"""
Tracker Optimizer: simulate agrivoltaic shading scenarios.
Uses FarquharModel + ShadowModel to compute A at different tracker tilt
angles, then finds the optimal energy/crop tradeoff.
"""

from __future__ import annotations

import sys
from pathlib import Path

import numpy as np
import pandas as pd

PROJECT_ROOT = Path(__file__).resolve().parent.parent
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from src.farquhar_model import FarquharModel
from src.solar_geometry import ShadowModel

_model = FarquharModel()
_shadow = ShadowModel()


def load_sensor_data() -> pd.DataFrame:
    """Load sensor sample, filter daytime PAR > 50, add helper columns."""
    from src.sensor_data_loader import SensorDataLoader

    loader = SensorDataLoader()
    df = loader.load()
    df = loader.filter_daytime(df)
    df["time"] = pd.to_datetime(df["time"], utc=True)
    df["hour"] = df["time"].dt.hour + df["time"].dt.minute / 60
    df["date"] = df["time"].dt.date
    df["delta_t"] = df["Air1_leafTemperature_ref"] - df["Air1_airTemperature_ref"]
    return df


def compute_stress_heatmap(df: pd.DataFrame) -> pd.DataFrame:
    """Pivot table: hour-of-day (int) vs date, values = mean deltaT.
    Restricted to daytime hours (5:00-19:00 UTC) — no stress at night."""
    tmp = df.copy()
    tmp["hour_int"] = tmp["time"].dt.hour
    # Keep only daytime hours (sunrise ~5 UTC, sunset ~19 UTC for Sde Boker)
    tmp = tmp[(tmp["hour_int"] >= 5) & (tmp["hour_int"] <= 19)]
    pivot = tmp.pivot_table(
        values="delta_t", index="hour_int", columns="date", aggfunc="mean",
    )
    # Ensure all daytime hours are represented even if some have no data
    full_hours = list(range(5, 20))
    pivot = pivot.reindex(full_hours)
    return pivot


def _compute_A_at_par(row: pd.Series, par_factor: float) -> float:
    """Compute A for a single row with PAR scaled by par_factor."""
    par = float(row["Air1_PAR_ref"]) * par_factor
    if par <= 0:
        return 0.0
    return _model.calc_photosynthesis(
        PAR=par,
        Tleaf=float(row["Air1_leafTemperature_ref"]),
        CO2=float(row["Air1_CO2_ref"]),
        VPD=float(row["Air1_VPD_ref"]),
        Tair=float(row["Air1_airTemperature_ref"]),
    )


def _compute_A_at_par_value(row: pd.Series, par_value: float) -> float:
    """Compute A for a single row with an absolute PAR value."""
    if par_value <= 0:
        return 0.0
    return _model.calc_photosynthesis(
        PAR=par_value,
        Tleaf=float(row["Air1_leafTemperature_ref"]),
        CO2=float(row["Air1_CO2_ref"]),
        VPD=float(row["Air1_VPD_ref"]),
        Tair=float(row["Air1_airTemperature_ref"]),
    )


def simulate_tilt_angles(
    df: pd.DataFrame,
    angles: list[int] | None = None,
) -> pd.DataFrame:
    """
    For each tilt angle offset from astronomical, compute mean A and energy
    fraction across the dataset using the shadow model.
    Returns DataFrame with columns: angle, energy_pct, mean_A, A_pct.
    """
    if angles is None:
        angles = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]

    # Precompute solar positions for all timestamps
    times = pd.DatetimeIndex(df["time"])
    solar_pos = _shadow.get_solar_position(times)

    # Baseline A at astronomical tracking (offset=0)
    baseline_A_values = []
    for idx, (_, row) in enumerate(df.iterrows()):
        elev = solar_pos["solar_elevation"].iloc[idx]
        azim = solar_pos["solar_azimuth"].iloc[idx]
        if elev <= 2:
            baseline_A_values.append(0.0)
            continue
        tracker = _shadow.compute_tracker_tilt(azim, elev)
        theta_astro = tracker["tracker_theta"]
        mask = _shadow.project_shadow(elev, azim, theta_astro)
        par_dist = _shadow.compute_par_distribution(
            float(row["Air1_PAR_ref"]), mask,
            solar_elevation=elev, solar_azimuth=azim, tracker_tilt=theta_astro)
        # Canopy-average PAR (weighted by LAI)
        par_avg = float(np.average(par_dist, weights=_shadow.lai_weights, axis=0).mean())
        baseline_A_values.append(_compute_A_at_par_value(row, par_avg))
    baseline_A = np.mean(baseline_A_values)

    results = []
    for angle_offset in angles:
        A_values = []
        energy_factors = []
        for idx, (_, row) in enumerate(df.iterrows()):
            elev = solar_pos["solar_elevation"].iloc[idx]
            azim = solar_pos["solar_azimuth"].iloc[idx]
            if elev <= 2:
                A_values.append(0.0)
                energy_factors.append(1.0)
                continue
            tracker = _shadow.compute_tracker_tilt(azim, elev)
            theta_astro = tracker["tracker_theta"]
            aoi_astro = tracker["aoi"]

            # Apply offset
            theta_shade = theta_astro + angle_offset
            mask = _shadow.project_shadow(elev, azim, theta_shade)
            par_dist = _shadow.compute_par_distribution(
                float(row["Air1_PAR_ref"]), mask,
                solar_elevation=elev, solar_azimuth=azim, tracker_tilt=theta_shade)
            par_avg = float(np.average(par_dist, weights=_shadow.lai_weights, axis=0).mean())
            A_values.append(_compute_A_at_par_value(row, par_avg))

            # Energy: cos(aoi) ratio between offset and astronomical
            cos_astro = max(0.0, np.cos(np.radians(aoi_astro)))
            cos_offset = max(0.0, np.cos(np.radians(aoi_astro + angle_offset)))
            energy_factors.append(
                cos_offset / cos_astro if cos_astro > 0.01 else 1.0)

        mean_A = np.mean(A_values)
        energy_pct = np.mean(energy_factors) * 100
        A_pct = (mean_A / baseline_A * 100) if baseline_A > 0 else 0
        results.append({
            "angle": angle_offset,
            "energy_pct": energy_pct,
            "mean_A": mean_A,
            "A_pct": A_pct,
        })
    return pd.DataFrame(results)


def compute_daily_schedule(
    df: pd.DataFrame,
    stress_threshold: float = 2.0,
    shade_angle: int = 20,
) -> pd.DataFrame:
    """
    For each 15-min slot: compute astronomical tracking angle (pvlib),
    and if deltaT > threshold, offset by shade_angle to shade the vine.
    Computes A for both strategies using the shadow model.
    """
    times = pd.DatetimeIndex(df["time"])
    solar_pos = _shadow.get_solar_position(times)

    records = []
    for idx, (_, row) in enumerate(df.iterrows()):
        dt = float(row["delta_t"]) if pd.notna(row["delta_t"]) else 0.0
        stressed = dt > stress_threshold
        elev = solar_pos["solar_elevation"].iloc[idx]
        azim = solar_pos["solar_azimuth"].iloc[idx]

        if elev <= 2:
            records.append({
                "time": row["time"],
                "hour": row["hour"],
                "delta_t": dt,
                "stressed": stressed,
                "tracker_angle": 0.0,
                "recommended_angle": 0.0,
                "A_baseline": 0.0,
                "A_smart": 0.0,
                "energy_fraction": 1.0,
            })
            continue

        # Astronomical tracking angle (full sun-following)
        tracker = _shadow.compute_tracker_tilt(azim, elev)
        theta_astro = tracker["tracker_theta"]
        aoi_astro = tracker["aoi"]

        # Baseline: full astronomical tracking
        mask_baseline = _shadow.project_shadow(elev, azim, theta_astro)
        par_dist_baseline = _shadow.compute_par_distribution(
            float(row["Air1_PAR_ref"]), mask_baseline,
            solar_elevation=elev, solar_azimuth=azim, tracker_tilt=theta_astro)
        par_avg_baseline = float(
            np.average(par_dist_baseline, weights=_shadow.lai_weights, axis=0).mean())
        A_baseline = _compute_A_at_par_value(row, par_avg_baseline)

        # Smart: offset by shade_angle when stressed
        if stressed:
            theta_smart = theta_astro + shade_angle
            mask_smart = _shadow.project_shadow(elev, azim, theta_smart)
            par_dist_smart = _shadow.compute_par_distribution(
                float(row["Air1_PAR_ref"]), mask_smart,
                solar_elevation=elev, solar_azimuth=azim, tracker_tilt=theta_smart)
            par_avg_smart = float(
                np.average(par_dist_smart, weights=_shadow.lai_weights, axis=0).mean())
            A_smart = _compute_A_at_par_value(row, par_avg_smart)

            cos_astro = max(0.0, np.cos(np.radians(aoi_astro)))
            cos_offset = max(0.0, np.cos(np.radians(aoi_astro + shade_angle)))
            energy_frac = cos_offset / cos_astro if cos_astro > 0.01 else 1.0
        else:
            theta_smart = theta_astro
            A_smart = A_baseline
            energy_frac = 1.0

        records.append({
            "time": row["time"],
            "hour": row["hour"],
            "delta_t": dt,
            "stressed": stressed,
            "tracker_angle": theta_astro,
            "recommended_angle": theta_smart,
            "A_baseline": A_baseline,
            "A_smart": A_smart,
            "energy_fraction": energy_frac,
        })
    return pd.DataFrame(records)


def compute_season_summary(schedule: pd.DataFrame) -> dict:
    """Aggregate season totals from the daily schedule."""
    total_slots = len(schedule)
    stress_slots = schedule["stressed"].sum()
    stress_hours = stress_slots * 0.25  # 15-min slots

    energy_baseline = total_slots  # each slot = 1 unit at full tracking
    energy_smart = schedule["energy_fraction"].sum()
    energy_pct = (energy_smart / energy_baseline * 100) if energy_baseline > 0 else 100

    A_baseline_total = schedule["A_baseline"].sum()
    A_smart_total = schedule["A_smart"].sum()

    A_change_pct = ((A_smart_total - A_baseline_total) / A_baseline_total * 100) if A_baseline_total > 0 else 0

    # Water savings estimate: each stress hour shaded reduces transpiration demand
    water_savings_pct = min(30.0, stress_hours * 0.08)

    return {
        "energy_pct": energy_pct,
        "A_baseline_total": A_baseline_total,
        "A_smart_total": A_smart_total,
        "A_change_pct": A_change_pct,
        "stress_hours": stress_hours,
        "total_hours": total_slots * 0.25,
        "water_savings_pct": water_savings_pct,
    }