Package Health Score

69 / 100

security
No known security issues
popularity
Small
maintenance
Sustainable
community
Sustainable

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Popularity

Small

GitHub Stars: 70
Forks: 11
Contributors: 6

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package celerite2, we found that it has been starred 70 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jun 23, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 25
Open PR: 3
Last Release: 5 months ago
Last Commit: 11 days ago

Further analysis of the maintenance status of celerite2 based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that celerite2 demonstrates a positive version release cadence with at least one new version released in the past 12 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 6
Funding: No

This project has seen only 10 or less contributors.

We found a way for you to contribute to the project! Looks like celerite2 is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.9

Age: 4 years
Latest Release: 5 months ago
Dependencies: 1 Direct
Versions: 18
Maintainers: 1
Wheels: Linux, macOS, Windows

Readme

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.

celerite2 dependencies

numpy

FAQs

What is celerite2?

Fast and scalable Gaussian Processes in 1D. Visit Snyk Advisor to see a full health score report for celerite2, including popularity, security, maintenance & community analysis.

Is celerite2 popular?

The python package celerite2 receives a total of 3,455 weekly downloads. As such, celerite2 popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is celerite2 well maintained?

We found indications that celerite2 maintenance is sustainable demonstrating some project activity. We saw a total of 6 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is celerite2 safe to use?

The python package celerite2 was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 18 November-2024, at 08:19 (UTC).

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celerite2

Fast and scalable Gaussian Processes in 1D For more information about how to use this package see README

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celerite2

celerite2 dependencies

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Readme

celerite2

celerite2 dependencies

FAQs

What is celerite2?

Is celerite2 popular?

Is celerite2 well maintained?

Is celerite2 safe to use?

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Source Code

Open Source

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