5 Commits

Include renames

Author	SHA1	Message	Date
grabowski	24998b033c	fix(deps): python-dotenv 1.2.2 (PYSEC-2026-2270, set_key symlink overwrite) ... Now a direct dependency, pip-audit sees it; it used to come in through uvicorn. The .env parser is unchanged: rust/collector/testdata/gen_envfile_cases.py regenerates the 614-case fixture byte-identical under 1.2.2, so envfile.rs still matches.	2026-10-18 17:31:41 +07:00
grabowski	7e642555b4	chore: remove the Python service; Python is the training stack only ... Phase 4 of docs/RUST_REWRITE.md. Since 2026-10-18 ping-collector (rust/) collects, forecasts, notifies (ntfy + Matrix) and serves the website/API; nothing in production ran the Python service code any more. Removed (~17k lines): the FastAPI app (web_api, schemas, health_check, metrics, postgres_history, forecast_history, thaiwater), the Python RID collector and CLI (water_scraper_v3, main, run.py), the notifier and Matrix alerting (notify, alerting), the multi-backend adapters (SQLite, MySQL, InfluxDB, VictoriaMetrics) with demo databases, models/validators/exceptions/ logging/rate limiter, ml/skill.py (served by Rust now), the migration tooling (api_parity, collector_parity, e2e_notify, install_collector.sh, gen_notify_cases.py, spikes/rust-forecast), Docker/compose/Grafana files, sql/, the release workflow and the docs for all of that. Their tests went with them. Kept in Python: src/ml (training, evaluation harness, export; predict.py as the reference the Rust forecaster is tested against), the HII and RID-dam clients/stores for the archive backfills (their live collectors removed), the backfill scripts, retrain.sh + timer, forecast_parity.py. - PostgreSQL only: src/config.py is database_url() from POSTGRES_* / .env; the stores lost their sqlite/mysql branches (and the 2026-08 column migration). The database tests run against a throwaway schema from TEST_DATABASE_URL (tests/conftest.py pg_url, skipped without it), as the Rust tests do; CI gets a PostgreSQL service. 100 tests (66 without a DB). - The notifier settings moved from src/notify.py into src/ml/export.py; flood_config.json regenerates byte-identical. - tests/test_dashboard.py checks every path the dashboard fetches against ping-collector's router. - Dependencies: fastapi, uvicorn, pydantic, influxdb, pymysql, psutil, schedule, sphinx, pyinstaller, mypy, pytest-asyncio dropped; python-dotenv 1.1.1 explicit (it came in through uvicorn[standard]). - Formatting contract extended to scripts/ and tests/ (reformatted once). - scripts/install.sh is the one installer (fresh host or update, in place in a checkout without touching its ownership): binary, units, uv sync, .env defaults, removes the retired water-monitor.service and any old --alert-check cron job, dry run + forecast check, restart, readiness wait. - Docs, CLAUDE.md, CONTRIBUTING.md, .env.example, Makefile, unit comments and Rust doc comments updated to the Rust service.	2026-10-18 17:15:07 +07:00
grabowski	b03318210c	security: pip-audit + bandit gates that can fail; patch 29 known CVEs ... security.yml previously ran safety/bandit/semgrep with `|| true` and could not go red. Now: pip-audit on requirements.txt is a hard gate (dev deps reported only), bandit HIGH fails (B104 bind-all skipped: intended behind Cloudflare/Caddy), pip-licenses uploaded as a report. Weekly + on dependency/source changes. Running it locally found 29 advisories, all in pinned-and-forgotten runtime deps: starlette 0.27 (7, incl. Host-header path confusion and form DoS), fastapi 0.104, requests 2.31 (3), pymysql 1.1. Bumped to current: fastapi 0.141.1 / starlette 1.6.0, pydantic 2.13.5, uvicorn 0.52.4, requests 2.34.2, pymysql 1.2.0; dev: pytest 9.1.1, black 26.5.1. pip-audit is now clean. requires-python narrowed to 3.11 (the truth: psycopg2-binary 2.9.9 fails on 3.13; pandas 2.0.3 has no 3.12 wheels). Full suite passes; API smoke-tested (health, stations, forecast, history, stats, docs, openapi) on the new stack. black 26 reformatted 8 files.	2026-09-11 23:44:43 +02:00
grabowski	4358d52d55	feat: ML flood-event forecasting from 8 years of gauge history ... Add src/ml/ package predicting, per station and per 6/12/24 h horizon, the probability of exceeding warning (3.0 m) and danger (4.5 m) levels plus expected peak level, trained on the 592k-row PostgreSQL history: - features.py: hourly grid with coverage gating and no future leakage; upstream stations enter at empirically measured travel-time lags (P.20 +17h ... P.103 +1h vs P.1); hour-of-day deliberately excluded (it encodes the scrape schedule, not hydrology) - train.py: HistGradientBoosting regression + warn/danger classifier heads per station x horizon, >=30-positives gate with calibrated sigmoid-on-regression fallback, strict temporal splits, per-event lead-time evaluation; guards against sklearn 1.9.0 crash on degenerate feature columns - predict.py: bundle loading with feature-name checks, heuristic fallback tier, get_latest_forecasts() for the API; raises when no models are trained so the endpoint 503s instead of serving persistence output as forecasts - data.py: Postgres-first loader (FLOOD_ML_DB_URL override), HTTP API fallback (flagged: that path backfills synthetic discharge), csv.gz cache - /forecast endpoint (15-min TTL cache) + dashboard flood-risk panel (hidden until models exist) - docs/FLOOD_FORECASTING.md: full system doc with measured deployment numbers (~335 MB RSS, CPU negligible, ~6 min full retrain) and retraining policy Validation: out-of-sample backtest of the record 2024 flood season (train <= Aug 2024) alerted 24-48 h ahead of the Oct 5 peak; 2025-26 test split: P.1 6h PR-AUC 0.974, recall 98.3% at 1% false-alarm rate. Also: fix P.81 station coordinates (was Ban Pong/Ratchaburi, 493 km out of basin; now 18.6936 N 99.0819 E per RID station page), pin scikit-learn==1.9.0 and numpy<2, gitignore model artifacts (~100 MB, train on the server via scripts/train_flood_model.py).	2026-08-10 12:49:47 +07:00
grabowski	af62cfef0b	Initial commit: Northern Thailand Ping River Monitor v3.1.0 ... Features: - Real-time water level monitoring for Ping River Basin (16 stations) - Coverage from Chiang Dao to Nakhon Sawan in Northern Thailand - FastAPI web interface with interactive dashboard and station management - Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics) - Comprehensive monitoring with health checks and metrics collection - Docker deployment with Grafana integration - Production-ready architecture with enterprise-grade observability CI/CD & Automation: - Complete Gitea Actions workflows for CI/CD, security, and releases - Multi-Python version testing (3.9-3.12) - Multi-architecture Docker builds (amd64, arm64) - Daily security scanning and dependency monitoring - Automated documentation generation - Performance testing and validation Production Ready: - Type safety with Pydantic models and comprehensive type hints - Data validation layer with range checking and error handling - Rate limiting and request tracking for API protection - Enhanced logging with rotation, colors, and performance metrics - Station management API for dynamic CRUD operations - Comprehensive documentation and deployment guides Technical Stack: - Python 3.9+ with FastAPI and Pydantic - Multi-database architecture with adapter pattern - Docker containerization with multi-stage builds - Grafana dashboards for visualization - Gitea Actions for CI/CD automation - Enterprise monitoring and alerting Ready for deployment to B4L infrastructure!	2025-08-12 15:40:24 +07:00