.gitlab-ci.yml

@@ -71,6 +71,25 @@ basic-3.10:
   <<: *test-python
   image: python:3.10
 
+.test-samplers-import: &test-samplers-import
+  stage: initial
+  script:
+    - python -m pip install .
+    - python -m pip list installed
+    - python test/test_samplers_import.py
+
+import-samplers-3.8:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python38
+
+import-samplers-3.9:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
+
+import-samplers-3.10:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+
 .precommits: &precommits
   stage: initial
   script:
@@ -97,13 +116,12 @@ precommits-py3.9:
     CACHE_DIR: ".pip39"
     PYVERSION: "python39"
 
-# FIXME: when image builds for 3.10 change this back.
-#precommits-py3.10:
-#  <<: *precommits
-#  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
-#  variables:
-#    CACHE_DIR: ".pip310"
-#    PYVERSION: "python310"
+precommits-py3.10:
+  <<: *precommits
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+  variables:
+    CACHE_DIR: ".pip310"
+    PYVERSION: "python310"
 
 install:
   stage: initial
@@ -146,19 +164,16 @@ python-3.9:
       - htmlcov/
     expire_in: 30 days
 
-# add back when 3.10 image is available
-#python-3.10:
-#  <<: *unit-test
-#  needs: ["basic-3.10", "precommits-py3.10"]
-#  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+python-3.10:
+  <<: *unit-test
+  needs: ["basic-3.10", "precommits-py3.10"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
 .test-sampler: &test-sampler
   stage: test
   script:
     - python -m pip install .
-    - python -m pip install schwimmbad
     - python -m pip list installed
-
     - pytest test/integration/sampler_run_test.py --durations 10 -v
 
 python-3.8-samplers:
@@ -171,11 +186,10 @@ python-3.9-samplers:
   needs: ["basic-3.9", "precommits-py3.9"]
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
 
-# add back when 3.10 image is available
-#python-3.10-samplers:
-#  <<: *test-sampler
-#  needs: ["basic-3.10", "precommits-py3.10"]
-#  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+python-3.10-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.10", "precommits-py3.10"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
 
 integration-tests-python-3.9:
   stage: test
@@ -209,11 +223,10 @@ plotting-python-3.9:
   image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
   needs: ["basic-3.9", "precommits-py3.9"]
 
-# add back when 3.10 image is available
-#plotting-python-3.10:
-#  <<: *plotting
-#  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
-#  needs: ["basic-3.10", "precommits-py3.10"]
+plotting-python-3.10:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+  needs: ["basic-3.10", "precommits-py3.10"]
 
 # ------------------- Docs stage -------------------------------------------
 
@@ -257,8 +270,11 @@ pages:
   stage: deploy
   image: docker:20.10.12
   needs: ["containers"]
-  only:
-    - schedules
+  except:
+    refs:
+      - schedules
+    changes:
+      - containers/*
   script:
     - cd containers
     - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
@@ -276,11 +292,10 @@ build-python39-container:
   variables:
     PYVERSION: "python39"
 
-# add back when 3.10 image is available
-#build-python310-container:
-#  <<: *build-container
-#  variables:
-#    PYVERSION: "python310"
+build-python310-container:
+  <<: *build-container
+  variables:
+    PYVERSION: "python310"
 
 pypi-release:
   stage: deploy

AUTHORS.md

@@ -29,6 +29,7 @@ Hector Estelles
 Ignacio Magaña Hernandez
 Isobel Marguarethe Romero-Shaw
 Jack Heinzel
+Jacob Golomb
 Jade Powell
 James A Clark
 Jeremy G Baier

bilby/bilby_mcmc/proposals.py

@@ -547,6 +547,7 @@ class GMMProposal(DensityEstimateProposal):
     def _sample(self, nsamples=None):
         return np.squeeze(self.density.sample(n_samples=nsamples)[0])
 
+    @staticmethod
     def check_dependencies(warn=True):
         if importlib.util.find_spec("sklearn") is None:
             if warn:
@@ -593,12 +594,15 @@ class NormalizingFlowProposal(DensityEstimateProposal):
             fallback=fallback,
             scale_fits=scale_fits,
         )
-        self.setup_flow()
-        self.setup_optimizer()
-
+        self.initialised = False
         self.max_training_epochs = max_training_epochs
         self.js_factor = js_factor
 
+    def initialise(self):
+        self.setup_flow()
+        self.setup_optimizer()
+        self.initialised = True
+
     def setup_flow(self):
         if self.ndim < 3:
             self.setup_basic_flow()
@@ -699,6 +703,9 @@ class NormalizingFlowProposal(DensityEstimateProposal):
         self.trained = True
 
     def propose(self, chain):
+        if self.initialised is False:
+            self.initialise()
+
         import torch
 
         self.steps_since_refit += 1
@@ -728,6 +735,7 @@ class NormalizingFlowProposal(DensityEstimateProposal):
 
         return theta, float(log_factor)
 
+    @staticmethod
     def check_dependencies(warn=True):
         if importlib.util.find_spec("nflows") is None:
             if warn:
@@ -1094,10 +1102,6 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
         ]
         if GMMProposal.check_dependencies(warn=warn):
             plist.append(GMMProposal(priors, weight=big_weight, scale_fits=L1steps))
-        if NormalizingFlowProposal.check_dependencies(warn=warn):
-            plist.append(
-                NormalizingFlowProposal(priors, weight=big_weight, scale_fits=L1steps)
-            )
 
     plist = remove_proposals_using_string(plist, string)
     return ProposalCycle(plist)

bilby/bilby_mcmc/sampler.py

@@ -6,6 +6,7 @@ from pathlib import Path
 
 import numpy as np
 import pandas as pd
+from scipy.optimize import differential_evolution
 
 from ..core.result import rejection_sample
 from ..core.sampler.base_sampler import (
@@ -114,6 +115,13 @@ class Bilby_MCMC(MCMCSampler):
     evidence_method: str, [stepping_stone, thermodynamic]
         The evidence calculation method to use. Defaults to stepping_stone, but
         the results of all available methods are stored in the ln_z_dict.
+    initial_sample_method: str
+        Method to draw the initial sample. Either "prior" (a random draw
+        from the prior) or "maximize" (use an optimization approach to attempt
+        to find the maximum posterior estimate).
+    initial_sample_dict: dict
+        A dictionary of the initial sample value. If incomplete, will overwrite
+        the initial_sample drawn using initial_sample_method.
     verbose: bool
         Whether to print diagnostic output during the run.
 
@@ -144,6 +152,8 @@ class Bilby_MCMC(MCMCSampler):
         fixed_tau=None,
         tau_window=None,
         evidence_method="stepping_stone",
+        initial_sample_method="prior",
+        initial_sample_dict=None,
     )
 
     def __init__(
@@ -188,6 +198,8 @@ class Bilby_MCMC(MCMCSampler):
         self.proposal_cycle = self.kwargs["proposal_cycle"]
         self.pt_rejection_sample = self.kwargs["pt_rejection_sample"]
         self.evidence_method = self.kwargs["evidence_method"]
+        self.initial_sample_method = self.kwargs["initial_sample_method"]
+        self.initial_sample_dict = self.kwargs["initial_sample_dict"]
 
         self.printdt = self.kwargs["printdt"]
         check_directory_exists_and_if_not_mkdir(self.outdir)
@@ -239,8 +251,8 @@ class Bilby_MCMC(MCMCSampler):
     @staticmethod
     def add_data_to_result(result, ptsampler, outdir, label, make_plots):
         result.samples = ptsampler.samples
-        result.log_likelihood_evaluations = result.samples[LOGLKEY]
-        result.log_prior_evaluations = result.samples[LOGPKEY]
+        result.log_likelihood_evaluations = result.samples[LOGLKEY].to_numpy()
+        result.log_prior_evaluations = result.samples[LOGPKEY].to_numpy()
         ptsampler.compute_evidence(
             outdir=outdir,
             label=label,
@@ -287,6 +299,8 @@ class Bilby_MCMC(MCMCSampler):
             pool=self.pool,
             use_ratio=self.use_ratio,
             evidence_method=self.evidence_method,
+            initial_sample_method=self.initial_sample_method,
+            initial_sample_dict=self.initial_sample_dict,
         )
 
     def get_setup_string(self):
@@ -522,9 +536,13 @@ class BilbyPTMCMCSampler(object):
         pool,
         use_ratio,
         evidence_method,
+        initial_sample_method,
+        initial_sample_dict,
     ):
         self.set_pt_inputs(pt_inputs)
         self.use_ratio = use_ratio
+        self.initial_sample_method = initial_sample_method
+        self.initial_sample_dict = initial_sample_dict
         self.setup_sampler_dictionary(convergence_inputs, proposal_cycle)
         self.set_convergence_inputs(convergence_inputs)
         self.pt_rejection_sample = pt_rejection_sample
@@ -572,10 +590,12 @@ class BilbyPTMCMCSampler(object):
         betas = self.get_initial_betas()
         logger.info(
             f"Initializing BilbyPTMCMCSampler with:"
-            f"ntemps={self.ntemps},"
-            f"nensemble={self.nensemble},"
-            f"pt_ensemble={self.pt_ensemble},"
-            f"initial_betas={betas}\n"
+            f"ntemps={self.ntemps}, "
+            f"nensemble={self.nensemble}, "
+            f"pt_ensemble={self.pt_ensemble}, "
+            f"initial_betas={betas}, "
+            f"initial_sample_method={self.initial_sample_method}, "
+            f"initial_sample_dict={self.initial_sample_dict}\n"
         )
         self.sampler_dictionary = dict()
         for Tindex, beta in enumerate(betas):
@@ -591,6 +611,8 @@ class BilbyPTMCMCSampler(object):
                     convergence_inputs=convergence_inputs,
                     proposal_cycle=proposal_cycle,
                     use_ratio=self.use_ratio,
+                    initial_sample_method=self.initial_sample_method,
+                    initial_sample_dict=self.initial_sample_dict,
                 )
                 for Eindex in range(n)
             ]
@@ -1077,6 +1099,8 @@ class BilbyMCMCSampler(object):
         Tindex=0,
         Eindex=0,
         use_ratio=False,
+        initial_sample_method="prior",
+        initial_sample_dict=None,
     ):
         self.beta = beta
         self.Tindex = Tindex
@@ -1086,12 +1110,24 @@ class BilbyMCMCSampler(object):
         self.parameters = _sampling_convenience_dump.priors.non_fixed_keys
         self.ndim = len(self.parameters)
 
-        full_sample_dict = _sampling_convenience_dump.priors.sample()
-        initial_sample = {
-            k: v
-            for k, v in full_sample_dict.items()
-            if k in _sampling_convenience_dump.priors.non_fixed_keys
-        }
+        if initial_sample_method.lower() == "prior":
+            full_sample_dict = _sampling_convenience_dump.priors.sample()
+            initial_sample = {
+                k: v
+                for k, v in full_sample_dict.items()
+                if k in _sampling_convenience_dump.priors.non_fixed_keys
+            }
+        elif initial_sample_method.lower() in ["maximize", "maximise", "maximum"]:
+            initial_sample = get_initial_maximimum_posterior_sample(self.beta)
+        else:
+            raise ValueError(
+                f"initial sample method {initial_sample_method} not understood"
+            )
+
+        if initial_sample_dict is not None:
+            initial_sample.update(initial_sample_dict)
+
+        logger.info(f"Using initial sample {initial_sample}")
         initial_sample = Sample(initial_sample)
         initial_sample[LOGLKEY] = self.log_likelihood(initial_sample)
         initial_sample[LOGPKEY] = self.log_prior(initial_sample)
@@ -1266,6 +1302,42 @@ class BilbyMCMCSampler(object):
         return samples
 
 
+def get_initial_maximimum_posterior_sample(beta):
+    """A method to attempt optimization of the maximum likelihood
+
+    This uses a simple scipy optimization approach, starting from a number
+    of draws from the prior to avoid problems with local optimization.
+
+    """
+    logger.info("Finding initial maximum posterior estimate")
+    likelihood = _sampling_convenience_dump.likelihood
+    priors = _sampling_convenience_dump.priors
+    search_parameter_keys = _sampling_convenience_dump.search_parameter_keys
+
+    bounds = []
+    for key in search_parameter_keys:
+        bounds.append((priors[key].minimum, priors[key].maximum))
+
+    def neg_log_post(x):
+        sample = {key: val for key, val in zip(search_parameter_keys, x)}
+        ln_prior = priors.ln_prob(sample)
+
+        if np.isinf(ln_prior):
+            return -np.inf
+
+        likelihood.parameters.update(sample)
+
+        return -beta * likelihood.log_likelihood() - ln_prior
+
+    res = differential_evolution(neg_log_post, bounds, popsize=100, init="sobol")
+    if res.success:
+        sample = {key: val for key, val in zip(search_parameter_keys, res.x)}
+        logger.info(f"Initial maximum posterior estimate {sample}")
+        return sample
+    else:
+        raise ValueError("Failed to find initial maximum posterior estimate")
+
+
 # Methods used to aid parallelisation:
 
 

bilby/core/sampler/__init__.py

@@ -6,7 +6,7 @@ import bilby
 from bilby.bilby_mcmc import Bilby_MCMC
 
 from ..prior import DeltaFunction, PriorDict
-from ..utils import command_line_args, loaded_modules_dict, logger
+from ..utils import command_line_args, env_package_list, loaded_modules_dict, logger
 from . import proposal
 from .base_sampler import Sampler, SamplingMarginalisedParameterError
 from .cpnest import Cpnest
@@ -175,6 +175,7 @@ def run_sampler(
     likelihood.outdir = outdir
     meta_data["likelihood"] = likelihood.meta_data
     meta_data["loaded_modules"] = loaded_modules_dict()
+    meta_data["environment_packages"] = env_package_list(as_dataframe=True)
 
     if command_line_args.bilby_zero_likelihood_mode:
         from bilby.core.likelihood import ZeroLikelihood

bilby/core/sampler/pymc.py

@@ -61,7 +61,6 @@ class Pymc(MCMCSampler):
         n_init=200000,
         initvals=None,
         trace=None,
-        chain_idx=0,
         chains=2,
         cores=1,
         tune=500,

bilby/core/utils/io.py

@@ -292,8 +292,6 @@ def encode_for_hdf5(key, item):
         output = json.dumps(item._get_json_dict())
     elif isinstance(item, pd.DataFrame):
         output = item.to_dict(orient="list")
-    elif isinstance(item, pd.Series):
-        output = item.to_dict()
     elif inspect.isfunction(item) or inspect.isclass(item):
         output = dict(
             __module__=item.__module__, __name__=item.__name__, __class__=True

bilby/core/utils/log.py

+import json
 import logging
 from pathlib import Path
+import subprocess
 import sys
 
 logger = logging.getLogger('bilby')
@@ -70,3 +72,60 @@ def loaded_modules_dict():
         if "." not in str(key):
             vdict[key] = str(getattr(sys.modules[key], "__version__", "N/A"))
     return vdict
+
+
+def env_package_list(as_dataframe=False):
+    """Get the list of packages installed in the system prefix.
+
+    If it is detected that the system prefix is part of a Conda environment,
+    a call to ``conda list --prefix {sys.prefix}`` will be made, otherwise
+    the call will be to ``{sys.executable} -m pip list installed``.
+
+    Parameters
+    ----------
+    as_dataframe: bool
+        return output as a `pandas.DataFrame`
+
+    Returns
+    -------
+    pkgs : `list` of `dict`, or `pandas.DataFrame`
+        If ``as_dataframe=False`` is given, the output is a `list` of `dict`,
+        one for each package, at least with ``'name'`` and ``'version'`` keys
+        (more if `conda` is used).
+        If ``as_dataframe=True`` is given, the output is a `DataFrame`
+        created from the `list` of `dicts`.
+    """
+    prefix = sys.prefix
+
+    # if a conda-meta directory exists, this is a conda environment, so
+    # use conda to print the package list
+    if (Path(prefix) / "conda-meta").is_dir():
+        pkgs = json.loads(subprocess.check_output([
+            "conda",
+            "list",
+            "--prefix", prefix,
+            "--json"
+        ]))
+
+    # otherwise try and use Pip
+    else:
+        try:
+            import pip  # noqa: F401
+        except ModuleNotFoundError:  # no pip?
+            # not a conda environment, and no pip, so just return
+            # the list of loaded modules
+            modules = loaded_modules_dict()
+            pkgs = [{"name": x, "version": y} for x, y in modules.items()]
+        else:
+            pkgs = json.loads(subprocess.check_output([
+                sys.executable,
+                "-m", "pip",
+                "list", "installed",
+                "--format", "json",
+            ]))
+
+    # convert to recarray for storage
+    if as_dataframe:
+        from pandas import DataFrame
+        return DataFrame(pkgs)
+    return pkgs

bilby/gw/conversion.py

@@ -192,14 +192,14 @@ def convert_to_lal_binary_black_hole_parameters(parameters):
 
     converted_parameters = parameters.copy()
     original_keys = list(converted_parameters.keys())
-
-    if 'redshift' in converted_parameters.keys():
-        converted_parameters['luminosity_distance'] = \
-            redshift_to_luminosity_distance(parameters['redshift'])
-    elif 'comoving_distance' in converted_parameters.keys():
-        converted_parameters['luminosity_distance'] = \
-            comoving_distance_to_luminosity_distance(
-                parameters['comoving_distance'])
+    if 'luminosity_distance' not in original_keys:
+        if 'redshift' in converted_parameters.keys():
+            converted_parameters['luminosity_distance'] = \
+                redshift_to_luminosity_distance(parameters['redshift'])
+        elif 'comoving_distance' in converted_parameters.keys():
+            converted_parameters['luminosity_distance'] = \
+                comoving_distance_to_luminosity_distance(
+                    parameters['comoving_distance'])
 
     for key in original_keys:
         if key[-7:] == '_source':

containers/dockerfile-template

@@ -30,15 +30,7 @@ RUN conda install -n ${{conda_env}} -c conda-forge scikit-image celerite george
 
 # Install dependencies and samplers
 RUN pip install corner healpy cython tables
-RUN conda install -n ${{conda_env}} -c conda-forge dynesty emcee nestle ptemcee
-RUN conda install -n ${{conda_env}} -c conda-forge pymultinest ultranest
-RUN conda install -n ${{conda_env}} -c conda-forge cpnest kombine dnest4 zeus-mcmc
-RUN conda install -n ${{conda_env}} -c conda-forge ptmcmcsampler
-RUN conda install -n ${{conda_env}} -c conda-forge pytorch
-RUN conda install -n ${{conda_env}} -c conda-forge theano-pymc
-RUN conda install -n ${{conda_env}} -c conda-forge pymc3
-RUN conda install -n ${{conda_env}} -c conda-forge pymc pymc-base
-RUN pip install nessai
+RUN conda install -n ${{conda_env}} {conda_samplers} -c conda-forge -c pytorch
 
 # Install Polychord
 RUN apt-get update --allow-releaseinfo-change

containers/v3-dockerfile-test-suite-python310

+# 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>"
+
+RUN conda update -n base -c defaults conda
+
+ENV conda_env python310
+
+RUN conda create -n ${conda_env} python=3.10
+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 conda info
+RUN python --version
+
+# Install conda-installable programs
+RUN conda install -n ${conda_env} -y matplotlib numpy scipy pandas astropy flake8
+RUN conda install -n ${conda_env} -c anaconda coverage configargparse future dill
+RUN conda install -n ${conda_env} -c conda-forge black pytest-cov deepdish arviz
+
+# Install pip-requirements
+RUN pip install --upgrade pip
+RUN pip install --upgrade setuptools coverage-badge parameterized
+
+# Install documentation requirements
+RUN pip install sphinx numpydoc nbsphinx sphinx_rtd_theme sphinx-tabs autodoc
+
+# Install testing requirements
+RUN conda install -n ${conda_env} -c conda-forge scikit-image celerite george
+
+# Install dependencies and samplers
+RUN pip install corner healpy cython tables
+RUN conda install -n ${conda_env} dynesty emcee nestle ptemcee pymultinest ultranest cpnest kombine dnest4 zeus-mcmc pytorch pymc nessai ptmcmcsampler -c conda-forge -c pytorch
+
+# Install Polychord
+RUN apt-get update --allow-releaseinfo-change
+RUN apt-get install -y build-essential
+RUN apt-get install -y libblas3 libblas-dev
+RUN apt-get install -y liblapack3 liblapack-dev
+RUN apt-get install -y libatlas3-base libatlas-base-dev
+RUN apt-get install -y gfortran
+
+RUN git clone https://github.com/PolyChord/PolyChordLite.git \
+&& (cd PolyChordLite && python setup.py --no-mpi install)
+
+# Install GW packages
+RUN conda install -n ${conda_env} -c conda-forge python-lalsimulation bilby.cython
+RUN pip install ligo-gracedb gwpy ligo.skymap
+
+# 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/v3-dockerfile-test-suite-python38

@@ -32,15 +32,7 @@ RUN conda install -n ${conda_env} -c conda-forge scikit-image celerite george
 
 # Install dependencies and samplers
 RUN pip install corner healpy cython tables
-RUN conda install -n ${conda_env} -c conda-forge dynesty emcee nestle ptemcee
-RUN conda install -n ${conda_env} -c conda-forge pymultinest ultranest
-RUN conda install -n ${conda_env} -c conda-forge cpnest kombine dnest4 zeus-mcmc
-RUN conda install -n ${conda_env} -c conda-forge ptmcmcsampler
-RUN conda install -n ${conda_env} -c conda-forge pytorch
-RUN conda install -n ${conda_env} -c conda-forge theano-pymc
-RUN conda install -n ${conda_env} -c conda-forge pymc3
-RUN conda install -n ${conda_env} -c conda-forge pymc pymc-base
-RUN pip install nessai
+RUN conda install -n ${conda_env} dynesty emcee nestle ptemcee pymultinest ultranest cpnest kombine dnest4 zeus-mcmc pytorch pymc nessai ptmcmcsampler -c conda-forge -c pytorch
 
 # Install Polychord
 RUN apt-get update --allow-releaseinfo-change

containers/v3-dockerfile-test-suite-python39

@@ -32,15 +32,7 @@ RUN conda install -n ${conda_env} -c conda-forge scikit-image celerite george
 
 # Install dependencies and samplers
 RUN pip install corner healpy cython tables
-RUN conda install -n ${conda_env} -c conda-forge dynesty emcee nestle ptemcee
-RUN conda install -n ${conda_env} -c conda-forge pymultinest ultranest
-RUN conda install -n ${conda_env} -c conda-forge cpnest kombine dnest4 zeus-mcmc
-RUN conda install -n ${conda_env} -c conda-forge ptmcmcsampler
-RUN conda install -n ${conda_env} -c conda-forge pytorch
-RUN conda install -n ${conda_env} -c conda-forge theano-pymc
-RUN conda install -n ${conda_env} -c conda-forge pymc3
-RUN conda install -n ${conda_env} -c conda-forge pymc pymc-base
-RUN pip install nessai
+RUN conda install -n ${conda_env} dynesty emcee nestle ptemcee pymultinest ultranest cpnest kombine dnest4 zeus-mcmc pytorch pymc nessai ptmcmcsampler -c conda-forge -c pytorch
 
 # Install Polychord
 RUN apt-get update --allow-releaseinfo-change

containers/write_dockerfiles.py

@@ -3,13 +3,31 @@ from datetime import date
 with open("dockerfile-template", "r") as ff:
     template = ff.read()
 
-python_versions = [(3, 8), (3, 9)]
+python_versions = [(3, 8), (3, 9), (3, 10)]
 today = date.today().strftime("%Y%m%d")
 
+conda_sampler_dict = dict(
+    python38=[
+        "dynesty", "emcee", "nestle", "ptemcee", "pymultinest", "ultranest",
+        "cpnest", "kombine", "dnest4", "zeus-mcmc",
+        "pytorch", "pymc", "nessai", "ptmcmcsampler",
+    ],
+    python39=[
+        "dynesty", "emcee", "nestle", "ptemcee", "pymultinest", "ultranest",
+        "cpnest", "kombine", "dnest4", "zeus-mcmc",
+        "pytorch", "pymc", "nessai", "ptmcmcsampler",
+    ],
+    python310=[
+        "dynesty", "emcee", "nestle", "ptemcee", "pymultinest", "ultranest",
+        "cpnest", "kombine", "dnest4", "zeus-mcmc",
+        "pytorch", "pymc", "nessai", "ptmcmcsampler",
+    ]
+)
+
 for python_major_version, python_minor_version in python_versions:
+    key = f"python{python_major_version}{python_minor_version}"
     with open(
-        "v3-dockerfile-test-suite-python"
-        f"{python_major_version}{python_minor_version}",
+        f"v3-dockerfile-test-suite-{key}",
         "w"
     ) as ff:
         ff.write(
@@ -19,5 +37,6 @@ for python_major_version, python_minor_version in python_versions:
         ff.write(template.format(
             date=today,
             python_major_version=python_major_version,
-            python_minor_version=python_minor_version
+            python_minor_version=python_minor_version,
+            conda_samplers=" ".join(conda_sampler_dict[key])
         ))

docs/installation.txt

@@ -10,7 +10,7 @@ Installation
 
           $ conda install -c conda-forge bilby
 
-      Supported python versions: 3.6+.
+      Supported python versions: 3.8-3.10.
 
    .. tab:: Pip
 
@@ -18,7 +18,7 @@ Installation
 
           $ pip install bilby
 
-      Supported python versions: 3.6+.
+      Supported python versions: 3.8-3.10.
 
 
 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 3.6+. In the
+:code:`bilby` is developed and tested with Python 3.8-3.10. 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
@@ -65,6 +65,15 @@ Clone the repository, install the requirements, and then install the software:
 Once you have run these steps, you have :code:`bilby` installed. You can now
 try to run the examples.
 
+.. note::
+   The bilby version is determined by the tags in the git repository. Therefore, you may need to run
+
+   .. code-block:: console
+
+      git fetch --all --tags
+
+   to fetch the tags so that when you install from source your version information is up to date. 
+
 .. note::
    If you do not have a git.ligo account, and receive an error message:
 
@@ -116,7 +125,7 @@ file, you can do this from the link above, or run the command
 
    $ wget https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh
 
-this will download an installer for python 3.6, for other versions check
+this will download a linux installer for python, for other versions check
 the `anaconda page <https://www.anaconda.com/download/#linux>`_.
 Then, `run the command
 <https://conda.io/docs/user-guide/install/linux.html>`_

setup.py

@@ -87,6 +87,7 @@ setup(
     classifiers=[
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],

test/bilby_mcmc/test_proposals.py

@@ -165,29 +165,33 @@ class TestProposals(TestBaseProposals):
     def test_NF_proposal(self):
         priors = self.create_priors()
         chain = self.create_chain(10000)
-        prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
-        prop.steps_since_refit = 9999
-        start = time.time()
-        p, w = prop(chain)
-        dt = time.time() - start
-        print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
-        self.assertTrue(prop.trained)
-
-        self.proposal_check(prop)
+        if proposals.NormalizingFlowProposal.check_dependencies():
+            prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
+            prop.steps_since_refit = 9999
+            start = time.time()
+            p, w = prop(chain)
+            dt = time.time() - start
+            print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
+            self.assertTrue(prop.trained)
+            self.proposal_check(prop)
+        else:
+            print("nflows not installed, unable to test NormalizingFlowProposal")
 
     def test_NF_proposal_15D(self):
         ndim = 15
         priors = self.create_priors(ndim)
         chain = self.create_chain(10000, ndim=ndim)
-        prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
-        prop.steps_since_refit = 9999
-        start = time.time()
-        p, w = prop(chain)
-        dt = time.time() - start
-        print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
-        self.assertTrue(prop.trained)
-
-        self.proposal_check(prop, ndim=ndim)
+        if proposals.NormalizingFlowProposal.check_dependencies():
+            prop = proposals.NormalizingFlowProposal(priors, first_fit=10000)
+            prop.steps_since_refit = 9999
+            start = time.time()
+            p, w = prop(chain)
+            dt = time.time() - start
+            print(f"Training for {prop.__class__.__name__} took dt~{dt:0.2g} [s]")
+            self.assertTrue(prop.trained)
+            self.proposal_check(prop, ndim=ndim)
+        else:
+            print("nflows not installed, unable to test NormalizingFlowProposal")
 
 
 if __name__ == "__main__":

test/core/sampler/nessai_test.py

@@ -21,6 +21,7 @@ class TestNessai(unittest.TestCase):
             plot=False,
             skip_import_verification=True,
             sampling_seed=150914,
+            npool=None,  # TODO: remove when support for nessai<0.7.0 is dropped
         )
         self.expected = self.sampler.default_kwargs
         self.expected['output'] = 'outdir/label_nessai/'
@@ -59,10 +60,13 @@ class TestNessai(unittest.TestCase):
         assert self.expected["seed"] == 150914
 
     def test_npool_max_threads(self):
+        # TODO: remove when support for nessai<0.7.0 is dropped
         expected = self.expected.copy()
         expected["n_pool"] = None
+        expected["max_threads"] = 1
         new_kwargs = self.sampler.kwargs.copy()
         new_kwargs["n_pool"] = 1
+        new_kwargs["max_threads"] = 1
         self.sampler.kwargs = new_kwargs
         self.assertDictEqual(expected, self.sampler.kwargs)
 

test/core/sampler/pymc_test.py

@@ -33,7 +33,6 @@ class TestPyMC(unittest.TestCase):
             n_init=200000,
             initvals=None,
             trace=None,
-            chain_idx=0,
             chains=2,
             cores=1,
             tune=500,
@@ -57,7 +56,6 @@ class TestPyMC(unittest.TestCase):
             n_init=200000,
             initvals=None,
             trace=None,
-            chain_idx=0,
             chains=2,
             cores=1,
             tune=500,

test/gw/utils_test.py

@@ -104,7 +104,7 @@ class TestGWUtils(unittest.TestCase):
         strain = gwutils.read_frame_file(
             filename, start_time=None, end_time=None, channel=channel
         )
-        self.assertEqual(strain.channel.name, channel)
+        self.assertEqual(strain.name, channel)
         self.assertTrue(np.all(strain.value == data[:-1]))
 
         # Check reading with time limits