def test_use_norm(self):
x = y = np.array([0, 0, 0])
z = np.array([2, 2, 2])
normalized_z = np.array([3, 4, 5])
point_cloud = create_point_cloud(x, y, z, normalized_z=normalized_z)
neighborhood = [[0, 1, 2]]
extractor = RangeFeatureExtractor(data_key=keys.normalized_height)
_max, _min, _range = extractor.extract(point_cloud, neighborhood, None, None, None)
self.assertAlmostEquals(_max, 5)
self.assertAlmostEquals(_min, 3)
self.assertAlmostEquals(_range, 2)
def test_height_stats_without_neighbors(self):
pc_in = read_las.read("testdata/AHN2.las")
neighborhood = []
max_z, min_z, range_z = RangeFeatureExtractor().extract(pc_in, neighborhood, pc_in, None, None)
assert np.isnan(range_z)
assert np.isnan(max_z)
assert np.isnan(min_z)
def test_height_stats(self):
pc_in = read_las.read("testdata/AHN2.las")
neighborhood = [89664, 23893, 30638, 128795, 62052, 174453, 29129, 17127, 128215, 29667, 116156, 119157, 98591,
7018,
61494, 65194, 117931, 62971, 10474, 90322]
max_z, min_z, range_z = RangeFeatureExtractor().extract(pc_in, neighborhood, None, None, None)
np.testing.assert_allclose(range_z, 5.5)
np.testing.assert_allclose(max_z, 5.979999973773956)
np.testing.assert_allclose(min_z, 0.47999997377395631)
def test_normalized_z_provides_correct(self):
feature_names = RangeFeatureExtractor(
data_key=keys.normalized_height).provides()
self.assertIn('min_normalized_height', feature_names)
self.assertIn('max_normalized_height', feature_names)
self.assertIn('range_normalized_height', feature_names)
def test_default_provides_correct(self):
feature_names = RangeFeatureExtractor().provides()
self.assertIn('min_z', feature_names)
self.assertIn('max_z', feature_names)
self.assertIn('range_z', feature_names)