# pavlin-policar/openTSNE

Extensible, parallel implementations of t-SNE

Repository: https://github.com/pavlin-policar/openTSNE
Canonical: https://ross.abutalabs.com/products/opentsne
Homepage: https://opentsne.rtfd.io
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: tsne, visualization, machine-learning, dimensionality-reduction, embedding
Last push: 2026-08-02T07:14:28+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 8, longevity 100
- inputs: {"age_days": 3008, "days_push": 31, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1621, forks 176 (observed 2026-08-28T04:05:12.451564+00:00)

## What it is
openTSNE is a modular Python library implementing t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualizing high-dimensional data. It includes parallelized, fast implementations that scale to millions of points and supports adding new data points to existing embeddings.

## Use cases
- visualize high-dimensional datasets in 2D
- embed single-cell transcriptomics data
- reduce dimensionality of large datasets with millions of points
- add new data points to an existing t-SNE embedding
- preserve global structure in t-SNE visualizations
- speed up t-SNE with parallel computation

## When to choose
- you need a fast, scalable t-SNE implementation in Python
- you want to transform new data into an existing embedding
- you work with large datasets like single-cell RNA-seq
- you need better global alignment of t-SNE clusters

## When to avoid
- you need general-purpose dimensionality reduction like PCA or UMAP alternatives
- you need GPU-accelerated t-SNE specifically
- you need a non-Python environment

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-visualization, math
- domain: machine-learning, data-visualization, data-science
- platform: python, cross-platform
- tags: tsne, dimensionality-reduction, embedding, parallel-computing

## Member repositories
- pavlin-policar/openTSNE (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.451564+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:49:15.455617+00:00, confidence not recorded.
  - readme: https://github.com/pavlin-policar/openTSNE (fetched 2026-08-28T04:05:12.451564+00:00, sha cc342dac4f48)
  - registry_pypi: https://pypi.org/pypi/opentsne/json (fetched 2026-08-29T11:22:19.577827+00:00, sha 927548fd96db)
- Data as of 2026-08-30T08:39:29.467469+00:00.