class_docstr: Class for fitting the Diffusion Kurtosis Microstructural Model 
class_docstr_tok: ["Class","for","fitting","the","Diffusion","Kurtosis","Microstructural","Model"]
docstr: Given a DKI microstructural model fit, predict the signal on the
  vertices of a gradient table
  
  gtab : a GradientTable class instance
      The gradient table for this prediction
  
  S0 : float or ndarray (optional)
      The non diffusion-weighted signal in every voxel, or across all
      voxels. Default: 1
  
  Notes
  -----
  The predicted signal is given by:
  
  $S(\theta, b) = S_0 * [f * e^{-b ADC_{r}} + (1-f) * e^{-b ADC_{h}]$,
  where $ADC_{r}$ and $ADC_{h}$ are the apparent diffusion coefficients
  of the diffusion hindered and restricted compartment for a given
  direction $\theta$, $b$ is the b value provided in the GradientTable
  input for that direction, $f$ is the volume fraction of the restricted
  diffusion compartment (also known as the axonal water fraction).
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code: def predict(self, gtab, S0=1.0):
      r"""Given a DKI microstructural model fit, predict the signal on the
      vertices of a gradient table
  
      gtab : a GradientTable class instance
          The gradient table for this prediction
  
      S0 : float or ndarray (optional)
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Notes
      -----
      The predicted signal is given by:
  
      $S(\theta, b) = S_0 * [f * e^{-b ADC_{r}} + (1-f) * e^{-b ADC_{h}]$,
      where $ADC_{r}$ and $ADC_{h}$ are the apparent diffusion coefficients
      of the diffusion hindered and restricted compartment for a given
      direction $\theta$, $b$ is the b value provided in the GradientTable
      input for that direction, $f$ is the volume fraction of the restricted
      diffusion compartment (also known as the axonal water fraction).
      """
      self._is_awfonly()
      return dkimicro_prediction(self.model_params, 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 10 for calling the KurtosisMicrostructuralFit obj's underlying member method code with example usage: obj.predict(gtab, S0) and returns: dkimicro_prediction