function main()
%{
function main()
Main function to analyze the unknown signal

Tasks:
    - Load signal
    - Plot raw signal
    - Perform and plot power spectrum

Author: Tikea TE
Date: 16/04/2025
%}

% ==== Load signal ====
load unknownsignal.mat  
Fs = 200;                % given sampling frequency (Hz)

% ==== Plot the raw signal ====
plotRawSignal(X, Fs);

% ==== Analyze frequency spectrum ====
[f, power] = frequencySpectrum(X, Fs, 1);  % set 1 to plot

% ======== Apply a bandpass filter ==============
% [filteredSignal, Z, P] = iirFilter(10, [5 20], X, Fs, 1);

% ======== Apply FirFilter ===============
filteredSignal = firFilter(30, [5 20], X, 200);  % for a 30th-order bandpass FIR filter

% ============ plot the filtered signal ============
plotFilteredSignal(filteredSignal, Fs);
plotRawVsFiltered(X, filteredSignal, Fs);

% ==== Power spectrum of the filtered signal ====
[f_filtered, power_filtered] = frequencySpectrum(filteredSignal, Fs, 1);

% === Create time vector for windowing ===
t = (0:length(filteredSignal)-1) / Fs;

% === Choose window duration ===
duration_signal = length(filteredSignal) / Fs;
win_duration = 2;  % seconds

% === Create and apply Hanning window ===
win_hanning = createtemporalWindow(t, duration_signal, win_duration, Fs, 2);
x_hanning = applyTemporalwindow(filteredSignal, t, win_hanning);

% === Plot raw vs windowed ===
figure;
plot(t, filteredSignal, 'r'); hold on;
plot(t, x_hanning, 'b');
legend('Filtered signal', 'Hanning-windowed');
xlabel('Time (s)');
ylabel('Amplitude');
title('Windowing Effect on Filtered Signal');
grid on;


end