Mailman 3 February 2023 - NumPy-Discussion

ANN: pvlib-0.9.4 released
by Dr. Mark Alexander Mikofski PhD 05 Feb '23

05 Feb '23

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 v0.9.4:* ** *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 and https://anaconda.org/conda-forge/pvlib-python *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *v0.9.4 Highlights:* * Fitting <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> and prediction <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> functions for the ADR PV efficiency model, along with several gallery examples <https://pvlib-python.readthedocs.io/en/stable/gallery/index.html#adr-model-…> . * An extension of the Faiman temperature model <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tem…> that includes a radiative loss term. * Efficient direct <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> and diffuse <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> IAM models based on the Schlick approximation to the Fresnel equations. * A convenience function <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> to calculate one of DNI, DHI, GHI from the other two. * The Hay-Davies <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> transposition model can now return individual components in addition to global tilted irradiance. * An implementation of ASTM E1036 <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.ivt…> for extracting the basic characteristics of an I-V curve. * There were several new contributors. *Thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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NumPy 1.24. 2 Release
by Charles R Harris 05 Feb '23

05 Feb '23

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.24.2. NumPy 1.24.2 is a maintenance release that fixes bugs and regressions discovered after the 1.24.1 release. The Python versions supported by this release are 3.8-3.11 Note that 32 bit wheels are only provided for Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.24.2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.24.2>. *Contributors* A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Khem Raj + - Mark Harfouche - Matti Picus - Panagiotis Zestanakis + - Peter Hawkins - Pradipta Ghosh - Ross Barnowski - Sayed Adel - Sebastian Berg - Syam Gadde + - dmbelov + - pkubaj + *Pull requests merged* A total of 17 pull requests were merged for this release. - #22965: MAINT: Update python 3.11-dev to 3.11. - #22966: DOC: Remove dangling deprecation warning - #22967: ENH: Detect CPU features on FreeBSD/powerpc64* - #22968: BUG: np.loadtxt cannot load text file with quoted fields separated... - #22969: TST: Add fixture to avoid issue with randomizing test order. - #22970: BUG: Fix fill violating read-only flag. (#22959) - #22971: MAINT: Add additional information to missing scalar AttributeError - #22972: MAINT: Move export for scipy arm64 helper into main module - #22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang - #22989: BUG: Ensure correct loop order in sin, cos, and arctan2 - #23030: DOC: Add version added information for the strict parameter in... - #23031: BUG: use ``_Alignof`` rather than ``offsetof()`` on most compilers - #23147: BUG: Fix for npyv__trunc_s32_f32 (VXE) - #23148: BUG: Fix integer / float scalar promotion - #23149: BUG: Add missing <type_traits> header. - #23150: TYP, MAINT: Add a missing explicit ``Any`` parameter to the ``npt.ArrayLike``... - #23161: BLD: remove redundant definition of npy_nextafter [wheel build] Cheers, Charles Harris

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Efficiently represent 128-bit integers
by Tim Candid 03 Feb '23

03 Feb '23

Hello, I wonder what are the alternatives to efficiently represent 128-bit integers? I have found a GitHub issue that proposed a support for int128 data type - https://github.com/numpy/numpy/issues/9992 - but it is closed now, and I was wondering what are the options meanwhile. The context of my question: in the end, I am looking for an efficient way to work UUID values in Pandas (sorry, it goes down to that). Since I am not doing computations on this data, but only use it as identifiers, I am mostly looking into the efficient memory usage, I guess (unless I can get a performance hit for some other related reasons too?). With Python's regular integral number type, 128-bit integers use 44 bytes (according to the `sys.getsizeof'). Interestingly, a 16-byte array in Python uses 49 bytes, so I'm still better off with an integer. I understand that even with a theoretical np.int128 I'd get 40 bytes (24 + 16), but hey, it is less than 44 anyway. I could artificially split 128-integers into two int64 numbers (high 64 bits and low 64 bits) and consider the set of these two as a single value for all meaningful usages, but this is quite inconvenient and actually will use more space in the end. So I wonder are there other options? From the GitHub issue I understood that NumPy internally uses 128-bit integers for some purpose, where can I find out more about it? Thank you!

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GSoC Participation
by Rohit Goswami 03 Feb '23

03 Feb '23

Last year we took with SciPy under the PSF with Ralf taking care of admin duties. We had a great contributor (Namami [1]) and I think solid progress was made for F2PY. SciPy is currently considering applying again this year [1], with Hameer stepping up to take the administrative burden if there is enough interest. I think even though GSoC (and Google in general) has been a little weird about the program requirements, its still good PR and is a good vehicle for onboarding contributors. I would be willing to mentor a student towards finishing up the F2PY derived type support [2] which mostly needs examples and finding rough edges in the design. It would be great to have it through for NumPy 2.0 Other project ideas would be much appreciated as well. [1] https://namamishanker.github.io/posts/gsoc-closing-remarks/ [2] https://mail.python.org/archives/list/scipy-dev@python.org/thread/A4KU7WVR7… [3] https://github.com/HaoZeke/f2py_derived_nep

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