def test_bilby_to_lalinference(self):
mass_1 = [1, 20]
mass_2 = [1, 20]
chirp_mass = [1, 5]
mass_ratio = [0.125, 1]
bilby_prior = BBHPriorDict(dictionary=dict(
chirp_mass=Uniform(name='chirp_mass', minimum=chirp_mass[0], maximum=chirp_mass[1]),
mass_ratio=Uniform(name='mass_ratio', minimum=mass_ratio[0], maximum=mass_ratio[1]),
mass_2=Constraint(name='mass_2', minimum=mass_1[0], maximum=mass_1[1]),
mass_1=Constraint(name='mass_1', minimum=mass_2[0], maximum=mass_2[1])))
lalinf_prior = BBHPriorDict(dictionary=dict(
mass_ratio=Constraint(name='mass_ratio', minimum=mass_ratio[0], maximum=mass_ratio[1]),
chirp_mass=Constraint(name='chirp_mass', minimum=chirp_mass[0], maximum=chirp_mass[1]),
mass_2=Uniform(name='mass_2', minimum=mass_1[0], maximum=mass_1[1]),
mass_1=Uniform(name='mass_1', minimum=mass_2[0], maximum=mass_2[1])))
nsamples = 5000
bilby_samples = bilby_prior.sample(nsamples)
bilby_samples, _ = conversion.convert_to_lal_binary_black_hole_parameters(
bilby_samples)
# Quicker way to generate LA prior samples (rather than specifying Constraint)
lalinf_samples = []
while len(lalinf_samples) < nsamples:
s = lalinf_prior.sample()
if s["mass_1"] < s["mass_2"]:
s["mass_1"], s["mass_2"] = s["mass_2"], s["mass_1"]
if s["mass_2"] / s["mass_1"] > 0.125:
lalinf_samples.append(s)
lalinf_samples = pd.DataFrame(lalinf_samples)
lalinf_samples["mass_ratio"] = lalinf_samples["mass_2"] / lalinf_samples["mass_1"]
# Construct fake result object
result = bilby.core.result.Result()
result.search_parameter_keys = ["mass_ratio", "chirp_mass"]
result.meta_data = dict()
result.priors = bilby_prior
result.posterior = pd.DataFrame(bilby_samples)
result_converted = bilby.gw.prior.convert_to_flat_in_component_mass_prior(result)
if "plot" in sys.argv:
# Useful for debugging
plt.hist(bilby_samples["mass_ratio"], bins=50, density=True, alpha=0.5)
plt.hist(result_converted.posterior["mass_ratio"], bins=50, density=True, alpha=0.5)
plt.hist(lalinf_samples["mass_ratio"], bins=50, alpha=0.5, density=True)
plt.show()
# Check that the non-reweighted posteriors fail a KS test
ks = ks_2samp(bilby_samples["mass_ratio"], lalinf_samples["mass_ratio"])
print("Non-reweighted KS test = ", ks)
self.assertFalse(ks.pvalue > 0.05)
# Check that the non-reweighted posteriors pass a KS test
ks = ks_2samp(result_converted.posterior["mass_ratio"], lalinf_samples["mass_ratio"])
print("Reweighted KS test = ", ks)
self.assertTrue(ks.pvalue > 0.001)
priors['RM2_radm2'] = bilby.prior.Uniform(
minimum=-1100.0,
maximum=1100.0,
name="RM2_radm2",
latex_label="$\phi_2$ (rad m$^{-2}$)",
)
priors['sigmaRM1_radm2'] = bilby.prior.Uniform(
minimum=0.0,
maximum=1000.0,
name="sigmaRM1_radm2",
latex_label="$\sigma_{RM,1}$ (rad m$^{-2}$))",
)
priors['sigmaRM2_radm2'] = bilby.prior.Uniform(
minimum=0.0,
maximum=1000.0,
name="sigmaRM2_radm2",
latex_label="$\sigma_{RM,2}$ (rad m$^{-2}$)",
)
priors['delta_RM1_RM2_radm2'] = Constraint(
minimum=0,
maximum=2200.0,
name="delta_RM1_RM2_radm2",
latex_label="$\Delta\phi_{1,2}$ (rad m$^{-2}$)",
)
priors['sum_p1_p2'] = Constraint(
minimum=0.001,
maximum=1,
name="sum_p1_p2",
latex_label="$p_1+p_2$)",
)