Mailman 3 September 2023 - SciPy-Dev

RFC: decouple doctesting from refguide-check
by Evgeni Burovski Feb. 23, 2024

Feb. 23, 2024

Hi, Code examples in the SciPy and NumPy documentation are doctested, with a modified doctesting machinery which understands floating point, numpy formatting and some details of our documentation and API. Our modified doctesting machinery is buried in the refguide-check utility, which also does several unrelated things, all of which are tightly coupled to each other and to the libraries themselves. It has been sometimes described as hard to understand or work with or extend due to this tight coupling and a lack of a dedicated test suite. The numpy and scipy versions of the utility are both vendored, and the numpy version has diverged from the scipy version somewhat. Following a discussion in https://github.com/numpy/numpy/issues/21070 I did a small experiment to decouple the doctesting into a separate package, so that it's easier to consolidate the two versions. Plus, a separate repo is generally easier to maintain, configure, and possibly extend or adopt to other projects. The work-in-progress result is here: https://github.com/ev-br/scpdt It currently can run the full doctesting of the scipy API documentation (docstrings of objects) --- see https://github.com/ev-br/scpdt/pull/33 (the log of a test run with warnings turned to errors is in the GH actions: https://github.com/ev-br/scpdt/runs/6743881766?check_suite_focus=true). Note that it shows, among other things, a couple of deprecation warnings our docs have accumulated :-). The API of the tool closely follows that of the standard library doctest module and provides (nearly) drop-in replacements for doctest checking, parsing, finding and running. Various configuration options for our modifications are collected into a single bag object which is internally passed around. This way, it's user-configurable all the way from a plain standard doctest module behavior to what refguide-check does now. There are a couple of wrinkles to iron out; overall it does what refguide-check does already. One missing bit is doctesting rst or other text files, but it's coming soon. The current plan is to: - verify that the standalone version does not miss things checked by the refguide-check - plumb it through the SciPy dev interface and rip out the refguide-check bundled doctesting utilities. - Sync changes that NumPy version of refguide-check accumulated over time - Make sure it correctly tests the NumPy docs, too. - Better document the internals, there is currently only a readme file. If someone's interested to join me working on these, great, the more the merrier :-). I think it could make sense to move the tool's repository to the scipy github org (or maybe even numpy org?). I'm offering to maintain it regardless of the location. Thoughts? Cheers, Evgeni

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Deprecate scipy.misc and Add scipy.datasets submodule
by Anirudh Dagar Jan. 7, 2024

Jan. 7, 2024

Hi all, I just filed https://github.com/scipy/scipy/pull/15607 after an offline discussion with Ralf earlier this month. The summary of that PR is: This is a good time to get rid of the `scipy.misc` submodule since it houses only a handful of functions. As of today, most of the methods have already been deprecated from the `misc` module. They have generally moved under some other submodule. In this case, it is best to create a `scipy.datasets` submodule that can be home to the dataset functions (ascent, face, ecg). Adding a datasets submodule is not a new idea, and was proposed a couple of years ago by Warren (see https://github.com/scipy/scipy/pull/8707), but unfortunately, it never went ahead. Adding this submodule is not only sensible from an organizational/structural point of view but will also help reduce the wheel size of SciPy package. This is possible if we decouple the dataset files from the SciPy repository making use of Pooch to download and cache datasets kept in separate repos. See the PR for more details. I also filed https://github.com/scipy/scipy/issues/15608 Out of the 5 methods in `scipy.misc`, three dataset functions can be moved to this datasets submodule and will slowly be deprecated from misc. The question about the other two methods i.e (derivative, central_diff_weights) is up for discussion and I'd like to understand what will be the best way to deprecate them. Should we move them to a submodule like integrate (keeping derivative makes sense to me)? On the other hand, considering these two methods are not used extensively so one possibility is to deprecate both of them completely and remove them from SciPy. Please share your opinion/concerns, and comment if you see a potential issue with this. Cheers, Anirudh

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Let's make it official then... recommend max line length 88
by Warren Weckesser Nov. 1, 2023

Nov. 1, 2023

SciPy is documented to follow the PEP 8 style guidelines; see https://docs.scipy.org/doc/scipy/dev/contributor/pep8.html#pep8-scipy PEP 8 recommends a maximum line length of 79 characters. The degree of care with which we follow this guideline has varied quite a bit over the years, and there is currently a lot code where the guideline is violated. Lately, in particular, the enforcement of this guideline has been relaxed, and folks seem to be settling on 88 characters as a reasonable maximum. This is partly a side effect of the replacement of `pycodestyle` in CI with `ruff`; the default maximum line length in `ruff` is 88. This means new lines of code with lengths between 80 and 88 are not being flagged in CI. (I don't recall if CI currently fails with lines longer than 88 characters.) If everyone is satisfied with a maximum of 88 characters, let's make it official and update our developer documents to state this explicitly. What do you say, yea or nay? Warren

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ANN: SciPy 1.11.3
by Tyler Reddy Sept. 28, 2023

Sept. 28, 2023

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.11.3, 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/v1.11.3 One of a few ways to install this release with pip: pip install scipy==1.11.3 ====================== SciPy 1.11.3 Release Notes ====================== SciPy 1.11.3 is a bug-fix release with no new features compared to 1.11.2. Authors ======= * Name (commits) * Jake Bowhay (2) * CJ Carey (1) * Colin Carroll (1) + * Anirudh Dagar (2) * drestebon (1) + * Ralf Gommers (5) * Matt Haberland (2) * Julien Jerphanion (1) * Uwe L. Korn (1) + * Ellie Litwack (2) * Andrew Nelson (5) * Bharat Raghunathan (1) * Tyler Reddy (37) * Søren Fuglede Jørgensen (2) * Hielke Walinga (1) + * Warren Weckesser (1) * Bernhard M. Wiedemann (1) A total of 17 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 1.11.3 -------------------------------- * `#15093 <https://github.com/scipy/scipy/issues/15093>`__: BUG: scipy.optimize's trust-constr algorithm hangs when keep-feasible... * `#15273 <https://github.com/scipy/scipy/issues/15273>`__: freqz: suboptimal performance for worN=2\*\*n+1, include_nyquist=True... * `#17269 <https://github.com/scipy/scipy/issues/17269>`__: Bug in scipy.sparse.csgraph.min_weight_full_bipartite_matching * `#17289 <https://github.com/scipy/scipy/issues/17289>`__: BUG: Different results between numpy.fft.rfft and scipy.signal.freqz * `#18716 <https://github.com/scipy/scipy/issues/18716>`__: Buffer dtype mismatch, expected 'ITYPE_t' but got 'long' * `#18782 <https://github.com/scipy/scipy/issues/18782>`__: BUG: johnsonsu distribution no longer accepts integer \`b\` parameter * `#18922 <https://github.com/scipy/scipy/issues/18922>`__: BUG: dev.py has \`distutils\` usage * `#19101 <https://github.com/scipy/scipy/issues/19101>`__: BUG: mesonpy embeds random path in .pyx files * `#19103 <https://github.com/scipy/scipy/issues/19103>`__: BUG: Regression in 1.11.2: optimize.least_squares with method='trf'... * `#19132 <https://github.com/scipy/scipy/issues/19132>`__: BUG: Build fails on latest commit * `#19149 <https://github.com/scipy/scipy/issues/19149>`__: BUG: scipy.sparse.csgraph.laplacian raises AttributeError on... * `#19197 <https://github.com/scipy/scipy/issues/19197>`__: BUG: Incorrect sampling from zero rank covariance Pull requests for 1.11.3 ------------------------------- * `#17633 <https://github.com/scipy/scipy/pull/17633>`__: BUG: add infeasibility checks to min_weight_full_bipartite_matching * `#18784 <https://github.com/scipy/scipy/pull/18784>`__: BUG: Allow johnsonsu parameters to be floats * `#18913 <https://github.com/scipy/scipy/pull/18913>`__: BUG: sparse.csgraph: Support int64 indices in traversal.pyx * `#18924 <https://github.com/scipy/scipy/pull/18924>`__: BUG: Fix python3.12 distutils dev.py build * `#18956 <https://github.com/scipy/scipy/pull/18956>`__: BUG: trust-constr Bounds exclusive * `#19076 <https://github.com/scipy/scipy/pull/19076>`__: MAINT: should not be using np.float64() on arrays * `#19084 <https://github.com/scipy/scipy/pull/19084>`__: REL, MAINT: prep for 1.11.3 * `#19111 <https://github.com/scipy/scipy/pull/19111>`__: BUG: Fixes #19103 by adding back make_strictly_feasible to lsq... * `#19123 <https://github.com/scipy/scipy/pull/19123>`__: BLD: Avoid absolute pathnames in .pyx files * `#19135 <https://github.com/scipy/scipy/pull/19135>`__: MAINT: signal: Remove the cval parameter from the private function... * `#19139 <https://github.com/scipy/scipy/pull/19139>`__: BLD: revert to using published wheels [wheel build] * `#19156 <https://github.com/scipy/scipy/pull/19156>`__: BUG: Support sparse arrays in scipy.sparse.csgraph.laplacian * `#19199 <https://github.com/scipy/scipy/pull/19199>`__: MAINT: stats.CovViaEigendecomposition: fix \`_LA\` attribute... * `#19200 <https://github.com/scipy/scipy/pull/19200>`__: TST: fix \`TestODR.test_implicit\` test failure with tolerance... * `#19208 <https://github.com/scipy/scipy/pull/19208>`__: BUG: freqz rfft grid fix * `#19280 <https://github.com/scipy/scipy/pull/19280>`__: MAINT: newton, make sure x0 is an inexact type * `#19286 <https://github.com/scipy/scipy/pull/19286>`__: BUG: stats: fix build failure due to incorrect Boost policies... * `#19290 <https://github.com/scipy/scipy/pull/19290>`__: BLD: add float.h include to \`_fpumode.c\`, fixes Clang on Windows... * `#19299 <https://github.com/scipy/scipy/pull/19299>`__: MAINT: fix libquadmath licence Checksums ========= MD5 ~~~ b940b7bab3db8f80d6f79f214ff48ec9 Changelog f744557fb5b0f5094635a35255561195 README.txt df92549e1d2832c0685d851303685301 scipy-1.11.3-cp310-cp310-macosx_10_9_x86_64.whl 989c613e807e1ec39b9ce7aced5b7f6d scipy-1.11.3-cp310-cp310-macosx_12_0_arm64.whl ee99c50f95ce57cb08ea701f5fa9e0b2 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Do special functions need to have all kwargs/attributes/methods of ufuncs?
by Matt Haberland Sept. 21, 2023

Sept. 21, 2023

Hi Team, gh-19023 <https://github.com/scipy/scipy/pull/19023> added alternative backend support to some SciPy special functions; e.g., when `scipy.special.ndtr` receives a CuPy array, it calls `cupyx.scipy.special.ndtr` to perform the calculation and returns the resulting CuPy array. Currently, this functionality is opt-in only (via the SCIPY_ARRAY_API environment variable) because of a backward incompatibility issue: Technically, many `scipy.special` functions are universal functions, and without special care, these functions will lose some of these features (e.g. `reduce`) when we wrap them. SciPy doesn't actually document any of these features (e.g. compare `ndtr` documentation <https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.ndtr.html> vs ufunc documentation <https://numpy.org/doc/stable/reference/ufuncs.html>), but does anyone know of code that relies on them and would be impacted by the change? If you have thoughts about which ufunc features need to be preserved, please join the conversation in gh-19252. Thanks! Matt

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SciPy Community Meeting this Wednesday, September 20 at 2pm UTC
by Jake Bowhay Sept. 19, 2023

Sept. 19, 2023

Hi all, The next SciPy Community Meeting will happen on* Wednesday September 20th at 2pm (noon) UTC*. PLEASE NOTE THIS IS A CHANGE FROM THE PREVIOUS 12am TIME. Here's the hackmd we are using to take notes: https://hackmd.io/pyhudrC5TgSwdJtpPldHgQ If you wish to join on Zoom, use this link: https://us06web.zoom.us/j/6345425936?pwd=aDVFQzVmbk9SVU5jU0Jwc0s3YWUrdz09 Our community calendar is also available here: https://scientific-python.org/calendars/ Cheers, Jake

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[Proposal] Extension to scipy.spatial.transform for 3D Translations and Proper Rigid Transformations
by wsshambaugh＠gmail.com Sept. 18, 2023

Sept. 18, 2023

Hello SciPy dev community, I've opened a GitHub issue proposing an extension to the scipy.spatial.transform module. The idea is to add 3D translation capabilities, as well as full proper rigid transformations that combine 3D rotations and translations. This allows for defining arbitrary coordinate frames in 3D space. Please see the detailed proposal at this link, and below I've summarized the key points. https://github.com/scipy/scipy/issues/19254 What's Proposed? * A Translation class to handle 3D translational offsets. * A Transformation class for 3D rigid transformations, built up from composing any number of Rotations and Translations. * Top level interpolation methods for both, analagous to Slerp and RotationSpline. Why? Defining and manipulating 3D coordinate frames is indispensable in the fields of robotics, aerospace, and computer graphics, and I believe is more broadly applicable over many more domains as well. Quick API Example ``` t = Translation.from_vector([x, y, z]) t.apply([a, b, c]) == np.array([a + x, b + y, c + z]) t.inv() == Translation.from_vector([-x, -y, -z]) r = Rotation.from_rotvec([u, v, w]) T1 = t * r # Generate a Transformation by composing translations and rotations with __mul__ T1.apply([a, b, c]) == np.array([x, y, z]) + r.apply([a, b, c]) T1.inv() == r.inv() * t.inv() T1 * T2.inv() # Coordinate frame T1 as measured in reference frame T2 ``` What's Next? * Are you in favor of inclusion of this capability into SciPy? * Suggestions on API / naming / functionality? * Suggestions on how to conduct this work? One large PR vs building up via chunks? Thank you very much for considering this, and I look forward to hearing your feedback. Cheers, Scott Shambaugh

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NumPy 1.26.0 released
by Charles R Harris Sept. 17, 2023

Sept. 17, 2023

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.26.0. The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle with the addition of Python 3.12.0 support. Python 3.12 dropped distutils, consequently supporting it required finding a replacement for the setup.py/distutils based build system NumPy was using. We have chosen to use the Meson build system instead, and this is the first NumPy release supporting it. This is also the first release that supports Cython 3.0 in addition to retaining 0.29.X compatibility. Supporting those two upgrades was a large project, over 100 files have been touched in this release. The changelog doesn't capture the full extent of the work, special thanks to Ralf Gommers, Sayed Adel, Stéfan van der Walt, and Matti Picus who did much of the work in the main development branch. The highlights of this release are: - Python 3.12.0 support. - Cython 3.0.0 compatibility. - Use of the Meson build system - Updated SIMD support - f2py fixes, meson and bind(x) support - Support for the updated Accelerate BLAS/LAPACK library The Python versions supported by this release are 3.9-3.12. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.26.0/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.26.0> *Contributors* A total of 20 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - Albert Steppi + - Bas van Beek - Charles Harris - Developer-Ecosystem-Engineering - Filipe Laíns + - Jake Vanderplas - Liang Yan + - Marten van Kerkwijk - Matti Picus - Melissa Weber Mendonça - Namami Shanker - Nathan Goldbaum - Ralf Gommers - Rohit Goswami - Sayed Adel - Sebastian Berg - Stefan van der Walt - Tyler Reddy - Warren Weckesser *Build system changes* In this release, NumPy has switched to Meson as the build system and meson-python as the build backend. Installing NumPy or building a wheel can be done with standard tools like ``pip`` and ``pypa/build``. The following are supported: - Regular installs: ``pip install numpy`` or (in a cloned repo) ``pip install .`` - Building a wheel: ``python -m build`` (preferred), or ``pip wheel .`` - Editable installs: ``pip install -e . --no-build-isolation`` - Development builds through the custom CLI implemented with spin <https://github.com/scientific-python/spin>: ``spin build``. All the regular ``pip`` and ``pypa/build`` flags (e.g., ``--no-build-isolation``) should work as expected. *NumPy-specific build customization* Many of the NumPy-specific ways of customizing builds have changed. The ``NPY_*`` environment variables which control BLAS/LAPACK, SIMD, threading, and other such options are no longer supported, nor is a ``site.cfg`` file to select BLAS and LAPACK. Instead, there are command-line flags that can be passed to the build via ``pip``/``build``'s config-settings interface. These flags are all listed in the ``meson_options.txt`` file in the root of the repo. Detailed documented will be available before the final 1.26.0 release; for now, see the SciPy building from source <http://scipy.github.io/devdocs/building/index.html> docs since most build customization works in an almost identical way in SciPy as it does in NumPy. *Build dependencies* While the runtime dependencies of NumPy have not changed, the build dependencies have. Because we temporarily vendor Meson and meson-python, there are several new dependencies - please see the ``[build-system]`` section of ``pyproject.toml`` for details. *Troubleshooting* This build system change is quite large. In case of unexpected issues, it is still possible to use a ``setup.py``-based build as a temporary workaround (on Python 3.9-3.11, not 3.12), by copying ``pyproject.toml.setuppy`` to ``pyproject.toml``. However, please open an issue with details on the NumPy issue tracker. We aim to phase out ``setup.py`` builds as soon as possible, and therefore would like to see all potential blockers surfaced early on in the 1.26.0 release cycle. Cheers, Charles Harris

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Exposing ODEintWarning in scipy.integrate
by Jake Bowhay Sept. 12, 2023

Sept. 12, 2023

Hi all It was reported in [1] that the only way to import ODEintWarning is through the deprecated scipy.integrate.odepack namespace and not scipy.integrate. This is annoying as this leads to a deprecation warning. Other custom warnings used in SciPy, such as IntegrationWarning, as importable from their respective namespace so it seems logical that we do the same for ODEintWarning to allow for warning filtering etc. One potential argument against this is that ODEintWarning arises from the legacy odeint interface so normally we wouldn't make any modifications. However, since this is just a custom warning I don't think there is really any maintenance burden to worry about. Please feel free to join in the discussion on my pr [2]. Regards Jake [1] https://github.com/scipy/scipy/issues/17771 [2] https://github.com/scipy/scipy/pull/19224

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Request for Review: Addition of Triangular Waveform Functionality to _waveforms.py
by muzakkirhussain011＠gmail.com Sept. 11, 2023

Sept. 11, 2023

Dear Scipy-Dev Community, I hope this message finds you well. I would like to propose the addition of a new feature to the _waveforms.py module in SciPy, and I believe it could be a valuable enhancement to our library. The feature I'm suggesting is the inclusion of a function for generating triangular waveforms. Triangular waves are widely used in various fields, including signal processing, electronics, and communications. They have practical applications in modulation, testing, and analysis. This addition would offer users a convenient and efficient way to generate triangular waves with customizable frequencies and amplitudes, improving the versatility of SciPy in handling waveform generation tasks. I have already implemented this feature and thoroughly tested it to ensure accuracy and performance. However, before finalizing and merging the code, I would greatly appreciate your input, suggestions, and feedback. Your expertise and insights can help refine this feature and ensure it aligns with the high standards of the SciPy library. If you have any comments, questions, or concerns about this proposal or the code implementation, please feel free to share them. Your feedback is invaluable in shaping the future of SciPy. Thank you for your time and consideration. I look forward to your feedback and collaboration in making SciPy even more powerful and user-friendly. Best regards

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