Mailman 3 November 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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OrderedCounter and OrderedDefaultDict
by Ian Foote Feb. 12, 2016

Feb. 12, 2016

I recently wanted to use an OrderedCounter and an OrderedDefaultDict. After a bit of googling I discovered that OrderedCounter is quite easy to implement: from collections import Counter, OrderedDict class OrderedCounter(Counter, OrderedDict): 'Counter that remembers the order elements are first seen' def __repr__(self): return '%s(%r)' % (self.__class__.__name__, OrderedDict(self)) def __reduce__(self): return self.__class__, (OrderedDict(self),) from https://rhettinger.wordpress.com/2011/05/26/super-considered-super/ Unfortunately an OrderedDefaultDict did not seem so easy. I did find http://stackoverflow.com/questions/6190331/can-i-do-an-ordered-default-dict… which suggests: |fromcollections importOrderedDict,CallableclassDefaultOrderedDict(OrderedDict):# Source: http://stackoverflow.com/a/6190500/562769def__init__(self,default_factory=N… isnotNoneandnotisinstance(default_factory,Callable)):raiseTypeError('first argument must be callable')OrderedDict.__init__(self,*a,**kw)self.default_factory =default_factory def__getitem__(self,key):try:returnOrderedDict.__getitem__(self,key)exceptKeyError:returnself.__missing__(key)def__missing__(self,key):ifself.default_factory isNone:raiseKeyError(key)self[key]=value =self.default_factory()returnvalue def__reduce__(self):ifself.default_factory isNone:args =tuple()else:args =self.default_factory,returntype(self),args,None,None,self.items()defcopy(self):returnself.__copy__()def__copy__(self):returntype(self)(self.default_factory,self)def__deepcopy__(self,memo):importcopy returntype(self)(self.default_factory,copy.deepcopy(self.items()))def__repr__(self):return'OrderedDefaultDict(%s, %s)'%(self.default_factory,OrderedDict.__repr__(self))| This seems to me both easy to get wrong and hard to discover. It also risks getting out of sync with updates to collections. I'm wondering if this is sufficient justification to add OrderedCounter and OrderedDict to collections, either directly or as recipes. Thanks, Ian F

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Adding collections.abc.Ordered
by Amir Rachum Dec. 27, 2015

Dec. 27, 2015

I am suggesting the addition of a collections abstract base class called "Ordered". Its meaning is that a collection's iteration order is part of its API. The bulk of this mail describes a use case for this. The reason I believe that such abstract base class is required is that there is no way to test this behavior in a given class. An ordered collection has the exact same interface as an unordered collection (e.g, dict and OrderedDict), other than a _promise_ of the API that the order in which this collection will be iterated has some sort of meaning (In OrderedDict, it is the order in which keys were added to it.) As examples, set, frozenset, dict and defaultdict should *not* be considered as ordered. list, OrderedDict, deque and tuple should be considered ordered. Before I dive into the use case I am presenting, I would like to point out that a similar discussion was already done on the subject (suggesting at first that OrderedDict would abstract-subclass from Sequence) at the following thread* - http://code.activestate.com/lists/python-ideas/29532/. That thread was abandoned largely from a lack of a clear use case, which I hope will be more clear in this suggestion. I am working on package called basicstruct ( https://pypi.python.org/pypi/basicstruct). The way it works is that you define an object that inherits from basicstruct.BasicStruct, define __slots__ , and automatically get behaviors such as a nice __init__, __str__, comparison functions, attribute access, dict-like access, pickleability, etc. It is similar to namedtuple, except that it is mutable. In the Python documentation, the following is said regarding __slots__: Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key. Here's how the current__init__ method of BasicStruct looks like: class BasicStruct(object): """Class for holding struct-like objects.""" __slots__ = () # should be extended by deriving classes def __init__(self, *args, **kwargs): arg_pairs = zip(self.__slots__, args) for key, value in chain(arg_pairs, six.iteritems(kwargs)): setattr(self, key, value) for key in self.__slots__: if not hasattr(self, key): setattr(self, key, None) Notice the following line: arg_pairs = zip(self.__slots__, args) It assumes that __slots__ defines attributes in a certain order. So as a use I would expect that the following code class MyStruct(BasicStruct): __slots__ = ('x', 'y') MyStruct(0, 1) ... will create a struct in which x is 0 and y is 1. However, if I define __slots__ as a set, or dict, the result is undefined. class MyStruct(BasicStruct): __slots__ = {'x', 'y'} # No order is defined here MyStruct(0, 1) # Which is which? So, In BasicStruct's __init__ method it is required to test whether __slots__ was defined with a collection that has a meaningful order. If it was _not_, using non-keyword arguments in __init__ should be forbidden, since the order of __slots__ is arbitrary. Here is how I wish the code would look like: class BasicStruct(object): """Class for holding struct-like objects.""" __slots__ = () # should be extended by deriving classes def __init__(self, *args, **kwargs): ordered = isinstance(self.__slots__, Ordered) if args and not ordered: raise ValueError("Can't pass non-keyword arguments to {}, since " "__slots__ was declared with an unordered " "iterable.".format(self.__class__.__name__)) arg_pairs = zip(self.__slots__, args) for key, value in chain(arg_pairs, six.iteritems(kwargs)): setattr(self, key, value) for key in self.__slots__: if not hasattr(self, key): setattr(self, key, None) Thanks, Amir Rachum * The author of the original thread is Ram Rachum. To avoid some confusion - yes, we are related - Ram is my big brother. It's just so happens that I was looking around for this issue and found that he did so in the past as well.

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Re: [Python-ideas] [Python-ideos] Dedicated overloadable boolean operators
by Henry Schreiner Dec. 1, 2015

Dec. 1, 2015

Sorry, the order was incorrect in that last message. It should be: When x and y is written, if x has __booland__, then x.__booland__(y) is returned. If not, then return x if x==False else if x==True, then if y has y.__rbooland__(x), that is returned. If not, then return y. On Monday, November 30, 2015 at 10:11:03 AM UTC-6, Henry Schreiner wrote: > > It seems to me there are two ideas being discussed. One is adding 2 new > operators, something like && and ||, and providing them for some sort of > "element wise" or other boolean context, and giving them a better order of > evaluations than bitwise ops. This would have the advantage of being usable > on the built in python lists/tuples if needed, but otherwise doesn't seem > to solve all the issues above, including expressions like -1 < mat < 1, > which work for python variables but not for things like Numpy arrays. (for > example, selecting values from an array works like this: mat[(-1<mat) & > (mat<1)] but not like this: mat[-1 < mat < 1] or this: mat[-1<mat & > mat<1] ). > > The common "general purpose infix notation" pops up here, of course, but > I'd consider it a variant of the first idea. > > The other idea is that overloads could be added to the existing and and or > operators. This, I believe, would solve all the main problems, and would > not add more to the language for a newcomer to learn. (In fact, most people > could forget about the current bitwise and and or, since that's not what > most people need, at least those using databases and Numpy). The downside > is that the current and and or operators are short-circuiting, and adding > a way to overload a short-circuiting operator is tricky/ugly/expensive > (though the last one might not be that bad anymore). But, as I think Andrew > was mentioning, what about a mixed solution? One that provides a > non-short-circuiting overload, but the original short-circuiting behavior > is default? It would look like this: > > Two new magic methods would be added, for now called __booland__ and > __boolor__, along with __rbooland__ and __rboolor__. > > > > Evaluation would work like this, using and for an example: > When x and y is written, if x has __booland__, then y.__booland__(y) is > called. If it does not, then if y exists and has y.__rbooland__(x), that > is called. If neither, then x and y is performed in a normal, > short circuiting manner, calling bool(x) then calling bool(y) only if x > == True. (The second line here might be the hard part due to the way > short circuiting works) > > > > This would allow object that want custom and/or behavior to implement > these magic functions. They would lose (explicit) short-circuiting, but > they would gain custom behavior. Most of these special objects do not need > or expect the short circuit behavior (Only if an array was all True or > False would it even make sense in numpy , and this would allow > expressions like -1 < mat < 1 to work as expected in Python. This would > be easy to teach to newcomers, too, since only objects that have a definite > True/False should short circuit, others have the special methods. The > old behavior could be forced on overriding objects by wrapping them in > bool(). > > > Would something like that be possible and better than PEP 335? > > PS: > Either type of solution would also be useful for the Plumbum library, by > the way, as it would be useful to add and-ing and or-ing to pipelines, but > the bitwise operators have a different meaning for Plumbum. Due to the lazy > evaluation of Plumbum, even the above solution (or any solution) would > still allow short-circuiting in the pipeline. > > >

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Re: [Python-ideas] [Python-ideos] Dedicated overloadable boolean operators
by Henry Schreiner Nov. 30, 2015

Nov. 30, 2015

My proposal, I believe, is exactly the same as the “Reduced Special Methods set” of PEP 335; that’s the phase two only part. I believe the __r*__ methods there are the same as my proposal. A portion of the complexity of PEP 335 is used in the “phase 1” special methods, which are only helpful if you want to overload the shortcutting behavior itself on a per object basis, which I don’t think there is much need for. Assuming your methods are part of the class, as Python usually requires anyway, this would allow a class to either be short circuiting, boolean only (not containing __and2__ or __sand__ or __xand__ or whatever it would be called), or would have them and therefore would have customized non-short-circuiting behavior. (But, due to the fact that the class controls how much calculation happens, this generally can still limit computation, as in my Plumbum example; the only requirement is that the variable exist so it can be passed to the function). If it looks like this would be acceptable to the NumPy folks, then maybe PEP 335 could be changed to make the Reduced Special Methods the main suggestion, with extending to the two phase method as a side note? Henry Schreiner > On Nov 30, 2015, at 2:28 PM, Andrew Barnert <abarnert(a)yahoo.com> wrote: > > PEP 335 uses the standard rules for 'r' methods, so y.__rand2__ will only be used if issubclass(type(y), type(x)) or if x.__and2__ doesn't exist. Wouldn't having different rules for 'r' methods in this case than for all other operators be confusing? (Maybe not; the comparison operators are the closest analogs, and they have special rules too...) > > Meanwhile, looking at PEP 335 again, it seems like we could very easily add just the "phase-2" part today (which would allow non-short-circuiting overloads, while preserving short-circuiting for types that don't overload), and then add the "phase-1" part (which would add optional short-circuiting overloads) later if needed, or just never add it if not. >

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Re: [Python-ideas] The AttributeError/__getattr__ mechanism
by 王珺 Nov. 30, 2015

Nov. 30, 2015

> A dedicated subclass of *Attr*ibuteError to trigger __get*attr*__ is more natural. I mean it's unnatural to convert AttributeError to AttributeDynamicError by property or descriptor. Why not use RuntimeError as that of PEP479? And there's no need to write custom property-like descriptor factory when you want to trigger __getattr__, although it may be only a few lines of code.

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