python-peps/pep-0252.txt

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, and their instances more like
    class instances.  For example, type(x) will be equivalent to
    x.__class__ for most built-in types.  When C is x.__class__,
    x.meth(a) will generally be equivalent to C.meth(x, a), and
    C.__dict__ contains x's methods and other attributes.

    This 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.

    Based on the generalized descriptor API, this PEP also introduces
    a way to declare class methods and static methods.


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.  This is the
    classic Python attribute lookup rule.  Note that PEP 253 will
    propose to change the attribute lookup order, and if accepted,
    this PEP will follow suit.)

    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 would
    show 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; a clumsy but descriptive term that fits
    both is "meta-object".)

    (XXX static and dynamic are not great terms to use here, because
    "static" attributes may actually behave quite dynamically, and
    because they have nothing to do with static class members in C++
    or Java.)

    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 (f.f_code,
    c.co_filename, etc.).  When an object with dynamic attributes
    exposes these through its __dict__ attribute, __dict__ is a static
    attribute.

    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; the latter are easily discovered by
    inspection of obj.__dict__.

    In the discussion below, I distinguish two kinds of objects:
    regular objects (e.g. lists, ints, functions) and meta-objects.
    Types and classes and meta-objects.  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 other 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.

    Together, these not only tell us about *all* attributes defined by
    a meta-object, but they also help us calculate the value of a
    specific attribute of a given object.

    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_key().  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 usually has a __class__ attribute.  If it
       does, this references a meta-object.  A meta-object can define
       static attributes for the regular object whose __class__ it
       is.  This is normally done through the following mechanism:

    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 (a mapping but
       not necessarily a dictionary).  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
       directed acyclic graph.  (It is not necessarily a tree, since
       multiple classes can have the same base class.)  The __dict__
       attributes of the meta-objects in the inheritance graph supply
       attribute descriptors for the regular object whose __class__ is
       at the top of the inheritance graph.

    5. Precedence rules

       When two meta-objects in the inheritance graph for a given
       regular object both define an attribute descriptor with the
       same name, the left-to-right depth-first rule applies.  (This
       is the classic Python attribute lookup rule.  Note that PEP 253
       will propose to change the attribute lookup order, and if
       accepted, this PEP will follow suit.)

       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 graph rooted at the regular
       object's __class__), the static attribute has precedence if it
       is a descriptor that defines a __set__ method (see below);
       otherwise (if there is no __set__ method) the dynamic attribute
       has precedence.

       Rationale: 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__.  Presence of a __set__ method on a descriptor
       indicates that this is a data descriptor.  (Even read-only data
       descriptors have a __set__ method: it always raises an
       exception.)  Absence of a __set__ method on a descriptor
       indicates that the descriptor isn't interested in intercepting
       assignment, and then the classic rule applies: an instance
       variable with the same name as a method hides the method until
       it is deleted.

    6. Attribute descriptors

       This is where it gets interesting -- and messy.  Attribute
       descriptors (descriptors for short) are stored in the
       meta-object's __dict__ (or in the __dict__ of one of its
       ancestors), and have two uses: a descriptor can be used to get
       or set the corresponding attribute value on the (regular,
       non-meta) object, and it has an additional interface that
       describes the attribute for documentation and 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 certain
       minimal 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 dynamic data
       attribute.  Backwards compatibility also dictates that (in the
       absence of a __setattr__ method) it is legal to assign to an
       attribute corresponding to a 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.  A future 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 may 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.  All
    attributes are optional -- a specific attribute may or may not be
    present on a given descriptor.  An absent attribute means that the
    corresponding information is not available or the corresponding
    functionality is not implemented.

    - __name__: the attribute name.  Because of aliasing and renaming,
      the attribute may (additionally or exclusively) 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.  This may be
      None.

    - __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.

    - __get__(): a function callable with one or two arguments that
      retrieves the attribute value from an object.  With one
      argument, X, this either (for data attributes) retrieves the
      attribute value from X or (for method attributes) binds the
      attribute to X (returning some form of "bound" object that
      receives an implied first argument of X when called).  With two
      arguments, X and T, T must be a meta-object that restricts the
      type of X.  X must either be an instance of T (in which the
      effect is the same as when T is omitted), or None.  When X is
      None, this should be a method descriptor, and the result is an
      *unbound* method restricted to objects whose type is (a
      descendent of) T.  (For methods, this is called a "binding"
      operation, even if X==None.  Exactly what is returned by the
      binding operation depends on the semantics of the descriptor;
      for example, class methods ignore the instance and bind to the
      type instead.)

    - __set__(): a function of two arguments that sets the attribute
      value on the object.  If the attribute is read-only, this method
      raises a TypeError exception.  (Not an AttributeError!)
      Example: C.ivar.set(x, y) ~~ x.ivar = y.

    Method attributes may also be callable; in this case they act as
    unbound method.  Example: C.meth(C(), x) ~~ C().meth(x).


C API

    XXX


Discussion

    XXX


Examples

    XXX


Backwards compatibility

    XXX


Warnings and Errors

    XXX


Implementation

    A partial implementation of this PEP is available from CVS as a
    branch named "descr-branch".  To experiment with this
    implementation, proceed to check out Python from CVS according to
    the instructions at http://sourceforge.net/cvs/?group_id=5470 but
    add the arguments "-r descr-branch" to the cvs checkout command.
    (You can also start with an existing checkout and do "cvs update
    -r descr-branch".)  For some examples of the features described
    here, see the file Lib/test/test_descr.py.

    Note: the code in this branch goes way beyond this PEP; it is also
    the experimentation area for PEP 253 (Subtyping Built-in Types).


References

    XXX


Copyright

    This document has been placed in the public domain.


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