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malice
2ac005ab16Co-authored-by: mj <mj@k7zvx.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1304 lines
58 KiB
Python
1304 lines
58 KiB
Python
"""Hermetic tests for navi-offroute (extraction #8) — service-shape, not routing
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correctness. OffrouteRouter is mocked; MVUM uses a tiny fixture SQLite; admin
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probes (Valhalla/PG/osmium) are mocked. No live PostGIS/Valhalla/osmium/DEM.
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"""
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import sqlite3
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import pytest
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from shapely import wkb
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from shapely.geometry import Point
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import services.navi_offroute.offroute_route as route_mod
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import services.navi_offroute.admin as admin_mod
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from services.navi_offroute.app import create_app
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AUTH = {'X-Authentik-Username': 'matt'}
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@pytest.fixture
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def client():
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return create_app().test_client()
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# ── /api/offroute — mocked router ─────────────────────────────────────────
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class FakeRouter:
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instances = []
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route_result = {'status': 'ok', 'route': {'type': 'FeatureCollection', 'features': []},
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'summary': {'total_distance_km': 1.2, 'total_effort_minutes': 30,
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'barrier_crossings': 0, 'mvum_closed_crossings': 0}}
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raise_on_init = False
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raise_on_route = False
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def __init__(self):
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if FakeRouter.raise_on_init:
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raise RuntimeError('router init boom')
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self.closed = False
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FakeRouter.instances.append(self)
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def route(self, **kwargs):
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if FakeRouter.raise_on_route:
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raise RuntimeError('route boom')
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return FakeRouter.route_result
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def close(self):
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self.closed = True
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@pytest.fixture
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def fake_router(monkeypatch):
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FakeRouter.instances = []
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FakeRouter.raise_on_init = False
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FakeRouter.raise_on_route = False
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FakeRouter.route_result = {'status': 'ok', 'route': {'type': 'FeatureCollection', 'features': []},
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'summary': {'total_distance_km': 1.2, 'total_effort_minutes': 30,
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'barrier_crossings': 0, 'mvum_closed_crossings': 0}}
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monkeypatch.setattr(route_mod, 'OffrouteRouter', FakeRouter)
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return FakeRouter
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def _post(client, body):
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return client.post('/api/offroute', json=body)
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def test_offroute_empty_body_400(client, fake_router):
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# Body parses to a falsy value (JSON null) → the "No JSON body provided" 400
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# branch. (A *malformed* body makes Flask's get_json() raise BadRequest, which
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# the outer except turns into 500 — faithful to recon; not this branch.)
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r = client.post('/api/offroute', data='null', content_type='application/json')
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assert r.status_code == 400 and r.get_json()['message'] == 'No JSON body provided'
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def test_offroute_missing_coords_400(client, fake_router):
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assert _post(client, {'start': [43.6, -116.2]}).status_code == 400
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def test_offroute_bad_start_shape_400(client, fake_router):
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assert _post(client, {'start': [1, 2, 3], 'end': [4, 5]}).status_code == 400
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def test_offroute_bad_mode_400(client, fake_router):
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3], 'mode': 'spaceship'})
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assert r.status_code == 400 and 'mode must be' in r.get_json()['message']
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def test_offroute_bad_boundary_mode_400(client, fake_router):
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3], 'boundary_mode': 'yolo'})
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assert r.status_code == 400 and 'boundary_mode must be' in r.get_json()['message']
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def test_offroute_happy_path_shape(client, fake_router):
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3], 'mode': 'foot',
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'boundary_mode': 'strict'})
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assert r.status_code == 200
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d = r.get_json()
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assert d['status'] == 'ok'
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assert d['route']['type'] == 'FeatureCollection'
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# the summary keys the UI reads (ManeuverList / DirectionsPanel)
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assert {'total_distance_km', 'total_effort_minutes', 'barrier_crossings',
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'mvum_closed_crossings'} <= set(d['summary'])
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assert fake_router.instances[0].closed is True # always closed
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def test_offroute_router_status_error_is_400(client, fake_router):
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fake_router.route_result = {'status': 'error', 'message': 'no route found'}
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3]})
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assert r.status_code == 400 and r.get_json()['message'] == 'no route found'
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def test_offroute_router_init_raises_is_500(client, fake_router):
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fake_router.raise_on_init = True
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3]})
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assert r.status_code == 500 and r.get_json()['status'] == 'error'
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def test_offroute_close_called_even_when_route_raises(client, fake_router):
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fake_router.raise_on_route = True
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r = _post(client, {'start': [43.6, -116.2], 'end': [43.7, -116.3]})
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assert r.status_code == 500 # outer except -> 500
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assert fake_router.instances[0].closed is True # finally still closed it
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# ── /api/mvum — fixture SQLite ─────────────────────────────────────────────
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_ROAD_COLS = ['ogc_fid', 'id', 'name', 'forestname', 'districtname', 'symbol',
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'operationalmaintlevel', 'surfacetype', 'seasonal', 'jurisdiction',
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'passengervehicle', 'passengervehicle_datesopen',
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'highclearancevehicle', 'highclearancevehicle_datesopen',
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'atv', 'atv_datesopen', 'motorcycle', 'motorcycle_datesopen',
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'fourwd_gt50inches', 'fourwd_gt50_datesopen',
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'twowd_gt50inches', 'twowd_gt50_datesopen',
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'e_bike_class1', 'e_bike_class1_dur', 'e_bike_class2', 'e_bike_class2_dur',
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'e_bike_class3', 'e_bike_class3_dur', 'shape']
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_TRAIL_COLS = ['ogc_fid', 'id', 'name', 'forestname', 'districtname', 'symbol',
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'seasonal', 'jurisdiction', 'trailclass', 'trailsystem',
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'passengervehicle', 'passengervehicle_datesopen',
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'highclearancevehicle', 'highclearancevehicle_datesopen',
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'atv', 'atv_datesopen', 'motorcycle', 'motorcycle_datesopen',
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'fourwd_gt50inches', 'fourwd_gt50_datesopen',
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'twowd_gt50inches', 'twowd_gt50_datesopen',
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'e_bike_class1', 'e_bike_class1_dur', 'e_bike_class2', 'e_bike_class2_dur',
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'e_bike_class3', 'e_bike_class3_dur', 'shape']
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def _make_table(conn, table, cols, row):
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conn.execute(f"CREATE TABLE {table} ({', '.join(cols)})")
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if row is not None:
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conn.execute(f"INSERT INTO {table} ({', '.join(cols)}) VALUES ({', '.join(['?'] * len(cols))})",
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[row.get(c) for c in cols])
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conn.commit()
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def _shape_at(lat, lon):
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return wkb.dumps(Point(lon, lat))
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def _mvum_db(tmp_path, monkeypatch, roads=None, trails=None):
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db = tmp_path / 'navi.db'
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conn = sqlite3.connect(db)
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if roads is not None:
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_make_table(conn, 'mvum_roads', _ROAD_COLS, roads)
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if trails is not None:
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_make_table(conn, 'mvum_trails', _TRAIL_COLS, trails)
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conn.close()
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monkeypatch.setenv('NAVI_OFFROUTE_NAVI_DB', str(db))
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return db
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def test_mvum_road_happy_path(client, tmp_path, monkeypatch):
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_mvum_db(tmp_path, monkeypatch, roads={
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'ogc_fid': 1, 'id': 'FR 123', 'name': 'Some Forest Road',
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'forestname': 'Sawtooth National Forest', 'districtname': 'Ketchum RD',
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'surfacetype': 'NAT', 'operationalmaintlevel': '2 - HIGH CLEARANCE VEHICLES',
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'seasonal': 'Seasonal', 'symbol': 2,
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'passengervehicle': 'Open', 'passengervehicle_datesopen': '06/15-10/15',
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'atv': 'Open', 'shape': _shape_at(43.6150, -116.2023)})
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r = client.get('/api/mvum?lat=43.6150&lon=-116.2023&radius=500')
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assert r.status_code == 200
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d = r.get_json()
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assert d['status'] == 'ok'
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f = d['feature']
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assert f['id'] == 'FR 123' and f['forest'] == 'Sawtooth National Forest'
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assert f['maintenance_level'] == 2 # parsed from "2 - HIGH…"
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assert f['access']['passenger_vehicle'] == {'status': 'Open', 'dates': '06/15-10/15'}
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assert set(f['access']) == {'passenger_vehicle', 'high_clearance', 'atv', 'motorcycle',
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'4wd_gt50', '2wd_gt50', 'e_bike_class1', 'e_bike_class2', 'e_bike_class3'}
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def test_mvum_falls_back_to_trails(client, tmp_path, monkeypatch):
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# No mvum_roads table → roads query returns None → trails consulted.
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_mvum_db(tmp_path, monkeypatch, trails={
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'ogc_fid': 1, 'id': 'TR 7', 'name': 'Goat Trail', 'forestname': 'Sawtooth NF',
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'trailclass': '2', 'trailsystem': 'Alpine', 'atv': 'Open',
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'shape': _shape_at(43.6150, -116.2023)})
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f = client.get('/api/mvum?lat=43.6150&lon=-116.2023&radius=500').get_json()['feature']
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assert f['id'] == 'TR 7' and f['trail_system'] == 'Alpine'
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def test_mvum_no_match_returns_null_feature(client, tmp_path, monkeypatch):
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# Road exists but far outside the radius → null feature.
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_mvum_db(tmp_path, monkeypatch, roads={
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'ogc_fid': 1, 'id': 'FR 999', 'name': 'Far Road',
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'shape': _shape_at(0.0, 0.0)})
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d = client.get('/api/mvum?lat=43.6150&lon=-116.2023&radius=50').get_json()
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assert d == {'status': 'ok', 'feature': None}
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def test_mvum_missing_coords_400(client):
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assert client.get('/api/mvum?lat=43.6').status_code == 400
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def test_friction_reader_raises_file_not_found_when_missing(tmp_path):
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"""The FileNotFoundError pre-check (review fix #2) fires before rasterio sees
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the path — consistent with the barriers/trails readers."""
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from services.navi_offroute.friction import FrictionReader
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reader = FrictionReader(tmp_path / 'does-not-exist.vrt')
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with pytest.raises(FileNotFoundError) as exc:
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reader._open()
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assert 'Friction VRT not found' in str(exc.value)
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# ── admin-info — mocked probes ─────────────────────────────────────────────
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def _mock_probes_ok(monkeypatch):
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class _Resp:
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status_code = 200
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monkeypatch.setattr(admin_mod.requests, 'get', lambda *a, **k: _Resp())
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class _Cur:
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def execute(self, *a): pass
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def fetchone(self): return (1,)
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def __enter__(self): return self
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def __exit__(self, *a): return False
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class _Conn:
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def cursor(self): return _Cur()
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def close(self): pass
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monkeypatch.setattr(admin_mod.psycopg2, 'connect', lambda *a, **k: _Conn())
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monkeypatch.setattr(admin_mod.subprocess, 'check_output', lambda *a, **k: 'osmium version 1.16.0\n')
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def test_admin_info_auth_required(client):
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assert client.get('/api/admin/navi-offroute/info').status_code == 401
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def test_admin_info_no_secrets_and_probes(client, monkeypatch):
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_mock_probes_ok(monkeypatch)
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d = client.get('/api/admin/navi-offroute/info', headers=AUTH).get_json()
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assert d['service'] == 'navi-offroute' and d['port'] == 8428
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# No masked secrets anywhere (Phase A §10 — none exist; DSN is peer-auth).
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assert all('...' not in str(e['value']) and e['value'] != '****' for e in d['env'])
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assert all('password' not in e['name'].lower() for e in d['env'])
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names = {dep['name'] for dep in d['dependencies']}
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assert names == {'valhalla', 'padus-postgis', 'osmium-tool'}
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# cheap file probes only (no row_count/size-of-db enrichment)
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fs_names = {f['name'] for f in d['filesystem']}
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assert {'dem', 'osm_pbf', 'navi_db', 'barriers_tif', 'wilderness_tif',
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'trails_tif', 'friction_vrt'} == fs_names
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assert all(set(f) == {'name', 'path', 'exists', 'readable'} for f in d['filesystem'])
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# ── OffrouteRouter._route_auto — eligible-mode-set selection ──────────────
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# Tested in isolation on a bare router (no __init__/readers): OffrouteRouter.route is
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# monkeypatched per-mode; eligibility comes from category hints or a stubbed spatial
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# fallback. Exercises _route_auto directly, not the Flask blueprint.
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from services.navi_offroute.router import OffrouteRouter, AUTO_MODE_PRIORITY
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ALL_MODES = frozenset({"vehicle", "4w", "2w", "foot"})
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def _stub_route(per_mode, calls):
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def stub(self, start_lat, start_lon, end_lat, end_lon, mode="foot", boundary_mode="pragmatic", **kwargs):
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calls.append(mode)
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return dict(per_mode[mode])
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return stub
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def _all_ok():
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return {m: {"status": "ok"} for m in AUTO_MODE_PRIORITY}
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# ── _eligible_modes_from_category ─────────────────────────────────────────
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def test_eligible_modes_exact_match():
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r = object.__new__(OffrouteRouter)
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assert r._eligible_modes_from_category("highway:residential") == ALL_MODES
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assert r._eligible_modes_from_category("highway:track") == frozenset({"4w", "2w", "foot"})
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assert r._eligible_modes_from_category("highway:path") == frozenset({"2w", "foot"})
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assert r._eligible_modes_from_category("highway:footway") == frozenset({"foot"})
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def test_eligible_modes_wildcard_match():
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r = object.__new__(OffrouteRouter)
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# building:house -> building:* -> all modes
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assert r._eligible_modes_from_category("building:house") == ALL_MODES
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assert r._eligible_modes_from_category("amenity:cafe") == ALL_MODES
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# natural:* -> foot only
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assert r._eligible_modes_from_category("natural:peak") == frozenset({"foot"})
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def test_eligible_modes_none_or_unknown():
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r = object.__new__(OffrouteRouter)
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assert r._eligible_modes_from_category(None) is None
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assert r._eligible_modes_from_category("") is None
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assert r._eligible_modes_from_category("bogus:thing") is None
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# ── probe-iteration logic (both endpoints typed -> no spatial calls) ──────
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def _typed_all(monkeypatch):
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monkeypatch.setattr(OffrouteRouter, "_eligible_modes_from_category",
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lambda self, cat: ALL_MODES)
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def test_route_auto_picks_capability_mode(monkeypatch):
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# Classify-once: typed road endpoints -> intersection = all modes -> the first
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# AUTO_MODE_PRIORITY mode (vehicle) is picked and routed ONCE (no 4-mode contest).
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_typed_all(monkeypatch)
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calls = []
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monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
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r = object.__new__(OffrouteRouter)
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out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic")
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assert out["status"] == "ok"
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assert out["selected_mode"] == "vehicle"
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assert out["selected_mode_set"] == sorted(ALL_MODES)
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assert calls == ["vehicle"] # ONE route call, not four
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def test_route_auto_falls_back_to_foot_on_error(monkeypatch):
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# Foot-as-last-resort: the capability-picked mode (vehicle) fails, so Auto retries
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# foot ONCE and ships it, tagging auto_fallback_from for the UI.
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_typed_all(monkeypatch)
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calls = []
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per_mode = {m: {"status": "error", "message": f"{m} failed"} for m in AUTO_MODE_PRIORITY}
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per_mode["foot"] = {"status": "ok"}
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monkeypatch.setattr(OffrouteRouter, "route", _stub_route(per_mode, calls))
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r = object.__new__(OffrouteRouter)
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out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic")
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assert out["status"] == "ok"
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assert out["selected_mode"] == "foot"
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assert out["auto_fallback_from"] == "vehicle"
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assert out["selected_mode_set"] == sorted(ALL_MODES) # original eligibility, not foot-only
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assert calls == ["vehicle", "foot"]
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def test_route_auto_returns_error_when_picked_and_foot_both_fail(monkeypatch):
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# Picked mode AND the foot fallback both fail -> original error surfaces, exactly
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# two attempts (picked, then foot).
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_typed_all(monkeypatch)
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calls = []
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per_mode = {m: {"status": "error", "message": f"{m} failed"} for m in AUTO_MODE_PRIORITY}
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monkeypatch.setattr(OffrouteRouter, "route", _stub_route(per_mode, calls))
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r = object.__new__(OffrouteRouter)
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out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic")
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assert out["status"] == "error"
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assert "selected_mode" not in out
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assert out["selected_mode_set"] == sorted(ALL_MODES)
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assert calls == ["vehicle", "foot"]
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def test_route_auto_no_fallback_when_picked_is_foot(monkeypatch):
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# When the picked mode is already foot (foot-only intersection), there is no second
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# attempt -- foot cannot fall back to itself.
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calls = []
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per_mode = {"foot": {"status": "error", "message": "foot failed"}}
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monkeypatch.setattr(OffrouteRouter, "route", _stub_route(per_mode, calls))
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r = object.__new__(OffrouteRouter)
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out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
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start_category="building:house", end_category="natural:peak") # -> {foot}
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assert out["status"] == "error"
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assert out["selected_mode_set"] == ["foot"]
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assert calls == ["foot"] # no fallback attempt
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def test_route_auto_tagged_road_to_road_no_spatial_probe(monkeypatch):
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# Tagged road endpoints -> pure category classification, the spatial probe must
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# NOT fire, and exactly one route call (mode=vehicle) is made.
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calls = []
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spatial_calls = []
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monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
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monkeypatch.setattr(OffrouteRouter, "_spatial_eligible_modes",
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lambda self, lat, lon, sc: spatial_calls.append((lat, lon)) or ALL_MODES)
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r = object.__new__(OffrouteRouter)
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out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
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start_category="building:house", end_category="highway:residential")
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assert out["selected_mode"] == "vehicle"
|
|
assert calls == ["vehicle"]
|
|
assert spatial_calls == [] # no spatial probe for tagged endpoints
|
|
|
|
|
|
def test_route_auto_untagged_spatial_called_once_per_endpoint(monkeypatch):
|
|
# One untagged endpoint -> _spatial_eligible_modes fires exactly once (for that
|
|
# endpoint only); the tagged endpoint stays a dict lookup.
|
|
calls = []
|
|
spatial_calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
monkeypatch.setattr(OffrouteRouter, "_spatial_eligible_modes",
|
|
lambda self, lat, lon, sc: spatial_calls.append((lat, lon)) or ALL_MODES)
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
|
|
start_category="building:house", end_category=None) # end untagged
|
|
assert spatial_calls == [(42.5, -114.5)] # exactly once, for the untagged end
|
|
assert calls == ["vehicle"]
|
|
|
|
|
|
# ── _route_auto with category type hints (real _eligible_modes_from_category) ──
|
|
|
|
def test_route_auto_address_to_address_picks_vehicle(monkeypatch):
|
|
calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
|
|
start_category="building:house", end_category="highway:residential")
|
|
assert out["selected_mode"] == "vehicle"
|
|
assert calls == ["vehicle"]
|
|
|
|
|
|
def test_route_auto_address_to_trailhead_picks_atv(monkeypatch):
|
|
calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
|
|
start_category="building:house", end_category="highway:trailhead")
|
|
assert out["selected_mode"] == "4w"
|
|
assert calls == ["4w"] # capability pick, single call
|
|
assert out["selected_mode_set"] == sorted({"4w", "2w", "foot"})
|
|
|
|
|
|
def test_route_auto_address_to_peak_picks_foot(monkeypatch):
|
|
calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic",
|
|
start_category="building:house", end_category="natural:peak")
|
|
assert out["selected_mode"] == "foot"
|
|
assert calls == ["foot"]
|
|
assert out["selected_mode_set"] == ["foot"]
|
|
|
|
|
|
def test_route_auto_both_unknown_uses_spatial_fallback(monkeypatch):
|
|
calls = []
|
|
spatial_calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
|
|
def fake_spatial(self, lat, lon, snap_cache):
|
|
spatial_calls.append((lat, lon))
|
|
return ALL_MODES
|
|
|
|
monkeypatch.setattr(OffrouteRouter, "_spatial_eligible_modes", fake_spatial)
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic") # no categories
|
|
assert len(spatial_calls) == 2 # both endpoints resolved spatially
|
|
assert out["selected_mode"] == "vehicle"
|
|
assert calls == ["vehicle"] # one route after classification
|
|
|
|
|
|
# ── _spatial_eligible_modes — Valhalla classification.classification + .use ──
|
|
# _locate_on_network is monkeypatched to inject snap fixtures (road_class + use).
|
|
|
|
def _stub_locate_fixed(snap):
|
|
def stub(self, lat, lon, mode="vehicle"):
|
|
return dict(snap)
|
|
return stub
|
|
|
|
|
|
def test_spatial_service_other_picks_vehicle(monkeypatch):
|
|
# (a) paved grade "service_other" close in -> vehicle eligible (tight tier)
|
|
snap = {"snap_distance_m": 3.0, "road_class": "service_other", "use": "road"}
|
|
monkeypatch.setattr(OffrouteRouter, "_locate_on_network", _stub_locate_fixed(snap))
|
|
r = object.__new__(OffrouteRouter)
|
|
modes = r._spatial_eligible_modes(43.6, -116.2, {})
|
|
assert "vehicle" in modes
|
|
assert modes == frozenset({"vehicle", "4w", "2w", "foot"})
|
|
|
|
|
|
def test_spatial_use_track_picks_atv_mtb_foot(monkeypatch):
|
|
# (b) no road grade, use="track" -> atv/mtb/foot, NOT vehicle
|
|
snap = {"snap_distance_m": 20.0, "road_class": None, "use": "track"}
|
|
monkeypatch.setattr(OffrouteRouter, "_locate_on_network", _stub_locate_fixed(snap))
|
|
r = object.__new__(OffrouteRouter)
|
|
modes = r._spatial_eligible_modes(43.6, -116.2, {})
|
|
assert modes == frozenset({"4w", "2w", "foot"})
|
|
assert "vehicle" not in modes
|
|
|
|
|
|
def test_spatial_use_footway_picks_mtb_foot(monkeypatch):
|
|
# (c) no road grade, use="footway" -> mtb/foot (path, not track -> no atv)
|
|
snap = {"snap_distance_m": 20.0, "road_class": None, "use": "footway"}
|
|
monkeypatch.setattr(OffrouteRouter, "_locate_on_network", _stub_locate_fixed(snap))
|
|
r = object.__new__(OffrouteRouter)
|
|
modes = r._spatial_eligible_modes(43.6, -116.2, {})
|
|
assert modes == frozenset({"2w", "foot"})
|
|
|
|
|
|
def test_spatial_no_class_no_use_picks_foot(monkeypatch):
|
|
# (d) nothing recognized -> foot only
|
|
snap = {"snap_distance_m": 20.0, "road_class": None, "use": None}
|
|
monkeypatch.setattr(OffrouteRouter, "_locate_on_network", _stub_locate_fixed(snap))
|
|
r = object.__new__(OffrouteRouter)
|
|
modes = r._spatial_eligible_modes(43.6, -116.2, {})
|
|
assert modes == frozenset({"foot"})
|
|
|
|
|
|
# ── EntryPointIndex.has_entry_points — EXISTS guard (replaces COUNT(*)) ────
|
|
# Bare index (no __init__/DB); table_exists + _get_conn monkeypatched.
|
|
|
|
from services.navi_offroute.router import EntryPointIndex
|
|
|
|
|
|
class _FakeCur:
|
|
def __init__(self, row):
|
|
self._row = row
|
|
def __enter__(self):
|
|
return self
|
|
def __exit__(self, *a):
|
|
return False
|
|
def execute(self, q, *a):
|
|
self.q = q
|
|
def fetchone(self):
|
|
return self._row
|
|
|
|
|
|
class _FakeConn:
|
|
def __init__(self, row):
|
|
self._row = row
|
|
def cursor(self):
|
|
return _FakeCur(self._row)
|
|
|
|
|
|
def _bare_index(monkeypatch, table_exists, row=None):
|
|
monkeypatch.setattr(EntryPointIndex, "table_exists", lambda self: table_exists)
|
|
monkeypatch.setattr(EntryPointIndex, "_get_conn", lambda self: _FakeConn(row))
|
|
return object.__new__(EntryPointIndex)
|
|
|
|
|
|
def test_has_entry_points_table_missing(monkeypatch):
|
|
idx = _bare_index(monkeypatch, table_exists=False)
|
|
assert idx.has_entry_points() is False
|
|
|
|
|
|
def test_has_entry_points_empty(monkeypatch):
|
|
idx = _bare_index(monkeypatch, table_exists=True, row=(False,))
|
|
assert idx.has_entry_points() is False
|
|
|
|
|
|
def test_has_entry_points_rows(monkeypatch):
|
|
idx = _bare_index(monkeypatch, table_exists=True, row=(True,))
|
|
assert idx.has_entry_points() is True
|
|
|
|
|
|
# ── EntryPointIndex.query_radius — k-NN <-> ordering + radius soft cap ─────
|
|
|
|
class _FakeCurQ:
|
|
def __init__(self, rows, capture):
|
|
self._rows, self._capture = rows, capture
|
|
def __enter__(self):
|
|
return self
|
|
def __exit__(self, *a):
|
|
return False
|
|
def execute(self, q, params=None):
|
|
self._capture["query"] = q
|
|
self._capture["params"] = params
|
|
def fetchall(self):
|
|
return self._rows
|
|
|
|
|
|
class _FakeConnQ:
|
|
def __init__(self, rows, capture):
|
|
self._rows, self._capture = rows, capture
|
|
def cursor(self, cursor_factory=None):
|
|
return _FakeCurQ(self._rows, self._capture)
|
|
|
|
|
|
def test_query_radius_uses_knn_sql(monkeypatch):
|
|
cap = {}
|
|
monkeypatch.setattr(EntryPointIndex, "table_exists", lambda self: True)
|
|
monkeypatch.setattr(EntryPointIndex, "_get_conn", lambda self: _FakeConnQ([], cap))
|
|
idx = object.__new__(EntryPointIndex)
|
|
idx.query_radius(44.1, -115.0, 50, limit=10)
|
|
assert "<->" in cap["query"]
|
|
assert "LIMIT" in cap["query"]
|
|
assert "ST_DWithin" not in cap["query"] # radius scan removed
|
|
|
|
|
|
def test_query_radius_soft_cap_filters_beyond_radius(monkeypatch):
|
|
rows = [
|
|
{"id": 1, "distance_m": 100.0},
|
|
{"id": 2, "distance_m": 50000.0},
|
|
{"id": 3, "distance_m": 200000.0}, # beyond 50km cap -> dropped
|
|
]
|
|
monkeypatch.setattr(EntryPointIndex, "table_exists", lambda self: True)
|
|
monkeypatch.setattr(EntryPointIndex, "_get_conn", lambda self: _FakeConnQ(rows, {}))
|
|
idx = object.__new__(EntryPointIndex)
|
|
out = idx.query_radius(44.1, -115.0, 50, limit=10) # 50 km = 50000 m cap
|
|
assert [r["id"] for r in out] == [1, 2]
|
|
|
|
|
|
# ── Anisotropic A* pathfinder (#17+#18) ───────────────────────────────────
|
|
import numpy as _np
|
|
import services.navi_offroute.router as _router_mod
|
|
from services.navi_offroute.astar import (
|
|
_speed_kmh, astar_multigoal, inflate_cost_multiplier,
|
|
)
|
|
from services.navi_offroute.cost import compute_cost_multiplier_grid
|
|
|
|
_MG = float(_np.tan(_np.radians(40.0)))
|
|
|
|
|
|
def test_signed_tobler_asymmetry():
|
|
# Same magnitude, opposite sign -> downhill faster than uphill; peak near -0.05.
|
|
up = _speed_kmh(0.2, 0, 6.0, _MG)
|
|
down = _speed_kmh(-0.2, 0, 6.0, _MG)
|
|
assert down > up
|
|
peak = _speed_kmh(-0.05, 0, 6.0, _MG)
|
|
assert peak >= _speed_kmh(0.0, 0, 6.0, _MG)
|
|
assert peak >= _speed_kmh(-0.15, 0, 6.0, _MG)
|
|
|
|
|
|
def test_inflation_bumps_neighbors_and_preserves_inf():
|
|
grid = _np.ones((30, 30), dtype=_np.float64)
|
|
grid[15, 15] = 100.0 # high finite cost
|
|
out = inflate_cost_multiplier(grid)
|
|
assert out[15, 16] > 1.0 # neighbor inflated
|
|
assert out[0, 0] < 1.05 # far corner ~baseline
|
|
|
|
grid2 = _np.ones((30, 30), dtype=_np.float64)
|
|
grid2[15, 15] = _np.inf # impassable
|
|
out2 = inflate_cost_multiplier(grid2)
|
|
assert _np.isinf(out2[15, 15]) # inf preserved exactly
|
|
assert _np.isfinite(out2[15, 16]) and out2[15, 16] > 1.0 # neighbor bumped, not inf
|
|
|
|
|
|
def _flat_inputs(n):
|
|
elev = _np.zeros((n, n), dtype=_np.float64)
|
|
mult = _np.ones((n, n), dtype=_np.float64)
|
|
trail = _np.zeros((n, n), dtype=_np.uint8)
|
|
lookup = _np.full(256, _np.inf, dtype=_np.float64)
|
|
barr = _np.zeros((n, n), dtype=_np.uint8)
|
|
return elev, mult, trail, lookup, barr
|
|
|
|
|
|
def test_astar_small_synthetic_shortest_path():
|
|
elev, mult, trail, lookup, barr = _flat_inputs(30)
|
|
gr = _np.array([29], dtype=_np.int64)
|
|
gc = _np.array([29], dtype=_np.int64)
|
|
idx, path, cost = astar_multigoal(
|
|
mult, elev, 30.0, 30.0, _MG, 0, 6.0, trail, lookup, barr, 2, 0, 0, gr, gc)
|
|
assert idx == 0
|
|
assert tuple(path[0]) == (0, 0)
|
|
assert tuple(path[-1]) == (29, 29)
|
|
assert len(path) == 30 # pure diagonal on a flat grid
|
|
assert cost > 0 and _np.isfinite(cost)
|
|
|
|
|
|
def test_astar_multigoal_picks_cheaper():
|
|
elev, mult, trail, lookup, barr = _flat_inputs(30)
|
|
gr = _np.array([0, 20], dtype=_np.int64) # goal0 at (0,5) near; goal1 at (20,20) far
|
|
gc = _np.array([5, 20], dtype=_np.int64)
|
|
idx, path, cost = astar_multigoal(
|
|
mult, elev, 30.0, 30.0, _MG, 0, 6.0, trail, lookup, barr, 2, 0, 0, gr, gc)
|
|
assert idx == 0
|
|
assert tuple(path[-1]) == (0, 5)
|
|
|
|
|
|
def test_compute_cost_multiplier_grid_math():
|
|
elev = _np.zeros((4, 4), dtype=_np.float64)
|
|
friction = _np.full((4, 4), 2.0, dtype=_np.float64)
|
|
# mtb override: grass(30)=2.0, water(80)=inf
|
|
fr = _np.full((4, 4), 30, dtype=_np.uint8)
|
|
fr[0, 0] = 80
|
|
mult = compute_cost_multiplier_grid(
|
|
elev, 30.0, 30.0, friction=friction, friction_raw=fr, wilderness=None, mode="2w")
|
|
assert mult[1, 1] == 4.0 # 2.0 friction * 2.0 grass override
|
|
assert _np.isinf(mult[0, 0]) # water impassable
|
|
|
|
|
|
# ── _pathfind_wilderness mode wiring (mtb profile -> herzog + mtb trail set) ──
|
|
|
|
class _FakeDEM:
|
|
def get_elevation_grid(self, south, north, west, east):
|
|
return _np.zeros((10, 10), dtype=_np.float64), {"cell_size_m": 30.0}
|
|
def latlon_to_pixel(self, lat, lon, meta):
|
|
return (0, 0) if lat == 44.0 else (9, 9)
|
|
def pixel_to_latlon(self, row, col, meta):
|
|
return (44.0 + row * 0.001, -115.0 + col * 0.001)
|
|
|
|
|
|
class _FakeGrid:
|
|
def __init__(self, val, dtype):
|
|
self.val, self.dtype = val, dtype
|
|
def _grid(self, **k):
|
|
return _np.full((10, 10), self.val, dtype=self.dtype)
|
|
|
|
|
|
def test_pathfind_wilderness_always_uses_foot_effort(monkeypatch):
|
|
# Even when called with mode="2w", the wilderness cost is computed as foot:
|
|
# compute_cost_multiplier_grid receives mode="foot", and A* gets the foot speed
|
|
# function (tobler=0), foot base speed (6.0), and foot trail friction.
|
|
captured = {}
|
|
|
|
def fake_mult(elevation, cell_size_lat_m, cell_size_lon_m,
|
|
friction=None, friction_raw=None, wilderness=None, mode="foot"):
|
|
captured["mult_mode"] = mode
|
|
return _np.ones((10, 10), dtype=_np.float64)
|
|
|
|
def fake_astar(cost_mult, elevation, clat, clon, max_grade, sfid, base, trails,
|
|
lookup, barriers, bmid, orow, ocol, grows, gcols):
|
|
captured["speed_function_id"] = sfid
|
|
captured["base_speed"] = base
|
|
captured["lookup"] = lookup
|
|
return 0, _np.array([[0, 0], [9, 9]], dtype=_np.int64), 6.0
|
|
|
|
monkeypatch.setattr(_router_mod, "compute_cost_multiplier_grid", fake_mult)
|
|
monkeypatch.setattr(_router_mod, "astar_multigoal", fake_astar)
|
|
monkeypatch.setattr(OffrouteRouter, "_init_readers", lambda self: None)
|
|
|
|
r = object.__new__(OffrouteRouter)
|
|
r.dem_reader = _FakeDEM()
|
|
r.friction_reader = type("F", (), {"get_friction_grid": lambda self, **k: _np.full((10, 10), 30, dtype=_np.uint8)})()
|
|
r.barrier_reader = type("B", (), {"get_barrier_grid": lambda self, **k: _np.zeros((10, 10), dtype=_np.uint8)})()
|
|
r.trail_reader = type("T", (), {"get_trails_grid": lambda self, **k: _np.zeros((10, 10), dtype=_np.uint8)})()
|
|
r.wilderness_reader = None # foot is not wilderness_impassable -> not loaded anyway
|
|
|
|
ep = [{"lat": 44.001, "lon": -115.001, "highway_class": "track", "name": "t", "land_status": "open"}]
|
|
out = r._pathfind_wilderness(44.0, -115.0, 44.001, -115.001, ep, "pragmatic", "start", mode="2w")
|
|
|
|
assert out["status"] == "ok"
|
|
assert captured["mult_mode"] == "foot" # cost grid built as foot despite mode=2w
|
|
assert captured["speed_function_id"] == 0 # tobler (foot)
|
|
assert captured["base_speed"] == 6.0 # foot base speed
|
|
assert captured["lookup"][5] == 0.1 # foot road
|
|
assert captured["lookup"][15] == 0.3 # foot track
|
|
assert captured["lookup"][25] == 0.5 # foot foot-trail
|
|
|
|
|
|
# ── Smooth max_grade penalty (#19) ────────────────────────────────────────
|
|
from services.navi_offroute.astar import SLOPE_PENALTY_CAP as _CAP # noqa: F401
|
|
|
|
|
|
def test_smooth_max_grade_penalty():
|
|
# 5x5 wall across row 2 with one crossing gap at (2,0) (the cliff cell); diagonal
|
|
# dodges of (2,0) are blocked so the gap can only be crossed via the penalised
|
|
# vertical edges. A second gap at (2,4) is opened only for the "routes around" case.
|
|
mg = float(_np.tan(_np.radians(40.0)))
|
|
|
|
def run(bump, second_gap):
|
|
elev = _np.zeros((5, 5), dtype=_np.float64)
|
|
elev[2, 0] = bump
|
|
mult = _np.ones((5, 5), dtype=_np.float64)
|
|
for c in (1, 2, 3):
|
|
mult[2, c] = _np.inf
|
|
if not second_gap:
|
|
mult[2, 4] = _np.inf # close the detour: (2,0) is the only crossing
|
|
mult[1, 1] = _np.inf # block diagonal dodge into (2,0)
|
|
mult[3, 1] = _np.inf # block diagonal dodge out of (2,0)
|
|
trail = _np.zeros((5, 5), dtype=_np.uint8)
|
|
lookup = _np.full(256, _np.inf, dtype=_np.float64)
|
|
barr = _np.zeros((5, 5), dtype=_np.uint8)
|
|
gr = _np.array([4], dtype=_np.int64)
|
|
gc = _np.array([0], dtype=_np.int64)
|
|
return astar_multigoal(mult, elev, 30.0, 30.0, mg, 0, 6.0,
|
|
trail, lookup, barr, 2, 0, 0, gr, gc)
|
|
|
|
def cells(path):
|
|
return {tuple(int(x) for x in p) for p in path}
|
|
|
|
# Flat control (only gap): crosses (2,0).
|
|
idx0, p0, c0 = run(0.0, second_gap=False)
|
|
assert idx0 == 0 and (2, 0) in cells(p0)
|
|
|
|
# Moderate cliff (grade ~0.87 > max 0.84), no alternative: the smooth penalty lets A*
|
|
# TRAVERSE it (a hard cliff would have returned no path) at a finite, raised cost.
|
|
idxm, pm, cm = run(26.0, second_gap=False)
|
|
assert idxm == 0 and (2, 0) in cells(pm)
|
|
assert _np.isfinite(cm) and cm > c0
|
|
|
|
# Absurd grade (~10) past the cap is still truly impassable: no alternative -> no path.
|
|
idxa, pa, ca = run(300.0, second_gap=False)
|
|
assert idxa == -1
|
|
|
|
# ...but when an alternative exists, A* routes AROUND the impassable cliff cell.
|
|
idxr, pr, cr = run(300.0, second_gap=True)
|
|
assert idxr == 0 and (2, 0) not in cells(pr) and (2, 4) in cells(pr)
|
|
|
|
|
|
def test_signed_grade_at_max_threshold():
|
|
# A cell at EXACTLY max_grade incurs no penalty (the ramp is on the overshoot).
|
|
mg = 0.5
|
|
elev = _np.zeros((2, 1), dtype=_np.float64)
|
|
elev[1, 0] = mg * 30.0 # rise/run = 15/30 = 0.5 == mg exactly
|
|
mult = _np.ones((2, 1), dtype=_np.float64)
|
|
trail = _np.zeros((2, 1), dtype=_np.uint8)
|
|
lookup = _np.full(256, _np.inf, dtype=_np.float64)
|
|
barr = _np.zeros((2, 1), dtype=_np.uint8)
|
|
gr = _np.array([1], dtype=_np.int64)
|
|
gc = _np.array([0], dtype=_np.int64)
|
|
idx, path, cost = astar_multigoal(mult, elev, 30.0, 30.0, mg, 0, 6.0,
|
|
trail, lookup, barr, 2, 0, 0, gr, gc)
|
|
expected = 30.0 * 3.6 / _speed_kmh(mg, 0, 6.0, mg) # penalty = 1.0 at threshold
|
|
assert idx == 0
|
|
assert abs(cost - expected) < 1e-6
|
|
|
|
|
|
# ── Tighter wilderness bbox (#20): 5 entry points + 1.5 km pad ─────────────
|
|
|
|
def test_route_a_slices_entry_points_to_five(monkeypatch):
|
|
# query_radius yields 8; _route_A must hand _pathfind_wilderness at most 5.
|
|
eps = [{"lat": 44.0 + i * 0.001, "lon": -115.0, "highway_class": "track",
|
|
"name": str(i), "land_status": "open"} for i in range(8)]
|
|
captured = {}
|
|
|
|
def fake_pf(self, olat, olon, dlat, dlon, entry_points, boundary_mode, label, mode="foot"):
|
|
captured["n"] = len(entry_points)
|
|
return {"status": "error", "message": "stop"}
|
|
|
|
monkeypatch.setattr(OffrouteRouter, "_pathfind_wilderness", fake_pf)
|
|
r = object.__new__(OffrouteRouter)
|
|
r.entry_index = type("I", (), {
|
|
"has_entry_points": lambda self: True,
|
|
"query_radius": lambda self, *a, **k: list(eps),
|
|
})()
|
|
out = r._route_A_wilderness_to_network(44.0, -115.0, 44.5, -115.5, "foot", "pragmatic")
|
|
assert out["status"] == "error"
|
|
assert captured["n"] == 5
|
|
|
|
|
|
def test_pathfind_wilderness_bbox_pad_is_1_5km(monkeypatch):
|
|
# Capture the bbox passed to get_elevation_grid; with origin == the single entry point,
|
|
# the span is purely the padding -> 0.015 deg (~1.5 km) on each side.
|
|
bounds = {}
|
|
|
|
class _CapDEM:
|
|
def get_elevation_grid(self, south, north, west, east):
|
|
bounds.update(south=south, north=north, west=west, east=east)
|
|
return _np.zeros((10, 10), dtype=_np.float64), {"cell_size_m": 30.0}
|
|
def latlon_to_pixel(self, lat, lon, meta):
|
|
return (0, 0)
|
|
def pixel_to_latlon(self, row, col, meta):
|
|
return (44.0 + row * 0.001, -115.0 + col * 0.001)
|
|
|
|
monkeypatch.setattr(_router_mod, "compute_cost_multiplier_grid",
|
|
lambda *a, **k: _np.ones((10, 10), dtype=_np.float64))
|
|
monkeypatch.setattr(_router_mod, "astar_multigoal",
|
|
lambda *a, **k: (0, _np.array([[0, 0]], dtype=_np.int64), 5.0))
|
|
monkeypatch.setattr(OffrouteRouter, "_init_readers", lambda self: None)
|
|
|
|
r = object.__new__(OffrouteRouter)
|
|
r.dem_reader = _CapDEM()
|
|
r.friction_reader = type("F", (), {"get_friction_grid": lambda self, **k: _np.full((10, 10), 30, dtype=_np.uint8)})()
|
|
r.barrier_reader = type("B", (), {"get_barrier_grid": lambda self, **k: _np.zeros((10, 10), dtype=_np.uint8)})()
|
|
r.trail_reader = type("T", (), {"get_trails_grid": lambda self, **k: _np.zeros((10, 10), dtype=_np.uint8)})()
|
|
r.wilderness_reader = None
|
|
|
|
ep = [{"lat": 44.0, "lon": -115.0, "highway_class": "track", "name": "t", "land_status": "open"}]
|
|
out = r._pathfind_wilderness(44.0, -115.0, 44.0, -115.0, ep, "pragmatic", "start", mode="foot")
|
|
assert out["status"] == "ok"
|
|
assert abs((bounds["north"] - 44.0) - 0.015) < 1e-9
|
|
assert abs((44.0 - bounds["south"]) - 0.015) < 1e-9
|
|
assert abs((bounds["east"] - (-115.0)) - 0.015) < 1e-9
|
|
|
|
|
|
# ── Auto classify-once priority pick (replaces the old min-time contest) ──
|
|
|
|
def test_route_auto_picks_priority_not_min_time(monkeypatch):
|
|
# Classify-once picks the first AUTO_MODE_PRIORITY mode in the intersection
|
|
# (vehicle) and routes ONCE -- it no longer probes all modes to find a faster one.
|
|
# (The old min-time contest, where a slower-priority mode could win, is gone -- a
|
|
# documented PR1 trade-off in auto-rewrite-plan.md.)
|
|
_typed_all(monkeypatch)
|
|
calls = []
|
|
per_mode = {
|
|
"vehicle": {"status": "ok", "summary": {"total_effort_minutes": 200.0}},
|
|
"4w": {"status": "ok", "summary": {"total_effort_minutes": 50.0}},
|
|
"2w": {"status": "ok", "summary": {"total_effort_minutes": 120.0}},
|
|
"foot": {"status": "ok", "summary": {"total_effort_minutes": 800.0}},
|
|
}
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(per_mode, calls))
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic")
|
|
assert out["status"] == "ok"
|
|
assert out["selected_mode"] == "vehicle" # first priority, picked by capability
|
|
assert calls == ["vehicle"] # routed once, no contest
|
|
|
|
|
|
def test_route_auto_per_leg_breakdown():
|
|
# Scenario A (_build_response): foot wilderness leg + network leg -> both > 0.
|
|
r = object.__new__(OffrouteRouter)
|
|
ws = [[-116.20, 43.60], [-116.21, 43.61]]
|
|
ws_stats = {"effort_minutes": 12.0, "distance_km": 1.0, "elevation_gain_m": 10.0,
|
|
"elevation_loss_m": 5.0, "on_trail_pct": 50.0, "barrier_crossings": 0}
|
|
net = {"distance_km": 60.0, "duration_minutes": 45.0, "maneuvers": [],
|
|
"coordinates": [[-116.21, 43.61], [-116.30, 43.70]]}
|
|
out = r._build_response(ws, ws_stats, None, net, None, None, None,
|
|
"vehicle", "pragmatic", None, None, "A", 0.0, None)
|
|
assert out["status"] == "ok"
|
|
summ = out["summary"]
|
|
assert summ["wilderness_minutes"] > 0
|
|
assert summ["network_minutes"] > 0
|
|
# approx adds up to total
|
|
assert abs((summ["wilderness_minutes"] + summ["network_minutes"]) - summ["total_effort_minutes"]) < 1e-6
|
|
|
|
|
|
def test_route_auto_annotates_picked_mode_once(monkeypatch):
|
|
# Classify-once routes the picked mode with annotate_mvum=False, then
|
|
# _annotate_network_segments runs exactly once, on that picked mode (vehicle).
|
|
_typed_all(monkeypatch)
|
|
calls = []
|
|
monkeypatch.setattr(OffrouteRouter, "route", _stub_route(_all_ok(), calls))
|
|
annotated = []
|
|
monkeypatch.setattr(OffrouteRouter, "_annotate_network_segments",
|
|
lambda self, result, mode: annotated.append(mode))
|
|
r = object.__new__(OffrouteRouter)
|
|
out = r._route_auto(42.0, -114.0, 42.5, -114.5, "pragmatic")
|
|
assert out["selected_mode"] == "vehicle"
|
|
assert calls == ["vehicle"]
|
|
assert annotated == ["vehicle"] # annotated once, on the picked mode
|
|
|
|
|
|
# ── Layer 3b: parking as a hybrid transition candidate source ──
|
|
|
|
def _hybrid_ok_leg(distance_km, minutes):
|
|
return {
|
|
"status": "ok",
|
|
"route": {"type": "FeatureCollection", "features": [
|
|
{"type": "Feature",
|
|
"properties": {"segment_type": "network", "network_mode": "x"},
|
|
"geometry": {"type": "LineString",
|
|
"coordinates": [[-114.0, 44.0], [-114.1, 44.0]]}},
|
|
]},
|
|
"summary": {"total_distance_km": distance_km, "total_effort_minutes": minutes,
|
|
"network_distance_km": distance_km, "network_duration_minutes": minutes,
|
|
"wilderness_distance_km": 0.0, "wilderness_effort_minutes": 0.0,
|
|
"scenario": "D"},
|
|
}
|
|
|
|
|
|
def _hybrid_winning_single_mode(distance_km, minutes):
|
|
return {
|
|
"status": "ok",
|
|
"route": {"type": "FeatureCollection", "features": [
|
|
{"type": "Feature", "properties": {"segment_type": "combined"},
|
|
"geometry": {"type": "LineString",
|
|
"coordinates": [[-114.0, 44.0], [-114.5, 44.0]]}},
|
|
]},
|
|
"summary": {"total_distance_km": distance_km, "total_effort_minutes": minutes,
|
|
"scenario": "D"},
|
|
"selected_mode": "vehicle",
|
|
}
|
|
|
|
|
|
class _FakeParking:
|
|
def __init__(self, records):
|
|
self._records = records
|
|
|
|
def query_parking_near_line(self, coords, buffer_m=2000):
|
|
return list(self._records)
|
|
|
|
|
|
def test_hybrid_consumes_parking_candidates(monkeypatch):
|
|
# Only the parking index supplies candidates (no trailhead index, no surface
|
|
# changes); the parking lot must be probed as a leg-1 destination and win.
|
|
parking = {"lat": 44.0, "lon": -114.25, "name": "BLM Trailhead Lot",
|
|
"road_class": "parking", "parking_type": "surface", "access": None}
|
|
monkeypatch.setattr("services.navi_offroute.router.get_surface_change_candidates",
|
|
lambda coords, url: [])
|
|
|
|
seen_dests = []
|
|
|
|
def fake_route(self, s_lat, s_lon, e_lat, e_lon, mode="foot",
|
|
boundary_mode="pragmatic", annotate_mvum=True, **k):
|
|
seen_dests.append((round(e_lat, 4), round(e_lon, 4)))
|
|
if mode == "vehicle":
|
|
return _hybrid_ok_leg(12.0, 20.0)
|
|
return _hybrid_ok_leg(4.0, 30.0)
|
|
monkeypatch.setattr(OffrouteRouter, "route", fake_route)
|
|
|
|
r = object.__new__(OffrouteRouter)
|
|
r.spatial_index = None
|
|
r.trailhead_index = None # no trailheads -> parking must still be gathered
|
|
r.parking_index = _FakeParking([parking])
|
|
|
|
best = _hybrid_winning_single_mode(distance_km=30.0, minutes=120.0)
|
|
out = r._try_hybrid_auto(44.0, -114.0, 44.0, -114.5, "pragmatic",
|
|
best, 120.0, frozenset({"vehicle", "4w", "2w", "foot"}))
|
|
assert out is not None
|
|
assert out["selected_mode"] == "hybrid"
|
|
# the parking lot was probed as a drive-to (leg-1) destination
|
|
assert (round(parking["lat"], 4), round(parking["lon"], 4)) in seen_dests
|
|
trans = next(f for f in out["route"]["features"]
|
|
if f["properties"].get("kind") == "transition")
|
|
assert trans["properties"]["name"] == "BLM Trailhead Lot"
|
|
|
|
|
|
# ── Multi-mode A* kernel (unified-graph Phase 2; spec §2.3 / §10 / §11) ───────
|
|
from services.navi_offroute.astar import astar_multigoal_multimode as _mm
|
|
from services.navi_offroute.cost import MODE_PROFILES as _PROFILES
|
|
|
|
_MODE_ORDER = ["foot", "2w", "4w", "vehicle"] # spec §2.1 fixed index order
|
|
_SFID = {"tobler": 0, "herzog": 1, "linear": 2}
|
|
|
|
|
|
def _mode_param_arrays():
|
|
"""Per-mode 1D param arrays (foot,2w,4w,vehicle order) + trail_friction_stack
|
|
[n_modes,256], built faithfully from MODE_PROFILES."""
|
|
n = len(_MODE_ORDER)
|
|
max_grade = _np.empty(n, dtype=_np.float64)
|
|
sfid = _np.empty(n, dtype=_np.int64)
|
|
base = _np.empty(n, dtype=_np.float64)
|
|
tfs = _np.full((n, 256), _np.inf, dtype=_np.float64)
|
|
for mi, name in enumerate(_MODE_ORDER):
|
|
p = _PROFILES[name]
|
|
max_grade[mi] = float(_np.tan(_np.radians(p.max_slope_deg)))
|
|
sfid[mi] = _SFID[p.speed_function]
|
|
base[mi] = p.base_speed_kmh
|
|
for tv, fr in p.trail_friction.items():
|
|
tfs[mi, tv] = _np.inf if fr is None else float(fr)
|
|
return max_grade, sfid, base, tfs
|
|
|
|
|
|
def _empty_trans():
|
|
z = _np.empty(0, dtype=_np.int64)
|
|
return z, z.copy(), z.copy(), z.copy(), _np.empty(0, dtype=_np.float64)
|
|
|
|
|
|
def test_multimode_foot_only_parity():
|
|
# foot-only, no transitions: the multimode kernel (1-mode stack) must reproduce
|
|
# astar_multigoal exactly -- it is a strict superset.
|
|
n = 8
|
|
elev, mult, trail, lookup, barr = _flat_inputs(n)
|
|
foot_mg = float(_np.tan(_np.radians(_PROFILES["foot"].max_slope_deg)))
|
|
gr = _np.array([n - 1], dtype=_np.int64)
|
|
gc = _np.array([n - 1], dtype=_np.int64)
|
|
idx1, path1, cost1 = astar_multigoal(
|
|
mult, elev, 30.0, 30.0, foot_mg, 0, 6.0, trail, lookup, barr, 2, 0, 0, gr, gc)
|
|
|
|
stack = mult.reshape(n, n, 1).copy()
|
|
tr, tc, tf, tt, tcost = _empty_trans()
|
|
idx2, path2, cost2 = _mm(
|
|
stack, elev, 30.0, 30.0,
|
|
_np.array([foot_mg]), _np.array([0], dtype=_np.int64), _np.array([6.0]),
|
|
trail, lookup.reshape(1, 256).copy(), barr, 2,
|
|
0, 0, _np.array([0], dtype=_np.int64), gr, gc, _np.array([0], dtype=_np.int64),
|
|
tr, tc, tf, tt, tcost)
|
|
|
|
assert idx2 == idx1 == 0
|
|
assert cost2 == pytest.approx(cost1, rel=1e-9, abs=1e-9)
|
|
assert _np.array_equal(path2[:, :2], path1) # same (row,col) sequence
|
|
assert _np.all(path2[:, 2] == 0) # all foot
|
|
|
|
|
|
def test_multimode_parking_switch():
|
|
# Forest corridor (foot-only) -> parking cell -> open field where vehicle is
|
|
# fast and foot is slow. The optimizer must switch foot->vehicle at the parking
|
|
# cell and beat foot-only. The cost advantage is TERRAIN-driven (no trails / no
|
|
# friction<1), which keeps the §10 heuristic admissible -- a road's <1 friction
|
|
# would make effective speed exceed base speed and break the heuristic (a known
|
|
# property of the inherited single-mode kernel too).
|
|
rows, cols, road_start = 3, 50, 25
|
|
elev = _np.zeros((rows, cols), dtype=_np.float64)
|
|
n_modes = 4
|
|
stack = _np.full((rows, cols, n_modes), _np.inf, dtype=_np.float64)
|
|
stack[:, :, 0] = 1.0 # foot: passable everywhere off-trail
|
|
stack[:, road_start:cols, 3] = 1.0 # vehicle: drivable only in the open field
|
|
trail = _np.zeros((rows, cols), dtype=_np.uint8) # no trails anywhere
|
|
max_grade, sfid, base, tfs = _mode_param_arrays()
|
|
barr = _np.zeros((rows, cols), dtype=_np.uint8)
|
|
|
|
gr = _np.array([1], dtype=_np.int64)
|
|
gc = _np.array([cols - 1], dtype=_np.int64)
|
|
pr, pc = 1, road_start # parking cell, foot<->vehicle, 60 s each way
|
|
tr = _np.array([pr, pr], dtype=_np.int64)
|
|
tc = _np.array([pc, pc], dtype=_np.int64)
|
|
tf = _np.array([0, 3], dtype=_np.int64)
|
|
tt = _np.array([3, 0], dtype=_np.int64)
|
|
tcost = _np.array([60.0, 60.0], dtype=_np.float64)
|
|
om = _np.array([0], dtype=_np.int64) # start on foot
|
|
gm = _np.array([3, 0], dtype=_np.int64) # finish vehicle or foot
|
|
|
|
idx, path, cost = _mm(
|
|
stack, elev, 30.0, 30.0, max_grade, sfid, base, trail, tfs, barr, 1,
|
|
1, 0, om, gr, gc, gm, tr, tc, tf, tt, tcost)
|
|
|
|
assert idx == 0
|
|
modes = path[:, 2]
|
|
assert modes[0] == 0 and modes[-1] == 3 # foot start, vehicle finish
|
|
switches = [k for k in range(1, len(path)) if modes[k] != modes[k - 1]]
|
|
assert len(switches) == 1 # exactly one mode change
|
|
sk = switches[0]
|
|
assert modes[sk - 1] == 0 and modes[sk] == 3 # foot -> vehicle
|
|
assert tuple(path[sk, :2]) == (pr, pc) # at the parking cell
|
|
assert tuple(path[sk - 1, :2]) == (pr, pc) # same cell, mode-change edge
|
|
|
|
tr0, tc0, tf0, tt0, tcost0 = _empty_trans()
|
|
_, _, cost_foot_only = _mm(
|
|
stack, elev, 30.0, 30.0, max_grade, sfid, base, trail, tfs, barr, 1,
|
|
1, 0, _np.array([0], dtype=_np.int64), gr, gc, _np.array([0], dtype=_np.int64),
|
|
tr0, tc0, tf0, tt0, tcost0)
|
|
assert cost < cost_foot_only
|
|
|
|
|
|
def test_multimode_no_transitions_independent():
|
|
# No transitions: the 4-mode search degrades to 4 independent single-mode
|
|
# searches -- the winning path never changes mode, and its cost equals the
|
|
# min over the four single-mode runs (vehicle wins on flat passable terrain).
|
|
# Off-trail only (no friction<1) keeps the heuristic admissible.
|
|
n = 12
|
|
elev = _np.zeros((n, n), dtype=_np.float64)
|
|
n_modes = 4
|
|
stack = _np.ones((n, n, n_modes), dtype=_np.float64) # all modes passable off-trail
|
|
trail = _np.zeros((n, n), dtype=_np.uint8) # no trails
|
|
max_grade, sfid, base, tfs = _mode_param_arrays()
|
|
barr = _np.zeros((n, n), dtype=_np.uint8)
|
|
gr = _np.array([n - 1], dtype=_np.int64)
|
|
gc = _np.array([n - 1], dtype=_np.int64)
|
|
all_modes = _np.array([0, 1, 2, 3], dtype=_np.int64)
|
|
tr, tc, tf, tt, tcost = _empty_trans()
|
|
|
|
idx, path, cost = _mm(
|
|
stack, elev, 30.0, 30.0, max_grade, sfid, base, trail, tfs, barr, 2,
|
|
0, 0, all_modes, gr, gc, all_modes, tr, tc, tf, tt, tcost)
|
|
|
|
assert _np.all(path[:, 2] == path[0, 2]) # single mode the whole way
|
|
winning_mode = int(path[0, 2])
|
|
|
|
single_costs = []
|
|
for mi in range(4):
|
|
_, _, c1 = astar_multigoal(
|
|
stack[:, :, mi].copy(), elev, 30.0, 30.0,
|
|
float(max_grade[mi]), int(sfid[mi]), float(base[mi]),
|
|
trail, tfs[mi].copy(), barr, 2, 0, 0, gr, gc)
|
|
single_costs.append(c1)
|
|
assert cost == pytest.approx(min(single_costs), rel=1e-9, abs=1e-9)
|
|
assert winning_mode == int(_np.argmin(single_costs)) # vehicle (fastest base)
|
|
|
|
|
|
def test_multimode_heuristic_admissibility():
|
|
# §10 admissibility: h(r,c,m) must never exceed the true optimal remaining cost.
|
|
# vehicle (global-fastest base) is an allowed goal mode, so max_goal_speed is the
|
|
# global max -> h is a true lower bound. No trails (friction<1 would let a road
|
|
# beat base speed), so effective speed <= base speed everywhere.
|
|
rows, cols = 6, 10
|
|
rng = _np.random.RandomState(0)
|
|
elev = (rng.rand(rows, cols) * 20.0).astype(_np.float64)
|
|
n_modes = 4
|
|
stack = _np.ones((rows, cols, n_modes), dtype=_np.float64)
|
|
stack[2:4, 3:6, 1] = _np.inf # a forest block impassable to wheeled modes
|
|
stack[2:4, 3:6, 2] = _np.inf
|
|
stack[2:4, 3:6, 3] = _np.inf
|
|
trail = _np.zeros((rows, cols), dtype=_np.uint8)
|
|
max_grade, sfid, base, tfs = _mode_param_arrays()
|
|
barr = _np.zeros((rows, cols), dtype=_np.uint8)
|
|
gr = _np.array([rows - 1], dtype=_np.int64)
|
|
gc = _np.array([cols - 1], dtype=_np.int64)
|
|
gm = _np.array([0, 3], dtype=_np.int64) # foot or vehicle finish
|
|
tr = _np.array([0, 0], dtype=_np.int64) # one foot<->vehicle transition
|
|
tc = _np.array([5, 5], dtype=_np.int64)
|
|
tf = _np.array([0, 3], dtype=_np.int64)
|
|
tt = _np.array([3, 0], dtype=_np.int64)
|
|
tcost = _np.array([60.0, 60.0], dtype=_np.float64)
|
|
|
|
max_goal_speed = max(float(base[g]) for g in gm)
|
|
|
|
sampled = 0
|
|
for r in range(0, rows, 2):
|
|
for c in range(0, cols, 3):
|
|
for m in (0, 3):
|
|
d = float(_np.hypot((r - (rows - 1)) * 30.0, (c - (cols - 1)) * 30.0))
|
|
h = d * 3.6 / max_goal_speed
|
|
# Oracle: same kernel with the heuristic disabled (Dijkstra), seeded
|
|
# only from this state -> exact true remaining cost.
|
|
_, _, true_cost = _mm(
|
|
stack, elev, 30.0, 30.0, max_grade, sfid, base, trail, tfs, barr, 2,
|
|
r, c, _np.array([m], dtype=_np.int64), gr, gc, gm,
|
|
tr, tc, tf, tt, tcost, True)
|
|
if not _np.isfinite(true_cost):
|
|
continue
|
|
assert h <= true_cost + 1e-6
|
|
sampled += 1
|
|
assert sampled > 0 # the sweep actually exercised reachable states
|
|
|
|
|
|
# ── PHASE 3 — unified cost layers + transition cells (cost.py + transitions.py) ──
|
|
import os as _os
|
|
import time as _time
|
|
import math as _math
|
|
import numpy as _p3np
|
|
from services.navi_offroute.cost import (
|
|
compute_unified_cost_layers as _cu_layers,
|
|
compute_cost_multiplier_grid as _ccmg,
|
|
)
|
|
from services.navi_offroute.astar import inflate_cost_multiplier as _inflate
|
|
import services.navi_offroute.transitions as _trans
|
|
|
|
|
|
def _p3_meta(rows, cols, cell_m=30.0):
|
|
"""Synthetic DEMReader-shape meta near lat 40 (mirrors shared/dem.py meta keys)."""
|
|
dlat = cell_m / 111000.0
|
|
dlon = cell_m / (111000.0 * _math.cos(_math.radians(40.0)))
|
|
return {
|
|
"bounds": (40.0, 40.0 + rows * dlat, -111.0, -111.0 + cols * dlon),
|
|
"pixel_size_lat": -dlat,
|
|
"pixel_size_lon": dlon,
|
|
"origin_lat": 40.0 + rows * dlat, # top edge (row 0)
|
|
"origin_lon": -111.0,
|
|
"cell_size_m": cell_m,
|
|
"shape": (rows, cols),
|
|
}
|
|
|
|
|
|
def test_unified_cost_layers_per_mode_parity():
|
|
"""cost_mult[mode] == inflate(compute_cost_multiplier_grid(mode)) for each mode."""
|
|
rows, cols = 24, 30
|
|
rng = _p3np.random.default_rng(7)
|
|
elevation = (1000.0 + rng.normal(0, 30, (rows, cols))).astype(_p3np.float64)
|
|
elevation[3, 4] = _p3np.nan # exercise inf handling
|
|
friction = (1.0 + rng.random((rows, cols))).astype(_p3np.float64)
|
|
friction_raw = rng.choice([10, 20, 30, 60], size=(rows, cols)).astype(_p3np.uint8)
|
|
trails = _p3np.zeros((rows, cols), _p3np.uint8); trails[10, :] = 5
|
|
wilderness = _p3np.zeros((rows, cols), _p3np.uint8); wilderness[0:3, 0:3] = 255
|
|
meta = _p3_meta(rows, cols)
|
|
cm = float(meta["cell_size_m"])
|
|
layers = _cu_layers(
|
|
elevation, friction, friction_raw, trails, wilderness, meta,
|
|
modes=("foot", "2w", "4w", "vehicle"), boundary_mode="pragmatic",
|
|
endpoint_line=None)
|
|
assert set(layers["cost_mult"]) == {"foot", "2w", "4w", "vehicle"}
|
|
assert layers["meta"]["boundary_mode"] == "pragmatic"
|
|
for mode in ("foot", "2w", "4w", "vehicle"):
|
|
expected = _inflate(_ccmg(
|
|
elevation, cell_size_lat_m=cm, cell_size_lon_m=cm,
|
|
friction=friction, friction_raw=friction_raw,
|
|
wilderness=wilderness, mode=mode))
|
|
got = layers["cost_mult"][mode]
|
|
assert _p3np.array_equal(_p3np.isinf(got), _p3np.isinf(expected))
|
|
fin = ~_p3np.isinf(expected)
|
|
assert _p3np.allclose(got[fin], expected[fin])
|
|
|
|
|
|
def test_road_terminus_transitions_pure_raster():
|
|
"""A road row ending mid-grid yields foot↔vehicle termini at 60 s, no DB."""
|
|
rows, cols = 10, 10
|
|
trail_grid = _p3np.zeros((rows, cols), _p3np.uint8)
|
|
trail_grid[5, 0:6] = 5 # road cols 0..5; col 6 is off-network
|
|
meta = _p3_meta(rows, cols)
|
|
tuples = _trans.road_terminus_transitions(meta, trail_grid)
|
|
cells = {}
|
|
for (lat, lon, fm, tm, cost_s) in tuples:
|
|
cells.setdefault(_trans._latlon_to_pixel(lat, lon, meta), []).append((fm, tm, cost_s))
|
|
assert set(cells) == {(5, c) for c in range(6)} # all row-5 road cells border off-network
|
|
f, v = _trans.MODE_INDEX["foot"], _trans.MODE_INDEX["vehicle"]
|
|
for edges in cells.values():
|
|
assert sorted(edges) == sorted([(f, v, 60.0), (v, f, 60.0)])
|
|
|
|
|
|
def test_transition_cap_closest_15(monkeypatch):
|
|
""">15 parking lots within 5 km -> only the closest 15 (by perp distance) survive."""
|
|
line = ((40.0, -111.0), (40.0, -110.0)) # ~east-west; lat offset = perp distance, all <5 km
|
|
records = [{"lat": 40.0 + 0.0005 * k, "lon": -110.5, "name": f"P{k}", "access": "yes"}
|
|
for k in range(1, 21)]
|
|
|
|
class _StubParking:
|
|
def query_parking_near_line(self, coords, buffer_m=2000):
|
|
return records
|
|
monkeypatch.setattr(_trans, "load_parking_index", lambda *a, **k: _StubParking())
|
|
raw = _trans.parking_transitions_near_line(line, buffer_m=5000)
|
|
capped = _trans._cap_candidates(raw, line)
|
|
surviving_lats = sorted({round(t[0], 6) for t in capped})
|
|
expected_lats = sorted({round(40.0 + 0.0005 * k, 6) for k in range(1, 16)})
|
|
assert surviving_lats == expected_lats # the closest 15 points
|
|
assert len(capped) == 15 * 6 # 6 directed tuples per lot
|
|
|
|
|
|
def test_compute_unified_cost_layers_perf():
|
|
"""≤1 s to build 4 cost layers + transition cells for a ~50 km bbox (spec §5 gate).
|
|
Requires the real parking/trailhead DBs + a reachable Valhalla; skips otherwise."""
|
|
from services.navi_offroute.mvum_parking import parking_db_path
|
|
from services.navi_offroute.mvum import navi_db_path
|
|
|
|
valhalla_url = _os.environ.get("NAVI_OFFROUTE_VALHALLA_URL", "http://localhost:8002")
|
|
if not (_os.path.exists(parking_db_path()) and _os.path.exists(navi_db_path())):
|
|
pytest.skip("parking/trailhead DBs not present locally — skipping perf gate")
|
|
try:
|
|
import requests
|
|
requests.get(f"{valhalla_url}/status", timeout=1).raise_for_status()
|
|
except Exception as e:
|
|
pytest.skip(f"Valhalla not reachable at {valhalla_url}: {e}")
|
|
|
|
cell_m = 30.0
|
|
n = int(50_000 / cell_m) # ~50 km / 30 m
|
|
elevation = _p3np.full((n, n), 1000.0, dtype=_p3np.float64)
|
|
friction = _p3np.ones((n, n), dtype=_p3np.float64)
|
|
friction_raw = _p3np.full((n, n), 30, dtype=_p3np.uint8)
|
|
trails = _p3np.zeros((n, n), _p3np.uint8); trails[n // 2, :] = 5
|
|
wilderness = _p3np.zeros((n, n), _p3np.uint8)
|
|
meta = _p3_meta(n, n, cell_m)
|
|
south, north, west, east = meta["bounds"]
|
|
t0 = _time.perf_counter()
|
|
layers = _cu_layers(
|
|
elevation, friction, friction_raw, trails, wilderness, meta,
|
|
modes=("foot", "2w", "4w", "vehicle"), boundary_mode="pragmatic",
|
|
endpoint_line=((south, west), (north, east)), valhalla_url=valhalla_url)
|
|
elapsed = _time.perf_counter() - t0
|
|
assert set(layers["cost_mult"]) == {"foot", "2w", "4w", "vehicle"}
|
|
assert elapsed <= 1.0, f"unified cost layers build took {elapsed:.3f}s > 1.0s"
|