diff --git a/bilby/gw/conversion.py b/bilby/gw/conversion.py
index d52e05ae8d21f7bff9054da180dcc3fb1ecbeb6d..b494dca9f4a947f63cce12004bae181c478e5c7e 100644
--- a/bilby/gw/conversion.py
+++ b/bilby/gw/conversion.py
@@ -838,12 +838,8 @@ def _generate_all_cbc_parameters(sample, defaults, base_conversion,
output_sample = fill_from_fixed_priors(output_sample, priors)
output_sample, _ = base_conversion(output_sample)
if likelihood is not None:
- if (
- hasattr(likelihood, 'phase_marginalization') or
- hasattr(likelihood, 'time_marginalization') or
- hasattr(likelihood, 'distance_marginalization') or
- hasattr(likelihood, 'calibration_marginalization')
- ):
+ marginalized_parameters = getattr(likelihood, "_marginalized_parameters", list())
+ if len(marginalized_parameters) > 0:
try:
generate_posterior_samples_from_marginalized_likelihood(
samples=output_sample, likelihood=likelihood, npool=npool)
@@ -854,10 +850,17 @@ def _generate_all_cbc_parameters(sample, defaults, base_conversion,
"interpretation.".format(e)
)
if priors is not None:
- for par, name in zip(
- ['distance', 'phase', 'time'],
- ['luminosity_distance', 'phase', 'geocent_time']):
- if getattr(likelihood, '{}_marginalization'.format(par), False):
+ misnamed_marginalizations = dict(
+ distance="luminosity_distance",
+ time="geocent_time",
+ calibration="recalib_index",
+ )
+ for par in marginalized_parameters:
+ name = misnamed_marginalizations.get(par, par)
+ if (
+ getattr(likelihood, f'{par}_marginalization', False)
+ and name in likelihood.priors
+ ):
priors[name] = likelihood.priors[name]
if (
@@ -1296,10 +1299,8 @@ def generate_posterior_samples_from_marginalized_likelihood(
sample: DataFrame
Returns the posterior with new samples.
"""
- if not any([likelihood.phase_marginalization,
- likelihood.distance_marginalization,
- likelihood.time_marginalization,
- likelihood.calibration_marginalization]):
+ marginalized_parameters = getattr(likelihood, "_marginalized_parameters", list())
+ if len(marginalized_parameters) == 0:
return samples
# pass through a dictionary
@@ -1382,11 +1383,8 @@ def generate_posterior_samples_from_marginalized_likelihood(
[np.array(val) for key, val in cached_samples_dict.items() if key != "_samples"]
)
- samples['geocent_time'] = new_samples[:, 0]
- samples['luminosity_distance'] = new_samples[:, 1]
- samples['phase'] = new_samples[:, 2]
- if likelihood.calibration_marginalization:
- samples['recalib_index'] = new_samples[:, 3]
+ for ii, key in enumerate(marginalized_parameters):
+ samples[key] = new_samples[:, ii]
return samples
@@ -1413,13 +1411,8 @@ def generate_sky_frame_parameters(samples, likelihood):
def fill_sample(args):
ii, sample, likelihood = args
+ marginalized_parameters = getattr(likelihood, "_marginalized_parameters", list())
sample = dict(sample).copy()
likelihood.parameters.update(dict(sample).copy())
new_sample = likelihood.generate_posterior_sample_from_marginalized_likelihood()
-
- if not likelihood.calibration_marginalization:
- return new_sample["geocent_time"], new_sample["luminosity_distance"],\
- new_sample["phase"]
- else:
- return new_sample["geocent_time"], new_sample["luminosity_distance"],\
- new_sample["phase"], new_sample['recalib_index']
+ return (new_sample[key] for key in marginalized_parameters)
diff --git a/bilby/gw/likelihood/base.py b/bilby/gw/likelihood/base.py
index d4d8eca345bb8f6dfca921f4cd20bfcbc3c5cb6c..fd2aea3ea7365ba7ccf3a23f57f162879800ee2c 100644
--- a/bilby/gw/likelihood/base.py
+++ b/bilby/gw/likelihood/base.py
@@ -448,8 +448,7 @@ class GravitationalWaveTransient(Likelihood):
This involves a deepcopy of the signal to avoid issues with waveform
caching, as the signal is overwritten in place.
"""
- if any([self.phase_marginalization, self.distance_marginalization,
- self.time_marginalization, self.calibration_marginalization]):
+ if len(self._marginalized_parameters) > 0:
signal_polarizations = copy.deepcopy(
self.waveform_generator.frequency_domain_strain(
self.parameters))
diff --git a/test/gw/conversion_test.py b/test/gw/conversion_test.py
index 54dd064f08e956587debb4951dcf08270efaa612..e89bdce00164bc24a2440f18dd2a21dc25b41c02 100644
--- a/test/gw/conversion_test.py
+++ b/test/gw/conversion_test.py
@@ -465,6 +465,39 @@ class TestGenerateAllParameters(unittest.TestCase):
for key in expected:
self.assertIn(key, new_parameters)
+ def test_generate_bbh_paramters_with_likelihood(self):
+ priors = bilby.gw.prior.BBHPriorDict()
+ priors["geocent_time"] = bilby.core.prior.Uniform(0.4, 0.6)
+ ifos = bilby.gw.detector.InterferometerList(["H1"])
+ ifos.set_strain_data_from_power_spectral_densities(duration=1, sampling_frequency=256)
+ wfg = bilby.gw.waveform_generator.WaveformGenerator(
+ frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole
+ )
+ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
+ interferometers=ifos,
+ waveform_generator=wfg,
+ priors=priors,
+ phase_marginalization=True,
+ time_marginalization=True,
+ reference_frame="H1L1",
+ )
+ self.parameters["zenith"] = 0.0
+ self.parameters["azimuth"] = 0.0
+ del self.parameters["ra"], self.parameters["dec"]
+ converted = bilby.gw.conversion.generate_all_bbh_parameters(
+ sample=self.parameters, likelihood=likelihood, priors=priors
+ )
+ extra_expected = [
+ "geocent_time",
+ "phase",
+ "H1_optimal_snr",
+ "H1_matched_filter_snr",
+ "ra",
+ "dec",
+ ]
+ for key in extra_expected:
+ self.assertIn(key, converted)
+
class TestDistanceTransformations(unittest.TestCase):
def setUp(self):
diff --git a/test/gw/likelihood_test.py b/test/gw/likelihood_test.py
index a4528e623dee9729ae769812af3fc049fa992683..3489dab6872591e68f491ab7b9493457f34732d8 100644
--- a/test/gw/likelihood_test.py
+++ b/test/gw/likelihood_test.py
@@ -1,3 +1,4 @@
+import itertools
import os
import pytest
import unittest
@@ -648,6 +649,43 @@ class TestMarginalizations(unittest.TestCase):
prior=prior,
)
+ @parameterized.expand(
+ itertools.product(["regular", "roq"], *itertools.repeat([True, False], 3)),
+ name_func=lambda func, num, param: (
+ f"{func.__name__}_{num}__{param.args[0]}_" + "_".join([
+ ["D", "P", "T"][ii] for ii, val
+ in enumerate(param.args[1:]) if val
+ ])
+ )
+ )
+ def test_marginalization_reconstruction(self, kind, distance, phase, time):
+ if time and kind == "roq":
+ pytest.skip("Time reconstruction not supported for ROQ likelihood")
+ marginalizations = dict(
+ geocent_time=time,
+ luminosity_distance=distance,
+ phase=phase,
+ )
+ like = self.get_likelihood(
+ kind=kind,
+ distance_marginalization=distance,
+ time_marginalization=time,
+ phase_marginalization=phase,
+ )
+ params = self.parameters.copy()
+ reference_values = dict(
+ luminosity_distance=self.priors["luminosity_distance"].rescale(0.5),
+ geocent_time=self.interferometers.start_time,
+ phase=0.0,
+ )
+ for key in marginalizations:
+ if marginalizations[key]:
+ params[key] = reference_values[key]
+ like.parameters.update(params)
+ output = like.generate_posterior_sample_from_marginalized_likelihood()
+ for key in marginalizations:
+ self.assertFalse(marginalizations[key] and reference_values[key] == output[key])
+
class TestROQLikelihood(unittest.TestCase):
def setUp(self):