"""Decimal-string discipline (docs/11 §3.3) at the skill boundary.

Contracts carry money/energy/prices as fixed-point decimal *strings*. Inside a
skill we compute with numpy floats, then quantize back to strings with an
explicit scale — and any headline figure (revenue, energy) is re-derived with
``decimal.Decimal`` from the already-quantized curves, so what a report cites
is exactly reproducible from the curves it cites.
"""

from __future__ import annotations

from decimal import ROUND_HALF_EVEN, Decimal
from typing import Iterable

import numpy as np

INTERVALS = 96
INTERVAL_HOURS = Decimal("0.25")

SCALE_MW = 3
SCALE_MWH = 3
SCALE_PRICE = 2
SCALE_MONEY = 2


def to_array(values: Iterable[str]) -> np.ndarray:
    return np.array([float(Decimal(v)) for v in values], dtype=float)


def quantize(x: float | Decimal, scale: int) -> str:
    q = Decimal(1).scaleb(-scale)
    d = x if isinstance(x, Decimal) else Decimal(repr(float(x)))
    out = d.quantize(q, rounding=ROUND_HALF_EVEN)
    if out == 0:
        out = abs(out)  # normalise "-0.000"
    return format(out, "f")


def curve(values: np.ndarray | list[str], date: str, scale: int) -> dict:
    if isinstance(values, np.ndarray):
        vals = [quantize(float(v), scale) for v in values.tolist()]
    else:
        vals = list(values)
    if len(vals) != INTERVALS:
        raise ValueError(f"curve must have {INTERVALS} values, got {len(vals)}")
    return {"interval_minutes": 15, "date": date, "values": vals}


def dsum(values: Iterable[str]) -> Decimal:
    return sum((Decimal(v) for v in values), Decimal(0))


def dot(a: Iterable[str], b: Iterable[str]) -> Decimal:
    """Exact Σ a_i·b_i over decimal strings."""
    return sum((Decimal(x) * Decimal(y) for x, y in zip(a, b, strict=True)), Decimal(0))