def test_cost_functions():
lena = scipy.misc.lena()
blurred = scipy.ndimage.gaussian_filter(lena, 3.0)
fp = af.FocusPoint(0, 0, 10, 10)
stack = np.dstack((lena, blurred))
assert af.discriminate(af.cost_frequencies, stack, fp, 0, 1) == 0
assert af.discriminate(af.cost_sobel, stack, fp, 0, 1) == 0
assert af.discriminate(af.cost_gradient, stack, fp, 0, 1) == 0
def test_discriminator_result():
fp = af.FocusPoint(0, 0, 10, 10)
stack = np.random.randn(2 * 100).reshape(10, 10, 2)
r = af.discriminate(af.cost_stddev, stack, fp, 0, 1)
assert r == 0 or r == 1
