def test_normalize_tiny_unequal_point_cloud(self):
point_cloud = create_point_cloud([0, 0, 0], [0, 0, 0], [1, 2, 3])
normalized_point_cloud = normalize(point_cloud)
normalized_values = get_attribute_value(normalized_point_cloud,
range(3), normalized_height)
np.testing.assert_allclose(normalized_values,
np.array([0, 1, 2]),
atol=1e-7)
def test_normalize_tiny_unequal_point_cloud_multiple_cells(self):
"""Last of the 3 points is not in the neighborhood of the others."""
point_cloud = create_point_cloud([0, 0, 5], [0, 0, 0], [1, 2, 3])
normalized_point_cloud = normalize(point_cloud, cell_size=2)
normalized_values = get_attribute_value(normalized_point_cloud,
range(3), normalized_height)
np.testing.assert_allclose(normalized_values,
np.array([0, 1, 0]),
atol=1e-7)
def test_normalize_provenance_data_present(self):
"""Last of the 3 points is not in the neighborhood of the others."""
point_cloud = create_point_cloud([0, 0, 5], [0, 0, 0], [1, 2, 3])
point_cloud.pop(keys.provenance, None) # Remove any provenance data
normalized_point_cloud = normalize(point_cloud, cell_size=2)
self.assertTrue(keys.provenance in normalized_point_cloud)
def test_normalize_empty_point_cloud(self):
point_cloud = create_point_cloud([], [], [])
normalized_point_cloud = normalize(point_cloud)
self.assertTrue(normalized_height in normalized_point_cloud[point])