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>
2026-05-04 23:35:22 +02:00
parent f5c651d1f1
commit dabc945eda
193 changed files with 13146 additions and 3 deletions
@@ -0,0 +1,53 @@
+"""Plot a log-scale 2D histogram heatmap on a matplotlib Axes."""
+
+from __future__ import annotations
+
+
+def plot_heatmap_log(
+    ax: "Axes",
+    xs: "list[float] | np.ndarray",
+    ys: "list[float] | np.ndarray",
+    extent: "tuple[float, float, float, float]",
+    bins: int = 200,
+    cmap: str = "hot",
+    alpha: float = 0.6,
+) -> None:
+    """Plot a log-scale 2D density heatmap using histogram binning.
+
+    Computes a 2D histogram over the given points within ``extent``, applies
+    log1p to compress the dynamic range, and renders the result as an image
+    overlay on the Axes.
+
+    Args:
+        ax: matplotlib Axes to draw on.
+        xs: X coordinates (longitude or projected x).
+        ys: Y coordinates (latitude or projected y).
+        extent: Bounding box as (minx, maxx, miny, maxy).
+        bins: Number of histogram bins along each axis. Default 200.
+        cmap: Matplotlib colormap name. Default "hot".
+        alpha: Opacity of the heatmap overlay (0–1). Default 0.6.
+    """
+    import numpy as np  # type: ignore
+
+    xs_arr = np.asarray(xs, dtype=float)
+    ys_arr = np.asarray(ys, dtype=float)
+
+    minx, maxx, miny, maxy = extent
+
+    counts, _xedges, _yedges = np.histogram2d(
+        xs_arr,
+        ys_arr,
+        bins=bins,
+        range=[[minx, maxx], [miny, maxy]],
+    )
+
+    log_counts = np.log1p(counts.T)
+
+    ax.imshow(
+        log_counts,
+        extent=[minx, maxx, miny, maxy],
+        origin="lower",
+        cmap=cmap,
+        alpha=alpha,
+        aspect="auto",
+    )