mirror of
https://github.com/radio95-rnt/rds95.git
synced 2026-02-26 20:33:53 +01:00
125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
PLOT = True
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FFT = PLOT and True
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import math
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import io, os
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if PLOT: import matplotlib.pyplot as plt
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if FFT: import numpy as np # Import numpy for FFT
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DATA_RATE = 1187.5
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SIZE_RATIO = 2
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ratio = 16
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sample_rate = DATA_RATE*ratio
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print(f"{sample_rate=}")
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if not sample_rate.is_integer(): raise ValueError("Need a even value")
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# this is modified from ChristopheJacquet's pydemod
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def rrcosfilter(NumSamples):
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T_delta = 1/float(sample_rate)
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sample_num = list(range(NumSamples))
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h_rrc = [0.0] * NumSamples
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SymbolPeriod = 1/(2*DATA_RATE)
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for x in sample_num:
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t = (x-NumSamples/2)*T_delta
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if t == 0.0:
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h_rrc[x] = 1.0 - 1 + (4/math.pi)
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elif t == SymbolPeriod/4:
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h_rrc[x] = (1/math.sqrt(2))*(((1+2/math.pi)* \
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(math.sin(math.pi/4))) + ((1-2/math.pi)*(math.cos(math.pi/4))))
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elif t == -SymbolPeriod/4:
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h_rrc[x] = (1/math.sqrt(2))*(((1+2/math.pi)* \
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(math.sin(math.pi/4))) + ((1-2/math.pi)*(math.cos(math.pi/4))))
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else:
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h_rrc[x] = (4*(t/SymbolPeriod)*math.cos(math.pi*t*2/SymbolPeriod))/ \
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(math.pi*t*(1-(4*t/SymbolPeriod)*(4*t/SymbolPeriod))/SymbolPeriod)
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return h_rrc
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def convolve(a, b):
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out = [0] * (len(a) + len(b) - 1)
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for i in range(len(a)):
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for j in range(len(b)):
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out[i+j] += a[i] * b[j]
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return out
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PATH = os.path.dirname(os.path.abspath(__file__))
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outc = io.open(f"{PATH}/src/waveforms.c", mode="w", encoding="utf8")
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outh = io.open(f"{PATH}/src/waveforms.h", mode="w", encoding="utf8")
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header = u"""
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/* This file was automatically generated by "gen_wave.py".
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(C) 2014 Christophe Jacquet.
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(C) 2023 Anthony96922.
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(C) 2025 kuba201.
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Released under the GNU GPL v3 license.
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*/
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"""
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outc.write(header)
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outh.write(header)
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def generate():
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l = ratio // 2
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sample = [0.0] * (16*l)
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sample[l] = 1
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sample[2*l] = -1
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sf = rrcosfilter(l*16)
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shapedSamples = convolve(sample, sf)
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lowest = 0
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lowest_idx = 0
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highest = 0
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highest_idx = 0
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for i,j in enumerate(shapedSamples):
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if j < lowest:
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lowest = j
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lowest_idx = i
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if j > highest:
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highest = j
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highest_idx = i
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middle = int((lowest_idx+highest_idx)/2)
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out = shapedSamples[middle-int(ratio*SIZE_RATIO):middle+int(ratio*SIZE_RATIO)]
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out = [2 * (i - min(out)) / (max(out) - min(out)) - 1 for i in out]
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if max(out) > 1 or min(out) < -1: raise Exception("Clipped")
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print(f"{len(out)=} {len(out)/sample_rate=} {(len(out)/sample_rate)/(1/DATA_RATE)=} {1/DATA_RATE=}")
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if PLOT:
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# Plot the waveform
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plt.plot(out, label="out")
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plt.legend()
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plt.grid(True)
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plt.show()
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if FFT:
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# Compute the FFT of the waveform
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N = len(out)
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fft_out = np.fft.fft(out)
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fft_freqs = np.fft.fftfreq(N, d=1/sample_rate)
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# Plot the magnitude of the FFT
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plt.figure(figsize=(10, 6))
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plt.plot(fft_freqs[:N//2], np.abs(fft_out)[:N//2]) # Plot only the positive frequencies
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plt.xlim(0,DATA_RATE*3)
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plt.title("FFT of the waveform")
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plt.xlabel("Frequency (Hz)")
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plt.ylabel("Magnitude")
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plt.grid(True)
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plt.show()
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outc.write(u"float waveform_biphase[{size}] = {{{values}}};\n\n".format(
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values = u", ".join(map(str, out)),
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size = len(out)))
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outh.write(u"extern float waveform_biphase[{size}];\n".format(size=len(out)))
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generate()
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outc.close()
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outh.close() |