Compare revisions

8fd92475 · 3dda0972 · 36131cd7 · fcbf77cb · 18b3e164 · eb06dc2e
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,3 +3,4 @@ omit =
    test/integration/example_test.py
    test/integration/noise_realisation_test.py
    test/integration/other_test.py
+    bilby/_version.py
--- a/.gitignore
+++ b/.gitignore
@@ -13,5 +13,6 @@ MANIFEST
 *.dat
 *.version
 *.ipynb_checkpoints
-outdir/*
+**/outdir
 .idea/*
+bilby/_version.py
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -17,39 +17,51 @@ stages:
 # ------------------- Initial stage -------------------------------------------
+.list-env: &list-env
+  - PREFIX="$(dirname $(which python))/.."
+  - if [ -d "${PREFIX}/conda-meta" ]; then
+      conda list --prefix "${PREFIX}" --show-channel-urls;
+    else
+      python -m pip list installed;
+    fi
 # Check author list is up to date
 authors:
  stage: initial
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
  script:
    - python test/check_author_list.py
 # Test containers scripts are up to date
 containers:
  stage: initial
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
  script:
    - cd containers
    - python write_dockerfiles.py #HACK
    # Fail if differences exist. If this fails, you may need to run
    # write_dockerfiles.py and commit the changes.
    - git diff --exit-code
+    - cp env-template.yml env.yml
+    - echo "  - python=3.11" >> env.yml
+    - mamba env create -f env.yml -n test --dry-run
 .test-python: &test-python
  stage: initial
  image: python
-  before_script:
-    # this is required because pytables doesn't use a wheel on py37
-    - apt-get -yqq update
-    - apt-get -yqq install libhdf5-dev
  script:
    - python -m pip install .
+    - *list-env
    - python -c "import bilby"
+    - python -c "import bilby.bilby_mcmc"
    - python -c "import bilby.core"
    - python -c "import bilby.core.prior"
    - python -c "import bilby.core.sampler"
+    - python -c "import bilby.core.utils"
    - python -c "import bilby.gw"
    - python -c "import bilby.gw.detector"
+    - python -c "import bilby.gw.eos"
+    - python -c "import bilby.gw.likelihood"
    - python -c "import bilby.gw.sampler"
    - python -c "import bilby.hyper"
    - python -c "import cli_bilby"
@@ -58,173 +70,173 @@ containers:
          ${script} --help;
      done
-# Test basic setup on python 3.7
+basic-3.10:
-basic-3.7:
  <<: *test-python
-  image: python:3.7
+  image: python:3.10
-# Test basic setup on python 3.8
+basic-3.11:
-basic-3.8:
  <<: *test-python
-  image: python:3.8
+  image: python:3.11
-# Test basic setup on python 3.9
+basic-3.12:
-basic-3.9:
  <<: *test-python
-  image: python:3.9
+  image: python:3.12
+.test-samplers-import: &test-samplers-import
-precommits-py3.7:
  stage: initial
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
  script:
-    - source activate python37
+    - python -m pip install .
-    - mkdir -p .pip37
+    - *list-env
-    - pip install --upgrade pip
+    - pytest test/test_samplers_import.py -v
-    - pip --cache-dir=.pip37 install --upgrade bilby
-    - pip --cache-dir=.pip37 install .
+import-samplers-3.10:
-    - pip --cache-dir=.pip37 install pre-commit
+  <<: *test-samplers-import
-    # Run precommits (flake8, spellcheck, isort, no merge conflicts, etc)
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
-    - pre-commit run --all-files --verbose --show-diff-on-failure
-precommits-py3.8:
+import-samplers-3.11:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+import-samplers-3.12:
+  <<: *test-samplers-import
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python312
+.precommits: &precommits
  stage: initial
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python38
  script:
-    - source activate python38
+    - source activate $PYVERSION
-    - mkdir -p .pip38
+    - mkdir -p $CACHE_DIR
    - pip install --upgrade pip
-    - pip --cache-dir=.pip38 install --upgrade bilby
+    - pip --cache-dir=$CACHE_DIR install --upgrade bilby
-    - pip --cache-dir=.pip38 install .
+    - pip --cache-dir=$CACHE_DIR install .
-    - pip --cache-dir=.pip38 install pre-commit
    # Run precommits (flake8, spellcheck, isort, no merge conflicts, etc)
    - pre-commit run --all-files --verbose --show-diff-on-failure
-precommits-py3.9:
+precommits-py3.11:
+  <<: *precommits
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+  variables:
+    CACHE_DIR: ".pip311"
+    PYVERSION: "python311"
+install:
  stage: initial
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
+  parallel:
+    matrix:
+      - EXTRA: [gw, mcmc, all]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
  script:
-    - source activate python39
+    - pip install .[$EXTRA]
-    - mkdir -p .pip38
-    - pip install --upgrade pip
-    - pip --cache-dir=.pip39 install --upgrade bilby
-    - pip --cache-dir=.pip39 install .
-    - pip --cache-dir=.pip39 install pre-commit
-    # Run precommits (flake8, spellcheck, isort, no merge conflicts, etc)
-    - pre-commit run --all-files --verbose --show-diff-on-failure
 # ------------------- Test stage -------------------------------------------
-# test example on python 3.7 and build coverage
+.unit-tests: &unit-test
-python-3.7:
  stage: test
-  needs: ["basic-3.7", "precommits-py3.7"]
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
  script:
    - python -m pip install .
+    - *list-env
-    # Run pyflakes
-    - flake8 .
-    # Run tests and collect coverage data
    - pytest --cov=bilby --durations 10
-    - coverage html
-    - coverage-badge -o coverage_badge.svg -f
+python-3.10:
+  <<: *unit-test
+  needs: ["basic-3.10"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+python-3.11:
+  <<: *unit-test
+  needs: ["basic-3.11", "precommits-py3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+  after_script:
+    - coverage html
+    - coverage xml
+  coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
  artifacts:
+    reports:
+      coverage_report:
+        coverage_format: cobertura
+        path: coverage.xml
    paths:
-      - coverage_badge.svg
      - htmlcov/
+    expire_in: 30 days
-# test example on python 3.8
+python-3.12:
-python-3.8:
+  <<: *unit-test
-  stage: test
+  needs: ["basic-3.12"]
-  needs: ["basic-3.8", "precommits-py3.8"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python312
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python38
-  script:
-    - python -m pip install .
-    - pytest
-# test example on python 3.9
-python-3.9:
-  stage: test
-  needs: ["basic-3.9", "precommits-py3.9"]
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
-  script:
-    - python -m pip install .
-    - pytest
-# test samplers on python 3.7
-python-3.7-samplers:
-  stage: test
-  needs: ["basic-3.7", "precommits-py3.7"]
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
-  script:
-    - python -m pip install .
-    - pytest test/integration/sampler_run_test.py --durations 10
-# test samplers on python 3.8
-python-3.8-samplers:
-  stage: test
-  needs: ["basic-3.8", "precommits-py3.8"]
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python38
-  script:
-    - python -m pip install .
-    - pytest test/integration/sampler_run_test.py --durations 10
-# test samplers on python 3.9
+.test-sampler: &test-sampler
-python-3.9-samplers:
  stage: test
-  needs: ["basic-3.9", "precommits-py3.9"]
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python39
  script:
-    - python -m pip install .
+    - python -m pip install .[all]
+    - *list-env
-    - pytest test/integration/sampler_run_test.py --durations 10
+    - pytest test/integration/sampler_run_test.py --durations 10 -v
-integration-tests-python-3.7:
+python-3.10-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.10"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+python-3.11-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.11", "precommits-py3.11"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+python-3.12-samplers:
+  <<: *test-sampler
+  needs: ["basic-3.12"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python312
+integration-tests-python-3.11:
  stage: test
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
-  needs: ["basic-3.7", "precommits-py3.7"]
+  needs: ["basic-3.11", "precommits-py3.11"]
  only:
    - schedules
  script:
    - python -m pip install .
+    - *list-env
    # Run tests which are only done on schedule
    - pytest test/integration/example_test.py
-plotting-python-3.7:
+.plotting: &plotting
  stage: test
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
-  needs: ["basic-3.7", "precommits-py3.7"]
  only:
    - schedules
  script:
    - python -m pip install .
-    - python -m pip install ligo.skymap
+    - *list-env
    - pytest test/gw/plot_test.py
+plotting-python-3.10:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
+  needs: ["basic-3.10"]
+plotting-python-3.11:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
+  needs: ["basic-3.11", "precommits-py3.11"]
+plotting-python-3.12:
+  <<: *plotting
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python312
+  needs: ["basic-3.12"]
 # ------------------- Docs stage -------------------------------------------
 docs:
  stage: docs
-  needs: ["python-3.7"]
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python311
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
+  before_script:
+    - python -m ipykernel install
  script:
    # Make the documentation
-    - apt-get -yqq install pandoc
    - python -m pip install .
-    - cd docs
+    - python -m pip install myst_parser # only for testing purposes - remove once test image is generating correctly
-    - pip install ipykernel ipython jupyter
+    - cd examples/tutorials
-    - cp ../examples/tutorials/*.ipynb ./
+    - jupyter nbconvert --to notebook --execute *.ipynb --output-dir ../../docs
-    - rm basic_ptmcmc_tutorial.ipynb
+    - cd ../../docs
-    - rm compare_samplers.ipynb
-    - rm visualising_the_results.ipynb
-    - jupyter nbconvert --to notebook --execute *.ipynb --inplace
    - make clean
    - make html
@@ -234,13 +246,12 @@ docs:
 # ------------------- Deploy stage -------------------------------------------
-deploy-docs:
+pages:
  stage: deploy
-  needs: ["docs", "python-3.7"]
+  needs: ["docs", "python-3.11"]
  script:
    - mkdir public/
    - mv htmlcov/ public/
-    - mv coverage_badge.svg public/
    - mv docs/_build/html/* public/
  artifacts:
    paths:
@@ -249,56 +260,50 @@ deploy-docs:
  only:
    - master
-# Build the containers
+.build-container: &build-container
-build-python37-container:
  stage: deploy
-  image: docker:19.03.12
+  image: docker:20.10.23
  needs: ["containers"]
-  only:
+  rules:
-    - schedules
+    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
+      changes:
+        compare_to: 'refs/heads/main'
+        paths:
+          - containers/*
+      when: manual
+    - if: $CI_PIPELINE_SOURCE == "schedule"
  script:
    - cd containers
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
-    - docker build --tag v3-bilby-python37 - < v3-dockerfile-test-suite-python37
+    - cp v3-dockerfile-test-suite-$PYVERSION Dockerfile
-    - docker image tag v3-bilby-python37 containers.ligo.org/lscsoft/bilby/v2-bilby-python37:latest
+    - docker build --tag v3-bilby-$PYVERSION .
-    - docker image push containers.ligo.org/lscsoft/bilby/v2-bilby-python37:latest
+    - docker image tag v3-bilby-$PYVERSION containers.ligo.org/lscsoft/bilby/v2-bilby-$PYVERSION:latest
+    - docker image push containers.ligo.org/lscsoft/bilby/v2-bilby-$PYVERSION:latest
-build-python38-container:
+build-python310-container:
-  stage: deploy
+  <<: *build-container
-  image: docker:19.03.12
+  variables:
-  needs: ["containers"]
+    PYVERSION: "python310"
-  only:
-    - schedules
-  script:
-    - cd containers
-    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
-    - docker build --tag v3-bilby-python38 - < v3-dockerfile-test-suite-python38
-    - docker image tag v3-bilby-python38 containers.ligo.org/lscsoft/bilby/v2-bilby-python38:latest
-    - docker image push containers.ligo.org/lscsoft/bilby/v2-bilby-python38:latest
-build-python39-container:
+build-python311-container:
-  stage: deploy
+  <<: *build-container
-  image: docker:19.03.12
+  variables:
-  needs: ["containers"]
+    PYVERSION: "python311"
-  only:
-    - schedules
+build-python312-container:
-  script:
+  <<: *build-container
-    - cd containers
+  variables:
-    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
+    PYVERSION: "python312"
-    - docker build --tag v3-bilby-python39 - < v3-dockerfile-test-suite-python39
-    - docker image tag v3-bilby-python39 containers.ligo.org/lscsoft/bilby/v2-bilby-python39:latest
-    - docker image push containers.ligo.org/lscsoft/bilby/v2-bilby-python39:latest
 pypi-release:
  stage: deploy
-  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python37
+  image: containers.ligo.org/lscsoft/bilby/v2-bilby-python310
  variables:
    TWINE_USERNAME: $PYPI_USERNAME
    TWINE_PASSWORD: $PYPI_PASSWORD
  before_script:
-    - pip install twine
+    - python -m build --sdist --wheel --outdir dist/ .
-    - python setup.py sdist
  script:
    - twine upload dist/*
  only:
-  - tags
+    - tags
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,25 +5,38 @@ repos:
    -   id: check-merge-conflict # prevent committing files with merge conflicts
    -   id: flake8 # checks for flake8 errors
 -   repo: https://github.com/psf/black
-    rev: 20.8b1
+    rev: 22.3.0
    hooks:
      - id: black
        language_version: python3
-        files: ^bilby/bilby_mcmc/
+        files: '^(bilby/bilby_mcmc/|bilby/core/sampler/|examples/)'
 -   repo: https://github.com/codespell-project/codespell
-    rev: v1.16.0
+    rev: v2.1.0
    hooks:
    -   id: codespell
        args: [--ignore-words=.dictionary.txt]
-   repo: https://github.com/asottile/seed-isort-config
-    rev: v1.3.0
-    hooks:
-    -   id: seed-isort-config
-        args: [--application-directories, 'bilby/']
-        files: ^bilby/bilby_mcmc/
 -   repo: https://github.com/pre-commit/mirrors-isort
-    rev: v4.3.21
+    rev: v5.10.1
    hooks:
    -   id: isort # sort imports alphabetically and separates import into sections
        args: [-w=88, -m=3, -tc, -sp=setup.cfg ]
-        files: ^bilby/bilby_mcmc/
+        files: '^(bilby/bilby_mcmc/|bilby/core/sampler/|examples/)'
+-   repo: https://github.com/datarootsio/databooks
+    rev: 0.1.14
+    hooks:
+      - id: databooks
+        name: databooks
+        description:
+          "Remove notebook metadata using `databooks`."
+        entry: databooks meta
+        language: python
+        minimum_pre_commit_version: 2.9.2
+        types: [ jupyter ]
+        args: [-w]
+-   repo: local
+    hooks:
+      - id: jupyter-nb-clear-output
+        name: jupyter-nb-clear-output
+        files: \.ipynb$
+        language: system
+        entry: jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace
--- a/AUTHORS.md
+++ b/AUTHORS.md
@@ -9,11 +9,14 @@ Aditya Vijaykumar
 Andrew Kim
 Andrew Miller
 Antoni Ramos-Buades
+Apratim Ganguly
 Avi Vajpeyi
+Ben Patterson
 Bruce Edelman
 Carl-Johan Haster
 Cecilio Garcia-Quiros
 Charlie Hoy
+Chentao Yang
 Christopher Philip Luke Berry
 Christos Karathanasis
 Colm Talbot
@@ -24,18 +27,26 @@ Eric Thrane
 Ethan Payne
 Francisco Javier Hernandez
 Gregory Ashton
+Hank Hua
 Hector Estelles
 Ignacio Magaña Hernandez
+Isaac McMahon
 Isobel Marguarethe Romero-Shaw
+Jack Heinzel
+Jacob Golomb
 Jade Powell
 James A Clark
 Jeremy G Baier
 John Veitch
 Joshua Brandt
+Josh Willis
+Karl Wette
 Katerina Chatziioannou
 Kaylee de Soto
 Khun Sang Phukon
+Kruthi Krishna
 Kshipraa Athar
+Kyle Wong
 Leslie Wade
 Liting Xiao
 Maite Mateu-Lucena
@@ -49,19 +60,24 @@ Michael Puerrer
 Michael Williams
 Monica Rizzo
 Moritz Huebner
+Nico Gerardo Bers
 Nicola De Lillo
 Nikhil Sarin
 Nirban Bose
+Noah Wolfe
 Olivia Wilk
 Paul Easter
 Paul Lasky
 Philip Relton
 Rhys Green
+Rhiannon Udall
 Rico Lo
 Roberto Cotesta
 Rory Smith
 S. H. Oh
 Sacha Husa
+Sama Al-Shammari
+Samson Leong
 Scott Coughlin
 Serguei Ossokine
 Shanika Galaudage
@@ -69,9 +85,21 @@ Sharan Banagiri
 Shichao Wu
 Simon Stevenson
 Soichiro Morisaki
+Soumen Roy
+Stephen R Green
 Sumeet Kulkarni
 Sylvia Biscoveanu
 Tathagata Ghosh
+Teagan Clarke
+Tomasz Baka
+Will M. Farr
 Virginia d'Emilio
 Vivien Raymond
 Ka-Lok Lo
+Isaac Legred
+Marc Penuliar
+Andrew Fowlie
+Martin White
+Peter Tsun-Ho Pang
+Alexandre Sebastien Goettel
+Ann-Kristin Malz
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
 # All notable changes will be documented in this file
-## [1.1.4] 2021-10-08
+## [Unreleased]
+## [2.3.0] - 2024-05-30
+### Added
+- Add support for sampler plugins via entry points (!1340, !1355)
+- Add `bilby.core.sampler.get_implemented_samplers` and `bilby.core.get_sampler_class` (!1340)
+- Add `bilby.core.utils.entry_points.get_entry_points` (!1340)
+- Add support for reading results from PathLike objects (!1342)
+- Add `snrs_as_sample` property to `bilby.gw.likelihood.base.GravitationalWaveTransient` (!1344)
+- Add `get_expected_outputs` method to the sampler classes (!1336)
+### Changed
+- Change `bilby_mcmc` to use `glasflow` instead of `nflows` (!1332)
+- Sampler classes in are no longer imported in `bilby.core.sampler` (!1340)
+- Sampler classes in `bilby.core.sampler.IMPLEMENTED_SAMPLERS` must now be loaded before use (!1340)
+- `bilby.core.sampler.IMPLEMENTED_SAMPLERS` is now an instance of `bilby.core.sampler.ImplementedSampler` instead of a dictionary (!1355)
+- Updates to support numpy v2 (!1362)
+### Fixed
+- Include final frequency point in relative binning integration (!1310)
+- Address various deprecation warnings and deprecated keyword arguments (!1316, !1326, !1343)
+- Fix typo in logging statement in `bilby.gw.source` (!1325)
+- Fix missing import in `bilby.gw.detector.load_data_from_cache_file` (!1327)
+- Fix bug where `linestyle` was ignored in `bilby.core.result.plot_multiple` (!1238)
+- Fix `soft_init` sampler keyword argument with `dynesty` (!1335)
+- Fix ZeroDivisionError when using the `dynesty` with `act-walk` and large values of `nact` (!1346)
+- Fix custom prior loading from result file (!1360)
+## [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
+This is a bugfix release reverting a change from 2.2.1
+### Changes
+- Revert !1284 (!1306)
+## [2.2.1] - 2023-1111
+Version 2.2.1 release of Bilby
+This release is a bugfix release.
+### Changes
+- Ensure inteferometer metadata is not empty (!1281)
+- Make interrupted pools exit more quickly (!1284)
+- Fix conditional sampling with DeltaFunction conditions (!1289)
+- The triangular prior raised an error with numpy (!1294)
+- Make sure strain data resampling works (!1295)
+- Dynesty logging (!1296)
+- A bug with saving lists that contain None (!1301)
+- Preparatory fix an upcoming change in dynesty (!1302)
+## [2.2.0] - 2023-07-24
+Version 2.2.0 release of Bilby
+This release contains one new feature and drops support for Python 3.8.
+### Added
+- New waveform interface to support the SEOBNRv5 family of waveforms (!1218)
+- Enable default noise + injection function for non-CBC signals (!1263)
+- Fallback to result pickle loading to match result writing (!1291)
+### Changes
+- Additional error catching for plotting (!1261, !1271)
+- Improve plotting options for corner plots (!1270)
+- Fix bugs in closing the pool for emcee (!1274)
+- Generalize MPI support (!1278)
+- Fix a bug with saving hdf5 results when conda isn't present (!1290)
+### Deprecated
+- Drop support for py38 (!1277)
+## [2.1.2] - 2023-07-17
+Version 2.1.2 release of Bilby
+This is a bugfix release.
+Note that one of the changes will have a significant impact on scripts that rely on
+a seed for random data generation.
+Where users have previously used `np.random.seed` they should now call
+`bilby.core.utils.random.seed`.
+### Changes
+- Fix issues related to random number generation with multiprocessing (!1273)
+- Enable cosmological priors to be written/read in our plain text format (!1258)
+- Allow posterior reweighting to be performed when changing the likelihood and the prior (!1260)
+## [2.1.1] - 2023-04-28
+Version 2.1.1 release of Bilby
+Bugfix release
+### Changes
+- Fix the matched filter SNR phase for the multiband likelihood (!1253)
+- Bugfix for Fisher matrix proposals in `bilby_mcmc` (!1251)
+- Make the changes to the spline calibration backward compatible, 2.0.2 resume files can't be read with 2.1.0 (!1250)
+## [2.1.0] - 2023-04-12
+Version 2.1.0 release of Bilby
+Minor feature improvements and bug fixes
+### Additions
+- Additional parameterizations for equation-of-state inference (!1083, !1240)
+- Add Fisher matrix posterior estimator (!1242)
+### Changes
+- Improvements to the bilby-mcmc sampler including a Fisher Information Matrix proposal (!1242)
+- Optimize spline interpolation of calibration uncertainties (!1241)
+- Update LIGO India coordinates record to public DCC (!1246)
+- Make logger disabling work in redundancy test (!1245)
+- Make sure nested samples are data frame (!1244)
+- Minor improvements to the result methods including moving to top level imports (!1243)
+- Fix a bug in the slabspike prior (!1235)
+- Reduce verbosity when setting strain data (!1233)
+- Fix issue with cached results class (!1223)
+### Deprecated
+- Reading/writing ROQ weights to json (!1232)
+## [2.0.2] - 2023-03-21
+Version 2.0.2 release of Bilby
+This is a bugfix release after the last major update.
+### Changes
+- Fix to bilby-MCMC implementation of prior boundary (!1237)
+- Fix to time calibration (!1234)
+- Fix nessai sampling time (!1236)
+## [2.0.1] - 2023-03-13
+Version 2.0.1 release of Bilby
+This is a bugfix release after the last major update.
+Users may notice changes in inferred binary neutron star masses after updating to match [lalsuite](https://git.ligo.org/lscsoft/lalsuite/-/merge_requests/1658).
+### Changes
+- Make sure quantities that need to be conserved between dynesty iterations are class-level attributes (!1225).
+- Fix massive memory usage in post-processing calculation of SNRs (!1227).
+- Update value for the solar mass (!1229).
+- Make `scikit-learn` an explicit dependence of `bilby[GW]` (!1230).
+## [2.0.0] - 2023-02-29
+Version 2.0.0 release of Bilby
+This major version release has a significant change to the behaviour of the `dynesty` wrapper.
+There are also a number of bugfixes and some new features in sampling and GW utilities.
+### Added
+- Add marginalized time reconstruction for the ROQ likelihood (!1196)
+- Generate the `dynesty` posterior using rejection sampling by default (!1203)
+- Add optimization for mass ratio prior peaking at equal masses (!1204)
+- Add option to sample over a number of precomputed calibration curves (!1215)
+### Changes
+- Optimize weight calculation for `MultiBandGravitationalWaveTransient` (!1171)
+- Add compatibility with pymc 5 (!1191)
+- A bug fix of the stored prior when using a marginalized likelihood (!1193)
+- Various bug fixes to improve the reliability of the `RelativeBinningGravitationalWaveTransient` (!1198, !1211)
+- A hack fix for samplers that are not compatible with `numpy>1.23` (!1194)
+- Updates to some reference noise curves (!1206, !1207)
+- Fix the broken time+calibration marginalization (!1201)
+- Fix a bug when reading GW frame files (!1202)
+- Fix the normalization of the whitened strain attribute of `Interferometer` (!1205)
+- Optimize ROQ waveform and calibration calls (!1216)
+- Add different proposal distribution and MCMC length for `dynesty` (!1187, !1222)
+## [1.4.1] - 2022-12-07
+Version 1.4.1 release of Bilby
+This is a bugfix release to address some minor issues identified after v1.4.0.
+### Changes
+- Documentation updates (!1181, !1183)
+- Fix some of the examples in the repository (!1182)
+- Make sure conversion to symmetric mass ratio always returns a valid value (!1184)
+- Provide a default nlive for dynamic dynesty (!1185)
+- Enable the relative binning likelihood to be initialized with ra/dec when sampling in a different sky parameterization (!1186)
+- Make sure that all dumping pickle files is done safely (!1189)
+- Make error catching for `dynesty` checkpointing more robust (!1190)
+## [1.4.0] - 2022-11-18
+Version 1.4.0 release of Bilby
+The main changes in this release are support for more recent versions of `dynesty` (!1138)
+and `nessai` (!1161) and adding the
+`RelativeBinningGravitationalWaveTransientLikelihood` (!1105)
+(see [arXiv:1806.08792](https://arxiv.org/abs/1806.08792)) for details.
+### Added
+- Per-detector likelihood calculations (!1149)
+- `bilby.gw.likelihood.relative.RelativeBinningGravitationalWaveTransient` (!1105)
+### Changes
+- Reset the timer for `PyMultiNest` when overwriting an existing checkpoint directory (!1163)
+- Cache the computed the noise log likelihood for the `GravitationalWaveTransient` (!1179)
+- Set the reference chirp mass for the multi banded likelihood from the prior when not specified (!1169)
+- Bugfix in the name of the saved ASD file in `Interferometer.save_data` (!1176)
+- Modify the window length for stationarity tests for `ptemcee` (!1146)
+- Explicit support for `nessai>=0.7.0` (!1161)
+- Allow prior arguments read from a string to be functions (!1144)
+- Support `dynesty>=1.1.0` (!1138)
+## [1.3.0] - 2022-10-23
+Version 1.3.0 release of Bilby
+This release has a major change to a sampler interface, `pymc3` is no longer supported, users should switch to `pymc>=4`.
+This release also adds a new top-level dependency, `bilby-cython`.
+This release also contains various documentation improvements.
+### Added
+- Improved logging of likelihood information when starting sampling (!1148)
+- Switch some geometric calculations to use optimized bilby-cython package (!1053)
+- Directly specify the starting point for `bilby_mcmc` (!1155)
+- Allow a signal to be specified to only be present in a specific `Interferometer` (!1164)
+- Store time domain model function in CBCResult metadata (!1165)
+### Changes
+- Switch from `pymc3` to `pymc` (!1117)
+- Relax equality check for distance marginalization lookup to allow cross-platform use (!1150)
+- Fix to deal with non-checkpointing `bilby_mcmc` analyses (!1151)
+- Allow result objects with different analysis configurations to be combined (!1153)
+- Improve the storing of environment information (!166)
+- Fix issue when specifying distance and redshfit independently (!1154)
+- Fix a bug in the storage of likelihood/prior samples for `bilby_mcmc` (!1156)
+## [1.2.1] - 2022-09-05
+Version 1.2.1 release of Bilby
+This release contains a few bug fixes following 1.2.0.
+### Changes
+- Improve how sampling seed is handled across samplers (!1134)
+- Make sure labels are included when evidences are in corner plot legend (!1135)
+- Remove calls to `getargspec` (!1136)
+- Make sure parameter reconstruction cache is not mangled when reading (!1126)
+- Enable the constant uncertainty calibration spline to have a specifiable boundary condition (!1137)
+- Fix a bug in checkpointing for `bilby_mcmc` (!1141)
+- Fix the `LALCBCWaveformGenerator` (!1140)
+- Switch to automatic versioning with `setuptools_scm` (!1125)
+- Improve the stability of the multivariate normal prior (!1142)
+- Extend mass conversions to include source-frame parameters (!1131)
+- Fix prior ranges for GW150914 example (!1129)
+## [1.2.0] - 2022-08-15
+Version 1.2.0 release of Bilby
+This is the first release that drops support for `Python<3.8`.
+This release involves major refactoring, especially of the sampler implementations.
+Additionally, there are a range of improvements to how information is passed
+with multiprocessing.
+### Added
+- Time marginalized ROQ likelihood (!1040)
+- Multiple and multi-banded ROQ likelihood (!1093)
+- Gaussian process likelihoods (!1086)
+- `CBCWaveformGenerator` added with CBC specific defaults (!1080)
+### Changes
+- Fixes and improvements to multi-processing (!1084, !1043, !1096)
+- Major refactoring of sampler implementations (!1043)
+- Fixes for reading/writing priors (!1103, !1127, !1128)
+- Fixes/updates to exmample scripts (!1050, !1031, !1076, !1081, !1074)
+- Fixes to calibration correction in GW likelihoods (!1114, !1120, !1119)
+### Deprecated/removed
+- Require `Python>=3.8`
+- Require `astropy>=5`
+- `bilby.core.utils.conversion.gps_time_to_gmst`
+- `bilby.core.utils.spherical_to_cartesian`
+- `bilby.core.utils.progress`
+- Deepdish IO for `Result`, `Interferometer`, and `InterferometerList`
+## [1.1.5] - 2022-01-14
+Version 1.1.5 release of Bilby
+### Added
+- Option to enforce that a GW signal fits into the segment duration (!1041)
+- Remove the save `.dat` samples file with `dynesty` (!1028)
+- Catch corrupted `json` result result files being passed (!1034)
+### Changes
+- Fixes to conversion function for `nessai` and `cpnest` (!1055)
+- Workaround for `astropy` v5 (!1054)
+- Various fixes to testing system (!1038, !1044, !1045, !1048)
+- Updated defaults for `nessai` (!1042)
+- Small bug fixes (!1032, !1036, !1039, !1046, !1052)
+- Bug fix in the definition of some standard interferometers (!1037)
+- Improvements to the multi-banded GWT likelihood (!1026)
+- Improve meta data comparison (!1035)
+## [1.1.4] - 2021-10-08
 Version 1.1.4 release of bilby
 ### Added
@@ -20,7 +333,7 @@ Version 1.1.4 release of bilby
 - Typo fix in eart light crossing (!1003)
 - Fix zero spin conversion (!1002)
-## [1.1.3] 2021-07-02
+## [1.1.3] - 2021-07-02
 Version 1.1.3 release of bilby
 ### Added
@@ -50,7 +363,7 @@ Version 1.1.3 release of bilby
 - Restructured utils module into several submodules. API remains backwards compatible (!873)
 - Changed number of default walks in `dynesty` from `10*self.ndim` to `100` (!961)
-## [1.1.2] 2021-05-05
+## [1.1.2] - 2021-05-05
 Version 1.1.2 release of bilby
 ### Added
@@ -80,13 +393,13 @@ Version 1.1.2 release of bilby
 - Fixed issues with pickle saving and loading (!932)
 - Fixed an issue with the `_base_roq_waveform` (!959)
-## [1.1.1] 2021-03-16
+## [1.1.1] - 2021-03-16
 Version 1.1.1 release of bilby
 ### Changes
 - Added `include requirements.txt` in `MANIFEST.in` to stop the pip installation from breaking
-## [1.1.0] 2021-03-15
+## [1.1.0] - 2021-03-15
 Version 1.1.0 release of bilby
 ### Added
@@ -125,7 +438,7 @@ Version 1.1.0 release of bilby
 - Fixed the likelihood count in `dynesty` (!853)
 - Changed the ordering of keyword arguments for the `Sine` and `Cosine` constructors (!892)
-## [1.0.4] 2020-11-23
+## [1.0.4] - 2020-11-23
 Version 1.0.4 release of bilby
 ### Added
@@ -134,7 +447,7 @@ Version 1.0.4 release of bilby
 ### Changes
 - Fixed issue in the CI
-## [1.0.3] 2020-10-23
+## [1.0.3] - 2020-10-23
 Version 1.0.3 release of bilby
@@ -153,7 +466,7 @@ Version 1.0.3 release of bilby
 - Typo fixes (!878, !887, !879)
 - Minor bug fixes (!888)
-## [1.0.2] 2020-09-14
+## [1.0.2] - 2020-09-14
 Version 1.0.2 release of bilby
@@ -175,7 +488,7 @@ Version 1.0.2 release of bilby
 - Clean up of code (!854)
 - Various minor bug, test and plotting fixes (!859, !874, !872, !865)
-## [1.0.1] 2020-08-29
+## [1.0.1] - 2020-08-29
 Version 1.0.1 release of bilby
@@ -200,7 +513,7 @@ Version 1.0.1 release of bilby
 - Various minor bug fixes and improvements to the documentation (!820)(!823)(!837)
 - Various testing improvements (!833)(!847)(!855)(!852)
-## [1.0.0] 2020-07-06
+## [1.0.0] - 2020-07-06
 Version 1.0 release of bilby
@@ -751,3 +1064,33 @@ First `pip` installable version https://pypi.org/project/BILBY/ .
 ### Removed
 - All chainconsumer dependency as this was causing issues.
+[Unreleased]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.3.0...master
+[2.3.0]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.2.3...v2.3.0
+[2.2.3]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.2.2...v2.2.3
+[2.2.2]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.2.1...v2.2.2
+[2.2.1]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.2.0...v2.2.1
+[2.2.0]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.1.2...v2.2.0
+[2.1.2]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.1.1...v2.1.2
+[2.1.1]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.1.0...v2.1.1
+[2.1.0]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.0.2...v2.1.0
+[2.0.2]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.0.1...v2.0.2
+[2.0.1]: https://git.ligo.org/lscsoft/bilby/-/compare/v2.0.0...v2.0.1
+[2.0.0]: https://git.ligo.org/lscsoft/bilby/-/compare/v1.4.1...v2.0.0
+[1.4.1]: https://git.ligo.org/lscsoft/bilby/-/compare/v1.4.0...v1.4.1
+[1.4.0]: https://git.ligo.org/lscsoft/bilby/-/compare/1.3.0...v1.4.0
+[1.3.0]: https://git.ligo.org/lscsoft/bilby/-/compare/1.2.1...1.3.0
+[1.2.1]: https://git.ligo.org/lscsoft/bilby/-/compare/1.2.0...1.2.1
+[1.2.0]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.5...1.2.0
+[1.1.5]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.4...1.1.5
+[1.1.4]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.2...1.1.4
+[1.1.3]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.2...1.1.3
+[1.1.2]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.1...1.1.2
+[1.1.1]: https://git.ligo.org/lscsoft/bilby/-/compare/1.1.0...1.1.1
+[1.1.0]: https://git.ligo.org/lscsoft/bilby/-/compare/1.0.4...1.1.0
+[1.0.4]: https://git.ligo.org/lscsoft/bilby/-/compare/1.0.3...1.0.4
+[1.0.3]: https://git.ligo.org/lscsoft/bilby/-/compare/1.0.2...1.0.3
+[1.0.2]: https://git.ligo.org/lscsoft/bilby/-/compare/1.0.1...1.0.2
+[1.0.1]: https://git.ligo.org/lscsoft/bilby/-/compare/1.0.0...1.0.1
+[1.0.0]: https://git.ligo.org/lscsoft/bilby/-/compare/0.6.9...1.0.0
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
 # Contributing to bilby
-This is a short guide to contributing to bilby aimed at general LVC members who
+This is a short guide to contributing to bilby aimed at general LVK members who
 have some familiarity with python and git.  
 1. [Code of conduct](#code-of-conduct)
 2. [Code style](#code-style)
-3. [Code relevance](#code-relevance)
+3. [Automated Code Checking](#automated-code-checking)
-4. [Merge requests](#merge-requests)
+4. [Unit Testing](#unit-testing)
-5. [Typical workflow](#typical-workflow)
+5. [Code relevance](#code-relevance)
-6. [Hints and tips](#hints-and-tips)
+6. [Merge requests](#merge-requests)
-7. [Code overview](#code-overview)
+7. [Typical workflow](#typical-workflow)
+8. [Hints and tips](#hints-and-tips)
+9. [Code overview](#code-overview)
 ## Code of Conduct
@@ -17,7 +19,7 @@ have some familiarity with python and git.
 Everyone participating in the bilby community, and in particular in our issue
 tracker, merge requests, and chat channels, is expected to treat other people
 with respect and follow the guidelines articulated in the [Python Community
-Code of Conduct](https://www.python.org/psf/codeofconduct/).
+Code of Conduct](https://www.python.org/psf/codeofconduct/). Furthermore, members of the LVK collaboration must follow the [LVK Code of Conduct](https://dcc.ligo.org/LIGO-M1900037/public).
 ## Code style
@@ -46,8 +48,8 @@ def my_new_function(x, y, print=False):
 ```
 3. Avoid inline comments unless necessary. Ideally, the code should make it obvious what is going on, if not the docstring, only in subtle cases use comments
 4. Name variables sensibly. Avoid using single-letter variables, it is better to name something `power_spectral_density_array` than `psda`.
-5. Don't repeat yourself. If code is repeated in multiple places, wrap it up into a function.
+5. Don't repeat yourself. If code is repeated in multiple places, wrap it up into a function. This also helps with the writing of robust unit tests (see below).
-6. Add tests. The C.I. is there to do the work of "checking" the code, both now and into the future. Use it.
 ## Automated code checking
@@ -76,6 +78,50 @@ If you experience any issues with pre-commit, please ask for support on the
 usual help channels.
+## Unit Testing
+Unit tests are an important part of code development, helping to minimize the number of undetected bugs which may be present in a merge request. They also greatly expedite the review of code, and can even help during the initial development if used properly. Accordingly, bilby requires unit testing for any changes with machine readable inputs and outputs (i.e. pretty much everything except plotting). 
+Unit testing is integrated into the CI/CD pipeline, and uses the builtin unittest package. Tests should be written into the `test/` directory which corresponds to their location within the package, such that, for example, a change to `bilby/gw/conversion.py` should go into `test/gw/conversion_test.py`. To run a single test locally, one may simply do `pytest /path/to/test TestClass.test_name`, whereas to run all the tests in a given test file one may omit the class and function.
+For an example of what a test looks like, consider this test for the fft utils in bilby:
+```
+class TestFFT(unittest.TestCase):
+    def setUp(self):
+        self.sampling_frequency = 10
+    def tearDown(self):
+        del self.sampling_frequency
+    def test_nfft_sine_function(self):
+        injected_frequency = 2.7324
+        duration = 100
+        times = utils.create_time_series(self.sampling_frequency, duration)
+        time_domain_strain = np.sin(2 * np.pi * times * injected_frequency + 0.4)
+        frequency_domain_strain, frequencies = bilby.core.utils.nfft(
+            time_domain_strain, self.sampling_frequency
+        )
+        frequency_at_peak = frequencies[np.argmax(np.abs(frequency_domain_strain))]
+        self.assertAlmostEqual(injected_frequency, frequency_at_peak, places=1)
+    def test_nfft_infft(self):
+        time_domain_strain = np.random.normal(0, 1, 10)
+        frequency_domain_strain, _ = bilby.core.utils.nfft(
+            time_domain_strain, self.sampling_frequency
+        )
+        new_time_domain_strain = bilby.core.utils.infft(
+            frequency_domain_strain, self.sampling_frequency
+        )
+        self.assertTrue(np.allclose(time_domain_strain, new_time_domain_strain))
+```
+`setUp` and `tearDown` handle construction and deconstruction of the test, such that each of the other test functions may be run independently, in any order. The other two functions each make an intuitive test of the functionality of and fft/ifft function: that the fft of a sine wave should be a delta function, and that an ifft should be an inverse of an fft. 
+For more information on how to write effective tests, see [this guide](https://docs.python-guide.org/writing/tests/), and many others.
 ## Code relevance
 The bilby code base is intended to be highly modular and flexible. We encourage
@@ -153,7 +199,19 @@ $ git clone git@git.ligo.org:albert.einstein/bilby.git
 ```
 replacing the SSH url to that of your fork. This will create a directory
-`/bilby` containing a local copy of the code. From this directory, you can run
+`/bilby` containing a local copy of the code.
+It is strongly advised to perform development with a dedicated conda environment.
+In depth instructions for creating a conda environment may be found at the relevant
+[conda docs](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment),
+but for most purposes the commands
+```bash
+$ conda create -n my-environment-name python=3.X
+$ conda activate my-environment-name
+```
+will produce the desired results. Once this is completed, one may proceed to the `/bilby` directory and run
 ```bash
 $ pip install -e .
@@ -202,8 +260,25 @@ $ git checkout -b my-new-feature lscsoft/master
 ### Step d) Hack away
 1. Develop the changes you would like to introduce, using `git add` to add files with changes. Ideally commit small units of change often, rather than creating one large commit at the end, this will simplify review and make modifying any changes easier.
-2. Commit the changes using `git commit`. This will prompt you for a commit message. Commit messages should be clear, identifying which code was changed, and why. Common practice (see e.g. [this blog](https://chris.beams.io/posts/git-commit/)) is to use a short summary line (<50 characters), followed by a blank line, then more information in longer lines.
+2. Commit the changes using `git commit`. This will prompt you for a commit message. Commit messages should be clear, identifying which code was changed, and why. Bilby is adopting the use of scipy commit format, specified [here](https://docs.scipy.org/doc/scipy/dev/contributor/development_workflow.html#writing-the-commit-message). Commit messages take a standard format of `ACRONYM: Summary message` followed by a more detailed description. For example, an enhancement would look like:
-3. Push your changes to the remote copy of your fork on git.ligo.org
+```
+ENH: Add my awesome new feature
+This is a very cool change that makes parameter estimation run 10x faster by changing a single line.
+```
+Similarly a bugfix:
+```
+BUG: Fix type error in my_awesome_feature.py
+Correct a typo at L1 of /bilby/my_awesome_feature.py which returned a dictionary instead of a string.
+```
+For more discussion of best practices, see e.g. [this blog](https://chris.beams.io/posts/git-commit/).
+4. Push your changes to the remote copy of your fork on git.ligo.org
 ```bash
 git push origin my-new-feature

--- a/MANIFEST.in
+++ b/MANIFEST.in
 include README.rst
 include LICENSE.md
 include requirements.txt
+include gw_requirements.txt
+include mcmc_requirements.txt
+include optional_requirements.txt
+include sampler_requirements.txt
+include bilby/_version.py
 recursive-include test *.py *.prior
--- a/README.rst
+++ b/README.rst
 |pipeline status| |coverage report| |pypi| |conda| |version|
 =====
-Bilby
+Bilby development has moved to `GitHub <https://github.com/bilby-dev/bilby>`__!
 =====
+Please open any new issues or pull requests there. A full migration guide will be provided soon. Links below here may no longer be active.
+====
 A user-friendly Bayesian inference library.
 Fulfilling all your Bayesian dreams.
@@ -30,50 +32,11 @@ us directly. For advice on contributing, see `the contributing guide <https://gi
 Citation guide
 --------------
-If you use :code:`bilby` in a scientific publication, please cite
+Please refer to the `Acknowledging/citing bilby guide <https://lscsoft.docs.ligo.org/bilby/citing-bilby.html>`__.
-* `Bilby: A user-friendly Bayesian inference library for gravitational-wave
-  astronomy
-  <https://ui.adsabs.harvard.edu/#abs/2018arXiv181102042A/abstract>`__
-* `Bayesian inference for compact binary coalescences with BILBY: validation and application to the first LIGO-Virgo gravitational-wave transient catalogue <https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.3295R/abstract>`__
-The first of these papers introduces the software, while the second introduces advances in the sampling approaches and validation of the software.
-If you use the :code:`bilby_mcmc` sampler, please additionally cite
-* `BILBY-MCMC: an MCMC sampler for gravitational-wave inference <https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.2037A/abstract>`__
-Additionally, :code:`bilby` builds on a number of open-source packages. If you
-make use of this functionality in your publications, we recommend you cite them
-as requested in their associated documentation.
-**Samplers**
-* `dynesty <https://github.com/joshspeagle/dynesty>`__
-* `nestle <https://github.com/kbarbary/nestle>`__
-* `pymultinest <https://github.com/JohannesBuchner/PyMultiNest>`__
-* `cpnest <https://github.com/johnveitch/cpnest>`__
-* `emcee <https://github.com/dfm/emcee>`__
-* `ptemcee <https://github.com/willvousden/ptemcee>`__
-* `ptmcmcsampler <https://github.com/jellis18/PTMCMCSampler>`__
-* `pypolychord <https://github.com/PolyChord/PolyChordLite>`__
-* `PyMC3 <https://github.com/pymc-devs/pymc3>`_
-**Gravitational-wave tools**
-* `gwpy <https://github.com/gwpy/gwpy>`__
-* `lalsuite <https://git.ligo.org/lscsoft/lalsuite>`__
-* `astropy <https://github.com/astropy/astropy>`__
-**Plotting**
-* `corner <https://github.com/dfm/corner.py>`__ for generating corner plot
-* `matplotlib <https://github.com/matplotlib/matplotlib>`__ for general plotting routines
 .. |pipeline status| image:: https://git.ligo.org/lscsoft/bilby/badges/master/pipeline.svg
   :target: https://git.ligo.org/lscsoft/bilby/commits/master
-.. |coverage report| image:: https://lscsoft.docs.ligo.org/bilby/coverage_badge.svg
+.. |coverage report| image:: https://git.ligo.org/lscsoft/bilby/badges/master/coverage.svg
   :target: https://lscsoft.docs.ligo.org/bilby/htmlcov/
 .. |pypi| image:: https://badge.fury.io/py/bilby.svg
   :target: https://pypi.org/project/bilby/

--- a/bilby/__init__.py
+++ b/bilby/__init__.py
@@ -23,8 +23,12 @@ from . import core, gw, hyper
 from .core import utils, likelihood, prior, result, sampler
 from .core.sampler import run_sampler
 from .core.likelihood import Likelihood
+from .core.result import read_in_result, read_in_result_list
-__version__ = utils.get_version_information()
+try:
+    from ._version import version as __version__
+except ModuleNotFoundError:  # development mode
+    __version__ = 'unknown'
 if sys.version_info < (3,):

--- a/bilby/bilby_mcmc/chain.py
+++ b/bilby/bilby_mcmc/chain.py
-from distutils.version import LooseVersion
 import numpy as np
 import pandas as pd
+from packaging import version
 from ..core.sampler.base_sampler import SamplerError
 from ..core.utils import logger
@@ -137,12 +136,14 @@ class Chain(object):
    @property
    def _random_idx(self):
+        from ..core.utils.random import rng
        mindex = self._last_minimum_index[1]
        # Check if mindex exceeds current position by 10 ACT: if so use a random sample
        # otherwise we draw only from the chain past the minimum_index
        if np.isinf(self.tau_last) or self.position - mindex < 10 * self.tau_last:
            mindex = 0
-        return np.random.randint(mindex, self.position + 1)
+        return rng.integers(mindex, self.position + 1)
    @property
    def random_sample(self):
@@ -158,7 +159,7 @@ class Chain(object):
    @property
    def minimum_index(self):
-        """This calculated a minimum index from which to discard samples
+        """This calculates a minimum index from which to discard samples
        A number of methods are provided for the calculation. A subset are
        switched off (by `if False` statements) for future development
@@ -253,7 +254,7 @@ class Chain(object):
    @property
    def tau(self):
-        """ The maximum ACT over all parameters """
+        """The maximum ACT over all parameters"""
        if self.position in self.max_tau_dict:
            # If we have the ACT at the current position, return it
@@ -272,7 +273,7 @@ class Chain(object):
    @property
    def tau_nocache(self):
-        """ Calculate tau forcing a recalculation (no cached tau) """
+        """Calculate tau forcing a recalculation (no cached tau)"""
        tau = max(self.tau_dict.values())
        self.max_tau_dict[self.position] = tau
        self.cached_tau_count = 0
@@ -280,7 +281,7 @@ class Chain(object):
    @property
    def tau_last(self):
-        """ Return the last-calculated tau if it exists, else inf """
+        """Return the last-calculated tau if it exists, else inf"""
        if len(self.max_tau_dict) > 0:
            return list(self.max_tau_dict.values())[-1]
        else:
@@ -288,7 +289,7 @@ class Chain(object):
    @property
    def _tau_for_full_chain(self):
-        """ The maximum ACT over all parameters """
+        """The maximum ACT over all parameters"""
        return max(self._tau_dict_for_full_chain.values())
    @property
@@ -297,11 +298,11 @@ class Chain(object):
    @property
    def tau_dict(self):
-        """ Calculate a dictionary of tau (ACT) for every parameter """
+        """Calculate a dictionary of tau (ACT) for every parameter"""
        return self._calculate_tau_dict(self.minimum_index)
    def _calculate_tau_dict(self, minimum_index):
-        """ Calculate a dictionary of tau (ACT) for every parameter """
+        """Calculate a dictionary of tau (ACT) for every parameter"""
        logger.debug(f"Calculating tau_dict {self}")
        # If there are too few samples to calculate tau
@@ -343,7 +344,12 @@ class Chain(object):
    @property
    def nsamples(self):
        nuseable_steps = self.position - self.minimum_index
-        return int(nuseable_steps / (self.thin_by_nact * self.tau))
+        n_independent_samples = nuseable_steps / self.tau
+        nsamples = int(n_independent_samples / self.thin_by_nact)
+        if nuseable_steps >= nsamples:
+            return nsamples
+        else:
+            return 0
    @property
    def nsamples_last(self):
@@ -408,12 +414,20 @@ class Chain(object):
        for ax, key in zip(axes[:, 1], self.keys):
            if all_samples is not None:
                yy_all = all_samples[key]
-                ax.hist(yy_all, bins=50, alpha=0.6, density=True, color="k")
+                if np.any(np.isinf(yy_all)):
+                    logger.warning(
+                        f"Could not plot histogram for parameter {key} due to infinite values"
+                    )
+                else:
+                    ax.hist(yy_all, bins=50, alpha=0.6, density=True, color="k")
            yy = self.get_1d_array(key)[nburn : self.position : self.thin]
-            ax.hist(yy, bins=50, alpha=0.8, density=True)
+            if np.any(np.isinf(yy)):
+                logger.warning(
-            ax.set_xlabel(self._get_plot_label_by_key(key, priors))
+                    f"Could not plot histogram for parameter {key} due to infinite values"
+                )
+            else:
+                ax.hist(yy, bins=50, alpha=0.8, density=True)
+                ax.set_xlabel(self._get_plot_label_by_key(key, priors))
        # Add x-axes labels to the traceplots
        axes[-1, 0].set_xlabel(r"Iteration $[\times 10^{3}]$")
@@ -512,7 +526,7 @@ class Sample(object):
 def calculate_tau(x, autocorr_c=5):
    import emcee
-    if LooseVersion(emcee.__version__) < LooseVersion("3"):
+    if version.parse(emcee.__version__) < version.parse("3"):
        raise SamplerError("bilby-mcmc requires emcee > 3.0 for autocorr analysis")
    if np.all(np.diff(x) == 0):

--- a/bilby/bilby_mcmc/flows.py
+++ b/bilby/bilby_mcmc/flows.py
 import torch
-from nflows.distributions.normal import StandardNormal
+from glasflow.nflows.distributions.normal import StandardNormal
-from nflows.flows.base import Flow
+from glasflow.nflows.flows.base import Flow
-from nflows.nn import nets as nets
+from glasflow.nflows.nn import nets as nets
-from nflows.transforms import (
+from glasflow.nflows.transforms import (
    CompositeTransform,
    MaskedAffineAutoregressiveTransform,
    RandomPermutation,
 )
-from nflows.transforms.coupling import (
+from glasflow.nflows.transforms.coupling import (
    AdditiveCouplingTransform,
    AffineCouplingTransform,
 )
-from nflows.transforms.normalization import BatchNorm
+from glasflow.nflows.transforms.normalization import BatchNorm
 from torch.nn import functional as F
 # Turn off parallelism

--- a/bilby/bilby_mcmc/proposals.py
+++ b/bilby/bilby_mcmc/proposals.py
@@ -6,9 +6,10 @@ import numpy as np
 from scipy.spatial.distance import jensenshannon
 from scipy.stats import gaussian_kde
+from ..core.fisher import FisherMatrixPosteriorEstimator
 from ..core.prior import PriorDict
 from ..core.sampler.base_sampler import SamplerError
-from ..core.utils import logger, reflect
+from ..core.utils import logger, random, reflect
 from ..gw.source import PARAMETER_SETS
@@ -18,7 +19,7 @@ class ProposalCycle(object):
        self.weights = [prop.weight for prop in self.proposal_list]
        self.normalized_weights = [w / sum(self.weights) for w in self.weights]
        self.weighted_proposal_list = [
-            np.random.choice(self.proposal_list, p=self.normalized_weights)
+            random.rng.choice(self.proposal_list, p=self.normalized_weights)
            for _ in range(10 * int(1 / min(self.normalized_weights)))
        ]
        self.nproposals = len(self.weighted_proposal_list)
@@ -61,6 +62,9 @@ class BaseProposal(object):
            self.parameters = [p for p in self.parameters if p in subset]
            self._str_attrs.append("parameters")
+        if len(self.parameters) == 0:
+            raise ValueError("Proposal requested with zero parameters")
        self.ndim = len(self.parameters)
        self.prior_boundary_dict = {key: priors[key].boundary for key in priors}
@@ -129,9 +133,16 @@ class BaseProposal(object):
        val_normalised_reflected = reflect(np.array(val_normalised))
        return minimum + width * val_normalised_reflected
-    def __call__(self, chain):
+    def __call__(self, chain, likelihood=None, priors=None):
-        sample, log_factor = self.propose(chain)
-        sample = self.apply_boundaries(sample)
+        if getattr(self, "needs_likelihood_and_priors", False):
+            sample, log_factor = self.propose(chain, likelihood, priors)
+        else:
+            sample, log_factor = self.propose(chain)
+        if log_factor == 0:
+            sample = self.apply_boundaries(sample)
        return sample, log_factor
    @abstractmethod
@@ -208,7 +219,7 @@ class FixedGaussianProposal(BaseProposal):
        sample = chain.current_sample
        for key in self.parameters:
            sigma = self.prior_width_dict[key] * self.sigmas[key]
-            sample[key] += sigma * np.random.randn()
+            sample[key] += sigma * random.rng.normal(0, 1)
        log_factor = 0
        return sample, log_factor
@@ -245,15 +256,15 @@ class AdaptiveGaussianProposal(BaseProposal):
    def propose(self, chain):
        sample = chain.current_sample
        self.update_scale(chain)
-        if np.random.random() < 1e-3:
+        if random.rng.uniform(0, 1) < 1e-3:
            factor = 1e1
-        elif np.random.random() < 1e-4:
+        elif random.rng.uniform(0, 1) < 1e-4:
            factor = 1e2
        else:
            factor = 1
        for key in self.parameters:
            sigma = factor * self.scale * self.prior_width_dict[key] * self.sigmas[key]
-            sample[key] += sigma * np.random.randn()
+            sample[key] += sigma * random.rng.normal(0, 1)
        log_factor = 0
        return sample, log_factor
@@ -295,13 +306,13 @@ class DifferentialEvolutionProposal(BaseProposal):
        theta = chain.current_sample
        theta1 = chain.random_sample
        theta2 = chain.random_sample
-        if np.random.rand() > self.mode_hopping_frac:
+        if random.rng.uniform(0, 1) > self.mode_hopping_frac:
            gamma = 1
        else:
            # Base jump size
-            gamma = np.random.normal(0, 2.38 / np.sqrt(2 * self.ndim))
+            gamma = random.rng.normal(0, 2.38 / np.sqrt(2 * self.ndim))
            # Scale uniformly in log between 0.1 and 10 times
-            gamma *= np.exp(np.log(0.1) + np.log(100.0) * np.random.rand())
+            gamma *= np.exp(np.log(0.1) + np.log(100.0) * random.rng.uniform(0, 1))
        for key in self.parameters:
            theta[key] += gamma * (theta2[key] - theta1[key])
@@ -336,7 +347,7 @@ class UniformProposal(BaseProposal):
        for key in self.parameters:
            width = self.prior_width_dict[key]
            if np.isinf(width) is False:
-                sample[key] = np.random.uniform(
+                sample[key] = random.rng.uniform(
                    self.prior_minimum_dict[key], self.prior_maximum_dict[key]
                )
            else:
@@ -458,7 +469,7 @@ class DensityEstimateProposal(BaseProposal):
        # Print a log message
        took = time.time() - start
-        logger.info(
+        logger.debug(
            f"{self.density_name} construction at {self.steps_since_refit} finished"
            f" for length {chain.position} chain, took {took:0.2f}s."
            f" Current accept-ratio={self.acceptance_ratio:0.2f}"
@@ -479,7 +490,7 @@ class DensityEstimateProposal(BaseProposal):
                fail_parameters.append(key)
        if len(fail_parameters) > 0:
-            logger.info(
+            logger.debug(
                f"{self.density_name} construction failed verification and is discarded"
            )
            self.density = current_density
@@ -492,7 +503,10 @@ class DensityEstimateProposal(BaseProposal):
        # Check if we refit
        testA = self.steps_since_refit >= self.next_refit_time
        if testA:
-            self.refit(chain)
+            try:
+                self.refit(chain)
+            except Exception as e:
+                logger.warning(f"Failed to refit chain due to error {e}")
        # If KDE is yet to be fitted, use the fallback
        if self.trained is False:
@@ -547,6 +561,7 @@ 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 +608,15 @@ class NormalizingFlowProposal(DensityEstimateProposal):
            fallback=fallback,
            scale_fits=scale_fits,
        )
-        self.setup_flow()
+        self.initialised = False
-        self.setup_optimizer()
        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()
@@ -651,7 +669,7 @@ class NormalizingFlowProposal(DensityEstimateProposal):
        return np.power(max_js, 2)
    def train(self, chain):
-        logger.info("Starting NF training")
+        logger.debug("Starting NF training")
        import torch
@@ -682,14 +700,14 @@ class NormalizingFlowProposal(DensityEstimateProposal):
                    validation_samples, training_samples_draw
                )
                if max_js_bits < max_js_threshold:
-                    logger.info(
+                    logger.debug(
                        f"Training complete after {epoch} steps, "
                        f"max_js_bits={max_js_bits:0.5f}<{max_js_threshold}"
                    )
                    break
        took = time.time() - start
-        logger.info(
+        logger.debug(
            f"Flow training step ({self.steps_since_refit}) finished"
            f" for length {chain.position} chain, took {took:0.2f}s."
            f" Current accept-ratio={self.acceptance_ratio:0.2f}"
@@ -699,6 +717,9 @@ class NormalizingFlowProposal(DensityEstimateProposal):
        self.trained = True
    def propose(self, chain):
+        if self.initialised is False:
+            self.initialise()
        import torch
        self.steps_since_refit += 1
@@ -707,7 +728,10 @@ class NormalizingFlowProposal(DensityEstimateProposal):
        # Check if we retrain the NF
        testA = self.steps_since_refit >= self.next_refit_time
        if testA:
-            self.train(chain)
+            try:
+                self.train(chain)
+            except Exception as e:
+                logger.warning(f"Failed to retrain chain due to error {e}")
        if self.trained is False:
            return self.fallback.propose(chain)
@@ -728,11 +752,12 @@ class NormalizingFlowProposal(DensityEstimateProposal):
        return theta, float(log_factor)
+    @staticmethod
    def check_dependencies(warn=True):
-        if importlib.util.find_spec("nflows") is None:
+        if importlib.util.find_spec("glasflow") is None:
            if warn:
                logger.warning(
-                    "Unable to utilise NormalizingFlowProposal as nflows is not installed"
+                    "Unable to utilise NormalizingFlowProposal as glasflow is not installed"
                )
            return False
        else:
@@ -753,14 +778,72 @@ class FixedJumpProposal(BaseProposal):
    def propose(self, chain):
        sample = chain.current_sample
        for key, jump in self.jumps.items():
-            sign = np.random.randint(2) * 2 - 1
+            sign = random.rng.integers(2) * 2 - 1
            sample[key] += sign * jump + self.epsilon * self.prior_width_dict[key]
        log_factor = 0
        return sample, log_factor
    @property
    def epsilon(self):
-        return self.scale * np.random.normal()
+        return self.scale * random.rng.normal()
+class FisherMatrixProposal(AdaptiveGaussianProposal):
+    needs_likelihood_and_priors = True
+    """Fisher Matrix Proposals
+    Uses a finite differencing approach motivated by BayesWave (see, e.g.
+    https://arxiv.org/abs/1410.3835). The inverse Fisher Information Matrix
+    is calculated from the current sample, then proposals are drawn from a
+    multivariate Gaussian and scaled by an adaptive parameter.
+    """
+    def __init__(
+        self,
+        priors,
+        subset=None,
+        weight=1,
+        update_interval=100,
+        scale_init=1e0,
+        fd_eps=1e-4,
+        adapt=False,
+    ):
+        super(FisherMatrixProposal, self).__init__(
+            priors, weight, subset, scale_init=scale_init
+        )
+        self.update_interval = update_interval
+        self.steps_since_update = update_interval
+        self.adapt = adapt
+        self.mean = np.zeros(len(self.parameters))
+        self.fd_eps = fd_eps
+    def propose(self, chain, likelihood, priors):
+        sample = chain.current_sample
+        if self.adapt:
+            self.update_scale(chain)
+        if self.steps_since_update >= self.update_interval:
+            fmp = FisherMatrixPosteriorEstimator(
+                likelihood, priors, parameters=self.parameters, fd_eps=self.fd_eps
+            )
+            try:
+                self.iFIM = fmp.calculate_iFIM(sample.dict)
+            except (RuntimeError, ValueError, np.linalg.LinAlgError) as e:
+                logger.warning(f"FisherMatrixProposal failed with {e}")
+                if hasattr(self, "iFIM") is False:
+                    # No past iFIM exists, return sample
+                    return sample, 0
+            self.steps_since_update = 0
+        jump = self.scale * random.rng.multivariate_normal(
+            self.mean, self.iFIM, check_valid="ignore"
+        )
+        for key, val in zip(self.parameters, jump):
+            sample[key] += val
+        log_factor = 0
+        self.steps_since_update += 1
+        return sample, log_factor
 class BaseGravitationalWaveTransientProposal(BaseProposal):
@@ -814,11 +897,11 @@ class CorrelatedPolarisationPhaseJump(BaseGravitationalWaveTransientProposal):
        alpha = sample["psi"] + phase
        beta = sample["psi"] - phase
-        draw = np.random.random()
+        draw = random.rng.random()
        if draw < 0.5:
-            alpha = 3.0 * np.pi * np.random.random()
+            alpha = 3.0 * np.pi * random.rng.random()
        else:
-            beta = 3.0 * np.pi * np.random.random() - 2 * np.pi
+            beta = 3.0 * np.pi * random.rng.random() - 2 * np.pi
        # Update
        sample["psi"] = (alpha + beta) * 0.5
@@ -853,7 +936,7 @@ class PhaseReversalProposal(BaseGravitationalWaveTransientProposal):
    @property
    def epsilon(self):
        if self.fuzz:
-            return np.random.normal(0, self.fuzz_sigma)
+            return random.rng.normal(0, self.fuzz_sigma)
        else:
            return 0
@@ -918,7 +1001,7 @@ class StretchProposal(BaseProposal):
 def _stretch_move(sample, complement, scale, ndim, parameters):
    # Draw z
-    u = np.random.rand()
+    u = random.rng.uniform(0, 1)
    z = (u * (scale - 1) + 1) ** 2 / scale
    log_factor = (ndim - 1) * np.log(z)
@@ -930,14 +1013,15 @@ def _stretch_move(sample, complement, scale, ndim, parameters):
 class EnsembleProposal(BaseProposal):
-    """ Base EnsembleProposal class for ensemble-based swap proposals """
+    """Base EnsembleProposal class for ensemble-based swap proposals"""
    def __init__(self, priors, weight=1):
        super(EnsembleProposal, self).__init__(priors, weight)
    def __call__(self, chain, chain_complement):
        sample, log_factor = self.propose(chain, chain_complement)
-        sample = self.apply_boundaries(sample)
+        if log_factor == 0:
+            sample = self.apply_boundaries(sample)
        return sample, log_factor
@@ -961,7 +1045,7 @@ class EnsembleStretch(EnsembleProposal):
    def propose(self, chain, chain_complement):
        sample = chain.current_sample
        completement = chain_complement[
-            np.random.randint(len(chain_complement))
+            random.rng.integers(len(chain_complement))
        ].current_sample
        return _stretch_move(
            sample, completement, self.scale, self.ndim, self.parameters
@@ -975,7 +1059,7 @@ def get_default_ensemble_proposal_cycle(priors):
 def get_proposal_cycle(string, priors, L1steps=1, warn=True):
    big_weight = 10
    small_weight = 5
-    tiny_weight = 0.1
+    tiny_weight = 0.5
    if "gwA" in string:
        # Parameters for learning proposals
@@ -983,9 +1067,12 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
            first_fit=1000, nsamples_for_density=10000, fit_multiplier=2
        )
+        all_but_cal = [key for key in priors if "recalib" not in key]
        plist = [
-            AdaptiveGaussianProposal(priors, weight=small_weight),
+            AdaptiveGaussianProposal(priors, weight=small_weight, subset=all_but_cal),
-            DifferentialEvolutionProposal(priors, weight=small_weight),
+            DifferentialEvolutionProposal(
+                priors, weight=small_weight, subset=all_but_cal
+            ),
        ]
        if GMMProposal.check_dependencies(warn=warn) is False:
@@ -996,15 +1083,15 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
        if priors.intrinsic:
            intrinsic = PARAMETER_SETS["intrinsic"]
            plist += [
-                AdaptiveGaussianProposal(priors, weight=big_weight, subset=intrinsic),
+                AdaptiveGaussianProposal(priors, weight=small_weight, subset=intrinsic),
                DifferentialEvolutionProposal(
-                    priors, weight=big_weight, subset=intrinsic
+                    priors, weight=small_weight, subset=intrinsic
                ),
                KDEProposal(
-                    priors, weight=big_weight, subset=intrinsic, **learning_kwargs
+                    priors, weight=small_weight, subset=intrinsic, **learning_kwargs
                ),
                GMMProposal(
-                    priors, weight=big_weight, subset=intrinsic, **learning_kwargs
+                    priors, weight=small_weight, subset=intrinsic, **learning_kwargs
                ),
            ]
@@ -1013,13 +1100,13 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
            plist += [
                AdaptiveGaussianProposal(priors, weight=small_weight, subset=extrinsic),
                DifferentialEvolutionProposal(
-                    priors, weight=big_weight, subset=extrinsic
+                    priors, weight=small_weight, subset=extrinsic
                ),
                KDEProposal(
-                    priors, weight=big_weight, subset=extrinsic, **learning_kwargs
+                    priors, weight=small_weight, subset=extrinsic, **learning_kwargs
                ),
                GMMProposal(
-                    priors, weight=big_weight, subset=extrinsic, **learning_kwargs
+                    priors, weight=small_weight, subset=extrinsic, **learning_kwargs
                ),
            ]
@@ -1030,6 +1117,11 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
                GMMProposal(
                    priors, weight=small_weight, subset=mass, **learning_kwargs
                ),
+                FisherMatrixProposal(
+                    priors,
+                    weight=small_weight,
+                    subset=mass,
+                ),
            ]
        if priors.spin:
@@ -1039,13 +1131,23 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
                GMMProposal(
                    priors, weight=small_weight, subset=spin, **learning_kwargs
                ),
+                FisherMatrixProposal(
+                    priors,
+                    weight=big_weight,
+                    subset=spin,
+                ),
            ]
-        if priors.precession:
+        if priors.measured_spin:
-            measured_spin = ["chi_1", "chi_2", "a_1", "a_2", "chi_1_in_plane"]
+            measured_spin = PARAMETER_SETS["measured_spin"]
            plist += [
                AdaptiveGaussianProposal(
                    priors, weight=small_weight, subset=measured_spin
                ),
+                FisherMatrixProposal(
+                    priors,
+                    weight=small_weight,
+                    subset=measured_spin,
+                ),
            ]
        if priors.mass and priors.spin:
@@ -1073,6 +1175,21 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
                CorrelatedPolarisationPhaseJump(priors, weight=tiny_weight),
                PhasePolarisationReversalProposal(priors, weight=tiny_weight),
            ]
+        if priors.sky:
+            sky = PARAMETER_SETS["sky"]
+            plist += [
+                FisherMatrixProposal(
+                    priors,
+                    weight=small_weight,
+                    subset=sky,
+                ),
+                GMMProposal(
+                    priors,
+                    weight=small_weight,
+                    subset=sky,
+                    **learning_kwargs,
+                ),
+            ]
        for key in ["time_jitter", "psi", "phi_12", "tilt_2", "lambda_1", "lambda_2"]:
            if key in priors.non_fixed_keys:
                plist.append(PriorProposal(priors, subset=[key], weight=tiny_weight))
@@ -1088,13 +1205,10 @@ def get_proposal_cycle(string, priors, L1steps=1, warn=True):
            DifferentialEvolutionProposal(priors, weight=big_weight),
            UniformProposal(priors, weight=tiny_weight),
            KDEProposal(priors, weight=big_weight, scale_fits=L1steps),
+            FisherMatrixProposal(priors, weight=big_weight),
        ]
        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)
@@ -1111,6 +1225,7 @@ def remove_proposals_using_string(plist, string):
        GM=GMMProposal,
        PR=PriorProposal,
        UN=UniformProposal,
+        FM=FisherMatrixProposal,
    )
    for element in string.split("no")[1:]:

--- a/bilby/bilby_mcmc/sampler.py
+++ b/bilby/bilby_mcmc/sampler.py
 import datetime
 import os
-import signal
 import time
 from collections import Counter
+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 MCMCSampler, ResumeError, SamplerError
+from ..core.sampler.base_sampler import (
-from ..core.utils import check_directory_exists_and_if_not_mkdir, logger, safe_file_dump
+    MCMCSampler,
+    ResumeError,
+    SamplerError,
+    _sampling_convenience_dump,
+    signal_wrapper,
+)
+from ..core.utils import (
+    check_directory_exists_and_if_not_mkdir,
+    logger,
+    random,
+    safe_file_dump,
+)
 from . import proposals
 from .chain import Chain, Sample
 from .utils import LOGLKEY, LOGPKEY, ConvergenceInputs, ParallelTemperingInputs
@@ -108,6 +120,18 @@ 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.
+    normalize_prior: bool
+        When False, disables calculation of constraint normalization factor
+        during prior probability computation. Default value is True.
+    verbose: bool
+        Whether to print diagnostic output during the run.
    """
@@ -129,14 +153,16 @@ class Bilby_MCMC(MCMCSampler):
        autocorr_c=5,
        L1steps=100,
        L2steps=3,
-        npool=1,
        printdt=60,
+        check_point_delta_t=1800,
        min_tau=1,
        proposal_cycle="default",
        stop_after_convergence=False,
        fixed_tau=None,
        tau_window=None,
        evidence_method="stepping_stone",
+        initial_sample_method="prior",
+        initial_sample_dict=None,
    )
    def __init__(
@@ -148,10 +174,11 @@ class Bilby_MCMC(MCMCSampler):
        use_ratio=False,
        skip_import_verification=True,
        check_point_plot=True,
-        check_point_delta_t=1800,
        diagnostic=False,
        resume=True,
        exit_code=130,
+        verbose=True,
+        normalize_prior=True,
        **kwargs,
    ):
@@ -169,9 +196,9 @@ class Bilby_MCMC(MCMCSampler):
        self.check_point_plot = check_point_plot
        self.diagnostic = diagnostic
        self.kwargs["target_nsamples"] = self.kwargs["nsamples"]
-        self.npool = self.kwargs["npool"]
        self.L1steps = self.kwargs["L1steps"]
        self.L2steps = self.kwargs["L2steps"]
+        self.normalize_prior = normalize_prior
        self.pt_inputs = ParallelTemperingInputs(
            **{key: self.kwargs[key] for key in ParallelTemperingInputs._fields}
        )
@@ -181,31 +208,24 @@ 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"]
+        self.check_point_delta_t = self.kwargs["check_point_delta_t"]
        check_directory_exists_and_if_not_mkdir(self.outdir)
        self.resume = resume
-        self.check_point_delta_t = check_point_delta_t
        self.resume_file = "{}/{}_resume.pickle".format(self.outdir, self.label)
        self.verify_configuration()
+        self.verbose = verbose
-        try:
-            signal.signal(signal.SIGTERM, self.write_current_state_and_exit)
-            signal.signal(signal.SIGINT, self.write_current_state_and_exit)
-            signal.signal(signal.SIGALRM, self.write_current_state_and_exit)
-        except AttributeError:
-            logger.debug(
-                "Setting signal attributes unavailable on this system. "
-                "This is likely the case if you are running on a Windows machine"
-                " and is no further concern."
-            )
    def verify_configuration(self):
        if self.convergence_inputs.burn_in_nact / self.kwargs["target_nsamples"] > 0.1:
            logger.warning("Burn-in inefficiency fraction greater than 10%")
    def _translate_kwargs(self, kwargs):
+        kwargs = super()._translate_kwargs(kwargs)
        if "printdt" not in kwargs:
            for equiv in ["print_dt", "print_update"]:
                if equiv in kwargs:
@@ -214,11 +234,16 @@ class Bilby_MCMC(MCMCSampler):
            for equiv in self.npool_equiv_kwargs:
                if equiv in kwargs:
                    kwargs["npool"] = kwargs.pop(equiv)
+        if "check_point_delta_t" not in kwargs:
+            for equiv in self.check_point_equiv_kwargs:
+                if equiv in kwargs:
+                    kwargs["check_point_delta_t"] = kwargs.pop(equiv)
    @property
    def target_nsamples(self):
        return self.kwargs["target_nsamples"]
+    @signal_wrapper
    def run_sampler(self):
        self._setup_pool()
        self.setup_chain_set()
@@ -240,8 +265,8 @@ 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_likelihood_evaluations = result.samples[LOGLKEY].to_numpy()
-        result.log_prior_evaluations = result.samples[LOGPKEY]
+        result.log_prior_evaluations = result.samples[LOGPKEY].to_numpy()
        ptsampler.compute_evidence(
            outdir=outdir,
            label=label,
@@ -271,8 +296,7 @@ class Bilby_MCMC(MCMCSampler):
        return result
    def setup_chain_set(self):
-        if os.path.isfile(self.resume_file) and self.resume is True:
+        if self.read_current_state() and self.resume is True:
-            self.read_current_state()
            self.ptsampler.pool = self.pool
        else:
            self.init_ptsampler()
@@ -288,6 +312,9 @@ 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,
+            normalize_prior=self.normalize_prior,
        )
    def get_setup_string(self):
@@ -303,8 +330,8 @@ class Bilby_MCMC(MCMCSampler):
        self._steps_since_last_print = 0
        self._time_since_last_print = 0
        logger.info(f"Drawing {self.target_nsamples} samples")
-        logger.info(f"Checkpoint every {self.check_point_delta_t}s")
+        logger.info(f"Checkpoint every check_point_delta_t={self.check_point_delta_t}s")
-        logger.info(f"Print update every {self.printdt}s")
+        logger.info(f"Print update every printdt={self.printdt}s")
        while True:
            t0 = datetime.datetime.now()
@@ -353,10 +380,26 @@ class Bilby_MCMC(MCMCSampler):
            os.remove(self.resume_file)
    def read_current_state(self):
+        """Read the existing resume file
+        Returns
+        -------
+        success: boolean
+            If true, resume file was successfully loaded, otherwise false
+        """
+        if os.path.isfile(self.resume_file) is False or not os.path.getsize(
+            self.resume_file
+        ):
+            return False
        import dill
        with open(self.resume_file, "rb") as file:
-            self.ptsampler = dill.load(file)
+            ptsampler = dill.load(file)
+            if not isinstance(ptsampler, BilbyPTMCMCSampler):
+                logger.debug("Malformed resume file, ignoring")
+                return False
+            self.ptsampler = ptsampler
            if self.ptsampler.pt_inputs != self.pt_inputs:
                msg = (
                    f"pt_inputs has changed: {self.ptsampler.pt_inputs} "
@@ -364,7 +407,6 @@ class Bilby_MCMC(MCMCSampler):
                )
                raise ResumeError(msg)
            self.ptsampler.set_convergence_inputs(self.convergence_inputs)
-            self.ptsampler.proposal_cycle = self.proposal_cycle
            self.ptsampler.pt_rejection_sample = self.pt_rejection_sample
        logger.info(
@@ -372,32 +414,14 @@ class Bilby_MCMC(MCMCSampler):
            f"with {self.ptsampler.position} steps "
            f"setup:\n{self.get_setup_string()}"
        )
+        return True
-    def write_current_state_and_exit(self, signum=None, frame=None):
-        """
-        Make sure that if a pool of jobs is running only the parent tries to
-        checkpoint and exit. Only the parent has a 'pool' attribute.
-        """
-        if self.npool == 1 or getattr(self, "pool", None) is not None:
-            if signum == 14:
-                logger.info(
-                    "Run interrupted by alarm signal {}: checkpoint and exit on {}".format(
-                        signum, self.exit_code
-                    )
-                )
-            else:
-                logger.info(
-                    "Run interrupted by signal {}: checkpoint and exit on {}".format(
-                        signum, self.exit_code
-                    )
-                )
-            self.write_current_state()
-            self._close_pool()
-            os._exit(self.exit_code)
    def write_current_state(self):
        import dill
+        if not hasattr(self, "ptsampler"):
+            logger.debug("Attempted checkpoint before initialization")
+            return
        logger.debug("Check point")
        check_directory_exists_and_if_not_mkdir(self.outdir)
@@ -408,9 +432,10 @@ class Bilby_MCMC(MCMCSampler):
            logger.info("Written checkpoint file {}".format(self.resume_file))
        else:
            logger.warning(
-                "Cannot write pickle resume file! "
+                "Cannot write pickle resume file! Job may not resume if interrupted."
-                "Job will not resume if interrupted."
            )
+            # Touch the file to postpone next check-point attempt
+            Path(self.resume_file).touch(exist_ok=True)
        self.ptsampler.pool = _pool
    def print_long_progress(self):
@@ -486,7 +511,8 @@ class Bilby_MCMC(MCMCSampler):
            rse = 100 * count / nsamples
            msg += f"|rse={rse:0.2f}%"
-        print(msg, flush=True)
+        if self.verbose:
+            print(msg, flush=True)
    def print_per_proposal(self):
        logger.info("Zero-temperature proposals:")
@@ -529,38 +555,28 @@ class Bilby_MCMC(MCMCSampler):
                        all_samples=ptsampler.samples,
                    )
-    def _setup_pool(self):
+    @classmethod
-        if self.npool > 1:
+    def get_expected_outputs(cls, outdir=None, label=None):
-            logger.info(f"Setting up multiproccesing pool with {self.npool} processes")
+        """Get lists of the expected outputs directories and files.
-            import multiprocessing
-            self.pool = multiprocessing.Pool(
-                processes=self.npool,
-                initializer=_initialize_global_variables,
-                initargs=(
-                    self.likelihood,
-                    self.priors,
-                    self._search_parameter_keys,
-                    self.use_ratio,
-                ),
-            )
-        else:
-            self.pool = None
-        _initialize_global_variables(
+        These are used by :code:`bilby_pipe` when transferring files via HTCondor.
-            likelihood=self.likelihood,
-            priors=self.priors,
-            search_parameter_keys=self._search_parameter_keys,
-            use_ratio=self.use_ratio,
-        )
-    def _close_pool(self):
+        Parameters
-        if getattr(self, "pool", None) is not None:
+        ----------
-            logger.info("Starting to close worker pool.")
+        outdir : str
-            self.pool.close()
+            The output directory.
-            self.pool.join()
+        label : str
-            self.pool = None
+            The label for the run.
-            logger.info("Finished closing worker pool.")
+        Returns
+        -------
+        list
+            List of file names.
+        list
+            List of directory names. Will always be empty for bilby_mcmc.
+        """
+        filenames = [os.path.join(outdir, f"{label}_resume.pickle")]
+        return filenames, []
 class BilbyPTMCMCSampler(object):
@@ -573,10 +589,15 @@ class BilbyPTMCMCSampler(object):
        pool,
        use_ratio,
        evidence_method,
+        initial_sample_method,
+        initial_sample_dict,
+        normalize_prior=True,
    ):
        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.normalize_prior = normalize_prior
        self.setup_sampler_dictionary(convergence_inputs, proposal_cycle)
        self.set_convergence_inputs(convergence_inputs)
        self.pt_rejection_sample = pt_rejection_sample
@@ -592,7 +613,7 @@ class BilbyPTMCMCSampler(object):
        self._nsamples_dict = {}
        self.ensemble_proposal_cycle = proposals.get_default_ensemble_proposal_cycle(
-            _priors
+            _sampling_convenience_dump.priors
        )
        self.sampling_time = 0
        self.ln_z_dict = dict()
@@ -607,9 +628,9 @@ class BilbyPTMCMCSampler(object):
        elif pt_inputs.Tmax is not None:
            betas = np.logspace(0, -np.log10(pt_inputs.Tmax), pt_inputs.ntemps)
        elif pt_inputs.Tmax_from_SNR is not None:
-            ndim = len(_priors.non_fixed_keys)
+            ndim = len(_sampling_convenience_dump.priors.non_fixed_keys)
            target_hot_likelihood = ndim / 2
-            Tmax = pt_inputs.Tmax_from_SNR ** 2 / (2 * target_hot_likelihood)
+            Tmax = pt_inputs.Tmax_from_SNR**2 / (2 * target_hot_likelihood)
            betas = np.logspace(0, -np.log10(Tmax), pt_inputs.ntemps)
        else:
            raise SamplerError("Unable to set temperature ladder from inputs")
@@ -624,10 +645,12 @@ class BilbyPTMCMCSampler(object):
        betas = self.get_initial_betas()
        logger.info(
            f"Initializing BilbyPTMCMCSampler with:"
-            f"ntemps={self.ntemps},"
+            f"ntemps={self.ntemps}, "
-            f"nensemble={self.nensemble},"
+            f"nensemble={self.nensemble}, "
-            f"pt_ensemble={self.pt_ensemble},"
+            f"pt_ensemble={self.pt_ensemble}, "
-            f"initial_betas={betas}\n"
+            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):
@@ -643,6 +666,9 @@ 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,
+                    normalize_prior=self.normalize_prior,
                )
                for Eindex in range(n)
            ]
@@ -653,7 +679,7 @@ class BilbyPTMCMCSampler(object):
    @property
    def sampler_list(self):
-        """ A list of all individual samplers """
+        """A list of all individual samplers"""
        return [s for item in self.sampler_dictionary.values() for s in item]
    @sampler_list.setter
@@ -750,13 +776,19 @@ class BilbyPTMCMCSampler(object):
    @property
    def samples(self):
+        cached_samples = getattr(self, "_cached_samples", (False,))
+        if cached_samples[0] == self.position:
+            return cached_samples[1]
        sample_list = []
        for sampler in self.zerotemp_sampler_list:
            sample_list.append(sampler.samples)
        if self.pt_rejection_sample:
            for sampler in self.tempered_sampler_list:
                sample_list.append(sampler.samples)
-        return pd.concat(sample_list)
+        samples = pd.concat(sample_list, ignore_index=True)
+        self._cached_samples = (self.position, samples)
+        return samples
    @property
    def position(self):
@@ -799,7 +831,7 @@ class BilbyPTMCMCSampler(object):
    @staticmethod
    def _get_sample_to_swap(sampler):
-        if sampler.chain.converged is False:
+        if not (sampler.chain.converged and sampler.stop_after_convergence):
            v = sampler.chain[-1]
        else:
            v = sampler.chain.random_sample
@@ -825,7 +857,7 @@ class BilbyPTMCMCSampler(object):
                with np.errstate(over="ignore"):
                    alpha_swap = np.exp(dbeta * (logli - loglj))
-                if np.random.uniform(0, 1) <= alpha_swap:
+                if random.rng.uniform(0, 1) <= alpha_swap:
                    sampleri.chain[-1] = vj
                    samplerj.chain[-1] = vi
                    self.sampler_dictionary[Tindex][Eindex] = sampleri
@@ -859,7 +891,7 @@ class BilbyPTMCMCSampler(object):
                        - curr[LOGPKEY]
                    )
-                    if np.random.uniform(0, 1) <= alpha:
+                    if random.rng.uniform(0, 1) <= alpha:
                        sampler.accept_proposal(prop, proposal)
                    else:
                        sampler.reject_proposal(curr, proposal)
@@ -913,7 +945,7 @@ class BilbyPTMCMCSampler(object):
            ln_z, ln_z_err = self.compute_evidence_per_ensemble(method, kwargs)
            self.ln_z_dict[key] = ln_z
            self.ln_z_err_dict[key] = ln_z_err
-            logger.info(
+            logger.debug(
                f"Log-evidence of {ln_z:0.2f}+/-{ln_z_err:0.2f} calculated using {key} method"
            )
@@ -1028,7 +1060,7 @@ class BilbyPTMCMCSampler(object):
        ln_z_realisations = []
        try:
            for _ in range(repeats):
-                idxs = [np.random.randint(i, i + ll) for i in range(steps - ll)]
+                idxs = [random.rng.integers(i, i + ll) for i in range(steps - ll)]
                ln_z_realisations.append(
                    self._calculate_stepping_stone(betas, ln_likes[idxs, :])[0]
                )
@@ -1129,19 +1161,38 @@ class BilbyMCMCSampler(object):
        Tindex=0,
        Eindex=0,
        use_ratio=False,
+        initial_sample_method="prior",
+        initial_sample_dict=None,
+        normalize_prior=True,
    ):
        self.beta = beta
        self.Tindex = Tindex
        self.Eindex = Eindex
        self.use_ratio = use_ratio
+        self.normalize_prior = normalize_prior
-        self.parameters = _priors.non_fixed_keys
+        self.parameters = _sampling_convenience_dump.priors.non_fixed_keys
        self.ndim = len(self.parameters)
-        full_sample_dict = _priors.sample()
+        if initial_sample_method.lower() == "prior":
-        initial_sample = {
+            full_sample_dict = _sampling_convenience_dump.priors.sample()
-            k: v for k, v in full_sample_dict.items() if k in _priors.non_fixed_keys
+            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)
+        if self.beta == 1:
+            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)
@@ -1163,7 +1214,10 @@ class BilbyMCMCSampler(object):
                warn = False
            self.proposal_cycle = proposals.get_proposal_cycle(
-                proposal_cycle, _priors, L1steps=self.chain.L1steps, warn=warn
+                proposal_cycle,
+                _sampling_convenience_dump.priors,
+                L1steps=self.chain.L1steps,
+                warn=warn,
            )
        elif isinstance(proposal_cycle, proposals.ProposalCycle):
            self.proposal_cycle = proposal_cycle
@@ -1180,17 +1234,20 @@ class BilbyMCMCSampler(object):
        self.stop_after_convergence = convergence_inputs.stop_after_convergence
    def log_likelihood(self, sample):
-        _likelihood.parameters.update(sample.sample_dict)
+        _sampling_convenience_dump.likelihood.parameters.update(sample.sample_dict)
        if self.use_ratio:
-            logl = _likelihood.log_likelihood_ratio()
+            logl = _sampling_convenience_dump.likelihood.log_likelihood_ratio()
        else:
-            logl = _likelihood.log_likelihood()
+            logl = _sampling_convenience_dump.likelihood.log_likelihood()
        return logl
    def log_prior(self, sample):
-        return _priors.ln_prob(sample.parameter_only_dict)
+        return _sampling_convenience_dump.priors.ln_prob(
+            sample.parameter_only_dict,
+            normalized=self.normalize_prior,
+        )
    def accept_proposal(self, prop, proposal):
        self.chain.append(prop)
@@ -1213,7 +1270,11 @@ class BilbyMCMCSampler(object):
        while internal_steps < self.chain.L1steps:
            internal_steps += 1
            proposal = self.proposal_cycle.get_proposal()
-            prop, log_factor = proposal(self.chain)
+            prop, log_factor = proposal(
+                self.chain,
+                likelihood=_sampling_convenience_dump.likelihood,
+                priors=_sampling_convenience_dump.priors,
+            )
            logp = self.log_prior(prop)
            if np.isinf(logp) or np.isnan(logp):
@@ -1238,7 +1299,7 @@ class BilbyMCMCSampler(object):
                    - curr[LOGPKEY]
                )
-            if np.random.uniform(0, 1) <= alpha:
+            if random.rng.uniform(0, 1) <= alpha:
                internal_accepted += 1
                proposal.accepted += 1
                curr = prop
@@ -1288,8 +1349,10 @@ class BilbyMCMCSampler(object):
        zerotemp_logl = hot_samples[LOGLKEY]
        # Revert to true likelihood if needed
-        if _use_ratio:
+        if _sampling_convenience_dump.use_ratio:
-            zerotemp_logl += _likelihood.noise_log_likelihood()
+            zerotemp_logl += (
+                _sampling_convenience_dump.likelihood.noise_log_likelihood()
+            )
        # Calculate normalised weights
        log_weights = (1 - beta) * zerotemp_logl
@@ -1311,35 +1374,45 @@ class BilbyMCMCSampler(object):
        return samples
-# Methods used to aid parallelisation:
+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.
-def call_step(sampler):
+    """
-    sampler = sampler.step()
+    logger.info("Finding initial maximum posterior estimate")
-    return sampler
+    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))
-_likelihood = None
+    def neg_log_post(x):
-_priors = None
+        sample = {key: val for key, val in zip(search_parameter_keys, x)}
-_search_parameter_keys = None
+        ln_prior = priors.ln_prob(sample)
-_use_ratio = False
+        if np.isinf(ln_prior):
+            return -np.inf
-def _initialize_global_variables(
+        likelihood.parameters.update(sample)
-    likelihood,
-    priors,
+        return -beta * likelihood.log_likelihood() - ln_prior
-    search_parameter_keys,
-    use_ratio,
+    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)}
-    Store a global copy of the likelihood, priors, and search keys for
+        logger.info(f"Initial maximum posterior estimate {sample}")
-    multiprocessing.
+        return sample
-    """
+    else:
-    global _likelihood
+        raise ValueError("Failed to find initial maximum posterior estimate")
-    global _priors
-    global _search_parameter_keys
-    global _use_ratio
+# Methods used to aid parallelisation:
-    _likelihood = likelihood
-    _priors = priors
-    _search_parameter_keys = search_parameter_keys
+def call_step(sampler):
-    _use_ratio = use_ratio
+    sampler = sampler.step()
+    return sampler
--- a/bilby/core/__init__.py
+++ b/bilby/core/__init__.py
-from . import grid, likelihood, prior, result, sampler, series, utils
+from . import grid, likelihood, prior, result, sampler, series, utils, fisher
--- a/bilby/core/fisher.py
+++ b/bilby/core/fisher.py
+import numpy as np
+import pandas as pd
+import scipy.linalg
+from scipy.optimize import minimize
+class FisherMatrixPosteriorEstimator(object):
+    def __init__(self, likelihood, priors, parameters=None, fd_eps=1e-6, n_prior_samples=100):
+        """ A class to estimate posteriors using the Fisher Matrix approach
+        Parameters
+        ----------
+        likelihood: bilby.core.likelihood.Likelihood
+            A bilby likelihood object
+        priors: bilby.core.prior.PriorDict
+            A bilby prior object
+        parameters: list
+            Names of parameters to sample in
+        fd_eps: float
+            A parameter to control the size of perturbation used when finite
+            differencing the likelihood
+        n_prior_samples: int
+            The number of prior samples to draw and use to attempt estimatation
+            of the maximum likelihood sample.
+        """
+        self.likelihood = likelihood
+        if parameters is None:
+            self.parameter_names = priors.non_fixed_keys
+        else:
+            self.parameter_names = parameters
+        self.fd_eps = fd_eps
+        self.n_prior_samples = n_prior_samples
+        self.N = len(self.parameter_names)
+        self.prior_samples = [
+            priors.sample_subset(self.parameter_names) for _ in range(n_prior_samples)
+        ]
+        self.prior_bounds = [(priors[key].minimum, priors[key].maximum) for key in self.parameter_names]
+        self.prior_width_dict = {}
+        for key in self.parameter_names:
+            width = priors[key].width
+            if np.isnan(width):
+                raise ValueError(f"Prior width is ill-formed for {key}")
+            self.prior_width_dict[key] = width
+    def log_likelihood(self, sample):
+        self.likelihood.parameters.update(sample)
+        return self.likelihood.log_likelihood()
+    def calculate_iFIM(self, sample):
+        FIM = self.calculate_FIM(sample)
+        iFIM = scipy.linalg.inv(FIM)
+        # Ensure iFIM is positive definite
+        min_eig = np.min(np.real(np.linalg.eigvals(iFIM)))
+        if min_eig < 0:
+            iFIM -= 10 * min_eig * np.eye(*iFIM.shape)
+        return iFIM
+    def sample_array(self, sample, n=1):
+        from .utils.random import rng
+        if sample == "maxL":
+            sample = self.get_maximum_likelihood_sample()
+        self.mean = np.array(list(sample.values()))
+        self.iFIM = self.calculate_iFIM(sample)
+        return rng.multivariate_normal(self.mean, self.iFIM, n)
+    def sample_dataframe(self, sample, n=1):
+        samples = self.sample_array(sample, n)
+        return pd.DataFrame(samples, columns=self.parameter_names)
+    def calculate_FIM(self, sample):
+        FIM = np.zeros((self.N, self.N))
+        for ii, ii_key in enumerate(self.parameter_names):
+            for jj, jj_key in enumerate(self.parameter_names):
+                FIM[ii, jj] = -self.get_second_order_derivative(sample, ii_key, jj_key)
+        return FIM
+    def get_second_order_derivative(self, sample, ii, jj):
+        if ii == jj:
+            return self.get_finite_difference_xx(sample, ii)
+        else:
+            return self.get_finite_difference_xy(sample, ii, jj)
+    def get_finite_difference_xx(self, sample, ii):
+        # Sample grid
+        p = self.shift_sample_x(sample, ii, 1)
+        m = self.shift_sample_x(sample, ii, -1)
+        dx = .5 * (p[ii] - m[ii])
+        loglp = self.log_likelihood(p)
+        logl = self.log_likelihood(sample)
+        loglm = self.log_likelihood(m)
+        return (loglp - 2 * logl + loglm) / dx ** 2
+    def get_finite_difference_xy(self, sample, ii, jj):
+        # Sample grid
+        pp = self.shift_sample_xy(sample, ii, 1, jj, 1)
+        pm = self.shift_sample_xy(sample, ii, 1, jj, -1)
+        mp = self.shift_sample_xy(sample, ii, -1, jj, 1)
+        mm = self.shift_sample_xy(sample, ii, -1, jj, -1)
+        dx = .5 * (pp[ii] - mm[ii])
+        dy = .5 * (pp[jj] - mm[jj])
+        loglpp = self.log_likelihood(pp)
+        loglpm = self.log_likelihood(pm)
+        loglmp = self.log_likelihood(mp)
+        loglmm = self.log_likelihood(mm)
+        return (loglpp - loglpm - loglmp + loglmm) / (4 * dx * dy)
+    def shift_sample_x(self, sample, x_key, x_coef):
+        vx = sample[x_key]
+        dvx = self.fd_eps * self.prior_width_dict[x_key]
+        shift_sample = sample.copy()
+        shift_sample[x_key] = vx + x_coef * dvx
+        return shift_sample
+    def shift_sample_xy(self, sample, x_key, x_coef, y_key, y_coef):
+        vx = sample[x_key]
+        vy = sample[y_key]
+        dvx = self.fd_eps * self.prior_width_dict[x_key]
+        dvy = self.fd_eps * self.prior_width_dict[y_key]
+        shift_sample = sample.copy()
+        shift_sample[x_key] = vx + x_coef * dvx
+        shift_sample[y_key] = vy + y_coef * dvy
+        return shift_sample
+    def get_maximum_likelihood_sample(self, initial_sample=None):
+        """ 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.
+        Note: this approach works well in small numbers of dimensions when the
+        posterior is narrow relative to the prior. But, if the number of dimensions
+        is large or the posterior is wide relative to the prior, the method fails
+        to find the global maximum in high dimensional problems.
+        """
+        minlogL = np.inf
+        for i in range(self.n_prior_samples):
+            initial_sample = self.prior_samples[i]
+            x0 = list(initial_sample.values())
+            def neg_log_like(x, self, T=1):
+                sample = {key: val for key, val in zip(self.parameter_names, x)}
+                return - 1 / T * self.log_likelihood(sample)
+            out = minimize(
+                neg_log_like,
+                x0,
+                args=(self, 1),
+                bounds=self.prior_bounds,
+                method="L-BFGS-B",
+            )
+            if out.fun < minlogL:
+                minout = out
+        return {key: val for key, val in zip(self.parameter_names, minout.x)}
--- a/bilby/core/grid.py
+++ b/bilby/core/grid.py
 import json
 import os
-from collections import OrderedDict
 import numpy as np
@@ -352,7 +351,7 @@ class Grid(object):
            'label', 'outdir', 'parameter_names', 'n_dims', 'priors',
            'sample_points', 'ln_likelihood', 'ln_evidence',
            'ln_noise_evidence']
-        dictionary = OrderedDict()
+        dictionary = dict()
        for attr in save_attrs:
            try:
                dictionary[attr] = getattr(self, attr)
@@ -437,7 +436,7 @@ class Grid(object):
        grid: bilby.core.grid.Grid
        Raises
-        =======
+        ======
        ValueError: If no filename is given and either outdir or label is None
                    If no bilby.core.grid.Grid is found in the path

--- a/bilby/core/likelihood.py
+++ b/bilby/core/likelihood.py
@@ -4,7 +4,7 @@ import numpy as np
 from scipy.special import gammaln, xlogy
 from scipy.stats import multivariate_normal
-from .utils import infer_parameters_from_function
+from .utils import infer_parameters_from_function, infer_args_from_function_except_n_args
 class Likelihood(object):
@@ -108,13 +108,14 @@ class Analytical1DLikelihood(Likelihood):
        value is given).
    """
-    def __init__(self, x, y, func):
+    def __init__(self, x, y, func, **kwargs):
        parameters = infer_parameters_from_function(func)
-        super(Analytical1DLikelihood, self).__init__(dict.fromkeys(parameters))
+        super(Analytical1DLikelihood, self).__init__(dict())
        self.x = x
        self.y = y
        self._func = func
-        self._function_keys = list(self.parameters.keys())
+        self._function_keys = [key for key in parameters if key not in kwargs]
+        self.kwargs = kwargs
    def __repr__(self):
        return self.__class__.__name__ + '(x={}, y={}, func={})'.format(self.x, self.y, self.func.__name__)
@@ -164,11 +165,11 @@ class Analytical1DLikelihood(Likelihood):
    @property
    def residual(self):
        """ Residual of the function against the data. """
-        return self.y - self.func(self.x, **self.model_parameters)
+        return self.y - self.func(self.x, **self.model_parameters, **self.kwargs)
 class GaussianLikelihood(Analytical1DLikelihood):
-    def __init__(self, x, y, func, sigma=None):
+    def __init__(self, x, y, func, sigma=None, **kwargs):
        """
        A general Gaussian likelihood for known or unknown noise - the model
        parameters are inferred from the arguments of function
@@ -190,7 +191,7 @@ class GaussianLikelihood(Analytical1DLikelihood):
            to that for `x` and `y`.
        """
-        super(GaussianLikelihood, self).__init__(x=x, y=y, func=func)
+        super(GaussianLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
        self.sigma = sigma
        # Check if sigma was provided, if not it is a parameter
@@ -229,7 +230,7 @@ class GaussianLikelihood(Analytical1DLikelihood):
 class PoissonLikelihood(Analytical1DLikelihood):
-    def __init__(self, x, y, func):
+    def __init__(self, x, y, func, **kwargs):
        """
        A general Poisson likelihood for a rate - the model parameters are
        inferred from the arguments of function, which provides a rate.
@@ -251,10 +252,10 @@ class PoissonLikelihood(Analytical1DLikelihood):
            fixed value is given).
        """
-        super(PoissonLikelihood, self).__init__(x=x, y=y, func=func)
+        super(PoissonLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
    def log_likelihood(self):
-        rate = self.func(self.x, **self.model_parameters)
+        rate = self.func(self.x, **self.model_parameters, **self.kwargs)
        if not isinstance(rate, np.ndarray):
            raise ValueError(
                "Poisson rate function returns wrong value type! "
@@ -286,7 +287,7 @@ class PoissonLikelihood(Analytical1DLikelihood):
 class ExponentialLikelihood(Analytical1DLikelihood):
-    def __init__(self, x, y, func):
+    def __init__(self, x, y, func, **kwargs):
        """
        An exponential likelihood function.
@@ -302,10 +303,10 @@ class ExponentialLikelihood(Analytical1DLikelihood):
            value is given). The model should return the expected mean of
            the exponential distribution for each data point.
        """
-        super(ExponentialLikelihood, self).__init__(x=x, y=y, func=func)
+        super(ExponentialLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
    def log_likelihood(self):
-        mu = self.func(self.x, **self.model_parameters)
+        mu = self.func(self.x, **self.model_parameters, **self.kwargs)
        if np.any(mu < 0.):
            return -np.inf
        return -np.sum(np.log(mu) + (self.y / mu))
@@ -328,7 +329,7 @@ class ExponentialLikelihood(Analytical1DLikelihood):
 class StudentTLikelihood(Analytical1DLikelihood):
-    def __init__(self, x, y, func, nu=None, sigma=1.):
+    def __init__(self, x, y, func, nu=None, sigma=1, **kwargs):
        """
        A general Student's t-likelihood for known or unknown number of degrees
        of freedom, and known or unknown scale (which tends toward the standard
@@ -357,7 +358,7 @@ class StudentTLikelihood(Analytical1DLikelihood):
            Set the scale of the distribution. If not given then this defaults
            to 1, which specifies a standard (central) Student's t-distribution
        """
-        super(StudentTLikelihood, self).__init__(x=x, y=y, func=func)
+        super(StudentTLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
        self.nu = nu
        self.sigma = sigma
@@ -406,7 +407,7 @@ class Multinomial(Likelihood):
    Likelihood for system with N discrete possibilities.
    """
-    def __init__(self, data, n_dimensions, label="parameter_"):
+    def __init__(self, data, n_dimensions, base="parameter_"):
        """
        Parameters
@@ -415,19 +416,21 @@ class Multinomial(Likelihood):
            The number of objects in each class
        n_dimensions: int
            The number of classes
+        base: str
+            The base of the parameter labels
        """
        self.data = np.array(data)
        self._total = np.sum(self.data)
        super(Multinomial, self).__init__(dict())
        self.n = n_dimensions
-        self.label = label
+        self.base = base
        self._nll = None
    def log_likelihood(self):
        """
        Since n - 1 parameters are sampled, the last parameter is 1 - the rest
        """
-        probs = [self.parameters[self.label + str(ii)]
+        probs = [self.parameters[self.base + str(ii)]
                 for ii in range(self.n - 1)]
        probs.append(1 - sum(probs))
        return self._multinomial_ln_pdf(probs=probs)
@@ -567,5 +570,166 @@ class JointLikelihood(Likelihood):
        return sum([likelihood.noise_log_likelihood() for likelihood in self.likelihoods])
+def function_to_celerite_mean_model(func):
+    from celerite.modeling import Model as CeleriteModel
+    return _function_to_gp_model(func, CeleriteModel)
+def function_to_george_mean_model(func):
+    from celerite.modeling import Model as GeorgeModel
+    return _function_to_gp_model(func, GeorgeModel)
+def _function_to_gp_model(func, cls):
+    class MeanModel(cls):
+        parameter_names = tuple(infer_args_from_function_except_n_args(func=func, n=1))
+        def get_value(self, t):
+            params = {name: getattr(self, name) for name in self.parameter_names}
+            return func(t, **params)
+        def compute_gradient(self, *args, **kwargs):
+            pass
+    return MeanModel
+class _GPLikelihood(Likelihood):
+    def __init__(self, kernel, mean_model, t, y, yerr=1e-6, gp_class=None):
+        """
+            Basic Gaussian Process likelihood interface for `celerite` and `george`.
+            For `celerite` documentation see: https://celerite.readthedocs.io/en/stable/
+            For `george` documentation see: https://george.readthedocs.io/en/latest/
+            Parameters
+            ==========
+            kernel: Union[celerite.term.Term, george.kernels.Kernel]
+                `celerite` or `george` kernel. See the respective package documentation about the usage.
+            mean_model: Union[celerite.modeling.Model, george.modeling.Model]
+                Mean model
+            t: array_like
+                The `times` or `x` values of the data set.
+            y: array_like
+                The `y` values of the data set.
+            yerr: float, int, array_like, optional
+                The error values on the y-values. If a single value is given, it is assumed that the value
+                applies for all y-values. Default is 1e-6, effectively assuming that no y-errors are present.
+            gp_class: type, None, optional
+                GPClass to use. This is determined by the child class used to instantiate the GP. Should usually
+                not be given by the user and is mostly used for testing
+        """
+        self.kernel = kernel
+        self.mean_model = mean_model
+        self.t = np.array(t)
+        self.y = np.array(y)
+        self.yerr = np.array(yerr)
+        self.GPClass = gp_class
+        self.gp = self.GPClass(kernel=self.kernel, mean=self.mean_model, fit_mean=True, fit_white_noise=True)
+        self.gp.compute(self.t, yerr=self.yerr)
+        super().__init__(parameters=self.gp.get_parameter_dict())
+    def set_parameters(self, parameters):
+        """
+        Safely set a set of parameters to the internal instances of the `gp` and `mean_model`, as well as the
+        `parameters` dict.
+        Parameters
+        ==========
+        parameters: dict, pandas.DataFrame
+            The set of parameters we would like to set.
+        """
+        for name, value in parameters.items():
+            try:
+                self.gp.set_parameter(name=name, value=value)
+            except ValueError:
+                pass
+            self.parameters[name] = value
+class CeleriteLikelihood(_GPLikelihood):
+    def __init__(self, kernel, mean_model, t, y, yerr=1e-6):
+        """
+            Basic Gaussian Process likelihood interface for `celerite` and `george`.
+            For `celerite` documentation see: https://celerite.readthedocs.io/en/stable/
+            For `george` documentation see: https://george.readthedocs.io/en/latest/
+            Parameters
+            ==========
+            kernel: celerite.term.Term
+                `celerite` or `george` kernel. See the respective package documentation about the usage.
+            mean_model: celerite.modeling.Model
+                Mean model
+            t: array_like
+                The `times` or `x` values of the data set.
+            y: array_like
+                The `y` values of the data set.
+            yerr: float, int, array_like, optional
+                The error values on the y-values. If a single value is given, it is assumed that the value
+                applies for all y-values. Default is 1e-6, effectively assuming that no y-errors are present.
+        """
+        import celerite
+        super().__init__(kernel=kernel, mean_model=mean_model, t=t, y=y, yerr=yerr, gp_class=celerite.GP)
+    def log_likelihood(self):
+        """
+        Calculate the log-likelihood for the Gaussian process given the current parameters.
+        Returns
+        =======
+        float: The log-likelihood value.
+        """
+        self.gp.set_parameter_vector(vector=np.array(list(self.parameters.values())))
+        try:
+            return self.gp.log_likelihood(self.y)
+        except Exception:
+            return -np.inf
+class GeorgeLikelihood(_GPLikelihood):
+    def __init__(self, kernel, mean_model, t, y, yerr=1e-6):
+        """
+            Basic Gaussian Process likelihood interface for `celerite` and `george`.
+            For `celerite` documentation see: https://celerite.readthedocs.io/en/stable/
+            For `george` documentation see: https://george.readthedocs.io/en/latest/
+            Parameters
+            ==========
+            kernel: george.kernels.Kernel
+                `celerite` or `george` kernel. See the respective package documentation about the usage.
+            mean_model: george.modeling.Model
+                Mean model
+            t: array_like
+                The `times` or `x` values of the data set.
+            y: array_like
+                The `y` values of the data set.
+            yerr: float, int, array_like, optional
+                The error values on the y-values. If a single value is given, it is assumed that the value
+                applies for all y-values. Default is 1e-6, effectively assuming that no y-errors are present.
+        """
+        import george
+        super().__init__(kernel=kernel, mean_model=mean_model, t=t, y=y, yerr=yerr, gp_class=george.GP)
+    def log_likelihood(self):
+        """
+        Calculate the log-likelihood for the Gaussian process given the current parameters.
+        Returns
+        =======
+        float: The log-likelihood value.
+        """
+        for name, value in self.parameters.items():
+            try:
+                self.gp.set_parameter(name=name, value=value)
+            except ValueError:
+                raise ValueError(f"Parameter {name} not a valid parameter for the GP.")
+        try:
+            return self.gp.log_likelihood(self.y)
+        except Exception:
+            return -np.inf
 class MarginalizedLikelihoodReconstructionError(Exception):
    pass
--- a/bilby/core/prior/analytical.py
+++ b/bilby/core/prior/analytical.py
@@ -28,6 +28,7 @@ class DeltaFunction(Prior):
                                            minimum=peak, maximum=peak, check_range_nonzero=False)
        self.peak = peak
        self._is_fixed = True
+        self.least_recently_sampled = peak
    def rescale(self, val):
        """Rescale everything to the peak with the correct shape.
@@ -1430,8 +1431,8 @@ class Categorical(Prior):
                 unit=None, boundary="periodic"):
        """ An equal-weighted Categorical prior
-        Parameters:
+        Parameters
-        -----------
+        ==========
        ncategories: int
            The number of available categories. The prior mass support is then
            integers [0, ncategories - 1].
@@ -1517,3 +1518,107 @@ class Categorical(Prior):
            idxs = np.isin(val, self.categories)
            probs[idxs] = self.lnp
            return probs
+class Triangular(Prior):
+    """
+    Define a new prior class which draws from a triangular distribution.
+    For distribution details see: wikipedia.org/wiki/Triangular_distribution
+    Here, minimum <= mode <= maximum,
+    where the mode has the highest pdf value.
+    """
+    def __init__(self, mode, minimum, maximum, name=None, latex_label=None, unit=None):
+        super(Triangular, self).__init__(
+            name=name,
+            latex_label=latex_label,
+            unit=unit,
+            minimum=minimum,
+            maximum=maximum,
+        )
+        self.mode = mode
+        self.fractional_mode = (self.mode - self.minimum) / (
+            self.maximum - self.minimum
+        )
+        self.scale = self.maximum - self.minimum
+        self.rescaled_minimum = self.minimum - (self.minimum == self.mode) * self.scale
+        self.rescaled_maximum = self.maximum + (self.maximum == self.mode) * self.scale
+    def rescale(self, val):
+        """
+        'Rescale' a sample from standard uniform to a triangular distribution.
+        This maps to the inverse CDF. This has been analytically solved for this case.
+        Parameters
+        ==========
+        val: Union[float, int, array_like]
+            Uniform probability
+        Returns
+        =======
+        Union[float, array_like]: Rescaled probability
+        """
+        below_mode = (val * self.scale * (self.mode - self.minimum)) ** 0.5
+        above_mode = ((1 - val) * self.scale * (self.maximum - self.mode)) ** 0.5
+        return (self.minimum + below_mode) * (val < self.fractional_mode) + (
+            self.maximum - above_mode
+        ) * (val >= self.fractional_mode)
+    def prob(self, val):
+        """
+        Return the prior probability of val
+        Parameters
+        ==========
+        val: Union[float, int, array_like]
+        Returns
+        =======
+        float: Prior probability of val
+        """
+        between_minimum_and_mode = (
+            (val < self.mode)
+            * (self.minimum <= val)
+            * (val - self.rescaled_minimum)
+            / (self.mode - self.rescaled_minimum)
+        )
+        between_mode_and_maximum = (
+            (self.mode <= val)
+            * (val <= self.maximum)
+            * (self.rescaled_maximum - val)
+            / (self.rescaled_maximum - self.mode)
+        )
+        return 2.0 * (between_minimum_and_mode + between_mode_and_maximum) / self.scale
+    def cdf(self, val):
+        """
+        Return the prior cumulative probability at val
+        Parameters
+        ==========
+        val: Union[float, int, array_like]
+        Returns
+        =======
+        float: prior cumulative probability at val
+        """
+        return (
+            (val > self.mode)
+            + (val > self.minimum)
+            * (val <= self.maximum)
+            / (self.scale)
+            * (
+                (val > self.mode)
+                * (self.rescaled_maximum - val) ** 2.0
+                / (self.mode - self.rescaled_maximum)
+                + (val <= self.mode)
+                * (val - self.rescaled_minimum) ** 2.0
+                / (self.mode - self.rescaled_minimum)
+            )
+        )
--- a/bilby/core/prior/base.py
+++ b/bilby/core/prior/base.py
@@ -7,8 +7,13 @@ import numpy as np
 import scipy.stats
 from scipy.interpolate import interp1d
-from bilby.core.utils import infer_args_from_method, BilbyJsonEncoder, decode_bilby_json, logger, \
+from ..utils import (
-    get_dict_with_properties
+    infer_args_from_method,
+    BilbyJsonEncoder,
+    decode_bilby_json,
+    logger,
+    get_dict_with_properties,
+)
 class Prior(object):
@@ -124,7 +129,9 @@ class Prior(object):
        float: A random number between 0 and 1, rescaled to match the distribution of this Prior
        """
-        self.least_recently_sampled = self.rescale(np.random.uniform(0, 1, size))
+        from ..utils.random import rng
+        self.least_recently_sampled = self.rescale(rng.uniform(0, 1, size))
        return self.least_recently_sampled
    def rescale(self, val):
@@ -161,14 +168,14 @@ class Prior(object):
    def cdf(self, val):
        """ Generic method to calculate CDF, can be overwritten in subclass """
-        from scipy.integrate import cumtrapz
+        from scipy.integrate import cumulative_trapezoid
        if np.any(np.isinf([self.minimum, self.maximum])):
            raise ValueError(
                "Unable to use the generic CDF calculation for priors with"
                "infinite support")
        x = np.linspace(self.minimum, self.maximum, 1000)
        pdf = self.prob(x)
-        cdf = cumtrapz(pdf, x, initial=0)
+        cdf = cumulative_trapezoid(pdf, x, initial=0)
        interp = interp1d(x, cdf, assume_sorted=True, bounds_error=False,
                          fill_value=(0, 1))
        return interp(val)
@@ -214,22 +221,13 @@ class Prior(object):
        """
        prior_name = self.__class__.__name__
+        prior_module = self.__class__.__module__
        instantiation_dict = self.get_instantiation_dict()
-        args = ', '.join(['{}={}'.format(key, repr(instantiation_dict[key]))
+        args = ', '.join([f'{key}={repr(instantiation_dict[key])}' for key in instantiation_dict])
-                          for key in instantiation_dict])
+        if "bilby.core.prior" in prior_module:
-        return "{}({})".format(prior_name, args)
+            return f"{prior_name}({args})"
+        else:
-    @property
+            return f"{prior_module}.{prior_name}({args})"
-    def _repr_dict(self):
-        """
-        Get a dictionary containing the arguments needed to reproduce this object.
-        """
-        property_names = {p for p in dir(self.__class__) if isinstance(getattr(self.__class__, p), property)}
-        subclass_args = infer_args_from_method(self.__init__)
-        dict_with_properties = self.__dict__.copy()
-        for key in property_names.intersection(subclass_args):
-            dict_with_properties[key] = getattr(self, key)
-        return {key: dict_with_properties[key] for key in subclass_args}
    @property
    def is_fixed(self):
@@ -333,7 +331,7 @@ class Prior(object):
    @classmethod
    def from_repr(cls, string):
-        """Generate the prior from it's __repr__"""
+        """Generate the prior from its __repr__"""
        return cls._from_repr(string)
    @classmethod
@@ -405,6 +403,10 @@ class Prior(object):
            The string is interpreted as a call to instantiate another prior
            class, Bilby will attempt to recursively construct that prior,
            e.g., Uniform(minimum=0, maximum=1), my.custom.PriorClass(**kwargs).
+        - Else If the string contains a ".":
+            It is treated as a path to a Python function and imported, e.g.,
+            "some_module.some_function" returns
+            :code:`import some_module; return some_module.some_function`
        - Else:
            Try to evaluate the string using `eval`. Only built-in functions
            and numpy methods can be used, e.g., np.pi / 2, 1.57.
@@ -429,9 +431,9 @@ class Prior(object):
            val = None
        elif re.sub(r'\'.*\'', '', val) in ['r', 'u']:
            val = val[2:-1]
-        elif "'" in val:
+        elif val.startswith("'") and val.endswith("'"):
            val = val.strip("'")
-        elif '(' in val:
+        elif '(' in val and not val.startswith(("[", "{")):
            other_cls = val.split('(')[0]
            vals = '('.join(val.split('(')[1:])[:-1]
            if "." in other_cls:
@@ -445,10 +447,17 @@ class Prior(object):
            try:
                val = eval(val, dict(), dict(np=np, inf=np.inf, pi=np.pi))
            except NameError:
-                raise TypeError(
+                if "." in val:
-                    "Cannot evaluate prior, "
+                    module = '.'.join(val.split('.')[:-1])
-                    "failed to parse argument {}".format(val)
+                    func = val.split('.')[-1]
-                )
+                    new_val = getattr(import_module(module), func, val)
+                    if val == new_val:
+                        raise TypeError(
+                            "Cannot evaluate prior, "
+                            f"failed to parse argument {val}"
+                        )
+                    else:
+                        val = new_val
        return val
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