# TutteInstitute/datamapplot

Creating beautiful plots of data maps

Repository: https://github.com/TutteInstitute/datamapplot
Canonical: https://ross.abutalabs.com/products/datamapplot
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-23T21:24:43+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 86, longevity 70
- inputs: {"age_days": 989, "days_push": 10, "days_rel": 94, "gap_med": 11.0, "n_releases_24m": 15}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1023, forks 75 (observed 2026-08-28T04:03:16.176655+00:00)

## What it is
DataMapPlot is a Python library for creating beautiful, presentation- and publication-ready plots of data maps (2D embeddings of clustered data). It automates aesthetic choices so users only need to label point clusters, producing static or simple interactive plots with extensive customization options.

## Use cases
- visualize clustered 2D embeddings of a dataset
- create publication-ready figures of data maps for papers
- make attractive data map plots for presentations and posters
- label clusters in a UMAP or t-SNE scatter plot automatically
- generate dark-mode styled data map visualizations
- create word-cloud style labeled data maps
- build simple interactive data map plots

## When to avoid
- you need fully interactive dashboards or drill-down exploration of data maps
- you need general-purpose plotting beyond clustered 2D data maps
- you require fine-grained manual control over every plot element with no automation

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, charts
- domain: data-visualization, data-science, machine-learning
- platform: python, cross-platform
- tags: data-maps, scatter-plot, cluster-labeling, publication-figures, matplotlib, interactive-plots

## Member repositories
- TutteInstitute/datamapplot (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:16.176655+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 2026-08-30T07:08:17.251391+00:00, confidence not recorded.
  - readme: https://github.com/TutteInstitute/datamapplot (fetched 2026-08-28T04:03:16.176655+00:00, sha 390d5678b72f)
  - registry_pypi: https://pypi.org/pypi/datamapplot/json (fetched 2026-08-29T13:08:45.740897+00:00, sha 1344c9bbb9dd)
- Data as of 2026-08-30T08:39:29.467469+00:00.