# RF Propagation API

HTTP API for radio visibility, terrain profiles, Fresnel/LOS checks, link budget,
viewshed, and coverage calculations.

This repository follows `SPEC.md`. The first implementation pass creates the full
service skeleton, pure RF/math kernels, and Copernicus DEM sampling. Integrations
that require PostGIS data, pycraf, ITM/P.1812, landcover/canopy rasters, or
external viewshed binaries are exposed through stable interfaces and return
explicit "not implemented" responses until the corresponding data pipeline is
connected.

## Layout

- `api/` - FastAPI service, core calculations, service orchestration, tests.
- `scripts/` - data bootstrap/import entry points.
- `data/` - mounted data volume for DEM, landcover, and canopy rasters.
- `docker-compose.yml` - local stack with API, worker, Redis, PostGIS, optional tiler.

## Quick Start

```bash
cp .env.example .env
docker compose up --build api redis postgis
```

The API is served at `http://localhost:8000`, with OpenAPI docs at `/docs`.

## DEM Bootstrap

Download Copernicus DEM GLO-30 COG tiles for Saint Petersburg and Leningrad Oblast:

```bash
python scripts/bootstrap_dem.py --bbox 27.3,58.4,35.8,61.4 --output-dir data/dem
docker compose restart api worker
```

The API samples all `.tif`/`.tiff` files under `DEM_PATH` recursively. In Docker,
the default `DEM_PATH=/data/dem` points to the mounted `./data/dem` directory.

## Buildings Bootstrap

Download an OSM PBF extract and import building polygons into PostGIS:

```bash
mkdir -p data/osm
wget -O data/osm/northwestern-fed-district-latest.osm.pbf \
  https://download.geofabrik.de/russia/northwestern-fed-district-latest.osm.pbf
sh scripts/load_buildings.sh data/osm/northwestern-fed-district-latest.osm.pbf
docker compose restart api worker
```

The script builds a local `radio-osm2pgsql:latest` image from
`scripts/Dockerfile.osm2pgsql` on first run. It imports the PBF through standard
`osm2pgsql` tables, then normalizes `planet_osm_polygon` into the API `buildings`
table with `db/sql/normalize_buildings.sql`.

The import writes a `buildings` table with `geom`, `height_m`, `levels`,
`building_type`, and `source`. Heights come from `height`, then
`building:levels * 3.0`, then an estimated default by building type.

Check the API:

```bash
curl -s -X POST http://localhost:8000/api/v1/buildings/query \
  -H 'Content-Type: application/json' \
  -d '{"bbox":[30.30,59.93,30.33,59.95]}' | jq '.features | length'
```

For local Python development:

```bash
cd api
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
python -m pytest
python -m ruff check .
```