> You have a million 32-bit floating point numbers that are in the > thousands. Thus you are exceeding the 32-bitfloat precision and, if you > can, you need to increase precision of the accumulator in np.mean() or > change the input dtype: >>>> a.mean(dtype=np.float32) # default and lacks precision > 3067.0243839999998 >>>> a.mean(dtype=np.float64) > 3045.747251076416 >>>> a.mean(dtype=np.float128) > 3045.7472510764160156 >>>> b=a.astype(np.float128) >>>> b.mean() > 3045.7472510764160156 > > Otherwise you are left to using some alternative approach to calculate > the mean. > > Bruce
Interesting -- I knew that float64 accumulators were used with integer arrays, and I had just assumed that 64-bit or higher accumulators would be used with floating-point arrays too, instead of the array's dtype. This is actually quite a bit of a gotcha for floating-point imaging-type tasks -- good to know! Zach _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
