This is an adaptation of the matlab mTRF-toolbox using only basic Python and Numpy. It aims to implement the same methods as the original toolbox and advance them. This documentation provides tutorial-like demonstrations of the core functionalities like model fitting, visualization and optimization as well as a comprehensive reference documentation.
You can get the stable release from PyPI:
pip install mtrf Or get the latest version from this repo:
pip install git+https://github.com/powerfulbean/mTRFpy.gitWhile mTRFpy only depends on numpy, matplotlib is an optional dependency used to visualize models. It can also be installed via pip:
pip install matplotlibWe also provide an optional interface to MNE-Python so it might be useful to install mne as well.
For a little tutorial on the core features of mTRFpy, have a look at our online documentation
- Please use the issue search to check if the issue has already been reportet.
- Try to reproduce problem using the latest
masterbranch. - Create an issue with a minimal example that reproduces the problem.
Feature requests are welcome. But take a moment to find out whether your idea fits with the scope and aims of the project. It's up to you to make a strong case to convince the project's developers of the merits of this feature. Please provide as much detail and context as possible.
Great! Please take a moment to read the before you do.
Bialas et al., (2023). mTRFpy: A Python package for temporal response function analysis. Journal of Open Source Software, 8(89), 5657, https://doi.org/10.21105/joss.05657
@article{Bialas2023,
doi = {10.21105/joss.05657},
url = {https://doi.org/10.21105/joss.05657},
year = {2023}, publisher = {The Open Journal},
volume = {8},
number = {89},
pages = {5657},
author = {Ole Bialas and Jin Dou and Edmund C. Lalor},
title = {mTRFpy: A Python package for temporal response function analysis},
journal = {Journal of Open Source Software} }