old_contents: import numpy as np
  import scipy.stats.stats as stat
  
  from laserchicken.feature_extractor.abc import AbstractFeatureExtractor
  from laserchicken.keys import point
  
  
  class MinZFeatureExtractor(AbstractFeatureExtractor):
      """Extracts the maximum value on the z axis."""
      DEFAULT_MIN = float('NaN')
  
      @classmethod
      def requires(cls):
          return []
  
      @classmethod
      def provides(cls):
          return ['max_z']
  
      def extract(self, sourcepc, neighborhood, targetpc, targetindex, volume_description):
          if neighborhood:
              z = sourcepc[point]['z']['data'][neighborhood]
              min_z = np.min(z) if len(z) > 0 else self.DEFAULT_MIN
          else:
              min_z = np.NaN
          return min_z
  
new_contents: import numpy as np
  import scipy.stats.stats as stat
  
  from laserchicken.feature_extractor.abc import AbstractFeatureExtractor
  from laserchicken.keys import point
  
  
  class MinZFeatureExtractor(AbstractFeatureExtractor):
      """Extracts the minimum value on the z axis."""
      DEFAULT_MIN = float('NaN')
  
      @classmethod
      def requires(cls):
          return []
  
      @classmethod
      def provides(cls):
          return ['min_z']
  
      def extract(self, sourcepc, neighborhood, targetpc, targetindex, volume_description):
          if neighborhood:
              z = sourcepc[point]['z']['data'][neighborhood]
              min_z = np.min(z) if len(z) > 0 else self.DEFAULT_MIN
          else:
              min_z = np.NaN
          return min_z
  
subject: fix provided name and comment in min_z