Python-ideas February 2012

python-ideas@python.org

82 participants
62 discussions

@classproperty, @abc.abstractclasspropery, etc.

by K. Richard Pixley

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 months, 1 week

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 year, 8 months

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

8 years, 12 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.

9 years

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:

9 years

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

9 years, 7 months

Re: [Python-ideas] doctest

by Ian Bicking

On Feb 17, 2012 4:12 PM, "Nick Coghlan" <ncoghlan(a)gmail.com> wrote: > > On Sat, Feb 18, 2012 at 7:57 AM, Mark Janssen <dreamingforward(a)gmail.com> wrote: > > Anyway... of course patches welcome, yes... ;^) > > Not really. doctest is for *testing code example in docs*. If you try > to use it for more than that, it's likely to drive you up the wall, so > proposals to make it more than it is usually don't get a great > reception (docs patches to make it's limitations clearer are generally > welcome, though). The stdib solution for test driven development is > unittest (the vast majority of our own regression suite is written > that way - only a small proportion uses doctest). This pessimistic attitude is why doctest is challenging to work with at times, not anything to do with doctest's actual model. The constant criticisms of doctest keep contributors away, and keep its many resolvable problems from being resolved. > An interesting third party alternative that has been created recently > is behave: http://crate.io/packages/behave/ This style of test is why it's so sad that doctest is ignored and unmaintained. It's based on testing patterns developed by people who care to promote what they are doing, but I'm of the strong opinion that they are inferior to doctest. Ian

9 years, 7 months

revisit pep 377: good use case?

by Craig Yoshioka

So I've recently been trying to implement something for which I had hoped the 'with' statement would be perfect, but it turns out, won't work because Python provides no mechanism by which to skip the block of code in a with statement. I want to create some functionality to make it easy too wrap command line programs in a caching architecture. To do this there are some things that need to happen before and after the wrapped CLI program is called, a try,except,finally version might look like this: def cachedcli(*args): try: hashedoutput = hashon(args) if iscached(): return hashedoutput acquirelock() cli(*args,hashedoutput) iscached(True) return hashedoutput except AlreadyLocked: while locked: wait() return example(*args) finally: releaselock() the 'with' version would look like def cachedcli(*args) hashedpath = hashon(args) with cacheon(hashedpath): cli(hashedpath,*args) return hashedpath So obviously the 'with' statement would be a good fit, especially since non-python programmers might be wrapping their CLI programs... unfortunately I can't use 'with' because I can't find a clean way to make the with block code conditional. PEP377 suggested some mechanics that seemed a bit complicated for getting the desired effect, but I think, and correct me if I'm wrong, that the same effect could be achieved by having the __enter__ function raise a StopIteration that would be caught by the context and skip directly to the __exit__ function. The semantics of this even make some sense too me, since the closest I've been able to get to what I had hoped for was using an iterator to execute the appropriate code before and after the loop block: def cachedcli(*args) hashedpath = hashon(args) for _ in cacheon(hashedpath): cli(hashedpath,*args) return hashedpath this still seems non-ideal to me...

9 years, 7 months

doctest

by Mark Janssen

I find myself wanting to use doctest for some test-driven development, and find myself slightly frustrated and wonder if others would be interested in seeing the following additional functionality in doctest: 1. Execution context determined by outer-scope doctest defintions. 2. Smart Comparisons that will detect output of a non-ordered type (dict/set), lift and recast it and do a real comparison. Without #1, "literate testing" becomes awash with re-defining re-used variables which, generally, also detracts from exact purpose of the test -- this creates testdoc noise and the docs become less useful. Without #2, "readable docs" nicely co-aligning with "testable docs" tends towards divergence. Perhaps not enough developers use doctest to care, but I find it one of the more enjoyable ways to develop python code -- I don't have to remember test cases nor go through the trouble of setting up unittests. AND, it encourages agile development. Another user wrote a while back of even having a built-in test() method. Wouldn't that really encourage agile developement? And you wouldn't have to muddy up your code with "if __name__ == "__main__": import doctest, yadda yadda". Anyway... of course patches welcome, yes... ;^) mark

9 years, 7 months

More helpers in reprlib (was Re: doctest)

by Nick Coghlan

On Wed, Feb 29, 2012 at 8:19 AM, Barry Warsaw <barry(a)python.org> wrote: > On Feb 27, 2012, at 05:44 PM, Ian Bicking wrote: > >>Doctest needs reliable repr's more than reversable repr's, and you can create >>them using that. You'll still get a lot of <foobar.Foobar object at >>0x391a9df> strings, which suck... but if you are committed to doctest then >>maybe better to provide good __repr__ methods on your custom objects! > > +1 even if you don't use doctests! I can't tell you how many times adding a > useful repr has vastly improved debugging. I urge everyone to flesh out your > reprs with a little bit of useful information so you can quickly identify your > instances at a pdb prompt. Since this question came up recently, what do you think of adding some more helpers to reprlib to make this even easier to do? I know I just added some utility functions to PulpDist [1] to avoid reinventing that particular wheel for each of my class definitions. Cheers, Nick. [1] http://git.fedorahosted.org/git/?p=pulpdist.git;a=blob;f=src/pulpdist/core/… Cheers, Nick. -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

9 years, 8 months

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Python-ideas February 2012