@mytec: iter1.3 ready for test
This commit is contained in:
104
backend/app/api/routes/coverage.py
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104
backend/app/api/routes/coverage.py
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@@ -0,0 +1,104 @@
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from fastapi import APIRouter, HTTPException, BackgroundTasks
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from typing import List, Optional
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from pydantic import BaseModel
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from app.services.coverage_service import (
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coverage_service,
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CoverageSettings,
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SiteParams,
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CoveragePoint
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)
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router = APIRouter()
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class CoverageRequest(BaseModel):
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"""Request body for coverage calculation"""
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sites: List[SiteParams]
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settings: CoverageSettings = CoverageSettings()
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class CoverageResponse(BaseModel):
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"""Coverage calculation response"""
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points: List[CoveragePoint]
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count: int
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settings: CoverageSettings
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stats: dict
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@router.post("/calculate")
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async def calculate_coverage(request: CoverageRequest) -> CoverageResponse:
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"""
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Calculate RF coverage for one or more sites
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Returns grid of RSRP values with terrain and building effects
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"""
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if not request.sites:
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raise HTTPException(400, "At least one site required")
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if len(request.sites) > 10:
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raise HTTPException(400, "Maximum 10 sites per request")
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# Validate settings
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if request.settings.radius > 50000:
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raise HTTPException(400, "Maximum radius 50km")
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if request.settings.resolution < 50:
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raise HTTPException(400, "Minimum resolution 50m")
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# Calculate
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if len(request.sites) == 1:
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points = await coverage_service.calculate_coverage(
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request.sites[0],
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request.settings
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)
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else:
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points = await coverage_service.calculate_multi_site_coverage(
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request.sites,
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request.settings
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)
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# Calculate stats
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rsrp_values = [p.rsrp for p in points]
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los_count = sum(1 for p in points if p.has_los)
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stats = {
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"min_rsrp": min(rsrp_values) if rsrp_values else 0,
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"max_rsrp": max(rsrp_values) if rsrp_values else 0,
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"avg_rsrp": sum(rsrp_values) / len(rsrp_values) if rsrp_values else 0,
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"los_percentage": (los_count / len(points) * 100) if points else 0,
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"points_with_buildings": sum(1 for p in points if p.building_loss > 0),
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"points_with_terrain_loss": sum(1 for p in points if p.terrain_loss > 0),
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}
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return CoverageResponse(
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points=points,
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count=len(points),
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settings=request.settings,
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stats=stats
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)
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@router.get("/buildings")
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async def get_buildings(
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min_lat: float,
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min_lon: float,
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max_lat: float,
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max_lon: float
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):
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"""
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Get buildings in bounding box (for debugging/visualization)
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"""
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from app.services.buildings_service import buildings_service
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# Limit bbox size
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if (max_lat - min_lat) > 0.1 or (max_lon - min_lon) > 0.1:
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raise HTTPException(400, "Bbox too large (max 0.1 degrees)")
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buildings = await buildings_service.fetch_buildings(
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min_lat, min_lon, max_lat, max_lon
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)
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return {
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"count": len(buildings),
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"buildings": [b.model_dump() for b in buildings]
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}
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@@ -4,7 +4,7 @@ from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from app.core.database import connect_to_mongo, close_mongo_connection
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from app.api.routes import health, projects, terrain
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from app.api.routes import health, projects, terrain, coverage
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@asynccontextmanager
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@@ -17,7 +17,7 @@ async def lifespan(app: FastAPI):
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app = FastAPI(
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title="RFCP Backend API",
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description="RF Coverage Planning Backend",
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version="1.2.0",
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version="1.3.0",
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lifespan=lifespan,
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)
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@@ -34,11 +34,12 @@ app.add_middleware(
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app.include_router(health.router, prefix="/api/health", tags=["health"])
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app.include_router(projects.router, prefix="/api/projects", tags=["projects"])
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app.include_router(terrain.router, prefix="/api/terrain", tags=["terrain"])
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app.include_router(coverage.router, prefix="/api/coverage", tags=["coverage"])
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@app.get("/")
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async def root():
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return {"message": "RFCP Backend API", "version": "1.2.0"}
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return {"message": "RFCP Backend API", "version": "1.3.0"}
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if __name__ == "__main__":
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255
backend/app/services/buildings_service.py
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255
backend/app/services/buildings_service.py
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@@ -0,0 +1,255 @@
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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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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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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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))
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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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331
backend/app/services/coverage_service.py
Normal file
331
backend/app/services/coverage_service.py
Normal file
@@ -0,0 +1,331 @@
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import numpy as np
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import asyncio
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from typing import List, Optional, Tuple
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from pydantic import BaseModel
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from app.services.terrain_service import terrain_service, TerrainService
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from app.services.los_service import los_service
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from app.services.buildings_service import buildings_service, Building
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class CoveragePoint(BaseModel):
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lat: float
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lon: float
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rsrp: float # dBm
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distance: float # meters from site
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has_los: bool
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terrain_loss: float # dB
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building_loss: float # dB
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class CoverageSettings(BaseModel):
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radius: float = 10000 # meters
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resolution: float = 200 # meters
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min_signal: float = -120 # dBm threshold
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use_terrain: bool = True
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use_buildings: bool = True
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class SiteParams(BaseModel):
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lat: float
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lon: float
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height: float = 30 # antenna height meters
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power: float = 43 # dBm (20W)
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gain: float = 15 # dBi
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frequency: float = 1800 # MHz
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azimuth: Optional[float] = None # degrees, None = omni
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beamwidth: Optional[float] = 65 # degrees
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class CoverageService:
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"""
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RF Coverage calculation with terrain and buildings
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"""
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EARTH_RADIUS = 6371000
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def __init__(self):
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self.terrain = terrain_service
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self.buildings = buildings_service
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self.los = los_service
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async def calculate_coverage(
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self,
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site: SiteParams,
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settings: CoverageSettings
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) -> List[CoveragePoint]:
|
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"""
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Calculate coverage grid for a single site
|
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|
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Returns list of CoveragePoint with RSRP values
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"""
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points = []
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# Generate grid
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grid = self._generate_grid(
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site.lat, site.lon,
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settings.radius,
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settings.resolution
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)
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# Fetch buildings for coverage area (if enabled)
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buildings = []
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if settings.use_buildings:
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# Calculate bbox with margin
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lat_delta = settings.radius / 111000 # ~111km per degree
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lon_delta = settings.radius / (111000 * np.cos(np.radians(site.lat)))
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buildings = await self.buildings.fetch_buildings(
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site.lat - lat_delta, site.lon - lon_delta,
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site.lat + lat_delta, site.lon + lon_delta
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)
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# Calculate coverage for each point
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for lat, lon in grid:
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point = await self._calculate_point(
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site, lat, lon,
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settings, buildings
|
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)
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|
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if point.rsrp >= settings.min_signal:
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points.append(point)
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return points
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|
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async def calculate_multi_site_coverage(
|
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self,
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sites: List[SiteParams],
|
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settings: CoverageSettings
|
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) -> List[CoveragePoint]:
|
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"""
|
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Calculate combined coverage from multiple sites
|
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Best server (strongest signal) wins at each point
|
||||
"""
|
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if not sites:
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return []
|
||||
|
||||
# Get all individual coverages
|
||||
all_coverages = await asyncio.gather(*[
|
||||
self.calculate_coverage(site, settings)
|
||||
for site in sites
|
||||
])
|
||||
|
||||
# Combine by best signal
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||||
point_map: dict[Tuple[float, float], CoveragePoint] = {}
|
||||
|
||||
for coverage in all_coverages:
|
||||
for point in coverage:
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key = (round(point.lat, 6), round(point.lon, 6))
|
||||
|
||||
if key not in point_map or point.rsrp > point_map[key].rsrp:
|
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point_map[key] = point
|
||||
|
||||
return list(point_map.values())
|
||||
|
||||
def _generate_grid(
|
||||
self,
|
||||
center_lat: float, center_lon: float,
|
||||
radius: float, resolution: float
|
||||
) -> List[Tuple[float, float]]:
|
||||
"""Generate coverage grid points"""
|
||||
points = []
|
||||
|
||||
# Convert resolution to degrees
|
||||
lat_step = resolution / 111000
|
||||
lon_step = resolution / (111000 * np.cos(np.radians(center_lat)))
|
||||
|
||||
# Calculate grid bounds
|
||||
lat_delta = radius / 111000
|
||||
lon_delta = radius / (111000 * np.cos(np.radians(center_lat)))
|
||||
|
||||
lat = center_lat - lat_delta
|
||||
while lat <= center_lat + lat_delta:
|
||||
lon = center_lon - lon_delta
|
||||
while lon <= center_lon + lon_delta:
|
||||
# Check if within radius (circular, not square)
|
||||
dist = TerrainService.haversine_distance(center_lat, center_lon, lat, lon)
|
||||
if dist <= radius:
|
||||
points.append((lat, lon))
|
||||
lon += lon_step
|
||||
lat += lat_step
|
||||
|
||||
return points
|
||||
|
||||
async def _calculate_point(
|
||||
self,
|
||||
site: SiteParams,
|
||||
lat: float, lon: float,
|
||||
settings: CoverageSettings,
|
||||
buildings: List[Building]
|
||||
) -> CoveragePoint:
|
||||
"""Calculate RSRP at a single point"""
|
||||
|
||||
# Distance
|
||||
distance = TerrainService.haversine_distance(site.lat, site.lon, lat, lon)
|
||||
|
||||
if distance < 1:
|
||||
distance = 1 # Avoid division by zero
|
||||
|
||||
# Base path loss (Okumura-Hata for urban)
|
||||
path_loss = self._okumura_hata(
|
||||
distance, site.frequency, site.height, 1.5 # 1.5m receiver height
|
||||
)
|
||||
|
||||
# Antenna pattern loss (if directional)
|
||||
antenna_loss = 0.0
|
||||
if site.azimuth is not None and site.beamwidth:
|
||||
antenna_loss = self._antenna_pattern_loss(
|
||||
site.lat, site.lon, lat, lon,
|
||||
site.azimuth, site.beamwidth
|
||||
)
|
||||
|
||||
# Terrain loss (LoS check)
|
||||
terrain_loss = 0.0
|
||||
has_los = True
|
||||
|
||||
if settings.use_terrain:
|
||||
los_result = await self.los.check_line_of_sight(
|
||||
site.lat, site.lon, site.height,
|
||||
lat, lon, 1.5 # receiver at 1.5m
|
||||
)
|
||||
has_los = los_result["has_los"]
|
||||
|
||||
if not has_los:
|
||||
# Add diffraction loss based on clearance
|
||||
clearance = los_result["clearance"]
|
||||
terrain_loss = self._diffraction_loss(clearance, site.frequency)
|
||||
|
||||
# Building loss
|
||||
building_loss = 0.0
|
||||
|
||||
if settings.use_buildings and buildings:
|
||||
for building in buildings:
|
||||
intersection = self.buildings.line_intersects_building(
|
||||
site.lat, site.lon, site.height + await self.terrain.get_elevation(site.lat, site.lon),
|
||||
lat, lon, 1.5 + await self.terrain.get_elevation(lat, lon),
|
||||
building
|
||||
)
|
||||
if intersection is not None:
|
||||
# Building penetration loss (~20dB for concrete)
|
||||
building_loss += 20.0
|
||||
has_los = False
|
||||
break # One building is enough
|
||||
|
||||
# Calculate RSRP
|
||||
# RSRP = Tx Power + Tx Gain - Path Loss - Antenna Loss - Terrain Loss - Building Loss
|
||||
rsrp = site.power + site.gain - path_loss - antenna_loss - terrain_loss - building_loss
|
||||
|
||||
return CoveragePoint(
|
||||
lat=lat,
|
||||
lon=lon,
|
||||
rsrp=rsrp,
|
||||
distance=distance,
|
||||
has_los=has_los,
|
||||
terrain_loss=terrain_loss,
|
||||
building_loss=building_loss
|
||||
)
|
||||
|
||||
def _okumura_hata(
|
||||
self,
|
||||
distance: float, # meters
|
||||
frequency: float, # MHz
|
||||
tx_height: float, # meters
|
||||
rx_height: float # meters
|
||||
) -> float:
|
||||
"""
|
||||
Okumura-Hata path loss model (urban)
|
||||
|
||||
Returns path loss in dB
|
||||
"""
|
||||
d_km = distance / 1000
|
||||
|
||||
if d_km < 0.1:
|
||||
d_km = 0.1 # Minimum distance
|
||||
|
||||
# Mobile antenna height correction (urban)
|
||||
a_hm = (1.1 * np.log10(frequency) - 0.7) * rx_height - (1.56 * np.log10(frequency) - 0.8)
|
||||
|
||||
# Path loss
|
||||
L = (69.55 + 26.16 * np.log10(frequency) - 13.82 * np.log10(tx_height) - a_hm +
|
||||
(44.9 - 6.55 * np.log10(tx_height)) * np.log10(d_km))
|
||||
|
||||
return L
|
||||
|
||||
def _antenna_pattern_loss(
|
||||
self,
|
||||
site_lat: float, site_lon: float,
|
||||
point_lat: float, point_lon: float,
|
||||
azimuth: float, beamwidth: float
|
||||
) -> float:
|
||||
"""Calculate antenna pattern attenuation"""
|
||||
# Calculate bearing from site to point
|
||||
bearing = self._calculate_bearing(site_lat, site_lon, point_lat, point_lon)
|
||||
|
||||
# Angle difference from main lobe
|
||||
angle_diff = abs(bearing - azimuth)
|
||||
if angle_diff > 180:
|
||||
angle_diff = 360 - angle_diff
|
||||
|
||||
# Simple cosine pattern approximation
|
||||
# 3dB beamwidth = angle where power drops to half
|
||||
half_beamwidth = beamwidth / 2
|
||||
|
||||
if angle_diff <= half_beamwidth:
|
||||
# Within main lobe - minimal loss
|
||||
loss = 3 * (angle_diff / half_beamwidth) ** 2
|
||||
else:
|
||||
# Outside main lobe - significant loss
|
||||
loss = 3 + 12 * ((angle_diff - half_beamwidth) / half_beamwidth) ** 2
|
||||
loss = min(loss, 25) # Cap at 25dB (back lobe)
|
||||
|
||||
return loss
|
||||
|
||||
def _calculate_bearing(
|
||||
self,
|
||||
lat1: float, lon1: float,
|
||||
lat2: float, lon2: float
|
||||
) -> float:
|
||||
"""Calculate bearing from point 1 to point 2 (degrees)"""
|
||||
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
|
||||
|
||||
dlon = lon2 - lon1
|
||||
|
||||
x = np.sin(dlon) * np.cos(lat2)
|
||||
y = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(dlon)
|
||||
|
||||
bearing = np.degrees(np.arctan2(x, y))
|
||||
|
||||
return (bearing + 360) % 360
|
||||
|
||||
def _diffraction_loss(self, clearance: float, frequency: float) -> float:
|
||||
"""
|
||||
Knife-edge diffraction loss
|
||||
|
||||
Args:
|
||||
clearance: Clearance in meters (negative = obstructed)
|
||||
frequency: Frequency in MHz
|
||||
|
||||
Returns:
|
||||
Additional loss in dB
|
||||
"""
|
||||
if clearance >= 0:
|
||||
return 0.0 # No obstruction
|
||||
|
||||
# Fresnel parameter approximation
|
||||
# v ~ clearance * sqrt(2 / (lambda * d))
|
||||
# Simplified: use clearance directly
|
||||
|
||||
v = abs(clearance) / 10 # Normalize
|
||||
|
||||
# Knife-edge loss approximation
|
||||
if v <= 0:
|
||||
loss = 0
|
||||
elif v < 2.4:
|
||||
loss = 6.02 + 9.11 * v - 1.27 * v**2
|
||||
else:
|
||||
loss = 13.0 + 20 * np.log10(v)
|
||||
|
||||
return min(loss, 40) # Cap at 40dB
|
||||
|
||||
|
||||
# Singleton
|
||||
coverage_service = CoverageService()
|
||||
Reference in New Issue
Block a user