Using nanmean in retime when sampling intervals don't perfectly line up
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I have a number of short time-period high resolution timetables that I want to retime to match a longer, lower resolution timeseries. I have done this using the following (contained within a for loop that also does some other stuff):
% Smoothed Obs
a_Obs = Obs_Timetables.(fnm)(:,[2:3,14:15,27]);
a_SmoothedObs = retime(a_Obs,time(min_time_idx:max_time_idx),@(x)mean(x,'omitnan')); % still showing final row as nan
Obs_SmoothedObs.(fnm) = a_SmoothedObs;
where Obs_Timetables is a structure containing the short, high resolution timetables, time is the datetime variable used to construct the low resolution timetable, and min_ (max_) time_idx is the index in time of the first (last) datetime value in the high resolution timetable. Having used both the format shown above and @nanmean in place of @x ... here, the retime step sets all values in the final row of my new short, low resolution timetables (a_SmoothedObs) to NaN. I think this is because the final time in the a_Obs and the time at time(max_time_idx) are not the same (with a_Obs.time(end) typically smaller than time(max_time_idx), although this is happenstance). I would like it to give me the nanmean as if a_Obs.time did extend to the time at time(max_idx), but all the values between that and the current final value were nan. Is there a way to do this that doesn't involve tacking an additional nan line onto the end of a_Obs and then removing it afterwards?
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SAI SRUJAN
2024-3-27
Hi Victoria,
I understand that you are facing an issue with retiming high-resolution time series data to align with a lower-resolution timeline using the 'retime' MATLAB function.
When we retime high-resolution time series to match a lower-resolution, the last interval in the high-resolution series seems not to align perfectly with the lower-resolution series causing the 'retime' function to produce 'NaN' values for the final row because it doesn't find any corresponding data points within that final time period.
You can create a custom function that handles the aggregation and treats missing intervals, please go through the following code sample to proceed further,
% Define your custom aggregation function
function result = customNanMean(x)
if isempty(x)
result = NaN;
else
result = mean(x, 'omitnan');
end
end
a_SmoothedObs = retime(a_Obs, time(min_time_idx:max_time_idx), @(x) customNanMean(x));
For a comprehensive understanding of the 'retime' MATLAB function, please go through the following documentation.
I hope this helps!
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