# jonnyhyman/Chaos

Visualizations of the connections between chaos theory and fractals through the logistic map; made for Veritasium YouTube video

Repository: https://github.com/jonnyhyman/Chaos
Canonical: https://ross.abutalabs.com/products/jonnyhyman-chaos
Language: Python
License: MIT
License Family: permissive
Last push: 2023-10-17T18:53:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2410, "days_push": 1051, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1878, forks 307 (observed 2026-08-28T04:05:48.426006+00:00)

## What it is
A collection of standalone Python scripts that visualize the connections between chaos theory, fractals, and the logistic map, created for a Veritasium YouTube video. It includes an interactive cobweb/bifurcation plot explorer and a 3D volume rendering of the Mandelbrot set.

## Use cases
- explore the logistic map interactively with cobweb and bifurcation plots
- visualize the relationship between the Mandelbrot set and chaos theory in 3D
- create zoom animations of the logistic map bifurcation diagram
- learn chaos theory and fractals through interactive visualizations
- reproduce visuals from the Veritasium chaos theory video

## When to avoid
- you need a reusable Python library or API for chaos theory computations
- you require real-time rendering of the high-resolution 3D Mandelbrot volume
- you need actively maintained, well-structured software with no code duplication

## Facets
- artifact type: application
- maturity: maintenance
- function: data-visualization, simulation, graphics
- domain: mathematics, data-visualization, education
- platform: python, cross-platform
- tags: chaos-theory, fractals, logistic-map, mandelbrot, bifurcation, visualization-scripts, veritasium

## Member repositories
- jonnyhyman/Chaos (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:48.426006+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 2026-08-30T03:14:39.798244+00:00, confidence not recorded.
  - readme: https://github.com/jonnyhyman/Chaos (fetched 2026-08-28T04:05:48.426006+00:00, sha d2a8c4903ba8)
- Data as of 2026-08-30T08:39:29.467469+00:00.