import numpy as np
import matplotlib.pyplot as plt

def sig_fft(file1, file2):
    # Load data from CSV files
    data = np.genfromtxt(file1, delimiter=',')[2:]
    data2 = np.genfromtxt(file2, delimiter=',')[2:]
    
    # find where data is nan
    n_index = np.argmax(np.isnan(data[:,1]))
    n_index2 = np.argmax(np.isnan(data2[:,1]))
    # Define each column
    time = data[:n_index, 0]
    rx_signal = data[:n_index, 1]
    tx_signal = data[:n_index, 2]
    num_samples = time.size
    fs1 = 1 / (time[1] - time[0])  # Set sampling frequency
    
    # Define each column for data 2
    time2 = data2[:n_index2, 0]
    rx_signal2 = data2[:n_index2, 1]
    tx_signal2 = data2[:n_index2, 2]
    num_samples2 = time2.size
    fs2 = 1 / (time2[1] - time2[0])  # Set sampling frequency
    # Plot raw data
    plt.figure(1)
    plt.plot(time, rx_signal, label='Data 1')
    plt.plot(time2, rx_signal2, label='Data 2')
    plt.title('Unfiltered Data')
    plt.legend()
    
    # fft for data 1
    sigfft = np.fft.fft(rx_signal)
    freq = np.fft.fftfreq(rx_signal.shape[-1], 1/(fs1))
    # fft for data 2
    sigfft2 = np.fft.fft(rx_signal2)
    freq2 = np.fft.fftfreq(rx_signal2.shape[-1], 1/(fs2))
    # plot fft
    plt.figure(2)
    plt.plot(freq, np.abs(sigfft), label='Data 1')
    plt.plot(freq2, np.abs(sigfft2), label='Data 2')
    plt.title('FFT of Data')
    plt.xlim(0, 20000)
    
    plt.show()