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Integral average of y values with respect to x values

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I have two vectors (x and y) of the same size with x having more than one value in the y direction. For example, x is [0.02 0.02 0.03 0.03 0.03 0.04 0.04 0.05 0.05 0.05] and y is [0.23 0.40 0.12 0.16 0.09 0.50 0.02 0.33 0.10 0.08]. I want to convert the attached scattered plot of the two vectors to a simple continuous curve, by evaluating the integral average of y values with respect to x values. Take into account that the x vector is already ordered from small to large values. How could I do this? Thank you in advance.
  1 Comment
Image Analyst
Image Analyst on 5 Oct 2015
"I have two vectors (x and y) of the same size with x having more than one value in the y direction" Huh? Does it have one more, or are x and y the same length? Not sure what you're saying. Are you saying that for any given x value, it may be in the x array more than once, with each occurrence of that x value having a different y value stored at corresponding indexes in the y array?

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Accepted Answer

Star Strider
Star Strider on 4 Oct 2015
I’m not exactly certain what you want, but one approach would be to use the cumtrapz function:
x = [0.02 0.02 0.03 0.03 0.03 0.04 0.04 0.05 0.05 0.05];
y = [0.23 0.40 0.12 0.16 0.09 0.50 0.02 0.33 0.10 0.08];
I = cumtrapz(x,y); % Integrated Vector Of ‘y’ w.r.t. ‘x’
figure(1)
plot(x, I)
grid
  7 Comments
Star Strider
Star Strider on 5 Oct 2015
I would experiment with the filter order and use filtfilt if you have the Signal Processing Toolbox. This gave what appeared to me to be close to what you want:
N = 50; % Length Of Filter
b = ones(1,N); % Filter Coefficients
a = N; % Filter Coefficients
sf = filtfilt(b, a, Vsorted); % Filter Data
I experimented with filter lengths ranging from 50 to 200 and it seemed to give the result you illustrated. It is not possible to design a typical low-pass filter because your data are not regularly sampled. It would be necessary to interpolate your data (using interp1 if your data meet its requriements) to a regularly-sampled time base (here Rsorted) to design a filter for it. That could make the curve smoother, but I can’t promise that it would be.
The first step after interpolation would be to do a fft in order to determine what frequencies you want to exclude as noise. When you decide on a cutoff frequency for your low-pass filter, I would then use the filter design procedure I outlined here.

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More Answers (1)

arich82
arich82 on 5 Oct 2015
Edited: arich82 on 5 Oct 2015
[edited to fix typo in comments, and clean-up useless clutter]
Perhaps try combining the filter solution with the accumarray (or filtfilt, if you have the appropriate toolbox, which I do not):
x = Rsorted(:).';
y = Vsorted(:).';
% compute average y for each unique x
[c, ic, ia] = unique(x);
sums = accumarray(ia, y);
wgts = accumarray(ia, ones(size(y))); % weights, i.e. number of reps
avgs = sums./wgts;
% interpolate data on regular grid
n = numel(c); % or some other number
xq = linspace(c(1), c(end), n);
yq = interp1(c, avgs, xq);
% filter gridded data
windowSize = 50;
a = windowSize;
b = ones(1, windowSize);
yf = filter(b, a, yq);
% plot results
hold all;
plot(xq, yf, 'r-', 'LineWidth', 3)
  4 Comments
Star Strider
Star Strider on 7 Oct 2015
In my latest Comment (5 Oct 2015 at 21:09), I used filtfilt.

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