From tyler.je.reddy at gmail.com Tue Dec 1 23:00:43 2020 From: tyler.je.reddy at gmail.com (Tyler Reddy) Date: Tue, 1 Dec 2020 21:00:43 -0700 Subject: [SciPy-Dev] Proposed 1.6.0 Release Schedule In-Reply-To: References:

Message-ID: There are only 6 PRs left with the 1.6.0 milestone: There are two "major feature" PRs that can always use a set of eyes: https://github.com/scipy/scipy/pull/10730 https://github.com/scipy/scipy/pull/12541 There are two stats PRs left, but I believe the "bandwidth" is a bit better with core devs reviewing those, and they could always be bumped I think. There are two "dueling" optimize PRs: https://github.com/scipy/scipy/pull/13143 https://github.com/scipy/scipy/pull/12889 So, maybe we are within a single digit number of days from branching 1.6.0 now? Many thanks to several core devs (of NumPy and SciPy projects) for assisting with efforts to migrate off Travis CI due to the recent restrictions imposed there. Best wishes, Tyler On Sun, 22 Nov 2020 at 13:24, Ilhan Polat wrote: > I don't mean to disrupt your flow but please feel free to assign some > dummy work to us if need be. Given the pandemic and all, it's much easier > to get overwhelmed with stuff these days. > > On Sun, Nov 22, 2020 at 8:14 PM Tyler Reddy > wrote: > >> We may need to delay the release a bit. Mostly because of the disruption >> to the Travis CI service (not running/credit limit), which would probably >> affect the wheels repo before a final release too. I've tried to move a few >> of the simpler jobs to Azure, but probably running out of steam for that >> effort this weekend. >> >> I've reached out to Travis CI support and asked NumFOCUS informally what >> we might do here. A few kind folks have offered to chip in to keep the CI >> running short-term. Let's see what happens with the former inquiries first? >> >> Open PR count is currently 26 for the 1.6.0 milestone, though probably a >> few more I can bump to next milestone. >> >> Best wishes, >> Tyler >> >> >> >> On Mon, 9 Nov 2020 at 00:12, Evgeni Burovski >> wrote: >> >>> How about shifting the release towards January then? >>> >>> ??, 9 ????. 2020 ?., 1:43 Tyler Reddy : >>> >>>> Ok, I'll bump it by a week then. >>>> >>>> - November 24: branch maintenance/1.6.x >>>> - November 27: rc1 >>>> - December 8: rc2 (if needed) >>>> - December 17: final release >>>> >>>> >>>> I was trying to avoid the overlap with US Thanksgiving and a final >>>> release in late December near Winter Break, but I'll manage. >>>> >>>> Tyler >>>> >>>> On Sun, 8 Nov 2020 at 14:53, Ralf Gommers >>>> wrote: >>>> >>>>> >>>>> >>>>> On Sat, Nov 7, 2020 at 10:48 PM Tyler Reddy >>>>> wrote: >>>>> >>>>>> Hi, >>>>>> >>>>>> SciPy 1.5.0 was released June 21 (~5 months ago), and I think we'd >>>>>> like to keep a roughly biannual release cadence. >>>>>> >>>>>> I'd like to propose the following schedule for 1.6.0: >>>>>> - November 17: branch 1.6.x >>>>>> - November 20: rc1 >>>>>> - December 1: rc2 (if needed) >>>>>> - December 10: final release >>>>>> >>>>>> As always, it is a good idea to start tagging things that should be >>>>>> in 1.6.0 & please do help with reviewing PRs/issues that are >>>>>> tagged--current counts are: >>>>>> >>>>>> - PRs: 45 open with 1.6.0 milestone >>>>>> - issues: 25 open with 1.6.0 milestone >>>>>> >>>>>> While helping with that, also great if the release notes wiki is >>>>>> updated for appropriate changes: >>>>>> https://github.com/scipy/scipy/wiki/Release-note-entries-for-SciPy-1.6.0 >>>>>> >>>>>> >>>>>> Thoughts/objections for the schedule? >>>>>> >>>>> >>>>> >>>>> It does seem like there's a lot of PRs open, having only one weekend >>>>> left seems optimistic. The majority can be bumped to 1.7.0, but there's a >>>>> bit of a backlog of nice PRs that have been ready for a while and would be >>>>> nice to get in. For example: >>>>> >>>>> - KDTree/cKDTree feature parity: >>>>> https://github.com/scipy/scipy/pull/12852 >>>>> - HiGHS solver as linprog method: >>>>> https://github.com/scipy/scipy/pull/12043 >>>>> - Balanced cut tree: https://github.com/scipy/scipy/pull/10730 >>>>> - Andrew's set of optimize PRs that he brought up on the mailing list >>>>> recently >>>>> - Greg's set of ndimage PRs, complex kernels and the boundary handling >>>>> ones (#12725, #12767, #12776) >>>>> >>>>> If it's possible, given your time constraints, to bump the schedule by >>>>> one week then that may be useful. >>>>> >>>>> Cheers, >>>>> Ralf >>>>> >>>>> >>>>> >>>>> >>>>> _______________________________________________ >>>>> SciPy-Dev mailing list >>>>> SciPy-Dev at python.org >>>>> https://mail.python.org/mailman/listinfo/scipy-dev >>>>> >>>> _______________________________________________ >>>> SciPy-Dev mailing list >>>> SciPy-Dev at python.org >>>> https://mail.python.org/mailman/listinfo/scipy-dev >>>> >>> _______________________________________________ >>> SciPy-Dev mailing list >>> SciPy-Dev at python.org >>> https://mail.python.org/mailman/listinfo/scipy-dev >>> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at python.org >> https://mail.python.org/mailman/listinfo/scipy-dev >> > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralf.gommers at gmail.com Wed Dec 2 07:01:30 2020 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Wed, 2 Dec 2020 12:01:30 +0000 Subject: [SciPy-Dev] Proposed 1.6.0 Release Schedule In-Reply-To: References:

Message-ID: On Wed, Dec 2, 2020 at 4:01 AM Tyler Reddy wrote: > There are only 6 PRs left with the 1.6.0 milestone: > > There are two "major feature" PRs that can always use a set of eyes: > https://github.com/scipy/scipy/pull/10730 > This one is basically done, should be good to merge after moving some tests around. https://github.com/scipy/scipy/pull/12541 > This one, for a balanced cut tree hierarchical clustering function, could really use another set of eyes. Even a user of the existing cut_tree function that wants to play with this new function would be helpful. > There are two stats PRs left, but I believe the "bandwidth" is a bit > better with core devs reviewing those, and they could always be bumped I > think. > > There are two "dueling" optimize PRs: > https://github.com/scipy/scipy/pull/13143 > https://github.com/scipy/scipy/pull/12889 > > So, maybe we are within a single digit number of days from branching 1.6.0 > now? > > Many thanks to several core devs (of NumPy and SciPy projects) for > assisting with efforts to migrate off Travis CI due to the recent > restrictions imposed there. > +10 Cheers, Ralf > Best wishes, > Tyler > > On Sun, 22 Nov 2020 at 13:24, Ilhan Polat wrote: > >> I don't mean to disrupt your flow but please feel free to assign some >> dummy work to us if need be. Given the pandemic and all, it's much easier >> to get overwhelmed with stuff these days. >> >> On Sun, Nov 22, 2020 at 8:14 PM Tyler Reddy >> wrote: >> >>> We may need to delay the release a bit. Mostly because of the disruption >>> to the Travis CI service (not running/credit limit), which would probably >>> affect the wheels repo before a final release too. I've tried to move a few >>> of the simpler jobs to Azure, but probably running out of steam for that >>> effort this weekend. >>> >>> I've reached out to Travis CI support and asked NumFOCUS informally what >>> we might do here. A few kind folks have offered to chip in to keep the CI >>> running short-term. Let's see what happens with the former inquiries first? >>> >>> Open PR count is currently 26 for the 1.6.0 milestone, though probably a >>> few more I can bump to next milestone. >>> >>> Best wishes, >>> Tyler >>> >>> >>> >>> On Mon, 9 Nov 2020 at 00:12, Evgeni Burovski >>> wrote: >>> >>>> How about shifting the release towards January then? >>>> >>>> ??, 9 ????. 2020 ?., 1:43 Tyler Reddy : >>>> >>>>> Ok, I'll bump it by a week then. >>>>> >>>>> - November 24: branch maintenance/1.6.x >>>>> - November 27: rc1 >>>>> - December 8: rc2 (if needed) >>>>> - December 17: final release >>>>> >>>>> >>>>> I was trying to avoid the overlap with US Thanksgiving and a final >>>>> release in late December near Winter Break, but I'll manage. >>>>> >>>>> Tyler >>>>> >>>>> On Sun, 8 Nov 2020 at 14:53, Ralf Gommers >>>>> wrote: >>>>> >>>>>> >>>>>> >>>>>> On Sat, Nov 7, 2020 at 10:48 PM Tyler Reddy >>>>>> wrote: >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> SciPy 1.5.0 was released June 21 (~5 months ago), and I think we'd >>>>>>> like to keep a roughly biannual release cadence. >>>>>>> >>>>>>> I'd like to propose the following schedule for 1.6.0: >>>>>>> - November 17: branch 1.6.x >>>>>>> - November 20: rc1 >>>>>>> - December 1: rc2 (if needed) >>>>>>> - December 10: final release >>>>>>> >>>>>>> As always, it is a good idea to start tagging things that should be >>>>>>> in 1.6.0 & please do help with reviewing PRs/issues that are >>>>>>> tagged--current counts are: >>>>>>> >>>>>>> - PRs: 45 open with 1.6.0 milestone >>>>>>> - issues: 25 open with 1.6.0 milestone >>>>>>> >>>>>>> While helping with that, also great if the release notes wiki is >>>>>>> updated for appropriate changes: >>>>>>> https://github.com/scipy/scipy/wiki/Release-note-entries-for-SciPy-1.6.0 >>>>>>> >>>>>>> >>>>>>> Thoughts/objections for the schedule? >>>>>>> >>>>>> >>>>>> >>>>>> It does seem like there's a lot of PRs open, having only one weekend >>>>>> left seems optimistic. The majority can be bumped to 1.7.0, but there's a >>>>>> bit of a backlog of nice PRs that have been ready for a while and would be >>>>>> nice to get in. For example: >>>>>> >>>>>> - KDTree/cKDTree feature parity: >>>>>> https://github.com/scipy/scipy/pull/12852 >>>>>> - HiGHS solver as linprog method: >>>>>> https://github.com/scipy/scipy/pull/12043 >>>>>> - Balanced cut tree: https://github.com/scipy/scipy/pull/10730 >>>>>> - Andrew's set of optimize PRs that he brought up on the mailing list >>>>>> recently >>>>>> - Greg's set of ndimage PRs, complex kernels and the boundary >>>>>> handling ones (#12725, #12767, #12776) >>>>>> >>>>>> If it's possible, given your time constraints, to bump the schedule >>>>>> by one week then that may be useful. >>>>>> >>>>>> Cheers, >>>>>> Ralf >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> _______________________________________________ >>>>>> SciPy-Dev mailing list >>>>>> SciPy-Dev at python.org >>>>>> https://mail.python.org/mailman/listinfo/scipy-dev >>>>>> >>>>> _______________________________________________ >>>>> SciPy-Dev mailing list >>>>> SciPy-Dev at python.org >>>>> https://mail.python.org/mailman/listinfo/scipy-dev >>>>> >>>> _______________________________________________ >>>> SciPy-Dev mailing list >>>> SciPy-Dev at python.org >>>> https://mail.python.org/mailman/listinfo/scipy-dev >>>> >>> _______________________________________________ >>> SciPy-Dev mailing list >>> SciPy-Dev at python.org >>> https://mail.python.org/mailman/listinfo/scipy-dev >>> >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at python.org >> https://mail.python.org/mailman/listinfo/scipy-dev >> > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From jacobr at ethz.ch Wed Dec 2 07:27:17 2020 From: jacobr at ethz.ch (Romain Jacob) Date: Wed, 2 Dec 2020 13:27:17 +0100 Subject: [SciPy-Dev] Proposed 1.6.0 Release Schedule In-Reply-To: References:

Message-ID: Hi everyone, > There are two stats PRs left, but I believe the "bandwidth" is a bit > better with core devs reviewing those, and they could always be bumped > I think. Could this stat PR (https://github.com/scipy/scipy/pull/12680) be considered for the 1.6.0 milestone? I made the suggested changes and there has not been any further comments in a while; so, just wandering :-) If not, it would be cool to know what else I am expected to provide to conclude PR. Cheers, -- Romain > > There are two "dueling" optimize PRs: > https://github.com/scipy/scipy/pull/13143 > https://github.com/scipy/scipy/pull/12889 > > So, maybe we are within a single digit number of days from branching > 1.6.0 now? > > Many thanks to several core devs (of NumPy and SciPy projects) for > assisting with efforts to migrate off Travis CI due to the recent > restrictions imposed there. > > Best wishes, > Tyler > > On Sun, 22 Nov 2020 at 13:24, Ilhan Polat > wrote: > > I don't mean to disrupt your flow but please feel free to assign > some dummy work to us if need be. Given the pandemic and all, it's > much easier to get overwhelmed with stuff these days. > > On Sun, Nov 22, 2020 at 8:14 PM Tyler Reddy > > wrote: > > We may need to delay the release a bit. Mostly because of the > disruption to the Travis CI service (not running/credit > limit), which would probably affect the wheels repo before a > final release too. I've tried to move a few of the simpler > jobs to Azure, but probably running out of steam for that > effort this weekend. > > I've reached out to Travis CI support and asked NumFOCUS > informally what we might do here. A few kind folks have > offered to chip in to keep the CI running short-term. Let's > see what happens with the former inquiries first? > > Open PR count is currently 26 for the 1.6.0 milestone, though > probably a few more I can bump to next milestone. > > Best wishes, > Tyler > > > > On Mon, 9 Nov 2020 at 00:12, Evgeni Burovski > > wrote: > > How about shifting the release towards January then? > > ??, 9 ????. 2020 ?., 1:43 Tyler Reddy > >: > > Ok, I'll bump it by a week then. > > * November 24: branch maintenance/1.6.x > * November 27: rc1 > * December 8: rc2 (if needed) > * December 17: final release > > I was trying to avoid the overlap with US Thanksgiving > and a final release in late December near Winter > Break, but I'll manage. > Tyler > > On Sun, 8 Nov 2020 at 14:53, Ralf Gommers > > wrote: > > > > On Sat, Nov 7, 2020 at 10:48 PM Tyler Reddy > > wrote: > > Hi, > > SciPy 1.5.0 was released June 21 (~5 months > ago), and I think we'd like to keep a roughly > biannual release cadence. > > I'd like to propose the following schedule for > 1.6.0: > - November 17: branch 1.6.x > - November 20: rc1 > - December 1: rc2 (if needed) > - December 10: final release > > As always, it is a good idea to start tagging > things that should be in 1.6.0 & please do > help with reviewing PRs/issues that are > tagged--current counts are: > > - PRs: 45 open with 1.6.0 milestone > - issues: 25 open with 1.6.0 milestone > > While helping with that, also great if the > release notes wiki is updated for appropriate > changes: > https://github.com/scipy/scipy/wiki/Release-note-entries-for-SciPy-1.6.0 > > > Thoughts/objections for the schedule? > > > > It does seem like there's a lot of PRs open, > having only one weekend left seems optimistic. The > majority can be bumped to 1.7.0, but there's a bit > of a backlog of nice PRs that have been ready for > a while and would be nice to get in. For example: > > - KDTree/cKDTree feature parity: > https://github.com/scipy/scipy/pull/12852 > - HiGHS solver as linprog method: > https://github.com/scipy/scipy/pull/12043 > - Balanced cut tree: > https://github.com/scipy/scipy/pull/10730 > - Andrew's set of optimize PRs that he brought up > on the mailing list recently > - Greg's set of ndimage PRs, complex kernels and > the boundary handling ones (#12725, #12767, #12776) > > If it's possible, given your time constraints, to > bump the schedule by one week then that may be useful. > > Cheers, > Ralf > > > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > > > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev -- Romain Jacob Postdoctoral Researcher ETH Zurich - Networked Systems Group (NSG) www.romainjacob.net @RJacobPartner Gloriastrasse 35, ETZ G78 8092 Zurich +41 7 68 16 88 22 -------------- next part -------------- An HTML attachment was scrubbed... URL: From warren.weckesser at gmail.com Wed Dec 2 18:15:27 2020 From: warren.weckesser at gmail.com (Warren Weckesser) Date: Wed, 2 Dec 2020 18:15:27 -0500 Subject: [SciPy-Dev] Adding the function `relative_risk` to SciPy Message-ID: Hey all, In the pull request https://github.com/scipy/scipy/pull/13048 I propose adding the function 'relative_risk' to the scipy.stats.contingency namespace. Relative risk (https://en.wikipedia.org/wiki/Relative_risk; also known as the risk ratio) is one of the fundamental statistics used when analyzing 2x2 contingency tables. (Other widely used statistics include the risk difference and the odds ratio.) Given the current pandemic, it is especially pertinent, as the relative risk is often used to summarize the result of a study that compares outcomes for treated and untreated subjects in medical research. For example, the main statistic quoted in the abstract of https://www.mdpi.com/2072-6643/12/12/3642/htm is the relative risk. The calculation of the relative risk for a given contingency table is trivial. The more useful part of the PR is the calculation of the confidence interval for the relative risk. I've had feedback from Ralf and Lucas; additional comments and suggestions would be welcome. Warren From charlesr.harris at gmail.com Thu Dec 3 16:34:40 2020 From: charlesr.harris at gmail.com (Charles R Harris) Date: Thu, 3 Dec 2020 14:34:40 -0700 Subject: [SciPy-Dev] NumPy 1.20.0rc1 released Message-ID: Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.20.0rc1. This NumPy release is the largest to date, containing some 654 merged pull requests contributed by 182 people. See the list of highlights below. The Python versions supported for this release are 3.7-3.9, support for Python 3.6 has been dropped. Wheels can be downloaded from PyPI ; source archives, release notes, and wheel hashes are available on Github . Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Highlights* - Annotations for NumPy functions. This work is ongoing and improvements can be expected pending feedback from users. - Wider use of SIMD to increase execution speed of ufuncs. Much work has been done in introducing universal functions that will ease use of modern features across different hardware platforms. This work is ongoing. - Preliminary work in changing the dtype and casting implementations in order to provide an easier path to extending dtypes. This work is ongoing but enough has been done to allow experimentation and feedback. - Extensive documentation improvements comprising some 185 PR merges. This work is ongoing and part of the larger project to improve NumPy's online presence and usefulness to new users. - Further cleanups related to removing Python 2.7. This improves code readability and removes technical debt. - Preliminary support for the upcoming Cython 3.0. *Contributors* A total of 182 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Meurer + - Abhilash Barigidad + - Abhinav Reddy + - Abhishek Singh + - Al-Baraa El-Hag + - Albert Villanova del Moral + - Alex Leontiev + - Alex Rockhill + - Alex Rogozhnikov - Alexander Belopolsky - Alexander Kuhn-Regnier + - Allen Downey + - Andras Deak - Andrea Olivo andryandrew at gmail.com andryandrew + - Andrew Eckart + - Anirudh Subramanian - Anthony Byuraev + - Antonio Larrosa + - Ashutosh Singh + - Bangcheng Yang + - Bas van Beek + - Ben Derrett + - Ben Elliston + - Ben Nathanson + - Bharat Medasani + - Bharat Raghunathan - Bijesh Mohan + - Bradley Dice + - Brandon David + - Brandt Bucher - Brian Soto + - Brigitta Sipocz - Cameron Blocker + - Carl Leake + - Charles Harris - Chris Brown + - Chris Vavaliaris + - Chunlin Fang - CloseChoice + - Daniel G. A. Smith + - Daniel Hrisca - Daniel Vanzo + - David Pitchford + - Davide Dal Bosco + - Dima Kogan + - Dmitry Kutlenkov + - Douglas Fenstermacher + - Dustin Spicuzza + - E. Madison Bray + - Elia Franzella + - Enrique Mat?as S?nchez (Quique) + - Erfan Nariman | Veneficus + - Eric Larson - Eric Moore - Eric Wieser - Erik M. Bray - EthanCJ-git + - Etienne Guesnet + - Felix Divo - Frankie Robertson + - Ganesh Kathiresan - Gengxin Xie - Gerry Manoim + - Guilherme Leobas - Hassan Kibirige - Hugo Mendes + - Hugo van Kemenade - Ian Thomas + - InessaPawson + - Isabela Presedo-Floyd + - Isuru Fernando - Jakob Jacobson + - Jakob Jakobson + - Jakub Wilk - James Myatt + - Jesse Li + - John Hagen + - John Zwinck - Joseph Fox-Rabinovitz - Josh Wilson - Jovial Joe Jayarson + - Julia Signell + - Jun Kudo + - Karan Dhir + - Kaspar Thommen + - Kerem Halla? - Kevin Moore + - Kevin Sheppard - Klaus Zimmermann + - LSchroefl + - Laurie + - Laurie Stephey + - Levi Stovall + - Lisa Schwetlick + - Lukas Geiger + - Madhulika Jain Chambers + - Matthias Bussonnier - Matti Picus - Melissa Weber Mendon?a - Michael Hirsch - Nick R. Papior - Nikola Forr? - Noman Arshad + - Paul YS Lee + - Pauli Virtanen - Pawe? Redzy?ski + - Peter Andreas Entschev - Peter Bell - Philippe Ombredanne + - Phoenix Meadowlark + - Piotr Gai?ski - Raghav Khanna + - Raghuveer Devulapalli - Rajas Rade + - Rakesh Vasudevan - Ralf Gommers - Raphael Kruse + - Rashmi K A + - Robert Kern - Rohit Sanjay + - Roman Yurchak - Ross Barnowski - Royston E Tauro + - Ryan C Cooper + - Ryan Soklaski - Safouane Chergui + - Sahil Siddiq + - Sarthak Vineet Kumar + - Sayed Adel - Sebastian Berg - Sergei Vorfolomeev + - Seth Troisi - Sidhant Bansal + - Simon Gasse - Simon Graham + - Stefan Appelhoff + - Stefan Behnel + - Stefan van der Walt - Steve Dower - Steve Joachim + - Steven Pitman + - Stuart Archibald - Sturla Molden - Susan Chang + - Takanori H + - Tapajyoti Bose + - Thomas A Caswell - Tina Oberoi - Tirth Patel - Tobias Pitters + - Tyler Reddy - Veniamin Petrenko + - Wansoo Kim + - Warren Weckesser - Wei Yang + - Wojciech Rzadkowski - Yang Hau + - Yogesh Raisinghani + - Yu Feng - Yuya Unno + - Zac Hatfield-Dodds - Zuhair Ali-Khan + - @abhilash42 + - @bernie gray + - @danbeibei + - @dojafrat - @dpitch40 + - @forfun + - @iamsoto + - @jbrockmendel + - @leeyspaul + - @mitch + - @prateek arora + - @qiyu8 + - @serge-sans-paille + - @skywalker + - @stphnlyd + - @xoviat - @??? + - @JMFT + - @Jack + - @Neal C + Cheers, Charles Harris -------------- next part -------------- An HTML attachment was scrubbed... URL: From mishaogan1 at hotmail.com Tue Dec 8 08:41:55 2020 From: mishaogan1 at hotmail.com (Mikheil Oganesyan) Date: Tue, 8 Dec 2020 13:41:55 +0000 Subject: [SciPy-Dev] New method of PSD estimation: pyulear Message-ID: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> Hi everyone Very excited to have joined this community! I was thinking of adding a new method of estimating power spectral density (PSD) - namely using an autoregressive (AR) model. There?s an equivalent function in MATLAB called pyulear (link: https://uk.mathworks.com/help/signal/ref/pyulear.html) Reasoning is the following: Scipy already contains two standard methods of PSD estimation that are non-parametric - periodogram and Welch?s method. However there isn?t an implementation of a parametric approach. The AR method is the most widely used in the category. The function would be useful in a variety of contexts related to signal processing and statistics. Given that scipy.signal is the go-to package for signal processing in Python, I strongly feel that this function belongs here. Currently, the way to implement AR PSD estimation involves using either statsmodels + scipy, or numpy + scipy. Thus it only makes sense to have this out of the box. What are your thoughts on this? I would be happy to implement it given a green light. Best regards and excited to contribute, Mikheil -------------- next part -------------- An HTML attachment was scrubbed... URL: From jfoxrabinovitz at gmail.com Tue Dec 8 16:08:36 2020 From: jfoxrabinovitz at gmail.com (Joseph Fox-Rabinovitz) Date: Tue, 8 Dec 2020 16:08:36 -0500 Subject: [SciPy-Dev] New method of PSD estimation: pyulear In-Reply-To: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> References: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> Message-ID: I'd be very happy to see this. Joe On Tue, Dec 8, 2020, 08:42 Mikheil Oganesyan wrote: > Hi everyone > > Very excited to have joined this community! > > I was thinking of adding a new method of estimating power spectral density > (PSD) - namely using an autoregressive (AR) model. > There?s an equivalent function in MATLAB called pyulear (link: > https://uk.mathworks.com/help/signal/ref/pyulear.html) > > Reasoning is the following: > > Scipy already contains two standard methods of PSD estimation that are > non-parametric - periodogram and Welch?s method. > However there isn?t an implementation of a parametric approach. The AR > method is the most widely used in the category. > > The function would be useful in a variety of contexts related to signal > processing and statistics. > Given that scipy.signal is the go-to package for signal processing in > Python, I strongly feel that this function belongs here. > > Currently, the way to implement AR PSD estimation involves using either > statsmodels + scipy, or numpy + scipy. Thus it only makes sense to have > this out of the box. > > What are your thoughts on this? I would be happy to implement it given a > green light. > > > Best regards and excited to contribute, > > Mikheil > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From tyler.je.reddy at gmail.com Tue Dec 8 21:44:17 2020 From: tyler.je.reddy at gmail.com (Tyler Reddy) Date: Tue, 8 Dec 2020 19:44:17 -0700 Subject: [SciPy-Dev] master branch is open for development of SciPy 1.7.0 Message-ID: Hi, This evening I branched maintenance/1.6.x and prepared the master branch for the development of SciPy 1.7.0. Best wishes, Tyler -------------- next part -------------- An HTML attachment was scrubbed... URL: From david.mikolas1 at gmail.com Tue Dec 8 22:48:39 2020 From: david.mikolas1 at gmail.com (David Mikolas) Date: Wed, 9 Dec 2020 11:48:39 +0800 Subject: [SciPy-Dev] New method of PSD estimation: pyulear In-Reply-To: References: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> Message-ID: I would as well, does this currently work on 2D also, or can it be modified to do so? On Wed, Dec 9, 2020 at 5:09 AM Joseph Fox-Rabinovitz < jfoxrabinovitz at gmail.com> wrote: > I'd be very happy to see this. > > Joe > > On Tue, Dec 8, 2020, 08:42 Mikheil Oganesyan > wrote: > >> Hi everyone >> >> Very excited to have joined this community! >> >> I was thinking of adding a new method of estimating power spectral >> density (PSD) - namely using an autoregressive (AR) model. >> There?s an equivalent function in MATLAB called pyulear (link: >> https://uk.mathworks.com/help/signal/ref/pyulear.html) >> >> Reasoning is the following: >> >> Scipy already contains two standard methods of PSD estimation that are >> non-parametric - periodogram and Welch?s method. >> However there isn?t an implementation of a parametric approach. The AR >> method is the most widely used in the category. >> >> The function would be useful in a variety of contexts related to signal >> processing and statistics. >> Given that scipy.signal is the go-to package for signal processing in >> Python, I strongly feel that this function belongs here. >> >> Currently, the way to implement AR PSD estimation involves using either >> statsmodels + scipy, or numpy + scipy. Thus it only makes sense to have >> this out of the box. >> >> What are your thoughts on this? I would be happy to implement it given a >> green light. >> >> >> Best regards and excited to contribute, >> >> Mikheil >> _______________________________________________ >> SciPy-Dev mailing list >> SciPy-Dev at python.org >> https://mail.python.org/mailman/listinfo/scipy-dev >> > _______________________________________________ > SciPy-Dev mailing list > SciPy-Dev at python.org > https://mail.python.org/mailman/listinfo/scipy-dev > -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralf.gommers at gmail.com Wed Dec 9 04:50:11 2020 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Wed, 9 Dec 2020 10:50:11 +0100 Subject: [SciPy-Dev] New method of PSD estimation: pyulear In-Reply-To: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> References: <70E1FED2-4942-4DEA-8D21-5FF7F61D0A4C@hotmail.com> Message-ID: On Tue, Dec 8, 2020 at 2:42 PM Mikheil Oganesyan wrote: > Hi everyone > > Very excited to have joined this community! > > I was thinking of adding a new method of estimating power spectral density > (PSD) - namely using an autoregressive (AR) model. > There?s an equivalent function in MATLAB called pyulear (link: > https://uk.mathworks.com/help/signal/ref/pyulear.html) > > Reasoning is the following: > > Scipy already contains two standard methods of PSD estimation that are > non-parametric - periodogram and Welch?s method. > However there isn?t an implementation of a parametric approach. The AR > method is the most widely used in the category. > > The function would be useful in a variety of contexts related to signal > processing and statistics. > Given that scipy.signal is the go-to package for signal processing in > Python, I strongly feel that this function belongs here. > > Currently, the way to implement AR PSD estimation involves using either > statsmodels + scipy, or numpy + scipy. Thus it only makes sense to have > this out of the box. > > What are your thoughts on this? I would be happy to implement it given a > green light. > Not having heard of this method, I looked up the literature for it. The first paper for it is titiled "Why Yule-Walker should not be used for autoregressive modelling" [1]. And the plots in the Matlab docs don't look great for spectra that shouldn't be all that hard to estimate. So is this actually useful, and aren't there better methods? [1] https://www.sciencedirect.com/science/article/pii/0306454995001263 Cheers, Ralf -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralf.gommers at gmail.com Wed Dec 9 06:19:09 2020 From: ralf.gommers at gmail.com (Ralf Gommers) Date: Wed, 9 Dec 2020 12:19:09 +0100 Subject: [SciPy-Dev] master branch is open for development of SciPy 1.7.0 In-Reply-To: References: Message-ID: On Wed, Dec 9, 2020 at 3:44 AM Tyler Reddy wrote: > Hi, > > This evening I branched maintenance/1.6.x and prepared the master branch > for the development of SciPy 1.7.0. > Nice! Before you push a commit to build RC1 wheels, I'd like to put one commit into 1.6.x with a setuptools version pin (a small subset of https://github.com/scipy/scipy/pull/12862). PR will follow shortly. Cheers, Ralf -------------- next part -------------- An HTML attachment was scrubbed... URL: From tyler.je.reddy at gmail.com Thu Dec 10 21:49:21 2020 From: tyler.je.reddy at gmail.com (Tyler Reddy) Date: Thu, 10 Dec 2020 19:49:21 -0700 Subject: [SciPy-Dev] ANN: SciPy 1.6.0rc1 -- please test Message-ID: Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.6.0rc1. Please help us test this pre-release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.6.0rc1 One of a few ways to install the release candidate with pip: pip install scipy==1.6.0rc1 ========================== SciPy 1.6.0 Release Notes ========================== Note: Scipy 1.6.0 is not released yet! 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 Cram?r-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 * R?diger Busche + * Matthias Bussonnier * Dominic C + * Corallus Caninus + * CJ Carey * Thomas A Caswell * chapochn + * Luc?a Cheung * Zach Colbert + * Coloquinte + * Yannick Copin + * Devin Crowley + * Terry Davis + * Micha?l Defferrard + * devonwp + * Didier + * divenex + * Thomas Duvernay + * Eoghan O'Connell + * G?k?en 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 * Lo?c Est?ve * 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 Forr? + * odidev * ofirr + * Sambit Panda * Dima Pasechnik * Tirth Patel + * Pawe? Redzy?ski + * Vladimir Philipenko + * Philipp Th?lke + * 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 + * S?ren Fuglede J?rgensen * 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 121 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 `__: ndimage.shift destroys data from edges (Trac #796) * `#1892 `__: using rptfile= with an existing file causes a Fortran runtime... * `#1903 `__: ndimage.rotate misses some values (Trac #1378) * `#1930 `__: scipy.io.wavfile should be able to read 24 bit signed wave (Trac... * `#3158 `__: Odd casting behaviour of signal.filtfilt * `#3203 `__: interpolation.zoom incorrect output for certain cases * `#3645 `__: BUG: stats: mstats.pearsonr calculation is wrong if the masks... * `#3665 `__: Return Bunch objects from stats functions * `#4922 `__: unexpected zero output values from zoom * `#5202 `__: BUG: stats: Spurious warnings from the pdf method of several... * `#5223 `__: Zoom does not return the same values when resizing a sub-array... * `#5396 `__: scipy.spatial.distance.pdist documention bug * `#5489 `__: ValueError: failed to create intent(cache|hide)|optional array--... * `#6096 `__: loadmat drops dtype of empty arrays when squeeze_me=True * `#6713 `__: sicpy.ndimage.zoom returns artefacts and boundaries in some cases * `#7125 `__: Impossible to know number of dimensions in c function used by... * `#7324 `__: scipy.ndimage.zoom bad interpolation when downsampling (zoom... * `#8131 `__: BUG: geometric_transform wrap mode possible bug * `#8163 `__: LSMR fails on some random values when providing an x0 * `#8210 `__: Why should I choose order > 1 for scipy.ndimage.zoom? * `#8465 `__: Unexpected behavior with reflect mode of ndimage.rotate * `#8776 `__: cdist behavior with Minkowsky and np.inf * `#9168 `__: documentation of pearson3 in scipy.stats unclear * `#9223 `__: Faster implementation of scipy.sparse.block_diag * `#9476 `__: Invalid index in signal.medfilt2d's QUICK_SELECT * `#9857 `__: scipy.odr.Output.sd_beta is not standard error * `#9865 `__: Strange behavior of \`ndimage.shift\` and \`ndimage.affine_transform\` * `#10042 `__: Consider support for multivariate student-t distribution? * `#10134 `__: gausshyper distribution accepts invalid parameters * `#10179 `__: str+bytes concatenation error in test_lapack.py * `#10216 `__: cKDTree.query_ball_point speed regression * `#10463 `__: ENH: vectorize scipy.fft for more CPU architectures * `#10593 `__: Rename \`sum\` ndimage function * `#10595 `__: scipy.stats.ttest_1samp should support alternative hypothesis * `#10610 `__: ndimage.interpolation.spline_filter1d default value of mode * `#10620 `__: ndimage.interpolation.zoom() option to work like skimage.transform.resize() * `#10711 `__: Array Shapes Not Aligned Bug in scipy.optimize._lsq.lsq_linear.py * `#10782 `__: BUG: optimize: methods unknown to \`scipy.optimize.show_options\` * `#10892 `__: Possible typo in an equation of optimize/dual_annealing * `#11020 `__: signal.fftconvolve return a tuple including lag information * `#11093 `__: scipy.interpolate.interp1d can not handle datetime64 * `#11170 `__: Use manylinux2014 to get aarch64/ppc64le support * `#11186 `__: BUG: stats: pearson3 CDF and SF functions incorrect when skew... * `#11366 `__: DeprecationWarning due to invalid escape sequences * `#11403 `__: Optimize raises "ValueError: \`x0\` violates bound constraints"... * `#11558 `__: ENH: IIR comb filter * `#11559 `__: BUG: iirdesign doesn't fail for frequencies above Nyquist * `#11567 `__: scipy.signal.iirdesign doesn't check consistency of wp and ws... * `#11654 `__: ENH: Add Negative Hypergeometric Distribution * `#11720 `__: BUG: stats: wrong shape from median_absolute_deviation for arrays... * `#11746 `__: BUG: stats: pearson3 returns size 1 arrays where other distributions... * `#11756 `__: Improve and fix \*Spline docstrings and code * `#11758 `__: BUG: of scipy.interpolate.CubicSpline when \`bc_type' is set... * `#11925 `__: MAINT: remove character encoding check in CI? * `#11963 `__: Test failures - TestLinprogIPSparseCholmod * `#12102 `__: incorrect first moment of non central t-distribution * `#12113 `__: scipy.stats.poisson docs for rate = 0 * `#12152 `__: ENH: signal.gauss_spline should accept a list * `#12157 `__: BUG: Iteration index initialisation is wrong in scipy.optimize.linesearch.scalar_search_wolfe2 * `#12162 `__: Storing Rotation object in NumPy array returns an array with... * `#12176 `__: cannot modify the slice of an array returned by \`wavfile.read\` * `#12190 `__: retrieve leave colors from dendrogram * `#12196 `__: PERF: scipy.linalg.pinv is very slow compared to numpy.linalg.pinv * `#12222 `__: Interpolating categorical data (interp1d) * `#12231 `__: Is the p-value of the Kruskal-Wallis test two-sided? * `#12249 `__: ENH: least_squares: should not re-instanciate csr_matrix if already... * `#12264 `__: DOC: optimize: linprog method-specific function signature * `#12290 `__: DOC: Convex Hull areas are actually perimeters for 2-dimensional... * `#12308 `__: integrate.solve_ivp with DOP853 method fails when yDot = 0 * `#12326 `__: BUG: stats.exponnorm.pdf returns 0 for small K * `#12337 `__: scipy.sparse.linalg.eigsh documentation is misleading * `#12339 `__: scipy.io.wavfile.write documentation has wrong example * `#12340 `__: sparse.lil_matrix.tocsr() fails silently on matrices with nzn... * `#12350 `__: Create a 2-parameter version of the gamma distribution * `#12369 `__: scipy.signal.correlate has an error in the documentation, examples... * `#12373 `__: interp1d returns incorrect values for step functions * `#12378 `__: interpolate.NearestNDInterpolator.__call__ & LinearNDInterpolator.__call__... * `#12411 `__: scipy.stats.spearmanr mishandles nan variables with "propogate" * `#12413 `__: DOC: Remove the "Basic functions" section from the SciPy tutorial. * `#12415 `__: scipy.stats.dirichlet documentation issue * `#12419 `__: least_squares ValueError with 'lm' method - regression from 1.4.1... * `#12431 `__: Request for Python wrapper for LAPACK's ?pptrf (Cholesky factorization... * `#12458 `__: spearmanr with entire NaN columns produces errors * `#12477 `__: WIP: Addition of MLE for stats.invgauss/wald * `#12483 `__: reading .wav fails when the file is too big on python 3.6.0 * `#12490 `__: BUG: stats: logistic and genlogistic logpdf overflow for large... * `#12499 `__: LinearNDInterpolator raises ValueError when value array has writeable=False... * `#12523 `__: Wrong key in __odrpack.c * `#12547 `__: typo in scipy/cluster/_hierarchy.pyx * `#12549 `__: DOC: least_squares return type is poorly formatted. * `#12578 `__: TST: test_bounds_infeasible_2 failing on wheels repo cron jobs * `#12585 `__: ENH: Add Multivariate Hypergeometric Distribution * `#12604 `__: unintuitive conversion in \`scipy.constants.lambda2nu\` * `#12606 `__: DOC: Invalid syntax in example. * `#12665 `__: List of possible bugs found by automated code analysis * `#12696 `__: scipy.optimize.fminbound, numpy depreciation warning Creating... * `#12699 `__: TestProjections.test_iterative_refinements_dense failure * `#12701 `__: TestDifferentialEvolutionSolver::test_L4 failing * `#12719 `__: Misleading scipy.signal.get_window() docstring with 'exponential'... * `#12740 `__: circstd doesn't handle R = hypot(S, C) > 1 * `#12749 `__: ENH: interp1d Matlab compatibility * `#12773 `__: Meta-issue: ndimage spline boundary handling (NumFOCUS proposal) * `#12813 `__: optimize.root(method="krylov") fails if options["tol_norm"] expects... * `#12815 `__: stats.zscore inconsistent behavior when all values are the same * `#12840 `__: scipy.signal.windows.dpss docstring typo * `#12874 `__: Rotation.random vs stats.special_ortho_group * `#12881 `__: FFT - documentation - examples - linspace construction * `#12904 `__: BUG: parsing in loadarff() * `#12917 `__: GitHub Actions nightly build triggered on forks * `#12919 `__: BUG: numerical precision, use gammaln in nct.mean * `#12924 `__: Rename Sample Based Integration Methods to Comply with Code of... * `#12940 `__: Should the minimum numpy for AIX be bumped to 1.16.5 * `#12951 `__: A possible typo in scipy.stats.weightedtau * `#12952 `__: [Documentation question] Would it be more precise to specify... * `#12970 `__: Documentation presents second order sections as the correct choice... * `#12982 `__: Calculate standard error of the intercept in linregress * `#12985 `__: Possible wrong link in scipy.stats.wilcoxon doc * `#12991 `__: least_squares broken with float32 * `#13001 `__: \`OptimizeResult.message\` from \`L-BFGS-B\` is a bytes, not... * `#13030 `__: BUG: lint_diff.py still fails for backport PRs * `#13077 `__: CI: codecov proper patch diffs * `#13085 `__: Build failing on main branch after HiGHS solver merge * `#13088 `__: BLD, BUG: wheel builds failure with HiGHS/optimize * `#13099 `__: Wrong output format for empty sparse results of kron * `#13108 `__: TST, CI: GitHub Actions MacOS Failures * `#13111 `__: BUG, DOC: refguide check is failing * `#13127 `__: ODR output file writing broken in conda env with system compilers * `#13134 `__: FromTravis migration tracker * `#13140 `__: BUG: signal: \`ss2tf\` incorrectly truncates output to integers. * `#13179 `__: CI: lint is failing because of output to stderr * `#13182 `__: Key appears twice in \`test_optimize.test_show_options\` * `#13191 `__: \`scipy.linalg.lapack.dgesjv\` overwrites original arrays if... * `#13207 `__: TST: Erratic test failure in test_cossin_separate Pull requests for 1.6.0 ------------------------------ * `#8032 `__: ENH: Add in taylor window common in Radar processing * `#8779 `__: CI: Run benchmarks * `#9361 `__: ENH: Add Kendall's tau-a and tau-c variants to scipy.stats.kendalltau() * `#11068 `__: ENH: Adds correlation_lags function to scipy.signal * `#11119 `__: ENH: add Cramer-von-Mises (one-sample) test to scipy.stats * `#11249 `__: ENH: optimize: interpolative decomposition redundancy removal... * `#11346 `__: ENH: add fast toeplitz matrix multiplication using FFT * `#11413 `__: ENH: Multivariate t-distribution (stale) * `#11563 `__: ENH: exact p-value in stats.kendalltau() for sample sizes > 171 * `#11691 `__: ENH: add a stack of reversal functions to linprog * `#12043 `__: ENH: optimize: add HiGHS methods to linprog - continued * `#12061 `__: Check parameter consistensy in signal.iirdesign * `#12067 `__: MAINT: Cleanup OLDAPI in ndimage/src/_ctest.c * `#12069 `__: DOC: Add developer guidelines for implementing the nan_policy... * `#12077 `__: MAINT: malloc return value checks for cython * `#12080 `__: MAINT: Remove suppress_warnings * `#12085 `__: ENH: special: support ILP64 Lapack * `#12086 `__: MAINT: Cleanup PyMODINIT_FUNC used during 2to3 * `#12097 `__: ENH: stats: override stats.rayleigh.fit with analytical MLE * `#12112 `__: DOC: Improve integrate.nquad docstring * `#12125 `__: TST: Add a test for stats.gmean with negative input * `#12139 `__: TST: Reduce flakiness in lsmr test * `#12142 `__: DOC: add a note in poisson distribution when mu=0 and k=0 in... * `#12144 `__: DOC: Update ndimage.morphology.distance_transform\* * `#12154 `__: ENH: scipy.signal: allow lists in gauss_spline * `#12170 `__: ENH: scipy.stats: add negative hypergeometric distribution * `#12177 `__: MAINT: Correctly add input line to ValueError * `#12183 `__: ENH: Use fromfile where possible * `#12186 `__: MAINT: generalize tests in SphericalVoronoi * `#12198 `__: TST: Fix str + bytes error * `#12199 `__: ENH: match np.result_type behaviour in some scipy.signal functions * `#12200 `__: ENH: add FIR and IIR gammatone filters to scipy.signal * `#12204 `__: ENH: Add overwrite argument for odr.ODR() and its test. * `#12206 `__: MAINT:lstsq: Switch to tranposed problem if the array is tall * `#12208 `__: wavfile bugfixes and maintenance * `#12214 `__: DOC: fix docstring of "sd_beta" of odr.Output. * `#12234 `__: MAINT: prevent divide by zero warnings in scipy.optimize BFGS... * `#12235 `__: REL: set version to 1.6.0.dev0 * `#12237 `__: BUG: Fix exit condition for QUICK_SELECT pivot * `#12242 `__: ENH: Rename ndimage.sum to ndimage.sum_labels (keep sum as alias) * `#12243 `__: EHN: Update SuperLU * `#12244 `__: MAINT: stats: avoid spurious warnings in ncx2.pdf * `#12245 `__: DOC: Fixed incorrect default for mode in scipy.ndimage.spline_filter1d * `#12248 `__: MAINT: clean up pavement.py * `#12250 `__: ENH: Replaced csr_matrix() by tocsr() and complemented docstring * `#12253 `__: TST, CI: turn on codecov patch diffs * `#12259 `__: MAINT: Remove duplicated test for import cycles * `#12263 `__: ENH: Rename LocalSearchWrapper bounds * `#12265 `__: BUG optimize: Accept np.matrix in lsq_linear * `#12266 `__: BUG: Fix paren error in dual annealing accept_reject calculation * `#12269 `__: MAINT: Included mismatched shapes in error messages. * `#12279 `__: MAINT: \`__array__\` and array protocols cannot be used in sparse. * `#12281 `__: DOC: update wheel DL docs * `#12283 `__: ENH: odr: ILP64 Blas support in ODR * `#12284 `__: ENH: linalg: support for ILP64 BLAS/LAPACK in f2py wrappers * `#12286 `__: ENH: Cythonize scipy.spatial.transform.Rotation * `#12287 `__: ENH: Read arbitrary bit depth (including 24-bit) WAVs * `#12292 `__: BLD: fix musl compilation * `#12293 `__: MAINT: Fix a DeprecationWarning in validate_runtests_log.py. * `#12296 `__: DOC: Clarify area/volume in scipy.spatial.ConvexHull docstrings * `#12302 `__: CI: Run travis builds on master to keep cache up to date * `#12305 `__: TST: Cleanup print statements in tests * `#12323 `__: ENH: Add a Bunch-like class to use as a backwards compatible... * `#12324 `__: BUG: io: Fix an error that occurs when attempting to raise a... * `#12327 `__: DOC: clarify docstrings of \`query_ball_tree\` and \`query_pairs\` * `#12334 `__: PERF: Improve cKDTree.query_ball_point constant time cython overhead * `#12338 `__: DOC: improve consistency and clarity of docs in linalg and sparse/linalg * `#12341 `__: DOC: add Examples for KDTree query_ball_tree and query_pairs * `#12343 `__: DOC: add examples for special.eval_legendre() * `#12349 `__: BUG: avoid overflow in sum() for 32-bit systems * `#12351 `__: DOC: Fix example wavfile to be 16bit * `#12352 `__: [BUG] Consider 0/0 division in DOP853 error estimation * `#12353 `__: Fix exception causes in vq.py * `#12354 `__: MAINT: Cleanup unneeded void\* cast in setlist.pxd * `#12355 `__: TST: Remove hack for old win-amd64 bug * `#12356 `__: ENH: Faster implementation of scipy.sparse.block_diag (#9411... * `#12357 `__: MAINT,TST: update and run scipy/special/utils/convert.py * `#12358 `__: TST: Check mstat.skewtest pvalue * `#12359 `__: TST: Sparse matrix test with int64 indptr and indices * `#12363 `__: DOC: ref. in CloughTocher2DInterpolator * `#12364 `__: DOC: \`sparse_distance_matrix\` and \`count_neighbors\` examples * `#12371 `__: MAINT, CI: bump to latest stable OpenBLAS * `#12372 `__: MAINT: Minor cleanup of (c)KDTree tests * `#12374 `__: DEP: Deprecate \`distance.wminkowski\` * `#12375 `__: ENH: Add fast path for minkowski distance with p=1,2 and support... * `#12376 `__: Fix exception causes in most of the codebase * `#12377 `__: DOC: Quick fix - adds newline to correlation_lags docstring Examples... * `#12381 `__: BENCH: remove obsolete goal_time param * `#12382 `__: ENH: Replace KDTree with a thin wrapper over cKDTree * `#12385 `__: DOC: improve docstrings of interpolate.NearestNDInterpolator.__call__... * `#12387 `__: DOC/STY: add example to scipy.signal.correlate * `#12393 `__: CI: Replace the existing check for non-ASCII characters with... * `#12394 `__: CI: arm64 numpy now available * `#12395 `__: ENH: improve stats.binned_statistic_dd performance * `#12396 `__: DOC, MAINT: forward port 1.5.0 relnotes * `#12398 `__: API: Disable len() and indexing of Rotation instances with single... * `#12399 `__: MAINT: Replace some Unicode dash-like chars with an ASCII hyphen. * `#12402 `__: update .mailmap * `#12404 `__: MAINT: io: Change the encoding comment of test_mio.py to utf-8. * `#12416 `__: CI: cache mingw, azure pipelines * `#12427 `__: BUG: logic error in loop unrolling (cKDTree) * `#12432 `__: DOC: Remove the "Basic functions" section from the SciPy tutorial. * `#12434 `__: ENH:linalg: Add LAPACK wrappers pptrf/pptrs/ppsv/pptri/ppcon * `#12435 `__: DOC: fix simplex math for scipy.stats.dirichlet documentation * `#12439 `__: DOC: add API methods summary for NdPPoly * `#12443 `__: BUG: stats: Improve calculation of exponnorm.pdf * `#12448 `__: DOC: stats: Add "Examples" to the ansari docstring. * `#12450 `__: ENH: add \`leaves_color_list\` for cluster.dendrogram dictionary. * `#12451 `__: MAINT: remove "blacklist" terminology from code base * `#12452 `__: DOC: clarify the meaning of whitening for cluster.vq.whiten() * `#12455 `__: MAINT: clearer error message in setup.py * `#12457 `__: ENH: stats: override stats.pareto.fit with analytical MLE * `#12460 `__: check if column in spearman rho is entirely NaN or Inf * `#12463 `__: DOC: improve and clean up \*Spline docstrings in fitpack2.py * `#12474 `__: ENH: linalg: speedup _sqrtm_triu by moving tight loop to Cython * `#12476 `__: ENH: add IIR comb filter to scipy.signal * `#12484 `__: Fix documentation for minimize * `#12486 `__: DOC: add a note in poisson distribution when mu=0 and k=0 in... * `#12491 `__: MAINT: forward port 1.5.1 release notes * `#12508 `__: Fix exception causes all over the codebase * `#12514 `__: ENH: stats: override stats.invgauss.fit with analytical MLE * `#12519 `__: PERF: Avoid np.zeros when custom initialization is needed anyway * `#12520 `__: DOC: Minor RST section renaming. * `#12521 `__: MAINT: Remove unused imports * `#12522 `__: PERF: Get rid of unnececssary allocation in VarReader5.cread_fieldnames * `#12524 `__: DOC: special: Set Axes3D rect to avoid clipping labels in plot. * `#12525 `__: Fix large sparse nnz * `#12526 `__: DOC: Remove double section and too long header underline. * `#12527 `__: Improve error message for wrong interpolation type * `#12530 `__: Move redundant logic outside loop for conditional speedup in... * `#12532 `__: ENH: Add norm={"forward", "backward"} to \`scipy.fft\` * `#12535 `__: MAINT: Avoid sphinx deprecated aliases for SeeAlso and Only * `#12540 `__: BUG: fix odr.output.work_ind key bug and add its test. * `#12541 `__: ENH: add solver for minimum weight full bipartite matching * `#12550 `__: PERF: pickling speed of rv\* * `#12551 `__: DOC: fix typo in cluster/_hierarchy.pyx * `#12552 `__: CI: Cleanup travis pip installs * `#12556 `__: BUG: Fix problem with Scipy.integrate.solve_bvp for big problems * `#12557 `__: MAINT: Use extern templates to improve sparsetools compile time * `#12558 `__: MAINT: Remove hack to allow scipy.fft to act like a function * `#12563 `__: MAINT: Remove unused mu0 in special/orthogonal.py * `#12564 `__: DOC: fix return type docstring for least_squares * `#12565 `__: DOC: stats: respond to query about Kruskal-Wallis test being... * `#12566 `__: BUG: Interpolate: use stable sort * `#12568 `__: Updated documentation for as_quat * `#12571 `__: DEP: remove deprecated slepian window * `#12573 `__: DEP: remove \`frechet_l\` and \`frechet_r\` * `#12575 `__: BUG: stats: fix multinomial.pmf NaNs when params sum > 1 * `#12576 `__: MAINT: remove warning from LSQSphereBivariateSpline * `#12582 `__: ENH: Multivariate t-distribution * `#12587 `__: ENH: speed up rvs of gengamma in scipy.stats * `#12588 `__: DOC: add Examples add see also sections for LinearNDInterpolator,... * `#12597 `__: ENH: Add single-sided p-values to t-tests * `#12599 `__: Small update to scipy FFT tutorial * `#12600 `__: ENH: disjoint set data structure * `#12602 `__: BUG: add const for Read-only views in interpnd.pyx * `#12605 `__: BUG: correct \`np.asanyarray\` use in \`scipy.constants.lambda2nu\` * `#12610 `__: MAINT: forward port 1.5.2 release notes * `#12612 `__: MAINT: stats: Use explicit keyword parameters instead of \`\*\*kwds\`. * `#12616 `__: DOC: make explicit docstring that interpolate.interp1d only accepts... * `#12618 `__: DOC: Minor doc formatting. * `#12640 `__: MAINT: stats: fix issues with scipy.stats.pearson3 docs, moment,... * `#12647 `__: TST: Add Boost ellipr[cdfgj]_data test data * `#12648 `__: DOC: Update special/utils/README with instructions * `#12649 `__: DOC: simplified pip quickstart guide * `#12650 `__: DOC: stats: Fix boxcox docstring: lambda can be negative. * `#12655 `__: DOC: update Steering Council members listed in governance docs * `#12659 `__: rv_sample expect bug * `#12663 `__: DOC: optimize: try to fix linprog method-specific documentation * `#12664 `__: BUG: stats: Fix logpdf with large negative values for logistic... * `#12666 `__: MAINT: Fixes from static analysis * `#12667 `__: ENH: Adding Modified Rodrigues Parameters to the Rotation class * `#12670 `__: DOC: Update documentation for Gamma distribution * `#12673 `__: API: Unconditionally return np.intp from cKDTree.query * `#12677 `__: MAINT: Add Autogenerated notice to ufuncs.pyi * `#12682 `__: MAINT: Remove _util._valarray * `#12688 `__: MAINT: add f2py-generated scipy.integrate files to .gitignore * `#12689 `__: BENCH: simplify benchmark setup, remove benchmarks/run.py * `#12694 `__: scipy/stats: Add laplace_asymmetric continuous distribution * `#12695 `__: DOC: update Ubuntu quickstart; conda compilers now work! * `#12698 `__: MAINT: Replace np.max with np.maximum * `#12700 `__: TST: bump test precision for constrained trustregion test * `#12702 `__: TST: bump test tolerance for \`DifferentialEvolutionSolver.test_L4\` * `#12703