diff --git a/tnc/modem.py b/tnc/modem.py
index ae82e8f0..6ffeb5fe 100644
--- a/tnc/modem.py
+++ b/tnc/modem.py
@@ -22,6 +22,7 @@ import static
 import data_handler
 
 #import Hamlib
+import numpy as np
 import rigctld
 rigctld = rigctld.Rigctld()
 
@@ -458,7 +459,7 @@ class RF():
                 
                 data_in = self.stream_rx.read(nin, exception_on_overflow=False)
                 
-                #self.calculate_fft(data_in)
+                self.calculate_fft(data_in)
                 
                 
                 data_in = audioop.ratecv(data_in,2,1,static.AUDIO_SAMPLE_RATE_RX, static.MODEM_SAMPLE_RATE, None) 
@@ -643,70 +644,16 @@ class RF():
 
                             
     def calculate_fft(self, data_in):
-        data_in_array = np.frombuffer(data_in, dtype=np.int16)
-                #print(fft_raw)
-        #fft_raw = fft(data_in_array)
-                #print(fft_raw)
-                #fft_raw = data_in.hex()
-                #print(fft_raw)
-                #static.FFT = fft_raw.tolist()
-        #fft_raw = fft_raw.tobytes()
-        
-        #rate = 48000
-        #M = 1024
-        #freqs, times, Sx = signal.spectrogram(data_in_array, fs=rate, window='hanning', nperseg=1024, noverlap=M - 100, detrend=False, scaling='spectrum', return_onesided=True) 
-        
-        #freqs, times, Sx = signal.spectrogram(data_in_array, fs=rate, return_onesided=True, axis=-1) 
-        
-        
-        
-        
-        #print(Sx)
-        
-        #fft_raw = Sx.tobytes()
-        #print(fft_raw)
-        #static.FFT = fft_raw.hex()
-        #static.FFT = fft_raw
-        #data_in = np.frombuffer(data_in, dtype=np.int16)
-        #data = fft(data_in)
-        #print(data)
-        #data = getFFT(data_in, 48000, 2048)
-        #print(data)
-
-        #data = abs(data) * 2 / np.sum(8192)
-        
-        #data = abs(data) // np.sum(1024)
-
-        
-        #data = np.frombuffer(data_in, dtype=np.int16)
-        #data.resize((1,2048))
-        #data = np.delete(data,0)
-        
-        
-        #data = data.tobytes()
-        #print(data)
-        #static.FFT = data.tolist()
-        #static.FFT = data.hex()
-        
-def getFFT(data, rate, chunk_size, log_scale=False):
-    data = data * np.hamming(len(data))
-    try:
-        FFT = np.abs(np.fft.rfft(data)[1:])
-    except:
-        FFT = np.fft.fft(data)
-        left, right = np.split(np.abs(FFT), 2)
-        FFT = np.add(left, right[::-1])
-
-    #fftx = np.fft.fftfreq(chunk_size, d=1.0/rate)
-    #fftx = np.split(np.abs(fftx), 2)[0]
-
-    if log_scale:
-        try:
-            FFT = np.multiply(20, np.log10(FFT))
-        except Exception as e:
-            print('Log(FFT) failed: %s' %str(e))
-
-    return FFT
+    
+        # https://gist.github.com/ZWMiller/53232427efc5088007cab6feee7c6e4c
+        audio_data = np.fromstring(data_in, np.int16)
+        # Fast Fourier Transform, 10*log10(abs) is to scale it to dB
+        # and make sure it's not imaginary
+        dfft = 10.*np.log10(abs(np.fft.rfft(audio_data)))
 
+        print(dfft.tolist())
+        dfftlist = dfft.tolist()
         
 
+        static.FFT = dfftlist[:512]
+        return dfft