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

Marco Thank you, this is extremely helpful. Funny you mention over-sampled. You should see the original data, which is sampled 12 times even more frequently. This is telemetry data from a large machine, and high-frequency sampling is required to resolve certain mechanical responses, but certainly not thermal responses. The data example I supplied here is an example of a thermal response. Kindest regards,

Rich

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:

I have a 1-D list of data with about 3 million elements. The data looks like noise to the naked eye, but it is not noise. If I apply FindPeaks to a 60k-element sub-list Taken from somewhere in the midst of the original data, FindPeaks[sublist,315,0] does a pretty decent job of finding the peaks at the scale I need, finding 22 peaks. If I do FindPeaks[originallist,315,0], I get 25 peaks and none are in the subset. The found peaks are clustered at the front end of the original list. Any clues what is going on? I wish I could share the notebook, but I'm not sure how to do that with 3M points.