feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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>

python/functions/geo/haversine_km.md

@@ -0,0 +1,47 @@
+---
+id: haversine_km_py_geo
+name: haversine_km
+kind: function
+lang: py
+domain: geo
+version: "1.0.0"
+purity: pure
+signature: "haversine_km(lon1: float, lat1: float, lon2: float, lat2: float) -> float"
+description: "Calcula la distancia en kilometros entre dos puntos lon/lat usando la formula de Haversine con R=6371.0."
+tags: [geo, distance, haversine, coordinates]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: ["math"]
+example: |
+  from geo.haversine_km import haversine_km
+  d = haversine_km(-3.7038, 40.4168, 2.1686, 41.3874)  # Madrid -> Barcelona ~504 km
+tested: true
+tests: ["madrid_barcelona_aproximado", "misma_coordenada_es_cero"]
+test_file_path: "python/functions/geo/tests/test_haversine_km.py"
+file_path: "python/functions/geo/haversine_km.py"
+params:
+  - {name: lon1, desc: "longitud del primer punto en grados decimales"}
+  - {name: lat1, desc: "latitud del primer punto en grados decimales"}
+  - {name: lon2, desc: "longitud del segundo punto en grados decimales"}
+  - {name: lat2, desc: "latitud del segundo punto en grados decimales"}
+output: "distancia en kilometros entre los dos puntos"
+source_repo: "internal:footprint_aurgi"
+source_license: "internal-aurgi"
+source_file: "zonas_mapas_aurgi/backend/app.py:668"
+---
+
+## Ejemplo
+
+```python
+from geo.haversine_km import haversine_km
+
+d = haversine_km(-3.7038, 40.4168, 2.1686, 41.3874)
+# d ≈ 504.0 km
+```
+
+## Notas
+
+Funcion pura. Usa R=6371.0 km (radio medio de la Tierra). No maneja NaN ni Inf.