Finalize demod flow

1 files changed, 53 insertions, 28 deletions

diff --git a/numpy_fm_demod.py b/numpy_fm_demod.py
index 44d232d..f689064 100644
--- a/numpy_fm_demod.py
+++ b/numpy_fm_demod.py
@@ -24,47 +24,72 @@ class NumpyFmDemod():
         self.samples = sdr.read_samples(self.sample_count)
         sdr.close()
 
-    def demod(self):
-        # Convert samples to a numpy array
-        x1 = np.array(self.samples).astype('complex64')
+    def samples_to_np(self):
+        '''
+        Convert samples to a complex numpy array
+        '''
+        self.samples = np.array(self.samples).astype('complex64')
 
-        # Mix the samples back down to avoid DC offset
-        fc1 = np.exp(-1.0j * 2.0 * np.pi * self.dc_offset / self.sample_rate * np.arange(len(x1)))
-        x2 = x1 * fc1
+    def mix_down_dc_offset(self):
+        '''
+        Mix the samples back down to account for DC offset
+        '''
+        fc1 = np.exp(-1.0j * 2.0 * np.pi * self.dc_offset / self.sample_rate * np.arange(len(self.samples)))
+        self.samples *= fc1
 
-        # Downsample the signal to catch only the target frequency
+    def lowpass_filter(self):
+        '''
+        Apply low-pass filter to catch only the target frequency
+        '''
         BANDWIDTH = 200000  # wideband FM signal is always 200kHz
-        TAPS = 64
-
-        # Use Remez algorithm to design filter coefficients
-        lpf = signal.remez(TAPS, [0, BANDWIDTH, BANDWIDTH + (self.sample_rate / 2 - BANDWIDTH) / 4, self.sample_rate / 2], [1,0], Hz=self.sample_rate)
-        x3 = signal.lfilter(lpf, 1.0, x2)
-
         decimation_rate = int(self.sample_rate / BANDWIDTH)
-        x4 = x3[0::decimation_rate]
-        decimated_rate = self.sample_rate / decimation_rate
+        self.samples = signal.decimate(self.samples, decimation_rate)
+        self.sample_rate /= dec_rate
 
-        y5 = x4[1:] * np.conj(x4[:-1])
-        x5 = np.angle(y5)
+    def polar_discriminator(self):
+        '''
+        Apply a polar discriminator to demodulate the FM signal
+        '''
+        self.samples = np.angle(self.samples[1:] * np.conj(self.samples[:-1]))
 
-        d = decimated_rate * 75e-6  # Calculate the # of samples to hit the -3dB point
-        x = np.exp(-1/d)  # Calculate the decay between each sample
-        b = [1-x]  # Create the filter coefficients
-        a = [1,-x]
-        x6 = signal.lfilter(b,a,x5)
+    def de_emphasis_filter(self):
+        '''
+        Apply a de-emphasis filter
+        Still need to figure out exactly what's going on here
+        '''
+        d = self.sample_rate * 75e-6
+        x = np.exp(-1/d)
+        b, a = [1-x], [1,-x]
+        self.samples = signal.lfilter(b, a, self.samples)
 
+    def mono_decimate(self):
+        '''
+        Decimate the signal to catch the mono transmission
+        '''
         audio_freq = 44100.0
-        dec_audio = int(decimated_rate / audio_freq)
-        Fs_audio = decimated_rate / dec_audio
+        decimation_rate = int(self.sample_rate / audio_freq)
+        audio_rate = self.sample_rate / decimation_rate
+        self.samples = signal.decimate(self.samples, decimation_rate)
 
-        x7 = signal.decimate(x6, dec_audio)
+    def scale_volume(self):
+        '''
+        Scale samples to adjust volume
+        '''
+        self.samples *= 10000 / np.max(np.abs(self.samples))
 
-        x7 *= 10000 / np.max(np.abs(x7))
-        x7.astype('int16').tofile('wbfm-mono.raw')
+    def output_file(self, filename):
+        self.samples.astype('int16').tofile(filename)
 
     def run(self):
         self.capture_samples()
-        self.demod()
+        self.samples_to_np()
+        self.mix_down_dc_offset()
+        self.lowpass_filter()
+        self.polar_discriminator()
+        self.de_emphasis_filter()
+        self.mono_decimate()
+        self.scale_volume()
+        self.output_file()
 
 if __name__ == '__main__':
     nfd = NumpyFmDemod(frequency=96.3e6)