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)], targets, ['sigma_z'],
InfiniteCylinder(10))
np.testing.assert_almost_equal(targets[keys.point]['sigma_z']['data'][0],
expected)
def test_eigenvalues_of_too_few_points_results_in_0():
"""If there are too few points to calculate the eigen values don't output NaN or inf."""
a = np.array([5])
pc = create_point_cloud(a, a, a)
compute_features(pc, [[0]], pc, ["eigenv_1", "eigenv_2", "eigenv_3"],
InfiniteCylinder(5))
eigen_val_123 = np.array(
[pc[keys.point]['eigenv_{}'.format(i)]['data'] for i in [1, 2, 3]])
assert not np.any(np.isnan(eigen_val_123))
assert not np.any(np.isinf(eigen_val_123))
def test_with_neighborhood_generator():
"""Should run for all extractors without error meaning that neighborhood generator is only iterated once.
Using actual feature extractors here because test feature extractors don't use neighborhoods. """
n = 200
feature_names = ['vectorized1', 'test1_a', 'median_z', 'mean_z']
x = np.ones(n)
y = np.ones(n)
z = np.ones(n)
target = test_tools.create_point_cloud(x, y, z)
neighborhoods = ([] for _ in range(len(target["vertex"]["x"]["data"])))
feature_extraction.compute_features({}, neighborhoods, target,
feature_names, Sphere(5))
def test_eigenvalues_in_cylinders(self):
"""Test provenance added (This should actually be part the general feature extractor test suite)."""
random.seed(102938482634)
point_cloud = load(os.path.join('testdata', 'AHN3.las'))
num_all_pc_points = len(point_cloud[keys.point]["x"]["data"])
rand_indices = [
random.randint(0, num_all_pc_points) for _ in range(20)
]
target_point_cloud = utils.copy_point_cloud(point_cloud, rand_indices)
radius = 2.5
neighbors = compute_neighbors.compute_cylinder_neighborhood(
point_cloud, target_point_cloud, radius)
compute_features(point_cloud, neighbors, target_point_cloud,
["eigenv_1", "eigenv_2", "eigenv_3"],
InfiniteCylinder(5))
self.assertEqual(
"laserchicken.feature_extractor.eigenvals_feature_extractor",
target_point_cloud[keys.provenance][-1]["module"])
def _compute_features(target, feature_names):
neighborhoods = [[] for _ in range(len(target["vertex"]["x"]["data"]))]
feature_extraction.compute_features({}, neighborhoods, target,
feature_names, Sphere(5))
return target