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