Merge branch 'github-main' into patch-1

test/core/result_test.py

@@ -751,5 +751,71 @@ def test_pp_plot_raises_error_with_wrong_number_of_confidence_intervals(self):
             )
 
 
+class SimpleGaussianLikelihood(bilby.core.likelihood.Likelihood):
+    def __init__(self, mean=0, sigma=1):
+        """
+        A very simple Gaussian likelihood for testing
+        """
+        from scipy.stats import norm
+        super().__init__(parameters=dict())
+        self.mean = mean
+        self.sigma = sigma
+        self.dist = norm(loc=mean, scale=sigma)
+
+    def log_likelihood(self):
+        return self.dist.logpdf(self.parameters["mu"])
+
+
+class TestReweight(unittest.TestCase):
+
+    def setUp(self):
+        self.priors = bilby.core.prior.PriorDict(dict(
+            mu=bilby.core.prior.TruncatedNormal(0, 1, minimum=-5, maximum=5),
+        ))
+        self.result = bilby.core.result.Result(
+            search_parameter_keys=list(self.priors.keys()),
+            priors=self.priors,
+            posterior=pd.DataFrame(self.priors.sample(1000)),
+            log_evidence=-np.log(10),
+        )
+
+    def _run_reweighting(self, sigma):
+        likelihood_1 = SimpleGaussianLikelihood()
+        likelihood_2 = SimpleGaussianLikelihood(sigma=sigma)
+        original_ln_likelihoods = list()
+        for ii in range(len(self.result.posterior)):
+            likelihood_1.parameters = self.result.posterior.iloc[ii]
+            original_ln_likelihoods.append(likelihood_1.log_likelihood())
+        self.result.posterior["log_prior"] = self.priors.ln_prob(self.result.posterior)
+        self.result.posterior["log_likelihood"] = original_ln_likelihoods
+        self.original_ln_likelihoods = original_ln_likelihoods
+        return bilby.core.result.reweight(
+            self.result, likelihood_1, likelihood_2, verbose_output=True
+        )
+
+    def test_reweight_same_likelihood_weights_1(self):
+        """
+        When the likelihoods are the same, the weights should be 1.
+        """
+        _, weights, _, _, _, _ = self._run_reweighting(sigma=1)
+        self.assertLess(min(abs(weights - 1)), 1e-10)
+
+    def test_reweight_different_likelihood_weights_correct(self):
+        """
+        Test the known case where the target likelihood is a Gaussian with
+        sigma=0.5. The weights can be calculated analytically and the evidence
+        should be close to the original evidence within statistical error.
+        """
+        from scipy.stats import norm
+        new, weights, _, _, _, _ = self._run_reweighting(sigma=0.5)
+        expected_weights = (
+            norm(0, 0.5).pdf(self.result.posterior["mu"])
+            / norm(0, 1).pdf(self.result.posterior["mu"])
+        )
+        self.assertLess(min(abs(weights - expected_weights)), 1e-10)
+        self.assertLess(abs(new.log_evidence - self.result.log_evidence), 0.05)
+        self.assertNotEqual(new.log_evidence, self.result.log_evidence)
+
+
 if __name__ == "__main__":
     unittest.main()

test/gw/likelihood/relative_binning_test.py

@@ -78,13 +78,13 @@ def setUp(self):
             duration=duration, sampling_frequency=sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=fmin, minimum_frequency=fmin, approximant=approximant)
+                reference_frequency=fmin, minimum_frequency=fmin, waveform_approximant=approximant)
         )
         bin_wfg = bilby.gw.waveform_generator.WaveformGenerator(
             duration=duration, sampling_frequency=sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole_relative_binning,
             waveform_arguments=dict(
-                reference_frequency=fmin, approximant=approximant, minimum_frequency=fmin)
+                reference_frequency=fmin, waveform_approximant=approximant, minimum_frequency=fmin)
         )
         ifos.inject_signal(
             parameters=self.test_parameters,

test/gw/likelihood_test.py

@@ -341,7 +341,7 @@ def setUp(self):
             waveform_arguments=dict(
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -360,7 +360,7 @@ def setUp(self):
                 frequency_nodes_quadratic=fnodes_quadratic,
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -597,7 +597,7 @@ def test_rescaling(self):
                 frequency_nodes_quadratic=fnodes_quadratic,
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -1240,7 +1240,7 @@ def tearDown(self):
         ("IMRPhenomHM", False, 4, True, 1e-3)
     ])
     def test_matches_original_likelihood(
-        self, approximant, linear_interpolation, highest_mode, add_cal_errors, tolerance
+        self, waveform_approximant, linear_interpolation, highest_mode, add_cal_errors, tolerance
     ):
         """
         Check if multi-band likelihood values match original likelihood values
@@ -1249,7 +1249,7 @@ def test_matches_original_likelihood(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, waveform_approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         self.ifos.inject_signal(parameters=self.test_parameters, waveform_generator=wfg)
@@ -1258,7 +1258,7 @@ def test_matches_original_likelihood(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, waveform_approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
@@ -1284,12 +1284,12 @@ def test_large_accuracy_factor(self):
         """
         Check if larger accuracy factor increases the accuracy.
         """
-        approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomD"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, waveform_approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         self.ifos.inject_signal(parameters=self.test_parameters, waveform_generator=wfg)
@@ -1298,7 +1298,7 @@ def test_large_accuracy_factor(self):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, waveform_approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
@@ -1330,7 +1330,7 @@ def test_reference_chirp_mass_from_prior(self):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomD"
             )
         )
         likelihood1 = bilby.gw.likelihood.MBGravitationalWaveTransient(
@@ -1352,7 +1352,7 @@ def test_no_reference_chirp_mass(self):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomD"
             )
         )
         with self.assertRaises(TypeError):
@@ -1368,7 +1368,7 @@ def test_cannot_determine_reference_chirp_mass(self):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomD"
             )
         )
         for key in ["chirp_mass", "mass_1", "mass_2"]:
@@ -1385,12 +1385,12 @@ def test_inout_weights(self, linear_interpolation):
         Check if multiband weights can be saved as a file, and a likelihood object constructed from the weights file
         produces the same likelihood value.
         """
-        approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomD"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         self.ifos.inject_signal(
@@ -1401,7 +1401,7 @@ def test_inout_weights(self, linear_interpolation):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         likelihood_mb = bilby.gw.likelihood.MBGravitationalWaveTransient(
@@ -1424,7 +1424,7 @@ def test_inout_weights(self, linear_interpolation):
                 duration=self.duration, sampling_frequency=self.sampling_frequency,
                 frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
                 waveform_arguments=dict(
-                    reference_frequency=self.fmin, approximant=approximant
+                    reference_frequency=self.fmin, waveform_approximant=waveform_approximant
                 )
             )
             likelihood_mb_from_weights = bilby.gw.likelihood.MBGravitationalWaveTransient(
@@ -1441,12 +1441,12 @@ def test_from_dict_weights(self, linear_interpolation):
         """
         Check if a likelihood object constructed from dictionary-like weights produce the same likelihood value
         """
-        approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomD"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         self.ifos.inject_signal(
@@ -1457,7 +1457,7 @@ def test_from_dict_weights(self, linear_interpolation):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         likelihood_mb = bilby.gw.likelihood.MBGravitationalWaveTransient(
@@ -1474,7 +1474,7 @@ def test_from_dict_weights(self, linear_interpolation):
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         weights = likelihood_mb.weights
@@ -1492,7 +1492,7 @@ def test_from_dict_weights(self, linear_interpolation):
         ("IMRPhenomHM", False, 4, False, 5e-3),
     ])
     def test_matches_original_likelihood_low_maximum_frequency(
-        self, approximant, linear_interpolation, highest_mode, add_cal_errors, tolerance
+        self, waveform_approximant, linear_interpolation, highest_mode, add_cal_errors, tolerance
     ):
         """
         Test for maximum frequency < sampling frequency / 2
@@ -1504,7 +1504,7 @@ def test_matches_original_likelihood_low_maximum_frequency(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         self.ifos.inject_signal(parameters=self.test_parameters, waveform_generator=wfg)
@@ -1513,7 +1513,7 @@ def test_matches_original_likelihood_low_maximum_frequency(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.binary_black_hole_frequency_sequence,
             waveform_arguments=dict(
-                reference_frequency=self.fmin, approximant=approximant
+                reference_frequency=self.fmin, waveform_approximant=waveform_approximant
             )
         )
         likelihood = bilby.gw.likelihood.GravitationalWaveTransient(

test/gw/source_test.py

@@ -306,7 +306,7 @@ def setUp(self):
             frequency_nodes_quadratic=fnodes_quadratic,
             reference_frequency=50.0,
             minimum_frequency=20.0,
-            approximant="IMRPhenomPv2",
+            waveform_approximant="IMRPhenomPv2",
         )
         self.frequency_array = bilby.core.utils.create_frequency_series(2048, 4)