| interpolating missing data [message #25349] |
Wed, 06 June 2001 00:38 |
ignore_this_adress
Messages: 3
Registered: June 2001
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Junior Member |
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Hello,
I just came across a problem that should be relatively easy to solve. I
read a time series of a slowly varying scalar. However, in my original
data, some timepoints are missing. My read rountine fills them up with
zeroes (all normal values are well above 0). When the rountine finishes
I would like to automatically correct the data for the missing
timepoints. When using interpolate with the MISSING value, I end up with
the original dataset (syntax: pip = INTERPOLATE(p1, INDGEN(8197),
MISSING=0); where p1 is a float array[8197] with incidental zero
values).
Am I using wrong syntax, or should I try a different approach. I
understand that I could do (linear) fitting around the missing data,
however, that would require more or less arbitrary choices on what data
to use, etc. I would greatly appreciate any suggestion for a more
wholistic approach :-) .
Mika
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