# deepcharles/ruptures

ruptures: change point detection in Python

Repository: https://github.com/deepcharles/ruptures
Canonical: https://ross.abutalabs.com/products/ruptures
Language: Python
License: BSD-2-Clause
License Family: permissive
Topics: signal-processing, changepoint, python, scientific-computing, science, change-point-detection
Last push: 2026-07-06T22:04:14+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 15, longevity 100
- inputs: {"age_days": 3147, "days_push": 58, "days_rel": 357, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2076, forks 190 (observed 2026-08-28T04:06:11.222226+00:00)

## What it is
ruptures is a Python library for offline change point detection in non-stationary signals. It provides exact and approximate detection algorithms across parametric and non-parametric models with a consistent, modular, well-documented interface.

## Use cases
- detect change points in a time series
- segment a non-stationary signal into regimes
- find structural breaks in sensor data
- split a signal where its statistical properties change
- analyze piecewise stationary signals in Python
- detect regime shifts in financial or physiological time series

## When to choose
- you need offline (batch) change point detection in Python
- you want a choice of many detection algorithms and cost models with a unified API
- you need a well-documented, citable scientific library for signal segmentation

## When to avoid
- you need online/streaming change point detection in real time
- you work outside Python or need deep-learning-based segmentation
- you only need simple anomaly or outlier detection rather than regime changes

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, math
- domain: data-science, analytics
- platform: python
- tags: change-point-detection, signal-processing, segmentation, time-series, scientific-computing, algorithms

## Member repositories
- deepcharles/ruptures (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:11.222226+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 2026-08-30T02:56:11.106752+00:00, confidence not recorded.
  - readme: https://github.com/deepcharles/ruptures (fetched 2026-08-28T04:06:11.222226+00:00, sha 6f33f73f3300)
- Data as of 2026-08-30T08:39:29.467469+00:00.