Hi all, I have a problem, and am looking for help... I am trying to use Python as a glue language for passing some very large numeric arrays in and out of various C libraries. These arrays can contain NaN values to indicate missing elements. As long as on the Python level I only use Numeric to pass these arrays around as opaque data containers, there is no problem: - From C library FOO I obtain the huge array 'data'; - Using the PyArray_FromDimsAndData() constructor from the Numeric C API, I create a Numeric array that references 'data'; - In Python, I can pass the Numeric array on to e.g. VTKPython for visualisation. VTK has no problem with the NaNs -- everything works. The problem arises because I want to allow people to manipulate these arrays from within Python as well. As is mentioned in its documentation, Numeric does not support NaNs, and instead advises to use Masked Arrays instead. These would indeed seem to be well-suited for the job (setting aside the possible issues of performance and user-friendliness), but my problem is that I do not understand how I can create an appropriate mask for my array in the first place. Something as simple as: import MA nanv = 1e30000/1e30000 a = MA.masked_array([0,nanv,nanv,nanv,4], mask=[0,1,1,1,0]) print MA.filled(2 * MA.sin(a)) works quite well, but explicit enumeration is clearly not an option for the huge pre-existing arrays I'm dealing with. So I would want to do something similar to: a = MA.masked_object([0,1,nanv,3,4], nanv) but this simply leads to a.mask() returning None. At first I thought this was because 'nanv == nanv' always evaluates to False, but it turns out that in Python 2.3.2 it actually evaluates to True -- presumably because Python's own IEEE 754 support is lacking (if I understand PEP 754 correctly). So why doesn't the masked_object() constructor work? Beats me... It *does* work if I use e.g. '4' as the value parameter. I tried many other approaches as well, including downloading the fpconst package mentioned in PEP 754 and trying to use its IsNaN() as a condition to the MA.masked_where() constructor -- which doesn't work either, and gives me an exception somewhere deep within the bowels of MA. At this point I think I've now reached the end of my rope. Does anybody reading this have any ideas on how I might beat MA into submission, or if there are any other solutions I could try that would allow me to manipulate large NaN-containing arrays efficiently (or even *at all*!) from within Python? Or am I perhaps simply (hopefully) missing something obvious? I am eagerly looking forward to any help or advice. Many thanks in advance, -- Leo Breebaart <leo@lspace.org>