在基于函数的环境构建的。通过使用非数据描述器，这两方面完成了无缝融合。

在调用时，存储在类词典中的函数将被转换为方法。方法与常规函数的不同之处仅在于对象实例被置于其他参数之前。方法与常规函数的不同之处仅在于第一个参数是为对象实例保留的。按照惯例，实例引用称为 self ，但也可以称为 this 或任何其他变量名称。

可以使用 types.MethodType 手动创建方法，其行为基本等价于：

class MethodType:
    "Emulate PyMethod_Type in Objects/classobject.c"

    def __init__(self, func, obj):
        self.__func__ = func
        self.__self__ = obj

    def __call__(self, *args, **kwargs):
        func = self.__func__
        obj = self.__self__
        return func(obj, *args, **kwargs)

    def __getattribute__(self, name):
        "Emulate method_getset() in Objects/classobject.c"
        if name == '__doc__':
            return self.__func__.__doc__
        return object.__getattribute__(self, name)

    def __getattr__(self, name):
        "Emulate method_getattro() in Objects/classobject.c"
        return getattr(self.__func__, name)

    def __get__(self, obj, objtype=None):
        "Emulate method_descr_get() in Objects/classobject.c"
        return self

To support automatic creation of methods, functions include the __get__() method for binding methods during attribute access. This means that functions are non-data descriptors that return bound methods during dotted lookup from an instance. Here's how it works:

class Function:
    ...

    def __get__(self, obj, objtype=None):
        "Simulate func_descr_get() in Objects/funcobject.c"
        if obj is None:
            return self
        return MethodType(self, obj)

在解释器中运行以下类，这显示了函数描述器的实际工作方式：

class D:
    def f(self):
         return self

class D2:
    pass

该函数具有 qualified name 属性以支持自省：

>>> D.f.__qualname__
'D.f'

Accessing the function through the class dictionary does not invoke __get__(). Instead, 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 interesting behavior occurs during dotted access from an instance. The dotted lookup calls __get__() which returns a bound method object:

>>> d = D()
>>> d.f
<bound method D.f of <__main__.D object at 0x00B18C90>>

绑定方法在内部存储了底层函数和绑定的实例：

>>> d.f.__func__
<function D.f at 0x00C45070>

>>> d.f.__self__
<__main__.D object at 0x00B18C90>

如果你曾好奇常规方法中的 self 或类方法中的 cls 是从什么地方来的，就是这里了！

方法的种类

非数据描述器为把函数绑定为方法的通常模式提供了一种简单的机制。

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 cls.f(*args) becomes f(*args).

下表总结了绑定及其两个最有用的变体：

转换形式	通过对象调用	通过类调用
function -- 函数	f(obj, *args)	f(*args)
静态方法	f(*args)	f(*args)
类方法	f(type(obj), *args)	f(cls, *args)

静态方法

静态方法返回底层函数，不做任何更改。调用 c.f 或 C.f 等效于通过 object.__getattribute__(c, "f") 或 object.__getattribute__(C, "f") 查找。这样该函数就可以从对象或类中进行相同的访问。

适合作为静态方法的是那些不引用 self 变量的方法。

For instance, a statistics package may include a container class for experimental data. The class provides normal methods for computing the average, mean, median, and other descriptive statistics that depend on the data. However, there may be useful functions which are conceptually related but do not depend on the data. For instance, erf(x) is handy conversion routine that comes up in statistical work but does not directly depend on a particular dataset. It can be called either from an object or the class: s.erf(1.5) --> 0.9332 or Sample.erf(1.5) --> 0.9332.

由于静态方法返回的底层函数没有任何变化，因此示例调用也是意料之中：

class E:
    @staticmethod
    def f(x):
        return x * 10

>>> E.f(3)
30
>>> E().f(3)
30

使用非数据描述器，纯 Python 版本的 staticmethod() 如下所示：

import functools

class StaticMethod:
    "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f
        functools.update_wrapper(self, f)

    def __get__(self, obj, objtype=None):
        return self.f

    def __call__(self, *args, **kwds):
        return self.f(*args, **kwds)

    @property
    def __annotations__(self):
        return self.f.__annotations__

The functools.update_wrapper() call adds a __wrapped__ attribute that refers to the underlying function. Also it carries forward the attributes necessary to make the wrapper look like the wrapped function, including __name__, __qualname__, and __doc__.

类方法

与静态方法不同，类方法在调用函数之前将类引用放在参数列表的最前。无论调用方是对象还是类，此格式相同：

class F:
    @classmethod
    def f(cls, x):
        return cls.__name__, x

>>> F.f(3)
('F', 3)
>>> F().f(3)
('F', 3)

当方法仅需要具有类引用并且确实依赖于存储在特定实例中的数据时，此行为就很有用。类方法的一种用途是创建备用类构造函数。例如，类方法 dict.fromkeys() 从键列表创建一个新字典。纯 Python 的等价实现是：

class Dict(dict):
    @classmethod
    def fromkeys(cls, iterable, value=None):
        "Emulate dict_fromkeys() in Objects/dictobject.c"
        d = cls()
        for key in iterable:
            d[key] = value
        return d

现在可以这样构造一个新的唯一键字典：

>>> d = Dict.fromkeys('abracadabra')
>>> type(d) is Dict
True
>>> d
{'a': None, 'b': None, 'r': None, 'c': None, 'd': None}

使用非数据描述器协议，纯 Python 版本的 classmethod() 如下：

import functools

class ClassMethod:
    "Emulate PyClassMethod_Type() in Objects/funcobject.c"

    def __init__(self, f):
        self.f = f
        functools.update_wrapper(self, f)

    def __get__(self, obj, cls=None):
        if cls is None:
            cls = type(obj)
        return MethodType(self.f, cls)

ClassMethod 中的 functools.update_wrapper() 调用增加了一个指向下层函数的 __wrapped__ 属性。 它还会向前传递必要的属性以使此包装器看起来像是被包装的函数: __name__, __qualname__, __doc__ 以及 __annotations__。

成员对象和 __slots__

当一个类定义了 __slots__，它会用一个固定长度的 slot 值数组来替换实例字典。 从用户的视角看，效果是这样的：

1. Provides immediate detection of bugs due to misspelled attribute assignments. Only attribute names specified in __slots__ are allowed:

class Vehicle:
    __slots__ = ('id_number', 'make', 'model')

>>> auto = Vehicle()
>>> auto.id_nubmer = 'VYE483814LQEX'
Traceback (most recent call last):
    ...
AttributeError: 'Vehicle' object has no attribute 'id_nubmer'

2. Helps create immutable objects where descriptors manage access to private attributes stored in __slots__:

class Immutable:

    __slots__ = ('_dept', '_name')          # Replace the instance dictionary

    def __init__(self, dept, name):
        self._dept = dept                   # Store to private attribute
        self._name = name                   # Store to private attribute

    @property                               # Read-only descriptor
    def dept(self):
        return self._dept

    @property
    def name(self):                         # Read-only descriptor
        return self._name

>>> mark = Immutable('Botany', 'Mark Watney')
>>> mark.dept
'Botany'
>>> mark.dept = 'Space Pirate'
Traceback (most recent call last):
    ...
AttributeError: property 'dept' of 'Immutable' object has no setter
>>> mark.location = 'Mars'
Traceback (most recent call last):
    ...
AttributeError: 'Immutable' object has no attribute 'location'

3. Saves memory. On a 64-bit Linux build, an instance with two attributes takes 48 bytes with __slots__ and 152 bytes without. This flyweight design pattern likely only matters when a large number of instances are going to be created.

4. Improves speed. Reading instance variables is 35% faster with __slots__ (as measured with Python 3.10 on an Apple M1 processor).

5. Blocks tools like functools.cached_property() which require an instance dictionary to function correctly:

from functools import cached_property

class CP:
    __slots__ = ()                          # Eliminates the instance dict

    @cached_property                        # Requires an instance dict
    def pi(self):
        return 4 * sum((-1.0)**n / (2.0*n + 1.0)
                       for n in reversed(range(100_000)))

>>> CP().pi
Traceback (most recent call last):
  ...
TypeError: No '__dict__' attribute on 'CP' instance to cache 'pi' property.

要创建一个一模一样的纯 Python 版的 __slots__ 是不可能的，因为它需要直接访问 C 结构体并控制对象内存分配。 但是，我们可以构建一个非常相似的模拟版，其中作为 slot 的实际 C 结构体由一个私有的 _slotvalues 列表来模拟。 对该私有结构体的读写操作将由成员描述器来管理：

null = object()

class Member:

    def __init__(self, name, clsname, offset):
        'Emulate PyMemberDef in Include/structmember.h'
        # Also see descr_new() in Objects/descrobject.c
        self.name = name
        self.clsname = clsname
        self.offset = offset

    def __get__(self, obj, objtype=None):
        'Emulate member_get() in Objects/descrobject.c'
        # Also see PyMember_GetOne() in Python/structmember.c
        if obj is None:
            return self
        value = obj._slotvalues[self.offset]
        if value is null:
            raise AttributeError(self.name)
        return value

    def __set__(self, obj, value):
        'Emulate member_set() in Objects/descrobject.c'
        obj._slotvalues[self.offset] = value

    def __delete__(self, obj):
        'Emulate member_delete() in Objects/descrobject.c'
        value = obj._slotvalues[self.offset]
        if value is null:
            raise AttributeError(self.name)
        obj._slotvalues[self.offset] = null

    def __repr__(self):
        'Emulate member_repr() in Objects/descrobject.c'
        return f'<Member {self.name!r} of {self.clsname!r}>'

The type.__new__() method takes care of adding member objects to class variables:

class Type(type):
    'Simulate how the type metaclass adds member objects for slots'

    def __new__(mcls, clsname, bases, mapping, **kwargs):
        'Emulate type_new() in Objects/typeobject.c'
        # type_new() calls PyTypeReady() which calls add_methods()
        slot_names = mapping.get('slot_names', [])
        for offset, name in enumerate(slot_names):
            mapping[name] = Member(name, clsname, offset)
        return type.__new__(mcls, clsname, bases, mapping, **kwargs)

object.__new__() 方法负责创建具有 slot 而非实例字典的实例。 以下是一个纯 Python 的粗略模拟版：

class Object:
    'Simulate how object.__new__() allocates memory for __slots__'

    def __new__(cls, *args, **kwargs):
        'Emulate object_new() in Objects/typeobject.c'
        inst = super().__new__(cls)
        if hasattr(cls, 'slot_names'):
            empty_slots = [null] * len(cls.slot_names)
            object.__setattr__(inst, '_slotvalues', empty_slots)
        return inst

    def __setattr__(self, name, value):
        'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
        cls = type(self)
        if hasattr(cls, 'slot_names') and name not in cls.slot_names:
            raise AttributeError(
                f'{cls.__name__!r} object has no attribute {name!r}'
            )
        super().__setattr__(name, value)

    def __delattr__(self, name):
        'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
        cls = type(self)
        if hasattr(cls, 'slot_names') and name not in cls.slot_names:
            raise AttributeError(
                f'{cls.__name__!r} object has no attribute {name!r}'
            )
        super().__delattr__(name)

To use the simulation in a real class, just inherit from Object and set the metaclass to Type:

class H(Object, metaclass=Type):
    'Instance variables stored in slots'

    slot_names = ['x', 'y']

    def __init__(self, x, y):
        self.x = x
        self.y = y

这时，metaclass 已经为 x 和 y 加载了成员对象：

>>> from pprint import pp
>>> pp(dict(vars(H)))
{'__module__': '__main__',
 '__doc__': 'Instance variables stored in slots',
 'slot_names': ['x', 'y'],
 '__init__': <function H.__init__ at 0x7fb5d302f9d0>,
 'x': <Member 'x' of 'H'>,
 'y': <Member 'y' of 'H'>}

当实例被创建时，它们将拥有一个用于存放属性的 slot_values 列表：

>>> h = H(10, 20)
>>> vars(h)
{'_slotvalues': [10, 20]}
>>> h.x = 55
>>> vars(h)
{'_slotvalues': [55, 20]}

错误拼写或未赋值的属性将引发一个异常：

>>> h.xz
Traceback (most recent call last):
    ...
AttributeError: 'H' object has no attribute 'xz'