code: def predict_steadystate(x, F=1, u=0, B=1):
      
  
      if np.isscalar(F):
          F = np.array(F)
      x = dot(F, x) + dot(B, u)
  
      return x
docs: Predict next state (prior) using the Kalman filter state propagation
      equations. This steady state form only computes x, assuming that the
      covariance is constant.
  
      Parameters
      ----------
  
      x : numpy.array
          State estimate vector
  
      P : numpy.array
          Covariance matrix
  
      F : numpy.array()
          State Transition matrix
  
      u : numpy.array, Optional, default 0.
          Control vector. If non-zero, it is multiplied by B
          to create the control input into the system.
  
      B : numpy.array, optional, default 0.
          Control transition matrix.
  
      Returns
      -------
  
      x : numpy.array
          Prior state estimate vector