Mailman 3 February 2024 - NumPy-Discussion

next NumPy Optimization Team meeting
by Inessa Pawson March 1, 2024

March 1, 2024

The next NumPy Optimization Team meeting will be held on Monday, November 20th at 4pm UTC. Join us via Zoom: https://numfocus-org.zoom.us/j/88162122074?pwd=a3h0cTZubzlLcTZzMVhCL1AxUVFM… . 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/dVdSlQ0TThWkOk0OkmGsmw?both. For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/optim_team_meetings. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

1 5

ENH: Add 'axis' kwarg to np.tile
by evan＠evanw.org Feb. 29, 2024

Feb. 29, 2024

Hoping to get some more feedback on my recent PR [0] which has stagnated a bit for the past few weeks. This adds an `axis` keyword argument to np.tile which may be an int or tuple of ints, much like np.sum or np.roll. This is my first contribution to numpy :) Thanks, Evan [0]: https://github.com/numpy/numpy/pull/25703

3 2

builing numpy on windows
by Ganesh Rajora Feb. 27, 2024

Feb. 27, 2024

Hi Team, I am Ganesh working for an MNC here in India and I am working on customised Python where I build set of python modules with python. I do not install them directly from web but I build each and everything from source code. this is because security concerns at the organisation. In similar line I want to get some help from you on building numpy on windows, as I did not find any direct reference on how to do that on any of numpy's official web. I am facing lots of issues to build that, If you could help me out with it or could give me a right point of contact to discuss the issue would be great help. I have posted the issue here on stackoverflow - Issues in buildingnumpy from source on Windows Thanks,Ganesh

3 2

ANN: DataLab v0.12
by Pierre Raybaut Feb. 26, 2024

Feb. 26, 2024

Hello everyone, We're thrilled to announce the release of DataLab v0.12! https://datalab-platform.github.io DataLab is an open-source platform for scientific and technical data processing and visualization, based on NumPy, SciPy, ... and Qt. Once again, this release is the result of the feedback and contributions of our early adopters. We're grateful for your support and look forward to your continued insights. 💥 New Features: - New tour (when starting DataLab for the first time) and demo features. - New "Text file import assistant" feature for easily importing data from arbitrary text files. - New statistics calculations, intensity profile graphical features, ... 🚀 New Binder environment to test DataLab online without installing anything: https://mybinder.org/v2/gh/DataLab-Platform/DataLab/binder-environments?url… 📚 Documentation: - New text tutorials are available: "Measuring Laser Beam Size", "DataLab and Spyder: a perfect match" - New "Contributing" section explaining how to contribute to DataLab, whether you are a developer or not - New "Macros" section explaining how to create and use macros in DataLab 💡 You have a project or a use case in mind? (we can even write a tutorial for you...) You want to contribute? We'd love to hear from you! So please, don't hesitate to contact us or to open an issue ( https://github.com/Codra-Ingenierie-Informatique/DataLab/issues/new/choose) on our GitHub project for any request or question. Thank you for taking the time to read my announcement. Looking forward to your thoughts. Best, Pierre Raybaut

2 1

Need help in numpy building from source on windows.
by rajoraganesh＠ymail.com Feb. 26, 2024

Feb. 26, 2024

detailed proble can be found at - https://stackoverflow.com/questions/78059816/issues-in-buildingnumpy-from-s…

2 1

Converting array to string
by Dom Grigonis Feb. 25, 2024

Feb. 25, 2024

Hello, I am seeking a bit of help. I need a fast way to transfer numpy arrays in json format. Thus, converting array to list is not an option and I need something similar to: a = np.ones(10000000) %timeit a.tobytes() 17.6 ms This is quick, fast and portable. In other words I am very happy with this, but... Json does not support bytes. Any method of subsequent conversion from bytes to string is number of times slower than the actual serialisation. So my question is: Is there any way to serialise directly to string? I remember there used to be 2 methods: tobytes and tostring. However, I see that tostring is deprecated and its functionality is equivalent to `tobytes`. Is it completely gone? Or is there a chance I can still find a performant version of converting to and back from `str` type of non-human readable form? Regards, DG

2 4

Re: Introducing quarterly date units to datetime64 and timedelta64
by Stefano Miccoli Feb. 24, 2024

Feb. 24, 2024

Actually quarters (3 months sub-year groupings) are already supported as ‘M8[3M]’ and ‘m8[3M]’: >>> np.datetime64('2024-05').astype('M8[3M]') - np.datetime64('2020-03').astype('M8[3M]') numpy.timedelta64(17,'3M') So explicitly introducing a ‘Q’ time unit is only to enable more intuitive representation/parsing of dates and durations. I’m moderately negative on this proposal: - there is no native support of quarters in Python - ISO 8601-1 does not support sub-year groupings - the ISO 8601-2 extensions representing sub-year groupings is not sufficiently widespread to be adopted by numpy. (E.g. '2001-34' expresses "second quarter of 2001”, but I suppose nobody would guess this meaning) In other words, without a clear normative reference, implementing quarters in numpy would risk to introduce a custom/arbitrary notation. Stefano

2 2

Introducing quarterly date units to datetime64 and timedelta64
by Oyibo Feb. 24, 2024

Feb. 24, 2024

Dear Numpy Team, I am writing to propose the addition of quarterly date units (Q) to the datetime64 and timedelta64 data types in Numpy. While Numpy currently supports various date units, including years, months, weeks, and days, the absence of quarterly units limits its usability in numerous applications. Users are often forced to use cumbersome workarounds such as writing custom code or relying on external libraries, which detracts from the simplicity and efficiency of using Numpy. Quarters are a fundamental temporal unit, particularly used in finance, business, and economics (it is probably the most analyzed frequency in macroeconometrics). Many time series datasets are reported at quarterly intervals, making native support for quarterly units essential for simplifying data handling and manipulation within Numpy. Although alternative methods exist for representing quarters, native support would improve consistency across codebases and enhance interoperability with other libraries and frameworks. Ultimately it leads to improving the overall user experience using dates data types in Numpy. I would like to discuss this proposal further and would be happy to open an issue on GitHub to initiate the conversation if the proposal sounds reasonable. Your feedback would be appreciated. Best regards, Oyibo

1 0

16ᵗʰ Advanced Scientific Programming in Python in Heraklion, Crete, Greece, 25 August – 1 September, 2024
by Tiziano Zito Feb. 22, 2024

Feb. 22, 2024

ASPP2024: 16ᵗʰ Advanced Scientific Programming in Python Summer School ====================================================================== https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are interactive and allow students to acquire direct hands-on experience with the topics. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Python is the standard tool for the programming scientist due to clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization. This school is targeted at PhD students, postdocs and more senior researchers from all areas of science. Competence in Python or in another language such as Java, JavaScript, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and git or another version control system is assumed. Participants without any prior experience with Python or git should work through the proposed introductory material before the course. We care for diversity and inclusion, and strive for a welcoming atmosphere to programming scientists of all levels. In particular, we have focused on recruiting an international and gender-balanced pool of students. Date & Location =============== 25 August – 1 September, 2024. Heraklion, Crete, Greece Application =========== You can apply online: https://aspp.school Application deadline: 23:59 UTC, Wednesday 1 May, 2024. There will be no deadline extension, so be sure to apply on time. Invitations and notification of rejection will be sent by Sunday 26 May, 2024. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program ======= • Large-scale collaborative scientific code development with git and GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Scientific programming patterns in Python • Writing parallel applications in Python • Profiling and speeding up scientific code • Programming in teams Faculty ======= • Aitor Morales-Gregorio, Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich Germany • Jenni Rinker, Department of Wind and Energy Systems, Technical University of Denmark, Lyngby Denmark • Lisa Schwetlick, Laboratory of Psychophysics, EPFL, Lausanne Switzerland • Pamela Hathway, YPOG, Berlin/Nürnberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, innoCampus, Technische Universität Berlin Germany • Verjinia Metodieva, NeuroCure, Charité – Universitätsmedizin Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers ========== Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, innoCampus, Technische Universität Berlin Germany Organization team in Heraklion: • Sara Moberg, Department of Biology, Humboldt-Universität zu Berlin Germany • Athanasia Papoutsi, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Maria Diamantaki, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Zampeta Kalogeropoulou, Digital Science & Research Solutions Ltd., Heraklion Greece Sponsors ======== We are able to hold this year's ASPP school thanks to the financial support of the Tübingen AI Center. The Institute of Molecular Biology & Biotechnology of the Foundation for Research and Technology – Hellas is hosting us in Heraklion and is taking care of the local organization. Website: https://aspp.school Contact: info(a)aspp.school

1 0

Resources of newcomers and some questions about my first PR
by Amogh Sood Feb. 20, 2024

Feb. 20, 2024

Hi all. I want to thank you all for your contributions and efforts in maintaining numpy. numpy has been an indispensable tool during both my graduate and undergraduate studies. While I am no software wizard, going forward, I do want to make efforts to improve my skills and and also contribute to projects that I use regularly and benefit from (in my spare time). PR Specific Questions: The docs linked here https://numpy.org/doc/stable/dev/ have been helpful in learning how to contribute. I have also taken some time to review open issues and attempt my first PR. Feedback and criticism are more than welcome. I tried following the outlined practices to the best ability. https://github.com/numpy/numpy/pull/25844 After submitting the PR, I am looking to remedy the following: (i) Github actions `Some checks were not successful 1 cancelled, 3 skipped, 59 successful, and 1 failing checks` specifically `BLAS tests (Linux) / Test Linux (nightly OpenBLAS) (pull_request)` failed. (ii) I see an unverified tag on the commits associated with my PR ? I couldn't find the process to remedy this in the docs. General Questions a) What other resources are available for newcomers (if any) ? Specifically with respect to mentorship, code review, feedback channels ? b) Are there lists of beginner friend BUG/ENH that are maintained somewhere ? (e.g. a good first issue tag?) I look forward to learning from and collaborating with this community. Many thanks for your time and efforts.

2 1