% Load Signal
pkg load signal
data = csvread('unknownsignal.csv');
signal=data';
Fs=300
N=length(signal)
% Apply the Hamming Filter
signal_Windowed = Hamming_Windower(signal);

% Spectral Analysis (FFT)
% Compute the FFT of the windowed signal
signal_Windowed_fft = fft(signal_Windowed, N);

% Normalize the FFT
signal_Windowed_fft_norm = signal_Windowed_fft / N;

% Compute the magnitude spectrum
magnitude_spectrum = abs(signal_Windowed_fft_norm);

% Generate the frequency vector
frequencies = linspace(0, Fs/2, N/2);

% Plot the magnitude spectrum in the frequency domain
figure;
plot(frequencies, magnitude_spectrum(1:N/2));
xlabel('Frequency (Hz)');
ylabel('Magnitude');
title('Magnitude Spectrum of the Windowed Signal');
grid on;


% Define the frequency range of interest
low_freq = 27;
high_freq = 66;

% Normalize the frequencies (Nyquist criterion)
low_freq_norm = low_freq / (Fs/2);
high_freq_norm = high_freq / (Fs/2);

% Design a Butterworth bandpass filter
filter_order = 4; % can be adjusted to modify rolloff
[b, a] = butter(filter_order, [low_freq_norm, high_freq_norm], 'bandpass');

% Apply the Butterworth filter to the windowed signal
filtered_signal = filter(b, a, signal_Windowed);

% Plot the filtered signal in the time domain
t = linspace(0, (N-1)/Fs, N); % Time vector
figure;
plot(t, filtered_signal);
xlabel('Time (s)');
ylabel('Amplitude');
title('Filtered Signal in Time Domain');
grid on;

% Compute the FFT of the filtered signal and normalize it
fft_filtered_signal = fft(filtered_signal, N);
fft_filtered_signal_normalized = fft_filtered_signal / N;

% Compute the magnitude spectrum
magnitude_spectrum_filtered = abs(fft_filtered_signal_normalized);

% Generate the frequency vector
frequencies = linspace(0, Fs/2, N/2);

% Plot the magnitude spectrum of the filtered signal in the frequency domain
figure;
plot(frequencies, magnitude_spectrum_filtered(1:N/2));
xlabel('Frequency (Hz)');
ylabel('Magnitude');
title('Magnitude Spectrum of the Filtered Signal');
grid on;