# FilterPy

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

Repository: https://github.com/rlabbe/filterpy
Canonical: https://ross.abutalabs.com/products/filterpy
Language: Python
License: MIT
License Family: permissive
Last push: 2024-02-07T10:05:31+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4433, "days_push": 938, "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 3862, forks 678 (observed 2026-08-28T04:08:27.578178+00:00)

## What it is
An open-source Jupyter Notebook book teaching Kalman and Bayesian filters with intuition-first explanations, runnable Python code, and solved exercises. It accompanies FilterPy, a Python library for Kalman filtering and optimal estimation.

## Use cases
- learn kalman filtering from scratch
- understand bayesian filters intuitively
- implement sensor fusion in python
- track noisy sensor data like gps
- study extended and unscented kalman filters
- learn particle filters with code examples
- teach state estimation to a team

## When to choose
- you want an intuition-driven introduction rather than formal proofs
- you want runnable, modifiable code in Jupyter notebooks
- you need practical Python implementations of Kalman, EKF, UKF, and particle filters

## When to avoid
- you need rigorous mathematical proofs or academic treatment
- you need a production-grade, actively maintained filtering library
- you need filters beyond Bayesian/state estimation techniques

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science, simulation, math
- domain: machine-learning, education, tutorials, robotics, computer-vision
- platform: python, cross-platform
- tags: kalman-filter, bayesian-filters, state-estimation, jupyter-notebook, signal-processing, sensor-fusion, particle-filters, textbook

## Member repositories
- rlabbe/filterpy (main) score 23
- rlabbe/Kalman-and-Bayesian-Filters-in-Python (docs) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:27.578178+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 2026-08-29T17:00:24.205110+00:00, confidence not recorded.
  - readme: https://github.com/rlabbe/filterpy (fetched 2026-08-28T04:08:27.578178+00:00, sha 98e5a6f5f5df)
  - registry_pypi: https://pypi.org/pypi/filterpy/json (fetched 2026-08-29T07:58:43.413926+00:00, sha 1d3409bbc098)
- Data as of 2026-08-30T08:39:29.467469+00:00.