Fast and scalable Gaussian Processes in 1D

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Development Status
- 4 - Beta
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- Developers
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python
- Python :: 3

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

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Project links

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GitHub Statistics

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Development Status
- 4 - Beta
Intended Audience
- Developers
- Science/Research
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python
- Python :: 3

Release history Release notifications | RSS feed

This version

0.3.1

Mar 6, 2024

0.3.1rc1 pre-release

Mar 6, 2024

0.3.0

Nov 28, 2023

0.3.0rc3 pre-release

Nov 21, 2023

0.3.0rc1 pre-release

Oct 20, 2022

0.2.1

Jan 11, 2022

0.2.1rc1 pre-release

Jan 11, 2022

0.2.0

Mar 15, 2021

0.2.0rc2 pre-release

Mar 12, 2021

0.1.0

Nov 2, 2020

0.0.2

Oct 3, 2020

0.0.1

Sep 18, 2020

0.0.1rc1 pre-release

Aug 10, 2020

0.0.1.dev2 pre-release

Aug 4, 2020

0.0.1.dev1 pre-release

Jul 8, 2020

0.0.1.dev0 pre-release

Jul 7, 2020

0.0.0

Aug 10, 2020

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Hashes for celerite2-0.3.1.tar.gz

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Hashes for celerite2-0.3.1-cp312-cp312-win_amd64.whl
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Algorithm	Hash digest
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Hashes for celerite2-0.3.1-cp311-cp311-win_amd64.whl
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Hashes for celerite2-0.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Hashes for celerite2-0.3.1-cp311-cp311-macosx_11_0_arm64.whl
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Hashes for celerite2-0.3.1-cp39-cp39-win_amd64.whl
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MD5	`c5347dcdcd629f0a3ea2df0eee6d9aca`
BLAKE2b-256	`a2c8a5b73346e2e3be009537a47dbcb984da6940075c0e5feb994ba8fb6b30a1`

Hashes for celerite2-0.3.1-cp37-cp37m-win_amd64.whl

Hashes for celerite2-0.3.1-cp37-cp37m-win_amd64.whl
Algorithm	Hash digest
SHA256	`7d3250022a7a5226f6594da40afde15e52f90333d2879601550aac9df2b8ab01`
MD5	`4817325007e3010f3d63c44e45433055`
BLAKE2b-256	`289b48c191bf7ad5850d7372bd8fe605d0d7eb21c064bc6eb88ffc9340b010c5`

Hashes for celerite2-0.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for celerite2-0.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`554c2954eea12734f34093d05e86255dcadb81f2606d6d1c9458cb3bc6986b1c`
MD5	`46771ee32aeac752f2322ffe37423921`
BLAKE2b-256	`bc9cc7ce163fd7e958b3314e771113d410aebd70280b9a875b0b517780f1fb7c`

Hashes for celerite2-0.3.1-cp37-cp37m-macosx_10_9_x86_64.whl

Hashes for celerite2-0.3.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`ec0aa15f9cd9788d85d129e479124749e0bc1501c388c2197b9213578c090f71`
MD5	`955fabb44c167c6aa8bdc6b5fcc93c4b`
BLAKE2b-256	`593109005ec59c088079c121da2f629f8eaab1d6d7b9b25456438b4c95c33065`

Hashes for celerite2-0.3.1-cp36-cp36m-win_amd64.whl

Hashes for celerite2-0.3.1-cp36-cp36m-win_amd64.whl
Algorithm	Hash digest
SHA256	`171b539eef57068d0d0e2a725d20c5f71c54d26dbc26226d7d6406d49c4590ea`
MD5	`1d16e90bff0b0de82e34af303dc03b53`
BLAKE2b-256	`d16baeabb8d68abb55ba9be951f7a1922edc033e60395ae61232a57d44688655`

Hashes for celerite2-0.3.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for celerite2-0.3.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`f7c76d12017c181ad6ca3757a40601ac4628ff93379f13392407076275f044ab`
MD5	`aeb9a677051da36b62fcb007506aa583`
BLAKE2b-256	`39aacd52f43ec839c8319e81de6693826169601a18e5c57d962b663d5ff2f8b1`

Hashes for celerite2-0.3.1-cp36-cp36m-macosx_10_9_x86_64.whl

Hashes for celerite2-0.3.1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`2d5e865d7fccaa5229d0036381f0b8849eb0f395dbf086006e6f7122407c5224`
MD5	`785e1910e757e1a4905c3d3719f7f6a0`
BLAKE2b-256	`8a44f54f84569d938846a27b8d52964c78ce4c7631fa1993485685701f8011a9`