CliffordSpeed.ipynb

@chrisjldoran There's another reason to consider matrices: Someone else has already done the coding! :) But yes, the goal is certainly to get to a better implementation of the products.

@hugohadfield Have you tried these timings on your installation? In particular, I'm wondering if the @ operator really is faster for many configurations, or if it's something peculiar to mine.

@moble Hi guys, I'm also really keen to see better performance in the clifford library. Just chiming in to say I executed the notebook and and the @ is fastest for me too (dot 56 µs ± 1.29 µs, einsum 64.4 µs ± 804 ns, @ 43.1 µs ± 457 ns).
Output of show_config():

blas_mkl_info: NOT AVAILABLE blis_info: NOT AVAILABLE openblas_info: libraries = ['libopenblas_v0.2.20_mingwpy', 'libopenblas_v0.2.20_mingwpy'] library_dirs = ['c:\\opt\\64\\lib'] language = c define_macros = [('HAVE_CBLAS', None)] blas_opt_info: libraries = ['libopenblas_v0.2.20_mingwpy', 'libopenblas_v0.2.20_mingwpy'] library_dirs = ['c:\\opt\\64\\lib'] language = c define_macros = [('HAVE_CBLAS', None)] lapack_mkl_info: NOT AVAILABLE openblas_lapack_info: libraries = ['libopenblas_v0.2.20_mingwpy', 'libopenblas_v0.2.20_mingwpy'] library_dirs = ['c:\\opt\\64\\lib'] language = c define_macros = [('HAVE_CBLAS', None)] lapack_opt_info: libraries = ['libopenblas_v0.2.20_mingwpy', 'libopenblas_v0.2.20_mingwpy'] library_dirs = ['c:\\opt\\64\\lib'] language = c define_macros = [('HAVE_CBLAS', None)]