egutierrez
dabc945eda
Extrae al registry funciones del proyecto interno footprint_aurgi: - core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb - geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket - geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout - valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n - datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull - datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column - datascience viz (2): plot_kde_2d, plot_heatmap_log - infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest - pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone - types geo (4): LonLat, BBox, IsochroneRequest, Centro Incluye: - apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose) - 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH) - Issue tracker dev/issues/0052-footprint-aurgi-extraction.md - Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi - Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines) Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
"""Isócronas de múltiples puntos en paralelo via Valhalla (async)."""
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from __future__ import annotations
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import asyncio
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import httpx
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async def valhalla_isochrones_async(
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requests: list[dict],
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base_url: str = "http://localhost:8002",
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costing: str = "auto",
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concurrency: int = 6,
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timeout_s: float = 120.0,
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denoise: float = 0.6,
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generalize_m: int = 50,
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) -> list[dict | None]:
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"""Calcula isócronas para múltiples puntos en paralelo usando Valhalla.
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Args:
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requests: Lista de dicts con 'lat' (float), 'lon' (float), 'minutes' (int)
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y opcionalmente 'id' (str). Cada elemento genera una isócrona.
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base_url: URL base del servidor Valhalla.
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costing: Modelo de coste ('auto', 'bicycle', 'pedestrian', etc.).
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concurrency: Número máximo de requests simultáneas.
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timeout_s: Timeout en segundos por request.
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denoise: Factor de suavizado del contorno (0-1).
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generalize_m: Tolerancia de generalización en metros.
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Returns:
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Lista paralela a 'requests' con GeoJSON dict o None por cada punto.
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Preserva el orden de entrada.
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"""
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url = f"{base_url.rstrip('/')}/isochrone"
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sem = asyncio.Semaphore(concurrency)
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timeout = httpx.Timeout(timeout_s)
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async def _fetch_one(
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client: httpx.AsyncClient,
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req: dict,
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) -> dict | None:
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payload = {
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"locations": [{"lat": float(req["lat"]), "lon": float(req["lon"])}],
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"costing": costing,
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"contours": [{"time": int(req["minutes"])}],
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"polygons": True,
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"denoise": denoise,
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"generalize": generalize_m,
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"format": "geojson",
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}
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try:
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async with sem:
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r = await client.post(url, json=payload)
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r.raise_for_status()
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return r.json()
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except Exception:
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return None
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async with httpx.AsyncClient(timeout=timeout) as client:
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tasks = [_fetch_one(client, req) for req in requests]
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return list(await asyncio.gather(*tasks))
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