code: def cov_from_scales(self, scales):
          
  
          # Get array of scales
          ord_sc = []
          for stochastic in self.stochastics:
              ord_sc.append(np.ravel(scales[stochastic]))
          ord_sc = np.concatenate(ord_sc)
  
          if np.squeeze(ord_sc).shape[0] != self.dim:
              raise ValueError("Improper initial scales, dimension don't match",
                               (np.squeeze(ord_sc), self.dim))
  
          # Scale identity matrix
          return np.eye(self.dim) * ord_sc
docs: Return a covariance matrix built from a dictionary of scales.
  
          `scales` is a dictionary keyed by stochastic instances, and the
          values refer are the variance of the jump distribution for each
          stochastic. If a stochastic is a sequence, the variance must
          have the same length.