Mailman 3 April 2022 - 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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An extension of the .npy file format
by Michael Siebert Aug. 25, 2022

Aug. 25, 2022

Dear all, originally, I have planned to make an extension of the .npy file format a dedicated follow-up pull request, but I have upgraded my current request instead, since it was not as difficult to implement as I initially thought and probably a more straight-forward solution: https://github.com/numpy/numpy/pull/20321/ What is this pull request about? It is about appending to Numpy .npy files. Why? I see two main use cases: 1. creating .npy files larger than the main memory. They can, … [View More]once finished, be loaded as memory maps 2. creating binary log files, which can be processed very efficiently without parsing Are there not other good file formats to do this? Theoretically yes, but practically they can be pretty complex and with very little tweaking .npy could do efficient appending too. Use case 1 is already covered by the Pip/Conda package npy-append-array I have created and getting the functionality directly into Numpy was the original goal of the pull request. This would have been possible without introducing a new file format version, just by adding some spare space in the header. During the pull request discussion it turned out that rewriting the header after each append would be desirable in case the writing program crashes to minimize data loss. Use case 2 however would highly profit from a new file format version as it would make rewriting the header unnecessary: since efficient appending can only take place along one axis, setting shape[-1] = -1 in case of Fortran order or shape[0] = -1 otherwise (default) in the .npy header on file creation could indicate that the array size is determined by the file size: when np.load (typically with memory mapping on) gets called, it constructs the ndarray with the actual shape by replacing the -1 in the constructor call. Otherwise, the header is not modified anymore, neither on append nor on file write finish. Concurrent appends to a single file would not be advisable and should be channeled through a single AppendArray instance. Concurrent reads while writes take place however should work relatively smooth: every time an np.load (ideally with mmap) is called, the ndarray would provide access to all data written until that time. Currently, my pull request provides: 1. A definition of .npy version 4.0 that supports -1 in the shape 2. implementations for fortran order and non-fortran order (default) including test cases 3. Updated np.load 4. The AppendArray class that does the actual appending Although there is a certain hassle with introducing a new .npy version, the changes themselves are very small. I could also implement a fallback mode for older Numpy installations, if someone is interested. What do you think about such a feature, would it make sense? Anyone available for some more code review? Best from Berlin, Michael PS thank you so far, I could improve my npy-append-array module as well and from what I have seen so far the Numpy code readability exceeded my already high expectations. [View Less]

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copy="never" discussion and no deprecation cycle?
by Sebastian Berg July 21, 2022

July 21, 2022

Hi all, (sorry for the length, details/discussion below) On the triage call, there seemed a preference to just try to skip the deprecation and introduce `copy="never"`, `copy="if_needed"`, and `copy="always"` (i.e. string options for the `copy` keyword argument). Strictly speaking, this is against the typical policy (one year of warning/errors). But nobody could think of a reasonable chance that anyone actually uses it. (For me just "policy" will be enough of an argument to just take it … [View More]slow.) BUT: If nobody has *any* concerns at all, I think we may just end up introducing the change right away. The PR is: https://github.com/numpy/numpy/pull/19173 ## The Feature There is the idea to add `copy=never` (or similar). This would modify the existing `copy` argument to make it a 3-way decision: * `copy=always` or `copy=True` to force a copy * `copy=if_needed` or `copy=False` to prefer no-copy behavior * `copy=never` to error when no-copy behavior is not possible (this ensures that a view is returned) this would affect the functions: * np.array(object, copy=...) * arr.astype(new_dtype, copy=...) * np.reshape(arr, new_shape, copy=...), and the method arr.reshape() * np.meshgrid and possibly Where `reshape` currently does not have the option and would benefit by allowing for `arr.reshape(-1, copy=never)`, which would guarantee a view. ## The Options We have three options that are currently being discussed: 1. We introduce a new `np.CopyMode` or `np.<something>.Copy` Enum with values `np.CopyMode.NEVER`, `np.CopyMode.IF_NEEDED`, and `np.CopyMode.ALWAYS` * Plus: No compatibility concerns * Downside(?): This would be a first in NumPy, and is untypical API due to that. 2. We introduce `copy="never"`, `copy="if_needed"` and `copy="always"` as strings (all other strings will be a `TypeError`): * Problem: `copy="never"` currently means `copy=True` (the opposite) Which means new code has to take care when it may run on older NumPy versions. And in theory could make old code return the wrong thing. * Plus: Strings are the typical for options in NumPy currently. 3. Same as 2. But we take it very slow: Make strings an error right now and only introduce the new options after two releases as per typical deprecation policy. ## Discussion We discussed it briefly today in the triage call and we were leaning towards strings. I was honestly expecting to converge to option 3 to avoid compatibility issues (mainly surprises with `copy="never"` on older versions). But considering how weird it is to currently pass `copy="never"`, the question was whether we should not change it with a release note. The probability of someone currently passing exactly one of those three (and no other) strings seems exceedingly small. Personally, I don't have a much of an opinion. But if *nobody* voices any concern about just changing the meaning of the string inputs, I think the current default may be to just do it. Cheers, Sebastian [View Less]

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NumPy Newcomers' Hour – Thursday, March 24th
by Inessa Pawson July 10, 2022

July 10, 2022

Our next Newcomers' Hour will be held tomorrow, March 24th, at 4 pm UTC. We have no agenda this time. Stop by to ask questions or just to say hi. Join the meeting via Zoom: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson NumPy Contributor Experience Lead

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Changes to `np.loadtxt`
by Sebastian Berg June 29, 2022

June 29, 2022

Hi all, just a brief heads up that: https://github.com/numpy/numpy/pull/20580 is now merged. This moves `np.loadtxt` to C. Mainly making it much faster. There are also some other improvements and changes though: * It now supports `quotechar='"'` to support Excel dialect CSV. * Parsing some numbers is stricter (e.g. removed support for `_` or hex float parsing by default). * `max_rows` now actually counts rows and not lines. A warning is given if this makes a difference (blank … [View More]

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Numpy binary wheels and CI for win/arm64 platform
by Niyas Sait June 22, 2022

June 22, 2022

Hello, Numpy v1.21.2 <https://github.com/numpy/numpy/releases/tag/v1.21.2> added support for windows/arm64 platforms but we still don't have any systems in place to produce binary wheels or test win/arm64 packages. I think it will be good to start looking into this. CPython has an official buildbot worker running for win/arm64 and official python support for the platform will be available from the 3.11 release. It is not yet clear to me how the build and CI system for numpy is deployed … [View More]

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NEP 29 and the faster Python release cadence
by Aaron Meurer May 27, 2022

May 27, 2022

Was the faster CPython release cadence (PEP 602 https://peps.python.org/pep-0602/) ever discussed in relation to NEP 29 (https://numpy.org/neps/nep-0029-deprecation_policy.html)? NEP 29 currently says: "The current Python release cadence is 18 months so a 42 month window ensures that there will always be at least two minor versions of Python in the window." However, as of PEP 602, the release cadence of CPython is now 12 months, not 18 months. This schedule now means supporting 3-4 versions … [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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