mytec
defa3ad440
Major refactoring of RFCP backend: - Modular propagation models (8 models) - SharedMemoryManager for terrain data - ProcessPoolExecutor parallel processing - WebSocket progress streaming - Building filtering pipeline (351k → 15k) - 82 unit tests Performance: Standard preset 38s → 5s (7.6x speedup) Known issue: Detailed preset timeout (fix in 3.1.0)
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""
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Worker functions for parallel coverage calculation.
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These run in separate processes and access shared memory data.
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"""
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from typing import List, Dict, Optional
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from app.parallel.manager import SharedTerrainData, SharedBuildingData
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def process_chunk(
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chunk: List[tuple],
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terrain_cache: dict,
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buildings: list,
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osm_data: dict,
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config: dict,
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) -> List[dict]:
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"""
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Process a chunk of grid points.
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This is the standard worker function used by both Ray and ProcessPoolExecutor.
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It re-uses the existing coverage calculation logic.
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"""
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# Inject terrain cache into the module-level singleton
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from app.services.terrain_service import terrain_service
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terrain_service._tile_cache = terrain_cache
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# Build spatial index
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from app.services.spatial_index import SpatialIndex
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spatial_idx = SpatialIndex()
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if buildings:
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spatial_idx.build(buildings)
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# Process points using existing calculator
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from app.services.coverage_service import CoverageService, SiteParams, CoverageSettings
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site = SiteParams(**config['site_dict'])
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settings = CoverageSettings(**config['settings_dict'])
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svc = CoverageService()
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timing = {
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"los": 0.0, "buildings": 0.0, "antenna": 0.0,
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"dominant_path": 0.0, "street_canyon": 0.0,
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"reflection": 0.0, "vegetation": 0.0,
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}
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precomputed = config.get('precomputed')
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results = []
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for lat, lon, point_elev in chunk:
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pre = precomputed.get((lat, lon)) if precomputed else None
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point = svc._calculate_point_sync(
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site, lat, lon, settings,
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buildings, osm_data.get('streets', []),
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spatial_idx, osm_data.get('water_bodies', []),
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osm_data.get('vegetation_areas', []),
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config['site_elevation'], point_elev, timing,
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precomputed_distance=pre.get('distance') if pre else None,
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precomputed_path_loss=pre.get('path_loss') if pre else None,
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)
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if point.rsrp >= settings.min_signal:
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results.append(point.model_dump())
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return results
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