def nonreducing_reducer(reducing_func, arr, axis):
reduced = reducing_func(arr, axis=axis)
shape = list(reduced.shape)
axis = axis % len(arr.shape)
shape.insert(axis, 1)
reduced.shape = tuple(shape)
return reduced
Cute !
Here is another one:
1dfunc = lambda x: atleast_1d(reducing_function(x))
apply_along_axis(1dfunc, axis, arr)
Is it something people often need ? Would it be worth, (for numpy 1.1 ?), to consider complex axis arguments as a wish to preserve the rank of the array?
>>> a.shape
(n,m,o,p)
>>> a.sum(axis=1j)
(n,1,o,p)
>>> a.sum(axis=1)
(n,o,p)
Thanks,
David
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