Fast and scalable Gaussian Processes in 1D
Project description
celerite2
celerite is an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension and this library, celerite2 is a re-write of the original celerite project to improve numerical stability and integration with various machine learning frameworks. Documentation for this version can be found here. This new implementation includes interfaces in Python and C++, with full support for PyMC (v3 and v4) and JAX.
This documentation won't teach you the fundamentals of GP modeling but the best resource for learning about this is available for free online: Rasmussen & Williams (2006). Similarly, the celerite algorithm is restricted to a specific class of covariance functions (see the original paper for more information and a recent generalization for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, GPyTorch might be a good choice.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for celerite2-0.3.2-cp312-cp312-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | da59f1563fb912967025e2ac4e49def0e260f6705574344be8ad04a9a11ddb61 |
|
MD5 | ca95d49ab865290f498f9e284e8de54b |
|
BLAKE2b-256 | 0a4b7030c369dbc62f1deeaefc262205ee8fc8f62735eed28f7ec03729511672 |
Hashes for celerite2-0.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9bf284866ea1a5e94c27bc66cb526086ae8f4cb6e6f0a7747e289e5872ddb269 |
|
MD5 | dcbd617bfdd6de83d2aee75658b59c59 |
|
BLAKE2b-256 | f158de7d2b8daa50715ac3bd48bdd285502f27b79eb6fe1285c7d1ce95de26bb |
Hashes for celerite2-0.3.2-cp312-cp312-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cf8b80ea8c465508ace18c511f681a7f979c88495fe02b1e93472df32e0a9e5 |
|
MD5 | 7540d8ddf760c612b3df6b15f867c2ee |
|
BLAKE2b-256 | 9cf90ad3912ef8e436feda76492f7ef150ee10ba449c6cea5aba3580e9dd5f45 |
Hashes for celerite2-0.3.2-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53781c7b2e8759bc6a9dc8e86965c6a77da1d2f584cf9c0e3822e20af4c64e3a |
|
MD5 | 6b855a853cea94153dc1cacd4986803a |
|
BLAKE2b-256 | 4fd6213ad2a9066d5d2127d5bee329b275a3bb9d1b5a87486db809e1674731ba |
Hashes for celerite2-0.3.2-cp311-cp311-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2cc48985e35b258b5ee7317d737e80d40e9579bdd5d6a93ba9294374db55ec4a |
|
MD5 | 0ce501e1e1220d4d1756efc42b368c4e |
|
BLAKE2b-256 | fddd76a3f61158879a52117fbdf46ef7088bed3192996d116426a57bb1ba49ff |
Hashes for celerite2-0.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 496811917fc85e550b9f86d6b93b97e2b08c179721a11781409d9f4d5d87153b |
|
MD5 | 234561db57bad6cda0dc607ad8f0c3a8 |
|
BLAKE2b-256 | 142326792653f96e8f9e91987b8171c0e8b540251d61f44f4dc4318dec4eb882 |
Hashes for celerite2-0.3.2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 67b89c27e8d34e0d9e5fafe89864b7a5899f700e40723ebf9fc3ee78d0a13537 |
|
MD5 | 0e86d0114c9166de5199b674a73e88e9 |
|
BLAKE2b-256 | fc04d781fd51859b5762ddff3d91fba0bd70397251af48bbbbaf015472d5aed0 |
Hashes for celerite2-0.3.2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 837ba24c3472789111b95ef437ff7caaafd648ef5a6818c092cda0f69b5774a6 |
|
MD5 | 2f2482ead4dec2421e3780a49ed625fa |
|
BLAKE2b-256 | 823f0d35cae3b7aeba07e73732d063bfac66f360574f0b543a80dd0e28fce674 |
Hashes for celerite2-0.3.2-cp310-cp310-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6ed527e41c2da7c0248478f0203421db5fbb12748426f04b65cc866c1f32a888 |
|
MD5 | f011bfc8531fe01c54f0ea27882626c1 |
|
BLAKE2b-256 | d469802324d6603a35e5671ebe3e32ab9aec4113c004c30b818157c34c550380 |
Hashes for celerite2-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 308700056975396676a668847947bbcf91a1a2320999060428f974713edf63aa |
|
MD5 | 294a1487ab797e65c7737eab9f848e16 |
|
BLAKE2b-256 | 7ea441a5d43678955a631d829c34630728ab2cca0acedbd7e1e3abf83fa6bac5 |
Hashes for celerite2-0.3.2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 65069c52c94ff4780b89253cad326ba12cd58633f6af29290f0abe46498e8755 |
|
MD5 | cbc80542575d2ce5f2885aad892be537 |
|
BLAKE2b-256 | 650a965fd88ea8fd2f36e0ea49de9cca4b882db1718d9333ab4cfc2e54b73f93 |
Hashes for celerite2-0.3.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5e71c92831bad82c2162e36d7a825bf969c28ed6ff2aed63b0f9661603f1ec1e |
|
MD5 | 2f260e02c8a2e0bc9b5e7004f77e817a |
|
BLAKE2b-256 | 58afec95425f3b2497c5f899b9a3b35be8fa1e99b6b61405c91d7aaa47090a71 |
Hashes for celerite2-0.3.2-cp39-cp39-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4a3cbaf48ddb82ea2b858de88609781c91fc94639317360e8e40ecccb1cf94bc |
|
MD5 | ea29a2b50c3f1ce388e27cf3456916b7 |
|
BLAKE2b-256 | 4095bfaef2f543331a0334cb2ca6510d493b8317a298816c49c8df00cad0db1f |
Hashes for celerite2-0.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7aa822eba82f9798d5cc3863201f2a69f5f1e984a5b55e446a99ffdcdf8b679b |
|
MD5 | 6528be04fca79e6a4bb66a0299c33510 |
|
BLAKE2b-256 | aff4ea74639266e1d8e131665d8f9fed8f7eb5251c7741cd169d1c21e184bb16 |
Hashes for celerite2-0.3.2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 18dbb7799cafe8f4b50f09d49be2bcd0c589e3edb0db98318cc3fba0984dc33f |
|
MD5 | fc4f0c7707f3a91052849167ec54be17 |
|
BLAKE2b-256 | 4cd0f89e2eb321c4e08793d7ba46d2b1324257ad0ebd0794ccc74f473fea1fff |
Hashes for celerite2-0.3.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4e3b72bbe56787d91baa08007c239368bed5709c8e336dfc937116f033552320 |
|
MD5 | 073c9261d8e04b8ce96822e179e24aa4 |
|
BLAKE2b-256 | 70d8550aaa3ee57c47ef7e31a17102c61bb4929fef8bc9e566c0507ce9d0328b |