I posted the following inquiry to python-list@python.org earlier this week, but got no responses, so I thought I'd try a more focused group. I assume MA module falls under NumPy area. I am using 2 (and more) dimensional masked arrays with some numeric data, and using the reduce functionality on the arrays. I use the masking because some of the values in the arrays are 'missing' and should not be included in the results of the reduction. For example, assume a 5 x 2 array, with masked values for the 4th entry for both of the 2nd dimension cells. If I want to sum along the 2nd dimension, I would expect to get a 'missing' value for the 4th entry because both of the entries for the sum are 'missing'. Instead, I get 0, which might be a valid number in my data space, and the returned 1 dimensional array has no mask associated with it. Is this expected behavior for masked arrays or a bug or am I misusing the mask concept? Does anyone know how to get the reduction to produce a masked value? Example Code:
import MA a = MA.array([[1,2,3,-99,5],[10,20,30,-99,50]]) a [[ 1, 2, 3,-99, 5,] [ 10, 20, 30,-99, 50,]] m = MA.masked_values(a, -99) m array(data = [[ 1, 2, 3,-99, 5,] [ 10, 20, 30,-99, 50,]], mask = [[0,0,0,1,0,] [0,0,0,1,0,]], fill_value=-99)
r = MA.sum(m) r array([11,22,33, 0,55,]) t = MA.getmask(r) print t None