I’m not certain what plot I’m looking at in your Question. I could not figure out how to reproduce it in my code, so I will let you plot it.
I did manage to interpolate and then take the means and standard deviations what I believe to be the variables you are interested in. If they are not, my code should at least provide a way for you to do the interpolations and other calculations on the correct variables. I used the longest ‘t_discharge’ vector as the basis of the interpolation, so the ‘LoadV_discharge’ vectors (matrix) are all interpolated to the same times, that being ‘t_discharge{Idx}’.
The code:
load('Michel de Jongh ICR1865022P_Testdata.mat'); % Load Data Into Workspace
vars_t_d = whos('t_discharge*'); % Get ‘t_discharge’ Names
vars_LV_d = whos('LoadV_discharge*'); % Get ‘LoadV_discharge’ Names
for k1 = 1:length(vars_t_d)
t_discharge{k1} = eval(vars_t_d(k1).name); % Create ‘t_discharge’ Cell Array
LoadV_discharge{k1} = eval(vars_LV_d(k1).name); % Create ‘LoadV_discharge’ Cell Array
vctlen(k1) = length(t_discharge{k1}); % Vector Lengths
end
[LenMax,Idx] = max(vctlen); % Longest Vector & Index
for k1 = 1:length(vars_t_d) % Interpolation Loop
LoadV_disch_intrp(:,k1) = interp1(t_discharge{k1}, LoadV_discharge{k1}, t_discharge{Idx}, 'spline', 'extrap');
end
figure(1)
plot(t_discharge{Idx}/3600, LoadV_disch_intrp)
grid
LoadV_disch_mean = mean(LoadV_disch_intrp, 2); % Column Vectors, So Take Means Across Rows
LoadV_disch_std = std(LoadV_disch_intrp, [], 2); % Column Vectors, So Take Standard Deviations Across Rows
figure(2)
plot(t_discharge{Idx}/3600, LoadV_disch_mean, '-r')
hold on
plot(t_discharge{Idx}/3600, LoadV_disch_mean+1.96*LoadV_disch_std, '-g')
plot(t_discharge{Idx}/3600, LoadV_disch_mean-1.96*LoadV_disch_std, '-g')
hold off
grid
I apologise for the delay. I was doing my best to reproduce your plot to check my calculations, and cannot figure out what you are plotting. (I used your code as a basis for mine, but did not run it.) The benefit of my code is also that it creates a matrix of your dynamically-named variables and then does all the calculations on the matrix. You might also find a 'linear' interpolation to be preferable to 'spline'.
I would retain the 'extrap' option in the interp1 call regardless. (It requires that you specify a method.) It will avoid errors if your ‘t_discharge’ variables do not all begin and end at the same times.
