added FFT using numpy

test.py

@@ -87,10 +87,13 @@ Query('WAVEFORM:FORMAT?')
 [status, wfLen] = Query('WAVEFORM:STATUS?').split(',')
 if status == "IDLE" and int(wfLen) >= 0 :
   print("---- get waveform data")
+  start_time = time.time()
   osc.write('WAVEFORM:DATA?')
   binary = osc.read_bytes(int(wfSize) +11 + 1) # 
+  end_time = time.time()
   # print(binary)
-  
+duration = end_time - start_time
+print("Duration for obtaining waveform:", duration, "seconds")
 
 #---------- read data from internal memory, 1.12 million points, take like 
 # Write('STOP')
@@ -133,5 +136,48 @@ header = binary[:11]
 data_points = binary[11:]
 # Decode the 1-byte data points
 decoded_data = [byte for byte in data_points]
-print("Decoded data:", decoded_data)
+# print("Decoded data:", decoded_data)
+
+
+#===================== FFT
+import numpy as np
+signal = np.array(decoded_data)
+signal = signal[:-1]
+
+timeStep = float(para[4])
+sampling_rate = 1./timeStep  # Sampling rate (Hz)
+duration = wfSize * timeStep  # Duration of signal (seconds)
+num_samples = wfSize 
+timeList = np.linspace(0, duration, num_samples)
+
+# Perform FFT
+fft_result = np.fft.fft(signal)
+frequencies = np.fft.fftfreq(num_samples, 1 / sampling_rate)
+
+# Plot
+import matplotlib.pyplot as plt
+
+# Plot signal
+plt.figure(figsize=(14, 6))
+plt.subplot(1, 2, 1)
+plt.plot(timeList*1e6, signal)
+plt.title('Signal')
+plt.xlabel('Time (us)')
+plt.ylabel('Amplitude')
+plt.grid(True)
+
+# Plot FFT
+plt.subplot(1, 2, 2)
+# plt.plot(frequencies[:num_samples//2] / 1e6, np.abs(fft_result)[:num_samples//2])
+plt.semilogy(frequencies[:num_samples//2] / 1e6, np.abs(fft_result)[:num_samples//2])
+plt.title('FFT of Signal')
+plt.xlabel('Frequency (MHz)')
+plt.ylabel('Magnitude')
+plt.grid(True)
+
+plt.xlim(0, 100)  # Set the X-range from 0 to 10 MHz
+
+
+plt.tight_layout()
+plt.show()