code: def predict_steadystate(self, u=0, B=None):
          
  
          if B is None:
              B = self.B
  
          # x = Fx + Bu
          if B is not None:
              self.x = dot(self.F, self.x) + dot(B, u)
          else:
              self.x = dot(self.F, self.x)
  
          # save prior
          self.x_prior = self.x.copy()
          self.P_prior = self.P.copy()
docs: Predict state (prior) using the Kalman filter state propagation
          equations. Only x is updated, P is left unchanged. See
          update_steadstate() for a longer explanation of when to use this
          method.
  
          Parameters
          ----------
  
          u : np.array
              Optional control vector. If non-zero, it is multiplied by B
              to create the control input into the system.
  
          B : np.array(dim_x, dim_z), or None
              Optional control transition matrix; a value of None
              will cause the filter to use `self.B`.