class_docstr: Base Class for multivariate elliptical distributions, normal and t
  
  contains common initialization, and some common methods
  subclass needs to implement at least rvs and logpdf methods
class_docstr_tok: ["Base","Class","for","multivariate","elliptical","distributions",",","normal","and","t","contains","common","initialization",",","and","some","common","methods","subclass","needs","to","implement","at","least","rvs","and","logpdf","methods"]
docstr: normalize the random variable, i.e. subtract mean and rescale
  
  The distribution will have zero mean and sigma equal to correlation
  
  Parameters
  ----------
  x : array_like, 1d or 2d
      Data to be whitened, if 2d then each row contains an independent
      sample of the multivariate random vector
  
  Returns
  -------
  (x - self.mean)/std_sigma
  
  Notes
  -----
  
  
  See Also
  --------
  whiten : rescale random variable, standardize without subtracting mean.
code: def normalize(self, x):
      """normalize the random variable, i.e. subtract mean and rescale
  
      The distribution will have zero mean and sigma equal to correlation
  
      Parameters
      ----------
      x : array_like, 1d or 2d
          Data to be whitened, if 2d then each row contains an independent
          sample of the multivariate random vector
  
      Returns
      -------
      (x - self.mean)/std_sigma
  
      Notes
      -----
  
  
      See Also
      --------
      whiten : rescale random variable, standardize without subtracting mean.
  
  
      """
      std_ = np.atleast_2d(self.std_sigma)
      return (x - self.mean) / std_
  
imports: import numpy
  from scipy import special
  from statsmodels.sandbox.distributions.multivariate import mvstdtprob
  from .extras import mvnormcdf
label_desc: Use image node_id 10 for calling the MVElliptical obj's underlying member method code with example usage: obj.normalize(x) and returns: unknown