how to calculate fast Fourier transform with a 128-point window on these data with non-uniform sampling frequency

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Hi everyone
I have provided you with my MATLAB code and data. These samples were recorded at a non-uniform sampling frequency, so I used the NUFFT command to convert the Fourier.
Now, if I want to use fast Fourier transform with a 128-point window on these data with non-uniform sampling frequency to calculate the power spectrum, and then divide the frequency range of all power spectra into eight equal parts and divide the area under the 8-channel curve. What calculator code should I use to calculate?THANKS SO MUCH
This is my code:
%% load Data
DATA3 = [];DATA4 = [];FFT3 =[];M7=[];
for j =1:13
data3 = load(strcat(strcat('als',num2str(j)),'.ts'));
DATA3 = [DATA3;data3];
t3 = data3(:,1); f3 = (length(data3)/300))*(0:(length(t3)-1))/length(t3);
fft3 = nufft(data3(:,2:13),t3,f3);FFT3 = [FFT3;fft3]; M6 = abs(fft3);M7 = [M7;M6];
end
FFT4=[];M9=[];
for j = 1:16
data4 = load(strcat(strcat('control',num2str(j)),'.ts'));
DATA4 = [DATA4;data4];
t4 = data4(:,1); f4 = (length(data4)/300))*(0:(length(t4)-1))/length(t4);
fft4 = nufft(data4(:,2:13),t4,f4);FFT4 = [FFT4;fft4]; M8 = abs(fft4);M9 =[M9;M8];
% f4 = (0.8167/2)*(0:(length(DATA4)-1))/length(DATA4);
end

Accepted Answer

Mathieu NOE
Mathieu NOE on 5 Jul 2022
hello
see my suggestion below
the result is in Area
clc
clearvars
load('DATA3.mat');
t3 = data3(:,1);
%% uniform resampling of data on 128 samples
nfft = 128; % fft length
newt3 = linspace(min(t3),max(t3),nfft); % equally spaced time vector
dt = mean(diff(newt3)); % new time increment
Fs = 1/dt; % sampling frequency
newdata3_2_13 = interp1(t3,data3(:,2:13),newt3,'linear');
%% fft
fft_spectrum = abs(fft(newdata3_2_13))/nfft;
% one sidded fft spectrum % Select first half
if rem(nfft,2) % nfft odd
select = (1:(nfft+1)/2)';
else
select = (1:nfft/2+1)';
end
fft_spectrum = fft_spectrum(select,:);
freq_vector = (select - 1)*Fs/nfft;
figure,semilogy(freq_vector,fft_spectrum);
%% divide the frequency range of all power spectra into eight equal parts
freq_points = linspace(min(freq_vector),max(freq_vector),8+1); % equally spaced freq vector
for cj = 1:numel(freq_points)-1
start = freq_points(cj);
stop = freq_points(cj+1);
ind = find(freq_vector>=start & freq_vector<=stop);
Area(cj,:) = trapz(freq_vector(ind),fft_spectrum(ind,:)); % Area under curve :
% rows = 8 (as many as parts) , cols = 12 , as many as signals
end
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