I'm a little perplexed why reduceat was made to behave like this:
In [26]: arr = np.ones((10, 4), dtype=bool)
In [27]: arr
Out[27]:
array([[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True]], dtype=bool)
In [30]: np.add.reduceat(arr, [0, 3, 3, 7, 9], axis=0)
Out[30]:
array([[3, 3, 3, 3],
[1, 1, 1, 1],
[4, 4, 4, 4],
[2, 2, 2, 2],
[1, 1, 1, 1]])
this does not seem intuitively correct. Since we have:
In [33]: arr[3:3].sum(0)
Out[33]: array([0, 0, 0, 0])
I would expect
array([[3, 3, 3, 3],
[0, 0, 0, 0],
[4, 4, 4, 4],
[2, 2, 2, 2],
[1, 1, 1, 1]])
Obviously I can RTFM and see why it does this ("if ``indices[i] >=
indices[i + 1]``, the i-th generalized "row" is simply
``a[indices[i]]``"), but it doesn't make much sense to me, and I need
work around it. Suggestions?
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