BUG: fix ma.median for empty ndarrays

[juliantaylor]

return nan as it did in 1.11 and same as normal median.
closes gh-8703

[juliantaylor]

returning a masked nan would make more sense to me, but should we change the behaviour instead of keeping the old?

[mhvk]

Agreed that a masked nan might be more logical, but since np.ma.mean also just returns nan, it is probably best to keep the behaviour as is.

[eric-wieser]

Not a huge fan of this special-casing of index building

Can we just do something like:

indexer = [slice(None)] * asorted.ndim
if asorted.shape[axis] == 0:
    indexer[axis] = []
elif asorted.shape[axis] % 2 == 1:
    indexer[axis] = [h]
else:
    indexer[axis] = [h-1, h]

# ....

return np.ma.mean(asorted[indexer], axis=axis)

Edit: no, that doesn't work since the counts are different on each dimension

[eric-wieser]

I think I meant:

indexer = np.ix_(*(np.arange(x) for x in asorted.shape))
if asorted.shape[axis] == 0:
    indexer[axis] = # empty of the right shape
elif:
    indexer[axis] = np.stack([h-1, h], axis=axis)

[eric-wieser]

What does this return for a fully-masked matrix? nan or masked?

[juliantaylor]

hm right you can index with mixed slices and indices, might be nicer

it returns unmasked nan array of the input shape % the reduced axis

[eric-wieser]

hm right you can index with mixed slices and indices, might be nicer

Doesn't work when indexing both h and h-1 and the same time. But I'd replace the empty slice with a empty array, for consistency. So remove all the slices, rather than all the arrays

[juliantaylor]

not really nicer due to the way the mean works at the end, which is ugly but I don't really want to change it for a bugfix

[eric-wieser]

Fair enough, I might do some maintenance on this another time

[eric-wieser]

@juliantaylor: re index-building, #8708