(S,varS),(T,varT) = scf.compute_coeffs_discrete(xyz, mass=mass, r_s=1.,
                                                nmax=10, lmax=4,
                                                compute_var=True)

signal_to_noise = np.sqrt(S**2 / varS)

plt.figure(figsize=(6,4))
for l in range(S.shape[1]):
    plt.semilogy(signal_to_noise[:,l,0], marker=None, lw=2,
                 alpha=0.5, label='l={}'.format(l))
plt.axhline(1., linestyle='dashed')
plt.xlabel("$n$")
plt.ylabel("$S/N$")
plt.legend()
plt.tight_layout()