python-peps/pep-0362.txt

PEP: 362
Title: Function Signature Object
Version: $Revision$
Last-Modified: $Date$
Author: Brett Cannon <brett@python.org>, Jiwon Seo <seojiwon@gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 21-Aug-2006
Python-Version: 2.6
Post-History: 05-Sep-2007


Abstract
========

Python has always supported powerful introspection capabilities,
including that for functions and methods (for the rest of this PEP the
word "function" refers to both functions and methods).  Taking a
function object, you can fully reconstruct the function's signature.
Unfortunately it is a little unruly having to look at all the
different attributes to pull together complete information for a
function's signature.

This PEP proposes an object representation for function signatures.
This should help facilitate introspection on functions for various
uses.  The introspection information contains all possible information
about the parameters in a signature (including Python 3.0 features).

This object, though, is not meant to replace existing ways of
introspection on a function's signature.  The current solutions are
there to make Python's execution work in an efficient manner.  The
proposed object representation is only meant to help make application
code have an easier time to query a function on its signature.


Purpose
=======

An object representation of a function's call signature should provide
an easy way to introspect what a function expects as arguments.  It
does not need to be a "live" representation, though; the signature can
be inferred once and stored without changes to the signature object
representation affecting the function it represents (but this is an
`Open Issues`_).

Indirecation of signature introspection can also occur.  If a
decorator took a decorated function's signature object and set it on
the decorating function then introspection could be redirected to what
is actually expected instead of the typical ``*args, **kwargs``
signature of decorating functions.


Signature Object
================

The overall signature of an object is represented by the Signature
object.  This object is to store a `Parameter object`_ for each
parameter in the signature.  It is also to store any information
about the function itself that is pertinent to the signature.

A Signature object has the following structure attributes:

* name : str
    Name of the function.  This is not fully qualified because
    function objects for methods do not know the class they are
    contained within.  This makes functions and methods
    indistinguishable from one another when passed to decorators,
    preventing proper creation of a fully qualified name.
* var_args : str
    Name of the variable positional parameter (i.e., ``*args``), if
    present, or the empty string.
* var_kw_args : str
    Name of the variable keyword parameter (i.e., ``**kwargs``), if
    present, or the empty string.
* var_annotations: dict(str, object)
    Dict that contains the annotations for the variable parameters.
    The keys are of the variable parameter with values of the
    annotation.  If an annotation does not exist for a variable
    parameter then the key does not exist in the dict.
* return_annotation : object
    If present, the attribute is set to the annotation for the return
    type of the function.
* parameters : list(Parameter)
    List of the parameters of the function as represented by
    Parameter objects in the order of its definition (keyword-only
    arguments are in the order listed by ``code.co_varnames``).
* bind(\*args, \*\*kwargs) -> dict(str, object)
    Create a mapping from arguments to parameters.  The keys are the
    names of the parameter that an argument maps to with the value
    being the value the parameter would have if this function was
    called with the given arguments.

Signature objects also have the following methods:

* __getitem__(self, key : str) -> Parameter
    Returns the Parameter object for the named parameter.
* __iter__(self)
    Returns an iterator that returns Parameter objects in their
    sequential order based on their 'position' attribute.

The Signature object is stored in the ``__signature__`` attribute of
a function.  When it is to be created is discussed in
`Open Issues`_.


Parameter Object
================

A function's signature is made up of several parameters.  Python's
different kinds of parameters is quite large and rich and continues to
grow.  Parameter objects represent any possible parameter.

Originally the plan was to represent parameters using a list of
parameter names on the Signature object along with various dicts keyed
on parameter names to disseminate the various pieces of information
one can know about a parameter.  But the decision was made to
incorporate all information about a parameter in a single object so
as to make extending the information easier.  This was originally put
forth by Talin and the preferred form of Guido (as discussed at the
2006 Google Sprint).

The structure of the Parameter object is:

* name : (str | tuple(str))
    The name of the parameter as a string if it is not a tuple.  If
    the argument is a tuple then a tuple of strings is used.
* position : int
    The position of the parameter within the signature of the
    function (zero-indexed).  For keyword-only parameters the position
    value is arbitrary while not conflicting with positional
    parameters.  The suggestion of setting the attribute to None or -1
    to represent keyword-only parameters was rejected to prevent
    variable type usage and as a possible point of errors,
    respectively.
* default_value : object
    The default value for the parameter, if present, else the
    attribute does not exist.
* keyword_only : bool
    True if the parameter is keyword-only, else False.
* annotation
    Set to the annotation for the parameter.  If ``has_annotation`` is
    False then the attribute does not exist to prevent accidental use.


Implementation
==============

An implementation can be found in Python's sandbox [#impl]_.
There is a function named ``signature()`` which
returns the value stored on the ``__signature__`` attribute if it
exists, else it creates the Signature object for the
function and sets ``__signature__``.  For methods this is stored
directly on the im_func function object since that is what decorators
work with.


Examples
========

Annotation Checker
------------------
::

    def quack_check(fxn):
        """Decorator to verify arguments and return value quack as they should.

        Positional arguments.
        >>> @quack_check
        ... def one_arg(x:int): pass
        ... 
        >>> one_arg(42)
        >>> one_arg('a')
        Traceback (most recent call last):
            ...
        TypeError: 'a' does not quack like a <type 'int'>


        *args
        >>> @quack_check
        ... def var_args(*args:int): pass
        ... 
        >>> var_args(*[1,2,3])
        >>> var_args(*[1,'b',3])
        Traceback (most recent call last):
            ...
        TypeError: *args contains a a value that does not quack like a <type 'int'>

        **kwargs
        >>> @quack_check
        ... def var_kw_args(**kwargs:int): pass
        ... 
        >>> var_kw_args(**{'a': 1})
        >>> var_kw_args(**{'a': 'A'})
        Traceback (most recent call last):
            ...
        TypeError: **kwargs contains a value that does not quack like a <type 'int'>

        Return annotations.
        >>> @quack_check
        ... def returned(x) -> int: return x
        ... 
        >>> returned(42)
        42
        >>> returned('a')
        Traceback (most recent call last):
            ...
        TypeError: the return value 'a' does not quack like a <type 'int'>

        """
        # Get the signature; only needs to be calculated once.
        sig = Signature(fxn)
        def check(*args, **kwargs):
            # Find out the variable -> value bindings.
            bindings = sig.bind(*args, **kwargs)
            # Check *args for the proper quack.
            try:
                duck = sig.var_annotations[sig.var_args]
            except KeyError:
                pass
            else:
                # Check every value in *args.
                for value in bindings[sig.var_args]:
                    if not isinstance(value, duck):
                        raise TypeError("*%s contains a a value that does not "
                                        "quack like a %r" %
                                        (sig.var_args, duck))
                # Remove it from the bindings so as to not check it again.
                del bindings[sig.var_args]
            # **kwargs.
            try:
                duck = sig.var_annotations[sig.var_kw_args]
            except (KeyError, AttributeError):
                pass
            else:
                # Check every value in **kwargs.
                for value in bindings[sig.var_kw_args].values():
                    if not isinstance(value, duck):
                        raise TypeError("**%s contains a value that does not "
                                        "quack like a %r" %
                                        (sig.var_kw_args, duck))
                # Remove from bindings so as to not check again.
                del bindings[sig.var_kw_args]
            # For each remaining variable ...
            for var, value in bindings.items():
                # See if an annotation was set.
                try:
                    duck = sig[var].annotation
                except AttributeError:
                    continue
                # Check that the value quacks like it should.
                if not isinstance(value, duck):
                    raise TypeError('%r does not quack like a %s' % (value, duck))
            else:
                # All the ducks quack fine; let the call proceed.
                returned = fxn(*args, **kwargs)
                # Check the return value.
                try:
                    if not isinstance(returned, sig.return_annotation):
                        raise TypeError('the return value %r does not quack like '
                                        'a %r' % (returned,
                                            sig.return_annotation))
                except AttributeError:
                    pass
                return returned
        # Full-featured version would set function metadata.
        return check


Open Issues
===========

When to construct the Signature object?
---------------------------------------

The Signature object can either be created in an eager or lazy
fashion.  In the eager situation, the object can be created during
creation of the function object.  In the lazy situation, one would
pass a function object to a function and that would generate the
Signature object and store it to ``__signature__`` if
needed, and then return the value of ``__signature__``.


Should ``Signature.bind`` return Parameter objects as keys?
-----------------------------------------------------------

Instead of returning a dict with keys consisting of the name of the
parameters, would it be more useful to instead use Parameter
objects?  The name of the argument can easily be retrieved from the
key (and the name would be used as the hash for a Parameter object).


Have ``var_args`` and ``_var_kw_args`` default to ``None``?
------------------------------------------------------------

It has been suggested by Fred Drake that these two attributes have a
value of ``None`` instead of empty strings when they do not exist.
The answer to this question will influence what the defaults are for
other attributes as well.


Deprecate ``inspect.getargspec()`` and ``.formatargspec()``?
-------------------------------------------------------------

Since the Signature object replicates the use of ``getargspec()``
from the ``inspect`` module it might make sense to deprecate it in
2.6.  ``formatargspec()`` could also go if Signature objects gained a
__str__ representation.

Issue with that is types such as ``int``, when used as annotations,
do not lend themselves for output (e.g., ``"<type 'int'>"`` is the
string represenation for ``int``).  The repr representation of types
would need to change in order to make this reasonable.


Have the objects be "live"?
---------------------------

Jim Jewett pointed out that Signature and Parameter objects could be
"live".  That would mean requesting information would be done on the
fly instead of caching it on the objects.  It would also allow for
mutating the function if the Signature or Parameter objects were
mutated.


References
==========

.. [#impl] pep362 directory in Python's sandbox
   (http://svn.python.org/view/sandbox/trunk/pep362/)


Copyright
=========

This document has been placed in the public domain.



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