.gitlab-ci.yml

@@ -75,6 +75,10 @@ basic-3.10:
   <<: *test-python
   image: python:3.10
 
+basic-3.11:
+  <<: *test-python
+  image: python:3.11
+
 .test-samplers-import: &test-samplers-import
   stage: initial
   script:
@@ -90,6 +94,10 @@ import-samplers-3.10:
   <<: *test-samplers-import
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
+import-samplers-3.11:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 .precommits: &precommits
   stage: initial
   script:
@@ -156,6 +164,11 @@ python-3.10:
       - htmlcov/
     expire_in: 30 days
 
+python-3.11:
+  <<: *unit-test
+  needs: ["basic-3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 .test-sampler: &test-sampler
   stage: test
   script:
@@ -173,6 +186,11 @@ python-3.10-samplers:
   needs: ["basic-3.10", "precommits-py3.10"]
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
+python-3.11-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+
 integration-tests-python-3.10:
   stage: test
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
@@ -204,6 +222,11 @@ plotting-python-3.10:
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
   needs: ["basic-3.10", "precommits-py3.10"]
 
+plotting-python-3.11:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+  needs: ["basic-3.11"]
+
 # ------------------- Docs stage -------------------------------------------
 
 docs:
@@ -271,6 +294,11 @@ build-python310-container:
   variables:
     PYVERSION: "python310"
 
+build-python311-container:
+  <<: *build-container
+  variables:
+    PYVERSION: "python311"
+
 pypi-release:
   stage: deploy
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310

CHANGELOG.md

 # All notable changes will be documented in this file
 
+## [2.2.3] 2024-02-24
+Version 2.2.3 release of Bilby
+
+This is a bugfix release 
+
+There are also a number of testing/infrastructure updates.
+
+### Changes
+- Fix a bug when the specified maximum frequency is too low for the multibanding likelihood (!1279)
+- Allow the `DirichletElement` prior to be pickled (!1312)
+- Add the ability to change the pool size when resuming a `dynesty` job (!1315)
+- Fix how the random seed is passed to `dynesty` (!1319)
+
 ## [2.2.2] 2023-11-29
 Version 2.2.2 release of Bilby
 

containers/v3-dockerfile-test-suite-python311

+# This dockerfile is written automatically and should not be modified by hand.
+
+FROM containers.ligo.org/docker/base:conda
+LABEL name="bilby CI testing" \
+maintainer="Gregory Ashton <gregory.ashton@ligo.org>, Colm Talbot <colm.talbot@ligo.org>"
+
+COPY env-template.yml env.yml
+RUN echo "  - python==3.11" >> env.yml
+ENV conda_env python311
+
+RUN mamba env create -f env.yml -n ${conda_env}
+RUN echo "source activate ${conda_env}" > ~/.bashrc
+ENV PATH /opt/conda/envs/${conda_env}/bin:$PATH
+RUN /bin/bash -c "source activate ${conda_env}"
+RUN mamba info
+RUN python --version
+
+# Add the ROQ data to the image
+RUN mkdir roq_basis \
+    && cd roq_basis \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/B_linear.npy \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/B_quadratic.npy \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_linear.npy \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/fnodes_quadratic.npy \
+    && wget https://git.ligo.org/lscsoft/ROQ_data/raw/master/IMRPhenomPv2/4s/params.dat \
+    && wget https://git.ligo.org/soichiro.morisaki/roq_basis/raw/main/IMRPhenomD/16s_nospins/basis_addcal.hdf5 \
+    && wget https://git.ligo.org/soichiro.morisaki/roq_basis/raw/main/IMRPhenomD/16s_nospins/basis_multiband_addcal.hdf5

containers/write_dockerfiles.py

@@ -3,7 +3,7 @@ from datetime import date
 with open("dockerfile-template", "r") as ff:
     template = ff.read()
 
-python_versions = [(3, 9), (3, 10)]
+python_versions = [(3, 9), (3, 10), (3, 11)]
 today = date.today().strftime("%Y%m%d")
 
 for python_major_version, python_minor_version in python_versions:

docs/installation.txt

@@ -10,7 +10,7 @@ Installation
 
           $ conda install -c conda-forge bilby
 
-      Supported python versions: 3.8-3.10.
+      Supported python versions: 3.9-3.11.
 
    .. tab:: Pip
 
@@ -18,7 +18,7 @@ Installation
 
           $ pip install bilby
 
-      Supported python versions: 3.8-3.10.
+      Supported python versions: 3.9-3.11.
 
 
 This will install all requirements for running :code:`bilby` for general
@@ -47,7 +47,7 @@ wave inference, please additionally run the following commands.
 Install bilby from source
 -------------------------
 
-:code:`bilby` is developed and tested with Python 3.8-3.10. In the
+:code:`bilby` is developed and tested with Python 3.9-3.11. In the
 following, we assume you have a working python installation, `python pip
 <https://packaging.python.org/tutorials/installing-packages/#use-pip-for-installing)>`_,
 and `git <https://git-scm.com/>`_. See :ref:`installing-python` for our

setup.py

@@ -66,7 +66,7 @@ setup(
         "bilby.gw.detector": ["noise_curves/*.txt", "detectors/*"],
         "bilby.gw.eos": ["eos_tables/*.dat"],
     },
-    python_requires=">=3.8",
+    python_requires=">=3.9",
     install_requires=get_requirements(),
     extras_require={
         "gw": get_requirements("gw"),
@@ -87,6 +87,7 @@ setup(
     classifiers=[
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],

test/core/result_test.py

@@ -751,5 +751,71 @@ class TestPPPlots(unittest.TestCase):
             )
 
 
+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 @@ 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,

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(

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)