class_docstr: Class for fitting the Correlation Tensor Model 
class_docstr_tok: ["Class","for","fitting","the","Correlation","Tensor","Model"]
docstr: Returns the isotropic Source of Kurtosis (K_iso)
  
  Notes
  -----
  The K_iso is defined as :
  
  :math::
      $$ K_{\text{iso}} = 3 \cdot \frac{V(\overline{D}^c)}{\overline{D}^2} $$
  
  where: $ K_{\text{iso}} $ is the isotropic kurtosis,
      \(V({\overline{D}^c})\) represents the variance of the diffusion
      tensor raised to the power c,
      \(\overline{D}\) is the mean of the diffusion tensor.
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code: def K_iso(self):
      r"""Returns the isotropic Source of Kurtosis (K_iso)
  
      Notes
      -----
      The K_iso is defined as :
  
      :math::
          $$ K_{\text{iso}} = 3 \cdot \frac{V(\overline{D}^c)}{\overline{D}^2} $$
  
      where: $ K_{\text{iso}} $ is the isotropic kurtosis,
          \(V({\overline{D}^c})\) represents the variance of the diffusion
          tensor raised to the power c,
          \(\overline{D}\) is the mean of the diffusion tensor.
  
      """
      C = self.ct
      mean_D = self.md
      Variance = (
          1
          / 9
          * (
              C[..., 0]
              + C[..., 1]
              + C[..., 2]
              + 2 * C[..., 5]
              + 2 * C[..., 4]
              + 2 * C[..., 3]
          )
      )
      if mean_D == 0:
          K_iso = 0
      else:
          K_iso = 3 * (Variance / (mean_D**2))
      return K_iso
  
imports: import numpy
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.utils import cti_design_matrix
  from dipy.reconst.dki import DiffusionKurtosisFit
  from dipy.reconst.dti import decompose_tensor, from_lower_triangular, lower_triangular, MIN_POSITIVE_SIGNAL
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 5 for calling the CorrelationTensorFit obj's underlying member method code with example usage: obj.K_iso() and returns: K_iso