Dear Richard,

that is indeed, interesting. And the mechanical responses might be on a much faster time scale than the thermal ones. A long time ago I looked at data from milling and had rather high resolution measurements. I believe that Mathematica should be able to deal with this kind of data. (You might use alternatives to the Import function though...)

Do you need the results in (near) real-time?

Best wishes,

Marco

Another thing is that the time series appears to be hopelessly oversampled. Everything becomes much faster if you first resample:

Show[ListLinePlot[MovingAverage[ArrayResample[testData, 2500], 50]], 
 ListPlot[FindPeaks[MovingAverage[ArrayResample[testData, 2500], 50], 25], PlotStyle -> Red]]

One can put a bit more effort (calculate window sizes in the resampled data) into this, but basically one can then also determine where the peaks are for the original sampling:

peaksresample = N /@ FindPeaks[MovingAverage[ArrayResample[testData, 2500], 50], 25];
{Length[testData]/2500.*#[[1]], #[[2]]} & /@ peaksresample

Cheers,

Marco

See attached notebook with 1/12 the data. I am sure there is an explanation, but I don't understand the behavior difference between FindPeaks[ , 400], 500, and 600. Perhaps I am simply asking too much of FindPeaks. FindPeaks[ ,500] gets closest to the result I am seeking, in terms of scale.

Attachments:

Hi Jos,

the way you calculate the peaks you get a result in the form {{index (!), value}, ...}. One possibility is to define your data as a time series and do it like so (compare with documentation on FindPeaks - under "Details and Options"):

ts = Transpose[test];
peaks = Normal@FindPeaks[TimeSeries[ts[[2]], {ts[[1]]}]]; 

Regards -- Henrik