diff --git a/scope_fft/scope_fft.py b/scope_fft/scope_fft.py
new file mode 100644
index 0000000..7a7cbe5
--- /dev/null
+++ b/scope_fft/scope_fft.py
@@ -0,0 +1,45 @@
+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()