class_docstr: generate randomised scaling along each dim for data augmentation
docstr: Compute optimal standard deviation for Gaussian kernel.
  
      Cardoso et al., "Scale factor point spread function matching:
      beyond aliasing in image resampling", MICCAI 2015
code: def _get_sigma(self, zoom):
      """
      Compute optimal standard deviation for Gaussian kernel.
  
          Cardoso et al., "Scale factor point spread function matching:
          beyond aliasing in image resampling", MICCAI 2015
      """
      k = 1 / zoom
      variance = (k**2 - 1**2) * (2 * np.sqrt(2 * np.log(2))) ** (
          -2
      )
      sigma = np.sqrt(variance)
      return sigma
  
file_path: NiftyNet/niftynet/layer/rand_spatial_scaling.py
imports: from __future__ import absolute_import, print_function
  import warnings
  import numpy
  import scipy.ndimage
  from niftynet.layer.base_layer import RandomisedLayer
label_desc: Use image node_id 3 for calling the RandomSpatialScalingLayer obj's underlying member method code with example usage: obj._get_sigma(zoom) and returns: sigma