from statistics import mean, median, pstdev
from typing import Any, Dict, Iterable, List, Mapping, Union, cast


def _extract_results(simulation_results: Iterable[object]) -> List[float]:
    values: List[float] = []
    for item in simulation_results:
        if not isinstance(item, Mapping):
            continue
        mapping_item = cast(Mapping[str, Any], item)
        value = mapping_item.get("result")
        if isinstance(value, (int, float)):
            values.append(float(value))
    return values


def _percentile(values: List[float], percentile: float) -> float:
    if not values:
        return 0.0
    sorted_values = sorted(values)
    if len(sorted_values) == 1:
        return sorted_values[0]
    index = (percentile / 100) * (len(sorted_values) - 1)
    lower = int(index)
    upper = min(lower + 1, len(sorted_values) - 1)
    weight = index - lower
    return sorted_values[lower] * (1 - weight) + sorted_values[upper] * weight


def generate_report(
    simulation_results: List[Dict[str, float]],
) -> Dict[str, Union[float, int]]:
    """Aggregate basic statistics for simulation outputs."""

    values = _extract_results(simulation_results)

    if not values:
        return {
            "count": 0,
            "mean": 0.0,
            "median": 0.0,
            "min": 0.0,
            "max": 0.0,
            "std_dev": 0.0,
            "variance": 0.0,
            "percentile_10": 0.0,
            "percentile_90": 0.0,
            "percentile_5": 0.0,
            "percentile_95": 0.0,
            "value_at_risk_95": 0.0,
            "expected_shortfall_95": 0.0,
        }

    summary: Dict[str, Union[float, int]] = {
        "count": len(values),
        "mean": mean(values),
        "median": median(values),
        "min": min(values),
        "max": max(values),
        "percentile_10": _percentile(values, 10),
        "percentile_90": _percentile(values, 90),
        "percentile_5": _percentile(values, 5),
        "percentile_95": _percentile(values, 95),
    }

    std_dev = pstdev(values) if len(values) > 1 else 0.0
    summary["std_dev"] = std_dev
    summary["variance"] = std_dev**2

    var_95 = summary["percentile_5"]
    summary["value_at_risk_95"] = var_95

    tail_values = [value for value in values if value <= var_95]
    if tail_values:
        summary["expected_shortfall_95"] = mean(tail_values)
    else:
        summary["expected_shortfall_95"] = var_95

    return summary