Two review points from @dancafc:
1) Rename the `Comparable` dataclass → `ComparableProperty` (it models one
comparable *property*; the collection stays `ComparableProperties`). Applied
across domain, repositories, orchestration, harness, scripts, and tests with a
word-boundary rename so `ComparableProperties` is untouched.
2) Move `PredictionTarget` out of comparable_properties.py into prediction_target.py
(where `PredictionTargetAttributes` + `build_prediction_target` already live).
comparable_properties.py now imports it; no import cycle (prediction_target no
longer depends on comparable_properties). Importers updated.
92 tests pass across the touched suites; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a `source` discriminator (lodged | predicted) to the EPC store so a Property
holds a lodged EPC and a predicted one (EPC Prediction gap-fill) at once
(ADR-0031). EpcRepository.save gains source="lodged"; idempotent delete is now
per-source (a predicted save no longer wipes lodged, and vice versa);
get_for_property/get_for_properties filter lodged; new get_predicted_for_property
/ get_predicted_for_properties read predicted. PropertyPostgresRepository.get +
get_many hydrate Property.predicted_epc, so the predicted picture reaches the
modelling read (both load via get_many). FakeEpcRepo mirrors the dual slot.
EpcPropertyModel gains `source` (default "lodged"); the test DB builds from the
SQLModel mirror so this is exercised without the prod migration. The matching
Drizzle change (column + per-(property_id,source) uniqueness) is the team's to
action before merge — docs/MIGRATION_NOTE_predicted_epc_source.md.
3 store tests (coexist, idempotent predicted re-save leaves lodged, lodged-only
has no predicted) + property-repo wiring; 85 pass across affected suites; new
code pyright-clean (2 pre-existing wwhrs errors in epc_property_table untouched).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Build the cohort IO port ADR-0029 deferred (ADR-0031 slice-5b):
`ComparablePropertiesRepository.candidates_for(postcode) -> list[Comparable]`,
with an EPC-API + geospatial adapter that lists the postcode's lodged certs
(search_by_postcode), fetches + maps each (get_by_certificate_number), and
resolves their UPRNs to coordinates in ONE batched read. Register metadata the
cert doesn't carry (address, registration date) is threaded off the search row;
a UPRN-less or unparseable-date cert is kept, just uncoordinated / unweighted.
The domain select_comparables then filters these candidates into the cohort.
Thin CohortEpcClient / CohortGeospatial Protocols keep the adapter testable
against fakes; EpcClientService + GeospatialS3Repository satisfy them
structurally (no changes). 3 tests; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds GeospatialRepository.coordinates_for_uprns(uprns) -> dict — a batch
coordinate lookup returning only covered UPRNs. The S3 adapter overrides it
to read the meta once, group UPRNs by their covering partition, and read each
partition once for all the UPRNs it covers; co-located (closely-numbered)
UPRNs share a partition, so an EPC Prediction cohort is typically one or two
reads instead of one per neighbour. Default port impl is a per-UPRN loop.
Feeds the EPC Prediction geo-proximity work: a cohort's UPRNs resolve to
coordinates in a couple of reads (validated at corpus scale: 170 partition
reads for 2683 UPRNs).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Flat per-dwelling decommission price (sample_catalogue \£250) + 0.25 contingency
(covers unknown heater count / hard-wired-vs-plugged / repaint extent). The JSON
repo joins the contingency from config, proven by the new repo test. No composite
Products machinery — a lodged secondary is one roughly-fixed job, not room-scaled.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ProductPostgresRepository.get took .first() with no ORDER BY, so when a
measure type has several active material rows (the live catalogue holds 74
solar_pv, 5 high_heat_retention_storage_heaters) the chosen row — hence the
cost and material_id — depended on the database's physical row order. Order by
id so a re-seed prices the same product every time.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Tighten the recommendation/plan vocabulary off generic str:
MeasureOption.measure_type and PlanMeasure.measure_type are now MeasureType
(also _GlazingTarget.measure_type, MeasureDependency.triggers ->
frozenset[MeasureType], and the optimiser's chosen/required-type locals).
Because MeasureType is a StrEnum the change is transparent to persistence
(the `recommendation` varchar column), the optimiser group-by key, and every
`== "solar_pv"`-style comparison — so pyright now enforces the enum at every
construction site with no runtime behaviour change.
The catalogue boundary stays str: ProductRepository.get(measure_type: str)
and Product.measure_type are unchanged (they map arbitrary DB/JSON rows), so
the fake product repos in tests need no edit. Test construction helpers coerce
their str arg via MeasureType(...); direct constructions use members.
Suite green: tests/domain/modelling + orchestration + harness 253 pass + 3
xfail; pyright clean on production + tests (pre-existing moto + property-
override-rowcount baselines untouched).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PR feedback (dancafc): the SQLModel column was Optional[str], but the
domain `SapBuildingPart.wall_insulation_thickness` is Optional[Union[str,
int]] — `_api_resolve_wall_insulation_thickness` returns an int mm when the
API lodges `wall_insulation_thickness == "measured"` (SAP 10.2 §5.7 /
Table 8). The plain str column round-trips that int back as the string
"100", corrupting the Table 8 insulated-wall U-value lookup.
This column was missed in the round-trip-fidelity §1 JSONB sweep
(#1129) — its `Union[str, int]` sibling `roof_insulation_thickness` was
converted, but `wall_insulation_thickness` was not, and no 21.0.0/21.0.1
fixture lodges "measured" so the gap stayed latent. Convert to JSONB
(matching `roof_insulation_thickness` / `flat_roof_insulation_thickness`),
align the column type to Optional[Union[str, int]] (also removes a pyright
type-mismatch), record it in the migration doc §1, and add a round-trip
guard test asserting an int survives as an int (fails as '100' == 100 on
the old str column).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3c.5. `PropertyPostgresRepository` takes an injected `SpatialRepository`
and hydrates `Property.planning_restrictions` by UPRN (bulk in `get_many`,
single in `get`). A UPRN with no cached row — or a property with no UPRN —
defaults to unrestricted, matching legacy `empty_spatial_df` (ADR-0020). This
closes the loop: Ingestion caches the protections, Modelling reads them off the
Property to gate solid-wall EWI/IWI (ADR-0019).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3c.3. Ingestion writes the OS spatial reference cache through the same
unit it persists the EPC/solar enrichments with, so `UnitOfWork` declares a
`spatial` repo, `PostgresUnitOfWork` binds a `SpatialPostgresRepository` to the
session, and `FakeUnitOfWork` gains a `FakeSpatialRepo` (seedable for read
tests, recording writes for ingestion-side assertions).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3c.2. The OS Open-UPRN reference set is too large to host in Postgres, so
it lives in S3 and is cached per-UPRN in the existing `property_details_spatial`
table (ADR-0020). `PropertyDetailsSpatialRow` mirrors that table (uprn unique);
`SpatialRepository` / `SpatialPostgresRepository` upsert one shared row per UPRN
and read the planning protections back by UPRN (a null flag reads as
unrestricted; absent UPRNs are omitted so the caller defaults them).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3c.1. Ingestion will persist a UPRN's coordinates and planning
protections together as a write-through cache, so resolve them in a single
partition read rather than two. `SpatialReference` bundles the coordinates
(which drive the Solar fetch) and the `PlanningRestrictions` (which gate wall
insulation per ADR-0019/ADR-0020); `GeospatialRepository.spatial_for(uprn)`
returns it, and `coordinates_for`/`planning_restrictions_for` now delegate to
the one lookup.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3a (ADR-0020). PlanningRestrictions relocated out of the solid-wall
generator into domain/geospatial/ as the shared, Property-level value object
(three distinct flags + measure-specific blocks_external/blocks_internal).
GeospatialRepository gains a non-abstract planning_restrictions_for defaulting
to None (sources without the flags need not implement it); GeospatialS3Repository
reads conservation_status/is_listed_building/is_heritage_building from the same
Open-UPRN partition as the coordinates (legacy column names — to confirm in the
S3 deep-dive). Shared _row_for helper dedups the partition lookup.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Apply the deep-research off-gas figures so oil/smokeless/wood sit on the
same NEP-Apr-2026 retail / DESNZ DUKES gross-CV basis as the new coal
proxy (fuel-input, not useful-heat): OIL 9.16 -> 12.11 (prior value was
materially low vs current kerosene), SMOKELESS 10.0 -> 8.69, WOOD_LOGS
8.83 -> 8.25, WOOD_PELLETS 7.99 -> 7.38. SEG (15.0, Solar Energy UK) and
LPG (17.61, bottled-propane) kept; gas/electricity (Ofgem cap) unchanged.
CV arithmetic recorded in the snapshot _assumptions. OIL pin updated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Coal and heat networks have no national retail/cap rate, so the snapshot
left them null and BillDerivation raised UnpricedFuel — dropping those
certs from an offline cohort run. Add researched proxy rates (fuel-input
basis, sources + arithmetic in the JSON _note/_gaps): COAL 7.13 p/kWh
(NEP Nov 2025 coal uprated + DESNZ DUKES house-coal GCV) and HEAT_NETWORK
16.0 p/kWh + 69.4 p/day (Insite Energy operator sample; indicative, schemes
vary ~8-30). Both flagged proxy/indicative — sense-check estimates, not
market rates. Existing curated fuels are unchanged.
Replaces the unpriced-raises pin for these two with a positive rate pin;
off-peak stays unpriced pending the day/night accessor. Golden cohort now
runs 57/57 offline with zero errors.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Move the scenario and installed_measure tables into
infrastructure/postgres/modelling/ as full-parity SQLModel definitions
(ScenarioModel, InstalledMeasureModel + MeasureType), completing the cluster
consolidation. backend/app/db/models/recommendations.py is now a pure
re-export shim.
ScenarioModel.goal is the PortfolioGoal enum (legacy planning branches on it),
sourced from domain/modelling/portfolio_goal.py; the repo's to_domain maps it to
its value string, so domain Scenario.goal is now the value ("Increasing EPC")
consistent with the orchestrator's check — fixing the latent name-vs-value
inconsistency the old str column masked (the scenario repo test stored the enum
*name*). Parity columns are nullable (mirror convention; live NOT-NULLs owned by
Drizzle).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Standardise the modelling persistence classes on the …Model suffix (PlanModel,
RecommendationModel, RecommendationMaterialModel) — matching the epc_property
precedent and the legacy names the rest of backend/ already imports, so the
shim's plan re-export becomes literal (no alias) and the eventual shim deletion
needs zero renames. The …Row→…Model sweep for the non-cluster tables
(Property/Task/Material/…) waits until their live legacy …Model counterparts
are retired, to avoid reintroducing dual-definition collisions. No behaviour
change.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Move the live plan, recommendation, recommendation_materials and (retiring)
plan_recommendations tables into a new infrastructure/postgres/modelling/
subpackage as single SQLModel definitions (the epc_property pattern), absorbing
the rebuild's partial PlanRow/RecommendationRow mirrors and carrying full
legacy column parity plus recommendation.plan_id. Out-of-cluster references are
plain indexed ints (mirror convention); the live FKs are owned by the Drizzle
schema. backend/app/db/models/recommendations.py becomes a re-export shim
(ScenarioModel/InstalledMeasure stay for a later slice).
Fix the export conftest to create SQLModel-first (so Base funding_package's FK
to the now-SQLModel plan resolves) and skip the redundant drop_all on its
function-scoped throwaway DB (the epc enum type is now shared across both
metadatas). Resolves the pre-existing dual-definition collision: the rebuild
and legacy export suites are now co-runnable. No behaviour change.
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>
Bill / EnergyBreakdown / BillDerivation / sap_fuel were under
domain/property_baseline/ only because Baseline was built first. The Modelling
stage now needs them too, so move them (and their tests) to a neutral
domain/billing/ — Fuel/FuelRates already live in the shared domain/fuel_rates/.
Avoids a modelling -> property_baseline cross-stage import and a package name
that wrongly implies ownership (ADR-0011, ADR-0014 amendment). Pure git mv +
import rewrite across 10 files; 40 billing/baseline/repo tests pass, pyright
strict clean. CONTEXT.md Bill Derivation location updated.
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>
Slice 4a. The Modelling stage reads the Scenario + Product catalogue and
writes the Plan + its Plan Measures on one session, committed once
(ADR-0012/0017). Adds uow.scenario / uow.product / uow.plan to the
UnitOfWork port and constructs them in PostgresUnitOfWork.__enter__.
Additive — existing stages and the bare-stub Modelling wiring are
unaffected. Wiring test asserts the unit exposes the three ports.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 3 of #1157. Persists a Plan and its Plan Measures to the live
plan / recommendation tables via SQLModel mirrors (ADR-0017).
- infrastructure/postgres/plan_table.py: PlanRow (`plan`) + RecommendationRow
(`recommendation`) mirrors. RecommendationRow adds the new `plan_id` FK
(ON DELETE CASCADE) linking each Plan Measure to its Plan, replacing the
plan_recommendations m2m for new writes. from_domain mappers convert CO2
kg → tonnes to match the live column contract and derive post_epc_rating
from the rounded SAP. Only the impact + cost + identity columns the tracer
fills are declared; energy/bill, U-value, valuation, labour, plan_type are
left to later slices.
- PlanRepository port + PlanPostgresRepository.save(plan, *, property_id,
scenario_id, portfolio_id, is_default) -> plan id. Idempotent replace:
deleting the Plan cascades to its recommendation rows via plan_id, so a
re-run overwrites (ADR-0012). No commit — the UoW owns the transaction.
2 tests (persist + idempotent re-run); pyright strict clean; 73 pass across
repositories/modelling/orchestration with no regressions.
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>
Adds the file-backed Product catalogue — the stopgap source for costs
the ETL does not yet supply, behind the same ProductRepository port as
ProductPostgresRepository. The JSON file maps each Measure Type to its
fully-loaded unit cost; the per-Measure-Type contingency is joined from
config (not stored in the file), so config stays the single source of
truth for contingency — mirroring the Postgres repo's mapping.
Strict-raises (ValueError) on an absent measure type, a non-object
entry, or a missing/non-numeric unit_cost_per_m2, matching the
repo-wide strict-no-silent-default convention. tmp_path-backed tests,
no DB fixture needed.
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>
The PropertyBaselineOrchestrator now reads the current Fuel Rates snapshot
once per batch, builds a BillDerivation, and prices each scored property's
SapResult -> EnergyBreakdown into a Bill carried on PropertyBaselinePerformance
(None only on the stub no-calculator path). The Bill is flattened onto nullable
bill_* flat columns (per-section kwh+cost, standing charges, SEG credit, total)
on the postgres table, with bill_total_annual_bill_gbp as the not-null
discriminator on read-back. Section absent from the bill stays None, not 0.
Updated all four orchestrator construction sites to inject the FuelRatesRepository
port (handler + three test sites), and the FE migration doc to reflect the
prefixed columns and that they are now populated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PR feedback: adapters here are <aggregate>_<backend>_repository (e.g.
property_baseline_postgres_repository). Rename the fuel-rates adapter to
match — file static_file_fuel_rates_repository.py ->
fuel_rates_static_file_repository.py and class StaticFileFuelRatesRepository
-> FuelRatesStaticFileRepository, plus its test. git mv preserves history.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 1 of Bill Derivation — the reference-data foundation that later slices
price the calculator's per-end-use kWh against:
- Fuel enum (canonical billing fuels; the join key between the calculator's
SAP-code fuels and the rates snapshot). COAL + HEAT_NETWORK are members with
no national rate.
- FuelRates value object: unit_rate_p_per_kwh / standing_charge_p_per_day /
seg_export_p_per_kwh; raises UnpricedFuel on a fuel it has no rate for rather
than billing at a wrong default.
- FuelRatesRepository port (ADR-0011 Repo-reads-stored-reference-data) +
StaticFileFuelRatesRepository reading a committed JSON snapshot.
- Snapshot fuel_rates_2026_q2.json: GB national, Apr-Jun 2026 Ofgem cap
(gas/electricity) + DESNZ/NEP May 2026 (off-gas). Carries the full researched
data; the value object exposes single-rate fuels this slice. Off-peak
(day/night), house coal and heat network raise UnpricedFuel until later slices.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final slice of ADR-0012: collapse the per-property read round-trips a batch
made (Baseline hydrated ~8 queries x 30 properties one at a time) into a
handful of per-table IN queries.
- EpcPostgresRepository: extracted a shared `_compose(rows)` from `get` (the
windows + floor-dim fetches are now passed in, not fetched inline), so both
`get` and the new `get_for_properties(property_ids)` build EpcPropertyData
from pre-fetched rows. `get_for_properties` fetches each child table once
(`WHERE epc_property_id IN ...`), groups in memory, and composes — load-whole
per ADR-0002.
- PropertyRepository.get_many(property_ids) -> Properties: one query for the
property rows + one bulk EPC hydration, composed in input order.
- BaselineOrchestrator / IngestionOrchestrator read the batch via get_many
instead of N x get.
- Ports + fakes gain the bulk methods.
The #1129 round-trip fidelity test stays green (the compose extraction is
behaviour-preserving). New tests: bulk hydration correctness + round-trips are
constant w.r.t. batch size (one-per-table, proven by query count). 123 pass;
pyright strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Re-runs of a First Run batch re-save a property's data; that must replace,
not duplicate (ADR-0012 idempotent batch writes).
- `EpcPostgresRepository.save` deletes the property's existing EPC graph
(parent + all child tables, floor-dims via their building parts) before
inserting, when a `property_id` is given. Anonymous saves still insert.
- `BaselinePostgresRepository.save` deletes the existing row for the
`property_id` before inserting — no more unique-constraint violation on
re-save; also what the re-score-on-override path needs.
- Solar already upserts, so it's unchanged.
The #1129 round-trip fidelity test stays green (delete-first is a no-op on
a first save). 2 new tests (re-save replaces, not duplicates). pyright
strict clean; AAA.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
