"""Tests para kmeans_segments."""

import numpy as np

from kmeans_segments import kmeans_segments


def _three_blobs(seed: int = 0, per_blob: int = 40):
    """Genera 3 blobs gaussianos bien separados en 2D, alineados por fila."""
    rng = np.random.default_rng(seed)
    centers = [(0.0, 0.0), (12.0, 12.0), (0.0, 12.0)]
    xs: list[float] = []
    ys: list[float] = []
    for cx, cy in centers:
        pts = rng.normal(loc=(cx, cy), scale=0.4, size=(per_blob, 2))
        xs.extend(float(p[0]) for p in pts)
        ys.extend(float(p[1]) for p in pts)
    return {"x": xs, "y": ys}


def test_three_separated_blobs_finds_k3():
    columns = _three_blobs(seed=0, per_blob=40)
    result = kmeans_segments(columns, k_min=2, k_max=8)

    assert result["best_k"] == 3
    assert result["silhouette"] > 0.5
    assert result["n_features"] == 2
    assert result["n_rows_used"] == 120
    assert sum(result["cluster_sizes"]) == 120
    assert len(result["centers"]) == 3
    # scores_by_k cubre todo el rango probado.
    ks = [s["k"] for s in result["scores_by_k"]]
    assert ks == list(range(2, 9))


def test_insufficient_rows_returns_note():
    # Solo 3 filas válidas, k_min*2 = 4 -> insuficiente.
    columns = {"x": [1.0, 2.0, 3.0], "y": [1.0, 2.0, 3.0]}
    result = kmeans_segments(columns, k_min=2, k_max=8)

    assert result["best_k"] == 0
    assert result["note"] == "datos insuficientes"


def test_insufficient_numeric_columns_returns_note():
    # Una sola columna numérica; la otra es texto -> menos de 2 numéricas.
    columns = {
        "x": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
        "label": ["a", "b", "c", "d", "e", "f"],
    }
    result = kmeans_segments(columns, k_min=2, k_max=8)

    assert result["best_k"] == 0
    assert result["note"] == "datos insuficientes"


def test_rows_with_none_are_dropped():
    columns = _three_blobs(seed=1, per_blob=40)
    # Inyectar None en una fila; debe descartarse, dejando 119.
    columns["x"][0] = None
    result = kmeans_segments(columns, k_min=2, k_max=8)

    assert result["best_k"] == 3
    assert result["n_rows_used"] == 119