I encourage one to merge into e.g. {NumPy, SciPy, }; are there PRs? Though SymPy.physics only yet supports X,Y,Z vectors and doesn't mention e.g. "jaccard"?, F

I encourage one to merge into e.g. {NumPy, SciPy, }; are there PRs? Though SymPy.physics only yet supports X,Y,Z vectors and doesn't mention e.g. "jaccard"?, FWIW: https://docs.sympy.org/latest/modules/physics/vector/vectors.html https://docs.sympy.org/latest/modules/physics/vector/vectors... https://docs.sympy.org/latest/modules/physics/vector/fields.html#curl https://docs.sympy.org/latest/modules/physics/vector/fields.... #cfd include/simsimd/simsimd.h: https://github.com/ashvardanian/SimSIMD/blob/main/include/simsimd/simsimd.h https://github.com/ashvardanian/SimSIMD/blob/main/include/si... conda-forge maintainer docs > Switching BLAS implementation: https://conda-forge.org/docs/maintainer/knowledge_base.html#switching-blas-implementation https://conda-forge.org/docs/maintainer/knowledge_base.html#... : conda install "libblas=*=*mkl" conda install "libblas=*=*openblas" conda install "libblas=*=*blis" conda install "libblas=*=*accelerate" conda install "libblas=*=*netlib" numpy-feedstock: https://github.com/conda-forge/numpy-feedstock/blob/main/recipe/meta.yaml https://github.com/conda-forge/numpy-feedstock/blob/main/rec... scipy-feedstock: https://github.com/conda-forge/scipy-feedstock/blob/main/recipe/meta.yaml https://github.com/conda-forge/scipy-feedstock/blob/main/rec... pysimdjson-feedstock: https://github.com/conda-forge/pysimdjson-feedstock/blob/main/recipe/meta.yaml https://github.com/conda-forge/pysimdjson-feedstock/blob/mai... simdjson-feedstock: https://github.com/conda-forge/simdjson-feedstock/blob/main/recipe/meta.yaml https://github.com/conda-forge/simdjson-feedstock/blob/main/... mkl_random-feedstock: https://github.com/conda-forge/mkl_random-feedstock https://github.com/conda-forge/mkl_random-feedstock https://github.com/google/paranoid_crypto/tree/main/paranoid_crypto/lib/randomness_tests https://github.com/google/paranoid_crypto/tree/main/paranoid... : > NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL blas: https://github.com/conda-forge/blas-feedstock/blob/main/recipe/meta.yaml https://github.com/conda-forge/blas-feedstock/blob/main/reci... xtensor-blas-feedstock: https://github.com/conda-forge/xtensor-blas-feedstock https://github.com/conda-forge/xtensor-blas-feedstock xtensor-fftw (FFT with xtensor (c++)) could probably be AVX-512 and SVE -optimized as well? https://github.com/xtensor-stack/xtensor-fftw https://github.com/xtensor-stack/xtensor-fftw ggml_cpu_has_avx512() https://github.com/search?q=repo%3Aggerganov%2Fggml%20AVX&type=code https://github.com/search?q=repo%3Aggerganov%2Fggml%20AVX&ty... https://github.com/search?q=repo%3Aggerganov%2Fllama.cpp%20avx&type=code https://github.com/search?q=repo%3Aggerganov%2Fllama.cpp%20a... CuPy would also be an impactful place to merge and defend these optimizations; though no GPUs have AVX-512 or SVE? cupyx.scipy.spatial.distance: https://docs.cupy.dev/en/stable/reference/scipy_spatial_distance.html https://docs.cupy.dev/en/stable/reference/scipy_spatial_dist... https://docs.cupy.dev/en/stable/reference/comparison.html https://docs.cupy.dev/en/stable/reference/comparison.html