# How to calculate wavenumber, for fft2 of a 2D array?

84 vues (au cours des 30 derniers jours)
Jay Ghosh le 27 Mai 2023
Commenté : Hiro Yoshino le 14 Juin 2023
I have perfomed fft2 on an image/2D-array (361x211). When I plot the result, using imagesc, I would like to label the axes with wavenumbers. However, for the fft2 output, I do not know how to calculate the wavenumbers (I commented out my this part of my code). Is it related to the image resolution (dpi)?
My code and output plots (amplitude and phase) are attached below. Thanks!
x = data(:,1); % longitude
y = data(:,2); % latitude
z = data(:,3); % anomaly (nano Tesla)
% Find the size (row,col) of the xyz file
row = length(unique(x));
col = length(unique(y));
% Reshape and transpose z values to get correct data orientaion
zm = reshape(z,row,col);
zm = fliplr(zm);
zm = zm';
% Perform fft2 on z-values (nT)
fft2_zm = fftshift(log(abs(fft2(zm)))); % amplitude
% phase_zm = angle(fftshift(fft2(zm))); % phase
% % Calculate wavenumbers
% Nc = col; Nr = row;
% dp = 1/300; % dpi = 300?
% kx = (-Nr/2:Nr/2-1)/(Nr*dp);
% ky = (-Nc/2:Nc/2-1)/(Nc*dp);
figure(1);
imagesc(fft2_zm); colorbar;
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Star Strider le 28 Mai 2023
The spatial frequency would be in terms of . I am not certain that it would be possible to expres anything about your data in terms of wavenumber.
I deleted my Answer because it became obvious to me that it was not going to provide the result you want, essentially because I am not certain that is possible.
Jay Ghosh le 30 Mai 2023

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### Réponse acceptée

Hiro Yoshino le 27 Mai 2023
We use "normalized frequency" with FFT and this is a linear transformation between the time space and the frequency space. So the number of points along the x-axis and the y-axis corresponds to the wavenumber.
<< This is what I called "normalized frequency".
x = data(:,1); % longitude
y = data(:,2); % latitude
z = data(:,3); % anomaly (nano Tesla)
% Find the size (row,col) of the xyz file
row = length(unique(x));
col = length(unique(y));
% Reshape and transpose z values to get correct data orientaion
zm = reshape(z,row,col);
zm = fliplr(zm);
zm = zm';
% Perform fft2 on z-values (nT)
fft2_zm = fftshift(log(abs(fft2(zm)))); % amplitude
% phase_zm = angle(fftshift(fft2(zm))); % phase
% % Calculate wavenumbers
% Nc = col; Nr = row;
% dp = 1/300; % dpi = 300?
% kx = (-Nr/2:Nr/2-1)/(Nr*dp);
% ky = (-Nc/2:Nc/2-1)/(Nc*dp);
figure(1);
%imagesc(fft2_zm); colorbar;
x_axis = linspace(-pi,pi,size(fft2_zm,1));
y_axis = linspace(-pi,pi,size(fft2_zm,2));
imagesc('XData',x_axis,'YData',y_axis,'CData',fft2_zm);
xlim([x_axis(1),x_axis(end)]);
ylim([y_axis(1),y_axis(end)]);
ax = gca;
ax.XTick = [-pi, -pi/2, 0, pi/2, pi];
ax.YTick = [-pi, -pi/2, 0, pi/2, pi];
ax.XTickLabel = {"-\pi", "-\pi/2", "0","\pi/2", "/pi"};
ax.YTickLabel = {"-\pi", "-\pi/2", "0","\pi/2", "/pi"};
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Jay Ghosh le 13 Juin 2023
Thank you @Hiro Yoshino. I have a follow up question/comment. Why is it that here we see that the highest energy corrsponds to the lowest spatial frequency? I thought the higher the energy in kx-ky space, the higher the frequency. In another word, high energy is associated with high frequency. But, for both the transforations above (tr86 and lena512), we see that the highest energy (in the centre) is associated with lowest frequency. Thanks.
Hiro Yoshino le 14 Juin 2023
The power spectrum we got is typical. We often see the highest power at lower frequencies.
Unless we have specific high frequencies in the data, we normally observe similar powe spectrums.
The energy at 0 frequency corresponds to the bias. So you can remove it by subtracting the mean value from the data.

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