PLOT = True
FFT = PLOT and True

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

DATA_RATE = 1187.5
SIZE_RATIO = 1

ratio = 16
sample_rate = DATA_RATE*ratio
print(f"{sample_rate=}")
if not sample_rate.is_integer(): raise ValueError("Need a even value")

# 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*DATA_RATE)

	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)
	blackman = [0.42 + 0.5*math.cos(math.pi*i/(NumSamples-1)) + 0.08*math.cos(2.0*math.pi*i/(NumSamples-1)) for i in range(NumSamples)]
	h_rrc = [h_rrc[i] * blackman[i] for i in range(NumSamples)]

	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():
	t = [i / sample_rate for i in range(ratio)]
	out = [math.sin(2 * math.pi * DATA_RATE * time) for time in t]
	print(f"{len(out)=} {len(out)/sample_rate=} {1/DATA_RATE=}")

	if PLOT:
		plt.plot(out*4, label="out")
		plt.legend()
		plt.axvline(x=len(out)*2, color='r', linestyle='--', label='center')
		plt.grid(True)
		plt.show()

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

		plt.figure(figsize=(10, 6))
		plt.plot(fft_freqs[:N//2], np.abs(fft_out)[:N//2])
		plt.xlim(0,DATA_RATE*2.5)
		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()