OK, my specific problem with masked arrays is as follows:
a = numpy.array([1,numpy.nan,2]) aa = numpy.ma.masked_where(numpy.isnan(a),a) aa array(data = [ 1.00000000e+00 1.00000000e+20 2.00000000e+00], mask = [False True False], fill_value=1e+020)
numpy.ma.set_fill_value(aa,0) aa array(data = [ 1. 0. 2.], mask = [False True False], fill_value=0)
OK, so this looks like I want it to, however:
[v for v in aa] [1.0, array(data = 999999, mask = True, fill_value=999999) , 2.0]
Two questions:
1. why am I not getting my NaN's back?
when iterating over a masked array, you get the "ma.masked" constant for elements that were masked (same as what you would get if you indexed the masked array at that element). If you are referring specifically to the .data portion of the array... it looks like the latest version of the numpy.ma sub-module preserves nan's in the data portion of the masked array, but the old version perhaps doesn't based on the output you are showing.
2. why is the wrong fill value being used here?
the second element in the array iteration here is actually the numpy.ma.masked constant, which always has the same fill value (which I guess is 999999). This is independent of the fill value for your specific array. - Matt