import matplotlib.pyplot as plt

import numpy as np
from sklearn.metrics import accuracy_score
from models import basic
from models.basic import AWGNChannel, BPSKDemod, BPSKMod, BypassChannel, MaryMod, MaryDemod
import misc

def show_constellation(mod, chan, demod, samples=1000):
    x = misc.generate_random_bit_array(samples)
    x_mod = mod.forward(x)
    x_chan = chan.forward(x_mod)
    x_demod = demod.forward(x_chan)

    x_mod_rect = misc.polar2rect(x_mod)
    x_chan_rect = misc.polar2rect(x_chan)
    plt.plot(x_chan_rect[:, 0][x], x_chan_rect[:, 1][x], '+')
    plt.plot(x_chan_rect[:, 0][~x], x_chan_rect[:, 1][~x], '+')
    plt.plot(x_mod_rect[:, 0], x_mod_rect[:, 1], 'ro')
    axes = plt.gca()
    axes.set_xlim([-2, +2])
    axes.set_ylim([-2, +2])
    plt.grid()
    plt.show()
    print('Accuracy : ' + str())


def get_ber(mod, chan, demod, samples=1000):
    x = misc.generate_random_bit_array(samples)
    x_mod = mod.forward(x)
    x_chan = chan.forward(x_mod)
    x_demod = demod.forward(x_chan)
    return 1 - accuracy_score(x, x_demod)


def get_AWGN_ber(mod, demod, samples=1000, start=-8, stop=5, steps=30):
    ber_x = np.linspace(start, stop, steps)
    ber_y = []
    for noise in ber_x:
        ber_y.append(get_ber(mod, AWGNChannel(noise), demod, samples=samples))
    return ber_x, ber_y


if __name__ == '__main__':

    plt.plot(*get_AWGN_ber(MaryMod(6, 10e6, gray=True), MaryDemod(6, 10e6), samples=12000, start=-15), '-', label='64-QAM')
    plt.plot(*get_AWGN_ber(MaryMod(5, 10e6, gray=True), MaryDemod(5, 10e6), samples=12000, start=-15), '-', label='32-QAM')
    plt.plot(*get_AWGN_ber(MaryMod(4, 10e6, gray=True), MaryDemod(4, 10e6), samples=12000, start=-15), '-', label='16-QAM')
    plt.plot(*get_AWGN_ber(BPSKMod(10e6), BPSKDemod(10e6, 10e3), samples=12000), '-', label='BPSK')
    plt.yscale('log')
    plt.gca().invert_xaxis()
    plt.grid()
    plt.xlabel('Noise dB')
    plt.ylabel('BER')
    plt.legend()
    plt.show()

    pass