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An explanation for the atol difference between numpy and math by the author of math.isclose():
https://github.com/numpy/numpy/issues/10161#issuecomment-350...
plus a ton of flogging/benchmarking common libraries (numpy, scipy, scikit-learn)
and also this gem:
import numpy as np
I remember having to write something similar for Lua, because there was no math.approximately() function and I was dealing with networking floats and comparing values over the wire to predicted movement in 2D space.
https://github.com/Planimeter/grid-sdk/blob/master/engine/sh...
https://github.com/JuliaMath/DoubleFloats.jl
Of course, if you are calling BLAS/LAPACK, you are constrained to use floats, but the recommendation on DoubleFloats is clear: if you know you algorithms, use the increased precision only in the parts that matter