code: def errors(self):
          
  
          n = self.n
          dt = self.dt
          order = self._order
          sigma = self.sigma
  
          error = np.zeros(order + 1)
          std = np.zeros(order + 1)
  
          if n == 0:
              return (error, std)
  
          if order == 0:
              error[0] = sigma/sqrt(n)
              std[0] = sigma/sqrt(n)
  
          elif order == 1:
              if n > 1:
                  error[0] = sigma * sqrt(2*(2*n-1) / (n*(n+1)))
                  error[1] = sigma * sqrt(12. / (n*(n*n-1)*dt*dt))
              std[0] = sigma * sqrt((2*(2*n-1)) / (n*(n+1)))
              std[1] = (sigma/dt) * sqrt(12. / (n*(n*n-1)))
  
          elif order == 2:
              dt2 = dt * dt
  
              if n >= 3:
                  error[0] = sigma * sqrt(3*(3*n*n-3*n+2) / (n*(n+1)*(n+2)))
                  error[1] = sigma * sqrt(12*(16*n*n-30*n+11) /
                                          (n*(n*n-1)*(n*n-4)*dt2))
                  error[2] = sigma * sqrt(720/(n*(n*n-1)*(n*n-4)*dt2*dt2))
  
              std[0] = sigma * sqrt((3*(3*n*n - 3*n + 2)) / (n*(n+1)*(n+2)))
              std[1] = (sigma/dt) * sqrt((12*(16*n*n - 30*n + 11)) /
                                         (n*(n*n - 1)*(n*n - 4)))
              std[2] = (sigma/dt2) * sqrt(720 / (n*(n*n-1)*(n*n-4)))
  
          return error, std
docs: Computes and returns the error and standard deviation of the
          filter at this time step.
  
          Returns
          -------
  
          error : np.array size 1xorder+1
          std : np.array size 1xorder+1