old_contents: import unittest
  
  import numpy as np
  
  from laserchicken import keys
  from laserchicken.feature_extractor.density_absolute_mean_z_feature_extractor import \
      DensityAbsoluteMeanFeatureExtractor
  from laserchicken.keys import point
  from laserchicken.test_tools import create_point_cloud
  
  
  class TestDensityAbsoluteMeanZFeatureExtractorArtificialData(unittest.TestCase):
      def test_simle_case_correct(self):
          """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
          ground = 2  # Ground tag
          veg = 4  # Medium vegetation tag
          x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
          point_cloud = create_point_cloud(x, y, z)
          point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
                                                      'type': 'double'}
          neighborhood = list(range(len(x)))
  
          density_absolute_mean = self.extractor.extract(point_cloud, neighborhood, None, None, None)
  
          self.assertAlmostEqual(density_absolute_mean, 25)
  
      def test_default_provides_correct(self):
          feature_names = self.extractor.provides()
          self.assertIn('density_absolute_mean_z', feature_names)
  
      def setUp(self):
          self.extractor = DensityAbsoluteMeanFeatureExtractor()
  
  
  class TestDensityAbsoluteMeanNormZFeatureExtractorArtificialData(unittest.TestCase):
      def test_simle_case_correct(self):
          """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
          ground = 2  # Ground tag
          veg = 4  # Medium vegetation tag
          x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
          point_cloud = create_point_cloud(x, y, np.zeros_like(z), normalized_z=z)
          point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
                                                      'type': 'double'}
          neighborhood = list(range(len(x)))
  
          density_absolute_mean = self.extractor.extract(point_cloud, neighborhood, None, None, None)
  
          self.assertAlmostEqual(density_absolute_mean, 25)
  
      def test_default_provides_correct(self):
          feature_names = self.extractor.provides()
          self.assertIn('density_absolute_mean_normalized_height', feature_names)
  
      def setUp(self):
          self.extractor = DensityAbsoluteMeanFeatureExtractor(data_key=keys.normalized_height)
  
new_contents: import unittest
  
  import numpy as np
  
  from laserchicken import keys
  from laserchicken.feature_extractor.density_absolute_mean_feature_extractor import DensityAbsoluteMeanFeatureExtractor
  from laserchicken.keys import point
  from laserchicken.test_tools import create_point_cloud
  
  
  class TestDensityAbsoluteMeanZFeatureExtractorArtificialData(unittest.TestCase):
      def test_simle_case_correct(self):
          """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
          ground = 2  # Ground tag
          veg = 4  # Medium vegetation tag
          x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
          point_cloud = create_point_cloud(x, y, z)
          point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
                                                      'type': 'double'}
          neighborhood = list(range(len(x)))
  
          density_absolute_mean = self.extractor.extract(point_cloud, neighborhood, None, None, None)
  
          self.assertAlmostEqual(density_absolute_mean, 25)
  
      def test_default_provides_correct(self):
          feature_names = self.extractor.provides()
          self.assertIn('density_absolute_mean_z', feature_names)
  
      def setUp(self):
          self.extractor = DensityAbsoluteMeanFeatureExtractor()
  
  
  class TestDensityAbsoluteMeanNormZFeatureExtractorArtificialData(unittest.TestCase):
      def test_simle_case_correct(self):
          """Check that one out of 4 points above mean of only vegetation points yields a value of 25"""
          ground = 2  # Ground tag
          veg = 4  # Medium vegetation tag
          x = y = z = np.array([10, 10, 10, 1, 1, 1, 2])
          point_cloud = create_point_cloud(x, y, np.zeros_like(z), normalized_z=z)
          point_cloud[point]['raw_classification'] = {'data': np.array([ground, ground, ground, veg, veg, veg, veg]),
                                                      'type': 'double'}
          neighborhood = list(range(len(x)))
  
          density_absolute_mean = self.extractor.extract(point_cloud, neighborhood, None, None, None)
  
          self.assertAlmostEqual(density_absolute_mean, 25)
  
      def test_default_provides_correct(self):
          feature_names = self.extractor.provides()
          self.assertIn('density_absolute_mean_normalized_height', feature_names)
  
      def setUp(self):
          self.extractor = DensityAbsoluteMeanFeatureExtractor(data_key=keys.normalized_height)
  
subject: fix import