code: def sde(self):
          
          variance = float(self.variance.values)
          lengthscale = float(self.lengthscale)
  
          F  = np.array(((-1.0/lengthscale,),))
          L  = np.array(((1.0,),))
          Qc = np.array( ((2.0*variance/lengthscale,),) )
          H = np.array(((1.0,),))
          Pinf = np.array(((variance,),))
          P0 = Pinf.copy()
  
          dF = np.zeros((1,1,2));
          dQc = np.zeros((1,1,2));
          dPinf = np.zeros((1,1,2));
  
          dF[:,:,0] = 0.0
          dF[:,:,1] = 1.0/lengthscale**2
  
          dQc[:,:,0] = 2.0/lengthscale
          dQc[:,:,1] = -2.0*variance/lengthscale**2
  
          dPinf[:,:,0] = 1.0
          dPinf[:,:,1] = 0.0
  
          dP0 = dPinf.copy()
  
          return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
docs: Return the state space representation of the covariance.