(I got distracted by some numerical accuracy checks. np.polyfit looks good in NIST test.) Does numpy have something like this? def lre(actual, desired): '''calculate log relative error, number of correct significant digits not an informative function name Parameters ---------- actual : array_like actual values desired : array_like desired values Returns ------- lre : ndarray number of significant digits, uses relative absolute difference if desired is not zero, and absolute difference if desired is zero. References ---------- http://en.wikibooks.org/wiki/Statistics:Numerical_Methods/Numerical_Comparis... http://digilander.libero.it/foxes/StRD_Benchmarks/X_NIST_StRD_Benchmarks.htm ''' actual = np.atleast_1d(np.asarray(actual)) desired = np.atleast_1d(np.asarray(desired)) mask_zero = desired == 0 dig = np.zeros(desired.shape) dig[mask_zero] = -np.log10(np.abs(actual[mask_zero])) dig[~mask_zero] = -np.log10(np.abs(actual[~mask_zero] - desired[~mask_zero]) / np.abs(desired[~mask_zero])) if np.size(dig) == 1: dig = np.squeeze(dig)[()] return dig Josef