Mailman 3 October 2011 - Python-ideas

@classproperty, @abc.abstractclasspropery, etc.
by K. Richard Pixley 16 Dec '20

16 Dec '20

There's a whole matrix of these and I'm wondering why the matrix is currently sparse rather than implementing them all. Or rather, why we can't stack them as: class foo(object): @classmethod @property def bar(cls, ...): ... Essentially the permutation are, I think: {'unadorned'|abc.abstract}{'normal'|static|class}{method|property|non-callable attribute}. concreteness implicit first arg type name comments {unadorned} {unadorned} method def foo(): exists now {unadorned} {unadorned} property @property exists now {unadorned} {unadorned} non-callable attribute x = 2 exists now {unadorned} static method @staticmethod exists now {unadorned} static property @staticproperty proposing {unadorned} static non-callable attribute {degenerate case - variables don't have arguments} unnecessary {unadorned} class method @classmethod exists now {unadorned} class property @classproperty or @classmethod;@property proposing {unadorned} class non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract {unadorned} method @abc.abstractmethod exists now abc.abstract {unadorned} property @abc.abstractproperty exists now abc.abstract {unadorned} non-callable attribute @abc.abstractattribute or @abc.abstract;@attribute proposing abc.abstract static method @abc.abstractstaticmethod exists now abc.abstract static property @abc.staticproperty proposing abc.abstract static non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract class method @abc.abstractclassmethod exists now abc.abstract class property @abc.abstractclassproperty proposing abc.abstract class non-callable attribute {degenerate case - variables don't have arguments} unnecessary I think the meanings of the new ones are pretty straightforward, but in case they are not... @staticproperty - like @property only without an implicit first argument. Allows the property to be called directly from the class without requiring a throw-away instance. @classproperty - like @property, only the implicit first argument to the method is the class. Allows the property to be called directly from the class without requiring a throw-away instance. @abc.abstractattribute - a simple, non-callable variable that must be overridden in subclasses @abc.abstractstaticproperty - like @abc.abstractproperty only for @staticproperty @abc.abstractclassproperty - like @abc.abstractproperty only for @classproperty --rich

10 15

Specify number of items to allocate for array.array() constructor
by Sven Rahmann 22 Feb '20

22 Feb '20

At the moment, the array module of the standard library allows to create arrays of different numeric types and to initialize them from an iterable (eg, another array). What's missing is the possiblity to specify the final size of the array (number of items), especially for large arrays. I'm thinking of suffix arrays (a text indexing data structure) for large texts, eg the human genome and its reverse complement (about 6 billion characters from the alphabet ACGT). The suffix array is a long int array of the same size (8 bytes per number, so it occupies about 48 GB memory). At the moment I am extending an array in chunks of several million items at a time at a time, which is slow and not elegant. The function below also initializes each item in the array to a given value (0 by default). Is there a reason why there the array.array constructor does not allow to simply specify the number of items that should be allocated? (I do not really care about the contents.) Would this be a worthwhile addition to / modification of the array module? My suggestions is to modify array generation in such a way that you could pass an iterator (as now) as second argument, but if you pass a single integer value, it should be treated as the number of items to allocate. Here is my current workaround (which is slow): def filled_array(typecode, n, value=0, bsize=(1<<22)): """returns a new array with given typecode (eg, "l" for long int, as in the array module) with n entries, initialized to the given value (default 0) """ a = array.array(typecode, [value]*bsize) x = array.array(typecode) r = n while r >= bsize: x.extend(a) r -= bsize x.extend([value]*r) return x

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i18n and Python tracebacks
by Andre Roberge 28 Oct '12

28 Oct '12

I think it would be a good idea if Python tracebacks could be translated into languages other than English - and it would set a good example. For example, using French as my default local language, instead of >>> 1/0 Traceback (most recent call last): File "<stdin>", line 1, in <module> ZeroDivisionError: integer division or modulo by zero I might get something like >>> 1/0 Suivi d'erreur (appel le plus récent en dernier) : Fichier "<stdin>", à la ligne 1, dans <module> ZeroDivisionError: division entière ou modulo par zéro André

9 15

Re: [Python-ideas] Cofunctions - Back to Basics
by Mark Shannon 21 Oct '12

21 Oct '12

Greg Ewing wrote: > Mark Shannon wrote: > >> Why not have proper co-routines, instead of hacked-up generators? > > What do you mean by a "proper coroutine"? > A parallel, non-concurrent, thread of execution. It should be able to transfer control from arbitrary places in execution, not within generators. Stackless provides coroutines. Greenlets are also coroutines (I think). Lua has them, and is implemented in ANSI C, so it can be done portably. See: http://www.jucs.org/jucs_10_7/coroutines_in_lua/de_moura_a_l.pdf (One of the examples in the paper uses coroutines to implement generators, which is obviously not required in Python :) ) Cheers, Mark.

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Cofunctions PEP - Revision 4
by Greg Ewing 13 Oct '12

13 Oct '12

Here's an updated version of the PEP reflecting my recent suggestions on how to eliminate 'codef'. PEP: XXX Title: Cofunctions Version: $Revision$ Last-Modified: $Date$ Author: Gregory Ewing <greg.ewing(a)canterbury.ac.nz> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 13-Feb-2009 Python-Version: 3.x Post-History: Abstract ======== A syntax is proposed for defining and calling a special type of generator called a 'cofunction'. It is designed to provide a streamlined way of writing generator-based coroutines, and allow the early detection of certain kinds of error that are easily made when writing such code, which otherwise tend to cause hard-to-diagnose symptoms. This proposal builds on the 'yield from' mechanism described in PEP 380, and describes some of the semantics of cofunctions in terms of it. However, it would be possible to define and implement cofunctions independently of PEP 380 if so desired. Specification ============= Cofunction definitions ---------------------- A cofunction is a special kind of generator, distinguished by the presence of the keyword ``cocall`` (defined below) at least once in its body. It may also contain ``yield`` and/or ``yield from`` expressions, which behave as they do in other generators. From the outside, the distinguishing feature of a cofunction is that it cannot be called the same way as an ordinary function. An exception is raised if an ordinary call to a cofunction is attempted. Cocalls ------- Calls from one cofunction to another are made by marking the call with a new keyword ``cocall``. The expression :: cocall f(*args, **kwds) is evaluated by first checking whether the object ``f`` implements a ``__cocall__`` method. If it does, the cocall expression is equivalent to :: yield from f.__cocall__(*args, **kwds) except that the object returned by __cocall__ is expected to be an iterator, so the step of calling iter() on it is skipped. If ``f`` does not have a ``__cocall__`` method, or the ``__cocall__`` method returns ``NotImplemented``, then the cocall expression is treated as an ordinary call, and the ``__call__`` method of ``f`` is invoked. Objects which implement __cocall__ are expected to return an object obeying the iterator protocol. Cofunctions respond to __cocall__ the same way as ordinary generator functions respond to __call__, i.e. by returning a generator-iterator. Certain objects that wrap other callable objects, notably bound methods, will be given __cocall__ implementations that delegate to the underlying object. Grammar ------- The full syntax of a cocall expression is described by the following grammar lines: :: atom: cocall | <existing alternatives for atom> cocall: 'cocall' atom cotrailer* '(' [arglist] ')' cotrailer: '[' subscriptlist ']' | '.' NAME Note that this syntax allows cocalls to methods and elements of sequences or mappings to be expressed naturally. For example, the following are valid: :: y = cocall self.foo(x) y = cocall funcdict[key](x) y = cocall a.b.c[i].d(x) Also note that the final calling parentheses are mandatory, so that for example the following is invalid syntax: :: y = cocall f # INVALID New builtins, attributes and C API functions -------------------------------------------- To facilitate interfacing cofunctions with non-coroutine code, there will be a built-in function ``costart`` whose definition is equivalent to :: def costart(obj, *args, **kwds): try: m = obj.__cocall__ except AttributeError: result = NotImplemented else: result = m(*args, **kwds) if result is NotImplemented: raise TypeError("Object does not support cocall") return result There will also be a corresponding C API function :: PyObject *PyObject_CoCall(PyObject *obj, PyObject *args, PyObject *kwds) It is left unspecified for now whether a cofunction is a distinct type of object or, like a generator function, is simply a specially-marked function instance. If the latter, a read-only boolean attribute ``__iscofunction__`` should be provided to allow testing whether a given function object is a cofunction. Motivation and Rationale ======================== The ``yield from`` syntax is reasonably self-explanatory when used for the purpose of delegating part of the work of a generator to another function. It can also be used to good effect in the implementation of generator-based coroutines, but it reads somewhat awkwardly when used for that purpose, and tends to obscure the true intent of the code. Furthermore, using generators as coroutines is somewhat error-prone. If one forgets to use ``yield from`` when it should have been used, or uses it when it shouldn't have, the symptoms that result can be extremely obscure and confusing. Finally, sometimes there is a need for a function to be a coroutine even though it does not yield anything, and in these cases it is necessary to resort to kludges such as ``if 0: yield`` to force it to be a generator. The ``cocall`` construct address the first issue by making the syntax directly reflect the intent, that is, that the function being called forms part of a coroutine. The second issue is addressed by making it impossible to mix coroutine and non-coroutine code in ways that don't make sense. If the rules are violated, an exception is raised that points out exactly what and where the problem is. Lastly, the need for dummy yields is eliminated by making it possible for a cofunction to call both cofunctions and ordinary functions with the same syntax, so that an ordinary function can be used in place of a cofunction that yields zero times. Record of Discussion ==================== An earlier version of this proposal required a special keyword ``codef`` to be used in place of ``def`` when defining a cofunction, and disallowed calling an ordinary function using ``cocall``. However, it became evident that these features were not necessary, and the ``codef`` keyword was dropped in the interests of minimising the number of new keywords required. The use of a decorator instead of ``codef`` was also suggested, but the current proposal makes this unnecessary as well. It has been questioned whether some combination of decorators and functions could be used instead of a dedicated ``cocall`` syntax. While this might be possible, to achieve equivalent error-detecting power it would be necessary to write cofunction calls as something like :: yield from cocall(f)(args) making them even more verbose and inelegant than an unadorned ``yield from``. It is also not clear whether it is possible to achieve all of the benefits of the cocall syntax using this kind of approach. Prototype Implementation ======================== An implementation of an earlier version of this proposal in the form of patches to Python 3.1.2 can be found here: http://www.cosc.canterbury.ac.nz/greg.ewing/python/generators/cofunctions.h… If this version of the proposal is received favourably, the implementation will be updated to match. Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

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Avoiding nested for try..finally: atexit for functions?
by Nikolaus Rath 12 Dec '11

12 Dec '11

Hello, I often have code of the form: def my_fun(): allocate_res1() try: # do stuff allocate_res2() try: # do stuff allocate_res3() try: # do stuff finally: cleanup_res3() finally: cleanup_res2() finally: cleanup_res1() return With increasing number of managed resources, the indentation becomes really annoying, there is lots of line noise, and I don't like the fact that the cleanup is so far away from the allocation. I would much rather have something like this: def my_fun(): allocate_res1() atreturn.register(cleanup_res1) # do stuff allocate_res2() atreturn.register(cleanup_res2) # do stuff allocate_res3() atreturn.register(cleanup_res3) # do stuff return Has the idea of implementing such "on return" handlers ever come up? Maybe there is some tricky way to do this with function decorators? Best, -Nikolaus -- »Time flies like an arrow, fruit flies like a Banana.« PGP fingerprint: 5B93 61F8 4EA2 E279 ABF6 02CF A9AD B7F8 AE4E 425C

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PEP 3155 - Qualified name for classes and functions
by Antoine Pitrou 27 Nov '11

27 Nov '11

Hello, I would like to propose the following PEP for discussion and, if possible, acceptance. I think the proposal shouldn't be too controversial (I find it quite simple and straightforward myself :-)). I also have a draft implementation that's quite simple (http://hg.python.org/features/pep-3155). Regards Antoine. PEP: 3155 Title: Qualified name for classes and functions Version: $Revision$ Last-Modified: $Date$ Author: Antoine Pitrou <solipsis(a)pitrou.net> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 2011-10-29 Python-Version: 3.3 Post-History: Resolution: TBD Rationale ========= Python's introspection facilities have long had poor support for nested classes. Given a class object, it is impossible to know whether it was defined inside another class or at module top-level; and, if the former, it is also impossible to know in which class it was defined. While use of nested classes is often considered poor style, the only reason for them to have second class introspection support is a lousy pun. Python 3 adds insult to injury by dropping what was formerly known as unbound methods. In Python 2, given the following definition:: class C: def f(): pass you can then walk up from the ``C.f`` object to its defining class:: >>> C.f.im_class <class '__main__.C'> This possibility is gone in Python 3:: >>> C.f.im_class Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'function' object has no attribute 'im_class' >>> dir(C.f) ['__annotations__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__'] This limits again the introspection capabilities available to the user. It can produce actual issues when porting software to Python 3, for example Twisted Core where the issue of introspecting method objects came up several times. It also limits pickling support [1]_. Proposal ======== This PEP proposes the addition of a ``__qname__`` attribute to functions and classes. For top-level functions and classes, the ``__qname__`` attribute is equal to the ``__name__`` attribute. For nested classed, methods, and nested functions, the ``__qname__`` attribute contains a dotted path leading to the object from the module top-level. The repr() and str() of functions and classes is modified to use ``__qname__`` rather than ``__name__``. Example with nested classes --------------------------- >>> class C: ... def f(): pass ... class D: ... def g(): pass ... >>> C.__qname__ 'C' >>> C.f.__qname__ 'C.f' >>> C.D.__qname__ 'C.D' >>> C.D.g.__qname__ 'C.D.g' Example with nested functions ----------------------------- >>> def f(): ... def g(): pass ... return g ... >>> f.__qname__ 'f' >>> f().__qname__ 'f.g' Limitations =========== With nested functions (and classes defined inside functions), the dotted path will not be walkable programmatically as a function's namespace is not available from the outside. It will still be more helpful to the human reader than the bare ``__name__``. As the ``__name__`` attribute, the ``__qname__`` attribute is computed statically and it will not automatically follow rebinding. References ========== .. [1] "pickle should support methods": http://bugs.python.org/issue9276 Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

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Cofunctions - Back to Basics
by Greg Ewing 08 Nov '11

08 Nov '11

Still feeling in a peppish mood after the last round of PEP 335, I decided to revisit my cofunctions proposal. Last time round, I allowed myself to be excessively swayed by popular opinion, and ended up with something that lacked elegance and failed to address the original issues. So, I've gone right back to my initial idea. The current version of the draft PEP can be found here: http://www.cosc.canterbury.ac.nz/greg.ewing/python/generators/cd_current/co… together with updated examples here: http://www.cosc.canterbury.ac.nz/greg.ewing/python/generators/cd_current/Ex… and prototype implementation available from: http://www.cosc.canterbury.ac.nz/greg.ewing/python/generators/cofunctions.h… In a nutshell, the 'cocall' syntax is gone. Inside a cofunction, if an object being called implements __cocall__, then a cocall (implicit yield-from) is performed automatically, otherwise a normal call is made. -- Greg

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Changing str(someclass) to return only the class name
by Éric Araujo 02 Nov '11

02 Nov '11

Hi everyone, I’ve sometimes wished that str(someclass) were the natural way to get a class name, for example when writing __repr__ methods. Guido expressed the same wish recently, so I’ve made a patch for Python 3.3. In the whole standard library, only one module (optparse) and three test files (which were parsing the output of str(someclass) or using doctests) had to be updated because of this change: http://bugs.python.org/issue13224 One can say that this change would break code and should thus be rejected; one could argue that the previous behavior of str(someclass) was undefined, or an implementation detail. What say you? Cheers

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Draft PEP for the regularization of Python install layouts
by VanL 01 Nov '11

01 Nov '11

In part inspired by the virtualenv-in-the-stdlib PEP, I figured that it might be a good time to draft up a PEP to fix one of my regular annoyances: the ever-so-slightly different layouts for Python between platforms. For someone who develops on Windows and a Mac and deploys on Linux, this is a major pain in the rear - so much so that I always change my system Python install to have matching environments. I harbor no illusions that this is necessarily a thorn in anyone else's side, or that this will go anywhere. However, I have written it up and I would love any feedback. Thanks, Van ===================================== PEP: XXX Title: Consistent Python Environment Layout Across Platforms Version: $Revision$ Last-Modified: $Date$ Author: Van Lindberg <van.lindberg at gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 28-Oct-2011 Python-Version: 3.3 Post-History: 28-Oct-2011 Abstract ======== Python currently uses different environment layouts based upon the underlying operating system and the interpreter implementation language. This PEP proposes to regularize the directory layout for Python environments. Motivation ========== One of Python's strengths is its cross-platform appeal. Carefully- written Python programs are frequently portable between operating systems and Python implementations with very few changes. Over the years, substantial effort has been put into maintaining platform parity and providing consistent interfaces to available functionality, even when different underlying implementations are necessary (such as with ntpath and posixpath). One place where Python is unnecessarily different, however, is in the layout and organization of the Python environment. This is most visible in the name of the directory for binaries on the Windows platform ("Scripts") versus the name of the directory for binaries on every other platform ("bin"), but a full listing of the layouts shows substantial differences in layout and capitalization across platforms. Sometimes the include is capitalized ("Include"), and sometimes not; and the python version may or may not be included in the path to the standard library or not. The differences between platforms become particularly noticable when attempting to do cross-platform development in an isolated environment like a `virtualenv`_ or the proposed Python Virtual Environments. Differences between platforms get hard-coded in or worked around so that the same code will just work without having to artificially take the platform into account. This information can also be made available to Python tools (like virtualenv, `distribute`_, or `pip`_) so that environment directories can be more easily found and changed. Regularizing the Python environment layout across Python versions will lower the differences between Python versions, making it more consistent for all users, and enforcing a unified API for virtual environments and installers rather than ad-hoc detection or hard-coded values. .. _virtualenv: http://www.virtualenv.org .. _distribute: http://packages.python.org/distribute/ .. _pip: http://www.pip-installer.org/ Specification ============= When the Python binary is executed, it imports both sysconfig and distutils.command.install. These two modules contain slightly different versions of the environment layout. They are: From sysconfig: _INSTALL_SCHEMES = { 'posix_prefix': { 'stdlib': '{base}/lib/python{py_version_short}', 'platstdlib': '{platbase}/lib/python{py_version_short}', 'purelib': '{base}/lib/python{py_version_short}/site-packages', 'platlib': '{platbase}/lib/python{py_version_short}/site-packages', 'include': '{base}/include/python{py_version_short}', 'platinclude': '{platbase}/include/python{py_version_short}', 'scripts': '{base}/bin', 'data': '{base}', }, 'posix_home': { 'stdlib': '{base}/lib/python', 'platstdlib': '{base}/lib/python', 'purelib': '{base}/lib/python', 'platlib': '{base}/lib/python', 'include': '{base}/include/python', 'platinclude': '{base}/include/python', 'scripts': '{base}/bin', 'data' : '{base}', }, 'nt': { 'stdlib': '{base}/Lib', 'platstdlib': '{base}/Lib', 'purelib': '{base}/Lib/site-packages', 'platlib': '{base}/Lib/site-packages', 'include': '{base}/Include', 'platinclude': '{base}/Include', 'scripts': '{base}/Scripts', 'data' : '{base}', }, 'os2': { 'stdlib': '{base}/Lib', 'platstdlib': '{base}/Lib', 'purelib': '{base}/Lib/site-packages', 'platlib': '{base}/Lib/site-packages', 'include': '{base}/Include', 'platinclude': '{base}/Include', 'scripts': '{base}/Scripts', 'data' : '{base}', }, 'os2_home': { 'stdlib': '{userbase}/lib/python{py_version_short}', 'platstdlib': '{userbase}/lib/python{py_version_short}', 'purelib': '{userbase}/lib/python{py_version_short}/site-packages', 'platlib': '{userbase}/lib/python{py_version_short}/site-packages', 'include': '{userbase}/include/python{py_version_short}', 'scripts': '{userbase}/bin', 'data' : '{userbase}', }, 'nt_user': { 'stdlib': '{userbase}/Python{py_version_nodot}', 'platstdlib': '{userbase}/Python{py_version_nodot}', 'purelib': '{userbase}/Python{py_version_nodot}/site-packages', 'platlib': '{userbase}/Python{py_version_nodot}/site-packages', 'include': '{userbase}/Python{py_version_nodot}/Include', 'scripts': '{userbase}/Scripts', 'data' : '{userbase}', }, 'posix_user': { 'stdlib': '{userbase}/lib/python{py_version_short}', 'platstdlib': '{userbase}/lib/python{py_version_short}', 'purelib': '{userbase}/lib/python{py_version_short}/site-packages', 'platlib': '{userbase}/lib/python{py_version_short}/site-packages', 'include': '{userbase}/include/python{py_version_short}', 'scripts': '{userbase}/bin', 'data' : '{userbase}', }, 'osx_framework_user': { 'stdlib': '{userbase}/lib/python', 'platstdlib': '{userbase}/lib/python', 'purelib': '{userbase}/lib/python/site-packages', 'platlib': '{userbase}/lib/python/site-packages', 'include': '{userbase}/include', 'scripts': '{userbase}/bin', 'data' : '{userbase}', }, } From distutils.command.install: if sys.version < "2.2": WINDOWS_SCHEME = { 'purelib': '$base', 'platlib': '$base', 'headers': '$base/Include/$dist_name', 'scripts': '$base/Scripts', 'data' : '$base', } else: WINDOWS_SCHEME = { 'purelib': '$base/Lib/site-packages', 'platlib': '$base/Lib/site-packages', 'headers': '$base/Include/$dist_name', 'scripts': '$base/Scripts', 'data' : '$base', } INSTALL_SCHEMES = { 'unix_prefix': { 'purelib': '$base/lib/python$py_version_short/site-packages', 'platlib': '$platbase/lib/python$py_version_short/site-packages', 'headers': '$base/include/python$py_version_short/$dist_name', 'scripts': '$base/bin', 'data' : '$base', }, 'unix_home': { 'purelib': '$base/lib/python', 'platlib': '$base/lib/python', 'headers': '$base/include/python/$dist_name', 'scripts': '$base/bin', 'data' : '$base', }, 'unix_user': { 'purelib': '$usersite', 'platlib': '$usersite', 'headers': '$userbase/include/python$py_version_short/$dist_name', 'scripts': '$userbase/bin', 'data' : '$userbase', }, 'nt': WINDOWS_SCHEME, 'nt_user': { 'purelib': '$usersite', 'platlib': '$usersite', 'headers': '$userbase/Python$py_version_nodot/Include/$dist_name', 'scripts': '$userbase/Scripts', 'data' : '$userbase', }, 'os2': { 'purelib': '$base/Lib/site-packages', 'platlib': '$base/Lib/site-packages', 'headers': '$base/Include/$dist_name', 'scripts': '$base/Scripts', 'data' : '$base', }, 'os2_home': { 'purelib': '$usersite', 'platlib': '$usersite', 'headers': '$userbase/include/python$py_version_short/$dist_name', 'scripts': '$userbase/bin', 'data' : '$userbase', }, } There is an API call (sysconfig.get_config_var) that is used to resolve these various variables, but there is no way to set them other than directly clobbering _INSTALL_SCHEMES/INSTALL_SCHEME dicts holding the information (in both sysconfig and in distutils.command.install). This PEP proposes to change the default installation layout as follows: - Define a new config var, py_binaries, and a corresponding module-level variable, _PY_BINARIES with the default value 'bin' - Change sysconfig's _INSTALL_SCHEMES to INSTALL_SCHEMES, and make it part of the public API. A default scheme equivalent to the current posix_prefix would be added and made to be the default. - To support virtual environments, a special key "current" would be added to INSTALL_SCHEMES. The value associated with "current" would be the name of the currently active scheme. During activation, a virtual environment could place a custom layout in the INSTALL_SCHEMES dict and set "current" to the custom scheme. This could also be used to support legacy or custom layouts. This would be done as follows:: from _collections import defaultdict _DEFAULT_SCHEME = { 'stdlib': '{userbase}/lib/python{py_version_short}', 'platstdlib': '{userbase}/lib/python{py_version_short}', 'purelib': '{userbase}/lib/python{py_version_short}/site-packages', 'platlib': '{userbase}/lib/python{py_version_short}/site-packages', 'include': '{userbase}/include/python{py_version_short}', 'headers': '{userbase}/include/python{py_version_short}', 'scripts': '{userbase}/{py_binaries}', 'data' : '{userbase}', } INSTALL_SCHEMES = defaultdict(lambda: _DEFAULT_SCHEME.copy()) INSTALL_SCHEMES['install'] = _DEFAULT_SCHEME INSTALL_SCHEMES['current'] = 'install' - Change distutils.command.install to use information from INSTALL_SCHEMES instead of embedding its own copy of the dict. By using a defaultdict, prior users of the API could transparently query the dict using the platform value and receive back a reasonable response. The current virtual environment would be retrievable by getting INSTALL_SCHEMES[INSTALL_SCHEMES['current']], and the installed scheme retrievable by getting INSTALL_SCHEMES['install']. Open Issues ========== By default, OS X Framework installs use a format that is different than the one specified above. Possible responses would be to change the layout just for OS X, but special-casing as little as possible. The double indirection to get the current install scheme is a little ugly, but I am not sure what would be better. This explicitly removes the compatibility code in distutils for 2.x install layouts (particularly layouts pre-2.3). This is not considered a major issue, as 2.2 code will largely not be compatible with 3.3, and an individual user could add a custom layout if needed. Based on actual use, some of the distinctions that appeared necessary when distutils and sysconfig were first written (platlib, purelib, include, headers) seem to be duplicative. As written above, the distinctions are maintained for ease of moving to the standardized layout, but it may be worth choosing just one name and sticking with it.

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