Slice 1 of solid-wall insulation. BuildingPartOverlay gains a
wall_insulation_thickness field; the generic applicator already folds it onto
SapBuildingPart by name. With wall_insulation_type=1 (EWI) / 3 (IWI) + 100 mm,
the calculator derives the post-insulation U-value (§5.8 documentary path,
λ=0.04 default) — and for IWI also lowers the thermal-mass parameter. Two new
Elmhurst before/after cascade pins (solid-brick EWI + IWI, cert 001431)
reproduce the re-lodged after at abs(diff) <= 1e-4 across SAP/CO2/PE.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The Plan derives its Valuation Uplift (ADR-0018) from its baseline -> post
band jump and works+contingency cost, given one external input — the
Property's current market value (a Property Valuation, mostly absent).
`Plan.valuation` / `Plan.baseline_epc_rating` are derived like the other
headline figures; `PlanModel.from_domain` maps the £ forms to the live
plan.valuation_* columns (NULL when no value — the percentage is not
persisted on those columns). `Property.current_market_value` is the new
optional source; the orchestrator threads it onto the Plan. `run_one`
takes a `current_market_value` so the harness can value the uplift, and
the sense-check table shows the average % (always) plus the £ forms when
known.
Sourcing the current market value (upload / default) remains deferred
(ADR-0018); it is None throughout until that lands, so the columns stay
NULL at scale.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The financial-uplift model per ADR-0018. `estimate_valuation_uplift(
current_band, target_band, current_value=None, total_cost=None)` returns
a `ValuationUplift`: band-transition uplift compounded from four broker
tables (MoneySupermarket / Lloyds per-step, Knight Frank / Rightmove
whole-jump), taking min/max/mean across the covering sources. Always a
percentage; absolute £ forms (increase at each bound + post-retrofit
value) only when a current market value is supplied; the 2x ROI cap
rescales the percentages and can only bite once a value is known. A
non-improving jump is a clean 0% no-op.
Pure function, no external dependency. Persisting it (where the value
lands) and sourcing the current market value stay deferred (ADR-0018).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Expand half of the recommendation_materials retirement (ADR-0017). A
Plan Measure installs a single Product, so thread its catalogue id end to
end — Product.id -> MeasureOption.material_id -> PlanMeasure.material_id
-> recommendation.material_id — replacing the per-material BOM child
table with one nullable column on the row. ProductPostgresRepository
reads the id from MaterialRow; the four fabric generators set it on their
Option; the orchestrator carries it onto the Plan Measure; the mirror
declares + maps the column. Optional throughout (the JSON stopgap
catalogue carries no ids -> NULL).
The multi-measure integration test now pins each persisted measure's
material_id to its seeded MaterialRow id. Migration spec (live column
must be added before this deploys; contraction is the owner's next step)
in docs/migrations/recommendation-material-id.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PortfolioGoal is domain vocabulary (a Scenario's goal — legacy planning branches
on PortfolioGoal.INCREASING_EPC), so it belongs in domain/ co-located with
scenario.py, mirroring how domain/epc/wall_type.py holds an enum that
infrastructure/ imports. This lets the consolidated ScenarioModel (next slice)
source the goal enum from domain without an infra→backend dependency.
portfolio.py keeps a re-export so every existing
`from ...portfolio import PortfolioGoal` caller is unaffected.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
`_plan_for` now scores the baseline + every cumulative prefix once
(`cascade_scores`, best-practice order) and reuses those Scores for both the
role-3 marginal attribution and a per-measure bill cascade: bill each prefix at
one Fuel Rates snapshot and take consecutive Bill deltas as each measure's
marginal delivered-kWh and £ saving. Saving is signed (ventilation is
negative) and telescopes exactly to the Plan headline savings, because the
Plan's baseline/post Bills are now the same cascade endpoints (`bills[0]` /
`bills[-1]`) — which also drops the redundant standalone baseline `calculate`.
`recommendation.kwh_savings` / `energy_cost_savings` are filled from these.
Adds `Bill.total_consumption_kwh` (shared by Plan + the orchestrator). Pinned
end-to-end on the real calculator: Σ per-measure savings == the Plan totals
(ADR-0014 amendment).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
`PlanMeasure` grows optional `kwh_savings` (delivered energy) and
`energy_cost_savings` (£) — its slice of the telescoping bill cascade, signed
so positive is a saving and `None` until billing runs. `RecommendationRow`
declares the matching live `recommendation.kwh_savings` /
`energy_cost_savings` columns and maps them in `from_domain` (None → NULL).
The vestigial `recommendation.energy_savings` stays undeclared (legacy = 0).
No FE migration — the columns already exist on the live table (ADR-0014 / 0017).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pull the cumulative-prefix scoring out of `marginal_impacts` into a reusable
`cascade_scores(scorer, baseline, overlays) -> list[Score]` (index 0 the
baseline, one calculator run per prefix) plus a pure `marginals_from_scores`.
Each Score carries its SapResult, so the next slice's telescoping per-measure
bill cascade can re-bill the same prefixes the role-3 attribution already
scores — no extra `calculate` calls (ADR-0014 / ADR-0016). `marginal_impacts`
now delegates; behaviour unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Plan gains optional baseline_bill / post_bill (the Bills derived for the
unmodified and post-package end-states at one Fuel Rates snapshot) and derives
the four plan-level columns: post_energy_bill (post total), energy_bill_savings
(baseline - post), post_energy_consumption (Σ post section kWh), and
energy_consumption_savings (baseline - post delivered kWh). All return None until
billing runs (persisted as NULL), so existing Plan construction and the
not-yet-wired orchestrator stay green. Plan-level only; per-measure savings are a
later slice (ADR-0014 amendment).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Score gains sap_result: Optional[SapResult], populated by PackageScorer with the
calculator output its headline figures came from. This lets the Modelling stage
price the post-package (and baseline) end-state via Bill Derivation reusing a
SapResult already computed by the optimiser's re-score / the orchestrator's
baseline score — no second calculate (ADR-0014 amendment). The optimiser reads
only sap_continuous, so it stays domain-agnostic and the stub scorers (which omit
sap_result) are unaffected — all optimiser tests pass unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The warm-start (and max-gain fallback) now price each forced Measure Dependency
the candidate triggers, not just inject it afterwards: optimise/optimise_min_cost
fold dependencies into each candidate's cost+gain via _augmented_cost_gain, and
optimise_package scores each dependency's true role-1 signal (_with_role1_signals)
instead of the 0.0 placeholder. This stops the min-cost objective (i) ignoring the
~£900 a wall drags in (a wall-free package reaching target can be cheaper) and
(ii) picking a small-gain wall whose mandatory ventilation (down to -5 SAP) makes
it net-negative, which repair cannot un-pick.
Budget is now a hard envelope: the constraint applies to the augmented (measure +
its ventilation) cost, so a wall that fits alone but whose ventilation would bust
the budget is DROPPED rather than forced over budget. This reverses the earlier
'forced regardless of budget' call (which made sense when selection was
ventilation-blind). Safety invariant intact — presence still injected on every
path; we just never recommend a wall we can't afford to ventilate. ADR-0016
amendment updated. 94 modelling+orchestration tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Rewire the objective per the ADR-0016 amendment. With a target_sap (Increasing
EPC): warm-start optimise_min_cost (cheapest package reaching target_gain =
target_sap - baseline within budget) -> inject dependencies -> re-score ->
repair toward target; if the warm-start is infeasible or the repaired package
still falls short on the true score, fall back to max-gain-within-budget (best
effort). Without a target_sap: max-gain (unchanged). The min-cost objective
stops at the target without overshooting into a higher band; surplus budget is
left unspent. Extracted _max_gain_package (no-target path + fallback) and
_repair_to_target (inject + re-score + greedy repair). Dependency injection and
the repair loop are preserved; all prior optimiser + dependency tests pass
unchanged. Ventilation-aware *selection* is the next slice; injection is still
post-warm-start here.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Exact-enumeration sibling to optimise(): pick <=1 option per group to minimise
total cost subject to total gain >= target_gain and cost <= budget (None =
unconstrained). Ties broken toward higher gain ('recommend more'). Returns None
when no package within budget reaches the target (caller falls back to
max-gain); a non-positive target is met by the empty package. This is the
warm-start objective for an Increasing EPC goal per the ADR-0016 amendment
(least-cost-to-target, not max-gain). Dependency-blind for now; ventilation-aware
selection lands in a later slice.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
measure_dependency.py now owns only the selection semantics: the trigger set and
the forced-edge wrapping. It delegates production (detection + pricing) to
recommend_ventilation and wraps the returned Recommendation into the
MeasureDependency, picking the cheapest Option (one MEV today; readies the seam
for MEV-c / MVHR). The orchestrator's _measure_dependencies call is unchanged.
Trimmed the now-redundant option-detail assertions — those live in
test_ventilation_recommendation. 138 pass, behaviour-preserving.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recommend_ventilation(epc, products) does the same two jobs as wall/roof/floor —
detect applicability (the has_ventilation guard) and price the work (2 MEV units
+ contingency) — and returns a Recommendation. Ventilation is a Recommendation
like the others; what makes it special (forced when fabric is selected, excluded
from the free pool) stays in the Measure Dependency layer. Detect + price now
live in generators/, not inline in measure_dependency.py. Note it is NOT run by
the candidate-pool runner — it is consumed only by the dependency path.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
domain/modelling/ had grown to 15 flat modules. Group the behavioural ones into
subpackages — generators/ (wall/roof/floor Recommendation Generators), scoring/
(overlay applicator, package scorer, role-1/3 scoring), optimisation/ (optimiser
+ measure dependency) — and leave the shared value-object vocabulary
(recommendation, plan, scenario, product, contingencies, simulation) flat at the
top, since it is imported everywhere. Pure move + import-path rewrite across 89
import sites; no behaviour change. 136 pass, pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ModellingOrchestrator builds the ventilation dependency per Property
(suppressed when already mechanically ventilated) and passes it to
optimise_package, so a selected wall measure forces MEV into the package before
the re-score. Ventilation joins the role-3 cascade in best-practice order
(walls -> roof -> ventilation -> floor) and persists as a Plan Measure carrying
its real negative marginal and its cost. Added the mechanical_ventilation
contingency rate (0.26, per legacy Costs.CONTINGENCIES). Integration test now
seeds the ventilation Product and asserts the forced measure persists with
<=0 SAP and 2x900 cost; the full-pipeline test seeds the Product too (the
dependency is built for every not-yet-ventilated dwelling). On 000490 the real
calculator scores MEV at -1.275 SAP.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ventilation_dependency(epc, products) returns the forced 'fabric requires
ventilation' edge: triggers = MEASURES_NEEDING_VENTILATION (cavity/internal/
external wall, mirroring legacy assumptions.measures_needing_ventilation), and a
required Option installing decentralised MEV (mechanical_ventilation_kind=
EXTRACT_OR_PIV_OUTSIDE), priced at two fully-loaded units. Returns None when the
dwelling already lodges a mechanical ventilation kind (legacy has_ventilation
guard), so MEV is never forced onto an already-ventilated dwelling.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
MeasureDependency(triggers, required) is a data-declared 'A requires B' edge.
optimise_package gains a dependencies param: after the warm-start it injects any
dependency whose triggers intersect the selected measure-types, BEFORE the
whole-package re-score, so the dependency's (negative) SAP lands in the truthful
figure and the undershoot/repair decision (ADR-0016). Forced — injected
regardless of budget — but its cost counts toward package spend, so repair sees
less headroom. Repair candidates fold in any dependency they newly trigger, so
their marginal SAP-per-£ and incremental cost are truthful. The dependency never
competes in the optimiser pool. Returned selected includes the injected deps.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
VentilationOverlay (all-optional partial of SapVentilation) + EpcSimulation.
ventilation; apply_simulations folds it onto sap_ventilation, creating one when
the baseline lodged none. This is the surface a Measure Dependency (ventilation)
writes — whole-dwelling, no building part.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 2 of #1160 — the ADR-0016 truth step on top of the warm-start
knapsack. optimise_package(groups, scorer, baseline_epc, budget,
target_sap) -> OptimisedPackage:
warm-start optimise() (role-1 signal) → re-score the chosen package on
the real scorer (role-2 truth) → while the true SAP undershoots
target_sap and budget remains, greedy-add the untreated-group Option
with the best *marginal* SAP-per-£ (re-scored, not the role-1 signal),
re-score, repeat until the target is met, nothing positive-marginal is
affordable, or the budget is spent.
`Scorer` is a structural Protocol (PackageScorer satisfies it) so the
repair loop is tested with a stub scorer — no calculator, runs on ARM.
The key case: role-1 under-counts roof so the warm-start skips it, the
re-score undershoots, and repair adds roof back to hit the target. 3
repair tests + the 6 core tests; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 1 of #1160. Recycles the GainOptimiser/CostOptimiser formulation
(≤1 Option per Recommendation, maximise SAP gain subject to budget) as a
clean typed DDD function — but as an exact pure-Python multiple-choice
knapsack rather than the legacy `mip` MILP, since mip's CBC backend does
not load on aarch64 (so the legacy solver path can't run / be tested
here). At retrofit scale the candidate space Π(|group|+1) is tiny, so
exhaustive enumeration is exact and instant; ADR-0016 only needs the
knapsack as a warm-start signal anyway (the truthful figure comes from
the whole-package re-score + repair, next slice).
`optimise(groups, budget) -> list[ScoredOption]`: maximise total gain,
tie-break toward lower cost, skip-per-group covers "select none". 6 tests
(budget-bound selection, ≤1/group, unconstrained, budget-too-small,
empty groups, partial-affordability); pyright strict clean.
Multi-phase remains descoped (ADR-0005) — single-phase optimiser.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 2 of #1157. The per-Property output of one Scenario's modelling
run, per ADR-0017.
- PlanMeasure: a selected Measure Option frozen with its installed Cost
and role-3 (final-package cascade) attributed MeasureImpact — the
output counterpart of a Recommendation's candidate Option.
- Plan: the selected Plan Measures + baseline/post-retrofit Scores.
Single-phase (ADR-0005); derives the persisted headline figures —
cost_of_works, contingency_cost, co2_savings_kg_per_yr (kg; the mapper
converts to tonnes), post_sap_continuous, and post_epc_rating (band
from the rounded SAP via Epc.from_sap_score).
1 unit test, pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 1 of the #1157 build. The FE creates a Scenario and passes only
its id to the pipeline; the Modelling stage reads it back here.
- domain/modelling/scenario.py: thin `Scenario(id, goal, goal_value,
budget, is_default)` — the slice the stage uses today (goal/budget for
the Optimiser later; is_default drives plan.is_default). No phases
(ADR-0005); legacy file-path/aggregate columns not modelled.
- infrastructure/postgres/scenario_table.py: `ScenarioRow` SQLModel
mirror of the live `scenario` table (ADR-0017), declaring only the
read columns; goal mapped as its string value.
- ScenarioPostgresRepository.get_many(scenario_ids) -> list[Scenario]:
bulk read, input-order-preserving, raises on a missing id.
The method shape lives on the concrete repo for now; it is promoted to
an @abstractmethod on the port when the real orchestrator is wired and
the bare-stub instantiations retire (keeps the stubbed Modelling wiring
composing meanwhile). 2 tests, pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Completes #1159 end-to-end with solid and suspended-floor before/after
cascade pins on cert 001431, both closing at delta 0.000000.
Adds floor_insulation_type_str to BuildingPartOverlay (the generic
field-fold applicator picks it up with no change) and has
recommend_floor_insulation set it to "Retro-fitted". Insulating an
as-built floor re-lodges its insulation as retro-fitted; the calculator
keys on this for a suspended timber floor's sealed/unsealed
determination (cert_to_inputs.py: "retro" + no U-value supplied →
sealed). Without it the suspended-floor cascade left a +1.40 SAP gap
(the floor stayed "unsealed", wrong U-value); with it the cascade
closes exactly. Solid floors are unaffected by the seal logic and stay
at delta 0; both Elmhurst after-certs lodge "Retro-fitted", so setting
it uniformly is faithful.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Completes #1158 end-to-end. recommend_loft_insulation now emits a
300 mm overlay (was 270 mm). The Elmhurst before/after re-lodgement of
the loft-insulation measure on cert 001431 lodges the after-cert at
300 mm roof insulation; pinning before→overlay→after requires the
overlay to match that depth — at 270 mm the cascade left a +0.173 SAP
residual, at 300 mm it closes at delta 0.000000 on SAP/CO2/PE.
Adds test_loft_overlay_reproduces_the_relodged_after and updates the
roof generator unit test's thickness assertion to 300.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recommend_floor_insulation(epc, products) detects an uninsulated ground floor
(SapBuildingPart.floor_insulation_thickness blank/zero) and its construction
from floor_construction_type — 'Suspended timber' -> suspended_floor_insulation,
'Solid' -> solid_floor_insulation — emitting the matching single Option (a
floor is one construction, like a cavity wall) with the overlay
(floor_insulation_thickness = 100 mm) and a priced Cost (ground-floor area x
the Product's fully-loaded unit cost + contingency).
- building_geometry.ground_floor_area(epc, identifier): the lowest floor's
(floor == 0) area. Pinned 14.85 m^2 on 000490 MAIN.
- BuildingPartOverlay gains floor_insulation_thickness (generic Applicator
writes it unchanged). suspended (0.20) / solid (0.26) floor contingencies.
Progress on #1159 (generator + geometry); end-to-end + Elmhurst pin pending
the orchestrator (#1157) and parser. Four behaviour tests (suspended / solid
/ none / cost) + geometry pin. pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recommend_loft_insulation(epc, products) detects an uninsulated main loft
(SapBuildingPart.roof_insulation_thickness == 0) and emits a
Recommendation("Roof") with one loft_insulation Option carrying the overlay
(roof_insulation_thickness = 270 mm, the recommended top-up) and a priced
Cost (roof area x the Product's fully-loaded unit cost + contingency).
- building_geometry.roof_area(epc, identifier): the part's greatest
per-storey floor area (RdSAP 10 §3.8). Pinned 14.85 m^2 on 000490 MAIN.
- BuildingPartOverlay gains roof_insulation_thickness; the generic Overlay
Applicator writes it with NO change (validated by the tracer) — the
deep-module field-fold paying off.
- loft_insulation contingency (0.10) added.
Progress on #1158 (generator + geometry); end-to-end + Elmhurst pin pending
the orchestrator (#1157) and the parser fix. Four behaviour tests
(geometry pin; detect / none / cost). pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
scoring.py adds the telescoping marginal cascade that serves two of the three
ADR-0016 scoring roles:
- marginal_impacts(scorer, baseline, overlays): applies overlays cumulatively
in order and reports each measure's marginal MeasureImpact (sap_points +
carbon/energy savings). Role 3 (final-package attribution) — the marginals
telescope EXACTLY to the whole-package total.
- independent_option_impacts(scorer, baseline, options): role 1 — scores each
Option's overlay independently vs baseline, scoring each DISTINCT overlay
once (Options sharing an overlay reuse the result). Approximate signal for
the optimiser; never surfaced as a measure's true impact.
Role 2 (whole-package re-score) is PackageScorer.score directly. Three
behaviour tests on the real Sap10Calculator / a counting stand-in (hand-built
EPD): single-overlay marginal == improvement-over-baseline; two-overlay
marginals telescope to the package total; per-Option dedup scores each
distinct overlay once. Closes#1156. pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PackageScorer(calculator: SapCalculator).score(baseline, simulations) folds
the Simulation Overlays onto the baseline via the Overlay Applicator and
scores the throwaway EpcPropertyData on the injected deterministic SAP
calculator, returning Score(sap_continuous, co2_kg_per_yr,
primary_energy_kwh_per_yr). Depends on the SapCalculator abstraction, not a
concrete engine. This is the reusable scoring primitive (ADR-0016) — the
same call serves the optimiser's whole-package re-score and a future live
re-score of a user-assembled plan.
Two behaviour tests against the real Sap10Calculator on a hand-built EPD:
filling the main cavity improves SAP (right-directional through the real
physics); an empty package scores the unmodified baseline (pins the
SapResult->Score mapping). The Elmhurst before/after cascade PIN (#1154's
acceptance) lands once cert 001431 parses (external _extract_windows fix).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recommend_cavity_wall now takes a ProductRepository and prices the Measure
Option: Cost(total = gross_heat_loss_wall_area(MAIN) x product.unit_cost_per_m2,
contingency_rate = product.contingency_rate). Detection is unchanged and runs
before pricing, so ineligible walls still return None without a catalogue hit.
Completes #1155 — the cavity-wall Recommendation Generator now detects an
uninsulated main cavity wall and emits a priced Option carrying the filled-
cavity overlay. Four behaviour tests (detection x3 + fully-loaded cost).
pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Product(measure_type, unit_cost_per_m2, contingency_rate). ProductRepository
is the DDD port abstracting the catalogue source; ProductPostgresRepository
reads the externally-owned material table (defensive SQLModel view
MaterialRow) and maps an active row to a Product — total_cost becomes the
fully-loaded unit_cost_per_m2 — joining the per-measure-type contingency
(contingencies.py, mirrors Costs.CONTINGENCIES; cavity 0.10). Strict-raise
on missing/inactive row. A JSON-backed impl will follow behind the same
port for ETL-gap costs.
Two DB tests against an ephemeral Postgres (map active row; raise on
inactive-only). Toward #1155 cost (4b). Also generalises the CONTEXT
Simulation Overlay wording: windows are targeted by index, building-part
association carried via window_location (_window_bp_index). pyright clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
recommend_cavity_wall(epc) detects an uninsulated main cavity wall
(wall_construction=4, wall_insulation_type=4) and emits a Recommendation
whose single Measure Option carries the Simulation Overlay setting MAIN
wall_insulation_type=2 (Table 6 'Filled cavity'; cf. domain/sap10_ml/
rdsap_uvalues.py u_wall). Returns None for already-insulated or
non-cavity main walls.
Recommendation/MeasureOption reshaped per design review: the target is
encoded in the Option's overlay (addresses a building part / window /
system), not a typed key on Recommendation — generalises to glazing and
heating without changing the type. CONTEXT partition wording generalised
to match.
Three behaviour tests (hand-built EPD, no PDF). Cost (behaviour 4 of
#1155) outstanding — needs net heat-loss wall area + ProductRepository.
WIP on #1155. pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
EpcSimulation is the Simulation Overlay — a narrow all-optional partial
mirror of EpcPropertyData/SapBuildingPart (wall surface first), targeting
building parts by BuildingPartIdentifier (composition, not inheritance).
apply_simulations(baseline, simulations) deep-copies the baseline, folds
overlays in order (later wins on a shared field) via a generic non-None
field write, and returns a throwaway EpcPropertyData for the calculator;
the baseline is never mutated.
Four behaviour tests (hand-built EPD from the 000490 fixture, no PDF):
targeted-write-leaves-others-untouched, empty-overlay no-op, sequential
last-wins, baseline-immutability. pyright strict clean.
Slice 1 of the Modelling stage rebuild (ADR-0016). Closes#1153.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
