Python-ideas November 2011

python-ideas@python.org

55 participants
28 discussions

Specify number of items to allocate for array.array() constructor

by Sven Rahmann

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

1 month, 1 week

i18n and Python tracebacks

by Andre Roberge

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

7 years, 5 months

Re: [Python-ideas] Cofunctions - Back to Basics

by Mark Shannon

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.

7 years, 5 months

Cofunctions PEP - Revision 4

by Greg Ewing

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:

7 years, 5 months

Avoiding nested for try..finally: atexit for functions?

by Nikolaus Rath

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

8 years, 3 months

Is there a reason why file.readlines() doesn't/can't return an iterator?

by Giampaolo Rodolà

This is problably too late and I'm probably missing something but given amount of generators/iterators introduced in python 3.X (http://docs.python.org/release/3.0.1/whatsnew/3.0.html#views-and-iterators-…) file.readlines() seems a good case where an iterator can be more appropriate than a list. I realized it while writing this recipe: http://code.activestate.com/recipes/577968-log-watcher-tail-f-log/ In this specific case, having readlines() yield a single line at a time would save a lot of memory. Maybe we can introduce a new parameter to do this? Regards, --- Giampaolo http://code.google.com/p/pyftpdlib/ http://code.google.com/p/psutil/

8 years, 4 months

Making min and max behave more like any and all

by Jakob Bowyer

I just had something pointed out to me in #python that min and max accept *args, I know for a fact that any and all only use a single iterable password. Shouldn't we allow either one of the two ideas? Either max/min take only iterable arguments. OR Allow any/all to use *args In the second case this becomes legal any(1 in a, 2 in b, 3 in c) In the first case this becomes illegal max(1,2,3,4,5)

8 years, 4 months

PEP 3155 - Qualified name for classes and functions

by Antoine Pitrou

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:

8 years, 4 months

proto-pep capturing the concurrency discusion

by Mike Meyer

I tried to capture all the ideas generated during the concurrency discussion early this month in the form of an informational PEP. I believe that information is worth preserving where others can find it, and a PEP seems like a logical place. Since it doesn't make a specific proposal, it's informational PEP. Possibly this is an abuse of the PEP mechanism, in which case I'll find another forum for it. The point this time is not to debate the ideas proposed, but to check for errors and omissions. If there's an alternative I missed, let me know. If there's a problem with some alternative I missed, let me know. If I think it's already in the document, I'll probably point it out and ask for suggestions on how to make it more obvious. Thanks, <mike

8 years, 4 months

Except block for with-statement

by Carl M. Johnson

I was looking through the "What's New for Python 3.3" file and I saw this example code: try: with open("document.txt") as f: content = f.read() except FileNotFoundError: print("document.txt file is missing") except PermissionError: print("You are not allowed to read document.txt") and I thought it would be more elegant if you could drop the try. with open("document.txt") as f: content = f.read() except FileNotFoundError: print("document.txt file is missing") except PermissionError: print("You are not allowed to read document.txt") I assume that this has already been proposed and rejected. Does anyone have a good explanation of why? ISTM that the with-statement is mostly just a way of abstracting out try-except blocks, so it might be nice to have a way of handling errors that pop up during the initialization phase instead of just the code-block phase. I guess one obvious counter argument is that naive programmers might think that the "except" applies to things in the block instead of just things in the initializer. But naive programmers think lots of wrong things. ;-)

8 years, 4 months

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Python-ideas November 2011