"""
ROIService: budget utilisation, intervention statistics, and LER computation.

Provides the data layer for the ROI Dashboard tab.
"""

from __future__ import annotations

import logging
from dataclasses import dataclass, field
from datetime import date, datetime, timedelta
from typing import Dict, List, Optional

from config.settings import (
    MAX_ENERGY_REDUCTION_PCT,
    TARGET_LER,
)

logger = logging.getLogger(__name__)


# ---------------------------------------------------------------------------
# Data containers
# ---------------------------------------------------------------------------

@dataclass
class InterventionStats:
    """Statistics for a single period (day/week/month)."""

    period_label: str
    total_slots: int = 0          # number of 15-min slots
    intervention_slots: int = 0   # slots where offset > 0
    avg_offset_deg: float = 0.0   # mean offset across intervention slots
    max_offset_deg: float = 0.0
    energy_sacrificed_kwh: float = 0.0
    budget_allocated_kwh: float = 0.0
    budget_utilisation_pct: float = 0.0  # sacrificed / allocated * 100

    @property
    def intervention_rate_pct(self) -> float:
        if self.total_slots == 0:
            return 0.0
        return self.intervention_slots / self.total_slots * 100


@dataclass
class LERResult:
    """Land Equivalent Ratio computation."""

    energy_fraction: float    # E_agri / E_mono (agrivoltaic / monoculture PV)
    crop_fraction: float      # Y_agri / Y_mono (agrivoltaic / monoculture vineyard)
    ler: float                # energy_fraction + crop_fraction
    meets_target: bool = False

    def summary(self) -> str:
        status = "MEETS" if self.meets_target else "BELOW"
        return (
            f"LER = {self.ler:.2f} ({status} target {TARGET_LER:.1f}): "
            f"energy {self.energy_fraction:.2f} + crop {self.crop_fraction:.2f}"
        )


@dataclass
class BudgetStatus:
    """Current budget utilisation snapshot."""

    annual_budget_kwh: float
    annual_spent_kwh: float
    annual_remaining_kwh: float
    monthly_budget_kwh: float = 0.0
    monthly_spent_kwh: float = 0.0
    weekly_budget_kwh: float = 0.0
    weekly_spent_kwh: float = 0.0
    daily_budget_kwh: float = 0.0
    daily_spent_kwh: float = 0.0

    @property
    def annual_utilisation_pct(self) -> float:
        if self.annual_budget_kwh == 0:
            return 0.0
        return self.annual_spent_kwh / self.annual_budget_kwh * 100

    def is_over_budget(self) -> bool:
        return self.annual_spent_kwh > self.annual_budget_kwh


# ---------------------------------------------------------------------------
# ROI Service
# ---------------------------------------------------------------------------

class ROIService:
    """Compute budget utilisation, intervention stats, and LER from tick logs.

    Parameters
    ----------
    annual_generation_kwh : float
        Expected total annual PV generation (kWh).
        Default: 48 kW × 1800 peak-sun-hours ≈ 86,400 kWh.
    max_reduction_pct : float
        Maximum energy sacrifice ceiling (%).
    """

    def __init__(
        self,
        annual_generation_kwh: float = 86_400.0,
        max_reduction_pct: float = MAX_ENERGY_REDUCTION_PCT,
    ):
        self.annual_gen = annual_generation_kwh
        self.annual_budget = annual_generation_kwh * max_reduction_pct / 100.0
        self._tick_log: List[dict] = []

    def load_tick_log(self, tick_log: List[dict]) -> None:
        """Load a tick log (list of TickResult.to_dict() entries)."""
        self._tick_log = list(tick_log)
        logger.info("Loaded %d tick entries", len(self._tick_log))

    # ------------------------------------------------------------------
    # Budget status
    # ------------------------------------------------------------------

    def get_budget_status(
        self,
        target_date: Optional[date] = None,
    ) -> BudgetStatus:
        """Compute current budget utilisation from the tick log."""
        today = target_date or date.today()
        year = today.year

        annual_spent = 0.0
        monthly_spent = 0.0
        weekly_spent = 0.0
        daily_spent = 0.0

        week_start = today - timedelta(days=today.weekday())

        for tick in self._tick_log:
            cost = tick.get("energy_cost_kwh", 0.0) or 0.0
            ts = tick.get("timestamp", "")
            try:
                if isinstance(ts, str):
                    tick_date = datetime.fromisoformat(ts).date()
                elif isinstance(ts, datetime):
                    tick_date = ts.date()
                else:
                    continue
            except (ValueError, AttributeError):
                continue

            if tick_date.year == year:
                annual_spent += cost
            if tick_date.year == year and tick_date.month == today.month:
                monthly_spent += cost
            if tick_date >= week_start:
                weekly_spent += cost
            if tick_date == today:
                daily_spent += cost

        return BudgetStatus(
            annual_budget_kwh=self.annual_budget,
            annual_spent_kwh=annual_spent,
            annual_remaining_kwh=max(0, self.annual_budget - annual_spent),
            monthly_spent_kwh=monthly_spent,
            weekly_spent_kwh=weekly_spent,
            daily_spent_kwh=daily_spent,
        )

    # ------------------------------------------------------------------
    # Intervention statistics
    # ------------------------------------------------------------------

    def compute_intervention_stats(
        self,
        start_date: Optional[date] = None,
        end_date: Optional[date] = None,
        label: str = "period",
    ) -> InterventionStats:
        """Compute intervention statistics for a date range."""
        stats = InterventionStats(period_label=label)

        for tick in self._tick_log:
            ts = tick.get("timestamp", "")
            try:
                if isinstance(ts, str):
                    tick_date = datetime.fromisoformat(ts).date()
                elif isinstance(ts, datetime):
                    tick_date = ts.date()
                else:
                    continue
            except (ValueError, AttributeError):
                continue

            if start_date and tick_date < start_date:
                continue
            if end_date and tick_date > end_date:
                continue

            stats.total_slots += 1
            offset = tick.get("plan_offset_deg", 0.0) or 0.0
            if offset > 0:
                stats.intervention_slots += 1
                stats.max_offset_deg = max(stats.max_offset_deg, offset)

            stats.energy_sacrificed_kwh += tick.get("energy_cost_kwh", 0.0) or 0.0

        if stats.intervention_slots > 0:
            # Recompute average from individual entries
            total_offset = sum(
                (t.get("plan_offset_deg", 0.0) or 0.0)
                for t in self._tick_log
                if (t.get("plan_offset_deg", 0.0) or 0.0) > 0
            )
            stats.avg_offset_deg = total_offset / stats.intervention_slots

        return stats

    # ------------------------------------------------------------------
    # LER computation
    # ------------------------------------------------------------------

    def compute_ler(
        self,
        actual_energy_kwh: float,
        mono_energy_kwh: Optional[float] = None,
        actual_crop_yield: float = 1.0,
        mono_crop_yield: float = 1.0,
    ) -> LERResult:
        """Compute Land Equivalent Ratio.

        LER = (E_agri / E_mono) + (Y_agri / Y_mono)

        LER > 1.0 means the combined system is more productive
        than growing either crop alone.

        Parameters
        ----------
        actual_energy_kwh : float
            Actual PV generation under agrivoltaic operation.
        mono_energy_kwh : float, optional
            Theoretical generation without any shading interventions.
            Defaults to annual_generation_kwh.
        actual_crop_yield : float
            Agrivoltaic crop yield (any unit, must match mono).
        mono_crop_yield : float
            Monoculture crop yield (same unit).
        """
        e_mono = mono_energy_kwh or self.annual_gen
        e_frac = actual_energy_kwh / e_mono if e_mono > 0 else 0.0
        c_frac = actual_crop_yield / mono_crop_yield if mono_crop_yield > 0 else 0.0
        ler = e_frac + c_frac

        return LERResult(
            energy_fraction=e_frac,
            crop_fraction=c_frac,
            ler=ler,
            meets_target=ler >= TARGET_LER,
        )

    # ------------------------------------------------------------------
    # Summary
    # ------------------------------------------------------------------

    def summary(self, target_date: Optional[date] = None) -> dict:
        """Return a combined summary dict for the dashboard."""
        budget = self.get_budget_status(target_date)
        stats = self.compute_intervention_stats(label="all_time")

        return {
            "budget": {
                "annual_budget_kwh": budget.annual_budget_kwh,
                "annual_spent_kwh": budget.annual_spent_kwh,
                "annual_remaining_kwh": budget.annual_remaining_kwh,
                "utilisation_pct": budget.annual_utilisation_pct,
                "over_budget": budget.is_over_budget(),
            },
            "interventions": {
                "total_slots": stats.total_slots,
                "intervention_slots": stats.intervention_slots,
                "intervention_rate_pct": stats.intervention_rate_pct,
                "avg_offset_deg": stats.avg_offset_deg,
                "max_offset_deg": stats.max_offset_deg,
                "energy_sacrificed_kwh": stats.energy_sacrificed_kwh,
            },
        }