Skip to main content

simnibs-analyze

Cohort-level analysis and visualisation of SimNIBS e-field simulations, driven by YAML config.

simnibs-analyze turns SimNIBS outputs into ROI statistics and publication figures across subjects and conditions, for non-invasive brain stimulation studies (TMS/tDCS).

PyPI version Docs License: MIT

📖 Full documentation → ICM-Frontlab-CDPR.github.io/simnibs-analyze


Install & run

pip install simnibs-analyze

Two config-driven commands:

simnibs-analyze --config config-analyze.yaml   # features + statistics
simnibs-viz     --config config-viz.yaml       # figures + cohort montages

Ready-made configs in docs/examples/config/, full key reference in the configuration guide.


Ecosystem

simnibs-analyze is built on:

It consumes results produced by:


Metadata

Release files for simnibs-analyze 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for simnibs-analyze 0.1.1
File Size Uploaded Provenance Actions
simnibs_analyze-0.1.1.tar.gz 29.3 kB Without provenance Details for simnibs_analyze-0.1.1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for simnibs-analyze 0.1.1
File Interpreter ABI Platform Actions
simnibs_analyze-0.1.1-py3-none-any.whl Python 3 none any Details for simnibs_analyze-0.1.1-py3-none-any.whl

Total release size: 62.4 kB

Release files / simnibs_analyze-0.1.1.tar.gz

Download URL simnibs_analyze-0.1.1.tar.gz
Size 29.3 kB
Tags Source
SHA-256 checksum
How to use checksums
c3f7f8164a77e009306eff328457daddf702e4730f2315b91249c7f6de40441e
BLAKE2b-256 checksum
How to use checksums
1e8f4ac1d7211d59f439e40f7ef52acc5e87f644a5630026f53df2fd148fdbae
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.6

Release files / simnibs_analyze-0.1.1-py3-none-any.whl

Download URL simnibs_analyze-0.1.1-py3-none-any.whl
Size 33.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8c3642154df5082eed79e0a95c60e2aba3c933e1dc22b83effabded85ef2024f
BLAKE2b-256 checksum
How to use checksums
fdbd526cd5ae42f3919ee6a0c130ff46599bc95be7e2773381570f2bedf10374
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.6

Security for simnibs-analyze 0.1.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page