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/best_central_tendency.md

@@ -0,0 +1,68 @@
+---
+id: best_central_tendency_py_datascience
+name: best_central_tendency
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def best_central_tendency(values: list[float], dist_type: str) -> tuple[str, float]"
+description: "Selects the most appropriate central tendency measure for a given distribution type. Returns (label, value) pair."
+tags: [statistics, central-tendency, distribution, robust, mean, median]
+uses_functions:
+  - geometric_mean_py_datascience
+  - trimmed_mean_py_datascience
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: [math, numpy]
+example: |
+  from best_central_tendency import best_central_tendency
+  label, value = best_central_tendency([1, 2, 3, 4, 5], "normal-ish")
+  # ("mean", 3.0)
+tested: true
+tests:
+  - "test_best_central_tendency_normal_ish"
+  - "test_best_central_tendency_right_skewed"
+  - "test_best_central_tendency_left_skewed"
+  - "test_best_central_tendency_lognormal_ish"
+  - "test_best_central_tendency_heavy_tail"
+  - "test_best_central_tendency_empty"
+  - "test_best_central_tendency_default"
+test_file_path: "python/functions/datascience/tests/test_best_central_tendency.py"
+file_path: "python/functions/datascience/best_central_tendency.py"
+params:
+  - name: values
+    desc: "List of numeric values to summarize."
+  - name: dist_type
+    desc: "Distribution type string, typically from detect_distribution_type. One of: normal-ish, lognormal-ish, heavy-tail, right-skewed, left-skewed, other, too_few_samples."
+output: >
+  Tuple (label, value) where label is one of "mean", "median", "geometric_mean",
+  "trimmed_mean_5%", and value is the computed central tendency. Returns ("median", math.nan) for empty input.
+source_repo: "internal:footprint_aurgi"
+source_license: "internal-aurgi"
+source_file: "aurgi_mapas/generar_pdf_reporte.py:196"
+---
+
+## Ejemplo
+
+```python
+from best_central_tendency import best_central_tendency
+
+best_central_tendency([1, 2, 3, 4, 5], "normal-ish")    # ("mean", 3.0)
+best_central_tendency([1, 2, 3, 4, 5], "right-skewed")  # ("median", 3.0)
+best_central_tendency([1, 2, 4, 8], "lognormal-ish")    # ("geometric_mean", ~2.83)
+best_central_tendency([1, 2, 3, 100], "heavy-tail")     # ("trimmed_mean_5%", ...)
+```
+
+## Mapeo de tipos a medidas
+
+| dist_type       | Medida           | Funcion interna       |
+|-----------------|------------------|-----------------------|
+| normal-ish      | mean             | numpy.mean            |
+| lognormal-ish   | geometric_mean   | geometric_mean()      |
+| heavy-tail      | trimmed_mean_5%  | trimmed_mean(0.05)    |
+| right-skewed    | median           | numpy.median          |
+| left-skewed     | median           | numpy.median          |
+| otros / default | median           | numpy.median          |