# ContextLab/hypertools

A Python toolbox for gaining geometric insights into high-dimensional data

Repository: https://github.com/ContextLab/hypertools
Canonical: https://ross.abutalabs.com/products/hypertools
Homepage: http://hypertools.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: data-visualization, high-dimensional-data, python, topic-modeling, text-vectorization, data-wrangling, visualization, time-series
Last push: 2026-07-24T13:35:38+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 62, longevity 100
- inputs: {"age_days": 3627, "days_push": 40, "days_rel": 40, "gap_med": 227.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1887, forks 164 (observed 2026-08-28T04:05:49.177014+00:00)

## What it is
HyperTools is a Python library for visualizing and exploring high-dimensional data via dimensionality reduction, built on matplotlib, scikit-learn, and seaborn. It reduces datasets and produces plots in a single function call, with optional interactive plotly backends and tools for clustering, alignment, and text vectorization.

## Use cases
- visualize high-dimensional data in 2D or 3D
- reduce dimensionality of a dataset and plot it in one call
- explore time-series trajectories in a lower-dimensional space
- cluster high-dimensional data with mixture models
- hyperalign multiple datasets to a common space
- visualize topic models of text corpora
- create interactive plots of embeddings in Colab or Kaggle

## When to choose
- you want quick, single-call dimensionality-reduction visualizations of numeric or text data
- you work in Python with scikit-learn-style models and want plotting integrated with them
- you need to compare or align multiple high-dimensional datasets
- you want both static matplotlib and interactive plotly output

## When to avoid
- you need production-grade dashboards or business intelligence reporting rather than exploratory plots
- you need fine-grained control over every plot aesthetic - use matplotlib or seaborn directly
- you work outside Python or need GPU-accelerated visualization of massive datasets
- you need real-time or streaming data visualization

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, machine-learning, nlp
- domain: data-visualization, data-science, machine-learning
- platform: python, cross-platform
- tags: dimensionality-reduction, high-dimensional-data, matplotlib, plotly, clustering, hyperalignment, pca, umap, time-series, topic-modeling

## Member repositories
- ContextLab/hypertools (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:49.177014+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 2026-08-30T03:13:31.792055+00:00, confidence not recorded.
  - readme: https://github.com/ContextLab/hypertools (fetched 2026-08-28T04:05:49.177014+00:00, sha 6b2040b8857b)
  - registry_pypi: https://pypi.org/pypi/hypertools/json (fetched 2026-08-29T10:52:58.269861+00:00, sha 56c70a353ee1)
- Data as of 2026-08-30T08:39:29.467469+00:00.