import math
from dataclasses import fields

from app.sample import Sample

RowAgg = dict[str, float | int | None]


def window(snap: list[Sample], max_points: int) -> list[tuple[float, dict[str, RowAgg]]]:
    """Window-average a sample list down to at most `max_points` points.

    The samples are split into consecutive chunks of ceil(n / max_points)
    and each numeric Sample field is reduced to {avg, min, max} per chunk;
    whole-number fields (byte counts) stay ints, fractional fields are
    rounded to 0.1. Each point is stamped with the timestamp of the last
    sample in its chunk.

    Args:
        snap: samples oldest first (HistoryStore.snapshot).
        max_points: maximum number of points to emit.

    Returns:
        (timestamp, field aggregations) pairs, oldest first.
    """
    n = len(snap)
    w = max(1, math.ceil(n / max_points))
    out: list[tuple[float, dict[str, RowAgg]]] = []
    for start in range(0, n, w):
        chunk = snap[start : start + w]
        vals: dict[str, list[int | float]] = {}
        for sample in chunk:
            for f in fields(sample):
                if f.name == "ts":
                    continue
                v = getattr(sample, f.name)
                if isinstance(v, (int, float)) and not isinstance(v, bool):
                    vals.setdefault(f.name, []).append(v)
        row: dict[str, RowAgg] = {}
        for k, lst in vals.items():
            ints = all(isinstance(v, int) for v in lst)
            avg = sum(lst) / len(lst)
            row[k] = {
                "avg": round(avg) if ints else round(avg, 1),
                "min": min(lst) if ints else round(min(lst), 1),
                "max": max(lst) if ints else round(max(lst), 1),
            }
        out.append((chunk[-1].ts, row))
    return out