class_docstr: Class for fitting the Correlation Tensor Model 
class_docstr_tok: ["Class","for","fitting","the","Correlation","Tensor","Model"]
docstr: Returns the total excess kurtosis.
  
  Notes
  -----
  $K_total$ is defined as :
  
  :math::
      \[\Psi = \frac{2}{5} \cdot \frac{D_{11}^2 + D_{22}^2 + D_{33}^2
                                       + 2D_{12}^2 + 2D_{13}^2 +
                                       2D_{23}^2{\overline{D}^2} -
                                       \frac{6}{5} \]
      \[{\overline{W}} = \frac{1}{5} \cdot (W_{1111} + W_{2222}
                                            + W_{3333} + 2W_{1122}
                                            + 2W_{1133} + 2W_{2233})\
        ]
  
  where \(\Psi\) is a variable representing a part of the total
  excess kurtosis,
  \(D_{ij}\) are elements of the diffusion tensor,
  \(\overline{D}\) is the mean of the diffusion tensor.
  \{\overline{W}} is the mean kurtosis,
  \(W_{ijkl}\) are elements of the kurtosis tensor.
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code: def K_total(self):
      r""" Returns the total excess kurtosis.
  
              Notes
              -----
              $K_total$ is defined as :
  
              :math::
                  \[\Psi = \frac{2}{5} \cdot \frac{D_{11}^2 + D_{22}^2 + D_{33}^2
                                                   + 2D_{12}^2 + 2D_{13}^2 +
                                                   2D_{23}^2{\overline{D}^2} -
                                                   \frac{6}{5} \]
                  \[{\overline{W}} = \frac{1}{5} \cdot (W_{1111} + W_{2222}
                                                        + W_{3333} + 2W_{1122}
                                                        + 2W_{1133} + 2W_{2233})\
                    ]
  
              where \(\Psi\) is a variable representing a part of the total
              excess kurtosis,
              \(D_{ij}\) are elements of the diffusion tensor,
              \(\overline{D}\) is the mean of the diffusion tensor.
              \{\overline{W}} is the mean kurtosis,
              \(W_{ijkl}\) are elements of the kurtosis tensor.
          """
  
      mean_K = self.mkt()
      D = self.quadratic_form
      mean_D = self.md
      if mean_D == 0:
          psi = 0
      else:
          psi = 2 / 5 * (
              (
                  D[..., 0, 0] ** 2
                  + D[..., 1, 1] ** 2
                  + D[..., 2, 2] ** 2
                  + 2 * D[..., 0, 1] ** 2
                  + 2 * D[..., 0, 2] ** 2
                  + D[..., 1, 2] ** 2
              )
              / (mean_D**2)
          ) - (6 / 5)
  
      return mean_K + psi
  
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 6 for calling the CorrelationTensorFit obj's underlying member method code with example usage: obj.K_total() and returns: unknown