Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Junifer logo

junifer - JUelich NeuroImaging FEature extractoR

PyPI PyPI - Python Version PyPI - Wheel Anaconda-Server Badge GitHub Codecov Code style: black Ruff pre-commit

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 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

You can also install via conda, like so:

conda install -c conda-forge 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:

junifer, FZJuelich AML neuroimaging feature extraction library. Copyright (C) 2022, 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/.

Metadata

Release files for junifer 0.0.3.dev125

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

Source distribution (sdist)

Source distribution for junifer 0.0.3.dev125
File Size Uploaded
junifer-0.0.3.dev125.tar.gz 562.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for junifer 0.0.3.dev125
File Interpreter ABI Platform
junifer-0.0.3.dev125-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / junifer-0.0.3.dev125.tar.gz

Download URL junifer-0.0.3.dev125.tar.gz
Size 562.0 kB
Tags Source
SHA-256 checksum
How to use checksums
0a5a4e74cceb544f9980d57bbe67e957c0faaa54a6587fc7a9de9b816f723059
BLAKE2b-256 checksum
How to use checksums
192f96c4cc9d92dfe8204f463fde9694b4f63460cae761abddfe7408e7ed3d40
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.11.4

Release files / junifer-0.0.3.dev125-py3-none-any.whl

Download URL junifer-0.0.3.dev125-py3-none-any.whl
Size 535.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
f3c0cbaf148c95452c1824ed0a055676da3ea46b71a7931bdac0a291b8cfb600
BLAKE2b-256 checksum
How to use checksums
70196a7d6dc083c1e6fefe7e9a41afc211b57604a008efadca6188789b96c9b1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.11.4

Release history Release notifications | RSS feed

0.0.7

2 release files

0.0.6

2 release files

0.0.5

2 release files

0.0.4

2 release files

0.0.3

2 release files

This release

0.0.3.dev125 This release

2 release files

0.0.2

2 release files

0.0.1

2 release files

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