# spd-metrics-id: A Python Package for SPD-Aware Distance Metrics in Connectome Fingerprinting and Beyond

arXiv: https://arxiv.org/abs/2510.04438 · HTML: https://mlsanity.com/paper/2510.04438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [TWSSenti: A Novel Hybrid Framework for Topic-Wise Sentiment Analysis on Social Media Using Transformer Models](https://mlsanity.com/paper/2504.09896/) (2025)
- [Improving Functional Connectome Fingerprinting with Degree-Normalization](https://mlsanity.com/paper/2011.10079/) (2020)
- [Alpha-Z divergence unveils further distinct phenotypic traits of human brain connectivity fingerprint](https://mlsanity.com/paper/2507.23116/) (2025)
- [Tangent functional connectomes uncover more unique phenotypic traits](https://mlsanity.com/paper/2212.06394/) (2022)

## Abstract (arXiv)

We present spd-metrics-id, a Python package for computing distances and divergences between symmetric positive-definite (SPD) matrices. Unlike traditional toolkits that focus on specific applications, spd-metrics-id provides a unified, extensible, and reproducible framework for SPD distance computation. The package supports a wide variety of geometry-aware metrics, including Alpha-z Bures-Wasserstein, Alpha-Procrustes, affine-invariant Riemannian, log-Euclidean, and others, and is accessible both via a command-line interface and a Python API. Reproducibility is ensured through Docker images and Zenodo archiving. We illustrate usage through a connectome fingerprinting example, but the package is broadly applicable to covariance analysis, diffusion tensor imaging, and other domains requiring SPD matrix comparison. The package is openly available at https://pypi.org/project/spd-metrics-id/.