def METHOD_NAME(freqs, omega_p, autovis, expectation):
    dlst = np.pi / 180
    wrapped_lsts = np.linspace(2 * np.pi - dlst, 2 * np.pi + dlst, 50)
    integration_time = (
        np.mean(np.diff(wrapped_lsts)) * units.day.to("s") * units.rad.to("cycle")
    )
    wrapped_lsts %= 2 * np.pi
    channel_width = np.mean(np.diff(freqs)) * units.GHz.to("Hz")
    expected_SNR = np.sqrt(integration_time * channel_width)
    Trx = 0
    if autovis is not None:
        autovis = np.ones((wrapped_lsts.size, freqs.size), dtype=complex)
    noise_sim = noise.ThermalNoise(
        Tsky_mdl=noise.HERA_Tsky_mdl["xx"],
        omega_p=omega_p,
        Trx=Trx,
        autovis=autovis,
    )
    with expectation:
        vis = noise_sim(lsts=wrapped_lsts, freqs=freqs)
        assert np.isclose(np.std(vis), 1 / expected_SNR, rtol=1 / np.sqrt(vis.size))