View libNeuroML on Snyk Open Source Advisor

Package Health Score

63 / 100

security
Security review needed
popularity
Small
maintenance
Sustainable
community
Sustainable

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Popularity

Small

GitHub Stars: 39
Forks: 33
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package libNeuroML, we found that it has been starred 39 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Sep 13, 2024
Direct Vulnerabilities: 0
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0
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Indirect Vulnerabilities: 0
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0
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0
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License Risk: 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

Sustainable

Commit Frequency

Open Issues: 10
Open PR: 4
Last Release: 3 months ago
Last Commit: 4 months ago

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

We found that libNeuroML 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

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 20
Funding: No

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

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

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Package

Python Versions Compatibility: ==3.10.*, ==3.11.*, ==3.12.*, ==3.8.*, ==3.9.*

Age: 12 years
Latest Release: 3 months ago
Dependencies: 4 Direct / 8 Total
Versions: 50
Maintainers: 0
Wheels: OS Independent

Readme

Introduction

This package provides Python libNeuroML, for working with neuronal models specified in NeuroML 2.

For more about libNeuroML see:

Michael Vella, Robert C. Cannon, Sharon Crook, Andrew P. Davison, Gautham Ganapathy, Hugh P. C. Robinson, R. Angus Silver and Padraig Gleeson, libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience Frontiers in Neuroinformatics 2014, doi: 10.3389/fninf.2014.00038

PLEASE CITE THE PAPER ABOVE IF YOU USE libNeuroML!

Documentation is available at http://readthedocs.org/docs/libneuroml/en/latest/

For installation instructions, see http://readthedocs.org/docs/libneuroml/en/latest/install.html

For an overview of all NeuroML related libraries/documentation/publications see https://docs.neuroml.org

pyNeuroML

A related package, pyNeuroML builds on this and provides functionality, scripts and modules for reading, writing, simulating and analysing NeuroML2/LEMS models.

pyNeuroML builds on: libNeuroML & PyLEMS and wraps functionality from jNeuroML.

Development process for libNeuroML

Most of the work happens in the development branch. That branch is kept up to date with the development branches for NeuroML 2 and related libraries. See https://docs.neuroml.org/ for an overview of the various NeuroML libraries.

Changelog

version 0.5.8

drop py3.7, add py3,12
fix loader to check for given path and fall back to relative path
extend get_segment_groups_from_substring to also include an unbranched filter
more type hint/doc fixes

version 0.5.7

more documentation for writes/loaders

version 0.5.6

documentation fixes to writer modules

version 0.5.5

update schema, changes for NML 2.3 release

version 0.5.4

use natsort to improve sorting of segments/groups when optimising

version 0.5.3

add links to schema documentation
move from legacy setup.py to pyproject.toml build system

version 0.5.2

explicitly depend on numpy

version 0.5.1

updates to GHA

version 0.5.0

enable CI on py3.11
fix to loaders
format code with black
add graph representation for morphology, and methods to calculate distances b/w segments

version 0.4.1

add multiple cell builder utility functions
performance improvements in generic helper functions
documentation fixes/improvements
add type annotations to all nml classes to aid users
add level 1 validation method
add generic component inspection methods

version 0.4.0

update to use schema version 2.3
drop python 2 support

version 0.3.1

include schema documentation in generated nml.py API
introduce generic methods to add child/children elements to components

version 0.2.58

multiple documentation fixes

version 0.2.57

Enable Python 3.10 support
Regenerate nml.py with generateDS using Python 3
Add generic add method to all NeuroML ComponentType classes that allows users to easily construct their NeuroML documents.
Improve unit tests
DEPRECATION notice: append_to_element will be deprecated in future releases, please use the add method instead

version 0.2.56

Documentation updates for RTD and other minor fixes.

version 0.2.55

Patch release with minor changes under the hood.
Use PyTest for testing.
Enable CI on GitHub Actions

version 0.2.54

Using Schema for NeuroML v2.1. Better compatibility with Python 3

version 0.2.50

Updated to use the final stable Schema for NeuroML v2.0

version 0.2.47

Updated to use the final stable Schema for NeuroML v2beta5

version 0.2.18

Updated to use the final stable Schema for NeuroML v2beta4
Tested with Python 3

version 0.2.4

Updated to use the Schema for NeuroML v2beta4

version 0.2.2

Updated to use the Schema for NeuroML v2beta3
Ensures numpy & pytables are only required when using non-XML loaders/writers

version 0.2.0

Updated to use the Schema for NeuroML v2beta2

version 0.1.9

Minor release: Update to latest schema

version 0.1.8

Several Bug fixes and small enhamcements
Support for latest NeuroML schema (see change outline)
JSON serialization
MongoDB backend
HDF5 serialization
Improved installation process
All usage examples are now run on the Travis-CI continuous integration server to confirm that that they do not error.
Schema validation utility
Improved documentation and documentation new look

libNeuroML dependencies

lxml natsort networkx six

FAQs

What is libNeuroML?

A Python library for working with NeuroML descriptions of neuronal models. Visit Snyk Advisor to see a full health score report for libNeuroML, including popularity, security, maintenance & community analysis.

Is libNeuroML popular?

The python package libNeuroML receives a total of 2,702 weekly downloads. As such, libNeuroML popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is libNeuroML well maintained?

We found indications that libNeuroML maintenance is sustainable demonstrating some project activity. We saw a total of 20 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is libNeuroML safe to use?

While scanning the latest version of libNeuroML, 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 14 December-2024, at 04:02 (UTC).

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