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>
