diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 6089160269d5ea613fae7036f8d4c1e480cab4f9..f426e45d175031e9f8654244aecb303c1d3fb16f 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -51,7 +51,7 @@ python-3.7:
- flake8 .
# Run tests and collect coverage data
- - pytest --cov=bilby
+ - pytest --cov=bilby --durations 10
- coverage html
- coverage-badge -o coverage_badge.svg -f
@@ -75,10 +75,28 @@ python-3.6:
- pytest
+# test samplers on python 3.7
+python-3.7-samplers:
+ stage: test
+ image: bilbydev/v2-dockerfile-test-suite-python37
+ script:
+ - python -m pip install .
+
+ - pytest test/sampler_test.py --durations 10
+
+# test samplers on python 3.6
+python-3.6-samplers:
+ stage: test
+ image: bilbydev/v2-dockerfile-test-suite-python36
+ script:
+ - python -m pip install .
+
+ - pytest test/sampler_test.py
+
# Tests run at a fixed schedule rather than on push
scheduled-python-3.7:
stage: test
- image: bilbydev/bilby-test-suite-python37
+ image: bilbydev/v2-dockerfile-test-suite-python37
only:
- schedules
script:
@@ -104,3 +122,17 @@ pages:
expire_in: 30 days
only:
- master
+
+deploy_release:
+ stage: deploy
+ image: bilbydev/v2-dockerfile-test-suite-python37
+ variables:
+ TWINE_USERNAME: $PYPI_USERNAME
+ TWINE_PASSWORD: $PYPI_PASSWORD
+ before_script:
+ - pip install twine
+ - python setup.py sdist
+ script:
+ - twine upload dist/*
+ only:
+ - tags
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5fbd2aa5a933201df222ab0c94fc0ba8ff1a34f8..c69ba9b2dfbbabb1e1c734ce39257b7db1234690 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,23 @@
# All notable changes will be documented in this file
+## [0.6.5] 2020-02-14
+### Changes
+- Fix for time reconstruction bug (!714)
+- Resolved errors Waveform longer than the frequency array (!710)
+- Prior reading clean-up (!715)
+- More efficient dynesty restarting (!713)
+- PP tests show 123 sigma bounds by default (!726)
+
+### Added
+- HealPixPrior (!651)
+- GW prior documentation (!720)
+- Multiple contours to PP tests plots (!721)
+- Distance marginalization for non-luminosity-distance parameters (!719)
+
+### Removed
+- Pipenv (!724)
+
+
## [0.6.4] 2020-01-30
### Changes
- Discontinue python2.7 support (!697)
diff --git a/Pipfile b/Pipfile
deleted file mode 100644
index a543565eb9540cc24838229c8574a5ae778fd8d4..0000000000000000000000000000000000000000
--- a/Pipfile
+++ /dev/null
@@ -1,34 +0,0 @@
-[[source]]
-url = "https://pypi.org/simple"
-verify_ssl = true
-name = "pypi"
-
-[packages]
-future = "*"
-corner = "*"
-numpy = "==1.15.2"
-ligotimegps = "<=1.2.3"
-matplotlib = "<3"
-scipy = ">=0.16"
-pandas = "==0.23.0"
-deepdish = "==0.3.6"
-mock = "*"
-astropy = "<3"
-gwpy = "*"
-theano = "*"
-lalsuite = "*"
-dill = "*"
-
-# cpnest = "*"
-dynesty = "*"
-emcee = "*"
-nestle = "*"
-ptemcee = "*"
-pymc3 = "*"
-
-[requires]
-
-[dev-packages]
-
-[pipenv]
-allow_prereleases = false
diff --git a/Pipfile.lock b/Pipfile.lock
deleted file mode 100644
index add2830ca920575ab49a8200fa48a9b41e6928ce..0000000000000000000000000000000000000000
--- a/Pipfile.lock
+++ /dev/null
@@ -1,619 +0,0 @@
-{
- "_meta": {
- "hash": {
- "sha256": "eb20a6f752cbf495151f478087441410ee5bb4eb3a064f72f5a31aec85c1032d"
- },
- "pipfile-spec": 6,
- "requires": {},
- "sources": [
- {
- "name": "pypi",
- "url": "https://pypi.org/simple",
- "verify_ssl": true
- }
- ]
- },
- "default": {
- "asn1crypto": {
- "hashes": [
- "sha256:2f1adbb7546ed199e3c90ef23ec95c5cf3585bac7d11fb7eb562a3fe89c64e87",
- "sha256:9d5c20441baf0cb60a4ac34cc447c6c189024b6b4c6cd7877034f4965c464e49"
- ],
- "version": "==0.24.0"
- },
- "astropy": {
- "hashes": [
- "sha256:0836c2b08496e98859551fc3f82f78a24276a67d8da2a8a606dbc8d1e438c136",
- "sha256:0b57d343f898fc0d0b9f1f63cc23c0d9f32a4c6ffa326f2bdcd1a15de63ad468",
- "sha256:0f234986be6e4717eab9a1a67e72686665c22e8d2b9dde6731260a4e660943f8",
- "sha256:10091619a67c17824821426deaa8eef0d70b37ecfd99add8bd5ed059783071ed",
- "sha256:2b01d9c8972d2c1b103364bfc3a2a005d0ce09cd28ba96659f5cef3907141953",
- "sha256:314ed424551a06cc466ca5f6f0105daed6d5dfc32fce752e4ccd7b505ddb74ed",
- "sha256:322c05aa8bd7f371ff43e9aebca88a425ca9b6787fbda433bd32c540cc77cb75",
- "sha256:46c07a8893c6be3c6d0cc5aa3d9003614af2c416042f22475779839181d98b42",
- "sha256:5fcb553002f2782c9cb0e738813d2758c9c027b29cb1f1ea72c8d82ef8dbb467",
- "sha256:64caa0955f3c09686103faeae04c77162513b80df1258d500e04d7426dafaf07",
- "sha256:6abde36755f9aa46d972fe3868030f0388edaaae420f0640bc2902e18870fe56",
- "sha256:7a11b9b35b9309832b991f74ea0eef95bc4f0288255df7ac2da26e7f654ee697",
- "sha256:81bae35320d7c72ae8569eeabc596e3a5ed416249b3fb2de9b40f30673085d0b",
- "sha256:98d59d9faf8c7a766b7da1e2741c7d7b7b72107588d94b0811f306245bd66c49",
- "sha256:b39db24461e25a7711cbf491ca83defe5bc67d88d3b62649fbc2bfce245db0f4",
- "sha256:b981ca97ce0b8ac32fdb7d8ee0a72bea1fe528104542c29a763b3da4ff137e40",
- "sha256:ba0f47ebf6b922a9a3e8d5c66c9fdc3d2a03fdcfe9d2a4dd4d89f91880fcf3c8",
- "sha256:bdf62bb686bb26cfc972bbb3b6d9760d7dbf56f6c30960987de73d45b50479ff",
- "sha256:c813f5f3fe2b51621a0ec1d7d61fb6049a36c7bba53cadb91a987e28fc290444",
- "sha256:cd7aeced7fbfad6d6b5f5f88aa1055a24e5a3aa8874b3c7dd9cd655c6d9578ab",
- "sha256:d85727d114c6d0fd1a3ab9b9e59c60500c7f2bd47637ec88e81f9f690c178001",
- "sha256:dd40c7e782c6f91584fb75a589c54d332ad80011b43e150dc8f5ff15e6317e71",
- "sha256:f3f8f7b3832288bec74f1937791d75386879a23da5c21de33739138990cf6bd2"
- ],
- "index": "pypi",
- "version": "==2.0.12"
- },
- "atomicwrites": {
- "hashes": [
- "sha256:03472c30eb2c5d1ba9227e4c2ca66ab8287fbfbbda3888aa93dc2e28fc6811b4",
- "sha256:75a9445bac02d8d058d5e1fe689654ba5a6556a1dfd8ce6ec55a0ed79866cfa6"
- ],
- "version": "==1.3.0"
- },
- "attrs": {
- "hashes": [
- "sha256:69c0dbf2ed392de1cb5ec704444b08a5ef81680a61cb899dc08127123af36a79",
- "sha256:f0b870f674851ecbfbbbd364d6b5cbdff9dcedbc7f3f5e18a6891057f21fe399"
- ],
- "version": "==19.1.0"
- },
- "cffi": {
- "hashes": [
- "sha256:00b97afa72c233495560a0793cdc86c2571721b4271c0667addc83c417f3d90f",
- "sha256:0ba1b0c90f2124459f6966a10c03794082a2f3985cd699d7d63c4a8dae113e11",
- "sha256:0bffb69da295a4fc3349f2ec7cbe16b8ba057b0a593a92cbe8396e535244ee9d",
- "sha256:21469a2b1082088d11ccd79dd84157ba42d940064abbfa59cf5f024c19cf4891",
- "sha256:2e4812f7fa984bf1ab253a40f1f4391b604f7fc424a3e21f7de542a7f8f7aedf",
- "sha256:2eac2cdd07b9049dd4e68449b90d3ef1adc7c759463af5beb53a84f1db62e36c",
- "sha256:2f9089979d7456c74d21303c7851f158833d48fb265876923edcb2d0194104ed",
- "sha256:3dd13feff00bddb0bd2d650cdb7338f815c1789a91a6f68fdc00e5c5ed40329b",
- "sha256:4065c32b52f4b142f417af6f33a5024edc1336aa845b9d5a8d86071f6fcaac5a",
- "sha256:51a4ba1256e9003a3acf508e3b4f4661bebd015b8180cc31849da222426ef585",
- "sha256:59888faac06403767c0cf8cfb3f4a777b2939b1fbd9f729299b5384f097f05ea",
- "sha256:59c87886640574d8b14910840327f5cd15954e26ed0bbd4e7cef95fa5aef218f",
- "sha256:610fc7d6db6c56a244c2701575f6851461753c60f73f2de89c79bbf1cc807f33",
- "sha256:70aeadeecb281ea901bf4230c6222af0248c41044d6f57401a614ea59d96d145",
- "sha256:71e1296d5e66c59cd2c0f2d72dc476d42afe02aeddc833d8e05630a0551dad7a",
- "sha256:8fc7a49b440ea752cfdf1d51a586fd08d395ff7a5d555dc69e84b1939f7ddee3",
- "sha256:9b5c2afd2d6e3771d516045a6cfa11a8da9a60e3d128746a7fe9ab36dfe7221f",
- "sha256:9c759051ebcb244d9d55ee791259ddd158188d15adee3c152502d3b69005e6bd",
- "sha256:b4d1011fec5ec12aa7cc10c05a2f2f12dfa0adfe958e56ae38dc140614035804",
- "sha256:b4f1d6332339ecc61275bebd1f7b674098a66fea11a00c84d1c58851e618dc0d",
- "sha256:c030cda3dc8e62b814831faa4eb93dd9a46498af8cd1d5c178c2de856972fd92",
- "sha256:c2e1f2012e56d61390c0e668c20c4fb0ae667c44d6f6a2eeea5d7148dcd3df9f",
- "sha256:c37c77d6562074452120fc6c02ad86ec928f5710fbc435a181d69334b4de1d84",
- "sha256:c8149780c60f8fd02752d0429246088c6c04e234b895c4a42e1ea9b4de8d27fb",
- "sha256:cbeeef1dc3c4299bd746b774f019de9e4672f7cc666c777cd5b409f0b746dac7",
- "sha256:e113878a446c6228669144ae8a56e268c91b7f1fafae927adc4879d9849e0ea7",
- "sha256:e21162bf941b85c0cda08224dade5def9360f53b09f9f259adb85fc7dd0e7b35",
- "sha256:fb6934ef4744becbda3143d30c6604718871495a5e36c408431bf33d9c146889"
- ],
- "version": "==1.12.2"
- },
- "corner": {
- "hashes": [
- "sha256:102e22797ee75d1432b6dc66aa2850f61388996ece66fd6600508742d2a7b88f"
- ],
- "index": "pypi",
- "version": "==2.0.1"
- },
- "cryptography": {
- "hashes": [
- "sha256:066f815f1fe46020877c5983a7e747ae140f517f1b09030ec098503575265ce1",
- "sha256:210210d9df0afba9e000636e97810117dc55b7157c903a55716bb73e3ae07705",
- "sha256:26c821cbeb683facb966045e2064303029d572a87ee69ca5a1bf54bf55f93ca6",
- "sha256:2afb83308dc5c5255149ff7d3fb9964f7c9ee3d59b603ec18ccf5b0a8852e2b1",
- "sha256:2db34e5c45988f36f7a08a7ab2b69638994a8923853dec2d4af121f689c66dc8",
- "sha256:409c4653e0f719fa78febcb71ac417076ae5e20160aec7270c91d009837b9151",
- "sha256:45a4f4cf4f4e6a55c8128f8b76b4c057027b27d4c67e3fe157fa02f27e37830d",
- "sha256:48eab46ef38faf1031e58dfcc9c3e71756a1108f4c9c966150b605d4a1a7f659",
- "sha256:6b9e0ae298ab20d371fc26e2129fd683cfc0cfde4d157c6341722de645146537",
- "sha256:6c4778afe50f413707f604828c1ad1ff81fadf6c110cb669579dea7e2e98a75e",
- "sha256:8c33fb99025d353c9520141f8bc989c2134a1f76bac6369cea060812f5b5c2bb",
- "sha256:9873a1760a274b620a135054b756f9f218fa61ca030e42df31b409f0fb738b6c",
- "sha256:9b069768c627f3f5623b1cbd3248c5e7e92aec62f4c98827059eed7053138cc9",
- "sha256:9e4ce27a507e4886efbd3c32d120db5089b906979a4debf1d5939ec01b9dd6c5",
- "sha256:acb424eaca214cb08735f1a744eceb97d014de6530c1ea23beb86d9c6f13c2ad",
- "sha256:c8181c7d77388fe26ab8418bb088b1a1ef5fde058c6926790c8a0a3d94075a4a",
- "sha256:d4afbb0840f489b60f5a580a41a1b9c3622e08ecb5eec8614d4fb4cd914c4460",
- "sha256:d9ed28030797c00f4bc43c86bf819266c76a5ea61d006cd4078a93ebf7da6bfd",
- "sha256:e603aa7bb52e4e8ed4119a58a03b60323918467ef209e6ff9db3ac382e5cf2c6"
- ],
- "version": "==2.6.1"
- },
- "cycler": {
- "hashes": [
- "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
- "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
- ],
- "version": "==0.10.0"
- },
- "deepdish": {
- "hashes": [
- "sha256:2b1d3d2a32356909f2618c382a1829347990ad81b63aaecc87b35a046cd3034c",
- "sha256:7858b674ef15ee79828de4892dadd2f58773ead527f3a62aaf1e8ce5a595a074"
- ],
- "index": "pypi",
- "version": "==0.3.6"
- },
- "dill": {
- "hashes": [
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- ],
- "index": "pypi",
- "version": "==0.2.9"
- },
- "dqsegdb2": {
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- ],
- "version": "==1.0.1"
- },
- "dynesty": {
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- "sha256:eb9aed4f856d084551e54e9939e295f50d232e089286c8d93954afe443cb37d8"
- ],
- "index": "pypi",
- "version": "==0.9.5.3"
- },
- "emcee": {
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- ],
- "index": "pypi",
- "version": "==2.2.1"
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- "future": {
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- ],
- "index": "pypi",
- "version": "==0.17.1"
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- "gwdatafind": {
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- "sha256:3b7b586a2fbe87dc3867c38699c50ad8028f8403a6689b21ac6229e0ac127492",
- "sha256:5ab0fb818b22a9f10064cfa072967727a8072d7f7d0a3b96c83907e8ce7bd5b6"
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- "version": "==1.0.4"
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- "gwosc": {
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- "sha256:f707bdc3b3f9fd6b8b2dceabe770b2fec6a488a1f57b8edc91089d8dac9bf5e3"
- ],
- "version": "==0.4.3"
- },
- "gwpy": {
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- "sha256:45c4f124cc43d8284520b73f3c35ca571221a00fe3953dd34714d0b859ffd820"
- ],
- "index": "pypi",
- "version": "==0.14.2"
- },
- "h5py": {
- "hashes": [
- "sha256:05750b91640273c69989c657eaac34b091abdd75efc8c4824c82aaf898a2da0a",
- "sha256:082a27208aa3a2286e7272e998e7e225b2a7d4b7821bd840aebf96d50977abbb",
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- "sha256:1e9fb6f1746500ea91a00193ce2361803c70c6b13f10aae9a33ad7b5bd28e800",
- "sha256:2cca17e80ddb151894333377675db90cd0279fa454776e0a4f74308376afd050",
- "sha256:30e365e8408759db3778c361f1e4e0fe8e98a875185ae46c795a85e9bafb9cdf",
- "sha256:3206bac900e16eda81687d787086f4ffd4f3854980d798e191a9868a6510c3ae",
- "sha256:3c23d72058647cee19b30452acc7895621e2de0a0bd5b8a1e34204b9ea9ed43c",
- "sha256:407b5f911a83daa285bbf1ef78a9909ee5957f257d3524b8606be37e8643c5f0",
- "sha256:4162953714a9212d373ac953c10e3329f1e830d3c7473f2a2e4f25dd6241eef0",
- "sha256:5fc7aba72a51b2c80605eba1c50dbf84224dcd206279d30a75c154e5652e1fe4",
- "sha256:713ac19307e11de4d9833af0c4bd6778bde0a3d967cafd2f0f347223711c1e31",
- "sha256:71b946d80ef3c3f12db157d7778b1fe74a517ca85e94809358b15580983c2ce2",
- "sha256:8cc4aed71e20d87e0a6f02094d718a95252f11f8ed143bc112d22167f08d4040",
- "sha256:9d41ca62daf36d6b6515ab8765e4c8c4388ee18e2a665701fef2b41563821002",
- "sha256:a744e13b000f234cd5a5b2a1f95816b819027c57f385da54ad2b7da1adace2f3",
- "sha256:b087ee01396c4b34e9dc41e3a6a0442158206d383c19c7d0396d52067b17c1cb",
- "sha256:b0f03af381d33306ce67d18275b61acb4ca111ced645381387a02c8a5ee1b796",
- "sha256:b9e4b8dfd587365bdd719ae178fa1b6c1231f81280b1375eef8626dfd8761bf3",
- "sha256:c5dd4ec75985b99166c045909e10f0534704d102848b1d9f0992720e908928e7",
- "sha256:d2b82f23cd862a9d05108fe99967e9edfa95c136f532a71cb3d28dc252771f50",
- "sha256:e58a25764472af07b7e1c4b10b0179c8ea726446c7141076286e41891bf3a563",
- "sha256:f3b49107fbfc77333fc2b1ef4d5de2abcd57e7ea3a1482455229494cf2da56ce"
- ],
- "version": "==2.9.0"
- },
- "joblib": {
- "hashes": [
- "sha256:11cdfd38cdb71768149e1373f2509e9b4fc1ec6bc92f874cb515b25f2d69f8f4",
- "sha256:142e74bee7b9dfad75c7dad24c4295b9084a6788116dd149e2fc7daf0db25675"
- ],
- "version": "==0.12.5"
- },
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- "sha256:341ec10361b64a24accaec3c7ba5f7d5ee1ca4cebea30f76fad3dd12db9f0541",
- "sha256:952c0389db115437f966c4c2079ae9d54714b9455190e56acebe14e8c38a7efa"
- ],
- "version": "==3.6.4"
- },
- "python-dateutil": {
- "hashes": [
- "sha256:7e6584c74aeed623791615e26efd690f29817a27c73085b78e4bad02493df2fb",
- "sha256:c89805f6f4d64db21ed966fda138f8a5ed7a4fdbc1a8ee329ce1b74e3c74da9e"
- ],
- "version": "==2.8.0"
- },
- "pytz": {
- "hashes": [
- "sha256:303879e36b721603cc54604edcac9d20401bdbe31e1e4fdee5b9f98d5d31dfda",
- "sha256:d747dd3d23d77ef44c6a3526e274af6efeb0a6f1afd5a69ba4d5be4098c8e141"
- ],
- "version": "==2019.1"
- },
- "scipy": {
- "hashes": [
- "sha256:014cb900c003b5ac81a53f2403294e8ecf37aedc315b59a6b9370dce0aa7627a",
- "sha256:281a34da34a5e0de42d26aed692ab710141cad9d5d218b20643a9cb538ace976",
- "sha256:588f9cc4bfab04c45fbd19c1354b5ade377a8124d6151d511c83730a9b6b2338",
- "sha256:5a10661accd36b6e2e8855addcf3d675d6222006a15795420a39c040362def66",
- "sha256:628f60be272512ca1123524969649a8cb5ae8b31cca349f7c6f8903daf9034d7",
- "sha256:6dcc43a88e25b815c2dea1c6fac7339779fc988f5df8396e1de01610604a7c38",
- "sha256:70e37cec0ac0fe95c85b74ca4e0620169590fd5d3f44765f3c3a532cedb0e5fd",
- "sha256:7274735fb6fb5d67d3789ddec2cd53ed6362539b41aa6cc0d33a06c003aaa390",
- "sha256:78e12972e144da47326958ac40c2bd1c1cca908edc8b01c26a36f9ffd3dce466",
- "sha256:790cbd3c8d09f3a6d9c47c4558841e25bac34eb7a0864a9def8f26be0b8706af",
- "sha256:79792c8fe8e9d06ebc50fe23266522c8c89f20aa94ac8e80472917ecdce1e5ba",
- "sha256:865afedf35aaef6df6344bee0de391ee5e99d6e802950a237f9fb9b13e441f91",
- "sha256:870fd401ec7b64a895cff8e206ee16569158db00254b2f7157b4c9a5db72c722",
- "sha256:963815c226b29b0176d5e3d37fc9de46e2778ce4636a5a7af11a48122ef2577c",
- "sha256:9726791484f08e394af0b59eb80489ad94d0a53bbb58ab1837dcad4d58489863",
- "sha256:9de84a71bb7979aa8c089c4fb0ea0e2ed3917df3fb2a287a41aaea54bbad7f5d",
- "sha256:b2c324ddc5d6dbd3f13680ad16a29425841876a84a1de23a984236d1afff4fa6",
- "sha256:b86ae13c597fca087cb8c193870507c8916cefb21e52e1897da320b5a35075e5",
- "sha256:ba0488d4dbba2af5bf9596b849873102d612e49a118c512d9d302ceafa36e01a",
- "sha256:d78702af4102a3a4e23bb7372cec283e78f32f5573d92091aa6aaba870370fe1",
- "sha256:def0e5d681dd3eb562b059d355ae8bebe27f5cc455ab7c2b6655586b63d3a8ea",
- "sha256:e085d1babcb419bbe58e2e805ac61924dac4ca45a07c9fa081144739e500aa3c",
- "sha256:e2cfcbab37c082a5087aba5ff00209999053260441caadd4f0e8f4c2d6b72088",
- "sha256:e742f1f5dcaf222e8471c37ee3d1fd561568a16bb52e031c25674ff1cf9702d5",
- "sha256:f06819b028b8ef9010281e74c59cb35483933583043091ed6b261bb1540f11cc",
- "sha256:f15f2d60a11c306de7700ee9f65df7e9e463848dbea9c8051e293b704038da60",
- "sha256:f31338ee269d201abe76083a990905473987371ff6f3fdb76a3f9073a361cf37",
- "sha256:f6b88c8d302c3dac8dff7766955e38d670c82e0d79edfc7eae47d6bb2c186594"
- ],
- "index": "pypi",
- "version": "==1.2.1"
- },
- "six": {
- "hashes": [
- "sha256:3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c",
- "sha256:d16a0141ec1a18405cd4ce8b4613101da75da0e9a7aec5bdd4fa804d0e0eba73"
- ],
- "version": "==1.12.0"
- },
- "tables": {
- "hashes": [
- "sha256:08ebb38c46a3833acb297f4d9fbb256ed28e0671cb0edef7e4ab6b29134d2f9d",
- "sha256:102e55c6c85790ae58a202d3e1e82b78c8f3bf2ab38490f2b01d7ec9010165de",
- "sha256:1fcf8802f8715731424277ffdd1faf4428371f40aefee1f8b1a14ef8d775ded0",
- "sha256:221f1734ce6318cbf95994c574d78a3f56d57635effd474911116af20d9158cb",
- "sha256:2fcfd88df0a081e7624d80dd991f25ba23f9300950289aa5766b8625db70ec3a",
- "sha256:3354d8d7e9638b6afc8e86b8858576314abac7e8479081c55f43907532866f37",
- "sha256:39c9a942cf1179504d9fe73d00d07c3ae7b434bab822b1ad07dc345442eeb39e",
- "sha256:3f744c006dfc105a0e4674ba4bdad68c2cb9ea2dfd2bb5cb4eb43d87602e8a28",
- "sha256:45987da933b549ae5592a4da9b5bb4e6519eaeada01f2838c301c0ee5b0bc774",
- "sha256:51032a5976f65fcb076b29cd7337b272539bf034aeb1dc3e9c0d28df7d94e2e1",
- "sha256:56967b8497b1aa898b43525e95e2e8b3085f20c30f2f6638af3e59e8cdf340ac",
- "sha256:5b6035ecfff22deb7b82c35e34caa21a92134704af9b60ab006e562061f119f1",
- "sha256:5fc2813c1ef24c88483e0accfd3ce971cfa6e47bbc00538c6d5b06c9ec2675e9",
- "sha256:66880416323b9e00c88f372f57210bc716d14c63eec8afc5dbeed7701955e754",
- "sha256:6aced515f538c2c45afb3c7951df3cea3e9f37df5ef7743e233b35905b7a1d58",
- "sha256:779d51bf969626b92738847686a44aeb9d48a92af9f38b94ad7b9f455fd65f58",
- "sha256:81d036e5c1f3912698507f176499a820e0c6f1f5bf7fa135acd5dc331de95204",
- "sha256:88e2f3be1f143febc8bf8a7fe49ad51fc12518d6a1ac4eb641778d93e5dc2039",
- "sha256:9d65f20af462e1e735414937225d50e126ba4cdca91d5908b0994a3f7d457666",
- "sha256:b17af3f07c9b1afc723742c9f067fa8b3bd034676ca4eefaca8d251f22693342",
- "sha256:b22284e1d9fd4bd3ea4d357ce939078c44af4b23b9a2a3f1189a54a67a93f57a",
- "sha256:d8f4091ca8f521ddea0e7702269b87a721443439981f9ab06544e3dcd6ed314b",
- "sha256:fb7a873a86dfc801256cd295547d7fae2632a09cf37a349f9951a593a178bdc3"
- ],
- "version": "==3.5.1"
- },
- "theano": {
- "hashes": [
- "sha256:35c9bbef56b61ffa299265a42a4e8f8cb5a07b2997dabaef0f8830b397086913"
- ],
- "index": "pypi",
- "version": "==1.0.4"
- },
- "tqdm": {
- "hashes": [
- "sha256:d385c95361699e5cf7622485d9b9eae2d4864b21cd5a2374a9c381ffed701021",
- "sha256:e22977e3ebe961f72362f6ddfb9197cc531c9737aaf5f607ef09740c849ecd05"
- ],
- "version": "==4.31.1"
- }
- },
- "develop": {}
-}
diff --git a/bilby/core/prior/dict.py b/bilby/core/prior/dict.py
index 3324daf3b04fb6589f36326c180072c46ce6672e..a375625b3082ecf54d9005ff85fef766671386b2 100644
--- a/bilby/core/prior/dict.py
+++ b/bilby/core/prior/dict.py
@@ -1,14 +1,12 @@
from importlib import import_module
from io import open as ioopen
import json
-import numpy as np
import os
from future.utils import iteritems
from matplotlib.cbook import flatten
+import numpy as np
-# keep 'import *' to make eval() statement further down work consistently
-from bilby.core.prior.analytical import * # noqa
from bilby.core.prior.analytical import DeltaFunction
from bilby.core.prior.base import Prior, Constraint
from bilby.core.prior.joint import JointPrior
@@ -142,7 +140,6 @@ class PriorDict(dict):
comments = ['#', '\n']
prior = dict()
- mvgdict = dict(inf=np.inf) # evaluate inf as np.inf
with ioopen(filename, 'r', encoding='unicode_escape') as f:
for line in f:
if line[0] in comments:
@@ -151,39 +148,8 @@ class PriorDict(dict):
elements = line.split('=')
key = elements[0].replace(' ', '')
val = '='.join(elements[1:]).strip()
- cls = val.split('(')[0]
- args = '('.join(val.split('(')[1:])[:-1]
- try:
- prior[key] = DeltaFunction(peak=float(cls))
- logger.debug("{} converted to DeltaFunction prior".format(
- key))
- continue
- except ValueError:
- pass
- if "." in cls:
- module = '.'.join(cls.split('.')[:-1])
- cls = cls.split('.')[-1]
- else:
- module = __name__.replace('.' + os.path.basename(__file__).replace('.py', ''), '')
- cls = getattr(import_module(module), cls, cls)
- if key.lower() in ["conversion_function", "condition_func"]:
- setattr(self, key, cls)
- elif (cls.__name__ in ['MultivariateGaussianDist',
- 'MultivariateNormalDist']):
- if key not in mvgdict:
- mvgdict[key] = eval(val, None, mvgdict)
- elif (cls.__name__ in ['MultivariateGaussian',
- 'MultivariateNormal']):
- prior[key] = eval(val, None, mvgdict)
- else:
- try:
- prior[key] = cls.from_repr(args)
- except TypeError as e:
- raise TypeError(
- "Unable to parse dictionary file {}, bad line: {} "
- "= {}. Error message {}".format(
- filename, key, val, e))
- self.update(prior)
+ prior[key] = val
+ self.from_dictionary(prior)
@classmethod
def _get_from_json_dict(cls, prior_dict):
@@ -218,22 +184,61 @@ class PriorDict(dict):
return obj
def from_dictionary(self, dictionary):
+ eval_dict = dict(inf=np.inf)
for key, val in iteritems(dictionary):
- if isinstance(val, str):
+ if isinstance(val, Prior):
+ continue
+ elif isinstance(val, (int, float)):
+ dictionary[key] = DeltaFunction(peak=val)
+ elif isinstance(val, str):
+ cls = val.split('(')[0]
+ args = '('.join(val.split('(')[1:])[:-1]
try:
- prior = eval(val)
- if isinstance(prior, (Prior, float, int, str)):
- val = prior
- except (NameError, SyntaxError, TypeError):
- logger.debug(
- "Failed to load dictionary value {} correctly"
- .format(key))
+ dictionary[key] = DeltaFunction(peak=float(cls))
+ logger.debug("{} converted to DeltaFunction prior".format(key))
+ continue
+ except ValueError:
pass
+ if "." in cls:
+ module = '.'.join(cls.split('.')[:-1])
+ cls = cls.split('.')[-1]
+ else:
+ module = __name__.replace(
+ '.' + os.path.basename(__file__).replace('.py', ''), ''
+ )
+ cls = getattr(import_module(module), cls, cls)
+ if key.lower() in ["conversion_function", "condition_func"]:
+ setattr(self, key, cls)
+ elif isinstance(cls, str):
+ if "(" in val:
+ raise TypeError("Unable to parse prior class {}".format(cls))
+ else:
+ continue
+ elif (cls.__name__ in ['MultivariateGaussianDist',
+ 'MultivariateNormalDist']):
+ if key not in eval_dict:
+ eval_dict[key] = eval(val, None, eval_dict)
+ elif (cls.__name__ in ['MultivariateGaussian',
+ 'MultivariateNormal']):
+ dictionary[key] = eval(val, None, eval_dict)
+ else:
+ try:
+ dictionary[key] = cls.from_repr(args)
+ except TypeError as e:
+ raise TypeError(
+ "Unable to parse prior, bad entry: {} "
+ "= {}. Error message {}".format(key, val, e)
+ )
elif isinstance(val, dict):
logger.warning(
'Cannot convert {} into a prior object. '
'Leaving as dictionary.'.format(key))
- self[key] = val
+ else:
+ raise TypeError(
+ "Unable to parse prior, bad entry: {} "
+ "= {} of type {}".format(key, val, type(val))
+ )
+ self.update(dictionary)
def convert_floats_to_delta_functions(self):
""" Convert all float parameters to delta functions """
diff --git a/bilby/core/result.py b/bilby/core/result.py
index 630830dfc3830de9f6e338e88f469523bfa3f988..19b372096ce367f30f0f85d88da8b5f492a0ad86 100644
--- a/bilby/core/result.py
+++ b/bilby/core/result.py
@@ -20,7 +20,7 @@ from . import utils
from .utils import (logger, infer_parameters_from_function,
check_directory_exists_and_if_not_mkdir,)
from .utils import BilbyJsonEncoder, decode_bilby_json
-from .prior import Prior, PriorDict, DeltaFunction, ConditionalPriorDict
+from .prior import Prior, PriorDict, DeltaFunction
def result_file_name(outdir, label, extension='json', gzip=False):
@@ -276,15 +276,15 @@ class Result(object):
if getattr(self, 'posterior', None) is not None:
if getattr(self, 'log_noise_evidence', None) is not None:
return ("nsamples: {:d}\n"
- "log_noise_evidence: {:6.3f}\n"
- "log_evidence: {:6.3f} +/- {:6.3f}\n"
- "log_bayes_factor: {:6.3f} +/- {:6.3f}\n"
+ "ln_noise_evidence: {:6.3f}\n"
+ "ln_evidence: {:6.3f} +/- {:6.3f}\n"
+ "ln_bayes_factor: {:6.3f} +/- {:6.3f}\n"
.format(len(self.posterior), self.log_noise_evidence, self.log_evidence,
self.log_evidence_err, self.log_bayes_factor,
self.log_evidence_err))
else:
return ("nsamples: {:d}\n"
- "log_evidence: {:6.3f} +/- {:6.3f}\n"
+ "ln_evidence: {:6.3f} +/- {:6.3f}\n"
.format(len(self.posterior), self.log_evidence, self.log_evidence_err))
else:
return ''
@@ -299,7 +299,7 @@ class Result(object):
@priors.setter
def priors(self, priors):
if isinstance(priors, dict):
- if isinstance(priors, ConditionalPriorDict):
+ if isinstance(priors, PriorDict):
self._priors = priors
else:
self._priors = PriorDict(priors)
@@ -389,6 +389,22 @@ class Result(object):
def posterior(self, posterior):
self._posterior = posterior
+ @property
+ def log_10_bayes_factor(self):
+ return self.log_bayes_factor / np.log(10)
+
+ @property
+ def log_10_evidence(self):
+ return self.log_evidence / np.log(10)
+
+ @property
+ def log_10_evidence_err(self):
+ return self.log_evidence_err / np.log(10)
+
+ @property
+ def log_10_noise_evidence(self):
+ return self.log_noise_evidence / np.log(10)
+
@property
def version(self):
return self._version
@@ -1526,8 +1542,9 @@ def plot_multiple(results, filename=None, labels=None, colours=None,
return fig
-def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
+def make_pp_plot(results, filename=None, save=True, confidence_interval=[0.68, 0.95, 0.997],
lines=None, legend_fontsize='x-small', keys=None, title=True,
+ confidence_interval_alpha=0.1,
**kwargs):
"""
Make a P-P plot for a set of runs with injected signals.
@@ -1540,8 +1557,8 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
The name of the file to save, the default is "outdir/pp.png"
save: bool, optional
Whether to save the file, default=True
- confidence_interval: float, optional
- The confidence interval to be plotted, defaulting to 0.9 (90%)
+ confidence_interval: (float, list), optional
+ The confidence interval to be plotted, defaulting to 1-2-3 sigma
lines: list
If given, a list of matplotlib line formats to use, must be greater
than the number of parameters.
@@ -1549,6 +1566,8 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
The font size for the legend
keys: list
A list of keys to use, if None defaults to search_parameter_keys
+ confidence_interval_alpha: float, list, optional
+ The transparency for the background condifence interval
kwargs:
Additional kwargs to pass to matplotlib.pyplot.plot
@@ -1576,18 +1595,26 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
x_values = np.linspace(0, 1, 1001)
- # Putting in the confidence bands
N = len(credible_levels)
- edge_of_bound = (1. - confidence_interval) / 2.
- lower = scipy.stats.binom.ppf(1 - edge_of_bound, N, x_values) / N
- upper = scipy.stats.binom.ppf(edge_of_bound, N, x_values) / N
- # The binomial point percent function doesn't always return 0 @ 0,
- # so set those bounds explicitly to be sure
- lower[0] = 0
- upper[0] = 0
fig, ax = plt.subplots()
- ax.fill_between(x_values, lower, upper, alpha=0.2, color='k')
+ if isinstance(confidence_interval, float):
+ confidence_interval = [confidence_interval]
+ if isinstance(confidence_interval_alpha, float):
+ confidence_interval_alpha = [confidence_interval_alpha] * len(confidence_interval)
+ elif len(confidence_interval_alpha) != len(confidence_interval):
+ raise ValueError(
+ "confidence_interval_alpha must have the same length as confidence_interval")
+
+ for ci, alpha in zip(confidence_interval, confidence_interval_alpha):
+ edge_of_bound = (1. - ci) / 2.
+ lower = scipy.stats.binom.ppf(1 - edge_of_bound, N, x_values) / N
+ upper = scipy.stats.binom.ppf(edge_of_bound, N, x_values) / N
+ # The binomial point percent function doesn't always return 0 @ 0,
+ # so set those bounds explicitly to be sure
+ lower[0] = 0
+ upper[0] = 0
+ ax.fill_between(x_values, lower, upper, alpha=alpha, color='k')
pvalues = []
logger.info("Key: KS-test p-value")
@@ -1617,7 +1644,7 @@ def make_pp_plot(results, filename=None, save=True, confidence_interval=0.9,
len(results), pvals.combined_pvalue))
ax.set_xlabel("C.I.")
ax.set_ylabel("Fraction of events in C.I.")
- ax.legend(linewidth=1, labelspacing=0.25, fontsize=legend_fontsize)
+ ax.legend(linewidth=1, handlelength=2, labelspacing=0.25, fontsize=legend_fontsize)
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
fig.tight_layout()
diff --git a/bilby/core/sampler/base_sampler.py b/bilby/core/sampler/base_sampler.py
index 9d72464c2584dbd280249ab27c56b802301d76c6..197a6de7653c15f35751295082a1e7ed7826c232 100644
--- a/bilby/core/sampler/base_sampler.py
+++ b/bilby/core/sampler/base_sampler.py
@@ -439,7 +439,10 @@ class Sampler(object):
return np.array(unit_cube), np.array(parameters), np.array(likelihood)
def check_draw(self, theta, warning=True):
- """ Checks if the draw will generate an infinite prior or likelihood
+ """
+ Checks if the draw will generate an infinite prior or likelihood
+
+ Also catches the output of `numpy.nan_to_num`.
Parameters
----------
@@ -452,11 +455,12 @@ class Sampler(object):
True if the likelihood and prior are finite, false otherwise
"""
- if np.isinf(self.log_prior(theta)):
+ bad_values = [np.inf, np.nan_to_num(np.inf), np.nan]
+ if abs(self.log_prior(theta)) in bad_values:
if warning:
logger.warning('Prior draw {} has inf prior'.format(theta))
return False
- if np.isinf(self.log_likelihood(theta)):
+ if abs(self.log_likelihood(theta)) in bad_values:
if warning:
logger.warning('Prior draw {} has inf likelihood'.format(theta))
return False
diff --git a/bilby/core/sampler/cpnest.py b/bilby/core/sampler/cpnest.py
index 1ed64e89c23b1588c88083043e3b74d64989ad0f..4c46e02f29b35a0a1dc5615c52b38ad67cab072e 100644
--- a/bilby/core/sampler/cpnest.py
+++ b/bilby/core/sampler/cpnest.py
@@ -38,7 +38,7 @@ class Cpnest(NestedSampler):
{self.outdir}/cpnest_{self.label}/
"""
- default_kwargs = dict(verbose=1, nthreads=1, nlive=500, maxmcmc=1000,
+ default_kwargs = dict(verbose=3, nthreads=1, nlive=500, maxmcmc=1000,
seed=None, poolsize=100, nhamiltonian=0, resume=True,
output=None, proposals=None, n_periodic_checkpoint=8000)
diff --git a/bilby/core/sampler/dynamic_dynesty.py b/bilby/core/sampler/dynamic_dynesty.py
index 0f2a8013dae906749aabe7f426cc48d5eafb1203..4e7e1f56dfa5134b25ef9e50c9cb83f3dc2b8e36 100644
--- a/bilby/core/sampler/dynamic_dynesty.py
+++ b/bilby/core/sampler/dynamic_dynesty.py
@@ -44,9 +44,6 @@ class DynamicDynesty(Dynesty):
Other Parameters
----------------
- npoints: int, (250)
- The number of live points, note this can also equivalently be given as
- one of [nlive, nlives, n_live_points]
bound: {'none', 'single', 'multi', 'balls', 'cubes'}, ('multi')
Method used to select new points
sample: {'unif', 'rwalk', 'slice', 'rslice', 'hslice'}, ('rwalk')
diff --git a/bilby/core/sampler/dynesty.py b/bilby/core/sampler/dynesty.py
index 7d6e511cdb62958e94192d04e22c7dc66f4c5eaf..d11334ee6d8174d31c4646bd5ec18cec8d1a3d99 100644
--- a/bilby/core/sampler/dynesty.py
+++ b/bilby/core/sampler/dynesty.py
@@ -329,7 +329,6 @@ class Dynesty(NestedSampler):
sampler_kwargs['add_live'] = True
self.start_time = datetime.datetime.now()
while True:
- sampler_kwargs['maxcall'] += self.n_check_point
self._run_nested_wrapper(sampler_kwargs)
if self.sampler.ncall == old_ncall:
break
diff --git a/bilby/gw/conversion.py b/bilby/gw/conversion.py
index 31eef6eea8e0217c34487d4919fd3a44245163a0..469eef80e594f6a06013be2945d1262366855fcd 100644
--- a/bilby/gw/conversion.py
+++ b/bilby/gw/conversion.py
@@ -56,7 +56,7 @@ def luminosity_distance_to_comoving_distance(distance, cosmology=None):
def bilby_to_lalsimulation_spins(
theta_jn, phi_jl, tilt_1, tilt_2, phi_12, a_1, a_2, mass_1, mass_2,
reference_frequency, phase):
- if tilt_1 in [0, np.pi] and tilt_2 in [0, np.pi]:
+ if (a_1 == 0 or tilt_1 in [0, np.pi]) and (a_2 == 0 or tilt_2 in [0, np.pi]):
spin_1x = 0
spin_1y = 0
spin_1z = a_1 * np.cos(tilt_1)
diff --git a/bilby/gw/likelihood.py b/bilby/gw/likelihood.py
index 45078af08199b935ec0f230bc13c650c451482d8..78626248189be863e5bd45f8bd0ad08f7225b1d4 100644
--- a/bilby/gw/likelihood.py
+++ b/bilby/gw/likelihood.py
@@ -22,7 +22,7 @@ from ..core.utils import (
speed_of_light, radius_of_earth)
from ..core.prior import Interped, Prior, Uniform
from .detector import InterferometerList
-from .prior import BBHPriorDict, CBCPriorDict
+from .prior import BBHPriorDict, CBCPriorDict, Cosmological
from .source import lal_binary_black_hole
from .utils import noise_weighted_inner_product, build_roq_weights, blockwise_dot_product
from .waveform_generator import WaveformGenerator
@@ -143,6 +143,9 @@ class GravitationalWaveTransient(Likelihood):
for distance in self._distance_array])
self._setup_distance_marginalization(
distance_marginalization_lookup_table)
+ for key in ['redshift', 'comoving_distance']:
+ if key in priors:
+ del priors[key]
priors['luminosity_distance'] = float(self._ref_dist)
self._marginalized_parameters.append('luminosity_distance')
@@ -214,6 +217,18 @@ class GravitationalWaveTransient(Likelihood):
self.priors[key] = Uniform(
self.interferometers.start_time,
self.interferometers.start_time + self.interferometers.duration)
+ elif key == 'luminosity_distance':
+ for key in ['redshift', 'comoving_distance']:
+ if key in self.priors:
+ if not isinstance(self.priors[key], Cosmological):
+ raise TypeError(
+ "To marginalize over {}, the prior must be specified as a "
+ "subclass of bilby.gw.prior.Cosmological.".format(key)
+ )
+ self.priors['luminosity_distance'] = self.priors[key].get_corresponding_prior(
+ 'luminosity_distance'
+ )
+ del self.priors[key]
else:
self.priors[key] = BBHPriorDict()[key]
diff --git a/bilby/gw/prior.py b/bilby/gw/prior.py
index 5a07c8e944970071b4b08667e5717d34f9c2a137..c8472ebf90334d62128cb758aa0c92a571e729ba 100644
--- a/bilby/gw/prior.py
+++ b/bilby/gw/prior.py
@@ -136,31 +136,7 @@ class Cosmological(Interped):
@minimum.setter
def minimum(self, minimum):
- cosmology = get_cosmology(self.cosmology)
- self._minimum[self.name] = minimum
- if self.name == 'redshift':
- self._minimum['luminosity_distance'] =\
- cosmology.luminosity_distance(minimum).value
- self._minimum['comoving_distance'] =\
- cosmology.comoving_distance(minimum).value
- elif self.name == 'luminosity_distance':
- if minimum == 0:
- self._minimum['redshift'] = 0
- else:
- self._minimum['redshift'] = cosmo.z_at_value(
- cosmology.luminosity_distance, minimum * self.unit)
- self._minimum['comoving_distance'] = self._minimum['redshift']
- elif self.name == 'comoving_distance':
- if minimum == 0:
- self._minimum['redshift'] = 0
- else:
- self._minimum['redshift'] = cosmo.z_at_value(
- cosmology.comoving_distance, minimum * self.unit)
- self._minimum['luminosity_distance'] = self._minimum['redshift']
- try:
- self._update_instance()
- except (AttributeError, KeyError):
- pass
+ self._set_limit(value=minimum, limit_dict=self._minimum)
@property
def maximum(self):
@@ -168,21 +144,44 @@ class Cosmological(Interped):
@maximum.setter
def maximum(self, maximum):
+ self._set_limit(value=maximum, limit_dict=self._maximum)
+
+ def _set_limit(self, value, limit_dict):
+ """
+ Set either the limits for redshift luminosity and comoving distances
+
+ Parameters
+ ----------
+ value: float
+ Limit value in current class' parameter
+ limit_dict: dict
+ The limit dictionary to modify in place
+ """
cosmology = get_cosmology(self.cosmology)
- self._maximum[self.name] = maximum
+ limit_dict[self.name] = value
if self.name == 'redshift':
- self._maximum['luminosity_distance'] = \
- cosmology.luminosity_distance(maximum).value
- self._maximum['comoving_distance'] = \
- cosmology.comoving_distance(maximum).value
+ limit_dict['luminosity_distance'] = \
+ cosmology.luminosity_distance(value).value
+ limit_dict['comoving_distance'] = \
+ cosmology.comoving_distance(value).value
elif self.name == 'luminosity_distance':
- self._maximum['redshift'] = cosmo.z_at_value(
- cosmology.luminosity_distance, maximum * self.unit)
- self._maximum['comoving_distance'] = self._maximum['redshift']
+ if value == 0:
+ limit_dict['redshift'] = 0
+ else:
+ limit_dict['redshift'] = cosmo.z_at_value(
+ cosmology.luminosity_distance, value * self.unit)
+ limit_dict['comoving_distance'] = (
+ cosmology.comoving_distance(limit_dict['redshift']).value
+ )
elif self.name == 'comoving_distance':
- self._maximum['redshift'] = cosmo.z_at_value(
- cosmology.comoving_distance, maximum * self.unit)
- self._maximum['luminosity_distance'] = self._maximum['redshift']
+ if value == 0:
+ limit_dict['redshift'] = 0
+ else:
+ limit_dict['redshift'] = cosmo.z_at_value(
+ cosmology.comoving_distance, value * self.unit)
+ limit_dict['luminosity_distance'] = (
+ cosmology.luminosity_distance(limit_dict['redshift']).value
+ )
try:
self._update_instance()
except (AttributeError, KeyError):
@@ -767,22 +766,30 @@ ConditionalUniformSourceFrame = conditional_prior_factory(UniformSourceFrame)
class HealPixMapPriorDist(BaseJointPriorDist):
- def __init__(self, hp_file, names=None, bounds=None, distance=False):
- """
- Class defining prior according to given HealPix Map, defaults to 2D in ra and dec but can be set to include
- Distance as well. This only works with skymaps that include the 2D joint probability in ra/dec and that use the
- normal LALInference type skymaps where each pixel has a DISTMU, DISTSIGMA, and DISTNORM defining the conditional
- distance distribution along a given line of sight.
-
- hp_file: file path to .fits file
- .fits file that containes the 2D or 3D Healpix Map
- names: list (optional)
- list of names of parameters included in the JointPriorDist, defaults to ['ra', 'dec']
- bounds: dict or list (optional)
- dictionary or list with given prior bounds. defaults to normal bounds on ra, dev and 0, inf for distance
- if this is for a 3D map
- """
+ """
+ Class defining prior according to given HealPix Map, defaults to 2D in ra and dec but can be set to include
+ Distance as well. This only works with skymaps that include the 2D joint probability in ra/dec and that use the
+ normal LALInference type skymaps where each pixel has a DISTMU, DISTSIGMA, and DISTNORM defining the conditional
+ distance distribution along a given line of sight.
+
+ Parameters
+ ----------
+
+ hp_file : file path to .fits file
+ .fits file that containes the 2D or 3D Healpix Map
+ names : list (optional)
+ list of names of parameters included in the JointPriorDist, defaults to ['ra', 'dec']
+ bounds : dict or list (optional)
+ dictionary or list with given prior bounds. defaults to normal bounds on ra, dev and 0, inf for distance
+ if this is for a 3D map
+
+ Returns
+ -------
+ PriorDist : `bilby.gw.prior.HealPixMapPriorDist`
+ A JointPriorDist object to store the joint prior distribution according to passed healpix map
+ """
+ def __init__(self, hp_file, names=None, bounds=None, distance=False):
self.hp = self._check_imports()
self.hp_file = hp_file
if names is None:
@@ -854,14 +861,14 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- samp: float, int
+ samp : float, int
must take in single value for pixel on unitcube to recale onto ra, dec (distance), for the map Prior
- kwargs: dict
+ kwargs : dict
kwargs are all passed to _rescale() method
Returns
- ----------
- array_like
+ -------
+ rescaled_sample : array_like
sample to rescale onto the prior
"""
if self.distance:
@@ -889,12 +896,15 @@ class HealPixMapPriorDist(BaseJointPriorDist):
JointPrior Parameters. This function updates the current distance pdf, inverse_cdf, and sampler according to
given pixel or line of sight.
+ Parameters
----------
- pix_idx: int
+ pix_idx : int
pixel index value to create the distribtuion for
+
Returns
- ----------
- None - just updates these functions at new pixel values
+ -------
+ None : None
+ just updates these functions at new pixel values
"""
self.distance_pdf = lambda r: self.distnorm[pix_idx] * norm(
loc=self.distmu[pix_idx], scale=self.distsigma[pix_idx]
@@ -918,12 +928,12 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- array: array_like
+ array : array_like
input array we want to renormalize if not already normalized
Returns
- ---------
- array_like:
+ -------
+ normed_array : array_like
returns input array normalized
"""
norm = np.linalg.norm(array, ord=1)
@@ -940,14 +950,14 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- size: int
+ size : int
number of samples we want to draw
- kwargs: dict
+ kwargs : dict
kwargs are all passed to be used
Returns
- ----------
- array_like
+ -------
+ sample : array_like
sample of ra, and dec (and distance if 3D=True)
"""
pixel_choices = np.arange(self.npix)
@@ -971,9 +981,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Method to recursively draw a distance value from the given set distance distribution and check that it is in
the bounds
- Returns
+ Parameters
----------
- dist: sample drawn from the distance distribution at set pixel index
+
+ pix : int
+ integer for pixel to draw a distance from
+
+ Returns
+ -------
+ dist : float
+ sample drawn from the distance distribution at set pixel index
"""
if self.distmu[pix] == np.inf or self.distmu[pix] <= 0:
return 0
@@ -990,16 +1007,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- ra: float, int
+ ra : float, int
value drawn for rightascension
- dec: float, int
+ dec : float, int
value drawn for declination
- pix: int
+ pix : int
pixel index for given pixel we want to get ra, and dec from
Returns
- ---------
- tuple:
+ -------
+ ra_dec : tuple
this returns a tuple of ra, and dec sampled uniformly that are in the pixel given
"""
if not self.check_in_pixel(ra, dec, pix):
@@ -1017,16 +1034,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- ra: float, int
+ ra : float, int
rightascension value to check
- dec: float, int
+ dec : float, int
declination value to check
- pix: int
+ pix : int
index for pixel we want to check in
Returns
- --------
- bool:
+ -------
+ bool :
returns True if values inside pixel, False if not
"""
for val, name in zip([ra, dec], self.names):
@@ -1042,16 +1059,16 @@ class HealPixMapPriorDist(BaseJointPriorDist):
Parameters
----------
- samp: array_like
+ samp : array_like
samples of ra, dec to evaluate the lnprob at
- lnprob: array_like
+ lnprob : array_like
array of correct length we want to populate with lnprob values
- outbounds: boolean array
+ outbounds : boolean array
boolean array that flags samples that are out of the given bounds
Returns
- ----------
- array_like
+ -------
+ lnprob : array_like
lnprob values at each sample
"""
for i in range(samp.shape[0]):
diff --git a/bilby/gw/source.py b/bilby/gw/source.py
index 6fc9d354f4741abe875a6a94f12c56d08c901482..c6f7101801bbe671bc0785da8d217a94262503ee 100644
--- a/bilby/gw/source.py
+++ b/bilby/gw/source.py
@@ -396,6 +396,13 @@ def _base_lal_cbc_fd_waveform(
h_plus *= frequency_bounds
h_cross *= frequency_bounds
+ if wf_func == lalsim_SimInspiralFD:
+ dt = 1. / delta_frequency + (hplus.epoch.gpsSeconds + hplus.epoch.gpsNanoSeconds * 1e-9)
+ h_plus *= np.exp(
+ -1j * 2 * np.pi * dt * frequency_array)
+ h_cross *= np.exp(
+ -1j * 2 * np.pi * dt * frequency_array)
+
return dict(plus=h_plus, cross=h_cross)
diff --git a/docs/gw_prior.txt b/docs/gw_prior.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1167ec7edf9aa407575b4a37e5e60796095e6874
--- /dev/null
+++ b/docs/gw_prior.txt
@@ -0,0 +1,32 @@
+.. gw_prior:
+
+===================================
+Transient Graviatiaonal wave priors
+===================================
+
+A Cosmological GW prior, :code:`Cosmological`:
+
+.. autoclass:: bilby.gw.prior.Cosmological
+ :members:
+
+Uniform in Comoving Volume GW Prior (inherited from Cosmological) :code:`UniformComovingVolume`:
+
+.. autoclass:: bilby.gw.prior.UniformComovingVolume
+ :members:
+
+Uniform in Source Frame GW Prior :code:`UniformSourceFrame`:
+
+.. autoclass:: bilby.gw.prior.UniformSourceFrame
+ :members:
+
+Aligned Spine GW Prior :code:`AlignedSpin`:
+
+.. autoclass:: bilby.gw.prior.AlignedSpin
+ :members:
+
+HealPixMap JointPriorDist (See JointPriors in bilby.core.prior.joint) :code:`HealPixMapPriorDist`:
+
+.. autoclass:: bilby.gw.prior.HealPixMapPriorDist
+ :members:
+
+
diff --git a/docs/index.txt b/docs/index.txt
index eb0cb84acb78e42e0d323f60d552fecd8c9dd440..45c4b119810b707ae6e36bea30752ea2815d231f 100644
--- a/docs/index.txt
+++ b/docs/index.txt
@@ -20,6 +20,7 @@ Welcome to bilby's documentation!
compact-binary-coalescence-parameter-estimation
transient-gw-data
gw_likelihood
+ gw_prior
conversion
gw_references
writing-documentation
diff --git a/examples/core_examples/logo/sample_logo.py b/examples/core_examples/logo/sample_logo.py
index 9340e34f5b4d1ede09d562650fa752ccf974b4f2..20b591a39c0e091c9e1ac660f89b572939c37650 100644
--- a/examples/core_examples/logo/sample_logo.py
+++ b/examples/core_examples/logo/sample_logo.py
@@ -8,14 +8,14 @@ from skimage import io
class Likelihood(bilby.Likelihood):
def __init__(self, interp):
self.interp = interp
- self.parameters = dict(x=None, y=None)
+ super().__init__(parameters=dict(x=None, y=None))
def log_likelihood(self):
return -1 / (self.interp(self.parameters['x'], self.parameters['y'])[0])
for letter in ['B', 'I', 'L', 'Y']:
- img = 1 - io.imread('{}.png'.format(letter), as_grey=True)[::-1, :]
+ img = 1 - io.imread('{}.png'.format(letter), as_gray=True)[::-1, :]
x = np.arange(img.shape[0])
y = np.arange(img.shape[1])
interp = si.interpolate.interp2d(x, y, img.T)
diff --git a/setup.cfg b/setup.cfg
index a7a69fe9f033787df2a7eab72d3f56e6f3f6db48..fad8e426f7aece3c1095427bd83748ca9d1255a1 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -9,6 +9,7 @@ addopts =
--ignore test/gw_example_test.py
--ignore test/example_test.py
--ignore test/sample_from_the_prior_test.py
+ --ignore test/sampler_test.py
[metadata]
license_file = LICENSE.md
diff --git a/setup.py b/setup.py
index 1f7aaa45edf181789169728c1725efcc27427732..1c07598b11fbdd7b9fafccf0f0c53ffba74b94db 100644
--- a/setup.py
+++ b/setup.py
@@ -64,7 +64,7 @@ def readfile(filename):
return filecontents
-VERSION = '0.6.4'
+VERSION = '0.6.5'
version_file = write_version_file(VERSION)
long_description = get_long_description()
diff --git a/test/prior_test.py b/test/prior_test.py
index d579e31a64ec1319c4856ee76795aa7c47c24922..832956928bc2c44217395afd287cc564868d4325 100644
--- a/test/prior_test.py
+++ b/test/prior_test.py
@@ -929,7 +929,7 @@ class TestFillPrior(unittest.TestCase):
self.likelihood = Mock()
self.likelihood.parameters = dict(a=0, b=0, c=0, d=0, asdf=0, ra=1)
self.likelihood.non_standard_sampling_parameter_keys = dict(t=8)
- self.priors = dict(a=1, b=1.1, c='string', d=bilby.core.prior.Uniform(0, 1))
+ self.priors = dict(a=1, b=1.1, c="string", d=bilby.core.prior.Uniform(0, 1))
self.priors = bilby.core.prior.PriorDict(dictionary=self.priors)
self.default_prior_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'prior_files/binary_black_holes.prior')
diff --git a/test/sampler_test.py b/test/sampler_test.py
index 7a80eac6fd8d838604b59aca2d873e7328db5120..1bc4b59168c0bb82888d34b70b6efa070eb46375 100644
--- a/test/sampler_test.py
+++ b/test/sampler_test.py
@@ -112,14 +112,14 @@ class TestCPNest(unittest.TestCase):
del self.sampler
def test_default_kwargs(self):
- expected = dict(verbose=1, nthreads=1, nlive=500, maxmcmc=1000,
+ expected = dict(verbose=3, nthreads=1, nlive=500, maxmcmc=1000,
seed=None, poolsize=100, nhamiltonian=0, resume=True,
output='outdir/cpnest_label/', proposals=None,
n_periodic_checkpoint=8000)
self.assertDictEqual(expected, self.sampler.kwargs)
def test_translate_kwargs(self):
- expected = dict(verbose=1, nthreads=1, nlive=250, maxmcmc=1000,
+ expected = dict(verbose=3, nthreads=1, nlive=250, maxmcmc=1000,
seed=None, poolsize=100, nhamiltonian=0, resume=True,
output='outdir/cpnest_label/', proposals=None,
n_periodic_checkpoint=8000)
@@ -552,7 +552,8 @@ class TestRunningSamplers(unittest.TestCase):
def test_run_dynamic_dynesty(self):
_ = bilby.run_sampler(
likelihood=self.likelihood, priors=self.priors, sampler='dynamic_dynesty',
- nlive=100, save=False)
+ nlive_init=100, nlive_batch=100, dlogz_init=1.0, maxbatch=0,
+ maxcall=100, bound="single", save=False)
def test_run_emcee(self):
_ = bilby.run_sampler(