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Northern-Thailand-Ping-Rive…/CLAUDE.md
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grabowski 2e19974fad docs: README describes the project that exists; CLAUDE.md for agents
The README was the original template: P.1 "in Nakhon Sawan", P.103 "in
Bangkok", VictoriaMetrics as the recommended database, Docker/Grafana
sections, github.com/your-username support links. Rewritten around what
runs: sources, gap fill, the forecast and its 13 h result, the live API
table, systemd deployment with the retrain timer, repository layout,
real docs links, data-source credits. Other DB adapters are mentioned as
supported-but-not-production.

CLAUDE.md replaces the untracked Ruflo boilerplate with project rules:
Python 3.11, format/test gates, Bangkok timestamps, harness-first model
changes judged on lead, the RainUnavailableError guard, no git add -A.
2026-09-11 23:44:43 +02:00

41 lines
2.1 KiB
Markdown

# CLAUDE.md
Guidance for AI coding agents working in this repository.
## What this is
Flood monitoring and forecasting for the Ping River, Chiang Mai. Public dashboard and
API at https://water.buildfor.life/ (never publish the server's private/Tailscale IP).
Production: one systemd unit on a small VPS, `/opt/thailand-water-monitor`, user
`water-monitor`, interpreter `.venv/bin/python` (uv-managed), updated by `git pull`.
## Rules
- Python 3.11 only. `uv sync --python 3.11`; run everything as `uv run ...`.
- `make format` (black 88 / isort black profile, config in pyproject.toml) before
committing; CI fails on formatting. `make test` must stay green — tests are
synthetic-data only, never add one that needs the DB or network.
- Timestamps everywhere are Asia/Bangkok wall-clock with no offset. The dashboard
parses them with `parseTs()` and renders with `timeZone: TZ`; keep it that way.
- Model changes go through the rolling-origin harness (`scripts/evaluate_variants.py`)
and are judged on first-alert LEAD and false alarms, not MAE. Record results, positive
or negative, in `docs/FLOOD_FORECASTING.md` section 5. Do not change what is deployed
(`rise_rain` / hgb-v3) without a harness result that beats it on lead.
- `train_all()` must never silently produce a gauge-only (v2) model; the guard that
raises `RainUnavailableError` stays.
- No `git add -A`: zero-byte shell-accident files (`#`, `$(wc`, ...) have been committed
before. Stage files by name.
- Do not add Co-Authored-By trailers.
- The dashboard is a single file, `src/static/dashboard.html`, EN + TH via the `t()`
table: every user-visible string needs both languages.
## Where things are
- `src/web_api.py` FastAPI app; `src/water_scraper_v3.py` RID collector;
`src/hii_collector.py` ThaiWater/HII; `src/ml/` features/train/evaluate/predict,
`rain.py` (Open-Meteo), `dam.py`, `hii_rain.py`.
- `scripts/retrain.sh` + `water-monitor-retrain.timer`: monthly retrain with staged
promote. `scripts/dev_proxy.py`: serve the working-copy dashboard against the live API.
- `docs/FLOOD_FORECASTING.md` is the authoritative model write-up; `docs/DATA_SOURCES.md`
the source catalog.