Mailman 3 March 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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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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Steering Council membership updates
by Ralf Gommers March 31, 2021

March 31, 2021

Hi all, On behalf of the NumPy Steering Council (SC) I have a number of membership changes to announce. We're excited to welcome Inessa Pawson and Melissa Mendonça as new SC members. Inessa has been contributing for close to two years, and has been a driving force behind the new website, the user survey, and other content and community initiatives. Melissa has been contributing since the start of last year, she leads the documentation team, co-maintains f2py, does a lot of mentoring, and is … [View More]

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Dot + add operation
by Guillaume Bethouart March 31, 2021

March 31, 2021

Is it possible to add a method to perform a dot product and add the result to an existing matrix in a single operation ? Like C = dot_add(A, B, C) equivalent to C += A @ B.This behavior is natively proposed by the Blas *gemm primitive. The goal is to reduce the peak memory consumption. Indeed, during the computation of C += A @ B, the maximum allocated memory is twice the size of C.Using *gemm to add directly the result , the maximum memory consumption is less than 1.5x the size of C. This … [View More]

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NumPy Community Meeting Wednesday (no DST: for those e.g. in the EU)
by Sebastian Berg March 30, 2021

March 30, 2021

Hi all, There will be a NumPy Community meeting Wednesday March 3rd 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 PS: As the subject says, we will stay on UTC 20:00, so for those in the EU and anyone else who had daylight saving times switches the time will have shifted.

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Expanding the scope of numpy.unpackbits and numpy.packbits to include more than uint8 type
by Rashiq Azhan March 30, 2021

March 30, 2021

I would like this feature to be added since I think it can very useful when there is a need to process data that cannot be included in uint8. One of my personal requirements is modifying a 10-bit, per channel, images held in a NumPy array but I cannot do that using the specified functions. They are eloquent solution and works well with the with NumPy functions as long as the data is uint8.

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ANN: SfePy 2021.1
by Robert Cimrman March 29, 2021

March 29, 2021

I am pleased to announce the release of SfePy 2021.1. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by finite element methods. It is distributed under the new BSD license. Home page: https://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------… [View More]

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Unreliable crash when converting using numpy.asarray via C buffer interface
by Friedrich Romstedt March 29, 2021

March 29, 2021

Hi, This is with Python 3.8.2 64-bit and numpy 1.19.2 on Windows 10. I'd like to be able to convert some C++ extension type to a numpy array by using ``numpy.asarray``. The extension type implements the Python buffer interface to support this. The extension type, called "Image" here, holds some chunk of ``double``, C order, contiguous, 2 dimensions. It "owns" the buffer; the buffer is not shared with other objects. The following Python code crashes:: image = <... Image production .… [View More]

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Japanese translation of numpy.org complete - proofreader help wanted
by Ralf Gommers March 28, 2021

March 28, 2021

Hi all, We have our first complete translation of the numpy.org content, Japanese, thanks to Atsushi Sakai. It would be really helpful if someone else who speaks Japanese can proofread the translations. Then it's ready to deploy I think - we just need to enable the language switcher widget, the code for that was tested already. Proofreading is done in Crowdin, which is a friendly interface for translators see - https://github.com/numpy/numpy/wiki/Translations-of-the-NumPy-website. This should … [View More]

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NumPy 1.20.2 released.
by Charles R Harris March 27, 2021

March 27, 2021

Charles R Harris <charlesr.harris(a)gmail.com> Sun, Feb 7, 2:23 PM to numpy-discussion, SciPy, SciPy-User, bcc: python-announce-list Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.20.2. NumPy 1,20.2 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.1 release. The Python versions supported for this release are 3.7-3.9. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.20.2/>; source … [View More]

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ANN: SciPy 1.6.2
by Tyler Reddy March 24, 2021

March 24, 2021

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.6.2, 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/v1.6.2 <https://github.com/scipy/scipy/releases/tag/v1.6.1> One of a few ways to install this release with pip: pip install scipy==1.6.2 ===================== SciPy 1.6.2 Release Notes ===================== SciPy 1.6.2 is a bug-fix … [View More]release with no new features compared to 1.6.1. This is also the first SciPy release to place upper bounds on some dependencies to improve the long-term repeatability of source builds. Authors ====== * Pradipta Ghosh + * Tyler Reddy * Ralf Gommers * Martin K. Scherer + * Robert Uhl * Warren Weckesser A total of 6 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 1.6.2 ------------------------------ * `#13512 <https://github.com/scipy/scipy/issues/13512>`__: \`stats.gaussian_kde.evaluate\` broken on S390X * `#13584 <https://github.com/scipy/scipy/issues/13584>`__: rotation._compute_euler_from_matrix() creates an array with negative... * `#13585 <https://github.com/scipy/scipy/issues/13585>`__: Behavior change in coo_matrix when dtype=None * `#13686 <https://github.com/scipy/scipy/issues/13686>`__: delta0 argument of scipy.odr.ODR() ignored Pull requests for 1.6.2 ------------------------------ * `#12862 <https://github.com/scipy/scipy/pull/12862>`__: REL: put upper bounds on versions of dependencies * `#13575 <https://github.com/scipy/scipy/pull/13575>`__: BUG: fix \`gaussian_kernel_estimate\` on S390X * `#13586 <https://github.com/scipy/scipy/pull/13586>`__: BUG: sparse: Create a utility function \`getdata\` * `#13598 <https://github.com/scipy/scipy/pull/13598>`__: MAINT, BUG: enforce contiguous layout for output array in Rotation.as_euler * `#13687 <https://github.com/scipy/scipy/pull/13687>`__: BUG: fix scipy.odr to consider given delta0 argument Checksums ========= MD5 ~~~ fc81d43879a28270d593aaea37c74ff8 scipy-1.6.2-cp37-cp37m-macosx_10_9_x86_64.whl 9213533bfd3c2f1563d169009c39825c scipy-1.6.2-cp37-cp37m-manylinux1_i686.whl 2ddd03b89efdb1619fa995da7b83aa6f scipy-1.6.2-cp37-cp37m-manylinux1_x86_64.whl d378f725958bd6a83db7ef23e8659762 scipy-1.6.2-cp37-cp37m-manylinux2014_aarch64.whl 87bc2771b8a8ab1f10168b1563300415 scipy-1.6.2-cp37-cp37m-win32.whl 861dab18fe41e82c08c8f585f2710545 scipy-1.6.2-cp37-cp37m-win_amd64.whl d2e2002b526adeebf94489aa95031f54 scipy-1.6.2-cp38-cp38-macosx_10_9_x86_64.whl 2dc36bfbe3938c492533604aba002c17 scipy-1.6.2-cp38-cp38-manylinux1_i686.whl 0114de2118d41f9440cf86fdd67434fc scipy-1.6.2-cp38-cp38-manylinux1_x86_64.whl ede6db56b1bf0a7fed0c75acac7dcb85 scipy-1.6.2-cp38-cp38-manylinux2014_aarch64.whl 191636ac3276da0ee9fd263b47927b73 scipy-1.6.2-cp38-cp38-win32.whl 8bdf7ab041b9115b379f043bb02d905f scipy-1.6.2-cp38-cp38-win_amd64.whl 608c82b227b6077d9a7871ac6278e64d scipy-1.6.2-cp39-cp39-macosx_10_9_x86_64.whl 4c0313b2cccc85666b858ffd692a3c87 scipy-1.6.2-cp39-cp39-manylinux1_i686.whl 92da8ffe165034dbbe5f098d0ed58aec scipy-1.6.2-cp39-cp39-manylinux1_x86_64.whl b4b225fb1deeaaf0eda909fdd3bd6ca6 scipy-1.6.2-cp39-cp39-manylinux2014_aarch64.whl 662969220eadbb6efec99030e4d00268 scipy-1.6.2-cp39-cp39-win32.whl f19186d6d91c7e37000e9f6ccd9b9b60 scipy-1.6.2-cp39-cp39-win_amd64.whl cbcb9b39bd9d877ad3deeccc7c37bb7f scipy-1.6.2.tar.gz b56e705c653ad808a9725dfe840d1258 scipy-1.6.2.tar.xz 6f615549670cd3d312dc9e4359d2436a scipy-1.6.2.zip SHA256 ~~~~~~ 77f7a057724545b7e097bfdca5c6006bed8580768cd6621bb1330aedf49afba5 scipy-1.6.2-cp37-cp37m-macosx_10_9_x86_64.whl e547f84cd52343ac2d56df0ab08d3e9cc202338e7d09fafe286d6c069ddacb31 scipy-1.6.2-cp37-cp37m-manylinux1_i686.whl bc52d4d70863141bb7e2f8fd4d98e41d77375606cde50af65f1243ce2d7853e8 scipy-1.6.2-cp37-cp37m-manylinux1_x86_64.whl adf7cee8e5c92b05f2252af498f77c7214a2296d009fc5478fc432c2f8fb953b scipy-1.6.2-cp37-cp37m-manylinux2014_aarch64.whl e3e9742bad925c421d39e699daa8d396c57535582cba90017d17f926b61c1552 scipy-1.6.2-cp37-cp37m-win32.whl ffdfb09315896c6e9ac739bb6e13a19255b698c24e6b28314426fd40a1180822 scipy-1.6.2-cp37-cp37m-win_amd64.whl 6ca1058cb5bd45388041a7c3c11c4b2bd58867ac9db71db912501df77be2c4a4 scipy-1.6.2-cp38-cp38-macosx_10_9_x86_64.whl 993c86513272bc84c451349b10ee4376652ab21f312b0554fdee831d593b6c02 scipy-1.6.2-cp38-cp38-manylinux1_i686.whl 37f4c2fb904c0ba54163e03993ce3544c9c5cde104bcf90614f17d85bdfbb431 scipy-1.6.2-cp38-cp38-manylinux1_x86_64.whl 96620240b393d155097618bcd6935d7578e85959e55e3105490bbbf2f594c7ad scipy-1.6.2-cp38-cp38-manylinux2014_aarch64.whl 03f1fd3574d544456325dae502facdf5c9f81cbfe12808a5e67a737613b7ba8c scipy-1.6.2-cp38-cp38-win32.whl 0c81ea1a95b4c9e0a8424cf9484b7b8fa7ef57169d7bcc0dfcfc23e3d7c81a12 scipy-1.6.2-cp38-cp38-win_amd64.whl c1d3f771c19af00e1a36f749bd0a0690cc64632783383bc68f77587358feb5a4 scipy-1.6.2-cp39-cp39-macosx_10_9_x86_64.whl 50e5bcd9d45262725e652611bb104ac0919fd25ecb78c22f5282afabd0b2e189 scipy-1.6.2-cp39-cp39-manylinux1_i686.whl 816951e73d253a41fa2fd5f956f8e8d9ac94148a9a2039e7db56994520582bf2 scipy-1.6.2-cp39-cp39-manylinux1_x86_64.whl 1fba8a214c89b995e3721670e66f7053da82e7e5d0fe6b31d8e4b19922a9315e scipy-1.6.2-cp39-cp39-manylinux2014_aarch64.whl e89091e6a8e211269e23f049473b2fde0c0e5ae0dd5bd276c3fc91b97da83480 scipy-1.6.2-cp39-cp39-win32.whl d744657c27c128e357de2f0fd532c09c84cd6e4933e8232895a872e67059ac37 scipy-1.6.2-cp39-cp39-win_amd64.whl e9da33e21c9bc1b92c20b5328adb13e5f193b924c9b969cd700c8908f315aa59 scipy-1.6.2.tar.gz 8fadc443044396283c48191d48e4e07a3c3b6e2ae320b1a56e76bb42929e84d2 scipy-1.6.2.tar.xz 2af283054d91865336b4579aa91f9e59d648d436cf561f96d4692008f795c750 scipy-1.6.2.zip [View Less]

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Programmatically contracting multiple tensors
by Michael Lamparski March 24, 2021

March 24, 2021

Greetings, I have something in my code where I can receive an array M of unknown dimensionality and a list of "labels" for each axis. E.g. perhaps I might get an array of shape (2, 47, 3, 47, 3) with labels ['spin', 'atom', 'coord', 'atom', 'coord']. For every axis that is labeled "coord", I want to multiply in some rotation matrix R. So, for the above example, this could be done with the following handwritten line: return np.einsum('Cc,Ee,abcde->abCdE', R, R, M) But since I want to do … [View More]

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg March 23, 2021

March 23, 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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PyData/Sparse 0.12.0 Release Announcement
by Hameer Abbasi March 19, 2021

March 19, 2021

Apologies in advance for the cross-post! I’m happy to announce the release of PyData/Sparse 0.12.0! PyData/Sparse provides sparse arrays with a NumPy-like API for the PyData ecosystem. This is a large release with GCXS support, preliminary CSR/CSC support and extensions to DOK, as well as bugfixes. Changelog: https://sparse.pydata.org/en/stable/changelog.html Documentation: https://sparse.pydata.org/ Source: https://github.com/pydata/sparse/ Best regards, Hameer Abbasi -- Sent from … [View More]

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Google Season of Docs 2021
by Melissa Mendonça March 16, 2021

March 16, 2021

Hello, folks! NumPy is hoping to participate again in Google Season of Docs this year, and we have a couple of project ideas listed here: https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2021-Project-Ideas This year, GSoD has a different structure: we must choose only one project idea (ideally, the one prospective technical writers are most interested in) and submit a sort of grant proposal (details are here: https://developers.google.com/season-of-docs/docs/admin-guide). If we are … [View More]

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NumPy Community Meeting Wednesday (no DST: for those in the US, one hour later)
by Sebastian Berg March 16, 2021

March 16, 2021

Hi all, There will be a NumPy Community meeting Wednesday March 3rd 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 PS: As the subject says, we will stay on UTC 20:00, so for those in the US and anyone else who had daylight saving times switches the time will have shifted.

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Exposing experimental C-API for DTypes
by Sebastian Berg March 16, 2021

March 16, 2021

Hi all, For DTypes, it may soon make sense to expose API publicly for testing/experimentation. But, right now I don't really want to get roped into discussing API details too much and slowing down potential revamps. Do we have any idea for exposing an "experimental" API? The first option would be "symbols" with an underscore in the name and an understanding that using them might just break if you don't use the exact version you wrote the code for and compiled with (i.e. no API/ABI guarantee)… [View More]

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NEP: array API standard adoption (NEP 47)
by Ralf Gommers March 15, 2021

March 15, 2021

Hi all, Here is a NEP, written together with Stephan Hoyer and Aaron Meurer, for discussion on adoption of the array API standard ( https://data-apis.github.io/array-api/latest/). This will add a new numpy.array_api submodule containing that standardized API. The main purpose of this API is to be able to write code that is portable to other array/tensor libraries like CuPy, PyTorch, JAX, TensorFlow, Dask, and MXNet. We expect this NEP to remain in draft state for quite a while, while we're … [View More]gaining experience with using it in downstream libraries, discuss adding it to other array libraries, and finishing some of the loose ends (e.g., specifications for linear algebra functions that aren't merged yet, see https://github.com/data-apis/array-api/pulls) in the API standard itself. See https://mail.python.org/pipermail/numpy-discussion/2020-November/081181.html for an initial discussion about this topic. Please keep high-level discussion here and detailed comments on https://github.com/numpy/numpy/pull/18456. Also, you can access a rendered version of the NEP from that PR (see PR description for how), which may be helpful. Cheers, Ralf Abstract -------- We propose to adopt the `Python array API standard`_, developed by the `Consortium for Python Data API Standards`_. Implementing this as a separate new namespace in NumPy will allow authors of libraries which depend on NumPy as well as end users to write code that is portable between NumPy and all other array/tensor libraries that adopt this standard. .. note:: We expect that this NEP will remain in a draft state for quite a while. Given the large scope we don't expect to propose it for acceptance any time soon; instead, we want to solicit feedback on both the high-level design and implementation, and learn what needs describing better in this NEP or changing in either the implementation or the array API standard itself. Motivation and Scope -------------------- Python users have a wealth of choice for libraries and frameworks for numerical computing, data science, machine learning, and deep learning. New frameworks pushing forward the state of the art in these fields are appearing every year. One unintended consequence of all this activity and creativity has been fragmentation in multidimensional array (a.k.a. tensor) libraries - which are the fundamental data structure for these fields. Choices include NumPy, Tensorflow, PyTorch, Dask, JAX, CuPy, MXNet, and others. The APIs of each of these libraries are largely similar, but with enough differences that it’s quite difficult to write code that works with multiple (or all) of these libraries. The array API standard aims to address that issue, by specifying an API for the most common ways arrays are constructed and used. The proposed API is quite similar to NumPy's API, and deviates mainly in places where (a) NumPy made design choices that are inherently not portable to other implementations, and (b) where other libraries consistently deviated from NumPy on purpose because NumPy's design turned out to have issues or unnecessary complexity. For a longer discussion on the purpose of the array API standard we refer to the `Purpose and Scope section of the array API standard < https://data-apis.github.io/array-api/latest/purpose_and_scope.html>`__ and the two blog posts announcing the formation of the Consortium [1]_ and the release of the first draft version of the standard for community review [2]_. The scope of this NEP includes: - Adopting the 2021 version of the array API standard - Adding a separate namespace, tentatively named ``numpy.array_api`` - Changes needed/desired outside of the new namespace, for example new dunder methods on the ``ndarray`` object - Implementation choices, and differences between functions in the new namespace with those in the main ``numpy`` namespace - A new array object conforming to the array API standard - Maintenance effort and testing strategy - Impact on NumPy's total exposed API surface and on other future and under-discussion design choices - Relation to existing and proposed NumPy array protocols (``__array_ufunc__``, ``__array_function__``, ``__array_module__``). - Required improvements to existing NumPy functionality Out of scope for this NEP are: - Changes in the array API standard itself. Those are likely to come up during review of this NEP, but should be upstreamed as needed and this NEP subsequently updated. Usage and Impact ---------------- *This section will be fleshed out later, for now we refer to the use cases given in* `the array API standard Use Cases section < https://data-apis.github.io/array-api/latest/use_cases.html>`__ In addition to those use cases, the new namespace contains functionality that is widely used and supported by many array libraries. As such, it is a good set of functions to teach to newcomers to NumPy and recommend as "best practice". That contrasts with NumPy's main namespace, which contains many functions and objects that have been superceded or we consider mistakes - but that we can't remove because of backwards compatibility reasons. The usage of the ``numpy.array_api`` namespace by downstream libraries is intended to enable them to consume multiple kinds of arrays, *without having to have a hard dependency on all of those array libraries*: .. image:: _static/nep-0047-library-dependencies.png Adoption in downstream libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The prototype implementation of the ``array_api`` namespace will be used with SciPy, scikit-learn and other libraries of interest that depend on NumPy, in order to get more experience with the design and find out if any important parts are missing. The pattern to support multiple array libraries is intended to be something like:: def somefunc(x, y): # Retrieves standard namespace. Raises if x and y have different # namespaces. See Appendix for possible get_namespace implementation xp = get_namespace(x, y) out = xp.mean(x, axis=0) + 2*xp.std(y, axis=0) return out The ``get_namespace`` call is effectively the library author opting in to using the standard API namespace, and thereby explicitly supporting all conforming array libraries. The ``asarray`` / ``asanyarray`` pattern ```````````````````````````````````````` Many existing libraries use the same ``asarray`` (or ``asanyarray``) pattern as NumPy itself does; accepting any object that can be coerced into a ``np.ndarray``. We consider this design pattern problematic - keeping in mind the Zen of Python, *"explicit is better than implicit"*, as well as the pattern being historically problematic in the SciPy ecosystem for ``ndarray`` subclasses and with over-eager object creation. All other array/tensor libraries are more strict, and that works out fine in practice. We would advise authors of new libraries to avoid the ``asarray`` pattern. Instead they should either accept just NumPy arrays or, if they want to support multiple kinds of arrays, check if the incoming array object supports the array API standard by checking for ``__array_namespace__`` as shown in the example above. Existing libraries can do such a check as well, and only call ``asarray`` if the check fails. This is very similar to the ``__duckarray__`` idea in :ref:`NEP30`. .. _adoption-application-code: Adoption in application code ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The new namespace can be seen by end users as a cleaned up and slimmed down version of NumPy's main namespace. Encouraging end users to use this namespace like:: import numpy.array_api as xp x = xp.linspace(0, 2*xp.pi, num=100) y = xp.cos(x) seems perfectly reasonable, and potentially beneficial - users get offered only one function for each purpose (the one we consider best-practice), and they then write code that is more easily portable to other libraries. Backward compatibility ---------------------- No deprecations or removals of existing NumPy APIs or other backwards incompatible changes are proposed. High-level design ----------------- The array API standard consists of approximately 120 objects, all of which have a direct NumPy equivalent. This figure shows what is included at a high level: .. image:: _static/nep-0047-scope-of-array-API.png The most important changes compared to what NumPy currently offers are: - A new array object which: - conforms to the casting rules and indexing behaviour specified by the standard, - does not have methods other than dunder methods, - does not support the full range of NumPy indexing behaviour. Advanced indexing with integers is not supported. Only boolean indexing with a single (possibly multi-dimensional) boolean array is supported. An indexing expression that selects a single element returns a 0-D array rather than a scalar. - Functions in the ``array_api`` namespace: - do not accept ``array_like`` inputs, only NumPy arrays and Python scalars - do not support ``__array_ufunc__`` and ``__array_function__``, - use positional-only and keyword-only parameters in their signatures, - have inline type annotations, - may have minor changes to signatures and semantics of individual functions compared to their equivalents already present in NumPy, - only support dtype literals, not format strings or other ways of specifying dtypes - DLPack_ support will be added to NumPy, - New syntax for "device support" will be added, through a ``.device`` attribute on the new array object, and ``device=`` keywords in array creation functions in the ``array_api`` namespace, - Casting rules that differ from those NumPy currently has. Output dtypes can be derived from input dtypes (i.e. no value-based casting), and 0-D arrays are treated like >=1-D arrays. - Not all dtypes NumPy has are part of the standard. Only boolean, signed and unsigned integers, and floating-point dtypes up to ``float64`` are supported. Complex dtypes are expected to be added in the next version of the standard. Extended precision, string, void, object and datetime dtypes, as well as structured dtypes, are not included. Improvements to existing NumPy functionality that are needed include: - Add support for stacks of matrices to some functions in ``numpy.linalg`` that are currently missing such support. - Add the ``keepdims`` keyword to ``np.argmin`` and ``np.argmax``. - Add a "never copy" mode to ``np.asarray``. Functions in the ``array_api`` namespace ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Let's start with an example of a function implementation that shows the most important differences with the equivalent function in the main namespace:: def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False ) -> array: """ Array API compatible wrapper for :py:func:`np.max <numpy.max>`. """ return np.max._implementation(x, axis=axis, keepdims=keepdims) This function does not accept ``array_like`` inputs, only ``ndarray``. There are multiple reasons for this. Other array libraries all work like this. Letting the user do coercion of lists, generators, or other foreign objects separately results in a cleaner design with less unexpected behaviour. It's higher-performance - less overhead from ``asarray`` calls. Static typing is easier. Subclasses will work as expected. And the slight increase in verbosity because users have to explicitly coerce to ``ndarray`` on rare occasions seems like a small price to pay. This function does not support ``__array_ufunc__`` nor ``__array_function__``. These protocols serve a similar purpose as the array API standard module itself, but through a different mechanisms. Because only ``ndarray`` instances are accepted, dispatching via one of these protocols isn't useful anymore. This function uses positional-only parameters in its signature. This makes code more portable - writing ``max(x=x, ...)`` is no longer valid, hence if other libraries call the first parameter ``input`` rather than ``x``, that is fine. The rationale for keyword-only parameters (not shown in the above example) is two-fold: clarity of end user code, and it being easier to extend the signature in the future with keywords in the desired order. This function has inline type annotations. Inline annotations are far easier to maintain than separate stub files. And because the types are simple, this will not result in a large amount of clutter with type aliases or unions like in the current stub files NumPy has. DLPack support for zero-copy data interchange ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ability to convert one kind of array into another kind is valuable, and indeed necessary when downstream libraries want to support multiple kinds of arrays. This requires a well-specified data exchange protocol. NumPy already supports two of these, namely the buffer protocol (i.e., PEP 3118), and the ``__array_interface__`` (Python side) / ``__array_struct__`` (C side) protocol. Both work similarly, letting the "producer" describe how the data is laid out in memory so the "consumer" can construct its own kind of array with a view on that data. DLPack works in a very similar way. The main reasons to prefer DLPack over the options already present in NumPy are: 1. DLPack is the only protocol with device support (e.g., GPUs using CUDA or ROCm drivers, or OpenCL devices). NumPy is CPU-only, but other array libraries are not. Having one protocol per device isn't tenable, hence device support is a must. 2. Widespread support. DLPack has the widest adoption of all protocols, only NumPy is missing support. And the experiences of other libraries with it are positive. This contrasts with the protocols NumPy does support, which are used very little - when other libraries want to interoperate with NumPy, they typically use the (more limited, and NumPy-specific) ``__array__`` protocol. Adding support for DLPack to NumPy entails: - Adding a ``ndarray.__dlpack__`` method - Adding a ``from_dlpack`` function, which takes as input an object supporting ``__dlpack__``, and returns an ``ndarray``. DLPack is currently a ~200 LoC header, and is meant to be included directly, so no external dependency is needed. Implementation should be straightforward. Syntax for device support ~~~~~~~~~~~~~~~~~~~~~~~~~ NumPy itself is CPU-only, so it clearly doesn't have a need for device support. However, other libraries (e.g. TensorFlow, PyTorch, JAX, MXNet) support multiple types of devices: CPU, GPU, TPU, and more exotic hardware. To write portable code on systems with multiple devices, it's often necessary to create new arrays on the same device as some other array, or check that two arrays live on the same device. Hence syntax for that is needed. The array object will have a ``.device`` attribute which enables comparing devices of different arrays (they only should compare equal if both arrays are from the same library and it's the same hardware device). Furthermore, ``device=`` keywords in array creation functions are needed. For example:: def empty(shape: Union[int, Tuple[int, ...]], /, *, dtype: Optional[dtype] = None, device: Optional[device] = None) -> array: """ Array API compatible wrapper for :py:func:`np.empty <numpy.empty>`. """ return np.empty(shape, dtype=dtype, device=device) The implementation for NumPy may be as simple as setting the device attribute to the string ``'cpu'`` and raising an exception if array creation functions encounter any other value. Dtypes and casting rules ~~~~~~~~~~~~~~~~~~~~~~~~ The supported dtypes in this namespace are boolean, 8/16/32/64-bit signed and unsigned integer, and 32/64-bit floating-point dtypes. These will be added to the namespace as dtype literals with the expected names (e.g., ``bool``, ``uint16``, ``float64``). The most obvious omissions are the complex dtypes. The rationale for the lack of complex support in the first version of the array API standard is that several libraries (PyTorch, MXNet) are still in the process of adding support for complex dtypes. The next version of the standard is expected to include ``complex64`` and ``complex128`` (see `this issue < https://github.com/data-apis/array-api/issues/102>`__ for more details). Specifying dtypes to functions, e.g. via the ``dtype=`` keyword, is expected to only use the dtype literals. Format strings, Python builtin dtypes, or string representations of the dtype literals are not accepted - this will improve readability and portability of code at little cost. Casting rules are only defined between different dtypes of the same kind. The rationale for this is that mixed-kind (e.g., integer to floating-point) casting behavior differs between libraries. NumPy's mixed-kind casting behavior doesn't need to be changed or restricted, it only needs to be documented that if users use mixed-kind casting, their code may not be portable. .. image:: _static/nep-0047-casting-rules-lattice.png *Type promotion diagram. Promotion between any two types is given by their join on this lattice. Only the types of participating arrays matter, not their values. Dashed lines indicate that behaviour for Python scalars is undefined on overflow. Boolean, integer and floating-point dtypes are not connected, indicating mixed-kind promotion is undefined.* The most important difference between the casting rules in NumPy and in the array API standard is how scalars and 0-dimensional arrays are handled. In the standard, array scalars do not exist and 0-dimensional arrays follow the same casting rules as higher-dimensional arrays. See the `Type Promotion Rules section of the array API standard < https://data-apis.github.io/array-api/latest/API_specification/type_promoti… >`__ for more details. .. note:: It is not clear what the best way is to support the different casting rules for 0-dimensional arrays and no value-based casting. One option may be to implement this second set of casting rules, keep them private, mark the array API functions with a private attribute that says they adhere to these different rules, and let the casting machinery check whether for that attribute. This needs discussion. Indexing ~~~~~~~~ An indexing expression that would return a scalar with ``ndarray``, e.g. ``arr_2d[0, 0]``, will return a 0-D array with the new array object. There are several reasons for that: array scalars are largely considered a design mistake which no other array library copied; it works better for non-CPU libraries (typically arrays can live on the device, scalars live on the host); and it's simply a consistent design. To get a Python scalar out of a 0-D array, one can simply use the builtin for the type, e.g. ``float(arr_0d)``. The other `indexing modes in the standard < https://data-apis.github.io/array-api/latest/API_specification/indexing.html >`__ do work largely the same as they do for ``numpy.ndarray``. One noteworthy difference is that clipping in slice indexing (e.g., ``a[:n]`` where ``n`` is larger than the size of the first axis) is unspecified behaviour, because that kind of check can be expensive on accelerators. The lack of advanced indexing, and boolean indexing being limited to a single n-D boolean array, is due to those indexing modes not being suitable for all types of arrays or JIT compilation. Their absence does not seem to be problematic; if a user or library author wants to use them, they can do so through zero-copy conversion to ``numpy.ndarray``. This will signal correctly to whomever reads the code that it is then NumPy-specific rather than portable to all conforming array types. The array object ~~~~~~~~~~~~~~~~ The array object in the standard does not have methods other than dunder methods. The rationale for that is that not all array libraries have methods on their array object (e.g., TensorFlow does not). It also provides only a single way of doing something, rather than have functions and methods that are effectively duplicate. Mixing operations that may produce views (e.g., indexing, ``nonzero``) in combination with mutation (e.g., item or slice assignment) is `explicitly documented in the standard to not be supported < https://data-apis.github.io/array-api/latest/design_topics/copies_views_and… >`__. This cannot easily be prohibited in the array object itself; instead this will be guidance to the user via documentation. The standard current does not prescribe a name for the array object itself. We propose to simply name it ``ndarray``. This is the most obvious name, and because of the separate namespace should not clash with ``numpy.ndarray``. Implementation -------------- .. note:: This section needs a lot more detail, which will gradually be added when the implementation progresses. A prototype of the ``array_api`` namespace can be found in https://github.com/data-apis/numpy/tree/array-api/numpy/_array_api. The docstring in its ``__init__.py`` has notes on completeness of the implementation. The code for the wrapper functions also contains ``# Note:`` comments everywhere there is a difference with the NumPy API. Two important parts that are not implemented yet are the new array object and DLPack support. Functions may need changes to ensure the changed casting rules are respected. The array object ~~~~~~~~~~~~~~~~ Regarding the array object implementation, we plan to start with a regular Python class that wraps a ``numpy.ndarray`` instance. Attributes and methods can forward to that wrapped instance, applying input validation and implementing changed behaviour as needed. The casting rules are probably the most challenging part. The in-progress dtype system refactor (NEPs 40-43) should make implementing the correct casting behaviour easier - it is already moving away from value-based casting for example. The dtype objects ~~~~~~~~~~~~~~~~~ We must be able to compare dtypes for equality, and expressions like these must be possible:: np.array_api.some_func(..., dtype=x.dtype) The above implies it would be nice to have ``np.array_api.float32 == np.array_api.ndarray(...).dtype``. Dtypes should not be assumed to have a class hierarchy by users, however we are free to implement it with a class hierarchy if that's convenient. We considered the following options to implement dtype objects: 1. Alias dtypes to those in the main namespace. E.g., ``np.array_api.float32 = np.float32``. 2. Make the dtypes instances of ``np.dtype``. E.g., ``np.array_api.float32 = np.dtype(np.float32)``. 3. Create new singleton classes with only the required methods/attributes (currently just ``__eq__``). It seems like (2) would be easiest from the perspective of interacting with functions outside the main namespace. And (3) would adhere best to the standard. TBD: the standard does not yet have a good way to inspect properties of a dtype, to ask questions like "is this an integer dtype?". Perhaps this is easy enough to do for users, like so:: def _get_dtype(dt_or_arr): return dt_or_arr.dtype if hasattr(dt_or_arr, 'dtype') else dt_or_arr def is_floating(dtype_or_array): dtype = _get_dtype(dtype_or_array) return dtype in (float32, float64) def is_integer(dtype_or_array): dtype = _get_dtype(dtype_or_array) return dtype in (uint8, uint16, uint32, uint64, int8, int16, int32, int64) However it could make sense to add to the standard. Note that NumPy itself currently does not have a great for asking such questions, see `gh-17325 <https://github.com/numpy/numpy/issues/17325>`__. [View Less]

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Looking for a difference between Numpy 0.19.5 and 0.20 explaining a perf regression with Pythran
by PIERRE AUGIER March 15, 2021

March 15, 2021

Hi, I'm looking for a difference between Numpy 0.19.5 and 0.20 which could explain a performance regression (~15 %) with Pythran. I observe this regression with the script https://github.com/paugier/nbabel/blob/master/py/bench.py Pythran reimplements Numpy so it is not about Numpy code for computation. However, Pythran of course uses the native array contained in a Numpy array. I'm quite sure that something has changed between Numpy 0.19.5 and 0.20 (or between the corresponding wheels?) … [View More]

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Numpy 1.20.1 availability
by dan_patterson March 15, 2021

March 15, 2021

Any idea why the most recent version isn't available on the main anaconda channel. conda-forge and building are not options for a number of reasons. I posted a package request there but double digit days have gone by it just got a thumbs up and package-request tag https://github.com/ContinuumIO/anaconda-issues/issues/12309 I realize it could be the "times" or maybe no one is aware of its absence. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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How to get Boolean matrix for similar lists in two different-size numpy arrays of lists
by Ali Sheikholeslam March 14, 2021

March 14, 2021

I have written a question in: https://stackoverflow.com/questions/66623145/how-to-get-boolean-matrix-for-… It was recommended by numpy to send this subject to the mailing lists. The question is as follows. I would be appreciated if you could advise me to solve the problem: At first, I write a small example of to lists: F = [[1,2,3],[3,2,7],[4,4,1],[5,6,3],[1,3,7]] # (1*5) 5 lists S = [[1,3,7],[6,8,1],[3,2,7]] # (1*3) 3 lists I want to get Boolean … [View More]

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Pi day easter egg
by Neal Becker March 14, 2021

March 14, 2021

There's a little pi day easter egg for all math fans. Google for pi to find it.

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg March 9, 2021

March 9, 2021

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, March 10th at 11 am Pacific Time (19:00 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 PS: We will probably schedule the community meeting in UTC next week probably, shifting to avoid shifting … [View More]

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NumPy Community Meeting Wednesday (Today)
by Sebastian Berg March 8, 2021

March 8, 2021

Hi all, There will be a NumPy Community meeting Wednesday March 3rd at 12pm Pacific Time (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 PS: Sorry for the late reminder.

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Using logfactorial instead of loggamma in random_poisson sampler
by zoj613 March 8, 2021

March 8, 2021

Hi All, I noticed that the transformed rejection method for generating Poisson random variables used in numpy makes use of the `random_loggam` function which directly calculates the log-gamma function. It appears that a log-factorial lookup table was added a few years back which could be used in place of random_loggam since the input is always an integer. Is there a reason for not using this table instead? See link below for the line of code: https://github.com/numpy/numpy/blob/… [View More]

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guide for downstream package authors & setting version constraints
by Ralf Gommers March 8, 2021

March 8, 2021

Hi all, We now have a guide for downstream package authors, talking about API and ABI stability, NumPy versioning, testing against NumPy master, and how to add build time and runtime dependencies for numpy: https://numpy.org/devdocs/user/depending_on_numpy.html Especially the version constraints and setting upper bounds for install_requires correctly is important - almost no packages do this correctly (or at all really). If your package depends on NumPy and you deal with packaging it, please … [View More]

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String accessor methods
by Todd March 7, 2021

March 7, 2021

Currently. working with strings in numpy is not very convenient. You have to use a separate set of functions in a separate namespace, and those functions are relatively limited and poorly-documented. A solution several other projects, including pandas [0] and xarray [1], have found are string accessor methods. These are a set of methods attached to a `str` attribute of the class. These have the advantage that they are always available and have a well-defined object they operate on. On non-… [View More]

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Development branches renamed
by Stefan van der Walt March 4, 2021

March 4, 2021

Hi everyone, The development branches of most of the repositories on github.com/numpy have been renamed to `main` (this is the GitHub default for newly created repositories). The move has not yet been made for sub-projects such as `numpydoc` or `numpy.org`, but those should follow soon. We were able to preserve all PRs, other than those for which the original branches have been deleted. You can update your locally cloned repository to have a `main` branch as follows: git branch -m master … [View More]

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ANN: NumExpr 2.7.3
by Robert McLeod March 3, 2021

March 3, 2021

======================== Announcing NumExpr 2.7.3 ======================== Hi everyone, This is a maintenance release to make use of the oldest supported NumPy version when building wheels, in an effort to alleviate issues seen on Windows machines that do not have the latest Windows MSVC runtime installed. It also adds wheels built via GitHub Actions for ARMv8 platforms. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.7.2 to 2.7.3 ----------------------… [View More]

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Possible
by Michal Radwanski Feb. 28, 2021

Feb. 28, 2021

Hello, I'm not sure if it's expected behaviour or a bug, so I decided to write here. First an example: In [4]: array([2**63]) Out[4]: array([9223372036854775808], dtype=uint64) In [5]: array([2**63-1, 2**63]) Out[5]: array([9.22337204e+18, 9.22337204e+18]) The docs for `numpy.array` mention, that: dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. … [View More]

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