Mailman 3 December 2020 - NumPy-Discussion

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

18 Feb '23

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

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Documentation Team meeting - Monday June 8th
by Melissa Mendonça 04 Dec '22

04 Dec '22

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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[Feature Request] Add alias of np.concatenate as np.concat
by Iordanis Fostiropoulos 10 May '22

10 May '22

In regard to Feature Request: https://github.com/numpy/numpy/issues/16469 It was suggested to sent to the mailing list. I think I can make a strong point as to why the support for this naming convention would make sense. Such as it would follow other frameworks that often work alongside numpy such as tensorflow. For backward compatibility, it can simply be an alias to np.concatenate I often convert portions of code from tf to np, it is as simple as changing the base module from tf to np. e.g. np.expand_dims -> tf.expand_dims. This is done either in debugging (e.g. converting tf to np without eager execution to debug portion of the code), or during prototyping, e.g. develop in numpy and convert in tf. I find myself more than at one occasion to getting syntax errors because of this particular function np.concatenate. It is unnecessarily long. I imagine there are more people that also run into the same problems. Pandas uses concat (torch on the other extreme uses simply cat, which I don't think is as descriptive).

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

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Addition of new distributions: Polya-gamma
by Zolisa Bleki 27 Jan '21

27 Jan '21

Hi All, I would like to know if Numpy accepts addition of new distributions since the implementation of the Generator interface. If so, what is the criteria for a particular distribution to be accepted? The reason why i'm asking is because I would like to propose adding the Polya-gamma distribution to numpy, for the following reasons: 1) Polya-gamma random variables are commonly used as auxiliary variables during data augmentation in Bayesian sampling algorithms, which have wide-spread usage in Statistics and recently, Machine learning. 2) Since this distribution is mostly useful for random sampling, it since appropriate to have it in numpy and not projects like scipy [1]. 3) The only python/C++ implementation of the sampler available is licensed under GPLv3 which I believe limits copying into packages that choose to use a different license [2]. 4) Numpy's random API makes adding the distribution painless. I have done preliminary work on this by implementing the distribution sampler as decribed in [3]; see: https://github.com/numpy/numpy/compare/master...zoj613:polyagamma . There is a more efficient sampling algorithm described in a later paper [4], but I chose not to start with that one unless I know it is worth investing time in. I would appreciate your thoughts on this proposal. Regards, Zolisa Refs: [1] https://github.com/scipy/scipy/issues/11009 [2] https://github.com/slinderman/pypolyagamma [3] https://arxiv.org/pdf/1205.0310v1.pdf [4] https://arxiv.org/pdf/1405.0506.pdf Disclaimer - University of Cape Town This email is subject to UCT policies and email disclaimer published on our website at http://www.uct.ac.za/main/email-disclaimer or obtainable from +27 21 650 9111. If this email is not related to the business of UCT, it is sent by the sender in an individual capacity. Please report security incidents or abuse via https://csirt.uct.ac.za/page/report-an-incident.php.

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updated backwards compatibility and deprecation policy NEP
by Ralf Gommers 26 Jan '21

26 Jan '21

Hi all, Here is a long overdue update of the draft NEP about backwards compatibility and deprecation policy: https://github.com/numpy/numpy/pull/18097 - This is NEP 23: https://numpy.org/neps/nep-0023-backwards-compatibility.html - Link to the previous mailing list discussion: https://mail.python.org/pipermail/numpy-discussion/2018-July/078432.html It would be nice to get this NEP to Accepted status. Main changes are: - Removed all examples that people objected to - Removed all content regarding versioning - Restructured sections, and added "Strategies related to deprecations" (using suggestions by @njsmith and @shoyer). - Added concrete examples of deprecations, and a more thorough description of how to go about adding warnings incl. Sphinx directives, using `stacklevel`, etc. As always, feedback here or on the PR is very welcome! Cheers, Ralf Abstract -------- In this NEP we describe NumPy's approach to backwards compatibility, its deprecation and removal policy, and the trade-offs and decision processes for individual cases where breaking backwards compatibility is considered. Motivation and Scope -------------------- NumPy has a very large user base. Those users rely on NumPy being stable and the code they write that uses NumPy functionality to keep working. NumPy is also actively maintained and improved -- and sometimes improvements require, or are made much easier by, breaking backwards compatibility. Finally, there are trade-offs in stability for existing users vs. avoiding errors or having a better user experience for new users. These competing needs often give rise to long debates and to delays in accepting or rejecting contributions. This NEP tries to address that by providing a policy as well as examples and rationales for when it is or isn't a good idea to break backwards compatibility. In scope for this NEP are: - Principles of NumPy's approach to backwards compatibility. - How to deprecate functionality, and when to remove already deprecated functionality. - Decision making process for deprecations and removals. Out of scope are: - Making concrete decisions about deprecations of particular functionality. - NumPy's versioning scheme. General principles ------------------ When considering proposed changes that are backwards incompatible, the main principles the NumPy developers use when making a decision are: 1. Changes need to benefit users more than they harm them. 2. NumPy is widely used so breaking changes should by default be assumed to be fairly harmful. 3. Decisions should be based on data and actual effects on users and downstream packages rather than, e.g., appealing to the docs or for stylistic reasons. 4. Silently getting a wrong answer is much worse than getting a loud error. When assessing the costs of proposed changes, keep in mind that most users do not read the mailing list, do not look at deprecation warnings, and sometimes wait more than one or two years before upgrading from their old version. And that NumPy has millions of users, so "no one will do or use this" is very likely incorrect. Benefits include improved functionality, usability and performance, as well as lower maintenance cost and improved future extensibility. Fixes for clear bugs are exempt from this backwards compatibility policy. However in case of serious impact on users (e.g. a downstream library doesn't build anymore or would start giving incorrect results), even bug fixes may have to be delayed for one or more releases. Strategies related to deprecations ---------------------------------- Getting hard data on the impact of a deprecation of often difficult. Strategies that can be used to assess such impact include: - Use a code search engine ([1]_) or static ([2]_) or dynamic ([3]_) code analysis tools to determine where and how the functionality is used. - Testing prominent downstream libraries against a development build of NumPy containing the proposed change to get real-world data on its impact. - Making a change in master and reverting it, if needed, before a release. We do encourage other packages to test against NumPy's master branch, so this often turns up issues quickly. If the impact is unclear or significant, it is often good to consider alternatives to deprecations. For example discouraging use in documentation only, or moving the documentation for the functionality to a less prominent place or even removing it completely. Commenting on open issues related to it that they are low-prio or labeling them as "wontfix" will also be a signal to users, and reduce the maintenance effort needing to be spent. Implementing deprecations and removals -------------------------------------- Deprecation warnings are necessary in all cases where functionality will eventually be removed. If there is no intent to remove functionality, then it should not be deprecated either. A "please don't use this" in the documentation or other type of warning should be used instead. Deprecations: - shall include the version number of the release in which the functionality was deprecated. - shall include information on alternatives to the deprecated functionality, or a reason for the deprecation if no clear alternative is available. - shall use ``VisibleDeprecationWarning`` rather than ``DeprecationWarning`` for cases of relevance to end users. For cases only relevant to downstream libraries, a regular ``DeprecationWarning`` is fine. *Rationale: regular deprecation warnings are invisible by default; library authors should be aware how deprecations work and test for them, but we can't expect this from all users.* - shall be listed in the release notes of the release where the deprecation is first present. - shall set a ``stacklevel``, so the warning appears to come from the correct place. - shall be mentioned in the documentation for the functionality. A ``.. deprecated::`` directive can be used for this. Examples of good deprecation warnings: .. code-block:: python warnings.warn('np.asscalar(a) is deprecated since NumPy 1.16.0, use ' 'a.item() instead', DeprecationWarning, stacklevel=3) warnings.warn("Importing from numpy.testing.utils is deprecated " "since 1.15.0, import from numpy.testing instead.", DeprecationWarning, stacklevel=2) # A change in NumPy 1.14.0 for Python 3 loadtxt/genfromtext, slightly # tweaked in this NEP (original didn't have version number). warnings.warn( "Reading unicode strings without specifying the encoding " "argument is deprecated since NumPy 1.14.0. Set the encoding, " "use None for the system default.", np.VisibleDeprecationWarning, stacklevel=2) Removal of deprecated functionality: - shall be done after at least 2 releases (assuming the current 6-monthly release cycle; if that changes, there shall be at least 1 year between deprecation and removal). - shall be listed in the release notes of the release where the removal happened. - can be done in any minor (but not bugfix) release. For backwards incompatible changes that aren't "deprecate and remove" but for which code will start behaving differently, a ``FutureWarning`` should be used. Release notes, mentioning version number and using ``stacklevel`` should be done in the same way as for deprecation warnings. A ``.. versionchanged::`` directive can be used in the documentation to indicate when the behavior changed: .. code-block:: python def argsort(self, axis=np._NoValue, ...): """ Parameters ---------- axis : int, optional Axis along which to sort. If None, the default, the flattened array is used. .. versionchanged:: 1.13.0 Previously, the default was documented to be -1, but that was in error. At some future date, the default will change to -1, as originally intended. Until then, the axis should be given explicitly when ``arr.ndim > 1``, to avoid a FutureWarning. """ ... warnings.warn( "In the future the default for argsort will be axis=-1, not the " "current None, to match its documentation and np.argsort. " "Explicitly pass -1 or None to silence this warning.", MaskedArrayFutureWarning, stacklevel=3) Decision making ~~~~~~~~~~~~~~~ In concrete cases where this policy needs to be applied, decisions are made according to the `NumPy governance model <https://docs.scipy.org/doc/numpy/dev/governance/index.html>`_. All deprecations must be proposed on the mailing list, in order to give everyone with an interest in NumPy development to be able to comment. Removal of deprecated functionality does not need discussion on the mailing list. Functionality with more strict deprecation policies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - ``numpy.random`` has its own backwards compatibility policy, see `NEP 19 <http://www.numpy.org/neps/nep-0019-rng-policy.html>`_. - The file format for ``.npy`` and ``.npz`` files must not be changed in a backwards incompatible way. Example cases ------------- We now discuss a few concrete examples from NumPy's history to illustrate typical issues and trade-offs. **Changing the behavior of a function** ``np.histogram`` is probably the most infamous example. First, a new keyword ``new=False`` was introduced, this was then switched over to None one release later, and finally it was removed again. Also, it has a ``normed`` keyword that had behavior that could be considered either suboptimal or broken (depending on ones opinion on the statistics). A new keyword ``density`` was introduced to replace it; ``normed`` started giving ``DeprecationWarning`` only in v.1.15.0. Evolution of ``histogram``:: def histogram(a, bins=10, range=None, normed=False): # v1.0.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=False): #v1.1.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): #v1.2.0 def histogram(a, bins=10, range=None, normed=False, weights=None): #v1.5.0 def histogram(a, bins=10, range=None, normed=False, weights=None, density=None): #v1.6.0 def histogram(a, bins=10, range=None, normed=None, weights=None, density=None): #v1.15.0 # v1.15.0 was the first release where `normed` started emitting # DeprecationWarnings The ``new`` keyword was planned from the start to be temporary. Such a plan forces users to change their code more than once, which is almost never the right thing to do. Instead, a better approach here would have been to deprecate ``histogram`` and introduce a new function ``hist`` in its place. **Disallowing indexing with floats** Indexing an array with floats is asking for something ambiguous, and can be a sign of a bug in user code. After some discussion, it was deemed a good idea to deprecate indexing with floats. This was first tried for the v1.8.0 release, however in pre-release testing it became clear that this would break many libraries that depend on NumPy. Therefore it was reverted before release, to give those libraries time to fix their code first. It was finally introduced for v1.11.0 and turned into a hard error for v1.12.0. This change was disruptive, however it did catch real bugs in, e.g., SciPy and scikit-learn. Overall the change was worth the cost, and introducing it in master first to allow testing, then removing it again before a release, is a useful strategy. Similar deprecations that also look like good examples of cleanups/improvements: - removing deprecated boolean indexing (in 2016, see `gh-8312 < https://github.com/numpy/numpy/pull/8312>`__) - deprecating truth testing on empty arrays (in 2017, see `gh-9718 < https://github.com/numpy/numpy/pull/9718>`__) **Removing the financial functions** The financial functions (e.g. ``np.pmt``) had short non-descriptive names, were present in the main NumPy namespace, and didn't really fit well within NumPy's scope. They were added in 2008 after `a discussion < https://mail.python.org/pipermail/numpy-discussion/2008-April/032353.html>`_ on the mailing list where opinion was divided (but a majority in favor). The financial functions didn't cause a lot of overhead, however there were still multiple issues and PRs a year for them which cost maintainer time to deal with. And they cluttered up the ``numpy`` namespace. Discussion on removing them happened in 2013 (gh-2880, rejected) and then again in 2019 (:ref:`NEP32`, accepted without significant complaints). Given that they were clearly outside of NumPy's scope, moving them to a separate ``numpy-financial`` package and removing them from NumPy after a deprecation period made sense. Alternatives ------------ **Being more aggressive with deprecations.** The goal of being more aggressive is to allow NumPy to move forward faster. This would avoid others inventing their own solutions (often in multiple places), as well as be a benefit to users without a legacy code base. We reject this alternative because of the place NumPy has in the scientific Python ecosystem - being fairly conservative is required in order to not increase the extra maintenance for downstream libraries and end users to an unacceptable level. Discussion ---------- - `Mailing list discussion on the first version of this NEP in 2018 < https://mail.python.org/pipermail/numpy-discussion/2018-July/078432.html>`__ References and Footnotes ------------------------ - `Issue requesting semantic versioning < https://github.com/numpy/numpy/issues/10156>`__ .. [1] https://searchcode.com/ .. [2] https://github.com/Quansight-Labs/python-api-inspect .. [3] https://github.com/data-apis/python-record-api

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Comment published in Nature Astronomy about The ecological impact of computing with Python
by PIERRE AUGIER 26 Jan '21

26 Jan '21

Hi, I recently took a bit of time to study the comment "The ecological impact of high-performance computing in astrophysics" published in Nature Astronomy (Zwart, 2020, https://www.nature.com/articles/s41550-020-1208-y, https://arxiv.org/pdf/2009.11295.pdf), where it is stated that "Best however, for the environment is to abandon Python for a more environmentally friendly (compiled) programming language.". I wrote a simple Python-Numpy implementation of the problem used for this study (https://www.nbabel.org) and, accelerated by Transonic-Pythran, it's very efficient. Here are some numbers (elapsed times in s, smaller is better): | # particles | Py | C++ | Fortran | Julia | |-------------|-----|-----|---------|-------| | 1024 | 29 | 55 | 41 | 45 | | 2048 | 123 | 231 | 166 | 173 | The code and a modified figure are here: https://github.com/paugier/nbabel (There is no check on the results for https://www.nbabel.org, so one still has to be very careful.) I think that the Numpy community should spend a bit of energy to show what can be done with the existing tools to get very high performance (and low CO2 production) with Python. This work could be the basis of a serious reply to the comment by Zwart (2020). Unfortunately the Python solution in https://www.nbabel.org is very bad in terms of performance (and therefore CO2 production). It is also true for most of the Python solutions for the Computer Language Benchmarks Game in https://benchmarksgame-team.pages.debian.net/benchmarksgame/ (codes here https://salsa.debian.org/benchmarksgame-team/benchmarksgame#what-else). We could try to fix this so that people see that in many cases, it is not necessary to "abandon Python for a more environmentally friendly (compiled) programming language". One of the longest and hardest task would be to implement the different cases of the Computer Language Benchmarks Game in standard and modern Python-Numpy. Then, optimizing and accelerating such code should be doable and we should be able to get very good performance at least for some cases. Good news for this project, (i) the first point can be done by anyone with good knowledge in Python-Numpy (many potential workers), (ii) for some cases, there are already good Python implementations and (iii) the work can easily be parallelized. It is not a criticism, but the (beautiful and very nice) new Numpy website https://numpy.org/ is not very convincing in terms of performance. It's written "Performant The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code." It's true that the core of Numpy is well-optimized C code but to seriously compete with C++, Fortran or Julia in terms of numerical performance, one needs to use other tools to move the compiled-interpreted boundary outside the hot loops. So it could be reasonable to mention such tools (in particular Numba, Pythran, Cython and Transonic). Is there already something planned to answer to Zwart (2020)? Any opinions or suggestions on this potential project? Pierre PS: Of course, alternative Python interpreters (PyPy, GraalPython, Pyjion, Pyston, etc.) could also be used, especially if HPy (https://github.com/hpyproject/hpy) is successful (C core of Numpy written in HPy, Cython able to produce HPy code, etc.). However, I tend to be a bit skeptical in the ability of such technologies to reach very high performance for low-level Numpy code (performance that can be reached by replacing whole Python functions with optimized compiled code). Of course, I hope I'm wrong! IMHO, it does not remove the need for a successful HPy! -- Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels BP53, 38041 Grenoble Cedex, France tel:+33.4.56.52.86.16

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NumPy EOL Versions?
by Symphoni Bush - NOAA Affiliate 13 Jan '21

13 Jan '21

I am trying to find out if there are any end-of-life versions for NumPy, and if so, when do these versions typically become EOL/unsupported? Thanks.

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proposal for NumPy sponsorship guidelines (NEP 46)
by Ralf Gommers 03 Jan '21

03 Jan '21

Hi all, I just opened https://github.com/numpy/numpy/pull/18084, "NumPy Sponsorship Guidelines". Below are the most important parts for review (for Related Work, References, etc. see the PR). Please bring up broader points here, and small/textual feedback on the PR. Cheers, Ralf Abstract -------- This NEP provides guidelines on how the NumPy project will acknowledge financial and in-kind support. Motivation and Scope -------------------- In the past few years the NumPy project has gotten significant financial support, as well as dedicated work time for maintainers to work on NumPy. There is a need to acknowledge that support - funders and organizations expect or require it, it's helpful when looking for new funding, and it's the right thing to do. Furthermore, having a clear policy for how NumPy acknowledges support is helpful when searching for new support. This NEP is aimed at both the NumPy community - who can use it when looking for support and acknowledging existing support - and at past, current and prospective sponsors, who often want or need to know what they get in return for their support (other than a healthier NumPy). The scope of this proposal includes: - direct financial support, employers providing paid time for NumPy maintainers and regular contributors, and in-kind support such as free hardware resources or services. - where and how NumPy acknowledges support (e.g., logo placement on the website). - the amount and duration of support which leads to acknowledgement. - who in the NumPy project is responsible for sponsorship related topics, and how to contact them. How NumPy will acknowledge support ---------------------------------- There will be two different ways to acknowledge financial and in-kind support, one to recognize significant active support, and another one to recognize support received in the past and smaller amounts of support. Entities who fall under "significant active supporter" we'll call Sponsor. The minimum level of support given to NumPy to be considered a Sponsor are: - $30,000/yr for unrestricted financial contributions - $60,000/yr for financial contributions for a particular purpose - $100,000/yr for in-kind contributions The rationale for the above levels is that unrestricted financial contributions are typically the most valuable for the project, and the hardest to obtain. The opposite is true for in-kind contributions. The dollar value of the levels also reflect that NumPy's needs have grown to the point where we need at least a few paid developers in order to effectively support our user base and continue to move the project forward. Financial support at or above these levels is needed to be able to make a significant difference. Sponsors will get acknowledged through: - a small logo displayed on the front page of the NumPy website - prominent logo placement on https://numpy.org/about/ - logos displayed in talks about NumPy by maintainers - announcements of the sponsorship on the NumPy mailing list and the numpy-team Twitter account In addition to Sponsors, we already have the concept of Institutional Partner (defined in NumPy's `governance document <https://numpy.org/devdocs/dev/governance/index.html >`__), for entities who employ a NumPy maintainer and let them work on NumPy as part of their official duties. The governance document doesn't currently define a minimum amount of paid maintainer time needed to be considered for partnership. Therefore we propose that level here, roughly in line with the sponsorship levels: - 6 person-months/yr of paid work time for one or more NumPy maintainers or regular contributors Institutional Partners get the same benefits as Sponsors, in addition to what is specified in the NumPy governance document. Finally, a new page on the website (https://numpy.org/funding/, linked from the About page) will be added to acknowledge all current and previous sponsors, partners, and any other entities and individuals who provided $5,000 or more of financial or in-kind support. This page will include relevant details of support (dates, amounts, names and purpose); no logos will be used on this page. The rationale for the $5,000 minimum level is to keep the amount of work maintaining the page reasonable; the level is the equivalent of, e.g., one GSoC or a person-week's worth of engineering time in a Western country, which seems like a reasonable lower limit. Implementation -------------- The following content changes need to be made: - Add a section with small logos towards the bottom of the `numpy.org <https://numpy.org/>`__ website. - Create a full list of historical and current support and deploy it to https://numpy.org/funding. - Update the NumPy governance document for changes to Institutional Partner eligibility requirements and benefits. - Update https://numpy.org/about with details on how to get in touch with the NumPy project about sponsorship related matters (see next section). A NumPy Funding Team ~~~~~~~~~~~~~~~~~~~~ At the moment NumPy has only one official body, the Steering Council, and no good way to get in touch with either that body or any person or group responsible for funding and sponsorship related matters. The way this is typically done now is to somehow find the personal email of a maintainer, and email them in private. There is a need to organize this more transparently - a potential sponsor isn't likely to inquire through the mailing list, nor is it easy for a potential sponsor to know if they're reaching out to the right person in private. https://numpy.org/about/ already says that NumPy has a "funding and grants" team, however that is not the case. We propose to organize this team, name team members on it, and add the names of those team members plus a dedicated email address for the team to the About page. Status before this proposal --------------------------- Acknowledgement of support ~~~~~~~~~~~~~~~~~~~~~~~~~~ At the time of writing (Dec 2020), the logos of the four largest financial sponsors and two institutional partners are displayed on https://numpy.org/about/. The `Nature paper about NumPy < https://www.nature.com/articles/s41586-020-2649-2>`__ mentions some early funding. No comprehensive list of received funding and in-kind support is published anywhere. Decisions on which logos to list on the website have been made mostly by the website team. Decisions on which entities to recognize as Institutional Partner have been made by the NumPy Steering Council. NumPy governance, decision-making and financial oversight ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ *This section is meant as context for the reader, to help put the rest of this NEP in perspective, and perhaps answer questions the reader has when reading this as a potential sponsor.* NumPy has a formal governance structure defined in `this governance document < https://numpy.org/devdocs/dev/governance/index.html>`__). Decisions are made by consensus among all active participants in a discussion (typically on the mailing list), and if consensus cannot be reached then the Steering Council takes the decision (also by consensus). NumPy is a sponsored project of NumFOCUS, a US-based 501(c)3 nonprofit. NumFOCUS administers NumPy funds, and ensures they are spent in accordance with its mission and nonprofit status. In practice, NumPy has a NumFOCUS subcommittee (with its members named in the NumPy governance document) who can authorize financial transactions. Those transactions, for example paying a contractor for a particular activity or deliverable, are decided on by the NumPy Steering Council. Alternatives ------------ *Tiered sponsorship levels.* We considered using tiered sponsorship levels, and rejected this alternative because it would be more complex, and not necessarily communicate the right intent - the minimum levels are for us to determine how to acknowledge support that we receive, not a commercial value proposition. Entities typically will support NumPy because they rely on the project or want to help advance it, and not to get brand awareness through logo placement. *Listing all donations*. Note that in the past we have received many smaller donations, mostly from individuals through NumFOCUS. It would be great to list all of those contributions, but given the way we receive information on those donations right now, that would be quite labor-intensive. If we manage to move to a more suitable platform, such as `Open Collective < https://opencollective.com/>`__, in the future, we should reconsider listing all individual donations.

2 2

ANN: SciPy 1.6.0
by Tyler Reddy 31 Dec '20

31 Dec '20

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.6.0. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.6.0 One of a few ways to install this release with pip: pip install scipy==1.6.0 ===================== SciPy 1.6.0 Release Notes ===================== SciPy 1.6.0 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 1.6.x branch, and on adding new features on the master branch. This release requires Python 3.7+ and NumPy 1.16.5 or greater. For running on PyPy, PyPy3 6.0+ is required. Highlights of this release --------------------------------- - `scipy.ndimage` improvements: Fixes and ehancements to boundary extension modes for interpolation functions. Support for complex-valued inputs in many filtering and interpolation functions. New ``grid_mode`` option for `scipy.ndimage.zoom` to enable results consistent with scikit-image's ``rescale``. - `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the ``HiGHS`` library. - `scipy.stats` improvements including new distributions, a new test, and enhancements to existing distributions and tests New features ========== `scipy.special` improvements --------------------------------------- `scipy.special` now has improved support for 64-bit ``LAPACK`` backend `scipy.odr` improvements ---------------------------------- `scipy.odr` now has support for 64-bit integer ``BLAS`` `scipy.odr.ODR` has gained an optional ``overwrite`` argument so that existing files may be overwritten. `scipy.integrate` improvements ------------------------------------------ Some renames of functions with poor names were done, with the old names retained without being in the reference guide for backwards compatibility reasons: - ``integrate.simps`` was renamed to ``integrate.simpson`` - ``integrate.trapz`` was renamed to ``integrate.trapezoid`` - ``integrate.cumtrapz`` was renamed to ``integrate.cumulative_trapezoid`` `scipy.cluster` improvements --------------------------------------- `scipy.cluster.hierarchy.DisjointSet` has been added for incremental connectivity queries. `scipy.cluster.hierarchy.dendrogram` return value now also includes leaf color information in `leaves_color_list`. `scipy.interpolate` improvements -------------------------------------------- `scipy.interpolate.interp1d` has a new method ``nearest-up``, similar to the existing method ``nearest`` but rounds half-integers up instead of down. `scipy.io` improvements -------------------------------- Support has been added for reading arbitrary bit depth integer PCM WAV files from 1- to 32-bit, including the commonly-requested 24-bit depth. `scipy.linalg` improvements ------------------------------------- The new function `scipy.linalg.matmul_toeplitz` uses the FFT to compute the product of a Toeplitz matrix with another matrix. `scipy.linalg.sqrtm` and `scipy.linalg.logm` have performance improvements thanks to additional Cython code. Python ``LAPACK`` wrappers have been added for ``pptrf``, ``pptrs``, ``ppsv``, ``pptri``, and ``ppcon``. `scipy.linalg.norm` and the ``svd`` family of functions will now use 64-bit integer backends when available. `scipy.ndimage` improvements ------------------------------------------ `scipy.ndimage.convolve`, `scipy.ndimage.correlate` and their 1d counterparts now accept both complex-valued images and/or complex-valued filter kernels. All convolution-based filters also now accept complex-valued inputs (e.g. ``gaussian_filter``, ``uniform_filter``, etc.). Multiple fixes and enhancements to boundary handling were introduced to `scipy.ndimage` interpolation functions (i.e. ``affine_transform``, ``geometric_transform``, ``map_coordinates``, ``rotate``, ``shift``, ``zoom``). A new boundary mode, ``grid-wrap`` was added which wraps images periodically, using a period equal to the shape of the input image grid. This is in contrast to the existing ``wrap`` mode which uses a period that is one sample smaller than the original signal extent along each dimension. A long-standing bug in the ``reflect`` boundary condition has been fixed and the mode ``grid-mirror`` was introduced as a synonym for ``reflect``. A new boundary mode, ``grid-constant`` is now available. This is similar to the existing ndimage ``constant`` mode, but interpolation will still performed at coordinate values outside of the original image extent. This ``grid-constant`` mode is consistent with OpenCV's ``BORDER_CONSTANT`` mode and scikit-image's ``constant`` mode. Spline pre-filtering (used internally by ``ndimage`` interpolation functions when ``order >= 2``), now supports all boundary modes rather than always defaulting to mirror boundary conditions. The standalone functions ``spline_filter`` and ``spline_filter1d`` have analytical boundary conditions that match modes ``mirror``, ``grid-wrap`` and ``reflect``. `scipy.ndimage` interpolation functions now accept complex-valued inputs. In this case, the interpolation is applied independently to the real and imaginary components. The ``ndimage`` tutorials (https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been updated with new figures to better clarify the exact behavior of all of the interpolation boundary modes. `scipy.ndimage.zoom` now has a ``grid_mode`` option that changes the coordinate of the center of the first pixel along an axis from 0 to 0.5. This allows resizing in a manner that is consistent with the behavior of scikit-image's ``resize`` and ``rescale`` functions (and OpenCV's ``cv2.resize``). `scipy.optimize` improvements ----------------------------------------- `scipy.optimize.linprog` has fast, new methods for large, sparse problems from the ``HiGHS`` C++ library. ``method='highs-ds'`` uses a high performance dual revised simplex implementation (HSOL), ``method='highs-ipm'`` uses an interior-point method with crossover, and ``method='highs'`` chooses between the two automatically. These methods are typically much faster and often exceed the accuracy of other ``linprog`` methods, so we recommend explicitly specifying one of these three method values when using ``linprog``. `scipy.optimize.quadratic_assignment` has been added for approximate solution of the quadratic assignment problem. `scipy.optimize.linear_sum_assignment` now has a substantially reduced overhead for small cost matrix sizes `scipy.optimize.least_squares` has improved performance when the user provides the jacobian as a sparse jacobian already in ``csr_matrix`` format `scipy.optimize.linprog` now has an ``rr_method`` argument for specification of the method used for redundancy handling, and a new method for this purpose is available based on the interpolative decomposition approach. `scipy.signal` improvements -------------------------------------- `scipy.signal.gammatone` has been added to design FIR or IIR filters that model the human auditory system. `scipy.signal.iircomb` has been added to design IIR peaking/notching comb filters that can boost/attenuate a frequency from a signal. `scipy.signal.sosfilt` performance has been improved to avoid some previously- observed slowdowns `scipy.signal.windows.taylor` has been added--the Taylor window function is commonly used in radar digital signal processing `scipy.signal.gauss_spline` now supports ``list`` type input for consistency with other related SciPy functions `scipy.signal.correlation_lags` has been added to allow calculation of the lag/ displacement indices array for 1D cross-correlation. `scipy.sparse` improvements --------------------------------------- A solver for the minimum weight full matching problem for bipartite graphs, also known as the linear assignment problem, has been added in `scipy.sparse.csgraph.min_weight_full_bipartite_matching`. In particular, this provides functionality analogous to that of `scipy.optimize.linear_sum_assignment`, but with improved performance for sparse inputs, and the ability to handle inputs whose dense representations would not fit in memory. The time complexity of `scipy.sparse.block_diag` has been improved dramatically from quadratic to linear. `scipy.sparse.linalg` improvements ----------------------------------------------- The vendored version of ``SuperLU`` has been updated `scipy.fft` improvements -------------------------------- The vendored ``pocketfft`` library now supports compiling with ARM neon vector extensions and has improved thread pool behavior. `scipy.spatial` improvements --------------------------------------- The python implementation of ``KDTree`` has been dropped and ``KDTree`` is now implemented in terms of ``cKDTree``. You can now expect ``cKDTree``-like performance by default. This also means ``sys.setrecursionlimit`` no longer needs to be increased for querying large trees. ``transform.Rotation`` has been updated with support for Modified Rodrigues Parameters alongside the existing rotation representations (PR gh-12667). `scipy.spatial.transform.Rotation` has been partially cythonized, with some performance improvements observed `scipy.spatial.distance.cdist` has improved performance with the ``minkowski`` metric, especially for p-norm values of 1 or 2. `scipy.stats` improvements ------------------------------------ New distributions have been added to `scipy.stats`: - The asymmetric Laplace continuous distribution has been added as `scipy.stats.laplace_asymmetric`. - The negative hypergeometric distribution has been added as `scipy.stats.nhypergeom`. - The multivariate t distribution has been added as `scipy.stats.multivariate_t`. - The multivariate hypergeometric distribution has been added as `scipy.stats.multivariate_hypergeom`. The ``fit`` method has been overridden for several distributions (``laplace``, ``pareto``, ``rayleigh``, ``invgauss``, ``logistic``, ``gumbel_l``, ``gumbel_r``); they now use analytical, distribution-specific maximum likelihood estimation results for greater speed and accuracy than the generic (numerical optimization) implementation. The one-sample Cramér-von Mises test has been added as `scipy.stats.cramervonmises`. An option to compute one-sided p-values was added to `scipy.stats.ttest_1samp`, `scipy.stats.ttest_ind_from_stats`, `scipy.stats.ttest_ind` and `scipy.stats.ttest_rel`. The function `scipy.stats.kendalltau` now has an option to compute Kendall's tau-c (also known as Stuart's tau-c), and support has been added for exact p-value calculations for sample sizes ``> 171``. `stats.trapz` was renamed to `stats.trapezoid`, with the former name retained as an alias for backwards compatibility reasons. The function `scipy.stats.linregress` now includes the standard error of the intercept in its return value. The ``_logpdf``, ``_sf``, and ``_isf`` methods have been added to `scipy.stats.nakagami`; ``_sf`` and ``_isf`` methods also added to `scipy.stats.gumbel_r` The ``sf`` method has been added to `scipy.stats.levy` and `scipy.stats.levy_l` for improved precision. `scipy.stats.binned_statistic_dd` performance improvements for the following computed statistics: ``max``, ``min``, ``median``, and ``std``. We gratefully acknowledge the Chan-Zuckerberg Initiative Essential Open Source Software for Science program for supporting many of these improvements to `scipy.stats`. Deprecated features ================ `scipy.spatial` changes ------------------------------- Calling ``KDTree.query`` with ``k=None`` to find all neighbours is deprecated. Use ``KDTree.query_ball_point`` instead. ``distance.wminkowski`` was deprecated; use ``distance.minkowski`` and supply weights with the ``w`` keyword instead. Backwards incompatible changes ========================== `scipy` changes --------------------- Using `scipy.fft` as a function aliasing ``numpy.fft.fft`` was removed after being deprecated in SciPy ``1.4.0``. As a result, the `scipy.fft` submodule must be explicitly imported now, in line with other SciPy subpackages. `scipy.signal` changes ------------------------------- The output of ``decimate``, ``lfilter_zi``, ``lfiltic``, ``sos2tf``, and ``sosfilt_zi`` have been changed to match ``numpy.result_type`` of their inputs. The window function ``slepian`` was removed. It had been deprecated since SciPy ``1.1``. `scipy.spatial` changes ------------------------------- ``cKDTree.query`` now returns 64-bit rather than 32-bit integers on Windows, making behaviour consistent between platforms (PR gh-12673). `scipy.stats` changes ----------------------------- The ``frechet_l`` and ``frechet_r`` distributions were removed. They were deprecated since SciPy ``1.0``. Other changes ============= ``setup_requires`` was removed from ``setup.py``. This means that users invoking ``python setup.py install`` without having numpy already installed will now get an error, rather than having numpy installed for them via ``easy_install``. This install method was always fragile and problematic, users are encouraged to use ``pip`` when installing from source. - Fixed a bug in `scipy.optimize.dual_annealing` ``accept_reject`` calculation that caused uphill jumps to be accepted less frequently. - The time required for (un)pickling of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete`, and `scipy.stats.rv_frozen` has been significantly reduced (gh12550). Inheriting subclasses should note that ``__setstate__`` no longer calls ``__init__`` upon unpickling. Authors ======= * @endolith * @vkk800 * aditya + * George Bateman + * Christoph Baumgarten * Peter Bell * Tobias Biester + * Keaton J. Burns + * Evgeni Burovski * Rüdiger Busche + * Matthias Bussonnier * Dominic C + * Corallus Caninus + * CJ Carey * Thomas A Caswell * chapochn + * Lucía Cheung * Zach Colbert + * Coloquinte + * Yannick Copin + * Devin Crowley + * Terry Davis + * Michaël Defferrard + * devonwp + * Didier + * divenex + * Thomas Duvernay + * Eoghan O'Connell + * Gökçen Eraslan * Kristian Eschenburg + * Ralf Gommers * Thomas Grainger + * GreatV + * Gregory Gundersen + * h-vetinari + * Matt Haberland * Mark Harfouche + * He He + * Alex Henrie * Chun-Ming Huang + * Martin James McHugh III + * Alex Izvorski + * Joey + * ST John + * Jonas Jonker + * Julius Bier Kirkegaard * Marcin Konowalczyk + * Konrad0 * Sam Van Kooten + * Sergey Koposov + * Peter Mahler Larsen * Eric Larson * Antony Lee * Gregory R. Lee * Loïc Estève * Jean-Luc Margot + * MarkusKoebis + * Nikolay Mayorov * G. D. McBain * Andrew McCluskey + * Nicholas McKibben * Sturla Molden * Denali Molitor + * Eric Moore * Shashaank N + * Prashanth Nadukandi + * nbelakovski + * Andrew Nelson * Nick + * Nikola Forró + * odidev * ofirr + * Sambit Panda * Dima Pasechnik * Tirth Patel + * Matti Picus * Paweł Redzyński + * Vladimir Philipenko + * Philipp Thölke + * Ilhan Polat * Eugene Prilepin + * Vladyslav Rachek * Ram Rachum + * Tyler Reddy * Martin Reinecke + * Simon Segerblom Rex + * Lucas Roberts * Benjamin Rowell + * Eli Rykoff + * Atsushi Sakai * Moritz Schulte + * Daniel B. Smith * Steve Smith + * Jan Soedingrekso + * Victor Stinner + * Jose Storopoli + * Diana Sukhoverkhova + * Søren Fuglede Jørgensen * taoky + * Mike Taves + * Ian Thomas + * Will Tirone + * Frank Torres + * Seth Troisi * Ronald van Elburg + * Hugo van Kemenade * Paul van Mulbregt * Saul Ivan Rivas Vega + * Pauli Virtanen * Jan Vleeshouwers * Samuel Wallan * Warren Weckesser * Ben West + * Eric Wieser * WillTirone + * Levi John Wolf + * Zhiqing Xiao * Rory Yorke + * Yun Wang (Maigo) + * Egor Zemlyanoy + * ZhihuiChen0903 + * Jacob Zhong + A total of 122 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 1.6.0 ------------------------------- * `#1323 <https://github.com/scipy/scipy/issues/1323>`__: ndimage.shift destroys data from edges (Trac #796) * `#1892 <https://github.com/scipy/scipy/issues/1892>`__: using rptfile= with an existing file causes a Fortran runtime... * `#1903 <https://github.com/scipy/scipy/issues/1903>`__: ndimage.rotate misses some values (Trac #1378) * `#1930 <https://github.com/scipy/scipy/issues/1930>`__: scipy.io.wavfile should be able to read 24 bit signed wave (Trac... * `#3158 <https://github.com/scipy/scipy/issues/3158>`__: Odd casting behaviour of signal.filtfilt * `#3203 <https://github.com/scipy/scipy/issues/3203>`__: interpolation.zoom incorrect output for certain cases * `#3645 <https://github.com/scipy/scipy/issues/3645>`__: BUG: stats: mstats.pearsonr calculation is wrong if the masks... * `#3665 <https://github.com/scipy/scipy/issues/3665>`__: Return Bunch objects from stats functions * `#4922 <https://github.com/scipy/scipy/issues/4922>`__: unexpected zero output values from zoom * `#5202 <https://github.com/scipy/scipy/issues/5202>`__: BUG: stats: Spurious warnings from the pdf method of several... * `#5223 <https://github.com/scipy/scipy/issues/5223>`__: Zoom does not return the same values when resizing a sub-array... * `#5396 <https://github.com/scipy/scipy/issues/5396>`__: scipy.spatial.distance.pdist documention bug * `#5489 <https://github.com/scipy/scipy/issues/5489>`__: ValueError: failed to create intent(cache|hide)|optional array--... * `#6096 <https://github.com/scipy/scipy/issues/6096>`__: loadmat drops dtype of empty arrays when squeeze_me=True * `#6713 <https://github.com/scipy/scipy/issues/6713>`__: sicpy.ndimage.zoom returns artefacts and boundaries in some cases * `#7125 <https://github.com/scipy/scipy/issues/7125>`__: Impossible to know number of dimensions in c function used by... * `#7324 <https://github.com/scipy/scipy/issues/7324>`__: scipy.ndimage.zoom bad interpolation when downsampling (zoom... * `#8131 <https://github.com/scipy/scipy/issues/8131>`__: BUG: geometric_transform wrap mode possible bug * `#8163 <https://github.com/scipy/scipy/issues/8163>`__: LSMR fails on some random values when providing an x0 * `#8210 <https://github.com/scipy/scipy/issues/8210>`__: Why should I choose order > 1 for scipy.ndimage.zoom? * `#8465 <https://github.com/scipy/scipy/issues/8465>`__: Unexpected behavior with reflect mode of ndimage.rotate * `#8776 <https://github.com/scipy/scipy/issues/8776>`__: cdist behavior with Minkowsky and np.inf * `#9168 <https://github.com/scipy/scipy/issues/9168>`__: documentation of pearson3 in scipy.stats unclear * `#9223 <https://github.com/scipy/scipy/issues/9223>`__: Faster implementation of scipy.sparse.block_diag * `#9476 <https://github.com/scipy/scipy/issues/9476>`__: Invalid index in signal.medfilt2d's QUICK_SELECT * `#9857 <https://github.com/scipy/scipy/issues/9857>`__: scipy.odr.Output.sd_beta is not standard error * `#9865 <https://github.com/scipy/scipy/issues/9865>`__: Strange behavior of \`ndimage.shift\` and \`ndimage.affine_transform\` * `#10042 <https://github.com/scipy/scipy/issues/10042>`__: Consider support for multivariate student-t distribution? * `#10134 <https://github.com/scipy/scipy/issues/10134>`__: gausshyper distribution accepts invalid parameters * `#10179 <https://github.com/scipy/scipy/issues/10179>`__: str+bytes concatenation error in test_lapack.py * `#10216 <https://github.com/scipy/scipy/issues/10216>`__: cKDTree.query_ball_point speed regression * `#10463 <https://github.com/scipy/scipy/issues/10463>`__: ENH: vectorize scipy.fft for more CPU architectures * `#10593 <https://github.com/scipy/scipy/issues/10593>`__: Rename \`sum\` ndimage function * `#10595 <https://github.com/scipy/scipy/issues/10595>`__: scipy.stats.ttest_1samp should support alternative hypothesis * `#10610 <https://github.com/scipy/scipy/issues/10610>`__: ndimage.interpolation.spline_filter1d default value of mode * `#10620 <https://github.com/scipy/scipy/issues/10620>`__: ndimage.interpolation.zoom() option to work like skimage.transform.resize() * `#10711 <https://github.com/scipy/scipy/issues/10711>`__: Array Shapes Not Aligned Bug in scipy.optimize._lsq.lsq_linear.py * `#10782 <https://github.com/scipy/scipy/issues/10782>`__: BUG: optimize: methods unknown to \`scipy.optimize.show_options\` * `#10892 <https://github.com/scipy/scipy/issues/10892>`__: Possible typo in an equation of optimize/dual_annealing * `#11020 <https://github.com/scipy/scipy/issues/11020>`__: signal.fftconvolve return a tuple including lag information * `#11093 <https://github.com/scipy/scipy/issues/11093>`__: scipy.interpolate.interp1d can not handle datetime64 * `#11170 <https://github.com/scipy/scipy/issues/11170>`__: Use manylinux2014 to get aarch64/ppc64le support * `#11186 <https://github.com/scipy/scipy/issues/11186>`__: BUG: stats: pearson3 CDF and SF functions incorrect when skew... * `#11366 <https://github.com/scipy/scipy/issues/11366>`__: DeprecationWarning due to invalid escape sequences * `#11403 <https://github.com/scipy/scipy/issues/11403>`__: Optimize raises "ValueError: \`x0\` violates bound constraints"... * `#11558 <https://github.com/scipy/scipy/issues/11558>`__: ENH: IIR comb filter * `#11559 <https://github.com/scipy/scipy/issues/11559>`__: BUG: iirdesign doesn't fail for frequencies above Nyquist * `#11567 <https://github.com/scipy/scipy/issues/11567>`__: scipy.signal.iirdesign doesn't check consistency of wp and ws... * `#11654 <https://github.com/scipy/scipy/issues/11654>`__: ENH: Add Negative Hypergeometric Distribution * `#11720 <https://github.com/scipy/scipy/issues/11720>`__: BUG: stats: wrong shape from median_absolute_deviation for arrays... * `#11746 <https://github.com/scipy/scipy/issues/11746>`__: BUG: stats: pearson3 returns size 1 arrays where other distributions... * `#11756 <https://github.com/scipy/scipy/issues/11756>`__: Improve and fix \*Spline docstrings and code * `#11758 <https://github.com/scipy/scipy/issues/11758>`__: BUG: of scipy.interpolate.CubicSpline when \`bc_type' is set... * `#11925 <https://github.com/scipy/scipy/issues/11925>`__: MAINT: remove character encoding check in CI? * `#11963 <https://github.com/scipy/scipy/issues/11963>`__: Test failures - TestLinprogIPSparseCholmod * `#12102 <https://github.com/scipy/scipy/issues/12102>`__: incorrect first moment of non central t-distribution * `#12113 <https://github.com/scipy/scipy/issues/12113>`__: scipy.stats.poisson docs for rate = 0 * `#12152 <https://github.com/scipy/scipy/issues/12152>`__: ENH: signal.gauss_spline should accept a list * `#12157 <https://github.com/scipy/scipy/issues/12157>`__: BUG: Iteration index initialisation is wrong in scipy.optimize.linesearch.scalar_search_wolfe2 * `#12162 <https://github.com/scipy/scipy/issues/12162>`__: Storing Rotation object in NumPy array returns an array with... * `#12176 <https://github.com/scipy/scipy/issues/12176>`__: cannot modify the slice of an array returned by \`wavfile.read\` * `#12190 <https://github.com/scipy/scipy/issues/12190>`__: retrieve leave colors from dendrogram * `#12196 <https://github.com/scipy/scipy/issues/12196>`__: PERF: scipy.linalg.pinv is very slow compared to numpy.linalg.pinv * `#12222 <https://github.com/scipy/scipy/issues/12222>`__: Interpolating categorical data (interp1d) * `#12231 <https://github.com/scipy/scipy/issues/12231>`__: Is the p-value of the Kruskal-Wallis test two-sided? * `#12249 <https://github.com/scipy/scipy/issues/12249>`__: ENH: least_squares: should not re-instanciate csr_matrix if already... * `#12264 <https://github.com/scipy/scipy/issues/12264>`__: DOC: optimize: linprog method-specific function signature * `#12290 <https://github.com/scipy/scipy/issues/12290>`__: DOC: Convex Hull areas are actually perimeters for 2-dimensional... * `#12308 <https://github.com/scipy/scipy/issues/12308>`__: integrate.solve_ivp with DOP853 method fails when yDot = 0 * `#12326 <https://github.com/scipy/scipy/issues/12326>`__: BUG: stats.exponnorm.pdf returns 0 for small K * `#12337 <https://github.com/scipy/scipy/issues/12337>`__: scipy.sparse.linalg.eigsh documentation is misleading * `#12339 <https://github.com/scipy/scipy/issues/12339>`__: scipy.io.wavfile.write documentation has wrong example * `#12340 <https://github.com/scipy/scipy/issues/12340>`__: sparse.lil_matrix.tocsr() fails silently on matrices with nzn... * `#12350 <https://github.com/scipy/scipy/issues/12350>`__: Create a 2-parameter version of the gamma distribution * `#12369 <https://github.com/scipy/scipy/issues/12369>`__: scipy.signal.correlate has an error in the documentation, examples... * `#12373 <https://github.com/scipy/scipy/issues/12373>`__: interp1d returns incorrect values for step functions * `#12378 <https://github.com/scipy/scipy/issues/12378>`__: interpolate.NearestNDInterpolator.__call__ & LinearNDInterpolator.__call__... * `#12411 <https://github.com/scipy/scipy/issues/12411>`__: scipy.stats.spearmanr mishandles nan variables with "propogate" * `#12413 <https://github.com/scipy/scipy/issues/12413>`__: DOC: Remove the "Basic functions" section from the SciPy tutorial. * `#12415 <https://github.com/scipy/scipy/issues/12415>`__: scipy.stats.dirichlet documentation issue * `#12419 <https://github.com/scipy/scipy/issues/12419>`__: least_squares ValueError with 'lm' method - regression from 1.4.1... * `#12431 <https://github.com/scipy/scipy/issues/12431>`__: Request for Python wrapper for LAPACK's ?pptrf (Cholesky factorization... * `#12458 <https://github.com/scipy/scipy/issues/12458>`__: spearmanr with entire NaN columns produces errors * `#12477 <https://github.com/scipy/scipy/issues/12477>`__: WIP: Addition of MLE for stats.invgauss/wald * `#12483 <https://github.com/scipy/scipy/issues/12483>`__: reading .wav fails when the file is too big on python 3.6.0 * `#12490 <https://github.com/scipy/scipy/issues/12490>`__: BUG: stats: logistic and genlogistic logpdf overflow for large... * `#12499 <https://github.com/scipy/scipy/issues/12499>`__: LinearNDInterpolator raises ValueError when value array has writeable=False... * `#12523 <https://github.com/scipy/scipy/issues/12523>`__: Wrong key in __odrpack.c * `#12547 <https://github.com/scipy/scipy/issues/12547>`__: typo in scipy/cluster/_hierarchy.pyx * `#12549 <https://github.com/scipy/scipy/issues/12549>`__: DOC: least_squares return type is poorly formatted. * `#12578 <https://github.com/scipy/scipy/issues/12578>`__: TST: test_bounds_infeasible_2 failing on wheels repo cron jobs * `#12585 <https://github.com/scipy/scipy/issues/12585>`__: ENH: Add Multivariate Hypergeometric Distribution * `#12604 <https://github.com/scipy/scipy/issues/12604>`__: unintuitive conversion in \`scipy.constants.lambda2nu\` * `#12606 <https://github.com/scipy/scipy/issues/12606>`__: DOC: Invalid syntax in example. * `#12665 <https://github.com/scipy/scipy/issues/12665>`__: List of possible bugs found by automated code analysis * `#12696 <https://github.com/scipy/scipy/issues/12696>`__: scipy.optimize.fminbound, numpy depreciation warning Creating... * `#12699 <https://github.com/scipy/scipy/issues/12699>`__: TestProjections.test_iterative_refinements_dense failure * `#12701 <https://github.com/scipy/scipy/issues/12701>`__: TestDifferentialEvolutionSolver::test_L4 failing * `#12719 <https://github.com/scipy/scipy/issues/12719>`__: Misleading scipy.signal.get_window() docstring with 'exponential'... * `#12740 <https://github.com/scipy/scipy/issues/12740>`__: circstd doesn't handle R = hypot(S, C) > 1 * `#12749 <https://github.com/scipy/scipy/issues/12749>`__: ENH: interp1d Matlab compatibility * `#12773 <https://github.com/scipy/scipy/issues/12773>`__: Meta-issue: ndimage spline boundary handling (NumFOCUS proposal) * `#12813 <https://github.com/scipy/scipy/issues/12813>`__: optimize.root(method="krylov") fails if options["tol_norm"] expects... * `#12815 <https://github.com/scipy/scipy/issues/12815>`__: stats.zscore inconsistent behavior when all values are the same * `#12840 <https://github.com/scipy/scipy/issues/12840>`__: scipy.signal.windows.dpss docstring typo * `#12874 <https://github.com/scipy/scipy/issues/12874>`__: Rotation.random vs stats.special_ortho_group * `#12881 <https://github.com/scipy/scipy/issues/12881>`__: FFT - documentation - examples - linspace construction * `#12904 <https://github.com/scipy/scipy/issues/12904>`__: BUG: parsing in loadarff() * `#12917 <https://github.com/scipy/scipy/issues/12917>`__: GitHub Actions nightly build triggered on forks * `#12919 <https://github.com/scipy/scipy/issues/12919>`__: BUG: numerical precision, use gammaln in nct.mean * `#12924 <https://github.com/scipy/scipy/issues/12924>`__: Rename Sample Based Integration Methods to Comply with Code of... * `#12940 <https://github.com/scipy/scipy/issues/12940>`__: Should the minimum numpy for AIX be bumped to 1.16.5 * `#12951 <https://github.com/scipy/scipy/issues/12951>`__: A possible typo in scipy.stats.weightedtau * `#12952 <https://github.com/scipy/scipy/issues/12952>`__: [Documentation question] Would it be more precise to specify... * `#12970 <https://github.com/scipy/scipy/issues/12970>`__: Documentation presents second order sections as the correct choice... * `#12982 <https://github.com/scipy/scipy/issues/12982>`__: Calculate standard error of the intercept in linregress * `#12985 <https://github.com/scipy/scipy/issues/12985>`__: Possible wrong link in scipy.stats.wilcoxon doc * `#12991 <https://github.com/scipy/scipy/issues/12991>`__: least_squares broken with float32 * `#13001 <https://github.com/scipy/scipy/issues/13001>`__: \`OptimizeResult.message\` from \`L-BFGS-B\` is a bytes, not... * `#13030 <https://github.com/scipy/scipy/issues/13030>`__: BUG: lint_diff.py still fails for backport PRs * `#13077 <https://github.com/scipy/scipy/issues/13077>`__: CI: codecov proper patch diffs * `#13085 <https://github.com/scipy/scipy/issues/13085>`__: Build failing on main branch after HiGHS solver merge * `#13088 <https://github.com/scipy/scipy/issues/13088>`__: BLD, BUG: wheel builds failure with HiGHS/optimize * `#13099 <https://github.com/scipy/scipy/issues/13099>`__: Wrong output format for empty sparse results of kron * `#13108 <https://github.com/scipy/scipy/issues/13108>`__: TST, CI: GitHub Actions MacOS Failures * `#13111 <https://github.com/scipy/scipy/issues/13111>`__: BUG, DOC: refguide check is failing * `#13127 <https://github.com/scipy/scipy/issues/13127>`__: ODR output file writing broken in conda env with system compilers * `#13134 <https://github.com/scipy/scipy/issues/13134>`__: FromTravis migration tracker * `#13140 <https://github.com/scipy/scipy/issues/13140>`__: BUG: signal: \`ss2tf\` incorrectly truncates output to integers. * `#13179 <https://github.com/scipy/scipy/issues/13179>`__: CI: lint is failing because of output to stderr * `#13182 <https://github.com/scipy/scipy/issues/13182>`__: Key appears twice in \`test_optimize.test_show_options\` * `#13191 <https://github.com/scipy/scipy/issues/13191>`__: \`scipy.linalg.lapack.dgesjv\` overwrites original arrays if... * `#13207 <https://github.com/scipy/scipy/issues/13207>`__: TST: Erratic test failure in test_cossin_separate * `#13221 <https://github.com/scipy/scipy/issues/13221>`__: BUG: pavement.py glitch * `#13239 <https://github.com/scipy/scipy/issues/13239>`__: Segmentation fault with \`eigh(..., driver="evx")\` for 10x10... * `#13248 <https://github.com/scipy/scipy/issues/13248>`__: ndimage: improper cval handling for complex-valued inputs Pull requests for 1.6.0 ------------------------------ * `#8032 <https://github.com/scipy/scipy/pull/8032>`__: ENH: Add in taylor window common in Radar processing * `#8779 <https://github.com/scipy/scipy/pull/8779>`__: CI: Run benchmarks * `#9361 <https://github.com/scipy/scipy/pull/9361>`__: ENH: Add Kendall's tau-a and tau-c variants to scipy.stats.kendalltau() * `#11068 <https://github.com/scipy/scipy/pull/11068>`__: ENH: Adds correlation_lags function to scipy.signal * `#11119 <https://github.com/scipy/scipy/pull/11119>`__: ENH: add Cramer-von-Mises (one-sample) test to scipy.stats * `#11249 <https://github.com/scipy/scipy/pull/11249>`__: ENH: optimize: interpolative decomposition redundancy removal... * `#11346 <https://github.com/scipy/scipy/pull/11346>`__: ENH: add fast toeplitz matrix multiplication using FFT * `#11413 <https://github.com/scipy/scipy/pull/11413>`__: ENH: Multivariate t-distribution (stale) * `#11563 <https://github.com/scipy/scipy/pull/11563>`__: ENH: exact p-value in stats.kendalltau() for sample sizes > 171 * `#11691 <https://github.com/scipy/scipy/pull/11691>`__: ENH: add a stack of reversal functions to linprog * `#12043 <https://github.com/scipy/scipy/pull/12043>`__: ENH: optimize: add HiGHS methods to linprog - continued * `#12061 <https://github.com/scipy/scipy/pull/12061>`__: Check parameter consistensy in signal.iirdesign * `#12067 <https://github.com/scipy/scipy/pull/12067>`__: MAINT: Cleanup OLDAPI in ndimage/src/_ctest.c * `#12069 <https://github.com/scipy/scipy/pull/12069>`__: DOC: Add developer guidelines for implementing the nan_policy... * `#12077 <https://github.com/scipy/scipy/pull/12077>`__: MAINT: malloc return value checks for cython * `#12080 <https://github.com/scipy/scipy/pull/12080>`__: MAINT: Remove suppress_warnings * `#12085 <https://github.com/scipy/scipy/pull/12085>`__: ENH: special: support ILP64 Lapack * `#12086 <https://github.com/scipy/scipy/pull/12086>`__: MAINT: Cleanup PyMODINIT_FUNC used during 2to3 * `#12097 <https://github.com/scipy/scipy/pull/12097>`__: ENH: stats: override stats.rayleigh.fit with analytical MLE * `#12112 <https://github.com/scipy/scipy/pull/12112>`__: DOC: Improve integrate.nquad docstring * `#12125 <https://github.com/scipy/scipy/pull/12125>`__: TST: Add a test for stats.gmean with negative input * `#12139 <https://github.com/scipy/scipy/pull/12139>`__: TST: Reduce flakiness in lsmr test * `#12142 <https://github.com/scipy/scipy/pull/12142>`__: DOC: add a note in poisson distribution when mu=0 and k=0 in... * `#12144 <https://github.com/scipy/scipy/pull/12144>`__: DOC: Update ndimage.morphology.distance_transform\* * `#12154 <https://github.com/scipy/scipy/pull/12154>`__: ENH: scipy.signal: allow lists in gauss_spline * `#12170 <https://github.com/scipy/scipy/pull/12170>`__: ENH: scipy.stats: add negative hypergeometric distribution * `#12177 <https://github.com/scipy/scipy/pull/12177>`__: MAINT: Correctly add input line to ValueError * `#12183 <https://github.com/scipy/scipy/pull/12183>`__: ENH: Use fromfile where possible * `#12186 <https://github.com/scipy/scipy/pull/12186>`__: MAINT: generalize tests in SphericalVoronoi * `#12198 <https://github.com/scipy/scipy/pull/12198>`__: TST: Fix str + bytes error * `#12199 <https://github.com/scipy/scipy/pull/12199>`__: ENH: match np.result_type behaviour in some scipy.signal functions * `#12200 <https://github.com/scipy/scipy/pull/12200>`__: ENH: add FIR and IIR gammatone filters to scipy.signal * `#12204 <https://github.com/scipy/scipy/pull/12204>`__: ENH: Add overwrite argument for odr.ODR() and its test. * `#12206 <https://github.com/scipy/scipy/pull/12206>`__: MAINT:lstsq: Switch to tranposed problem if the array is tall * `#12208 <https://github.com/scipy/scipy/pull/12208>`__: wavfile bugfixes and maintenance * `#12214 <https://github.com/scipy/scipy/pull/12214>`__: DOC: fix docstring of "sd_beta" of odr.Output. * `#12234 <https://github.com/scipy/scipy/pull/12234>`__: MAINT: prevent divide by zero warnings in scipy.optimize BFGS... * `#12235 <https://github.com/scipy/scipy/pull/12235>`__: REL: set version to 1.6.0.dev0 * `#12237 <https://github.com/scipy/scipy/pull/12237>`__: BUG: Fix exit condition for QUICK_SELECT pivot * `#12242 <https://github.com/scipy/scipy/pull/12242>`__: ENH: Rename ndimage.sum to ndimage.sum_labels (keep sum as alias) * `#12243 <https://github.com/scipy/scipy/pull/12243>`__: EHN: Update SuperLU * `#12244 <https://github.com/scipy/scipy/pull/12244>`__: MAINT: stats: avoid spurious warnings in ncx2.pdf * `#12245 <https://github.com/scipy/scipy/pull/12245>`__: DOC: Fixed incorrect default for mode in scipy.ndimage.spline_filter1d * `#12248 <https://github.com/scipy/scipy/pull/12248>`__: MAINT: clean up pavement.py * `#12250 <https://github.com/scipy/scipy/pull/12250>`__: ENH: Replaced csr_matrix() by tocsr() and complemented docstring * `#12253 <https://github.com/scipy/scipy/pull/12253>`__: TST, CI: turn on codecov patch diffs * `#12259 <https://github.com/scipy/scipy/pull/12259>`__: MAINT: Remove duplicated test for import cycles * `#12263 <https://github.com/scipy/scipy/pull/12263>`__: ENH: Rename LocalSearchWrapper bounds * `#12265 <https://github.com/scipy/scipy/pull/12265>`__: BUG optimize: Accept np.matrix in lsq_linear * `#12266 <https://github.com/scipy/scipy/pull/12266>`__: BUG: Fix paren error in dual annealing accept_reject calculation * `#12269 <https://github.com/scipy/scipy/pull/12269>`__: MAINT: Included mismatched shapes in error messages. * `#12279 <https://github.com/scipy/scipy/pull/12279>`__: MAINT: \`__array__\` and array protocols cannot be used in sparse. * `#12281 <https://github.com/scipy/scipy/pull/12281>`__: DOC: update wheel DL docs * `#12283 <https://github.com/scipy/scipy/pull/12283>`__: ENH: odr: ILP64 Blas support in ODR * `#12284 <https://github.com/scipy/scipy/pull/12284>`__: ENH: linalg: support for ILP64 BLAS/LAPACK in f2py wrappers * `#12286 <https://github.com/scipy/scipy/pull/12286>`__: ENH: Cythonize scipy.spatial.transform.Rotation * `#12287 <https://github.com/scipy/scipy/pull/12287>`__: ENH: Read arbitrary bit depth (including 24-bit) WAVs * `#12292 <https://github.com/scipy/scipy/pull/12292>`__: BLD: fix musl compilation * `#12293 <https://github.com/scipy/scipy/pull/12293>`__: MAINT: Fix a DeprecationWarning in validate_runtests_log.py. * `#12296 <https://github.com/scipy/scipy/pull/12296>`__: DOC: Clarify area/volume in scipy.spatial.ConvexHull docstrings * `#12302 <https://github.com/scipy/scipy/pull/12302>`__: CI: Run travis builds on master to keep cache up to date * `#12305 <https://github.com/scipy/scipy/pull/12305>`__: TST: Cleanup print statements in tests * `#12323 <https://github.com/scipy/scipy/pull/12323>`__: ENH: Add a Bunch-like class to use as a backwards compatible... * `#12324 <https://github.com/scipy/scipy/pull/12324>`__: BUG: io: Fix an error that occurs when attempting to raise a... * `#12327 <https://github.com/scipy/scipy/pull/12327>`__: DOC: clarify docstrings of \`query_ball_tree\` and \`query_pairs\` * `#12334 <https://github.com/scipy/scipy/pull/12334>`__: PERF: Improve cKDTree.query_ball_point constant time cython overhead * `#12338 <https://github.com/scipy/scipy/pull/12338>`__: DOC: improve consistency and clarity of docs in linalg and sparse/linalg * `#12341 <https://github.com/scipy/scipy/pull/12341>`__: DOC: add Examples for KDTree query_ball_tree and query_pairs * `#12343 <https://github.com/scipy/scipy/pull/12343>`__: DOC: add examples for special.eval_legendre() * `#12349 <https://github.com/scipy/scipy/pull/12349>`__: BUG: avoid overflow in sum() for 32-bit systems * `#12351 <https://github.com/scipy/scipy/pull/12351>`__: DOC: Fix example wavfile to be 16bit * `#12352 <https://github.com/scipy/scipy/pull/12352>`__: [BUG] Consider 0/0 division in DOP853 error estimation * `#12353 <https://github.com/scipy/scipy/pull/12353>`__: Fix exception causes in vq.py * `#12354 <https://github.com/scipy/scipy/pull/12354>`__: MAINT: Cleanup unneeded void\* cast in setlist.pxd * `#12355 <https://github.com/scipy/scipy/pull/12355>`__: TST: Remove hack for old win-amd64 bug * `#12356 <https://github.com/scipy/scipy/pull/12356>`__: ENH: Faster implementation of scipy.sparse.block_diag (#9411... * `#12357 <https://github.com/scipy/scipy/pull/12357>`__: MAINT,TST: update and run scipy/special/utils/convert.py * `#12358 <https://github.com/scipy/scipy/pull/12358>`__: TST: Check mstat.skewtest pvalue * `#12359 <https://github.com/scipy/scipy/pull/12359>`__: TST: Sparse matrix test with int64 indptr and indices * `#12363 <https://github.com/scipy/scipy/pull/12363>`__: DOC: ref. in CloughTocher2DInterpolator * `#12364 <https://github.com/scipy/scipy/pull/12364>`__: DOC: \`sparse_distance_matrix\` and \`count_neighbors\` examples * `#12371 <https://github.com/scipy/scipy/pull/12371>`__: MAINT, CI: bump to latest stable OpenBLAS * `#12372 <https://github.com/scipy/scipy/pull/12372>`__: MAINT: Minor cleanup of (c)KDTree tests * `#12374 <https://github.com/scipy/scipy/pull/12374>`__: DEP: Deprecate \`distance.wminkowski\` * `#12375 <https://github.com/scipy/scipy/pull/12375>`__: ENH: Add fast path for minkowski distance with p=1,2 and support... * `#12376 <https://github.com/scipy/scipy/pull/12376>`__: Fix exception causes in most of the codebase * `#12377 <https://github.com/scipy/scipy/pull/12377>`__: DOC: Quick fix - adds newline to correlation_lags docstring Examples... * `#12381 <https://github.com/scipy/scipy/pull/12381>`__: BENCH: remove obsolete goal_time param * `#12382 <https://github.com/scipy/scipy/pull/12382>`__: ENH: Replace KDTree with a thin wrapper over cKDTree * `#12385 <https://github.com/scipy/scipy/pull/12385>`__: DOC: improve docstrings of interpolate.NearestNDInterpolator.__call__... * `#12387 <https://github.com/scipy/scipy/pull/12387>`__: DOC/STY: add example to scipy.signal.correlate * `#12393 <https://github.com/scipy/scipy/pull/12393>`__: CI: Replace the existing check for non-ASCII characters with... * `#12394 <https://github.com/scipy/scipy/pull/12394>`__: CI: arm64 numpy now available * `#12395 <https://github.com/scipy/scipy/pull/12395>`__: ENH: improve stats.binned_statistic_dd performance * `#12396 <https://github.com/scipy/scipy/pull/12396>`__: DOC, MAINT: forward port 1.5.0 relnotes * `#12398 <https://github.com/scipy/scipy/pull/12398>`__: API: Disable len() and indexing of Rotation instances with single... * `#12399 <https://github.com/scipy/scipy/pull/12399>`__: MAINT: Replace some Unicode dash-like chars with an ASCII hyphen. * `#12402 <https://github.com/scipy/scipy/pull/12402>`__: update .mailmap * `#12404 <https://github.com/scipy/scipy/pull/12404>`__: MAINT: io: Change the encoding comment of test_mio.py to utf-8. * `#12416 <https://github.com/scipy/scipy/pull/12416>`__: CI: cache mingw, azure pipelines * `#12427 <https://github.com/scipy/scipy/pull/12427>`__: BUG: logic error in loop unrolling (cKDTree) * `#12432 <https://github.com/scipy/scipy/pull/12432>`__: DOC: Remove the "Basic functions" section from the SciPy tutorial. * `#12434 <https://github.com/scipy/scipy/pull/12434>`__: ENH:linalg: Add LAPACK wrappers pptrf/pptrs/ppsv/pptri/ppcon * `#12435 <https://github.com/scipy/scipy/pull/12435>`__: DOC: fix simplex math for scipy.stats.dirichlet documentation * `#12439 <https://github.com/scipy/scipy/pull/12439>`__: DOC: add API methods summary for NdPPoly * `#12443 <https://github.com/scipy/scipy/pull/12443>`__: BUG: stats: Improve calculation of exponnorm.pdf * `#12448 <https://github.com/scipy/scipy/pull/12448>`__: DOC: stats: Add "Examples" to the ansari docstring. * `#12450 <https://github.com/scipy/scipy/pull/12450>`__: ENH: add \`leaves_color_list\` for cluster.dendrogram dictionary. * `#12451 <https://github.com/scipy/scipy/pull/12451>`__: MAINT: remove "blacklist" terminology from code base * `#12452 <https://github.com/scipy/scipy/pull/12452>`__: DOC: clarify the meaning of whitening for cluster.vq.whiten() * `#12455 <https://github.com/scipy/scipy/pull/12455>`__: MAINT: clearer error message in setup.py * `#12457 <https://github.com/scipy/scipy/pull/12457>`__: ENH: stats: override stats.pareto.fit with analytical MLE * `#12460 <https://github.com/scipy/scipy/pull/12460>`__: check if column in spearman rho is entirely NaN or Inf * `#12463 <https://github.com/scipy/scipy/pull/12463>`__: DOC: improve and clean up \*Spline docstrings in fitpack2.py * `#12474 <https://github.com/scipy/scipy/pull/12474>`__: ENH: linalg: speedup _sqrtm_triu by moving tight loop to Cython * `#12476 <https://github.com/scipy/scipy/pull/12476>`__: ENH: add IIR comb filter to scipy.signal * `#12484 <https://github.com/scipy/scipy/pull/12484>`__: Fix documentation for minimize * `#12486 <https://github.com/scipy/scipy/pull/12486>`__: DOC: add a note in poisson distribution when mu=0 and k=0 in... * `#12491 <https://github.com/scipy/scipy/pull/12491>`__: MAINT: forward port 1.5.1 release notes * `#12508 <https://github.com/scipy/scipy/pull/12508>`__: Fix exception causes all over the codebase * `#12514 <https://github.com/scipy/scipy/pull/12514>`__: ENH: stats: override stats.invgauss.fit with analytical MLE * `#12519 <https://github.com/scipy/scipy/pull/12519>`__: PERF: Avoid np.zeros when custom initialization is needed anyway * `#12520 <https://github.com/scipy/scipy/pull/12520>`__: DOC: Minor RST section renaming. * `#12521 <https://github.com/scipy/scipy/pull/12521>`__: MAINT: Remove unused imports * `#12522 <https://github.com/scipy/scipy/pull/12522>`__: PERF: Get rid of unnececssary allocation in VarReader5.cread_fieldnames * `#12524 <https://github.com/scipy/scipy/pull/12524>`__: DOC: special: Set Axes3D rect to avoid clipping labels in plot. * `#12525 <https://github.com/scipy/scipy/pull/12525>`__: Fix large sparse nnz * `#12526 <https://github.com/scipy/scipy/pull/12526>`__: DOC: Remove double section and too long header underline. * `#12527 <https://github.com/scipy/scipy/pull/12527>`__: Improve error message for wrong interpolation type * `#12530 <https://github.com/scipy/scipy/pull/12530>`__: Move redundant logic outside loop for conditional speedup in... * `#12532 <https://github.com/scipy/scipy/pull/12532>`__: ENH: Add norm={"forward", "backward"} to \`scipy.fft\` * `#12535 <https://github.com/scipy/scipy/pull/12535>`__: MAINT: Avoid sphinx deprecated aliases for SeeAlso and Only * `#12540 <https://github.com/scipy/scipy/pull/12540>`__: BUG: fix odr.output.work_ind key bug and add its test. * `#12541 <https://github.com/scipy/scipy/pull/12541>`__: ENH: add solver for minimum weight full bipartite matching * `#12550 <https://github.com/scipy/scipy/pull/12550>`__: PERF: pickling speed of rv\* * `#12551 <https://github.com/scipy/scipy/pull/12551>`__: DOC: fix typo in cluster/_hierarchy.pyx * `#12552 <https://github.com/scipy/scipy/pull/12552>`__: CI: Cleanup travis pip installs * `#12556 <https://github.com/scipy/scipy/pull/12556>`__: BUG: Fix problem with Scipy.integrate.solve_bvp for big problems * `#12557 <https://github.com/scipy/scipy/pull/12557>`__: MAINT: Use extern templates to improve sparsetools compile time * `#12558 <https://github.com/scipy/scipy/pull/12558>`__: MAINT: Remove hack to allow scipy.fft to act like a function * `#12563 <https://github.com/scipy/scipy/pull/12563>`__: MAINT: Remove unused mu0 in special/orthogonal.py * `#12564 <https://github.com/scipy/scipy/pull/12564>`__: DOC: fix return type docstring for least_squares * `#12565 <https://github.com/scipy/scipy/pull/12565>`__: DOC: stats: respond to query about Kruskal-Wallis test being... * `#12566 <https://github.com/scipy/scipy/pull/12566>`__: BUG: Interpolate: use stable sort * `#12568 <https://github.com/scipy/scipy/pull/12568>`__: Updated documentation for as_quat * `#12571 <https://github.com/scipy/scipy/pull/12571>`__: DEP: remove deprecated slepian window * `#12573 <https://github.com/scipy/scipy/pull/12573>`__: DEP: remove \`frechet_l\` and \`frechet_r\` * `#12575 <https://github.com/scipy/scipy/pull/12575>`__: BUG: stats: fix multinomial.pmf NaNs when params sum > 1 * `#12576 <https://github.com/scipy/scipy/pull/12576>`__: MAINT: remove warning from LSQSphereBivariateSpline * `#12582 <https://github.com/scipy/scipy/pull/12582>`__: ENH: Multivariate t-distribution * `#12587 <https://github.com/scipy/scipy/pull/12587>`__: ENH: speed up rvs of gengamma in scipy.stats * `#12588 <https://github.com/scipy/scipy/pull/12588>`__: DOC: add Examples add see also sections for LinearNDInterpolator,... * `#12597 <https://github.com/scipy/scipy/pull/12597>`__: ENH: Add single-sided p-values to t-tests * `#12599 <https://github.com/scipy/scipy/pull/12599>`__: Small update to scipy FFT tutorial * `#12600 <https://github.com/scipy/scipy/pull/12600>`__: ENH: disjoint set data structure * `#12602 <https://github.com/scipy/scipy/pull/12602>`__: BUG: add const for Read-only views in interpnd.pyx * `#12605 <https://github.com/scipy/scipy/pull/12605>`__: BUG: correct \`np.asanyarray\` use in \`scipy.constants.lambda2nu\` * `#12610 <https://github.com/scipy/scipy/pull/12610>`__: MAINT: forward port 1.5.2 release notes * `#12612 <https://github.com/scipy/scipy/pull/12612>`__: MAINT: stats: Use explicit keyword parameters instead of \`\*\*kwds\`. * `#12616 <https://github.com/scipy/scipy/pull/12616>`__: DOC: make explicit docstring that interpolate.interp1d only accepts... * `#12618 <https://github.com/scipy/scipy/pull/12618>`__: DOC: Minor doc formatting. * `#12640 <https://github.com/scipy/scipy/pull/12640>`__: MAINT: stats: fix issues with scipy.stats.pearson3 docs, moment,... * `#12647 <https://github.com/scipy/scipy/pull/12647>`__: TST: Add Boost ellipr[cdfgj]_data test data * `#12648 <https://github.com/scipy/scipy/pull/12648>`__: DOC: Update special/utils/README with instructions * `#12649 <https://github.com/scipy/scipy/pull/12649>`__: DOC: simplified pip quickstart guide * `#12650 <https://github.com/scipy/scipy/pull/12650>`__: DOC: stats: Fix boxcox docstring: lambda can be negative. * `#12655 <https://github.com/scipy/scipy/pull/12655>`__: DOC: update Steering Council members listed in governance docs * `#12659 <https://github.com/scipy/scipy/pull/12659>`__: rv_sample expect bug * `#12663 <https://github.com/scipy/scipy/pull/12663>`__: DOC: optimize: try to fix linprog method-specific documentation * `#12664 <https://github.com/scipy/scipy/pull/12664>`__: BUG: stats: Fix logpdf with large negative values for logistic... * `#12666 <https://github.com/scipy/scipy/pull/12666>`__: MAINT: Fixes from static analysis * `#12667 <https://github.com/scipy/scipy/pull/12667>`__: ENH: Adding Modified Rodrigues Parameters to the Rotation class * `#12670 <https://github.com/scipy/scipy/pull/12670>`__: DOC: Update documentation for Gamma distribution * `#12673 <https://github.com/scipy/scipy/pull/12673>`__: API: Unconditionally return np.intp from cKDTree.query * `#12677 <https://github.com/scipy/scipy/pull/12677>`__: MAINT: Add Autogenerated notice to ufuncs.pyi * `#12682 <https://github.com/scipy/scipy/pull/12682>`__: MAINT: Remove _util._valarray * `#12688 <https://github.com/scipy/scipy/pull/12688>`__: MAINT: add f2py-generated scipy.integrate files to .gitignore * `#12689 <https://github.com/scipy/scipy/pull/12689>`__: BENCH: simplify benchmark setup, remove benchmarks/run.py * `#12694 <https://github.com/scipy/scipy/pull/12694>`__: scipy/stats: Add laplace_asymmetric continuous distribution * `#12695 <https://github.com/scipy/scipy/pull/12695>`__: DOC: update Ubuntu quickstart; conda compilers now work! * `#12698 <https://github.com/scipy/scipy/pull/12698>`__: MAINT: Replace np.max with np.maximum * `#12700 <https://github.com/scipy/scipy/pull/12700>`__: TST: bump test precision for constrained trustregion test * `#12702 <https://github.com/scipy/scipy/pull/12702>`__: TST: bump test tolerance for \`DifferentialEvolutionSolver.test_L4\` * `#12703 <https://github.com/scipy/scipy/pull/12703>`__: BUG: Improve input validation for sepfir2d * `#12708 <https://github.com/scipy/scipy/pull/12708>`__: MAINT: fix a typo in scipy.sparse * `#12709 <https://github.com/scipy/scipy/pull/12709>`__: BUG: bvls can fail catastrophically to converge * `#12711 <https://github.com/scipy/scipy/pull/12711>`__: MAINT: Use platform.python_implementation to determine IS_PYPY * `#12713 <https://github.com/scipy/scipy/pull/12713>`__: TST: Fix flaky test_lgmres * `#12716 <https://github.com/scipy/scipy/pull/12716>`__: DOC: add examples and tutorial links for interpolate functions... * `#12717 <https://github.com/scipy/scipy/pull/12717>`__: DOC: Fix Issue #5396 * `#12725 <https://github.com/scipy/scipy/pull/12725>`__: ENH: Support complex-valued images and kernels for many ndimage... * `#12729 <https://github.com/scipy/scipy/pull/12729>`__: DEP: remove setup_requires * `#12732 <https://github.com/scipy/scipy/pull/12732>`__: BENCH: skip benchmarks instead of hiding them when SCIPY_XSLOW=0 * `#12734 <https://github.com/scipy/scipy/pull/12734>`__: CI: Don't ignore line-length in the lint_diff check. * `#12736 <https://github.com/scipy/scipy/pull/12736>`__: DOC: Fix signal.windows.get_window() 'exponential' docstring * `#12737 <https://github.com/scipy/scipy/pull/12737>`__: ENH: stats: override stats.gumbel_r.fit and stats.gumbel_l.fit... * `#12738 <https://github.com/scipy/scipy/pull/12738>`__: ENH: stats: override stats.logistic.fit with system of equations... * `#12743 <https://github.com/scipy/scipy/pull/12743>`__: BUG: Avoid negative variances in circular statistics * `#12744 <https://github.com/scipy/scipy/pull/12744>`__: Prevent build error on GNU/Hurd * `#12746 <https://github.com/scipy/scipy/pull/12746>`__: TST: parameterize the test cases in test_ndimage.py * `#12752 <https://github.com/scipy/scipy/pull/12752>`__: DOC: Add examples for some root finding functions. * `#12754 <https://github.com/scipy/scipy/pull/12754>`__: MAINT, CI: Azure windows deps multiline * `#12756 <https://github.com/scipy/scipy/pull/12756>`__: ENH: stats: Add an sf method to levy for improved precision in... * `#12757 <https://github.com/scipy/scipy/pull/12757>`__: ENH: stats: Add an sf method to levy_l for improved precision. * `#12765 <https://github.com/scipy/scipy/pull/12765>`__: TST, MAINT: infeasible_2 context * `#12767 <https://github.com/scipy/scipy/pull/12767>`__: Fix spline interpolation boundary handling for modes reflect... * `#12769 <https://github.com/scipy/scipy/pull/12769>`__: DOC: syntax error in scipy.interpolate.bspl * `#12770 <https://github.com/scipy/scipy/pull/12770>`__: ENH: add nearest-up rounding to scipy.interpolate.interp1d * `#12771 <https://github.com/scipy/scipy/pull/12771>`__: TST: fix invalid input unit test for scipy.signal.gammatone * `#12775 <https://github.com/scipy/scipy/pull/12775>`__: ENH: Adds quadratic_assignment with two methods * `#12776 <https://github.com/scipy/scipy/pull/12776>`__: ENH: add grid-constant boundary handling in ndimage interpolation... * `#12777 <https://github.com/scipy/scipy/pull/12777>`__: Add Taylor Window function - Common in Radar DSP * `#12779 <https://github.com/scipy/scipy/pull/12779>`__: ENH: Improvements to pocketfft thread pool and ARM neon vectorization * `#12788 <https://github.com/scipy/scipy/pull/12788>`__: API: Rename cKDTree n_jobs argument to workers * `#12792 <https://github.com/scipy/scipy/pull/12792>`__: DOC: remove THANKS.txt file in favor of scipy.org * `#12793 <https://github.com/scipy/scipy/pull/12793>`__: Add new flag to authors tool * `#12802 <https://github.com/scipy/scipy/pull/12802>`__: BENCH: add scipy.ndimage.interpolation benchmarks * `#12803 <https://github.com/scipy/scipy/pull/12803>`__: Do not pin the version of numpy in unsupported python versions * `#12810 <https://github.com/scipy/scipy/pull/12810>`__: CI: fix 32-bit Linux build failure on Azure CI runs * `#12812 <https://github.com/scipy/scipy/pull/12812>`__: ENH: support interpolation of complex-valued images * `#12814 <https://github.com/scipy/scipy/pull/12814>`__: BUG: nonlin_solve shouldn't pass non-vector dx to tol_norm * `#12818 <https://github.com/scipy/scipy/pull/12818>`__: Update ckdtree.pyx * `#12822 <https://github.com/scipy/scipy/pull/12822>`__: MAINT: simplify directed_hausdorff * `#12827 <https://github.com/scipy/scipy/pull/12827>`__: DOC: Fix wrong name w being used instead of worN in docs. * `#12831 <https://github.com/scipy/scipy/pull/12831>`__: DOC: fix typo in sparse/base.py * `#12835 <https://github.com/scipy/scipy/pull/12835>`__: MAINT: stats: Improve vonmises PDF calculation. * `#12839 <https://github.com/scipy/scipy/pull/12839>`__: ENH: scipy.stats: add multivariate hypergeometric distribution * `#12843 <https://github.com/scipy/scipy/pull/12843>`__: changed M to N in windows.dpss * `#12846 <https://github.com/scipy/scipy/pull/12846>`__: MAINT: update minimum NumPy version to 1.16.5 * `#12847 <https://github.com/scipy/scipy/pull/12847>`__: DOC: Unify the formula in docs of scipy.stats.pearsonr() * `#12849 <https://github.com/scipy/scipy/pull/12849>`__: DOC: polish QAP docs for consistency and readability * `#12852 <https://github.com/scipy/scipy/pull/12852>`__: ENH, MAINT: Bring KDTree interface to feature-parity with cKDTree * `#12858 <https://github.com/scipy/scipy/pull/12858>`__: DOC: use :doi: and :arxiv: directives for references * `#12872 <https://github.com/scipy/scipy/pull/12872>`__: lazily import multiprocessing.Pool in MapWrapper * `#12878 <https://github.com/scipy/scipy/pull/12878>`__: DOC: document ScalarFunction * `#12882 <https://github.com/scipy/scipy/pull/12882>`__: MAINT: stats: Change a test to use <= instead of strictly less... * `#12885 <https://github.com/scipy/scipy/pull/12885>`__: numpy.linspace calls edited to ensure correct spacing. * `#12886 <https://github.com/scipy/scipy/pull/12886>`__: DOC: stats: Add 'versionadded' to cramervonmises docstring. * `#12899 <https://github.com/scipy/scipy/pull/12899>`__: TST: make a couple of tests expected to fail on 32-bit architectures * `#12903 <https://github.com/scipy/scipy/pull/12903>`__: DOC: update Windows build guide and move into contributor guide * `#12907 <https://github.com/scipy/scipy/pull/12907>`__: DOC: clarify which array the precenter option applies to * `#12908 <https://github.com/scipy/scipy/pull/12908>`__: MAINT: spatial: Remove two occurrences of unused variables in... * `#12909 <https://github.com/scipy/scipy/pull/12909>`__: ENH: stats: Add methods gumbel_r._sf and gumbel_r._isf * `#12910 <https://github.com/scipy/scipy/pull/12910>`__: CI: travis: Remove some unnecessary code from .travis.yml. * `#12911 <https://github.com/scipy/scipy/pull/12911>`__: Minor fixes to dendrogram plotting * `#12921 <https://github.com/scipy/scipy/pull/12921>`__: CI: don't run GitHub Actions on fork or in cron job * `#12927 <https://github.com/scipy/scipy/pull/12927>`__: MAINT: rename integrate.simps to simpson * `#12934 <https://github.com/scipy/scipy/pull/12934>`__: MAINT: rename trapz and cumtrapz to (cumulative\_)trapezoid * `#12936 <https://github.com/scipy/scipy/pull/12936>`__: MAINT: fix numerical precision in nct.stats * `#12938 <https://github.com/scipy/scipy/pull/12938>`__: MAINT: fix linter on master * `#12941 <https://github.com/scipy/scipy/pull/12941>`__: Update minimum AIX pinnings to match non AIX builds * `#12955 <https://github.com/scipy/scipy/pull/12955>`__: BUG: Fixed wrong NaNs check in scipy.stats.weightedtau * `#12958 <https://github.com/scipy/scipy/pull/12958>`__: ENH: stats: Implement _logpdf, _sf and _isf for nakagami. * `#12962 <https://github.com/scipy/scipy/pull/12962>`__: Correcting that p should be in [0,1] for a variety of discrete... * `#12964 <https://github.com/scipy/scipy/pull/12964>`__: BUG: added line.strip() to split_data_line() * `#12968 <https://github.com/scipy/scipy/pull/12968>`__: ENH: stats: Use only an analytical formula or scalar root-finding... * `#12971 <https://github.com/scipy/scipy/pull/12971>`__: MAINT: Declare support for Python 3.9 * `#12972 <https://github.com/scipy/scipy/pull/12972>`__: MAINT: Remove redundant Python < 3.6 code * `#12980 <https://github.com/scipy/scipy/pull/12980>`__: DOC: Update documentation on optimize.rosen * `#12983 <https://github.com/scipy/scipy/pull/12983>`__: ENH: improvements to stats.linregress * `#12990 <https://github.com/scipy/scipy/pull/12990>`__: DOC: Clarify that using sos as output type for iirdesign can... * `#12992 <https://github.com/scipy/scipy/pull/12992>`__: DOC: capitalization and formatting in lsmr * `#12995 <https://github.com/scipy/scipy/pull/12995>`__: DOC: stats: Several documentation fixes. * `#12996 <https://github.com/scipy/scipy/pull/12996>`__: BUG: Improve error messages for \`range\` arg of binned_statistic_dd * `#12998 <https://github.com/scipy/scipy/pull/12998>`__: MAINT: approx_derivative with FP32 closes #12991 * `#13004 <https://github.com/scipy/scipy/pull/13004>`__: TST: isinstance(OptimizeResult.message, str) closes #13001 * `#13006 <https://github.com/scipy/scipy/pull/13006>`__: Keep correct dtype when loading empty mat arrays. * `#13009 <https://github.com/scipy/scipy/pull/13009>`__: MAINT: clip SLSQP step within bounds * `#13012 <https://github.com/scipy/scipy/pull/13012>`__: DOC: fix bilinear_zpk example labels * `#13013 <https://github.com/scipy/scipy/pull/13013>`__: ENH: Add \`subset\` and \`subsets\` methods to \`DisjointSet\`... * `#13029 <https://github.com/scipy/scipy/pull/13029>`__: MAINT: basinhopping callback for initial mininmisation * `#13032 <https://github.com/scipy/scipy/pull/13032>`__: DOC: fix docstring errors in in stats.wilcoxon * `#13036 <https://github.com/scipy/scipy/pull/13036>`__: BUG: forward port lint_diff shims * `#13041 <https://github.com/scipy/scipy/pull/13041>`__: MAINT: dogbox ensure x is within bounds closes #11403 * `#13042 <https://github.com/scipy/scipy/pull/13042>`__: MAINT: forward port 1.5.4 release notes * `#13046 <https://github.com/scipy/scipy/pull/13046>`__: DOC: Update optimize.least_squares doc for all tolerance must... * `#13052 <https://github.com/scipy/scipy/pull/13052>`__: Typo fix for cluster documentation * `#13054 <https://github.com/scipy/scipy/pull/13054>`__: BUG: fix \`scipy.optimize.show_options\` for unknown methods.... * `#13056 <https://github.com/scipy/scipy/pull/13056>`__: MAINT: fft: Fix a C++ compiler warning. * `#13057 <https://github.com/scipy/scipy/pull/13057>`__: Minor fixes on doc of function csr_tocsc * `#13058 <https://github.com/scipy/scipy/pull/13058>`__: DOC: stats: Replace np.float with np.float64 in a tutorial file. * `#13059 <https://github.com/scipy/scipy/pull/13059>`__: DOC: stats: Update the "Returns" section of the linregress docstring. * `#13060 <https://github.com/scipy/scipy/pull/13060>`__: MAINT: clip_x_for_func should be private * `#13061 <https://github.com/scipy/scipy/pull/13061>`__: DOC: signal.win -> signal.windows.win in Examples * `#13063 <https://github.com/scipy/scipy/pull/13063>`__: MAINT: Add suite-sparse and sksparse installation check * `#13070 <https://github.com/scipy/scipy/pull/13070>`__: MAINT: stats: Remove a couple obsolete comments. * `#13073 <https://github.com/scipy/scipy/pull/13073>`__: BUG: Fix scalar_search_wolfe2 to resolve #12157 * `#13078 <https://github.com/scipy/scipy/pull/13078>`__: CI, MAINT: migrate Lint to Azure * `#13081 <https://github.com/scipy/scipy/pull/13081>`__: BLD: drop Python 3.6 support (NEP 29) * `#13082 <https://github.com/scipy/scipy/pull/13082>`__: MAINT: update minimum NumPy version to 1.16.5 in a couple more... * `#13083 <https://github.com/scipy/scipy/pull/13083>`__: DOC: update toolchain.rst * `#13086 <https://github.com/scipy/scipy/pull/13086>`__: DOC: Update the Parameters section of the correlation docstring * `#13087 <https://github.com/scipy/scipy/pull/13087>`__: ENH:signal: Speed-up Cython implementation of _sosfilt * `#13089 <https://github.com/scipy/scipy/pull/13089>`__: BLD, BUG: add c99 compiler flag to HiGHS basiclu library * `#13091 <https://github.com/scipy/scipy/pull/13091>`__: BUG: Fix GIL handling in _sosfilt * `#13094 <https://github.com/scipy/scipy/pull/13094>`__: DOC: clarify "location" in docstring of cKDTree.query * `#13095 <https://github.com/scipy/scipy/pull/13095>`__: Zoom resize update * `#13097 <https://github.com/scipy/scipy/pull/13097>`__: BUG: fix CubicSpline(..., bc_type="periodic") #11758 * `#13100 <https://github.com/scipy/scipy/pull/13100>`__: BUG: sparse: Correct output format of kron * `#13107 <https://github.com/scipy/scipy/pull/13107>`__: ENH: faster linear_sum_assignment for small cost matrices * `#13110 <https://github.com/scipy/scipy/pull/13110>`__: CI, MAINT: refguide/asv checks to azure * `#13112 <https://github.com/scipy/scipy/pull/13112>`__: CI: fix MacOS CI * `#13113 <https://github.com/scipy/scipy/pull/13113>`__: CI: Install word list package for refguide-check * `#13115 <https://github.com/scipy/scipy/pull/13115>`__: BUG: add value range check for signal.iirdesign() * `#13116 <https://github.com/scipy/scipy/pull/13116>`__: CI: Don't report name errors after an exception in refguide-check * `#13117 <https://github.com/scipy/scipy/pull/13117>`__: CI: move sdist/pre-release test Azure * `#13119 <https://github.com/scipy/scipy/pull/13119>`__: Improve error message on friedmanchisquare function * `#13121 <https://github.com/scipy/scipy/pull/13121>`__: Fix factorial() for NaN on Python 3.10 * `#13123 <https://github.com/scipy/scipy/pull/13123>`__: BLD: Specify file extension for language standard version tests * `#13128 <https://github.com/scipy/scipy/pull/13128>`__: TST: skip Fortran I/O test for ODR * `#13130 <https://github.com/scipy/scipy/pull/13130>`__: TST: skip factorial() float tests on Python 3.10 * `#13136 <https://github.com/scipy/scipy/pull/13136>`__: CI:Add python dbg run to GH Actions * `#13138 <https://github.com/scipy/scipy/pull/13138>`__: CI: Port coverage, 64-bit BLAS, GCC 4.8 build to azure * `#13139 <https://github.com/scipy/scipy/pull/13139>`__: Fix edge case for mode='nearest' in ndimage.interpolation functions * `#13141 <https://github.com/scipy/scipy/pull/13141>`__: BUG: signal: Fix data type of the numerator returned by ss2tf. * `#13144 <https://github.com/scipy/scipy/pull/13144>`__: MAINT: stats: restrict gausshyper z > -1 * `#13146 <https://github.com/scipy/scipy/pull/13146>`__: typo in csr.py * `#13148 <https://github.com/scipy/scipy/pull/13148>`__: BUG: stats: fix typo in stable rvs per gh-12870 * `#13149 <https://github.com/scipy/scipy/pull/13149>`__: DOC: spatial/stats: cross-ref random rotation matrix functions * `#13151 <https://github.com/scipy/scipy/pull/13151>`__: MAINT: stats: Fix a test and a couple PEP-8 issues. * `#13152 <https://github.com/scipy/scipy/pull/13152>`__: MAINT: stats: Use np.take_along_axis in the private function... * `#13154 <https://github.com/scipy/scipy/pull/13154>`__: ENH: stats: Implement defined handling of constant inputs in... * `#13156 <https://github.com/scipy/scipy/pull/13156>`__: DOC: maintain equal display range for ndimage.zoom example * `#13159 <https://github.com/scipy/scipy/pull/13159>`__: CI: Azure: Don't run tests on merge commits, except for coverage * `#13160 <https://github.com/scipy/scipy/pull/13160>`__: DOC: stats: disambiguate location-shifted/noncentral * `#13161 <https://github.com/scipy/scipy/pull/13161>`__: BUG: DifferentialEvolutionSolver.__del__ can fail in garbage... * `#13163 <https://github.com/scipy/scipy/pull/13163>`__: BUG: stats: fix bug in spearmanr nan propagation * `#13167 <https://github.com/scipy/scipy/pull/13167>`__: MAINT: stats: Fix a test. * `#13169 <https://github.com/scipy/scipy/pull/13169>`__: BUG: stats: Fix handling of misaligned masks in mstats.pearsonr. * `#13178 <https://github.com/scipy/scipy/pull/13178>`__: CI: testing.yml --> macos.yml * `#13181 <https://github.com/scipy/scipy/pull/13181>`__: CI: fix lint * `#13190 <https://github.com/scipy/scipy/pull/13190>`__: BUG: optimize: fix a duplicate key bug for \`test_show_options\` * `#13192 <https://github.com/scipy/scipy/pull/13192>`__: BUG:linalg: Add overwrite option to gejsv wrapper * `#13194 <https://github.com/scipy/scipy/pull/13194>`__: BUG: slsqp should be able to use rel_step * `#13199 <https://github.com/scipy/scipy/pull/13199>`__: [skip travis] DOC: 1.6.0 release notes * `#13203 <https://github.com/scipy/scipy/pull/13203>`__: fix typos * `#13209 <https://github.com/scipy/scipy/pull/13209>`__: TST:linalg: set the seed for cossin test * `#13212 <https://github.com/scipy/scipy/pull/13212>`__: [DOC] Backtick and directive consistency. * `#13217 <https://github.com/scipy/scipy/pull/13217>`__: REL: add necessary setuptools and numpy version pins in pyproject.toml... * `#13226 <https://github.com/scipy/scipy/pull/13226>`__: BUG: pavement.py file handle fixes * `#13249 <https://github.com/scipy/scipy/pull/13249>`__: Handle cval correctly for ndimage functions with complex-valued... * `#13253 <https://github.com/scipy/scipy/pull/13253>`__: BUG,MAINT: Ensure all Pool objects are closed * `#13255 <https://github.com/scipy/scipy/pull/13255>`__: BUG:linalg: Fix heevx wrappers and add new tests * `#13260 <https://github.com/scipy/scipy/pull/13260>`__: CI: fix macOS testing * `#13269 <https://github.com/scipy/scipy/pull/13269>`__: CI: github actions: In the linux dbg tests, update apt before... * `#13279 <https://github.com/scipy/scipy/pull/13279>`__: MAINT: 1.6.0 rc2 backports Checksums ========= MD5 ~~~ 8fd605d90e571560d33eecb3c2263abe scipy-1.6.0-cp37-cp37m-macosx_10_9_x86_64.whl ab345b6f61228d8d797083fbe4ecb9df scipy-1.6.0-cp37-cp37m-manylinux1_i686.whl 6df40fd888aacc66c7fa2f905fdc3c8b 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NumPy-Discussion December 2020