def test_convolve2d_adaptive_checks(self):
bad_shape_kernel = np.random.random((3, 3))
bad_shape_image = np.random.random((100, ))
kernel = np.random.random((3, 3, 1))
with self.assertRaises(ValueError):
ois.convolve2d_adaptive(self.ref_img, bad_shape_kernel, 0)
with self.assertRaises(ValueError):
ois.convolve2d_adaptive(bad_shape_image, kernel, 0)
ois.convolve2d_adaptive(self.ref_img.astype('int32'), kernel, 0)
ois.convolve2d_adaptive(self.ref_img, kernel.astype('int32'), 0)
def test_subtractongrid_adaptive_ri(self):
deg = 2
bkg_deg = None
k_side = 3
k_shape = (k_side, k_side)
pol_dof = (deg + 1) * (deg + 2) / 2
kernel = np.random.random((k_side, k_side, pol_dof))
image = ois.convolve2d_adaptive(self.ref_img, kernel, deg)
subt_img, o, k, b = ois.subtractongrid(image, self.ref_img,
kernelshape=k_shape,
bkgdegree=bkg_deg,
gridshape=(1, 1),
method="AdaptiveBramich",
poly_degree=deg)
norm_diff = np.linalg.norm(subt_img) / np.linalg.norm(image)
self.assertLess(norm_diff, 1E-10)
self.assertFalse(isinstance(subt_img, np.ma.MaskedArray))
self.assertFalse(isinstance(o, np.ma.MaskedArray))
self.assertFalse(isinstance(b, np.ma.MaskedArray))
def test_convolve2d_array_dims(self):
with self.assertRaises(ValueError):
ois.convolve2d_adaptive(np.zeros((10, 10, 2)), np.ones((3, 3, 6)), 2)
with self.assertRaises(ValueError):
ois.convolve2d_adaptive(np.zeros((10, 10)), np.ones((9, 6)), 2)
def test_convolve2d_adaptive_dtype_check(self):
kernel = np.random.random((3, 3, 1))
ois.convolve2d_adaptive(np.random.randint(low=0, high=100, size=(100, 100), dtype='int32'), kernel, 0)
ois.convolve2d_adaptive(np.random.random((100, 100)), kernel.astype('int32'), 0)
