python-peps/pep-0252.txt

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PEP: 252
Title: Making Types Look More Like Classes
Version: $Revision$
Author: guido@python.org (Guido van Rossum)
Status: Draft
Type: Standards Track
Python-Version: 2.2
Created: 19-Apr-2001
Post-History:
Abstract
This PEP proposes changes to the introspection API for types that
makes them look more like classes. For example, type(x) will be
equivalent to x.__class__ for most built-in types. When C is
x.__class__, x.meth(a) will be equivalent to C.meth(x, a), and
C.__dict__ contains descriptors for x's methods and other
attributes.
The PEP also introduces a new approach to specifying attributes,
using attribute descriptors, or descriptors for short.
Descriptors unify and generalize several different common
mechanisms used for describing attributes: a descriptor can
describe a method, a typed field in the object structure, or a
generalized attribute represented by getter and setter functions.
Introduction
One of Python's oldest language warts is the difference between
classes and types. For example, you can't directly subclass the
dictionary type, and the introspection interface for finding out
what methods and instance variables an object has is different for
types and for classes.
Healing the class/type split is a big effort, because it affects
many aspects of how Python is implemented. This PEP concerns
itself with making the introspection API for types look the same
as that for classes. Other PEPs will propose making classes look
more like types, and subclassing from built-in types; these topics
are not on the table for this PEP.
Introspection APIs
Introspection concerns itself with finding out what attributes an
object has. Python's very general getattr/setattr API makes it
impossible to guarantee that there always is a way to get a list
of all attributes supported by a specific object, but in practice
two conventions have appeared that together work for almost all
objects. I'll call them the class-based introspection API and the
type-based introspection API; class API and type API for short.
The class-based introspection API is used primarily for class
instances; it is also used by Jim Fulton's ExtensionClasses. It
assumes that all data attributes of an object x are stored in the
dictionary x.__dict__, and that all methods and class variables
can be found by inspection of x's class, written as x.__class__.
Classes have a __dict__ attribute, which yields a dictionary
containing methods and class variables defined by the class
itself, and a __bases__ attribute, which is a tuple of base
classes that must be inspected recursively. Some assumption here
are:
- attributes defined in the instance dict override attributes
defined by the object's class;
- attributes defined in a derived class override attributes
defined in a base class;
- attributes in an earlier base class (meaning occurring earlier
in __bases__) override attributes in a later base class.
(The last two rules together are often summarized as the
left-to-right, depth-first rule for attribute search.)
The type-based introspection API is supported in one form or
another by most built-in objects. It uses two special attributes,
__members__ and __methods__. The __methods__ attribute, if
present, is a list of method names supported by the object. The
__members__ attribute, if present, is a list of data attribute
names supported by the object.
The type API is sometimes combined by a __dict__ that works the
same was as for instances (e.g., for function objects in Python
2.1, f.__dict__ contains f's dynamic attributes, while
f.__members__ lists the names of f's statically defined
attributes).
Some caution must be exercised: some objects don't list theire
"intrinsic" attributes (e.g. __dict__ and __doc__) in __members__,
while others do; sometimes attribute names that occur both in
__members__ or __methods__ and as keys in __dict__, in which case
it's anybody's guess whether the value found in __dict__ is used
or not.
The type API has never been carefully specified. It is part of
Python folklore, and most third party extensions support it
because they follow examples that support it. Also, any type that
uses Py_FindMethod() and/or PyMember_Get() in its tp_getattr
handler supports it, because these two functions special-case the
attribute names __methods__ and __members__, respectively.
Jim Fulton's ExtensionClasses ignore the type API, and instead
emulate the class API, which is more powerful. In this PEP, I
propose to phase out the type API in favor of supporting the class
API for all types.
One argument in favor of the class API is that it doesn't require
you to create an instance in order to find out which attributes a
type supports; this in turn is useful for documentation
processors. For example, the socket module exports the SocketType
object, but this currently doesn't tell us what methods are
defined on socket objects. Using the class API, SocketType shows
us exactly what the methods for socket objects are, and we can
even extract their docstrings, without creating a socket. (Since
this is a C extension module, the source-scanning approach to
docstring extraction isn't feasible in this case.)
Specification of the class-based introspection API
Objects may have two kinds of attributes: static and dynamic. The
names and sometimes other properties of static attributes are
knowable by inspection of the object's type or class, which is
accessible through obj.__class__ or type(obj). (I'm using type
and class interchangeably, because that's the goal of the
exercise.)
(XXX static and dynamic are lousy names, because the "static"
attributes may actually behave quite dynamically.)
The names and values of dynamic properties are typically stored in
a dictionary, and this dictionary is typically accessible as
obj.__dict__. The rest of this specification is more concerned
with discovering the names and properties of static attributes
than with dynamic attributes.
Examples of dynamic attributes are instance variables of class
instances, module attributes, etc. Examples of static attributes
are the methods of built-in objects like lists and dictionaries,
and the attributes of frame and code objects (c.co_code,
c.co_filename, etc.). When an object with dynamic attributes
exposes these through its __dict__ attribute, __dict__ is a static
attribute.
In the discussion below, I distinguish two kinds of objects:
regular objects (e.g. lists, ints, functions) and meta-objects.
Meta-objects are types and classes. Meta-objects are also regular
objects, but we're mostly interested in them because they are
referenced by the __class__ attribute of regular objects (or by
the __bases__ attribute of meta-objects).
The class introspection API consists of the following elements:
- the __class__ and __dict__ attributes on regular objects;
- the __bases__ and __dict__ attributes on meta-objects;
- precedence rules;
- attribute descriptors.
1. The __dict__ attribute on regular objects
A regular object may have a __dict__ attribute. If it does,
this should be a mapping (not necessarily a dictionary)
supporting at least __getitem__, keys(), and has_item(). This
gives the dynamic attributes of the object. The keys in the
mapping give attribute names, and the corresponding values give
their values.
Typically, the value of an attribute with a given name is the
same object as the value corresponding to that name as a key in
the __dict__. In othe words, obj.__dict__['spam'] is obj.spam.
(But see the precedence rules below; a static attribute with
the same name *may* override the dictionary item.)
2. The __class__ attribute on regular objects
A regular object may have a __class__ attributes. If it does,
this references a meta-object. A meta-object can define static
attributes for the regular object whose __class__ it is.
3. The __dict__ attribute on meta-objects
A meta-object may have a __dict__ attribute, of the same form
as the __dict__ attribute for regular objects (mapping, etc).
If it does, the keys of the meta-object's __dict__ are names of
static attributes for the corresponding regular object. The
values are attribute descriptors; we'll explain these later.
(An unbound method is a special case of an attribute
descriptor.)
Becase a meta-object is also a regular object, the items in a
meta-object's __dict__ correspond to attributes of the
meta-object; however, some transformation may be applied, and
bases (see below) may define additional dynamic attributes. In
other words, mobj.spam is not always mobj.__dict__['spam'].
(This rule contains a loophole because for classes, if
C.__dict__['spam'] is a function, C.spam is an unbound method
object.)
4. The __bases__ attribute on meta-objects
A meta-object may have a __bases__ attribute. If it does, this
should be a sequence (not necessarily a tuple) of other
meta-objects, the bases. An absent __bases__ is equivalent to
an empty sequece of bases. There must never be a cycle in the
relationship between meta objects defined by __bases__
attributes; in other words, the __bases__ attributes define an
inheritance tree, where the root of the tree is the __class__
attribute of a regular object, and the leaves of the trees are
meta-objects without bases. The __dict__ attributes of the
meta-objects in the inheritance tree supply attribute
descriptors for the regular object whose __class__ is at the
top of the inheritance tree.
5. Precedence rules
When two meta-objects in the inheritance tree both define an
attribute descriptor with the same name, the left-to-right
depth-first rule applies. (XXX define rigorously.)
When a dynamic attribute (one defined in a regular object's
__dict__) has the same name as a static attribute (one defined
by a meta-object in the inheritance tree rooted at the regular
object's __class__), the dynamic attribute *usually* wins, but
for some attributes the meta-object may specify that the static
attribute overrides the dynamic attribute.
(We can't have a simples rule like "static overrides dynamic"
or "dynamic overrides static", because some static attributes
indeed override dynamic attributes, e.g. a key '__class__' in
an instance's __dict__ is ignored in favor of the statically
defined __class__ pointer, but on the other hand most keys in
inst.__dict__ override attributes defined in inst.__class__.
The mechanism whereby a meta-object can specify that a
particular attribute has precedence is not yet specified.)
6. Attribute descriptors
This is where it gets interesting -- and messy. Attribute
descriptors (descriptors for short) are stored in the
meta-object's __dict__, and have two uses: a descriptor can be
used to get or set the corresponding attribute value on the
(non-meta) object, and it has an additional interface that
describes the attribute for documentation or introspection
purposes.
There is little prior art in Python for designing the
descriptor's interface, neither for getting/setting the value
nor for describing the attribute otherwise, except some trivial
properties (e.g. it's reasonable to assume that __name__ and
__doc__ should be the attribute's name and docstring). I will
propose such an API below.
If an object found in the meta-object's __dict__ is not an
attribute descriptor, backward compatibility dictates
semantics. This basically means that if it is a Python
function or an unbound method, the attribute is a method;
otherwise, it is the default value for a data attribute.
Backwards compatibility also dictates that (in the absence of a
__setattr__ method) it is legal to assign to an attribute of
type method, and that this creates a data attribute shadowing
the method for this particular instance. However, these
semantics are only required for backwards compatibility with
regular classes.
The introspection API is a read-only API. We don't define the
effect of assignment to any of the special attributes (__dict__,
__class__ and __bases__), nor the effect of assignment to the
items of a __dict__. Generally, such assignments should be
considered off-limits. An extension of this PEP may define some
semantics for some such assignments. (Especially because
currently instances support assignment to __class__ and __dict__,
and classes support assignment to __bases__ and __dict__.)
Specification of the attribute descriptor API
Attribute descriptors have the following attributes. In the
examples, x is an object, C is x.__class__, x.meth() is a method,
and x.ivar is a data attribute or instance variable.
- name: the original attribute name. Note that because of
aliasing and renaming, the attribute may be known under a
different name, but this is the name under which it was born.
Example: C.meth.name == 'meth'.
- doc: the attribute's documentation string.
- objclass: the class that declared this attribute. The
descriptor only applies to objects that are instances of this
class (this includes instances of its subclasses). Example:
C.meth.objclass is C.
- kind: either "method" or "data". This distinguishes between
methods and data attributes. The primary operation on a method
attribute is to call it. The primary operations on a data
attribute are to get and to set it. Example: C.meth.kind ==
'method'; C.ivar.kind == 'data'.
- default: for optional data attributes, this gives a default or
initial value. XXX Python has two kinds of semantics for
referencing "absent" attributes: this may raise an
AttributeError, or it may produce a default value stored
somewhere in the class. There could be a flag that
distinguishes between these two cases. Also, there could be a
flag that tells whether it's OK to delete an attribute (and what
happens then -- a default value takes its place, or it's truly
gone).
- attrclass: for data attributes, this can be the class of the
attribute value, or None. If this is not None, the attribute
value is restricted to being an instance of this class (or of a
subclass thereof). If this is None, the attribute value is not
constrained. For method attributes, this should normally be
None (a class is not sufficient information to describe a method
signature). If and when optional static typing is added to
Python, this the meaning of this attribute may change to
describe the type of the attribute.
- signature: for methods, an object that describes the signature
of the method. Signature objects will be described further
below.
- readonly: Boolean indicating whether assignment to this
attribute is disallowed. This is usually true for methods.
Example: C.meth.readonly == 1; C.ivar.kind == 0.
- get(): a function of one argument that retrieves the attribute
value from an object. Examples: C.ivar.get(x) ~~ x.ivar;
C.meth.get(x) ~~ x.meth.
- set(): a function of two arguments that sets the attribute value
on the object. If readonly is set, this method raises a
TypeError exception. Example: C.ivar.set(x, y) ~~ x.ivar = y.
- call(): for method descriptors, this is a function of at least
one argument that calls the method. The first argument is the
object whose method is called; the remaining arguments
(including keyword arguments) are passed on to the method.
Example: C.meth.call(x, 1, 2) ~~ x.meth(1, 2).
- bind(): for method descriptiors, this is a function of one
argument that returns a "bound method object". This in turn can
be called exactly like the method should be called (in fact this
is what is returned for a bound method). This is the same as
get(). Example: C.meth.bind(x) ~~ x.meth.
For convenience, __name__ and __doc__ are defined as aliases for
name and doc. Also for convenience, calling the descriptor can do
one of three things:
- Calling a method descriptor is the same as calling its call()
method. Example: C.meth(x, 1, 2) ~~ x.meth(1, 2).
- Calling a data descriptor with one argument is the same as
calling its get() method. Example: C.ivar(x) ~~ x.ivar.
- Calling a data descriptor with two arguments is the same as
calling its set() method. Example: C.ivar(x, y) ~~ x.ivar = y.
Note that this specification does not define how to create
specific attribute descriptors. This is up to the individual
attribute descriptor implementations, of which there may be many.
Specification of the signature object API
XXX
Discussion
XXX
Examples
XXX
Backwards compatibility
XXX
Compatibility of C API
XXX
Warnings and Errors
XXX
Implementation
XXX
References
XXX
Copyright
This document has been placed in the public domain.
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