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>
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
"""best_central_tendency — Select the best central tendency measure for a distribution type."""
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import math
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import numpy as np
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try:
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from .geometric_mean import geometric_mean
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from .trimmed_mean import trimmed_mean
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except ImportError:
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from geometric_mean import geometric_mean # type: ignore
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from trimmed_mean import trimmed_mean # type: ignore
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def best_central_tendency(values: list[float], dist_type: str) -> tuple[str, float]:
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"""Return the most appropriate central tendency measure given the distribution type.
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Mapping:
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"normal-ish" -> ("mean", arithmetic mean)
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"lognormal-ish" -> ("geometric_mean", geometric mean of positives)
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"heavy-tail" -> ("trimmed_mean_5%", trimmed mean at 5%)
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"right-skewed" -> ("median", median)
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"left-skewed" -> ("median", median)
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default -> ("median", median)
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Args:
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values: List of numeric values.
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dist_type: Distribution type string (from detect_distribution_type).
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Returns:
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Tuple (label: str, value: float). Value is math.nan if values is empty.
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"""
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if not values:
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return ("median", math.nan)
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arr = np.array(values, dtype=float)
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if dist_type == "normal-ish":
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return ("mean", float(np.mean(arr)))
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elif dist_type == "lognormal-ish":
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return ("geometric_mean", geometric_mean(values))
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elif dist_type == "heavy-tail":
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return ("trimmed_mean_5%", trimmed_mean(values, trim=0.05))
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else:
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# right-skewed, left-skewed, other, too_few_samples, unknown
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return ("median", float(np.median(arr)))
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