Update Numpy Array Where Not Masked

My question is twofolded First, lets say I've two numpy arrays, that are partially masked array_old [[-- -- -- --] [10 11 -- --] [12 14 -- --] [-- -- 17 --]] array_update [[--

Solution 1:

Use a[~b.mask] = b.compressed().

a[~b.mask] selects all the values in a where b is not masked. b.compressed() is a flattened array with all the non-masked values in b.

Example:

>>> a = np.ma.masked_equal([[0, 0, 0, 0], [10, 11, 0, 0], [12, 14, 0, 0], [0, 0, 17, 0]], 0)
>>> b = np.ma.masked_equal([[0, 5, 0, 0], [0, 0, 9, 0], [0, 15, 8, 13], [0, 0, 19, 16]], 0)
>>> a[~b.mask] = b.compressed()
>>> a
[[-- 5 -- --]
[10 11 9 --]
[12 15 8 13]
[-- -- 19 16]]

This should work with 3d arrays too.