import math
import io, os
import matplotlib.pyplot as plt

sample_rate = 9500

# next 2 funcs are 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)  # 190 khz = 760
    count = int(offset / 10**(len(str(offset)) - 1))  # 760 / 100 = 7
    l = int(sample_rate / 1187.5) // 2  # 16/2 = 8
    if l == 1: raise Exception("Sample rate too small")
    print(f"{offset=} {count=} {l=}")

    sample = [0.0] * (count*l)
    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]
    
    plt.plot(sf, label="sf") 
    plt.plot(shapedSamples, label="shapedSamples") 
    plt.plot(out, label="out")
    plt.legend()
    plt.grid(True)
    plt.show()

    outc.write(u"float waveform_biphase[{size}] = {{{values}}};\n\n".format(
        values = u", ".join(map(str, [val / 2.5 for val in out])),
        size = len(out)))
        # note: need to limit the amplitude so as not to saturate when the biphase
        # waveforms are summed

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


generate()

outc.close()
outh.close()