From 3362a0a4bba0827300fffc64d37777928a0a5f75 Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Fri, 23 Feb 2024 15:50:58 +0000
Subject: [PATCH] TEST: fix waveform typos in likelihood tests

---
 test/gw/likelihood/relative_binning_test.py |  4 +-
 test/gw/likelihood_test.py                  | 46 ++++++++++-----------
 test/gw/source_test.py                      |  2 +-
 3 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/test/gw/likelihood/relative_binning_test.py b/test/gw/likelihood/relative_binning_test.py
index 6eeca4643..3f4af1e21 100644
--- a/test/gw/likelihood/relative_binning_test.py
+++ b/test/gw/likelihood/relative_binning_test.py
@@ -78,13 +78,13 @@ class TestRelativeBinningLikelihood(unittest.TestCase):
             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,
diff --git a/test/gw/likelihood_test.py b/test/gw/likelihood_test.py
index 08ba958af..718e18696 100644
--- a/test/gw/likelihood_test.py
+++ b/test/gw/likelihood_test.py
@@ -341,7 +341,7 @@ class TestROQLikelihood(unittest.TestCase):
             waveform_arguments=dict(
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -360,7 +360,7 @@ class TestROQLikelihood(unittest.TestCase):
                 frequency_nodes_quadratic=fnodes_quadratic,
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -597,7 +597,7 @@ class TestRescaledROQLikelihood(unittest.TestCase):
                 frequency_nodes_quadratic=fnodes_quadratic,
                 reference_frequency=20.0,
                 minimum_frequency=20.0,
-                approximant="IMRPhenomPv2",
+                waveform_approximant="IMRPhenomPv2",
             ),
         )
 
@@ -1240,7 +1240,7 @@ class TestMBLikelihood(unittest.TestCase):
         ("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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
         """
         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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
         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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
                 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 @@ class TestMBLikelihood(unittest.TestCase):
         """
         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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
         ("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 @@ class TestMBLikelihood(unittest.TestCase):
             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 @@ class TestMBLikelihood(unittest.TestCase):
             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(
diff --git a/test/gw/source_test.py b/test/gw/source_test.py
index 979699b34..5f5199fd1 100644
--- a/test/gw/source_test.py
+++ b/test/gw/source_test.py
@@ -306,7 +306,7 @@ class TestROQBBH(unittest.TestCase):
             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)
 
-- 
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