The problem I'm dealing with is the removal of test equipment artifacts from experimentally derived datasets. In layman's terms, the sensing circuit has a tendency to oscillate a little occasionally; which introduces inflections into the data which must be removed before the data can be used for the next part of the processing which requires monotonic data.

This is a case where a picture is worth a thousand words: http://oi63.tinypic.com/2z72xbr.jpg. The blue is (a small subset of) the raw data and the red is the desired output.

This code performs the cleanup. (It runs as a pipe filter, and I've adapted it to use __DATA__ for the small subset shown in the graphic for posting purposes.):

<Reveal this spoiler or all spoilers in this node or all in this thread>

The basic mechanism is to compare the points as successive pairs and look for a negative slope. When a negative slope is found, *both* points are removed from the dataset.

The complication is that pairwise comparisons have to continue with the next point against the *previous* unremoved point.

To (I hope) clarify, in the following:

                               H

                        G
                F

                     E
              D

                    C
            B            
    A

[download]

Resultant dataset: A, B, G, H.

The above code works; but is there a better way of coding it?

I'm not going to define "better"; I'd simply like to see a range of alternative codings.

(Please note: this is not about filtering or smoothing or interpolating the data; just removing spurious values.)

The output from the sample code:

<Reveal this spoiler>
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In reply to How would you code this? by BrowserUk

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