TST: change all likelihood and waveform tests to use more recent waveforms (#791)

test/gw/likelihood_test.py

@@ -331,7 +331,7 @@ def setUp(self):
         self.priors = bilby.gw.prior.BBHPriorDict()
         self.priors.pop("mass_1")
         self.priors.pop("mass_2")
-        # Testing is done with the 4s IMRPhenomPV2 ROQ basis
+        # Testing is done with the 4s IMRPhenomPv2 ROQ basis
         self.priors["chirp_mass"] = bilby.core.prior.Uniform(12.299703, 45)
         self.priors["mass_ratio"] = bilby.core.prior.Uniform(0.125, 1)
         self.priors["geocent_time"] = bilby.core.prior.Uniform(1.19, 1.21)
@@ -583,7 +583,7 @@ def test_rescaling(self):
         self.priors = bilby.gw.prior.BBHPriorDict()
         self.priors.pop("mass_1")
         self.priors.pop("mass_2")
-        # Testing is done with the 4s IMRPhenomPV2 ROQ basis
+        # Testing is done with the 4s IMRPhenomPv2 ROQ basis
         self.priors["chirp_mass"] = bilby.core.prior.Uniform(
             12.299703 / scale_factor, 45 / scale_factor
         )
@@ -631,7 +631,7 @@ def setUp(self):
         self.sampling_frequency = 2048
         self.duration = 16
         self.reference_frequency = 20.0
-        self.waveform_approximant = "IMRPhenomD"
+        self.waveform_approximant = "IMRPhenomXAS"
         # The SNRs of injections are 130-160 for roq_scale_factor=1 and 70-80 for roq_scale_factor=2
         self.injection_parameters = dict(
             mass_ratio=0.8,
@@ -942,7 +942,7 @@ def test_from_hdf5(self, basis_linear, basis_quadratic):
         duration = 16
         geocent_time = 1.2
         reference_frequency = 20.0
-        waveform_approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomXAS"
         mc_range = [8, 14]
 
         priors = bilby.gw.prior.BBHPriorDict()
@@ -1194,7 +1194,7 @@ def create_likelihood_multiple_bases(self, multiband):
         duration = 16
         geocent_time = 1.2
         reference_frequency = 20.0
-        waveform_approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomXAS"
         mc_range = [8, 14]
 
         priors = bilby.gw.prior.BBHPriorDict()
@@ -1310,10 +1310,10 @@ def tearDown(self):
         )
 
     @parameterized.expand([
-        ("IMRPhenomD", True, 2, False, 1.5e-2),
-        ("IMRPhenomD", True, 2, True, 1.5e-2),
-        ("IMRPhenomD", False, 2, False, 5e-3),
-        ("IMRPhenomD", False, 2, True, 6e-3),
+        ("IMRPhenomXAS", True, 2, False, 1.5e-2),
+        ("IMRPhenomXAS", True, 2, True, 1.5e-2),
+        ("IMRPhenomXAS", False, 2, False, 5e-3),
+        ("IMRPhenomXAS", False, 2, True, 6e-3),
         ("IMRPhenomHM", False, 4, False, 8e-4),
         ("IMRPhenomHM", False, 4, True, 1e-3)
     ])
@@ -1362,7 +1362,7 @@ def test_large_accuracy_factor(self):
         """
         Check if larger accuracy factor increases the accuracy.
         """
-        waveform_approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomXAS"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
@@ -1408,7 +1408,7 @@ 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, waveform_approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomXAS"
             )
         )
         likelihood1 = bilby.gw.likelihood.MBGravitationalWaveTransient(
@@ -1430,7 +1430,7 @@ 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, waveform_approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomXAS"
             )
         )
         with self.assertRaises(TypeError):
@@ -1446,7 +1446,7 @@ 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, waveform_approximant="IMRPhenomD"
+                reference_frequency=self.fmin, waveform_approximant="IMRPhenomXAS"
             )
         )
         for key in ["chirp_mass", "mass_1", "mass_2"]:
@@ -1463,7 +1463,7 @@ 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.
         """
-        waveform_approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomXAS"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
@@ -1519,7 +1519,7 @@ def test_from_dict_weights(self, linear_interpolation):
         """
         Check if a likelihood object constructed from dictionary-like weights produce the same likelihood value
         """
-        waveform_approximant = "IMRPhenomD"
+        waveform_approximant = "IMRPhenomXAS"
         wfg = bilby.gw.WaveformGenerator(
             duration=self.duration, sampling_frequency=self.sampling_frequency,
             frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
@@ -1565,8 +1565,8 @@ def test_from_dict_weights(self, linear_interpolation):
         self.assertAlmostEqual(llr, llr_from_weights)
 
     @parameterized.expand([
-        ("IMRPhenomD", True, 2, False, 1e-2),
-        ("IMRPhenomD", True, 2, True, 1e-2),
+        ("IMRPhenomXAS", True, 2, False, 1e-2),
+        ("IMRPhenomXAS", True, 2, True, 1e-2),
         ("IMRPhenomHM", False, 4, False, 5e-3),
     ])
     def test_matches_original_likelihood_low_maximum_frequency(

test/gw/plot_test.py

@@ -21,7 +21,7 @@ def setUp(self):
                 frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
                 time_domain_source_model=None,
                 waveform_arguments=dict(
-                    reference_frequency=20.0, waveform_approximant="IMRPhenomPv2"
+                    reference_frequency=20.0, waveform_approximant="IMRPhenomXPHM"
                 ),
                 interferometers=dict(
                     H1=dict(optimal_SNR=1, parameters=injection_parameters),

test/gw/result_test.py

@@ -27,7 +27,7 @@ def setUp(self):
                 time_marginalization=True,
                 frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
                 waveform_arguments=dict(
-                    reference_frequency=20.0, waveform_approximant="IMRPhenomPv2"
+                    reference_frequency=20.0, waveform_approximant="IMRPhenomXPHM"
                 ),
                 interferometers=dict(
                     H1=dict(optimal_SNR=1, parameters=injection_parameters),

test/gw/source_test.py

@@ -26,7 +26,7 @@ def setUp(self):
             phase=0.0,
         )
         self.waveform_kwargs = dict(
-            waveform_approximant="IMRPhenomPv2",
+            waveform_approximant="IMRPhenomXPHM",
             reference_frequency=50.0,
             minimum_frequency=20.0,
             catch_waveform_errors=True,
@@ -212,7 +212,7 @@ def setUp(self):
             lambda_2=100.0,
         )
         self.waveform_kwargs = dict(
-            waveform_approximant="IMRPhenomPv2_NRTidal",
+            waveform_approximant="IMRPhenomPv2_NRTidalv2",
             reference_frequency=50.0,
             minimum_frequency=20.0,
         )
@@ -479,7 +479,7 @@ def setUp(self):
         self.full_frequencies_to_sequence = self.frequency_array >= self.minimum_frequency
         self.frequencies = self.frequency_array[self.full_frequencies_to_sequence]
         self.waveform_kwargs = dict(
-            waveform_approximant="IMRPhenomPv2_NRTidal",
+            waveform_approximant="IMRPhenomPv2_NRTidalv2",
             reference_frequency=50.0,
         )
 
@@ -538,14 +538,14 @@ def setUp(self):
             phase=0.0,
         )
         self.waveform_kwargs_fiducial = dict(
-            waveform_approximant="IMRPhenomPv2",
+            waveform_approximant="IMRPhenomXPHM",
             reference_frequency=50.0,
             minimum_frequency=20.0,
             catch_waveform_errors=True,
             fiducial=True,
         )
         self.waveform_kwargs_binned = dict(
-            waveform_approximant="IMRPhenomPv2",
+            waveform_approximant="IMRPhenomXPHM",
             reference_frequency=50.0,
             minimum_frequency=20.0,
             catch_waveform_errors=True,
@@ -653,13 +653,13 @@ def setUp(self):
             lambda_2=100.0,
         )
         self.waveform_kwargs_fiducial = dict(
-            waveform_approximant="IMRPhenomPv2_NRTidal",
+            waveform_approximant="IMRPhenomPv2_NRTidalv2",
             reference_frequency=50.0,
             minimum_frequency=20.0,
             fiducial=True,
         )
         self.waveform_kwargs_binned = dict(
-            waveform_approximant="IMRPhenomPv2_NRTidal",
+            waveform_approximant="IMRPhenomPv2_NRTidalv2",
             reference_frequency=50.0,
             minimum_frequency=20.0,
             fiducial=False,

test/gw/utils_test.py

@@ -189,7 +189,7 @@ def test_lalsim_SimInspiralChooseFDWaveform(self):
             1000,
             20,
             None,
-            lalsim.IMRPhenomPv2,
+            lalsim.IMRPhenomXPHM,
         )
         self.assertEqual(len(a), 2)
         self.assertEqual(type(a[0]), lal.COMPLEX16FrequencySeries)

test/integration/make_standard_data.py

@@ -22,7 +22,7 @@
     luminosity_distance=100.0,
     theta_jn=0.4,
     phase=1.3,
-    waveform_approximant="IMRPhenomPv2",
+    waveform_approximant="IMRPhenomXPHM",
     reference_frequency=50.0,
     ra=1.375,
     dec=-1.2108,

test/integration/sample_from_the_prior_test.py

@@ -42,7 +42,7 @@ def test_fifteen_dimensional_cbc(self):
         bilby.core.utils.random.seed(8817021)
 
         waveform_arguments = dict(
-            waveform_approximant="IMRPhenomPv2",
+            waveform_approximant="IMRPhenomXPHM",
             reference_frequency=50.0,
             minimum_frequency=20.0,
         )

test/integration/test_waveforms.py

@@ -46,20 +46,20 @@ def setUp(self):
             lambda_2=1500,
         )
 
-    def test_IMRPhenomPv2(self):
-        waveform_approximant = "IMRPhenomPv2"
+    def test_IMRPhenomXPHM(self):
+        waveform_approximant = "IMRPhenomXPHM"
         self.run_for_approximant(waveform_approximant, source="bbh")
 
-    def test_IMRPhenomD(self):
-        waveform_approximant = "IMRPhenomD"
+    def test_IMRPhenomXAS(self):
+        waveform_approximant = "IMRPhenomXAS"
         self.run_for_approximant(waveform_approximant, source="bbh")
 
-    def test_IMRPhenomPv2_NRTidal(self):
-        waveform_approximant = "IMRPhenomPv2_NRTidal"
+    def test_IMRPhenomPv2_NRTidalv2(self):
+        waveform_approximant = "IMRPhenomPv2_NRTidalv2"
         self.run_for_approximant(waveform_approximant, source="bns")
 
-    def test_IMRPhenomD_NRTidal(self):
-        waveform_approximant = "IMRPhenomD_NRTidal"
+    def test_IMRPhenomD_NRTidalv2(self):
+        waveform_approximant = "IMRPhenomD_NRTidalv2"
         self.run_for_approximant(waveform_approximant, source="bns")
 
     def test_TaylorF2(self):