Add native scalar Clenshaw-Curtis grid transforms - #534
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Hi, thanks for the PR. Could you please provide a human readable summary as to why this PR is necessary, what Clenshaw-Curtis grid transforms are, references to the technique, what the advantages are over the grids and transforms that we are using now, etc.? And what is the maximum degree that this approach can handle? This is a big PR and will take considerable time to review. If possible, I would prefer that it be divided into several smaller PRs, but if not, that is ok. Also note that I will soon be moving this repo to Codeberg, and will be asking all contributors to abide by their new AI policies. |
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Hi @MarkWieczorek, this is not ready for review yet, as I am still trying to contain the scope and to conform to the repo standard. I have been working on my private repo for a while and opened the PR as a draft for visibility. My original intention was to update the DUCC wrapper only and add CC grid support to the Python API, as I had already implemented DUCC directly in my project and the work was mostly done. After I started to include Fortran changes the scope grew quite a bit. And I am also proposing moving ducc0.sht.experimental.* functions to the stable ducc0.sht.* API in 0.33. Updated the PR description on splitting the PR. |
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developbranch: themasterbranch is used only when releasing new versions.make checkto ensure that the python code follows standard formatting conventions.AI Disclaimer
I designed the detailed specification and used Codex/GPT-5.6 Terra for implementation. I review and test all changes.