def sde(self): 
        """ 
        Return the state space representation of the covariance. 
        """ 
        
        variance = float(self.variance.values)
        lengthscale = float(self.lengthscale.values)
        
        foo  = lengthscale/np.sqrt(3.) 
        F    = np.array(((0, 1.0), (-foo**2, -2*foo))) 
        L    = np.array(( (0,), (1.0,) ))
        Qc   = np.array(((12.*np.sqrt(3) / lengthscale**3 * variance,),)) 
        H    = np.array(((1.0, 0),)) 
        Pinf = np.array(((variance, 0.0), (0.0, 3.*variance/(lengthscale**2))))
        P0 = Pinf.copy()
        
        # 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 
        dP0 = dPinf.copy()
        
        return (F, L, Qc, H, Pinf, P0, dF, dQc, dPinf, dP0)
dataset/ETHPy150Open SheffieldML/GPy/GPy/kern/_src/sde_matern.py/sde_Matern32.sde/swapped operands of `/`