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

Author:
   Raymond Hettinger

Contact:
   <python at rcn dot com>


Contents
^^^^^^^^

* Descriptor HowTo Guide

  * Primer

    * Simple example: A descriptor that returns a constant

    * Dynamic lookups

    * Managed attributes

    * Customized names

    * Closing thoughts

  * Complete Practical Example

    * Validator class

    * Custom validators

    * Practical application

  * Technical Tutorial

    * Abstract

    * Definition and introduction

    * Descriptor protocol

    * Overview of descriptor invocation

    * Invocation from an instance

    * Invocation from a class

    * Invocation from super

    * Summary of invocation logic

    * Automatic name notification

    * ORM example

  * Pure Python Equivalents

    * Properties

    * Functions and methods

    * Kinds of methods

    * Static methods

    * Class methods

    * Member objects and __slots__

*Descriptors* let objects customize attribute lookup, storage, and
deletion.

This guide has four major sections:

1. The "primer" gives a basic overview, moving gently from simple
   examples, adding one feature at a time.  Start here if you're new
   to descriptors.

2. The second section shows a complete, practical descriptor example.
   If you already know the basics, start there.

3. The third section provides a more technical tutorial that goes into
   the detailed mechanics of how descriptors work.  Most people don't
   need this level of detail.

4. The last section has pure Python equivalents for built-in
   descriptors that are written in C.  Read this if you're curious
   about how functions turn into bound methods or about the
   implementation of common tools like "classmethod()",
   "staticmethod()", "property()", and *__slots__*.


Primer
======

In this primer, we start with the most basic possible example and then
we'll add new capabilities one by one.


Simple example: A descriptor that returns a constant
----------------------------------------------------

The "Ten" class is a descriptor that always returns the constant "10"
from its "__get__()" method:

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

To use the descriptor, it must be stored as a class variable in
another class:

   class A:
       x = 5                       # Regular class attribute
       y = Ten()                   # Descriptor instance

An interactive session shows the difference between normal attribute
lookup and descriptor lookup:

   >>> a = A()                     # Make an instance of class A
   >>> a.x                         # Normal attribute lookup
   5
   >>> a.y                         # Descriptor lookup
   10

In the "a.x" attribute lookup, the dot operator finds the key "x" and
the value "5" in the class dictionary.  In the "a.y" lookup, the dot
operator finds a descriptor instance, recognized by its "__get__"
method, and calls that method which returns "10".

Note that the value "10" is not stored in either the class dictionary
or the instance dictionary.  Instead, the value "10" is computed on
demand.

This example shows how a simple descriptor works, but it isn't very
useful. For retrieving constants, normal attribute lookup would be
better.

In the next section, we'll create something more useful, a dynamic
lookup.


Dynamic lookups
---------------

Interesting descriptors typically run computations instead of
returning constants:

   import os

   class DirectorySize:

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

   class Directory:

       size = DirectorySize()              # Descriptor instance

       def __init__(self, dirname):
           self.dirname = dirname          # Regular instance attribute

An interactive session shows that the lookup is dynamic — it computes
different, updated answers each time:

   >>> s = Directory('songs')
   >>> g = Directory('games')
   >>> s.size                              # The songs directory has twenty files
   20
   >>> g.size                              # The games directory has three files
   3
   >>> os.remove('games/chess')            # Delete a game
   >>> g.size                              # File count is automatically updated
   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*.


Managed attributes
------------------

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.

In the following example, *age* is the public attribute and *_age* is
the private attribute.  When the public attribute is accessed, the
descriptor logs the lookup or update:

   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()             # Descriptor instance

       def __init__(self, name, age):
           self.name = name                # Regular instance attribute
           self.age = age                  # Calls __set__()

       def birthday(self):
           self.age += 1                   # Calls both __get__() and __set__()

An interactive session shows that all access to the managed attribute
*age* is logged, but that the regular attribute *name* is not logged:

   >>> 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

One major issue with this example is that the private name *_age* is
hardwired in the *LoggedAgeAccess* class.  That means that each
instance can only have one logged attribute and that its name is
unchangeable.  In the next example, we'll fix that problem.


Customized names
----------------

When a class uses descriptors, it can inform each descriptor about
which variable name was used.

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()                # First descriptor instance
       age = LoggedAccess()                 # Second descriptor instance

       def __init__(self, name, age):
           self.name = name                 # Calls the first descriptor
           self.age = age                   # Calls the second descriptor

       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'}

The new class now logs access to both *name* and *age*:

   >>> 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

The two *Person* instances contain only the private names:

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


Closing thoughts
----------------

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.)

Descriptors get invoked by the dot "operator" during attribute lookup.
If a descriptor is accessed indirectly with
"vars(some_class)[descriptor_name]", the descriptor instance is
returned without invoking it.

Descriptors only work when used as class variables.  When put in
instances, they have no effect.

The main motivation for descriptors is to provide a hook allowing
objects stored in class variables to control what happens during
attribute lookup.

Traditionally, the calling class controls what happens during lookup.
Descriptors invert that relationship and allow the data being looked-
up to have a say in the matter.

Descriptors are used throughout the language.  It is how functions
turn into bound methods.  Common tools like "classmethod()",
"staticmethod()", "property()", and "functools.cached_property()" are
all implemented as descriptors.


Complete Practical Example
==========================

In this example, we create a practical and powerful tool for locating
notoriously hard to find data corruption bugs.


Validator class
---------------

A validator is a descriptor for managed attribute access.  Prior to
storing any data, it verifies that the new value meets various type
and range restrictions.  If those restrictions aren't met, it raises
an exception to prevent data corruption at its source.

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.


Custom validators
-----------------

Here are three practical data validation utilities:

1. "OneOf" verifies that a value is one of a restricted set of
   options.

2. "Number" verifies that a value is either an "int" or "float".
   Optionally, it verifies that a value is between a given minimum or
   maximum.

3. "String" verifies that a value is a "str".  Optionally, it
   validates a given minimum or maximum length.  It can validate a
   user-defined predicate as well.

   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}'
               )


Practical application
---------------------

Here's how the data validators can be used in a real class:

   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

The descriptors prevent invalid instances from being created:

   >>> 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


Technical Tutorial
==================

What follows is a more technical tutorial for the mechanics and
details of how descriptors work.


Abstract
--------

Defines descriptors, summarizes the protocol, and shows how
descriptors are called.  Provides an example showing how object
relational mappings work.

Learning about descriptors not only provides access to a larger
toolset, it creates a deeper understanding of how Python works.


Definition and introduction
---------------------------

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*.

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 method resolution
order of "type(a)". 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.  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 "__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).

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.


Overview of descriptor invocation
---------------------------------

A descriptor can be called directly with "desc.__get__(obj)" or
"desc.__get__(None, cls)".

But it is more common for a descriptor to be invoked automatically
from attribute access.

The expression "obj.x" looks up the attribute "x" in the chain of
namespaces for "obj".  If the search finds a descriptor outside of the
instance "__dict__", its "__get__()" method is invoked according to
the precedence rules listed below.

The details of invocation depend on whether "obj" is an object, class,
or instance of super.


Invocation from an instance
---------------------------

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.

If a descriptor is found for "a.x", then it is invoked with:
"desc.__get__(a, type(a))".

The logic for a dotted lookup is in "object.__getattribute__()".  Here
is a pure Python equivalent:

   def object_getattribute(obj, name):
       "Emulate PyObject_GenericGetAttr() in Objects/object.c"
       null = object()
       objtype = type(obj)
       cls_var = getattr(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)

Interestingly, attribute lookup doesn't call
"object.__getattribute__()" directly.  Instead, both the dot operator
and the "getattr()" function perform attribute lookup by way of 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__

So if "__getattr__()" exists, it is called whenever
"__getattribute__()" raises "AttributeError" (either directly or in
one of the descriptor calls).

Also, if a user calls "object.__getattribute__()" directly, the
"__getattr__()" hook is bypassed entirely.


Invocation from a class
-----------------------

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*.

If a descriptor is found, it is invoked with "desc.__get__(None, A)".

The full C implementation can be found in "type_getattro()" and
"_PyType_Lookup()" in Objects/typeobject.c.


Invocation from super
---------------------

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

A dotted lookup such as "super(A, obj).m" searches
"obj.__class__.__mro__" for the base class "B" immediately following
"A" and then returns "B.__dict__['m'].__get__(obj, A)".  If not a
descriptor, "m" is returned unchanged.

The full C implementation can be found in "super_getattro()" in
Objects/typeobject.c.  A pure Python equivalent can be found in
Guido's Tutorial.


Summary of invocation logic
---------------------------

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

The important points to remember are:

* Descriptors are invoked by the "__getattribute__()" method.

* Classes inherit this machinery from "object", "type", or "super()".

* 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.

* Data descriptors always override instance dictionaries.

* Non-data descriptors may be overridden by instance dictionaries.


Automatic name notification
---------------------------

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.

The implementation details are in "type_new()" and "set_names()" in
Objects/typeobject.c.

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 example
-----------

The following code is simplified skeleton showing how data descriptors
could be used to implement an object relational mapping.

The essential idea is that the data is stored in an external database.
The Python instances only hold keys to the database's tables.
Descriptors take care of lookups or updates:

   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'                    # Table name
       key = 'title'                       # Primary key
       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

To use the models, first connect to the database:

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

An interactive session shows how data is retrieved from the database
and how it can be updated:

   >>> 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'


Pure Python Equivalents
=======================

The descriptor protocol is simple and offers exciting possibilities.
Several use cases are so common that they have been prepackaged into
built-in tools. Properties, bound methods, static methods, class
methods, and __slots__ are all based on the descriptor protocol.


Properties
----------

Calling "property()" is a succinct way of building a data descriptor
that triggers a function call upon access to an attribute.  Its
signature is:

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

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:
       ...

       @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.


Functions and methods
---------------------

Python's object oriented features are built upon a function based
environment. Using non-data descriptors, the two are merged
seamlessly.

Functions stored in class dictionaries get turned into methods when
invoked. Methods only differ from regular functions in that the object
instance is prepended to the other arguments.  By convention, the
instance is called *self* but could be called *this* or any other
variable name.

Methods can be created manually with "types.MethodType" which is
roughly equivalent to:

   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)

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)

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

   class D:
       def f(self, x):
            return x

The function has a *qualified name* attribute to support
introspection:

   >>> 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>>

Internally, the bound method stores the underlying function and the
bound instance:

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

   >>> d.f.__self__
   <__main__.D object at 0x1012e1f98>

If you have ever wondered where *self* comes from in regular methods
or where *cls* comes from in class methods, this is it!


Kinds of 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 "cls.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(cls, *args)      |
   +-------------------+------------------------+--------------------+


Static methods
--------------

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 static methods return the underlying function with no changes,
the example calls are unexciting:

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

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

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


Class methods
-------------

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

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

This behavior is useful whenever the method only needs to have a class
reference and does not rely on data stored in a specific instance.
One use for class methods is to create alternate class constructors.
For example, the classmethod "dict.fromkeys()" creates a new
dictionary from a list of keys.  The pure Python equivalent is:

   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

Now a new dictionary of unique keys can be constructed like this:

   >>> d = Dict.fromkeys('abracadabra')
   >>> type(d) is Dict
   True
   >>> d
   {'a': None, 'b': None, 'r': 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, cls=None):
           if cls is None:
               cls = type(obj)
           if hasattr(type(self.f), '__get__'):
               return self.f.__get__(cls)
           return MethodType(self.f, cls)

The code path for "hasattr(type(self.f), '__get__')" was added in
Python 3.9 and makes it possible for "classmethod()" to support
chained decorators.  For example, a classmethod and property could be
chained together:

   class G:
       @classmethod
       @property
       def __doc__(cls):
           return f'A doc for {cls.__name__!r}'

   >>> G.__doc__
   "A doc for 'G'"


Member objects and __slots__
----------------------------

When a class defines "__slots__", it replaces instance dictionaries
with a fixed-length array of slot values.  From a user point of view
that has several effects:

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: can't set attribute
   >>> 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. 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.

It is not possible to create an exact drop-in pure Python version of
"__slots__" because it requires direct access to C structures and
control over object memory allocation.  However, we can build a mostly
faithful simulation where the actual C structure for slots is emulated
by a private "_slotvalues" list.  Reads and writes to that private
structure are managed by member descriptors:

   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
           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):
           'Emuluate 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)

The "object.__new__()" method takes care of creating instances that
have slots instead of an instance dictionary.  Here is a rough
simulation in pure Python:

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

       def __new__(cls, *args):
           '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'{type(self).__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'{type(self).__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

At this point, the metaclass has loaded member objects for *x* and
*y*:

   >>> 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'>}

When instances are created, they have a "slot_values" list where the
attributes are stored:

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

Misspelled or unassigned attributes will raise an exception:

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