def solve_toeplitz(c_or_cr, b, check_finite=True):
    # If numerical stability of this algorithm is a problem, a future
    # developer might consider implementing other O(N^2) Toeplitz solvers,
    # such as GKO (http://www.jstor.org/stable/2153371) or Bareiss.
    if isinstance(c_or_cr, tuple):
        c, r = c_or_cr
        c = _asarray_validated(c, check_finite=check_finite).ravel()
        r = _asarray_validated(r, check_finite=check_finite).ravel()
    else:
        c = _asarray_validated(c_or_cr, check_finite=check_finite).ravel()
        r = c.conjugate()

    # Form a 1D array of values to be used in the matrix, containing a reversed
    # copy of r[1:], followed by c.
    vals = np.concatenate((r[-1:0:-1], c))
    if b is None:
        raise ValueError('illegal value, `b` is a required argument')
    if vals.shape[0] != (2*b.shape[0] - 1):
        raise ValueError('incompatible dimensions')

    b = _asarray_validated(b)
    if np.iscomplexobj(vals) or np.iscomplexobj(b):
        vals = np.asarray(vals, dtype=np.complex128, order='c')
        b = np.asarray(b, dtype=np.complex128)

    else:
        vals = np.asarray(vals, dtype=np.double, order='c')
        b = np.asarray(b, dtype=np.double)

    if b.ndim == 1:
        x, _ = levinson(vals, np.ascontiguousarray(b))
    else:
        b_shape = b.shape
        b = b.reshape(b.shape[0], -1)
        x = np.column_stack(
            (levinson(vals, np.ascontiguousarray(b[:, i]))[0])
            for i in range(b.shape[1]))
        x = x.reshape(*b_shape)

    return x
Predict the magnification factor as
dataset/ETHPy150Open scipy/scipy/scipy/linalg/basic.py/solve_toeplitz--crossover with--dataset/ETHPy150Open SheffieldML/GPy/GPy/core/gp.py/GP.predict_magnification