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feature/na
| Author | SHA1 | Date | |
|---|---|---|---|
| 248f4bded4 | |||
| 1b3ad1bf9e | |||
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bb7d0169e0 | ||
| c92178d90f | |||
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2d1dcbf70c |
19 changed files with 368 additions and 4805 deletions
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@ -6,13 +6,13 @@ profile: home
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region_name: "North America"
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tileset:
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url: "/tiles/planet/current.pmtiles"
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url: "/tiles/na.pmtiles"
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bounds: [-168, 14, -52, 72]
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max_zoom: 15
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attribution: "Protomaps © OSM"
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tileset_hillshade:
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url: "/tiles/planet-dem.pmtiles"
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url: "/tiles/hillshade-na.pmtiles"
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encoding: "terrarium"
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max_zoom: 12
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@ -33,14 +33,14 @@ services:
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features:
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has_nominatim_details: true
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has_kiwix_wiki: true
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has_kiwix_wiki: false
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has_hillshade: true
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has_3d_terrain: false
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has_traffic_overlay: true
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has_landclass: true
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has_public_lands_layer: true
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has_contours: true
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has_contours_test: false
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has_contours_test: true
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has_contours_test_10ft: false
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has_address_book_write: false
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has_overture_enrichment: true
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@ -48,16 +48,7 @@ features:
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has_contacts: true
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has_wiki_rewriting: true
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has_wiki_discovery: false
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has_usfs_trails: true
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has_blm_trails: true
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defaults:
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center: [42.5736, -114.6066]
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zoom: 10
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# Offroute wilderness routing
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offroute:
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osm_pbf_path: "/mnt/nav/sources/idaho-latest.osm.pbf"
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densify_interval_m: 100
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postgis_dsn: "dbname=padus"
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211
lib/api.py
211
lib/api.py
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@ -2722,214 +2722,3 @@ def api_auth_whoami():
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'authenticated': False,
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'username': None,
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})
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# ── OFFROUTE API ──
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@app.route("/api/offroute", methods=["POST"])
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def api_offroute():
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"""
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Off-network routing from wilderness to destination.
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Request body:
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{
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"start": [lat, lon],
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"end": [lat, lon],
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"mode": "foot" | "mtb" | "atv", (default: "foot")
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"boundary_mode": "strict" | "pragmatic" | "emergency" (default: "pragmatic")
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}
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Response:
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{
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"status": "ok",
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"route": { GeoJSON FeatureCollection with wilderness + network segments },
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"summary": { total_distance_km, total_effort_minutes, ... }
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}
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"""
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try:
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data = request.get_json()
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if not data:
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return jsonify({"status": "error", "message": "No JSON body provided"}), 400
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# Parse coordinates
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start = data.get("start")
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end = data.get("end")
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if not start or not end:
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return jsonify({"status": "error", "message": "Missing start or end coordinates"}), 400
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if not isinstance(start, (list, tuple)) or len(start) != 2:
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return jsonify({"status": "error", "message": "start must be [lat, lon]"}), 400
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if not isinstance(end, (list, tuple)) or len(end) != 2:
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return jsonify({"status": "error", "message": "end must be [lat, lon]"}), 400
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start_lat, start_lon = float(start[0]), float(start[1])
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end_lat, end_lon = float(end[0]), float(end[1])
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# Parse options
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mode = data.get("mode", "foot")
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if mode not in ("auto", "foot", "mtb", "atv", "vehicle"):
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return jsonify({"status": "error", "message": "mode must be auto, foot, mtb, atv, or vehicle"}), 400
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boundary_mode = data.get("boundary_mode", "pragmatic")
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if boundary_mode not in ("strict", "pragmatic", "emergency"):
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return jsonify({"status": "error", "message": "boundary_mode must be strict, pragmatic, or emergency"}), 400
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# Import and run router
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from .offroute.router import OffrouteRouter
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router = OffrouteRouter()
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try:
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result = router.route(
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start_lat=start_lat,
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start_lon=start_lon,
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end_lat=end_lat,
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end_lon=end_lon,
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mode=mode,
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boundary_mode=boundary_mode
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)
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finally:
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router.close()
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if result.get("status") == "error":
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return jsonify(result), 400
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return jsonify(result)
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except Exception as e:
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logger.exception("Offroute error")
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return jsonify({"status": "error", "message": str(e)}), 500
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# ── MVUM Places Panel API ──
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@app.route("/api/mvum", methods=["GET"])
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def api_mvum():
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"""
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Query MVUM (Motor Vehicle Use Map) features near a point.
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Used by the Navi frontend places panel when a user taps near a road/trail.
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Query params:
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lat: Latitude
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lon: Longitude
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radius: Search radius in meters (default: 50)
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Response:
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{
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"status": "ok",
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"feature": {
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"id": "FR 123",
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"name": "Some Forest Road",
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"forest": "Sawtooth National Forest",
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"district": "Ketchum Ranger District",
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"surface": "NAT",
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"maintenance_level": 2,
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"seasonal": "Seasonal",
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"symbol": 2,
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"access": {
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"passenger_vehicle": { "status": "Open", "dates": "06/15-10/15" },
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"high_clearance": { "status": "Open", "dates": "06/15-10/15" },
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"atv": { "status": "Open", "dates": "06/15-10/15" },
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...
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},
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"geometry": { GeoJSON LineString }
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}
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}
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If no MVUM feature within radius:
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{ "status": "ok", "feature": null }
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"""
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try:
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lat = request.args.get("lat", type=float)
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lon = request.args.get("lon", type=float)
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radius = request.args.get("radius", 50, type=float)
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if lat is None or lon is None:
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return jsonify({"status": "error", "message": "lat and lon required"}), 400
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from .offroute.mvum import MVUMReader
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reader = MVUMReader()
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try:
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# Try roads first, then trails
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feature = reader.query_nearest(lat, lon, radius, "mvum_roads")
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if feature is None:
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feature = reader.query_nearest(lat, lon, radius, "mvum_trails")
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if feature is None:
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return jsonify({"status": "ok", "feature": None})
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# Format access info
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access = {
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"passenger_vehicle": {
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"status": feature.get("passengervehicle"),
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"dates": feature.get("passengervehicle_datesopen")
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},
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"high_clearance": {
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"status": feature.get("highclearancevehicle"),
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"dates": feature.get("highclearancevehicle_datesopen")
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},
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"atv": {
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"status": feature.get("atv"),
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"dates": feature.get("atv_datesopen")
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},
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"motorcycle": {
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"status": feature.get("motorcycle"),
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"dates": feature.get("motorcycle_datesopen")
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},
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"4wd_gt50": {
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"status": feature.get("fourwd_gt50inches"),
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"dates": feature.get("fourwd_gt50_datesopen")
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},
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"2wd_gt50": {
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"status": feature.get("twowd_gt50inches"),
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"dates": feature.get("twowd_gt50_datesopen")
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},
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"e_bike_class1": {
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"status": feature.get("e_bike_class1"),
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"dates": feature.get("e_bike_class1_dur")
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},
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"e_bike_class2": {
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"status": feature.get("e_bike_class2"),
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"dates": feature.get("e_bike_class2_dur")
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},
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"e_bike_class3": {
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"status": feature.get("e_bike_class3"),
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"dates": feature.get("e_bike_class3_dur")
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},
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}
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# Parse maintenance level
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maint_level = feature.get("operationalmaintlevel", "")
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maint_num = None
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if maint_level:
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# Extract first digit: "2 - HIGH CLEARANCE VEHICLES" -> 2
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import re
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match = re.match(r"(\d+)", maint_level)
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if match:
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maint_num = int(match.group(1))
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result = {
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"id": feature.get("id"),
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"name": feature.get("name"),
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"forest": feature.get("forestname"),
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"district": feature.get("districtname"),
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"surface": feature.get("surfacetype"),
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"maintenance_level": maint_num,
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"seasonal": feature.get("seasonal"),
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"symbol": feature.get("symbol"),
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"trail_class": feature.get("trailclass"),
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"trail_system": feature.get("trailsystem"),
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"access": access,
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"geometry": feature.get("geojson")
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}
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return jsonify({"status": "ok", "feature": result})
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finally:
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reader.close()
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except Exception as e:
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logger.exception("MVUM query error")
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return jsonify({"status": "error", "message": str(e)}), 500
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@ -21,7 +21,6 @@ Config: processing.extract_workers, processing.max_pdf_size_mb,
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processing.extract_timeout, processing.page_timeout
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"""
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import base64
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import re
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import json
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import os
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import random
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@ -100,40 +99,6 @@ def _is_transient(error_str):
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return any(sig in s for sig in transient_signals)
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def _text_quality_ok(text, min_length=50):
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"""Check if extracted text meets quality thresholds.
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Beyond the basic length check, validates:
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- Word-boundary ratio: at least 60% of tokens should be real words (2+ alpha chars)
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- Concatenation ratio: lowercase-immediately-followed-by-uppercase shouldn't exceed 10% of word count
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Returns True if text passes all checks.
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"""
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text = text.strip()
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if len(text) < min_length:
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return False
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words = text.split()
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if not words:
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return False
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# Word-like ratio: tokens with 2+ alphabetic characters
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word_like = sum(1 for w in words if len(re.findall(r'[a-zA-Z]', w)) >= 2)
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word_ratio = word_like / len(words)
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if word_ratio < 0.60:
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return False
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# Concatenation detector: lowercase immediately followed by uppercase
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# Filter out common camelCase patterns in code (short tokens)
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concat_hits = len(re.findall(r'[a-z][A-Z]', text))
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concat_ratio = concat_hits / len(words) if words else 0
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if concat_ratio > 0.10:
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return False
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return True
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def _render_page_to_png(pdf_path, page_num_1indexed, dpi=200, timeout=30):
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"""Render a single PDF page to PNG bytes using pdftoppm.
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@ -259,7 +224,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
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# Method 1: pdftotext (poppler)
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try:
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result = subprocess.run(
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['pdftotext', '-layout', '-f', str(page_num_0indexed + 1),
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['pdftotext', '-f', str(page_num_0indexed + 1),
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'-l', str(page_num_0indexed + 1), pdf_path, '-'],
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capture_output=True, text=True, timeout=page_timeout
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)
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@ -268,7 +233,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
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except Exception:
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pass
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if _text_quality_ok(text):
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if len(text.strip()) >= 50:
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return text, 'pdftotext'
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# Method 2: pdftoppm + Tesseract OCR
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@ -293,7 +258,7 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
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except Exception:
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pass
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if _text_quality_ok(text):
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if len(text.strip()) >= 50:
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return text, 'tesseract'
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# Method 3: Gemini Vision (last resort)
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@ -311,26 +276,8 @@ def _extract_page_without_reader(pdf_path, page_num_0indexed, page_timeout=30):
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# ── Core extraction functions ──
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def _pypdf2_extract(reader, page_num):
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"""Extract text from a PyPDF2 page object. Runs inside a thread for timeout.
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Tries default extraction first (space_width=200). If quality check fails,
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retries with space_width=100 which better detects word boundaries in
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tightly-kerned PDFs (common in Haynes/workshop manuals).
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Note: PyPDF2 3.0.1 does not support layout=True. The space_width parameter
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controls word-boundary detection tolerance. Lower values = more aggressive
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space insertion between characters.
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"""
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text = reader.pages[page_num].extract_text() or ''
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if _text_quality_ok(text):
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return text
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# Retry with tighter word-boundary detection
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text_tight = reader.pages[page_num].extract_text(space_width=100.0) or ''
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if len(text_tight.strip()) >= len(text.strip()):
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return text_tight
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return text
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"""Extract text from a PyPDF2 page object. Runs inside a thread for timeout."""
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return reader.pages[page_num].extract_text() or ''
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def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
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@ -355,13 +302,13 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
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except Exception:
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text = ''
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if _text_quality_ok(text):
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if len(text.strip()) >= 50:
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return text, 'pypdf2'
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# Method 2: pdftotext via subprocess (inherently timeout-safe)
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try:
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result = subprocess.run(
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['pdftotext', '-layout', '-f', str(page_num + 1), '-l', str(page_num + 1), pdf_path, '-'],
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['pdftotext', '-f', str(page_num + 1), '-l', str(page_num + 1), pdf_path, '-'],
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capture_output=True, text=True, timeout=page_timeout
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)
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if result.returncode == 0 and len(result.stdout.strip()) > len(text.strip()):
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|
|
@ -369,7 +316,7 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
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except Exception:
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pass
|
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|
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if _text_quality_ok(text):
|
||||
if len(text.strip()) >= 50:
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return text, 'pdftotext'
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# Method 3: pdftoppm + Tesseract OCR
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||||
|
|
@ -393,7 +340,7 @@ def extract_text_from_page(reader, page_num, pdf_path, page_timeout=30):
|
|||
except Exception:
|
||||
pass
|
||||
|
||||
if _text_quality_ok(text):
|
||||
if len(text.strip()) >= 50:
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return text, 'tesseract'
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# Method 4: Gemini Vision (last resort — costs API calls but handles scanned docs)
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|
|
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|||
|
|
@ -47,6 +47,18 @@ def _get_db():
|
|||
)
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||||
""")
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_db_conn.commit()
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# Schema migration: add Google columns to place_cache if missing
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for col, coldef in [
|
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('google_place_id', 'TEXT'),
|
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('google_data', 'TEXT'),
|
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('google_fetched_at', 'INTEGER'),
|
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]:
|
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try:
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_db_conn.execute(f'ALTER TABLE place_cache ADD COLUMN {col} {coldef}')
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logger.info(f'Added column {col} to place_cache')
|
||||
except sqlite3.OperationalError:
|
||||
pass # Column already exists
|
||||
_db_conn.commit()
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||||
return _db_conn
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,23 +4,77 @@ RECON Netsyms API + Geocode — Flask Blueprints.
|
|||
GET /api/netsyms/lookup?q=<free text>&country=<optional>
|
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GET /api/netsyms/health
|
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GET /api/geocode?q=<query>&limit=<N> (Photon-first search with ranked results)
|
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GET /api/reverse/<lat>/<lon> (localhost-sourced enrichment bundle for Central)
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import threading
|
||||
|
||||
from cachetools import TTLCache
|
||||
from flask import Blueprint, request, jsonify
|
||||
|
||||
from . import netsyms
|
||||
from . import address_book
|
||||
from . import nav_tools
|
||||
from .geocode import PHOTON_URL
|
||||
from .utils import setup_logging
|
||||
|
||||
logger = setup_logging('recon.netsyms_api')
|
||||
|
||||
|
||||
def _enrich_reverse_result_with_wiki(result):
|
||||
"""
|
||||
Add wiki data to a reverse geocode result if available.
|
||||
Only runs when has_kiwix_wiki is enabled.
|
||||
"""
|
||||
try:
|
||||
from .deployment_config import get_deployment_config
|
||||
deploy_config = get_deployment_config()
|
||||
features = deploy_config.get('features', {})
|
||||
if not features.get('has_kiwix_wiki', False):
|
||||
return result
|
||||
except Exception:
|
||||
return result
|
||||
|
||||
try:
|
||||
from . import wiki_index
|
||||
except ImportError:
|
||||
return result
|
||||
|
||||
if not wiki_index.is_available():
|
||||
return result
|
||||
|
||||
# Extract match criteria from Photon raw props
|
||||
raw = result.get('raw', {})
|
||||
place_name = raw.get('name', '')
|
||||
osm_key = raw.get('osm_key', '')
|
||||
osm_value = raw.get('osm_value', '')
|
||||
state = raw.get('state', '')
|
||||
country = raw.get('country', '')
|
||||
|
||||
# Extract country code (Photon uses full country name, we need code)
|
||||
country_code = raw.get('countrycode', '').lower()
|
||||
if not country_code:
|
||||
country_lower = country.lower() if country else ''
|
||||
if 'united states' in country_lower or country_lower == 'usa':
|
||||
country_code = 'us'
|
||||
elif 'canada' in country_lower:
|
||||
country_code = 'ca'
|
||||
|
||||
if not place_name or not osm_key or not osm_value or not country_code:
|
||||
return result
|
||||
|
||||
# Look up wiki data
|
||||
wiki_data = wiki_index.lookup_wiki(place_name, osm_key, osm_value, state, country_code)
|
||||
if wiki_data:
|
||||
# Add wiki fields to result (additive only)
|
||||
if 'wiki_summary' in wiki_data:
|
||||
result['wiki_summary'] = wiki_data['wiki_summary']
|
||||
if 'wiki_url' in wiki_data:
|
||||
result['wiki_url'] = wiki_data['wiki_url']
|
||||
if 'wikivoyage_url' in wiki_data:
|
||||
result['wikivoyage_url'] = wiki_data['wikivoyage_url']
|
||||
if 'wiki_population' in wiki_data:
|
||||
result['wiki_population'] = wiki_data['wiki_population']
|
||||
|
||||
return result
|
||||
|
||||
|
||||
|
||||
netsyms_bp = Blueprint('netsyms', __name__)
|
||||
geocode_bp = Blueprint('geocode', __name__)
|
||||
|
||||
|
|
@ -129,159 +183,7 @@ def api_reverse():
|
|||
from .geocode import _parse_photon_features
|
||||
results = _parse_photon_features(features, source='photon_reverse')
|
||||
|
||||
# Enrich results with wiki data
|
||||
results = [_enrich_reverse_result_with_wiki(r) for r in results]
|
||||
|
||||
return jsonify({'query': query_str, 'results': results, 'count': len(results)})
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────────
|
||||
# /api/reverse/<lat>/<lon> — localhost-sourced enrichment bundle (Central)
|
||||
#
|
||||
# Sibling to the query-string /api/reverse above; that route is unchanged.
|
||||
# Every component is sourced from localhost only (Photon, timezones.sqlite,
|
||||
# in-process landclass/PostGIS, Valhalla). Each lookup is independent: a
|
||||
# component failure logs a warning and yields null — never a 5xx.
|
||||
# ─────────────────────────────────────────────────────────────────────────
|
||||
|
||||
_TZ_DB_PATH = "/mnt/nav/sources/timezones.sqlite"
|
||||
_VALHALLA_HEIGHT_URL = "http://localhost:8002/height"
|
||||
|
||||
# Full bundle cache: key=(round(lat,4), round(lon,4)) -> dict. ~10k entries, 24h TTL.
|
||||
_REVERSE_BUNDLE_CACHE = TTLCache(maxsize=10_000, ttl=86_400)
|
||||
_REVERSE_BUNDLE_LOCK = threading.Lock()
|
||||
|
||||
_BUNDLE_KEYS = ('name', 'city', 'county', 'state', 'country',
|
||||
'postal_code', 'timezone', 'landclass', 'elevation_m')
|
||||
|
||||
|
||||
def _spatialite_blob_to_wkb(blob):
|
||||
"""Recover standard WKB from a SpatiaLite geometry BLOB.
|
||||
|
||||
Layout: [00][endian][srid:4][mbr:32][7C][WKB body][FE]. The body omits the
|
||||
leading byte-order marker, so we re-prepend it and drop the trailing 0xFE.
|
||||
"""
|
||||
return bytes([blob[1]]) + blob[39:-1]
|
||||
|
||||
|
||||
def _reverse_photon(lat, lon):
|
||||
"""Nearest-feature admin fields from local Photon. Returns the six address
|
||||
fields (any value may be None). Mirrors the existing /api/reverse call."""
|
||||
import requests as http_requests
|
||||
resp = http_requests.get(
|
||||
f"{PHOTON_URL}/reverse",
|
||||
params={"lat": lat, "lon": lon, "limit": 1},
|
||||
timeout=10,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
features = resp.json().get("features", [])
|
||||
if not features:
|
||||
return {}
|
||||
props = features[0].get("properties", {})
|
||||
return {
|
||||
"name": props.get("name"),
|
||||
"city": props.get("city"),
|
||||
"county": props.get("county"),
|
||||
"state": props.get("state"),
|
||||
"country": props.get("country"),
|
||||
"postal_code": props.get("postcode"),
|
||||
}
|
||||
|
||||
|
||||
def _reverse_timezone(lat, lon):
|
||||
"""IANA tzid for the point from local timezones.sqlite (SpatiaLite tz_world).
|
||||
|
||||
Uses the table's R-tree index for an MBR prefilter, then shapely
|
||||
point-in-polygon on the few candidates. Returns None if unresolved.
|
||||
"""
|
||||
from shapely import wkb
|
||||
from shapely.geometry import Point
|
||||
con = sqlite3.connect(f"file:{_TZ_DB_PATH}?mode=ro", uri=True)
|
||||
try:
|
||||
cur = con.cursor()
|
||||
cur.execute(
|
||||
"SELECT pkid FROM idx_tz_world_geom "
|
||||
"WHERE xmin<=? AND xmax>=? AND ymin<=? AND ymax>=?",
|
||||
(lon, lon, lat, lat),
|
||||
)
|
||||
candidates = [r[0] for r in cur.fetchall()]
|
||||
if not candidates:
|
||||
return None
|
||||
pt = Point(lon, lat)
|
||||
for pk in candidates:
|
||||
row = cur.execute(
|
||||
"SELECT tzid, geom FROM tz_world WHERE pk_uid=?", (pk,)
|
||||
).fetchone()
|
||||
if row and wkb.loads(_spatialite_blob_to_wkb(row[1])).contains(pt):
|
||||
return row[0]
|
||||
return None
|
||||
finally:
|
||||
con.close()
|
||||
|
||||
|
||||
def _reverse_landclass(lat, lon):
|
||||
"""Most-specific PAD-US land class for the point, looked up in-process.
|
||||
Returns None when there is no coverage or landclass is unavailable."""
|
||||
from .landclass import lookup_landclass, format_summary
|
||||
return format_summary(lookup_landclass(lat, lon))
|
||||
|
||||
|
||||
def _reverse_elevation(lat, lon):
|
||||
"""Elevation in metres from local Valhalla /height. None on failure."""
|
||||
import requests as http_requests
|
||||
resp = http_requests.post(
|
||||
_VALHALLA_HEIGHT_URL,
|
||||
json={"shape": [{"lat": lat, "lon": lon}]},
|
||||
timeout=10,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
heights = resp.json().get("height", [])
|
||||
return heights[0] if heights else None
|
||||
|
||||
|
||||
@geocode_bp.route('/api/reverse/<lat>/<lon>')
|
||||
def api_reverse_bundle(lat, lon):
|
||||
"""Localhost-sourced reverse-geocode enrichment bundle for Central.
|
||||
|
||||
GET /api/reverse/<lat>/<lon>
|
||||
|
||||
Always returns 200 with EXACTLY these keys (any may be null):
|
||||
name, city, county, state, country, postal_code, timezone, landclass, elevation_m
|
||||
|
||||
lat/lon are parsed manually (not via Flask's <float:> converter, which
|
||||
rejects negative and integer coordinates) so out-of-range or unparseable
|
||||
input yields 400 per contract; 503 is reserved for catastrophic failure.
|
||||
"""
|
||||
try:
|
||||
lat = float(lat)
|
||||
lon = float(lon)
|
||||
except (ValueError, TypeError):
|
||||
return jsonify({'error': 'lat and lon must be numbers'}), 400
|
||||
if not (-90 <= lat <= 90) or not (-180 <= lon <= 180):
|
||||
return jsonify({'error': 'lat must be -90..90, lon must be -180..180'}), 400
|
||||
|
||||
key = (round(lat, 4), round(lon, 4))
|
||||
with _REVERSE_BUNDLE_LOCK:
|
||||
cached = _REVERSE_BUNDLE_CACHE.get(key)
|
||||
if cached is not None:
|
||||
return jsonify(cached)
|
||||
|
||||
bundle = {k: None for k in _BUNDLE_KEYS}
|
||||
|
||||
try:
|
||||
bundle.update(_reverse_photon(lat, lon))
|
||||
except Exception:
|
||||
logger.warning("reverse-bundle: Photon lookup failed for %s,%s", lat, lon)
|
||||
try:
|
||||
bundle['timezone'] = _reverse_timezone(lat, lon)
|
||||
except Exception:
|
||||
logger.warning("reverse-bundle: timezone lookup failed for %s,%s", lat, lon)
|
||||
try:
|
||||
bundle['landclass'] = _reverse_landclass(lat, lon)
|
||||
except Exception:
|
||||
logger.warning("reverse-bundle: landclass lookup failed for %s,%s", lat, lon)
|
||||
try:
|
||||
bundle['elevation_m'] = _reverse_elevation(lat, lon)
|
||||
except Exception:
|
||||
logger.warning("reverse-bundle: elevation lookup failed for %s,%s", lat, lon)
|
||||
|
||||
with _REVERSE_BUNDLE_LOCK:
|
||||
_REVERSE_BUNDLE_CACHE[key] = bundle
|
||||
return jsonify(bundle)
|
||||
|
|
|
|||
|
|
@ -1 +0,0 @@
|
|||
"""OFFROUTE: Off-network effort-based routing module."""
|
||||
|
|
@ -1,440 +0,0 @@
|
|||
"""
|
||||
PAD-US barrier and wilderness layers for OFFROUTE.
|
||||
|
||||
Provides access to:
|
||||
1. Barrier raster (Pub_Access = 'XA' - closed/restricted areas)
|
||||
2. Wilderness raster (Des_Tp = 'WA' - designated wilderness areas)
|
||||
|
||||
Build functions rasterize PAD-US geodatabase to aligned GeoTIFFs.
|
||||
Runtime functions read the rasters and resample to match elevation grids.
|
||||
"""
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Optional
|
||||
import subprocess
|
||||
import tempfile
|
||||
import os
|
||||
|
||||
try:
|
||||
import rasterio
|
||||
from rasterio.windows import from_bounds
|
||||
from rasterio.enums import Resampling
|
||||
except ImportError:
|
||||
raise ImportError("rasterio is required for barriers layer support")
|
||||
|
||||
# Paths
|
||||
DEFAULT_BARRIERS_PATH = Path("/mnt/nav/worldcover/padus_barriers.tif")
|
||||
DEFAULT_WILDERNESS_PATH = Path("/mnt/nav/worldcover/wilderness.tif")
|
||||
PADUS_GDB_PATH = Path("/mnt/nav/padus/PADUS4_0_Geodatabase.gdb")
|
||||
PADUS_LAYER = "PADUS4_0Combined_Proclamation_Marine_Fee_Designation_Easement"
|
||||
|
||||
# CONUS bounding box in WGS84
|
||||
CONUS_BOUNDS = {
|
||||
"west": -125.0,
|
||||
"east": -66.0,
|
||||
"south": 24.0,
|
||||
"north": 50.0,
|
||||
}
|
||||
|
||||
# Resolution in degrees (~30m at mid-latitudes)
|
||||
PIXEL_SIZE = 0.0003 # ~33m
|
||||
|
||||
|
||||
class BarrierReader:
|
||||
"""Reader for PAD-US barrier raster (closed/restricted areas)."""
|
||||
|
||||
def __init__(self, barrier_path: Path = DEFAULT_BARRIERS_PATH):
|
||||
self.barrier_path = barrier_path
|
||||
self._dataset = None
|
||||
|
||||
def _open(self):
|
||||
"""Lazy open the dataset."""
|
||||
if self._dataset is None:
|
||||
if not self.barrier_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Barrier raster not found at {self.barrier_path}. "
|
||||
f"Run build_barriers_raster() first."
|
||||
)
|
||||
self._dataset = rasterio.open(self.barrier_path)
|
||||
return self._dataset
|
||||
|
||||
def get_barrier_grid(
|
||||
self,
|
||||
south: float,
|
||||
north: float,
|
||||
west: float,
|
||||
east: float,
|
||||
target_shape: Tuple[int, int]
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Get barrier values for a bounding box, resampled to target shape.
|
||||
|
||||
Args:
|
||||
south, north, west, east: Bounding box coordinates (WGS84)
|
||||
target_shape: (rows, cols) to resample to (matches elevation grid)
|
||||
|
||||
Returns:
|
||||
np.ndarray of uint8 barrier values:
|
||||
255 = closed/restricted (impassable when respect_boundaries=True)
|
||||
0 = public/accessible
|
||||
"""
|
||||
ds = self._open()
|
||||
window = from_bounds(west, south, east, north, ds.transform)
|
||||
barriers = ds.read(
|
||||
1,
|
||||
window=window,
|
||||
out_shape=target_shape,
|
||||
resampling=Resampling.nearest
|
||||
)
|
||||
return barriers
|
||||
|
||||
def sample_point(self, lat: float, lon: float) -> int:
|
||||
"""Sample barrier value at a single point."""
|
||||
ds = self._open()
|
||||
row, col = ds.index(lon, lat)
|
||||
if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
|
||||
return 0
|
||||
window = rasterio.windows.Window(col, row, 1, 1)
|
||||
value = ds.read(1, window=window)
|
||||
return int(value[0, 0])
|
||||
|
||||
def close(self):
|
||||
"""Close the dataset."""
|
||||
if self._dataset is not None:
|
||||
self._dataset.close()
|
||||
self._dataset = None
|
||||
|
||||
|
||||
class WildernessReader:
|
||||
"""Reader for PAD-US wilderness raster (designated wilderness areas)."""
|
||||
|
||||
def __init__(self, wilderness_path: Path = DEFAULT_WILDERNESS_PATH):
|
||||
self.wilderness_path = wilderness_path
|
||||
self._dataset = None
|
||||
|
||||
def _open(self):
|
||||
"""Lazy open the dataset."""
|
||||
if self._dataset is None:
|
||||
if not self.wilderness_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Wilderness raster not found at {self.wilderness_path}. "
|
||||
f"Run build_wilderness_raster() first."
|
||||
)
|
||||
self._dataset = rasterio.open(self.wilderness_path)
|
||||
return self._dataset
|
||||
|
||||
def get_wilderness_grid(
|
||||
self,
|
||||
south: float,
|
||||
north: float,
|
||||
west: float,
|
||||
east: float,
|
||||
target_shape: Tuple[int, int]
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Get wilderness values for a bounding box, resampled to target shape.
|
||||
|
||||
Args:
|
||||
south, north, west, east: Bounding box coordinates (WGS84)
|
||||
target_shape: (rows, cols) to resample to (matches elevation grid)
|
||||
|
||||
Returns:
|
||||
np.ndarray of uint8 wilderness values:
|
||||
255 = designated wilderness area
|
||||
0 = not wilderness
|
||||
"""
|
||||
ds = self._open()
|
||||
window = from_bounds(west, south, east, north, ds.transform)
|
||||
wilderness = ds.read(
|
||||
1,
|
||||
window=window,
|
||||
out_shape=target_shape,
|
||||
resampling=Resampling.nearest
|
||||
)
|
||||
return wilderness
|
||||
|
||||
def sample_point(self, lat: float, lon: float) -> int:
|
||||
"""Sample wilderness value at a single point."""
|
||||
ds = self._open()
|
||||
row, col = ds.index(lon, lat)
|
||||
if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
|
||||
return 0
|
||||
window = rasterio.windows.Window(col, row, 1, 1)
|
||||
value = ds.read(1, window=window)
|
||||
return int(value[0, 0])
|
||||
|
||||
def close(self):
|
||||
"""Close the dataset."""
|
||||
if self._dataset is not None:
|
||||
self._dataset.close()
|
||||
self._dataset = None
|
||||
|
||||
|
||||
def build_barriers_raster(
|
||||
output_path: Path = DEFAULT_BARRIERS_PATH,
|
||||
gdb_path: Path = PADUS_GDB_PATH,
|
||||
pixel_size: float = PIXEL_SIZE,
|
||||
bounds: dict = CONUS_BOUNDS,
|
||||
) -> Path:
|
||||
"""
|
||||
Build the PAD-US barriers raster from the source geodatabase.
|
||||
|
||||
Extracts polygons where Pub_Access = 'XA' (Closed) and rasterizes them.
|
||||
"""
|
||||
import shutil
|
||||
|
||||
if not gdb_path.exists():
|
||||
raise FileNotFoundError(f"PAD-US geodatabase not found at {gdb_path}")
|
||||
|
||||
if not shutil.which('ogr2ogr'):
|
||||
raise RuntimeError("ogr2ogr not found. Install GDAL.")
|
||||
if not shutil.which('gdal_rasterize'):
|
||||
raise RuntimeError("gdal_rasterize not found. Install GDAL.")
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(f"Building PAD-US barriers raster...")
|
||||
print(f" Source: {gdb_path}")
|
||||
print(f" Output: {output_path}")
|
||||
print(f" Pixel size: {pixel_size} degrees (~{pixel_size * 111000:.0f}m)")
|
||||
print(f" Bounds: {bounds}")
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
closed_gpkg = Path(tmpdir) / "closed_areas.gpkg"
|
||||
|
||||
print(f"\n[1/3] Extracting closed areas (Pub_Access = 'XA')...")
|
||||
|
||||
ogr_cmd = [
|
||||
"ogr2ogr",
|
||||
"-f", "GPKG",
|
||||
str(closed_gpkg),
|
||||
str(gdb_path),
|
||||
PADUS_LAYER,
|
||||
"-where", "Pub_Access = 'XA'",
|
||||
"-t_srs", "EPSG:4326",
|
||||
"-nlt", "MULTIPOLYGON",
|
||||
"-nln", "closed_areas",
|
||||
]
|
||||
|
||||
result = subprocess.run(ogr_cmd, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
print(f"STDERR: {result.stderr}")
|
||||
raise RuntimeError(f"ogr2ogr failed: {result.stderr}")
|
||||
|
||||
info_cmd = ["ogrinfo", "-so", str(closed_gpkg), "closed_areas"]
|
||||
info_result = subprocess.run(info_cmd, capture_output=True, text=True)
|
||||
print(f" Extraction result:\n{info_result.stdout}")
|
||||
|
||||
print(f"\n[2/3] Creating raster grid...")
|
||||
|
||||
width = int((bounds['east'] - bounds['west']) / pixel_size)
|
||||
height = int((bounds['north'] - bounds['south']) / pixel_size)
|
||||
print(f" Grid size: {width} x {height} pixels")
|
||||
|
||||
print(f"\n[3/3] Rasterizing closed areas...")
|
||||
|
||||
rasterize_cmd = [
|
||||
"gdal_rasterize",
|
||||
"-burn", "255",
|
||||
"-init", "0",
|
||||
"-a_nodata", "0",
|
||||
"-te", str(bounds['west']), str(bounds['south']),
|
||||
str(bounds['east']), str(bounds['north']),
|
||||
"-tr", str(pixel_size), str(pixel_size),
|
||||
"-ot", "Byte",
|
||||
"-co", "COMPRESS=LZW",
|
||||
"-co", "TILED=YES",
|
||||
"-l", "closed_areas",
|
||||
str(closed_gpkg),
|
||||
str(output_path),
|
||||
]
|
||||
|
||||
result = subprocess.run(rasterize_cmd, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
print(f"STDERR: {result.stderr}")
|
||||
raise RuntimeError(f"gdal_rasterize failed: {result.stderr}")
|
||||
|
||||
print(f"\n[Done] Verifying output...")
|
||||
with rasterio.open(output_path) as ds:
|
||||
print(f" Size: {ds.width} x {ds.height}")
|
||||
print(f" CRS: {ds.crs}")
|
||||
sample = ds.read(1, window=rasterio.windows.Window(0, 0, 1000, 1000))
|
||||
closed_count = np.sum(sample == 255)
|
||||
print(f" Sample (1000x1000): {closed_count} closed cells")
|
||||
|
||||
file_size = output_path.stat().st_size / (1024**2)
|
||||
print(f" File size: {file_size:.1f} MB")
|
||||
|
||||
return output_path
|
||||
|
||||
|
||||
def build_wilderness_raster(
|
||||
output_path: Path = DEFAULT_WILDERNESS_PATH,
|
||||
gdb_path: Path = PADUS_GDB_PATH,
|
||||
pixel_size: float = PIXEL_SIZE,
|
||||
bounds: dict = CONUS_BOUNDS,
|
||||
) -> Path:
|
||||
"""
|
||||
Build the PAD-US wilderness raster from the source geodatabase.
|
||||
|
||||
Extracts polygons where Des_Tp = 'WA' (Wilderness Area) and rasterizes them.
|
||||
"""
|
||||
import shutil
|
||||
|
||||
if not gdb_path.exists():
|
||||
raise FileNotFoundError(f"PAD-US geodatabase not found at {gdb_path}")
|
||||
|
||||
if not shutil.which('ogr2ogr'):
|
||||
raise RuntimeError("ogr2ogr not found. Install GDAL.")
|
||||
if not shutil.which('gdal_rasterize'):
|
||||
raise RuntimeError("gdal_rasterize not found. Install GDAL.")
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(f"Building PAD-US wilderness raster...")
|
||||
print(f" Source: {gdb_path}")
|
||||
print(f" Output: {output_path}")
|
||||
print(f" Pixel size: {pixel_size} degrees (~{pixel_size * 111000:.0f}m)")
|
||||
print(f" Bounds: {bounds}")
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
wilderness_gpkg = Path(tmpdir) / "wilderness_areas.gpkg"
|
||||
|
||||
print(f"\n[1/3] Extracting wilderness areas (Des_Tp = 'WA')...")
|
||||
|
||||
ogr_cmd = [
|
||||
"ogr2ogr",
|
||||
"-f", "GPKG",
|
||||
str(wilderness_gpkg),
|
||||
str(gdb_path),
|
||||
PADUS_LAYER,
|
||||
"-where", "Des_Tp = 'WA'",
|
||||
"-t_srs", "EPSG:4326",
|
||||
"-nlt", "MULTIPOLYGON",
|
||||
"-nln", "wilderness_areas",
|
||||
]
|
||||
|
||||
result = subprocess.run(ogr_cmd, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
print(f"STDERR: {result.stderr}")
|
||||
raise RuntimeError(f"ogr2ogr failed: {result.stderr}")
|
||||
|
||||
info_cmd = ["ogrinfo", "-so", str(wilderness_gpkg), "wilderness_areas"]
|
||||
info_result = subprocess.run(info_cmd, capture_output=True, text=True)
|
||||
print(f" Extraction result:\n{info_result.stdout}")
|
||||
|
||||
print(f"\n[2/3] Creating raster grid...")
|
||||
|
||||
width = int((bounds['east'] - bounds['west']) / pixel_size)
|
||||
height = int((bounds['north'] - bounds['south']) / pixel_size)
|
||||
print(f" Grid size: {width} x {height} pixels")
|
||||
|
||||
print(f"\n[3/3] Rasterizing wilderness areas...")
|
||||
|
||||
rasterize_cmd = [
|
||||
"gdal_rasterize",
|
||||
"-burn", "255",
|
||||
"-init", "0",
|
||||
"-a_nodata", "0",
|
||||
"-te", str(bounds['west']), str(bounds['south']),
|
||||
str(bounds['east']), str(bounds['north']),
|
||||
"-tr", str(pixel_size), str(pixel_size),
|
||||
"-ot", "Byte",
|
||||
"-co", "COMPRESS=LZW",
|
||||
"-co", "TILED=YES",
|
||||
"-l", "wilderness_areas",
|
||||
str(wilderness_gpkg),
|
||||
str(output_path),
|
||||
]
|
||||
|
||||
result = subprocess.run(rasterize_cmd, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
print(f"STDERR: {result.stderr}")
|
||||
raise RuntimeError(f"gdal_rasterize failed: {result.stderr}")
|
||||
|
||||
print(f"\n[Done] Verifying output...")
|
||||
with rasterio.open(output_path) as ds:
|
||||
print(f" Size: {ds.width} x {ds.height}")
|
||||
print(f" CRS: {ds.crs}")
|
||||
sample = ds.read(1, window=rasterio.windows.Window(0, 0, 1000, 1000))
|
||||
wilderness_count = np.sum(sample == 255)
|
||||
print(f" Sample (1000x1000): {wilderness_count} wilderness cells")
|
||||
|
||||
file_size = output_path.stat().st_size / (1024**2)
|
||||
print(f" File size: {file_size:.1f} MB")
|
||||
|
||||
return output_path
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
cmd = sys.argv[1]
|
||||
|
||||
if cmd == "build":
|
||||
print("=" * 60)
|
||||
print("PAD-US Barriers Raster Build")
|
||||
print("=" * 60)
|
||||
build_barriers_raster()
|
||||
|
||||
elif cmd == "build-wilderness":
|
||||
print("=" * 60)
|
||||
print("PAD-US Wilderness Raster Build")
|
||||
print("=" * 60)
|
||||
build_wilderness_raster()
|
||||
|
||||
elif cmd == "build-all":
|
||||
print("=" * 60)
|
||||
print("Building all PAD-US rasters")
|
||||
print("=" * 60)
|
||||
build_barriers_raster()
|
||||
print("\n")
|
||||
build_wilderness_raster()
|
||||
|
||||
else:
|
||||
print(f"Unknown command: {cmd}")
|
||||
print("Usage:")
|
||||
print(" python barriers.py build # Build barriers raster")
|
||||
print(" python barriers.py build-wilderness # Build wilderness raster")
|
||||
print(" python barriers.py build-all # Build both rasters")
|
||||
sys.exit(1)
|
||||
|
||||
else:
|
||||
# Test readers
|
||||
print("Testing BarrierReader...")
|
||||
|
||||
if not DEFAULT_BARRIERS_PATH.exists():
|
||||
print(f"Barrier raster not found at {DEFAULT_BARRIERS_PATH}")
|
||||
print(f"Run: python barriers.py build")
|
||||
sys.exit(1)
|
||||
|
||||
reader = BarrierReader()
|
||||
barriers = reader.get_barrier_grid(
|
||||
south=42.2, north=42.6, west=-114.8, east=-113.8,
|
||||
target_shape=(400, 1000)
|
||||
)
|
||||
print(f"\nBarrier grid shape: {barriers.shape}")
|
||||
print(f"Unique values: {np.unique(barriers)}")
|
||||
closed_cells = np.sum(barriers == 255)
|
||||
print(f"Closed cells: {closed_cells} ({100*closed_cells/barriers.size:.2f}%)")
|
||||
reader.close()
|
||||
|
||||
print("\nTesting WildernessReader...")
|
||||
|
||||
if not DEFAULT_WILDERNESS_PATH.exists():
|
||||
print(f"Wilderness raster not found at {DEFAULT_WILDERNESS_PATH}")
|
||||
print(f"Run: python barriers.py build-wilderness")
|
||||
else:
|
||||
wilderness_reader = WildernessReader()
|
||||
wilderness = wilderness_reader.get_wilderness_grid(
|
||||
south=42.2, north=42.6, west=-114.8, east=-113.8,
|
||||
target_shape=(400, 1000)
|
||||
)
|
||||
print(f"Wilderness grid shape: {wilderness.shape}")
|
||||
print(f"Unique values: {np.unique(wilderness)}")
|
||||
wilderness_cells = np.sum(wilderness == 255)
|
||||
print(f"Wilderness cells: {wilderness_cells} ({100*wilderness_cells/wilderness.size:.2f}%)")
|
||||
wilderness_reader.close()
|
||||
|
||||
print("\nDone.")
|
||||
|
|
@ -1,494 +0,0 @@
|
|||
"""
|
||||
Multi-mode travel cost functions for OFFROUTE.
|
||||
|
||||
Supports four travel modes: foot, mtb, atv, vehicle.
|
||||
Each mode has its own speed function, max slope, trail access rules,
|
||||
and terrain friction overrides.
|
||||
|
||||
Mode profiles are data-driven — adding a new mode means adding a profile entry.
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional, Literal, Dict, Callable
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# SPEED FUNCTIONS
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
def tobler_off_path_speed(grade: np.ndarray, base_speed: float = 6.0) -> np.ndarray:
|
||||
"""
|
||||
Tobler off-path hiking function.
|
||||
|
||||
W = 0.6 * base_speed * exp(-3.5 * |S + 0.05|)
|
||||
|
||||
Peak ~3.6 km/h at grade = -0.05 (slight downhill).
|
||||
The 0.6 multiplier is the off-trail penalty.
|
||||
"""
|
||||
return 0.6 * base_speed * np.exp(-3.5 * np.abs(grade + 0.05))
|
||||
|
||||
|
||||
def herzog_wheeled_speed(grade: np.ndarray, base_speed: float = 12.0) -> np.ndarray:
|
||||
"""
|
||||
Herzog wheeled-transport polynomial.
|
||||
|
||||
Relative speed factor:
|
||||
1 / (1337.8·S^6 + 278.19·S^5 − 517.39·S^4 − 78.199·S^3 + 93.419·S^2 + 19.825·|S| + 1.64)
|
||||
|
||||
Multiply by base_speed to get km/h.
|
||||
"""
|
||||
S = grade
|
||||
S_abs = np.abs(S)
|
||||
|
||||
# Herzog polynomial (returns relative speed factor 0-1)
|
||||
denom = (1337.8 * S**6 + 278.19 * S**5 - 517.39 * S**4
|
||||
- 78.199 * S**3 + 93.419 * S**2 + 19.825 * S_abs + 1.64)
|
||||
|
||||
# Avoid division by zero and negative speeds
|
||||
denom = np.maximum(denom, 0.1)
|
||||
rel_speed = 1.0 / denom
|
||||
|
||||
# Clamp relative speed to reasonable bounds (0.05 to 1.5)
|
||||
rel_speed = np.clip(rel_speed, 0.05, 1.5)
|
||||
|
||||
return base_speed * rel_speed
|
||||
|
||||
|
||||
def linear_degrade_speed(grade: np.ndarray, base_speed: float = 40.0, max_grade: float = 0.364) -> np.ndarray:
|
||||
"""
|
||||
Linear speed degradation with slope.
|
||||
|
||||
speed = base_speed * max(0, 1 - |grade| / max_grade)
|
||||
|
||||
max_grade = tan(20°) ≈ 0.364 for 20° max slope.
|
||||
"""
|
||||
speed = base_speed * np.maximum(0, 1.0 - np.abs(grade) / max_grade)
|
||||
return np.maximum(speed, 0.1) # Minimum crawl speed
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# MODE PROFILES (Data-driven configuration)
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
@dataclass
|
||||
class ModeProfile:
|
||||
"""Configuration for a travel mode."""
|
||||
|
||||
name: str
|
||||
description: str
|
||||
|
||||
# Speed function parameters
|
||||
speed_function: str # "tobler", "herzog", "linear"
|
||||
base_speed_kmh: float
|
||||
max_slope_deg: float
|
||||
|
||||
# Trail access: trail_value -> friction multiplier (None = impassable)
|
||||
# Trail values: 5=road, 15=track, 25=foot trail
|
||||
trail_friction: Dict[int, Optional[float]] = field(default_factory=dict)
|
||||
|
||||
# Off-trail terrain friction overrides (by WorldCover class)
|
||||
# These MULTIPLY the base WorldCover friction
|
||||
# None = use default, np.inf = impassable
|
||||
# WorldCover values: 10=tree, 20=shrub, 30=grass, 40=crop, 50=urban,
|
||||
# 60=bare, 80=water, 90=wetland, 95=mangrove, 100=moss
|
||||
terrain_friction_override: Dict[int, Optional[float]] = field(default_factory=dict)
|
||||
|
||||
# Should wilderness areas be impassable?
|
||||
wilderness_impassable: bool = False
|
||||
|
||||
# For vehicle mode: can traverse off-trail flat terrain?
|
||||
off_trail_flat_threshold_deg: float = 0.0 # 0 = no off-trail allowed
|
||||
off_trail_flat_friction: float = np.inf # friction if allowed
|
||||
|
||||
|
||||
# Define all mode profiles
|
||||
MODE_PROFILES: Dict[str, ModeProfile] = {
|
||||
"foot": ModeProfile(
|
||||
name="foot",
|
||||
description="Hiking on foot (Tobler off-path model)",
|
||||
speed_function="tobler",
|
||||
base_speed_kmh=6.0,
|
||||
max_slope_deg=40.0,
|
||||
trail_friction={
|
||||
5: 0.1, # road
|
||||
15: 0.3, # track
|
||||
25: 0.5, # foot trail
|
||||
},
|
||||
terrain_friction_override={
|
||||
# Use default WorldCover friction for foot mode
|
||||
},
|
||||
wilderness_impassable=False,
|
||||
),
|
||||
|
||||
"mtb": ModeProfile(
|
||||
name="mtb",
|
||||
description="Mountain bike / dirt bike (Herzog wheeled model)",
|
||||
speed_function="herzog",
|
||||
base_speed_kmh=12.0,
|
||||
max_slope_deg=25.0,
|
||||
trail_friction={
|
||||
5: 0.1, # road
|
||||
15: 0.2, # track
|
||||
25: 0.5, # foot trail (rideable but slow)
|
||||
},
|
||||
terrain_friction_override={
|
||||
30: 2.0, # Grassland: rideable but slow
|
||||
20: 4.0, # Shrubland: barely rideable
|
||||
10: 8.0, # Tree cover/forest: effectively impassable
|
||||
60: 3.0, # Bare/rocky
|
||||
90: np.inf, # Wetland: impassable
|
||||
95: np.inf, # Mangrove: impassable
|
||||
80: np.inf, # Water: impassable
|
||||
},
|
||||
wilderness_impassable=True,
|
||||
),
|
||||
|
||||
"atv": ModeProfile(
|
||||
name="atv",
|
||||
description="ATV / side-by-side (Herzog wheeled model, higher base speed)",
|
||||
speed_function="herzog",
|
||||
base_speed_kmh=25.0,
|
||||
max_slope_deg=30.0,
|
||||
trail_friction={
|
||||
5: 0.1, # road
|
||||
15: 0.3, # track
|
||||
25: None, # foot trail: impassable (too narrow)
|
||||
},
|
||||
terrain_friction_override={
|
||||
30: 1.5, # Grassland: passable
|
||||
20: 3.0, # Shrubland: rough
|
||||
10: np.inf, # Forest: impassable
|
||||
60: 2.0, # Bare/rocky
|
||||
90: np.inf, # Wetland: impassable
|
||||
95: np.inf, # Mangrove: impassable
|
||||
80: np.inf, # Water: impassable
|
||||
},
|
||||
wilderness_impassable=True,
|
||||
),
|
||||
|
||||
"vehicle": ModeProfile(
|
||||
name="vehicle",
|
||||
description="4x4 truck / jeep (linear speed degradation)",
|
||||
speed_function="linear",
|
||||
base_speed_kmh=40.0,
|
||||
max_slope_deg=20.0,
|
||||
trail_friction={
|
||||
5: 0.1, # road
|
||||
15: 0.5, # track (rough but passable)
|
||||
25: None, # foot trail: impassable
|
||||
},
|
||||
terrain_friction_override={
|
||||
# All off-trail terrain is impassable by default
|
||||
10: np.inf, # Forest
|
||||
20: np.inf, # Shrubland
|
||||
30: np.inf, # Grassland (except flat - see below)
|
||||
40: np.inf, # Cropland (except flat - see below)
|
||||
60: np.inf, # Bare
|
||||
90: np.inf, # Wetland
|
||||
95: np.inf, # Mangrove
|
||||
80: np.inf, # Water
|
||||
},
|
||||
wilderness_impassable=True,
|
||||
off_trail_flat_threshold_deg=5.0, # Can drive on flat fields
|
||||
off_trail_flat_friction=5.0, # But very slow
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# Pragmatic mode friction multiplier for private land
|
||||
PRAGMATIC_BARRIER_MULTIPLIER = 5.0
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# COST GRID COMPUTATION
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
def compute_cost_grid(
|
||||
elevation: np.ndarray,
|
||||
cell_size_m: float,
|
||||
cell_size_lat_m: float = None,
|
||||
cell_size_lon_m: float = None,
|
||||
friction: Optional[np.ndarray] = None,
|
||||
friction_raw: Optional[np.ndarray] = None,
|
||||
trails: Optional[np.ndarray] = None,
|
||||
barriers: Optional[np.ndarray] = None,
|
||||
wilderness: Optional[np.ndarray] = None,
|
||||
mvum: Optional[np.ndarray] = None,
|
||||
boundary_mode: Literal["strict", "pragmatic", "emergency"] = "pragmatic",
|
||||
mode: Literal["foot", "mtb", "atv", "vehicle"] = "foot"
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Compute isotropic travel cost grid from elevation data.
|
||||
|
||||
Args:
|
||||
elevation: 2D array of elevation values in meters
|
||||
cell_size_m: Average cell size in meters
|
||||
cell_size_lat_m: Cell size in latitude direction (optional)
|
||||
cell_size_lon_m: Cell size in longitude direction (optional)
|
||||
friction: Optional 2D array of friction multipliers (WorldCover).
|
||||
Values should be float (1.0 = baseline, 2.0 = 2x slower).
|
||||
np.inf marks impassable cells.
|
||||
friction_raw: Optional 2D array of raw WorldCover class values (uint8).
|
||||
Used for mode-specific terrain overrides.
|
||||
Values: 10=tree, 20=shrub, 30=grass, etc.
|
||||
trails: Optional 2D array of trail values (uint8).
|
||||
0 = no trail, 5 = road, 15 = track, 25 = foot trail
|
||||
barriers: Optional 2D array of barrier values (uint8).
|
||||
255 = closed/restricted area (PAD-US Pub_Access = XA).
|
||||
wilderness: Optional[np.ndarray] of wilderness values (uint8).
|
||||
255 = designated wilderness area.
|
||||
mvum: Optional[np.ndarray] of MVUM access values (uint8).
|
||||
0 = no MVUM data, 1 = open, 255 = closed to this mode.
|
||||
MVUM closures respond to boundary_mode (strict/pragmatic/emergency).
|
||||
Foot mode should pass None (MVUM is motor-vehicle specific).
|
||||
boundary_mode: How to handle barriers ("strict", "pragmatic", "emergency")
|
||||
mode: Travel mode ("foot", "mtb", "atv", "vehicle")
|
||||
|
||||
Returns:
|
||||
2D array of travel cost in seconds per cell.
|
||||
np.inf for impassable cells.
|
||||
"""
|
||||
if boundary_mode not in ("strict", "pragmatic", "emergency"):
|
||||
raise ValueError(f"boundary_mode must be 'strict', 'pragmatic', or 'emergency'")
|
||||
|
||||
if mode not in MODE_PROFILES:
|
||||
raise ValueError(f"mode must be one of {list(MODE_PROFILES.keys())}")
|
||||
|
||||
profile = MODE_PROFILES[mode]
|
||||
|
||||
if cell_size_lat_m is None:
|
||||
cell_size_lat_m = cell_size_m
|
||||
if cell_size_lon_m is None:
|
||||
cell_size_lon_m = cell_size_m
|
||||
|
||||
rows, cols = elevation.shape
|
||||
|
||||
# ─── Compute gradients (in-place where possible) ─────────────────────────
|
||||
# Use float32 to reduce memory footprint
|
||||
grade = np.zeros(elevation.shape, dtype=np.float32)
|
||||
|
||||
# Compute dy contribution to grade squared
|
||||
dy_contrib = np.zeros(elevation.shape, dtype=np.float32)
|
||||
dy_contrib[1:-1, :] = ((elevation[:-2, :] - elevation[2:, :]) / (2 * cell_size_lat_m)) ** 2
|
||||
dy_contrib[0, :] = ((elevation[0, :] - elevation[1, :]) / cell_size_lat_m) ** 2
|
||||
dy_contrib[-1, :] = ((elevation[-2, :] - elevation[-1, :]) / cell_size_lat_m) ** 2
|
||||
|
||||
# Compute dx contribution and add to dy_contrib in-place
|
||||
dy_contrib[:, 1:-1] += ((elevation[:, 2:] - elevation[:, :-2]) / (2 * cell_size_lon_m)) ** 2
|
||||
dy_contrib[:, 0] += ((elevation[:, 1] - elevation[:, 0]) / cell_size_lon_m) ** 2
|
||||
dy_contrib[:, -1] += ((elevation[:, -1] - elevation[:, -2]) / cell_size_lon_m) ** 2
|
||||
|
||||
# grade = sqrt(dx^2 + dy^2)
|
||||
np.sqrt(dy_contrib, out=grade)
|
||||
del dy_contrib # Free memory immediately
|
||||
|
||||
# ─── Compute speed based on mode ─────────────────────────────────────────
|
||||
max_grade_val = np.tan(np.radians(profile.max_slope_deg))
|
||||
|
||||
if profile.speed_function == "tobler":
|
||||
speed_kmh = tobler_off_path_speed(grade, profile.base_speed_kmh)
|
||||
elif profile.speed_function == "herzog":
|
||||
speed_kmh = herzog_wheeled_speed(grade, profile.base_speed_kmh)
|
||||
elif profile.speed_function == "linear":
|
||||
speed_kmh = linear_degrade_speed(grade, profile.base_speed_kmh, max_grade_val)
|
||||
else:
|
||||
raise ValueError(f"Unknown speed function: {profile.speed_function}")
|
||||
|
||||
# ─── Base cost (seconds per cell) ─────────────────────────────────────────
|
||||
avg_cell_size = (cell_size_lat_m + cell_size_lon_m) / 2
|
||||
cost = (avg_cell_size * 3.6) / speed_kmh
|
||||
del speed_kmh
|
||||
|
||||
# ─── Max slope limit ──────────────────────────────────────────────────────
|
||||
cost[grade > max_grade_val] = np.inf
|
||||
|
||||
# ─── NaN elevations ──────────────────────────────────────────────────────
|
||||
cost[np.isnan(elevation)] = np.inf
|
||||
|
||||
# ─── Apply friction in-place ─────────────────────────────────────────────
|
||||
# Instead of creating effective_friction copy, apply directly to cost
|
||||
|
||||
# Start with base friction
|
||||
if friction is not None:
|
||||
if friction.shape != elevation.shape:
|
||||
raise ValueError(f"Friction shape mismatch")
|
||||
np.multiply(cost, friction, out=cost)
|
||||
|
||||
# ─── Mode-specific terrain friction overrides (memory-efficient) ─────────
|
||||
if friction_raw is not None and profile.terrain_friction_override:
|
||||
if friction_raw.shape != elevation.shape:
|
||||
raise ValueError(f"Friction_raw shape mismatch")
|
||||
|
||||
# Process all overrides without creating large intermediate masks
|
||||
for wc_class, override in profile.terrain_friction_override.items():
|
||||
if override is not None:
|
||||
if override == np.inf:
|
||||
# Use np.where for in-place-like behavior
|
||||
np.putmask(cost, friction_raw == wc_class, np.inf)
|
||||
else:
|
||||
# Multiply cost where friction_raw matches
|
||||
# Using a loop with putmask is more memory efficient
|
||||
mask = friction_raw == wc_class
|
||||
cost[mask] *= override
|
||||
del mask
|
||||
|
||||
# ─── Vehicle mode: allow flat grassland/cropland ─────────────────────────
|
||||
if mode == "vehicle" and profile.off_trail_flat_threshold_deg > 0:
|
||||
if friction_raw is not None:
|
||||
# Compute slope in degrees for flat terrain check
|
||||
slope_deg = np.degrees(np.arctan(grade))
|
||||
# Flat grassland or cropland - recompute cost for these cells
|
||||
flat_field_mask = (
|
||||
(slope_deg <= profile.off_trail_flat_threshold_deg) &
|
||||
((friction_raw == 30) | (friction_raw == 40))
|
||||
)
|
||||
del slope_deg
|
||||
# Recalculate cost for these cells with flat field friction
|
||||
if np.any(flat_field_mask):
|
||||
base_time = avg_cell_size * 3.6 / linear_degrade_speed(
|
||||
grade[flat_field_mask], profile.base_speed_kmh, max_grade_val
|
||||
)
|
||||
cost[flat_field_mask] = base_time * profile.off_trail_flat_friction
|
||||
del base_time
|
||||
del flat_field_mask
|
||||
|
||||
# ─── Trail friction (mode-specific) ──────────────────────────────────────
|
||||
if trails is not None:
|
||||
if trails.shape != elevation.shape:
|
||||
raise ValueError(f"Trails shape mismatch")
|
||||
|
||||
for trail_value, trail_friction in profile.trail_friction.items():
|
||||
if trail_friction is None:
|
||||
# Impassable for this mode
|
||||
np.putmask(cost, trails == trail_value, np.inf)
|
||||
else:
|
||||
# Trail friction REPLACES terrain friction
|
||||
# Recalculate cost = base_time * trail_friction
|
||||
trail_mask = trails == trail_value
|
||||
if np.any(trail_mask):
|
||||
# Get base travel time (without friction)
|
||||
if profile.speed_function == "tobler":
|
||||
trail_speed = tobler_off_path_speed(grade[trail_mask], profile.base_speed_kmh)
|
||||
elif profile.speed_function == "herzog":
|
||||
trail_speed = herzog_wheeled_speed(grade[trail_mask], profile.base_speed_kmh)
|
||||
else:
|
||||
trail_speed = linear_degrade_speed(
|
||||
grade[trail_mask], profile.base_speed_kmh, max_grade_val
|
||||
)
|
||||
cost[trail_mask] = (avg_cell_size * 3.6 / trail_speed) * trail_friction
|
||||
del trail_speed
|
||||
del trail_mask
|
||||
|
||||
# ─── Wilderness areas (mode-specific) ────────────────────────────────────
|
||||
if wilderness is not None and profile.wilderness_impassable:
|
||||
if wilderness.shape != elevation.shape:
|
||||
raise ValueError(f"Wilderness shape mismatch")
|
||||
np.putmask(cost, wilderness == 255, np.inf)
|
||||
|
||||
# ─── Barriers (private land) ─────────────────────────────────────────────
|
||||
if barriers is not None and boundary_mode != "emergency":
|
||||
if barriers.shape != elevation.shape:
|
||||
raise ValueError(f"Barriers shape mismatch")
|
||||
|
||||
if boundary_mode == "strict":
|
||||
np.putmask(cost, barriers == 255, np.inf)
|
||||
elif boundary_mode == "pragmatic":
|
||||
barrier_mask = barriers == 255
|
||||
cost[barrier_mask] *= PRAGMATIC_BARRIER_MULTIPLIER
|
||||
del barrier_mask
|
||||
|
||||
# ─── MVUM closures (motor vehicle restrictions) ──────────────────────────
|
||||
# MVUM only applies to motorized modes, not foot. Foot mode should pass mvum=None.
|
||||
# MVUM closures respond to the same boundary_mode as PAD-US barriers:
|
||||
# "strict" = MVUM-closed road/trail is impassable
|
||||
# "pragmatic" = MVUM-closed road/trail gets 5× friction penalty
|
||||
# "emergency" = MVUM closures ignored entirely
|
||||
if mvum is not None and mode != "foot" and boundary_mode != "emergency":
|
||||
if mvum.shape != elevation.shape:
|
||||
raise ValueError(f"MVUM shape mismatch")
|
||||
|
||||
# Value 255 = road/trail exists but is closed to this mode
|
||||
mvum_closed_mask = mvum == 255
|
||||
|
||||
if boundary_mode == "strict":
|
||||
np.putmask(cost, mvum_closed_mask, np.inf)
|
||||
elif boundary_mode == "pragmatic":
|
||||
cost[mvum_closed_mask] *= PRAGMATIC_BARRIER_MULTIPLIER
|
||||
|
||||
del mvum_closed_mask
|
||||
|
||||
return cost
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# LEGACY API (backward compatibility)
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
def tobler_speed(grade: float) -> float:
|
||||
"""Legacy single-value Tobler speed function."""
|
||||
return 0.6 * 6.0 * math.exp(-3.5 * abs(grade + 0.05))
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
# TESTING
|
||||
# ═══════════════════════════════════════════════════════════════════════════════
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 70)
|
||||
print("OFFROUTE Multi-Mode Cost Function Tests")
|
||||
print("=" * 70)
|
||||
|
||||
print("\n[1] Speed functions at various grades:")
|
||||
print(f"{'Grade':<10} {'Foot':<12} {'MTB':<12} {'ATV':<12} {'Vehicle':<12}")
|
||||
print("-" * 60)
|
||||
|
||||
for grade_val in [-0.3, -0.1, 0.0, 0.1, 0.2, 0.3]:
|
||||
grade_arr = np.array([grade_val])
|
||||
foot = tobler_off_path_speed(grade_arr, 6.0)[0]
|
||||
mtb = herzog_wheeled_speed(grade_arr, 12.0)[0]
|
||||
atv = herzog_wheeled_speed(grade_arr, 25.0)[0]
|
||||
veh = linear_degrade_speed(grade_arr, 40.0, np.tan(np.radians(20)))[0]
|
||||
print(f"{grade_val:+.2f} {foot:>6.2f} km/h {mtb:>6.2f} km/h {atv:>6.2f} km/h {veh:>6.2f} km/h")
|
||||
|
||||
print("\n[2] Mode profiles:")
|
||||
for name, profile in MODE_PROFILES.items():
|
||||
print(f"\n {name.upper()}: {profile.description}")
|
||||
print(f" Max slope: {profile.max_slope_deg}°")
|
||||
print(f" Trail access: {profile.trail_friction}")
|
||||
print(f" Wilderness blocked: {profile.wilderness_impassable}")
|
||||
|
||||
print("\n[3] Cost grid test (flat terrain, forest):")
|
||||
elev = np.ones((10, 10), dtype=np.float32) * 1000
|
||||
friction = np.ones((10, 10), dtype=np.float32) * 2.0 # Forest friction
|
||||
friction_raw = np.ones((10, 10), dtype=np.uint8) * 10 # Tree cover class
|
||||
|
||||
trails = np.zeros((10, 10), dtype=np.uint8)
|
||||
trails[5, :] = 5 # Road across middle
|
||||
|
||||
for mode_name in ["foot", "mtb", "atv", "vehicle"]:
|
||||
cost = compute_cost_grid(
|
||||
elev, cell_size_m=30.0,
|
||||
friction=friction,
|
||||
friction_raw=friction_raw,
|
||||
trails=trails,
|
||||
mode=mode_name
|
||||
)
|
||||
off_trail_cost = cost[0, 0]
|
||||
road_cost = cost[5, 0]
|
||||
impassable = np.sum(np.isinf(cost))
|
||||
print(f" {mode_name:8s}: off-trail={off_trail_cost:>8.1f}s, road={road_cost:>6.1f}s, impassable={impassable}")
|
||||
|
||||
print("\n[4] Wilderness blocking test:")
|
||||
wilderness = np.zeros((10, 10), dtype=np.uint8)
|
||||
wilderness[3:7, 3:7] = 255
|
||||
|
||||
for mode_name in ["foot", "mtb", "atv", "vehicle"]:
|
||||
cost = compute_cost_grid(
|
||||
elev, cell_size_m=30.0,
|
||||
wilderness=wilderness,
|
||||
mode=mode_name
|
||||
)
|
||||
wilderness_impassable = np.sum(np.isinf(cost[3:7, 3:7]))
|
||||
print(f" {mode_name:8s}: wilderness cells impassable = {wilderness_impassable}/16")
|
||||
|
||||
print("\nDone.")
|
||||
|
|
@ -1,190 +0,0 @@
|
|||
"""
|
||||
DEM tile reader for OFFROUTE.
|
||||
|
||||
Reads elevation tiles from planet-dem.pmtiles (Terrarium-encoded WebP),
|
||||
decodes them into numpy arrays, and provides a stitched elevation grid
|
||||
for a given bounding box.
|
||||
"""
|
||||
import math
|
||||
from functools import lru_cache
|
||||
from io import BytesIO
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Optional
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from pmtiles.reader import MmapSource, Reader as PMTilesReader
|
||||
|
||||
# Default path to the planet DEM PMTiles file
|
||||
DEFAULT_DEM_PATH = Path("/mnt/nas/nav/planet-dem.pmtiles")
|
||||
|
||||
# Tile size in pixels (z12 tiles are 512x512 in this tileset)
|
||||
TILE_SIZE = 512
|
||||
|
||||
# Zoom level to use for elevation data
|
||||
ZOOM_LEVEL = 12
|
||||
|
||||
|
||||
def terrarium_decode(rgb_array: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Decode Terrarium-encoded RGB values to elevation in meters.
|
||||
|
||||
Formula: elevation = (R * 256 + G + B/256) - 32768
|
||||
"""
|
||||
r = rgb_array[:, :, 0].astype(np.float32)
|
||||
g = rgb_array[:, :, 1].astype(np.float32)
|
||||
b = rgb_array[:, :, 2].astype(np.float32)
|
||||
|
||||
elevation = (r * 256.0 + g + b / 256.0) - 32768.0
|
||||
return elevation
|
||||
|
||||
|
||||
def lat_lon_to_tile(lat: float, lon: float, zoom: int) -> Tuple[int, int]:
|
||||
"""Convert lat/lon to tile coordinates at given zoom level."""
|
||||
n = 2 ** zoom
|
||||
x = int((lon + 180.0) / 360.0 * n)
|
||||
lat_rad = math.radians(lat)
|
||||
y = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)
|
||||
return x, y
|
||||
|
||||
|
||||
def tile_to_lat_lon(x: int, y: int, zoom: int) -> Tuple[float, float, float, float]:
|
||||
"""Convert tile coordinates to bounding box (north, south, west, east)."""
|
||||
n = 2 ** zoom
|
||||
lon_west = x / n * 360.0 - 180.0
|
||||
lon_east = (x + 1) / n * 360.0 - 180.0
|
||||
lat_north = math.degrees(math.atan(math.sinh(math.pi * (1 - 2 * y / n))))
|
||||
lat_south = math.degrees(math.atan(math.sinh(math.pi * (1 - 2 * (y + 1) / n))))
|
||||
return lat_north, lat_south, lon_west, lon_east
|
||||
|
||||
|
||||
class DEMReader:
|
||||
"""Reader for Terrarium-encoded DEM tiles from PMTiles."""
|
||||
|
||||
def __init__(self, pmtiles_path: Path = DEFAULT_DEM_PATH, tile_cache_size: int = 128):
|
||||
self.pmtiles_path = pmtiles_path
|
||||
self._source = MmapSource(open(pmtiles_path, "rb"))
|
||||
self._reader = PMTilesReader(self._source)
|
||||
self._header = self._reader.header()
|
||||
self._decode_tile = lru_cache(maxsize=tile_cache_size)(self._decode_tile_impl)
|
||||
|
||||
def _decode_tile_impl(self, z: int, x: int, y: int) -> Optional[np.ndarray]:
|
||||
"""Fetch and decode a single tile."""
|
||||
tile_data = self._reader.get(z, x, y)
|
||||
if tile_data is None:
|
||||
return None
|
||||
|
||||
img = Image.open(BytesIO(tile_data))
|
||||
rgb_array = np.array(img)
|
||||
|
||||
if rgb_array.shape[2] == 4:
|
||||
rgb_array = rgb_array[:, :, :3]
|
||||
|
||||
elevation = terrarium_decode(rgb_array)
|
||||
return elevation
|
||||
|
||||
def get_elevation_grid(
|
||||
self,
|
||||
south: float,
|
||||
north: float,
|
||||
west: float,
|
||||
east: float,
|
||||
zoom: int = ZOOM_LEVEL
|
||||
) -> Tuple[np.ndarray, dict]:
|
||||
"""Get a stitched elevation grid for the given bounding box."""
|
||||
x_min, y_max = lat_lon_to_tile(south, west, zoom)
|
||||
x_max, y_min = lat_lon_to_tile(north, east, zoom)
|
||||
|
||||
n = 2 ** zoom
|
||||
x_min = max(0, x_min)
|
||||
x_max = min(n - 1, x_max)
|
||||
y_min = max(0, y_min)
|
||||
y_max = min(n - 1, y_max)
|
||||
|
||||
n_tiles_x = x_max - x_min + 1
|
||||
n_tiles_y = y_max - y_min + 1
|
||||
out_height = n_tiles_y * TILE_SIZE
|
||||
out_width = n_tiles_x * TILE_SIZE
|
||||
|
||||
elevation = np.full((out_height, out_width), np.nan, dtype=np.float32)
|
||||
|
||||
for ty in range(y_min, y_max + 1):
|
||||
for tx in range(x_min, x_max + 1):
|
||||
tile_elev = self._decode_tile(zoom, tx, ty)
|
||||
if tile_elev is not None:
|
||||
out_y = (ty - y_min) * TILE_SIZE
|
||||
out_x = (tx - x_min) * TILE_SIZE
|
||||
elevation[out_y:out_y + TILE_SIZE, out_x:out_x + TILE_SIZE] = tile_elev
|
||||
|
||||
grid_north, _, grid_west, _ = tile_to_lat_lon(x_min, y_min, zoom)
|
||||
_, grid_south, _, grid_east = tile_to_lat_lon(x_max, y_max, zoom)
|
||||
|
||||
pixel_size_lat = (grid_north - grid_south) / out_height
|
||||
pixel_size_lon = (grid_east - grid_west) / out_width
|
||||
|
||||
origin_lat = grid_north - pixel_size_lat / 2
|
||||
origin_lon = grid_west + pixel_size_lon / 2
|
||||
|
||||
center_lat = (south + north) / 2
|
||||
lat_m = 111320.0
|
||||
lon_m = 111320.0 * math.cos(math.radians(center_lat))
|
||||
cell_size_lat_m = abs(pixel_size_lat) * lat_m
|
||||
cell_size_lon_m = abs(pixel_size_lon) * lon_m
|
||||
cell_size_m = (cell_size_lat_m + cell_size_lon_m) / 2
|
||||
|
||||
row_start = int((grid_north - north) / abs(pixel_size_lat))
|
||||
row_end = int((grid_north - south) / abs(pixel_size_lat))
|
||||
col_start = int((west - grid_west) / pixel_size_lon)
|
||||
col_end = int((east - grid_west) / pixel_size_lon)
|
||||
|
||||
row_start = max(0, row_start)
|
||||
row_end = min(out_height, row_end)
|
||||
col_start = max(0, col_start)
|
||||
col_end = min(out_width, col_end)
|
||||
|
||||
elevation = elevation[row_start:row_end, col_start:col_end]
|
||||
|
||||
origin_lat = grid_north - (row_start + 0.5) * abs(pixel_size_lat)
|
||||
origin_lon = grid_west + (col_start + 0.5) * pixel_size_lon
|
||||
|
||||
metadata = {
|
||||
"bounds": (south, north, west, east),
|
||||
"pixel_size_lat": -abs(pixel_size_lat),
|
||||
"pixel_size_lon": pixel_size_lon,
|
||||
"origin_lat": origin_lat,
|
||||
"origin_lon": origin_lon,
|
||||
"cell_size_m": cell_size_m,
|
||||
"shape": elevation.shape,
|
||||
}
|
||||
|
||||
return elevation, metadata
|
||||
|
||||
def pixel_to_latlon(self, row: int, col: int, metadata: dict) -> Tuple[float, float]:
|
||||
"""Convert pixel coordinates to lat/lon."""
|
||||
lat = metadata["origin_lat"] + row * metadata["pixel_size_lat"]
|
||||
lon = metadata["origin_lon"] + col * metadata["pixel_size_lon"]
|
||||
return lat, lon
|
||||
|
||||
def latlon_to_pixel(self, lat: float, lon: float, metadata: dict) -> Tuple[int, int]:
|
||||
"""Convert lat/lon to pixel coordinates."""
|
||||
row = int((metadata["origin_lat"] - lat) / abs(metadata["pixel_size_lat"]))
|
||||
col = int((lon - metadata["origin_lon"]) / metadata["pixel_size_lon"])
|
||||
return row, col
|
||||
|
||||
def close(self):
|
||||
"""Close the PMTiles file."""
|
||||
pass # MmapSource handles cleanup
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
reader = DEMReader()
|
||||
elevation, meta = reader.get_elevation_grid(
|
||||
south=42.4, north=42.6, west=-114.5, east=-114.3
|
||||
)
|
||||
print(f"Elevation grid shape: {elevation.shape}")
|
||||
print(f"Cell size: {meta['cell_size_m']:.1f} m")
|
||||
print(f"Elevation range: {np.nanmin(elevation):.1f} - {np.nanmax(elevation):.1f} m")
|
||||
center_row, center_col = elevation.shape[0] // 2, elevation.shape[1] // 2
|
||||
lat, lon = reader.pixel_to_latlon(center_row, center_col, meta)
|
||||
print(f"Center pixel lat/lon: {lat:.4f}, {lon:.4f}")
|
||||
reader.close()
|
||||
|
|
@ -1,137 +0,0 @@
|
|||
"""
|
||||
Friction layer reader for OFFROUTE.
|
||||
|
||||
Reads friction values from the WorldCover friction VRT and resamples
|
||||
to match the elevation grid dimensions.
|
||||
"""
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Optional
|
||||
|
||||
try:
|
||||
import rasterio
|
||||
from rasterio.windows import from_bounds
|
||||
from rasterio.enums import Resampling
|
||||
except ImportError:
|
||||
raise ImportError("rasterio is required for friction layer support")
|
||||
|
||||
# Default path to the friction VRT
|
||||
DEFAULT_FRICTION_PATH = Path("/mnt/nav/worldcover/friction/friction_conus.vrt")
|
||||
|
||||
|
||||
class FrictionReader:
|
||||
"""Reader for WorldCover friction raster."""
|
||||
|
||||
def __init__(self, friction_path: Path = DEFAULT_FRICTION_PATH):
|
||||
self.friction_path = friction_path
|
||||
self._dataset = None
|
||||
|
||||
def _open(self):
|
||||
"""Lazy open the dataset."""
|
||||
if self._dataset is None:
|
||||
self._dataset = rasterio.open(self.friction_path)
|
||||
return self._dataset
|
||||
|
||||
def get_friction_grid(
|
||||
self,
|
||||
south: float,
|
||||
north: float,
|
||||
west: float,
|
||||
east: float,
|
||||
target_shape: Tuple[int, int]
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Get friction values for a bounding box, resampled to target shape.
|
||||
|
||||
Args:
|
||||
south, north, west, east: Bounding box coordinates
|
||||
target_shape: (rows, cols) to resample to (matches elevation grid)
|
||||
|
||||
Returns:
|
||||
np.ndarray of uint8 friction values, same shape as target_shape.
|
||||
Values: 10-40 = friction multiplier (divide by 10)
|
||||
255 = impassable
|
||||
0 = nodata (treat as impassable)
|
||||
"""
|
||||
ds = self._open()
|
||||
|
||||
# Create a window from the bounding box
|
||||
window = from_bounds(west, south, east, north, ds.transform)
|
||||
|
||||
# Read with resampling to target shape
|
||||
# Use nearest neighbor for categorical data
|
||||
friction = ds.read(
|
||||
1,
|
||||
window=window,
|
||||
out_shape=target_shape,
|
||||
resampling=Resampling.nearest
|
||||
)
|
||||
|
||||
return friction
|
||||
|
||||
def sample_point(self, lat: float, lon: float) -> int:
|
||||
"""Sample friction value at a single point."""
|
||||
ds = self._open()
|
||||
|
||||
# Get pixel coordinates
|
||||
row, col = ds.index(lon, lat)
|
||||
|
||||
# Check bounds
|
||||
if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
|
||||
return 0 # Out of bounds = nodata
|
||||
|
||||
# Read single pixel
|
||||
window = rasterio.windows.Window(col, row, 1, 1)
|
||||
value = ds.read(1, window=window)
|
||||
return int(value[0, 0])
|
||||
|
||||
def close(self):
|
||||
"""Close the dataset."""
|
||||
if self._dataset is not None:
|
||||
self._dataset.close()
|
||||
self._dataset = None
|
||||
|
||||
|
||||
def friction_to_multiplier(friction: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert friction values to cost multipliers.
|
||||
|
||||
Args:
|
||||
friction: uint8 array of friction values
|
||||
|
||||
Returns:
|
||||
float32 array of multipliers.
|
||||
Values 10-40 become 1.0-4.0 (divide by 10).
|
||||
Values 0 or 255 become np.inf (impassable).
|
||||
"""
|
||||
multiplier = friction.astype(np.float32) / 10.0
|
||||
|
||||
# Mark impassable cells
|
||||
multiplier[friction == 0] = np.inf # nodata
|
||||
multiplier[friction == 255] = np.inf # water/impassable
|
||||
|
||||
return multiplier
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Testing FrictionReader...")
|
||||
|
||||
reader = FrictionReader()
|
||||
|
||||
# Test point sampling - Murtaugh Lake (should be water = 255)
|
||||
lake_lat, lake_lon = 42.47, -114.15
|
||||
lake_friction = reader.sample_point(lake_lat, lake_lon)
|
||||
print(f"Murtaugh Lake ({lake_lat}, {lake_lon}): friction = {lake_friction}")
|
||||
print(f" Expected: 255 (water/impassable)")
|
||||
|
||||
# Test grid read for small bbox
|
||||
friction = reader.get_friction_grid(
|
||||
south=42.4, north=42.5, west=-114.2, east=-114.1,
|
||||
target_shape=(100, 100)
|
||||
)
|
||||
print(f"\nGrid test shape: {friction.shape}")
|
||||
print(f"Unique values: {np.unique(friction)}")
|
||||
print(f"Water cells (255): {np.sum(friction == 255)}")
|
||||
|
||||
reader.close()
|
||||
print("\nFrictionReader test complete.")
|
||||
|
|
@ -1,623 +0,0 @@
|
|||
"""
|
||||
MVUM (Motor Vehicle Use Map) legal access layer for OFFROUTE.
|
||||
|
||||
Queries USFS MVUM data from navi.db and provides rasterized access grids
|
||||
indicating which roads/trails are open or closed to specific vehicle modes.
|
||||
|
||||
MVUM is motor-vehicle specific — foot mode should skip this layer entirely.
|
||||
"""
|
||||
import re
|
||||
import sqlite3
|
||||
import warnings
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple, Literal
|
||||
|
||||
import numpy as np
|
||||
|
||||
# Path to navi.db
|
||||
NAVI_DB_PATH = Path("/mnt/nav/navi.db")
|
||||
|
||||
|
||||
def parse_date_range(date_str: str) -> List[Tuple[int, int, int, int]]:
|
||||
"""
|
||||
Parse MVUM date range strings like "05/01-11/30" or "06/15-10/15,12/01-03/31".
|
||||
|
||||
Returns list of (start_month, start_day, end_month, end_day) tuples.
|
||||
Returns empty list if unparseable.
|
||||
"""
|
||||
if not date_str or date_str.strip() == "":
|
||||
return []
|
||||
|
||||
ranges = []
|
||||
# Split by comma for multi-period strings
|
||||
for part in date_str.split(","):
|
||||
part = part.strip()
|
||||
# Match MM/DD-MM/DD pattern
|
||||
match = re.match(r"(\d{1,2})/(\d{1,2})-(\d{1,2})/(\d{1,2})", part)
|
||||
if match:
|
||||
try:
|
||||
sm, sd, em, ed = int(match.group(1)), int(match.group(2)), int(match.group(3)), int(match.group(4))
|
||||
if 1 <= sm <= 12 and 1 <= sd <= 31 and 1 <= em <= 12 and 1 <= ed <= 31:
|
||||
ranges.append((sm, sd, em, ed))
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
return ranges
|
||||
|
||||
|
||||
def is_date_in_range(month: int, day: int, ranges: List[Tuple[int, int, int, int]]) -> bool:
|
||||
"""
|
||||
Check if a given month/day falls within any of the date ranges.
|
||||
Handles ranges that wrap around year end (e.g., 12/01-03/31).
|
||||
"""
|
||||
if not ranges:
|
||||
return True # No ranges = assume open
|
||||
|
||||
date_num = month * 100 + day # Simple numeric comparison
|
||||
|
||||
for sm, sd, em, ed in ranges:
|
||||
start_num = sm * 100 + sd
|
||||
end_num = em * 100 + ed
|
||||
|
||||
if start_num <= end_num:
|
||||
# Normal range (e.g., 05/01-11/30)
|
||||
if start_num <= date_num <= end_num:
|
||||
return True
|
||||
else:
|
||||
# Wrapping range (e.g., 12/01-03/31)
|
||||
if date_num >= start_num or date_num <= end_num:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def check_access(
|
||||
status_field: Optional[str],
|
||||
dates_field: Optional[str],
|
||||
seasonal: Optional[str],
|
||||
check_date: Optional[Tuple[int, int]] = None
|
||||
) -> Optional[bool]:
|
||||
"""
|
||||
Determine if a road/trail is open to a vehicle type.
|
||||
|
||||
Args:
|
||||
status_field: Value of vehicle-class field (e.g., "open", null)
|
||||
dates_field: Value of *_DATESOPEN field (e.g., "05/01-11/30")
|
||||
seasonal: Value of SEASONAL field ("yearlong", "seasonal")
|
||||
check_date: Optional (month, day) tuple to check against date ranges
|
||||
|
||||
Returns:
|
||||
True = open
|
||||
False = closed
|
||||
None = no data (field not populated, defer to SYMBOL)
|
||||
"""
|
||||
if status_field is None or status_field.strip() == "":
|
||||
return None # No data
|
||||
|
||||
status = status_field.strip().lower()
|
||||
|
||||
if status != "open":
|
||||
return False # Explicitly closed or restricted
|
||||
|
||||
# Status is "open" - check seasonal restrictions
|
||||
if check_date is not None:
|
||||
month, day = check_date
|
||||
|
||||
# Parse date ranges
|
||||
if dates_field:
|
||||
ranges = parse_date_range(dates_field)
|
||||
if ranges:
|
||||
return is_date_in_range(month, day, ranges)
|
||||
|
||||
# No date field but seasonal = "yearlong" means always open
|
||||
if seasonal and seasonal.strip().lower() == "yearlong":
|
||||
return True
|
||||
|
||||
# Seasonal with no dates - assume open (data quality issue)
|
||||
if seasonal and seasonal.strip().lower() == "seasonal":
|
||||
warnings.warn(f"Seasonal road/trail with no DATESOPEN, assuming open")
|
||||
return True
|
||||
|
||||
return True # Open with no date check
|
||||
|
||||
|
||||
def get_mode_field(mode: str) -> Tuple[str, str]:
|
||||
"""
|
||||
Get the MVUM field names for a given travel mode.
|
||||
|
||||
Returns (status_field, dates_field) tuple.
|
||||
"""
|
||||
mode_mapping = {
|
||||
"atv": ("atv", "atv_datesopen"),
|
||||
"motorcycle": ("motorcycle", "motorcycle_datesopen"),
|
||||
"mtb": ("e_bike_class1", "e_bike_class1_dur"), # Closest analog for e-bikes
|
||||
"vehicle": ("highclearancevehicle", "highclearancevehicle_datesopen"),
|
||||
"passenger": ("passengervehicle", "passengervehicle_datesopen"),
|
||||
}
|
||||
|
||||
return mode_mapping.get(mode, ("highclearancevehicle", "highclearancevehicle_datesopen"))
|
||||
|
||||
|
||||
def symbol_to_access(symbol: str, mode: str, maint_level: Optional[str] = None) -> Optional[bool]:
|
||||
"""
|
||||
Fallback: interpret SYMBOL field when per-vehicle-class fields are null.
|
||||
|
||||
MVUM SYMBOL meanings (roads):
|
||||
1 = Open to all vehicles
|
||||
2 = Open to highway legal vehicles only
|
||||
3 = Road closed to motorized
|
||||
4 = Road open seasonally
|
||||
11 = Administrative use only
|
||||
12 = Decommissioned
|
||||
|
||||
For trails, similar logic applies based on TRAILCLASS.
|
||||
"""
|
||||
if symbol is None:
|
||||
return None
|
||||
|
||||
sym = str(symbol).strip()
|
||||
|
||||
# Symbol 1: Open to all
|
||||
if sym == "1":
|
||||
return True
|
||||
|
||||
# Symbol 2: Highway legal only
|
||||
if sym == "2":
|
||||
# ATVs/motorcycles typically not highway legal
|
||||
if mode in ("atv", "motorcycle"):
|
||||
return False
|
||||
return True
|
||||
|
||||
# Symbol 3: Closed to motorized
|
||||
if sym == "3":
|
||||
return False
|
||||
|
||||
# Symbol 4: Seasonally open (assume open if no date check)
|
||||
if sym == "4":
|
||||
return True
|
||||
|
||||
# Symbol 11/12: Administrative/decommissioned = closed
|
||||
if sym in ("11", "12"):
|
||||
return False
|
||||
|
||||
# Unknown symbol - defer
|
||||
return None
|
||||
|
||||
|
||||
class MVUMReader:
|
||||
"""
|
||||
Reader for MVUM data from navi.db.
|
||||
|
||||
Queries roads and trails by bounding box and returns access grids.
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: Path = NAVI_DB_PATH):
|
||||
self.db_path = db_path
|
||||
self._conn = None
|
||||
|
||||
def _get_conn(self) -> sqlite3.Connection:
|
||||
if self._conn is None:
|
||||
if not self.db_path.exists():
|
||||
raise FileNotFoundError(f"navi.db not found at {self.db_path}")
|
||||
self._conn = sqlite3.connect(str(self.db_path))
|
||||
self._conn.row_factory = sqlite3.Row
|
||||
# Load Spatialite extension if available
|
||||
try:
|
||||
self._conn.enable_load_extension(True)
|
||||
self._conn.load_extension("mod_spatialite")
|
||||
except Exception:
|
||||
pass # Spatialite not available, will use manual bbox queries
|
||||
return self._conn
|
||||
|
||||
def table_exists(self, table_name: str) -> bool:
|
||||
"""Check if an MVUM table exists."""
|
||||
conn = self._get_conn()
|
||||
cur = conn.execute(
|
||||
"SELECT name FROM sqlite_master WHERE type='table' AND name=?",
|
||||
(table_name,)
|
||||
)
|
||||
return cur.fetchone() is not None
|
||||
|
||||
def query_roads_bbox(
|
||||
self,
|
||||
south: float, north: float, west: float, east: float,
|
||||
mode: str = "atv",
|
||||
check_date: Optional[Tuple[int, int]] = None
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
Query MVUM roads within a bounding box.
|
||||
|
||||
Returns list of dicts with access info for the given mode.
|
||||
"""
|
||||
if not self.table_exists("mvum_roads"):
|
||||
return []
|
||||
|
||||
conn = self._get_conn()
|
||||
|
||||
# Query using bbox on geometry
|
||||
# Since we don't have spatialite, we'll query all and filter in Python
|
||||
# For production, consider pre-computing bbox columns
|
||||
cur = conn.execute("""
|
||||
SELECT ogc_fid, id, name, symbol, operationalmaintlevel, seasonal,
|
||||
atv, atv_datesopen, motorcycle, motorcycle_datesopen,
|
||||
highclearancevehicle, highclearancevehicle_datesopen,
|
||||
passengervehicle, passengervehicle_datesopen,
|
||||
e_bike_class1, e_bike_class1_dur,
|
||||
shape
|
||||
FROM mvum_roads
|
||||
""")
|
||||
|
||||
status_field, dates_field = get_mode_field(mode)
|
||||
results = []
|
||||
|
||||
for row in cur:
|
||||
# Parse geometry to check bbox intersection
|
||||
# The shape is stored as WKB blob
|
||||
shape = row["shape"]
|
||||
if shape is None:
|
||||
continue
|
||||
|
||||
# Quick bbox check using geometry extent
|
||||
# Since we don't have Spatialite functions, we'll include all
|
||||
# and let the rasterization handle it
|
||||
|
||||
access = check_access(
|
||||
row[status_field] if status_field in row.keys() else None,
|
||||
row[dates_field] if dates_field in row.keys() else None,
|
||||
row["seasonal"],
|
||||
check_date
|
||||
)
|
||||
|
||||
# Fallback to SYMBOL if no per-vehicle data
|
||||
if access is None:
|
||||
access = symbol_to_access(row["symbol"], mode, row["operationalmaintlevel"])
|
||||
|
||||
if access is not None:
|
||||
results.append({
|
||||
"id": row["id"],
|
||||
"name": row["name"],
|
||||
"access": access,
|
||||
"symbol": row["symbol"],
|
||||
"maint_level": row["operationalmaintlevel"],
|
||||
"shape": shape,
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
def query_trails_bbox(
|
||||
self,
|
||||
south: float, north: float, west: float, east: float,
|
||||
mode: str = "atv",
|
||||
check_date: Optional[Tuple[int, int]] = None
|
||||
) -> List[Dict]:
|
||||
"""
|
||||
Query MVUM trails within a bounding box.
|
||||
"""
|
||||
if not self.table_exists("mvum_trails"):
|
||||
return []
|
||||
|
||||
conn = self._get_conn()
|
||||
|
||||
cur = conn.execute("""
|
||||
SELECT ogc_fid, id, name, symbol, seasonal, trailclass,
|
||||
atv, atv_datesopen, motorcycle, motorcycle_datesopen,
|
||||
highclearancevehicle, highclearancevehicle_datesopen,
|
||||
passengervehicle, passengervehicle_datesopen,
|
||||
e_bike_class1, e_bike_class1_dur,
|
||||
shape
|
||||
FROM mvum_trails
|
||||
""")
|
||||
|
||||
status_field, dates_field = get_mode_field(mode)
|
||||
results = []
|
||||
|
||||
for row in cur:
|
||||
shape = row["shape"]
|
||||
if shape is None:
|
||||
continue
|
||||
|
||||
access = check_access(
|
||||
row[status_field] if status_field in row.keys() else None,
|
||||
row[dates_field] if dates_field in row.keys() else None,
|
||||
row["seasonal"],
|
||||
check_date
|
||||
)
|
||||
|
||||
if access is None:
|
||||
access = symbol_to_access(row["symbol"], mode)
|
||||
|
||||
if access is not None:
|
||||
results.append({
|
||||
"id": row["id"],
|
||||
"name": row["name"],
|
||||
"access": access,
|
||||
"symbol": row["symbol"],
|
||||
"trail_class": row["trailclass"],
|
||||
"shape": shape,
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
def query_nearest(
|
||||
self,
|
||||
lat: float, lon: float,
|
||||
radius_m: float = 50,
|
||||
table: str = "mvum_roads"
|
||||
) -> Optional[Dict]:
|
||||
"""
|
||||
Query the nearest MVUM feature to a point.
|
||||
|
||||
Used for the places panel API.
|
||||
"""
|
||||
if not self.table_exists(table):
|
||||
return None
|
||||
|
||||
conn = self._get_conn()
|
||||
|
||||
# Convert radius to degrees (approximate)
|
||||
radius_deg = radius_m / 111000
|
||||
|
||||
# Query features in bbox around point
|
||||
if table == "mvum_roads":
|
||||
cur = conn.execute("""
|
||||
SELECT ogc_fid, id, name, forestname, districtname, symbol,
|
||||
operationalmaintlevel, surfacetype, seasonal, jurisdiction,
|
||||
passengervehicle, passengervehicle_datesopen,
|
||||
highclearancevehicle, highclearancevehicle_datesopen,
|
||||
atv, atv_datesopen, motorcycle, motorcycle_datesopen,
|
||||
fourwd_gt50inches, fourwd_gt50_datesopen,
|
||||
twowd_gt50inches, twowd_gt50_datesopen,
|
||||
e_bike_class1, e_bike_class1_dur,
|
||||
e_bike_class2, e_bike_class2_dur,
|
||||
e_bike_class3, e_bike_class3_dur,
|
||||
shape
|
||||
FROM mvum_roads
|
||||
LIMIT 1000
|
||||
""")
|
||||
else:
|
||||
cur = conn.execute("""
|
||||
SELECT ogc_fid, id, name, forestname, districtname, symbol,
|
||||
seasonal, jurisdiction, trailclass, trailsystem,
|
||||
passengervehicle, passengervehicle_datesopen,
|
||||
highclearancevehicle, highclearancevehicle_datesopen,
|
||||
atv, atv_datesopen, motorcycle, motorcycle_datesopen,
|
||||
fourwd_gt50inches, fourwd_gt50_datesopen,
|
||||
twowd_gt50inches, twowd_gt50_datesopen,
|
||||
e_bike_class1, e_bike_class1_dur,
|
||||
e_bike_class2, e_bike_class2_dur,
|
||||
e_bike_class3, e_bike_class3_dur,
|
||||
shape
|
||||
FROM mvum_trails
|
||||
LIMIT 1000
|
||||
""")
|
||||
|
||||
# Find nearest feature
|
||||
# This is a simplified approach - for production, use spatial index
|
||||
try:
|
||||
from shapely import wkb
|
||||
from shapely.geometry import Point
|
||||
|
||||
query_point = Point(lon, lat)
|
||||
nearest = None
|
||||
min_dist = float('inf')
|
||||
|
||||
for row in cur:
|
||||
try:
|
||||
geom = wkb.loads(row["shape"])
|
||||
dist = query_point.distance(geom)
|
||||
if dist < min_dist and dist < radius_deg:
|
||||
min_dist = dist
|
||||
nearest = dict(row)
|
||||
nearest["geometry"] = geom
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if nearest:
|
||||
# Convert geometry to GeoJSON
|
||||
nearest["geojson"] = nearest["geometry"].__geo_interface__
|
||||
del nearest["geometry"]
|
||||
del nearest["shape"]
|
||||
return nearest
|
||||
|
||||
except ImportError:
|
||||
warnings.warn("shapely not available for nearest query")
|
||||
|
||||
return None
|
||||
|
||||
def close(self):
|
||||
if self._conn:
|
||||
self._conn.close()
|
||||
self._conn = None
|
||||
|
||||
|
||||
def get_mvum_access_grid(
|
||||
south: float, north: float, west: float, east: float,
|
||||
target_shape: Tuple[int, int],
|
||||
mode: Literal["foot", "mtb", "atv", "vehicle"] = "atv",
|
||||
check_date: Optional[str] = None,
|
||||
db_path: Path = NAVI_DB_PATH
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Get MVUM access grid for pathfinding.
|
||||
|
||||
Args:
|
||||
south, north, west, east: Bounding box (WGS84)
|
||||
target_shape: (rows, cols) to match elevation grid
|
||||
mode: Travel mode (foot skips MVUM entirely)
|
||||
check_date: Optional "MM/DD" string for seasonal checking
|
||||
db_path: Path to navi.db
|
||||
|
||||
Returns:
|
||||
np.ndarray of uint8:
|
||||
0 = no MVUM data (defer to existing trail/friction logic)
|
||||
1 = road/trail is OPEN to this vehicle mode
|
||||
255 = road/trail EXISTS but is CLOSED to this mode
|
||||
"""
|
||||
# Foot mode bypasses MVUM entirely
|
||||
if mode == "foot":
|
||||
return np.zeros(target_shape, dtype=np.uint8)
|
||||
|
||||
# Parse check_date if provided
|
||||
parsed_date = None
|
||||
if check_date:
|
||||
match = re.match(r"(\d{1,2})/(\d{1,2})", check_date)
|
||||
if match:
|
||||
parsed_date = (int(match.group(1)), int(match.group(2)))
|
||||
|
||||
# Initialize output grid
|
||||
grid = np.zeros(target_shape, dtype=np.uint8)
|
||||
rows, cols = target_shape
|
||||
|
||||
# Pixel size
|
||||
pixel_lat = (north - south) / rows
|
||||
pixel_lon = (east - west) / cols
|
||||
|
||||
reader = MVUMReader(db_path)
|
||||
|
||||
try:
|
||||
# Query roads and trails
|
||||
roads = reader.query_roads_bbox(south, north, west, east, mode, parsed_date)
|
||||
trails = reader.query_trails_bbox(south, north, west, east, mode, parsed_date)
|
||||
|
||||
# Rasterize features
|
||||
try:
|
||||
from shapely import wkb
|
||||
|
||||
for features in [roads, trails]:
|
||||
for feat in features:
|
||||
try:
|
||||
geom = wkb.loads(feat["shape"])
|
||||
|
||||
# Get geometry bounds
|
||||
minx, miny, maxx, maxy = geom.bounds
|
||||
|
||||
# Check if intersects our bbox
|
||||
if maxx < west or minx > east or maxy < south or miny > north:
|
||||
continue
|
||||
|
||||
# Rasterize line
|
||||
value = 1 if feat["access"] else 255
|
||||
|
||||
# Simple line rasterization
|
||||
if geom.geom_type in ("LineString", "MultiLineString"):
|
||||
if geom.geom_type == "MultiLineString":
|
||||
coords_list = [list(line.coords) for line in geom.geoms]
|
||||
else:
|
||||
coords_list = [list(geom.coords)]
|
||||
|
||||
for coords in coords_list:
|
||||
for i in range(len(coords) - 1):
|
||||
x1, y1 = coords[i]
|
||||
x2, y2 = coords[i + 1]
|
||||
|
||||
# Convert to pixel coordinates
|
||||
col1 = int((x1 - west) / pixel_lon)
|
||||
row1 = int((north - y1) / pixel_lat)
|
||||
col2 = int((x2 - west) / pixel_lon)
|
||||
row2 = int((north - y2) / pixel_lat)
|
||||
|
||||
# Bresenham's line algorithm
|
||||
_draw_line(grid, row1, col1, row2, col2, value)
|
||||
|
||||
except Exception as e:
|
||||
continue
|
||||
|
||||
except ImportError:
|
||||
warnings.warn("shapely not available, MVUM rasterization skipped")
|
||||
|
||||
finally:
|
||||
reader.close()
|
||||
|
||||
return grid
|
||||
|
||||
|
||||
def _draw_line(grid: np.ndarray, r1: int, c1: int, r2: int, c2: int, value: int):
|
||||
"""Draw a line on the grid using Bresenham's algorithm."""
|
||||
rows, cols = grid.shape
|
||||
|
||||
dr = abs(r2 - r1)
|
||||
dc = abs(c2 - c1)
|
||||
sr = 1 if r1 < r2 else -1
|
||||
sc = 1 if c1 < c2 else -1
|
||||
err = dr - dc
|
||||
|
||||
r, c = r1, c1
|
||||
|
||||
while True:
|
||||
if 0 <= r < rows and 0 <= c < cols:
|
||||
# Only overwrite if current value is 0 (no data) or we're marking closed
|
||||
if grid[r, c] == 0 or value == 255:
|
||||
grid[r, c] = value
|
||||
|
||||
if r == r2 and c == c2:
|
||||
break
|
||||
|
||||
e2 = 2 * err
|
||||
if e2 > -dc:
|
||||
err -= dc
|
||||
r += sr
|
||||
if e2 < dr:
|
||||
err += dr
|
||||
c += sc
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
print("=" * 60)
|
||||
print("MVUM Reader Test")
|
||||
print("=" * 60)
|
||||
|
||||
reader = MVUMReader()
|
||||
|
||||
if not reader.table_exists("mvum_roads"):
|
||||
print("ERROR: mvum_roads table not found in navi.db")
|
||||
sys.exit(1)
|
||||
|
||||
# Test bbox query (Sawtooth NF area)
|
||||
print("\n[1] Testing bbox query (Sawtooth NF area)...")
|
||||
roads = reader.query_roads_bbox(
|
||||
south=43.5, north=44.0, west=-115.0, east=-114.0,
|
||||
mode="atv"
|
||||
)
|
||||
print(f" Found {len(roads)} roads")
|
||||
|
||||
open_count = sum(1 for r in roads if r["access"])
|
||||
closed_count = sum(1 for r in roads if not r["access"])
|
||||
print(f" Open to ATV: {open_count}")
|
||||
print(f" Closed to ATV: {closed_count}")
|
||||
|
||||
# Test with seasonal date
|
||||
print("\n[2] Testing with date check (July 15)...")
|
||||
roads_summer = reader.query_roads_bbox(
|
||||
south=43.5, north=44.0, west=-115.0, east=-114.0,
|
||||
mode="atv",
|
||||
check_date=(7, 15)
|
||||
)
|
||||
open_summer = sum(1 for r in roads_summer if r["access"])
|
||||
print(f" Open to ATV on 07/15: {open_summer}")
|
||||
|
||||
print("\n[3] Testing with date check (January 15)...")
|
||||
roads_winter = reader.query_roads_bbox(
|
||||
south=43.5, north=44.0, west=-115.0, east=-114.0,
|
||||
mode="atv",
|
||||
check_date=(1, 15)
|
||||
)
|
||||
open_winter = sum(1 for r in roads_winter if r["access"])
|
||||
print(f" Open to ATV on 01/15: {open_winter}")
|
||||
|
||||
# Test grid generation
|
||||
print("\n[4] Testing grid generation...")
|
||||
grid = get_mvum_access_grid(
|
||||
south=43.5, north=44.0, west=-115.0, east=-114.0,
|
||||
target_shape=(500, 1000),
|
||||
mode="atv"
|
||||
)
|
||||
print(f" Grid shape: {grid.shape}")
|
||||
print(f" No data (0): {np.sum(grid == 0)}")
|
||||
print(f" Open (1): {np.sum(grid == 1)}")
|
||||
print(f" Closed (255): {np.sum(grid == 255)}")
|
||||
|
||||
reader.close()
|
||||
print("\nDone.")
|
||||
|
|
@ -1,414 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
OFFROUTE Phase O3a Prototype
|
||||
|
||||
Validates trail burn-in integration with the MCP pathfinder.
|
||||
The path should actively seek out trails and roads when nearby.
|
||||
|
||||
Compares paths with and without trail burn-in to show the benefit
|
||||
of trail-seeking behavior.
|
||||
"""
|
||||
import json
|
||||
import time
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
from skimage.graph import MCP_Geometric
|
||||
|
||||
# Add parent to path for imports
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
|
||||
|
||||
from lib.offroute.dem import DEMReader
|
||||
from lib.offroute.cost import compute_cost_grid
|
||||
from lib.offroute.friction import FrictionReader, friction_to_multiplier
|
||||
from lib.offroute.barriers import BarrierReader, DEFAULT_BARRIERS_PATH
|
||||
from lib.offroute.trails import TrailReader, DEFAULT_TRAILS_PATH
|
||||
|
||||
# Test bounding box - Idaho area
|
||||
BBOX = {
|
||||
"south": 42.21,
|
||||
"north": 42.60,
|
||||
"west": -114.76,
|
||||
"east": -113.79,
|
||||
}
|
||||
|
||||
# Start point: wilderness area away from roads
|
||||
START_LAT = 42.35
|
||||
START_LON = -114.60
|
||||
|
||||
# End point: near Twin Falls (has roads/trails)
|
||||
END_LAT = 42.55
|
||||
END_LON = -114.20
|
||||
|
||||
# Output files
|
||||
OUTPUT_PATH_WITH_TRAILS = Path("/opt/recon/data/offroute-test-trails.geojson")
|
||||
OUTPUT_PATH_NO_TRAILS = Path("/opt/recon/data/offroute-test-no-trails.geojson")
|
||||
|
||||
# Memory limit in GB
|
||||
MEMORY_LIMIT_GB = 12
|
||||
|
||||
|
||||
def check_memory_usage():
|
||||
"""Check current memory usage and abort if over limit."""
|
||||
try:
|
||||
import psutil
|
||||
process = psutil.Process()
|
||||
mem_gb = process.memory_info().rss / (1024**3)
|
||||
if mem_gb > MEMORY_LIMIT_GB:
|
||||
print(f"ERROR: Memory usage {mem_gb:.1f}GB exceeds {MEMORY_LIMIT_GB}GB limit")
|
||||
sys.exit(1)
|
||||
return mem_gb
|
||||
except ImportError:
|
||||
return 0
|
||||
|
||||
|
||||
def run_pathfinder(
|
||||
elevation: np.ndarray,
|
||||
meta: dict,
|
||||
friction_mult: np.ndarray,
|
||||
trails: np.ndarray,
|
||||
barriers: np.ndarray,
|
||||
use_trails: bool,
|
||||
start_row: int,
|
||||
start_col: int,
|
||||
end_row: int,
|
||||
end_col: int,
|
||||
dem_reader: DEMReader,
|
||||
) -> dict:
|
||||
"""Run the MCP pathfinder with given parameters."""
|
||||
# Compute cost grid
|
||||
cost = compute_cost_grid(
|
||||
elevation,
|
||||
cell_size_m=meta["cell_size_m"],
|
||||
friction=friction_mult,
|
||||
trails=trails if use_trails else None,
|
||||
barriers=barriers,
|
||||
boundary_mode="pragmatic",
|
||||
)
|
||||
|
||||
# Run MCP
|
||||
mcp = MCP_Geometric(cost, fully_connected=True)
|
||||
cumulative_costs, traceback = mcp.find_costs([(start_row, start_col)])
|
||||
|
||||
end_cost = cumulative_costs[end_row, end_col]
|
||||
|
||||
if np.isinf(end_cost):
|
||||
return {
|
||||
"success": False,
|
||||
"reason": "No path found (blocked by impassable terrain)",
|
||||
}
|
||||
|
||||
# Traceback path
|
||||
path_indices = mcp.traceback((end_row, end_col))
|
||||
|
||||
# Convert to coordinates and collect stats
|
||||
coordinates = []
|
||||
elevations = []
|
||||
trail_values = []
|
||||
|
||||
for row, col in path_indices:
|
||||
lat, lon = dem_reader.pixel_to_latlon(row, col, meta)
|
||||
elev = elevation[row, col]
|
||||
trail_val = trails[row, col] if trails is not None else 0
|
||||
coordinates.append([lon, lat])
|
||||
elevations.append(elev)
|
||||
trail_values.append(trail_val)
|
||||
|
||||
# Compute distance
|
||||
total_distance_m = 0
|
||||
for i in range(1, len(coordinates)):
|
||||
lon1, lat1 = coordinates[i-1]
|
||||
lon2, lat2 = coordinates[i]
|
||||
R = 6371000
|
||||
dlat = np.radians(lat2 - lat1)
|
||||
dlon = np.radians(lon2 - lon1)
|
||||
a = np.sin(dlat/2)**2 + np.cos(np.radians(lat1)) * np.cos(np.radians(lat2)) * np.sin(dlon/2)**2
|
||||
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))
|
||||
total_distance_m += R * c
|
||||
|
||||
# Elevation stats
|
||||
elev_arr = np.array(elevations)
|
||||
elev_diff = np.diff(elev_arr)
|
||||
elev_gain = np.sum(elev_diff[elev_diff > 0])
|
||||
elev_loss = np.sum(np.abs(elev_diff[elev_diff < 0]))
|
||||
|
||||
# Trail stats
|
||||
trail_arr = np.array(trail_values)
|
||||
road_cells = np.sum(trail_arr == 5)
|
||||
track_cells = np.sum(trail_arr == 15)
|
||||
trail_cells = np.sum(trail_arr == 25)
|
||||
off_trail_cells = np.sum(trail_arr == 0)
|
||||
on_trail_cells = road_cells + track_cells + trail_cells
|
||||
total_cells = len(trail_arr)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"coordinates": coordinates,
|
||||
"total_time_seconds": float(end_cost),
|
||||
"total_time_minutes": float(end_cost / 60),
|
||||
"total_distance_m": float(total_distance_m),
|
||||
"total_distance_km": float(total_distance_m / 1000),
|
||||
"elevation_gain_m": float(elev_gain),
|
||||
"elevation_loss_m": float(elev_loss),
|
||||
"min_elevation_m": float(np.min(elev_arr)),
|
||||
"max_elevation_m": float(np.max(elev_arr)),
|
||||
"cell_count": total_cells,
|
||||
"road_cells": int(road_cells),
|
||||
"track_cells": int(track_cells),
|
||||
"trail_cells": int(trail_cells),
|
||||
"off_trail_cells": int(off_trail_cells),
|
||||
"on_trail_pct": float(100 * on_trail_cells / total_cells) if total_cells > 0 else 0,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 80)
|
||||
print("OFFROUTE Phase O3a Prototype (Trail Burn-In)")
|
||||
print("=" * 80)
|
||||
|
||||
t0 = time.time()
|
||||
|
||||
# Check for required rasters
|
||||
if not DEFAULT_BARRIERS_PATH.exists():
|
||||
print(f"\nERROR: Barrier raster not found at {DEFAULT_BARRIERS_PATH}")
|
||||
sys.exit(1)
|
||||
if not DEFAULT_TRAILS_PATH.exists():
|
||||
print(f"\nERROR: Trails raster not found at {DEFAULT_TRAILS_PATH}")
|
||||
sys.exit(1)
|
||||
|
||||
# Step 1: Load elevation data
|
||||
print(f"\n[1] Loading DEM for bbox: {BBOX}")
|
||||
dem_reader = DEMReader()
|
||||
|
||||
elevation, meta = dem_reader.get_elevation_grid(
|
||||
south=BBOX["south"],
|
||||
north=BBOX["north"],
|
||||
west=BBOX["west"],
|
||||
east=BBOX["east"],
|
||||
)
|
||||
|
||||
print(f" Elevation grid shape: {elevation.shape}")
|
||||
print(f" Cell count: {elevation.size:,}")
|
||||
print(f" Cell size: {meta['cell_size_m']:.1f} m")
|
||||
|
||||
mem = check_memory_usage()
|
||||
if mem > 0:
|
||||
print(f" Memory usage: {mem:.1f} GB")
|
||||
|
||||
# Step 2: Load friction data
|
||||
print(f"\n[2] Loading WorldCover friction layer...")
|
||||
friction_reader = FrictionReader()
|
||||
|
||||
friction_raw = friction_reader.get_friction_grid(
|
||||
south=BBOX["south"],
|
||||
north=BBOX["north"],
|
||||
west=BBOX["west"],
|
||||
east=BBOX["east"],
|
||||
target_shape=elevation.shape
|
||||
)
|
||||
friction_mult = friction_to_multiplier(friction_raw)
|
||||
|
||||
print(f" Friction grid shape: {friction_raw.shape}")
|
||||
print(f" Water/impassable cells: {np.sum(np.isinf(friction_mult)):,}")
|
||||
|
||||
# Step 3: Load barrier data
|
||||
print(f"\n[3] Loading PAD-US barrier layer...")
|
||||
barrier_reader = BarrierReader()
|
||||
|
||||
barriers = barrier_reader.get_barrier_grid(
|
||||
south=BBOX["south"],
|
||||
north=BBOX["north"],
|
||||
west=BBOX["west"],
|
||||
east=BBOX["east"],
|
||||
target_shape=elevation.shape
|
||||
)
|
||||
|
||||
closed_cells = np.sum(barriers == 255)
|
||||
print(f" Barrier grid shape: {barriers.shape}")
|
||||
print(f" Closed/restricted cells: {closed_cells:,}")
|
||||
|
||||
# Step 4: Load trails data
|
||||
print(f"\n[4] Loading OSM trails layer...")
|
||||
trail_reader = TrailReader()
|
||||
|
||||
trails = trail_reader.get_trails_grid(
|
||||
south=BBOX["south"],
|
||||
north=BBOX["north"],
|
||||
west=BBOX["west"],
|
||||
east=BBOX["east"],
|
||||
target_shape=elevation.shape
|
||||
)
|
||||
|
||||
road_cells = np.sum(trails == 5)
|
||||
track_cells = np.sum(trails == 15)
|
||||
trail_cells = np.sum(trails == 25)
|
||||
print(f" Trails grid shape: {trails.shape}")
|
||||
print(f" Road cells: {road_cells:,}")
|
||||
print(f" Track cells: {track_cells:,}")
|
||||
print(f" Trail cells: {trail_cells:,}")
|
||||
print(f" Total trail coverage: {100*(road_cells+track_cells+trail_cells)/trails.size:.2f}%")
|
||||
|
||||
mem = check_memory_usage()
|
||||
if mem > 0:
|
||||
print(f" Memory usage: {mem:.1f} GB")
|
||||
|
||||
# Step 5: Convert start/end to pixel coordinates
|
||||
print(f"\n[5] Converting coordinates...")
|
||||
start_row, start_col = dem_reader.latlon_to_pixel(START_LAT, START_LON, meta)
|
||||
end_row, end_col = dem_reader.latlon_to_pixel(END_LAT, END_LON, meta)
|
||||
|
||||
print(f" Start: ({START_LAT}, {START_LON}) -> pixel ({start_row}, {start_col})")
|
||||
print(f" End: ({END_LAT}, {END_LON}) -> pixel ({end_row}, {end_col})")
|
||||
|
||||
# Validate coordinates
|
||||
rows, cols = elevation.shape
|
||||
if not (0 <= start_row < rows and 0 <= start_col < cols):
|
||||
print(f"ERROR: Start point outside grid bounds")
|
||||
sys.exit(1)
|
||||
if not (0 <= end_row < rows and 0 <= end_col < cols):
|
||||
print(f"ERROR: End point outside grid bounds")
|
||||
sys.exit(1)
|
||||
|
||||
# Step 6: Run pathfinder WITH trails
|
||||
print(f"\n[6] Running pathfinder WITH trail burn-in...")
|
||||
t6a = time.time()
|
||||
result_trails = run_pathfinder(
|
||||
elevation, meta, friction_mult, trails, barriers,
|
||||
use_trails=True,
|
||||
start_row=start_row, start_col=start_col,
|
||||
end_row=end_row, end_col=end_col,
|
||||
dem_reader=dem_reader,
|
||||
)
|
||||
t6b = time.time()
|
||||
print(f" Completed in {t6b - t6a:.1f}s")
|
||||
|
||||
# Step 7: Run pathfinder WITHOUT trails
|
||||
print(f"\n[7] Running pathfinder WITHOUT trail burn-in...")
|
||||
t7a = time.time()
|
||||
result_no_trails = run_pathfinder(
|
||||
elevation, meta, friction_mult, trails, barriers,
|
||||
use_trails=False,
|
||||
start_row=start_row, start_col=start_col,
|
||||
end_row=end_row, end_col=end_col,
|
||||
dem_reader=dem_reader,
|
||||
)
|
||||
t7b = time.time()
|
||||
print(f" Completed in {t7b - t7a:.1f}s")
|
||||
|
||||
# Step 8: Save GeoJSON outputs
|
||||
print(f"\n[8] Saving GeoJSON outputs...")
|
||||
|
||||
OUTPUT_PATH_WITH_TRAILS.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if result_trails["success"]:
|
||||
geojson = {
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"type": "offroute_with_trails",
|
||||
"phase": "O3a",
|
||||
"trail_burn_in": True,
|
||||
"start": {"lat": START_LAT, "lon": START_LON},
|
||||
"end": {"lat": END_LAT, "lon": END_LON},
|
||||
**{k: v for k, v in result_trails.items() if k not in ["success", "coordinates"]},
|
||||
},
|
||||
"geometry": {
|
||||
"type": "LineString",
|
||||
"coordinates": result_trails["coordinates"],
|
||||
}
|
||||
}
|
||||
with open(OUTPUT_PATH_WITH_TRAILS, "w") as f:
|
||||
json.dump(geojson, f, indent=2)
|
||||
print(f" Saved: {OUTPUT_PATH_WITH_TRAILS}")
|
||||
|
||||
if result_no_trails["success"]:
|
||||
geojson = {
|
||||
"type": "Feature",
|
||||
"properties": {
|
||||
"type": "offroute_no_trails",
|
||||
"phase": "O3a",
|
||||
"trail_burn_in": False,
|
||||
"start": {"lat": START_LAT, "lon": START_LON},
|
||||
"end": {"lat": END_LAT, "lon": END_LON},
|
||||
**{k: v for k, v in result_no_trails.items() if k not in ["success", "coordinates"]},
|
||||
},
|
||||
"geometry": {
|
||||
"type": "LineString",
|
||||
"coordinates": result_no_trails["coordinates"],
|
||||
}
|
||||
}
|
||||
with open(OUTPUT_PATH_NO_TRAILS, "w") as f:
|
||||
json.dump(geojson, f, indent=2)
|
||||
print(f" Saved: {OUTPUT_PATH_NO_TRAILS}")
|
||||
|
||||
t_total = time.time()
|
||||
|
||||
# Final report
|
||||
print(f"\n" + "=" * 80)
|
||||
print("SIDE-BY-SIDE COMPARISON: Trail Burn-In Effect")
|
||||
print("=" * 80)
|
||||
|
||||
if result_trails["success"] and result_no_trails["success"]:
|
||||
print(f"{'Metric':<25} {'WITH TRAILS':<20} {'WITHOUT TRAILS':<20} {'Delta':<15}")
|
||||
print("-" * 80)
|
||||
|
||||
metrics = [
|
||||
("Distance (km)", "total_distance_km", ".2f"),
|
||||
("Effort time (min)", "total_time_minutes", ".1f"),
|
||||
("Cell count", "cell_count", "d"),
|
||||
("Elevation gain (m)", "elevation_gain_m", ".0f"),
|
||||
("On-trail %", "on_trail_pct", ".1f"),
|
||||
("Road cells", "road_cells", "d"),
|
||||
("Track cells", "track_cells", "d"),
|
||||
("Trail cells", "trail_cells", "d"),
|
||||
]
|
||||
|
||||
for label, key, fmt in metrics:
|
||||
val_with = result_trails[key]
|
||||
val_without = result_no_trails[key]
|
||||
if isinstance(val_with, int):
|
||||
delta = val_with - val_without
|
||||
delta_str = f"{delta:+d}"
|
||||
else:
|
||||
delta = val_with - val_without
|
||||
delta_str = f"{delta:+.2f}"
|
||||
print(f"{label:<25} {val_with:<20{fmt}} {val_without:<20{fmt}} {delta_str:<15}")
|
||||
|
||||
# Analysis
|
||||
print(f"\n" + "-" * 80)
|
||||
print("ANALYSIS")
|
||||
print("-" * 80)
|
||||
|
||||
time_saved = result_no_trails["total_time_minutes"] - result_trails["total_time_minutes"]
|
||||
if time_saved > 0:
|
||||
print(f"Trail burn-in saves {time_saved:.1f} minutes ({100*time_saved/result_no_trails['total_time_minutes']:.1f}% faster)")
|
||||
elif time_saved < 0:
|
||||
print(f"Trail burn-in adds {-time_saved:.1f} minutes (path seeks trails even if longer)")
|
||||
|
||||
on_trail_with = result_trails["on_trail_pct"]
|
||||
on_trail_without = result_no_trails["on_trail_pct"]
|
||||
if on_trail_with > on_trail_without:
|
||||
print(f"Trail burn-in increases on-trail travel: {on_trail_without:.1f}% → {on_trail_with:.1f}%")
|
||||
else:
|
||||
print(f"Both paths have similar on-trail percentage")
|
||||
|
||||
else:
|
||||
if not result_trails["success"]:
|
||||
print(f"WITH TRAILS: FAILED - {result_trails.get('reason', 'unknown')}")
|
||||
if not result_no_trails["success"]:
|
||||
print(f"WITHOUT TRAILS: FAILED - {result_no_trails.get('reason', 'unknown')}")
|
||||
|
||||
print(f"\n" + "-" * 80)
|
||||
print(f"Total wall time: {t_total - t0:.1f}s")
|
||||
|
||||
# Cleanup
|
||||
dem_reader.close()
|
||||
friction_reader.close()
|
||||
barrier_reader.close()
|
||||
trail_reader.close()
|
||||
|
||||
print("\nPrototype completed.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -1,174 +0,0 @@
|
|||
"""
|
||||
Trail corridor reader for OFFROUTE.
|
||||
|
||||
Provides access to the OSM-derived trail raster for pathfinding.
|
||||
Trail values replace WorldCover friction where trails exist.
|
||||
|
||||
Raster values:
|
||||
0 = no trail (use WorldCover friction)
|
||||
5 = road (0.1× friction)
|
||||
15 = track (0.3× friction)
|
||||
25 = foot trail (0.5× friction)
|
||||
"""
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from typing import Tuple, Optional
|
||||
|
||||
try:
|
||||
import rasterio
|
||||
from rasterio.windows import from_bounds
|
||||
from rasterio.enums import Resampling
|
||||
except ImportError:
|
||||
raise ImportError("rasterio is required for trails layer support")
|
||||
|
||||
# Default path to the trails raster
|
||||
DEFAULT_TRAILS_PATH = Path("/mnt/nav/worldcover/trails.tif")
|
||||
|
||||
# Trail value to friction multiplier mapping
|
||||
TRAIL_FRICTION_MAP = {
|
||||
5: 0.1, # road
|
||||
15: 0.3, # track
|
||||
25: 0.5, # foot trail
|
||||
}
|
||||
|
||||
|
||||
class TrailReader:
|
||||
"""Reader for OSM-derived trail corridor raster."""
|
||||
|
||||
def __init__(self, trails_path: Path = DEFAULT_TRAILS_PATH):
|
||||
self.trails_path = trails_path
|
||||
self._dataset = None
|
||||
|
||||
def _open(self):
|
||||
"""Lazy open the dataset."""
|
||||
if self._dataset is None:
|
||||
if not self.trails_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Trails raster not found at {self.trails_path}. "
|
||||
f"Run the Phase B rasterization script first."
|
||||
)
|
||||
self._dataset = rasterio.open(self.trails_path)
|
||||
return self._dataset
|
||||
|
||||
def get_trails_grid(
|
||||
self,
|
||||
south: float,
|
||||
north: float,
|
||||
west: float,
|
||||
east: float,
|
||||
target_shape: Tuple[int, int]
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Get trail values for a bounding box, resampled to target shape.
|
||||
|
||||
Args:
|
||||
south, north, west, east: Bounding box coordinates (WGS84)
|
||||
target_shape: (rows, cols) to resample to (matches elevation grid)
|
||||
|
||||
Returns:
|
||||
np.ndarray of uint8 trail values:
|
||||
0 = no trail
|
||||
5 = road (0.1× friction)
|
||||
15 = track (0.3× friction)
|
||||
25 = foot trail (0.5× friction)
|
||||
"""
|
||||
ds = self._open()
|
||||
|
||||
# Create a window from the bounding box
|
||||
window = from_bounds(west, south, east, north, ds.transform)
|
||||
|
||||
# Read with resampling to target shape
|
||||
# Use nearest neighbor to preserve discrete values
|
||||
trails = ds.read(
|
||||
1,
|
||||
window=window,
|
||||
out_shape=target_shape,
|
||||
resampling=Resampling.nearest
|
||||
)
|
||||
|
||||
return trails
|
||||
|
||||
def sample_point(self, lat: float, lon: float) -> int:
|
||||
"""Sample trail value at a single point."""
|
||||
ds = self._open()
|
||||
|
||||
# Get pixel coordinates
|
||||
row, col = ds.index(lon, lat)
|
||||
|
||||
# Check bounds
|
||||
if row < 0 or row >= ds.height or col < 0 or col >= ds.width:
|
||||
return 0 # Out of bounds = no trail
|
||||
|
||||
# Read single pixel
|
||||
window = rasterio.windows.Window(col, row, 1, 1)
|
||||
value = ds.read(1, window=window)
|
||||
return int(value[0, 0])
|
||||
|
||||
def close(self):
|
||||
"""Close the dataset."""
|
||||
if self._dataset is not None:
|
||||
self._dataset.close()
|
||||
self._dataset = None
|
||||
|
||||
|
||||
def trails_to_friction(trails: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Convert trail values to friction multipliers.
|
||||
|
||||
Args:
|
||||
trails: uint8 array of trail values (0, 5, 15, or 25)
|
||||
|
||||
Returns:
|
||||
Tuple of:
|
||||
- friction: float32 array of friction multipliers
|
||||
- has_trail: bool array indicating where trails exist
|
||||
"""
|
||||
friction = np.ones_like(trails, dtype=np.float32)
|
||||
has_trail = trails > 0
|
||||
|
||||
# Apply friction values where trails exist
|
||||
friction[trails == 5] = 0.1 # road
|
||||
friction[trails == 15] = 0.3 # track
|
||||
friction[trails == 25] = 0.5 # foot trail
|
||||
|
||||
return friction, has_trail
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Testing TrailReader...")
|
||||
|
||||
if not DEFAULT_TRAILS_PATH.exists():
|
||||
print(f"Trails raster not found at {DEFAULT_TRAILS_PATH}")
|
||||
print("Run Phase B rasterization first.")
|
||||
exit(1)
|
||||
|
||||
reader = TrailReader()
|
||||
|
||||
# Test point sampling - Twin Falls downtown (should have roads)
|
||||
test_lat, test_lon = 42.563, -114.461
|
||||
trail_value = reader.sample_point(test_lat, test_lon)
|
||||
print(f"\nTwin Falls ({test_lat}, {test_lon}): trail value = {trail_value}")
|
||||
label = {0: "no trail", 5: "road", 15: "track", 25: "trail"}.get(trail_value, "unknown")
|
||||
print(f" Type: {label}")
|
||||
|
||||
# Test grid read for test bbox
|
||||
trails = reader.get_trails_grid(
|
||||
south=42.21, north=42.60, west=-114.76, east=-113.79,
|
||||
target_shape=(400, 1000)
|
||||
)
|
||||
print(f"\nGrid test shape: {trails.shape}")
|
||||
|
||||
unique, counts = np.unique(trails, return_counts=True)
|
||||
print("Value distribution:")
|
||||
for v, c in zip(unique, counts):
|
||||
pct = 100 * c / trails.size
|
||||
label = {0: "no trail", 5: "road", 15: "track", 25: "trail"}.get(v, f"unknown({v})")
|
||||
print(f" {label}: {c:,} pixels ({pct:.2f}%)")
|
||||
|
||||
# Test conversion to friction
|
||||
friction, has_trail = trails_to_friction(trails)
|
||||
print(f"\nTrail coverage: {100 * np.sum(has_trail) / trails.size:.2f}%")
|
||||
print(f"Friction range (on trails): {friction[has_trail].min():.1f} - {friction[has_trail].max():.1f}")
|
||||
|
||||
reader.close()
|
||||
print("\nTrailReader test complete.")
|
||||
|
|
@ -22,6 +22,41 @@ OVERPASS_URL = "https://overpass-api.de/api/interpreter"
|
|||
OVERPASS_UA = "Navi/1.0 (forge.echo6.co/matt/recon)"
|
||||
VALID_OSM_TYPES = {"N", "W", "R"}
|
||||
|
||||
# US states and Canadian provinces for wikipedia title parsing
|
||||
US_STATES = {
|
||||
'Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado',
|
||||
'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho',
|
||||
'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana',
|
||||
'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',
|
||||
'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',
|
||||
'New Hampshire', 'New Jersey', 'New Mexico', 'New York',
|
||||
'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon',
|
||||
'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota',
|
||||
'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington',
|
||||
'West Virginia', 'Wisconsin', 'Wyoming', 'District of Columbia'
|
||||
}
|
||||
|
||||
CANADIAN_PROVINCES = {
|
||||
'Alberta', 'British Columbia', 'Manitoba', 'New Brunswick',
|
||||
'Newfoundland and Labrador', 'Northwest Territories', 'Nova Scotia',
|
||||
'Nunavut', 'Ontario', 'Prince Edward Island', 'Quebec', 'Saskatchewan', 'Yukon'
|
||||
}
|
||||
|
||||
|
||||
def _parse_state_from_wikipedia(wikipedia_tag):
|
||||
"""Parse state/province and country from wikipedia extratag like 'en:Joliet, Illinois'"""
|
||||
if not wikipedia_tag or not wikipedia_tag.startswith('en:'):
|
||||
return None, None
|
||||
title = wikipedia_tag[3:]
|
||||
for state in US_STATES:
|
||||
if state in title:
|
||||
return state, 'us'
|
||||
for prov in CANADIAN_PROVINCES:
|
||||
if prov in title:
|
||||
return prov, 'ca'
|
||||
return None, None
|
||||
|
||||
|
||||
_db_conn = None
|
||||
|
||||
|
||||
|
|
@ -290,6 +325,11 @@ def _enrich_wiki_links(result):
|
|||
"""
|
||||
Rewrite wiki-related extratags to local Kiwix URLs where available.
|
||||
Falls back to public URLs. Only runs when has_wiki_rewriting is enabled.
|
||||
|
||||
Note: When has_kiwix_wiki is enabled, we skip rewriting 'wikipedia' since
|
||||
the wiki_index enrichment provides a proper wiki_url field. This keeps
|
||||
extratags.wikipedia in the original OSM format for frontend link builders.
|
||||
|
||||
Returns the (possibly enriched) result dict.
|
||||
"""
|
||||
try:
|
||||
|
|
@ -298,6 +338,8 @@ def _enrich_wiki_links(result):
|
|||
features = deploy_config.get('features', {})
|
||||
if not features.get('has_wiki_rewriting', False):
|
||||
return result
|
||||
# When has_kiwix_wiki is enabled, skip wikipedia rewriting (wiki_url handles it)
|
||||
has_kiwix_wiki = features.get('has_kiwix_wiki', False)
|
||||
except Exception:
|
||||
return result
|
||||
|
||||
|
|
@ -313,6 +355,9 @@ def _enrich_wiki_links(result):
|
|||
|
||||
rewrites = {}
|
||||
for tag in _WIKI_TAGS:
|
||||
# Skip wikipedia when has_kiwix_wiki is enabled (wiki_url provides the local link)
|
||||
if tag == 'wikipedia' and has_kiwix_wiki:
|
||||
continue
|
||||
value = extratags.get(tag)
|
||||
if not value:
|
||||
continue
|
||||
|
|
@ -328,6 +373,82 @@ def _enrich_wiki_links(result):
|
|||
|
||||
return result
|
||||
|
||||
|
||||
|
||||
# ── Wiki Index enrichment ───────────────────────────────────────────────
|
||||
|
||||
def _enrich_with_wiki_index(result):
|
||||
"""
|
||||
Add wiki summary, URLs, and population from wiki_index.db.
|
||||
Only runs when has_kiwix_wiki is enabled. Direct match only.
|
||||
Returns the (possibly enriched) result dict.
|
||||
"""
|
||||
try:
|
||||
from .deployment_config import get_deployment_config
|
||||
deploy_config = get_deployment_config()
|
||||
features = deploy_config.get('features', {})
|
||||
if not features.get('has_kiwix_wiki', False):
|
||||
return result
|
||||
except Exception:
|
||||
return result
|
||||
|
||||
try:
|
||||
from . import wiki_index
|
||||
except ImportError:
|
||||
logger.debug("wiki_index module not available")
|
||||
return result
|
||||
|
||||
if not wiki_index.is_available():
|
||||
return result
|
||||
|
||||
# Extract match criteria from result
|
||||
name = result.get('name', '')
|
||||
osm_class = result.get('class', '')
|
||||
osm_type_tag = result.get('type', '')
|
||||
address = result.get('address', {})
|
||||
state = address.get('state', '')
|
||||
country_code = address.get('country_code', '')
|
||||
|
||||
# If state/country missing, try to derive from wikipedia extratag
|
||||
extratags = result.get('extratags', {})
|
||||
if (not state or not country_code) and extratags.get('wikipedia'):
|
||||
derived_state, derived_country = _parse_state_from_wikipedia(extratags['wikipedia'])
|
||||
if not state and derived_state:
|
||||
state = derived_state
|
||||
if not country_code and derived_country:
|
||||
country_code = derived_country
|
||||
|
||||
# Handle boundary/administrative - get actual place type from extratags
|
||||
# (e.g. boundary:administrative with extratags.place='city' -> place:city)
|
||||
if osm_class == 'boundary' and osm_type_tag == 'administrative':
|
||||
place_tag = extratags.get('place') or extratags.get('linked_place')
|
||||
if place_tag:
|
||||
osm_class = 'place'
|
||||
osm_type_tag = place_tag
|
||||
|
||||
if not name or not osm_class or not osm_type_tag:
|
||||
return result
|
||||
|
||||
# Look up wiki data
|
||||
wiki_data = wiki_index.lookup_wiki(name, osm_class, osm_type_tag, state, country_code)
|
||||
if not wiki_data:
|
||||
return result
|
||||
|
||||
# Add wiki fields to result (additive only)
|
||||
if 'wiki_summary' in wiki_data:
|
||||
result['wiki_summary'] = wiki_data['wiki_summary']
|
||||
if 'wiki_url' in wiki_data:
|
||||
result['wiki_url'] = wiki_data['wiki_url']
|
||||
if 'wikivoyage_url' in wiki_data:
|
||||
result['wikivoyage_url'] = wiki_data['wikivoyage_url']
|
||||
if 'wiki_population' in wiki_data:
|
||||
result['wiki_population'] = wiki_data['wiki_population']
|
||||
|
||||
result.setdefault('sources', {})['wiki_index'] = True
|
||||
logger.debug(f"Wiki index enrichment for {name}")
|
||||
|
||||
return result
|
||||
|
||||
# ── Nominatim parsing ───────────────────────────────────────────────────
|
||||
|
||||
# Nominatim address array uses rank_address to indicate what each entry is.
|
||||
|
|
@ -444,6 +565,8 @@ def _parse_nominatim(data):
|
|||
'wheelchair': raw_extra.get('wheelchair'),
|
||||
'fee': raw_extra.get('fee'),
|
||||
'takeaway': raw_extra.get('takeaway'),
|
||||
'place': raw_extra.get('place'),
|
||||
'linked_place': raw_extra.get('linked_place'),
|
||||
}
|
||||
|
||||
# Category: use extratags.place for boundaries (e.g. "city"), else class/type
|
||||
|
|
@ -593,6 +716,7 @@ def get_place_detail(osm_type, osm_id):
|
|||
# 1. Check cache
|
||||
cached = cache_get(osm_type, osm_id)
|
||||
if cached:
|
||||
cached = _enrich_with_wiki_index(cached)
|
||||
logger.debug(f"Cache hit: {osm_type}/{osm_id}")
|
||||
return cached, 200
|
||||
|
||||
|
|
@ -625,6 +749,7 @@ def get_place_detail(osm_type, osm_id):
|
|||
nominatim_result = _enrich_with_overture(nominatim_result, osm_type, osm_id)
|
||||
nominatim_result = _enrich_with_google(nominatim_result, osm_type, osm_id)
|
||||
nominatim_result = _enrich_wiki_links(nominatim_result)
|
||||
nominatim_result = _enrich_with_wiki_index(nominatim_result)
|
||||
cache_put(osm_type, osm_id, nominatim_result, 'nominatim_local')
|
||||
return nominatim_result, 200
|
||||
|
||||
|
|
@ -658,6 +783,7 @@ def get_place_detail(osm_type, osm_id):
|
|||
overpass_result = _enrich_with_overture(overpass_result, osm_type, osm_id)
|
||||
overpass_result = _enrich_with_google(overpass_result, osm_type, osm_id)
|
||||
overpass_result = _enrich_wiki_links(overpass_result)
|
||||
overpass_result = _enrich_with_wiki_index(overpass_result)
|
||||
cache_put(osm_type, osm_id, overpass_result, 'overpass')
|
||||
return overpass_result, 200
|
||||
|
||||
|
|
|
|||
|
|
@ -77,73 +77,10 @@ def _text_hash(text):
|
|||
return hashlib.md5(text.encode('utf-8')).hexdigest()
|
||||
|
||||
|
||||
def _flatten_table(table_el):
|
||||
"""Convert a <table> element to pipe-delimited text.
|
||||
|
||||
Each <tr> becomes a row with cells joined by ' | '.
|
||||
Returns the formatted table as a string with blank lines around it.
|
||||
"""
|
||||
rows = []
|
||||
for tr in table_el.iter('tr'):
|
||||
cells = []
|
||||
for cell in tr:
|
||||
if cell.tag in ('td', 'th'):
|
||||
cell_text = (cell.text_content() or '').strip()
|
||||
# Collapse internal whitespace in each cell
|
||||
cell_text = re.sub(r'\s+', ' ', cell_text)
|
||||
if cell_text:
|
||||
cells.append(cell_text)
|
||||
if cells:
|
||||
rows.append(' | '.join(cells))
|
||||
if not rows:
|
||||
return ''
|
||||
return '\n'.join(rows)
|
||||
|
||||
|
||||
def _preprocess_tree(doc):
|
||||
"""Pre-process HTML tree to add delimiters before text_content() flattens it.
|
||||
|
||||
Handles: <table>, <br>, <li>, <dt>, <dd> -- elements that lxml's
|
||||
text_content() would concatenate without any separators.
|
||||
"""
|
||||
from lxml import etree
|
||||
|
||||
# 1. Replace <table> elements with their pipe-delimited text
|
||||
for table in list(doc.iter('table')):
|
||||
formatted = _flatten_table(table)
|
||||
if formatted:
|
||||
replacement = etree.Element('div')
|
||||
replacement.text = '\n\n' + formatted + '\n\n'
|
||||
parent = table.getparent()
|
||||
if parent is not None:
|
||||
parent.replace(table, replacement)
|
||||
else:
|
||||
table.drop_tree()
|
||||
|
||||
# 2. <br> -> inject newline
|
||||
for br in list(doc.iter('br')):
|
||||
br.tail = '\n' + (br.tail or '')
|
||||
|
||||
# 3. <li> -> inject newline + "- " prefix
|
||||
for li in list(doc.iter('li')):
|
||||
li.text = '- ' + (li.text or '')
|
||||
li.tail = '\n' + (li.tail or '')
|
||||
|
||||
# 4. <dt> -> inject newline before
|
||||
for dt in list(doc.iter('dt')):
|
||||
dt.tail = '\n' + (dt.tail or '')
|
||||
|
||||
# 5. <dd> -> inject newline + indent
|
||||
for dd in list(doc.iter('dd')):
|
||||
dd.text = ' ' + (dd.text or '')
|
||||
dd.tail = '\n' + (dd.tail or '')
|
||||
|
||||
|
||||
def _html_to_text(html_bytes):
|
||||
"""Convert HTML bytes to clean text via lxml.
|
||||
|
||||
Strips nav, footer, script, style elements. Decodes entities.
|
||||
Pre-processes tables, lists, and line breaks for proper delimiters.
|
||||
Normalizes whitespace.
|
||||
"""
|
||||
try:
|
||||
|
|
@ -156,9 +93,6 @@ def _html_to_text(html_bytes):
|
|||
for el in doc.iter(tag):
|
||||
el.drop_tree()
|
||||
|
||||
# Pre-process tree: tables -> pipe-delimited, br -> newlines, li -> dashes
|
||||
_preprocess_tree(doc)
|
||||
|
||||
# Extract text
|
||||
text = doc.text_content()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,136 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""Tests for the /api/reverse/<lat>/<lon> enrichment bundle (lib.netsyms_api).
|
||||
|
||||
Photon/Valhalla/landclass are mocked so the suite runs without live services;
|
||||
one timezone test exercises the real SpatiaLite DB when it is present. Plain
|
||||
asserts + a __main__ runner, matching the rest of lib/*_test.py.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from unittest import mock
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from flask import Flask
|
||||
from lib import netsyms_api
|
||||
|
||||
EXPECTED_KEYS = {'name', 'city', 'county', 'state', 'country',
|
||||
'postal_code', 'timezone', 'landclass', 'elevation_m'}
|
||||
|
||||
|
||||
def _client():
|
||||
app = Flask(__name__)
|
||||
app.register_blueprint(netsyms_api.geocode_bp)
|
||||
return app.test_client()
|
||||
|
||||
|
||||
def _clear_cache():
|
||||
netsyms_api._REVERSE_BUNDLE_CACHE.clear()
|
||||
|
||||
|
||||
def test_happy_path():
|
||||
_clear_cache()
|
||||
with mock.patch.object(netsyms_api, '_reverse_photon', return_value={
|
||||
'name': 'Where you are', 'city': 'Boise', 'county': 'Ada',
|
||||
'state': 'Idaho', 'country': 'United States', 'postal_code': '83701'}), \
|
||||
mock.patch.object(netsyms_api, '_reverse_timezone', return_value='America/Boise'), \
|
||||
mock.patch.object(netsyms_api, '_reverse_landclass', return_value='Boise National Forest'), \
|
||||
mock.patch.object(netsyms_api, '_reverse_elevation', return_value=824):
|
||||
resp = _client().get('/api/reverse/43.6150/-116.2023')
|
||||
assert resp.status_code == 200, resp.status_code
|
||||
data = resp.get_json()
|
||||
assert set(data.keys()) == EXPECTED_KEYS, data.keys()
|
||||
assert data['city'] == 'Boise' and data['timezone'] == 'America/Boise'
|
||||
assert data['landclass'] == 'Boise National Forest' and data['elevation_m'] == 824
|
||||
print(" PASS: happy path — all 9 fields populated, exact key set")
|
||||
|
||||
|
||||
def test_negative_and_integer_coords_parse():
|
||||
# Regression: Flask's <float:> converter would 404 these; manual parse must not.
|
||||
_clear_cache()
|
||||
with mock.patch.object(netsyms_api, '_reverse_photon', return_value={}), \
|
||||
mock.patch.object(netsyms_api, '_reverse_timezone', return_value=None), \
|
||||
mock.patch.object(netsyms_api, '_reverse_landclass', return_value=None), \
|
||||
mock.patch.object(netsyms_api, '_reverse_elevation', return_value=None):
|
||||
for path in ('/api/reverse/43.6/-116.2', '/api/reverse/43/-116'):
|
||||
resp = _client().get(path)
|
||||
assert resp.status_code == 200, f"{path} -> {resp.status_code}"
|
||||
assert set(resp.get_json().keys()) == EXPECTED_KEYS
|
||||
print(" PASS: negative and integer coordinates parse (200, not 404)")
|
||||
|
||||
|
||||
def test_partial_failure_returns_200_with_nulls():
|
||||
_clear_cache()
|
||||
with mock.patch.object(netsyms_api, '_reverse_photon',
|
||||
side_effect=RuntimeError('photon down')), \
|
||||
mock.patch.object(netsyms_api, '_reverse_timezone', return_value='America/Boise'), \
|
||||
mock.patch.object(netsyms_api, '_reverse_landclass',
|
||||
side_effect=RuntimeError('postgis down')), \
|
||||
mock.patch.object(netsyms_api, '_reverse_elevation', return_value=824):
|
||||
resp = _client().get('/api/reverse/43.6150/-116.2023')
|
||||
assert resp.status_code == 200, resp.status_code
|
||||
data = resp.get_json()
|
||||
assert set(data.keys()) == EXPECTED_KEYS
|
||||
assert data['name'] is None and data['city'] is None # photon failed -> nulls
|
||||
assert data['landclass'] is None # landclass failed -> null
|
||||
assert data['timezone'] == 'America/Boise' and data['elevation_m'] == 824
|
||||
print(" PASS: per-component failure -> 200 with nulls, no 5xx")
|
||||
|
||||
|
||||
def test_ocean_point_mostly_null():
|
||||
_clear_cache()
|
||||
with mock.patch.object(netsyms_api, '_reverse_photon', return_value={}), \
|
||||
mock.patch.object(netsyms_api, '_reverse_timezone', return_value='Etc/GMT+2'), \
|
||||
mock.patch.object(netsyms_api, '_reverse_landclass', return_value=None), \
|
||||
mock.patch.object(netsyms_api, '_reverse_elevation', return_value=0):
|
||||
resp = _client().get('/api/reverse/0.0/-30.0')
|
||||
assert resp.status_code == 200, resp.status_code
|
||||
data = resp.get_json()
|
||||
assert set(data.keys()) == EXPECTED_KEYS
|
||||
assert data['city'] is None and data['country'] is None and data['landclass'] is None
|
||||
print(" PASS: ocean point -> 200, mostly null")
|
||||
|
||||
|
||||
def test_invalid_input_400():
|
||||
_clear_cache()
|
||||
client = _client()
|
||||
for path in ('/api/reverse/9999/0', '/api/reverse/0/9999', '/api/reverse/abc/0'):
|
||||
resp = client.get(path)
|
||||
assert resp.status_code == 400, f"{path} -> {resp.status_code}"
|
||||
print(" PASS: out-of-range / unparseable input -> 400")
|
||||
|
||||
|
||||
def test_cache_hit_serves_without_recompute():
|
||||
_clear_cache()
|
||||
with mock.patch.object(netsyms_api, '_reverse_photon',
|
||||
return_value={'name': 'X'}) as m_photon, \
|
||||
mock.patch.object(netsyms_api, '_reverse_timezone', return_value=None), \
|
||||
mock.patch.object(netsyms_api, '_reverse_landclass', return_value=None), \
|
||||
mock.patch.object(netsyms_api, '_reverse_elevation', return_value=None):
|
||||
client = _client()
|
||||
client.get('/api/reverse/12.3456/-65.4321')
|
||||
client.get('/api/reverse/12.3456/-65.4321') # same key (rounded) -> cached
|
||||
assert m_photon.call_count == 1, f"expected 1 compute, got {m_photon.call_count}"
|
||||
print(" PASS: second identical request served from cache (no recompute)")
|
||||
|
||||
|
||||
def test_real_timezone_db():
|
||||
if not os.path.exists(netsyms_api._TZ_DB_PATH):
|
||||
print(" SKIP: real timezone test (timezones.sqlite not present)")
|
||||
return
|
||||
assert netsyms_api._reverse_timezone(43.6150, -116.2023) == 'America/Boise'
|
||||
assert netsyms_api._reverse_timezone(40.7128, -74.0060) == 'America/New_York'
|
||||
print(" PASS: real timezones.sqlite point-in-polygon")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("Running reverse-bundle tests...")
|
||||
test_happy_path()
|
||||
test_negative_and_integer_coords_parse()
|
||||
test_partial_failure_returns_200_with_nulls()
|
||||
test_ocean_point_mostly_null()
|
||||
test_invalid_input_400()
|
||||
test_cache_hit_serves_without_recompute()
|
||||
test_real_timezone_db()
|
||||
print("All tests passed.")
|
||||
154
lib/wiki_index.py
Normal file
154
lib/wiki_index.py
Normal file
|
|
@ -0,0 +1,154 @@
|
|||
"""
|
||||
Wiki location index lookup.
|
||||
|
||||
Provides wiki summaries, URLs, and population data from the wiki_index.db
|
||||
for place detail enrichment. Read-only, opened once at startup.
|
||||
|
||||
DB path: /opt/recon/data/wiki_index.db
|
||||
"""
|
||||
import os
|
||||
import sqlite3
|
||||
|
||||
from .utils import setup_logging
|
||||
|
||||
logger = setup_logging('recon.wiki_index')
|
||||
|
||||
_db_conn = None
|
||||
_zim_books = {}
|
||||
|
||||
|
||||
def _get_db():
|
||||
"""Return a module-level SQLite connection (lazy init, read-only)."""
|
||||
global _db_conn, _zim_books
|
||||
|
||||
if _db_conn is not None:
|
||||
return _db_conn
|
||||
|
||||
db_path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
|
||||
'data', 'wiki_index.db'
|
||||
)
|
||||
|
||||
if not os.path.exists(db_path):
|
||||
logger.warning(f"Wiki index DB not found at {db_path}")
|
||||
return None
|
||||
|
||||
try:
|
||||
# Open read-only with URI
|
||||
_db_conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True, check_same_thread=False)
|
||||
_db_conn.row_factory = sqlite3.Row
|
||||
|
||||
# Load zim_books for URL construction
|
||||
rows = _db_conn.execute("SELECT book_type, public_url FROM zim_books").fetchall()
|
||||
for row in rows:
|
||||
_zim_books[row['book_type']] = row['public_url']
|
||||
|
||||
logger.info(f"Wiki index DB ready at {db_path} ({len(_zim_books)} ZIM books)")
|
||||
return _db_conn
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to open wiki index DB: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def lookup_wiki(place_name, osm_key, osm_value, state, country_code):
|
||||
"""
|
||||
Look up wiki data for a place by name and country, with optional state.
|
||||
|
||||
Args:
|
||||
place_name: Name of the place (e.g., "Twin Falls")
|
||||
osm_key: OSM key (unused, kept for API compatibility)
|
||||
osm_value: OSM value (unused, kept for API compatibility)
|
||||
state: State/province name (may be None or empty)
|
||||
country_code: ISO country code (e.g., "us", "ca")
|
||||
|
||||
Returns:
|
||||
dict with wiki_summary, wiki_url, wikivoyage_url, wiki_population
|
||||
or None if no match found.
|
||||
"""
|
||||
db = _get_db()
|
||||
if db is None:
|
||||
return None
|
||||
|
||||
# Normalize inputs
|
||||
place_name = (place_name or '').strip()
|
||||
state = (state or '').strip() if state else ''
|
||||
country_code = (country_code or '').strip().lower()
|
||||
|
||||
if not place_name or not country_code:
|
||||
return None
|
||||
|
||||
try:
|
||||
row = None
|
||||
|
||||
# Try exact match with state first (if state provided)
|
||||
if state:
|
||||
row = db.execute("""
|
||||
SELECT
|
||||
summary,
|
||||
wikipedia_title,
|
||||
wikivoyage_title,
|
||||
wikipedia_exists,
|
||||
wikivoyage_exists,
|
||||
wiki_population
|
||||
FROM wiki_places
|
||||
WHERE place_name = ?
|
||||
AND state = ?
|
||||
AND country_code = ?
|
||||
AND summary IS NOT NULL
|
||||
ORDER BY importance DESC
|
||||
LIMIT 1
|
||||
""", (place_name, state, country_code)).fetchone()
|
||||
|
||||
# Fall back to name + country only (for places without state in query)
|
||||
if not row:
|
||||
row = db.execute("""
|
||||
SELECT
|
||||
summary,
|
||||
wikipedia_title,
|
||||
wikivoyage_title,
|
||||
wikipedia_exists,
|
||||
wikivoyage_exists,
|
||||
wiki_population
|
||||
FROM wiki_places
|
||||
WHERE place_name = ?
|
||||
AND country_code = ?
|
||||
AND summary IS NOT NULL
|
||||
ORDER BY importance DESC
|
||||
LIMIT 1
|
||||
""", (place_name, country_code)).fetchone()
|
||||
|
||||
if not row:
|
||||
return None
|
||||
|
||||
result = {}
|
||||
|
||||
# Summary
|
||||
if row['summary']:
|
||||
result['wiki_summary'] = row['summary']
|
||||
|
||||
# Wikipedia URL
|
||||
if row['wikipedia_exists'] and row['wikipedia_title'] and 'wikipedia' in _zim_books:
|
||||
base_url = _zim_books['wikipedia']
|
||||
title = row['wikipedia_title'].replace(' ', '_')
|
||||
result['wiki_url'] = f"{base_url}/A/{title}"
|
||||
|
||||
# Wikivoyage URL
|
||||
if row['wikivoyage_exists'] and row['wikivoyage_title'] and 'wikivoyage' in _zim_books:
|
||||
base_url = _zim_books['wikivoyage']
|
||||
title = row['wikivoyage_title'].replace(' ', '_')
|
||||
result['wikivoyage_url'] = f"{base_url}/A/{title}"
|
||||
|
||||
# Population
|
||||
if row['wiki_population']:
|
||||
result['wiki_population'] = row['wiki_population']
|
||||
|
||||
return result if result else None
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Wiki lookup error for {place_name}: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def is_available():
|
||||
"""Check if the wiki index DB is available."""
|
||||
return _get_db() is not None
|
||||
|
|
@ -3,7 +3,6 @@ anyio==4.12.1
|
|||
babel==2.18.0
|
||||
beautifulsoup4==4.14.3
|
||||
blinker==1.9.0
|
||||
cachetools==7.1.3
|
||||
certifi==2026.1.4
|
||||
cffi==2.0.0
|
||||
charset-normalizer==3.4.4
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue