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.  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_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 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.readonly == 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

    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 beyond this PEP; it is also
    on the way to implementing pep-0253 (Subtyping Built-in Types).

References

    XXX

Copyright

    This document has been placed in the public domain.


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