junifer - JUelich NeuroImaging FEature extractoR

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About

junifer is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.

It is curently 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 with plot_, otherwise, with run_.
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.
- markers: Markers module.
- pipeline: Pipeline module.
- preprocess: Preprocessing module.
- storage: Storage module.
- testing: Testing components module.
- utils: Utilities module (e.g. logging)

Installation

Use pip to install from PyPI like so:

pip install junifer

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 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:

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/.

Metadata

Release files for junifer 0.0.2.dev353

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Source distribution (sdist)

Source distribution for junifer 0.0.2.dev353
File	Size	Uploaded
junifer-0.0.2.dev353.tar.gz	673.1 kB	Mar 30, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for junifer 0.0.2.dev353
File	Interpreter	ABI	Platform	Reset
junifer-0.0.2.dev353-py3-none-any.whl 531.6 kB Mar 30, 2023	Python 3	none	any	Details

Total release size: 1.2 MB

Release files / junifer-0.0.2.dev353.tar.gz

Download URL	junifer-0.0.2.dev353.tar.gz
Size	673.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`fa395a849a0d5fee75ab7aaa227cfdf982213c07609dc4c2e0a116fa18cd784c`
BLAKE2b-256 checksum How to use checksums	`a687407a006c455ca152fb2ead8dfd22163f4f8037331d27bcf2e70708e68134`
Upload date	Mar 30, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.11.2`

Release files / junifer-0.0.2.dev353-py3-none-any.whl

Download URL	junifer-0.0.2.dev353-py3-none-any.whl
Size	531.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b10d1eaf90a583b7ecaac066c6d5adf69a9878aa9a54449f4d956976af714e6b`
BLAKE2b-256 checksum How to use checksums	`ab48347e787564ea3ed3dc87bbbb916d8e2e356f63edcb0889816d4fbeaa3bc4`
Upload date	Mar 30, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.11.2`