Model/domain/modelling/generators/solar_recommendation.py

"""The Solar PV Recommendation Generator (ADR-0026).

Offers competing whole-array PV Options built from real Google Solar imagery
(a typed `SolarPotential`), not an estimate. Unlike the heating bundles, the
SAP scoring side is already mature — the calculator does Appendix M β-split,
G4 diverter, SEG export, batteries and monthly E_PV — so this generator fixes
the *recommendation* side: where the array config comes from, how it is
conservatively sized, the new PV Overlay surface, and the composite cost.

This slice covers the generation-calibrated overshading derivation; config
selection, the overlay and `recommend_solar` land in later slices.
"""

from __future__ import annotations

from typing import Optional

from datatypes.epc.domain.epc_property_data import (
    EpcPropertyData,
    PhotovoltaicArray,
    PvBatteries,
    PvBattery,
)
from datatypes.epc.domain.field_mappings import PROPERTY_TYPE_LOOKUP
from domain.geospatial.planning_restrictions import PlanningRestrictions
from domain.modelling.products import Products, SolarCostInputs
from domain.modelling.measure_type import MeasureType
from domain.modelling.recommendation import Cost, MeasureOption, Recommendation
from domain.modelling.simulation import EpcSimulation, SolarOverlay
from domain.modelling.solar_potential import (
    SolarPanelConfiguration,
    SolarPotential,
    SolarRoofSegment,
)
from domain.sap10_calculator.rdsap.cert_to_inputs import (
    pv_annual_solar_radiation_kwh_per_m2,
)
from repositories.product.product_repository import ProductRepository

_SOLAR_SURFACE = "Solar PV"
_SOLAR_MEASURE_TYPE = MeasureType.SOLAR_PV

# The fixed, representative battery capacity for the with-battery variant
# (ADR-0026) — a flagged estimate (see the rate sheet), 5 kWh.
_BATTERY_CAPACITY_KWH = 5.0
# Watts → kilowatts for peak-power.
_WATTS_PER_KW = 1000.0
# The dwelling's PV connects to its own meter (the after-cert §19 "Connected to
# the dwelling's meter: Yes"). Non-load-bearing for the SAP cascade; carried for
# fidelity. 1 = connected, the modal install case.
_PV_CONNECTED_TO_DWELLING = 1

# A roof plane within this many degrees of due north (0°/360°, Google compass
# convention) is dropped: it generates little and is not worth panelling. The
# legacy `GoogleSolarApi.NORTH_FACING_AZIMUTH_RANGE` used the same ±30° band.
_NORTH_AZIMUTH_HALF_WIDTH = 30.0
# Cap usable panels at ~70% of Google's maxArrayPanelsCount — imagery misses
# obstructions (flues, dormers) and MCS wants a ~0.3 m edge setback, so the
# theoretical maximum is optimistic.
_USABLE_PANEL_FRACTION = 0.70
# At most this many competing configs go to the Optimiser (× battery on/off).
_MAX_CONFIGS = 5

# Google Solar inverter DC→AC efficiency — the canonical rate the legacy
# `GoogleSolarApi.dc_to_ac_rate` uses (mid of the 93–98% range); distinct from
# the unrelated no-API `MEDIAN_WATTAGE_TO_AC` fallback.
_DC_TO_AC_RATE = 0.955
# SAP 10.2 Appendix M PV annual output: E = 0.8 × kWp × S × ZPV. The 0.8 is the
# in-system performance factor; back-solving for ZPV isolates the effective
# overshading once orientation (S) and size (kWp) are divided out.
_SAP_PV_PERFORMANCE_FACTOR = 0.8

# ADR-0026 overshading cutpoints — the lower bound of each RdSAP bucket's ZPV
# midpoint band {1:1.0, 2:0.8, 3:0.5, 4:0.35}: ≥0.90→1, 0.65–0.90→2,
# 0.425–0.65→3, <0.425→4. ZPV > 1 (Google beats SAP's unshaded model) clamps
# to 1 via the ≥0.90 branch. RdSAP10 has no "Severe" 5th bucket.
_OVERSHADING_LOWER_BOUNDS: tuple[tuple[float, int], ...] = (
    (0.90, 1),
    (0.65, 2),
    (0.425, 3),
)
_OVERSHADING_HEAVY_CODE = 4


def overshading_code_from_zpv(zpv_target: float) -> int:
    """Snap a back-solved effective shading factor ZPV to the RdSAP overshading
    code (1 = very little/none … 4 = heavy), per the ADR-0026 cutpoints."""
    for lower_bound, code in _OVERSHADING_LOWER_BOUNDS:
        if zpv_target >= lower_bound:
            return code
    return _OVERSHADING_HEAVY_CODE


def segment_overshading_code(
    segment: SolarRoofSegment, panel_capacity_watts: float
) -> int:
    """Derive a roof segment's RdSAP overshading code from Google's expected
    generation (ADR-0026). Google's `yearlyEnergyDcKwh` already encodes real
    orientation, tilt and shading; dividing its AC equivalent by SAP's own
    unshaded annual output (0.8 × kWp × S) cancels orientation/tilt and leaves
    the effective overshading factor ZPV, which snaps to the bucket."""
    kwp: float = segment.panels_count * panel_capacity_watts / 1000.0
    s: float = pv_annual_solar_radiation_kwh_per_m2(
        segment.sap_orientation, segment.sap_pitch_code
    )
    unshaded_ac_kwh: float = _SAP_PV_PERFORMANCE_FACTOR * kwp * s
    if unshaded_ac_kwh <= 0.0:
        # No panels, or an orientation the calculator scores as zero — nothing
        # to shade; the modal "no shading" code.
        return 1
    generation_ac_kwh: float = segment.yearly_energy_dc_kwh * _DC_TO_AC_RATE
    zpv_target: float = generation_ac_kwh / unshaded_ac_kwh
    return overshading_code_from_zpv(zpv_target)


def _is_north_facing(azimuth_degrees: float) -> bool:
    """Whether a roof plane faces within 30° of due north (Google compass: 0°/
    360° = N), handling the 360° wrap."""
    return (
        azimuth_degrees <= _NORTH_AZIMUTH_HALF_WIDTH
        or azimuth_degrees >= 360.0 - _NORTH_AZIMUTH_HALF_WIDTH
    )


def _drop_north_segments(config: SolarPanelConfiguration) -> SolarPanelConfiguration:
    """Trim a configuration to its non-north planes, recomputing the array's
    panel count and expected generation to the usable subset."""
    kept: tuple[SolarRoofSegment, ...] = tuple(
        segment
        for segment in config.segments
        if not _is_north_facing(segment.azimuth_degrees)
    )
    return SolarPanelConfiguration(
        panels_count=sum(segment.panels_count for segment in kept),
        yearly_energy_dc_kwh=sum(segment.yearly_energy_dc_kwh for segment in kept),
        segments=kept,
    )


def select_conservative_configs(
    potential: SolarPotential,
) -> tuple[SolarPanelConfiguration, ...]:
    """Choose up to five conservatively-sized array configs for the Optimiser
    (ADR-0026): drop north-facing planes, cap usable panels at ~70% of
    maxArrayPanelsCount, then sample five spanning min→max by expected
    generation (the size-suitability proxy) so the size/cost choice is genuine.
    Returns an empty tuple when nothing usable remains."""
    panel_cap: float = _USABLE_PANEL_FRACTION * potential.max_array_panels_count
    feasible: list[SolarPanelConfiguration] = [
        trimmed
        for config in potential.configurations
        for trimmed in (_drop_north_segments(config),)
        if trimmed.segments and trimmed.panels_count <= panel_cap
    ]
    if not feasible:
        return ()
    # Collapse rungs that trimmed to the same usable size (north-drop can make
    # distinct original rungs coincide), keeping the higher-generation layout —
    # the Optimiser's dial is panel count (≈ kWp ≈ cost), so duplicates of the
    # same size add no choice.
    best_by_size: dict[int, SolarPanelConfiguration] = {}
    for config in feasible:
        incumbent = best_by_size.get(config.panels_count)
        if incumbent is None or config.yearly_energy_dc_kwh > incumbent.yearly_energy_dc_kwh:
            best_by_size[config.panels_count] = config
    unique: list[SolarPanelConfiguration] = sorted(
        best_by_size.values(), key=lambda c: c.yearly_energy_dc_kwh
    )
    if len(unique) > _MAX_CONFIGS:
        last: int = len(unique) - 1
        sampled_indices: list[int] = sorted(
            {round(i * last / (_MAX_CONFIGS - 1)) for i in range(_MAX_CONFIGS)}
        )
        unique = [unique[index] for index in sampled_indices]
    return tuple(sorted(unique, key=lambda c: c.panels_count))


def _array_for_segment(
    segment: SolarRoofSegment, panel_capacity_watts: float
) -> PhotovoltaicArray:
    """Project a chosen roof segment into a SAP `PhotovoltaicArray`: peak power
    from its panels, orientation/pitch from its geometry, and the
    generation-calibrated overshading code (ADR-0026)."""
    return PhotovoltaicArray(
        peak_power=segment.panels_count * panel_capacity_watts / _WATTS_PER_KW,
        pitch=segment.sap_pitch_code,
        orientation=segment.sap_orientation,
        overshading=segment_overshading_code(segment, panel_capacity_watts),
    )


def _solar_overlay(
    config: SolarPanelConfiguration,
    panel_capacity_watts: float,
    has_cylinder: bool,
    with_battery: bool,
) -> SolarOverlay:
    """Build the `SolarOverlay` for one array config variant: one
    `PhotovoltaicArray` per segment, export ensured, a diverter when the
    dwelling has a cylinder, and a battery for the with-battery variant."""
    return SolarOverlay(
        photovoltaic_arrays=[
            _array_for_segment(segment, panel_capacity_watts)
            for segment in config.segments
        ],
        # App G4 routes surplus PV to the cylinder immersion; a combi has nothing
        # to divert to, so leave the field unset (None) when there is no cylinder.
        pv_diverter_present=True if has_cylinder else None,
        pv_connection=_PV_CONNECTED_TO_DWELLING,
        is_dwelling_export_capable=True,
        pv_batteries=(
            PvBatteries(pv_battery=PvBattery(battery_capacity=_BATTERY_CAPACITY_KWH))
            if with_battery
            else None
        ),
    )


def _option(
    config: SolarPanelConfiguration,
    panel_capacity_watts: float,
    has_cylinder: bool,
    with_battery: bool,
    products: ProductRepository,
) -> MeasureOption:
    """Assemble one competing Solar PV Measure Option for a config variant."""
    peak_power_kwp: float = config.panels_count * panel_capacity_watts / _WATTS_PER_KW
    cost: Cost = Products().solar_bundle_cost(
        SolarCostInputs(
            peak_power_kwp=peak_power_kwp,
            has_cylinder=has_cylinder,
            has_battery=with_battery,
        )
    )
    battery_suffix: str = " with a 5 kWh battery" if with_battery else ""
    description: str = (
        f"Install a {peak_power_kwp:.1f} kWp roof-mounted solar PV array"
        f"{battery_suffix}, ensuring an export meter"
    )
    return MeasureOption(
        measure_type=_SOLAR_MEASURE_TYPE,
        description=description,
        overlay=EpcSimulation(
            solar=_solar_overlay(
                config, panel_capacity_watts, has_cylinder, with_battery
            )
        ),
        cost=cost,
        material_id=products.get(_SOLAR_MEASURE_TYPE).id,
    )


def recommend_solar(
    epc: EpcPropertyData,
    products: ProductRepository,
    solar_potential: Optional[SolarPotential],
    restrictions: PlanningRestrictions = PlanningRestrictions(),
) -> Optional[Recommendation]:
    """Return a "Solar PV" Recommendation of competing whole-array Options —
    up to five conservatively-sized configs × {no battery, battery} — for an
    eligible dwelling with feasible Google solar potential, else None
    (ADR-0026). A free Optimiser candidate; the Optimiser owns whether and at
    what size to install it."""
    if solar_potential is None or not _solar_eligible(epc, restrictions):
        return None
    configs: tuple[SolarPanelConfiguration, ...] = select_conservative_configs(
        solar_potential
    )
    if not configs:
        return None
    has_cylinder: bool = bool(epc.has_hot_water_cylinder)
    capacity: float = solar_potential.panel_capacity_watts
    options: list[MeasureOption] = [
        _option(config, capacity, has_cylinder, with_battery, products)
        for config in configs
        for with_battery in (False, True)
    ]
    return Recommendation(surface=_SOLAR_SURFACE, options=tuple(options))


def _solar_eligible(
    epc: EpcPropertyData, restrictions: PlanningRestrictions
) -> bool:
    """Solar PV suits a non-flat house/bungalow that is not fabric-protected and
    has no existing PV (ADR-0026). Eligibility encodes only physical/legal
    installability — the Optimiser owns the economics. A conservation area does
    NOT block PV (offered, installed sympathetically); a listed/heritage
    protection (`blocks_internal`) does — the same gate as ASHP."""
    if restrictions.blocks_internal:
        return False
    if not _is_house_or_bungalow(epc):
        return False
    return not _has_existing_pv(epc)


def _has_existing_pv(epc: EpcPropertyData) -> bool:
    """Whether the dwelling already has PV — the *existing* arrays on the EPC
    (existing-PV top-up is deferred), distinct from the Google potential."""
    arrays: Optional[list[PhotovoltaicArray]] = epc.sap_energy_source.photovoltaic_arrays
    return bool(arrays)


def _is_house_or_bungalow(epc: EpcPropertyData) -> bool:
    """Whether the dwelling is a house or bungalow (not a flat/maisonette). The
    Elmhurst path lodges the name; the API path a stringified RdSAP code
    (`PROPERTY_TYPE_LOOKUP`: 0 House, 1 Bungalow, 2 Flat, 3 Maisonette)."""
    raw: str = (epc.property_type or "").strip()
    if raw.lower() in ("house", "bungalow"):
        return True
    if raw.isdigit():
        return PROPERTY_TYPE_LOOKUP.get(int(raw)) in ("House", "Bungalow")
    return False