[Numpy-discussion] Using Reduce with Multi-dimensional Masked array
Sue Giller
sag at hydrosphere.com
Wed Nov 28 09:02:05 CST 2001
I posted the following inquiry to python-list at 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
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