Package Health Score

50 / 100

security
Security review needed
popularity
Limited
maintenance
Inactive
community
Limited

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Popularity

Limited

GitHub Stars: 8
Forks: 7
Contributors: 9

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package daops, we found that it has been starred 8 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Apr 10, 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
1
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 17
Open PR: 4
Last Release: 9 months ago
Last Commit: 8 months ago

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

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

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 9
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.7.0

Age: 5 years
Latest Release: 9 months ago
Dependencies: 4 Direct / 40 Total
Versions: 11
Maintainers: 0
Wheels: No

Readme

daops - data-aware operations

The daops library (pronounced "day-ops") provides a python interface to a set of operations suitable for working with climate simulation outputs. It is typically used with ESGF data sets that are described in NetCDF files. daops is unique in that it accesses a store of fixes defined for datasets that are irregular when compared with others in their population.

When a daops operation, such as subset, is requested, the library will look up a database of known fixes before performing and calculations or transformations. The data will be loaded and fixed using the xarray library before the any actual operations are sent to its sister library clisops.

Free software: BSD
Documentation:

Features

The package has the following features:

Ability to run data-reduction operations on large climate data sets.
Knowledge of irregularities/anomalies in some climate data sets.
Ability to apply fixes to those data sets before operating on them. This process is called normalisation of the data sets.

Credits

This package was created with Cookiecutter and the cedadev/cookiecutter-pypackage project template.

Cookiecutter:
cookiecutter-pypackage:

daops dependencies

aiohttp cftime fsspec netcdf4

FAQs

What is daops?

daops - data-aware operations. Visit Snyk Advisor to see a full health score report for daops, including popularity, security, maintenance & community analysis.

Is daops popular?

The python package daops receives a total of 120 weekly downloads. As such, daops popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is daops well maintained?

We found indications that daops is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is daops safe to use?

While scanning the latest version of daops, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 25 December-2024, at 06:05 (UTC).

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