Mailman 3 January 2022 - NumPy-Discussion

Fit 2D points to closed curve
by roger.davies＠geo-spatial.co.uk Jan. 21, 2022

Jan. 21, 2022

Hi, I use a CAD package called Rhino which lets users write python scripts that run in the cad environment. I have a closed curve that is a design surface border and I have a sparse data set of 2D surveyed points taken around that border, so some error is included in the 2D data set. I would like to know what python function I could use to do a 2D best fit of the points to the curve. Plenty of examples of fitting curves to points but not of best fitting points to a closed curve of which I do not know the formula. Any help or advice greatly appreciated.

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call for contributions – content writers for the NumPy newsletter
by Inessa Pawson Jan. 19, 2022

Jan. 19, 2022

Happy New Year, everyone! I hope you had a restful holiday break and are entering the new year refreshed and inspired. The NumPy documentation team and I are gearing up for the launch of the NumPy newsletter and currently looking for content writers. If you are new to contributing to open source or NumPy, this is a great way to start. You will deepen your knowledge about NumPy, as software and a community, and collaborate with other NumPy contributors. For more about our plans for the newsletter, visit https://github.com/numpy/numpy-newsletter. If you’d like to help or have questions about this project, please don’t hesitate to reach out. -- Every good wish, Inessa *Inessa Pawson* *Contributor Experience Lead | NumPy* inessa(a)albuscode.org

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NumPy Newcomers Hour – Thursday, January 13th
by Inessa Pawson Jan. 19, 2022

Jan. 19, 2022

The next NumPy Newcomers Hour is this *Thursday, January 13th at 4 pm UTC*. We are trying out a new format: a guest speaker presentation + Q&A. *Ganesh Kathiresan*, a NumPy maintainer, will show how to: – browse code with vim, ctags, and cscope; – the basics of how to debug NumPy core files. *Join us via Zoom*: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson NumPy Contributor Experience Lead

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NumPy 1.22.1 has been released.
by Charles R Harris Jan. 18, 2022

Jan. 18, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.1. NumPy 1.22.1 fixes several bugs discovered after the 1.22.0 release. Notable fixes are: - Fix for f2PY docstring problems (SciPy) - Fix for reduction type problems (AstroPy) - Fixes for various typing bugs. The Python versions supported in this release are 3.8-3.10. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.1>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Arryan Singh - Bas van Beek - Charles Harris - Denis Laxalde - Isuru Fernando - Kevin Sheppard - Matthew Barber - Matti Picus - Melissa Weber Mendonça - Mukulika Pahari - Omid Rajaei + - Pearu Peterson - Ralf Gommers - Sebastian Berg *Pull requests merged* A total of 20 pull requests were merged for this release. - #20702: MAINT, DOC: Post 1.22.0 release fixes. - #20703: DOC, BUG: Use pngs instead of svgs. - #20704: DOC: Fixed the link on user-guide landing page - #20714: BUG: Restore vc141 support - #20724: BUG: Fix array dimensions solver for multidimensional arguments... - #20725: TYP: change type annotation for ``__array_namespace__`` to ModuleType - #20726: TYP, MAINT: Allow ``ndindex`` to accept integer tuples - #20757: BUG: Relax dtype identity check in reductions - #20763: TYP: Allow time manipulation functions to accept ``date`` and ``timedelta``... - #20768: TYP: Relax the type of ``ndarray.__array_finalize__`` - #20795: MAINT: Raise RuntimeError if setuptools version is too recent. - #20796: BUG, DOC: Fixes SciPy docs build warnings - #20797: DOC: fix OpenBLAS version in release note - #20798: PERF: Optimize array check for bounded 0,1 values - #20805: BUG: Fix that reduce-likes honor out always (and live in the... - #20806: BUG: ``array_api.argsort(descending=True)`` respects relative... - #20807: BUG: Allow integer inputs for pow-related functions in ``array_api`` - #20814: DOC: Refer to NumPy, not pandas, in main page - #20815: DOC: Update Copyright to 2022 [License] - #20819: BUG: Return correctly shaped inverse indices in array_api set... Cheers, Charles Harris

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next NumPy Community meeting – January 19th at 18:00 (6 pm) UTC
by Inessa Pawson Jan. 18, 2022

Jan. 18, 2022

Our next NumPy Community meeting is scheduled for tomorrow, Wednesday, January 19th at 18:00 (6 pm) UTC.* Join us via Zoom: https://berkeley.zoom.us/j/762261535 As always, everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both *PLEASE NOTE that the schedule has slightly changed: starting tomorrow, we will be meeting an hour earlier. To convert the start time to the local time of the area you are in, use the following link: https://www.timeanddate.com/worldclock/fixedtime.html?msg=NumPy+Newcomers%2… Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy

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numpy 1.22.1 failed with "RuntimeError: Setuptools version is '60.5.0', version < '60.0.0' is required."
by s.srigowthami＠gmail.com Jan. 18, 2022

Jan. 18, 2022

Hi, We are using Openstack zuul ( Openstack CI ) to deploy devstack on IBM PPC64el hardware In https://review.opendev.org/c/openstack/requirements/+/824810/2/upper-constr… commit the numpy package version is updated from 1.22.0 to 1.22.1 When the version 1.22.0 of numpy was used setuptools-60.5.0 has successfully deployed. where as with the latest numpy 1.22.1 the setuptools package installation is failing with the below runtime error. Error: 2022-01-18 12:17:18.663446 | devstack-focal-newcloud | Collecting numpy 2022-01-18 12:17:18.763054 | devstack-focal-newcloud | Downloading numpy-1.22.1.zip (11.4 MB) 2022-01-18 12:17:24.048280 | devstack-focal-newcloud | Installing build dependencies: started 2022-01-18 12:17:32.883181 | devstack-focal-newcloud | Installing build dependencies: finished with status 'done' 2022-01-18 12:17:32.890122 | devstack-focal-newcloud | Getting requirements to build wheel: started 2022-01-18 12:17:33.282029 | devstack-focal-newcloud | Getting requirements to build wheel: finished with status 'error' 2022-01-18 12:17:33.282475 | devstack-focal-newcloud | ERROR: Command errored out with exit status 1: 2022-01-18 12:17:33.282499 | devstack-focal-newcloud | command: /usr/bin/python3.8 /usr/local/lib/python3.8/dist-packages/pip/_vendor/pep517/in_process/_in_process.py get_requires_for_build_wheel /tmp/tmpkr3xl1i_ 2022-01-18 12:17:33.282529 | devstack-focal-newcloud | cwd: /tmp/pip-install-c1eqnkjm/numpy_7e0d04ecfb1b47af806fb5707c655744 ....... 2022-01-18 12:17:33.283155 | devstack-focal-newcloud | RuntimeError: Setuptools version is '60.5.0', version < '60.0.0' is required. See pyproject.toml ... 2022-01-18 12:17:49.575387 | devstack-focal-newcloud | 2022-01-18 12:17:49.575401 | devstack-focal-newcloud | The conflict is caused by: 2022-01-18 12:17:49.575422 | devstack-focal-newcloud | websockify 0.10.0 depends on numpy 2022-01-18 12:17:49.575450 | devstack-focal-newcloud | The user requested (constraint) numpy===1.22.1 numpy: 1.22.1 ubuntu: Focal python : 3.8 Could someone help me with a way forward. Thanks Gowthami

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ndarray typing advise
by Vincent Schut Jan. 18, 2022

Jan. 18, 2022

Hi, I'm having a hard(ish) time adding the right type annotations to some numpy-using code. I hope someone more knowledgeable can give me some advise. Specifically, the following questions pop up: - How do I correctly type a ndarray as being an array of float64, such that mypy can infer that when I index this array, the result is actually a float (currently I consistently get "Any" after indexing)? - How can I get compatibility between np.floating and builtin.float? Currently, some numpy functions are typed to return np.floating[Any] (e.g. np.linalg.norm). When I feed this result into something that is supposed to receive a builtin.float, mypy complains. Is an explicit cast(float, <the np.floating[Any] thing>) the prefered way to gain compatibility here? versions: numpy: 1.22.0 python: 3.8.10 (unfortunately we're limited to this version) mypy: 0.931 Thanks! Vincent. -- Vincent Schut Remote Sensing Software Engineer +31 302272679 ~ Maliebaan 22 | 3581CP | Utrecht | Netherlands Linkedin <https://www.linkedin.com/company/satelligence/>~ satelligence.com <http://www.satelligence.com><http://www.satelligence.com>

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Importance of setting up SVML during build
by Sandro Tosi Jan. 16, 2022

Jan. 16, 2022

Hello, i'm preparing 1.22.1 for inclusion in Debian, and noticed how the new numpy has a git submodule to include numpy/SVML in its source tree. We are basing the debian package on the github tarball, and that does not include any submodule, so i'm wondering how important it is to have the SVML files around when building the packages. Given it's not immediately possible to include an external set of files, i'm just trying to balance the effort to inject them and their usefulness. At the same time, i'm wondering if https://github.com/numpy/numpy/pull/20695 (the PR that introduced NPY_DISABLE_SVML, also probably https://github.com/numpy/numpy/pull/20741 for the doc) should be backported to the 1.22.x branch, so that we can start using it in the release that introduced that feature. Thanks, -- Sandro "morph" Tosi My website: http://sandrotosi.me/ Me at Debian: http://wiki.debian.org/SandroTosi Twitter: https://twitter.com/sandrotosi

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MacPython travis funds have run out (again)
by Charles R Harris Jan. 14, 2022

Jan. 14, 2022

Hi All, Travis wheel build problems again. There is still a positive balance, but apparently it is insufficiently positive. Which is strange if ARM64 is free. Wheel builds have been failing since last weekend. Matti, could you ask for more credits? I'm thinking we really need to move off travis for ARM builds. Chuck

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np.convolve question
by cae＠live.in Jan. 14, 2022

Jan. 14, 2022

Hello, I am a mechanical engineer and am using Numpy Convolve to smoothen 1D data collected from physical testing for a diesel engine. (I found convolve the best after comparing with other smoothing methodologies) I tried reading the numpy manual and the wikipedia article referenced in it. But I would specifically like to ask/discuss what would happen if the second 1D input is not given? what type of window will the function pass the data through to smoothen it? Here are the articles I have read to implement my smoothing code: References: 1. https://numpy.org/doc/stable/reference/generated/numpy.convolve.html 2. https://en.wikipedia.org/wiki/Convolution 3. https://stackoverflow.com/questions/20618804/how-to-smooth-a-curve-in-the-r… NOTE: Apologies if I have unknowingly broken any rule of this forum - I can delete the thread if it doesn't meet any set guidelines of the forum. Best, Pushkar Sheth Bangalore, India.

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg Jan. 12, 2022

Jan. 12, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, January 12th at 17:00 UTC (9:00am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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Writing an array subclass in a compiled language with proper dispatch
by Juan Luis Cano Rodríguez Jan. 11, 2022

Jan. 11, 2022

Hi all, I am a long time user of astropy.units, which allows one to define quantities with physical units as follows: >>> from astropy import units as u >>> 10 << u.cm <Quantity 10. cm> >>> np.sqrt(4 << u.m ** 2) <Quantity 2. m> >>> ([1, 1, 0] << u.m) @ ([0, 10, 20] << u.cm / u.s) <Quantity 10. cm m / s> >>> (([1, 1, 0] << u.m) * ([0, 10, 20] << u.cm / u.s)).to(u.m ** 2 / u.s) <Quantity [0. , 0.1, 0. ] m2 / s> The mechanism works by subclassing numpy.ndarray and leveraging __array_function__ support aka NEP 18. Internally it is something like this: >>> v = np.array(10, dtype=np.float64, copy=False, order=None, subok=True, ndmin=0) >>> vu = v.view(u.Quantity) >>> vu._set_unit(u.cm) >>> vu <Quantity 10. cm> However, over the years I have been constantly annoyed by the fact that it is tremendously slow. I'm not critizing Astropy devs, the problem seems objectively difficult: although some code paths could be optimized at the cost of losing some syntactic sugar or breaking backwards compatibility, `isinstance` calls and introspection in general are slow. Setting aside the question of trying to make astropy.units faster (which may or may not be possible), I was thinking how feasible could it be to implement something similar, but using a compiled language instead (C, Cython, Rust, whatever) and leveraging "modern" dispatch mechanisms. But after reading about NEP 18, NEP 47, uarray, and various pull requests and issues here and there (https://labs.quansight.org/blog/2021/11/pydata-extensibility-vision/ and https://github.com/scipy/scipy/issues/10204#issuecomment-787067947 among others) I don't fully grasp the differences between the approaches, and I don't know if what I am proposing is feasible at all. Since IIUC the numpy function or ufunc is passed to __array_function__ and __array_ufunc__ respectively, I am not sure how would that interact with the code being in a foreign language (I assume the NumPy C API would have to be used). If folks have advice, ideas, or suggestions for a direction, I'll be happy to read them. Best!

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representation of valid float type range
by alejandro.giacometti＠gmail.com Jan. 10, 2022

Jan. 10, 2022

I am getting an interesting result, and I'm wondering if anyone would care to give me some intuition of why. The example is simple enough, I want to get a range of values that are representable by a type: ```python f64_info = np.finfo(np.float64) valid_range = np.linspace( start=f64_info.min, stop=f64_info.max, num=10 ) valid_range => array([ nan, inf, inf, inf, inf, inf, inf, inf, inf, 1.79769313e+308]) ``` The minimum value is representable by the type, I can see it: ```python f64_info.min => -1.7976931348623157e+308 ``` I thought that maybe the valid range cannot start with the minimun value, so I've tried a few alternatives: ```python valid_range = np.linspace( start=f64_info.min + f64_info.eps, stop=f64_info.max, num=10 ) valid_range => array([ nan, inf, inf, inf, inf, inf, inf, inf, inf, 1.79769313e+308]) valid_range = np.linspace( start=f64_info.min + f64_info.tiny, stop=f64_info.max, num=10 ) valid_range => array([ nan, inf, inf, inf, inf, inf, inf, inf, inf, 1.79769313e+308]) ``` I thought maybe the range is too wide, but I can do this: ```python valid_range = np.linspace( start=0, stop=f64_info.max, num=10 ) valid_range => array([0.00000000e+000, 1.99743682e+307, 3.99487363e+307, 5.99231045e+307, 7.98974727e+307, 9.98718408e+307, 1.19846209e+308, 1.39820577e+308, 1.59794945e+308, 1.79769313e+308]) ... valid_range = np.linspace( start=f64_info.tiny, stop=f64_info.max, num=10 ) valid_range => array([2.22507386e-308, 1.99743682e+307, 3.99487363e+307, 5.99231045e+307, 7.98974727e+307, 9.98718408e+307, 1.19846209e+308, 1.39820577e+308, 1.59794945e+308, 1.79769313e+308]) ... f32_info = np.finfo(np.float32) valid_range = np.linspace( start=f32_info.tiny, stop=f32_info.max, num=10, dtype=np.float32, ) valid_range => array([1.1754944e-38, 3.7809150e+37, 7.5618299e+37, 1.1342745e+38, 1.5123660e+38, 1.8904575e+38, 2.2685490e+38, 2.6466405e+38, 3.0247320e+38, 3.4028235e+38], dtype=float32) ``` I know that linear space is arbitrary, and perhaps not that useful. In fact this is valid: ```python valid_range = np.logspace( start=f64_info.minexp, stop=f64_info.maxexp, num=10, base=2, endpoint=False ) valid_range => array([2.22507386e-308, 8.67124674e-247, 3.37923704e-185, 1.31690901e-123, 5.13207368e-062, 2.00000000e+000, 7.79412037e+061, 3.03741562e+123, 1.18369915e+185, 4.61294681e+246]) ``` But I'm still confused on why linear space is invalid Thanks!

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mypy false positives
by Vincent Schut Jan. 7, 2022

Jan. 7, 2022

Hi all, I've got a question about numpy typing. We have our own internal codebase that heavily depends on numpy, and we're trying to get the number of mypy errors down. Many of the remaining mypy errors are actually numpy false positives. I hoped that with the numpy 1.22.0 release these would be fixed, as there ought to be many typing improvements in that version, but apparently they're not? I'm asking here to check if this is just still a work in progress and we should be patient, or if there's something wrong on our side...? Some typical false positives are: "finfo[float32]" has no attribute "tiny" Module has no attribute "count_nonzero" Module has no attribute "rot90" Module has no attribute "asfortranarray" Module has no attribute "isfinite" Module has no attribute "any" Module has no attribute "histogram"; maybe "histogramdd"? Module has no attribute "bitwise_or" Module has no attribute "bitwise_and" Module has no attribute "indices" Module has no attribute "pad" versions: python 3.8.10; numpy 1.22.0; mypy 0.930 -- Vincent Schut Remote Sensing Software Engineer +31 302272679 ~ Maliebaan 22 | 3581CP | Utrecht | Netherlands Linkedin <https://www.linkedin.com/company/satelligence/>~ satelligence.com <http://www.satelligence.com><http://www.satelligence.com>

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No credits left in travis for 1.22.0 release
by Charles R Harris Jan. 6, 2022

Jan. 6, 2022

Hi All, We need to get more credits in order to run MacPython/numpy-wheels builds on travis. Anyone recall how to go about this? Chuck

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NumPy Community Meeting Wednesday
by Sebastian Berg Jan. 5, 2022

Jan. 5, 2022

Hi all, There will be a NumPy Community meeting Wednesday January 5th at 19:00 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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MyGrad 2.1.0 has been released
by rsoklaski＠gmail.com Jan. 4, 2022

Jan. 4, 2022

Hi all, I am pleased to announce that MyGrad 2.1.0 has been released. In accordance with NEP 29, we have dropped support for NumPy < 1.19. Changelog: https://mygrad.readthedocs.io/en/latest/changes.html#v2-1-0 What the heck is MyGrad? MyGrad provides drop-in automatic differentiation to native NumPy code. Just pass MyGrad-tensors into your NumPy-based code (instead of arrays), and start differentiating! It accomplishes this by making keen use of NumPy's function-override mechanisms.

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Re: An article on numpy data types (Lev Maximov)
by Stefano Miccoli Jan. 2, 2022

Jan. 2, 2022

First of all, happy new 2022 UTC year! Let my add just a very brief note to this discussion: I opened https://github.com/numpy/numpy/issues/20675 <https://github.com/numpy/numpy/issues/20675> which addresses the shortcomings of the current documentation, which is in my opinion not sufficiently explicit in stating the datetime64 semantics. It is true that these are largely consistent with python ‘datetime.datetime’, but ‘explicit is better than implicit’. If nobody objects I will then open a doc-only PR adding a very short paragraph to the docs trying to explain the points discussed here. As what regards how much time UTC gained from 1970-01-01 up to today, you are right, it’s about 29 s. The UTC timescale was officially born in 1963 but it can be traced back at least up to 1956/1958, see https://github.com/skyfielders/python-skyfield/issues/679 <https://github.com/skyfielders/python-skyfield/issues/679> where this is discussed with reference to the timescales implemented in python-skyfield. Stefano > On 31 Dec 2021, at 13:27, numpy-discussion-request(a)python.org wrote: > > Hey, Stefano! > > The level of being pedantic is absolutely acceptable. > > I don't question any of your arguments. They are all perfectly valid. > > Except that I'd rather say it is ~29 seconds if measuring against 1970. Leap seconds were introduced in 1972 and there were > a total of 27 seconds since then, but TAI time was ticking since 1958 and gained 10 seconds by 1970 so it is approximately 0.83 second per year at which gives approx 28.67 sec between today and 1970. > So 1970 is a bad choice of epoch if you want to introduce a leap-second-aware datetime. > In GPS time they chose 1980. In TAI it is 1958, but that is somewhat worse than 1980 because it is not immediately clear how to perform the conversion timestamp<->timedelta between 1958 and 1970. > > Something like 'proleptic gps time' would be needed to estimate the number of leap seconds in the years before 1972 when they were introduced. Or maybe to limit the leap-second timescale > to start at 1972 and not to accept any timestamps before that date. > > The system that ignores the existence of the leap seconds has a right to exist. > But it just has limited applicability. > > np.datetime64 keeps time as a delta between the moment in time and a predefined epoch. > Which standard does it use to translate this delta to human-readable time in years, > months, and so on? > > If it is UTC, then it must handle times like 2016-12-31 23:59:60, because it is a valid UTC timestamp. > >>> np.datetime64('2016-12-31 12:59:60') > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > ValueError: Seconds out of range in datetime string "2016-12-31 12:59:60" > > Datetime also fails (so far) to handle it: > >>> dt(2016,12,31,23,59,60) > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > ValueError: second must be in 0..59 > > But `time` works. Well, at least it doesn't raise an exception: > >>> t = time.struct_time((2016,12,31,12,59,60,0,0,0)); t > time.struct_time(tm_year=2016, tm_mon=12, tm_mday=31, tm_hour=12, tm_min=59, tm_sec=60, tm_wday=0, tm_yday=0, tm_isdst=0) > >>> time.asctime(t) > 'Mon Dec 31 12:59:60 2016' > >>> time.gmtime(calendar.timegm(t)) > time.struct_time(tm_year=2017, tm_mon=1, tm_mday=1, tm_hour=1, tm_min=0, tm_sec=0, tm_wday=6, tm_yday=1, tm_isdst=0) > > Imagine a user that decides which library to use to store some (life critical!) measurements taken every 100 ms. He looks at NumPy datetime64, reads that it is capable of handling attosecods, and decides that it is a perfect fit. Now imagine that on 31 Dec 2022 the World Government decided to inject a leap second. The system will receive the announcement from the NTC servers and will > prepare to replay this second twice. As soon as this moment chimes in he'll run into a ValueError, which he won't notice because he's celebrating a New Year :) And guess whom he'll blame? ;) > > Actually the humanity has already got used to replaying timespans twice. It happens every year in the countries that observe daylight saving time. And the solution is to use a more linear scale than local time, namely, UTC. But now turns out that UTC is not linear enough and it also has certain timespans happening twice. > > The solution once again is use a _really_ linear time which is TAI. I think python 'time' library did a right thing to introduce time.CLOCK_TAI, after all. > > Astropy handles the UTC scale properly though: > >>> t = Time('2016-12-31 23:59:60') > <Time object: scale='utc' format='iso' value=2016-12-31 23:59:60.000> > >>> t0 = Time('2016-12-31 23:59:59') > <Time object: scale='utc' format='iso' value=2016-12-31 23:59:59.000> > >>> delta = t-t0 > <TimeDelta object: scale='tai' format='jd' value=1.1574074074038876e-05> > >>> delta.sec > 0.9999999999969589 > >>> t0 + delta > <Time object: scale='utc' format='iso' value=2016-12-31 23:59:60.000> > > So the solution for that particular person with regular intervals of time is to use astropy. I mention it in the article. > I made some corrections to the text. I'd be grateful if you had a look and pointed me to the particular sentences > that need improvement. > > Best regards, > Lev >

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NumPy 1.22.0 has been released
by Charles R Harris Jan. 1, 2022

Jan. 1, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.0. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across applications such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.0/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.0>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 153 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @Illviljan - @h-vetinari - @yan-wyb + - Aaron Meurer - Abel Aoun + - Adrian Gao + - Ahmet Can Solak + - Ajay DS + - Alban Colley + - Alberto Rubiales + - Alessia Marcolini + - Amit Kumar + - Andrei Batomunkuev + - Andrew Watson + - Anirudh Dagar + - Ankit Dwivedi + - Antony Lee - Arfy Slowy + - Arryan Singh + - Arun Palaniappen + - Arushi Sharma + - Bas van Beek - Brent Brewington + - Carl Johnsen + - Carl Michal + - Charles Harris - Chiara Marmo - Chris Fu (傅立业) + - Christoph Buchner + - Christoph Reiter + - Chunlin Fang - Clément Robert + - Constanza Fierro - Damien Caliste - Daniel Ching - David Badnar + - David Cortes + - David Okpare + - Derek Huang + - Developer-Ecosystem-Engineering + - Dima Pasechnik - Dimitri Papadopoulos + - Dmitriy Fishman + - Eero Vaher + - Elias Koromilas + - Eliaz Bobadilla + - Elisha Hollander + - Eric Wieser - Eskild Eriksen + - Evan Miller + - Fayas Noushad + - Gagandeep Singh + - Ganesh Kathiresan - Ghiles Meddour + - Greg Lucas - Gregory R. Lee - Guo Shuai + - Gwyn Ciesla + - Hameer Abbasi - Hector Martin + - Henry Schreiner + - Himanshu + - Hood Chatham + - Hugo Defois + - Hugo van Kemenade - I-Shen Leong + - Imen Rajhi + - Irina Maria Mocan + - Irit Katriel + - Isuru Fernando - Jakob Jakobson - Jerry Morrison + - Jessi J Zhao + - Joe Marshall + - Johan von Forstner + - Jonas I. Liechti + - Jonathan Reichelt Gjertsen + - Joshua Himmens + - Jérome Eertmans - Jérôme Kieffer + - KIU Shueng Chuan + - Kazuki Sakamoto + - Kenichi Maehashi - Kenny Huynh + - Kent R. Spillner + - Kevin Granados + - Kevin Modzelewski + - Kevin Sheppard - Lalit Musmade + - Malik Idrees Hasan Khan + - Marco Aurelio da Costa + - Margret Pax + - Mars Lee + - Marten van Kerkwijk - Matthew Barber + - Matthew Brett - Matthias Bussonnier - Matthieu Dartiailh - Matti Picus - Melissa Weber Mendonça - Michael McCann + - Mike Jarvis + - Mike McCann + - Mike Toews - Mukulika Pahari - Nick Pope + - Nick Wogan + - Niels Dunnewind + - Niko Savola + - Nikola Forró - Niyas Sait + - Pamphile ROY - Paul Ganssle + - Pauli Virtanen - Pearu Peterson - Peter Hawkins + - Peter Tillema + - Prathmesh Shirsat + - Raghuveer Devulapalli - Ralf Gommers - Robert Kern - Rohit Goswami + - Ronan Lamy - Ross Barnowski - Roy Jacobson + - Samyak S Sarnayak + - Sayantika Banik + - Sayed Adel - Sebastian Berg - Sebastian Schleehauf + - Serge Guelton - Shriraj Hegde + - Shubham Gupta + - Sista Seetaram + - Stefan van der Walt - Stephannie Jimenez Gacha + - Tania Allard - Theodoros Nikolaou + - Thomas Green + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Tom Tan + - Tyler Reddy - Vijay Arora + - Vinith Kishore + - Warren Weckesser - Yang Hau - Yashasvi Misra - Yuval Ofek + - Zac Hatfield-Dodds - Zhang Na + Cheers, Charles Harris

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Proposal to add method for string slicing to np.char
by Joseph Fox-Rabinovitz Jan. 1, 2022

Jan. 1, 2022

I made a PR for another new method `np.char.slice_` here: https://github.com/numpy/numpy/pull/20694 Here is an excerpt of the PR message: There are numerous examples of string slicing being a fairly requested feature: - https://stackoverflow.com/q/70547027/2988730 - https://stackoverflow.com/q/39042214/2988730 - https://stackoverflow.com/q/40976714/2988730 - https://stackoverflow.com/q/64981711/2988730 - https://stackoverflow.com/q/31387047/2988730 - https://stackoverflow.com/q/69856133/2988730 - ... I stopped searching around here Given the existence of the `char` module, there is no reason not to include a basic slicing operation that can work cheaper than making views and copies of a string, or switching to pandas for this one feature. This PR introduces such a function. It's written entirely in python, and does its absolute best not to make a copy of any data. The original inspiration for this is my answer to the first question in the list above. I've added a couple of features since then, like the ability to have a meaningful non-unit step and the ability to set the length of non-unit-step chunks. Please let me know your thoughts about the value of something like this. Regards, - Joe

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