How to interpret results of FFT/DFT?

Question

Dan Richter 2023-1-26

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1901465-how-to-interpret-results-of-fft-dft

评论： Star Strider 2023-2-4

I generate a stepped sinusoidal signal of 5 kHz each 2 µs from 100 data samples on the 32-bit MCU (ATSAMD21). How to properly interpret the results of the FFT/DFT calculated from an Excel CSV file acquired on the oscilloscope? I am mainly interested to find out the 5 kHz peak in the frequency characteristic. Do I need to somehow normalize the data before processing?

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Answer 1

Star Strider 2023-1-26

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1901465-how-to-interpret-results-of-fft-dft#answer_1157280

编辑：Star Strider 2023-1-27

在 MATLAB Online 中打开

The time vector needs to be scaled correctly if the frequency vector is to be correct.

Try this —

T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1275525/CSV_5k.csv')

T1 = 1200×2 table

Var1 Var2 ____ _____ 0 0.284 1 0.292 2 0.288 3 0.276 4 0.28 5 0.276 6 0.276 7 0.268 8 0.272 9 0.264 10 0.276 11 0.26 12 0.264 13 0.256 14 0.264 15 0.256

t = T1{:,1}*0.5E-6; % Added: 't' in 0.5 µs Steps

s = T1{:,2};

figure

plot(t, s)

grid

xlabel('Time (units)')

ylabel('Amplitude (units)')

Ts = mean(diff(t));

Fs = 1/Ts;

Fn = Fs/2;

L = size(T1,1);

NFFT = 2^nextpow2(L);

FTs = fft((s-mean(s)).*hann(L),NFFT)/L;

Fv = linspace(0, 1, NFFT/2+1)*Fn;

Iv = 1:numel(Fv);

[smax,idx] = max(abs(FTs(Iv))*2);

Frq = Fv(idx)

Frq = 4.8828e+03

figure

plot(Fv, abs(FTs(Iv))*2)

grid

xlabel('Frequency')

ylabel('Magnitude')

xline(Frq, '-r', sprintf('Frequency = %.4fx10^3 Hz',Frq*1E-3))

xlim([0 1E+4]) % Zoom To See Detail

EDIT — (27 Jan 2023 at 13:50)

Scaled ‘t’ to be appropriate by multiplying it by

so that the sampling interval ‘Ts’ is

.

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Dan Richter 2023-1-30

在 MATLAB Online 中打开

Any advice on how to modify attached C++ VS2022 program so I can use it on embedded 32-bit MCU (ATSAMD21) system?

I will get following results:

0.000000 0.000000

7.939106 1666.666748

16.939116 3333.333496

153.005859 5000.000244

15.244903 6666.666992

8.394692 8333.333740

5.840863 10000.000488

4.610853 11666.667236

...

How to calculate the peak frequency 4.8828e+03 more precisely?

C++ VS2022 program code:

// Source:
// https://www.geeksforgeeks.org/discrete-fourier-transform-and-its-inverse-using-c/
#include <iostream>
// C program for the above approach
#include <math.h>
#include <stdio.h>
const int len = 1200;
// Function to calculate the DFT
void calculateDFT()
{
	//int xn[len] = { 840, 920, 880, 760, 800, 760, 760, 680, 720, 640, 760, 600, 640, 560, 640, 560, 520, 640, 600, 480, 520, 480, 560, 440, 480, 440, 440, 480, 400, 480, 440, 360, 440, 360, 360, 440, 400, 360, 360, 440, 360, 440, 440, 360, 400, 400, 440, 360, 360, 480, 440, 360, 480, 440, 480, 400, 440, 480, 440, 560, 440, 560, 560, 480, 520, 600, 600, 520, 560, 640, 680, 600, 640, 760, 760, 680, 680, 840, 800, 880, 760, 880, 880, 960, 880, 000, 040, 960, 040, 960, 80, 160, 160, 80, 160, 240, 280, 200, 240, 400, 320, 400, 360, 520, 520, 440, 440, 640, 640, 560, 600, 760, 720, 800, 760, 920, 880, 920, 880, 80, 80, 040, 040, 240, 200, 240, 200, 320, 320, 400, 320, 520, 560, 480, 480, 680, 640, 760, 640, 800, 880, 800, 800, 920, 960, 040, 960, 80, 80, 200, 200, 120, 240, 360, 320, 400, 320, 520, 520, 440, 480, 720, 600, 680, 640, 840, 840, 760, 800, 000, 920, 000, 880, 120, 80, 160, 80, 280, 280, 240, 160, 400, 400, 320, 360, 560, 560, 480, 480, 680, 680, 600, 600, 760, 800, 720, 680, 880, 840, 920, 800, 000, 920, 040, 040, 920, 040, 120, 80, 040, 120, 160, 200, 120, 160, 320, 200, 320, 200, 400, 360, 280, 320, 440, 400, 320, 360, 480, 400, 480, 400, 520, 520, 440, 440, 560, 480, 560, 480, 560, 560, 480, 480, 600, 600, 480, 600, 520, 600, 520, 520, 600, 520, 600, 520, 600, 560, 480, 480, 560, 440, 520, 480, 560, 400, 520, 520, 400, 400, 480, 440, 360, 400, 320, 320, 400, 360, 240, 240, 320, 320, 160, 160, 240, 200, 80, 80, 160, 160, 000, 000, 80, 80, 880, 920, 960, 960, 800, 800, 880, 840, 680, 760, 680, 760, 560, 560, 640, 640, 440, 440, 520, 480, 320, 360, 280, 360, 200, 240, 120, 200, 120, 80, 000, 80, 000, 000, 840, 920, 840, 880, 680, 720, 760, 720, 520, 640, 520, 600, 400, 360, 480, 400, 240, 240, 320, 320, 80, 80, 160, 160, 920, 880, 960, 000, 720, 840, 720, 800, 600, 640, 600, 640, 480, 520, 400, 480, 280, 320, 240, 240, 320, 160, 120, 200, 80, 040, 960, 000, 920, 920, 800, 800, 920, 800, 680, 760, 640, 720, 520, 560, 520, 560, 400, 480, 400, 440, 280, 280, 360, 280, 160, 120, 240, 240, 040, 120, 040, 120, 960, 920, 000, 960, 840, 880, 800, 880, 760, 720, 840, 800, 680, 640, 720, 640, 720, 680, 600, 640, 560, 520, 560, 600, 480, 520, 440, 520, 480, 520, 400, 440, 480, 480, 400, 400, 440, 440, 360, 440, 360, 360, 440, 360, 400, 400, 360, 320, 440, 440, 360, 480, 360, 360, 440, 440, 400, 360, 480, 480, 400, 400, 480, 440, 520, 520, 440, 480, 600, 520, 560, 560, 600, 560, 640, 680, 600, 600, 720, 680, 760, 680, 760, 880, 760, 800, 840, 840, 960, 880, 960, 920, 040, 000, 040, 000, 160, 80, 160, 120, 280, 200, 280, 200, 400, 320, 400, 320, 520, 480, 440, 480, 640, 600, 680, 600, 760, 760, 720, 760, 920, 880, 960, 880, 040, 120, 000, 040, 200, 240, 160, 160, 280, 320, 400, 320, 480, 480, 560, 560, 480, 600, 720, 720, 640, 760, 880, 800, 920, 880, 040, 040, 960, 000, 200, 120, 200, 200, 320, 360, 280, 320, 520, 440, 520, 480, 680, 600, 720, 640, 840, 840, 760, 760, 960, 880, 000, 920, 160, 160, 040, 80, 240, 200, 280, 200, 440, 360, 400, 360, 560, 560, 480, 480, 640, 600, 680, 600, 760, 800, 720, 800, 760, 800, 960, 840, 920, 920, 000, 960, 000, 960, 120, 040, 120, 040, 200, 120, 240, 160, 320, 240, 320, 240, 400, 280, 360, 280, 400, 360, 440, 360, 480, 480, 400, 360, 520, 520, 400, 520, 440, 480, 560, 480, 600, 600, 480, 600, 480, 600, 520, 600, 520, 520, 560, 600, 520, 600, 520, 600, 520, 560, 480, 560, 480, 440, 560, 480, 520, 520, 400, 400, 520, 480, 360, 480, 360, 440, 320, 280, 360, 400, 240, 360, 240, 280, 160, 240, 160, 280, 120, 80, 200, 160, 000, 80, 000, 80, 920, 880, 960, 960, 760, 800, 880, 840, 720, 760, 640, 760, 560, 560, 600, 640, 520, 520, 400, 400, 520, 400, 280, 360, 280, 280, 160, 160, 240, 160, 000, 000, 120, 040, 840, 920, 840, 960, 680, 720, 800, 760, 560, 640, 560, 600, 400, 360, 480, 440, 240, 320, 200, 280, 80, 160, 040, 160, 920, 920, 960, 000, 760, 840, 760, 760, 640, 600, 680, 680, 520, 520, 400, 520, 360, 360, 280, 240, 360, 240, 80, 200, 80, 120, 960, 040, 960, 040, 840, 760, 880, 840, 640, 720, 680, 760, 560, 520, 600, 560, 400, 360, 480, 440, 280, 360, 240, 320, 160, 240, 120, 240, 040, 120, 040, 80, 960, 960, 920, 000, 840, 920, 800, 880, 800, 800, 720, 800, 680, 640, 760, 720, 680, 680, 600, 560, 640, 600, 520, 600, 480, 600, 480, 560, 480, 520, 400, 440, 480, 480, 400, 480, 400, 400, 480, 360, 440, 400, 400, 360, 440, 360, 440, 400, 360, 360, 440, 360, 440, 360, 480, 440, 360, 400, 480, 440, 520, 400, 480, 440, 520, 440, 520, 480, 560, 480, 600, 520, 640, 560, 640, 640, 600, 600, 720, 640, 720, 760, 680, 800, 760, 760, 840, 800, 920, 840, 960, 880, 040, 040, 960, 000, 160, 160, 80, 120, 280, 240, 160, 200, 360, 360, 280, 320, 520, 520, 440, 480, 600, 640, 560, 600, 760, 720, 800, 720, 880, 920, 840, 840, 000, 000, 120, 040, 120, 160, 240, 160, 240, 280, 360, 320, 400, 400, 560, 480, 560, 520, 720, 640, 760, 680, 880, 880, 760, 800, 040, 960, 040, 960, 200, 200, 120, 160, 360, 360, 280, 320, 560, 520, 440, 440, 680, 600, 680, 600, 840, 760, 840, 760, 000, 000, 920, 920, 120, 80, 160, 040, 240, 280, 200, 200, 360, 320, 440, 320, 440, 440, 560, 480, 560, 600, 680, 680, 600, 640, 800, 800, 680, 760, 880, 880, 840, 880, 040, 960, 040, 920, 120, 120, 040, 040, 200, 200, 120, 120, 240, 200, 320, 200, 360, 360, 280, 280, 400, 320, 440, 320, 480, 400, 480, 400, 520, 440, 520, 440, 560, 560, 440, 560, 480, 600, 480, 520, 560, 480, 600, 520, 600, 520, 600, 600, 520, 520, 600, 600, 480, 560, 480, 480, 560, 560, 440, 480, 560, 560, 440, 400, 520, 520, 360, 400, 480, 440, 320, 400, 280, 360, 240, 240, 360, 360, 160, 240, 160, 280, 120, 80, 160, 160, 000, 80, 000, 040, 920, 880, 000, 880, 960, 880, 800, 880, 760, 720, 640, 680, 760, 560, 640, 640, 560, 560, 400, 400, 520, 480, 280, 360, 280, 320, 120, 240, 160, 160, 000, 80, 000, 040, 880, 840, 960, 880, 680, 760, 680, 760, 560, 640, 520, 640, 400, 480, 400, 480, 280, 320, 240, 320, 120, 160, 040, 160, 000, 000, 880, 960, 840, 840, 720, 840, 720, 680, 560, 600, 680, 520, 440, 400, 480, 480, 240, 280, 360 };
	float xn[len] = { 0.284, 0.292, 0.288, 0.276, 0.280, 0.276, 0.276, 0.268, 0.272, 0.264, 0.276, 0.260, 0.264, 0.256, 0.264, 0.256, 0.252, 0.264, 0.260, 0.248, 0.252, 0.248, 0.256, 0.244, 0.248, 0.244, 0.244, 0.248, 0.240, 0.248, 0.244, 0.236, 0.244, 0.236, 0.236, 0.244, 0.240, 0.236, 0.236, 0.244, 0.236, 0.244, 0.244, 0.236, 0.240, 0.240, 0.244, 0.236, 0.236, 0.248, 0.244, 0.236, 0.248, 0.244, 0.248, 0.240, 0.244, 0.248, 0.244, 0.256, 0.244, 0.256, 0.256, 0.248, 0.252, 0.260, 0.260, 0.252, 0.256, 0.264, 0.268, 0.260, 0.264, 0.276, 0.276, 0.268, 0.268, 0.284, 0.280, 0.288, 0.276, 0.288, 0.288, 0.296, 0.288, 0.300, 0.304, 0.296, 0.304, 0.296, 0.308, 0.316, 0.316, 0.308, 0.316, 0.324, 0.328, 0.320, 0.324, 0.340, 0.332, 0.340, 0.336, 0.352, 0.352, 0.344, 0.344, 0.364, 0.364, 0.356, 0.360, 0.376, 0.372, 0.380, 0.376, 0.392, 0.388, 0.392, 0.388, 0.408, 0.408, 0.404, 0.404, 0.424, 0.420, 0.424, 0.420, 0.432, 0.432, 0.440, 0.432, 0.452, 0.456, 0.448, 0.448, 0.468, 0.464, 0.476, 0.464, 0.480, 0.488, 0.480, 0.480, 0.492, 0.496, 0.504, 0.496, 0.508, 0.508, 0.520, 0.520, 0.512, 0.524, 0.536, 0.532, 0.540, 0.532, 0.552, 0.552, 0.544, 0.548, 0.572, 0.560, 0.568, 0.564, 0.584, 0.584, 0.576, 0.580, 0.600, 0.592, 0.600, 0.588, 0.612, 0.608, 0.616, 0.608, 0.628, 0.628, 0.624, 0.616, 0.640, 0.640, 0.632, 0.636, 0.656, 0.656, 0.648, 0.648, 0.668, 0.668, 0.660, 0.660, 0.676, 0.680, 0.672, 0.668, 0.688, 0.684, 0.692, 0.680, 0.700, 0.692, 0.704, 0.704, 0.692, 0.704, 0.712, 0.708, 0.704, 0.712, 0.716, 0.720, 0.712, 0.716, 0.732, 0.720, 0.732, 0.720, 0.740, 0.736, 0.728, 0.732, 0.744, 0.740, 0.732, 0.736, 0.748, 0.740, 0.748, 0.740, 0.752, 0.752, 0.744, 0.744, 0.756, 0.748, 0.756, 0.748, 0.756, 0.756, 0.748, 0.748, 0.760, 0.760, 0.748, 0.760, 0.752, 0.760, 0.752, 0.752, 0.760, 0.752, 0.760, 0.752, 0.760, 0.756, 0.748, 0.748, 0.756, 0.744, 0.752, 0.748, 0.756, 0.740, 0.752, 0.752, 0.740, 0.740, 0.748, 0.744, 0.736, 0.740, 0.732, 0.732, 0.740, 0.736, 0.724, 0.724, 0.732, 0.732, 0.716, 0.716, 0.724, 0.720, 0.708, 0.708, 0.716, 0.716, 0.700, 0.700, 0.708, 0.708, 0.688, 0.692, 0.696, 0.696, 0.680, 0.680, 0.688, 0.684, 0.668, 0.676, 0.668, 0.676, 0.656, 0.656, 0.664, 0.664, 0.644, 0.644, 0.652, 0.648, 0.632, 0.636, 0.628, 0.636, 0.620, 0.624, 0.612, 0.620, 0.612, 0.608, 0.600, 0.608, 0.600, 0.600, 0.584, 0.592, 0.584, 0.588, 0.568, 0.572, 0.576, 0.572, 0.552, 0.564, 0.552, 0.560, 0.540, 0.536, 0.548, 0.540, 0.524, 0.524, 0.532, 0.532, 0.508, 0.508, 0.516, 0.516, 0.492, 0.488, 0.496, 0.500, 0.472, 0.484, 0.472, 0.480, 0.460, 0.464, 0.460, 0.464, 0.448, 0.452, 0.440, 0.448, 0.428, 0.432, 0.424, 0.424, 0.432, 0.416, 0.412, 0.420, 0.408, 0.404, 0.396, 0.400, 0.392, 0.392, 0.380, 0.380, 0.392, 0.380, 0.368, 0.376, 0.364, 0.372, 0.352, 0.356, 0.352, 0.356, 0.340, 0.348, 0.340, 0.344, 0.328, 0.328, 0.336, 0.328, 0.316, 0.312, 0.324, 0.324, 0.304, 0.312, 0.304, 0.312, 0.296, 0.292, 0.300, 0.296, 0.284, 0.288, 0.280, 0.288, 0.276, 0.272, 0.284, 0.280, 0.268, 0.264, 0.272, 0.264, 0.272, 0.268, 0.260, 0.264, 0.256, 0.252, 0.256, 0.260, 0.248, 0.252, 0.244, 0.252, 0.248, 0.252, 0.240, 0.244, 0.248, 0.248, 0.240, 0.240, 0.244, 0.244, 0.236, 0.244, 0.236, 0.236, 0.244, 0.236, 0.240, 0.240, 0.236, 0.232, 0.244, 0.244, 0.236, 0.248, 0.236, 0.236, 0.244, 0.244, 0.240, 0.236, 0.248, 0.248, 0.240, 0.240, 0.248, 0.244, 0.252, 0.252, 0.244, 0.248, 0.260, 0.252, 0.256, 0.256, 0.260, 0.256, 0.264, 0.268, 0.260, 0.260, 0.272, 0.268, 0.276, 0.268, 0.276, 0.288, 0.276, 0.280, 0.284, 0.284, 0.296, 0.288, 0.296, 0.292, 0.304, 0.300, 0.304, 0.300, 0.316, 0.308, 0.316, 0.312, 0.328, 0.320, 0.328, 0.320, 0.340, 0.332, 0.340, 0.332, 0.352, 0.348, 0.344, 0.348, 0.364, 0.360, 0.368, 0.360, 0.376, 0.376, 0.372, 0.376, 0.392, 0.388, 0.396, 0.388, 0.404, 0.412, 0.400, 0.404, 0.420, 0.424, 0.416, 0.416, 0.428, 0.432, 0.440, 0.432, 0.448, 0.448, 0.456, 0.456, 0.448, 0.460, 0.472, 0.472, 0.464, 0.476, 0.488, 0.480, 0.492, 0.488, 0.504, 0.504, 0.496, 0.500, 0.520, 0.512, 0.520, 0.520, 0.532, 0.536, 0.528, 0.532, 0.552, 0.544, 0.552, 0.548, 0.568, 0.560, 0.572, 0.564, 0.584, 0.584, 0.576, 0.576, 0.596, 0.588, 0.600, 0.592, 0.616, 0.616, 0.604, 0.608, 0.624, 0.620, 0.628, 0.620, 0.644, 0.636, 0.640, 0.636, 0.656, 0.656, 0.648, 0.648, 0.664, 0.660, 0.668, 0.660, 0.676, 0.680, 0.672, 0.680, 0.676, 0.680, 0.696, 0.684, 0.692, 0.692, 0.700, 0.696, 0.700, 0.696, 0.712, 0.704, 0.712, 0.704, 0.720, 0.712, 0.724, 0.716, 0.732, 0.724, 0.732, 0.724, 0.740, 0.728, 0.736, 0.728, 0.740, 0.736, 0.744, 0.736, 0.748, 0.748, 0.740, 0.736, 0.752, 0.752, 0.740, 0.752, 0.744, 0.748, 0.756, 0.748, 0.760, 0.760, 0.748, 0.760, 0.748, 0.760, 0.752, 0.760, 0.752, 0.752, 0.756, 0.760, 0.752, 0.760, 0.752, 0.760, 0.752, 0.756, 0.748, 0.756, 0.748, 0.744, 0.756, 0.748, 0.752, 0.752, 0.740, 0.740, 0.752, 0.748, 0.736, 0.748, 0.736, 0.744, 0.732, 0.728, 0.736, 0.740, 0.724, 0.736, 0.724, 0.728, 0.716, 0.724, 0.716, 0.728, 0.712, 0.708, 0.720, 0.716, 0.700, 0.708, 0.700, 0.708, 0.692, 0.688, 0.696, 0.696, 0.676, 0.680, 0.688, 0.684, 0.672, 0.676, 0.664, 0.676, 0.656, 0.656, 0.660, 0.664, 0.652, 0.652, 0.640, 0.640, 0.652, 0.640, 0.628, 0.636, 0.628, 0.628, 0.616, 0.616, 0.624, 0.616, 0.600, 0.600, 0.612, 0.604, 0.584, 0.592, 0.584, 0.596, 0.568, 0.572, 0.580, 0.576, 0.556, 0.564, 0.556, 0.560, 0.540, 0.536, 0.548, 0.544, 0.524, 0.532, 0.520, 0.528, 0.508, 0.516, 0.504, 0.516, 0.492, 0.492, 0.496, 0.500, 0.476, 0.484, 0.476, 0.476, 0.464, 0.460, 0.468, 0.468, 0.452, 0.452, 0.440, 0.452, 0.436, 0.436, 0.428, 0.424, 0.436, 0.424, 0.408, 0.420, 0.408, 0.412, 0.396, 0.404, 0.396, 0.404, 0.384, 0.376, 0.388, 0.384, 0.364, 0.372, 0.368, 0.376, 0.356, 0.352, 0.360, 0.356, 0.340, 0.336, 0.348, 0.344, 0.328, 0.336, 0.324, 0.332, 0.316, 0.324, 0.312, 0.324, 0.304, 0.312, 0.304, 0.308, 0.296, 0.296, 0.292, 0.300, 0.284, 0.292, 0.280, 0.288, 0.280, 0.280, 0.272, 0.280, 0.268, 0.264, 0.276, 0.272, 0.268, 0.268, 0.260, 0.256, 0.264, 0.260, 0.252, 0.260, 0.248, 0.260, 0.248, 0.256, 0.248, 0.252, 0.240, 0.244, 0.248, 0.248, 0.240, 0.248, 0.240, 0.240, 0.248, 0.236, 0.244, 0.240, 0.240, 0.236, 0.244, 0.236, 0.244, 0.240, 0.236, 0.236, 0.244, 0.236, 0.244, 0.236, 0.248, 0.244, 0.236, 0.240, 0.248, 0.244, 0.252, 0.240, 0.248, 0.244, 0.252, 0.244, 0.252, 0.248, 0.256, 0.248, 0.260, 0.252, 0.264, 0.256, 0.264, 0.264, 0.260, 0.260, 0.272, 0.264, 0.272, 0.276, 0.268, 0.280, 0.276, 0.276, 0.284, 0.280, 0.292, 0.284, 0.296, 0.288, 0.304, 0.304, 0.296, 0.300, 0.316, 0.316, 0.308, 0.312, 0.328, 0.324, 0.316, 0.320, 0.336, 0.336, 0.328, 0.332, 0.352, 0.352, 0.344, 0.348, 0.360, 0.364, 0.356, 0.360, 0.376, 0.372, 0.380, 0.372, 0.388, 0.392, 0.384, 0.384, 0.400, 0.400, 0.412, 0.404, 0.412, 0.416, 0.424, 0.416, 0.424, 0.428, 0.436, 0.432, 0.440, 0.440, 0.456, 0.448, 0.456, 0.452, 0.472, 0.464, 0.476, 0.468, 0.488, 0.488, 0.476, 0.480, 0.504, 0.496, 0.504, 0.496, 0.520, 0.520, 0.512, 0.516, 0.536, 0.536, 0.528, 0.532, 0.556, 0.552, 0.544, 0.544, 0.568, 0.560, 0.568, 0.560, 0.584, 0.576, 0.584, 0.576, 0.600, 0.600, 0.592, 0.592, 0.612, 0.608, 0.616, 0.604, 0.624, 0.628, 0.620, 0.620, 0.636, 0.632, 0.644, 0.632, 0.644, 0.644, 0.656, 0.648, 0.656, 0.660, 0.668, 0.668, 0.660, 0.664, 0.680, 0.680, 0.668, 0.676, 0.688, 0.688, 0.684, 0.688, 0.704, 0.696, 0.704, 0.692, 0.712, 0.712, 0.704, 0.704, 0.720, 0.720, 0.712, 0.712, 0.724, 0.720, 0.732, 0.720, 0.736, 0.736, 0.728, 0.728, 0.740, 0.732, 0.744, 0.732, 0.748, 0.740, 0.748, 0.740, 0.752, 0.744, 0.752, 0.744, 0.756, 0.756, 0.744, 0.756, 0.748, 0.760, 0.748, 0.752, 0.756, 0.748, 0.760, 0.752, 0.760, 0.752, 0.760, 0.760, 0.752, 0.752, 0.760, 0.760, 0.748, 0.756, 0.748, 0.748, 0.756, 0.756, 0.744, 0.748, 0.756, 0.756, 0.744, 0.740, 0.752, 0.752, 0.736, 0.740, 0.748, 0.744, 0.732, 0.740, 0.728, 0.736, 0.724, 0.724, 0.736, 0.736, 0.716, 0.724, 0.716, 0.728, 0.712, 0.708, 0.716, 0.716, 0.700, 0.708, 0.700, 0.704, 0.692, 0.688, 0.700, 0.688, 0.696, 0.688, 0.680, 0.688, 0.676, 0.672, 0.664, 0.668, 0.676, 0.656, 0.664, 0.664, 0.656, 0.656, 0.640, 0.640, 0.652, 0.648, 0.628, 0.636, 0.628, 0.632, 0.612, 0.624, 0.616, 0.616, 0.600, 0.608, 0.600, 0.604, 0.588, 0.584, 0.596, 0.588, 0.568, 0.576, 0.568, 0.576, 0.556, 0.564, 0.552, 0.564, 0.540, 0.548, 0.540, 0.548, 0.528, 0.532, 0.524, 0.532, 0.512, 0.516, 0.504, 0.516, 0.500, 0.500, 0.488, 0.496, 0.484, 0.484, 0.472, 0.484, 0.472, 0.468, 0.456, 0.460, 0.468, 0.452, 0.444, 0.440, 0.448, 0.448, 0.424, 0.428, 0.436 };
	float Xr[len];
	float Xi[len];
	int i, k, n, N = 1200;
	float x[len];
	double f[len];
	float mean, total_sum = 0, fmax;
	double fdelta;
	i = len;
	while(i --> 0) {
		total_sum += xn[i];
	}
	mean = total_sum / len;
	i = len;
	while (i --> 0) {
		x[i] = xn[i] - mean;
	}
	fmax = (double)(len - 1) / len / 0.5E-6;
	fdelta = fmax / (len - 1);
	i = len;
	f[0] = 0;
	for (i = 1; i < len; i++) {
		f[i] = f[i - 1] + fdelta;
	}
	for (k = 0; k < len; k++) {
		Xr[k] = 0;
		Xi[k] = 0;
		for (n = 0; n < len; n++) {
			Xr[k]
				= (Xr[k]
					+ x[n] * cos(2 * 3.141592 * k * n / N));
			Xi[k]
				= (Xi[k]
					- x[n] * sin(2 * 3.141592 * k * n / N));
		}
		//printf("(%f) + j(%f)\n", Xr[k], Xi[k]);
		printf("%f %f\n", sqrt(Xr[k] * Xr[k] + Xi[k] * Xi[k]), f[k]);
	}
}
// Driver Code
int main()
{
	calculateDFT();
	return 0;
}

Star Strider 2023-1-30

When I ran:

coder.Dictionary

the result was:

'coder.Dictionary' requires Embedded Coder.

I don’t have the Embedded Coder, so I have no experience with it.

It might be possible to use it here, since it is available using the Onlline Run feature (I checked). The problem is that anything using GUIs will not be possible here because the Online Run feature doesn’t support them. If it doesn’t require GUIs, then it could be possible to use it here. MATLAB Online is limited to what you have personal licences for, and it is also limited to applications that do not use GUis.

What you want to do may also require a specific Support Package. See Hardware Support Packages & Services and Third-Party Products & Services for that information. I’m not certain that using them would be possible with the Online Run feature here, although it’s certainly worth doing that experiment to see if it is. There are a number of Code Generation - Examples that could help.

This is far from my areas of expertise. This may be the limit of my ability to help with the C++ programming part of it.

.

Star Strider 2023-1-30

在 MATLAB Online 中打开

data_fft_display.m

As always, my pleasure!

I tried to do that with a function version of my code (attached), however it will not work with the Online Run feature —

type data_fft_display.m
function data_fft_display(filename)
T1 = readtable(filename)

t = T1{:,1}*0.5E-6;                             % Added: 't' in 0.5 µs Steps
s = T1{:,2};

figure
plot(t, s)
grid
xlabel('Time (units)')
ylabel('Amplitude (units)')

Ts = mean(diff(t));
Fs = 1/Ts;
Fn = Fs/2;
L = size(T1,1);
NFFT = 2^nextpow2(L);
FTs = fft((s-mean(s)).*hann(L),NFFT)/L;
Fv = linspace(0, 1, NFFT/2+1)*Fn;
Iv = 1:numel(Fv);

[smax,idx] = max(abs(FTs(Iv))*2);
Frq = Fv(idx)

figure
plot(Fv, abs(FTs(Iv))*2)
grid
xlabel('Frequency')
ylabel('Magnitude')
xline(Frq, '-r', sprintf('Frequency = %.4fx10^3 Hz',Frq*1E-3))
xlim([0 1E+4])                                                      % Zoom To See Detail
end
codegen data_fft_display -argx{filename}
Unable to run the 'fevalJSON' function because it calls the 'codegen' function, which is not supported for this product offering.

So apparently codegen isn’t available here, as I thought it would be. I’ll likely never use it (I haven’t needed it thus far, and it’s likely fairly expensive), however the Embedded Coder could be available as a trial if you want to go that route. That’s a one-off, so if I remember correctly, after the trial is over, another one isn’t possible. You’d need to buy it to keep using it.

It won’t work online here. At least now we know.

.

Star Strider 2023-2-3

在 MATLAB Online 中打开

Modifying it requires very few changes —

s = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1283561/ADC_5k_1024.txt')

s = 1024×1

1.2000 1.1600 1.0800 1.0200 0.9400 0.9700 1.0000 1.0900 1.1700 1.2900

L = numel(s) % Vector Length

L = 1024

Ts = 5.81E-6; % Sampling Interval

t = linspace(0, L-1, L)*Ts; % Create Time Vector

figure

plot(t, s)

grid

xlabel('Time (s)')

ylabel('Amplitude (V)')

% Ts = mean(diff(t));

Fs = 1/Ts;

Fn = Fs/2;

% L = size(s,1);

NFFT = 2^nextpow2(L);

FTs = fft((s-mean(s)).*hann(L),NFFT)/L;

Fv = linspace(0, 1, NFFT/2+1)*Fn;

Iv = 1:numel(Fv);

[smax,idx] = max(abs(FTs(Iv))*2);

Frq = Fv(idx)

Frq = 4.8744e+03

figure

plot(Fv, abs(FTs(Iv))*2)

grid

xlabel('Frequency')

ylabel('Magnitude')

xline(Frq, '-r', sprintf('Frequency = %.4fx10^3 Hz',Frq*1E-3))

xlim([0 1E+4]) % Zoom To See Detail

My apologies for the delay — off doing other things for a few minutes.

.

Dan Richter 2023-2-3

You are expert, thank you very much. I started to learn MATLAB.

Star Strider 2023-2-4

As always, my pleasure!

请先登录，再进行评论。

Answer 2

Dan Richter 2023-1-26

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1901465-how-to-interpret-results-of-fft-dft#answer_1157315

在 MATLAB Online 中打开

But how to scale the time vector to get frequency vector correctly? It looks like that time step for 1 200 data points is 500 µs.

Picture is my DFT calculation in C++ VS2022:

C++ code:

// https://www.geeksforgeeks.org/discrete-fourier-transform-and-its-inverse-using-c/
#include <iostream>
#include <math.h>
#include <stdio.h>
const int len = 1200;
// Function to calculate the DFT
void calculateDFT()
{
	int xn[len] = { 840, 920, 880, 760, 800, 760, 760, 680, 720, 640, 760, 600, 640, 560, 640, 560, 520, 640, 600, 480, 520, 480, 560, 440, 480, 440, 440, 480, 400, 480, 440, 360, 440, 360, 360, 440, 400, 360, 360, 440, 360, 440, 440, 360, 400, 400, 440, 360, 360, 480, 440, 360, 480, 440, 480, 400, 440, 480, 440, 560, 440, 560, 560, 480, 520, 600, 600, 520, 560, 640, 680, 600, 640, 760, 760, 680, 680, 840, 800, 880, 760, 880, 880, 960, 880, 000, 040, 960, 040, 960, 80, 160, 160, 80, 160, 240, 280, 200, 240, 400, 320, 400, 360, 520, 520, 440, 440, 640, 640, 560, 600, 760, 720, 800, 760, 920, 880, 920, 880, 80, 80, 040, 040, 240, 200, 240, 200, 320, 320, 400, 320, 520, 560, 480, 480, 680, 640, 760, 640, 800, 880, 800, 800, 920, 960, 040, 960, 80, 80, 200, 200, 120, 240, 360, 320, 400, 320, 520, 520, 440, 480, 720, 600, 680, 640, 840, 840, 760, 800, 000, 920, 000, 880, 120, 80, 160, 80, 280, 280, 240, 160, 400, 400, 320, 360, 560, 560, 480, 480, 680, 680, 600, 600, 760, 800, 720, 680, 880, 840, 920, 800, 000, 920, 040, 040, 920, 040, 120, 80, 040, 120, 160, 200, 120, 160, 320, 200, 320, 200, 400, 360, 280, 320, 440, 400, 320, 360, 480, 400, 480, 400, 520, 520, 440, 440, 560, 480, 560, 480, 560, 560, 480, 480, 600, 600, 480, 600, 520, 600, 520, 520, 600, 520, 600, 520, 600, 560, 480, 480, 560, 440, 520, 480, 560, 400, 520, 520, 400, 400, 480, 440, 360, 400, 320, 320, 400, 360, 240, 240, 320, 320, 160, 160, 240, 200, 80, 80, 160, 160, 000, 000, 80, 80, 880, 920, 960, 960, 800, 800, 880, 840, 680, 760, 680, 760, 560, 560, 640, 640, 440, 440, 520, 480, 320, 360, 280, 360, 200, 240, 120, 200, 120, 80, 000, 80, 000, 000, 840, 920, 840, 880, 680, 720, 760, 720, 520, 640, 520, 600, 400, 360, 480, 400, 240, 240, 320, 320, 80, 80, 160, 160, 920, 880, 960, 000, 720, 840, 720, 800, 600, 640, 600, 640, 480, 520, 400, 480, 280, 320, 240, 240, 320, 160, 120, 200, 80, 040, 960, 000, 920, 920, 800, 800, 920, 800, 680, 760, 640, 720, 520, 560, 520, 560, 400, 480, 400, 440, 280, 280, 360, 280, 160, 120, 240, 240, 040, 120, 040, 120, 960, 920, 000, 960, 840, 880, 800, 880, 760, 720, 840, 800, 680, 640, 720, 640, 720, 680, 600, 640, 560, 520, 560, 600, 480, 520, 440, 520, 480, 520, 400, 440, 480, 480, 400, 400, 440, 440, 360, 440, 360, 360, 440, 360, 400, 400, 360, 320, 440, 440, 360, 480, 360, 360, 440, 440, 400, 360, 480, 480, 400, 400, 480, 440, 520, 520, 440, 480, 600, 520, 560, 560, 600, 560, 640, 680, 600, 600, 720, 680, 760, 680, 760, 880, 760, 800, 840, 840, 960, 880, 960, 920, 040, 000, 040, 000, 160, 80, 160, 120, 280, 200, 280, 200, 400, 320, 400, 320, 520, 480, 440, 480, 640, 600, 680, 600, 760, 760, 720, 760, 920, 880, 960, 880, 040, 120, 000, 040, 200, 240, 160, 160, 280, 320, 400, 320, 480, 480, 560, 560, 480, 600, 720, 720, 640, 760, 880, 800, 920, 880, 040, 040, 960, 000, 200, 120, 200, 200, 320, 360, 280, 320, 520, 440, 520, 480, 680, 600, 720, 640, 840, 840, 760, 760, 960, 880, 000, 920, 160, 160, 040, 80, 240, 200, 280, 200, 440, 360, 400, 360, 560, 560, 480, 480, 640, 600, 680, 600, 760, 800, 720, 800, 760, 800, 960, 840, 920, 920, 000, 960, 000, 960, 120, 040, 120, 040, 200, 120, 240, 160, 320, 240, 320, 240, 400, 280, 360, 280, 400, 360, 440, 360, 480, 480, 400, 360, 520, 520, 400, 520, 440, 480, 560, 480, 600, 600, 480, 600, 480, 600, 520, 600, 520, 520, 560, 600, 520, 600, 520, 600, 520, 560, 480, 560, 480, 440, 560, 480, 520, 520, 400, 400, 520, 480, 360, 480, 360, 440, 320, 280, 360, 400, 240, 360, 240, 280, 160, 240, 160, 280, 120, 80, 200, 160, 000, 80, 000, 80, 920, 880, 960, 960, 760, 800, 880, 840, 720, 760, 640, 760, 560, 560, 600, 640, 520, 520, 400, 400, 520, 400, 280, 360, 280, 280, 160, 160, 240, 160, 000, 000, 120, 040, 840, 920, 840, 960, 680, 720, 800, 760, 560, 640, 560, 600, 400, 360, 480, 440, 240, 320, 200, 280, 80, 160, 040, 160, 920, 920, 960, 000, 760, 840, 760, 760, 640, 600, 680, 680, 520, 520, 400, 520, 360, 360, 280, 240, 360, 240, 80, 200, 80, 120, 960, 040, 960, 040, 840, 760, 880, 840, 640, 720, 680, 760, 560, 520, 600, 560, 400, 360, 480, 440, 280, 360, 240, 320, 160, 240, 120, 240, 040, 120, 040, 80, 960, 960, 920, 000, 840, 920, 800, 880, 800, 800, 720, 800, 680, 640, 760, 720, 680, 680, 600, 560, 640, 600, 520, 600, 480, 600, 480, 560, 480, 520, 400, 440, 480, 480, 400, 480, 400, 400, 480, 360, 440, 400, 400, 360, 440, 360, 440, 400, 360, 360, 440, 360, 440, 360, 480, 440, 360, 400, 480, 440, 520, 400, 480, 440, 520, 440, 520, 480, 560, 480, 600, 520, 640, 560, 640, 640, 600, 600, 720, 640, 720, 760, 680, 800, 760, 760, 840, 800, 920, 840, 960, 880, 040, 040, 960, 000, 160, 160, 80, 120, 280, 240, 160, 200, 360, 360, 280, 320, 520, 520, 440, 480, 600, 640, 560, 600, 760, 720, 800, 720, 880, 920, 840, 840, 000, 000, 120, 040, 120, 160, 240, 160, 240, 280, 360, 320, 400, 400, 560, 480, 560, 520, 720, 640, 760, 680, 880, 880, 760, 800, 040, 960, 040, 960, 200, 200, 120, 160, 360, 360, 280, 320, 560, 520, 440, 440, 680, 600, 680, 600, 840, 760, 840, 760, 000, 000, 920, 920, 120, 80, 160, 040, 240, 280, 200, 200, 360, 320, 440, 320, 440, 440, 560, 480, 560, 600, 680, 680, 600, 640, 800, 800, 680, 760, 880, 880, 840, 880, 040, 960, 040, 920, 120, 120, 040, 040, 200, 200, 120, 120, 240, 200, 320, 200, 360, 360, 280, 280, 400, 320, 440, 320, 480, 400, 480, 400, 520, 440, 520, 440, 560, 560, 440, 560, 480, 600, 480, 520, 560, 480, 600, 520, 600, 520, 600, 600, 520, 520, 600, 600, 480, 560, 480, 480, 560, 560, 440, 480, 560, 560, 440, 400, 520, 520, 360, 400, 480, 440, 320, 400, 280, 360, 240, 240, 360, 360, 160, 240, 160, 280, 120, 80, 160, 160, 000, 80, 000, 040, 920, 880, 000, 880, 960, 880, 800, 880, 760, 720, 640, 680, 760, 560, 640, 640, 560, 560, 400, 400, 520, 480, 280, 360, 280, 320, 120, 240, 160, 160, 000, 80, 000, 040, 880, 840, 960, 880, 680, 760, 680, 760, 560, 640, 520, 640, 400, 480, 400, 480, 280, 320, 240, 320, 120, 160, 040, 160, 000, 000, 880, 960, 840, 840, 720, 840, 720, 680, 560, 600, 680, 520, 440, 400, 480, 480, 240, 280, 360 };;
	float Xr[len];
	float Xi[len];
	int i, k, n, N = 0;
	printf("Enter the number of "
		"points in the DFT: ");
	scanf_s("%d", &N);
	for (k = 0; k < len; k++) {
		Xr[k] = 0;
		Xi[k] = 0;
		for (n = 0; n < len; n++) {
			Xr[k]
				= (Xr[k]
					+ xn[n] * cos(2 * 3.141592 * k * n / N));
			Xi[k]
				= (Xi[k]
					- xn[n] * sin(2 * 3.141592 * k * n / N));
		}
		//printf("(%f) + j(%f)\n", Xr[k], Xi[k]);
		printf("%f\n", sqrt(Xr[k] * Xr[k] + Xi[k] * Xi[k]));
	}
}
// Driver Code
int main()
{
	calculateDFT();
	return 0;
}