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/extract_graph_gliner2.md

@@ -0,0 +1,65 @@
+---
+name: extract_graph_gliner2
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def extract_graph_gliner2(text: str, entity_labels: list[str], relation_labels: list | dict, model: Any, threshold: float = 0.3, include_confidence: bool = False) -> dict"
+description: "Extrae entidades + relaciones en una sola pasada con GLiNER2. Wrapper de alto nivel: construye schema, ejecuta extraccion, normaliza a dict plano. No aplica post-filtrado ni coreference."
+tags: [gliner2, ner, relation-extraction, nlp, extraction, graph, zero-shot, datascience, python, apache2]
+uses_functions:
+  - gliner2_load_model_py_datascience
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [time, typing.Any]
+params:
+  - name: text
+    desc: "Texto a analizar. Recomendado hasta 1500 chars (pre-chunkeado con chunk_with_overlap). Textos mas largos degradan el recall de GLiNER2."
+  - name: entity_labels
+    desc: "Lista de strings con los tipos de entidad en minusculas snake_case. E.g. ['person', 'organization', 'location']. Labels en snake_case mejoran el recall segun notebook 08."
+  - name: relation_labels
+    desc: "Lista de strings o dict {label: description} con los tipos de relacion. E.g. ['works_at', 'ceo_of'] o {'works_at': 'person works at an organization'}."
+  - name: model
+    desc: "Instancia GLiNER2 cargada con gliner2_load_model. Inyectada por el caller (no se carga aqui)."
+  - name: threshold
+    desc: "Umbral de confianza entre 0 y 1. 0.3 validado empiricamente en notebook 04 (gliner_glirel_tuning). Valores mas bajos = mas recall, mas ruido."
+  - name: include_confidence
+    desc: "Si True, GLiNER2 devuelve scores internos por entidad y relacion. False por defecto para output mas limpio."
+output: "Dict con tres campos: 'entities' -> {type: [name, ...]}, 'relation_extraction' -> {rel_type: [(head, tail), ...]}, 'elapsed_s' -> float. Compatible con aggregate_extraction_results."
+tested: true
+tests:
+  - "output tiene claves entities relation_extraction elapsed_s"
+  - "stub model retorna shape correcto"
+test_file_path: "python/functions/datascience/tests/test_extract_graph_gliner2.py"
+file_path: "python/functions/datascience/extract_graph_gliner2.py"
+notes: |
+  LICENSE: GLiNER2 (fastino/gliner2-large-v1) es Apache 2.0 — uso comercial OK.
+
+  impure: invoca inferencia del modelo (side effect computacional + tiempo variable).
+  El model se inyecta externamente para permitir cache y reutilizacion entre llamadas.
+  Para textos largos usar chunk_with_overlap antes y llamar esta funcion por chunk,
+  luego agregar con aggregate_extraction_results.
+---
+
+## Ejemplo
+
+```python
+from datascience.gliner2_load_model import gliner2_load_model
+from datascience.extract_graph_gliner2 import extract_graph_gliner2
+
+model = gliner2_load_model(device="auto")
+
+result = extract_graph_gliner2(
+    text="Carlos Torres es presidente de BBVA, con sede en Bilbao.",
+    entity_labels=["person", "organization", "location"],
+    relation_labels=["president_of", "headquartered_in"],
+    model=model,
+    threshold=0.3,
+)
+# result["entities"]           -> {"person": ["Carlos Torres"], ...}
+# result["relation_extraction"]-> {"president_of": [("Carlos Torres", "BBVA")]}
+# result["elapsed_s"]          -> 0.234
+```