Mailman 3 February 2023 - NumPy-Discussion

next NumPy Newcomers' Hour - 8 pm UTC
by Inessa Pawson 20 Mar '23

20 Mar '23

Our next Newcomers' Hour will be held this Thursday, December 15th at 8 pm UTC. Stop by to ask questions or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09 -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

1 5

1D ndarray to java double[]
by Bill Ross 20 Mar '23

20 Mar '23

How best to write a 1D ndarray as a block of doubles, for reading in java as double[] or a stream of double? Maybe the performance of simple looping over doubles in python.write() and java.read() is fine, but maybe there are representational diffs? Maybe there's a better solution for the use case? Use case: I get the ndarray from keras, and it represents a 2D distance matrix. I want to find the top-50 matches for each item, per row and column. I'm looking at moving the top-50 task to java for its superior parallel threading. (Java doesn't fork processes with a copy of the array, which is ~5% of memory; rather one gets 1 process with e.g. 1475% CPU.) Thanks, Bill Ross -- Phobrain.com

2 5

next NumPy triage meeting - now starting at 5 pm UTC
by Inessa Pawson 19 Mar '23

19 Mar '23

The next NumPy triage meeting will be held this Wednesday, January 11th at 5 pm UTC. (Please note the new time!) This is a meeting where we synchronously triage prioritized PRs and issues. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… Everyone is welcome to attend and contribute to a conversation. Please notify us of issues or PRs that you’d like to have reviewed by adding a GitHub link to them in the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg. -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

1 5

next NumPy community meeting
by Inessa Pawson 12 Mar '23

12 Mar '23

The next NumPy community meeting will be held this Wednesday, August 17th at 18:00 (6 pm) UTC. Join us via Zoom: https://us06web.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdkZz09 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 For the notes from the previous meeting, visit: https://github.com/numpy/archive/blob/main/community_meetings/community-202… Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

1 15

Documentation team meeting - Monday 27 February at 12 PM UTC (noon)
by Mukulika Pahari 26 Feb '23

26 Feb '23

Hi all! Our next Documentation Team meeting will happen on *Monday, February 27* at ***12PM UTC***. We now alternate the meeting times to be a bit more inclusive. This means that we'll have a meeting at 12pm UTC every 28 days, and a meeting at 4pm UTC every 28 days. All are welcome - you don't need to already be a contributor to join. If you have questions or are curious about what we're doing, we'll be happy to meet you! If you wish to join on Zoom, use this (updated) link: https://numfocus-org.zoom.us/j/85016474448?pwd=TWEvaWJ1SklyVEpwNXUrcHV1YmFJQ... Here's the permanent hackmd document with the meeting notes (still being updated): https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg Hope to see you around! * You can also visit https://scientific-python.org/calendars to add the NumPy community calendar as an .ics file to your preferred calendar manager. * Best wishes, Mukulika

1 0

non normalised eigenvectors
by louis.petingi＠csi.cuny.edu 25 Feb '23

25 Feb '23

Dear all, I am not an expert in NumPy but my undergraduate student is having some issues with the way Numpy returns the normalized eigenvectors corresponding to the eigenvalues. We do understand that an eigenvector is divided by the norm to get the unit eigenvectors, however we do need the original vectors for the purpose of my research. This has been a really frustrated experience as NumPy returns the normalized vectors as a default. I appreciate any suggestions of how to go about this issue. This seems to be a outstanding issue from people using Numpy. Thanks LP

5 12

ANN: A new version (0.14.0) of sparse has been released.
by Hameer Abbasi 24 Feb '23

24 Feb '23

I’d like to announce the release of PyData/Sparse 0.14.0. PyData/Sparse is available on conda-forge and PyPI. PyData/Sparse is a package that provides sparse arrays conforming to the NumPy API, along with a subset of NumPy operations on sparse arrays. The highlights of this release include faster indexing, sampling and Sparse einsum support. The changelog from the last release is available at: https://sparse.pydata.org/en/0.14,0/changelog.html The code is available at: https://github.com/pydata/sparse/ And the latest documentation is available at https://sparse.pydata.org/

1 0

__array_function__ for assignment?
by Mark Harfouche 23 Feb '23

23 Feb '23

I've been working on creating a few lazy arrays for our applications. For large data volumes, it is often critical to avoid the use of bounce buffers. Essentially, I'm looking to see if there exists a function like `np.take` that would take: 1. A slice for the input array. 2. A slice for the output array. 3. Assign the slice of the input array to the output array. I could "slice" and assign, Pseudocode: s = MyFancyArray((3, 1024, 1024, 1024)) # A very big array a = np.zeros(s.shape, dtype=s.dtype) a[...] = s # I want this operation to be crafted carefully But that seems to: 1. Instantiate the full array `s` with `__array__`. 2. slice into s without passing me a reference to the array `a`. Is there something like `np.take` or `np.copyto` that is more general?

1 0

ANN: SciPy 1.10.1
by Tyler Reddy 19 Feb '23

19 Feb '23

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.10.1. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.10.1 One of a few ways to install this release with pip: pip install scipy==1.10.1 ====================== SciPy 1.10.1 Release Notes ====================== SciPy 1.10.1 is a bug-fix release with no new features compared to 1.10.0. Authors ======= * Name (commits) * alice (1) + * Matt Borland (2) + * Evgeni Burovski (2) * CJ Carey (1) * Ralf Gommers (9) * Brett Graham (1) + * Matt Haberland (5) * Alex Herbert (1) + * Ganesh Kathiresan (2) + * Rishi Kulkarni (1) + * Loïc Estève (1) * Michał Górny (1) + * Jarrod Millman (1) * Andrew Nelson (4) * Tyler Reddy (50) * Pamphile Roy (2) * Eli Schwartz (2) * Tomer Sery (1) + * Kai Striega (1) * Jacopo Tissino (1) + * windows-server-2003 (1) A total of 21 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 1.10.1 -------------------------------- * `#14980 <https://github.com/scipy/scipy/issues/14980>`__: BUG: Johnson's algorithm fails without negative cycles * `#17670 <https://github.com/scipy/scipy/issues/17670>`__: Failed to install on Raspberry Pi (ARM) 32bit in 3.11.1 * `#17715 <https://github.com/scipy/scipy/issues/17715>`__: scipy.stats.bootstrap broke with statistic returning multiple... * `#17716 <https://github.com/scipy/scipy/issues/17716>`__: BUG: interpolate.interpn fails with read only input * `#17718 <https://github.com/scipy/scipy/issues/17718>`__: BUG: RegularGridInterpolator 2D mixed precision crashes * `#17727 <https://github.com/scipy/scipy/issues/17727>`__: BUG: RegularGridInterpolator does not work on non-native byteorder... * `#17736 <https://github.com/scipy/scipy/issues/17736>`__: BUG: SciPy requires OpenBLAS even when building against a different... * `#17775 <https://github.com/scipy/scipy/issues/17775>`__: BUG: Asymptotic computation of ksone.sf has intermediate overflow * `#17782 <https://github.com/scipy/scipy/issues/17782>`__: BUG: Segfault in scipy.sparse.csgraph.shortest_path() with v1.10.0 * `#17795 <https://github.com/scipy/scipy/issues/17795>`__: BUG: stats.pearsonr one-sided hypothesis yields incorrect p-value... * `#17801 <https://github.com/scipy/scipy/issues/17801>`__: BUG: stats.powerlaw.fit: raises OverflowError * `#17808 <https://github.com/scipy/scipy/issues/17808>`__: BUG: name of cython executable is hardcoded in _build_utils/cythoner.py * `#17811 <https://github.com/scipy/scipy/issues/17811>`__: CI job with numpy nightly build failing on missing \`_ArrayFunctionDispatcher.__code__\` * `#17839 <https://github.com/scipy/scipy/issues/17839>`__: BUG: 1.10.0 tests fail on i386 and other less common arches * `#17896 <https://github.com/scipy/scipy/issues/17896>`__: DOC: publicly expose \`multivariate_normal\` attributes \`mean\`... * `#17934 <https://github.com/scipy/scipy/issues/17934>`__: BUG: meson \`__config__\` generation - truncated unicode characters * `#17938 <https://github.com/scipy/scipy/issues/17938>`__: BUG: \`scipy.stats.qmc.LatinHypercube\` with \`optimization="random-cd"\`... Pull requests for 1.10.1 ------------------------------- * `#17712 <https://github.com/scipy/scipy/pull/17712>`__: REL, MAINT: prepare for 1.10.1 * `#17717 <https://github.com/scipy/scipy/pull/17717>`__: BUG: allow readonly input to interpolate.interpn * `#17721 <https://github.com/scipy/scipy/pull/17721>`__: MAINT: update \`meson-python\` upper bound to <0.13.0 * `#17726 <https://github.com/scipy/scipy/pull/17726>`__: BUG: interpolate/RGI: upcast float32 to float64 * `#17735 <https://github.com/scipy/scipy/pull/17735>`__: MAINT: stats.bootstrap: fix BCa with vector-valued statistics * `#17743 <https://github.com/scipy/scipy/pull/17743>`__: DOC: improve the docs on using BLAS/LAPACK libraries with Meson * `#17777 <https://github.com/scipy/scipy/pull/17777>`__: BLD: link to libatomic if necessary * `#17783 <https://github.com/scipy/scipy/pull/17783>`__: BUG: Correct intermediate overflow in KS one asymptotic in SciPy.stats * `#17790 <https://github.com/scipy/scipy/pull/17790>`__: BUG: signal: fix check_malloc extern declaration type * `#17797 <https://github.com/scipy/scipy/pull/17797>`__: MAINT: stats.pearsonr: correct p-value with negative correlation... * `#17800 <https://github.com/scipy/scipy/pull/17800>`__: [sparse.csgraph] Fix a bug in dijkstra and johnson algorithm * `#17803 <https://github.com/scipy/scipy/pull/17803>`__: MAINT: add missing \`__init__.py\` in test folder * `#17806 <https://github.com/scipy/scipy/pull/17806>`__: MAINT: stats.powerlaw.fit: fix overflow when np.min(data)==0 * `#17810 <https://github.com/scipy/scipy/pull/17810>`__: BLD: use Meson's found cython instead of a wrapper script * `#17831 <https://github.com/scipy/scipy/pull/17831>`__: MAINT, CI: GHA MacOS setup.py update * `#17850 <https://github.com/scipy/scipy/pull/17850>`__: MAINT: remove use of \`__code__\` in \`scipy.integrate\` * `#17854 <https://github.com/scipy/scipy/pull/17854>`__: TST: mark test for \`stats.kde.marginal\` as xslow * `#17855 <https://github.com/scipy/scipy/pull/17855>`__: BUG: Fix handling of \`powm1\` overflow errors * `#17859 <https://github.com/scipy/scipy/pull/17859>`__: TST: fix test failures on i386, s390x, ppc64, riscv64 (Debian) * `#17862 <https://github.com/scipy/scipy/pull/17862>`__: BLD: Meson \`__config__\` generation * `#17863 <https://github.com/scipy/scipy/pull/17863>`__: BUG: fix Johnson's algorithm * `#17872 <https://github.com/scipy/scipy/pull/17872>`__: BUG: fix powm1 overflow handling * `#17904 <https://github.com/scipy/scipy/pull/17904>`__: ENH: \`multivariate_normal_frozen\`: restore \`cov\` attribute * `#17910 <https://github.com/scipy/scipy/pull/17910>`__: CI: use nightly numpy musllinux_x86_64 wheel * `#17931 <https://github.com/scipy/scipy/pull/17931>`__: TST: test_location_scale proper 32bit Linux skip * `#17932 <https://github.com/scipy/scipy/pull/17932>`__: TST: 32-bit tol for test_pdist_jensenshannon_iris * `#17936 <https://github.com/scipy/scipy/pull/17936>`__: BUG: Use raw strings for paths in \`__config__.py.in\` * `#17940 <https://github.com/scipy/scipy/pull/17940>`__: BUG: \`rng_integers\` in \`_random_cd\` now samples on a closed... * `#17942 <https://github.com/scipy/scipy/pull/17942>`__: BLD: update classifiers for Python 3.11 * `#17963 <https://github.com/scipy/scipy/pull/17963>`__: MAINT: backports/prep for SciPy 1.10.1 * `#17981 <https://github.com/scipy/scipy/pull/17981>`__: BLD: make sure macosx_x86_64 10.9 tags are being made on maintenance/1.10.x * `#17984 <https://github.com/scipy/scipy/pull/17984>`__: DOC: update link of the logo in the readme * `#17997 <https://github.com/scipy/scipy/pull/17997>`__: BUG: at least one entry from trial should be used in exponential... Checksums ========= MD5 ~~~ c9f5d246ec9ebbda9ed600d751cc427e Changelog b327ecc51bffee2c319687daaf87e127 README.txt 038b40489075536015cdfdd5b3448287 scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl 3666cb6db6e2205214f3b5f743f32162 scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl e3f6a817ff0a1d4ddf8c29f017930808 scipy-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl c529f1d6ff9d39ac7b5027778ad6055c scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cfb29e468d60cf7ba815bb82f84fabbe scipy-1.10.1-cp310-cp310-win_amd64.whl 00a473cd2d539dc122c7b37951659464 scipy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl 414c3e59fd6c0bec7626ed781cc699cf scipy-1.10.1-cp311-cp311-macosx_12_0_arm64.whl 7ce38386a60faeff9945764950afd31b scipy-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f18b34eb6c2d2fc6386448f79f983c5a scipy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 0611e707fc9601a221fe45734ee4c4a6 scipy-1.10.1-cp311-cp311-win_amd64.whl 40eed028ec0849127dce3333c97c443a scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl 87c12a8bc1b238202d086462d34838a3 scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl 1bb4ada4082c43e8312d11182f006d80 scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4e7919ee7b126817d566bb32d7f99e0e scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d1130585fa460fc8267ec3bff5e7c231 scipy-1.10.1-cp38-cp38-win_amd64.whl de8ee3fa36d67057ee1c0a58ede8072d scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl b767b83437990cadb8cebd5513fc17e0 scipy-1.10.1-cp39-cp39-macosx_12_0_arm64.whl c0547cc19e7f83fbb2cff56f1bc378de scipy-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b3b49cb6c274ed58f79c60e9f98282e4 scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b503abd182b6f2047b5af060e2df4c91 scipy-1.10.1-cp39-cp39-win_amd64.whl SHA256 ~~~~~~ ad652dda7a636fe49c19ab22d6c41bbab969f8e75a2c202930dd6ff1628a2abf Changelog ef6df1bfa21a442a7fbb2015a63d6470f27d17faf3e09866ff3c5f3772abc92b README.txt e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019 scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl 4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl 1553b5dcddd64ba9a0d95355e63fe6c3fc303a8fd77c7bc91e77d61363f7433f scipy-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4c0ff64b06b10e35215abce517252b375e580a6125fd5fdf6421b98efbefb2d2 scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1 scipy-1.10.1-cp310-cp310-win_amd64.whl 0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd scipy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5 scipy-1.10.1-cp311-cp311-macosx_12_0_arm64.whl aaea0a6be54462ec027de54fca511540980d1e9eea68b2d5c1dbfe084797be35 scipy-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 15a35c4242ec5f292c3dd364a7c71a61be87a3d4ddcc693372813c0b73c9af1d scipy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 43b8e0bcb877faf0abfb613d51026cd5cc78918e9530e375727bf0625c82788f scipy-1.10.1-cp311-cp311-win_amd64.whl 5678f88c68ea866ed9ebe3a989091088553ba12c6090244fdae3e467b1139c35 scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl 39becb03541f9e58243f4197584286e339029e8908c46f7221abeea4b749fa88 scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl bce5869c8d68cf383ce240e44c1d9ae7c06078a9396df68ce88a1230f93a30c1 scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415 scipy-1.10.1-cp38-cp38-win_amd64.whl cd9f1027ff30d90618914a64ca9b1a77a431159df0e2a195d8a9e8a04c78abf9 scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl 79c8e5a6c6ffaf3a2262ef1be1e108a035cf4f05c14df56057b64acc5bebffb6 scipy-1.10.1-cp39-cp39-macosx_12_0_arm64.whl 51af417a000d2dbe1ec6c372dfe688e041a7084da4fdd350aeb139bd3fb55353 scipy-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1b4735d6c28aad3cdcf52117e0e91d6b39acd4272f3f5cd9907c24ee931ad601 scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea scipy-1.10.1-cp39-cp39-win_amd64.whl

1 0

Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

5 4

NumPy-Discussion February 2023