nigsp 0.6.0

A python library (and toolbox!) to run Graph Signal Processing on multimodal MRI data.

NiGSP

A python library (and toolbox!) to run Graph Signal Processing on multimodal MRI data.

The project is currently under development stage alpha. Any suggestion/bug report is welcome! Feel free to open an issue.

This project follows the all-contributors specification. Contributions of any kind welcome!

Documentation

Full documentation coming soon!

Cite

If you use nigsp in your work, please cite either the all-time Zenodo DOI or the Zenodo DOI related to the version you are using. Please cite the following paper(s) too:

Preti, M.G., Van De Ville, D. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat Commun 10, 4747 (2019). https://doi.org/10.1038/s41467-019-12765-7.

If you are using the Couple/Decoupled Functional Connectivity, please cite also:

Griffa, A., et al. Brain structure-function coupling provides signatures for task decoding and individual fingerprinting. NeuroImage 250, 118970 (2022). https://doi.org/10.1016/j.neuroimage.2022.118970.

Installation

Install on any *nix system using python and pip, or clone this repository and install locally (run setup.py or pip). Currently, the only necessary dependency is numpy. However, to gain access to more features, other libraries might be required. It's easy to install them through pip. nigsp supports python versions 3.6+. However, please note that tests are run on python 3.7+.

Install with pip (recommended)

:exclamation::exclamation::exclamation: Please note that some systems might require to use pip3 instead of pip.

Basic installation:

For basic installation, simply run:

pip install nigsp

Richer installation

To install the dependencies to enable more features, you can append labels to nigsp, e.g:

pip install nigsp[nifti]

The possible features are:

• [mat]: to load and export MATLAB (.mat) files.
• [nifti]: to load and export nifti (.nii and .nii.gz) files.
• [viz]: to allow the creation of various plots during the workflow.
• [all]: to install all of the above.

Clone from Github / install without pip

:exclamation::exclamation::exclamation: Please note that nigsp is continuously deployed, i.e. the latest feature available are immediately released on PyPI. To install nigsp from Github, clone the repository first, then move to the cloned folder and run:

python setup.py install

Alternatively, pip can be used too:

pip install .

Developer installation

To be sure you have everything installed to develop (and test) nigsp, fork MIPLabCH/nigsp to your repository, then clone it locally and move inside the cloned folder. Finally, install with pip using the developer mode and the [dev] label:

pip install -e .[dev]

Run/use nigsp

You can run the nigsp workflow in a shell session (or in your code) - just follow the help:

nigsp --help

Alternatively, you can use nigsp as a module in a python session (or within your python script):

import nigsp

nigsp.__version__

Full API coming soon.

License

Copyright 2022, Stefano Moia.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.