# steward/core/time_range.py
"""Shared time-range utilities for scoping queries and sparkline data."""
from __future__ import annotations
from datetime import datetime, timedelta, timezone
from typing import TypeVar

RANGES: dict[str, timedelta] = {
    "1h":  timedelta(hours=1),
    "6h":  timedelta(hours=6),
    "24h": timedelta(hours=24),
    "7d":  timedelta(days=7),
    "30d": timedelta(days=30),
}

RANGE_OPTIONS: list[str] = list(RANGES.keys())
DEFAULT_RANGE = "24h"


def parse_range(range_str: str | None) -> tuple[datetime, str]:
    """Return (since_datetime, range_key) from a query-param string like '24h'."""
    key = range_str if range_str in RANGES else DEFAULT_RANGE
    since = datetime.now(timezone.utc) - RANGES[key]
    return since, key


def bucket_seconds(since: datetime, target_points: int = 80) -> int:
    """Return bucket width in seconds so that (now - since) / width ≈ target_points.

    Minimum is 60s (one poll interval) so raw data is never re-averaged below
    the collection resolution.
    """
    range_secs = (datetime.now(timezone.utc) - since).total_seconds()
    return max(60, int(range_secs / target_points))


T = TypeVar("T")


def subsample(seq: list[T], n: int = 80) -> list[T]:
    """Return at most n evenly-distributed elements from seq (preserves order)."""
    if len(seq) <= n:
        return seq
    indices = sorted({int(round(i * (len(seq) - 1) / (n - 1))) for i in range(n)})
    return [seq[i] for i in indices]