Mailman 3 June 2022 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

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

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Documentation Team meeting - Monday June 8th
by Melissa Mendonça Dec. 3, 2022

Dec. 3, 2022

Hi all! A reminder that on Monday, June 8, we have another documentation team meeting at 3PM UTC**. If you wish to join on Zoom, you need to use this link https://zoom.us/j/420005230 Here's the permanent hackmd document with the meeting notes: https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg <https://www.google.com/url?q=https%3A%2F%2Fhackmd.io%2FoB_boakvRqKR-_2jRV-Q…> Hope to see you around (especially if you want to introduce yourself or discuss ideas for Google Season of Docs). ** You can click this link to get the correct time at your timezone: https://www.timeanddate.com/worldclock/fixedtime.html?msg=NumPy+Documentati… - Melissa

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next NumPy Newcomers' Hour
by Inessa Pawson Oct. 2, 2022

Oct. 2, 2022

Our next Newcomers' Hour will be held this Thursday, June 16th, at 4 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://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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An extension of the .npy file format
by Michael Siebert Aug. 25, 2022

Aug. 25, 2022

Dear all, originally, I have planned to make an extension of the .npy file format a dedicated follow-up pull request, but I have upgraded my current request instead, since it was not as difficult to implement as I initially thought and probably a more straight-forward solution: https://github.com/numpy/numpy/pull/20321/ What is this pull request about? It is about appending to Numpy .npy files. Why? I see two main use cases: 1. creating .npy files larger than the main memory. They can, once finished, be loaded as memory maps 2. creating binary log files, which can be processed very efficiently without parsing Are there not other good file formats to do this? Theoretically yes, but practically they can be pretty complex and with very little tweaking .npy could do efficient appending too. Use case 1 is already covered by the Pip/Conda package npy-append-array I have created and getting the functionality directly into Numpy was the original goal of the pull request. This would have been possible without introducing a new file format version, just by adding some spare space in the header. During the pull request discussion it turned out that rewriting the header after each append would be desirable in case the writing program crashes to minimize data loss. Use case 2 however would highly profit from a new file format version as it would make rewriting the header unnecessary: since efficient appending can only take place along one axis, setting shape[-1] = -1 in case of Fortran order or shape[0] = -1 otherwise (default) in the .npy header on file creation could indicate that the array size is determined by the file size: when np.load (typically with memory mapping on) gets called, it constructs the ndarray with the actual shape by replacing the -1 in the constructor call. Otherwise, the header is not modified anymore, neither on append nor on file write finish. Concurrent appends to a single file would not be advisable and should be channeled through a single AppendArray instance. Concurrent reads while writes take place however should work relatively smooth: every time an np.load (ideally with mmap) is called, the ndarray would provide access to all data written until that time. Currently, my pull request provides: 1. A definition of .npy version 4.0 that supports -1 in the shape 2. implementations for fortran order and non-fortran order (default) including test cases 3. Updated np.load 4. The AppendArray class that does the actual appending Although there is a certain hassle with introducing a new .npy version, the changes themselves are very small. I could also implement a fallback mode for older Numpy installations, if someone is interested. What do you think about such a feature, would it make sense? Anyone available for some more code review? Best from Berlin, Michael PS thank you so far, I could improve my npy-append-array module as well and from what I have seen so far the Numpy code readability exceeded my already high expectations.

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NumPy community meeting
by Inessa Pawson July 31, 2022

July 31, 2022

The next NumPy community meeting will be held this Wednesday, May 25th at 18:00 (6 pm) UTC. Join us via Zoom: https://berkeley.zoom.us/j/762261535 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 Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy

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copy="never" discussion and no deprecation cycle?
by Sebastian Berg July 21, 2022

July 21, 2022

Hi all, (sorry for the length, details/discussion below) On the triage call, there seemed a preference to just try to skip the deprecation and introduce `copy="never"`, `copy="if_needed"`, and `copy="always"` (i.e. string options for the `copy` keyword argument). Strictly speaking, this is against the typical policy (one year of warning/errors). But nobody could think of a reasonable chance that anyone actually uses it. (For me just "policy" will be enough of an argument to just take it slow.) BUT: If nobody has *any* concerns at all, I think we may just end up introducing the change right away. The PR is: https://github.com/numpy/numpy/pull/19173 ## The Feature There is the idea to add `copy=never` (or similar). This would modify the existing `copy` argument to make it a 3-way decision: * `copy=always` or `copy=True` to force a copy * `copy=if_needed` or `copy=False` to prefer no-copy behavior * `copy=never` to error when no-copy behavior is not possible (this ensures that a view is returned) this would affect the functions: * np.array(object, copy=...) * arr.astype(new_dtype, copy=...) * np.reshape(arr, new_shape, copy=...), and the method arr.reshape() * np.meshgrid and possibly Where `reshape` currently does not have the option and would benefit by allowing for `arr.reshape(-1, copy=never)`, which would guarantee a view. ## The Options We have three options that are currently being discussed: 1. We introduce a new `np.CopyMode` or `np.<something>.Copy` Enum with values `np.CopyMode.NEVER`, `np.CopyMode.IF_NEEDED`, and `np.CopyMode.ALWAYS` * Plus: No compatibility concerns * Downside(?): This would be a first in NumPy, and is untypical API due to that. 2. We introduce `copy="never"`, `copy="if_needed"` and `copy="always"` as strings (all other strings will be a `TypeError`): * Problem: `copy="never"` currently means `copy=True` (the opposite) Which means new code has to take care when it may run on older NumPy versions. And in theory could make old code return the wrong thing. * Plus: Strings are the typical for options in NumPy currently. 3. Same as 2. But we take it very slow: Make strings an error right now and only introduce the new options after two releases as per typical deprecation policy. ## Discussion We discussed it briefly today in the triage call and we were leaning towards strings. I was honestly expecting to converge to option 3 to avoid compatibility issues (mainly surprises with `copy="never"` on older versions). But considering how weird it is to currently pass `copy="never"`, the question was whether we should not change it with a release note. The probability of someone currently passing exactly one of those three (and no other) strings seems exceedingly small. Personally, I don't have a much of an opinion. But if *nobody* voices any concern about just changing the meaning of the string inputs, I think the current default may be to just do it. Cheers, Sebastian

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NumPy Newcomers' Hour – Thursday, March 24th
by Inessa Pawson July 10, 2022

July 10, 2022

Our next Newcomers' Hour will be held tomorrow, March 24th, at 4 pm UTC. We have no agenda this time. Stop by to ask questions or just to say hi. Join the meeting via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson NumPy Contributor Experience Lead

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NEP 50: Promotion rules for Python scalars
by Sebastian Berg July 6, 2022

July 6, 2022

Hi all, I would like to share the first formal draft of NEP 50: Promotion rules for Python scalars with everyone. The full text can be found here: https://numpy.org/neps/nep-0050-scalar-promotion.html NEP 50 is an attempt to remove value-based casting/promotion. We wish to replace it with clearer rules for the resulting dtype when mixing NumPy arrays and Python scalars. As a brief example, the proposal allows the following (unchanged): >>> np.array([1, 2, 3], dtype=np.int8) + 100 np.array([101, 102, 103], dtype=np.int8) While clearing up confusion caused by the value-inspecting behavior that we see sometimes, such as: >>> np.array([1, 2, 3], dtype=np.int8) + 300 np.array([301, 302, 303], dtype=np.int16) # note the int16 Where 300 is too large to fit an ``int8``. As well as removing the special behavior of 0-D arrays or NumPy scalars: >>> res = np.array(1, dtype=np.int8) + 100 >>> res.dtype dtype('int64') This is the continuation of a long discussion (see the "Discussion" section), including the poll I once posted: https://discuss.scientific-python.org/t/poll-future-numpy-behavior-when-mix… I would be happy for any feadback, be it just editorial or fundamental discussion. There are many alternatives which I have tried to capture in the NEP. So lets discuss here, or on discuss: https://discuss.scientific-python.org/t/nep-50-promotion-rules-for-python-s… For smaller edits, don't hesitate to open a NumPy PR, or propose edits on my branch (you can use the edit button to create a PR): https://github.com/seberg/numpy/blob/nep50/doc/neps/nep-0050-scalar-promoti… An important part of moving forward will be assessing the real world impact. To start that process, I have created a branch as a draft PR (at this time): https://github.com/numpy/numpy/pull/21626 It is missing some parts, but should allow preliminary testing. The main missing part is that the integer warnings and errors are less strict than proposed in the NEP. It would be invaluable to get a better idea to what extent existing code, especially end-user code, is affected by the proposed changes. Thanks in advance for any input! This is a big, complicated proposal, but finding a way forward will hopefully clear up a source of confusion and inconsistencies that make both maintainers and users life harder. Cheers, Sebastian

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next NumPy Newcomers Hour
by Inessa Pawson July 5, 2022

July 5, 2022

The next NumPy Newcomers Hour will be held this Thursday, June 30th at 4 pm UTC. Ryan C. Cooper, an assistant professor-in-residence at the University of Connecticut (Mansfield, Connecticut, USA), will share how he uses NumPy in his Engineering classes, from individual student research to semester-long courses. We will talk about lessons learned and key strategies to motivate and engage new Python users. Join us via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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Fuzzing integration of Numpy into OSS-Fuzz
by david korczynski July 2, 2022

July 2, 2022

Hi Numpy maintainers, Would you be interested in integrating continuous fuzzing by way of OSS-Fuzz? Fuzzing is a way to automate test-case generation and has been heavily used for memory unsafe languages. Recently efforts have been put into fuzzing memory safe languages and Python is one of the languages where it would be great to use fuzzing. In this PR: https://github.com/google/oss-fuzz/pull/7681 I did an initial integration into OSS-Fuzz. Essentially, OSS-Fuzz is a free service run by Google that performs continuous fuzzing of important open source projects. If you would like to integrate, the only thing I need is a list of email(s) that will get access to the data produced by OSS-Fuzz, such as bug reports, coverage reports and more stats. Notice the emails affiliated with the project will be public in the OSS-Fuzz repo, as they will be part of a configuration file. There are already some important Python projects on OSS-Fuzz such as tensorflow-python (https://github.com/google/oss-fuzz/tree/master/projects/tensorflow-py) and it would be great to add Numpy to the list. Let me know your thoughts on this and if you have any questions as I’m happy to clarify or go more into details with fuzzing. Kind regards, David ADA Logics Ltd is registered in England. No: 11624074. Registered office: 266 Banbury Road, Post Box 292, OX2 7DL, Oxford, Oxfordshire , United Kingdom

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Feature request: function to get minimum and maximum values simultaneously (as a tuple)
by Ewout ter Hoeven July 1, 2022

July 1, 2022

A function to get the minimum and maximum values of an array simultaneously could be very useful, from both a convenience and performance point of view. Especially when arrays get larger the performance benefit could be significant, and even more if the array doesn't fit in L2/L3 cache or even memory. There are many cases where not either the minimum or the maximum of an array is required, but both. Think of clipping an array, getting it's range, checking for outliers, normalizing, making a plot like a histogram, etc. This function could be called aminmax() for example, and also be called like ndarray.minmax(). It should return a tuple (min, max) with the minimum and maximum values of the array, identical to calling (ndarray.min(), ndarray.max()). With such a function, numpy.ptp() and the special cases of numpy.quantile(a, q=[0,1]) and numpy.percentile(a, q=[0,100]) could also potentially be speeded up, among others. Potentially argmin and argmax could get the same treatment, being called argminmax(). There is also a very extensive post on Stack Overflow (a bit old already) with discussion and benchmarks: https://stackoverflow.com/questions/12200580/numpy-function-for-simultaneou…

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Setting C and linker flags for Windows build
by Matthew Brett June 30, 2022

June 30, 2022

Hi, I am very sorry - I feel I should know this, or be able to work it out, but is there a way of setting the flags to the C compiler and the linker, for the Numpy build, on Windows? I'm trying to set the flags for a build with Windows mingw-w64 - but I believe Numpy is ignoring $env:LDFLAGS, $env:CFLAGS and $env:OPT - and I can't see any way of setting these options from the command line. Am I missing something? Cheers, Matthew

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Changes to `np.loadtxt`
by Sebastian Berg June 29, 2022

June 29, 2022

Hi all, just a brief heads up that: https://github.com/numpy/numpy/pull/20580 is now merged. This moves `np.loadtxt` to C. Mainly making it much faster. There are also some other improvements and changes though: * It now supports `quotechar='"'` to support Excel dialect CSV. * Parsing some numbers is stricter (e.g. removed support for `_` or hex float parsing by default). * `max_rows` now actually counts rows and not lines. A warning is given if this makes a difference (blank lines). * Some exception will change, parsing failures now (almost) always give an informative `ValueError`. * `converters=callable` is now valid to provide a single converter for all columns. Please test, and let us know if there is any issue or followup you would like to see. We do have possible followups planned * Consider deprecating the `encoding="bytes"` default which exists for Python 2 compatibility. * Consider renaming `skip_rows` to the more precise `skip_lines`. Moving to C unlocks possible further improvement, such as full `csv.Dialect` support. We do not have this on the roadmap, but such contributions are possible now. Similarly, it should be possible to rewrite `genfromtxt` based on this work. Cheers, Sebastian

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Speeding up `unique` and adding "kind" parameter
by Miles Cranmer June 29, 2022

June 29, 2022

Dear all, There is a PR that adds a lookup table approach to `unique`, shown below. You can get up to ~16x speedup for large integer arrays, at the cost of potentially greater memory usage. https://github.com/numpy/numpy/pull/21843 This is controlled by a new `kind` parameter, which is described below. The current approach uses "sort" while the proposed change implements the "table" method. The default option None will automatically select "table" when possible and memory usage is not too large. ``` kind : {None, 'sort', 'table'}, optional The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations. * If 'sort', will use a mergesort-based approach. * If 'table', will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of `ar` plus the max-min value of `ar`. The options `return_index`, `return_inverse`, `axis`, and `equal_nan` are unavailable with this option. * If None, will automatically choose 'table' if possible, and the required memory allocation is less than or equal to 6 times the size of `ar`. Will otherwise will use 'sort'. This is done to not use a large amount of memory by default, even though 'table' may be faster in most cases. ``` The method and API are very similar to that merged last week for `isin`: https://github.com/numpy/numpy/pull/12065/. One difference is that `return_counts` required a slightly modified approach–using `bincount` seems to work well for this. I am eager to hear your comments on this new PR. Thanks! Miles

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg June 27, 2022

June 27, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 29th at 16:00 UTC (9:00am Pacific Time). 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, discussed, or reviewed. A calendar to can be found at: https://scientific-python.org/calendars/ please make sure to not import the static `ics` but the url. Best regards Sebastian

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ANN: NumExpr 2.8.3
by Robert McLeod June 25, 2022

June 25, 2022

Hi everyone, Please find here another maintenance release of NumExpr. Support for Python 3.6 has been dropped to enable support for NumPy 1.23 (and by extension Python 3.11 when it is released). Wheels for ARM64 multilinux should be available again after troubles with GitHub Actions and Apple Silicon wheels are also now available on PyPi for download. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.8.1 to 2.8.2 --------------------------- * Support for Python 3.6 has been dropped due to the need to substitute the flag `NPY_ARRAY_WRITEBACKIFCOPY` for `NPY_ARRAY_UPDATEIFCOPY`. This flag change was initiated in NumPy 1.14 and finalized in 1.23. The only changes were made to cases where an unaligned constant was passed in with a pre-allocated output variable: ``` x = np.empty(5, dtype=np.uint8)[1:].view(np.int32) ne.evaluate('3', out=x) ``` We think the risk of issues is very low, but if you are using NumExpr as a expression evaluation tool you may want to write a test for this edge case. * Thanks to Matt Einhorn (@matham) for improvements to the GitHub Actions build process to add support for Apple Silicon and aarch64. * Thanks to Biswapriyo Nath (@biswa96) for a fix to allow `mingw` builds on Windows. * There have been some changes made to not import `platform.machine()` on `sparc` but it is highly advised to upgrade to Python 3.9+ to avoid this issue with the Python core package `platform`. 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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No PDF documentation
by Charles R Harris June 24, 2022

June 24, 2022

Hi All, There is a Makefile PR <https://github.com/numpy/numpy/pull/21849> that, among other things, removes the generation of PDF files. This will require changes to the documentation files and I'm wondering exactly how we want to proceed with that. Note that the zipped html files are considerably larger than the two generated PDF file together, about 30 MB, and users will need to download that, unzip it, and click on the index file. Is there anyway to make that simpler? Do we need to? Chuck

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ANN: SciPy 1.9.0rc1 -- please test
by Tyler Reddy June 24, 2022

June 24, 2022

Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.9.0rc1. Please help us test this pre-release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.9.0rc1 One of a few ways to install the release candidate with pip: pip install scipy==1.9.0rc1 ========================== SciPy 1.9.0 Release Notes ========================== Note: Scipy 1.9.0 is not released yet! SciPy 1.9.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). Our development attention will now shift to bug-fix releases on the 1.9.x branch, and on adding new features on the main branch. This release requires Python 3.8+ and NumPy 1.18.5 or greater. For running on PyPy, PyPy3 6.0+ is required. ***************************** Highlights of this release ***************************** - We have modernized our build system to use ``meson``, substantially reducing our source build times - Added `scipy.optimize.milp`, new function for mixed-integer linear programming. - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data. - Tensor-product spline interpolation modes were added to `scipy.interpolate.RegularGridInterpolator`. - A new global optimizer (DIviding RECTangles algorithm) `scipy.optimize.direct` **************** New features **************** `scipy.interpolate` improvements ========================= - Speed up the ``RBFInterpolator`` evaluation with high dimensional interpolants. - Added new spline based interpolation methods for `scipy.interpolate.RegularGridInterpolator` and its tutorial. - `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn` now accept descending ordered points. - ``RegularGridInterpolator`` now handles length-1 grid axes. - The ``BivariateSpline`` subclasses have a new method ``partial_derivative`` which constructs a new spline object representing a derivative of an original spline. This mirrors the corresponding functionality for univariate splines, ``splder`` and ``BSpline.derivative``, and can substantially speed up repeated evaluation of derivatives. `scipy.linalg` improvements ===================== - `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved. - Minimum required LAPACK version is bumped to ``3.7.1``. `scipy.fft` improvements ================== - Added ``uarray`` multimethods for `scipy.fft.fht` and `scipy.fft.ifht` to allow provision of third party backend implementations such as those recently added to CuPy. `scipy.optimize` improvements ======================= - A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm) was added. For problems with inexpensive function evaluations, like the ones in the SciPy benchmark suite, ``direct`` is competitive with the best other solvers in SciPy (``dual_annealing`` and ``differential_evolution``) in terms of execution time. See `gh-14300 <https://github.com/scipy/scipy/pull/14300>`__ for more details. - Add a ``full_output`` parameter to `scipy.optimize.curve_fit` to output additional solution information. - Add a ``integrality`` parameter to `scipy.optimize.differential_evolution`, enabling integer constraints on parameters. - Add a ``vectorized`` parameter to call a vectorized objective function only once per iteration. This can improve minimization speed by reducing interpreter overhead from the multiple objective function calls. - The default method of `scipy.optimize.linprog` is now ``'highs'``. - Added `scipy.optimize.milp`, new function for mixed-integer linear programming. - Added Newton-TFQMR method to ``newton_krylov``. - Added support for the ``Bounds`` class in ``shgo`` and ``dual_annealing`` for a more uniform API across `scipy.optimize`. - Added the ``vectorized`` keyword to ``differential_evolution``. - ``approx_fprime`` now works with vector-valued functions. `scipy.signal` improvements ===================== - The new window function `scipy.signal.windows.kaiser_bessel_derived` was added to compute the Kaiser-Bessel derived window. - Single-precision ``hilbert`` operations are now faster as a result of more consistent ``dtype`` handling. `scipy.sparse` improvements ====================== - Add a ``copy`` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace computation with ``copy=False`` reduces the memory footprint. - Add a ``dtype`` parameter to `scipy.sparce.csgraph.laplacian` for type casting. - Add a ``symmetrized`` parameter to `scipy.sparce.csgraph.laplacian` to produce symmetric Laplacian for directed graphs. - Add a ``form`` parameter to `scipy.sparce.csgraph.laplacian` taking one of the three values: ``array``, or ``function``, or ``lo`` determining the format of the output Laplacian: * ``array`` is a numpy array (backward compatible default); * ``function`` is a pointer to a lambda-function evaluating the Laplacian-vector or Laplacian-matrix product; * ``lo`` results in the format of the ``LinearOperator``. `scipy.sparse.linalg` improvements =========================== - ``lobpcg`` performance improvements for small input cases. `scipy.spatial` improvements ====================== - Add an ``order`` parameter to `scipy.spatial.transform.Rotation.from_quat` and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format. `scipy.stats` improvements ===================== - `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis tests to assess whether a sample was drawn from a given distribution. Besides reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`, `scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample size limitations, it makes it possible to perform similar tests using arbitrary statistics and distributions. - Several `scipy.stats` functions support new ``axis`` (integer or tuple of integers) and ``nan_policy`` ('raise', 'omit', or 'propagate'), and ``keepdims`` arguments. These functions also support masked arrays as inputs, even if they do not have a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays, such as when axis-slices have no unmasked elements or entire inputs are of size zero, are handled consistently. - Add a ``weight`` parameter to `scipy.stats.hmean`. - Several improvements have been made to `scipy.stats.levy_stable`. Substantial improvement has been made for numerical evaluation of the pdf and cdf, resolving [#12658](https://github.com/scipy/scipy/issues/12658) and [#14944](https://github.com/scipy/scipy/issues/14994). The improvement is particularly dramatic for stability parameter ``alpha`` close to or equal to 1 and for ``alpha`` below but approaching its maximum value of 2. The alternative fast Fourier transform based method for pdf calculation has also been updated to use the approach of Wang and Zhang from their 2008 conference paper *Simpson’s rule based FFT method to compute densities of stable distribution*, making this method more competitive with the default method. In addition, users now have the option to change the parametrization of the Levy Stable distribution to Nolan's "S0" parametrization which is used internally by SciPy's pdf and cdf implementations. The "S0" parametrization is described in Nolan's paper [*Numerical calculation of stable densities and distribution functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's implementation is based. "S0" has the advantage that ``delta`` and ``gamma`` are proper location and scale parameters. With ``delta`` and ``gamma`` fixed, the location and scale of the resulting distribution remain unchanged as ``alpha`` and ``beta`` change. This is not the case for the default "S1" parametrization. Finally, more options have been exposed to allow users to trade off between runtime and accuracy for both the default and FFT methods of pdf and cdf calculation. More information can be found in the documentation here (to be linked). - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data. - The methods ``"pearson"`` and ``"tippet"`` from `scipy.stats.combine_pvalues` have been fixed to return the correct p-values, resolving [#15373](https://github.com/scipy/scipy/issues/15373). In addition, the documentation for `scipy.stats.combine_pvalues` has been expanded and improved. - Unlike other reduction functions, ``stats.mode`` didn't consume the axis being operated on and failed for negative axis inputs. Both the bugs have been fixed. Note that ``stats.mode`` will now consume the input axis and return an ndarray with the ``axis`` dimension removed. - Replaced implementation of `scipy.stats.ncf` with the implementation from Boost for improved reliability. - Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0 to 64 bits to compute the sequence. Default is ``None`` which corresponds to 30 for backward compatibility. Using a higher value allow to sample more points. Note: ``bits`` does not affect the output dtype. - Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling integers using any QMC sampler. - Improved the fit speed and accuracy of ``stats.pareto``. - Added ``qrvs`` method to ``NumericalInversePolynomial`` to match the situation for ``NumericalInverseHermite``. - Faster random variate generation for ``gennorm`` and ``nakagami``. - ``lloyd_centroidal_voronoi_tessellation`` has been added to allow improved sample distributions via iterative application of Voronoi diagrams and centering operations - Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling method. It guarantees that samples are separated from each other by a given ``radius``. - Add `scipy.stats.pmean` to calculate the weighted power mean also called generalized mean. ************************* Deprecated features ************************* - Due to collision with the shape parameter ``n`` of several distributions, use of the distribution ``moment`` method with keyword argument ``n`` is deprecated. Keyword ``n`` is replaced with keyword ``order``. - Similarly, use of the distribution ``interval`` method with keyword arguments ``alpha`` is deprecated. Keyword ``alpha`` is replaced with keyword ``confidence``. - The ``'simplex'``, ``'revised simplex'``, and ``'interior-point'`` methods of `scipy.optimize.linprog` are deprecated. Methods ``highs``, ``highs-ds``, or ``highs-ipm`` should be used in new code. - Support for non-numeric arrays has been deprecated from ``stats.mode``. ``pandas.DataFrame.mode`` can be used instead. - The function `spatial.distance.kulsinski` has been deprecated in favor of `spatial.distance.kulczynski1`. - The ``maxiter`` keyword of the truncated Newton (TNC) algorithm has been deprecated in favour of ``maxfun``. - The ``vertices`` keyword of ``Delauney.qhull`` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. - The ``extradoc`` keyword of ``rv_continuous``, ``rv_discrete`` and ``rv_sample`` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. ************************* Expired Deprecations ************************* There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - Object arrays in sparse matrices now raise an error. - Inexact indices into sparse matrices now raise an error. - Passing ``radius=None`` to `scipy.spatial.SphericalVoronoi` now raises an error (not adding ``radius`` defaults to 1, as before). - Several BSpline methods now raise an error if inputs have ``ndim > 1``. - The ``_rvs`` method of statistical distributions now requires a ``size`` parameter. - Passing a ``fillvalue`` that cannot be cast to the output type in `scipy.signal.convolve2d` now raises an error. - `scipy.spatial.distance` now enforces that the input vectors are one-dimensional. - Removed ``stats.itemfreq``. - Removed ``stats.median_absolute_deviation``. - Removed ``n_jobs`` keyword argument and use of ``k=None`` from ``kdtree.query``. - Removed ``right`` keyword from ``interpolate.PPoly.extend``. - Removed ``debug`` keyword from ``scipy.linalg.solve_*``. - Removed class ``_ppform`` ``scipy.interpolate``. - Removed BSR methods ``matvec`` and ``matmat``. - Removed ``mlab`` truncation mode from ``cluster.dendrogram``. - Removed ``cluster.vq.py_vq2``. - Removed keyword arguments ``ftol`` and ``xtol`` from ``optimize.minimize(method='Nelder-Mead')``. - Removed ``signal.windows.hanning``. - Removed LAPACK ``gegv`` functions from ``linalg``; this raises the minimally required LAPACK version to 3.7.1. - Removed ``spatial.distance.matching``. - Removed the alias ``scipy.random`` for ``numpy.random``. - Removed docstring related functions from ``scipy.misc`` (``docformat``, ``inherit_docstring_from``, ``extend_notes_in_docstring``, ``replace_notes_in_docstring``, ``indentcount_lines``, ``filldoc``, ``unindent_dict``, ``unindent_string``). - Removed ``linalg.pinv2``. *************************************** Backwards incompatible changes *************************************** - Several `scipy.stats` functions now convert ``np.matrix`` to ``np.ndarray``s before the calculation is performed. In this case, the output will be a scalar or ``np.ndarray`` of appropriate shape rather than a 2D ``np.matrix``. Similarly, while masked elements of masked arrays are still ignored, the output will be a scalar or ``np.ndarray`` rather than a masked array with ``mask=False``. - The default method of `scipy.optimize.linprog` is now ``'highs'``, not ``'interior-point'`` (which is now deprecated), so callback functions and some options are no longer supported with the default method. - For `scipy.stats.combine_pvalues`, the sign of the test statistic returned for the method ``"pearson"`` has been flipped so that higher values of the statistic now correspond to lower p-values, making the statistic more consistent with those of the other methods and with the majority of the literature. - `scipy.linalg.expm` due to historical reasons was using the sparse implementation and thus was accepting sparse arrays. Now it only works with nDarrays. For sparse usage, `scipy.sparse.linalg.expm` needs to be used explicitly. - The definition of `scipy.stats.circvar` has reverted to the one that is standard in the literature; note that this is not the same as the square of `scipy.stats.circstd`. - Remove inheritance to `QMCEngine` in `MultinomialQMC` and `MultivariateNormalQMC`. It removes the methods `fast_forward` and `reset`. - Init of `MultinomialQMC` now require the number of trials with `n_trials`. Hence, `MultinomialQMC.random` output has now the correct shape ``(n, pvals)``. - Several function-specific warnings (``F_onewayConstantInputWarning``, ``F_onewayBadInputSizesWarning``, ``PearsonRConstantInputWarning``, ``PearsonRNearConstantInputWarning``, ``SpearmanRConstantInputWarning``, and ``BootstrapDegenerateDistributionWarning``) have been replaced with more general warnings. ****************** Other changes ****************** - A draft developer CLI is available for SciPy, leveraging the ``doit``, ``click`` and ``rich-click`` tools. For more details, see [gh-15959](https://github.com/scipy/scipy/pull/15959). - The SciPy contributor guide has been reorganized and updated (see [#15947](https://github.com/scipy/scipy/pull/15947) for details). - QUADPACK Fortran routines in `scipy.integrate`, which power `scipy.integrate.quad`, have been marked as `recursive`. This should fix rare issues in multivariate integration (`nquad` and friends) and obviate the need for compiler-specific compile flags (`/recursive` for ifort etc). Please file an issue if this change turns out problematic for you. This is also true for ``FITPACK`` routines in `scipy.interpolate`, which power ``splrep``, ``splev`` etc., and ``*UnivariateSpline`` and ``*BivariateSpline`` classes. - the ``USE_PROPACK`` environment variable has been renamed to ``SCIPY_USE_PROPACK``; setting to a non-zero value will enable the usage of the ``PROPACK`` library as before Lazy access to subpackages ======================= Before this release, all subpackages of SciPy (`cluster`, `fft`, `ndimage`, etc.) had to be explicitly imported. Now, these subpackages are lazily loaded as soon as they are accessed, so that the following is possible (if desired for interactive use, it's not actually recommended for code, see :ref:`scipy-api`): ``import scipy as sp; sp.fft.dct([1, 2, 3])``. Advantages include: making it easier to navigate SciPy in interactive terminals, reducing subpackage import conflicts (which before required ``import networkx.linalg as nla; import scipy.linalg as sla``), and avoiding repeatedly having to update imports during teaching & experimentation. Also see [the related community specification document]( https://scientific-python.org/specs/spec-0001/). SciPy switched to Meson as its build system ================================== This is the first release that ships with [Meson](https://mesonbuild.com) as the build system. When installing with ``pip`` or ``pypa/build``, Meson will be used (invoked via the ``meson-python`` build hook). This change brings significant benefits - most importantly much faster build times, but also better support for cross-compilation and cleaner build logs. Note: This release still ships with support for ``numpy.distutils``-based builds as well. Those can be invoked through the ``setup.py`` command-line interface (e.g., ``python setup.py install``). It is planned to remove ``numpy.distutils`` support before the 1.10.0 release. When building from source, a number of things have changed compared to building with ``numpy.distutils``: - New build dependencies: ``meson``, ``ninja``, and ``pkg-config``. ``setuptools`` and ``wheel`` are no longer needed. - BLAS and LAPACK libraries that are supported haven't changed, however the discovery mechanism has: that is now using ``pkg-config`` instead of hardcoded paths or a ``site.cfg`` file. - The build defaults to using OpenBLAS. See :ref:`blas-lapack-selection` for details. The two CLIs that can be used to build wheels are ``pip`` and ``build``. In addition, the SciPy repo contains a ``python dev.py`` CLI for any kind of development task (see its ``--help`` for details). For a comparison between old (``distutils``) and new (``meson``) build commands, see :ref:`meson-faq`. For more information on the introduction of Meson support in SciPy, see `gh-13615 <https://github.com/scipy/scipy/issues/13615>`__ and `this blog post < https://labs.quansight.org/blog/2021/07/moving-scipy-to-meson/>`__. ********** Authors ********** * endolith (12) * Caio Agiani (2) + * Emmy Albert (1) + * Joseph Albert (1) * Tania Allard (3) * Carsten Allefeld (1) + * Kartik Anand (1) + * Virgile Andreani (2) + * Weh Andreas (1) + * Francesco Andreuzzi (5) + * Kian-Meng Ang (2) + * Gerrit Ansmann (1) * Ar-Kareem (1) + * Shehan Atukorala (1) + * avishai231 (1) + * Blair Azzopardi (1) * Sayantika Banik (2) + * Ross Barnowski (8) * Christoph Baumgarten (3) * Nickolai Belakovski (1) * Peter Bell (9) * Sebastian Berg (2) * Bharath (1) + * bobcatCA (2) + * boussoffara (2) + * Islem BOUZENIA (1) + * Jake Bowhay (41) + * Matthew Brett (11) * Dietrich Brunn (2) + * Michael Burkhart (2) + * Evgeni Burovski (96) * Matthias Bussonnier (20) * Dominic C (1) * Cameron (1) + * CJ Carey (3) * Thomas A Caswell (2) * Ali Cetin (2) + * Hood Chatham (5) + * Klesk Chonkin (1) * Craig Citro (1) + * Dan Cogswell (1) + * Luigi Cruz (1) + * Anirudh Dagar (5) * Brandon David (1) * deepakdinesh1123 (1) + * Denton DeLoss (1) + * derbuihan (2) + * Sameer Deshmukh (13) + * Niels Doucet (1) + * DWesl (8) * eytanadler (30) + * Thomas J. Fan (5) * Isuru Fernando (3) * Joseph Fox-Rabinovitz (1) * Ryan Gibson (4) + * Ralf Gommers (308) * Srinivas Gorur-Shandilya (1) + * Alex Griffing (2) * h-vetinari (3) * Matt Haberland (442) * Tristan Hearn (1) + * Jonathan Helgert (1) + * Samuel Hinton (1) + * Jake (1) + * Stewart Jamieson (1) + * Jan-Hendrik Müller (1) * Yikun Jiang (1) + * JuliaMelle01 (1) + * jyuv (12) + * Chris Keefe (1) + * Robert Kern (4) * Andrew Knyazev (11) * Matthias Koeppe (4) + * Sergey Koposov (1) * Volodymyr Kozachynskyi (1) + * Yotaro Kubo (2) + * Jacob Lapenna (1) + * Peter Mahler Larsen (8) * Eric Larson (4) * Laurynas Mikšys (1) + * Antony Lee (1) * Gregory R. Lee (2) * lerichi (1) + * Tim Leslie (2) * P. L. Lim (1) * Smit Lunagariya (43) * lutefiskhotdish (1) + * Cong Ma (12) * Syrtis Major (1) * Nicholas McKibben (17) * Melissa Weber Mendonça (10) * Mark Mikofski (1) * Jarrod Millman (13) * Harsh Mishra (6) * ML-Nielsen (3) + * Matthew Murray (1) + * Andrew Nelson (50) * Dimitri Papadopoulos Orfanos (1) + * Evgueni Ovtchinnikov (2) + * Sambit Panda (1) * Nick Papior (2) * Tirth Patel (43) * Petar Mlinarić (1) * petroselo (1) + * Ilhan Polat (64) * Anthony Polloreno (1) * Amit Portnoy (1) + * Quentin Barthélemy (9) * Patrick N. Raanes (1) + * Tyler Reddy (121) * Pamphile Roy (196) * Vivek Roy (2) + * Niyas Sait (2) + * Atsushi Sakai (25) * Mazen Sayed (1) + * Eduardo Schettino (5) + * Daniel Schmitz (6) + * Eli Schwartz (3) + * SELEE (2) + * Namami Shanker (4) * siddhantwahal (1) + * Gagandeep Singh (8) * Soph (1) + * Shivnaren Srinivasan (1) + * Scott Staniewicz (1) + * Leo C. Stein (4) * Albert Steppi (7) * Christopher Strickland (1) + * Kai Striega (4) * Søren Fuglede Jørgensen (1) * Aleksandr Tagilov (1) + * Masayuki Takagi (1) + * Sai Teja (1) + * Ewout ter Hoeven (2) + * Will Tirone (2) * Bas van Beek (7) * Dhruv Vats (1) * H. Vetinari (4) * Arthur Volant (1) * Samuel Wallan (5) * Stefan van der Walt (8) * Warren Weckesser (83) * Anreas Weh (1) * Nils Werner (1) * Aviv Yaish (1) + * Dowon Yi (1) * Rory Yorke (1) * Yosshi999 (1) + * yuanx749 (2) + * Gang Zhao (23) * ZhihuiChen0903 (1) * Pavel Zun (1) + * David Zwicker (1) + A total of 153 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 1.9.0 ************************** * `#1884 <https://github.com/scipy/scipy/issues/1884>`__: stats distributions fit problems (Trac #1359) * `#2047 <https://github.com/scipy/scipy/issues/2047>`__: derivatives() method is missing in BivariateSpline (Trac #1522) * `#2071 <https://github.com/scipy/scipy/issues/2071>`__: TST: stats: \`check_sample_var\` should be two-sided (Trac #1546) * `#2414 <https://github.com/scipy/scipy/issues/2414>`__: stats binom at non-integer n (Trac #1895) * `#2623 <https://github.com/scipy/scipy/issues/2623>`__: stats.distributions statistical power of test suite * `#2625 <https://github.com/scipy/scipy/issues/2625>`__: wilcoxon() function does not return z-statistic * `#2650 <https://github.com/scipy/scipy/issues/2650>`__: (2D) Interpolation functions should work with complex numbers * `#2834 <https://github.com/scipy/scipy/issues/2834>`__: ksone fitting * `#2868 <https://github.com/scipy/scipy/issues/2868>`__: nan and stats.percentileofscore * `#2877 <https://github.com/scipy/scipy/issues/2877>`__: distributions.ncf numerical issues * `#2993 <https://github.com/scipy/scipy/issues/2993>`__: optimize.approx_fprime & jacobians * `#3214 <https://github.com/scipy/scipy/issues/3214>`__: stats distributions ppf-cdf roundtrip * `#3758 <https://github.com/scipy/scipy/issues/3758>`__: discrete distribution defined by \`values\` with non-integer... * `#4130 <https://github.com/scipy/scipy/issues/4130>`__: BUG: stats: fisher_exact returns incorrect p-value * `#4897 <https://github.com/scipy/scipy/issues/4897>`__: expm is 10x as slow as matlab according to http://stackoverflow.com/questions/30048315 * `#5103 <https://github.com/scipy/scipy/issues/5103>`__: Docs suggest scipy.sparse.linalg.expm_multiply supports LinearOperator... * `#5266 <https://github.com/scipy/scipy/issues/5266>`__: Deprecated routines in Netlib LAPACK >3.5.0 * `#5890 <https://github.com/scipy/scipy/issues/5890>`__: Undefined behavior when using scipy.interpolate.RegularGridInterpolator... * `#5982 <https://github.com/scipy/scipy/issues/5982>`__: Keyword collision in scipy.stats.levy_stable.interval * `#6472 <https://github.com/scipy/scipy/issues/6472>`__: scipy.stats.invwishart does not check if scale matrix is symmetric * `#6551 <https://github.com/scipy/scipy/issues/6551>`__: BUG: stats: inconsistency in docs and behavior of gmean and hmean * `#6624 <https://github.com/scipy/scipy/issues/6624>`__: incorrect handling of nan by RegularGridInterpolator * `#6882 <https://github.com/scipy/scipy/issues/6882>`__: Certain recursive scipy.integrate.quad (e.g. dblquad and nquad)... * `#7469 <https://github.com/scipy/scipy/issues/7469>`__: Misleading interp2d documentation * `#7560 <https://github.com/scipy/scipy/issues/7560>`__: Should RegularGridInterpolator support length 1 dimensions? * `#8928 <https://github.com/scipy/scipy/issues/8928>`__: BUG: scipy.stats.norm wrong expected value of function when loc... * `#9231 <https://github.com/scipy/scipy/issues/9231>`__: infinite loop in stats.fisher_exact * `#9313 <https://github.com/scipy/scipy/issues/9313>`__: geometric distribution stats.geom returns negative values if... * `#9524 <https://github.com/scipy/scipy/issues/9524>`__: interpn returns nan with perfectly valid data * `#9591 <https://github.com/scipy/scipy/issues/9591>`__: scipy.interpolate.interp1d with kind=“previous” doesn't extrapolate... * `#9815 <https://github.com/scipy/scipy/issues/9815>`__: stats.mode's nan_policy 'propagate' not working? * `#9944 <https://github.com/scipy/scipy/issues/9944>`__: documentation for \`scipy.interpolate.RectBivariateSpline\` is... * `#9999 <https://github.com/scipy/scipy/issues/9999>`__: BUG: malloc() calls in Cython and C that are not checked for... * `#10096 <https://github.com/scipy/scipy/issues/10096>`__: Add literature reference for circstd (and circvar?) * `#10446 <https://github.com/scipy/scipy/issues/10446>`__: RuntimeWarning: invalid value encountered in stats.genextreme * `#10577 <https://github.com/scipy/scipy/issues/10577>`__: Additional discussion for scipy.stats roadmap * `#10821 <https://github.com/scipy/scipy/issues/10821>`__: Errors with the Yeo-Johnson Transform that also Appear in Scikit-Learn * `#10983 <https://github.com/scipy/scipy/issues/10983>`__: LOBPCG inefficinet when computing > 20% of eigenvalues * `#11145 <https://github.com/scipy/scipy/issues/11145>`__: unexpected SparseEfficiencyWarning at scipy.sparse.linalg.splu * `#11406 <https://github.com/scipy/scipy/issues/11406>`__: scipy.sparse.linalg.svds (v1.4.1) on singular matrix does not... * `#11447 <https://github.com/scipy/scipy/issues/11447>`__: scipy.interpolate.interpn: Handle ValueError('The points in dimension... * `#11673 <https://github.com/scipy/scipy/issues/11673>`__: intlinprog: integer linear program solver * `#11742 <https://github.com/scipy/scipy/issues/11742>`__: MAINT: stats: getting skewness alone takes 34000x longer than... * `#11806 <https://github.com/scipy/scipy/issues/11806>`__: Unexpectedly poor results when distribution fitting with \`weibull_min\`... * `#11828 <https://github.com/scipy/scipy/issues/11828>`__: UnivariateSpline gives varying results when multithreaded on... * `#11948 <https://github.com/scipy/scipy/issues/11948>`__: fitting discrete distributions * `#12073 <https://github.com/scipy/scipy/issues/12073>`__: Add note in documentation * `#12456 <https://github.com/scipy/scipy/issues/12456>`__: Add generalized mean calculation * `#12480 <https://github.com/scipy/scipy/issues/12480>`__: RectBivariateSpline derivative evaluator is slow * `#12485 <https://github.com/scipy/scipy/issues/12485>`__: linprog returns an incorrect message * `#12506 <https://github.com/scipy/scipy/issues/12506>`__: ENH: stats: one-sided p-values for statistical tests * `#12545 <https://github.com/scipy/scipy/issues/12545>`__: stats.pareto.fit raises RuntimeWarning * `#12548 <https://github.com/scipy/scipy/issues/12548>`__: scipy.stats.skew returning MaskedArray * `#12633 <https://github.com/scipy/scipy/issues/12633>`__: Offer simpler development workflow? * `#12658 <https://github.com/scipy/scipy/issues/12658>`__: scipy.stats.levy_stable.pdf can be inaccurate and return nan * `#12838 <https://github.com/scipy/scipy/issues/12838>`__: Accept multiple matrices in \`scipy.linalg.expm\` * `#12848 <https://github.com/scipy/scipy/issues/12848>`__: DOC: stats: multivariate distribution documentation issues * `#12870 <https://github.com/scipy/scipy/issues/12870>`__: Levy Stable Random Variates Code has a typo * `#12871 <https://github.com/scipy/scipy/issues/12871>`__: Levy Stable distribution uses parameterisation that is not location... * `#13200 <https://github.com/scipy/scipy/issues/13200>`__: Errors made by scipy.optimize.linprog * `#13462 <https://github.com/scipy/scipy/issues/13462>`__: Too many warnings and results objects in public API for scipy.stats * `#13582 <https://github.com/scipy/scipy/issues/13582>`__: ENH: stats: \`rv_continuous.stats\` with array shapes: use \`_stats\`... * `#13615 <https://github.com/scipy/scipy/issues/13615>`__: RFC: switch to Meson as a build system * `#13632 <https://github.com/scipy/scipy/issues/13632>`__: stats.rv_discrete is not checking that xk values are integers * `#13655 <https://github.com/scipy/scipy/issues/13655>`__: MAINT: stats.rv_generic: \`moment\` method falls back to \`_munp\`... * `#13689 <https://github.com/scipy/scipy/issues/13689>`__: Wilcoxon does not appropriately detect ties when mode=exact. * `#13835 <https://github.com/scipy/scipy/issues/13835>`__: Change name of \`alpha\` parameter in \`interval()\` method * `#13872 <https://github.com/scipy/scipy/issues/13872>`__: Add method details or reference to \`scipy.integrate.dblquad\` * `#13912 <https://github.com/scipy/scipy/issues/13912>`__: Adding Poisson Disc sampling to QMC * `#13996 <https://github.com/scipy/scipy/issues/13996>`__: Fisk distribution documentation typo * `#14035 <https://github.com/scipy/scipy/issues/14035>`__: \`roots_jacobi\` support for large parameter values * `#14081 <https://github.com/scipy/scipy/issues/14081>`__: \`scipy.optimize._linprog_simplex._apply_pivot\` relies on asymmetric... * `#14095 <https://github.com/scipy/scipy/issues/14095>`__: scipy.stats.norm.pdf takes too much time and memory * `#14162 <https://github.com/scipy/scipy/issues/14162>`__: Thread safety RectBivariateSpline * `#14267 <https://github.com/scipy/scipy/issues/14267>`__: BUG: online doc returns 404 - wrong \`reference\` in url * `#14313 <https://github.com/scipy/scipy/issues/14313>`__: ks_2samp: example description does not match example output * `#14418 <https://github.com/scipy/scipy/issues/14418>`__: \`ttest_ind\` for two sampled distributions with the same single... * `#14455 <https://github.com/scipy/scipy/issues/14455>`__: Adds Mixed Integer Linear Programming from highs * `#14462 <https://github.com/scipy/scipy/issues/14462>`__: Shapiro test returning negative p-value * `#14471 <https://github.com/scipy/scipy/issues/14471>`__: methods 'revised simplex' and 'interior-point' are extremely... * `#14505 <https://github.com/scipy/scipy/issues/14505>`__: \`Optimization converged to parameters that are outside the range\`... * `#14548 <https://github.com/scipy/scipy/issues/14548>`__: Add convention flag to quanternion in \`Scipy.spatial.transform.rotation.Rotation\` * `#14565 <https://github.com/scipy/scipy/issues/14565>`__: optimize.minimize: Presence of callback causes method TNC to... * `#14622 <https://github.com/scipy/scipy/issues/14622>`__: BUG: (sort of) mannwhitneyu hits max recursion limit with imbalanced... * `#14645 <https://github.com/scipy/scipy/issues/14645>`__: ENH: MemoryError when trying to bootstrap with large amounts... * `#14716 <https://github.com/scipy/scipy/issues/14716>`__: BUG: stats: The \`loguniform\` distribution is overparametrized. * `#14731 <https://github.com/scipy/scipy/issues/14731>`__: BUG: Incorrect residual graph in scipy.sparse.csgraph.maximum_flow * `#14745 <https://github.com/scipy/scipy/issues/14745>`__: BUG: scipy.ndimage.convolve documentation is incorrect * `#14750 <https://github.com/scipy/scipy/issues/14750>`__: ENH: Add one more derivative-free optimization method * `#14777 <https://github.com/scipy/scipy/issues/14777>`__: BUG: Wrong limit and no warning in stats.t for df=np.inf * `#14793 <https://github.com/scipy/scipy/issues/14793>`__: BUG: Missing pairs in cKDTree.query_pairs when coordinates contain... * `#14861 <https://github.com/scipy/scipy/issues/14861>`__: BUG: unclear error message when all bounds are all equal for... * `#14889 <https://github.com/scipy/scipy/issues/14889>`__: BUG: NumPy's \`random\` module should not be in the \`scipy\`... * `#14914 <https://github.com/scipy/scipy/issues/14914>`__: CI job with code coverage is failing (yet again) * `#14926 <https://github.com/scipy/scipy/issues/14926>`__: RegularGridInterpolator should be called RectilinearGridInterpolator * `#14994 <https://github.com/scipy/scipy/issues/14994>`__: BUG: Levy stable * `#15009 <https://github.com/scipy/scipy/issues/15009>`__: BUG: scipy.stats.multiscale_graphcorr p-values are computed differently... * `#15059 <https://github.com/scipy/scipy/issues/15059>`__: BUG: documentation inconsistent with code for find_peaks_cwt * `#15082 <https://github.com/scipy/scipy/issues/15082>`__: DOC: Sampling from the truncated normal * `#15125 <https://github.com/scipy/scipy/issues/15125>`__: Deprecate \`scipy.spatial.distance.kulsinski\` * `#15133 <https://github.com/scipy/scipy/issues/15133>`__: BUG: Log_norm description is incorrect and produces incorrect... * `#15150 <https://github.com/scipy/scipy/issues/15150>`__: BUG: RBFInterpolator is much slower than Rbf for vector data * `#15172 <https://github.com/scipy/scipy/issues/15172>`__: BUG: special: High relative error in \`log_ndtr\` * `#15195 <https://github.com/scipy/scipy/issues/15195>`__: BUGS: stats: Tracking issue for distributions that warn and/or... * `#15199 <https://github.com/scipy/scipy/issues/15199>`__: BUG: Error occured \`spsolve_triangular\` * `#15245 <https://github.com/scipy/scipy/issues/15245>`__: MAINT: scipy.stats._levy_stable should be treated as subpackage... * `#15252 <https://github.com/scipy/scipy/issues/15252>`__: DOC: Multivariate normal CDF docstring typo * `#15296 <https://github.com/scipy/scipy/issues/15296>`__: BUG: SciPy 1.7.x build failure on Cygwin * `#15308 <https://github.com/scipy/scipy/issues/15308>`__: BUG: OpenBLAS 0.3.18 support * `#15338 <https://github.com/scipy/scipy/issues/15338>`__: DOC: Rename \`\*args\` param in \`f_oneway\` to \`\*samples\` * `#15345 <https://github.com/scipy/scipy/issues/15345>`__: BUG: boschloo_exact gives pvalue > 1 (and sometimes nan) * `#15368 <https://github.com/scipy/scipy/issues/15368>`__: build warnings for \`unuran_wrapper.pyx\` * `#15373 <https://github.com/scipy/scipy/issues/15373>`__: BUG: Tippett’s and Pearson’s method for combine_pvalues are not... * `#15415 <https://github.com/scipy/scipy/issues/15415>`__: \`integrate.quad_vec\` missing documentation for \`limit\` parameter * `#15456 <https://github.com/scipy/scipy/issues/15456>`__: Segfault in HiGHS code when building with Mingw-w64 on Windows * `#15458 <https://github.com/scipy/scipy/issues/15458>`__: DOC: Documentation inaccuracy of scipy.interpolate.bisplev * `#15488 <https://github.com/scipy/scipy/issues/15488>`__: ENH: missing examples for scipy.optimize in docs * `#15507 <https://github.com/scipy/scipy/issues/15507>`__: BUG: scipy.optimize.linprog: the algorithm determines the problem... * `#15508 <https://github.com/scipy/scipy/issues/15508>`__: BUG: Incorrect error message in multivariate_normal * `#15541 <https://github.com/scipy/scipy/issues/15541>`__: BUG: scipy.stats.powerlaw, why should x ∈ (0,1)? x can exceed... * `#15551 <https://github.com/scipy/scipy/issues/15551>`__: MAINT: stats: deprecating non-numeric array support in \`stats.mode\` * `#15568 <https://github.com/scipy/scipy/issues/15568>`__: BENCH/CI: Benchmark timeout * `#15572 <https://github.com/scipy/scipy/issues/15572>`__: BUG: \`scipy.spatial.transform.rotation\`, wrong deprecation... * `#15575 <https://github.com/scipy/scipy/issues/15575>`__: BUG: Tests failing for initial build [arm64 machine] * `#15589 <https://github.com/scipy/scipy/issues/15589>`__: BUG: scipy.special.factorialk docstring inconsistent with behaviour * `#15601 <https://github.com/scipy/scipy/issues/15601>`__: BUG: Scalefactors for \`signal.csd\` with \`average=='median'\`... * `#15617 <https://github.com/scipy/scipy/issues/15617>`__: ENH: stats: all multivariate distributions should be freezable * `#15631 <https://github.com/scipy/scipy/issues/15631>`__: BUG: stats.fit: intermittent failure in doctest * `#15635 <https://github.com/scipy/scipy/issues/15635>`__: CI:ASK: Remove LaTeX doc builds? * `#15638 <https://github.com/scipy/scipy/issues/15638>`__: DEV: \`dev.py\` missing PYTHONPATH when building doc * `#15644 <https://github.com/scipy/scipy/issues/15644>`__: DOC: stats.ks_1samp: incorrect commentary in examples * `#15666 <https://github.com/scipy/scipy/issues/15666>`__: CI: CircleCI build_docs failure on main * `#15670 <https://github.com/scipy/scipy/issues/15670>`__: BUG: AssertionError in test__dual_annealing.py in test_bounds_class * `#15689 <https://github.com/scipy/scipy/issues/15689>`__: BUG: default value of shape parameter in fit method of rv_continuous... * `#15692 <https://github.com/scipy/scipy/issues/15692>`__: CI: scipy.scipy (Main refguide_asv_check) failure in main * `#15696 <https://github.com/scipy/scipy/issues/15696>`__: DOC: False information in docs - scipy.stats.ttest_1samp * `#15700 <https://github.com/scipy/scipy/issues/15700>`__: BUG: AssertionError in test_propack.py * `#15730 <https://github.com/scipy/scipy/issues/15730>`__: BUG: "terminate called after throwing an instance of 'std::out_of_range'"... * `#15732 <https://github.com/scipy/scipy/issues/15732>`__: DEP: execute deprecation of inexact indices into sparse matrices * `#15734 <https://github.com/scipy/scipy/issues/15734>`__: DEP: deal with deprecation of ndim >1 in bspline * `#15735 <https://github.com/scipy/scipy/issues/15735>`__: DEP: add actual DeprecationWarning for sym_pos-keyword of scipy.linalg.solve * `#15736 <https://github.com/scipy/scipy/issues/15736>`__: DEP: Remove \`debug\` keyword from \`scipy.linalg.solve_\*\` * `#15737 <https://github.com/scipy/scipy/issues/15737>`__: DEP: Execute deprecation of pinv2 * `#15739 <https://github.com/scipy/scipy/issues/15739>`__: DEP: sharpen deprecation for >1-dim inputs in optimize.minimize * `#15740 <https://github.com/scipy/scipy/issues/15740>`__: DEP: Execute deprecation for squeezing input vectors in spatial.distance * `#15741 <https://github.com/scipy/scipy/issues/15741>`__: DEP: remove spatial.distance.matching * `#15742 <https://github.com/scipy/scipy/issues/15742>`__: DEP: raise if fillvalue cannot be cast to output type in \`signal.convolve2d\` * `#15743 <https://github.com/scipy/scipy/issues/15743>`__: DEP: enforce radius for \`spatial.SphericalVoronoi\` * `#15744 <https://github.com/scipy/scipy/issues/15744>`__: DEP: sharpen deprecation of dual_annealing argument 'local_search_options' * `#15745 <https://github.com/scipy/scipy/issues/15745>`__: DEP: remove signal.windows.hanning * `#15746 <https://github.com/scipy/scipy/issues/15746>`__: DEP: remove k=None from KDTree.query * `#15747 <https://github.com/scipy/scipy/issues/15747>`__: DEP: stats: remove support for \`_rvs\` without \`size\` parameter * `#15750 <https://github.com/scipy/scipy/issues/15750>`__: DEP: remove \`n_jobs\` from kdtree * `#15751 <https://github.com/scipy/scipy/issues/15751>`__: DEP: remove ftol/xtol from neldermead * `#15752 <https://github.com/scipy/scipy/issues/15752>`__: DEP: remove right keyword from interpolate.PPoly.extend * `#15753 <https://github.com/scipy/scipy/issues/15753>`__: DEP: remove \`_ppform\` * `#15754 <https://github.com/scipy/scipy/issues/15754>`__: DEP: Remove mlab mode from dendrogram * `#15757 <https://github.com/scipy/scipy/issues/15757>`__: DEP: docstring-related deprecations * `#15758 <https://github.com/scipy/scipy/issues/15758>`__: DEP: remove LAPACK \*gegv functions * `#15759 <https://github.com/scipy/scipy/issues/15759>`__: DEP: remove old BSR methods * `#15760 <https://github.com/scipy/scipy/issues/15760>`__: DEP: remove py_vq2 * `#15761 <https://github.com/scipy/scipy/issues/15761>`__: DEP: remove stats.itemfreq * `#15762 <https://github.com/scipy/scipy/issues/15762>`__: DEP: remove stats.median_absolute_deviation * `#15773 <https://github.com/scipy/scipy/issues/15773>`__: BUG: iirfilter allows Wn[1] < Wn[0] for band-pass and band-stop... * `#15780 <https://github.com/scipy/scipy/issues/15780>`__: BUG: CI on Azure broken with PyTest 7.1 * `#15843 <https://github.com/scipy/scipy/issues/15843>`__: BUG: scipy.stats.brunnermunzel incorrectly returns nan for undocumented... * `#15854 <https://github.com/scipy/scipy/issues/15854>`__: CI: Windows Meson job failing sometimes on OpenBLAS binary download * `#15866 <https://github.com/scipy/scipy/issues/15866>`__: BUG/CI: Wrong python version used for tests labeled "Linux Tests... * `#15899 <https://github.com/scipy/scipy/issues/15899>`__: BUG: _calc_uniform_order_statistic_medians documentation example... * `#15927 <https://github.com/scipy/scipy/issues/15927>`__: BUG: Inconsistent handling of INF and NAN in signal.convolve * `#15931 <https://github.com/scipy/scipy/issues/15931>`__: BUG: scipy/io/arff/tests/test_arffread.py::TestNoData::test_nodata... * `#15960 <https://github.com/scipy/scipy/issues/15960>`__: BUG: Documentation Error in scipy.signal.lfilter * `#15961 <https://github.com/scipy/scipy/issues/15961>`__: BUG: scipy.stats.beta and bernoulli fails with float32 inputs * `#15962 <https://github.com/scipy/scipy/issues/15962>`__: Race condition in macOS Meson build between \`_matfuncs_expm\`... * `#15987 <https://github.com/scipy/scipy/issues/15987>`__: CI: \`np.matrix\` deprecation warning * `#16007 <https://github.com/scipy/scipy/issues/16007>`__: BUG: Confusing documentation in \`ttest_ind_from_stats\` * `#16011 <https://github.com/scipy/scipy/issues/16011>`__: BUG: typo in documentation for scipy.optimize.basinhopping * `#16020 <https://github.com/scipy/scipy/issues/16020>`__: BUG: dev.py FileNotFoundError * `#16027 <https://github.com/scipy/scipy/issues/16027>`__: jc should be (n-1)/2 * `#16031 <https://github.com/scipy/scipy/issues/16031>`__: BUG: scipy.sparse.linalg.norm does not work on sparse arrays * `#16036 <https://github.com/scipy/scipy/issues/16036>`__: Missing \`f\` prefix on f-strings * `#16054 <https://github.com/scipy/scipy/issues/16054>`__: Bug: Meson build with dev.py fails to detect SciPy with debian... * `#16065 <https://github.com/scipy/scipy/issues/16065>`__: BUG: Gitpod build with \`python runtests.py\` fails; move to... * `#16074 <https://github.com/scipy/scipy/issues/16074>`__: BUG: refguide check fails with \`numpydoc==1.3\` * `#16081 <https://github.com/scipy/scipy/issues/16081>`__: CI, MAINT: minor refguide failure with stats.describe * `#16121 <https://github.com/scipy/scipy/issues/16121>`__: DOC: scipy.interpolate.RegularGridInterpolator and interpn works... * `#16162 <https://github.com/scipy/scipy/issues/16162>`__: BUG: curve_fit gives wrong results with Pandas float32 * `#16171 <https://github.com/scipy/scipy/issues/16171>`__: BUG: scipy.stats.multivariate_hypergeom.rvs raises ValueError... * `#16219 <https://github.com/scipy/scipy/issues/16219>`__: \`TestSobol.test_0dim\` failure on 32-bit Linux job * `#16233 <https://github.com/scipy/scipy/issues/16233>`__: BUG: Memory leak in function \`sf_error\` due to new reference... * `#16254 <https://github.com/scipy/scipy/issues/16254>`__: DEP: add deprecation warning to \`maxiter\` kwarg in \`_minimize_tnc\` * `#16292 <https://github.com/scipy/scipy/issues/16292>`__: BUG: compilation error: no matching constructor for initialization... * `#16337 <https://github.com/scipy/scipy/issues/16337>`__: TST: stats/tests/test_axis_nan_policy.py::test_axis_nan_policy_full... * `#16347 <https://github.com/scipy/scipy/issues/16347>`__: TST, MAINT: 32-bit Linux test failures in wheels repo * `#16358 <https://github.com/scipy/scipy/issues/16358>`__: TST, MAINT: test_theilslopes_warnings fails on 32-bit Windows * `#16378 <https://github.com/scipy/scipy/issues/16378>`__: DOC: pydata-sphinx-theme v0.9 defaults to darkmode depending... * `#16381 <https://github.com/scipy/scipy/issues/16381>`__: BUG: bootstrap get ValueError for paired statistic * `#16382 <https://github.com/scipy/scipy/issues/16382>`__: BUG: truncnorm.fit does not fit correctly * `#16403 <https://github.com/scipy/scipy/issues/16403>`__: MAINT: NumPy main will require a few updates due to new floating... * `#16409 <https://github.com/scipy/scipy/issues/16409>`__: BUG: SIGSEGV in qhull when array type is wrong * `#16419 <https://github.com/scipy/scipy/issues/16419>`__: BUG: scipy.stats.nbinom.logcdf returns wrong results when some... * `#16426 <https://github.com/scipy/scipy/issues/16426>`__: BUG: stats.shapiro inplace modification of user array ************************** Pull requests for 1.9.0 ************************** * `#9523 <https://github.com/scipy/scipy/pull/9523>`__: ENH: improvements to the Stable distribution * `#11829 <https://github.com/scipy/scipy/pull/11829>`__: Fixes safe handling of small singular values in svds. * `#13490 <https://github.com/scipy/scipy/pull/13490>`__: DEV: stats: check for distribution/method keyword name collisions * `#13572 <https://github.com/scipy/scipy/pull/13572>`__: ENH: n-D and nan_policy support for scipy.stats.percentileofscore * `#13918 <https://github.com/scipy/scipy/pull/13918>`__: ENH: Poisson Disk sampling for QMC * `#13955 <https://github.com/scipy/scipy/pull/13955>`__: DOC: SciPy extensions for code style and docstring guidelines. * `#14003 <https://github.com/scipy/scipy/pull/14003>`__: DOC: clarify the definition of the pdf of \`stats.fisk\` * `#14036 <https://github.com/scipy/scipy/pull/14036>`__: ENH: fix numerical issues in roots_jacobi and related special... * `#14087 <https://github.com/scipy/scipy/pull/14087>`__: DOC: explain null hypotheses in ttest functions * `#14142 <https://github.com/scipy/scipy/pull/14142>`__: DOC: Add better error message for unpacking issue * `#14143 <https://github.com/scipy/scipy/pull/14143>`__: Support LinearOperator in expm_multiply * `#14300 <https://github.com/scipy/scipy/pull/14300>`__: ENH: Adding DIRECT algorithm to \`\`scipy.optimize\`\` * `#14576 <https://github.com/scipy/scipy/pull/14576>`__: ENH: stats: add one-sample Monte Carlo hypothesis test * `#14642 <https://github.com/scipy/scipy/pull/14642>`__: ENH: add Lloyd's algorithm to \`scipy.spatial\` to improve a... * `#14718 <https://github.com/scipy/scipy/pull/14718>`__: DOC: stats: adjust bootstrap doc to emphasize that batch controls... * `#14781 <https://github.com/scipy/scipy/pull/14781>`__: BUG: stats: handle infinite \`df\` in \`t\` distribution * `#14847 <https://github.com/scipy/scipy/pull/14847>`__: ENH: BLD: enable building SciPy with Meson * `#14877 <https://github.com/scipy/scipy/pull/14877>`__: DOC: ndimage convolve origin documentation (#14745) * `#15001 <https://github.com/scipy/scipy/pull/15001>`__: ENH: sparse.linalg: More comprehensive tests (Not only for 1-D... * `#15026 <https://github.com/scipy/scipy/pull/15026>`__: ENH: allow approx_fprime to work with vector-valued func * `#15079 <https://github.com/scipy/scipy/pull/15079>`__: ENH:linalg: expm overhaul and ndarray processing * `#15140 <https://github.com/scipy/scipy/pull/15140>`__: ENH: Make \`stats.kappa3\` work with array inputs * `#15154 <https://github.com/scipy/scipy/pull/15154>`__: DOC: a small bug in docstring example of \`lobpcg\` * `#15165 <https://github.com/scipy/scipy/pull/15165>`__: MAINT: Avoid using del to remove numpy symbols in scipy.__init__.py * `#15168 <https://github.com/scipy/scipy/pull/15168>`__: REL: set version to 1.9.0.dev0 * `#15169 <https://github.com/scipy/scipy/pull/15169>`__: DOC: fix formatting of Methods in multivariate distributions * `#15171 <https://github.com/scipy/scipy/pull/15171>`__: \`AttrDict\` raises \`AttributeError\` on missing attributes,... * `#15176 <https://github.com/scipy/scipy/pull/15176>`__: BUG: special: Clean up some private namespaces and fix \`special.__all__\` * `#15182 <https://github.com/scipy/scipy/pull/15182>`__: MAINT: fix typos principle -> principal * `#15184 <https://github.com/scipy/scipy/pull/15184>`__: MAINT: CI: Rename 'Nightly CPython' job to 'NumPy main' * `#15187 <https://github.com/scipy/scipy/pull/15187>`__: BUG: special: Fix numerical precision issue of log_ndtr * `#15188 <https://github.com/scipy/scipy/pull/15188>`__: MAINT: sparse.linalg: Using more concise and user-friendly f-string... * `#15190 <https://github.com/scipy/scipy/pull/15190>`__: MAINT: interpolate: speed up the RBFInterpolator evaluation with... * `#15196 <https://github.com/scipy/scipy/pull/15196>`__: BUG: stats: Fix handling of support endpoints in two distributions. * `#15197 <https://github.com/scipy/scipy/pull/15197>`__: MAINT: build dependency updates * `#15202 <https://github.com/scipy/scipy/pull/15202>`__: MAINT: special: Don't use macro for 'extern "C"' in strictly... * `#15205 <https://github.com/scipy/scipy/pull/15205>`__: BUG: stats: Fix spurious warnings generated by several distributions. * `#15207 <https://github.com/scipy/scipy/pull/15207>`__: MAINT: sparse.linalg: Using the interface with the trace of sparse... * `#15219 <https://github.com/scipy/scipy/pull/15219>`__: DOC: Corrected docstring of ndimage.sum_labels * `#15223 <https://github.com/scipy/scipy/pull/15223>`__: DOC: x0->x for finite_diff_rel_step docstring closes #15208 * `#15230 <https://github.com/scipy/scipy/pull/15230>`__: ENH: expose submodules via \`__getattr__\` to allow lazy access * `#15234 <https://github.com/scipy/scipy/pull/15234>`__: TST: stats: mark very slow tests as \`xslow\` * `#15243 <https://github.com/scipy/scipy/pull/15243>`__: DOC: stats: add reference for gstd * `#15244 <https://github.com/scipy/scipy/pull/15244>`__: Added example for morphology: binary_dilation and erosion * `#15250 <https://github.com/scipy/scipy/pull/15250>`__: ENH: Make \`stats.kappa4\` work with array * `#15251 <https://github.com/scipy/scipy/pull/15251>`__: [MRG] ENH: Update \`laplacian\` function introducing the new... * `#15255 <https://github.com/scipy/scipy/pull/15255>`__: MAINT: Remove \`distutils\` usage in \`runtests.py\` to fix deprecation... * `#15259 <https://github.com/scipy/scipy/pull/15259>`__: MAINT: optimize, special, signal: Use custom warnings instead... * `#15261 <https://github.com/scipy/scipy/pull/15261>`__: DOC: Add inline comment in Hausdorff distance calculation * `#15265 <https://github.com/scipy/scipy/pull/15265>`__: DOC: update .mailmap * `#15266 <https://github.com/scipy/scipy/pull/15266>`__: CI: remove coverage usage from Windows jobs * `#15269 <https://github.com/scipy/scipy/pull/15269>`__: BLD: add setup.py for \`stats/_levy_stable\` * `#15272 <https://github.com/scipy/scipy/pull/15272>`__: BUG: Fix owens_t function when a tends to infinity * `#15274 <https://github.com/scipy/scipy/pull/15274>`__: DOC: fix docstring in _cdf() function of _multivariate.py * `#15284 <https://github.com/scipy/scipy/pull/15284>`__: TST: silence RuntimeWarning from \`np.det\` in \`signal.place_poles\`... * `#15285 <https://github.com/scipy/scipy/pull/15285>`__: CI: simplify 32-bit Linux testing * `#15286 <https://github.com/scipy/scipy/pull/15286>`__: MAINT: Highs submodule CI issue - use shallow cloning * `#15289 <https://github.com/scipy/scipy/pull/15289>`__: DOC: Misc numpydoc formatting. * `#15291 <https://github.com/scipy/scipy/pull/15291>`__: DOC: some more docstring/numpydoc formatting. * `#15294 <https://github.com/scipy/scipy/pull/15294>`__: ENH: add integrality constraints for linprog * `#15300 <https://github.com/scipy/scipy/pull/15300>`__: DOC: Misc manual docs updates. * `#15302 <https://github.com/scipy/scipy/pull/15302>`__: DOC: More docstring reformatting. * `#15304 <https://github.com/scipy/scipy/pull/15304>`__: CI: fix Gitpod build by adding HiGHS submodule checkout * `#15305 <https://github.com/scipy/scipy/pull/15305>`__: BLD: update NumPy to >=1.18.5, setuptools to <60.0 * `#15309 <https://github.com/scipy/scipy/pull/15309>`__: CI: update OpenBLAS to 0.3.18 in Azure jobs * `#15310 <https://github.com/scipy/scipy/pull/15310>`__: ENH: signal: Add Kaiser-Bessel derived window function * `#15312 <https://github.com/scipy/scipy/pull/15312>`__: BUG: special: Fix loss of precision in pseudo_huber when r/delta... * `#15314 <https://github.com/scipy/scipy/pull/15314>`__: MAINT: changed needed after renaming \`master\` branch to \`main\` * `#15315 <https://github.com/scipy/scipy/pull/15315>`__: MAINT: account for NumPy master -> main renaming * `#15325 <https://github.com/scipy/scipy/pull/15325>`__: CI: reshuffle two Windows Azure CI jobs, and don't run 'full'... * `#15330 <https://github.com/scipy/scipy/pull/15330>`__: ENH: optimize: support undocumented option \`full_output\` for... * `#15336 <https://github.com/scipy/scipy/pull/15336>`__: DOC: update detailed roadmap * `#15344 <https://github.com/scipy/scipy/pull/15344>`__: MAINT:stats: Renamed \`\*args\` param to \`\*samples\` * `#15347 <https://github.com/scipy/scipy/pull/15347>`__: ENH: stats: add weights in harmonic mean * `#15352 <https://github.com/scipy/scipy/pull/15352>`__: BLD: put upper bound \`setuptools<60.0\` in conda environment... * `#15357 <https://github.com/scipy/scipy/pull/15357>`__: ENH: interpolate: add new methods for RegularGridInterpolator. * `#15360 <https://github.com/scipy/scipy/pull/15360>`__: MAINT: speed up rvs of nakagami in scipy.stats * `#15361 <https://github.com/scipy/scipy/pull/15361>`__: MAINT: sparse.linalg: Remove unnecessary operations * `#15366 <https://github.com/scipy/scipy/pull/15366>`__: Make signal functions respect input dtype. * `#15370 <https://github.com/scipy/scipy/pull/15370>`__: DOC: governance members moved to scipy.org * `#15371 <https://github.com/scipy/scipy/pull/15371>`__: MAINT: stats: fix unuran compile-time warnings * `#15378 <https://github.com/scipy/scipy/pull/15378>`__: MAINT: remove version pinning on gmpy2 * `#15380 <https://github.com/scipy/scipy/pull/15380>`__: ENH/MAINT: Version switcher from the sphinx theme * `#15385 <https://github.com/scipy/scipy/pull/15385>`__: DOC: fix typo * `#15387 <https://github.com/scipy/scipy/pull/15387>`__: MAINT: Fix a couple build warnings. * `#15388 <https://github.com/scipy/scipy/pull/15388>`__: DOC: interpolate: improve \`RectBivariateSpline\` doc * `#15391 <https://github.com/scipy/scipy/pull/15391>`__: ENH: graph Laplacian as LinearOperator, add dtype and symmetrized... * `#15392 <https://github.com/scipy/scipy/pull/15392>`__: ENH: integrality constraints for differential_evolution * `#15394 <https://github.com/scipy/scipy/pull/15394>`__: ENH: optimize: improvements to \`LinearConstraint\` class * `#15396 <https://github.com/scipy/scipy/pull/15396>`__: DOC: Git:// protocol on github pending removal. * `#15399 <https://github.com/scipy/scipy/pull/15399>`__: ENH: stats: add \`axis\` tuple and \`nan_policy\` to \`hmean\` * `#15400 <https://github.com/scipy/scipy/pull/15400>`__: MAINT: sparse.linalg: Move the test function of GMRES to the... * `#15401 <https://github.com/scipy/scipy/pull/15401>`__: MAINT: DOC: analytics from analytics.scientific-python * `#15402 <https://github.com/scipy/scipy/pull/15402>`__: DOC: update pip_quickstart (submodules) * `#15406 <https://github.com/scipy/scipy/pull/15406>`__: MAINT: use \`Rotation.Random\` instead of manual generation * `#15407 <https://github.com/scipy/scipy/pull/15407>`__: BLD: meson: split pyx->c and Python extension build * `#15408 <https://github.com/scipy/scipy/pull/15408>`__: MAINT: check for negative weights in \`Rotation.align_vectors\` * `#15410 <https://github.com/scipy/scipy/pull/15410>`__: ENH: add \`order\` parameter to specify quaternion format * `#15413 <https://github.com/scipy/scipy/pull/15413>`__: ENH: stats: add \`rvs\` method for \`gennorm\` * `#15424 <https://github.com/scipy/scipy/pull/15424>`__: ENH: bypass LinearOperator in lobpcg for small-size cases * `#15427 <https://github.com/scipy/scipy/pull/15427>`__: MAINT: Manage imports in \`sparse.linalg\` * `#15431 <https://github.com/scipy/scipy/pull/15431>`__: Revert "ENH: add \`order\` parameter to specify quaternion format" * `#15436 <https://github.com/scipy/scipy/pull/15436>`__: ENH: stats: fit: function for fitting discrete and continuous... * `#15439 <https://github.com/scipy/scipy/pull/15439>`__: ENH: differential_evolution vectorized kwd * `#15440 <https://github.com/scipy/scipy/pull/15440>`__: MAINT: Try to detect scipy path in \`runtests.py\` while not... * `#15442 <https://github.com/scipy/scipy/pull/15442>`__: MAINT: Fix meson build warnings on windows * `#15443 <https://github.com/scipy/scipy/pull/15443>`__: DOC, BUG: Fix error in heading remapping for custom \`scipy.optimize:function\` domain directive * `#15445 <https://github.com/scipy/scipy/pull/15445>`__: ENH: stats: add \`nnlf\` method for discrete distributions * `#15451 <https://github.com/scipy/scipy/pull/15451>`__: BLD: further refinement of Cython dependencies * `#15452 <https://github.com/scipy/scipy/pull/15452>`__: BUG/DOC/TST: combine_pvalues: fix Tippett and Pearson * `#15453 <https://github.com/scipy/scipy/pull/15453>`__: ENH: Make dual_annealing work with Bounds class * `#15454 <https://github.com/scipy/scipy/pull/15454>`__: BLD: remove dependency on libnpymath from \`spatial._distance_wrap\` * `#15455 <https://github.com/scipy/scipy/pull/15455>`__: ENH: Support Bounds class in shgo * `#15459 <https://github.com/scipy/scipy/pull/15459>`__: DOC: documents parameter \`limit\` for function \`integrate.quad_vec\`. * `#15460 <https://github.com/scipy/scipy/pull/15460>`__: ENH: optimize: milp: mixed integer linear programming * `#15462 <https://github.com/scipy/scipy/pull/15462>`__: CI: switch one macOS CI job from distutils to meson * `#15464 <https://github.com/scipy/scipy/pull/15464>`__: ENH: Performance improvements for \`linear_sum_assignment\` * `#15465 <https://github.com/scipy/scipy/pull/15465>`__: DOC: stats: add weights in formulas and examples for gmean and... * `#15466 <https://github.com/scipy/scipy/pull/15466>`__: MAINT: fix compile errors with CPython 3.11 * `#15469 <https://github.com/scipy/scipy/pull/15469>`__: MAINT: Remove \`distutils\` usage * `#15470 <https://github.com/scipy/scipy/pull/15470>`__: ENH: \`stats.qmc\`: faster hypercube point comparison and scrambling... * `#15472 <https://github.com/scipy/scipy/pull/15472>`__: ENH: stats: add \`axis\` tuple and \`nan_policy\` to \`skew\` * `#15485 <https://github.com/scipy/scipy/pull/15485>`__: BLD: updates to Meson build files for more correct linking and... * `#15487 <https://github.com/scipy/scipy/pull/15487>`__: MAINT: typo in bsplines.py * `#15496 <https://github.com/scipy/scipy/pull/15496>`__: DOC: signal: fixed parameter 'order' for butter bandpass * `#15497 <https://github.com/scipy/scipy/pull/15497>`__: MAINT: update vendored uarray * `#15499 <https://github.com/scipy/scipy/pull/15499>`__: CI: remove matplotlib from 32-bit linux job, it fails to build * `#15501 <https://github.com/scipy/scipy/pull/15501>`__: MAINT: Remove unused variable warnings * `#15502 <https://github.com/scipy/scipy/pull/15502>`__: DEV: meson: allow specifying build directory and install prefix * `#15512 <https://github.com/scipy/scipy/pull/15512>`__: MAINT: optimize.linprog: make HiGHS default and deprecate old... * `#15523 <https://github.com/scipy/scipy/pull/15523>`__: DOC: fixed the link for fluiddyn's transonic vision in dev/roadmap.html. * `#15526 <https://github.com/scipy/scipy/pull/15526>`__: MAINT: add qrvs method to NumericalInversePolynomial in scipy.stats * `#15529 <https://github.com/scipy/scipy/pull/15529>`__: DOC: forward port 1.8.0 relnotes * `#15532 <https://github.com/scipy/scipy/pull/15532>`__: TST: parametrize test_ldl_type_size_combinations * `#15546 <https://github.com/scipy/scipy/pull/15546>`__: DOC: missing section for metrics * `#15555 <https://github.com/scipy/scipy/pull/15555>`__: MAINT: make unuran clone shallow * `#15557 <https://github.com/scipy/scipy/pull/15557>`__: DOC: fixes inaccuracy in bisplev documentation * `#15559 <https://github.com/scipy/scipy/pull/15559>`__: BENCH: selection of linalg solvers to facilitate expansion * `#15560 <https://github.com/scipy/scipy/pull/15560>`__: DOC: types and return values for Bessel Functions * `#15561 <https://github.com/scipy/scipy/pull/15561>`__: MAINT: update HiGHS submodule to include fix for Windows segfault * `#15563 <https://github.com/scipy/scipy/pull/15563>`__: CI: add a Windows CI job on GitHub Actions using Meson * `#15564 <https://github.com/scipy/scipy/pull/15564>`__: DOC: stray backticks * `#15565 <https://github.com/scipy/scipy/pull/15565>`__: DOC: incorrect underline lenght in section. * `#15567 <https://github.com/scipy/scipy/pull/15567>`__: ENH: stats.pareto fit improvement for parameter combinations * `#15569 <https://github.com/scipy/scipy/pull/15569>`__: DOC: pip quickstart: setup.py -> meson * `#15570 <https://github.com/scipy/scipy/pull/15570>`__: MAINT: bump test tolerance in test_linprog * `#15571 <https://github.com/scipy/scipy/pull/15571>`__: DOC: Wrong underline length * `#15578 <https://github.com/scipy/scipy/pull/15578>`__: Make Windows Python setup more standard * `#15581 <https://github.com/scipy/scipy/pull/15581>`__: MAINT: clarify deprecation warning spatial.transform.rotation * `#15583 <https://github.com/scipy/scipy/pull/15583>`__: DOC: clarify O(N) SO(N) in random rotations * `#15586 <https://github.com/scipy/scipy/pull/15586>`__: ENH: stats: Add 'alternative' and confidence interval to pearsonr * `#15590 <https://github.com/scipy/scipy/pull/15590>`__: DOC: factorialk docstring inconsistent with code * `#15597 <https://github.com/scipy/scipy/pull/15597>`__: DOC: update \`hyp2f1\` docstring example based on doctest * `#15598 <https://github.com/scipy/scipy/pull/15598>`__: BUG/ENH: \`lsq_linear\`: fixed incorrect \`lsmr_tol\` in first... * `#15603 <https://github.com/scipy/scipy/pull/15603>`__: BENCH: optimize: milp: add MILP benchmarks * `#15606 <https://github.com/scipy/scipy/pull/15606>`__: MAINT: allow multiplication sign \`×\` * `#15611 <https://github.com/scipy/scipy/pull/15611>`__: BUG:signal: Fix median bias in csd(..., average="median") * `#15616 <https://github.com/scipy/scipy/pull/15616>`__: CI: pin asv to avoid slowdowns in 0.5/0.5.1 * `#15619 <https://github.com/scipy/scipy/pull/15619>`__: DOC: stats: update interval and moment method signatures * `#15625 <https://github.com/scipy/scipy/pull/15625>`__: MAINT: Clean up \`type: ignore\` comments related to third-party... * `#15626 <https://github.com/scipy/scipy/pull/15626>`__: TST, MAINT: ignore np distutils dep * `#15629 <https://github.com/scipy/scipy/pull/15629>`__: MAINT: stats: fix \`trim1\` \`axis\` behavior * `#15632 <https://github.com/scipy/scipy/pull/15632>`__: ENH: stats.wilcoxon: return z-statistic (as requested) * `#15634 <https://github.com/scipy/scipy/pull/15634>`__: CI: Improve concurrency to cancel running jobs on PR update * `#15645 <https://github.com/scipy/scipy/pull/15645>`__: DOC: Add code example to the documentation of \`sparse.linalg.cg\`. * `#15646 <https://github.com/scipy/scipy/pull/15646>`__: DOC: stats.ks_1samp: correct examples * `#15647 <https://github.com/scipy/scipy/pull/15647>`__: ENH: add variable bits to \`stats.qmc.Sobol\` * `#15648 <https://github.com/scipy/scipy/pull/15648>`__: DOC: Add examples to documentation for \`scipy.special.ellipr{c,d,f,g,j}\` * `#15649 <https://github.com/scipy/scipy/pull/15649>`__: DEV/DOC: remove latex/pdf documentation * `#15651 <https://github.com/scipy/scipy/pull/15651>`__: DOC: stats.ks_2samp/stats.kstest: correct examples * `#15652 <https://github.com/scipy/scipy/pull/15652>`__: DOC: stats.circstd: add reference, notes, comments * `#15655 <https://github.com/scipy/scipy/pull/15655>`__: REL: fix small issue in pavement.py for release note writing * `#15656 <https://github.com/scipy/scipy/pull/15656>`__: DOC: Fix example for subset_by_index in eigh doc * `#15661 <https://github.com/scipy/scipy/pull/15661>`__: DOC: Additional examples for optimize user guide * `#15662 <https://github.com/scipy/scipy/pull/15662>`__: DOC: stats.fit: fix intermittent failure in doctest * `#15663 <https://github.com/scipy/scipy/pull/15663>`__: DOC: stats.burr12: fix typo * `#15664 <https://github.com/scipy/scipy/pull/15664>`__: BENCH: Add benchmarks for special.factorial/factorial2/factorialk * `#15673 <https://github.com/scipy/scipy/pull/15673>`__: DOC: fix intersphinx links * `#15682 <https://github.com/scipy/scipy/pull/15682>`__: MAINT: sparse.linalg: Clear up unnecessary modules imported in... * `#15684 <https://github.com/scipy/scipy/pull/15684>`__: DOC: add formula and documentation improvements for scipy.special.chndtr... * `#15690 <https://github.com/scipy/scipy/pull/15690>`__: ENH: add uarray multimethods for fast Hankel transforms * `#15694 <https://github.com/scipy/scipy/pull/15694>`__: MAINT,CI: signal: fix failing refguide check * `#15699 <https://github.com/scipy/scipy/pull/15699>`__: DOC: stats.ttest_1samp: update example * `#15701 <https://github.com/scipy/scipy/pull/15701>`__: BUG: Fix dual_annealing bounds test * `#15703 <https://github.com/scipy/scipy/pull/15703>`__: BUG: fix test fail in test_propack.py (loosen atol) * `#15710 <https://github.com/scipy/scipy/pull/15710>`__: MAINT: sparse.linalg: \`bnorm\` only calculate once * `#15712 <https://github.com/scipy/scipy/pull/15712>`__: ENH: \`scipy.stats.qmc.Sobol\`: allow 32 or 64 bit computation * `#15715 <https://github.com/scipy/scipy/pull/15715>`__: ENH: stats: add _axis_nan_policy_factory to moment * `#15718 <https://github.com/scipy/scipy/pull/15718>`__: ENH: Migration of \`write_release_and_log\` into standalone script * `#15723 <https://github.com/scipy/scipy/pull/15723>`__: TST: stats: make \`check_sample_var\` two-sided * `#15724 <https://github.com/scipy/scipy/pull/15724>`__: TST: stats: simplify \`check_sample_mean\` * `#15725 <https://github.com/scipy/scipy/pull/15725>`__: DEV: Try to detect scipy from dev installed path * `#15728 <https://github.com/scipy/scipy/pull/15728>`__: ENH: changed vague exception messages to a more descriptive ones... * `#15729 <https://github.com/scipy/scipy/pull/15729>`__: ENH: stats: add weighted power mean * `#15763 <https://github.com/scipy/scipy/pull/15763>`__: ENH: stats: replace ncf with Boost non_central_f distribution * `#15766 <https://github.com/scipy/scipy/pull/15766>`__: BUG: improve exceptions for private attributes in refactored... * `#15768 <https://github.com/scipy/scipy/pull/15768>`__: [DOC] fix typo in cython optimize help example * `#15769 <https://github.com/scipy/scipy/pull/15769>`__: MAINT: stats: check integrality in \`_argcheck\` as needed * `#15771 <https://github.com/scipy/scipy/pull/15771>`__: MAINT: stats: resolve discrete rvs dtype platform dependency * `#15774 <https://github.com/scipy/scipy/pull/15774>`__: MAINT: stats: remove deprecated \`median_absolute_deviation\` * `#15775 <https://github.com/scipy/scipy/pull/15775>`__: DOC: stats.lognorm: rephrase note about parameterization * `#15776 <https://github.com/scipy/scipy/pull/15776>`__: DOC: stats.powerlaw: more explicit explanation of support * `#15777 <https://github.com/scipy/scipy/pull/15777>`__: MAINT: stats.shapiro: subtract median from shapiro input * `#15778 <https://github.com/scipy/scipy/pull/15778>`__: MAINT: stats: more specific error type from \`rv_continuous.fit\` * `#15779 <https://github.com/scipy/scipy/pull/15779>`__: CI: don't run meson tests on forks and remove skip flags * `#15782 <https://github.com/scipy/scipy/pull/15782>`__: DEPR: remove k=None in KDTree.query * `#15783 <https://github.com/scipy/scipy/pull/15783>`__: CI:Pin pytest version to 7.0.1 on Azure * `#15785 <https://github.com/scipy/scipy/pull/15785>`__: MAINT: stats: remove deprecated itemfreq * `#15786 <https://github.com/scipy/scipy/pull/15786>`__: DOC: Add examples of integrals to integrate.quadpack * `#15788 <https://github.com/scipy/scipy/pull/15788>`__: DOC: update macOS and Linux contributor docs to use Python 3.9 * `#15789 <https://github.com/scipy/scipy/pull/15789>`__: DOC, MAINT: Remove numpydoc submodule * `#15791 <https://github.com/scipy/scipy/pull/15791>`__: MAINT: add ShapeInfo to continuous distributions in scipy.stats * `#15795 <https://github.com/scipy/scipy/pull/15795>`__: DEP: remove n_jobs from cKDTree * `#15797 <https://github.com/scipy/scipy/pull/15797>`__: scipy/_lib/boost: Update to d8626c9d2d937abf6a38a844522714ad72e63281 * `#15799 <https://github.com/scipy/scipy/pull/15799>`__: DEP: add warning for documented-as-deprecated extradoc * `#15802 <https://github.com/scipy/scipy/pull/15802>`__: DOC: Import Error in examples * `#15803 <https://github.com/scipy/scipy/pull/15803>`__: DOC: error in TransferFunctionDiscrete example * `#15804 <https://github.com/scipy/scipy/pull/15804>`__: DEP: sharpen warning message on >1-dim for optimize.minimize * `#15805 <https://github.com/scipy/scipy/pull/15805>`__: DEP: specify version to remove dual_annealing argument 'local_search_options' * `#15809 <https://github.com/scipy/scipy/pull/15809>`__: DOC,MAINT: remove \`quad_explain\` that has become irrelevant. * `#15810 <https://github.com/scipy/scipy/pull/15810>`__: DOC: stats.mood: validity only when observations are unique * `#15811 <https://github.com/scipy/scipy/pull/15811>`__: DOC: fix evaluate_all_bspl example. * `#15812 <https://github.com/scipy/scipy/pull/15812>`__: DOC: Couple of single to double backticks * `#15813 <https://github.com/scipy/scipy/pull/15813>`__: DOC: information about skip on CircleCI * `#15817 <https://github.com/scipy/scipy/pull/15817>`__: MAINT: stats.fisher_exact: improve docs and fix bugs * `#15819 <https://github.com/scipy/scipy/pull/15819>`__: DEP: docstring-related deprecations (#15757) * `#15821 <https://github.com/scipy/scipy/pull/15821>`__: DEP: add actual DeprecationWarning for sym_pos-keyword of scipy.linalg.solve * `#15822 <https://github.com/scipy/scipy/pull/15822>`__: DEP: remove \`right\` from interpolate.PPoly.extend * `#15823 <https://github.com/scipy/scipy/pull/15823>`__: DOC: Interpolative tutorial - wrong matrix fill var * `#15824 <https://github.com/scipy/scipy/pull/15824>`__: BUG: Handle base case for scipy.integrate.simpson when span along... * `#15825 <https://github.com/scipy/scipy/pull/15825>`__: TST: stats: xfail_on_32bit studentized_range moment test * `#15827 <https://github.com/scipy/scipy/pull/15827>`__: DOC: change docs that specify the SNR ratio definition for find_peaks_cwt(). * `#15828 <https://github.com/scipy/scipy/pull/15828>`__: DEP: raise value error for object arrays * `#15830 <https://github.com/scipy/scipy/pull/15830>`__: MAINT: stats: collocate bootstrap/permutation_test/monte_carlo_test * `#15831 <https://github.com/scipy/scipy/pull/15831>`__: MAINT: stats.rv_generic: fix unnecessary call to \`_munp\` in... * `#15835 <https://github.com/scipy/scipy/pull/15835>`__: FIX: Incorect boschloo pvalue * `#15837 <https://github.com/scipy/scipy/pull/15837>`__: DOC: Simplify conda command * `#15840 <https://github.com/scipy/scipy/pull/15840>`__: DOC: special: Add 'Examples' for wrightomega. * `#15842 <https://github.com/scipy/scipy/pull/15842>`__: DOC: Add examples for \`CGS\`, \`GCROTMK\` and \`BiCGSTAB\` iterative... * `#15846 <https://github.com/scipy/scipy/pull/15846>`__: DOC: Add efficiency condition for CSC sparse matrix and remove... * `#15847 <https://github.com/scipy/scipy/pull/15847>`__: BUG: adds warning to scipy.stats.brunnermunzel * `#15848 <https://github.com/scipy/scipy/pull/15848>`__: DOC: fix interp2d docs showing wrong Z array ordering. * `#15850 <https://github.com/scipy/scipy/pull/15850>`__: MAINT: sparse.linalg: Missing tfqmr in the re-entrancy test * `#15853 <https://github.com/scipy/scipy/pull/15853>`__: DEP: remove the keyword debug from linalg.solve * `#15855 <https://github.com/scipy/scipy/pull/15855>`__: ENH: stats.rv_continuous.expect: split interval to improve reliability * `#15867 <https://github.com/scipy/scipy/pull/15867>`__: CI: fix python version matrix in linux workflow * `#15868 <https://github.com/scipy/scipy/pull/15868>`__: CI: fix Azure workflows * `#15872 <https://github.com/scipy/scipy/pull/15872>`__: DEP: remove mlab from dendrogram * `#15874 <https://github.com/scipy/scipy/pull/15874>`__: DEP: remove py_vq2 * `#15875 <https://github.com/scipy/scipy/pull/15875>`__: DEP: remove old BSR methods * `#15876 <https://github.com/scipy/scipy/pull/15876>`__: DEP: remove _ppform * `#15881 <https://github.com/scipy/scipy/pull/15881>`__: DEP: remove signal.windows.hanning * `#15882 <https://github.com/scipy/scipy/pull/15882>`__: DEP: enforced radius in spherical voronoi * `#15885 <https://github.com/scipy/scipy/pull/15885>`__: DOC: stats: clarify truncnorm shape parameter definition * `#15886 <https://github.com/scipy/scipy/pull/15886>`__: BUG: check that iirfilter argument Wn satisfies Wn[0] < Wn[1] * `#15887 <https://github.com/scipy/scipy/pull/15887>`__: DEP: remove ftol/xtol from neldermead * `#15894 <https://github.com/scipy/scipy/pull/15894>`__: [BUG] make p-values consistent with the literature * `#15895 <https://github.com/scipy/scipy/pull/15895>`__: CI: remove pin on Jinja2 * `#15898 <https://github.com/scipy/scipy/pull/15898>`__: DOC: stats: correct documentation of \`wilcoxon\`'s behavior... * `#15900 <https://github.com/scipy/scipy/pull/15900>`__: DOC: fix import in example in _morestats * `#15905 <https://github.com/scipy/scipy/pull/15905>`__: MAINT: stats._moment: warn when catastrophic cancellation occurs * `#15909 <https://github.com/scipy/scipy/pull/15909>`__: DEP: deal with deprecation of ndim >1 in bspline * `#15911 <https://github.com/scipy/scipy/pull/15911>`__: MAINT: stats: fix \`gibrat\` name * `#15914 <https://github.com/scipy/scipy/pull/15914>`__: MAINT: special: Clean up C style in ndtr.c * `#15916 <https://github.com/scipy/scipy/pull/15916>`__: MAINT: stats: adjust tolerance of failing TestTruncnorm * `#15917 <https://github.com/scipy/scipy/pull/15917>`__: MAINT: stats: remove support for \`_rvs\` without \`size\` parameter * `#15920 <https://github.com/scipy/scipy/pull/15920>`__: ENH: stats.mannwhitneyu: add iterative implementation * `#15923 <https://github.com/scipy/scipy/pull/15923>`__: MAINT: stats: attempt to consolidate warnings and errors * `#15932 <https://github.com/scipy/scipy/pull/15932>`__: MAINT: stats: fix and thoroughly test \`rv_sample\` at non-integer... * `#15933 <https://github.com/scipy/scipy/pull/15933>`__: TST: test_nodata respect endianness * `#15938 <https://github.com/scipy/scipy/pull/15938>`__: DOC: sparse.linalg: add citations for COLAMD * `#15939 <https://github.com/scipy/scipy/pull/15939>`__: Update _dual_annealing.py * `#15945 <https://github.com/scipy/scipy/pull/15945>`__: BUG/ENH: \`MultinomialQMC.random\` shape to (n, pvals) * `#15946 <https://github.com/scipy/scipy/pull/15946>`__: DEP: remove inheritance to \`QMCEngine\` in \`MultinomialQMC\`... * `#15947 <https://github.com/scipy/scipy/pull/15947>`__: DOC: Revamp contributor setup guides * `#15953 <https://github.com/scipy/scipy/pull/15953>`__: DOC: Add meson docs to use gcc, clang build in parallel and optimization... * `#15955 <https://github.com/scipy/scipy/pull/15955>`__: BUG Fix signature of D_IIR_forback(1,2) * `#15959 <https://github.com/scipy/scipy/pull/15959>`__: ENH: Developer CLI for SciPy * `#15965 <https://github.com/scipy/scipy/pull/15965>`__: MAINT: stats: ensure that \`rv_continuous._fitstart\` shapes... * `#15968 <https://github.com/scipy/scipy/pull/15968>`__: BUG: Fix debug and coverage arguments with dev.py * `#15970 <https://github.com/scipy/scipy/pull/15970>`__: BLD: specify \`cython_lapack\` dependency for \`matfuncs_expm\` * `#15973 <https://github.com/scipy/scipy/pull/15973>`__: DOC: Add formula renderings to integrate.nquad. * `#15981 <https://github.com/scipy/scipy/pull/15981>`__: ENH: optimize: Add Newton-TFQMR method and some tests for Newton-Krylov * `#15982 <https://github.com/scipy/scipy/pull/15982>`__: BENCH: stats: Distribution memory and CDF/PPF round trip benchmarks * `#15983 <https://github.com/scipy/scipy/pull/15983>`__: TST: sparse.linalg: Add tests for the parameter \`show\` * `#15991 <https://github.com/scipy/scipy/pull/15991>`__: TST: fix for np.kron matrix issue. * `#15992 <https://github.com/scipy/scipy/pull/15992>`__: DOC: Fixed \`degrees\` parameter in return section * `#15997 <https://github.com/scipy/scipy/pull/15997>`__: MAINT: integrate: add \`recursive\` to QUADPACK Fortran sources * `#15998 <https://github.com/scipy/scipy/pull/15998>`__: BUG: Fix yeojohnson when transformed data has zero variance * `#15999 <https://github.com/scipy/scipy/pull/15999>`__: MAINT: Adds doit.db.db to gitignore * `#16004 <https://github.com/scipy/scipy/pull/16004>`__: MAINT: rename MaximumFlowResult.residual to flow * `#16005 <https://github.com/scipy/scipy/pull/16005>`__: DOC: sparse.linalg: Fixed the description of input matrix of... * `#16010 <https://github.com/scipy/scipy/pull/16010>`__: MAINT: Add a check to verify all \`.pyi\` files are installed... * `#16012 <https://github.com/scipy/scipy/pull/16012>`__: DOC: Fix broken link and add python headers to contributing guide * `#16015 <https://github.com/scipy/scipy/pull/16015>`__: DEP: bump version for deprecating residual to flow. * `#16018 <https://github.com/scipy/scipy/pull/16018>`__: Doc: fix arch linux building from source local dependencies instructions * `#16019 <https://github.com/scipy/scipy/pull/16019>`__: DOC: fix conda env name in quickstart guide [skip ci] * `#16021 <https://github.com/scipy/scipy/pull/16021>`__: DOC: typos in basinhopping documentation * `#16024 <https://github.com/scipy/scipy/pull/16024>`__: CI: unpin pytest and pytest-xdist * `#16026 <https://github.com/scipy/scipy/pull/16026>`__: BUG: Allow \`spsolve_triangular\` to work with matrices with... * `#16029 <https://github.com/scipy/scipy/pull/16029>`__: BUG: Fix meson-info file errors and add more informative exception * `#16030 <https://github.com/scipy/scipy/pull/16030>`__: MAINT: stats: more accurate error message for \`multivariate_normal\` * `#16032 <https://github.com/scipy/scipy/pull/16032>`__: FIX: show warning when passing NAN into input of convolve method * `#16037 <https://github.com/scipy/scipy/pull/16037>`__: MAINT: fix missing \`f\` prefix on f-strings * `#16042 <https://github.com/scipy/scipy/pull/16042>`__: MAINT: stats.dirichlet: fix interface inconsistency * `#16044 <https://github.com/scipy/scipy/pull/16044>`__: DEV: do.py, adoption of pkg pydevtool (removed non SciPy specific... * `#16045 <https://github.com/scipy/scipy/pull/16045>`__: ENH: Use circleci-artifacts-redirector-action * `#16051 <https://github.com/scipy/scipy/pull/16051>`__: MAINT: Miscellaneous small changes to filter_design * `#16053 <https://github.com/scipy/scipy/pull/16053>`__: Mark fitpack sources as \`recursive\` * `#16055 <https://github.com/scipy/scipy/pull/16055>`__: MAINT: stats: replace \`np.var\` with \`_moment(..., 2)\` to... * `#16058 <https://github.com/scipy/scipy/pull/16058>`__: DEV: Fix meson debian python build * `#16060 <https://github.com/scipy/scipy/pull/16060>`__: MAINT: Allow all Latin-1 Unicode letters in the source code. * `#16062 <https://github.com/scipy/scipy/pull/16062>`__: DOC: Document QUADPACK routines used in \`\*quad\` * `#16067 <https://github.com/scipy/scipy/pull/16067>`__: DEP: remove spatial.distance.matching * `#16070 <https://github.com/scipy/scipy/pull/16070>`__: ENH: interpolate: handle length-1 grid axes in RegularGridInterpolator * `#16073 <https://github.com/scipy/scipy/pull/16073>`__: DOC: expand RegularGridInterpolator docstring * `#16075 <https://github.com/scipy/scipy/pull/16075>`__: CI: Fix refguidecheck failures; unpin Sphinx * `#16077 <https://github.com/scipy/scipy/pull/16077>`__: BUG: interpolate: RGI(nan) is nan * `#16078 <https://github.com/scipy/scipy/pull/16078>`__: DEV,BLD: Use Meson in Gitpod builds * `#16082 <https://github.com/scipy/scipy/pull/16082>`__: BUG: refguide-check: allow multiline namedtuples * `#16083 <https://github.com/scipy/scipy/pull/16083>`__: DOC: fixing a sign issue in FFTlog function documentation * `#16092 <https://github.com/scipy/scipy/pull/16092>`__: ENH: interpolate: Add functionality to accept descending points... * `#16095 <https://github.com/scipy/scipy/pull/16095>`__: MAINT: Remove old filtered warnings * `#16100 <https://github.com/scipy/scipy/pull/16100>`__: MAINT: Fix a couple compiler warnings. * `#16104 <https://github.com/scipy/scipy/pull/16104>`__: DOC: stats: symmetry not checked for (inv)wishart distributions * `#16111 <https://github.com/scipy/scipy/pull/16111>`__: BUG: Fix norm for sparse arrays * `#16115 <https://github.com/scipy/scipy/pull/16115>`__: MAINT: merge \`environment.yml\` and \`environment_meson.yml\` * `#16117 <https://github.com/scipy/scipy/pull/16117>`__: MAINT: stats.wilcoxon: return \`zstatistic\` only when \`method='approx'\` * `#16118 <https://github.com/scipy/scipy/pull/16118>`__: Download openblas binary from GH repo * `#16122 <https://github.com/scipy/scipy/pull/16122>`__: CI: Speed up ci build that keeps timing out * `#16125 <https://github.com/scipy/scipy/pull/16125>`__: DOC: interpolate: fix typos "the the" -> "the" * `#16126 <https://github.com/scipy/scipy/pull/16126>`__: DOC: interpolate: details rectilinear grids in docstrings * `#16128 <https://github.com/scipy/scipy/pull/16128>`__: BUG: interpolate: fix extrapolation behaviors of \`previous\`... * `#16130 <https://github.com/scipy/scipy/pull/16130>`__: Increase time to timeout on azure * `#16134 <https://github.com/scipy/scipy/pull/16134>`__: BUG: signal: Fix calculation of extended image indices in convolve2d. * `#16135 <https://github.com/scipy/scipy/pull/16135>`__: MAINT: sparse.linalg: A minor improvement with zero initial guess * `#16137 <https://github.com/scipy/scipy/pull/16137>`__: Clean up fitpack smoke tests * `#16138 <https://github.com/scipy/scipy/pull/16138>`__: TST: interpolate: mark rbf chunking tests as slow * `#16141 <https://github.com/scipy/scipy/pull/16141>`__: DOC: Plot poles as x and zeros as o in signal * `#16144 <https://github.com/scipy/scipy/pull/16144>`__: DEP: Execute deprecation for squeezing input vectors in spatial.distance * `#16145 <https://github.com/scipy/scipy/pull/16145>`__: ENH: Fix signal.iircomb w0 bugs, add support for both frequency... * `#16150 <https://github.com/scipy/scipy/pull/16150>`__: Add typing info for Rotation.concatenate * `#16165 <https://github.com/scipy/scipy/pull/16165>`__: BUG: fix extension module initialization, needs use of \`PyMODINIT_FUNC\` * `#16166 <https://github.com/scipy/scipy/pull/16166>`__: MAINT:linalg: Expose Cython functions for generic use * `#16167 <https://github.com/scipy/scipy/pull/16167>`__: ENH: Tweak theilslopes and siegelslopes to return a tuple_bunch * `#16168 <https://github.com/scipy/scipy/pull/16168>`__: BUG: special: Fix the test 'test_d' that is run when SCIPY_XSLOW... * `#16173 <https://github.com/scipy/scipy/pull/16173>`__: Adds note to the curve_fit() docstring to use float64. * `#16176 <https://github.com/scipy/scipy/pull/16176>`__: MAINT: remove questionable uses of \`Py_FatalError\` in module... * `#16177 <https://github.com/scipy/scipy/pull/16177>`__: MAINT: Cleanup unused code in meson-files * `#16180 <https://github.com/scipy/scipy/pull/16180>`__: DEV: do.py build. On setup checks if intro-buildoptions.json... * `#16181 <https://github.com/scipy/scipy/pull/16181>`__: BUG: stats: fix multivariate_hypergeom.rvs method * `#16183 <https://github.com/scipy/scipy/pull/16183>`__: ENH: Simplify return names in stats.theil/siegelslopes (and fix... * `#16184 <https://github.com/scipy/scipy/pull/16184>`__: DEP: raise if fillvalue cannot be cast to output type in signal.convolve2d * `#16185 <https://github.com/scipy/scipy/pull/16185>`__: BUG: stats: Fix handling of float32 inputs for the boost-based... * `#16187 <https://github.com/scipy/scipy/pull/16187>`__: BLD: default to Meson in pyproject.toml * `#16194 <https://github.com/scipy/scipy/pull/16194>`__: BLD: add a build option to force use of the g77 ABI with Meson * `#16198 <https://github.com/scipy/scipy/pull/16198>`__: DEP: sharpen deprecation in NumericalInverseHermite * `#16206 <https://github.com/scipy/scipy/pull/16206>`__: CI: Test NumPy main branch also with Python 3.11 * `#16220 <https://github.com/scipy/scipy/pull/16220>`__: Create a new spline from a partial derivative of a bivariate... * `#16223 <https://github.com/scipy/scipy/pull/16223>`__: MAINT: interpolate: move RGI to a separate file * `#16228 <https://github.com/scipy/scipy/pull/16228>`__: TST: interpolate: move test_spalde_scalar to other fitpack tests * `#16229 <https://github.com/scipy/scipy/pull/16229>`__: REL: DOC: fix documentation URLs * `#16230 <https://github.com/scipy/scipy/pull/16230>`__: BUG: fix extension module initialization, needs use of PyMODINIT_FUNC,... * `#16239 <https://github.com/scipy/scipy/pull/16239>`__: MAINT: tools: Add more output to a refguide-check error message. * `#16241 <https://github.com/scipy/scipy/pull/16241>`__: DOC: stats: update roadmap * `#16242 <https://github.com/scipy/scipy/pull/16242>`__: BUG: Make KDTree more robust against nans. * `#16245 <https://github.com/scipy/scipy/pull/16245>`__: DEP: Execute deprecation of pinv2 * `#16247 <https://github.com/scipy/scipy/pull/16247>`__: DOC:linalg: Remove references to removed pinv2 function * `#16248 <https://github.com/scipy/scipy/pull/16248>`__: DOC: prep 1.9.0 release notes * `#16249 <https://github.com/scipy/scipy/pull/16249>`__: Refguide check verbosity abs names * `#16257 <https://github.com/scipy/scipy/pull/16257>`__: DEP: Deprecation follow-ups * `#16259 <https://github.com/scipy/scipy/pull/16259>`__: Revert "CI: pin Pip to 22.0.4 to avoid issues with \`--no-build-isolation\`" * `#16261 <https://github.com/scipy/scipy/pull/16261>`__: DEP: add deprecation warning to maxiter kwarg in _minimize_tnc * `#16264 <https://github.com/scipy/scipy/pull/16264>`__: DOC: update the RegularGridInterpolator docstring * `#16265 <https://github.com/scipy/scipy/pull/16265>`__: DEP: deprecate spatial.distance.kulsinski * `#16267 <https://github.com/scipy/scipy/pull/16267>`__: DOC: broken donation link on GitHub * `#16273 <https://github.com/scipy/scipy/pull/16273>`__: DOC: remove deprecated functions from refguide * `#16276 <https://github.com/scipy/scipy/pull/16276>`__: MAINT: sparse.linalg: Update some docstrings. * `#16279 <https://github.com/scipy/scipy/pull/16279>`__: MAINT: stats: override \`loguniform.fit\` to resolve overparameterization * `#16282 <https://github.com/scipy/scipy/pull/16282>`__: BUG: special: DECREF scipy_special object before exiting sf_error(). * `#16283 <https://github.com/scipy/scipy/pull/16283>`__: Corrections To Docs * `#16287 <https://github.com/scipy/scipy/pull/16287>`__: BLD: sync pyproject.toml changes from oldest-supported-numpy * `#16289 <https://github.com/scipy/scipy/pull/16289>`__: MAINT: stats: remove function-specific warning messages * `#16290 <https://github.com/scipy/scipy/pull/16290>`__: BLD: fix issue with \`python setup.py install\` and \`_directmodule\` * `#16295 <https://github.com/scipy/scipy/pull/16295>`__: MAINT: move \`import_array\` before module creation in module... * `#16296 <https://github.com/scipy/scipy/pull/16296>`__: DOC: REL: fix \`make dist\` issue with missing dependencies * `#16303 <https://github.com/scipy/scipy/pull/16303>`__: MAINT: revert addition of multivariate_beta * `#16304 <https://github.com/scipy/scipy/pull/16304>`__: MAINT: add a more informative error message for broken installs * `#16309 <https://github.com/scipy/scipy/pull/16309>`__: BLD: CI: fix issue in wheel metadata, and add basic "build in... * `#16316 <https://github.com/scipy/scipy/pull/16316>`__: REL: update version switcher for 1.8.1 * `#16321 <https://github.com/scipy/scipy/pull/16321>`__: DOC: fix incorrect formatting of deprecation tags * `#16326 <https://github.com/scipy/scipy/pull/16326>`__: REL: update version switcher for 1.9 * `#16329 <https://github.com/scipy/scipy/pull/16329>`__: MAINT: git security shim for 1.9.x * `#16339 <https://github.com/scipy/scipy/pull/16339>`__: MAINT, TST: bump tol for _axis_nan_policy_test * `#16341 <https://github.com/scipy/scipy/pull/16341>`__: BLD: update Pythran requirement to 0.11.0, to support Clang >=13 * `#16353 <https://github.com/scipy/scipy/pull/16353>`__: MAINT: version bounds 1.9.0rc1 * `#16360 <https://github.com/scipy/scipy/pull/16360>`__: MAINT, TST: sup warning for theilslopes * `#16361 <https://github.com/scipy/scipy/pull/16361>`__: MAINT: SCIPY_USE_PROPACK * `#16370 <https://github.com/scipy/scipy/pull/16370>`__: MAINT: update Boost submodule to include Cygwin fix * `#16374 <https://github.com/scipy/scipy/pull/16374>`__: MAINT: update pydata-sphinx-theme * `#16379 <https://github.com/scipy/scipy/pull/16379>`__: DOC: dark theme css adjustments * `#16390 <https://github.com/scipy/scipy/pull/16390>`__: TST, MAINT: adjust 32-bit xfails for HiGHS * `#16414 <https://github.com/scipy/scipy/pull/16414>`__: BUG: spatial: Handle integer arrays in HalfspaceIntersection. * `#16420 <https://github.com/scipy/scipy/pull/16420>`__: MAINT: next round of 1.9.0 backports * `#16422 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1 0

StackOverflow Developer Survey
by Michael Siebert June 23, 2022

June 23, 2022

Hi all, just found some more survey data on Numpy, see (need to scroll down a little to „Other frameworks and libraries“): https://survey.stackoverflow.co/2022 Numpy seems to enjoy an exceptional position as a library over a wide spectrum of programming languages: rank 2 overall and among professionals and even rank 1 with beginners. Well deserved! Best, Michael

1 0

Speeding up isin1d and adding a "method" or similar
by Sebastian Berg June 23, 2022

June 23, 2022

Hi all, there is a PR to add a faster path to `np.isin`, that uses a look-up- table for all the elements that are included in the haystack (`test_elements`): https://github.com/numpy/numpy/pull/12065/files Such a table means that the memory overhead can be very significant, but the speedup as well, so there was the idea of adding an option to pick which version is used. The current documentation for this new `method` keyword argument would be. So the main questions are: * Is there any concern about adding such a new kwarg? * Is `method` the best name? Sorts uses `kind` which may also be good There is also the smaller question of what heuristic 'auto' would use, but that can be tweaked at any time. ``` method : {'auto', 'sort', 'dictionary'}, optional The algorithm to use. This will not affect the final result, but will affect the speed. Default is 'auto'. - If 'sort', will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of `ar1` and `ar2`, not accounting for size of dtypes. - If 'dictionary', will use a key-dictionary approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of `ar1` plus the max-min value of `ar2`. This tends to be the faster method if the following formula is true: `log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927`, but may use greater memory. - If 'auto', will automatically choose the method which is expected to perform the fastest, using the above formula. For larger sizes or smaller range, 'dictionary' is chosen. For larger range or smaller sizes, 'sort' is chosen.` ``` Cheers, Sebastian

2 3

NumPy 1.23.0 released
by Charles R Harris June 22, 2022

June 22, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.23.0. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of ``loadtxt`` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of structured dtypes. - Improvements to f2py. The Python versions supported in this release are 3.8-3.10, 3.11 will be supported when it comes out. 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. All 64 bit wheels are also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.0/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.0>. *Contributors* A total of 151 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @GalaxySnail + - @code-review-doctor + - @h-vetinari - Aaron Meurer - Alexander Shadchin - Alexandre de Siqueira - Allan Haldane - Amrit Krishnan - Andrei Batomunkuev - Andrew J. Hesford + - Andrew Murray + - Andrey Andreyevich Bienkowski + - André Elimelek de Weber + - Andy Wharton + - Arryan Singh - Arushi Sharma - Bas van Beek - Bharat Raghunathan - Bhavuk Kalra + - Brigitta Sipőcz - Brénainn Woodsend + - Burlen Loring + - Caio Agiani + - Charles Harris - Chiara Marmo - Cornelius Roemer + - Dahyun Kim + - Damien Caliste - David Prosin + - Denis Laxalde - Developer-Ecosystem-Engineering - Devin Shanahan + - Diego Wang + - Dimitri Papadopoulos Orfanos - Ding Liu + - Diwakar Gupta + - Don Kirkby + - Emma Simon + - Eric Wieser - Evan Miller + - Evgeni Burovski - Evgeny Posenitskiy + - Ewout ter Hoeven + - Felix Divo - Francesco Andreuzzi + - Ganesh Kathiresan - Gaëtan de Menten - Geoffrey Gunter + - Hans Meine - Harsh Mishra + - Henry Schreiner - Hood Chatham + - I-Shen Leong - Ilhan Polat - Inessa Pawson - Isuru Fernando - Ivan Gonzalez + - Ivan Meleshko + - Ivan Yashchuk + - Janus Heide + - Jarrod Millman - Jason Thai + - Jeremy Volkman + - Jesús Carrete Montaña + - Jhong-Ken Chen (陳仲肯) + - John Kirkham - John-Mark Gurney + - Jonathan Deng + - Joseph Fox-Rabinovitz - Jouke Witteveen + - Junyan Ou + - Jérôme Richard + - Kassian Sun + - Kazuki Sakamoto + - Kenichi Maehashi - Kevin Sheppard - Kilian Lieret + - Kushal Beniwal + - Leo Singer - Logan Thomas + - Lorenzo Mammana + - Margret Pax - Mariusz Felisiak + - Markus Mohrhard + - Mars Lee - Marten van Kerkwijk - Masamichi Hosoda + - Matthew Barber - Matthew Brett - Matthias Bussonnier - Matthieu Darbois - Matti Picus - Melissa Weber Mendonça - Michael Burkhart + - Morteza Mirzai + - Motahhar Mokf + - Muataz Attaia + - Muhammad Motawe + - Mukulika Pahari - Márton Gunyhó + - Namami Shanker + - Nihaal Sangha + - Niyas Sait - Omid Rajaei + - Oscar Gustafsson + - Ovee Jawdekar + - P. L. Lim + - Pamphile Roy + - Pantelis Antonoudiou + - Pearu Peterson - Peter Andreas Entschev - Peter Hawkins - Pierre de Buyl - Pieter Eendebak + - Pradipta Ghosh + - Rafael Cardoso Fernandes Sousa + - Raghuveer Devulapalli - Ralf Gommers - Raphael Kruse - Raúl Montón Pinillos - Robert Kern - Rohit Goswami - Ross Barnowski - Ruben Garcia + - Sadie Louise Bartholomew + - Saswat Das + - Sayed Adel - Sebastian Berg - Serge Guelton - Simon Surland Andersen + - Siyabend Ürün + - Somasree Majumder + - Soumya + - Stefan van der Walt - Stefano Miccoli + - Stephan Hoyer - Stephen Worsley + - Tania Allard - Thomas Duvernay + - Thomas Green + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Ting Sun + - Tirth Patel - Toshiki Kataoka - Tyler Reddy - Warren Weckesser - Yang Hau - Yoon, Jee Seok + Cheers, Charles Harris

1 1

Numpy binary wheels and CI for win/arm64 platform
by Niyas Sait June 22, 2022

June 22, 2022

Hello, Numpy v1.21.2 <https://github.com/numpy/numpy/releases/tag/v1.21.2> added support for windows/arm64 platforms but we still don't have any systems in place to produce binary wheels or test win/arm64 packages. I think it will be good to start looking into this. CPython has an official buildbot worker running for win/arm64 and official python support for the platform will be available from the 3.11 release. It is not yet clear to me how the build and CI system for numpy is deployed and how to enable support for a new platform like win/arm64. One of the main issues in supporting win/arm64 build would be due to the lack of win/arm64 VMs available on the cloud. But I see we have been producing binary wheels for Apple M1 platforms on pypi and conda repository for some time which also lacks the cloud VM support. I think we could take some learnings from Apple M1 support and look at how a similar strategy can be used for win/arm64. I would like to hear if anyone has any thoughts on this topic. Also, any pointers to understand numpy wheel generation and CI flow for similar platforms would be helpful as well. Thanks, Niyas

4 9

seed for new random number generator
by Pieter Eendebak June 21, 2022

June 21, 2022

Hi everyone, The new numpy random interface (e.g. r=numpy.random.default_rng; r.random) is much faster than the old one (e.g. np.random.random). When converting code from the old style to the new style I miss having a way to set the seed of the RNG I tried: rng.bit_generator = np.random.PCG64(seed=42) # fails, with good error message rng.bit_generator.state['state']['state']=42 # has no effect, perhaps make this dict read-only? Is there a way to set the seed without creating a new RNG object? With kind regards, Pieter Eendebak

3 4

next NumPy Documentation Team meeting
by Inessa Pawson June 18, 2022

June 18, 2022

The next NumPy Documentation Team meeting will be held this Monday, June 20th at 16:00 (4 pm) UTC. Join us via Zoom: https://zoom.us/j/96219574921?pwd=VTRNeGwwOUlrYVNYSENpVVBRRjlkZz09 Everyone is welcome to attend and contribute to a conversation. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

1 0

numpy build question
by Jesse Peet June 17, 2022

June 17, 2022

I am trying to build numpy 1.22.3 in container using a rhel8 host and the container being rhel7. My version of gcc is 4.8.5-44. Here is the error I get building library "npymath" sourcesWARN: Could not locate executable armflang _configtest.cxx: In function ‘int main()’: _configtest.cxx:1:17: warning: ‘auto’ changes meaning in C++11; please remove it [-Wc++0x-compat] int main(void){ auto x = 0.0; return static_cast<int>(x); } ^ _configtest.cxx:1:22: error: ‘x’ does not name a type int main(void){ auto x = 0.0; return static_cast<int>(x); } ^ _configtest.cxx:1:55: error: ‘x’ was not declared in this scope int main(void){ auto x = 0.0; return static_cast<int>(x); } ^ Traceback (most recent call last): File "setup.py", line 461, in <module> setup_package() File "setup.py", line 453, in setup_package setup(**metadata) File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/core.py", line 148, in setup dist.run_commands() File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/dist.py", line 966, in run_commands self.run_command(cmd) File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/dist.py", line 985, in run_command cmd_obj.run() File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/command/build.py", line 135, in run self.run_command(cmd_name) File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/cmd.py", line 313, in run_command self.distribution.run_command(command) File "/opt/rh/rh-python38/root/usr/lib64/python3.8/distutils/dist.py", line 985, in run_command cmd_obj.run() File "numpy/core/setup.py", line 708, in get_mathlib_info raise RuntimeError( RuntimeError: Broken toolchain: cannot link a simple C++ program. note: A compiler with support for C++11 language features is required. V/R Jesse S. Peet Configuration Manager Specialist/Engineer Email: jesse.peet(a)raytheon.com Cell: 719-322-4257

4 6

assert_array_equal scalar broadcast behaviour
by Jon Morris June 17, 2022

June 17, 2022

Hello all, I was recently tripped up by issue #9542<https://github.com/numpy/numpy/issues/9542>, where a call to assert_array_equal unexpectedly passed because a single scalar can be declared equal to an array if every value in the array is the same. I've created pull #21595<https://github.com/numpy/numpy/pull/21595> to address the problem - what does everyone think? Is this an acceptable solution or is there a better way to resolve this issue? Many thanks, Jon Jon Morris Software Developer e: Jon.Morris(a)jbarisk.com d:+44 (0)1756 587229 t: +44 (0)1756 799919 www.jbarisk.com Celebrating 10 Years of The Flood People All JBA Risk Management's email messages contain confidential information and are intended only for the individual(s) named. If you are not the named addressee you should not disseminate, distribute or copy this e-mail. Please notify the sender immediately by email if you have received this email by mistake and delete this email from your system. JBA Risk Management Limited is registered in England, company number 07732946, 1 Broughton Park, Old Lane North, Broughton, Skipton, North Yorkshire, BD23 3FD, England.

2 1

argmin/argmax underspecification?
by TODD ANDERSON June 16, 2022

June 16, 2022

The docs say "Returns the indices of the maximum values along an axis." When axis=None, I presume the implementation is actually "Returns the first index (assuming ravel ordering) of the maximum value." I also presume that people are now relying on the fact that it always returns the first such index. So, maybe the docs should be updated?

1 0

NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg June 14, 2022

June 14, 2022

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, June 15st at 16:00 UTC (9:00am Pacific Time). 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, discussed, or reviewed. A calendar to can be found at: https://scientific-python.org/calendars/ please make sure to not import the static `ics` but the url. Best regards Sebastian

1 0

Future of numpy.distutils
by Jerome Kieffer June 13, 2022

June 13, 2022

Dear Numpy developpers, We are developing a set of scientific tools (https://github.com/silx-kit) and all our build infrastructure is based on `numpy.distutils` which apparently is going to disappear in the coming years. Beside us, the `scipy` project was using it ... Ralf Gommers has ported `scipy` to build with `meson-python` and there are apparently some sharp edges remaining, especially under windows and macos. I wonder if you can comment on the sustainability of this approach or if you would advice us another build tool. Thanks for your insights Cheers, Jerome

4 7

NumPy 1.23.0rc3 released
by Charles R Harris June 11, 2022

June 11, 2022

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.23.0rc2. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of ``loadtxt`` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of structured dtypes. - Improvements to f2py. The Python versions supported in this release are 3.8-3.10, 3.11 will be supported when it comes out. 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. All 64 bit wheels are also linked with 64 bit OpenBLAS. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.0rc3/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.0rc3>. *Contributors* A total of 151 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @GalaxySnail + - @code-review-doctor + - @h-vetinari - Aaron Meurer - Alexander Shadchin - Alexandre de Siqueira - Allan Haldane - Amrit Krishnan - Andrei Batomunkuev - Andrew J. Hesford + - Andrew Murray + - Andrey Andreyevich Bienkowski + - André Elimelek de Weber + - Andy Wharton + - Arryan Singh - Arushi Sharma - Bas van Beek - Bharat Raghunathan - Bhavuk Kalra + - Brigitta Sipőcz - Brénainn Woodsend + - Burlen Loring + - Caio Agiani + - Charles Harris - Chiara Marmo - Cornelius Roemer + - Dahyun Kim + - Damien Caliste - David Prosin + - Denis Laxalde - Developer-Ecosystem-Engineering - Devin Shanahan + - Diego Wang + - Dimitri Papadopoulos Orfanos - Ding Liu + - Diwakar Gupta + - Don Kirkby + - Emma Simon + - Eric Wieser - Evan Miller + - Evgeni Burovski - Evgeny Posenitskiy + - Ewout ter Hoeven + - Felix Divo - Francesco Andreuzzi + - Ganesh Kathiresan - Gaëtan de Menten - Geoffrey Gunter + - Hans Meine - Harsh Mishra + - Henry Schreiner - Hood Chatham + - I-Shen Leong - Ilhan Polat - Inessa Pawson - Isuru Fernando - Ivan Gonzalez + - Ivan Meleshko + - Ivan Yashchuk + - Janus Heide + - Jarrod Millman - Jason Thai + - Jeremy Volkman + - Jesús Carrete Montaña + - Jhong-Ken Chen (陳仲肯) + - John Kirkham - John-Mark Gurney + - Jonathan Deng + - Joseph Fox-Rabinovitz - Jouke Witteveen + - Junyan Ou + - Jérôme Richard + - Kassian Sun + - Kazuki Sakamoto + - Kenichi Maehashi - Kevin Sheppard - Kilian Lieret + - Kushal Beniwal + - Leo Singer - Logan Thomas + - Lorenzo Mammana + - Margret Pax - Mariusz Felisiak + - Markus Mohrhard + - Mars Lee - Marten van Kerkwijk - Masamichi Hosoda + - Matthew Barber - Matthew Brett - Matthias Bussonnier - Matthieu Darbois - Matti Picus - Melissa Weber Mendonça - Michael Burkhart + - Morteza Mirzai + - Motahhar Mokf + - Muataz Attaia + - Muhammad Motawe + - Mukulika Pahari - Márton Gunyhó + - Namami Shanker + - Nihaal Sangha + - Niyas Sait - Omid Rajaei + - Oscar Gustafsson + - Ovee Jawdekar + - P. L. Lim + - Pamphile Roy + - Pantelis Antonoudiou + - Pearu Peterson - Peter Andreas Entschev - Peter Hawkins - Pierre de Buyl - Pieter Eendebak + - Pradipta Ghosh + - Rafael Cardoso Fernandes Sousa + - Raghuveer Devulapalli - Ralf Gommers - Raphael Kruse - Raúl Montón Pinillos - Robert Kern - Rohit Goswami - Ross Barnowski - Ruben Garcia + - Sadie Louise Bartholomew + - Saswat Das + - Sayed Adel - Sebastian Berg - Serge Guelton - Simon Surland Andersen + - Siyabend Ürün + - Somasree Majumder + - Soumya + - Stefan van der Walt - Stefano Miccoli + - Stephan Hoyer - Stephen Worsley + - Tania Allard - Thomas Duvernay + - Thomas Green + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Ting Sun + - Tirth Patel - Toshiki Kataoka - Tyler Reddy - Warren Weckesser - Yang Hau - Yoon, Jee Seok + Cheers, Charles Harris

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numpy-stl
by Frank Underdown June 10, 2022

June 10, 2022

Greetings: I am reaching out to see if anyone is familiar with using numpy-stl. I have some FDTD code, and I want to find a practical way to input a device, drawn in cad, into my fdtd engine. Thank you in advance for help. Cheers! Frank Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows

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Njumpy-stl
by frank.underdown＠quaise.energy June 9, 2022

June 9, 2022

Hello, I recently discovered numpy-stl. I am looking for documentations and tutorials to learn how to use this library. Also, if you know of any books where this library is discussed, I would appreciate you making a recommendation. Thank you in advance for your help. Cheers! Frank

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Importing Numpy when using libpython
by jensaxel＠soegaard.net June 9, 2022

June 9, 2022

Hi All, Hope this is the right forum. I am working on using Numpy from the programming language Racket. My plan of attack is to use Python via `libpython`. That is, from Racket I use `ffilib` to load `libpython` and from there I use the C API to control Python. Here is what works at the moment: 1. From Racket I can load `libpython` via `ffilib`. 2. It is possible to initialize a Python process and run Python programs in it. 3. It is possible to import modules written in Python. What doesn't work is importing `numpy`. The error I get when I run `import numpy` is: ImportError: dlopen(/usr/local/lib/python3.10/site-packages/numpy/core/_multiarray_umath.cpython-310-darwin.so, 0x0002): symbol not found in flat namespace '_PyBaseObject_Type' Right now, I am trying to figure out where things went wrong. First I checked that Numpy works in the terminal (it did): soegaard@mbp2 ~ % /usr/local/Cellar/python(a)3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/bin/python3.10 Python 3.10.4 (main, Apr 26 2022, 19:42:59) [Clang 13.1.6 (clang-1316.0.21.2)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy >>> numpy.array([[1, 2, 3], [4, 5, 6]], numpy.int32) array([[1, 2, 3], [4, 5, 6]], dtype=int32) I notice that Python is built with `clang`. Is this okay? I ask, partly because there is a cryptic note on GCC in the Numpy installation guide and partly because I have seen posts where static link problems where due to mixing gcc and clang binaries. I installed `python3.10` on my Intel mac using HomeBrew. Then I installed `numpy` using `pip`. Since XCode terminal commands are installed the system `gcc` actually invokes `clang`. In order to check that the paths are as expected, I made the embedded Python instance print out the paths before issuing the command `import numpy`: (ProgramName /usr/local/Cellar/python(a)3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/bin/python3.10) (Prefix /usr/local/Cellar/python(a)3.10/3.10.4/Frameworks/Python.framework/Versions/3.10) (ExecPrefix /usr/local/Cellar/python(a)3.10/3.10.4/Frameworks/Python.framework/Versions/3.10) (ProgramFullPath /usr/local/Cellar/python(a)3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/bin/python3.10) (Path /usr/local/Cellar/python@3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/lib/python310.zip:/usr/local/Cellar/python@3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/lib/python3.10:/usr/local/Cellar/python@3.10/3.10.4/Frameworks/Python.framework/Versions/3.10/lib/python3.10/lib-dynload) (PythonHome #f) The full error message was: Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/numpy/core/__init__.py", line 23, in <module> from . import multiarray File "/usr/local/lib/python3.10/site-packages/numpy/core/multiarray.py", line 10, in <module> from . import overrides File "/usr/local/lib/python3.10/site-packages/numpy/core/overrides.py", line 6, in <module> from numpy.core._multiarray_umath import ( ImportError: dlopen(/usr/local/lib/python3.10/site-packages/numpy/core/_multiarray_umath.cpython-310-darwin.so, 0x0002): symbol not found in flat namespace '_PyBaseObject_Type' Any help is appreciated. -- Jens Axel Søgaard

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NumPy-Numba interaction
by Ralf Gommers June 8, 2022

June 8, 2022

Hi all, In a few places I noticed recently that it has become harder for Numba to keep up with NumPy releases. Numba is very popular, so that's not an ideal situation for end users or for packages which have both NumPy and Numba as dependencies. If we can do things to make the life of the Numba team easier, we should consider doing them I'd think. As far as I can remember, we have never had a broad discussion on this. Numba devs have posted issues about individual APIs or behavior, but they're not labelled or prioritized in any way. Rather they just go in our 1000+ open issues backlog. And then other libraries/projects get to rediscover the same issues sometimes - because if it's an issue for Numba, it's likely to be an issue elsewhere too. Here's an example: https://github.com/numpy/numpy/issues/13718. Independently discovered by PyTorch, Warren's ufunclab, and in the Data APIs discussions at least (I suspect JAX/CuPy too, but they didn't link to that issue). Stuart Archibald wrote up a very nice overview of the main pain points Numba devs are seeing at https://github.com/numba/numba/issues/8008. It would be great if others could read those, and discuss the content in that issue in case things are unclear. And then we could circle back and perhaps add a dedicated topic like "Numba compatibility" on our roadmap under https://numpy.org/neps/roadmap.html#interoperability. Not everything will be fixable, but some things should be. And if we'd have to make a few minor technically backwards-incompatible changes (e.g. returning tuple-of-arrays rather than list-of-arrays from some functions), that'd be worth considering. Cheers, Ralf

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next NumPy Newcomers Hour
by Inessa Pawson June 5, 2022

June 5, 2022

The next NumPy Newcomers Hour will be held this Thursday, June 2nd at 4 pm UTC. Sebastian Berg, one of the NumPy core developers, will talk about deprecations in NumPy. Join us via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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Exponential function, sine function and cos function
by Brinley Patterson June 4, 2022

June 4, 2022

Hi, By using the exponential equation: exp(x) = (sum{k=0}{n} 1/ k! ) ^ x the speed and accuracy of calculating exponent greatly increases. Plus it makes it easier to use with imaginary numbers. I have the python function code if you are interested to learn more about this. This equation can then be used to calculate sine and cos more efficiently using the exponent form of them both. Kind regards, Brinley MSc Machine Learning BSc Mathematical Physics

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API Addition: Polynomial classes now have a "symbol" denoting the variable
by Sebastian Berg June 1, 2022

June 1, 2022

Hi all, just another small API announcement, that I merged: https://github.com/numpy/numpy/pull/16154 which adds `symbol="x"` to the polynomial classes. Ross' more detailed explanation is copied below. Cheers, Sebastian New attribute ``symbol`` added to polynomial classes ---------------------------------------------------- The polynomial classes in the ``numpy.polynomial`` package have a new ``symbol`` attribute which is used to represent the indeterminate of the polynomial. This can be used to change the value of the variable when printing:: >>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y") >>> print(P_y) 1.0 + 0.0·y¹ - 1.0·y² Note that the polynomial classes only support 1D polynomials, so operations that involve polynomials with different symbols are disallowed when the result would be multivariate:: >>> P = np.polynomial.Polynomial([1, -1]) # default symbol is "x" >>> P_z = np.polynomial.Polynomial([1, 1], symbol="z") >>> P * P_z Traceback (most recent call last) ... ValueError: Polynomial symbols differ The symbol can be any valid Python identifier. The default is ``symbol=x``, consistent with existing behavior.

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Small API addition: unique now has `equal_nan=False` keyword argument
by Sebastian Berg June 1, 2022

June 1, 2022

Hi all, this has been discussed before, so mainly a brief announcement that we merged a PR to add the `equal_nan` kwarg to `np.unique`. If set to False, multiple `NaN`s will be reported multiple times (which was the behavior prior to NumPy 1.21). The keyword argument name was chosen to match that of `np.array_equal` and the testing functions that. Since the actual release is a bit off and this has no backwards compatibility concerns, there is a chance that this may be backported into NumPy 1.23 (This is at Chucks discretion as the release manager). Cheers, Sebastian

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overhead of numpy scalars
by Pieter Eendebak June 1, 2022

June 1, 2022

Hi everyone, Operations such as np.sqrt or np.cos on numpy scalars are much slower than operations on an array of size 1 (benchmark below). The reason seems to be that a numpy scalar (e.g. np.float64(1.1)) is converted internally to an array, an array is created for the result of the operation and the result is converted back to a scalar (so three object constructions in total). Operating directly on an array creates an array for the result, which is returned (so one object construction in total). Has anyone looked into improving this performance before or has ideas how to tackle this? With kind regards, Pieter Eendebak Example benchmark: import numpy as np from numpy import sqrt import time v=np.array([1.1]) t0=time.perf_counter() x=np.float64(v) for kk in range(1_000_000): w1=sqrt(x) dt1=time.perf_counter()-t0 print(dt1) t0=time.perf_counter() x=v for kk in range(1_000_000): w2=sqrt(x) dt2=time.perf_counter()-t0 print(dt2) print(f'factor {dt1/dt2:.2f} faster') assert(float(w1)==float(w2)) Results in 1.0878291579974757 0.5115369699997245 factor 2.13 faster

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