Mailman 3 April 2021 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

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

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

Feb. 18, 2023

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How … [View More]

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

Dec. 3, 2022

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

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[Feature Request] Add alias of np.concatenate as np.concat
by Iordanis Fostiropoulos May 10, 2022

May 10, 2022

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. … [View More]

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell July 22, 2021

July 22, 2021

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 … [View More]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 [View Less]

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Add smallest_normal and smallest_subnormal attributes to finfo
by Stephannie Jiménez Gacha July 19, 2021

July 19, 2021

Good afternoon, Given the discussions happened in the Data API consortium when looking into the attributes of `finfo` used in the wild, we found that `tiny` is used regularly but in a good amount of cases not for its intended purpose but rather as "just give me a small number". Following this we are proposing the addition of `smallest_normal` and `smallest_subnormal` attributes. Personally, I think that the `tiny` name is a little bit odd and misleading, so it will be great to leave that as an … [View More]

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NEP 42 status
by Lee Johnston May 11, 2021

May 11, 2021

Is the work on NEP 42 custom DTypes far enough along to experiment with? Lee

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GSoD 2021 - Statement of Interest
by Mahesh S May 10, 2021

May 10, 2021

Hello, I am Mahesh from India. I am interested in doing Google Season of Docs with NumPy on the project HIGH-LEVEL RESTRUCTURING AND END-USER FOCUS - NumPy I have past experience in documentation writing with WordPress and have completed *Google Summer of Code 2018 *with KDE. I have been an open source enthusiast and contributor since 2017.My past experience with coding ,code documentation , understanding code bases ,will help achieve this task without much input from your end. I have about … [View More]

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Fwd: PhD position in the Netherlands; solar energy/Python
by Dr. Mark Alexander Mikofski PhD April 30, 2021

April 30, 2021

Hello, sharing a PhD position opening at Utrecht in Netherlands. Read more below... ---------- Forwarded message --------- From: Mark Mikofski <bwanamarko(a)gmail.com> Date: Fri, Apr 30, 2021, 12:29 PM Subject: Fwd: PhD position in the Netherlands; solar energy/Python To: Mark Alexander Mikofski <mikofski(a)berkeley.edu> PhD positions with Wilfried van Sark at Utrecht Netherlands ---------- Forwarded message --------- From: abedw...(a)googlemail.com <abedwayyad(a)googlemail.… [View More]com> Date: Monday, April 19, 2021 at 9:22:00 AM UTC-7 Subject: PhD position in the Netherlands; solar energy/Python To: pvlib-python <pvlib-python(a)googlegroups.com> Hello all, I did my MSc with Wilfried at Utrecht (the Netherlands). He's a truly wonderful mentor with an amazing and broad grasp of the knowledge. 10/10 would recommend doing a PhD there if you or someone you know would like to develop solar forecasting models. They are looking for pythonistas. All the best, Abed ---------- Forwarded message --------- From: dr. Wilfried van Sark - W.G.J.H....(a)uu.nl <blackboar...(a)uu.nl> Date: Wed, Apr 14, 2021 at 4:11 AM Subject: Master Energy Science: Looking for PhD/junior researchers To: Dear all, I am looking for talented and motivated junior researchers that would be interested to work in two projects: 1) ROBUST project: this recently granted project is focused on investigating options for flexibility in local electricity grids by using vehicle-to-grid technology in neighborhoods in Utrecht and Arnhem. We require experience with data analysis and simulation using Python. In this project we collaborate with WeDriveSolar and TUDelft. We have funding for at least 44 months available. 2) PVObs and SolFaSi projects. Both projects deal with photovoltaic performance and forecasting. The PVObs project aims to create a large database of PV systems in the Netherlands in order to assess their performance and from that determine with better accuracy the contribution of PV to the national electricity demand. Parnters in this project are CBS and SunData. The SolFaSi project aims to develop PV forecasting at high time and spatial resolutions using all-sky imagers (aka cloud cameras). We will install three cameras at the UU campus and one at KNMI. This network allows to construct a moving shadow field over the campus. This should be correlated with the power generation of the 4000+ PV panels at campus buildings. We require experience with (big) data analysis and simulation using Python. We have funding for 24 months available. If you are interested, or if you know someone who may be interested, please send me a mail at w.g.j.h....(a)uu.nl best regards Wilfried [View Less]

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NumPy Tutorials
by Melissa Mendonça April 30, 2021

April 30, 2021

Hello, all! Recently, we added a link to NumPy Tutorials to our main page ( https://numpy.org/numpy-tutorials) This set of tutorials has been built with the collaboration of a few contributors, who have put effort and dedication into them. Some of this content was generated as part of last year's Google Season of Docs program, with authors kubedoc and Ryan Cooper. These tutorials are meant to be not only about NumPy, but about using NumPy to solve concrete problems, which means more … [View More]

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NumPy Community Meeting Wednesday
by Sebastian Berg April 27, 2021

April 27, 2021

Hi all, There will be a NumPy Community meeting Wednesday April 28th at 20:00 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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Applying as a potential technical writer for 2021 Season of Docs
by NIKITA MAHOVIYA April 27, 2021

April 27, 2021

Dear Mentor, I am a 3rd-year B.Tech undergraduate pursuing Dual Degree in Electronics and Communication Engineering at National Institute of Technology Hamirpur, Himachal Pradesh, India. I aspire to contribute to the open-source community with my technical writing skills that I have acquired by contributing to various projects. I have hands-on experience in handling WordPress websites and am currently the technical writing head of the leading page of the largest student community I AM AN … [View More]NITIAN <https://m.facebook.com/iamannitian>. During my sophomore year, I undertook a remote internship activity at MATLAB Helper as a MATLAB Developer Intern for four and a half months from 27th April 2020 to 9th September 2020. My internship activity included working in the field of MATLAB & promotional content creation. I have contributed to the organization with my blogs on GUIDE, SIR Modelling and webinar on Image Processing using Fuzzy Logic Toolbox. I have also worked on the revision of the online course of Neural Network. During this internship, I have used tools like MATLAB, Slack, Zoom, Trello, Canva, etc. Here I am attaching links to my writing work Blog: Creating a GUI in MATLAB https://matlabhelper.com/blog/matlab/creating-a-gui-in-matlab/ Blog: Image Processing Using Fuzzy Logic Toolbox https://matlabhelper.com/blog/matlab/image-processing-using-fuzzy-logic-too… Blog: COVID-19 SIR Modelling for Andhra Pradesh, India https://matlabhelper.com/blog/matlab/covid-19-sir-modelling-for-andhra-prad… Blog: COVID-19 SIR Modelling for West Bengal, India https://matlabhelper.com/blog/matlab/covid-19-sir-modelling-for-west-bengal… Medium https://mahoviyanikita.medium.com/ Website https://iamannitian.com/ Open Source Contribution: How do object behaviours and attributes differ PR#1326 <https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes/pull/1326> Defining the correct usage of data types in Java PR#1340 <https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes/pull/1340> Updated the documentation to reflect the current state of implementation of Adorsys PR#1204 <https://github.com/adorsys/open-banking-gateway/pull/1204> Code for saying hello in different languages PR#5 <https://github.com/gregdelozier/temporary/pull/5> Updated the README file of scorelab/GSoC PR#116 <https://github.com/scorelab/GSoC/pull/116> Updated the README file of the squiral library PR#4 <https://github.com/sadikkuzu/squiral/pull/4> I am interested in working on the project idea of Creating high-level documentation, in the Tutorial or How-to format, covering topics that are missing from the official documentation, organizing the content which is currently scattered through the reference documentation thereby structuring the documentation. I have exposure to documenting projects which involve beginning from the very initial stage to explaining the various code snippets and their uses and reaching the conclusion as can be seen from the blogs which I have developed. I would like to write relevant and easily readable documentation that could help people to explore the full potential of the services. I would be honoured if I get an opportunity to work on the documentation project which will be a pillar for future developers. The documentation will provide an understanding of the entire landscape of solutions. Please find my resume as an attachment with this mail. Waiting in anticipation. Yours sincerely Nikita Mahoviya 3rd Year B.Tech Undergraduate Department of Electronics and Communication Engineering National Institute of Technology Hamirpur (H.P.) [View Less]

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ANN: SciPy 1.6.3
by Tyler Reddy April 25, 2021

April 25, 2021

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.6.3, which is a bug fix release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.6.3 <https://github.com/scipy/scipy/releases/tag/v1.6.1> One of a few ways to install this release with pip: pip install scipy==1.6.3 ===================== SciPy 1.6.3 Release Notes ===================== SciPy 1.6.3 is a bug-fix … [View More]release with no new features compared to 1.6.2. Authors ====== * Peter Bell * Ralf Gommers * Matt Haberland * Peter Mahler Larsen * Tirth Patel * Tyler Reddy * Pamphile ROY + * Xingyu Liu + A total of 8 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 1.6.3 ------------------------------- * `#13772 <https://github.com/scipy/scipy/issues/13772>`__: Divide by zero in distance.yule * `#13796 <https://github.com/scipy/scipy/issues/13796>`__: CI: prerelease_deps failures * `#13890 <https://github.com/scipy/scipy/issues/13890>`__: TST: spatial rotation failure in (1.6.3) wheels repo (ARM64) Pull requests for 1.6.3 ------------------------------ * `#13755 <https://github.com/scipy/scipy/pull/13755>`__: CI: fix the matplotlib warning emitted during builing docs * `#13773 <https://github.com/scipy/scipy/pull/13773>`__: BUG: Divide by zero in yule dissimilarity of constant vectors * `#13799 <https://github.com/scipy/scipy/pull/13799>`__: CI/MAINT: deprecated np.typeDict * `#13819 <https://github.com/scipy/scipy/pull/13819>`__: substitute np.math.factorial with math.factorial * `#13895 <https://github.com/scipy/scipy/pull/13895>`__: TST: add random seeds in Rotation module Checksums ========= MD5 ~~~ 3b75d493f6c93b662f927d6c2ac60053 scipy-1.6.3-cp37-cp37m-macosx_10_9_x86_64.whl ada6fa32f066dc58033ab47a4fbcd208 scipy-1.6.3-cp37-cp37m-manylinux1_i686.whl 29c6d6edbe9c2ba17dc4edd89ed31abb scipy-1.6.3-cp37-cp37m-manylinux1_x86_64.whl cde2f4824337fda5b2522795fff2135d scipy-1.6.3-cp37-cp37m-manylinux2014_aarch64.whl 27fa4101babcfa7928e4c23e33212e71 scipy-1.6.3-cp37-cp37m-win32.whl b0ef1b6ba4d3b54ad8f17dd10156c3f8 scipy-1.6.3-cp37-cp37m-win_amd64.whl 66b3ea5bb7869af010ae9d6a5015360b scipy-1.6.3-cp38-cp38-macosx_10_9_x86_64.whl 0ef8582f203fdd4afe1ca56fc5b309f5 scipy-1.6.3-cp38-cp38-manylinux1_i686.whl 1194e9d88ef98595619acd657439fae4 scipy-1.6.3-cp38-cp38-manylinux1_x86_64.whl 4b8fe1b85fd8d60bbc787a06c0d545ee scipy-1.6.3-cp38-cp38-manylinux2014_aarch64.whl 1d55aff23261c2fa8d7882bc7220c25d scipy-1.6.3-cp38-cp38-win32.whl e82a80c799f1e5190be0cbd25b554766 scipy-1.6.3-cp38-cp38-win_amd64.whl 8309d581e025539bafaa98ba9c3122a7 scipy-1.6.3-cp39-cp39-macosx_10_9_x86_64.whl 75a9ba6865ff2fefcb5df917a412d99d scipy-1.6.3-cp39-cp39-manylinux1_i686.whl fbfc53ba0ea23f56afb62c2a159e6303 scipy-1.6.3-cp39-cp39-manylinux1_x86_64.whl 4076d3cdb2b16f7d64229ac76c945b6f scipy-1.6.3-cp39-cp39-manylinux2014_aarch64.whl bd553432d6a55a6e139f76951766f31d scipy-1.6.3-cp39-cp39-win32.whl 6b9526aca2adf7b062ac92f184328147 scipy-1.6.3-cp39-cp39-win_amd64.whl 05b4ca400aa1157290abff69016f1cab scipy-1.6.3.tar.gz cef7aa950dfc7f5be79b8c630bfe0cc1 scipy-1.6.3.tar.xz f13ea8ab38fb03c17e38c3cda7877972 scipy-1.6.3.zip SHA256 ~~~~~~ 2a799714bf1f791fb2650d73222b248d18d53fd40d6af2df2c898db048189606 scipy-1.6.3-cp37-cp37m-macosx_10_9_x86_64.whl 9e3302149a369697c6aaea18b430b216e3c88f9a61b62869f6104881e5f9ef85 scipy-1.6.3-cp37-cp37m-manylinux1_i686.whl b79104878003487e2b4639a20b9092b02e1bad07fc4cf924b495cf413748a777 scipy-1.6.3-cp37-cp37m-manylinux1_x86_64.whl 44d452850f77e65e25b1eb1ac01e25770323a782bfe3a1a3e43847ad4266d93d scipy-1.6.3-cp37-cp37m-manylinux2014_aarch64.whl b30280fbc1fd8082ac822994a98632111810311a9ece71a0e48f739df3c555a2 scipy-1.6.3-cp37-cp37m-win32.whl 10dbcc7de03b8d635a1031cb18fd3eaa997969b64fdf78f99f19ac163a825445 scipy-1.6.3-cp37-cp37m-win_amd64.whl 1b21c6e0dc97b1762590b70dee0daddb291271be0580384d39f02c480b78290a scipy-1.6.3-cp38-cp38-macosx_10_9_x86_64.whl 1caade0ede6967cc675e235c41451f9fb89ae34319ddf4740194094ab736b88d scipy-1.6.3-cp38-cp38-manylinux1_i686.whl 19aeac1ad3e57338723f4657ac8520f41714804568f2e30bd547d684d72c392e scipy-1.6.3-cp38-cp38-manylinux1_x86_64.whl ad7269254de06743fb4768f658753de47d8b54e4672c5ebe8612a007a088bd48 scipy-1.6.3-cp38-cp38-manylinux2014_aarch64.whl d647757373985207af3343301d89fe738d5a294435a4f2aafb04c13b4388c896 scipy-1.6.3-cp38-cp38-win32.whl 33d1677d46111cfa1c84b87472a0274dde9ef4a7ef2e1f155f012f5f1e995d8f scipy-1.6.3-cp38-cp38-win_amd64.whl d449d40e830366b4c612692ad19fbebb722b6b847f78a7b701b1e0d6cda3cc13 scipy-1.6.3-cp39-cp39-macosx_10_9_x86_64.whl 23995dfcf269ec3735e5a8c80cfceaf384369a47699df111a6246b83a55da582 scipy-1.6.3-cp39-cp39-manylinux1_i686.whl fdf606341cd798530b05705c87779606fcdfaf768a8129c348ea94441da15b04 scipy-1.6.3-cp39-cp39-manylinux1_x86_64.whl f68eb46b86b2c246af99fcaa6f6e37c7a7a413e1084a794990b877f2ff71f7b6 scipy-1.6.3-cp39-cp39-manylinux2014_aarch64.whl 01b38dec7e9f897d4db04f8de4e20f0f5be3feac98468188a0f47a991b796055 scipy-1.6.3-cp39-cp39-win32.whl 3274ce145b5dc416c49c0cf8b6119f787f0965cd35e22058fe1932c09fe15d77 scipy-1.6.3-cp39-cp39-win_amd64.whl a75b014d3294fce26852a9d04ea27b5671d86736beb34acdfc05859246260707 scipy-1.6.3.tar.gz 3851fdcb1e6877241c3377aa971c85af0d44f90c57f4dd4e54e1b2bbd742635e scipy-1.6.3.tar.xz 0a723da627af61665c7793fdf869ad2606be9cf32e2c0abc0d230f62c4f4914f scipy-1.6.3.zip [View Less]

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Add pad_to_shape and pad_to_match for simpler padding if a target size is already known
by czorio4 April 24, 2021

April 24, 2021

Hello all, I'm only two weeks late with this message about my pull request <https://github.com/numpy/numpy/pull/18707> that adds functions that allow the user to pad to a target shape, instead of adding a certain amount to each axis. For example: x = np.ones((3, 3)) # We want an output shape of (10, 10) padded = np.pad_to_shape(x, (10, 10)) print(padded.shape) prints: (10, 10) Whereas with the current implementation of np.pad you'd need to first figure out the … [View More]

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GSOD
by KiranKhanna April 23, 2021

April 23, 2021

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NEP 49: Data allocation strategies
by Matti Picus April 21, 2021

April 21, 2021

I have submitted NEP 49 to enable user-defined allocation strategies for the ndarray.data homogeneous memory area. The implementation is in PR 17582 https://github.com/numpy/numpy/pull/17582 Here is the text of the NEP: Abstract -------- The ``numpy.ndarray`` requires additional memory allocations to hold ``numpy.ndarray.strides``, ``numpy.ndarray.shape`` and ``numpy.ndarray.data`` attributes. These attributes are specially allocated after creating the python object in ``__new__`` method. … [View More]The ``strides`` and ``shape`` are stored in a piece of memory allocated internally. This NEP proposes a mechanism to override the memory management strategy used for ``ndarray->data`` with user-provided alternatives. This allocation holds the arrays data and is can be very large. As accessing this data often becomes a performance bottleneck, custom allocation strategies to guarantee data alignment or pinning allocations to specialized memory hardware can enable hardware-specific optimizations. Motivation and Scope -------------------- Users may wish to override the internal data memory routines with ones of their own. Two such use-cases are to ensure data alignment and to pin certain allocations to certain NUMA cores. User who wish to change the NumPy data memory management routines will use :c:func:`PyDataMem_SetHandler`, which uses a :c:type:`PyDataMem_Handler` structure to hold pointers to functions used to manage the data memory. The calls are wrapped by internal routines to call :c:func:`PyTraceMalloc_Track`, :c:func:`PyTraceMalloc_Untrack`, and will use the :c:func:`PyDataMem_EventHookFunc` mechanism already present in NumPy for auditing purposes. Since a call to ``PyDataMem_SetHandler`` will change the default functions, but that function may be called during the lifetime of an ``ndarray`` object, each ``ndarray`` will carry with it the ``PyDataMem_Handler`` struct used at the time of its instantiation, and these will be used to reallocate or free the data memory of the instance. Internally NumPy may use ``memcpy` or ``memset`` on the data ``ptr``. Usage and Impact ---------------- The new functions can only be accessed via the NumPy C-API. An example is included later in the NEP. The added ``struct`` will increase the size of the ``ndarray`` object. It is one of the major drawbacks of this approach. We can be reasonably sure that the change in size will have a minimal impact on end-user code because NumPy version 1.20 already changed the object size. Backward compatibility ---------------------- The design will not break backward compatibility. Projects that were assigning to the ``ndarray->data`` pointer were already breaking the current memory management strategy (backed by ``npy_alloc_cache``) and should restore ``ndarray->data`` before calling ``Py_DECREF``. As mentioned above, the change in size should not impact end-users. Matti [View Less]

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ANN: MyGrad 2.0 - Drop-in autodiff for NumPy
by Ryan Soklaski April 20, 2021

April 20, 2021

All, I am excited to announce the release of MyGrad 2.0. MyGrad's primary goal is to make automatic differentiation accessible and easy to use across the NumPy ecosystem (see [1] for more detailed comments). Source: https://github.com/rsokl/MyGrad Docs: https://mygrad.readthedocs.io/en/latest/ MyGrad's only dependency is NumPy and (as of version 2.0) it makes keen use of NumPy's excellent protocols for overriding functions and ufuncs. Thus you can "drop in" a mygrad-tensor into your pure … [View More]

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg April 20, 2021

April 20, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, Arpil 21st at 11 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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Polynomial Improvements
by Robert April 20, 2021

April 20, 2021

Hi, I am new to contributing to open source projects and not sure where to begin (maybe emailing this distro?). In any case, the main improvement I would like to work on would be adding multivariate polynomials/differentials to numpy. I would love any insight into the current status and intensions of numpy polynomial implementations. 1) why do we have np.polynomial and np.lib.polynomial? which one is older? is there a desire to see these merged? 2) why does np.polynomial.Polynomial have a … [View More]

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two questions about `choose`
by Alan G. Isaac April 19, 2021

April 19, 2021

1. Is there a technical reason for `choose` not accept a `dtype` argument? 2. Separately, mypy is unhappy with my 2nd argument to `choose`: Argument 2 to "choose" has incompatible type "Tuple[int, Sequence[float]]"; expected "Union[Union[int, float, complex, str, bytes, generic], Sequence[Union[int, float, complex, str, bytes, generic]], Sequence[Sequence[Any]],_SupportsArray]" However, `choose` is happy to have e.g. `choices=(0,seq)` (and I hope it will remain so!). E.g., a = a = (0,1,1,0,0,… [View More]

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GSoD'21 Ideas Disussion
by Mukulika Pahari April 19, 2021

April 19, 2021

Hello everyone! I am Mukulika Pahari, a Computer Engineering student from India. I wanted to implement the following ideas for Google Summer of Docs 2021 if given the opportunity. I have referred to both- the GSoD proposal <https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2021-Project-Idea…> and the NEP 44 <https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html> proposal. Please give me feedback about the ideas! 1. Reorganising contents of the documentation … [View More]

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timing of abs(complex)
by fiolj April 19, 2021

April 19, 2021

Hi all, I recently noted that absolute values of complex arrays are very slow (about a factor of five compared to some straightforward implementations). I would like to understand the origin, but could not trace the code. Please, consider these timings: In [67]: z = np.random.random(10000) + 1j*np.random.random(10000) In [68]: %timeit np.abs(z)**2 215 µs ± 2.09 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [69]: %timeit ((np.sqrt((z.real**2 + z.imag**2)))**2) 78.2 µs ± 193 … [View More]

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Inaccurate documentation of the random c-api
by zoj613 April 18, 2021

April 18, 2021

Hi All, https://numpy.org/devdocs/reference/random/c-api.html has an inaccurate description of the API that goes as follows: "/zig in the name are based on a ziggurat lookup algorithm is used instead of calculating the log, which is significantly faster. The non-ziggurat variants are used in corner cases and for legacy compatibility./". It appears this is no longer the case and instead there are functions that have `inv` in the name to signal the use of the inverse method for sampling. I … [View More]

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Improving performance of the `numpy.any` function.
by zoj613 April 15, 2021

April 15, 2021

Hi All, I was using numpy's `any` function earlier and realized that it might not be as performant as I assumed. See the code below: ``` In [1]: import numpy as np In [2]: a = np.zeros(1_000_000) In [3]: a[100] = 1 In [4]: b = np.zeros(2_000_000) In [5]: b[100] = 1 In [6]: %timeit np.any(a) 1.33 ms ± 15.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [7]: %timeit np.any(b) 2.65 ms ± 71.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [8] %timeit any(a) 13.4 µ… [View More]

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savetxt -> gzip: nondeterministic because of time stamp
by Joachim Wuttke April 14, 2021

April 14, 2021

If argument fname of savetxt(fname, X, ...) ends with ".gz" then array X is not only converted to text, but also compressed using gzip. The format gzip [1] has a timestamp. The Python module gzip.py [2] sets the timestamp according to an optional constructor argument "mtime". By default, the current time is used. This makes the file written by savetxt(*.gz, ...) non-deterministic. This is unexpected and confusing in a numerics context. I let different versions of a program generate *.gz … [View More]

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NumPy Community Meeting Wednesday
by Sebastian Berg April 13, 2021

April 13, 2021

Hi all, There will be a NumPy Community meeting Wednesday April 14th at 20:00 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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Is there a defined way to "unpad" an array, and if not, should there be?
by Jeff Gostick April 13, 2021

April 13, 2021

I often find myself padding an array to do some processing on it (i.e. to avoid edge artifacts), then I need to remove the padding. I wish there was either a built in "unpad" function that accepted the same arguments as "pad", or that "pad" accepted negative numbers (e.g [-20, -4] would undo a padding of [20, 4]). This seems like a pretty obvious feature to me so maybe I've just missed something, but I have looked through all the open and closed issues on github and don't see anything related … [View More]

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files for 3D printing of NumPy logo
by Ralf Gommers April 11, 2021

April 11, 2021

Hi all, Andrej Krecker, who is a 3D printing engineer who likes NumPy, shared 3D printing files for the NumPy logo. It comes in two flavors, a really 3-D version, and a flatter (~0.5 cm thick) 2-D version. Check them out at https://www.thingiverse.com/thing:4821992. Cheers, Ralf

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Type resolver related deprecations and changes
by Sebastian Berg April 8, 2021

April 8, 2021

Hi all, I have to do some changes to the type resolution, and I started these here: https://github.com/numpy/numpy/pull/18718 There are four changes: * Deprecate `signature="l"` and `signature=("l",)`, these are confusing since the signature should include all inputs and outputs. To only provide the output use `dtype="l"`. * Using `dtype=` for comparisons (e.g. `np.equal`) used to be weird: np.equal(1, 2, dtype=object) -> returns boolean np.equal(None, 2, dtype=object) … [View More] -> returns object array The first one will now give a FutureWarning. Comparisons that provide a dtype other than object or bool give a DeprecationWarning (or fail). I hope the warning can be preserved when more refactoring happens. * NumPy *almost* always ignores any metadata, byte-order, time unit information from the `dtype` or `signature` arguments to ufuncs. Practically, the dtypes passed actually denote the DType types rather than the specific instance (which could be byte swapped). NumPy will now do this always and give a warning if byte-order or time unit is ignored! * It is THEORETICALLY possible to call `ufunc->type_resolver` in the C API (as opposed to providing it, which is somewhat OK). If someone does that they have to normalize the type tuple now, I don't really see a reason for keeping support, when NumPy will stop calling it itself almost always and anyone using it would probably be in trouble soon. To be clear: I have NOT found a single instance of such code in a code search. Even *providing* it – which is much more reasonable – is probably only done by astropy/pyerfa. ** Long example for the "time unit" dropping change ** For the third point, which is in theory the largest impact. Both pandas and astropy do not notice it (I also grepped scipy, its clean). These are the biggest changes: # The following will now warn on most systems (unchanged result): np.add(3, 5, dtype=">i32") # The biggest impact is for timedelta or datetimes: arr = np.arange(10, dtype="m8[s]") # The examples always ignored the time unit "ns" (using the # unit of `arr`. They now issue a warning: np.add(arr, arr, dtype="m8[ns]") np.maximum.reduce(arr, dtype="m8[ns]") # The following issue a warning but previously did return # a "ns" result. np.add(3, 5, dtype="m8[ns]") # Now return generic time units np.maximum(arr, arr, dtype="m8[ns]") # Now returns "s" (from `arr`) I doubt there is a good way to just keep the old behaviour. It is hopelessly inconsistent. (The result even depends on how you pass things, as the paths I deprecate align with `dtype=` but not with the `signature=(None, None, dtype)` equivalent call.) One thought I had just raising a hard error instead of a UserWarning right now. If you say that why not have `dtype=...` always be honored as the correct output dtype, I don't disagree. But it seems to me we probably would have wade through a FutureWarning first, so a warning is the "right direction". (Or just add an `output_dtypes` keyword argument.) Cheers, Sebastian [View Less]

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umPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg April 6, 2021

April 6, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, March 10th at 11 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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MAINT: Use of except-pass blocks
by Michael Dubravski April 6, 2021

April 6, 2021

Hello everyone, There are multiple instances of except-pass blocks within the codebase that to my knowledge are bad practices (Referencing This StackOverflow Article. For example in numpy/ma/core.py there is an except-pass block that catches all exceptions thrown. Another example of this can be found in numpy/core/function_base.py. I was wondering if it would be a good idea to add some print statements for logging the exceptions caught. Also for cases where except-pass blocks are needed, is … [View More]

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Beginner at open source
by Harsh Kumar Singh April 3, 2021

April 3, 2021

Hi all, As I am beginner at open source. I would be obliged if someone could guide me forward. Provided I know basics of numpy and other machine learning libraries.

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ANN: mplcairo 0.4 release
by Antony Lee April 2, 2021

April 2, 2021

Dear all, I am pleased to announce the release of mplcairo 0.4. mplcairo is a Matplotlib backend based on the well-known cairo library, supporting output to both raster (including interactively) and vector formats. In other words, it provides the functionality of Matplotlib's {,qt5,gtk3,wx,tk,macos}{agg,cairo}, pdf, ps, and svg backends. Per Matplotlib's standard API, the backend can be selected by calling matplotlib.use("module://mplcairo.qt") or setting your MPLBACKEND environment … [View More]

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