Package Health Score

60 / 100

security
Security review needed
popularity
Limited
maintenance
Sustainable
community
Sustainable

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Popularity

Limited

GitHub Stars: 122
Forks: 41
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package pyNIBS, we found that it has been starred 122 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Aug 2, 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: 2
H
1
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

GPL-3.0

Non-Permissive License

We noticed that this project uses a license which requires less permissive conditions such as disclosing the source code, stating changes or redistributing the source under the same license. It is advised to further consult the license terms before use.

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 25
Open PR: 1
Last Release: 3 months ago
Last Commit: 8 months ago

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

We found that pyNIBS 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: 20
Funding: No

With more than 10 contributors for the pyNIBS 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 pyNIBS is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=3.7

Age: 3 years
Latest Release: 3 months ago
Dependencies: 11 Direct / 63 Total
Versions: 16
Maintainers: 1
Wheels: OS Independent

Readme

pyNIBS

Preprocessing, postprocessing, and analyses routines for non-invasive brain stimulation experiments.

pyNIBS provides the functions to allow cortical mappings with transcranial magnetic stimulation (TMS) via functional analysis. pyNIBS is developed to work with SimNIBS, i.e. SimNIBS' meshes and FEM results can directly be used. Currently, SimNIBS 3.2.6 and SimNIBS >=4.0 are supported.

See the documentation for package details and our protocol publication for a extensive usage examples. Free view only version of the paper: https://t.co/uv7CmVw6tp.

Installation

Via PiP:

pip install pynibs

Or clone the source repository and install the development branch for the most recent version:

git clone https://gitlab.gwdg.de/tms-localization/pynibs
cd pynibs
git checkout dev
pip install -e .

See here for more detailed installation instructions.

To import CED Signal EMG data use the export to .mat feature of Signal. To read .cfs files exported with CED Signal you might need to manually compile the libbiosig package.

Bugs

For sure. Please open an issue or feel free to file a PR.

Citation

Please cite Numssen, O., Zier, A. L., Thielscher, A., Hartwigsen, G., Knösche, T. R., & Weise, K. (2021). Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression. NeuroImage, 245, 118654. doi:10.1016/j.neuroimage.2021.118654 when using this toolbox in your research.

References

Weise*, K., Numssen*, O., Thielscher, A., Hartwigsen, G., & Knösche, T. R. (2020). A novel approach to localize cortical TMS effects. NeuroImage, 209, 116486. doi: 10.1016/j.neuroimage.2019.116486
Numssen, O., Zier, A. L., Thielscher, A., Hartwigsen, G., Knösche, T. R., & Weise, K. (2021). Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression. NeuroImage, 245, 118654. doi:10.1016/j.neuroimage.2021.118654
Weise*, K., Numssen*, O., Kalloch, B., Zier, A. L., Thielscher, A., Hartwigsen°, G., Knösche°, T. R. (2023). Precise transcranial magnetic stimulation motor-mapping. Nature Protocols. doi:10.1038/s41596-022-00776-6
Jing, Y., Numssen, O., Weise, K., Kalloch, B., Buchberger, L., Haueisen, J., Hartwigsen, G., Knösche, T. (2023). Modeling the Effects of Transcranial Magnetic Stimulation on Spatial Attention. Physics in Medicine & Biology. doi: 10.1088/1361-6560/acff34
Numssen, O., Kuhnke, P., Weise, K., & Hartwigsen, G. (2024). Electric field based dosing for TMS. Imaging Neuroscience. doi: 10.1162/imag_a_00106

pyNIBS dependencies

asteval fmm3dpy fslpy jsonschema lmfit meshio scikit-image tables tvb-gdist uncertainties vtk

FAQs

What is pyNIBS?

A python toolbox to conduct non-invasive brain stimulation (NIBS) experiments. Visit Snyk Advisor to see a full health score report for pyNIBS, including popularity, security, maintenance & community analysis.

Is pyNIBS popular?

The python package pyNIBS receives a total of 134 weekly downloads. As such, pyNIBS popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is pyNIBS well maintained?

We found indications that pyNIBS 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 pyNIBS safe to use?

While scanning the latest version of pyNIBS, 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 November-2024, at 15:49 (UTC).

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pyNIBS

A python toolbox to conduct non-invasive brain stimulation (NIBS) experiments. For more information about how to use this package see README

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Package Health Score

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Popularity

Direct Usage Popularity

Security

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Non-Permissive License

Maintenance

Commit Frequency

Community

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Package

Readme

pyNIBS

Installation

Bugs

Citation

References

pyNIBS dependencies

FAQs

What is pyNIBS?

Is pyNIBS popular?

Is pyNIBS well maintained?

Is pyNIBS safe to use?

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Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Explore Similar Packages

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Snyk Vulnerability Scanner

Readme

pyNIBS

Installation

Bugs

Citation

References

pyNIBS dependencies

FAQs

What is pyNIBS?

Is pyNIBS popular?

Is pyNIBS well maintained?

Is pyNIBS safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

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