提供一套统一工具，计算对称正定矩阵间的几何感知距离。
spd-metrics-id: A Python Package for SPD-Aware Distance Metrics in Connectome Fingerprinting and Beyond
- 支持多种几何感知距离度量，如Bures-Wasserstein、对数欧氏等。
- 兼容命令行与Python API，可复现性强，附带Docker和存档。
- 适用于脑连接组指纹识别、扩散张量成像等需比较协方差矩阵的领域。
我们推出spd-metrics-id，一个用于计算对称正定（SPD）矩阵间距离与散度的Python工具包。不同于聚焦特定应用的传统工具箱，该工具包提供统一、可扩展且可复现的SPD距离计算框架。支持多种几何感知度量，包括Alpha-z Bures-Wasserstein、Alpha-Procrustes、仿射不变黎曼、对数欧氏等，并可通过命令行接口或Python API访问。通过Docker镜像和Zenodo归档确保可复现性。我们以脑连接组指纹识别为例展示使用方法，但该工具包广泛适用于协方差分析、扩散张量成像及其他需要对比SPD矩阵的领域。工具包开源可获取：https://pypi.org/project/spd-metrics-id/。
原文摘要 · Abstract (English)
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/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。