def test_units(observation):
ps = PowerSpectrum(observation=observation)
assert ps.horizon_buffer.to("littleh/Mpc").unit == units.littleh / units.Mpc
assert ps.k_21.to("littleh/Mpc").unit == units.littleh / units.Mpc
assert ps.delta_21.to("mK^2").unit == units.mK**2
assert callable(ps.p21)
assert ps.k_min.to("littleh/Mpc").unit == units.littleh / units.Mpc
assert ps.k_max.to("littleh/Mpc").unit == units.littleh / units.Mpc
assert ps.k1d.to("littleh/Mpc").unit == units.littleh / units.Mpc
assert isinstance(ps.power_normalisation(0.1 * units.littleh / units.Mpc), float)
assert ps.horizon_limit(10).to("littleh/Mpc").unit == units.littleh / units.Mpc
ps = PowerSpectrum(
observation=observation,
k_21=np.array([1, 2, 3]) * units.littleh / units.Mpc,
delta_21=np.array([1, 2, 3]) * units.mK**2,
)
ps2 = PowerSpectrum(
observation=observation,
k_21=np.array([1, 2, 3]) / units.Mpc,
delta_21=np.array([1, 2, 3]) * units.mK**2,
)
assert np.all(ps.k_21 < ps2.k_21)
def test_sensitivity_optimistic(observation):
ps = PowerSpectrum(observation=observation, foreground_model="optimistic")
assert ps.horizon_limit(10.0) > ps.horizon_limit(5.0)