Mailman 3 Dezembro de 2017 - Python-Dev

PEP 448 review
by Guido van Rossum 29 de Março de 2023

29 de Março de 2023

I'm back, I've re-read the PEP, and I've re-read the long thread with "(no subject)". I think Georg Brandl nailed it: """ *I like the "sequence and dict flattening" part of the PEP, mostly because itis consistent and should be easy to understand, but the comprehension syntaxenhancements seem to be bad for readability and "comprehending" what the codedoes.The call syntax part is a mixed bag on the one hand it is nice to be consistent with the extended possibilities in literals (flattening), but on the other hand there would be small but annoying inconsistencies anyways (e.g. the duplicate kwarg case above).* """ Greg Ewing followed up explaining that the inconsistency between dict flattening and call syntax is inherent in the pre-existing different rules for dicts vs. keyword args: {'a':1, 'a':2} results in {'a':2}, while f(a=1, a=2) is an error. (This form is a SyntaxError; the dynamic case f(a=1, **{'a': 1}) is a TypeError.) For me, allowing f(*a, *b) and f(**d, **e) and all the other combinations for function calls proposed by the PEP is an easy +1 -- it's a straightforward extension of the existing pattern, and anybody who knows what f(x, *a) does will understand f(x, *a, y, *b). Guessing what f(**d, **e) means shouldn't be hard either. Understanding the edge case for duplicate keys with f(**d, **e) is a little harder, but the error messages are pretty clear, and it is not a new edge case. The sequence and dict flattening syntax proposals are also clean and logical -- we already have *-unpacking on the receiving side, so allowing *x in tuple expressions reads pretty naturally (and the similarity with *a in argument lists certainly helps). From here, having [a, *x, b, *y] is also natural, and then the extension to other displays is natural: {a, *x, b, *y} and {a:1, **d, b:2, **e}. This, too, gets a +1 from me. So that leaves comprehensions. IIRC, during the development of the patch we realized that f(*x for x in xs) is sufficiently ambiguous that we decided to disallow it -- note that f(x for x in xs) is already somewhat of a special case because an argument can only be a "bare" generator expression if it is the only argument. The same reasoning doesn't apply (in that form) to list, set and dict comprehensions -- while f(x for x in xs) is identical in meaning to f((x for x in xs)), [x for x in xs] is NOT the same as [(x for x in xs)] (that's a list of one element, and the element is a generator expression). The basic premise of this part of the proposal is that if you have a few iterables, the new proposal (without comprehensions) lets you create a list or generator expression that iterates over all of them, essentially flattening them: >>> xs = [1, 2, 3] >>> ys = ['abc', 'def'] >>> zs = [99] >>> [*xs, *ys, *zs] [1, 2, 3, 'abc', 'def', 99] >>> But now suppose you have a list of iterables: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xss[0], *xss[1], *xss[2]] [1, 2, 3, 'abc', 'def', 99] >>> Wouldn't it be nice if you could write the latter using a comprehension? >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs for xs in xss] [1, 2, 3, 'abc', 'def', 99] >>> This is somewhat seductive, and the following is even nicer: the *xs position may be an expression, e.g.: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs[:2] for xs in xss] [1, 2, 'abc', 'def', 99] >>> On the other hand, I had to explore the possibilities here by experimenting in the interpreter, and I discovered some odd edge cases (e.g. you can parenthesize the starred expression, but that seems a syntactic accident). All in all I am personally +0 on the comprehension part of the PEP, and I like that it provides a way to "flatten" a sequence of sequences, but I think very few people in the thread have supported this part. Therefore I would like to ask Neil to update the PEP and the patch to take out the comprehension part, so that the two "easy wins" can make it into Python 3.5 (basically, I am accepting two-thirds of the PEP :-). There is some time yet until alpha 2. I would also like code reviewers (Benjamin?) to start reviewing the patch <http://bugs.python.org/issue2292>, taking into account that the comprehension part needs to be removed. -- --Guido van Rossum (python.org/~guido)

7 17

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com 18 de Maio de 2021

18 de Maio de 2021

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End:

8 14

congrats on 3.5! Alas, windows 7 users are having problems installing it
by Laura Creighton 15 de Setembro de 2019

15 de Setembro de 2019

webmaster has already heard from 4 people who cannot install it. I sent them to the bug tracker or to python-list but they seem not to have gone either place. Is there some guide I should be sending them to, 'how to debug installation problems'? Laura

5 6

Python startup time
by Victor Stinner 10 de Outubro de 2018

10 de Outubro de 2018

Hi, On Twitter, Raymond Hettinger wrote: "The decision making process on Python-dev is an anti-pattern, governed by anecdotal data and ambiguity over what problem is solved." https://twitter.com/raymondh/status/887069454693158912 About "anecdotal data", I would like to discuss the Python startup time. == Python 3.7 compared to 2.7 == First of all, on speed.python.org, we have: * Python 2.7: 6.4 ms with site, 3.0 ms without site (-S) * master (3.7): 14.5 ms with site, 8.4 ms without site (-S) Python 3.7 startup time is 2.3x slower with site (default mode), or 2.8x slower without site (-S command line option). (I will skip Python 3.4, 3.5 and 3.6 which are much worse than Python 3.7...) So if an user complained about Python 2.7 startup time: be prepared for a 2x - 3x more angry user when "forced" to upgrade to Python 3! == Mercurial vs Git, Python vs C, startup time == Startup time matters a lot for Mercurial since Mercurial is compared to Git. Git and Mercurial have similar features, but Git is written in C whereas Mercurial is written in Python. Quick benchmark on the speed.python.org server: * hg version: 44.6 ms +- 0.2 ms * git --version: 974 us +- 7 us Mercurial startup time is already 45.8x slower than Git whereas tested Mercurial runs on Python 2.7.12. Now try to sell Python 3 to Mercurial developers, with a startup time 2x - 3x slower... I tested Mecurial 3.7.3 and Git 2.7.4 on Ubuntu 16.04.1 using "python3 -m perf command -- ...". == CPython core developers don't care? no, they do care == Christian Heimes, Naoki INADA, Serhiy Storchaka, Yury Selivanov, me (Victor Stinner) and other core developers made multiple changes last years to reduce the number of imports at startup, optimize impotlib, etc. IHMO all these core developers are well aware of the competition of programming languages, and honesty Python startup time isn't "good". So let's compare it to other programming languages similar to Python. == PHP, Ruby, Perl == I measured the startup time of other programming languages which are similar to Python, still on the speed.python.org server using "python3 -m perf command -- ...": * perl -e ' ': 1.18 ms +- 0.01 ms * php -r ' ': 8.57 ms +- 0.05 ms * ruby -e ' ': 32.8 ms +- 0.1 ms Wow, Perl is quite good! PHP seems as good as Python 2 (but Python 3 is worse). Ruby startup time seems less optimized than other languages. Tested versions: * perl 5, version 22, subversion 1 (v5.22.1) * PHP 7.0.18-0ubuntu0.16.04.1 (cli) ( NTS ) * ruby 2.3.1p112 (2016-04-26) [x86_64-linux-gnu] == Quick Google search == I also searched for "python startup time" and "python slow startup time" on Google and found many articles. Some examples: "Reducing the Python startup time" http://www.draketo.de/book/export/html/498 => "The python startup time always nagged me (17-30ms) and I just searched again for a way to reduce it, when I found this: The Python-Launcher caches GTK imports and forks new processes to reduce the startup time of python GUI programs." https://nelsonslog.wordpress.com/2013/04/08/python-startup-time/ => "Wow, Python startup time is worse than I thought." "How to speed up python starting up and/or reduce file search while loading libraries?" https://stackoverflow.com/questions/15474160/how-to-speed-up-python-startin… => "The first time I log to the system and start one command it takes 6 seconds just to show a few line of help. If I immediately issue the same command again it takes 0.1s. After a couple of minutes it gets back to 6s. (proof of short-lived cache)" "How does one optimise the startup of a Python script/program?" https://www.quora.com/How-does-one-optimise-the-startup-of-a-Python-script-… => "I wrote a Python program that would be used very often (imagine 'cd' or 'ls') for very short runtimes, how would I make it start up as fast as possible?" "Python Interpreter Startup time" https://bytes.com/topic/python/answers/34469-pyhton-interpreter-startup-time "Python is very slow to start on Windows 7" https://stackoverflow.com/questions/29997274/python-is-very-slow-to-start-o… => "Python takes 17 times longer to load on my Windows 7 machine than Ubuntu 14.04 running on a VM" => "returns in 0.614s on Windows and 0.036s on Linux" "How to make a fast command line tool in Python" (old article Python 2.5.2) https://files.bemusement.org/talks/OSDC2008-FastPython/ => "(...) some techniques Bazaar uses to start quickly, such as lazy imports." -- So please continue efforts for make Python startup even faster to beat all other programming languages, and finally convince Mercurial to upgrade ;-) Victor

42 96

Clarifying Cygwin support in CPython
by Erik Bray 31 de Julho de 2018

31 de Julho de 2018

Hi folks, As some people here know I've been working off and on for a while to improve CPython's support of Cygwin. I'm motivated in part by a need to have software working on Python 3.x on Cygwin for the foreseeable future, preferably with minimal graft. (As an incidental side-effect Python's test suite--especially of system-level functionality--serves as an interesting test suite for Cygwin itself too.) This is partly what motivated PEP 539 [1], although that PEP had the advantage of benefiting other POSIX-compatible platforms as well (and in fact was fixing an aspect of CPython that made it unfriendly to supporting other platforms). As far as I can tell, the first commit to Python to add any kind of support for Cygwin was made by Guido (committing a contributed patch) back in 1999 [2]. Since then, bits and pieces have been added for Cygwin's benefit over time, with varying degrees of impact in terms of #ifdefs and the like (for the most part Cygwin does not require *much* in the way of special support, but it does have some differences from a "normal" POSIX-compliant platform, such as the possibility for case-insensitive filesystems and executables that end in .exe). I don't know whether it's ever been "officially supported" but someone with a longer memory of the project can comment on that. I'm not sure if it was discussed at all or not in the context of PEP 11. I have personally put in a fair amount of effort already in either fixing issues on Cygwin (many of these issues also impact MinGW), or more often than not fixing issues in the CPython test suite on Cygwin--these are mostly tests that are broken due to invalid assumptions about the platform (for example, that there is always a "root" user with uid=0; this is not the case on Cygwin). In other cases some tests need to be skipped or worked around due to platform-specific bugs, and Cygwin is hardly the only case of this in the test suite. I also have an experimental AppVeyor configuration for running the tests on Cygwin [3], as well as an experimental buildbot (not available on the internet, but working). These currently rely on a custom branch that includes fixes needed for the test suite to run to completion without crashing or hanging (e.g. https://bugs.python.org/issue31885). It would be nice to add this as an official buildbot, but I'm not sure if it makes sense to do that until it's "green", or at least not crashing. I have several other patches to the tests toward this goal, and am currently down to ~22 tests failing. Before I do any more work on this, however, it would be best to once and for all clarify the support for Cygwin in CPython, as it has never been "officially supported" nor unsupported--this way we can avoid having this discussion every time a patch related to Cygwin comes up. I could provide some arguments for why I believe Cygwin should supported, but before this gets too long I'd just like to float the idea of having the discussion in the first place. It's also not exactly clear to me how to meet the standards in PEP 11 for supporting a platform--in particular it's not clear when a buildbot is considered "stable", or how to achieve that without getting necessary fixes merged into the main branch in the first place. Thanks, Erik [1] https://www.python.org/dev/peps/pep-0539/ [2] https://github.com/python/cpython/commit/717d1fdf2acbef5e6b47d9b4dcf48ef182… [3] https://ci.appveyor.com/project/embray/cpython

3 6

Is static typing still optional?
by Raymond Hettinger 29 de Janeiro de 2018

29 de Janeiro de 2018

The make_dataclass() factory function in the dataclasses module currently requires type declarations. It would be nice if the type declarations were optional. With typing (currently works): Point = NamedTuple('Point', [('x', float), ('y', float), ('z', float)]) Point = make_dataclass('Point', [('x', float), ('y', float), ('z', float)]) Without typing (only the first currently works): Point = namedtuple('Point', ['x', 'y', 'z']) # underlying store is a tuple Point = make_dataclass('Point', ['x', 'y', 'z']) # underlying store is an instance dict This proposal would make it easy to cleanly switch between the immutable tuple-based container and the instancedict-based optionally-frozen container. The proposal would make it possible for instructors to teach dataclasses without having to teach typing as a prerequisite. And, it would make dataclasses usable for projects that have elected not to use static typing. Raymond

26 83

PEP 432 progress: Python initalization
by Victor Stinner 24 de Janeiro de 2018

24 de Janeiro de 2018

Hi, Serhiy Storchaka seems to be worried by the high numbers of commits in https://bugs.python.org/issue32030 "PEP 432: Rewrite Py_Main()", so let me explain the context of this work :-) To prepare CPython to implement my UTF-8 Mode PEP (PEP 540), I worked on the implementation of Nick Coghlan's PEP 432: PEP 432 -- Restructuring the CPython startup sequence https://www.python.org/dev/peps/pep-0432/ The startup sequence is a big pile of code made of multiple functions: main(), Py_Main(), Py_Initialize(), Py_Finalize()... and a lot of tiny "configuration" functions like Py_SetPath(). Over the years, many configuration options were added in the middle of the code. The priority of configuration options is not always correct between command line options, envrionment variables, configuration files (like "pyenv.cfg"), etc. For technical reasons, it's hard to impement properly the -E option (ignore PYTHON* environment variables). For example, the new PYTHONCOERCECLOCALE environment variable (of PEP 538) doesn't handle properly -E (it ignores -E), because it was too complex to support -E. -- I'm working on fixing this. Last weeks, I mostly worked on the Py_Main() function, Modules/getpath.c and PC/getpathp.c, to "refactor" the code: * Split big functions (300 to 500 lines) into multiple small functions (50 lines or less), to make it easily to follow the control flow and to allow to more easily move code * Replace static and global variables with memory allocated on the heap. * Reorganize how the configuration is read: populate a first temporary structure (_PyMain using wchar_t*), then create Python objects (_PyMainInterpreterConfig) to finish with the real configuration (like setting attributes of the sys module). The goal is to centralize all code reading configuration to fix the priority and to simplify the code. My motivation was to write a correct implementation of the UTF-8 Mode (PEP 540). Nick's motivation is to make CPython easily to embed. His plan for Python 3.8 is to give access to the new _PyCoreConfig and _PyMainInterpreterConfig structures to: * easily give access to most (if not all?) configuration options to "embedders" * allow to configure Python without environment variables, command line options, configuration files, but only using these structures * allow to configure Python using Python objects (PyObject*) rather than C types (like wchar_t*) (I'm not sure that I understood correctly, so please read the PEP 432 ;-)) IMHO the most visible change of the PEP 432 is to split Python initialization in two parts: * Core: strict minimum to use the Python C API * Main: everything else The goal is to introduce the opportunity to configure Python between Core and Main. The implementation is currently a work-in-progress. Nick will not have the bandwidth, neither do I, to update his PEP and finish the implementation, before Python 3.7. So this work remains private until at least Python 3.8. Another part of the work is to enhance the documentation. You can for example now find an explicit list of C functions which can be called before Py_Initialize(): https://docs.python.org/dev/c-api/init.html#before-python-initialization And also a list of functions that must not be called before Py_Initialize(), whereas you might want to call them :-) Victor

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Support of the Android platform
by Xavier de Gaye 23 de Janeiro de 2018

23 de Janeiro de 2018

The following note is a proposal to add the support of the Android platform. The note is easier to read with clickable links at https://github.com/xdegaye/cagibi/blob/master/doc/android_support.rst Motivations =========== * Android is ubiquitous. * This would be the first platform supported by Python that is cross-compiled, thanks to many contributors. * Although the Android operating system is linux, it is different from most linux platforms, for example it does not use GNU libc and runs SELinux in enforcing mode. Therefore supporting this platform would make Python more robust and also would allow testing it on arm 64-bit processors. * Python running on Android is also a handheld calculator, a successor of the slide rule and the `HP 41`_. Current status ============== * The Python test suite succeeds when run on Android emulators using buildbot strenuous settings with the following architectures on API 24: x86, x86_64, armv7 and arm64. * The `Android build system`_ is described in another section. * The `buildmaster-config PR 26`_ proposes to update ``master.cfg`` to enable buildbots to run a given Android API and architecture on the emulators. * The Android emulator is actually ``qemu``, so the test suites for x86 and x86_64 last about the same time as the test suite run natively when the processor of the build system is of the x86 family. The test suites for the arm architectures last much longer: about 8 hours for arm64 and 10 hours for armv7 on a four years old laptop. * The changes that have been made to achieve this status are listed in `bpo-26865`_, the Android meta-issue. * Given the cpu resources required to run the test suite on the arm emulators, it may be difficult to find a contributed buildbot worker. So it remains to find the hardware to run these buildbots. Proposal ======== Support the Android platform on API 24 [1]_ for the x86_64, armv7 and arm64 architectures built with NDK 14b. *API 24* * API 21 is the first version to provide usable support for wide characters and where SELinux is run in enforcing mode. * API 22 introduces an annoying bug on the linker that prints something like this when python is started:: ``WARNING: linker: libpython3.6m.so.1.0: unused DT entry: type 0x6ffffffe arg 0x14554``. The `termux`_ Android terminal emulator describes this problem at the end of its `termux-packages`_ gitlab page and has implemented a ``termux-elf-cleaner`` tool to strip the useless entries from the ELF header of executables. * API 24 is the first version where the `adb`_ shell is run on the emulator as a ``shell`` user instead of the ``root`` user previously, and the first version that supports arm64. *x86_64* It seems that no handheld device exists using that architecture. It is supported because the x86_64 Android emulator runs fast and therefore is a good candidate as a buildbot worker. *NDK 14b* This release of the NDK is the first one to use `Unified headers`_ fixing numerous problems that had been fixed by updating the Python configure script until now (those changes have been reverted by now). Android idiosyncrasies ====================== * The default shell is ``/system/bin/sh``. * The file system layout is not a traditional unix layout, there is no ``/tmp`` for example. Most directories have user restricted access, ``/sdcard`` is mounted as ``noexec`` for example. * The (java) applications are allocated a unix user id and a subdirectory on ``/data/data``. * SELinux is run in enforcing mode. * Shared memory and semaphores are not supported. * The default encoding is UTF-8. Android build system ==================== The Android build system is implemented at `bpo-30386`_ with `PR 1629`_ and is documented by its `README`_. It provides the following features: * To build a distribution for a device or an emulator with a given API level and a given architecture. * To start the emulator and + install the distribution + start a remote interactive shell + or run remotely a python command + or run remotely the buildbottest * Run gdb on the python process that is running on the emulator with python pretty-printing. The build system adds the ``Android/`` directory and the ``configure-android`` script to the root of the Python source directory on the master branch without modifying any other file. The build system can be installed, upgraded (i.e. the SDK and NDK) and run remotely, through ssh for example. The following external libraries, when they are configured in the build system, are downloaded from the internet and cross-compiled (only once, on the first run of the build system) before the cross-compilation of the extension modules: * ``ncurses`` * ``readline`` * ``sqlite`` * ``libffi`` * ``openssl``, the cross-compilation of openssl fails on x86_64 and arm64 and this step is skipped on those architectures. The following extension modules are disabled by adding them to the ``*disabled*`` section of ``Modules/Setup``: * ``_uuid``, Android has no uuid/uuid.h header. * ``grp`` some grp.h functions are not declared. * ``_crypt``, Android does not have crypt.h. * ``_ctypes`` on x86_64 where all long double tests fail (`bpo-32202`_) and on arm64 (see `bpo-32203`_). .. [1] On Wikipedia `Android version history`_ lists the correspondence between API level, commercial name and version for each release. It also provides information on the global Android version distribution, see the two charts on top. .. _`README`: https://github.com/xdegaye/cpython/blob/bpo-30386/Android/README.rst .. _`bpo-26865`: https://bugs.python.org/issue26865 .. _`bpo-30386`: https://bugs.python.org/issue30386 .. _`bpo-32202`: https://bugs.python.org/issue32202 .. _`bpo-32203`: https://bugs.python.org/issue32203 .. _`PR 1629`: https://github.com/python/cpython/pull/1629 .. _`buildmaster-config PR 26`: https://github.com/python/buildmaster-config/pull/26 .. _`Android version history`: https://en.wikipedia.org/wiki/Android_version_history .. _`termux`: https://termux.com/ .. _`termux-packages`: https://gitlab.com/jbwhips883/termux-packages .. _`adb`: https://developer.android.com/studio/command-line/adb.html .. _`Unified headers`: https://android.googlesource.com/platform/ndk.git/+/ndk-r14-release/docs/Un… .. _`HP 41`: https://en.wikipedia.org/wiki/HP-41C .. vim:filetype=rst:tw=78:ts=8:sts=2:sw=2:et:

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Concerns about method overriding and subclassing with dataclasses
by Ethan Smith 22 de Janeiro de 2018

22 de Janeiro de 2018

Hello all, I've recently been experimenting with dataclasses. They totally rock! A lot of the boilerplate for the AST I've designed in Python is automatically taken care of, it's really great! However, I have a few concerns about the implementation. In a few cases I want to override the repr of the AST nodes. I wrote a __repr__ and ran the code but lo and behold I got a type error. I couldn't override it. I quickly learned that one needs to pass a keyword to the dataclass decorator to tell it *not* to auto generate methods you override. I have two usability concerns with the current implementation. I emailed Eric about the first, and he said I should ask for thoughts here. The second I found after a couple of days sitting on this message. The first is that needing both a keyword and method is duplicative and unnecessary. Eric agreed it was a hassle, but felt it was justified considering someone may accidentally override a dataclass method. I disagree with this point of view as dataclasses are billed as providing automatic methods. Overriding via method definition is very natural and idiomatic. I don't really see how someone could accidentally override a dataclass method if methods were not generated by the dataclass decorator that are already defined in the class at definition time. The second concern, which I came across more recently, is if I have a base class, and dataclasses inherit from this base class, inherited __repr__ & co are silently overridden by dataclass. This is both unexpected, and also means I need to pass a repr=False to each subclass' decorator to get correct behavior, which somewhat defeats the utility of subclassing. Im not as sure a whole lot can be done about this though. I appreciate any thoughts folks have related to this. Cheers, ~>Ethan Smith

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PEP 567 v2
by Yury Selivanov 10 de Janeiro de 2018

10 de Janeiro de 2018

This is a second version of PEP 567. A few things have changed: 1. I now have a reference implementation: https://github.com/python/cpython/pull/5027 2. The C API was updated to match the implementation. 3. The get_context() function was renamed to copy_context() to better reflect what it is really doing. 4. Few clarifications/edits here and there to address earlier feedback. Yury PEP: 567 Title: Context Variables Version: $Revision$ Last-Modified: $Date$ Author: Yury Selivanov <yury(a)magic.io> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 12-Dec-2017 Python-Version: 3.7 Post-History: 12-Dec-2017, 28-Dec-2017 Abstract ======== This PEP proposes a new ``contextvars`` module and a set of new CPython C APIs to support context variables. This concept is similar to thread-local storage (TLS), but, unlike TLS, it also allows correctly keeping track of values per asynchronous task, e.g. ``asyncio.Task``. This proposal is a simplified version of :pep:`550`. The key difference is that this PEP is concerned only with solving the case for asynchronous tasks, not for generators. There are no proposed modifications to any built-in types or to the interpreter. This proposal is not strictly related to Python Context Managers. Although it does provide a mechanism that can be used by Context Managers to store their state. Rationale ========= Thread-local variables are insufficient for asynchronous tasks that execute concurrently in the same OS thread. Any context manager that saves and restores a context value using ``threading.local()`` will have its context values bleed to other code unexpectedly when used in async/await code. A few examples where having a working context local storage for asynchronous code is desirable: * Context managers like ``decimal`` contexts and ``numpy.errstate``. * Request-related data, such as security tokens and request data in web applications, language context for ``gettext``, etc. * Profiling, tracing, and logging in large code bases. Introduction ============ The PEP proposes a new mechanism for managing context variables. The key classes involved in this mechanism are ``contextvars.Context`` and ``contextvars.ContextVar``. The PEP also proposes some policies for using the mechanism around asynchronous tasks. The proposed mechanism for accessing context variables uses the ``ContextVar`` class. A module (such as ``decimal``) that wishes to store a context variable should: * declare a module-global variable holding a ``ContextVar`` to serve as a key; * access the current value via the ``get()`` method on the key variable; * modify the current value via the ``set()`` method on the key variable. The notion of "current value" deserves special consideration: different asynchronous tasks that exist and execute concurrently may have different values for the same key. This idea is well-known from thread-local storage but in this case the locality of the value is not necessarily bound to a thread. Instead, there is the notion of the "current ``Context``" which is stored in thread-local storage, and is accessed via ``contextvars.copy_context()`` function. Manipulation of the current ``Context`` is the responsibility of the task framework, e.g. asyncio. A ``Context`` is conceptually a read-only mapping, implemented using an immutable dictionary. The ``ContextVar.get()`` method does a lookup in the current ``Context`` with ``self`` as a key, raising a ``LookupError`` or returning a default value specified in the constructor. The ``ContextVar.set(value)`` method clones the current ``Context``, assigns the ``value`` to it with ``self`` as a key, and sets the new ``Context`` as the new current ``Context``. Specification ============= A new standard library module ``contextvars`` is added with the following APIs: 1. ``copy_context() -> Context`` function is used to get a copy of the current ``Context`` object for the current OS thread. 2. ``ContextVar`` class to declare and access context variables. 3. ``Context`` class encapsulates context state. Every OS thread stores a reference to its current ``Context`` instance. It is not possible to control that reference manually. Instead, the ``Context.run(callable, *args, **kwargs)`` method is used to run Python code in another context. contextvars.ContextVar ---------------------- The ``ContextVar`` class has the following constructor signature: ``ContextVar(name, *, default=_NO_DEFAULT)``. The ``name`` parameter is used only for introspection and debug purposes, and is exposed as a read-only ``ContextVar.name`` attribute. The ``default`` parameter is optional. Example:: # Declare a context variable 'var' with the default value 42. var = ContextVar('var', default=42) (The ``_NO_DEFAULT`` is an internal sentinel object used to detect if the default value was provided.) ``ContextVar.get()`` returns a value for context variable from the current ``Context``:: # Get the value of `var`. var.get() ``ContextVar.set(value) -> Token`` is used to set a new value for the context variable in the current ``Context``:: # Set the variable 'var' to 1 in the current context. var.set(1) ``ContextVar.reset(token)`` is used to reset the variable in the current context to the value it had before the ``set()`` operation that created the ``token``:: assert var.get(None) is None token = var.set(1) try: ... finally: var.reset(token) assert var.get(None) is None ``ContextVar.reset()`` method is idempotent and can be called multiple times on the same Token object: second and later calls will be no-ops. contextvars.Token ----------------- ``contextvars.Token`` is an opaque object that should be used to restore the ``ContextVar`` to its previous value, or remove it from the context if the variable was not set before. It can be created only by calling ``ContextVar.set()``. For debug and introspection purposes it has: * a read-only attribute ``Token.var`` pointing to the variable that created the token; * a read-only attribute ``Token.old_value`` set to the value the variable had before the ``set()`` call, or to ``Token.MISSING`` if the variable wasn't set before. Having the ``ContextVar.set()`` method returning a ``Token`` object and the ``ContextVar.reset(token)`` method, allows context variables to be removed from the context if they were not in it before the ``set()`` call. contextvars.Context ------------------- ``Context`` object is a mapping of context variables to values. ``Context()`` creates an empty context. To get a copy of the current ``Context`` for the current OS thread, use the ``contextvars.copy_context()`` method:: ctx = contextvars.copy_context() To run Python code in some ``Context``, use ``Context.run()`` method:: ctx.run(function) Any changes to any context variables that ``function`` causes will be contained in the ``ctx`` context:: var = ContextVar('var') var.set('spam') def function(): assert var.get() == 'spam' var.set('ham') assert var.get() == 'ham' ctx = copy_context() # Any changes that 'function' makes to 'var' will stay # isolated in the 'ctx'. ctx.run(function) assert var.get() == 'spam' Any changes to the context will be contained in the ``Context`` object on which ``run()`` is called on. ``Context.run()`` is used to control in which context asyncio callbacks and Tasks are executed. It can also be used to run some code in a different thread in the context of the current thread:: executor = ThreadPoolExecutor() current_context = contextvars.copy_context() executor.submit( lambda: current_context.run(some_function)) ``Context`` objects implement the ``collections.abc.Mapping`` ABC. This can be used to introspect context objects:: ctx = contextvars.copy_context() # Print all context variables and their values in 'ctx': print(ctx.items()) # Print the value of 'some_variable' in context 'ctx': print(ctx[some_variable]) asyncio ------- ``asyncio`` uses ``Loop.call_soon()``, ``Loop.call_later()``, and ``Loop.call_at()`` to schedule the asynchronous execution of a function. ``asyncio.Task`` uses ``call_soon()`` to run the wrapped coroutine. We modify ``Loop.call_{at,later,soon}`` and ``Future.add_done_callback()`` to accept the new optional *context* keyword-only argument, which defaults to the current context:: def call_soon(self, callback, *args, context=None): if context is None: context = contextvars.copy_context() # ... some time later context.run(callback, *args) Tasks in asyncio need to maintain their own context that they inherit from the point they were created at. ``asyncio.Task`` is modified as follows:: class Task: def __init__(self, coro): ... # Get the current context snapshot. self._context = contextvars.copy_context() self._loop.call_soon(self._step, context=self._context) def _step(self, exc=None): ... # Every advance of the wrapped coroutine is done in # the task's context. self._loop.call_soon(self._step, context=self._context) ... C API ----- 1. ``PyContextVar * PyContextVar_New(char *name, PyObject *default)``: create a ``ContextVar`` object. 2. ``int PyContextVar_Get(PyContextVar *, PyObject *default_value, PyObject **value)``: return ``-1`` if an error occurs during the lookup, ``0`` otherwise. If a value for the context variable is found, it will be set to the ``value`` pointer. Otherwise, ``value`` will be set to ``default_value`` when it is not ``NULL``. If ``default_value`` is ``NULL``, ``value`` will be set to the default value of the variable, which can be ``NULL`` too. ``value`` is always a borrowed reference. 3. ``PyContextToken * PyContextVar_Set(PyContextVar *, PyObject *)``: set the value of the variable in the current context. 4. ``PyContextVar_Reset(PyContextVar *, PyContextToken *)``: reset the value of the context variable. 5. ``PyContext * PyContext_New()``: create a new empty context. 6. ``PyContext * PyContext_Copy()``: get a copy of the current context. 7. ``int PyContext_Enter(PyContext *)`` and ``int PyContext_Exit(PyContext *)`` allow to set and restore the context for the current OS thread. It is required to always restore the previous context:: PyContext *old_ctx = PyContext_Copy(); if (old_ctx == NULL) goto error; if (PyContext_Enter(new_ctx)) goto error; // run some code if (PyContext_Exit(old_ctx)) goto error; Implementation ============== This section explains high-level implementation details in pseudo-code. Some optimizations are omitted to keep this section short and clear. For the purposes of this section, we implement an immutable dictionary using ``dict.copy()``:: class _ContextData: def __init__(self): self._mapping = dict() def get(self, key): return self._mapping[key] def set(self, key, value): copy = _ContextData() copy._mapping = self._mapping.copy() copy._mapping[key] = value return copy def delete(self, key): copy = _ContextData() copy._mapping = self._mapping.copy() del copy._mapping[key] return copy Every OS thread has a reference to the current ``_ContextData``. ``PyThreadState`` is updated with a new ``context_data`` field that points to a ``_ContextData`` object:: class PyThreadState: context_data: _ContextData ``contextvars.copy_context()`` is implemented as follows:: def copy_context(): ts : PyThreadState = PyThreadState_Get() if ts.context_data is None: ts.context_data = _ContextData() ctx = Context() ctx._data = ts.context_data return ctx ``contextvars.Context`` is a wrapper around ``_ContextData``:: class Context(collections.abc.Mapping): def __init__(self): self._data = _ContextData() def run(self, callable, *args, **kwargs): ts : PyThreadState = PyThreadState_Get() saved_data : _ContextData = ts.context_data try: ts.context_data = self._data return callable(*args, **kwargs) finally: self._data = ts.context_data ts.context_data = saved_data # Mapping API methods are implemented by delegating # `get()` and other Mapping calls to `self._data`. ``contextvars.ContextVar`` interacts with ``PyThreadState.context_data`` directly:: class ContextVar: def __init__(self, name, *, default=_NO_DEFAULT): self._name = name self._default = default @property def name(self): return self._name def get(self, default=_NO_DEFAULT): ts : PyThreadState = PyThreadState_Get() data : _ContextData = ts.context_data try: return data.get(self) except KeyError: pass if default is not _NO_DEFAULT: return default if self._default is not _NO_DEFAULT: return self._default raise LookupError def set(self, value): ts : PyThreadState = PyThreadState_Get() data : _ContextData = ts.context_data try: old_value = data.get(self) except KeyError: old_value = Token.MISSING ts.context_data = data.set(self, value) return Token(self, old_value) def reset(self, token): if token._used: return if token._old_value is Token.MISSING: ts.context_data = data.delete(token._var) else: ts.context_data = data.set(token._var, token._old_value) token._used = True class Token: MISSING = object() def __init__(self, var, old_value): self._var = var self._old_value = old_value self._used = False @property def var(self): return self._var @property def old_value(self): return self._old_value Implementation Notes ==================== * The internal immutable dictionary for ``Context`` is implemented using Hash Array Mapped Tries (HAMT). They allow for O(log N) ``set`` operation, and for O(1) ``copy_context()`` function, where *N* is the number of items in the dictionary. For a detailed analysis of HAMT performance please refer to :pep:`550` [1]_. * ``ContextVar.get()`` has an internal cache for the most recent value, which allows to bypass a hash lookup. This is similar to the optimization the ``decimal`` module implements to retrieve its context from ``PyThreadState_GetDict()``. See :pep:`550` which explains the implementation of the cache in a great detail. Summary of the New APIs ======================= * A new ``contextvars`` module with ``ContextVar``, ``Context``, and ``Token`` classes, and a ``copy_context()`` function. * ``asyncio.Loop.call_at()``, ``asyncio.Loop.call_later()``, ``asyncio.Loop.call_soon()``, and ``asyncio.Future.add_done_callback()`` run callback functions in the context they were called in. A new *context* keyword-only parameter can be used to specify a custom context. * ``asyncio.Task`` is modified internally to maintain its own context. Design Considerations ===================== Why contextvars.Token and not ContextVar.unset()? ------------------------------------------------- The Token API allows to get around having a ``ContextVar.unset()`` method, which is incompatible with chained contexts design of :pep:`550`. Future compatibility with :pep:`550` is desired (at least for Python 3.7) in case there is demand to support context variables in generators and asynchronous generators. The Token API also offers better usability: the user does not have to special-case absence of a value. Compare:: token = cv.get() try: cv.set(blah) # code finally: cv.reset(token) with:: _deleted = object() old = cv.get(default=_deleted) try: cv.set(blah) # code finally: if old is _deleted: cv.unset() else: cv.set(old) Rejected Ideas ============== Replication of threading.local() interface ------------------------------------------ Please refer to :pep:`550` where this topic is covered in detail: [2]_. Backwards Compatibility ======================= This proposal preserves 100% backwards compatibility. Libraries that use ``threading.local()`` to store context-related values, currently work correctly only for synchronous code. Switching them to use the proposed API will keep their behavior for synchronous code unmodified, but will automatically enable support for asynchronous code. Reference Implementation ======================== The reference implementation can be found here: [3]_. References ========== .. [1] https://www.python.org/dev/peps/pep-0550/#appendix-hamt-performance-analysis .. [2] https://www.python.org/dev/peps/pep-0550/#replication-of-threading-local-in… .. [3] https://github.com/python/cpython/pull/5027 Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

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