class_docstr: Class for fitting the Correlation Tensor Model 
class_docstr_tok: ["Class","for","fitting","the","Correlation","Tensor","Model"]
docstr: Given a CTI model fit, predict the signal on the vertices of a
  gradient table
  
  Parameters:
  -----------
  params: numpy.ndarray (...,43)
          All parameters estimated from the correlation tensor 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. Twenty-One elements of the covariance tensor
  gtab1: dipy.core.gradients.GradientTable
      A GradientTable class instance for first DDE diffusion epoch
  gtab2: dipy.core.gradients.GradientTable
      A GradientTable class instance for second DDE diffusion epoch
  S0 : float or ndarray (optional)
      The non diffusion-weighted signal in every voxel, or across all
      voxels. Default: 1
  
  Returns
  -------
  S : numpy.ndarray
      Predicted signal based on the CTI model
docstr_tok: ["Given","a","CTI","model","fit",",","predict","the","signal","on","the","vertices","of","a","gradient","table","Parameters",":","--","--","--","--","--","-","params",":","numpy.ndarray","(","...",",43",")","All","parameters","estimated","from","the","correlation","tensor","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",".","Twenty-One","elements","of","the","covariance","tensor","gtab1",":","dipy.core.gradients.GradientTable","A","GradientTable","class","instance","for","first","DDE","diffusion","epoch","gtab2",":","dipy.core.gradients.GradientTable","A","GradientTable","class","instance","for","second","DDE","diffusion","epoch","S0",":","float","or","ndarray","(","optional",")","The","non","diffusion-weighted","signal","in","every","voxel",",","or","across","all","voxels",".","Default",":","1","Returns","--","--","--","-","S",":","numpy.ndarray","Predicted","signal","based","on","the","CTI","model"]
code: def predict(self, gtab1, gtab2, S0=1):
      """Given a CTI model fit, predict the signal on the vertices of a
      gradient table
  
      Parameters:
      -----------
      params: numpy.ndarray (...,43)
              All parameters estimated from the correlation tensor 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. Twenty-One elements of the covariance tensor
      gtab1: dipy.core.gradients.GradientTable
          A GradientTable class instance for first DDE diffusion epoch
      gtab2: dipy.core.gradients.GradientTable
          A GradientTable class instance for second DDE diffusion epoch
      S0 : float or ndarray (optional)
          The non diffusion-weighted signal in every voxel, or across all
          voxels. Default: 1
  
      Returns
      -------
      S : numpy.ndarray
          Predicted signal based on the CTI model
      """
      return cti_prediction(self.model_params, gtab1, gtab2, S0)
  
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 3 for calling the CorrelationTensorFit obj's underlying member method code with example usage: obj.predict(gtab1, gtab2, S0) and returns: cti_prediction