Mailman 3 August 2015 - 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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Implicit string literal concatenation considered harmful?
by Guido van Rossum March 14, 2018

March 14, 2018

I just spent a few minutes staring at a bug caused by a missing comma -- I got a mysterious argument count error because instead of foo('a', 'b') I had written foo('a' 'b'). This is a fairly common mistake, and IIRC at Google we even had a lint rule against this (there was also a Python dialect used for some specific purpose where this was explicitly forbidden). Now, with modern compiler technology, we can (and in fact do) evaluate compile-time string literal concatenation with the '+' operator, so there's really no reason to support 'a' 'b' any more. (The reason was always rather flimsy; I copied it from C but the reason why it's needed there doesn't really apply to Python, as it is mostly useful inside macros.) Would it be reasonable to start deprecating this and eventually remove it from the language? -- --Guido van Rossum (python.org/~guido)

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proposal: "python -m foo" should bind sys.modules['foo']
by Cameron Simpson Jan. 18, 2017

Jan. 18, 2017

Hello all, This is a writeup of a proposal I floated here: https://mail.python.org/pipermail/python-list/2015-August/694905.html last Sunday. If the response is positive I wish to write a PEP. Briefly, it is a natural expectation in users that the command: python -m module_name ... used to invoke modules in "main program" mode on the command line imported the module as "module_name". It does not, it imports it as "__main__". An import within the program of "module_name" makes a new instance of the module, which causes cognitive dissonance and has the side effect that now the program has two instances of the module. What I propose is that the above command line _should_ bind sys.modules['module_name'] as well as binding '__main__' as it does currently. I'm proposing that the python -m option have this effect (python pseudocode): % python -m module.name ... runs: # pseudocode, with values hardwired for clarity import sys M = new_empty_module(name='__main__', qualname='module.name') sys.modules['__main__'] = M sys.modules['module.name'] = M # load the module code from wherever (not necessarily a file - CPython # already must do this phase) M.execfile('/path/to/module/name.py') Specificly, this would have the following two changes to current practice: 1) the module is imported _once_, and bound to both its canonical name and also to __main__. 2) imported modules acquire a new attribute __qualname__ (analogous to the recent __qualname__ on functions). This is always the conanoical name of the module as resolved by the importer. For most modules __name__ will be the same as __qualname__, but for the "main" module __name__ will be '__main__'. This change has the following advantages: The current standard boilerplate: if __name__ == '__main__': ... invoke "main program" here ... continues to work unchanged. Importantly, if the program then issues "import module_name", it is already there and the existing instance is found and used. The thread referenced above outlines my most recent encounter with this and the trouble it caused me. Followup messages include some support for this proposed change, and some criticism. The critiquing article included some workarounds for this multiple module situation, but they were (1) somewhat dependent on modules coming from a file pathname and (2) cumbersome and require every end user to adopt these changes if affected by the situation. I'd like to avoid that. Cheers, Cameron Simpson <cs(a)zip.com.au> The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man. - George Bernard Shaw

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solving multi-core Python
by Eric Snow June 3, 2016

June 3, 2016

tl;dr Let's exploit multiple cores by fixing up subinterpreters, exposing them in Python, and adding a mechanism to safely share objects between them. This proposal is meant to be a shot over the bow, so to speak. I plan on putting together a more complete PEP some time in the future, with content that is more refined along with references to the appropriate online resources. Feedback appreciated! Offers to help even more so! :) -eric -------- Python's multi-core story is murky at best. Not only can we be more clear on the matter, we can improve Python's support. The result of any effort must make multi-core (i.e. parallelism) support in Python obvious, unmistakable, and undeniable (and keep it Pythonic). Currently we have several concurrency models represented via threading, multiprocessing, asyncio, concurrent.futures (plus others in the cheeseshop). However, in CPython the GIL means that we don't have parallelism, except through multiprocessing which requires trade-offs. (See Dave Beazley's talk at PyCon US 2015.) This is a situation I'd like us to solve once and for all for a couple of reasons. Firstly, it is a technical roadblock for some Python developers, though I don't see that as a huge factor. Regardless, secondly, it is especially a turnoff to folks looking into Python and ultimately a PR issue. The solution boils down to natively supporting multiple cores in Python code. This is not a new topic. For a long time many have clamored for death to the GIL. Several attempts have been made over the years and failed to do it without sacrificing single-threaded performance. Furthermore, removing the GIL is perhaps an obvious solution but not the only one. Others include Trent Nelson's PyParallels, STM, and other Python implementations.. Proposal ======= In some personal correspondence Nick Coghlan, he summarized my preferred approach as "the data storage separation of multiprocessing, with the low message passing overhead of threading". For Python 3.6: * expose subinterpreters to Python in a new stdlib module: "subinterpreters" * add a new SubinterpreterExecutor to concurrent.futures * add a queue.Queue-like type that will be used to explicitly share objects between subinterpreters This is less simple than it might sound, but presents what I consider the best option for getting a meaningful improvement into Python 3.6. Also, I'm not convinced that the word "subinterpreter" properly conveys the intent, for which subinterpreters is only part of the picture. So I'm open to a better name. Influences ======== Note that I'm drawing quite a bit of inspiration from elsewhere. The idea of using subinterpreters to get this (more) efficient isolated execution is not my own (I heard it from Nick). I have also spent quite a bit of time and effort researching for this proposal. As part of that, a number of people have provided invaluable insight and encouragement as I've prepared, including Guido, Nick, Brett Cannon, Barry Warsaw, and Larry Hastings. Additionally, Hoare's "Communicating Sequential Processes" (CSP) has been a big influence on this proposal. FYI, CSP is also the inspiration for Go's concurrency model (e.g. goroutines, channels, select). Dr. Sarah Mount, who has expertise in this area, has been kind enough to agree to collaborate and even co-author the PEP that I hope comes out of this proposal. My interest in this improvement has been building for several years. Recent events, including this year's language summit, have driven me to push for something concrete in Python 3.6. The subinterpreter Module ===================== The subinterpreters module would look something like this (a la threading/multiprocessing): settrace() setprofile() stack_size() active_count() enumerate() get_ident() current_subinterpreter() Subinterpreter(...) id is_alive() running() -> Task or None run(...) -> Task # wrapper around PyRun_*, auto-calls Task.start() destroy() Task(...) # analogous to a CSP process id exception() # other stuff? # for compatibility with threading.Thread: name ident is_alive() start() run() join() Channel(...) # shared by passing as an arg to the subinterpreter-running func # this API is a bit uncooked still... pop() push() poison() # maybe select() # maybe Note that Channel objects will necessarily be shared in common between subinterpreters (where bound). This sharing will happen when the one or more of the parameters to the function passed to Task() is a Channel. Thus the channel would be open to the (sub)interpreter calling Task() (or Subinterpreter.run()) and to the new subinterpreter. Also, other channels could be fed into such a shared channel, whereby those channels would then likewise be shared between the interpreters. I don't know yet if this module should include *all* the essential pieces to implement a complete CSP library. Given the inspiration that CSP is providing, it may make sense to support it fully. It would be interesting then if the implementation here allowed the (complete?) formalisms provided by CSP (thus, e.g. rigorous proofs of concurrent system models). I expect there will also be a _subinterpreters module with low-level implementation-specific details. Related Ideas and Details Under Consideration ==================================== Some of these are details that need to be sorted out. Some are secondary ideas that may be appropriate to address in this proposal or may need to be tabled. I have some others but these should be sufficient to demonstrate the range of points to consider. * further coalesce the (concurrency/parallelism) abstractions between threading, multiprocessing, asyncio, and this proposal * only allow one running Task at a time per subinterpreter * disallow threading within subinterpreters (with legacy support in C) + ignore/remove the GIL within subinterpreters (since they would be single-threaded) * use the GIL only in the main interpreter and for interaction between subinterpreters (and a "Local Interpreter Lock" for within a subinterpreter) * disallow forking within subinterpreters * only allow passing plain functions to Task() and Subinterpreter.run() (exclude closures, other callables) * object ownership model + read-only in all but 1 subinterpreter + RW in all subinterpreters + only allow 1 subinterpreter to have any refcounts to an object (except for channels) * only allow immutable objects to be shared between subinterpreters * for better immutability, move object ref counts into a separate table * freeze (new machinery or memcopy or something) objects to make them (at least temporarily) immutable * expose a more complete CSP implementation in the stdlib (or make the subinterpreters module more compliant) * treat the main interpreter differently than subinterpreters (or treat it exactly the same) * add subinterpreter support to asyncio (the interplay between them could be interesting) Key Dependencies ================ There are a few related tasks/projects that will likely need to be resolved before subinterpreters in CPython can be used in the proposed manner. The proposal could implemented either way, but it will help the multi-core effort if these are addressed first. * fixes to subinterpreter support (there are a couple individuals who should be able to provide the necessary insight) * PEP 432 (will simplify several key implementation details) * improvements to isolation between subinterpreters (file descriptors, env vars, others) Beyond those, the scale and technical scope of this project means that I am unlikely to be able to do all the work myself to land this in Python 3.6 (though I'd still give it my best shot). That will require the involvement of various experts. I expect that the project is divisible into multiple mostly independent pieces, so that will help. Python Implementations =================== They can correct me if I'm wrong, but from what I understand both Jython and IronPython already have subinterpreter support. I'll be soliciting feedback from the different Python implementors about subinterpreter support. C Extension Modules ================= Subinterpreters already isolate extension modules (and built-in modules, including sys). PEP 384 provides some help too. However, global state in C can easily leak data between subinterpreters, breaking the desired data isolation. This is something that will need to be addressed as part of the effort.

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Re: [Python-ideas] Draft PEP on string interpolation
by Eric Fahlgren Aug. 30, 2015

Aug. 30, 2015

On Sat Aug 29 18:41:55 CEST 2015, Ron Adam wrote: > One is nested evaluations, but that may be fixable. > > Nesting arguments and more complex examples: > > >>> for align, text in zip('<^>', ['left', 'center', 'right']): > ... '{0:{fill}{align}16}'.format(text, fill=align, align=align) > ... > 'left<<<<<<<<<<<<' > '^^^^^center^^^^^' > '>>>>>>>>>>>right' > > I haven't gotten this example to work yet. I've deployed an implementation of my interpretation of PEP-498 in our 2.7 production code, tested in 3.4. Here's the function and your test case working properly: #!/bin/env python from __future__ import print_function, division import sys as _sys try: import _string # Py3 def _parseFormat(string): return _string.formatter_parser(string) unicode = str except ImportError: # Py2 def _parseFormat(string): return string._formatter_parser() def _stringInterpolater(s, depth=1, evalCallback=None, context=None, **kwds): """ $uuid:4cbb6191-464a-56dd-88e2-52f4f861527e$ Experimental implementation of the behavior described in https://www.python.org/dev/peps/pep-0498/ The first extension, ``depth``, allows for reimplementation from a function that looks deeper into the call stack for the "current" context (see ``printi``, below). The second extension, ``evalCallback``, allows us to intercept the value before formatting so that we can, for example, convert a Marker object to an Adams id (see Simulatable.formatFunction for details). ``context`` allows the user to pass a dictionary to completely replace the namespace calculations performed below. It too gets superseded by ``kwds``. """ # Local frame is needed for error reporting, so calculate it even # when the user supplies a context. localFrame = _sys._getframe() while depth: localFrame = localFrame.f_back depth -= 1 if context: localDict = kwds globalDict = context.copy() else: localDict = localFrame.f_locals.copy() # Must copy to avoid side effects. localDict.update(kwds) # Our **kwds override locals. globalDict = localFrame.f_globals def doFormat(formatString): """ $uuid:7c45191f-741b-51a0-b75a-a534e99f58cb$ """ result = list() for text, expression, formatSpec, conversion in _parseFormat(formatString): if text: result.append(text) if expression is None: break value = eval(expression, globalDict, localDict) if evalCallback: value = evalCallback(value) if conversion == "r": if isinstance(value, unicode): # Delete this check in Py3. value = str(value) # Eat the annoying "u" prefix in Py2. value = repr(value) elif conversion == "s": value = str(value) formatSpec = doFormat(formatSpec) # Recurse to evaluate embedded formats. try: result.append(format(value, formatSpec)) except ValueError as e: raise ValueError("{}, object named '{}'".format(str(e), expression), localFrame) return "".join(result) return doFormat(s) f = _stringInterpolater def printi(*args, **kwds): """ $uuid:15cccafc-f4ae-58df-ab3e-03553e567bf0$ """ sep = kwds.pop("sep", " ") # Py2 smell, in Py3 you'd just put them in the signature. end = kwds.pop("end", "\n") file = kwds.pop("file", _sys.stdout) newArgs = list() for arg in args: if isinstance(arg, (str, unicode)): newArgs.append(f(arg, depth=2, **kwds)) print(*newArgs, sep=sep, end=end, file=file) if __name__ == "__main__": strings = { "<" : "left ", "^" : " center ", ">" : " right", } width = 2*5 + max(len(s) for s in strings.values()) for align in strings: fill = align print(f("{strings[align]:{fill}{align}{width}}")) printi("{strings[align]:{fill}{align}{width}}")

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some useful descriptors + a classtools module (was: Properties for classes possible?)
by Eric Snow Aug. 29, 2015

Aug. 29, 2015

On Sat, Aug 29, 2015 at 3:21 PM, Eric Snow <ericsnowcurrently(a)gmail.com> wrote: > I advocate for #3 over the others. It provides for broader > application while not impacting the optimizations in classmethod (and > it has a more accurate name). It would work similarly to the naive > classmethod implementation (and work as a less efficient replacement > for classmethod): > > class classresolved: > def __init__(self, wrapped): > self.wrapped = wrapped > def __get__(self, obj, cls): > try: > getter = self.wrapped.__get__ > except AttributeError: > return self.wrapped > return getter(cls, type(cls)) > > Note that wrapped data descriptors (like property) would behave as > non-data descriptors since classresolved is itself a non-data > descriptor. The case for making it a data descriptor can be treated > separately, but I don't think that case is as strong. > > All this leads to some broader observations about useful, generic > descriptors in the stdlib. I'll open a new thread for that > conversation. [Just to be clear, I don't have much time to pursue this so it's more food-for-though than anything. Feel free to pick up the baton. <wink>] While we're on the topic, there are other generic descriptors that are worth considering for inclusion in the stdlib. I've written more than my fair share of descriptors in the past, many to meet real needs. [1] Along with "classresolved", here are some non-data descriptors that might be worth adding: * lazy - late bound attr that replaces itself on the class (or instance) * Attr - marks an attr as defined on instances of the class, e.g. to satisfy an ABC or as programmatic "documentation"; like binding a place-holder class attr but supports doc strings * rawattr - basically a synonym for staticmethod * classattr - equivalent to simply binding the wrapped object directly on the class, except that wrapped descriptors always resolve as though called on the class * classonly - like classattr, but lookup for instances results in AttributeError; this is like adding a method to a metaclass without using a metaclass * nondata - turns data descriptors into non-data descriptors * classunresolved - causes descriptors to resolve only against instances; like the inverse of a classonly/classattr combo [note that I already proposed "classonly" to this list a couple years ago [2]] While the implementation of many of these is relatively trivial, understanding of the descriptor protocol (and the attribute lookup machinery) is just outside the understanding of your average Python user. I'm all for changing that but in the meantime I think it's justifiable to provide useful (even if relatively trivial) descriptors in the stdlib. Keep in mind that the implementation of property, classmethod, and staticmethod have about the same level of complexity as the descriptors I've described above. Also, if it makes sense to add any of these (or other) useful descriptors then it might also make sense for them to live in a stdlib module rather than as builtins (as property/classmethod/staticmethod do). There really isn't a good fit for them currently. Consequently, I would advocate for adding a new "classtools" module, inspired by the existing functools and itertools modules. The new module would also be an appropriate place for more than descriptors, as there are likely other things that would fit well there. In fact there are a few things we've stuck in the stdlib (e.g. in inspect and types) that would have gone into a classtools module if it had existed. -eric [1] https://bitbucket.org/ericsnowcurrently/presentations/src/default/utpy-may2… [2] https://mail.python.org/pipermail/python-ideas/2013-March/019848.html

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Properties for classes possible?
by Thomas Güttler Aug. 29, 2015

Aug. 29, 2015

I think it would be great to have properties for classes in Python2 and Python3 There are some "patterns" to get this working: http://stackoverflow.com/questions/5189699/how-can-i-make-a-class-property-… http://stackoverflow.com/questions/128573/using-property-on-classmethods ... but an official solution would be more "zen of python". Do you think properties for classes would be useful? If it works for classes, then it could be used for modules, too? Regards, Thomas Güttler PS: my first post via google groups web interface did not arrive here. That's the second try. Now via mail. -- Thomas Guettler http://www.thomas-guettler.de/

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Draft PEP on string interpolation
by Mike Miller Aug. 29, 2015

Aug. 29, 2015

The ground seems to be settling on the issue, so I have tried my hand at a grand unified pep for string interpolation. I originally started writing thinking I would fight arbitrary expressions, though agreeing they would be very useful. In my research however, I discovered that they've become an industry standard of sorts. So, I pivoted and started thinking of mitigation strategies to reduce their downsides instead. There's still plenty to do and details to iron out, I'd appreciate your help. If this PEP doesn't stick I hope fragments of it can be useful for others. https://bitbucket.org/mixmastamyk/docs/src/default/pep/pep-05XX.rst (Pls excuse the inline links, I've not moved them to the footer yet.) -Mike

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String interpolation: environment variables, command substitution
by Mike Miller Aug. 29, 2015

Aug. 29, 2015

One of the remaining questions for the string interpolation subject is whether to allow for easy access to environment variables and output-capture of external processes (aka command-substitution) as bash does. While incredibly useful in use-cases such as shell-script replacements, the functionality is perceived to be, if not dangerous. More so than arbitrary expressions? Given that we are talking about string literals and not input, I'm not sure, so am looking for feedback. The idea is not unheard of in Python, there was a module that captured process output called commands in the old days, which was superseded at some point by subprocess.check_output() I believe. Here is some example syntax modeled on bash, though placed inside .format braces. Note both start with $ as the signal:: >>> x'Home folder: {$HOME}' # environment 'Home folder: /home/nobody' >>> x'Files: {$(/bin/ls .)}' # capture output 'foo foo1 foo2' For safety, command substitution should return output in a way analogous to the modern equivalent:: subprocess.check_output(['/bin/ls', '.'], shell=False).decode(encoding) -Mike

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