
Descriptor HowTo Guide
**********************

Author:
   Raymond Hettinger

Contact:
   <python at rcn dot com>


Contents
^^^^^^^^

* Descriptor HowTo Guide

  * Abstract

  * Definition and Introduction

  * Descriptor Protocol

  * Invoking Descriptors

  * Descriptor Example

  * Properties

  * Functions and Methods

  * Static Methods and Class Methods


Abstract
========

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``

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 both ``__get__()`` and ``__set__()``, 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.


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

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 call
``super(B, obj).m()`` searches ``obj.__class__.__mro__`` for the base
class ``A`` immediately following ``B`` and then returns
``A.__dict__['m'].__get__(obj, A)``.  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 and unbound 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(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
           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)

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


Functions and Methods
=====================

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`` and ``lambda``, the usual tools for
creating functions.  The only difference from regular functions is
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 or unbound
methods depending whether they are invoked from an object or a class.
In pure python, it works like this:

   class Function(object):
       . . .
       def __get__(self, obj, objtype=None):
           "Simulate func_descr_get() in Objects/funcobject.c"
           return types.MethodType(self, obj, objtype)

Running the interpreter shows how the function descriptor works in
practice:

   >>> class D(object):
        def f(self, x):
             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
   <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 ``PyMethod_Type`` in Objects/classobject.c is a
single object with two different representations depending on whether
the ``im_self`` field is set or is *NULL* (the C equivalent of
*None*).

Likewise, the effects of calling a method object depend on the
``im_self`` field. If set (meaning bound), the original function
(stored in the ``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 ``instancemethod_call()`` is only slightly more complex in that it
includes some type checking.


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 a ``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              |
   +===================+========================+====================+
   | function          | f(obj, *args)          | f(*args)           |
   +-------------------+------------------------+--------------------+
   | staticmethod      | f(*args)               | f(*args)           |
   +-------------------+------------------------+--------------------+
   | classmethod       | 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(object):
        def f(x):
             print(x)
        f = staticmethod(f)

   >>> print(E.f(3))
   3
   >>> print(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:
       . . .
       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
