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
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.
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...
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:
(reposting this from Google Group once more as the previous post missed
Mailing List, because I was not subscribed in Mailman)
*Static module/package inspection*
Abstract:
- static: without execution (as opposed to dynamic)
- module/package: .py or __init__.py file
- inspection: get an overview of the contents
*What should this do?*
*
*
The proposal to add a mechanism to Python interpreter to get an outline of
module/package contents without importing or executing module/package. The
outline includes names of classes, functions, variables. It also should
contain values for variables that could be provided without sophisticated
calculations (e.g. a string, integer, but probably not expressions as it
may lead to security leaks).
*Why?*
*
*
*user story PEPx.001:*
As a Python package maintainer, I find it bothersome to repeatedly write
bolierplate code (e.g. setup.py) to package my single file module. The
reason I should write setup.py is to provide version and description info.
This info is already available in my module source code. So I need to
either copy/paste the info from the module manually, or to import (and
hence execute) my module during packaging and installation, which I don't
want either, because modules are often installed with root privileges.
With this PEP, packing tool will be able to extract meta information from
my module without executing it or without me manually copying version
fields into some 'package configuration file'.
*user story PEPx.002:*
As a Python Application developer, I find it really complicated to provide
plugin extension subsystem for my users. Users need a mechanism to switch
between different versions of the plugin, and this mechanism is usually
provided by external tool such as setuptools to manage and install multiple
versions of plugins in local Python package repository. It is rather hard
to create an alternative approach, because you are forced to maintain
external meta-data about your plugin modules even in case it is already
available inside the module.
With this PEP, Python Application will be able to inspect
meta-data embedded inside of plugins before choosing which version to load.
This will also provide a standard mechanism for applications to check
modules returned by packaging tools without executing them. This will
greatly simplify writing and debugging custom plugins loaders on different
platforms.
*Feedback goal*
At this stage I'd like to a community response to two separate questions:
1. If everybody feels this functionality will be useful for Python
2. If the solution is technically feasible
Hi,
According to its own documentation, the merge() function in the heapq
module is similar to sorted(). However, it has none of the key and
reverse parameters that sorted() has. I think they could be just as
useful as in sorted().
http://docs.python.org/dev/library/heapq.html#heapq.mergehttp://docs.python.org/dev/library/functions.html#sorted
First of all what do you think of the idea?
I’m working on a patch for a key parameter. I think it can be pretty
straightforward, but I’ll measure if the "no key" case becomes slower
(calls to lambda x: x) At worst we can always duplicate the loop.
However, I am not sure how to implement reverse. Not all values have an
"opposite value" that reverses order, _nsmallest and _nlargest are quite
different, and merge uses neither.
Anyway, if I get this to work, it will be my first contribution to
CPython. I’m trying to follow the Developer’s guide, but is there
something else I should be aware of?
http://docs.python.org/devguide/
Regards,
--
Simon Sapin
I expected to find the answer to this question in FAQ, but because there is
no FAQ I ask it anyway.
How about adding a new standard dict-like container type that allows access
using . (dot) to its members instead of ['index']?
Why? It is convenient to write options.help instead of options['halp'] etc.
Example:
>>> mydict = container(someprop=somevalue)
>>> mydict['someprop']
somevalue
>>> mydict.someprop
somevalue
>>> mydict.otherprop
Exception KeyError ...
I know that it is easy to implement, but wouldn't it be nice to make it
available by default?
A side benefit of having this in stdlib is that newbies will be aware of
the behaviour of derived classes without having to understand the mechanics
of magic methods.
Is it a good idea to have a list of modules in tracker?
So that you can have a summary of all bugs per component similar to Trac's
http://trac.edgewall.org/milestone/0.12.3
--
anatoly t.
(redirecting to python-ideas - coroutine proposals are nowhere near
mature enough for python-dev)
On Wed, Jan 25, 2012 at 5:35 PM, Matt Joiner <anacrolix(a)gmail.com> wrote:
> If someone can explain what's stopping real coroutines being into
> Python (3.3), that would be great.
The general issues with that kind of idea:
- the author hasn't offered the code for inclusion and relicensing
under the PSF license (thus we legally aren't allowed to do it)
- complexity
- maintainability
- platform support
In the specific case of coroutines, you have the additional hurdle of
convincing people whether or not they're a good idea at all.
Cheers,
Nick.
--
Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia
Just running another half-baked "easy" idea up to see what others
think of it.
One of the cases where EAFP is clumsy is if the "forgiveness" portion
is actually retrying what failed after fixing things. I just ran into
this in real code, and the resulting discussion suggested this change.
Eiffel's exception-handling mechanism is built assuming that's the
*standard* way to do things. Seems to work ok, but if I had to choose
one of the two, I'll take try/except.
So here's the proposal:
A single new keyword/clause, "retry". It has the same syntax as an
"except" clause, can be used anywhere "except" can be used, and can be
intermingled with them in the same try statement. There's probably a
better syntax, but this is easy to describe.
The behavior change from except is that instead of exiting the "try"
statement when the "retry" clause ends, it restarts the try
clause. In python, this code:
try:
# try block
except:
# except block
retry:
# retry block
else:
# else block
finally:
# finally block
Would provide the same behavior as this code:
while True:
try:
# try block
except:
# except block
retry:
# retry block
else:
# else block
finally:
# finally block
break
Where except & retry can be repeated, include exceptions to check
against, and intermixed with each other. The break would propagate up
to the else block and any except blocks if there was no finally. If
there's no else, the break from it winds up at the end of the try.
The use case, as mentioned, is avoiding doing EAFP without repeating
the code in the exception handler. I.e., the choices without retry
are things like (LBYL):
if not isinstance(x, my_class):
x = fixup(x)
mangle(x)
other_stuff(x)
or EAFP:
try:
mangle(x)
except:
x = fixup(x)
mangle(x)
other_stuff(x)
or:
while True:
try:
mangle(x)
break
except:
x = fixup(x)
other_stuff(x)
with:
try:
mangle(x)
retry:
e = fixup(x)
other_stuff(x)
My gut reaction is cool, but not clear it's worth a new keyword. So
maybe there's a tweak to the except clause that would have the same
effect, but I don't see anything obvious.
<mike
I often find it useful to profile small sections to code in a running
application to pinpoint a known bottleneck. The cProfile and profile
modules don't make this easy as they stand, requiring the section of code
to be wrapped into a single statement with can be called as a string using
exec(). This always feels a bit clumsy to me, so my idea is simply to
provide __enter__ and __exit__ methods to cProfile.Profile() and
profile.Profile(). From a quick look at the code it seems that this should
be very easy for cProfile, and slightly less easy for profile (I can't
quite figure out how to deal with the set_cmd() line, or how important it
is - maybe someone can help?).
Any major objections to this?
David
