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/datascience/fuzzy_merge_adaptive.md

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+---
+name: fuzzy_merge_adaptive
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def fuzzy_merge_adaptive(left: list[dict], right: list[dict], left_key: str, right_key: str, thresholds: list[int] | None = None, how: str = 'left') -> list[dict]"
+description: "Fuzzy join adaptativo entre dos listas de dicts usando rapidfuzz.token_sort_ratio. Prueba thresholds de mayor a menor y asigna el mayor cumplido. Soporta how='left' (todos los de left) e how='inner' (solo con match). Campos colisionantes reciben sufijos _left/_right."
+tags: [fuzzy, matching, join, merge, rapidfuzz, string-similarity, datascience]
+params:
+  - name: left
+    desc: Lista de dicts (lado izquierdo del join).
+  - name: right
+    desc: Lista de dicts (lado derecho del join).
+  - name: left_key
+    desc: Clave en los dicts de left usada para matching de strings.
+  - name: right_key
+    desc: Clave en los dicts de right usada para matching de strings.
+  - name: thresholds
+    desc: Lista de thresholds enteros a probar en orden descendente. Default [90,80,70,60,50].
+  - name: how
+    desc: "'left' incluye todos los items de left; 'inner' solo los que tienen match."
+output: "Lista de dicts mergeados con campos de left + right (sufijos _left/_right si colisionan) + fuzzy_match (str|None), match_score (int), threshold_used (int|None)."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: ["rapidfuzz"]
+tested: true
+tests:
+  - "left join con typo"
+  - "inner join excluye sin match"
+  - "left join sin match devuelve none"
+  - "threshold adaptativo"
+  - "colision de claves usa sufijos"
+test_file_path: "python/functions/datascience/tests/test_fuzzy_merge_adaptive.py"
+file_path: "python/functions/datascience/fuzzy_merge_adaptive.py"
+source_repo: "internal:footprint_aurgi"
+source_license: "internal-aurgi"
+source_file: "fuzzy_joins/fuzzy_en_batches.py"
+---
+
+## Ejemplo
+
+```python
+from fuzzy_merge_adaptive import fuzzy_merge_adaptive
+
+left = [{"name": "Madrid"}, {"name": "Barclona"}]
+right = [{"name": "Madrid", "cp": "28"}, {"name": "Barcelona", "cp": "08"}]
+result = fuzzy_merge_adaptive(left, right, left_key="name", right_key="name")
+# result[1]["fuzzy_match"] == "Barcelona", result[1]["match_score"] >= 80
+```
+
+## Notas
+
+Migrado de thefuzz a rapidfuzz (API compatible, mayor velocidad). Sin pandas: el merge se implementa manualmente via dict lookup por right_key. Los thresholds se prueban de mayor a menor; el primero cumplido se asigna a threshold_used.