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>
54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
---
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id: trimmed_mean_py_datascience
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name: trimmed_mean
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "def trimmed_mean(values: list[float], trim: float = 0.05) -> float"
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description: "Arithmetic mean after cutting the bottom and top trim percentiles. Returns math.nan for empty input."
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tags: [statistics, mean, robust, trimming, outliers]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: [math, numpy]
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example: |
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from trimmed_mean import trimmed_mean
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result = trimmed_mean([1, 2, 3, 4, 5, 100], 0.1) # ~3.5
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tested: true
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tests:
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- "test_trimmed_mean_basic"
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- "test_trimmed_mean_empty_returns_nan"
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- "test_trimmed_mean_no_trim"
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- "test_trimmed_mean_single_element"
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- "test_trimmed_mean_uniform"
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test_file_path: "python/functions/datascience/tests/test_trimmed_mean.py"
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file_path: "python/functions/datascience/trimmed_mean.py"
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params:
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- name: values
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desc: "List of numeric values to average."
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- name: trim
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desc: "Fraction to cut from each tail before averaging (0 <= trim < 0.5). Default 0.05."
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output: "Trimmed arithmetic mean as float. Returns math.nan if values is empty or all values are trimmed away."
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source_repo: "internal:footprint_aurgi"
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source_license: "internal-aurgi"
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source_file: "aurgi_mapas/generar_pdf_reporte.py:117"
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---
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## Ejemplo
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```python
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from trimmed_mean import trimmed_mean
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trimmed_mean([1, 2, 3, 4, 5, 100], 0.1) # ~3.5 (100 is trimmed)
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trimmed_mean([], 0.05) # math.nan
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trimmed_mean([5.0, 5.0, 5.0], 0.0) # 5.0
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```
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## Notas
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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.
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