pyPTE is an open-source implementation of the Phase Transfer Entropy method based on the publications [Lobier2014] and [Hillebrand2016]. It is fully implemented in Python and requires only the Python libraries NumPy and SciPy.
The prerequisites for this package are:
Mandantory:
- a working Python installation, version 3.6 or higher
- git
- NumPy
- SciPy
Optional:
- mne-python
- pandas
- seaborn
Recommended:
To prevent Python module incompatibilities using a virtual environment like
- conda
- pyenv
is highly recommendable. If you are planning to use mne-python, an Anaconda3 installation is mandantory.
Clone into the public GitHub repository using: :: > git clone https://github.com/patrk/pyPTE.git
Build the pyPTE package and make it available to your Python interpreter by: :: > cd pyPTE > python setup.py install
To test the installation of pyPTE simply run: :: > cd test > py.test
- Hillebrand2016
Arjan Hillebrand, Preejaas Tewarie, Edwin van Dellen, Meichen Yu, Ellen W. S. Carbo, Linda Douw, Alida A. Gouw, Elisabeth C.W. van Straaten and Cornelies J. Stam. Direction of information flow in large-scale resting-state networks is frequency-dependent. Proceedings of the National Academy of Sciences of the United States of America, 113(14):3867-72, 2016. doi:10.1073/pnas.1515657113
- Lobier2014
Muriel Lobier, Felix Siebenhühner, Satu Palva and J. Matias Palva. Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. NeuroImage, 2014. doi:10.1016/j.neuroimage.2013.08.056