def test_k_trafo_cont():
def inner_of_box_1(x, y, z):
if (-1 < x < 1) and (-1 < y < 1) and (-1 < z < 1):
return 4.5
return 0
def inner_of_box_2(x, y, z):
rad_pos = (x**2 + y**2 + z**2)**0.5
if rad_pos <= 0.2:
return 1
if rad_pos <= 0.5:
return 0.5
if rad_pos <= 0.8:
return 0.1
return 0
cuboid_coordinates = {
'x1': -1,
'x2': 1,
'y1': -1,
'y2': 1,
'z1': -1,
'z2': 1
}
number_rays = {'dim_1': 2, 'dim_2': 2}
source_point = np.array([5, 0, 0])
middle = middle_of_cuboid(cuboid_coordinates)
diag = length_main_diagonal(cuboid_coordinates)
pv, nv = shadow_plane_maker(source_point, cuboid_coordinates)
vx, vy = get_parameter_plane_vectors(nv)
factor_x = length_vector_for_diag(vx, diag)
factor_y = length_vector_for_diag(vy, diag)
factor_array_x = divide_the_factor(factor_x, number_rays['dim_1'])
factor_array_y = divide_the_factor(factor_y, number_rays['dim_2'])
points = create_ray_points(factor_array_x, factor_array_y, vx, vy, middle)
rays = create_rays(source_point, points)
k_trafo_cont_value_1 = k_trafo_cont(rays, inner_of_box_1)
k_trafo_cont_value_2 = k_trafo_cont(rays, inner_of_box_2)
# for comparison
l1 = line_integral_cont(rays[0], inner_of_box_1)
l2 = line_integral_cont(rays[0], inner_of_box_2)
# since the functions are symmetric for the 4 rays we can just take exp(-l1)
# since we would sum up 4 times the same value and divide it by 4 afterwards
np.testing.assert_almost_equal(k_trafo_cont_value_1,
np.exp(-l1),
decimal=2)
np.testing.assert_almost_equal(k_trafo_cont_value_2,
np.exp(-l2),
decimal=2)
cuboid_coordinates = {
'x1': -1,
'x2': 1,
'y1': -1,
'y2': 1,
'z1': -1,
'z2': 1
}
fineness = 10**3
number_rays = {'dim_1': i, 'dim_2': i}
source_point = np.array([3, 0, 0])
diag = length_main_diagonal(cuboid_coordinates)
middle = middle_of_cuboid(cuboid_coordinates)
pv, nv = shadow_plane_maker(source_point, cuboid_coordinates)
vx, vy = get_parameter_plane_vectors(nv)
factor_x = length_vector_for_diag(vx, diag)
factor_y = length_vector_for_diag(vy, diag)
factor_array_x = divide_the_factor(factor_x, number_rays['dim_1'])
factor_array_y = divide_the_factor(factor_y, number_rays['dim_2'])
points = create_ray_points(factor_array_x, factor_array_y, vx, vy, middle)
rays = create_rays(source_point, points)
k_transform_result = k_trafo_cont(rays, inner_of_box, fineness)
results += [k_transform_result]
results = np.asarray(results)
np.save("results_different_number_arrays", results)
def test_divide_the_factor():
factor_array = divide_the_factor(10, 3)
np.testing.assert_equal(np.array([-5, 0, 5]), factor_array)
factor_array = divide_the_factor(1, 1)
np.testing.assert_equal(np.array([0]), factor_array)
def generate_everything(number_ktrans, cuboid_coordinates, number_rays, steps,
inner_of_box):
"""
Generates the source points and k-tra values by simply moving the
source around them.
we firmly assume here that the k-transforms lie at the same distance
in a circle (with radius 2) around the box
"""
source_point_d = {}
pv_d = {}
nv_d = {}
vx_d = {}
vy_d = {}
factor_x_d = {}
factor_y_d = {}
factor_x_array_d = {}
factor_y_array_d = {}
points_d = {}
rays_d = {}
ktra_v = np.zeros(number_ktrans)
middle = middle_of_cuboid(cuboid_coordinates)
diag = length_main_diagonal(cuboid_coordinates)
for i in np.arange(number_ktrans):
source_point = 2 * np.array([
np.sin(2 * np.pi * i / number_ktrans),
np.cos(2 * np.pi * i / number_ktrans), 0
])
source_point_d[i] = source_point
pv, nv = shadow_plane_maker(source_point, cuboid_coordinates)
pv_d[i] = pv
nv_d[i] = nv
vx, vy = get_parameter_plane_vectors(nv)
vx_d[i] = vx
vy_d[i] = vy
factor_x = length_vector_for_diag(vx, diag)
factor_y = length_vector_for_diag(vy, diag)
factor_x_d[i] = factor_x
factor_y_d[i] = factor_y
factor_array_x = divide_the_factor(factor_x, number_rays['dim_1'])
factor_array_y = divide_the_factor(factor_y, number_rays['dim_2'])
factor_x_array_d[i] = factor_array_x
factor_y_array_d[i] = factor_array_y
points = create_ray_points(factor_array_x, factor_array_y, vx, vy,
middle)
points_d[i] = points
rays = create_rays(source_point, points)
rays_d[i] = rays
x_cor, y_cor, z_cor = coordinate_discretization(
cuboid_coordinates, steps)
values = discretization_of_model(x_cor, y_cor, z_cor, inner_of_box,
steps)
line_matrices_d = lineintegral_matrices_maker(rays_d, steps, x_cor, y_cor,
z_cor)
big_line_matrix_array = np.array([
np.array(list(line_matrices_d[i].values()))
for i in range(number_ktrans)
])
ktra_v = k_transform(values, big_line_matrix_array)
touched = important_mini_cuboids(x_cor, y_cor, z_cor, steps, rays_d,
values)
return big_line_matrix_array, ktra_v, touched, values, x_cor, y_cor, z_cor, rays_d