egutierrez
faac610745
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>
37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
"""summary_stats — Compute descriptive statistics for a numeric list."""
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import math
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import numpy as np
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def summary_stats(values: list[float]) -> dict:
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"""Return basic descriptive statistics for a list of floats.
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Args:
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values: List of numeric values.
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Returns:
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Dict with keys:
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"n" (int): number of elements.
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"mean" (float): arithmetic mean, or math.nan if empty.
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"median" (float): median, or math.nan if empty.
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"p25" (float): 25th percentile, or math.nan if empty.
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"p75" (float): 75th percentile, or math.nan if empty.
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"""
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if not values:
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return {
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"n": 0,
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"mean": math.nan,
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"median": math.nan,
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"p25": math.nan,
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"p75": math.nan,
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}
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arr = np.array(values, dtype=float)
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return {
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"n": int(len(arr)),
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"mean": float(np.mean(arr)),
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"median": float(np.median(arr)),
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"p25": float(np.percentile(arr, 25)),
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"p75": float(np.percentile(arr, 75)),
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}
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