Conflicts due to poor backup

FFT.m

@@ -1,65 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%
-% Script task: Normalize RGB data and plot FFT using the power spectra
-%
-% Input : RGB_data.csv -> average RGB values of each image
-%
-% Output : Fast Fourier Transform of X(t): a graph representing the Single-Sided Amplitude Spectrum of X(t)
-%
-% Author: Maryne DEY (maryne.dey@ecam.fr)
-% Date: 07/02/2023
-%%%%%%%%%%%%%%%%%%%%%
-
-clear all
-close all
-clc
-
-%To be able to extract from external format (excel)
-pkg load io
-
-%Normalization of the data
-data = csvread('RGB_database/RGB_data.csv');
-standard_deviation = std(data);
-mean_value = mean(data);
-
-for i = 1:size(data,1)
-  normalized_data_G(i,1) = (data(i,2)-mean_value(2))/standard_deviation(2); %%2 and not 1 because green is the 2nd color
-endfor
-
-%Input characteristics
-Fs = 970/32;           % Sampling frequency = 970 images in 32 seconds                  
-T = 1/Fs;              % Sampling period       
-L = 970;               % Length of signal = 32 seconds
-t = (0:L-1)*T;         % Time vector
-
-X = normalized_data_G;
-
-%Plot of the RGB data in the time domain
-plot(t(1:970),X(1:970))
-title("Signal")
-xlabel("t (milliseconds)")
-ylabel("X(t)")
-
-%Finding power spectra P2 and P1
-Y = fft(X);
-P2 = abs(Y/L);
-P1 = P2(1:L/2+1);
-P1(2:end-1) = 2*P1(2:end-1);
-f = Fs*(0:(L/2))/L;
-
-% Finding boudaries
-start = find(f==0.75);
-stop = find(f==4);
-P1_graph = P1(start:stop);
-f_graph = f(start:stop);
-
-%Graph of FFT in function of the frequency
-plot(f_graph,P1_graph)
-title("Single-Sided Amplitude Spectrum of X(t)")
-xlabel("f (Hz)")
-ylabel("|P1(f)|")
-
-%Extracting the heart rate
-[heart_rate_Hz, index] = max(P1_graph);
-corresponding_frequency = f_graph(index);
-heart_rate_bpm = corresponding_frequency*60;
-fprintf("Your heart rate is about %d bpm.\n", heart_rate_bpm)