On Mar 30, 2018, at 08:31 AM, INADA Naoki <songofacandy(a)gmail.com> wrote:
Hi,
As far as I know, most amd64 and arm64 systems use only 48bit address spaces.
(except [1])
[1] https://software.intel.com/sites/default/files/managed/2b/80/5-level_paging…
It means there are some chance to compact some data structures.
I point two examples below.
My question is; can we use 48bit pointer safely?
Not really, at least some CPUs can also address more memory than that. See <https://lwn.net/Articles/717293/> which talks about Linux support for 57-bit virtual addresses and 52-bit physical addresses.
Ronald
On behalf of the Python development community and the Python 3.7 release
team, I'm happy to announce the availability of Python 3.7.0b3. b3 is
the third of four planned beta releases of Python 3.7, the next major
release of Python, and marks the end of the feature development phase
for 3.7. You can find Python 3.7.0b3 here:
https://www.python.org/downloads/release/python-370b3/
Among the new major new features in Python 3.7 are:
* PEP 538, Coercing the legacy C locale to a UTF-8 based locale
* PEP 539, A New C-API for Thread-Local Storage in CPython
* PEP 540, UTF-8 mode
* PEP 552, Deterministic pyc
* PEP 553, Built-in breakpoint()
* PEP 557, Data Classes
* PEP 560, Core support for typing module and generic types
* PEP 562, Module __getattr__ and __dir__
* PEP 563, Postponed Evaluation of Annotations
* PEP 564, Time functions with nanosecond resolution
* PEP 565, Show DeprecationWarning in __main__
* PEP 567, Context Variables
Please see "What’s New In Python 3.7" for more information.
Additional documentation for these features and for other changes
will be provided during the beta phase.
https://docs.python.org/3.7/whatsnew/3.7.html
Beta releases are intended to give you the opportunity to test new
features and bug fixes and to prepare their projects to support the
new feature release. We strongly encourage you to test your projects
with 3.7 during the beta phase and report issues found to
https://bugs.python.org as soon as possible.
While the release is feature complete entering the beta phase, it is
possible that features may be modified or, in rare cases, deleted up
until the start of the release candidate phase (2018-05-21). Our goal
is have no ABI changes after beta 3 and no code changes after rc1.
To achieve that, it will be extremely important to get as much exposure
for 3.7 as possible during the beta phase.
Attention macOS users: there is a new installer variant for
macOS 10.9+ that includes a built-in version of Tcl/Tk 8.6. This
variant is expected to become the default version when 3.7.0 releases.
Check it out! We welcome your feedback. As of 3.7.0b3, the legacy
10.6+ installer also includes a built-in Tcl/Tk 8.6.
Please keep in mind that this is a preview release and its use is
not recommended for production environments.
The next planned release of Python 3.7 will be 3.7.0b4, currently
scheduled for 2018-04-30. More information about the release schedule
can be found here:
https://www.python.org/dev/peps/pep-0537/
--
Ned Deily
nad(a)python.org -- []
Currently the repository contains bundled pip and setuptools (2 MB
total) which are updated with every release of pip and setuptools. This
increases the size of the repository by around 2 MB several times per
year. There were total 37 updates of Lib/ensurepip/_bundled, therefore
the repository contains up to 70 MB of unused blobs. The size of the
repository is 350 MB. Currently blobs takes up to 20% of the size of the
repository, but this percent will likely grow in future, because they
where added only 4 years ago.
Wouldn't be better to put them into a separate repository like Tcl/Tk
and other external binaries for Windows, and download only the recent
version?
Hi,
My name is Julia Hiyeon Kim.
My suggestion is to change the syntax for creating an empty set and an empty dictionary as following.
an_empty_set = {}
an_empty_dictionary = {:}
It would seem to make more sense.
Warm regards,
Julia Kim
Python 3.6.5 is now available. 3.6.5 is the fifth maintenance release of
Python 3.6, which was initially released in 2016-12 to great interest.
Detailed information about the changes made in 3.6.5 can be found in its
change log. You can find Python 3.6.5 and more information here:
https://www.python.org/downloads/release/python-365/
See the "What’s New In Python 3.6" document for more information about
features included in the 3.6 series. Detailed information about the
changes made in 3.6.5 can be found in the change log here:
https://docs.python.org/3.6/whatsnew/changelog.html#python-3-6-5-final
Attention macOS users: as of 3.6.5, there is a new additional installer
variant for macOS 10.9+ that includes a built-in version of Tcl/Tk 8.6.
This variant is expected to become the default variant in future
releases. Check it out!
The next maintenance release is expected to follow in about 3 months,
around the end of 2018-06.
Thanks to all of the many volunteers who help make Python Development and
these releases possible! Please consider supporting our efforts by
volunteering yourself or through organization contributions to the Python
Software Foundation:
https://www.python.org/psf/
--
Ned Deily
nad(a)python.org -- []
There is a feature request and patch to propagate the float.is_integer() API through rest of the numeric types ( https://bugs.python.org/issue26680 ).
While I don't think it is a good idea, the OP has been persistent and wants his patch to go forward.
It may be worthwhile to discuss on this list to help resolve this particular request and to address the more general, recurring design questions. Once a feature with a marginally valid use case is added to an API, it is common for us to get downstream requests to propagate that API to other places where it makes less sense but does restore a sense of symmetry or consistency. In cases where an abstract base class is involved, acceptance of the request is usually automatic (i.e. range() and tuple() objects growing index() and count() methods). However, when our hand hasn't been forced, there is still an opportunity to decline. That said, proponents of symmetry requests tend to feel strongly about it and tend to never fully accept such a request being declined (it leaves them with a sense that Python is disordered and unbalanced).
Raymond
---- My thoughts on the feature request -----
What is the proposal?
* Add an is_integer() method to int(), Decimal(), Fraction(), and Real(). Modify Rational() to provide a default implementation.
Starting point: Do we need this?
* We already have a simple, traditional, portable, and readable way to make the test: int(x) == x
* In the context of ints, the test x.is_integer() always returns True. This isn't very useful.
* Aside from the OP, this behavior has never been requested in Python's 27 year history.
Does it cost us anything?
* Yes, adding a method to the numeric tower makes it a requirement for every class that ever has or ever will register or inherit from the tower ABCs.
* Adding methods to a core object such as int() increases the cognitive load for everyday users who look at dir(), call help(), or read the main docs.
* It conflicts with a design goal for the decimal module to not invent new functionality beyond the spec unless essential for integration with the rest of the language. The reasons included portability with other implementations and not trying to guess what the committee would have decided in the face of tricky questions such as whether Decimal('1.000001').is_integer()
should return True when the context precision is only three decimal places (i.e. whether context precision and rounding traps should be applied before the test and whether context flags should change after the test).
Shouldn't everything in a concrete class also be in an ABC and all its subclasses?
* In general, the answer is no. The ABCs are intended to span only basic functionality. For example, GvR intentionally omitted update() from the Set() ABC because the need was fulfilled by __ior__().
But int() already has real, imag, numerator, and denominator, why is this different?
* Those attributes are central to the functioning of the numeric tower.
* In contrast, the is_integer() method is a peripheral and incidental concept.
What does "API Parsimony" mean?
* Avoidance of feature creep.
* Preference for only one obvious way to do things.
* Practicality (not craving things you don't really need) beats purity (symmetry and foolish consistency).
* YAGNI suggests holding off in the absence of clear need.
* Recognition that smaller APIs are generally better for users.
Are there problems with symmetry/consistency arguments?
* The need for guard rails on an overpass doesn't imply the same need on a underpass even though both are in the category of grade changing byways.
* "In for a penny, in for a pound" isn't a principle of good design; rather, it is a slippery slope whereby the acceptance of a questionable feature in one place seems to compel later decisions to propagate the feature to other places where the cost / benefit trade-offs are less favorable.
Should float.as_integer() have ever been added in the first place?
* Likely, it should have been a math module function like isclose() and isinf() so that it would not have been type specific.
* However, that ship has sailed; instead, the question is whether we now have to double down and have to dispatch other ships as well.
* There is some question as to whether it is even a good idea to be testing the results of floating point calculations for exact values. It may be useful for testing inputs, but is likely a trap for people using it other contexts.
Have we ever had problems with just accepting requests solely based on symmetry?
* Yes. The str.startswith() and str.endswith() methods were given optional start/end arguments to be consistent with str.index(), not because there were known use cases where code was made better with the new feature. This ended up conflicting with a later feature request that did have valid use cases (supporting multiple test prefixes/suffixes). As a result, we ended-up with an awkward and error-prone API that requires double parenthesis for the valid use case: url.endswith(('.html', '.css')).
https://bugs.python.org/issue33141 points out an interesting issue with
dataclasses and descriptors.
Given this code:
from dataclasses import *
class D:
"""A descriptor class that knows its name."""
def __set_name__(self, owner, name):
self.name = name
def __get__(self, instance, owner):
if instance is not None:
return 1
return self
@dataclass
class C:
d: int = field(default=D(), init=False)
C.d.name is not set, because d.__set_name__ is never called. However, in
this case:
class X:
d: int = D()
X.d.name is set to 'd' when d.__set_name__ is called during type.__new__.
The problem of course, is that in the dataclass case, when class C is
initialized, and before the decorator is called, C.d is set to a Field()
object, not to D(). It's only when the dataclass decorator is run that I
change C.d from a Field to the value of D(). That means that the call to
d.__set_name__(C, 'd') is skipped. See
https://www.python.org/dev/peps/pep-0487/#implementation-details for
details on how type.__new__ works.
The only workaround I can think of is to emulate the part of PEP 487
where __set_name__ is called. I can do this from within the @dataclass
decorator when I'm initializing C.d. I'm not sure how great this
solution is, since it's moving the call from class creation time to
class decorator time. I think in 99+% of cases this would be fine, but
you could likely write code that depends on side effects of being called
during type.__new__.
Unless anyone has strong objections, I'm going to make the call to
__set_name__ in the @datacalss decorator. Since this is such a niche use
case, I don't feel strongly that it needs to be in today's beta release,
but if possible I'll get it in. I already have the patch written. And if
it does get in but the consensus is that it's a bad idea, we can back it
out.
Eric
Hi Python-dev,
I'm one of the core attrs contributors, and I'm contemplating applying an
optimization to our generated __init__s. Before someone warns me python-dev
is for the development of the language itself, there are two reasons I'm
posting this here:
1) it's a very low level question that I'd really like the input of the
core devs on, and
2) maybe this will find its way into dataclasses if it works out.
I've found that, if a class has more than one attribute, instead of
creating an init like this:
self.a = a
self.b = b
self.c = c
it's faster to do this:
self.__dict__ = {'a': a, 'b': b, 'c': c}
i.e. to replace the instance dictionary altogether. On PyPy, their core
devs inform me this is a bad idea because the instance dictionary is
special there, so we won't be doing this on PyPy.
But is it safe to do on CPython?
To make the question simpler, disregard the possibility of custom setters
on the attributes.
Thanks in advance!
Hi Python Devs
I recently started testing Jedi with Python 3.7. Some tests broke. I
realized that one of the things that changed in 3.7 was the use of
argument clinic in methods like str.replace.
The issue is that the text signature doesn't contain a return annotation.
>>> str.replace.__text_signature__
'($self, old, new, count=-1, /)
In Python < 3.7 there was a `S.replace(old, new[, count]) -> str` at
the top of the __doc__. T
If the __text_signature__ was `'($self, old, new, count=-1, /) -> str`
a lot of tools would be able to have the information again.
Is this intentional or was this just forgotten? I'd like to note that
this information is insanely helpful (at least for Jedi) to pick up
type information. I really hope this information can make it back into
3.7, since it was there in earlier versions.
If you lack don't have time I might have some. Just give me some instructions.
~ Dave
PS: Don't get me wrong, I love argument clinic/inspect.signature and
am generally in favor of using it everywhere. It helps tools like
jedi, pycharm and others get accurate information about a builtin
function.
On Mar 24, 2018, at 16:13, Steve Dower <steve.dower(a)python.org> wrote:
> Or we could just pull the right version directly from PyPI? (Note that updating the version should be an explicit step, as it is today, but the file should be identical to what’s on PyPI, right? And a urlretrieve is easier than pulling from a git repo.)
I think the primary original rationale for having the pip wheel and its dependencies checked into the cpython repo was so that users would be able to install pip even if they did not have an Internet connection. But perhaps that requirement can be relaxed a bit if we say that the necessary wheels are vendored into all of our downloadable release items, that is, included in the packaging of source release files (the various tarballs) and the Windows and macOS binary installers. The main change would likely be making ensurepip a bit smarter to download if the bundled wheels are not present in the source directory. Assuming that people building from a cpython repo need to have a network connection if they want to run ensurepip, at least for the first time, is probably not an onerous requirement.
--
Ned Deily
nad(a)python.org -- []
