pyPTE

Phase transfer entropy for directed connectivity between oscillatory signals. Given two channels of EEG, MEG or any rhythmic time series, pyPTE answers which one is leading the other.

pyPTE recovers the direction of information flow between a pair of signals. It does not recover the wiring diagram of a network.

New here? Start with the tutorial, which builds the measure from phase histograms up, with every figure computed from a real run.

Installation

pip install pyPTE          # or: uv add pyPTE
pip install "pyPTE[mne]"   # MNE-Python adapter (also installs pandas)

The core needs only NumPy and SciPy, and Python 3.11 or newer.

Quickstart

PTE takes an (n_channels, n_samples) array and returns two matrices, where entry [i, j] describes flow from channel i to channel j.

import numpy as np
from pyPTE import PTE

rng = np.random.default_rng(0)
t = np.arange(8000) / 250.0
driver = np.sin(2 * np.pi * 10 * t) + 0.2 * rng.standard_normal(t.size)
target = 0.9 * np.roll(driver, 12) + 0.4 * rng.standard_normal(t.size)

dPTE, raw_PTE = PTE(np.vstack([driver, target]))
dPTE[0, 1]  # above 0.5: driver leads target

dPTE[i, j] + dPTE[j, i] == 1: above 0.5 means net flow from i to j, and 0.5 means no preferred direction. A dPTE value on its own is not a finding; test it as described in running a real analysis.

Based on Lobier et al. 2014, Phase transfer entropy, and Hillebrand et al. 2016, Direction of information flow in large-scale resting-state networks.