def test_target_number_matches_neighborhood_number(self):
_, points = create_points_in_xy_grid(lambda x, y: 10 * (x % 2))
environment_point_cloud = create_point_cloud(points[:, 0],
points[:, 1], points[:,
2])
assert_target_number_matches_neighborhood_number(
environment_point_cloud)
def test_cell_grid(self):
_, points = create_points_in_xy_grid(lambda x, y: np.random.rand())
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:,
2])
targets = create_point_cloud(np.array([4.5]), np.array([4.5]),
np.array([4.5])) # Center of grid
neighborhoods = compute_neighborhoods(point_cloud, targets, Cell(2))
neighborhood = next(neighborhoods)
assert_equal(len(neighborhood[0]), 4)
def assert_std_for_z_function_in_xy_grid(z_checkered, expected):
"""Assert that the standard deviation of z values in a grid of unit x and y"""
n_points, points = create_points_in_xy_grid(z_checkered)
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:, 2])
targets = create_point_cloud([0], [0], [0])
compute_features(point_cloud, [range(n_points)], 0, targets, ['sigma_z'],
InfiniteCylinder(10))
np.testing.assert_almost_equal(targets[keys.point]['sigma_z']['data'][0],
expected)
def test_cell_grid_origin(self):
_, points = create_points_in_xy_grid(lambda x, y: np.random.rand())
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:,
2])
targets = create_point_cloud(np.array([0]), np.array([0]),
np.array([0])) # Center of grid
neighborhoods = list(
compute_neighborhoods(point_cloud, targets, Cell(1.99)))
assert_equal(len(neighborhoods[0]), 1)
def test_cube_grid(self):
_, points = create_points_in_xy_grid(lambda x, y: 10 * (x % 2))
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:,
2])
targets = create_point_cloud(np.array([4.5]), np.array([4.5]),
np.array([0])) # Center of grid
neighborhoods = list(
compute_neighborhoods(point_cloud, targets, Cube(2)))
assert_equal(len(neighborhoods[0]), 2)
def test_cell_grid_larger_sample_size(self):
_, points = create_points_in_xy_grid(lambda x, y: np.random.rand())
point_cloud = create_point_cloud(points[:, 0], points[:, 1], points[:,
2])
targets = create_point_cloud(np.array([4.5]), np.array([4.5]),
np.array([4.5])) # Center of grid
neighborhoods = compute_neighborhoods(
point_cloud, targets, Cell(5),
sample_size=10000) # Result 36 neighbors
_ = next(neighborhoods)
