"""Shared fixtures: a spec matching Attachment 1 and helpers for tiny markets."""

from __future__ import annotations

import numpy as np
import pandas as pd
import pytest

from battery_dispatch.config import BatterySpec, RunConfig
from battery_dispatch.markets import MARKET_1, MARKET_2, Market

# Equal to Attachment 1, but stated literally so the tests do not depend on the
# workbook being present or unchanged.
SPEC = BatterySpec(
    max_charge_mw=2.0,
    max_discharge_mw=2.0,
    capacity_mwh=4.0,
    charge_efficiency=0.95,
    discharge_efficiency=0.95,
    lifetime_years=10.0,
    lifetime_cycles=5000.0,
    degradation_pct_per_cycle=0.001,
    capex_gbp=500_000.0,
    fixed_opex_gbp_per_year=5_000.0,
)


@pytest.fixture
def spec() -> BatterySpec:
    return SPEC


@pytest.fixture
def config() -> RunConfig:
    return RunConfig()


def markets_from(
    market_1_prices: list[float],
    market_2_prices: list[float] | None = None,
) -> list[Market]:
    """Build both markets from a half-hourly Market 1 price list.

    ``market_2_prices`` is given per *hour*; when omitted Market 2 is priced so
    low that it never trades, isolating Market 1 behaviour.
    """
    n = len(market_1_prices)
    if market_2_prices is None:
        broadcast = np.zeros(n)
    else:
        assert n == 2 * len(market_2_prices), "one Market 2 price per hour"
        broadcast = np.repeat(np.asarray(market_2_prices, dtype=float), 2)

    return [
        Market(MARKET_1, 1, np.asarray(market_1_prices, dtype=float)),
        Market(MARKET_2, 2, broadcast),
    ]


def prices_frame(
    market_1_prices: list[float],
    market_2_prices: list[float],
    start: str = "2018-01-01",
) -> pd.DataFrame:
    """A price frame shaped like the one ``load_prices`` returns."""
    n = len(market_1_prices)
    index = pd.date_range(start, periods=n, freq="30min")
    return pd.DataFrame(
        {
            "market_1_price": np.asarray(market_1_prices, dtype=float),
            "market_2_price": np.repeat(np.asarray(market_2_prices, dtype=float), 2),
            "hour_index": np.arange(n) // 2,
        },
        index=index,
    )