Caught live writing property_overrides on portfolio 796: the Python
override_component SAEnum lagged the DB enum, so reading a new-component row
back threw LookupError. Guard it with a consistency test.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Adds whole-dwelling property_type/built_form to EpcSimulation (folded by
apply_simulations) and maps those override components. property_type drives
party-wall heat loss + ASHP/solar/wall eligibility, so a landlord correction now
moves both the SAP calc and the measure menu; built_form has no calculator
consumer today (feeds the ML transform). Written as the landlord text value
(park-home check is text-only). Refines ADR-0032 dec-4.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extends WallType coverage to timber/stone/system-built/cob/park-home/curtain and
adds RoofType "Pitched, N mm loft insulation" -> roof_insulation_thickness. The
"(assumed) insulated"/"partial" wall states stay deferred (ambiguous code, needs
Elmhurst validation per ADR-0032); property_type/built_form carry no SAP weight.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The new pipeline left no per-Property record of a run (the old engine set
property.has_recommendations and populated property_details_epc). Restore the
marker: PropertyRepository.mark_modelled sets has_recommendations (true when the
Plan carries measures, mirroring the old engine) and bumps updated_at, so a
first-run under the new process is identifiable as updated_at >= 2026-06-01.
ModellingOrchestrator marks each Property after its Scenarios (true if any
Scenario yielded a measure); run_modelling_e2e's --persist path marks it too
(its compute runs on in-memory fakes, so the DB UoW sets it directly). Adds the
has_recommendations/updated_at columns to the PropertyRow mirror.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The measures a run considers should come from the Scenario, not a CLI flag.
The live scenario table persists exclusions only (no inclusions column), as a
Postgres text-array of exact MeasureType values.
- Scenario gains `exclusions: frozenset[MeasureType]` + `considered_measures()`
(all measures minus the excluded ones, or None when none are excluded).
- ScenarioModel.to_domain parses the `{a,b,c}` exclusions array into
MeasureTypes, raising on a token that is not an exact MeasureType value
(no high-level category expansion), per the strict-enum convention.
- ModellingOrchestrator._plan_for derives the allowlist from the Scenario's
exclusions, combined (intersection) with any explicit considered_measures
override via the new `combine_considered_measures`.
- run_modelling_e2e sources the allowlist from the Scenario; --measures /
--exclude-measures become optional overlays (e.g. the technical
secondary_heating_removal exclusion the catalogue cannot yet stock).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
