On Sat, Feb 7, 2026 at 2:50 AM Ilhan Polat via NumPy-Discussion < [email protected]> wrote:
> That's fantastic that you are working on it David. A good high-level > ARPACK is beneficial for all and possibly better to re-map to C if the > accuracy is higher. We can maybe replace the translated C code with it. > > There are a few places discussion took place already, a few of them below > and the references therein > > > https://discuss.scientific-python.org/t/a-policy-on-generative-ai-assisted-contributions/1702 > https://github.com/scientific-python/summit-2025/issues/35 > > I wish these models were available when I was translating all that Fortran > code because now I can scan my previous work and find the errors extremely > quickly when I am hunting for bugs. So just in a few months they leaped > forward from the pointless "this code uses Fortran let me compile with f2c, > hihi" to "I compiled with valgrind and on line 760, the Fortran has > out-of-bounds access which seems to cause an issue, I'll fix the translated > code". I think I wrote sufficient text in those sources, so I'll leave it > to others but regardless of the policy discussions, you have at least one > customer looking forward to it. > I missed that recent discussion, thanks. Seems to clarify the direction NumPy community may follow based on SymPy policy. On the actual code I am not implementing ARPACK (arnoldi w/ implicit restart/deflation), but Krylov-Schur, which has fewer quircks and simpler to implement: https://slepc.upv.es/release/_downloads/5229480744b7c2533563dee75c16dfde/str7.pdf. Until recently, claude/chatgpt were useful in "filling the blanks" on some implementation details not specified in those reports and other doc. Now, I am pretty sure they could write a good implementation w/ guidance. Last time I had 1--2 hours for it, it could find the bug that blocked me by running the code on different examples/situation. David > ilhan > > > On Fri, Feb 6, 2026 at 6:23 PM David Cournapeau via NumPy-Discussion < > [email protected]> wrote: > >> Hi, >> >> I know there has been discussions in the past on AI-generated >> contributions. Is there a current policy for NumPy ? E.g. do we request >> that contributors are the "sole contributor" to the written code, or do we >> allow code written by AI as long as it follows the usual quality >> requirements ? >> >> Context of my question: ~18 months ago I started in some spare time >> writing an ARPACK-replacement in numpy/scipy during scipy sprint. At that >> time, I used ChatGPT for the "research part" only: literature review, >> explain to me some existing BSD implementation in Julia for points I could >> not understand. I implemented the python code myself. There is still quite >> a bit of work needed to be a viable replacement for ARPACK. >> >> Seeing the progress of the AI tooling in my team at work, and how I >> myself use those tools for other hobby projects, I believe I could finish >> that replacement very quickly with those tools today. But I don't want to >> "taint" the work if this would risk the chances of integration into scipy >> proper. >> >> Thanks, >> David >> >> _______________________________________________ >> NumPy-Discussion mailing list -- [email protected] >> To unsubscribe send an email to [email protected] >> https://mail.python.org/mailman3//lists/numpy-discussion.python.org >> Member address: [email protected] >> > _______________________________________________ > NumPy-Discussion mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/numpy-discussion.python.org > Member address: [email protected] >
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