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/words_to_dataset.md

@@ -0,0 +1,55 @@
+---
+name: words_to_dataset
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def words_to_dataset(texts: Iterable[str | None], min_ocurrencias: int = 1, eliminar_stopwords: bool = False) -> list[dict]"
+description: "Extrae palabras y sus ocurrencias de un iterable de textos. Tokeniza con \\b\\w+\\b, convierte a mayusculas, cuenta con Counter, filtra por minimo de ocurrencias y opcionalmente elimina stopwords en espanol. Sin pandas."
+tags: [nlp, text, words, frequency, counter, stopwords, spanish, datascience]
+params:
+  - name: texts
+    desc: Iterable de strings o None. Los None se ignoran silenciosamente.
+  - name: min_ocurrencias
+    desc: Numero minimo de ocurrencias para incluir una palabra. Default 1.
+  - name: eliminar_stopwords
+    desc: Si True, filtra un conjunto embebido de stopwords comunes en espanol.
+output: "Lista de dicts {'palabra': str, 'ocurrencias': int} ordenada por ocurrencias descendente."
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests:
+  - "cuenta palabras repetidas"
+  - "eliminar stopwords filtra del"
+  - "min ocurrencias filtra"
+  - "none ignorados"
+  - "lista vacia"
+  - "orden descendente"
+test_file_path: "python/functions/datascience/tests/test_words_to_dataset.py"
+file_path: "python/functions/datascience/words_to_dataset.py"
+source_repo: "internal:footprint_aurgi"
+source_license: "internal-aurgi"
+source_file: "fuzzy_joins/arreglo_fuzzy.py"
+---
+
+## Ejemplo
+
+```python
+from words_to_dataset import words_to_dataset
+
+texts = ["calle mayor", "calle del sol", "avenida principal"]
+result = words_to_dataset(texts)
+# [{"palabra": "CALLE", "ocurrencias": 2}, {"palabra": "MAYOR", "ocurrencias": 1}, ...]
+
+result_clean = words_to_dataset(texts, eliminar_stopwords=True)
+# "DEL" no aparece
+```
+
+## Notas
+
+Stopwords embebidas (frozenset de ~40 palabras ES). Funcion pura: solo stdlib (re, collections.Counter). Tokens en mayusculas para unificar "Calle" y "CALLE".