Python-ideas January 2012

python-ideas@python.org

68 participants
33 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

6 months, 4 weeks

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, 10 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, 11 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, 11 months

Re: [Python-ideas] PEP x: Static module/package inspection

by anatoly techtonik

(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

8 years, 5 months

A key parameter for heapq.merge

by Simon Sapin

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.merge http://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

8 years, 7 months

Dict-like object with property access

by anatoly techtonik

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.

8 years, 7 months

Module field in tracker

by anatoly techtonik

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.

8 years, 7 months

Re: [Python-ideas] Coroutines and PEP 380

by Nick Coghlan

(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

8 years, 7 months

Retrying EAFP without DRY

by Mike Meyer

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

8 years, 7 months

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Python-ideas January 2012