code: def cov_from_value(self, scaling):
          
          rv = []
          for s in self.stochastics:
              rv.extend(np.ravel(s.value).copy())
  
          # Remove 0 values since this would lead to quite small jumps...
          arv = np.array(rv)
          arv[arv == 0] = 1.
  
          # Create a diagonal covariance matrix using the scaling factor.
          return np.eye(self.dim) * np.abs(arv) / scaling
docs: Return a covariance matrix for the jump distribution using
          the actual value of the stochastic as a guess of their variance,
          divided by the `scaling` argument.
  
          Note that this is likely to return a poor guess.