from __future__ import annotations

import numpy as np


def moving_average(values: list[float], window: int = 5) -> list[float]:
    if len(values) < window:
        return values
    arr = np.array(values, dtype=float)
    return np.convolve(arr, np.ones(window) / window, mode="valid").tolist()


def detect_anomalies(
    values: list[float], threshold: float = 2.0
) -> list[dict]:
    """Detect anomalies using Z-score method."""
    arr = np.array(values, dtype=float)
    mean = np.mean(arr)
    std = np.std(arr)

    if std == 0:
        return []

    z_scores = np.abs((arr - mean) / std)
    anomalies = []
    for i, (val, z) in enumerate(zip(values, z_scores)):
        if z > threshold:
            anomalies.append({"index": i, "value": val, "z_score": float(z)})
    return anomalies


def percentile_stats(values: list[float]) -> dict:
    arr = np.array(values, dtype=float)
    return {
        "p50": float(np.percentile(arr, 50)),
        "p90": float(np.percentile(arr, 90)),
        "p95": float(np.percentile(arr, 95)),
        "p99": float(np.percentile(arr, 99)),
    }