pySPFM 1.0.1

A python package for Paradigm Free Mapping (3dPFM and 3dMEPFM).

pySPFM

The Python version of AFNI's 3dPFM and 3dMEPFM with some extra features like the addition of a spatial regularization similar to the one used by Total Activation.

Development

This project uses uv for fast, reliable dependency management and tox for testing across multiple Python versions.

Setting up a development environment

1. Create a virtual environment and install dependencies (including tests) in editable mode:

   uv sync --extra tests
   

Running tests

Run tests for your current Python version:

pytest pySPFM/tests/

Run tests across all supported Python versions (3.10, 3.11, 3.12) using tox:

tox

Run tests for a specific Python version:

tox -e py310  # For Python 3.10

Run linting checks:

tox -e lint

Benefits of uv

• Fast: 10-100x faster than pip for dependency resolution and installation
• Deterministic: uv.lock ensures reproducible installations across all environments
• Reliable: Resolves dependencies consistently

References

• Caballero-Gaudes, C., Moia, S., Panwar, P., Bandettini, P. A., & Gonzalez-Castillo, J. (2019). A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping. NeuroImage, 202, 116081–116081. https://doi.org/10.1016/j.neuroimage.2019.116081
• Caballero Gaudes, C., Petridou, N., Francis, S. T., Dryden, I. L., & Gowland, P. A. (2013). Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses. Human Brain Mapping. https://doi.org/10.1002/hbm.21452
• Karahanoǧlu, F. I., Caballero-Gaudes, C., Lazeyras, F., & Van De Ville, D. (2013). Total activation: FMRI deconvolution through spatio-temporal regularization. NeuroImage. https://doi.org/10.1016/j.neuroimage.2013.01.067
• Uruñuela, E., Bolton, T. A., Van De Ville, D., & Caballero-Gaudes, C. (2023). Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work. Aperture Neuro, 3, 1-25. https://doi.org/10.52294/001c.87574