PS. my exact numbers are different from yours (probably a multithreaded thing?), but `ypad[:-2].mean()` agrees with the last 3 elements in `ypad` in my case and I'm sure this is true for yours too.
On Sun, Apr 29, 2018 at 11:36 PM, Andras Deak <deak.and...@gmail.com> wrote: >> 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