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/rebel_load_model.md

@@ -0,0 +1,65 @@
+---
+name: rebel_load_model
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def rebel_load_model(model_name: str = 'Babelscape/rebel-large') -> tuple[Any, Any]"
+description: "Carga (y cachea) el tokenizer y modelo REBEL (BART-based, ~1.5 GB). Solo ingles. Licencia Apache 2.0 — uso comercial permitido. Cache por model_name."
+tags: [rebel, relation-extraction, nlp, model, huggingface, english, seq2seq, apache2, datascience, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [transformers]
+params:
+  - name: model_name
+    desc: "ID del modelo en HuggingFace Hub (defecto: Babelscape/rebel-large, BART ~1.5 GB, solo EN)"
+output: "tupla (tokenizer, model) listos para inferencia, cacheados por model_name."
+tested: false
+tests: []
+test_file_path: ""
+file_path: "python/functions/datascience/rebel_load_model.py"
+notes: |
+  LICENCIA: Apache 2.0 — uso comercial permitido (a diferencia de mREBEL que es CC BY-NC-SA).
+  Solo funciona bien con texto en INGLES. Para espanol usar mrebel_load_model.
+  
+  REBEL usa el mismo wire format que mREBEL, por lo que parse_rebel_output es compatible.
+  Diferencia vs mREBEL: no emite el prefijo tp_XX de idioma en el output (parse_rebel_output
+  lo maneja porque ya hace .replace('tp_XX', '')).
+  
+  impure: descarga red/disco la primera vez, mantiene estado en _MODEL_CACHE.
+  Cache separada de mrebel_load_model (modulo distinto).
+---
+
+## Ejemplo
+
+```python
+from python.functions.datascience.rebel_load_model import rebel_load_model
+from python.functions.datascience.parse_rebel_output import parse_rebel_output
+
+tokenizer, model = rebel_load_model()
+
+text = "Pablo Isla is the CEO of Inditex, based in Arteixo."
+inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
+generated = model.generate(**inputs, num_beams=4, length_penalty=1.0, max_length=256)
+decoded = tokenizer.decode(generated[0], skip_special_tokens=False)
+triplets = parse_rebel_output(decoded)
+```
+
+## Comparacion REBEL vs mREBEL
+
+| | REBEL | mREBEL |
+|---|---|---|
+| Licencia | Apache 2.0 (comercial OK) | CC BY-NC-SA 4.0 (no comercial) |
+| Idiomas | Solo ingles | 30+ (es_XX, en_XX, fr_XX...) |
+| Tamanio | ~1.5 GB | ~2.4 GB (large) / ~900 MB (base) |
+| Base | BART | mBART-50 |
+
+## Tamanio y latencia
+
+- `Babelscape/rebel-large`: ~1.5 GB en disco.
+- Primera carga: 20-60 s en CPU.
+- Inferencia CPU: 3-10 s por frase (mas rapido que mREBEL por ser BART vs mBART).