Mailman 3 July 2020 - NumPy-Discussion

NumPy dtype API improvement suggestion
by Eyal Kutz 27 Jul '20

27 Jul '20

I am interested in suggesting an API improvement for NumPy. I wish to make it so that the following code: @np.dtype class Point: x: np.int16 y: np.int16 would be equivalent to the following code: Point = np.dtype([('x', np.int16), ('y', np.int16)]) I am willing to submit the code changes required to make this happen.

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Journey with NumPy in Docs Development
by Saber Tooth 27 Jul '20

27 Jul '20

Hello @Melissa Mendonça <melissawm(a)gmail.com> @Ralf Gommers <ralf.gommers(a)gmail.com> @Matti Picus <matti.picus(a)gmail.com> , My journey working with NumPy Docs team has been nothing short of Progressive , the insights that i have gained attending the Docs meeting has helped me refine my proposal . Attending the past 3 meetings have helped me remove some of redundant features in my proposal , letting me structure the Explanations , How-To's ,Tutorials in a better way keeping in mind the requirements as highlighted by NEP-44 and the mentors . I have really tried to work upon NumPy community bonding , i have been a part of some really good discussions , contributed to some ideas , raised some queries ☺ which so far i can say has been really progressive for me to start contributing , as today i understand what NumPy Docs is trying to deliver . I have really been enticed by the workflow of the community which encourages me to think and contribute to as much as I can. I have been mining the NumPy Discussion mailing list since last meeting using the archive as mentioned in Hackmd doc by @Ralf Gommers <ralf.gommers(a)gmail.com> , collecting topics for which we can frame Tutorials , How To's and Explanations . Discussion around Explanations has been really fruitful with @Melissa Mendonça <melissawm(a)gmail.com> and the community . Moreover I am working on a how-to nowadays for which I will soon open a PR . Contributing to NumPy in such a manner I believe will help me get started with Official Documentation Period in a more Efficient manner , as i had already worked upon Community Bonding which will continue to strengthen in upcoming days . Thank You for your encouragement and having me , I am looking into delivering much more and contributing to my GSoD Proposal and beyond that too . Thanks , Mrinal Tyagi

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Re: [Numpy-discussion] NumPy dtype API improvement suggestion
by Eyal Kutz 26 Jul '20

26 Jul '20

*Kevin Sheppard, I agree with you but* I don't know how to do this. do you?

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ANN: SciPy 1.5.2
by Tyler Reddy 24 Jul '20

24 Jul '20

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.5.2, which is a bug fix release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.5.2 One of a few ways to install this release with pip: pip install scipy==1.5.2 ========================== SciPy 1.5.2 Release Notes ========================== SciPy 1.5.2 is a bug-fix release with no new features compared to 1.5.1. Authors ====== * Peter Bell * Tobias Biester + * Evgeni Burovski * Thomas A Caswell * Ralf Gommers * Sturla Molden * Andrew Nelson * ofirr + * Sambit Panda * Ilhan Polat * Tyler Reddy * Atsushi Sakai * Pauli Virtanen A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 1.5.2 ------------------------------ * `#3847 <https://github.com/scipy/scipy/issues/3847>`__: Crash of interpolate.splprep(task=-1) * `#7395 <https://github.com/scipy/scipy/issues/7395>`__: splprep segfaults if fixed knots are specified * `#10761 <https://github.com/scipy/scipy/issues/10761>`__: scipy.signal.convolve2d produces incorrect values for large arrays * `#11971 <https://github.com/scipy/scipy/issues/11971>`__: DOC: search in devdocs returns wrong link * `#12155 <https://github.com/scipy/scipy/issues/12155>`__: BUG: Fix permutation of distance matrices in scipy.stats.multiscale_graphcorr * `#12203 <https://github.com/scipy/scipy/issues/12203>`__: Unable to install on PyPy 7.3.1 (Python 3.6.9) * `#12316 <https://github.com/scipy/scipy/issues/12316>`__: negative scipy.spatial.distance.correlation * `#12422 <https://github.com/scipy/scipy/issues/12422>`__: BUG: slsqp: ValueError: failed to initialize intent(inout) array... * `#12428 <https://github.com/scipy/scipy/issues/12428>`__: stats.truncnorm.rvs() never returns a scalar in 1.5 * `#12441 <https://github.com/scipy/scipy/issues/12441>`__: eigvalsh inconsistent eigvals= subset_by_index= * `#12445 <https://github.com/scipy/scipy/issues/12445>`__: DOC: scipy.linalg.eigh * `#12449 <https://github.com/scipy/scipy/issues/12449>`__: Warnings are not filtered in csr_matrix.sum() * `#12469 <https://github.com/scipy/scipy/issues/12469>`__: SciPy 1.9 exception in LSQSphereBivariateSpline * `#12487 <https://github.com/scipy/scipy/issues/12487>`__: BUG: optimize: incorrect result from approx_fprime * `#12493 <https://github.com/scipy/scipy/issues/12493>`__: CI: GitHub Actions for maintenance branches * `#12533 <https://github.com/scipy/scipy/issues/12533>`__: eigh gives incorrect results * `#12579 <https://github.com/scipy/scipy/issues/12579>`__: BLD, MAINT: distutils issues in wheels repo Pull requests for 1.5.2 ------------------------------- * `#12156 <https://github.com/scipy/scipy/pull/12156>`__: BUG: Fix permutation of distance matrices in scipy.stats.multiscale_graphcorr * `#12238 <https://github.com/scipy/scipy/pull/12238>`__: BUG: Use 64-bit indexing in convolve2d to avoid overflow * `#12256 <https://github.com/scipy/scipy/pull/12256>`__: BLD: Build lsap as a single extension instead of extension +... * `#12320 <https://github.com/scipy/scipy/pull/12320>`__: BUG: spatial: avoid returning negative correlation distance * `#12383 <https://github.com/scipy/scipy/pull/12383>`__: ENH: Make cKDTree.tree more efficient * `#12392 <https://github.com/scipy/scipy/pull/12392>`__: DOC: update scipy-sphinx-theme * `#12430 <https://github.com/scipy/scipy/pull/12430>`__: BUG: truncnorm and geninvgauss never return scalars from rvs * `#12437 <https://github.com/scipy/scipy/pull/12437>`__: BUG: optimize: cast bounds to floats in new_bounds_to_old/old_bounds_to_new * `#12442 <https://github.com/scipy/scipy/pull/12442>`__: MAINT:linalg: Fix for input args of eigvalsh * `#12461 <https://github.com/scipy/scipy/pull/12461>`__: MAINT: sparse: write matrix/asmatrix wrappers without warning... * `#12478 <https://github.com/scipy/scipy/pull/12478>`__: BUG: fix array_like input defects and add tests for all functions... * `#12488 <https://github.com/scipy/scipy/pull/12488>`__: BUG: fix approx_derivative step size. Closes #12487 * `#12500 <https://github.com/scipy/scipy/pull/12500>`__: CI: actions branch trigger fix * `#12501 <https://github.com/scipy/scipy/pull/12501>`__: CI: actions branch trigger fix * `#12504 <https://github.com/scipy/scipy/pull/12504>`__: BUG: cKDTreeNode use after free * `#12529 <https://github.com/scipy/scipy/pull/12529>`__: MAINT: allow graceful docs re-upload * `#12538 <https://github.com/scipy/scipy/pull/12538>`__: BUG:linalg: eigh type parameter handling corrected * `#12560 <https://github.com/scipy/scipy/pull/12560>`__: MAINT: truncnorm.rvs compatibility for \`Generator\` * `#12562 <https://github.com/scipy/scipy/pull/12562>`__: redo gh-12188: fix segfaults in splprep with fixed knots * `#12586 <https://github.com/scipy/scipy/pull/12586>`__: BLD: Add -std=c99 to sigtools to compile with C99 * `#12590 <https://github.com/scipy/scipy/pull/12590>`__: CI: Add GCC 4.8 entry to travis build matrix * `#12591 <https://github.com/scipy/scipy/pull/12591>`__: BLD: fix cython error on master-branch cython Checksums ========= MD5 ~~~ 2e046d26cdc4241a6a5b2907d57528df scipy-1.5.2-cp36-cp36m-macosx_10_9_x86_64.whl 902dea66453e2fa0616e9479970986f5 scipy-1.5.2-cp36-cp36m-manylinux1_i686.whl e130db080706d9f4ce22d8493c8e1ce2 scipy-1.5.2-cp36-cp36m-manylinux1_x86_64.whl 721f16bae600731e479a5b4e98ce9a97 scipy-1.5.2-cp36-cp36m-win32.whl a3171cfe38618d51acbfb8d1b39ac612 scipy-1.5.2-cp36-cp36m-win_amd64.whl c9f733d4d2e82c098c08760963dafaf8 scipy-1.5.2-cp37-cp37m-macosx_10_9_x86_64.whl 53ba6c502d09145b38e0e857b2d4a273 scipy-1.5.2-cp37-cp37m-manylinux1_i686.whl b9db33944ac4147936a7f42df8e95ad2 scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl be9e8bfdf0e5e0914d1e1605be26d9c0 scipy-1.5.2-cp37-cp37m-win32.whl 848fa7b82a25d0ce36710ccc47ebc2ca scipy-1.5.2-cp37-cp37m-win_amd64.whl 590cd3b70a2dc8664896d6b9e2e5fc6d scipy-1.5.2-cp38-cp38-macosx_10_9_x86_64.whl 7fdbb19c15702b98319ea4ea32df8458 scipy-1.5.2-cp38-cp38-manylinux1_i686.whl 301f3a873e1bfef70d6f594c489fafe8 scipy-1.5.2-cp38-cp38-manylinux1_x86_64.whl 8c08ac0f55810e89e336eb3bf5a7b337 scipy-1.5.2-cp38-cp38-win32.whl 711f5c47c801dc79bead7d40669fd8c9 scipy-1.5.2-cp38-cp38-win_amd64.whl 620fc39f371e04a76af5d0290f8d3753 scipy-1.5.2.tar.gz 5bc188f21054a2ecff74fae40dd298da scipy-1.5.2.tar.xz 17bc80802955d100f6c1335594eda29a scipy-1.5.2.zip SHA256 ~~~~~~ cca9fce15109a36a0a9f9cfc64f870f1c140cb235ddf27fe0328e6afb44dfed0 scipy-1.5.2-cp36-cp36m-macosx_10_9_x86_64.whl 1c7564a4810c1cd77fcdee7fa726d7d39d4e2695ad252d7c86c3ea9d85b7fb8f scipy-1.5.2-cp36-cp36m-manylinux1_i686.whl 07e52b316b40a4f001667d1ad4eb5f2318738de34597bd91537851365b6c61f1 scipy-1.5.2-cp36-cp36m-manylinux1_x86_64.whl d56b10d8ed72ec1be76bf10508446df60954f08a41c2d40778bc29a3a9ad9bce scipy-1.5.2-cp36-cp36m-win32.whl 8e28e74b97fc8d6aa0454989db3b5d36fc27e69cef39a7ee5eaf8174ca1123cb scipy-1.5.2-cp36-cp36m-win_amd64.whl 6e86c873fe1335d88b7a4bfa09d021f27a9e753758fd75f3f92d714aa4093768 scipy-1.5.2-cp37-cp37m-macosx_10_9_x86_64.whl a0afbb967fd2c98efad5f4c24439a640d39463282040a88e8e928db647d8ac3d scipy-1.5.2-cp37-cp37m-manylinux1_i686.whl eecf40fa87eeda53e8e11d265ff2254729d04000cd40bae648e76ff268885d66 scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl 315aa2165aca31375f4e26c230188db192ed901761390be908c9b21d8b07df62 scipy-1.5.2-cp37-cp37m-win32.whl ec5fe57e46828d034775b00cd625c4a7b5c7d2e354c3b258d820c6c72212a6ec scipy-1.5.2-cp37-cp37m-win_amd64.whl fc98f3eac993b9bfdd392e675dfe19850cc8c7246a8fd2b42443e506344be7d9 scipy-1.5.2-cp38-cp38-macosx_10_9_x86_64.whl a785409c0fa51764766840185a34f96a0a93527a0ff0230484d33a8ed085c8f8 scipy-1.5.2-cp38-cp38-manylinux1_i686.whl 0a0e9a4e58a4734c2eba917f834b25b7e3b6dc333901ce7784fd31aefbd37b2f scipy-1.5.2-cp38-cp38-manylinux1_x86_64.whl dac09281a0eacd59974e24525a3bc90fa39b4e95177e638a31b14db60d3fa806 scipy-1.5.2-cp38-cp38-win32.whl 92eb04041d371fea828858e4fff182453c25ae3eaa8782d9b6c32b25857d23bc scipy-1.5.2-cp38-cp38-win_amd64.whl 066c513d90eb3fd7567a9e150828d39111ebd88d3e924cdfc9f8ce19ab6f90c9 scipy-1.5.2.tar.gz 28d5d2e9af6ca5c0352cd83fb64191f2d8e883ab5287a221ba7a175c8cc2ccbe scipy-1.5.2.tar.xz a9054595a370f24d68f7a694037316b69ae80f5837323d567f76cde055189c08 scipy-1.5.2.zip

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NumPy 1.19.1 released.
by Charles R Harris 21 Jul '20

21 Jul '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.19.1 has been released. This release supports Python 3.6-3.8 and may be built with the latest Python 3.9 beta. It fixes several bugs found in the 1.19.0 release, replaces several functions deprecated in the upcoming Python-3.9 release, has improved support for AIX, and has a number of development related updates to keep CI working with recent upstream changes. Downstream developers should use Cython >= 0.29.21 when building for Python 3.9 and Cython >= 0.29.16 when building for Python 3.8. OpenBLAS >= 3.7 is needed to avoid wrong results on the Skylake architecture. The NumPy Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.19.1/>, source archives, release notes, and wheel hashes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.19.1>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Abhinav Reddy + - Anirudh Subramanian - Antonio Larrosa + - Charles Harris - Chunlin Fang - Eric Wieser - Etienne Guesnet + - Kevin Sheppard - Matti Picus - Raghuveer Devulapalli - Roman Yurchak - Ross Barnowski - Sayed Adel - Sebastian Berg - Tyler Reddy *Pull requests merged* A total of 25 pull requests were merged for this release. - #16649: MAINT, CI: disable Shippable cache - #16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength. - #16654: REL: Fix outdated docs link - #16656: BUG: raise IEEE exception on AIX - #16672: BUG: Fix bug in AVX complex absolute while processing array of... - #16693: TST: Add extra debugging information to CPU features detection - #16703: BLD: Add CPU entry for Emscripten / WebAssembly - #16705: TST: Disable Python 3.9-dev testing. - #16714: MAINT: Disable use_hugepages in case of ValueError - #16724: BUG: Fix PyArray_SearchSorted signature. - #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src - #16772: MAINT: Remove unneeded call to PyUnicode_READY - #16776: MAINT: Fix deprecated functions in scalarapi.c - #16779: BLD, ENH: Add RPATH support for AIX - #16780: BUG: Fix default fallback in genfromtxt - #16784: BUG: Added missing return after raising error in methods.c - #16795: BLD: update cython to 0.29.21 - #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it - #16872: BUG: Validate output size in bin- and multinomial - #16875: BLD, MAINT: Pin setuptools - #16904: DOC: Reconstruct Testing Guideline. - #16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's... - #16906: BUG, DOC: Fix bad MPL kwarg. - #16916: BUG: Fix string/bytes to complex assignment - #16922: REL: Prepare for NumPy 1.19.1 release Cheers, Charles Harris

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NumPy Community Meeting Wednesday
by Sebastian Berg 21 Jul '20

21 Jul '20

Hi all, There will be a NumPy Community meeting Wednesday July 22nd at 1pm Pacific Time (20:00 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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Augment unique method
by Amin Sadeghi 16 Jul '20

16 Jul '20

It would be handy to add "atol" and "rtol" optional arguments to the "unique" method. I'm proposing this since uniqueness is a bit vague for floats. This change would be clearly backwards-compatible.

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Discussion around How-to ,Tutorials.
by Saber Tooth 16 Jul '20

16 Jul '20

Hello Melissa , Are we having our Docs meeting on 20th July ? If yes , then could we have some discussion around the structure of tutorials ,how-to sections . I wanted to discuss how we are planning to distinguish between the two , should the How-to be straightforward solutions like here <https://matplotlib.org/3.2.2/gallery/lines_bars_and_markers/bar_stacked.htm…> or something else . Tutorials should be structured as in here <https://matplotlib.org/3.2.2/tutorials/introductory/usage.html#sphx-glr-tut…> or something else . The general belief is when we hear the word "How to" is that we are greeted to just the solution of the problem like above , while when we hear Tutorials we are greeted to Detailed solutions with explanations for most of the steps . Thanks , Mrinal

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An alternative to vectorize that lets you access the array?
by Ram Rachum 15 Jul '20

15 Jul '20

Hi everyone, Here's a problem I've been dealing with. I wonder whether NumPy has a tool that will help me, or whether this could be a useful feature request. In the upcoming EuroPython 20200, I'll do a talk about live-coding a music synthesizer. It's going to be a fun talk, I'll use the sounddevice <https://github.com/spatialaudio/python-sounddevice/> module to make a program that plays music. Do attend, or watch it on YouTube when it's out :) There's a part in my talk that I could make simpler, and thus shave 3-4 minutes of cumbersome explanations. These 3-4 minutes matter a great deal to me. But for that I need to do something with NumPy and I don't know whether it's possible or not. The sounddevice library takes an ndarray of sound data and plays it. Currently I use `vectorize` to produce that array: output_array = np.vectorize(f, otypes='d')(input_array) And I'd like to replace it with this code, which is supposed to give the same output: output_array = np.ndarray(input_array.shape, dtype='d') for i, item in enumerate(input_array): output_array[i] = f(item) The reason I want the second version is that I can then have sounddevice start playing `output_array` in a separate thread, while it's being calculated. (Yes, I know about the GIL, I believe that sounddevice releases it.) Unfortunately, the for loop is very slow, even when I'm not processing the data on separate thread. I benchmarked it on both CPython and PyPy3, which is my target platform. On CPython it's 3 times slower than vectorize, and on PyPy3 it's 67 times slower than vectorize! That's despite the fact that the Numpy documentation says "The `vectorize` function is provided primarily for convenience, not for performance. The implementation is essentially a `for` loop." So here are a few questions: 1. Is there something like `vectorize`, except you get to access the output array before it's finished? If not, what do you think about adding that as an option to `vectorize`? 2. Is there a more efficient way of writing the `for` loop I've written above? Or any other kind of solution to my problem? Thanks for your help, Ram Rachum.

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NumPy Development Meeting Today - Triage Focus
by Sebastian Berg 15 Jul '20

15 Jul '20

Hi all, Our bi-weekly triage-focused NumPy development meeting is tomorrow (Wednesday, July 15th) at 11 am Pacific Time (18:00 UTC). 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 or simply discussed briefly. Just comment on it so we can label it, or add your PR/issue to this weeks topics for discussion. Best regards Sebastian

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