src/shading/tradeoff_engine.py

"""
TradeoffEngine: InterventionGate + minimum-dose search for SolarWine 2.0.

Design philosophy
-----------------
The tracker's default is ALWAYS full astronomical tracking (maximum energy
generation). Shading is an exception, not a rule.

The decision logic has two clean responsibilities:

  InterventionGate — "Is the vine significantly stressed?"
    Asks only physiological questions — time of day and month are NOT checked
    here. The sun geometry handles those cases naturally:
      1. Is there meaningful sunlight? (GHI > MIN_MEANINGFUL_GHI — night/cloud guard)
      2. Is the leaf temperature above the Rubisco transition? (Tleaf ≥ 30°C)
      3. Is water stress confirmed by sensors? (CWSI ≥ 0.4)
      4. Is irradiance high enough to cause real heat load? (GHI ≥ 400 W/m²)
      5. Does the FvCB model agree shading would help? (shading_helps = True)

  TradeoffEngine — "Does any offset actually help the fruiting zone right now?"
    Uses the 3D ShadowModel to ray-trace each candidate offset and checks:
      (a) fruiting-zone PAR drops below FRUITING_ZONE_TARGET_PAR (400 µmol)
      (b) top-canopy PAR stays ≥ 70% of ambient (apical leaves remain productive)
      (c) energy sacrifice ≤ remaining per-slot budget
    Returns the SMALLEST offset satisfying all three, or offset=0 (stay put).

Why manual time/month rules are replaced by geometry
----------------------------------------------------
- Morning (9:00, sun in east): astronomical tilt already faces east. No positive
  offset places the shadow over the fruiting zone; find_minimum_dose() returns
  no effective dose naturally.
- May (fruit-set, low stress): CWSI < 0.4 and Tleaf < 30°C → gate blocks.
  In rare extreme heat: gate passes, but if the geometry still doesn't deliver
  shade to the fruiting zone, no dose is selected.
- Overcast (GHI < 100 W/m²): MIN_MEANINGFUL_GHI guard fires.
- Every other edge case: geometry decides, not the calendar.
"""

from __future__ import annotations

import math
from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Optional

from config.settings import (
    CANDIDATE_OFFSETS,
    FRUITING_ZONE_INDEX,
    FRUITING_ZONE_TARGET_PAR,
    MIN_MEANINGFUL_GHI,
    SHADE_ELIGIBLE_CWSI_ABOVE,
    SHADE_ELIGIBLE_GHI_ABOVE,
    SHADE_ELIGIBLE_TLEAF_ABOVE,
)

# Top-canopy zone index in the 3-zone ShadowModel (0=basal, 1=fruiting, 2=apical)
_TOP_ZONE_INDEX = 2

# Relative thresholds used in find_minimum_dose (relative to astronomical baseline)
# The panel at astronomical tracking already shades the canopy substantially at high
# sun elevations. These thresholds compare offset vs. baseline, not vs. ambient.
_FRUITING_IMPROVEMENT_MIN = 0.85  # sun-side fruiting PAR must drop ≥ 15% below astro baseline
_TOP_CANOPY_TOLERANCE = 0.85      # top canopy must retain ≥ 85% of its astro-baseline PAR


# ---------------------------------------------------------------------------
# InterventionGate
# ---------------------------------------------------------------------------

@dataclass
class GateDecision:
    """Result of the InterventionGate evaluation."""

    passed: bool
    rejection_reason: Optional[str] = None

    # Diagnostic flags (for SimulationLog explainability)
    no_meaningful_sun: bool = False
    tleaf_below_threshold: bool = False
    cwsi_below_threshold: bool = False
    ghi_below_threshold: bool = False
    biology_says_shade_helps: bool = False

    def decision_tags(self) -> List[str]:
        if not self.passed and self.rejection_reason:
            return [f"gate_blocked:{self.rejection_reason}"]
        return ["gate_passed"]


class InterventionGate:
    """
    Physiological pass/fail check — answers "is the vine stressed enough to
    consider intervention?"

    Time of day, month, and sun angle are NOT evaluated here.
    The 3D ShadowModel in TradeoffEngine determines whether a candidate offset
    can geometrically deliver shade to the fruiting zone for any given sun position.

    Default answer: NO (full astronomical tracking).
    Gate opens only when ALL stress conditions are simultaneously met.
    """

    def __init__(
        self,
        min_meaningful_ghi: float = MIN_MEANINGFUL_GHI,
        shade_eligible_tleaf_above: float = SHADE_ELIGIBLE_TLEAF_ABOVE,
        shade_eligible_cwsi_above: float = SHADE_ELIGIBLE_CWSI_ABOVE,
        shade_eligible_ghi_above: float = SHADE_ELIGIBLE_GHI_ABOVE,
    ) -> None:
        self.min_meaningful_ghi = min_meaningful_ghi
        self.shade_eligible_tleaf_above = shade_eligible_tleaf_above
        self.shade_eligible_cwsi_above = shade_eligible_cwsi_above
        self.shade_eligible_ghi_above = shade_eligible_ghi_above

    # ------------------------------------------------------------------
    # Individual gate checks (pipeline pattern)
    # ------------------------------------------------------------------

    def _check_meaningful_sun(self, ghi_w_m2: Optional[float], dec: GateDecision) -> Optional[GateDecision]:
        """Block if GHI too low (night / deep overcast)."""
        if ghi_w_m2 is not None and ghi_w_m2 < self.min_meaningful_ghi:
            dec.no_meaningful_sun = True
            dec.rejection_reason = (
                f"no_meaningful_sun:GHI={ghi_w_m2:.0f} W/m² "
                f"< {self.min_meaningful_ghi:.0f}"
            )
            return dec
        return None

    def _check_heat_stress(self, tleaf_c: Optional[float], dec: GateDecision) -> Optional[GateDecision]:
        """Block if leaf temperature below Rubisco transition (vine is light-limited)."""
        if tleaf_c is not None and tleaf_c < self.shade_eligible_tleaf_above:
            dec.tleaf_below_threshold = True
            dec.rejection_reason = (
                f"no_heat_stress:Tleaf={tleaf_c:.1f}°C "
                f"< {self.shade_eligible_tleaf_above:.0f}°C (Rubisco transition)"
            )
            return dec
        return None

    def _check_water_stress(self, cwsi: Optional[float], dec: GateDecision) -> Optional[GateDecision]:
        """Block if CWSI below threshold (vine not water-stressed)."""
        if cwsi is not None and cwsi < self.shade_eligible_cwsi_above:
            dec.cwsi_below_threshold = True
            dec.rejection_reason = (
                f"no_water_stress:CWSI={cwsi:.2f} "
                f"< {self.shade_eligible_cwsi_above:.2f}"
            )
            return dec
        return None

    def _check_radiation_load(self, ghi_w_m2: Optional[float], dec: GateDecision) -> Optional[GateDecision]:
        """Block if radiation too low for meaningful heat build-up."""
        if ghi_w_m2 is not None and ghi_w_m2 < self.shade_eligible_ghi_above:
            dec.ghi_below_threshold = True
            dec.rejection_reason = (
                f"low_radiation:GHI={ghi_w_m2:.0f} W/m² "
                f"< {self.shade_eligible_ghi_above:.0f} W/m²"
            )
            return dec
        return None

    def _check_biology(self, shading_helps: Optional[bool], dec: GateDecision) -> Optional[GateDecision]:
        """Block if FvCB model says shading would hurt (RuBP-limited)."""
        dec.biology_says_shade_helps = bool(shading_helps)
        if not shading_helps:
            dec.rejection_reason = (
                "biology:shading_helps=False — vine is RuBP-limited despite high Tleaf; "
                "possibly declining afternoon PAR or unusual conditions"
            )
            return dec
        return None

    # ------------------------------------------------------------------
    # Main evaluate (pipeline composition)
    # ------------------------------------------------------------------

    def evaluate(
        self,
        tleaf_c: Optional[float],
        ghi_w_m2: Optional[float],
        cwsi: Optional[float],
        shading_helps: Optional[bool],
        dt: Optional[datetime] = None,   # accepted but not used; preserved for logging
    ) -> GateDecision:
        """
        Evaluate whether the vine is significantly stressed.

        Runs a pipeline of 5 checks in order. First rejection stops the pipeline.
        Gate passes only when ALL stress conditions are simultaneously met.

        Parameters
        ----------
        tleaf_c       : leaf temperature (°C)
        ghi_w_m2      : global horizontal irradiance (W/m²)
        cwsi          : Crop Water Stress Index [0–1]
        shading_helps : output of FarquharModel
        dt            : slot datetime (optional; for logging only)
        """
        dec = GateDecision(passed=False)

        # Run checks as a pipeline — first rejection short-circuits
        checks = [
            lambda d: self._check_meaningful_sun(ghi_w_m2, d),
            lambda d: self._check_heat_stress(tleaf_c, d),
            lambda d: self._check_water_stress(cwsi, d),
            lambda d: self._check_radiation_load(ghi_w_m2, d),
            lambda d: self._check_biology(shading_helps, d),
        ]

        for check in checks:
            rejection = check(dec)
            if rejection is not None:
                return rejection

        # All stress conditions met
        dec.passed = True
        return dec


# ---------------------------------------------------------------------------
# TradeoffEngine
# ---------------------------------------------------------------------------

@dataclass
class DoseResult:
    """Result of the minimum-dose offset search."""

    success: bool
    chosen_offset_deg: float = 0.0
    offsets_tested: List[float] = field(default_factory=list)
    fruiting_par_at_chosen: Optional[float] = None   # µmol m⁻² s⁻¹
    top_par_fraction: Optional[float] = None          # top_par / ambient_par
    energy_sacrifice_fraction: Optional[float] = None # approx 1 − cos(offset)
    rationale: str = ""

    def decision_tags(self) -> List[str]:
        if self.success:
            tags = [f"dose:{self.chosen_offset_deg:.0f}deg"]
            if self.fruiting_par_at_chosen is not None:
                tags.append(f"fruiting_par:{self.fruiting_par_at_chosen:.0f}")
            return tags
        return ["no_effective_dose"]


class TradeoffEngine:
    """
    Minimum-effective-dose search over candidate tilt offsets.

    For each offset (smallest first), ray-traces the shadow at
    θ_astro + offset using the 3D ShadowModel and returns the FIRST offset
    that simultaneously:
      (a) reduces fruiting-zone PAR below FRUITING_ZONE_TARGET_PAR
      (b) keeps top-canopy PAR ≥ 70% of ambient (preserves apical productivity)
      (c) costs ≤ the available slot budget (energy sacrifice fraction)

    Falls back to offset=0 (stay at astronomical) if no offset qualifies.

    Conditions (a) and (b) are geometry-only — they naturally encode the
    morning/evening cases where the sun angle means any offset either
    over-shades the whole canopy or misses the fruiting zone entirely.
    """

    def __init__(
        self,
        shadow_model=None,
        candidate_offsets: Optional[List[float]] = None,
        fruiting_zone_target_par: float = FRUITING_ZONE_TARGET_PAR,
        fruiting_zone_index: int = FRUITING_ZONE_INDEX,
        top_canopy_min_sunlit: float = _TOP_CANOPY_TOLERANCE,
    ) -> None:
        self._shadow_model = shadow_model
        self.candidate_offsets = (
            [o for o in CANDIDATE_OFFSETS if o > 0]
            if candidate_offsets is None
            else candidate_offsets
        )
        self.fruiting_zone_target_par = fruiting_zone_target_par
        self.fruiting_zone_index = fruiting_zone_index
        self.top_canopy_min_sunlit = top_canopy_min_sunlit

    @property
    def shadow_model(self):
        if self._shadow_model is None:
            from src.solar_geometry import ShadowModel
            self._shadow_model = ShadowModel()
        return self._shadow_model

    def find_minimum_dose(
        self,
        ambient_par_umol: float,
        solar_elevation_deg: float,
        solar_azimuth_deg: float,
        astronomical_tilt_deg: float,
        max_sacrifice_fraction: float = 1.0,
        diffuse_fraction: float = 0.15,
    ) -> DoseResult:
        """
        Find the smallest tilt offset that meaningfully protects the fruiting zone
        without disproportionately sacrificing top-canopy productivity.

        Offset direction
        ----------------
        The offset is applied TOWARD HORIZONTAL — i.e. it reduces the absolute
        tilt angle. This is the direction that increases overhead shadow footprint
        on the vine below the panel.
          morning  (astro_tilt > 0, panel faces east):  trial = astro − offset
          afternoon (astro_tilt < 0, panel faces west): trial = astro + offset
          near-noon (astro_tilt ≈ 0): panel already near-horizontal; no beneficial offset.

        Conditions (evaluated relative to the astronomical-tracking baseline)
        -----------------------------------------------------------------------
        A. Sun-side fruiting-face PAR drops below FRUITING_ZONE_TARGET_PAR (400 µmol).
           "Sun-side" = whichever vertical face receives more direct beam right now.
           This is the face where sunburn risk is highest.

        B. Top canopy does not lose more than TOP_CANOPY_TOLERANCE (15%) of its
           astronomical-baseline PAR. Computed from the horizontal top face of the
           canopy (top[]) which is most sensitive to panel tilt changes.

        C. Energy sacrifice (1 − cos(offset)) ≤ max_sacrifice_fraction.

        Parameters
        ----------
        ambient_par_umol       : total above-canopy PAR (µmol m⁻² s⁻¹)
        solar_elevation_deg    : solar elevation above horizon (°)
        solar_azimuth_deg      : solar azimuth (°)
        astronomical_tilt_deg  : sun-following tilt from pvlib (°, +east / −west)
        max_sacrifice_fraction : per-slot energy budget ceiling (fraction of max gen)
        diffuse_fraction       : diffuse fraction of ambient PAR (default 0.15)
        """
        if ambient_par_umol <= 0 or solar_elevation_deg <= 2:
            return DoseResult(
                success=False,
                rationale="Solar elevation ≤ 2° or PAR = 0; no shading meaningful.",
            )

        # Near-noon: panel already near-horizontal; moving toward horizontal
        # adds negligible additional shade. Skip entirely.
        if abs(astronomical_tilt_deg) < 3.0:
            return DoseResult(
                success=False,
                rationale=(
                    f"Near-noon: astro_tilt={astronomical_tilt_deg:.1f}° already near-horizontal; "
                    "no beneficial offset direction."
                ),
            )

        # Geometric feasibility pre-check:
        # The panel (width=panel_w, center height=panel_h) can only intercept
        # direct beam on the vine's vertical face (fruiting zone at fruiting_z)
        # when the required horizontal reach is ≤ half the panel width.
        # Below this elevation threshold, the direct side-beam always bypasses
        # the panel and tilt offsets cannot reduce fruiting-face PAR.
        # For this site (panel_w=1.13m, panel_h=2.05m, fruiting_z=0.6m):
        #   min_elevation ≈ arctan((2.05-0.6)/(1.13/2)) ≈ 68.6°
        panel_half = self.shadow_model.panel_width / 2.0
        panel_height = self.shadow_model.panel_height
        from config.settings import FRUITING_ZONE_HEIGHT_M
        min_elev_for_side_block = math.degrees(
            math.atan((panel_height - FRUITING_ZONE_HEIGHT_M) / max(panel_half, 0.001))
        )
        if solar_elevation_deg < min_elev_for_side_block:
            return DoseResult(
                success=False,
                rationale=(
                    f"Solar elevation {solar_elevation_deg:.1f}° < {min_elev_for_side_block:.1f}° "
                    f"— direct beam bypasses panel (panel half-width {panel_half:.3f}m reaches only "
                    f"{(panel_height - FRUITING_ZONE_HEIGHT_M) / math.tan(math.radians(solar_elevation_deg)):.2f}m "
                    f"vs {panel_half:.3f}m needed). Tracker stays at θ_astro; passive overhead "
                    "shading provides all available protection."
                ),
            )

        # Baseline at astronomical tracking
        try:
            astro_pz = self.shadow_model.compute_face_par_zones(
                total_par=ambient_par_umol,
                solar_elevation=solar_elevation_deg,
                solar_azimuth=solar_azimuth_deg,
                tracker_tilt=astronomical_tilt_deg,
                diffuse_fraction=diffuse_fraction,
            )
        except Exception as exc:
            return DoseResult(
                success=False,
                rationale=f"Shadow model error at baseline: {exc}",
            )

        # Sun-side face: the face receiving more direct beam has higher sunburn risk
        east_astro = float(astro_pz["east"][self.fruiting_zone_index])
        west_astro = float(astro_pz["west"][self.fruiting_zone_index])
        sun_side = "west" if west_astro >= east_astro else "east"
        fruiting_par_astro = max(east_astro, west_astro)

        # Top canopy baseline: maximum across the horizontal top face
        top_astro = float(max(astro_pz["top"]))

        # Offset direction: toward horizontal to increase overhead shadow
        sign_astro = 1 if astronomical_tilt_deg > 0 else -1

        tested: List[float] = []

        for offset in self.candidate_offsets:
            tested.append(offset)

            # Apply offset toward horizontal
            trial_tilt = astronomical_tilt_deg - sign_astro * offset

            try:
                trial_pz = self.shadow_model.compute_face_par_zones(
                    total_par=ambient_par_umol,
                    solar_elevation=solar_elevation_deg,
                    solar_azimuth=solar_azimuth_deg,
                    tracker_tilt=trial_tilt,
                    diffuse_fraction=diffuse_fraction,
                )
            except Exception:
                continue

            east_trial = float(trial_pz["east"][self.fruiting_zone_index])
            west_trial = float(trial_pz["west"][self.fruiting_zone_index])
            fruiting_par_trial = east_trial if sun_side == "east" else west_trial
            top_par_trial = float(max(trial_pz["top"]))

            top_par_fraction = top_par_trial / ambient_par_umol
            sacrifice_fraction = 1.0 - math.cos(math.radians(offset))

            # Condition A: sun-side fruiting face meaningfully shaded
            #   - Below absolute sunburn threshold, AND
            #   - At least 15% reduction from astronomical baseline
            cond_a = (
                fruiting_par_trial < self.fruiting_zone_target_par
                and fruiting_par_trial <= fruiting_par_astro * _FRUITING_IMPROVEMENT_MIN
            )

            # Condition B: top canopy doesn't lose more than tolerance% vs astronomical
            #   (panel at astro already shades canopy top substantially; we must not
            #    make it significantly worse, but the absolute fraction is not the goal)
            cond_b = (
                top_astro <= 0  # astronomical already zero → no further degradation check
                or top_par_trial >= top_astro * _TOP_CANOPY_TOLERANCE
            )

            # Condition C: energy sacrifice within budget
            cond_c = sacrifice_fraction <= max_sacrifice_fraction

            if cond_a and cond_b and cond_c:
                return DoseResult(
                    success=True,
                    chosen_offset_deg=float(offset),
                    offsets_tested=tested,
                    fruiting_par_at_chosen=round(fruiting_par_trial, 1),
                    top_par_fraction=round(top_par_fraction, 3),
                    energy_sacrifice_fraction=round(sacrifice_fraction, 5),
                    rationale=(
                        f"Offset {offset}° (trial_tilt={trial_tilt:.1f}°): "
                        f"{sun_side}-face fruiting PAR {fruiting_par_trial:.0f} µmol "
                        f"(astro={fruiting_par_astro:.0f}, target <{self.fruiting_zone_target_par:.0f}), "
                        f"top canopy {top_par_trial:.0f}/{top_astro:.0f} µmol "
                        f"({top_par_trial / max(top_astro, 1) * 100:.0f}% of baseline), "
                        f"sacrifice {sacrifice_fraction * 100:.2f}%."
                    ),
                )

        # No offset qualified — build diagnostic
        rationale_parts = [
            f"No offset in {self.candidate_offsets}° (toward-horizontal) satisfied conditions. "
            f"Baseline: {sun_side}-face fruiting={fruiting_par_astro:.0f} µmol, "
            f"top={top_astro:.0f} µmol. Staying at θ_astro."
        ]
        if tested:
            last = tested[-1]
            last_tilt = astronomical_tilt_deg - sign_astro * last
            try:
                pz = self.shadow_model.compute_face_par_zones(
                    total_par=ambient_par_umol,
                    solar_elevation=solar_elevation_deg,
                    solar_azimuth=solar_azimuth_deg,
                    tracker_tilt=last_tilt,
                    diffuse_fraction=diffuse_fraction,
                )
                fp = pz["east"][self.fruiting_zone_index] if sun_side == "east" else pz["west"][self.fruiting_zone_index]
                tp = max(pz["top"])
                sf = 1.0 - math.cos(math.radians(last))
                fails = []
                if not (fp < self.fruiting_zone_target_par and fp <= fruiting_par_astro * _FRUITING_IMPROVEMENT_MIN):
                    fails.append(
                        f"fruiting {fp:.0f} µmol (need <{self.fruiting_zone_target_par:.0f} "
                        f"and ≤{fruiting_par_astro * _FRUITING_IMPROVEMENT_MIN:.0f})"
                    )
                if top_astro > 0 and tp < top_astro * _TOP_CANOPY_TOLERANCE:
                    fails.append(
                        f"top canopy {tp:.0f} µmol < {top_astro * _TOP_CANOPY_TOLERANCE:.0f} "
                        f"({_TOP_CANOPY_TOLERANCE * 100:.0f}% of baseline {top_astro:.0f})"
                    )
                if sf > max_sacrifice_fraction:
                    fails.append(f"sacrifice {sf * 100:.2f}% > budget {max_sacrifice_fraction * 100:.2f}%")
                rationale_parts.append(f"At {last}°: {'; '.join(fails) or 'unknown'}.")
            except Exception:
                pass

        return DoseResult(
            success=False,
            chosen_offset_deg=0.0,
            offsets_tested=tested,
            rationale=" ".join(rationale_parts),
        )

    @staticmethod
    def energy_sacrifice_fraction(offset_deg: float) -> float:
        """Approximate per-slot energy sacrifice: 1 − cos(offset_deg)."""
        return 1.0 - math.cos(math.radians(offset_deg))


# ---------------------------------------------------------------------------
# CLI smoke test
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    from src.solar_geometry import ShadowModel

    # --- InterventionGate tests ---
    gate = InterventionGate()
    print("=== InterventionGate (geometry-first) ===\n")

    cases = [
        # (tleaf, ghi, cwsi, helps, label)
        (33.0, 800, 0.5, True,  "All stress conditions met → PASS (geometry decides next)"),
        (25.0, 800, 0.5, True,  "Tleaf < 30°C → no heat stress"),
        (33.0, 50,  0.5, True,  "GHI < 100 → night/deep cloud"),
        (33.0, 800, 0.2, True,  "CWSI < 0.4 → vine healthy"),
        (33.0, 300, 0.5, True,  "GHI < 400 → low radiation load"),
        (33.0, 800, 0.5, False, "FvCB says shading hurts → RuBP-limited"),
        # Cases that were previously hard-blocked by time/month rules:
        (33.0, 800, 0.5, True,  "9:00 morning (no longer blocked — geometry will decide)"),
        (33.0, 800, 0.5, True,  "May heat wave (no longer blocked — geometry will decide)"),
    ]
    for tleaf, ghi, cwsi, helps, label in cases:
        dec = gate.evaluate(tleaf_c=tleaf, ghi_w_m2=ghi, cwsi=cwsi, shading_helps=helps)
        status = "PASS" if dec.passed else "BLOCK"
        reason = dec.rejection_reason or "—"
        print(f"  [{status}] {label}")
        print(f"          {reason}\n")

    # --- TradeoffEngine test with real Sde Boker summer day ---
    print("=== TradeoffEngine — real July-15 Sde Boker trajectories ===\n")
    import pandas as pd
    import pvlib

    shadow = ShadowModel()
    engine = TradeoffEngine(shadow_model=shadow)

    loc = pvlib.location.Location(30.87, 34.79, tz='Asia/Jerusalem', altitude=475)
    times = pd.date_range('2025-07-15 06:00', '2025-07-15 19:00', freq='2h', tz='Asia/Jerusalem')
    sol = loc.get_solarposition(times)

    print(f"  {'Time':>6}  {'Elev':>6}  {'Azim':>6}  {'Astro':>6}  {'Result':>12}  Notes")
    print(f"  {'-'*70}")
    for t in times:
        elev = float(sol.loc[t, 'apparent_elevation'])
        azim = float(sol.loc[t, 'azimuth'])
        if elev < 5:
            continue
        tr = shadow.compute_tracker_tilt(azim, elev)
        astro = tr['tracker_theta']
        par = min(elev * 15, 1100)
        res = engine.find_minimum_dose(
            ambient_par_umol=par,
            solar_elevation_deg=elev,
            solar_azimuth_deg=azim,
            astronomical_tilt_deg=astro,
            max_sacrifice_fraction=0.08,
        )
        status = f"offset={res.chosen_offset_deg:.0f}°" if res.success else "no dose"
        print(f"  {t.strftime('%H:%M'):>6}  {elev:>6.1f}  {azim:>6.1f}  {astro:>6.1f}  {status:>12}  {res.rationale[:60]}")

    print()
    print("  Energy sacrifice by offset:")
    for off in [0, 3, 5, 8, 10, 15, 20]:
        s = TradeoffEngine.energy_sacrifice_fraction(off)
        print(f"    {off:2d}°  →  {s * 100:.2f}%")