set the sample rate to ratio 10

gen_wave.py

@@ -6,7 +6,7 @@ import io, os
 if PLOT: import matplotlib.pyplot as plt
 if FFT: import numpy as np  # Import numpy for FFT
 
-sample_rate = 9500
+sample_rate = 11875
 
 # this is modified from ChristopheJacquet's pydemod
 def rrcosfilter(NumSamples):
@@ -57,9 +57,9 @@ outc.write(header)
 outh.write(header)
 
 def generate():
-    offset = int(sample_rate*0.004)  # 190 khz = 760
-    count = int(offset / 10**(len(str(offset)) - 1))  # 760 / 100 = 7
-    l = int(sample_rate / 1187.5) // 2  # 16/2 = 8
+    offset = int(sample_rate*0.004)
+    count = int(offset / 10**(len(str(offset)) - 1))
+    l = int(sample_rate / 1187.5) // 2
     if count*l > l*16: raise Exception("Sample rate too small")
     print(f"{offset=} {count=} {l=}")
 
@@ -67,13 +67,11 @@ def generate():
     sample[l] = 1
     sample[2*l] = -1
 
-    # Apply the data-shaping filter
     sf = rrcosfilter(l*16)
     shapedSamples = convolve(sample, sf)
 
-    # Slice the array like numpy would
     out = shapedSamples[offset-l*count:offset+l*count]
-    out = [i/(max(out)) for i in out]
+    out = [2 * (i - min(out)) / (max(out) - min(out)) - 1 for i in out]
     if max(out) > 1 or min(out) < -1: raise Exception("Clipped")
 
     if PLOT:
@@ -92,6 +90,7 @@ def generate():
         # Plot the magnitude of the FFT
         plt.figure(figsize=(10, 6))
         plt.plot(fft_freqs[:N//2], np.abs(fft_out)[:N//2])  # Plot only the positive frequencies
+        plt.xlim(0,1187.5*3)
         plt.title("FFT of the waveform")
         plt.xlabel("Frequency (Hz)")
         plt.ylabel("Magnitude")