Spherical Harmonic bugs/enhancements

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#16 创建于 2017年5月18日 33 条评论 0 个 reaction 已指派 0 人在 GitHub 查看

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首先查看 plan_sph2fourier 以及此 issue 中剩余的检查清单项。未解决的工作包括类型稳定性、复数数据方法，以及针对非均匀点的 NUFFT 变体；完成意味着所请求的球谐功能已实现并通过验证。

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bug enhancement

This issue is a placeholder for spherical harmonic bugs/enhancements that will be part of a patch:

the way the butterfly algorithm is currently implemented, it only works for dimensions that are powers of two, though nothing stops this from working for general dimensions. This afflicts FastSphericalHarmonicPlan and ThinSphericalHarmonicPlan. For the time being, it's recommended to just pad with zeros to the nextpow2.
the commands sph2fourier and fourier2sph should work on different band limits in l and m.
the command plan_sph2fourier is not type-stable as it very crudely dispatches to a different SphericalHarmonicPlan based on the bandlimit. This is negligible compared to pre-computation costs for large degrees (and is somewhat likened to Base's factorize).
the convenience constructors sphrand, sphrandn, etc... take m and n and create an array with dimensions m and 2n-1 which is arguably misleading.
it would be great to have an allocation-free FFTW override for converting the bivariate Fourier array to function values on the sphere at tensor product grids equispaced in angle. (Added starting with 67a6b560735ef8f82e1c6a7f82dad3e1a843fc8e)
remove segmentation fault when using an even number of columns, thanks to @meggart for finding this. (Added starting with c0e4a546c3b7507efa44b905fb5bafa6379e20e0)
add methods for complex data,
and, NUFFT variants for nonuniform point.

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Loading FastTransforms.jl can make FFTs via FFTW.jl 100x slower due to threading conflicts
可能已有人在做 @dlfivefifty 于 96 天前认领。未关闭
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JuliaApproximation/FastTransforms.jl#267 · 2 个 reaction · 已指派 2 人 · 2026年4月9日创建
plan_transform fails with 3D Ultraspherical tensor product
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难度 3/5 1-2 天新手友好度 38/100

JuliaApproximation/FastTransforms.jl#266 · 1 条评论 · 2026年2月28日创建
LibFastTransforms crashing in tests on 1.12
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难度 4/5 3-5 天新手友好度 42/100

JuliaApproximation/FastTransforms.jl#263 · 2 条评论 · 2025年10月9日创建
Unwanted sine modes appearing in convolution using FFT
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难度 4/5 3-5 天新手友好度 35/100

JuliaApproximation/FastTransforms.jl#257 · 1 条评论 · 2025年1月15日创建
Allocating lmul!
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难度 4/5 3-5 天新手友好度 25/100

JuliaApproximation/FastTransforms.jl#253 · 7 条评论 · 2024年9月12日创建