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/translate_es_to_en.py

@@ -0,0 +1,68 @@
+"""Traduce texto espanol a ingles usando MarianMT, frase a frase."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+
+# Patron de split por oraciones: punto, exclamacion, interrogacion seguidos de espacio.
+_SENTENCE_RE = re.compile(r"(?<=[.!?])\s+")
+
+
+def translate_es_to_en(
+    text: str,
+    tokenizer: Any,
+    model: Any,
+    max_length: int = 512,
+    num_beams: int = 4,
+) -> str:
+    """Translate Spanish text to English, sentence by sentence.
+
+    Splits the input on sentence boundaries (after ``.``, ``!``, ``?``),
+    translates each sentence independently, and rejoins with a single space.
+    Processing sentence by sentence preserves proper nouns (names, companies,
+    locations) better than passing the full paragraph in a single call, because
+    the translation model can focus on shorter context windows.
+
+    Args:
+        text: Spanish text to translate. Can be a single sentence or a
+            multi-sentence paragraph.
+        tokenizer: MarianMT tokenizer loaded with ``marianmt_es_en_load_model``.
+        model: MarianMT model loaded with ``marianmt_es_en_load_model``.
+        max_length: Maximum token length for each sentence during tokenization
+            and generation. Sentences longer than this are truncated.
+        num_beams: Number of beams for beam search. Higher = better quality,
+            slower. Default 4 is a good tradeoff.
+
+    Returns:
+        Translated English text. Sentences joined with a single space.
+        Returns an empty string if ``text`` is empty or whitespace-only.
+
+    Raises:
+        RuntimeError: if model.generate fails (propagated from transformers).
+    """
+    if not text or not text.strip():
+        return ""
+
+    sentences = _SENTENCE_RE.split(text.strip())
+    sentences = [s.strip() for s in sentences if s.strip()]
+    if not sentences:
+        return ""
+
+    translated_parts: list[str] = []
+    for sentence in sentences:
+        inputs = tokenizer(
+            sentence,
+            return_tensors="pt",
+            max_length=max_length,
+            truncation=True,
+        )
+        generated = model.generate(
+            **inputs,
+            num_beams=num_beams,
+            max_length=max_length,
+        )
+        decoded = tokenizer.decode(generated[0], skip_special_tokens=True)
+        translated_parts.append(decoded.strip())
+
+    return " ".join(translated_parts)