I think that the new implementation is making a copy of the data with indexing a MA. This is different from both ndarray and the existing numpy ma version.
e.g. testma = ma.array([[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5]], mask=ma.nomask) testma2 = testma[1] testma2[1] = 20 print testma print testma2 --output-- [[1 2 3 4 5] [1 2 3 4 5] [1 2 3 4 5]] [ 1 20 3 4 5] Having subviews of the mask seems complicated with the mask being nomask. What happens if the view sets a new masked value and hence changes from nomask to an boolean array? How does the parent mask get updated? I think the numpy implementation gets away with this by returning a view of only the _data part if the ma mask is nomask - I don't like this solution as I would expect a ma to be returned. Also I suspect that if the ma is to be a view of another ma, then in __new__ a mask that is a boolean array of all False cannot be converted to nomask. I like the new implementation of maskedarray, especially the focus on simplicity. The only simple solution I see is to have the mask be a boolean array at all times. Or at least as soon as a view is required of a ma if mask is nomask then it should be converted to a boolean array and then both a view of the boolean array mask as well as the data is used in the view ma returned. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
