class_docstr: Mean signal Diffusion Kurtosis Model
      
class_docstr_tok: ["Mean","signal","Diffusion","Kurtosis","Model"]
docstr: Predict a signal for this MeanDiffusionKurtosisModel class instance
  given parameters.
  
  Parameters
  ----------
  msdki_params : ndarray
      The parameters of the mean signal diffusion kurtosis model
  S0 : float or ndarray
      The non diffusion-weighted signal in every voxel, or across all
      voxels. Default: 1
  
  Returns
  -------
  S : (..., N) ndarray
      Simulated mean signal based on the mean signal diffusion kurtosis
      model
  
  Notes
  -----
  The predicted signal is given by:
      $MS(b) = S_0 * exp(-bD + 1/6 b^{2} D^{2} K)$, where $D$ and $K$ are
      the mean signal diffusivity and mean signal kurtosis.
  
  References
  ----------
  .. [1] Henriques, R.N., 2018. Advanced Methods for Diffusion MRI Data
         Analysis and their Application to the Healthy Ageing Brain
         (Doctoral thesis). Downing College, University of Cambridge.
         https://doi.org/10.17863/CAM.29356
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code: def predict(self, msdki_params, S0=1.0):
      """
      Predict a signal for this MeanDiffusionKurtosisModel class instance
      given parameters.
  
      Parameters
      ----------
      msdki_params : ndarray
          The parameters of the mean signal diffusion kurtosis model
      S0 : float or ndarray
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Returns
      -------
      S : (..., N) ndarray
          Simulated mean signal based on the mean signal diffusion kurtosis
          model
  
      Notes
      -----
      The predicted signal is given by:
          $MS(b) = S_0 * exp(-bD + 1/6 b^{2} D^{2} K)$, where $D$ and $K$ are
          the mean signal diffusivity and mean signal kurtosis.
  
      References
      ----------
      .. [1] Henriques, R.N., 2018. Advanced Methods for Diffusion MRI Data
             Analysis and their Application to the Healthy Ageing Brain
             (Doctoral thesis). Downing College, University of Cambridge.
             https://doi.org/10.17863/CAM.29356
      """
      return msdki_prediction(msdki_params, self.gtab, S0)
  
imports: import numpy
  import scipy.optimize
  from dipy.core.gradients import check_multi_b, unique_bvals_magnitude, round_bvals
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.dti import MIN_POSITIVE_SIGNAL
  from dipy.core.ndindex import ndindex
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 3 for calling the MeanDiffusionKurtosisModel obj's underlying member method code with example usage: obj.predict(msdki_params, S0) and returns: msdki_prediction