Replace sas_temper_engine.py

    # print("Jacobian")

    # print(J)

    # This is an approximation of the Hessian

    Hess = np.matmul(J_T,J)

    print("Hessian")

    print(Hess)

    # this is the singlular value decomposition of J

    # see https://github.com/bumps/bumps/blob/master/bumps/lsqerror.py lines 237-239

    # The tolerance shown cuts down on singlular values

    # bumps as a whole may not use this directly.  have to give it a try, though

    U, S, V = np.linalg.svd(J, 0)

    tol = 1e-8

    S[S <= tol] = tol

    # Invert it to get the covariance matrix

    Cov = np.linalg.inv(Hess)

    print("Covariance Matrix")

    print(Cov)

    # This is the work around to avoid the direct inversion of the psuedo-Hessian

    Cov = np.dot(V.T.conj() / S**2, V)

    # the diagonal should be only as long as the number of parameters

    errs = np.sqrt(abs(np.diag(Cov)))

    errs = np.sqrt(np.diag(Cov))

    # these final values need to be inserted into the structure to be returned

    # note that fixed parameters have their uncertainty set to 0.0 here to avoid