clc; clear; close all;

% Load your data (Replace this with actual loading)
N = 10000;  % Total number of signals
M = 400;    % Number of samples per signal
time = linspace(0, 100, M); % Adjust time axis if needed
signals = randn(N, M) * 0.01; % Example noise (Replace with real data)

% Filtering parameters
threshold = 0.02;  % Define threshold to detect rectangular pulses
window_size = 5;    % Median filter window size

% Initialize cleaned signals storage
cleaned_signals = zeros(N, M);

parfor i = 1:N  % Use parallel processing for speedup
    signal = signals(i, :);
    
    % Step 1: Median Filtering to Remove Spikes
    signal_filtered = medfilt1(signal, window_size);
    
    % Step 2: Identify Pulse (Assuming Rectangular Shape)
    pulse_idx = signal_filtered > threshold; % Find regions above threshold
    
    % Step 3: Keep Only Rectangular Part
    if any(pulse_idx)
        cleaned_signal = zeros(1, M);
        cleaned_signal(pulse_idx) = signal_filtered(pulse_idx);
        cleaned_signals(i, :) = cleaned_signal;
    end
end

% Plot example results (first 10 signals for visualization)
figure;
hold on;
for i = 1:10
    plot(time, cleaned_signals(i, :));
end
hold off;

title('Rectangular Pulses Only (Noise & Spikes Removed)');
xlabel('Time (ns)');
ylabel('Amplitude (V)');
legend('Processed Signals');