import math
import numpy as np
from dipy.core import wavelet


def adaptive_soft_matching(ima, fimau, fimao, sigma):
    r"""Adaptive Soft Coefficient Matching

    Combines two filtered 3D-images at different resolutions and the original
    image. Returns the resulting combined image.

    Parameters
    ----------
    ima : the original (not filtered) image
    fimau : 3D double array,
        filtered image with optimized non-local means using a small block
        (suggested:3x3), which corresponds to a "high resolution" filter.
    fimao : 3D double array,
        filtered image with optimized non-local means using a small block
        (suggested:5x5), which corresponds to a "low resolution" filter.
    sigma : the estimated standard deviation of the Gaussian random variables
        that explain the rician noise. Note: In P. Coupe et al. the
        rician noise was simulated as sqrt((f+x)^2 + (y)^2) where f is
        the pixel value and x and y are independent realizations of a
        random variable with Normal distribution, with mean=0 and
        standard deviation=h

    Returns
    -------
    fima : 3D double array
        output denoised array which is of the same shape as that of
        the input

    References
    ----------
    .. [Coupe11] Pierrick Coupe, Jose Manjon, Montserrat Robles, Louis Collins.
                 "Multiresolution Non-Local Means Filter for 3D MR Image
                 Denoising" IET Image Processing, Institution of Engineering
                 and Technology,
                 2011

    """

    s = fimau.shape
    p = [int(2**math.ceil(math.log(s[i], 2))) for i in range(3)]
    pad1 = np.zeros((p[0], p[1], p[2]))
    pad2 = np.zeros((p[0], p[1], p[2]))
    pad3 = np.zeros((p[0], p[1], p[2]))
    pad1[:s[0], :s[1], :s[2]] = fimau[:, :, :]
    pad2[:s[0], :s[1], :s[2]] = fimao[:, :, :]
    pad3[:s[0], :s[1], :s[2]] = ima[:, :, :]
    af = np.array([[0, -0.01122679215254],
                   [0, 0.01122679215254],
                   [-0.08838834764832, 0.08838834764832],
                   [0.08838834764832, 0.08838834764832],
                   [0.69587998903400, -0.69587998903400],
                   [0.69587998903400, 0.69587998903400],
                   [0.08838834764832, -0.08838834764832],
                   [-0.08838834764832, -0.08838834764832],
                   [0.01122679215254, 0],
                   [0.01122679215254, 0]])
    sf = np.array(af[::-1, :])
    w1 = wavelet.dwt3D(pad1, 1, af)
    w2 = wavelet.dwt3D(pad2, 1, af)
    w3 = wavelet.dwt3D(pad3, 1, af)
    for i in range(7):
        tmp = np.array(w3[0][i])
        tmp = tmp[:(s[0] // 2), :(s[1] // 2), :(s[2] // 2)]
        sigY = np.std(tmp, ddof=1)
        sigX = (sigY * sigY) - sigma * sigma
        if sigX < 0:
            T = abs(w3[0][i]).max()
        else:
            T = (sigma * sigma) / (sigX**0.5)
        w3[0][i] = abs(w3[0][i])
        dist = np.array(w3[0][i]) - T
        dist = np.exp(-0.01 * dist)
        dist = 1. / (1 + dist)
        w3[0][i] = dist * w1[0][i] + (1 - dist) * w2[0][i]
    w3[1] = w1[1]
    fima = wavelet.idwt3D(w3, 1, sf)
    fima = fima[:s[0], :s[1], :s[2]]
    return fima
