Metadata-Version: 2.1
Name: pydfc
Version: 1.0.4
Summary: This package aims to provide a comprehensive framework for assessing dynamic functional connectivity (dFC) using multiple methods and comparing results across methods.
Author: Mohammad Torabi
Maintainer-email: Mohammad Torabi <mohammad.torabi@mail.mcgill.ca>
License: MIT
License-File: LICENSE
Keywords: dFC package,neuroimaging,python
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Requires-Dist: h5py
Requires-Dist: hmmlearn
Requires-Dist: ksvd
Requires-Dist: matplotlib
Requires-Dist: networkx
Requires-Dist: nilearn!=0.10.3,>=0.10.2
Requires-Dist: pyclustering
Requires-Dist: pycwt
Requires-Dist: seaborn
Requires-Dist: statsmodels
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/x-rst

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10161176.svg
    :target: https://zenodo.org/doi/10.5281/zenodo.10161176

pydfc
=======
An implementation of several well-known dynamic Functional Connectivity (dFC) assessment methods.

Simply do these steps in the main repository directory to learn how to use the dFC functions:
  * ``conda create --name pydfc_env python=3.11``
  * ``conda activate pydfc_env``
  * ``pip install -e '.'``
  * run the code cells in demo jupyter notebooks

The ``dFC_methods_demo.ipynb`` illustrates how to load data and apply each of the dFC methods implemented in the ``pydfc`` toolbox individually.
The ``multi_analysis_demo.ipynb`` illustrates how to use the ``pydfc`` toolbox to apply multiple dFC methods at the same time on a dataset and compare their results.

For more details about the implemented methods and the comparison analysis see `our paper <https://www.biorxiv.org/content/10.1101/2023.07.13.548883v2>`_.

  * Torabi M, Mitsis GD, Poline JB. On the variability of dynamic functional connectivity assessment methods. bioRxiv. 2023:2023-07. 
