def sde(self): 
        """ 
        Return the state space representation of the covariance. 
        """ 
        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)
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/_src/sde_stationary.py/sde_Exponential.sde/original