5 Commits

Include renames

Author	SHA1	Message	Date
grabowski	f6570ac10f	chore: declutter the repo ... Removes 26 tracked files that no longer describe or serve the running system, verified one by one against the whole repo (source, tests, docs, README, Makefile, Dockerfile, .gitea workflows, pyproject, packaging spec) plus dynamic-reference paths, before deletion. Root (11): one-off launch/setup write-ups from the project's first weeks that document events which never happened the way they describe — a github.com publication (the remote is self-hosted Gitea) and a 15-minute scheduler (the service runs hourly). Also .gitlab-ci.yml (unused, CI is .gitea/), .env.postgres and setup.py.backup (a placeholder env file and a backup in version control), and the PyInstaller packaging trio build_executable.py / build_simple.py / ping-river-monitor.spec, which bundled docs that no longer exist and is not how this deploys. docs (9): stale guides superseded by DATABASE_DEPLOYMENT_GUIDE, GITEA_WORKFLOWS, FLOOD_FORECASTING and DATA_SOURCES, plus two snapshots (PROJECT_STATUS, PROJECT_STRUCTURE) describing a 4-file src/ that is now 39. scripts (5): one-shot bootstrap tools already run — init_git.sh/.bat, generate_badges.py, migrate_geolocation.py, encode_password.py. Every inbound reference was fixed rather than left dangling: README doc index and migration section, three Makefile targets, the docs.yml summary step, and the GITEA_WORKFLOWS resource list. src/ is deliberately untouched. The audit proposed removing several live modules; verification showed those proposals were mis-scoped and would have broken production. .gitignore now covers the agent tooling dirs, model eval output and editor/shell leftovers — the working tree had collected 56 zero-byte files named after fragments of shell commands. 136 tests pass; production modules import clean.	2026-08-14 13:45:03 +07:00
grabowski	0b885e572d	chore: gitignore .playwright-mcp browser artifacts	2026-08-10 17:45:21 +07:00
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	4bc3d82773	Persist station CRUD across restarts via JSON config ... Station CRUD via the API previously mutated the scraper's in-memory station_mapping only, so changes were lost on restart (and the systemd service auto-restarts). - Extract the 130-line hardcoded station_mapping into bundled defaults at src/data/stations.json; the scraper loads from a runtime-writable config file (STATION_CONFIG_PATH, default stations.json) and falls back to the bundled defaults to seed it. - Add scraper.save_stations() with an atomic temp-file + os.replace write. - create/update/delete station endpoints now persist and roll back the in-memory change if the write fails; re-raise HTTPException so persistence errors surface as real 500s instead of being swallowed. - Backend-agnostic (works for the VictoriaMetrics deployment, which has no relational stations table). Runtime stations.json is gitignored. Also clears pre-existing flake8 debt in water_scraper_v3.py (unused imports, long lines, duplicate logging import) and dedupes the User-Agent to Config.USER_AGENT.	2026-07-22 12:51:29 +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