Mailman 3 November 2010 - Python-ideas

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

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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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Bring back callable()
by Antoine Pitrou 07 Mar '11

07 Mar '11

Hello, Python 3 has removed callable() under the justification that's it's not very useful and duck typing (EAFP) should be used instead. However, it has since been felt by many people that it was an annoying loss; there are situations where you truly want to know whether something is a callable without actually calling it (for example when writing sophisticated decorators, or simply when you want to inform the user of an API misuse). The substitute of writing `isinstance(x, collections.Callable)` is not good, 1) because it's wordier 2) because collections is really not an intuitive place where to look for a Callable ABC. So, I would advocate bringing back the callable() builtin, which was easy to use, helpful and semantically sane. Regards Antoine.

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Possible PEP 380 tweak
by Guido van Rossum 11 Dec '10

11 Dec '10

[Changed subject] > On 2010-10-25 04:37, Guido van Rossum wrote: >> This should not require threads. >> >> Here's a bare-bones sketch using generators: [...] On Mon, Oct 25, 2010 at 3:19 AM, Jacob Holm <jh(a)improva.dk> wrote: > If you don't care about allowing the funcs to raise StopIteration, this > can actually be simplified to: [...] Indeed, I realized this after posting. :-) I had several other ideas for improvements, e.g. being able to pass an initial value to the reduce-like function or even being able to supply a reduce-like function of one's own. > More interesting (to me at least) is that this is an excellent example > of why I would like to see a version of PEP380 where "close" on a > generator can return a value (AFAICT the version of PEP380 on > http://www.python.org/dev/peps/pep-0380 is not up-to-date and does not > mention this possibility, or even link to the heated discussion we had > on python-ideas around march/april 2009). Can you dig up the link here? I recall that discussion but I don't recall a clear conclusion coming from it -- just heated debate. Based on my example I have to agree that returning a value from close() would be nice. There is a little detail, how multiple arguments to StopIteration should be interpreted, but that's not so important if it's being raised by a return statement. > Assuming that "close" on a reduce_collector generator instance returns > the value of the StopIteration raised by the "return" statements, we can > simplify the code even further: > > > def reduce_collector(func): > try: > outcome = yield > except GeneratorExit: > return None > while True: > try: > val = yield > except GeneratorExit: > return outcome > outcome = func(outcome, val) > > def parallel_reduce(iterable, funcs): > collectors = [reduce_collector(func) for func in funcs] > for coll in collectors: > next(coll) > for val in iterable: > for coll in collectors: > coll.send(val) > return [coll.close() for coll in collectors] > > > Yes, this is only saving a few lines, but I find it *much* more readable... I totally agree that not having to call throw() and catch whatever it bounces back is much nicer. (Now I wish there was a way to avoid the "try..except GeneratorExit" construct in the generator, but I think I should stop while I'm ahead. :-) The interesting thing is that I've been dealing with generators used as coroutines or tasks intensely on and off since July, and I haven't had a single need for any of the three patterns that this example happened to demonstrate: - the need to "prime" the generator in a separate step - throwing and catching GeneratorExit - getting a value from close() (I did have a lot of use for send(), throw(), and extracting a value from StopIteration.) In my context, generators are used to emulate concurrently running tasks, and "yield" is always used to mean "block until this piece of async I/O is complete, and wake me up with the result". This is similar to the "classic" trampoline code found in PEP 342. In fact, when I wrote the example for this thread, I fumbled a bit because the use of generators there is different than I had been using them (though it was no doubt thanks to having worked with them intensely that I came up with the example quickly). So, it is clear that generators are extremely versatile, and PEP 380 deserves several good use cases to explain all the API subtleties. BTW, while I have you, what do you think of Greg's "cofunctions" proposal? -- --Guido van Rossum (python.org/~guido)

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Detecting circular imports ?
by Tarek Ziadé 06 Dec '10

06 Dec '10

Hello, This morning I tried to fix an issue for a while before I realized I had a circular import. This issue is not obvious because you get a cryptic error, like an AttributeError and it can tak a while before finding out. I don't know of this was mentioned before, or how hard it would be. But it would be nice if Python had a specific "CircularImportError" raised in that case, or something.. That would be a fabulous hint for developers. Cheers Tarek -- Tarek Ziadé | http://ziade.org

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`issubclass` shouldn't be raising exceptions for non-type inputs
by cool-RR 04 Dec '10

04 Dec '10

`issubclass(1, list)` raises an Exception, complaining that `1` is not a class. This is wrong in my opinion. It should just return False. Use case: I have an object which can be either a list, or a string, or a callable, or a type. And I want to check whether it's a sub-class of some base class. So I don't think I should be taking extra precautions before using `issubclass`: If my object is not a subclass of the given base class, I should just get `False`. Ram.

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Adding `Unpicklable` to the `collections` module
by cool-RR 04 Dec '10

04 Dec '10

Hello. Recently I had the need to filter objects based on whether they're picklable or not: http://stackoverflow.com/questions/4080688/python-pickling-a-dict-with-some… I'm not sure what's a good way to check for a specific object whether it's picklable. <http://stackoverflow.com/questions/4080688/python-pickling-a-dict-with-some…>This led me to think: Maybe we should have an `Unpicklable` abstract base class in the `collections` module? Then various unpicklable classes, like locks, files or widgets, could inherit from this class to signify that they cannot be pickled. What do you think? Ram.

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Truly international Python
by Dima Tisnek 02 Dec '10

02 Dec '10

Let's recall Guido's old Computer Programming for Everybody (CP4E) proposal. Nowadays that Python is established, it's high time to push Python into education, especially first programming language education. I think, in the modern world it means pre-school. Now the larger part of the world's children doesn't learn English before school, therefore we need to have truly localized Python. Some might recall a Python derivative demo with unicode variable names (link anyone?). I think we ought to go further. For example, consider imaginary language pig latin: """This does that""" --> """Thiso acto thato""" # docstrings __version__ = (1,2,3) --> __versio__ = (1,2,3) # variable names import time --> importo chrono # standard module names def foo(): pass --> defo foo(): passo # Python keywords "foo".upper() --> "foo".uppero() # standard library raise Xx("undefined") --> raisio Xx("indifinito") # errors #!/usr/bin/python --> #!/usr/bin/pythono # executable name #!/usr/bin/python --> #!/usero/binaro/pythono # name and path Of course there are concerns for many languages: Each language needs to establish stable translations for keywords, basic types, standard modules, methods in standard modules, etc. Some languages don't support word spaces natively Some languages have different punctuation rules, e.g. comma for decimal point Some languages use different quotes RTL languages spell words RTL yet (some/all?) spell numbers LTR Hopefully none has to recreate 10,000-separator system ;-) Anyhow, it's not the issue of core Python to support particular languages, what is needed is: the concept that this is needed, and the base where from a particular localization can evolve from Here, a fun example, how Python might look like in google-translate-simplified-chinese. Blame google, not me as I know very little about this language. """This does that""" --> """这是""" # docstrings __version__ = (1,2,3) --> __版本__ = (1,2,3) # variable names import time --> 进口时间 # standard module names def foo(): pass --> 业美孚（）：通过 # Python keywords "foo".upper() --> “富” 上层（） # standard library raise Xx("undefined") --> 提高。二十（“未定义”） # errors #!/usr/bin/python --> #!/usr/bin/蛇 # executable name #!/usr/bin/python --> #!/用户/二进制/蛇 # name and path I track this here and will update with the received feedback: http://pythonic-wisdom.blogspot.com/2010/11/truly-international-python.html

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In-process interpreters
by Dima Tisnek 30 Nov '10

30 Nov '10

Perhaps you can clarify what exactly you want to do. I can see at least 2 distict cases 1. Multithreaded web server (or even browser) * interpreters need separate imports at least pure python modules should be loaded and unloaded, different versions of same python modules could be used by different interpreters if different versions of C extensions are needed, different dynamic loading might be needed btw, is it still the case that C extensions cannot be unloaded? * interpreters need separate memory regions so that individual interpreters can be killed quickly 2. Long-running process that executes many small independent user scripts (e.g. phone) * memory could be shared as long as cross-references are forbidden garbage collector hopefully kill circular references after interpreter is terminated * long-running process might want to reload a C extension, ouch * a possible workaround would be execute only one interpreter at a time, somehow pickling user script state?

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gc callbacks
by Kristján Valur Jónsson 29 Nov '10

29 Nov '10

Hi there. Some months back I mentioned that I had other stuff in store for GC. Here is an idea for you. This particular idea is just a generalization of a system we've used in EVE for years now: garbage collection callbacks. The need, originally, was to be able to quantify the time spent in garbage collection runs. Since they occur out of direct control of the application, we needed to have python tell us about it somehow. We added gc.register_callback(), a function that added a callable to an internal list of functions that get called on two occations: 1) When a garbage collection run Is about to start 2) When a garbage collection run has finished. The cases are distinguished using an integer argument. The callbacks are invoked from gc with gc inhibited from reentry, so that the callbacks cannot themselves cause another gc run to commence. What we traditionally use this for is to start and stop a performance timer and other stats. More recently though, we have found another very important use for this. When gc finds uncollectable objects, they are put in the gc.garbage list. This then needs to be handled by the application. However, there is no particularly good way for the application to do this, except to periodically check this list. With the above callback, modules that create uncollectable objects, such as classes with __del__ methods, can register their callback. At the end of a gc run, they can then walk gc.garbage and take appropriate action for those objects it recognizes. So, what do you think? This is a very simple addition to gc, orthogonal to everything and easily implemented. I also think it is very useful. K

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