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>
2.7 KiB
2.7 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 | |||||||||||||||||||||||||||
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| best_central_tendency_py_datascience | best_central_tendency | function | py | datascience | 1.0.0 | pure | def best_central_tendency(values: list[float], dist_type: str) -> tuple[str, float] | Selects the most appropriate central tendency measure for a given distribution type. Returns (label, value) pair. |
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false |
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from best_central_tendency import best_central_tendency label, value = best_central_tendency([1, 2, 3, 4, 5], "normal-ish") # ("mean", 3.0) | true |
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python/functions/datascience/tests/test_best_central_tendency.py | python/functions/datascience/best_central_tendency.py |
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Tuple (label, value) where label is one of "mean", "median", "geometric_mean", "trimmed_mean_5%", and value is the computed central tendency. Returns ("median", math.nan) for empty input. | internal:footprint_aurgi | internal-aurgi | aurgi_mapas/generar_pdf_reporte.py:196 |
Ejemplo
from best_central_tendency import best_central_tendency
best_central_tendency([1, 2, 3, 4, 5], "normal-ish") # ("mean", 3.0)
best_central_tendency([1, 2, 3, 4, 5], "right-skewed") # ("median", 3.0)
best_central_tendency([1, 2, 4, 8], "lognormal-ish") # ("geometric_mean", ~2.83)
best_central_tendency([1, 2, 3, 100], "heavy-tail") # ("trimmed_mean_5%", ...)
Mapeo de tipos a medidas
| dist_type | Medida | Funcion interna |
|---|---|---|
| normal-ish | mean | numpy.mean |
| lognormal-ish | geometric_mean | geometric_mean() |
| heavy-tail | trimmed_mean_5% | trimmed_mean(0.05) |
| right-skewed | median | numpy.median |
| left-skewed | median | numpy.median |
| otros / default | median | numpy.median |