from scipy.spatial import Voronoi, voronoi_plot_2d
import matplotlib.pyplot as plt
import numpy as np


def get_gray_code(n: int):
    return n ^ (n >> 1)


def difference(sym0: int, sym1: int):
    return bit_count(sym0 ^ sym1)


def bit_count(i: int):
    """
    Hamming weight algorithm, just counts number of 1s
    """
    assert 0 <= i < 0x100000000
    i = i - ((i >> 1) & 0x55555555)
    i = (i & 0x33333333) + ((i >> 2) & 0x33333333)
    return (((i + (i >> 4) & 0xF0F0F0F) * 0x1010101) & 0xffffffff) >> 24


def compute_optimal(points, show_graph=False):
    available = set(range(len(points)))
    map = {}

    vor = Voronoi(points)

    if show_graph:
        voronoi_plot_2d(vor)
        plt.show()
    pass


if __name__ == '__main__':
    a = np.array([[-1, -1], [-1, 1], [1, 1], [1, -1]])
    # a = basic.load_alphabet('16qam', polar=False)
    compute_optimal(a, show_graph=True)
    pass