Mailman 3 October 2013 - 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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while conditional in list comprehension ??
by Wolfgang Maier Feb. 21, 2014

Feb. 21, 2014

Dear all, I guess this is so obvious that someone must have suggested it before: in list comprehensions you can currently exclude items based on the if conditional, e.g.: [n for n in range(1,1000) if n % 4 == 0] Why not extend this filtering by allowing a while statement in addition to if, as in: [n for n in range(1,1000) while n < 400] Trivial effect, I agree, in this example since you could achieve the same by using range(1,400), but I hope you get the point. This intuitively understandable extension would provide a big speed-up for sorted lists where processing all the input is unnecessary. Consider this: some_names=["Adam", "Andrew", "Arthur", "Bob", "Caroline","Lancelot"] # a sorted list of names [n for n in some_names if n.startswith("A")] # certainly gives a list of all names starting with A, but . [n for n in some_names while n.startswith("A")] # would have saved two comparisons Best, Wolfgang

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os.path.join
by anatoly techtonik Nov. 29, 2013

Nov. 29, 2013

>>> os.path.join('/static', '/styles/largestyles.css') '/styles/largestyles.css' Is it only me who thinks that the code above is wrong?

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A nice __repr__ for the ast.* classes?
by Haoyi Li Nov. 19, 2013

Nov. 19, 2013

Wouldn't it be nice if this >>> import ast >>> print repr(ast.parse("(1 + 1)").body[0].value) <_ast.BinOp object at 0x0000000001E94B38> printed something more useful? >>> print repr(ast.parse("(1 + 1)").body[0].value) BinOp(left=Num(n=1), op=Add(), right=Num(n=1)) I've been doing some work on macropy <https://github.com/lihaoyi/macropy>, which uses the ast.* classes extensively, and it's annoying that we have to resort to dirty-tricks like monkey-patching the AST classes (for CPython 2.7) or even monkey-patching __builtin__.repr (to get it working on PyPy) just to get eval(repr(my_ast)) == my_ast to hold true. And a perfectly good solution already exists in the ast.dump() method, too! (It would also be nice if "==" did a structural comparison on the ast.* classes too, but that's a different issue). -Haoyi

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Gzip and zip extra field
by Serhiy Storchaka Nov. 17, 2013

Nov. 17, 2013

Gzip files can contains an extra field [1] and some applications use this for extending gzip format. The current GzipFile implementation ignores this field on input and doesn't allow to create a new file with an extra field. ZIP file entries also can contains an extra field [2]. Currently it just saved as bytes in the `extra` attribute of ZipInfo. I propose to save an extra field for gzip file and provide structural access to subfields. f = gzip.GzipFile('somefile.gz', 'rb') f.extra_bytes # A raw extra field as bytes # iterating over all subfields for xid, data in f.extra_map.items(): ... # get Apollo file type information f.extra_map[b'AP'] # (or f.extra_map['AP']?) # creating gzip file with extra field f = gzip.GzipFile('somefile.gz', 'wb', extra=extrabytes) f = gzip.GzipFile('somefile.gz', 'wb', extra=[(b'AP', apollodata)]) f = gzip.GzipFile('somefile.gz', 'wb', extra={b'AP': apollodata}) # change Apollo file type information f.extra_map[b'AP'] = ... Issue #17681 [3] has preliminary patches. There is some open doubt about interface. Is not it over-engineered? Currently GzipFile supports seamless reading a sequence of separately compressed gzip files. Every such chunk can have own extra field (this is used in dictzip for example). It would be desirable to be able to read only until the end of current chunk in order not to miss an extra field. [1] http://www.gzip.org/format.txt [2] http://www.pkware.com/documents/casestudies/APPNOTE.TXT [3] http://bugs.python.org/issue17681

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Stdlib YAML evolution (Was: PEP 426, YAML in the stdlib and implementation discovery)
by anatoly techtonik Nov. 14, 2013

Nov. 14, 2013

FWIW, I am +1 on for the ability to read YAML based configs Python without dependencies, but waiting for several years is hard. Maybe try an alternative data driven development process as opposed to traditional PEP based all-or-nothing style to speed up the process? It is possible to make users happy incrementally and keep development fun without sacrificing too much on the Zen side. If open source is about scratching your own itches, then the most effective way to implement a spec would be to allow people add support for their own flavor without disrupting works of the others. For some reason I think most people don't need full YAML speccy, especially if final implementation will be slow and heavy. So instead of: import yaml I'd start with something more generic and primitive: from datatrans import yamlish Where `datatrans` is data transformation framework taking care of usual text parsing (data partitioning), partition mapping (structure transformation) and conversion (binary to string etc.) trying to be as fast and lightweight as possible, bringing vast field for future optimizations on algorithmic level. `yamlish` is an implementation which is not vastly optimized (datatrans to yamlish is like RPython to PyPy) and can be easily extended to cover more YAML (hopefully). Therefore the name - it doesn't pretend to parse YAML - it parses some supported subset, which is improved over time by different parties (if datatrans is done right to provide readable (maintainable + extensible) code for implementation). There is an exisiting package called `yamlish` on PyPI - I am not talking about it - it is PyYAML based, which is not an option for now as I see it. So I stole its name. Sorry. This PyPI package was used to parse TAP format, which is again, a subset. Subset.. It appears that YAML is good for humans for its subsets. It leaves an impression (maybe it's just an illusion) that development work for subset support can also be partitioned. If `datatrans` "done right" is possible, it will allow incremental addition of new YAML features as the need for them arises (new data examples are added). Or it can help to build parsers for YAML subsets that are intentionally limited to make them performance efficient. Because `datatrans` is a package isolating parsing, mapping and conversion parts of the process to make it modular and extensible, it can serve as a reference point for various kinds of (scientific) papers including the ones that prove that such data transformation framework is impossible. As for `yamlish` submodule, the first important paper covering it will be a reference table matrix of supported features. While it all sounds too complicated, driving development by data and real short term user needs (as opposed to designing everything upfront) will make the process more attractive. In data driven development, there are not many things that can break - you either continue parsing previous data or not. The output from the parsing process may change over time, but it may be controlled by configuring the last step of data transformation phase. `Parsing AppEngine config file` or `reading package meta data` are good starting points. Once package meta data subset is parsed, it is done and won't break. The implementation for meta data parsing may mature in distutils package, for AppEngine in its SDK, and merged in either of those, sending patches for `datastrans` to stdlib. The question is only to design output format for the parse stage. I am not sure everything should be convertible into Python objects using the "best fit" technique. I will be pretty comfortable if target format will not be native Python objects at all. More than that - I will even insist to avoid converting to native Python object from the start. The ideal output for the first version should be generic tree structure with defined names for YAML elements. The tree that can be represented as XML where these names are tags. The tree can be therefore traversed and selected using XPath/JQuery syntax. It will take several years for implementation to mature and in the end there will be a plenty of backward compatibility matters with the API, formatting and serializing. So the first thing I'd do is [abandon serialization]. From the point of view of my self-proclaimed data transformation theory, the input and output formats are data. If output format is not human readable - as some linked Python data structures in memory - it wastes time and hinders development. Serializing Python is a problem of different level, which is an example of binary, abstract, memory-only output format - a lot of properties that you don't want to deal with while working with data. To summarize: 1. full spec support is no goal 2. data driven (using real world examples/stories) 3. incremental (one example/story at a time) 4. research based (beautiful ideas vs ugly real world limitations) 5. maintainable (code is easy to read and understand the structure) 6. extensible (easy to find out the place to be modified) 7. core "generic tree" data structure as an intermediate format and "yaml tree" data structure as a final format from parsing process P.S. I am willing to wok on this "data transformation theory" stuff and prototype implementation, because it is generally useful in many areas. But I need support. -- anatoly t.

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Support os.path.join for Windows paths on Posix
by Ryan Gonzalez Nov. 2, 2013

Nov. 2, 2013

The recent thread/post/whatever on os.path.join has gotten me thinking. Say I wanted to join a Windows path...on Ubuntu. This is what I get: ryan@DevPC-LX:~$ python Python 2.7.3 (default, Sep 26 2013, 20:03:06) [GCC 4.6.3] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import os >>> os.path.join('C:\\', 'x.jpg') 'C:\\/x.jpg' >>> Isn't something wrong there? My idea: check for \'s in the path. If there are any, assume \ is the path separator, not /. -- Ryan

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Where did we go wrong with negative stride?
by Guido van Rossum Nov. 2, 2013

Nov. 2, 2013

In the comments of http://python-history.blogspot.com/2013/10/why-python-uses-0-based-indexing… were some complaints about the interpretation of the bounds for negative strides, and I have to admin it feels wrong. Where did we go wrong? For example, "abcde"[::-1] == "edcba" as you'd expect, but there is no number you can put as the second bound to get the same result: "abcde"[:1:-1] == "edc" "abcde"[:0:-1] == "edcb" but "abcde":-1:-1] == "" I'm guessing it all comes from the semantics I assigned to negative stride for range() long ago, unthinkingly combined with the rules for negative indices. Are we stuck with this forever? If we want to fix this in Python 4 we'd have to start deprecating negative stride with non-empty lower/upper bounds now. And we'd have to start deprecating negative step for range() altogether, recommending reversed(range(lower, upper)) instead. Thoughts? Is NumPy also affected? -- --Guido van Rossum (python.org/~guido)

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