This release is a pre-release and may not be stable for production use.
junifer - JUelich NeuroImaging FEature extractoR
About
junifer is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.
It is currently being developed and maintained at the Applied Machine Learning group at Forschungszentrum Juelich, Germany. Although the library is designed for people working at Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), it is designed to be as modular as possible thus enabling others to extend it easily.
The documentation is available at https://juaml.github.io/junifer.
Repository Organization
docs: Documentation, built using sphinx.examples: Examples, using sphinx-gallery. File names of examples that create visual output must start withplot_, otherwise, withrun_.junifer: Main library directory.api: User API module.configs: Module for pre-defined configs for most used computing clusters.data: Module that handles data required for the library to work (e.g. parcels, coordinates).datagrabber: DataGrabber module.datareader: DataReader module.external: Module for external libraries and tools.markers: Markers module.onthefly: Transformation components (on-the-fly) module.pipeline: Pipeline module.preprocess: Preprocessing module.storage: Storage module.testing: Testing components module.typing: Type hints module.utils: Utilities module (e.g. logging).
Installation
Use pip to install from PyPI like so:
pip install junifer
You can also install via conda, like so:
conda install -c conda-forge junifer
Optional dependencies
junifer supports a few optional dependencies to enable certain features. You can
install them by specifying a comma separated list within square brackets, like so:
pip install "junifer[bct,dev]"
bctinstalls bctpy to enable use ofontheflymodule.neurokit2installs neurokit2 to enable use of complexity markers.allincludes all of the above.devinstalls packages needed for development.docsinstalls packages needed for building documentation.
Citation
If you use junifer in a scientific publication, we would appreciate if you cite our work. Currently, we do not have a publication, so feel free to use the project's Zenodo URL.
Funding
We thank the Helmholtz Imaging Platform and
SMHB for supporting development of junifer.
(The funding sources had no role in the design, implementation and evaluation of the pipeline.)
Contribution
Contributions are welcome and greatly appreciated. Please read the guidelines to get started.
License
junifer is released under the AGPL v3 license:
junifer, FZJuelich AML neuroimaging feature extraction library. Copyright (C) 2023, authors of junifer.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see http://www.gnu.org/licenses/.
Release files for junifer 0.0.6.dev418
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| junifer-0.0.6.dev418.tar.gz | 1.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| junifer-0.0.6.dev418-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.3 MB
Release files / junifer-0.0.6.dev418.tar.gz
| Download URL | junifer-0.0.6.dev418.tar.gz |
|---|---|
| Size | 1.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
18daa32f10d90641fdf087a6b863fceac322aeaacbdb77d3f95e5f6c1132608f
|
|
BLAKE2b-256 checksum How to use checksums |
a6d7bdb939955031eae2453635dc9c0c5c1c510ca5b0fee08decf97209cd3775
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.8
|
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.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jan 26, 2025.
Transparency logRelease files / junifer-0.0.6.dev418-py3-none-any.whl
| Download URL | junifer-0.0.6.dev418-py3-none-any.whl |
|---|---|
| Size | 1.7 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
558540080f5dd8cf827944c78845fadbbf3d1378409e9e6126458a75a8e2f04b
|
|
BLAKE2b-256 checksum How to use checksums |
ac5b0ac6386756f3748de26e31fd59e032d8dedb4014225660b2307816eb15f4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.8
|
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.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jan 26, 2025.
Transparency log
