F1 Telemetry Analyzer

Turns raw F1 22 lap telemetry into compact Markdown reports you can paste into an LLM for analysis. A full raw lap is ~1M tokens; these reports are 1–35k.

# from the tool/ folder
python -m telemetry technique "races/Zandvoort/Practice/zandvoort_P1_L7_74.074_ferrari.csv"
# -> races/Zandvoort/Practice/reports/zandvoort_P1_L7_74.074_ferrari_technique.md   (~35k tokens)

Or just double-click run-gui.bat and click Generate report.

Install

Python 3.10+ (uses only the standard library — no pip install).
Tk is required for the GUI; it ships with the standard Python installer on Windows/macOS. The CLI works without it.

Download or clone the repository. Keep your telemetry CSVs in the parent folder of tool/ (see folder layout).

What it does

Six modes. Five produce reports, one renames files.

Mode	Input	Use it to analyse
`technique`	one lap	how you drive — braking points, apex speeds, throttle application, lockups, resource cost
`setup`	one lap	what is set — balance (US/OS), tyre temps, brakes, suspension rake & roll
`compare`	two+ laps	where one lap is faster — per-corner and cumulative delta, plus a deterministic `Verdict` (fuel-corrected)
`race`	a folder of laps	the whole race — pace, stint degradation, tyre wear, ERS per lap
`profile`	a folder of laps	cheap (~400 tokens) cross-lap driver tendencies per tyre compound (median coast/lock/spin per corner, flagged relative to the session) + auto-picked laps to deep-dive. Track-agnostic: corners are clustered by apex distance, not index
`rename`	files/folder	insert session type and lap number into each filename (`..._74.074_...` → `..._P1_L7_74.074_...`)

Reports include a legend explaining every column and have the analysis prompt auto-appended at the end — just drop the file into an LLM. Prompts come in RU and EN; pick with --lang en on the CLI or the language radio in the GUI.

Flags: --no-prompt omits the embedded analysis prompt (smaller files for agent pipelines; in the GUI it's the Include prompt checkbox). --force regenerates even when an up-to-date report already exists (runs are otherwise skipped as cached).

Typical optimized flow: run profile first to see chronic tendencies and which laps matter, then generate technique/setup/compare only for those laps with --no-prompt.

Folder layout

Telemetry/
  races/
    Zandvoort/
      Practice/                        <- CSVs go here after renaming
        zandvoort_P1_L7_74.074_ferrari.csv
        reports/                       <- technique/setup/compare reports land here
      Qualifying/
        zandvoort_Q1_L3_74.897_ferrari.csv
        reports/
      Race/
        zandvoort_R_L7_74.152_ferrari.csv
        reports/
          Race_race.md                 <- race mode report
  tool/                                <- this repository
    telemetry/
    prompts/
    run-gui.bat

Handy things

GUI — double-click run-gui.bat (or run-gui.pyw for no console window, or run python -m telemetry gui). Pick a mode and language (RU/EN), browse to the file/folder, click Generate. The file browser remembers the last used directory per mode. The Rename unprocessed button adds the session type and lap number to files that don't have them yet and leaves the rest alone.

CLI — from the tool/ folder:

python -m telemetry technique races/Zandvoort/Practice/lap.csv
python -m telemetry setup     races/Zandvoort/Practice/lap.csv
python -m telemetry compare   races/Zandvoort/Practice/lap_a.csv races/Zandvoort/Practice/lap_b.csv
python -m telemetry race      races/Zandvoort/Race
python -m telemetry profile   races/Zandvoort/Race        # cheap cross-lap tendencies + lap picks
python -m telemetry rename    races/Zandvoort/Practice   # or individual files

# choose prompt language (default: ru)
python -m telemetry technique races/Zandvoort/Practice/lap.csv --lang en

# agent pipeline: drop the prompt, reuse fresh reports
python -m telemetry technique races/Zandvoort/Practice/lap.csv --no-prompt
python -m telemetry technique races/Zandvoort/Practice/lap.csv --force   # ignore cache

Every report run prints the output path and a token estimate:

C:\...\Telemetry\reports\lap_technique.md
~33834 tokens (budget 60k)

Rename inserts tokens AND sorts into folders. Point it at a folder containing CSVs (e.g., D:\Games\Telemetry\Data\lapdata\f1_2022) — the script:

Recursively scans all subdirectories
Reads metadata from each CSV (track, session type)
Inserts P1_L7 into filenames that don't have both tokens yet
Moves files to races/<track>/Practice|Qualifying|Race/
Even already-renamed files are moved if they're not in the right folder
Only skips files that are already in the correct location with the correct name

Re-running on the same folder is safe — in-place files are left alone.

Input format

F1 22 UDP telemetry exported to CSV (tested with F1Laps). UTF-8, ~1800 rows per lap (≈29 Hz), 108 channels. One file = one lap. For race mode, drop all of a race's lap files into one folder.

For developers

Pure-stdlib Python. Pipeline: parser → segments (+ corner_metrics) → resample → report_*. See ARCHITECTURE.md.

cd tool
python -m pytest -q        # run the test suite
python -m telemetry gui    # launch the GUI

Adding a mode: drop a telemetry/report_<mode>.py exposing generate(...) -> (abs_path, tokens) and wire one line into telemetry/__main__.py. The parser, corner detection, adaptive trace, and shared report blocks are reused.

Credits

Built for analysing personal F1 22 hotlaps. Telemetry export format from F1Laps.

License

MIT — see LICENSE.

5.9 KiB Raw Permalink Blame History Unescape Escape