Python-ideas June 2017

python-ideas@python.org

96 participants
39 discussions

Specify number of items to allocate for array.array() constructor

by Sven Rahmann

At the moment, the array module of the standard library allows to create arrays of different numeric types and to initialize them from an iterable (eg, another array). What's missing is the possiblity to specify the final size of the array (number of items), especially for large arrays. I'm thinking of suffix arrays (a text indexing data structure) for large texts, eg the human genome and its reverse complement (about 6 billion characters from the alphabet ACGT). The suffix array is a long int array of the same size (8 bytes per number, so it occupies about 48 GB memory). At the moment I am extending an array in chunks of several million items at a time at a time, which is slow and not elegant. The function below also initializes each item in the array to a given value (0 by default). Is there a reason why there the array.array constructor does not allow to simply specify the number of items that should be allocated? (I do not really care about the contents.) Would this be a worthwhile addition to / modification of the array module? My suggestions is to modify array generation in such a way that you could pass an iterator (as now) as second argument, but if you pass a single integer value, it should be treated as the number of items to allocate. Here is my current workaround (which is slow): def filled_array(typecode, n, value=0, bsize=(1<<22)): """returns a new array with given typecode (eg, "l" for long int, as in the array module) with n entries, initialized to the given value (default 0) """ a = array.array(typecode, [value]*bsize) x = array.array(typecode) r = n while r >= bsize: x.extend(a) r -= bsize x.extend([value]*r) return x

6 months, 4 weeks

Asynchronous exception handling around with/try statement borders

by Erik Bray

Hi folks, I normally wouldn't bring something like this up here, except I think that there is possibility of something to be done--a language documentation clarification if nothing else, though possibly an actual code change as well. I've been having an argument with a colleague over the last couple days over the proper way order of statements when setting up a try/finally to perform cleanup of some action. On some level we're both being stubborn I think, and I'm not looking for resolution as to who's right/wrong or I wouldn't bring it to this list in the first place. The original argument was over setting and later restoring os.environ, but we ended up arguing over threading.Lock.acquire/release which I think is a more interesting example of the problem, and he did raise a good point that I do want to bring up. </prologue> My colleague's contention is that given lock = threading.Lock() this is simply *wrong*: lock.acquire() try: do_something() finally: lock.release() whereas this is okay: with lock: do_something() Ignoring other details of how threading.Lock is actually implemented, assuming that Lock.__enter__ calls acquire() and Lock.__exit__ calls release() then as far as I've known ever since Python 2.5 first came out these two examples are semantically *equivalent*, and I can't find any way of reading PEP 343 or the Python language reference that would suggest otherwise. However, there *is* a difference, and has to do with how signals are handled, particularly w.r.t. context managers implemented in C (hence we are talking CPython specifically): If Lock.__enter__ is a pure Python method (even if it maybe calls some C methods), and a SIGINT is handled during execution of that method, then in almost all cases a KeyboardInterrupt exception will be raised from within Lock.__enter__--this means the suite under the with: statement is never evaluated, and Lock.__exit__ is never called. You can be fairly sure the KeyboardInterrupt will be raised from somewhere within a pure Python Lock.__enter__ because there will usually be at least one remaining opcode to be evaluated, such as RETURN_VALUE. Because of how delayed execution of signal handlers is implemented in the pyeval main loop, this means the signal handler for SIGINT will be called *before* RETURN_VALUE, resulting in the KeyboardInterrupt exception being raised. Standard stuff. However, if Lock.__enter__ is a PyCFunction things are quite different. If you look at how the SETUP_WITH opcode is implemented, it first calls the __enter__ method with _PyObjet_CallNoArg. If this returns NULL (i.e. an exception occurred in __enter__) then "goto error" is executed and the exception is raised. However if it returns non-NULL the finally block is set up with PyFrame_BlockSetup and execution proceeds to the next opcode. At this point a potentially waiting SIGINT is handled, resulting in KeyboardInterrupt being raised while inside the with statement's suite, and finally block, and hence Lock.__exit__ are entered. Long story short, because Lock.__enter__ is a C function, assuming that it succeeds normally then with lock: do_something() always guarantees that Lock.__exit__ will be called if a SIGINT was handled inside Lock.__enter__, whereas with lock.acquire() try: ... finally: lock.release() there is at last a small possibility that the SIGINT handler is called after the CALL_FUNCTION op but before the try/finally block is entered (e.g. before executing POP_TOP or SETUP_FINALLY). So the end result is that the lock is held and never released after the KeyboardInterrupt (whether or not it's handled somehow). Whereas, again, if Lock.__enter__ is a pure Python function there's less likely to be any difference (though I don't think the possibility can be ruled out entirely). At the very least I think this quirk of CPython should be mentioned somewhere (since in all other cases the semantic meaning of the "with:" statement is clear). However, I think it might be possible to gain more consistency between these cases if pending signals are checked/handled after any direct call to PyCFunction from within the ceval loop. Sorry for the tl;dr; any thoughts?

1 year, 12 months

Positional-only parameters

by Victor Stinner

Hi, For technical reasons, many functions of the Python standard libraries implemented in C have positional-only parameters. Example: ------- $ ./python Python 3.7.0a0 (default, Feb 25 2017, 04:30:32) >>> help(str.replace) replace(self, old, new, count=-1, /) # <== notice "/" at the end ... >>> "a".replace("x", "y") # ok 'a' >>> "a".replace(old="x", new="y") # ERR! TypeError: replace() takes at least 2 arguments (0 given) ------- When converting the methods of the builtin str type to the internal "Argument Clinic" tool (tool to generate the function signature, function docstring and the code to parse arguments in C), I asked if we should add support for keyword arguments in str.replace(). The answer was quick: no! It's a deliberate design choice. Quote of Yury Selivanov's message: """ I think Guido explicitly stated that he doesn't like the idea to always allow keyword arguments for all methods. I.e. `str.find('aaa')` just reads better than `str.find(needle='aaa')`. Essentially, the idea is that for most of the builtins that accept one or two arguments, positional-only parameters are better. """ http://bugs.python.org/issue29286#msg285578 I just noticed a module on PyPI to implement this behaviour on Python functions: https://pypi.python.org/pypi/positional My question is: would it make sense to implement this feature in Python directly? If yes, what should be the syntax? Use "/" marker? Use the @positional() decorator? Do you see concrete cases where it's a deliberate choice to deny passing arguments as keywords? Don't you like writing int(x="123") instead of int("123")? :-) (I know that Serhiy Storshake hates the name of the "x" parameter of the int constructor ;-)) By the way, I read that "/" marker is unknown by almost all Python developers, and [...] syntax should be preferred, but inspect.signature() doesn't support this syntax. Maybe we should fix signature() and use [...] format instead? Replace "replace(self, old, new, count=-1, /)" with "replace(self, old, new[, count=-1])" (or maybe even not document the default value?). Python 3.5 help (docstring) uses "S.replace(old, new[, count])". Victor

2 years

Implicit string literal concatenation considered harmful?

by Guido van Rossum

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)

2 years, 6 months

JavaScript-Style Object Creation in Python (using a constructor function instead of a class to create objects)

by Simon Ramstedt

Hi, do you have an opinion on the following? Wouldn't it be nice to define classes via a simple constructor function (as below) instead of a conventional class definition? *conventional*: class MyClass(ParentClass): def __init__(x): self._x = x def my_method(y): z = self._x + y return z *proposed*: def MyClass(x): self = ParentClass() def my_method(y): z = x + y return z self.my_method = my_method # that's cumbersome (see comments below) return self Here are the pros and cons I could come up with for the proposed method: (+) Simpler and more explicit. (+) No need to create attributes (like `self._x`) just to pass something from `__init__` to another method. (+) Default arguments / annotations for methods could be different for each class instance. Adaptive defaults wouldn't have to simulated with a None. (+) Class/instance level imports would work. (-/+) Speed: The `def`-based objects take 0.6 μs to create while the `class`-based objects take only 0.4 μs. For method execution however the closure takes only 0.15 μs while the proper method takes 0.22 μs (script <https://gist.github.com/rmst/78b2b0f56a3d9ec13b1ec6f3bd50aa9c>). (-/+) Checking types: In the proposed example above the returned object wouldn't know that it has been created by `MyClass`. There are a couple of solutions to that, though. The easiest to implement would be to change the first line to `self = subclass(ParentClass())` where the subclass function looks at the next item in the call stack (i.e. `MyClass`) and makes it the type of the object. Another solution would be to have a special rule for functions with capital first letter returning a single object to append itself to the list of types of the returned object. Alternatively there could be a special keyword e.g. `classdef` that would be used instead of `def` if we wouldn't want to rely on the name. (-) The current syntax for adding a function to an object is cumbersome. That's what is preventing me from actually using the proposed pattern. But is this really the only reason for not using it? And if so, wouldn't that be a good argument for enabling something like below? *attribute function definitions*: def MyClass(x): self = ParentClass() def self.my_method(y): z = x + y return z return self or alternatively *multiline lambdas*: def MyClass(x): self = ParentClass() self.my_method = (y): z = x + y return z return self Cheers, Simon

3 years, 2 months

Improving Catching Exceptions

by Sven R. Kunze

Hi folks, just one note I'd like to dump here. We usually teach our newbies to catch exceptions as narrowly as possible, i.e. MyModel.DoesNotExist instead of a plain Exception. This works out quite well for now but the number of examples continue to grow where it's not enough. There are at least three examples I can name off the top of my head: 1) nested StopIteration - PEP 479 2) nested ImportError 3) nested AttributeError 1) is clear. 2) usually can be dealt with by applying the following pattern: try: import user except ImportError: import sys if sys.exc_info()[2].tb_next: raise Chris showed how to deal with 3). Catching nested exception is not what people want many times. Am I the only one getting the impression that there's a common theme here? Regards, Sven

3 years, 2 months

socket module: plain stuples vs named tuples

by Thomas Güttler

AFAIK the socket module returns plain tuples in Python3: https://docs.python.org/3/library/socket.html Why not use named tuples? Regards, Thomas Güttler -- I am looking for feedback for my personal programming guidelines: https://github.com/guettli/programming-guidelines

3 years, 2 months

Runtime types vs static types

by Koos Zevenhoven

There has been some discussion here and there concerning the differences between runtime types and static types (mypy etc.). What I write below is not really an idea or proposal---just a perspective, or a topic that people may want to discuss. Since the discussion on this is currently very fuzzy and scattered and not really happening either AFAICT (I've probably missed many discussions, though). Anyway, I thought I'd give it a shot: Clearly, there needs to be some sort of distinction between runtime classes/types and static types, because static types can be more precise than Python's dynamic runtime semantics. For example, Iterable[int] is an iterable that contains integers. For a static type checker, it is clear what this means. But at runtime, it may be impossible to figure out whether an iterable is really of this type without consuming the whole iterable and checking whether each yielded element is an integer. Even that is not possible if the iterable is infinite. Even Sequence[int] is problematic, because checking the types of all elements of the sequence could take a long time. Since things like isinstance(it, Iterable[int]) cannot guarantee a proper answer, one easily arrives at the conclusion that static types and runtime classes are just two separate things and that one cannot require that all types support something like isinstance at runtime. On the other hand, there are many runtime things that can or could be done using (type) annotations, for example: Multidispatch (example with hypothetical syntax below): @overload def concatenate(parts: Iterable[str]) -> str: return "".join(parts) @overload def concatenate(parts: Iterable[bytes]) -> bytes: return b"".join(parts) @overload def concatenate(parts: Iterable[Iterable]) -> Iterable: return itertools.chain(*parts) or runtime type checking: @check_types def load_from_file(filename: Union[os.PathLike, str, bytes]): with open(filename) as f: return do_stuff_with(f.read()) which would automatically give a nice error message if, say, a file object is given as argument instead of a path to a file. However useful (and efficient) these things might be, the runtime type checks are problematic, as discussed above. Furthermore, other differences between runtime and static typing may emerge (or have emerged), which will complicate the matter further. For instance, the runtime __annotations__ of classes, modules and functions may in some cases contain something completely different from what a type checker thinks the type should be. These and other incompatibilities between runtime and static typing will create two (or more) different kinds of type-annotated Python: runtime-oriented Python and Python with static type checking. These may be incompatible in both directions: a static type checker may complain about code that is perfectly valid for the runtime folks, and code written for static type checking may not be able to use new Python techniques that make use of type hints at runtime. There may not even be a fully functional subset of the two "languages". Different libraries will adhere to different standards and will not be compatible with each other. The split will be much worse and more difficult to understand than Python 2 vs 3, peoples around the world will suffer like never before, and programming in Python will become a very complicated mess. One way of solving the problem would be that type annotations are only a static concept, like with stubs or comment-based type annotations. This would also be nice from a memory and performance perspective, as evaluating and storing the annotations would not occupy memory (although both issues and some more might be nicely solved by making the annotations lazily ealuated). However, leaving out runtime effects of type annotations is not the approach taken, and runtime introspection of annotations seems to have some promising applications as well. And for many cases, the traditional Python class actually acts very nicely as both the runtime and static type. So if type annotations will be both for runtime and for static checking, how to make everything work for both static and runtime typing? Since a writer of a library does not know what the type hints will be used for by the library users, it is very important that there is only one way of making type annotations which will work regardless of what the annotations are used for in the end. This will also make it much easier to learn Python typing. Regarding runtime types and isinstance, let's look at the Iterable[int] example. For this case, there are a few options: 1) Don't implement isinstance This is problematic for runtime uses of annotations. 2) isinstance([1, '2', 'three'], Iterable[int]) returns True This is in fact now the case. This is ok for many runtime situations, but lacks precision compared to the static version. One may want to distinguish between Iterable[int] and Iterable[str] at runtime (e.g. the multidispatch example above). 3) Check as much as you can at runtime There could be something like Reiterable, which means the object is not consumed by iterating over it, so one could actually check if all elements are instances of int. This would be useful in some situations, but not available for every object. Furthermore, the check could take an arbitrary amount of time so it is not really suitable for things like multidispatch or some matching constructs etc., where the performance overhead of the type check is really important. 4) Do a deeper check than in (2) but trust the annotations For example, an instance of a class that has a method like def __iter__(self) -> Iterator[int]: some code could be identified as Iterable[int] at runtime, even if it is not guaranteed that all elements are really integers. On the other hand, an object returned by def get_ints() -> Iterable[int]: some code does not know its own annotations, so the check is difficult to do at runtime. And of course, there may not be annotations available. 5) Something else? And what about PEP544 (protocols), which is being drafted? The PEP seems to aim for having type objects that represent duck-typing protocols/interfaces. Checking whether a protocol is implemented by an object or type is clearly a useful thing to do at runtime, but it is not really clear if isinstance would be a guaranteed feature for PEP544 Protocols. So one question is, is it possible to draw the lines between what works with isinstance and what doesn't, and between what details are checked by isinstance and what aren't? -- Or should insinstance be reserved for a more limited purpose, and add another check function, say `implements(...)`, which would perhaps guarantee some answer for all combinations of object and type? I'll stop here---this email is probably already much longer than a single email should be ;) -- Koos -- + Koos Zevenhoven + http://twitter.com/k7hoven +

3 years, 2 months

Re: [Python-ideas] Allow function to return multiple values

by rymg19＠gmail.com

IIRC I'm pretty sure the OP just didn't know about the existence of tuple unpacking and the ability to use that to return multiple values. -- Ryan (ライアン) Yoko Shimomura, ryo (supercell/EGOIST), Hiroyuki Sawano >> everyone elsehttp://refi64.com On Jun 25, 2017 at 6:09 PM, <Mikhail V <mikhailwas(a)gmail.com>> wrote: joannah nanjekye wrote: > [...] > >Today I was writing an example snippet for the book and needed to write a >function that returns two values something like this: > >def return_multiplevalues(num1, num2): > return num1, num2 > > I noticed that this actually returns a tuple of the values which I did not >want in the first place.I wanted python to return two values in their own >types so I can work with them as they are but here I was stuck with working >around a tuple. It was quite puzzling at first what was the actual idea but probably I can guess why this question came up by you. It seems to me (I am just intuitively guessing that) that you were about to write a procedure which operates on global variables. If so you should use the keyword "global" for that. E.g. if you want to work with the variables defined in other part of the code you can simply do it: x = 0 y = 0 def move(): global x, y x = x + 10 y = y + 20 move() print (x,y) This function will change x and y (global variables in this case). Note that without the line with the "global" statement this will not work. Another typical usage is initialising variables inside a procedure: def init_coordinates(): global x,y x=0 y=0 init_coordinates() print (x,y) So for some reason it seemed to me that you are trying to do something like that. >My proposal is we provide a way of functions returning multiple values. >This has been implemented in languages like Go and I have found many cases >where I needed and used such a functionality. I wish for this convenience >in python so that I don't have to suffer going around a tuple. So if using globals as in the above examples you certinly don't have to suffer going around a tuple. Mikhail _______________________________________________ Python-ideas mailing list Python-ideas(a)python.org https://mail.python.org/mailman/listinfo/python-ideas Code of Conduct: http://python.org/psf/codeofconduct/

3 years, 2 months

+ operator on generators

by lucas

Hello ! I often use generators, and itertools.chain on them. What about providing something like the following: a = (n for n in range(2)) b = (n for n in range(2, 4)) tuple(a + b) # -> 0 1 2 3 This, from user point of view, is just as how the __add__ operator works on lists and tuples. Making generators works the same way could be a great way to avoid calls to itertools.chain everywhere, and to limits the differences between generators and other "linear" collections. I do not know exactly how to implement that (i'm not that good at C, nor CPython source itself), but by seeing the sources, i imagine that i could do something like the list_concat function at Objects/listobject.c:473, but in the Objects/genobject.c file, where instead of copying elements i'm creating and initializing a new chainobject as described at Modules/itertoolsmodule.c:1792. (In pure python, the implementation would be something like `def __add__(self, othr): return itertools.chain(self, othr)`) Best regards, --lucas

3 years, 2 months

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Python-ideas June 2017