docs: def getVariances(self):
          """
          Returns the estimated variances as a n_terms x P matrix
          each row of the output represents a term and its P values represent the variance corresponding variance in each trait
          """
          if self.P>1:
              RV=SP.zeros((self.n_terms,self.P))
              for term_i in range(self.n_terms):
                  RV[term_i,:]=self.vd.getTerm(term_i).getTraitCovar().K().diagonal()
          else:
              RV=self.getScales()**2
          return RV