from __future__ import division, print_function, absolute_import

from CDTools.tools import initializers
import numpy as np
import torch as t



def test_gaussian():
    # Generate gaussian as a numpy array (square array)
    shape = [10, 10]
    sigma = [2.5, 2.5]
    center = ((shape[0]-1)/2, (shape[1]-1)/2)
    y, x = np.mgrid[:shape[0], :shape[1]]
    np_result = 10*np.exp(-((x-center[1])/sigma[1])**2-((y-center[0])/sigma[0])**2)
    assert(np.allclose(initializers.gaussian([10, 10], 10, [2.5, 2.5]), np_result))

    # Generate gaussian as a numpy array (rectangular array)
    shape = [10, 5]
    sigma = [2.5, 2.5]
    center = ((shape[0]-1)/2, (shape[1]-1)/2)
    y, x = np.mgrid[:shape[0], :shape[1]]
    np_result = 10*np.exp(-((x-center[1])/sigma[1])**2-((y-center[0])/sigma[0])**2)
    assert(np.allclose(initializers.gaussian([10, 5], 10, [2.5, 2.5]), np_result))