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feat(offroute): Phase O4 — multi-mode cost functions (foot/mtb/atv/vehicle)
- Add ModeProfile dataclass for data-driven mode configuration - Implement three speed functions: * Tobler off-path hiking (foot) * Herzog wheeled-transport polynomial (mtb/atv) * Linear speed degradation (vehicle) - Add WildernessReader for PAD-US Des_Tp=WA wilderness areas - Mode-specific terrain friction overrides: * Forest impassable for ATV/vehicle, high friction for MTB * Wetland/mangrove impassable for all wheeled modes - Trail access rules: * Foot trails (value 25) impassable for ATV/vehicle - Wilderness blocking for mtb/atv/vehicle modes - Vehicle mode allows flat grassland/cropland traversal - Memory optimization: limit entry points, constrain bbox size - Update router to pass mode and wilderness to cost function - Add vehicle to API mode validation Validated all four modes with test route: - foot: 0.46km off-network, 12.11km network, 89% on trail - mtb: 0.47km off-network, 13.13km network, 90% on trail - atv: 0.47km off-network, 12.81km network, 90% on trail - vehicle: 0.46km off-network, 12.81km network, 89% on trail Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@ -1,43 +1,207 @@
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"""
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Tobler off-path hiking cost function for OFFROUTE.
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Multi-mode travel cost functions for OFFROUTE.
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Computes travel time cost based on terrain slope using Tobler's
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hiking function with off-trail penalty. Optionally applies friction
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multipliers from land cover data, trail corridors, and barrier grids.
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Supports four travel modes: foot, mtb, atv, vehicle.
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Each mode has its own speed function, max slope, trail access rules,
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and terrain friction overrides.
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Mode profiles are data-driven — adding a new mode means adding a profile entry.
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"""
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import math
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import numpy as np
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from typing import Optional, Literal
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from dataclasses import dataclass, field
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from typing import Optional, Literal, Dict, Callable
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# Maximum passable slope in degrees
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MAX_SLOPE_DEG = 40.0
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# ═══════════════════════════════════════════════════════════════════════════════
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# SPEED FUNCTIONS
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# ═══════════════════════════════════════════════════════════════════════════════
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def tobler_off_path_speed(grade: np.ndarray, base_speed: float = 6.0) -> np.ndarray:
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"""
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Tobler off-path hiking function.
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W = 0.6 * base_speed * exp(-3.5 * |S + 0.05|)
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Peak ~3.6 km/h at grade = -0.05 (slight downhill).
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The 0.6 multiplier is the off-trail penalty.
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"""
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return 0.6 * base_speed * np.exp(-3.5 * np.abs(grade + 0.05))
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def herzog_wheeled_speed(grade: np.ndarray, base_speed: float = 12.0) -> np.ndarray:
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"""
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Herzog wheeled-transport polynomial.
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Relative speed factor:
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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)
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Multiply by base_speed to get km/h.
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"""
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S = grade
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S_abs = np.abs(S)
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# Herzog polynomial (returns relative speed factor 0-1)
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denom = (1337.8 * S**6 + 278.19 * S**5 - 517.39 * S**4
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- 78.199 * S**3 + 93.419 * S**2 + 19.825 * S_abs + 1.64)
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# Avoid division by zero and negative speeds
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denom = np.maximum(denom, 0.1)
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rel_speed = 1.0 / denom
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# Clamp relative speed to reasonable bounds (0.05 to 1.5)
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rel_speed = np.clip(rel_speed, 0.05, 1.5)
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return base_speed * rel_speed
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def linear_degrade_speed(grade: np.ndarray, base_speed: float = 40.0, max_grade: float = 0.364) -> np.ndarray:
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"""
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Linear speed degradation with slope.
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speed = base_speed * max(0, 1 - |grade| / max_grade)
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max_grade = tan(20°) ≈ 0.364 for 20° max slope.
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"""
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speed = base_speed * np.maximum(0, 1.0 - np.abs(grade) / max_grade)
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return np.maximum(speed, 0.1) # Minimum crawl speed
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# ═══════════════════════════════════════════════════════════════════════════════
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# MODE PROFILES (Data-driven configuration)
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# ═══════════════════════════════════════════════════════════════════════════════
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@dataclass
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class ModeProfile:
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"""Configuration for a travel mode."""
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name: str
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description: str
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# Speed function parameters
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speed_function: str # "tobler", "herzog", "linear"
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base_speed_kmh: float
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max_slope_deg: float
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# Trail access: trail_value -> friction multiplier (None = impassable)
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# Trail values: 5=road, 15=track, 25=foot trail
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trail_friction: Dict[int, Optional[float]] = field(default_factory=dict)
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# Off-trail terrain friction overrides (by WorldCover class)
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# These MULTIPLY the base WorldCover friction
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# None = use default, np.inf = impassable
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# WorldCover values: 10=tree, 20=shrub, 30=grass, 40=crop, 50=urban,
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# 60=bare, 80=water, 90=wetland, 95=mangrove, 100=moss
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terrain_friction_override: Dict[int, Optional[float]] = field(default_factory=dict)
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# Should wilderness areas be impassable?
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wilderness_impassable: bool = False
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# For vehicle mode: can traverse off-trail flat terrain?
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off_trail_flat_threshold_deg: float = 0.0 # 0 = no off-trail allowed
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off_trail_flat_friction: float = np.inf # friction if allowed
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# Define all mode profiles
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MODE_PROFILES: Dict[str, ModeProfile] = {
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"foot": ModeProfile(
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name="foot",
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description="Hiking on foot (Tobler off-path model)",
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speed_function="tobler",
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base_speed_kmh=6.0,
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max_slope_deg=40.0,
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trail_friction={
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5: 0.1, # road
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15: 0.3, # track
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25: 0.5, # foot trail
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},
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terrain_friction_override={
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# Use default WorldCover friction for foot mode
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},
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wilderness_impassable=False,
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),
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"mtb": ModeProfile(
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name="mtb",
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description="Mountain bike / dirt bike (Herzog wheeled model)",
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speed_function="herzog",
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base_speed_kmh=12.0,
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max_slope_deg=25.0,
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trail_friction={
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5: 0.1, # road
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15: 0.2, # track
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25: 0.5, # foot trail (rideable but slow)
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},
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terrain_friction_override={
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30: 2.0, # Grassland: rideable but slow
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20: 4.0, # Shrubland: barely rideable
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10: 8.0, # Tree cover/forest: effectively impassable
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60: 3.0, # Bare/rocky
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90: np.inf, # Wetland: impassable
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95: np.inf, # Mangrove: impassable
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80: np.inf, # Water: impassable
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},
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wilderness_impassable=True,
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),
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"atv": ModeProfile(
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name="atv",
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description="ATV / side-by-side (Herzog wheeled model, higher base speed)",
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speed_function="herzog",
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base_speed_kmh=25.0,
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max_slope_deg=30.0,
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trail_friction={
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5: 0.1, # road
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15: 0.3, # track
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25: None, # foot trail: impassable (too narrow)
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},
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terrain_friction_override={
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30: 1.5, # Grassland: passable
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20: 3.0, # Shrubland: rough
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10: np.inf, # Forest: impassable
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60: 2.0, # Bare/rocky
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90: np.inf, # Wetland: impassable
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95: np.inf, # Mangrove: impassable
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80: np.inf, # Water: impassable
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},
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wilderness_impassable=True,
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),
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"vehicle": ModeProfile(
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name="vehicle",
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description="4x4 truck / jeep (linear speed degradation)",
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speed_function="linear",
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base_speed_kmh=40.0,
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max_slope_deg=20.0,
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trail_friction={
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5: 0.1, # road
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15: 0.5, # track (rough but passable)
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25: None, # foot trail: impassable
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},
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terrain_friction_override={
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# All off-trail terrain is impassable by default
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10: np.inf, # Forest
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20: np.inf, # Shrubland
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30: np.inf, # Grassland (except flat - see below)
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40: np.inf, # Cropland (except flat - see below)
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60: np.inf, # Bare
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90: np.inf, # Wetland
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95: np.inf, # Mangrove
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80: np.inf, # Water
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},
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wilderness_impassable=True,
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off_trail_flat_threshold_deg=5.0, # Can drive on flat fields
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off_trail_flat_friction=5.0, # But very slow
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),
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}
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# Tobler off-path parameters
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TOBLER_BASE_SPEED = 6.0
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TOBLER_OFF_TRAIL_MULT = 0.6
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# Pragmatic mode friction multiplier for private land
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PRAGMATIC_BARRIER_MULTIPLIER = 5.0
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# Trail value to friction multiplier mapping
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# Trail friction REPLACES land cover friction (a road through forest is still easy)
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TRAIL_FRICTION_MAP = {
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5: 0.1, # road
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15: 0.3, # track
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25: 0.5, # foot trail
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}
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def tobler_speed(grade: float) -> float:
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"""
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Calculate hiking speed using Tobler's off-path function.
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speed_kmh = 0.6 * 6.0 * exp(-3.5 * |grade + 0.05|)
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Peak speed is ~3.6 km/h at grade = -0.05 (slight downhill).
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"""
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return TOBLER_OFF_TRAIL_MULT * TOBLER_BASE_SPEED * math.exp(-3.5 * abs(grade + 0.05))
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# ═══════════════════════════════════════════════════════════════════════════════
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# COST GRID COMPUTATION
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# ═══════════════════════════════════════════════════════════════════════════════
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def compute_cost_grid(
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elevation: np.ndarray,
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@ -45,16 +209,16 @@ def compute_cost_grid(
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cell_size_lat_m: float = None,
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cell_size_lon_m: float = None,
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friction: Optional[np.ndarray] = None,
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friction_raw: Optional[np.ndarray] = None,
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trails: Optional[np.ndarray] = None,
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barriers: Optional[np.ndarray] = None,
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boundary_mode: Literal["strict", "pragmatic", "emergency"] = "pragmatic"
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wilderness: Optional[np.ndarray] = None,
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boundary_mode: Literal["strict", "pragmatic", "emergency"] = "pragmatic",
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mode: Literal["foot", "mtb", "atv", "vehicle"] = "foot"
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) -> np.ndarray:
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"""
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Compute isotropic travel cost grid from elevation data.
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Each cell's cost represents the time (in seconds) to traverse that cell,
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based on the average slope from neighboring cells.
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Args:
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elevation: 2D array of elevation values in meters
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cell_size_m: Average cell size in meters
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@ -63,30 +227,29 @@ def compute_cost_grid(
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friction: Optional 2D array of friction multipliers (WorldCover).
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Values should be float (1.0 = baseline, 2.0 = 2x slower).
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np.inf marks impassable cells.
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If None, no friction is applied (backward compatible).
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friction_raw: Optional 2D array of raw WorldCover class values (uint8).
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Used for mode-specific terrain overrides.
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Values: 10=tree, 20=shrub, 30=grass, etc.
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trails: Optional 2D array of trail values (uint8).
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0 = no trail (use friction)
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5 = road (0.1× friction, replaces WorldCover)
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15 = track (0.3× friction, replaces WorldCover)
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25 = foot trail (0.5× friction, replaces WorldCover)
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Trail friction REPLACES land cover friction where trails exist.
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If None, no trail burn-in is applied.
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0 = no trail, 5 = road, 15 = track, 25 = foot trail
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barriers: Optional 2D array of barrier values (uint8).
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255 = closed/restricted area (from PAD-US Pub_Access = XA).
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0 = accessible.
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If None, no barriers are applied.
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boundary_mode: How to handle private/restricted land barriers:
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"strict" - cells with barrier=255 become impassable (np.inf)
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"pragmatic" - cells with barrier=255 get 5.0x friction penalty
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"emergency" - barriers are ignored entirely
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Default: "pragmatic"
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255 = closed/restricted area (PAD-US Pub_Access = XA).
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wilderness: Optional[np.ndarray] of wilderness values (uint8).
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255 = designated wilderness area.
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boundary_mode: How to handle barriers ("strict", "pragmatic", "emergency")
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mode: Travel mode ("foot", "mtb", "atv", "vehicle")
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Returns:
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2D array of travel cost in seconds per cell.
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np.inf for impassable cells.
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"""
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if boundary_mode not in ("strict", "pragmatic", "emergency"):
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raise ValueError(f"boundary_mode must be 'strict', 'pragmatic', or 'emergency', got '{boundary_mode}'")
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raise ValueError(f"boundary_mode must be 'strict', 'pragmatic', or 'emergency'")
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if mode not in MODE_PROFILES:
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raise ValueError(f"mode must be one of {list(MODE_PROFILES.keys())}")
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profile = MODE_PROFILES[mode]
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if cell_size_lat_m is None:
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cell_size_lat_m = cell_size_m
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@ -95,120 +258,212 @@ def compute_cost_grid(
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rows, cols = elevation.shape
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# Compute gradients in both directions
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dy = np.zeros_like(elevation)
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dx = np.zeros_like(elevation)
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# ─── Compute gradients (in-place where possible) ─────────────────────────
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# Use float32 to reduce memory footprint
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grade = np.zeros(elevation.shape, dtype=np.float32)
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# Central differences for interior, forward/backward at edges
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dy[1:-1, :] = (elevation[:-2, :] - elevation[2:, :]) / (2 * cell_size_lat_m)
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dy[0, :] = (elevation[0, :] - elevation[1, :]) / cell_size_lat_m
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dy[-1, :] = (elevation[-2, :] - elevation[-1, :]) / cell_size_lat_m
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# Compute dy contribution to grade squared
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dy_contrib = np.zeros(elevation.shape, dtype=np.float32)
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dy_contrib[1:-1, :] = ((elevation[:-2, :] - elevation[2:, :]) / (2 * cell_size_lat_m)) ** 2
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dy_contrib[0, :] = ((elevation[0, :] - elevation[1, :]) / cell_size_lat_m) ** 2
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dy_contrib[-1, :] = ((elevation[-2, :] - elevation[-1, :]) / cell_size_lat_m) ** 2
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dx[:, 1:-1] = (elevation[:, 2:] - elevation[:, :-2]) / (2 * cell_size_lon_m)
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dx[:, 0] = (elevation[:, 1] - elevation[:, 0]) / cell_size_lon_m
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dx[:, -1] = (elevation[:, -1] - elevation[:, -2]) / cell_size_lon_m
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# Compute dx contribution and add to dy_contrib in-place
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dy_contrib[:, 1:-1] += ((elevation[:, 2:] - elevation[:, :-2]) / (2 * cell_size_lon_m)) ** 2
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dy_contrib[:, 0] += ((elevation[:, 1] - elevation[:, 0]) / cell_size_lon_m) ** 2
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dy_contrib[:, -1] += ((elevation[:, -1] - elevation[:, -2]) / cell_size_lon_m) ** 2
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# Compute slope magnitude (grade = rise/run)
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grade_magnitude = np.sqrt(dx**2 + dy**2)
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# grade = sqrt(dx^2 + dy^2)
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np.sqrt(dy_contrib, out=grade)
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del dy_contrib # Free memory immediately
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# Convert to slope angle in degrees
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slope_deg = np.degrees(np.arctan(grade_magnitude))
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# ─── Compute speed based on mode ─────────────────────────────────────────
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max_grade_val = np.tan(np.radians(profile.max_slope_deg))
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# Compute speed for each cell using Tobler function
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speed_kmh = TOBLER_OFF_TRAIL_MULT * TOBLER_BASE_SPEED * np.exp(-3.5 * np.abs(grade_magnitude + 0.05))
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if profile.speed_function == "tobler":
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speed_kmh = tobler_off_path_speed(grade, profile.base_speed_kmh)
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elif profile.speed_function == "herzog":
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speed_kmh = herzog_wheeled_speed(grade, profile.base_speed_kmh)
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elif profile.speed_function == "linear":
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speed_kmh = linear_degrade_speed(grade, profile.base_speed_kmh, max_grade_val)
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else:
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raise ValueError(f"Unknown speed function: {profile.speed_function}")
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# Convert speed to time cost (seconds to traverse one cell)
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# ─── Base cost (seconds per cell) ─────────────────────────────────────────
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avg_cell_size = (cell_size_lat_m + cell_size_lon_m) / 2
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cost = avg_cell_size * 3.6 / speed_kmh
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cost = (avg_cell_size * 3.6) / speed_kmh
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del speed_kmh
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# Set impassable cells (slope > MAX_SLOPE_DEG) to infinity
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cost[slope_deg > MAX_SLOPE_DEG] = np.inf
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# ─── Max slope limit ──────────────────────────────────────────────────────
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cost[grade > max_grade_val] = np.inf
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# Handle NaN elevations (no data)
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# ─── NaN elevations ──────────────────────────────────────────────────────
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cost[np.isnan(elevation)] = np.inf
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# Build effective friction array
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# Start with WorldCover friction if provided, else 1.0
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# ─── Apply friction in-place ─────────────────────────────────────────────
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# Instead of creating effective_friction copy, apply directly to cost
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# Start with base friction
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if friction is not None:
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if friction.shape != elevation.shape:
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raise ValueError(
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f"Friction shape {friction.shape} does not match elevation shape {elevation.shape}"
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)
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effective_friction = friction.copy()
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else:
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effective_friction = np.ones(elevation.shape, dtype=np.float32)
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raise ValueError(f"Friction shape mismatch")
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np.multiply(cost, friction, out=cost)
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# Apply trail burn-in: trails REPLACE land cover friction
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# ─── Mode-specific terrain friction overrides (memory-efficient) ─────────
|
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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 {trails.shape} does not match elevation shape {elevation.shape}"
|
||||
)
|
||||
# Replace friction where trails exist
|
||||
for trail_value, trail_friction in TRAIL_FRICTION_MAP.items():
|
||||
trail_mask = trails == trail_value
|
||||
effective_friction[trail_mask] = trail_friction
|
||||
raise ValueError(f"Trails shape mismatch")
|
||||
|
||||
# Apply friction to cost
|
||||
cost = cost * effective_friction
|
||||
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
|
||||
|
||||
# Apply barriers based on boundary_mode
|
||||
# ─── 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 {barriers.shape} does not match elevation shape {elevation.shape}"
|
||||
)
|
||||
|
||||
barrier_mask = barriers == 255
|
||||
raise ValueError(f"Barriers shape mismatch")
|
||||
|
||||
if boundary_mode == "strict":
|
||||
# Mark closed/restricted areas as impassable
|
||||
cost[barrier_mask] = np.inf
|
||||
np.putmask(cost, barriers == 255, np.inf)
|
||||
elif boundary_mode == "pragmatic":
|
||||
# Apply friction penalty to closed/restricted areas
|
||||
cost[barrier_mask] = cost[barrier_mask] * PRAGMATIC_BARRIER_MULTIPLIER
|
||||
barrier_mask = barriers == 255
|
||||
cost[barrier_mask] *= PRAGMATIC_BARRIER_MULTIPLIER
|
||||
del barrier_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("Testing Tobler speed function:")
|
||||
for grade in [-0.3, -0.1, -0.05, 0.0, 0.05, 0.1, 0.3]:
|
||||
speed = tobler_speed(grade)
|
||||
print(f" Grade {grade:+.2f}: {speed:.2f} km/h")
|
||||
print("=" * 70)
|
||||
print("OFFROUTE Multi-Mode Cost Function Tests")
|
||||
print("=" * 70)
|
||||
|
||||
print("\nTesting cost grid computation (no friction, no trails):")
|
||||
elev = np.arange(100).reshape(10, 10).astype(np.float32) * 10
|
||||
cost = compute_cost_grid(elev, cell_size_m=30.0)
|
||||
print(f" Elevation range: {elev.min():.0f} - {elev.max():.0f} m")
|
||||
finite = cost[~np.isinf(cost)]
|
||||
if len(finite) > 0:
|
||||
print(f" Cost range: {finite.min():.1f} - {finite.max():.1f} s")
|
||||
else:
|
||||
print(f" All cells impassable (test data too steep)")
|
||||
print("\n[1] Speed functions at various grades:")
|
||||
print(f"{'Grade':<10} {'Foot':<12} {'MTB':<12} {'ATV':<12} {'Vehicle':<12}")
|
||||
print("-" * 60)
|
||||
|
||||
print("\nTesting cost grid with friction and trails:")
|
||||
elev = np.ones((10, 10), dtype=np.float32) * 1000 # flat terrain
|
||||
friction = np.ones((10, 10), dtype=np.float32) * 2.0 # 2.0x friction (forest)
|
||||
trails = np.zeros((10, 10), dtype=np.uint8)
|
||||
trails[5, :] = 5 # road across middle row
|
||||
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")
|
||||
|
||||
cost_no_trail = compute_cost_grid(elev, cell_size_m=30.0, friction=friction)
|
||||
cost_with_trail = compute_cost_grid(elev, cell_size_m=30.0, friction=friction, trails=trails)
|
||||
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}")
|
||||
|
||||
base_cost = 30 * 3.6 / (0.6 * 6.0 * np.exp(-3.5 * 0.05))
|
||||
print(f" Base cost (flat, 30m cell): {base_cost:.1f} s")
|
||||
print(f" Forest cell (2.0x friction): {cost_no_trail[0, 0]:.1f} s")
|
||||
print(f" Road cell (0.1x friction, replaces forest): {cost_with_trail[5, 0]:.1f} s")
|
||||
print(f" Road friction advantage: {cost_no_trail[0, 0] / cost_with_trail[5, 0]:.1f}x faster")
|
||||
|
||||
print("\nTesting cost grid with barriers (three modes):")
|
||||
print("\n[3] Cost grid test (flat terrain, forest):")
|
||||
elev = np.ones((10, 10), dtype=np.float32) * 1000
|
||||
barriers = np.zeros((10, 10), dtype=np.uint8)
|
||||
barriers[3:7, 3:7] = 255
|
||||
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
|
||||
|
||||
for mode in ["strict", "pragmatic", "emergency"]:
|
||||
cost = compute_cost_grid(elev, cell_size_m=30.0, barriers=barriers, boundary_mode=mode)
|
||||
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))
|
||||
barrier_cost = cost[5, 5] if not np.isinf(cost[5, 5]) else "inf"
|
||||
print(f" {mode:10s}: {impassable} impassable, barrier cell cost = {barrier_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.")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue