Skip to content

topometry-nosc

Provenance

topometry-nosc is an independently maintained, heavily modified fork of TopOMetry by David S Oliveira (original copyright and MIT license preserved). Its API, internals and behaviour may differ substantially from upstream. It is not an official release of, affiliated with, or endorsed by the original project. The import package is still named topo, so it cannot be installed alongside the upstream topometry distribution in the same environment. Please cite both this fork and the original work — see Citation.

topometry-nosc is a geometry-aware Python toolkit for exploring high-dimensional data via diffusion/Laplacian operators. It learns neighborhood graphs → Laplace–Beltrami–type operators → spectral scaffolds → refined graphs and then builds low-dimensional layouts for analysis and visualization.

  • scikit-learn–style transformers with a high-level TopOGraph orchestrator
  • Fixed-time & multiscale spectral scaffolds
  • Operator-native metrics to quantify geometry preservation and Riemannian diagnostics to evaluate distortion in visualizations
  • Designed for large, diverse datasets

For background, see the original paper: https://doi.org/10.7554/eLife.100361.2

Status

Under active development. Interfaces may still change.

Geometry-first rationale

We approximate the Laplace–Beltrami operator (LBO) by learning well-weighted similarity graphs and their Laplacian/diffusion operators. The eigenfunctions of these operators form an orthonormal basis — the spectral scaffold — that captures the dataset's intrinsic geometry across scales. This view connects to Diffusion Maps, Laplacian Eigenmaps, and related kernel eigenmaps, and enables downstream tasks such as clustering and graph-layout optimization with geometry preserved.

When to use it

  • Geometry-faithful representations beyond variance maximization (e.g., PCA)
  • Robust low-dimensional views and clustering from operator-grounded features
  • Quantitative operator-native metrics to compare methods and parameter choices
  • Reproducible, non-destructive pipelines

When not to use it

  • Very small sample sizes where the manifold hypothesis is weak
  • Workflows needing streaming/online updates or inverse transforms (embedding new points without recomputing operators is not currently supported)

Next steps