Two-pass org_ref-matched builder for property_overrides (classify via ChatGPT
into the landlord ledger, validate+apply user edits, write idempotently);
ephemeral-Postgres smoke proves the one-property chain without creds.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
The FE-owned `material.type` pgEnum cannot carry `secondary_heating_removal`,
so pricing it through the DB catalogue raises a DataError that poisons the
session — the modelling pipeline crashed on any property with a lodged
secondary heater unless the measure was excluded on the Scenario.
Realise the `ProductRepository` docstring's intent (DB catalogue today, a JSON
file for costs the ETL does not yet supply, behind the same port): add a
`CompositeProductRepository` that resolves an override source first, then the
catalogue. Checking the override first keeps that Measure Type away from the DB
entirely; every other type misses the override and falls through unchanged.
- off_catalogue_costs.json prices it at £270 flat per-dwelling — the legacy
`Costs.heater_removal` ported to the new flat model (ADR-0028):
(£25 + £200 baseline) x 1.2 VAT, for the single fixed secondary a cert lodges.
Contingency (0.25) is joined from config, not the file.
- Wire the composite into PostgresUnitOfWork.product and run_modelling_e2e, so
the first-run pipeline and the local runner both honour the overlay.
- Integration test: drop the unrealistic seeded secondary_heating_removal DB
rows (the pgEnum can't hold the type) and assert it is JSON-sourced
(material_id is None, cost £270) end-to-end through a real Unit of Work.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
An uninsulated stone wall of lodged thickness, age bands A-E, is billed by
the RdSAP 10 §5.6 Table-12 formula on its measured thickness — sandstone /
limestone U = 54.876·W^-0.561, granite / whinstone U = 45.315·W^-0.513. Two
bugs suppressed it:
1. CAP: the as-built branch capped the formula result at the Table-6
typical-thickness default (`if u0 >= 1.7: return 1.7`). But the formula
only dips below 1.7 past ~488 mm (sandstone) / ~640 mm (granite), so the
cap nullified §5.6 for essentially every real-thickness stone wall,
under-counting fabric loss and over-rating. A measured 400 mm sandstone
age-B wall is 1.90 (Elmhurst-confirmed), not 1.70.
2. NO INSULATION-STATE GATE: the branch fired for any stone wall with a
lodged thickness, including ones whose insulation is "Unknown". RdSAP
treats an unknown-insulation wall via the Table-6 typical-thickness
default, NOT as bare stone of the lodged thickness — so a 50 mm granite
wall with Unknown insulation must read 1.70 (worksheet), not the formula's
6.09. Gated on `wall_insulation_type is not None`.
Fixes the 2 long-standing stone-U unit tests (granite 120 mm → 3.8871,
sandstone 120 mm → 3.7408 — they were correct red tests, not "known fails").
Corpus within-0.5 70.3% -> 71.6% (MAE 0.833 -> 0.822); ratcheted floors to
0.71 / 0.83. Worksheet fixture 000565 (granite 50 mm Unknown → 1.70) still
passes via the insulation gate. The two stone groups (sandstone/limestone vs
granite/whinstone) keep their distinct §5.6 coefficients.
pyright not installed in this codespace (strict gate not run locally).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The SAP rating is spec-floored at 1 ("if the result of the calculation is
less than 1, the rating is 1"). `sap_rating_integer` already clamps, but the
continuous `sap_score_continuous` did not — so a degenerate dwelling could
emit a physically impossible negative SAP. Apply the same max(1, …) floor to
the continuous value (the un-rounded part is for sensitivity near real
ratings, not for negative ratings).
Removes a -12.3 accuracy outlier on the committed corpus (cert 422000111926,
lodged at the floor of 1, was computing -11.3): within-0.5 70.2% -> 70.3%,
MAE 0.845 -> 0.833. Ratcheted the corpus MAE ceiling to 0.84. Unit-pinned in
test_calculator.
pyright not installed in this codespace (strict gate not run locally).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The gov-API lodges secondary fuel as an enum whose value can COLLIDE with a
different same-valued RdSAP 10 Table 32 / SAP 10.2 Table 12 fuel code:
- enum 9 = "dual fuel (mineral and wood)" vs Table code 9 = LPG SC11F
- enum 5 = "anthracite" vs Table code 5 = LPG (bulk)
The main-fuel boundary already canonicalises these (`_GOV_API_COLLISION_
FUELS`), but the SECONDARY-heating cost + CO2/PE paths never did — they took
the bare same-value lookup, so a dual-fuel room heater was priced as LPG
(3.48 vs dual-fuel 3.99 p/kWh) and emitted as LPG (CO2 0.241 vs 0.087),
and an anthracite secondary as bulk LPG (12.19 vs 3.64 p/kWh). The price
under-count over-rates SAP; the CO2 over-count inflates emissions.
Fix: add enum 9 to `_GOV_API_COLLISION_FUELS` (5 and 33 were already there)
and canonicalise the secondary fuel code on both the cost
(`_secondary_fuel_cost_gbp_per_kwh`) and factor (`_secondary_fuel_code`)
paths, mirroring the main-fuel boundary. canonical_fuel_code only touches
{5,9,33}, so genuinely Table-coded secondaries (House coal 11, wood logs 20,
community fuels 30-32) are left unchanged — confirmed by a full-map audit.
Corpus: within-0.5 69.7% -> 70.2% (MAE 0.854 -> 0.845; dual-fuel-secondary
cohort 42.9% -> 49.0%, signed +0.55 -> +0.41) and CO2 MAE 0.12 -> 0.08 t/yr
(bias +0.04 -> 0.00). Ratcheted the corpus floors (within 0.70, MAE 0.85,
CO2 0.09, PE 4.0). A prior session deferred enum 9 ("direction not
understood") while the EPC PE/CO2 lens was confounded by the climate-cascade
bug (fc7c4d2d); on the corrected lens the over-rate direction is clear.
pyright not installed in this codespace (strict gate not run locally).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The SAP/EI rating is computed on UK-average weather (Appendix U Tables
U1-U3 region 0) so ratings are nationally comparable, but Appendix U
paragraph 1 (PDF p.124) requires that "other calculations (such as for
energy use and costs on EPCs) are done using local weather. Weather data
for each postcode district are taken from the PCDB". `Sap10Calculator.
calculate` ran ONE cascade (UK-average) and fed it to SAP, CO2 AND primary
energy, so every cert's EPC-displayed CO2/PE were computed on the wrong
climate. Because most of England is warmer than the UK-average, this
systematically OVER-counted heating demand on the emissions/PE outputs.
The two cascades (`cert_to_inputs` rating, `cert_to_demand_inputs`
postcode) already existed; this wires the demand cascade into the
production entry point and grafts its CO2/PE onto the rating result (SAP
unchanged). The corpus gauge's longstanding +5% CO2/PE over-estimate was
mostly this climate bug, NOT (as previously diagnosed) per-cert mapper
fidelity:
CO2 MAE 0.26 -> 0.12 t/yr (bias +0.18 -> +0.04)
PE MAE 13.6 -> 3.8 kWh/m2 (bias +9.0 -> +0.24)
SAP within-0.5 = 69.7% (rating cascade, unchanged)
Worksheet-validated to 1e-4 on simulated case 45 (heat-pump ground-floor
flat, postcode W6): the P960 prints the current dwelling twice — Block 1
on UK-average weather (SAP 60.5318, CO2 692.13) and Block 2 on postcode
weather (CO2 626.78, PE 6581.59). Both reproduce exactly. Added a tracked
case-45 Summary fixture + two-cascade cascade pin as a permanent guard,
and ratcheted the corpus CO2/PE ceilings to 0.13 / 4.2. The e2e Elmhurst
suite (Block-1 line refs) now pins the rating cascade directly; the two
Vaillant overlay snapshots refreshed to demand-cascade CO2/PE.
pyright not installed in this codespace (strict gate not run locally);
change is type-trivial (dataclasses.replace over SapResult).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
