"""Tests para describe_numeric."""

import os
import sys

sys.path.insert(0, os.path.dirname(__file__))

from describe_numeric import describe_numeric

# Keys that every result dict must always contain (the eda numeric_sub contract).
_EXPECTED_KEYS = {
    "min", "max", "mean", "median", "mode", "std", "variance", "cv",
    "p1", "p5", "p25", "p50", "p75", "p95", "p99", "iqr",
    "skew", "kurtosis", "n_outliers", "outlier_pct",
    "zero_pct", "negative_pct", "distribution_type", "histogram",
}


def test_lista_con_outlier_y_none():
    """Lista con outlier claro y None descartado."""
    # Tight cluster around 2-4 plus a None to drop and a clear extreme outlier.
    # A wide cluster (n=40) keeps std small so the extreme value's z-score
    # exceeds the 3.0 threshold used by detect_outliers.
    cluster = [1, 2, 2, 3, 4] * 8  # 40 numeric values, mode == 2
    values = cluster + [None, 1000]
    result = describe_numeric(values)

    # Contract: all keys present.
    assert set(result.keys()) == _EXPECTED_KEYS

    # Non-numeric / missing dropped: 41 numeric values remain.
    assert result["min"] == 1.0
    assert result["max"] == 1000.0

    # mean/median reasonable: median sits in the cluster, mean pulled up by 1000.
    assert result["median"] < result["mean"]
    assert 0.0 < result["median"] <= 5.0
    assert result["mean"] > result["median"]

    # mode = most frequent (2 appears twice per block).
    assert result["mode"] == 2.0

    # At least one z-score outlier detected (the 1000).
    assert result["n_outliers"] >= 1
    assert result["outlier_pct"] > 0.0

    # Histogram non-empty and counts cover every numeric value.
    assert len(result["histogram"]) > 0
    total = sum(bucket["count"] for bucket in result["histogram"])
    assert total == 41
    for bucket in result["histogram"]:
        assert "lo" in bucket and "hi" in bucket and "count" in bucket

    # No zeros, no negatives in this sample.
    assert result["zero_pct"] == 0.0
    assert result["negative_pct"] == 0.0


def test_lista_vacia_todo_none():
    """Lista vacia (o sin numericos) devuelve todas las claves en None."""
    result = describe_numeric([None, "abc", float("nan")])

    assert set(result.keys()) == _EXPECTED_KEYS
    for key in _EXPECTED_KEYS - {"histogram"}:
        assert result[key] is None, f"{key} debe ser None"
    assert result["histogram"] == []


def test_cv_none_cuando_mean_cero():
    """cv es None cuando la media es 0."""
    # Symmetric around zero so mean == 0.
    result = describe_numeric([-2, -1, 0, 1, 2])
    assert result["mean"] == 0.0
    assert result["cv"] is None
    assert result["zero_pct"] == 20.0
    assert result["negative_pct"] == 40.0


def test_iqr_y_percentiles():
    """iqr = p75 - p25 y percentiles coherentes."""
    result = describe_numeric(list(range(1, 101)))  # 1..100
    assert result["iqr"] == result["p75"] - result["p25"]
    assert result["p1"] <= result["p25"] <= result["p50"] <= result["p75"] <= result["p99"]
    assert result["min"] == 1.0
    assert result["max"] == 100.0