diff --git a/test/gw/likelihood/relative_binning_test.py b/test/gw/likelihood/relative_binning_test.py
index 6eeca464366bba63dfc9292719b8483e7bea5a83..3f4af1e21a94a7435edb5918ed0a4a852e68470f 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 08ba958afdd821ba56da52d864134085715a4635..718e186968303dfb5f549d90ef50e2905bc63d30 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 979699b34ead35eeb742e6c9a35282230894fcbd..5f5199fd10698993036f21fdfe5517cbf7dcbff7 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)