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/gliner2_load_model.md

@@ -0,0 +1,67 @@
+---
+name: gliner2_load_model
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def gliner2_load_model(model_name: str = 'fastino/gliner2-large-v1', device: str = 'auto') -> Any"
+description: "Carga (y cachea por (model_name, device)) un modelo GLiNER2 (NER+RE joint). GLiNER2 extrae entidades y relaciones en una sola pasada con schema unificado. ~2x mas rapido que GLiNER + GLiREL separados. LICENSE: Apache 2.0."
+tags: [gliner2, ner, relation-extraction, nlp, model, huggingface, zero-shot, joint, datascience, python, apache2]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [gliner2]
+params:
+  - name: model_name
+    desc: "ID del modelo en HuggingFace Hub. Default: fastino/gliner2-large-v1. Alternativas: fastino/gliner2-base-v1 (mas ligero)."
+  - name: device
+    desc: "'auto' usa CUDA si disponible, sino CPU. Valores: 'cpu', 'cuda', 'cuda:0', 'cuda:1'. auto es el default recomendado."
+output: "Instancia GLiNER2 cacheada por (model_name, device). Tiene metodos .create_schema().entities(...).relations(...) y .extract(text, schema=schema, threshold=0.3)."
+tested: true
+tests:
+  - "cache devuelve la misma instancia con los mismos parametros"
+  - "device=auto resuelve a cpu si torch no esta instalado"
+  - "ImportError si gliner2 no esta instalado"
+test_file_path: "python/functions/datascience/tests/test_gliner2_load_model.py"
+file_path: "python/functions/datascience/gliner2_load_model.py"
+notes: |
+  LICENSE: fastino/gliner2-large-v1 es Apache 2.0 — uso comercial OK.
+  Diferencia con gliner_load_model: GLiNER hace solo NER, GLiNER2 hace NER+RE
+  en una sola pasada (joint schema). Para pipelines de grafo usar GLiNER2
+  cuando se necesiten ambas tareas simultaneamente.
+
+  impure: descarga red/disco la primera vez, mantiene estado en _MODEL_CACHE.
+  Tamanio: fastino/gliner2-large-v1 ~500 MB. Primera carga 15-30s en CPU.
+  Inferencia CPU: 10-50 KB texto/s con schema tipico (3 entity + 8 relation labels).
+---
+
+## Ejemplo
+
+```python
+from datascience.gliner2_load_model import gliner2_load_model
+
+model = gliner2_load_model(device="auto")
+
+schema = (model.create_schema()
+          .entities(["person", "organization", "location"])
+          .relations(["works_at", "ceo_of", "located_in"]))
+
+result = model.extract(
+    "Pablo Isla es el CEO de Inditex, empresa con sede en Arteixo.",
+    schema=schema,
+    threshold=0.3,
+)
+# result["entities"] -> {"person": ["Pablo Isla"], "organization": ["Inditex"], ...}
+# result["relation_extraction"] -> {"ceo_of": [("Pablo Isla", "Inditex")], ...}
+```
+
+## Instalacion
+
+```bash
+cd python && uv pip install gliner2
+# o con el extra NLP completo:
+cd python && uv pip install -e '.[nlp]'
+```