class_docstr: Class for the Diffusion Kurtosis Microstructural Model
      
class_docstr_tok: ["Class","for","the","Diffusion","Kurtosis","Microstructural","Model"]
docstr: Predict a signal for the DKI microstructural model class instance
  given parameters.
  
  Parameters
  ----------
  params : ndarray (x, y, z, 40) or (n, 40)
      All parameters estimated from the diffusion kurtosis
      microstructural model.
      Parameters are ordered as follows:
          1) Three diffusion tensor's eigenvalues
          2) Three lines of the eigenvector matrix each containing the
             first, second and third coordinates of the eigenvector
          3) Fifteen elements of the kurtosis tensor
          4) Six elements of the hindered diffusion tensor
          5) Six elements of the restricted diffusion tensor
          6) Axonal water fraction
  S0 : float or ndarray (optional)
      The non diffusion-weighted signal in every voxel, or across all
      voxels. Default: 1
  
  Notes
  -----
  In the original article of DKI microstructural model [1]_, the hindered
  and restricted tensors were defined as the intra-cellular and
  extra-cellular diffusion compartments respectively.
  
  References
  ----------
  .. [1] Fieremans, E., Jensen, J.H., Helpern, J.A., 2011. White Matter
         Characterization with Diffusion Kurtosis Imaging. Neuroimage
         58(1): 177-188. doi:10.1016/j.neuroimage.2011.06.006
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code: def predict(self, params, S0=1.0):
      """Predict a signal for the DKI microstructural model class instance
      given parameters.
  
      Parameters
      ----------
      params : ndarray (x, y, z, 40) or (n, 40)
          All parameters estimated from the diffusion kurtosis
          microstructural model.
          Parameters are ordered as follows:
              1) Three diffusion tensor's eigenvalues
              2) Three lines of the eigenvector matrix each containing the
                 first, second and third coordinates of the eigenvector
              3) Fifteen elements of the kurtosis tensor
              4) Six elements of the hindered diffusion tensor
              5) Six elements of the restricted diffusion tensor
              6) Axonal water fraction
      S0 : float or ndarray (optional)
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Notes
      -----
      In the original article of DKI microstructural model [1]_, the hindered
      and restricted tensors were defined as the intra-cellular and
      extra-cellular diffusion compartments respectively.
  
      References
      ----------
      .. [1] Fieremans, E., Jensen, J.H., Helpern, J.A., 2011. White Matter
             Characterization with Diffusion Kurtosis Imaging. Neuroimage
             58(1): 177-188. doi:10.1016/j.neuroimage.2011.06.006
      """
      return dkimicro_prediction(params, self.gtab, S0)
  
imports: import numpy
  from dipy.reconst.dti import lower_triangular, from_lower_triangular, decompose_tensor, trace, mean_diffusivity, radial_diffusivity, axial_diffusivity, MIN_POSITIVE_SIGNAL
  from dipy.reconst.dki import split_dki_param, _positive_evals, directional_kurtosis, directional_diffusion, kurtosis_maximum, DiffusionKurtosisModel, DiffusionKurtosisFit
  from dipy.reconst.dti import design_matrix
  from dipy.core.ndindex import ndindex
  from dipy.reconst.vec_val_sum import vec_val_vect
  from dipy.data import get_sphere
  import dipy.core.sphere
label_desc: Use image node_id 3 for calling the KurtosisMicrostructureModel obj's underlying member method code with example usage: obj.predict(params, S0) and returns: dkimicro_prediction