Numpy Float128 Polyfit
You may use logarithmic version of your variables (np.log10), so when dealing with something like 1e-200 you will have -200, less memory and more efficiency.
I'm using numpy's polyfit to find a best fit curve for a set of data. However, numpy's polyfit returns an array of float64 and because the calculated coefficients are so large/small (i.e. 1e-200), it's returning an overflow error that's encountered in multiply :
RuntimeWarning: overflow encountered in multiply
  scale = NX.sqrt((lhs*lhs).sum(axis=0))
I've tried casting the initial array to be float128, but that does not seem to work. Is there any way around this overflow issue / any way to handle such large coefficients?