# ttrftech/NanoVNA

Very Tiny Palmtop Vector Network Analyzer

Repository: https://github.com/ttrftech/NanoVNA
Canonical: https://ross.abutalabs.com/products/nanovna
Language: C
License Family: other
Topics: nanovna, stm32, cortex-m0, chibios, sdr, instruments, firmware, i2s, ili9341, si5351a, jupyter-notebook, python, vna
Last push: 2020-11-11T21:02:22+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": 3514, "days_push": 2121, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1409, forks 368 (observed 2026-08-28T04:04:38.679074+00:00)

## What it is
Firmware source for the NanoVNA, a tiny handheld vector network analyzer built on an STM32 Cortex-M0 with a Si5351A RF synthesizer and LCD display. It includes Python/Jupyter tooling for controlling the device and analyzing measurements from a PC.

## Use cases
- measure antenna SWR and impedance
- build and flash NanoVNA firmware
- analyze RF filter response
- control a NanoVNA from Python or Jupyter
- tune ham radio antennas
- characterize RF components on a budget

## When to choose
- you own or are building a NanoVNA device and need its firmware
- you want an open-source, portable RF measurement instrument
- you want to script VNA measurements via Python

## When to avoid
- you need a full-featured lab-grade VNA with wide frequency range
- you want a PC-only application without the hardware
- you need a maintained project with an explicit license

## Facets
- artifact type: application
- maturity: maintenance
- function: embedded, sdk, developer-tools
- domain: hardware, developer-tools
- platform: embedded, python, cross-platform
- tags: vector-network-analyzer, rf-instrument, stm32, firmware, ham-radio, sdr, si5351a, chibios

## Member repositories
- ttrftech/NanoVNA (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.679074+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 2026-08-30T04:38:33.841450+00:00, confidence not recorded.
  - readme: https://github.com/ttrftech/NanoVNA (fetched 2026-08-28T04:04:38.679074+00:00, sha faf2a9dd7400)
- Data as of 2026-08-30T08:39:29.467469+00:00.