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by thatcherc
615 days ago
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Yup, my data is nicely gridded so I can use the convolution approach pretty easily. Agreed though - missing data at the edges or in the interior is annoying. For a while I was thinking I should recompute the SG coefficients every time I hit a missing data point so that they just "jump over" the missing values, giving me a derivative at the missing point based on the values that come before and after it, but for now I'm just throwing away any convolutions that hit a missing value. |
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We had, in our geophysics application, a "pre computed" coefficient cache - the primary filters (central symmetric smoothing at various lengths) were common choices and almost always there to grab - missing values were either cheaply "faked" for Quick'NDirty displays or infilled by prediction filters that were S-G's computed to use existing points within the range to replace the missing value, that was either a look up from indexed filter cache or a fresh filter generation to use and stash in cache.
It's a complication (in the mechanical watch sense) to add, but with code to generate coefficients already existing it's really just looking at the generation times versus the hassle of indexing and storing them as created and the frequency of reuse of "uncommon" patterns.