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navi-offroute: vectorize road_terminus_transitions (O2b, perf) (#51)
Replace the per-road-cell Python 8-neighbour scan with a single 3x3 binary dilation of the off-network mask (scipy.ndimage), AND'd with the road/track mask. `border_value=0` treats out-of-bounds neighbours as on-network, matching the scalar version's OOB skip -- NOT np.roll, which would wrap the raster edges and fabricate phantom neighbours. A road cell is never off-network itself, so dilating with the centre included is equivalent to the loop's strict-neighbour test; the surviving cell set and the 2 directed foot<->vehicle tuples per cell are identical (only emission order differs; cap/kernel are order-independent). Synthetic eyeball benchmark (1234x470, dense road block, worst case for the loop's early-break): scalar 843 ms -> vector 9.3 ms; set-equal True. Targets the ~3.75s road_terminus stage of Route B's gather_transition_cells. Adds test_road_terminus_dilation_no_wrap (np.roll regression guard); existing test_road_terminus_transitions_pure_raster + gather/cost parity tests unchanged. Co-authored-by: mj <mj@k7zvx.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2 changed files with 32 additions and 22 deletions
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@ -960,6 +960,19 @@ def test_road_terminus_transitions_pure_raster():
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assert sorted(edges) == sorted([(f, v, 60.0), (v, f, 60.0)])
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def test_road_terminus_dilation_no_wrap():
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"""O2b safety net: the 3×3 dilation must NOT wrap the raster edges. The only road cell sits
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at corner (0,0) with all real in-bounds neighbours on-network; the only off-network cell is
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the opposite corner (4,4). With border_value=0 the corner road cell has no off-network
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neighbour -> no termini. np.roll would wrap (4,4) into (0,0)'s neighbourhood and falsely fire."""
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rows, cols = 5, 5
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trail_grid = _p3np.full((rows, cols), 10, _p3np.uint8) # all on-network (trail), non-road
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trail_grid[0, 0] = 5 # the only road cell, at the corner
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trail_grid[4, 4] = 0 # the only off-network cell, opposite corner
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meta = _p3_meta(rows, cols)
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assert _trans.road_terminus_transitions(meta, trail_grid) == []
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def test_transition_cap_closest_15(monkeypatch):
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""">15 parking lots within 5 km -> only the closest 15 (by perp distance) survive."""
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line = ((40.0, -111.0), (40.0, -110.0)) # ~east-west; lat offset = perp distance, all <5 km
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@ -10,6 +10,7 @@ DEMReader.latlon_to_pixel (shared/dem.py).
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import math
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import numpy as np
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import scipy.ndimage as ndi
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from .cost import (
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TRANSITION_COST_PARKING_S,
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@ -143,32 +144,28 @@ def trailhead_transitions_near_line(line, buffer_m=5000):
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def road_terminus_transitions(meta, trail_grid, elevation=None):
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"""Road-terminus mode switches from the trail raster (spec §4): a road(5)/track(15) cell
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with a passable off-network 8-neighbour (value 0; finite elev when `elevation` given).
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foot↔vehicle at TRANSITION_COST_ROAD_TERMINUS_S. Pure raster scan, no DB — fixes §1."""
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rows, cols = trail_grid.shape
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foot↔vehicle at TRANSITION_COST_ROAD_TERMINUS_S. Pure raster scan, no DB — fixes §1.
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O2b: the per-road-cell 8-neighbour Python loop is replaced by one 3×3 binary dilation of
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the off-network mask (scipy.ndimage). `border_value=0` treats out-of-bounds neighbours as
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on-network, matching the scalar version's OOB skip — NOT np.roll, which would wrap the
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raster edges and fabricate phantom neighbours. A road cell is never off-network itself, so
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dilating with the centre included is equivalent to the loop's strict-neighbour test. The
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surviving cell set (and the 2 directed tuples per cell) is identical; only emission order
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differs, and the cap / kernel consume the cells order-independently."""
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pairs = (("foot", "vehicle"),)
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road = (trail_grid == 5) | (trail_grid == 15)
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rs, cs = np.nonzero(road)
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offnet = (trail_grid == 0)
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if elevation is not None:
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offnet &= np.isfinite(elevation)
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offnet_neighbour = ndi.binary_dilation(
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offnet, structure=np.ones((3, 3), dtype=bool), border_value=0)
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terminus = road & offnet_neighbour # road cell with ≥1 off-network 8-neighbour
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rs, cs = np.nonzero(terminus)
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out = []
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for r, c in zip(rs.tolist(), cs.tolist()):
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is_terminus = False
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for dr in (-1, 0, 1):
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for dc in (-1, 0, 1):
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if dr == 0 and dc == 0:
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continue
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nr, nc = r + dr, c + dc
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if nr < 0 or nr >= rows or nc < 0 or nc >= cols:
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continue
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if trail_grid[nr, nc] != 0:
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continue
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if elevation is not None and not np.isfinite(elevation[nr, nc]):
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continue
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is_terminus = True
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break
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if is_terminus:
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break
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if is_terminus:
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lat, lon = _pixel_to_latlon(r, c, meta)
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out.extend(_bidir(lat, lon, pairs, TRANSITION_COST_ROAD_TERMINUS_S))
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lat, lon = _pixel_to_latlon(r, c, meta)
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out.extend(_bidir(lat, lon, pairs, TRANSITION_COST_ROAD_TERMINUS_S))
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return out
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