# How to store an interpolated data to get the mean for each new point in X?

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Pedro Oliveira on 13 Nov 2019
Edited: Prabhan Purwar on 21 Jan 2020
I am trying to find the mean for each point in a series of 16 profiles. The code is as following
a= table2array (DataTot);
b= standardizeMissing(a,nan);
b = reshape(a,[181,4,16]);
for i=1:16
xx= b(:,1,i);
yy=b(:,4,i);
yyc = yy;
[maxHValue, indexAtMaxH] = findpeaks(yyc);
xshift=xx(yyc==max(maxHValue));
xxs=xx - xshift;
xxs = rmmissing(xxs);
yyc = rmmissing(yy);
XYint = interp1(xxs,yyc,-100:1:200);
L(i)= XYint %ERROR
end
When I try to store in L it doesn't allow me to do so(ERROR="unable to perform assignment because the indices on the left side are not compatible with the size of the right side"). Is there a way to find the mean while inside the loop for the interpolated values or should I store it to use the data after the loop?
Thanks for the help.

David Hill on 13 Nov 2019
if the size of XYint changes, you could use a cell array.
L{i}= XYint;
Pedro Oliveira on 13 Nov 2019
It did work, thank you!
but the problem now is how the data from x keep stored. It only store a 1x301 cell. And I lose how to plot the x to y later on with the mean value.

Prabhan Purwar on 21 Jan 2020
Edited: Prabhan Purwar on 21 Jan 2020
Hey,
The mean of the interpolated values could be found out using the mean() function in the loop.
You may make use of Cell arrays to store variable size datasets as shown in the following code:
L{1,i}=XYint; %y data
L{2,i}=xxs; %x data
L{3,i}=xshift; %x shift

R2018b

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