def sde(self): 
        variance = float(self.variance.values)
        lengthscale = float(self.lengthscale.values)
        foo  = np.sqrt(3.)/lengthscale 
        F    = np.array([[0, 1], [-foo**2, -2*foo]]) 
        L    = np.array([[0], [1]]) 
        Qc   = np.array([[12.*np.sqrt(3) / lengthscale**3 * variance]]) 
        H    = np.array([[1, 0]]) 
        Pinf = np.array([[variance, 0],  
        [0,              3.*variance/(lengthscale**2)]]) 
        # Allocate space for the derivatives 
        dF    = np.empty([F.shape[0],F.shape[1],2])
        dQc   = np.empty([Qc.shape[0],Qc.shape[1],2]) 
        dPinf = np.empty([Pinf.shape[0],Pinf.shape[1],2]) 
        # The partial derivatives 
        dFvariance       = np.zeros([2,2]) 
        dFlengthscale    = np.array([[0,0], 
        [6./lengthscale**3,2*np.sqrt(3)/lengthscale**2]]) 
        dQcvariance      = np.array([12.*np.sqrt(3)/lengthscale**3]) 
        dQclengthscale   = np.array([-3*12*np.sqrt(3)/lengthscale**4*variance]) 
        dPinfvariance    = np.array([[1,0],[0,3./lengthscale**2]]) 
        dPinflengthscale = np.array([[0,0], 
        [0,-6*variance/lengthscale**3]]) 
        # Combine the derivatives 
        dF[:,:,0]    = dFvariance 
        dF[:,:,1]    = dFlengthscale 
        dQc[:,:,0]   = dQcvariance 
        dQc[:,:,1]   = dQclengthscale 
        dPinf[:,:,0] = dPinfvariance 
        dPinf[:,:,1] = dPinflengthscale 

        return (F, L, Qc, H, Pinf, dF, dQc, dPinf)
Return the state space representation of the covariance.
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/src/stationary.py/Matern32.sde