PLOT = True
FFT = PLOT and True

import math
import io, os
if PLOT: import matplotlib.pyplot as plt
if FFT: import numpy as np  # Import numpy for FFT

sample_rate = 11875

# this is modified from ChristopheJacquet's pydemod
def rrcosfilter(NumSamples):
    T_delta = 1/float(sample_rate)
    sample_num = list(range(NumSamples))
    h_rrc = [0.0] * NumSamples
    SymbolPeriod = 1/(2*1187.5)

    for x in sample_num:
        t = (x-NumSamples/2)*T_delta
        if t == 0.0:
            h_rrc[x] = 1.0 - 1 + (4/math.pi)
        elif t == SymbolPeriod/4:
            h_rrc[x] = (1/math.sqrt(2))*(((1+2/math.pi)* \
                    (math.sin(math.pi/4))) + ((1-2/math.pi)*(math.cos(math.pi/4))))
        elif t == -SymbolPeriod/4:
            h_rrc[x] = (1/math.sqrt(2))*(((1+2/math.pi)* \
                    (math.sin(math.pi/4))) + ((1-2/math.pi)*(math.cos(math.pi/4))))
        else:
            h_rrc[x] = (4*(t/SymbolPeriod)*math.cos(math.pi*t*2/SymbolPeriod))/ \
                    (math.pi*t*(1-(4*t/SymbolPeriod)*(4*t/SymbolPeriod))/SymbolPeriod)

    return h_rrc

def convolve(a, b):
    out = [0] * (len(a) + len(b) - 1)
    for i in range(len(a)):
        for j in range(len(b)):
            out[i+j] += a[i] * b[j]
    return out

PATH = os.path.dirname(os.path.abspath(__file__))

outc = io.open(f"{PATH}/src/waveforms.c", mode="w", encoding="utf8")
outh = io.open(f"{PATH}/src/waveforms.h", mode="w", encoding="utf8")

header = u"""
/* This file was automatically generated by "gen_wave.py".
   (C) 2014 Christophe Jacquet.
   (C) 2023 Anthony96922.
   (C) 2025 kuba201.
   Released under the GNU GPL v3 license.
*/

"""

outc.write(header)
outh.write(header)

def generate():
    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=}")

    sample = [0.0] * (count*l)
    sample[l] = 1
    sample[2*l] = -1

    sf = rrcosfilter(l*16)
    shapedSamples = convolve(sample, sf)

    out = shapedSamples[offset-l*count:offset+l*count]
    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:
        # Plot the waveform
        plt.plot(out, label="out")
        plt.legend()
        plt.grid(True)
        plt.show()

    if FFT:
        # Compute the FFT of the waveform
        N = len(out)
        fft_out = np.fft.fft(out)
        fft_freqs = np.fft.fftfreq(N, d=1/sample_rate)

        # 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")
        plt.grid(True)
        plt.show()

    outc.write(u"float waveform_biphase[{size}] = {{{values}}};\n\n".format(
        values = u", ".join(map(str, out)),
        size = len(out)))

    outh.write(u"extern float waveform_biphase[{size}];\n".format(size=len(out)))

generate()

outc.close()
outh.close()