% locations of all, indiscriminate peaks locs(1:3) = [0.1740 2.7050 3.4340] % Peaks amplitude/size at set locations pks(1:3) = [3.5 4.2 8.9]
There is an additional criteria, that the peaks fall within certain ranges or windows of time. These selected windows are based on the value of another signal.
% These bounding vectors all fit within the time series of the peaks above % May be a different number of "windows" than the peak location vector lower_bounds(1:5) = [1.9930 2.8470 3.6940 4.5320 5.3670] upper_bounds(1:5) = [2.6930 3.5470 4.3940 5.2320 6.0670]
Each peak is only considered to be True if it falls within these bounds. For this trivial example, only locs(3) falls within an acceptable range. The actual vectors are of course much longer. I can't figure out how to do this in a practical sense. I'd like the output to be a new vector of locations that meet the windows criteria. I'd then like to use these new locations to identify which peaks are to be selected. This last step I imagine is likely a combination of vector indexing, which I can probalby figure out. Any help would be appreciated. I've already tried for loops (but got lost in the vector storage and space allocation issues), as well as bounding the location vector between the two bounding vectors (however the sizes aren't the same, so there becomes an issue).
loc_is_in_range = any(locs >= lower_bounds(:) & locs <= upper_bounds(:));
This depends upon locs being a row vector (not a column vector). This code should be usable from R2016b onwards; for earlier releases it would have to be modified.
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