---
id: kde_density_levels_py_datascience
name: kde_density_levels
kind: function
lang: py
domain: datascience
version: "1.0.0"
purity: pure
signature: "def kde_density_levels(xs: list[float], ys: list[float], bw_adjust: float = 0.6, abs_quantile: float = 0.1, dense_quantile: float = 0.85, bins: int = 80) -> dict | None"
description: "Estimates 2-D density via KDE (scipy) or histogram fallback (numpy) and returns per-point density values plus absolute and dense quantile thresholds."
tags: [statistics, kde, density, spatial, geospatial, scipy, numpy, pendiente-usar]
uses_functions: []
uses_types: []
returns: []
returns_optional: false
error_type: ""
imports: [numpy, scipy]
example: |
  from kde_density_levels import kde_density_levels
  import numpy as np
  rng = np.random.default_rng(42)
  result = kde_density_levels(rng.normal(0,1,50).tolist(), rng.normal(0,1,50).tolist())
  # {"method": "kde", "densities": array(...), "abs_level": ..., "dense_level": ...}
tested: true
tests:
  - "test_kde_density_levels_returns_dict_for_50_points"
  - "test_kde_density_levels_none_for_few_points"
  - "test_kde_density_levels_none_for_4_points"
  - "test_kde_density_levels_levels_ordered"
  - "test_kde_density_levels_mismatched_lengths"
test_file_path: "python/functions/datascience/tests/test_kde_density_levels.py"
file_path: "python/functions/datascience/kde_density_levels.py"
params:
  - name: xs
    desc: "X-coordinates of the 2-D point cloud."
  - name: ys
    desc: "Y-coordinates of the 2-D point cloud. Must have same length as xs."
  - name: bw_adjust
    desc: "Bandwidth adjustment factor for gaussian_kde. Default 0.6."
  - name: abs_quantile
    desc: "Quantile of density values used as the absolute (sparse) threshold. Default 0.1."
  - name: dense_quantile
    desc: "Quantile of density values used as the dense cluster threshold. Default 0.85."
  - name: bins
    desc: "Number of bins per axis for the histogram fallback. Default 80."
output: "Dict with method (str), densities (np.ndarray of per-point density), abs_level (float), dense_level (float). Returns None if len(xs) < 5 or lengths differ."
source_repo: "internal:footprint_aurgi"
source_license: "internal-aurgi"
source_file: "ponderacion_isochronas/src/recomendador_centros.py:305"
---

Funcion pura que no escribe nada en disco. returns_optional=true porque devuelve None cuando hay menos de 5 puntos.
