from scipy import ndimage

import numpy as np

def norm_minmax(d):

    return -1 + 2 * (d - d.min()) / (d.max() - d.min())

def laplace(d):

    return ndimage.laplace(d)

def laplace_norm(d):

    return norm_minmax(laplace(d))

from scipy.ndimage import gaussian_gradient_magnitude

def grad(d):

    return gaussian_gradient_magnitude(d, sigma=(1,1,1))

    #grads = np.zeros_like(d)

    #for a in range(d.squeeze().ndim):

    #    grads += np.power(ndimage.sobel(d.squeeze(), axis=a), 2)

    #return np.sqrt(grads)

def grad_norm(d):

    return norm_minmax(grad(d))