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>
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1.7 KiB
id, name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, example, tested, tests, test_file_path, file_path, params, output, source_repo, source_license, source_file
| id | name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | example | tested | tests | test_file_path | file_path | params | output | source_repo | source_license | source_file | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| trimmed_mean_py_datascience | trimmed_mean | function | py | datascience | 1.0.0 | pure | def trimmed_mean(values: list[float], trim: float = 0.05) -> float | Arithmetic mean after cutting the bottom and top trim percentiles. Returns math.nan for empty input. |
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false |
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from trimmed_mean import trimmed_mean result = trimmed_mean([1, 2, 3, 4, 5, 100], 0.1) # ~3.5 | true |
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python/functions/datascience/tests/test_trimmed_mean.py | python/functions/datascience/trimmed_mean.py |
|
Trimmed arithmetic mean as float. Returns math.nan if values is empty or all values are trimmed away. | internal:footprint_aurgi | internal-aurgi | aurgi_mapas/generar_pdf_reporte.py:117 |
Ejemplo
from trimmed_mean import trimmed_mean
trimmed_mean([1, 2, 3, 4, 5, 100], 0.1) # ~3.5 (100 is trimmed)
trimmed_mean([], 0.05) # math.nan
trimmed_mean([5.0, 5.0, 5.0], 0.0) # 5.0
Notas
Usa numpy.percentile para calcular los umbrales lo y hi, luego filtra valores dentro del rango [lo, hi]. Util para calcular promedios robustos cuando hay valores extremos en la distribucion.