Mailman 3 April 2021 - NumPy-Discussion

NumPy Community Meeting Wednesday
by Sebastian Berg April 28, 2021

April 28, 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 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.)

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ANN: SciPy 1.6.3
by Tyler Reddy April 26, 2021

April 26, 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 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

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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 difference in axis sizes. The proposed function would perform this step for you. As this function passes to np.pad internally, I made sure to include the arguments in the signature that np.pad uses for padding modes. Finally, I've added a logical extension of this function; pad_to_match, this takes another array and pads the input array to match. These calls would be equivalent: x = np.ones((3, 3)) y = np.zeros((10, 10)) padded_to_shape = np.pad_to_shape(x, y.shape) padded_to_match = np.pad_to_match(x, y) For additional functionality description, I refer to the pull request. I'm not too familiar with mailing lists, so I hope this is how things work. Kind Regards, Mathijs

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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 22, 2021

April 22, 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. 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

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ANN: MyGrad 2.0 - Drop-in autodiff for NumPy
by Ryan Soklaski April 21, 2021

April 21, 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 NumPy code and compute derivatives through it. Ultimately, MyGrad could be extended to bring autodiff to other array-based libraries like CuPy, Sparse, and Dask. For full release notes see [2]. Feedback, critiques, and ideas are welcome! Cheers, Ryan Soklaski [1] MyGrad is not meant to "compete" with the likes of PyTorch and JAX, which are fantastically-fast and powerful autodiff libraries. Rather, its emphasis is on being lightweight and seamless to use in NumPy-centric workflows. [2] https://mygrad.readthedocs.io/en/latest/changes.html#v2-0-0

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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 domain and window? they appear to have no implementation (except the chebyshev interpolation) as far as I can tell? what is their intended use? why is their no variable representation implemented like in np.polynomial and is not installed to numpy directly, @set_module('numpy')? 3) how many improvements are allowed to be done all at once? is there an expectation of waiting before improving implementations or code? Thinking further down the road, how much is too much to add to numpy? I would like to see multivariate implemented, but after that it would be nice to have a collection of known polynomials like standard, pochhammer, cyclotomic, hermite, lauguerre, legendre, chebyshev, jacobi. After that it would be of interest to extend .diff() to really be a differential operator (implemented as sequences of polynomials) and to allow differential constructions. Then things like this would be possible: x = np.poly1d([1,0]) D = np.diff1d([1,0]) assert Hermite(78) == (2*x - D)(Hermite(77)) Or if series were implemented then we would see things like: assert Hermite(189) == (-1)**189 * Exp(x**2)*(D**189)(Exp(-x**2)) assert Hermite(189) == Exp(-D**2)(x**189)(2*x) 4) at what point does numpy polynomial material belong in scipy material and visa-versa? is numpy considered the backend to scipy or visa-versa? at times it seems like things are double implemented in numpy and scipy, but in general should not implement things in numpy that already exist in scipy? Thanks, Robert -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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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 = (0,1,1,0,0,0,1,1) #binary array np.choose((0,range(8)) #array([0, 1, 2, 0, 0, 0, 6, 7]) Thanks, Alan Isaac

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