I have to perform a median filter on only selected elements in an array and am
wondering if using a masked array will speed things up by preventing the computation
of the median on those elements where it's not needed.
I'm using a Fortran code to fill up a (NX,NY,NZ) array where (NZ) is the number of
points in the median filter window and NX and NY are the array dimensions themselves.
If a filter is not required at a certain (I,J) element, I set all the elements along
the NZ dimension to a fill value, and then create a masked array where the "mask"
argument is set to True for all fill values.
So, I am hoping that the numpy.median(data, axis=2) call will not even try to compute
the median for those (I,J) elements where all the data in the NZ direction are masked
out, thus saving me valuable computational time.
Is this a valid assumption? Or does numpy go ahead and calculate the median for all
(I,J) elements irregardless of whether they're masked out or not?
What is the recommended way of doing a fast median filter on an array where only
certain elements of the array need to be calculated? I'm trying to avoid a
nested loop over all (I,J) elements.