# 修飾器 HowTo 指南

作者

  Raymond Hettinger

联系方式

  <python at rcn dot com>

目錄

- 修飾器 HowTo 指南

  - 摘要

  - 定义和简介

  - 描述器协议

  - 发起调用描述符

  - 描述符示例

  - Properties

  - 函数和方法

  - Static Methods and Class Methods

## 摘要

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.

学习描述器不仅能提供接触到更多工具集的方法，还能更深地理解 Python 工作的原理并更加体会到其设计的优雅性。

## 定义和简介

一般地，一个描述器是一个包含 “绑定行为” 的对象，对其属性的存取被描述器协议中定义的方法覆盖。这些方法有：`__get__()`，`__set__()` 和 `__delete__()`。如果某个对象中定义了这些方法中的任意一个，那么这个对象就可以被称为一个描述器。

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。例如，`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)` 的基类，不包括元类。 如果找到的值是定义了某个描述器方法的对象，则 Python 可能会重载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。

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.

## 描述器协议

`descr.__get__(self, obj, type=None) -> value`

`descr.__set__(self, obj, value) -> None`

`descr.__delete__(self, obj) -> None`

That is all there is to it. Define any of these methods and an object is considered a descriptor and can override default behavior upon being looked up as an attribute.

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).

Data and non-data descriptors differ in how overrides are calculated with respect to entries in an instance's dictionary. If an instance's dictionary has an entry with the same name as a data descriptor, the data descriptor takes precedence. If an instance's dictionary has an entry with the same name as a non-data descriptor, the dictionary entry takes precedence.

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.

## 发起调用描述符

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

The important points to remember are:

- descriptors are invoked by the `__getattribute__()` method

- overriding `__getattribute__()` prevents automatic descriptor calls

- `object.__getattribute__()` and `type.__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__()`.

## 描述符示例

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:
        """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:
    ...     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.

## Properties

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:
        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:
        "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__)

The `property()` builtin helps whenever a user interface has granted attribute access and then subsequent changes require the intervention of a method.

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

## 函数和方法

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 `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:
        . . .
        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:
    ...     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,
    # 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

Non-data descriptors provide a simple mechanism for variations on the usual patterns of binding functions into methods.

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)`.

This chart summarizes the binding and its two most useful variants:

> Transformation | Called from an Object | Called from a Class
> --- | --- | ---
> 函数 | f(obj, *args) | f(*args)
> 静态方法 | f(*args) | f(*args)
> 类方法 | f(type(obj), *args) | f(klass, *args)

Static methods return the underlying function without changes. Calling either `c.f` or `C.f` is the equivalent of a direct lookup into `object.__getattribute__(c, "f")` or `object.__getattribute__(C, "f")`. As a result, the function becomes identically accessible from either an object or a class.

Good candidates for static methods are methods that do not reference the `self` variable.

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) --> .9332` or `Sample.erf(1.5) --> .9332`.

Since staticmethods return the underlying function with no changes, the example calls are unexciting:

    >>> class E:
    ...     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:
        "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:
    ...     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:
        . . .
        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:
        "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