          """ get warped image, target image and target mask
              From DFAKER plugin """
          logger.trace("Randomly warping landmarks")
          size = image.shape[0]
          coverage = self.get_coverage(image)
  
          p_mx = size - 1
          p_hf = (size // 2) - 1
  
          edge_anchors = [(0, 0), (0, p_mx), (p_mx, p_mx), (p_mx, 0),
 
</s> remove         coverage = self.get_coverage(image)
 </s> add         coverage = self.get_coverage(image) // 2 </s> remove         slices = slice(size // 2 - coverage // 2, size // 2 + coverage // 2)
 </s> add         slices = slice(size // 2 - coverage, size // 2 + coverage) </s> remove         radius = max(1, round(mask_diameter * radius_ratio / 100))
        kernel_size = int((radius * 2) + 1)
 </s> add         radius = round(max(1., mask_diameter * radius_ratio / 100.))
        kernel_size = radius * 2 + 1 </s> remove         range_ = np.linspace(height // 2 - coverage // 2,
                             height // 2 + coverage // 2,
                             5, dtype='float32')
 </s> add         range_ = np.linspace(height // 2 - coverage, height // 2 + coverage, 5, dtype='float32') </s> remove             """
            Function to mimic the 'fspecial' gaussian MATLAB function.
            """
 </s> add             """ Function to mimic the 'fspecial' gaussian MATLAB function. """

        coverage = self.get_coverage(image) // 2
          """ get warped image, target image and target mask
              From DFAKER plugin """
          logger.trace("Randomly warping landmarks")
          size = image.shape[0]
        coverage = self.get_coverage(image) // 2
  
          p_mx = size - 1
          p_hf = (size // 2) - 1
  
          edge_anchors = [(0, 0), (0, p_mx), (p_mx, p_mx), (p_mx, 0),
keep keep keep keep replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
lib/training_data.py