Mailman 3 March 2012 - Python-ideas

@classproperty, @abc.abstractclasspropery, etc.
by K. Richard Pixley Dec. 16, 2020

Dec. 16, 2020

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

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Specify number of items to allocate for array.array() constructor
by Sven Rahmann Feb. 22, 2020

Feb. 22, 2020

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

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Yielding through context managers
by Joshua Bartlett Jan. 9, 2013

Jan. 9, 2013

I'd like to propose adding the ability for context managers to catch and handle control passing into and out of them via yield and generator.send() / generator.next(). For instance, class cd(object): def __init__(self, path): self.inner_path = path def __enter__(self): self.outer_path = os.getcwd() os.chdir(self.inner_path) def __exit__(self, exc_type, exc_val, exc_tb): os.chdir(self.outer_path) def __yield__(self): self.inner_path = os.getcwd() os.chdir(self.outer_path) def __send__(self): self.outer_path = os.getcwd() os.chdir(self.inner_path) Here __yield__() would be called when control is yielded through the with block and __send__() would be called when control is returned via .send() or .next(). To maintain compatibility, it would not be an error to leave either __yield__ or __send__ undefined. The rationale for this is that it's sometimes useful for a context manager to set global or thread-global state as in the example above, but when the code is used in a generator, the author of the generator needs to make assumptions about what the calling code is doing. e.g. def my_generator(path): with cd(path): yield do_something() do_something_else() Even if the author of this generator knows what effect do_something() and do_something_else() have on the current working directory, the author needs to assume that the caller of the generator isn't touching the working directory. For instance, if someone were to create two my_generator() generators with different paths and advance them alternately, the resulting behaviour could be most unexpected. With the proposed change, the context manager would be able to handle this so that the author of the generator doesn't need to make these assumptions. Naturally, nested with blocks would be handled by calling __yield__ from innermost to outermost and __send__ from outermost to innermost. I rather suspect that if this change were included, someone could come up with a variant of the contextlib.contextmanager decorator to simplify writing generators for this sort of situation. Cheers, J. D. Bartlett

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i18n and Python tracebacks
by Andre Roberge Oct. 28, 2012

Oct. 28, 2012

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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Re: [Python-ideas] Cofunctions - Back to Basics
by Mark Shannon Oct. 21, 2012

Oct. 21, 2012

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.

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Cofunctions PEP - Revision 4
by Greg Ewing Oct. 13, 2012

Oct. 13, 2012

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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sys.implementation
by Eric Snow April 26, 2012

April 26, 2012

In October 2009 there was a short flurry of interest in adding "sys.implementation" as an object to encapsulate some implementation-specific information [1]. Does anyone recollect where this proposal went? Would anyone object to reviving it (or a variant)? -eric [1] http://mail.python.org/pipermail/python-dev/2009-October/092893.html

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Re: [Python-ideas] make __closure__ writable
by Mark Shannon April 10, 2012

April 10, 2012

Yury Selivanov wrote: > On 2012-03-16, at 2:57 PM, Yury Selivanov wrote: > >> Decorators can be nested, and what you can do in this case is to >> find the most inner-wrapped function by traversing the '__wrapped__' >> attributes (and check that the function you found is the actual >> original function). After that you can play with its attributes, >> but you can't simply substitute the function object, as the inner >> decorator won't use it. So sometimes you have to work with the >> function object without a way of substituting it. > > And that applies to the situations where decorators are not enough > and you have to work on the opcode level. Which you can do with a decorator. Would this do what you want? def f_with_new_closure(f, closure): return types.FunctionType(f.__code__, f.__globals__, f.__name__, f.__defaults__, closure) Cheers, Mark.

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Thread stopping
by Andrew Svetlov April 2, 2012

April 2, 2012

I propose to add Thread.interrupt() function. th.interrupt() will set a flag in ThreadState structure. When interpreter switches to next thread it will check that flag. If flag is on then ThreadInterruptionError will be raised in thread context. If thread has blocked via threading locks (Lock, RLock, Condition, Semaphore etc) — exception is raised also. Of course we cannot interrupt thread if it has been locked by C Extension call or just waiting for blocking IO. But, I think, the way to force stopping of some thread can be useful and has no incompatibility effect. The standard way to stop thread is the sending some message which is the signal to thread for termination. Pushing None or sentinel into thread message queue for example. Other variants: — check 'interrupted' state explicitly by call threading.current_thread().interrupted() than do what you want. — do the same as boost.threading does: check state in direct interruption point and locks if interruption is enabled. BTW, we can disable interruption mechanic by default and use it only if switched on by threading.enable_interruption() What do you think? -- Thanks, Andrew Svetlov

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Implied try blocks
by Calvin Spealman March 29, 2012

March 29, 2012

Given that for some time the try/except/else form is standard and encouraged, I've found the following pattern really common in my code: try: r = some_single_statement() except TypeError: print "oh no!" raise OhNoException() else: p = prepare(r) print "I got", p Where the try block ever doing more than one thing feels dangerous or sloppy, because I want to make sure I know exactly where the exception comes from. The else block becomes the long tail and the try is just the head. This makes the try block itself seem heavy. What if we allowed this to be implied and except/else blocks bound to the previous statement? A try block would be an optional form, and mostly left for multi-block try's r = some_single_statement() except TypeError: print "oh no!" raise OhNoException() else: p = prepare(r) print "I got", p I think it reads acceptably. With a try: block your eye leads up right to that one statement. There is no ambiguity to deal with, that I can tell. I'm not sure if this is a great idea, but I don't dislike it. -- Read my blog! I depend on your acceptance of my opinion! I am interesting! http://techblog.ironfroggy.com/ Follow me if you're into that sort of thing: http://www.twitter.com/ironfroggy

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