metatensor

v0.2.0

Self-describing sparse tensor data format for atomistic machine learning and beyond For more information about how to use this package see README

Latest version published 10 months ago

License: BSD-3-Clause

PyPI

GitHub

Package Health Score

73 / 100

security
No known security issues
popularity
Limited
maintenance
Sustainable
community
Active

Popularity

Limited

GitHub Stars: 52
Forks: 18
Contributors: 30

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package metatensor, we found that it has been starred 52 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jan 26, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
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License Risk: 0
H
0
M
0
L

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

License

BSD-3-Clause

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 68
Open PR: 4
Last Release: 10 months ago
Last Commit: 14 hours ago

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

We found that metatensor 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

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 30
Funding: No

With more than 10 contributors for the metatensor repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: >=3.8

Age: 1 year
Latest Release: 10 months ago
Dependencies: 3 Direct
Versions: 4
Maintainers: 4
Wheels: OS Independent

Readme

Metatensor

Metatensor is a self-describing sparse tensor data format for atomistic machine learning and beyond; storing values and gradients of these values together. Think numpy ndarray or pytorch Tensor equipped with extra metadata for atomic systems and other point clouds data. The core of this library is written in Rust and we provide API for C, C++, and Python.

The main class of metatensor is the TensorMap data structure, defining a custom block-sparse data format. If you are using metatensor from Python, we additionally provide a collection of mathematical, logical and other utility operations to make working with TensorMaps more convenient.

Documentation

For details, tutorials, and examples, please have a look at our documentation.

Contributors

Thanks goes to all people that make metatensor possible:

We always welcome new contributors. If you want to help us take a look at our contribution guidelines and afterwards you may start with an open issue marked as good first issue.

metatensor dependencies

metatensor-core metatensor-learn metatensor-operations

FAQs

What is metatensor?

Self-describing sparse tensor data format for atomistic machine learning and beyond. Visit Snyk Advisor to see a full health score report for metatensor, including popularity, security, maintenance & community analysis.

Is metatensor popular?

The python package metatensor receives a total of 585 weekly downloads. As such, metatensor popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is metatensor well maintained?

We found indications that metatensor maintenance is sustainable demonstrating some project activity. We saw a total of 30 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is metatensor safe to use?

The python package metatensor 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 7 November-2024, at 23:36 (UTC).

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