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>
2.3 KiB
2.3 KiB
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, params, output, tested, tests, test_file_path, file_path, source_repo, source_license, source_file
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | params | output | tested | tests | test_file_path | file_path | source_repo | source_license | source_file | |||||||||||||||||||||||||||||||||||||||||||||
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| plot_heatmap_log | function | py | datascience | 1.0.0 | impure | 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 | Dibuja un heatmap 2D con escala log1p sobre un Axes de matplotlib. Usa np.histogram2d con el extent dado y ax.imshow para renderizar. |
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false | error_go_core |
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None. Modifica el Axes in-place añadiendo el heatmap como imagen con ax.imshow. | true |
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python/functions/datascience/tests/test_plot_heatmap_log.py | python/functions/datascience/plot_heatmap_log.py | internal:footprint_aurgi | internal-aurgi | zonas_mapas_aurgi/examples/generar_reporte_madrid.py:62 |
Ejemplo
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from datascience.plot_heatmap_log import plot_heatmap_log
rng = np.random.default_rng(42)
xs = rng.uniform(-4.0, -3.5, 500)
ys = rng.uniform(40.3, 40.6, 500)
fig, ax = plt.subplots()
plot_heatmap_log(ax, xs, ys, extent=(-4.0, -3.5, 40.3, 40.6), bins=100)
fig.savefig("heatmap.png")
Notas
Aplica np.log1p a las cuentas del histograma para comprimir el rango dinámico y hacer visibles tanto zonas densas como dispersas. El histograma se transpone (counts.T) antes de pasar a imshow para alinear correctamente los ejes x/y. aspect="auto" permite que la imagen se estire al aspecto del Axes.