navi/backend/scripts/ingest_parking.py
malice f9f2eb9b8f
MVUM Layer 3b: OSM parking as multi-modal Auto transition candidates (#31)
* MVUM Layer 3b: OSM parking as multi-modal Auto transition candidates

Adds OSM parking lots as a third multi-modal-Auto transition source alongside
MVUM trailheads (3a) and surface-change points (3c), so Auto can suggest
"drive to a parking lot, switch to foot/2w/4w" trips where no MVUM trailhead
exists -- BLM/state land, urban edges, anywhere OSM has parking but the USFS
trailhead layer does not. Backend-only; consumes the already-ingested
/mnt/nav/osm-parking.db read-only (no data-pipeline change).

- mvum_parking.py: OSMParkingIndex (process-wide singleton via load_parking_index)
  over a shapely STRtree of parking points, mirroring MVUMSpatialIndex /
  TrailheadIndex. Read-only SQLite. Drops access in (private,no,permit) at load.
  query_parking_near_line(coords, buffer_m=2000) with the same coarse-bbox +
  precise-distance filter as TrailheadIndex. Records carry
  {lat, lon, name, road_class="parking", parking_type, access}.
  Perf note: the ingest already stored representative_point() in lat/lon, so the
  STRtree is built straight from those columns -- parsing the 1.6M WKB blobs at
  boot would add minutes for an identical point.
- router.py: _try_hybrid_auto generalized to gather candidates from each AVAILABLE
  source (trailhead index if present + surface-change always + parking index if
  present) instead of hard-returning when trailhead_index is None, so parking-only
  candidates still work. Combined list keeps the existing closest-first sort +
  HYBRID_MAX_TRAILHEADS cap. Signature unchanged; record shape already compatible.
- app.py / offroute_route.py: load + inject the OSM parking singleton, mirroring
  MVUM_SPATIAL_INDEX / MVUM_TRAILHEAD_INDEX. Failure logs a warning, degrades None.
- admin.py: GET /api/admin/osm-parking/info -> {count, build_time_seconds,
  memory_estimate_mb}, mirroring /api/admin/mvum-spatial/info.
- backend/scripts/ingest_parking.py + README-osm-parking-ingest.md: the
  data-pipeline ingest lifted to the repo with argparse (--geojsonseq/--db, no
  /tmp) + the download/filter/export/ingest/restart refresh recipe.

Tests: test_mvum_parking.py (loads, near-line close-only, private/no/permit
filtered, null-access kept) + test_offroute.py::test_hybrid_consumes_parking_
candidates (parking-only source probed as a leg-1 destination). Full offroute
suite: 82 passed.

Real-DB sanity (not deployed): index loads 1,489,054 usable parking objects
(182,945 access-blocked dropped) in ~12 s using ~950 MB RSS per worker; a Redfish
Lake/Sawtooth corridor query returns 8 lots. The ~950 MB/worker memory cost is
notable -- flagging for review.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* Numpy-pack OSMParkingIndex coords + lazy records to cut RSS (~950->~570 MB/worker)

Store parking coords as packed float64 numpy arrays (_lats/_lons) and the
attribute columns as interned lists (_names/_parking_types/_accesses), and build
candidate record dicts lazily in query_parking_near_line instead of materializing
1.5M dicts + 1.5M shapely Point objects up front. road_class is the constant
"parking" so it is not stored per row.

Measured on the real /mnt/nav/osm-parking.db (1,489,054 usable rows):
RSS/worker ~950 MB -> ~570 MB (~40%), build ~11 s. Across 2 gunicorn workers that
is ~1.9 GB -> ~1.14 GB.

NOTE: this does NOT reach the ~250 MB originally targeted. The remaining cost is
the shapely STRtree itself: it permanently retains the input geometries
(tree.geometries len == row count), so the transient `del points` does not free
them. Attribution on the real DB: columns-only 137 MB, retained Point objects
+230 MB, STRtree index +110 MB. Reaching ~250 MB would require dropping the
shapely STRtree for a coordinate-only structure (e.g. scipy cKDTree over the
lon/lat arrays), which changes the line-buffer query into a per-vertex radius
query -- a behavior change beyond this fix-up's scope. Flagged for a follow-up.

Tests unchanged except one assertion (`len(idx.records) == idx.count`); full
offroute suite 82 passed.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Matt <mj@k7zvx.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-26 14:48:35 -06:00

107 lines
4.1 KiB
Python

#!/usr/bin/env python3
"""Ingest OSM amenity=parking into the OSM parking SQLite DB consumed by
navi-offroute's Layer 3b (mvum_parking.OSMParkingIndex).
Pipeline (see README-osm-parking-ingest.md):
geofabrik <region>-latest.osm.pbf
-> osmium tags-filter nwr/amenity=parking -> <region>-parking.osm.pbf
-> osmium export -f geojsonseq -a type,id -> <region>-parking.geojsonseq
-> THIS SCRIPT -> osm-parking.db
Reads the GeoJSONSeq stream (one RFC-8142 record per line, 0x1e-prefixed),
keeps only features tagged amenity=parking, and writes one row per parking
object: a Point for nodes, a (Multi)Polygon for closed-area ways/relations.
osmium export emits each closed area-way TWICE -- once as the raw LineString and
once as the assembled (Multi)Polygon -- so we drop Line geometries to keep a
single geometry per object (rare genuinely-open parking ways, which are data
errors, are dropped). lat/lon store an interior representative_point used by the
index to build its STRtree without re-parsing the WKB.
"""
import argparse
import json
import sqlite3
from shapely.geometry import shape
from shapely import to_wkb
DEFAULT_DB = "/mnt/nav/osm-parking.db"
def parse_capacity(v):
"""Leading-integer parse of an OSM capacity value (e.g. '120', '12;disabled'); None if non-numeric."""
if v is None:
return None
digits = ""
for ch in str(v):
if ch.isdigit():
digits += ch
else:
break
return int(digits) if digits else None
def main():
ap = argparse.ArgumentParser(description="Ingest amenity=parking GeoJSONSeq into osm-parking.db")
ap.add_argument("--geojsonseq", required=True,
help="input GeoJSONSeq from `osmium export -f geojsonseq -a type,id`")
ap.add_argument("--db", default=DEFAULT_DB,
help=f"output SQLite DB (default {DEFAULT_DB})")
args = ap.parse_args()
con = sqlite3.connect(args.db)
con.execute("PRAGMA journal_mode=OFF")
con.execute("PRAGMA synchronous=OFF")
con.execute("DROP TABLE IF EXISTS parking")
con.execute("""CREATE TABLE parking (
id INTEGER PRIMARY KEY,
osm_id TEXT, osm_type TEXT, name TEXT,
capacity INTEGER, access TEXT, parking_type TEXT,
lat REAL, lon REAL, shape BLOB)""")
INS = ("INSERT INTO parking (osm_id,osm_type,name,capacity,access,parking_type,lat,lon,shape)"
" VALUES (?,?,?,?,?,?,?,?,?)")
batch, n, skipped, bad = [], 0, 0, 0
with open(args.geojsonseq, "rb") as f:
for raw in f:
raw = raw.strip().lstrip(b"\x1e").strip()
if not raw:
continue
try:
feat = json.loads(raw)
except Exception:
bad += 1; continue
props = feat.get("properties") or {}
if props.get("amenity") != "parking":
skipped += 1; continue
gj = feat.get("geometry")
if not gj:
skipped += 1; continue
try:
geom = shape(gj)
if geom.is_empty:
bad += 1; continue
# Drop the duplicate LineString osmium emits for each closed area-way.
if geom.geom_type in ("LineString", "MultiLineString"):
skipped += 1; continue
rep = geom.representative_point()
wkb = to_wkb(geom, output_dimension=2, byte_order=1)
except Exception:
bad += 1; continue
batch.append((str(props.get("@id")), props.get("@type"), props.get("name"),
parse_capacity(props.get("capacity")), props.get("access"),
props.get("parking"), rep.y, rep.x, wkb))
n += 1
if len(batch) >= 5000:
con.executemany(INS, batch); batch = []
if batch:
con.executemany(INS, batch)
con.execute("CREATE INDEX idx_parking_latlon ON parking(lat, lon)")
con.commit()
print(f"inserted={n} skipped_non_parking_or_line={skipped} bad_geom={bad}")
con.close()
if __name__ == "__main__":
main()