Mailman 3 October 2022 - NumPy-Discussion

How will Google DeepMind's AlphaTensor effect numpy
by canhtart＠gmail.com 26 Oct '22

26 Oct '22

Hello! I was curious on how AlphaTensor will effect NumPy and other similar applications considering it has found a way to perform 3x3 matrix multiplication efficiently. https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor. I am not even sure how NumPy does this under the hood, is it 2x2? Is anyone working on implementing this 3x3 algorithm for NumPy? Is it too early, and if so why? Are there any concerns about this algorithm? I am new to the mailing list, it is nice to be here! Long-time user of NumPy in my projects, thank you for your amazing work! Can

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ANN: NumExpr 2.8.4 Release
by Robert McLeod 26 Oct '22

26 Oct '22

======================== Announcing NumExpr 2.8.4 ======================== Hi everyone, This is a maintenance and bug-fix release for NumExpr. In particular, now we have added Python 3.11 support. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.8.3 to 2.8.4 --------------------------- * Support for Python 3.11 has been added. * Thanks to Tobias Hangleiter for an improved accuracy complex `expm1` function. While it is 25 % slower, it is significantly more accurate for the real component over a range of values and matches NumPy outputs much more closely. * Thanks to Kirill Kouzoubov for a range of fixes to constants parsing that was resulting in duplicated constants of the same value. * Thanks to Mark Harfouche for noticing that we no longer need `numpy` version checks. `packaging` is no longer a requirement as a result. What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod robbmcleod(a)gmail.com robert.mcleod(a)hitachi-hightech.com

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Passing output array to bincount
by ntessore＠pm.me 22 Oct '22

22 Oct '22

Hello, I would like to propose adding the `out` array as an optional parameter to `bincount`. This makes `bincount` very useful when iteratively tallying data with large indices. Consider this example tallying batches of values from some fictional source of data: >>> tally = np.zeros(10000**2) >>> for indices, weights in read_sensor_data(): ... tally += np.bincount(indices, weights, 10000**2) # slow: repeatedly adding large arrays This could be trivially sped up: >>> tally = np.zeros(10000**2) >>> for indices, weights in read_sensor_data(): ... np.bincount(indices, weights, out=tally) # fast: plain sum-loop in C As far as I can see, there is no equivalent numpy functionality. In fact, as far as I'm aware, there isn't any fast alternative outside of C/Cython/numba/... It also fits the purpose of `bincount` nicely, and does not change existing functionality. One might argue about the exact semantics if both `minlength` and `out` are given, but I think that a sensible answer exists in requiring `len(out) >= max(list.max(), minlength)`.

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Out paramter for argsort?
by Paul Rudin 22 Oct '22

22 Oct '22

Argsort returns its result in a new array. It would be useful if allocation of the new array could be avoided when a suitable array is already available. Is there a reason why this wouldn't be useful or problematic to implement?

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NEP 50 and cast safety for scalar assignment/conversions
by Sebastian Berg 22 Oct '22

22 Oct '22

Hi all, I am happy that we have the correct integer handling for NEP 50 merged, so the relevant parts of the proposal can now be tested. [1] However, this has highlighted that NumPy has problems with applying the "cast safety" logic to scalars. We had discussed this a bit yesterday, and this is an attempt to summarize the issue and thoughts on how to "resolve" it. This mainly affects Python int, float, and complex due to their special handling with NEP 50. NumPy has the cast safety concept for converting between different dtypes: https://numpy.org/doc/stable/reference/generated/numpy.can_cast.html It uses "same-kind" in ufuncs (users do not usually notice this unless `out=` or `dtype=` is used). NumPy otherwise tends to use "unsafe" for casts and assignments by default which can lead to undefined/strange results at times. Since casts/assignment use "unsafe" casting, scalars are often converted in a non-safe way. However, there are certain exceptions: np.arange(5)[3] = np.nan # Errors (an unsafe cast would not) More importantly, NEP 50 requires the following to error: np.uint8(3) + 5000 # 5000 cannot be converted to uint8 And we just put in a deprecation that would always disallow the above! But what would the answer to: np.can_cast(5000, np.uint8, casting="safe/same_kind/unsafe") be? And how to resolve the fact that casting scalars and arrays has a different notion of "safety"? I could imagine two main approaches: * cast-safety doesn't apply to scalar conversions, they are whatever they currently are (sometimes unsafe, sometimes same-kind, but strictly safe for integer assignments). `np.can_cast(5000, np.uint8)` just errors out. We have an assignment "safety" that is independent of casting safety. For `np.add(np.uint8(5), 100, casting="safe")` the "safe" (or other modes) simply doesn't make sense for the `100` since effectively the assignment "safety" is used. * Scalar conversions also have a cast-safety and it may inspect the value. The problem with defining cast-safety for scalar conversion is not implementing it, but rather how to (not?) resolve the inconsistencies. Even if we change the default casting for assignments to "same kind" (a deprecation also applied to arrays): int8_arr[3] = 5000 should presumably be an error (not even "unsafe"), but: np.can_cast(np.int64, np.int8, casting="same_kind") returns `True` (an int64 could be 5000 as well), and `same_kind` is what ufuncs also use. I don't have a clear plan on this right now, my best thought is that we live with the inconsistency: np.can_cast(100, np.int8) would be "safe" while: np.can_cast(100., np.int8) would be "unsafe" (and other conversions through `__int__`). And: np.can_cast(1000, dtype=np.int8) would always return `False` (the assignment would fail), even though that is not what would happen when casting integers. More confusingly, maybe: np.can_cast(1000., dtype=npint8) is "unsafe" and making it an error (completely unsafe) might be a future deprecation. That would add a cast-safety that is slightly inconsistent between Python integer and NumPy integers Cheers, Sebastian [1] The NEP: https://numpy.org/neps/nep-0050-scalar-promotion.html The new part is mainly that `np.uint8(5) + 300` will now give the proposed error (when opting in). Calls that use `casting=` or `can_cast()` may not have the fully correct future behavior, but these should be very niche. [2] A bit tricky to define, but right now: arr.astype(new_dtype, casting="safe").astype(arr.dtype) should always round-trip correctly.

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ANN: SciPy 1.9.3
by Tyler Reddy 20 Oct '22

20 Oct '22

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.9.3, 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/v1.9.3 ===================== SciPy 1.9.3 Release Notes ===================== SciPy 1.9.3 is a bug-fix release with no new features compared to 1.9.2. Authors ====== * Jelle Aalbers (1) * Peter Bell (1) * Jake Bowhay (3) * Matthew Brett (3) * Evgeni Burovski (5) * drpeteb (1) + * Sebastian Ehlert (1) + * GavinZhang (1) + * Ralf Gommers (2) * Matt Haberland (15) * Lakshaya Inani (1) + * Joseph T. Iosue (1) * Nathan Jacobi (1) + * jmkuebler (1) + * Nikita Karetnikov (1) + * Lechnio (1) + * Nicholas McKibben (1) * Andrew Nelson (1) * o-alexandre-felipe (1) + * Tirth Patel (1) * Tyler Reddy (51) * Martin Reinecke (1) * Marie Roald (1) + * Pamphile Roy (2) * Eli Schwartz (1) * serge-sans-paille (1) * ehsan shirvanian (1) + * Mamoru TASAKA (1) + * Samuel Wallan (1) * Warren Weckesser (7) * Gavin Zhang (1) + A total of 31 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 1.9.3 ------------------------------- * `#3691 <https://github.com/scipy/scipy/issues/3691>`__: scipy.interpolate.UnivariateSpline segfault * `#5286 <https://github.com/scipy/scipy/issues/5286>`__: BUG: multivariate_normal returns a pdf for values outside its... * `#6551 <https://github.com/scipy/scipy/issues/6551>`__: BUG: stats: inconsistency in docs and behavior of gmean and hmean * `#9245 <https://github.com/scipy/scipy/issues/9245>`__: running scipy.interpolate.tests.test_fitpack::test_bisplev_integer_overflow... * `#12471 <https://github.com/scipy/scipy/issues/12471>`__: test_bisplev_integer_overflow: Segmentation fault (core dumped) * `#13321 <https://github.com/scipy/scipy/issues/13321>`__: Bug: setting iprint=0 hides all output from fmin_l_bfgs_b, but... * `#13730 <https://github.com/scipy/scipy/issues/13730>`__: \`scipy.stats.mood\` does not correct for ties * `#14019 <https://github.com/scipy/scipy/issues/14019>`__: ks_2samp throws \`RuntimeWarning: overflow encountered in double_scalars\` * `#14589 <https://github.com/scipy/scipy/issues/14589>`__: \`shgo\` error since scipy 1.8.0.dev0+1529.803e52d * `#14591 <https://github.com/scipy/scipy/issues/14591>`__: Input data validation for RectSphereBivariateSpline * `#15101 <https://github.com/scipy/scipy/issues/15101>`__: BUG: binom.pmf - RuntimeWarning: divide by zero * `#15342 <https://github.com/scipy/scipy/issues/15342>`__: BUG: scipy.optimize.minimize: Powell's method function evaluated... * `#15964 <https://github.com/scipy/scipy/issues/15964>`__: BUG: lombscargle fails if argument is a view * `#16211 <https://github.com/scipy/scipy/issues/16211>`__: BUG: Possible bug when using winsorize on pandas data instead... * `#16459 <https://github.com/scipy/scipy/issues/16459>`__: BUG: stats.ttest_ind returns wrong p-values with permutations * `#16500 <https://github.com/scipy/scipy/issues/16500>`__: odr.Model default meta value fails with __getattr__ * `#16519 <https://github.com/scipy/scipy/issues/16519>`__: BUG: Error in error message for incorrect sample dimension in... * `#16527 <https://github.com/scipy/scipy/issues/16527>`__: BUG: dimension of isuppz in syevr is mistranslated * `#16600 <https://github.com/scipy/scipy/issues/16600>`__: BUG: \`KDTree\`'s optional argument \`eps\` seems to have no... * `#16656 <https://github.com/scipy/scipy/issues/16656>`__: dtype not preserved with operations on sparse arrays * `#16751 <https://github.com/scipy/scipy/issues/16751>`__: BUG: \`stats.fit\` on \`boltzmann\` expects \`bound\` for \`lambda\`,... * `#17012 <https://github.com/scipy/scipy/issues/17012>`__: BUG: Small oversight in sparse.linalg.lsmr? * `#17020 <https://github.com/scipy/scipy/issues/17020>`__: BUG: Build failure due to problems with shebang line in cythoner.py * `#17088 <https://github.com/scipy/scipy/issues/17088>`__: BUG: stats.rayleigh.fit: returns \`loc\` that is inconsistent... * `#17104 <https://github.com/scipy/scipy/issues/17104>`__: BUG? Incorrect branch in \`LAMV\` / \`_specfunc.lamv\` * `#17196 <https://github.com/scipy/scipy/issues/17196>`__: DOC: keepdims in stats.mode is incorrectly documented Pull requests for 1.9.3 ------------------------------ * `#5288 <https://github.com/scipy/scipy/pull/5288>`__: BUG: multivariate_normal returns a pdf for values outside its... * `#13322 <https://github.com/scipy/scipy/pull/13322>`__: Bug: setting iprint=0 hides all output from fmin_l_bfgs_b, but... * `#13349 <https://github.com/scipy/scipy/pull/13349>`__: BUG: stats: Reformulate loggamma._rvs to handle c << 1. * `#13411 <https://github.com/scipy/scipy/pull/13411>`__: BUG: fix out-of-bound evaluations in optimize.minimize, powell... * `#15363 <https://github.com/scipy/scipy/pull/15363>`__: BUG: fix powell evaluated outside limits * `#15381 <https://github.com/scipy/scipy/pull/15381>`__: BUG: fix stats.rv_histogram for non-uniform bins * `#16212 <https://github.com/scipy/scipy/pull/16212>`__: stats.mood: correct for when ties are present * `#16288 <https://github.com/scipy/scipy/pull/16288>`__: BUG: fix a crash in \`fpknot\` * `#16318 <https://github.com/scipy/scipy/pull/16318>`__: MAINT: stats: fix _contains_nan on Pandas Series * `#16460 <https://github.com/scipy/scipy/pull/16460>`__: Fix ttest permutations * `#16506 <https://github.com/scipy/scipy/pull/16506>`__: MAINT: fix SHGO extra arguments * `#16521 <https://github.com/scipy/scipy/pull/16521>`__: BUG: Fix error in error message for incorrect sample dimension... * `#16525 <https://github.com/scipy/scipy/pull/16525>`__: MAINT: stats.ks_2samp: always emit warning when exact method... * `#16528 <https://github.com/scipy/scipy/pull/16528>`__: BUG: fix syevr series segfault by explicitly specifying operator... * `#16562 <https://github.com/scipy/scipy/pull/16562>`__: BUG: optimize: Fix differential_evolution error message. * `#16573 <https://github.com/scipy/scipy/pull/16573>`__: FIX: \`odr.Model\` error with default \`meta\` value * `#16588 <https://github.com/scipy/scipy/pull/16588>`__: FIX: stats: ignore divide-by-zero warnings from Boost binom impl * `#16590 <https://github.com/scipy/scipy/pull/16590>`__: MAINT: stats.vonmises: wrap rvs to -pi, pi interval * `#16630 <https://github.com/scipy/scipy/pull/16630>`__: BUG: eps param no effect fixed * `#16645 <https://github.com/scipy/scipy/pull/16645>`__: MAINT: Ensure Pythran input for lombscargle are contiguous * `#16649 <https://github.com/scipy/scipy/pull/16649>`__: Detect integer overflow in bivariate splines in fitpackmodule.c,... * `#16657 <https://github.com/scipy/scipy/pull/16657>`__: BUG: sparse: Fix indexing sparse matrix with empty index arguments. * `#16669 <https://github.com/scipy/scipy/pull/16669>`__: FIX: spurious divide error with \`gmean\` * `#16701 <https://github.com/scipy/scipy/pull/16701>`__: BUG: fix mutable data types as default arguments in \`ord.{Data,RealData}\` * `#16752 <https://github.com/scipy/scipy/pull/16752>`__: MAINT: stats.boltzmann: correct _shape_info typo * `#16780 <https://github.com/scipy/scipy/pull/16780>`__: BUG: interpolate: sanity check x and y in make_interp_spline(x,... * `#16836 <https://github.com/scipy/scipy/pull/16836>`__: MAINT: avoid \`func_data\`, it conflicts with system header on... * `#16872 <https://github.com/scipy/scipy/pull/16872>`__: BUG: interpolate: work array sizes for RectSphereBivariateSpline * `#16965 <https://github.com/scipy/scipy/pull/16965>`__: BUG: linalg: Fix the XSLOW test test_sgesdd_lwork_bug_workaround() * `#17043 <https://github.com/scipy/scipy/pull/17043>`__: MAINT: fix small LSMR problem * `#17090 <https://github.com/scipy/scipy/pull/17090>`__: MAINT: stats.rayleigh: enforce constraint on location * `#17105 <https://github.com/scipy/scipy/pull/17105>`__: FIX: special: use intended branching for \`lamv\` implementation * `#17166 <https://github.com/scipy/scipy/pull/17166>`__: MAINT: stats.rv_discrete.pmf: should be zero at non-integer argument * `#17176 <https://github.com/scipy/scipy/pull/17176>`__: REL: Prep for SciPy 1.9.3 * `#17190 <https://github.com/scipy/scipy/pull/17190>`__: BUG: special: Fix two XSLOW test failures. * `#17193 <https://github.com/scipy/scipy/pull/17193>`__: MAINT: update meson.build to make it work on IBM i system * `#17200 <https://github.com/scipy/scipy/pull/17200>`__: BLD: fix issue with incomplete threads dependency handling * `#17204 <https://github.com/scipy/scipy/pull/17204>`__: Keepdims incorrectly documneted fix * `#17209 <https://github.com/scipy/scipy/pull/17209>`__: MAINT: Handle numpy's deprecation of accepting out-of-bound integers. * `#17210 <https://github.com/scipy/scipy/pull/17210>`__: BLD: fix invalid shebang for build helper script Checksums ========= MD5 ~~~ 4f24f8e360b9ad4e9e73368d2a45e093 Changelog 7887e0d95aa5a8ffc13c916ffe50b954 README.txt 7cc7a68a35baa1410401cd17c0c1adef scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl befec66a3e966689b06deaab36738d80 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Creating community standards for meta arrays (arrays that wrap other arrays)
by github＠thielen.science 18 Oct '22

18 Oct '22

The Scientific Python community has seen some amazing work in the past several years around community standards for array providing libraries to streamline compatibility within array consuming libraries. However, there are libraries that do both (provide and consume arrays), which we can refer to as *meta array* libraries, and standardization work in this area (especially in concerns arising with using multiple meta arrays in combination) has languished. To motivate discussion in this area, I've created a post in the Scientific Python Discourse forum: https://discuss.scientific-python.org/t/creating-community-standards-for-me…. I'd greatly appreciate any and all feedback on that post from interested community members!

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new proposed NumPy Newcomers' Hour schedule
by Inessa Pawson 18 Oct '22

18 Oct '22

I’m considering alternating the NumPy Newcomers’ Hour start time between 12:00 UTC and 20:00 UTC to better accommodate our contributors from every part of the world. Please share your thoughts on the proposed. -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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ANN: pvlib-0.9.3 released
by Dr. Mark Alexander Mikofski PhD 13 Oct '22

13 Oct '22

Dear Pythonistas and solar power enthusiasts, The maintainers are happy to announce a new release of pvlib python: software for simulating performance of photovoltaic solar energy systems. *See what's new for v0.9.3:* ** *https://pvlib-python.readthedocs.io/en/stable/whatsnew.html *Releases are available from PyPI and the conda-forge channel:* * https://pypi.org/project/pvlib/ * https://anaconda.org/conda-forge/pvlib * https://anaconda.org/conda-forge/pvlib-python *Note: you can now install from conda-forge using either "pvlib" or "pvlib-python"* *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *Highlights:* * Anton Driesse has joined the maintainers. Anton has been a consistent contributor, a frequent poster on Google Groups, and a fixture in the PV modeling community for many years. We gladly welcome Anton and are grateful for his support. * Several new functions to calculate spectral mismatch <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.spe…> of photovoltaic devices between incident and reference spectra. Compare this to the existing FSLR spectral mismatch function. * The Townsend snow loss model <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.sno…> is now available. Compare this with the existing snow loss model from Marion. * A new generic linear temperature model <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tem…> function and class that can convert between PVsyst, Faiman, and SAPM thermal model coefficients. * A new altitude look-up function <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.loc…> using public data from Mapzen, a project for open map data hosted by AWS. * There were four new first time contributors! Thank you! * There was a training using JupyterHub at PVPMC 2022 <https://pvsc-python-tutorials.github.io/PVPMC_2022>. Check it out! *Thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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NumPy 1.23.4 released.
by Charles R Harris 12 Oct '22

12 Oct '22

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.23.4. NumPy 1.23.4 is a maintenance release that fixes bugs discovered after the 1.23.3 release and keeps the build infrastructure current. The main improvements are fixes for some annotation corner cases, a fix for a long time *nested_iters* memory leak, and a fix of complex vector dot for very large arrays. The Python versions supported for this release are 3.8-3.11. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.4>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.4>. Note that the mypy version needs to be 0.981+ if you test using Python 3.10.7, otherwise the typing tests will fail. *Contributors* A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Matthew Barber - Matti Picus - Ralf Gommers - Ross Barnowski - Sebastian Berg - Sicheng Zeng + *Pull requests merged* A total of 13 pull requests were merged for this release. - #22368: BUG: Add ``__array_api_version__`` to ``numpy.array_api`` namespace - #22370: MAINT: update sde toolkit to 9.0, fix download link - #22382: BLD: use macos-11 image on azure, macos-1015 is deprecated - #22383: MAINT: random: remove ``get_info`` from "extending with Cython"... - #22384: BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements - #22387: REV: Loosen ``lookfor``'s import try/except again - #22388: TYP,ENH: Mark ``numpy.typing`` protocols as runtime checkable - #22389: TYP,MAINT: Change more overloads to play nice with pyright - #22390: TST,TYP: Bump mypy to 0.981 - #22391: DOC: Update delimiter param description. - #22392: BUG: Memory leaks in numpy.nested_iters - #22413: REL: Prepare for the NumPy 1.23.4 release. - #22424: TST: Fix failing aarch64 wheel builds. Cheers, Charles Harris

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