"""Per-frame descriptive statistics"""

from __future__ import annotations
import math
from dataclasses import dataclass


@dataclass(frozen=True)
class Stats:
    min: float; min_index: int
    max: float; max_index: int
    diff: float; avg: float
    sigma: float; three_sigma: float


def compute_stats(values: list[float]) -> Stats:
    clean = [(i, v) for i, v in enumerate(values) if not math.isnan(v)]
    if not clean:
        return Stats(0.0, -1, 0.0, -1, 0.0, 0.0, 0.0, 0.0)

    min_index, min_v = min(clean, key=lambda iv: iv[1])
    max_index, max_v = max(clean, key=lambda iv: iv[1])
    nums = [v for _, v in clean]
    avg = sum(nums) / len(nums)
    variance = sum((v - avg) ** 2 for v in nums) / len(nums)
    sigma = math.sqrt(variance)

    return Stats(
        min=min_v,
        min_index=min_index,
        max=max_v,
        max_index=max_index,
        diff=max_v - min_v,
        avg=avg,
        sigma=sigma,
        three_sigma=3 * sigma,
    )