267 lines
8.5 KiB
Python
267 lines
8.5 KiB
Python
import httpx
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import asyncio
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from typing import List, Optional
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from pydantic import BaseModel
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from functools import lru_cache
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import hashlib
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import json
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from pathlib import Path
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class Building(BaseModel):
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"""Single building footprint"""
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id: int
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geometry: List[List[float]] # [[lon, lat], ...]
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height: float # meters
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levels: Optional[int] = None
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building_type: Optional[str] = None
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material: Optional[str] = None # Detected material type
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tags: dict = {} # Store all OSM tags for material detection
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class BuildingsService:
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"""
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OpenStreetMap buildings via Overpass API
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"""
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OVERPASS_URL = "https://overpass-api.de/api/interpreter"
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DEFAULT_LEVEL_HEIGHT = 3.0 # meters per floor
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DEFAULT_BUILDING_HEIGHT = 9.0 # 3 floors if unknown
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def __init__(self, cache_dir: str = "/opt/rfcp/backend/data/buildings"):
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self.cache_dir = Path(cache_dir)
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self.cache_dir.mkdir(exist_ok=True, parents=True)
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self._memory_cache: dict[str, List[Building]] = {}
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self._max_cache_size = 50 # bbox regions
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def _bbox_key(self, min_lat: float, min_lon: float, max_lat: float, max_lon: float) -> str:
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"""Generate cache key for bbox"""
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# Round to 0.01 degree (~1km) grid for cache efficiency
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key = f"{min_lat:.2f},{min_lon:.2f},{max_lat:.2f},{max_lon:.2f}"
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return hashlib.md5(key.encode()).hexdigest()[:12]
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async def fetch_buildings(
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self,
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min_lat: float, min_lon: float,
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max_lat: float, max_lon: float,
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use_cache: bool = True
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) -> List[Building]:
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"""
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Fetch buildings in bounding box from OSM
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Args:
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min_lat, min_lon, max_lat, max_lon: Bounding box
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use_cache: Whether to use cached results
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Returns:
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List of Building objects with height estimates
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"""
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cache_key = self._bbox_key(min_lat, min_lon, max_lat, max_lon)
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# Check memory cache
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if use_cache and cache_key in self._memory_cache:
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return self._memory_cache[cache_key]
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# Check disk cache
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cache_file = self.cache_dir / f"{cache_key}.json"
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if use_cache and cache_file.exists():
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try:
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with open(cache_file, 'r') as f:
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data = json.load(f)
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buildings = [Building(**b) for b in data]
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self._memory_cache[cache_key] = buildings
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return buildings
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except Exception:
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pass # Fetch fresh if cache corrupted
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# Fetch from Overpass API
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query = f"""
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[out:json][timeout:30];
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(
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way["building"]({min_lat},{min_lon},{max_lat},{max_lon});
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relation["building"]({min_lat},{min_lon},{max_lat},{max_lon});
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);
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out body;
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>;
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out skel qt;
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"""
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try:
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(
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self.OVERPASS_URL,
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data={"data": query}
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)
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response.raise_for_status()
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data = response.json()
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except Exception as e:
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print(f"Overpass API error: {e}")
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return []
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# Parse response
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buildings = self._parse_overpass_response(data)
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# Cache results
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if buildings:
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# Disk cache
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with open(cache_file, 'w') as f:
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json.dump([b.model_dump() for b in buildings], f)
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# Memory cache (with size limit)
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if len(self._memory_cache) >= self._max_cache_size:
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oldest = next(iter(self._memory_cache))
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del self._memory_cache[oldest]
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self._memory_cache[cache_key] = buildings
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return buildings
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def _parse_overpass_response(self, data: dict) -> List[Building]:
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"""Parse Overpass JSON response into Building objects"""
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buildings = []
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# Build node lookup
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nodes = {}
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for element in data.get("elements", []):
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if element["type"] == "node":
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nodes[element["id"]] = (element["lon"], element["lat"])
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# Process ways (building footprints)
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for element in data.get("elements", []):
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if element["type"] != "way":
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continue
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tags = element.get("tags", {})
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if "building" not in tags:
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continue
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# Get geometry
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geometry = []
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for node_id in element.get("nodes", []):
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if node_id in nodes:
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geometry.append(list(nodes[node_id]))
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if len(geometry) < 3:
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continue # Invalid polygon
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# Estimate height
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height = self._estimate_height(tags)
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# Detect material from tags
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material_str = None
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if "building:material" in tags:
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material_str = tags["building:material"]
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elif "building:facade:material" in tags:
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material_str = tags["building:facade:material"]
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buildings.append(Building(
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id=element["id"],
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geometry=geometry,
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height=height,
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levels=int(tags.get("building:levels", 0)) or None,
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building_type=tags.get("building"),
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material=material_str,
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tags=tags
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))
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return buildings
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def _estimate_height(self, tags: dict) -> float:
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"""Estimate building height from OSM tags"""
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# Explicit height tag
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if "height" in tags:
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try:
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h = tags["height"]
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# Handle "10 m" or "10m" format
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if isinstance(h, str):
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h = h.replace("m", "").replace(" ", "")
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return float(h)
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except (ValueError, TypeError):
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pass
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# Calculate from levels
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if "building:levels" in tags:
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try:
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levels = int(tags["building:levels"])
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return levels * self.DEFAULT_LEVEL_HEIGHT
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except (ValueError, TypeError):
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pass
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# Default based on building type
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building_type = tags.get("building", "yes")
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type_heights = {
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"house": 6.0,
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"residential": 12.0,
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"apartments": 18.0,
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"commercial": 12.0,
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"industrial": 8.0,
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"warehouse": 6.0,
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"garage": 3.0,
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"shed": 2.5,
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"roof": 3.0,
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"church": 15.0,
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"cathedral": 30.0,
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"hospital": 15.0,
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"school": 12.0,
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"university": 15.0,
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"office": 20.0,
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"retail": 6.0,
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}
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return type_heights.get(building_type, self.DEFAULT_BUILDING_HEIGHT)
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def point_in_building(self, lat: float, lon: float, building: Building) -> bool:
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"""Check if point is inside building footprint (ray casting)"""
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x, y = lon, lat
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polygon = building.geometry
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n = len(polygon)
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inside = False
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j = n - 1
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for i in range(n):
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xi, yi = polygon[i]
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xj, yj = polygon[j]
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if ((yi > y) != (yj > y)) and (x < (xj - xi) * (y - yi) / (yj - yi) + xi):
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inside = not inside
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j = i
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return inside
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def line_intersects_building(
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self,
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lat1: float, lon1: float, height1: float,
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lat2: float, lon2: float, height2: float,
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building: Building
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) -> Optional[float]:
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"""
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Check if line segment intersects building
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Returns:
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Distance along path where intersection occurs, or None
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"""
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# Simplified 2D check + height comparison
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# For accurate 3D intersection, would need proper ray-polygon intersection
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from app.services.terrain_service import TerrainService
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# Sample points along line
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num_samples = 20
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for i in range(num_samples):
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t = i / num_samples
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lat = lat1 + t * (lat2 - lat1)
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lon = lon1 + t * (lon2 - lon1)
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height = height1 + t * (height2 - height1)
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if self.point_in_building(lat, lon, building):
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# Check if signal height is below building
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if height < building.height:
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# Calculate distance
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dist = t * TerrainService.haversine_distance(lat1, lon1, lat2, lon2)
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return dist
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return None
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# Singleton instance
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buildings_service = BuildingsService()
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