Mailman 3 December 2022 - NumPy-Discussion

next NumPy Newcomers' Hour - 12 pm UTC
by Inessa Pawson 31 Oct '23

31 Oct '23

Our next Newcomers' Hour will be held tomorrow, December 1st, at 12 pm UTC. Stop by to ask questions or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09 -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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next NumPy community meeting
by Inessa Pawson 22 Oct '23

22 Oct '23

The next NumPy community meeting will be held this Wednesday, August 17th at 18:00 (6 pm) UTC. Join us via Zoom: https://us06web.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdkZz09 Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both For the notes from the previous meeting, visit: https://github.com/numpy/archive/blob/main/community_meetings/community-202… Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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reminder: put an upper bound on setuptools if you use numpy.distutils!
by Ralf Gommers 31 Aug '23

31 Aug '23

Hi all, After setuptools 65.0 was released a few days ago, all users of numpy.distutils had their builds broken. This is already fixed in setuptools 65.0.2 because the breakage was particularly bad. However, the next breakage may not be fixed anymore (and more breakages *are* expected). So this is a good time to remind you all that you should put an upper bound on the setuptools version you allow in the releases of your package - to the last version that is known to work with your package. Our official stance here is that setuptools versions >=60 are not supported - see the "deprecating numpy.distutils" thread: https://mail.python.org/archives/list/numpy-discussion@python.org/message/P…. Newer versions may work for you, depending on what features you use. They don't for NumPy and for SciPy; both projects pin to 59.X to avoid problems. For the recent issue with setuptools 65.0.0, see https://github.com/numpy/numpy/issues/22135. We have also made the warnings about this topic in our docs more explicit, see https://github.com/numpy/numpy/pull/22154. Cheers, Ralf

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Adding bit_count ufunc
by Doug Turnbull 13 Aug '23

13 Aug '23

👋Long time numpy user, and big fan of all your work TL; DR - there's a `bit_count` method on numpy scalars, and Python ints, I'm advocating for a `ufunc` `bit_count` as has been implemented in this PR: https://github.com/numpy/numpy/pull/21429 A number of people have requested this as a numpy feature and there's a lot of great discussion at this issue https://github.com/numpy/numpy/issues/16325 A bit count comes up in certain similarity situations (like hamming distance). This involves an xor with numpy arrays and a bit count, where the bit count is currently the bottleneck by a few orders of magnitude in my local benchmarking. Currently there's a number of not particularly fast, workarounds for doing this with numpy arrays, like the bit-twiddling solutions here https://stackoverflow.com/a/68943135/8123 https://stackoverflow.com/a/109025/8123 So I'd love to be able to continue the work of bit_count -> numpy array ufunc to get performance gains at the array level 🙏

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mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

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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Representation of NumPy scalars
by Sebastian Berg 09 Jun '23

09 Jun '23

TL;DR: NumPy scalars representation is e.g. `34.3` instead of `float32(34.3)`. So the representation is missing the type information. What are your thoughts on changing that? Hi all, I am thinking about the next steps for NEP 50 (The NEP wants to fix the NumPy promotion rules, especially with respect to scalars): https://numpy.org/neps/nep-0050-scalar-promotion.html In relation to that, there was one point that Stéfan brought up previously. The NumPy scalars (representation) currently print as numbers: >>> np.float32(34.3) 34.3 >>> np.uint8(5) 5 That can already be confusing now. However, it gets more problematic if NEP 50 is introduced since the behavior between a Python `34.3` and `np.float32(34.3)` would differ more than it does now (please refer to the NEP). The change would be that we should print as: float64(34.3) (or similar?) This Email is mainly to ask for any feedback or concern on such a change. I suspect we may have to write a very brief NEP about it. If there is little concern, maybe we could move forward such a change promptly. Otherwise it could be moved forward together with NEP 50 and take effect in a "major" release [1]. Cheers, Sebastian [1] Note that for me, even a major release would hopefully not affect the majority of users or be very disruptive.

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next NumPy Newcomers' Hour - 8 pm UTC
by Inessa Pawson 28 May '23

28 May '23

Our next Newcomers' Hour will be held this Thursday, December 15th at 8 pm UTC. Stop by to ask questions or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09 -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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

18 Feb '23

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

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Introducing Arm Optimized Routines
by Chris Sidebottom 08 Feb '23

08 Feb '23

Hello, Here at Arm, we've been investigating how we can improve performance on AArch64. One way in which we can improve performance is by integrating some existing optimized routines (https://github.com/ARM-software/optimized-routines), similar to the SVML methods for AVX512 that are currently included as a git submodule. Our intent is to include the optimized routines repository as an additional submodule which we can then use to provide routines on AArch64 for ASIMD, SVE and beyond. Currently, we're targeting 4-ULP as this aligns with libmvec (https://sourceware.org/glibc/wiki/libmvec) and the SVML integration (https://github.com/numpy/numpy/pull/19478). This is alongside adding sufficient error handling to pass the Numpy test suite, meeting the test requirements highlighted in the SVML integration (https://github.com/numpy/numpy/pull/19478#issuecomment-893001722). We've already started curating the necessary functions, let us know if you have any feedback. Cheers, Chris IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.

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Advanced indexing doesn't follow the Principle of least astonishment
by Diogo Valada 31 Dec '22

31 Dec '22

Hi all, New to the mailing list, so I hope I'm creating a discussion in the right place. Am I the only one that thinks that Advanced indexing in numpy doesn't follow the principle of minimum astonishment? for example ```python a = np.random.rand(100, 100) a[(2,4)] #this yields the element at [2,4] a[[2,4]] #this yields the rows at position 2 and 4 a[1, (2,4)] #this yields the 2nd and 4th elements of row 1. (So actually does advanced indexing) a[1, [2,4]] # Works the same way as the previous one. ``` Worst of all, it's very easy for someone do a mistake and not notice it: it seems to me that the first method, a[(2,4)], should not be allowed, and instead only a[*(2,4)] should work. How checked how it works in Julia (which has a similar syntax), and a[(2,4)] would yield an error, which makes sense to me. Could it be an idea to deprecate a[(2,4)]-like usages?

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