Update auto-tune.py fix name

groundstation/auto-tune.py

@@ -16,44 +16,39 @@ if __name__ == "__main__":
           graph = 'y'  
         
 
-# Create a sample signal (sum of two sine waves)
-sampling_rate = 1024e3 # 250e3 # Hz
-duration = 65536/sampling_rate # 1          # seconds
-# t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False)
-t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False)
-# frequency1 = 50       # Hz
-# frequency2 = 120      # Hz
-# signal = 0.7 * np.sin(2 * np.pi * frequency1 * t) + np.sin(2 * np.pi * frequency2 * t)
+	sampling_rate = 1024e3 # 250e3 # Hz
+	duration = 65536/sampling_rate # 1          # seconds
+	t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False)
 	
-sdr = RtlSdr()
+	sdr = RtlSdr()
 	
-# configure device
-sdr.sample_rate = sampling_rate # 250e3 # 2.4e6
-#center_frequency = 434.8e6
-sdr.center_freq = center_frequency
-sdr.gain = 4
-sdr.direct_sampling = False
+	# configure device
+	sdr.sample_rate = sampling_rate # 250e3 # 2.4e6
+	#center_frequency = 434.8e6
+	sdr.center_freq = center_frequency
+	sdr.gain = 4
+	sdr.direct_sampling = False
 	
-# signal = sdr.read_samples(64*1024) #256
-signal = sdr.read_samples(duration*sampling_rate).real #256
+	# signal = sdr.read_samples(64*1024) #256
+	signal = sdr.read_samples(duration*sampling_rate).real #256
 	
-print(f"Center frequency is {center_frequency}")
+	print(f"Center frequency is {center_frequency}")
 	
-sdr.close()
+	sdr.close()
 	
-# Compute the FFT
-fft_result = np.fft.fft(signal)
+	# Compute the FFT
+	fft_result = np.fft.fft(signal)
 	
-# Calculate the frequencies corresponding to the FFT output
-n = len(signal)
-frequencies = np.fft.fftfreq(n, d=1/sampling_rate)
+	# Calculate the frequencies corresponding to the FFT output
+	n = len(signal)
+	frequencies = np.fft.fftfreq(n, d=1/sampling_rate)
 	
-# Take the absolute value for amplitude spectrum and consider only the positive frequencies
-positive_frequencies_indices = np.where(frequencies >= 0)
-positive_frequencies = frequencies[positive_frequencies_indices]
-amplitude_spectrum = 2/n * np.abs(fft_result[positive_frequencies_indices]) # Normalize for amplitude
+	# Take the absolute value for amplitude spectrum and consider only the positive frequencies
+	positive_frequencies_indices = np.where(frequencies >= 0)
+	positive_frequencies = frequencies[positive_frequencies_indices]
+	amplitude_spectrum = 2/n * np.abs(fft_result[positive_frequencies_indices]) # Normalize for amplitude
 	
-if (graph == 'y'):
+	if (graph == 'y'):
 	  # Plotting the results
 	  plt.figure(figsize=(12, 6))
 	  
@@ -73,24 +68,24 @@ if (graph == 'y'):
 	  plt.tight_layout()
 	  plt.show()
 	
-# print(amplitude_spectrum)
-x = amplitude_spectrum 
-# print(x)
-min_value = min(x)
-max_value = max(x)
+	# print(amplitude_spectrum)
+	x = amplitude_spectrum 
+	# print(x)
+	min_value = min(x)
+	max_value = max(x)
 	
-#freq_min = np.argmax(min_value)
-# print(np.argmax(x))
-# print(np.argmax(x)*(150e3 - 10e3)/(9770 - 709))
-# print(sampling_rate)
-# print(center_frequency)
+	#freq_min = np.argmax(min_value)
+	# print(np.argmax(x))
+	# print(np.argmax(x)*(150e3 - 10e3)/(9770 - 709))
+	# print(sampling_rate)
+	# print(center_frequency)
 	
-offset = (np.argmax(x)*(150e3 - 10e3)/(9770 - 709))
-freq_max = center_frequency + offset 
+	offset = (np.argmax(x)*(150e3 - 10e3)/(9770 - 709))
+	freq_max = center_frequency + offset 
 	
-print(f" {freq_max} {max_value}")
-#print(f"The minimum signal is {min_value} at frequency {freq_min}")
+	print(f" {freq_max} {max_value}")
+	#print(f"The minimum signal is {min_value} at frequency {freq_min}")
 	
-#print(min_value)
-#print(max_value)
+	#print(min_value)
+	#print(max_value)