> mean(y): -1.3778013372117948e-16 > ypad: > [-1.37780134e-16 -1.37780134e-16 -1.37780134e-16 0.00000000e+00 > 3.09016994e+00 5.87785252e+00 8.09016994e+00 9.51056516e+00 > 1.00000000e+01 9.51056516e+00 8.09016994e+00 5.87785252e+00 > 3.09016994e+00 1.22464680e-15 -3.09016994e+00 -5.87785252e+00 > -8.09016994e+00 -9.51056516e+00 -1.00000000e+01 -9.51056516e+00 > -8.09016994e+00 -5.87785252e+00 -3.09016994e+00 -2.44929360e-15 > -7.40148683e-17 -7.40148683e-17] > > The left pad is correct, but the right pad is different and not the mean of > y) --- why?
This is how np.pad computes mean padding: https://github.com/numpy/numpy/blob/01541f2822d0d4b37b96f6b42e35963b132f1947/numpy/lib/arraypad.py#L1396-L1400 elif mode == 'mean': for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \ in enumerate(zip(pad_width, kwargs['stat_length'])): newmat = _prepend_mean(newmat, pad_before, chunk_before, axis) newmat = _append_mean(newmat, pad_after, chunk_after, axis) That is, first the mean is prepended, then appended, and in the latter step the updates (front-padded) array is used for computing the mean again. Note that with arbitrary precision this is fine, since appending n*`mean` to an array with mean `mean` should preserve the mean. But with doubles you can get errors on the order of the machine epsilon, which is what happens here: In [16]: ypad[3:-2].mean() Out[16]: -1.1663302849022412e-16 In [17]: ypad[:-2].mean() Out[17]: -3.700743415417188e-17 So the prepended values are `y.mean()`, but the appended values are `ypad[:-2].mean()` which includes the near-zero padding values. I don't think this error should be a problem in practice, but I agree it's surprising. AndrĂ¡s _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion