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