import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
import scipy
import scipy.fftpack as sfft


def deg_2_rad(winkel):
    return winkel/180.0 * np.pi


# all units in angstrom
base_a_m = 5.75
base_b_m = 4.5
base_c_m = 5.38

base_c_r = 2.856
base_b_r = 4.554
base_a_r = base_b_r

alpha_m = 122.64  # degree


def mono_2_rutile(c_m, a_m):
    a_r = np.cos(deg_2_rad(alpha_m - 90)) * c_m * base_c_m
    c_r = (a_m) * base_a_m + np.sin(deg_2_rad(alpha_m - 90)) * c_m * base_c_m
    return a_r, c_r


class Lattice:

    def _get_rutile(self, X, Y):
        self.atom_x_rut = X * base_c_r + np.mod(Y, 4) * 0.5 * base_c_r
        self.atom_y_rut = Y * 0.5 * base_a_r

    def _get_mono(self, X, Y):
        offset_a_m = 0.25 - 0.23947
        offset_c_m = 0.02646

        offset_a_r, offset_c_r = mono_2_rutile(offset_c_m, offset_a_m)

        print("A_r: ", offset_a_r, "C_r: ", offset_c_r)

        self.atom_x_mono = offset_a_r + X * \
            base_c_r + np.mod(Y, 4) * 0.5 * base_c_r
        self.atom_x_mono[np.mod(X, 2) == 0] -= 2 * offset_a_r

        self.atom_y_mono = offset_c_r + 0.5 * Y * base_a_r
        self.atom_y_mono[np.mod(X, 2) == 0] -= 2 * offset_c_r

    def _generate_vec(self, x_len: int, y_len: int):
        x = np.arange(x_len)
        y = np.arange(y_len)
        X, Y = np.meshgrid(x, y)
        X[np.mod(Y, 4) == 3] = X[np.mod(Y, 4) == 3]-1
        X[np.mod(Y, 4) == 2] = X[np.mod(Y, 4) == 2]-1
        assert np.mod(x.size, 2) == 0
        assert np.mod(y.size, 2) == 0

        return X, Y

    def __init__(self, x_len: int, y_len: int):
        X, Y = self._generate_vec(x_len*2, y_len*2)
        self._get_mono(X, Y)
        self._get_rutile(X, Y)

    def get_from_mask(self, maske: np.array, inplace_pos_x=None, inplace_pos_y=None):
        if inplace_pos_x is None:
            inplace_pos_x = np.zeros_like(self.atom_x_mono)
        if inplace_pos_y is None:
            inplace_pos_y = np.zeros_like(self.atom_x_mono)

        mask = np.empty_like(self.atom_x_mono, dtype=bool)
        print(mask.shape, maske.shape)
        mask[0::2, 0::2] = maske
        mask[1::2, 0::2] = maske
        mask[0::2, 1::2] = maske
        mask[1::2, 1::2] = maske

        inplace_pos_x[mask] = self.atom_x_rut[mask]
        inplace_pos_y[mask] = self.atom_y_rut[mask]
        mask = np.invert(mask)
        inplace_pos_x[mask] = self.atom_x_mono[mask]
        inplace_pos_y[mask] = self.atom_y_mono[mask]
        return inplace_pos_x, inplace_pos_y


def test_lattice():
    lat = Lattice(10, 10)
    maske = np.zeros((10, 10), dtype=bool)
    x, y = lat.get_from_mask(maske)

    plt.scatter(x, y)
    maske = np.invert(maske)
    x, y = lat.get_from_mask(maske)
    plt.scatter(x, y)

    maske[:3, :5] = False
    x, y = lat.get_from_mask(maske)
    plt.scatter(x, y)
    plt.show()


RESOLUTION = 0.1


def image_from_pos(pos_x, pos_y):
    length_x = np.max(pos_x) + RESOLUTION
    length_y = np.max(pos_y) + RESOLUTION
    x_ind = np.arange(0, length_x, RESOLUTION)  # angstrom
    y_ind = np.arange(0, length_y, RESOLUTION)  # angstrom
    img = np.zeros((x_ind.size, y_ind.size))
    xind = np.searchsorted(x_ind, pos_x)
    yind = np.searchsorted(y_ind, pos_y)
    img[xind, yind] = 1
    return img


def test_img():
    lat = Lattice(10, 10)
    maske = np.ones((10, 10), dtype=bool)
    x, y = lat.get_from_mask(maske)
    img = image_from_pos(x, y)
    plt.imshow(img.T, origin="lower", extent=(0, np.max(x), 0, np.max(y)))
    plt.scatter(x, y)
    plt.show()


def fft(img):
    Z_fft = sfft.fft2(img)
    Z_shift = sfft.fftshift(Z_fft)
    fft_freqx = sfft.fftfreq(img.shape[0], RESOLUTION)
    fft_freqy = sfft.fftfreq(img.shape[1], RESOLUTION)
    fft_freqx_clean = sfft.fftshift(fft_freqx)
    fft_freqy_clean = sfft.fftshift(fft_freqy)
    return fft_freqx_clean, fft_freqy_clean, np.abs(Z_shift) ** 2


def gaussian(img):
    ratio = img.shape[0] / img.shape[1]
    x = np.linspace(-ratio, ratio, img.shape[0])
    y = np.linspace(-1, 1, img.shape[1])
    X, Y = np.meshgrid(x, y)
    sigma = 0.3
    z = (1/(2*np.pi*sigma*sigma) * np.exp(-(X**2/(2*sigma**2)
                                            + Y**2/(2*sigma**2))))
    return np.multiply(img, z.T)


def main():
    lat = Lattice(10, 10)
    maske = np.ones((10, 10), dtype=bool)
    x, y = lat.get_from_mask(maske)
    img = image_from_pos(x, y)

    fig, [axs,axs2] = plt.subplots(2, 3)
    axs[0].imshow(img)
    img = gaussian(img)
    axs[1].imshow(img)
    plt.pause(.1)

    freqx, freqy, intens = fft(img)
    axs[2].imshow(intens, extent = (np.min(freqx), np.max(
        freqx), np.min(freqy), np.max(freqy)))
    
    maske = np.zeros((10, 10), dtype=bool)
    x, y = lat.get_from_mask(maske)
    img = image_from_pos(x, y)

    axs2[0].imshow(img)
    img = gaussian(img)
    axs2[1].imshow(img)
    plt.pause(.1)

    freqx, freqy, intens = fft(img)
    axs2[2].imshow(intens, extent = (np.min(freqx), np.max(
        freqx), np.min(freqy), np.max(freqy)))
    plt.show()


if __name__ == "__main__":
    main()