code: def getVarianceComps(self, univariance=False):
          
          RV=sp.zeros((self.P,self.n_randEffs))
          for term_i in range(self.n_randEffs):
              RV[:,term_i] = self.getTraitCovar(term_i).diagonal()
          if univariance:
              RV /= RV.sum(1)[:,sp.newaxis]
          return RV
docs: Return the estimated variance components
  
          Args:
              univariance:   Boolean indicator, if True variance components are normalized to sum up to 1 for each trait
          Returns:
              variance components of all random effects on all phenotypes [P, n_randEffs matrix]